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Education's role in rural areas of Latin America
Economics
of Education
0272-7757188 $3.00 + 0.00 Pergamon Press plc
Review, Vol. I, No. 3, pp. 333-343,1988.
Printed in Great Britain.
Education’s Role in Rural Areas of Latin America KENNETH Department
of Economics,
University
P. JAMESON*
of Notre
Dame,
Notre
Dame,
IN 46556,
U.S.A.
Abstract -
Large rural surveys from Bolivia, the Dominican Republic, Guatemala, and Paraguay are used to examine the role of education. Three frameworks which explain the effects of education are explored: the human capital-productivity approach, the modernizing environment approach, and the political economic-social differentiation framework. The main conclusion is that education has a consistently important effect which differs across countries. It is strongest in the modernizing environment context and is consistently related to social differentiation, except in Guatemala. Education has a positive and significant relation with rural productivity in Bolivia and the Dominican Republic, though not in Paraguay or Guatemala.
INTRODUCTION THERE IS
a long tradition of research on education and rural productivity in Third World countries. Its modern roots are found in the more general work of Denison (1962), of Welch (1970) and of Schultz (1964). The ample empirical work was summarized in a survey of 18 studies by Lockheed et al. (1980, p. 46) as follows: “We have hypothesized that education will have a positive effect on farmer efficiency; overall, we find confirmation for this hypothesis”. An active research program has used this conclusion as its starting point in the effort to improve understanding of the relation of education and rural productivity. For example, Moock’s (1981) study of Kenya corn producers relaxed the common assumption that education is an additive factor and investigated its interaction with other factors of production in an effort to reach greater realism and better explanation. Phillips and Marble (1985) relaxed the assumption that there is a common production technology, the “best practice”, used by all producers in their efforts to maximize profits. They estimated a frontier production function for Guatemala which admits
variability in actual practice. Again their results supported education’s contribution to rural productivity. The current research program at the World Bank adds the dimension of quality to the more familiar quantity measures of education. Again where this correction is made, the explanatory power of education variables is strengthened (Heyneman and Loxley, 1983; Behrman and Birdsall, 1983). The approach of this paper in understanding education and rural productivity differs. Its starting point is a skepticism about the likelihood of finding a stable and general relation of education and rural productivity across countries and across time, even with the use of more sophisticated theoretical and technical approaches. There are three reasons for this skepticism. The first is methodological. The recent work of McCloskey (1983) questions the methodology implicit in the earlier studies and the basis it provides for claims on the role of education. His argument suggests that while much can be learned about education and productivity, the effort must be much broader than usually conceived, taking into account a variety of approaches. The second reason is empirical. A closer examination of the empirical
*I would like to acknowledge the assistance of August0 de la Torre, Joe Phillips, and Pat Rooney and the comments of many colleagues, including the participants in the ECIEL project on Rural Productivity and Education. Partial support was received from the Jesse Jones Faculty Research Travel Fund of the University of Notre Dame. [Manuscript received 16 March 1986; revision accepted for publication 30 November 1987.1
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results, for example in Lockheed or Moock, shows that on statistical grounds they are generally not strong, and efforts to replicate them on other sets of data are generally not very successful in providing a consistent and strong relation (Jameson, 1985). Finally, there are theoretical reasons to question the possibility of finding a simple relation of education and productivity; observation and other literature suggest that education plays a multifaceted role, certain to differ across countries, only one element of which is to increase productivity. One of the most important areas of education’s influence is the sociopolitical (Bowles, 1978; Simmons, 1979). In one sense the starting point of this paper is the same as that of previous studies, the hypothesis that education plays a significant role in rural areas. It differs from much of that literature in maintaining that education’s role in rural areas can be multiple and that it is likely to differ in different situations, e.g. across countries. To be sure these suggestions are not novel. The paper’s contribution is in using four extensive and comparable rural surveys from different countries to test for the influence of education as viewed from three different perspectives. EMPIRICAL
PROCEDURE
The empirical procedure relies upon four extensive surveys from the Latin American countries of Bolivia, the Dominican Republic, Guatemala, and Paraguay. These were large, carefully collected and well-designed studies which were comparable across countries, and which thus provide an extremely strong data base for examining the role of education in this broader context. (See the Appendix for a more complete description of the surveys and of the data.) From a broad theoretical standpoint, education can be expected to contribute to rural development in one or more of three ways. Human capital theory suggests that increases in education should increase productivity directly or in interaction with the other production inputs (Welch, 1970; Moock, 1981). Again this is the most familiar framework for viewing the role of education, comprising an extensive theoretical and empirical corpus which provides the starting point for the empirical tests in this paper. A political-economic approach locates education’s contribution to rural development in its
Review
effect on the “social differentiation” of the country, or of the rural areas, which moves the historical pattern of relationships from feudalism toward the more productive capitalist form (Warren, 1979; Bowles, 1978; de Janvry and Deere, 1979). The starting point in this framework is the Marxist conception of development as the movement from more primitive and less productive modes of production, such as slavery and feudalism, to more advanced and productive modes such as capitalism and socialism. de Janvry and Deere (1979) operationalized this framework for rural development in Latin America by examining social differentiation in the rural area. Social differentiation can be measured by differences in household characteristics, in integration into the market economy and in measures of economic performance. As social differentiation increases, the historical movement from feudalism to capitalism advances. In this context education contributes to development by encouraging social differentiation, by providing the basic skills and knowledge to allow some farmers to alter their family structure, their integration into the market, and their success in the application of agricultural techniques. A third perspective, Schultz’s suggestion that education’s effect is strongest in a modernizing environment, can be considered as intermediate between the other two (Schultz, 1964, 1975). He suggests that the role of education is enhanced when external factors generate disequilibrium in a system, for the more educated will be better able to operate in this environment and to take advantage of the opportunities provided by the disequilibrium. ’ The theories have their critics and certainly do not exhaust the possible effects of education in rural areas. They do provide a useful theoretical and empirical departure point for the empirical effort of this paper, which proceeds in four steps. The first is the estimation, for each country, of the most commonly used human capital model, the CobbDouglas production function with inputs of land, labor, machinery, animal power, seed, fertilizer, and finally of years of education. The sign and significance of the education variable is used to assess the importance of education from this perspective. The second step is to estimate a more sophisticated human capital equation which allows education to interact with the other production factors, a more complete representation of education’s
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Education in Latin America Table 1. Cobb-Douglas
Paraguay Intercept Labor Land Seed Fertilizer Machinery Animal Educ (log) F statistic RSQUARE
7.68 (;.;&; (3:51) 0.297 (1.90) 0.274 (2.81) -0.050 (-1.61) -0.071 (-1.71) -0.007 (-1.31) -0.006 (-;I;;) 0.08
production function results Guatemala
Bolivia
3.62 (34.5) 0.029 (3.1) 0.84 (38.3) -0.002 -(“d.“3’
1.97 (15.2) 0.259
(9.8) 0.014 (4.13) 0.009 (2.9) 0.006 (1.9) 337.6 0.59
(3.4) 0.037 (11.9) 0.010 (3.7) 0.001 (0.4) 407.6 0.66
6.91 (10.4) 0.05 (6.6) 0.339 (6.67) 0.005 (0.7) 0.039 (5.3) 0.077 (1.8) 0.000 (0.0) 0.138 (2.36) 22.0 0.29
Dominican Republic
WW9 (19.9) 0.183 (W8
*r-Statistics are in parentheses. Countries were placed on the positive side in Fig. 1 if the education coefficient was positive and significant at the 6.01 level.
