The Economics of Labor Force Participation 9781400874774

Table of contents :
Preface
Table of Contents
List of Tables
List of Figures
Part I. Framework of the Study
CHAPTER 1. Introduction: Scope, Organization, and the Labor Force Concept
CHAPTER 2. The Conceptual Framework and Method of Analysis
Part II. Explaining Labor Force Participation
CHAPTER 3. Prime-Age Males: Individual Characteristics
CHAPTER 4. Prime-Age Males: Labor Market Conditions
CHAPTER 5. Married Women: Individual Characteristics
CHAPTER 6. Married Women: Labor Market Conditions
CHAPTER 7. Trends in the Participation of Married Women
CHAPTER 8. Single Women 25-54
CHAPTER 9. Older Persons: Individual Characteristics
CHAPTER 10. Older Persons: Labor Market Conditions
CHAPTER 11. Trends in the Participation of Older Persons
CHAPTER 12. Younger Persons: Individual Characteristics
CHAPTER 13. Younger Persons: Labor Market Conditions
CHAPTER 14. Younger Males: Trends in Participation, Enrollment, and Activity Rates
Part III. Unemployment and Labor Force Participation
CHAPTER 15. Unemployment and Labor Force Participation: Cross-Sectional Relations
CHAPTER 16. Unemployment and Labor Force Participation: Time-Series Relations
CHAPTER 17. Unemployment and Labor Force Participation: Some Evidence from the 1963-1967 Boom
Chapter Appendices
General Appendices
Index

Citation preview

THE ECONOMICS OF LABOR FORCE PARTICIPATION

THE ECONOMICS OF LABOR FORCE PARTICIPATION By William G. Bowen and T. Aldrich Finegan

Princeton, New Jersey Princeton University Press 1969

Copynght © 1969 by Princeton University Press ALL RIGHTS RESERVED

L.C. Card Number: 69-17396 Printed in the United States of America by Princeton University Press, Princeton, N.J. This book was composed in Linofilm Times Roman

Preface The authors of any study as quantitative as this one are apt to be re­ garded with suspicion by people who know that "looking at the numbers" for too long a time can lead to interpretations which fail to come close to the whole truth. Perhaps we can allay this suspicion at least in part by the very act of recognizing the grounds for it—and by citing one (nonsubstan­ tive) case in point. According to our records, we have been working on this study for six years; the number six, however, seems a hopelessly inadequate description of the longevity of the project! The enterprise started innocently enough when one of us showed the other a scattergram relating labor force participation rates for married women to unem­ ployment rates in large metropolitan areas just at a time the other was thinking more generally about the relevance of labor force data for fiscal policy. It quickly took on a life of its own, resisted vigorously all efforts to curb its expansionist tendencies and its appetite for computer time, and finally ended only when the authors were faced with a now-or-never ultimatum posed by the approach on the horizon of a huge wave of new data (the 1970 Census). The Introduction and the Table of Contents together provide all that is needed in the way of a reader's guide to this volume. But perhaps we should, in this preface, call attention to one important change in emphasis which occurred during the life-span of the study. When we started out we were concerned mainly with the sensitivity of labor force participation rates to the tightness of labor markets. It soon became apparent, however, that in order to do a tolerably good job of isolating the effects of varying degrees of labor market tightness on participation rates, we would have to invest considerable effort in controlling for many other important varia­ bles. And, as we worked to develop more general models capable of ex­ plaining labor force decisions, we realized that there were many other relationships which were as interesting in their own right as the relation­ ship we originally set out to study. For example, we became interested in the differential effects of educa­ tional attainment on the participation of whites and Negroes; in the inter­ related effects of wage levels and the availability of certain kinds of jobs on the participation of many population groups (especially male teen­ agers); in the effects of Social Security regulations and other institutional factors on the retirement decision as contrasted with the effects of income per se and earnings opportunities; in the effects on the participation of married women of the ages of their children and their own work experi­ ence as well as permanent and transitory elements of family income; and in the effects of health and other personal characteristics on the labor force participation of men and older persons in general.

vi

PREFACE

Consequently, while our original (and continuing) interest in the sensi­ tivity of participation rates to the tightness of the labor market is reflected in the special attention devoted to this topic in Part III, the main part of the book presents a more comprehensive analysis of the effects of in­ dividual characteristics and labor market conditions on the labor force decisions of all major population groups in the United States. We are well aware that many chapters contain so much detail that the patience of even the most dedicated reader will be sorely tried. This re­ sults in part from a conscious decision not to gloss over results which we cannot fully explain—and from the fact that it generally requires more space to describe a puzzle than to report evidence consistent with a straightforward hypothesis. Hopefully, the presentation of detailed explanations of methods and the inclusion of some discussions which are frankly labeled conjectural will be useful to other persons interested in studying labor force partici­ pation. This is a subject in which each successive study has been built on earlier contributions; as the footnotes to this book indicate, we have benefitted from the work of many predecessors, some of whom in turn have profited from the still earlier studies of Woytinsky, Douglas, and others. Thus, we have felt a particularly strong obligation to do what we could to facilitate further work. In the course of our research, we have received direct help from a great many people. We are heavily indebted to Mr. Orley Ashenfelter, formerly a graduate student in economics at Princeton and now a Lec­ turer, who has served as our primary advisor on statistical and econo­ metric questions, and who has written a part of Appendix B (dealing with heteroscedasticity in a model employing dichotomous variables) which will be of independent interest to readers concerned with technical issues. Professors Stanley Black, William Branson, Stephen Goldfeld, and Richard Quandt, all of the Princeton Economics Department, have also provided advice on econometric problems. Dr. Michael Godfrey, formerly in the Statistics Department at Princeton and now a member of the staff of the Bell Laboratories, played a critical role at two stages of our work: he developed a seasonal adjustment program which ex­ pedited our time-series analysis, and he also made it possible for us to process the data from the 1/1000 Sample of the 1960 Census in an effi­ cient way at the University Computer Center. We are also indebted to Mr. Orin Merrill, then a Princeton undergraduate, who developed the original program making use of the 1/1000 Sample. Among our other Princeton colleagues, we are glad to acknowledge the advice provided by Professors Ansley Coale and Charles Westoff on demographic problems, the helpful comments of Professors Richard Lester and Frederick Harbison, and the virtuosity of a friend of ours in the Department of Politics, Professor Stanley Kelley, Jr., who not only knows all about party politics, but who also has a surprising gift for ex­ plaining economic phenomena. In particular, however, we want to ac-

PREFACE

vii

knowledge the help provided by Professor Albert Rees, now serving as Director of the Industrial Relations Section. Professor Rees generously found time in a hectic schedule to read every chapter with uncommon care and to make many suggestions which significantly enhanced our understanding of labor force participation. Colleagues at other universities have also been very kind in their will­ ingness to review early drafts of the manuscript and to send us written comments. In this regard, we wish to thank Professors Glen Cain and Lee Hansen of the University of Wisconsin, Professor Jacob Mincer of Co­ lumbia University, Professor Stanley Lebergott of Wesleyan University, and Mrs. Gertrude Bancroft McNally, formerly of the Bureau of the Census and the Department of Labor. Special thanks are due Mr. Daniel Rubinfeld, a Princeton undergradu­ ate who is now a graduate student in Economics at M.I.T. Mr. Rubinfeld displayed a rare blend of ingenuity and persistence in doing much of the complicated programming required by the way we chose to use the 1/1000 Sample; he also did much of the preliminary work on trends in the par­ ticipation of older persons and prepared an initial draft of that chap­ ter. We have been fortunate throughout the period of this research in hav­ ing the assistance of exceedingly capable research assistants. During the first three years of the study Mrs. Virginia Gebhardt served as chief research assistant, and she not only discharged all of her ordinary re­ sponsibilities with diligence and skill but also made many important sub­ stantive suggestions; more generally her persistence and loyalty did much to keep this project going during some difficult times. When Mrs. Gebhardt left Princeton, she was succeeded by Miss Judy Livingston (now Mrs. John Aklonis), who brought her own blend of intelligence, conscien­ tiousness, and good humor to the task. Mrs. Clelia Casey has been our chief research assistant for the last two and one-half years, and she too has done an extraordinarily good job of running regressions, preparing tables, querying some of our not-so-obvious propositions, and attempting to improve our grammar. In addition, she has been a source of good cheer and a force for sanity. The preparation of this manuscript was an unusually demanding task because of both its length and the large number of tables and appendices. Here too we have had excellent assistance which it is a pleasure, not a duty, to acknowledge. Miss Doris McBride, Mrs. Alberta Martin, and Mrs. Vickye Kivett all helped with parts of the typing. Major credit, however, belongs to Miss Evelyn Parker who did most of the typing herself, including almost all of the tables, and who assumed full responsi­ bility for shepherding the final manuscript to the Press. It would be hard to exaggerate how much her steadiness, reliability, and skill have con­ tributed to the completion of this project. We are also pleased to acknowledge the help we have received from the Princeton University Press, and especially from Mrs. Dorothy Holl-

PREFACE viii mann, who deserves the reader's thanks as well as our own for many edi­ torial improvements. The last individual we wish to mention is Mary Ellen Bowen, who deserves the thanks of her husband for bearing the agonies of this study with her usual patience and understanding. For financial assistance, we are indebted to several organizations: the U.S. Department of Labor, whose grant to Professor Finegan made pos­ sible an additional term of leave from Vanderbilt University at an im­ portant stage of the work;1 the Ford Foundation, whose faculty research fellowship permitted Professor Bowen to devote an entire year to this study, free from other duties; and the National Science Foundation, whose support of computing at Princeton contributed directly to this re­ search and whose institutional grant to Princeton paid for the acquisition of the 1/1000 Sample of the 1960 Census. Beyond these forms of special assistance, a significant share of the general costs of the project were carried by the Research Program on Unemployment conducted by the Institute of Industrial Relations, University of California, Berkeley, under a grant from the Ford Foundation. We are grateful to the codirectors of this project, Professors Arthur Ross and R. A. Gordon, for their continued support and encouragement. Finally, we are glad to acknowledge the substantial financial support provided by the Industrial Relations Section of Princeton University. IN CONCLUDING this preface we wish to express the hope that no one

who reads this book will waste time attempting to discover which parts of the work were written by each author. We have worked on this study so closely and over such a long period of time that we could not possibly disentangle our individual contributions, even if there were anything to be gained by doing so. "Group" or "team" research is both much in vogue and much condemned these days. It would be absurd to base sweeping comments on this controversy on the experience of any one study, but we can say that without a collaborative effort this book would never have been written. We do not mean simply that there was too much work for one person to do, though that is surely true. Nor do we mean simply that by discussing issues and findings we achieved an analysis which is superior to the sum of what we could have produced inde­ pendently, though we also believe this to be true. The additional element is the encouragement—and pleasure—that comes from sharing an in­ tellectual experience with a good friend. WGB TAF Princeton, New Jersey September 1968 ^More specifically, this grant was made by the Manpower Administration of the U.S. Department of Labor under the authority of Title I of the Manpower Development and Training Act of 1962. We hasten to add, however, that the opinions and conclusions stated in this study do not necessarily represent the official position or policy of the De­ partment of Labor.

Table of Contents ν

Preface List of Tables List of Figures

XVU XXlV

PART I: FRAMEWORK OF THE STUDY CHAPTER 1

Introduction: Scope, Organization, and the Labor Force Concept The Subject and Its Scope Organization of the Study The Labor Force Concept and Its Measurement

3 3 4 7

2

CHAPTER

The Conceptual Framework and Method of Analysis Application of the General Theory of Choice Tastes, 16 —Expected market "earnings" rates, 17 —Expected nonmarket "earnings" rates, 18—The resource constraint, 18 The Measurable Variables Tastes, 19 —Expected market earnings rates: substitution effects, 24 — Expected non-market earnings rates, 26 —Family resources, 27 Constructing Testable Models Data and Methods

16 16

18

29

32

PART II: EXPLAINING LABOR FORCE PARTICIPATION CHAPTER

3

Prime-Age Males: Individual Characteristics Marital Status Color Schooling Health Other Income Age CHAPTER

39 40 49 53 62 67 71

4

Prime-Age Males: Labor Market Conditions Cross-Sectional Results for All Urban, Prime-Age Males in I960 Results for Urban Subgroups in 1960

75 75

80

X

TABLE OF CONTENTS

Results for Rural Nonfarm Residents "Comparability" Results for 1940, 1950, and Time-Series Results CHAPTER

1960

5

Married Women: Individual Characteristics Color White-Negro differentials, 90 —Other nonwhites, 94 Children General results for all married women, 98 —Implications of hours of work, 100 — Results for married women with children under 6: effects of age of youngest child, 102 —Results for Negro married women, 103 Housing Age Schooling General effects of schooling on the participation of married women, 115 —Sources of the positive relationship between schooling and L M W 1 4 _5 4 : wage incentives versus non-wage factors, 118 — Schooling and the participation of women with and without young children, 122 — Schooling and the participation of Negro married women, 122 Occupation of the Wife Other Family Income The family tax effect, 136 —The relationship between OFI and Lmwi4-54 examined in more detail, 138-Comparisons with intercity results, 145 Employment Status of the Husband Occupational Status of the Husband CHAPTER

The Intercity Regressions in General Wages of Domestic Servants Earnings of Females Female Jobs and Female Jobseekers Unemployment Results of the intercity regressions for 1960, 179 —The relative importance of the additional-worker and discouraged-worker effects, 181 — Results for rural, nonfarm residents, 186—Time-series results, 187 "Comparability" Results for 1940 and 1950

89 96

106 108 114

127 132

147 154

159 159 164 172 174 178

190

7

Trends in the Participation of Married Women DEMOGRAPHIC

88

6

Married Women: Labor Market Conditions

CHAPTER

82 83 86

FACTORS

Age and the Presence

197 198

of Young Children

198

TABLE OF CONTENTS

Color Rural-Urban Residence The Demographic Factors Combined INCOME AND JOB-INCENTIVE EFFECTS

Implications of the 1960 Cross-Sectional Coefficients for the Postwar Trend in LMW14_54

Xl

204 204 205 206 207

General results, 207 —Effects of individual variables, 210

Implications of the 1950 and 1960 Cross-Sectional Coefficients for the Postwar Trend in LMWI4+ Implications of the 1940 and 1950 Cross-Sectional Coefficients for the Increase in LMW14+ Between 1940 and 1950 OTHER FACTORS

Hours of Work Home Goods: Prices and Methods of Production Income Aspirations

219 222 226 227 231 234

CHAPTER 8

Single Women 25-54 INDIVIDUAL CHARACTERISTICS

Marital Status and Related Characteristics

242 242 242

The taste for market work, 244 —The presence of young children in the home, 245 —Family status, 246 —Financial resources, 248 —Ex­ pected market wage rates, 249

Color Schooling Other Income Age LABOR MARKET CONDITIONS TRENDS

CHAPTER

251 254 260 262 263 267

9

Older Persons: Individual Characteristics Age

270 271

The adjusted relationships between age and participation, 273 —The Social Security System, private benefit plans, and the drop in partici­ pation for men around the age of 65, 279 —The jump in participation for older men at age 72 and the earnings test once again, 285

Marital Status

286

Females, 288 —Males, 289

Color Family Responsibilities and Housing Characteristics Schooling Health

290 295 296 304

XII

TABLE OF CONTENTS

Other Income and Other Family Income Males 55-64 and 65-74, 309 — Single women, 318 — Married women, 319 Employment Status of Husband Occupation and Related Characteristics Married women 55-64, husband present, 326 —Single women 55-64, not in a family, 326-Males 55-64 and 65-74, 327 CHAPTER

Results for Urban Groups in 1960 Migration, 337 —Industry mix and group supply, 340 —Earnings, 341 — Unemployment, 342 —Wages of domestic servants and the participation of older married women, 343 Results for Older Males Classified by Marital Status Results for Older Males in Rural Nonfarm Areas Comparability Regressions for 1960, 1950, and 1940 Results of Time Series Regressions for Older Persons

65

AND

OVER

335

344 346 346 348

WOMEN

65

351 353

Demographic Factors Income and Job-Incentive Effects Other income, 355 —Earnings, 358 —Schooling, 359 —Unemployment, 360 —Measures of supply and occupational mix, 361 Other Factors Hours of work, 364 —Health, 367 —Asset holdings, 368 —Income aspirations, 370 —Compulsory retirement, 371 Summary MARRIED

335

11

Trends in the Participation of Older Persons MALES

323 325

10

Older Persons: Labor Market Conditions

CHAPTER

309

AND

OVER

353 355

363

373 375

Previous Work Experience, 376 —The Participation of Husbands, 376 —Schooling, 377 —Health and the Taste for Leisure, 377 —Income Aspirations, 378 CHAPTER

12

Younger Persons: Individual Characteristics A Preview YOUNGER

of the Next Three Chapters

PERSONS

IN SCHOOL

Age Income Effects Other family income and the participation of 14-17-year-olds, 386 — Size of family and the participation of 14-17-year-olds, 388 —Other income and the participation of college-age students, 389

380 380 381 382

386

TABLE OF CONTENTS Characteristics of Students and Schools Students living away from home versus other students, 391—Kinds of school, 394 —The student-teacher ratio, 394 Family Characteristics Presence of both parents, 397 —Schooling of the family head, 397 — Occupation of the family head, 398 — Labor force status of the family head, 399 Color ACTIVITY AND ENROLLMENT RATES AND THE PARTICIPATION OF YOUNGER PERSONS NOT IN SCHOOL The Size of the Inactive Group Age Family and Marital Status Schooling Socioeconomic Characteristics of the Family Color

Χΐΐΐ 391

396

401 406 406 408 411 413 415 416

CHAPTER 13

Younger Persons: Labor Market Conditions

419

LABOR FORCE PARTICIPATION RATES OF YOUNGER MALES Some General Findings Unemployment The interaction between enrollment rates and participation rates, 424—Time-series results, 430 Earnings The meaning of the negative relationship, 432 —Addendum, 438 — Differential effects by enrollment status and age, 440 Industry Mix The Supply of Teenage Males ENROLLMENT AND ACTIVITY RATES OF YOUNGER MALES General Patterns of Enrollment Rates and Activity Rates Unemployment Earnings of Teenage Males Industry Mix The Supply of Teenage Males

420 420 423

431

441 443 445 446 449 451 453 453

CHAPTER 14

Younger Males: Trends in Participation, Enrollment and Activity Rates Activity Rates Trends in activity rates: the historical record, 458 — Effects of general demographic and socioeconomic factors, 460 —Effects of labor market factors, 461—Effects of the minimum wage, 463 —Effects of military manpower requirements, 465—The "hippies," 466

458 458

xiv

TABLE OF CONTENTS

Enrollment Rates Participation Rates The historical record, 469 —Changes in the average "quality" of the not-enrolled group, 469 —Changes in the socioeconomic characteris­ tics of the enrolled group, 472 —Other factors affecting the participa­ tion of the enrolled group, 473 PART III: UNEMPLOYMENT AND LABOR FORCE PARTICIPATION

CHAPTER

15

Unemployment and Labor Force Participation: CrossSectional Relations 479 A Preview of the Next Three Chapters Sensitivity Estimates for Urban and Rural, Nonfarm Groups in 1960 Results for urban groups, 480 —Sensitivity estimates for rural, nonfarm groups, 487

Results for Five Urban Groups: 1940, 1950, and 1960 Estimates of "Hidden Unemployment" in 1960 HU estimates for the urban sector, 491 -HU estimates for the rural, nonfarm sector, 493

Sources of Bias in Cross-Sectional Sensitivity Estimates Seasonal forces, 495 — Migration, 499 — Spurious associations and the choice of labor market predictors, 502

CHAPTER

16

Unemployment and Labor Force Participation: TimeSeries Relations THE BASIC REGRESSION MODEL

The Variables and the Regression Equation Results of Basic Regressions: An Overview SENSITIVITY ESTIMATES FROM THE BASIC MODEL RESULTS OF OTHER TIME-SERIES STUDIES AND IMPLICATIONS OF ALTERNATIVE LABOR MARKET PREDICTORS

Studies by Telia and Dernburg and Strand Implications of Alternative Measures of Labor Market Tight­ ness The structure of the experiments, 516 — Results of the tests, 517 — I s ElP a reliable measure of labor demand?—521 EXPERIMENTS WITH OTHER MODELS AND VARIABLES

Experiments with Simple Lags and Distributed Lags Experiments with Supplementary Variables

TABLE OF CONTENTS CHAPTER

XV

17

Unemployment and Labor Force Participation: Some Evidence from the 1963-67 Boom 530 Estimation Procedures and Results InterpretationofResults

531 533

APPENDICES

Chapter Appendices APPENDICES TO CHAPTER 1

I-A I-B 1-C

Patterns of Participation: A Descriptive Survey 541 Labor Force Parts of Current Population Survey Form Used Prior to January 1967 566 Labor Force Parts of Monthly Labor Survey Form Used in November 1966 and Incorporated into the Current Popu­ lation Survey in January 1967 567 2 A Simple Model of the Allocation of the Time of Members of a Household among Work in the Market, Work at Home, and Leisure 569

APPENDIX TO CHAPTER

2-A

APPENDIX TO CHAPTER

5

Tables 5-A through 5-H

571

7 Derivations and Sources of Measures of Changes Between 1948 and 1965 in the Variables to Which Cross-Sectional Coefficients Are Applied in Table 7-2 Tables 7-A through 7-C

583 585

APPENDIX TO CHAPTER

7-A

APPENDIX TO CHAPTER 8

Tables 8-A through 8-D

589

13 Labor Force Participation Rates of Younger Never-Married Women 592 Tables 13-A through 13-E 594 APPENDIX TO CHAPTER

13-A

APPENDIX TO CHAPTER 14 Tables 14-A through 14-D

16 A Review of the Time-Series Studies by Telia and by Dernburg and Strand

605

APPENDIX TO CHAPTER

16-A

APPENDIX TO CHAPTER

Table 17-A

17

609

XVl

TABLE OF CONTENTS

General Appendices List of General Appendix Tables

631

APPENDIX A

Regression Analysis of the 1/1000 Sample of the 1960 Census.

641

APPENDIX B

Intercity Regressions.

772

APPENDIX C

Interstate Regressions.

856

APPENDIX D

Time-Series Regressions.

861

Index

889

List of Tables Table 3-1: Table 3-2:

Table 3-3:

Table 3-4: Table 3-5:

Table 3-6: Table 3-7: Table 3-8:

Table 4-1:

Table 4-2:

Table 4-3:

Table 5-1: Table 5-2:

Table 5-3:

Table 5-4:

Marital Status and Labor Force Participation: PrimeAge Males in Urban Areas, Census Week of 1960 Labor Force Participation Rates, by Age and Detailed Marital Status Categories: Prime-Age Males (Total and Nonwhite), Census Week of 1960 Color and Labor Force Participation: Prime-Age Males (Total and "Single") in Urban Areas, Census Week of 1960 Labor Force Participation Rates, by Color: Males 35-44, Census Weeks of 1940, 1950, & 1960 Schooling Attainment and Labor Force Participation: Prime-Age Males (Total, "Single," and Negro) in Urban Areas, Census Week of 1960 Schooling and Labor Force Participation: Males 3544, Census Weeks of 1940, 1950, and 1960 Health, Education, and Labor Force Participation: Males 45-64, July 1961-June 1963 Other Income and Labor Force Participation: PrimeAge Males (Total, "Single," and Negro) in Urban Areas, Census Week of 1960 Multiple Regression Analysis of Intercity Differences in Laqor Force Participation Rates of Males 25-54, Census Week of 1960 Effects of Labor Market Conditions on Labor Force Participation Rates of Males 25-34, 35-44, and 4554: Intercity Regressions, Census Week of 1960 Effects of Labor Market Conditions on Labor Force Participation Rates of Prime-Age Males: Intercity Regressions, Census Weeks of 1960, 1950, 1940

41

45

51 53

55 60 62

68

77

81

84

Color and Labor Force Participation: Married Women 14-54 in Urban Areas, Census Week of 1960 90 Children and Labor Force Participation: Married Women 14-54 (Total and Negro) in Urban Areas, 97 Census Week of 1960 Labor Force Participation Rates, Hours Worked, and Full-Time-Equivalent Participation Rates: Married Women 14-54 in Urban Areas (by Presence of Children of Various Ages), Census Week of 1960 101 Age and Labor Force Participation: Married Women

XVlll

Table 5-5:

Table 5-6:

Table 5-7:

Table 5-8:

Table 5-9:

Table 5-10:

Table 6-1:

Table 6-2:

Table 6-3:

Table 6-4:

Table 6-5:

Table 7-1:

LIST OF TABLES

14-54 in Urban Areas (Total, Negro, and by Pres­ ence and Absence of Children Under 6), Census Week of 1960 Schooling and Labor Force Participation: Married Women 14-54 in Urban Areas (Total, Negro, and by Presence and Absence of Children Under 6), Census Week of 1960 Relative Importance of Wage and Non-Wage Factors Underlying the Association Between Schooling and the Labor Force Participation of Married Women 14-54, Census Week of 1960 Occupation and Labor Force Participation: Urban Married Women 30-54 with an "Occupation," Cen­ sus Week of 1960 Other Family Income and Labor Force Participa­ tion: Married Women 14-54 in Urban Areas (Total, Negro, and by Presence and Absence of Children Under 6), Census Week of 1960 Effects of Other Family Income on Labor Force Par­ ticipation Rates of Married Women 14-54, at Differ­ ent Levels of Other Family Income, Census Week of 1960 Employment Status of Husband and Labor Force Participation: Married Women 14-54 in Urban Areas (Total, Negro, and by Presence and Absence of Children Under 6), Census Week of 1960 Multiple Regression Analysis of Intercity Differ­ ences in Labor Force Participation Rates of All Married Women 14-54, Census Week of 1960 Effects of Labor Market Conditions on Labor Force Participation Rates of Married Women, by Presence and Absence of Children Under 6: Intercity Regres­ sions, Census Week of 1960 Effects of Labor Market Conditions on Labor Force Participation Rates of Married Women 14-54, by Age; Intercity Regressions, Census Week of 1960 Estimates of the Labor Force Sensitivity of Females 25-54 Based on Time-Series Regressions for the Postwar Period and Intercity Regressions for 1960 Effects of Labor Market Conditions on Labor Force Participation Rates of Married Women: Intercity Regressions, Census Weeks of 1960, 1950, and 1940 Summary of Effects of Demographic Factors on Changes in Labor Force Participation Rates of

109

116

119

128

133

142

150

161

163

164

188 191

LIST OF TABLES

Table 7-2:

Table 7-3:

Table 7-4:

Table 7-5:

Table 7-6:

Table 8-1:

Table 8-2: Table 8-3:

Table 8-4: Table 8-5:

Table 8-6:

Table 8-7:

Married Women 14-64, 1948 to 1965 and 1940 to 1950 Predicted Change in the Labor Force Participation Rate of Married Women 14-54, Between 1948 and 1965, Using the Income and Job-Incentive Coeffi­ cients from the 1960 Intercity Regression Predicted Change in the Labor Force Participation Rate of Married Women 14-54, Between 1948 and 1965, Using the Income and Job-Incentive Coeffi­ cients from the 1960 Intercity Regression with Wages of Domestics Dropped Predicted Change in the Labor Force Participation Rate of Married Women 14 and Over, Between 1948 and 1955 and Between 1955 and 1965, Using the Income and Job-Incentive Coefficients from 1950 and 1960 Intercity "Comparability" Regressions Predicted Change in the Labor Force Participation Rate of Married Women 14 and Over, Between 1940 and 1950, Using Income and Job-Incentive Coeffi­ cients from 1950 and 1940 Intercity "Comparabil­ ity" Regressions Domestic Service, Kitchen and Other Household Appliances, and Total Personal Consumption: Price Changes and Units Purchased, 1940 to 1965 Marital Status and Labor Force Participation: Single Women 25-54 in Urban Areas, Census Week of 1960 Color and Labor Force Participation: Single Women 25-54 in Urban Areas, Census Week of 1960 Schooling and Labor Force Participation: Single Women 25-54 in Urban Areas, Census Week of 1960 Schooling and Labor Force Participation (Unad­ justed): Single Women 25-54 with No Children Under 6 Years Old, by Color, Census Week of 1960 Other Income and Labor Force Participation: Single Women 25-54 in Urban Areas, Census Week of 1960 Eifects of Labor Market Conditions on Labor Force Participation Rates of Three Groups of Single Women: Intercity Regressions, Census Week of 1960 Participation Rates for Single Women 25-54, by Marital Status and Age: Census Weeks of 1940, 1950, and 1960

XlX

206

208

218

220

223

232

243 251

255 258

261

265

268

xx

LIST OF TABLES

Table 9-1: Table 9-2: Table 9-3: Table 9-4: Table 9-5:

Table 9-6:

Table 9-7: Table 9-8:

Table

9~9:

Table 9-10:

Table 9-11:

Table 9-12:

Table 9-13:

Table 9-14:

Table 10-1:

Table 10-2:

Age and Labor Force Participation of Older Persons in Urban Areas, Census Week of 1960 Marital Status and Labor Force Participation: Older Persons in Urban Areas, Census Week of 1960 Color and Labor Force Participation: Older Persons in Urban Areas, Census Week of 1960 Schooling and Labor Force Participation: Older Persons in Urban Areas, Census Week of 1960 Schooling and Labor Force Participation: Selected Groups of Older Persons, by Color, All U.S., Census Week of 1960 Percentage of Older Workers in "More Desirable" Occupations, by Age, Sex, Color, and Years of Schooling: Total U.S., Census Week of 1960 Health, Education, and Labor Force Participation: Males 65 Years Old and Over,July 1961-June 1963 Effects of Dropping Persons "Unable to Work" from Population Base on Labor Force Participation Rates of Older Persons in Urban Areas, by Age, Sex, Marital Status, and Color: Census Week of 1950 Other Income and Labor Force Participation: Older Males and Older Single Women in Urban Areas, Census Week of 1960 Other Family Income and Labor Force Participation: Older Married Women in Urban Areas, Census Week of 1960 Effects of Other Family Income on Labor Force Participation Rates of Married Women 55-64, at Different Levels of Other Family Income, Census Week of 1960 Employment Status of Husband and Labor Force Participation: Older Married Women in Urban Areas, Census Week of 1960 Occupation and Labor Force Participation: Urban Males 55-64 and 65-74 with an "Occupation," Census Week of 1960 Self-Employment and Labor Force Participation: Urban Males 55-64 and 65-74 with an "Occupation," Census Week of 1960

274 287 291 297

302

303 305

308

310

311

322

324

328

334

Effects of Labor Market Conditions on Participation Rates of Older Persons: Intercity Regressions, 336 Census Week of 1960 Effects of Labor Market Conditions on Participation Rates of Older Males, by Age and Marital Status: 345 Intercity Regressions, Census Week of 1960

LIST OF TABLES

Table 10-3:

Table 10-4:

Effects of Labor Market Conditions on Labor Force Participation Rates of Males 65 and Over: Inter­ city Regressions, Census Weeks of 1960,1950,1940 Estimates of the Labor Force Sensitivity of Older Persons Based on Time-Series Regressions for the Postwar Period and Intercity Regressions for 1960

Labor Force Participation Rates of Older Persons, 1940-1965 Table 11-2: Summary of Effects of Demographic Factors on Changes in Labor Force Participation Rates of Males 65 and Over and Married Women 65 and Over, Husband Present, 1948 to 1965 and 1940 to 1950 Table 11-3: Predicted Change in the Labor Force Participation Rate of Males 65 and Over Between 1948 and 1965, Using the Income and Job-Incentive Coefficients from the 1960 Intercity and 1/1000 Sample Re­ gressions Table 11-4: Summary of Changes (in Percentage Points) Between 1948 and 1965 in the Labor Force Participation Rate of Males 65 and Over Attributed to Various Factors

xxi

348

350

Table 11-1:

Table 12-1:

Table 12-2:

Table 12-3:

Table 12-4:

Table 12-5:

Table 12-6:

Table 12-7:

Table 12-8:

Age and Labor Force Participation: Younger Per­ sons Enrolled in School in Urban Areas, Census Week of 1960 Age, Participation, and Hours of Work: Younger Persons Enrolled in School in Urban Areas, Census Week of 1960 Other Family Income and LaborForce Participation: Males 14-17 in School in Urban Areas, Census Week of 1960 Family Size and Labor Force Participation: Younger Teenagers in School in Urban Areas, Census Week of 1960 Other Income and Labor Force Participation: Per­ sons 18-24 in School in Urban Areas, Census Week of 1960 Marital and Family Status and Labor Force Partici­ pation: 18-24-Year-Old Persons in School in Urban Areas, Census Week of 1960 Kind of School Attended and Labor Force Partici­ pation: Persons 18-24 in School in Urban Areas, Census Week of 1960 Family Status and Labor Force Participation:

351

354

364

374

383

385

387

388 390

393 395

XXll

Table 12-9:

Table 12-10:

Table 12-11:

Table 12-12:

Table 12-13:

Table 12-14:

Table 13-1:

Table 13-2:

Table 13-3:

Table 13-4:

Table 13-5:

Table 13-6:

Table 13-7:

Table 13-8:

LIST OF TABLES

Younger Teenagers in School in Urban Areas, Cen­ sus Week of 1960 Labor Force Status of Family Head and Labor Force Participation: Younger Persons in School in Urban Areas, Census Week of 1960 Color and Labor Force Participation: Younger Per­ sons Enrolled in School in Urban Areas, Census Week of 1960 Age, Enrollment, Labor Force Participation, and Activity Rates: Younger Persons in Urban Areas, Census Week of 1960 Family and Marital Status, Enrollment, Labor Force Participation, and Activity Rates: Younger Persons in Urban Areas, Census Week of 1960 Schooling and Labor Force Participation: Younger Persons Not Enrolled in School, in Urban Areas, Census Week of 1960 Color, Enrollment, Labor Force Participation, and Activity Rates: Younger Persons in Urban Areas, Census Week of 1960 Effects of Labor Market Conditions on the Participa­ tion Rates of Males 16-24 Enrolled in School: Inter­ city Regressions, Census Week of 1960 Effects of Labor Market Conditions on the Partici­ pation Rates of Males 16-24 Not Enrolled in School: Intercity Regressions, Census Week of 1960 Overall Results of Intercity Regressions Predicting Enrollment and Activity Rates of Males 16-24, Cen­ sus Week of 1960 Effects of Unemployment on School Enrollment, Participation, and Activity Rates of Males 16-24: Intercity Regressions, Census Week of 1960 Effects of Earnings of Teenage Males on School En­ rollment, Participation, and Activity Rates of Males 16-24: Intercity Regressions, Census Week of 1960 Effects of Male Industry Mix on School Enrollment, Participation, and Activity Rates it might seem likely that the effects of labor market conditions on the participation rate of prime-age males which our theorizing leads us to expect would be too weak to be statistically significant. This was our suspicion, but it turned out to be unfounded, as the regression results reported in Table 4-1 demonstrate.2 1 We have also invested a great deal of effort in time-series analysis of the relation be­ tween economy-wide unemployment rates and labor force participation rates, using sea­ sonally adjusted, quarterly data for the period 1948-1965 (and for sub-periods). This analysis is useful primarily in telling us about the short-run sensitivity of participation rates to cyclical changes in labor market conditions and thus is discussed most conveniently in Part III, where we bring together all of our findings concerning the relationships between labor market conditions and participation rates and discuss some of the differences between our cross-sectional and time-series findings. For reasons explained in Part III (especially Chapter 16), we have less confidence in our time-series analysis than in our cross-sectional work. Nevertheless, we do discuss our time-series results for prime-age males in the last section of this chapter, and we also call attention to time-series results for other population groups when they are directly relevant"to a point under discussion. We do not, however, summarize all of the time-series results for each group in these chapters because this would require more space (and more patience on the part of the reader) than it would be worth. 2 Since our concern in this section is solely with the effects of labor market conditions on participation rates, we shall not comment at any length on the variables other than unem-

76

PRIME-AGE MALES

The evidence presented in this table is strikingly consistent with the proposition that labor force participation rates of prime-age males are significantly influenced by local labor market conditions. The regression coefficients for unemployment, industry mix, and earnings all have the expected signs, and all are easily significant at the 1 percent level. To­ gether with the control variables, these three measures of labor market conditions account for over 60 percent of the variance in the participation rates of prime-age males among large metropolitan areas in 1960. We interpret the overall unemployment rate3 in the metropolitan area as a rather straightforward measure of the probability that a potential labor force entrant will be able to find a job in the area within a given period of time. Thus, the highly significant negative association between the unemployment rate and the participation of prime-age males suggests that high unemployment does indeed discourage some men from looking for work. In quantitative terms, the net regression coefficient of—0.31 (Regression n, Table 4-1) tells us that a metropolitan area "typical" in terms of the other variables, but with an unemployment rate one percent­ age point above the all-SMSA average, could have been expected to have a labor force participation rate for prime-age males about three-tenths of a percentage point below the all-SMSA average. Given the overall unemployment rate, the representative prime-age male should have an easier time finding a job if he lives in an area which ployment, industry mix, and earnings which were included in this pair of regressions. The coefficients, standard errors, and /-values of the five other variables included in Regression ι are shown in the table so that the reader will have a complete picture of what other factors were taken into account. It is perhaps worth repeating that four of these variables — other income, schooling, color, and marital status —are best viewed as characteristics of individuals; therefore, their effects on labor force participation rates were explored by using the 1/1000 Sample. The main reason for including these variables in the intercity regressions was to control for the possibility that substantial differences among metropolitan areas in these respects might distort our estimates of the effects of labor market conditions on par­ ticipation. Thus, the values of the coefficients of these control variables are not of great intrinsic interest, and it is neither surprising nor worrisome that one of these variables which was strongly related to the participation of prime-age males in the 1/1000 analysis (marital status) fails to be significantly related to LM25 -54 in the intercity regression. It is, if anything, more surprising that the other three control variables all behave so much in accord with our a priori expectations and our 1/1000 results. The last variable listed on Table 4-1, migration, plays a special role in this analysis, which will be described later in this chapter. 3 The reasons for using the overall unemployment rate rather than the age-sex-specific rate are discussed in general in Chapter 1, since the same issue applies to all of our popula­ tion groups. In brief, we use the overall rate because: (1) it permits us to take account of the possibilities of substitution among labor force groups; (2) we feel that we can analyze the effects of labor market conditions peculiar to particular population groups more success­ fully by including other variables (industry mix and male earnings in this case) in our multiple regression analysis than by using the age-sex-specific unemployment rate, which has the disadvantage of reflecting "feedback" relations from labor force decisions to un­ employment rates so strongly that serious problems of interpretation can arise; (3) from the standpoint of interpreting the significance of our findings for economic policy, it is con­ venient to have direct measures of the associations between the overall unemployment rate and the labor force participation rate for each population group.

TABLE 4-1 Multiple Regression Analysis of Intercity DiflFerences in Labor Force Participation Rates of Males 25-54, Census Week of 1960 Regression ι Independent Variables

b

(s)

Regression π a t

b

(s)

t

LABOR MARKET VARIABLES:

Unemployment (%) Industry mix, male (#) Earnings ($100/yr.)

-0.32 (0.06) 5.41 ** 0.20 (0.05) 4.20 ** 0.05 (0.02) 2.34 *

-0.31 (0.06) 5.41 ** 0.18 (0.04) 4.23 ** 0.06 (0.02) 2.79 **

-0.36 0.26 -0.03 -0.05 0.01

-0.29 (0.09) 3.05 ** 0.24 (0.11) 2.23 * -0.03 (0.01) 3.01 **

CONTROL VARIABLES:

Other income ($100/yr.) Schooling (years) Color (% nonwhite) Marital status (% married) Migration (#)

(0.13) (0.12) (0.01) (0.04) (0.02)

2.72 ** 2.24* 3.27 ** 1.30 0.53

OTHER DATA

Dependent variable (L -S ): Mean Standard deviation Numberofobservations R2 M25

4

96.4 1.2 100 .62**

96.4 1.2 100 .61

Source: 1960 Census. See Appendix B for an overall discussion of the intercity regression analysis. For ease of reference, this table is reproduced there as Appendix Table B-1, along with similar tables for other population groups. Notation: Units of measurement are shown in parentheses following variables. A # means that the unit cannot be readily abbreviated; see definitions of variables below. b = net (partial) regression coefficient. (s) = standard error of the regression coefficient. t = /-value of the regression coefficient (bis)·, (-values may differ from bis ratios in table owing to rounding. ** Significant at the 1 percent level. * Significant at the 5 percent level. t Significant at the 10 percent level. Definitions of variables: LM25-54: percentage of males aged 25 to 54 years in the civilian, noninstitutional popula­ tion who were in the civilian labor force during the census week. Unemployment: percentage of the civilian labor force unemployed during the census week. Industry mix: a measure of the percentage of jobs in each SMSA which we might expect to be held by men; based on the industry mix of the SMSA, as explained in detail in Appendix B. Earnings: median income in 1959 of all males who worked 50-52 weeks. Other income: mean income from nonemployment sources in 1959 per recipient of any kind of income. Schooling: median number of years of school completed by all males aged 25 years and older. Color: percentage of all persons in households who were nonwhite. Marital status: percentage of all males aged 25 to 54 years in the civilian, noninstitutional population who were married with wife present. Migration: net migration (+ = in, — = out) of all males aged 30 to 54 years between 1955 and the 1960 census week, divided by the total population of the group in the 1960 census week. (Members and former members of the Armed Forces and inmates of institutions are included in both numerator and denominator.) a Includes only these variables significant at the 10 percent level (or better) in regres­ sion I.

78

PRIME-AGE MALES

has an industry mix conducive to male employment (such as Birmingham) than if he lives in an area having an above-average proportion of clerical and other jobs customarily filled by women (such as Washington, D.C.). In essence, the industry-mix variable used in the regression reported in Table 4-1 is a measure of what the ratio of male to total employment in the SMSA would have been in the census week of 1960 had this ratio depended solely on the industry mix of the SMSA and the ratio of male to total employment in each industry for the country as a whole.4 The net regression coefficient of 0.18 for this variable is to be inter­ preted as follows: in 1960, each one percentage point difference among otherwise similar SMSA'S in their expected ratios of male to total employ­ ment was accompanied, on the average, by a difference of about twotenths of a percentage point in LM25-54· The statistical power of this variable has certainly convinced us that the job outlook for primeage males —and thus their participation rate—does depend on condi­ tions in their own sub-market, as well as on general labor market condi­ tions.5 Whereas the unemployment and industry-mix variables serve as in­ dicators of the odds that a prime-age male will find a job, the earnings variable 6 presumably measures what a man could expect to earn in his 4 For a detailed explanation of calculation procedures, see Appendix B. Calculatingthese predicted employment ratios for each of our 100 SMSA'S was an extremely laborious process, and it would have been much easier to use the actual employment ratios. Using the actual ratios would have been improper, however, because one of the determinants of the actual ratio of male to total employment in an SMSA is the labor force participation rate of primeage males —which is, of course, what we are trying to explain. 5 As our earlier published paper on intercity differences in participation rates indicates ("Labor Force Participation and Unemployment," in A. M. Ross, ed., Employment Policy and the Labor Market, University of California Press, 1965, pp. 115-116), we needed to be convinced. No industry mix variable was included in the regressions for prime-age males reported in that paper, although a precisely equivalent industry mix variable was used successfully in the regressions for married women. (By definition, for each SMSA the ex­ pected ratio of male employment to total employment equals one minus the expected ratio of female employment to total employment.) It is thanks to the stimulating comments of colleagues and students on a preliminary version of the present chapter that we finally included the industry-mix variable in our analysis of the prime-age male population group. Thinking back on our failure to include this variable —whose values, we had already implicitly calculated! —in our original work, the only explanation we can offer is that we were so pessimistic about our chances of getting any measure of labor market conditions to succeed in explaining intercity differences in LM25_M that we never contemplated trying anything but the overall unemployment rate. The moral of the story is obvious — let data, not vague feelings of optimism or pessimism, test hypotheses. Including the industry-mix variable was not only a good thing in its own right, it also helped the schooling variable take on its expected sign, increased the /-value of the unem­ ployment coefficient, reduced the coefficient of the earnings variable to a more believable level, and raised the R2 for the multiple regression as a whole from .53 to .61. β Unfortunately, what we are really measuring here is not just earnings from employ­ ment, but the median total income in the preceding year of all males 14 years of age and older who worked 50-52 weeks. The inclusion of some nonlabor income is not very serious, however, because employment income is such a large proportion of the total (well over 90 percent), because we use a median, which will presumably be less affected by nonlabor in­ come than a mean (assuming that the distribution of nonlabor income among men 25-54

LABOR MARKET CONDITIONS

79

locality if he were employed. Though also significant at the 1 percent level, the earnings variable has the smallest /-value of our three indices of labor market conditions. This ordering is consistent with the notion that prime-age males are expected to work almost regardless of the wage and that their labor force status is apt to be more strongly influenced by the availability of jobs than by the expected wage rate. Indeed, in the context of the prime-age male group, it may be best to view the earnings variable simply as another measure of job opportunities for men—as a measure which supplements the information provided by the unemployment and industry-mix variables. The industry-mix variable is not designed to reflect short-run changes in job opportunities for men, and the unemployment rate is something less than a perfectly sensitive barometer of local labor market conditions, especially when it has already reached a rather low level. In such circumstances, in particular, differ­ ences among areas in the tightness of labor market conditions may be reflected at least as fully in differential levels of earnings as in differential unemployment rates. In interpreting associations between labor force participation rates and indices of labor market conditions, it is important to be aware of the possible influence of migration. As Mincer has pointed out, "migration to better job opportunities is an alternative to dropping out of the labor force. . . ."7 Thus, if the effects of migration are not somehow im­ pounded, a negative regression coefficient for the unemployment variable could reflect, in part at least, the selective character of migration—the tendency for the abler, more aggressive members of a population group to move from relatively depressed metropolitan areas to areas having better job prospects, leaving behind in the depressed areas groups of men with weaker propensities for labor force participation. It was in an effort to test the quantitative importance of this hypothesis — and to control for its influence—that we included in the prime-age male regressions reported here a migration variable, defined as the net number of males 30-54 years of age who migrated into each SMSA between 1955 years old is more skewed toward the higher incomes than the distribution of income from employment) and because we do have a separate variable in the multiple regression meas­ uring "other income," which can be expected to pick up most of the effect of nonlabor in­ come on labor force participation. In our "comparability regressions" for 1940 (discussed below), we use the median wage or salary income in 1939 of all males 14 years of age and older who received at least $100 of such income. This measure has the advantage of being restricted to earnings from employment; it has, however, the disadvantage of including the earnings of men who worked only part-time and thus is Eiffected by the number of weeks worked as well as by rates of pay per week. 7See his article in Prosperity and Unemployment, 1966, pp. 79-81. Mincer was par­ ticularly interested in migration as a factor which might reconcile some of the differences between cross-sectional and time-series estimates of the sensitivity of labor force participa­ tion rates to labor market conditions; all our results germane to this question are sum­ marized in Chapter 16. We were derelict in not discussing} migration in our earlier paper on "Labor Force Participation and Unemployment," and we are indebted to both Mincer and Lee Hansen for calling this omission to our attention.

80

PRIME-AGE MALES

and 1960, expressed as a percentage of all males 30-54 who resided in the SMSA in I960.8 As can be seen from the last line of Table 4-1, this migration variable was insignificantly different from zero.9 Thus, in the absence of contrary evidence, we interpret the highly significant coefficients for all three indices of labor market conditions as a straightforward indication that prime-age males have withdrawn from the labor force in metropolitan areas where job opportunities have been relatively scarce.

Results for Urban Subgroups in 1960 Within the prime-age male group, we might expect the labor force status of some groups of men to be more sensitive to local labor market conditions than that of others. Presumably those groups who are likely to be near the end of any hiring queue will have their employment pros­ pects affected most adversely by a general easing of labor market pres­ sures, and such groups are therefore most likely to be discouraged from even seeking work when job opportunities in general are scarce. As one test of this hypothesis, we subdivided the prime-age males into three age groups (25-34, 35-44, 45-54) and ran separate intercity re­ gressions for each group. After a person is past his early thirties, his chances of finding a job once he is out of work decline markedly with his age, since employers generally prefer to hire younger workers; for this reason, we thought that the participation rates of men 45-54 would be more sensitive to differences among metropolitan areas in labor market conditions than the participation rates of men in the younger age groups. The results of this analysis of the interactions between age and labor market conditions (summarized in Table 4-2) support these expectations. Only the industry-mix variable has more of an impact on the participa­ tion rates of the younger prime-age males than on the participation rates of the two older subgroups. The coefficient of the earnings variable is insignificantly different from zero in the case of men 25-34, +0.10 (i = 3.45 **) in the case of men 35-44, and +0.16 (t = 4.36 **) in the case of men 45-54. The coefficient of the unemployment variable is neg­ ative and significant at the 1 percent level for all three groups, but both the coefficient and its f-value are appreciably larger in the regression for 8 The reason for taking the age group 30-54 rather than 25-54 was to guard against the possibility that geographical movements of men enrolled in school might distort the measure. 9 Professor Rees has suggested one defect in the basic hypothesis which may explain, in part, why the migration variable failed to have any discernible impact. His point is that recent migrants may require some time to get settled in a new area, and that while they are finding a place to live they may not be looking for work. Thus a large number of new migrants could depress rather than raise an area's labor force participation rate if this transitional nonparticipation effect outweighed the selective-character-of-migration effect noted above. A more general explanation for the poor performance of the migration variables may be that migration is simply less oriented to overall labor market conditions that we sometimes assume. Among the 100 largest SMSA'S in 1960, the simple correlation between unemploy­ ment and the net migration variable was only —.07.

81

LABOR MARKET CONDITIONS

TABLE 4-2 Effects of Labor Market Conditions on Labor Force Participation Rates of Males 25-34, 35-44, and 45-54; Intercity Regressions, Census Week of 1960 Males 25-34 Variables measuring labor market conditions

a

Unemployment (%) Industry mix, male (#) Earnings ($ 100/yr.)

b

(s)

Males 35--44 t

-0.23 (0.12) 2.78** 0.46 (0.07) 6.29 ** Not significant at 10%

Males 45--54

b

(s)

t

b

W

t

-0.35 0.20 0.10

(0.09) (0.06) (0.03)

4.03 ** 3.29 ** 3.45 **

-0.53 0.26 0.16

(0.11) (0.08) (0.04)

4.68 ** 3.17 ** 4.36 **

OTHER DATA

Dependent variable (L1): Mean Standard deviation Number of observations R2 for multiple regression

95.4 2.0 100 .29 **

96.1 1.6 100 .45 **

93.9 2.2 100 .50 **

Source: The complete multiple regression equations from which these results have been taken are presented in Appendix Tables B-2, B-3, and B-4 (regression Ii in each case). Notation and definition of variables: See notes to Table 4-1. a The other explanatory variables included in these regressions (in addition to unemployment, industry mix, and earnings) are the same as the variables included in the regression for all males 25-54 summarized in Table 4-1.

the group 45-54 years old than in the regressions for the two younger groups. In interpreting this overall pattern of results, it should be rec­ ognized that it is the unemployment and earnings variables which are likely to reflect most accurately the "current" (including the recent past) state of the local labor market; the industry mix of an area presumably changes more slowly and in response to considerations of a longer-run character.10 From the standpoint of analyzing differential responses of prime-age males to labor market conditions, another interesting way of breaking up the group is according to marital status. One of the principal findings in Chapter 3 was that prime-age males who are married have a much stronger propensity for labor force participation than prime-age males who are single. We concluded that the lower participation rate of the single men was attributable to a combination of lesser family responsi­ bilities and personal traits which inhibit both marriage and labor force participation. Both these explanations suggest that the labor force par­ ticipation rates of single males should be more sensitive to differences among metropolitan areas in labor market conditions than the participa­ tion rates of married men. Their lesser family responsibilities enable single males to withdraw from the labor force more readily than married men when jobs are scarce; their personal traits make many of them likely to end up near the end of most hiring queues, which in turn means that a 10 It should also be recognized that the greater sensitivity of the older group to labor market conditions may be due in part to a lower level of educational attainment, and not just to employer attitudes toward age per se. Unfortunately, the Census data do not permit us to run separate intercity regressions for persons with different educational attainments, and so we are unable to pin down this conjecture.

82

PRIME-AGE MALES

small difference in the overall unemployment rate may make a dispro­ portionate difference in the degree of difficulty experienced by the typi­ cal single male in finding a job. The data bear out these expectations with a vengeance. In the case of the married men, the coefficient of the unemployment variable is only —0.23 (i = 1.96 t), whereas in the case of the single men the coefficient of the unemployment variable is a whopping 1.16 (i = 3.16 **). More generally, almost none of the inter-SMSA variance in the partici­ pation rates of married men is explained by our regressions. In the run containing ten independent variables, the R2 is .09 and the unemploy­ ment variable is the only one significant at even the 10 percent level. In contrast, 63 percent of the variance in the participation rates of single men is accounted for in the run limited to the significant variables, and the earnings variable is the only usually dependable performer which fails to be significant. One other finding from the regression for single males worth highlight­ ing is the behavior of the marital status variable: the higher the per­ centage of married men in a metropolitan area, the lower the participa­ tion rate of single men. We interpret this highly significant association (t = 4.02 **) as supporting a widely held proposition about employer preferences and the functioning of labor markets —it suggests that em­ ployers prefer married men and that the more married men there are in an area, the harder it is for a single man to find a job.11

Results for Rural, Nonfarm Residents Prime-age males living in rural, nonfarm residences constitute another special group which has had an above-average degree of difficulty finding jobs. We noted in the course of our general survey of patterns of labor force participation in the appendix to Chapter 1 that the participation rates for these men were substantially lower than the rates for other prime-age males, and we suggested that this finding might well be due to more limited job opportunities. The overall unemployment rate among prime-age males classified as belonging to the rural, nonfarm population was 5.1 in 1960, compared with unemployment rates of 4.0 among their counterparts in urban areas and 2.1 among men living on farms.12 An analysis of differences among areas (states, in this case, not SMSA'S) in the participation rates of prime-age males living in rural, nonfarm residences shows that their participation rates were, in fact, much more sensitive to labor market conditions than were the participation rates of prime-age males living in metropolitan areas. The net regression coeffi­ cient for unemployment in the regression for rural, nonfarm areas was 11 For the complete intercity regressions for married men and single men, see Appendix Tables B-5 and B-6, respectively. 12 U.S. Census of Population: I960, Detailed Characteristics, U.S. Summary, Table 194, pp. 487-498.

LABOR MARKET CONDITIONS

83

**),13

—0.68 (t = 4.92 whereas the comparable coefficient in the regres­ sion for metropolitan areas was —0.31 (t = 5.41 **). As in the analysis of metropolitan areas, it is important to consider the possibility that the coefficient for unemployment in the rural, nonfarm regression is picking up mainly the effect of migration. Indeed, it seemed to us reasonable to expect selective migration to be more significant in the rural, nonfarm analysis, given the well-known tendency for persons from depressed rural areas to move to the cities. As a crude test of the migration hypothesis, we added a populationchange variable14 to our rural, nonfarm regression. The results can be stated succinctly. The population-change variable itself was insignifi­ cantly different from zero (/ = 0.37), and the inclusion of this variable had no noticeable effect on the unemployment coefficient—which changed from —0.68 (t = 4.92 **) to —0.67 (t = 4.76 **)! These findings certainly offer no basis for doubting that the association between high unem­ ployment rates and low participation rates for prime-age males in rural, nonfarm areas simply means that a number of prime-age males in such areas have withdrawn from the labor force.

"Comparability" Results for 1940, 1950, and 1960 All the cross-section results reported thus far have pertained to re­ lationships between participation and labor market conditions in 1960, the most recent year for which cross-sectional data are available. In an effort to obtain additional evidence concerning the nature of these rela­ tionships, and more particularly to find out if the sensitivity of labor force participation rates to labor market conditions has changed over time, we also ran "comparability" regressions for the last three decennial census years.15 The full results of these regressions are presented in Appendix Table B-100, and the results for the labor-market variables are summarized in 13The complete multiple regression equation for prime-age males living in rural, non­ farm areas is presented in Appendix Table C-1. The variables other than unemployment in­ cluded in this regression are schooling and color (both significant), and the R2 is .65. In the case of this population group, the unemployment variable was defined as the percentage of the rural nonfarm labor force in the state which was unemployed, since this is the unem­ ployment rate which presumably best reflects relevant labor market conditions. 14 Percentage change in the number of men 25-years-of-age-and-over in the rural, nonfarm population of each state between 1950 and 1960. The values of this variable were calculated from Table 20 in each state volume of U.S. Census of Population: 1950, Vol. II, Character­ istics of the Population: and Table 47 in each state volume, U.S. Census of Population: 1960, General Social and Economic Characteristics. The unavailability of comparable data prevented the use of the somewhat more refined net and gross migration rates used in the analysis of SMSA'S. 15 See Appendix B for a discussion of procedures. In general, we tried to include the same explanatory variables in the intercity regressions for all three census years, so that changes in the net regression coefficient for a particular variable from one census year to another would not be attributable to changes in the roster of variables included in the regression. Because of the enormous amount of computational work involved, we ran comparability regressions only for all males 25-54 in metropolitan areas.

84

PRIME-AGE MALES

TABLE 4-3 Effects of Labor Market Conditions on Labor Force Participation Rates of Prime-Age Males: Intercity Regressions, Census Weeks of 1960, 1950, 1940

Unemployment (%) Industry mix, male (#) Earnings ($100/year)c

b

-0.31 0.18 0.06

1940

1950

I960" Variables measuring labor market conditionsa

CO

t

b

(0

t

b

(S)

t

(0.06) (0.04) (0.02)

5.41 ** 4.23 **

-0.24 0.06 0.18

(0.08) (0.05) (0.05)

2.93 ** 1.08 3.82 **

-0.02 0.09 0.05"

(0.04) (0.03) (0.03)

0.43 2.49 * 1.50

2 79 **

OTHER DATA

Dependent variable (L1): Mean Standard deviation Number of observations R 2 for multiple regression

96.4 1.2 100 .61 "

94.1 1.5 78 51 * *

95.1 1.0 92 .33 **

Source: The complete multiple regression equations from which these results have been taken are presented in Appendix Table B-100. Notation and definition of variables: See notes to Table 4-1. a The other explanatory variables included in these regressions (in addition to unemployment, industry mix, and earnings) are other income, schooling, and color. b The results for 1960 in this table are not exactly the same as the results in Table 4-1 because the multiple regres­ sion equation from which these results were taken included the "comparability" variables listed in note a whether or not they were statistically significant at the 10 percent level; the regression from which the results in Table 4-1 were taken included only significant variables. Nevertheless, the results in the two tables are very similar, which is encouraging in that it suggests that the coefficients of the unemployment, industry-mix, and earnings vari­ ables are rather insensitive to the inclusion or exclusion of these other variables. c Measured in constant (1959) dollars. d The definition of the earnings variable for 1940 differs slightly from the definition for 1950 and 1960. In 1940 it was defined as "median wage or salary income in 1939 of all males who received at least $100 of such income"; in 1950 and 1960 it was defined as "median income in preceding year of all males who worked 50-52 weeks." The pros and cons of these two definitions are discussed in a footnote in the text.

Table 4-3. Though there are some aspects of these regressions which are rather curious (e.g., in 1950 the industry-mix variable loses its signifi­ cance and the earnings variable takes on greater importance —why?), there is no mistaking the general tenor of the findings. Participation rates of prime-age males in metropolitan areas have been much more sensitive to labor market conditions in the postwar census years than they were in 1940. The pattern of the unemployment coefficients is the most striking: differences among metropolitan areas in unemployment rates had somewhat more effect on Lm25-S4 in 1960 than in 1950, but in both years the coefficients were highly significant, whereas in 1940 the unemployment variable had no discernible impact whatsoever. Many factors may have contributed to the nonsignificance of unem­ ployment in 1940. By any criteria, the economic situation was extremely abnormal—marked, as it was, by a very high overall level of unemploy­ ment (11.2 percent in the cities in our analysis, with an additional 3.3 percent reported as being on public emergency work) and by intercity unemployment differentials of uncertain origin and duration. It is cer­ tainly plausible that the discouragement effect associated with an inter­ city difference of one percentage point in the unemployment rate should be smaller when this rate is 11 percent than when it is 5 percent.

LABOR MARKET CONDITIONS

85

It is also possible that the greater sensitivity of the participation of prime-age males to unemployment in more recent years is explainable in part in terms of the growing affluence of our society. Rising income levels may have enabled more prime-age males (especially those having health handicaps) to stay out of the labor force when employment pros­ pects are bleak. Finally, there is the ever-present possibility that changes in Census definitions and data-gathering procedures have also contrib­ uted to these results. We think, however, that the main explanation lies in the large-scale program of public emergency work that was in effect in 1940. This pro­ gram, sponsored by the Work Projects Administration (W.P. A.) and other welfare agencies, was designed to provide temporary employment to persons who had been unemployed for many months and whose financial need was so great as to entitle them to public relief. At the time of the census week of 1940, some 3.4 million persons were on the payrolls of the federal emergency work agencies,16 and perhaps one million more has been certified for W.P.A. work and were waiting assignment to a project.17 (In contrast, the total number of wholly unemployed persons during the census week of 1940 was about 5.1 million, or 9.6 percent of the U.S. labor force.) Furthermore, this program was especially important in the case of prime-age males, inasmuch as "heads of families were very generally given preference in assignments to projects." 18 The very existence of an emergency work program of this magnitude must have affected the relationship between unemployment and Lm25-S4 by preventing many discouraged jobseekers from being reported as out of the labor force. Those persons who were doing public emergency work were automatically classified as in the labor force, and persons who hoped to secure such work in the future were no doubt also classified as in the labor force. To become eligible for public emergency work, a per­ son was required to maintain active registration with the U.S. Employ­ ment Service. Such registration is, of course, one form of job seeking, and therefore individuals who were hoping to be assigned to public emergency work at the time of the census week would have been reported as members of the labor force. For these reasons, we have a strong suspicion that the emergency work programs of the Great Depression largely nullified the discouraged worker effect of high unemployment during the census week of 1940, 16 Due to underreporting of emergency work (vis-a-vis other employment) and a misclassification of some persons who did report such work, only 2.5 million persons were recorded in this category by the Census enumerators. (See p. 8 of the Special Report on Employment and Personal Characteristics, 1940 Census of Population.) "U.S. Work Projects Administration, Final Report on the W.P.A. Program, 1935-43 (Washington, D.C., 1947), p. 30. 18 W.P.A. Final Report, p. 17. The employment statistics from the 1940 Census show the results of this preferential treatment. In all urban areas combined, prime-age males ac­ counted for only 38 percent of total unemployment but for 49 percent of all employment on emergency-work projects. (Calculated from the U.S. Census of Population: 1940, Vol. IV, Characteristics by Age, Part 1, Table 24.)

86

PRIME-AGE MALES

not only for prime-age males, but for most other demographic groups as well. Married women constitute an important exception to this proposi­ tion, for very few of them were eligible for emergency work. And, as we show in Chapter 6, their participation was highly sensitive to unemploy­ ment in 1940. Since the only one of our population groups largely in­ eligible for public emergency work is also the only one whose participa­ tion rate was sensitive to unemployment in 1940, we regard this parallelism as offering considerable support for our interpretation of the role of public emergency work in erasing the sensitivity of prime-age male participation to the unemployment variable in 1940. Time-Series Results Having presented a whole series of results based on cross-sectional regressions, all of which suggest that the labor force participation of prime-age males is sensitive to the tightness of the labor market, we are obliged to report that the results of our time-series regressions support no such conclusion. As the results in Appendix Tables D-I and D-3 indicate, quarterly variations in LM -S4 over the postwar period have borne no statistically significant relationship to the overall rate of unem­ ployment or to an industry-mix variable. Furthermore, we are unable to detect evidence of any induced increase in LM25-54 stemming from the economic expansion of 1963-1967.19 We have no ready explanation for these contradictory results. For reasons explained at length elsewhere in this book (see especially Chapter 16), we have more general confidence in our cross-sectional regressions than in our time-series work. And in the case of prime-age males, our cross-sectional regressions reveal: (1) high /-values for all males 25-54 in both 1950 and 1960, (2) a set of results for subgroups classified according to age and marital status which are entirely consistent with the hypothesis that labor market conditions matter, and (3) evidence of a highly signifi­ cant relationship between unemployment and participation among rural, nonfarm residents in different states. These cross-sectional results do not seem to stem from interarea mi­ gration—at least the migration that took place between 1955 and 1960 — nor can they be attributed to seasonal forces.20 It is of course always pos­ sible that there is some unknown variable which correlates closely with unemployment in cross-sections and which is really responsible for the apparent relationships between unemployment and Lm25-J4. But we have been unable to identify any such variable; nor does it seem likely that the whole set of statistically significant relationships reported in this chapter can be explained away by the unearthing of some variable so mysterious 25

19See Chapter 17 for a detailed discussion of the methods of analysis used to measure induced participation over this period and Appendix Table 17-A for the results for all pop­ ulation groups. 20 See the discussion of this issue in Chapter 15.

