Economic Development Within the Philadelphia Metropolitan Area [Reprint 2016 ed.] 9781512807813

Table of contents :
Contents
Preface and Acknowledgments
List of Tables in Text
One. Introduction
Two. 1986 Update
Three. Overall Economic Trends in the Counties
Four. Shifts among the Counties in Jobs and Resident Workers, 1960-1980
Five. Shifts in Population among Localities: 1970-1980
Six. New Firm Activity and Employment Changes among the Localities in the Philadelphia Area, 1980-1983
Seven. Industrial Strengths of the Counties
Eight. High Technology in the Region
Nine. Intergovernmental Aid in the Philadelphia PMSA: Who Gets How Much?
Ten. Summary and Policy Implications
List of Appendices and Tables
Appendix A: Bibliography
Appendix B: Supplementary Materials to Chapters 4–9
Appendix C: Comparative County Data for the Philadelphia PMSA
Appendix D: Philadelphia PMSA Economic Statistics
Index

Citation preview

Economic Development within the Philadelphia Metropolitan Area

Economic Development within the Philadelphia Metropolitan Area

Anita A. Summers and Thomas F. Luce

University of Pennsylvania Press PHILADELPHIA

Copyright © 1987 by the University of Pennsylvania Press All rights reserved Library of Congress Cataloging-in-Publication Data Summers, Anita A. Economic development within the Philadelphia metropolitan area. Bibliography: p. Includes index. 1. Philadelphia Metropolitan Area (Pa.)—Economic conditions. 2. Philadelphia Metropolitan Area (Pa.)— Economic policy. I. Luce, Thomas F. II. Title. HC108.P5S85 1987 338.9748'H 86-25091 ISBN 0-8122-1231-2 Printed in the United States of America

Contents Preface and Acknowledgments List of Tables in Text One

Two

Three

Four

Five

ix xiii

Introduction

1

Objectives of this Economic Report Executive Summary: Findings and Implications Summary Policy Implications Methods and Data Outline of the Report

1 2 2 3 5 7

1986 Update

9

Update on Overall Economic Trends Update on the Economic Environment The Turnaround in the 1980s

9 10 12

Overall Economic Trends in the Counties

IS

Pre-World War II: Development into a Metropolitan Area Post-World War II: Decentralization of the Metropolitan Area Accompanying Socioeconomic Profile

15 18 21

Shifts among the Counties in Jobs and Resident Workers Mark Alan Hughes and Janice F. Madden

24

Decreasing Daily Interaction between City and Suburbs Changes in Intercounty Commuting, 1960-1980 Proportion of City Commuters in Each County, 1960-1980 Ratio of Local Workers to City Commuters Do People Follow Jobs or Do Jobs Follow People? Changes in Occupational Structure In the Region Among the Counties Summary and Policy Implications

24 25 27 28 29 30 31 31 34

Shifts in Population among Localities: 1970-1980

37

The Diversity of Communities within the Counties Intracounty Patterns in 1970 Intracounty Patterns in 1980 Population Dispersal within the Counties between 1970 and 1980 Total Population Movements Minority Population Movements The Distribution of Poverty Populations

38 38 38 41 41 43 44

vi

Contents

Six

The Determinants of Population Location Explaining Population Growth Patterns Summary and Policy Implications

46 48 49

New Firm Activity and Employment Changes among the Localities in the Philadelphia Area, 1980-83

51

Joseph Gyourko

Seven

Eight

Previous Findings: An Overview What Influences the Location of New Firms in the Philadelphia PMSA? Manufacturing Starts Business Service Starts What Influences the Net Changes in Employment in the Philadelphia PMSA? Summary and Policy Implications Notes

54

Industrial Strengths of the Counties

61

Size in the County Size in the Region Position in the Nation Relative Growth Rates in the County Relative Growth Rates in the Region Growth Rates Relative to the U.S. Rates From the 1970s to the 1980s Components of Employment Growth Subsector Employment Developments Central City vs. Suburban Development, Here and Elsewhere Multiplier Effects in the City vs. the Surrounding Counties Summary: Economic Report Card for the Counties

62 64 66 68 68 71 72 73 75 78 81 83

High Technology in the Region

88

55 56 57 58 58 59

Thomas F. Luce Defining High Tech Alternative Definitions Selected Definition Advantages and Disadvantages of Promoting High Tech Advantages Disadvantages Philadelphia PMSA High Tech in the National Context High Tech in the Region Total PMSA High Tech Largest Sectors Large Sectors Medium and Small Sectors Summary of PMSA Trends High Tech in the Counties Total High Tech Growth in the Counties

89 89 90 90 91 92 93 95 95 98 98 98 99 99 100

Contents

County Patterns in the PMSA's Largest Sectors County Patterns in Large Sectors County Patterns in Medium and Small Sectors Sources of Growth Differentials Summary of County Trends Future Prospects High Tech Prospects for the PMSA High Tech Prospects in the Counties Summary and Policy Conclusions

Nine

102 102 102 102 106 106 107 109 110

Intergovernmental Aid in the Philadelphia PMSA: Who Gets How Much? 112 Janet Rothenberg Pack

Ten

Why Intergovernmental Aid? Revenue Side Arguments Expenditure Side Arguments Combinations of Expenditure and Revenue Arguments The Local Response to Intergovernmental Aid Intergovernmental Aid in the Philadelphia PMSA Distribution of Aid across the Counties Federal Aid to the Counties by Type of Aid The Determinants of Federal Aid The Determinants of State Aid Intergovernmental Aid and Local Public Finance The Share of Intergovernmental Revenues in Local Revenues Intergovernmental Aid, Local Taxes, and Expenditures Summary and Policy Conclusions Notes

112 113 113 114 114 115 115 122 126 126 127 127 129 131 132

Summary and Policy Implications

134

Summary 1986 Update Overall Economic Trends in the Counties Shifts among the Counties in Jobs and Resident Workers Shifts in Population among Localities New Firm Activity and Employment Changes in the Localities Relative Industrial Strengths of the Counties High Technology in the Region Intergovernmental Aid: Who Gets How Much in the Region? Policy Implications Stimulating the Economic Environment in the Region Stimulating Local Population Growth Stimulating Local Employment Growth Regional Economic Impact of the Needs of the Disadvantaged Intergovernmental Aid Allocation of Public Economic Development Funds Conclusion

134 134 135 136 137 138 138 140 141 142 142 143 143 144 145 145 146

List of Appendices and Appendix Tables

149

Contenti

Appendix A:

Appendix B:

Bibliography

153

A.1 Statistical Bibliography A.2 General Bibliography

153 155

Supplementary Materials to Chapters 4-9

165

B.l B.2 B.3 B.4 B.5 B.6

165 170 174 182 191 199

Chapter Chapter Chapter Chapter Chapter Chapter

4 5 6 7 8 9

Appendix C:

Comparative County Data for the Philadelphia PMSA

208

Appendix D:

Philadelphia PMSA Economic Statistics

230

Index

261

Preface and Acknowledgments In 1984 an ongoing independent economic monitoring process was initiated in the Philadelphia region. Support for the first year's study came from a start-up grant from the Fels Fund, a major grant from the Greater Philadelphia First Corporation, and a supplementing grant from the William Penn Foundation. Support for this second report (and for next year's) was provided by the William Penn Foundation. Helen A. Davis, Program Associate at the Foundation, has been a regular and valued supporter of the project, and a guardian of the independence of the investigators. We are genuinely indebted to her. The research activity associated with this 1986 study also benefited from a wide range of resources from within the University of Pennsylvania: United Parcel Services provided some computer support; the Wharton School provided space; very bright undergraduates were available as research assistants at "slave" wages; an independent study by a General Honors student contributed to the analysis in Chapter 8 of high technology industries; the Mellon Foundation, as part of its support of a Penn interdisciplinary seminar on "Cities," provided computer support for Joseph A. Gyourko, the contributor of Chapter 6; a senior thesis enabled the meticulous assembling of a difficult data set; and the enthusiasm of many administrators at having the University of Pennsylvania contribute to understanding the region's economy provided a productively supportive environment. The Rockfeller Foundation provided a very special research grant to one of us, Anita A. Summers, by an appointment as a Resident Scholar at their Bellagio Study and Conference Center in Italy. It made possible a month of uninterrupted work on the project. The physical and mental environment at the Center made this month an extremely productive and memorable one. Our research assistants were real troupers. Henry A. Eskin, a veteran of the first year's study, was the linchpin of this support group. He provided superb programming expertise, and a senior thesis that contributed to the section in Chapter 7 devoted to comparing city-suburban development in Philadelphia with that of other metropolitan areas in the United States. Jeffrey H. Smith and Michael E. Chernew were also veterans of the 1985 report. This year both worked on whatever needed to be done throughout the year, but Smith prepared a number of background papers on the history of economic development in the counties, and Chernew organized the extensive data set needed for the analysis in Chapter 5 of the population shifts. Jed Sherwindt wrote a General Honors independent study paper involving a literature review of the economics of the high technology industry, which has been incorporated into Chapter 8. Alok Gupta helped

X

Preface and Acknowledgment!

assemble the interregional high technology data set during the spring semester, and Alan Scheiner helped with the project during the summer. We had first-rate administrative assistance on this project. Roberta E. Fallon gets our thanks for the meticulous and interested care she gave to the manuscript, background files, and mailing list—and she handled the many outside inquiries with knowledge and grace. Caroline K. McCarthy was our savior during the final crunch. She came in on weekends and marched through the last changes with cheer and skill. This year's study was greatly facilitated by drawing upon the relevant research interests of three members of the Penn faculty (one working with a finishing doctoral student) who were doing work closely associated with the subject of intraregional economic development in the Philadelphia region. Mark A. Hughes (now Assistant Professor at the Woodrow Wilson School at Princeton) worked with Janice F. Madden, Associate Professor of Regional Science, on the movements of jobs and resident workers in the region over the past twenty years. Joseph A. Gyourko, Assistant Professor of Finance and specialist in urban economics, brought his research interests to determining the role of local economic policies and characteristics in explaining local changes in employment activity and new firm start-ups in the early 1980s. Janet Rothenberg Pack, Associate Professor of Public Policy and Management, brought her extensive research expertise in fiscal federalism to the question of the role of federal and state grants to local governments. In addition, Janusz M. Szyrmer, Assistant Director of Penn's Social Science Data Center, supplemented his project work of last year in translating the input-output data from the Regional Science Research Institute into useful regional data. We are indebted to all of these scholars for their willingness to participate. They enabled us to give a depth and thoroughness to the study that would not otherwise have been feasible. We are indebted to Helen F. Ladd and John Yinger, of the John F. Kennedy School of Government at Harvard University for providing us with tax capacity estimates for a large group of cities. These were used in the analysis supporting parts of Chapter 7. Finally, we wish to thank the Advisory Committee to the project. In addition to their participation in the committee meetings, three members— John P. Claypool, Theodore A. Crone, and A. Gilbert Heebner—provided us with many insightful comments on the draft. John M. L. Gruenstein, former Vice President and head of the Urban Section of the Department of Research of the Federal Reserve Bank of Philadelphia, chaired the Committee until his resignation this spring. He gave us very constructive critical comment and support in that role. We are fortunate that Dr. Heebner, Executive Vice President and Economist at Philadelphia National Bank, was willing to take over the chair. His co-chair is Dr. Crone, current head of Urban Research at the Federal Reserve Bank, who has shown consistent interest in our work for several years. Dr. Heebner has been involved in this project from its earliest inception. He has advised, cautioned, and supported. We are grateful that he is willing to give us some of his economic expertise and general wisdom. A. A. S. and T. F. L.

Prdact and Acknowledgment!

xi

Two authors of signed chapters wish to make their own acknowledgments. Joseph G. Gyourko: I thank Connie Lee, Steve Phillips, and Ken Younkins for their diligence in helping collect and organize the data. Sekyung Oh provided very able computer assistance. Anita Summers and Thomas Luce made most helpful comments that improved the chapter. Janet Rothenberg Pack: I thank Joon Han Kim and Charles Neivert for diligent, as well as imaginative, research assistance. I am also indebted to the Pennsylvania Intergovernmental Council, Mr. John King in particular, for providing the detailed federal aid data for the Pennsylvania counties for 1984 and 1985. Anita Summers' editorial advice and substantive comments are gready appreciated and have undoubtedly improved the chapter.

List of Tables in Text This list includes all tables appearing in the main text of this volume. Tables appearing in the appendices are listed on pp. 149-152. 2.1 2.2

Annual Employment Growth Rates: Philadelphia PMSA and U.S., 1952-1986

9

Recent Developments in the Economic Environment: Philadelphia PMSA

11

2.3 The Turnaround in the '80s: Philadelphia PMSA 3.1

County Population and Employment Shares: Philadelphia PMSA, 1950-1980

13 19

3.2

Changed Socioeconomic Profile of the Counties: Philadelphia PMSA, 1960-1980 22

4.1

Percentage Changes in Work and Residence Location among the Counties: Philadelphia PMSA, 1960-1970, 1970-1980

26

Percentage of Workers Commuting to Philadelphia by County of Residence: Philadelphia PMSA, 1960-1980

28

Ratio of Local Workers to Philadelphia Commuters by County of Residence: Philadelphia PMSA, 1960-1980

29

Occupational Distribution of Workers: Philadelphia PMSA, Philadelphia and Surrounding Counties, 1970-1980

31

Effects of Relocation on Jobs and Resident Workers by Occupation and County: Philadelphia PMSA, 1970-1980

33

Variations among Municipalities within Counties: Philadelphia PMSA, 1970

39

The Distribution and Growth of Population within the Counties: Philadelphia PMSA, 1970, 1980

42

The Distribution and Growth of Minority Population within the Counties: Philadelphia PMSA, 1970, 1980

45

5.4

Poverty Rates within the Counties: Philadelphia PMSA, 1970, 1980

47

6.1

Variations in New Firms and Net Changes in Employment within Counties for Selected Manufacturing and Business Service Sectors: Pennsylvania Counties, Philadelphia PMSA, 1980-1Q to 1983-1Q

52

Variation in Selected Economic Characteristics within Counties: Pennsylvania Counties, Philadelphia PMSA, 1981

53

Employment Levels and Shares by Major Sectors within Counties: Philadelphia PMSA, 1985-3Q

63

County Shares of Regional Employment by Major Sectors: Philadelphia PMSA, 1985-3Q

65

4.2 4.3 4.4 4.5 5.1 5.2 5.3

6.2 7.1 7.2

List of Table« in Text

7.3 7.4

7.5 7.6 7.7

7.8 7.9 8.1 8.2 8.3 8.4

9.1

County Shares of U.S. Employment by Major Sectors: Philadelphia PMSA, 1985-3Q

67

Annual Employment Growth Rates in Major Sectors: Philadelphia PMSA, Component Counties, and the U.S., 1975-1Q to 1980-1Q, 1980-1Q to 1985-3Q

70

Components of Employment Change: Pennsylvania Counties of the Philadelphia PMSA, 1975-1Q to 1980-1Q, 1980-1Q to 1983-2Q

74

Employment and Growth in Leading Industries in the Counties: Philadelphia PMSA, 1975-1Q to 1980-1Q, 1980-1Q to 1985-3Q

76

Distribution of Employment between Central Cities and Their Surrounding Counties by Major Sectors: Philadelphia PMSA and 42 Other PMSAs, 1970, 1980

79

Employment and Output Multipliers by Major Sectors: Philadelphia and Rest of SMS A, 1981

82

Economic Report Card for the Counties of the Philadelphia PMSA by Major Industrial Sectors

84

Employment and Growth in High Technology Sectors: 10 Largest Centers and the U.S., 1977, 1982

94

Employment and Growth in High Technology Sectors: Philadelphia PMSA and the U.S., 1975-1Q, 1980-1Q, 1985-3Q

96

Employment and Growth in High Technology Sectors by County: Philadelphia PMSA, 1975-1Q, 1980-1Q, 1985-3Q

101

Sources of Growth Differentials with the Nation in High Tech: Philadelphia PMSA and the Counties, 1975-1Q to 1980-1Q, and 1980-1Q to 1985-3Q

104

Total and Per Capita Intergovernmental Aid: All U.S. PMSAs, Philadelphia PMSA and Component Counties, 1965, 1983

116

9.2

Total and Per Capita Federal Aid: Philadelphia PMSA, 1965, 1970, 1975, 1980, 1983 118

9.3

Total and Per Capita State Aid: Philadelphia PMSA, 1965, 1970, 1975, 1980, 1983

119

9.4

State Aid to the Counties: Philadelphia PMSA, 1965, 1983

121

9.5

State Government Responses to Federal Aid Declines: Philadelphia PMSA, 1977-1983 123

9.6

Federal Grants to the Counties by Department: Philadelphia PMSA, 1980, 1984, 1985 125

9.7

Percentage of Total County Revenue from Intergovernmental Revenue: Philadelphia PMSA, 1965, 1970, 1975, 1980-1983

128

Annual Rates of Change in Total and Intergovernmental Revenue to the Counties: Philadelphia PMSA, 1975-1983

130

9.8

Economic Development within the Philadelphia Metropolitan Area

/ " V .

Base map courtesy of the Delaware Valley Regional Planning Commission

One

Introduction This report is the second of a planned series of objective assessments of the economic climate in the Philadelphia region. Numerous individual economic analyses have been undertaken in Philadelphia, as in other urban areas, in response to specific and urgent problems, but here as elsewhere there are few examples of the consistent monitoring that is essential to strategic economic planning and avoidance of fiscal crises. This series is designed to provide the Philadelphia region with one form of ongoing, independent economic monitoring.

Objective« of This Economic Report

The Philadelphia region is one of the group of older midwestern and northeastern metropolitan areas in the United States that have shared the experience of relatively stressful economic conditions in the last two decades. The first volume in this series, Economic Report on the Philadelphia Metropolitan Area, 1985 (Summers and Luce, 1985) identified, for the region as a whole, the recent historical sweep of economic events, the relatively strong industries (the "winners"), and the strengths and weaknesses in the economic climate. Although all parts of the region share in its economic fortunes, they do not necessarily share equally. All ships may rise with the tide, but that analogy is not apt to describe interregional or intraregional economic developments. The major objective of the present study has been to gain an understanding of the nature and origin of the disparate economic development patterns among the eight counties of the Philadelphia metropolitan area. Policy options flow from the conflict between the common economic interests within the region and the political independence of its parts. The major findings of the analysis and their policy implications are summarized in the next section. A brief discussion of the methods and data underlying the findings follow. An additional objective, one consistent with the spirit behind the ongoing project, is to encourage informed discourse among the target groups listed in the 1985 report—local elected and appointed government officials, businesses in the area, financial institutions, neighborhood organizations, news media, civic organizations, and specialists in urban economic affairs. All of these groups have been represented by the purchasers of the report, and all have been in the audiences when the many presentations and discussions of its contents have taken place. Many economic reports and news articles have drawn upon its contents; it has been used in some academic courses; academic papers based on the work have been presented at professional meetings; and the project itself is being examined by some other regions as a model. It is the aim of the project to deepen these exchanges with the additional analysis of this report.

2

Economic Development within the Philadelphia Metropolitan Atea

Executive Summary: Findings and Implications

The evidence is strong that the Philadelphia metropolitan area has emerged from a difficult period of adjustment. The period of slow growth for the region—decline for the city—induced some of the adjustments producing the strengths of the recent few years. But the economic characteristics of the region are not equally distributed among its parts.

Summary Briefly, the major findings of the analysis by the Wharton Philadelphia Economic Monitoring Project are these: • The region has substantially completed its accommodation to changed market forces and government policies, but the shakeout process in the city's manufacturing sector has not completely run its course. • It is the counties outside the city that have now emerged as the expanding and flourishing parts of the region. • There has been a marked redistribution of jobs and workers over the last twenty years—both have decentralized: jobs appear to have followed people into the suburbs; daily interaction between the suburban counties and Philadelphia has decreased; professional groups have suburbanized less and blue collar workers have suburbanized more. • Apart from any inducements resulting from job availability or government policies, people have chosen to live less closely together. Minority populations, as well as whites, have suburbanized, but the poor show much less mobility. • When new manufacturing firms choose locations within the region, they are attracted to population density, business land availability, and proximity to interstate highways, but they are discouraged bv high property taxes. • When new firms delivering business services choose locations within the region, they are attracted to population density but are unaffected by local zoning and fiscal policies. • Comparisons between county and national employment growth rates indicated that the city's were lower than those of the nation in every industry except legal services. The city and Delaware County were the only parts of the region that lagged behind the nation, but Delaware had positive and only slightly lower growth rates. • Over the last decade, almost all the counties improved their economies relative to the nation, suggesting that significant industrial accommodation had already taken place. Philadelphia shared in these changes—it reduced its growth lag in almost every sector, including manufacturing.

Introduction

3

• A comparison of the dispersal pattern of this region with that of 42 other metropolitan areas indicates that Philadelphia lost more jobs relative to its suburbs than the national average—it wasn't the national dispersal trend alone that accounted for the shrinking of the metropolitan nucleus. • An economic "report card," using ten criteria graded each of the major industrial sectors in each of the counties: the city received the largest number of low grades; Bucks, Burlington, and Montgomery counties received the largest number of high grades. • The Philadelphia metropolitan area is one of the nation's largest centers of high-technology employment, and the region enters the second half of the 1980s in a good position to share in the nation's future growth in high-tech industries. • Philadelphia has received far more aid from federal and state governments than have the other counties, and it has been subject to less fluctuation and reduction. Though its responsibilities for disadvantaged groups have been much higher than in other parts of the region, placing more emphasis on economic development could have resulted in lower taxes and more of the type of public services that would have stimulated employment. • For the region as a whole, intergovernmental aid has improved markedly in the last twenty years relative to other metropolitan areas. By 1983, the Philadelphia PMSA's aid equalled others—a big change from the 1965 picture when it received less than half the average. • There was substantially less federal aid in 1985 than in 1980, and economic development funding has been particularly hard hit.

Policy implications There are many policy implications associated with the major findings of this study—the substantial difference in economic strength between Philadelphia and its surrounding areas; the domination of market forces in the accommodation of people and jobs in the parts of the region; and the powerful effect on the region's economic map of the nationwide preference for lower density living. Public policies can aid or hinder economic development, but they will not have sustained effectiveness if they counter strong international and national competitive forces or strong majority decisions by people on how they want to live. These policy implications are consistent with the major findings: • If the region is to hold on to its stronger position, it must improve its relatively poor infrastructure, encourage state participation in expanding the availability of venture capital to the region, and evaluate its high business taxes. It must hold on to and further develop one of its major economic assets—its amenities.

4

Economic Development within the Philadelphia Metropolitan Area • For Philadelphia, the ability to keep or expand population by using local policies to counter people's personal preferences for lower density living is minimal, though lower tax rates and improved amenities are likely to help. • Suburban communities trying to maintain low-density environments should design land-use measures to counteract pressures for job development and coordinate land-use policies that affect commercial development (which attracts population) and industrial development (which repels population). • Communities trying to attract new manufacturing firms will find that low property taxes, good access to interstate highway systems, and available population density are selling points—but, most of all, they must also recognize that employment growth in manufacturing is at the mercy of broadly based national forces. • For Philadelphia, stimulating employment growth through increased specialization in what the city does best is important. Dollars spent on infrastructure, tax benefits, and preparation of industrial land should be directed to the industries in which it has comparative advantages. • Economic development funds should not be designed solely to promote startups. Those that affect existing firms are an equally important part of a productive strategy. • Suburban communities must recognize that some of the major factors underlying the strength of one of their successful sets of industries—high technology—are centered in the city. Helping the city receive state money for its universities and federal money for technology-oriented defense spending will help their hightechnology industries. • Suburban communities must also recognize that, to a large extent, the image the region projects to the rest of the world is a reflection of the socioeconomic conditions in the city. Infrastructure improvements and regional labor market exchange programs, which would ameliorate the city's unemployment problems, warrant "yea" votes by suburban legislators, not just because they help their fellow citizens, but because improved conditions in the city enhance the region's attractiveness. • In the city, recognition of the region's interdependence is vital. Efforts should be directed to enabling its residents to participate in the employment growth of the rest of the region by means of improved outbound commuter transportation networks, labor information exchanges, and skills training. • City and suburban communities alike may want to lobby for increased replacement aid by their states as federal aid declines.

Introduction

S

• Most important, for Philadelphia, is that it should be almost as aggressive in seeking intergovernmental funds for the region as it is for the city itself. • The city should regularly review all possibilities for focusing additional expenditures enabled by intergovernmental aid to economic development. In sum, Philadelphia's policies must address its smaller size, and policies in the surrounding counties must recognize the extent to which their economic well-being flows from being part of a metropolitan area with access to the unique assets of a central city. Although the region is crisscrossed with political boundaries, genuine economic interdependencies link the fortunes of the individual communities together in many important ways.

Methods and Data

Economic assessment requires the development of extensive data bases and the analysis of the data with a variety of empirical and theoretical techniques.

Methods The general approach of this report involves a comparative analysis of employment patterns by industrial sector—across the counties and across the country's large urban areas—and econometric analyses of the factors that underlie the variations in employment and population patterns. Techniques employed include simple comparisons of growth rates and levels among the eight counties, simple comparisons of the suburbanization patterns in this region with those of other comparable regions, analyses of cross tabulations of economic data, regression procedures, and inputoutput calculations. Four comparison groups are developed: (1) the municipal civil divisions (MCDs), or a sample of them, in the eight-county area; (2) the eight counties; (3) other large metropolitan areas (42 others for the comparison of suburbanization trends, 44 others for the comparison on high-technology performance); (4) the United States as a whole. The Philadelphia eight-county region is officially labeled a Primary Metropolitan Statistical Area (PMSA). The former government designation for the same eight counties was Standard Metropolitan Statistical Area (SMSA), though the labor market area concept underlying the label remains the same. It is defined as "a geographic area consisting of a large population nucleus together with adjacent communities having a high degree of economic and social integration with that nucleus" (1980 Census ofPopulation: Metropolitan Statistical Areas, Supplementary Report, December, 1984, U.S. Department of Commerce, p.7). Another designation, Consolidated Metropolitan Statistical Area (CMSA), includes Wilmington,

6

Economic Development within the Philadelphia Metropolitan Arca Trenton, Allentown, and Bethlehem. As regular data for the Philadelphia CMSA develop, future analyses will involve the larger region when appropriate. The Philadelphia PMSA consists of eight counties: Bucks, Chester, Delaware, Montgomery, and Philadelphia in Pennsylvania, and Burlington, Camden, and Gloucester in New Jersey. The criteria for the selection of MCDs and comparison PMSAs are described in the relevant sections.

Data For intraregional investigations, the problem of compiling up-to-date, complete, and comparable data at the county or local level of disaggregation are complex and cannot be achieved with perfect accuracy. The problems have been exacerbated in recent years by the reduction in the coverage and frequency of data from federal sources. For the Philadelphia PMSA, the comparability problems are further complicated for data acquired at the state level by the fact that three of the eight counties in the region are in New Jersey, and five are in Pennsylvania. Meticulous attention is paid to these problems in this report. Discrepant data and their resolutions are footnoted throughout the text and appendix tables. A complete listing of the sources is given in the Statistical Bibliography (Appendix A.l). Several of the data sets warrant special mention. For the comparisons of employment patterns involving the Philadelphia PMSA as a whole, Philadelphia, the rest of the PMSA, and the United States, the Bureau of Labor Statistics (BLS) time series of employment based on establishment survey data is used. These data are supplemented by county level data from the Bureau of the Census. For the more detailed employment comparisons by industry, compilations of employment by three-digit Standard Industrial Classification (SIC) codes for the five Pennsylvania counties in the PMSA were acquired from the Institute for Public Policy Studies, Temple University. These more disaggregated employment files were compiled from firm level data, collected by the Office of Employment Security (OES) of the Pennsylvania Department of Labor and Industry. Data for the three New Jersey counties were compiled from unpublished data provided by the New Jersey Office of Employment Security and from a variety of federal government sources. A number of specialized employment data sets are used in the study: the detailed employment data on high-technology industries, analyzed in Chapter 8, are drawn from the sources listed in the preceding paragraph and are supplemented by data from the 1972, 1977, and 1982 Census of Manufactures, Bureau of the Census; data compiled by the Temple University Institute of Public Policy Studies are used for the intra-PMSA analysis of the components of employment change, and for the zip-code level analysis supporting Chapter 6. The Census of Population, Bureau of the Census, is the source of the decennial population data for all the geographic categories. These were updated by data in Current Population Reports. Compilations of much of the MCD-level population data were purchased from the Delaware Valley Regional Planning Commission.

Introduction

7

County level tax data came from the Census Bureau's Local Government Finances in Selected Metropolitan Areas and Large Cities. MCD-level tax data were provided by the Pennsylvania State Tax Equalization Board and the Commonwealth Land Title Insurance Company for Pennsylvania localities; for New Jersey, they were collected from annual publications of the New Jersey Taxpayers' Association. Data on the changes in location of jobs and resident workers are from the chapters of the Subject Reports of the Census of Population entitled "Journey to Work." These reports also were the source for the comparative central city/outside central city analysis in Chapter 7. Intergovernmental revenue data came from the Census Bureau's Local Government Finances in Selected Metropolitan Areas and Large Cities, the Federal Assistance Awards Data System, and the General Services Administration's Geographic Distribution of Federal Funds Report. Finally, two types of input-output multipliers were computed for both Philadelphia and the aggregate of the surrounding counties for each major industrial sector. These were computed from data acquired from the Regional Science Research Institute. A description of the procedures and model is provided in Appendix B.4. Outline of the Report

The contents of this volume build on the assessment of the region as a whole described in the first report of the Wharton Philadelphia Economic Monitoring Project, the Economic Report on the Philadelphia Metropolitan Area, 1985. In that report, the transformation of the economy of the Philadelphia region was documented and analyzed. The regional economy was described as "fundamentally more robust," reflecting "considerable accommodation of the industrial structure." This volume takes the analysis one step further. The geographical details of this industrial and demographic transformation are examined. Political boundaries are largely defined by these smaller geographical units, rather than by a PMSA. An understanding of the units' specific economic developments is essential, therefore, to effective government and business policy decision-making. In this report, Philadelphia refers to the city, while the eight-county area is referred to as the Philadelphia PMSA, or the metropolitan area. Chapter 2 updates last year's report on the overall economic trends and environment of the region. Chapter 3 describes the broad sweep of economic developments in and among the eight counties in the Philadelphia PMSA, particularly over the last three decades. Chapter 4 examines the specific changes among the counties in where people work in relation to where they live. Chapter 5 analyzes the nature of and factors underlying the population dispersal, and Chapter 6 considers the factors underlying the employment patterns (growth, new firm startups) across the localities in the area. Chapter 7 develops ten measures of industrial strength for the industries in each county, summarizing the results in a "report card" that identifies the strongest ones in each county and the county location of the strongest industries. Chapter 8 examines an important group of industries in which there is considerable interest—high technology—and assesses

8

Economic Development within the Philadelphia Metropolitan Area both the past growth in the sectors and their prospects for future growth in the various counties of the region. Chapter 9 looks at the role of intergovernmental revenue in the development of the current regional economic map. Chapter 10 consists of a summary of the report, with a review of the policy options flowing from its major conclusions. The appendices contain the sources, techniques, and much of the data underlying the tables in the text. They also contain general data on the Philadelphia PMSA and its component counties. Appendix A provides a detailed bibliography of statistical and general sources. Appendix B describes the data and empirical procedures used in each of the chapters. Appendix C is a rich source of comparative county data for this region. Appendix D updates the Philadelphia PMSA data compilation published in the 1985 Economic Report's Appendix.

Two

19S6 Update The general conclusions of the 1985 Economic Report were that the region has undergone structural transformations, has been involved in industrial accommodations to changed market forces and government policies, and, therefore, is and will be experiencing stronger economic development. These conclusions have been strengthened by the evaluation of subsequently available data.

Update on Overall Economic Trend*

Documentation of the relative improvement in the Philadelphia regional economy is presented in Table 2.1. The very recent annual growth rates, in relation to those of earlier years, are shown for the area as a whole, and for the central city and non-central city components. Data for the nation are provided as benchmarks. The PMSA has maintained a high employment growth rate in recent years, though the rate declined slightly between Table 2.1 ANNUAL EMPLOYMENT GROWTH RATES: PHILADELPHIA PMSA AND U.S., 1952-1986

1952-72

(a)

1972-83

1983-1Q -1984-1Q

1984-1Q -1985-1Q

1985-1Q -1986-1Q

3.57Z 2.22 3.92

3.55Z 1.82 3.99

2.80Z -0.57 3.64

PMSA Total Baployaent Manufacturing Nonmanufactuclng

l.OOZ -0.82 1.95

0.60Z -2.36 1.55

Philadelphia Total Employment Manufacturing Nonmanufacturing

-0.61 -2.82 0.35

-1.54 -5.66 -0.56

0.42 -2.33 0.90

-0.14 -3.36 0.41

1.08 -3.66 1.86

Rest of PMSA Total lteployaent Manufacturing Nonmanufacturing

3.31 1.35 4.64

2.30 -0.58 3.43

5.66 4.09 6.16

5.87 3.83 6.51

3.83 0.55 4.82

United States Total Employment Manufacturing Nonmanufacturing

2.08 0.71 2.67

1.85 -0.32 2.51

4.37 7.12 3.67

4.16 1.11 4.97

2.99 -0.65 3.92

SOURCE: Computations based on data from Employment and Earnings, Bureau of Labor Statistics, U.S. Department of Labor; and County Business Patterns, Bureau of the Census, U.S. Department of Commerce. (a) Total R(SA growth computed from Employment and Earnings. Philadelphia and Rest of PMSA employment for 1952 computed from total PMSA employment and county employment shares from County Business Patterns, 1951.