contribution to rural development. These regressions are reported in Table 2. The next step moves away from regression analysis to difference of means tests designed to assess the relation of education and modernization, i.e. is education associated with the decision of a farmer to adopt more modern practices such as fertilizer, machinery, pesticides, high yielding varieties or animal power? This is not a direct test of the Schultz hypothesis such as Jamison and Moock (1984) undertook by differentiating two areas of Nepal in terms of modernization. Rather the assumption is that in any modernizing environment there will be an association between education and measures of modernization. The absence of this association can be taken as evidence that the environment is not modernizing. The results are reported in Table 3. The fourth step examines the role of education in social differentiation in the rural areas of these countries, drawing upon the framework suggested by de Janvry and Deere (1979). Since education is viewed as a contributor to social differentiation, the procedure is to calculate measures of association between education and indicators of household characteristics, of market integration, and of economic performance, on the hypothesis that greater educational activity will be associated with greater
social differentiation in rural areas. Table 4 contains these results. These different tests of education’s role provide information which is summarized in Fig. 1. Reading across the top, education can either have a significant and positive effect in statistical terms. The most common alternative is that there is no significant association between education and a particular measure. Reading down the left hand side are the four perspectives on education. Based upon the tests, the countries are located in this schema; the pattern differs substantially across countries. One clear result is that education and modernization are associated, the only exception being Paraguay where differences in amounts of education have no significant relation with changes in agricultural practices. There is similarly a strong association of education with social differentiation, the only exception in the case of Guatemala. In Paraguay the only significant relation between education and the production process is in social differentiation.* Education plays a positive role in the other countries especially in Bolivia where it is significant in all dimensions except the interaction regressions. In the Dominican Republic it has a direct role in production as seen in the Cobb-Douglas estimates as well as in modernization and social differentiation. In Guatemala the role is less important, appearing
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Economics of Education Review 2. Cobb-Douglas
Table
Paraguay
interaction Dominican
6.84 (6.4) 0.562 (4.1) 0.542 (4.3) 0.512
Intercept Labor Land Seed
‘8::k
Fertilizer Machinery Animal E*Labor E*Land E*Seed E*Fert E’Mach E*Animal F statistic
(1.4) -0.093 (2.3) -0.021 (-1.5) -0.052 (-2.1) 0.044 (2.0) -0.006 (-0.3) -0.010 (-2.1) -0.011 (-2.5) 0.000 (0.0) 16.0
Republic
3.56 (34.6) 0.059 (4.81) 0.792 (32.8) -0.007 (-1.3) 0.028 (6.91) 0.012 (2.81) 0.007 (1.71) -0.004 (-2.8) 0.011 (4.31) -0.007 (-1.3) 0.0008 (0.8) 0.0006 (0.5) 0.001 (1.4) 201.3
0.21
RSQUARE
production
0.59
t-Statistics are in parentheses. Countries were placed on the positive side in Fig. 1 if there significant coefficients. If these were accompanied by negative country was omitted.
only through its interaction, particularly with the labor variable, and in modernization. So as suggested above, education is found to play a complex role in rural areas, generally supportive of “development”, but with substantial variation across countries. A realistic view of education and development must take this variation into account. The remainder of the paper presents the specific results of these tests. PRODUCTION
FUNCTION
-
COBB-DOUGLAS
A Cobb-Douglas production function was estimated with six inputs - labor, land, machinery, seed, fertilizer, and animal power - and with the log of years of education entering as an additive factor. Despite quite well-documented theoretical and empirical difficulties with this specification (Just et al., 1983), it remains the most commonly used
function
results
Guatemala
Bolivia 5.81
1.96 (15.1) 0.154 (3.6) 0.501 (13.0) 0.221 (7.4) 0.007
(4.9) 0.014 (1.4) 0.028
(1.7) 0.033
(2.6) 0.011
(6.3) 0.005 (1.1) 0.158
(0.2) 0.002 (0.0) -0.001 (-0.1) -0.006 (-0.1) -0.002 (-1.9) 0.018
(3.1) -0.078 (-1.7) -0.055 (-1.6) 0.002 (0.491) 0.006 (1.1) 0.008 (1.3) 240.0 0.65
(Z2 (4.8) 0.333
(1.3) 0.092 (1.5) -0.004 (,;:;) 0.32
were one or more positive and significant coefficients,
and the
form. The estimates presented in Table 1 indicate that the overall equation is significant in all four cases, though the R2 of 0.08 in Paraguay is quite low. For the most part the parameter estimates correspond with expectations, though a number of them are not significantly different from zero. The estimates for the education variable are quite mixed. In the Dominican Republic and in Bolivia, education had a positive and significant effect on production, with elasticities of 0.006 and 0.138 respectively. Although both elasticity estimates are quite small, given that the average education levels are quite low (2.9 years in the Dominican Republic and 1.8 years in Bolivia), small increases in the average number of years of education represent large percentage increases and thus could translate into significant increases in output. Lockheed (1980) calculated the mean gain in production for 4 years of education and found that it was 8.7%. The same
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Education in Latin America Table 3. Modern practice difference of mean education tests Paraguay
Dominican Republic
Guatemala
Bolivia
1.96 2.52 (-1.19)
24.9 (0.001)
Fertilizer use mean No fertilizer mean (r-stat)
5.53 2.57 (2.53)
2.89 2.06 (5.6)
Machinery use mean No machinery mean (t-stat)
3.18 2.59 (1.12)
3.02 2.77 (4.14)
2.25 1.85 (2.93)
20.7 (0.001)
Pesticide use mean No pesticide mean (f-stat)
2.91 2.46 (3.28)
2.70 2.24 (2.96)
2.19 1.75 (3.6)
31.4 (0.001)
HYV use mean No HYV mean (r-stat)
2.88 2.56 (1.56)
*
2.12 1.71 (3.24)
10.5 (0.001)
Animal use mean No animal mean (r-stat)
2.67 2.54 (0.97)
2.27 2.38 (-0.86)
1.81 2.07 (-2.14)
14.9 (0.002)
*No information was collected for this variable. For Bolivia we used a x2 test whose value and level of significance are reported. r-Statistics are in parentheses. Countries were placed on the positive side in Fig. 1 if a majority of these tests were significant. Table 4. Social differentiation Variable
Paraguay
and education correlations
Dominican Republic
Guatemala
Bolivia?
Family characteristics
Literacy? Male head? No. persons on farm % of land owned Migration experience
0.33 0.10 0.06* 0.05* -0.01*
0.81 0!03* -0.01* $
-0.01 $ -0.01* -0.05* $
0.91 0.24 0.11 0.03* 0.04*
Market integration
Distance to market Technical assistance? Use of credit? Days of hired labor
0.06 0.09
0.11
0.12 0.0.5*
-0.03* 0.07 0.00* 0.18
0.21 0.23 0.07 0.17 0.14 9 2 2 5
0.14 0.19 0.16 0.39 0.07 9 1 3 5
0.16 0.09 -0.05* 0.31 -0.02* 5 0 2 3
0.10 0.06* 0.03*
-0.22 0.15 0.13 0.33
Economic performance
Revenue from crops Cost of hired labor Net farm income Non-farm income Value of animals Total significant relations Family characteristics Market integration Economic performance *Indicates tbrdicates SIndicates Countries
0.35 0.35 0.18 0.20 0.05* 11 3 4 4
non-significance at the 0.01 level. that a Spearman correlation test was used because variables were not continuous. that data were not collected on this indicator. were placed on the positive side in Fig. 1 if more than 50% of the associations were positive and significant.
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Figure 1.
Cobb-Douglas Interaction Modernization Education differentiation
Empirical pattern of education’s role Positive and significant
Insignificant
Bolivia, Dominican Republic Guatemala Bolivia, Guatemala, Dominican Republic Bolivia, Paraguay, Dominican Republic
Paraguay, Guatemala Bolivia Paraguay Guatemala
calculation for Bolivia gives a value of 32% while that for the Dominican Republic is only 0.8%. These values emphasize that increases in rural education may permit substantial increases in rural production, though that appears to be the exception. However, in these two countries education clearly makes a positive and significant contribution to increasing production.3 In contrast, the education elasticity was not significantly different from zero in Guatemala, and was negative in Paraguay, though not significant.4 PRODUCTION FUNCTION - EDUCATION INTERACTIVE FACTOR
there is a significant positive interaction with land but education appears as a substitute for labor, fertilizer, and machinery. So the only clear case in which education augments the productivity of other inputs as expected is Guatemala, though there are positive interactions with land in Paraguay and the Dominican Republic.’ These results reinforce those of the simple CobbDouglas estimates, indicating in general that higher levels of education in rural areas contribute to higher production; but there are exceptions and the overall strength of the relationship is not great.
