Research and Productivity in Asian Agriculture 9781501734328

Table of contents :
Contents
General Introduction
Part I. Institutional Development of Research and Extension Programs
1. Investment in Agricultural Research and Extension Programs: A Quantitative Assessment
2. The Development of Asian Research Institutions: Underinvestment, Allocation of Resources, and Productivity
Part II. Agricultural Research and Productivity Change
3. Returns from Agricultural Research and Extension in Wheat and Maize in Pakistan
4. Research and Agricultural Productivity Growth in Bangladesh
5. Rice Productivity and the Returns to Rice Research in Indonesia
6. Research, Extension, Infrastructure, and Productivity Change in Indian Agriculture
Part III. Research, Output Supply, Factor Demand, and Incomes
7. Technology, Infrastructure, Output Supply, and Factor Demand in Philippine Agriculture
8. Technology, Infrastructure, Output Supply, and Factor Demand in Thai Agriculture
9. Technology, Infrastructure, Output Supply, and Factor Demand in North Indian Agriculture
10. The Distribution of Income in India’s Northern Wheat Region
11. Agricultural Technology, Population Growth, Infrastructure, and Real Incomes in North India
Part IV. International Perspectives on Research and Extension Programming
12. International Technology Transfer and Agricultural Productivity
13. IARC, NARC and Extension Investment, and Field Crop Productivity: An International Assessment
14. lARCs, Aid, and Investment in National Research and Extension Programs
15. Research Effectiveness and the Support Base for National and International Agricultural Research and Extension Programs
Index

Citation preview

Research and Productivity in Asian Agriculture

Food Systems and Agrarian Change

Edited by Frederick H. Buttel, Billie R. DeWalt, and Per Pinstrup-Andersen

Research and Productivity in Asian Agriculture by Robert E. Evenson and Carl E. Pray

Searching for Rural Development: Labor Migration and Employment in Rural Mexico by Merilee S. Grindle

Networking in International Agricultural Research by Donald L. Plucknett, Nigel J. H. Smith, and Selcuk Ozgediz

Transforming Agriculture in Taiwan: The Experience of the Joint Commission on Rural Reconstruction by Joseph A. Yager

RESEARCH AND PRODUCTIVITY IN ASIAN AGRICULTURE Robert E. Evenson Carl E. Pray With the assistance of Ahmed Hans P. Binswanger James K. Boyce A. Steven Englander Devendra Gupta M. Ann Judd

James W. McKinsey, Jr. Joseph G. Nagy Jaime Quizon David C. Salmon Suthad Setboonsarng

Cornell University Press ITHACA AND LONDON

Copyright © 1991 by Cornell University All rights reserved. Except for brief quotations in a review, this book, or parts thereof, must not be reproduced in any form without permission in writing from the publisher. For information, address Cornell University Press, 124 Roberts Place, Ithaca, New York 14850. First published 1991 by Cornell University Press. International Standard Book Number 0-8014-2535-2 Fibrary of Congress Catalog Card Number 90-55750 Printed in the United States of America

Librarians: Library of Congress cataloging information appears on the last page of the book.

0

The paper in this book meets the minimum requirements of the American National Standard for Information Sciences— Permanence of Paper for Printed Fibrary Materials, ANSI Z39.48-1984.

Contents

General Introduction

2

I. Institutional Development of Research and Extension Programs

5

Part

1

Investment in Agricultural Research and Extension Programs: A Quantitative Assessment M. Ann Judd, James K. Boyce, and Robert E. Evenson The Development of Asian Research Institutions: Underinvestment, Allocation of Resources, and Productivity Carl E. Pray

2

II. Agricultural Research and Productivity Change Returns from Agricultural Research and Extension in Wheat and Maize in Pakistan Joseph G. Nagy

Part

3

4

5

6

V

7

81

93

Research and Agricultural Productivity Growth in Bangladesh Carl E. Pray and Zafar Ahmed Rice Productivity and the Returns to Rice Research in Indonesia David C. Salmon

233

Research, Extension, Infrastructure, and Productivity Change in Indian Agriculture Robert E. Evenson and James W. McKinsey, Jr.

158

II4

vi

Contents

Part III. Research, Output Supply, Factor Demand, and

Incomes 7 Technology, Infrastructure, Output Supply, and Factor Demand in Philippine Agriculture Robert E. Evenson and Jaime Quizon 8 Technology, Infrastructure, Output Supply, and Factor Demand in Thai Agriculture Suthad Setboonsarng and Robert E. Evenson 9 Technology, Infrastructure, Output Supply, and Factor Demand in North Indian Agriculture Robert E. Evenson 10 The Distribution of Income in India’s Northern Wheat Region Jaime Quizon, Hans P. Binswanger, and Devendra Gupta 11 Agricultural Technology, Population Growth, Infrastructure, and Real Incomes in North India Robert E. Evenson Part IV. International Perspectives on Research and Extension Programming 12 International Technology Transfer and Agricultural Productivity A. Steven Englander 13 I ARC, NARC and Extension Investment, and Field Crop Productivity: An International Assessment Robert E. Evenson 14 lARCs, Aid, and Investment in National Research and Extension Programs Robert E. Evenson 15

Research Effectiveness and the Support Base for National and International Agricultural Research and Extension Programs Carl E. Pray and Robert E. Evenson

Index

185

195

206

217

233

265

287

291

314

330

355 373

/

Research and Productivity in Asian Agriculture

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Institutional Development of Research and Extension Programs

billion U.S. dollars; that of public-sector extension was approximately 3.4 billion dollars. These data do not include spending by the lARCs of approximately 140 million dollars in 1980. National research systems increased real spending by a factor of 3.68 since 1959 (and by 1.4 since 1970). Scientist man-years increased by a multiple of 3.14 since 1959, reflecting the rise in expenditures per scientist over the period. The comparable multiples for extension spending and manpower were 2.50 and 2.05, respectively. From Table 1.1 it is evident that the industrialized regions of the world—Western Europe, Eastern Europe/USSR, and North America/ Oceania—continued to spend the most on agricultural research. In addition, China appeared to have become, by 1970, one of the “big spenders” in this area.“ However, if one were to calculate expenditure shares, these would reveal the declining importance of both North America/Oceania and Eastern Europe/USSR relative to the rest of the world and the increasing importance of Western Europe, Latin America, and Asia. When China is included, Asia’s share of world expenditures almost doubled between 1959 and 1980 (rising from 12.7 to 24.3 percent); without China, Asia’s share increased from 10.3 to 17.1 per¬ cent. Latin America’s share rose from 3.9 to 6.3 percent, and Western Europe’s share increased from 13.3 to 20.2 percent. At the same time, the share of Eastern Europe/USSR dropped from 27.5 to 20.2 percent, while that of North America/Oceania declined sharply from 36.9 to 23.3 percent. Africa’s share of total expenditures remained virtually unchanged during the period, indicating that the region had generally not experienced an expansion in its research programs comparable to that of Latin America and Asia. The patterns revealed by the scientist man-year (SMY) figures are somewhat different from those that characterize the expenditure figures. Eastern Europe/USSR and Asia accounted for over 60 percent of the world’s SMYs in both 1959 and 1980 (this is true even if one excludes China). The share of Eastern Europe/USSR declined somewhat between 1959 and 1980 (from 37.5 to 34.9 percent), but it remained several times larger than that of either Western Europe (13.2 percent in 1980) or North America/Oceania (9.2 percent in 1980), a reflection of the rela¬ tive capital-intensity of the research programs in these latter regions.^ Although Africa’s share of world SMYs increased only from 4.1 to 5.5 2. It should be noted, however, that the data for Cdiina are subject to a relatively high degree of uncertainty owing to the paucity of source material. 3. Problems of definitional consistency in enumerating scientists may, however, account for part of the difference.

Investment in Agricultural Research and Extension Programs

13

percent between 1959 and 1980, SMYs in 1980 were over four times what they had been in 1959. Many African countries went through a postcolonial adjustment period during which highly paid British and French civil servants were replaced by somewhat lower paid national scientists. Therefore, these countries were able to increase the number of SMYs devoted to agricultural research at a faster rate than they ex¬ panded expenditures. The increase in the number of SMYs in Latin America follows the same pattern as the increase in expenditures. Tropical South America (mainly Brazil) and the Caribbean/Central America (mainly Mexico) experienced the greatest increases. South Asia and Southeast Asia both increased SMYs more slowly than expenditures."^ Table 1.3 shows the international distribution of public-sector re¬ search and extension spending and manpower. It reveals that in 1980 the international distribution of extension spending was remarkably even—Western Europe accounted for 15.6 percent of worldwide expen¬ ditures; Eastern Europe/USSR, 21.0 percent; North America/Oceania, 22.4 percent; Latin America, 12.7 percent; Africa, 14.8 percent; and Asia, 13.5 percent. Although the industrialized regions spent slightly more than other regions, differences in extension expenditure levels were less significant than differences in research expenditure levels. This points out clearly that the industrialized regions placed more emphasis on research than on extension, while the developing regions tended to do the opposite. The most dramatic change between 1959 and 1980 was the expansion of extension expenditures in Latin America (from 4.3 percent to 12.7 percent). Africa’s share decreased slightly during the period, but the region continued to spend more on extension than either Latin America or Asia (excluding China). The data on extension workers show a very different distribution pattern. In 1959, almost half of the world’s extension workers were in Asia, and this share was still substantial in 1980, 45.1 percent. If extension data were available for China, Asia’s share would, of course, be much larger. The number of extension workers in Latin America increased dramatically between 1959 and 1980—from 3,353 to almost

4. In compiling data on research spending, it was usually not possible to distinguish between capital and operating expenditures. Rapid periods of growth in national program expenditures often reflect increased capital spending that occurs when a country engages in program building. While program-building expenditures may be used in part to increase the nurnbers of scientists engaged in research, they are more often used to improve laboratory acilities or to raise the salary levels of current research personnel. It is therefore not surprising that SMYs often increase at a rate slower than expenditures.

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164

Agricultural Research and Productivity Change

the data.^ Districts with minor production of the crop were excluded from the regressions. Total production growth can be accounted for by area growth plus yield growth.^ The proportion accounted for by yield growth has generally increased over time. It is of interest to note that productivity growth rates for the early Green Revolution period, 196373, are exceptionally high only for wheat. Note that area growth in this analysis includes growth in multiple cropping. Even with multiple¬ cropping growth included in area growth, these data show that yield increases have dominated area in production growth in all commodities except the pulses. For the two major foodgrains, rice and wheat, yield increases account for 54 and 57 percent of total production growth. Jowar and bajra area has declined. (This is also the case for cotton.) It is relevant to point out that area itself responds to the availability of new technology, and this responsiveness limits the relevance of this type of productivity accounting.^ Table 6.3 reports these yield trends by state and crop for the entire period. It shows that the Punjab recorded the highest yield trends in three of the five major foodgrains: rice, jowar, and bajra. These yield trends are not very meaningful for states where production shares of the commodities are low. The reader should interpret these data with that in mind. It is easier for a state to raise average yields for crops with relatively low levels of production.

Total Factor Productivity Growth The total factor productivity (TFP) index is a more general measure of productivity. The methodology for the construction of this measure is set forth in the introduction to Part II. A Fisher’s chained index number for crop outputs and for crop inputs was constructed for each district. (The Fisher’s chained index is a “superlative” index number [Diewert 1978] and approximates the Divisia index.) Five major crops (rice, wheat, jowar, bajra, and maize) and ten minor crops were included in the output index. Farm harvest prices were used to aggregate outputs. Input data covered land, irrigation, labor, animal labor, tractors, and fertilizer. Farm rental prices were used to aggregate inputs.^ Table 6.4 reports the mean values of the production, input, and total 4. The state dummy variables control for some systematic biases in measurement and related phenomenon. 5. Virtually all trend coefficients were statistically significant. 6. This is taken into consideration in the yield analysis reported below. 7. These included rental prices for irrigated and nonirrigated land (see McKinsey et al. 1988).

