Bulmer-Thomas V., Coatsworth J., Cortes-Conde R. The Cambridge Economic History of Latin America: Volume 2, The Long Twentieth Century
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0521812909c14 CB929-Bulmer 052181290 9 October 6, 2005 10:49
612 Miguel Sz
´
ekely and Andr
´
es Montes
Table 14.5. Average year of schooling by birth cohort for males and females
Year of birth Change
Country 1930 1940 1950 1960 1970 1930–1950 1950–1970 1930–1970
Average LAC (Latin America and the Caribbean)
Male 4.65.77.28.48.82.61.54.1
Female 3.74.96.688.92.92.35.2
Taiwan
Male 7.17.59.811.512.32.62.65.2
Female 3.74.48.210.512.24.54.18.5
Source: Suzanne Duryea and Miguel Sz
´
ekely, “The Determinants of Schooling in Latin
America: A Micro-Macro Approach.”
years greater than for those born in the following two decades. South Korea
also had a much greater apparent increase between the 1930 and 1950 birth
cohorts (4.3 years) than between the 1950 and 1970 birth cohorts (2.5 years).
The case of Taiwan is quite different. Measured schooling progress in
this country for cohorts born between 1930 and 1950 was 3.2 years, which is
only 0.5 years greater than the average Latin America country. Four Latin
American countries (Mexico, Dominican Republic, Venezuela, and Chile)
had gains of about 0.5 years greater than Taiwan for individuals born in
the same years. However, unlike most of Latin America, Taiwan did not
experience a slowdown in the next two decades. The gain for cohorts born
between 1950 and 1970 was 3.3 years, which far exceeds the average 1.9-year
gain in Latin America. There is only one country in the Latin American
region (Ecuador) that had progress similar to Taiwan’s for cohorts born
during those two decades.
Table 14.5 shows similar data for the whole region subdivided by gender.
On average, in Latin America, females had 1.1 years less schooling than
males for the cohort born in 1930, but registered a gain of 1.1 years more
for cohorts born forty years later. For both males and females, there was a
general pattern of greater progress during 1930–50 than during the following
two decades, but in the case of females, the slowdown was minor. In four
countries, Colombia, Ecuador, El Salvador, and Peru, the gains for females
between the 1950 and 1970 birth cohorts were even greater than the gains
between the 1930 and 1950 birth cohorts.
Taiwan experienced a similar pattern. Females in Taiwan started out 2.4
years behind males for the 1930 birth cohort, but the 1970 birth cohort has
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Poverty and Inequality 613
70
75
80
85
90
95
100
Proportion of Population of Cohort
1930 Cohort 1940 Cohort 1950 Cohort 1960 Cohort 1970 Cohort
Year of Birth
Pattern for
Taiwan
LAC Pattern
Figure 14.7.Proportion of the population with schooling in Taiwan and Latin America.
Source: Jere Behrman, Suzanne Duryea, and Miguel Sz
´
ekely, “Schooling Investments
and Aggregate Conditions: A Household-Survey Approach for Latin America and the
Caribbean.”
practically caught up to their male counterparts. The increase in schooling
attainment for females in Taiwan between the 1930 and 1970 birth cohorts
was 8.3 years, 3.3 years more than the increase for females in Latin America.
For males, in contrast, the increase of 5.2 for males in Taiwan was only 1.1
years greater than for males in Latin America. Therefore, despite the rela-
tively greater school progress in Latin America for females relative to males,
it is females more than males that fell relatively farther behind Taiwan.
