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Agricultural development and the size distribution of personal income: The tropical African experience
World bweloptmr.
Vol. 16, No. 4, PQ. 482-488. 1988.
Printed in Great Britain.
0305-750x/88 s3.M) + 0.00 @ 1988 Pergamon Press plc
Agricultural Development and the Size Distribution of Personal Income: The Tropical African Experience KWABENA GYIMAH-BREMPONG* New College of the University of South Florida, Sarasota We use data from a sample of countries in Tropical Africa to investigate the relationship between relative income and growth rate of the agricultural sector and income inequality. We find that an increase in the income of the agricultural sector relative to that of the non-agricultural sector decreases income inequality. The growth rate of agricultural production and the mmortion of the labor force in the non-agricultural sector were also found to be negatively cokelsted with income inequality. Summary. -
development policies that generate employment for large numbers of unskilled laborers are likely to improve income distribution more than policies that generate the employment of capital or skill-intensive labor. In a LewisJFei-Ranis type dualistic economy, change in inequality comes from two sources inequality within the sectors and inequality between sectors. Development policies that focus attention on increasing productivity in the large lower income sector not only decrease the intersectoral income inequality but may decrease the intra-sectoral inequality. 3*4On the other hand, if policies lead to the employment of capital and skill-intensive labor, not only will intersectoral inequality increase, but intrasectoral inequality will increase as welL5 Empirical studies have found a relationship between sectoral emphasis in development policy and relative income distribution. Fei, Ranis and Kuo (1978a, 1978b) find that a large part of the reduction in relative income inequality in Taiwan during the early phase of its economic development can be attributed to improved distribution in the rural areas, itself a function of land reform and agricultural development; Bequele and Van der Hoeven (1980) cite evidence to show that in
1. INTRODUCTION Tropical African countries witnessed respectable economic growth rates in the 196Os, though these growth rates declined in the 1970s with the onset of the international oil crisis. The performance of the agricultural sector during this period of growth varied a great deal among countries in Tropical Africa. In some countries (e.g., Ivory Coast, Malawi, Cameroon and Zimbabwe), growth was, by and large, led by the agricultural sector, while in other countries (e.g., Ghana, Guinea, and Tanzania) the agricultural sector stagnated or in some cases witnessed an absolute decline. Neglect of the agricultural sector may, in part, account for the food problems that surfaced in these countries in the early 1980s. In addition to differences in the performance of the agricultural sector, there was a marked variation among these nations in the distribution of the benefits of economic growth among various income classes. Some countries have reduced relative income inequality with growth while others have increased relative income inequality with economic growth. Early studies of the relationship between income distribution and economic growth would predict that inequality will increase with economc growth, at least during the initial phase of growth.’ Recent empirical studies, however, suggest that Kuznets’ hypothesis is not inevitable.2 They suggest that relative as well as absolute income inequality is determined by the character of economic growth rather than growth per se. They also suggest that
*Research work was done while the author was a Visiting Scholar at The Center for African Studies, University of Florida. The paper has benefited from the comments of two anonymous referees of this journal. The author, however, is solely responsible for any remaining errors. 483
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Tropical African countries. where agricultural production has stagnated. income distribution has become more unequal. Chow and Paperneck (1981) find that labor-intensive growth has improved income distribution in Hong Kong. There is ample evidence to suggest a strong relationship between relative factor intensity (and hence sectoral composition of growth) and income inequality. In Tropical Africa, the labor-intensive sector is the large agricultural sector, yet no attempt has been made, empirically, to link income distribution directly to the performance of the agricultural sector. This paper attempts to link relative income inequality to the performance of the agricultural sector during the early transition growth period in Tropical Africa.’ In open dualistic land surplus economies, like those of Tropical Africa where an overwhelming majority of the people live from subsistence farming, development policy based on increasing agricultural productivity increases incomes of the majority of people. This is more so in the case of Tropical Africa, where the land tenure system is not an impediment to entry into agriculture and where agriculture uses very little capital.’ Since it is easy to enter the azricultural sector, this sector can be improved w%hout increasing inequality in that sector while decreasing intersectoral income differentials. While most studies in income distribution have looked at one country at a particular moment in time or a country over time, this paper will look at the distribution of income in a cross-section of nations. Though there is an inherent danger of cross-national comparison of income distributions, it is hoped that the advantages gained by looking at the importance of sectoral influences in the initial phase of development may outweigh problems caused by cultural and historical differences among nations. The remainder of this paper is organized as follows: Section 2 discusses the theoretical foundations of the paper; Section 3 examines the data and Section 4 reports the statistical results. Section 5 concludes the paper.
spectively, while intrasectoral inequality are G” and G’. Total income inequality is a function of intrasectoral inequality, average sectoral income and the distribution of the population between sectors. G = G(W,w’,~,~,G“,G’)
(la)
where G = index of total income inequality and$
aci aci
aw
W > w”
+ p = 1.
aw’, < 0,
aci
aca,
am
act > 0
act apl = - ac/ ap 3 0.