Research and Productivity Change in Indian Agriculture

165

'

“ Table 6.3. Estimated trends in yields for major crops in India by state, 1956-83 11-

llate ndhra Pradesh aryana ijiadhya Pradesh laharashtra 'arnataka mjab imil Nadu ttar Pradesh lujarat bjasthan "•n states total

Rice

Wheat

Jowar

Bajra

Maize

Sugar

Cotton

Pulses

.0206 .0265 -.00009 .0259 .0271 .0537 .0133 .0163 .0192 .0118 .0172

.0460 .0312 .0238 -.0182 .0619 .0445

.0079 .0183 .0029 .0172 .0445 .0520 .0073 .0096 .0333 .0156 .0166

.0086 .0216 -.00006 .0195 .0285 .0501 .0227 .0086 .0394 .0021 .0158

.0399 -.0148 .0084 .0600 .0732 .0123 .0116 -.0051 .0106 -.0059 .0115

-.0096 .0112 .0016 .0323 .0080 .0283 .0161 .0143 .0024 .0686 .0158

.0565 .0058 -.0051 .0137 .0181 -.0035 .0293 .0135 .0311 .0152 .0189

.0181 -.0029 .0027 -.0145 .0154 -.0052 .0041 .0018 -.0144 -.0002 .0011

NA

.0313 .0552 .0217 .0281

I Source: Government of India, Ministry of Agriculture, Directorate of Economics and Statistics, Area and ■oduction of Principal Crops in India, 1985-86 (New Delhi, 1987). Note: NA, not available.

Table 6.4. Mean annual crop production, crop inputs, and total factor productivity indices in India for ten states

Year

Production

Inputs

1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983

100 95 116 112 119 128 122 119 141 112 116 141 130 148 174 168 145 173 159 199 181 208 212 170 200 221 221 253

100 101 102 103 104 105 107 108

no 111 114 116 119 122 125 127 128 130 130 132 134 136 142 146 151 157 163 169

Total factor productivity 100 95 114 109 116 122 115 111 128 102 102 122 111 123 142 135 115 135 124 153 138 155 153 118 136 145 140 156

166

Agricultural Research and Productivity Change

Table 6.5. Estimated rates of change in total factor productivity index

by state, India State

1956-63

1963-73

1973-83

1956-83

Andhra Pradesh

.0161 (.0072) .0263 (.0106) .0119 (.0066) .0108 (.0069) .0330 (.0065) .0231 (.0066) .0266 (.0068) .0212 (.0041) .0556 (.0108) —.0062 (.0129)

.0138 (.0055) .0555 (.0087) .0108 (.0049) -.0252 (.0061) .0141 (.0077) .0419 (.0062) .0328 (.0074) .0151 (.0031) .0166 (.0091) .0484 (.0111)

.0029 (.0066) .0094 (.0138) .0104 (.0048) .0045 (.0066) -.0037 (.0078) .0157 (.0065) -.0028 (.0117) .0284 (.0076) .0163 (.0093) -.0162 (.0088)

.0112 (.0014) .0268 (.0025) .0034 (.0011) .0091 (.0016) .0126 (.0017) .0320 (.0013) .0042 (.0023) .0133 (.0008) .0179 (.0021) .0162 (.0023)

Haryana Madhya Pradesh Maharashtra Karnataka Punjab Tamil Nadu Uttar Pradesh Gujarat Rajasthan

Note: The numbers in parentheses are standard errors.

factor productivity (production/inputs x 100) indices by year for the ten states. Table 6.5 reports estimated rates of change in the TFP index by state for selected periods. (These trends were estimated in the same way that the yield trends were estimated.) Table 6.4 shows that TFP grew at a steady rate over time with significant weather variations; negative growth occurred in the drought years 1965, 1966, and 1979. The variation in the index is due to changes in production or output. Input levels change relatively slowly and smoothly over time. Rates of change by state (see Table 6.5) clearly show the Punjab to be the leading state with Haryana second. The TFP growth rates of these two states, in excess of 2.5 percent, rank high by world standards (note that both had extraordinary growth rates in the Green Revolution period, 1963—73). (Only a few U.S. states have performed this well over the 1956—83 period. Average TFP growth in U.S. agriculture was slightly below 2 percent for this period.)^ Gujarat and Rajasthan produced modest rates of TFP growth (1.5— 8. A recent study by Evenson (1988) reports comparable TEP growth rates for U.S. agriculture. Annual TFP growth is estimated to be 1.84 percent. The best performing U.S. states (Mississippi, Alabama, Cieorgia) had annual rates of TFP change comparable to those realized in the Punjab and Haryana.

Research and Productivity Change in Indian Agriculture

167

2.0 percent). Uttar Pradesh, Karnataka, and Andhra Pradesh experi¬ enced low rates of TFP growth (1.0-1.4 percent). Maharashtra, Tamil Nadu, and Madyha Pradesh recorded poor TFP growth at less than 1 percent per year.

Productivity Decomposition: Methods and Specificity Several studies using statistical methods to measure the contributions of research and extension to productivity change have been made (see Chapter 5 for a review). Typically, statistical methods are used when no direct measures of inventions adopted or of the invention-productivity link are available. These methods rely on estimating the link, not be¬ tween research output and productivity, but between investments (i.e, inputs) in research, extension, and schooling, and productivity. Statisti¬ cal analysis requires variables measured for a unit of observation. The unit may be a farm or an aggregate of farms. Production, prices, and factors of production must be measured for each observation. This is usually straightforward. The key problems of statistical productivity analysis are the measurement of research, extension, and related invest¬ ment variables for the unit of observation. This requires attention to the following: 1. Functional form questions 2. Simultaneity of investment and productivity changes 3. Time-shapes (i.e., the timing between investment and productivity impact) 4. Spatial relationships between the location of the investment and the location of production in the unit of observation 5. Deflators The general TFP decomposition specification relates TFP to the fol¬ lowing variables: human capital (schooling of farmers, public-sector extension); technology (public-sector research, private-sector research, public-sector extension); and infrastructure (geoclimate factors, weath¬ er variables, government policy).

Functional Form Issues

Three decisions regarding functional form are required for TFP de¬ composition. The first is whether the dependent variable should be in

168

Agricultural Research and Productivity Change

the form of an annual rate of change or in the form of a cumulated rate of change from a base period. The second is whether the base should reflect cross-sectional variation in productivity or efficiency. The third concerns the actual specification—i.e., logarithmic, quadratic, etc. The determination of the specification of the dependent variable will, of course, influence the specification of the independent variables. On the first question there is a strong argument for the cumulative specification based on error cancellation. Suppose that weather errors are affecting productivity measures. The annual rate of change measure for t - 1 to t incorporates two errors. The first is due to weather in period t. The second is due to the previous period’s weather. The cumulated index has only the current error term and the error term for the base period that can be averaged out over several years. The second issue is more problematic. TFP indices were calculated for each district. Each of these indices was expressed relative to the base period in each district. It is possible to construct such indices relative to a state or national average base. If all outputs and inputs were measured in constant quality units this would be a true cross-sectional index. Unfortunately, it is difficult to make the case for such measures, and such measures were not attempted here for India. The issue of the appropriate functional form is not one where a strong appeal to an underlying model can be made for the TFP decomposition specification. (However, for metafunction studies, there are strong model implications for the functional form. See Chapter 9.) The public and private investments in research and extension are the result of complex political decisions and investment analysis. There is little rea¬ son to suppose that a maximizing process is taking place to such an extent that standard cross-equation and related restrictions should be imposed on the estimates. The fact that the TFP measure is a dimension-free measure, however, has implications for the deflator used for research, to be discussed below.

Simultaneity between Investments and Productivity

Simultaneity in economic models occurs when the independent vari¬ ables in a regression are not exogenous in the model. If research invest¬ ment, for example, were made in response to productivity change the causality between research investment and productivity change would be confounded. Two factors arc relevant to questions of simultaneity in TFP decomposition. The first is that different “actors” are producing the

Research and Productivity Change in Indian Agriculture

169

TFP and the investment data. Farmer actions produce the TFP data. Individual farms have no control over the investments in research. (They do control their own investments in experimentations and infor¬ mation purchase, but these variables are not considered in this study.) This does not mean that the public-sector investment does not respond to productivity change. (See Otsuka 1979 for an analysis.) Perhaps more important, there is a substantial time lag between the relevant investments and productivity change. In the case of research investment this may be ten years or longer. Given this time lag, a recursiveness argument can be made. Even if research investment is responding to productivity change, this response is for current invest¬ ment. However, it is past investment that is affecting current productiv¬ ity change. A recursiveness argument is relied on in this chapter as the basis for inferring causality between investment and productivity.

Timing

Most of the variables affecting TFP do so with a time lag that is typically “distributed” over time with different time weights. These time weights will depend on the variable and on the form of the TFP measure. A research project may begin at time t. If it is directed toward the invention of new technology and is successful, new technology will be developed in one or more periods later than t. The technology then requires testing, further modification, and release to farmers. Farmers will then experiment with the new technology and fit it into their production activities. There may then be a further period of learning by farmers before the full impact of the research investment will be real¬ ized. Some research projects are unsuccessful. Some produce new technology, but also produce new intellectual capital that enhances further research projects. Some are not designed to produce technology per se, but have pretechnology science objectives.^ Furthermore, technology once adopted by farmers may experience a real depreciation in value or TFP impact. This stems from two sources. The first, and probably the most important in agriculture, is through real deterioration from exposure to pests and pathogens. This is a common problem with new crop varieties and to some extent with animal improvements as well. The second is through replacements with incomplete additivity. New inventions are continuously replacing older inventions because the new inventions are superior. In some cases they 9. See Huffman and Evenson 1989: chap. 6, for a fuller development.

170

Agricultural Research and Productivity Change

build upon or add to the older invention. In these cases the TFP impact of the older, replaced invention does not deteriorate, but is an actual part of the new invention. However, this additivity may not be com¬ plete. The new invention may have emerged from a different technology core or sequence of inventions. In this case it will not contain the full effect of the replaced invention. From the perspective of specifying a research variable to be associated with TFP change in a given time period one must look backward in time and include the research investment or activities that are effectively contained in the TFP index. If the TFP index is in the form of an annual change this can include negative weights. In this study five alternative time weights were constructed. Mini¬ mum mean square error criteria were used to estimate the optimal weights. Segment a is the years of rising weights, b the years of constant weights, and c the years of declining weights. The mean square error test called for set 5 as the best. Segment Set

a

b

c

13 2 3 3 3 4 6 5 9

3 3 6 6 9

3 6 6 6 9

Extension programs also have a time lag, but it differs considerably from the research lag. Extension programs have direct and relatively quick impacts because of direct contact with farmers. Because of an education and learning process, these impacts will have a rising compo¬ nent over time. They will also have a falling component because there are good substitutes for public extension programs. Markets supply information to farmers; private firms also supply information. Much of the public extension effect is to enable the processing and conversion of technical and price information into managerial decision making to occur earlier and more effectively. Alternative sources of information are of the replacement with incomplete additivity type. Hence, much, perhaps most, of the extension impacts deteriorate within a relatively short period. Given the burden of estimation of other parameters in this study the extension time weights were not estimated. Instead they were imposed to last only three periods with time weights each period of 0.5, 0.25, and 0.25, respectively. Literacy impacts were specified to be permanent.

