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Income inequality in southeast asian and latin american countries, its impact on economic growth, and the extent of poverty in the region. Data on income distribution, gini coefficients, and the relationship between income distribution and economic growth.
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Income of the 5% wealthiest
$2,000 $4,000 $6,000 $8,000 $10,000 $12,000 $14, GDP per capita
(Percent of total income)
Source: IDB calculations based on Deininger and Squire (1996).
Africa
Latin America
Rest of Asia
Southeast Asia
Developed 7
8
9
10
11
12
13
Income of the poorest 30%
$2,000 $4,000 $6,000 $8,000 $10,000 $12,000 $14, GDP per capita
Figure 1.2. Income Received by the Poorest 30 Percent of the Population (Percent of total income)
Source: IDB calculations based on Deininger and Squire (1996).
Africa
Latin America
Rest of Asia Southeast Asia
Developed
(^1) The source for these comparisons is the database in Deininger and Squire (1996), which has information on income distribution based on reliable house- hold surveys in 108 countries. Southeast Asia includes only Hong Kong, Ko- rea, Singapore and Taiwan. (^2) Henceforth, the IDB’s own calculations based on the most recent house- hold surveys are used, except for Colombia, Guatemala, Jamaica and the Do- minican Republic, for which the most recent findings according Deininger and Squire (1996a) are used. Appendix 1.2 describes the characteristics of the sur- veys and the main socioeconomic indicators by income deciles and country.
12 CHAPTER 1
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 100
A
B
Gini coefficient
5 10 15 20 25 30 Income of 5th quintile/1st quintile
*Countries with urban data only. Source: IDB calculations based on recent household surveys, and Deininger and Squire (1996a).
Mexico Panama
Peru Costa Rica
Bolivia*
Uruguay*
Paraguay
El Salvador
Ecuador
Argentina*
Honduras
Brazil
Venezuela
Chile
Bahamas
Trinidad and Tobago
If incomes were distributed in a fully equitable manner, each person would receive the same share of income. The Gini index measures how far real distribution is from such a hypothetical reference point. Real income distribution (or that of any other vari- able) can be represented as a cumulative curve showing what percentage of total income is received by each per- centage of the population, lined up with its income level (which is called a Lorenz Curve). Consider points A and B in Figure 1. Point A indicates that the poorest 20 percent of the population receive 4 percent of income and that 90 percent receive 60 percent, which is actually more or less what happens in Latin American economies. Inasmuch as the diagonal represents the case of perfect distribution, the Gini coefficient is simply the area separating the Lorenz curve from the diagonal, divided by the area under the di- agonal. In theory, the Gini coefficient can vary between zero—perfect distribution—and one—complete concen- tration in a single person. In practice, Gini coefficients of per capita income vary between 0.25 and 0.60. Of the countries for which comparable information is available, only five have Gini coefficients outside of this range. In- equality indices in Latin America are on average 0.52, with a minimum of 0.43 for Uruguay and a maximum of 0.59 for Brazil. There are other measurements of income inequal- ity, such as the Theil index or the logarithmic income vari- ance. Each inequality measure assigns a different weight to observations by income level, which can be interpreted as a way of aggregating individuals in order to obtain an overall measurement of social welfare. The empirical evi- dence shows that all the usual measurements of inequal- ity produce highly correlated results, and hence for pur- poses of comparative analysis between countries, either one is adequate. Instead of the Gini or one of the other measure- ments of concentration, some economists prefer to refer simply to the income gaps between the groups at either end, such as between the wealthiest 20 percent and the poorest 20 percent of the population (called the first and fifth quintiles, respectively), or between the wealthiest and poorest 10 percent (tenth and first deciles). This measure- ment is easy to understand, and it relates quite closely to the Gini coefficient (Figure 1). It is, however, a rather crude measurement, for it is based simply on comparing two points in the distribution curve, and that can be decep- tive. For example, Ecuador and Panama have income gaps greater than Brazil’s, but their Gini coefficients are better because the distribution among their middle groups is better.