LABOR MARKET CONDITIONS

87

that it has not as yet occurred to us or to any of the numerous people who have read drafts of this discussion. At the same time we are also unable to see any solid ground for ques­ tioning the lack of any association between unemployment and the par­ ticipation of prime-age males over time. We know that we lack an adequate set of control variables in our time-series regressions, and per­ haps this deficiency prevents us from detecting what would in any case be a relatively mild sensitivity of L _ 4 to changes in the unemployment rate. But this is only a conjecture, and we see no alternative but to end this chapter by leaving the reader with a conundrum.21 Happily, the prob­ lem of reconciling cross-sectional and time-series relations is not nearly so great in the case of our other major population groups. M25

5

21 The results of an experiment made after this chapter was written have narrowed con­ siderably the chasm between our cross-sectional and "induced-participation" estimates of labor force sensitivity for prime-age males. They also offer at least a glimmer of hope that further research will bring about a more complete reconciliation of these estimates. While examining the data in Employment and Earnings for males 25-54 who were not in the labor force, we were struck by the large increase between 1963 and 1967 in the number who were reported "unable to work" —this subgroup increased from 316,000 to 404,000 (annual averages) over this short period. The reasons for this sharp rise in the incidence of disabil­ ity—assuming the figures can be taken at face value—are not obvious; we note in passing that the increase in question was much larger for males 45-54 than for males 25-44 —and still larger for men 55-64 (about 170,000). At any rate, we decided to recalculate our prime-age male participation rates for 1963 and 1967 dropping those persons in the "unableto-work" category from the population base. When the adjusted rates are substituted for the original ones, our estimate of induced participation for prime-age males between 1963 and 1967 rises from nil (see Appendix Table 17-A) to about 115,000. (We used the same pro­ cedures for estimating induced participation here as set forth in Chapter 17, inasmuch as the increase in reported disability during base period of 1959-1963 was very small —about 30,000.) The estimate of 115,000 —a little less than 0.4 percent of the prime-age male population in 1967 — is exactly one-half the amount of induced participation predicted by our three intercity regressions for prime-age males (230,000), but half a loaf is better than none. Furthermore, were we to use the unemployment coefficient from the 1960 intercity regres­ sion for all males 25-54 (—0.31) instead of a weighted average of the (higher) coefficients from the subset runs for males 25-34, 35-44, and 45-54 (i.e., —0.40), the revised induced participation estimate would be two-thirds as large as the intercity prediction. The results of this experiment provide a good illustration of the pitfalls one is likely to encounter in any analysis of changes in participation over time when controls for demo­ graphic factors are lacking—as they generally are.

CHAPTER 5

Married Women: Individual Characteristics In March of 1967, approximately 16 million married women were in the United States labor force—one out of every five participants was a married woman. And, lest anyone think that these women tend to be mainly part-time workers, it should be noted that roughly three-fourths of them were classified as full-time participants.1 The earnings of these women serve as a rough measure of their direct contribution to the pro­ duction of marketable goods and services, and we estimate that in 1965 they earned a total of approximately 36 billion dollars.2 To say that married women have not always played such an important role in the labor force is an understatement. So much has been written about the extraordinary increases over the last half-century in the labor force participation of married women that no detailed recounting of these changes is necessary here. Still, the basic numbers are sufficiently impres­ sive to merit being mentioned once again: around 1900, the labor force participation rate was about 5 percent; in 1940 it was 15 percent; by 1950 it had risen to 24 percent; and by 1967 it had reached 37 per­ cent.3 Neither the economic nor the social significance of the role currently played by married women in the labor force needs belaboring. In any case, our immediate task is not to evaluate the consequences of the labor force participation of married women, but rather to analyze the factors affecting their labor force status. In organizing this discussion we have followed the pattern established in our analysis of prime-age males. The present chapter is devoted to an analysis of the relationship between the participation of married women at a point in time (usually the census week of 1960) and their individual and household characteristics. Then, in the following chapter, we con­ sider the effects of labor market conditions (including what they have to pay to have others perform their "home tasks") on their participation. Both these chapters are based mainly on our cross-sectional results for 1960, though we do include (in Chapter 6) some discussion of the dif1 This calculation is based on data for persons employed in nonagricultural industries presented in Tables A-20 and A-23 of the Manpower Report of the President, April 1968. Full-time participants include both women working "full-time" (35 hours per week or more) and women who usually work full-time but who worked part-time during the survey week because of economic conditions. The other figures in this paragraph were calculated from Table B-I of the same publication. 2Calculated from Current Population Survey, Consumer Income, Series P-60, No. 51, "Income in 1965 of Families and Persons in the United States," Table 19. 3The estimate for 1900 was made by Long (see the citation in Appendix Table 1-C). The rates for 1940, 1950, and 1967 are from the CPS surveys for March of each year (see Appendix Table 5-A and the 1968 Manpower Report, Table B-2.

INDIVIDUAL CHARACTERISTICS

89

ferential impact of certain labor market variables in 1940, 1950, and 1960, and some discussion of time-series results. Finally, trends in the participation rate of married women over the postwar period are analyzed in Chapter 7. It should also be noted that these three chapters are concerned pri­ marily with the labor force participation rate of married women between the ages of 14 and 54 (sometimes abbreviated LMW14_54). Certain data are available only for all married women 14 and over, or for married women 14-64, and results based on such data are included in these chapters; however, all results pertaining specifically to married women 55 and over are presented as part of the more general discussion of older persons (Chapters 9, 10, and 11). We are now ready to begin our analysis of individual characteristics. As explained elsewhere (especially in Appendix A and the first part of Chapter 3), the basic model we use to analyze the characteristics of in­ dividual households is a linear probability model applied to observations drawn from the 1/1000 Sample of the 1960 Census. Here we are con­ cerned with the probability that a wife 14-54 years old will be in the labor force, and this dependent variable is related, via multiple regres­ sion analysis, to sets of dummy variables representing: (1) color of wife; (2) children living in the household; (3) housing circumstances; (4) age of wife; (5) schooling of wife; (6) occupation of wife; (7) other family income (total family income less earnings of wife); (8) employment status of husband; and (9) occupation of the husband. In the case of married women, we have found it useful to run not only an overall regression for all married women 14-54 years of age, but also separate regressions for three important subgroups: Negro married women 14-54; married women 14-54 with no children under 6 years of age; and married women 14-54 with children under 6. Furthermore, we have run some special regressions, restricted to married women who were employed, in which hours of work is the dependent variable—the objective here being to analyze the effects of the various sets of dummy variables on the degree of participation in the labor force. Finally, we have run some special regressions designed to illuminate the relation­ ships between a woman's occupational work experience and the probabil­ ity that she will be in the labor force. As in Chapter 3, the complete multiple regression equations based on the 1/1000 Sample are presented in Appendix A, while the findings germane to each set of variables are discussed seriatim in the text.

Color The simple association between color and the labor force status of married women can be described succinctly: in every year for which data are available, the participation rate for nonwhite married women has been considerably higher than the rate for white married women. Over

90

MARRIED WOMEN

TABLE 5-1 Color and Labor Force Participation: Married Women 14-54 in Urban Areas, Census Week of 1960 Labor force participation rates

Married women 14-54 by color

Number in sample

Percent of population

Unadjusted

Adjusted3

Whites Negroes Other nonwhites Total F-ratio

19,966 1,885 170 22,021 22.2**

90.7 8.6 0.8 100.0

34.7 47.0 41.8 35.8

35.2 42.0 43.6 35.8

Source: 1/1000 Sample. See Appendix Table A-8. a For the effects of wife's schooling and age, other family income, presence of children in specific age intervals, and employment status of husband. ** Significant at the 1 percent level.

the postwar years, the absolute size of this differential has been remark­ ably constant (in a 10-13 point range).4 Perhaps the best way to start to disentangle the relationships respon­ sible for the association between color and Lmwi4-S4 is by studying this association at one point in time on as much of an other-things-equal basis as possible. Table 5-1, which has the same format as the tables in Chapter 3 based on multiple regression analysis of the 1/1000 Sample, enables us to compare the participation rates of white, Negro, and other nonwhite married women as of the census week of 1960. White-Negro differentials. We are primarily interested in the differ­ ences between white and Negro married women, and we see from Table 5-1 that in the census week of 1960, Negro married women 14-54 had a crude (unadjusted) participation rate 12.3 points higher than the rate for white married women.5 However, about half this differential disappears when we adjust for the effects of the other variables included in the mul­ tiple regression analysis —age and schooling of the wife, children, em­ ployment status of the husband, and other family income. The adjusted participation rate for Negro married women is 6.8 points higher than the adjusted participation rate for white married women, rather than 12.3 points. To explain the compression in this differential which occurs when we take other variables into account, it is necessary to anticipate one finding 4 See Appendix Table 5-B. S. Lebergott (Manpower in Economic Growth, McGraw-Hill, 1964, p. 519) presents figures indicating that in 1890 the participation rate for nonwhite married women was 20 percentage points higher than the participation rate for white married women. 5 In terms of absolute level, all the unadjusted participation rates in Table 5-1 are higher than the rates for 1960 shown in Appendix Table 5-B. The explanation is that we limited the set of observations from the 1/1000 Sample to those married women under 55 years of age in the noninstitutional population who lived in urban areas. Also, we excluded from this analysis married women under 35 years of age who were enrolled in school.

INDIVIDUAL CHARACTERISTICS

91

described in a later section of this chapter: there is a strong inverse as­ sociation between other family income and LMWi4-54· Also, the wellknown tendency for Negro families to be heavily concentrated at the bottom of the income distribution is accentuated when the measure of family income is defined to exclude the earnings of the wife, as it must be here if we are to avoid feedback effects from the wife's employment status to her labor force participation. Cross-tabulations for our sample of married women indicate that 38 percent of Negro families had less than $3,000 of other family income, compared with 10 percent of white families and with 12 percent of other nonwhite families. At the other end of the distribution, only 3 percent of Negro families had more than $9,000 of other family income, compared with 19 percent of white families and the same fraction of other nonwhite families. While it is the other-family-income variable which appears to be doing the bulk of the work in reducing the crude white-Negro differential, the age and employment-status-of-husband variables also do some compress­ ing. On the other hand, taking account of the wife's schooling serves to widen the differential in that the educational attainment of the typical Negro woman is much lower than the educational attainment of her white counterpart; as we shall see later, there is a strong positive association between schooling and LMW14_54. The fact that the participation rate of Negro married women is still almost 7 points higher than the participation rate of white married women after we have allowed for these demographic and economic differences is at least as important as the finding that a comparison of unadjusted participation rates exaggerates the effect of color per se on participation. The ί-value corresponding to the difference in adjusted participation rates between white and Negro women is over 6, which implies that there is a highly significant differential still to be explained. Before having recourse to the fall-back explanation of differences in "tastes" or "attitudes," somehow or other defined, it is important to explore the question of to what extent this difference in participation rates is offset by differences in hours worked by white and Negro women.6 6 One of the major contributions of Glen Cain's study (Married Women in the Labor Force, University of Chicago Press, 1966, especially pp. 80-81) is that he emphasizes the importance of differences in the degree of participation and provides various kinds of evi­ dence to show that Negro women do considerably more part-time work than white women. Cain did not, however, incorporate the hours data from the 1/1000 Sample into his general analysis; consequently, he had no firm basis for estimating how much of the differential was attributable to differences in the degree of participation. (He does offer some rough estimates of the adjusted differential in Appendix D of his book, based on data collected by the Survey Research Center at the University of Michigan.) Hours worked is not, of course, the only measure of the degree of labor force participa­ tion—weeks worked is another, when one is dealing with the amount of labor supplied by an individual. However, when one is explaining the amount of labor supplied by a group (as is the case here), variations in weeks worked, given the participation rate in a typical week, affect only the number of different individuals who participate at some time or other during the course of the year, not the total amount of labor supplied by the group.

92

MARRIED WOMEN

In an effort to analyze this question systematically, we ran a separate multiple regression, confined to the 7,202 married women in our sample who were "at work" during the census week of 1960, in which we re­ lated hours worked to the same sets of independent variables used to explain labor force participation.7 It is the relationship between hours worked and color which is relevant here, and we found, after controlling for the effects of other variables, that Negro married women who were at work during the census week of 1960 worked fewer hours than comparable white married women. The adjusted mean hours worked by Negro married women was 32.9, com­ pared with an adjusted mean of 35.7 for white married women.8 To develop a full explanation for these adjusted differences in hours worked would require another study. Probably the main consideration is the greater relative importance among Negro married women of jobs in domestic service. Hours worked per week tend to be more flexibly determined in this occupation than in most occupations.9 In any event, our immediate concern is with the implications of differ­ ences in hours worked for the total amount of labor supplied by white and Negro married women; having obtained the estimates of adjusted hours worked from a multiple regression comparable (in terms of the sample and the other variables included) to the multiple regression used to obtain the adjusted participation rates, we are able to combine the two sets of results. Specifically, we can calculate a more general measure of labor input, which reflects both the labor-force-participation and hours-worked dimensions of labor supply. This can be done by expressing the adjusted participation rates in terms of some fixed ("full-time") unit of hours worked per week. Thus, if we take 40 hours as a "full-time" work week,10 7Since hours worked, unlike labor force participation, is not a dichotomous variable, some modifications were necessary in our basic regression program. Each respondent in the 1960 Census who worked "last week" was assigned to one of seven categories depending on the number of hours he worked, the categories being: 1-14 hours, 15-29 hours, 30-34 hours, 35-39 hours,'40 hours, 41-48 hours, 49-59 hours, and 60-or-more hours. We took the mid-point of each category as the representative value for that category (except that we arbitrarily assigned the value of 62 to the 60-and-over category) and then treated hours of work as a continuous dependent variable. Thus, this multiple regression equation (presented in full in Appendix Table A-12) shows the number of hours worked by married women as a function of the various sets of independent variables. 8The t-value associated with this differential is 6.1—easily significant at the 1 percent level. (See Appendix Table A-12 for the full matrix of r-values, as well as the regression coefficients.) The differential between the unadjusted means was slightly smaller, the re­ spective means being 33.4 for Negro married women and 35.6 for white married women. This is what one would expect, given the lower other family income of the Negro women and the fact that low other family income tends to increase the number of hours worked by the wife. (Relationships between hours worked and the other independent variables are dis­ cussed in subsequent sections of this chapter, in conjunction with the discussions of the relationships between participation rates and these variables.) 9 For supporting evidence see Table 5-7 below. 10 For present purposes, it makes little diiference what number of hours worked we take as equivalent to "full-time." Substituting some other number for 40 would change the length of our full-time-equivalent measuring rod but would have no effect on the estimate of the relative amounts of labor supplied by white and Negro women.

INDIVIDUAL CHARACTERISTICS

93

we find that out of every 100 white married women 14-54, there were 31.4 full-time-equivalent participants. In the case of Negro married women, on the other hand, out of every 100 having the same character­ istics (other family income, schooling, etc.) as the white women, there were 34.5 full-time-equivalent participants.11 The substantive conclusions to emerge from this analysis can be sum­ marized as follows: The unadjusted difference between the participation rates of Negro and white married women of 12.3 points (47.0 — 34.7) exaggerates greatly the effect of color per se on the amount of labor supplied. Taking account of differences in participation rates attributable to dif­ ferences between the two groups of married women in other family in­ come, schooling, children, age, and employment status of husbands re­ duces the differential to 6.8 points (42.0 — 35.2). Taking account of the tendency for Negro married women to work shorter hours than a comparable group of white women brings about a further reduction in the gap between the Negro and the white participa­ tion rates (now measured in terms of full-time-equivalent participants per week), the final differential being 3.1 points (34.5 — 31.4). To state these results another way, while the typical Negro married woman did allocate a larger proportion of her time to labor market pur­ suits during the census week of 1960 than a comparable white woman, the Negro woman did not allocate 35 percent more time to such pursuits than her white counterpart—this being the size of the differential sug­ gested by a comparison of the unadjusted participation rates (47.0/34.7 = 1.35). According to our analysis, the amount of market labor supplied by the typical Negro married woman in the census week of 1960 ex­ ceeded the amount supplied by the comparable white woman by 10 per­ cent (34.5/31.4 = 1.10), not 35 percent. What accounts for this remaining differential of 10 percent? We shall mention three possible explanations (all suggested by Cain) without, at this point, attempting any assessment of the quantitative importance of each. One factor is the lesser degree of job discrimination against Negro women than Negro men, reflected in a higher ratio of wives' earnings to husbands' earnings for Negroes than whites, which serves to encourage a Negro household to make some substitution of market work on the part of the wife for market work on the part of the husband. A second factor is differences among Negro and white families in housing patterns, with the 11 These numbers were obtained by multiplying the respective adjusted participation rates by the respective ratios of adjusted hours worked to 40 hours. For example, the figure of 31.4 for white married women was obtained by multiplying the adjusted participa­ tion rate of 35.2 by 35.7/40.0. It must be emphasized that these full-time equivalent figures are "adjusted," in that they are estimates of what the numbers of full-time-equivalent white and Negro participants would have been had these two groups been the same in terms of other family income, schooling, children, and the other variables included in the mul­ tiple regressions.

94

MARRIED WOMEN

greater incidence of crowding and doubling-up among Negro families reducing the amount of "home tasks" to be performed by the wife. A third factor is the much discussed matriarchal family structure of Negro society, which Cain relates to the greater degree of marital instability of Negro families and the attendant incentive for Negro wives to main­ tain close ties to the labor market as a protection against the danger of being left self-supporting.12 Other nonwhites. Since married women 14-54 are one of the few popu­ lation groups which include enough "other nonwhites" to enable us to say anything significant about this subgroup, it seems appropriate to end this discussion of the effect of color on L _ 4 by commenting briefly on the differences between the labor force participation of these women and the participation of their white and Negro counterparts. Table 5-1 indicates that the unadjusted participation rate for the other nonwhites is inter­ mediate between the unadjusted rates for white and Negro married women (41.8 for the other nonwhites, versus 34.7 for whites and 47.0 for Negroes). Controlling for the other variables included in the mul­ tiple regression analysis alters this ranking, however, in that the ad­ justed participation rate for the other nonwhite women is higher than the adjusted rates for both Negro and the white married women. The adjust­ ment process serves to lower the participation rate of the Negro married women relative to the other nonwhite women, but to leave the differen­ tial between the white women and the other nonwhite women much the same as it was before. This result of the adjustment process may seem somewhat surprising at first inspection because we have been conditioned by the publication of so many sets of data lumping Negroes and other nonwhites together into a single "nonwhite" category to assume that other nonwhites must be much like Negroes. The reader will recall (from our discussion of other nonwhites in Chapter 3) that the other nonwhite population liv­ ing in urban areas in 1960 consisted mainly of persons of Oriental descent, with persons of Japanese ancestry alone comprising almost half of this group. (The American Indians are the other significant element, and they comprise only 20 percent of the total.) In terms of general eco­ nomic and social characteristics, the other nonwhites as a group are in fact much more like the white population than the Negro population,13 MW14

5

12See Cain, Married Women in the Labor Force, especially pp. 83, 85-89, lOlff. Cain considers each of the factors mentioned in this paragraph in some detail, and our comments here are nothing more than a cryptic summary of hypotheses he advances and supports. 13 We already noted, in commenting on the low level of other family income characteris­ tic of Negro families, that almost exactly the same percentage of other nonwhite women as white women in our sample had an other family income of $9,000 or more (the percentages being 19.4 and 19.2, respectively, compared with 3.3 for Negro women), and that the pro­ portion of other nonwhite women having an other family income below $3,000 was only slightly higher than the proportion for white women —and far lower than the percentage for Negro women. Cross-tabulations by years of school completed by the wife reveal that the educational attainments of other nonwhite married women are much more similar to the edu-

INDIVIDUAL CHARACTERISTICS

95

and this explains why the adjustment process treats the white and other nonwhite groups in much the same way. What is most interesting, however, is that even after controlling for the usual demographic and economic variables, the participation rate for the other nonwhite married women is slightly higher than the rate for Negro women and over 8 points higher than the rate for white married women—the latter differential being large enough to be statistically sig­ nificant (see Appendix Table A-8) in spite of the small number of other nonwhites in the sample. Our first inclination was to regard this differ­ ential as a good illustration of the effects of culturally inspired dif­ ferences in tastes for market work. We calculated a crude participation rate for Japanese women 14-64 years old who were neither enrolled in school nor confined to institutions during the census week of 1960, and this rate (55.0 percent) turned out to be nearly identical to the rate for females 20-64 living in Japan in 1960 (54.6 percent).14 This may well be too facile an explanation, however. One thing that is absolutely clear is that adding the hours-of-work di­ mension to this analysis increases markedly the differential to be ex­ plained, rather than reducing it as it did in the case of the white-Negro differential. The adjusted mean hours worked by other nonwhite married women is appreciably higher than the adjusted mean for white or Negro women—the figure for other nonwhites is 38.1 hours per week, whereas the comparable figures for whites and Negroes are 35.7 and 32.9 respec­ tively. Hence, taking account of differences in hours worked strength­ ens considerably the claim of the other nonwhites to top ranking in terms of the amount of market labor supplied per married woman, as a comparison of the full-time-equivalent participation rates indicates. Whereas the full-time-equivalent participation rates for white and Negro married women are 31.4 and 34.5, respectively, the full-time-equivalent rate for other nonwhite married women is 41.5. Why should the other nonwhite married women devote so much more of their time to work in the market sector than white and Negro married women who are comparable in terms of other family income, schooling, and so on? The three possible explanations cited above for the higher full-time-equivalent participation rate of Negro wives relative to white wives do not seem particularly applicable to the other nonwhites—and, cational attainments of the white women than the Negro women. Whereas 38 percent of the Negro married women in our sample had never gone to high school, only 19 percent of the white women fell in this category and only 24 percent of the other nonwhites. At the top of the educational ladder, 8 percent of the Negro women in our sample had completed 13 or more years of schooling, compared with 18 percent of the whites and 23 percent of the other nonwhites. 14 The data for the U.S. were calculated from U.S. Census of Population: I960, Subject Report PC(2)-1C, Nonwhite Population by Race. This source also contains much additional descriptive information about the nonwhite population. The data for Japan were calculated from figures published by the International Labour Organization (Yearbook of Labour Sta­ tistics, 1964, Table 2).

96

MARRIED WOMEN

in any event, they could hardly be used to explain why other nonwhite married women are more active in the labor market than Negro married women. It is necessary, therefore, to consider other factors. The "culturally-inspired-differences-in-tastes" explanation is not easily rejected— or supported, which is what makes it so slippery. While we have been unable to come up with an alternative explanation of equal promise, there is one secondary hypothesis which does seem worth mentioning at this point. It concerns the relative importance of self-employment among wives of Chinese extraction. We have the impression that family laundries, restaurants, and similar enterprises provide special employment oppor­ tunities for many of these wives.15 The quantitative importance of this consideration is limited, however, by the fact that only 21 percent of all "other nonwhites" are of Chinese origin, and it is doubtful if self-employ­ ment is of comparable importance among other Orientals living in this country (mainly Japanese).

Children The presence of children can be expected to influence the labor force participation of a married woman in three ways: (1) by increasing the amount of work to be done in the home; (2) by increasing the family's need for money income; and (3) in the case of older children, by provid­ ing a source of assistance with home tasks. These considerations pull in different directions, consideration (1) serving to reduce the probabil­ ity that the wife will seek market work and considerations (2) and (3) increasing the likelihood that she will participate in the labor force. The quantitative impact of each of these considerations, and thus the overall net effect of children, should be expected to vary according to both the ages and number of children, and also to vary as a result of interactions with other variables. In analyzing this complex set of relationships on the basis of the 1/1000 Sample of the 1960 Census, we first constructed eight categories to be used in describing the presence of children of different ages. These cate­ gories are listed in the left-hand column of Table 5-2. A three-fold age15 To test this hypothesis rigorously, we would need a breakdown of self-employment in the total United States by urban-rural residence and detailed race (white, Negro, and other nonwhite groups). Such data, unfortunately, are not available from the 1960 Census. As an alternative, we have gone to the 1960 Census Tracts for New York City and San Francisco -Oakland, where there are relatively large concentrations of Orientals. And these data are consistent with our assertion: at comparable income levels, the rate of self-employment among residents of these tracts containing a majority of "other nonwhites" (that is, the areas locally known in both SMSA'S as "Chinatown") is almost always higher than the rate in tracts containing a majority of Negroes or a majority of whites. The percentage of un­ paid family workers (many of whom work in the same family businesses in which the hus­ band is classified as self-employed) also is higher on the whole in largely Oriental areas and is exceptionally low in Negro areas. (Of the 19 heavily Negro tracts we examined in the New York City and San Francisco-Oakland areas, ranging in median income from $1,880 to $5,608, only three had any unpaid family workers at all, and in each case the proportion was less than one-half of one percent.)

97

INDIVIDUAL CHARACTERISTICS

TABLE 5-2 Children and Labor Force Participation: Married Women 14-54 (Total and Negro) in Urban Areas Census Week of 1960 Presence-ofchildren category !

14-54 1. With children under 6 only (CHxoo) ,N 2. With children under 6 and chil­ dren 6-13 but no children 1417 (CHxxo) 3. With children under6 and chil­ dren 14-17 but no children 613 (CHxox) 4. With children under 6 and chil­ dren 6-13 and children 14-17 (CHxxx) 5. With children 6-13 only (CHoxo) 6. With children 6-13 and chil­ dren 14-17 (CHoxx) 7. With children 14-17 only (CHoox) 8. With no children under 18 (CH000) Total F-ratio

Number in sample

Percent of population

Labor force participation rate Unadjusted

Adjustedb

I. ALL MARRIED WOMEN

II.

NEGRO MARRIED WOMEN 14-54 1. With children under 6 only (CHx00) 2. With children under 6 and chil­ dren 6-13, but no children 14-17 (CHxxo) 3. With children under6 and chil­ dren 14-17, but no children 613 (CHxox) 4. With children under6 and chil­ dren 6-13 and children 14-17 (CHxxx) 5. With children 6-13 only (CHoxo) 6. With children 6-13 and chil­ dren 14-17 (CHoxx) 7. With children 14-17 only (CHoox) 8. With no children under 18 (CH000) Total F-ratio

4,218

19.2

19.3

13.2

2,937

13.3

17.8

15.0

476

2.2

24.2

23.6

1,208

5.5

21.9

20.7

1,755

8.0

36.6

36.2

2,187

9.9

34.8

36.5

2,972

13.5

48.1

53.3

6,268

28.5 100.0

53.4 35.8

56.1 35.8

341

18.1

32.3

30.2

217

11.5

30.4

28.7

45

2.4

37.8

36.6

158

8.4

36.7

33.5

125

6.6

55.2

54.2

123

6.5

52.9

52.6

173

11.5

54.9

57.3

37.3 100.0

57.8 47.0

59.7 47.0

22,021

331.6

:

703 1,885 15.6**

Source: 1/1000 Sample. See Appendix Table A-8 for the derivation of the figures for all married women and Appendix Table A-9 for Negro married women. a The verbal description of each category has been translated into the symbols shown in parentheses, with the first subscript indicating whether there are children in the under-6 interval (X = yes, 0 = no), the second subscript referring to the 6-13 interval, and the third subscript referring to the 14-17 interval. These are the symbols used in the multiple regression equations presented in Ap­ pendix Tables A-8 and A-9. b For the effects of color, age, schooling, other family income, and employment status of husband. ** Significant at the 1 percent level.

98

MARRIED WOMEN

of-children breakdown is used (less than 6, 6-13, 14-17), and the mar­ ried women in the sample have been classified according to whether or not they had any children living at home in each of the three age intervals. That is, the emphasis in this classification scheme is on the effects on LMwi4-54 of having children in the various combinations of the three age intervals, not on the significance of the number of children in each age interval. General results for all married women. Panel I of Table 5-2 contains results for our overall sample of 22,021 married women 14-54 years old. It is worth considering these results in some detail before comparing them with the results in panel II for the 1,885 Negro married women who constitute a subsample of the entire group. At the most general level of discussion, it is plain from the enormous size of the F-ratio at the bottom of the panel (331.6) that ages of chil­ dren have a great deal to do with determining the labor force status of married women. Stated in such general terms, this conclusion will sur­ prise no one. It is the detailed pattern formed by the adjusted participation rates shown in the right-hand column, and the sizes of the differentials, which are instructive. Four specific findings stand out. First, it is the presence of children under 6 which takes precedence over all other aspects of the age distribution of children in determining the mother's labor force status. In our table, women in categories 1-4 share the attribute of having at least one child under 6, though they differ in terms of the presence of children in the two older age intervals. The im­ portant finding is that the adjusted participation rate for women in each of these categories is significantly different from—and lower than—the adjusted participation rate for women in each of the other categories.16 No equally clear-cut statement can be made concerning the participation rates of women grouped according to the presence of children 6-12, whatever the ages of their other children (categories 2, 4, 5, 6), or for women grouped according to the presence of children 13-17 (categories 3,4, 6, 7). Pre-school children obviously require more care than older children, and this characteristic seems to outweigh all effects associated with the presence or absence of older children. As of 1960, the probability that a married woman with a child or children under 6 (who was "average" in terms of the other variables included in the multiple regression) would have been in the labor force was approximately one-seventh; the compa­ rable probability for all other married women combined was one-half.17 16 In this set of 16 ί-values, the lowest is 5.49, in a context in which a /-value of 2.58 is significant at the 1 percent level. The full matrix of /-values is presented as a part of Ap­ pendix Table A-8, which also contains the multiple regression equation used to derive the adjusted participation rates. "Calculated by averaging the adjusted participation rates for women in categories 1-4

INDIVIDUAL CHARACTERISTICS

99

A second result is that among those married women who did not have children under 6, the adjusted participation rates for women who had children 6-13 years old (categories 5 and 6) were found to be significantly lower than the rate for women who only had children 14-17 years old (category 7) and lower than the rate for women who had no children under 18 at all (category 8). So, the presence of children in this "middle" age range does seem to have inhibited labor force participation to some extent, but not nearly as much as the presence of children of pre-school age. Third, the adjusted participation rate for women who had only 14-17year-old children living at home is slightly lower—by about 3 percentage points — than the adjusted rate for women with no children at all, and this difference, too, is statistically significant. It is, however, very much smaller than the 17 point gap separating the adjusted rates for women with only 14-17-year-old children and those with only 6-13-year-olds, or the 23 point gap between women in the latter category and those whose only children were under 6 years of age. The inference is that it can be mis­ leading to speak of children in general as discouraging labor force par­ ticipation—it is mainly the presence of children under 14, and especially children under 6, which seems to have this effect. The fourth finding reinforces this last comment and adds a new per­ spective to it. Let us return to the four categories of women who did have children under 6 and compare the adjusted participation rates for those who did and did not also have children in the two older age intervals. The rate for women who also had children 6-13, but had no children 14-17, is insignificantly different from the rate for women who only had chil­ dren under 6. What is most intriguing, however, is not this comparison, but the comparisons which highlight the effects of a 14-17-year-old child in a household which also includes a pre-school child. The clear­ est way of isolating the significance of the presence of children 14-17 years old is by comparing category 3 with category 1, and category 4 with category 2. In both comparisons we find that the added presence of 14-17-year-old children raises the adjusted participation rate of married women by a statistically significant amount.18 Apparently, the presence and for women in categories 5-8, using the numbers of women in the individual categories as weights in both cases. Expressed as percentages, the average adjusted participation rates for these two general groups are 0.15 and 0.50, respectively. In drawing this sharp contrast between the participation of wives with and without chil­ dren under 6, we do not wish to suggest that this particular age has any magical influence on participation decisions. As we show later in this section, the participation of wives rises monotonically with the age of the youngest child from under 1 to 5, and there is every reason to think that the same pattern holds at higher ages — at least up to a point. The point we are prepared to defend is that age 6 does have more intrinsic significance than any other single age —representing the age at which school attendance (and hence limited day-care of chil­ dren) most commonly begins; hence a dichotomous classification based on this milestone makes more sense than drawing the line somewhere else. l8The two /-values are 4.64 and 3.67, respectively.

100

MARRIED WOMEN

of older children encourages a mother of young children to enter the labor force by providing a source of assistance with home tasks.19 Implications of hours of work. We saw in the previous section that tak­ ing account of differences in the degree of participation, as reflected in hours worked per week, led to some significant modifications in the con­ clusions concerning the relationship between color and the amount of labor supplied by married women. The results of some analogous compu­ tations, which serve to incorporate the hours-of-work dimension into our analysis of the effects of the presence of children of various ages on the participation of married women, are summarized in Table 5-3. In this case, taking account of differences in hours worked (by those wives who were at work in the census week) makes little difference. The variations in hours worked associated with the presence of children of various ages are so much smaller than the variations in participation that the set of full-time-equivalent rates is dominated by the relationships be­ tween the children variables and the adjusted participation rates. None of the four conclusions reached above needs to be modified, although the magnitudes of certain differentials are altered somewhat—interestingly enough, always so as to strengthen the conclusions. Working women with no children did average nearly two hours more work per week than working women who only had children 14-17, who in turn averaged about 2 hours more work per week than all women with children under 14. This means that the presence of children under 14 (and especially the presence of children under 6) had a somewhat greater inhibiting effect on the amount of labor supplied by married women than one would have thought by looking solely at the adjusted participa­ tion rates. There is one other finding regarding hours of work which deserves special mention, and it concerns working mothers who had children under 6 and children 6-13, but no children 14-17. These women worked sig­ nificantly fewer hours than their counterparts with children in all three age intervals — indeed, fewer hours than any other group of married women. This result strengthens appreciably the fourth finding stated above, i.e., that the presence of a 14-17-year-old child in a family with younger children serves to increase the mother's propensity to seek market work. Comparing full-time-equivalent participation rates we now find that the relative amount of labor supplied by mothers who had children in all three age brackets (category 4) compared with the amount of labor supplied by mothers having children in the two younger age brackets only (category 2), is considerably greater than when the com19 The other possible explanation is of course that the presence of older children increases the family's need for money income (and thus the mother's inclination to seek market work), perhaps in anticipation of expenses for college education in at least some instances. How­ ever, the plausibility of this explanation is weakened by the fact that the added presence of 14-17-year-old children in households without pre-school children does not seem to lead to a higher adjusted participation rate. (The rates for categories 5 and 6 are nearly identical.)

101

INDIVIDUAL CHARACTERISTICS

TABLE 5-3 Labor Force Participation Rates, Hours Worked, and Full-Time-Equivalent Participation Rates: Married Women 14-54 in Urban Areas (by Presence of Children of Various Ages), Census Week of 1960

Presence of children category MARRIED WOMEN 14-54 1. With children under 6 only (CHxoo) 2. With children under 6 and children 6-13 but no chil­ dren 14-17 (CHxxo) 3. With children under 6 and children 14-17 but no chil­ dren 6-13 (CHxox) 4. With children under 6 and children 6-13 and children 14-17 (CHxxx) 5. With children 6-13 only (CHoxo) 6. With children 6-13 and children 14-17 (CH0XX) 7. With children 14—17 only (CHoox) 8. With no children under 18 (CH000)

Adjusted " full-time equivalent" labor force participation rate

Adjusted a labor force participation rate

Adjusted a hours worked (per week)

13.2

33.2

11.0

15.0

30.5

11.4

23.6

33.1

19.5

20.7

34.0

17.6

36.2

34.2

31.0

36.5

34.0

31.0

53.3

35.6

47.4

56.1

37.3

52.3

Source: 1/1000 Sample. See Table 5-2 for the adjusted labor force participation rates. The adjusted hours-worked figures were derived from the multiple regression shown in Ap­ pendix Table A-12. The full-time-equivalent participation rates were calculated from the data in the other two columns as explained in footnote b. a For the effects of color, age, schooling, other family income, and employment status of the husband. b Assuming that 40 hours per week is "full-time." These rates were calculated by multiply­ ing the adjusted labor force participation rate for each category of the children variable by the ratio of the adjusted mean hours worked per week by women in that category to 40 hours. A fuller explanation is given earlier in this chapter.

parison was limited to the regular adjusted participation rages.20 This is precisely what one would expect to find, in that an older child can help take care of younger children after school and thus enable the mother to work longer hours than her counterpart who lacks an "inside" mother's helper.21 20 The ratio of the two full-time-equivalent participation rates is 1.54 :1.00; the ratio of the two regularly adjusted participation rates is 1.38 :1.00. 21 While the above findings indirectly take some account of the total number of children in a household (since, for example, a family with children in all three age intervals is ob­ viously likely to have a larger number of children at home than a family with children in only one or two of the age intervals), it would be good to have a more direct test of the sig,-

102

MARRIED WOMEN

Results for married women with children under 6: effects of age of youngest child. Whether we do or do not control separately for number of children, and whether we look only at the usual adjusted participation rates or at the full-time-equivalent rates, we always find that it is the presence of at least one pre-school child which has the dominant impact on the wife's labor force status. This being the case, we decided to under­ take a more intensive analysis of the relationship between presence of young children and the labor force status of the mother by running a sep­ arate regression limited to married women with children under 6 which would include a new set of dummy variables for age of youngest child. The results are tabulated in the text table, an abbreviated version of the kind of table used to summarize all our results from the 1/1000 Sam­ ple: 22 AGE OF YOUNGEST CHILD

(among married women 14-54 with children under 6) Less than one One Two Three Four Five

LABOR FORCE PARTICIPATION RATE

Unadjusted

Adjusted

12.2 19.5 20.9 22.3 25.5 27.0

10.5 18.8 21.6 23.4 27.5 28.5

nificance for L 4_ of number of children per se. To this end, we added a new set of dummy variables which measure the number of related persons under 18 in a household. When used with the variables indicating the presence or absence of children in the specific age intervals, this new set of variables showed a mildly negative association be­ tween number of children under 18 and LMW14_54, but the new variables neither added much to the overall explanatory power of the multiple regression nor led to any appreciable changes in the adjusted participation rates reported in Table 5-2. Therefore, it seemed un­ necessary to present the regression including the variables for the number of children under 18 in Appendix A. The fact that this straightforward measure of number of children under 18 turned out to be a much less powerful predictor of laborforce status than the set of variables measuring the presence of children of specified ages offers additional support for the proposition that it is young children, not children in general, who inhibit most sig­ nificantly the labor force participation of married women. 22 See Appendix Table A-IO for the full multiple regression from which the adjusted par­ ticipation rates shown here were derived. The N for this regression is 8,808 married women 14-54 with children under 6; at the risk of spoiling the fun for the really dedicated, detective-type reader, we must point out that adding this number to the number of married women 14-54 in the subset with no children under 6 included in Appendix Table A-Il (13,182) leaves us 31 short of the total number of married women 14-54 (22,021) included in the basic regression for this population group. As a result of a minor mechanical mis­ hap, we simply lost 31 women with children under 6 somewhere in the computer center. By comparing various cross-tabulations, we were able to determine the characteristics of the 31 missing women, and we were reassured to note that they have no single character­ istic in common—it looks as if they were simply the first (or at last) 31 women on the tape. Given this fact, and given the exceedingly small reduction in the size of the sample in­ volved, it seemed pointless to spend the time and money which would have been required to recapture these wayward souls. MW1

54

INDIVIDUAL CHARACTERISTICS

103

The first conclusion to be drawn from this set of results is that the labor force status of this subset of married women with children under 6 is affected markedly by the age of the youngest child.23 The largest differ­ ence in adjusted participation rates is between women having children under 1 and women having children ranging from 1 to 5 years of age—a result explainable in part in terms of the fact that a number of women who had children under 1 at the time of the Census must have just had their babies (indeed, some of them were presumably still in hospitals). Thus, it is hardly surprising that only about 10 percent of these women were in the labor force, compared with over 25 percent of all women with children 1 to 5 years old. While the adjusted participation rate does increase monotonically with the age of the youngest child over the entire range from under 1 to 5 years of age, not all the differences in adjusted participation rates are significant. The statistically significant increases occur between the Iessthan-1 and 1-year-old categories, between the 1- and 2-year-old catego­ ries, and between the 3- and 4-year-old categories.24 The statistically significant jumps in participation rates which occur at the bottom of this scale are no doubt attributable in large part to the increased willingness of mothers to leave their children in the care of others as the children pass out of the infancy stage. However, the personal experience of the wife of one of the authors leads him to reject categor­ ically the proposition that 2-year-old children are easier to care for than 1-year-olds—the higher participation rate among mothers of 2-year-olds must be due either to greater confidence that the child will be all right if left in the care of someone else or to a desperate effort to escape from the child for at least part of the day. Another interesting facet of this entire set of results is the fact that the statistically significant jump in participation at the upper end of the age scale occurs between the 3- and 4-year-old categories, rather than between the 2- and 3-year-old or the 4- and 5-year-old intervals. We sus­ pect that the explanation rests on the incremental changes in nurseryschool enrollment and in the use of day-care centers which are associated with each increase in the age of the youngest child, but we have not at­ tempted to explore this question. Results for Negro married women. Let us now shift our attention to the results for Negro married women reported way back in panel π of Table 5-2.25 The eight adjusted participation rates for these women fall 23 The F-ratio for this set of dummy variables is 40.1 — in a context in which an /-'-ratio of 2.9 is significant at the 1 percent level. 24 See Appendix Table A-IO for the complete matrix of /-values. 25The fact that only 1,885 Negro married women 14-54 are included in our sample of course makes it more difficult to obtain statistically meaningful results in their case than in the case of all married women, and this is especially true when we attempt to run special re­ gressions limited to a subset or in which hours worked is the dependent variable. Conse­ quently, we shall report here only the associations between participation rates and the basic set of eight presence-of-children categories used in the initial part of our analysis for all married women.

104

MARRIED WOMEN

into two extraordinarily homogeneous sets, with the presence or absence of children under 6 years old serving to define the boundary. As in the case of all married women, the adjusted participation rates for all four categories of Negro women with children under 6 are significantly lower (about 20 to 30 points lower) than the rates for women in each of the other four categories. Indeed, in the case of Negro women, the presence or ab­ sence of pre-school children is the only characteristic of our set of dummy variables for children which makes any difference at all.26 These results also throw additional light on the differences in levels of participation rates between Negro married women and other women as these differences are affected by the presence and absence of children of various ages. Among women with no children and among women who only had children 14-17, the adjusted participation rates for Negro women are nearly the same as the adjusted rates for all women. For these two categories combined (7 and 8 in Table 5-2), the ratio of the average rate for Negro women to the average rate for all married women is only 1.1. Taking women with some children 6-13, but no younger children, as a second combined group (categories 5 and 6), we find that the average adjusted participation rate for the Negroes is 1.5 times greater than the comparable rate for all married women. Finally, in the case of women having children under 6 (categories 1-4), the average Negro rate is ex­ actly twice as high as the average rate for all married women—30.8 com­ pared with 15.4.27 Thus, we conclude that the tendency for Negro married women to have a higher labor force participation rate than other married women, even after allowances are made for the effects of such variables as age, school­ ing, other family income, and employment status of the husband, is (1) most apparent in the case of women with children under 6; (2) still quite apparent in the case of women whose youngest child is between 6 and 13 28 As the matrix of t-values in Appendix Table A-9 indicates, there are no significant differences in participation rates associated with the presence or absence of children 6-13 or 14-17. However, this result may be due to the relatively small number of observations in most of these categories. 27 The ratios presented above are weighted averages of the adjusted rates for women in the relevant categories of Table 5-2, the number of women in each category serving as the weights. The tenor of these results is entirely consistent with the interrelationships among color, labor force participation, and presence of children under 6 revealed by our subset regressions. As a comparison of Appendix Tables A-IO and A-Il indicates, the differential in adjusted participation rates between white and Negro married women is far greater within the subset having children under 6 than within the subset having no children under 6. The sizes of the differentials in favor of Negro women are smaller, however, in the presence-ofchildren subset runs than in the separate runs for all married women and Negro married women reported in the text paragraph. The reason is that differences between Negroes and whites in other family income, schooling, etc., are taken into account in the presence-ofchildren subset runs but not in the separate runs for all married women and Negro married women. On the other hand, differences in OFI, etc., within the Negro group (and within the all-married-women group) between women having children of various ages are taken into ac­ count in the separate runs for all married women and Negro married women but not in the presence-of-children subset runs.

INDIVIDUAL CHARACTERISTICS

105

years of age; and (3) almost nonexistent among married women 14-54 who had no children at home under 14 years of age. It should be noted that this set of conclusions concerning the interac­ tion among participation rates, the presence of children of various ages, and the color of the wife differs markedly from the published conclusions reached by Cain on the basis of his analysis of essentially the same set of data.28 28 Cain (Married Women in the Labor Force, p. 120) writes: ". . . the multiple regression with the l-in-1,000 sample indicated that, with control over many important variables, the presence of children under 3 years of age was about as deterring to work among Negro wives as among white wives; that the presence of children under 6 years of age was only slightly less deterring to Negro wives; and only when children aged 7 to 11 were present did Negro wives work more readily than white wives. . . . And the observed higher work rates of the Negro mothers of pre-school age children seem to be accounted for by the other economic and demographic variables in my analyses." In defining his population group Cain did use somewhat different selection criteria than ours in that: (1) he included in one group all married women (with husband present) between the ages of 14 and 64, whereas we included married women over 54 in a separate analysis of older persons; (2) he included some non-urban residents, whereas we limited this part of our analysis to the urban population; and (3) he excluded all wives whose husbands were out of the labor force, whereas we did not. These differences in the characteristics of the two samples are relatively minor, however, and do not seem to account for the differ­ ences in conclusions. Nor does the explanation lie in differences in the other variables included in the mul­ tiple regression equations, since Cain used almost exactly the same set of independent variables we did. There are differences in the way the samples were stratified before the re­ gressions were run, in that Cain made separate runs for owners and for renters within each color group, but inspection of the differences in results according to housing status also fails to explain the differences in conclusions. Rather, the explanation seems to lie in the way Cain defined his dummy variables for children and in the way he interpreted his regression coefficients. He used only three dummy variables which were not mutually exclusive: (1) no children under 3; (2) no chil­ dren under 6; and (3) no children between 7 and 11. The fact that these categories overlap makes it difficult to interpret the net regression coefficients, and the fact that the reference groups differ for each dummy variable (wives who did have children under 3, under 6, and 7-11, respectively) makes it exceedingly difficult to compare the coefficients for the three dummy variables. Also, the reference groups are extremely heterogeneous, and since each regression coefficient is an estimate of the difference between the participation rate for its group and for all other wives, each coefficient must be interpreted in the light of what one knows about the overall participation rate of the "other," extremely diverse, group. In the case of Cain's dummy variable for "no children between 7 and 11," for example, the reference group of "other women" consists of (1) wives who had children under 7, as well as between 7 and 11; (2) wives who only had children between 7 and 11; (3) wives who had children over 11 as well as between 7 and 11; and (4) wives who had children under 7 and over 11 as well as between 7 and 11. Cain gets a small coefficient for this variable in his runs for Negro women and a relatively large coefficient in the runs for white women (see his Table 34), but in order to interpret this difference it is necessary to have informa­ tion about the participation rates of all the "other" Negro women and all the "other" white women. Given the data in our Table 5-2, it is possible to interpret the small coefficients for the "no children between 7 and 11" dummy in Cain's Negro runs as indicating that the group of other Negro wives who did have children between 7 and 11 contained offsetting sub­ groups, one having a below-average participation rate (those women who also had children under 7) and the other having an above-average participation rate (those who had no children under 7), and that the combined participation rates of these two subgroups of

106

MARRIED WOMEN

Housing We discuss housing next because, like children, it is a characteristic of the family setting in which the wife's participation is determined. Housing differs from children, however, in that we have only negative things to say about this variable, and they can be said briefly. As has already been pointed out in Chapter 2, there is an a priori case to be made for both a positive and a negative association between size of housing unit and the probability that the wife will be in the labor force. One can argue that the larger the number of rooms in the dwelling, the greater (in general) the more or less fixed expenditures of the household, the greater the need for money income, and therefore the greater the probability that the wife will participate in the labor force, other things (especially other family income) equal. The case for expecting a nega­ tive association rests mainly on the proposition that the larger the num­ ber of rooms in the dwelling, the more housework there is to be done, and therefore the lower the probability that the wife will engage in market work. A second reason for expecting a negative association between size of dwelling and LMW14_54 is that wives with strong preferences for careers other women just happened to average out to approximately the participation rate for those Negro women with no children 7-11 years old. The larger coefficient for this dummy in the white runs can be interpreted, again with the aid of the data in Table 5-2, as showing mainly that the subgroup of other white women who also had children under 7 had a much lower participation rate than their Negro counterparts, and that therefore the participa­ tion rate for all the "other" white women was lower than the participation rate for white women with no children 7-11 years old. In short, we view Cain's smaller coefficient for Negro women with no children 7-11 years old as reflecting mainly the relatively high par­ ticipation of those Negro wives with children 7-11 years old who also had younger chil­ dren. There is also direct evidence in Cain's Table 34 to support our main finding —that the ad­ justed participation rate for Negro wives with children under 6 is considerably higher than the adjusted participation rate for white wives with children under 6. The net regression co­ efficients of Cain's dummy variable "no children under 6" are almost twice as large for white wives as for Negro wives in his regressions for home-owners and renters. This rela­ tionship is remarkedly consistent with the comparable ratio for our sample —and hard to reconcile with Cain's own conclusion quoted earlier, namely, that "the observed higher work rates of the Negro mothers of preschool age children seem to be accounted for by the other economic and demographic variables in my analyses." In ending this commentary, we wish to emphasize that our purpose has not been to criti­ cize Cain's study in general. On the contrary, we have a very high opinion of his book, as is evident from the many laudatory footnote references scattered throughout this chapter and the next one, and from the review of his book published by one of us in the American Economic Review (September 1967). Indeed, it is because we are confident that Cain's study of the labor force participation of married women will be used so extensively that we thought it necessary to call attention to one of the few instances in which he seems to have stated a wrong conclusion. Finally, we wish to thank Professor Cain for his helpful correspondence on this issue, and for having provided us with an extensive and painstaking reinterpretation of the re­ sults in his Table 34. On the basis of an ingenious reworking of his data, Cain has demon­ strated that his regressions lead to conclusions consistent with the ones reached in this study, and he wishes to withdraw the contrary conclusion published in his book. Cain has constructed a new table summarizing this further development of his work, which he will be glad to supply to readers upon request.

107

INDIVIDUAL CHARACTERISTICS

may be more likely than their otherwise similar counterparts to choose to live in relatively small dwellings; analogously, wives who have a strong taste for staying home, working in the garden, and so on, might be ex­ pected to encourage their husbands to live in a house rather than an apart­ ment. While theory offers no firm basis for predicting which of these two sets of effects is likely to predominate, our intuitive feeling was that the rea­ sons for expecting a negative association were more convincing. The data indicate that we would have been wiser to maintain a "wait and see" at­ titude. We included in the 1/1000 regression for all married women 14-54 a set of dummy variables measuring number of rooms in each household's dwelling unit, along with the other variables already mentioned. The simple association between number of rooms and L was, in fact, inverse; but when the effects of the other variables were allowed for, there was no significant association left. Number of rooms and other family income are of course strongly correlated, and the inverse simple association between size of dwelling unit and L seems to have been reflecting mainly the effect of other family income on the wife's labor force status. The lack of a significant net association between size of dwelling unit and Lmww-54 may be telling us, in part, that number of rooms is a poor proxy for the amount of work to be done in the home. The "feeding-thefamily" component of home work (which involves shopping, cooking, and doing dishes, and which may well be more time-consuming than cleaning) bears no direct relationship to number of rooms in the dwell­ ing. Furthermore, the time required to clean a home may well be higher per room in a small dwelling than in a large dwelling because of both (1) the "start-up" costs and (2) the tendency for certain key areas such as kitchen, bathroom, dining, and living areas, which are common to dwell­ ings of all sizes, to take a disproportionate amount of time to clean. In another effort to probe the relationship between housing circum­ stances and labor force participation, we tried a dummy variable which indicated whether the household owned its home or rented. Again, no significant results were obtained.29 We conclude that, while housing conditions undoubtedly exert an in­ dependent influence on the labor force decisions of some wives, this in­ fluence can operate in either direction, and the overall net effect of hous­ ing on LMWi4—54 is relatively unimportant. Among the seven sets of vari­ ables included in our analysis of the effects of individual characteristics on the labor force participation of married women, only the housing vari­ ables were insignificant. MW14 _ 5 4

MW14 _ 5 4

29 Because both housing variables we tried were insignificant, they were omitted from the multiple regressions for married women reported in Appendix A. Cain (Married Women in the Labor Force, especially pp. 101-110) makes a number of efforts to relate participation rates to housing conditions, but, except in the case of Negro women, he too finds little evidence of significant associations.

108

MARRIED WOMEN

Age We must confess that in planning the empirical work for this chapter we regarded age mainly as a control variable and did not give much thought to the expected characteristics of the relationship between the participation of married women and their age (in the 14-54 range). This was a mistake, because the age-profiles turn out to be rather interesting, and we now find ourselves in the uncomfortable position of being able to offer only ex post explanations. Looking first at the results for all married women, reported in panel ι of Table 5-4, we see that the pattern of adjusted participation rates dif­ fers appreciably from the pattern of unadjusted rates, especially at the ends of the age distribution. Whereas the unadjusted participation rate for married teenagers is about the same as the unadjusted rates for mar­ ried women in the next two age categories, the adjusted rate for the teen­ agers is significantly lower than the adjusted rates for wives in the 20-44 age range. Similarly, the adjusted rates for married women in the 45-49 and 50-54 age categories are significantly lower than the adjusted rates for women in the 20-44 age range. However, the unadjusted rates for the older women are actually higher than the unadjusted rates for most of the younger age groups.30 Among the variables included in the multiple regression equation used to derive the adjusted participation rates, it is apparent that controlling for the association between age and presence of pre-school children is mainly responsible for these pronounced differences in patterns. A very small proportion of the older women have children under 6. Another im­ portant factor leading to higher unadjusted participation rates for the two younger groups of married women (14-24) than for those 25-34 is the relatively low incomes of the husbands of the younger women. The "pure" age-profile revealed by the adjusted participation rates for all married women 14-54 is a relatively smooth curve, having the general shape of an inverted "U" (see Figure 5-1). The significant de­ clines in adjusted rates which occur in the 45-49 and 50-54 age brack­ ets presumably reflect a tendency for women to begin retiring from the labor force at an earlier age than men. The relatively low adjusted par­ ticipation rates for married women 14-19 are probably the result of a combination of factors: (1) the generally greater difficulty which teen­ agers encounter in finding jobs, as a consequence both of limited experi­ ence and of the existence of laws restricting what minors can do; (2) a tendency (suggested by one of our secretaries who was a teenage bride) for some teenage brides to live in "dreamland" for a time, under the impression that their husbands will earn enough to satisfy their needs; (3) the willingness of some parents to help support newly married 30 For the /-values used to test for the significance of differences between pairs of ad­ justed participation rates, see Appendix Table A-8. All the assertions about statistical sig­ nificance made in this paragraph hold at the 1 percent level.

TABLE 5-4 Age and Labor Force Participation: Married Women 14-54 in Urban Areas (Total, Negro, and by Presence and Absence of Children Under 6), Census Week of 1960 Population group, by age

Number in sample

Percent of population

481 2,313 2,904 3,497 3,856 3,414 3,063 2,493 22,021 30.7 **

Labor force participation rate Unadjusted

Adjusted"

2.2 10.5 13.2 15.9 17.5 15.5 13.9 11.3 100.0

31.2 32.9 28.9 29.4 36.3 41.7 43.6 38.2 35.8

28.7 38.6 39.5 38.7 39.2 37.2 32.3 23.4 35.8

3.3 11.5 14.2 17.0 16.6 14.5 12.2 10.7 100.0

38.1 37.0 40.1 47.4 51.9 57.3 49.6 44.6 47.0

42.4 47.2 47.3 52.1 50.6 51.7 41.4 34.4 47.0

1.7 4.7 4.9 9.8 17.2 21.0 22.0 18.7 100.0

49.6 66.2 61.5 49.7 47.7 46.8 44.7 38.3 46.8

36.6 50.5 55.2 53.1 52.1 49.5 44.6 36.4 46.8

2.9 19.2 25.6 25.0 17.9 7.3 1.8 0.3 100.0

14.9 20.8 19.5 17.6 19.8 20.4 23.4 32.0 19.4

17.4 21.5 20.1 18.2 18.7 17.6 18.0 28.9 19.4

I. ALL MW14-54

1. 14-19 2. 20-24 3. 25-29 4. 30-34 5. 35-39 6. 40-44 7. 45-49 8. 50-54 Total F-ratio II. NEGRO MW14-54

1. 14-19 2. 20-24 3. 25-29 4. 30-34 5. 35-39 6. 40-44 7. 45-49 8. 50-54 Total F-ratio

63 216 267 321 312 274 230 202 1,885 3 1**

HI . MW14-54, NO CHILDREN UNDER 6

1. 14-19 2. 20-24 3. 25-29 4. 30-34 5. 35-39 6. 40-44 7. 45-49 8. 50-54 Total F-ratio

224 618 644 1,290 2,271 2,766 2,902 2,467 13,182 26.8 **

iv. MW14-54, WITH CHILDREN UNDER 6

1. 14-19 2. 20-24 3. 25-29 4. 30-34 5. 35-39 6. 40-44 7. 45-49 8. 50-54 Total F-ratio

255 1,691 2,255 2,202 1,577 643 159 26 8,808 1.4

Source: 1/1000 Sample. See Appendix Table A-8 for the derivation of the figures for all married women, Appendix Table A-9 for Negro married women, Appendix Table A-IO for married women with children under 6, and Appendix Table A-Il for married women with no children under 6. MW = married woman. a For the effects of color, presence of children, schooling, other family income, and employ­ ment status of husband. ** Significant at the 1 percent level.

110

MARRIED WOMEN

Negro married \Homen ~ ~

50

,;'"

Q)

'0

a::

~/

-

c .9-

.. .. ..

~~

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c::

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40

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,

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"'

30

0 I.L.

0 .0 C

...J

20

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Ui

:::J "Q

.~

63 d 44.9

Persons with some activity limitation Number (000) 2,641

d d

3,855 (52.6%)

Percent in labor force 18.9 >25 d >35 d 21.1

All persons Number (000) 4,655

d d

7,333 (100.0%)

Weighted Percent in percent in labor force labor forcec 27.5 >37 a >52 d 32.4

28.3 35.4 48.3 32.4

Source: Calculated from special tabulation of National Health Survey data supplied by the U.S. Public Health Service. a "Health" is defined here in terms of whether or not a person had a chronic condition causing some activity limitation. See footnote 40 of Chapter 3 for more details and Health Statistics, Series B, No. 36, pp. 31-32 for a full definition. b Defined in terms of usual activity status during 12 month period prior to week of interview. c Calculated by assuming equal incidence of activity limitations among educational attainment cate­ gories. Thus, the percent-in-labor-force figure for each educational attainment category is a weighted average of the percent-in-labor-force figure for persons in the educational attainment category (1) having no activity limitation and (2) having some activity limitation, the respective weights being the percent of all persons having no activity limitation (47.4) and the percent of all persons having some activity limitation (52.6). d Number of persons not given because the sampling error of the estimate for one component (un­ employment) was too high. Percentage in labor force estimated from reported data on employed persons and persons not in the labor force; omission of unemployed from numerator and denom­ inator makes these estimates too low. e Includes a small number of persons whose educational attainment is unknown and thus exceeds the sum of the persons in the three known categories.

one might have expected them to be. A comparison of these two sets of figures suggests that, as of 1962, only about one-fifth of the simple association between schooling and labor force participation of older males was due to the inverse relation between schooling and chronic activity limitations. By way of contrast, the comparable fraction that we estimated for males 45-64 was one-half. Having used the National Health Survey data to examine the interre­ lationships among health, schooling, and labor force participation, let us now return to the broader question of the overall importance of poor health as a deterrent to participation and compare the figures from the National Health Survey with the quite different figures collected as part of the Current Population Survey. As noted in Chapter 3, the CPS is a second source of information on the incidence of disability among dif­ ferent age-sex groups, although this information is of a less precise and more subjective nature than that presented in the National Health Survey. In short, persons not in the labor force are classified by the reason they re-

306

OLDER PERSONS

port for this status, and one category is "unable to work because of longterm physical or mental illness."47 In the latter half of 1962, there were some 5.3 million males 65 and over in the total noninstitutional population who were not in the labor force; of these, only about 460,000, or 9 percent, were classified as "un­ able to work."48 During the same period, 61 percent of older males in the National Health Survey who were not in the labor force were reported to have some chronic activity limitation. The two disability concepts do not purport to measure the same thing, and it would be cause for concern if they were to lead to the same numerical estimates —still, the extent of the difference between them is so great that it is hard to know what to make of either set of figures. The 61 percent disability ratio reported by the National Health Sur­ vey may seem incredibly high, but other research studies have reached similar (although less precise) conclusions. In her survey volume deal­ ing with the whole field of welfare policies, Margaret S. Gordon cites several of these studies and concludes that "the great majority of elderly persons who are out of the labor force do not consider themselves well enough to work, or, in the case of women, have had no work experience since before age 50."48 She also refers to a recent study by the Survey Re­ search Center of the University of Michigan which found that almost one-half the spending-unit heads 65 and over reported having a physical, mental, or nervous condition that limited their ability to work. To pursue this matter further, we examined an earlier National Health Survey conducted from July 1959 to June 1961 in which individuals with activity limitations resulting from chronic conditions were classified by the extent to which these conditions limited the major activity and physical movements of the persons concerned. On the basis of this sur­ vey, it was estimated that 3.4 million men 65 and over experienced some limitation in activity due to a chronic condition—49.8 percent of all men in this age bracket. Of these 3.4 million men, about 1.6 million (45 per­ cent) were classified as "unable to carry on major activity" — and for this population group, "major activity" is synonymous with "work" in the National Health Survey jargon. Another 45 percent were limited "in amount or kind of major activity," and the remaining 10 percent had some limitation that did not affect their major activity.50 Suppose we make the plausible assumption that all of the 1.6 million 47 The other categories are "engaged in own home housework," "in school," and "other." The great majority of older persons fall in the last category. 48Calculated from Employment and Earnings, issues for August 1962 through January 1963, Table A-3. The comparable figure for older women not in the laborforce is 5 percent. 49 The Economics of Welfare Policies (Columbia University Press, 1963), especially p. 38. 50 Ibid., Table 10. Unfortunately, no cross-tabulations of these figures by labor force status or years of schooling are available.

INDIVIDUAL CHARACTERISTICS

307

males "unable to carry on major activity" (i.e., unable to work) were not in the labor force. If that were so, men in this health category would have accounted for roughly 34 percent of the entire group of older males classified as not in the labor force in 1960. But the average number of older males who were "unable to work" according to the Current House­ hold Survey that year was just under 480,000 —only 10 percent of those not in the labor force.51 Thus, there remains an astonishingly large dif­ ference in the number of older persons who are reported by these two sources as unable to work. We are in no position to say which is the better estimate, but we do feel strongly that the question is of sufficient impor­ tance to justify a serious effort by the Bureau of the Census and the U.S. Public Health Service to relate the results of their separate surveys. Another important question involving health is the extent to which dif­ ferences in the incidence of chronic disability among older whites and nonwhites affect the relative labor force participation rates of these two groups. At several points we have suggested that a larger fraction of older nonwhites are unable to work, and it would be helpful to have some quantitative evidence on this point. The 1960 Census asked no questions at all about ability to work, but the 1950 Census did; the Special Report on Employment and Personal Characteristics based on the 1950 Census shows the number of persons, cross-classified by age, sex, color, and marital status, who stated they were unable to work because of a long-term physical or mental illness or disability. These data probably overstate considerably the number of persons with this condition. For one thing, the Bureau of the Census con­ cedes that "some persons were included as 'unable to work' who were only temporarily ill or who, although elderly, were not permanently dis­ abled."52 For another, the total number of persons over 65 classified as "unable to work" in the 1950 Census was roughly three times the number so reported by the Current Household Survey in 1960, and it is hard to believe that the health of older persons improved that much in a decade. Nonetheless, the 1950 Census data do afford some basis for assessing the comparative effects of ill health on the participation of older whites and older nonwhites. In Table 9-8 we show what happens to the rates for urban residents if those in the "unable to work" category are omitted from the population base. The results are striking. In the case of males 55-64, the substantially higher rates for whites in both marital catego­ ries (married and "single") all but disappears; and for males 65-74, the differentials are actually reversed by a substantial margin. That is, omit­ ting persons reported as unable to work makes the participation rate of 51 Calculated from Employment and Earnings, issues for February 1960-January 1961, Table A-3. 52Special report cited above, p. 1A-6. Also, as noted in Chapter 3, some persons who were unemployed were inadvertently classifled as "unable to work" by Census enumerators.