Research and Productivity Change in Indian Agriculture

171

Spatial-Geoclimate Dimensions: Spill-in of Technology

Since the unit of observation is productivity in a specific time period, for this study, research, extension, and other variables must be matched with the unit of observation. For some variables it may be argued that there is no appreciable spatial issue, because the variable is closely associated with farm producers. This is the case for schooling and also for extension, but not for research. If one could directly measure technology in use by farmers, one could possibly trace it to its origins. For example, technology in use in a given state may have originated (i.e., been invented) in another state or even in another country. If so, it can be said to have spilled in to the state in question and spilled out of the origin state. Using this spill-in and spillout information, one could attribute the value of technology to its originating institution. Some technology spills far and wide. For example, a chemical herbi¬ cide may be more valuable than the next best alternative in every Indian state. In economic terms it is the best technology in a broad range of locations. If all agricultural technology had this characteristic one would specify a single national (or international) research stock using the time shape weights noted above. But most agricultural technology does not spill far and wide. Spilling is inhibited by soil, climate, and even eco¬ nomic factors. The biological performance of a variety of corn, for example, is inhibited by changes in day length and length of growing season. As crop and animal husbandry priorities were developed, husbandry selection modified many crop and animal species through selection for economically valuable characteristics. Considerable improvement in economic species occurred over the centuries prior to the modern agri¬ cultural research period. Some of the natural inhibitors were reduced in scope and importance so that economic species exhibited much less fine tailoring to small niches than noneconomic species. Nonetheless, the basic pattern of tailoring through location-specific husbandry selection was maintained. With the advent of modern plant breeding and research practices, further selection to reduce inhibiting effects has taken place (e.g., mod¬ ern high-yielding rice varieties in Asia have been selected for lower photoperiod sensitivity). At the same time, the existence of inhibitors (sometimes referred to as genotype-environment intervention [see Evenson et al. 1979]) has become a central feature of the organization and design of agricultural research systems (Chapter 12). In the Indian system, this principle, which can be thought of as a factor on the supply

172

Agricultural Research and Productivity Change

side of research, has combined with demand factors to encourage the development of state stations and branch, or sub-, stations. Of course, some technology spills in directly from one state to an¬ other. This is particularly true for agrochemical technology. Were it the case, however, that all technology spilled broadly across soil and climate inhibitors, only a few of the state programs would be productive. The technology tailoring that states do engage in productively attests to limited direct spill-in, but much of this activity can be thought of as indirect spill-in. To deal with this problem we defined research variables for the geoclimate regions in India identified by Evenson and Kislev (1975). This was done by allocating public-sector research on a commodity in a state to the regions in the state based on the proportion of production in the region. Stocks were summed over states to obtain a research stock variable for the geoclimate region. This variable was then assigned to each district in the geoclimate region. These variables were not deflated for the PEP (partial productivity analysis) reported below.

Commodity Spill-in and the Deflator for TFP Analysis

Since the TEP indices to be analyzed are available only for aggregate crop production, the matching of research stock variables must also be aggregated over crop research categories. In addition, since the TEP measures are measured in rate of change or index number form, state research (and extension) stocks should be consistent with this specifica¬ tion. This requires a deflator that effectively deals with the size issue— i.e., that makes a small state comparable to a large state and that also deals with geoclimate and aggregate commodity heterogeneity. Consider the case where a single commodity is being produced in a single homogeneous region with no spill-in. In this case a research stock should not be deflated at all (as in the PEP analysis). The form of the productivity specification would depend on whether the dependent variable was measured as a rate of change or as an absolute quantity (e.g., output-inputs). It would not matter, however, how large the ho¬ mogeneous region in question was. Now suppose that the region is not homogeneous, but that there are subregions in it, and that there are two states each with a different number of subregions. Each state has a station that seeks to tailor technology to each subregion. How can a meaningful research stock variable be defined for the two states? Consider two extremes. One is that the subregion characteristics do not inhibit technology spilling from one region to the other. In this case

Research and Productivity Change in Indian Agriculture

173

the subregions would not matter. At the other extreme, no significant spillover, even indirectly, takes place between subregions. In this case each subregion would require a separate research program and the aggregate program research stock could be defined as

2s,R|

(6.2)

where S, is the share of production in the ith subregion. This deflated aggregate research stock presupposes not only that no spillover between subregions occurs, but that the system is optimally allocating research between subregions in proportion to the size of the subregions. Because the Indian regions are not homogeneous, each commodity stock was deflated by the gross cropped area (GCA) in the state. This commodity variable can then be expressed on a state or geoclimate region basis. Aggregation over crops used district share weights.

Genetic Improvements and Nongenetic Improvements Research programs produce several types of inventions or new technology. These include genetic or varietal improvements, improvements in chemicals, improvements m mechanical processes, and improvements in management. Measures of high-yielding variety adoption available at the district level for the five major foodgrain crops are summarized in Table 6.6. These data can be used to separate the contributions of genetic improvements from the contribution of chemical, mechanical, and management improvements. At the farm level, HYV adoption is determined by individual farmer choice. At the district level, aggregate HYV adop¬ tion is determined largely by the characteristics of the HYV materials and their interactions with soil and climate conditions in the district. Accordingly, HYV adoption can be considered to be exogenously deter¬ mined at the district level. include both imported and domestically produced varieties. The early rice and wheat HYVs summarized m Table 6.6 were im¬ ported: wheat varieties from CIMMYT, rice varieties from IRRI and several other research programs. After the early Green Revolution years (1966-72) almost all strictly imported HYVs in India were replaced by domestically produced HYVs. Most of these HYVs incorporated im¬ ported genetic resources through parent material,

10. Gollin and Evenson (1989) document the genetic resources used in rice varieties in India.

174

Agricultural Research and Productivity Change

Table 6.6. Area (000 hectares) planted to high-yielding varieties

Year 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 Percent of gross cropped area, 1986

Total

Rice

Wheat

Jowar

Bajra

Maize

1,886 6,036 9,297 11,413 15,383 18,173 22,321 26,038 27,337 31,888 33,560 38,930 40,134 38,383 43,039 46,491 47,491 53,739 54,140 55,422 54,038

888 1,785 2,681 4,342 5,588 7,412 8,168 9,981 11,208 12,443 13,337 16,122 16,882 15,991 18,234 19,687 18,842 21,736 22,778 23,374 23,480 60

541 2,942 4,793 4,910 6,480 7,861 10,177 11,027 11,194 13,458 14,522 15,803 15,899 15,027 16,104 16,751 17,837 19,387 19,090 19,175 19,019 78

191 603 690 555 802 688 868 1,155 1,312 1,958 2,370 3,140 3,069 3,052 3,500 3,882 4,373 5,283 5,077 6,082 4,945 30

59 419 745 1,155 2,051 1,775 2,503 3,003 2,529 2,897 2,268 2,631 2,938 2,961 3,600 4,573 4,713 5,422 5,168 4,992 4,675 42

207 287 388 451 462 437 605 872 1,094 1,132 1,063 1,234 1,346 1,352 1,601 1,598 1,726 1,911 2,027 1,799 1,919 23

Source: The Fertilizer Association of India, Fertilizer Statistics, 1986-87 (New Delhi,

1987).

Private research programs are of relatively recent origins in India, but they are quite important. Private-sector research expenditures in the chemical (insecticides, herbicides, and fertilizers) and power (farm ma¬ chinery) sectors of the private economy were estimated, and a stock variable comparable to the public research stock was estimated. The HYV variables are designed to measure genetic improvement contributions to productivity change. The public and private research variables are designed to measure nongenetic contributions.^^

Specifications and Variable Means

Table 6.7 reports a summary of means and a brief description for the variables used in the PFP and TFP analyses.

11. See McKinsey and Evenson 1983 for details.

n 0 4-H 00 00

0 0 ON 0

0 NO 00 00 lo (N (N 0 I/O ^ r4 rsi

u o

bG

(/5

c/5 1> H u >- 4^ u c H > X H X O c/5 3 &. -) kJ

.. "aj w ot)

_u

S

3J t-

3/

i

ON

3

O-^ n■ O O 3 £ -n *(U -E ^ ^.-xc'-^ 04 ^ OJ 3/ •- (/, C/5 C/5 OJ

1/5 J= ON £ ^ rt -C rt C biD ^ ‘7^ 3J 3 . 3

M-l

C/5

rr q

oo

4-1

n

rsi

3

E

3 ^

3 II >4 04 3 V 3 =0 "O os ..

c

•3

^

Q


—'

0/

E O

oc

V

bj

c I 8S tu ON

q

c

4~»

c 03 iC

00

0

35 c 03 p X UJ

03

■4-< bn ~o

NO NO |o to to |o 10 1 1 1 1 op NO NO |o |o |o to ON ON ON ON '—1 ’—1

oo to 1 to to ON

ON to 1 00 |o ON

0 00 ) ON to ON

00 1 0 00 ON

2^ ^ -ii O 2 o UJ

"O0/ Includes sorghum, corn, and spi Anticipated achievement.

1/5 QJ

HYV area

'-

CJ u

c

(/5

rt

^

L.

■u.

Sa. OI- o u Cci

IL>

u

j=

D.

c

CQ

ro ON

Tf rr)

NO O (N ^ to rsl NO

— T-.

d d

O d

(N

NO

rs| [\

|\ fN

NO OC 30 ON lO O ON NO

ON CC

d d

d r-.’ rsj d d

^

c

o

Z

o

s'

(U

/2

c c

qj

t-

C

.

2 r" -= ■" j a ■::

iS"^

O

Cj

I

sC O —

rr _ _, oc

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I

w

'z: r-

— ri = o — ^ ^ :/3 C O'

w

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— 3S o O

3s — CO O sC

3s

oc rs| — Lo O I\ rs| — — lo Lo —

^TTTTT”

I

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rsl sC oc

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T

CO rsl (ss. O sC CO

^T I 7

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00 3^1^0

ly, 3^ hs. O

I

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sC CO — rr

rsl — sC -Tj- — rsl I^s o O — (N

oc O O O ~

sC

—■ O O rs| rf rf.

O oc

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^ ^ ^ rs^ — ~

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^

a

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a

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• —

«J]s2 Es2 — .^

d Ef J:

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^ 2 « 13 C£ « —

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■ -C 5J c :d ^ u


•• 'A'.-riv PM ’ ’, i v \

■

Vf”*

„ ;.

i p?

;i lifti b ■ '

‘

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'”■••; P:^ 14

ttjtji

rymy^^

j

.'

,,., ;:^'n l*i»l :„Wv. . .., *V '■ ■''^•.

h: ..