14 CHAPTER 1
Gini coefficient of total income
0.42 0.44 0.46 0.48 0.50 0.52 0.54 0.56 0. Gini coefficient of urban income
Argentina*
Bolivia* Brazil Chile
Costa Rica
Ecuador
Honduras
Mexico
Panama
Peru
Paraguay
Uruguay* El Salvador
Venezuela
Figure 1.5. Total and Urban Income Concentration
*Actual data cover urban incomes only. Gini for total incomes are estimates. Source: IDB calculations based on recent household surveys.
Urban/rural income
Brazil Mexico
Ecuador Paraguay
Panama El Salvador
Peru Honduras
Chile Costa Rica
Venezuela
Figure 1.6. Urban-Rural Income Gap
Source: IDB calculations based on recent household surveys.
(^4) Approximations based on available information for the population of 13 countries that together make up 83 percent of the Latin American population (Londoño and Székely, 1997). Note that the Gini inequality index for the entire region is not the same as the average of Ginis by countries, since it is based on the incomes of all individuals combined. The Gini for the region is higher due to differences of average income between countries. Since these estimates are based on partial evidence of the evolution of distribution in each country, they should not be considered an exact description of the behavior of this variable over time.
Magnitude of Inequalities 15
Gini coefficient
1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994
Figure 1.7. Income Concentration in Latin America, 1970-
Source: Londoño and Székely (1997).
1970 1975 1980 1985 1990 1995
Poorest 20% Middle quintiles Wealthiest 20%
Figure 1.8. Participation of Each Income Group in Total Income, 1970- (Percent of normalized income)
Source: Londoño and Székely (1997).
(^1982 )
(^5) The countries mentioned are those for which there are at least five original observations since 1980. The interpolations and calculations used in the fig- ures are by Londoño and Székely (1997).
Magnitude of Inequalities 17
Who Are the Rich?
Information on income distribution comes mainly from the sur- veys made regularly by governments of representative samples of households on a national level. Besides information on the income of each household, such surveys provide information on education, age, occupation and other characteristics of each household member. They are therefore a rich source for study- ing the relationship between income distribution, work activity, participation in the education system, and the size and make- up of households. Hence, they are currently the most effective instrument for designing and evaluating all kinds of social pro- grams. Even though this tool is now used around the world, sur- veys are still not carried out in some Latin American countries (Guatemala, Guyana and Haiti), while others perform surveys that leave out rural or nonmetropolitan areas (Argentina, Bo- livia and Uruguay). For the sake of studying income distribution, the most common shortcoming in some Latin American surveys is the lack of information on nonlabor sources of income (such as rent- ing out of housing, noncash incomes, etc.) and on complemen- tary variables that help establish the socioeconomic level of fami- lies (access to services, for example). Because nonlabor incomes are often reported at less than their true value, especially among upper-income families, sur- veys tend to underestimate the level of real inequality in coun- tries. If adjustments are made to correct this shortcoming, the results are subject to the arbitrary character of the method used. For Mexico in 1994, for example, Gini coefficients ranged from 0.477 to 0.60, depending on the adjustment method used.
The household surveys used in this study are of high qual- ity and in keeping with international standards (Deininger and Squire, 1996a). Nevertheless, they do not all apply the same methodology for data gathering and sampling, and in a few cases with regard to the observation unit.^1 Since it is never possible to attain perfect comparability between surveys of different coun- tries, differences in methodology do not necessarily invalidate the making of comparisons (Atkinson, 1995). In this study we have used reasonably comparable surveys, avoided any kind of subjective adjustment to the primary information, and applied identical methods of processing and statistical analysis to all surveys used. (See the Appendix to this chapter for the main features of the surveys used.) In order to deal with the shortcomings in coverage and quality of the surveys and make them more relevant for policy design, the Inter-American Development Bank has implemented the Improvement of Surveys and Measurement of Living Stan- dards (Mecovi) Program, in coordination with governments and other international agencies. The program provides financing and technical assistance for carrying out household surveys.^2
(^1) See Berry, et al (1983), Atkinson and Micklewright (1992), and Gottschalk and Smeeding (1997). (^2) The program has provided funding to carry out surveys in Argentina, El Salvador, Paraguay and Peru and will expand its operations to Bolivia and Nicaragua in the near future.
18 CHAPTER 1
Decile 10 Decile 9 Deciles 1-
Figure 1.13. Percentage of Professionals and Executives in Rich and Poor Families
*Countries with urban data only. Source: IDB calculations based on recent household surveys.
