ï»¿ WPS6384
Policy Research Working Paper 6384
Fifteen Years of Inequality in Latin America
How Have Labor Markets Helped?
JoÃ£o Pedro Azevedo
MarÃa Eugenia DÃ¡valos
Carolina Diaz-Bonilla
Bernardo Atuesta
Raul Andres CastaÃ±eda
The World Bank
Poverty Reduction and Economic Management Network
Poverty, Gender and Equity Unit
March 2013
Policy Research Working Paper 6384
Abstract
Household income inequality has declined in Latin effect (other components, within skill groups, affecting
America in the past decades, contributing significantly labor income). The results show that falling returns to
to poverty reduction in the region. Although available skills for both education and experience is, on average,
evidence shows that changes in the labor income are driving the decline in labor income inequality in Latin
among the main factors behind these inequality trends, America. The quantity effect, in turn, has contributed
few studies have analyzed more closely the labor market little to inequality reduction, mostly attributable to a
dynamics that have led to a decline in total income larger dispersion in years of experience, possibly linked
inequality in some countries, but also to an increase to the regionâ€™s demographic transition and to significant
in others. Using household survey data for a sample of increases in female labor force participation. Additional
15 countries in Latin America from 1995 to 2010, this findings show that wage inequality, still high in the
paper uses an extension of the Juhn-Murphy-Pierce region, is coupled with inequality in terms of hours
methodology to decompose changes in labor income worked. The paper complements the existing literature by
inequality (hourly wages) into a quantity effect (capturing presenting separate results for males and females, as well
changes in the distribution of workersâ€™ skills), price as formal and informal sector workers as an attempt to
effect (reflecting returns to skills), and unobservables control for secular shifts in these characteristics.
This paper is a product of the Poverty, Gender and Equity Unit, Poverty Reduction and Economic Management Network.
It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development
policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.
org. The author may be contacted at jazevedo@worldbank.org.
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Produced by the Research Support Team
Fifteen Years of Inequality in Latin America
How Have Labor Markets Helped? 1
JoÃ£o Pedro Azevedo** MarÃa Eugenia DÃ¡valos** Carolina Diaz-Bonilla*
Bernardo Atuesta+ Raul Andres CastaÃ±eda*
Keywords: Inequality; Decomposition; Labor Income; Latin-America
JEL Codes: Q15; I24; J30
Sector Board: Poverty Reduction (POV)
1 The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not
necessarily represent the views of the International Bank for Reconstruction and Development, World Bank and its
affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. The
authorsâ€™ benefitted from comments from Louise J. Cord, Augusto de la Torre, Leonardo Gasparini, Luis Felipe LÃ³pez Calva,
Samuel Freije, Julian Messina, Amparo Ballivian, Maurice D. Kugler, JosÃ© Antonio Cuesta Leiva, Nora Luistig, Jamele
Rigolini, Francisco Ferreira, Sergei Soares, Miguel Foguel, and Leopoldo Tornarolli. The authorâ€™s would also like to thank
the comments received from Catherine Porter and other participants of the 32nd IARIW General Conference held in
Boston, 2012. The usual disclaimer applies.
* authorâ€™s from the Poverty, Gender and Equity Unit from the Poverty Reduction and Economic Management Team
(LCSPP) in the Latin America and Caribbean Region from the World Bank; (**) author from Poverty Reduction and
Economic Management Team from the Europe and Central Asia Region of the World Bank; (+) Paris School of Economics.
Corresponding author: jazevedo@worldbank.org
I. Introduction
In the past decades, Latin America has seen reductions in both poverty and inequality. From 1995
to 2010, the region achieved a decline in poverty of around 18 percentage points, with the
moderate poverty rate going from 46 to 28 in this period. Most of the reduction took place in the
last decade when the rate of decline significantly accelerated (Figure 1). Similarly, the Gini
coefficient of total household income per capita declined by 9 percent from 1995 and 2010, going
from 0.57 to 0.52. 2 Consistent with the sharper decline in poverty in the last decade, inequality also
declined more rapidly from 2000 to 2010 than in previous periods (Table 1 and Figure A1).
The fall in inequality has indeed played an important role in reducing poverty in the region.
Decomposing changes in poverty into a growth and a redistributive component (Datt and Ravallion,
1992) shows that for the past decade inequality had a significantly larger contribution to poverty
reduction than that from economic growth alone. In fact, between 2000-2005 and 2005-2010, the
decline in inequality accounted for 58 and 37 percent, respectively, of the total poverty reduction in
Latin America, and close to 44 and 40 percent, for extreme poverty (Table 2). This is true for many
countries in the region. For example, in Brazil, 60 percent of the total change in poverty between
2000 and 2005 is due to redistributive effects. In the same period, growth contributed to poverty
increases in Argentina, which were offset by strong redistributive effects.
With a break from a historically high and persistent inequality in Latin America, it is key to better
understand what has driven the declining trend. A number of authorâ€™s have shown most of the
income inequality in the Latin American region is generated in the labor market. The analysis of
household survey of 15 countries from 1995 to 2010 suggests that although, on average, labor
income inequality has reduced its contribution to total household income inequality in Latin
America, reducing from 77 to 74 percent of total per capita household income inequality from 1995
to 2010 (Figure 2). Labor income still accounts for the highest share of total per capita household
income in the region (Figure A2) and remains the main contributor to inequality.
Given the importance of earnings in driving the overall inequality trends in the region, this paper
aims at disentangling the factors behind the decline in labor income inequality (hourly wages) in
the past fifteen years in Latin America. Using an extension of the methodology by Juhn-Murphy-
Pierce (1993) which decomposes labor income inequality into a quantity, price and unobservable
2
Gini calculated pooling data for all countries in the sample and excluding zero values. Other specifications
are presented in Table 1.
2
(residuals) effects, we explain the trends in and drivers of labor income inequality in the region and
highlight the differences in patterns across Latin American countries. Using four measures of
inequality, including the commonly used Gini coefficient and the Theil-T index, findings show that
the price effect, which captures returns to skills (education and experience), has been, on average,
the main driver of the inequality decline.
Understanding the factors behind the declining labor income inequality has important policy
implications. First, it helps determine, at least partially, how the region broke with its persistent
inequality. Second, the analysis can be useful for tackling inequality in countries that have not yet
joined the declining trend, both in Latin American and possibly in other regions as well. Finally, it
can better inform policymakers on (i) whether the decline is likely to be sustainable over time, (ii)
the possible threats to the path towards further reducing inequality and (iii) policy options that
could contribute to further falls in inequality.
The next section briefly reviews some of the recent literature exploring the declining trend in
inequality in the region, including labor income inequality. Section III details the JMP methodology
and the adaptations employed in this paper. Section IV describes the data and the empirical
strategy, while Section V and VI provide detailed results for the region and for each country in the
sample. Conclusions are presented in Section VII.
II. Literature Review
Latin America has been singled out as the world's most unequal region. As such, a growing
literature has tried to understand the historic reasons behind its persistent and high inequality, as
well as the determinants behind the recent declining trends. This section provides a brief overview
of the most recent work on inequality in the region.
Putting the recent decline in inequality into a historical perspective, Lustig and Gasparini (2011)
note that inequality trends in Latin American countries have undergone two distinct periods in the
past three decades. During the crisis of the 1980s and 1990s, and the period of structural reforms of
the 1990s, most of the countries in their analysis experienced an increase in inequality. This trend
seems to be related to the macroeconomic crises that took place in those two decades, coupled with
inexistent or inefficient social safety nets and regressive effects of structural adjustment programs.
3
A number of authors have been recently analyzing the main factors behind the recent inequality
decline in the region. LÃ³pez-Calva and Lustig (2010) compile a detailed analysis of the inequality
trends in four countries in the region: Argentina, Brazil, Mexico, and Peru. Results show that the
decline in inequality in these countries can indeed be attributed to two main factors: first, a
shrinking earnings gap between skilled and low-skilled workers, from an expansion in education in
the last decades. This effect was not compensated, as in the 1980s and part of the 1990s, by a higher
demand for skilled labor. Second, from an equalizing effect of government transfers, related to
larger and better targeted conditional cash transfer programs in these countries. Evidence in Figure
A3 shows that transfers (public and private) have the highest inequality-reducing marginal effect of
the various household income sources, at -2.2 percent in 2010.
Azevedo, Inchausete and Sanfelice (2012) decompose the recent observed changes in inequality in
16 Latin American countries over the last decade in order to find the main contributors to the
observed reduction in inequality. In contrast to methods that focus on aggregate summary
statistics, their method generates entire counterfactual distributions, allowing them to account for
changes due to demographics, labor income, transfers, pensions and other non-labor income
sources. The results shows that for most countries in the sample, the most important contributor to
the observed decline in inequality has been the relatively strong growth in labor income at the
bottom of the income distribution (over 43 percent of the total change in the Gini for the period).
Other factors are also linked to the falling inequality. For instance, recent studies refer to the role of
social-democratic political regimes in the region during the past decade, and how the policies put in
place by them had a more pronounced redistributive effect (Cornia, 2010; Birdsall, Lustig and
McLeod, 2011). Moreover, the shrinking wage gap between skilled and unskilled workers in
Argentina, for example, seems to be related to factors such as the commodity boom of the last
decade, the exchange rate devaluation, and the role of labor unions, all of which pushed up the
demand for unskilled labor relative to skilled labor (LÃ³pez-Calva and Lustig, 2010).
These and other studies point to labor income as one key factor of inequality changes. However,
most of the existing literature analyzing income inequality in Latin America focuses on total income
inequality, and more in-depth labor markets analyses are only available for a limited number of
countries. A few important exception in this literature is the work of BattistÃ³n et al. (2011) and
Gasparini et al (2011) which also addresses the issue of earnings inequality.
BattistÃ³n et al. (2011) focus on the determinants of the level of inequality and not on the change of
inequality over the recent years, their work does confirm the presence of the so called â€œparadox of
4
progressâ€?: changes in education induced higher income inequality levels (through the highly convex
structure of returns) in most countries in the region, and that the changes were more unequalizing
in the 1990s than in the 2000s.
Gasparini et al (2011) apply the Murphy-Katz decomposition to establish which factor related to
the demand and suply of labor by skill level can account can account for this differential evolution
of inequality in 16 Latin American countries over the decades of the 1990s and the 2000s,
concentrating on the change in the distribution in terms of return to skills (as proxied by
educational attainment). Their paper disentangled the relative contribution of supply and demand
factors in explaining wage premia evolution. In a context of constant increase in the relative supply
of skilled and semi-skilled workers, Tinbergenâ€™s framework suggests that differential evolution
indicates a strong shift in demand towards skilled labor in the 1990s and a deceleration of this
relative demand in the second period.