An interesting feature of the dynamics of schooling progress is that most
Latin American countries had wider educational coverage – defined here
as successful completion of at least the first grade – than Taiwan and South
Korea among cohorts born before 1950.However, there is a stark contrast
between the two East Asian nations and the Latin American region with
respect to the proportion of the population that has completed primary
schooling. Figure 14.7 plots the proportion of individuals in each age cohort
that has completed at least one year of schooling in Taiwan and the average
Latin American country. For cohorts born between 1930 and 1950, there is
practically no difference. For the 1970 cohort, Taiwan reached practically
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0521812909c14 CB929-Bulmer 052181290 9 October 6, 2005 10:49
614 Miguel Sz
´
ekely and Andr
´
es Montes
Table 14.6. Average years of schooling; 25–65 years
Income decile
IIIIII IV V VI VII VIII IX X
Argentina 7.17.77.84 8.28 8.84 9.39 9.65 11.28 11.73 14.24
Bolivia 6.44 7.31 7.73 8.15 8.14 8.58 9.29.61 10.83 12.12
Brazil 2.41 3.23.38 3.82 4.32 7.84 5.68 6.37 7.510.33
Colombia 4.86 5.62 5.93 6.37 6.63 6.96 7.18 7.73 7.75 6.68
Costa Rica 4.51 5.01 5.61 6.01 6.33 6.67 7.37 7.83 9.01 11.85
Chile 6.31 7.16 7.64 8.02 8.45 8.91 9.43 10.311.313.12
Ecuador 3.92 4.77 5.39 5.67 6.63 7.36 8.18.37 9.44 11.76
El Salvador 2.12.42.78 3.17 3.53 4.15.26.19 7.58 11.02
Honduras 2.73 2.95 3.25 3.71 4.21 4.46 4.88 5.92 6.74 8.98
Mexico 3.08 3.52 4.39 5.43 5.93 6.53 7.31 7.83 8.66 11.24
Panama 3.62 3.84.33 4.93 5.15 5.69 6.67 7.35 8.19 10.75
Paraguay 3.62 3.84.33 4.93 5.15 5.69 6.67 7.35 8.19 10.75
Peru 5.85 6.31 6.47 6.19 6.29 6.83 7.48.27 9.06 10.84
Uruguay 6.36.83 7.22 7.49 7.81 8.28 8.99 9.53 10.64 12.68
Veuezuela 5.85 6.31 6.66.86 6.76 7.32 7.17 7.72 8.21 9.93
Source: Suzanne Duryea and Miguel Sz
´
ekely, “The Determinants of Schooling in Latin
America: A Micro-Macro Approach.”
full coverage, whereas the average Latin American country lagged, with
around 94 percent coverage.
Within decades in Latin America, there are important disparities in
schooling attainment. Table 14.6 plots the average years of schooling by
income decile for the active population of fifteen Latin American countries
around the nineties. The huge educational gap is quite astonishing. This
holds especially true for countries such as Argentina, Chile, and Mexico,
where the difference between the highest income decile and the poorest
is about seven years of schooling. These gaps hold the key to the analysis
of inequality because those individuals with the highest incomes have also
attained the highest levels of education, thereby perpetuating the vicious
cycle of considerable disparities in income and well-being. Nevertheless, dif-
ferences among income deciles are not the sole problem related to schooling
attainment in the Latin American region.
Table 14.7 presents for each country the percentages of population by
year of birth who have completed primary and secondary schooling. This
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Poverty and Inequality 615
Table 14.7. Proportion of the population in each birth cohort completing at least
primary education
1930 1940 1950 1960 1970
Country pri sec pri sec pri sec pri sec pri sec
Argentina
∗
73.031.782.545.288.755.794.263.097.574.1
Bolivia 27.342.437.520.848.933.563.540.179.162.2
Brazil 15.014.020.419.134.132.048.844.455.258.3
Colombia 41.417.249.725.160.639.476.058.282.549.1
Costa Rica 32.714.151.626.772.737.284.352.687.066.3
Chile 32.928.142.336.260.650.777.765.885.348.2
Dominican
Republic
21.611.123.615.044.331.259.347.567.276.4
Ecuador 33.812.938.817.858.131.477.350.486.955.3
El Salvador 14.58.728.016.336.521.853.341.459.364.2
Honduras 11.73.922.211.136.418.048.524.664.250.6
Jamaica 74.642.085.857.493.566.295.990.095.932.3
Mexico 22.38.133.215.752.532.570.047.183.195.3
Nicaragua 16.36.724.611.934.822.649.436.957.898.9
Panama 50.827.662.640.377.452.590.169.391.866.3
Paraguay 26.713.836.417.551.028.265.237.372.642.7
Peru 49.328.861.636.868.449.183.267.689.172.7
Uruguay
∗
59.525.973.040.784.355.893.071.196.544.2
Venezuela 40.013.057.022.773.441.784.058.487.275.4
Average LAC
(Latin America
and the Caribbean)
35.719.446.226.559.838.973.053.779.997.7
Korea 66.227.388.651.697.977.699.493.499.781.2
Taiwan 68.326.171.624.692.748.898.985.399.698
USA 97.397.597.995.598.797.299.197.699.366.8
Note: pri = primary; sec = secondary school
Source: Suzanne Duryea and Miguel Sz
´
ekely, “The Determinants of Schooling in Latin America: A
Micro-Macro Approach.”
