Department of Social Systems and Management
Discussion Paper Series

No.1273

Income Distribution Dynamics and the
Decline of Inequality in Mexico(1989-2006)

byAlberto J. INIGUEZ-MONTIEL

July 2011

UNIVERSITY OF TSUKUBA
Tsukuba, Ibaraki 305-8573
JAPAN

INCOME DISTRIBUTION DYNAMICS AND THE
DECLINE OF INEQUALITY IN MEXICO (1989–2006) ★
Alberto J. Iniguez-Montiel
Graduate School of Systems and Information Engineering,
University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8573 Japan.
E-mail: iniguez@sk.tsukuba.ac.jp
Tel.: +81-29-853-5175 / Fax: +81-29-853-5070
Abstract
This paper investigates the determinants of the income inequality in Mexico between
1989 and 2006, as well as the factors that caused the inequality decline in the 2000s. By
applying a dynamic decomposition methodology, which is a variant of the micro
simulation method in Almeida dos Reis and Paes de Barros (1991) and Bourguignon etal. (2001), we show that most of the forces behind the increase in the level of inequality
in Mexico in the years following 1984 are now working in the opposite direction(equalizing effect). In particular, educational-related changes and the betterment in therates of return to the rural areas and, especially, the south of the country were found to
mainly explain the recent distributional improvement. Moreover, we identify anadditional factor: financial assets, associated with Mexico’s poor economic performanceafter 2000, the 2001–2002 USA recession and the sharp reduction in the nationalrisk-free rate in the 2000s, which were responsible for about 38% of the fall in the Gini
coefficient in 2000-2006. Since financial-asset changes were associated more withtemporal rather than permanent changes, we argue that the declining trend in theincome inequality in Mexico, which has occurred through a combination of market
forces and government policy intervention, may be reversed in the short to medium run.
Our findings enable us to recommend policies aiming at more drastic inequality andpoverty reduction, as well as faster, equitable growth through a more equal distributionof income in Mexico.
Keywords: Inequality; poverty; decomposition; Mexico; Latin America.
JEL Classification: D31, O15, O54.
★

This paper is based on part of my doctoral dissertation. I am particularly grateful to Yoko Kijima
for her superb supervision of my research. I also wish to thank Daiji Kawaguchi, Georgios Katsenos,
Kaori Miyazaki, Mari Minowa, Seki Asano and Yoshimi Kuroda, as well as seminar participants of
the Graduate Program in Economic and Public Policy Management at the University of Tsukuba, for
helpful comments and suggestions. Any remaining errors are my own.
1

1. INTRODUCTION
Mexico’s income distribution could be considered among the most unequal in the world.
According to ECLAC (2008), the country’s inequality level ranks third among
economies with a similar level of development in Latin America (the less equitableregion in terms of income and assets (World Bank, 2009)), just after Brazil and Chile.
It is well known that Mexico’s history of high inequality and sharp social contrasts has
been a very long one, possibly going back to colonial times (from the 1520s to the early
1800s) and later on in 1910, the year of the Mexican Revolution.1 These problems havebeen definitely present since the mid 20th century (Altimir (1982), Nugent and
Tarawneh (1983) and Szekely (2005))2. More recently, as shown in Figure 1, income
inequality rose sharply between 1984 and 1989,3 the Gini coefficient increasing from0.49 to 0.54,4 remained high during the 1990s, and, declining between 2000 and 2002,
the Gini coefficient dropping by 3 percentage points to 0.52, remained approximately
constant thereafter.
Following a worldwide pattern of rising inequality since the 1980’s (Atkinson (1999,2004), Addison and Cornia (2001)), the income distribution literature on Mexico has
primarily focused on the study of wage inequality in order to explain the observed
increases in the skill premium (SP),5 which apparently occurred right after the insertion
1

According to Scott (2009), the Independence from Spain in 1821 and the Mexican Revolution a
century later were conflicts associated with extreme concentrations of wealth, land and politicalpower, which actually shaped the construction of the modern Mexican State over the last two
centuries. Likewise, de Ferranti et al. (2004) argue that the highly unequal distribution of land,
education and political power that existed in the Latin American region, at least until the late 1800s,
was due to the exclusionary institutions that were set up during the European conquest.
2
Despite its relatively high standards of living, rich human and physical capital, and advanced
institutions, Mexico lags far behind OECD averages. Moreover, there is a further dichotomy within
the country because of the high degree of inequality across individuals and across regions (World
Bank, 2007).
3
It is argued that income inequality rose in the years following 1984, after declining continuously
since the mid 1960s (Szekely, 2005), due to Mexico’s economic-model change and the drastic
economic policies implemented as a consequence of the 1982 financial crisis. For excellent readingon this, see Lustig (1992), Szekely (1998), Hernandez-Laos and Velazquez-Roa (2003) andRueda-Peiro (2009).
4
It should be noted that the sharp increase in income inequality after 1984 is related in great part toan increase in wage inequality, which occurred in Mexico from the mid-1980s to the mid-1990s. It
has also been attributed to changes in the returns to education, the penalty of living in the rural areas
and the southern region of the country, the deterioration in the returns to the household
characteristics of those living in these areas, and the changes in the distribution of household
endowments (Bouillon et al., 2003).
5
The skill premium (SP) may be defined as the wage gap between low– and high–skilled workers
2

of the economy into the global market and until 1996.6 In particular, there are threecompeting hypotheses about the documented rise in wage inequality in Mexico (Cortez,
2001). The first explanation relates to an increase in the demand for skilled labor, which
could have been caused by trade liberalization (Robertson, 2004), skilled-biased
technological change (Cragg and Epelbaum (1996), Esquivel and Rodriguez-Lopez(2003)), foreign direct investment (Feenstra and Hanson, 1997) or a process ofquality-upgrading due to an increase in exports (Verhoogen, 2008). The second
hypothesis is associated with changes in the relative supply of labor or in the
distribution of education (Legovini et al. (2005), Lopez-Acevedo (2006)).7 Finally,other theories emphasize the importance of changes in labor market institutions, namely,
minimum wages and unionization rates (Cortez (2001), Fairris (2003), Bosch andManacorda (2008), Fairris et al. (2008)), as well as those suggesting that the mid-1980s
reduction in tariffs disproportionally affected industries employing a larger proportionof unskilled workers (Hanson and Harrison, 1999), which were usually the most
unionized sectors in the country.
On the other hand, the post-1996 reduction in wage inequality has been attributed to
not-mutually-exclusive hypotheses like general supply-side factors (Montes-Rojas,
2006); an increase in the demand for low-skilled workers (Robertson, 2007); a rise in
the number of skilled workers that was not matched with a parallel increase inhigh-quality jobs (Campos–Vazquez, 2008); trade liberalization in accordance with the
Stolper–Samuelson theorem (Acosta and Montes-Rojas (2008), Chiquiar (2008)); andchanges in the composition of the workforce, particularly, a sharp reduction in thenumber of unskilled workers (Esquivel, 2010).
Moreover, even though the issue of income inequality in Mexico has not been studied as
extensively as the distribution of labor income, a fair amount of research on Mexico has
also concentrated on the study of either monetary or total income inequality, particularly
for the period after 1994.8 Among the most relevant studies found in the literature, theor the ratio of white-collar workers to blue-collar workers.
6
The SP grew steadily during the 1980s and 1990s in Mexico, which implemented a large tradereform in the mid-1980s and was continually exposed to other forms of globalization, such as
outsourcing or FDI, for the next two decades (Goldberg & Pavcnik, 2007).
7
Education is by far the variable that accounts for the largest share of earnings inequality in Mexico,
in terms of both its gross and its marginal contribution (Lopez-Acevedo, 2006)8
There is a rich Spanish literature related to income inequality in Mexico after the NAFTA period.
For a reference, see the articles and/or books by Cortes (2000a, 2000b, 2001, 2003, 2006), Boltvinik
and Hernandez-Laos (2006), Hernandez-Laos (1998, 2000a, 2000b, 2003, 2005), Hernandez-Laosand Velazquez-Roa (2003), Rueda-Peiro (2009), Szekely (2005) and Tuiran-Gutierrez (2005).
3

ones of Esquivel (2010) and Esquivel et al. (2009)9 analyze income inequality in
Mexico during 1994–2006 and 1996–2006, respectively, by using the methodology
proposed by Lerman and Yitzhaki (1985) to decompose the Gini coefficient of monetary
income. They attribute the recent inequality reduction to changes in three sources ofincome, namely, labor income, remittances and transfers. In particular, they identify adecline in the skill premium (SP) between low- and high-skilled workers, a rise in the
proportion of income received from abroad, and an increase in the proportion of
government transfers accruing to low-income households mainly from theOportunidades program. 10 Furthermore, Jensen and Rosas (2007) analyze therelationship between FDI and income inequality in Mexico’s thirty-two states between
1990 and 2000, applying an instrumental variables approach. The authors conclude that
inflows of FDI into Mexican states are associated with a reduction of inequality at thestate level, and speculate that this effect occurs through a localized increased demand
for labor relative to that of capital (Jensen and Rosas, 2007).
Surprisingly, no single study can be found in the published literature addressing theissue of the inequality in the distribution of total income in Mexico and its determinants
after 1994. Arguably, total per-capita income is the most relevant definition to beconsidered for the analysis of inequality, not only because it includes all sources ofincome (monetary and non-monetary) that are available to households (people), but also
because it may be the most appropriate definition for analyzing the extent of poverty ina country as well as the impact of inequality on growth (Aghion et al., 1999).11
Consequently, the present paper intends to fill this gap in the literature and to study
further the income inequality in Mexico by applying a dynamic decomposition
technique introduced in Bouillon et al. (2003)12, who applied a variant of the micro
9

According to these authors, the income inequality decline in Mexico occurred right after 1994.
Nevertheless, our calculations, which are based on total household per capita income and in line withthose of Hernandez-Laos and Velazquez-Roa (2003), World Bank (2004), Szekely (2005) and
ECLAC (2008), do not support theirs.
10
An earlier study by Cortes (2003), who also analyzed the distribution of monetary income in
Mexico during 1977–2000 and decomposed the Gini into its main sources of income (labor, own
businesses, property rents and transfers), following Leibrandt et al. (1996), found that wages andincome from own businesses were the sources that determined (by at least 88%) the changes in theGini coefficient in Mexico.
11
Ideally, the most relevant inequality measure is that of wealth (Aghion et al., 1999). However, in
the absence of data on the distribution of wealth, researchers are forced to use proxies in empiricalwork. The most common approach is to use data on income inequality as a proxy for wealthinequality (Aghion et al., 1999).
12
As explained in Bourguignon et al. (2005), this parametric microeconomic approach possesses
4

simulation method used in Almeida dos Reis and Paes de Barros (1991) and
Bourguignon et al. (2001)13 to an estimated reduced-form household income regression
model for Mexico during 1984–94.14 By doing so, we wish to identify the deeper,
microeconomic factors, along with their specific contributions, which determined the
distribution of income in 1989–2006 and that were responsible for the sudden decline ofincome inequality in the country after 2000. Additionally, we wish to find whether the
improvement along the distribution of income has been partially caused by any
particular policy, structural and/or economic change.
The analysis was carried out for 1989–2006 and three different and consecutive periods
in between the former, namely, 1989–1994, 1994–2000, and 2000–2006, exactly
corresponding to three different presidential administrations in Mexico.15 According toour findings, most of the forces behind the increasing and high levels of inequality after
1984 are now working in the opposite direction (equalizing) and, therefore, contributing
to the reduction of the high income disparities that persisted through the 1990s. In
particular, the determinants mainly associated with the income distributional
improvement in Mexico are the changes in the levels of and rates of return to education,and the betterment in the rates of return to the rural areas and, especially, the South ofthe country.
clear and important advantages over the static methodologies employed so far in the case of Mexico(Cortes (2003), Esquivel (2010), Esquivel et al. (2009)). First, it can shed light on the evolution of
the entire distribution of income, rather than merely on the path of summary statistics. Second, by
simulating counterfactual distributions, it can decompose any change in the incomes of a set ofhouseholds into three fundamental sources: changes in the amounts of resources at their disposal(reflected in the population or endowments effect), changes in how the markets remunerate thoseresources (reflected in the price or returns effect), and changes in the distribution and/or
remuneration of factors not included directly into the model but considered through the residual term
(reflected in the unobservables effect). Moreover, this methodology allows uncovering the strong
countervailing forces that are hidden in aggregate indices of inequality. Finally, since it analyzes thewhole distribution of income, it is possible to assess how different factors affect different parts of the
distribution.
13
The micro simulation method mentioned here builds on the seminal papers by Blinder (1973) andOaxaca (1973).
14
There are two other methodologies that are quite similar to the simulation and decomposition
technique in Bouillon et al. (2003). The first one is the semi-parametric approach proposed by
DiNardo et al. (1996); the second one is a parametric approach introduced by Machado and Mata(2005), applied in the context of quantile regression analysis and the study of wage distribution in
Portugal.
15
In order to facilitate the analysis and due to space limitations, only the results regarding theperiods 1989–2006 and 2000–06 are covered in here. However, it should be noted that the
1989–1994 and 1994–2000 decomposition results resemble those obtained for the period 1989–2006,
either with respect to the magnitude of the impact of each microeconomic factor considered as wellas the direction followed by them in each period. Decomposition results are available from theauthor upon request.
5However, the identification of an additional factor: financial assets (rates of return anddistribution), responsible for about 38% of the fall in the Gini coefficient in 2000–06
and regarded more as a temporal rather than a permanent change, makes us quite
suspicious about the relative improvement that occurred along the distribution of thetotal household per-capita income during the 2000s. According to our analysis, which isconsistent with Cortes (2003, 2006), that change is greatly related to the Mexican and
USA recessions in the early 2000s,16 as well as the sharp decline of the nationalrisk-free (governmental bond) rate after 2000, which may be regarded as a sign of thepoor economic performance of the Mexican economy and the world financial crisis
overall. Therefore, unless there are real (permanent) changes in the structure of theeconomy,17 which shift the gains from growth towards those with lower incomes, wethink that income inequality in Mexico may be reversed in the short to medium run,once the world exits the financial crisis and the Mexican economy recovers.
Given the important role that the redistribution (lower inequality) plays in the reductionof poverty in Mexico, as corroborated in Iniguez-Montiel (2011), and thegrowth-enhancing effect that is attributed to lower levels of inequality, especially, inhighly unequal societies, like Mexico, according to the literature,18 this study aims, first,
at updating our understanding of the income distribution dynamics in Mexico,continuing from Bouillon et al. (2003),19 and, second, at informing policy making
16