10

Economic Development within the Philadelphia Metropolitan Area

1985 and 1986. In addition, the difference between the national and regional growth rates has diminished steadily. The strength of the improvement is measurable not only for the region as a whole. Philadelphia is experiencing, most recently, a small positive growth rate of a shade over 1%. The surrounding counties are enjoying large growth rates—substantially outperforming the nation in all the recent years. Developments in the major industrial categories, manufacturing and nonmanufacturing, warrant attention. In the city, manufacturing continues to decline at a rapid rate. There are some manufacturing industries in which the city has strength, but the overall numbers indicate that it has not yet reached an optimal degree of specialization. Nonmanufacturing industries in the city, in contrast, are experiencing an acceleration in employment growth (from 0.4% for 1984-85 to 1.86% for 1985-86). The significance of this acceleration comes, in part, from the contrast with the declining growth rates for the United States (from 4.97% for 1984-85 to about half, 3.92% for 1985-86). The counties surrounding the city of Philadelphia have growth rates in manufacturing that exceed the nation: an increase in the level of employment in the most recent year contrasts with a decline for the country. In nonmanufacturing, also, the suburban counties are growing more rapidly than the nation. The most recent data, then, confirm the increased robustness of the regional economy. The exception to this significant improvement is the manufacturing sector in Philadelphia, which is still showing substantial declines in employment.

Update on the economic environment A number of factors that the literature on industrial location and expansion decisions evaluates as influential were assessed in the 1985 Economic Report. New data suggest that some have improved, some became more disadvantageous, some are unchanged, and, for some, no new data are available. Specific information on each of these factors is listed in Table 2.2.

Only one dramatic change in these factors occurred over the last year: the decline in the value of the dollar. The industrial structure of this region suggests that this decline will improve its competitive position in the nation, since it has a relatively high proportion of its employment in export industries. For the Ports of Philadelphia, however, the deterrent effect on imports is not a favorable factor, since the volume of imports has traditionally been much higher than the volume of exports. One other aspect of the region's economic environment warrants special mention—the availability of venture capital. This appears to be particularly important to small firms without track records. Parts of several sectors in which the region is strong—high technology, trade, services, and FIRE (finance, insurance and real estate)—are characterized by small firms, and

198« Update

11 Table 2.2

R E C E N T D E V E L O P M E N T S I N T H E E C O N O M I C E N V I R O N M E N T : P H I L A D E L P H I A PMSA

Factor

Update

Comments

Mfg. Labor Costs

1982: 100.7% of U.S. Avg. 1985: 102.6% of U.S. Avg.

Not a major factor, but has slightly worsened.

Cost of Living

(U.S. = 100 ) Phila 17 Largest Northeast

Still advantaged relative to NE; position about the same relative to large PMSAs and country.

Unionization

Same data as 1985 report

Lower than N E - M W PMSAs; higher rates than South.

Taxes

Same data as 1985 report

Business taxes high, as measured by tax effort and per capita collections.

Amenities

Same data as 1985 report

Advantaged, particularly in education, health care and environment, and the arts.

Venture Capital

Total in Region 1983 $22.3 million 1984 35.9 million 1985 20.4 million

Use of venture capital funding: decline in levels and % of U.S. between 1984 and 1985.

Infrastructure Quality

Same data as 1985 report

Office Space Costs

1984 - R a n k among 18 in CBD 6 -Phila. CBD $20/ Rate sq. ft - R a n k among 18 outside CBD 8 -Phila Outside $16/ CBD Rate sq. ft.

1984 104 103 113

1985 105 103 114

% of U.S. 0.8 1.2 0.8

N o additional evidence. Recent DVRPC/PEL/UI report affirms seriousness. 1985

City's position is less favorable; surrounding areas are more favorable.

11 23

6 17

State Economic Development Policies

Continued expansion of Ben Franklin Partnership

Favorable position continued, except in venture capital stimulation.

Intergovernmental Assistance

See Chapter 9.

Improving position of city.

Value of U.S. Dollar

25% decline between Feb. 1985 and Feb. 1986.

Helps manufacturing employment. Hurts import-related industries.

12

Economic Development within the Philadelphia Metropolitan Area their strength can be enhanced or deterred by their access to venture capital. Estimates of the amount received by industries in the Philadelphia PMSA, developed by Venture Economics, Inc., show declines in most of the last few years. These estimates reflect funding from private venture capital funds. (Detailed data are given in Appendix C, Table C.l.) The Commonwealth of Pennsylvania has become more energetic in filling this void, but it is not on the list of states that give loan guarantees, or have state-chartered or state-funded venture capital corporations. The region has improved its competitive footing in recent years. If it is to hold and further improve its market position, then it will be important that it not be deterred by a poor infrastructure, sparsity of venture capital, or high business taxes. Further, it will be important to hold on to the advantages of relatively low living and office space costs, and the quality of its amenities. The high office occupancy rate in Philadelphia undoubtedly underlies the change to a less favorable office-cost position for the city. It is a factor to be watched, though not one amenable to public policies. The final resolution of the trash disposal problem in Philadelphia, an issue of considerable current interest, is going to be relevant to future assessments of its amenities and to tax rates. It, and other amenities, should be evaluated, in part, in terms of the eventual impact on the economic development of the city.

The Tarnaroniid in the 1980s

Most regions in the country, over their history, have experienced both periods of strong long-term economic growth and periods of long-term decline. The regions that rise from the periods of decline are those that have been able to adjust to changed national and international market conditions. The Philadelphia region appears to be one of those. The declines of the 1960s became the steeper declines of the 1970s, but, by many indicators, the 1980s are a turnaround time. Data documenting this turnaround have been assembled in Table 2.3. For three time periods—1960 to 1970, 1970 to 1980, and 1980 to 1986— annual growth rates in employment and population are shown for the region as a whole, the city, and the surrounding counties. The second column in each time period shows the difference between national and regional rates. A minus sign indicates that Philadelphia ranks lower than the nation as a whole—a preferred outcome only for the unemployment rate. The changes in the 1980s are apparent from the numbers. With the exception of the very small decline in growth in the non-central city part of the PMSA, every entry shows the relative improvement in the region's economy between the seventies and the eighties. This is mirrored in the shrinking differences with national growth rates during the more recent period. For the region as a whole, employment growth was 1.8 percentage points per year lower than for the United States in the seventies, but less than a half a percentage point (0.3) lower in the later period. For the city, the difference was reduced from 4.1 percentage points per year lower to 2.1 lower. The city's improved performance in the 1980s, as measured by increases

13

1986 Update

or very small decreases in employment growth rates, has made a significant contribution to the region's cheerier economic picture. Though still slightly negative, the annual growth rate for the city from 1980 to 1986 improved over the 1970 to 1980 period more than for the United States or for the suburban counties. In essence, the dwindling difference between the growth rates of the Philadelphia PMSA and the nation was due to a substantial reduction in the city's employment declines (moving to actual employment Table 2.3 THE TURNAROUND IN THE '80s: PHILADELPHIA PMSA 1960-70 Annual Growth

Phila. Minus U.S.

1970-80 Annual Growth

Phila. Minus U.S.

1980-86 Annual Growth

Phila. Minus U.S

PMSA Total Employment Manufacturing Nonmanufacturing Population .. Unemployment rate

1.8% -0.1 2.8 1.0 [3.3]

-0.9 -1.6 -0.5 -0.2 -1.1

0.7% -2.2 1.8 -0.2 [7.7]

-1.8 -2.7 -1.4 -1.3 1.2

1.2% -0.3 -1.8 -0.9 2.0, « -0.1 0.3 Q ; -0.8 [5.8] -1.3

Philadelphia^ c ) Total Employment Manufacturing Nonmanufacturing Population ... Unemployment rate

0.2 -2.5 1.3 -0.3 [4.6]

-2.5 -4.0 -1.9 -1.5

-1.6 -5.4 -0.6 -1.4 [11.4]

-4.1 -5.9 -3.7 -2.5 4.9

-0.6 -4.8

1.1

2.7 -0.3 4.1 0.5 [5.9]

0.2 -0.8 1.0

Rest of P M S A ( C ) Total Employment Manufacturing Nonmanufacturing Population (b) Unemployment rate

[2.3]

United States Total Employment Manufacturing Nonmanufacturing Population (b) Unemployment rate

2.7 1.4 3.3 1.3 [4.4]

3.9

0.2

2.2

0.8

2.1

0.8

4.9

1.7 -2.1

2.5 0.5 3.1 1.1 [6.5]

-0.6

-0.6

-2.1 -3.9 -2.0 0.1 - 0 . 6 (a) -1.7 -0.6 [6.5] 2.3

-0.6

0.8

0.3

1.2 3.3 (a) 0.8 X ~' -0.3 [5.4] -1.7

1.5 -0.9

2.1 1.1 (a)

_

[7.1]

SOURCES: Computations based on data from several sources. Employment data: Employment and Earnings, Bureau of Labor Statistics, U.S. Department of Labor; County Business Patterns, Bureau of the Census, U.S. Department of Commerce. Population and unemployment data: Census of Population, 1960, 1970, 1980, and unpublished data, Bureau of the Census, U.S. Department of Commerce. (a) Population growth rates are from 1980 to 1985. (b) Unemployment rates, in brackets, are rates at the end of the time periods, not annual growth. (c) 1960-1970 Philadelphia and Rest of IMSA employment growth rates are derived from county data for 1959.

14

Economic Development within the Philadelphia Metropolitan Area

increases in some years) and a slowing down in national employment growth rates. The counties surrounding Philadelphia have consistently outperformed the nation. Overall employment growth rates have been higher over the 25 years. Nonmanufacturing rates have been higher, and for most of the period—including the 1980s—manufacturing has also grown more rapidly or declined less in this region than in the country as a whole. These employment changes mirrored population changes. On the whole, population growth in the region, and in both the city and the suburbs, has been less than for the whole United States, but the difference has been shrinking. As population has decentralized, employment has followed. The net effect of those adjustments is reflected in the region's unemployment rates, which are now substantially and increasingly lower than those of the nation. The first quarter 1986 unemployment rate of 7.1% for the nation has, as its regional counterparts, 5.8% for the PMSA, 6.5% for Philadelphia, and 5.4% for the rest of the PMSA. The evidence that the Philadelphia metropolitan area has emerged from its difficult period of adjustment appears to be strong. The industries in which the region could not compete have shrunk, the industries in which the region has comparative advantage have grown, and employment and population shifts have become sufficiently synchronized to produce unemployment rates lower than those of the nation. The major exception to this pattern of substantially completed accommodation is the city's manufacturing sector. The data suggest that, in this sector, specialization in the industries for which Philadelphia is best suited is far from having been achieved. The shakeout process in manufacturing has not completely run its course in the city.

Three

Overall Economic Trends in the Counties Historically, economic development patterns within a given region are very closely tied to such indigenous characteristics as its population and geography. In the development of the Philadelphia metropolitan area, as elsewhere, access to rivers was a major asset for industry, and, when transportation and communication networks were sparse, population density was a significant factor in achieving economies in obtaining inputs and selling outputs. Revolutionary changes in these networks have taken place over the last half century, however. Choice of location for most industries is no longer so dependent on the natural qualities of an area. Other characteristics dominate the choice. For the Philadelphia region, this change has been reflected in a significant reduction in the city's domination of the region's industry. Philadelphia flourished and expanded in an era where economies flowing from proximity to markets (agglomeration economies) dominated cost, enabling the city to share in the urban industrialization of America. But it is the counties outside the central city that have now emerged as the expanding and flourishing parts of this region—a pattern shared in common with many of America's urban areas.

Pre-World War II: Development into a Metropolitan Area

The major economic transformation in the Philadelphia region prior to World War II was its welding into a coherent labor market area dominated by a relatively large metropolis. It developed into one of the nation's largest metropolitan areas. Different parts of the region originally had very different economic characteristics. It is particularly interesting to note the past wide variations among the counties, because it is these which have narrowed considerably. Bucks County was once a predominantly rural county with a prosperous farming industry and scattered small-scale industry. Agriculture was not a large employer, but over 90% of the county's land was devoted to crops, pastures, and forests. The soil was rich, an indigenous characteristic, and the location was proximate to markets. Bucks was among the smallest counties in the region, with a population of less than 108,000 in 1940. Manufacturing employed about 13,500, of which 3,000 were in the apparel industry, 2,600 were in textiles, 1,400 in chemicals, and 1,200 in instruments and precision goods. Other industries, which grew rapidly subsequently, employed only handfuls of people in 1940—fabricated metals employed 340, and electric machinery employed 26! Prior to World War II, then, Bucks County was essentially a homogeneous agrarian community with some pockets of industrial activity and was only tangentially part of the Philadelphia metropolitan area.

16

Ecmmmuc Development withia the Philadelphia Metropolitan Area Burlington County, one of the smallest counties in the region with a 1940 population of 97,000, was also overwhelmingly agricultural prior to World War II. It is situated north of Camden along the Delaware River. What nonagricultural industry it had, developed mostly along the riverfront, and most of the county's population was also situated close to the Delaware River. Foundries, serving machine operations, dye houses, shoe factories, and flour mills were the manufacturing industries, but farming was the primary economic activity. In the 1930s, Burlington County, apart from being a supplier of agricultural products, was not significandy integrated into the Philadelphia regional economy. Camden County, situated directly across the Delaware River from Philadelphia, has, historically, had close economic and cultural ties with it. The transformation of Camden County from a rural, agrarian area to an area with an industrialized center and suburban development took place before World War II—much earlier than in Burlington and Gloucester counties. The county's industrial development was strongly influenced by the ownership of much of the land along the Delaware River by railroads. These railroads connected Camden with major cities on the east coast, which encouraged factories to locate there and influenced the patterns of industrial growth. Prior to World War II, the city of Camden and its immediate surroundings were a thriving industrial community. Residential suburbanization, including some moderate residential movement from Philadelphia to Camden's suburbs, began as early as 1920, encouraged by the construction of highways, trolley lines, and, in 1926, the Ben Franklin Bridge. Camden County, prior to 1940, was closely connected to the Philadelphia metropolitan area. Its population was relatively large—256,000 in 1940. Residents and workers moved between the county and the city of Philadelphia, and its economic development patterns were much like those of the city. Chester County, immediately prior to World War II, was still largely an agricultural area, despite its proximity to Philadelphia. Dairy farming and mushroom crops used large portions of the county's land area. Out of a 1940 population of 136,000, manufacturing employed 11,550 workers. The largest manufacturing sector, by far, in 1940 was primary metals, which employed 4,800. Paper, textiles, and food each employed over 1,000, and fabricated metals had 750 workers. Before 1940, Chester County was a dominantly agrarian economy, with one relatively large manufacturing sector. Delaware County, situated south and proximate to Philadelphia, was the largest of Philadelphia's surrounding counties in 1940, with a population of 311,000. Its position on the shores of the Delaware River and its extensive railroad service led to intensive industrialization in the pre-World War II years. The advantage of having access to both a deep, navigable river and train tracks led to a heavy concentration of industry along the Delaware River, with accompanying population expansion. There was high-density usage of the county's land. Delaware County had a relatively large manufacturing sector, employing

Overall Ecooenic Trends in the Coontkf

17

over 34,000. In 1940, the largest industry in the county was transportation equipment, with over 11,000 employees. Textiles employed almost 4,800 and nonelectric machinery over 4,300. Petroleum and chemicals were the only other industries employing over 3,000. Prior to 1940, Delaware County was closely linked to the city of Philadelphia. Residents and workers moved extensively between them, and the county shared in the nation's industrial expansion. Gloucester County, situated south of Camden County across the Delaware River from Delaware County, was, except for a concentration of heavy industry along the river, a rural area. It was the smallest county in the region, with a 1940 population of 72,000. The county's major population concentration was south of the city of Camden, and its industrial activity consisted primarily of petroleum and chemical plants located across from Philadelphia along the Delaware. Mobil Oil's refinery operations began there in 1917. Prior to World War II, then, Gloucester County had an industrial sector that was close to Philadelphia, and accompanying links between the two were established. The rest of the county—farm and vacant land—was more loosely connected. Montgomery County's transition from an exclusively rural county to one with substantial commercial development occurred early in the twentieth century. During the 1920s, population growth accelerated, as the late nineteenth century railroad boroughs developed commuting as a means of connecting residents and jobs. The placement of the railroad in Montgomery County, it should be noted, was not in response to the county's needs. It was because the county lay along routes from Philadelphia to elsewhere—to connect with Pittsburgh's coal and steel, for example. Also, during this period, roads between the city and the contiguous parts of the county improved, thus encouraging more dense settlement in townships close to Philadelphia. In 1940, Montgomery County's population was the second largest in the surrounding counties, 289,000. A relatively high proportion of its population, 41,000, was employed in manufacturing. Most of the county's industrial employment during the pre-World War II period, was in textiles and apparel, and primary and fabricated metals. Dairy farming was the primary agricultural endeavor of the county. As industry developed, agriculture declined in importance. The number of farms and amount of land devoted to farming decreased substantially. As downtown shopping districts evolved in the railroad boroughs, commercial centers developed, first close to the railroads, and then along major roads as they were constructed. Montgomery County, then, began to lose its rural character early in the twentieth century. The presence of the railroad was the significant factor in its development as a "bedroom" community. The growth of that population had important implications for the subsequent development of industry in the county. Philadelphia, of course, was the hub of the metropolitan area. The 1940 population in the city was almost two-thirds of the PMSA's population, most of the employment in the region was in the city, and it was the intellectual and cultural center of the area. The city was, in terms of people

18

Economic Development within die Philadelphia Metropolitan Afea

and jobs, most of the region. It shared in the urban industrialization of America in an era where agglomeration economies were a major cost factor. Most of the workers were employed in heavy manufacturing. The correct description of the pre-World War II Philadelphia regional map is that it was uninucleated. It had an overwhelmingly dominant core— the city of Philadelphia. In 1940 three counties were closely linked to this nucleus. Delaware County (with a population slightly more than one-sixth that of Philadelphia) had relatively intensively developed its manufacturing industry; Montgomery County (with a population slightly less than oneseventh of Philadelphia's) had developed into the city's bedroom community, and Camden County (with slightly less than one-eighth of Philadelphia's population) had developed both industrial and commuting links. The other four counties, Bucks, Burlington, Chester, and Gloucester, were basically not identifiable as links in the development of the Philadelphia metropolitan economy.

Post-World War II: Decentralization of the Metropolitan Area

World War II and its aftermath accelerated changes in many of the factors underlying the location decisions of people and jobs. Profit maximization is clearly a driving force for firms choosing a region to locate in, and where in the region to locate. Many factors enter into that calculation. Land and building costs, labor costs, transportation costs of inputs, transportation costs of outputs, and taxes all affect revenues and expenses. And these costs may significantly vary between a region's central city and its surrounding areas. During and after World War II, highways were built, mass transit was developed, new communications technologies emerged, suburban housing was heavily subsidized relative to urban housing, and educational opportunity became more equalized across the nation. All these developments affected the relative costs and prices associated with locating in the city versus the suburbs. Legal or other professional establishments catering to the needs of city-located firms found it advantageous to be in the city with its relatively high density of people and jobs. But the legal or professional firm catering to the expanded employment and population in the suburbs found it most beneficial to locate nearer the newly developed density. Manufacturing establishments using one-floor production processes also found it advantageous to locate in a suburban area where land was cheaper, since transportation networks became so supportive outside of central cities. The metropolitan map of America changed during the post-World War II period. Uninucleated maps became multinucleated as decentralization took place. Philadelphia was no exception. The details of this movement compared to developments in other areas are described and analyzed in Chapter 7, but the broad aspects of this movement, discussed in the following paragraphs, underlie all analysis of the current regional economy. The dramatic features of the decentralization are shown in Table 3.1, which presents the percentage shares of population and employment in each of the eight counties in the Philadelphia PMSA for each decade

Overall Economic Trends in the Counties

19

between 1950 and 1980. The summary observation is clear: there have been relative and absolute declines in employment and population in Philadelphia, and large increases in the surrounding counties (particularly in Bucks, Burlington, and Montgomery). Philadelphia's share of the PMSA's population dropped from 56.4% to 35.8% between 1950 and 1980, accompanied by an even more precipitous drop in employment share from 67.5% to 38.6%. The absolute declines were over 380,000 in population and about 190,000 in employment. During the 1950s only services and FIRE employment increased. During the 1960s, construction along with transportation, communications, and public utilities (TCPU) also increased, but in the 1970s only services continued to expand. (Sectoral details on employment in each of the counties for the 1951-1980 period are shown in Appendix Table C.2.) The counterpart of the Philadelphia story is the drama of the suburban development. Every one of those counties showed increases in employment and population shares between 1950 and 1980. Some were particularly large. Bucks County's population share increased from 3.9% to 10.2%, and its employment share from 2.9% to 9.1%. A major episode in its industrial and residential development was the decision of U.S. Steel to build a major steel mill in the lower portion of the county. The Fairless Works went into operation at the end of 1952. An influx of new employees and residential development took place rapidly—population and employment doubled between 1950 and 1960. Both continued to expand in the next two decades, but somewhat more slowly. Burlington's population share increased from 3.7% to 7.7%, and its employment share from 1.8% to 5.0%. Its population became substantially suburbanized during the 1950s, reflecting the increased emigration from Table 3.1 COUNTY POPULATION AND EMPLOYMENT SHARES: PHILADELPHIA PMSA, 1950-1980 Population County Bucks Burlington Camden Chester Delaware Gloucester Montgomery Philadelphia PMSA

Employment

1950

1960

1970

1980

1951

1959

1970

1980

3.9% 3.7 8.2 4.3 11.3 2.5 9.6 56.4

7.1% 5.2 9.0 4.8 12.7 3.1 11.9 46.1

8.6% 6.7 9.5 5.8 12.5 3.6 12.9 40.4

10.2% 7.7 10.0 6.7 11.8 4.2 13.6 35.8

2.9% 1.8 7.0 2.9 7.7 1.2 9.0 67.5

4.2% 2.6 7.8 3.8 8.5 1.6 11.2 60.2

6.1% 3.6 7.8 4.8 9.0 1.9 15.6 51.2

9.1% 5.0 8.7 6.6 9.8 2.7 19.4 38.6

100.0

100.0

100.0

100.0

100.0

100.0

100.0

100.0

SOURCE: County Business Patterns, 1951, 1959, 1970, 1980 and Census of Population, 1950, 1960, 1970, 1980, Bureau of the Census, U.S. Department of Commerce.

2«

Economic Dcrdopment within the Philadelphia Metropolitan Area Philadelphia and Camden. Population density shifted from the riverfront region, which historically had the highest concentrations of population, to the newer suburban communities to the east. Industry suburbanized in the 1970s. The old plants along the riverfront were replaced by new plants along the highways. (Union Carbide and American Honda are examples.) Montgomery, already a bedroom community before 1940, increased its population share from 9.6% to 13.6%, and its employment share went from the largest 1951 suburban share of 9.0% to the overwhelmingly largest 1980 suburban share of 19.4%. It was the largest gainer from the region's employment decentralization. The building of the Schuylkill Expressway connecting downtown Philadelphia to the Pennsylvania Turnpike, the construction of that turnpike across Montgomery County during the 1940s, and the attachment of the county to the interstate highway system were major developments. Access to this transportation network changed a variety of cost factors. The increased preference for trucking over rail transport was facilitated by the easy highway access. Workers were freed from having to reside near plants, so worksites could be more decentralized. The advantages of lower cost land for one-story manufacturing were not counteracted by higher transportation costs. And the development of Montgomery County as a bedroom community was further facilitated by reduced commuting time. Delaware, with a land area already heavily utilized before World War II, showed a small increase in population and employment shares. In general, it was "too crowded" at the outset of the period of decentralization to benefit gready from it. It had already been the site of the first beginnings of suburbanization. A heavy concentration of industry had already developed along the Delaware, and in the state its population density was second only to Philadelphia. Camden, with a substantially developed industrial sector in 1940, also experienced only relatively small increases in its population and employment shares. The construction of the Pennsylvania Turnpike also benefited Chester County. Residential suburbanization accelerated in the 1960s, followed by an acceleration in employment in the 1970s. Gloucester County's suburbanization occurred throughout the period. Its share of regional population increased steadily over the thirty years. Its employment share increased slowly from 1950 to 1970, then jumped substantially during the 1970s. The reduction in the role of the central city and the parallel expansion in the surrounding areas that occurred in the Philadelphia PMSA were not unique to the area. Many of the nation's metropolitan areas shared the experience. Underlying market forces, increased residential preference for lower density living, and improved communications technology interacted with government policies (greatly expanded highway network, housing subsidies) to produce a new map characterized by smaller central cities and larger concentrations of people and jobs outside them. These changes have been a long time coming, and they now represent well-rooted structural transformations in the local economies. The need for new policies to accompany this transformation arises from the fact that the

Overall Economic Treads in the Cmmtks

21

associated movement of economic strength crossed government boundaries. When the city of Philadelphia was suburbanizing within its boundaries—from the Center City areas to the Northeast, for example— government expenditures and revenues benefited and were collected from the same people. But, when local government boundaries are crossed, the implications are significant. The gainers have access to more revenue and more growth potential. The central city, the loser, faces reduced revenue and lower potential. City policymakers have to address its smaller size, and suburban policymakers have to address the extent to which their strength flows from the existence of a center city.

Accompanying Socioeconomic Profile

The dispersal of jobs and people across the counties composing the metropolitan map might also be accompanied by changes in the socioeconomic characteristics of these counties. Growth areas might have different profiles from no growth areas. Income levels, the size of the poverty population, educational attainments, and occupational breakdowns are descriptors of the resident population that might affect, and be affected by, the significant density changes. In fact, for the Philadelphia PMSA, the evidence indicates that no dramatic change occurred in the standings of the suburban counties relative to each other. Philadelphia's profile, in contrast, worsened significantly relative to the suburban counties, as it lost employment and population. The changes in a number of socioeconomic characteristics, along with the changes in population and employment, are shQwn, for the 1960 to 1980 period, in Table 3.2. In general, the seven counties surrounding Philadelphia divide themselves into two groups. There is the high-growth group of Bucks, Burlington, Chester, and Gloucester, characterized by significant increases in population and employment over the two decades. And there is the more moderate growth group—ranging from Montgomery, with very high employment increases and modest population increases, to Camden County, with modest employment and modest population increases, to Delaware, with virtually no population increase and modest employment growth. Philadelphia is in a separate league—both population and employment declined. Several features emerge from the data in Table 3.2. The high-growth counties had the largest increases in real per capita income between 1960 and 1980—from 35.4% for Gloucester to 50.7% for Chester. The moderategrowth counties experienced moderate income changes of around 26%. Philadelphia, standing alone, had only a 17.7% increase. There was no clear association, however, between 1960 income levels and income growth in the following twenty years. The counties with the highest real per capita incomes in 1960, Montgomery and Delaware, had moderate increases in income, but so did Camden, starting from a lower income level. Chester, with virtually the same 1960 income level as Camden, had the largest twodecade income growth. Clearly, the income composition of the populations in the counties underwent some change. Chester and Bucks attracted an increasing proportion of high-income residents, moving from counties

22

Economic Development within the Philadelphia Metropolitan Atea Table 3.2 CHANGED SOCIOECONOMIC PROFILE OF THE COUNTIES: PHILADELPHIA PMSA, 1960-1980 z (b)

x

White Collar

Blue Collar

% 4 or 4+ Yrs. College

51.0 (e) 92.8 149.8 193.8%

41.9 50.6 56.2 34.1%

44.5 36.2 32.9 -26.1%

8.9 12.1 13.6 *4.7

31.9 (e) 54.1 82.5 158.7%

43.8 55.4 59.7 36.3%

40.6 29.6 27.1 -33.3%

8.0 12.6 18.4 *10.4

93.7 (e) 118.1 142.2 51.7%

45.7 52.7 59.6 30.4%

41.2 32.0 28.2 -31.6%

6.9 9.8 16.2 *9.3

*0.2

46.2 (e) 72.5 107.3 132.5%

40.5 51.8 58.0 43.2%

38.3 31.5 28.1 -26.6%

11.4 17.1 26.3 *14.9

$2,617 3,344 3,317 26.8%

n.a. 4.6 5*8-,» *1.2

102.9 (e) 137.0 160.7 56.2%

52.7 58.7 61.4 16.5%

34.3 28.1 26.4 -23.0%

11.4 13.9 18.9 *7.5

134.8 172.7 199.9 48.3%

$2,113 2,714 2,861 35.4%

n.a. 5.7

19.8 (e) 28.4 44.9 126.9%

37.4 44.9 51.6 38.0%

46.3 38.3 35.2 -24.0%

6.1 8.0 13.1 *7.0

1960 1970 1980 Change 1960-80

516.7 623.8 643.6 24.6%

$3,181 3,943 4,014 26.2%

n.a. 3.3

135.1 (e) 236.7 318.4 135.7%

51.3 58.4 62.7 22.2%

35.1 29.1 26.3 -25.1%

13.6 17.2 24.9 *11.3

1960 1970 1980 Change 1960-80

2002.5 1948.6 1688.2 -15.7%

$2,121 2,722 2,496 17.7%

n.a. 11.2

725.3 (e) 775.5 632.1 -12.9%

40.8 47.5 54.4 33.3%

39.1 33.1 29.6 -24.3%

5.1 6.8 11.1 *6.0

Pop. (000)

Real (a) Per Cap. Income

308.6 415.1 479.2 55.3%

$2,267 3,059 3,319 46.4%

n.a. 4.1

1960 1970 1980 Change 1960-80

225.1 323.1 362.5 61.0%

$2,256 2,953 3,163 40.2%

n.a. 5.2

1960 1970 1980 Change 1960-80

392.0 456.3 471.7 20.3%

$2,364 2,996 3,001 26.9%

n.a. 6.8

1960 1970 1980 Change 1960-80

210.6 278.3 316.7 50.4%

$2,398 3,287 3,614 50.7%

n.a. 4.5

1960 1970 1980 Change 1960-80

553.2 600.0 555.0 0.3%

1960 1970 1980 Change 1960-80

Bocks

1960 1970 1980 Change 1960-80

Burlington

Caaden

Chester

Delaware

Gloucester

Montgomery

Philadelphia

% of Families In Pov.

*0.6

*-0.1

*2.8

*0.7

*0.0

16 6

' fH>>

*5.4

Empi. (000)

SOURCES: Census of Population, 1960, 1970 and 1980, County Business Patterns, 1959, 1970, 1980, Bureau of the Census, U.S. Department of Commerce, CPI Detailed Report, 1985, Bureau of Labor Stalstlcs, U.S. Department of Labor. (a) (b) (c) (d) (e)

Real per capita Income In 1967 dollars. Deflator from Appendix Table D.4. "White Collar": Prof. & Tech., Managers & Admin., Sales, and Clerical. "Blue Collar": Craftsmen, Foremen, Operatives, Trans. Operatives, and Laborers. Change from 1970 to 1980. 1960 employment figures are for 1959. *: Percentage point change.

Overall E c t m k Trend* in the Comities

23

which had the third and fifth highest income levels in 1960 to the second and third highest in 1980. Camden and Delaware, on the other hand, attracted or kept more lower income residents, moving from fourth and second positions in 1960 to sixth and fourth positions in 1980. But the rich stayed rich—Montgomery County remained with the top income level—and the poor got poorer—Philadelphia went from the second lowest in 1960 to the lowest in 1980. The high-growth counties tended to have the smallest increases (less than one percentage point) in the proportion offamilies with incomes below the poverty line between 1970 and 1980. The more moderate-growth counties had slightly higher increases, though Montgomery was an exception, with no change. Philadelphia experienced a very substantial 5.4 percentage point increase. The proportion of blue collar workers declined around 25% in every county in the Philadelphia region, as it did in the nation. The change here mirrored that of the nation, and was felt similarly throughout the metropolitan area. The country-wide increase in the proportion of white collar workers was experienced here as well, but less evenly: Chester had very high increases, Delaware very low ones. It is interesting to note that Philadelphia, though losing substantial amounts of employment (12.9%), had substantial increases in the proportion of its employment in white collar jobs (33.3%). Although education levels increased in the region, the relative education levels among the counties did not undergo any dramatic changes over the two decades. The proportions of the twenty-five-years-and-over population with four or more years of college more than doubled in the region. Philadelphia and Gloucester had the smallest proportion of highly educated residents in 1960, as they did in 1980, though the levels were much higher. Chester and Montgomery had the highest in 1960 and they had the highest in 1980. Using several measures, the general conclusion is that changes in the socioeconomic profiles of the counties mirrored the major change in the region—the decline in the population and employment in the city, and the burgeoning growth of the surrounding counties. There were variations among the suburbs, but the big changes were between city and suburb. The differences between the two in proportions of families in poverty, real per capita income, and proportion of highly educated adults were far greater than the differences among the counties. And, between 1960 and 1980, these differences widened. The legacy of the regional dispersal of population and employment is spelled out in these widening differentials, which translated into serious fiscal burdens and very high unemployment for the city.