Uruguay* Costa Rica
Peru
Venezuela
Argentina
El Salvador
Honduras Bolivia*
Mexico
Panama
Chile
Ecuador
Paraguay
Brazil
0
10
20
30
40
Figure 1.14. Percentage of Employers in Rich and Poor Families
*Countries with urban data only. Source: IDB calculations based on recent household surveys.
Uruguay* Costa Rica
Peru
Venezuela
Argentina
El Salvador
Honduras Bolivia*
Mexico
Panama
Chile
Ecuador
Paraguay
Brazil
Decile 10 Decile 9 Deciles 1-
0
5
10
15
20
25
Decile 10 Decile 9^ Deciles 1-
Figure 1.12. Years of Schooling in Rich and Poor Families
*Countries with urban data only. Source: IDB calculations based on recent household surveys.
Uruguay* Costa Rica
Peru
Venezuela
Argentina
El Salvador
Honduras Bolivia*
Mexico
Panama
Chile
Ecuador
Paraguay
Brazil
2
4
6
8
10
12
14
20 CHAPTER 1
Social Justice
Percent of employers, deciles 9-
0.40 0.45 0.50 0.55 0. Gini coefficient of per capita income
Uruguay*
Costa Rica
Venezuela
Peru (^) Argentina*
El Salvador Bolivia*
Mexico
Honduras
Chile
Ecuador
Paraguay
Panama
Brazil
Figure 1.18. Income Concentration and Gap in the Proportion of Employers among the Rich
*Countries with urban data only. Source: IDB calculations based on recent household surveys.
0
Children per family, deciles 9-
0.40 0.45 0.50 0.55 0. Gini coefficient of per capita income
Uruguay* (^) Costa Rica
Venezuela
Peru
Argentina*
El Salvador
Bolivia*
Mexico
Honduras
Chile
Ecuador Paraguay
Panama
Brazil
Figure 1.19. Income Concentration and Number of Children among the Rich
*Countries with urban data only. Source: IDB calculations based on recent household surveys.
Figure 1.20. Concentration of Total and Labor Income
Gini coefficient of labor income
*Countries with urban data only. Source: IDB calculations based on recent household surveys.
Gini coefficient of household income
0.2 0.3 0.4 0.5 0.
0.2 Germany
UK
USA
Uruguay*
Argentina
Peru
Costa Rica
Venezuela
El Salvador
Panama
Honduras
Bolivia* Mexico
Paraguay
Ecuador
Chile Brazil
Magnitude of Inequalities 21
Equality and Economic Development
Since Adam Smith, most economists have considered growth and equality to be largely incompatible. Only recently has a new school of theoreticians come to argue what seems plain to the eye: that high levels of economic development are found in rela- tively egalitarian societies where accumulating physical, human and social capital is attractive. For classical economists, the capital accumulation needed for growth could only be obtained from the savings of capitalists, because workers would always consume their entire earnings. Equity and growth were incompatible. A similar con- flict emerged within the pure neoclassical vision, but it was rooted in microeconomics: any redistribution would ultimately be at the expense of a productive factor, thereby lowering eco- nomic efficiency. Even a decade ago, these two visions had a great deal of influence on the position of economists on the issue of redistribution. Both implied that a shortage of produc- tive resources would become sharper if a portion were set aside for redistribution purposes, thereby constituting an expenditure, not an investment. Because redistribution policies would have nothing to offer for economic growth or development according to these traditional approaches, income distribution was for a long time absent from the central agenda of economic theory and policy (Atkinson, 1997). Over the past decade, the relationship between growth and equity has come back to the center of the academic debate, thanks to theories of endogenous growth and political economy. The central argument is that poor income distribution can weaken the pace of the accumulation of physical and human capital or affect productivity growth, which are the sources of economic growth.^1 Education can be the channel through which poor income distribution lowers the possibilities of growth: families with lim- ited resources are not in a position to put aside money for edu- cation, even though such an effort could be socially and eco- nomically profitable. Moreover, families with little education and few possibilities for future education for their children will pre- fer to have more children than those in the opposite situation, thereby reinforcing the vicious circle of inequality and poverty (see Banerjee and Newman, 1991; Galor and Zeira, 1993; Aghion and Bolton, 1997; Piketty, 1997; and Dahan and Tsiddon, 1998).