According to Cortes (2003, 2006), there is a positive relationship between the economic cycle andincome inequality in Mexico. Therefore, right after the 1982 and 1994–95 crises, the distribution ofincome in the country improved significantly, and the same occurred after 2000 during the economic
recession in 2001–2003 (Cortes, 2006). This economic regularity, identified by Cortes (2003, 2006),
is corroborated in our study as well and can be clearly observed in Figure 1 for the years 1996 and2002.
17Such as systematic increases in the over 30-year depreciated minimum wage, on which so many
households at the lower tail of the income distribution depend for their living, or a more progressive
allocation of government expenditure, which drastically redistributes income and addresses thepervasive market and government failures so characteristic of Mexico’s imperfect markets.
18
For a reference on the positive impact of reduced inequality on poverty and economic growth, see
Ravallion (1997, 2001, 2005, 2007), Deininger and Squire (1998), Aghion et al. (1999), Thorbecke& Charumilind (2002), Bourguignon (2004), Dagdeviren et al. (2004), Culpeper (2005), Lopez andServen (2006), Perry et al. (2006), Easterly (2007), World Bank (2009) among others.
19
Despite the similar questions the two papers ask, there are clear differences between their study
and ours. First, an income-distribution analysis is provided. Second, not only has the period ofanalysis been extended in order to cover as many years as surveys were available, but thedecomposition analysis was made for three different periods in between the main one, allowing us toaccount in a better way the income-inequality changes that occurred in the country in 1989–2006.
Furthermore, some additional variables and sub-categories were included in the household income
regression model in order to improve it. Finally, and perhaps most importantly, our study is not only
limited to the identification of the specific factors that affected the distribution of income through thereturns effect, but it also includes the analysis of the endowments and unobservables effects in detail6aiming at inequality and poverty reduction as well as fostering economic growththrough a more equal distribution of income (Aghion et al. (1999), Dagdeviren et al.
(2004), Culpeper (2005)) or policies for equitable growth (World Bank, 2007).
The following section discusses the data and applied methodology, and explains thehousehold income model, along with its estimated reduced form, and the specificvariables that were included in the regression exercise. Next, an income distribution
analysis for the period 1984–2006 is provided in section 3, and the 1989 and 2006 OLS
regression results are shown and discussed in section 4. Section 5 explains the main
decomposition results, while section 6 interprets them, based on the relevant literature,summarizes the findings and discusses some policy implications. Finally, section 7
concludes.

2. DATA, HOUSEHOLD INCOME MODEL & METHODOLOGYDATA
The data sets used in this study come from the National Survey of Household Income
and Expenditure (ENIGH), a nationally representative survey, covering both the ruraland urban populations in the 32 Mexican states, conducted by the National Institute of
Statistics, Geography and Informatics (INEGI) in 1984, 1989, and every two years since
1992 including 2005. ENIGH is based on a probabilistic sample and has been carriedout applying similar questionnaires and sampling methodologies.
For the income distribution analysis, we rely on all available ENIGHs until 2006. On
the other hand, for the regression, simulation and decomposition analyses, only the
ENIGHs for the years 1989, 1994, 2000 and 2006 are used. Incomes are deflated by thenational consumer price index in order to account for the effects of inflation across time.
It should be noted that the income definition that is been used through out this paper isthe one of total household per capita income, including all monetary and non-monetary
sources of income, except for other incomes (“otros ingresos”) as the selling ofhousehold assets is considered among this type of monetary income in the ENIGHs.

so as to make it a more comprehensive study.
7

HOUSEHOLD INCOME SPECIFICATION 20
Let household income be the sum of each household member’s wage earnings, self
employment income and other incomes:nY¦ L w 3 Y
w
i

i

0

i

i 1

where Y is total household income,household member

1 ,..., n

Lwi is hours of work in wage employment by

i , wi is the wage rate received by household member i , Ȇ isincome from self employment, and Y0 is income from other sources.
The present study estimates a reduced form of the household income model given by
the previous equation as follows:y

Xr  H

where y is the log of per capita household income adjusted for differences in inflation,
X is a vector of household characteristics, r is the vector of returns to thosecharacteristics and ǆ is the regression residual that captures the effect of unobservablecharacteristics and is assumed to be normally distributed. The equation is estimated for
four points in time (1989, 1994, 2000 and 2006). The X vector of household
characteristics include household demographics (number of family members in eachage/gender category), male and female education (number of family members in eacheducation/age/gender category; excluded categories: individuals with no education ineach age-gender category), dummies for house ownership and household financial andreal assets, dummies for urban and rural location interacted with regional dummies
other than the dummy for the southern region, 21 and a southern region dummy

20

The following subsections draw heavily on Bouillon et al. (2003).
North-West region includes Baja California, Baja California Norte, Sinaloa, Sonora, and Nayarit;
North-East region includes Tamaulipas and Nuevo Leon; North region includes Coahuila, Chihuahua,
San Luis Potosi, Zacatecas, and Durango; Centre region includes Hidalgo, Queretaro, Tlaxcala,
Mexico, Morelos, and Puebla. The Federal District is included as a separate region, and the excluded
category is the Centre-West region that includes Aguascalientes, Colima, Guanajuato, Jalisco, andMichoacan – for urban and rural sectors.

21

8

interacted with urban and rural dummies,22 household demographics, male and female
education, house ownership, and financial and real assets.
SIMULATION AND DECOMPOSITION METHOD
The methodology applied in this study is a regression-based simulation anddecomposition technique, which builds on the seminal papers by Oaxaca (1973) andBlinder (1973). By simulating the impact of changes in the returns and changes inobservable and unobservable characteristics, the micro-simulation method decomposes
the observed changes in the distribution of income into three main effects: the returns
effect, the population or endowments effect, and the effect of unobservables.
The returns effect captures the contribution of how endowments are remunerated in themarket as well as that related to the reward or penalty facing households for living indifferent geographical locations. The endowments effect determines the contribution ofchanges in the levels and distribution of household characteristics and location overall.
Finally, the ‘effect of unobservables’ captures the contribution of changes in the
distribution and/or remuneration of unobservable characteristics, such as
entrepreneurship abilities, specific governmental programs directed to households orother human capital factors of family members.
Simulation Method
Once the household income regression model is estimated, counterfactual distributionsof income need to be simulated in order to estimate each of the abovementioned effects.
For the returns-effect, the estimated returns (parameter coefficients) of the initial (final)
year of the analyzed period should be substituted by those of the final (initial) year tosimulate the distribution of income that would have been observed in the initial (final)
year if the rates of return to endowments had been those observed in the final (initial)
year. Therefore, to measure the overall returns effect, all coefficients must be changed
simultaneously to generate a new ‘simulated’ vector of household per capita incomes.
For example, once the vector of the log of household per capita income for 1989 (initialyyear in this study) is estimated ( 89X89rˆ89  Hˆ89 ), the parameter coefficients

22

The southern region dummy includes the South (Tabasco and Veracruz), the South-East (Chiapas,
Guerrero, and Oaxaca), and the South-West (Campeche, Quintana Roo, and Yucatan).
9r̂89 are replaced with the 2006-estimated parameters, r̂06 , and, then, the vector y must
be recalculated as follows:r06
y89

X 89rˆ06  Hˆ89

Likewise, it is possible to decompose the overall returns effect into the effect of changesof specific returns, which are calculated by replacing one by one the coefficients of1989 with those obtained for 2006 and vice versa. In other words, we repeat theprocedure described above for every single coefficient in our model at a time to measure
the effect of changes in the returns of each household characteristic (demographics,
education, financial and real assets, and home ownership), and regional returns onhousehold per capita income.
On the other hand, the effect of unobservables is estimated by modifying the
distribution of residuals, so as to match the distribution of 2006 residuals with that of
the 1989 residuals and vice versa. This is done by scaling the error terms in one year by
the ratio of standard deviations, as follows:23
H 06
y89

X 89 rˆ89 

Vˆ 06
Hˆ
Vˆ 89 89

Finally, the endowments effect is calculated as the difference between the total change
in household per capita income distribution and the simulated effects of returns andunobservable characteristics.
These simulations produce a whole set of simulated vectors of household per capita
income, or counterfactual distributions of income, which represent the incomes that thehouseholds could have received under different assumptions. With these counterfactual
distributions of income, we proceed to calculate the measures of inequality (or poverty)of interest and carry out the decomposition to estimate the specific contribution of theindividual effects.
Decomposition Method
23This assumes the residuals are normally distributed. Juhn et al. (1993) followed a different
procedure, which is equivalent to the one applied in this study, based on the actual income percentile
of a household in a particular year and the average cumulative distribution over time (Bouillon et al.,2003).
10

The decomposition exercise consists in estimating the specific contribution of the
returns (overall and individual variables), endowments and unobservables effects to theobserved changes in the distribution of income during the analyzed period. This willallow us to determine the impact of the different variables included in the model, as wellas those considered through the residual term, on the improvement or worsening of theinequality measure.
The methodology can be summarized as follows: let D ( y ) be the income distribution
measure (inequality index) of interest, which can be rewritten as D ( r , X , H ) . Let y
be the income in year 0 and yc income in year 1. We are interested in explaining thechange in household per capita income distribution (ǻD) between year 1 and year 0:'D

D (r', X ',H ' )  D (r, X ,H )This can be decomposed into the effect of changing returns, the effect of changing
unobservables (after having changed prices), the effect of changing household
characteristics (after having changed returns and unobservables). This can be stated as:'D [ D ( r ' , X ' , H ' )  D ( r , X ' , H ' )]  [ D ( r , X ' , H ' )  D ( r , X ' , H )]
 [ D ( r , X ' , H )  D ( r , X , H )]which, with simplifying notation, can be expressed as:'D

D r ( X ' , H ' )  DH ( r , X ' )  D X ( r , H )where each D subscript represents the difference in the distribution from changing thesubscript variable. This is an exact ‘sequential’ decomposition of
returns,
unobservables, and endowments. This decomposition does not, however, keep final
conditions (in our case, 2006) constant at each step of the simulation. To keep final
conditions unchanged and measure all the effects using year 1 variables; we apply asimple transformation and rearrange terms to obtain:'D

Dr ( X ' , H ' )  DH ( r ' , X ' )  DX ( r, H )  [ DH ( r, X ' )  DH ( r ' , X ' )](returns effect) + (effect of unobservables) + (endowments effect) + [remainder]

11

(1)

Or alternatively, to keep initial conditions (in our case, 1989) unchanged:

 'D

Dr ( X , H )  DH ( r, X )  DX ( r ' , H ' )  [ DH ( r ' , X )  DH ( r, X )](2)

Equations (1) and (2) say that the total change in the distribution of household per
capita income can be expressed as the sum of the effect of changes in returns andchanges in the distribution of unobservables – given final (initial) conditions – plus
the effect of changes in the distribution of endowments – given initial (final) conditions– plus a remainder.24 The methodology assumes that the results for each effectobtained from decompositions given final and initial conditions represent the upper andlower bounds for the estimates of the effects. Using this assumption, the analysis of theresults will be based on the average of the upper and lower bounds.