Four

Shifts among the Counties in Jobs and Resident Workers, 1960-1980 Mark Alan Hughes and Janice F. Madden

Various chapters of this study document the fact that, since World War II, jobs and residents have been dispersing from central cities into surrounding suburbs. Since I960, this movement has resulted in most older central cities experiencing an absolute decline in both employment and population. The Philadelphia metropolitan area has not been an exception. By 1980, the city of Philadelphia had over 20% fewer jobs and nearly 22% fewer resident workers than in 1960. During this same period, however, the number of people living and working in the metropolitan area increased by more than 12%. Clearly, there has been a marked redistribution of jobs and workers within the area. The specific characteristics of this changed distribution of employment and residence in the Philadelphia metropolitan area are considered in the following sections. First, the data on the geographic redistribution of jobs and resident workers among the eight counties of the Philadelphia PMSA are presented. These data document the decentralization of jobs and workers in the region. Second, the growth of suburban local economies in which people both live and work is examined. This growth documents the decreasing daily interaction between the suburban counties and the central city. More people now commute among suburban counties than commute to the city. Third, the timing of changes in the suburbanization of employment and residents is considered as evidence of whether jobs are following residents to the suburbs or residents are following jobs. The changes from 1960 to 1980 indicate that, in the Philadelphia area, jobs are following people into the suburbs. Fourth, the locational shifts of jobs and workers in specific occupations are described. The evidence is that professional groups suburbanized less and blue-collar workers more than other occupational groups. Finally, the Philadelphia decentralization patterns are compared to those of other metropolitan areas, and policy implications are considered.

Decreasing Daily Interaction between City and Suburbs

Decentralization is shifting much of the economic activity of the metropolitan area toward the suburban counties. One consequence of this decentralization is the decreasing daily interaction between the central city and the suburban counties. In this section, the extent of this decreasing daily interaction is documented by reporting three related measures: the percentage change in each of the intercounty commutes from 1960 to

Shifts in J«tx and Retidenl Worker*

25

1980, the proportion of city commuters in each county from 1960 to 1980, and the ratio of local workers to city commuters in each county from 1960 to 1980.

Changes in intercounty commuting, 1960-1980 Table 4.1 reports the percentage change in the number of jobs and resident workers in each county between 1960 and 1970 and between 1970 and 1980. These are based on cross-tabulated data from the "Journey to Work" subject reports of the 1960-1980 U.S. Census of Population. (These crosstabulations for 1960, 1970, and 1980 are described and presented in full detail in Appendix B.2.) The percentages in the main diagonal of the table (in bold print) represent the change in the numbers of local workers, those who both live and work in the county. There was a 19.9% increase (column 7, row 13) between 1960 and 1970 in the number of people who both lived and resided in Montgomery County, for example. The entries off the main diagonal of the table represent the change in the numbers of commuters, those who live and work in different counties. For example, there was almost a fourfold increase between 1960 and 1970 in the number of people who lived in Burlington County and worked in Montgomery County (column 7, row 3). The final column (column 9) represents the increases in the total number of workers who live in each county and who, work in any of the counties. Between 1970 and 1980, for example, there was a 43.9% increase (column 9, row 2) in the number of Bucks County residents who were working in the Philadelphia PMSA. The bottom rows (17 and 18) show the percentage increase in the numbers of jobs in each county. Between 1970 and 1980, for example, there was a 55% increase (column 4, row 18) in the number of jobs in Chester County, but an 8.4% decline (column 8, row 18) in Philadelphia. In contrast to the loss of resident workers and employment in Philadelphia, the suburban counties gained considerably during both periods. For example, between 1960 and 1970, employment in Montgomery County increased by 35.1% (column 7, row 17). This was accompanied by increased commuting into the county, including a 78.1% increase (column 7, row 15), between 1960 and 1970, in workers commuting from Philadelphia. (Total outbound commuting from Philadelphia increased from 51,905 to 72,248 workers between 1960 and 1980, an increase of 39%.) Between 1960 and 1970, most of the suburban counties had large increases in the number of either jobs or resident workers. For example, Montgomery and Delaware had roughly twice the increase in jobs as in resident workers—35.1% compared with 14.4%, and 10.9% compared with 4.6%. On the other hand, Burlington and Chester had roughly twice as great an increase in resident workers as in jobs—17.8% compared with 5.7%, and 14.5% compared with 6.7%. Except in Bucks County, there was little growth (less than 20%) in local workers in the suburban counties between 1960 and 1970 (main diagonal). Between 1970 and 1980, however, there was increased growth in local

Economic DcTclopmnt within the Philadelphia Metropolitan Area

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64

Economic Development within the Philadelphia Metropolitan Area • Trade, services, and manufacturing are about equally important in Bucks. • Trade dominates Burlington's economy. • Trade and services dominate the Camden County economy. • Services are a big employer in Chester County, but so is manufacturing. • Delaware is strongly dominated by services, particularly health services. • Gloucester's economy is strongly dominated by trade, its services sector employs a relatively small share, and its manufacturing a relatively large share. • Montgomery County's economy is fairly equally divided among services, manufacturing, and trade, but FIRE is also a relatively large sector. • Philadelphia's employment is dominated by services, is low in manufacturing, and has the proportionately largest FIRE sector in the region. Those industries, then, show evidence of strength in their counties: the location has been demonstrably favorable, since they employ proportionately large numbers.

Size in the Region

A second way of assessing the relative strength of an industry in a county is to determine its position in the industry of the whole region. If employment in an industry in a county were a significant part of the region's employment in that industry, though it was but a small portion of the county's total employment, that would be a characteristic of strength. It would mean that the county location was relatively attractive to the industry, though the industry might not be a large employer. There are many examples of this in the data given in Table 7.2, which shows the share of employment in each county for each industry. Agriculture and mining, for example, employs only 1.296 of Montgomery County's total employed (see Table 7.1), but 24.3% of the PMSA's agricultural and mining jobs are in Montgomery. Similarly, Philadelphia's banking industry employs 3.4% (see Table 7.1) of those employed in the city, but almost 60% of the region's banking employees! Certain strengths are clear from this table. Philadelphia and Montgomery counties have, by far, the largest shares of the region's total employment—35% and 20%, respectively. With the sole exception of agriculture and mining, they have the largest shares of each major industrial sector. But, there are many examples of other counties having shares in individual sectors that are much larger than their total employment shares would suggest:

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Honmannfacturing Construction TCPU Trade FIRE Services

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80.1 72.0 65.6 81.7 75.3 84.5

19.9 28.0 34.4 18.3 24.7 15.5

86.4 81.7 78.9 87.7 80.0 89.1

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6.3 9.7 13.3 6.1 4.7 4.7

SOURCE: Analysis of data from the Regional Science Research Institute and the Bureau of Economic Analysis, U.S. Department of Commerce. See Appendix B.4 for the description of the data and methods employed.

Industrial Strength« of the Conntie«

83

rates is made. Again, for durables, 10.5 percentage points more are retained by the suburbs than by Philadelphia. This result is fairly evenly distributed across industries. In the lower half of the table, the same type of estimates are shown for the employment multiplier. In all cases, the retention rates of the surrounding counties are higher than those in the central city. For these multipliers, however, the results are not distributed evenly across industries. For durables and TCPU, the suburbs capture a much higher proportion than the city; for FIRE and services, the proportion is higher, but less so. The overall message is, therefore, that all the counties in the region are bound together in very important ways. If the only employment effects considered by a local government are those that occur within its boundaries, the region will be the loser. The full impact of any economic development program will be underassessed, probably significantly. If the parts of the region are to enjoy maximum employment growth, then local economic developers should value the effects of their efforts on the whole region and create' a level of labor mobility that would allow workers to participate in employment expansion wherever it takes place.

Summary: Economic Report Card for the Comities

The combination of all the measurements of employment strength appraised in the preceding sections of this chapter yields an economic profile for each county and each major industrial sector in the region. It provides the information for one important element of public and private decision making by clarifying which industries are strongest in each county and which counties are strongest in each industry. These results are summarized in an economic "report card" for the region in Table 7.9. There are four dominant features of this table: (1) Philadelphia receives the largest number of low grades; (2) Bucks, Burlington, and Montgomery counties receive the largest number of high grades; (3) the differences between Philadelphia and the surrounding counties are far greater than the differences among the latter; (4) trade and services, particularly business services, are the strongest industries in most of the suburbs; banking and legal services are the strongest in the city. The "grade" associated with each industrial sector for each county was derived from an extensive set of "grade" assignments. For each major sector, an evaluation was made of ten criteria. If the sector showed above average strength (its growth rate within the county was relatively high, for example), it received two stars; if it showed average strength, one star; and if it showed below average strength, no star. (For some of the two-digit sectors, less information was available, and the number of criteria, therefore, was fewer.) The precise bases for the assignment of stars are described in Appendix B.4. The following ten criteria discussed in the preceding sections were used to develop these assessments.

Economic Development within the Philadelphia Metropolitan Area

84

Table 7.9 ECONOMIC REPORT CARD FOR THE COUNTIES OF THE PHILADELPHIA FMSA BY MAJOR INDUSTRIAL SECTORS

Bucks

Burlington

Manufacturing Durables Nondurables

45Z 44 56

65Z 72 61

65% 61 67

50Z 61 39

Homanufactoring Ag. & Mining Construction TCPU Trade Wholesale Retail FIRE Banking Other FIRE Services Business Legal Health Education Other Services

80 93 70 15 80 79 79 40 57 50 75 75 21 64 29 57

75 71 70 50 85 64 86 35 79 0 75 81 21 43 7 14

60 50 40 25 60 86 50 50 29 71 70 88 57 71 21 29

75 86 70 40 50 21 43 55 57 71 80 81 43 43 29 79

Sectors

Delaware

Gloucester

Montgomery

Philadelphia

Sectors

Camden

Chester

Manufacturing Durables Nondurables

40% 33 44

65Z 33 72

55Z 61 56

15Z 17 33

•omianufacturing Ag. & Mining Construction TCPU Trade Wholesale Retail FIRE Banking Other FIRE Services Business Legal Health Education Other Services

70 71 70 60 45 79 21 25 0 36 70 44 50 50 43 79

60 71 55 20 85 79 71 25 29 43 50 63 36 14 50 14

80 71 65 35 70 71 71 60 57 86 80 75 29 57 14 71

45 50 30 35 45 29 36 50 64 50 45 31 86 50 43 36

SOURCE: Data in Tables 7.1 - 7.8 were combined to calculate summary scores. Details of the calculations are described in Appendix B.4.

Industrial Strengths of the Counties

85

1. Share of total employment in the county 2. Share of total employment in the PMSA 3. Share of total employment in the nation 4. Relative growth rate in the county 5. Relative growth rate in the PMSA 6. Growth rates relative to the nation 7. Change in growth rates relative to the nation between the 1970s and the 1980s 8. Strength of central city vs. suburban development relative to other metropolitan areas 9. Percent of output retained if the initial demand increase occurs in Philadelphia vs. outside the city 10. Percent of employment retained if the initial employment increase occurs in Philadelphia vs. outside the city If an industry is in this region—in the Philadelphia PMSA—in which counties is it flourishing most? Potential developers and allocators of economic development funds presumably have a strong interest in that information. The report card gives these answers: Total manufacturing: Burlington, Camden, and Gloucester lead, but all the suburban counties do very much better than the city's grade of 15%. Durables: Burlington, Camden, Chester, and Montgomery lead, but all the suburban counties do very much better than the city. Nondurables: Suburban counties do better than the city, though Chester has less of an advantage, and the city is not as disadvantaged as it is in durables. Total nonmanufacturing: The suburban counties do much better than the city and perform similarly to each other. Agriculture and mining: Bucks and Chester lead, Camden and Philadelphia lag. Construction: The suburban counties, except Camden, do very much better than the city. TCPU: In Delaware, the industry flourishes; in Bucks, Camden, and Gloucester it does not. Trade: All the suburban counties do much better than the city (Bucks, Burlington, and Gloucester are the strongest, and Chester and Delaware are the weakest in the suburbs).

86

Economic Development within the Philadelphia Metropolitan Afea

Wholesale trade: Six of the suburban counties do very much better than the city, but Chester has the least flourishing wholesale trade sector in the region. Retail trade: Burlington, Bucks, Montgomery, and Gloucester lead in the PMSA, and Delaware has the weakest retail trade sector in the region. FIRE: Montgomery leads the region, closely followed by Chester, Camden, and Philadelphia; in Delaware and Gloucester the industry is not strong. Banking: Burlington leads the region, followed by Philadelphia, then Bucks, Chester, and Montgomery; the sector is weak in Camden, Delaware, and Gloucester. Non-banking FIRE: Montgomery is the leader by far, Camden and Chester are also ahead, and the others are all behind. Total services: All the suburban counties are strong relative to Philadelphia, though Gloucester is the least so. Business services: Delaware and Philadelphia lag behind the other counties; Camden is a leader. Legal services: Philadelphia is the clear leader, with no close second. Health services: Camden leads, Bucks is relatively strong, and Philadelphia and the other suburban counties do about equally as well (except for Gloucester, where the industry is not active). Education services: In Gloucester, Delaware, and Philadelphia, the industry does about equally well. Other services: Montgomery, Delaware, and Chester do about equally well and are much stronger than Philadelphia and the New Jersey counties. For each of the eight counties, which industries are flourishing most? If they are engaged in targeting strategies, allocators of local economic development resources would, presumably, find these answers useful: Bucks has a number of industries that have high scores: trade (both wholesale and retail), services (particularly business), and construction; its agricultural and mining sector has the highest grade in the region. Burlington is flourishing in trade (particularly retail), banking and services (particularly business); its durables sectors are among the strongest in the region. Camden is strongest in services (particularly business and health) and wholesale trade, and it also has a strong nondurables sector. Chester is particularly strong in services (especially business and miscellaneous), and its durables and agriculture and mining sectors are among the strongest in the region.

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87

Delaware is relatively strong in wholesale trade, services (particularly miscellaneous), and construction. Gloucester, like Burlington, is flourishing in trade (particularly wholesale) and has the strongest nondurables industry in the region. Montgomery has a large number of relatively strong sectors—durables, trade, services (particularly business, health, and miscellaneous), and it is the region's leader in non-banking FIRE. Philadelphia's greatest relative strength is in legal services; banking is a strong sector; health services and non-banking FIRE are areas of comparative strength. These designations are intended to be used as targets for particular attention by decision makers. For government decision makers, they suggest where public expenditures (infrastructure development, for example) might be most productive. They provide guidelines for deploying scarce economic development resources geographically, and to industries. For private decision makers, they suggest where capital investment or real estate development might be particularly successful. They do not, of course, provide guidelines for directing resources to particular companies. Such decisions require firm-level data.

Eight

High Technology in the Region Thomas F. Luce

The promotion of high-technology development has become one of the most common components of regional development strategies in the United States. In the early years of this decade, regional planners and decisionmakers from all over the country jumped on the "high-tech" bandwagon. The fundamental reason for this is clear. In the past 10 to 15 years, a group of industries clearly associated with the frontier of technological development has bucked the national trend toward slower growth. During the relatively slow-growth years of the 1970s, various hightechnology industries helped places like California's Silicon Valley to maintain regional growth at levels like (or even exceeding) those prevalent during the long expansion period of the 1960s. In other places, such as Boston's Route 128 Corridor, growth in technology-related industries cushioned the effects of the deep recessions of the early 1980s and served as the focal point of robust growth in the current economic expansion. What is the Philadelphia area's standing in the national high-tech picture? Did the region reap any of the benefits of the high-tech boom and is it now in a good position to capture a significant share of future growth in these industries? Have any specific locations in the region had concentrations of high tech in the past or does any stand ready to capture a large share of future growth in the region? These are the questions addressed in this chapter. The Philadelphia PMSA has long been, and remains, one of the nation's largest centers of high-technology employment. Throughout the 1970s and into the early 1980s, the region was among the five largest metropolitan areas in the country in terms of employment in technology-related sectors. Although the PMSA's growth in these industries lagged behind national averages in the early 1970s, by the late 1970s and early 1980s, the gap had disappeared. At the end of 1985, the region was home to about 2.6% of national employment in high-tech industries. This was greater than its share of either total employment (2.1%) or manufacturing employment (2.0%). The area's greatest concentrations of high-tech employment are in sectors that have been growing in the nation, a very positive sign in view of the extent to which regional markets in these sectors are linked to national and international conditions. In addition, the region's three largest high-tech sectors—computer services and data processing, communications equipment, and pharmaceuticals—grew more quickly here than in the nation in the first half of this decade. Although some more recent occurrences—merger activity and signs of encroachment of foreign com-

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89

petition in U.S. markets—have cast some uncertainty on prospects for future growth in particular counties, the region as a whole entered the second half of the 1980s in a good position to share in future growth at the national level in high-tech industries. Within the PMSA, Montgomery County dominates the high-tech picture, with employment levels more than twice that of the second largest county, Philadelphia. In 1985, 34% of regional employment in high tech was in Montgomery County. Between 1975 and 1985, Montgomery also led the region in job creation in high tech, gaining more than 9,000 jobs over the period. Burlington, Chester, and Camden counties were close behind. Philadelphia and Delaware County, with greater than average concentrations in high-tech sectors in decline in the nation, each lost hightech jobs during this period—nearly 17,000 jobs in Philadelphia, and more than 3,000 in Delaware. Based on their past performance, Montgomery, Burlington, Chester, and Camden stand to gain the most from future hightech growth. However, Delaware has also shown signs of strength in the 1980s, and may emerge in the near future as a significant factor in regional high-tech growth. Defining High Tech

Defining high tech has been a major problem for both researchers and development planners. The central problem is that firms and sectors (the units by which readily available data are gathered) are not homogeneous. Individual firms often produce many different products, some of which may involve state-of-the-art knowledge or technology while others do not—pharmaceutical firms research, produce, and market cough drops as well as cancer treatments. In addition, almost any industrial sector that meets some criteria for high tech will include individual firms engaged in activities (or producing products) that do not meet the criteria, no matter how finely the sector is defined. The sector "office, computing, and accounting machines," for instance, includes firms that produce standard typewriters as well as those that produce computers.

Alternative definitions Because of these ambiguities, there is no single, universally accepted, definition of high tech. The many definitions that have been used fall into two broad categories. One type of definition groups industries (categorized by the output produced by firms in the sector) according to spending on research and development and the proportion of technology-oriented workers in the work force. This kind of definition has two major advantages. First, it relies on readily available data, which means that tracking sectoral performance over time and among places is straightforward. Second, it classifies sectors using clear-cut criteria that do not rely on subjective judgments about what terms like "state-of-the-art" or "rapid technological change" mean. The clear disadvantage is that this kind of definition is likely to overstate high-tech employment in sectors that meet the criteria (by including all employment in those sectors) and understate

90

Economic Development within the Philadelphia Metropolitan Atea

it in sectors that do not meet the criteria (by excluding all employment in the sectors). The second type of definition attempts to address the weaknesses of the first by distinguishing among the different kinds of activities taking place in individual firms and sectors. Employment in an individual firm that is associated with research and development activities, for instance, would be counted as high-tech employment, while production-level jobs associated with producing a standard, "non-high tech," product would not. Although it avoids most of the pitfalls of the first category of definitions, this approach presents the investigator with its own set of problems. One is still left with the problem of defining what kinds of activities, products, or firms qualify as high tech. And, more importantly, the time and costs involved in data collection at this level of detail are likely to limit the extent to which comparative analysis across regions and sectors is possible.

Selected definition The definition chosen for use in this chapter is one of the most commonly used definitions from the first category. In addition to the advantages common to the category as a whole, the chosen definition matches, or very nearly matches, that used in the majority of empirical research on the topic. The definition employs two criteria—research and development spending and the proportion of technology-oriented workers in the workforce—to classify sectors as high tech or not. It includes 26 manufacturing sectors (defined at the three-digit SIC level) and one service sector. (See Appendix B.5 for a more complete description of the definition.) Despite the fact that a major advantage of the chosen categorization scheme is its use of standard industry classifications, data problems remain. These are described in Appendix B. As a result of these problems, the national data used for comparisons with other large PMSAs are not compatible with the regional data used in the rest of the chapter. In addition, some data for specific sectors in the New Jersey counties had to be estimated because of incomplete reporting by the Bureau of the Census.

Advantage* and Disadvantages of Promoting High Tech

The fascination that high tech holds for regional development planners is not surprising. High-tech industries embody a group of characteristics that make them very attractive targets for local development efforts. However, they are also characterized by features that make them elusive targets. On the positive side, technology related industries: • include the nation's most competitive manufacturing industries in world markets • provide an attractive alternative to workers displaced by the decline of many traditional manufacturing industries • produce for national and international markets • generate high productivity, high-wage jobs

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91

On the negative side, high-technology sectors: • are very diverse in nature • are often dominated by a few large multi-plant firms

Advantages Nation's competitive advantage. Perhaps the single most attractive feature of high-tech industries to local planners is the growth that these sectors have enjoyed. At the national level, the group of 27 high-tech sectors grew at an annual rate of 2.5% between 1975 and 1985, matching the rate of growth of total employment in the nation. This growth occurred during a period when employment in low- and medium-technology manufacturing sectors was virtually stagnant. The major reason for this dichotomy, of course, was the nation's changing role in the international economy. For a variety of reasons, the United States maintained a comparative advantage relative to its trading partners in many high-tech sectors over the time period. This contrasted with the increasing competitive pressures experienced in the more traditional sectors, where the nation began the period with very small advantages, if any, over its competitors. (See Paul Krugman's 1984 article for a good discussion of the relationship between high tech and the American position in world markets.) The same kind of competitive pressure may inhibit domestic employment growth in some high-tech sectors in the future, as other countries, notably Japan, narrow their technological and productivity gaps with the United States. Indeed, most analysts feel that this is happening (or has already happened) in parts of the electronic components sector. However, it is clear that, where the United States maintains a competitive edge in manufacturing, it is most likely to be in high labor productivity, technology-oriented sectors like those coming under the high-tech umbrella. Alternative for displaced workers. Regional growth patterns set high tech apart from other, more traditional, industries, as well. At the regional level, a few PMSAs have experienced explosive growth in high-tech employment. Growth rates actually exceeded 10% per year in some years in places like San Jose and Minneapolis. To areas that were losing substantial numbers of jobs in the traditional manufacturing sectors that had formed the basis of the local economy for decades, high-tech development, therefore, represented a potential means to turn local economies around. Market orientation. The fact that high-tech firms engage, for the most part, in manufacturing activities serving national and international markets also enhances their attractiveness as policy targets. Estimates in the 1985 Economic Report on the Philadelphia Metropolitan Area indicated that, in 1981, approximately 85% of the output produced by manufacturing concerns in the Philadelphia PMSA was exported from the region to the rest of the country or the world. This compared with a figure of about 23% for nonmanufacturing output. Jobs added in firms in export-oriented indus-

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Economic Development within the Philadelphia Metropolitan Area

tries like high tech are much more likely to result in net gains in total employment in a given region than those added in firms serving local markets. The gains in one firm are much less likely to be displacing jobs in other firms in the same region. Wage levels. High-tech sectors, because they rely to a large extent on skilled labor, also generate high-wage jobs. In 1982, average hourly earnings for production workers in high-tech sectors in the United States exceeded the average in the other manufacturing sectors by 16% and by 26% in the Philadelphia PMSA. Wages in total manufacturing, in turn, exceeded those in services sectors by more than 20% in the United States. These high wages not only support higher living standards, but they also generate more substantial secondary effects within regional economies through multiplier effects. Research done for last year's report indicates that for each new manufacturing job in the Philadelphia PMSA, an additional 0.9 jobs are created through secondary effects, compared to an additional 0.5 jobs for each new nonmanufacturing job. If public sector incentives are equally effective for manufacturing and nonmanufacturing (a big if, in many cases), then high-tech sectors provide a bigger "bang for the buck." Disadvantages Diversity of sectors. Counterbalancing these advantages are some distinct disadvantages to targeting high-tech development. The definitional issues discussed in the previous section make high tech an elusive target and accentuate the diversity of sectors coming under any definition. The diversity means that one cannot assume that local incentives for businesses will affect different high-tech sectors in the same way. Firms in a sector like computer and data processing services almost surely seek out different kinds of local economic environments than firms in sectors like electronic components or space vehicles. Domination by large, multi-plant firms. The fact that many high-tech sectors are dominated by a few large, multi-plant firms also holds some peril for local economies. First, resources in such firms are likely to be more geographically mobile than those in locally based, single-plant companies, putting local jobs at more risk over the long term. Second, branch plants are likely to have fewer linkages to other sectors or firms in the regional economy, reducing the local multiplier effects. And, finally, as a general rule, dependence on a few large firms for growth by a locality or region implies greater risk over the long term, since a single decision made by businessmen in a different part of the country can have substantial local repercussions. These repercussions may be positive as well as negative, of course, but the downside risks for the local economy can be substantial. In the Philadelphia area, at least four of the region's largest high-tech sectors contain much of their employment in a few large firms. On the New Jersey side, employment in communications equipment is largely dependent on employment in two large RCA plants (now merged with General Electric) in Camden and Burlington counties. Similarly, on the Pennsylvania side, pharmaceuticals, office and computing equipment, and

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93

aircraft and parts rely heavily on a few firms—Rohm and Haas, and SmithKline Beckman in pharmaceuticals, Commodore Business Machines and Sperry in computers, and Boeing-Vertol in aircraft. The implications of this concentration for these sectors are not all negative, of course, especially since three of the six mentioned firms are headquartered in the region. However, in the long term, diversity within industrial sectors (in terms of the number of firms and their specialties) is as valuable an asset to a region as general diversity across industries. In sum, the promotion of high tech in regional economies holds negative as well as positive prospects. Growth in high tech in the present holds some clear advantages over growth in other kinds of industries—good prospects for future growth in the nation as a whole, high average wage rates, and greater than average secondary effects. But that growth may imply greater risks as well—uncertain success of local incentives, susceptibility to international market swings, and greater than average dependence on the performance and decisions of individual firms.

Philadelphia PMSA High Tech in the National Context

Discussions of high-technology industries in the nation often begin and end with stories about a few distinct high-tech regions—the Silicon Valley or California as a whole, Boston's Route 128 Corridor, North Carolina's High Tech Triangle, and Minneapolis come immediately to mind. One rarely hears the Philadelphia area mentioned as a high-tech center. However, the numbers in Table 8.1 document that this is an omission. The Philadelphia PMSA had the fifth largest concentration of employment among all PMSAs in 1982 in the 27 industrial sectors defined as high tech in this chapter. The region also showed a greater than average concentration of high-tech employment (relative to total employment) and outgrew the nation in these sectors between 1977 and 1982. Its standing among the ten largest high-tech PMSAs in the country was not quite as impressive. It lagged behind the average in growth (4.3% per year compared with an average of 5.2%) and high-tech share of total employment (6.4% compared to 7.6%). The Los Angeles PMSA showed by far the greatest employment in high-tech sectors in 1982, exceeding the second largest area (San Jose) by more than 150,000 jobs (by 85%). Following Los Angeles were two clusters of PMSAs—San Jose and Chicago with employment at about 180,000, and then Dallas, Philadelphia, Boston, and Anaheim in the 110,000 to 125,000 range. New York, Newark, and Minneapolis finished out the group of ten. In 1982, these ten PMSAs contained roughly 29% of total employment in the nation in high tech, up from about 26% in 1977. The group, as a whole, outgrew the nation in these sectors by about 2 percentage points per year between 1977 and 1982. (This difference should be regarded as an upper bound, however, because of differences in disclosure conventions between the two years that affected the PMSA employment estimates, but not the national numbers. See note a, Table 8.1.) Thus, high-tech employment was becoming more concentrated in large high-tech centers during the time period. This trend is also evident in a sample of the 44 largest high-tech centers (representing about half of

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94

national high-tech employment in 1982) used for the empirical analysis described in Appendix B.5. High-tech employment in the Philadelphia P M S A grew between 1977 and 1982 at a rate in excess of the national rate, but less than the average for the ten P M S A s together. Philadelphia outgrew four (including L o s Angeles) of the ten P M S A s and trailed five. Data for earlier years show that this performance followed a five-year period, from 1972 to 1977, when the region lost high-tech jobs and trailed both the nation and the majority of the ten centers. N o t surprisingly, high tech represented a greater proportion of total employment in the group of ten P M S A s than in the nation. However, the share of high-tech employment in total employment differed widely a m o n g the P M S A s in the group, varying from 26.2% to 2.5%. San Jose stands out by this measure of concentration with a share that was nearly five times greater than the average for the nation in 1982. Philadelphia ranked above the national average but below the average for the ten P M S A s . In sum, the Philadelphia P M S A is home to a very substantial number of

Table 8.1 EMPLOYMENT AND GROWTH IN HIGH TECHNOLOGY SECTORS: 10 LARGEST CENTERS AND THE U.S., 1977, 1982

1982

Region Los Angeles San Jose Chicago Dallas Philadelphia Boston Anaheim New York Newark Minneapolis

(a)

1977

High Tech Share of Empi. Total Rank (000) PMSA Empi. 1 2 3 4 5 6 7 8 9 10

337.6 182.1 180.0 122.9 121.5 115.3 113.5 96.3 85.0 82.2

9.5% 26.2 5.8 8.0 6.4 7.9 13.3 2.5 9.0 7.7

1977-82

High Tech Share of Empi. Total Rank (000) PMSA Empi. 1 3 2 6 4 8 9 5 7 11

278.4 105.4 186.7 88.6 98.4 74.8 70.7 90.0 78.9 44.2

Annual Growth

8.6% 19.6 6.1 7.4 5.4 5.7 10.5 2.7 8.9 4.6

3.93% 11.56 -0.73 6.76 4.31 9.04 9.93 1.36 1.50 13.21

Total - 10 PMSAs

1,436.4

7.6

1,116.1

6.5

5.18

United States

5,004.1

5.6

4,274.6

5.2

3.20

SOURCE: Census of Manufactures, Census of Services, 1977, 1982, Bureau of the Census, U.S. Department of Commerce. (a) 1982 employment totals Include estimates of employment in sectors where precise data were not reported. Estimates were the mean of the employment range reported by the Bureau of the Census. Similar estimates were not available for 1977. Growth rates from 1977 to 1982 should, therefore, be viewed as upper limits. The net effects of the disclosure differences between the two years are greatest for Boston and Chicago.

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95

high-tech jobs. Between 1977 and 1982, high-tech sectors grew in the region at a rate exceeding their growth in the nation, on a par with the average for the ten largest high-tech centers in the country, and in excess of four of the ten (including the largest high-tech center in the country). While the region cannot be ranked among the fastest growing high-tech places, it is clearly a major center of technology-related industries in the country.

High Tech in the Region

In 1985, nine of the 27 high-tech sectors dominated the employment picture in the Philadelphia area. Three of the nine (computer services and data processing, communications equipment, and pharmaceuticals) employed a total of 55,000 workers—more than a third of regional high-tech employment. The next six largest sectors contained another third. High tech, as a whole, has grown slightly more rapidly in the 1980s in the region than in the late 1970s. This trend is in direct opposition to what happened in the nation as a whole, where growth in the 1980s trailed that in the 1970s by more than 2.5 percentage points per year. As a result, the substantial gap between regional growth and national growth that existed in the late 1970s disappeared in the early 1980s. The nine largest sectors in the region showed substantially greater employment growth than the other 18 high-tech sectors in both the region and the nation during the past ten years. Growth in the region's three largest sectors was greater in the region than in the nation during the early 80s, and, by 1985, the PMSA was home to nearly 4% of national employment in these sectors. The details of these patterns are shown in Table 8.2.

Philadelphia PMSA employment totals for the 27 sectors included in the selected definition of high-technology industries are shown for 1975, 1980, and 1985. The sectors are ranked according to 1985-3Q employment and are divided into four groups based on size. The grouping was done in order to facilitate the discussion of the county level data in subsequent sections. At the county level, confidentiality requirements did not allow reporting employment for all sectors in all counties (including some sectors with substantial employment totals).