Hence, this paper contributes to the literature by providing a regional perspective on how labor
income inequality has shifted in the region. The analytical framework chosen in this paper allow us
to more clearly disentangle how the educational and demographic (here define of years of
experience), components have contributed to the changes of inequality over time, as well as the
residual wage inequalityâ€”that is, the wage dispersion within demographic and skill groups
increased simultaneously. Moreover, this approach can also help us understand if the observed
changes were driven by changes in returns or the composition. One last, important contribution of
this analysis, are the separate results by males and females; as well as, formal and informal sector
works as an attempt to control for secular shifts in these characteristics.
III. Methodology
This paper uses the Juhn-Murphy-Pierce (JMP, 1993) methodology to decompose labor income
inequality, with an extension proposed by Foguel and Azevedo (2007) that allows for a
counterfactual interpretation of inequality changes over time. One advantage of this methodology
vis-Ã -vis alternative methods, such as the Oxaca-Binder decompositon, is the possibility to account
for within-group inequality, captured by the inclusion of the residual term in the counterfactual
distribution. This is particularly important for the decomposition of distributional sensitive
measures such as the ones analyzed in this paper.
The Juhn-Murphy-Pierce methodology
5
The JMP approach is based on Mincer-type Ordinary Least Squares (OLS) regressions that allow
decomposing labor income inequality, using any measure of inequality, in three parts. First, a
quantity effect which refers to the distribution of observable workersâ€™ characteristics, such as
education and labor market experience, and are included as regressors in the equation. Second, a
price effect which captures changes in returns to observed characteristics through the regressionâ€™s
coefficients. Third, the regression residual reflects changes in inequality within education and
experience groups driven by unobserved factors.
The starting point is a Mincerian equation:
y=
it X it Î²t + uit , (1)
where i represents a worker observed in time t , yit is the log of labor income, X it represents the
vector of the workerâ€™s observable characteristics, Î² t the vector of coefficients for time t, and uit the
error term assumed to have zero mean (i.e. E[uit | X it ] =
0 ).
Let Ft (. | X it ) be the conditional cumulative distribution of the residuals for period t. Denoting Î¸ it
as the percentile of individual i at time t in the residuals distribution, equation (1) can be expressed
as:
yit = X it Î²t + Ft âˆ’1 (Î¸it | X it ). (2)
Changes in earnings over time can occur from (i) changes in the distribution of workersâ€™ observable
characteristics, X it , known as the quantity effect; (ii) changes in returns to these observed
characteristics, Î² t , or the price effect,; and, finally, (iii) changes in the distribution of unobservables
( F âˆ’1 (. | X ) ).
This framework allows us to simulate the distribution of earnings for each period t by keeping
some components fixed, i.e., by substituting one or more of the right-hand side components with
their mean over time. Particularly, let Î² be the vector of observable characteristics for a regression
including all years; similarly; F (. | X it ) is the conditional distribution of the residuals of that
regression. By rewriting equation (2) with these components as
1
yit = X it Î² + F âˆ’1(Î¸it | X it ). , (3)
6
it can now be interpreted as the distribution of labor income in period t when keeping prices and
residuals constant, so that only the observable characteristics, Xs, change over time.
Following a similar approach, we can once more rewrite equation (2) to simulate the distribution of
earnings by letting both quantities and prices vary over time, while keeping the distribution of
residuals fixed. This equation will be
2
yit = X it Î²t + F âˆ’1(Î¸it | X it ). (4)
A third and final simulation allows for all components to change over time, reflecting the original
distribution of earnings, so that
y=
3
it X it Î²t + Ft âˆ’1 (Î¸it | X it ) â‰¡ yit , . (5)
With all three simulated labor income distributions in place, the concept of inequality is introduced.
Let D (.) be any measure of inequality, such as the Gini coefficient or the Theil index. If
Yitk = exp ( yit
k
) , k=1,2,3, the contribution of quantities, prices and unobservables to total inequality
in period t (i.e., Tt = D (Yit ) )can be expressed as
Qt D(Yit1 ),
= (6)
=Pt D(Yit2 ) âˆ’ D(Yit1 ) (7)
and
Rt = D(Yit3 ) âˆ’ D(Yit2 ). (8)
The sum of each of these components in period t equals total inequality namely
Rt D(Y=
Qt + Pt + = 3
it ) D(Y=
it ) Tt , so that total inequality is decomposed into contributions of the
quantity, price and unobservables effects.
The JMP methodolgy just described has been widely used and allows for the decomposition to be
interpreted as the contribution of each component to inequality in a particular year. However, a
7
limitation of this approach is that the overall methodology is not suited for comparisons of how
each effect contributes to inequality over time. More specifically, letâ€™s consider two time periods,
âˆ’1 âˆ’1
Ï„ â€² and Ï„ â€²â€² , and simplify the notation of F (Î¸it | X it ) =
F and Ft (Î¸it | X it ) =
âˆ’1
Ft âˆ’1 . Taking time
differences for Qt , Pt and Rt we arrive at the following:
=
QÏ„ â€²â€² ( ) (
âˆ’ QÏ„ â€² D exp ( X iÏ„ â€²â€² Î² + F âˆ’1) âˆ’ D exp ( X iÏ„ â€² Î² + F âˆ’1) , ) (9)
=
PÏ„ â€²â€²
âˆ’PÏ„â€² ( )
ï£® D exp ( X â€²â€² Î² â€²â€² + F âˆ’1) âˆ’ D exp ( X â€²â€² Î² + F âˆ’1) ï£¹
ï£° iÏ„ Ï„ iÏ„ ( ï£» )
( ) ( )
âˆ’ ï£® D exp ( X iÏ„ â€² Î²Ï„ â€² + F âˆ’1) âˆ’ D exp ( X iÏ„ â€² Î² + F âˆ’1) ï£¹
ï£° ï£»
(10)
and
RÏ„ â€²â€² âˆ’ RÏ„ â€² ï£® D ( exp ( X iÏ„ â€²â€² Î²Ï„ â€²â€² + FÏ„âˆ’
=
ï£°
1
(
â€²â€² ) ) âˆ’ D exp ( X â€²â€² Î² â€²â€² + F
iÏ„ Ï„
âˆ’1
) ï£¹
ï£» )
âˆ’ ï£® D ( exp ( X iÏ„ â€² Î²Ï„ â€² + FÏ„âˆ’
ï£° (
â€² ) ) âˆ’ D exp ( X â€² Î² â€² + F
1
iÏ„ Ï„
âˆ’1
)
) ï£¹.
ï£» (11)
As mentioned before, JMP is limited in providing information on changes over time in the
contributions to inequality of each component. The exception is the first component, the quantity
effect, expressed in (9). More specifically, the time differences in (9) show that the only component
âˆ’1
that changes between Ï„ â€² and Ï„ â€²â€² is the observable characteristics, while the Î² and F remain
fixed. Therefore, this difference in fact reflects the effect of changes in quantities between the two
time periods.
Conversely, expressions (10) and (11) fail to provide a temporal interpretation. In (10), for
instance, the time difference in the price component cannot be interpreted as the contribution of
the price effect to changes in inequality. This is because it is not only the prices (i.e. the Î² s) that
change in PÏ„ â€²â€²
âˆ’PÏ„â€²
, but also the Xs . Unless the distribution of quantities remains fixed over time,
JMP is limited in providing a counterfactual interpretation of the price effect. A similar analysis
8
leads us to the conclusion that a counterfactual analysis cannot be derived from RÏ„ â€²â€² âˆ’ RÏ„ â€² , given
âˆ’1 âˆ’1
that changes over time cannot be only attributed to changes between FÏ„ â€² and FÏ„ â€²â€² .
Adapting JMP for a counterfactual interpretation
This study presents a modification to the original JMP method by Foguel and Azevedo (2007), so
that it allows for a counterfactual interpretation over time. By letting s be a fixed time period (e.g.,
2000) we can rewrite equations (3), (4) and (5) as follows:
âˆ—1
yit = X it Î² s + Fsâˆ’1 (Î¸it | X it ), (12)
âˆ—2
yit =X it Î²t + Fsâˆ’1 (Î¸it | X it ) (13)
and
= X it Î²t + Ft âˆ’1 (Î¸it | X it ) â‰¡ yit ,
âˆ—3
yit (14)
where Fs (Î¸it | X
âˆ’1
= it ) Fsâˆ’1 ( F (uit | X it )) , denoted as Fsâˆ’1 for simplicity.
Equation (12) simulates labor income allowing quantities to change over time, but keeping prices
and residuals fixed at a reference period s. The difference with (3) is therefore straightforward:
while (12) leaves prices and residuals fixed at a specific period, (3) uses the mean of prices and
residuals for all periods under consideration. Similarly, equation (13) simulates a distribution of
labor income where quantities and prices vary over time (as in equation (4) in the JMP
methodology), but in which the distribution fo residuals is that from s. As equation (14) allows all
components to vary it is identical to (5).
âˆ—k âˆ—k
Following the same steps of JMP of (6), (7) and (8), and with Yit = exp ( yit ) , k = 1, 2, 3 , the
quantity, price and unobservable components for period t are as defined follows:
Qtâˆ— D(Yitâˆ—1 ),
= (15)
9
=Pt âˆ— D(Yitâˆ—2 ) âˆ’ D(Yitâˆ—1 ) (16)
and
Rtâˆ— = D(Yitâˆ—3 ) âˆ’ D(Yitâˆ—2 ). (17)
As before, the sum of the three components equals total labor income inequality, i.e.
âˆ—
Qtâˆ— + Pt âˆ— + =
Rtâˆ— D(Yit 3 ) â‰¡ D(Y
=it ) Tt . Note also that for t = s , Qsâˆ— = Ts and P
=s
âˆ—
=
Rs
âˆ—
0.