3. INCOME DISTRIBUTION ANALYSISThis section presents an analysis of the evolution of the total per-capita income share ofhouseholds in order to clearly appreciate the changes that occurred along the
distribution of income in Mexico during 1984 to 2006. Additionally, we present our own
calculations of the Gini coefficient, which is the inequality measure most widely used in
the literature and selected in this study as our preferred index.
HOUSEHOLD PER-CAPITA INCOME DISTRIBUTION, 1984–2006
A first glance at Table 1 and Figure 1, gives us the possibility of identifying the year inwhich household per-capita income inequality was at its lowest point in the last 25 years,
and to understand the economic reasons associated with that level. As it is possible tocorroborate in Table 1, the relative income shares of households at the first nine deciles
were the highest of the whole series in 1984, while the one related to the richest
households (decile X) was the lowest in that same year. As a result, income inequality in
1984, according to the Gini, was 48.82%, and the ratio between the per-capita income
shares of the richest versus the poorest households was only 24.5 times.25
24

The remainder terms are the interaction between different factors being simulated. In other words,
the combined effect of modifying two or more factors at the same time – say returns and household
characteristics – is not equal to the sum of the components: that is, the effect of changing returns
keeping households characteristics and the effect of changing households characteristics keeping
returns fixed (Bouillon et al., 2003).
25
As mentioned above, the 1984 level of inequality constitutes the lowest in Mexico since 1950
(Szekely 1998, 2005). According to Cortes (2003), the improvement along the distribution of
12

Contrastingly, in 1989, income inequality increased quite sharply to 54.18%, and thedeterioration was caused by a disproportionate rise in the income share of the richest 10
per cent of households, from 36.9 to 43.5%, at the expense of the remaining 9 deciles.26
As it is possible to verify in Table 2, all households below the 90th percentile of theincome distribution experienced an income-share loss, which affected the poorest 10 per
cent of households the most, by 15.9%, and the rest of households by 11% on average.
However, households at the top decile were the only benefited ones by changes in the
distribution of income during 1984–89 by 17.7%. Furthermore, it should be noted, thatwhile overall average income increased by 15.7% during the period, average income fell
for the poorest 20 per cent, but increased quite sharply for households at the top decile
by 45.3%. All of these aspects contributed to the worsening of the distribution ofhousehold per-capita income in Mexico.
On the other hand, in 1989–94, income shares remained almost constant in absoluteterms. Nevertheless, the relative shares of the second to sixth deciles deteriorated,
including a slight reduction in that of households at the tenth decile, accompanied by arelative income-share improvement in the rest of household deciles, which caused theinequality in the distribution of household per-capita income to increase once more to54.30%, representing an 11.22% peak rise in the Gini coefficient since the middle 1980s.
In 1994, the income-share ratio between the top and the bottom 10 per cent ofhouseholds improved slightly, as compared to 1989, but the ratios between the top andthe bottom 20 per cent, and the top 10 and bottom 40 per cent of households rose.
It is interesting to note, that, in 1996, there is an improvement along the distribution ofincome, which caused an increase in the income share of households below the 90th
percentile, and a decline to that of the richest 10 per cent of households by 3.35% from43.25 to 41.80%. It may be possible to infer that the reduction in household per-capita
income inequality in 1996 was associated with the 1994–95 crisis, in a similar way towhat occurred in 1984 due to the 1982 crisis (Cortes, 2003).27
household per-capita income in 1984, was the result of the 1982 crisis, which contracted the incomeof all households, but increased the relative income share of households at the first eight deciles,
while reducing the ones of households above the 80th percentile. In addition, Cortes and Rubalcava(1991) explain that the inequality reduction in 1984 took place in a context of generalized
impoverishment.
26
Our results and conclusions regarding the period 1984–89 are in line with those of Szekely(1998).
27
These results and conclusions are similar to the ones shown in Cortes (2003). He also documented
the improvement along the distribution of monetary income that occurred in 1996, and affirmed that
it was the immediate result of the 1994–95 financial crisis.
13

Moreover, it is also possible to see in Tables 1 and 2 that the relative income share ofhouseholds at the first, second and seventh to ninth deciles deteriorated in 1994–2000
by 8.6, 1.8, 0.9, 0.2 and 2.6% respectively. Additionally, it should be noted that thelowest income shares of the series for households below the 20th percentile are observedin 1998 and 2000 after consecutively falling since 1984.28 However, the income shareof households at the top decile increased about 1.1% during the period, while, in 1998
and 2000, it rose to 43.74 and 43.73% respectively, contributing to the inequality rise inboth years.29 As shown in Table 2, real average income fell for households at all deciles
during the period, but especially for the poorest 10 per cent by 16.9%, and this is
attributed to the direct income effect of the 1994–95 crisis.30
After 2000, an evident and interesting change along the distribution of household
per-capita income emerges from the data. It can be seen in Table 1 that the downward
pattern in the income share of households at the lower and middle tail of the income
distribution that followed since 1984 was reversed since 2002, contributing invariably
to the income-distributional improvement that was recently observed in Mexico. As
shown in Table 2, the income-share increase was most evident for the poorest
households in the country (deciles 1–3), with positive changes during 2000–06 of about
25.6, 15.5 and 10.7% respectively. Contrastingly, only the richest households
experienced a fall in their income share of about 5.4%. Furthermore, it is also possible
to see in the same table that, in 2000–06, changes in the real average income werepositive for households at all deciles, but households below the 90th percentile of the
distribution had above-average increases in their incomes, with those below the 40th
28

There is an important issue to be observed from the data. According to the ENIGHs, households
at the lower tail of the income distribution, but in particular the ones below the 20th percentile, were
only able to increase their average income level from that in 1984 until 2002. This means that almost
two decades were lost for the poor in terms of economic growth and development. Due to space
restrictions, the table of monthly average total income by deciles is not included; instead, two
additional columns (“During 84–00”) were added to Table 2 to show this point in some detail.
29
The highest income per capita shares for the richest households in Mexico, along with the highest
inequality levels of the whole series, are observed in 1998 and 2000. According to the Gini
coefficient, household per-capita income inequality rose by 3.74%, from 52.72 to 54.69% in
1996–98, decreasing slightly to 54.64% in 2000. It is interesting to note, that if the income-shareratio between the richest and the poorest households is considered, no income-distributional
difference seems to emerge in 1998 and 2000. Nevertheless, when the ratio between the top and thebottom 20 per cent of households is chosen, inequality is apparently higher in 1998, but, when theratio between the richest 10% and the poorest 40% of households is selected, then inequality seems
to be higher in 2000.
30
According to Nicita (2009), also the real per capita expenditures showed a decline in the wealthof Mexican households following the financial crisis of 1995, and the purchasing power of mosthouseholds was still slightly lower than that of 1989 by 2000.
14

percentile rising their incomes by 42% on average.
Consequently, given the positive changes that occurred along the distribution ofhousehold per-capita income since 2002, income inequality has been declining after
2000 and reached a level of 51.96%, as measured by the Gini coefficient, in 2006. This
represents an inequality reduction of about 4.1, 4.3 and 4.9% with respect to 1989, 1994
and 2000 respectively. Unfortunately, as it is possible to corroborate in Table 1 andFigure 1, the inequality level in 2006 has not yet fallen, or is even close, to the one
observed in 1984, which is the lowest level recorded in the last 6 decades. Our estimatesindicate that that hypothetical goal is still far from being reached.
Finally, we have included two additional columns to our calculations in Table 2, in order
to be able to identify the changes in the distribution of income that occurred during
1989–2006, which is the main period of analysis of the present study. It is interesting tonote in that table, that similar changes, as the ones found for the 2000–06 period, in theincome per-capita shares and the real average income of households at all deciles arealso observed in 1989–2006 although they are smaller in magnitude. This is definitely
no coincidence, and the changes that occurred in 2000–06 had, consequently, a positive
distributional impact on the long-run period.
In the next sections, we devote our attention to identifying the microeconomic factorsthat caused the improvement along the distribution of total household per-capita incomethat occurred after 2000 and further reaffirmed in 2006, as well as the variables thatwere inequality–reducing or –augmenting since 1989 in Mexico. For that purpose, weapply the micro-simulation and decomposition methodology explained in the previous
section.

4. REGRESSION ANALYSIS: 1989–2006
As it can be seen in Table 3, the household regressions have substantial explanatory
power. The R-squared for the regression is 52.91 and 57.56% in 1989 and 2006
respectively. Additionally, as shown by the F-test statistic, the overall significance ofboth regressions is high at the 1% level, and almost all individual variables are highly
significant as well.

15

Results are reported in reference to a household in the center-west region31 that does
not own a house or any real and financial assets, and whose household head is present,
and between 14 and 65 years old and with no education. (These are the excluded
categories for obtaining a non-singular matrix.)
Household characteristics and regional effects are found to be highly significant.
Interaction terms measuring the marginal effects of returns in the south on household
characteristics are significant as a group in both years, meaning that the returns tocharacteristics of households living in the southern region were significantly different
from those of the rest of the country in 1989 and 2006.32
Household Characteristics
Demographics
Age and gender categories of the household are found to be very significant in
explaining per capita household income. The estimated coefficients measure themarginal effect of adding a person to the household. In the case of adults (who aredefined as individuals that are 14 years or older), the coefficients measure the value ofadding a household member with no education. These marginal effects are found to be
negative for both children and adults, meaning that an additional individual contributes
less than average income to the family.
Furthermore, as we can see in Table 3, the negative effect of adding a person with noeducation to the family increased for all variables, except for the elderly and females
between 60 and 65 years old. This means that marginal additions were more costly in2006, therefore, the cost of raising a child increased, and also that the marginal adultwould bring relatively less earnings to the household in that year than in 1989. This has
been caused in great part by the strong depreciation of the minimum salary,33 which,31

The center-west region includes the states of Aguascalientes, Colima, Guanajuato, Jalisco, andMichoacán, for urban and rural sectors.
32
The gains of households in the South of Mexico are lower because of a higher reliance on income
originating from sales of agricultural products and a relatively less skilled labor force. Other reasonsthat have limited the effect of trade liberalization in southern areas are the large share of the rural
population and the pervasive subsistence economy that greatly isolates households’ budgets fromany price effect (Nicita, 2009).
33
Perhaps, the Mexican government let the minimum wage deteriorate – by simply leaving its
nominal value unchanged in the face of inflation – for fear that this might impede readjustment in
the face of macroeconomic shocks, or because of increasing pressure towards inequality in market
16

from the year 1989 to date, has lost its value by more than 30% in real terms (ILO
(2007) and Bayon (2009))34, and the deterioration in the real incomes of the rural sector(Hernandez-Laos (2006) and World Bank (2007)). On the other hand, it is possible tosay that the number of old people in the household is increasing in part because of
improved life expectancy, and, therefore, these people, on average, may be healthier andmore productive and/or have larger pensions than the same cohorts 18 years earlier.35
Education
The educational-attainment level of household members is highly significant as well inaccounting for differences in household income in both years. The coefficients measure
the marginal effect of replacing an adult with no education by an adult with educationfor each specified category. With the exception of the coefficients for males with someprimary education in both years and females with some primary education in 2006 only,
the rest of the returns to education are positive and significant. They tend to increase
with educational level, and the increase is monotonic at least with respect to‘completed’ levels of education.
It should be noted that the large differences between the returns to lower and higher
education that were present in the year 1989 are disappearing. In the case of males, rates
of return are much smaller for all educational completed levels in the year 2006, andparticularly so for postgraduate education. This means that there was actually anarrowing income gap between skilled and unskilled workers that, as we will see in thedecomposition analysis, constituted one of the main factors contributing to the reductionof inequality in Mexico.
On the other hand, in the case of females, a similar situation to that of males was foundat all educational levels, except for the case of some-college education, where the rate of
return was higher in the year 2006. This increase could be the result of a natural
wages (Bosch and Manacorda, 2008).
34
Between 1980 and 2000, the minimum wage lost 70% of its purchasing power, while industrial
wages fell by 35% in real terms (Bayon, 2009). Unlike most Latin American countries, where
average weighted real minimum wages at the end of 2007 were 76% higher than in 1990, Mexico is
one of the few countries of the region where minimum wages have barely recovered the purchasing
power they commanded 17 years ago (ILO, 2007).
35
The results and conclusions regarding the rates of return to demographics are similar to thosefound in Bouillon et al. (2003) for Mexico during 1984–94. In addition, the increase in the numberof elderly people from 1989 to 2006 was corroborated with the data; however, due to space
limitations the corresponding table is not included.
17

correction to the parameter coefficient estimated for the year 1989, which shows anunderestimated rate of return at that level of education for females,36 or to the presenceof female-work discrimination in that year.37
As we will corroborate in the decomposition analysis, the changes in the returns to
education for females worked out in the opposite direction to those for males. Therefore,
this factor was, though to a small degree, an unequalizing determinant of the
distribution of income in Mexico during 1989–2006.
Physical and Financial Assets
Real and financial assets and home ownership contributed positively to householdincome in both years as expected. It should be noted that, between 1989 and 2006, thereturns to house ownership declined, while those for real and financial assets increased.
The latter was partly caused by an increase in the number of households that declared
having financial assets at the top seven deciles of the income distribution, while theproportion of those at the bottom three deciles declined. Additionally, it is important tonote that the highest increase in financial assets was achieved by households at the top
deciles (8 to 10), whose assets in this category increased on average by 142%, and theincrease was especially higher for those families above the 90th percentile of the income
distribution (see Table 4).
On the other hand, the increase in the returns to real assets is related to the decline in thenumber of households possessing that type of assets at the bottom 9 deciles of theincome distribution, accompanied by the increase in the number of households who
declared having real assets above the 90th percentile at all income levels. Therefore, we
can infer that real and financial assets are strongly correlated with higher levels of
income in Mexico.
Finally, the reduction in the rate of return to house ownership is due to the fact that thepercentage of households that owned a house in 1989, 74.33%, declined to 69.36% in
2006, a 6.7% change. Moreover, as it is possible to corroborate in Table 4, even though
the home-ownership decline experienced in Mexico during the period was a generalized
36

This can be corroborated by the higher rates of return to secondary and college education
observed in that year (see Table 3).
37
It is possible to see in Table 3 that the rate of return for females with some college education isdefinitely lower than that for males in 1989, which could be attributed to some sort of wage
discrimination for female workers at that level of education only.
18phenomenon for households at all income levels, except for families at the 8th decile ofthe income distribution, it should also be noted that the smaller reductions in theownership of the middle- and higher-income households, but especially the ones of thewealthiest families (deciles 8 to 10), prevented the returns to this covariate to fall evenfurther in 2006.
Regional Effects
Relative to the center-west region (the region of reference), fixed effects, in the year
2006, were positive for urban areas in all regions except for the south, and negative inrural areas in all regions, but the strongest effect was observed in the southern part ofthe country. As for the year 1989, the situation in the regions was a bit different. In
urban and rural areas, fixed effects were negative for three regions: the north, the centerand the south. However, the strongest negative effect was also felt in the southern
region.38
As for the ranking, urban areas in the north-west and the capital fare the best in both
years, and there is a substantial improvement in the relative position of all regions in theyear 2006. For rural areas in 1989, the regions that fare the worst were the south and thecenter, followed by the north. However, there was a considerable improvement in therelative position of the center and northern regions in the year 2006, which, as we will
see in the decomposition analysis, was one of the factors that contributed to the
reduction of inequality in Mexico during the analyzed period.