'

!'Cr ' »*"j - iJw

''''V''''’^fe lA .‘'\ii,’.iiS^ '

•K-

12 International Technology Transfer and Agricultural Productivity A. Steven Englander Federal Reserve Bank of New York

Most analysts believe that technology transfer and development are important and desirable parts of the development process. Indeed, the United Nations (UNCTAD 1978) even placed them at the heart of economic development. ^ Despite this almost universal recognition, rela¬ tively little analysis of the process of technology transfer has emerged. Even the empirical work has for the most part consisted of relatively narrow case studies. The limited scope of these studies is not surprising in light of the great difficulties in measuring technology transfer. Be¬ cause of these limitations, however, few answers have emerged to the questions of whether and how technology transfer can be increased, and, perhaps more important, to the question of why it should be increased.^ This chapter is based on my Ph.E). dissertation at Yale University. I thank my advisors, Robert Evenson, Gustav Ranis, and Brian Wright, for helpful discussions. I am also grateful for the comments of T. W. Schultz, Menaham Prywes, and Richard Nelson. The opinions expressed are mine and do not necessarily reflect those of the Federal Reserve Bank of New York or the Federal Reserve System. 1. To quote UNCTAD (1978:148), A new phase is beginning in the developing coun¬ tries a phase marked by a radical shift of vision and the search for new policies. The peripheral policies of the past, involving minor modification to existing forms of relationships, are being replaced by a search for fresh patterns drawing upon economic, social and cultural resources indigenous to the territories of the Third World. The strengthening of national technological capabilities is assuming a central place in development plans and policies.” 2. Krugman (1979), Rodriquez (1975), Kmenta (1967), and Nelson (1968) provide notable

291

292

International Perspectives on Research and Extension

In this chapter 1(1) provide direct measures of the transferability of agricultural technology and (2) show that agricultural technology can be made more transferable to other countries by the country initially developing the technology. This increased transferability comes at a cost, however. The yield of the newly developed varieties in the home country is lower than it would have been had no effort been made at increasing transferability. Moreover, the more yield is improved in foreign countries, the greater is the sacrifice of yield in the home coun¬ try. I call this trade-off between foreign and home yields the “technology transfer frontier.” National or regional research systems have little incentive to exploit this trade-off by subordinating local goals to those of some distant environment. Partly because of this, a system of international agricul¬ tural research centers has been established. The stated goal of these centers is the production of technologies that are widely transferable to developing countries. The first of these centers was founded by the Rockefeller Foundation in Mexico and later became known familiarly by its Spanish abbreviation, CIMMYT. CIMMYT was responsible for the development of the Green Revolution wheat varieties, as the Inter¬ national Rice Research Institute (IRRI) in the Philippines was responsi¬ ble for the rice varieties. Later we will use data on the wheat varieties developed by CIMMYT to estimate the trade-off between domestic and foreign productivity. The rest of this chapter is divided into five sections. The first section provides a deterministic model of the research problem in a single region and shows how the research priorities of a region will affect the transferability of its research to other regions. The second section links this model to the stochastic model of research developed by Evenson and Kislev (1975). This synthesis provides the technology transfer fron¬ tier that links home-country research productivity and the transferabil¬ ity of technology. The third section estimates the technology transfer frontier between CIMMYT (Mexico) and the rest of the world. A fourth section examines the gains from local research aimed at improving and adapting foreign varietal technology to local conditions relative to di¬ rect adoption of foreign varieties. A final section summarizes the chap¬ ter and discusses some of its policy implications.

exceptions, but even in these works it is assumed that the transfer of technology is a function only of the level of research in the developed country and can be accomplished without cost.

International Technology Transfer and Agricultural Productivity

293

A Simple Model of Induced Innovation Underlying models of induced innovation is the notion that the direc¬ tion of technological progress is both responding to economic incentives and constrained by engineering and technical improvement possibili¬ ties. The names associated with the development of this theory are Ahmad (1966), Fellner (1971), Kennedy (1964), Hayami and Ruttan (1971), and Binswanger (1974). A model following this tradition, but taking as its starting point work on the value of genetic improvements by Melton and Ladd (1979) and Carlson (1979), is developed in this section. To start, assume a general production function of the form

? Qr.; Gj, G 2

Q0 = Qo(Qi,Q2,-

(12.1)

?

where Qq is output of the final product, Qj is the level of the jth variable input, and is the level of the kth genetic characteristic. To the farmer in any period, the set of Gj^s from which he can choose is limited to the available varieties. Once he selects a variety, the genetic composition is fixed and the farmer’s problem is to maximize profits (tt) as measured by

^ = PoQo - 2 P^Q,

(12.2)

i=l

given

Gk = G|,; k = 1, . . . , m

where

Pg = output price Pj = price of the ith input

Introducing a vector of Lagrangian multipliers, (X, X,, . . . , X.„) and forming the Lagrangian n

(Q, G, X) = PgQo -

2

m

PiQi -

i=l

where

Q = (Qi, Q2, • • • , Qn)

G = (Gj, G2, . . ., G^) ^ ~ (^1? ^2? • • • ? ^m)

2 j= l

Xj(Gi - G:) ’

'

(12.3)

294

International Perspectives on Research and Extension

one can obtain the necessary conditions for an optimum by setting all partial derivatives of the Lagrangian equal to zero. The derivative with respect to the kth genetic characteristic at the optimum, aL

aG,^

Pq ^Qo

aG,^

(12.4)

indicates the value of a small improvement in the kth characteristic.^ Research organizations in principle use these X.s in order to maximize the benefits from their research to local farmers. For a given set of research possibilities they select the combination valued most highly by the farmers of their region. While this problem, as presented, is very stylized, the control that a region’s farmers exercise over research con¬ ducted there often implicitly communicates to the researchers the rela¬ tive priorities in the region. (For a much richer discussion of the determi¬ nation of research priorities, see Ruttan 1982.) Given these priorities the researchers must estimate the combinations of improvements that they can expect to accomplish. The problem for the research system can be rewritten as max V = y(AGi, AG2, . . ., AGm)

subject to

(12.5)

r(AGi, AG2, . . . , AG^n) = 0 where AGj^ is the improvement in the kth characteristic, r(-) is the transformation surface of possible genetic improvements for a given level of resources devoted to research, and y(') is the benefit (yield increase, say) that results from any set of genetic improvements.^ For small improvements

V=EXi(AGi)

(12.6)

In general the optimal vector of genetic improvements, G"'', can be written as a function of research priorities and constraints: G'^ = W[\, r(-)]

(12.7)

3. For a more elaborate derivation see Melton and Ladd 1979. 4. For brevity the notation (•) will be used to represent the set of arguments previously specified for a given function.

International Technology Transfer and Agricultural Productivity

295

Thus the direction of genetic improvement can be formulated in terms of research priorities and constraints, \vhich are determined by the availability of resources for research, the initial state of technology, and the environment. If either the priorities of research or the opportunity set of potential improvements changes, then so v^ill the vector of opti¬ mal genetic improvements. Since we assume that other regions can use the varieties produced by the first region, they will be affected by the change in the vector of genetic improvements. Figure 12.1 illustrates some of the possibilities regarding the transfer of technology. Curves r^ and ^2 indicate the possibilities for genetic improvements for region 1 and region 2. Note that each region has a comparative advantage in producing the im-

Figure 12.1. Possibilities for transfer of technology (genetic improvement) be¬ tween two regions, and r2 are the combinations of improvements that can be achieved by researchers in regions 1 and 2, respectively, y j, yf and y] represent three levels of benefit to region 1. Aj and A| represent possibilities of technology transfer from region 2 to region 1; A-^ and B represent possibilities of techology transfer from region 1 to region 2.

Improvement in second genetic characteristic (AG2)

296

International Perspectives on Research and Extension

provement that it values more highly. The lines y{, y|, and y] represent combinations of genetic improvements that provide three levels of bene¬ fits to region 1. Similarly each point on y\ represents a combination of genetic improvements that provides a fixed level of benefits to region 2. Three possibilities suggest themselves: (1) the two regions conduct research independently; (2) one region conducts research for both itself and the other region; and (3) the regions cooperate in research. Al¬ though each of the three arrangements occurs, the first two are most prevalent. Most national systems operate independently, choosing their research goals according to domestic priorities. In Figure 12.1 this is represented by the choice of by region 1 as the optimal set of genetic improvements, while region 2 selects A2. Note, however, that if region 1 did not conduct research, it would still obtain y\ benefits from the genetic improvements selected by region 2. These benefits are direct spillovers of region 2 research between nations. Within nations, however, research systems generally adopt compro¬ mise goals for the areas they serve. The research station located in a particular county must generally also serve other counties. Figure 12.1 illustrates an extreme example. If region 2 attempted to maximize region I’s benefits from region 2’s research it w^ould adopt the region 1 priorities and would select point A| on r2. As compared with the situation in which region 2 maximized its own benefits there would be more improvement in the first genetic characteristic and less in the second, and region 1 would obtain y\ rather than y^ benefits. In terms of the lARCs this example is not unreasonable. Their mis¬ sion is to provide agricultural technology for many countries, and the improvement of local varieties is one priority among many. As in Figure 12.1, however, the local constraints on the type of research that can be conducted and the sets of improvements that may be achieved probably ensure that a large percentage of the benefits will accrue to the region that hosts the research.

A Stochastic Model of Technology Transfer An important simplification in the discussion above is the determinis¬ tic nature of the research process. Research stations could select that exact point on the research possibilities frontier that maximized their benefits. In fact, the research process, in particular that of breeding plant varieties, is subject to chance. This greatly affects the possibilities for research transfer.

International Technology Transfer and Agricultural Productivity i

[ j I

i

! I !

297

In this section the stochastic model of Evenson and Kislev (E-K) (1975) is extended to allow for direct transfer of agricultural technology.^ In that model each research experiment consists of a draw from a probability distribution of outcomes, the highest representing the best technology developed. (In our context, higher ys are identified with higher yields, and each experiment is a new cross of varieties.) The probability that n experiments produce a best variety that yields less than some y is H(y,n) = [E(y)]n

(12.8)

where E(*) is the cumulative distribution of potential outcomes. We assume that each of two locations has a different and independent probability distribution of outcomes. Because we are modeling the direct transfer of technology, the best variety for a region is the highest yielding one, whether produced do¬ mestically or abroad. Independent distributions of the outcomes in the two regions imply that the choices made by each region will not affect the distribution of outcomes to the other region. Just as we estimate expected improvements in technology as a function of the amount of research a region conducts for itself, we can also estimate the improve¬ ments in technology accruing to a second region from the research performed in the first region. The second region evaluates the research of the first region in terms of its own priorities. However, as we saw in Eigure 12.1, the second region can alter the relevance of its research to the first region by selecting A| rather than A2 as its research goal. In the deterministic case, however, a region would not alter its goals unless it was going to provide a better variety than the other region already possesses or produces through its own research, because the contribu¬ tion of the effort producing the second-best variety is both predictable and zero. In the more general stochastic model this is not the case. Two sets of experiments on the same varieties, seeking the same goals and making the same crosses, can produce different results. Because of this randomness, even if region 1 pursues its own ends uniquely and these goals differ markedly from those of region 2, there remains a chance that region 1 will nevertheless produce the best variety for region 2. The expected yield of region 2’s best variety will thus be positively related to region I’s research activity, even if region I’s research seems 5. Gumbel (1958) and Epstein (1960) provide an earlier discussion of the statistics of extreme values, which underlies Evenson and Kislev’s work.

International Perspectives on Research and Extension

scarcely related to the interests of region 2. However, as region I’s goals approach those of region 2 they are more likely to produce the best variety for region 2. In Figure 12.1, for example, assume that region I’s research has produced a variety with the improvements shown as B. In the deterministic case there is no benefit to region 1 from having region 2 incorporate region I’s priorities at that level of research since the best variety that region 2 could produce would be no better than the variety produced at B. Both B and AJ lie on yf, giving region 1 the same benefit. The stochastic case is different, however. Here r^ and ^2 must be regarded as the expected values of the research product of the two regions. This means that generally there will be some probability that efforts directed at either A2 or even A2 will produce varieties that perform better than B in region 1. This possibility may make it worth¬ while for region 2 to aim at A| rather than A2 since, unless the proba¬ bility distribution is much more concentrated around AJ, the A| targets provide a much greater chance of providing better varieties for region 1. This type of targeting can be considered formally in the E-K analysis. We can write the probability that region 2’s experiments produce for itself a variety yielding less than some yield, y, as H22(y,n2) = [F22(y)]"2

(12.9)

Similarly the probability that region 1 produces a best variety after Uj experiments that yields less than y in region 2 can be written as Hi2(y,ni) = [Fi2(y)]"i

(12.10)

Since the two regions conduct their experiments independently, the probability that the best variety for region 2 yields less than y is the product of the two individual probabilities. We can write this proba¬ bility as P2(y, ni, n2) = [F22(y)]"2

[Fi2(y)]".