AS
Moock (1981) correctly noted that modelling education as an additive factor of production is likely to understate its total effect on production, for education seems clearly to interact with other elements of the production process by increasing the effectiveness of other inputs, i.e. raising their marginal products. The case can be made most clearly with the labor variable where an increase in education should increase the marginal product of each man-hour. The expectation is that education would complement the other inputs, that the coefficient estimate of the interaction term would be positive. To take account of this suggestion, another Cobb-Douglas production function was estimated in which the education variable was entered in interaction with each of the other factors. The results presented in Table 2 are again quite inconsistent. The Guatemala case provides the best results; the interaction of education with labor is positive and significant, and though education appears to substitute for land and seed, no other interaction is significant. In Bolivia the only significant interaction is the substitution relation with seed inputs. In the Dominican Republic the complementary interaction with land is countered by the opposite interaction with labor; and in Paraguay
EDUCATION AND MODERNIZATION Schultz (1964, 1975) suggested that education is most likely to be effective in a modernizing environment , that modernization engenders disequilibria and education plays an important role in developing the capacities for dealing with disequilibrium. Lockheed et al. (1980) examined this hypothesis based on the studies that they reviewed and found on average that a modernizing environment increased the effect of education by some 10%. They may also have found in it an explanation for some of the weakness of the education variable, for in the cases of non-modernizing environments “the mean percentage increase (in productivity as a result of education) may even be negative” (p. 57). This relation appears quite strong, though several difficulties remain. The first is the definition of a modernizing environment. Take the Lockheed definitions: (non-modern environment criteria) “included primitive technology, traditional farming practices and crops, and little reported innovation or exposure to new methods”; (modern environment criteria) “included the availability of new crop varieties, innovative planting methods, erosion control, and the availability of capital inputs such as insecticides, fertilizers, and tractors or machines” (pp. 55-56). This is clearly a loose definition and is open to a good deal of judgment. It may be useful in
Education in Latin America a cross country context, though at least in Latin America there are few countries which would be categorized as non-modernizing on these criteria. For the four countries of this study, based solely on the descriptive statistics of the Appendix, Bolivia would certainly be the least modernizing environment, followed by Paraguay. Guatemala would be the most modernizing followed by the Dominican Republic. However, Bolivia and the Dominican Republic show the clearest positive effect of education, with Guatemala exhibiting only a positive interaction with labor. This does not correspond with the expected pattern. Part of the problem is that education and other indicators of modernization are generally highly correlated, that education will increase as will availability of improved seed and of machinery, etc. This is particularly true in Latin America, for there are few regions that have not begun some process of modernization, making the “modernizing-nonmodernizing” distinction very unclear (see de Janvry, 1981). In this context, as education increases, access to these inputs will also increase because access means knowledge, ability to obtain the inputs, and ability to use them. Thus it is difficult in Latin America to ascribe causality to the modernizing environment or to education. An alternative perspective is to view education as one component of a process of modernization, and to see its role as a contributor to a whole series of changes sparked by modernization. This requires examination of differences in the use of modern practices in agriculture which are associated with educational differences.6 The statistical tests appropriate for this question are differences of means tests and a Chi-square test for Bolivia since its education variable is not continuous. In all of the countries except Paraguay there is a significant difference in the education levels of those adopting modern techniques and the non-adopters. In Bolivia education levels are different across each of the five elements of modern production. In the Dominican Republic, the only exception is the use of animal power, and this may simply indicate that production techniques have moved beyond this simple innovation. This result is mirrored by the significantly lower education of those using animal power in Guatemala, where the differences of means are significant with the exception of fertilizer. In Paraguay on the other hand mean education differs in only two of the five modernization
339
indicators: the use of fertilizer, and the use of pesticides. There is no significant difference in the other three cases.’ The overall conclusion is that there is a general association between education and the indicators of modern agricultural practice. Increasing education is part of a broader and complex process of modernization. For example, although the education variable was significant in only one of the production function tests for Guatemala, its clear association with the elements of modernization may indicate that there are important limits on the contribution which modernization can make to increasing production. The Paraguay case with only two of the five indicators significant is anomalous, which suggests that other elements may dominate the production process in that country, a result consistent with the earlier tests. SOCIAL DIFFERENTIATION AND EDUCATION de Janvry and Deere (1979) have suggested a very different manner of viewing development in rural areas, that modernization hides the reality of rural development by omitting political and power relationships and the role which classes play. The key to rural development is the historical process of the destruction of rural feudal classes, who are initially supplanted by a higher degree of social differentiation, followed by a consolidation of classes into some modern capitalist form. The key construct in understanding contemporary Latin American agriculture is social differentiation, a broadening and reorganization of class relations in rural areas. In operational terms they suggest that the size of landholding can be used as a measure of social differentiation, and that it should be associated with differences in a whole series of indicators of rural life and activity, ranging from family size to type of income obtained, to agricultural practices. Their criterion for accepting that social differentiation affects a particular variable is the existence of a unilinear relation across the land size categories. Our hypothesis is that education contributes to social differentiation and therefore to development. We test that by calculating measures of association between education and indicators of social differentiation. There are three groups of these indicators.8 First are family characteristics: literacy, whether the household is headed by a male, family size,
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migration experience, and percent of land owned. Next are measures of integration into the market economy: distance to market, whether the farm received technical assistance, whether the farm used consumer credit, and the amount of labor used in the primary crop. Finally are measures of economic performance: revenue from crop production, cost of hired labor, net farm income, off farm income, and value of animals owned. Pearson correlation coefficients were utilized as the measure of association unless the variables were categoric, in which case Spearman correlations were calculated. Table 4 presents the results. It can be seen that more than half of the associations are significant except in Guatemala where only 5 of 12 fall in that category. So the overall pattern is that education does play an important role in rural social differentiation, in development in the political economic context. Additional information on the social differentiation process is provided by examining the three categories separately. It becomes clear that household characteristics are least affected by the educational process. This is not surprising since they are demographically driven and thus would change more gradually; and they may also be more resistant to change because of cultural influences. Education affects economic performance most clearly, with market integration in an intermediate situation. This may imply that social differentiation initially shifts production and economic activity away from household production and toward efforts to increase economic results; next markets develop and are expanded to include these former household producers. SUMMARY When
all the results
OF RESULTS are examined
and summar-
ized in Fig. 1, it is clear that education has a complex role in rural development, but one that generally is a significant contributor to a broadly defined process of development. Thus, although the understanding of the role of education in development is different from Lockheed’s, our conclusion can be stated in parallel to their conclusion: “we have hypothesized that education will have a positive effect on rural development; overall, we find confirmation for this hypothesis”. In two countries, Bolivia and the Dominican Republic, the traditional human capital understanding of education’s contribution is corroborated. The more complex human capital approach gives very mixed results and none are particularly strong, though the interaction with labor in Guatemala is significant. The role of education in modernization is again positive in Bolivia, Guatemala, and in the Dominican Republic. There is very little evidence of a significant role in the case of Paraguay. Finally, social differentiation through the educational process is a notable element of rural development in Bolivia, Paraguay, and the Dominican Republic. When all of these elements are added together, the claim that education does have an important development role to play receives confirmation in every country. However, the mechanism by which education affects development differs across the countries. In Bolivia and the Dominican Republic there is a positive effect in all dimensions. In Guatemala the effect is seen only in its interaction with labor and in the modernization process. Finally in Paraguay education’s effect is confined to social differentiation, with little contribution directly to production nor to the modernization of agriculture.