5. DECOMPOSITION RESULTS
I. Decomposition: 1989–2006
As shown in Table 5, the two effects that contributed the most to the reduction of
inequality in Mexico from 1989 to the year 2006 are the effect of unobservables and theendowments effect, which contributed almost in the same proportion to reducing
38

There is a regional per-capita output divergence in Mexico originated after 1985 that was notreversed with the enactment of NAFTA (Chiquiar, 2005). Apparently, regional income dispersion ismainly the result of physical and human capital differences between Mexican states as well as
location-related externalities. Therefore, according to Chiquiar (2005), southern states, whose labor
force is more concentrated in traditional agriculture and which have the greatest deficiencies inhuman capital and basic infrastructure, seem to be the losers from trade liberalization as opposed to,especially, the states in the border region with the U.S. market.
19

income inequality in the country, by 92.17 and 107.96% respectively as measured by theGini coefficient. On the other hand, the returns effect contributed negatively to theinequality reduction in Mexico by 96.75%, which means that the rates of return tohousehold characteristics and geographical location, considered as a whole, were an
unequalizing factor in the income distribution process. Nevertheless, as we will see laterin the analysis, some particular returns had an equalizing effect, and played an
important role in the reduction of inequality in Mexico during the last two decades.
Returns to Household Characteristics & Geographical Location
Among the different returns to household characteristics, the ones with the highest
negative contribution to the reduction of inequality are the returns to assets, followed by
the returns to demographics (see Table 5). These two factors are the ones that
continuously prevented inequality to decline further in the last 18 years.39 In the case of
returns to demographics, its negative impact (61.12%) was relatively high compared tothe main positive effects. It should be noted that, as explained in the previous section,
the rates of return to demographics for adults measure the marginal effect of adding aperson with no education to the household. Based on the assumption that those adults
earn, on average, the minimum wage for their labor, this finding suggests that theinequality-increasing effect of the returns to demographics was in great part the result of
the 30% depreciation in the real value of the minimum salary that occurred in
1989–2006 (ILO, 2007) and the deterioration in the real incomes of the rural sector(Hernandez-Laos (2006) and World Bank (2007)), which consequently caused theabsolute fall in real incomes and have its stronger effects at the lower tail of the income
and educational distributions.
Rates of return to assets contributed negatively to income inequality reduction by
79.18%. From that number, as shown in Table 5, 31.53 and 49.27% correspond to thenegative contribution of the returns to real and financial assets respectively. This issomething not surprising as the estimated returns to those assets both increased from1989 to 2006 by 320 and 51% respectively,40 benefiting higher income classes the most,39

When the decomposition analysis is carried out for the three periods in between 1989–2006(1989–1994, 1994–2000, and 2000–2006), there is an invariable negative contribution of the rates ofreturn to demographics and assets to income inequality in Mexico, with the exception of the period
2000–06 where the returns to assets were an important equalizing factor contributing to the reductionof inequality during that period. This is explained in detail later in this section.
40
See Table 3. According to Jensen & Rosas (2007), the increase in the returns to financial assets
could be attributed in part to the liberalization of the financial sector that took place during the20which are the ones that possess the majority of financial assets, and the ones that also
hold real assets in a higher proportion as compared to lower and middle-income groups(see Table 4).
Furthermore, rates of return to education were one of the main factors contributing to
the reduction of inequality in Mexico. Considered as a whole, they are able to explain
54% of the reduction in the inequality measure (see Table 5).41 However, in particular,
the reduction in the skill premium (SP) between low and high-skilled male workers was
the factor responsible for 60.52% of the change in the Gini. This narrowing income gap,
associated with male skills, was actually one of the most equalizing forces behind theinequality situation in Mexico during the analyzed period.42
On the other hand, returns to geographical location also contributed negatively toincome-inequality reduction in Mexico during the last decades. However, the overall
impact of that effect (regional effects), which is the result of equalizing andunequalizing forces, is relatively small (9.02%). Among the regional-equalizing factors,
it was possible to identify the positive contribution of rural-fixed effects in regions otherthan the south. These effects contributed to 20.30% to the decline in the inequality index,
and are just second in importance among the rates of return, preceded only by returns to
education.43 This constitutes another important finding of this study, which contrasts
with the results in Bouillon et al. (2003) for Mexico during 1984–94, and that suggeststhat conditions in rural areas, except for the south, are improving and contributing to
reduce the high-income disparities that prevail across regions and between rural andurban areas in the country.44
Endowments

1990s.
41
Changes in the relative earnings among educational groups are always the leading force behindchanges in inequality in Mexico (Lopez-Acevedo, 2006).
42
This finding is in line with those in Acosta and Montes-Rojas (2008), Campos-Vazquez (2008),
Esquivel (2009), Esquivel and Rodriguez-Lopez (2003), Montes-Rojas (2006) and Robertson(2004).
43
Apart from the returns to the rural areas in regions other than the south, the rest of the regional
effects in 1989–2006 were unequalizing. Most notably, we could mention the negative contribution(19.6%) of the urban fixed effects in regions other than the south, which almost offset theinequality-reducing effects of the returns to living in the rural areas mentioned here (see Table 5).
44This statement applies only to the long-run period. In fact, rates of return to the Southern region
were identified as an equalizing factor during 2000–06, implying that conditions in the south of
Mexico are also improving (see subsection II: “Decomposition 2000–2006”).
21

The change in the distribution of endowments (education, home ownership, real andfinancial assets, families’ demographic composition, among others), or population effect,was, certainly, the factor that contributed the most to income-inequality reduction in
Mexico, with an overall equalizing impact of 107.96% on the decline in the Gini
coefficient.
As explained in the methodological section, the endowments effect is calculated in thisstudy as a residual and it is not decomposed into its explicative factors. For that reason,a descriptive-statistics analysis of all the model’s household characteristics was carriedout in order to understand and make inferences about the changes in the distribution ofendowments that took place within the last two decades in Mexico. However, becauseof space limitations, only the analysis of the main equalizing endowments is presented
below.
x

Education

The most important contribution to the reduction of inequality during the last two
decades is definitely that of education. As it can be seen in Table 6, the overall change in
the average educational level of households in Mexico increased by 42% in 1989–2006.
However, it was the rural sector and the low-income classes the ones whose levels of
education increased the most, in relative terms, in the last two decades by 60%, or more
as it was the case of the bottom 5 percentile of the income distribution. But, if theincrease in the absolute level of education is considered, it is possible to see that theextra number of years that the adult population achieved in the last 18 years, at all levels
of income and different locations in the country, was actually similar, about 2.5 years on
average, with the exception of the bottom 1 and 5 percentiles whose educational
increment was much smaller by 1.7 and 0.9 years respectively. 45 Moreover, it isimportant to note that the wealthiest families were able to increase their average
education level the most in absolute terms by 2.7 years, or more as it was the case ofhouseholds at the top 5 percentile of the income distribution.46
45

It should be noted that it was just until the year 2006 that the poorest households were able toobtain an average education level equivalent to that of elementary school. This level of education
gives them no chance to compete at all with the other higher-income level households in the labour
market, and forces them to stay poor for an indefinite number of years or, as it is most likely, forgenerations. According to Mayer-Foulkes (2008), there is a low human-capital development trap in
Mexico; the poverty trap exists if a high enough proportion of the population suffers strong enoughbarriers to its access to human capital (education, health, nutrition among others).
46
According to the World Bank (2007), the high level of education inequality that exists in the
country contributes to the high level of income inequality and limits the pool of skilled workers,
22

Although the wealthiest households in Mexico attained a higher level of educational
improvement in absolute terms, it is the majority of the population (those below the 90th
percentile of the income distribution or around 22 million households) the ones who
benefited the most by the changes in the educational distribution that occurred during
1989–2006 as it can be corroborated in Table 7. In there, it is possible to see that thepercentage of adults with a level of education equal or lower than elementary school
decreased considerably, while the percentage of adults with higher levels of education,
especially those with junior-high school, some high school, some college and somepostgraduate education, invariably increased.47
The changes in the percentage of adults with higher levels of educational attainment, but,in particular, the ones that occurred from high-school level, and the sharp decreases in
the proportion of adults with zero formal education and some elementary education,
along with the changes in the returns to tertiary education, are important to understandthe improvement in the distribution of household per-capita income during the last
decades for, at least, three reasons. First, the 2 percentage point increase (30.7% change)in the number of adults with tertiary education or composition effect, shown in Table 7,means that there were less unskilled workers in 2006 than in 1989, and, therefore, thatan additional number of adults were able to earn, on average, higher incomes for thatlevel of education. Second, the reduction in the number of adults with less than somemiddle education, and the rise in the number of adults at all levels of secondary schooland some college education but, in particular, the changes that occurred above somehigh-school education, imply that the number of semi-skilled workers increased in the
country (from 12.8 to 18.1% in 1989 and 2006 respectively), giving them a chance toincrease their level of income consequently. 48 Finally, because the changes in theultimately limiting the potential of Mexico to compete with other knowledge-driven economies.
Additionally, the same World Bank report mentions that educational inequality in Mexico is thehighest in the OECD.
47
This finding is consistent with Scott (2009). According to the author, the schooling concentration
coefficient in Mexico, remained broadly flat between 1984 and 1994, but declined rapidly thereafter
from 0.347 to 0.276 in 1994 and 2006 respectively. This decline is mostly explained by the rapid
educational expansion, demographic dynamics and the truncated nature of the indicator, as
increasingly educated cohorts enter the adult population, while the older, least educated cohorts exit
it (Scott, 2009).
48
According to INEGI and other authors (Jensen & Rosas (2007), Lopez-Acevedo et al. (2005)
among others), around 10% of the population, or less, is estimated to be high-skilled workers. The
other 90% of Mexicans are considered either unskilled (people with no education, some orcompleted elementary school, some or completed junior-high school, or some high-school
education) or semi-skilled workers (people with a technical degree, besides junior-high school
education, completed high-school, or some college education). These two groups form thelow-skilled population in Mexico. In Table 7, it is actually possible to corroborate this information,
23

distribution of educational-attainment were accompanied by decreasing returns totertiary education as well, there was a corresponding reduction in the level of inequality
in Mexico due to a considerable decline in the incomes of the highly-skilled populationin the year 2006.49
Therefore, in the case of Mexico, changes in the stock of education,50 along with the
reductions in the SP that were identified, contributed in an equalizing manner and to arather high degree to improving per-capita household income inequality in the last two
decades.
x

Household Demographic Composition

According to the data, there was a 60.9% increase in the proportion of elderly people inhouseholds at the lower tail of the income distribution, which constitutes an equalizing
factor. There are at least two reasons for this to be the case. First, as it was discussed in
the previous section, the negative rates of return for the elderly were smaller for males
and females in 2006 than in 1989. This is attributed to elderly people being healthierand more productive and / or having larger pensions than the same cohorts 18 years
earlier.51 Additionally, in support of that finding, since January 2006, there has been a
direct cash transfer provided to the elderly (70 years or older) in extremely poor
households (those below the 25th percentile of the income distribution) by the Mexican
and, according to the data, up to 50% of the adult population did not have more than some-middle
school education (with the vast majority of that group of people (46%) with an educational level
equal or lower than primary school), 23% no more than some high-school education, 18% no morethan some-college education (semi-skilled population group), and only 8.6% of adults held auniversity degree in 2006.
49
According to Esquivel (2009), the most important factor contributing to the reduction in the skill
premium (SP) from 1994 to 2006 in Mexico was the reduction in the number of unskilled workers,
or workers with less than lower secondary education, which, in 1989, represented almost 55% of theworkforce population as opposed to only 35% in 2006, a 20 percentage-point fall in the supply ofthis group of workers. Additionally, the author also supports the findings in Campos-Vazquez (2008)
and Robertson (2007), which relate the decline in the SP to the increase in the supply of skilledworkers and the increase in the demand for unskilled workers respectively.
50
The improvement in the distribution of education in Mexico could be primarily explained by theeducational policy change, which brought about the 1990’s and 2000’s educational expansion
identified in this study (see Table 7). As documented in Scott (2009), the allocation of public
spending in the 1970’s and 1980’s was heavily biased towards higher education. However, the bias
was reversed with an increasing reallocation of educational spending towards basic education(primary and middle-secondary school) after 1988. Consequently, between 1992 and 2002, spending
per student expanded in real terms by only 7.5% in the case of tertiary education, but by 63% in thecase of primary education (Scott, 2009).
51
According to the National Institute of Statistics, Geography, and Informatics in Mexico (INEGI),
life expectancy increased from 70.6 to 74.8 years in the period 1990 to 2006.
24

government through the Oportunidades program. Therefore, the increase in the numberof old people in the household has contributed positively to the reduction of inequality
in Mexico during the last decades.
Unobservables
As explained above, the effect of unobservables was the second most important factor in
explaining the reduction in the degree of inequality in Mexico. It contributed to 92.17%
to the change in the inequality index during 1989–2006. In this section, we discuss
briefly some of the variables that could be responsible for this equalizing effect.52
x