308

OLDER PERSONS

TABLE 9-8 Effects of Dropping Persons "Unable to Work" a from Population Base on Labor Force Participation Rates of Older Persons in Urban Areas, by Age, Sex, Marital Status, and Color: Census Week of 1950

Population group, by sex and marital status

55-64 Married, wife present "Single"c MALES 65-74 Married, wife present "Single"c MARRIED WOMEN 55-64

Labor force participation rates,b persons "unable to work" included in population base

Labor force participation rates,b persons "unable to work" excluded from population base Nonwhites

Whites

Nonwhites

88.5 74.0

84.6 69.1

3.9 4.9

93.5 81.8

92.8 81.4

0.7 0.4

55.6 37.7

51.8 34.8

3.8 2.9

68.8 51.7

73.5 57.8

-4.7 -6.1

13.6

25.7

-12.1

13.8

27.0

-13.2

66.6

52.1

14.5

70.0

58.1

11.9

38.4

40.2

-1.8

40.9

47.6

-6.7

Difference Whites

Difference

MALES

HUSBAND PRESENT NEVER-MARRIED WOMEN

55-64 "OTHER" WOMEN

55-64

d

Source: 1950 Census of Population, Special Report on Employment and Personal Charac­ teristics, Table 10. a Only those persons unable to work because of a long-term physical or mental illness or disability were supposed to be included in this category, but some others were counted as well. For the explanation, see the text. b Inmates of institutions excluded from population base. c Includes men who were widowed, divorced, separated, married with wife absent, and never married. d Includes women who were widowed, divorced, separated, and married with husband ab­ sent.

nonwhite men 65-74 within each marital category higher than the rate for their white counterparts.53 The effects on our three female groups are in the same direction, but smaller in magnitude, with no signs being reversed. The last test we wish to report concerning the effects of ill health on participation involves our intercity analysis for males 65 and over dur­ ing the 1960 census week. Operating on the premise that in cities where the death rates of older males were higher than average, disability rates 53Commencing with the report of the Current Household Survey for January 1967, separate tabulations of persons "unable to work" are reported by age, sex, and color. When the experiment in Table 9-8 is repeated for white and nonwhite males 65 and over (without regard to marital status) in the latter month, the relative size of the two partici­ pation rates is again reversed, but the color differentials are much smaller. Calculated in the usual way, the participation rate of whites in January 1967 was 25.8 percent, compared to 24.6 percent for nonwhites. After dropping those disabled (a much smaller number in the CPS data than in the 1950 Census, as noted above), the rate for nonwhites rises to 28.6 percent, compared to 27.9 percent for whites.

INDIVIDUAL CHARACTERISTICS

309

should also have been relatively high, we inserted in our regression a variable measuring the number of deaths of males 65 and over during 1960 per 10,000 population of these males in the SMSA during the census week.54 The results were ludicrous, as the death-rate variable turned up with a positive sign, and to make matters worse it was significant at the 5 percent level. Furthermore, the net regression coefficient suggested that a city with a death rate of older males which was 4 per thousand above average had a participation rate of 10 per thousand above average. It seems fairly certain that the death of 4 more elderly men per year did not cause 10 other elderly men to be in the labor market: entrance into the morticians' trade is not that easy. A more plausible explanation for the bizarre result is that the cities with relatively high death rates also tend to have inhospitable climates. And these tend to be the cities that elderly couples with financial means (and, therefore, better health) are likely to forsake for more pleasant (and more healthful) climes.55 It is also possible that the strain associated with participation itself causes higher death rates among older males, but we are not prepared to accept this hypothesis solely on the basis of the regression results de­ scribed above.

Other Income and Other Family Income One would surely expect the labor force participation of older persons to be inversely related to the amount of other income (other family in­ come for married women) they receive. Indeed, if this hypothesis were refuted, our confidence in the relevance of economic variables to the participation decision of older persons would be badly shaken. It turns out, however, that there is no cause for alarm on this score; the results reported for males and single females (not in a family) in Table 9-9 and for married women with husband present in Table 9-10 all support the generalization that larger amounts of other income tend to be associated with lower participation rates, other things equal. Males 55-64 and 65-74. We begin our more detailed examination of this set of relationships with the results for older males, which are sum­ marized in graphical form on Figure 9-6. The first thing to note about the relationships for older males 55-64 and 65-74 is that in both cases a curious reversal of the otherwise in­ verse relationship between other income and participation occurs at the top level of other income. For the 55-64 group, the adjusted participa­ tion rate for those reporting $5,000 or more of other income is 6.5 points higher than the adjusted participation rate for those with $3,000 to $4,999; 54The number of deaths in each SMSA was reported in the U.S. Public Health Service, Vital Statistics of the United States: I960, Vol. II, Part 3, Table 9-5. 55 There is a significant inverse association between death rates of older males and the mean temperature of the SMSA in January. The effect of climate on participation is dis­ cussed briefly in the next chapter; see pp. 337-340.

310

OLDER PERSONS

TABLE 9-9 Other Income and Labor Force Participation: Older Males and Older Single Women in Urban Areas, Census Week of 1960 Population group, by amount of other income in 1959

Number in sample

Percent of population

MALES 55 -64 Less than $500 b $500-999 $1,000-1,999 $2,000-2,999 $3,000-4,999 $5,000 or more Total F-ratio

3,737 453 377 162 125 113 4,967 156.0**

65-74 Less than $500 b $500-999 $1,000-1,999 $2,000-2,999 $3,000-4,999 $5,000 or more Total F-ratio

Labor force participation rate Unadjusted

Adjusted8

75.2 9.1 7.6 3.3 2.5 2.3 100.0

92.2 71.3 56.8 60.5 58.4 66.4 85.1

92.3 73.3 57.1 58.2 55.5 62.0 85.1

1,080 536 1,028 414 219 115 3,392 129.3 **

31.8 15.8 30.3 12.2 6.5 3.4 100.0

67.4 31.0 24.2 22.7 16.9 33.0 36.9

65.8 34.7 26.9 21.0 10.7 24.9 36.9

564 205 205 139 49 53 1,215 65.4 **

46.4 16.9 16.9 11.4 4.0 4.4 100.0

80.7 78.5 42.0 29.5 24.5 17.0 62.9

79.3 78.3 45.0 31.3 25.2 15.9 62.9

200 169 416 289 68 61 40 1,243 25.9 **

16.1 13.6 33.5 23.3 5.5 4.9 3.2 100.0

50.5 39.1 21.6 11.8 10.3 9.8 5.0 24.6

47.4 37.4 24.8 12.9 6.3 3.7 2.7 24.6

MALES

SINGLE WOMEN 55-64, NOT IN A FAMILY c

$0 or loss $1-499 $500-999 $1,000-1,999 $2,000-2,999 $3,000 or more Total F-ratio SINGLE WOMEN

65-74

NOT IN A FAMILY c

$0 or loss $1-499 $500-999 $1,000-1,999 $2,000-2,999 $3,000-4,999 $5,000 or more Total F-ratio

Source: 1/1000 Sample. Adjusted figures for groups i -iv were derived from Appendix Tables A-14 to A-17. aFor the effects of marital status, age, color, and schooling. b Includes persons with a net loss. c Includes all women not married with husband present living alone or with nonrelatives only. ** Significant at the 1 percent level.

311

INDIVIDUAL CHARACTERISTICS

TABLE 9-10 Other Family Income and Labor Force Participation: Older Married Women in Urban Areas, Census Week of 1960 Age group, by amount of other family income in 1959 I. MARRIED WOMEN

Number in sample

Percent of population

175 314 350 341 404 408 333 429 238 239 153 80 3,464 15.8 **

115 334 300 206 149 130 96 171 67 84 73 1,725

Labor force participation rate Unadjusted

Adjusted"

5.1 9.1 10.1 9.8 11.7 11.8 9.6 12.4 6.9 6.9 4.4 2.3 100.0

45.1 33.1 30.6 32.8 30.0 32.8 26.1 25.4 16.8 20.1 20.3 3.8 28.2

49.7 39.2 35.5 34.2 29.5 31.5 24.2 22.9 15.4 17.2 15.6 —4.7c 28.2

6.7 19.4 17.4 11.9 8.6 7.5 5.6 9.9 3.9 4.9 4.2 100.0

11.3 6.3 8.7 4.9 3.4 7.7 10.4 6.4 6.0 10.7 11.0 7.4

14.1 9.5 10.2 4.6 2.2 5.8 8.3 4.5 2.9 7.5 6.1 7.4

55-64,

HUSBAND PRESENT

Less than $1,000 " $1,000-1,999 $2,000-2,999 $3,000-3,999 $4,000-4,999 $5,000-5,999 $6,000-6,999 $7,000-8,999 $9,000-10,999 $11,000-14.999 $15,000-24,999 $25,000 or more Total F-ratio MARRIED WOMEN

65-74,

HUSBAND PRESENT

Less than $1,000 $1,000-1,999 $2,000-2,999 $3,000-3,999 $4,000-4,999 $5,000-5,999 $6,000-6,999 $7,000-8,999 $9,000-10,999 $11,000-14,999 $15,000 or more Total F-ratio

b

2 93 **

Source: 1/1000 Sample. Adjusted figures for groups ι and Ii were derived from Appendix Tables A-18 and A-19, respectively. a For the effects of age, color, schooling, and employment status of husband. b Includes some persons with a net loss. c A negative participation rate is, of course, impossible. But given the additive nature of our adjustment process, the presence of nonlinear interactions between certain independ­ ent variables and the dependent variables can cause our estimate of the adjusted partici­ pation rate to be negative, and that is what has happened here. For a more complete ex­ planation of this point, see Appendix A. ** Significant at the 1 percent level.

90

80

70

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Moles 55-64

tJ

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10

/ /

""

/

/"

(1500)

(2500)

(4000)

Loss 500

1000

2000

3000

I

I

I

1999

2999

4999

I

500 999

./

, / " Moles 65-74

(250)(750) I

/'

(6000) 5000+

Other Income ($) FIGURE 9-6. Other income and adjusted labor force participation rates, older males in urban areas, census week of 1960. Source: Table 9-9, panels I and II.

INDIVIDUAL CHARACTERISTICS

313

and for males 65-74, the adjusted participation rate for those with $5,000 or more of other income is over 14 points higher than the rate for those in the $3,000 to $4,999 range.56 We suspect that this reversal is the product of three related factors: (1) the personal characteristics of the men in the top other income bracket, (2) the kinds of work open to them, and (3) the fact that elderly persons with high earnings opportunities and large amounts of other in­ come from non-OASDi sources are less likely to be deterred from working by the retirement test of the oasdi than are less affluent older men. Each of these considerations requires a word of explanation. It seems quite plausible that the ranks of older males who have reached the top 3 percent of the other income distribution include a dispropor­ tionate number of individuals with exceptional ability and ambition and a strong taste for work. To be sure, a good many men in this top category must have gotten there by inheriting wealth rather than by accumulating it over the years, but this fact poses no problem: we are concerned with why the participation rate for this category is as high as 25 percent, and there is plenty of room within the ranks of the 75 percent who are nonparticipants for those whose high level of other income is not associated with the kinds of personal traits listed above. The second point is that persons with work experience in the more in­ teresting, pleasant, and prestigious occupations are also overrepresented in the top other income bracket, and these individuals are more likely to continue work after 65 than those in duller, dirtier, and less-remunerative trades. The 1/1000 Sample contains information on each person's occu­ pation—his current occupation if he was employed during the census week, and his most recent occupation if he was not working during that week but had done some work during the preceding decade. An analysis of these data shows that 47 percent of the men 65-74 with $5,000 or more of other income had occupations in the professional and managerial categories, compared with 32 percent of those in the $3,000-4,999 interval and 17 percent of all older males in our sample. (The same pat­ tern is evident for men 55-64, where the comparable fractions were 55, 44, and 22 percent, respectively.)57 The propensity to continue work in later years is especially strong among those who are self-employed, partly because of the nature of the work, partly because no compulsory retirement provisions apply, and partly because those who are self-employed have much more freedom in deciding how long (and when) to work than do wage or salary workers. 56 This latter differential is significant at the 1 percent level, the /-value being 2.86. The differential for males 55-64 is not quite significant at the 10 percent level, the t-value being 1.55. However, the similarity in the results for the two subgroups of older males leads us to attach a good deal more significance to the results for both subgroups than a straightforward reading of the two /-values would warrant. 57 The relationship between occupational group and labor force status for older men and women is analyzed in detail in the final section of this chapter.

314

OLDER PERSONS

It turns out that those who had previously been self-employed are also overrepresented at the top of the other-income scale. Among men 55-64 in our sample, 38 percent in the top other-income category had work ex­ perience of this kind, compared with 28 percent in the adjacent interval and 15 percent overall. For men 65-74, the respective percentages were 24, 15, and 15.58 The final point, concerning the effects of the OASDI retirement test, pertains only to those men over 65. The earnings-test feature of the OASDI system, which discouraged eligible persons from working during the census week of 1960, has been described in detail earlier in this chapter. It can be shown with conventional indifference curve analysis that the more an older person earns, the less likely he is to be deterred from working by the prospective loss of his Social Security benefits.59 And it has just been noted that the fraction of older men with work ex­ perience in the higher-paying occupations is of course much greater at the top of the other-income pyramid. It is also true that the more other income an older person receives from sources other than Social Security benefits, the less powerful the deterring effect of the prospective loss of these benefits. It appears that from one-fourth to one-third of those 65-74-year-old males in the top other-income (oi) bracket received over $10,000 of such income in 1959.60 Another interesting aspect of the oi-participation curves for both groups of older males in Figure 9-6 is that the steepest declines in par­ ticipation occur between the two lowest oi intervals. Could it really be that having $500 to $999 of oi (rather than $499 or less) in 1959 enabled nearly 20 percent of males 55-64 and over 30 percent of those 65-74 to retire even temporarily? This hardly seems likely, and we suspect that these steep declines ought to be viewed in part as reflecting the re­ ceipt of welfare-type payments by men who are more-or-less unable to work. The relation between oi and schooling for older males in our sample is 58Since we have drawn on occupational and self-employment data to help explain a number of features in the relations between participation and other variables, the reader may be wondering why we did not include sets of dummy variables for these characteristics in our overall analysis. The reason is that a substantial fraction of each group reported no occupation—i.e., no work experience during the preceding decade —and this group's par­ ticipation rate is, of course, virtually zero. Thus, the inclusion of a set of dummies for occu­ pation greatly increases the explained variance, but in an illegitimate and mechanical way, since it tends to "short-circuit" the more fundamental determinants (such as schooling and marital status). It is meaningful, however, to introduce occupation and class of worker dummies into an analysis of that subset of the population with some occupation; this is what we have done in the subsequent section on "Occupation and Related Characteristics." 59See Gallaway, The Retirement Decision, pp. 13-17. 60 Precise figures are not available. The estimate offered above is based on the fraction of older males with family income minus subject's earnings of $ 11,000 or more. Some of this income may have come from the earnings of other family members, but the participation rates of married women in the relevant age and other-income categories are very low in­ deed—in the neighborhood of 5 percent.

315

INDIVIDUAL CHARACTERISTICS

consistent with this view. We would expect these two variables to be positively related to the extent that 01 measures Social Security bene­ fits, payments under private pension plans, and income from various kinds of financial assets. What we find, however, is that the fraction of men with eight years of schooling or less fails to decline perceptibly as we go up the first two steps of the Oi ladder—in fact, for males 65-74, this fraction is actually higher among those with $500-999 and $1,0001,999 of oi than among those with less than $500, suggesting a higher incidence of welfare payments in the lower oi categories than at the top. The fraction of men with little education does, of course, become smaller at higher oi levels.61 The problem of interpreting the oi-participation relationship for males 65-74 is further complicated all along the line by the OASDI earnings test and by the compulsory retirement programs of employers. The existence of the earnings test means that the Social Security benefits received by an older man and his labor force status have to be viewed as simultane­ ously determined. This interdependence between the receipt of other in­ come and nonparticipation is even more pronounced in the case of pen­ sions paid by establishments with compulsory retirement programs.62 Hence, it would be a mistake to think of the oi-participation profile as showing only—or perhaps even primarily—the pure "income effect" of nonlabor income on participation decisions. We know that a good many older persons who have retired under these programs would have pre­ ferred to continue working for a time.63 Consequently, not all of the lower participation associated with larger amounts of other income is really voluntary, and this fact may be quite important in explaining the association between these variables in the case of older males.64 61 The

specific figures on the relation in question are shown below:

AMOUNT OF OTHER INCOME

oj -I^

ΚΛ

H O O Z Q H δ Z

m

*

70

*3

r > CD O

346

OLDER PERSONS

older men, whatever their marital status, no doubt view their labor force status as a live issue, to be determined in part by the tightness of local labor markets.

Results for Older Males in Rural, Nonfarm Areas Having discussed the effects of labor market conditions on the partici­ pation rates of various groups of older males living in urban areas, let us now consider briefly the effects on males 65 and over who resided in rural, nonfarm areas during the census week of 1960. The participation rate for these rural, nonfarm residents was appreciably lower than the rate for their urban counterparts (24.8 versus 31.4), a differential pre­ sumably due in large measure to the greater degree of difficulty in finding jobs experienced by rural, nonfarm residents. For the same reason, we might expect to find that the participation decisions of rural, nonfarm residents tend to be more sensitive to interarea differences in labor market conditions than the participation decisions of otherwise similar urban residents. This is indeed what we found in the cases of prime-age males and married women.16 In the case of the older males, however, it turns out that the degree of sensitivity appears to be slightly lower among rural, nonfarm dwellers. The regression coefficient for the latter group is —1.22, compared to —1.34 for urban residents, and both are easily significant at the 1 percent level.17 However, the fact that many important control variables were not available for the rural, nonfarm regression (only 41 percent of the variance in participation was explained, compared to 78 percent in the intercity analysis) should serve as a warning not to attach great weight to the similar magnitude of these coefficients.

Comparability Regressions for 1960, 1950, and 1940 The intercity regressions discussed above provide a panoramic view of the effects of local labor market conditions on the participation of older persons at one point in time—the census week of 1960. They tell us noth­ ing, however, about how these effects have changed over time. One way of finding out is to run the same kinds of regressions for the census weeks of 1950 and 1940; we have tried to do this for one group of older persons — males 65 and over in large metropolitan areas.18 To make our 1960 results more comparable to those for 1950 and 1940, 16 See

Appendix Tables C-I and C-2. coefficient of —1.22 for the rural, nonfarm residents is taken from Appendix Table C-3, which contains the full regression. The coefficient of—1.34 for the urban resi­ dents is from Table 10-1. 18 As explained in Chapter 2, the 1950 analysis used 78 Standard Metropolitan Areas as observations (i.e., all those with a total population of 250,000 or more), whereas 92 cities (all those with 100,000 population or more) served as observations in the 1940 regression. 17The

LABOR MARKET CONDITIONS

347

we reran our 1960 regressions, dropping all the variables that were not available from the two previous censuses. Unfortunately, migration was one of the casualties, and its loss not only reduces the R2 of the 1960 regression for older males from .78 to .61, but knocks out the groupsupply and earnings variables as well. Consequently, the 1960 regression coefficients for the latter two predictors, as shown in Table 10-3, are mis­ leading; one can only speculate concerning the extent to which the ab­ sence of a migration control has also had a distorting effect on the supply and earnings coefficients for the earlier census years.19 At the same time, the unemployment and occupational-mix coefficients for 1960 were very little affected by the removal of migration, so a com­ parison of their magnitudes in different census cross-sections may be more meaningful. What the comparison reveals is very clear: (1) unem­ ployment had almost as great an impact on participation in 1950 as in 1960 but had no perceptible effect at all in 1940; and (2) occupational mix was significant only in 1960. The reader of Chapter 4 will recall that we also found no association between unemployment and the participation of prime-age males in 1940 and that we attributed this finding, for the most part, to the large-scale program of public emergency work in effect during that year. We believe this same line of reasoning applies with equal force to older males. To be sure, the policy of giving preference to heads of families in the allocation of emergency work meant that older males were underrepresented on these jobs relative to prime-age males; nevertheless, over 3 percent of males 65 and over who were in the urban labor force were on emergency work during the census week, and this shows that the program had a substantial impact on this group. An additional 11 percent of these men were wholly unemployed, but some of them were on the waiting list for emergency jobs.20 Indeed, more unemployed older males may have been induced to remain in the labor force by the hope of getting a W.P.A. job than by the amount of emergency work that they actually received. 19 The reader will recall that in our final intercity regression for older males in 1960, earn­ ings had a negative regression coefficient that was significant at the 1 percent level —an un­ expected finding that we think is attributable to a positive association between wage levels and the extent of compulsory retirement programs. When migration is dropped the earnings coefficient assumes its usual positive sign, although it is not significant—a plausible result when one remembers that most of the retirement cities in Florida and Arizona have lowerthan-average wage levels, so that failing to control for migration imparts a positive bias to the earnings-participation relationship. The fact that the 1950 earnings coefficient is much larger than its 1960 counterpart may be due to the lesser importance of compulsory retire­ ment plans that year. Similarly, the decline in the size of the negative regression coefficient for group supply (in the absence of a migration control) is probably related to an increase in retirement-oriented migration since 1940. 20 The source of these figures is the U.S. Census of Population: 1940, Vol. IV, Charac­ teristics by Age, Part 1, Table 24. It was estimated that 1.2 million jobseekers in February 1940 were eligible for W.P.A. employment and waiting to be assigned to an emergency work project, but how many of these persons were men 65 and over is unknown. (See Donald S. Howard, The W.P.A. and Federal Relief Policy [New York, 1943], pp. 612-613.)

348

OLDER PERSONS

TABLE 10-3 Effects of Labor Market Conditions on Labor Force Participation Rates of Males 65 and Over: Intercity Regressions, Census Weeks of 1960, 1950, 1940 Variables measuring labor market conditions a

I960" b

W

Unemployment (%) Earnings ($100/yr.)c Occupational mix (%)' Group supply (%)'

-1.47 0.10 0.26 -0.18

(0.24) (0.08) (0.12) (0.20)

1950 t

b

6.05 ** -1.20 0.47 1.30 0.08 2.16 * -0.55 0.94

1940

(S)

t

(0.35) (0.20) (0.23) (0.42)

3.42 ** 2.33 * 0.34 1.32

b

(S)

0.08 (0.17) 0.09 d (0.19) -0.10 (0.17) -0.69 (0.37)

t

0.47 0.47 0.61 1.86 t

OTHER DATA

Dependent variable (LM65+): Mean Standard deviation Number of observations R2 for complete multiple regression

30.7 4.6 100 .61 * *

41.0 5.8 78 .45 * *

38.2 5.0 92 .35 * *

Source: 1960, 1950, 1940 Censuses. The complete multiple regression equations from which these results have been taken are presented in Appendix Table B-102. Notation: See Table 10-1. a The other explanatory variables in these regressions are schooling, color, and other income. bThe results for 1960 in this table —particularly, the coefficients for earnings and group supply and the overall R2 — differ substantially from those in panel Ii of Table 10-1, due mainly to the omission of migration. (For a discussion of the effects of this omission, see the text.) Minor differences may also arise from the retention of nonsignificant control variables in the comparability run, whereas such variables were omitted from our basic runs. c Measured in constant (1959) dollars. dThe definition of the earnings variable for 1940 —the median wage or salary income in 1939 of ail males who received at least $100 of such income that year—diifers somewhat from the definition for 1950 and 1960: i.e., the median income in the preceding year of all males who worked 50-52 weeks that year. cThe percentage of all males employed as farmers, managers, sales workers, and service workers during the census week. This variable was used as a substitute for industry mix, since the latter was nonsignificant in 1960. 'Percentage of all males 14 years old and over in the civilian, noninstitutional population who were 65 and over. ** Significant at the 1 percent level. * Significant at the 5 percent level. t Significant at the 10 percent level.

Results of Time-Series Regressions for Older Persons We conclude our discussion of the effects of labor market conditions on the participation decisions of older persons with a brief summary of the findings of our postwar time-series regressions for these groups. These regressions are discussed more fully in Chapter 16; the complete regres­ sion equations are presented in Appendix D. In this analysis, quarterly averages of the seasonally adjusted labor force participation rates of four groups of older persons in the civilian, noninstitutional population—males 55-64 and 65 and over, and females 55-64 and 65 and over—were regressed on the overall rate of unemploy-

LABOR MARKET CONDITIONS

349

ment in the economy and the ratio of manufacturing employment to total employment (both seasonally adjusted and lagged one quarter), with two time-trend variables and a special dummy variable included as controls. Two sets of regressions were run—one for the period from 1949II to 1965III, the second for 1954IV to 1965III. From these regressions, adjusted coefficients for unemployment were derived; these coefficients, which are shown in panel ι of Table 10-4, purport to show the average (percentage-point) change in group participation during the specified period generated by a one percentage point change in the overall unem­ ployment rate and the associated cyclical change in the manufacturingemployment ratio, after accounting for trends in participation.21 To provide a benchmark for evaluating the time-series sensitivity esti­ mates, we have reproduced the net regression coefficients for unemploy­ ment from the 1960 intercity regressions for each of our two males groups and have constructed weighted averages of the unemployment coefficients for our various subsets of older women. These cross-sectional sensitivity estimates appear in panel n of the table. For reasons developed in Chapter 16, we believe that the time-series regressions for 1954IV-1965III give a more accurate impression of the cyclical sensitivity of participation decisions during recent years than do the runs which include the earlier postwar period. And except in the case of males 55-64, where special factors have been at work, the sensitivity estimates for 1954IV-1965III are quite believable — in terms of both sign and magnitude. First of all, the coefficients for males 65 and over, females 55-64, and females 65 and over are all negative, indicating that over this period the participation of these groups increased at a slower rate—or declined at a faster rate—when jobs were hard to find than when they were relatively plentiful. These time-series estimates provide further evidence that the discouragement effect of higher unemployment has been stronger than the additional-worker effect for all three groups in recent years. Second, the time-series sensitivity estimates for all three groups are substantially smaller than the comparable cross-sectional estimates from our intercity regressions for 1960. This is as it should be, since time-series regressions are unlikely to pick up the long-run adjustment of participa­ tion decisions to changes in unemployment that persist for several years — and the long-run adjustment is presumably larger than the cyclical response. Finally, the rank order of the time-series and cross-sectional coefficients for the three groups in question is the same—a symmetry which enhances the credibility of both sets of coefficients. The absence of any time-series sensitivity for males 55-64 is the one troublesome result in this set of comparisons, and we think we have 21 The coefficients shown in the table are based on second runs containing only significant variables. Where the manufacturing-employment ratio was dropped, the unadjusted co­ efficient for unemployment was used instead of the adjusted coefficients described in the text.

350

OLDER PERSONS

TABLE 10-4 Estimates of the Labor Force Sensitivity of Older Persons Based on Time-Series Regressions for the Postwar Period and Intercity Regressions for 1960 Estimates of labor force sensitivity based on I. Time-Series Regressions a Population group Males 55-64 Males 65+ Females 55-64 Females 65+

II. Intercity Regressions

1949II—1965III

1954IV-1965III

1960 census week

Not significant at 10% -0.12 0.13 Not significant at 10%

Not significant at 10% -0.49 -0.41 -0.20

-1.27 -1.34 -0.87" -0.58 "

Sources: CoeflScients from time-series regressions have been taken from the final runs in Appendix Tables D-I through D-4; coefficients from intercity regressions are from Tables 10-1 and 8-6. (For the derivation of female intercity coefficients, see note b below.) a Estimates of labor force sensitivity from time-series regressions show the average joint effect of a one percentage point change in the overall rate of unemployment and the asso­ ciated change in the ratio of manufacturing employment to total employment (where this variable is significant) on the labor force participation rate of the subject group over the indicated period, after impounding trends. Standard errors are not available for these esti­ mates; hence, no significance indicators are shown. However, no sensitivity estimate is shown in those cases where the partial regression coefficient for unemployment is not significant at the 10 percent level or better. (For an explanation of how these sensitivity estimates were constructed, see Chapter 16.) 0 A population-weighted average of the regression coefficients for female subsets in Tables 8-6 and 10-1.

found a possible explanation for it. Between 1963IV and 1965III the number of 55-64-year-old men who were reported "unable to work" by the Current Population Survey because of long-term physical or mental illness rose from about 220,000 to 305,000—an increase of 85,000 per­ sons, or 1 percent of the civilian, noninstitutional population of this group in 1965.22 While the reasons for this marked increase in disability of males 55-64 are not readily apparent, it obviously pulled down the group's participation rate. Moreover, the fact that this exogenous decline took place during a period of steadily falling unemployment means that it must have imparted a positive bias to our time-series sensitivity esti­ mate for this group during the 1954IV-1963III period. And since the ob­ served relation between unemployment and LM55_64 over this period was nil, it seems very likely that the "true" cyclical association (after abstract­ ing from the effects of changes in the fraction of men unable to work) was negative, as our intercity results lead us to expect. 22 Calculated from data in Table A-18 of Employment and Earnings for October 1963January 1964 and for August-October 1965. As noted in Chapter 4, footnote 21, a similar increase was observed for males 45-54 over the period from 1963 to 1967.

CHAPTER 1 1

Trends in the Participation of Older Persons * The postwar record reveals a sharp contrast between the trends in labor force participation rates for older men and older women. As can be seen from Table 11-1 and Figure 11-1, the proportion of older men in the labor force has declined markedly at the same time that the proportion of older women in the labor force has, in general, tended to rise. TABLE 11-1 Labor Force Participation Rates of Older Persons, 1940-1965 Population group

Labor force participation rates a 1940

I. CENSUS DATA 1950

1960

83.9 41.8

83.4 41.4

83.3 30.6

7.1 2.8

13.1 4.5

25.2 6.8

47.2 16.9

57.2 19.7

64.8 23.0

26.8 6.2

35.8 7.8

47.6 10.6

MALES

55-64 65 and over MARMED WOMEN, HUSBAND PRESENT

55-64 65 and over NEVER-MARRIED WOMEN

55-64 65 and over OTHER WOMEN B

55-64 65 and over 1948

π. CPS DATA (selected years) 1950 1955 1960

1965

MALES

55-64 65 and over

89.5 46.8

86.9 45.8

87.9 39.6

86.8 33.1

84.7 27.9

24.3 9.1

27.0 9.7

32.5 10.6

37.2 10.8

41.1 10.0

6.1

6.4

7.5

5.9

7.6

FEMALES (TOTAL)

55-64 65 and over MARRIED WOMEN, HUSBAND PRESENT

65 and over

Source: (1) Census data: 1960 Census of Population, Employment Status and Work Ex­ perience, Table 6; (2) CPS data: Manpower Report of the President, March 1966, Table B-2 for married women, and Table A-2 for all other subgroups. aParticipation rates from Census data are for the total U.S. population (including inmates of institutions) during the census week of the stated year. CPS rates pertain to the U.S. civilian noninstitutional population and are annual averages of data from monthly surveys, except that the rates for married women pertain only to a single month (April or March). b Comprises women who are widowed, divorced, separated, or married with husband absent. * We wish to acknowledge the special contribution to this chapter made by Mr. Daniel Rubinfeld, who is now a graduate student at the Massachusetts Institute of Technology. Mr. Rubinfeld did much of the preliminary work for this chapter, including the prepara­ tion of a first draft.

352

OLDER PERSONS

45

...............

40

.

_.

~ales

65+

'"

~ -; 35

c;

a::: c:

'-....

""-..

.~

c;

""-

930 .!:!

""-

::;

.

Q. tJ

::; 25

'"

._._.

u.

...

o o

.D

.J

10 Married women 65 +

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

Year

11-1. Labor force participation rates, males 65+ and married women with husband present 65+, 1948-1965. Source: Table 11-1 and sources cited therein.

FIGURE

It is the large decrease in labor force participation on the part of men 65 and over-their participation rate having fallen by almost 20 percentage points between 1948 and 1965 - that is of greatest interest, and we shall devote by far the largest part of the present chapter to this population group. Considerations of space, repetitiveness, and lack of data all suggest that it would be unwise to attempt a detailed analysis of postwar trends in the participation rates of any of the other subgroups of older persons. However, we shall also comment, in the last section of this chapter, on some of the factors which may explain why the participation rate of mar-

6~

353 ried women 65 and over has behaved so differently from the participation rate of the older men—and from the participation rate of younger married women. As in the earlier chapter which analyzed trends in the participation rates of younger married women (Chapter 7), most of this analysis will consist of attempts to see what our cross-sectional findings can con­ tribute to an understanding of changes over time in participation rates.1 The organization of this material also parallels that of Chapter 7, in that we shall begin by examining the effects of demographic factors, shall then consider income and job-incentive effects of various kinds, and shall end by discussing other factors (principally hours of work, health, and com­ pulsory retirement). TRENDS

MALES 65 AND OVER

Demographic Factors The standardization procedures used to analyze the effects of demo­ graphic factors on trends in the participation of older persons are exactly the same as the procedures described at length in Chapter 7; a brief synopsis of findings is all that is needed here. The results (for married women 65 and over as well as for males 65 and over) are summarized in Table 11-2. In the case of men 65 and over, the net effect of standardizing for changes in age distribution, marital status, color, and rural-urban residence is to dampen somewhat the downtrend in participation over the period 1948-1965. To be more specific, we estimate that the combined effect of these demographic factors has been to reduce the participation rate of older men by 2.9 points over this period. It follows that, had these demographic changes not occurred, the participation rate for these men would have fallen 16.0 points between 1948 and 1965, as compared with the drop of 18.9 points actually recorded. Over the decade spanning World War II, demographic factors had a somewhat smaller absolute effect on the participation rate of older men, tending to reduce the participation rate of this group by 2.3 percentage points. In marked contrast, however, to the precipitous drop in the actual participation rate which occurred between 1948 and 1965, the actual decline between 1940 and 1950 was less than one-half of a per­ centage point. The implication is that between 1940 and 1950 demo­ graphic factors and other factors pulled in opposite directions, and our estimates suggest that the participation of men 65 and over would have 1The results of our attempts at time-series analysis are discussed in Chapter 16. Unfor­ tunately, we were unable to develop the time-series analysis in such a way as to make it helpful in disentangling factors responsible for trends.

354

OLDER PERSONS

TABLE 11-2 Summary of Effects of Demographic Factors on Changes in Labor Force Participation Rates of Males 65 and Over and Married Women, Husband Present, 65 and Over, 1943Γ to 1965 and 1940 to 1950 1948 to 1965

1940 to 1950

—18.9

—0.4

MALES 65 AND OVER

ActualchangeinLM65+ Effect on actual change in LM65+ of correcting for changes in:a Age Color Rural-urban residence MaritEil status Subtotal Change in LM65+ after correcting for all four demo­ graphic factors

+0.8 0.0 +2.7 —0.6 +2.9

+0.6 0.0 +2.0 —0.3 +2.3

—16.0

+1.9

MARRIED WOMEN 65 AND OVER, HUSBAND PRESENT Actual change in LMW65+

+1.5

+1.7

+0.2 —0.1 —0.1 0.0

+0.1 0.0 0.0 +0.1

+1.5

+1.8

Effect on actual change in LMW65+ of correcting for changes in: a Age Color Rural-urban residence Subtotal Change in Lmw6S+ after correcting for all three demographic factors

Sources: The actual changes for the period 1948-1965 were calculated from CPS data presented in the Manpower Report of the President, March 1966. The actual changes for the period 1940-1950 were calculated from the decennial census data. The weights used to correct for the effects of changes in the various demographic factors were calculated from data presented in various Special Labor Force Reports as well as in the decennial censuses. a In making the correction for, say, age for the period 1948-1965, we first calculated what the overall participation rate for persons 65 and over would have been in 1960: (1) had the 1948 age distribution prevailed then; and (2) had the 1965 age distribution prevailed then. Next, we subtracted the 1965 result from the 1948 result to obtain the correction. The cor­ rections for the 1940-1950 period were made analogously, with the participation rates in 1950 calculated by using 1940 and 1950 weights. For a discussion of various methods of standardizing, see the section on 'Demographic Factors" in Chapter 7, especially foot­ note 2. The results reported in Chapter 7 suggest that the correction factors are relatively insensitive to the choice of reference dates.

risen by about 2 percentage points during this decade had the demo­ graphic characteristics of the group remained the same. Clearly the most important demographic change in both periods was in residence — the causal factor being off-farm migration. Older males living on farms are much more likely to be in the labor force than are their nonfarm counterparts, and we estimate that the change in the residential characteristics of males over 64 (taken by itself) would have reduced their overall participation rate by 2.7 points between 1948 and 1965, and by 2.0 points between 1940 and 1950.

355 The aging of older males that has occurred since 1940 has also served to reduce their participation, but not nearly so much. According to our calculations, the rise in the fraction of males 65 and over who were 75 and over was large enough to produce a reduction of eight-tenths of a point in the participation rate between 1948 and 1965, and a reduction of six-tenths of a point during the wartime decade. On the other side of the ledger, the secular increase in the fraction of older males who are married with wife present has tended to increase the participation of older males—by about six-tenths of a point in the post­ war period, and by about half that much between 1940 and 1950. Finally, we should note that the slight increase in the fraction of older males who are nonwhite evidently had no perceptible effect on the partici­ pation of older males during either period. TRENDS

Income and Job-Incentive Effects We found in the preceding section that, after adjusting for demographic changes, the labor force participation rate of males 65 and over declined by 16 percentage points between 1948 and 1965. Ourtask now is to see how much of this decline can be explained by associated changes in various income and job-incentive variables: changes in the amount of other income received by older males, increases in their educational attainment and earnings, changes in occupation mix and in the relative number of older persons, and variations in the overall tightness of labor markets. We shall proceed by discussing these variables one at a time and then presenting a tabular summary of our findings.2 Other income. There has, of course, been a very substantial increase during the postwar period in the amount of other income per capita re­ ceived by older persons. The sharp rise in the fraction of older persons eligible for Social Security retirement benefits, numerous increases in the level of these benefits, the spread of private pension plans, and increases in the accumulated savings of individuals — all these developments have served to increase the financial resources at the disposal of older persons. We would normally expect higher levels of other income to be associated with lower participation rates, other things equal, and our analysis of the 1/1000 Sample provides strong support for this hypothesis, at least up to a certain (rather high) level of other income. Thus, at least part of the sharp decline in the participation of males 65 and over which occurred between 1948 and 1965 surely ought to be attributed directly to the operation of an ordinary income effect. The more difficult—and more interesting—question concerns the magnitude of this effect. The first problem involved in estimating the magnitude stems from the rather curious shape of the cross-sectional relation between other 2 The reader who wishes to see this summary without further ado may turn directly to Table 11-3 on page 364.

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OLDER PERSONS

income and participation. If one looks at the adjusted relationship for males 65-74 in Figure 9-6, he sees at once that the graphed relation is quadratic rather than linear. For reasons set out in Chapter 9, we believe that special considerations account for the unusually steep decline in participation between the two lowest oi intervals, as well as for the in­ crease in participation between the two highest intervals.3 Therefore, we have used the near-linear intermediate segment (from $500-999 to $3,000-4,999) as the basis for estimating the impact of a rise in oi over time on this group's participation. This procedure leads to a conservative estimate of the income effect on participation, since the presence of wel­ fare recipients in the $500-999 oi interval pulls down the participation rate for this group; that is, the segment in question would be steeper if disabled persons on welfare were eliminated from the sample. As it stands, the slope of the segment under scrutiny is roughly —0.75 (with other income expressed in units of $100).4 Hence, a rise in oi of $100 would lead, under these assumptions, to a fall in participation of three-fourths of one percentage point. The second step in the estimating procedure consists of calculating the magnitude of the increase between 1948 and 1965 in the median amount of other income (expressed in 1959 dollars5) received by all males 65 and over. Unfortunately, this requires some guesswork because there are simply no continuous time-series data on the total amount of other income per capita. There is also surprisingly little information on many of its com­ ponents, which collectively comprise all sources of income other than wages, salaries, and earnings from self-employment. There are, however, some rather good statistics on Social Security benefits and coverage for older males, and these can be used as the springboard for a rough esti­ mate of the change in total other income. According to the Social Security Administration, the average annual retirement benefit paid under the Social Security system to men in "re­ tired-worker families" increased from $310 in 1948 to $1,086 in 1965. In 1959 dollars, the respective figures are $378 and $1,000. Overthe same period, the fraction of all males 65 and over receiving retirement benefits from Social Security rose even more dramatically—from 14 percent to 76 3 To recapitulate the argument, we suspect that welfare payments to the disabled are very important in the $500-999 bracket, whereas the very top Oi bracket ($5,000 or more) in­ cludes a disproportionately large number of men with exceptional ambition, unusually re­ warding occupations, and enough other income from non-OASDi sources to make the workdeterring effect of the earnings test of little import. (For further discussion of these points, see the section on "Other Income and Other Family Income: Males 55-64 and 64-74" in Chapter 9.) 4 This estimate was derived by dividing the drop in participation between the $500-999 and $3,000-4,999 intervals (24 points) by the associated rise in oi between the midpoints" of these intervals ($3,250) and then converting the quotient to $100 units. Fitting a straight line to all of the points on this segment would yield almost exactly the same slope. 5 Since the oi-participation relation in Figure 9-6 pertains to other income received in 1959, it is necessary to estimate the increase in oi in 1959 dollars.

TRENDS

357

percent. If we multiply the average benefit for each year by the fraction of elderly males receiving such benefits, we obtain an estimate of the average annual retirement income from Social Security for the entire population of males over 64. This works out to about $53 in 1948 and $760 in 1965 —an increase of roughly $700 per capita.6 If Social Security benefits per older male rose by about $700 (in 1959 dollars) between 1948 and 1965, how large was the average increase over this period in all other forms of non-labor income received by older males? Given the lack of data, any answer is somewhat conjectural, but $600 strikes us as a plausible estimate.7 It would therefore appear that the median increase in other income re­ ceived by males 65 and over from all sources over the period from 1948 to 1965 was in the neighborhood of $1,300 (in 1959 dollars). To find the predicted change in the participation rate of these men that a rise of $1,300 would produce, we multiply this figure by the estimated slope of the oi-participation function, namely —0.75 (per hundred dollars). The answer is an implied decline in participation of 9.8 percentage points, or about three-fifths of the actual decline (after adjusting for demographic factors) that took place during these years. This result leaves little doubt 6 The data on benefits and coverage were obtained from the U.S. Department of Health, Education, and Welfare, Social Security Bulletin, "Annual Statistical Supplement for 1965," Table 26, and from the Social Security Administration, Quarterly Summary of Earnings, Employment, and Benefit Data, February 1966, Table 7. We assume that the median increase in these benefits would be quite similar to the mean increment estimated above. 7 This estimate was obtained in the following manner: First, we calculated the mean amount of asset income (interest, dividends, and rent), pen­ sions (including veterans' benefits) from all sources other than Social Security, and public assistance payments received by males 65 and over (and their wives) in 1962. (Source: U.S. Department of Health, Education, and Welfare, The Aged Population of the United States: The 1963 Social Security Survey of the Aged, Table 3.6.) The figure was $1,040 (in 1962 dollars). Next, we attempted to secure a comparable figure for 1951 on the basis of data gathered in the Follow-up Survey of Persons 65 and Over, a special survey conducted in April 1952 by the Bureau of the Census. (Source: Peter O. Steiner and Robert Dorfman, The Economic Status of the Aged, Appendix Tables 109, 111, and 112.) The initial estimate of $620 in­ cludes Social Security benefits, for which separate data are not shown in this survey. Social Security data indicate that about 25 percent of older males received such benefits in 1951, and that the average benefit per retired male recipient was about $43 per month, or $516 per year. This implies an annual per-capita benefit for all males over 64 which was onefourth as large, or about $130. Subtracting the latter figure from our initial figure of $620 leaves a residual of $490 —our estimate of mean asset income, pensions other than Social Security, and assistance payments received by elderly men in 1951. Converted to 1959 dollars, the estimates work out to roughly $550 for 1951 and $1,000 for 1962 —an increase of $450. The final step is to estimate the size of the average increase in other income minus Social Security benefits for the longer period of 1948-1965. If we assume roughly the same rate of increase during the longer postwar period as we found for 1951-1962, the figure that emerges is very close to $700 (in 1959 dollars). However, we think that a figure of $600 is more defensible, since the increase in the mean amount of other income (excluding Social Security benefits) has probably exceeded the increase in the median level by a considerable amount, and the latter measure is presumably more relevant to changes in the labor force participation rate of the whole population of older males.

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that rising levels of other income — and particularly the increases in Social Security benefits and coverage — have played a major role in reducing the participation of older males during the postwar years.8 There is one final point of some importance to be made: there is good reason to believe that the foregoing result overstates, to some unknown extent, the pure income effect of rising other income. As we explained in Chapter 9 (see especially page 315), the relation between oi and par­ ticipation in the 1/1000 Sample is not altogether free of the effects of "compulsory retirement" and the OASDI "earnings test." Both factors make the participation of elderly men with moderate amounts of other in­ come lower than would otherwise be the case. (We shall return, later in this chapter, to the broader question of the overall effects of various institutional arrangements on the decline in the participation of older men.) Earnings. Having considered the effects of increasing real income on the trend in the labor force participation of older men, we turn now to the first of our job-incentive factors, earnings. Surprisingly, this is the most difficult single variable to incorporate into the analysis, and we end up having little to say about it. The major source of difficulty is the performance of the earnings varia­ ble in the 1960 intercity regression analysis. These regressions provide no evidence of the expected positive relationship between earnings and the participation of males 65 and over. On the contrary, the earnings coeffi­ cient is negative and highly significant in the run for all males 65 and over and in the runs for both marital subsets.8 We have argued that this nega­ tive relation is due, in the main, to a greater incidence of compulsory re­ tirement policies in high-wage cities;10 hence it would certainly not make sense to apply the intercity earnings coefficient for this group to the in­ crease in full-year male wages which has occurred over time. We are also reluctant, however, to insist on substituting some (arbi­ trary) positive coefficient for the negative coefficient. To be sure, rising wages do increase the opportunity cost of retirement and thus certainly ought to be expected to lead, ceteris paribus, to some increase in partici­ pation. But work beyond age 65 in a society where retirement at 65 is the norm may be viewed as a special kind of "overtime," and the willingness of a person to work overtime may turn not just on the absolute remunera­ tion he will receive, but also on the ratio of his earnings during the over­ time period to what he has earned previously. With few exceptions, men 8 This conclusion might appear to be at variance with Long's view that "social security and pensions were far from being the main force (though they doubtless helped) in bringing about the withdrawal of elderly persons from the labor market" (Clarence D. Long, The Labor Force under Changing Income and Employment, p. 163). But Long was primarily concerned with reductions in participation that occurred prior to 1950, and for this earlier era his conclusion is no doubt correct. 9SeeTables 10-1 and 10-2. 10See "Results for Urban Groups in 1960: Earnings" in Chapter 10.

TRENDS

359

well past 65 who continue working no doubt experience a decline in earnings, partly because of the frequent necessity to find new jobs, and partly because of the loss in productivity associated with aging. If the earnings opportunities for the elderly have risen no faster during the postwar period than have the (higher) earnings of men 55-64 (unfor­ tunately, we have been unable to find reliable data bearing on this issue), it is not clear that increases in the general wage level have caused an appreciable rise in the fraction of elderly men who would prefer to con­ tinue working beyond the usual retirement age. The conclusion we have reached is that in estimating the quantitative effects of our several income and job-incentive variables, it is best to assume that earnings per se has had a negligible impact. There are three reasons for this conclusion: (1) the observed negative relationship be­ tween earnings and participation found in the intercity cross-sections seems inapplicable to the analysis of trends, and yet there is no definite evidence that one ought to project a positive relationship; (2) it may be that the relative earnings of older males (relative, that is, to what they earned before reaching 65) are at least as important as the absolute level of past-65 earnings in affecting participation decisions, and we doubt that these relative earnings have risen significantly, if at all, over the postwar period; (3) whatever positive effect the increasing absolute level of earnings may have had on participation may well be captured by our esti­ mate of the effect of the increased schooling of older persons on their participation (discussed immediately below). Schooling. Between 1948 and 1965, the median educational attain­ ment of males 65 and over rose from about 7.6 years of schooling to about 8.3 years —an increase of seven-tenths of a year.11 Since we found a positive and highly significant association between schooling and the participation of older males in both the intercity regressions and in the analysis of data from the 1/1000 Sample, there is a prima facie case for expecting the rise in schooling over time to have exerted some upward pressure on participation. Not all of the factors that underlie the cross-sectional relationship, however, can be expected to operate over time. At any point in time, older males with more schooling are less likely to be unemployed and, if working, are more likely to hold the more interesting, better-paying jobs. But over time hiring standards with respect to the minimum amount of schooling needed for most jobs have risen along with the general level of education. Schooling at a point in time is also associated to some degree with a number of basic personal characteristics, such as intelligence and motivation, which probably change very little in the population at large over the span of two decades (or longer). For all these reasons, it seems safer to use the coefficient for schooling 11 Estimatedfrom data for April 1947 and March 1965 in the Current Population Survey, Series P-20, Reports No. 15 and 158.

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OLDER PERSONS

from the intercity regression than to use the slope of the schoolingparticipation relation based on the 1/1000 Sample. Schooling and per­ sonal characteristics are likely to be associated less strongly when making comparisons among cities rather than among individuals. The intercity coefficient for schooling in 1960 (as shown in Appendix Table B-21) was 1.89. When applied to the rise of 0.7 years in the median attainment of elderly men, it leads to a predicted increase of about 1.3 percentage points in LMe5+ over the 1948-1965 period. As noted at the end of our discussion of earnings, this predicted increase may reflect, in part at least, the effect on participation of the increased earnings levels associated with more education. Unemployment. The findings in Chapter 10 make it very clear that the participation of males 65 and over is highly sensitive to the overall rate of unemployment. Our intercity regressions for 1960, the comparability regressions for 1950, and the time-series regressions for 1954IV-1965III all support this conclusion, even though they lead to different estimates of the slope of the relationship. The overall unemployment rate averaged 3.8 percent in 1948 and 4.5 percent in 1965. If one multiplies the difference of 0.7 points by the net re­ gression coefficient for unemployment in the final regression for I960,12 namely —1.34, he obtains a predicted fall in participation of 0.9 per­ centage points.13 We believe, however, that the above procedure leads to a considerable underestimate of the extent to which high unemployment during the late 1950's and early 1960's reduced the labor force participation rate of older males in 1965 vis-a-vis 1948. It is important to recognize that the unemployment rate averaged 5.8 percent in the seven years preceding the terminal year of 1965, when it fell to 4.5 percent. And we doubt seriously that the cumulative effects of high unemployment over the period 1958-1964 were erased entirely by the tightening of the labor market in 1965.14 The participation of older males would certainly have been higher in 1965 if the (relatively low) unemployment rate that year had prevailed during the preceding seven years. Given the greater impact of long-run changes in labor market condi­ tions on participation decisions, this argument for not placing much stress on the 1965 unemployment rate would presumably hold for all demographic groups. But it operates with special force in the case of older persons since the decision to retire is often irreversible. That is, relatively 12This

regression is shown in panel n of Table 10-1. the sensitivity in question may have changed over the postwar period, a case can be made for using some average of the unemployment coefficients from the comparability regressions for 1950 and 1960 instead of the coefficient employed above. But this alterna­ tive approach would yield exactly the same result, for the mean of the two coefficients in question (—1.47 and —1.20, respectively) is —1.34. 14 For some strong evidence in support of this position, see our estimates of "induced participation" in Chapter 17. 13 Since

TRENDS

361

few older workers who lose their jobs and drop out of the labor force during a prolonged recession are likely to re-enter the labor market several years later, even if jobs are easier to find at that time. In the mean­ time these persons have grown accustomed to the routine of not working; both their interest in work and their job skills have deteriorated to some extent; and unless the labor market where they live is very tight, em­ ployers may still be unwilling to hire them. We hasten to add that we have no convincing empirical evidence to offer in support of this "irreversi­ bility" hypothesis, but it seems so intuitively plausible that we are in­ clined to attach a fair degree of importance to it. If this hypothesis is right, what ought to be compared is not the unem­ ployment rates in 1948 and 1965, but rather the unemployment rates over two longer periods which can be approximated by 1945-1948 and 1961-1965. If one calculates a simple (unweighted) average of the annual unemployment rates during these two periods, he obtains a figure of 3.5 percent for 1945-1948 and 5.5 percent for 1961-1965.15 Multi­ plying the 2-point increase in unemployment by the intercity coefficient of —1.34 yields a much more plausible estimate, in our judgment, of the reduction in the participation of elderly males between 1948 and 1965 which can be attributed to the chronic slack that prevailed in our economy from 1958 to 1964 —namely, a decline of about 2.7 percentage points. We do not claim that this simple averaging procedure is precisely the right way of taking the irreversibility phenomenon into account. But, in the absence of evidence which could provide the basis for a more sophisti­ cated approach, it seems defensible. Measures of supply and occupational mix. Our index of the supply of older males (the percentage of all males 14 and over in the civilian, noninstitutional population who were 65 and over) increased by 2.3 per­ centage points between 1948 and 1965.16 The resulting increase in the competition for jobs among older males presumably had some depressing effect on their participation, but the intercity coefficient for this variable in 1960 (—0.30) suggests that the effect was small — roughly seven-tenths of a percentage point. Postwar trends in the occupational distribution of employment may also have contributed to changes in the participation of older males. As of 1960, SMSA'S with a larger fraction of male employment in four major occupational groups—farmers, managers, sales workers and service 15 The figures for 1945 and 1946 are based on data in Table B-20 of the Economic Report of the President, January 1967. Since the figures for these two years are based on the old definitions of labor force and unemployment, we have increased them by 0.3 points to make them more comparable with the statistics for more recent years. We use 1945-1948, rather than 1944-1948, for the earlier period so as not to give undue emphasis to the highly unusual labor market conditions that prevailed during World War II. 16 The ratios for these two years (calculated from the 1966 Manpbwer Report of the Presi­ dent) were 9.8 percent and 12.1 percent, respectively.

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OLDER PERSONS

workers — tended to have higher participation rates for males over 64,17 and there has been a significant postwar decline in the fraction of total male employment accounted for by these occupations. However, a straightforward application of our usual estimation procedure (i.e., multiplying the change in the variable by its regression coefficient) would be a mistake. Most of the change in this variable is due to the decline in agricultural employment, and we have already taken this shift into account by adjusting our participation rates for changes in residence. 18 An alternate approach uses the adjusted participation rates for males 65-74 in major nonfarm occupational groups, as shown in panel IB of Table 9-13. As we explained in Chapter 9, these rates were taken from a special regression which was confined to older males in the 1/1000 Sample "with an occupation"—those with some work experience during the preceding ten years. These rates reflect such occupation-specific factors as the ease with which older men can continue working in the same job after age 65, the returns (pecuniary and psychic) from such work, and similar considerations. We computed two weighted averages of these adjusted rates for 1960, using as weights the nonfarm employ­ ment of men 55-64 in 1950 and 1965, respectively.19 (The reason for using employment data for men 55-64 rather than for men 65 and over is that we wish to know the change between 1948 and 1965 in the occu­ pational distribution of men approaching the usual retirement age, not the change in the distribution of men still working beyond that age.) The predicted participation rate (for men 65-74 with an occupation) is 46.1 percent using 1950 employment weights, and 46.6 percent using 1965 employment weights. We conclude that the change between 1948 and 1965 in the occupational distribution of (nonfarm) men approaching retirement age, taken by itself, would have produced an increase in the participation rate of males 65 and over of about one-half of one per­ centage point.20 Finally, in light of our finding that self-employed men have a much higher propensity to continue working past 65 than do their wage-andsalary counterparts, account should be taken of the fact that the ratio of self-employment to total employment in the nonagricultural sector21 has 17 The regression coefficient for this index of occupation mix was 0.26 and significant at the 1 percent level; see Appendix Table B-21. 18 See the discussion of demographic factors in the preceding section. 19 Data on the employment of males 55-64 by major occupational group for 1950 were obtained from the 1950 Census of Population, Detailed Characteristics, U.S. Summary, Table 127. (Similar data for 1948 are not available.) Comparable data for 1965 were taken from the B.L.S., Special Labor Force Report No. 69, Labor Force and Employment in 1965, Table C-8. Men with farm occupations were excluded both years. 20We recognize that the estimation procedures used here are less than ideal. It would have been better to use as weights the distribution of men 65 and over in 1948 and 1965 according to the occupation they had at, say, age 60. But information of this kind is not available. 21 Again we exclude the agricultural sector to avoid duplicating our earlier adjustment for changes in residence.

TRENDS

363

declined appreciably during the postwar years. In the case of males 5564, this decline amounted to about 5 percentage points during the 19481965 period.22 How large a decline in the participation of elderly men does a reduction of this magnitude in self-employment imply? Again, our special 1/1000 regression for males 65-74 with an occupation contains the ingredients for making a reasonable guess. During the 1960 census week, the adjusted participation rate for those males whose most recent work experience had been in a self-employed status was 60.8 percent, compared to 43.3 percent for those who had last worked as wage-or-salary workers.23 This difference of 17.5 percentage points suggests that a one-point de­ cline in the self-employment ratio for men approaching retirement age would ultimately lead to a fall of 0.175 percentage points in the partici­ pation rate of these men during ages 65-74. A five-point decline should have five times as great an effect, and we therefore estimate that the longrun trend away from nonagricultural self-employment tended to make the participation rate of males 65 and over about nine-tenths of a point lower in 1965 than in 1948.

Table 11-3 recapitulates the predicted change in LM65+ between 1948 and 1965 generated by changes in the income and job-incentive variables discussed above. The total (net) predicted change is —12.3 percentage points, slightly over three-fourths of the actual reduction in participa­ tion of males 65 and over, corrected for changes in demographic factors.

Other Factors With so large a fraction of the postwar decline in LM65+ already "ex­ plained" by the income and job-incentive variables (about 75 percent), one might possibly conclude that the remaining, less tangible consider­ ations have had relatively little impact. Such a conclusion need not fol­ low, however. First, there is nothing in our method of analysis which guarantees that the actual change in participation will not be overpredicted. Secondly, even if the other factors as a group have had a rather small net impact, this may be because their individual effects have been strong but largely offsetting. Indeed, we shall argue that several of the "other factors" examined in this section—hours of work, health, as22 This inference is drawn from Census data for April 1950 (Special Report on Industrial Characteristics, Tables 3, 4, and 19) and CPS data for April 1967 in Employment and Earn­ ings (May 1967, Table A-18). Statistics for 1948 and 1965 are evidently not available, but the trend for 1950-1967 should be very similar. The calculated self-employment ratio for nonfarm males 55-64 was 17.3 percent in April 1950 and 14.1 percent in April 1967. Since a special study by the Bureau of the Cen­ sus indicated that self-employment of males in nonagricultural industries was underreported by roughly 10 percent in the 1950 Census (see the U.S. President's Committee to Appraise Employment and Unemployment Statistics, Measuring Employment and Unemployment, Appendix J, Table J-4), we raised the estimate for April 1950 from 17.3 percent to 19.0 percent. 23These results appear in Table 9-14, panel π.

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TABLE 11-3 Predicted Change in the Labor Force Participation Rate of Males 65 and Over Between 1948 and 1965, Using the Income and Job-Incentive Coefficients from the 1960 Intercity and 1/1000 Sample Regressions Income and job-incentive variables

Regression coefficient for the variable" (1)

Other income ($100 per year) -0.75 ** C Earnings of males + 1.89 ** Schooling of older males (yrs.) Unemployment rate (%) — 1.34 ** Supply of older males (%) -0.30 * Occupation mixc +0.18 ** Self-employment mix (%)' Total predicted change Actual change in LMe5+, corrected for changes in demographic factorsg Predicted change as a percent of actual change

Estimated change in the variable, 1948-1965 (2)

Predicted change in I-M65+ [(D X (2)] (3)

+13.0"

-9.8 0.0 + 1.3 -2.7 -0.7 +0.5 -0.9 -12.3

c

+0.7 +2.0 +2.3

a

e

-5.0

-16.0 77%

Sources: The regression coefficients shown in column (1) for schooling, unemployment, and supply are from the multiple regression presented in panel Ii of Table 10-1 and in Appendix Table B-21 (run n). The coefficients for other income and self-employment mix are based on the results of the regression using the 1/1000 Sample for males 65-74 pre­ sented in Tables 9-9 (panel π) and 9-14 (panel n), respectively. The sources and changes over time in the values of the variables shown in column (2) are explained in the text and footnotes thereto. The source of the predicted change in LM65+ due to occupation mix is indicated in note e below. a The symbols after each variable indicate the level of significance of the regression coeffi­ cient (** = 1 percent, * = 5 percent). b Measured in hundreds of 1959 dollars. 0 The regression coefficient for this variable in Table 10-1 (panel n) is negative (—0.18) and significant at 1 percent. For reasons explained in the text, we do not believe this co­ efficient can be applied to changes in earnings over time, and it is not clear whether the coefficient relevant to such changes is positive or negative. Therefore a predicted change of zero has been (arbitrarily) assigned to this variable. " This figure is the change in the average unemployment rate between 1945-1948 and 1961 — 1965; the reason for using these periods is explained in the text. e The results for this characteristic are based on the difference between two weighted aver­ ages of the adjusted occupational participation rates for nonfarm males 65-74 with an oc­ cupation, as shown in Table 9-13, panel IB. The first average was constructed using the employment in April 1950 of males 55-64 in these occupation groups as weights; the second average used the employment of these males in April 1965. For further details, see the text. 'This variable is defined as the ratio of self-employment to total employment of males 5564 in nonagricuitural industries. 8 See Table 11-2.

set holdings, income aspirations, and compulsory retirement—have in fact had marked effects on the participation rate for older men. Hours of work. Over the period from 1948 to 1965, we estimate that the average work week of employed males 65 and over fell by about five

365

TRENDS

36.4.24

hours, or 12 percent—specifically, from 41.5 hours to This re­ duction in hours of work means, of course, that the quantity of labor sup­ plied by older males has declined a good deal more than their labor force participation rate. Whereas the latter rate fell from 46.8 percent in 1948 to 27.9 percent in 1965, a participation rate expressed in 41.5-hour "work units" fell from 46.8 percent to 24.5 percent, a decline of 3.4 percentage points more. In the present context, the main issue raised by this trend toward shorter hours is its impact on the participation decisions of elderly men. We are quite confident about the direction of impact: the decline in the work week has no doubt served to increase the participation of men 65 and over. The observed decline in LM65+ would surely have been even greater had not hours of work also declined. Two lines of argument lead to this conclusion about direction of im­ pact. First, if one envisions individuals (or family units) deciding to allocate a certain fraction of their lifetime hours to labor force partici­ pation, given tastes, income, wage rates, etc., and if the hours worked per week when at work are then reduced, some compensating increase in the fraction of time spent in the labor force would seem to be appro­ priate. The logic of this argument, especially as it relates to the ap­ plicability of cross-section analysis (when the economy-wide pattern of hours is more or less fixed) to an explanation of trends (when there are general changes in hours worked), was discussed at some length in Chap­ ter 7 (especially under "Other Factors: Hours of work") where we con­ sidered the effects of changing work schedules on the trend in the partici­ pation of married women. The second line of argument relates more specifically to the effects of required work schedules on the willingness of an older man to participate in the labor force at any point in time. In deciding whether to retire or to continue in the labor force, every individual presumably has in mind some maximum number of weekly hours which he is willing to work on a regu­ lar basis. (Presumably there is also some minimum number of hours per week necessary to make holding a job worthwhile, but it seems unlikely that this constraint is of practical importance in many cases.) The pref­ erences of individuals regarding maximum work weeks form one fre­ quency distribution (with X 0 individuals willing to work no more than H 0 hours, etc.), which can be called Hmax. Employers, of course, also have preferences regarding regular hours of work, and let us call the frequency distribution formed by these preferences Him (where Y0 jobs require H0 hours, etc.). Now, the argument that a reduction in hours of work leads to greater 24The figure for 1948 is based on a backward extrapolation of the trend in Census data between April 1950 and April 1960; the figure for 1965 is from the Current Population Sur­ vey as reported in the May issue of Employment and Earnings, Table A-22.