NOTES 1. The conceptual difference between the modernization and the political economy approaches is quite marked. Their empirical distinction is less clear, for many of the indicators of modernization, e.g. the use of hybrid seeds, could also be taken to evidence social differentiation. Separate sets of indicators will be utilized in the empirical sections, though the distinction will not be as neat as it appears there. 2. Although it would go far beyond what can be concluded from the data, the dominance of the country over the last 30 years by the dictatorship of Gen. Stroessner may play some role in generating this poor performance for education. That education has played a different role in that country may be signalled by the name of the first education ministry at the turn of the century, the Ministry of Justice and Education. 3. McCloskey (1985) correctly emphasizes that the magnitude of the coefficients is often more
Education
in Latin America
important in an economic sense than their significance. His criterion would suggest that education is not an important factor in the Dominican Republic. 4. The equation was also estimated using a O-l variable for use of technical assistance, a measure of non-formal education. The variable was significant only in the Dominican Republic. 5. Cases of negative and significant coefficient estimates for the education variable are not unknown. For example, Moock (1981) found a negative effect of education on farm yield. He suggested that “an a posteriori justification would need to show that those who begin school but drop out before earning over the first credential are dull from the start, or that they become demoralized or, conversely, that they develop a self-importance that blinds them to their technical incompetence” (pp. 732-733). for the major factors 6. The test using several indicators of modernization attempts to “control” which could affect education’s role. As McCloskey (1985, p. 14) notes, the inability to control all factors even in scientific experiments vitiate the attempt to falsify hypotheses. Thus in this case there may be other factors which affect the relation of education and modernization - but that problem is not specific to such bivariate comparisons. 7. Other factors which may influence the adoption of modern practices such as land tenure or elements of social differentiation may be correlated with education, and so education’s role may be overstated. One way of controlling for these effects was the use of a logit model as in Jamison and Moock (1984). We chose not to use the same approach because it reduces modernization to one dimension, e.g. the use of chemical fertilizer, whereas there are many elements to modernizing agricultural practices. These can be included in the approach used here. 8. de Janvry and Deere (1979) used different specific indicators though they related to the same three categories. They provide an extensive treatment of the theoretical bases for these indicators. Their main categorization of different social strata was by amount of land used. When this was used in our four countries, only Paraguay exhibited significant social differentiation in these terms. REFERENCES BEHRMAN, J. and BIRDSALL, N. (1983) The quality of schooling: quantity alone is misleading. Am. econ. Rev. 73, 928-946. BOWLES, S. (1978) Capitalist development and educational structures. World Dev. 6, 783-796. DE JANVRY, A. (1981) The Agrarian Question and Reformism in Latin America. Baltimore: Johns Hopkins. DE JANVRY, A. and DEERE, C. (1979) A conceptual framework for the empirical analysis of peasants. Am. J. agric. Econ. 61, 601-611. DENISON, E. (1962) The Sources of Economic Growth in the United States and The Alternatives Before Vs. NY: Committee for Economic Development. HEYNEMAN, S. and LOXLEY, W. (1983) The effect of primary school quality on academic achievement across twenty-nine high- and low-income countries. The Am. J. Social. 88, 1162-1194. JAMESON, K. (1985) Education and development: rhetoric and reality, Typescript. JAMESON, K., Peasants, productivity and pedogogy: Paraguay. In Latin America Educarion: A Quesr for Idenrity, NANCY NYSTROM (Editor), pp. 157-174. Tulane University, 1985. JAMISON, D. and MOOCK, P. (1984) Farmer education and farm efficiency in Nepal: the role of schooling, extension services, and cognitive skills. World Dev. 12, 67-86. JUST, R., ZILBERMAN, D. and HOCHMAN, E. (1983) Estimation of multicrop production functions. Am. .I. agric. Econ. 65, 770-780. LOCKHEED, M. et al. (1980) Farmer education and farm efficiency: a survey. Economic Developmenr and Cultural Change 29, 37-75. MCCLOSKEY, D. (1983) The Rhetoric of Economics. J. econ. Lit. XXI, 481-517. MCCLOSKEY, D. (1985) The Rhetoric of Economics. Madison: University of Wisconsin Press. MELLOR, J. (1976) The New Economics of Growrh. Ithaca: Cornell Universitv Press. 1976. MoocK,‘~. (1981)‘Education and technical efficiency in small farm production: Economic Developmenf and Cultural Change 29, 723-739. PHILLIPS, J. and MARBLE, R. (1986) Farmer education and efficiency: a frontier production function approach. Econ. educ. Rev. 5, 257-264. SCHULTZ, T. (1964) Transforming Traditional Agriculture. New Haven: Yale University Press. SCHULTZ, T. (1975) The value of the ability to deal with disequilibrium. J. econ. Lit. 13, 827-846. SIMMONS, J. (1979) Education for development, reconsidered. World Dev. 7, 1005-1016. WARREN, B. The postwar economic experience of the Third World. In Toward a New Strategy __ .for Development, CHAPEL, R. (Editor), pp. 144-168. N.Y: Pergamon. WELCH, F. (1970) Education in production. J. uolit. Econ. 78, 35-59. WILBER, C.K. (1978) The methodological basis of institutional economics: pattern model, storytelling and holism. J. econ. Issues XII. 61-89.
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Economics of Education Review APPENDIX Four farm level surveys of Latin American countries are used for the estimates in this paper. The countries are Paraguay, Bolivia, the Dominican Republic, and Guatemala. Each survey was the result of a co-operative venture between the U.S. Agency for International Development (USAID) and national agriculture agencies. Although the survey instrument and techniques are not identical, the variations are small enough to facilitate comparisons between countries. Each survey was based on interviews at the farm level, so data are available on a series of socio-economic characteristics and on the inputs and outputs of five major crops and three varieties of livestock. The farm level data are the observations used in all of the statistical testing. The Paraguay survey is stratified by farm size and is a random sample of 1053 farms in the eastern portion of the country. The survey was administered by the Technical Cabinet of the Ministry of Agriculture and Livestock together with USAID. It was conducted in August and September, 1976, for the July 1975-June 1976 crop year. The main focus of the survey was to study farm production (crop and animal) as well as migration and credit utilization. The Dominican Republic survey was conducted by the Secretariat of State for Agriculture in conjunction with USAID. The survey covers 1802 farms from across the country and was taken in March and April of 1976 for the July 1975-April1976 crop year. The focus of the survey was farm production, employment, and income. The Guatemala survey was conducted by Ministry of Agriculture personnel with technical support from USAID. It is a paired sample with 1548 farm level observations from across the country (excluding the Peten region). The survey was taken in the first three months of 1974 for the 1973 crop year. Its goal was to analyze the effect of credit on income, output, and employment. The Bolivia survey was a joint effort of AID and the Ministry of Rural Affairs and Agriculture, carried out in April and May of 1977; the sample includes 750 farms from the Departments of Chiquisaca, Tarija and Potosi. It concentrated on farm income. Table A presents descriptive statistics from the four surveys as a basis for comparison. The indicators are calculated from the data in which individual farms are the level of observation. These figures show that average years of formal schooling is low but that literacy is relatively high. There is substantial variation between countries in the receipt of technical assistance and in credit. The extreme case of Guatemala is a result of the nature of that sample which paired recipients and non-recipients of supervised credit; and credit recipients at that time were also receiving technical assistance. Table A shows substantial variation in input usage between countries. Usage of such inputs as fertilizer, pesticide, machinery, hired labor, and HYV seeds is higher in Guatemala. It is lowest in all
Table A. Descriptive
Average years schooling Average years of schooling Range (years) Variance % No schooling % Literate % Receiving technical assistance % Using cridit % Usine HYVs Averagg distance to market Average age of producer Average farm size % Using chemical fertilizer % Using machinery % Using pesticide % Using hired labor % Using animal power
statistics
Paraguay
Dominican Republic
2.60
2.87
O-16 4.4 22.5% 76.5% 5.2% 29.1% 14.1% 11.1 km 48.0 yrs 11.6 ha 13.3% 4.5% 22.2% 6.1% 45.2%
o-21 8.2 41.4% 62.4% 20.8% 31.2% 32.3% 9.3 km 52.6 yrs 13.9 ha 33.0%7
32.6% 40.6%
Guatemala
Bolivia
1.97
1.8*
O-18 5.8 43.2% 64.8% 51.6% 52.3% 64.2% 12.4 km 44.6 yrs 10.9 ha 68.8% 33.2% 50.4% 88.8% 38.1%
48.9% 51.07% 2.1% 6.7% 3.6% 9.0 km 45.6 yrs 3.9 ha 7.9% 0.6% 11.7% 26.9% 78.3%
*The Bolivian data are categoric. This lowers the mean and makes the other statistics misleading. tNo separate breakdown for fertilizer and pesticide.
Education in Latin America
343
cases for Bolivia. The one exception is that animal power is used most frequently in Bolivia. This allows a scaling of the modernizing environment (Schultz, 1964) across countries. Bolivia is at the low end, and for this sample Guatemala would be at the high end, with the other two in an intermediate position. In Table B is the farm size distribution in the various samples. In each of the surveys the bulk of farms are less than 35 ha and above 0.7 ha, meaning that the surveys deal with small and medium sized farms. In the Dominican Republic and in Bolivia there is a larger sampling of “microfincas” (less than 0.7 ha). Table C shows farm tenancy patterns in the three surveys. There is substantial variation. In Bolivia, Guatemala and the Dominican Republic most farms are owner operated. In Paraguay farms are either rented or a mix of owned and rented land. Table D describes the variables used in the analysis.
Table B. Land size distributions Farm size
Paraguay
Dominican Republic
Guatemala
Bolivia
Less than 0.7 ha 0.7-4.0 ha 4.0-7.0 ha 7.0-35.0 ha 35.0-350.0 ha 350 ha +
4.0% 23.8 12.6 54.9 4.4 0.0
24.5% 36.0 12.3 20.5 6.8 0.1
2.9% 43.0 21.7 27.5 5.1 0.2
23.6% 51.4 12.8 11.4 0.7 0.0
Table C. Farm tenancy patterns
Status
Paraguay
Dominican Republic
Guatemala
Bolivia
Owned Cash rent Crop rent Mixed Other
32.5% 13.4 17.9 30.7 5.3
73.7% 2.6 2.8
74.1% 7.9 1.0 12.5 4.4
95.4% 0.5 1.3 0.4 2.4
20.7
Table D. Description of variables in regressions Output - value of crop and animal output (in country currency). Labor - quantity of labor used in production (man-days). Land - quantity of land used in production in hectares. Seed-quantity or value of seed used in production (Paraguay - kilograms, Dominican Republic pesos, Guatemala - kilograms). Fertilizer - quantity of fertilizer used in production (Paraguay - kilograms, Dominican Republic pounds, Guatemala - kilograms). Machinery - quantity or value of machinery used in crop production (Paraguay - initial cost guaranis, Dominican Republic - pesos, Guatemala - passes over field). Animal use - animal days used in crop production; Paraguay - cost of animals rented. Education - years of formal schooling, except in Bolivia where the following categories were used: school; l-3 years of school; more than three years of school. Technical assistance (dummy) - no assistance = 0, assistance = 1.
in no
of Education
0272-7757188 $3.00 + 0.00 Pergamon Press plc
Review, Vol. I, No. 3, pp. 333-343,1988.