With w’ > w” (ci la LewisIFei-Ranis), an increase in W, G”, and G’, all things equal, will increase inequality. An increase in W’, all things equal, decrease inquality. Changes in W and w” affect total inequality through their effects on intersectoral income differentials. An increase in the proportion of the population in the higher income sector, all things equal. cannot increase inequality if inequality in the high income sector is not higher than inequality in the low income sector. An increase in the proportion of the population in the low income sector cannot decrease income inequality. Equation (1) can be written as: G’ = G’ (D.~,G”.G’)
(lb)
where D = W’/w’ and we have taken into account the fact that r = 1 - p. aG’/ aD c 0. All other variables and their relationship with G’ remain as in (1). Equation (lb) implies that the relative income inequality depends upon agricultural income relative to non-agricultural income, the relative size of the non-agricultural sector, and inequality within the agricultural and non-agricultural sectors, respectively. Changes in relative inequality will depend upon changes in these variables. This implies that:
act/ at = w[
am
at,
aj? at, z:( ackrat)]
2. THE MODEL
k = a,i.
The theoretical base of this study is provided by the income inequality accounting framework of Fei, Ranis and Kuo (1978a) and Fields (1980). We do not repeat the models here but just mention the essentials.* We assume that the economy is divided into two sectors, agriculture (A) and non-agriculture (I).; average income in these sectors are Up and W’, respectively. The proportion of the population in each sector are p and p, re-
Equations (lb) and (2) are estimated in the statistical section.
(2)
3. THE DATA SOURCES The measure of relative income inequality adopted in this paper is the Gini coefficient of income distribution (GINI). We adopt the Gini as
AGRICULTURAL
DEVELOPMENT
the measure of income inequality because it is the one measure that has been calculated for African countries and hence is available to us. Relative agricultural income (AGRINC) is measured as the ratio of real per capita income in the agricultural sector to the real per capita income in the non-agricultural sector. This measure accounts for population sizes and changes in population in these sectors. The share .of the non-agricultural sector in the economy Cp) is represented by the proportion of the labor force employed on nonagricultural occupations during the year in which the Gini coefficient was calculated for a country. We label this variable INEMP. It was not possible to obtain data in sectoral inequality in most of the countries in our sample. Nor could we find any suitable proxy. We therefore excluded these variables from the regression analysis.’ There have been many references in the literature to the notion that a high overall economic growth rate makes possible the redistribution of income to reduce income inequality. To test the validity of this hypothesis for Tropical African countries, the average growth rate of real per capita income between 1960 and 1979 (GRATE) was included in the regression equation. It is possible that GRATE’s influence on income inequality could have its sources in the growth of the agricultural sector or growth in the nonagricultural sector. If GRATE is found to be a significant determinant of GINI, we reestimate the GIN1 equation, breaking GRATE into growth rate of the agricultural sector (AGRO) and growth rate of the non-agricultural sector (NONAGRO) to find the source of the growth effect in GINI. Data from 13 African countries were used in the regression analysis. to Because some countries had more than one observation, the total number of observations was 28. The data are not annual data but are collected at irregular intervals. GRATE, AGRO, NONAGRO, and INEMP were obtained from the World Bank’s World Tables 1980 and 1974 and the Economic Surveys of the various countries. The GRATE data obtained were also checked against those in World Bank’s World Development Report, 1983. AGRINC was calculated from the International Labor Organization’s Yearbook of Labor Stattitfcs for the various years. Data on GIN1 were collected from various sources. For the Ivory Coast, data were collected from World Bank (1978); for Ghana, Killick (1978); for Zambia, IL0 (1977). For all other countries and one observation for Zambia, the GIN1 data were from Jain (1975). Summary statistics of the data used for the study are presented in Table 1. From Table 1, it is clear
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485
Table 1. Summary statistics of sample data
Variable
Mean value
GIN1 GRATE AGRINC INEMP AGRO NONAGRO CGINI CGRATE CAGRINC CINEMP
0.476 2.290 0.473 22.14 2.07 2.53 0.009 1.34 -0.001 3.92
Number of observations
= 28.
Standard deviation 0.114 5.260 0.240 12.50 5.82 6.05 0.078 4.22 0.113 5.501
that there is a lot of variation in the sample data. It has not been possible to standardize definitions of income, spatial coverage, the income earning unit, or the time of coverage for the calculations of the GIN1 variable. The sample period also differed from country to country. However, every observation was either in the 1960s or the 1970s - by which time Tropical African countries had obtained their political independence, and had had time to chart their own development paths. Though the quality of the data is low, we hope that our approach will provoke more research in this field. Because of our interest in the dynamic behavior of income inequality as well as the static accounting of relative income inequality, two equations - (lc) and (2b) - are first estimated in this paper. The equations we estimate are: GIN1 = a0 + al AGRINC + a2 INEMP + a3 GRATE + e (lc) CGINI = bo + bl CAGRINC + b2 CINEMP + b3 CGRATE + v (2b) where CGINI, CAGRINC, CINEMP, and CGRATE are the first difference in GINI, AGRINC, INEMP, and GRATE, respectively; and e, v are error terms assumed to obey classical assumptions. Because we took the first difference in the CGINI equation, we lose 13 observations, leaving us with only 15 observations for equation (2b). As indicated in Section 2, the coefficient of AGRINC and INEMP are expected to be negative, while the coefficient of GRATE is expected to be negative if the redistribution through growth hypothesis is valid for Tropical Africa and is positive otherwise.