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OLDER PERSONS

participation by older males rests on two key premises. The first is that part of the Hmax distribution lies to the left of the Htetl distribution—i.e., that in general there are not enough short-hour jobs to go around, so that some persons on the short-hour side of the Hmax distribution elect not to participate in the labor force. The second premise is that the reduction in hours is not confined to long-work-week jobs (for which there may be no excess demand), but that the number of short-hour jobs which are in short supply also increases. In short, if the whole frequency distribution of Hrm shifts to the left, so that a larger fraction of those seeking shorthour jobs are now able to fine them, the participation rate will rise. The first of these two premises seems plausible enough, and there is some empirical evidence to support the second. During the period from April 1950 to April 1960, the fraction of employed urban males 65 and over working part-time (less than 35 hours per week) rose from 15 percent to 29 percent, while the percentage working more than 40 hours fell from 38 percent to 29 percent.25 CPS data for the more recent period from 1957 to 1965 show that the fraction of all employed males 65 and over, includ­ ing those in rural areas, who were working part-time for voluntary reasons increased from 19 percent to 30 percent, while the fraction on full-time schedules (35 hours or more) declined from 76 percent to 67 percent.26 These figures surely suggest that there has been a substantial increase during the postwar period in the number of part-time jobs open to elderly men,27 and we would be surprised indeed if this development has not, in turn, encouraged a significant number of older men to continue partici­ pating in the labor force. But how many older men have had their labor force status affected by changes in work schedules? Has the decline in hours caused the partici­ pation of these men to rise, ceteris paribus, by a less-than-proportional, exactly-proportional, or more-than-proportional amount? Unfortunately, 25These figures were calculated from Subject Report PC(2)-6A of the 1960 Census, Employment Status and Work Experience, Table 12, and from Special Report P-E No. IA of the 1950 Census, Employment and Personal Characteristics, Table 13. Persons at work for whom hours were not reported and persons with a job but not at work were excluded from the base. 26 Calculated from Tables A-20 and A-22 of the 1966 Manpower Report of the President. The fraction of these men on part-time schedules for economic reasons declined from 5 to 3 percent. 27 Strictly speaking, the fact that a larger fraction of older men are now working less than 35 hours does not prove that there has been a substantial increase in the overall number of part-time jobs open to older men. The increase in the ratio of part-time employment to total employment could also be the result of (1) a reduction in hours worked by older men who are in a position to control their own hours (mainly the self-employed) and (2) a greater increase in the incidence of compulsory retirement among full-time employees than among part-time employees. While part of the increase in the part-time employment ratio for older males is probably due to these factors, we doubt that most of it is. The fact that the rise in part-time job holding has been so widespread (see the figures on female employment presented in Chapter 7 and the figures for younger persons in Chapter 14) certainly suggests that this is a general phenomenon which has affected nearly all population groups.

TRENDS

367

we have been unable to uncover any evidence that bears directly on this issue, but our intuitive feeling is that an elasticity in the vicinity of —1 (implying a proportionate rise in participation) may be fairly close to the mark. Consequently, we have assumed that the decline between 1948 and 1965 in the work week of elderly men caused their participation rate to fall by 3.5 percentage points less than it otherwise would have fallen. (See the calculations presented at the beginning of this section.) This implies that the unexplained reduction in the participation rate shown in Table 11-3 is increased from 3.7 percentage points to 7.2 when allowance is made" for the decline in average hours worked. We wish to emphasize, however, that this calculation is based on little more than a guess concerning the quantitative impact of shorter hours on the partici­ pation of older men. Health. In view of the important role that health plays in the retire­ ment decisions of older males,28 we need to consider whether there has been any significant change during the postwar period in the fraction of older males whose activities are limited because of chronic illness or disability. In view of the numerous medical advances during the period in ques­ tion, one might have expected to observe some decline in the relative number of persons handicapped by poor health. The best available evi­ dence, however, indicates otherwise. According to data gathered by the National Health Survey, the fraction of men 65 and over in the civilian, noninstitutional population reporting some chronic activity limitation actually increased from 43.6 percent during fiscal 1958 to 51.3 percent in fiscal 1966.29 Some part of this increase may be due to changes in the questionnaire and interviewing procedures used in the National Health Survey.30 Another part—and a very small one at that—may be related to the aging of the population of older men which occurred during this period.31 While we are reluctant to assert that these two considerations account for most of the increase, it is difficult to come up with additional explanatory factors. There is one other possible factor which does seem worth mentioning, however—the First World War. American casualties in that war were heavy: in addition to the 116,500 men who lost their lives, 204,000 were 28 See

the section on "Health" in Chapter 9. former statistic is from the U.S. Department of Health, Education, and Welfare, Health Statistics, Series B, No. 11, Table 9; the latter figure is from a forthcoming report by the above department and was kindly supplied by Elijah L. White of the National Center for Health Statistics. Comparable figures for earlier years are not available. 30 According to Mr. White (identified in the previous footnote), such changes "have re­ sulted in a continual increase in the number of [chronic] conditions reported as well as in the number of persons reporting [such] conditions." 31 The fraction of males over 64 who were over 74 rose by about 2.5 percentage points (from 32.2 to 34.7) during the period from 1957 to 1965. 29 The

368

OLDER PERSONS

wounded, and thousands more must have suffered injuries and contracted illnesses of a chronic nature in connection with their military service.32 Most of these men were 18 to 25 years old at this time (1917-1918). Those who lived to riper years were 58-65 in 1957 (the year of the first health survey) and 66-73 in 1965 (the year of the most recent one). Thus, part of the higher disability rate in the latter year may simply re­ flect the imprints of World War I borne by some of its survivors. It is interesting to note that the fraction of elderly women reporting chronic activity limitations declined from 42.8 percent in 1957-1958 to 40.2 percent in 1965-1966. The fact that the ratio moved in opposite directions for men and women is, of course, consistent with the "effectsof-war" hypothesis advanced above. (It also casts doubt on the notion that the increase for men is wholly statistical.) It should be added that the war hypothesis also has testable implications with respect to the kinds of disability for which the largest increases were reported in the older male population between 1957 and 1965. Unfortunately, lacking the complete findings of the 1965-1966 survey, we are unable to check these inferences. At any rate, if we accept the stated increase in the fraction of elderly men with a chronic activity limitation, it implies a reduction in LM65+ of 1.8 percentage points between 1957-1958 and 1965-1966. This estimate was obtained by calculating two weighted averages of the 1961-1963 participation rates of men with and without chronic activity limitations — the first average based on the percentage of elderly males in each of these two categories in 1957-1958, the second based on these percentages in 1965-1966 —and then subtracting the latter mean from the former.33 In view of the statistical doubts attending this result, plus the possi­ bility that it may be partly rooted in an exogenous historical event, we do not feel justified in extrapolating the decline for this period backward to 1948. Acceptance of the result for the more recent period (1957-1958 to 1965-1966) does mean, however, that the unexplained decline in the participation of older males during the postwar years is reduced by 1.8 percentage points —that is, from 7.2 points to 5.4. Asset holdings. There has been considerable improvement in the asset holdings of elderly couples and elderly single men during the postwar period. In 1951, about 42 percent of such couples had no financial assets at all (defined as cash, bank deposits, marketable securities, and loans to others); by 1962, this ratio had fallen to 28 percent. Over the same span, the fraction of single men 65 and over with no liquid assets fell from 62 percent to 41 percent. (In view of these sharp increases in the relative number of older persons with some financial assets, it is not surprising that the median amount of assets held by persons with some assets has 32 The

source of the casualty figures is the Information Please Almanac for 1968, p. 703. participation rates for each group in 1961-1963 are from the results of the Na­ tional Health Survey conducted during that period, as reported in Chapter 9 (see Table 9-7). 33The

TRENDS

369

changed relatively little: measured in 1959 dollars, this median rose from about $3,100 to $3,500for couples, and it fell from about $2,800 to $2,600 for single males.) In all probability, sizeable gains have also been reg­ istered in the number of older persons holding assets of other kinds (nota­ bly equity in homes), but reliable data are lacking.34 There is every reason to think that the asset holdings and labor force participation of older males are inversely related—just as we expected to find (and did find) an inverse relationship between other income and participation. Financial assets are, of course, one source of other income, but they are not an important source except for the well-to-do. Further­ more, most of the postwar growth in other income which has already been taken into account has not been the by-product of a growth in assets (as usually measured), but of Social Security benefits and private pensions. By considering both increases in assets and increases in other income as factors affecting participation, we are not, therefore, in danger of doublecounting to any serious degree. What is unclear, however, is how much the growth in asset holdings has reduced participation, for we lack the cross-sectional data needed to obtain some notion of the slope of the relation in question. But, in view of the rather small absolute increase in the average holdings per house­ hold during this period (a gain of perhaps $800 in 1959 dollars for mar­ ried couples and around $600 for single men),35 it seems unlikely that this factor has made a large contribution (say, more than one or two per­ centage points) to the reduction in participation of older males.36 34The figures reported above are drawn from two sources cited earlier: the 1963 Social Security Survey of the Aged (Table 4-5), and Steiner and Dorfman, The Economic Status of the Aged (Table 9.5, p. 133). The raw data for 1951 were gathered from a survey of OASDi beneficiaries, whereas those for 1962 relate to the total population of elderly persons. However, Steiner and Dorfman have adjusted the oasdi data to make them more represent­ ative of the total population. The medians for 1951 were estimated from frequency distri­ butions in Table 9.5 of Steiner and Dorfman and are therefore subject to a rather large mar­ gin of error. 35 These figures represent rough estimates of the increase in the average asset holdings of each group (including those with no assets) based on the sources cited above, with the me­ dian holding of those with some assets being treated as if it were the mean for that subset. 36 On the basis of the slope of the relation between L 5+ and other income, plus one truly M6 heroic assumption, we can make a very crude estimate of the impact of increased financial assets. We have estimated the slope of the middle segment of the relation between oi and LM65+ as —0.75, where oi is measured in hundreds of 1959 dollars. Now, if we had some idea about the rate of discount which older persons apply to future income, we could estimate the amount of assets which they would be willing to accept in exchange for a $100-per-year reduction in other income. There are good reasons to think this rate of discount is much higher than the market rate of interest on high grade corporate bonds. The low incomes of older persons, their short life expectancy, and the advantages of assets over market-equiva­ lent income streams in meeting financial emergencies — all these factors tend to drive a wedge between the two rates. Suppose we assume that the "average" rate of discount of elderly men is 15 percent (this is the heroic assumption). Given this premise, an elderly man would be indifferent between receiving a lump sum gift of $1,000 and a lifetime annuity of $150 per year. Now let us make the further assumption that each of these equally durable windfalls would have the same ef­ fect on his participation decisions. Under these two assumptions, a rise in asset holdings

370

OLDER PERSONS

Income aspirations. In using data from the 1/1000 Sample to estimate how much of the postwar reduction in LM65+ was due to the rise in other income that occurred during these years, we implicitly assumed that the income aspirations of older men remained the same. Specifically, this premise means that the prospect of receiving a given retirement income (measured in dollars of constant purchasing power) would have been just as likely to induce an elderly man of given demographic characteristics to retire in 1965 as in 1948. Stated this way, the assumption is clearly untenable: the rising standard of living of society at large must surely have eroded somewhat the satisfaction afforded by a retirement income of given size. And to the extent that the income aspirations of the elderly have risen during these years, we have overestimated the negative impact of rising other income on L 5+· If the direction of this bias is unambiguous, its magnitude certainly is not. In fact, the box of relevant data turns out to be empty. All we can do is offer some intuitive reasons for thinking that increases in income aspi­ rations have had a considerably smaller effect on participation in the case of older males than in the case of younger married women. Consider, first of all, the extent to which a desire to acquire "luxury" goods and services may have encouraged greater labor force participa­ tion. In the case of younger wives, many of the items in question are wanted, in large measure, because of the presence of children (e.g., wash­ ing machines, larger homes, a second car, a suburban environment, and college educations). But most elderly couples live alone. Also, we suspect that the "demonstration effect" associated with the introduction of new goods and services operates with considerably less effect on the elderly than on those in the prime of life—partly because the advertising which accompanies these innovations is usually "pitched" toward the younger generation, partly because older persons tend to be more "set in their ways," and partly because they are more reluctant to draw down their savings or go into debt (even temporarily) to buy ex­ pensive things lest some unforeseen emergency place them in dire straits. Finally, in the context of a life-cycle model of household behavior, sacrificing leisure in order to increase the level of consumption is less practicable for older couples than for younger ones. To the extent that the income aspirations experienced during old age are foreseen, it must seem advantageous to many to make provision for them by working harder during those years when one's earnings are relatively high, rather M6

37

of $750 for all males 65 and over would reduce their participation rate by the same amount as an increase in oi of $112 per year, or slightly less than one percentage point. The main point of this exercise is not to derive the specific estimate just noted, but rather to show that unless the rate of discount is extremely high (say 25 percent or more), very little of the unexplained reduction in the participation of older males can be accounted for by the growth in their asset holdings during the postwar period. 37 The general theoretical underpinning for this line of argument has been discussed at some length in Chapter 7 (see pp. 234-240) and need not be repeated here.

TRENDS

371

than by prolonging one's working years. We suspect that the desire to ensure a comfortable life in later years — or at least to avoid a miserable one—is one reason (among many) why younger wives take market jobs and why their husbands are so willing to work overtime. On the other hand, to the extent that the income aspirations and con­ sumption needs of old age are not foreseen until well after the retirement milestone has been passed, they probably have very little impact on par­ ticipation decisions, given the imposing set of obstacles that most retired men would face in finding remunerative work. In short, anticipated in­ creases in income aspirations during old age are likely to have their main impact on work-leisure decisions before age 65, whereas unanticipated desires for greater income are not likely to have much effect on partici­ pation decisions beyond that age. What does this discussion imply about the as yet unexplained portion of the reduction in LM65+? At last report, this figure stood at 5.4 percent­ age points. If forced to pick a number, we would surmise that higher in­ come aspirations have raised the participation rate of elderly men some­ what more than larger asset holdings have lowered it, but not by much more—say, one percentage point. This would place the residual reduction — with only one more factor to consider—at 6.4 percentage points, or at about 40 percent of the actual reduction (net of adjustments for demo­ graphic factors) of 16 points. Compulsory retirement. Having come this far, one is naturally tempted to assign all of the (as yet) unexplained reduction in LM65+ to the last explanatory variable, which in this case is the widely noted spread of com­ pulsory retirement provisions in union contracts and company personnel programs. (As noted in Chapter 9, these rules do not, of course, require the older worker to leave the labor force, but they do make it necessary for him to find a new job — sometimes in an entirely different industry, and nearly always at lower wages and less advantageous conditions of employment.) This temptation is especially hard to resist when the re­ sidual factor is clearly one of considerable importance, and when its impact on participation is obviously in the "right" direction. Two postwar surveys provide at least some basis for deciding whether or not to succumb to this temptation. The first of these two surveys was conducted by the Bureau of the Census in April 1952; it indicated that 7 percent of all males 65 and over in the civilian, noninstitutional popu­ lation reported they had stopped working (or looking for work) because of compulsory retirement policies.38 The second survey, conducted by the Social Security Administration in January and February of 1963, classified male wage and salary workers 65 and over who had "retired" 38Steiner and Dorfman, The Economic Status of the Aged, Table 4.7. This figure does not include the relatively few men who had been "retired" under these programs and had subsequently returned to work —roughly 1 out of every 8 men whose original jobs were terminated due to compulsory retirement provisions (ibid., Table 4.8).

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OLDER PERSONS

since 1957 by the reasons they gave for retirement; 19 percent of this group indicated company separations due to age.39 Quite obviously these two percentages are not at all comparable as they stand; furthermore, all of the adjustments that would have to be made in the 1963 figure to make it comparable to the figure for 1952 would tend to reduce the apparent difference of 12 points between the two percentages. That is, (1) adding persons who had retired since 1957 from the ranks of the self-employed to the population base would lower the ratio; (2) adding persons who had reached retirement age since 1957 but who were still at work in early 1963 would operate in the same direc­ tion; and (3) extending the scope of the analysis to include those persons who had retired (or had the option to retire) in 1957 or earlier—i.e., to all persons 65 and over—would probably result in a further reduction in the estimated compulsory retirement ratio, since we know that fewer workers were subject to compulsory retirement programs in earlier years. We have no way of making the third adjustment enumerated above, but we have recalculated the ratio for 1963 to take into account the first two considerations. The revised ratio —our estimate of the number of compulsorily retired males 65 and over in early 1963 who had quit work since 1957, expressed as a percentage of all males of this age who had reached retirement age since 1957 —works out to 11 percent, or just 4 percentage points higher than the ratio for April 1952. 40 If we were so bold as to apply the same annual rate of increase in the incidence of compulsory retirement to the longer period from 1948 to 1965, we would obtain an estimated change for the entire period of 6.5 percentage points —a figure that is remarkably (some might say suspi­ ciously) close to the unexplained reduction in the participation rate of elderly men of about 7 points. But given the strong probability that the adjusted ratio for 1963 is too high (since persons over 70 have, for the most part, been excluded), a somewhat smaller estimate — perhaps 5 points — would be more defensible. Before recording this estimate as our best guess, there are two other relevant considerations, pulling in opposite directions, which should be mentioned. First, Slavick's finding that compulsory retirement pro­ visions are more common in establishments that have adopted formal 39 1963 Social Security Survey of the Aged, Table 8.4. It should be noted that for the purposes of this tabulation, "retirement" was defined to mean "not working at a regular full-time job (35 hours or more a week for 6 or more consecutive months)." Thus, parttime workers and those who worked less than 6 consecutive months in 1962 were included among the "retired." 40The adjusted ratio was obtained by adding the following persons to the denominator of the original ratio (which originally included only male wage-and-salary workers 65 and over who had retired since 1957): (1) 440,000 male self-employed workers of the same age who had also retired since 1957, and (2) an additional 700,000 males aged 65-69 (an esti­ mate of the number of males in this age bracket who worked at regular full-time jobs in 1962). (Recall that all part-time workers and those full-time workers who worked less than six months were classified as retired in the 1963 tabulation.)

TRENDS

373

pension plans —also more common under plans providing higher bene­ fits 41 — suggests that our adjustment for the rise in other income has al­ ready captured a portion of the effects of greater compulsory retirement. On the other hand, it can be argued tfiat the spread of compulsory re­ tirement plans reflects a broader set of attitudes which has also affected older persons working in many areas besides those in which formal pro­ visions for compulsory retirement are common. That is, the growth of compulsory retirement policies may well have been accompanied by an increased tendency for other employers to decide against retaining older employees — a tendency which has probably also been encouraged by the increased coverage of Social Security, in the sense that the availability of benefits no doubt reduces the humanitarian qualms an employer may feel about retiring an old employee. We have no basis for assigning quantitative values to either of these last two (countervailing) considerations, so we end by repeating our earlier estimate — namely, that compulsory retirement policies have prob­ ably led to a decrease of something like 5 percentage points between 1948 and 1965 in the participation rate of males 65 and over.

Summary Our confidence in any one of the estimates included in this analysis of postwar changes in the participation of older men depends somewhat on the credibility of the whole set of estimates. Consequently, it seems appropriate to conclude this discussion by bringing together all the ele­ ments of the analysis; this we have done in Table 11-4. As we have tried to make clear, some of these estimates are much "harder" than others; nonetheless, it has seemed a good discipline to force ourselves to present a specific estimate for each factor. According to this analysis, the principal impetus toward lower partici­ pation rates for older men has come from the increased ability to afford leisure, as measured in terms of increases in non-wage (or salary) in­ come. A second, closely related, factor is compulsory retirement poli­ cies, which have spread in concert with the growing availability of pri­ vate and public pensions; indeed, the quantitative impact of compulsory retirement policies cannot be disentangled with any precision from the pure income effect. The movement of population from rural to urban areas and the rela­ tively high unemployment rates which prevailed during the late 1950's and early 1960's are two other factors which have been important in bringing about lower participation; we estimate that each of these fac­ tors has been responsible for a drop of almost 3 points in LM65+ between 1948 and 1965. Comparatively small amounts of additional downward 41

Fred Slavick, Compulsory and Flexible Retirement in the American Economy, pp.

12-13, 22-23.

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OLDER PERSONS

TABLE 11-4 Summary of Changes (in Percentage Points) Between 1948 and 1965 in the Labor Force Participation Rate of Males 65 and Over Attributed to Various Factors I. ACTUAL CHANGE IN L M6 5+ BETWEEN

1948

AND

1965

-18.9

II. CHANGES ATTRIBUTED TO:

A. Demographic factors 1. Age 2. Color 3. Rural-urban residence 4. Marital status Subtotal B. Income and job-incentive factors 1. Other income 2. Earnings of males 3. Schooling of older males 4. Unemployment 5. Supply of older males 6. Occupation mix 7. Self-employment mix Subtotal C. "Other factors" 1. Hoursofwork 2. Health 3. Asset holdings Ί 4. Income aspirations J 5. Compulsory retirement Subtotal

—0.8 0.0 —2.7 -1-0.6 —2.9 —9.8 0.0 +1.3 —2.7 —0.7 +0.5 —0.9 —12.3 +3.5 -1.8 +1.0

—5.0

TOTAL CHANGE ATTRIBUTED TO ABOVE FACTORS III. CHANGE IN L M65+ NOT ATTRIBUTED TO ABOVE FACTORS

—2.3 —17.5 —1.4

Sources: See text.

pressure on participation can be attributed to the aging of the popula­ tion, the increasing relative number of older persons, the apparent in­ crease in the incidence of chronic health conditions, and the decline in self-employment. If we had considered only the effects of factors serving to depress the participation rate of males 65 and over, we would have "over-explained" the decline in participation which has in fact occurred. The increasing percentage of older men who are married with wife present, the increas­ ing educational attainment of men 65 and over, rising income aspirations, changes in the occupational mix, and decreases in hours of work—all of these factors have tended to keep the participation rate from falling as much as it otherwise would have. However, according to our estimates, these "positive" factors have had relatively small quantitative effects, with the notable exception of hours of work. In our view, the reduction in the average work week, which has resulted in good part from a marked increase in the relative number of part-time jobs, has offset downward pressures on the participation rate equivalent to roughly 3.5 percentage points.

TRENDS

375

As the last lines of Table 11-4 indicate, when the negative and posi­ tive elements are combined, we arrive at an overall estimate of the change in LM65+ of—17.5 percentage points, which falls a little short of the actual decrease of 18.9 points. What is surely significant, however, is not the exact size of the residual, nor even whether we end up under- or overexplaining the change, but that the results seem to be in the right range. MARRIED WOMEN 65 AND OVER In the first section of this chapter, we presented data showing that the labor force participation rate of married women 65 and over had risen slightly during the postwar period (from 6.1 percent in April 1948 to 7.6 percent in March 1965) and that changes in demographic character­ istics of these women have apparently had no net effect on this trend (see panel n of Table 11-2). Our task now is to suggest some reasons why the trend in this group's participation has been so different from the trends for elderly men (just discussed) and for married women 14-54 (discussed in Chapter 7). Our approach here is both more "free-wheeling" and less precise than the approach employed in analyzing changes in the participation rates of older men and younger married women. We decided not to attempt to be as rigorous and quantitative in dealing with the trend for elderly wives, in part because of the lack of information about certain key explanatory variables (such as earnings). More importantly, it seemed to us that to labor at great length over an increase in participation of 1.5 percentage points might constitute analytical overkill. The contrast between the mild uptrend for older married women and the sharp downtrend for older men is comparatively easy to explain. Quite obviously, the rate for elderly wives could not have fallen very much, at least in absolute terms, because (unlike the rate for elderly men) it was never high. But the direction of movement might have been downward, and undoubtedly certain factors (such as the rise in family income) must have tended to pull it down. That it actually rose, if only a little, must be explained by much the same constellation of factors that propelled the participation of younger wives to new heights during the postwar years. The more interesting question, in our view, is why the rate for elderly wives rose so much less (both absolutely and proportionately) than the rate for their younger counterparts. To sharpen the issue, how can one ex­ plain the fact that the participation rate for wives 65 and over rose by only 1.5 points (or 20 percent) during the same period (1948-1965) that the rate for wives 55-64 rose by roughly 17 points (or 125 percent)?42 In 42 We estimate the rate for married women 55-64 to have been about 14 percent in April 1948, compared with the CPS figure of 31.4 percent in March 1965. The former figure was obtained by assuming that the rate for wives 55-64 in April 1948 (for which CPS data are not available) bore the same relation to the rate for wives 45-64 as it did in April 1952 (the first month both rates are available). The source of these data is the 1966 Manpower Re­ port of the President, Table B-2.

376

OLDER PERSONS

the pages that follow, we shall attempt to describe, in somewhat specula­ tive terms, the main factors responsible for this pattern. Previous work experience. It seems reasonable to suppose that the par­ ticipation decisions of elderly wives are strongly influenced by their previous work experience. While a few women may succeed in finding new work after 65, we suspect that most wives who are still in the labor force after passing that chronological milestone have had a long history of work in a particular occupation, and perhaps in a particular establish­ ment as well. Thus, part of the smaller postwar rise in LMW65+ may be due to the comparatively small increase in opportunities for market work early in their adult lives experienced by women in this age interval, compared with younger women. Wives who were 65-74 in 1948 spent much of their adult lives in the 1920's and 1930's, when employment opportunities for women were scarce, and these women were (for the most part) rather old to begin working during World War II. Much the same thing can be said of those who were 55-64 in 1948, although some of the younger members of this cohort no doubt did hold wartime jobs. But neither cohort faced abundant job opportunities until well past the age of 40. By 1965, however, the relative amount of past work experience of wives in these two age inter­ vals had diverged substantially. Those 65-74 in 1965 were 41-50 at the outbreak of World War II, whereas those 55-64 in 1965 were 31-40 at that time. A substantial fraction of the younger cohort had husbands in the Armed Forces during the war years, whereas few of the older group did. We submit that the participation rate of the younger wives rose substantially during the war and that their rate remained higher than it would otherwise have been throughout the postwar period (when they passed through their 40's and 50's), whereas the lifetime participation of those who were 65-74 in 1965 was relatively unaffected by the war and the subsequent postwar expansion. In short, we attribute part of the progressive widening of the gap be­ tween LMW55_e4 and LMw65+ to a steady increase in the gap between the early lifetime work experiences of wives in these two age intervals. This hypothesis has an interesting implication: namely, we ought to observe a larger increase (ceteris paribus) in the participation rate of wives 65 and over in future years. The ranks of this venerable group will, after all, be filled more and more by women who were under 40 at the time of Pearl Harbor. The participation of husbands. As a rule, when a trend in one group's participation is advanced to explain the trend in another group's partici­ pation, there is cause for suspicion. We believe, however, that the pres­ ent case is an exception—that what has happened to the participation of husbands in the 65-74 age range compared with husbands in the 55-64 age range has had a direct impact on the patterns of participation of the two groups of wives.

TRENDS

377

During the period 1948 to 1965, the participation rate for married men 65 and over fell from around 53 percent to 31 percent.43 Since we learned in Chapter 9 that, even on an other-things-equal basis, an elderly wife is much less likely to work if her husband has retired (see Table 9-12 and the accompanying discussion), the marked decline cited above may have dampened somewhat the increase in the participation of wives over 64. In contrast, the participation rate for married men 55-64 fell only about three percentage points; hence the dampening effect on the rising partici­ pation of wives 55-64 has been very much smaller—probably negligible. We realize that there is some tendency for wives to be younger than their husbands, so that the rates for married men cited above do not cor­ respond precisely with the rates for the husbands of the wives in question; but this is presumably a minor discrepancy. We are also aware of the oversimplification involved in treating the husband's participation status as if it were determined independently of the wife's; but the main ex­ planation for the downtrend in the participation of elderly husbands surely lies in the social and economic factors discussed earlier in this chapter and not in a change in the tastes and preferences of their wives. In short, we are confident that the retirement of the husband exerts a truly independent influence on the labor force status of the wife. (In this connection it should be noted that the increasing tendency for older couples to move to a new residence after the husband has retired usually serves to sever any ties the wife may have had with the local labor market.) Schooling. In 1950 there was scarcely any difference between the median educational attainments of women 55-64 and those 65 and over; the medians were 8.5 and 8.3 years, respectively. By 1965, the differen­ tial had grown to a year and one-half. While the median years of school completed by women 55-64 was advancing to 10.1 years, the comparable figure for those 65 and over rose by a much smaller amount—to only 8.6 years.44 Given the strong positive relationship between the schooling and participation of older married women (especially those 55-64) as revealed by the 1/1000 Sample,45 it seems reasonable to attribute some part of the lesser gain in participation by elderly wives to the relative decline in their educational attainments — not only with respect to younger wives, but also vis-a-vis the population as a whole. Health and the taste for leisure. We consider these two very different factors jointly, not because we believe that there has been a marked post­ war change in either one, so far as elderly wives are concerned, but rather 43 The figure for 1948 is a guess based on the ratios for April 1947 (54.5) and April 1949 (51.9). Source: 1966 Manpower Report Table B-2. 44The figures for 1950 were estimated from the 1950 Census, Detailed Characteristics, U.S. Summary, Table 115. Data for 1965 are from the Current Population Survey, Series P-20, No. 158, Table 4. Figures for married women in these age intervals are not available, but the trends for them should be very similar. 45 See Table 9-4, panels ν and vi, and footnote 40 of Chapter 9.

378

OLDER PERSONS

because both considerations operate to put a rather low ceiling on the potential participation rate of older women. According to the U.S. National Health Survey for July 1957 to June 1958, 43 percent of all females 65 and over reported at least one chronic condition resulting in some limitation of activity, whereas the comparable ratio for females 55-64 was 22 percent.46 The participation rate of elderly wives with activity limitations is not available, but it is presumably close to zero. Even among older women who are completely well, the preference to remain at home must surely increase greatly with advancing years, es­ pecially beyond the middle 60's. (It is quite possible that husbands also have strong preferences that their wives not work in old age.) To what ex­ tent these preferences have held down the participation rate of elderly wives is, of course, a matter of conjecture. But some clue as to the force of these considerations is given by the fact that among 65-74-year-old wives with some graduate training (17 years of schooling or more), a group with access to interesting and relatively well-paid work, the partici­ pation rate was only 24.1 percent during the census week of 1960. The comparable rate for 55-64-year-old wives with some graduate training was 57.3.47 Taken together, a high disability rate and strong preferences for leisure may have substantially held down the growth of participation by married women over 64. Income aspirations. In an earlier section of this chapter, we offered some reasons for believing that men over 65 have experienced a smaller rise in income aspirations than have married women in the 14-54 age range. The same considerations would seem to apply, albeit with less force, to wives over 65 vis-a-vis wives 55-64. The latter group is still young enough to have children of college age. Furthermore, the 55-64year-old wives are apt to have stronger desires for new durable goods and, in all likelihood, more mortgage payments to make. To SUM up, we think that the much slower rate of increase in the partici­ pation of wives 65 and over, compared with the rate of increase in the participation of younger wives, can be explained in good part in terms of the following factors. (1) The limited work experience of the 65-and-over group during the years they were under 40—from this standpoint, those who were over 64 in 1965 are the last pre-World War II cohort. (2) The pronounced decline in the participation of husbands of women 16Source: U.S. Department of Health, Education and Welfare, Health Statistics, Series B, No. 11, Table 9 and 10. Again, separate data for wives are not available. 47Calculated from Table 5 of Educational Attainment, Subject Report PC(2)-5B of the 1960 Census. We hasten to add that many of the nonparticipants in the 65-and-over group may have been forced to retire as a result of compulsory retirement provisions.

TRENDS

379

in the 65-and-over age range, combined with the increasing tendency for retirement to be accompanied by a change of residence. (3) The decline in the relative educational attainment of elderly wives vis-a-vis younger wives. (4) The constraining effects of poor health and strong preferences for leisure, both of which seem to be particularly characteristic of wives in the 65-and-over age interval. (5) The likelihood that the income aspirations of older persons have undergone less radical change than the aspirations of younger people.

CHAPTER 12

Younger Persons: Individual Characteristics A Preview of the Next Three Chapters The final three chapters of Part I are concerned with persons 14-24 years old—"younger persons" for short. Appraising the determinants of this group's participation is greatly complicated by the fact that a large fraction of younger persons are in school. Not surprisingly, those enrolled in school are less inclined to seek market work than those who are not in school. The crux of the problem is that the enrollment and participation decisions of an individual are inter­ related, and some factors have quite different effects of each one con­ sidered separately. For example, a rise in unemployment may discourage participation by those not in school, yet at the same time it may also pro­ vide youngsters with an incentive to remain in school. Or, children in large families may have a lower probability of school enrollment than children in small families (other things equal), but for those who are en­ rolled the probability of participation may increase with family size. The ideal solution to these problems would be to develop a simultaneous-equation model that would predict enrollment and participation decisions jointly, allowing full play to the kinds of interactions noted above. We think this approach holds great promise, and we hope that someone will pursue it. Our own comparative advantage, however, lies in the more orthodox methods of analysis employed in the previous chap­ ters. Fortunately, these methods yield many useful insights into the participation and enrollment decisions of younger persons. Moreover, as explained below, we believe we have found a way to take some of the interactions into account. The present chapter analyzes the relations between the participation decisions of younger persons and their individual and family charac­ teristics. It also explores the impact of some of these characteristics on "enrollment ratios" and "activity rates." The enrollment ratio is simply the fraction of a particular demographic group enrolled in school. The activity rate, a new concept, is the fraction of a group either attending school or in the labor force or both. It is by using this concept that we are able to examine the simultaneous effects of individual and family charac­ teristics on enrollment decisions and on the labor force status of persons not enrolled in school. Chapter 13 contains a similar analysis of the effects of labor market conditions on the participation, enrollment, and activity rates of younger persons. Finally, trends in participation rates and ac­ tivity rates are examined in Chapter 14. Since the participation decisions of students and of persons not in school are influenced by somewhat different sets of individual and family

INDIVIDUAL CHARACTERISTICS

381

characteristics, we have elected to present the findings for these two groups of younger persons in separate sections. The results for students are presented first. While our primary interest is, of course, in the rela­ tions between individual characteristics and labor force participation, we also report statistically significant relations (as revealed by separate multiple regressions) between these same variables and another im­ portant dimension of labor supply — the number of hours worked by em­ ployed students during the census week of I960.1 The second part of the chapter contains our participation-rate findings for the out-of-school subset as well as the observed relations between certain individual and family characteristics and the enrollment and ac­ tivity rates of younger persons. This part of our analysis is constrained by the fact that Census data on the socioeconomic characteristics of parents are available only for those younger persons still living at home. Most of the findings reported here are drawn from the 1/1000 Sample. We have classified younger persons in this sample by sex and age, using 14-17 and 18-24 as our age intervals. The subset of 18-24-year-old men includes those who are married (wife present) as well as those in other marital categories, but the subset of women 18-24 is confined to those who have never married. Finally, we have further limited our sample of 14-17-year-old boys and girls to never-married persons living in families; as noted above, this restriction is necessary if we are to relate the partici­ pation of these youngsters to family characteristics. YOUNGER PERSONS IN SCHOOL All of the younger persons discussed in this section have one thing in common: they were enrolled in a "regular" school for some part of the period between February 1, 1960, and the census week. The Census de­ fines "regular" schooling as "formal education obtained in public and private (denominational or nondenominational) kindergartens, graded schools, colleges, universities, or professional schools, whether day or night school, and whether attendance was full time or part time."2 Under this definition, a person holding a full-time job and working toward his degree by taking only one course a term is counted as enrolled in school. Since our purpose in this section is to analyze the participation decisions of younger persons who view themselves primarily as students, we want to exclude these "incidental" students from our sample. Unfor­ tunately, the Census data contain no direct information on the "extent of schooling activities"; however, there is information on hours worked, and we have dropped from this sample all persons 18-24 who worked 40 hours or more during the census week. While some of these people may 1 We have not undertaken a similar analysis for younger persons who were out of school, the main reason being that the hours worked by these persons are more likely to reflect demand-side considerations and the conventions in particular occupations and industries than the preferences of individuals. 2 1960 Census of Population, Detailed Characteristics, U.S. Summary, p. xvi.

382

YOUNGER PERSONS

have also carried full course loads in school, most of them were presum­ ably part-time students. In organizing the discussion of individual and family characteristics which affect the labor force status of younger persons enrolled in school, we first consider the relationship between single year of age and labor force status. Next, we discuss various direct measures of family income and other income available to students. This is followed immediately by an analysis of variables representing characteristics of student life (living arrangements, kinds of schools attended, etc.); we regard the results of this part of our work as both interesting in their own right and indicative of effects of family income on participation which are not captured by the more straightforward measures of economic circumstances. Finally, after discussing briefly the effects of other socioeconomic characteristics of the family (schooling and employment status of the head, presence of both a mother and a father in the household, etc.), we end by describing the dif­ ferences in labor force status of whites, Negroes, and other nonwhites which remain after all the other variables have been taken into account.

Age No one will be surprised to learn that 14-year-old school children are less likely to be in the labor force than 24-year-old college students. The restrictions imposed by child labor laws, the hiring standards of em­ ployers, and the tendency for older children to want progressively more spending money than their parents are willing or able to provide all play a role in producing the expected positive relation between the participa­ tion of youngsters in school and their age. The findings for each of our four groups appear in Table 12-1. The only feature of these profiles that calls for special comment is the contrast between the steady and highly significant advance of participa­ tion with age for both sexes within the 14-17 interval and the erratic changes which occur as one moves from 18 to 24. While most of the interval-to-interval changes for the two older groups are probably the result of sampling variations, the failure of male participation to rise very much over the 18-24 age interval deserves to be taken seriously; indeed, the age variable is wholly devoid of statistical significance for the older group of males. The explanation for this phenomenon is not obvious. One reason may be that somewhat more time is required for academic work at higher levels of education, or that those who drop out along the way are less academically motivated than those who persevere. To obtain a fuller picture of the relationship between age and the amount of labor supplied by younger persons enrolled in school, it is helpful to take account of variations in hours worked as well as in participation rates. Indeed, looking only at the relations between age and participation leads to a substantial underestimate of the overall impact of this characteristic on the amount of labor supplied by all but one of our

TABLE 12-1 Age and Labor Force Participation: Younger Persons Enrolled in School in Urban Areas, Census Week of 1960 Population group a by age

Labor force participation rate

Number in sample

Percent of popu­ lation

(1)

(2)

Unadjusted (3)

Adjustedb (4)

863 888 754 694 3,199 63.6 * *

27.0 27.8 23.6 21.7 100.0

11.4 17.5 26.7 38.3 22.5

11.7 17.3 26.7 38.0 22.5

832 762 819 662 3,075 68.1 **

27.1 24.8 26.6 21.5 100.0

5.1 6.8 14.4 26.7 12.7

5.0 6.6 14.7 26.7 12.7

443 289 186 179 133 89 72 1,391 0.66

31.9 20.8 13.4 12.9 9.6 6.4 5.2 100.0

38.6 40.5 33.9 36.9 39.9 41.6 48.6 39.0

36.9 42.3 35.9 38.8 41.5 38.2 43.4 39.0

43.7 23.2 13.8 10.2 5.6 2.7 0.8 100.0

25.9 30.6 37.1 30.4 34.0 50.0

25.7 30.8 37.5 30.4 34.7 49.1

XM MALES 14-17, IF

14 15 16 17 Total F-ratio II. XM FEMALES 14-17, IF

14 15 16 17 Total F-ratio III. MALES 18-24

18 19 20 21 22 23 24 Total F-ratio IV. XM FEMALES

18-24 18 19 20 21 22 23 24 Total F-ratio

393 209 124 92 50 24 7 899 2.33 *

c

30.4

c

30.4

Source: 1/1000 Sample. The complete multiple regression equations for these groups will be found in Appendix Tables A-26 through A-29. a Symbols for population groups: XM = never-married IF = living in a family. b Results for groups ι and II have been adjusted for the effects of color, other family income, family status, family size, schooling of family head, and labor force status of family head. Results for groups m and iv have been adjusted for the effects of color, marital and family status, other income, level of enrollment, and kind of school. c Results not shown due to insufficient observations. * * Significant at the 1 percent level. * Significant at the 5 percent level.

384

YOUNGER PERSONS

subgroups, for the number of hours worked by the typical student who does participate also rises with age. The figures presented in Table 12-2 document this proposition. The first column shows the adjusted number of hours worked during the census week of 1960 by those students in each age interval who held part-time jobs.3 In the second column we have reproduced the adjusted participation rates for each group from Table 12-1. Finally, in column (3) we have converted the participation rates in column (2) into 15hour units. This conversion involved dividing the adjusted average work week in each interval by 15, and then multiplying the quotient by the adjusted participation rate. (The use of a 15-hour benchmark is arbitrary but not unreasonable, in that the mean numbers of hours worked by the various enrolled-in-school subgroups range around this number.) For boys and girls 14-17, the age-related increase in average hours per worker is much less pronounced than the rise in their participation rate; still, the net relation between hours and age for both groups is highly significant. Consequently, the index of labor supply [in column (3)] rises at a markedly faster rate than does either hours or participation consid­ ered separately. The most dramatic rise in the overall measure of labor supply is seen in panel iv, for never-married women 18-24. The index for this group nearly quadruples between ages 18 and 24. Closer inspection of these data reveals, however, that a large part of the sharp increase in the amount of labor supplied by these women occurs at the very top of this age range—after age 22. The results for males 18-24 provide a striking contrast, in that neither hours nor participation are systematically re­ lated to age at the 10 percent level of confidence (see panel m). We suspect that much of the explanation for these divergent age-profiles is related to differences between men and women in methods of financing graduate (or professional) study. Most persons over age 22 who are still enrolled in school are presumably studying for graduate or professional degrees, and our hunch is that the women fitting this description participate more actively in the labor force than men—in part because less financial assistance is available to the women. Some of the men receive support from working wives, whereas only nevermarried women have been included in this sample. Furthermore, it may well be that financial aid, in the form of scholarships and loans, has been less readily available to the women students than to the men. Differences between men and women in the kinds of postgraduate training most commonly pursued may also help explain the greater 3 The same set of explanatory variables was used in the first runs of the hours-worked and participation regressions for each group of students. The final multiple regression equations predicting hours worked (from which certain nonsignificant variables have been dropped) will be found in Appendix Tables A-32 through A-35.

385

INDIVIDUAL CHARACTERISTICS

TABLE 12-2 Age, Participation, and Hours of Work: Younger Persons Enrolled in School in Urban Areas, Census Week of 1960 Population group a

Adjusted mean hours worked b (1)

Adjusted participation ratec (2)

Index of labor supply d (3)

10.0 10.6 12.5 14.7 (F = 11.0 **)

11.7 17.3 26.7 38.0 (F = 63.6**)

7.8 12.2 22.3 37.2

XM MALES 14-17, IF

14 15 16 17 II. XM FEMALES 14-17, IF

14 15 16 17 III. MALES

8.3 10.9 13.8 11.8 (F = 5.16**)

5.0 6.6 14.7 26.7 (F = 68.1 **)

2.8 4.8 13.6 21.0

15.3 17.8 16.7 19.1 17.6 20.0 18.2 (F= 1.76)

36.9 42.3 35.9 38.8 41.5 38.2 43.4 (F = 0.66)

37.6 50.2 40.0 49.4 48.7 50.9 52.7

12.3 16.7 16.0 14.8 19.3 24.9

25.7 30.8 37.5 30.4 34.7 49.1

21.1 34.3 40.0 30.0 44.6 81.5

18-24

18 19 20 21 22 23 24 IV. XM FEMALES

18-24 18 19 20 21 22 23 24

e

(F = 6.00**)

e

e

(F = 2.33 *)

Source: 1/1000 Sample. See notes b, c, and d for specific sources. "Symbols for population groups are given in Table 12-1. b Derived from the multiple regression equations in Appendix Tables A-32 through A-35. Hours data pertain to the census week of 1960. c Reproduced from column (4) of Table 12-1. dAdjusted participation rates expressed in units of 15 hours of work per week. These figures were obtained by dividing the mean hours in column (1) by 15, and then multiply­ ing this quotient by the participation rates in column (2). e Not shown for lack of sufficient observations. * * Significant at the 1 percent level. * Significant at the 5 percent level.

386

YOUNGER PERSONS

amount of labor supplied by the women than the men. A higher propor­ tion of the women may have been engaged in professional programs (e.g., nursing) in which a significant amount of part-time work is a part of the course of study. This observation also relates back to the question of financial aid, since loans and scholarships are relatively hard to obtain in such professional programs.

Income Effects Income is one of the two basic economic variables which we would expect to exert the strongest influence on the labor force status of younger persons enrolled in school (earnings opportunities, discussed in the next chapter, being the other). Unfortunately, it is much easier to assert that income should matter than it is to define and measure the relevant concepts for persons 18-24 enrolled in school, some of whom are mar­ ried and many of whom live away from their parents. In studying this college-age subset, we shall have to supplement what little direct infor­ mation on income is available with several proxy variables. It is much easier to study the relationship between income and participation for 14-17-year-old teenagers living in families, and we shall begin this part of our analysis with these high-school-age students. Other family income and the participation of 14-17-year-olds. For this group, other family income (defined as total family income less the earnings of the teenager in question) is the most relevant concept, and since information on the income of all members of a family is available on the 1/1000 tapes, it was easy enough to include a set of dummies representing various levels of OFI in the multiple regression equations used to analyze the labor force status of males and females in the 14-17 age range. To economize on space we present in Table 12-3 only the re­ sults for males; the results for females (which will be found in Appendix Table A-27) are quite similar. The first thing we observe is a decline in participation from about 26 percent for enrolled males in families with less than $2,000 to about 20 percent for males in families with $4,000 to $5,999. Presumably this de­ cline is a straightforward income effect: there is less need for children to work as family income rises. But then comes the surprise: at the $6,0006,999 level, participation jumps back up to 25 percent, and it does not begin to decline again until we reach the $11,000-14,999 bracket—and even there it falls by only two points.4 Not until we reach the $15,00024,999 level of OFI is the participation of school-attending teenagers lower than in the $4,000-5,999 interval (13 percent versus 20). Finally, among 4 Participation in the $6,000-6,999, $7,000-8,999 and $9,000-10,999 brackets is sig­ nificantly higher than in the $5,000-5,999 interval at the 10, 5, and 5 percent levels, re­ spectively. (If the former three intervals were combined, the level of statistical confidence would undoubtedly be greater.)

387

INDIVIDUAL CHARACTERISTICS TABLE 12-3

Other Family Income and Labor Force Participation: Males 14-17 in School in Urban Areas, Census Week of 1960 Males 14-17 in school, by other family income

Number in sample

Less than $1,000 $1,000-1,999 $2,000-2,999 $3,000-3,999 $4,000-4,999 $5,000-5,999 $6,000-6,999 $7,000-8,999 $9,000-10,999 $11,000-14,999 $15,000-24,999 $25,000 or more Total /-'-ratio

110 112 168 218 284 370 386 559 464 311 164 53 3,199 2.92 **

Percent of population 3.4 3.5 5.3 6.8 8.9 11.6 12.1 17.5 14.5 9.7 5.1 1.7 100.0

Labor force participation rate Unadjusted

Adjusted"

20.0 23.2 19.6 19.7 18.7 19.7 24.6 25.9 27.8 24.8 14.0 1.9 22.5

25.2 27.9 23.2 22.1 19.9 19.5 24.8 24.9 25.2 23.1 12.8 2.4 22.5

Source: 1/1000 Sample. The complete multiple regression equation is presented in Ap­ pendix Table A-26. "For the effects of color, age, family status, family size, schooling of family head, and labor force status of family head. * * Significant at the 1 percent level.

children in families at the very top of the income ladder ($25,000 or more), the propensity to participate is virtually zero.5 What accounts for the intriguing jump in the participation of teenage boys in upper-middle-income and moderately well-to-do families? We have not studied this issue in depth, and we certainly do not claim to have found the complete answer. But we suspect that part of the explana­ tion turns on the comparative advantage that youngsters in these families have in finding part-time jobs. For one thing, their parents are more fre­ quently able to help, mainly as a result of their business and social con­ tacts. For another thing, a youngster who grows up in a better neighbor­ hood and attends a better school may have more success in finding a job on his own than his less fortunate counterpart on the other side of town. A related possibility is that there may be more part-time jobs available in and around these wealthier neighborhoods. (Opportunities for lawnmowing and babysitting are two cases in point.) These same considerations presumably apply with equal force to youngsters in wealthy homes. Thus, the decline in participation that oc­ curs at the top of the OFI ladder must be due to some combination of 5 Even though there are only 53 observations in the $25,000+ OFI category, the adjusted participation rate of 2.4 percent is significantly different from the 23 percent rate in the $11,000-14,999 interval at the 1 percent level.

388

YOUNGER PERSONS

larger allowances for these youngsters and less parental pressure on them to find after-school jobs. Could it be that it is the upper-middleincome parents who attach the greatest importance to having their chil­ dren obtain work experience early in life? Size of family and the participation of 14-17-year-olds. As the notes to Table 12-3 indicate, one of the other family characteristics impounded in order to obtain the adjusted relationship between OFI and participa­ tion is family size. Direct inspection of the relationship between this variable and the labor force status of teenagers enrolled in school, now holding OFI constant, as well as age, color, and so on, provides another perspective on the effects of family needs on the incentive for children in school to hold part-time jobs. The results reported in Table 12-4 indi­ cate that, as one would expect, the percentage of children in the labor force increases with family size (i.e., with decreasing per capita income). The adjusted rate for 14-17-year-old males in families of 6 or more is seen to be about 8 percentage points (or 45 percent) higher than the rate for males in families of 2 or 3, and the comparable differential for females is 5 percentage points (or 50 percent). With regard to hours worked, there is no association whatever with TABLE 12-4 Family Size and Labor Force Participation: Younger Teenagers in School in Urban Areas, Census Week of 1960 Population group by number of persons in the family a

Number in sample

Percent of population

Labor force participation rate Unadjusted

Adjusted"

I. XM MALES,

14-17, IF 2 or 3 4 or 5 6 or more Total F-ratio

701 1,577 921 3,199 7.85 **

21.9 49.3 28.8 100.0

18.8 24.0 22.7 22.5

17.2 23.4 25.0 22.5

601 1,559 915 3,075 5.54 **

19.5 50.7 29.8 100.0

12.2 12.1 14.0 12.7

10.4 11.8 15.6 12.7

II. XM FEMALES, 14-17, IF

2 or 3 4 or 5 6 or more Total F-ratio

Source: 1/1000 Sample. For the complete multiple regression equations from which these results are taken, see Appendix Tables A-26 and A-27. a Symbols for population groups are defined in Table 12-1. b For the effects of color, age, other family income, family status, schooling of family head, and labor force status of family head. ** Significant at the 1 percent level.

INDIVIDUAL CHARACTERISTICS

389

family size for female students 14-17 holding part-time jobs, but a rela­ tion of this kind does appear for male students of this age. The following figures show the adjusted hours, adjusted participation, and our measure of overall labor supply (again expressed in units of 15 hours per week) for this group, classified by the number of persons in the family: 6 Family size 2 or 3 persons 4 or 5 persons 6 persons or more

Adjusted mean hours

Adjusted

12.3 14.2 11.8 ( F = 3.33*)

17.2 23.4 25.0 (F = 7.85**)

LFPR

Index of labor supply 14.1 22.2 19.9

The interesting feature of these results is the markedly fewer hours worked by youngsters in the largest families (6 persons or more) com­ pared to the hours reported for boys in 4-or-5 person families. The drop in hours more than offsets the small rise in adjusted participation between the 4-or-5 and 6+ person intervals, as the associated decline in the labor supply index shows. Perhaps this differential in hours worked is due to the greater family responsibilities (including care of younger children) that 14-17-year-old boys in very large families commonly bear. Other income and the participation of college-age students. Ideally, we would like to know the incomes of the parents of college-age students and the number of other children in their families, just as we do in the case of younger teenagers. However, such information is available only for those students who continue to live with their parents, and hence it is impossible to relate the labor force status of college-age students in general to the OFI and the size-of-family variables used for the 14-17year-olds. We are able to relate the laborforce status of the 18-24-yearolds to their own "other income," but we confess at the outset that we are rather suspicious of these results. The definition of other income is, of course, the same for students as for other groups, but only two components of this conglomerate are of any great importance to this group. These are "veterans' payments" (to students enrolled under the post-Korean G.I. Bill) and "periodic con­ tributions for support from persons who are not members of the house­ hold." 7 Both these items are relevant to college students living away from home, and if these forms of other income were measured satisfac­ torily, we would have a direct measure of what parents and others in fact provided in the way of income. Furthermore, this variable would then be superior to the OFI variable, which is, after all, only an imperfect measure of what parents could provide in the way of financial support. The diffi6 The figures for hours are from Appendix Table A-32; construction of the labor supply index was explained earlier in this chapter and in note d of Table 12-2. 7 1960 Census, Detailed Characteristics, U.S. Summary, p. xl. Bear in mind that a stu­ dent living away from home is not counted as a member of his parents' household.

390

YOUNGER PERSONS

TABLE 12-5 Other Income and Labor Force Participation: Persons 18-24 in School in Urban Areas, Census Week of 1960 Population group by other income in 1959 a

Number in sample

18-24 Nonec 1,195 $1-499 123 $500 or more 73 Total 1,391 F-ratio 1.59

Percent of population

Labor force participation rate Unadjusted

Adjusted"

85.9 8.8

39.8 35.0

39.8 36.6

5.3 100.0

32.9 39.0

29.6 39.0

89.4 7.9

30.4 33.8

30.5 32.4

2.7 100.0

20.8 30.4

19.7 30.4

I. MALES

II. XM FEMALES

18-24 Nonec $1-499 $500 or more Total F-ratio

804 71 24 899 0.72

Source: 1/1000 Sample. For the complete multiple regression equations from which these results are taken, see Appendix Tables A-28 and A-29. aSymbols for population groups are defined in Table 12-1. b For the effects of age, color, marital and family status, kind of school, and level of en­ rollment. c May include a few persons with net losses.

culty, as we shall see, is that there is good reason to suspect that much parental support of this kind was not reported in the census and thus is not captured by the other-income variable. One would expect the adjusted association between other income and students' participation to be inverse, and so it seems to be (see Table 12-5). For both never-married women and all males aged 18-24, the adjusted participation rate of those with $500 or more of other income was about 10 percentage points lower than the rate for those with no such income at all. Due, however, to the small number of observations in the former interval, this rather sizeable differential is statistically significant at the 10 percent level only in the case of males.8 8The

respective t-values for this deferential are 1.70 for males and 1.13 for females. According to our hours-of-work regressions for students 18-24 holding part-time jobs, there was no net association whatever between other income and hours worked by males during the census week of 1960; consequently, this variable was dropped from the final run for this group shown in Appendix Table A-34. In the case of females, however, an in­ verse relation did appear: those working students with $1 or mor^ of other income in 1959 put in an (adjusted) average of 12.9 hours of work —three hours less than their fellow stu­ dents with no other income that year. The f-value for this differential (1.52) is not quite significant at the 10 percent level. (For the complete results of this regression, see Ap­ pendix Table A-35.)

INDIVIDUAL CHARACTERISTICS

391

What makes us suspicious of these results, however, is the fact that 86 percent of the men and 89 percent of the never-married women who were 18-24 and enrolled in school during the census week reported re­ ceiving no other income at all in 1959 (see the second column of Table 12-5). In view of the fact that two-fifths of these students were living away from home,9 we find it hard to believe that these figures are an ac­ curate measure of the periodic financial support that these students re­ ceived from their parents. In fact, the fraction of students living away from home who reported no other income is almost as large—83 percent of the men, and 87 percent of the women. The explanation has to be that a great many of these students did not report amounts of money given to them by their parents. The reason for the underreporting is not hard to find: the "periodic contributions" item cited above was not mentioned in the specific census question pertaining to other income.10 As a conse­ quence, many students must have assumed that such contributions were not to be reported. Indeed, this assumption may have been correct after all, since it is not clear how the Bureau of the Census would distinguish in this case between "periodic contributions" (which are to be reported) and "gifts" (which are not). At any rate, it is dear that other income is not a very satisfactory measure of the amount of parental support—direct and indirect— that college students receive. In our view, certain other variables, re­ lated to school attendance, do a better job of controlling for this im­ portant factor.

Characteristics of Students and Schools Students living away from home versus other students. One indirect way of getting at the effects of family income on the participation of 1824-year-olds enrolled in school is by including in the multiple regres9The latter ratio was estimated by adding the number of enrolled single males 18-24 who were not in families to the number of enrolled never-married females 18-24 not in families and dividing the sum by the total number of persons 18-24 who were enrolled in school. The fraction of enrolled never-married females who were not in a family was con­ siderably higher than the comparable ratio for all males (49 percent versus 36 percent), partly because married men are included in the male totals, and partly because the fraction of unmarried male students living away from home is smaller than the comparable fraction of never-married females. It should be noted that some of the students living away from home during the census week of 1960 no doubt lived at home during part or all of 1959 —and thus may not have received other income then even though they did receive other income during the census week. But the existence of this (relatively small) group surely does not explain away the extraordinarily large number of students reporting no other income in 1959. 10The question (P 34) reads as follows: "Last year (1959), did this person receive any income from—Social Security, Pensions, Veteran's payments, Rent (minus expenses), Interest or dividends, Unemployment insurance, Welfare payments, Any other source not already entered." (1960 Census, Detailed Characteristics, U.S. Summary, p. xxxix). The last item was intended to include periodic support from persons not in the household, but under the self-enumeration system widely used in 1960, students might well not have known this.

392

YOUNGER PERSONS

sion a variable for living arrangements. We assigned each of our male students in the 18-24 age range to one of three categories: (1) married and living with his wife; (2) not married and living in a family (i.e., with his parents or relatives); and (3) not married and not living in a family. For present purposes, the key distinction is between the two categories of unmarried students. The great majority of unmarried students living away from home (included in category 3 above) reside in college dormi­ tories, fraternity houses, and rooming houses. Since one can safely as­ sume that these students have wealthier parents, in the main, than un­ married students who continue to live at home while attending nearby schools, a comparison of the participation rates for the two groups is relevant to the question of the relationship between family income and labor force status. We find (see panel ι of Table 12-6) that the adjusted participation rate of those unmarried male students 18-24 not living in families was signifi­ cantly lower than the adjusted participation rate for those unmarried students living at home, the respective participation rates being 29.3 and 42.2 —a finding which is, of course, consistent with the incomeeffect hypothesis. (We also find that the adjusted participation rate of the married students was higher than the rate of either unmarried group, and we interpret this result as additional evidence concern­ ing the effect of financial circumstances on the labor force status of students. While in some instances the presence of a wife no doubt eases the financial pressures on the student to work, we would expect the greater financial needs associated with marriage to increase the likeli­ hood that the husband will be in the labor force in most cases; this ex­ pectation certainly seems to be borne out by the results reported in Table 12-6.) Our 1960 intercity regressions for enrolled males 18-19 and 20-24 provide further evidence that college students who live on campus are less likely to hold part-time jobs than students who live at home. One of the explanatory variables in these regressions was the ratio of persons residing in college dormitories, fraternity and sorority houses, etc., to total college enrollment in the SMSA. The net regression coefficient was negative and significant at the 1 percent level in both runs.11 Does the same relation hold for unmarried women who are enrolled in school? The findings in panel Ii of Table 12-6 suggest that it does, but to a much lesser degree. The adjusted participation rate for 18-24-yearold never-married females in families was 5 percentage points higher than the rate for their on-campus counterparts, but this differential was only about two-fifths as large as the comparable difference for single males and was not statistically significant at the 10 percent level. The results of our hours-of-work regressions for 18-24-year-old 11 As we point out in Chapter 13, the college dormitory ratio in our intercity regressions may also be viewed in part as a measure of the competition for part-time jobs among college students. (See "The Supply of Teenage Males.")

393

INDIVIDUAL CHARACTERISTICS

TABLE 12-6 Marital and Family Status and Labor Force Participation: 18-24-Year-Old Persons in School in Urban Areas, Census Week of 1960 Population group by marital and family status a

Number in sample

Labor force participation rate

Percent of population

Unadjusted

Adjusted"

121 767 503 1,391 23.3 **

8.7 55.1 36.2 100.0

58.7 42.6 28.6 39.0

58.5 42.2 29.3 39.0

461 438 899 1.92

51.3 48.7 100.0

31.0 29.7 30.4

32.8 27.8 30.4

I. MALES 18-24

Married, wife present Single, IF Single, not IF Total F-ratio II. XM FEMALES 18-24 IF Not IF

Total F-ratio

Source: 1/1000 Sample. For the complete multiple regression equations from which these results are taken, see Appendix Tables A-28 and A-29. aSymbols for population groups are the same as in Table 12-1. Single males include all those not married with wife present. 6 For the effects of color, age, other income, level of enrollment, and kind of school. ** Significant at the 1 percent level.

students reinforce one of the participation patterns discussed above but partly offset another. Among never-married students with part-time employment, those who were living in families worked considerably longer hours than did those living away from home. This differential was about 8 hours for women and about 4 hours for men, and it produces a stronger relation between family status and labor supply—especially in the case of women—than the participation findings alone would sug­ gest. On the other hand, the much higher participation of married men vis-a-vis single men in families has no counterpart in the data on hours. In fact, student jobholders in the former group worked about two hours less, on the average, than did those in the latter category, but this dif­ ference falls short of statistical significance at the 10 percent level. The figures shown in the text table (p. 394) recapitulate our main find­ ings on the sensitivity of participation and hours worked to living arrange­ ments in the case of students 18-24.12 While differences in participation rates are more important for the men and differences in hours worked are more important for the women, the general pattern, measured by our overall index of labor supply, is re12The adjusted participation rates are taken from panels ι and n of Table 12-6; he ad­ justed hours means are derived from Appendix Tables A-34 and A-35; and the index of labor supply (which expresses the adjusted participation rates in units of 15 hours of work per week) has been constructed by methods explained earlier in this chapter and in note d of Table 12-2.

394

YOUNGER PERSONS

markably similar for both men and women; 18-24-year-old students living away from home devoted only about half as much time (per capita) to labor force participation as did their counterparts who lived at home.