Printed in Great Britain.
Education’s Role in Rural Areas of Latin America KENNETH Department
of Economics,
University
P. JAMESON*
of Notre
Dame,
Notre
Dame,
IN 46556,
U.S.A.
Abstract -
Large rural surveys from Bolivia, the Dominican Republic, Guatemala, and Paraguay are used to examine the role of education. Three frameworks which explain the effects of education are explored: the human capital-productivity approach, the modernizing environment approach, and the political economic-social differentiation framework. The main conclusion is that education has a consistently important effect which differs across countries. It is strongest in the modernizing environment context and is consistently related to social differentiation, except in Guatemala. Education has a positive and significant relation with rural productivity in Bolivia and the Dominican Republic, though not in Paraguay or Guatemala.
INTRODUCTION THERE IS
a long tradition of research on education and rural productivity in Third World countries. Its modern roots are found in the more general work of Denison (1962), of Welch (1970) and of Schultz (1964). The ample empirical work was summarized in a survey of 18 studies by Lockheed et al. (1980, p. 46) as follows: “We have hypothesized that education will have a positive effect on farmer efficiency; overall, we find confirmation for this hypothesis”. An active research program has used this conclusion as its starting point in the effort to improve understanding of the relation of education and rural productivity. For example, Moock’s (1981) study of Kenya corn producers relaxed the common assumption that education is an additive factor and investigated its interaction with other factors of production in an effort to reach greater realism and better explanation. Phillips and Marble (1985) relaxed the assumption that there is a common production technology, the “best practice”, used by all producers in their efforts to maximize profits. They estimated a frontier production function for Guatemala which admits
variability in actual practice. Again their results supported education’s contribution to rural productivity. The current research program at the World Bank adds the dimension of quality to the more familiar quantity measures of education. Again where this correction is made, the explanatory power of education variables is strengthened (Heyneman and Loxley, 1983; Behrman and Birdsall, 1983). The approach of this paper in understanding education and rural productivity differs. Its starting point is a skepticism about the likelihood of finding a stable and general relation of education and rural productivity across countries and across time, even with the use of more sophisticated theoretical and technical approaches. There are three reasons for this skepticism. The first is methodological. The recent work of McCloskey (1983) questions the methodology implicit in the earlier studies and the basis it provides for claims on the role of education. His argument suggests that while much can be learned about education and productivity, the effort must be much broader than usually conceived, taking into account a variety of approaches. The second reason is empirical. A closer examination of the empirical
*I would like to acknowledge the assistance of August0 de la Torre, Joe Phillips, and Pat Rooney and the comments of many colleagues, including the participants in the ECIEL project on Rural Productivity and Education. Partial support was received from the Jesse Jones Faculty Research Travel Fund of the University of Notre Dame. [Manuscript received 16 March 1986; revision accepted for publication 30 November 1987.1
333
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Economics
of Education
results, for example in Lockheed or Moock, shows that on statistical grounds they are generally not strong, and efforts to replicate them on other sets of data are generally not very successful in providing a consistent and strong relation (Jameson, 1985). Finally, there are theoretical reasons to question the possibility of finding a simple relation of education and productivity; observation and other literature suggest that education plays a multifaceted role, certain to differ across countries, only one element of which is to increase productivity. One of the most important areas of education’s influence is the sociopolitical (Bowles, 1978; Simmons, 1979). In one sense the starting point of this paper is the same as that of previous studies, the hypothesis that education plays a significant role in rural areas. It differs from much of that literature in maintaining that education’s role in rural areas can be multiple and that it is likely to differ in different situations, e.g. across countries. To be sure these suggestions are not novel. The paper’s contribution is in using four extensive and comparable rural surveys from different countries to test for the influence of education as viewed from three different perspectives. EMPIRICAL
PROCEDURE
The empirical procedure relies upon four extensive surveys from the Latin American countries of Bolivia, the Dominican Republic, Guatemala, and Paraguay. These were large, carefully collected and well-designed studies which were comparable across countries, and which thus provide an extremely strong data base for examining the role of education in this broader context. (See the Appendix for a more complete description of the surveys and of the data.) From a broad theoretical standpoint, education can be expected to contribute to rural development in one or more of three ways. Human capital theory suggests that increases in education should increase productivity directly or in interaction with the other production inputs (Welch, 1970; Moock, 1981). Again this is the most familiar framework for viewing the role of education, comprising an extensive theoretical and empirical corpus which provides the starting point for the empirical tests in this paper. A political-economic approach locates education’s contribution to rural development in its
Review
effect on the “social differentiation” of the country, or of the rural areas, which moves the historical pattern of relationships from feudalism toward the more productive capitalist form (Warren, 1979; Bowles, 1978; de Janvry and Deere, 1979). The starting point in this framework is the Marxist conception of development as the movement from more primitive and less productive modes of production, such as slavery and feudalism, to more advanced and productive modes such as capitalism and socialism. de Janvry and Deere (1979) operationalized this framework for rural development in Latin America by examining social differentiation in the rural area. Social differentiation can be measured by differences in household characteristics, in integration into the market economy and in measures of economic performance. As social differentiation increases, the historical movement from feudalism to capitalism advances. In this context education contributes to development by encouraging social differentiation, by providing the basic skills and knowledge to allow some farmers to alter their family structure, their integration into the market, and their success in the application of agricultural techniques. A third perspective, Schultz’s suggestion that education’s effect is strongest in a modernizing environment, can be considered as intermediate between the other two (Schultz, 1964, 1975). He suggests that the role of education is enhanced when external factors generate disequilibrium in a system, for the more educated will be better able to operate in this environment and to take advantage of the opportunities provided by the disequilibrium. ’ The theories have their critics and certainly do not exhaust the possible effects of education in rural areas. They do provide a useful theoretical and empirical departure point for the empirical effort of this paper, which proceeds in four steps. The first is the estimation, for each country, of the most commonly used human capital model, the CobbDouglas production function with inputs of land, labor, machinery, animal power, seed, fertilizer, and finally of years of education. The sign and significance of the education variable is used to assess the importance of education from this perspective. The second step is to estimate a more sophisticated human capital equation which allows education to interact with the other production factors, a more complete representation of education’s
335
Education in Latin America Table 1. Cobb-Douglas
Paraguay Intercept Labor Land Seed Fertilizer Machinery Animal Educ (log) F statistic RSQUARE
7.68 (;.;&; (3:51) 0.297 (1.90) 0.274 (2.81) -0.050 (-1.61) -0.071 (-1.71) -0.007 (-1.31) -0.006 (-;I;;) 0.08
production function results Guatemala
Bolivia
3.62 (34.5) 0.029 (3.1) 0.84 (38.3) -0.002 -(“d.“3’
1.97 (15.2) 0.259
(9.8) 0.014 (4.13) 0.009 (2.9) 0.006 (1.9) 337.6 0.59
(3.4) 0.037 (11.9) 0.010 (3.7) 0.001 (0.4) 407.6 0.66
6.91 (10.4) 0.05 (6.6) 0.339 (6.67) 0.005 (0.7) 0.039 (5.3) 0.077 (1.8) 0.000 (0.0) 0.138 (2.36) 22.0 0.29
Dominican Republic
WW9 (19.9) 0.183 (W8
*r-Statistics are in parentheses. Countries were placed on the positive side in Fig. 1 if the education coefficient was positive and significant at the 6.01 level.