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4. ECONOMETRIC
RESULTS
The dependent variable, GINI, is regressed on the independent variables using ordinary least squares (OLS) technique. The error structure is assumed to follow classical assumptions. This assumption may not be realistic in view of the fact that we are dealing with cross-sectional data. The error structure may therefore be heteroskedastic. The structure of the error terms has to be determined from the data in such a case. We therefore had to test for heterodasticity. A Park test could not reject the null hypothesis that the error structure is homoskedastic. The estimated equations are reported in equations (3a) and (4a), respectively, below: GINI
= 0.880 -0.006 AGRINC (9.76)* (6.20) -0.006 GRATE -0.004 INEMP (3a) (3.43) (2.411 F = 11.21 R = 0.627 I?’ = 0.576 N = 28 SEE = 0.148 *absolute value of I-statistics in parentheses.
The regression results show that the three variables explain a fairly high proportion of the variations in GIN1 in our sample. The three variables explain over 60% of the variance in GINI. The F statistic and other statistics indicate an overall good fit to the data. AGRINC and INEMP have the expected signs and are significantly different from zero at the 0.01 level of significance. This is consistent with our hypothesis that increased relative income of the agricultural sector decreases relative income inequality in less developed countries (LDCs). GRATE has a negative and significant coefficient, indicating that the data support the redistribution through growth hypothesis. Because of our interest in the coefficient of AGRINC, we did some experiments to test the stability of this coefficient. We did a stepwise regression starting with AGRINC and added the other variables (one at a time). The coefficient of AGRINC was remarkably stable throughout these experiments. We also deleted some observation and reestimated the full equation. In this regression, the coefficient of AGRINC decreased slightly, but it maintained its sign and significance. The first difference version of the regression is presented in equation (4a): CGINI
= 0.004 -0.013 CAGRINC (2.76)* (1.91) -0.018 CGRATE -0.015 (1.08) (2.90)
CINEMP (4a)
F = 13.64 R2 = 0.752 I?’ = 0.684 N = 15 SEE = 0.017 *absolute value of t-statistics in parentheses. Equation (4a) is very similar to the results obtained and discussed in equation (3a). We will therefore not spend much time discussing it. We observe, however, that in dynamic terms AGRINC still has a negative and significant relationship with relative income inequality. The estimated equations indicate that a higher economic growth rate is associated with lower income inequality. Growth rate of national income, however, is a weighted average of the growth rates of agricultural and non-agricultural sectors, respectively. This means the reduction in inequality associated with a higher economic growth rate could be caused by a high growth rate in the agricultural sector, by a higher growth rate in the non-agricultural sector, or both. To find out the growth in which sector causes lower inequality, we reestimate the GIN1 equation, separating GRATE into the growth rate of the agricultural sector (AGRO) and growth rate of the non-agricultural sector (NONAGRO). The estimated equation is presented below. GIN1 = 0.609 -0.2991 AGRINC (6.203)* (2.544) -0.0009 INEMP -0.0076 AGRO (1.342) (2.70) + 0.00008 NONAGRO (0.189) F = 14.71 R2 = 0.719 R2’ = 0.670 N = 28 SEE = 0.101 *absolute value of f-statistics in parentheses. The coefficient of AGRINC increases sharply in absolute terms while that of INEMP decreases in absolute terms. However, both of them maintain their negative signs and are statistically significant at the 0.1 significance level. AGRO has a negative and statistically significant coefficient. NONAGRO, however, has a positive but statistically insignificant coefficient. This result implies that it is the growth rate of the agricultural sector, rather than the growth rate of the whole economy, which reduces income inequality in our sample. This result is consistent with theoretical expectations since a high growth rate in the agricultural sector, all things equal, decreases the intersectoral income inequality and hence the overall inequality. 5. CONCLUSION This paper has investigated
the relationship
.
AGRICULTURAL
DEVELOPMENT
between relative income and growth rate in the agricultural sector and income inequality in Tropical Africa. The hypothesis that growth polities that emphasize a relative increase in agricultural income decrease relative income inequality was supported by data from 13 countries in Tropical Africa. We also find that increased employment in the non-agricultural sector and a high growth rate in the agricultural sector decrease income inequality. This implies that increased inequality during the initial phase of economic growth is not inevitable. Inequality is, at least in part, a matter of development strategy. Certain policy implications flow from this re-
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suit. It appears that African countries can not only solve their food problems by paying more attention to increasing productivity in the agricultural sector, but they can also reduce relative income inequality through such a growth process. Within Tropical Africa, such productivity increases could be achieved through the use of price incentives, the provision of rural infrastructure such as roads, and the provision of simple farm inputs at the appropriate farming seasons. With a very large proportion of the agricultural population poor, such a development strategy will not only decrease relative inequality; it will also decrease absolute poverty.”