The Oportunidades Program

An important factor that is highly related to the reduction of poverty in Mexico in thelast decade is the Oportunidades program.53 Oportunidades consists of three main
components that operate simultaneously to exploit their complementarity: (1) education,(2) health, and (3) food-nutrition. (Levy and Rodriguez, 2004). It should be noted thatthe elements of the program that are associated with the effect of unobservables, which
are analyzed in this section, are health, nutrition and the government-income transfer
itself. The former two components are explained further in the following subsections.
As for the Oportunidades income transfer, it should be mentioned that it is able toreduce poverty and inequality in the short run.54 However, according to our simulations,the impact of the program on income inequality is relatively small as it contributed tothe decline of the inequality index by 0.63% on average in 2002 to 2006.55 Most
probably, the inequality-decreasing impact of the conditional-cash transfer program ismuch more important in the rural areas as suggested by Esquivel (2009) and Esquivel et52

Due to space limitations, only the most representative unobservable factors are covered in here.
However, the complete analysis includes, besides the variables mentioned below, social security,
government redistributive expenditure, and physical household characteristics as well.
53
Oportunidades is a conditional cash and in-kind transfer program of the federal government thataims at the reduction and alleviation of extreme poverty, through the development of the humancapital of poor households. It was launched in the year 1997 with a total beneficiary population of
300,000 households. Since the year 2004, the program benefits up to 5 million families in Mexico.
54
According to CONEVAL (2008), Oportunidades does not have an important effect on poverty
reduction in urban areas in the short run. However, in the rural areas, the program has contributed
substantially to the reduction of poverty since 2002. In 2006, poverty decreased from 28.4 to 24.5%
in rural areas, representing 1.7 million people out of extreme poverty, due to the Oportunidades cashtransfer (CONEVAL, 2008).
55
We estimated the level of inequality for the years 2002, 2004, 2005 and 2006 without the
Oportunidades transfer, and the Gini coefficient increased slightly by 0.32, 0.29, 0.34 and 0.37
percentage points to 52.35, 53.08, 53.16 and 52.33% respectively.
25

al. (2009), and considering the significant effect that it has on the reduction of poverty
in that sector (CONEVAL, 2008).
x

Human-Capital Development

HealthIt should be noted that the health of the poorest households in Mexico has improved in
the last 12 years, and most probably at a higher rate than the national average, due to the
Oportunidades program, which focuses on preventive-health care and regular check-ups
in order to increase the health condition of all household-beneficiary members. 56
Additionally, in 2001, the federal government introduced the Seguro Popular or Popular
Health Insurance (PHI) as a pilot program, eventually becoming part of the Mexican
health-system legislation in 2003. The PHI offers access to publicly subsidized healthinsurance to all households that are currently not part of any existing social-security
program. In 2006, there were around 3 million beneficiary households in the PHI
program, and almost all those families belonged to the bottom of the income
distribution (Lustig, 2006).57
Without doubt, health plays a crucial role in the income-distribution and development
process, and will continue to be an important equalizing factor as long as the healthinequities among all Mexicans become smaller,58 the quality and coverage of the healthservices provided mainly by the government to the majority of the population increase
evenly across all regions, and social security in Mexico becomes universal rather thanbeing a luxury commodity.59
Nutrition & Food Consumption
Nutrition is another human-capital factor that, along with health, has had a positive56

The Oportunidades program has shown to be very effective in improving the health and nutritionof children and mothers of beneficiary households (Lustig, 2006). For a good review of studies
related with health and Oportunidades up to the year 2006, the reader is referred to Levy (2006).
57
The government’s goal is to achieve universal coverage by 2010 to all households not covered byany social-security program in Mexico. By the time the program reaches full coverage, Mexico’s
health system will move from 144th (out of 191 countries included in the study done by the World
Health Organization) to 44th place in terms of fairness in financing of health care (Lustig, 2006).
58
Health indicators in Mexico are very unequal with poorer socioeconomic groups andmunicipalities showing health levels similar to those found in some sub-Saharan African and South
Asian countries (Lustig, 2006).
59
According to CONEVAL (2008), around 50% of all households in Mexico were not covered byany social-security program in 2006.
26

impact on the reduction of poverty and inequality in the country. According to
CONEVAL (2008), nutritional indicators showed important improvements in 2006 ascompared to 1988 and 1999. In particular, the prevalence of low weight, low height, andemaciation in children 5 years old or younger has decreased by 64.79, 45.18 and73.33% respectively in 1988–2006. Moreover, according to the same Council, while
per-capita food consumption has declined by 1.45% for the country as a whole, it has
increased by 16.62% in real terms for the poorest 20% of the population in 1992–2006.
In addition, the percentage of the population that could not afford a minimum foodbasket, even if spending their total income on it, decreased during the same period from
21.4 to 13.8%.60
xMigration & Remittances

Migration and remittances are two interrelated variables considered as unobservables inthis study.61 Several studies point towards the positive effects of these two factors on
inequality, poverty and development.62 Given the high and increasing migration rates
from Mexico to the United States,63 and the considerable amount of resources that aretransferred to Mexico in the form of remittances,64 both factors must play an importantrole in the distribution of income in the country.
According to Mckenzie and Rapoport (2007), there is indeed a negative relationshipbetween migration and inequality in Mexico, especially at high levels of migration
prevalence. Additionally, the authors found suggestive evidence for a Kuznets
60

It should be noted that these important and positive changes in nutrition and food consumption for
the whole population, but, in particular, for the poorest households in Mexico, are related in greatpart to the direct impact of the Oportunidades program since beneficiary households receive anutritional supplement every month for vulnerable members and a monthly food stipend to increase
the dietary intake of the family.
61
Labour migration can be analyzed as an exchange of labour force for remittances. Althoughmigration can have other benefits and costs, a country loses a certain amount of population, while it(through migrants’ families) receives remittances in exchange. Mexico is among the top three
remittance-receiving countries in the world (Escobar-Latapi, 2009).
62
See for example, Adams and Page (2005), Escobar-Latapi (2009), Escobar-Latapi and Janssen(2006), Mckenzie and Rapoport (2007), Mishra (2007), Stark et al. (1986) and Taylor and Wyatt(1996).
63
Mishra (2007) estimated that around 16% of the Mexican labor force were emigrants to theUnited States in 2000, whereas they constituted 11 and 3% in 1990 and 1970 respectively.
64
Remittances accounted for over 3.5% of Mexico’s GDP by 2005, reaching 23.74 and 23.97 billion
dollars in 2006 and 2007 respectively (Bank of Mexico, 2008), more than foreign direct investment(FDI) and equivalent to one-eight of all exports. In social terms, remittances were roughly 7 times
the largest government cash-transfer program (Oportunidades) to aid the poor (Escobar-Latapi,2009).
27

relationship in Mexican-rural communities, with migration increasing inequality atlower levels of migration stock, and then reducing it at higher levels.
On the other hand, there is an important contribution of remittances to the reduction ofpoverty (particularly in rural areas (CONEVAL, 2008))65 and inequality in Mexico. Our
simulations indicate that income inequality declined by 1.1% on average, from 2000 to
2006, because of the positive effect of remittances,66 whose impact on the distributionof income has increased over time (Esquivel, 2010).
II. Decomposition: 2000–2006
Between the period 2000 to 2006, income inequality decreased by 4.9% from 54.64 to
51.96%, an absolute reduction in the Gini coefficient of 2.68 percentage points. This is aconsiderable fall in the inequality index, given the fact that income inequality remained
pretty stable (around 0.54) from the late 1980s to 2000 and, actually, had a tendency toincrease since 1989 after rising quite sharply in the mid 1980s. 67 This important
income-inequality reduction could be attributed to a particular policy, structural and/or
economic change, which may be possible to identify in the decomposition analysis, orby making further investigation on the issue.
By looking at the decomposition results in Table 8, it is possible to see that the returns
65

According to the estimations made by CONEVAL (2008), extreme poverty in rural areas would
have increased from 13.8 to 15.9%, representing a poverty reduction of about 2.3 million people in2006, had it not been for the remittances received by poor households in that sector. In addition, it isestimated that remittances increased by 405.5% among extremely poor households living in rural
areas in Mexico during 1992–2006 (CONEVAL, 2008). In this case, the definition of extreme
poverty refers to the one of food poverty.
66
By deducting the amount of remittances received from total household per-capita income and thencalculating the Gini index for different years in Mexico, we could corroborate that theinequality-reducing impact of remittances is actually stronger than the Oportunidades cash transfer.
This is a natural result, as remittances accounted for over 3.5% of Mexico’s GDP by 2005 (Bank of
Mexico, 2008), while Oportunidades represented only 0.36% of GDP in 2006 (Scott, 2009).
Consequently, according to our simulations, the Gini coefficient in Mexico decreased by 0.63, 0.54,
0.30, 0.67 and 0.76 percentage points in 2000, 2002, 2004, 2005 and 2006, respectively, due toremittances from abroad.
67
According to the World Bank (2004), the Gini coefficients for Mexico for the years 1992, 1994,
1996, 1998 and 2000 are 0.53, 0.54, 0.52, 0.54 and 0.54 respectively. These estimates were
calculated by using the methodology for the calculation of the per-capita income variable that wasapproved by the Technical Committee for the Measurement of Poverty (2002) in Mexico. It should
be noted that it is only in 1996 that inequality in Mexico decreased to a similar level to that of 2006.
However, the inequality decline in 1996 was the reflection of the 1994–95 financial crisis in the
country (Cortes, 2003). Apparently, the circumstances, under which the reduction in the level ofinequality took place in 2006, were quite different from those in 1996. Let us find out the answer tothis below.
28effect was no longer an unequalizing factor in the distribution of income in Mexicofrom 2000 to 2006, as it was the case of that effect in the long-run period or any of theprevious analyzed periods (1989–94 and 1994–2000). Moreover, it should be noted thatthe returns effect was the most important contributor to the reduction of inequality in
Mexico, with a positive impact of 57.82% on the change in the Gini coefficient, which
is an interesting finding that greatly differs from the previous decompositions. The
contribution of the other two effects (unobservables and endowments) did not change ascompared to the period 1994–2000. Therefore, the former effect was unequalizing but
rather small (9.73%), and the endowments effect was equalizing, with a positive impactof 51.16% on the distribution of income in Mexico.
Now, let us have a closer look at the factors that were behind the sudden directionalchange in the returns effect in this period. First, it can be immediately seen that thereturns to household characteristics and regional effects are both equalizing, with apositive impact of 40.24 and 17.72% respectively, something that again differs from anyother analyzed period. Furthermore, it is important to note that there were only two
unequalizing factors among the rates of return. One is the returns to demographics witha small, negative contribution of just 3.17%, and the other one is the returns to urban
areas in regions other than the south with a negative impact on inequality of about 15%.
The rest of the returns contributed positively to the reduction of inequality during theperiod, which is one of the most important reasons behind the positive change along the
distribution of income in Mexico in 2006.
As for the equalizing rates of return, it is possible to identify that the returns to
education, considered as a whole, continued to have a positive and strong impact(23.65%), which makes it the most important contributor to the reduction of income
inequality in Mexico during the period and in the longer one as well. However, as
shown in Table 8, only the returns to education for males had a positive effect on the
distribution of household per-capita income, while the returns for females were slightly
unequalizing.
Moreover, there were important changes in the returns to assets that contributed an
estimated 20% to the reduction in the Gini coefficient. This is something that contrasts
with the findings in previous decompositions, where rates of return to assets werehighly unequalizing in either 1989–94 or 1994–2000. As it can be seen in Table 8, thefactor that was responsible for this positive, overall change was the returns to financial

29

assets, which contributed to an estimated 28% to the decline in the inequality index
from 2000 to 2006. This factor is also one of the most important forces in explaining theincome-distributional improvement that took place during the latter period.
As mentioned above, the returns to geographical location also contributed positively tothe reduction of inequality in Mexico. From those returns, it is the contribution of thesouth the one with the highest impact, which accounts to 19.41% of the change in theinequality index. Furthermore, it is possible to identify that, from that effect, 16.12%
corresponds to the positive impact of the bettering returns to household characteristics
relative to returns in other regions, and the rest of the effect (3.29%) is related to fixed
effects in urban and rural areas in the region.
Finally, it should be noted that the effect of returns to rural areas in regions other than
the south was also considerably significant in the equalizing process, with an overall
contribution of about 13% to the reduction in the Gini. These last two findings point
towards an improvement in the distribution of income that is taking place in the rural
areas and the South of the country at last due to positive changes in the rates of return in
Mexico from 2000 to 2006.
As for the endowments effect, there were primarily three factors whose distributionalchanges during the period yielded a remarkable inequality-reducing impact on theincome distribution, namely financial and real assets and education. As shown in Table
9, real and financial assets were no longer unequalizing factors in the distribution ofincome in Mexico during 2000–06. It is particularly notable the positive contribution of
the changes in the distribution of financial assets to the reduction of inequality. Perhaps,
this is one of the most important determinants of the equalization process that occurredin the country.68 In addition, regarding the stock of real assets, the proportion ofhouseholds holding this type of assets increased for households at all income levels and68

According to the data, the proportion of households with financial assets rose slightly by 1.1% on
average during the period. However, there was around a 50% increase in the proportion ofhouseholds with financial assets below the 90th percentile of the income distribution, but especiallyfor those at the lower- and middle-part of it. Contrastingly, the proportion of households holding
financial assets at the top 10, 5 and 1 percentile of the income distribution fell by 5, 8 and 20%
respectively. It is important to mention that the sudden change (improvement) in the distribution offinancial assets occurred since 2002 due in great part to the 2001–03 economic recession in Mexico.
However, it was possible to corroborate that the improvement along the distribution of financial
assets remained in 2004 and 2006 in the absence of any crisis or recession in the country. We believethat this was possible because of the sharp decline of the national risk-free rate after 2000, which had
an important equalizing impact at the upper tail of the financial-asset distribution.
30

areas in the country, rising by 29.7% for low-income families, a better than averagechange for this group during the period.
With respect to the changes in the stock of education, it is possible to see in Table 10that the average educational attainment level of households increased by 13.32% during
2000–06. However, the rise was much higher, in relative and absolute terms, forhouseholds at the lower tail of the income distribution (below the 50th percentile) thanfor the middle- and higher-income families, and this also applies to households in rural
areas and the South of the country with respect to the urban sector.
Moreover, as shown in Table 11, important changes took place regarding the decline in
the proportion of adults with a level of education equal or lower than elementary school.
By comparing Tables 7 and 11, it can be corroborated that the greatest reductions in thepercentage of adults with those levels of education occurred in 2000–06. Additionally, it
is also possible to see that the highest increase in the proportion of adults with somecollege education was definitely achieved during 2000–06. Therefore, changes in thestock of education were also an important inequality-decreasing factor during thisperiod.
Finally, as mentioned above, the effect of unobservables was fairly small, and, therefore,
did not play such an important role in the determination of household per-capita income
inequality during the period. Although the overall impact of this effect was a negative
one, there must have been several equalizing forces or unobservable factors that greatly
counteracted the unequalizing ones as it should be the case, especially, of theOportunidades program, the PHI program, and migration and remittances in 2000–06.