This modification of the original JMP provides a counterfactual interpretation of changes in labor
income inequality over time between any time period t and time period s. This is derived from the
following expressions:
Qtâˆ— âˆ’ Qsâˆ— D ( exp ( X it Î² s + Fsâˆ’1 ) ) âˆ’ D ( exp ( X is Î² s + Fsâˆ’1 ) ) ,
=
(18)
ï£° D ( exp ( X it Î²t + Fs ) ) âˆ’ D ( exp ( X it Î² s + Fs ) ) ï£»
Pt âˆ— âˆ’ Psâˆ— ï£®
= âˆ’1 âˆ’1
ï£¹
ï£° D ( exp ( X is Î² s + Fs ) ) âˆ’ D ( exp ( X is Î² s + Fs ) ) ï£»
âˆ’ï£® âˆ’1 âˆ’1
ï£¹
= D ( exp ( X it Î²t + Fsâˆ’1 ) ) âˆ’ D ( exp ( X it Î² s + Fsâˆ’1 ) )
(19)
and
ï£° D ( exp ( X it Î²t + Ft ) ) âˆ’ D ( exp ( X it Î²t + Fs ) ) ï£»
Rtâˆ— âˆ’ Rsâˆ— ï£®
= âˆ’1 âˆ’1
ï£¹
ï£° D ( exp ( X is Î² s + Fs ) ) âˆ’ D ( exp ( X is Î² s + Fs ) ) ï£»
âˆ’ï£® âˆ’1 âˆ’1
ï£¹
= D ( exp ( X it Î²t + Ft âˆ’1 ) ) âˆ’ D ( exp ( X it Î²t + Fsâˆ’1 ) ) .
(20)
10
The difference in (18) shows that only the Xs change between t and s, so that it can be interpreted
as the effect of changes in quantities on inequality between this two periods. This interpretation
can also be derived from the original JMP, with the difference that in JMP the reference period in
which prices and unobservables are kept fixed for evaluating changes in quantities over time, is the
mean of all periods instead of s.
The main difference comes when evaluating (19) and (20). In (19), for example, the difference
âˆ— âˆ—
between Pt and Ps can now only be attributed to changes in prices between t and s, as the second
term in brackets will equal zero. In sum, (19) provides a counterfactual interpretation of changes in
total labor income inequality between t and s from price changes between those two time periods.
A similar interpretation is derived from (20) for the case of unobservables, capturing only the effect
of changes in the unobservable component in changes of total labor income inequality between t
and the reference period s.
As described above, this adaptation of the JMP methodology allows for a counteractual
interpretation of the quantity, price and unobservables effects between s and t. It is important to
keep in mind, however, that it does not allow for the evaluation of these effectsâ€™ contributions to
total inequality between any two periods Ï„ â€² and Ï„ â€²â€² , where none of them are the reference s.
IV. Data and Empirical Strategy
The data used in this paper are from a harmonized database of household surveys from 15 Latin
American countries compiled in the Socio-Economic Database for Latin America and the Caribbean
(SEDLAC), a joint effort of the Centro de Estudios Distributivos Laborales y Sociales of the Universidad
Nacional de La Plata and the World Bankâ€™s poverty group for Latin America and the Caribbean. The
countries included in this analysis are Argentina, Bolivia (urban), Brazil, Chile, Colombia, Costa Rica,
Dominican Republic, Ecuador, El Salvador, Honduras, Mexico, Panama, Paraguay, Peru, and Uruguay
(Montevideo-urban). So as to make time periods comparable across time we use the circa criteria
for the years 1995, 2000, 2005 and 2010. Tables A1 and A2 provide more detail of the countries,
years and surveys included in this study.
Labor income is calculated as individual hourly wages (only for individuals with positive wages in
the dataset), and all regional estimations are computed as working population-weighted averages.
For the variables of observed workersâ€™ characteristics ( X it ) we use two measures of skills: years of
education and potential experience in the labor market. The latter is measured as age minus years
11
of education minus six. All regressions are estimated using OLS, and workersâ€™ characteristics are
included in the regression as dummies, with years of education covering a range from 0 to 17 years;
experience including the following categories of potential experience (as in JMP, 1993): 0-10, 11-20,
21-30 and 31 or more; and finally, an interaction term of the years of education and potential
experience dummies. The JMP methodology does not require for the same individual to be followed
over time, i.e., panel data, so we use four years of cross-sectional data (1995, 2000, 2005 and 2010)
to run the decompositions. The reference period chosen for this study is the year 2000, so that all
results are interpreted as deviations of inequality (or each of the components) from that year.
Following Foguel and Azevedo (2007) we estimate equation (1) for each available year. The
regression residuals are ranked in ascending order for each year and divided in percentiles. For
each percentile in each year we estimate the mean to create a discrete empirical approximation of
Ft âˆ’1 (Î¸it | X it ) . We employ this discrete distribution to construct the earnings of each individual (i)
í µí±—
in year (t) as í µí±‹í µí±–í µí±¡ í µí»½í µí±¡ + í µí°¹í µí±¡âˆ’1 (í µí¼ƒí µí±–í µí±¡ |í µí±‹í µí±–í µí±¡ ), where í µí»½í µí±¡ is the vector of estimated coefficients in year t and
Î¸ j is the mean value of residuals in percentile Î¸ j in which the individual was located. For
Fsâˆ’1 ( F (uit | X it )) , we use the mean residuals for each percentile in the reference year (2000). For
more details see Foguel and Azevedo (2007).
Finally, the simulations of labor income of (12), (13) and (14) are calculated using the estimated
coefficients and the discrete distribution of residuals, which in turn are used to estimate the final
decomposition in (15), (16) and (17). We conduct our methods using four measures of inequality,
namely the Gini coefficient, the Theil-T (GE(1)) index of inequality and the ratios of mean labor
income between the 90th and 10th deciles (90/10), and the 80th and 20th deciles (80/20).
One issue that is often raised in analyses of this nature is the importance of selection into the labor
market, which can potentially bias the estimated coefficients of the earnings equation. Attempting
to methodologically address this issue is beyond the scope of the current work. However, in order
to test the robustness of the findings and explore the extent to which selection might be biasing our
results, this paper also estimates separate models for Males-Females as well as Formal-Informal
workers. This choice was based on two relevant trends in LAC: the increase in female labor force
participation in the region between 1995-2010 (World Bank, 2012), as well as the expansion of
12
formal employment 3. As further discussed below, the findings of this paper support a qualitatively
consistent story across these groups.
V. Results
5.1 Decomposing Labor Income Inequality
As a starting point to assessing changes in labor income inequality in Latin America, Table 3
presents the evolution of labor income inequality measures in the past fifteen years. All four
measures suggest a monotonic decline, on average, in labor income inequality for the region. More
specifically, the regional Gini (working-population-weighted averages of countries in the sample)
declined at an average rate of -0.6 percent per year and the Theil by -1.3 percent from 1995 to
2010. However, not all countries joined the declining trend in labor income inequality in this
period. The labor income Gini increased for Costa Rica, Honduras, Panama and Uruguay, while the
Theil also increased in those four countries in addition to El Salvador and Bolivia. The fastest fall in
the Gini took place in Brazil, with a -0.75 percent annualized rate from 1995 to 2010 (Figure 3).
To explore the factors behind the regional fall in labor income inequality, Figure 4 and 5 present the
adapted JMP decomposition results for Latin America. The first panel of each figure (panel A) shows
the observed total changes in inequality from 1995 to 2010, with 2000 as the reference year. The
rest of the panels (B-D) decompose the total changes into quantity, price and unobservable
(residuals) effects. Moreover, Table 4 through 7 present detailed decomposition results for each
country and each inequality measure for the period of the fastest inequality decline (2000-2010). A
negative sign denotes a contribution to inequality decline, while a positive sign indicates that the
component was inequality-increasing over this period.
Quantity effect: focusing on the quantity effect (i.e., the contribution of changes in the composition
of skills to labor income inequality, ceteris paribus), shows that in most measures (the exception
being the 90/10 ratio) the quantity effect further reduced its already low contribution to inequality
decline in 1995 (panel B of Figures 4 and 5), resulting in a very small share of inequality falls
attributable to this factor by 2010. Results by country presented in Tables 4-7 for the last decade
(2000-2010) show that in 5 out of 15 countries, the quantity component contributed to increasing
the labor income Gini and the Theil index. The decompositions of the 90/10 and 80/20 measures
3Data on informality from SEDLAC (CEDLAS and The World Bank) show a decline in informality in most
countries in the region.
13
show, however, a positive contribution of the quantity effect to labor income inequality in 11 and 9
countries, respectively.
Price effect: the driving factor behind labor income inequality declines in the past fifteen years,
independent of the measure of inequality used, was the falling returns to skills (Panel C), also
known as the price effect. Between 2000 and 2010, for example, around 64 percent of the total
change in the Gini coefficient can be attributed to declining returns to skills. This result is consistent
in most countries in the sample, with an inequality-reducing effect of the skill premia in 12 out of
15 countries for the Gini, Theil index and 90/10 ratio (13 countries for the 80/20 ratio). In fact, one
of the highest achiever in terms of declines in the labor income Gini coefficient and Theil Index from
2000 to 2010, Brazil, can attribute around 61 percent and 72 percent of the changes, respectively,
to falling returns to years of education and experience. Conversely, in Costa Rica, for example, were
the Gini coefficient of labor income increased in the past decade (2000 to 2010), both the quantity
and the price effects were inequality-increasing.
Other factors effect: The role of unobservables (within skill-group inequality, measured by the
residual) is very heterogeneous across countries. This effect could be capturing a wide range of
things not accounted for in our empirical strategy, such as quality of education, changes across
sectors or occupations (including changes in demand for workers in specific sectors), among others.
On average, inequality within groups decreased over time, although its contribution to total labor
income inequality changes was relatively small by 2010. Nonetheless, this effect was particularly
strong in some countries in terms of enhancing inequality, fully offsetting the role of the price and
quantity effects. This is the case, for example, of Paraguay, where the Gini coefficient would have
fallen between 2000 and 2010 driven by the reductions in returns, if it had not been more than
compensated by the larger and positive contribution to inequality of within-group changes.
To summarize the patterns across countries of the quantity, price and unobservables effects, Table
8 presents a typology of countries based on whether the various components were inequality-
increasing or inequality-decreasing in the last decade in terms of the Gini coefficient. Only in four
countries in the sample, i.e., Argentina, Brazil, Mexico and Peru, all three components moved in the
same direction, thus enhancing the overall change in labor income inequality.
5.2 Decomposing Labor Income Inequality by Gender and Sector
14
Overall regional and country trends in labor income inequality could be masking differences across
subgroups. We therefore apply the adapted JMP methodology to subsamples of the working
population by gender and by formal/informal sector workers. To simplify the analysis, we focus on
the Gini coefficient of labor income inequality for all four subgroups (Annex tables A3-A6) in the
period 2000 to 2010.