Total PMSA high tech Total high-tech employment in the PMSA increased between the first quarter of 1975 and the third quarter of 1985 by about 9,000 jobs. This growth occurred during a time when the region lost more than" 60,000 jobs in manufacturing as a whole. High-tech growth was greater between the early 1980s and the late 1970s by about 0.9% per year (1.0% per year compared to 0.1%). Thus, growth in high-tech sectors increased between two periods when annual growth rates in manufacturing and total employment declined. In the nation, on the other hand, the data at the top of the columns headed "U.S. Annual Growth" indicate that high-tech growth rates roughly matched those for total employment in both time periods. The substantial gap in the late 1970s between PMSA growth in high tech

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96

Table 8.2 EMPLOYMENT AND GROWTH IN HIGH TECHNOLOGY SECTORS: PHILADELPHIA PMSA AND THE U.S., 1975-1Q, 1980-1Q, 1985-3Q Philadelphia PMSA

Sector (a) Total Employmentv ' Total Manufacturing

1975

Employment 1980

1985

1,780,500 1,922,800 2,046,600 458,662 447,654 393,716

Annual Growth 1975-80 1980-85 1.55% -0.48

1.26% -2.54

146,127

146,827

154,983

0.10

0.99

Largest Sectors Comp. Serv. & Data Proc. Communications Equip. Pharmaceuticals Sub-total

5,914 13,917 13,714 33,545

9,843 11,762 15,165 36,770

19,407 18,255 17,998 55,660

10.73 -3.31 2.03 1.85

13.14 8.32 3.16 7.83

Large Sectors Off. & Comp. Mach. Meas. Devices Elec. Components Petrol. Refining Space Vehicles Aircraft & Parts Sub-total

11,872 12,517 5,165 8,551 3,803 5,751 47,659

11,746 14,533 8,172 8,623 5,605 6,080 54,759

13,166 10,405 8,978 8,932 7,565 7,454 56,500

-0.21 3.03 9.61 0.17 8.07 1.12 2.82

2.10 -5.89 1.72 0.64 5.60 3.77 0.57

Medium Sectors Plastics & Synthetics Indust. Org. Chem. Elec. Trans. Equip. Spec. Indust. Mach. Elec. Indust. Equip. Medical Instr. Misc. Chem. Eng. & Lab Equip. Indust. Inorg. Chem. Sub-total

9,757 2,210 10,825 5,931 3,466 2,815 3,444 1,463 3,502 43,413

4,847 4,687 7,260 5,491 4,033 4,225 3,680 1,044 3,485 38,752

5,644 4,764 3,915 3,576 3,278 3,200 3,149 2,784 2,240 32,550

-13.06 16.23 -7.68 -1.53 3.08 8.46 1.33 -6.53 -0.10 -2.25

2.81 0.30 -10.62 -7.50 -3.70 -4.93 -2.79 19.52 -7.72 -3.12

Small Sectors Engines & Turbines Soaps, Cleaners, etc. Paint Radio & TV Equip. Misc. Elec. Mach. Photo. Equip. Ordnance & Access. Agric. Chem. Optical Instr. Sub-total

7,940 1,926 2,823 2,303 2,410 741 1,284 1,081 1,002 21,510

5,047 1,977 2,152 2,022 1,642 750 871 813 1,272 16,546

1,821 1,592 1,448 1,438 1,435 886 734 524 395 10,273

-8.66 0.52 -5.28 -2.57 -7.39 0.24 -7.47 -5.54 4.89 -5.11

-16.92 -3.86 -6.95 -6.01 -2.42 3.08 -3.06 -7.68 -19.15 -8.30

Total High Tech

(a) Total Nonagricultural Employment. (b) Complete 1975 and 1980 employment data are not available for Communications Equipment due to disclosure limitations in the New Jersey counties. 1980 employment levels in the New Jersey counties were estimated from the 1975 estimates and the complete 1985 data, assuming constant growth between the two years.

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Table 8.2 (continued)

Sector (a) Total Employment Total Manufacturing

Ü.S. Annual Growth 1975-80 1980-85

PMSA Growth Minus U.S. Growth 1975-80 1980-85

PMSA Share of U.S. Empi. 1985

3.58% 2.06

1.65% -0.69

-2.03 -2.54

-0. AO -1.84

2.1%

3.85

1.27

-3.75

-0.28

2.6

15.67 2.29 2.99 5.37

12.44 3.95 0.90 6.22

-4.95 -5.60 -0.96 -3.52

0.69 4.37 2.26 1.61

3.5 2.8 8.8 3.9

6.98 6.36 9.26 2.30 2.43 3.50 5.67

2.96 1.00 2.62 -4.06 10.37 0.38 1.86

-7.19 -3.33 0.35 -2.13 5.64 -2.38 -2.85

-0.86 -6.90 -0.90 4.70 -4.77 3.39 -1.28

2.7 4.1 1.4 6.7 4.2 1.1 2.4

Medium Sectors Plastics & Synthetics Indus t. Org. Chem. Elec. Trans. Equip. Spec. Indust. Mach. Elec. Indust. Equip. Medical Instr. Misc. Chem. Eng. & Lab Equip. Indust. Inorg. Chem. Sob-total

-0.79 2.42 0.85 1.18 1.96 6.33 1.18 2.72 1.37 1.69

-4.38 -0.80 -1.93 -4.20 -4.60 2.43 -0.06 1.82 -2.42 -2.09

-12.27 13.81 -8.53 -2.71 1.11 2.13 0.15 -9.25 -1.46 -3.93

7.19 1.10 -8.69 -3.30 0.90 -7.36 -2.73 17.70 -5.30 -1.03

3.4 2.9 3.4 2.2 1.7 1.8 3.4 3.3 1.6 2.5

Small Sectors Engines & Turbines Soaps, Cleaners, etc. Paint Radio & TV Equip. Misc. Elec. Mach. Photo. Equip. Ordnance & Access. Agric. Chem. Optical Instr. Sab-total

2.36 2.79 1.55 0.11 4.30 1.20 -2.74 1.92 7.70 1.95

-5.54 1.64 -0.87 -4.86 -1.53 -1.18 4.83 -3.79 0.82 -1.45

-11.02 -2.27 -6.83 -2.68 -11.69 -0.96 -4.73 -7.46 -2.81 -7.06

-11.38 -5.50 -6.09 -1.15 -0.89 4.25 -7.89 -3.89 -19.98 -6.85

1.7 1.1 2.3 1.7 1.0 0.7 0.9 0.9 1.2 1.2

Total High-Tech Largest Sectors Comp. Serv. & Data Proc. Communications Equip. Pharmaceut icals Sub-total Large Sectors Off. & Comp. Mach. Meas. Devices Elec. Components Petrol. Refining Space Vehicles Aircraft & Parts Sub-total

2.0

SOURCES: Computations based on data from several sources. Pennsylvania Counties: Pennsylvania Office of Employment Security data, compiled by the Institute for Public Policy Studies, Temple University. New Jersey counties: compiled from unpublished data provided by the New Jersey Department of Labor (1985), and County Business Patterns, (1975 and 1980). PMSA total nonagricultural employment and U.S. data: Employment and Earnings, BLS, U.S. Dept. of Labor.

98

Economic Development within the Philadelphia Metropolitan Area and national growth narrowed substantially, dropping from - 3 . 8 % per year to - 0 . 3 % per year in the 1980s.

Largest sectors T h e region's largest high-tech sector in 1985, computer services and data processing, was also the fastest growing in the previous ten years, when nearly 14,000 jobs were added in the sector. Pharmaceuticals, the third largest sector in 1985, showed steady growth over the ten years as well, adding more than 4 , 0 0 0 jobs. T h e region's second largest sector, communications equipment, showed a more erratic pattern, declining by more than 2 , 0 0 0 jobs between 1975 and 1980, and then adding about 6,500 jobs in the 1980s. (A portion of this erratic behavior is an artifact of the data, resulting from the fact that employment in the New Jersey counties can only be estimated in 1975 and 1980. However, the reported decline between 1975 and 1980 was largely the result of jobs lost on the Pennsylvania side of the PMSA and is not due to disclosure limitations.) Employment in these three sectors as a group increased by more than 2 0 , 0 0 0 in the ten-year period, with growth in the later period exceeding that in the earlier by a substantial margin (7.8% per year compared to 1.9% per year). Growth in the later period also exceeded the national rate in these sectors by about 1.6% per year.

Large sectors T h e second group of sectors showed a much different pattern. As a group, they grew more quickly in the late 1970s than in the 1980s. In the region, five of the six sectors grew over the ten years, while one (measuring devices) gained 2 , 0 0 0 jobs between 1975 and 1980, but then lost more than 4 , 0 0 0 jobs in the early 1980s. In the 1980s, space vehicles, aircraft, and office and computing machinery each added more than 1,400 jobs. The second group, as a whole, followed the national pattern more closely than the group of largest sectors, but narrowed the gap between PMSA growth and national growth from - 2 . 9 % per year between 1975 and 1980 to - 1 . 3 % in the 1980s.

Medium and small sectors T h e sectors in the top two groups are the kinds of sectors that one typically thinks of when high tech is mentioned (with the possible exception of petroleum refining). This is not the case, however, for the 18 sectors in the bottom two groups. These groups are much more of a mixture of emerging sectors (medical instruments, for example), more traditional manufacturing sectors that have some high-tech components (such as electric transmission equipment), and some basic manufacturing sectors that have traditionally devoted greater than average resources to research and development (soaps and cleaners, or paints, for example). T h e growth rates for these

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two groups reflect this mixture. Growth rates in the region were consistently declining, were less than national rates (which themselves were less than the average for total high tech), were less than the average for high tech in the region, and, for many of the sectors, were actually less than for total manufacturing in the region. The exceptions to this pattern were industrial organic chemicals, and engineering and laboratory equipment, which added 2,500 and 1,300 jobs, respectively, over the ten years. One other sector in the group, plastics and synthetics, showed reasonable growth in the 1980s after losing nearly 5,000 jobs between 1975 and 1980.

Summary of PMSA trends In sum, the overall picture in the region is largely positive. Growth in high tech improved between the late 1970s and the early 1980s; By 1985, the region closed the growth gap that had existed between it and the rest of the country, largely by maintaining growth during a period when national growth rates were declining. The greatest concentrations of high-tech employment in the region are now in sectors that have been growing nationally. And, finally, the numbers suggest that a "shakeout," much like the one that has occurred in manufacturing as a whole, has largely run its course. Employment levels in many of the "lower tech" high-tech sectors have declined to the point that they play a much smaller role in the regional economy than they did ten years ago. Much of the region's improvement relative to the rest of the country has been the result of regional growth rates remaining stable while national rates declined. It remains to be seen if the region can increase the proportion of national growth in high tech that it captures in boom times as well as in slowdowns. High Tech in the Counties

Just as discussions about high tech in the nation tend to begin and end with a few outstanding regions, analyses of the Philadelphia area often focus on Montgomery County, and the Route 202 Corridor. This tendency may be more justified at the regional level than at the national level. Montgomery dominates the high-tech picture in the PMSA to a much greater extent than any region dominates the national picture. Montgomery was home to 34% of the PMSA's total employment in high-tech sectors in 1985, up from 30% in 1975. Philadelphia's role, on the other hand, declined significantly over the ten years from 28% to 15%. All of the other counties, except Delaware, increased their share of regional high-technology employment. The growth patterns across sectors in the counties mirrored those in the PMSA, for the most part. The "largest" and "large" categories (fully defined in Table 8.2) grew, in general, while the "medium" and "small" categories declined. In most of the counties, the three sectors in the "largest" category showed growth rates in the 1980s exceeding those in the late 1970s. The six sectors in the "large" category grew over the full ten years in most of the counties, but at greater rates in the 1970s than in the 1980s. Growth in the top two categories tended to be concentrated in

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Montgomery, Camden, Burlington, and Chester. The majority of the decline in the bottom two categories occurred in Philadelphia and Delaware. The reduced role of these two counties in the regional high-tech picture in 1985 was entirely due to significant employment losses in these sectors during the previous ten years. Because of the strong link between the county and regional growth patterns across sectors, and the similar link between regional and national growth patterns, the extent to which individual counties had concentrations in sectors growing in the nation had a significant impact on the overall growth differentials among the counties, particularly in the 1980s. Montgomery, Burlington, Chester, and Camden counties benefited from greater than average concentrations of employment in sectors growing in the nation as a whole, while the high-tech employment mixes in Philadelphia, Delaware, Gloucester, and Bucks were a detriment to growth. However, only Philadelphia, Delaware, and Bucks grew more slowly than the nation in high tech between 1980 and 1985. Table 8.3 shows the employment levels and growth rates in high tech between 1975 and 1985 in the counties. Sectoral data are shown only for the four size categories established for Table 8.2 to avoid problems with the confidentiality regulations of the state agencies that provide the data. (All disclosable data for the individual sectors in the counties are contained in Appendix Table C.7.) Each category contains the same sectors in each county. The industries in each category were determined by sector size in the PMSA as a whole.

Total high-tech growth in the counties In 1975, Montgomery and Philadelphia dominated the high-tech picture in the region. Each contained approximately twice as many high-tech jobs as the third largest county (Delaware), and together they represented nearly 60% of regional employment. By 1985, however, Philadelphia's role had declined substantially—high-tech employment in the city dropped off by nearly 17,000 jobs during the ten years, and the city's share of regional employment fell to 15% (from 28% in 1975). Montgomery County, on the other hand, gained more high-tech jobs between 1975 and 1985 (over 9,000) than any of the other counties, and increased its share of regional employment from 30% to 34%. Of the other six counties, only Delaware's share declined over the ten years. With the exception of the increasing concentration of high-tech jobs in Montgomery County, the general trend between 1975 and 1985 was toward a more uniform spread of high-tech jobs through the PMSA. The four counties with the lowest employment levels in 1975 (Burlington, Camden, Chester, and Gloucester) showed the greatest growth rates in the subsequent ten years. The share of total PMSA high-tech jobs in these counties increased from 19% in 1975 to 30% in 1985, with the largest absolute increases occurring in Burlington (7,300 jobs), Chester (5,700), and Camden (4,500).

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Table 8.3 EMPLOYMENT AND GROWTH IN HIGH TECHNOLOGY SECTORS^ BY COUNTY: PHILADELPHIA PMSA, 1975-1Q, 1980-1Q, 1985-3Q

1975

Employment 1980 1985

Share of PMSA Empi.,1985

Annual Growth 1975-80 1980-85

Total High-Tech Largest Large Medium Small

12,483

14,708

13,551

780 5,933 4,558 1,212

1,261 6,175 6,153 1,119

2,171 5,182 5,337 861

Total High-Tech Largest Large Medium Small

5,100

8,148

12,465

8.0

9.82

8.04

2,132 1,255 1,611 102

4,559 1,496 1,973 120

8,649 1,807 1,760 249

15.5 3.2 5.4 2.4

16.42 3.58 4.14 3.30

12.35 3.49 -2.06 14.19

Total High-Tech Largest Large Medium Small

8,652

10,539

13,100

4,456 1,364 1,638 1,194

6,309 1,540 1,893 797

9,064 1,681 1,698 657

Total High-Tech Largest Large Medium Small

9,188

11,126

14,829

2,817 3,721 1,830 820

3,304 4,140 2,609 1,073

4,948 5,083 4,541 257

Total High-Tech Largest Large Medium Small

21,803

19,347

18,286

11.8

-2.36

-1.02

1,298 8,683 3,001 8,821

1,768 9,314 2,855 5,410

2,761 10,596 2,677 2,252

5.0 18.8 8.2 21.9

6.38 1.41 -0.99 -9.31

8.44 2.37 -1.16 -14.73

Total High-Tech Largest Large Medium Small

4,766

5,453

6,095

3.9

375 870 1,771 1,750

531 1,865 1,636 1,421

927 2,462 1,624 1,082

1.7 4.4 5.0 10.5

2.73 7.20 16.48 -1.57 -4.07

10.66 5.18 -0.13 -4.84

Total High-Tech Largest Large Medium Small

43,659

47,179

52,893

34.1

1.56

2.10

13,784 19,147 7,888 2,840

13,461 21,833 8,844 3,041

19,361 22,976 7,760 2,796

34.8 40.7 23.8 27.2

-0.47 2.66 2.31 1.38

6.83 0.93 -2.35 -1.52

Philadelphia Total High-Tech Largest Large Medium Small

40,476

30,327

23,764

15.3

-5.61

-4.34

7,903 6,686 21,116 4,771

5,577 8,396 12,789 3,565

14.0 11.9 22.0 20.6

-6.73 4.66 -9.54 -5.66

6.24 -3.99 -10.03 -9.02

Bucks

Burlington

Camden

Chester

Delaware

Gloucester

Montgomery

7,779 6,713 7,153 2,119

8.7Z

3.9 9.2 16.4 8.4

3.33Z

10.08 0.80 6.18 -1.58

-1.48Z

10.38 -3.14 -2.55 -4.65

8.5

4.02

4.03

16.3 3.0 5.2 6.4

7.20 2.46 2.94 -7.77

6.81 1.61 -1.96 -3.45

9.6

3.90

5.36

8.9 9.0 14.0 2.5

3.24 2.16 7.35 5.53

SOURCE: See Table 8.2. (a) The categories — largest, large, medium, small — are based on employment size in the PMSA. See Table 8.2 for the specific sectors in each category.

7.62 3.80 10.60 -22.88

2.04

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County patterns in the PMSA's largest sectors The PMSA pattern of increasing employment in the group representing the largest sectors in the region carried over into nearly all of the counties. The only significant exception over the ten-year perioa was in Philadelphia where employment in the three sectors in the category declined. The majority of the total regional increase of 22,000 in the region's three largest sectors occurred in Montgomery, Camden, and Burlington counties. In Montgomery, all of the increase in these sectors was attributable to growth in computer services and data processing (5,700 jobs added) and pharmaceuticals (1,800 jobs added). In Camden and Burlington, the bulk of the increase was due to growth in communications equipment, although disclosure limitations make it impossible to determine the exact magnitude of the increase. Growth rates in these three sectors, as a group, exceeded the national rate in all eight counties between 1980 and 1985.

County patterns in large sectors Seven of the eight counties also gained jobs in the second category ("large" sectors) between 1975 and 1985. The lone exception was Bucks. In addition, four of the eight (Burlington, Chester, Delaware, and Gloucester) grew more rapidly than the nation in these sectors in the 1980s. Most of the growth (in terms of numbers of jobs) in the region occurred in Montgomery, Delaware, Gloucester, and Chester. Electronic components (in Montgomery) and aircraft and parts (in Delaware) were the growth leaders in these counties and sectors.

County patterns in medium and small sectors The PMSA pattern of decline in the "medium" and "small" categories also carried over into most of the counties. The single significant exception was in Chester, which added more than 2,700 jobs in the "medium" sectors over the period. More than 1,100 of them came in a single sector, engineering and laboratory equipment. It was also in the "medium" sectors that Philadelphia absorbed most of its job losses in high tech. The city lost 14,000 jobs in this category, more than 12,000 of them in just two sectors—electrical transmission equipment and plastics and synthetics. Similarly, Delaware County lost over 6,000 jobs in sectors in the "small" category, with the major part of the loss coming in engines and turbines. As a rule, growth in the counties lagged behind national rates in the bottom two categories by significant amounts.

Sources of growth differentials The strong link between national and regional growth in high tech, combined with the diversity in growth rates among the sectors at the

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national level, had very important implications for the fortunes of the individual counties. Counties that had greater than average proportions of their high-tech employment in sectors that were growing in the nation clearly benefited from their employment mix, especially in the 1980s. Analysis of the way that the county-level employment patterns interacted with the widely varying performances of different high-tech sectors in the national economy sheds a great deal of light on the growth differentials among the counties in the PMSA. (See Appendix B.5 for a discussion of the analysis and the empirical findings.) Table 8.4 shows the results of decomposing the gap between countylevel growth and national growth into two components. The first component (the second column) is the part of the total growth differential that was attributable to growth rate differences between the counties and the nation in the individual sectors. This component indicates the extent to which an individual county possessed a comparative advantage over the rest of the country in high-tech sectors. The second component (third column) is the part of the total growth gap that was due to different employment levels across sectors in the counties (the "employment mix"). It shows the extent to which a county's growth differential was determined by a good match between its employment concentrations across sectors and national growth rates across sectors. PMSA total. For the PMSA as a whole, employment mix had a relatively small impact on the overall growth gap with the nation. Between 1975 and 1980, it had essentially no effect (-0.02% per year compared to the overall differential of -3.75% per year), and in the 1980s, the effect, although positive, was very small. The major reason that the region closed the growth gap with the nation was improvement, sector by sector, in the region's growth rates relative to the nation. An important implication of this is that the area is not overly dependent on the pattern of growth across sectors in the nation. Its ability to capture a share of national growth in high tech as a whole is, therefore, not overly vulnerable to the ups and downs that these sectors may experience in international markets. (The absolute rates at which these sectors grow in the region are, of course, linked to national and international conditions.) Montgomery. Among the counties, Montgomery, in particular, benefited substantially from the fact that its greatest concentrations of employment were in sectors growing in the nation. Between 1975 and 1980, when the county grew more slowly than the nation as a whole, the county's advantageous employment mix made the growth gap considerably narrower than it would have been if Montgomery's mix had matched the nation's. Between 1980 and 1985, when Montgomery outgrew the nation in high tech, the effect was even more substantial, accounting for virtually all of the difference between the county's growth rate in high tech as a whole and that of the nation's. The downside implication of this finding is that Montgomery's high-tech growth was not the result of any comparative advantages that the county held relative to the rest of the nation. Sector by sector, its growth rates did not exceed those in the nation; the entries for Montgomery in the second column of Table 8.4 are both negative. Its

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growth rate in total high tech exceeded the nation's because it had unusually large concentrations of employment in growth sectors. Its continued position as the region's largest generator of high-tech jobs is, therefore, likely to be very dependent on whether future growth in high tech in the nation will continue to be distributed among the individual high-tech sectors in a way that matches the county's largest high-tech sectors.

Table 8.4 SOURCES OF GROWTH DIFFERENTIALS WITH THE NATION IN HIGH TECH: PHILADELPHIA PMSA AND THE COUNTIES, 1975-1Q TO 1980-1Q, AND 1980-1Q TO 1985-3Q

1975-1980

Bucks Burlington Camden Chester Delaware Gloucester Montgomery Philadelphia PMSA

County Growth Minus U.S. Growth

Annual Growth Difference Due To Comparative Advantages In Individual Sectors

-0.52% 5.97 0.17 0.05 -6.21 -1.12 -2.29 -9.46 -3.75

-0.67% 5.71 -0.06 -1.20 -5.27 1.20 -3.37 -8.38 -3.74

Annual Growth Difference Due To County Employment Mix 0.15% 0.27 0.24 1.25 -0.95 -2.32 1.08 -1.08 -0.02

1980-1985

County Growth Minus U.S. Growth Bucks Burlington Camden Chester Delaware Gloucester Montgomery Philadelphia PMSA

-2.75% 6.77 2.76 4.09 -2.29 0.77 0.83 -5.61 -0.18

Annual Growth Difference Due To Comparative Advantages In Individual Sectors -1.95% 4.45 0.91 3.62 0.17 3.79 -1.06 -4.96 -0.40

SOURCE: Computed from Tables 8.2 and 8.3.

Annual Growth Difference Due To County Employment Mix -0.80% 2.32 1.85 0.47 -2.46 -3.02 1.89 -0.65 0.22

See Appendix B.5.

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Burlington, Camden, and Chester. Burlington, Camden, and Chester counties also have benefited from their high-tech employment mixes. Both New Jersey counties grew more rapidly than the nation during the tenyear period. In the 1980s, a substantial portion of the differences (onethird in Burlington and two-thirds in Camden) was the result of their beneficial employment mixes. The rest of the gap was attributable to the counties' comparative advantage in high tech. Burlington, especially, showed substantial advantages in both time periods. However, the implications of this are clouded by the fact that, in both counties, employment is highly concentrated in a single sector. This sector, communications equipment, is dominated by two branch plants of the same company, RCA, which was recently involved in a major merger with General Electric. Although the merger will not necessarily have a negative on effect employment levels at the two New Jersey plants, it does cast some uncertainty on their future growth paths. The merger of two companies that previously competed with each other (as GE and RCA did in some markets) often leads to employment reductions in some plants as the new, larger, company rationalizes its changed overall structure. Chester County grew more rapidly than the nation in the late 1970s and in the 1980s. In recent years, the gap was primarily due to its overall comparative advantage in high tech. Its employment mix, although advantageous, was responsible for only about 10% of its overall growth advantage. Philadelphia. Philadelphia grew significandy more slowly than the nation between 1975 and 1985. In both time periods shown in Table 8.4, between 10% and 20% of the growth gap was the result of a disadvantageous employment mix. Although the city was harmed by its employment mix, the great majority of its growth shortfall was the result of the comparative disadvantages of central city locations for high-tech firms. The magnitude of these disadvantages was substantial in both time periods. Delaware. Delaware County showed a pattern similar to Philadelphia's in the late 1970s. Growth in high tech trailed the nation by a substantial margin, and only about 15% of the gap was due to a disadvantageous employment mix. The picture reversed dramatically in the 1980s, however. Then, all of the county's growth shortfall was due to its greater than average concentrations in sectors declining, or growing more slowly than average, in the nation. Sector by sector, growth rates roughly matched those in the nation. Delaware improved its competitive position relative to the rest of the country between the two periods by more than any of the other seven counties. Gloucester and Bucks. Gloucester and Bucks also have had disadvantageous high-tech employment mixes in the 1980s. However, in Gloucester, this disadvantage was more than offset by the county's comparative advantage in individual sectors. High tech as a whole grew more rapidly in Gloucester than in the nation between 1980 and 1985, despite its heavier than average concentrations in sectors growing slowly, or declining, in the nation. Bucks County, on the other hand, trailed the nation in total growth,

10«

Economic Development within the Philadelphia Metropolitan Area

with about a fourth of the shortfall coming because of its employment distribution.

Summary of county trends In sum, two contrasting trends have been at work, affecting the spread of high tech among the counties in the PMSA. On the one hand, the region's largest high-tech county (Montgomery) increased its role in the high-tech picture during the last decade. On the other hand, in the rest of the region, high-tech employment conformed to the general pattern of decentralization that the economy as a whole followed. The role of Philadelphia declined, while the importance of the smallest counties increased. The region's largest sectors have shown positive growth in virtually all of the counties in the 1980s, while the smallest sectors (in 1985) have declined everywhere. However, in terms of numbers of jobs, growth in the large sectors has been concentrated in Montgomery, Burlington, Camden, and Chester counties, while job losses in the declining sectors hit Philadelphia and Delaware counties the hardest. The decomposition of county-level growth into two components, one representing the county's comparative advantage relative to the rest of the country, and the other representing the effects of the county's high-tech employment mix, reveals that the sources of growth were different in different counties. In the 1980s, sector by sector growth rates were responsible for Burlington, Chester, and Gloucester counties outperforming the nation. Montgomery and Camden counties, on the other hand, outgrew the nation primarily because they began the 1980s with advantageous employment mixes. Of the counties that trailed the nation in high-tech growth, Philadelphia and Bucks grew less primarily because of lower growth rates sector by sector, while Delaware County's shortfall in the 1980s was entirely due to its undesirable employment mix in 1980.

Future Prospect«

The prospects for regional growth in high-technology sectors are closely tied to how well the nation as a whole can compete in international markets and to how much of the nation's growth the PMSA can capture. What is important to the region as a whole is its attractiveness to high-tech firms. Regional prospects, therefore, depend on the answers to two questions. First, what factors are most important to high-tech firms when they are deciding what part of the country to locate in (or which branch plants, in which regions, should expand or contract)? Second, how well is the region endowed with attributes that are favored (or not favored) by these kinds of firms? The answers to these questions provide an important clue to how much of future national growth the region is likely to capture. How the regional growth that does occur is distributed among the individual counties in the region is likely to depend on the answers to a different set of questions. What parts of the region provide the best access for high-tech firms to their most important input and output markets? Where in the region are the sites with the best physical attributes—sites

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107

with adequate space that are well connected to the area's basic infrastructure? This section examines these questions in more detail in order to evaluate growth prospects in the region and its counties.

High-tech prospects for the PMSA The future path of high-technology development in the region as a whole will depend fundamentally on two things: what happens in the nation as a whole in high-tech sectors, and how much of the future growth in the nation the region can capture. Predicting what will happen in the nation to the specific high-tech sectors is a risky business and beyond the scope of this analysis. However, economic theory and recent trends clearly suggest grounds for optimism. High-technology sectors represent many of the nation's highest labor productivity industries and are the portion of manufacturing in which the United States is most likely to compete successfully in international markets. Among high-tech sectors, there will certainly be sectors growing now that will decline in the future, and others that are now declining that will grow in the future. However, for the near-term future, recent growth rates in the nation can serve as reasonable measures of the likely future path of high-tech sectors. The employment mix component of total growth, shown in the third column of Table 8.4, can serve as an indicator of whether the region is well positioned in terms of the high-tech sectors that it specializes in. The findings suggest that the region's position in this regard has improved, but that it is unlikely to be the dominant source of future regional growth. The region's ability to capture an increasing share of national employment growth is, therefore, likely to determine whether the PMSA's hightech future is one of stability, growth, or decline. The comparative advantage component of the growth breakdown in Table 8.4 provides one summary measure of the region's recent growth, sector by sector, relative to the nation. The major improvement in this measure between the late 1970s and the early 1980s is a very encouraging sign. However, this kind of summary measure tells us nothing about which features of the region are attractive to high-tech firms and which are not. Research into the question of what regional attributes attract or repel high-techology firms has met with only limited success. (For good examples of statistical investigations, see the 1984 studies by A. K. Glasmeier et al. and C. Armington et al. For results based on Philadelphia area firms, see the 1984 Delaware Valley Regional Planning Commission study based on a survey of firms in the Route 202 Corridor.) These studies, however, have not been devoid of findings. One clear pattern is the finding that regional growth differentials in high-tech sectors are not affected by many of the factors that past research has shown to affect regional growth patterns in manufacturing as a whole. For instance, there is little evidence that wage differentials among regions affect regional growth patterns in high tech. Nor does the evidence suggest that regional differences in taxes or other cost factors (energy costs, for example) have a substantial effect on high-tech growth.

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Economic Development within the Philadelphia Metropolitan Afea

Appendix B.5 contains the results of research done for this chapter that tests the effects of some of these factors, in addition to some factors that the empirical literature suggests actually do influence regional high-tech growth patterns. The results further document the lack of influence of the more traditional cost factors, but confirm the influence of the newly perceived factors—education, defense spending, and employment concentrations. The results imply that high-tech employment growth between 1977 and 1982 in a group of 44 large high-tech PMSAs was most likely to occur where regional work forces were more educated, where employment concentrations in individual sectors already existed, where the local share of total high-tech employment in total employment was higher, and, where there was more defense spending by the federal government. During the period, high-tech growth was also lower where firms were larger, on average. Local unemployment rates, unionization rates, wage rates, relative energy costs, tax rates, regional transportation facilities, climate, and relative housing costs showed no significant impact on regional employment growth in high-technology sectors. What do these findings imply for the Philadelphia region? In general, the implications are positive. The region tends to do well relative to other places in most of the factors that the research suggests are important. (See Table B.5.2.) The PMSA as a whole has a higher than average high-tech share of total employment and is home to high levels of employment in the nation's fastest growing high-tech sectors. The region thus has clear advantages in two of the five factors found to have stimulative influences on regional growth differentials in high tech. The region's standing in two of the other three important factors is also positive, but less clearly so. Although it captures a lower than average share, per capita, of defense contracts, the region does contain a very important source of federal defense spending for high-tech-related products (the Philadelphia Navy Yard) and one of the Army's major training centers (Fort Dix). The PMSA has had roughly average concentrations of workers with science and engineering Ph.D.s (the measure of education levels in the workforce used in the research), and, in addition, the area contains an extensive university system. It is doubtful, therefore, that lack of access to an adequately educated workforce repels potential high-tech firms from the region or inhibits the growth of existing firms, a contention supported by the 1984 DVRPC survey of Route 202 Corridor firms. Finally, on average, high-tech firms in the PMSA are larger than the average for the 44 PMSA sample. The results suggest that this is a negative attribute. The region does, in fact, depend on a few large firms in some of its most important sectors—pharmaceuticals, aircraft, and communications equipment, in particular. The implications of this are unclear, because the sectors in the Philadelphia area that have large firm sizes tend also to be sectors that have greater than average firm sizes in the other PMSAs. These results suggest that the Philadelphia area has many attributes that high-tech firms regard as desirable and that it is reasonable to expect that the recent improvement in regional growth rates relative to the national

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109

averages can be maintained or improved. Whether improvement will continue at a rate like the one implied by the PMSA-level measure of comparative advantage shown in Table 8.4 is more problematic. The dramatic improvement between the late 1970s and the early 1980s may mean only that the region loses less than other areas when growth rates in the nation decline, as they did between the two periods. Evidence from only two time periods is not adequate to make a good judgment about whether a real structural change has occurred in the regional high-tech economy that will enable it to capture greater shares of national growth in boom times as well as during periods of slow growth.