(12.11)

Note that as either n2 or n, rises, the end product of research is more likely to be a higher yielding variety. The more important point, how¬ ever, is that Fj2 depends on the priorities of region 1. If region 2 moves its target from A2 to A2 in Figure 12.1, and the distribution of outcomes around the targets remains the same, then the probability of producing better yielding varieties for region 2 also increases.

International Technology Transfer and Agricultural Productivity

299

The expected value of the highest yielding variety in region 2, E(y2), can be obtained by differentiating Eq. 12.11 with respect to y and integrating over the range of possible ys. E(y2) = /[(n2

+ (rii

E22"2-1

f22

F12".)

Ei2"i~^ -- fi2 -- F22)]ydy

— v(n|, n2, F22

F22)

(12.12)

where f22 and 1^2 are derivatives of F22 and F^2 with respect to y. The term in brackets on the right-hand side of the first equality is the probability density function of y2, the maximum yield in region 2 of varieties produced by the two research programs. If the probability that an experiment in region 1 produces a best variety for region 2 is zero (i.e., f|2 = 0) or if n^ = 0 then Eq. 12.12 reduces to the standard expression for the expected value of the maximum. This section generalized the E-K model to the case of two countries and related it to the Hayami-Ruttan model of induced innovation. Within the range of relevant research goals (for region 2, between and A| in Figure 12.1) there was a negative trade-oR between expected improvements in the two regions. For the case illustrated, there were diminishing marginal returns in orienting region 2’s research to region 1. In the next section I examine whether these results have some empiri¬ cal relevance.

Measurement of the Technology Transfer Frontier The research conducted at CIMMYT permits empirical investigation of the technology transfer frontier. Before being released or widely disseminated, new varieties are generally tested in yield trials—a com¬ mon set of varieties is planted at a number of locations and observation is made of the yields and the conditions at the location. In CIMMYT’s case the locations are scattered throughout the world. These yield trials enable us to observe whether there is a trade-off between yield in Mexico (where CIMMYT is located) and yield in other countries. If increased yield abroad requires some sacrifice of domestic yield, evi¬ dence is provided for the existence of the technology transfer frontier.^ 6. In associating yield with technological attainment I am following Evenson and Kislev (1975), Binswanger and Barah (1980), Hardwick and Wood (1972), and many other students of genotype x environmental interactions and agricultural development. Implicitly, the same

300

International Perspectives on Research and Extension

There are two steps. The first is to explain the yields of the varieties in the yield trials as a function of fixed location effects, treatment effects, and the technology embodied in the varieties. In particular, for each CIMMYT variety we wish to identify the varietal effect on yield when the variety is planted in Mexico and when it is planted abroad. The second step is to relate the Mexican and foreign yield effects and see whether there are significant trade-offs. Data

The data are provided by CIMMYT (1970, 1972, 1976, 1978a, 1978b), which publishes the results of the annual series of international spring wheat yield trials that it has conducted since 1964. In each trial a common set of forty-nine varieties is planted in forty to eighty experi¬ ment stations. The varieties are contributed by both CIMMYT and national research systems and represent a combination of the most promising experimental varieties and best performing cultivars avail¬ able. The set of varieties changes from trial to trial, as does the set of planting sites, but some varieties and sites are entered in more than one trial. The data published by CIMMYT are uneven in quality and coverage. Almost all sites provide data on their location, altitude, and the yields of the varieties planted at the trial. Most sites provide information on whether or not fertilizer has been applied, but many do not report the quantities applied. Some locations also indicate whether or not their fields were irrigated and the amount of rainfall during the test. association is made by the CIMMYT and IRRI, since they use yield trials to select superior varieties. Several reservations should be noted, however. Farmers may prefer lower yielding varieties whose yields are more stable to higher yielding varieties with less-stable yields. Binswanger and Barah (1980) stress that both characteristics are important to farmers, although they identify yield with expected returns. A second point is that the levels of irrigation, fertilizer application, and other controllable inputs used in yield trials will not be optimal for all varieties. The yield rankings of two varieties may be reversed, for example, in two trials in which different levels of fertilizer are applied. If the relative yields of the set of varieties being tested are not sensitive to variations of input applications around their optimal levels, this will not be a major problem. The use of experiment station data carries advantages and disadvantages. The advantages are that the varieties and location are carefully identified, and inputs can in theory be more accurately measured. The major disadvantage is that neither an experiment station’s location nor its treatment of the varieties is necessarily typical of farmer practices (or what would be optimal). As Barker and Herdt (1979) note, there are still large discrepancies between experiment station’s yields and farmers’ yields. Nevertheless, experiment station trials are used extensively to identify superior varieties, and the prevalence of the practice provides implicit evidence of its usefulness.

International Technology Transfer and Agricultural Productivity

301

Data from five of the first thirteen trials for which data have been published by CIMMYT are used in this analysis. These are the third, sixth, tenth, twelfth, and thirteenth. They provided 17,000 yield obser¬ vations, of which about 15,800 are usable. (A usable observation con¬ sists of a measurement of the yield of a particular variety at a particular site in a trial as well as measurement of the corresponding environmen¬ tal variables.) Each site was classified according to the Papadakis (1966) inter¬ national climate classification (ten geoclimatic zones, which are further subdivided into regions and subregions). Also used by Evenson and Kislev, this classification is useful because it is oriented toward agricul¬ tural production and thus provides additional environmental data. The regions are defined in Table 12.1. Table 12.1 also lists and defines the variables entering the regressions. The dependent variable yield measures the yield in kilograms per hec¬ tare of each variety x location x trial combination. To capture en¬ vironmental effects, sets of dummy variables indicate the Papadakis re¬ gion (pap) and the fifty-six countries of planting (lcntr). The dnitro, DPHOSPH, DPOTAS dummy variables are used rather than levels of fertilizer application to maximize the number of usable observations. (None of the thirteen trial locations had any data on levels of applica¬ tion, and the reporting format used in the early trials was not standard¬ ized. Thus, quantitative measures could not be used.) The three vari¬ ables have a value of one if the particular fertilizer was applied and a value of zero if it was not. The variables elev and alat indicate the elevation and the absolute value of the latitude of each planting site. The other variables relate to the varieties and the technology that they embody, dom has a value of one if the variety was planted in the country in which it was developed and of zero if it was not. intrial is a set of dummy variables that indicates the first trial in which a variety was entered and is a proxy for the vintage of technology embodied in the variety. The set of dummy variables cym indicates the type of breeding program that developed each variety. After the development of the semidwarf wheat varieties, the more active national research organiza¬ tions rapidly integrated these varieties into breeding programs. These dummy variables help correct for the differing composition of each trial’s set of varieties, mexvar is a set of dummies that identifies each Mexican variety, and vcntr is a set of dummies identifying which of twenty-two countries developed each variety.

302

International Perspectives on Research and Extension

Table 12.1. Variables used in regression and their definitions Variable

Definition

Dependent variable YIELD

Yield in kilograms per hectare of each variety entered into yield trial

Independent variable Climate LCNTR PAP ALAT ELEV

A set of dummy variables indicating the country in which the varieties are planted A set of dummy variables indicating the one-digit Papadakis climatic classification of location The absolute value of the latitude at which the variety is planted Elevation in meters above sea level for each location

Treatment DNITRO DPHOSPH DPOTAS

A dummy variable with a value of one if nitrogen fertilizer was applied at a site A dummy variable with a value of one if phosphorus fertilizer was applied at a site A dummy variable with a value of one if potassium fertilizer was applied at a site

Varietal-technological DOM INTRIAL CYM

VCNTR MEX MEXVAR

A dummy variable with a value of one if the variety is planted in the country in which it was developed A set of dummy variables indicating the initial trial in which a variety was entered A set of dummy variables indicating whether a variety was traditional, a local cross using a CIMMYT variety, a local reselection of a CIMMYT variety, or a CIMMYT variety A set of dummy variables indicating the country in which a variety was developed A dummy variable with a value of one if the variety was bred by CIMMYT in Mexico A set of dummy variables identifying each of the Mexican-bred CIMMYT varieties entering the trials

Note: The countries comprising the planting and origin countries in lcntr and vcntr are identified by their common abbreviations. The cym dummies are identified as follows: cym-i, a traditional variety; cym-2, local cross using a CIMMYT variety; CYM-3, local reselection of a CIMMYT variety; CYM-4, a CIMMYT variety. The Papadakis (1966) classification comprises ten geoclimate zones: pap-2, tropical; pap-i, tropical highland; PAP-3, desert; PAP-4, subtropical, PAP-5, pampean; pap-6, mediterranean; PAP-7, marine; pap-8, humid continental; PAP-9, steppe; pap-io, arctic.

The Measurement of the Technology Transfer Frontier

I use a statistical model based on Hardwick and Wood 1972 that corrects as much as possible for environmental effects on yields and for the changing composition of the set of varieties planted in each trial. My focus in on yield variation that can be explained by factors associated with the origins of the varieties. The model can be written in its most simple form as

International Technology Transfer and Agricultural Productivity Y,j = f(Ej, Tj, Gjj) + error term

where

303 (12.13)

Y,j = yield of wheat variety i in location j Ej = a vector (Ej^, Ej2, • • • , Ej^) characterizing the environ¬ ment at location j Tj = a vector (Tj^, T,2,. . ., Tjn) characterizing the technology embodied in variety i Gij = a vector (Gjji, Gij2, • • . , G,jq) characterizing the inter¬ actions between the technology and the environment

A linear version of this model is estimated: YIELD = b® + b^ +

ALAT + b^ 'i- ELEV + b^ALAT

ELEV

ALAT + b^

+ 2bf

LCNTR, + Sb^

+ 2b^

PAPi

-h Sb^O

PAPj

+ error

ELEV

PAPj

DNITRO + Sbf

PAP,

DPHOSPH + Eb,l^

+ 2b,CYM + 2b,l^ + 2b,l^

ELEV

ALAT

INTRIALj

VCNTR, + b^"^

MEXVARj + 2b,l^

DPOTAS

MEXVAR^

DOM DOM

(12.14)

The entire set of coefficient estimates and their t statistics are reported in Englander 1981. The size of the data set and the number of variables, more than 200, put limitations on the sophistication of the specification of the model. However, experiments with different specifications of the environmen¬ tal variables did not substantially alter the results on the coefficients of primary economic interest—those relating to the yield effects of dif¬ ferent technologies in different environments. The question we wish to examine is whether an increase in the transferability of Mexican (i.e., CIMMYT) varieties is accomplished at the cost of a decline in their yields in Mexico, where they were bred. One piece of evidence comes from a comparison of domestic and foreign productivities of Mexican and unimproved non-Mexican varieties. Un¬ less otherwise indicated, the relative yields of Zimbabwe’s varieties outside of Zimbabwe is used as the benchmark and is arbitrarily set to zero. An unimproved non-Mexican variety is a variety developed out¬ side of Mexico that contains no CIMMYT genetic matrix. For non-Mexican varieties the estimated domestic yield bonus is 373 kg/ha, more than 10 percent of average yield. Relative to Zim-