At a regional level, results show a larger decline in inequality for male workers compared to female
workers from 2000 to 2010. From a country perspective, while labor income inequality declined in
12 out of the 15 countries for males, it declined in only seven countries for females. The
decomposition results show that the price and unobservables effects were inequality-reducing for
both groups, but much more powerful for males. The quantity effect, on the contrary, contributed to
increasing inequality only for females, as the new women joining the labor market had on average
more experience (age) and education (its effect on pushing down male labor income inequality,
nonetheless, is very small).
Labor income inequality in Latin America declined more in the informal than in the formal sector
from 2000 to 2010. Results are very heterogeneous across countries; while inequality declined
relatively more in the formal sector in Argentina, Bolivia and Peru, it fell relatively sharper in the
informal sector for Brazil and Mexico. The price effect across sectors is very similar. The
unobserved effects have a four times larger contribution to reducing inequality in the informal
sector than in the formal sector, even if the quantity effect is inequality-increasing for the informal
only. Looking at returns to skills and unobservables, returns to experience have declined relatively
more for formal workers.
5.3 Price Effect: Unbundling returns to education and experience
Given that falling returns to skills seem to be dominating, on average, inequality changes over time,
we try to unbundle this price effect to better understand its dynamics. Figure 6 presents the mean
returns to education, experience and unobservables over time (captured by the residual). 4. Results
4
Returns to education and experience are calculated from the coefficients of the Mincer equation (1) for each
characteristic. The mean return to education for year t, for example, is calculated as a weighted average (by
population share with each level of education) of the return to each level of education, divided by the
weighted average (by population share with each level of education) of each level of education. A similar
approach is taken for experience level, and in both cases the interaction terms are also included in the
estimations. For the unobservables, equation (1) is rewritten as yit = X it Î²t + Ïƒ t Îµ it , where,
= X it Î²t + uit
assuming that Îµ it is a random independently and identically distributed variable (iid) following a normal
15
in Figure 6 show that returns to all three factors declined during the period of analysis and that the
pace of reduction accelerated after 2005. Overall, mean returns to years of education and
experience declined a total of 30 and 20 percent, between 1995 and 2010. Detailed results for
males and females (Figures A4- A6) show that trends for both groups in returns to education are
similar. In experience, we observe a decline for both groups, but slightly larger for males. It is
important to notice that part of the price story captured in this decomposition is the effect of
intertemporal changes on the quantities (supply of educated people, who can change the skill
premium). An important implication to this, is that the after-mentioned quantity effect can be
interpreted as a composition effect, as it is net of any quantity effect that might have picked up by
the price effect (i.e. changes in the returns).
The decline in returns to skills has been driven by a larger supply of experienced and educated
workers in the region. Both mean years of education and experience have increased in the region
for the working population (Figure 7), more sharply for education. Mean years of education and
experience have increased for both sexes (Figures A5 and A6). For education, for example,
investments in the past decades have resulted in a significant average increase in educational
attainment of the population (1.7 additional years on average in Latin America). For experience,
changes in the mean could be driven by an increase in female labor force participation and by the
aging of the population (further discussed in section 5.4).
In all countries in the sample, except for the Dominican Republic and Ecuador, average education
levels of workers increased in the period (Table 10). The largest expansion took place in Brazil and
Mexico, where years of education of workers increased a total of 35 percent and 26 percent,
respectively, from 1995 to 2010. Similarly, mean years of experience also rose in all countries
(except for a slight decrease in Bolivia), although at slower rates.
Changes in returns to education show a very mixed picture across countries (Table 9). While
returns to years of education declined a total of 43 percent in Brazil from 1995 to 2010, they
increased in Argentina (40 percent) and Chile (83 percent). Similarly, while returns to experience
fell by 34 percent in Mexico and 28 percent in Chile in the period under study, they increased by 38
percent in Honduras.
distribution, N (0,1) , and Ïƒt is a factor (standard-deviation) that alters the dispersion of the distribution of
errors, Ïƒt can be interpreted as capturing the â€œpriceâ€? of unobservables. For more details see Foguel and
Azevedo (2007).
16
5.4 Quantity Effect: Unbundling inequality of education and experience
Previous results showed that on average, the quantity effect contributed very little to the reduction
of inequality in the region. It is important to keep in mind that quantity, in particular education, still
play 5 an important role in explaining the high level of inequality in the region (BattistÃ³n, et al
2011). This subsection explores the factors behind the quantity effect by looking at mean levels of
education and experience and the dispersion in these characteristics over time. In other words, we
further explore the composition of skills among workers, all else equal.
The abovementioned expansion in years of education and experience has not been uniform across
the population, resulting in changes in the distribution of these skills among the working
population. On the one hand, the evolution of the standard deviation of years of education suggests
that inequality in education slightly decreased (by around 2 percent) in the 2000s. This seems to be
primarily driven by falling educational inequality of women (Figure A5). Overall, the reduction in
educational inequality reflects a catch up from those at the bottom of the education distribution.
For instance, the bottom income quintile in Latin America achieved an additional 1.8 years of
education from 1995-2009, while the top quintile increased by 1.3 years. 6
On the other hand, the changing composition in years of experience has led to higher inequality of
experience in workers in the past fifteen years (total increase of 1.8 percent from 1995-2010). In
fact, in 11 out of 15 countries in the sample there was an increase in the standard deviation of years
of experience among workers (Table 11). Both the mean and the standard deviation of experience
have increased for men and women over time. By 2010, mean years of experience for women had
increased more for than for men.
Looking at the workforce by sector, informal workers have increased their mean education
significantly more than formal workers; the changing composition of education has resulted,
however, in a growing dispersion in education for informal workers and a decline in educational
inequality for the formal sector. For the formal sector, the decline in educational inequality of the
formal sector is likely offset by a sharp increase in inequality of experience, not observed in the
informal sector (Figures A5 and A6).
5
Since the seminal work of Langoni (1973) several authors have found the effect of educational expansion was to
increase inequality, including Bourguignon et al (2005), Reis and Barros (1991), Knight and Sabot (1983), Reyes
(1988), and Lam (1999).
6 World Bank (2011).
17
As the education and experience effect are working in opposite directions, the overall quantity
effects (net effect) is, on average, small. This suggests that the experience component is, on average,
dominating in the overall JMP quantity effect. The question then arises of what is driving the
dispersion in experience levels?
Given that our experience variable (reflecting potential experience) is a construct including age,
years of education and a constant, and given that years of education have increased and are less
dispersed among the working population, it seems likely that the explanation behind a higher
experience inequality lies in the aging of the working population. The age profile of people in the
labor markets is likely related to two factors: (i) the demographic transition in the region, which
has resulted in a bulge of newcomers into the labor market since 2000 and (ii) the increase of
female labor force participation in the region.
From 1995 to 2000 alone, occupied workers between 19-24 years old increased by around 14
percent in LAC (much higher than increases in subsequent periods at 11 percent from 2000 to 2005
and 5 percent from 2005 to 2010), as the largest birth cohort of the region enters the labor market
(Cotlear 2010). As this cohort aged through 2010 and gained more experience in the labor market:
(i) overall mean experience increased in the region, pushing down returns to experience and (ii)
the dispersion of experience also increased, more so as they joined employment with very little
experience in the first five years of our sample. The increase in experience inequality has persisted
over time, but at lower rates.
The demographic transition story is complemented by a generalized increase in female labor force
participation in the region. The ratio of male/female ratio of workers rate went from 1.9 in 1995 to
1.5 in 2010. Figure A7 presents the growth rate of male and female workers from 2000 to 2010 in
the region and by age. As shown, the increase in women workers is significantly higher than that of
men, particularly for women in their late forties and early fifties. This is possible linked to, first, the
higher increase in mean experience for women compared to men (Figure A6) (given that
experience is an age construct). Second, it could also result in the lower dispersion in experience
observed than for men, as the bulge of young workers entering the workforce with little experience
(from the demographic transition) is partially offset by a relatively older group of women
(estimated to have more experience) joining the labor markets.
VI. Earnings Inequality
18
So far the analysis has concentrated in the evolution and factors behind inequality of hourly wages.
This section aims at more explicitly linking inequality in hourly wages to total household income
inequality. Following Juhn, Murphy and Pierce (1993), we use annual earnings as a proxy for
income under the assumption that hourly wages hold a stronger link to annual earnings than to
family income per-capita. Using annual earnings as a proxy for income is reasonable in this context,
given that this source of income represents around three-quarters of total household income for
Latin American households.
To assess the contribution of inequality in hourly wages to that of annual earnings, we calculate the
annual earnings as the product of the hourly wage and the number of hours worked per year.
Departing from í µí±¦ = â„Ž + í µí±¤, where í µí±¦ is the log of annual earnings, í µí±¤ is the log of hourly wages and â„Ž
is the log of hours worked per year 7, the variance of log annual earnings, Ïƒ2
y , is
2 2
Ïƒ2 2
y = Ïƒh + Ïƒw + 2Ïƒhw ,
2 2
where Ïƒ2
w is the variance of log hourly wages, Ïƒh is the variance of log hours worked, and Ïƒhw is the
covariance of log hourly wages and log hours worked.
Figure 8 shows the variance of log annual earnings and its components for the population-
weighted average of countries in our sample. Over the period as a whole, the movements of the
annual earnings variance depended mostly on the hourly wage variance. The variance of the weekly
hours worked also contributed to the annual earnings variance, especially in the increase of both
2005 and 2010. An interesting finding is that the covariance of hourly wages and weeks worked is
negative for all countries (contrary to JMP, 1993, results for the United States). This means that the
higher the hourly wage, the smaller the amount of hours worked per week. Although this negative
covariance is slowly approaching zero for almost every country in the period under study, this
result is the reflection of the high inequality not only in terms of hourly wages but also in terms of
hours worked in Latin American countries. In other words, people that earn less per hour also work
more hours per week.
Finally, although wages are a key component of changes in total earnings, only 50% of the increase
in the variance of annual earnings from 1995 to 2010 is due to the increase in the variance of
hourly wages. This fact highlights the difference between the concepts of earnings and wage
inequality, something that should be kept in mind when analyzing inequality trends.
7 Due to data constraints, we assume that all individuals worked 52 weeks per year.
19
VII. Conclusions
Latin America is finally on a path towards reducing income inequality. To better understand the
factors behind this trend and given that labor income contributes the most to total household
income and to total income inequality, this paper explores the drivers of labor income inequality
changes. The results show that a more equitable distribution of labor market income has been the
main force behind falling inequality. The decline in labor income inequality, in turn, has been
mainly driven by falling returns to education and experience.