contribution to rural development. These regressions are reported in Table 2. The next step moves away from regression analysis to difference of means tests designed to assess the relation of education and modernization, i.e. is education associated with the decision of a farmer to adopt more modern practices such as fertilizer, machinery, pesticides, high yielding varieties or animal power? This is not a direct test of the Schultz hypothesis such as Jamison and Moock (1984) undertook by differentiating two areas of Nepal in terms of modernization. Rather the assumption is that in any modernizing environment there will be an association between education and measures of modernization. The absence of this association can be taken as evidence that the environment is not modernizing. The results are reported in Table 3. The fourth step examines the role of education in social differentiation in the rural areas of these countries, drawing upon the framework suggested by de Janvry and Deere (1979). Since education is viewed as a contributor to social differentiation, the procedure is to calculate measures of association between education and indicators of household characteristics, of market integration, and of economic performance, on the hypothesis that greater educational activity will be associated with greater
social differentiation in rural areas. Table 4 contains these results. These different tests of education’s role provide information which is summarized in Fig. 1. Reading across the top, education can either have a significant and positive effect in statistical terms. The most common alternative is that there is no significant association between education and a particular measure. Reading down the left hand side are the four perspectives on education. Based upon the tests, the countries are located in this schema; the pattern differs substantially across countries. One clear result is that education and modernization are associated, the only exception being Paraguay where differences in amounts of education have no significant relation with changes in agricultural practices. There is similarly a strong association of education with social differentiation, the only exception in the case of Guatemala. In Paraguay the only significant relation between education and the production process is in social differentiation.* Education plays a positive role in the other countries especially in Bolivia where it is significant in all dimensions except the interaction regressions. In the Dominican Republic it has a direct role in production as seen in the Cobb-Douglas estimates as well as in modernization and social differentiation. In Guatemala the role is less important, appearing
336
Economics of Education Review 2. Cobb-Douglas
Table
Paraguay
interaction Dominican
6.84 (6.4) 0.562 (4.1) 0.542 (4.3) 0.512
Intercept Labor Land Seed
‘8::k
Fertilizer Machinery Animal E*Labor E*Land E*Seed E*Fert E’Mach E*Animal F statistic
(1.4) -0.093 (2.3) -0.021 (-1.5) -0.052 (-2.1) 0.044 (2.0) -0.006 (-0.3) -0.010 (-2.1) -0.011 (-2.5) 0.000 (0.0) 16.0
Republic
3.56 (34.6) 0.059 (4.81) 0.792 (32.8) -0.007 (-1.3) 0.028 (6.91) 0.012 (2.81) 0.007 (1.71) -0.004 (-2.8) 0.011 (4.31) -0.007 (-1.3) 0.0008 (0.8) 0.0006 (0.5) 0.001 (1.4) 201.3
0.21
RSQUARE
production
0.59
t-Statistics are in parentheses. Countries were placed on the positive side in Fig. 1 if there significant coefficients. If these were accompanied by negative country was omitted.
only through its interaction, particularly with the labor variable, and in modernization. So as suggested above, education is found to play a complex role in rural areas, generally supportive of “development”, but with substantial variation across countries. A realistic view of education and development must take this variation into account. The remainder of the paper presents the specific results of these tests. PRODUCTION
FUNCTION
-
COBB-DOUGLAS
A Cobb-Douglas production function was estimated with six inputs - labor, land, machinery, seed, fertilizer, and animal power - and with the log of years of education entering as an additive factor. Despite quite well-documented theoretical and empirical difficulties with this specification (Just et al., 1983), it remains the most commonly used
function
results
Guatemala
Bolivia 5.81
1.96 (15.1) 0.154 (3.6) 0.501 (13.0) 0.221 (7.4) 0.007
(4.9) 0.014 (1.4) 0.028
(1.7) 0.033
(2.6) 0.011
(6.3) 0.005 (1.1) 0.158
(0.2) 0.002 (0.0) -0.001 (-0.1) -0.006 (-0.1) -0.002 (-1.9) 0.018
(3.1) -0.078 (-1.7) -0.055 (-1.6) 0.002 (0.491) 0.006 (1.1) 0.008 (1.3) 240.0 0.65
(Z2 (4.8) 0.333
(1.3) 0.092 (1.5) -0.004 (,;:;) 0.32
were one or more positive and significant coefficients,
and the
form. The estimates presented in Table 1 indicate that the overall equation is significant in all four cases, though the R2 of 0.08 in Paraguay is quite low. For the most part the parameter estimates correspond with expectations, though a number of them are not significantly different from zero. The estimates for the education variable are quite mixed. In the Dominican Republic and in Bolivia, education had a positive and significant effect on production, with elasticities of 0.006 and 0.138 respectively. Although both elasticity estimates are quite small, given that the average education levels are quite low (2.9 years in the Dominican Republic and 1.8 years in Bolivia), small increases in the average number of years of education represent large percentage increases and thus could translate into significant increases in output. Lockheed (1980) calculated the mean gain in production for 4 years of education and found that it was 8.7%. The same
337
Education in Latin America Table 3. Modern practice difference of mean education tests Paraguay
Dominican Republic
Guatemala
Bolivia
1.96 2.52 (-1.19)
24.9 (0.001)
Fertilizer use mean No fertilizer mean (r-stat)
5.53 2.57 (2.53)
2.89 2.06 (5.6)
Machinery use mean No machinery mean (t-stat)
3.18 2.59 (1.12)
3.02 2.77 (4.14)
2.25 1.85 (2.93)
20.7 (0.001)
Pesticide use mean No pesticide mean (f-stat)
2.91 2.46 (3.28)
2.70 2.24 (2.96)
2.19 1.75 (3.6)
31.4 (0.001)
HYV use mean No HYV mean (r-stat)
2.88 2.56 (1.56)
*
2.12 1.71 (3.24)
10.5 (0.001)
Animal use mean No animal mean (r-stat)
2.67 2.54 (0.97)
2.27 2.38 (-0.86)
1.81 2.07 (-2.14)
14.9 (0.002)
*No information was collected for this variable. For Bolivia we used a x2 test whose value and level of significance are reported. r-Statistics are in parentheses. Countries were placed on the positive side in Fig. 1 if a majority of these tests were significant. Table 4. Social differentiation Variable
Paraguay
and education correlations
Dominican Republic
Guatemala
Bolivia?
Family characteristics
Literacy? Male head? No. persons on farm % of land owned Migration experience
0.33 0.10 0.06* 0.05* -0.01*
0.81 0!03* -0.01* $
-0.01 $ -0.01* -0.05* $
0.91 0.24 0.11 0.03* 0.04*
Market integration
Distance to market Technical assistance? Use of credit? Days of hired labor
0.06 0.09
0.11
0.12 0.0.5*
-0.03* 0.07 0.00* 0.18
0.21 0.23 0.07 0.17 0.14 9 2 2 5
0.14 0.19 0.16 0.39 0.07 9 1 3 5
0.16 0.09 -0.05* 0.31 -0.02* 5 0 2 3
0.10 0.06* 0.03*
-0.22 0.15 0.13 0.33
Economic performance
Revenue from crops Cost of hired labor Net farm income Non-farm income Value of animals Total significant relations Family characteristics Market integration Economic performance *Indicates tbrdicates SIndicates Countries
0.35 0.35 0.18 0.20 0.05* 11 3 4 4
non-significance at the 0.01 level. that a Spearman correlation test was used because variables were not continuous. that data were not collected on this indicator. were placed on the positive side in Fig. 1 if more than 50% of the associations were positive and significant.
Economics of Education Review
338
Figure 1.
Cobb-Douglas Interaction Modernization Education differentiation
Empirical pattern of education’s role Positive and significant
Insignificant
Bolivia, Dominican Republic Guatemala Bolivia, Guatemala, Dominican Republic Bolivia, Paraguay, Dominican Republic
Paraguay, Guatemala Bolivia Paraguay Guatemala
calculation for Bolivia gives a value of 32% while that for the Dominican Republic is only 0.8%. These values emphasize that increases in rural education may permit substantial increases in rural production, though that appears to be the exception. However, in these two countries education clearly makes a positive and significant contribution to increasing production.3 In contrast, the education elasticity was not significantly different from zero in Guatemala, and was negative in Paraguay, though not significant.4 PRODUCTION FUNCTION - EDUCATION INTERACTIVE FACTOR
there is a significant positive interaction with land but education appears as a substitute for labor, fertilizer, and machinery. So the only clear case in which education augments the productivity of other inputs as expected is Guatemala, though there are positive interactions with land in Paraguay and the Dominican Republic.’ These results reinforce those of the simple CobbDouglas estimates, indicating in general that higher levels of education in rural areas contribute to higher production; but there are exceptions and the overall strength of the relationship is not great.