NOTES 1. For example, see Adelman and Robinson (1978); Kuznets (1%3); and Paukert (1973). 2. See, for example, Adelman and Robinson (1978); Brutton (1977); Blitzer (1974); Chenery er al. (1974); Chow and Paperneck (1981); Fei, Ranis and Kuo (1978a, 1978b); Fields (1980); Frank and Webb (1977). 3. Fields (1980) calls growth that increases income in the traditional agricultural sector, all things equal, traditional sector enrichment growth. The growth strategy that we describe here is related to but not the same as traditional sector enrichment growth. While Fields’ traditional sector enrichment growth requires incomes in the traditional sector to grow, incomes in other sectors remaining constant. the growth we characterize requires incomes in all sectors to grow. We only require that incomes in the agricultural sector grow fasterthan growth in other sectors. 4. Bequele and Van der Hoeven (1980) cite evidence to indicate that in African countries where agricultural production has stagnated, relative income inequality has increased. 5. This occurs because very little labor is absorbed by the industrial sector while economic rents of capitalists increase. Fei and Ranis have shown that labor’s share of industrial output will increase in the initial phase of industrialization if and only if the labor absorption bias in technical change is large enough to overwhelm the innovation intensity effect. See Fei and Ranis (1964), Chap. 3. In African countries where all industrial technological change is embodied in machinery imported from labor-scarce western industrial nations, the labor absorption bias is not likely to exceed the innovation intensity bias.
6. Transition growth period is the period between the colonial growth epoch and modem growth epoch during which a former colony prepares itself for modem economic growth. For detailed definition and characterization of transition growth, see Paauw and Fei (1973). 7. In most Tropical African countries, land is communally held. Every citizen has use-right to land but not ownership right. This, in part, accounts for the low land concentration ratios in Africa. For more on land tenure systems and how it affects agricultural production, see USDA (1981). 8.
See Fei ef al. (1978a) and Fields (1980) for details.
9. One could also make the assumption that the ratio of inequality in the agricultural sector to that of the non-agricultural sector remains constant across countries to justify the exclusion of these variables. Such an assumption is too restrictive and may not be justified. We do not pursue this alternative approach. 10. The countries are Benin, Botswana, Gabon, Ghana, Ivory Coast, Kenya, Malawi, Senegal, Sierra Leone, Sudan, Tanzania, Uganda, and Zambia. Data availability dictated the countries to be included in the sample. The only exceptions are countries whose income distribution would be affected by economic and political domination by a racial minority (e.g., Zimbabwe) or where natural and man-made disasters have precluded any meaningful economic development (e.g., Chad). 11. See International 1979).
Labour
Organization
(1976;
REFERENCES Acharya, A. N., “Perspectives and problems of development in Subsaharan Africa,” World Development Vol. 9, No. 2 (1981).
Adelman, I., and S. Robinson, Income Distribution Policy in Developing Countries: A Cae Study of Korea (New York: Oxford University Press, 1978).
. * 488
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Ahluwalia, M., N. Carter, and H. Chenery; “Growth and poverty in developing countries.” Journal of Developmenf Economics (September 1979). Bequele, A., and R. Van der Hoeven, “Poverty and inequality in subsaharan Africa,” fnrernarional Labor Review, Vol. 119 (May/June 1980). Blitzer, C. R., “Development and income distribution in a dual economy,” Journal of Development Economics (September 1979). Brutton, H. J., “Industrialization policy and income distribution.” in C. Frank Jr. and R. Webb (Eds.), Income Distribution and Growth in Less Developed Counrries (Washington. DC: Brookings Institution,
1977). Chenery, H. et al., Redistribution With Growth (London: Oxford University Press, 1974). Chow, S. C., and G. F. Paperneck, “Laissez faire growth and equity: The Hong Kong case,” Economic Journal (June 1981). Fei, J. C. H., and G. Ranis, Development of the Labor Surplus Economy: Theory and Policy (Homewood, IL: Irwin, 1964). Fei, J. C. H., G. Ranis, and S. Kuo, “Growth and the family distribution of income by factor components,” Quarrerlv Journal of Economics (February 1978a). Fee J. C.-H., G. Ranis, and S. l&o, Gr&vfh Wiih Equity: The Taiwan Case (New York: Oxford University Press, 1978b). Fields, G., Poverty, Inequality and Development (New York: Cambridge Universitv Press. 1980). Fields, G., and J.-C. H. Fei, *‘On inequalit; comparisons,” Econometrica Vol. 64, No. 2 (March 1978). Frank Jr., C., and R. Webb, “Causes of income distribution and growth in less developed countries: Some reflections on the relation between theory and policy,” in C. Frank Jr., and R. Webb (Eds.), Income Distribution and Growth in Less Developed Countries (Washington, DC: Brookings Institution, 1977).