As inequality in the region remains high, two things should be considered in the path towards
further inequality reduction. First, improved access to education, which has been a key driver
falling inequality, needs to be coupled with improvements in skills. The educational system needs to
strengthen its ability to generate the skills that are valued in the labor market (Aedo and Walker,
2012); in other words, quality of education and skills are the new margin for inequality. A recent
study tests for the intergenerational persistence of inequality using PISA scores 8 and finds that
Latin American countries have relatively higher rates of intergenerational persistence of inequality
in educational achievements than, for example, countries in Asia (Ferreira and Gignoux, 2010). Also
employing PISA data, the Human Opportunity Index for quality of education is consistently lower
for science, mathematics and reading for Latin American countries than countries in Europe and
North America (Molinas et al., 2010).
Second, Latin America is currently undergoing a demographic transition with a larger proportion of
working-age adults. As a result, the region is likely to generate a demographic dividend that can
provide resources to be geared towards inequality and poverty-reducing investments. This
favorable scenario is projected to continue until around 2020, when this ratio of workers/retirees
should reach its maximum, before starting to decline again, this time due to the growing proportion
of older persons and a relatively smaller workforce. It is important to notice that while such
demographic transition lasted for over a century in developed countries, similar changes are
occurring much more quickly in Latin America and other developing countries today. France had
115 years to accommodate the doubling of its elderly population from 7 percent to 14 percent; in
Latin America this process is happening much more quickly and the adjustment will likewise need
8
PISA refers to the OECD Programme for International Student Assessment, which is an internationally comparable
dataset that assess competencies in math, reading and science for 15 year old students in many countries.
20
to be quicker. Chile is projected to face this change in 26 years, Brazil in 21, and Colombia in 19
years. 9
Going forward it is important to continue to invest in country specific analysis, to deepen the
understanding of the channels and mechanisms under which many of the stylized facts presented in
this paper operate. In particular, it might be important to better understand the roles of some of the
factors within particular occupational choices, and greater attention should be devoted to changes
in the distribution of skills and characteristics over time.
9
Cotlear, Daniel (Editor) â€œPopulation Aging: Is Latin America Ready?â€? The World Bank: Washington, DC.
21
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24
Figure 1. Headcount Poverty Ratio in Latin America, US$ 2.5/day and US$ 4/day (2005 PPP) and
GDP per Capita PPP (constant 2005 international $), 1995-2010
50.0 11,000
GDP per capita, PPP (constant 2005 international $)
45.0 10,500
46.0
44.9
43.6 44.0
42.3 43.0 42.7
40.0 41.5 41.4 41.0 10,000
Poverty headcount (%)
38.1
35.0
9,500
34.1
30.0 31.6
30.6 30.4
9,000
27.9 27.7 28.0
25.027.5 26.8 26.7 26.7
25.1 25.7
24.5 24.1 8,500
20.0 21.9
18.6
15.0 17.2 8,000
16.4 16.1
14.0
10.0 7,500
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Extreme Poverty Moderate Poverty GDP per capita PPP (constant 2005 international $)
Source: â€œOn the Edge of Uncertainty, Poverty Reduction in LAC during the Great Recession and Beyondâ€? by the Poverty and Gender Unit
in LAC, The World Bank (2012).
25
Table 1. Gini and Theil-T index of Total Household Income Per Capita, circa 1995-2010
Gini
Country 1995 2000 2005 2010
Argentina 0.48 0.50 0.49 0.44
Brazil 0.59 0.59 0.56 0.54
Bolivia 0.53 0.54 0.52 0.51
Chile 0.55 0.55 0.52 0.52
Colombia 0.55 0.59 0.55 0.55
Costa Rica 0.45 0.46 0.47 0.50
Dominican Republic 0.47 0.52 0.50 0.47
Ecuador 0.50 0.55 0.53 0.49
El Salvador 0.50 0.52 0.50 0.48
Honduras 0.55 0.54 0.59 0.55
Mexico 0.54 0.54 0.51 0.47
Panama 0.55 0.56 0.54 0.52
Paraguay 0.58 0.57 0.53 0.52
Peru 0.54 0.56 0.52 0.48
Uruguay 0.41 0.44 0.46 0.47
Data without zeros
LAC (pooled data) 0.57 0.57 0.54 0.52
LAC (population weighted average) 0.56 0.56 0.54 0.51
Data with zeros
LAC (pooled data) 0.58 0.58 0.55 0.53
LAC (population weighted average) 0.57 0.57 0.54 0.52
Theil
Country 1995 2000 2005 2010
Argentina 0.43 0.46 0.45 0.35
Brazil 0.71 0.71 0.65 0.59
Bolivia 0.57 0.60 0.56 0.54
Chile 0.62 0.65 0.56 0.58
Colombia 0.69 0.80 0.65 0.65
Costa Rica 0.37 0.38 0.41 0.49
Dominican Republic 0.42 0.55 0.49 0.41
Ecuador 0.50 0.65 0.75 0.49
El Salvador 0.49 0.53 0.47 0.44
Honduras 0.64 0.57 0.70 0.61
Mexico 0.61 0.59 0.55 0.45
Panama 0.58 0.61 0.54 0.52
Paraguay 0.69 0.69 0.60 0.65
Peru 0.58 0.66 0.52 0.44
Uruguay 0.29 0.34 0.39 0.40
Data without zeros
LAC (pooled data) 0.67 0.66 0.61 0.55
LAC (population weigthed average) 0.65 0.66 0.60 0.54
Data with zeros
LAC (pooled data) 0.70 0.70 0.63 0.57
LAC (population weigthed average) 0.68 0.70 0.63 0.56
Source: Authorâ€™s calculations with data from SEDLAC (CEDLAS and The World Bank).
26
Table 2. Decomposing poverty changes: % of total poverty changes from growth and redistribution
a. Poverty at $4/day
Poverty Headcount Poverty Gap Poverty Gap Squared
1995- 2000- 2005- 1995- 2000- 2005- 1995- 2000- 2005-
Countries 2000 2005 2010 2000 2005 2010 2000 2005 2010
Argentina
Growth 0.43 0.30 -0.56 0.31 0.23 -0.49 0.30 0.16 -0.44
Distribution 0.57 -1.30 -0.44 0.69 -1.23 -0.51 0.70 -1.16 -0.56
Bolivia
Growth 0.60 -0.90 0.23 0.47 -0.61 2.00 0.33 -0.42 0.32
Distribution 0.40 -0.10 -1.23 0.53 -0.39 -1.00 0.67 -0.58 0.68
Brazil
Growth 2.00 -0.41 -0.68 0.97 -0.23 -0.63 0.53 -0.19 -0.63
Distribution -1.00 -0.59 -0.32 0.03 -0.77 -0.37 0.47 -0.81 -0.37
Chile
Growth -1.00 -0.43 -0.80 -0.96 -0.38 -1.00 -1.10 -0.34 -1.66
Distribution 0.00 -0.57 -0.20 -0.04 -0.62 0.00 0.10 -0.66 0.66
Colombia
Growth 0.68 -0.64 -1.04 0.59 -0.47 -1.02 0.53 -0.39 -0.94
Distribution 0.32 -0.36 0.04 0.41 -0.53 0.02 0.47 -0.61 -0.06
Costa Rica
Growth -1.59 -1.35 -1.54 -1.38 -0.96 -1.32 -0.97 -0.72 -1.35
Distribution 0.59 0.35 0.54 0.38 -0.04 0.32 -0.03 -0.28 0.35
Dominican Rep.
Growth -2.00 1.33 -0.73 -2.00 1.60 -0.49 -2.00 1.97 -0.40
Distribution 1.00 -0.33 -0.27 1.00 -0.60 -0.51 1.00 -0.97 -0.60
Ecuador
Growth -2.00 -0.87 -0.46 -1.00 -0.88 -0.36 -1.00 -0.87 -0.30
Distribution 1.00 -0.13 -0.54 2.00 -0.12 -0.64 2.00 -0.13 -0.70
El Salvador
Growth -1.77 1.00 -0.35 -1.00 0.63 -0.17 -0.51 0.38 -0.12
Distribution 0.77 -2.00 -0.65 2.00 -1.63 -0.83 1.51 -1.38 -0.88
Honduras
Growth -0.55 -2.00 -0.79 -0.94 -1.00 -0.78 -2.00 -1.00 -0.78
Distribution -0.45 1.00 -0.21 -0.06 2.00 -0.22 1.00 2.00 -0.22
Mexico
Growth -0.97 -0.44 1.00 -0.87 -0.40 0.56 -0.81 -0.38 0.46
Distribution -0.03 -0.56 -2.00 -0.13 -0.60 -1.56 -0.19 -0.62 -1.46
Panama
Growth 0.90 -0.60 -0.60 2.00 -0.38 -0.47 0.93 -0.28 -0.39
Distribution 0.10 -0.40 -0.40 -1.00 -0.62 -0.53 -1.93 -0.72 -0.61
Paraguay
Growth 1.75 0.31 -0.82 1.31 0.14 -0.92 0.87 0.10 -1.00
Distribution -0.75 -1.31 -0.18 -0.31 -1.14 -0.08 0.13 -1.10 0.00
Peru
Growth -2.00 0.60 -0.69 -2.00 0.29 -0.74 -1.44 0.23 -0.78
Distribution 1.00 -1.60 -0.31 1.00 -1.29 -0.26 0.44 -1.23 -0.22
Uruguay
Growth 0.34 0.72 -1.02 0.45 0.70 -0.89 0.57 0.70 -0.84
Distribution 0.66 0.28 0.02 0.55 0.30 -0.11 0.43 0.30 -0.16
LAC
Growth -1.22 -0.42 -0.63 -1.36 -0.30 -0.59 -1.59 -0.26 -0.58
Distribution 0.22 -0.58 -0.37 0.36 -0.70 -0.41 0.59 -0.74 -0.42
27
b. Poverty at $2.5/day
Poverty Headcount Poverty Gap Poverty Gap Squared
1995- 2000- 2005- 1995- 2000- 2005- 1995- 2000- 2005-
Countries 2000 2005 2010 2000 2005 2010 2000 2005 2010
Argentina
Growth 0.27 0.24 -0.46 0.28 0.16 -0.42 0.28 0.08 -0.37
Distribution 0.73 -1.24 -0.54 0.72 -1.16 -0.58 0.72 -1.08 -0.63
Bolivia
Growth 0.56 -0.72 1.00 0.30 -0.36 0.22 0.19 -0.24 0.09
Distribution 0.44 -0.28 -2.00 0.70 -0.64 0.78 0.81 -0.76 0.91
Brazil
Growth 1.00 -0.25 -0.66 0.49 -0.18 -0.62 0.27 -0.15 -0.62
Distribution -2.00 -0.75 -0.34 0.51 -0.82 -0.38 0.73 -0.85 -0.38
Chile
Growth -0.78 -0.37 -1.05 -1.39 -0.31 -2.00 -2.00 -0.27 -1.00
Distribution -0.22 -0.63 0.05 0.39 -0.69 1.00 1.00 -0.73 2.00
Colombia
Growth 0.62 -0.54 -1.10 0.51 -0.37 -0.93 0.44 -0.28 -0.79
Distribution 0.38 -0.46 0.10 0.49 -0.63 -0.07 0.56 -0.72 -0.21
Costa Rica
Growth -2.00 -1.05 -1.18 -0.86 -0.63 -1.33 -0.51 -0.45 -1.72
Distribution 1.00 0.05 0.18 -0.14 -0.37 0.33 -0.49 -0.55 0.72
Dominican Rep.
