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Impact of Income Inequality on Growth and Poverty in SE Asia and Latin America, Papers of Comparative Law and Politics

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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Chapter 1
MAGNITUDE OF INEQUALITIES
Latin America and the Caribbean have the greatest dis-
parities in income distribution in the world. A quarter of
all national income is received by a mere 5 percent of the
population, and the top 10 percent receive 40 percent. Such
proportions are comparable only to those found in some
African countries, whose per capita income levels are half
those of Latin America, and they are considerably higher
than those of any other group of countries (Figure 1.1). In
Southeast Asian countries, the wealthiest 5 percent receive
16 percent of all national income on average, while in the
developed countries they receive 13 percent.1
The counterpart to the great concentration of income
in the hands of the wealthy is found at the other end of the
income scale in Latin America: the poorest 30 percent of
the population receive only 7.5 percent of total income,
less than anywhere else in the world, where it is over 10
percent. (Figure 1.2). This income concentration applies
more to Latin America—which is the focus of this study
due to the availability of information—than to the English-
speaking Caribbean, where disparities are more moderate.
The indicator most commonly used to measure in-
come inequality is the Gini index, which draws together
information about the breakdown of income among all
12
14
16
18
20
22
24
26
Income of the 5% wealthiest
$2,000 $4,000 $6,000 $8,000 $10,000 $12,000 $14,000
GDP per capita
Figure 1.1. Income Received by the
Wealthiest 5 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
7
8
9
10
11
12
13
Income of the poorest 30%
$2,000 $4,000 $6,000 $8,000 $10,000 $12,000 $14,000
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.
population groups (Box 1.1). The Gini average for all the
countries in the world for which there is reliable informa-
tion on income distribution is 0.4. A perfectly equal dis-
tribution would produce an index of zero, but in fact the
best instances of distribution such as Spain, Finland and
some other European countries show Gini indices of be-
tween 0.25 and 0.3 (Figure 1.3). At the opposite extreme,
the indices of greatest income inequality are around 0.6,
which are found almost solely in Latin America and the
Caribbean. With the exception of Jamaica, whose inequal-
ity index of 0.38 is closer to European than to Latin Ameri-
can patterns, the other countries in the region for which
there is reliable information for the 1990s show inequal-
ity levels higher than the world average, and 11 of them
have indices higher than 0.5.2
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Chapter 1

MAGNITUDE OF INEQUALITIES

Latin America and the Caribbean have the greatest dis-

parities in income distribution in the world. A quarter of

all national income is received by a mere 5 percent of the

population, and the top 10 percent receive 40 percent. Such

proportions are comparable only to those found in some

African countries, whose per capita income levels are half

those of Latin America, and they are considerably higher

than those of any other group of countries (Figure 1.1). In

Southeast Asian countries, the wealthiest 5 percent receive

16 percent of all national income on average, while in the

developed countries they receive 13 percent.^1

The counterpart to the great concentration of income

in the hands of the wealthy is found at the other end of the

income scale in Latin America: the poorest 30 percent of

the population receive only 7.5 percent of total income,

less than anywhere else in the world, where it is over 10

percent. (Figure 1.2). This income concentration applies

more to Latin America—which is the focus of this study

due to the availability of information—than to the English-

speaking Caribbean, where disparities are more moderate.

The indicator most commonly used to measure in-

come inequality is the Gini index, which draws together

information about the breakdown of income among all

12

14

16

18

20

22

24

26

Income of the 5% wealthiest

$2,000 $4,000 $6,000 $8,000 $10,000 $12,000 $14, GDP per capita

Figure 1.1. Income Received by the

Wealthiest 5 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 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.

population groups (Box 1.1). The Gini average for all the

countries in the world for which there is reliable informa-

tion on income distribution is 0.4. A perfectly equal dis-

tribution would produce an index of zero, but in fact the

best instances of distribution such as Spain, Finland and

some other European countries show Gini indices of be-

tween 0.25 and 0.3 (Figure 1.3). At the opposite extreme,

the indices of greatest income inequality are around 0.6,

which are found almost solely in Latin America and the

Caribbean. With the exception of Jamaica, whose inequal-

ity index of 0.38 is closer to European than to Latin Ameri-

can patterns, the other countries in the region for which

there is reliable information for the 1990s show inequal-

ity levels higher than the world average, and 11 of them

have indices higher than 0.5.^2

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

Figure 1. Typical Lorenz Curve

in Latin America

(In percent)

A

B

Gini coefficient

5 10 15 20 25 30 Income of 5th quintile/1st quintile

Figure 2. Gini Coefficients and

Income Gaps

*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.