MARITAL AND FAMILY STATUS

Males 18-24 Married, wife present Single, living in a family Single, not in a family N ever-married females 18-24 Living in a family Not in a family

ADJUSTED MEAN HOURS

ADJUSTED LFPR

INDEX OF LABOR SUPPLY

16.8 58.5 18.6 42.2 14.4 29.3 (F = 7.35 **) (F = 23.3 **)

65.5 52.3 28.1

19.7 11.4 (F = 40.5 **)

43.1 21.1

32.8 27.8 ( F = 1.92)

Kind of school. Another indirect way of attempting to analyze the re­ lationship between family income and labor force status involves classify­ ing students according to the public versus private school dichotomy. One would expect that 18-24-year-old students in private schools would be less inclined to participate than their counterparts in public schools, the premise being that the private school students are less dependent, on average, on their own earnings than are public school students. As can be seen from Table 12-7, this conjecture is confirmed for men at the 1 percent level, but is not supported in the case of single women. In fact, the adjusted participation rate for single women in private schools is actually three points higher than the rate for those in state- and citysupported schools. This differential is not statistically significant, how­ ever, which is just as well in that we have no ready explanation for it.13 The student-teacher ratio. Our intercity analysis of males 18-19 and 20-24 enrolled in school afforded the opportunity to see what relation prevailed in 1960 between the participation of these students and another characteristic of schools — the ratio of college students to college teachers in the SMSA.14 We expected this relation to be positive, partly because the 13 The t- value for this differential is 0.96. Single women 18-24 in private schools who held part-time jobs during the census week also worked about two hours longer that week (on the average) than did similar women who were attending public schools. The adjusted means (derived from Appendix Table A-35) were 16.6 and 14.9 hours, respectively; the /-value for this pair of means is 1.33 (not significant at 10 percent). The comparable figures for males 18-24 were virtually identical; hence this characteristic was omitted from the final run for this group in Appendix Table A-34. 14 This experimental variable was calculated l>y dividing the total college enrollment in the SMSA by the number of "college presidents, professors, and instructors" in the SMSA. (A single value based on the number of students and teachers in Miami and Ft. LauderdaleHollywood combined was assigned to both SMSA'S to take account of the fact that most

395

INDIVIDUAL CHARACTERISTICS TABLE 12-7

Kind of School Attended and Labor Force Participation: Persons 18-24 in School in Urban Areas, Census Week of 1960

Population group by kind of schoolab

Number in sample

Labor force participation rate

Percent of population

Unadjusted

Adjusted'

477 914 1,391 7.54**

34.3 65.7 100.0

33.1 42.0 39.0

34.2 41.5 39.0

352 547 899 1.18

39.2 60.8 100.0

32.7 28.9 30.4

32.3 29.1 30.4

I. MALES 18-24

Private school Public school Total F-ratio 11. XM FEMALES 18-24

Private school Public school Total F-ratio

Source: 1/1000 Sample. The complete multiple regression equations are in Appendix Ta­ bles A-28 and A-29. a Symbols for population groups Eire defined in Table 12-1. bAll persons aged 18-24 in our sample enrolled in "regular" schools, regardless of the level at which they were enrolled, are included in these figures, except persons who worked 40 hours or more during the census week. As explained earlier, the elimination of persons who worked 40 hours or more is intended to confine the analysis to full-time students, but some part-time students undoubtedly remain in our subset. According to the Census, "a 'public' school is defined as any school which is controlled and supported pri­ marily by a local, State, or Federal governmental agency, whereas 'private' schools are defined as schools supported by private persons or organizations, including those sup­ ported by a religious organization" (1960 Census, Subject Report on School Enrollment, p. ix). c For the effects of age, color, other income, marital and family status, and level of enroll­ ment. ** Significant at the 1 percent level.

student-teacher ratio ought to be an inverse index of the average cost of attending college in the SMSA (and thus an inverse index of the incomes of the parents of students enrolled), and partly on the hunch that this ratio would also serve as a crude inverse measure of the average quality of higher education in the community, including the average amount of time that students must devote to academic pursuits. In other words, the student-teacher ratio should be inversely related to the amount of finan­ cial support students receive and directly related to the amount of time they have available for extracurricular activities. college students residing in the latter SMSA were evidently attending the University of Miami or other schools in the Miami SMSA.) The all-SMSA mean for this variable was 19.9; the standard deviation was 5.0; and the values ranged from a high of 36.0 in Bakersfield (California) to a low of 9.3 in New Haven (Connecticut). Part of this large dispersion is accounted for by intercity differences in the fraction of college students who are enrolled on a part-time basis and in the fraction of in­ structors who teach only part-time.

396

YOUNGER PERSONS

Given these preconceptions, it was encouraging to find that the regres­ sion coefficient for this variable was positive and statistically significant in at least one run for both age groups.15 These intercity results, while not terribly impressive in and of themselves, are consistent with the general pattern suggested by the results based on the 1/1000 Sample, and for this reason we have more confidence in them than we would have if we had to view them in isolation. All in all, we think that by including these various schooling character­ istics in the regressions we have managed to do a considerably better job of taking into account the effects of family income on the participation of older students than would have been possible had we relied solely on the direct measure of other income. This is of some importance in that it in­ creases the reliability of our estimates of the "other-things-equal" rela­ tionships between labor force status and other variables — most notably color, which we shall discuss in detail after describing briefly what we have learned about the relationships between the labor force status of students and other socioeconomic characteristics of their families.16

Family Characteristics In the next few pages, we report briefly on the relationships between the participation of younger persons in school and four family charac­ teristics: (1) presence of parents; (2) schooling of family head; (3) occu­ pation of family head; and (4) employment status of family head. These relationships pertain only to 14-17-year-olds living in families. For reasons explained earlier, the Census data do not permit similar kinds of analyses for 18-24-year-olds. In our work, these variables serve mainly as controls. Economic theory has little to say about the expected relationships between these variables and labor force status, and we have tried to avoid the temptation to en­ gage in what could only be described as the speculations of amateurs con­ cerning sociological and psychological propositions. Consequently, what follows consists mainly of descriptive statements, supplemented occa15 In the first run for males 18-19, the regression coefficient was +0.25 with a /-value of 1.79, significant at 10 percent. However, when the nonsignificant variables were dropped in the second run, the student-teacher ratio also fell below the 10 percent level of signifi­ cance. In the first run for males 20-24, the respective values were+0.25 and 1.58, not quite significant at 10 percent. Dropping four nonsignificant variables with lower t- values from the second run made the student-teacher ratio significant at the 5 percent level (b = 0.30, t = 2.02). (The rationale for this departure from our usual rule about dropping all nonsignificant variables is explained in footnote a to Appendix Table B-32.) 16One additional schooling characteristic was included in the analysis of the 1/1000 Sample —namely, the level of scfiool in which the student was enrolled. This variable took the value of 1 for college or above and 0 for high school or below. In the case of males 18-24, the unadjusted participation rate for students in higher education was about 8 points lower than for other students, whereas for never-married women 18-24 an opposite differen­ tial of about 4 points appeared. However, after age, kind of school, marital and family status, etc., were taken into account, the differences in participation associated with level of enrollment were very much smaller and statistically nonsignificant; this being the case, we decided not to discuss this school characteristic in the text.

INDIVIDUAL CHARACTERISTICS

397

sionally by comments concerning the kinds of explanations which are likely to occur to economists. Presence of both parents. In our analysis of the participation decisions of 14-17-year-old never-married students living in families, we included a variable which took the value of 1 if both parents were present, and 0 in all other cases. As Table 12-8 reveals, enrolled teenagers from broken homes were more likely to be in the labor force than their more fortunate classmates. The differential is more pronounced (and significant at a higher level) for females than for males. In both cases, the adjusted dif­ ferential is considerably larger than the unadjusted one; indeed, the im­ pact of this factor on male participation is not at all evident until the other variables in the regression have been taken into account—a result which is presumably due to the overrepresentation of Negroes among the children in broken homes.17 Schooling of the family head. This widely used measure of socio­ economic status bears no consistent relationship to the labor force status of teenage children enrolled in school. For this reason, we do not present the usual tabular summary in the text. (The coefficients for the schooling dummies can be found in Appendix Tables A-26 and A-27.) There is, however, one feature of these results which we feel we should mention, even though we have no good explanation for it. In the cases of both boys and girls, those 14-17-year-olds whose parents were college dropouts (i.e., had completed 13-15 years of school) had appreciably higher partici­ pation rates than other children. This pattern exists in the unadjusted data and after the participation rates are adjusted for differences among families in other family income, employment status of the head, color, and so on. A related measure —the median years of schooling of all persons 25 and over—was included in our 1960 intercity regressions for younger persons, with strikingly different results. In fact, the performance of this variable is so robust that we suspect it is measuring something besides the associa­ tion between parents' schooling and children's participation. According to the regression coefficients from these runs, a difference of one year in the median schooling of adults was positively associated with a difference of 3.7 percentage points in the participation of enrolled males 16-17, 1.9 points in the rate for enrolled males 18-19, and 4.3 points for those 20-24. The comparable differentials for never-married females were 2.7 points for those 16-17, 2.5 points for those 18-19, and 2.6 points for those 2024.18 One possible explanation for these associations is that cities where the 17 Negroes accounted for 24 percent of the 14-17-year-old males in our sample who were living in broken homes but for only 8 percent of those living with both parents. The com­ parable ratios for females 14-17 are very similar. 18 These coefficients, all of which are highly significant, are taken from Appendix Tables B-30, B-31, and B-32 (for enrolled males), and from Tables B-36, B-37, and B-38 (for all never-married females).

398

YOUNGER PERSONS

TABLE 12-8 Family Status and Labor Force Participation: Younger Teenagers in School in Urban Areas, Census Week of 1960

Population group by family statusa

Number in sample

Labor force participation rate

Percent of population

Unadjusted

Adjustedb

2,697 502 3,199 2.801

84.3 15.7 100.0

22.5 22.7 22.5

21.9 25.8 22.5

2,587 488 3,075 10.8 **

84.1 15.9 100.0

12.0 16.0 12.7

11.7 17.9 12.7

I. XM MALES 14-17, IF

Both parents present Otherc Total F-ratio II. XM FEMALES 14-17, IF

Both parents present Otherc Total F-ratio

Source: 1/1000 Sample. For the complete multiple regression equations from which these results were taken, see Appendix Tables A-26 and A-27. a Symbols for population groups are the same as those in Table 12-1. b For the effects of color, age, other family income, family size, schooling of family head, and labor force status of family head. c Includes persons living with only one parent or with other relatives. ** Significant at the 1 percent level. t Significant at the 10 percent level.

average educational attainment is higher have better job opportunities for students. This might be the case if the occupational or industry mix in such communities were more favorable to the employment of these youngsters. A different hypothesis is that the quality of schooling tends to be higher in metropolitan areas with better-educated taxpayers, and that firms are therefore more willing to employ the youngsters in these cities. The schools in these cities may also have provided more assistance in finding jobs for their students. Occupation of the family head. In our preliminary analysis of the participation decisions of enrolled persons 14-17, we included a set of dummy variables for the major occupational group of the family head. Several interesting findings emerged. First, boys in families where the head was a sales worker were more likely to be in the labor force than other youngsters. The adjusted partici­ pation rate for this group was 30.7 percent, compared to the all-person mean of 22.5 percent.19 Since employed teenage boys are overrepresented in retail trade, this differential suggests to us that parents who are em19 The difference between the sales-worker rate and the rates for most other groups was significant at the 10 percent level or better.

INDIVIDUAL CHARACTERISTICS

399

ployed in this industry are often instrumental in getting part-time jobs for their children in the same establishment. In the comparable regression for 14-17-year-old girls, those in families where the head was a service worker had the highest participation rate — 16.7 percent, vis-a-vis an overall rate of 12.7 percent. Although this dif­ ferential is of only marginal statistical significance, the pattern fits the same "parental influence" mold suggested above.20 These findings notwithstanding, we decided to drop occupation of family head from our final set of regressions for 14-17-year-old persons in school. Two considerations led to this decision: (1) in no case was the overall F-ratio for this characteristic statistically significant at the 5 per­ cent level, and (2) there is a rather large overlap between family heads with no occupation and those who were not in the labor force. Since we are especially interested in the eifects on children's participation of the labor force status of the family head, it seemed best to avoid any statisti­ cal overlap between these determinants. Labor force status of the family head. Do children enrolled in school sometimes assume the role of "additional workers" when the chief bread­ winner of the family loses his job? Table 12-9 returns an uncertain verdict Among two of the groups— 14-17-year-old girls and our subset of nevermarried males 18-24 living at home—students in families where the head was unemployed did have higher adjusted participation rates than their counterparts in families where the head was employed. However, neither of these differentials is statistically significant at the 10 percent level.21 More interesting are the results for 14-17-year-old males. This is the one group for which we do find a statistically significant differential, and a most surprising one at that: the adjusted participation rate for youngsters with unemployed fathers22 is nearly 10 percentage points lower than the 20 A special run for Negro girls, 14-17 years old, provides further evidence to support this hypothesis. It turns out that the children of domestic servants had by far the highest rate (about 23 percent), followed by the children of other service workers (15 percent); the over­ all mean for this group was 9.6 percent. In spite of the small number of observations in this regression (324), the F-ratio for occupation of the family head (1.93) was significant at the 10 percent level. In another experimental run for 14-17-year-old Negro boys, rather similar results were ob­ tained. These boys were most likely to be working when the family head was engaged in domestic service; the adjusted participation rate for boys in this category was 25.4 percent, compared to an overall mean for this subset of 14.9 percent; however, the differences be­ tween this participation rate and those for other occupational categories fell short of significance at the 10 percent level. Due to the absence of any other interesting findings for these two Negro subsets, no fur­ ther discussion of these regressions is presented here. 21 The differential for males 18-24 just misses this mark; its t-value is 1.61. The differen­ tial for females 14-17 has a t- value of 1.07. 22 While not all family heads in this subset are fathers, the great majority are. And, since a separate variable for the presence of both parents has been included in the regression, it seems quite legitimate to use the terms "father" and "family head" as if they were synony­ mous; we have followed this practice throughout the chapter.

400

YOUNGER PERSONS

TABLE 12-9 Labor Force Status of Family Head and Labor Force ParticipationiYounger Persons in School in Urban Areas, Census Week of 1960 Population group by labor force status Offamilyheada

Labor force participation rate

Percent of population

Unadjusted

Adjustedb

2,861 102 236 3,199 4.96 **

89.4 3.2 7.4 100.0

23.2 11.7 18.2 22.5

23.3 13.6 16.8 22.5

Head employed Head unemployed Head not in labor force Total F-ratio

2,769 96 210 3,075 1.75

90.1 3.1 6.8 100.0

12.6 16.7 11.0 12.7

12.8 16.4 9.4 12.7

18-24, IF c Head employed Head unemployed Head not in labor force Total F-ratio

752 30 85 867 1.51

86.7 3.5 9.8 100.0

52.9 66.7 50.6 53.2

53.0 68.4 49.7 53.2

447 13 44 504 0.57

88.7 2.6 8.7 100.0

36.9

37.0

14-17, IF Head employed Head unemployed Head not in labor force Total F-ratio

Number in sample

I. XM MALES

XM FEMALES 14-17, IF

XM MALES

XM FEMALES 18-24, IF c

Head employed Head unemployed Head not in labor force Total F-ratio

d

d

45.5 37.9

44.1 37.9

Source: 1/1000 Sample. For the complete multiple regression equations, see Appendix Tables A-26, A-27, A-30, and A-31. a Population symbols are defined in Table 12-1. b For the effects of color, age, family status, family size, other family income, and schooling of family head. c The overall participation rates shown for these special subsets of older students in families are not comparable with the results in other tables for all males and all never-married women (18-24) since students working 40 hours or more are included in these subsets but not in the overall groups. d Not shown for lack of sufficient observations. ** Significant at the 1 percent level.

rate for youngsters whose fathers were employed. In relative terms, the participation of the former group was only about three-fifths as high as that of the latter. This unexpected result makes it clear that the usual added-worker effect does not operate via a net increase in the participation of these

INDIVIDUAL CHARACTERISTICS

401

14-17-year-old boys still enrolled in school.23 But what can account for the opposite relation? Why do we find such a much higher participation rate, other things equal, for students whose fathers were employed than for students whose fathers were unemployed? The best we can do in attempting to answer this question is to record three conjectures which may serve as points of departure for further re­ search. (1) To the extent that fathers do play an important role in getting part-time jobs for their teenage sons, the sons of unemployed fathers are obviously at a disadvantage in this respect. (2) When a father loses his job, this event may come as a shock to his teenage son, reducing the son's willingness to look for work—an intra-household "discouragement effect," if you will. (3) Since there is a sizeable additional-worker response in the case of married women (see Chapter 5), this may mean that there is more work for the teenage children of unemployed fathers to do at home. Having listed these three possible explanations, it is necessary to point out one important difficulty: none of them explains why, as noted above, the expected additional-worker response seems to prevail for 14-17year-old girls. Indeed, conjecture 3 would appear to be much more appli­ cable to girls than to boys, and girls should also be affected by the first two considerations.

Color We come finally to the association between the participation of younger persons in school and their color. Our findings are recorded in Table 12-10, and the main conclusions can be stated simply: (1) within each group, Negroes have substantially lower participation rates than whites; (2) taking account of differences in the individual, family, and schooling characteristics already discussed in this chapter tends to widen this dif­ ferential, except in the case of 14-17-year-old boys. The absolute differences between the adjusted participation rates of whites and Negroes are impressive enough, varying from about 5 per­ centage points for 14-17-year-old girls to nearly 12 points in the case of 18-24-year-old males; all four differentials are statistically significant at the 5 percent level or better. But when viewed in relation to the low abso­ lute levels of participation for whites, these differentials are even more striking. The ratio of the adjusted participation rate for Negroes to that 23This evidence does not, of course, rebut the proposition that unemployment of the family head should be expected to lead to a net increase in the participation of all 14-17year-old boys as a result of changes in school enrollment. That is, children might react to the unemployment of the family head by quitting school altogether and entering the labor force. To evaluate this possibility, it is necessary to examine the relationships between em­ ployment status of the family head, enrollment status of children, and the labor force status of children not enrolled in school. This chain of relationships is analyzed near the end of the next section of this chapter.

402

YOUNGER PERSONS

TABLE 12-10 Color and Labor Force Participation: Younger Persons Enrolled in School in Urban Areas, Census Week of 1960 Number in sample

Labor force participation rate

Percent of popu­ lation

Unadjusted

Adjusted11

2,828 342 29 3,199 6.16**

88.4 10.7 0.9 100.0

23.6 14.9 10.3 22.5

23.5 15.3 13.6 22.5

Whites Nonwhitesc Total F-ratio

2,727 348 3,075 7.42 **

88.7 11.3 100.0

13.1 9.5 12.7

13.3 7.9 12.7

18-24 Whites Negroes Other nonwhites Total F-ratio

1,275 96 20 1,391 3.98 *

91.7 6.9 1.4 100.0

39.5 32.3 35.0 39.0

39.8 28.3 35.5 39.0

811 80 8 899 3.08 *

90.2 8.9 0.9 100.0

30.8 23.8

31.0 22.6

Population group by color a 14-17, Whites Negroes Other nonwhites Total F-ratio

I. XM MALES

II. XM FEMALES

IF

14-17,

IF

III. MALES

18-24 Whites Negroes Other nonwhites Total F-ratio

IV. XM FEMALES

d

d

W4

304

Source: 1/1000 Sample. The complete multiple regression equations from which these re­ sults are taken will be found in Appendix Tables A-26 through A-29. a Symbols for population groups are the same as in Table 12-1. b Results for groups ι and n have been -adjusted for the effects of age, other family income, family status, family size, schooling of family head, and labor force status of family head. Results for groups HI and iv (which include persons not in a family) have been adjusted for the effects of age, marital and family status, other income, level of enrollment (college or high school), and kind of school (public or private). c Due to a mistake in programming, Negroes and other nonwhites in this group were not classified separately. d Results not shown due to small number of observations in this category. ** Significant at the 1 percent level. * Significant at the 5 percent level.

for whites is between 60 and 65 percent for 14-17-year-old boys and girls in school and around 70 percent for students of both sexes in the 18-24 age bracket. These ratios are much lower than for any other population group examined in this study, save only for females 14-17 not in school.24 24Since this important conclusion rests on relatively few observations, we decided to check it against the published data for younger persons in school as shown in the 1960 Cen­ sus (Detailed Characteristics, U.S. Summary, Table 197) —data drawn from a 25 percent sample of all census returns. These figures reinforce the conclusion. They show that the

403

INDIVIDUAL CHARACTERISTICS

As we observed earlier, the adjusted participation rates for the 14-17year-old groups do take family characteristics into account—specifically other family income, family size, and the schooling and labor force status of the family head—whereas it is not possible to control for these charac­ teristics, at least directly, in the overall regressions for older students (18-24), since many of the latter live away from home. However, lest the reader think that part of the gap between the participation rates of collegeage whites and Negroes in school is due to this deficiency, we report here the adjusted rates for a subset of these students, namely, those nevermarried persons enrolled in school but living at home. After controlling for differences in all of the family characteristics mentioned above, we ob­ tain an adjusted participation rate of 55.6 percent for white males 18-24, compared with 33.6 percent for Negro males of the same age. The comparable adjusted rates for never-married female students of the same age who were living at home were 39.2 percent for whites and 30.0 per­ cent for nonwhites.25 We think there is a very simple explanation for the much lower partici­ pation of nonwhite students, and that is the much greater difficulty they face in finding part-time jobs. As the figures in the text table show, the rate of unemployment among nonwhites enrolled in school during the UNEMPLOYMENT RATE, PERSONS ENROLLED IN SCHOOL

(excluding full-time workers) Males 14-17 18-24 Females 14-17 18-24

CENSUS WEEK OF

1960

Nonwhites

Whites

Ratio

18.2 20.7

9.4 10.0

1.9 2.1

22.5 20.8

9.2 7.6

2.5 2.7

ratio of the unadjusted participation rate for nonwhite students to the comparable rate for white students was 77 percent for males 18-24, 67 percent for females 18-24, 55 percent for males 14-17, and 43 percent for females 14-17. (Persons holding full-time jobs were excluded from these ratios.) 25 Because these two subset regressions are hampered by relatively few observations and by a probable selection bias, we do not discuss the results for most of the variables in the text. However, the complete multiple regression equations are recorded in Appendix Tables A-30 and A-31. The essence of the selection bias is that parents in the higher socioeconomic strata are more likely to send their children to residential colleges and universities than are their middle-class counterparts —mainly because they can better afford to do so. As a result, a smaller fraction of college students from well-to-do families live at home. Under these cir­ cumstances, it is not clear what interpretation to attach (say) to the relation between other family income (or the schooling of the family head) and the participation of 18-24-year-old college students who are living at home. However, we do present the results for this subset pertaining to the labor force status of the family head. No problem of a selection bias arises in the case of students enrolled at the high-school level (or below), since the latter were recorded as living at home even if they were actually away at boarding school.

404

YOUNGER PERSONS

1960 census week was more than twice as high as the jobless rate for their white counterparts,26 Indeed, we are unable to advance any other important reason for the differences in participation of these two groups. In contrast with the multitude of uncontrolled factors associated with color in the case of prime-age males, married women, single women 25-54, and older per­ sons, the differences in the participation of whites and Negroes enrolled in school seem to us to be due overwhelmingly to the greater reluctance of employers to hire Negro students at the prevailing wage rates for parttime jobs. It is not clear, however, to what extent this reluctance reflects pure discrimination against Negroes as opposed to lower expected pro­ ductivity in the jobs concerned. The generally poorer quality of schooling that Negroes obtain undoubtedly does put them at a comparative disad­ vantage in competing for highly skilled jobs, but most part-time jobs that students hold appear to fall in the less-demanding sales- and serviceworker categories.27 Whatever the underlying explanation for the greater difficulty that Negro students have in finding jobs, one implication is absolutely clear: this handicap must surely tend to make the fraction of Negroes enrolled in school—particularly at the college level—lower than it would other­ wise be. How long a youngster from a poor family can afford to remain in school obviously depends on a great many things, but his ability to find a suitable part-time job must surely tip the balance in many cases.28 Our 1960 intercity regressions reported in Chapter 13 provide addi­ tional evidence that Negro students have lower participation rates than white students. In each of the separate analyses for enrolled males and all never-married females in the 16-17, 18-19, and 20-24 age intervals, the net regression coefficient for the nonwhite population ratio was nega­ tive, and this relation was statistically significant for every group except male students 20-24.29 One aspect of these results, however, is both puzzling and intriguing. This concerns the size of the regression coefficient for enrolled males 16 to 17 years old: —0.32. This highly significant statistic (its /-value is over 26These figures were calculated from the 1960 Census, Detailed Characteristics, U.S. Summary, Table 197. For reasons explained above, persons enrolled in school who worked 35 hours or more during the census week were excluded from the labor force. This exclu­ sion raises the absolute unemployment rate for each group, but has very little impact on the ratio of nonwhite unemployment to white. 27 This conclusion rests mainly on casual observation. 28There is no contradiction between this argument and our finding in Chapter 13 that, other things equal, cities with above-average unemployment in 1960 tended to have aboveaverage enrollment ratios for teenage males. While higher overall unemployment in the com­ munity does serve to discourage students from dropping out of school, a rise in the rate of joblessness among students alone would presumably have the opposite effect. The point here is simply that the relatively small number of jobs open to Negro students serves to in­ crease the net cost to Negro families of keeping their children in school beyond the minimum school-leaving age, especially at the college level. 29 For the specific findings, see Appendix Tables B-30 through B-32 (for enrolled males) and B-36 through B-38 (for all never-married females).

INDIVIDUAL CHARACTERISTICS

405

seven times as large as its standard error) indicates that a city with a nonwhite population ratio 10 points above the national average could be ex­ pected to have had a participation rate for enrolled males in this age group roughly 3 percentage points below the all-SMSA mean of 31 percent during the census week. This differential is more than twice as large as one would have expected to find on the basis of the data in the 1/1000 Sample.30 The inference seems to be that high school students who live in metro­ politan areas with relatively large nonwhite populations have much lower participation rates than their individual characteristics can explain. Whether the lower participation of students in these cities is due to greater discrimination by employers, to lower productivity of these students in part-time jobs, to adverse parental influences, or to still other characteristics of the cities in question, we are unable to say. But we think the result itself is very interesting and demonstrates once again the need for careful sociological as well as economic analysis of "group phe­ nomena" of this kind.31 We conclude this part of our analysis by commenting briefly on the net relationship between color and the number of hours worked by employed students during the census week. Only for males 18-24 is this relation significant, and it shows that Negroes worked about four hours longer than whites. The adjusted means are 20.8 and 16.9 hours, respectively.32 30 Panel ι of Table 12-10 shows an adjusted differential of about 8 percentage points for white and Negro males 14-17 years old, but presumably the differential for those 16 to 17 would be somewhat larger. According to the published Census data for 1960 (Detailed Characteristics, U.S. Summary, Table 197), enrolled white males 16-17 in urban areas had a participation rate 14 percentage points higher than enrolled nonwhites. Let us assume that the adjusted and unadjusted differentials for this age group are roughly the same (as was the case for males 14-17 in Table 12-10). Then a 14-point color differential for this group suggests that if city A has a nonwhite student-population ratio 10 percentage points higher than city B, but is similar in other respects, the overall participation rate for all students 16-17 should be 1.4 percentage points lower in A — a differential less than half as large as the 3.2-point differential^implied by our intercity regression for this group. 31 It should be noted that a comparison of the coefficients for color from the intercity and 1/1000 regressions predicting the activity rates of younger males and participation of younger males not enrolled in school fails to reveal a similar pattern. For example, the net regression coefficient for the nonwhite population ratio is —0.03 in the first run of the activity rate regression for each age group (16-17, 18-19, and 20-24), which would be con­ sistent with an adjusted participation rate differential of 3 percentage points in the 1/1000 Sample. The actual white-Negro differentials (shown inTable 12-17) are remarkably similar, namely, 2.0 points for males 14-17 and 2.6 points for males 18-24. In the case of younger teenage males not enrolled in school, the discrepancy is con­ siderably larger (the intercity coefficient for males 16-17 is —0.16, whereas the adjusted participation rate differential for males 14-17 is only 8.5 points). But the interpretation of this discrepancy is clouded by the fact that the intercity nonwhite ratio pertains to all teenage males rather than those who are not in school, and it is not clear what difference in the color ratio for dropouts is associated with a one percentage point difference in the ratio for all persons. 32 The complete multiple regression equation is shown in Appendix Table A-34. Variables measuring marital and family status and age were also included in the final run. In addition, other income, kind of school, and level of enrollment were tried in the first run, but proved to be wholly nonsignificant. Their omission from the final run had no appreciable effect on the results for color and age.

406

YOUNGER PERSONS

The longer hours worked by Negro students of college age is undoubtedly rooted in their greater need for income to meet the costs of attending school. Their longer hours may also be viewed as partially offsetting the handicap of lower participation. To determine the extent of this offset, we combined the participation rate results and the hours results into an overall index of labor supply (ex­ pressed in units of 15 hours of work per week).

Males 18-24

Adjusted hours worked

Whites Negroes

20.8

16.9

Adjusted labor force partici­ pation rate

Index of labor supply

39.8 28.3

44.9 39.3

The above figures show that after taking account of differences in hours worked as well as in participation rates, Negro males 18-24 enrolled in school supplied 88 percent as much labor as a comparable group of white students. (If we had looked only at participation rates — and thus tacitly assumed that there were no differences in hours worked—the percentage would have been 71.) The point to remember, however, is that even after taking account of the longer hours worked by those Negro students who did hold part-time jobs during the census week, Negro students as a whole still participated less actively in the labor market than did comparable white students. ACTIVITY AND ENROLLMENT RATES AND THE PARTICIPATION OF YOUNGER PERSONS NOT IN SCHOOL

The Size of the Inactive Group During the census week of 1960, roughly 645,000 males aged 14-24 in the noninstitutional population were reported as not in school, not em­ ployed, and not seeking work—they were "inactive" in our terminology.33 33This figure was obtained by subtracting the number of males 14-24 who were inmates of institutions and not enrolled in school (reported in the 1960 Census Subject Report on Inmates of Institutions, Table 21) from the number of males 14-24 in the total population who were neither enrolled in school nor in the labor force (reported in the 1960 Census, Detailed Characteristics, U.S. Summary, Table 197). It should be noted that the CPS esti­ mate of the number of males 14-24 who were neither enrolled in school nor in the labor force as of April 1960 differs appreciably from the decennial census estimate cited in the text. According to the CPS, there were 474,000 such persons, not 645,000 as reported by the decennial census. It is hard to say which is the better estimate. The greater experience of the CPS interviewers would lead us to expect them to have done a better job of classifying people according to labor force status than the decennial census (which relied heavily on self-enumeration in 1960). On the other hand, the relatively small size of the CPS sample renders their estimates for a narrow group of the kind we are discussing here subject to a relatively large sampling error. The extraordinary year-to-year changes in the CPS count of the number of males 14-24 who were neither in school nor in the labor force certainly sug­ gests the presence of large sampling variations. For example, the April CPS figures for years 1962 through 1967 were (in thousands): 339, 639, 327, 672, 637 and 457. (These figures were calculated from various issues of the Monthly Report in the Labor Force.)

INDIVIDUAL CHARACTERISTICS

407

That so many youngsters appeared to be "doing nothing" — at least noth­ ing they (or their parents) were willing to report—is startling and disturb­ ing, to say the least. This inactive group contained about 5 percent of all males 14-24 and almost 11 percent of all not-enrolled males 14-24. The number of males 14-24 who were not enrolled in school and not in the labor force was 1.4 times as large as the number of not-enrolled males in this age range who were unemployed. Worse still, there is good reason to believe that the actual number of inactive males was substantially larger than the reported figures suggest. There was a substantial undercount of certain groups in 1960, and those males 14-24 who were neither in school nor in the labor force are probably more likely than most other groups to have been missed by Census ennumerators. Jacob S. Siegel of the Census Bureau has esti­ mated that 689,000 males 15-24 were not counted in I960.34 Unfor­ tunately, there is no direct estimate of the percentage of this missing group who were neither in school nor in the labor force, but from what we do know about the selective character of persons missed by the census (e.g., Siegel's data indicate that nonwhite males in this age range were three to four times as likely to be missed as white males), we are entitled to suspect that the percentage could have been very high indeed. It is con­ ceivable that as many as half these missing males were both out of school and out of the labor force; if this were true, the actual number of inactive males 14-24 as of the census week of 1960 would have exceeded the re­ ported number by 345,000. Thus, under this assumption, the total number of inactive males in this age range would have been approximately one million. It is much harder to arrive at a meaningful estimate of the number of in­ active females 14-24 than of the number of inactive males. According to the 1960 Census, there were almost 4 million females 14-24 who were neither enrolled in school nor in the labor force. However, by no means all these persons can be regarded as inactive, since a substantial number must have been engaged in keeping house, taking care of children, and so on. In short, when discussing females it is necessary to modify the defini­ tion of "inactive" to take account of non-market work done in the home. Unfortunately, this principle is easier to state than to implement, since the Census categories provide no direct information concerning unpaid work done around the house. The Census data concerning marital and family status, however, when related to data on school enrollment and labor force status, do permit at least a crude estimate of the number we are looking for. As of the census 34 This figure was calculated from Table 2 of his paper "Completeness of Coverage of the Nonwhite Population in the 1960 Census and Current Estimates and Some Implications," prepared for presentation at the Conference on Social Statistics and the City, June 22-23 [1967]. While Siegel's main concern is with white-nonwhite differences in undercounts (a subject to which we return near the end of this chapter), the figure of 689,000 cited in the text is for all males 15-24, regardless of color.

408

YOUNGER PERSONS

week of 1960, there were 840,000 females 16-24 years of age who were not in school, not in the labor force, and not married with husband present.35 To be sure, some of these women were no doubt engaged in unpaid work in the home which consumed so much of their time that participa­ tion in the labor force would have been impossible on this ground alone (both some never-married women caring for parents or relatives and some once-married women taking care of their own children no doubt fit this description), and to this extent the figure of 840,000 overestimates the number of women who were genuinely "inactive." On the other hand, the Census undercount, while smaller for females than males within this age range, was still substantial, and this consideration of course pulls in the opposite direction. In view of these conflicting considerations, the absence of better information, and the haziness of the dividing line between active and inactive status for females, we are inclined to round off the figure given above and speak of somewhere between f and 1 million inactive females 14-24 as of the census week of 1960. Thus, all told, we estimate that in April of 1960 perhaps Ii to 2 million persons 14-24 years of age were not enrolled in school, not employed, not looking for work, and (in the case of the females) not married. The large number of persons in this amorphous combination of cate­ gories surely merits careful investigation, and in the rest of this chapter we use the limited data available to describe their individual and family characteristics. Attention is given to the relationships between these characteristics and (1) enrollment ratios (i.e., the percentage of, say, males 14-17 enrolled in school), (2) participation rates among those not en­ rolled in school, and (3) activity rates (the percentage of, say, males 14-17 who were either enrolled in school or in the labor force). The organization of this material parallels the preceding discussion of the enrolled-in-school group. We begin with age, then discuss marital status, the person's own schooling, and various characteristics of the families of the members of this group still living at home. We end with an examination of the differences associated with color which remain after these other variables have been taken into account.

Age The extent to which enrollment ratios decline with age can be seen from the first column of Table 12-11,36 and there is no need to elaborate 35Calculated from U.S. Census of Population: 1960, Subject Report PC(2)-5A, School Enrollment, Table 8. The reason for using 16-24 rather than 14-24 as the age range is simply that this is the only way the data are presented. Similarly, the classification scheme used in the construction of this table forces us to lump together all women except those who were married with spouse present. 36 This table differs in format from all of the earlier tables presenting results of regressions based on the 1/1000 Sample, the reason being that it is helpful in this instance to summarize on one page the results of three separate regressions: regressions predicting enrollment

INDIVIDUAL CHARACTERISTICS

409

on these results. It is necessary, however, to point out that the enrollment rates for males 18-24, unlike those for boys and girls 14-17, are unad­ justed rates. The reason we are unable to show adjusted enrollment rates for persons 18-24 is that a high proportion of these individuals live away from home, and the Census data do not permit us to relate their enroll­ ment status to the characteristics of their families. (In Table 12-11 and the other tables of this section, unadjusted rates are placed in parenthe­ ses.) The second column of Table 12-11 shows the adjusted relationships be­ tween age and labor force status for those persons in each group who were not enrolled in school, and a few comments are in order concern­ ing this set of findings. For all three not-enrolled groups for whom re­ sults are reported in the table, the labor force participation rate rises appreciably with age, and it is undoubtedly true that some of the same considerations responsible for a similar association among students — notably, child labor laws and the hiring policies of firms —also operate here. But two other factors must also be mentioned. First, while we tend to think of younger persons who are not in school as having "quit school," this is not always the case. Some youngsters are not enrolled in school because of health problems; such people are not likely to be in the labor force either. We suspect that a substantial fraction of 14- and 15-year-olds who are not in school fall in this "dropout-due-to-disability" category and that it is mainly for this reason that the participation rates for these boys and girls are so extremely low. Above age 15, however, the relative importance of this reason for nonenrollment no doubt declines markedly. The second point is that as one ascends the age ladder (at least beyond age 16), the fraction of not-enrolled youngsters who left school (for good) within the last year surely declines. The longer the period since leaving school, the more information the individual is likely to have about the kinds of jobs that are available in the locality (even if he is not actively seeking work), and the more realistic he is likely to be in setting his em­ ployment sights. Furthermore, the family and social pressures on him to look hard for work of some kind must also increase significantly after he has been out of school for some time. To put this point another way, we would expect that many younger persons who are neither working nor status, labor force status for persons not enrolled in school, and activity rates. If, in addi­ tion to the three sets of adjusted rates, we had also tried to show all of the information on cell size and unadjusted relationships contained on previous tables, we would have had 12 columns of numbers. In an effort to retain some of the more important data pertaining to cell size, we have used the symbol "

ίν lies within one standard error of the value of b for 13 groups out of 18. There are, however, fewer statistically significant associations between participation and unemployment in our distributed-lag runs than in our basic runs, as a comparison of columns (1) and (4) makes clear. We at­ tribute this difference to the relative inefficiency of distributed-lag esti­ mates based on least-squares techniques; we have therefore drawn our main conclusions (tentative as they are) about time-series relations from the results of our basic runs, which are also less subject to bias.

Experiments with Supplementary Variables In one important set of experimental runs, we added a measure of long-term unemployment—the percentage of all persons in the civilian labor force who had been unemployed for 27 weeks or longer ( U 27 JL) — to our basic regressions for 1949II—1965III. Like the overall unem­ ployment rate, U27JL was lagged one quarter. Our purpose here was to see whether the long-term unemployment rate would pick up any entry of additional workers into the labor market—as well it might if such persons generally deferred their entry until the unemployed bread­ winner in the family had remained unemployed for a considerable time. In short, the long-term unemployment rate was supposed to capture the additional-worker effect while the overall rate was supposed to reveal the discouraged-worker effect.25 The results make short shrift of these expectations. For only 5 of the 18 groups does U21JL have a positive sign, and not one of these 5 coeffi­ cients is significant at the 10 percent level. (The one that comes closest is for a group which must have very few additional workers indeed— males 25-34!) On the other side of the ledger, of the 13 negative coeffi25 A precedent for using separate variables to isolate these two gross flows was established by Dernburg and Strand in two articles: "Cyclical Variation in Civilian Labor Force Par­ ticipation," Review of Economics and Statistics, Vol. 46 (November 1964), pp. 378391; and "Hidden Unemployment 1953-62: A Quantitative Analysis by Age and Sex," American Economic Review, Vol. 56 (March 1966), pp. 71-95. The additional-worker pre­ dictor they selected—new unemployment compensation exhaustions as a percentage of total population-is, no doubt, very highly correlated with our own (C/27+/L), and in their model it performs beautifully, turning up with positive signs that, in most cases, are highly significant. However, for reasons developed at length in Appendix 16-A, we think that much of this predictor's success is due to a nearly tautological relationship, and that the regres­ sion coefficients are simply too large to be believed. One purpose in trying out a similar predictor in a less tautological regression was to subject our interpretation of the Dernburg-Strand results to a rough empirical test.

526

UNEMPLOYMENT AND LABOR FORCE PARTICIPATION

cients for long-term unemployment, 6 are significant at the 5 percent level.26 The fact that U 2 7 JL is negatively associated with participation— where it works at all—makes it clear that it is not catching delayed in­ flows of additional workers. But what about delayed withdrawals of dis­ couraged workers? There are, unfortunately, some difficulties with this interpretation of the results. For two of the six groups concerned (males 45-54 and 55-64), adding U 2 7 JL makes the regression coefficient for UlL significantly positive. Can it really be that, for these men, a rise in unemployment causes first a net inflow of additional workers, and much later a withdrawal of dis­ couraged workers? This hardly seems likely. Dropping UjL from the runs for these two groups causes U27JL to lose its significance, and con­ versely — suggesting collinearity. For two other groups (males 35-44 and females 20-24), U 2 7 JL is significantly negative and UjL is nonsignificant—a reasonable result. But when U/L is dropped, the coefficient for U27JL is reduced to marginal significance. In the two final cases (males 65 or over and females 35-44), both UlL and U27JL are significantly negative when run concurrently — also a credible result. But here these two predictors seem to be fighting each other over the same variance in LJPi: the net regression coefficient for U 2 7 JL and its standard error are both over five times as large when UlL is in each run than when it is not, whereas the presence of U27JL tends to make the regression coefficient for UlL smaller—and its standard error larger—than when the latter variable operates by itself. In short, the increment to the R2 from a second unemployment variable is very small in both cases. To sum up: the addition of a long-term unemployment rate to our basic regression model does not enable us to sort out the gross flows of dis­ couraged and additional workers. Where this rate has a negative sign (as it does in a majority of cases), it often seems to be doing little more than replacing some part of the effect of our overall measure of unemploy­ ment. In view of the high intercorrelation between these predictors (r = +.84 for 1949II to 1965III), this result is not surprising. Instead of add­ ing a variable for the long-term unemployment rate to our basic model, one could of course substitute such a variable for the regular unemploy­ ment rate in this model. However, when we did this we continued to find a negative net association between the unemployment variable (which now measures the long-term rate) and participation for almost all popu­ lation groups.27 26These six groups are males 35-44, 45-54, 55-64, and 65 or over, plus females 20-24 and 35-44. 27 Since the long-term unemployment rate plays a key role in the labor force equations developed for the Brookings model by Professor Stanley Lebergott (see his chapter, "The Labor Force and Marriages as Endogenous Factors," in James S. Duesenberry, et al.

TIME-SERIES RELATIONS

527

In the last of this series of tests we explored the net relation between participation rates and the direction of change in the rate of unemploy­ ment. Two alternative measures of this factor were used in successive runs: (1) a dummy variable which took the value of 1 if unemployment was higher in period / than in t — 1, zero otherwise; (2) the arithmetic value of (UlL)t — (UjL)t-x. Neither variable was statistically significant in the great majority of runs; where significant coefficients did appear they were generally positive, suggesting a lag in response to changes in labor market conditions. (The other predictors used in these tests were the unlagged overall unemployment rate, T and T2.) In short, the variable did not perform consistently enough to warrant retaining it in our final time-series runs. In another series of experiments we considered an alternative version of our industry-mix variable. The successful performance of EjE in our basic runs (especially those for the longer postwar period) made us wonder if a more sophisticated measure of employment opportunities for secondary labor force groups would work even better. To find out, we constructed a new index of industry mix based on establishment data for some 30 industry groups at the two- and three-digit level. By matching these industry groups with their Census counterparts, we were able to [editors], The Brookings Quarterly Econometric Model of the United States [Chicago: Rand McNally & Co., 1965], pp. 335-371), we think it appropriate to point out here that our conclusions are radically different from his. Lebergott finds a positive net relationship between the long-term unemployment rate and participation rates for females 20 years old and over, from which he infers that the additional-worker effect outweighs the discouragedworker effect for this group. Some special regressions of our own have convinced us that this conclusion is erroneous and that the source of the difficulty is the absence in Lebergott's equations of a satisfactory control for the pronounced uptrend in female participation rates. In the first test we looked at the simple correlations between U 27 JL and the participation rates of our 18 age-sex groups over the period from 1949II to 1965III; for many of the female subsets a significant positive relation was evident —a finding which is consistent with Lebergott's results. However, when we eliminated the common trends in U27JL and in the group values of LJPi by adding T and T2 to the analysis, the net regression coeffi­ cient for U27JL had a negative sign for 17 groups out of 18, and 9 of these negative coeffi­ cients were statistically significant. (The one positive coefficient was not significant.) To make sure that this pattern of results was not due to the difference in the period of time covered by our regressions and his, we reran our regressions for the period from 19491 to 1960IV. Again, the positive simple association between U27JL and LJP1 for our female groups were converted into negative coefficients when the time-trend variables were added — as they must be if the cyclical associations are not to be swamped by common trends. Later on in his multivariate analysis Lebergott does attempt to take account of trends, but we are not convinced that the variables employed for this purpose are themselves wholly trend-free. In one set of regressions, he uses as his dependent variable the deviations of the actual female labor force (20 and over) from the interpolated values based on the 1950 to 1960 labor force projections of John Durand. In our opinion, the trouble with this approach is that Durand (along with everyone else) grossly underestimated the postwar rise in female participation rates, so there must be a fairly strong time-trend in Lebergott's residuals. It seems to us that the same limitation besets the second set of runs, where his dependent variable is the ratio of the actual female labor force to the trend in the weighted average of employment in those major industry groups employing large numbers of women, since the denominator of this ratio appears to take no account of the marked substitution of women for men which has occurred within these industry groups during the postwar period.

528

UNEMPLOYMENT AND LABOR FORCE PARTICIPATION

estimate the fraction of total employment in each category in 1960 con­ sisting of "secondary workers" (defined here as females of all ages plus males 14-19 and 65 or over). After identifying those industries with secondary worker ratios above the national average, we then proceeded to calculate the ratio of aggregate employment in these industries to total nonagricultural employment from seasonally adjusted establishment data for each quarter during the postwar period.28 This alternative measure of industry mix, which we shall designate EsJE, has two conceptual advantages over EJE: (1) it is sensitive to changes over time in secondary-worker job opportunities within the manufacturing and non-manufacturing sectors of the economy, and (2) it is not nearly so intercorrelated with unemployment in the postwar period.29 Unfortunately, EsJE also has a serious flaw, in that it suffers from a much higher collinearity with T (our time-trend variable) than does EJE: r = .91 for 1949II-1965III and .98 for 1954IV-1965III. As a result, when the new industry-mix variable is substituted for the old, the regression coefficients for EsJE are somewhat less "well behaved" and less frequently significant at high levels than the comparable coefficients for EJE in our basic model. Nevertheless, the adjusted regression co­ efficients for unemployment based on the runs using EsJE turn out to be virtually identical with the adjusted coefficients from our basic runs de­ scribed below! These results suggest to us that the strong collinearity between UIL and EJE has not introduced a serious bias into our esti­ mates of the cyclical sensitivity of group participation rates. They also suggest (with less certainty) that, despite its limitations, EjE is a toler­ ably good measure of employment opportunities for secondary labor force groups. Hence we elected to retain EjE in our final set of time-series regressions. The impressive performance of the industry-mix variable also encour­ aged us to construct a set of group-supply variables — specifically, the ratio of the civilian, noninstitutional population (CNP) of each of our 18 age-sex groups to the total CNP, 14 years old and over, which we des­ ignated P J P. We hoped PJP would measure changes over time in the competition for jobs faced by individuals in each group, much as the ratio of female population to total population served to capture inter­ city differences in the potential supply of female workers. As a supply variable, PJP was expected to have a negative sign. 28 The establishment data were calculated from U.S. Department of Labor, Employment and Earnings Statistics for the United States 1909-65 (Bulletin No. 1312-3), pp. 660-674. The following industries had above-average secondary worker ratios: miscellaneous manu­ facturing industries; tobacco manufactures; textile mill products; apparel and related products; printing, publishing, and allied industries; leather and leather products; retail trade; finance, insurance, and real estate; service and miscellaneous; and state and local government. 29The simple correlation between UlL and E JE is .65 for 1949II-1965III and .48 for s 1954IV-1965III. The comparable coefficients for UIL and EJE are —.86 and —.83, re­ spectively.

TIME-SERIES RELATIONS

529

When quarterly values of P J P were added to our regressions for the 1949II—1965III period, the new predictor had a negative coefficient in only 8 runs out of 18; and only one of these negative coefficients (the one for females 35-44) was statistically significant.30 Worse still, in 6 instances PJP was positive and significant at the 5 percent level or below!31 Being unable to think of a plausible explanation for these curi­ ous results, we decided not to include the supply variable in our basic model. THIS completes the discussion of the detailed aspects of our time-series

regressions. There is, however, a very basic question which deserves further consideration: Are the differences between the sensitivity esti­ mates generated by our time-series and cross-sectional analyses of more or less the right magnitude to be consistent with an interpretation which views the time-series coefficients as reflecting short-run effects and the cross-sectional coefficients as indicative of longer-run sensitivities? The next chapter represents an attempt to develop at least a provisional answer to this question by examining the effects of the 1963-1967 ex­ pansion on labor force participation rates. 30 In this case, P J P attained significance at the expense of E J E . When the former was dropped from the run, the latter regained significance, and the R2 fell by only one point. 31 These six groups were males 35-44 and 65 or over, and females 14-15, 18-19, 25-34, and 45-54. In some of these cases the addition of PJP substantially raised the R1', in oth­ ers, the new variable markedly reduced the significance of other predictors but had little effect o n R 2 .

CHAPTER 17

Unemployment and Labor Force Participation: Some Evidence from the 1963-1967 Boom From 1958 to 1963, the American economy experienced two sharp re­ cessions and two incomplete recoveries. During this period of chronic slack, the overall unemployment rate never fell below 5.0 percent and averaged 5.9 percent. The next four years witnessed a sustained eco­ nomic expansion that reduced the jobless rate from 5.7 percent in 1963, to 5.2 percent in 1964, to 4.5 percent in 1965, and finally to 3.8 percent in 1966 and 1967—the lowest rate since the Korean War. In this chapter we shall attempt to assess the effects of tighter labor markets on labor force participation during this prolonged expansion. This analysis is interesting both in its own right and because of what it can contribute to an understanding of the different sensitivity estimates generated by our cross-sectional and time-series regressions. If a prolonged recession produces "hidden unemployment," then a sub­ sequent boom should have the opposite effect on participation decisions. We have dubbed the latter effect "induced participation," which we de­ fine as the actual change in participation during a given economic ex­ pansion minus the projected change under the premise of (continued) high unemployment. The sensitivity estimates obtained from the timeseries and cross-sectional regressions presented in the two previous chapters can, of course, also be used to derive estimates of the volume of induced participation during the period 1963-1967, and we shall pre­ sent the results of such calculations later in the chapter. We prefer, however, to begin our analysis of the effect of the 1963-1967 boom on labor force participation by using a simpler and more direct method to construct estimates of induced participation. In brief, instead of study­ ing the net association between participation and unemployment over this period by means of time-series regressions, we propose to compare the actual change in labor force participation between 1963 and 1967 with an estimate of what would have happened to participation if the unemployment rate had remained close to 5.6 percent throughout this period. As will be explained shortly, the estimate of "what would have happened" is based simply on an extrapolation of recent trends in par­ ticipation rates calculated in such a way as to minimize the impact of cyclical forces. This approach has several advantages. It provides a better measure of the long-run effects on participation of a fall in unemployment than ordinary time-series regressions, which pick up mainly short-run associa­ tions. It also enables us to focus on a particular period of special inter-

SOME EVIDENCE FROM THE 1963-1967 BOOM

531

est and to obtain some idea of how much induced participation occurred from year to year during that period. Against these advantages, two limi­ tations should be noted. First, the results for any given boom may have very little applicability to other economic expansions (past or future). Moreover, the estimates of induced participation for any period are, of course, no better than the projections of what would have happened to participation rates had unemployment not declined, and projections of this nature are subject to wide margins of error—especially in the case of individual groups. In fact, the same considerations that made it im­ possible to control with precision for the effects of demographic factors on group participation rates in our time-series regressions also prevent us from making precise projections of this kind.

Estimation Procedures and Results We derived our basic estimates of induced participation in the follow­ ing manner. First, the participation rate for each of our 16 age-sex popu­ lation groups was regressed on a time-trend variable in special regres­ sions using only those quarters from 19591 to 1963IV in which the over­ all rate of unemployment was at least 5.2 percent but no higher than 5.9 percent.1 Those quarters in which unemployment was higher than 5.9

percent were omitted in order that the 1960-1961 recession would not produce a bias in projections which were meant to show what would have happened to participation rates had a more or less steady unemployment rate of approximately 5.6 percent prevailed from 1963 through 1967. However, the lapse in time associated with these omitted quarters was, of course, taken into account via the values of T assigned to each period (19591 = 1, 1959II = 2, . . . , 1963IV = 20). The regression coefficient for T obtained from the regression for each population group is interpreted as showing the average quarterly trend in the group's participation rate over the period in question. We next obtained the projected "high-unemployment" participation rates for 1964, 1965, 1966, and 1967 by simply adding the trend factor (multi­ plied by the appropriate number of quarters) to the actual participation rate in the base year of 1963. The amount of induced participation between 1963 and each subse­ quent year was then calculated by taking the difference between the ac1 Fifteen quarters passed this test: 1959I-IV; 19601, II, and III; 1962I-IV; and 19631IV. In an earlier set of regressions, we also included dummy variable D in an attempt to con­ trol for the effects of adjustments in labor force and population totals stemming from the introduction of 1960 Census data into the CPS estimation procedures. (For a definition of this variable, see Chapter 16.) However, the correlation between T and D over the period involved was quite high and the inclusion of D caused certain regression coefficients for T to assume intuitively implausible values; hence D was dropped. It should be added that while omitting D did alter some group values of induced participation, it had very little effect on the aggregate estimates shown below.

532

UNEMPLOYMENT AND LABOR FORCE PARTICIPATION TABLE 17-1 Estimates of Induced Participation by Sex: 1963-1967 Estimates of induced participation (thousands of persons)

Period

Absolute change in overall unemployment rate

Males 16+

1963-1964 1964-1965 1965-1966 1966-1967

-0.5 -0.7 -0.7 0

130 144 113 242

85 111 438 368

215 255 551 610

1963-1967

-1.9

629

1,002

1,631

Females 16+

All persons 16+

Source: Appendix Table 17-A.

tual and projected participation rates in the year in question and multi­ plying this difference by the civilian, noninstitutional population of the group in that year. Finally, estimates of the amount of induced participa­ tion which occurred during subperiods were obtained by subtraction. (For example, the amount of induced participation between 1964 and 1965 was estimated by subtracting the estimate for the period 1963 to 1964 from the estimate for the period 1963 to 1965.) The estimating procedure described above was used to obtain esti­ mates for each of our 16 age-sex population groups for the entire period 1963-1967 and for each yearly subperiod. These results are presented in Appendix Table 17-A. In the case of individual population groups, our crude method of projecting trends in participation rates can lead to sub­ stantial errors, because no allowance is made for demographic factors, legislative changes, or a multitude of other forces which have special relevance for a particular population group. Thus, we do not have great confidence in these detailed estimates — some appear implausibly high and others appear implausibly low. However, we see no a priori reason why these errors should be systematically associated with the actual amounts of induced participation by these groups. If this is so, it is not unreasonable to suppose that many of these estimating errors will cancel each other out, and that aggregate estimates will be considerably more re­ liable. The aggregate estimates for all males 16 and over and all females 16 and over are shown in Table 17-1. It is these estimates which are of pri­ mary interest from the standpoint of our general concern with the ef­ fect of a tightening labor market on labor supply. These same results (expressed this time, however, as rates rather than as numbers of persons2) are shown graphically on Figure 17-1. We have also shown the year-to-year changes in the overall unemployment rate 2 The participation rates for each sex shown on this figure are population-weighted aver­ ages of the rates for the eight component age groups. We used fixed weights, based on the age distribution in 1967, to prevent shifts in age distribution from affecting the results.

SOME EVIDENCE FROM THE 1963-1967 BOOM

533

in both Table 17-1 and Figure 17-1, for reasons which will be explained when we discuss the interpretation of these results.3

Interpretation of Results One important conclusion to be gleaned from Table 17-1 and Figure 17-1 is that during the first half of the 1963-1967 expansion only a rel­ atively small amount of induced participation seems to have occurred. According to our estimates, it amounted to only 215,000 persons between 1963 and 1964, and to only 255,000 more between 1964 and 1965 —a total of 470,000. This seems a small amount of induced participation when compared with the estimates for the second half of the expansion (discussed below), especially when we consider the behavior of the un­ employment rate over this period. Most of the decline in unemployment between 1963 and 1967 took place during the first two years of this fouryear period, the unemployment rate having fallen from 5.7 percent in 1963 to 5.2 percent in 1964 to 4.5 percent in 1965. As was noted at the beginning of this chapter, in constructing these estimates of induced participation we were interested not only in the re­ sults per se, but also in what these results might tell us about the dis­ parate estimates of labor force sensitivity obtained from our time-series and cross-sectional regressions. If we had attempted to predict the amount of induced participation betweeen 1963 and 1965 on the basis of our cross-sectional coefficients, we would have obtained a far higher fig­ ure than 470,000—about 1,200,000.4 On the other hand, the amount of induced participation predicted by our "best" set of time-series re­ gressions, 395,000,5 turns out to be somewhat lower than the 470,000 3 Before discussing interpretations, we should report the results of an alternative proce­ dure used to estimate induced participation. Since the estimates depend directly on the "high-unemployment" projections of group participation rates, we thought it advisable to see how our results would be affected if we used a different —and somewhat simpler—pro­ cedure for making projections. Instead of regressing group participation rates on T in se­ lected quarters of the 1959-1963 period, we simply calculated the absolute change in each group's participation rate between 1959 and 1963 (using annual data), and then extrapo­ lated this change from 1963 to 1967. The alternative estimates of induced participation based on these projections are very similar to the original ones. The estimate for males is virtually identical (about 625,000 persons) under both trend-projection procedures, while the alternative estimate for females (927,000) is slightly smaller than the original (1,002,000). The two grand totals differ by less than 90,000 persons. 4Obtained by multiplying 0.01 times the unemployment coefficients for each population group reported in the right-hand column of Table 16-1, by 1.2 times the civilian, noninstitutional population of the group in 1965. We used a multiplier of 1.2 because the un­ employment rate fell 1.2 points between 1963 and 1965. 5Obtained by multiplying 0.01 times the population-weighted average of adjusted re­ gression coefficients for unemployment in the runs for 1954IV-1965III, as shown in Table 16-1, by 1.2 (the amount of the decline in the unemployment rate between 1963 and 1965) times the civilian, noninstitutional population, 16 years old and over, in 1965. Our timeseries regressions for 194911- 1965III would have predicted a smaller volume of induced participation over this interval — about 120,000 persons. But for reasons cited in Chapter 16, we believe that the sensitivity estimates from these runs have been distorted by exogenous disturbances in the early postwar years (especially around the time of the Korean War) and hence are not applicable to the period under consideration here.

UNEMPLOYMENT AND LABOR FORCE PARTICIPATION

534 83

82

Actual male LFPR

Induced male 'participation

80 Projected

male LFPR

79

Actual female LFPR

40 39

Induced female participation

38 Projected female LPPR 37 36

Overall unemployment rate

1959

I960

1961

1962

1963

1964

1965

1966

1967

Year

FIGURE 17-1. Estimates of induced participation, by sex: 1963-1967. Source: See text. * On the assumption of continued high unemployment.

SOME EVIDENCE FROM THE 1963-1967 BOOM

535

figure—but not so much lower as to suggest different orders of magnitude. In short, for the first half of the expansion, the time-series coefficients generate estimates of changes in the labor force which are fairly close to our ex post estimates of induced participation. However, before awarding the time-series coefficients any badge con­ noting consistently good performance, it is necessary to look carefully at the figures for the latter half of our four-year period of expansion. As can be seen clearly from Figure 17-1, the gap between actual and pro­ jected participation rates for both men and women is appreciably wider in 1966 and 1967 than in 1965 and 1966. The much greater volume of induced participation which seems to have occurred between 1965 and 1967 is indicated clearly by the figures for absolute numbers of persons shown in Table 17-1. We estimate that tighter labor markets caused a net expansion in the labor force of 550,000 persons between 1965 and 1966 and a further expansion of 610,000 persons between 1966 and 1967 — an increase of nearly 1.2 million persons during this two-year interval. It is particularly interesting to note that whereas the amount of in­ duced participation appears to have been more than twice as great be­ tween 1965 and 1967 as between 1963 and 1965, unemployment fell by a much smaller amount during the more recent interval — specifically, from 4.5 percent in 1965 to 3.8 percent in 1966 and 1967. This lack of a close association between the magnitude of the decline in unemployment and the volume of induced participation is of course consistent with the looseness of the time-series relationships. At the same time, the large volume of induced participation between 1965 and 1967 has a much more important implication: it clearly suggests that the long-run impact on participation rates of a pronounced and pro­ longed change in labor market conditions is very large indeed—certainly much larger than our time-series regressions would suggest.6 6 There is another hypothesis which might also be used to explain this pattern of results. The heavy concentration of induced participation in 1966 and 1967 might be construed as evidence of a "threshold effect," whereby improvements in labor market conditions have relatively little effect on participation decisions, even in the long run, until the unemploy­ ment rate falls below some critical level —presumably somewhere below 4.5 percent (the average rate in 1965). In an effort to test this hypothesis we re-examined the residuals from our cross-sectional (intercity) regressions for 1960. Since a linear relationship was posited between unemployment and the participation rate in our intercity regressions, if the thresh­ old effect operates, our intercity regressions should tend to underpredict participation for smsa's which had very low unemployment rates in the census week of 1960. We plotted the residuals against the unemployment rates in the various smsa's for four population groups — married women 14-54, married women 45-54, males 65+, and males 25-54. We found no semblance of support for the threshold hypothesis in the data for any of these groups. This result is also consistent with a more general finding obtained early in our research: there is no evidence of any pronounced curvilinear relationships between unemployment and labor force participation. (The series of tests which led to this conclusion were conducted by Mrs. Virginia Gebhardt, who was then working as our research assistant.) To balance this account, we should add that we also tried to use the intercity results to devise tests for the hypothesis proposed in the text—that it is prolonged periods of low un­ employment that have a pronounced impact on participation rates. We were unable to devise any tests which satisfied us, however, in part because of the lack of good data on unemploy-

536

UNEMPLOYMENT AND LABOR FORCE PARTICIPATION

Let us now consider the induced participation estimates for the entire expansion of 1963 to 1967. These figures are equal, of course, to the sum of the incremental estimates for the four one-year intervals. We obtain a grand total of slightly more than 1.6 million persons (one million fe­ males and 600,000 males). What is remarkable about this estimate is that it is similar to the amount of induced participation predicted by our crosssectional regressions: roughly 1.9 million persons (1,040,000 females and 850,000 males)!7 There is, to be sure, no reason why these two sets of estimates ought to be identical. For one thing, still more induced participation may be forth­ coming between 1967 and 1968, assuming labor markets remain tight. More important, cross-sectional estimates of labor force sensitivity are not strictly applicable to time-series phenomena, since certain factors that are associated with differences in unemployment rates among cities at a moment in time may not vary in the same manner with changes in unemployment over time. For example, part of the lower participation rates of older groups in cities with high unemployment is presumably due to early retirement—a decision that may often be irreversible. On the other hand, a prolonged economy-wide recession may cause the participa­ tion rates of certain younger groups to be higher in the subsequent boom than they would otherwise have been—a kind of "compensation effect" which is presumably much less important in cross-sectional data. Nonetheless, we would certainly not expect the actual estimates and the cross-sectional predictions of indiced participation to be of different orders of magnitude. And the fact that they are quite similar greatly in­ creases our confidence in the cross-sectional coefficients of labor force sensitivity presented in Chapter 15, as well as in our interpretation of these coefficients. They do seem to indicate the long-run effects of a large and sustained change in labor market conditions.

I N CONCLUSION, we estimate that the recovery and boom of 1963-1967 induced about one million females and 600,000 males to enter (or remain ment rates over time for SMSA'S (the B.E.S. data are the only source, and they are subject to well-known limitations), and in part because we had already included in our intercity re­ gressions several variables likely to reflect previous economic conditions (husband's in­ come in 1959) and the sensitivity of the SMSA to cyclical swings (industry mix). 7The estimate for 1963-1967 based on the cross-sectional coefficients was obtained in the same way as the estimate for 1963-1965, which was explained earlier. The only special point to note is that in calculating the change in the unemployment rate (1.8 points) to be used as the multiplier in these calculations, we chose 1962-1963 as our reference period. The selection of this as the reference period for high unemployment is somewhat arbitrary; one might reasonably argue that the longer period of 1958-1963 should have been used in­ stead. The difficulty with the latter approach is that it is likely to give undue weight to the ab­ normally high unemployment that prevailed during 1958 and 1961. If we view the intercity regression coefficients as long-run sensitivity estimates, then it would seem proper to omit these two years in estimating the average level of unemployment during this period. The mean rate of joblessness for 1959, 1960, 1962, and 1963 is 5.55 percent—virtually the same as that for 1962-1963 (5.6 percent).