Growth -2.00 1.36 -0.51 -2.00 2.00 -0.37 -2.00 2.00 -0.30
Distribution 1.00 -0.36 -0.49 1.00 -1.00 -0.63 1.00 -1.00 -0.70
Ecuador
Growth -1.00 -0.93 -0.35 -1.00 -0.89 -0.28 -1.00 -0.83 -0.24
Distribution 2.00 -0.07 -0.65 2.00 -0.11 -0.72 2.00 -0.17 -0.76
El Salvador
Growth -1.00 0.90 -0.19 -0.41 0.34 -0.10 -0.23 0.23 -0.07
Distribution 2.00 -1.90 -0.81 1.41 -1.34 -0.90 1.23 -1.23 -0.93
Honduras
Growth -0.59 -2.00 -0.79 -2.00 -1.00 -0.78 -1.00 -1.00 -0.79
Distribution -0.41 1.00 -0.21 1.00 2.00 -0.22 2.00 2.00 -0.21
Mexico
Growth -0.89 -0.44 0.58 -0.79 -0.36 0.39 -0.71 -0.33 0.41
Distribution -0.11 -0.56 -1.58 -0.21 -0.64 -1.39 -0.29 -0.67 -1.41
Panama
Growth 1.11 -0.43 -0.49 0.91 -0.26 -0.36 0.28 -0.19 -0.31
Distribution -0.11 -0.57 -0.51 -1.91 -0.74 -0.64 -1.28 -0.81 -0.69
Paraguay
Growth 2.00 0.13 -1.12 0.77 0.08 -1.03 0.52 0.07 -1.12
Distribution -1.00 -1.13 0.12 0.23 -1.08 0.03 0.48 -1.07 0.12
Peru
Growth -2.00 0.47 -0.77 -1.12 0.21 -0.81 -0.76 0.17 -0.80
Distribution 1.00 -1.47 -0.23 0.12 -1.21 -0.19 -0.24 -1.17 -0.20
Uruguay
Growth 0.55 0.67 -0.85 0.83 0.69 -0.79 1.75 0.73 -0.77
Distribution 0.45 0.33 -0.15 0.17 0.31 -0.21 -0.75 0.27 -0.23
LAC
Growth -1.13 -0.34 -0.60 -1.69 -0.24 -0.57 -2.00 -0.20 -0.56
Distribution 0.13 -0.66 -0.40 0.69 -0.76 -0.43 1.00 -0.80 -0.44
Source: Authorâ€™s calculations with data from SEDLAC (CEDLAS and The World Bank). Notes: Decomposition follows Datt and Ravallion
(1992). A negative sign indicates a contribution to increasing poverty, a positive sign indicates a contribution to poverty reduction.
28
Figure 2. Decomposition of Latin American household income inequality, by share attributable to
each source of income, 1995 and 2010
1.000
0.140 0.124 0.120 0.119
0.900
0.013 0.020 0.020 0.014
0.800 0.075 0.115 0.124 0.131
0.700
0.600
0.500
0.400 0.771 0.742 0.736 0.736
0.300
0.200
0.100
0.000
1995 2000 2005 2010
labor pensions transfers other
Source: Authorâ€™s calculations with data from SEDLAC (CEDLAS and The World Bank). Note: Calculated using total household income per
capita.
29
Table 3. Labor Income Inequality Indices in Latin America, 1995-2010
Gini coefficient Theil Index
Country 1995 2000 2005 2010 1995 2000 2005 2010
Argentina 0.414 0.432 0.435 0.400 0.322 0.341 0.373 0.300
Bolivia 0.535 0.567 0.548 0.525 0.570 0.670 0.606 0.611
Brazil 0.581 0.571 0.548 0.519 0.709 0.712 0.670 0.613
Chile 0.597 0.558 0.548 0.547 0.873 0.727 0.713 0.690
Colombia 0.512 0.547 0.514 0.508 0.556 0.671 0.568 0.562
Costa Rica 0.418 0.425 0.445 0.453 0.338 0.354 0.431 0.407
Dom. Rep. 0.474 0.488 0.479 0.469 0.449 0.467 0.475 0.415
Ecuador 0.461 0.517 0.472 0.449 0.411 0.572 0.447 0.409
El Salvador 0.467 0.469 0.469 0.442 0.451 0.430 0.485 0.382
Honduras 0.539 0.529 0.609 0.576 0.658 0.573 0.889 0.821
Mexico 0.538 0.534 0.507 0.484 0.629 0.606 0.560 0.485
Panama 0.470 0.491 0.492 0.472 0.414 0.469 0.467 0.451
Paraguay 0.545 0.506 0.521 0.507 0.623 0.503 0.536 0.558
Peru 0.524 0.576 0.529 0.510 0.562 0.778 0.564 0.525
Uruguay 0.438 0.434 0.469 0.459 0.374 0.371 0.421 0.430
LAC 0.547 0.546 0.524 0.500 0.638 0.649 0.601 0.546
90/10 ratio 80/20 ratio
Country 1995 2000 2005 2010 1995 2000 2005 2010
Argentina 14.8 17.2 20.3 15.3 8.1 9.2 10.1 8.2
Bolivia 33.4 47.1 40.7 34.0 16.5 20.0 17.6 15.4
Brazil 39.5 39.4 34.5 28.8 19.4 17.8 15.2 12.7
Chile 36.0 29.2 26.4 24.9 17.8 14.4 13.3 12.8
Colombia 35.7 39.8 35.4 32.2 14.5 17.3 14.9 14.1
Costa Rica 16.0 16.9 17.9 17.5 8.1 8.4 9.1 9.2
Dom. Rep. 21.1 19.3 20.0 19.6 10.7 10.7 10.7 10.5
Ecuador 21.8 33.8 24.5 22.6 10.8 14.9 11.5 10.4
El Salvador 22.0 22.0 20.7 17.6 11.0 11.0 10.4 9.1
Honduras 35.3 40.4 82.4 54.9 16.5 17.6 29.3 22.1
Mexico 43.3 42.6 37.9 31.4 17.5 17.0 15.1 13.0
Panama 23.4 35.8 37.4 28.4 11.5 15.1 15.8 12.6
Paraguay 48.6 35.8 41.8 36.4 19.6 15.4 17.5 15.1
Peru 45.8 62.9 43.6 42.2 17.8 24.0 18.4 16.7
Uruguay 16.9 16.2 21.0 20.0 9.0 8.8 11.0 10.3
LAC 38.3 39.3 34.7 29.7 17.3 17.0 14.9 13.0
Source: Authorsâ€™ calculations with data from SEDLAC (CEDLAS and The World Bank). LAC values refer to labor population weighted
averages.
30
Figure 3. Annualized changes in the Labor Income Gini, 1995-2010
0.6% 0.53%
0.45%
0.4% 0.32%
0.2%
0.03%
0.0%
-0.2% -0.06% -0.05%
-0.12%
-0.17%
-0.22% -0.18%
-0.4%
-0.37%
-0.6% -0.49%
-0.59% -0.58%
-0.8% -0.70%
-0.75%
-1.0%
LAC
El Salvador
Argentina
Dominican Rep.
Chile
Ecuador
Panama
Mexico
Costa Rica
Brazil
Paraguay
Bolivia
Colombia
Uruguay
Honduras
Peru
Source: Authorsâ€™ calculations with data from SEDLAC (CEDLAS and The World Bank). LAC values refer to labor population weighted
averages.
31
Figure 4. Decomposition of Labor Income (individual hourly wages) inequality changes, 1995-2010:
Gini coefficient and Theil Index
0.050 0.050
Observed Quantities
0.030 0.030
Changes in the Gini from 2000
0.010 0.010
-0.010 -0.010
-0.030 -0.030
-0.050
-0.050
-0.070
-0.070
-0.090
-0.090
-0.110
-0.110
1995 2000 2005 2010
1995 2000 2005 2010
Gini Theil Gini Theil
0.050
0.050 Unobservables
Prices 0.030
0.030
0.010
0.010
-0.010
-0.010
-0.030
-0.030
-0.050 -0.050
-0.070 -0.070
-0.090 -0.090
-0.110 -0.110
1995 2000 2005 2010 1995 2000 2005 2010
Gini Theil Gini Theil
Source: Authorâ€™s calculations with data from SEDLAC (CEDLAS and The World Bank) using labor population-weighted averages. The
decomposition follows Foguel and Azevedo (2007).
32
Figure 5. Decomposition of Labor Income (individual hourly wages) inequality changes, 1995-2010:
90/10 and 80/20 labor income ratios
15.0 15.0
Observed Quantities
Changes in the Gini from 2000
10.0 10.0
5.0 5.0
0.0 0.0
-5.0 -5.0
-10.0 -10.0
-15.0 -15.0
1995 2000 2005 2010 1995 2000 2005 2010
90/10 80/20 90/10 80/20
15.0
Prices
15.0
10.0 Unobservables
10.0
5.0
5.0
0.0
0.0
-5.0
-5.0
-10.0
-10.0
-15.0
1995 2000 2005 2010 -15.0
1995 2000 2005 2010
90/10 80/20
90/10 80/20
Source: Authorâ€™s calculations with data from SEDLAC (CEDLAS and The World Bank) using labor population-weighted averages. The
decomposition follows Foguel and Azevedo (2007).
