WPS4224
Accounting for Mexican Income Inequality
during the 1990s
Rafael E. De Hoyos*
Development Prospects Group
World Bank
Abstract
We implement several inequality decomposition methods to measure the extent to which
total household income disparities can be attributable to sectoral asymmetries and
differences in skill endowments. The results show that at least half of total household
inequality in Mexico is attributable to incomes derived from entrepreneurial activities, an
income source rarely scrutinized in the inequality literature. We show that education
(skills) endowments are unevenly distributed among the Mexican population, with
positive shifts in the market returns to schooling being associated with increases in
inequality. Asymmetries in the allocation of education explain around 20 percent of
overall household income disparities in Mexico during the 1990s. Moreover, the
proportion of inequality attributable to education endowments increases during stable
periods and reduces during the crisis. This pattern is mostly explained by shifts in returns
to schooling rather than changes in the distribution of skills. Applying the same
techniques to decompose within-sector income differences, we find that skill
endowments can account for as much as 25 percent of earnings disparities but as little as
5 percent of dispersion in other income sources.
JEL Classification: D33, F1
Keywords: Inequality, regression-based decomposition, Mexico
World Bank Policy Research Working Paper 4224, May 2007
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the
exchange of ideas about development issues. An objective of the series is to get the findings out quickly,
even if the presentations are less than fully polished. The papers carry the names of the authors and should
be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely
those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors,
or the countries they represent. Policy Research Working Papers are available online at
http://econ.worldbank.org.
*I am grateful to the comments and suggestions made by Paul Kattuman, Ajit Singh,
Jaime Ruiz-Tagle, Hans Timmer, and Adrian Wood. Email address:
rdehoyos@worldbank.org.
1 Introduction
During the 1990s, Mexico experienced profound economic changes. The decade started
with a new economic order placing the market at the center of the development strategy
and reducing the role played by the state. In the mid 1990s, the North American Free
Trade Agreement (NAFTA), a trilateral tariff reduction agreement between Canada,
Mexico and the US, was enacted. The agreement was seen as the starting point of
a long and sustained period of economic growth benefiting the less skilled laborers in
particular. At the end of 1994, the same year when NAFTA was enacted, the Mexican
peso suffered a massive devaluation which triggered the economic crisis of 1995. The
shrinking domestic economy, combined with an expensive foreign currency within the
framework of the new trade agreement turned the Mexican economy into an export-
oriented one. The sectoral composition of the economy changed significantly during
those years (see Figure 3). The proportion of the economy in the non-tradable service
sector grew steadily between 1990 and 1995. Nevertheless, after the peso crisis and
NAFTA, a redistribution equal to 2 points of the GDP occurred with the service sector
shrinking and the manufacturing sector expanding.1
The economic reforms undertaken at the beginning of the 1990s in addition to the
profound economic crisis of 1995 and its subsequent sectoral redistribution, could have
had a significant impact upon income distribution. As it is argued by Sz´ekely (1995),
the market-oriented reforms undertaken in Mexico during the second half of the 1980s
had an adverse effect on household income distribution. Undertaking conventional in-
equality decomposition analysis Sz´ekely (1995) concludes that the inequality-enhancing
impact is explained by the reduction in the government's scope for implementing redis-
tributive policies after the privatization/liberalization of the Mexican economy during
those years. Other studies that use decomposition methods in order to idenitify the fac-
tors that account for Mexican income inequality during the late 1980s and early 1990s
had stressed the importance played by the distribution of skills in reshaping distribution
(Lopez-Acevedo, 2001 and Legovini et al., 2005). Another strand of the literature had
concentrated in the impact that trade liberalization has had on relative wages in the
manufacturing sector. The results of this research point towards a worldwide skilled-
biased technological change as the explanation behind the unfavorable distributional
impact on countries that are relatively abundant in unskilled labor.2
Given the sectoral changes in the Mexican economy and the findings from previous
1The importance of the agricultural sector in GDP remained practically unchanged during this
period.
2See Feenstra and Hanson (1997), Revenga (1997), Wood (1997) and Harrison and Hanson (1999).
2
studies, we argue that a sound decomposition analysis aiming to account for inequality
in Mexico during the 1990s should, at least, consider the following aspects: (1) sectoral
asymmetries, (2) the distribution of skills, and (3) the market rewards for those skills.