High-tech prospects in the counties If the region does, in fact, continue to improve its "capture rate" of national growth, what parts of the metropolitan area are most likely to benefit? The choice of a site within a region presents different problems for firms than the interregional choice, and factors different from those showing strong effects in the empirical work previously described may come into play. For instance, the results suggest that transportation infrastructure does not play a major role in the interregional location decision. However, a recent survey study of high-tech sectors sponsored by the Pennsylvania Department of Transportation (James P. DeAngelis and H. G. R. Bullen, 1986) found that the two intraregional location factors most often cited by high-tech firms were (1) the physical attributes of specific sites, and (2) the access to transportation facilities. This is consistent with the intraregional high-tech development pattern that is most common around the country. Major highway systems (or improvements to older, regional arteries) have very often been associated with concentrations of high-tech firms. High-tech "corridors" have nearly always gotten their shape from major transportation arteries. The county growth patterns evident in Table 8.3 suggest that this has also been a major factor in the spread of high tech within the Philadelphia area. The three counties that added the most high-tech jobs between 1975 and 1985— Montgomery, Burlington, and Chester—are the counties in the PMSA with extensive access to either the Pennsylvania Turnpike (all three counties) or to Route 202 (Chester and Montgomery counties). In addition, the next two largest growing counties—Camden and Gloucester—are served by Interstate 295 and the New Jersey Turnpike (as is Burlington). On the other hand, the two slowest growing of the suburban counties in the PMSA (Bucks and Delaware) had the least access to the region's major highways between 1975 and 1985. However, the recent completion of Interstate 95 and resumption of construction of Interstate 476 (the "Blue Route") have changed, or will change, this situation, especially for Delaware County. The indications are that future growth within the region in high tech is likely to follow a pattern similar to that of the past ten years. On the Pennsylvania side, the status of the 202 Corridor as a major center of employment and growth in the region is likely not only to continue, but

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Economic Development within the Philadelphia Metropolitan Area

will be enhanced if and when the Blue Route is completed. Delaware County, which was already showing signs of a turnaround in its competitiveness as a location for high-tech in the early 1980s, stands to gain the most from the recent and planned improvements in the region's transportation networks. A major factor for Delaware is likely to be whether desirable sites are available. This is particularly important, since the county is more densely populated than Montgomery and Chester. The prospects for the New Jersey side of the PMSA are more difficult to gauge because of the uncertainties associated with predicting the fortunes of the individual large firms that dominate. The transportation improvement most likely to affect the status of the New Jersey counties is the Schuylkill Expressway reconstruction, which, by improving its access to the region's major east-west highway (the Pennsylvania Turnpike), will enhance Camden's position in the regional highway network.

Summary and Policy Conclusion*

The Philadelphia PMSA has been, and remains, a major center of hightechnology employment in the nation. High tech grew more rapidly in the region in the early 1980s than in the late 1970s, and at a rate roughly commensurate with national high-tech growth. The region is, in general, well endowed with the attributes that research has shown to be most attractive to high-technology firms. It is, therefore, well positioned to maintain, and enhance, the improvement in the area's economic fortunes that has occurred in the 1980s. Within the region, Montgomery County is home to the greatest concentrations of high-tech jobs and is likely to continue as the major focus of local high-tech growth. In the rest of the region, growth patterns resulted in a more uniform spread of high-tech employment. The roles of Philadelphia, Bucks, and Delaware have been declining, while those of the other four counties have increased. This pattern is also likely to continue, with one possible exception. Delaware County has shown signs in the 1980s of a turnaround in its high-tech fortunes, and stands to benefit the most from ongoing and planned improvements to the region's transportation network. The policy implications of these findings differ, depending on the perspective of the policy maker. From a regional point of view, there is clearly potential for opportunities to be lost. The benefits of some policies helpful to high tech in the full region, but enacted in only one county, will inevitably spill over into other parts of the region. Therefore, the county financing the policy (or spending political capital in state or national legislatures) will not be ready to finance it to the extent that the total regional benefits warrant. The clearest example of this arises from the fact that educational levels of the workforce and defense spending—two factors important to high tech—are strongly dependent on circumstances in the city. Philadelphia is the hub of the region's higher education system, and the Philadelphia Navy Yard is within the city limits, but it is unlikely to be able to capture a share of future high-tech growth commensurate with its contributions to these helpful factors. Clear reasons exist, therefore, for

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111

city policy makers to undervalue policies that may enhance the region's competitiveness in high tech relative to the rest of the country. Policy makers in other parts of the region, particularly in those areas that stand to gain the most from high-tech growth, should be aware that their constituents stand to gain from some policies that impact most directly on the city. This is especially true for the region's representatives in the state and national legislatures, since they are the source of the policies that affect higher education and the Navy Yard most substantially. The need for a unified regional voice in these policy arenas is clear. Within the PMSA, access to transportation networks and specific site characteristics appear to be the most important location factors considered by high-tech firms. Localities wishing to enhance their attractiveness to these kinds of firms are likely to do best by ensuring that sites with good access to basic infrastructure and the region's highways and airports are available. There is little local policy makers can do regarding the region's highways, since they are largely in place. But site preparation, in the form of industrial park development, and enhancement of other local amenities, are well within their normal policy bounds. They warrant serious attention by the region's localities if the Philadelphia metropolitan area is to continue, or enhance, its important position in the nation's high-technology industries.

Nine

Intergovernmental Aid in the Philadelphia PMSA: Who Gets How Much? Janet Rothenberg Pack

Among the conclusions that emerge from the analysis of the disparate population and employment patterns of the region is that taxes, amenities, and transportation systems play very significant roles. These are factors that are particularly affected by decisions of higher levels of government. What role have the federal and state governments played in contributing to making the economic development patterns within the Philadelphia region more or less disparate? Has intergovernmental aid had an equalizing effect? Analysis of the patterns of this aid in the eight counties over the past twenty years and their effects on local government expenditures reveals that equalizing has, indeed, occurred: the city of Philadelphia's share of the intergovernmental aid coming to this region has increased substantially. Moreover, the city of Philadelphia did not suffer the losses in federal aid that began in most of the suburban counties around 1979 or 1980. The findings also show that there has been a relatively low offset by the Commonwealth of Pennsylvania in response to these declines. As a result of all these factors, public expenditure disparities between the city and the surrounding areas appear to have widened, but tax rate disparities have changed very little. This chapter examines the motivations for intergovernmental aid, the general response of localities to the receipt of state and federal aid, the distribution of the aid within the Philadelphia PMSA, the types of grants given and their distribution across the region, and the implications of these patterns for the city and the surrounding counties. Detailed intergovernmental aid data are contained in Appendix B.6.

Why Intergovernmental Aid?

Different rationales have been used to justify different types of intergovernmental aid. As a result, grant-in-aid programs show a great deal of diversity in the requirements they place on recipient governments. How a particular aid program is structured depends on the reasons for it being undertaken. Some programs, such as general revenue sharing, are defended primarily by arguments regarding the qualities of the revenue instruments available to different levels of government. Others, such as matching grant programs for public education, are a result of the fact that the benefits from some public expenditures extend beyond the borders of the jurisdiction doing the spending. Finally, some grant-in-aid programs, such as

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113

categorical grants targeted for poverty-level populations, are justified by both revenue and expenditure side arguments.

Revenue side arguments Quality of revenue instruments. In a federal system, such as that of the United States, there are many bases for deciding which level of government is best suited to collect the taxes needed to finance local area needs. The revenue instruments available to higher levels of government are regarded as superior to those available to local governments in many ways—more diverse, more elastic, more progressive. The federal government can use an income tax, for example, but this source is available to few local governments. Relocation incentives, Local governments are inhibited, far more than state and federal governments, by the ability of their taxpayers to relocate if taxes are out of line with other jurisdictions. For this reason, the income tax, for example, has long been the preserve of the federal government. More recently, however, it has been taken up by most state governments, while local governments, fearing the departure of their residents, use it far less frequently. The property tax, the principal source of revenue for local governments, appears to produce less of this kind of response, but its yield does not increase automatically with the economy, as does the income tax yield.

Expenditure side arguments Efficiency. Although on the revenue side there are good arguments for centralizing responsibilities at higher levels of government, there are, at the same time, strong efficiency arguments for not centralizing expenditures. There is no reason to expect that it is more cost-effective to deliver local services from a central authority, that is, to expect nationwide economies of scale. Indeed, because local authorities know more about the special characteristics of individual localities, they should be better able to provide local services efficiently. This general argument underlies all forms of intergovernmental aid, regardless of whether there are strings attached to the aid or not. Externalities. Some benefits of a particular expenditure are enjoyed beyond the boundaries of the jurisdiction making the expenditure decision. The benefits of city dollars going to the Philadelphia Art Museum accrue to suburbanites also, for example. If externalities such as these are generated by particular kinds of local spending, local authorities can be expected to finance less of the activity than is optimal. The decision-making unit, left to its own, will appropriately consider only the benefits to its own residents and not the benefits to others. There are many possible ways of addressing these interjurisdictional spillovers of benefits. One common response is the formation of special districts, which embrace numerous local jurisdictions. Water and sewer districts, coastal zone commissions, and transit authorities are examples. Still another response is for a higher

114

Economic Development within the Philadelphia Metropolitan Area

level of government to subsidize expenditures that involve externalities, with the subsidy being equal to the proportion of total benefits accruing to those outside the local jurisdiction.1

Combinations of expenditure and revenue arguments Grants-in-aid for programs that are targeted for specific portions of the population, such as people living in poverty or the physically handicapped, are based on both expenditure and revenue side arguments. On the expenditure side, the provision of services to special target groups is usually most efficiently located with local agencies possessing the most indepth knowledge about local conditions and preferences. In addition, important externalities are associated with concentrations of poverty populations, or other groups with special needs. This redistribution of income from the rich to the poor is regarded as a responsibility of the nation. On the revenue side, financing redistributive programs with local taxes is very difficult. They create relocation incentives that can work to an individual area's detriment. If the tax burden for such programs is felt in some areas but not in others, some local tax bases will suffer as more mobile, higherincome residents flee to lower tax areas. For all these reasons, the communities of the Philadelphia metropolitan area are the recipients of a wide variety of intergovernmental revenues. These funds come in different forms—as revenue sharing, as categorical grants, with or without matching requirements, closed or open-ended. Some require local governments to apply for funding and are considered on a case-by-case basis. Others are allocated by formula and come automatically. The categorical grants, with or without matching requirements, carry restrictions of varying stringency. They may have very narrow scope (public transit or energy assistance to the elderly, for example), or be broader (community development block grants, which may be spent on economic development, on housing rehabilitation, or on urban infrastructure, for example).

The Local Response to Intergovernmental Aid

Regardless of the form that intergovernmental aid takes, local government officials view it as a very desirable form of revenue. It is relatively painless, since the taxes financing it are collected at a different level of government. The most desirable form of aid from the point of view of the recipient government is the unrestricted grant—general revenue sharing is the principal example. Such funds may be used at the discretion of local authorities to increase expenditures, for tax relief, or both. Clearly, federal and state aid received by a community does not automatically become a one-to-one addition to the expenditures of the local government; local taxes might be less than they would have been in the absence of the aid. The theoretical literature on the expected response of local governments to intergovernmental aid is straightforward. Lump sum grants, such as revenue sharing, are expected to affect local expenditure decisions in the same way as any other increase in local income: if local

Intergovernmental Aid

US

communities spend 5% to 10% of each additional dollar in income on the public sector, they would be expected to spend an additional dollar of intergovernmental aid in the same way. The empirical literature, however, reveals a very different pattern. Various studies of revenue sharing have found that 75% to 85% of these funds were used to increase local expenditures and only 15% to 25% for tax reduction! 2 This greater propensity for public spending out of intergovernmental aid, rather than tax reduction, has come to be known as the flypaper effect—money sticks where it hits. 3 Most intergovernmental aid is not, however, no-strings, lump sum, but is categorical—directed to specific purposes, such as energy assistance for elderly households. This kind of aid is even more likely to be used for expenditure increases rather than tax relief, especially if local governments are required to match, or partially match, the aid. If the intent of a higher level of government is to increase local expenditures for particular programs, then intergovernmental aid appears to do the job.4 Intergovernmental Aid in the Philadelphia PMSA

It has been a long time since local governments were in full charge of their fiscal affairs. Federal and state fiscal assistance make up a substantial fraction of local revenues. The expenditure mandates and regulations of these higher levels of government influence the amount and composition of expenditures of local governments. It is, therefore, very likely that they have played, and will play, an important role in the economic growth patterns of the nation's metropolitan areas. What has been the Philadelphia experience? In this section, several characteristics of intergovernmental aid in the region are examined: the changes in both federal and state aid to the Philadelphia metropolitan area and its counties over the period 1965 through 1983 (the last year for which county figures are available), shifts in the locus of aid within the metropolitan area from city to suburban counties, and the types of aid coming to the region and to each of the counties. Briefly, by 1983, the region's receipts of state and federal aid nearly equaled the average per capita receipts of the nation's metropolitan areas. The city of Philadelphia received substantially more aid than did any of the other counties; the city's share of the region's aid had increased over time; and it had become relatively and absolutely more dependent on intergovernmental aid.

Distribution of aid across the counties Who gets how much total aid? The region's position with respect to intergovernmental aid has improved markedly in the last twenty years relative to other PMSAs. In fiscal year 1983 nearly $2.5 billion of intergovernmental revenues were received in the Philadelphia PMSA. This was more than thirteen times the 1965 figure of about $181 million. In per capita terms, the Philadelphia metropolitan area was near parity with the average for all PMSAs in the United States in 1983 ($524 vs. $580). This was a big change from the 1965 picture, when it received less than half that of all PMSAs ($39 vs. $83).

Economic Development within the Philadelphia Metropolitan Afea

116

Given the variation in size and income among the counties of the PMSA, it is not surprising to find enormous differences in the amount of intergovernmental aid they receive, since population (an indicator of need) and per capita income (an indicator of local capacity to finance needed expenditures) are important determinants of the amount of aid received. Table 9.1 shows the aid pattern in the region. Philadelphia stands at the Table 9.1 TOTAL AND PER CAPITA INTERGOVERNMENTAL AID: ALL U.S. PMSAs, PHILADELPHIA PMSA AND COMPONENT COUNTIES, 1965, 1983

Total Aid ($000,000) County All U.S. PMSAs Philadelphia PMSA

1965 $6,527.0

1983 $69,997.0

Per Capita Aid %

Change 972.4% 1,274.1

1965

1983

Change

$83.22

$580.26

597.3%

39.25

524.32

1,235.7

181.0 (100.0%)

2,486.5 (100.0%)

Bucks

15.3 (8.5%)

153.2 (6.2%)

899.4

43.54

305.45

601.5

Burlington

14.8 (8.2%)

181.7 (7.3%)

1131.2

55.75

486.47

772.6

Camden

18.8 (10.4%)

311.7 (12.5%)

1560.8

43.56

649.99

1,392.1

Chester

12.9 (7.2%)

89.9 (3.6%)

594.4

53.85

272.52

406.1

Delaware

11.3 (6.3%)

165.2 (6.6%)

1,358.1

19.37

299.74

1,447.8

Gloucester

7.3 (4.0%)

93.8 (3.8%)

1,186.3

47.31

457.63

867.4

Montgomery

8.2 (4.5%)

147.8 (5.9%)

1,713.6

14.28

228.97

1,503.0

92.4 (51.1%)

1,343.2 (54.0%)

1,353.9

45.91

810.98

1,666.3

Philadelphia

SOURCES: Local Government Finances In Selected Metropolitan Areas and Large Cities, Bureau of the Census, U.S. Department of Commerce, 1965, 1983; Current Population Report, Bureau of the Census, U.S. Department of Commerce, 1965, 1983. Note: Figures in parentheses are county shares of total EMSA aid.

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117

top of the list in total aid received in 1983. It obtained $1.3 billion dollars, 54% of the PMSA total. In comparison, Gloucester and Chester counties (with relatively small populations) were close contenders for last place with about $90 million each—less than 4% of the total. Much, but certainly not all, of this enormous difference disappears when per capita figures are compared: Philadelphia still stands in first place, receiving more than $800, and it is the county with the highest per capita income, Montgomery, which is in last place, at $230 per capita. Shifts over time in total aid. The expansion in intergovernmental aid between 1965 and 1983, the expansion of social assistance programs for the poor, and the changes in the relative population of the counties within the metropolitan area all suggest that some substantial shifts in the geographical locus of aid have occurred. In particular, conventional wisdom would suggest that there would have been increasing aid to the growing and the poorer parts of the PMSA. In fact, a look at the overall distribution of aid across the counties indicates that the shifts were modest—a few percentage points here and there. However, some of the smaller counties, Chester and Bucks in particular, lagged far behind the rest of the PMSA in overall growth of intergovernmental aid. In Chester County, total intergovernmental aid was only about 600% larger in 1983 than in 1965, compared with a PMSA increase of nearly 1300%. At the high end, total intergovernmental aid to Montgomery County increased 1700%. In the two central city counties, Philadelphia and Camden, the increases were at or above the average increase—up by slightly less than 14-fold in Philadelphia and 16-fold in Camden. In per capita terms the differences were, surprisingly, even larger. The largest overall increase in per capita aid accrued to Philadelphia, an increase of nearly 1700%, compared with only 400% in Chester. Bucks, Chester, Burlington, and Gloucester counties all had increases of less than tenfold. By comparison, Chester and Burlington had stood at the top of the per capita aid distribution in 1965. Who gets how much federal aid? In 1983, nearly one-fourth of the intergovernmental aid received in the Philadelphia PMSA came directly from the federal government. Table 9.2 presents details on federal aid to the region. The data indicate that federal aid to the Philadelphia metropolitan area is overwhelmingly concentrated in the city of Philadelphia. It received over 78% of the area's total in 1983 ($502.6 million out of $641.4 million), compared with 54% (see Table 9.1.) of combined federal and state aid. Each of the remaining counties received between 0.6% and 5% of the total. In per capita terms, the differences were equally striking. Philadelphia received $303.42, nearly sixteen times Gloucester's $18.29, and the difference between Philadelphia and Camden (the second largest recipient of federal aid) in per capita terms was more than fourfold. Shifts over time in federal aid. The $641 million received by the Philadelphia metropolitan area in 1983 was about 21 times what it had been in 1965. In per capita terms, the region moved from a figure about 10% below the national average in 1965 ($6.64 vs. $7.33) to a figure that exceeded

118

Economic Development within the Philadelphia Metropolitan Area Table 9.2 TOTAL AND PER CAPITA FEDERAL AID: PHILADELPHIA PMSA, 1965, 1970, 1975 , 1980, 1983 Peak. . Year

1965

1970

1975

PMSA Total Aid ($000,000) % of PMSA Total Aid X of Aid to All PMSAs Per Capita Aid ($)

30.6 100.0 5.3 6.64

63.2 100.0 3.5 13.10

260.3 100.0 3.8 54.64

641.4 100.0 4.6 135.25

641.4 100.0 4.6 135.25

Bucks Total Aid ($000,000) % of PMSA Total Aid Per Capita Aid ($)

0.6 1.9 1.62

2.2 3.5 5.33

11.6 4.5 25.38

31.0 6.5 62.52

29.3 4.6 58.46

Burlington Total Aid ($000,000) % of PMSA Total Aid Per Capita Aid ($)

3.7 12.0 13.83

5.3 8.3 16.33

10.7 4.1 31.07

28.7 7.4 79.35

9.9 1.5 26.51

Caaden Total Aid ($000,000) % of PMSA Total Aid Per Capita Aid ($)

2.2 7.2 5.14

2.2 3.5 4.85

11.1 4.2 23.32

40.2 10.4 84.94

32.0 5.0 66.83

Chester Total Aid ($000,000) % of PMSA Total Aid Per Capita Aid ($)

0.2 0.5 0.68

0.3 0.4 0.96

4.6 1.8 15.70

24.7 5.2 82.14

10.4 1.6 31.45

Delaware Total Aid ($000,000) % of PMSA Total Aid Per Capita Aid ($)

1.4 4.7 2.46

2.1 3.4 3.52

15.4 5.9 26.53

38.7 8.2 67.69

28.2 4.4 51.24

Gloucester Total Aid ($000,000) % of PMSA Total Aid Per Capita Aid ($)

0.8 2.5 5.06

0.5 0.9 3.12

7.5 2.9 41.00

10.0 2.2 53.91

3.7 0.6 18.29

Montgomery Total Aid ($000,000) % of PMSA Total Aid Per Capita Aid ($)

0.4 1.5 0.79

0.8 1.2 1.21

11.6 4.4 18.15

40.8 9.6 63.47

25.2 3.9 39.06

Philadelphia Total Aid ($000,000) % of PMSA Total Aid Per Capita Aid ($)

21.3 69.7 10.60

49.8 78.8 25.53

187.8 72.2 104.75

502.6 78.4 303.42

502.6 78.4 303.42

SOURCES: See Table 9.1. (a) Peak years: PMSA, 1983; Bucks, 1982; Burlington, 1979; Camden, 1979; Chester, 1977; Delaware, 1977; Gloucester, 1976; Montgomery, 1980; Philadelphia, 1983.

1983

Intergovernmental Aid

119

the national PMSA average by about 16% ($135.25 vs. $116.34). The overall patterns of change in federal aid were very similar for all the counties. Between 1965 and 1972, there was very little change; from 1972 to the mid-to-late 1970s federal aid increased fairly steadily. Around 1979 or 1980 there was a substantial loss in federal aid in all of the counties, except Bucks and Philadelphia. In some counties this was followed by still further losses, and in no case had the earlier peak been reattained by 1983. In Chester County these fluctuations have been extreme since 1976. Much of the year-to-year fluctuation in total intergovernmental aid to the region was due to the federal component. The concentration of federal aid in the city of Philadelphia increased between 1965 and 1983, although even in 1965 it was nearly 70% of the total. The principal difference of note between the two years is the large decrease in the share received by Burlington—down from nearly 12% to only 1.5%. The very pronounced shift to Philadelphia over the period is clearest from the per capita figures. In 1965 Philadelphia was second to Burlington in federal aid per capita, receiving $10.60 compared to Burlington's $13.83. By 1983, Philadelphia not only stood in first place with $303.42, but the next highest recipient, Camden, received less than 25% of that amount, $66.83. Who gets how much state aid? State aid flowing to the Philadelphia metropolitan area increased more than twelvefold between 1965 and 1983, to over $1.8 billion. Table 9.3 presents details on state aid to the region. The most striking feature of the state aid totals is in how the patterns differ in the two states. In 1983, per capita state aid to the New Jersey counties exceeded aid to the Pennsylvania counties by 45%. Since the mid1970s, when per capita state aid was about the same on both sides of the PMSA, the difference has been increasing. Table 9.3 TOTAL AND PER CAPITA STATE AID: PHILADELPHIA PMSA, 1965, 1970, 1975, 1980, 1983

Year 1965 1970 1975 1980 1983

Phila. PMSA

PA Counties

NJ Counties

Total Per Capita State Aid State (000,000) Aid

Total Per Capita State Aid State (000,000) Aid

Total Per Capita State Aid State (000,000) Aid

$150.4 457.0 1004.2 1682.6 1845.1

$32.61 94.73 210.83 356.74 389.07

$116.2 355.7 794.6 1272.5 1303.6

$30.90 91.86 211.23 345.54 353.82

$34.2 101.3 209.6 410.2 541.5

$40.20 106.44 209.32 396.64 511.80

SOURCES: State Government Finances, 1965, 1970, 1975, 1980, 1983, Bureau of the Census, U.S. Department of Commerce. See Table 9.1 for additional sources.

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Economic Development within the Philadelphia Metropolitan Area

County details on state aid are shown in Table 9.4. Philadelphia was by far the recipient of the largest amount of state aid. In 1983 it received 46% of the region's state aid. Some $840 million came from the state to Philadelphia in 1983. Chester received the least total state aid, $79 million. On the New Jersey side of the PMSA, Camden was the largest beneficiary with nearly $280 million. As one might expect—since the total amount of aid is related to population size—the spread among the counties in per capita state aid was far smaller than in total aid. In Pennsylvania, Philadelphia stood at the top of the distribution with over $500 per capita, compared with Montgomery's $190. The three New Jersey counties were far more similar, with Camden receiving nearly $600 per capita, compared with $460 for Burlington and almost $440 for Gloucester. Shifts over time in state aid. Changes in the distribution of state aid between 1965 and 1983 favored the central city counties in both states, although the shifts were not very large. Philadelphia's share of state aid to the five Pennsylvania counties increased from 61.2% to 64.5%. In New Jersey, Camden's share increased from 48.4% to 51.6% of the three-county total. Montgomery, Delaware, Burlington, and Gloucester also gained increased shares of total state aid to the eight counties. The largest decline occurred in Chester (down from 8.5% to 4.3% of the total), and there was a more modest shift away from Bucks. The per capita changes illustrate clearly that the overall shifts described above are not attributable simply to population shifts. If population changes were the main determinant of aid shifts, similar changes in per capita aid would be expected in each county. However, the increase over the period in per capita state aid was about 1300% in Philadelphia, Delaware, and Montgomery counties, compared with less than 400% in Chester and just under 500% for Bucks. In New Jersey, the shifts in per capita terms were more similar: a 1400% increase in Camden compared with a 900% increase in Gloucester. Thus, the combined effects of the overall distribution of state aid, and the growth and intrametropolitan shifts during the 1960s and 1970s, did not favor suburban counties. The aggregate shifts moved in the direction of both of the central cities of the region, Philadelphia and Camden. State aid: "compensatory" change? Some time in the late 1970s, or early 1980s for most counties, the steady increases in federal aid became more erratic. How did the state respond to such fluctuations? Did states compensate, or try to compensate, for such changes by increasing their own assistance to local governments? Specifically, did changes in aid from Pennsylvania and New Jersey state governments compensate for the declines in federal aid when they occurred? Understanding the historical response is relevant to assessing future state responses to the changing view of federal responsibilities for local activities. It is useful, then, to look at whether or not the percentage increase in state aid to a county exceeded the increase in the previous year when federal aid declined, and whether the dollar amount of the increase in state aid exceeded the dollar loss in federal aid.5 "Yes" or "No" answers to these questions are given for each of the

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Intergovernmental Aid

Table 9.4 STATE AID TO THE COUNTIES: PHILADELPHIA PMSA, 1965, 1983

Amount ($000,000)

Total State Aid County County Share of Share of State's Aid PMSA Total to Region(a)

Bucks 1965 1983 % Change

14.8 123.9 739.22

Burlington 1965 1983 % Change

171.8 1448.0%

7.4 9.3 Î.9 (b)

Camden 1965 1983 % Change

16.6 279.6 1589.6%

Chester 1965 1983 % Change Delaware 1965 1983 % Change

12.71% 9.50 (b) -3.2

$41.90 247.00 489.1%

32.50 31.74

(b)

41.90 460.00 997.2%

11.0 15.2 4.2 (b)

48.45 51.64 3.2 (b)

38.40 583.20 1418.0%

12.8 79.5 522.1%

8.5 4.3 -4.2(b)

11.00

9.9 136.9 1285.0%

11.1

9.8% 6.7 (b) -3.1

Per Capita State Aid

-4.9(b)

53.20 241.10 353.4%

6.6 7.4 (b) 0.8

8.51 10.50 2.0 (b)

16.90 248.50 1370.2%

19.06 16.62 -2.4(b)

42.20 439.30 940.0%

6.63 9.41 2.8 (b)

13.50 189.90 1307.0%

Gloucester 1965 1983 % Change

1282.8%

4.3 4.9 (b) 0.6

Montgomery 1965 1983 % Change

7.7 122.6 1491.9%

5.1 6.7 1.5(b)

Philadelphia 1965 1983 X Change

71.1 840.7 1083.0%

47.3 45.6 -1.7(b)

6.5 90.0

-0.8

6.10

61.16

64.49 (b) 3.3

35.30 507.60 1337.2%

SOURCE: See Table 9.1. (a) Bucks, Chester, Delaware, Montgomery, Philadelphia: Percent of Pennsylvania state aid to the five counties; Burlington, Camden, Gloucester: Percent of New Jersey state aid to the three counties. (b) Percentage point change, 1965 to 1983.

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Economic Development within the Philadelphia Metropolitan Area

counties in Table 9.5. In the Pennsylvania counties, there were fifteen cases (over the five counties in the years since 1977) of decline in federal aid. In ten of the fifteen cases of loss of federal aid, the state tried to provide a cushion. (These are the cases marked "Yes" in the first column.) However, in only seven of these ten was the increase in state aid as large or greater than the loss of federal aid (marked "Yes" in the second column). Moreover, all of the failures of effort and four of the seven failures in effect have occurred since 1981. In New Jersey, the pattern was quite different. Of the ten instances of federal aid decline (in the three counties since 1977), seven were marked by an increased growth rate in state aid during the year. (The three exceptions occurred in 1978, 1980, and 1981.) In all years, the dollar increase in state aid exceeded the dollar decrease in federal aid. The New Jersey counties, then, have had their losses in federal aid offset more by their state than have the Pennsylvania counties.

Federal aid to the counties by type of aid Local public finances and economic development efforts are affected differently by different kinds of intergovernmental aid. It is of some importance, therefore, to look at the types of aid being received in the region, how the mix differs among counties, and how it has changed over time. Of particular interest is the question: Are the suburban counties primarily receiving economic development assistance, while the central cities receive mostly support for their poor populations? Examining this issue is very difficult because of the erratic nature of the data. Two incompatible sources of data are available—incompatible with each other and with the aggregate data for 1965-83. One covers the years 1977-80 for all the PMSA's counties. The other, for 1984 and 1985, includes only the Pennsylvania portion of the PMSA. Details on these sources and their limitations are described in Appendix B.6. Given the substantial differences among the counties in the income of their population, their social conditions, and the pace of economic activity, identical patterns of federal aid are hardly to be expected. For example, aid from the Department of Health, Education, and Welfare (HEW) should not be nearly as important in wealthy Montgomery County as in Philadelphia. (In 1980 HEW was divided into two new departments, Health and Human Services (HHS) and Education.) However, it is not so clear what expectations should be from other departments whose aid is more centered on economic development. Perhaps the Department of Housing and Urban Development (HUD) is more like HEW/HHS than is the Department of Transportation (DOT) or the Environmental Protection Agency (EPA). Types offederal aid to the region: 1977-80. The data show (in Appendix Table B.6.1) that in 1977, HHS/HEW was the largest source of aid by far; the Departments of Labor, Agriculture, and Transportation were next; and smaller amounts came from HUD, General Revenue Sharing (GRS) from the Department of the Treasury, and EPA. Did the suburban counties get disproportionate amounts of the economic development funds? It does not

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123

look that way: the central cities, Philadelphia and Camden, received high proportions of the HUD budget. And transportation funds—widely viewed as critical to the economic development of the central city—have been very heavily concentrated in Philadelphia, which received between 81% and 88% of the DOT total in three of the four years.

Table 9.5 STATE GOVERNMENT RESPONSES TO FEDERAL AID DECLINES: PHILADELPHIA PMSA, 1977-1983

/

a

\

State Aid Increase (%) Greater than Prior Year Increase

State Aid Increase ($) Greater than Federal Aid Decrease

County

Year

Bucks

1978 1983

Yes Yes

Yes Yes

Chester

1978 1980 1981

Yes Yes No

No Yes No

Delaware

1978 1979 1980 1981

Yes Yes Yes No

No Yes Yes No

Montgomery

1981 1982 1983

No Yes No

Yes Yes No

Philadelphia

1977 1979 1982

Yes Yes No

No Yes No

Burlington

1978 1980 1981 1983

No Yes Yes Yes

Yes Yes Yes Yes

Camden

1980 1981

Yes< b > No

Yes Yes

Gloucester

1977 1979 1980 1982

Yes Yes No Yes

Yes Yes Yes Yes

SOURCES: See Table 9.1. (a) Years between 1977 and 1983 in which federal aid to the county declined. (b) Although slightly smaller than the previous year, the increases in both 1979 and 1980 were unusually large.