International Perspectives on Research and Extension

babwe’s varieties, which are used as a standard, the estimated produc¬ tivities of the other countries’ varieties average 190 kg/ha. Thus, the non-Mexican varieties average 563 kg/ha more when they are planted domestically and 190 kg/ha more when they are planted abroad than the Zimbabwean benchmark. In contrast, for the fifty-six Mexican varieties, the average domestic yield bonus is 279 kg/ha, almost 100 kg/ha less than for unimproved non-Mexican varieties.^ The productivity abroad of the Mexican va¬ rieties averages 250 kg/ha, relative to the Zimbabwean benchmark. Hence, as illustrated in Figure 12.2, Mexican varieties grown in Mex¬ ico yield less than unimproved non-Mexican varieties grown in their countries of origin. However, Mexican varieties yield more when they are planted outside Mexico than do non-Mexican varieties planted in foreign soil. These data are consistent with the hypothesis that, relative to other nations, CIMMYT has raised the foreign productivity of its varieties at a cost of foregoing some domestic productivity. A more direct way of investigating this possibility is by relating the domestic yield bonus (DYB) of each Mexican variety to its productivity abroad (FYM). To measure the productivity of each Mexican variety abroad we sum the general yield effect of a Mexican variety and the spe¬ cific varietal effect. The domestic yield bonus of the Mexican varieties is the sum of the general yield effect for domestically produced varieties and the additional yield of each variety when it is planted in Mexico. This latter effect indicates how much more each Mexican-bred variety yields in Mexico than it does abroad under similar conditions. More¬ over, it reflects directly the yield trade-off between focusing uniquely on local goals and incorporating foreign goals into research priorities. Table 12.2 provides summary statistics on the mean and range of DYB and FYM. Note that both variables can have negative values. Negative values of DYB imply that the variety yields less in Mexico than abroad, while negative values of FYM imply that the variety performs very poorly abroad. While all linear and quadratic regressions produced a significantly negative relationship, the best-fitting in terms of adjusted was DYB = 625 - 2.77

10“^

FYM^

(3.61) adjusted

= .18; F(l,54) = 9.69

7. Of the fifty-six Mexican varieties, two are traditional varieties that do not incorporate the high-yielding genetic material that characteri/.es CIMMYT varieties. Their removal does not alter the results to any significant degree.

International Technology Transfer and Agricultural Productivity

JOS

Figure 12.2. Relative yields of fifty-six improved Mexican varieties of wheat versus unimproved non-Mexican varieties planted at home and abroad (Zimbabwe yield in foreign environment = 0)

Table 12.2. Summary statistics on domestic yield bonus (DYB) and Mexican varietal effects (FYM) for fifty-six Mexican varieties

Mean Standard deviation Minimum value Maximum value

DYB (kg/ha)

FYM (kg/ha)

279.3 829.8 -2,336.7 1,407.4

249.7 252.4 -247.9 793.2

Source: Englander 1981, chapter 5.

As the theoretical model suggested, there would be a range over which domestic and foreign yields are positively correlated. (For exam¬ ple, in Figure 12.1 if region 1 initially chose goals to the right of A| or the left of A2 and subsequently moved toward A|' or A2, then both domestic and foreign yields would increase.) Since DYB is the yield bonus, the value of FYM at which overall domestic yields falls is the point at which

306

International Perspectives on Research and Extension

dDYB dFYM “ This occurs when FYM =181 kg/ha. We saw above, however, that the mean foreign productivity of non-Mexican varieties (as always mea¬ sured relative to Zimbabwe’s) was 190 kg/ha. If Mexico is similar to the other countries in the sample in terms of the inherent transferability of its varieties, this result indicates that overall domestic yields begin to fall immediately when foreign yields are made to exceed those that occur by chance. At the mean level of FYM, overall domestic yield is reduced by 0.37 kg/ha for each kilogram per hectare increase abroad. As foreign productivity increases, the trade-off becomes increasingly steep. At for¬ eign productivity levels of 450 kg/ha, domestic productivity falls by 1.5 kg/ha for every kilogram per hectare increase abroad. It is clear that CIMMYT’s program of increasing yields abroad has been very successful. Of the fifty-six Mexican varieties in our sample, al¬ most a quarter have foreign productivity effects greater than 450 kg/ha, and two-thirds exceed the 190 kg/ha that the other countries averaged. However, the trade-offs are quite steep and grow much steeper as the level of foreign productivity increases. It is easy to see why countries may not make much effort to increase the transferability of their own vari¬ eties to other nations.

Is Increasing Technology Transfer the Best Policy? The previous section provided evidence that indicated that wheat varieties could be made much more adaptable to regions other than the one in which they are bred. This would seem to provide a cheap alterna¬ tive to a policy of strengthening national research systems: one of supporting research institutes dedicated to providing agricultural tech¬ nology to poor nations. Obviously, such a policy is optimal for nations utterly lacking in the resources or stability with which to create and maintain a research system. There are other countries that are not in such desperate straits to whom such a research policy may seem attrac¬ tive. In these cases the question is simply whether what they get out of a national research system is worth the cost. A preliminary answer can be obtained by summing the country effects (vcntr) and the domestic varietal effect (dom) and comparing the total with the yields of the Mexican varieties abroad. This measure is not very

International Technology Transfer and Agricultural Productivity

307

compelling, though, as neither the Mexican varietal effects nor the domestic yield effects are allowed to differ across countries. (The limita¬ tions of the data made it impossible to estimate specific variety x country effects.) However, it is possible to estimate yield effects for Mexican varieties planted in each country and specific domestic varietal effects for each country.^ These are presented in columns 2a and 2b of Table 12.3. For example, the regressions indicate that the yield of a Mexican variety planted in Argentina exceeds that of a typical nonMexican, non-Argentinian variety by 573 kg/ha. A domestic Argentin¬ ian variety is estimated to yield 371 kg/ha more in Argentina than abroad. One striking result is that all countries but one showed positive domestic yield bonuses—that is, varieties virtually always yielded more at home than abroad. While this result is expected on average, its pervasiveness is remarkable, given the crudeness of the available data. This suggests that varieties tend to be highly specialized to local condi¬ tions. It is also possible to determine from these data the benefits to a country of having a vigorous agricultural research system. When the CIMMYT varieties became available and proved to be highly success¬ ful, many countries cross-bred CIMMYT varieties with their local vari¬ eties in the hope of obtaining varieties that performed even better locally. Other countries made selections locally among crosses that had originally been bred in Mexico by CIMMYT. The varieties entered in the trials were put into four classes depending on whether they were CIMMYT varieties, non-Mexican crosses using a CIMMYT parent variety, reselections of CIMMYT crosses, or varieties that do not use CIMMYT material. Varieties that do not incorporate a CIMMYT par¬ ent were estimated to yield 479 kg/ha less than varieties reselected from crosses originally made in Mexico and 430 kg/ha less than crosses with a CIMMYT parent bred to a domestic parent. The 479 kg/ha differential was used to construct two measures of the productivity of domestic varieties. The first measure sums the individual

8. The model is the same as that of Eq. 12.14 except that the variables on the right-hand side that relate to the yields of specific Mexican varieties are dropped and are replaced by the variables mex lcntr, and vcntr -- dom where, as before, lcntr is a dummy indicating the country of planting, mex is a dummy variable that indicates whether the variety is Mexican, VCNTR is a set of dummies indicating the country in which each variety was developed, and DOM is a dummy indicating whether or not the variety is planted in the country in which it was developed. The coefficients of mex lcntr give the average additional yield enjoyed by Mexican varieties m each country, while the coefficients of dom =!■ vcntr reveal the average yield bonus enjoyed by domestically produced varieties in each country.

308

International Perspectives on Research and Extension

'Table 12,3. Transferability of Mexican wheat technology and performance relative to domestic technology (2)

Yield bonus (kg/ha)

Planting country South America Argentina Bolivia Brazil Chile Colombia Ecuador Paraguay Peru Uruguay Asian subcontinent Bangladesh India Nepal Pakistan North Africa, Iberia Algeria Egypt Libya Morocco Portugal Senegal Spain Tunisia Middle East (high) Israel Lebanon Turkey Middle East (low) Afghanistan Iran Iraq Jordan Saudia Arabia Syria Yemen Central Europe, Balkans Austria Bulgaria Hungary Romania Yugoslavia

(a) Mexican varieties

573 388 466 208 1,261 -492 200 356 -24 282 623 295 772 1,331 665 -238 415 747 878 1,291 1,185 1,338 669 680 304 431 430 236 -482 -183 453 547 330 376 1,209 924

(b) Domestic varieties

(3)Yield bonus of domestic varieties relative to yield bonus of Mexican varieties in planting country (kg/ha) (a) Traditional

(b) Modern

Average yield of all varieties

1.042

2,074 1,108 1,709 3,398 2,583 1,876 869 3,333 1,665

371

.58

839 727 1,263

1.29 3.99 1.21

2.31 6.35 1.59

16

-.14

1.21

273

.50

1.27

520

.72

1.34

328

.93

1.65

290

.24

.65

1,262 1,164

.99 1.73

1.33 2.45

2,515

1,092

.13

1.16

(4)

1.25

1.68

2,911 3,125 4,110 3,038 4,600 4,186 3,610 2,196 3,562 5,467 3,101 3,483 5,543 3,053 3,221 3,933 3,655 3,181 2,969 1,459 3,267 4,219 4,842 3,341 2,794 4,042 2,705

International Technology Transfer and Agricultural Productivity

309

Table 12.3. Continued (2)

(1)

(3)-

Yield bonus (kg/ha)

Planting country Eastern Europe Einland Poland Russia Western Europe France Germany Norway United Kingdom Mediterranean Europe Cyprus Greece Italy Horn of Africa Ethiopia Kenya Somalia Sudan Southern Africa Angola Malawi South Africa Zimbabwe North and Central America Canada Guatemala United States Mexico Oceania Australia New Zealand Far East Japan Korea Philippines

(a)

(b)

Mexican varieties

Domestic varieties

(4)

Yield bonus of domestic varieties relative to yield bonus of Mexican varieties in planting country _(kg/ha)_ (a) Traditional

(b) Modern

Average yield of all varieties

311 235 251

3,327 3,774 694

324 -424 243 -67

3,647 10,649 3,526 2.954

518 498 472

2,074 4.955 6,406

431 930 -55 378

842

.33

.91

501

2.54

3.81

2,339 2,494 5,017 1,931 2,354 557 2,480 6,275

261 260 643 1,363

652 1,092

.39 .60

1.14 .96

433 661 540 899

452 228 269 899

-.88 .22 .05

.22 .95 .94

2,953 2,743 3,806 4,329

455 339

204

.66

1.72

1,765 3,932

271 206 194

2,824 4,160 941

Source: Englander 1981, chapter 5. ^Modern varieties are varieties that incorporate CIMMYT genetic material, but that have been selected domestically. Traditional varieties are varieties that do not incorporate CIMMYT genetic material. The relative yields in column 3 can be calculated only for countries for which there is an observation of a local variety planted domestically.