By combining orthodox non-parametric inequality decomposition with a more recent
semi-parametric approach, the present study measures the amount of total household
income inequality that can be accounted by these three components.
We revisit the existing methods of inequality decomposition, pointing out their strengths
and pitfalls. Following our methodological discussion, as a first approach, we undertake
orthodox, non-parametric inequality decompositions. The advantage of this method
over other decomposition techniques is that it does not impose any a-priori functional
form on the income generating process. Moreover using the square of the coefficient
of variation as our inequality index, we show that the proportion of inequality that is
attributable to a particular income factor by the orthodox non-parametric decomposi-
tion rule is actually equal to the more integrated Shapley value decomposition method
developed in Shorrocks (1999). The main shortcoming of the orthodox approach is
the lack of economic structure behind the decomposition, which makes its interpre-
tation somewhat difficult. Therefore to complement the non-parametric analysis, we
implement a recent methodology developed by Morduch and Sicular (2002) and Fields
(2003) which combines the traditional inequality decomposition by income factors with
a regression-based estimation determining household income.
Our results show that earnings in the manufacturing and service sectors, together with
incomes from agricultural and informal self-employment activities account for less than
50 percent of total Mexican household income inequality. These leaves more than half
of total inequality without any structural interpretation, opening a mandatory line
for future research. Regarding skill endowments, the regression-based decomposition
shows that this asset can account for, at most, 25 percent of total income dispersion.
We found that returns to schooling have a positive impact on, both, total inequality and
income disparities within-sectors. Moreover, the sectoral asymmetries brought about
by NAFTA and the peso devaluation, made skill endowments a more important factor
contributing--positively--to income inequality in the tradable sectors. These results
corroborate previous findings suggesting that the liberalizing reforms had a positive
effect on relative (skilled/unskilled) wages in the tradable sector (Hanson, 2003).
This paper contributes to our understanding of the relative importance played by the
most relevant factors explaining income disparities during times of liberalizing reforms.3
3As we stated above, although the present study includes the factors that, according to the liter-
ature are the most important determinants of inequality, we can only account for 50 percent of total
3
For instance, a considerably large literature had concentrated its analysis in the impact
of trade reforms on relative wages in the manufacturing sector, using the methodol-
ogy presented in this paper, we can quantify the proportion of total household income
inequality that can be explained by relative wages in the manufacturing sector. Uncov-
ering these issues can inform policy makers on the extent to which household income
inequality can be deemed to be a problem associated with the distribution of skills or
an outcome of sectoral asymmetries. Furthermore, our results can prompt scholars to
move towards a new research agenda where income factors different from relative wages
are used to explore the distributional impact of liberalizing or other types of economic
reforms.
The paper is organized as follows. To give the reader an idea of the macroeconomic con-
text prevailing during the 1990s, in Section 2 we present a brief description of the major
macroeconomic changes which took place in Mexico during that period. In the same
section, we show inequality trends and major changes in tradable and non-tradable
labor markets. A revision and discussion of existing decomposition methodologies is
presented in Section 3. The inequality decomposition results are shown in Section 4.
Finally the conclusions can be found in the last section.
2 Macroeconomic and Inequality Performance
2.1 Macroeconomic Changes
Mexico had suffered from instability and a lack of sustained growth starting from the
debt crisis in 1982 and continuing to the peso crisis of 1994-95. The macroeconomic
performance during those years had ups and downs. Figure 1 shows the rate of growth of
GDP and yearly inflation from 1981 to 2000. During the 1980s there were 2 recessions,
the first one deriving from the debt crisis of 1982 and the second one starting with
the devaluation of the Mexican peso in 1985. From 1989 to 1994 the macroeconomic
performance was stable. Inflation reached a peak of 153 percent in 1988, but by 1994
it was within the one-digit figures. In December 1994 the Mexican peso devaluated
by more than 60 percent against the US dollar. This was the beginning of the 1995
crisis where GDP decreased by almost 8 per cent and inflation jumped to 42 percent.
The last period under analysis (1996-2000) was characterized by a rapid recovery with
GDP growing at an average yearly rate of more than 5 percent and annual price changes
reducing to 7 percent.
household income variation.