Restricted access to capital markets is another channel that perpetuates poor income distribution. Because access to credit requires being able to provide guarantees, those who ini- tially have a higher level of wealth have more opportunity to invest in physical and human capital. Hence, in societies where wealth is very concentrated, many investments that could be profitable at the individual and social level cannot be made, thereby impeding growth (Aghion and Bolton, 1992; Galor and Zeira, 1993; and De Gregorio and Kim, 1994). The connection between inequality and growth can also take place through various political and economic channels. The first is political participation, through which voters express their preferences for policies of economic redistribution. In a society where wealth and income are highly concentrated, most people will support redistribution policies financed with taxes on capi- tal or similar measures that will discourage investment and pro- ductivity. The “median voter” is less inclined toward such poli- cies to the extent that wealth is better distributed (Alesina and Rodrik, 1992 and 1994; Persson and Tabellini, 1992 and 1994; Alessina and Perotti, 1993; and Perotti, 1994). A second connec- tion is that poor income distribution causes distributional and social tensions that lead to political instability and uncertain- ties that hinder investment. A third possibility is that the power groups that arise in unequal societies can erode genuinely dis- tributive policies by seizing government institutions and other income-producing activities, perpetuating inequality and low growth (Benhabib and Rustichini, 1996; and Birdsall and Londoño, 1998). Education, restricted access to capital markets, and po- litical and economic mechanisms conditioning government poli- cies are thus different channels by which income distribution affects growth.
(^1) Alesina and Perotti (1994), Alesina and Rodrik (1994) and Solimano (1998) contain excellent reviews of this literature.
Magnitude of Inequalities 23
The relationship between income distribution and economic growth has been a recurring topic of discussion among econo- mists. In theory, arguments can be found to justify both a direct and an inverse relationship between the two variables. What does the empirical evidence say? The prevailing conclusion in recent empirical studies is that poor income distribution harms economic growth and that therefore, instead of the conflict between equality and develop- ment that used to be regarded as a serious constraint on distri- bution policies, the relationship is mutually reinforcing. Studies by Clarke (1992), Alessina and Perotti (1993 and 1994), Persson and Tabellini (1994), Birdsall, Ross and Sabot (1995), Deininger and Squire (1996b) and Perotti (1996) conclude that poor income distribution reduces prospects for economic growth. This conclusion remains valid even after taking into ac- count the influence on development of other factors, such as initial per capita income, education levels or other economic, political or demographic variables. Such empirical studies are no doubt open to question not only because of shortcomings in the quality of information, but also because of the possibility that the relationship between growth and equality over time by countries is different from what can be seen in these cross-sec-
tional comparisons between countries (Forbes, 1997). Neverthe- less, the empirical studies that have focused on the channels connecting distribution with growth also support the conclu- sion that these variables tend to be mutually reinforcing rather than in conflict. Econometric models that simply include income distri- bution as an extra variable explaining growth do not shed light on the channels that can determine this relationship. But sev- eral studies have tried to fill this vacuum. Perotti (1996) shows that joint decisions made by families regarding education and number of children may perpetuate a society’s high inequality and low growth. Another possible channel is political instabil- ity: poor income distribution often goes hand in hand with po- litical unrest that affects investment and growth (Alesina and Perotti, 1993 and 1994; Perotti, 1996). On a worldwide scale there is little evidence for the thesis of the “median voter,” namely that poor distribution leads to higher tax levels that hinder growth. Hence, even though the empirical evidence is not that of complete consensus, it does tend to favor those hypotheses according to which good income distribution tends to stimulate economic growth.
(^8) These surveys have been carried out annually in 17 countries since 1996 by Latinobarómetro, a private, nonpartisan and independent agency. Presented here are results for those countries that have comparable income distribution statistics.