124

Economic Development within the Philadelphia Metropolitan Area The major patterns emerging from the 1977 to 1980 data are: • The largest proportion of federal aid to the Philadelphia PMSA, 43% to 46%, came from HEW. (These percentages are calculated from the data in Table B.6.1.) The central cities received a very steady proportion of these funds over the four years, with Philadelphia receiving between 72% and 74% of the total and Camden about 8%. As expected, the individual counties show substantial differences in the sources of federal aid. In 1977, for example, only 25% of the federal aid received by Montgomery came from HEW, compared with 51% in Philadelphia. Nor are such large differences limited to single years. In 1980, 22% of Montgomery's federal aid came from HHS, compared with 37% in Philadelphia. • Equally stable, but far smaller, was the proportion of aid to the region from HUD—5% to 6%. In this case, too, the distribution of aid to the central city counties was very stable over the period— 66% to 68% went to Philadelphia, 7% to 8% to Camden. • Not surprisingly, funding from DOT and EPA was much more erratic. These sources generally cover discrete project costs, rather than operating budgets. Thus, once a project is completed, the funding ends. From 1977 through 1980 the region received between 7.5% and 19% of its total intergovernmental aid from DOT and between 2% and 8% from EPA. Philadelphia, for example, received only 59% of the region's DOT grants in 1977, but 81% to 88% in the next three years. Camden was the recipient of 32% of DOT funds coming to the region in 1977, compared with only 2% in 1978. Sources of federal aid to the region, 1984-85. Strict data comparability with earlier data is not possible to achieve for any of the categories for the years 1984 and 1985. In addition, these more recent data are available only for the Pennsylvania counties. 6 Despite these shortcomings, it is worth looking at the trends in the data shown in Table 9.6. Several developments stand out in the county comparisons of 1980 with 1984 and 1985. • Philadelphia is the only one of the counties that experienced a decrease in funds from HEW/HHS. 7 In all other Pennsylvania counties there were steady increases over the period, with 1985 figures 16% to 23% greater than 1980. This may reflect some decentralization of the poor out of the city of Philadelphia between 1980 and 1984. 8 • Economic development funds from HUD declined very substantially. By 1984, HUD funds received in the Pennsylvania counties (with the exception of Bucks) had declined by 20% to 40%.

Intergovernmental Aid

12S

This pattern was reversed, however, in 1985, when HUD grants generally increased by small amounts. • DOT funds have been very erratic. Indeed, with the exception of Philadelphia, aid from DOT has practically disappeared. • EPA funding continued its highly erratic year-to-year fluctuations. Table 9.6 FEDERAL GRANTS TO THE COUNTIES BY DEPARTMENT:^ PHILADELPHIA PMSA, 1980, 1984, 1985 ($000,000)

County

Health & Human Services

Bucks 1980 1984 1985

$24.8 27.6 29.6

$5.2 7.5 1.1

$4.7 0.2 0.0

$6.6 6.6 6.6

Chester 1980 1984 1985

19.8 22.8 24.5

4.0 2.9 3.0

5.0 0.1 0.9

Delaware 1980 1984 1985

41.1 47.3 50.6

14.5 8.1 8.4

Montgomery 1980 1984 1985

25.6 27.6 29.7 619.5 560.4 594.0

Philadelphia 1980 1984 1985

Five County Total 1980 730.8 1984 685.7 1985 728.4

Housing & Urban TransDevelopment portation

Environ. Protect. Treasury^ Agency

Labor

Agriculture

$1.6 9.1 3.7

$17.0 n.a. n.a

$11.4 n.a. n.a.

3.7 3.7 3.9

0.2 12.8 0.2

6.6 n.a. n.a.

7.5 n.a. n.a.

4.1 0.0 0.5

8.6 9.2 9.0

(c) 0.0 0.8

22.8 n.a. n.a.

18.5 n.a. n.a.

7.5 6.0 11.3

31.5 0.0 0.6

7.6 7.4 7.4

1.1 5.7 0.8

19.7 n.a. n.a.

9.7 n.a. n.a.

92.4 65.7 71.2

306.9 150.0 109.8

45.4 45.9 45.0

156.8 5.0 69.3

109.1 n.a. n.a.

182.3 n.a. n.a.

123.6 90.5 95.0

352.2 150.6 111.8

71.9 72.8 71.9

175.2 n.a. n.a.

229.4 n.a. n.a.

159.7 32.6 74.8

SOURCES: 1984 and 1985: Special Tabulations by Pennsylvania Intergovernmental Council from Federal Assistance Awards Data System (FAADS); 1980: Geographic Distribution of Federal Funds Report, General Service Administration. (a) Data for 1977-1979 are in Appendix Table B.6.1. (b) General Revenue Sharing. (c) Less than $100,000.

126

Economic Development within the Philadelphia Metropolitan Area

In sum, except for HEW/HHS and general revenue sharing (which has remained just about constant), most counties are receiving substantially less federal aid in 1985 than in 1980 for the programs for which comparable data are available—housing, transportation, environment. Economic development funding—from DOT and HUD—has been particularly hard hit in both Philadelphia and the suburban counties.

The determinants of federal aid The amount of aid a community receives should be related to both local population and local income. Lump sum revenue-sharing aid should be allocated in relation to population, while aid which is designed to assist the disadvantaged should be allocated according to income. Empirical estimates of these relationships for the Philadelphia region are described in Appendix B.6.9 Consistent with expectations, larger populations are found to be associated with both higher total and higher per capita federal aid. Total aid would be predictably higher because the number of recipients is higher; per capita aid is expected to be higher because increased population densities bring special problems of crime, transportation, and pollution with them. Higher per capita income should, of course, reduce the need for external aid, and it is in fact found to be associated with lower total and lower per capita federal aid. 10 Thus, for example, the $33 difference between per capita federal aid to Gloucester and aid to Delaware in 1983 ($18 vs. $51 in Table 9.2), is composed of two effects: Gloucester's much smaller population (a difference of 346,100) induced an estimated per capita aid difference of - $ 5 6 and its much lower per capita income (about $2,575 less) induced an estimated additional per capita aid of +$23. For Philadelphia, relatively low per capita income has had a particularly strong effect on per capita aid. Thus, in 1983, approximately $61, nearly 25%, of the difference of $264 between Philadelphia and Montgomery counties in federal aid per capita was due to Philadelphia's lower per capita income. This close tie between income and aid is an important part of the explanation why Philadelphia has had more sustained increases in aid over time than have the suburban communities.

The determinants of state aid State aid to local governments should also be related to population and to local income: higher where population is larger and lower where incomes are higher. In addition, state aid might be expected to increase as federal aid decreases, perhaps because of local pressures on the state legislature to compensate for lost federal funds. Empirical estimates of actual relationships for the Philadelphia region are described in Appendix B.6. In contrast to federal aid, increases in population have been associated with decreases in per capita state aid in Pennsylvania. This implies an assumption on the part of the state that there are economies of scale in the provision of local services—it costs less per capita to provide services for larger populations. Finally, there is an interesting relationship between

Intergovernmental Aid

127

state and federal aid: in general, state aid increases when federal aid decreases, and state aid decreases when federal aid increases. Thus, since most of the period 1965-83 was characterized by substantial year-to-year increases in federal aid, the effect was to displace state aid to some extent. However, federal aid displaced state aid to suburban governments by far more than it displaced aid to Philadelphia. Indeed, there appears to have been little or no displacement for Philadelphia: it had the benefit of increased federal aid without related losses in state aid. This analysis of the determinants of both state and federal aid points to some striking inferences concerning the favored treatment of Philadelphia compared with the other counties. State aid to Philadelphia did not decline when federal aid increased. However, for every additional dollar in federal aid per capita received by suburban governments, on average, they received $.35 less in state aid per capita; they were still better off, but not by the full amount of the increase in per capita federal aid. For Philadelphia, the displacement was something less than $.10 per dollar of increased per capita federal aid. It barely lost any state aid when federal aid increased— and federal aid increased substantially. Moreover, increases in federal aid to Philadelphia have been more sustained over time than increases to the suburbs, and Philadelphia's low-income population has brought it disproportionately higher per capita aid than in suburban counties. In the analysis of New Jersey, neither changes in population, nor per capita income, nor federal aid appear to influence state aid to local governments. It may be that the more rapid and sustained year-to-year increases in state aid in New Jersey during this period dominate these other influences. (It may also be that the three New Jersey counties included in the study are not representative enough of the population and income differences in the state to support the analysis.)

Intergovernmental Aid and Local Public Finance

What has been the fiscal impact of intergovernmental aid in the Philadelphia metropolitan area? There are numerous facets to the answer to so seemingly simple a question. How large a role does aid play in local budgets? Has it stimulated expenditures or helped to keep taxes down? Have the effects differed among the counties of the region? Has it helped to ameliorate or exacerbate fiscal disparities? These are enormously difficult issues to disentangle. For the Philadelphia region, the answers seem to be that intergovernmental aid plays a more substantial role in the budgets of the three New Jersey counties and of Philadelphia than those of the other four Pennsylvania counties. In addition, the pattern of aid appears to have widened the expenditure disparities between the central city and all its suburbs to a greater extent than it has narrowed tax disparities.

The share of intergovernmental revenues in local revenues The proportion of local expenditures financed by intergovernmental aid has increased very substantially since 1965 in the Philadelphia region as

Economic Development within the Philadelphia Metropolitan Arca

128

well as in other PMSAs. (See Table 9.7.) Until 1970 the Philadelphia PMSA raised far more of its local government revenue on its own than did all PMSAs on average. (Alternatively, the Philadelphia PMSA received relatively less intergovernmental aid.) In 1965, the nation's PMSAs received, on average, 27.8% of total revenues from higher levels of government. The Philadelphia PMSA received 18.5%. In later years, however, the ratios have been much more similar. Although the increases have not been continuous, the trend has been toward greater dependence on intergovernmental aid until the late 1970s or early 1980s, and reduced dependence since then. Thus, simply comparing 1965 and 1983 does not tell the whole story. In the Philadelphia PMSA in 1965,14 cents (Delaware County) to 30 cents (Chester) of =ech dollar in local revenues came from intergovernmental aid. 11 In 1983, the comparable range was about 50% higher, 23 cents (Montgomery) to 45 cents (Philadelphia). In 1983, the counties fell into two distinct groups: Philadelphia and the three New Jersey counties, with 42% to 45% of their total revenues derived from intergovernmental aid, and the other four Pennsylvania counties, with proportions of 23% to 30%. Although the levels were quite different in 1965, the overall spread among the counties was about the same. In the 1980s, both central city counties (Camden and Philadelphia) received nearly half of their local revenue from state and federal govern-

Table 9.7 PERCENTAGE OF TOTAL COUNTY REVENUE FROM INTERGOVERNMENTAL REVENUE: PHILADELPHIA PMSA, 1965, 1970, 1975, 1980-1983

Year 1965 1970 1975 1980 1981 1982 1983 Year 1965 1970 1975 1980 1981 1982 1983

All PMSAs

Philadelphia PMSA

Bucks

Burlington

Camden

27.8% 34.5 40.7 43.0 42.4 40.3 38.9

18.5% 28.8 38.8 40.5 41.8 38.8 39.1

20.8% 31.3 34.5 32.1 31.3 28.2 29.2

23.9% 35.8 33.9 41.0 46.1 44.7 45.2

16.3% 27.6 34.6 45.0 46.0 44.5 43.5

Chester

Delaware

Gloucester

Montgomery

Philadelphia

30.3% 35.6 34.5 32.6 33.2 29.4 29.9

14.4% 24.6 33.5 36.5 36.4 31.9 29.5

25.4% 33.7 37.9 39.1 40.3 41.5 42.2

7.7% 19.2 22.6 26.2 27.2 24.3 22.8

19.6% 29.7 46.2 45.6 46.5 43.9 44.9

Source : Local Government Finances in Selected Metropolitan Areas and Large Cities, 1965, 1970, 1975, 1980, 1983, Bureau of the Census, U.S. Department of Commerce.

Intergovernmental Aid

129

ments. In the suburban counties the levels have been lower, especially on the Pennsylvania side. Around 1980, the suburban counties, particularly those in the Pennsylvania part of the PMSA, began to raise higher proportions of local revenue on their own. In Philadelphia, the ratio has also increased somewhat, but by a much smaller proportion. What is the import of these ratios and changes? What can be said without further analysis is that, with 23% to 45% of local government budgets financed by intergovernmental aid, these governments have lost a substantial degree of their fiscal independence. If state or federal governments cut back the levels of aid, regulate its use, change its composition, or increase matching requirements, local governments will be forced to make difficult decisions—to cut back expenditures that have become a regular feature of the public service package, or to spend their money differendy, or to increase taxes. Of course, the aid received in the past has permitted higher expenditures and/or lower taxes. Continued aid does the same. How has the difference between Philadelphia and its Pennsylvania suburbs come about? Has intergovernmental revenue increased more in Philadelphia, or have tax revenues increased less? Changes in total and intergovernmental revenue for the region, since 1966, are shown in Table 9.8. The answer is that, on the whole, the percentage change in intergovernmental revenues flowing to Philadelphia in the 1980s has substantially exceeded the increases in the suburban counties and its increases in total revenues have also been higher. However, in Philadelphia, the percentage increase in the former has, in all but one of the years between 1979 and 1983, exceeded the increases in the latter. In the suburban counties, even though the percentage increases in total revenues have been lower than in Philadelphia, they have been far higher than the increases in intergovernmental revenues. Thus, in contrast to the suburban counties, Philadelphia is financing more rapidly growing expenditures, with greater than average increases in both intergovernmental assistance and local revenues. In sum, there are three major characteristics of intergovernmental aid in the Philadelphia PMSA. All of the counties in the region have shown growth in intergovernmental and locally raised revenues way beyond what inflation alone would produce. The growth rates for both sources have been lower in the suburbs than in Philadelphia. Local revenue growth has exceeded intergovernmental revenue growth in the suburbs, but the opposite relationship is observed in Philadelphia.

Intergovernmental aid, local taxes, and expenditures How has intergovernmental aid affected taxes and public spending in the Philadelphia region? Estimates have been developed based upon assumptions about how the local governments use intergovernmental revenues, suggested by the literature reviewed earlier in the chapter. It is generally accepted that there are greater unmet public needs in a large central city than there are in smaller, relatively higher income

Economic Development within the Philadelphia Metropolitan Aita

130

suburban communities, and that local political leaders in the central city are pressed to turn, and can more easily turn, additional aid into expenditure increases rather than tax decreases. If, then, for example, Philadelphia spends an average 60 cents of each dollar of intergovernmental aid it receives and uses 40 cents for tax reduction, and the suburbs use only 30 cents of each intergovernmental aid dollar to increase expenditures but 70 cents for tax reduction, several implications follow. (The details of the underlying empirical analysis using these assumptions, and an alternative set, are in Appendix B.6 and Appendix Table B.6.5.) In 1983, when Philadelphia received 45% of its total revenue from intergovernmental aid and the suburban counties received 30%, these inferences may be drawn: • In Philadelphia, 1983 expenditures were about 37% higher than they would have been in the absence of aid. Locally raised revenues were about 25% lower than they would have been. Table 9.8 ANNUAL RATES OF CHANGE IN TOTAL AND INTERGOVERNMENTAL (I-G) REVENUE TO THE COUNTIES: PHILADELPHIA PMSA, 1975-1983 Bucks

Burlington

Camden

Chester

Year

Total Rev.

I-G Rev.

Total Rev 4

I-G Rev.

Total Rev.

I-G Rev.

Total Rev.

I-G Rev.

1975 1976 1977 1978 1979 1980 1981 1982 1983

10.6% 5.7 12.0 10.9 13.5 9.4 1.4 17.4 5.4

10.6% 8.0 -1.3 4.0 22.6 11.6 -1.1 5.9 9.0

16.6% 12.6 17.0 2.8 12.7 4.4 8.7 6.3 6.2

41.5% 7.7 34.0 11.0 15.3 4.2 22.3 3.1 7.4

9.2% 15.2 8.0 15.0 5.3 3.0 5.5 11.8 6.8

25.8% 11.9 15.8 12.0 24.3 12.1 7.7 8.3 4.3

7.5% 16.2 26.3 -0.7 6.0 7.7 -1.6 27.1 0.2

10.7% 11.4 32.5 -16.1 25.3 1.5 0.1 12.5 2.1

Delaware Year

Total Rev.

I-G Rev.

1975 1976 1977 1978 1979 1980 1981 1982 1983

-6.0% -12.7% 13.5 18.0 15.3 16.8 2.0 -0.5 17.2 23.5 8.6 9.2 -0.1 -0.3 15.6 1.4 6.5 -1.8

Gloucester

Montgomery

Philadelphia

Total Rev.

I-G Rev.

Total Rev.

I-G Rev.

Total Rev.

I-G Rev.

8.0% 11.3 11.2 4.2 17.5 8.3 6.3 8.7 5.1

10.7% 4.6 19.7 4.8 23.0 4.8 9.4 12.0 6.8

9.1% 8.0 6.6 9.9 12.4 10.1 -3.7 18.3 4.2

13.0% 13.8 -4.2 6.3 31.2 19.4 0.2 5.5 -2.1

10.5% 10.6 11.0 9.3 7.8 9.5 9.0 2.9 8.2

20.2% 21.9 -1.5 8.4 2.8 16.8 11.2 -2.9 10.7

SOURCE: Local Government Finances In Selected Metropolitan Areas and Large Cities, 1975-1983, Bureau of the Census, U.S. Department of Commerce.

Intergovernmental Aid

131

• In the suburban counties, expenditures were about 10% higher and taxes 23% lower. • If intergovernmental aid were to fall to zero, expenditures in Philadelphia would fall by 27% and taxes rise by 33%. In the suburbs, expenditures would fall by 9% and taxes would have to rise by 23%. On the basis of these assumptions, tax disparities between city and suburbs appear to have been changed very little by intergovernmental aid; in both, taxes are lower by about the same percentage as a result of intergovernmental assistance. However, the effect on expenditures has been far greater in the central city than in the suburbs.

Summary and Policy Conclusions

It is clear that Philadelphia receives more favorable treatment from both federal and state governments than do the suburban counties, including Camden. It receives far more aid per capita. Aid to Philadelphia has been subject to less fluctuation and reduction. Relatively low incomes in Philadelphia counted more heavily than they did in the suburban communities in the distribution of aid. Philadelphia's larger population has also had a disproportionately large effect on aid, in comparison with the suburban counties. In addition, while federal aid has a substantial displacement effect on state aid to the suburban counties, it displaces little or none of the state aid to Philadelphia. An implication of the displacement findings is that suburban jurisdictions may want to lobby state officials more vigorously for "maintenance of state aid" during periods of rising federal aid. More relevant now, when federal aid is declining, or growing much more slowly, is that both city and suburban communities may wish to lobby state government for increased "replacement" aid. Whether or not such efforts are ultimately desirable from the point of view of the local governments depends upon how state aid increases are financed (whether by increased taxes, decreases in other types of expenditures, or by shifts from other jurisdictions). The net result will vary for each jurisdiction. The link between intergovernmental aid and economic development occurs via its effects on taxes and public expenditures. Both the amount of and types of aid received and what the jurisdiction does with the funds— reduce taxes, increase expenditures, increase particular types of expenditures—are critical. In terms of dollar amounts, the city of Philadelphia has certainly not been short-changed vis-a-vis the suburbs, on either federal or state aid, nor has the mix of aid to the city been biased against economic development funds. Thus, the potential for influencing new firm location by keeping taxes down (relative to the suburbs) was available, as was the potential for increasing infrastructure expenditures, for example, to improve conditions for general economic development. However, this disproportionately high aid to Philadelphia does not tell the whole story. Philadelphia has the overwhelming proportion of the region's disadvantaged and direct responsibility, therefore, for their sub-

132

Economic Development within tbt Philadelphia Metropolitan Area

stantial needs. The fact that it received relatively large shares is not equivalent to the judgment that it was not shortchanged relative to its needs. But, what can be said is that, had economic development been a primary objective, taxes could have been lower, and expenditures could have been more focused on services that were particularly enabling to economic development. City government decision makers have to sort out the tensions between the immediate socioeconomic needs of its residents and the extent to which these needs will be met by facilitating economic growth. If they focus more of the additional expenditures made possible by intergovernmental aid on economic development, some of the significant growth differentials between Philadelphia and its surrounding counties will be reduced. For the suburbs, continuous evaluation of balancing the effects on economic development of tax decreases vs. expenditure increases will be an important priority.

Notes 1. If the desired distribution of income were achieved by the national tax and transfer system, if local public goods were financed through benefit taxation, and if land markets fully capitalized the differences among jurisdictions in the price of public goods, the arguments for redistributive grants would be undercut. 2. Cases against each of these arguments for intergovernmental fiscal assistance may be made and are being made more and more often. A recent exposition is Edward M. Gramlich's, "Reforming U.S. Federal Fiscal Arrangements," in John M. Quigley and Daniel L. Rubinfeld, American Domestic Priorities: An Economic Appraisal, Berkeley: University of California Press, 1985. Many of the counterarguments have been picked up by proponents of radical changes in the intergovernmental system, favoring turn backs of both revenue sources and expenditure responsibilities from the federal government to state and local governments. Nonetheless, the current system has been built on the basis of the reasoning outlined in the text. 3. Gramlich's 1977 survey puts the difference in a somewhat smaller range; 45% of intergovernmental grants devoted to public expenditures, compared with 10% of local income. See Edward Gramlich, "Intergovernmental Grants: A Review of the Empirical Literature," in Wallace Oates, ed., The Political Economy of Fiscal Federalism, Boston: W. C. Heath, 1977. 4. Numerous explanations of this phenomenon have been put forth. See discussion and citations in George Break, Financing Government in a Federal System, Washington: The Brookings Institution, 1980, p. 160. 5. For a survey of this literature, see Robert P. Inman, "Fiscal Performance of Local Governments," in Peter Mieszkowski and Mahlon Straszheim, Current Issues in Urban Economics, Baltimore: Johns Hopkins University Press, 1979. 6. The first perspective tells something about whether the change in state aid in that year was "exceptional" or not, and the second simply indicates whether the positive change in state aid exceeded the negative change in federal aid. The latter, of course, does not by itself indicate compensation, since the normal pattern of state aid is year-to-year increase. A compensation parameter in a simple multivariate model of state aid is estimated in the equations described in Appendix B.6. 7. The differences are described in Appendix B.6. 8. Note that because 1980 aid figures include some pass-through from the state government, some decrease between 1980 and 1984 might have been expected, just because this component is not included in the 1984 figures. However, this decrease is observed only in Philadelphia. 9. However, this inference should be taken with caution, given the fact that the data for 1984 and 1985 are not entirely comparable with those for 1980. In

Intergovernmental Aid

133

addition, the data presented in Chapter 5 show no such trend between 1970 and 1980. 10. Since the data do not allow for the disaggregation of total federal aid into these categories, the relationships between aggregate federal aid, population, and local income are estimated. Details are in Appendix B.6. 11. The only exception is that per capita income differences among the suburban counties (including Camden) have not been important determinants of differences in total federal aid. When Philadelphia County is included in the comparison, however, its lower per capita income does appear to increase its total federal aid. Thus, there is a disproportionate influence of low incomes in Philadelphia, as compared with low incomes in the other counties. 12. For Montgomery County we are using the 1966 figure, since the difference between 1965 and 1966 was unusually large, 8 cents compared to 21 cents.

Ten

Summary and Policy Implications The evidence that the Philadelphia metropolitan area has emerged from its difficult period of adjustment appears to be strong. The period of slow growth for the region—decline for the city—induced some of the adjustments producing the strengths of the recent few years. But different parts of the region accommodated in different ways to these changes. The result is a regional economy very different from the one of the mid-sixties. The major objective of this study has been to gain an understanding of the nature and origin of the disparate economic development patterns among the eight counties of the Philadelphia metropolitan area. This is the second of a planned series of objective assessments of the economic climate of the region, funded by the William Penn Foundation with the intent to provide ongoing, independent economic monitoring. A summary of the analyses underlying the major findings and their policy implications follows.

Summary

The Philadelphia region has been part of the group of older midwestern and northeastern metropolitan areas in the United States that have shared the experience of relatively stressful economic conditions in the last two decades. The 1985 report identified, for the region as a whole, the recent historical sweep of economic events, the relatively strong industries (the "winners"), and the strengths and weaknesses in the economic climate. Though all parts of the region share in the region's economic fortunes, they do not necessarily share equally. All ships may rise with the tide, but that analogy is not apt to describe interregional or intraregional economic developments. The transformation of the Philadelphia regional economy has produced changes in each of the parts of the region. This report identifies these changes, assesses how the prospects for future growth are likely to be distributed, and explores the policy options flowing from the analysis for the city and suburbs.

1986 update The general conclusions of the 1985 Economic Report—that the region has undergone structural transformations, has been involved in industrial accommodations to changed market forces and governmental policies, and, therefore, is and will be experiencing stronger economic development— have been strengthened by the evaluation of the subsequently available data. The PMSA has maintained the high employment growth rate of a few years ago, though at a slightly lower rate. Growth from early 1985 to early 1986 (2.80%), in fact, was barely less than that of the United States (2.99%).

Summary and Policy Implication*

135

Philadelphia is sharing in this expansion: it is experiencing, after a long period of decline, a small positive growth in employment of a shade over 1%. The significant exception to this improvement is the city's manufacturing sector, which is still showing substantial decline in employment. The surrounding counties are enjoying large growth rates—substantially exceeding that of the nation as a whole. For the Philadelphia region the declines of the 1960s became the steeper declines of the 1970s—but, by many indicators, the 1980s are a turnaround time. Employment growth was 1.8 percentage points per year lower than the United States in the 1970s, but only 0.3 points lower in the 1980s. And the city made a significant contribution to this cheerier picture—its annual growth rate improved more than either the change in its suburbs or in the nation. These employment changes mirrored population changes. As population decentralized, employment followed. The net effect of those adjustments is reflected in the region's unemployment rates, which are now substantially and increasingly lower than those of the nation—the 1986 unemployment rate of 7.1% for the nation has, as its regional counterparts, 5.8% for the PMSA, 6.5% for Philadelphia, and 5.4% for the rest of the PMSA. The evidence that the Philadelphia metropolitan area has emerged from its difficult period of adjustment appears to be strong. The major exception to this pattern of substantially completed accommodation is the city's manufacturing sector. Specialization in the industries for which it is best suited has not yet been achieved. The shakeout process in manufacturing has not completely run its course in the city.

Overall economic trends in the counties Philadelphia flourished and expanded in an era where advantages flowing from proximity to markets (agglomeration economies) dominated other costs, enabling the city to share in the urban industrialization of America. But, it is the counties outside the central city that have now emerged as the expanding and flourishing parts of this region—a pattern shared with many of America's other urban areas. The correct description of the pre-World War II Philadelphia regional map is that it was uninucleated. It had an overwhelmingly dominant core— the city of Philadelphia. Three counties were closely linked to this nucleus. Delaware County (with slighdy more than one-sixth of the population size of Philadelphia in 1940) had a relatively intensively developed manufacturing industry; Montgomery County (with slightly less than one-seventh of the population size of Philadelphia in 1940) had developed into the city's bedroom community; and Camden County (with slightly less than one-eighth of Philadelphia's population) had developed both industrial and commuting links with the city. The other four counties, Bucks, Burlington, Chester, and Gloucester, were not identifiable as important parts of the Philadelphia metropolitan economy. World War II and its aftermath induced accelerated changes in many of

136

Economic Development within the Philadelphia Metropolitan Area

the factors underlying the location decisions of people and jobs. The metropolitan map of America changed. Uninucleated maps became multinucleated as decentralization took place. In the Philadelphia region, there were relative and absolute declines in employment and population in Philadelphia and large increases in the surrounding counties (particularly in Bucks, Burlington, and Montgomery). The city's share of the PMSA's population dropped from 56% to 36% between 1950 and 1980, accompanied by an even more precipitous drop in employment share from 68% to 39%. The counterpart of the Philadelphia story was the drama of the suburban development—every one of those counties increased their shares of employment and population. These changes have been a long time coming, and they now represent well-rooted structural transformations in the local economies. Changes in the socioeconomic profiles of the counties mirrored the decline in the population and employment in the city, and the burgeoning growth of the surrounding counties. There were variations among the suburbs, but the big changes were between city and suburb. The differences between the two in the proportions of families in poverty, real per capita income, and proportion of highly educated adults were far greater than the differences among the suburban counties. And, between 1960 and 1980, these differences widened. The legacy of the regional dispersal of population and employment is spelled out in these widening differentials.

Shifts among the counties in jobs and resident workers Between 1960 and 1980 the dispersal brought about a marked redistribution of jobs and workers within the region. Decentralization shifted much of the region's economic activity toward the suburban counties. One consequence has been decreased daily interaction between Philadelphia and its surrounding counties. Several measures document this: between 1970 and 1980, there was increased growth in local workers (those who both live and work in the same county) in all of the suburban counties, but Philadelphia's numbers declined rapidly; most of the surrounding counties had sustained declines in the proportion of their jobholders who commuted to the city; by 1980, there were larger numbers of workers commuting among the suburban counties than there were commuting from suburban counties to Philadelphia; Montgomery county joined Philadelphia as a net daily importer of workers, and Delaware and Camden have come closer to being net importers. The evidence is that jobs have followed people to the Philadelphia suburbs. Between 1960 and 1980, decentralization seems to have been characterized by these phases: a phase of early residential conversion from rural land use with increasing ties to city employment (the Chester County case), a phase of continued residential suburbanization and later a tipping point at which employment begins to suburbanize more rapidly than residential development (the Burlington, Camden, and Gloucester cases); and, finally, a phase of sustained déconcentration during which jobs suburbanize at high rates and there are large increases in outbound

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commuting toward the new suburban employment centers (the Bucks, Delaware, and Montgomery cases). Occupational redistribution occurred as well. Although jobs and workers in all occupations shifted from the central city into the suburban counties, the rates of this suburbanization differed among occupations. One of the fastest growing occupations was the category of professional, managerial, and technical employment. This occupational group was also one of the most centralized and experienced relatively less of a locational shift out of the central city. This large growth in jobs that are among the most centralized has mitigated some of the loss of jobs and workers in Philadelphia, as blue collar jobs have simultaneously declined in the region and shifted into the suburbs. It has also created a central city that is much more oriented to white collar, service sector employment than the PMSA as a whole.

Shifts in population among localities It is clear that the dominant force in the changed economic map of the Philadelphia metropolitan area (and other areas) has been the collective impact of individual decisions to live outside the central city. The area outside Philadelphia is not a homogeneous one, of course. There are many local communities with very different characteristics. Understanding the population dispersal involves looking more closely at these. Several important patterns emerge from examination of the population data for municipalities. First, the decentralization process at work in the PMSA as a whole, and in the nation, also altered the maps of the individual counties. Populations shifted outward, toward the boundaries of the PMSA, in all of the suburban counties. People chose to live less closely together. Second, different demographic groups suburbanized at different rates. The poor, in particular, showed less mobility and remained concentrated in the region's central areas—in Philadelphia and Camden City, in particular. Minority populations, as well as whites, suburbanized, although the overwhelming majority of minorities still resided in or near the city in 1980. Finally, the data indicate that how population concentrates among the individual local communities in the suburban counties has, in fact, been influenced by factors in the local environment, such as taxes and land use patterns, that are at least partially amenable to public policies. This contrasts with the dominant influence of the preference for low-density living that resulted in the large movements of population from central cities to suburbs. Analysis of the socioeconomic patterns among the localities in the counties reemphasizes the pervasiveness of the tendency toward population decentralization. Dispersal occurred within the suburban counties as well as between the city and the suburbs. The outer areas of the counties grew more quickly than the areas closest to Philadelphia. Indeed, actual declines have not been limited to the major urban centers in the region. Many suburban areas, especially those closest to Philadelphia, have had to deal with decreasing population, as well. For many, here as elsewhere in the country, urban living has become less attractive.

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Economic Development within the Philadelphia Metropolitan Atea The population movements of the 1960s and 1970s reflected strong preferences for lower-density living arrangements in more amenable environments, but these were tastes that very low income people could not satisfy.

New firm activity and employment changes in the localities Just as local community characteristics might be relevant to where people choose to live, so might they be relevant to the startup of new firms and to the growth in employment (from expansions as well as startups). The patterns of new firms and employment changes across the municipalities of the Pennsylvania portion of the Philadelphia region were examined for two sectors: manufacturing and the very important business services component of the services industry. Manufacturers, drawing on national markets and more capital intensive, are different from business services firms, which draw more on local markets and are relatively labor intensive. It is not surprising, then, to find different factors influencing their behavior. Relatively high effective property tax rates were found to depress the density of new manufacturing firms in communities within some of the suburban counties. Across the wider five-county region, relatively high property tax rates also appeared to depress the density of new manufacturers, but that effect was not particularly strong. Other factors, such as density of population and the area's percentage of business land acreage, were more important in establishing the general boundaries of where a potential firm will conduct a search for a site. Tax differentials appear to become relevant only after the search area has been narrowed. Additionally, the net benefits of a central city location were negative. Urban sites were less attractive to potential manufacturing starts, even apart from negative influences flowing from tax differentials. Differences in fiscal climates among the communities were not found to influence the location of new business services producers. For startups, population density (providing agglomeration economies) was the only local characteristic to systematically affect the location of these new firms— though locating in Philadelphia was not seen to be a plus. Changes in the number of jobs were almost uniformly found to be insensitive to most local fiscal and demographic traits. Jobs, as well as new firms, were attracted to locations with dense population and were repelled by proximity to the general characteristics of a central city location. More broadly based national factors, influencing the demand for the firms' products, were the prime determinants of manufacturing employment changes. For business services, firm size was a key factor, with larger net increases in employment tending to occur in smaller firms.