4
Figure 1: Macroeconomic Overview
10
150
100
5
50
GDP Prices
in in
0 0
Change
Changes
% -50
%
-5
-100
-10 -150
1980 1985 1990 1995 2000
Year...
GDP Growth Inflation
Data source: Banco de Mexico
Figure 2: External Sector
10
60
8
50
6
dollar
for
40
(X+M)/GDP 4
Pesos
30 2
20 0
1980 1985 1990 1995 2000
Year...
Total Trade Exchange Rate
Data source: World Bank and Banco de Mexico
During the same period major market-oriented reforms took place. In 1985 Mexico
joined the General Agreement on Tariff and Trade (GATT). Between 1985 and 1988,
the average tariff dropped from 25 percent to 11 percent and the coverage of import
licensing decreased from 92.2 percent to 23.3 percent of total tradable goods. The
reduction on trade barriers made Mexican products more competitive in international
markets. In Figure 2 we can see that total trade (measured as the sum of exports plus
imports as the proportion of GDP) increased from 26 percent in 1985 to 36 percent in
1988. The second major trade reform came in 1994 when the North American Trade
Agreement (NAFTA) was enacted. As we mentioned above, in December of that year
5
the Mexican peso suffered a major devaluation making the exporting sector (mostly
the manufacturing one) the most dynamic sector in the economy.4 Between 1994 and
1996 the importance of international trade in the Mexican economy almost doubled,
passing from a pre-crisis/NAFTA level of 38 percent to 63 percent in 1996.
Figure 3: Sectoral Composition
.2
.775
.77
.195
.765
.19
.76
.755 .185
Services/GDP .75
.18
Manufacturing/GDP
.745
.175
.74
.735 .17
1990 1992 1994 1996 1998 2000
Year...
Services Manufacturing
Data source: INEGI
The peso devaluation together with the trade opportunities brought about by NAFTA,
had a significant sectoral redistribution impact on the Mexican economy. The propor-
tion of GDP that was generated in the tradable manufacturing sector experienced an
average annual expansion of 3 percent between 1994 and 1998. By the year 2000 the
manufacturing sector accounted for 20 percent of the economy compared with a ratio
of 17.5 percent in 1994. The counterpart of this increase was a reduction of the same
scale in the proportion of GDP that was generated in the service sector, passing from
77 per cent in 1994 to 75 percent in 2000 (see Figure 3).
2.2 Inequality Levels
The huge macroeconomic turbulence affected microeconomic agents (i.e. households,
firms and individuals) especially through changes in overall income distribution. In this
section, we will describe the distributional changes that took place during the 1990s
and propose possible explanations for them. In the following section, we will quantify
how much inequality is accounted for by these possible explanations.
4Between 1994 and 2000 manufacturing exports accounted for 95 percent of total exports.
6
To compute and decompose total income inequality, we use micro data from household
income surveys (ENIGH) conducted by INEGI, the National Institute of Statistics in
Mexico. ENIGH surveyed more than 11,000 households during years 1989, 1992, 1994,
1996, 1998 and 2000. The survey allows us to identify every individual in the household
and their position within it (head, spouse, etc). We can also learn the source of income
of every member of the household, their occupation, the industry they work in, hours
worked, age, gender and many other variables of interest.
While constructing all statistics and inequality indexes ENIGH's survey design was
taken into account. Therefore all the figures presented in this paper account for
ENIGH's stratification, clustering and expansion factors. Following the literature, our
inequality measures used the household as the unit of analysis (Cowell 2000). Given
the difficulty of identifying intra-household distribution, our preferred income measure
is household per capita incomes hence we assume no intra-household economies of scale
and constant costs across adults and children within the household.5
Table 1: Income Inequality Indexes
1989 1992 1994 1996 1998 2000
Gini 0.518 0.537 0.534 0.515 0.527 0.528
Theil 0.593 0.598 0.568 0.531 0.559 0.548
Entropy( =-1) 0.724 0.769 0.751 0.694 0.796 0.782
Entropy( =2) 2.855 1.655 1.211 1.284 1.369 1.121
Data source: Own calculations with data from ENIGH
Table 1 shows the value of four popular income inequality indexes: the Gini coefficient,
the Theil and the General Entropy with inequality aversion parameter equal to -1 and
2, respectively. Table 1 shows that, although there was a distributional improvement
between 1992 and 1994, we can't conclude anything about inequality changes occur-
ring between 1989 and 1994. Our inference about the distributional changes observed
between 1989 and 1994 depend on the weight given to the different parts of the income
distribution, in other words there is no stochastic- or Lorenz-dominance.6 Surprisingly,
three our of our four inequality measures imply that the severe peso crisis of 1994-95
had a favorable distributive effect. 7 Between 1996 and 1998, there was a consider-
5For a more detailed description of ENIGH's survey design and the methodology followed to con-
struct inequality indexes see De Hoyos (2005a).