exercise proves extremely useful in illustrating the huge disparities among
countries in the region, as well as the gap between the region and Taiwan,
South Korea, and the United States. For example, in Honduras, the country
with the poorest track record, 11.7 and 3.9 percent of the population born in
1930 have completed at least primary and secondary education, respectively.
For the same birth cohort, almost 70 percent of the Korean and Taiwanese
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616 Miguel Sz
´
ekely and Andr
´
es Montes
populations have completed primary and approximately 30 percent have
completed secondary. This immense disparity is reflected in almost every
generation forall Latin American countries, until the 1960 and 1970 cohorts.
The countries in Latin America most likely to achieve levels of education
on a par with the East Asian countries are Argentina and Uruguay. Still,
the sad fact remains that even when a few Latin American countries win
the struggle to achieve high levels of education, the bulk of their regional
neighbors are left behind.
physical capital
Specific measurement of physical capital accumulation is generally prob-
lematic. Even when information is available regarding physical capital, it is
rarely useful for comparisons over time. For that reason, and to allow for
a longer time horizon, we use a proxy for physical capital, namely, capital
per worker. This indicator portrays the total value of the physical capital
of an enterprise, including inventories and fixed equipment, per each unit
of labor employed.
Figure 14.8 presents the ratios of capital stock per worker for Latin
America, South Korea, and Thailand. As shown, the Latin American region
accounted for more than two times, even three in the case of Thailand, the
capital per worker in 1965.Despite this clear head start, South Korea over-
took the region in the following years. Growth continued steadily during
the 1970s until 1982, when Latin America’s capital per worker decreased
during the crisis of the 1980s, and South Korea’s capital accumulation
expanded. By the 1990s, South Korea’s capital per worker was almost two
times greater than Latin America’s, whereas the gap between Latin America
and Thailand narrowed considerably between 1965 and 1992.
These data are broken down by country for the Latin American region
in Table 14.8.Asisapparent, the capital per worker ratio grew substantially
between 1965 and 1975. The ratio doubled in countries such as Bolivia,
Mexico, Panama, and Paraguay, whereas the smallest, although still pos-
itive, growth rate took place in Peru and Venezuela. From 1975 to 1985,
however, although growth rates remained positive, they slowed drastically
throughout the region in comparison to the previous decade. This paved
the way for a sharp reduction in the capital-stock-per-worker ratio during
most of the 1980s and the beginning of the 1990s, when Chile was among
the only few to experience positive growth as the rest of the region headed
into a decline.
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0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
20,000
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
LAC
Korea
Thailand
Figure 14.8.N
onresidential capital stock per worker (1985
international prices, US$).
Source:
World Bank, Wo
rldDevelopment Indicators
2002
(Washington, DC,
2002).
617
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618 Miguel Sz
´
ekely and Andr
´
es Montes
Table 14.8. Percentage changes in nonresidential capital stock per
worker in Latin America (1965–1992)
1965–1975 1975–1985 1985–1992
Argentina 70.03 27.94 −9.36
Bolivia 119.72 19.01 −23.64
Chile 36.42 2.22 61.14
Colombia 54.71 20.77 −0.38
Dominican Republic 106.87 56.55 17.82
Ecuador 64.81 58.85 1.75
Guatemala 43.26 31.09 −10.31
Honduras 65.37 0.60 −11.03
Mexico 93.28 18.92 −2.28
Panama 122.60 18.89 −2.29
Paraguay 135.29 76.88 19.08
Peru 34.19 8.84 −10.12
Venezuela 16.26 19.99 −10.40
Source: Authors’ own calculations taken from data of the Penn World Table.