Box 1.1. Gini: A Simple Way to Measure Income Inequality

14 CHAPTER 1

Note that inequality of total income is greater than

the average of the urban and rural inequalities taken sepa-

rately, except in Chile, where the total and the urban Gini

are the same (Figure 1.5). If the average income levels in

the countryside and the city were similar, the inequality

of total income would be located at some point between

the separate concentrations of one region and the other.

But urban incomes are substantially higher than rural

ones, which means that, taken together, income inequal-

ity will be greater than in each region taken separately.

The largest gaps are found in Brazil, where per capita in-

come in urban areas is three times what it is in rural ar-

eas, and the smallest in Costa Rica and Venezuela, where

urban incomes are 75 percent higher than rural ones (Fig-

ure 1.6). Due to these great differences in per capita in-

come, total income inequality is greater than the sepa-

rate concentration in the countryside and in the city. On

average, the total Gini indices are 3.3 points higher than

separate Ginis for rural and urban areas. It is important

to keep this difference in mind, inasmuch as for three of

the countries analyzed in this study—Argentina, Bolivia

and Uruguay—information is not available on rural in-

come distribution (and hence they do not appear in these

comparisons). For example, the Ginis reported for coun-

tries that cover only urban areas (and only Greater Buenos

Aires in the case of Argentina) tend to be underestimated.

In Figure 1.5, we have assumed that the underestimate is

by these 3.3 points. With this correction, Uruguay dis-

plays the same inequality index as Costa Rica, Argentina

comes close to El Salvador, and Bolivia remains at in-

equality levels similar to those of Chile or Panama.

AS BAD AS BEFORE

Regardless of the measurement used, Latin America

stands out among all regions for its high inequality. In-

come distribution has not improved during the 1990s,

and according to the limited information available, it is

as high today as it was two decades ago (Figure 1.7).

The period of rapid growth in the region, which be-

gan in the 1960s and lasted until the outbreak of the debt

crisis in 1982, led to a notable improvement in income

distribution. Between 1970 and 1982 the region’s Gini

coefficient^4 fell by 5 points (that is, by 10 percent), and

the income ratio gap between the wealthiest 20 percent

of the population and the poorest 20 percent fell from 23

to 18 during that same period. While low-income groups

apparently improved their income share by around 10

percent, the highest groups stood still or fell, especially

between 1980 and 1982 (Figure 1.8). The wealthiest 10

percent saw their share of income fall by 6 percent dur-

ing this period and middle groups gained significantly.

But these improvements in distribution were short-lived.

During the 1980s, the decile with the highest incomes

increased its share by over 10 percent at the cost of all

other income deciles. The poorest 10 percent in Latin

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

America suffered a 15 percent drop in their share of in-

come. Thus the gaps widened again and the improve-

ments in distribution from before the debt crisis were

wiped out.

The economies of the region have undergone great

changes in the 1990s. High inflation rates have been

halted, deep economic reforms have been adopted to fa-

cilitate market operations, and productivity and economic

growth have been restored. All these changes have brought

about shifts of wealth and income. Concentration, how-

ever, has remained practically unchanged: the region’s Gini

has stood at around 0.58. What explains this apparent con-

tradiction is that the changes have affected some groups

differently from others. The poorest 10 percent in the re-

gion saw a 15 percent loss of their share in income be-

tween 1990 and 1995, and the next 10 percent a loss of 4

percent. The richest 10 percent also suffered a relative set-

back, while those who gained were the remaining groups

in the middle. Hence, although the indicators of average

concentration for the region have changed very little dur-

ing the 1990s, distribution has by no means stood still.

Moreover, movement has not been homogeneous

between countries. In Brazil, Chile and Mexico, income

inequality worsened in the 1980s, but that was halted in

the 1990s. In Colombia and Costa Rica, distribution pat-

terns have remained quite stable, and indices of concen-

tration in the 1990s have stood at levels similar to what

they were a decade ago. In Honduras and Jamaica, in-

come distribution worsened in the early 1990s, but in re-

cent years it has been better than in the 1980s. In Ve-

nezuela, there have been periods of sharp decline, but

they have been transitory.^5

WHERE INEQUALITY IS FOUND

One of the most striking features of the poor income dis-

tribution in Latin America is the huge gap between the

families in the highest income decile and everyone else.