33
Table 4. Decomposition of changes in the labor income Gini Coefficient, 2000-2010
Country Observed Quantities Prices Unobservables
Argentina -0.032 -0.002 -0.027 -0.002
Bolivia -0.042 -0.011 -0.041 0.010
Brazil -0.052 -0.001 -0.032 -0.019
Chile -0.011 -0.013 -0.015 0.016
Colombia -0.039 0.004 -0.032 -0.011
Costa Rica 0.027 0.014 0.016 -0.003
Dominican Rep. -0.019 -0.017 0.001 -0.003
Ecuador -0.069 0.000 -0.016 -0.053
El Salvador -0.027 0.005 -0.019 -0.013
Honduras 0.047 -0.001 0.001 0.047
Mexico -0.050 -0.005 -0.033 -0.013
Panama -0.019 0.016 -0.031 -0.003
Paraguay 0.001 -0.002 -0.017 0.019
Peru -0.067 -0.009 -0.034 -0.023
Uruguay 0.026 -0.003 0.013 0.015
LAC -0.045 -0.003 -0.029 -0.014
Source: Authorsâ€™ calculations with data from SEDLAC (CEDLAS and The World Bank). LAC values refer to labor population weighted
averages.
Table 5. Decomposition of changes in the labor income Theil Index, 2000-2010
Country Observed Quantities Prices Unobservables
Argentina -0.041 -0.005 -0.043 0.007
Bolivia -0.059 -0.058 -0.082 0.082
Brazil -0.099 -0.001 -0.071 -0.026
Chile -0.038 -0.071 -0.023 0.056
Colombia -0.109 0.005 -0.077 -0.037
Costa Rica 0.053 0.024 0.032 -0.002
Dominican Rep. -0.052 -0.045 0.007 -0.014
Ecuador -0.163 -0.004 -0.039 -0.120
El Salvador -0.049 0.000 -0.032 -0.017
Honduras 0.248 0.008 0.010 0.229
Mexico -0.121 -0.001 -0.083 -0.037
Panama -0.018 0.046 -0.064 0.000
Paraguay 0.055 -0.015 -0.027 0.096
Peru -0.253 -0.093 -0.095 -0.065
Uruguay 0.058 -0.007 0.023 0.043
LAC -0.101 -0.010 -0.068 -0.023
Source: Authorsâ€™ calculations with data from SEDLAC (CEDLAS and The World Bank). LAC values refer to labor population weighted
averages.
34
Table 6. Decomposition of changes in the labor income 90/10 ratio, 2000-2010
Country Observed Quantities Prices Unobservables
Argentina -1.844 0.250 -2.582 0.488
Bolivia -13.122 -1.790 -10.904 -0.428
Brazil -10.599 2.148 -8.683 -4.064
Chile -4.323 -2.142 -2.700 0.519
Colombia -7.633 0.903 -7.056 -1.480
Costa Rica 0.676 0.558 1.601 -1.484
Dominican Rep. 0.338 -1.142 0.122 1.358
Ecuador -11.174 0.478 -2.976 -8.676
El Salvador -4.410 0.879 -2.575 -2.714
Honduras 14.473 1.050 0.504 12.919
Mexico -11.197 -1.481 -7.692 -2.024
Panama -7.461 1.462 -6.321 -2.601
Paraguay 0.600 0.653 -4.254 4.200
Peru -20.745 -1.826 -12.269 -6.650
Uruguay 3.769 0.012 1.328 2.430
LAC -9.646 0.498 -7.381 -2.763
Source: Authorsâ€™ calculations with data from SEDLAC (CEDLAS and The World Bank). LAC values refer to labor population weighted
averages.
Table 7. Decomposition of changes in the labor income 80/20 ratio, 2000-2010
Country Observed Quantities Prices Unobservables
Argentina -0.979 0.278 -1.247 -0.009
Bolivia -4.594 -0.080 -4.496 -0.018
Brazil -5.099 -0.178 -3.272 -1.649
Chile -1.666 -0.737 -1.317 0.387
Colombia -3.216 0.439 -2.830 -0.825
Costa Rica 0.774 0.490 0.650 -0.366
Dominican Rep. -0.221 -0.566 -0.054 0.399
Ecuador -4.507 0.062 -1.181 -3.387
El Salvador -1.893 0.284 -1.202 -0.975
Honduras 4.509 0.531 0.127 3.852
Mexico -3.958 -0.538 -2.639 -0.781
Panama -2.509 0.647 -2.420 -0.737
Paraguay -0.238 0.496 -1.796 1.062
Peru -7.234 -0.804 -4.075 -2.355
Uruguay 1.512 0.040 0.602 0.869
LAC -4.074 -0.203 -2.738 -1.132
Source: Authorsâ€™ calculations with data from SEDLAC (CEDLAS and The World Bank). LAC values refer to labor population weighted
averages.
35
Table 8. Typology of Changes in the Gini coefficient of Labor Income (individual hourly wages),
between 2000 and 2010
Inequality-reducing Inequality-increasing
Argentina Colombia
Bolivia Costa Rica
Brazil Ecuador
Chile El Salvador
Dom. Rep. Panama
Quantity effect
Honduras
Mexico
Paraguay
Peru
Uruguay
Argentina Costa Rica
Bolivia Dominican Rep.
Brazil Honduras
Chile Uruguay
Colombia
Ecuador
Price effect
El Salvador
Mexico
Panama
Paraguay
Peru
Argentina Bolivia
Brazil Uruguay
Colombia Chile
Costa Rica Paraguay
Dominican Rep. Honduras
Other Factors
Ecuador
El Salvador
Mexico
Panama
Peru
Source: Authorsâ€™ calculations with data from SEDLAC (CEDLAS and The World Bank).
36
Figure 6. Mean returns to years of education, experience and other factors, 1995-2010
105.0
100.0
95.0
Index 2000=100
90.0
85.0
80.0
75.0
70.0
1995 2000 2005 2010
Education Experience Other Factors
Source: Authorâ€™s calculations with data from SEDLAC (CEDLAS and The World Bank) using population-weighted averages. The
aggregation of the returns follows Foguel and Azevedo (2007).
Table 9. Mean returns to education, experience and other factors, 1995-2010 (2000=100)
Education Experience Unobservables
1995 2000 2005 2010 1995 2000 2005 2010 1995 2000 2005 2010
Argentina 52 100 114 72 93 100 90 66 94 100 108 102
Bolivia 146 100 108 40 216 100 194 165 93 100 98 97
Brazil 112 100 87 63 103 100 97 85 102 100 97 94
Chile 89 100 57 132 113 100 90 81 108 100 102 101
Colombia 106 100 83 76 79 100 88 80 103 100 101 99
Costa Rica 159 100 114 121 111 100 89 78 97 100 97 94
Dom. Rep. 117 100 109 89 105 100 95 95 109 100 104 104
Ecuador 138 100 130 94 127 100 109 68 87 100 91 89
El Salvador 99 100 71 69 85 100 74 81 96 100 100 91
Honduras 97 100 111 95 106 100 94 147 94 100 119 106
Mexico 85 100 102 71 104 100 82 69 104 100 100 99
Panama 76 100 104 100 107 100 101 81 90 100 103 96
Paraguay 86 100 58 25 131 100 145 98 108 100 109 102
Peru 87 100 68 69 99 100 141 108 103 100 94 96
Uruguay 136 100 118 158 106 100 96 92 103 100 106 106
LAC 101 100 92 71 103 100 96 83 102 100 99 97
Source: Authorsâ€™ calculations with data from SEDLAC (CEDLAS and The World Bank). LAC values refer to labor population weighted
averages.
37
Figure 7. Mean and Standard deviation of education and experience (1995-2010)
Education Experience
120
105
115 104
Index (2000=100)
Index (2000=100)
110 103
105 102
101
100
100
95
99
90 98
1995 2000 2005 2010 1995 2000 2005 2010
Standard Deviation Mean Standard Deviation Mean
Source: Authorâ€™s calculations with data from SEDLAC (CEDLAS and The World Bank) using labor population-weighted
averages.
Table 10. Mean education and experience, 1995-2010 (2000=100)
Mean Years of Education Mean Years of Experience
1995 2000 2005 2010 1995 2000 2005 2010
Argentina 96 100 106 110 98 100 100 100
Bolivia 98 100 99 117 101 100 102 99
Brazil 88 100 109 119 99 100 100 100
Chile 101 100 108 111 94 100 103 104
Colombia 88 100 101 107 98 100 102 103
Costa Rica 97 100 107 114 96 100 103 104
Dom. Rep. 102 100 103 98 99 100 102 109
Ecuador 122 100 103 108 89 100 103 106
El Salvador 93 100 108 109 99 100 99 100
Honduras 100 100 104 105 100 100 104 106
Mexico 93 100 110 117 100 100 102 102
Panama 105 100 104 109 93 100 102 104
Paraguay 96 100 111 119 96 100 97 97
Peru 87 100 98 105 99 100 104 104
Uruguay 97 100 108 105 99 100 102 102
LAC 91 100 107 114 99 100 101 102
Source: Authorsâ€™ calculations with data from SEDLAC (CEDLAS and The World Bank). LAC values refer to labor population weighted
averages.
38
Table 11. Standard deviation of education and experience, 1995-2010 (2000=100)
Standard Deviation of Education Standard Deviation of Experience
1995 2000 2005 2010 1995 2000 2005 2010
Argentina 98 100 99 95 99 100 103 103
Bolivia 103 100 100 98 100 100 102 102
Brazil 99 100 99 98 100 100 102 102
Chile 101 100 91 89 101 100 102 103
Colombia 99 100 103 104 99 100 101 103
Costa Rica 97 100 100 103 101 100 102 104
Dom. Rep. 99 100 97 98 101 100 99 101
Ecuador 96 100 101 101 98 100 100 101
El Salvador 99 100 100 98 101 100 99 99
Honduras 99 100 103 101 99 100 99 100
Mexico 99 100 100 97 100 100 99 99
Panama 97 100 100 99 100 100 102 104
Paraguay 98 100 105 104 99 100 102 104
Peru 94 100 101 100 101 100 101 103
Uruguay 100 100 102 100 102 100 100 100
LAC 99 100 100 98 100 100 101 102
Source: Authorsâ€™ calculations with data from SEDLAC (CEDLAS and The World Bank). LAC values refer to labor population weighted
averages.