537 in) the labor force—a net increase of 1.6 million persons. More than twothirds of this increase in the labor force took place, however, during the latter half of the expansion, after the overall unemployment rate declined below the 4.5 percent level. Comparison of this pattern with the predictions generated by our timeseries and cross-sectional regressions provides at least some basis for reconciling these very different sets of coefficients. The time-series coeffi­ cients seem more applicable than the cross-sectional coefficients to shortrun associations between changes in the tightness of the labor market and the immediate response of participation rates, as illustrated by the 19631965 interval. The cross-sectional coefficients, on the other hand, seem to be surprisingly good indicators of the effect of a prolonged change in labor market conditions on participation rates, as illustrated by the results for the entire period 1963-1967. We are well aware that these conclusions should be qualified in many ways. But this is the last page of the last chapter of this book, and having finally reached this point, we feel entitled to end on a more positive note— with a small leap of faith in the interpretation of this final set of results. SOME EVIDENCE FROM THE 1963-1967 BOOM

CHAPTER APPENDICES APPENDICES TO CHAPTER 1

I-A I-B 1-C

Patterns of Participation: A Descriptive Survey 541 Labor Force Parts of Current Population Survey Form Used Prior to January 1967 566 Labor Force Parts of Monthly Labor Survey Form Used in November 1966 and Incorporated into the Current Popula­ tion Survey in January 1967 567 2 A Simple Model of the Allocation of the Time of Members of a Household Among Work in the Market, Work at Home, and Leisure 569

APPENDIX TO CHAPTER

2-A

APPENDIX TO CHAPTER 5 Tables 5-A through 5-H; Figure 5-A

571

7 Derivations and Sources of Measures of Changes Between 1948 and 1965 in the Variables to Which Cross-Sectional Coefficients are Applied in Table 7-2 583 Tables 7-A through 7-C 585 APPENDIX TO CHAPTER

7-A

APPENDIX TO CHAPTER 8

Tables 8-A through 8-D

589

13 13-A Labor Force Participation Rates of Younger Never-Married Women 592 Tables 13-A through 13-E 594 APPENDIX TO CHAPTER

APPENDIX TO CHAPTER 14 Tables 14-A through 14-D

605

APPENDIX TO CHAPTER 16 16-A A Review of the Time-Series Studies by Telia and by Dernburg and Strand 609 APPENDIX TO CHAPTER

Table 17-A

17 627

APPENDICES TO CHAPTER 1

1-A. Patterns of Participation: A Descriptive Survey This descriptive survey consists of three parts: (1) an account of pat­ terns of labor force participation of population groups (classified accord­ ing to such demographic characteristics as age, sex, color, and region) in the United States in 1965; (2) a brief discussion of differences in patterns of participation among countries at various stages of economic develop­ ment; and (3) a few comments on long-period trends in participation, es­ pecially in the United States.

The United States: 1965 In the United States, in 1965, the total labor force participation rate was 57.5 percent.1 Like most numbers presented in isolation, this per­ centage in and of itself is neither provocative nor even very interesting. Only when compared with total participation rates in earlier years or in other countries, or when broken down into more readily identifiable com­ ponents, does it come alive. Let us begin by describing the patterns of labor force participation rates according to sex and age which are depicted in Figure 1-A. If one looks at this figure long enough he may begin to visualize any­ thing from an anteater to an elephant without front legs. At a more ele­ mentary level of interpretation, the first thing to note is that the male participation rate is significantly higher than the female rate at all age intervals. The male rate is also more regular in its behavior than the female rate. Rising steadily during the school-age years, as increasing numbers of boys finish their schooling and enter the labor market, it reaches a plateau of about 97 percent at the 25 to 44 age levels, declines only very slightly during the 45-54 interval, then drops to about 85 percent in the 55-64 age bracket as early retirements start to occur, and finally falls to an average of about 30 percent among men 65 and over. The curve showing the relation between age and labor force participa­ tion for females also rises steadily during the teenage period, as increas­ ing proportions of girls leave school and seek employment, and declines significantly during the 55-64 and 65+ intervals as retirements occur. It is over the middle age range that its shape differs significantly from the shape of the curve for the males. There is a double peak in the female curve, the first occurring around the ages of 18-19 and 20-24, and the second occurring at the 45-54 age level. At each of these two peaks, roughly half of all women are in the labor force. The most pronounced 1 Manpower Report of the President, March 1966, Table A-l. This is the percentage of the noninstitutional population 14 years of age and over in the total labor force (including Armed Forces). It is an average of the observations obtained from the twelve monthly household surveys conducted in 1965.

542

APPENDICES TO CHAPTER 1

part of the intervening dip comes at the 25-34 age level, when homemaking, childbearing, and the raising of young children tend to discourage the labor force participation of American women. The effects of marital status on the labor force participation of women are portrayed more directly in Figure 1-B. As one would expect, nevermarried women have much higher participation rates than married women with husband present in all age intervals except 14-19. Never-married girls are likely to continue in school longer than their married counter­ parts, and this is no doubt the main explanation for the higher participation rate among married teenagers than among never-married girls. The peak periods of labor force participation also differ significantly according to marital status, with never-married women reaching their peak in the 25-34 age interval and married women reaching their peak much later, at the 45-54 level. The participation rates for other women—the widoweddivorced-separated group—are intermediate between the rates for the never-married women and the married women, except in the case of teenagers, where the participation rate of other women is highest of all. We would also expect marital status to affect the labor force participa­ tion rates of school-age males, since many of those who are married presumably have financial responsibilities which either require them to drop out of school altogether or to hold part-time jobs while in school. This expectation is confirmed by Figure 1-C, which shows that roughly 95 percent of married2 males between the ages of 14 and 19 were in the labor force, compared with 32 percent of the single teenagers. A more surprising pattern is the consistently lower participation rates of single males at all ages. Men who are widowed, divorced, and separated also share this tendency of single men to be less active in the labor market than married men. Comparing Figures I-B and 1-C, there is one remarkable similarity to be noted. The age profile of participation for single men is almost identical with the age profile of participation for single women. Indeed, except for the 14-19 and 35-44 age intervals, the participation rates of these two groups are always within three percentage points of each other, which suggests that once family responsibilities are held constant, sex per se has little effect on participation. Still another perspective on patterns of labor force participation rates is obtained by classifying the population according to color (Figure 1-D). Although the educational gap between whites and nonwhites has steadily declined, nonwhite teenagers still leave school at an earlier age (on the average) than white teenagers, and for this reason one might expect labor force participation rates to be higher among nonwhite teenagers than 2 Strictly speaking, we should say "married, spouse present"; but repeating all this be­ comes tiresome, and so from here on we shall use the single word "married" to refer to the married, spouse-present category. Persons who are widowed, divorced, and separated will be referred to as such.

FIGURE 1-A. Labor force participation rates by sex and age, annual averages, 1965. Source: Manpower Report of the President, 1966.

FIGURE 1-B. Female labor force participation rates by age and marital status, March 1965. Source: Manpower Report of the President, 1966.

1966.

FIGURE 1-C. Male labor force participation rates by age and marital status, March 1965. Source: Manpower Report of the President,

0

10

20

30

40

50

60

70

14-19

20-24

I.

25-34

/------

35-44 Age Group

...........

45-54

------------

Nonwhite femoles

"""" ......

...... ......

""- "- "-

55 - 64

...... ......

"-

"-

"-

"-

"-

65+

""

FIGURE I-D. White and nonwhite labor force participation rates by sex and age, annual averages, 1965. Source: Manpower Report of the President, 1966.

oJ

0

0 .Q

u..

0

~

4>

Q.

... 0

~

9-

-0

!?

c:

a:

-0

4>

~

80

90

100

PATTERNS OF PARTICIPATION

547

among white teenagers. This is not the case, however. Apparently the ef­ fect of school enrollment is more than offset by differences in marital status, job opportunities and other socioeconomic factors. The 20-24 age category is the only one in which participation is clearly higher among both male and female nonwhites — due, presumably, to the much lower fraction of nonwhites going to college. In general, participation rates are higher for nonwhite females than for white females, but they tend to be lower for nonwhite males than for white males. The differences among the women are especially pronounced in the middle age ranges, where the rate for nonwhite females does not drop off significantly. Differences in both economic circumstances and in family structure contribute to this sharp difference in patterns of participation, as is seen in Chapter 5. Among the variety of other ways in which we could classify the United States population for the purpose of comparing labor force behavior, only geographical patterns will be included in this discussion. Variations according to income, occupation, employment status of other members of the household, size of family, educational attainment, and so on, are dis­ cussed in the chapters which seek to explain the determinants of labor force participation for particular population groups. The pattern of participation rates according to place of residence is displayed in Figure I-E which, unlike the preceding sets of data, is for the census week of 1960. (Data of this kind are available only from the decennial censuses.) Generally, the male participation rate is much lower among the rural, nonfarm population than among either the urban popu­ lation or the rural, farm population. The overall female rate is also lower among rural, nonfarm residents than among urban dwellers, but the par­ ticipation rate of women living on farms is lower yet.3 Looking at the figures for specific age groups within the male popula­ tion, the two most striking findings are the very high participation rate for males 65 and over living on farms (49.7 percent compared with 30.4 percent for urban residents) and the abnormally low participation rate among rural, nonfarm residents in the 55-64 category. The tendency of farmers to continue working longer than other workers is no doubt due in large part to the nature of farm work (including the prevalence of selfemployment) and the lack of Social Security coverage. But what is the explanation for the relatively low participation of males living in rural areas but not on farms? The relation between their participation rates and the economic conditions of the areas in which they reside is examined in Chapters 4 and 10. Among the females, the most intriguing group is again the rural, non­ farm population, but this time it is the pattern of participation in the middle age ranges that is most unusual. What accounts for the relatively 3 However as Gertrude Bancroft McNally has pointed out (in correspondence), this may be a seasonal phenomenon. She notes that when annual averages are calculated, rates for farm women are not lower.

FIGURE 1-E. Labor force participation rates by place of residence, sex, and age, census week of 1960. Source: 1960 Census.

549

PATTERNS OF PARTICIPATION Table I-A Labor Force Participation Rates, by Region, Place of Residence, and Sex Census Week of 1960

Northeast

North Central

South

West

Urban Males 14+

78.5

79.4

77.6

79.6

Females 14+

37.2

36.7

38.0

37.1

Rural Nonfarm Males 14+

75.3

73.4

71.3

75.5

Females 14+

31.0

28.5

28.1

28.0

Males 14+

81.2

81.4

73.7

82.3

Females 14+

28.8

22.2

22.3

24.2

Rural Farm

Source:

U.S. Census of Population: 1960, U.S. Summary, Detailed Characteristics, Table 251

modest decline in participation between the early 20's and the usual period of heavy child-care responsibilities (spanning the ages 25 to 39)? Is it attributable to special marital status or fertility characteristics, to the economic circumstances of the areas, or to something else? Apart from differences associated with varying ratios of urban to farm to rural nonfarm populations, there are relatively few clear-cut differ­ ences in participation rates among the four main regions of the United States. The numbers in Table I-A show that, except for a tendency for male participation rates in the South to be slightly below participation rates elsewhere, there is no discernible regional pattern of participation in urban areas. In rural areas, male participation is again lowest in the South, with the difference being especially pronounced on the farms, where the southern male rate is about 74 percent compared with rates over 80 percent in all the other regions. Female participation in the rural areas is relatively high in the Northeast, but otherwise the rates are re­ markably similar, as are the urban rates for females in all four regions. As of 1960, 70 percent of the population of the United States resided in urban areas; 22 percent were reported living in rural, nonfarm resi­ dences, and the remaining 8 percent lived on farms.4 Because of the quan­ titative importance of the urban sector and the greater availability of data for it, most of our subsequent analysis is confined to this part of the economy. Indeed, the 100 largest Standard Statistical Metropolitan 4

U.S. Census of Population: 1960, Detailed Characteristics, U.S. Summary, Table 181.

550

APPENDICES TO CHAPTER 1

Areas (SMSA'S), which contained 77 percent of the urban population in 1960,5 serve as the locus of much of our inter-area cross-sectional work. Given the pronounced similarity in urban participation rates among re­ gions noted above, it is worth noting at this juncture that these nearly uni­ form regional averages conceal marked differences in participation rates among metropolitan areas. For instance, the participation rates for males 14+ in 1960 were as low as 65.5 percent in Tampa-St. Petersburg (Florida), 70.9 percent in Huntington-Ashland (W. Virginia-Kentucky), 71.3 percent in Johns­ town (Pennsylvania), 74.1 percent in Knoxville (Tennessee), 74.5 percent in Duluth-Superior (Minnesota), 75.7 percent in Spokane (Washington), 76.3 percent in Birmingham (Alabama), and 76.8 percent in AlbanySchenectady-Troy (New York). At the upper end of the spectrum, par­ ticipation rates were as high as 84.2 percent in Wichita (Kansas), 83.8 percent in El Paso (Texas), 82.8 percent in Charlotte (North Carolina), and 82.4 percent in Indianapolis (Indiana).6 The metropolitan areas with relatively low participation rates for fe­ males 14+ in 1960 included Pittsburgh (Pennsylvania), Gary-HammondEast Chicago (Illinois), Charleston (W. Virginia), and YoungstownWarren (Ohio), all of which had rates under 30 percent. On the other hand, metropolitan areas with aggregate female participation rates over 40 percent included Atlanta (Georgia), Charlotte (North Carolina), Dallas (Texas), Des Moines (Iowa), Hartford (Connecticut), Trenton (New Jersey), and Washington (D.C.). Climate (via its effects on the age composition of the population and the predilection to retire) and job opportunities are two of the most obvious factors which explain some of these relatively extreme cases.

International Comparisons Moving from a comparison of participation rates in Johnstown, Penn­ sylvania, and Dallas, Texas to a consideration of differences in partici­ pation rates between Europe and Latin America surely justifies the use of the word "survey" in the title of this appendix, but a brief com­ mentary on intercountry patterns of labor force participation can also be justified on other grounds. However great the differences in economic conditions and cultural norms within the United States often seem, they are negligible in comparison with differences among regions of the world; 5CalcuIated from U.S. Census of Population; 1960, Detailed Characteristics, U.S. Summary, Tables 155 and 290. A small number of rural residents do live within the confines of SMSA'S but they probably comprise no more than 10 percent of the total population of the 100 largest SMSA'S. 6 U.S. Census of Population: I960, Detailed Characteristics, U.S. Summary, Table 295. These are total participation rates. The listing of cities with high and low participation rates in the above paragraph is not exhaustive, in that there are other cities with lower (higher) participation rates which were not listed. In compiling this list we wanted only to illustrate the variety of cities with high and low rates. The comments in this note also apply to the female participation rates by metropolitan area presented in the next paragraph.

PATTERNS OF PARTICIPATION

551

and even casual inspection of the associated patterns of participation provides insights into the important role such factors play. Detailed figures showing the age-sex profiles of labor force participa­ tion rates in 10 regions of the world are presented in Appendix Table 1-B. In the main, they represent conditions as of the early 1950's. While we know that some rather significant changes have occurred since then (trends are discussed in the last part of this chapter), it is very unlikely that the broad patterns revealed by these data have altered.7 The general pattern of male participation rates is simple and straight­ forward. In practically all countries, the participation rate rises steadily from the early teens up to the early twenties, stays on a high plateau (around 95 percent) between the ages of 25 and 45, then declines, first gradually, then sharply, as retirements occur. Differences among countries in male participation profiles are related, as one would expect, to degree of industrialization.8 The U.N. Report adopts the arbitrary but nonetheless useful procedure of classifying countries with less than 35 percent of active males engaged in agricul­ ture as "industrialized," countries with 35-59 percent of their males engaged in agriculture as "semi-industrialized," and countries with 60 percent or more in agriculture as "agricultural."9 As Figure I-F reveals, participation at the two extremes of the age distribution is much higher in the agricultural countries than in the industrialized countries, with the semi-industrialized countries occupying the intermediate position which continuity in the relationship would suggest. Actually, the visual impres­ sion given by Figure I-F tends to understate the magnitude of these dif7 As the source note to Appendix Table I-B indicates, these data come from a special report published by the United Nations in 1962. More recent data have been published by the International Labour Office in its Yearbook of Labour Statistics, but the figures pub­ lished by the I.L.O. are neither as detailed nor as reliable. Enormous problems of compara"bility beset any attempt at international comparisons, since both definitions of concepts and methods of enumeration vary widely. (The more serious of these problems are discussed in detail in the U.N. Report.) Since we were unable even to contemplate doing original re­ search in this area—research which would have required a painstaking analysis of the relia­ bility of the labor force data published by various countries and adjustments for differences in definitions and procedures —we decided to rely mainly on the data in the U.N. Report, which gives every indication of having been prepared with great care by persons thoroughly familiar with the pitfalls involved in work of this kind. After this chapter was written Mr. Tomas Frejka called our attention to a recent paper by Madame Nicole Dubrulle, "Sur L'Emploi Feminin en Europe," published in Conseil de l'Europe, Conference Demographique Europeenne, Strasbourg, 1966, Documents Officiels de la Conference, Vol. II, paper C-48. It does not appear that the data for years around 1960 contained in this paper change any of the general conclusions reported here. It should also be noted that Professor John Durand and his associates at the University of Pennsylvania are working on a comparative international study of labor force character­ istics which may well prove to be a major contribution. 8For a recent regression analysis of the relation between per capita income and wage levels on the one hand, and participation rates and hours of work on the other, see Gordon C. Winston, "An International Comparison of Income and Hours of Work," Review of Economics and Statistics, February 1966, pp. 28-39. Winston used I.L.O. data 9See Table A-14, p. 77, of the U.N. Report for a listing of the countries falling in each category.

SemiAustralia & Northtrest Central Southern Sorth . Latin West Indies^ Ceylon,India, Industrialized Iadustrialized Agricultural e a 6 -O Europe'c Africa America Hew Zealand Europe Europe" Ouyana Countries Countries" CountrlesJ Malava

Notes continued on following page.

Source: Uhited Nations. Sex and Age Patterns of Participation in Economic Activities, Population Studies, No. 33. Demographic Aspects of Manpower, Report I. (N.Y., 1962), Tables 3-2, 5-1, 5-2, A-2, A-3. (OUT calculations in some cases). The dates of the censuses from which these data were compiled range from about 19^7 to 1957, with a clustering between 1950 and 1952. The figures in the table are unweighted means. The author of the U. N. Report states that the countries included in each group had substantially similar patterns of participation and that therefore no great distortions are produced by averaging (p. 23). The^U. N. Report refers to figures of this kind as"fcctivity rates," and the I. L. 0., in its Yearbook of labour Statistics, refers to them as "percentage of the population economically active;" but since the definitions of economically active" seems to be the same a? the U. S. definition of "labor force" (see p. 1 of the I. L. 0. Yearbook and pp. 1-2 of the U. N. Report), we have used the labor force terminology.

Population Group

Selected Regions of the World, about 1952

Labor Force Participation Rates, by Age, Sex, and Degree of Industrialization

Table I-B

PATTERNS OF PARTICIPATION

553

Table I-B (continued) Footnotes a.

Belgium, Denmark, England-Wales, France, Ireland, Netherlands, Norway, Sweden, and Switzerland.

b.

Austria, Eastern Germany, Federal Republic of Germany, and Hungary.

c.

Greece, Italy, Portugal and Spain.

d.

Algeria (Moslems), Morocco (indigenous population), and Tunisia.

e.

Argentina, Brazil, Chile, Columbia, Costa Rica, El Salvador, Panama and Venezuela.

f.

Jamaica, Martinique and West Indies (UK).

g.

Male rates are unweighted means of the participation rates for 21 countries having less than 35 percent of active males engaged in agriculture and related activities. Female rates are for a subsample of 14 of these countries.

h.

Male rates are unweighted means of the participation rates for 30 countries having 35-59 percent of active males engaged in agriculture and related activities. No average female rates are given in the U.N. Report for this set of countries.

i.

Excluding England and Norway, which have a minimum age limit of 15 for inclusion in the economically active population.

j.

Male rates are unweighted means of the participation rates for 21 countries having 60 percent or more of active males engaged in agriculture and related activities. Female rates are for a subsample of 12 of these countries.

k.

Excluding countries which adopted a minimum age limit of 15 years for enumeration of the economically active population. There were 3 such cases among the industrialized countries, 2 among the semiindustrialized countries, and 3 among the agricultural countries.

ferences in participation rates at the edges of the age distribution, since it is the marked similarity in the shapes of the three curves which catches the eye, not the vertical distances between them at the lower and upper age intervals. For males 10-14 years of age, the average participation rates range from less than 5 percent in the industrialized countries, to just under 15 percent for the semi-industrialized countries, to nearly 25 percent in the agricultural countries. Presumably it is the combination of greater em­ ployment opportunities for youngsters in agricultural areas than in indus­ trialized areas and a lesser ability of families in poorer lands to send chil­ dren to school and do without their labor which accounts for this pattern.10 The lack of an equally consistent pattern in the 15-19 age interval is 10 The U.N. Report (p. 11) warns that the statistics on participation by children under 15 years of age are not exactly comparable, in large part because of differences in the minimum age at which persons are officially counted as in the labor force. The authors of this Report suggest that "the actual differences between the proportions of children working in the in­ dustrialized and in the under-developed countries may be somewhat greater than suggested by the data, since the minimum age adopted often does not correspond to the social reality in the less developed countries."

FIGURE 1-F. Male labor force participation rates by age: agricultural, semi-industrialized and industrial countries, about 1952. Source: Appendix Table 1-B.

555 surprising. Why isn't the average participation rate in the relatively af­ fluent industrialized countries well below the average participation rate for the semi-industrialized countries, rather than slightly above it? To be sure, participation rates for this group in the United States and Canada were much lower (45 percent and 59 percent, respectively) than in most semi-industrialized countries, as a direct consequence of the heavy in­ vestments made in schooling, but in Europe participation rates for male teenagers were as high, or higher, than in many of the less-developed countries. In the United Kingdom, for example, the labor force partici­ pation rate for males 15-19 was roughly 85 percent in 1951. In the intermediate range of ages there are no significant differences in participation related to degree of industrialization, but at the upper end of the age spectrum we see again the combined effects of low income levels and extensive self-employment in agricultural areas. The average participation rate for males 65 and over was almost twice as high in the agricultural countries (70 percent) as in the industrialized countries (38 percent). It is worth recalling that we observed exactly the same pattern in comparing rural and urban areas within the United States, though the absolute difference in participation rates was much smaller. International data on female participation rates are less comparable than data on male participation—mainly because of differences in the treatment of unpaid family workers—but much more interesting. Differ­ ences in age profiles for females between the industrialized and agricul­ tural countries are portrayed in Figure I-G.11 As in the case of the male populations, the participation rates for females at the youngest and oldest ages are higher in the agricultural countries than in the industrialized countries, probably for much the same reasons. It is between the ages of 15 and 44 that the striking differ­ ence in the female participation pattern occurs. There is a sharp contrast between the relative flatness of the age-participation relation for the agricultural countries and the tall peak at ages 15-24 which dominates the curve for the industrialized countries. In the industrialized countries many females enter the labor market in their teens and stay in either until they marry or until they have children, when their predilection for the labor market falls off markedly. The absence of this peak in the participa­ tion profile characteristic of the underdeveloped countries may be due in large part to the fact that organized labor markets for females are not well developed. As the U.N. Report states: "Much of production is carried out in household enterprises, and changes in a woman's marital status or in her responsibilities for the care of children do not have the PATTERNS OF PARTICIPATION

11 A smaller number of countries is included in these averages than in the male profiles according to degree of industrialization. The authors of the U.N. Report excluded from these calculations countries with data regarded as questionable either on account of the treatment of unpaid family workers or because the rates at certain critical ages had to be obtained by interpolation. There was so much variation among the semi-industrialized countries that no overall profile was calculated.

FIGURE 1-G. Female labor force participation rates by age: agricultural and industrialized countries, about 1952. Source: Appendix Table 1-B.

PATTERNS OF PARTICIPATION

557

same implications for her continuation in employment that they have in the industrialized countries, where most economic activity is centered outside the home." 12 Differences in age-fertility relationships may also be important: childbearing is spread over a longer period of years in most underdeveloped countries than in economically advanced countries, and this alone would make for a flatter relation between age and participation. In addition, the family structure of many primarily agricultural countries is such that the mother may have less personal responsibility for the care of the children. Finally, it may be easier to take a child along to an agricultural job than to a job in an office or factory.13 It would be a great mistake to think that the only differences in patterns of female participation in the labor force are those associated with the degree of industrialization. Among the agricultural countries, the average level of female participation is much lower in the Latin American coun­ tries than elsewhere. Even at their peak, female participation rates in Latin America are almost always below 30 percent and they are below 25 percent in a majority of cases. By way of contrast, female participation rates reach a peak of roughly 45 percent in Algeria (Moslem population only) and Morocco (indigenous population only), over 50 percent in the Philippines and India, and up in the 70-80 percent range in Turkey and Thailand.14 Very low levels of female participation are also found in Spain and Puerto Rico—but not in the British West Indies —which sug­ gests that it may be cultural factors associated with the Catholic tradition of Latin America which explain these results. In particular, the inclina­ tion of mothers in many Latin American countries to keep unmarried daughters under rather close surveillance may well inhibit labor force participation. The curious age-participation profile of females in India also deserves special mention. What is strange about the Indian data is not the average level of female participation, which is fairly normal for heavily agricul­ tural countries, but the fact that the participation rate rises steadily from the youngest age interval to the 35-44 interval. This pattern has been found in various sets of regional Indian statistics and also seems to hold for some other countries in which persons of Indian origin are numerous (Mauritius and British Guiana), and so cannot be written off as a statisti­ cal aberration. But why the participation rate should increase as the primary childbearing ages are reached remains a puzzle.15 12

U.N. Report, p. 23. are indebted to our colleagues Professors Coale, Levy, and Westoif for suggesting some of these observations as well as for helpful comments on other parts of this section. 14 See Appendix Table A-3 in the U.N. Report; the high rates for Turkey and Thailand, which reflect a large number of unpaid family workers, are not included in the overall average for agricultural countries depicted in Figure 1-G. 15 For a discussion of the various sets of data which are consistent with this pattern, see the U.N. Report, p. 23. The authors of the Report offer no explanation. 13 We

558

APPENDICES TO CHAPTER 1

Shifting now to comparisons among the European countries, one inter­ esting question not considered at all in the U.N. Report, for obvious reasons, is the relationship between political systems and participation. As a crude test, we compared female participation rates between the Eastern European and Western European countries in the 35-44 and 45-54 age intervals.16 The participation rates are fairly similar among the Western European countries and average just under 30 percent in both age intervals. There is much more variation within the Eastern European group, indeed so much variation that any formal measure of central tendency would give a specious impression of precision. But it is still possible to state unequivocally that the rates in the Eastern European countries are appreciably higher—perhaps 10 percent to 20 percent higher as a rough order of magnitude. Comparing only East Germany and West Germany, the East German female participation rate is 5 percent to 10 percent above the West German rate in this age range; and a rough comparison of the U.S. and the U.S.S.R. shows that female participation rates in the Soviet Union are perhaps 25 percentage points higher than in the United States.17 If the female age-participation profile for the United States alone (Figure 1-A) were superimposed on the combined profile for the set of industrialized countries illustrated in Figure 1-G, one other significant international variation would be highlighted. Female participation rates in the United States conform to the general pattern characteristic of the industrialized countries in that during the 25-34 age interval they fall sharply from the peak reached in the early twenties; but the U.S. profile differs from the broader pattern in that after the 25-34 age interval the participation rate climbs steadily until a definite secondary peak is reached in the 45-54 age interval. In most other industrialized countries, the female participation rate flattens out and, if anything, declines slowly over this age span. In Japan, France, Norway, and Sweden, however, we also see something of a secondary peak, and an important question for the future is whether this tendency for women to re-enter the labor market after their children reach school age will spread to other countries. The figures on trends reported in the next section of this appendix are relevant to this question, as is the detailed analysis in Chapter 5 of the interrela16 The reason for choosing these intervals is that there seems to be no systematic relation between participation and degree of industrialization over this range (see Figure 1-G), and so there is more hope of getting at the effects of political system per se than if other age intervals were used. The Western countries included in this exercise were Belgium, Den­ mark, France, West Germany, Ireland, Italy, Liechtenstein, Netherlands, Norway, Sweden, Switzerland, and England-Wales. The Eastern European countries were Bulgaria, East Germany, Hungary, Poland, and Yugoslavia. The USSR data are not broken down accord­ ing to these age intervals, but some gross comparisons are still possible. All of the country data are from Appendix Tables A-I and A-3 of the U.N. Report. 17 According to the I.L.O. Yearbook of Labour Statistics for 1964, (Table 2), the Soviet rate for females 20-59 years of age in 1959 was 65.7 percent. We calculated the comparable rate from the 1960 Census of Population in the United States; it was 41.6 percent.

PATTERNS OF PARTICIPATION

559

tions among participation rates of married women, ages of children, mother's education, and family income.

T rends If we now look at the pattern of labor force participation rates from a time perspective, the first thing to note about the record of American ex­ perience since 1900 is that the overall participation rate has varied within rather narrow bounds. It was 54.8 percent in 1900 and 55.3 percent in 1960, ranging from a low of 52.2 percent at the time of the 1940 Census to a high of 55.7 percent in 1910. As Richard Easterlin has pointed out, a change of "only" 1 or 2 percentage points in the overall participation rate can have a marked effect on the aggregate labor supply and is hardly to be dismissed as insignificant;18 nevertheless, compared with many other economic time series, the overall participation rate has remained remarkably constant, and it is for this reason that Klein and Kosobud have referred to it as one of the "great ratios of economics."19 At best, however, it is a "great ratio" for the United States, not for the world as a whole. An examination of trends in participation rates be­ tween about 1910 and 1955 for 10 industrialized countries other than the United States reveals a definite tendency for overall participation rates to decline, though this has not been a universal pattern.20 Even in the United States, the historical stability of the overall partici­ pation rate is probably best viewed as something of a fluke, without great meaning, unless someone can explain why it has settled at the particular level it seems to have chosen, rather than at a level charac­ teristic of one of the other industrialized countries. Furthermore, anyone inclined to attach great significance to the relative constancy of the overall rate ought to be able to explain why the various up and down pressures — exerted by changes in the relative importance of various demographic groups and by changes in participation rates for particular demographic groups — should have been almost exactly offsetting. The great variety of historical patterns to be found among the participa­ tion rates of selected demographic groups over the course of this century in the United States are depicted in Table I-C and Figure 1-H. 18 "Recent and Projected Changes in Labor Force Participation in the Light of Longer Term Experience," unpublished paper, September 1965, pp. 6-7. 19 L. R. Klein and R. F. Kosobud, "Some Econometrics of Growth: Great Ratios of Economics," Quarterly Journal of Economics, Vol. 75 (May 1961), p. 198. Clarence Long (The Labor Force Under Changing Income and Employment, 1958, esp. Chapter 7) has also emphasized the long-run stability of the overall participation rate. 20The 10 countries are Australia, Canada, England-Wales, France, Ireland, Japan, Netherlands, New Zealand, Sweden, and Switzerland. We calculated crude overall partici­ pation rates by assigning equal weights to the unadjusted male and female rates reported in the U.N. Report (Tables 3.5 and 5.3). The overall rates for 7 of the countries clearly de­ clined; in Australia the overall rate was slightly lower in 1955 than in 1910 and 1920, but slightly higher than in the intervening census years; and in Canada and the Netherlands the overall rate stayed roughly constant.

FIGURE 1-H. Labor force participation rates for selected groups: United States, 1900-1960. Source: Appendix Table 1-C.

560 APPENDICES TO CHAPTER 1

PATTERNS OF PARTICIPATION

561

Table I-C

Labor Force Participation Rates for Selected Age-Sex Groups United States, 1900-1960 Age-Sex Groups

1900

1910

1920

1930

1940

1950

I960

Males: 14-19 20-24 25-64 ¢5 + All Males 14+

:38.1 34.4 41.1 56.2 52.6 6l.l 39.9 86.2 88.0 82.8 89-9 91.1 90.9 91-7 (95.2) (95.8) (96.1) (95.9) (92.7) (91.2) !92.6. 41.6 (30.5) 41.5 58.3 60.1 68.3 58.1 87.3

86.3

86.5

84.1

79.0

79.0

77.4

Females: 14-19 20-24 25-64 65 + All Females 14+

18.8 22.5 (23.8; 28.4 22.8 26.8 28.1 42.5 (44.8 45.1 42.5 38.1 32.1 35.5 (l6.6) (19.5) (20.6) (22.8) (26.2) (31.0) (40.2'. 7-6 (10.3) 8.0 8.6 8.0 5.9 9.1

20.4

22.8

23.3

24.3

25.4

28.6

34.5

5.6

10.7

9.0

11.7

13.8

21.6

(30.7)

54.8

55.7

55.6

54.6

52.2

53.4

(55.3)

All married women 16 +

Total population 14 +-

Source: All figures, except for those in parentheses and those for I960, are taken from Clarence D. Long, The Labor Farce under Changing Inccme and Emplqynent. (Princeton, Princeton University Kress7 195¾), Tables A-2 and A-b. The figures in parentheses frcm 1900-1950, for males 25-64 and females 25-64, were calculated frcm Long's population and litbor force figures. Long adjusted the 1890-1920 figures for mis­ counts and differences in month of enumeration. His 1950 figures in­ clude a half million persons, abroad or at sea and not counted by the census: armed forces members, civilian employees of the U. S. government, families of the foregoing, and crews of merchant vessels. Figures for i960 were calculated frcm the U. S. Census of Population: I960, U. S. Summary. Detailed Characteristics, Tables 194 and 19t>· The total labor force is considered here, not the civilian, noninstitutional labor force.

The curve for males 25-64 was included on Figure I-H as much to balance the figure as for any other reason, and the lack of any major trend for this group requires no special comment.21 The rate for male teenagers declined sharply and steadily between 1900 and 1940, falling from over 60 percent at the turn of the century to about 35 percent in 1940. The pronounced downward slide in the participation of this group seems to have come to a halt in 1940, how21 The drop between the census weeks of 1930 and 1940 is no doubt due partly to the effects of the high unemployment of the 1930's, and the accompanying social legislation, on the participation of persons in their late 50's and early 60's. The further dip in 1950 is related to the abnormally high school enrollment ratio for males over 25 caused by the G.I. Bill. (See Gertrude Bancroft, The American Labor Force: Its Growth and Changing Composition, Wiley, 1958, p. 29.)

562

APPENDICES TO CHAPTER 1

ever, for in the two censuses since then their participation rate has hovered around 38-40 percent. The explanation is not that the long-run tendency for the school enrollment ratio to rise ended in 1940; the pro­ portion of males 14-19 enrolled in school was 63 percent in 1940, 68 percent in 1950, and 76 percent in I960.22 Other possible explanations, involving a general increase in part-time work, changes in labor market conditions, migration from farm to city, and increasing equality of access to education, are examined in detail in Chapter 14. This is an intriguing puzzle, especially in view of the fact that participation rates for male teenagers in other countries still seem to be falling steadily.23 At the other end of the age span, the participation rate of males 65+ has dropped even more precipitously than the participation rate of teenagers, having fallen from nearly 70 percent in 1900 to just 30 percent in 1960. At least as interesting as the magnitude of the decline is the irregular way in which it has occurred. There have been three sharp declines in the participation rate of older males—between 1900 and 1910, between 1930 and 1940, and between 1950 and 1960—with periods of stable rates inter­ vening. The most pronounced of the three drops occurred between 1930 and 1940 and is easily explained in terms of the special economic, social, and political circumstances of that period, including the passage of Social Security legislation. The general tendency for the depressed economic conditions of the 1930's to lead older people, in particular, to withdraw from the labor force can be clearly seen in the labor force statistics of many countries other than the United States —the most ex­ treme case is that of Australia in which the participation rate for males 65 and over was 55.1 percent in the Census of 1921 and 34.3 percent in the Census of 1933.24 In the United States, the manpower demands of World War II and its aftermath undoubtedly acted as something of a brake on the tendency of older men to leave the labor force, but after 1950 their participation rate resumed its downward course. It should also be noted that in other Western countries with almost no unemployment, labor force participa­ tion rates of older males have been declining markedly. Of the five population groups represented in Figure 1-H, the females 25-64 are unique in that they are the only group whose participation rate has increased every decade since 1900. And it has been increasing at an accelerating rate. Female participation rates have also increased in other countries, but in no other country for which we have data has the rate of increase been as sharp as in the United States.25 It is curious that the other industrialized countries which have also experienced increasing 22 Calculated from U.S. Census of Population: 1960, Detailed Characteristics, U.S. Sum­ mary, Table 166. 23 See U.N. Report, Appendix Table IV. 24 U.N. Report, Appendix Table A-5. 25 U.N. Report, Appendix Table A-6. We are told by Tomas Frejka, however, that the rate of increase has been even more pronounced in Czechoslovakia.

PATTERNS OF PARTICIPATION

563

participation rates for women in this age range are all English-speaking— Australia, Canada, England-Wales, and New Zealand. Female partici­ pation rates have been roughly constant in Japan, Ireland, Netherlands, Sweden, Switzerland, and France. From the standpoint of labor force participation rates, teenage girls reached their peak in 1920. As Figure I-H indicates, there is an inter­ esting scissors pattern in the relationship between participation rates for teenage girls and females 25-64. Between 1900 and 1930 the participa­ tion rate for teenage girls always exceeded the rate for older women, though the gap decreased steadily; in 1930 the two rates were identical; and from 1930 on the rate for teenage girls has been below the rate for older women by an even greater margin in each successive census. In historical perspective, the teenage girls occupy a position inter­ mediate between the teenage men and the older women: the girls' partici­ pation rate has been pushed downward by the increased school attend­ ance pressures which have had such a depressing influence on the participation of male teenagers, at least up until 1940; at the same time the girls' participation rate has been subject to the upward pull of many of the same factors which have caused participation on the part of females 25-64 to increase so dramatically. Over the 60-year span covered by our data, these countervailing pressures have come remarkably close to off­ setting each other exactly. The increasing importance of females over 25 years of age in the American labor force makes it worthwhile to look at the changing relation between age and participation for this group in more detail. Figure I-I shows age-participation profiles for the last three census years. Increases in participation have not occurred in equal measure at all ages, and as a result the shape of the age-participation curve has changed markedly. In 1940 the curve had a single peak at the 20-24 age level, with participation declining steadily thereafter. In 1950 the main peak is again at the 20-24 age level, but this time the curve turns up again and reaches a secondary peak at the 35-44 age level. In 1960 the tendency for the curve to turn up after the 25-34 age interval was both more pronounced and lasted longer, as reflected in the shift of the secondary peak from the 35-44 age level to the 45-54 range and in the fact that the secondary peak had reached the same level as the primary peak—the participation rate was approximately the same in the 45-54 age range and in the 20-24 range. These divergent trends in participation rates have combined with demographic developments to alter the composition of the American labor force markedly, as Table I-D indicates.26 Whereas in 1900 only 26 This table was originally published in William S. Peirce and William G. Bowen, "The United States Labor Force," in Labor and the National Economy (Norton, 1965), p. 16. We are grateful to Norton for permission to reproduce a slightly modified form of the table here. Some of the discussion of this section also first appeared in this same publication.

0

5

10

15

20

25

30

35

40

45

50

20-24

25-34

Age Group

35-44

45-54

55-64

l-I. Female labor force participation rates by age: United States, 1940, 1950, 1960. Source: Decennial censuses.

14-19

1940

1950

1960

65+

565

PATTERNS OF PARTICIPATION

Table I-D The Age-Sex Composition of the United States Labor Force, 1900-1960 (percent distribution) Census Years Age-Sex Groups Males Males Males Males

14-19 20-24 25-64 65

All Males 14 + Females Females Females Females

14-19 20-24 25-64 65

1900

1210

1920

1212

XSJiO

1950

1960

10 12 56 4

9 12 57 3

7 10 59 4

6 10 58 4

5 9 58 3

4 8 57 4

4 7 54 3

82

δο

80

78

76

73

68

5 4 9

4 4 12

3 5 13 1

2 5 16 1

2 4 20 1

3 4 24 1

27

32

(13)

(18)

-

4 4 10 1

All Females 14+

18

20

20

22

24

(All Married women 16+)

(3)

(5)

(5)

(6)

(7)

-

Sources: Clarence D. Long, The Labor Force Under Changing Income and Employment (Princeton University Press for the National Bureau of Economic Research, 1958), Tables A-2 and A-6. U.S. Census of Population I960. U.S. Summarv. Detailed Characteristics. Table 194.

18 out of every 100 members of the labor force were women (and only 3 of these were married), by 1960 this proportion had risen to 32 (including 18 married women). The increasing relative importance of women in the labor force has come primarily at the expense of younger males. Even though the partici­ pation rate for older males has decreased more sharply than the partici­ pation rate for any other group, their relative contribution to the labor force has changed surprisingly little. The explanation, of course, is that the number of men aged 65 and over has increased so rapidly that even though a much smaller percentage of them are working they still make up almost as large a proportion of the total labor force as formerly. In con­ trast, the proportion of males under 25 in the labor force declined from 22 out of every 100 in 1900 to 11 per hundred in 1960. Between 1950 and 1960 the share of males 25 to 64 in the total labor force also declined slightly, but this group still comprised over half of the total labor force. Actually, it is the group aged 25 to 54 who continue to constitute the "core" of the labor force, and in Chapters 3 and 4 of this study we begin our detailed analysis of individual population groups by analyzing the factors affecting their labor force participation.

1-B

Source: U.S. Dept. of Labor, Bureau of Labor Statistics (Harold Goldstein, Asst. Commissioner for Manpower & Employment Statistics)

Labor Force Parts of Current Population Survey Form Used Prior to January 1967

APPENDIX

1-C

Labor Force Parts of Monthly Labor Survey Form Used in November 1966 and Incorporated into the Current Population Survey in January 1967

APPENDIX

567

Source: U.S. Dept. of Labor, Bureau of Labor Statistics (Harold Goldstein, Asst. Commissioner for Manpower & Employment Statistics)

APPENDIX TO CHAPTER 2

2-A. A Simple Model of the Allocation of the Time of Members of a Household Among Work in the Market, Work at Home, and Leisure * Let us define three categories of consumables: "market goods" (pur­ chased for a price), "home goods" (child care, a clean house, etc., which are ordinarily regarded as involving "work" and which are produced and consumed at home), and "leisure" (hours not spent producing market goods or home goods). While it is difficult in principle (let alone in prac­ tice) to draw clean lines between these categories, these distinctions simplify the exposition without leading us far astray.1 If we now assume that the members of the household have collective "tastes" for market goods and home goods, for the amount of leisure available to each family member, and for the specific market and home tasks which could be per­ formed by each family member, then the decision-making task of the household is to maximize a utility function of the form U

· · • , Sni

**"ϊ

Gn, • • · j Gpq, Ηγι, · · . , H mq7 Li t . . . , Lq) (1)

where: gt is the quantity of the /th good purchased in the market; h, is the quantity of the jth good produced and consumed in the home; Gtk is the hours of labor in the /th market occupation supplied by the Ath family member; H j k is the hours of labor in the jth home occupation supplied by the Ath family member (we assume that each home good is produced by labor supplied to one and only one home occupation); L k is the hours of leisure of the Ath family member.

This function is to be maximized subject to a time constraint which says simply that for every member of the household the number of hours spent each day on market work, home work, and leisure must total 24; in short 2 G l k + 2 H j k + L k = 24 I

(2)

3

for each of the q members of the household. (As a result of this constraint * What follows is in the general spirit of the article by Gary Becker, "A Theory of the Allocation of Time," Economic Journal, Vol. 75 (September 1965), pp. 493-517. We make no contribution to the evolution of this general body of theory, however, and our presenta­ tion is less elegant and less detailed than Becker's treatment. 1 The fuzziness of the traditional division between leisure time and other time is discussed at length by Becker (Economic Journal, September 1965, especially p. 502), who demon­ strates the advantages of recognizing that time and other inputs are combined in the "pro­ duction" of almost all commodities.

570

APPENDIX TO CHAPTER 2

the Us need not appear explicitly in the utility function; the amount of leisure available to the fcth member of the household is determined im­ plicitly a l o n g w i t h G t k a n d H j k . ) There is also a money income constraint:2

JM. = / = /, + /.

(3)

i

where P i = the price of the /th market good

I = total family income I e = family income from market earnings [see (4) below]

I0 = other family income (from non-labor sources such as earnings from accumulated assets, inheritances, gifts, etc.). As Becker has shown,3 the time and money income constraints are not independent since time can be converted into income (and thus into market goods) by spending less time at home and more at work; therefore, it is possible to express (2) and (3) as a single constraint. He calls this total resource constraint "full income." To maximize (1) subject to (2) and (3), the only additional set of infor­ mation needed by the household is knowledge of the rates at which time can be transformed into money income (market goods) and into home goods by the various members of the household; that is, two sets of "pro­ duction functions" are Required. The time-into-money-income transfor­ mation can be written Ie = JdJjWlkClk I

(4)

k

where W i k is the wage rate per hour (assumed constant) received by the fcth member of the household for work performed in the /th occupation. And the time-into-home-goods transformation is K

fl(.Hll> · • • ? Hlg)> /½

ί2(//2** · • · j //20)' · · • » hm

frSflmU · · · , Hmo)

(^)

where we make the simplifying assumption that home goods are produced solely by the expenditure of the time of household members. 2 Here we assume, for the sake of simplicity, that no saving occurs, and that therefore ex­ penditures on market goods during the period equal total family income. However, one can easily include saving as one of the goods purchased by the family. 3 Economic Journal, September 1965, pp. 496-497.

-al

Source:

75+

70+

20.8 20.9 19.827.6 16.3 24.5 26.4 24.5 31.6 28.2 25.6 29.4 30.9 25.2 26.3 26.0 26.3 25.5 24.3 24.9 27.1 27.5 27.6 33.6 33.1 33.7 34.3 25.7 26.9 29.0 31.5 30.8 31.0 33.9 36.5 17.6 20.7 21.3 23.5 19.01 27.3 14.1a 18.6 a 6.1 6.4 8.5 8.9 6.6& 10.9 5.2& 5.8 6.0 5.4 7.5 7.8 4.3a 4.7 2.8a 1.3 2.6 2.8

24.025.928.1 25.3 27.8 27.5 29.8 31.1 12.0 13.2 14.1 14.0 20.4 17.1 16.0 17.4 28.1 29.7 32.1 29.4 30.1 30.3 33.1 34.4 30.2 30.7 30.6 30.0 32.4 31.6 33.2 36.6 27.1 27.4 28.5 27.7 29.2 29.4 30.0 30.6 26.2 26.9 27.4 26.8 28.8 28.5 29.4 28.8 28.0 28.0 29.4 28.6 29.5 30.2 30.5 32.3 35.7 36.7 36.9 36.2 38.4 39.0 39.8 39.4 32.2 32.6 33.9 34.2 37.3 37.2 38.9 39.5 37.2 38.240.340.542.442.544.444.8 24.6 23.8 24.0 24.3 29.3 29.0 30.4 31.3 28.6 28.0 28.0 28.9 35.1 35.3 36.2 37.9 19.2 18.4 19.0 18.1 21.7 20.7 22.6 22.6 7.3 7.4 7.4 8.3 8.2 8.4 8.8 11.1 10.4 10.7 12.2 5.6 5.9 5.6 6.3 6.7 6.4 5.9 7.3 7.6 6.4 7.6 4.4 5.0 4.4 2.9 3.3 4.8 2.5 3.5 2.6 3.6 2.5

7.6 6.8 4.7 3.5

10.8 10.7 6.6

27.034.3 30.6 17.6 15.9 29.637.9 35.6 38.1 41.1 32.1 32.5 35.0 31.8 31.0 32.3 34.0 40.641\.342.7 39.039.5 40.4 44.044.9 44.9 31.431.3 33.5 37.836.8 23.0 23.9

From the 1953 Current Population Survey. and have been revised.

Other 1953 figures are from the 1955 Current Population Survey

Manpower Report of the President. March 1966. Table B-2; B.L.S •• Special Labor Force Reports. No.2. 13. 20. 26, 40. 50. 64. and 80; Current Population Reports. Series P-50. No. 11. 22. 29. 39. 44. 50. 61. 73. 76. 87.

~

17.6 9.4 20.0 29.1 25.6

21.9 6.8 27.0 25.8 25.4 24.1 26.6 16.3 28.0 25.8 27.3 28.5 28.5 30.5 31.7 10.3 20.0 18.4 19.4 20.6 21.8 23.7 24.1 29.0 16.9 21.3 11.3 6.7 8.4 3.7 4.1 4.4 4.1 6.1 5.2 6.4 6.5 5.9 3.2 2.6

18.6 24.0 13.7 13.3 20.• 027.8 24.5 28.5 22.7 23.8

10.7

14-19 14-17 18-19 20-24 25-34 25-29 30-34 35-44 45-64 45-54 55-64 55-59 60-64 65-74 65-69 70-74 65+

21.2 16.5 14.5 16.9 8 6 27.5~0 9 22.2 • 19.7 • 24.9 9.0 21.8 9.3 22.2

14.7 21.7 20.0 22.0 22.5 23.8 25.2 25.3 26.3 26.6 27.7 29.0 29.6 30.2 30.9 30.5 32.7 32.7 33.7 34.4 34.735.4 36.8

1940 1944 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967

~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.

14+

Age

Appendix Table 5-A Labor Force Participation Rates for Married Women. Husband Present. By Fine Age Breakdowns. 1940. 1944. 1947-67

"C

tTl Vl

(")

>

I

Vl

~

(]Q ~

~

~

-.

:::c:.

I

VI

:;c

tTl

....,

> "C

:z::

0 Vl

X

::r-

~ (]Q

....,

0 ....

~

>

I

Z

> "C (D

........

CIl

~

!}

616

APPENDIX TO CHAPTER

16

Appendix Table 5-B Labor Force Participation ilates, Married Women with Husband Present, 14 and over, Whites and Nonwhites, 1940, 1947-1965

Year—^

Nonwhltes

Whites

Nonwhite Rate Minus White Rate

Nonwhite Rate Divided by White Rate

1940—^

24.3

10.8

13.5

2.25

1947

33.5

19.5

14.0

1.72

1948

30.6

21.3

9.3

1.44

1949

33.0

21.7

11.3

1.52

1950

37.0

22.8

14.2

1.62 1.48

1951

36.0

24.3

11.7

1952

33.6

24.5

9.1

1.37

1953

33.5

25.0

8.5

1.34

1954

36.9

26.0

10.9

1.42

1955

38.2

27.0

11.2

1.41

1956

40.6

28.0

12.6

1.45

1957

40.2

28.7

11.5

1.40

1958

42.0

29.2

12.8

1.44

1959

41.7

30.0

11.7

1.39

1960

40.8

29.6

11.2

1.38

1961

45.0

31.6

13.4

1.42

1962

46.5

31.5

15.0

1.48

1963

44.8

32.7

12.1

1.37

1964

45.4

33.4

12.0

1.36

1965

46.7

33.6

13.1

1.39

Sources:

a/ —

B.L.S., Special Labor Force Reports and Current Population Reports, various issues. Figures are for either March or April of every year. The labor force participation rates for married female workers in 1940 given by Lebergott (Manpower in Economic Growth, 1964, Table A-ll, p. 519) are markedly higher than those which we derived from the Current: Population Reports, Labor Force, Series P-50, No. 5 (14.6% versus our 10.8% for White married women, and 33.5% as against our 24.3% for nonwhltes). After double-checking our calculations, we have come to the conclusion that this discrepancy may be explainable in

Notes continued on following page.

APPENDIX TO CHAPTER 5

573

Appendix Table 5-B (continued)

terms of coverage: our data cover only those married couples with· the husband head of a household, and it seems likely that LebergotttS data may refer to all married women, not just to those with husband present. Unfortunately, due to lack of sufficient data in the Current Population Reports» we ate unable to test this conjecture by calculating our own rates for all married women. However, Lebergott does furnish figures for those he labels "Working Wives"(Table 2-4, p. 65) which seem consistent with this interpretation. These figures broken down by age and the presence of children under 6, are consider­ ably lower in the comparable age group when contrasted with those given In Table A-Il. Thus, anyone who wishes to link Lebergott's data for earlier years to our figures for 1940-1965 should be aware that there may be a bias caused by this difference in coverage. The actual long-term increase in the participation rate of married women may well be greeter than suggested by a direct comparison of LebergotttS figures for early years with the current figures for married women, husband present.

574

APPENDIX TO CHAPTER 5

Appendix Table 5-C Labor Force Participation Rates of Married Women 14-54, by Age and Presence of Children* Total and Nonwhite, Census Week of 1960

Children under 6

Women Having: Children 6-17 only

No Children under 16

14-54—'

19.2

39.3

49.8

14-19—'

15.2

20-24

19.8

40.4

63.9

25-34

18.9

40.9

64.8

25-29

19.0

40.9

66.6

30-34

18.7

40.9

62.7

19.6

40.7

54.6

19.2

41.1

58.0

Age Categories All Married Women:

35-44 35-39

37.5

20.3

40.3

52.7

23.3

36.0

42.7

45-49

22.8

37.3

46.6

50-54

26.6

33.5

39.5

14-54—7

30.9

51.1

54.9

14-19^

18.9

20-24

27.9

48.4

54.7 62.2

40-44 45-54

Nonwhite Married Women:

25-34

32.3

31.5

54.4

25-29

30.4

52.9

61.8

30-34

32.7

55.1

62.6

35-44

33.8

54.2

59.5

35-39

33.2

54.9

60.9

40-44

35.2

53.4

58.5

36.0

43.7

50.5

45-49

35.7

46.1

54.3

50-54

37.1

39.9

46.6

45-54

Source: a/ — —^

U.S. Census of Population 1960. Employment Status and Work Experience, Table 8, pp. 65-71. These rates were calculated on the assimption that married women 55-59 have no own children under 6 if only one own child was present. Ihese calculations assume that married women 14-19 have no own children over 6 years old.

XX.

I.

Appendix Table 5-D

5668

17+

+3.8

33.2

38.0

41.0

46.9

60.8

9-11

12

13-15

16

17+

Motes on following page.

+1.7

29.4

8

...

+13.9

+5.9

+3.0

+4.8

+8.5

27.7

5-7

(1)

19.2

0-4

Years of School Completed

+4.9

+3.3

+1.8

+2.1

+1.2

+1.7

+1.4

+9.0

+2.6

+1.2

+2.7

+2.6

+0.0

+7.1

Increments to Ad lusted Participation Rate . Due to Wage Due to Non, TotalEffect!' Wage Factors(2) (3) (4)

Improved Earnings Opportunities (the Wage Effect) and to non-Wage Factors.

Ad j. Labor Force Part. RateS/

3862

5507

4795

3992

26.3

35.4 44.4

30.7 35.6

32.8

27.4

28.9 31.6

25.6

23.5

26.3

20.6 22.3

19.2

19.2

"Corrected" Adjusted Participation Rates Showing Wage Effect Non-Wage Effects Only!/ Only—/ (5) (6)

54 Associated with each Schooling Category Attributable to

4632

16

Estimate of the "Shares' of the Increment in

3559 3941

3120

13-15

2876

8

9-11

12

2803 3048

2512

5-7

2393 2534

2211

0-4

Years of School Completed

a/ Mean Annual Earnings (in 1959) of Females Working Full-Time:All Females 14-54 Females 35-39 (1) (2)

Relationship Between Years of School Completed and Full-Time Earnings cf Females 14-54

Schooling, Earnings Opportunities, and Labor Force Participation: Married Women 14-54 in Drban Areas, Census Week of 1960

APPENDIX TO CHAPTER 5 575

576

APPENDIX TO CHAPTER 5

Appendix Table 5-D (continued) Source:

a/ —

All of the underlying data are from the 1/1000 Sample of the 1960 Census. The derivations of particular sets of numbers are explained in the following notes. "Full-time" is defined to include only persons who worked 35 hours or more in the census week and 50-52 weeks in 1959. ftMean annual earnings" is wage and salary income only. Xn calculating the means, we used the midpoints of the income intervals ($10 wide from $1 to $9,999,$1000 wide from $10,000 to $24,999) and a figure of $30,000 for the $25,000-or-more category·

—^

From Panel I of Table 5-5.

c/ —

Obtained directly from column (1).

—^

e/ —

—

Obtained by multiplying the increments to the annual earnings of females 14-54 at each level of Schooling (calculated from column (1) of the top panel of this table — the increment for the 5-7 years-of-schoollng category being $2512 - 2211 * $391, etc.) expressed in hundreds of dollars (e.g. 3.01), by the net regression coefficient for the Earnings variable of 40.47 (taken from the intercity regression--see Table 6-1). Thus, the share of the total increment at the 5-7 years-of-schooling level which we attribute to the wage effect is 40.47 times 3.01 » +1.41 (which we round to +1.4).

Obtained as a residual, by

subtracting column (3) from column (2).

Obtained by taking the rate for the reference group (19.2) and adding the cumulative increments due to the wage-effect (for column 5) and due to non-wage effects (for column 6).

577

APPENDIX TO CHAPTER 5 Appendix Table 5 - E Occupational Distribution of Harried Women 14-54 in Urban Areas (Total and Negro), by Years of School Completed Census VJeek of 1960

Years of School Completed

0-8

All Married Women % % % ProftTech. Mgr. Cler11

SubTotal

Negro Married Women % % % SubProfTTech. Mgr. ClertI Total

1.3

2.1

9.8

13.2

1.11

0.9

0..2

2.2

9-11 12 13-15

2.0 5.7 24.9

2.3 3.4

25.6 55.6

29.9 64.7

2.4 4.1

0.8 1.0

6 .8 21 . 8

10.0 26.9

4.0

52.0

80.9

24.6

1.6

39 .3

65.5

16+

66.0

3.2

23.5

92.7

79.3

0.0

13 .8

93.1

Total

10.8

3.0

38.0

51.8

7.1

0.9

10,.0

18.0

Source:

1/1000 Sample of 1960 Census.

Appendix Table 5 - F Years of School Completed and Income in 1959, Females 25 and over in Urban Areas, Total and Nonwhite1 Census Week of 1960

Y e a r s of School Completed

0

All Females Median3Income— Increment

Nonwhite Females Median^ Income— Increment

$ 781

$ 680

—

—

1-4

814

+$

83

745

+$

65

5-7

997

+

183

913

+

168 226

8

1299

+

302

1139

+

9-11

1764

+

465

1347

+

208

12

2363

+

599

1813

+

466 425

13-15

2571

+

208

2238

+

16+

3873

+ 1302

3809

+ 1571

Total

$1730

Source:

a/ —

$1205

U.S. Census of Population, i960; U.S. Summary, Detailed Characteris­ tics, Table 2 2 3 . These are medians for females who had some income in 1959.

Level of 0FIa/

2,030 2,930

6,980 8,780

4,500 5,500 6,500 7,500 8,500 10,000 12,000 15,000 22,000 32,000

2000-2999 (2500)

3000-3999 (3500)

4000-4999 (4500)

5000-5999 (5500)

6000-6999 (6500)

7000-8999 (8000)

9000-10999 (10,000)

11000-149S9 (13,000)

15000-24999 (20,000)

25000 and over (30,000)

7,995

4,525

2,585

1,883

1,456

1,159

961

766

586

406

225

46

Tax Liability on Total Family Income (4)

7,135

3,854

2,065

1,412

1,016

726

546

366

186

6

0

0

Tax Liability If Wife Had Earned Nothing (5)

a/ — Mid-point (used as point estimate in subsequent calculations) shown In parentheses.

26,780

17,780

11,480

5,630

4,730

3,830

1,130

3,500

1000-1999 (1500)

230

(3)

Taxable Income, Assuming 3.7 Exemptions and Deductions = 10% of 0FI

2,500

Total Gross Income of Family Assuming Wife Earns $2000 [(l)+$2000) (2)

Less than $1000 ($500)

(1)

Appendix Table 5-G

860

671

520

471

440

433

415

400

400

400

226

46

Tax Liability On Wife's Earnings [

'4

Sets of Indeoeodent Variables: Other Color Income

S

0-4

+11.3 S (2.1) S

11

F c - 0.C3

-24.6 01 (2.7) 1 0 0 0

//#»'

"X

Z

m

..,..,

:>

-....l VJ N

733

HOUSEHOLD REGRESSIONS Table A-31 (continued) Matrices of t-Values

Age 18

19

20

2.15*

19

21

22

23

3.74**

1.75+

0.66

1.08

1.87 +

0.23

0.26

0.26

2.17*

1.34

1.35

0.73

1.39

0.12

2.01*

20 21

0.38

22

Nonwhite

White

1.28

1000-1999 2000-2999 3000-3999 4000-4999 5000-5999 6000-6999 7000-8999 9000-10999 11000-14999

2.04* 1.67 +

Color

Loss-999

2.80**

0.39

23

Other Family Income

24

1000 2000 3000 4000 -1999 -2999 -3999 -4999 0.92

5000 6000 7000 -5999 -6999 -8999

9000 11000 -10999 -14999

15000+

1.49

0.86

1.82 +

1.17

0.96

0.42

1.17

1.89 +

1.31

0.48

0.18

0.80

0.17

0.09

0.65

0.12

0.90

0.33

0.36

0.38

0.71

1.41

0.47

0.50

0.17

1.27

0.46

0.14

0.63

0.43

1.46

0.66

0.87

1.27

2.19

1.04

0.15

0.62

0.38

1.29

0.08

1.09

0.24

0.92

0.34

1.54

0.61

1.41

2.66

1.53

1.35

0.36

0.80

*

0.84 Table continued on following page.

734

APPENDIX A

Table A-31 Matrices (continued)

Years of School Completed by Associated Person 0-6

7-8

9-10

11-12

13

1.80+

0.28

0.05

*

7-8

2.32

9-10 11-12

1.13

0.98

0.94

1.94+

0.31

0.36

0.38

0.42

0.59

1.53

1.32

1.26

0.86

1.36

1.14

1.07

1.60

1.47

1.42

0.07

0.09

16

0.02

Child of Head

No

Yes

0.08

«UN

15NL

0.78

0.80 0.23

«UN

Family Size

4 or 5

2 or 3

1.38

4 or 5

17+

0.74

*

14-15

hWK

16

2.35

13

Employment Status of Head

14-15

Significant at the 17, level. 6 or more 1.43 0.33

*

« Significant at the 5% level. = Significant at the 107· level

HOUSEHOLD REGRESSIONS

Appendix Table A-32 Multiple Regression Equation Used to Explain the Hours Worked by Never-Married Males 14-17 Enrolled in School, in Families

735

736

APPENDIX A

Table A-32 (continued)

Matrices of t-Values

Other Nonwhite

Color

Negro

White

0.16

0.33

Negro

0.28

Age

17

15

16

0.53

2.32

**

*

14

4.61

*

15

**

2.03

4.67 **

16

2.73

Years of School

Completed by Associated Person 0-7

8 0.87

9-11

12

1.51

1.68+

2.50

9-11

16+

0.47

1.44

2.75

1.37

2.22

0.15

1.02

0.27

1.21

0.17

*

**

*

8

13-15

12 13-15

1.05

Employment Status of Head «WK

«UN

«NL

0.69

1.45 1.35

«UN

Family Size

4 ox 5

2 or 3

2.20

6 or more

0.66 *

4 or 5

2.47 Significant at the 1% level. Significant at the 57. level. Significant at the 10% level.

HOUSEHOLD REGRESSIONS

Appendix Table A-33 Multiple Regression Equation Used to Explain the Hours Worked by Never-Married Females 14-17, Enrolled in School, in Families

737

7 3 8

APPENDIX A

HOUSEHOLD REGRESSIONS

Appendix Table A-34 Multiple Regression Equation Used to Explain the Hours Worked by Males 18-24 Enrolled in School

7 3 9

740

APPENDIX A Table A-34 (continued)

Matrices of t-Values

19

20

1.92+

0.85

2.29

1.34

2.34

1.37

0.68

0.74

0.10

1.07

0.18

0.49

1.50

0.67

0.73

0.43

0.38

1.05

0.25

Age 18 19 20

21

22

23

*

1.25

21

*

22 23

0.73

Color

Negro

White

1.91+

Negro

Marital and Family Status In Family, Married Spouse Present

Other Nonvhite 1.62 0.71

In Family, Single

1.10

In Family, Single

**

= Significant at the 1% level·

*

= Significant at the 5% level.