AS
Moock (1981) correctly noted that modelling education as an additive factor of production is likely to understate its total effect on production, for education seems clearly to interact with other elements of the production process by increasing the effectiveness of other inputs, i.e. raising their marginal products. The case can be made most clearly with the labor variable where an increase in education should increase the marginal product of each man-hour. The expectation is that education would complement the other inputs, that the coefficient estimate of the interaction term would be positive. To take account of this suggestion, another Cobb-Douglas production function was estimated in which the education variable was entered in interaction with each of the other factors. The results presented in Table 2 are again quite inconsistent. The Guatemala case provides the best results; the interaction of education with labor is positive and significant, and though education appears to substitute for land and seed, no other interaction is significant. In Bolivia the only significant interaction is the substitution relation with seed inputs. In the Dominican Republic the complementary interaction with land is countered by the opposite interaction with labor; and in Paraguay
EDUCATION AND MODERNIZATION Schultz (1964, 1975) suggested that education is most likely to be effective in a modernizing environment , that modernization engenders disequilibria and education plays an important role in developing the capacities for dealing with disequilibrium. Lockheed et al. (1980) examined this hypothesis based on the studies that they reviewed and found on average that a modernizing environment increased the effect of education by some 10%. They may also have found in it an explanation for some of the weakness of the education variable, for in the cases of non-modernizing environments “the mean percentage increase (in productivity as a result of education) may even be negative” (p. 57). This relation appears quite strong, though several difficulties remain. The first is the definition of a modernizing environment. Take the Lockheed definitions: (non-modern environment criteria) “included primitive technology, traditional farming practices and crops, and little reported innovation or exposure to new methods”; (modern environment criteria) “included the availability of new crop varieties, innovative planting methods, erosion control, and the availability of capital inputs such as insecticides, fertilizers, and tractors or machines” (pp. 55-56). This is clearly a loose definition and is open to a good deal of judgment. It may be useful in
Education in Latin America a cross country context, though at least in Latin America there are few countries which would be categorized as non-modernizing on these criteria. For the four countries of this study, based solely on the descriptive statistics of the Appendix, Bolivia would certainly be the least modernizing environment, followed by Paraguay. Guatemala would be the most modernizing followed by the Dominican Republic. However, Bolivia and the Dominican Republic show the clearest positive effect of education, with Guatemala exhibiting only a positive interaction with labor. This does not correspond with the expected pattern. Part of the problem is that education and other indicators of modernization are generally highly correlated, that education will increase as will availability of improved seed and of machinery, etc. This is particularly true in Latin America, for there are few regions that have not begun some process of modernization, making the “modernizing-nonmodernizing” distinction very unclear (see de Janvry, 1981). In this context, as education increases, access to these inputs will also increase because access means knowledge, ability to obtain the inputs, and ability to use them. Thus it is difficult in Latin America to ascribe causality to the modernizing environment or to education. An alternative perspective is to view education as one component of a process of modernization, and to see its role as a contributor to a whole series of changes sparked by modernization. This requires examination of differences in the use of modern practices in agriculture which are associated with educational differences.6 The statistical tests appropriate for this question are differences of means tests and a Chi-square test for Bolivia since its education variable is not continuous. In all of the countries except Paraguay there is a significant difference in the education levels of those adopting modern techniques and the non-adopters. In Bolivia education levels are different across each of the five elements of modern production. In the Dominican Republic, the only exception is the use of animal power, and this may simply indicate that production techniques have moved beyond this simple innovation. This result is mirrored by the significantly lower education of those using animal power in Guatemala, where the differences of means are significant with the exception of fertilizer. In Paraguay on the other hand mean education differs in only two of the five modernization
339
indicators: the use of fertilizer, and the use of pesticides. There is no significant difference in the other three cases.’ The overall conclusion is that there is a general association between education and the indicators of modern agricultural practice. Increasing education is part of a broader and complex process of modernization. For example, although the education variable was significant in only one of the production function tests for Guatemala, its clear association with the elements of modernization may indicate that there are important limits on the contribution which modernization can make to increasing production. The Paraguay case with only two of the five indicators significant is anomalous, which suggests that other elements may dominate the production process in that country, a result consistent with the earlier tests. SOCIAL DIFFERENTIATION AND EDUCATION de Janvry and Deere (1979) have suggested a very different manner of viewing development in rural areas, that modernization hides the reality of rural development by omitting political and power relationships and the role which classes play. The key to rural development is the historical process of the destruction of rural feudal classes, who are initially supplanted by a higher degree of social differentiation, followed by a consolidation of classes into some modern capitalist form. The key construct in understanding contemporary Latin American agriculture is social differentiation, a broadening and reorganization of class relations in rural areas. In operational terms they suggest that the size of landholding can be used as a measure of social differentiation, and that it should be associated with differences in a whole series of indicators of rural life and activity, ranging from family size to type of income obtained, to agricultural practices. Their criterion for accepting that social differentiation affects a particular variable is the existence of a unilinear relation across the land size categories. Our hypothesis is that education contributes to social differentiation and therefore to development. We test that by calculating measures of association between education and indicators of social differentiation. There are three groups of these indicators.8 First are family characteristics: literacy, whether the household is headed by a male, family size,
340
Economics of Education Review
migration experience, and percent of land owned. Next are measures of integration into the market economy: distance to market, whether the farm received technical assistance, whether the farm used consumer credit, and the amount of labor used in the primary crop. Finally are measures of economic performance: revenue from crop production, cost of hired labor, net farm income, off farm income, and value of animals owned. Pearson correlation coefficients were utilized as the measure of association unless the variables were categoric, in which case Spearman correlations were calculated. Table 4 presents the results. It can be seen that more than half of the associations are significant except in Guatemala where only 5 of 12 fall in that category. So the overall pattern is that education does play an important role in rural social differentiation, in development in the political economic context. Additional information on the social differentiation process is provided by examining the three categories separately. It becomes clear that household characteristics are least affected by the educational process. This is not surprising since they are demographically driven and thus would change more gradually; and they may also be more resistant to change because of cultural influences. Education affects economic performance most clearly, with market integration in an intermediate situation. This may imply that social differentiation initially shifts production and economic activity away from household production and toward efforts to increase economic results; next markets develop and are expanded to include these former household producers. SUMMARY When
all the results
OF RESULTS are examined
and summar-
ized in Fig. 1, it is clear that education has a complex role in rural development, but one that generally is a significant contributor to a broadly defined process of development. Thus, although the understanding of the role of education in development is different from Lockheed’s, our conclusion can be stated in parallel to their conclusion: “we have hypothesized that education will have a positive effect on rural development; overall, we find confirmation for this hypothesis”. In two countries, Bolivia and the Dominican Republic, the traditional human capital understanding of education’s contribution is corroborated. The more complex human capital approach gives very mixed results and none are particularly strong, though the interaction with labor in Guatemala is significant. The role of education in modernization is again positive in Bolivia, Guatemala, and in the Dominican Republic. There is very little evidence of a significant role in the case of Paraguay. Finally, social differentiation through the educational process is a notable element of rural development in Bolivia, Paraguay, and the Dominican Republic. When all of these elements are added together, the claim that education does have an important development role to play receives confirmation in every country. However, the mechanism by which education affects development differs across the countries. In Bolivia and the Dominican Republic there is a positive effect in all dimensions. In Guatemala the effect is seen only in its interaction with labor and in the modernization process. Finally in Paraguay education’s effect is confined to social differentiation, with little contribution directly to production nor to the modernization of agriculture.