Labour Organization, Employmenf, Growrh, and Basic Needs (Geneva: ILO, 1976). International Labour Organization, Zambia’s Income Distribufion in the Seventies (Geneva: ILO, 1977). International Labour Organization, Poverfy and fneauolirv in Africa (Geneva: ILO, i979). Jai;, S., kize D&rib&on of Income: A Compilafion of Data fwashineton. DC: World Bank, 1975). Killick, T., Devilopment Economics in Acrion (New International
York: St. Martin’s Press, 1978). Kuznets, S.; “Quantitative aspects of economic growth of nations III: Distribution of income by size,” Economic Development and Cultural Change, Vol. 11 (January 1963). Paauw, D. S., and J. C. H. Fei. The Transition in Open Dualistic Economies: Theory and Southeast Asian Experience (New Haven, CT: Yale University Press,
1973. Paukert. F.. “Income distribution at different levels of development: A survey of evidence,” International Labor Review. Vol. 108 (September/October 1973). Pyatt, G., “On the interpreiatibn and disaggregation of Gini coefficients,” Economic Journal (June 1976). Sahota, G. S., “Theories of personal income distribution: A survey article,” Journal of Economic Literature (March 1978). US Department of Agriculture, Food Problems in Subsaharan Africa in the 1980s (Washington. DC: GPO, 1981). Van Ginneken, W.. Rural and Urban income Inequalities in Indonesia, Tunisia (Geneva:
Mexico,
Pakistan,
Tanzania and
ILO, 1976).
World Bank, Ivory Coast: The Challenge of Success (Baltimore. MD: Johns Hopkins University Press, i978). World Bank, Accelerated Development in Subsaharan Africa (Washington, DC: World Bank Publications, 1981).
Vol. 16, No. 4, PQ. 482-488. 1988.
Printed in Great Britain.
0305-750x/88 s3.M) + 0.00 @ 1988 Pergamon Press plc
Agricultural Development and the Size Distribution of Personal Income: The Tropical African Experience KWABENA GYIMAH-BREMPONG* New College of the University of South Florida, Sarasota We use data from a sample of countries in Tropical Africa to investigate the relationship between relative income and growth rate of the agricultural sector and income inequality. We find that an increase in the income of the agricultural sector relative to that of the non-agricultural sector decreases income inequality. The growth rate of agricultural production and the mmortion of the labor force in the non-agricultural sector were also found to be negatively cokelsted with income inequality. Summary. -
development policies that generate employment for large numbers of unskilled laborers are likely to improve income distribution more than policies that generate the employment of capital or skill-intensive labor. In a LewisJFei-Ranis type dualistic economy, change in inequality comes from two sources inequality within the sectors and inequality between sectors. Development policies that focus attention on increasing productivity in the large lower income sector not only decrease the intersectoral income inequality but may decrease the intra-sectoral inequality. 3*4On the other hand, if policies lead to the employment of capital and skill-intensive labor, not only will intersectoral inequality increase, but intrasectoral inequality will increase as welL5 Empirical studies have found a relationship between sectoral emphasis in development policy and relative income distribution. Fei, Ranis and Kuo (1978a, 1978b) find that a large part of the reduction in relative income inequality in Taiwan during the early phase of its economic development can be attributed to improved distribution in the rural areas, itself a function of land reform and agricultural development; Bequele and Van der Hoeven (1980) cite evidence to show that in
1. INTRODUCTION Tropical African countries witnessed respectable economic growth rates in the 196Os, though these growth rates declined in the 1970s with the onset of the international oil crisis. The performance of the agricultural sector during this period of growth varied a great deal among countries in Tropical Africa. In some countries (e.g., Ivory Coast, Malawi, Cameroon and Zimbabwe), growth was, by and large, led by the agricultural sector, while in other countries (e.g., Ghana, Guinea, and Tanzania) the agricultural sector stagnated or in some cases witnessed an absolute decline. Neglect of the agricultural sector may, in part, account for the food problems that surfaced in these countries in the early 1980s. In addition to differences in the performance of the agricultural sector, there was a marked variation among these nations in the distribution of the benefits of economic growth among various income classes. Some countries have reduced relative income inequality with growth while others have increased relative income inequality with economic growth. Early studies of the relationship between income distribution and economic growth would predict that inequality will increase with economc growth, at least during the initial phase of growth.’ Recent empirical studies, however, suggest that Kuznets’ hypothesis is not inevitable.2 They suggest that relative as well as absolute income inequality is determined by the character of economic growth rather than growth per se. They also suggest that
*Research work was done while the author was a Visiting Scholar at The Center for African Studies, University of Florida. The paper has benefited from the comments of two anonymous referees of this journal. The author, however, is solely responsible for any remaining errors. 483
WORLD
48-t
DEVELOPMENT