6. INTERPRETATION OF RESULTS & POLICY RECOMMENDATIONSIn this section, we interpret the findings from the decomposition analysis, compare ourconclusions to those already drawn in the earlier literature, and make some policy
recommendations, with the intention of addressing the chronic problems of inequality
and poverty in Mexico.
As shown in Figure 1 (section 3), income inequality in Mexico increased quite sharply
after 1984, reaching a level (Gini) of 54.18 (%) since 1989, and remained high during

31

the 1990s. The data shows that the reduction in household per capita income inequality
took place after 2000, more specifically around 2002, declining to 52.03 and remaining
approximately constant thereafter.
Important equalizing forces were responsible for the fall in the level of inequality. In thelong run, the decomposition results show that only the endowments effect and the effectof unobservables contributed to the reduction of income inequality in Mexico. The
returns effect, considered as a whole, was found to be unequalizing. Nevertheless, ouranalysis also indicates that both changes in the levels of and returns to education wereboth the most important determinants in explaining the reduction of income inequality
in Mexico during 1989–2006. Our estimation is that education was responsible for morethan 50% of the improvement in the distribution of household per capita income, caused
mainly by the educational policy change, documented in Lopez-Acevedo (2006), Scott(2009) and World Bank (2007), that induced the important structural change in the
distribution of schooling, identified in our analysis, which affected the composition of
the labor force in such a way as to cause the decline in the SP between low- andhigh-skilled workers after 1994 (Esquivel and Rodriguez-Lopez (2003), Robertson(2004), Lopez-Acevedo (2006), Montes-Rojas (2006), Acosta and Montes-Rojas (2008),
Esquivel (2010)).
With respect to the decomposition results for the three periods in between 1989 and2006, it should be mentioned that the only factor that was continuously unequalizing
during the three analyzed periods was the returns to demographic characteristics. As
explained in section 4, this inequality-increasing factor is related in great part to thedepreciation of the minimum salary, which, from 1980 to 2006, lost its real value byaround 70% (ILO, 2007) and by 30% in 1990–2006 (Bayon, 2009). This strong
unequalizing factor, which is indeed captured in our decomposition exercises, is the
direct result of the adjustment process that followed the 1982 crisis (additionallycomplemented with the high-inflationary period that characterized the 1980s) and which
severely affected total real incomes and their distribution between wages and profits(Lustig, 1992).69 Therefore, in the long run, we consider that the strong and induced69

If adjustment policies (switching and expenditure-reduction mechanisms) have a contractionary
effect on the economy, as they did in Mexico, their impact on real incomes will be more pronounced(Lustig, 1992). Moreover, according to the same author, the costs of the adjustment process in
Mexico were borne by households at the low- and middle-income ranges, leaving the country with arelatively impoverished middle class, an increasing number of poor households, and the poor worse
off than before.
32

depreciation of the minimum salary that took place since the early 1980s, along with thedrastic adjustment policies implemented in Mexico, was one of the main factors
responsible for the high levels of household per capita income inequality that are
observed,70 and the second most unequalizing force after the rates of return to assets
during 1989–2006. After 2000, however, changes in the real value of the minimum
salary were only a minor, but still unequalizing, determinant of the level of inequality inthe country.
On the other hand, it should be noted that a completely different picture emerges fromthe 2000–06 decomposition results. First, it is possible to corroborate that the returns
effect was no longer unequalizing; rather, it constituted the main inequality-reducing
force, accounting for 53% of the inequality decline. Therefore, it can be concluded thatthe recent improvement that occurred along the distribution of income in Mexico was
due to the important contribution of the price effect overall. Moreover, it is also possible
to see that the rest of the equalizing change was the result of the positive impact of theendowments effect with a 47% contribution, and the effect of unobservables, althoughinequality-increasing, was fairly small during the period.
To be more specific, our analysis shows that the recent improvement that took place
along the distribution of total household per-capita income, after more than 15 years ofhigh and increasing inequality in Mexico, was greatly associated with three factors:
education (returns and distribution), financial assets (returns and distribution), and theimprovement in the rates of return to the rural areas and, in particular, the South of thecountry. Our estimation is that they contributed to about 33, 38 and 19%, respectively,of the reduction in inequality during 2000–06.
The 1990s educational expansion and the corresponding decline in the SP between lowand high-skilled workers have been both advanced in the literature as the main reasonsof the contribution of education to the equalization process in Mexico. Additionally, thereduction in the rural-urban income gap was mentioned in Esquivel (2010) as one of thefactors explaining the new income distributional dynamics in Mexico. However, no70

This is consistent with Bosch and Manacorda (2008), who show that the decline in the real valueof the minimum wage in Mexico was able to explain most, if not all, of the rise in urban inequality atthe bottom of the earnings distribution that was observed between the late 1980s and the late 1990s.
In addition, Szekely’s (2005) findings, regarding the positive and close association between
inequality and the level of inflation in Mexico, are clearly supportive of the great unequalizing
impact of the systematically depreciated minimum salary on the distribution of income that is foundin this study.
33

suggestion has been put forward regarding the impact of financial assets or the bettering
returns to living in and to household endowments in the southern region as possible
explanations of the recent decline in inequality in the country.
With respect to the latter of these factors and in sharp contrast with the findings in
Bouillon et al. (2003) for the period 1984–94, it is possible to say that conditions andincome opportunities in the South of Mexico may be finally improving and starting toconverge to those found in the rest of the country. The main reasons for this to be thecase should be related mostly to the educational inequality decline and the SP reduction
previously mentioned, as well as the overall effect that has been brought about by the
Oportunidades (Progresa) program since 1997.
As for the returns to and the distribution of financial assets, which, according to ouranalysis, were the most important determinants, together with education, of theincome-distributional betterment in Mexico in the latter period, we believe that these
temporal economic changes were the result of the economic recessions in Mexico andthe USA in 2001–2003 and the sharp decline of the national risk-free (governmentalbond) rate in the 2000s, which has definitely contributed to the financial-asset andincome equalization in the country. As it was possible to corroborate in this study, thefinancial-asset changes negatively affected the relative income share of the richestclasses in Mexico but, in particular, the one of households at the top decile of theincome distribution.
This latter finding is consistent with Cortes (2003, 2006), who explains that there is apositive relationship between the economic cycle and income inequality in Mexico.
Therefore, right after the 1980s and 1990s crises, the distribution of income in the
country improved significantly, and the same occurred after 2000 during the economic
recession in 2001–2003 (Cortes, 2006).71 Therefore, it may be possible to say that oneof the channels by which crises or recessions yield an equalizing impact on the
distribution of income in Mexico is through lower inequality in the distribution offinancial-asset holdings. However, this inequality-reducing effect persisted through
2004–2006 even though the economy was not in recession during that period. We
assume that this was possible because of the sharp reduction in the national risk-free
71

Cortes (2006) affirms that this economic regularity is given by a reduction in the relative income
share of households above the 90th percentile of the income distribution, but that this situation islater reversed once economic growth is attained, affecting positively the income share of the richest
households in Mexico.
34

rate after 2000,72 which may be regarded as a sign of the poor economic performanceof the Mexican economy and the adverse financial environment in the world.
Thus, we believe that the declining trend in the income inequality in Mexico may bereversed, in the same manner that occurred in the previous decades, once the Mexican
economy recovers and the world exits the financial crisis, unless there are real
(permanent) changes in the structure of the economy that make the distributional gains
from growth more pro-poor (DFID (2004), Ravallion & Chen (2003)).73
With hindsight and according to our findings, among the most important distributional
policies that could yield a doubly-beneficial impact in terms of equity and efficiency
and, consequently, induce some of the required structural changes in Mexico are thoseconcerned with: 1) increasing the educational attainment level of low- andmiddle-income households so that the huge educational gap between those classes andthe rich (see Tables 6 and 10) is gradually eliminated and, consequently, the strong andpositive correlation between poverty and low-educational levels, documented in
Iniguez-Montiel (2011), decreases overtime as well. In this matter, it is also of extreme
importance that the new educational policy incorporates the quality of public education
as one of its main pillars; 2) the further development of the financial system so that thebenefits provided by this important sector could be capitalized upon by all people andfirms in the country. Our study indicates that it is the households at the upper-tail of theincome distribution (particularly those at the top percentile (see Table 4)) the ones who
are mostly able to take advantage of the opportunities available through the financial
market, and this is also true in the case of the private sector, where large firms andcorporations are the main receivers of capital (Moreno-Brid et al., 2005); and 3)
increasing the over 70% depreciated minimum wage, which remains below subsistencelevels and affects mostly individuals at the lower tail of the income and educational
distributions, therefore, contributing to the high levels of inequality (Bosch andManacorda, 2008) and poverty that are observed. 74 Moreover, although indirectly
72

While the average risk-free rate in Mexico (the CETES) was 15.24% in 2000, it declined by 8%
points in 2006 to 7.19%. It is also possible to corroborate that the fall in the CETES occurred since2001, averaging 11.31% and coinciding with the beginning of the 2001–03 economic recession in
the country. Moreover, the CETES declined even further to 7.09, 6.23 and 6.82% in 2002, 2003 and2004 respectively. This information was obtained from the INEGI’s Economic Information Databaseat http://dgcnesyp.inegi.org.mx/bdiesi/bdie.html.
73
Growth is considered to be pro-poor if, in addition to reducing poverty, it decreases inequality as
well (Nissanke & Thorbecke, 2006).
74
It should be noted that the sharp and continuous reduction in the minimum salary, occurring since1978, is the policy that has been followed to control inflation and spur the competitiveness of the35

inferred from our results, our study suggests that policies aimed at developing thehuman-capital of the low-income households, like the Oportunidades and Popular
Health Insurance programs, and increasing the purchasing power of the poor, including
the elderly, through direct monetary transfers contribute greatly to improve the
distribution of income and assets in Mexico and, consequently, to reduce poverty. This
type of polices, therefore, should be strongly encouraged as well.75

7. CONCLUSIONSIn this study, we have analyzed the distribution of household per capita income in
Mexico from 1989 to 2006. By applying a dynamic decomposition methodology, which
is a variant of the micro simulation method in Almeida dos Reis and Paes de Barros(1991) and Bourguignon et al. (2001), we identify the main forces, along with their
specific contributions, which determined the distribution of income in the long run andthat were behind the inequality decline in the 2000s.
Among the determinants associated with the decline, it is possible to mention theimportant equalizing impact of education, financial assets, and the bettering of the
returns to living in and to household endowments in the rural areas and, in particular,
the South of Mexico. Without doubt, educational-related changes, which were caused by
the decline of educational inequality as well as the narrowing skill-premium gap
between low- and high-skilled workers, accounted for the bulk of the inequality
decrease in the long run. Furthermore, our analysis also indicates that the importantequalizing effect of unobservables identified in 1989–2006 is related, in particular, withcountry through the abundance of cheap labor (Lustig (1992), Hernandez-Laos and Velazquez-Roa(2003)).
75
There are two important issues to be considered when implementing this type of policies. Firstly,that some resources are made available to expand the supply of the services and, at least, maintainthe quality of them, so that overall quality is not sacrificed at the expense of the quantity provided.
Secondly, even though these policies are greatly conditional on financial constraints and politicalconsiderations, more effort should be made to increase the amount of money that is transferred to
low-income households so that the impact of the programs on inequality, poverty and economic
growth is more significant. As a reference, it should be noted that Oportunidades, the largest
government cash-transfer program in Mexico to aid the poor, represented about 0.4% of GDP or
1.8% of total government expenditure in 2008 (Scott, 2009), benefiting 5 million households or 26
million people. As documented in Scott (2009), the redistributive efficiency of the Oportunidades
program is remarkable. However, despite its expansion to benefit about 24% of the population since2004 (Levy, 2006), the program still represents a relatively small drop in the ocean of social
spending (Scott, 2009).
36

the Oportunidades program (considering its health, nutrition and income-transfer
components), the Popular Health Insurance program, and migration and remittances.
However, in the years following 2000, the income inequality reduction was primarily
caused by changes in the distribution of and the rates of return to financial assets. We
explain that these temporal changes are related in great part to Mexico’s poor economic
performance after 2000, the 2001–2002 USA recession and the sharp decline of thenational risk-free rate in the 2000s, affecting negatively households at the top decile ofthe income distribution the most. Therefore, the improvement in the distribution offinancial assets in Mexico in 2000–2006 seems to be partly the result of the 2001–2003
economic recession in the country, which is consistent with Cortes (2003, 2006), andprobably of rational decisions on the part of the richest families to allocate their capitalon higher-return investments (like real estate) than the ones available through thefinancial market since the early 2000s.
Two additional comments deserve special attention. First, the rather important
contribution of the endowments effect, either in the long run or the two latter,intermediate periods, suggests that the distribution of income in Mexico could begreatly improved through policies that redistribute assets (like land, health andeducation) to the low-income households so that their earnings potential is increased.
Second, given the net unequalizing impact of the effect of unobservables that was
identified for the period 1994–2006 and considering, in particular, the regressive effectof redistributive spending that was found in Scott (2009) for the period 1992–2008 in
Mexico, we suggest increasing and truthfully committed public-policy intervention inorder to achieve considerable progress in the reduction of the high and persistent
income disparities, which have prevailed in the country for centuries. In this respect, ourfindings indicate that the most important policies for reducing income inequality, as
well as poverty, in Mexico are definitely those related to the development of the humancapital of the low-income households; the promotion of a competitive financial systemthat benefits all members of a society and not only the economic elite; theencouragement of a highly progressive redistributive system that counteracts theexisting market and government failures; and the systematic increase in the over 30-year
depreciated minimum wage, which has adversely affected households at the lower tailof the income distribution.
As suggested in the literature (Ravallion (1997, 2005, 2007), Addison and Cornia37