6Distribution D1 Lorenz-dominates D2 if and only if all the points in the Lorenz curve correspond-
ing to D1 lie closer to the 45o line than the points corresponding to D2.
7Lopez-Acevedo and Salinas (1999) documented the possible causes behind the reduction in in-
equality during the 1995 economic crisis.
7
able increase in income dispersion (regardless of the inequality measure used) and it
remained like that up to the year 2000. To summarize, distribution was more or less
stable between 1989 and 1994, showing some marginal deterioration; the 1994-95 crises
had a favorable distributive effect which was eliminated during the recovery phase
1996-2000.
Empirical evidence shows that inequality indexes tend to be rather stable over time.
This does not necessarily mean that the underlying elements behind the inequality
measures are stable. To have a closer look at the changes in distribution, in the top
part of Figure 4 we show the percentage change in real average per capita household
income for the different income quintiles. Between 1989 and 1994, the poorest income
cohorts (Q1, Q2 and Q3) did not experience an increase in real incomes as fast as
the one taking place in Q4 and Q5. However, during the crisis period (1994-96), the
richest 20 percent of the population (Q5) were affected more than proportionally by
the negative shock with Q1 being the least affected one. This is the reason why all
inequality measures show a distributional improvement between 1994 and 1996. On
the other hand, between the years 1996 and 1998 the real incomes of the poorest 20
percent (Q1) remained practically constant, unlike the rising real incomes experienced
in all other income cohorts. This explains the documented deterioration in distribution
between 1996 and 1998. Despite the fact that by the year 2000, after a period of
strong growth, Q1 to Q3 had a real income level higher than that observed in 1994,
real income of the `richest' 40 percent of the population remained below the 1994 level.
Given the large proportion of the Mexican population living under poverty, the upper
40 percent of the income distribution actually covers the middle class.8 Trying to
capture heterogenous changes within the upper income cohorts, in the bottom part of
Figure 4 we show income changes for several uppercentiles. We can see that the income
changes of the richest 10 and 5 percent are quite different from what happened at the
top 1 percent of the distribution. Notice that the heterogeneity in performance among
the richest 5 percent of the population is significantly higher than the dissimilarities
among all the population. Between 1989 and 1994, real income of the richest 1 per
cent of the population hardly changes, however, after the 1994-96 crisis, incomes at the
very top of the distribution showed a much more volatile behaviour.
8Taking the poverty lines provided by the Mexican Ministry for Social Development, in the year
2000, 20 percent of the population could not satisfy their nutritional needs; 26 percent did not earn
enough to cover educational and health costs; and 49 percent could not cover transport or housing
costs (see De Hoyos 2005a).
8
Figure 4: % Change in Average Real Income per Percentile (1994=100)
110
(1994=100) 100
Incomes 90
Real
in
80
Change
% 70
1990 1995 2000
Year
Q1 Q2
Q3 Q4
Q5
110
100
(1994=100)
90
Incomes
in
80
Change
%
70
1990 1995 2000
Year
P20 P90
P95 P99
Data source: ENIGH
The persistent high income inequality and the changes registered after the peso crisis
might be explained by sectoral redistributions caused by the 1994-95 macro shocks (see
Figure 3). For instance, real incomes of heads of household working in the agricultural
sector could have been stagnant during the general recovery period of the economy
(1996-2000) and this might explain the increasing gap between the household incomes
of the poorest and richest cohorts during those years. To explore this possibility, let
us define 4 sectors in the economy: the urban manufacturing sector (tradable sector),
urban service sector (non-tradable sector), agricultural sector (rural tradable) and the
informal sector. In Figure 5 we show the average real personal income and the average
years of formal education of heads of household in each sector of the economy.