Tropical African countries. where agricultural production has stagnated. income distribution has become more unequal. Chow and Paperneck (1981) find that labor-intensive growth has improved income distribution in Hong Kong. There is ample evidence to suggest a strong relationship between relative factor intensity (and hence sectoral composition of growth) and income inequality. In Tropical Africa, the labor-intensive sector is the large agricultural sector, yet no attempt has been made, empirically, to link income distribution directly to the performance of the agricultural sector. This paper attempts to link relative income inequality to the performance of the agricultural sector during the early transition growth period in Tropical Africa.’ In open dualistic land surplus economies, like those of Tropical Africa where an overwhelming majority of the people live from subsistence farming, development policy based on increasing agricultural productivity increases incomes of the majority of people. This is more so in the case of Tropical Africa, where the land tenure system is not an impediment to entry into agriculture and where agriculture uses very little capital.’ Since it is easy to enter the azricultural sector, this sector can be improved w%hout increasing inequality in that sector while decreasing intersectoral income differentials. While most studies in income distribution have looked at one country at a particular moment in time or a country over time, this paper will look at the distribution of income in a cross-section of nations. Though there is an inherent danger of cross-national comparison of income distributions, it is hoped that the advantages gained by looking at the importance of sectoral influences in the initial phase of development may outweigh problems caused by cultural and historical differences among nations. The remainder of this paper is organized as follows: Section 2 discusses the theoretical foundations of the paper; Section 3 examines the data and Section 4 reports the statistical results. Section 5 concludes the paper.
spectively, while intrasectoral inequality are G” and G’. Total income inequality is a function of intrasectoral inequality, average sectoral income and the distribution of the population between sectors. G = G(W,w’,~,~,G“,G’)
(la)
where G = index of total income inequality and$
aci aci
aw
W > w”
+ p = 1.
aw’, < 0,
aci
aca,
am
act > 0
act apl = - ac/ ap 3 0.
With w’ > w” (ci la LewisIFei-Ranis), an increase in W, G”, and G’, all things equal, will increase inequality. An increase in W’, all things equal, decrease inquality. Changes in W and w” affect total inequality through their effects on intersectoral income differentials. An increase in the proportion of the population in the higher income sector, all things equal. cannot increase inequality if inequality in the high income sector is not higher than inequality in the low income sector. An increase in the proportion of the population in the low income sector cannot decrease income inequality. Equation (1) can be written as: G’ = G’ (D.~,G”.G’)
(lb)
where D = W’/w’ and we have taken into account the fact that r = 1 - p. aG’/ aD c 0. All other variables and their relationship with G’ remain as in (1). Equation (lb) implies that the relative income inequality depends upon agricultural income relative to non-agricultural income, the relative size of the non-agricultural sector, and inequality within the agricultural and non-agricultural sectors, respectively. Changes in relative inequality will depend upon changes in these variables. This implies that:
act/ at = w[
am
at,
aj? at, z:( ackrat)]
2. THE MODEL
k = a,i.
The theoretical base of this study is provided by the income inequality accounting framework of Fei, Ranis and Kuo (1978a) and Fields (1980). We do not repeat the models here but just mention the essentials.* We assume that the economy is divided into two sectors, agriculture (A) and non-agriculture (I).; average income in these sectors are Up and W’, respectively. The proportion of the population in each sector are p and p, re-
Equations (lb) and (2) are estimated in the statistical section.
(2)
3. THE DATA SOURCES The measure of relative income inequality adopted in this paper is the Gini coefficient of income distribution (GINI). We adopt the Gini as
AGRICULTURAL
DEVELOPMENT
the measure of income inequality because it is the one measure that has been calculated for African countries and hence is available to us. Relative agricultural income (AGRINC) is measured as the ratio of real per capita income in the agricultural sector to the real per capita income in the non-agricultural sector. This measure accounts for population sizes and changes in population in these sectors. The share .of the non-agricultural sector in the economy Cp) is represented by the proportion of the labor force employed on nonagricultural occupations during the year in which the Gini coefficient was calculated for a country. We label this variable INEMP. It was not possible to obtain data in sectoral inequality in most of the countries in our sample. Nor could we find any suitable proxy. We therefore excluded these variables from the regression analysis.’ There have been many references in the literature to the notion that a high overall economic growth rate makes possible the redistribution of income to reduce income inequality. To test the validity of this hypothesis for Tropical African countries, the average growth rate of real per capita income between 1960 and 1979 (GRATE) was included in the regression equation. It is possible that GRATE’s influence on income inequality could have its sources in the growth of the agricultural sector or growth in the nonagricultural sector. If GRATE is found to be a significant determinant of GINI, we reestimate the GIN1 equation, breaking GRATE into growth rate of the agricultural sector (AGRO) and growth rate of the non-agricultural sector (NONAGRO) to find the source of the growth effect in GINI. Data from 13 African countries were used in the regression analysis. to Because some countries had more than one observation, the total number of observations was 28. The data are not annual data but are collected at irregular intervals. GRATE, AGRO, NONAGRO, and INEMP were obtained from the World Bank’s World Tables 1980 and 1974 and the Economic Surveys of the various countries. The GRATE data obtained were also checked against those in World Bank’s World Development Report, 1983. AGRINC was calculated from the International Labor Organization’s Yearbook of Labor Stattitfcs for the various years. Data on GIN1 were collected from various sources. For the Ivory Coast, data were collected from World Bank (1978); for Ghana, Killick (1978); for Zambia, IL0 (1977). For all other countries and one observation for Zambia, the GIN1 data were from Jain (1975). Summary statistics of the data used for the study are presented in Table 1. From Table 1, it is clear
AND PERSONAL
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485
Table 1. Summary statistics of sample data
Variable
Mean value
GIN1 GRATE AGRINC INEMP AGRO NONAGRO CGINI CGRATE CAGRINC CINEMP
0.476 2.290 0.473 22.14 2.07 2.53 0.009 1.34 -0.001 3.92
Number of observations
= 28.