(2001), Bourguignon (2004), Dagdeviren et al. (2004), Lopez and Serven (2006), Perryet al. (2006), World Bank (2009)), equity should be seen as an important policy
objective for developing countries, especially high-inequality ones, in order to be able
to escape absolute poverty. Moreover, given the growth-enhancing impact of an
improved distribution of income (lower inequality level) that is identified in theliterature (Aghion et al. (1999), Thorbecke and Charumilind (2002), Dagdeviren et al.
(2004) and Culpeper (2005)), which overturns the conventional wisdom that there is atrade-off between equity and efficiency, the implementation of policies to redistributeincome and assets in favor of the poor (low-income households) should be strongly
encouraged (Oxfam (2000), Cornia et al. (2004), Cullity (2004), Dagdeviren et al.
(2004), Culpeper (2005), van der Hoeven (2008)). These important hypotheses are
particularly relevant for the case of Mexico, considering that its highly unequal
distribution of income could be partly blamed for the poor economic performance of the
Mexican economy during the last three decades (Arias et al. (2010), Chiquiar andRamos-Francia (2009), Levy and Walton (2009), OECD (2009)), and additionally, as
corroborated in Iniguez-Montiel (2011), for the rather high levels of poverty that
continue to prevail in the country. Indeed, it seems that a more equitable development
path (World Bank, 2007) is the answer to Mexico’s most pressing, underdevelopment
problems and, therefore, that a moderate- to low-income inequality level should beplaced at the forefront of the country’s political agenda, so that the overriding policy
objectives of poverty reduction (United Nations, 2000) and rapid, equitable(broad-based) growth (World Bank, 1993) can be successfully materialized in theforeseeable future.

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47

TABLE 1
Total Per Capita Income Distribution across Households, 1984-2006
Deciles

1984

1989

1992

1994

1996

1998

2000

2002

2004

2005

2006

I
II
III
IV
V
VI
VII
VIII
IX
X

1.51%
2.62%
3.49%
4.57%
5.77%
7.26%
9.32%
11.87%
16.67%
36.93%

1.27%
2.32%
3.17%
4.08%
5.10%
6.42%
8.07%
10.55%
15.56%
43.47%

1.25%
2.29%
3.17%
4.00%
5.12%
6.50%
8.30%
10.95%
16.26%
42.14%

1.28%
2.24%
3.04%
3.97%
5.02%
6.36%
8.09%
10.77%
15.99%
43.25%

1.34%
2.41%
3.26%
4.19%
5.26%
6.56%
8.27%
10.86%
16.04%
41.80%

1.17%
2.17%
3.14%
4.06%
5.09%
6.38%
8.06%
10.63%
15.57%
43.74%

1.17%
2.20%
3.08%
4.01%
5.10%
6.37%
8.02%
10.75%
15.57%
43.73%

1.42%
2.43%
3.32%
4.25%
5.32%
6.60%
8.35%
11.12%
16.22%
40.98%

1.40%
2.47%
3.35%
4.22%
5.27%
6.53%
8.19%
10.76%
15.58%
42.25%

1.33%
2.43%
3.30%
4.24%
5.30%
6.58%
8.27%
10.84%
15.53%
42.19%

1.47%
2.54%
3.41%
4.27%
5.32%
6.58%
8.27%
10.94%
15.84%
41.36%Gini
Top 10%/Bottom 10%
Top 20%/Bottom 20%
Top 10%/Bottom 40%

0.4882
24.5
13.0
3.0

0.5418
34.2
16.4
4.0

0.5354
33.7
16.5
3.9

0.5430
33.8
16.8
4.1

0.5272
31.2
15.4
3.7

0.5469
37.4
17.8
4.1

0.5464
37.4
17.6
4.2

0.5203
28.9
14.9
3.6

0.5279
30.2
14.9
3.7

0.5282
31.7
15.4
3.7

0.5196
28.1
14.3
3.5

Source : Own calculations based on the ENIGHs.

48

TABLE 2
Income Distribution Changes by Decile (1984-2006)
Income Share (%)

Change in Income Share (%)

Decile

1984

1989

1994

2000

2006

During
84-89

I
II
III
IV
V
VI
VII
VIII
IX
X

1.51
2.62
3.49
4.57
5.77
7.26
9.32
11.87
16.67
36.93

1.27
2.32
3.17
4.08
5.10
6.42
8.07
10.55
15.56
43.47

1.28
2.24
3.04
3.97
5.02
6.36
8.09
10.77
15.99
43.25

1.17
2.20
3.08
4.01
5.10
6.37
8.02
10.75
15.57
43.73

1.47
2.54
3.41
4.27
5.32
6.58
8.27
10.94
15.84
41.36

-15.9
-11.5
-9.2
-10.7
-11.6
-11.6
-13.4
-11.1
-6.7
17.7

Total

100

100

100

100

100

Change in Real Average Income (%)

During89-94

During94-00

During84-00

During00-06

During89-06

During84-89

During89-94

During94-00

During84-00

During00-06

During
89-06

0.8
-3.4
-4.1
-2.7
-1.6
-0.9
0.2
2.1
2.8
-0.5

-8.6
-1.8
1.3
1.0
1.6
0.2
-0.9
-0.2
-2.6
1.1

-22.5
-16.0
-11.7
-12.3
-11.6
-12.3
-13.9
-9.4
-6.6
18.4

25.6
15.5
10.7
6.5
4.3
3.3
3.1
1.8
1.7
-5.4

15.7
9.5
7.6
4.7
4.3
2.5
2.5
3.7
1.8
-4.9

-4.3
-2.7
1.1
2.0
0.9
4.8
2.4
6.5
10.3
45.3

8.7
0.5
0.1
0.5
-3.3
-0.4
0.6
4.2
0.1
7.5

-16.9
-13.1
-12.4
-11.8
-5.6
-10.6
-11.1
-9.6
-13.3
-14.0

-13.46-15.06-11.39
-9.54
-7.89
-6.66
-8.43
0.36
-3.39
34.25

44.0
40.7
42.0
41.0
36.2
34.9
37.0
26.3
37.9
22.4

30.1
22.8
24.5
25.0
24.3
20.1
22.5
19.0
20.8
13.2

15.7

14.1

-13.1

-4.1

24.6

23.5

Source : Own calculations based on the ENIGHs.

49

TABLE 3
REGRESSION RESULTS(with per capita household income as dependent variable)
Number of observations
F testR-squared
Root MSE

Age and Gender
Number of children
2 years old or younger
Number of children
between 3 and 5 years old
Number of children
between 6 and 10 years old
Number of children
between 11 and 13 years old
Number of malebetween 14 and 20 years old
Number of female
between 14 and 20 years old
Number of malebetween 21 and 59 years old
Number of female
between 21 and 59 years old
Number of malebetween 60 and 65 years old
Number of female
between 60 and 65 years old
Number of malebetween 66 and 100 years old
Number of female
between 66 and 100 years old

1989
11530
162.74***0.52910.65532006
20872
159.18***0.57560.59431989
Coefficientt statistic

2006
Coefficientt statistic-0.2091***-10.98-0.2399***-14.44-0.2213***-11.23-0.2456***-14.53-0.1901***-14.49-0.2347***-21.42-0.2061***-12.71-0.2378***-18.31-0.2191***-7.34-0.2552***-9.10-0.3496***-12.28-0.3505***-11.86-0.2058***-6.92-0.2202***-7.72-0.3129***-11.18-0.3309***-11.12-0.2006***-4.84-0.2486***-6.11-0.2789***-6.38-0.2251***-5.15-0.2341***-6.52-0.1655***-5.55-0.2561***-6.96-0.1608***-5.82

Education
Number of male household members between 14 and 65 years old
with some primary

0.05001.58

0.0381

1.21

with primary

0.1490***4.87

0.0783**

2.55

with some junior-high

0.0889***2.74

0.0890***3.60

with junior-high0.1764***5.78

0.1473***

5.17

with some high school

0.1724***4.39

0.1380***

4.33

with secondary education

0.2640***7.43

0.2132***

7.01

with some college

0.3948***8.38

0.3338***10.18

with college

0.6234***13.90

0.4942***

13.95

with postgraduate

1.1345***6.38

0.6641***

11.97

Number of female household members between 14 and 65 years old
with some primary

0.1110***3.79

0.0156

0.46

with primary

0.1676***

5.98

0.0949***3.07

with some junior-high0.1932***4.48

0.1209***4.48

with junior-high

0.2599***8.88

0.1552***

5.29

with some high school

0.2816***6.01

0.2285***

7.23

with secondary education

0.3786***11.34

0.3103***

10.09

with some college

0.3472***6.18

0.3728***

11.42

with college

0.7003***13.05

0.5018***14.06

with postgraduate

1.1779***2.96

0.7654***10.05

Dummy for house ownership

0.2320***9.43

0.1913***

11.81

Dummy for real assets

0.0751**2.01

0.3157***

11.86

Dummy for financial assets

0.4920***7.86

0.7429***20.49

North West region

0.2797***

6.45

0.2164***

9.40

North East region

0.1303***2.94

0.2011***

7.46

North region-0.0414-0.91

0.0295

1.31

Center region-0.0571-1.53

0.0476**

2.15

South region-0.1809**-2.10-0.1056**-2.27

Mexico City

0.1685***4.40

0.2706***

10.48

North West region

0.00290.05-0.1601***-4.28

North East region

0.04380.56-0.1742***-3.81

North region-0.3929***-9.05-0.2571***-7.40

Center region-0.4987***-12.04-0.2575***-7.85

South region-0.5841***-6.99-0.5939***-11.10AssetsRegional Fixed Effects
Urban

Rural

Note : *** Significance at the 1% level, and ** Significance at the 5% level.

50

TABLE 4
Change in Household Characteristics 1989-2006 (Assets)% of Households who own their House% of Households with Real Assets% of Households with Financial Assets

1989

1994

2000

2006

% Change(1989-2006)

1989

1994

2000

2006

% Change(1989-2006)

1989

1994

2000

2006

% Change(1989-2006)

Total

74.33

75.62

74.62

69.36

-6.7%

11.31

5.99

6.29

7.86

-30.5%

2.60

4.07

5.40

5.46

110.0%

Location
Urban
Rural

67.11
87.43

71.47
82.72

69.81
83.11

64.19
79.22

-4.4%
-9.4%

12.88
8.51

6.53
5.07

6.71
5.55

9.07
5.58

-29.6%-34.4%

3.13
1.62

5.24
2.07

6.66
3.16

6.80
2.91

117.3%
79.6%

SouthSouth-Urban
South-Rural

83.55
72.19
91.22

75.17
63.98
82.92

77.81
67.65
84.98

73.04
65.39
79.25

-12.6%-9.4%-13.1%

8.45
11.42
6.62

6.55
8.80
4.99

7.55
10.48
5.48

8.04
10.65
5.93

-4.9%
-6.7%
-10.4%

2.61
4.15
1.56

2.64
3.49
2.05

3.52
5.90
1.84

3.73
5.78
2.06

42.9%
39.3%
32.1%

Deciles
"1-3
"4-7
"8-10
Decile 8
Decile 9
Decile 10
Top 5 Percentile
Top 1 Percentile

77.13
73.41
72.69
70.08
74.61
73.42
73.94
81.14

79.98
72.66
75.81
74.21
74.22
78.63
81.75
82.76

79.64
72.32
73.28
71.26
74.09
74.27
73.69
80.49

66.79
68.81
72.24
72.43
73.34
71.02
72.56
78.51

-13.4%
-6.3%
-0.6%
3.4%
-1.7%
-3.3%
-1.9%
-3.2%

7.76
10.47
16.09
15.52
13.73
18.62
15.36
18.37

2.68
3.96
10.54
7.35
7.52
14.77
17.01
20.68

2.46
5.12
10.62
7.64
9.72
13.76
12.65
15.60

3.19
5.95
14.30
9.13
11.99
21.39
26.28
36.57

-58.9%-43.2%-11.1%-41.2%-12.7%
14.9%
71.1%
99.1%

0.60
1.70
5.87
4.09
4.17
8.93
11.75
22.98

0.51
1.06
9.87
2.82
6.15
19.00
25.08
51.76

0.36
1.24
14.03
3.70
6.34
28.63
39.61
67.15

0.53
1.94
14.23
5.32
9.57
27.13
36.46
53.15

-11.7%
14.1%
142.4%
30.1%
129.5%
203.8%
210.3%
131.3%

Source : Own calculations based on the ENIGHs.