310

International Perspectives on Research and Extension

country effect and its domestic yield bonus but subtracts the yield penalty for planting traditional varieties. This number is then divided by the yield effect of Mexican varieties in that country. For example, for Argentina the estimated overall effect for an Argentinian variety (186 kg/ha) is added to the estimated yield bonus of Argentinian vari¬ eties planted in Argentina (371 kg/ha). From this is subtracted the estimated yield differential for traditional varieties (-224 kg/ha). This total effect for a traditional Argentinian variety planted in Argentina (333 kg/ha) is divided by the estimated yield of CIMMYT varieties in Argentina (573 kg/ha). If this ratio exceeds one then a country’s tradi¬ tional varieties outperform the Mexican varieties. If the ratio is less than one, then Mexican varieties seem to yield more. The question here is whether the superior technology embodied in the Mexican varieties outweighs the advantages of local adaptation that the domestic varieties possess. The second measure again sums the individual country effects and the domestic yield bonus but this time adds the yield bonus to reselecting Mexican varieties domestically. Again this number is divided by the Mexican varietal yield effect in the country. If the ratio exceeds one, then adapting the CIMMYT technology to local conditions produces higher yields than simply planting the CIMMYT varieties. The results are presented in columns 3a and 3b of Table 12.3. In the sample, for fifteen of the twenty-one non-Mexican countries that contributed both traditional and modern varieties, the CIMMYT varieties are estimated to yield more than traditional, domestically bred varieties. In contrast, the modern varieties of fifteen countries are esti¬ mated to yield more than the CIMMYT varieties. Nine countries switch over—CIMMYT varieties are estimated to yield more than traditional varieties but less than modern varieties in these countries. On average for these nine countries the estimated excess of domestic modern vari¬ eties is 238 kg/ha, about 7.5 percent of the average yield in these countries. As all the varieties planted at any given location in a trial are treated the same, the 238 kg/ha is pure bonus. (Among the countries in which Mexican varieties seem to outperform the local modern varieties, several seem to represent exceptional cases. For example, the locations in Canada and the United States were in many cases atypical of the main wheat-growing regions, the eastern part of Canada and the south¬ western portion of the United States being overrepresented.) The results suggest that varieties that incorporate CIMMYT technol¬ ogy and are bred locally outperform both traditional varieties and CIMMYT varieties. However, the cost of this local research must be

International Technology Transfer and Agricultural Productivity

311

considered as well. Unfortunately few estimates apart from those of Evenson and Kislev (1975) and Boyce and Evenson (1975) have been made of research expenditures by national research systems, and even fewer have been made specifically on wheat-related research in develop¬ ing countries. However, the magnitude of the benefits seems to far outweigh the cost. Eor example, Dalrymple (1978) estimated that in 1972—73 India planted about 10 million hectares of modern wheat varieties (the vast majority of which had been bred or selected locally). The estimates of Table 12.3 imply that domestic Indian modern varieties yield 161 kg/ha more than CIMMYT wheats. Cutting this bonus by 40 percent to 100 kg/ha and applying the bonus to only half the planted area at the 1972—73 U.S. E.O.B. wheat price of about $90/metric ton yields benefits that exceed the cost of India’s entire agricultural research program. While this type of analysis is very crude, it is consistent with the results of more sophisticated analyses cited in Evenson and Jha 1973.

Conclusions and Policy Considerations Two major conclusions emerge from this analysis: (1) it is possible to increase the transferability of technology emerging from centers of agricultural research and (2) in order to maximize their benefits from new technology, countries must perform local research to adapt the technology to their local needs. The preferred role of international centers should be the development of new technologies rather than new varieties for direct use abroad. Countries that are too small or too poor to construct national re¬ search systems should be encouraged to engage in research cooper¬ atively. Nevertheless, it is not likely that this optimal role for inter¬ national centers will emerge soon. Eor the foreseeable future they are likely to be pursuing two goals, the development of innovative tech¬ nologies for countries that can adapt them and the development of new varieties for countries that are dependent on imported technologies.

References Ahmad, S. 1966. “On the Theory of Induced Innovation.” Economic Journal 76:344-57. Barker, R., and R. W. Herdt. 1979. “Establishing Priorities for Allocating Funds for

International Perspectives on Research and Extension Rice Research. Unpublished manuscript. Los Banos, Philippines: International Rice Research Institute. Binswanger, H. P. 1974. “A Microeconomic Approach to Innovation.” Economic Journal 84:940-58. Binswanger, H. P., and B. C. Barah. 1980. “Yield Risk, Risk Aversion and Genotype Selection: Conceptual Issues and Approaches.” Unpublished manuscript. Hyder¬ abad, India: International Center for Research in the Semi-Arid Tropics. Boyce, J. K., and R. E. Evenson. 1975. Agricultural Research and Extension Pro¬ grams. New York: Agricultural Development Council. Carlson, G. A. 1979. “Variability and Market Indicators of Breeding Values.” In Applications of Economics in Plant and Animal Breeding. Discussion Paper No. 98. Ames: Department of Economics, Iowa State University. CIMMYT (Centro Internacional de Mejoramento de Maize y Trigo. 1970. Results of the Third International Spring Wheat Yield Nursery, 1966-67. Research Bulletin No. 15. Mexico, D.E.: CIMMYT. -. 1972. Results of the Sixth International Spring Wheat Yield Nursery, 196970. Research Bulletin No. 23. Mexico, D.E.: CIMMYT. -. 1976. Results of the Tenth International Spring Wheat Yield Nursery, 197374. Research Bulletin No. 32. Mexico, D.E.: CIMMYT. -. 1978a. Results of the Twelfth International Spring Wheat Yield Nursery, 1975- 76. Research Bulletin No. 39. Mexico, D.E.: CIMMYT. -. 1978b. Results of the Thirteenth International Spring Wheat Yield Nursery, 1976- 77. Research Bulletin No. 40. Mexico, D.E.: CIMMYT. Dalrymple, D. D. 1978. Development and Spread of High Yielding Varieties of Wheat and Rice in Developing Nations. Washington, D.C.: USDA in cooperation with AID. Englander, A. S. 1981. “Technology Transfer and Development in Agricultural Research Programs.” Ph.D. diss., Yale University. Epstein, B. 1960. “Elements of the Theory of Extreme Values.” Technometrics 2:27-41. Evenson, R. E., and D. Jha. 1973. “The Contribution of the Agricultural Research System to Agricultural Production in India.” Indian Journal of Agricultural Eco¬ nomics 28(4). Evenson, R. E., and Y. Kislev. 1975. Agricultural Research and Productivity. New Haven: Yale Universitv Press. Eellner, W. 1971. “Empirical Support for the Theory of Induced Innovation.” Quarterly Journal of Economics 85:580—604. Gumbel, E. J. 1958. Statistics of Extremes. New York: Columbia University Press. Hardwick, R. C., and J. T. Wood. 1972. “Regression Methods for Studying Geno¬ type-Environment Interaction.” Heredity 28(2):209-22. Hayami, Y., and V. W. Ruttan. 1971. Agricultural Development: An International Perspective. Baltimore: Johns Hopkins University Press. Kennedy, C. 1964. “Induced Bias in Innovation and the Theory of Distribution.” Economic Journal 74:541—47. ¥

Kmenta, T. 1967. “Economic Theory and the Transfer of Technology.” In D. L. Spencer and A. Woroniak, eds.. The Transfer of Technology to Developing Countries. New York: Praeger.

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Krugman, P. 1979. “A Model of Innovation, Technology Transfer and the World Distribution of Income.” Journal of Political Economy 87:253-66. Melton, B., and G. W. Ladd. 1979. “Economic Value of Genetic Shifts in the Production Function.” In Applications of Economics in Plant and Animal Breed¬ ing. Discussion Paper No. 98. Ames: Department of Economics, Iowa State University. Nelson, R. R. 1968. “A Diffusion Model of International Productivity Differences in Manufacturing Industry.” American Economic Review 58:1219-48. Papadakis, J. 1966. Agricultural Climates of the World. Buenos Aires: published by the author. Rodriquez, C. 1975. “Trade in Technological Knowledge and the National Advan¬ tage.”/owrW of Political Economy 83:121-36. Ruttan, V. W. 1982. Agricultural Research Policy. Minneapolis: University of Min¬ nesota Press. UNCTAD (United Nations Conference on Trade and Development). 1978. Provi¬ sional Agenda for the 4th Session of UNCTAD. Doc TD/190. New York: United Nations.

13 lARC, NARC and Extension Investment, and Field Crop Productivity: An International Assessment Robert E. Evenson Yale University

A large number of studies showing relationships between agricultural productivity changes and investment in agricultural research programs in specific countries have been undertaken. (Norton and Davis 1981 and Ruttan 1982 provide reviews. Also, see Chapter 15 for a summary.) However, in spite of the voluminous literature on the Green Revolution, part of which was associated with International Agricultural Research Center (lARC) investments, little systematic study of lARC impact on productivity has been made, in part because it is international in charac¬ ter. Some particular country studies (e.g.. Chapters 3—6) have inferred lARC impact based on lARC high-yielding variety (HYV) data. These data, however, do not capture the full lARC impact because much of it is channeled through avenues other than HYVs and because it occurs in a number of countries. This chapter reports econometric estimates of impacts on crop productivity of national investment in crop-specific research, international investment in lARC research on the same com¬ modities, and national investment in extension. The estimates are based on international data. The crop productivity data are for five cereal grains (maize, millets, sorghum, rice, and wheat) and five staple crops (beans, cassava, groundnuts, potatoes, and sweet potatoes) for twenty314

I ARC, NARC and Extension Investment, and Crop Productivity

315

four countries for the period 1962—82. I ARC programs were initiated for each of these ten field crops at varying times over this period. ^ The first section of this chapter discusses the econometric specifica¬ tions used in the study. The second section reports the estimates of the key parameters. The last section discusses the economic and policy implications of the estimates.

Econometric Specification Since the focus of this study is on lARC effects, there are certain data limitations. It was necessary to pool data from several countries and to deal with commodity-specific data since the interest is in particular lARC programs rather than in their general or average impact. This means that the only real crop-specific productivity variables that can be observed are measures of production and area harvested.^ In addition, it is possible to measure irrigated area of all crops relative to all harvested area and fertilizer used on all crops. It is not really possible then to estimate a full production function or to compute a total factor productivity index by crop for each country. The practical alternative options are to estimate one of the following specifications:

PROD = a + bHA + cP=' + dF==- + eR

(13.1)

LN(PROD) = a' + b'LN(HA) + c'LN(r') + d'LN(F==-)

+ e'R

(13.2)

where prod is production in metric tons, ha is hectares harvested, T'* is the ratio of irrigated area to planted area for crops that are normally irrigated, F is the ratio of fertilizer used (valued at constant world prices) to acreage of crops normally fertilized, and R is a vector of research-extension variables.

1. Actually, I ARC programs for rice began in 1959 but were really only operational by 1962 or so. Also, the CIMMYT program on wheat and maize actually began earlier than 1959 with the predecessor Rockefeller Foundation program. 2. These data are from the FAO production yearbook, annual issues.