+=

24

Significant at the 10% level

Not in Family

1.49 **

3.81

HOUSEHOLD REGRESSIONS

Appendix Table A-35 Multiple Regression Equation Used to Explain the Hours Worked by Never-Married Females 18-24 Enrolled in School

7 4 1

7 4 2

APPENDIX A

Appendix Table B-100

Multiple Regression Equation Used to Explain the Labor Force Participation of Never-Married Males 14-17 in Families, Not Enrolled in School

HOUSEHOLD REGRESSIONS 743

744

APPENDIX A

Table A-36 (continued)

Matrices of t-Values Years of School Completed by Associated Person

7-8

9-10

0-6

1.24

0.49

0.43

1.31

0.59

0.71

0.62

0.08

0.98

7-8

11-12

9-10

1.08

11-12

Color

Negro

White

1.15

Age 14

15 0.90

15

16

17 *

2.52 + 1.83

Loss - 999 1000-1999

**

3.25 ** 2.75 1.13

16

Other Family Income

13f

1000 2000 3000 4000 5000 -1999 -2999 -3999 -4999 -5999 0.44

6000 -6999

7000 9000 -8999 -10999

11000+

0.57

0.56

0.34

1.17

0.35

0.74

0.24

1.27

1.18

0.12

1.93+

0.92

1.46

0.71

0.41

0.01

1.17

0.82

0.23

0.25

0.30

0.62

1.13

0.79

0.23

0.24

0.30

0.61

0.92

1.57

0.70

0.36

1.09

0.65

1.05

1.38

0.51

0.11

0.43

0.55

0.91

2000-2999 3000-3999

*

4000-4999

2.12

5000-5999 6000-6999 7000-8999 9000-10999

0.03

0.30

Child of Head

No

Yes

2.61

**

Table continued on following page.

HOUSEHOLD REGRESSIONS

45

Appendix Table B-100

Multiple Regression Equation Used to Explain the Labor Force Participation of Never-Married Females 14-17 in Families, Not Enrolled in School

746 APPENDIX A

747

HOUSEHOLD REGRESSIONS Table A-37 (continued)

Matrices of t-Values

Color

Monwhite irk

White

3.12

Other Family Incooe Loss-999

1000 -1999

2000 -2999

0.86

1000-1999

4000 -4999

5000 -5999

6000 -6999

2.30*

1.53

0.53

0.32

1.06

1.19

1.85+

1.62

1.29

0.58

0.36

0.61

0.12

0.21

0.94

0.72

0.74

1.84+

2.31

1.38

1.37

0.31

0.55

1.07

1.43

0.56

0.51

0.42

0.18

0.59

0.73

1.52

1.27

0.98

1.18

1.98*

1.71+

0.12

1.05

0.80

1.05

0.75

2000-2999 3000-3999 4000-4999

6000-6999 7000-8999

Family Size 2 or 3

No 0.18

4 or 5

6 or more

0.55

0.20

4 or 5

Employment Status of Head

1W «ON

11000+

0.25

9000-10999

Yes

*

0.28

5000-5999

Child of Head

9000 7000 -8999 -10999

3000 -3999

0.86

hDH

0.28

11NL

2.15* 1.59 Table continued on following page.

748

APPENDIX A Table A-37 Matrices (continued)

Years of School Completed by Associated Person

7-8 **

0-6

3.03

7-8

9-10 +

11-12 *

1.95

2.10

1.47

0.63

0.54

0.61

0.10

0.11

9-10 11-12

0.20

Age 14

15

16

0.15

15

17

1.39

2.41*

+

1.69

16 Years of School Completed by Subject

0-4 5-7 8

**

2.86 1.26

5-7 0.73

8

9-11

12+

0.17

1.39

2.41*

0.78

0.84 *

1.98

9-11

= Significant at the 1% level. *

= Significant at the 5% level*

+=

13+

Significant at the 10% level.

2.21* **

3.36

*

2.28

HOUSEHOLD REGRESSIONS

Appendix Table A-38 Multiple Regression Equation Used to Explain the Labor Force Participation of Males 18-24 Not Enrolled in School

7 4 9

7 5 0

APPENDIX A

HOUSEHOLD REGRESSIONS

Appendix Table A-39 Multiple Regression Equation Used to Explain the Labor Force Participation of Never-Married Females 18-24, Not Enrolled In School

751

7 5 2

APPENDIX A

• 57.4

L SM1S-24NES,IF

Child of Head

Employment Status of Head

-5.8 OFI6000_6999 (4.5)

+6.6 A24 (3.5) --------- -2.3 OFI7000_8999 F • 5.3** (4.3)

+24.1 S16+ (S.l)

FS • 7.3**

Significant at the 5% level.

Significant at the 1% level.

A

-4.7 OFI5000_5999 (4.4)

FOFI • 0.8

-6.7 OFI 15000+ (5.6)

-10.0 OFIl1000_l4999 (5.1)

-6.1 OFI9000_10999 (4.7)

-3.1 OFI4000_4999 (4.5)

-6.3 OFI3000_3999 (4.5)

+22.1 S9_ll --------- +13.2 A2l (6.4) FC - O.S (2.S) +24.2 S12 +S.4 A22 (6.3) (3.1) +14.9 A +1S.4 S13_15 (6.7) (3.2) 23

BwK

FS:AP = 0.3

---------.----

-0.3 S:AP16+ (5.5)

-0.4 S:AP9_10 (2.7) -2.2 S:AP _ U 12 (2.8) -6.1 S:AP13 (5.5) +0.6 S:AP _ 14 15 (4.8)

Schooling of Assoc. Person +(R) S:AP _ O 6 -0.3 S:AP _ 7 S (2.4)

Table continued on following page.

FH = 3.9* FFS· 0.4

~/"Sing1e" here includes all males in families except those married with wife present.

**. *-

Other Family Income

Sets of Independent Variables:

Family Size Color _____ ~J!e +(R) SO_4 +(R) CN +(R) Al8 +(R) OFILoss_999 +(R) CHDNO +(R) HNL +(R) FS 2_3 +1.2 FS _ +7.3 A +12.7 S5_7 +3.1 Cw -7.5 OFIlOOO_1999 -3.1 CHDyES +7.2 (2.1) 4 5 (2.6) (2.S) 19 (4.7) (2.1) (2.6) (7.0) +24.1 Ss +4.2 COh~ +7.6 A20 -3.7 OFI2000_2999 ----------- +5.0 HUN +2.1 FS6+ (2.4) (6.S) (S.O) (2.9) (4.5) FCHD = 2.2 (4.8)

LSM18-24NES,IF • 89.6 ** F ·2.1

N - 1,397

Constant Term

Dependent Variable

Schooling of Subject

Appendix Table A-40 Multiple Regre88io7 Equation Used to Explain the Labor Force Participation of Single! Males lS-24 in Families, Not Enrolled in School

-....l VI W

Z en

(3

en en

ttl

~

Cl

ttl

~

0

t""

0

::r:

ttl

en

c::

0

::r:

754

APPENDIX A

Table A-40 (continued) Matrices of t-Values Years of School Completed by Subject

5-7

8

+

**

1.82

0-4 5-7

9-11

3.53

*

2.55

8

12 **

**

3.46

*

3.83

**

**

2.74

2.46

3.12

1.30

1.80

0.02

1.39

0.01

1.01

1.15

0.37

2.00*

0.01

12 13-15

1.01

Color

Negro

White

1.19

Negro

19

18

2.61

21 22

Other Nonwhlte 0.15 0.53

Age

20

•*

2.99

0.59

9-11

19

16+

13-15

**

20 **

21

22

**

**

23 **

24

2.65

4.64

2.70

4 60

0.08

2.09*

0.36

2 38

2.00*

0.28

2 30

0.27

1.55

0 54

1.91+

*

*

1

91+

1.88+ 0.21

0.50 **

23

2.23

Other Family 1000 2000 3000 4000 5000 6000 7000 9000 11000 Income -1999 -2999 -3999 -4999 -5999 -6999 -8999 -10999 -14999 15000+ Loes-999

*

1.59 0.82 1.41 0.69 1.07 1.29

0.54

1.31

1.96

1.19

1000-1999

0.87 0.27 0.98 0.66 0.39

1.26

0.31

0.50

0.15

2000-2999

0.67 0.13 0.28 0.56

0.37

0.62

1.43

0.60

3000-3999

1.13

0.05

0.84

0.07

4000-4999

0.81 0.43 0.13 0.44 0.73

0.24

0.78

1.62

0.72

5000-5999

0.32

0.79

0.40

1.33

0.42

1.12

0.08

1.05

0.18

1.19

2.08

6000-6999 7000-8999 9000-10999 11000-14999

*

0.97

0.99 0.12 0.67

Table continued on following page.

755

HOUSEHOLD REGRESSIONS Table A-40 Matrices (continued)

Child of Head

No

Yes

1.49

Employment Status of Head

1W

«UN 0.49

4 or 5

2 or 3

0.56

4 or 5 Years of School Completed by Associated Person 0-6

7-8 9-10

NL **

2.76

1.04

hOH

Family Size

H

6 or more 0.86 0.44

16+

1,.10

0.12

0.06

1,.10

0.18

0.01

1,.06

0.20

0.004

0..72

0.60

0.35

9-10

11-12

13

0.11

0.13

0.80

0.04

0.83 0.71

11-12 13 14-15

= Significant at the 1% level. = Significant at the 57= level. +«

14-15

7-8

Significant at the 10% level.

1.01

0.81 0.14

1,091

Family Size

Employment Status of Head

(3.B)

Bl.2

-27.B S:AP 7+

+S.2 OFI7000_B999 (6.0)

FA =4.1

--------**

-17.7 S:AP16 (7.7)

+2.l0Fl lSO OO+ (B.O)

+7.50FIll000_14999 (7.0)

(6.B)

+7.00FI9000_10999

(6.3)

+8.S OFI6000_6999

+14.1 A24 (5.0)

FS .. 19.1**

l

Table continued on following page.

FS: AP " 2.3*

---------------

(7.9)

-6.1 S:AP _ 14 1S (7.2)

-4.0 S:AP 13 (B.6)

+4.1 OFI5000_5999 (6.2)

= 1.0

+16.6 A 23 (5.0)

-&.1 S:AP _ U 12 (3.7)

+25.6 Sl6+ (8.4)

FFS

FH-0.2

+3.7 ~ (&.3)

+1.2 OFI4000_4999 (6.1)

----------

(3.3)

-1.1 FS6+

+13.6 A22 (4.2)

= 0.2

-----------

+5.2 A20 +B.9 OFI2000_2999 (3.6) (6.2) FCHD

+25.6 S13_1S (6.2)

**

+(R) S:AP _ O6 -3.2 S:AP _ 7 B (3.3) -2.B S:AP _ 9 10

+2.3 OFI3000_3999 (6.2)

FC=25.5

Schooling of Assoc. Person

+9.2 A2l (4.0)

Significant at the 1% level. at the 5% level.

= Significant

***=

F - 5.S

**

LxMw18-24NES,IF

N ..

Age

Child of Head

+(R) C NW +(R) AlB +(R) OFlLoss_999 +(R) CHDNO +(R) FS 2_3 +(R) I\n. +16.9 ~ +1.5 A19 +10.4 OFII000_1999 +1.2 CHDyES +2.7 FS 4 _5 +1.5 ~K (3.5) (3.4) (6.9) (2.9) (2.B) (3.6)

Color

Other Family Income

Sets of Independent Variables:

+31.4 S12 (5.3)

(5.8)

+lB.6 S9_11

-13.0 SB (7.3)

+(R) SO_7

Schooling Constant of Term Subject

LxMwlB-24NES,IF· 31.7

Dependent Variable

Appendix Table A-4l

Multiple Regression Equation Used to Explain the Labor Force Participation of Never-Married Females lB-24, in Families, Not Enrolled in School

a X >

Z

tTl

> "'C "'C

0'\

VI

-.....l

757

HOUSEHOLD REGRESSIONS Table A-41 (continued) Matrices of t-Valueg Years of School Completed by Subject

8

0-7

1.77

9-11 +

**

3.22

**

8

5.07

12

13-15

**

**

3 88

12

1.51

0.95

1.52

0.84

13-15

0.003

Color

Nonwhite

White

4.80

**

19

20

21

0.45

1.47

2.31

Age 18

**

4.11 5 96 3.04 ** ** ** 7 73 5.85 4.42 **

9-11

16+

19 20 21 22 23 Other Family Income Loss-999

1.03

22 *

23 **

3.21

**

24 **

3.30

**

2.83

**

1.94+

2,84

3.00

2.56*

0.98

1.95+

2.24

1.77+

1.40

0.95

0.54

0.09

0.97

•

0.41 1000 2000 3000 4000 5000 6000 7000 9000 11000 -1999 -2999 -3999 -4999 -5999 -6999 -8999 -10999 -14999 1500CH-

1000-1999

1.51 1.44 0.38 0.19 0.65 1.36 0.87 1.03 0.23 1.25 1.48 0.99 0.29 0.87 0.50

0.42

1.03

2000-2999

1.17 1.44 0.88 0.07 0.73 0.32

0.23

0.92

1.07

0.27

3000-3999

0.23 0.34 1.19 0.60 0.83

0.87

0.02

4000-4999

0.61 1.53 0.94 1.13

1.16

0.15

5000-5999

0.92 0.26 0.57

0.63

0.29

6000-6999

0.78 0.30

0.19

0.96

7000-8999

0.39

9000-10999 11000-14999

0.46

0.49

0.09

0.71 0.76

Table continued on following page.

127

APPENDIX A

-

87.6

Constant Term Color

Sets of Independent Variables: Other Family Child of Empl. Status Income Head of Head Family Size

Schooling of Assoc. Person

Significant at the 11 level.

+(R) A14 -1.5 A1S (1.3)

------------------FOFI • 2.1 *

-0.4 OFIlSOOO_24999 (3.5) -2.B OFI 2S000+ (4.7)

-0.8 OFIIlOOO_14999 (3.2)

+0.3 OFI9000_10999 (3.0)

Table continued on following page.

+(R) C +(R) OFILoss_999 +(R) CIIDNO +(R) ~K +(R) FS 2_3 +(R) S:APO_6 N +2.5 Cw -7.9 OFIIOOO_1999 +S.3 (]IDYES -1.7 HUN -0.9 FS 4 _S +4.3 S:AP 7_B (1.6) (3.3) (1.2) (1.6) (1.5) (2.6) ----------+7.0 S:AP _ +10.0 C -8.2 A16 -1.6 '1n. -3.3 FS6+ -6.9 OFI2000_2999 9 10 (5.2) ONW (3.1) (1.3) (1.4) (1.7) FCHD -13.l ** (1.9) +9.1 S:AP _ -14.4 A17 ----------2.7 OFI3000_3999 U 12 F .. 2.6+ (1.3) (3.1) (1.6) c +11.4 S:APl3 -4.5 OFI4000_4999 (3.0) (2.B) S:AP 14 _ +11.0 -3.2 OF1SOOO_S999 1S (3.0) (2.3) +11.7 S:AP16 -2.3 OFI6000_6999 (3.0) (2.5) +11.1 S:AP17+ -2.4 OFI7000_8999 (3.0) (2.7)

Age

Significant at the 51 level. +- Significant at the 101 level.

***-

F- 12.1**

E~14-17IF - 91.3

N - 3,503

ESM14_17lF

Dependent Variable

Appendix Table A-42 Multiple Regression Equation Used to Explain the School Enrollment of Never-Married Males 14-17 in Families

c::

15 z '"

tTl

C)

tTl

'" ''"'""

r

::c o o

'"tTl

::c o

129

APPENDIX A

761

HOUSEHOLD REGRESSIONS Table A-42 Matrices (continued) Family Size 2 or 3

4 or 5

6 or more

0.73

2.41* *

4 or 5

Years of School Completed by Associated Person

2.18

7-8

9-10

11-12 At

2.65** 4.08* 5.71 1.81+

13

14-15

irk

Mr

17+

h

4.78 4.69 4.10 * * * * * * 3.63** 2.67* 3.18 3.19 2.67 1.47

4.07

16

1.61

1.84

1.97

1.57

0.87

0.94

1.16

0.80

0.11

0.11

0.09

0.24

0.01 0.21

**

• Significant at the 1% level.

•* Significant +«

at the 5% level.

Significant at the 10% level.

76.2

ESXHW14_l7IF·

** *•

**

Significant at the Significant at the

F • 9.5

ESXMW14_l7IF - 91.0

N • 3,378

Term

Constant

Dependent Variable

5~

l~

level. level.

-13.9 A17 (1.4)

------1-

FC·5.8

+7.8 OFI 2S000+ (4.5)

+6.5 OFI1SOOO_24999 (3.7)

+3.9 OFIllOOO_14999 (3.3)

+4.0 OFI9000_10999 (3.2)

+1.8 OFI7000_8999 (3.1)

+0.9 OFI 6000 6999 (3.3) -

+2.0 OFISOOO_5999 (3.2)

(3.3)

+4.5 OFI4000_4999

+4.0 OFI 3000 3999 (3.3) -

Table continued on following page.

+10.7 S:APl 7+ (2.7)

+12.4 S:AP13 (2.9) +10.2 S:AP _ 14 l5 (2.5) +8.2 S:AP16 (2.7)

+10.3 S:AP _ U 12 (1.8)

+8.5 S:AP _ 9 10 (1.9)

-1.0 FS6+ (1.5)

-3.2 A16 (1.3)

+0.7 OFI2000_2999 --------.-- +0.4 HUN (3.3) FCHD -3.9 (3.2)

+(R) S:APO_6 +5.1 S:AP7 8 (1. 7) -

+(R) FS _ 2 3 +2.0 FS _ 4 5 (1.3)

+(R) A14 +(R) CNW +(R) OFILoss_999 +(R) CHDNO +(R) HNL -1.3 A15 +3.7 Cw +5.0 OFI lOOO 1999 +3.0 CHDYES +2.6"wK (1.4) (3.6) (1.5) (2.0) (1.6)

Color

Schooling of Aasoc. Person

Age

Family Size

Sets of Independent Variables: Other Family Child of Empl. Status Income Head of Head

Appendix Table A-43 Multiple Regression Equation Used to Explain the School Enrollment of Never-Married Females 14-17 in Families

763

HOUSEHOLD REGRESSIONS Table A-43 (continued) Matrices of t-Values Age

15

14

0.98

16

17

2.43* 10.22**

15

1.40

**

9.08

**

16

7.91

Color

Nonwhite

Uhlce

2.32

Other Family Income Loss-999

*

1000 2000 3000 4000 5000 6000 7000 9000 11000 15000 -1999 -2999 -3999 -4999 -5999 -6999 -8999 -10999 -14999 -24999 25000* 1.39 0.21 1.21 1.37 0.62 0.28 0.58

1.24

1.17

1.79+ 1.73+

1000-1999

1.39 0.34 0.17 1.03 1.38 1.13

0.34

0.37

0.46 0.66

2000-2999

1.33

1.22

1.93+ 1.77+

3000-3999

1.23 1.45 0.52 0.08 0.47 0.21 0.84 1.30 1.00

0.02

0.03

0.90 1.00

4000-4999

1.12 1.60 1.33

0.22

0.26

0.74 0.88

5000-5999

0.53 0.11

1.02

0.89

1.74+ 1.58

6000-6999

0.49

1.57

1.40

2.16* 1.87+

1.29

1.11

1.97* 1.70+

0.07

1.01 1.06

7000-8999 9000-10999 11000-14999

1.03 1.08

15000-24999

0.33

Child of Head Yes Employment Statue of Head hWK

«UN

No 1.97*

«UN 0.82

hNL

1.29 0.13 Table continued on following page.

764

APPENDIX A

Table A-43 Matrices (continued)

Family Size 2 or 3

4 or 5 1.49

0-6

0.66 2.S8**

4 or

Years of School Completed by Associated Person

6 or more

7-8

9-10

11-12

14-15

16

2.92** 4.63 ** 5.87** 4.28** 4.16** 2.99 **

17+ 3.95 **

9-10

2.25* 3.78** 2.72** 2.33* 1.24 1.41 1.17 0.74 0.13

0.85

11-12

0.82

7-8

13

0.3.5

0.17

0.70

1.26

0.53 0.16 0.80

16.

** = Significant

2.27*

0.02

0.69

14-15

*•

13

at the 1% level.

Significant at the 5% level.

+. Significant at the 10% level.

3.S03

Significant at the 1t level. +. Significant at the 10% level.

**•

F • 3.9**

~14-17IF - 96.1

N •

+ FC = 2.4

-4.0 Al7 (0.9) FA • 8.4**

(3.7)

+S.9 C

-2.9 A 16 (0.9)

(1.1) ONW

Cw

-0.7 Al5 (0.9)

+2.0

+(R) CN

+(R) A14

9S.4

A~14-17IF

Color

Age

Constant Term

Dependent Variable

+6.0 S:AP16 (2.0) +S.2 S:AP17+

-2.0 OFI6000_6999 (2.1) -2.3 OFI7000-8999 (2.0)

-2.70FI 2S000+ (3.3)

FS: AP - 3.0**

(2.1)

Table continued on following page.

-0.9 OFI1S000_24999 (2.4)

-0.9 OFI11000_14999 (2.2)

-0.9 OFI9000_10999 (2.1)

(1. 7)

_ 13 15

+3.7 S:AP

(loS)

-3.4 OF1SOOO_S999 (2.1)

(loS)

+3.0 S:AP _11 9

(1.6)

+1.6 S:APS

+1.3 S:AP _ S 7 (1.6)

+(R) S:APO_4

Schooling of Assoc. Person

+4.6 S:AP12

-3.8 HNL (2.1)

(1.8)

+(R)

IiuN +0.02 iIwK

Empl. Status of Head

-1.7 OFI4000_4999 (2.1)

-2.6 OFI3000_3999 (2.2)

(2.2)

-4.S OFI2000_2999

-1.8 OFII000_1999 (2.4)

+(R) OFILoss_999

Other Family Income

Sets of Independent Variables:

Appendix Table A-44 Multiple Regression Equation Used to Explain the Activity Rate of Never-Married Males 14-17 in Families

c

VJ

o Z

VJ VJ

ttl

:;0:1

Q

ttl

:;0:1

t:1

r

::c o

ttl

VJ

::c o

766

APPENDIX A

Table A-44 (continued)

Matrices of t-Valuee

Age 14

16

15 0.74

IS

3.17** 4.33** 2.47*

16

Negro

Uhlte

1.83+

Hegro

Loss-999

3.64** 1.16

Color

Other Family Income

17

Other Nonwhlte 1.08 1.59

1000 2000 3000 4000 5000 6000 7000 9000 11000 15000 -1999 -2999 -3999 -4999 -5999 -6999 -8999 40999 44999-24999 25000«2.06* 1.21 0.78

1.61 0.97

1.15

0.42 0.40 0.37 0.83

1000-1999

1.31 0.40 0.06

0.81 0.12

0.29

0.47 0.45

2000-2999

1.06

1.37

2.18* 2.05* 1.74+ 0.60

0.75

1.65+ 0.70

1.50

0.38 0.29

3000-3999

0.58 0.48 0.38 0.18

1.12

1.05

4000-4999

1.19 0.25

0.56

0.52 0.40 0.37

0.51

0.86

0.04

5000-5999

1.01 0.83

1.91

1.73

6000-6999

0.26

0.89

0.81 0.62 0.25

1.25

7000-8999

1.11

1.36 0.23

0.84 0.14

0.01 0.02 0.66

9000-10999 11000-14999

0.02 0.65

15000-24999

0.60

Employment Statue of Head

bWK

«UN

11UN

0.01

hNL

2.95** 1.78 Table continued on following page.

767

HOUSEHOLD REGRESSIONS

Table A-44 Matrices (continued)

Years of School Completed by Associated Person 0-4 5-7

5-7 0.80

8

9-11

12

13-15

16

17+

1.03

2.04 *

3.03**

2.43 **

1.59

2.91 **

2.1S* 1.7S+

2.99**

0.29

2.73**

1.32

2.73 **

1.57 0.55

2.59** l.S3+

2.09* 1.94+

0.69

0.88

0.34

1.29

0.76 0.38

S 9-11 12 13-15 16

** -

Significant at the IX level. Significant at the 5X level. +~ Significant at the lOX level.

*-

1.61

1.21

** = Significant at the 1% level. +. Significant at the 10% level.

+3.8 OFI 25000+ (3.8)

+2.0 OFI15000_24999 (3.1)

+1.0 OFI9000_10999 (2.7)

= 3.9**

Table continued on following page.

F s : AP

+6.5 S:AP 17+ (2.6)

-O.S OFI6000_6999 (2.7)

+1.2 OFI5000_599~ (2.7)

+0.4 OFI7000_8999 (2.6)

+5.2 S:AP

+2.1 S:APS (2.0) +5.7 S:AP _ 9 11 (1.9)

+2.1 ~ (2.7)

+(R) S:AP _4 O +0.8 S:APS_7 (2.0)

~

+2.5 RwK (2.3) ,

+(R)

Empl. Status Schooling of of Head Assoc. Person

12 (2.0) +6.9 S:AP _ 13 15 (2.2) +3.9 S:AP 16 (2.6)

-2.2 CHDNO (1.3)

+(R) CHDyES

Child of Head

+2.6 OFI4000_4999 (2.7)

•

94.1

FC • 9.8**

+1.3 OFI11000_l4999 (2.8)

=

** FA = 10.5

(1.1)

+1.5 OFI3000_3999 (2.7)

-2.6 OFI2000_2999 (2.8)

+3.3 CONI{ (4.6)

-1.4 A16 (1.1)

-... _------

+2.6 OFllOOO_1999 (3.0)

+5.9 Cw (1.4)

-0.7 A'5 (1.1) •

-5.8 A17

+(R) OFltoss_999

+(R) C N

+(R) A14

Other Family Income

83.8

Color

Age

Sets of Independent Variables:

Constant Term

F • 4.9**

ARxMw14-l7IF

N • 3,382

ARxMw14-l7IF

Dependent Variable

Appendix Table A-45

Multiple Regression Equation Used to Explain the Activity Rate of Never-Married Females 14-17 in Families

769

HOUSEHOLD REGRESSIONS Table A-45 (continued) Matrices of t-Values Aee 14

15

16

0.61

1.25

15

17 **

5.10

**

0.62

4.41

•*

16 Color White

3.91 Uhlte

Other Nonuhlte

**

4.31

0 60

Negro

0 71

Other Family 1000 2000 3000 4000 5000 6000 7000 9000 11000 15000 Income -1999 -2999 -3999 -4999 -5999 -6999 -8999 -10999 •14999 24999 25000f Loss-999

0.87 0.93 0.55 0.94 0.43 0.31 0.17

0.37

0.47

0.66

0.99

1000-1999 2000-2999

1.99* 0.43 0.02 0.59 1.37 0.91

0.65

0.51

0.21

0.32

3000-3999

0.51 0.18 1.18 0.60

0.27

0.11

0.21

0.68

4000-4999

0.86

0.66

0.24

0.37

5000-5999

0.75 1.81+ 1.26 1.15 0.48

0.10

0.08

0.39

0.83

6000-6999

0.83

1.10

1.20

1.30

1.48

0.40

0.55

0.78

1.12

0.18

0.48

0.91

0.33

0.80

*

1.83+ 2.33 1.75+ 0.80 1.48

7000-8999 9000-10999 11000-14999 15000-24999

0.53

Child of Head

No

Yes

1.76+

Employment Status of Head

*U 11UN

1.68+ 1.73+ 1.78+ 1.86+

«UN 1.09

«NL 0.23 0.78 Table continued on following page.

770

APPENDIX A

Table A-45 Matricea (continued) Years of School Completed by Associated Person 0-4

8

5-7 0.40

1.06

5-7

0.85

8

9-11

12

13-15

0.43

9-11

12

e

17+ 2.52* 2.56* 2.0B*

0.79

0.90

0.40

1.12

0.65

0.65

1.37

O.lB

13-15 16

**

16

2.97** 2.63** 3.17** 1.47 3.46** 2.98** 3.48** 1.34 2.83** 2.35* 2.96** O.Bl

1.04

Significant at the 11. level.

*= Significant at the 5% level. += gignificant at the 10% level. Appendix Table A-46 Multiple Regression Equation Used to Explain the Activity Rate of Males 18-24 Sets of Independent VaE..:i=a.::.b;:,;le::.:s:..::_ _ __ Dependent Variable

Constant Term

Age

Color

Marital. and Family Status

A~18_24

95.4

+(R) AlB +1.3 A19 (1.0)

+(R) C N +2.6 ~ (0.9)

+(R) FIF ,MSP -3.9 FO (0.7)

+0.4 A (1.1) 20

+3.1 CONW (2.9)

+2.2 A21 (1.0)

FC

--------** 7.2 z

+0.4 A22 (1.1) +2.4 A (1.1) 23 +0.1 A24 (1.1)

II .4,753

Al\t18-24 • 96.1 F ·7.4**

**. Significant

at the 1'& level.

+. Significant at tbe 10'& level. Table continued on following page.

771

HOUSEHOLD REGRESSIONS

Table A-46 (continued)

Matrices of t-Va1ues

Age 18

19

20

1.27

0.33 0.89

19 20 21

21

2.11 *

0.36 0.82 0.86 1.71+ 0.03 1.71+

22 23

Color

Negro

White

2.84 **

Other Nonwhite 0.19

Negro

1.06

Marital 'H.d Family Status

Other

In Family, Married, Spouse Present

** = Significant at the

5.84**

n.

level.

* & Si.Gnificant at the 5t level. +

22

- Significant at the lOt level.

23

24

2.20*

0.12 1.06

0.98 1.86+ 0.19 1.89+

0.19 1.91+ 0.23 2.11+

APPENDIX B

Intercity Regressions As explained in Chapter 2, most of the conclusions reached in this study concerning the effects of labor market conditions on labor force participation rates are based on the results of our intercity regressions for 1960, 1950, and 1940. Since the characteristics of the regression model employed in this part of our work, the criteria for selecting the met­ ropolitan areas included in the analysis each year, the sources of data for these areas, and a description of the kinds of variables employed in the analysis have all been set out in Chapter 2, there is no reason to repeat that discussion here. Rather, the purpose of this appendix is three-fold: (1) to explain in greater detail how our measures of male and female industry mix were constructed and to present the values of the female index in 1960 for the 100 SMSA'S in our analysis; (2) to discuss the re­ sults of some experimental regressions in which the weight assigned to each SMSA was a function of its population, and (3) to present the com­ plete multiple regression equations and related results from our basic intercity analysis for all our population groups in 1960, our "comparabil­ ity regressions" for 1960, 1950, and 1940, and our experimental weighted regressions mentioned above.

Industry-Mix Variables As noted in Chapters 4 and 6, the industry-mix variables used in this study are designed to measure structural differences among metropolitan areas in the relative abundance of those jobs commonly held by females and males, respectively. These variables do not measure the actual ratio of male or female employment to total employment in SMSA'S during the census week, and it would be improper to use such a ratio in an analysis designed to predict group participation rates, since the level of participa­ tion is an important determinant of the actual employment ratio. Rather, our industry-mix variables measure the predicted ratio of male or female employment to total employment, the prediction being based on (1) the all-U.S. ratio of male or female employment to total employment within each of 36 two- and three-digit industry groups during the census week in question, and (2) the distribution of total employment among these industry groups within each SMSA. In other words, the value of the in­ dustry-mix variable for each SMSA is best viewed as a prediction of what the sex-employment ratio in that area would have been if that ratio had depended only on the national sex-employment ratios for each industry group and the area's industry mix. Of course, the actual ratios depend on many other considerations, including the specific employment policies of the particular firms in the metropolitan area. The following paragraphs describe the step-by-step construction of the

INTERCITY REGRESSIONS

773

female-industry-mix variable for 1960. The values of the male-industrymix variable were obtained by subtracting the values of the female index from 100; thus the two measures have different means but identical intercity variances in each year. In the first step we calculated the ratio of female employment to total employment within each of the following industry groups for the U.S. as a whole during the census week.1 Taken together, these industries contained all the employed persons in the civilian economy. Agriculture, forestry, and fisheries Mining Construction Lumber and wood products, except furniture Furniture and fixtures Stone, clay, and glass products Primary metal industries Fabricated metal industries Machinery, except electrical Electrical machinery, equipment, and supplies Transportation equipment Professional and photographic equipment and watches Miscellaneous (durable) manufacturing industries Food and kindred products Tobacco manufactures Textile mill products Apparel and other fabricated textile products Paper and allied products Printing, publishing, and allied industries Chemicals and allied products Petroleum and coal products Rubber and miscellaneous plastic products Leather and leather products Not specified manufacturing industries Transportation Communications Utilities and sanitary services Wholesale trade Retail trade Finance, insurance, and real estate Business and repair services Personal services Entertiiinment and recreation services Professional and related services Public administration Industry not reported 1

Source: 1960 Census, Detailed Characteristics, U.S. Summary, Table 211.

774

APPENDIX B

Second, for each metropolitan area, we multiplied the total employ­ ment (of both sexes combined) in each of the above industry groups by the national female-employment ratio for that group as calculated in step one. Finally, the products obtained by step two for each SMSA were summed and divided by the total civilian employment in the city. Thus, each quotient is simply an employment-weighted average of the national female-employment ratios for the industries located in the metropolitan area. Inasmuch as we have received a number of requests for the actual values of the female-industry-mix variable for the 100 SMSA'S used in our 1960 intercity regressions, we present these values below: Akron(Ohio) Albany-Schenectady-Troy (N.Y.) Albuquerque (N. Mex.) Allentown-Bethlehem-Easton (Pa.-N.J.) Atlanta (Ga.) Bakersfield (Calif.) Baltimore (Md.) Beaumont-Port Arthur (Texas) Birmingham (Ala.) Boston (Mass.) Bridgeport (Conn.) Buffalo (N.Y.) Canton (Ohio) Charleston (W. Va.) Charlotte (N.C.) Chattanooga (Tenn.) Chicago (111.) Cincinnati (Ohio-Ky.) Cleveland (Ohio) Columbia (S.C.) Columbus (Ohio) Dallas (Texas) Davenport-Rock Island-Moline (Iowa-Ill.) Dayton (Ohio) Denver (Colo.) Des Moines (Iowa) Detroit (Mich.) Duluth-Superior (Minn.-Wis.) El Paso (Texas) Erie (Pa.) Flint (Mich.) Fort Lauderdale-Hollywood (Fla.) Fort Worth (Texas)

31.5 35.7 34.0 33.7 35.2 30.0 32.9 30.2 32.3 36.7 31.4 30.1 28.2 31.3 36.0 34.8 32.5 32.8 31.0 38.8 34.5 34.3 30.3 31.9 34.3 35.0 29.6 30.2 35.5 30.5 26.8 35.4 32.0

775

INTERCITY REGRESSIONS

Fresno (Calif.) Gary-Hammond-East Chicago (Ind.) Grand Rapids (Mich.) Harrisburg (Pa.) Hartford (Conn.) Houston (Texas) Huntington-Ashland (W. Va.-Ky.-Ohio) Indianapolis (Ind.) Jacksonville (Fla.) Jersey City (N.J.) Johnstown (Pa.) Kansas City (Mo.-Kans.) Knoxville (Tenn.) Lancaster (Pa.) Lansing (Mich.) Los Angeles-Long Beach (Calif.) Louisville (Ky.-Ind.) Memphis (Tenn.) Miami (Fla.) Milwaukee (Wis.) Minneapolis-St. Paul (Minn.) Mobile (Ala.) Nashville (Tenn.) Newark (N.J.) New Haven (Conn.) New Orleans (La.) New York (N.Y.) Norfolk-Portsmouth (Va.) Oklahoma City (Okla.) Omaha (Nebr.-Iowa) Orlando (Florida) Paterson-Clifton-Passaic (N.J.) Peoria (111.) Philadelphia (Pa.-N.J.) Phoenix (Ariz.) Pittsburgh (Pa.) Portland (Oreg.-Wash.) Providence-Pawtucket (R.I.-Mass.) Reading (Pa.) Richmond (Va.) Rochester (N.Y.) Sacramento (Calif.) St. Louis (Mo.-Ill.) Salt Lake City (Utah) San Antonio (Texas)

30.8 24.7 31.6 33.5 32.1 32.1 30.3 32.6 34.9 34.5 29.5 33.2 34.1 33.6 32.6 33.3 33.5 35.3 37.4 31.6 33.8 33.8 37.0 34.2 35.0 34.4 38.1 33.7 34.6 33.0 33.1 34.2 29.8 34.3 33.2 29.3 32.9 35.2 33.5 36.3 35.7 32.2 32.7 32.8 35.7

776

APPENDIX B

San Bernardino-Riverside-Ontario (Calif.) San Diego (Calif.) San Francisco-Oakland (Calif.) San Jose (Calif.) Seattle (Wash.) Shreveport (La.) Spokane (Wash.) Springfield-Chicopee-Holyoke (Mass.) Syracuse (N.Y.) Tacoma (Wash.) Tampa-St. Petersburg (Fla.) Toledo(Ohio) Trenton (N.J.) Tucson (Ariz.) Tulsa (Okla.) Utica-Rome (N.Y.) Washington (D.C.-Md.-Va.) Wichita (Kans.) Wilkes-Barre-Hazleton (Pa.) Wilmington (Del.-NJ.) Worcester (Mass.) Youngstown-Warren (Ohio)

32.2 33.1 34.4 33.1 31.5 35.4 34.2 34.4 34.1 32.7 35.0 31.0 34.2 35.2 31.1 33.3 38.2 30.8 39.0 31.7 33.5 26.5

Our "femininity" indices for 1950 and 1940 were constructed in pre­ cisely the same way.

Weighted Regressions In all the intercity regressions presented in the text of this volume (and in Appendix Tables B-I through B-44 and B-100 through B-104), every metropolitan area has equal weight. We adopted this approach simply because it has been the one most widely used in empirical research of this kind; indeed we did not give serious consideration to an alternative procedure until nearly all our cross-sectional work had been completed. Thanks, however, to the comments of several of our colleagues,2 we became convinced that a good case could be made for using weighted re­ gressions, i.e., regressions in which the weight assigned to each obser­ vation is a (positive) function of the size of the sample on which the observation is based. The rationale for using weighted regressions is that they generate more efficient estimates of the slopes of the relations under scrutiny, mainly by giving less weight to those observations which are subject to relatively large sampling errors and more weight to those observations which are subject to relatively small errors of this kind. Put in more technical terms, the use of weighted regressions is designed 2 In

particular, Professors William Branson, Stephen Goldfeld, and Orley Ashenfelter.

INTERCITY REGRESSIONS

777

to reduce the amount of heteroscedasticity in the residuals of the re­ gression.3 While it was not practicable for us to run weighted regressions for all our population groups, we were able to do it for some of them. In these experimental runs, the value of each variable in the analysis was multi­ plied by y/PJLPi, where Pi was the total civilian population of the SMSA concerned in 1960 and XPi the population of all 100 SMSA'S combined. The results of these regressions (along with the comparable unweighted regressions) are presented in Appendix Tables B-200 through B-211. A detailed comparison of the results of the weighted and unweighted regressions—WR and UR, for short—leads to the following observations: The standard deviation of each dependent variable is always smaller in the WR than in the UR; nonetheless, the WR'S have larger /?2's in 10 cases out of 12 (males 25-54 and wives 14-54 are the exceptions). There are only seven net relations out of 123 (in the 12 regressions combined) that bear a different sign in the weighted and unweighted runs for a given group, and only one of regression coefficients in these seven cases is significant at the 10 percent level.4 While certain variables were significant more often in the UR than in the WR, and vice versa, the total number of statistically significant coefficients (at the 10 percent level or better) was almost identical in both sets of runs (i.e., 86 in WR, 87 in UR). However, more relations were significant at the 1 percent level in the WR than in the UR (56 ver­ sus 46, respectively). Ninety of the 123 regression coefficients in the set of WR fell within (plus or minus) one standard error of the comparable coefficient in the UR; 30 fell within two standard errors; and only three fell outside the latter range. Confining our attention to the regression coefficients for the labor market variables in these regressions, we find that 41 of the 57 relations were statistically significant at the 10 percent level or better in the WR, compared with 43 relations in the UR. Once again, however, slightly more coefficients were significant at the 1 percent level in the WR (28 versus 25). As the foregoing findings suggest, the general contour of relations in the weighted regressions is quite similar to the pattern of coefficients in the unweighted runs — a similarity which is most reassuring. It is appro­ priate, however, to conclude this discussion by pointing out three spe­ cific cases in which important differences do appear: 3 Heteroscedasticity refers to a situation in which the variance of the error term in the regression systematically changes from observation to observation. For further discussion of this problem, see J. Johnston, Econometric Methods, pp. 207-211, and E. Malinvaud, Statistical Methods of Econometrics, pp. 77, 254-258. 4 Interestingly, four of the seven cases involved a male marital-status variable: in each of these cases, the variable had the wrong sign (negative) in the unweighted run and the right sign in the weighted regression! (One of the right signs was significant at 10 percent.)

778

APPENDIX B

1. In all the regressions for younger males, earnings of teenage males has a smaller negative regression coefficient and lower i-value in the WR than in the UR. The coefficients for the supply of teenage males are also generally smaller in the WR. 2. The b for unemployment is smaller in the WR for prime-age males than in the UR (—0.20 and —0.32, respectively); but both relations are easily significant at 1 percent. At the same time, the b for earnings is considerably larger in the WR. 3. In the analysis of married women 14-54, the coefficient for earn­ ings is larger, and the coefficient for income of husbands is smaller, in the WR than in the UR. This implies that a somewhat larger share of the postwar rise in L _ 4 might be explained by the changes in these two variables (taken together) than our analysis in Chapter 7 would sug­ gest. MW14

5

Appendix Tables In the following pages we present the complete results of nearly all of the intercity regressions reported in this study. Tables B-I through B-44 contain the basic (unweighted) runs for the census week of 1960; the comparability regressions for 1960, 1950, and 1940 appear in Tables B-100 through B-104; and (as noted earlier) the experimental weighted regressions for 1960 will be found in Tables B-200 through B-211.

Appendix Table B-I Multiple Regression Equations Used to Explain Intercity Differences in Labor Force Participation Rates of Males 25-54 Census Week of 1960

Independent Variables

Unemployment (%)

b

Regression I (S) t

-0.32

(0.06)

b **

5.41 **

a/ Regression II— (S) t **

-0.31

(0.06)

5.41

Industry Mix, Male (#)

0.20

(0.05) 4.20

0.18

(0.04) 4.23

Earnings ($100/yr.)

0.05

(0.02)

0.06

(0.02)

2.79

-0.29

(0.09)

3.05

0.24

(0.11)

2.23

-0.03

(0.01)

3.01

Other Income ($100/yr.)

2.34* **

-0.36 (0.13)

2.72

0.26

(0.12)

2.24

Color (% nonwhite)

-0.03

(0.01)

3.27

Marital Status (% married)

-0.05

(0.04)

1.30

Schooling (yrs.)

Migration (#)

*

**

* **

**

0.01

**

(0.02) 0.53 Table continued on following page.

779

INTERCITY REGRESSIONS Table B-I (continued) Other Data Dependent Variable I^12S 541 Mean Standard Deviation Constant Term Standard Error of Estimate Number of Observations R

2

96.4

96.4

1.2

1.2

84.9

81.9

0.8

0.7

100 0.62

100 0.61

Notation: Units of measurement are shown in parentheses following variables. A # means that the unit cannot be abbreviated readily; see definitions of variables below. b s Net (partial) regression coefficient. (s)« Standard error of the regression coefficient. t * t-value of the regression coefficient (b/s); t-value may differ from b/s ratios in table owing to rounding. ** * Significant at the 1 percent level. * • Significant at the 5 percent level. + * Significant at the 10 percent level. Definition of variables: Ht25 54: PercentaSe of males aged 25 to 54 years in the civilian, non— i n s t i t u t i o n a l population who were in the civilian labor force during the Census Week. Unemployment: percentage of the civilian labor force unemployed during the Census Week. Industry Mix, Hale: a measure of the percentage of jobs in each SMSA which we might expect to be held by men; based on the industry mix of the SMSA. Earnings: mean income in 1959 of all males who worked 50*52 weeks. Other Income: median income from nonemployment sources in 1959 per recipient of any kind of income. Schooling: median number of years of school completed by all males aged 25 years and older. Color: percentage of all persons in households who were nonwhite. Marital status: percentage of all males aged 25 to 54 years in the civilian noninstitutional population who were married with wife present· Migration: net migration (+ • in, - • out) between 1955 and the 1960 Census Week of all males aged 30 to 54» divided by the total population of that group in the 1960 Census Week. (Members and former members of the armed forces and inmates of Institutions are included in both numerator and denominator.)

a/ — Includes only these variables significant at the 10% level (or better) in Regression I.

780

APPENDIX B

Appendix Table B-2 Multiple Regression Equations Used to Explain Intercity Differences in Labor Force Participation Rates of Males 25-34 Census Week of 1960

Independent Variables

Regression I b

Unemployment (%)

Reeression t

(S)

-0.32 (0.14)

b

t

(S)

*

2.30

**

-0.33 (0.12)

2.78

0.46 (0.07)

6.29

**

Industry Mix, Mala (#)

0.37 (0.11) 3.38

Earnings ($100/yr.)

0.04 (0.05) 0..84

Other Income ($100/yr.)

0.20 (0.30) 0.65

Schooling (yrs.)

-0.04 (0.27) 0,.15

Color (% nonwhite)

-0.03 (0.02)

1 ,

Marital Status (% married)

0.08 (0.08)

1 .

Migration (#)

0.004 (0.04) 0,.10

**

.16 .00

Other Data Dependent Variable 1^5 34: Mean Standard Deviation Constant Term Standard Error of Estimate Number of Observations S2

95.4

95.4

2.0

2.0

63.3

66.6

1.7

1.7

100

100

Q.34

Definition

0.29

of Variables:

Hl25- 34'

percentage of males ages 25 to 34 years in the civilian, noninstitutional population who were in the civilian labor force during the Census Week.

All other definitions are the same as in Appendix Table B-I. — Includes only these variables significant at the 10% level (or better) in Regression I.

781

INTERCITY REGRESSIONS

Appendix Table B-3 Multiple Regression Equations Used to Explain Intercity Differences in Labor Force Participation Rates of Males 35-44 Census Week of 1960

Independent Variables

b

Regression I (s) t

b

Regression lW (S) t

**

Unemployment (%)

-0.32 (0.10) 3,.28

**

-0.35 (0.09)

4.03

0.20 (0.06)

3.29

0.10 (0.03)

3.45

**

*rk

Industry Mix, Male (#)

0.22

(0.08)

2,.83

Earnings ($100/yr.)

0.08

(0.04)

2..23

Other Income ($100/yr.)

0.04 (0.22) 0.,20

Schooling (yrs.)

0.21 (0.19)

Color (% nonwhite) Marital Status (% married) Migration (#)

*

**

1.,U

-0.03

(0.02) 1.,95+

0.02

(0.06) 0..38

-0.04 (0.02)

2.39*

-0.02 (0.03) 0..62

Other Data Dependent Variable Mean Standard Deviation Constant Terra Standard Error of Estimate Number of Observations R2

96.1

96.1

1.6

1.6

74.7

79.3

1.2

1.2

100 ·

100

0.46

0.45

Definition of Variables: 44!

percentage of males aged 35 to 44 years in the civilian, noninstitutional population who were in the civilian labor force during the Census Week.

All other definitions are the same as in Appendix Table B-I. a/ — Includes only those variables significant at the 10% level (or better) in Regression I.

137

APPENDIX A

Appendix Table B-4 Multiple Regression Equations Used to Explain Intercity Differences in Labor Force Participation Rates of Males 45-54 Census Week of 1960

Definition of Variables: percentage of males aged 45 to 54 years in the civilian, noninstitutional population who were in the civilian labor force during the Census w a e k . All other definitions are the same as in Appendix Table B-l. a/ — Includes only these variables significant at the 1011 level (or better) to degression I.

783

INTERCITY REGRESSIONS

Appendix Table B-5 Multiple Regression Equations Used to Explain Intercity Differences in Labor Force Participation Rates of Married Men, VJife Present, 25-54 Census Week of 1960 Independent Variables

Regression I (s)

b

Unemployment (%)

-0.25

(0.15)

1,ji

0.08

(0.12)

0 ,.69

0.02

(0.06)

0 .36 .

-0.24

(0.33)

0 ..73

0.20

(0.31)

0 .65 .

Color (% nonwhite)

-0.01

(0.02)

0.61

Marital Status (% married)

-0.06

(0.09)

0 ,,72

Migration (#)

-0.005

"o O

Industry Mix, Male (#)

t

0.49

(0.96)

Earnings ($100/yr.) Other Income ($100/yr.) Schooling (yrs.)

Armed Forces (dunmy)

Regression II— (s) t -0.23 (0.12)

1.96

0 .,12 0 .,51

Other Data Dependent Variable Itm25.54: Mean

97,.6

Standard Deviation Constant Term Standard Error of Estimate Number of Observations S2

1.8

96,.6

98.8

1,.8 100 0.09

Definition

1,.8 100 0.04

of variables:

HlM25-54: 1

97.6

1,.8

PercentaSs married men, wife present, aged 25 to 54 years in the total population who were in the labor force during the Census Week.

Armed Forces: a dummy variable which was assigned the value of 1 if the percentage of males aged 14 years and over who were in the Armed Forces during the Census Week was 15% or over, and 0 other­ wise.

All other definitions are the same as in Appendix Table B-I. a/ — Includes only those variables significant at the 10% level (or better) in Regression I.

784

APPENDIX B

Appendix Table B-6 Multiple Regression Equations Used to Explain Intercity Differences in Labor Force Participation Rates of Never-Married Men 25-54 Census Week of 1960

Independent Variables

b

Regression I (s) t

b

a/ Regression II— (s) t **

**

Unemployment (%) Industry Mix, Male (#) Earnings ($100/yr.) Other Income ($100/yr.)

-1.14

(0.32)

3.55

0.51

(0.27)

1.91+

0.08

(0.13)

0.59

-0.43

(0.72)

0.60

1.47

(0.67)

2.18

*

Schooling (yrs.)

•k

Color (% nonwhite)

-0.13

(0.05)

2.47

Marital Status (% married)

-0.68

(0.20)

3.48

Migration (#)

0.04

(0.08)

0.42

Armed Forces (dummy)

2.56

(2.12)

1.21

-4.05

(0.42)

9.57

-1. 16

(0.30)

**

0 .,60 (0.19) 3.16

**

1 . 68 (0.48) 3.50

•k

- 0 .,12 (0.05) 2.51

**

•k-k

- 0 .,69

¢0.17)

4.02

-3.90

(0.41)

9.60

**

•kie

Institutional Population (%)

3.91

Other Data Dependent Variable Lgj^5 54: Mean

82.7

Standard Deviation Constant Term Standard Error of Estimate Number of Observations

82.7

6.3

6.3

100.8

95.3

4.0 100 0.64

3.9 100 0.63

Definition of Variables: LgM25

percentage of never-married males aged 25-54 years in the total population who were in the labor force during the Census Week.

Armed Forces: a dummy variable which was assigned the value of 1 if the percentage of males aged 14 years and over who were in the armed forces during the Census Week was 15 percent or over, and 0 otherwise. Institutional Population: percentage of males aged 14 and over who were inmates of institutions during the Census Week. All other definitions are the same as in Appendix Table B-I. a/ — Includes only those variables significant at the 10 percent level (or better) in Regression I.

785

INTERCITY REGRESSIONS

Appendix Table B-7 Multiple Regression Equations Used to Explain Intercity Differences in Labor Force Participation Rates of Never-Married Women 25-64 Census Week of 1960

Independent Variables

Regression

~ression

II~/

b

(s)

2.96**

-0.57

(0.20)

2.81 **

(0.10)

3.45**

0.37

(0.09)

4.24**

-0.15

(0.17)

0.90

Supply of Females (%)

-0.78

(0.33)

2.40*

-0.91

(0.28)

3.22**

Other Income ($lOO/yr)

-1.30

(0.51)

2.57*

-1.60

(0.33)

4.83**

2.77

(0.49)

5.71 **

2.65

(0.45)

5.89**

COlor (% nonwhite)

-0.18

(0.04)

4.46 **

-0.18

(0.03)

5.71 **

Age (% 55-64)

-0.04

(0.12)

0.29

2.93

(0.50)

5.83**

2.82

(0.35)

8.09**

Inmates of Institutions (%)

-1.31

(0.15)

8.81 **

-1.36

Migration (4/)

-0.04

,0.07)

0.53

b

(0)

-0.64

(0.22)

0.34

Industry Mix, Female (#)

Unemployment (%) Earnings of Females ($lOO/yr)

Schooling (yrs.)

Heads of Families (7,)

t

(0.13) 10.22**

Dependent Variable LXMW25-64: Mean Standard Deviation Constant Term Standard Error of Estimate Number of Observations

78.2

78.2

5.5

5.5

86.3

89.0

2.7

2.7

100 0.79

Notes on following page.

t

100 0.79

786

APPENDIX B

Table B-7 (continued)

Definition of Variables:

LXMW25 64' Percentage Of never-married women 25-64 in the total — — p o p u l a t i o n (including inmates) who were in the labor force during the Census Week.

Unemployment: percentage of the civilian labor force unemployed during the Census Week. Earnings of Females; median income in 1959 of females who worked 50-52 weeks that year. Industry Mix, Female: a measure of the percentage of jobs in each SMSA which we might expect to be held by women; based on the industry mix of the SMSA. Supply of Females: percentage of the civilian population 14+ who were females. Other Income: mean income from nonemployment sources in 1959 per recipient of any kind of income. Schooling:

Color; Age:

median years of school completed by all females aged 25 and over.

percent of never-married women 14+ who were nonwhite. percentage of never-married women 25-64 who were 55-64.

Heads of Families: percent of never-married women 14+ who were heads of families. Inmates of Institutions: percent of females 14+ not married with husband present who were inmates of institutions. Migration:

net migration (+ = in, - = out) between 1955 and the 1960 Census Week of all females aged 30 to 54, divided by the total population of that group in the 1960 Census Week. (Members and former members of the armed forces and inmates of institutions are included in both numerator and denominator.)

a/ — Includes only those variables significant at the 10 percent level (or better) in Regression I.

787

INTERCITY REGRESSIONS

Appendix Table B-8 Multiple Regression Equations Used to Explain Intercity Differences in Labor Force Participation Rates of Divorced Women 14+ Census Week of 1960 Independent Variables

b

a/ Regression II— b (s) t

Regression I (s) t

-0.68 (0.19) 3.61

Unemployment (%) Earnings of Females ($100/yr.)

**

0,.28 (0.08) 3,.62

Industry Mix, Female (#)

0,.14 (0.13) 1,.06

Supply of Females (%)

0,.19 (0.28) 0..67

Other Income ($100/yr.)

-1,.88 (0.44) 4..27**

-0,.82 (0.18) 4.65 **

0,.23 (0.07) 3..17

**

-1..75 (0.43) 4..08

**

•k

Schooling (yrs.)

0,.95 (0.38) 2,.53

Color (% nonwhite)

0,.03 (0.03) 1,.03

Age (% 65 and over)

-0..45 (0.16) 2,.81

Heads of Families (%) Inmates of Institutions (%)

**

**

-0..48 (0.08) 5..84

**

-0..54 (0.15) 3,.59

**

-0..52 (0.08) 6..46

•fc*

•k*

-0,.54 (0.13) 4..25

*

Migration (#)

1..06 (0.37) 2..84

0,.12 (0.05) 2,.30

-0,.54 (0.12) 4.45

*

0,.11 (0.05) 2,.05

Other Data Dependent Variable 1^ν^+Mean

72.2

Standard Deviation Constant Term Standard Error of Estimate Number of Observations R2

72.2

3.9

3.9

73.4

91.0

2.3 100

2.3 100

0.69

0.68

Definition of Variables: ——— Color: Age:

percentage of divorced women 14 and over in the population who were in the labor force during the Census Week. percentage of all divorced women 14+ who were nonwhite.

percentage of all divorced women 14+ who were 65+.

Heads of Families: percentage of all divorced women 14+ who were heads of families. All other definitions are the same as in Appendix Table B-7. a/ — Includes only those variables significant at the 10 percent level (or better) in Regression I 0

788

APPENDIX B

Appendix Table B-9 Multiple Regression Equations Used to Explain Intercity Differences in Labor Force Participation Rates of Separated Women and Married Women, Husband Absent, 14 and Over, Census Week of 1960 -

— Variables A

R

e

g

r

e

s

s

i b

o (s)

n

n I t

·

b

Unemployment (%)

-0.,44 (0.28) 1..55

Earnings of Females ($100/yr.)

-0,.08 (0.12) 0,.63

—

(s)

T T

— t

-0. .48 (0.28) 1..75+

**

Industry Mix, Female (#)

0.,66 (0.19) 3.42

Supply of Females (%)

**

0. .87 (0.15) 5..75

0. .66 (0.45) 1..45 •k

Other Income ($100/yr.)

-1. .44 (0.57) 2,.52 **

Schooling (yrs.)

1. .77 (0.55) 3,.18

Color (% nonwhite)

**

-1. .63 (0.55) 2..97

ick

1,.47 (0.47) 3. .11

-0,.01 (0.03) 0. .23

Marital Status (% separated)

**

0. .55 (0.07) 8,.23 **

Heads of Families (%)

-0. .26 (0.09) 2,.93

Inmates of Institutions (%)

0. .51 (0.04)11.46** **

-0. .23 (0.08) 2..81

ick

.97 (0.20) 4..94 -0.

ick

-1,.03 (0.19) 5. .39

**

Migration (#)

.22 (0.07) 3,.06 0,

**

0. .23 (0=07) 3..30

Other Data Dependent Variable Lg Mean

· 48.8

Standard Deviation Constant Term Standard Error of Estimate Number of Observations 2

48.8

7.1

7.1

-33.0

-2.6

3.3 100

3.3 100

0.80

0.80

Definition of Variables: L —

·

percentage of separated women and married women with husband absent, 14 and over, who were in the labor force during the Census Week.

Color;

percentage of all married women, 14+, with husband absent who were nonwhite.

Marital Status: percentage of all married women with husband absent who were separated. Heads of Families: percentage of all married women with husband absent who were heads of families. All other definitions are the same as in Appendix Table B-7. a/ —Includes only those variables significant at the 10 percent level (or better) in Regression I, except that Unemployment was included to see if it would become significant when Supply of Females (which had the wrong sign in the first run) was dropped.

789

INTERCITY REGRESSIONS

Appendix Table ΒΊΟ Multiple Regression Equations Used to Explain Intercity Differences in Labor Force Participation Rates of Married Women» Husband Present, 14-54 Census Week of 1960

Independent Variables

a/ Regression I— b t (S)

Unemployment (7»)

-0.94 (0.20) 4.71

Inccxne of Husbands ($100/yr.)

-0.20 (0.10) 1.97+

Earnings of Females ($100/yr.)

0.47 (0.14) 3.46** ** -1.40 (0.53) 2.65

**

Other Income ($100/yr.)

0.73 (0.41) 1.78+

Schooling (yrs.) Wages of Domestics ($100/yr.)

**

-0.75 (0.25) 3.04

**

Industry Mix, Female (#)

0.91 (0.16) 5.87

*

Supply of Females (%)

-0.64 (0.30) 2.10

Color (X nonwhite)

0.08 (0.04) 1.89+ ** -0.55 (0.11) 4.96

Children Under 6 (%)

*

Migration (#)

0.12 (0.06) 2.05

Other Data Dependent Variable

54*

Mean Standard Deviation Constant Term Standard Error of Estimate Number of Observations R2

Notes on following page.

34.1 4.1 61.3 2.4 100 0.69

790

APPENDIX B

Table B-IO (continued)

Definition of Variables: percentage of all married women, husband present, 14-54, in the total population, who were in the labor force during the Census Week.

LfciW14

•••

Unemployment: percentage of the civilian labor force unemployed during the Census Week. Income of Husbands: present.

median income in 1959 of all men married with wife

Earnings of Females: median income in 1959 of all females who worked 50-52 weeks that year. Other Income: mean income from nonemployment sources in 1959 per recipient of any kind of income. Schooling:

median years of school completed by all females aged 25 years and older.

Wages of Domestics: estimate of annual earnings obtained by calculating median weekly earnings in 1959 of female private household workers (living out) and multiplying this figure by 52. Industry Mix, Female: a measure of the percentage of jobs in each SMSA which we might expect to be held by women; based on the industry mix of the SMSA. Supply of Females: percentage of the total civilian population aged 14 years and older who were females. Color:

percentage of all married women who were nonwhite.

Children Under 6: percent of married couples with one child or more under 6 years of age. Migration:

net migration (+ = in, - = out) between 1955 and the 1960 Census Week of all females aged 30 to 54 years, divided by the total population of that group in the 1960 Census Week. (Members and former members of the armed forces and inmates of institutions are included in both numerator and denominator.)

a/ — Therei is only one regression for this group because all of the variables included in the first regression were significant.

791

INTERCITY REGRESSIONS

Appendix Table B-Il Multiple Regression Equations Used to Explain Intercity Differences in Labor Force Participation Rates of harried Women, Husband Present, 14+ with Children Under 6 Census Week of 1960

Independent Variables

b

a/ Regression I— (s) t **

Unemployment (¾)

-0.51

(0.17)

2.98

Income of Husbands ($100/yr.)

-0.44

(0.09)

5.14

Earnings of Females($100/yr.)

0.40

(0.12)

3.48

-0.81

(0.39)

2.07

1.05

(0.34)

3.08

-0.86

(0.21)

3.99

0.54

(0.13)

4.06

-0.68

(0.25)

2.75

0.18

(0.04)

** 5.16

-5.86

(2.26)

2.59

0.14

(0.05)

3.16

** **

Other Income ($100/yr.) Schooling (yrs.)

* ** *•

Wages of Domestics ($100/yr.)

**

Industry Mix, Female (#)

**

Supply of Females (%) Color (% nonwhite) Children Under 18 (#)

* **

Misration (#)

Other Data Dependent Variable I^14+(c (S) t

Reeresslon I (S) t b

Unemployment (%)

-0.77 (0.19) 4.01**

-0,,94 (0.16) 5.89**

Earnings of Males ($100/yr.)

-0.13 (0.08) 1.71+

-0,.20 (0.05) 3.78**

Industry Mix, Male (#)

-0.27 (0.15) 1.78+

Occupational Mix, Male (7.)

0.04 (0.11) 0.36

Supply of Older Males (7.)

-0.13 (0.15) 0.87

Other Income ($100/yr.)

0.10 (0.58) 0.17

Schooling (yrs.)

2.42 (0.76) 3.19**

Color (% nonwhlte)

-0.01 (0.04) 0.20

Age (7. 75 and oyer)

-0.27 (0.11) 2.42

* **

-0.29 (0.06) 5.16

Migration (#)

**

2,.70 (0.60) 4.53

**

-0. .31

(0.10) 3.02

-0, .29

(0.04) 7.54

**

Other Data Dependent Variable Mean

22.1

Standard Deviation Constsnt Term Standard Error of Estimate

22.1

3.4

3.4

39.9

25.8

2.2

Number of Observations

100

R2

2.2 100

0.62

0.59

Definition of Variables: ^SM65+:

a/

Percentage of single— males aged 65 and over in the civilian, noninstitutional population who were in the labor force during the Census Week.

All other definitions are the same as in Appendix Table B-21. a/ — "Single" here includes all men except those who are married with spouse present. —^Includes those variables significant at the 10 percent level (or better) in Regression I, except Male Industry Mix, which was dropped because it had the wrong sign (see text).

814

APPENDIX B

Appendix Table B-30 Multiple Regression Equations Used to Explain Intercity Differences In Labor Force Participation Rates of Males 16-17, Enrolled in School Census Ueek of 1960

Independent Variables

Industry Mix, Male (#) Supply of Teenage Males (7.)

-1,.81 (0.33) 5.55

-1.

(0.29) 6..17**

.32 (0.12) 2,.64*

0..48 (0.15) 3.16'

0,

0..30 (0.18) 1.68'

0,

.42 (0.16) 2..60*

-0.47 (0.36) 1.33

Earnings of Teenage Males ($l/wk.) -0,.97 (0.22) 4.47' Family Income ($l00/yr.)

j/ Regression II— b (s) t OC .-r

Unemployment (%) Industry Mix, Teenage Male (%)

Regression I b (s) t

-0.73 (0.19) 3.83**

0, .08 (0.10) 0.87 3,.38 (0.58) 5.84'

3.72 (0.49) 7.63**

Color (X nonuhite)

-0,.30 (0.04) 6.83'

-0.32 (0.04) 7.44**

Marital Status (% married)

-0..19 (1.00) 0.19

Inmates of Institutions (%)

-0.50 (0.35) 1.44

Schooling (yrs.)

Other Data Dependent Variable I^16.17BS: Mean Standard Deviation Constant Term Standard Error of Estimate Number of Observations R2

31.3

31.3

6.6

6.6

8.6

-9.0

3.7 100 0.71

3.8 100 0.69

—^Includes only those variables significant at the 10% level (or better) In Regression I. Notes continued on following page.