24 CHAPTER 1
Sample size
Income
Reference
Property
Capital
Non-
Imputed
Country
Year
Name of the survey
Coverage
month
Households
Individuals
Labor
rent
rent
Transfers
monetary
Rent
1
Argentina
80
Encuesta Permanente de Hogares
Greater Buenos Aires
October
3,
11,
X^
aX
aX
X^
na
na
96
Encuesta Permanente de Hogares
Greater Buenos Aires
April and May
3,
11,
X^
bX
bX
bX
na
na
2
Bolivia
86
Encuesta Permanente de Hogares
Urban
1986
2,
12,
X^
X^
na
na
na
na
95
Encuesta Integrada de Hogares
Urban
June
5,
25,
X^
X^
X^
X^
na
na
3
Brazil
81
Pesquisa Nacional por Amostra
National
September
103,
482,
X^
X^
X^
X^
na
na
de Domicilios
95
Pesquisa Nacional por Amostra
National
September
85,
334,
X^
X^
X^
X^
na
na
de Domicilios
4
Chile
94
Encuesta de Caracterización
National
November
45,
178,
X^
X^
X^
X^
X^
X
Socioeconómica Nacional
and December
5
Costa Rica
81
Encuesta Nacional de Hogares
National
July
6,
22,
X^
na
na
na
na
na
95
Encuesta de Hogares
National
July
9,
40,
X^
bX
bX
bX
na
na
de Propósitos Múltiples
6
Ecuador
95
Encuesta de Condiciones de Vida
Nacional
August
5,
26,
X^
X^
X^
X^
X^
na
to November
7
El Salvador
95
Encuesta de Hogares
National
1995
8,
40,
X^
bX
bX
bX
X^
X
de Propósitos Múltiples
8
Honduras
89
Encuesta Permanente de Hogares
National
September
8,
46,
X^
na
na
na
na
na
de Propósitos Múltiples
96
Encuesta Permanente de Hogares
National
September
6,
33,
X^
na
na
na
na
na
de Propósitos Múltiples
9
Mexico
84
Encuesta Nacional de Ingreso Gasto
National
Third quarter
4,
23,
X^
X^
X^
X^
X^
X
de los Hogares
94
Encuesta Nacional de Ingreso Gasto
National
Third quarter
12,
60,
X^
X^
X^
X^
X^
X
de los Hogares
10
Panama
95
Encuesta Continua de Hogares
National
August
9,
40,
X^
aX
aX
X^
na
na
11
Paraguay
95
Encuesta de Hogares - Mano de Obra
National
August
4,
21,
X^
X^
X^
X^
na
na
to November
12
Peru
85-
Encuesta Nacional de Hogares
National
July 1985
4,
26,
X^
X^
X^
X^
X^
na
sobre Medición de Niveles de Vida
to July 1986
96
Encuesta Nacional de Hogares
National
Fourth quarter
16,
88,
X^
aX
aX
X^
X^
na
sobre Niveles de Vida y Pobreza
13
Uruguay
81
Encuesta Nacional de Hogares
Urban
Second Semester
9,
32,
X^
X^
X^
X^
X^
X
95
Encuesta Continua de Hogares
Urban
1995
20,
64,
X^
X^
X^
X^
X^
X
14
Venezuela
81
Encuesta de Hogares por Muestra
National
Second Semester
45,
239,
X^
na
na
na
na
na
95
Encuesta de Hogares por Muestreo
National
Second Semester
16,
92,
X^
bX
bX
bX
na
na
a^ Cannot separate between property and capital rent.b^ Cannot separate between property rent, capital rent, and transfers.
A. Average size of household by income level
Deciles
(^1) The surveys for Argentina include only Greater Buenos Aires.
A. Average years of education for 25 year olds by income level
Deciles
B. Primary Completion Rates for 20-25 year olds by income level
C. Secondary completion rates for 20-25 year olds by income level
(^1) The surveys for Argentina include only Greater Buenos Aires.