316

International Perspectives on Research and Extension

These specifications are production function “proxies.” The variable HA actually has three roles in the specifications: 1. It measures productive services from land. 2. It measures land expansion-contraction effects (i.e., where land quality for new plantings may differ from the average land quality for the commodity). 3. It is correlated with other “left out” inputs such as labor and machine services, and it may thus “pick up” their effects. This study is not interested in the estimates of a', b', c', or d' per se. Nor is the exact functional form of the production function an impor¬ tant issue since no attempt is made to interpret coefficients as technical substitution parameters. The data available are not suited to addressing these relatively fine questions.^ The primary concern is with estimates of the e' vector of coefficients on the research-extension variables. Option 13.2 above was chosen as the more reasonable specification because left-out, unmeasured inputs are likely to be proportional to cropped area (ha). The coefficient b' will, of course, not be an estimate of the marginal product of land in that case, but, as noted, that is not of direct concern. The log-linear relationship between the researchextension variables and production is also consistent with some evi¬ dence on research productivity. Griliches (1958) found that hybrid corn varieties tended to improve yields proportionately rather than additively. The P*' and F"' variables are included only for those crops that are either irrigated or fertilized. These variables are not measured on a cropspecific basis, but they are likely to be proportional to actual cropspecific variables, and hence their inclusion can reduce bias. All specifications include country dummy variables. Thus “country effects” such as soil and climate factors, measurement errors, infrastruc¬ ture, and so on, that affect production or yield levels but not their change over time are picked up by these dummy variables. Specifica¬ tions that pool commodities also include commodity dummy variables. The most important variables in this analysis are the research and extension variables. The following factors require attention in develop¬ ing them: 1. A “stock” form is most appropriate since current production is affected not simply by current research and extension activities but by investments in the past. 2. Research and extension interact in that the effectiveness of one is affected by the other. There is also a kind of “hierarchy,” with lARCs 3. These questions require farm-level data from a reasonably homogeneous region.

lARC, NARC and Extension Investment, and Crop Productivity

317

being furthest from the farmer, national research next furthest, and extension closest. The lARCs produce new technology that can be either a substitute for or a complement to the technology produced by na¬ tional research programs. If lARC technology (such as a new crop variety) is well suited or “matched” to a nation’s producing environ¬ ment, it will be a substitute for technology produced by national re¬ search. lARC investment in this case will lower the marginal product of national research. It could be a complement to national extension by providing more technology to extend. On the other hand, easily identi¬ fied technology may enable farmers to bypass traditional extension services. If lARC technology is mismatched to national production conditions, it may complement national research because it provides national systems with scope for adaptive research, thus raising the marginal product of national research. 3. Appropriate deflators are required. In the case of extension, which is not measured on a commodity basis, a deflator measuring the general size and commodity mix is required. The national research stocks are to some extent deflated by their commodity-specific nature. 4. Simultaneity problems may exist if national research and exten¬ sion program investment responds to production and area (i.e., to yield). A number of studies have dealt with this by simply arguing that the relationship is “recursive.” That is, current research investment may respond to current yield performance, but current yields are responding to past research investments. In this study, the problem is dealt with formally by using a two-stage least squares procedure. The actual variables specified for this study are defined as follows:

pREsi, = .2r;Li + .4r;l2 + .6R;L3 1959

+ .8Rf_4 +

2 R;-i i = t —5

(13.3)

where R;' is predicted research spending in time t. The prediction is based on an investment analysis (see below). The weights used were indirectly estimated by constructing an alternative stock using weights rising to one by year t - 9. This stock was slightly inferior to the specified stock.^ EXTDIV = (.5Ext^ + .25Extf_j -I- .25Extf_2)DIVER

4. Inferior in that the regression had higher residual squared errors.

International Perspectives on Research and Extension

where Ext^ is actual spending in 1980 U.S. dollars on all agricultural ex¬ tension. DIVER = S Sf where S, is the share of total production of a speci¬ fic commodity in a specific geoclimate region. Livestock commodities are included in the construction of diver. Note that the weights for EXTDiv sum to one, implying that no long-term impact from extension is realized. The full impact is realized by the end of year t + 2. iNTRj = .2iarc,_i + .4iarc,_2 + .6iarc,_3 1959

+ .8iarc,_4 +

2

iarc,^5

(13.4)

i = t —5

where iarc, is spending by the lARC in 1980 U.S. dollars in time t. The following “interaction” variables are defined: EXTDPRES = EXTDIV INTRPRES = INTR INTREXT = INTR

PRESl PRESl EXTDIV

The use of predicted national research data to construct the research stock variable, presI, used in this analysis constitutes a form of twostage least squares designed to correct for simultaneity bias caused by the response of national governments in their investment decisions to productivity gains in the commodities. The predicting equation for national research was: RES = bo + bjPROD + b2AREA + b3CINTSP + b4UREARICE + b5RESNSR + b^ECONAG + byURBANPOP + bgEXPRAT + b9ARABLE + bjoDIVER + biiPRODDIVER + bi2VIOLD

(13.5)

where res is expenditures on research on the commodity, constructed by first estimating the share of total research spent on the commodity and multiplying the share by total spending. The share was estimated as the share of total standardized” publications devoted to research on the commodity. Publication data were from the Commonwealth Agricul¬ tural Bureau data. Publication data were standardized using data from Brazil to convert publications into constant spending units.** The inde.5. The Commonwealth Agricultural Bureau maintains an extensive series of abstracting formulas, abstracting several thousand agricultural )ournals from virtually every country m

lARC, NARC and Extension Investment, and Crop Productivity

319

pendent variables are: prod, production of the commodity; area, area harvested; cintsp, cumulated total lARC spending on the commodity; UREARiCE, the ratio of urea prices to rice prices (a measure of price intervention in markets); resnsr, scientist man-years devoted to the commodity in national research programs in similar geoclimate regions; ECONAG, the proportion of the labor force in agriculture; urbanpop, the proportion of the population in urban areas larger than 100,000 people; EXPRAT, the ratio of expenditures per research scientist to expenditures per extension w^orker (a proxy for the real costs of research); arable, the ratio of arable land currently to arable land six years previously (a measure of land exhaustion); and diver, a measure of geoclimate and commodity diversity. It is defined as 2 where Sj is the share of the ith commodity in the jth climate zo'fle in total production. Finally, PRODDiVER is PROD DIVER and viOLD is the proportion of the popula¬ tion killed by political violence in the past ten years. One further modification was made to take into account the differ¬ ence in lARC impacts in all countries in the data set. As a practical matter, it would be nearly impossible for lARC programs to have the same production impact in each of the twenty-four countries. The lARCs, in most cases, produce technology that is more closely matched to producing environments similar to their host country than to en¬ vironments that are dissimilar. This should not only affect the produc¬ tivity impact of the lARC program but its interaction with national research and extension programs as well. To take this into account, a variable, SR, is defined. This variable is equal to the proportion of the area planted to the commodity in the country of observation that is located in the same geochmate region as the lARC s central location. The geoclimate regions are defined by Papadakis (1966) and have been used in other studies of international productivity impact (Evenson and Kislev 1975 and Evenson 1983). Three interaction variables are then defined: INTRSR = INTR

SR

INTRESSR = INTRPRES INTREXSR = INTREXT

SR SR

the world. It is possible to use “keyword” sorting to obtain counts of research papers oriented to a particular commodity for years since 1972. For each of the countries in our data set, counts of publications oriented to some twenty-one commodities were obtained. The publica¬ tions were then standardized using Brazilian data (see Evenson 1982).

320

International Perspectives on Research and Extension

The coefficients on these variables measure added impacts in similar geoclimate regions. The reasoning offered above would lead to the expectation that direct lARC impact via the provision of matched technology will be higher in similar regions, while the indirect impact via the provision of mismatched technology will be larger outside the similar region. It is possible, of course, that both effects will be larger in similar regions.

Crop Productivity Impact Estimates The econometric analysis proceeded in three stages. In the first stage the predicting equations required for building the research stock vari¬ ables were estimated. In the second, crop productivity specifications were estimated for each of the ten commodities in the study using data for all twenty-four countries. In the third stage, regional estimates were obtained for maize, millets, and sorghum pooled, all cereals pooled, and all staple crops pooled. Stage 1 is not the central focus of this chapter. However, it is the focus of Chapter 14 and the reader may refer to Table 14.4 for these estimates. Stage 2 estimates are summarized in Table 13.1. In the left-hand panel actual coefficients for interaction terms are reported. In the right-hand panel the productivity elasticities of the national research, national extension, and lARC research variables are reported. The R^ statistics on each of these equations ranged from 0.94 to 0.99. The significance levels reported in the left-hand panel are for the coefficients. The signifi¬ cance levels reported for the general elasticities are based on joint significance tests of the set of coefficients on which the elasticities are based. Note that all equations included country dummy variables. Area, irrigation, and fertilizer variables were included where appropriate. (See the previous section.) Consider first the interaction effects. The first column shows that national research and extension programs are substitutes in the cereals. lARC research is also a substitute for extension in rice and wheat in similar geoclimate regions. This means that spending more on extension lowers the marginal product of research, and spending more on research lowers the marginal product of extension. For staples it appears that national research complements extension in cassava and sweet po¬ tatoes, where lARC research has not been effective. Where lARC re¬ search has been effective (as in cassava in similar regions), it tends to be a substitute for national extension.

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I ARC, NARC and Extension Investment, and Crop Productivity

325

elasticities for cereal crops are highest in Africa and lowest in Latin America. The reverse is true for staples. As a region, Asia does best with high productivity elasticities for all three forms of investment for all commodities. Latin America has bene¬ fited from all investments except those in staples. Africa has mixed results. lARC investment has been least productive in staples, while national investment has been most productive in the staple crops.

Investment Implications Estimates summarized in Tables 13.1 and 13.2 show that research directed toward the discovery and development of new agricultural technology has a significant impact on productivity growth. Not all research programs are successful, of course. In some cases, relatively new research programs may not be productive until a significant period of trial and error with scientific approaches and administrative and organizational change takes place. Most lARC programs are still quite young. Previous studies have documented high productivity of lARC research programs in wheat and rice, but relatively little systematic study of the impact on other commodities has been undertaken. The chief objective of this study was to use international crop produc¬ tivity data to measure lARC impacts in ten commodities. There were certain data limitations, and the study is not a substitute for moredetailed country studies. Nonetheless, the study does identify and mea¬ sure significant lARC impacts as well as national research and extension impacts on crop productivity. In addition, it identifies several inter¬ action and regional impacts of interest. (The study also deals with the simultaneous relationship between productivity and research and ex¬ tension investment.) The major findings are as follows: 1. Measurable positive lARC impacts on crop productivity were observed for all commodities except sweet potatoes. For pooled com¬ modity groups, grains, cereals, and staples, positive lARC impacts were measured for all groups in all regions. 2. lARC impacts are higher in countries in the same geoclimate region as the lARC central location. For most commodities, these lARC impacts lower the marginal product of both national research and national extension programs. The lARCs produce technology that to some extent substitutes for the products of national research and exten¬ sion. 3. Outside similar geoclimate regions, I ARC impacts complement

\ 326

International Perspectives on Research and Extension

national research programs in some commodities (maize, rice, beans) and substitute for others. 4. National research investment is highly productive in most com¬ modities and in most regions. 5. National research has a consistently negative interaction with national extension. Higher research spending reduces the impact of extension services. It appears that most extension services are not orga¬ nized to channel directly or to diffuse research products to farmers. 6. Extension services also are generally productive although their impacts are much more variable. Table 13.3 reports a summary of internal rates of return (IRR) to investment for national research programs based on estimates in Table 13.1. The more reliable estimates in Table 13.2 are used to compute returns to national research, national extension, and lARC research by region. The table reports elasticities, the share (S,) of the relevant invest¬ ment in production costs of the crop for the region of concern, and the internal rate of return. The converts the elasticity to a marginal product, and the time weights used in the construction of the variables are used to distribute the marginal product of an expenditure in time t through future periods. The internal rate of return computed is thus an estimated marginal internal rate of return. Note that the SjS for lARC research are very low because it is presumed that the lARC effect is relevant for all production in all twenty-four countries.^ It is clear from Table 13.3 that high rates of return have been realized for all forms of investment. The exceptions are for research and exten¬ sion programs for staples in Latin America (and this result is somewhat dubious since the extension programs have high rates of return in other commodities in Latin America) and for national research on cereals in Africa. Returns are generally highest in Asia, lowest in Africa. Returns to cereal research and related extension are higher than returns to the staples group. lARC research investments yield IRRs in excess of 100 percent in the cereals, and this is true for other programs as well. It is not very meaningful to dwell on the actual level of these extraordinarily high returns. It merits mention that even if the productivity elasticities are overestimated by a factor of ten, the research projects yielding IRRs above 50 percent would still yield a competitive return on investment. The policy questions to which these data speak are whether to expand the I ARC system; whether to continue expansion and development of national research systems; and whether to continue development of 8. In actuality the lARC impacts extend to otlier countries as well.

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