Relative industrial strengths of the counties This dominance of market forces on the location and growth of industrial employment means that it is of considerable importance to firms contem-

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plating expansion or new location, to financial and real estate investors choosing among alternatives, and to local and state governments trying to maximize the marginal impact of their incentives that they clearly identify these market "winners." Simple extrapolations of recent numbers is inadequate for this identification. Excessive weight is given to the immediate past, structural shifts are not analyzed, and the competitive position relative to other areas is not built into the assessment. A number of criteria are called for to increase the probability that the judgments on industrial strengths of the counties and industries are accurate. • The data on industrial employment shares in each county reveal a striking characteristic of the region. Philadelphia's profile is far more different from the other counties than they are from each other—manufacturing has a much smaller share, and FIRE and services represent much larger shares. • The county shares of regional employment are another measure of strength. Philadelphia and Montgomery have by far the largest shares of the region's total employment (35% and 20%, respectively) and have the largest shares of each major industrial sector except agriculture. • For many of the region's industries, the state of the industry in the nation is a very significant factor, because their role in national markets is not small. Each of the counties, except Burlington, has some industrial sectors that occupy a significantly larger part of the nation's employment than their total employment would suggest. • Some industries in each county have had employment growth rates strikingly higher than the county as a whole. For each of the counties, business and/or legal services displayed the most rapid climbs. Health services in Philadelphia and Camden were among the winners in this category. • Some counties had sizable industries whose growth rates have been strikingly higher than the regional experience—FIRE in Montgomery and business services in Bucks are examples. For Philadelphia, even the sectors with relatively strong growth had relatively weak rates in comparison with the other counties, though the legal services sector grew at competitive rates. • Comparisons between county and national growth rates indicate that, for Philadelphia, employment growth rates were lower than those of the nation in every industry except legal services. The city and Delaware County were the only parts of the region that lagged behind the nation, but Delaware had positive and only slightly lower growth rates. • Over the last decade, almost all the counties improved their economies relative to the nation, suggesting that significant industrial accommodation had already taken place. Philadelphia

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shared in these changes: it reduced its growth rate lag in almost every sector, including manufacturing. • An examination of subsectors revealed that there are industrial strengths that are not visible in the major sector totals: all the counties with declines in manufacturing in the 1980s (except Philadelphia and Gloucester) had some strong subsectors in the industry; computer and data processing is the business services subsector that stands out in Burlington, Chester, and Montgomery, but personnel supply services are strong in Camden and Philadelphia; the health industry is important in each of the counties except Burlington, with hospitals, in particular, playing a major role in Bucks, Delaware, and Philadelphia. • A comparison of the dispersal pattern of this region with that of 42 other metropolitan areas indicates that Philadelphia was more unattractive relative to its suburbs than the national average; it was not the national dispersal trend alone that accounted for the shrinking of the metropolitan nucleus. Its unusually high population density was the major factor. • Calculations of employment and output multipliers for the suburbs and for Philadelphia indicate that there is much less leakage from suburban expansion than from Philadelphia expansion. The suburbs, as a group, retain more of the benefits of their growth than does the city. Table 7.9 in Chapter 7 is an economic report card for the counties and industries in the region. "Grades" are given on the basis of these criteria. Philadelphia receives the largest number of low grades; Bucks, Burlington, and Montgomery receive the largest number of high grades.

High technology in the region The promotion of high-technology development has become one of the most common components of regional development strategies in the United States. In the early years of this decade, regional planners and decision makers from all over the country jumped on the "high-tech" bandwagon. What is the Philadelphia area's standing in the national hightech picture? The Philadelphia PMSA has been, and remains, a major center of hightechnology employment in the nation. High tech grew more rapidly in the region in the early 1980s than in the late 1970s, at a rate roughly commensurate with national high-tech growth. The region is, in general, well endowed with attributes such as large numbers of highly educated workers, that research has shown to be most attractive to high-technology firms. The PMSA is, therefore, well positioned to maintain, and perhaps enhance, the major improvement in the area's fortunes relative to the rest of the country that has occurred in the 1980s.

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Within the region, Montgomery County is home to the greatest concentrations of high-tech jobs and is likely to continue as the major focus of local high-tech growth. In the rest of the region, growth patterns have resulted in a more uniform spread of high-tech employment. The roles of Philadelphia, Bucks, and Delaware counties have been declining, while those of the other four counties have increased. This pattern is also likely to continue, with one possible exception. Delaware County has shown signs in the 1980s of a turnaround in its high-tech fortunes. It also stands to benefit the most from ongoing and planned improvements to the region's transportation network—an important factor in the determination of regional growth patterns.

Intergovernmental aid: who gets how much in the region? Among the conclusions that emerge from the analysis of the disparate population and employment patterns of the region is that taxes, amenities, and transportation systems play very significant roles. These are factors that are particularly affected by aid from higher levels of government. Analysis of the patterns of this aid in the eight counties over the past twenty years, and their effects on local government expenditures, reveals that there has been an equalizing effect. The city of Philadelphia's share of the intergovernmental aid coming to this region has increased substantially. Moreover, Philadelphia did not suffer the losses in federal aid that began in most of the suburban counties around 1979 or 1980. It receives far more aid per capita, and its aid has been subject to less fluctuation and reduction. For the region as a whole, intergovernmental aid has improved markedly in the last twenty years relative to other metropolitan areas. By 1983, the region's receipts of state and federal aid nearly equaled the average per capita receipts of the nation's metropolitan areas—a big change from the 1965 picture, when it received less than half the national average. The contribution of the State of New Jersey to this total picture was not trivial: the three New Jersey counties had their losses of federal aid offset much more by state aid than did the five Pennsylvania counties. From the point of view of economic development, however, the picture is less cheerful in the last few years. Except for General Revenue Sharing and funds from the Department of Health and Human Services, most counties received substantially less federal aid in 1985 than in 1980. Economic development funding—from the Departments of Transportation and Housing and Urban Development—has been particularly hard hit. The link between intergovernmental aid and economic development occurs via its effects on taxes and public expenditures. Both the amount of and types of aid received and what the jurisdiction does with the funds— reduce taxes, increase expenditures, increase particular types of expenditures—are critical. In terms of dollar amounts, the city of Philadelphia has certainly not been shortchanged vis-a-vis the suburbs in either federal or

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state aid, nor has the mix of aid to the city been biased against economic development funds. Thus, the potential for influencing new firm location by keeping taxes down (relative to the suburbs) was available, as was the potential for increasing infrastructure expenditures, for example, to improve conditions for general economic development. Dollar allocations do not, however, tell the whole story. Philadelphia has the overwhelming proportion of the region's disadvantaged, and direct responsibility, therefore, for their substantial needs. The fact that it received relatively large shares is not equivalent to the judgment that it was not shortchanged. But, what can be said is that, had economic development been a primary objective, taxes could have been lower, and expenditures could have been more focused on services that were particularly enabling to economic development.

Policy Implications

Fifteen years ago, death rites were being read in many meetings on the economic health of the Philadelphia metropolitan area. They were inaccurate. These diagnoses did not take into account the extent to which market forces induce accommodation, nor were they careful to distinguish the illness of Philadelphia, the city, from the health of its suburbs. Now that much, but not all, of the accommodation has taken place, it is time to reconsider policies that are appropriate to encouraging economic development. It is also time to underscore the realities of the concept of regionalism. Individual communities will not opt for policies that are not in their self interest. Each will enact policies that assist other political jurisdictions only if they see benefits for themselves. If the state or the nation is the beneficiary, then it is in these legislatures that such policies will be enacted. If the county is the beneficiary, then there is also an enacting body. But, if the beneficiary is a group of counties, there is only informal association as a mechanism, except in cases of such clearly recognized regional problems as waterways and pollution. There are many policy implications associated with the major findings of this study: the substantial difference in economic strength of Philadelphia and its surrounding areas; the domination of market forces in the accommodation of people and jobs in the parts of the region, and the powerful effect on the region's economic map of the nationwide preference for lower-density living. Public policies can aid or hinder economic development, but they will not have sustained effectiveness if they counter strong international and national competitive forces or strong majority decisions by people on how they want to live. Stimulating the economic environment in the region The competitive footing of the region has clearly improved in recent years. If it is to hold and improve its market position, it must not be deterred by its economic environment—by a poor infrastructure, sparsity of venture capital, or high business taxes. And it will be important to hold on to the

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advantages of relatively low living and office space costs. Inevitably, the faster the region grows, the greater the upward cost pressures will be. The quality of the region's amenities is a major economic asset and should be viewed in this light as well as in terms of the more immediate social and cultural benefits. The Philadelphia Art Museum and Orchestra are among the cultural treasures for the region's residents and visitors, but support of them should also be reviewed in terms of their contribution to employment growth.

Stimulating local population growth The Philadelphia region has decentralized—jobs and workers have dispersed, and different communities face different policy options. Their objectives and their capability for realizing them differ. Those who study the migration of population within regions have long debated whether jobs follow people or people follow jobs. If jobs suburbanize first, then communities like Philadelphia that are seeking to grow in employment and population should devote their efforts to developing and marketing local characteristics attractive to businesses: primarily tax relief and improved business land availability. If jobs follow people into the suburbs, then the amenities particularly attractive to a residential population are the first priority: clean streets, good educational systems, and tax relief. To the extent that the population dispersion reflects the nationwide preference for lower-density living—and this is clearly a large factor—policies to keep residents in the city are not likely to have a substantial effect. Some communities are actively trying to maintain low-density residential characteristics. Such communities should anticipate the pressures for job development and should design land-use measures accordingly. For Philadelphia, the major population and employment loser in the region, the ability to use local policies to counter people's personal preferences for lower-density living is minimal. Although property tax relief and improved amenities are likely to be helpful at the margin, increased total employment in the city is not likely to come from rising population. For other communities, there are some factors amenable to local government actions that affect how communities grow: tax rates and the regulation of commercial and industrial development. The observation that commercial development attracts population, while industrial development repels it suggests that local strategies regarding commercial, industrial, and residential development for communities wanting growth need to be carefully coordinated.

Stimulating local employment growth For policy makers wanting to expand employment in a locality, the message is clear: (1) relatively high property taxes are discouraging to potential manufacturing starts; (2) while local public-spending decisions may affect the nature of the population, those decisions will not be important factors in the location decisions of manufacturers and business

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Economic Development within the Philadelphia Metropolitan Area services firms; (3) good access to interstate highway systems appears to be attractive to new manufacturers; (4) economies achievable from being near dense populations and other business activity are important to potential firms, both in manufacturing and business services; new firms simply are not likely to locate in areas where other people or firms have not previously located; (5) to a large extent, however, overall employment growth in manufacturing is at the mercy of more broadly based national forces influencing demand for firms' output; (6) venture capital is likely to stimulate the growth of business services sectors, since small firms have grown most. There is little that local officials can do to influence national markets or the increased nationwide preference for decentralization. What they may be able to influence are new manufacturing firm startups through the property tax and—only marginally—amenities that affect residential preferences. Population density is a magnet for employment and new firm startups, but the ability to affect it is limited. For Philadelphia, it is increased specialization in the industries in which the city does best that provides the most viable strategy for employment growth. Increased employment of its residents can be accomplished by efforts in two directions. Improving the match between skills in the local population and the skills required by the city's changed job structure through well-targeted job training programs will reduce structural unemployment. Enabling residents to participate in the employment growth of the rest of the region will reduce transitional unemployment. Improved transportation networks (especially for outbound commuting to the suburbs), labor information exchanges, and skills training would serve these efforts.

Regional economic impact of the needs of the disadvantaged The need to address the problem of the city's relatively high unemployment rates is urgent. The increasing poverty rates in the region's central areas during the 1970s have important regional implications. The human costs associated with poverty fall most directly in the cities of Philadelphia and Camden, and so does the public responsibility to meet the special needs of the poor. However, other parts of the region are affected as well, both directly and indirectly. One direct effect comes from the central role that these areas, particularly Philadelphia, play in the region's social and economic environment. As people work and play in the city, they are affected by the problems associated with concentrations of people living in poverty. A second direct effect comes from the state and federal taxes that support the needs of the poverty population. To the extent that they are higher, because costs escalate where there are concentrations of high-need residents, then all are bearing higher costs. The indirect effects of problems in the central city, although they are often intangible, are important as well. To a large extent, the image the region projects to the rest of the world is a reflection of conditions in the

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city. The publicity arising from MOVE and the pileup of garbage taints the region's image as well as the city's. It is, therefore, in the interests of the suburban counties, as well as Philadelphia, to support programs that alleviate the effects of poverty on the city's environment and residents. Well-designed poverty programs, supported by state and federal governments, warrant some "yea" votes by suburban legislators—not just because they help their fellow citizens, but because improved conditions in the city enhance the regions attractiveness to people and business. And for the same reason, "yea" votes by suburban legislators are warranted for improvements in infrastructure and labor exchange information that would increase outbound commuting.

Intergovernmental aid The analysis of the patterns of intergovernmental aid to the region suggest that suburban jurisdictions may want to lobby state officials more vigorously for maintaining state aid during periods of rising federal aid. More relevant now, when federal aid is declining or growing much more slowly, is that both city and suburban communities may wish to lobby state government for increased replacement aid. Whether or not such efforts are ultimately desirable from the point of view of the local governments depends, of course, upon how state aid increases are financed. City government decision makers have to sort out the tensions between the immediate socioeconomic needs of its residents and the extent to which these needs will be met by facilitating economic growth. If they focus more of the additional expenditures enabled by intergovernmental aid on economic development, some of the significant growth differentials between Philadelphia and its surrounding counties are likely to be reduced. For the suburbs, continuous evaluation of balancing the effects on economic development of tax decreases versus expenditure increases will be an important priority.

Allocation of public economic development funds The primary allocation criterion for scarce public resources should involve, first, recognition that efforts to add impetus to market forces will have the greatest potential for stimulating employment growth. For a local community or county, improving the infrastructure, lowering the taxes, or preparing industrial land for firms in an industry that has demonstrated comparative advantage in the area, will have the highest probability of success. Second, for all localities—Philadelphia in particular—any action in the region that increases employment in the metropolitan area is a contribution to the job opportunities of its residents. It is important, therefore, for each community to identify its "winners" (listed in Table 7.6 and Table 7.9), but it is equally important that it marshal resources to stimulate the region's winners. If, for example, a large firm in the health care industry is strongly attracted to locating in one of the region's eight

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counties, other counties should consider cooperating in the marketing effort. Winners were identified in the 1985 Economic Report as chemicals (particularly pharmaceuticals), FIRE (particularly commercial banks and insurance subsectors), wholesale trade, food retailing, health services, and business services (particularly computer services and data processing, personnel supply services, and services to dwellings). In this 1986 report, another industrial grouping, high technology, was assessed and found strong. Some of the major factors underlying this strength—educational levels and defense spending—are centered on activities in the city. Policy makers in other parts of the region, particularly in those areas that stand to gain the most from high-technology growth, should be aware that their constituents stand to gain from some policies that impact most directly on Philadelphia. This is especially true for the region's representatives in the state and national legislatures, since they are the source of the policies that affect higher education and the Navy Yard most substantially. The need for a unified regional voice in these policy arenas is clear. For Philadelphia, the important message is that planners should focus on bringing jobs to the region and developing full access to the jobs wherever they are. Bringing jobs to the city is a secondary objective. For most of the suburbs, this ordering of objectives is less pressing. Third, the tendency to focus development funds on new firm startups should be modified in the light of the strong evidence that it is existing firms which contribute most to the region's overall growth. Policies should not be designed solely to promote startups. Those that affect existing firms should be viewed as equally important parts of any development strategy.

Conclusion

The regional map has become multinucleated, and this will not change in the foreseeable future. Underlying market forces (increased residential preference for lower-density living, improved communications technology) interacted with government policies (greatly expanded highway network, housing subsidies) to produce a new map characterized by smaller central cities and larger concentrations of people and jobs outside them. These changes have been a long time coming, and they now represent well-rooted structural transformations in the local and regional economies. These transformations have involved very disparate growth patterns in the region. But, it will be vital for the policy makers of the parts of the region to recognize how closely bound together their economic interests are. The recent economic and social history of the Philadelphia region has tended to accent the disparities and conflicts rather than the interdependencies. The interdependencies are real, however. There is a need for state and local public officials to consider new policies to accompany the transformation of the Philadelphia metropolitan area, because the movements of economic strength cross government boundaries. The implications of this are significant. The gainers have access to more revenue and more growth potential. The central city, the loser, faces reduced revenue and lower potential. Its policies have to address its smaller

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size. For the surrounding counties a major policy issue is the recognition of the extent to which their economic well-being flows from being part of a metropolitan area with access to the unique assets of a central city. There is a new economic development map in the region. Continued strong economic growth will depend on the ability of all the parts of the region to adapt.

List of Appendices Appendix A: Bibliography A.1 Statistical Bibliography A.2 General Bibliography Appendix B: Supplementary Materials to Chapters 4-9

153 155 165

B.1 Supplementary Materials to Chapter 4 165 B.l.l Data 165 B.1.2 Procedures 166 Table B.l.l County of Work and Residence for Workers: Philadelphia PMSA, 1960 167 Table B.1.2 County of Work and Residence for Workers: Philadelphia PMSA, 1970 168 Table B.1.3 County of Work and Residence for Workers: Philadelphia PMSA, 1980 169 B.2 Supplementary Materials to Chapter 5 170 Table B.2.1 Variable Key 172 Table B.2.2 MCD Population Growth Regression Results 173 B.3 Supplementary Materials to Chapter 6 174 B.3.1 Data 174 B.3.2 Procedures 175 Table B.3.1 SIC Sectors Included in Regressions 177 Table B.3.2 Variable Key 178 Table B.3.3 Regression Results: Determinants of New Manufacturing Firm Densities, 1980-1983 179 Table B.3.4 Regression Results: Determinants of New Business Services Firm Densities, 1980-1983 180 Table B.3.5 Regression Results: Determinants of Employment Change, 1980-1983 for Philadelphia PMSA, Pennsylvania Counties 181 B.4 Supplementary Materials to Chapter 7 182 B.4.1 Data 182 B.4.2 Procedures 183 B.4.3 Economic Report Card Calculations 186 Table B.4.1 Variable Key 189 Table B.4.2 Central City/Outside Central City Two Stage Least Squares Regression Results 190

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B.5 Supplementary Materials to Chapter 8 191 B.5.1 Data 191 B.5.2 Procedures 193 Table B.5.1 Variable Key 196 Table B.5.2 High Tech Regression Results 197 Table B.5.3 Summary Statistics for High Tech Variables 198 B.6 Supplementary Materials to Chapter 9 199 B.6.1 Data on Types of Federal Aid 199 B.6.2 Procedures for Analyzing the Determinants of Federal and State Aid 199 B.6.3 Procedures for Analyzing Intergovernmental Aid, Local Taxes, and Expenditures 202 Table B.6.1 Federal Grants to the Counties by Department: Philadelphia PMSA, 1977-1980 203 Table B.6.2 Regression Results: Determinants of Federal Aid to Counties, 1965 and 1983 204 Table B.6.3 Regression Results: Determinants of Federal Aid to Counties, 1965-1983 205 Table B.6.4 Regression Results: Determinants of Change in Per Capita State Aid, 1965-1983 206 Table B.6.5 Illustrative Example of Fiscal Effects of Intergovernmental Revenue 207

Appendix C: Comparative County Data for the Philadelphia PMSA

208

Table C.l Venture Capital Financing by Industry: Philadelphia PMSA, 1979-85 209 Table C.2 Employment by Major Sectors in the Counties: Philadelphia PMSA, 1951, 1959, 1970, 1980 210 Table C.3 Socioeconomic Profiles of the Counties: Philadelphia PMSA, 1960, 1970, 1980 211 Table C.4 Establishment-based Employment in Two-Digit SIC Sectors: Eight Counties of the Philadelphia PMSA, First Quarter 1975, 1980-1985, and Third Quarter 1985 214 Table C.5 Components of Employment Change by Major Sectors: Pennsylvania Counties of the Philadelphia PMSA, 1975-1Q to 1980-1Q, 1980-1Q to 1983-2Q 222 Table C.6 Distribution of Employment Between Central Cities and Surrounding Counties by Major

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Sectors: Philadelphia and 42 Other PMSAs, 1970-1980 225 Table C.7 Employment in High Technology Sectors in the Counties: Philadelphia PMS A, 1975-1Q, 1980-1Q, 1985-1Q 227 Table C.8 Per Capita Federal and State Aid to the Counties: Philadelphia PMSA, 1965-1983 229

Appendix D: Philadelphia PMSA Economic Statistics

230

Table D.l Unemployment Rates of the Civilian Labor Force by Sex, Age, Race, and Marital Status: Philadelphia PMSA and Central City, 1972-1985 231 Table D.2 Residence Based Civilian Labor Force, Total Employment, and Unemployment Rates: Philadelphia PMSA and Component Counties, 1977-1985 233 Table D.3 Establishment Based Total Employment by Two Digit Standard Industrial Classifications: Philadelphia PMSA, 1952-1985 234 Table D.4 Consumer Price Index for All Urban Wage Earners and Clerical Workers, CPI-W, by Expenditure Category, Commodity and Service Group, Annual Averages and Annual Percentage Change: Philadelphia PMSA, 1960, 1970, 1972-1985 237 Table D.5 Consumer Price Index for All Urban Consumers, CPI-U, by Expenditure Category, Commodity and Service Group, Annual Averages and Annual Percentage Change: Philadelphia PMSA, 1978-1985 241 Table D.6 Personal Income by Place of Residence and Place of Work: Philadelphia PMSA and Component Counties, 1965, 1970, 1972-1984 243 Table D.7 Population Estimates: Philadelphia PMSA and Component Counties, 1930, 1940, 1950, 1960, 1970-1985 252 Table D.8 Average Hourly Earnings by Selected Industrial Classifications: Philadelphia PMSA, 1972-1985 253 Table D.9 Value of Private Nonresidential Construction Authorized by Building Permits in Selected Permit-issuing Places by Type of Construction: Philadelphia PMSA, 1971-1985 254

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Table D.10 Number and Value of New Housing Units Authorized in Permit-issuing Places: Philadelphia PMSA, 1973-1985 255 Table D . l l Total Number of New Housing Units Authorized in Permit-issuing Places: Component Counties of Philadelphia PMSA, Excluding Philadelphia County, 1973-1984 256 Table D.12 Construction Cost and Building Cost Index: City of Philadelphia and 20 City Average, May of Every Year, 1967-1986 257 Table D.13 Operating Budget Summary: City of Philadelphia, Fiscal Years 1977-1986 258 Table D.14 General Governmental Revenues and Expenditures: School District of Philadelphia, Fiscal Years 1977-1985 259

Appendix A

Bibliography

A.1 Statistical Bibliography

Advisory Commission on Intergovernmental Relations. Measuring Metropolitan Fiscal Capacity and Effort: 1967-1980. Washington, DC: Working Paper no. 1, July 1983. Advisory Commission on Intergovernmental Relations. Tax Burden For Families Residing in the Largest Cities in Each State, 1982. Washington, DC: Working Paper no. 3R, August 1984. Boyer, Richard and David Savageau. Places Rated Almanac. Chicago: Rand McNally and Company, 1985. Bureau of Economic Analysis. The 1977 National Input-Output Table. Washington, DC: U.S. Department of Commerce, 1984. Bureau of Labor Statistics. Consumer Price Indexes. Washington, DC: U.S. Department of Labor, 1963-1984. Employment and Earnings, States and Areas. Washington, DC: U.S. Department of Labor, 1952-1984. Employment, Hours, and Earnings, 1909-1984. Washington, DC: U.S. Department of Labor, 1985. Geographic Profile of Employment and Unemployment, 1983. Washington, DC: U.S. Department of Labor, October 1984. Local Area Unemployment Statistics. Washington, DC: U.S. Department of Labor, 1976-1984. Monthly Labor Review. Washington, DC: U.S. Department of Labor, 1^63-1984. Bureau of the Census. Census of Business. Washington, DC: U.S. Department of Commerce, 1967. Census of Manufactures. Washington, DC: U.S. Department of Commerce, 1967, 1977. Census of Population. Washington, DC: U.S. Department of Commerce, 1950, 1960, 1970, 1980. Census of Population. Subject Reports: "Journey to Work" Data. Washington, DC: U.S. Department of Commerce, 1970, 1980. Census of Services. Washington, DC: U.S. Department of Commerce, 1977, 1982. Construction Reports—Housing Units Authorized by Building Permits and Public Contracts. Washington, DC: U.S. Department of Commerce, 1973-1983. County Business Patterns. Washington, DC: U.S. Department of Commerce, 1951, 1959, 1970, 1975, 1980-1983. Current Population Report. Washington, DC: U.S. Department of Commerce, 1965, 1983. Local Government Finances in Selected Metropolitan Areas and Large Cities. Washington, DC: U.S. Department of Commerce, 1965-1983. State Government Finances. Washington, DC: U.S. Department of Commerce, 1965-1983.

1S4

Lilt of

Appcndicct and Tablet

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Appendix B

Supplementary Materials to Chapters 4-9

B.1: Supplementary

B.l.l Data

(taTter^0

The basic data used in Chapter 4 were obtained from the "Journey to Work" subject reports of the 1960-80 U.S. Census of Population. The cross-tabulations of these data are shown in Tables B.l.l, B. 1.2, and B.1.3, which present the daily commuting flows of workers for 1960, 1970, and 1980 from county of residence to county of employment for each of the three census years. All of the data are for persons 16 years and older who live and work in the PMSA. Therefore, the unemployed, those outside the labor force, and those who commute to or from outside the PMSA are not counted in the tables. In addition, people with two or more jobs are counted only once, based on their primary job. The rows of these three tables refer to the county of residence and the columns refer to the county of employment. For example, in Table B.l.l, the entry "101" in the eleventh row and fourth column gives the number of Gloucester County residents who worked in Chester County in 1960. The entry "186,014" under the row totals gives the number of persons working in the PMSA who lived in Montgomery County and the entry "69,425" under the column totals gives the number of persons living in the PMSA who worked in Bucks County. For each cell, the percentage of the total workers in the PMSA (1,517,744 for 1960) represented by that cell is reported. Persons who live and work in the same county (the "main diagonal" of the tables) are labeled "local workers." Persons who live outside Philadelphia and work inside Philadelphia (the eighth column of the tables minus Philadelphia local workers) are referred to as "city commuters." Persons who live inside Philadelphia and work outside Philadelphia (the eighth row of the tables minus Philadelphia local workers) are labeled "out-commuters." These tables illustrate the region's transition from the "traditional" centralized pattern, in which a city is surrounded by bedroom suburbs populated by commuters, to a more decentralized pattern. A comparison of the column totals for each county (the number of jobs in each county) with the row totals (the number of workers residing in each county) shows that, in 1960, Philadelphia was the only county with more persons working than living within its borders. By 1970 and continuing into 1980, however, Montgomery County was also a net importer of workers. Another measure of decentralization is the decreasing share of residents and of jobs in the central city. Each county's share of PMSA residents is given in the percentages under the row totals. Philadelphia's share of PMSA residents decreased from 47.6% in 1960 to just 32.1% by 1980. Meanwhile, Philadelphia's share of PMSA jobs (the percentages under the column subtotals) decreased from 57.4% in 1960 to just 40.3% by 1980. The counties with the largest increase in PMSA share of residents were

166

Appends B: Supplementary Material to Chapten

Bucks (from 6.0% in 1960 to 10.5% in 1980) and Montgomery (from 12.3% in 1960 to 16.0% in 1980). The counties with the largest increase in PMSA share of jobs were, again, Bucks (from 4.6% in 1960 to 8.8% in 1980) and Montgomery (from 10.9% in 1960 to 17.0% in 1980). B . 1 . 2 Procedures The data in Table 4.5 in Chapter 4 in the column labeled "Jobs" and "Residents" are the numbers of jobs and of workers in the occupational group that were located in each county in 1980 as a result of the geographical dispersal of jobs and resident workers between 1970 and 1980. Estimates were derived from the following model: s

where

.80 _

E

.80 _

(r

.70

x

E

80)

E; 8 0 = the actual number of jobs (residents) in the occupation in county i in 1980; r™ =

the proportion of all PMSA jobs in the occupation in county i in 1970; and

E 8 0 = the total metropolitan employment in the occupation in 1980. A positive sign indicates the county has gained jobs or workers because of the geographic shifts in the 1970s; a negative sign indicates the county has lost jobs or workers. The numbers reflect changes due to geographical shifts only and do not indicate the total change in employment or residents. It is important to note that a county's gain or loss of residents or jobs due to the relocation of workers or jobs in the 1970s does not arise only from the movement of a residence or a firm. Increases or decreases in the labor force participation of immobile residents and the expansion, contraction, closing, and opening of firms also create shifts in workers and jobs across counties.

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co oo C M • —' O•

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o C r«M »«Oi• ON w lA

%

170

Appendix B: Supplementary Material to Chapten

B.2 Supplementary Materiab to Chapter S

Multiple regression analysis was used to support the discussion of the determinants of population growth among the 340 Municipal Civil Divisions (MCDs) in the Philadelphia PMSA in Chapter 5. The estimated model assumes that growth during the period from 1970 to 1980 in an MCD was a function of the MCD's characteristics in 1970. The variables describing the characteristics fall into four broad categories: land use characteristics, housing stock variables, property taxes, and demographic characteristics of the population. The selected variables (with sources) are listed in Table B.2.1. The regression results are reported in Table B.2.2. The chosen specification expresses population growth in numbers of people, rather than as a rate, in order to allow larger population changes in larger MCDs to carry greater weight. The relationship between numbers of people and land area was controlled for by including a combination of land use variables that added to total acreage in the MCD in all specifications. The major disadvantage of this procedure is that Philadelphia dominates the variance in the dependent variable because its population change between 1970 and 1980 was far in excess of that shown for any of the other MCDs. This dominance is a problem because many of the city's unique characteristics cannot be properly controlled for in the regression model. The results for three specifications of the model are, therefore, shown in Table B.2.2. Each treats the city in a different way. The first column of Table B.2.2 shows ordinary least squares estimates with Philadelphia included in the model, along with a dummy variable that takes on the value of one for Philadelphia and zero for all other MCDs. Column (1) can also be read as the results with Philadelphia excluded. The only difference in the statistics between the two specifications is in the R 2 , which fell to 0.26 when Philadelphia was excluded. Column (2) reports the results when Philadelphia was included with no dummy variable. The significance of some of the coefficients changed (although none of the signs change) but the effect on the coefficient for MFAMILY was most dramatic. (See Table 3.2.1. for key.) MFAMILY picked up most of the explanatory power that was lost when the dummy variable was dropped. This phenomenon carried over into other specifications of the model. Whenever the dummy variable was dropped, another variable, not always MFAMILY, tended to pick up Philadelphia's major contribution to the variance in the dependent variable. Since this tendency was not consistent—different variables were affected in different specifications—the results from column (1) were used for the discussion in the text. Many of the unique qualities of the city (the wage tax, for example) cannot be effectively included in the model, except through a dummy variable that captures the effects of any unexplained variation due to any of Philadelphia's unique features. The results in column (1), therefore, are best interpreted as explaining 26% of the variance in population changes in the suburban MCDs. The results for the land use variables were very robust across different specifications. Interactions among the dependent variables that had any effect on the estimates tended to center around the RENT variable. Surprisingly, these interactions were not strongest with the other housing stock variables (HCOST, LOTSIZE, and HOUSEAGE). RENT interacted

Appendix B: Supplementary Material to Chapter*

171

most strongly with POPDENS, RPTAX and POVERTY (although the coefficients for these variables were negative in all specifications). This suggests that RENT was acting as a proxy for local amenities, particularly those associated with low income populations, crowding, and public service provision. The positive and highly significant coefficient for RENT supports this interpretation. In general, the housing stock and land use variables that related most closely to multifamily and rental properties were much more discriminating than those related to single family, owneroccupied housing. The extent to which the reported results reflect the effects of important missing variables is very difficult to assess. The most obvious missing variable of relevance is a measure of employment by place of work. It is likely that population and employment changes are interlinked, implying that the ideal model would estimate at least two equations simultaneously. However, adequate establishment-based employment estimates are not available at the MCD level. In addition, the land use data do represent reasonable proxies for employment patterns. Finally, the Chapter 4 analysis suggests that jobs were following people to the suburbs in the PMSA during this period, implying that any simultaneity bias contained in the Table B.2.2 estimates is small. The model reported in column (1) was tested for heteroscedasticity, and the results were negative. Ordinary least squares estimation was, therefore, used in all specifications.