INTERCITY REGRESSIONS

815

Table B-30 (continued)

Definition

of Variables:

ljMie-UESt

••

PeccentaSe of males aged 16 to 17 years, enrolled In school, In the civilian population «ho were in the civilian labor force during the Census Week.

Unemployment; percentage of the civilian labor force unemployed during the Census Week. Industry Mi». Teenage Male: percentage of all employed persons 14 and over who were employed in agriculture and retail trade industries during the Census Week. Industry Mix. Male: a measure of the percentage of jobs In each SMSA which we might expect to be held by men; based on the industry mix of the SMSA during the Census Week. Supply of Teenage Males: percentage of the male, civilian, nonlnstitutional population aged 14 and over that was 14 to 19 during the Census Week. Earnings of Tfeenage Males: an estimate of the weekly earnings of males aged 14 to 19; based on their yearly income and mean number of weeks worked during 1959. Family Income: Schooling:

Color:

median family income in 1959 in each SMSA.

median nunber of years of school completed by all persons (male and female) aged 25 and over.

percentage of all males aged 14 to 19 who were nonwhlte.

Marital Status: percentage of all males aged 16 to 17 In the civilian, nonlnstitutional population who were married with wife present.

Inmates of Institutions: percentage of all males aged 14 to 19 in the civilian population who were inmates of institutions during the Census Week.

Appendix Table B-31

* -0.99 (0.49) 2.01 0.54 (0.21) 2.51*

-0.88 (0.52) 1.70+ 0.58 (0.22) 2.61*

Unemployment ('%.)

t

bl

111-

0.46 (0.12) 3.70** 1.91 (0.81) 2.36* -0.18 (0.07) 2.72 ** 0.57 (0.20) 2.82**

0.42 (0.12) 3.36** 2.33 (0.82) 2.84** -0.18 (0.07) 2.76** 0.44 (0.21) 2.10*

-0.14 (0.07) 1.89+ 0.43 (0.22) 1.91+ -0.85 (0.50) 1.71+ 0.09 (0.08) 1.11 0.25 (0.14) 1.79+

Marital Status ('%. married)

College Enrollment Ratio ('%.)

Student-Teacher Ratio (#)

Notes on following page.

0.19 (0.13) 1.52

44.5 8.2 19.3 5.3 100 0.65

0.64

100

44.5 8.2 13.9 5.2

44.5 8.2 14.5 5.3 100 0.62

_______________ :~!~~_!~!~~~_~!~! ** _________ :~!~~_!~!~~~_~!~~ ** _________ :~!~§_!~!~~~_~!~~ ** ______ _

Dependent Variable ~18-l9ES: Mean Standard Deviation Constant Term Standard Error of Estimate Number of Observations R2

Other Data

£~!!~8~_~~~~!~~~~_~~~!~_!;~

Inmates of Institutions ('%.)

Color ('%.nonwhite)

Schooling (yrs.)

-0.76 (0.49) 1.57

-0.73 (0.29) 2.53*

Earnings of Teenage Males ($l/wk.) Family Income ($lOO/yr.)

-0.82 (0.30) 2.75 **

-0.02 (0.29) 0.06

(s)

-0.87 (0.50) 1.74+ 0.60 (0.21) 2.79**

b

~ression

-0.40 (0.52) 0.77 -0.94 (0.31) 3.01 ** 0.35 (0.15) 2.28* 2.40 (0.86) 2.78**

Supply of Teenage Males ('%.)

Industry Mix, Teenage Male ('%.) Industry Mix, Male (#)

al Resression 11b (s) t

Re!!res-"ton I (u) t

b

Independent Variables

Multiple Regression Equations Used to Explain Intercity Differences in Labor Force Participation Rates of Males 18-19, Enrolled in School Census Week of 1960

0 '\

00

INTERCITY REGRESSIONS

817

Table B-31 (continued) Definition

of Variables;

1^118 jggg! "

percentage of males aged 18 to 19 years, enrolled in school, in the civilian population who were in the civilian labor force during the Census Week.

Marital Status; percentage of all males aged 18 to 19 in the civilian, noninstitutional population who were married with wife present during the Census Week. College Enrollment Ratio: the number of persons enrolled in college in each SMSA as a percentage of all persons aged 18 to 34 enrolled in school· Student-Teacher Ratio: ratio of total college enrollment to the number of college presidents, professors, and instructors. College Dormitory Ratio: percentage of all persons enrolled in college who were residing in college dormitories during the Census Week.

All other definitions are the same as in Appendix Table 8-30.

—^Includes only those variables significant at the 10% level (or better) in Regression I. —^Includes only those variables significant at the 10¾ level (or better) in Regression II.

818

APPENDIX B

Appendix Table B-32 MulCiple Regression Equations Used to Explain Intercity Differences in Labor Force Participation Rates of Males 20-24, Enrolled in School Census Week of 1960

Independent Variables

b

Regression I (s) t

a/ Regression II— b (a) t

Unemployment (%)

0.88 (0.63) 1.37

Industry Mix, Teenage Male (7.)

0.43 (0.26) 1.67+

0.45 (0.20) 2.27*

Industry Mix, Male (#)

0.51 (0.36) 1.44

0.84 (0.27) 3.14** Ά^ -0.87 (0.32) 2.73

Earnings of Teenage Males ($l/wk.)-0.85 (0.34) 2.53 Family Income ($100/yr.)

0.22 (0.17) 1.30

Schooling (yrs.) Color (¾ nonuhite)

3.26 (1.14) 2.86**

Marital Status (¾ married)

0.38 (0.14) 2.77**

Migration (#)

0.05 (0.05) 0.88

College Enrollment Ratio (7.)

0.29 (0.09) 3.14**

Student-Teacher Ratio (#)

0.24 (0.15) 1.57

College Dormitory Ratio (7.)

4.32 (0.78) 5.56**

-0.03 (0.09) 0.36

-0.27 (0.06) 4.34**

0.23 (0.10) 2.18*

0.34 (0.07) 4.68** 0.30 (0.15) 2.02* -0.31 (0.06) 5.56**

Other Data Dependent Variable Im20^4es = Mean Standard Deviation Constant Term Staodard Error of Estimate Number of Observations R2

43.2

43.2

9.5

9.5

-61.9

-73.6

5.9 100 0.66

5.9 100 0.65

— Due to the unusually large number of variables with t-values between 1.0 and 1.6 in Regression 1, we have not followed our usual practice of dropping all nonsignificant variables from Regression II. (Adherence to this rule here would result in a large drop in the R^ ard the omission of several important predictors·) Rather9 we have dropped the four nonsignificant variables with lowest t-values (Color» Migration» Unemployment, and Family Income); as a result, the two remaining variables that were shy of significance in Regression I (Male Industry Mix and the Student-Teacher Ratio) are highly significant in Regression II. Notes continued on following page.

INTERCITY REGRESSIONS

819

Table B-32 (continued)

Definition of Variables: Hl20-24ES: ————

Color:

Percentage of males aged 20-24 years, enrolled in school,

in the civilian, noninstitutional population who were in the civilian labor force, but workins less than forty hours per week, during the Census Week.

percentage of all males aged 20 to 24 who were nonwhite.

Marital Status: percentage of all males aged 20 to 24 in the civilian, noninstitutional population who were married with wife present.

Migration:

net migration (+ * Census Week of all population of that and former members tions are included

in, - « out) between 1955 and the 1960 males aged 20 to 24, divided by the total group in the 1960 Census Week. (Members of the armed forces and inmates of institu­ in both numerator and denominator.)

College Enrollment Ratio: the number of persons enrolled in college in each SMSA as a percentage of all persons a^ed 18 to 34 enrolled in school. Student-Teacher Ratio: ratio of total college enrollment to the number of college presidents, professors, and instructors. College Dormitory Ratio: percentage of all persons enrolled in college who were residing in college dormitories during the census week.

All other definitions are the same as in Appendix Table B-30.

820

APPENDIX B

Appendix Table B-33 Multiple Regression Equations Used to Explain Intercity Differences in Labor Force Participation Kates of Males 16-17, Not Enrolled in School Census Heek of 1960

Independent Variables

Regression Il~/ b (s) t

Regression I b (s)

Unemployment (%)

-3.43 (0.53) 6.43

Industry Mix, Teenage Male (%) Indus try Mix, Male (II)

1.00 (0.23) 4.27 -0.57 (0.28)

*I
er of Observations R2

8.0

171.7

139.5

4.9 100 0.65

Definition

57.0

8.0

5.5 100 0.55

of variables:

^XMWie 19 : —

percentage of never-married females aged 18 to 19 in the civilian, noninstitutional population who were in the civilian labor force during the Census Week.

All other definitions are the same as in Appendix Table B-36. e/ — Includes all those variables in Regression I significant at the 107. level (or better) except Female Industry Mix which, while statistically significant, has the wrong sign.

827

INTERCITY REGRESSIONS Appendix Table B-38 Multiple Regression Equations Used to Explain Intercity Differences in Labor Force Participation Rates of Never-Married Females 20-24 Census Week of 1960

Independent Variables

a/ Regression II— b (s) t

Regression I b (s) t

Unemployment (%)

-0.17 (0.25) 0..68

Industry Mix, Female (#)

-0.42 (0.22) 1 .91+

Supply of Females (%)

-0.66 (0.42) 1..59

Earnings of Females ($100/yr.)

0.14 (0.12) 1,.10

Heads of Families (%)

1.84 (0.59) 3..12

**

1..16 (0.56) 2.08* **

Schooling (yrs.)

-1.17 (0.16) 7..30 Λ^** 2.39 (0.59) 4..06

-1,.25 (0.16) 8.07 •k* 2..56 (0.53) 4.81

Color (% nonuhlte)

-0.23 (0.04) 5.22

-0..28 (0.05) 6.15 irt 0,.32 (0.09) 3.52

Enrollment (% enrolled)

**

Marital Status (7. single)

0.32 (0.09) 3.66**

Age (Χ 20 or 21)

-0.34 (0.14) 2.46*

Inmates of Institutions (%)

-0.24 (0.18) 1.35

-0,.35 (0.13) 2.73

Other Data Dependent Variable 1^20-241 Mean

75.9

Standard Deviation Constant Term Standard Error of Estimate Number of Observations

6.3

112.1

69.4

3.3 100

R2

0.76

Definition

75.9

6.3

3*6 100 0.70

of Variables:

XMW20-24"

L

percentage of never-married females aged 20-24 in the total population who were in the labor force during the Census Week.

Supply of Females: percentage of the civilian population aged 14 and older which was female. Heads of F**"* lies: percentage of all single women aged 14 and older who were heads of families during the Census Week. Enrollment:

percentage of all females aged 20 to 24 who were enrolled in school during the Census Week.

Marital Statua: Age:

percentage of all females aged 20 to 24 who were single.

percentage of single women aged 20 to 24 who were aged 20 to 21.

Inmates of Institutions: percentage of all females aged 14 and over not married with husband present, who were inmates of institutions. All other definitions are the same as in Appendix Table B-36.

-^Includes all variables In Regression I significant at the 10% level (or better) except Female Industry Mix9 which» while statistically significant» has the wrong sign.

828

APPENDIX B

Appendix Table 8-39 Multiple Regression Equations Used to Explain Intercity Differences in School Enrollment of Males 16-17 Census Week of 1960

Independent Variables

b

Re!lression 1 (s) t

Unemployment (7.) Industry Mix, Teenage Male (7.) Industry Mix, Male (#)

0.95 (0.26) 3.60** -0.06 (0.12) 0.52 0.35 (0.14) 2.47 *

Supply of Teenage Males (7.) Earnings of Teenage Males ($l/wk.)

-0.37 (0.29) 1.29

Family Income ($lOO/yr.) Schooling (yrs.) Color (7. nonwhite) Marital Status (7. married) Ilbates of Institutions (7.)

b

Re!lression ll!./ (8) t

0.96 (0.22) 4.40** 0.34 (0.13) 2.53 *

-0.24 (0.17) 1.40 0.10 (0.08) 1.30 3.05 (0.47) 6.53 **

0.14 (0.05) 2.72** 2.81 (0.40) 7.01 **

0.01 (0.04) 0.22 -0.20 (0.81) 0.24 -0.09 (0.28) 0.32

Other Data Dependent Variable ESM16_l7: Mean

84.1

84.1

Standard Deviation Constant Term

4.3 29.2

17.3

Standard Error of Estimate Number of Observations R2

Definition

4.3

3.0 100

3.0 100

0.56

0.54

of Variables: percentage of all civilian males aged 16 to 17 who were enrolled in school during the Census Week.

All other definitions are the same as in Appendix Table 8-30. a/ - Family Income is retained in Regression II despite its nODsignificance in RegressiOD I because its omission causes a large drop in the R2 1 the same cannot be said of any other variable that was not significant in Regression I. All other variables included in Regression 11 are those significant at the 107. level (or better) in Regression 1.

829

INTERCITY REGRESSIONS

Appendix Table 8-40 Multiple Regression Equations Used to Explain Intercity Differences in School Enrollment of Males 18-19 Census Week of 1960

Independent Variables

Resression I b (s) 1.73 (0.43) 4.05 **

Unemployment (%) Industry Mix, Teer.age Male (%) Industry Mix, Male (I.') Supply of Teennge

Mal~s

(%)

Earnings of Teenage Males ($l/wk.)

Resression II!/ b

(0)

1.51 (0.35) 4.29 **

-0.21 (0.18) 1.18 -0.28 (0.22) 1.24 1.86 (0.43) 4.33 **

1.50 (0.37) 4.03 **

0.86 (0.24) 3.53 **

0.91 (0.23) 4.02 **

Family Income ($lOO/yr.)

0.07 (0.12) 0.58

Schuoling (yrs.)

4.87 (0.70) 6.94 **

Color (% nonwhite)

-0.04 (0.05) 0.82

Marital Status (% married) College Dormitory Ratio (%)

-0.53 (0.18) 2.99 ** 0.23 (0.04) 5.57 **

5.06 (0.55) 9.25 ** -0.70 (0.15) 4.76 ** 0.24 (0.04) 6.22 **

Other Data Dependent Variable ESM18_19: Mean Standard Deviation Constant Term Standard Error of Estimate Number of Observations R2

Definition

51.8

51.8

7.3

7.3

-42.0

-57.7 4.4

4.4 100

100

0.68

0.66

of Variables: percentage of all civilian males aged 18 to 19 who were enrolled in school during the Census Week.

Marital Status: percentage of all males aged 18 to 19 in the civilian, noninstitutional population who were married with wife present. Col lese Dormitory Ratio: percentage of all persons enrolled in college who were residing in college dcrmitories during the Census Week. All other definitions are the same as in Appendix Table 8-30. !/Inc1udes only those variables significant at the 10% level (or better) in Regression 1.

830

APPENDIX B

Appendix Table B-41 Multiple Regression EquationsUsed to Explain Intercity Differences in School Enrollment of Males 20-24 Census Week of 1960

Independent Variables

b

Unemployment (%)

a/ ReRression I— (s) t

1.01 (0.42) 2.39

Industry Mix, Teenage Male (%)

-0.44 (0.17) 2.56*

Industry Mix, Male (#)

-0.44 (0.21) 2.03*

Supply of Teenage Males (%)

1.80 (0.41) 4.38 ,

**

Earnings of Teenage Males ($l/wk.) 0.99 (0.23) 4.29 Schooling (yrs.)

2.33 (0.69) 3.37

Color (¾ ncnwhite)

-0.26 (0.06) 4.62**

Marital Status (7» married)

-0.31 (0.09) 3.64**

Migration (#)

0.21 (0.04) 5.32**

College Dormitory flatlo (%)

0.17 (0.04) 4.17

**

Other Data Dependent Variable ES^_. _. t Mean Standard Deviation Constant Term Standard Error of Estimate Number of Observations R2

Definition

8.2 -10.3 4.2 100 0.76

of Variables:

ESM2Q_24: —— Color;

22.6

percentage of all civilian males aged 20 to 24 who were enrolled in school during the Census Week.

percentage of all males aged 20 to 24 who were nonwhite.

Marital Status: percentage of all males aged 20 to 24 in the civilian, noninstitutional population who were married with wife present. Migration: net migration (+ • in, - = out) between 1955 and Census Week 1960 of all males aged 20 to 24, divided by the total population of that group in the 1960 Census Week. (Members and former members of the armed forces and inmates of institutions are included in both numerator and denominator.) College Dormitory fratio: percentage of all persons enrolled in college viho were residing in college dormitories during the Census VJcek. All other definitions are the same as in Appendix Table B-30.

a/ !Io second run needed, since all variables in Regression I are statistically significant at the 10% level (or better).

831

INTERCITY REGRESSIONS

Appendix Table B-42 Multiple Regression Equations Used to Explain Intercity Differences in the Activity Rates of Males 16-17 Census Week of 1960

Independent Variables

a/ Regression II— b (s) t

Regression I b (s) t

Unemployment (%) Industry Mix, Teenage Male (7e) Industry Mix, Male (#)

-0.22

(o.ie)

1..44

0.19 (0.07) 2..55*

*

0.18 (0.07) 2..63

0.02 (0.09) 0..23

Supply of Teenage Males (%)

-0.37 (0.17) 2,.16*

-0.38 (0.17) 2,.25*

Earnings of Teenage Males ($l/wk.)

-0.29 (0.10) 2,.76**

-0.26 (0.10) 2,,65*

Family Income ($100/yr.)

**

1.41 (0.28) 5.08

Color (% nonwhite)

O Ot

Marital Status (7« married)

-0.12 (0.48) 0,.25

Inmates of Institutions (7»)

I O

Schooling (yrs.)

0.06 (0.05) 1..40

*

0.09 (0.04) 2,.23 ** 1.47 (0.26) 5,.60

(0.02) 1,.26

(0.17) 3.95**

**

-0.61 (0.16) 3,.88

Other Data Dependent Variable Mean

93.6

Standard Deviation Constant Term Standard Error of Estimate Number of Observations R2

2.5

85.0

82.3

1.8 100 0.54

Definition AR^ig —

93.6

2.5

1.8 100 0.52

of Variables: percentage of all males aged 16 to 17 in the civilian population who were either enrolled in school or in the civilian labor force during the Census Ueek.

All other definitions are the same as in Appendix Table 6-30. a/ — Includes all variables significant at the 10% level (or better) in Regression I plus Family Income. This variable was retained in Regression II despite its nonsignificance at the 107» level in Regression I. Dropping Marital Status, Male Industry Mix» and Color raised its t-value from 1.40 to 2.23. If Family Income is not included in the final run, the R^ falls from 0.52 to 0.50, and the regression coefficients for Earnings of Teenage Males and Teenage Male Industry Mix are substantially reduced in size, while the coefficient for Supply of Teenage Males is substantially increased.

832

APPENDIX B

Appendix Table B-43 Multiple Regression Equations Used to Explain Intercity Differences in the Activity Rates of Males 18-19 Census Week of 1960

Independent Variables

b

Unemployment (%)

a/ Regression II— b (s) t

Regression I (s) t **

-0.69 (0.20) 3.47

Industry Mix, Teenage Male (X)

0.29 (0.08) 3.45

Industry Mix, Male (P)

0..05 (0.11) 0.46

Supply of Teenage Males (%)

**

-0.45 (0.16) 2.81 **

**

0.28 (0.08) 3.40

-0.36 (0.20) 1.77+

-0.35 (0.20) 1.77+

Earnings of Teenage Males ($l/wk.) -0.25 (0.12) 2.11*

-0.26 (0.12) 2.22* **

Family Income ($100/yr.) Schooling (yrs.)

0. .13 (0.05) 2.40* **

1.15 (0.33) 3.49

Color (% nonwhlte)

-0.,03 (0.03) 1.27

Marital Status (% married)

-0.,15 (0.08) 1.73+

College Dormitory Ratio (X)

0.,03 (0.02) 1.68+

Inmates of Institutions (%)

0.18 (0.05) 4.08 **

1.05 (0.32) 3.34

*

0.05 (0.02) 2.65 **

**

-0..94 (0.19) 4.89

-0.90 (0.19) 4.77

Other Data Dependent Variable Mean

92,,5

Standard Deviation Constant Term Standard Error of Estimate Number of Observations R2

3.0

80, .8

79.4

2,.0 100 0.60

Definition AS^ig

92.5

3,,0

2.1 100 0.58

of Variables: percentage of all males aged 18 to 19 in the civilian population who were either enrolled in school or in the civilian labor force during the Census Week·

Marital Status: percentage of all males aged 18 to 19 in the civilian» noninstitutional population who were married with wife present. College Dormitory Ratio: percentage of all persons enrolled in college who were residing in dormitories during the Census Week. All other definitions are the same as in Appendix Table B-30. ^Includes all variables in Regression I significant at the 10% level (or b except Marital Status, which was dropped because it had the wrong sign.

833

INTERCITY REGRESSIONS Appendix Table B-44 Multiple Regression Equations Used to Explain Intercity Differences in the Activity Rates of Males 20-24 Census Week of 1960

Indapendent Varlables

a/ Regression XIb (s) t

Regression I b (s) t **

Unemploynent (7.)

-0.32 (0.10) 3.17

Industry Hlxt Teenage Male (Z)

0.02 (0.04) 0.59

Industry Mix, Male (#)

0.13 (0.05) 2.52

Supply of Teenage Males (X)

* **

-0.28 (0.10) 2.86

**

-0.33 (0.08) 4,.42

*

0.12 (0.05) 2.57

*

-0.20 (0.07) 2.32

Earnings of Teenage Males ($l/wk.) -0.08 (0.06) 1.54 **

Schooling (yrs.)

0.73 (0.17) 4.39

*

Color (% nonwhite)

-0.03 (0.01) 2.56

Marital Status (% married)

-0.00 (0.02) 0.02

**

0.66 (0.16) 4,.15

irk

-0.04 (0.01) 3.02

*·

Migration (#)

0.02 (0.01) 2.22

College Dormitory Ratio (7.)

0.01 (0.01) 1.32

**

0.02 (0.01) 2.68

Other Data Dependent Variable

AR 0-24: M2

Mean

96.1

Standard Deviation Constant Term Standard Error of Estimate Number of Observations R2

96.1

l.S

1.5

86.8

85.6

1.0 100 0.61

1.0 100 0.59

Definition of Variables:

ARm2q ~

Color:

24:

percentage of all males aged 20 to 24 in the civilian, noninstitutional population who were either enrolled in school or in the civilian labor force during the Census Week.

percentage of all males aged 20 to 24 who were nonwhite.

Marital Status: percentage of all males aged 20 to 24 in the civilian, noninstitutional population who were married with wife present. Migration: net migration (+ * in, - * out) between 1955 and the 1960 Census Week of all males aged 20 to 24, divided by the total population of that group in the 1960 Census Week. (Members and former members of the armed forces and inmates of institutions are included in both numerator and denominator.) College Dormitory Ratio: percentage of all persons enrolled in college who were residing in college dormitories during the Census Week. All other definitions are the same as in Appendix Table B-30. —''includes only those variables significant at the 10% level (or better) in Regression I.

0.06

Earnings ($lOO/yr.)

0.06 0.22

-0.62~/ (0.18) -0.07 -0.02

2.79** 3.05** 2.23* 3.01 **

0.51

78

1.1

89.2

1.5

94.1

(0.02)

(0.18)

(0.06)

(0.05)

(0.08)

-0.24

5.41** 4.23**

1950 (s)

b

t

1.45

0.41

3.48** 0.01

-0.22

0.33

92

0.9

92.4

1.0

95.1

(0.01)

(0.17)

0.1l~/ (0.07) -O.O~/ (0.02)

3.82**

(0.03)

(0.04)

1940 (s)

0.09

-0.02

b

1.08

2.93**

t

In cases of minor differences, this notation has been omitted.

Notes continued on following page.

definition of the variable.

~/Coefficient not entirely comparable with those for other years because of a difference in the

0.61

100

0.7

Number of Observations R2

Standard Error of Estimate

1.2

96.4

(0.01)

(0 .11)

81.9

-0.03

0.24

Constant Term

Standard Deviation

Mean

Dependent Variable \t25-54:

~~

Color (% nonwhite)

Schooling (yrs.)

(0.02)

(0.04)

(0.06)

1960 (s)

-0.2rj}./ (0.09)

0.18

Other Income ($lOO/yr.)

-0.31

Industry Mix, Male (iF)

b

Unemployment (%)

Independent Variables

Multiple Regression Equations Used to Explain Intercity Differences in Labor Force Participation Rates of Males 25-54 Census Weeks of 1960, 1950, 1940

Appendix Table 8-100

0.68

1.34

3.11**

1.50

2.49*

0.43

INTERCITY REGRESSIONS

835

Table B-IOO (continued)

Definition of Variables: Ηΐ25-54: "

PercentaSe of males aged 25 to 54 years in the civilian, noninstitutional population who were in the civilian labor force during the Census Week.

Unemployment: percentage of the civilian labor force unemployed during the Census Week, including persons on public emergency work in 1940. Industry Mix, Male: a measure of the percentage of jobs in each SMSA which we might expect to be held by men; based on the industry mix of the SMSA. Earnings:

1960 and 1950 -- median income in preceding year of all males who worked 50 to 52 weeks; 1940 -- median wage-or-salary income in 1939 of all males in the labor force who received at least $100 of such income.

Other Income: 1960 -- mean income from nonemployment sources in 1959 per recipient of any kind of income; 1950 -- median income in 1949 of all persons 14 years of age and over with income from nonemployment sources only; 1940 — percentage of all families who received some income from nonemployment sources in 1939. Schooling;

Color:

median number of years of school completed by all males aged 25 years and older.

1960 -- percentage of all persons in households who were nonwhite; 1950 and 1940 -- percentage of all males aged 21 years and over who were nonwhite.

Appendix Table 8-101

Feu~le (H)

(s)

1950 t

(s)

1940 t

0.13 (0.05) 2.56*

0.94 (0.16) 5.97 ~ -1.00 (0.38) 2.64**

0.25 (0.04) 5.89**

0.18 (0.46) 0.40

1.52 (0.32) 4.79** 0.82 (0.13) 6.21 ** -1.04 (0.35) 2.95 **

0.78 (0.14) 5.72~ -0.64 (0.27) 2.37* 0.10 (0.04) 2.66**

1.16 (0.50) 2.34 *

-0.0~tO.08) 1.19

-1.0~iO.36) 2.83**

-1.46!io.32) 4.51** 1.36 (0.35) 3.87**

-1.4~io.42) 3.41**

-0.64 (0.14) 4.73 **

b

-0.65 (0.17) 3.81** 1.11 (0.22) 4.96**

-0.62 (0.18) 3.49**

b

~,HP14+:

Estiu~te

Notes continued on following page.

~/See note ~/ in Appendix Table B-IOO.

Number of Observations R2

Standard Error of

Constant Term

Standard Deviation

Mean

Dependent Variable

Other Data

2.1

0.70

0.76

78

2.2 100

49.6

4.0

22.9

40.9

3.8

31.3

0.73

92

3.1

5.6 45.4

17.9

~~~~~~~-~~~~~-~-~:~--------------:~:~~-~~:~~~-~:~~~-------:~:~~-~~:~:~-::~:~-------:~:!~:~~:!!~-!:~!-------

Color (% nonwhite)

Supply of Females (7.)

Industry Mix,

Schooling (yr".)

Other Income ($lOO/yr.)

Unemployment (X) 0.40 (0.12) 3.27**

t

Income of Husbands ($lOO/yr.)

(s)

1960

Earnings of Females ($lOO/yr.)

b

-0.84 (0.17) 4.86** -0.25 (0.09) 2.76**

Independent Variables

Multiple Regression Equation Used to Explain Intercity Differences in Labor Force Participation Rates of Married Women, Husband Present, 14+, Census Weeks of 1960, 1950, 1940

INTERCITY REGRESSIONS

837

Table B-IOl (continued)

Definition of Variables:

HlW HP14+: —7

Percentage married women with husband present aged 14+ who were in the labor force during the Census Week.

Income of Husbands: 1960 and 1950 -- median income in preceding year of all men married with wife present; 1940 -- median wageor-salary income in 1939 of all males in the labor force who received at least $100 of such income. Earnings of Females: 1960 and 1950 -- median income in preceding year or all females who worked 50-52 weeks that year; 1940 -estimated median wage-or-salary income received in 1939 by all females in the experienced civilian labor force who worked 12 months and earned at least $100 of such income. Schooling:

median years of school completed by all females aged 25 and over.

Industry Mix, Female: a measure of the percentage of jobs in each SMSA which we might expect to be held by women; based on the industry mix of the SMSA. Supply of Females: percentage of the total civilian population aged 14 and over which was female. Color:

percentage of all married women who were nonwhite.

Children Under 6: 1960 and 1950 -- percentage of married couples with one or more children under 6 years of age; 1940 -- percentage of all families with a male head which had one or more children under 10 years of age. All other definitions are the same as in Appendix Tables B-100.

838

APPENDIX B

Appendix Table B-102 Multiple Regression Equations Used to Explain Intercity Differences in Labor Force participation Rates of Males 65+

Census Weeks of 1960, 1950, 1940

1960

Independent Variables Onenployment (X) Earnings ($100/yr.) Other Income ($100/yr.)

(s)

1950 (S)

-1.47 (0.24) 6.05

-1.20 (0.35) 3.42

0.10 (0.08) 1.30

n/ ** n/ ** -2.56-'(0.51) 5.05 -2.54-(0.69) 3.70

Color (¾ nonwhite)

0,08

(0.17) 0.47

0.19^0.40) 0.47 n/

-0.10 (0.17) 0.61

-0.55 (0.42) 1.32

-0.69 (0.37) 1.86

0.27 (0.96) 0.29

0.78 (0.87) 0.90

1.46 (1.22) 1.20

-0.06 (0.05) 1.10

-0.05 (0.09) 0.57

0.18 (0.08) 2.17*

Other Data Dependent Variable Mean Standard Deviation Constant Term Standard Error of Estimate Number of Observations „2

*

-0.26-(0.10) 2.53

0.08 (0.23) 0.34

Occupational Mix, Male 0.26 (0.12) 2.16 (¾) Supply of Older Males (%) -0.18 (0.20) 0.94 Schooling (yrs.)

0.58 (0.25) 2.33*

1940 (S)

30.7

41.0

38.2

4.6

5.8

5.0

36.9

43.5

40.1

3.0

4.5

4.2

100

0.61

-^See note n/ in Appendix Table B-100.

Notes continued on following page.

78 0.45

92 0.35

INTERCITY REGRESSIONS

839

Table B-102 (continued)

Definition of Variables: ^65+:

percentage of males aged 65 and over in the noninstitutional population who were in the labor force during the Census Veek.

Occupational Mix, Male; 1960 -- percent of all employed males in farmer, manager, sales, and service occupations (including private household workers) during the Census Week; 1950 and 1940 — percent of all employed males in farmer, manager, service (including domestic service), and not reported occupations during the Census Week. Supply of Older Males: 1960 -- percent of all males aged 14+ in the total noninstitutional population who were aged 65 and over; 1950 and 1940 — percent of all males in the civilian population (Including inmates) aged 14+ who were aged 65 and over· Schooling: 1960 and 1950 -- median number of years of school completed by all males 65 to 74 years old; 1940 -- median number of years of school COTipleted by all males aged 65 to 69. Color:

1960 and 1950 -- percentage of all males aged 65 and over who were nonwhite; 1940 -- percentage of all males aged 65 and over who were Negro.

All other definitions are the same as in Appendix Table B-100.

al

2.6

0.67

0.75

78

3.2

4.9

24.9

-0.13 (0.03) 3.89** 0.41 (0.10) 3.99**

0.36 (0.14) 2.48 * -0.05 (0.13) 0.34 -1.13 (0.64) 1.77+ -0.38 (0.07) 5.46**

43.5

100

t

-0.81

0.05

-0.01

-0.79

-0.09

0.91

92

70.7 1.7

5.3

23.9

0.35 (0.06) 12.89** (0.15)

(0.04) 18.11** (0.03) 0.24

Notes continued on following page.

!l/See note !ll in Appendix Table B-IOO.

0.52

2.46*

(0.17)

(0.08)

2.04 *

0.41

t

0.19

1940 (s)

-0.03 (0.08) 0.38!l/(0.18)

b

~/Except for 1940, where all females 14 to 19 are included, with a control for Marital Status.

Number o:f Observations R2

1950 (s)

-0.85 (0.19) 4.50**

b

32.6

5.3

Constant Term Standard Error of Estimate

24.7

Standard Deviation

0.46 (0.11) 4.05 **

Mean

Dependent Variable LXMW14-l9:-

Other Data

Marital Status (% married)

Age (% 18 or 19)

Color (% nonwhite)

School Enrollment (% enrolled)

-0.21 (0.09) 2.34 * -0.24 (0.04) 6.56**

-0.51 (0.34) 1.52

Females (%)

-0.02 (0.16) 0.15

~enage

Unemployment (%)

Industry Mix, Female (#)

t

Supply of

(3)

1960

Father's Income ($lOO/yr.)

b

-0.63 (0.24) 2.63* 0.21 (0.07) 2.84**

Independent Variables

Multiple Regression Equations Used to Explain Intercity Differences in Labor Force Participation Rates of Never-Married~1 Females 14-19 Census Weeks of 1960, 1950, 1940

Appendix Table B-l03 ~

o

00

INTERCITY REGRESSIONS

841

Table B-103 (continued) Definition of Variables: ant* 1950—percentage of never-married women 14 to 19 in ^ΧΜΝ14-19: ——— the total population (including inmates) in the labor force during the Census Week; 1940—percentage of all females aged 14 to 19 in the total population (including inmates) in the labor force during the Census Week (see Marital Status control variable, defined below)·

Unemployment: percentage of the civilian labor force unemployed during the Census Week, including persons on emergency public work in 1940. Father's Income: 1960 and 1950--median income in preceding year of all men married with wife present; 1940--median wage-or-salary income in 1939 of all males in the labor force νΛιο received at least $100 of such income. Industry Mix, Female: an index of the percentage of jobs in each SMSA which we might expect to be held by women; based on the industry mix of the SMSA. Supply of Teenage Females: 1960 and 1940--percentage of females aged 14 years and over who were aged 14 to 19; 1950--percentage of all persons aged 14 years and over who were single females aged 14 to 19. School Enrollment: 1960 and 1950--number of females aged 14 to 19 who were enrolled in school as a percentage of all single females aged 14 to 19; 1940--percentage of all females aged 14 to 19 who were enrolled in school. Color;

Age:

1960--percentage of all single women aged 14 and over who were nonwhite; 1950--percentage of all females aged 14 to 19 who were nonwhite; 1940--percentage of all persons aged 15 to 19 who were Negro.

1960 and 1950--percentage of all single women aged 14 to 19 who were 18 to 19 years old; 1940--percentage of all females aged 14 to 19 who were 18 to 19 years old.

Marital Status: percentage of all females aged 15 to 19 who were married. (This control variable is used in 1940 because separate labor force data for never-married females aged 14 to 19 were unavailable.)

Notes continued on following page.

-^See note n/ in Appendix Table B-100.

2

Number of Observations

Standard Error of Estimate

Constant Term

Standard Deviation

Mean

Dependent Variable 1^^

Other Data

1950 (s) t (0.06)

1940 (s)

(0.06)

1.3

0.58

0.41

0.94

92

4.0

3.3

7i>

5.1 78.1

28.5

24.6

30.4

(0.13)

4.9

0.24

33.6

100

1.07

1.38

(0.03) 20.62

(0.15)

3.64**

1.16

t

1.88+

0.003 (0.02) 0.19

-0.67

-0.16

-0.08

-0.47-^(0.13)

-0.06

b

4.9

-0.23 (0.19) 1.21

0.11 (0.06) 1.90+

-0.17 (0.12) 1.36

0.26 (0.46) 0.57

0.43 (0.11) 3.72

0.69 (0.21) 3.23**

-1.63 (0.34) 4.82

b

36.2

-0.001 (0.14) 0.01

Age (Ϊ 18 or 19)

(0.04) 4.96**

(0.10) 0.68

(0.32) 0.99

^ (0.12) 5.38

(0.08) 2.61*

-0.20

0.07

t

(0.27) 6.90

1960 (s)

Color (X nonwhite)

School Enrollment (% enrolled)

-0.32

0.63

Industry-Occupation Mix, Teenage Male (#)

Supply of Teenage Males (%)

0.21

-1.84

b

Father's Income ($100/yr.)

Unemployment (%)

Independent Variables

Multiple Regression Equations Used to Explain Intercity Differences in Labor Force Participation Rates of Males 14-19 Census Weeks of 1960, 1950, 1940

Appendix Table B-104

ig

05

>


^ JsJ

INTERCITY REGRESSIONS

843

Table B-104 (continued)

Definition of Variables: Hll4-19:

Percentage of males aged 14 to 19 in the civilian population

who were in the civilian labor force during the census week. Industry-Occupation Mix, Teenage Male: 1960--percentage of total civilian employment in each city in the "agriculture" and '^retail trade" industries; 1950 and 1940--an index of the fraction of jobs in each city which we might expect to be open to teenage males; based on the occupational mix of each city. Supply of Teenage Males: 1960--percentage of the male, civilian, noninstitutional population aged 14+ who were aged 14 to 19; 1950--percentage of the total civilian noninstitutional population aged 14 and older who were males aged 14 to 19; 1940--estimated percentage of the male, civilian population (including inmates) aged 14 and over who were aged 14 to 19. School Enrollment: percentage of all civilian males aged 14 to 19 who were enrolled in school. Color:

Age:

1960--percentage of all civilian males aged 14 to 19 who were nonwhite; 1950—percentage of all males aged 14 to 19 who were nonwhite; 1940--percentage of all persons aged 15 to 19 who were Negro. 1960 and 1950--percentage of civilian males aged 14 to 19 who were 18 or 19 years old; 1940--percentage of all males aged 14 to 19 who were 18 or 19 years old.

All other definitions are the same as in Appendix Table B-103.

844

APPENDIX B

Appendix Table B-200 A Comparlson of Weighted and Unweighted Intercity Regressions Explaining Participation Rates of Males 25-54 Census Week of 1960 Regression Coefficients

Independen t Variables

WeiSihted b (s2

Ru~7

UnweiSihted Ru.,£7 b t (s)

t

-0.20 (0.06) 3.56**

-0.32 (0.06) 5.41 **

Industry Mix, Male (IF)

0.07 (0.04) 1.89+

0.20 (0.05) 4.20 **

Earnings ($lOO/yr.)

0.08 (0.02) 4.45 **

0.05 (0.02) 2.34*

Unemp loyment (%)

Other Income ($lOO/yr.)

-0.27 (0.13)

2.04*

-0.36 (0.13) 2.72 **

Schooling (years)

0.08 (0.10) 0.83

0.26 (0.12) 2.24*

Color (% nonwhite)

-0.03 (0.01) 3.04**

-0.03 (0.01) 3.27**

Married Status (% married) Migration (IF)

0.05 (0.03) 1.60

-0.05 (0.04) 1.30

-0.00 (0.01) 0.05

0.01 (0.02) 0.53

Other Data Dependent Variable

~25-54:

Mean

96.4

Standard Deviation Constant Term Standard Error of Estimate Number of Observations R2

96.4

0.8

1.2

85.1

84.9

0.6

0.8

100

100

0.58

0.62

Notation and definitions of variables are the same as in Appendix Table B-1. !/In this run, each observation was multiplied by

~Pi/~Pi

' where Pi

is the civilian, noninstitutional population 14 years old and over in the

SMSA, and

~

Pi the aggregate population in all 100 SMSA' s.

For an

explanation of this procedure, see the text of Appendix B. £/The results of this run are the same as those in Regression I of Table B-1.

845

INTERCITY REGRESSIONS

Appendix Table B-201 A Comparison of Weighted and Unweighted Intercity Regressions Explaining Participation Rates of Married Women, Husband Present, 14-54 Census Week of 1960 Regression Coefficients

Weighted Run ~! b t ~s2

Independent Variables Unemployment (70)

-0.99 (0.23) 4.28 **

Income of Husbands ($lOO/yr.)

-0.14 (0.12) 1.20

Earnings of Females ($100!yr.) Other Income ($lOO!yr.)

0.43 (0.42) 1.04

Schooling (yrs. ) Wages of Domestics ($lOO/yr.) Industry Mix, Female (iF) Supply of Females (%) Color (% nonwhite)

-0.94 (0.20) 4.71** -0.20 (0.10) 1.97+ 0.47 (0.14) 3.46** -1.40 (0.53) 2.65* 0.73 (0.41) 1. 7B+

-0.62 (O.OB) 7.41 **

-0.75 (0.25) 3.04**

1.00 (0.19) 5.34**

0.91 (0.16) 5.B7 **

-0.90 (0.36) 0.13 (0.05)

Children Under 6 (;; ) Migration (iF)

Dependent Variable

0.59 (0.16) 3.69** -1.55 (0.59) 2.62*

Unweighted Run E.! b t (s2

-0.66 (0.14) 0.17 (0.06)

2.52* 2.93** 4.B3'** 2.65*

-0.64 (0.30) 2.10* 0.08 (0.04) 1.B9+ -0.55 (0.11) 4.96** 0.12 (0.06) 2.05*

~14-54:

Mean Standard Deviation Constant Tenn Standard Error of Estimate Number of Observations R2

33.B

34.1

3.7

4.1

77 .3

61.3

2.2 100 0.68

2.4 100 0.69

Notation and definitions of variables are the same as in Appendix Table B-IO.

~!See note ~! in Table B-200. E.!The results of this run are the same as those in RegreSSion I of Table B-IO.

846

APPENDIX B

Appendix Table B-202 A Comparison of Weighted and Unweighted Intercity Regressions Explaining Participation Rates of Males 65+ Census Week of 1960 Regression Coefficients a/ Unweighted Run —^

Weighted Run

Independent Variables

b

( £0

t

b

(s)

**

Unemployment (%)

-1.48 (0, .20) 7,.33

t **

-1.37 (0.20) 6,.94 •k

Earnings of Males ($100/yr.)

-0.14 (0.08) 1, .68+

-0.18 (0.08) 2,.25

Industry Mix, Male (#)

-0.23 (0. .15) 1,.57

Supply of Older Males (%)

-0.35 (0. .16) 2,.22

-0.07 (0.15) 0, .49 * -0.38 (0.15) 2,.46

•k

Other Income ($100/yr.)

0.17 (0.59) 0, .29 ** 0,30 (0, .11) 2..83

0.05 (0.58) 0, .09

Occupational Mix, Male (¾,) Schooling (yrs.)

1.46 (0, .77) 1, .90+

1.91 (0.76) 2,.50

0.26 (0.11) 2,.38 *

Color (% nonwhite)

-0.30 (0.04) 0,.76

-0.01 (0.04) 0..34

Age (% 75 and over)

-0.12 (0.09) 1,.37

-0.10 (0.11) 0, .89

Marital Status (% married)

-0.20 (0.09) 2,.27

*

**

Migration (#)

-0.42 (0.07) 6.43

*

-0.17 (0.08) 2,.07 ** -0.38 (0.06) 6,.02

Other Data: Dependent Variable

·

Mean Standard Deviation Constant Term Standard Error of Estimate Number of Observations R2

32,.2

30.7

4, .5

4.6

62,.9

48.8

1,.9 100 .84 0,

2.2 100 0.80

Notation and definitions of variables are the same as in Appendix Table B-21. a/ , — See note a/ in Table B-200. -^The results of this run are the same as those in Regression I of Table B-21,

847

· INTERCITY REGRESSIONS

Appendix Table B-203 A Comparison of Weighted and Unweighted Intercity Regressions Explaining Participation Rates of Males 16-17, Enrolled in School Census Week of 1960

Independent Variables Unemployment (%) Industry Mix, Teenage Male (%) Industry Mix, Male (#) Supply of Teenage Males (%)

Rearession Coefficients Weiahted Run !!!/ Unwei!!!!ted Run b t b (s) t (s~

'E./

-1.76 (0.34) 5.23**

-1.81 (0.33) 5.55**

3.10** 4.43**

0.48 (0.15) 3.16** 0.30 (0.18) 1.68+

0.59 (0.19) 0.75 (0.17)

-0.52 (0.42) 1.24

Earnings of Teenage Males ($l/wk.)-O.92 (0.21) 4.40**

-0.47 (0.36) 1.33 -0.97 (0.22) 4.47**

Family Income ($100/yr.)

0.12 (0.09) 1.36

Schooling (yrs.)

3.24 (0.58) 5.63**

3.38 (0.58) 5.84**

-0.31 (0.05) 6.45**

-0.30 (0.04) 6.83**

Color (% nonwhite) Marital Status (% married) Inmates of Institutions (%)

0.08 (0.10) 0.87

0.63 (1.05) 0.60

-0.19 (1.00) 0.19

-0.04 (0.41) 0.09

-0.50 (0.35) 1.44

30.2

31.3

Other Data Dependent Variable 1M16-l7ES: Mean Standard Deviation Constant Term Standard Error of Estimate Number of Observations R2

6.3

6.6

-26.4

8.6

3.2 100 0.77

3.7 100 0.71

Notation and definitions of variables are the same as in Appendix Table B-30.

~/See note ~/ in Table B-200. ~/The results of this run are the same as those in Regression I of Table 8-30.

848

APPENDIX B

Appendix Table B-204 A Comparison of Weighted and Unweighted Intercity Regressions Explaining Pa.ticipation Rates of Males 18-19, Enrolled in School Census Week of 1960

Independent Variables Unemployment (%) 'Industry Mix, Teenage Male (%) Industry Mix, Male (#)

Regression Coefficients _al b/ Unweighted Run -

Weighted Run b

(s)

t

-0.67 (0.51) 1.33 0.74 (0.26) 2.86**

b

(s)

t

-0.88 (0.52) 1.70+ 0.58 (0.22) 2.61*

0.10 (0.28) 0.36

-0.02 (0.29) 0.06

Supply of Teenage Males (%)

-0.17 (0.58) 0.30

-0.40 (0.52) 0.77

Earnings of Teenage Males ($l/wk.)

-0.71 (0.29) 2.44*

-0.94 (0.31) 3.01**

Family Income ($lOO/yr.)

0.52 (0.14) 3.80**

Schooling (yrs.)

1.47 (0.82) 1.80+

Color (% nonwhite) Marital Status (% married)

-0.22 (0.08) 2.92** 0.62 (0.22) 2.81**

0.35 (0.15) 2.28 * 2.40 (0.86) 2.78 ** -0.14 (0.07) 1.89+ 0.43 (0.22) 1.91+

Inmates of Institutions (%)

-0.21 (0.55) 0.38

-0.85 (0.50) 1.71+

College Enrollment Ratio (%)

0.02 (0.09) 0.20

0.09 (0.08) 1.11

Student-Teacher Ratio (#)

0.29 (0.15) 1.97+

0.25 (0.14) 1.79+

-0.24 (0.06) 3.99**

-0.24 (0.06) 4.31 **

College Dormitory Ratio (%) ~~

Dependent Variable \t18-19ES: Mean Standard Deviation Constant Term Standard Error of Estimate Number of Observations R2

45.4

44.5

7.2

8.2

0.2

19.3

4.3 100 0.70

5.3 100 0.65

Notation and definitions of variables are the same as in Appendix Table B-31.

~/See note ~/ in Table B-200.

~/The

results of this run are the same as those in Regression I of Table B-31.

849

INTERCITY REGRESSIONS

Appendix Table B-205 A Comparison of Weighted and Unweighted Intercity Regressions Explaining Participation Rates of Males 20-24, Enrolled in School~/ Census Week of 1960 RegreSSion Coefficients

Weighted Run 'E.7 t b (s)

Independent Variables Unemployment (%)

Unweighted Run ';;;.7 b t (s)

Industry Mix, Teenage Male (%)

0.68 (0.55) 1.25 0.53 (0.27) 1.94+

0.88 (0.63) 1.37 0.43 (0.26) 1.67+

Industry Mix, Male (#)

0.53 (0.32) 1.67+

0.51 (0.36) 1.44

Earnings of Teenage Males ($l/wk.)

-0.67 (0.28) 2.38 *

-0.85 (0.34) 2.53*

Family Income ($100/yr .)

0.33 (0.14) 2.46 *

0.22 (0.17) 1.30

Schooling (yrs .)

2.23 (0.91) 2.44*

3.26 (1.14) 2.86 **

-0.14 (0.08) 1.76+

Color (% nonwhite)

-0.03 (0.09) 0.36

Migration (iF)

0.39 (0.11) 3.58** 0.08 (0.05) 1.72+

0.05 (0.05) 0.88

College Enrollment Ratio (%)

0.22 (0.09) 2.39 *

0.29 (0.09) 3.14**

Student-Teacher Ratio (#)

0.17 (0.15) 1.13

0.24 (0.15) 1.57

-0.29 (0.06) 4.98 **

-0.27 (0.06) 4.34 **

Marital Status (% married)

College Dormitory Ratio (%)

0.38 (0.14) 2.77**

Other Data Dependent Variable Mean

~20-24ES:

Standard Deviation Constant Term Standard Error of Estimate Number of Observations 2 R

43.4

43.2

8.3

9.5

-57.1

-61.9

4.3 100 0.76

5.9 100 0.66

Notation and definitions of variables are the same as in Appendix Table B-32.

~/Males aged 20-24, enrolled in school, who were working forty hours per week or more are excluded.

~/See not ~/ in Table B-200. S/The results of this run are the same as those in RegreSSion I of Table B-32.

850

APPENDIX B

Appendix Table B-206 A Comparison of Weighted and Unweighted Intercity Regressions Explaining Participation Rates of Males 16-17, Not Enrolled in School Census Week of 1960 Regression Coefficients

Independent Variables Unemployment (%) Industry Mix, Teenage Males (%)

Weighted Run !!.I t b (s) -3.23 (0.44) 7.29** 0.93 (0.23) 4.13**

bl Unweighted Run b (s) -3.43 (0.53) 6.43** 1.00 (0.23) 4.27**

Industry Mix, Male (it)

-0.30 (0.20) 1.49

-0.57 (0.2B) 2.03*

Supply of Teenage Males ('.) Earnings of Teenage Males ($l/wk.)

-0.46 (0.55) 0.B3

-0.B3 (0.59) 1.40

-0.21 (0.2B) 0.77

-0.56 (0.35) 1.61

Schooling (yrs.) Color (% nonwhite) Marital Status (% married) Inmates of Institutions (%)

0.60 (0.61) 0.99 -O.lB (0.06) 2.B2 ** 0.30 (1.33) 0.23 -2.47 (0.55) 4.53**

1.55 (0.B4) 1.B5+ -0.16 (0.07) 2.22 * -0.40 (1.64) 0.25 -3.19 (0.59) 5.44**

Other Data Dependent Variable ~16-l7NES: Mean

Standard Deviation Constant Term Standard Error of Estimate Number of Observations R2

5B.9

59.6

6.4

9.0

90.1

113.7

4.3 100 0.59

6.3 100 0.55

Notation and definitions of variables are the same as in Appendix Table B-33.

!!./See note !!.I in Table B-2oo.

~/The

results of this run are the same as those in Regression I of Table B-33.

851

INTERCITY REGRESSIONS

Appendix Table B-207 A Comparison of Weighted and Unweighted Intercity Regressions Explaining Participation Rates of Males lS-19, Not Enrolled in School Census Week of 1960 Resression Coefficients

Independent Variables

Weishted Run !!./ b (s) t

Unweishted Run t b (s)

'!!../

-1.86 (0.25) 7.55**

-2.04 (0.31) 6.70**

Industry Mix, Teenage Male (%)

0.36 (0.13) 2.76**

0.46 (0.14) 3.26**

Industry Mix, Male (If)

0.46 (0.12) 3.68**

0.42 (0.17) 2.48*

Supply of Teenage Males (%)

-1.31 (0.30) 4.33**

-1.49 (0.34) 4.43**

Earnings of Teenage Males ($l/wk.) Schooling (yrs.)

-0.30 (0.16) 1.93+

-0.55 (0.20) 2.69**

Color (% nonwhite)

-0.08 (0.04)

Marital Status (% married)

-0.13 (0.11) 1.21

1.69 (0.48) 3.52** -O.OS (0.04) 1.91+ -0.25 (0.14) 1.82+

Inmates of Institutions (%)

-1.40 (0.31) 4.54**

-1.S4 (0.34) 5.35**

Unemployment (%)

1.23 (0.35)

3.56** 2.11*

Other Data Dependent Variable iMlS-19NES: Mean Standard Deviation Constant Term Standard Error of Estimate Number of Observations R2

S4.9

84.4

3.7

5.5

72.6

79.9

2.5 100 0.60

3.7 100

0.59

Notation and definitions of variables are the same as in Appendix Table B-34. !!./see note !!./ in Table B-200. '!!../The results of this run are the same as those in RegreSSion I of Table B-34.

852

APPENDIX B

Appendix Table B-208 A Comparison of Weighted and Unweighted Intercity Regressions Explaining Participation Rates of Males 20-24, Not Enrolled in School Census Week of 1960 Re~ression

Weighted Run ~7 t b (s2

Independent Variables Unemployment (%)

** -0.46 (0.09) 5.36

Coefficients

b/ Unweighted Run b (s2 -0.44 (0.11) 4.13 **

Industry Mix, Teenage Male (%)

0.01 (0.05) 0.14

0.05 (0.05) 0.98

Industry Mix, Male (It)

0.17 (0.05) 3.74**

0.18 (0.06) 2.95 **

Supply of Teenage Males (%)

-0.34 (0.10) 3.29 **

-0.46 (0.11) 4.06 **

Earnings of Teenage Males ($l/wk.)

-0.09 (0.05) 1. 76+

-0.16 (0.07) 2.43*

Schooling (yrs.) Color (% nonwhite)

0.39 (0.15) -0.05 (0.01)

Marital Status (% married)

0.06 (0.02)

Migration (It)

0.03 (0.01)

2.67** 3.79** 3.03** 3.17**

0.75 (0.20) 3.83 ** -0.03 (0.02) 2.02* 0.02 (0.02) 0.81 0.01 (0.01) 1.31

Other Data Dependent Variable 1M20-24NES: Mean

94.7

Standard Deviation Constant Term

Standard Error of Estimate Number of Observations R2

95.0

1.4

1.8

86.4

86.0

0.8 100 0.69

1.2 100 0.58

Notation and definitions of variables are the same as in Appendix Table B-35.

~/See

note

~/

~/The

results of this run are the same as those in Regression I of Table B-35.

in Table B-200.

853

INTERCITY REGRESSIONS

Appendix Table B-209 A Comparison of Weighted and Unweighted Intercity Regressions Explaining Activity Rates of Males 16-17 Census Week of 1960

Independent Variables Unemployment (%) Industry Mix, Teenage Male (Z) Industry Mix, Male (#)

Regression Coefficients a/ Weighted Run — Unweighted Run y b (s) t -ill-0.36 (0.13) 2.66

- 0 . 2 2 ( 0 . 1 6 ) 1.44 *

0.20 (0.08) 2.67*

0.19 (0.07) 2.55

0.07 (0.07) 1.10

Supply of Teenage Males(X)

-0.34 (0.17) 2.05*

0.02 (0.09) 0.23 •k -0.37 (0.17) 2 . 1 6

Earnings of Teenage Males ($l/wk.)

-0..14 (0, .08) 1,.69

-0.29 (0.10) 2.76

Family Income ($100/yr.)

0,.04 (0, .04) 1,.01

0.06 (0.05) 1.40

Schooling (yrs.)

1,.04 (0, .23) 4..55

1.41 (0.28) 5.08

*

•k

Color (% nonwhite)

-0,.04 (0, .02) 2,.20

-0.03 (0.02) 1 . 2 6

Marital Status (% married)

-0,.36 (0..41) 0..86

Inmates of Institutions (%)

-0..58 (0..16) 3,.571

-0.12 (0.48) 0.25 •k -0.65 (0.17) 3.95

Other Data Dependent Variable AR^g Mean Standard Deviation Constant Term Standard Error of Estimate Number of Observations 2

93..6

93.6

1.8

2.5

83.1

85.0

1.3 100 0.57

1.8 100 0.54

Notation and definitions of variables are the same as in Appendix Table B-42. a/ — See note a/ in Table B-200. -^The results of this run are the same as those in Regression I of Table B-42.

854

APPENDIX B

Appendix Table B-210 A Comparison of Weighted and Unweighted Intercity Regressions Explaining Activity Rates of Males 18-19 Census Week of 1960

Independent Variables

Rellression Coefficients Weillhted Run '!./ Unweillhted Run 'pj b b t t ~s} ~s} -0.64 (0.16) 3.98**

-0.69 (0.20) 3.47**

Industry Mix, Teenage Male (%)

0.28 (0.08) 3.44**

0.29 (0.08) 3.45 **

Industry Mix, Male UF)

0.05 (0.08) 0.67

Unemployment (%)

Supply of Teenage Males (%)

-0.25 (0.18) 1.34

0.05 (0.11) 0.46 -0.36 (0.20) 1.77+

Earnings of Teenage Males ($l/wk.)

-0.13 (0.09) 1.40

-0.25 (0.12) 2.11 *

Family Income ($lOO/yr.)

0.12 (0.04) 3.06**

Schooling (yrs.)

0.84 (0.26) 3.27**

Color (% nonwhite)

-0.05 (0.02) 2.47 *

Marital Status (% married) College Dormi tory Ratio (%)

-0.04 (0.07) 0.57 0.03 (0.02) 1.92+

Inmates of Institutions (%)

-0.80 (0.17) 4.61 **

0.13 (0.05) 2.40 * 1.15 (0.33) 3.49 ** -0.03 (0.03) 1.27 -0.15 (0.08) 1.73+ 0.03 (0.02) 1.68+ -0.94 (0.19) 4.89 **

Other Data Dependent

V~riable ~18-l9:

Mean Standard Deviation Constant Term Standard Error of Estimate Number of Observations R2

92.6

92.5

2.2

3.0

78.6

80.8

1.4 100 0.66

2.0 100

0.60

Notation and definitions of variables are the same as in Appendix Table B-43.

'!./See note '!./ in Table B-200.

~/The results of this run a~e the same as those in Regression I of Table B-43.

855

INTERCITY REGRESSIONS

Appendix Table B-211 A Comparison of Weighted and Unweighted Intercity Regressions Explaining Activity Rates of Males 20-24 Census Week of 1960 Regression Coefficients Weighted Run — /

Independent Variable

iBl

Unemployment (%)

-0.32 (0.08) 3.91

Industry Mix, Teenage Male (%)

-0.01 (0,04) 0.39

Industry Mix, Male (#)

b/ Unweighted Run — b (s) -0.32 (0.10) 3.17 0.02 (0.04) 0.59

0.11 (0.04) 3.00*

0.13 (0.05) 2.52*

Supply of Teenage Males (%)

-0.20 (0.09) 2.18*

-0.28 (0.10) 2.86*

Earnings of Teenage Males ($l/wk.)

-0.03 (0.04) 0.74

-0.08 (0.06) 1.54

Schooling (yrs.)

0.43 (0.12) 3.54*

0.73 (0.17) 4.39*

-0.05 (0.01) 4.33*

-0.03 (0.01) 2.56*

Marital Status (% married)

0.03 (0.01) 1.824

-0.00 (0.02) 0.02

Migration (#)

0.03 (0.01) 4.10

0.02 (0.01) 2.22*

College Dormitory Ratio (70)

0.01 (0.01) 1.42

0.01 (0.01) 1.32

Color (% nonwhite)

Other Data Dependent Variable ARj^q

'

Mean Standard Deviation Constant Term Standard Error of Estimate Number of Observations R2

95.9

96.1

1.2

1.5

88.0

86.8

0.7 100 0.71

1.0 100 0.61

Notation and definitions of variables are the same as in Appendix Table B-44. a/ — See note a/ in Table B-200. -^The results of this run are the same as those in Regression I of Table B-44.

APPENDIX C

Interstate Regressions As explained in the several chapters of Part II dealing with labor market conditions, our interstate regressions were designed to provide a very rough estimate of the relations between unemployment and the labor force participation of five selected groups residing in rural, nonfarm areas during the census week of 1960. In this analysis, the rural, nonfarm sector of each state serves as the unit of observation. Unfortunately, many of the control variables in our intercity analysis could not be ob­ tained for rural, nonfarm areas, and in view of the lesser (and diminish­ ing) importance of this sector of the economy, we concluded that an ex­ tensive analysis of the data that were available would not be worth the cost. One variable not reported in the following tables was taken into ac­ count in the first run for each group—namely, the percentage change in the number of men 25 and over in rural, nonfarm population during the decade from 1950 to 1960. This measure was used as a proxy for the net gain in population from migration (as data on migration were unavailable); but since it proved to be completely nonsignificant in every case, it was excused from the final runs presented below. The complete regression results appear in Tables C-I through C-5. Appendix Table C-I Multiple Regression Equation Used to Explain Interstate Differences in Labor Force Participation Rates of Males 25-54 Residing in Rural Nonfarm Areas Census Week of 1960

b

Regression (S)

Unemployment (%)

-0.68

(0.14)

4.92

Schooling (yrs.)

0.84

(0.26)

3.20

-0.07

(0.03)

2.42

Independent Variables

t **

**

Color (% nonwhite)

Other Data Dependent Variable ^ 2 5 - 5 4 ( R ) : Mean Standard Deviation Constant Term Standard Error of Estimate Number of Observations R2

Notes on following page.

94.9 2.4 91.7 1.5 48 .65

it*

857

INTERSTATE REGRESSIONS Table C-I (continued) Notation: Units of measurement are shown in parentheses following variables.

b = Net (partial) regression coefficient. (s)- Standard error of the regression coefficient. t = t-value of the regression coefficient (b/s); t-value may differ from b/s ratios due to rounding. ** = Significant at the V/o level, * = Significant at the 5% level, + = Significant at the 10% level Definition of Variables*. L^25 54/r·)·

·*—*·

percentage of rural nonfarm males aged 25 to 54 in the civilian, noninstitutional population who were in the civilian labor force during the Census Week.

Unemployment: percentage of the rural nonfarm civilian labor force unemployed during the Census Week. Schooling: Color:

median number of years of school completed by all rural nonfarm males aged 25 and over.

percentage of all rural nonfarm persons aged 14 and over who were nonwhite.

Appendix Table C-2 Multiple Regression Equation Used to Explain Interstate Differences in Labor Force Participation Rates of Married Women, Husband Present, 14+, Residing in Rural Nonfarm Areas

Census Week of 1960

Regression

Independent Variables

b

(s)

Unemployment (%)

-1.36

(0.33)

4.10

Income of Husbands ($100/yr.)

-0.1¾

(0.16)

0.92

Earnings of Females ($100/yr.)

0.29

(0.20)

1.46

Schooling (yrs.)

1.33

(0.72)

1.84"

0.14

(0.09)

1.62

-0.10

(0.18)

0.54

Color (% nonwhite) Children Under 6 (%)

Other Data Dependent Variable

^(R):

Mean Standard Deviation Constant Term Standard Error of Estimate Number of Observations R2

Notes on following page.

28,.4 4,.3 23,.8 3,.3 48 0,.48

t

858

APPENDIX C

Table C—2 (continued) Definition

of Variables: PercentaSe of rural nonfarm married women, husband present, aged 14 and over, In the noninstitutlonal population who were In the labor force during the Census Week.

^MW HPiRV r ''

Income of Husbands: median income in 1959 of ruial nonfarm males, married with wife present. Earnings of Females: median income in 1959 of rural nonfarm females who worked 50 to 52 weeks that year. Schooling: median number of years of school completed by all rural nonfarm females aged 25 years and over. Children Under 6: percentage of all rural nonfarm married women, husband present, with one or more children under 6 years old. All other definitions are the same as in Appendix Table C-I.

Appendix Table C-3 Multiple Regression Equation Used to Explain Interstate Differences in Labor Force Participation Rates of Males 65+, Residing in Rural Nonfarm Areas Census Week of 1960

Independent Variables

b

Regression (s)

t

Unemployment (7„)

-1.22

(0.39)

3.16**

Schooling (yrs.)

1.66

(0.85)

1.95+

-0.04

(0.11)

0.40

Color (% nonwhite)

Other Data Dependent Variable 1^5^: Mean

26.2

S tandard Deviat ion Constant Term

5.2 21.4

Standard Error of Estimate Number of Observations R

4.1 48 0.41

Definition of Variables: L —

•