NOTES 1. The conceptual difference between the modernization and the political economy approaches is quite marked. Their empirical distinction is less clear, for many of the indicators of modernization, e.g. the use of hybrid seeds, could also be taken to evidence social differentiation. Separate sets of indicators will be utilized in the empirical sections, though the distinction will not be as neat as it appears there. 2. Although it would go far beyond what can be concluded from the data, the dominance of the country over the last 30 years by the dictatorship of Gen. Stroessner may play some role in generating this poor performance for education. That education has played a different role in that country may be signalled by the name of the first education ministry at the turn of the century, the Ministry of Justice and Education. 3. McCloskey (1985) correctly emphasizes that the magnitude of the coefficients is often more
Education
in Latin America
important in an economic sense than their significance. His criterion would suggest that education is not an important factor in the Dominican Republic. 4. The equation was also estimated using a O-l variable for use of technical assistance, a measure of non-formal education. The variable was significant only in the Dominican Republic. 5. Cases of negative and significant coefficient estimates for the education variable are not unknown. For example, Moock (1981) found a negative effect of education on farm yield. He suggested that “an a posteriori justification would need to show that those who begin school but drop out before earning over the first credential are dull from the start, or that they become demoralized or, conversely, that they develop a self-importance that blinds them to their technical incompetence” (pp. 732-733). for the major factors 6. The test using several indicators of modernization attempts to “control” which could affect education’s role. As McCloskey (1985, p. 14) notes, the inability to control all factors even in scientific experiments vitiate the attempt to falsify hypotheses. Thus in this case there may be other factors which affect the relation of education and modernization - but that problem is not specific to such bivariate comparisons. 7. Other factors which may influence the adoption of modern practices such as land tenure or elements of social differentiation may be correlated with education, and so education’s role may be overstated. One way of controlling for these effects was the use of a logit model as in Jamison and Moock (1984). We chose not to use the same approach because it reduces modernization to one dimension, e.g. the use of chemical fertilizer, whereas there are many elements to modernizing agricultural practices. These can be included in the approach used here. 8. de Janvry and Deere (1979) used different specific indicators though they related to the same three categories. They provide an extensive treatment of the theoretical bases for these indicators. Their main categorization of different social strata was by amount of land used. When this was used in our four countries, only Paraguay exhibited significant social differentiation in these terms. REFERENCES BEHRMAN, J. and BIRDSALL, N. (1983) The quality of schooling: quantity alone is misleading. Am. econ. Rev. 73, 928-946. BOWLES, S. (1978) Capitalist development and educational structures. World Dev. 6, 783-796. DE JANVRY, A. (1981) The Agrarian Question and Reformism in Latin America. Baltimore: Johns Hopkins. DE JANVRY, A. and DEERE, C. (1979) A conceptual framework for the empirical analysis of peasants. Am. J. agric. Econ. 61, 601-611. DENISON, E. (1962) The Sources of Economic Growth in the United States and The Alternatives Before Vs. NY: Committee for Economic Development. HEYNEMAN, S. and LOXLEY, W. (1983) The effect of primary school quality on academic achievement across twenty-nine high- and low-income countries. The Am. J. Social. 88, 1162-1194. JAMESON, K. (1985) Education and development: rhetoric and reality, Typescript. JAMESON, K., Peasants, productivity and pedogogy: Paraguay. In Latin America Educarion: A Quesr for Idenrity, NANCY NYSTROM (Editor), pp. 157-174. Tulane University, 1985. JAMISON, D. and MOOCK, P. (1984) Farmer education and farm efficiency in Nepal: the role of schooling, extension services, and cognitive skills. World Dev. 12, 67-86. JUST, R., ZILBERMAN, D. and HOCHMAN, E. (1983) Estimation of multicrop production functions. Am. .I. agric. Econ. 65, 770-780. LOCKHEED, M. et al. (1980) Farmer education and farm efficiency: a survey. Economic Developmenr and Cultural Change 29, 37-75. MCCLOSKEY, D. (1983) The Rhetoric of Economics. J. econ. Lit. XXI, 481-517. MCCLOSKEY, D. (1985) The Rhetoric of Economics. Madison: University of Wisconsin Press. MELLOR, J. (1976) The New Economics of Growrh. Ithaca: Cornell Universitv Press. 1976. MoocK,‘~. (1981)‘Education and technical efficiency in small farm production: Economic Developmenf and Cultural Change 29, 723-739. PHILLIPS, J. and MARBLE, R. (1986) Farmer education and efficiency: a frontier production function approach. Econ. educ. Rev. 5, 257-264. SCHULTZ, T. (1964) Transforming Traditional Agriculture. New Haven: Yale University Press. SCHULTZ, T. (1975) The value of the ability to deal with disequilibrium. J. econ. Lit. 13, 827-846. SIMMONS, J. (1979) Education for development, reconsidered. World Dev. 7, 1005-1016. WARREN, B. The postwar economic experience of the Third World. In Toward a New Strategy __ .for Development, CHAPEL, R. (Editor), pp. 144-168. N.Y: Pergamon. WELCH, F. (1970) Education in production. J. uolit. Econ. 78, 35-59. WILBER, C.K. (1978) The methodological basis of institutional economics: pattern model, storytelling and holism. J. econ. Issues XII. 61-89.
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Economics of Education Review APPENDIX Four farm level surveys of Latin American countries are used for the estimates in this paper. The countries are Paraguay, Bolivia, the Dominican Republic, and Guatemala. Each survey was the result of a co-operative venture between the U.S. Agency for International Development (USAID) and national agriculture agencies. Although the survey instrument and techniques are not identical, the variations are small enough to facilitate comparisons between countries. Each survey was based on interviews at the farm level, so data are available on a series of socio-economic characteristics and on the inputs and outputs of five major crops and three varieties of livestock. The farm level data are the observations used in all of the statistical testing. The Paraguay survey is stratified by farm size and is a random sample of 1053 farms in the eastern portion of the country. The survey was administered by the Technical Cabinet of the Ministry of Agriculture and Livestock together with USAID. It was conducted in August and September, 1976, for the July 1975-June 1976 crop year. The main focus of the survey was to study farm production (crop and animal) as well as migration and credit utilization. The Dominican Republic survey was conducted by the Secretariat of State for Agriculture in conjunction with USAID. The survey covers 1802 farms from across the country and was taken in March and April of 1976 for the July 1975-April1976 crop year. The focus of the survey was farm production, employment, and income. The Guatemala survey was conducted by Ministry of Agriculture personnel with technical support from USAID. It is a paired sample with 1548 farm level observations from across the country (excluding the Peten region). The survey was taken in the first three months of 1974 for the 1973 crop year. Its goal was to analyze the effect of credit on income, output, and employment. The Bolivia survey was a joint effort of AID and the Ministry of Rural Affairs and Agriculture, carried out in April and May of 1977; the sample includes 750 farms from the Departments of Chiquisaca, Tarija and Potosi. It concentrated on farm income. Table A presents descriptive statistics from the four surveys as a basis for comparison. The indicators are calculated from the data in which individual farms are the level of observation. These figures show that average years of formal schooling is low but that literacy is relatively high. There is substantial variation between countries in the receipt of technical assistance and in credit. The extreme case of Guatemala is a result of the nature of that sample which paired recipients and non-recipients of supervised credit; and credit recipients at that time were also receiving technical assistance. Table A shows substantial variation in input usage between countries. Usage of such inputs as fertilizer, pesticide, machinery, hired labor, and HYV seeds is higher in Guatemala. It is lowest in all
Table A. Descriptive
Average years schooling Average years of schooling Range (years) Variance % No schooling % Literate % Receiving technical assistance % Using cridit % Usine HYVs Averagg distance to market Average age of producer Average farm size % Using chemical fertilizer % Using machinery % Using pesticide % Using hired labor % Using animal power
statistics
Paraguay
Dominican Republic
2.60
2.87
O-16 4.4 22.5% 76.5% 5.2% 29.1% 14.1% 11.1 km 48.0 yrs 11.6 ha 13.3% 4.5% 22.2% 6.1% 45.2%
o-21 8.2 41.4% 62.4% 20.8% 31.2% 32.3% 9.3 km 52.6 yrs 13.9 ha 33.0%7
32.6% 40.6%
Guatemala
Bolivia
1.97
1.8*
O-18 5.8 43.2% 64.8% 51.6% 52.3% 64.2% 12.4 km 44.6 yrs 10.9 ha 68.8% 33.2% 50.4% 88.8% 38.1%
48.9% 51.07% 2.1% 6.7% 3.6% 9.0 km 45.6 yrs 3.9 ha 7.9% 0.6% 11.7% 26.9% 78.3%
*The Bolivian data are categoric. This lowers the mean and makes the other statistics misleading. tNo separate breakdown for fertilizer and pesticide.
Education in Latin America
343
cases for Bolivia. The one exception is that animal power is used most frequently in Bolivia. This allows a scaling of the modernizing environment (Schultz, 1964) across countries. Bolivia is at the low end, and for this sample Guatemala would be at the high end, with the other two in an intermediate position. In Table B is the farm size distribution in the various samples. In each of the surveys the bulk of farms are less than 35 ha and above 0.7 ha, meaning that the surveys deal with small and medium sized farms. In the Dominican Republic and in Bolivia there is a larger sampling of “microfincas” (less than 0.7 ha). Table C shows farm tenancy patterns in the three surveys. There is substantial variation. In Bolivia, Guatemala and the Dominican Republic most farms are owner operated. In Paraguay farms are either rented or a mix of owned and rented land. Table D describes the variables used in the analysis.
Table B. Land size distributions Farm size
Paraguay
Dominican Republic
Guatemala
Bolivia
Less than 0.7 ha 0.7-4.0 ha 4.0-7.0 ha 7.0-35.0 ha 35.0-350.0 ha 350 ha +
4.0% 23.8 12.6 54.9 4.4 0.0
24.5% 36.0 12.3 20.5 6.8 0.1
2.9% 43.0 21.7 27.5 5.1 0.2
23.6% 51.4 12.8 11.4 0.7 0.0
Table C. Farm tenancy patterns
Status
Paraguay
Dominican Republic
Guatemala
Bolivia
Owned Cash rent Crop rent Mixed Other
32.5% 13.4 17.9 30.7 5.3
73.7% 2.6 2.8
74.1% 7.9 1.0 12.5 4.4
95.4% 0.5 1.3 0.4 2.4
20.7
Table D. Description of variables in regressions Output - value of crop and animal output (in country currency). Labor - quantity of labor used in production (man-days). Land - quantity of land used in production in hectares. Seed-quantity or value of seed used in production (Paraguay - kilograms, Dominican Republic pesos, Guatemala - kilograms). Fertilizer - quantity of fertilizer used in production (Paraguay - kilograms, Dominican Republic pounds, Guatemala - kilograms). Machinery - quantity or value of machinery used in crop production (Paraguay - initial cost guaranis, Dominican Republic - pesos, Guatemala - passes over field). Animal use - animal days used in crop production; Paraguay - cost of animals rented. Education - years of formal schooling, except in Bolivia where the following categories were used: school; l-3 years of school; more than three years of school. Technical assistance (dummy) - no assistance = 0, assistance = 1.
in no