Standard deviation 0.114 5.260 0.240 12.50 5.82 6.05 0.078 4.22 0.113 5.501
that there is a lot of variation in the sample data. It has not been possible to standardize definitions of income, spatial coverage, the income earning unit, or the time of coverage for the calculations of the GIN1 variable. The sample period also differed from country to country. However, every observation was either in the 1960s or the 1970s - by which time Tropical African countries had obtained their political independence, and had had time to chart their own development paths. Though the quality of the data is low, we hope that our approach will provoke more research in this field. Because of our interest in the dynamic behavior of income inequality as well as the static accounting of relative income inequality, two equations - (lc) and (2b) - are first estimated in this paper. The equations we estimate are: GIN1 = a0 + al AGRINC + a2 INEMP + a3 GRATE + e (lc) CGINI = bo + bl CAGRINC + b2 CINEMP + b3 CGRATE + v (2b) where CGINI, CAGRINC, CINEMP, and CGRATE are the first difference in GINI, AGRINC, INEMP, and GRATE, respectively; and e, v are error terms assumed to obey classical assumptions. Because we took the first difference in the CGINI equation, we lose 13 observations, leaving us with only 15 observations for equation (2b). As indicated in Section 2, the coefficient of AGRINC and INEMP are expected to be negative, while the coefficient of GRATE is expected to be negative if the redistribution through growth hypothesis is valid for Tropical Africa and is positive otherwise.
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4. ECONOMETRIC
RESULTS
The dependent variable, GINI, is regressed on the independent variables using ordinary least squares (OLS) technique. The error structure is assumed to follow classical assumptions. This assumption may not be realistic in view of the fact that we are dealing with cross-sectional data. The error structure may therefore be heteroskedastic. The structure of the error terms has to be determined from the data in such a case. We therefore had to test for heterodasticity. A Park test could not reject the null hypothesis that the error structure is homoskedastic. The estimated equations are reported in equations (3a) and (4a), respectively, below: GINI
= 0.880 -0.006 AGRINC (9.76)* (6.20) -0.006 GRATE -0.004 INEMP (3a) (3.43) (2.411 F = 11.21 R = 0.627 I?’ = 0.576 N = 28 SEE = 0.148 *absolute value of I-statistics in parentheses.
The regression results show that the three variables explain a fairly high proportion of the variations in GIN1 in our sample. The three variables explain over 60% of the variance in GINI. The F statistic and other statistics indicate an overall good fit to the data. AGRINC and INEMP have the expected signs and are significantly different from zero at the 0.01 level of significance. This is consistent with our hypothesis that increased relative income of the agricultural sector decreases relative income inequality in less developed countries (LDCs). GRATE has a negative and significant coefficient, indicating that the data support the redistribution through growth hypothesis. Because of our interest in the coefficient of AGRINC, we did some experiments to test the stability of this coefficient. We did a stepwise regression starting with AGRINC and added the other variables (one at a time). The coefficient of AGRINC was remarkably stable throughout these experiments. We also deleted some observation and reestimated the full equation. In this regression, the coefficient of AGRINC decreased slightly, but it maintained its sign and significance. The first difference version of the regression is presented in equation (4a): CGINI
= 0.004 -0.013 CAGRINC (2.76)* (1.91) -0.018 CGRATE -0.015 (1.08) (2.90)
CINEMP (4a)
F = 13.64 R2 = 0.752 I?’ = 0.684 N = 15 SEE = 0.017 *absolute value of t-statistics in parentheses. Equation (4a) is very similar to the results obtained and discussed in equation (3a). We will therefore not spend much time discussing it. We observe, however, that in dynamic terms AGRINC still has a negative and significant relationship with relative income inequality. The estimated equations indicate that a higher economic growth rate is associated with lower income inequality. Growth rate of national income, however, is a weighted average of the growth rates of agricultural and non-agricultural sectors, respectively. This means the reduction in inequality associated with a higher economic growth rate could be caused by a high growth rate in the agricultural sector, by a higher growth rate in the non-agricultural sector, or both. To find out the growth in which sector causes lower inequality, we reestimate the GIN1 equation, separating GRATE into the growth rate of the agricultural sector (AGRO) and growth rate of the non-agricultural sector (NONAGRO). The estimated equation is presented below. GIN1 = 0.609 -0.2991 AGRINC (6.203)* (2.544) -0.0009 INEMP -0.0076 AGRO (1.342) (2.70) + 0.00008 NONAGRO (0.189) F = 14.71 R2 = 0.719 R2’ = 0.670 N = 28 SEE = 0.101 *absolute value of f-statistics in parentheses. The coefficient of AGRINC increases sharply in absolute terms while that of INEMP decreases in absolute terms. However, both of them maintain their negative signs and are statistically significant at the 0.1 significance level. AGRO has a negative and statistically significant coefficient. NONAGRO, however, has a positive but statistically insignificant coefficient. This result implies that it is the growth rate of the agricultural sector, rather than the growth rate of the whole economy, which reduces income inequality in our sample. This result is consistent with theoretical expectations since a high growth rate in the agricultural sector, all things equal, decreases the intersectoral income inequality and hence the overall inequality. 5. CONCLUSION This paper has investigated
the relationship
.