51

TABLE 5
Decomposition of the Sources of Decreasing Household Income Inequality
on Per Capita Income (1989-2006)(% change in the Gini)
Base Year
1989

Base Year
2006

54.18

51.96

100

100

100

-131.93-61.57-96.75-115.33-61.31-88.32

Demographics-67.17-55.06-61.12

Education
Male
Female20.51
42.07-18.77

87.02
78.98
1.82

53.77
60.52
-8.47

AssetsHouse Ownership
Real Assets
Financial Assets-60.86
0.41-25.66-33.79-97.502.49-37.40-64.74-79.181.45-31.53-49.27

Remainder *

-7.81

4.24

-1.79

-22.82

4.78

-9.02

-10.93

12.41

0.74

-25.2314.32-13.97
26.27-19.6020.30

SouthFixed effects
Urban
Rural
Household Characteristics-13.18
-2.15
-0.83
-1.32
-10.60

-6.76
-1.29
-0.11
-1.18
-5.62

-9.97
-1.72
-0.47
-1.25
-8.11

Remainder *

1.29

-0.87

0.21

6.22

-5.05

0.59

97.27

87.08

92.17

135.0280.90

107.96

-0.36

-6.41

-3.38

GINI
TOTAL (I + II + III + IV)
I. Returns Effect

Dr ( X , H )a. Household Characteristics

b. Regional Effects
Regions otherthan the South
Urban
Rural

c. Remainder (a-b) *II. Effect of Unobservables

DH ( r, X )III. Endowments Effect **D X ( r c, H c)
IV. Remainder

DH ( r c, X )  DH ( r, X )Average
1989-2006

Notes: * Calculated as a difference between different factors.
** Calculated as a difference between total change and other components (I, II and IV).

52

TABLE 6YEARS OF SCHOOLING OF ADULTS, TOTAL, BY LOCATION AND BY LEVEL OF HOUSEHOLD INCOME 1989-2006
1989

Total
2006

% Change

1989

Males
2006 % Change

1989

Total

5.86

8.34

42.32

6.58

8.79

33.59

5.22

7.96

52.49

Urban
Rural

7.17
3.46

9.61
5.82

34.03
68.21

8.05
3.90

10.05
6.26

24.84
60.51

6.36
3.10

9.22
5.50

44.97
77.42

South
South-Urban
South-Rural

4.63
6.56
3.29

7.15
9.07
5.54

54.43
38.26
68.39

5.30
7.33
3.91

7.76
9.68
6.16

46.42
32.06
57.54

4.02
5.79
2.74

6.66
8.60
5.06

65.67
48.53
84.67

Bottom 1 Percentile
Bottom 5 Percentile
Deciles 1-3
Deciles 1-5
Deciles 1-9
Deciles 4-7
Deciles 6-9
Deciles 8-10
Deciles 10
Top 5 Percentile
Top 1 Percentile

2.15
2.02
3.18
3.85
5.27
5.57
7.15
9.13
11.03
11.91
12.96

3.00
3.61
5.38
6.10
7.70
7.81
9.53
11.66
13.84
14.87
15.64

39.53
78.71
69.18
58.44
46.11
40.22
33.29
27.71
25.48
24.85
20.68

2.59
2.30
3.62
4.38
5.92
6.27
8.05
10.45
12.58
13.28
14.18

3.57
3.95
5.84
6.51
8.14
8.21
10.07
12.38
14.64
15.68
16.09

37.84
71.74
61.33
48.63
37.50
30.94
25.09
18.47
16.38
18.07
13.47

1.70
1.67
2.76
3.38
4.70
5.03
6.52
8.31
10.06
10.91
12.28

2.62
3.26
5.08
5.84
7.40
7.60
9.27
11.32
13.49
14.33
15.01

54.12
95.21
84.06
72.78
57.45
51.09
42.18
36.22
34.10
31.35
22.23

Source : Own calculations based on the Household Income and Expenditure Surveys by INEGI.

53

Females
2006 % ChangeTABLE 7
PERCENTAGE OF ADULTS BY EDUCATIONAL LEVELNo Education
Some Elementary
Elementary SchoolSome Junior-High
Junior-High SchoolSome High School
High SchoolSome College
Tertiary Education
College Education
Postgraduate

1989

Total
2006 % Change

16.29
28.59
20.77
3.13
10.33
1.50
9.15
3.69
6.56
6.25
0.31

8.73
18.32
19.20
4.11
18.39
4.62
11.06
6.99
8.57
7.07
1.50

-46.4%-35.9%
-7.6%
31.3%
78.0%
208.5%
20.9%
89.4%
30.7%
13.1%
390.3%

1989

Males
2006 % Change

1989

Females
2006 % Change

13.75
28.04
20.38
4.00
9.80
2.12
7.52
5.23
9.15
8.64
0.51

7.03
17.77
18.94
4.53
18.76
4.56
10.39
7.69
10.33
8.41
1.92

18.57
29.09
21.13
2.34
10.80
0.93
10.62
2.30
4.22
4.10
0.12

10.19
18.79
19.42
3.75
18.08
4.68
11.64
6.39
7.06
5.92
1.14

-48.9%-36.6%
-7.1%
13.3%
91.4%
115.1%
38.2%
47.0%
12.9%
-2.7%
274.6%

Source : Own calculations based on the Household Income and Expenditure Surveys by INEGI.

54-45.1%-35.4%
-8.1%
60.3%
67.4%
401.5%
9.6%
177.8%
67.3%
44.4%
850.2%TABLE 8
Decomposition of the Sources of Decreasing Household Income Inequality
on Per Capita Income (2000-2006)(% change in the Gini)
Base Year
2000

Base Year
2006

54.64

51.96

100

100

100

64.45

51.19

57.82

48.65

31.82

40.24

Demographics

-3.91

-2.42

-3.17

Education
Male
Female

26.80
33.86
-6.24

20.49
28.03
-9.48

23.65
30.94
-7.86

AssetsHouse Ownership
Real Assets
Financial Assets

24.16
-3.03
-3.62
30.53

15.89
-2.56
-7.24
25.96

20.03
-2.80
-5.43
28.25

Remainder *

1.60

-2.14

-0.27

15.23

20.21

17.72

-6.06

2.74

-1.66

-18.31
12.05-12.03
14.93-15.17
13.49

South
Fixed effects
Urban
Rural
Household Characteristics

20.37
3.97
1.32
2.65
17.19

18.44
2.66
0.04
2.63
15.05

19.41
3.32
0.68
2.64
16.12

Remainder *

0.92

-0.97

-0.03

0.57

-0.84

-0.14

-9.12

-10.34

-9.73

43.97

58.35

51.16

0.70

0.81

0.75

GINI
TOTAL (I + II + III + IV)
I. Returns Effect

Dr ( X , H )a. Household Characteristics

b. Regional Effects
Regions otherthan the South
Urban
Rural

c. Remainder (a-b) *II. Effect of Unobservables

DH ( r, X )III. Endowments Effect **D X ( r c, H c)
IV. Remainder

DH ( r c, X )  DH ( r, X )Average
2000-2006

Notes: * Calculated as a difference between different factors.
** Calculated as a difference between total change and other components (I, II and IV).

55

TABLE 9
Change in Household Characteristics 2000-2006 (Assets)% of Households who
own their House% of Households with
Real Assets% of Households withFinancial Assets

2000

2006

% Change

2000

2006

% Change

2000

2006

% Change

Total

74.62

69.36

-7.0%

6.29

7.86

25.0%

5.40

5.46

1.1%

Location
Urban
Rural

69.81
83.11

64.19
79.22

-8.1%
-4.7%

6.71
5.55

9.07
5.58

35.2%
0.5%

6.66
3.16

6.80
2.91

2.1%
-7.9%

South
South-Urban
South-Rural

77.81
67.65
84.98

73.04
65.39
79.25

-6.1%
-3.3%
-6.7%

7.55
10.48
5.48

8.04
10.65
5.93

6.5%
1.6%
8.2%

3.52
5.90
1.84

3.73
5.78
2.06

6.0%
-2.0%
12.0%

Deciles
"1-3
"4-7
"8-10
Decile 8
Decile 9
Decile 10
Top 5 Percentile
Top 1 Percentile

79.64
72.32
73.28
71.26
74.09
74.27
73.69
80.49

66.79
68.81
72.24
72.43
73.34
71.02
72.56
78.51

-16.1%
-4.9%
-1.4%
1.6%
-1.0%
-4.4%
-1.5%
-2.5%

2.46
5.12
10.62
7.64
9.72
13.76
12.65
15.60

3.19
5.95
14.30
9.13
11.99
21.39
26.28
36.57

29.7%
16.2%
34.7%
19.5%
23.4%
55.5%
107.7%
134.4%

0.36
1.24
14.03
3.70
6.34
28.63
39.61
67.15

0.53
1.94
14.23
5.32
9.57
27.13
36.46
53.15

47.2%
56.5%
1.4%
43.8%
50.9%
-5.2%
-8.0%
-20.8%

Source : Own calculations based on the Household Income and Expenditure Surveys by INEGI.

56

TABLE 10YEARS OF SCHOOLING OF ADULTS, TOTAL, BY LOCATION AND BY LEVEL OF HOUSEHOLD INCOME (2000-2006)
2000

Total
2006

% Change

2000

Males
2006 % Change

2000

Total

7.36

8.34

13.32

7.95

8.79

10.57

6.87

7.96

15.87

Urban
Rural

8.69
4.99

9.61
5.82

10.59
16.63

9.30
5.50

10.05
6.26

8.06
13.82

8.12
4.63

9.22
5.50

13.55
18.79

South
South-Urban
South-Rural

5.87
8.25
4.17

7.15
9.07
5.54

21.81
9.94
32.85

6.53
9.15
4.72

7.76
9.68
6.16

18.84
5.79
30.51

5.31
7.62
3.71

6.66
8.60
5.06

25.42
12.86
36.39

Bottom 1 Percentile
Bottom 5 Percentile
Deciles 1-3
Deciles 1-5
Deciles 1-9
Deciles 4-7
Deciles 6-9
Deciles 8-10
Deciles 10
Top 5 Percentile
Top 1 Percentile

2.53
2.56
4.12
5.03
6.54
6.85
8.29
10.61
12.85
14.02
14.59

3.00
3.61
5.38
6.10
7.70
7.81
9.53
11.66
13.84
14.87
15.64

18.58
41.02
30.58
21.27
17.74
14.01
14.96
9.90
7.70
6.06
7.20

2.78
2.83
4.50
5.45
7.06
7.49
9.03
11.41
13.84
15.00
15.63

3.57
3.95
5.84
6.51
8.14
8.21
10.07
12.38
14.64
15.68
16.09

28.42
39.58
29.78
19.45
15.30
9.61
11.52
8.50
5.78
4.53
2.94

2.26
2.34
3.85
4.73
6.15
6.47
7.84
10.04
12.07
13.28
13.39

2.62
3.26
5.08
5.84
7.40
7.60
9.27
11.32
13.49
14.33
15.01

15.93
39.32
31.95
23.47
20.33
17.47
18.24
12.75
11.76
7.91
12.10

Source : Own calculations based on the Household Income and Expenditure Surveys by INEGI.

57

Females
2006 % ChangeTABLE 11
PERCENTAGE OF ADULTS BY EDUCATIONAL LEVEL (2000-2006)

No Education
Some Elementary
Elementary SchoolSome Junior-High
Junior-High SchoolSome High School
High SchoolSome College
Tertiary Education
College Education
Postgraduate

2000

Total
2006 % Change

11.26
23.63
20.91
3.69
15.35
2.06
10.71
3.88
8.53
7.76
0.77

8.73
18.32
19.20
4.11
18.39
4.62
11.06
6.99
8.57
7.07
1.50

-22.5%-22.5%
-8.2%
11.4%
19.8%
124.3%
3.3%
80.2%
0.5%
-8.9%
95.4%

2000

Males
2006 % Change

2000

Females
2006 % Change

9.59
22.88
19.94
4.36
15.47
2.84
9.52
4.92
10.47
9.32
1.15

7.03
17.77
18.94
4.53
18.76
4.56
10.39
7.69
10.33
8.41
1.92

12.73
24.28
21.76
3.10
15.24
1.36
11.76
2.96
6.82
6.39
0.43

10.19
18.79
19.42
3.75
18.08
4.68
11.64
6.39
7.06
5.92
1.14

-26.7%-22.3%
-5.0%
3.9%
21.3%
60.6%
9.1%
56.3%
-1.3%
-9.8%
67.1%

Source : Own calculations based on the Household Income and Expenditure Surveys by INEGI.

58-20.0%-22.6%-10.8%
21.0%
18.6%
244.1%-1.0%
115.9%
3.6%-7.4%
166.3%

0.56

Figure 1
Income Inequality in Mexico 1984-2006

0.54

0.52

0.50

0.48

0.46

0.44
1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
Gini CoefficientYear

Source: Own calculations based on the Household Income and Expenditure Surveys by INEGI.

59