Magnitude of Inequalities 29
Aghion, P., and P. Bolton. 1992. Distribution and Growth in Models of Imperfect Capital Markets. European Economic Review 36. __________. 1997. A Theory of Trickle-down Growth and Develop- ment. Review of Economic Studies 64(2) April: 151-72. Alesina, A., and R. Perotti. 1993. The Political Economy of Growth: A Critical Survey of Recent Literature and Some Results. World Bank. Mimeo. Alesina, A., and D. Rodrik. 1992. Income Distribution and Economic Growth: A Simple Theory and Empirical Evidence. In A. Cukierman, S. Hercovitz, and L. Leiderman, eds., The Political Economy of Business Cycles and Growth. Cambridge, MA: MIT Press. __________. 1994. Distributive Politics and Economic Growth: A Criti- cal Survey of the Recent Literature. The World Bank Economic Review 8(3). Atkinson, A. B. 1997. Bringing Income Distribution in From the Cold. The Economic Journal (March): 297-321. Atkinson, A. B., and J. Micklewright. 1992. Economic Transformation in Eastern Europe and the Distribution of Income. Cambridge: Cam- bridge University Press. Banerjee, A., and A. Newman. 1991. Risk Bearing and the Theory of Income Distribution. Review of Economic Studies 58: 211-35. Benabou, R. 1996. Inequality and Growth. Paper presented at Elev- enth Annual Macroeconomics Conference, Cambridge, MA. Benhabib, J., and A. Rustichini. 1996. Social Conflict, Growth and Income Distribution. Journal of Economic Growth 1(1): 125-42. Birdsall, N., and J.L. Londoño.1997. Asset Inequality Does Matter: Lessons from Latin America. American Economic Review 87(2) May. __________. 1998. No Tradeoff: Efficient Growth Via More Equal Human Capital Accumulation in Latin America. In Beyond Tradeoffs: Market Reforms and Equitable Growth in Latin America, Nancy Birdsall, Carol Graham and Richard Sabot, eds. Wash- ington, D.C.: The Brookings Institution and the Inter-Ameri- can Development Bank. Birdsall, N., D. Ross, and R. Sabot. 1995. Inequality and Growth Re- considered. World Bank Economic Review 9(3) September. Clarke, G. 1992. More Evidence on Income Distribution and Growth. World Bank Working Paper 1064. Dahan, M., and D. Tsiddon. 1998. Demographic Transition, Income Distribution and Economic Growth. Journal of Economic Growth. Forthcoming. De Gregorio, J., and S. Kim. 1994. Credit Markets with Differences in Abili- ties: Education, Distribution and Growth. IMF Working Paper 94/97. Deininger, K., and L. Squire. 1996a. New Ways of Looking at Old Is- sues: Inequality and Growth. World Bank. Unpublished.
__________. 1996b. A New Data Set Measuring Income Inequality. World Bank Economic Review 10(3), September: 565-91. Forbes J., K. 1997. A Reassessment of the Effect of Inequality on Growth. Massachusetts Institute of Technology. October. Un- published. Galor, O., and J. Zeira. 1993. Income Distribution and Macroeconom- ics. Review of Economic Studies 60. Gottschalk, P., and T. Smeeding. 1997. Cross-National Comparisons of Earnings and Income Inequality. Journal of Economic Litera- ture 35 (June): 633-87. Londoño, J. L., and M. Székely. 1997. Persistent Poverty and Excess In- equality: Latin America 1970-1995. Office of the Chief Econo- mist Working Paper Series No. 357, Inter-American Develop- ment Bank. Lustig, N. , and R. Deutch. 1998. The Inter-American Development Bank and Poverty Reduction: An Overview. Sustainable De- velopment Department, Inter-American Development Bank. Mimeo. Morley, Samuel. 1998. Poverty During Recovery and Reform in Latin America: 1985-1995. Inter-American Development Bank. Un- published. Perotti, R. 1993. Political Equilibrium, Income Distribution and Growth. Review of Economic Studies 60: 755-76. __________. 1996. Growth, Income Distribution and Democracy. Jour- nal of Economic Growth 1 (June): 149-87. Persson, T., and G. Tabellini. 1991. Is Inequality Harmful for Growth? Theory and Evidence. NBER Working Paper No. 3599. National Bureau of Economic Research, Cambridge, MA. __________. 1992. Growth, Distribution and Politics. European Eco- nomic Review 36. __________. 1994. Is Inequality Harmful for Growth? American Eco- nomic Review 84(3). Piketty, T. 1997. The Dynamics of the Wealth Distribution and the Interest Rate with Credit Rationing. Review of Economic Studies 64(2) April: 173-89. Solimano, Andrés. 1998. The End of Hard Choices? Revisiting the Relationship Between Income Distribution and Growth. In Solimano, A., ed., Social Inequality Values, Growth and the State. University of Michigan Press. Forthcoming.