AGRICULTURAL
DEVELOPMENT
between relative income and growth rate in the agricultural sector and income inequality in Tropical Africa. The hypothesis that growth polities that emphasize a relative increase in agricultural income decrease relative income inequality was supported by data from 13 countries in Tropical Africa. We also find that increased employment in the non-agricultural sector and a high growth rate in the agricultural sector decrease income inequality. This implies that increased inequality during the initial phase of economic growth is not inevitable. Inequality is, at least in part, a matter of development strategy. Certain policy implications flow from this re-
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suit. It appears that African countries can not only solve their food problems by paying more attention to increasing productivity in the agricultural sector, but they can also reduce relative income inequality through such a growth process. Within Tropical Africa, such productivity increases could be achieved through the use of price incentives, the provision of rural infrastructure such as roads, and the provision of simple farm inputs at the appropriate farming seasons. With a very large proportion of the agricultural population poor, such a development strategy will not only decrease relative inequality; it will also decrease absolute poverty.”
NOTES 1. For example, see Adelman and Robinson (1978); Kuznets (1%3); and Paukert (1973). 2. See, for example, Adelman and Robinson (1978); Brutton (1977); Blitzer (1974); Chenery er al. (1974); Chow and Paperneck (1981); Fei, Ranis and Kuo (1978a, 1978b); Fields (1980); Frank and Webb (1977). 3. Fields (1980) calls growth that increases income in the traditional agricultural sector, all things equal, traditional sector enrichment growth. The growth strategy that we describe here is related to but not the same as traditional sector enrichment growth. While Fields’ traditional sector enrichment growth requires incomes in the traditional sector to grow, incomes in other sectors remaining constant. the growth we characterize requires incomes in all sectors to grow. We only require that incomes in the agricultural sector grow fasterthan growth in other sectors. 4. Bequele and Van der Hoeven (1980) cite evidence to indicate that in African countries where agricultural production has stagnated, relative income inequality has increased. 5. This occurs because very little labor is absorbed by the industrial sector while economic rents of capitalists increase. Fei and Ranis have shown that labor’s share of industrial output will increase in the initial phase of industrialization if and only if the labor absorption bias in technical change is large enough to overwhelm the innovation intensity effect. See Fei and Ranis (1964), Chap. 3. In African countries where all industrial technological change is embodied in machinery imported from labor-scarce western industrial nations, the labor absorption bias is not likely to exceed the innovation intensity bias.
6. Transition growth period is the period between the colonial growth epoch and modem growth epoch during which a former colony prepares itself for modem economic growth. For detailed definition and characterization of transition growth, see Paauw and Fei (1973). 7. In most Tropical African countries, land is communally held. Every citizen has use-right to land but not ownership right. This, in part, accounts for the low land concentration ratios in Africa. For more on land tenure systems and how it affects agricultural production, see USDA (1981). 8.
See Fei ef al. (1978a) and Fields (1980) for details.
9. One could also make the assumption that the ratio of inequality in the agricultural sector to that of the non-agricultural sector remains constant across countries to justify the exclusion of these variables. Such an assumption is too restrictive and may not be justified. We do not pursue this alternative approach. 10. The countries are Benin, Botswana, Gabon, Ghana, Ivory Coast, Kenya, Malawi, Senegal, Sierra Leone, Sudan, Tanzania, Uganda, and Zambia. Data availability dictated the countries to be included in the sample. The only exceptions are countries whose income distribution would be affected by economic and political domination by a racial minority (e.g., Zimbabwe) or where natural and man-made disasters have precluded any meaningful economic development (e.g., Chad). 11. See International 1979).
Labour
Organization
(1976;
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