In relatively egalitarian societies such as Sweden or

Canada, an individual who belongs to the wealthiest

decile of the population earns on average 20 to 30 per-

cent more than someone in the next decile. The succeed-

ing differences in the next deciles are also lower, and

hence there are no sharp gaps between social strata. In

Latin America, the gaps within the middle-income groups

are not so pronounced, but between the wealthiest decile

and the next one there is an abyss: in the Dominican Re-

public or Chile, to cite the two most critical cases, the

income of someone who belongs to the tenth decile is

three times as great as in the previous decile and more

than 30 times greater than that of the poorest decile (Fig-

ure 1.9a). These differences are possibly even greater,

because income from capital, which is more important in

the richest decile of the population, is most certainly

underreported in the income surveys from which these

calculations are taken (Box 1.2).

At the other end of the income scale there are also

major gaps: because the incomes of the poorest 10 per-

cent of the population are really quite low, the next 10

percent receive twice as much income in most countries,

and in Ecuador and Panama close to triple the income of

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?

Among the many aspects that set the heads of house-

holds of the richest 10 percent of the population apart,

four features in particular stand out: their level of educa-

tion, the features of their work, where they live, and the

number of children they have. These characteristics are

valuable keys for determining the factors that cause and

perpetuate poor income distribution.

Education is the main productive resource on which

most people rely. This is even valid for the wealthiest 10

percent of the population. On average, in the 14 coun-

tries considered, the heads of household of the highest

income decile have 11.3 years of education. Although this

level amounts to slightly less than finishing secondary

school (12 years in most countries), it is 2.7 years higher

than the education level of the heads of household of

the next decile and almost seven years higher than the

heads of household of the poorest 30 percent of the popu-

lation. The most pronounced education gaps between the

two wealthiest deciles are found in Brazil, Mexico and

Box 1.2. Household Surveys: A Basic Source of Information on Inequality

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.

Honduras, where they are over three years, and only in

Peru are they less than two years (Figure 1.12). Between

the richest decile and the 30 percent at the bottom of the

income scale, the average schooling gaps are over nine

years in Mexico and between eight and nine years in Bra-

zil, Panama and El Salvador, all countries with high in-

come inequality. The lowest education gaps between rich

and poor are found in Uruguay, Venezuela and Peru, coun-

tries whose income inequality is moderate when com-

pared with patterns in the region. Thus, education is a

factor differentiating the rich, not so much because their

education level is so high, but rather because most oth-

ers have not spent much time in school.

A second distinctive feature of the wealthy is the

kind of work they do. A quarter of the heads of household

of the highest income decile work directly as profession-

als, technical personnel or senior executives of compa-

nies. The portions range from 18 percent in Honduras and

Paraguay to over 35 percent In Bolivia and Panama (Fig-

ure 1.13). In the next income decile, these proportions

drop off noticeably in most countries, and between the

18 CHAPTER 1

three lowest deciles only the tiniest number of heads of

household reach leadership positions or have technical

responsibilities. Most successful is Bolivia, where 6 per-

cent of the poorest heads of households occupy such a

position. Moreover, in most countries, between 10 per-

cent and 20 percent of the population of the highest in-

come decile are employers (Figure 1.14). Of the 14 coun-

tries for which such information is available, only in

Panama is this proportion under 10 percent, while in Ec-

uador it represents around 25 percent of the wealthiest

decile. In the next decile far fewer have the chance to

employ other workers. Differences with the wealthiest

decile are especially marked in the countries where in-

equality is greatest, such as Brazil, Paraguay, Ecuador and

Chile. The proportion of individuals who can employ oth-

ers is considerably less in lower income groups, even

though it is still somewhat significant in the lesser devel-

oped countries, such as El Salvador, Honduras, Bolivia

and Ecuador.

A third feature differentiating the rich is where they

live. In Latin America, poor households are found prima-

rily in the countryside, while most of the wealthy live in

cities. Among the countries in Figure 1.15, only in Brazil,

Chile and Venezuela are over half of the households of

the three lowest deciles in urban areas. By contrast, nine

out of ten of the households of the two highest income

declines are urban, except in Honduras, where the pro-

portion falls below eight.

Number of children is another characteristic that

varies between rich and poor households. In all the coun-

tries studied, the number of children in the highest in-

come decile is lower than in any other decile. In Hondu-

ras, where the families of the wealthy are the largest relative

to the wealthy in other countries, they do not even have

on average two children under 18. In Argentina and Uru-

guay, only one out of two families of the highest decile has

a minor child. The contrast with the poorest households is

striking: the average number of children in the three low-

est deciles is over two in all countries, reaches 3.5 in Mexico,

Peru and Venezuela, and is four children per household in

Paraguay (Figure 1.16). Hence, per capita income in each

home is higher in the top decile not only because heads of

household earn more, but because the number of persons

among whom it must be distributed is smaller.