Figure 8. Decomposition of Variance of Annual Earnings
1.4
1.2
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
1995 2000 2005 2010
Var hours worked Var hourly wage Covarience Hours-Wage
39
ANNEX
Table A1. The circa criteria
Country Circa 1995 Circa 2000 Circa 2005 Circa 2010
Argentina 1995 2000 2005 2010
Bolivia 1997 2000 2005 2008
Brazil 1995 2001 2005 2009
Chile 1996 2000 2006 2009
Colombia 1996 2002 2005 2010
Costa Rica 1995 2000 2005 2009
Dominican Rep. 1996 2000 2005 2010
Ecuador 1995 2003 2006 2010
El Salvador 1995 2000 2005 2009
Honduras 1995 1999 2005 2009
Mexico 1996 2000 2005 2010
Panama 1995 2001 2005 2009
Paraguay 1995 1999 2005 2010
Peru 1997 2002 2005 2010
Uruguay 1995 2000 2005 2010
Table A2. Surveys in the Sample
Country Circa 1995 Circa 2000 Circa 2005 Circa 2010
Argentina EPH EPH EPH-C EPH-C
Bolivia ENE ECH ECH ECH
Brazil PNAD PNAD PNAD PNAD
Chile CASEN CASEN CASEN CASEN
Colombia ENH-FT ECH ECH GEIH
Costa Rica EHPM EHPM EHPM EHPM
Dominican R. ENFT ENFT ENFT ENFT
Ecuador ECV ENEMDU ENEMDU ENEMDU
El Salvador EHPM EHPM EHPM EHPM
Honduras EPHPM EPHPM EPHPM EPHPM
Mexico ENIGH ENIGH ENIGH ENIGH
Panama EH EH EH EH
Paraguay EH EIH EPH EPH
Peru ENAHO ENAHO ENAHO ENAHO
Uruguay ECH ECH ECH ECH
40
Figure A1. Gini coefficient and Theil index of total household income per capita in LAC, 1995-2010
0.75
0.70
0.67 0.66
Gini and Theil coefficients
0.65
0.61
0.60 0.57 0.57
0.54 0.55
0.55
0.52
0.50
0.45
0.40
1995 2000 2005 2010
Theil Gini
Figure A2. Shares of income sources in total household income per capita in LAC, 1995-2010
1.000
0.128 0.112 0.113 0.108
0.900 0.026 0.036
0.017 0.029
0.800 0.076 0.106 0.113 0.116
0.700
0.600
0.500
0.400 0.780 0.756 0.745 0.740
0.300
0.200
0.100
0.000
1995 2000 2005 2010
labor pensions transfers other
Source: Authorâ€™s calculations with data from SEDLAC (CEDLAS and The World Bank).
41
Figure A3. Marginal effect on inequality by income source, 1995-2010
2.0%
1.4%
1.5% 1.3%
1.1% 1.2% 1.1%
1.0% 0.9%
0.7%
0.5%
0.0%
0.0%
-0.5% -0.3% -0.4%
-0.6%
-1.0% -0.9%
-0.9% -1.0%
-1.5%
-1.4%
-2.0%
-2.5% -2.2%
Labor Pensions Transfers Other
1995 2000 2005 2010
Source: Authorâ€™s calculations with data from SEDLAC (CEDLAS and The World Bank).
42
Table A3. Female: Decomposition of changes in the labor income Gini Coefficient, 2000-2010
Country Observed Quantities Prices Unobservables
Argentina -0.006 -0.003 -0.007 0.004
Bolivia 0.002 0.023 -0.054 0.032
Brazil -0.044 0.001 -0.027 -0.017
Chile 0.011 0.004 -0.010 0.017
Colombia -0.027 -0.004 -0.022 0.000
Costa Rica 0.012 0.003 0.020 -0.011
Dominican Rep. 0.003 -0.019 0.022 0.001
Ecuador -0.074 -0.010 -0.014 -0.049
El Salvador -0.022 0.012 -0.018 -0.016
Honduras 0.031 0.011 -0.032 0.052
Mexico -0.017 0.009 -0.025 -0.002
Panama 0.015 0.016 -0.028 0.026
Paraguay 0.067 0.004 -0.009 0.073
Peru -0.036 0.003 -0.022 -0.016
Uruguay 0.020 0.002 0.016 0.002
LAC -0.028 0.002 -0.022 -0.008
Source: Authorâ€™s calculations with data from SEDLAC (CEDLAS and The World Bank) using labor population-weighted
averages.
Table A4. Male: Decomposition of changes in the labor income Gini Coefficient, 2000-2010
Country Observed Quantities Prices Unobservables
Argentina -0.050 0.000 -0.042 -0.008
Bolivia -0.070 -0.012 -0.045 -0.013
Brazil -0.054 0.000 -0.035 -0.019
Chile -0.018 -0.016 -0.018 0.016
Colombia -0.044 0.009 -0.034 -0.019
Costa Rica 0.033 0.018 0.017 -0.003
Dominican Rep. -0.030 -0.020 -0.004 -0.006
Ecuador -0.070 -0.002 -0.015 -0.053
El Salvador -0.030 -0.001 -0.018 -0.012
Honduras 0.057 -0.003 0.012 0.048
Mexico -0.059 -0.001 -0.040 -0.018
Panama -0.032 0.013 -0.029 -0.016
Paraguay -0.024 -0.007 -0.026 0.008
Peru -0.086 -0.012 -0.045 -0.029
Uruguay 0.031 -0.006 0.015 0.021
LAC -0.052 -0.001 -0.034 -0.017
Source: Authorâ€™s calculations with data from SEDLAC (CEDLAS and The World Bank) using labor population-weighted
averages.
43
Table A5. Formal: Decomposition of changes in the labor income Gini Coefficient, 2000-2010
Gini Coefficient
Country Observed Quantities Prices Unobservables
Argentina -0.049 -0.008 -0.038 -0.002
Bolivia -0.044 -0.029 -0.034 0.019
Brazil -0.042 -0.005 -0.022 -0.016
Chile -0.014 -0.011 -0.010 0.007
Costa Rica 0.026 0.011 0.018 -0.002
Dominican Rep. -0.031 -0.029 -0.003 0.000
Ecuador -0.068 0.003 -0.021 -0.050
El Salvador -0.021 -0.003 -0.009 -0.009
Honduras 0.036 -0.006 -0.015 0.058
Mexico -0.032 -0.014 -0.026 0.008
Panama -0.003 0.016 -0.026 0.008
Paraguay -0.007 -0.017 -0.007 0.017
Peru -0.073 -0.019 -0.043 -0.011
Uruguay 0.002 0.004 0.021 -0.023
LAC -0.039 -0.009 -0.024 -0.006
Source: Authorâ€™s calculations with data from SEDLAC (CEDLAS and The World Bank) using labor population-weighted
averages.
Table A6. Informal: Decomposition of changes in the labor income Gini Coefficient, 2000-2010
Country Observed Quantities Prices Unobservables
Argentina -0.009 0.001 -0.012 0.002
Bolivia -0.005 0.003 -0.008 0.000
Brazil -0.057 0.007 -0.036 -0.028
Chile 0.034 -0.004 -0.007 0.045
Costa Rica 0.029 0.012 0.002 0.016
Dominican Rep. -0.024 -0.010 -0.004 -0.011
Ecuador -0.099 -0.009 -0.016 -0.074
El Salvador -0.037 0.003 -0.010 -0.031
Honduras 0.028 -0.001 0.006 0.023
Mexico -0.047 0.011 -0.029 -0.029
Panama -0.002 0.016 -0.015 -0.004
Paraguay 0.059 0.005 -0.004 0.059
Peru -0.049 -0.004 -0.006 -0.039
Uruguay 0.043 -0.021 -0.007 0.070
LAC -0.044 0.005 -0.025 -0.024
Source: Authorâ€™s calculations with data from SEDLAC (CEDLAS and The World Bank) using labor population-weighted
averages.
44
Figure A4. Mean returns to education, experience and others, 1995-2010, by gender and sector
110 Female 110 Male
105 105
100 100
Index 2000=100
Index 2000=100
95 95
90 90
85 85
80 80
75 75
70 70
65 65
1995 2000 2005 2010 1995 2000 2005 2010
Education Experience Other Factors Education Experience Other Factors
110 Formal 110 Informal
105 105
100 100
Index 2000=100
Index 2000=100
95 95
90 90
85 85
80 80
75 75
70 70
65 65
1995 2000 2005 2010 1995 2000 2005 2010
Education Experience Other Factors Education Experience Other Factors
Source: Authorâ€™s calculations with data from SEDLAC (CEDLAS and The World Bank) using labor population-weighted
averages.
45
Figure A5. Mean and Standard deviation of education (1995-2010), by sector and gender
125 Female 125 Male
120 120
115 115
Index (2000=100)
Index (2000=100)
110 110
105 105
100 100
95 95
90 90
85 85
1995 2000 2005 2010 1995 2000 2005 2010
Standard Deviation Mean Standard Deviation Mean
125 Formal 125 Informal
120 120
115 Index (2000=100) 115
Index (2000=100)
110 110
105 105
100 100
95 95
90 90
85 85
1995 2000 2005 2010 1995 2000 2005 2010
Standard Deviation Mean Standard Deviation Mean
Source: Authorâ€™s calculations with data from SEDLAC (CEDLAS and The World Bank) using labor population-weighted
averages.
46
Figure A6. Mean and Standard deviation of experience (1995-2010), by sector and gender
105 105
Female Male
104 104
103 103
Index (2000=100)
Index (2000=100)
102 102
101 101
100 100
99 99
98 98
97 97
1995 2000 2005 2010 1995 2000 2005 2010
Standard Deviation Mean Standard Deviation Mean
105 105
Formal Informal
104 104
103 103
Index (2000=100)
Index (2000=100)
102 102
101 101
100 100
99 99
98 98
97 97
1995 2000 2005 2010 1995 2000 2005 2010
Standard Deviation Mean Standard Deviation Mean
Source: Authorâ€™s calculations with data from SEDLAC (CEDLAS and The World Bank) using labor population-weighted
averages.
Figure A7. Growth in number of workers in LAC (2000-2010), percent
140%
120%
100%
80%
60%
40%
20%
0%
15
17
19
21
23
25
27
29
31
33
35
37
39
41
43
45
47
49
51
53
55
57
59
61
63
65
67
69
71
73
75
77
79
-20%
Males Females
Source: Authorâ€™s calculations with data from SEDLAC (CEDLAS and The World Bank) for workers 15 and older.
47