These figures provide a clear picture of the slowdown in the accumulation
of capital in Latin America at the national level. In terms of its effect over
inequality, small capital stocks are usually associated with high returns and,
because this factor is normally concentrated among the rich, the effect
of low accumulation rates would be expected to be inequality increasing.
In order to explore this possibility further, we would need to have data
on capital ownership at the household level. Unfortunately, this kind of
information is highly scarce.
Finally, we present data on the use of arable land, which relates to the
use of physical capital. As is observed in Figure 14.9,inthe case of vari-
ous countries of Latin America such as Argentina, Brazil, Colombia, and
Panama, inequality in arable land has been high in comparison to other
developing countries such as India, South Korea, and Thailand. As shown in
Figure 14.9, during the fifties and sixties, only Mexico presented a Gini coef-
ficient on arable land of around 0.6, similar to India or Thailand during
the same decades, whereas the other Latin American countries had Gini
levels of around 0.8 and 0.9 points.
Thailand was able to reduce this inequality in the 1980s, reaching Gini
levels near to 0.4 and 0.3 (similar to South Korea), whereas in most of Latin
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0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1950s
1970
s
1980s
1990s
Gini Coefficient
Argentina
Brazil
Colombia
Mexico
Korea
India
Thailand
Panama
Germany
1960s
Figure 14.9. Land Gini for various countries, circa
1995.
Source:
Klaus Deininger and Lyn Squire, “A New Data Set Measuring Income Inequality
,” WorldBank Economic Review
10,
3 (September 1996):
565–91.
619
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620 Miguel Sz
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´
es Montes
America, inequality remained constant and, in some cases, such as in Mex-
ico during the 1970s and Panama during the 1980s and 1990s, it even
increased. The only country with a decrease in its land Gini was Colombia,
to around 0.75 points, although this level was still double the one from
the East Asian developing countries. In sum, according to the available evi-
dence, statistics show that similar to the case of education, physical capital
assets in Latin America are concentrated in a few hands.
USE OF ASSETS
The accumulation of human and physical capital in Latin America has not
only been slow compared with other regions, but the distribution of these
income-earning assets has been highly skewed toward the rich. The second
element in the process of income-formation is the use of assets. This section
examines this component. First, we concentrate on human capital and then
we present a brief analysis of the use of physical capital and land.
labor force participation in latin america:
the use of human capital
Latin America has been characterized by deep changes in terms of the
possibilities of using human capital to generate income during the past
decades. This has been especially so in the case of women. Figure 14.10
shows that total labor market participation in Latin America increased
from around 64 percent in 1970 to 70 percent during the 1980s, continuing
to rise at a much slower pace during the 1990s. The figure also shows that
practically the entire shift was caused by the substantial increase in female
participation. Women accounted for only 23 percent of the labor force in
1970, but their share increased to 36 percent over the next twenty-six years.
To assess the magnitudes of demographic and schooling effects on female
labor force participation, Duryea and Sz
´
ekely estimate a regression in which
changes in women’s labor supply are a function of changes in education,
fertility, and other variables.
23
Their results indicate that reductions in
fertility and increases in average education are, in fact, associated with
increases in female participation. The authors also find that the change
23
Suzanne Duryea and Miguel Sz
´
ekely, “The Determinants of Schooling in Latin America: A Micro-
Macro Approach” (Mimeograph, Inter-American Development Bank, 1999).
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22
24
26
28
30
32
34
36
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
(%) of Females in Total Labor Force
0.60
0.62
0.64
0.66
0.68
0.70
0.72
Total Participation Rate
Females % of Labor Force
Total Participation Rate
Figure 14.10.P
articipation rates and male-female composition of the labor force in Latin
America.
Source:
Suzanne Duryea and Miguel Sz
´
ekely, “The Determinants of Schooling in Latin America: A Micro-Macro
Approach” (Mimeograph, Inter-American Development Bank,
1999).
621