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

index of 0.52 that is the inequality of per capita house-

hold income. This similarity can also be seen in each

country by itself, which indicates that it is not simply the

result of aggregating individual cases that could be very

dissimilar among themselves (Figure 1.20). The greatest

differences between one coefficient and another are found

in Bolivia and Panama, but even in these cases they only

reach five points. Similar differences between household

per capita income distribution and labor income per

worker are also found in developed countries.

The connection between labor income inequality

and how per capita income is distributed should not lead

to the conclusion that the impact of income from capital

(or from other sources) is secondary in income distribu-

tion. What it suggests is rather that such factors are not

independent of one another. As will be seen in Chapters

2 and 4, the differences in income between some workers

and others, or more precisely between workers who have

greater or lesser education, can be explained by the rela-

tive demands for different types of work, which depend

on the relative abundance of other factors and how they

interact among themselves and with the labor factor.

WHY SHOULD WE CARE?

Social Justice

Income distribution should be a cause for concern for

reasons of ethics and social justice. If income distribu-

tion reflected solely personal preferences about work,

effort and savings, there would be no reason for it to con-

stitute an ethical problem from the standpoint of distribu-

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.

tive justice. Inequality and poverty become an ethical

matter calling for outside intervention when it is acknowl-

edged that the conditions generating them are not the

choice of individuals but rather a result of circumstances

beyond their control or a legacy of past problems.

Once it is acknowledged that effort and attitudes

toward education, work, risk and savings are not inde-

pendent of each individual’s starting conditions, the way

is opened for other concerns. The point is not simply to

assure “equality of opportunity.” If equality of opportu-

nity is understood as equality of access (free basic edu-

cation), it will not be enough to assure equality of capa-

bility of use (attendance at school), let alone equality of

results (academic achievement). According to the social

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

justice objectives pursued by society in each area, policy

actions ought to be aimed at changing the distribution

of capabilities of use (school subsidies, for example) or

the distribution of results (leveling programs and other

kinds of support).

Equality and Economic Development

After having been neglected for years in the academic

debate among economists, the topic of income distribu-

tion has been regaining interest in recent years. The cur-

rent debate centers precisely on determining whether,

when governments try to improve distribution, they pro-

duce adverse consequences for the welfare of the popu-

lation. Until recently, most theories on the subject pre-

sumed there was a conflict between equity and growth

(Box 1.3). The usual arguments were either that greater

concentration would allow for the generation of more

savings, thereby facilitating investment and growth, or

that concentration was the other side of the coin of effort

and productivity. But empirical international evidence

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).

Box 1.3. Equality and Growth: Partners or Adversaries?

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-

Box 1.4. Inequality Hinders Growth: Summary of the Empirical Evidence

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.

Recent public opinion surveys in Latin America in-

dicate that income inequality can in fact change the way

democratic institutions operate.^8 Income inequality can

weaken acceptance of democratic institutions and prin-

ciples. Where income inequality is less pronounced, as

in Uruguay or Costa Rica, a high proportion of the popu-

lation believes that “democracy is preferable to any other

kind of government” (Figure 1.22). In the more unequal

countries there is a greater tendency to accept authori-

tarian governments, and more people think that “it makes

no difference whether a regime is democratic or non-

democratic.” There also tends to be less trust in these

countries in the institutions proper to a democracy, such

as government, civil service, political parties, the legisla-

ture, large companies and business associations.

The lack of confidence in democracy and its insti-

tutions associated with poor income distribution in Latin

America could have implications for national political sys-

tems. In fragmented societies where institutional confi-

dence is low, the process of aggregating individual pref-

erences is more complex and inexact, and conflicts over

distributing public resources are more intense. Moreover,

the social and economic integration of different groups

is difficult and the state apparatus is more susceptible to

the influence of interest groups, corruption and ineffi-

ciencies, all of which feed inequality.

24 CHAPTER 1

Appendix 1.1. Main Features of Household Surveys Used in this Study

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

  • Empleo y Desempleo

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.

Appendix Table 1.2.II. Demographics

A. Average size of household by income level

Deciles

(^1) The surveys for Argentina include only Greater Buenos Aires.

Appendix Table 1.2.III. Education

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

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