172

Appendix B: Supplementary Material to Chapters Table B.2.1 VARIABLE KEY

POPGRW:

Change in population in the MCD between 1970 and 1980. Source: Census of Population, 1970, 1980, (CP).

INDUSTRY:

MCD land acreage used in Manufacturing and TCPU in 1970. Source: Delaware Valley Regional Planning Commission, 1984 (DVRPC).

COMMERCIAL:

MCD land acreage used in commercial development in 1970. Source: DVRPC.

UNDEVELOPED:

MCD acreage in forests and undeveloped land in 1970. Source: DVRPC.

MFAMILY:

MCD acreage in use for multifamily residences in 1970. Source: DVRPC.

OTHERLAND:

Total MCD acreage in 1970 minus MFAMILY, UNDEVELOPED, COMMERCIAL, and INDUSTRY. Source: DVRPC.

HOUSEAGE:

Average age of housing in the MCD in 1970. Source: Computed from CP.

LOTSIZE:

Average number of acres per occupied housing unit in the MCD in 1970. Sources: Computed from DVRPC and CP.

HCOST:

Median value of owner-occupied housing in the MCD in 1970. Source: CP.

RENT:

Median contract rent in the MCD in 1970. Source: CP.

POVERTY:

Percent of total MCD population living in families below the poverty line in 1970. Source: CP.

INCOME:

Median family income in the MCD in 1970. Source: CP.

POPDENS:

Population per acre in the MCD in 1970. Source: CP and DVRPC.

RPTAX:

Real property tax (mills) in 1975 in the MCD. Source: Computed from unpublished data from the Pennsylvania State Tax Equalization Board and the Commonwealth Land Title Insurance Company.

PHILDUM:

Dummy variable taking on the value of one for Philadelphia and zero for all other MCDs.

For full source references, see Appendix A l .

Appendix B: Supplementary Material to Chapters

173

Table B.2.2 MCD POPULATION GROWTH REGRESSION RESULTS Dependent Variable: POPGRW Independent Variable Intercept INDUSTRY COMMERCIAL UNDEVELOPED MFAMILY OTHER LAND HOUSEAGE LOTSIZE HCOST RENT POVERTY INCOME POPDENS RPTAX PHILDIM Adjusted R 2 Degrees of Freedom

(2)

(1) 2633.8 (1.86) -1.73 (2.04) 7.39 (3.72) 0.08 (2.77) -6.45 (5. 35 0.03 (0.52) -175.3 (2.14) 223.6 (0.27) -0.04 (0.90) 16.87 (2.45) -61.13 (1.38) -0.02 (0. 18) -60.29 (1.83) -41.60 (1.85) -160690 (7.47)

« *« *« *»

»»

«»

ft«

ft « ft«

1294.2 (0.85) -1.92 (2.10) 7.89 (3.66) 0.08 (2.53) -14.47 (24.71) 0.09 (1.44) -133.6 (1.52) 778.2 (0.88) -0.04 (0. 78) 20.80 (2.80) -32.58 (0.68) -0.02 (0. 16) -16.69 (0.48) -25.81 (1.06)

0.97

0.96

321

322

»» «» »» *»

»*

t s t a t i s t i c s In parentheses« • : C o e f f i c i e n t s i g n i f i c a n t a t 90*. ««: C o e f f i c i e n t s i g n i f i c a n t a t 95*. Note:

Excluding R i l l a d e l p h l a and PHILDIM from model (1) y i e l d s Identical c o e f f i c i e n t s and t s t a t i s t i c s t o r a l l other c o e f f i c i e n t s and an adjusted R-squared =• «26.

Appendix B: Supplementary Material to Chapter«

174

B.3 Supplementary Chotei' "

0

B.3.1 Data With the heterogeneity in fiscal conditions across jurisdictions within the same urban area, fiscal and business activity data as finely detailed as possible are crucial for an adequate analysis of the influence of the local fiscal climate on the location of business activity. The data used here include information on fiscal conditions as well as other location-specific traits at the borough/township level for years between 1980 and 1983. Firm location and employment data are available by zip code location. The firm location and employment data are from the ESA-202 files collected by the Pennsylvania Department of Labor and Industry's Office of Employment Security. The Institute for Public Policy Studies at Temple University put the original data on tape. These data include the location by zip code of various manufacturing and nonmanufacturing firms and their employment levels during the first quarter of 1980 (1980-1) and the first quarter of 1983 (1983-1). It should be noted that 1980-1 marks a peak in the business cycle, while 1983-1 just follows a very deep trough that bottomed out in the fourth quarter of 1982. A new peak following that trough has not yet been reached. 1983-1 is the latest quarter for which firm data are available. Firms can be identified at the three-digit SIC level. Data on firms from a wide variety of manufacturing and nonmanufacturing SICs were analyzed. The two- and three-digit SICs used are listed in Table B.3.1. Several of the fast-growing business services industries (SIC 73) were included, along with other services associated with engineering firms (SIC 891) and accounting firms (SIC 893). This is in addition to the data on the selection of capital- and labor-intensive manufacturing industries included in most previous studies. Geographic coverage spans the five Pennsylvania counties (Bucks, Chester, Delaware, Montgomery, and Philadelphia) in the Philadelphia metropolitan area. Firm data are not available for the New Jersey counties. Consequently, differences in state fiscal climates do not have to be controlled for. Chester County firms are seriously underrepresented in the sample. Due to the lack of observations, it is difficult to conclude much about how local characteristics are influencing economic activity in that county. Numerous local fiscal variables, in addition to other locational attributes, were collected from a variety of sources. These variables include effective property tax rates, various local spending measures, and access to highway and rail transportation. Jurisdiction size variables relating to population, labor force, and land area are also included, as are variables describing the demographic make-up of the borough and township populations. Additionally, variables measuring firm and employment density were created from other variables. A complete list of the variables used in the empirical results is provided in Table B.3.2. It is important to note that all the fiscal and locational attributes are for individual boroughs and townships in the five-county area. Firms, however, are located by zip code in the data. Unfortunately, zip code and tax

Appendix B: Snpplementaiy Material to Chapter«

175

jurisdiction boundaries often do not coincide (except for Philadelphia). Where zip code areas span taxing jurisdictions, the fiscal and other locational attributes were averaged to provide mean measures of the fiscal and other locational conditions in the zip code areas. The averaging introduces obvious measurement error. Another method would have been to match individual firms from their zip codes into a specific borough or township. This method would have induced double counting (or more) of firms, since zip code areas often span more than one locality. Similarly, a firm could have been randomly assigned to one of the possible locales, given its zip code. There is measurement error under any method of matching the firm location data with the fiscal data. It is not obvious which methodology is best in these terms.

B.3.2 Procedures Models of the optimal spatial distribution of business activity are well understood in the urban and regional economics fields. Consequently, the model underlying the empirical work on the firm density summarized in Tables B.3.3 and B.3.4 will not be presented here. The interested reader can see A. H. Charney's 1983 article for a good theoretical description of why profit-maximizing firms are likely to choose relatively low tax jurisdictions, all else being constant. In the data base used in this study, a new firm could be a relocating firm or a spin-off from an existing firm or a branch plant. Unfortunately, the possibilities cannot be readily differentiated. Relocations are rare, even within narrow geographic boundaries such as a metropolitan area, so that most of the new firms are undoubtedly either spin-offs or branches. Due to sampling selection because of data restrictions, most of the new firms are probably spin-offs of existing firms and not branch plants. D. W. Carlton's 1979 study has a discussion of the differences between the two types of starts. Firm location is identified by zip code. A measure of new firm density (number of new firms per unit of land) was constructed for each zip code area. This was merged with a vector of local economic variables described in Table B.3.2. The measures of new manufacturing and business services firm density were then regressed on the vector of location-specific traits. Ordinary least squares results are presented in Tables B.3.3 and B.3.4. It should be noted that a variety of other specifications were investigated that employed different location characteristics than those presented. In particular, different population demographic characteristics were investigated. Those variables generally were not influential, and dropping them did not affect the fiscal and agglomeration coefficients, which are the ones of most importance. It is of interest to note that excluding areas with no manufacturing activity (as W. F. Fox did for the Cleveland area in his 1981 article) to address the supply of sites problem had no significant effect on the results. Tables B.3.3 and B.3.4 are not significantly changed by selecting out the

176

Appendix B: Supplementary Material to Chapten

zero (or very limited) manufacturing areas. The reported results are for the full sample. There were relatively few communities in the five-county region in which there was not land acreage devoted to manufacturing, although there were more with less than 1% of acreage going to manufacturers. Selected results on the change in employment are presented in Table B.3.5. Those results are for the net changes in employment in all firms— births, deaths, expansion, and contraction. The basic specification involved regressing the firm's change in employment over the 1980-1 to 1983-1 time frame on the same vector of location-specific variables used in Tables B.3.3 and B.3.4, the firm's beginning period level of employment, and the national growth rate of employment for the particular business sector the firm is in. The results in Table B.3.5 are from weighted-least squares regressions, a procedure needed to correct for heteroscedasticity problems. In the OLS runs, Glejser tests showed the residuals to be positively correlated with the beginning period employment level. The appropriate weighting was made. Details on the technique are in G. S. Maddala's 1977 book. Econometrics.

Appendix B: Supplementary Material to Chapter* Table B.3.1 SIC SECTORS INCLUDED IN REGRESSIONS

Manufacturing 20 23 25 26 28 33 34 35 36 38

-

Food Apparel Furniture Paper and Allied Products Chemicals Primary Metals Fabricated Metals Machinery, nonelectrical Electronic machinery Instruments

Business Services 731 732 734 737 739

-

Advertising Credit Reporting, Collection Services to Dwellings Computer and Data Processing Consulting

Others 891 - Engineering and Architecture 893 - Accounting and Auditing

177

178

Appendix B: Supplementary Material to Chapter* Table B.3.2 VARIABLE KEY

FIRMDEN

:

AEMPL

= Change in employment from 1980-1 to 1983-1.

PROPTAX

= Effective local property tax, 1980 data; the effective rate is the nominal rate adjusted with the sales-assessment ratio.

POLICE

= Local police expenditures per capita.

STREETS

:

POVERTY

' Percentage of local population below federal poverty line.

POPDEN

= Local population density, population/total land area.

BUSLAND

= Percentage of local acreage devoted to business purposes.

UNEMPL

= Percentage of local population unemployed.

PHILDIS

:

RAIL

• 0-1 dummy for major rail transport running through locality.

HIGHWAY

= 0-1 dummy for interstate highway running through locality.

PHILLY

= 0-1 dummy for Philadelphia.

BUCKS

1

New firm density, number of firms/usable land acreage.

Local streets and highway expenditures per capita.

Distance of borough or township to Philadelphia; all Philadelphia zips coded as a zero.

0 - 1 dummy for Bucks County.

CHESTER

= 0-1 dummy for Chester County.

DELAWARE

• 0-1 dummy for Delaware County.

MONTGOMERY

= 0-1 dummy for Montgomery County.

EMPLBEG

= Beginning of period, (1980-1), firm employment.

GROWTH

= National growth rate for appropriate SIC codes.

See Tables 6.1 and 6.2 for full source references.

17»

Appendix B: Supplementary Material to Chapters TABLE B.3.3 REGRESSION RESULTS: DETERMINANTS OF NEM MANUFACTURING FIRM DENSITIES, 1980-1983 Dependent Variable: New Firm Density (FIRMDEN) (4)

(5)

(6)

All Five Counties

(2) Four Suburban Counties

(3)

Independent Variables

Montgomery

Bucks

Delaware

Chester

INTERCEPT

-0.343»» (0.056)

0.0006 (0.001)

-0.0008 (0.002)

-0.0009 (0.002)

0.004 (0.004)

-0.0009 (0.005)

BUCKS

0.348*» (0.056)

CHESTER

0.342»» (0.056)

-0.0006 (0.0005)

DELAWARE

0.342«« (0.056)

-0.0018»* (0.0006)

MONTGOMERY

0.343«» (0.056)

-0.0009» (0.0005)

-0.038 (0.029)

-0.035 (0.031)

-0.043 (0.045)

-0. 107*» (0.055)

-0.209 (0.146)

-0.194 (0.224)

(1)

PROPTAX POLICE

-0.000005 -0.000002 (0.000008) (0.000009)

0.00001 (0.00001)

0.00004 (0.00001)

-0.00003 (0.00003)

-0.00001 (0.000041

STREETS

-0.00002*» -0.00003»» (0.00001) (0.00001)

-0.00008** 0.000001 (0.00003) (0.00002)

0.00002 (0.00009)

-0.00008 (0.0001)

POVERTY

0.022** (0.007)

0.02»» (0.008)

0.06** (0.027)

0.047 (0.036)

0.004 (0.055)

POPDEN

0.0004»» (0.00006)

0.0004»» (0.00007)

0.00065** 0.0013»» (0.0001) (0.0002)

0.0004»» (0.0001)

0.0002 (0.0002)

BUS LAND

0.002»» (0.0008)

0.002»* (0.0008)

0.0037** (0.001)

0.0038» (0.002)

-O.OOI (0.005)

0.0004 (0.007)

UNEMPLOY

-0.003 (0.017)

-0.0007 (0.018)

-0.036 (0.056)

0.019 (0.021)

0.0002 (0.073)

-0.091 (0.11)

PHILDIS

-0.00003 (0.00003)

-0.00003 (0.00003)

RAIL

0.00004 (0.00004)

-0.0005 (0.0004)

-0.003** (0.0007)

-0.0003 (0.0005)

0.0009 (0.001)

-0.001 (0.002)

HIGHWAY

0.0008»» (0.0004)

0.00056 (0.00043)

0.0017** (0.0006)

-0.001 (0.001)

0.0003 (0.003)

0.002 (0.005)

0.370

0. 394

0.459

0.646

0.347

0.029

9.17

9.23

5.90

12.14

3.12

1.12

0.0001

0.0001

0.0001

0.0001

0.3816

Adjusted R2: F: PROB > F:

Standard errors In parentheses »»: Coefficient significant at the .05 level »: Coefficient significant at the .10 level

-0.002 (0.013)

0.0001

Appendix B: Supplementary Material to Chaptcn

180

TABLE B.J.4 REGRESSION RESULTS: DETERMINANTS OF NEW BUSINESS SERVICES FIRM DENSITIES, 1980-1983 Dependent Variable:

New Firm Density (FIRMDEN)

All Five Counties

(2) Four Suburban Counties

Montgomery

Bucks

Delaware

Chester

1NTERCEPT

-0.286«» (0.105)

0.001 (0.001)

-0.0025 (0.0017)

0.0003 (0.002)

0.0003 (0.0002)

-0.0001 (0.0007)

BUCKS

0.287«* (0.104)

CHESTER

0.286»» (0. 105)

-0.0005 (0.0005)

DELAWARE

0.286** (0.104)

-0.0007 (0.0007)

MONTGOMERY

0.286*« (0.105)

-0.0012»* (0.0006)

0.034 (0.055)

0.035 (0.039)

0.008 (0.038)

0.04 (0.072)

-0.0038 (0.0046)

0.003 (0.014)

POLICE

-0.000008 (0.00001)

-0.000009 (0.00001)

0.00001 (0.00001)

-0.00002 (0.00002)

-3.16E-07 (0.000001)

0.000003 (0.000004)

STREETS

-0.000007 (0.00002)

-0.000006 (0.00001)

-0.00002 (0.00002)

-0.000005 (0.00003)

0.000001 (0.000002)

-0.000004 (0.000006)

POVERTY

0.011 (0.014)

0.011 (0.01)

0.02 (0.022)

0.008 (0.016)

-0.0005 (0.0006)

-0.0001 (0.002)

0.0003»» (0.0001)

0.0003** (0.0001)

0.0005** (0.0001)

0.0011** (0.0002)

0.00004* (0.00002)

0.0001* (0.00006)

BUS LAND

0.001 (0.001)

0.001 (0.001)

0.0028** (0.0008)

0.0011 (0.003)

-0.0002 (0.0002)

0.0009 (0.0007)

UNEMPLOY

-0.03 (0.032)

-0.031 (0.023)

-0.001 (0.047)

-0.063** (0.026)

-0.001 (0.002)

0.009 (0.008)

PHILDIS

-0.00006 (0.00005)

-0.00005 (0.00005)

RAIL

-0.0005 (0.00070)

-0.0006 (0.0005)

-0.0017»* (0.0006)

-0.0007 (0.0007)

-0.00004 (0.00005)

0.0001 (0.0001)

0.0002 (0.0008)

0.0003 (0.0005)

0.0008 (0.0005)

-0.0003 (0.0013)

-0.00009 (0.00005)

0.0006** (0.0002)

(1)

PROPTAX

POPDEN

HIGHWAY Adjusted R 2 : F: PROB > F:

0.163

0.353

3.85 0.0001

(3)

(4)

Independent Variables

(5)

(6)

0.393

0.521

0.017

7.90

4.73

7.65

1.06

2.21

0.0001

0.0002

0.4276

0.0621

Standard errors In parentheses **: Coefficient significant at the .05 level *: Coefficient significant at the .10 level

0.0001

0.260

Appendix B: Supplementary Material to Chapters

181

Table B . 3 . 5 REGRESSION RESULTS: DETERMINANTS OF EMPLOYMENT CHANGE, 1980-1983, FOR PHILADELPHIA H4SA, PENNSYLVANIA COUNTIES Dependent Variable: Independent Variables

Employment Change, I980-1Q t o 1983-1Q

Manufacturers

Business Services

0. 68

INTERCEPT

-1.17 (0.900)

(0.360)

BUCKS

-1.53 » (0.930)

-0.88 »* (0.370)

CHESTER

2.02

(1.270)

- 0 . 23 (0.480)

DELAWARE

-1.21 (1.160)

-0.96 (0.340)

MONTGOMERY

-1.19 (0.860)

- 0 . 6 5 •« (0.310)

PROPTAX

44.13 (137.250)

-27.77 (30.130)

-0.01 (0.056)

-0.01 (0.013)

(0.107)

-0.02

-0.01 (0.016)

-25.01 (54.890)

5.91 (12.600)

POPDEN

0.37 (0.416)

0.08 (0.084)

BUSLAND

3.46 (4.300)

-0.08 (0.909)

12.11 (140.220)

-14.45 (33.230)

POLICE STREETS POVERTY

UNWPLOY

(0. 120)

0.05 » (0.030)

RAIL

2.89 (3.190)

1.26 * * (0.640)

HIGHWAY

5.03 (3.200)

(0.574)

EMPLBEG

0.07 (0.069)

-0.36 « (0.036)

GROWTH

27.88 « (9.130)

-1.61 (1.850)

PHI LOIS

0.21 «

"

Adjusted R

0.019

F:

3.37

Prob. > F:

0.0001

Standard e r r o r s are In parentheses. ••: C o e f f i c i e n t s i g n i f i c a n t at the .05 l e v e l . * : C o e f f i c i e n t s i g n i f i c a n t at the .10 l e v e l .

-0.16

0.125 14.78 0.0001

182

Appendix B: Supplementary Material to Chapter*

B.4 Supplementary

B.4.1 Data

Mbitcriak to '

The county-level data used to derive Tables 7.1 through 7.6 and Appendix Tables C.4 and C.5 came from several sources. All data for the Pennsylvania counties were compiled from the Pennsylvania Office of Employment Security (OES) ESA-202 files by the Institute for Public Policy Studies, Temple University. These data include all employees covered by the Pennsylvania Unemployment Compensation Law. The data for the early 1970s suffer from two serious flaws. First, the SIC changes introduced in national data in 1972 were not incorporated into the OES files until 1975. Second, only about 80% of all employment in the state was covered by the law in 1972. By the late 1970s, coverage had increased to more than 95%. Because of these weaknesses, the analysis using these data was limited to the years after 1975. This eliminated all problems relating to SIC definition changes, but some coverage problems still remained. These problems were most significant in education services and government. Most employment in education services in 1975 was not covered by the unemployment compensation law. The data, therefore, significantly understate employment levels. By 1980 this was not the case. Employment in education services was excluded from all total employment and total services employment levels and growth-rate calculations to avoid biases resulting from coverage changes in this large sector. The sector was also excluded from all calculations based on national data in the tables using the OES data for the counties. Government data were excluded from all tables because the extent of coverage changes over years could not be reliably assessed. The New Jersey county data came from three sources. Data for nonagricultural employment from 1975 to 1981 were collected from County Business Patterns. Because this source seriously underreports employment in agriculture, Department of Commerce estimates, compiled by the Bureau of Economic Analysis Employment Service, were used for the New Jersey counties in these years. Data for the years from 1981 through 1984 were collected from annual issues of New Jersey Covered Employment Trends, published by the New Jersey Department of Labor, Office of Demographic and Economic Analysis. These data are compiled from the New Jersey Office of Employment Security (OES) ESA-202 files. Preliminary 1985 employment data were acquired directly from the OES. The comparative central city/outside central city (CC/OCC) data used to generate Table 7.7, and for the regression analysis supporting the discussion, were collected from the "Journey to Work" Subject Reports contained in the Census of Population, 1970 and 1980. It is the only nationwide compilation of employment within city boundaries. Other sources, such as County Business Patterns, report county-level data only. In only three or four major PMSAs, apart from Philadelphia, can county-level data be used to derive central city employment data based on the place of work. All PMSAs with population exceeding 600,000 in 1980 were included in the initial selection for the analysis. (Nassau-Suffolk was the only

Appendix B: Supplementary Material to Chapter*

183

exception. It was not included because it has no central city.) However, two major definitional issues resulted in the exclusion of several PMSAs. First, the official definition of PMSA boundaries used in the data changed between 1970 and 1980. Counties were added or subtracted from many of the nation's large PMSAs. County-level data reported in 1980 were used to alter the 1980 employment levels in the PMSAs to conform to 1970 definitions. (It was not possible to adjust 1970 data to 1980 boundaries because data were not reported in 1970 for all of the counties that were added to the PMSAs between the two years. 1980 data for all of the relevant counties were available.) This adjustment meant that three New England metropolitan areas (Boston, Hartford, and Providence) had to be excluded, since the additions and subtractions to the PMSA definitions were made at the township level in these areas. Second, many cities changed their boundaries through annexation during the ten years. Data showing the effects of this on reported employment levels are not available. All PMSAs where the area of the central city changed by more than 15% during the ten years were, therefore, excluded from the analysis. These included Birmingham, Charlotte, Columbus, Ohio, Dallas, Dayton, Denver, Fort Worth, Houston, Memphis, Miami, Orlando, Phoenix, Portland, Salt Lake City, San Antonio, and San Jose. The 43 PMSAs remaining after these deletions are listed in Table B.4.2. Several of the remaining PMSAs contain more than one central city (Minneapolis-St. Paul, for example). For these PMSAs, the definition of central city included all of the major cities within their boundaries for which data are reported. The CC/PMSA tax effort ratios used in the regression analysis were computed using data from two sources. The PMSA level tax effort measure is from Measuring Metropolitan Fiscal Capacity and Effort: 1967-1980, published by the Advisory Commission on Intergovernmental Relations (ACIR). The CC tax effort indexes were computed from unpublished tax capacity data generated by H. F. Ladd et. al. for their 1986 study and supplied by the authors. The estimation used a procedure that approximated the representative tax system method used in the ACIR source. The data are for 1977.

B.4.2 Procedures Compound annual growth rates. Annual growth rates reported throflghout the text were computed by {[(E r /E t ) 1/(r-,) ] - 1} x 100 where

E = employment, r = end year, and t = beginning year.

CC/OCC regression analysis. The regression analysis supporting the discussion of Table 7.7 involved simultaneous estimation (by two-stage least squares) of a two equation system. This was required to control for the fact that shifts in the central city share of PMSA population and shifts

184

Appendix B: Supplementary Material to Chapter*

in the employment share are interlinked in important ways. The employment shift was included as an independent variable in an equation explaining the population shift, and the population shift was included as an independent variable in an equation explaining the employment shift. Each was then estimated endogenously in the regression procedure. The system can be expressed by (1)

(EMPSHIFT 7 0 .8O) = f(POPSHIFT 7 0 . 8 o, X, DO

(2)

(POPSHIFT 7 0 .8O) = f ( E M P S H I F T 7 0 . 8 0 , X 7 0 , Y 7 0 )

where

EMPSHIFT70-8o

=

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=

the change in the CC percentage share of total PMSA employment between 1970 and 1980, the change in the CC percentage share of total PMSA population between 1970 and 1980,

X7o = a group of variables, measured in 1970, that explain both employment and population shifts, Y 70 = a group of variables, measured in 1970, that explain only population shifts, and D; = dummy variables controlling for differences across industrial sectors in employment shifts. The system is identified by the inclusion of the dummy variables in the employment equation and the inclusion of Yj in the population equation. The variables included in X 70 are listed in Table B.4.1, and the regression results are presented in Table B.4.2. Because the PMSA data were pooled across ten sectors, the significance levels and R 2 for the equations explaining changes in the Center City population shares are overstated. This is not the case for the employment shift equations because the dependent variable varies across sectors within a single PMSA (unlike the population shift, which is constant across sectors within individual PMSAs). Dummy variables were included in the employment equation for nine of the ten sectors to control for differences across sectors in the average change in the dependent variable. Manufacturing was the excluded sector. With one important exception, the estimated model is similar to that employed by W. Norton Grubb in his 1982 analysis of CC/OCC changes during the 1960s. Grubb simultaneously estimated end of period CC employment and population shares, using a formulation of the endogenous variables that controlled for the fact that CC/PMSA ratios at a given point in time are a function of the CC's share of total PMSA land area. This relationship is not as important when examining the factors underlying changes in the CC/PMSA ratio (since the CC land area share does not change). However, for comparison purposes, the model was estimated using Grubb's formulation of the endogenous variables and the results are shown in Table B.4.2 as "Specification II."

Appendix B: Supplementary Material to Chapter«

185

The specification results in sign changes for only two of the predetermined variables, the beginning of period population and employment ratios. Significance levels also change for the population density and poverty variables. These changes are consistent with the differences between the two formulations of the endogenous variables. The Grubb formulation enforces a particular density pattern on regional population and employment that is dependent on the relative size of the CC in the total region. Regions with greater than average CC shares are assumed to have steeper density gradients. It is, therefore, not surprising that the most affected coefficients are those for the variables most closely related to population density. Since the objective of the research supporting Chapter 7 was to uncover what features of the Philadelphia area influenced the region's greater than average decentralization rate during the 1970s, the results shown for Specification I are more appropriate. Specification I tests the extent to which greater centralization at the beginning of the period was an advantage for central cities or not, a question of interest for the discussion in Chapter 7. Input-output analysis. The analysis of the distribution of the secondary effects arising from employment and output increases presented in Table 7.8 was developed from the regional input-output system constructed for the 1985 Economic Report on the Philadelphia Metropolitan Area. A description of the procedures used to develop the system for the entire region can be found in Appendix B.2 of that volume. The spatial disaggregation of the regional input-output transactions table involved several steps with a few important assumptions. The regional table contained 492 business sectors, 4 value-added sectors (wages and salaries, state taxes, local taxes, and other value added), 2 final demand categories (household consumption, and investment and other final demand), and imports and exports. This was aggregated into 56 business sectors (value-added, final demand, and import/export categories remained the same). The 56 sector regional transactions table was divided between city and suburbs according to the employment ratios between the two areas in each sector. The two 56 sector tables were each then aggregated up to eight business sectors to simplify the multiplier and intraregional import/export calculations. These calculations were then made on the two-region table using the procedures described in Appendix B.2 of the 1985 Economic Report on the Philadelphia Metropolitan Area. Recent available data were used to rebenchmark the 56 sector regional table. As a result, the total regional multipliers reported in the table differ from those shown in the 1985 Economic Report for some sectors. These procedures involved three major assumptions. (1) The division of total output between city and suburbs is a linear function of the employment split between the two areas. (2) Input flows among business sectors within and between the two areas, and the division of value added and other final demand are linearly related to the division between city and suburbs of output in the sectors. (3) Household consumption patterns are identical in the two areas. This holds for both the pattern across goods and services, and for the regional distribution of purchases. In effect, spillovers

186

Appendix B: Supplementary Material to Chapter* between the two areas resulting from "induced" effects generated by increased household consumption are distributed between city and suburbs according to the city/suburb shares of output in consumption sectors. The alternative would have been to assume that suburbanites spend some percentage (different from city residents) of their income in the suburbs and the remainder in the city.

B.4.3. Economic Report Card Calculations Ten criteria were applied to each industry in each county to assess their relative strengths. These are the details of that application. 1. Share of total employment in the county. The percentage shares of total county employment during the third quarter of 1985 were calculated for each sector. (See Table 7.1 in Chapter 7.) For each industrial sector, the PMSA share was calculated. If a county entry was >125% of the PMSA share, it received two stars; if the entry was 75% and 75% and 125% of the county's total employment growth rate, it received two stars; if it was 75% and 75% and 70.4%), the sector received two stars; if the percent retained was in the middle range (68.4% to 70.3%), it received one star; and, if it was relatively low (< 68.0%), it received none. For the suburban counties in which the average percent retained was higher (79.7%), the sector received two stars if the percent retained exceeded 80.7%, one star if the percent retained was 78.7%, and no stars if the percent retained was 78.596), the sector received two stars; if the percent was >76.5% and

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229

Table C.8 PER CAPITA FEDERAL AND STATE AID TO THE COUNTIES: PHILADELPHIA WSA, 1965-1983 Bucks

Camden

Chester

Year

Federa 1 Aid

State Aid

Federa 1 Aid

State Aid

Federa 1 Aid

State Aid

Federal Aid

State Aid

1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983

$1.62 1.31 2.67 3.91 2.41 5.33 5.03 5.37 10.41 20.93 25.39 30. 58 36.38 35.79 41.89 47.03 55.30 62.52 58.47

$41.93 50.92 49.32 47.39 75.72 102.50 111.82 134.43 140.62 156.60 168.29 176.64 165.77 172.72 211.71 233.15 216.31 221.23 246.99

$13.83 10.38 14.01 16.70 14.75 16.33 14.97 17.90 22.13 30.07 31.07 49.87 56.58 53.63 79.35 41.44 36.04 40.78 26.51

$41.92 40.88 50.32 56.60 76.74 96.46 110.24 119.29 130.44 116.34 171.37 162.72 224.85 257.67 276.49 328.71 409.76 414.23 459. 97

$5.14 5.11 5.19 5.27 4.45 4.85 6.80 7.67 17.77 23.54 23.32 46.83 51.01 62.49 84.95 70.27 51.60 66.88 66.83

$38. 42 47.19 63.33 79. 12 78.94 116.24 138.50 185.24 194.20 189.60 244.15 253.29 297.19 328.49 398.03 472.82 527.83 557.66 583.16

$0.68 0.52 1.15 1.76 0.96 0. 96 0.77 1.96 6.62 17.25 15.70 27.08 82.14 31.99 62.23 30.43 14. 25 23.00 31.45

$53.17 50.90 50.91 50.94 78.53 101.38 102. 97 113.76 127.19 138.37 153.43 158.54 160.63 169.75 186. 94 216.19 228.45 247.00 241.07

Delaware

Gloucester

Montgomery

Phi ladelphla

Year

Federa 1 Aid

State Aid

Federa 1 Aid

State Aid

Federa 1 Aid

State Aid

Federa 1 Aid

State Aid

1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983

$2.46 1.80 4.32 6.80 3.74 3.52 8.63 5.38 12.11 24.16 26.53 35.41 67.69 55.74 46.62 46.44 43.95 49.99 51.24

$16.90 34.25 37.86 41.17 50.68 58.10 68.24 84.37 100. 29 152.19 128.09 148.32 149.33 164.11 227.06 253.08 256.64 254.93 248.49

$5.06 4.17 3.40 2.66 5. 10 3.12 3.40 5.75 9.88 47.33 41.00 53.91 46.60 41.97 25.78 18.48 19.40 17.79 18. 29

$42.25 48. 13 65.44 81.99 115.20 99.21 94. 14 109.94 143.20 160.70 190.51 185.39 236.41 246.55 321.93 340.05 367.89 441.74 439.34

$0.79 1.20 1.50 1.80 1.51 1.21 1.36 7.58 7.79 18.72 18. 15 28.06 25.35 25.14 35.24 63.47 57.82 41.05 39.07

$13.50 47.80 40.28 33.05 42.31 57.71 67.84 85.00 91.69 91.00 105.47 112.28 109.31 117.98 151.98 158.72 162. 75 190.76 189.91

$10.60 12.12 20.27 28.58 34.04 25.53 92.59 49.47 104.33 99.12 104.75 196.76 186.05 190.95 131.56 162. 10 213.30 212.69 303.42

$35.32 39.92 47.51 55.25 83.78 109.60 130.16 173.39 219. 11 228.77 296.16 298.33 308. 26 351.30 430.60 503.31 533. 86 515.61 507.56

SOURCES: See Table 9.1.

Appendix D

Philadelphia PMSA Economic Statistics

Appendix D: Philadelphia PMSA Economic Statuti« m! © I CT»

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