9
Figure 5: Real Personal Incomes and Formal Education by Sectors
Real Income
5000
4000
incomes
3000
Personal
2000
1000
1990 1995 2000
Year
Manufacturing Service
Agricultural Informal
Education
9
8
7
Schooling
of 6
Years 5
4
1990 1995 2000
Year
Manufacturing Service
Agricultural Informal
Data source: ENIGH
A couple of important points arise from Figure 5. Firstly, average income in the
informal and agricultural sectors (where poverty concentrates) were decreasing even
before the peso crisis. As a matter of fact, during the peso crisis (1994-96) real incomes
of heads of households working in the agricultural sector, the lowest remunerated sector,
decreased proportionally less than the large income reduction experienced by heads of
household working in the relatively better off manufacturing and service sectors. This
helps explain the improvements in distribution after the 1994-96 peso crisis. Secondly,
after the combination of the peso crisis and NAFTA (1994), in the middle of a recession
that caused an increase in the rate of unemployment, the years of education of the
representative laborer increased in all sectors, suggesting that the ones who kept their
jobs were the relatively better educated workers.
10
The descriptive statistics showed the important role played by disparities in sectoral
remunerations and education endowments as possible explanations of the persistently
high levels of household income inequality observed during the 1990s in Mexico. The
relatively stable incomes in the agricultural and informal sectors even in the presence
of a negative shock combined with a more-than-proportional reduction of incomes in
the highest income cohorts explains the reduction in inequality after the 1994-95 peso
crisis. On the other hand, the distribution of education endowments might be the source
behind the post-crisis/NAFTA deterioration in distribution. In order to explore these
hypotheses further, in the next section we will implement conventional non-parametric
decomposition methods to quantify the importance of sectoral disparities in accounting
for overall household income dispersion. Furthermore, we will combine orthodox non-
parametric decomposition by factor components with regression analysis to quantify
the importance of skills distribution and their market contribution in explaining the
high levels of household income inequality observed in Mexico during the 1990s.
3 Methodological Aspects
Let us define Y as a vector containing (1,...,N) household incomes as elements. In
turn, income of household i is defined as the sum of K different household income
components Yi = K
k=1 Yik. Assuming that the income components are mutually ex-
clusive, an income inequality index, I(Y ), measuring household income dispersion can
be defined as the sum of the contribution, Sk(Y ), made by the K different income
k
components:
K
I(Y ) = Sk(Y ) Y = (Y1 ,...,YNk)
k k k (1)
k=1
This type of decomposition can answer the question: what proportion of total income
inequality, I(Y ), is explained by income factor Y ? Several decomposition methods
k
had been developed, ranging from pure non-parametric ones (Shorrocks, 1982) to the
more sophisticated ones based on microeconometric modeling with endogenous behav-
ior (Bourguignon, Fournier and Gurgand, 2001). Shorrocks' (1982) seminal paper shows
that, given a set of desired decomposition properties and under several assumptions,
there is a unique factor decomposition rule. This decomposition rule is independent
of the inequality index used and defines the proportion of total inequality that is at-
tributable to income factor k in the following way:
11
cov(Y ,Y )
sk = k (2)
2(Y )
Where cov(Y ,Y ) is the covariance between total income and income from source k
k
and 2(Y ) is the variance of total income. The main advantage of this non-parametric
technique lies in the absence of assumptions about structural relationships, i.e. no
formal model or econometric estimation is involved. This advantage is, however, the
source of its weakness. In the absence of economic structure very little can be said about
the economic mechanisms driving the results. Two recent studies try to overcome this
problem while keeping Shorrocks' (1982) decomposition principle. Fields (2003) devel-
ops a semi-parametric method combining Shorrocks (1982) technique with regression
analysis. The author shows that income sources, Y , can be analogous to the market
k
value of personal characteristics within a human capital regression framework. For
instance, define the logarithm of income as a function of a matrix of observable charac-
teristics X, a vector of regression parameters, and a set of unobservable components
:
ln(Y ) = g(X,, ) (3)
Say that we are interested in the proportion of total income inequality that is at-
tributable to characteristic k, sk, then after the parameters in equation 3 have been
estimated, sk is defined as (Fields 2003):9
^k (Xk) cor(ln(Y ),Xk)
sk = (4)
[ln(Y )]
Where ^k is the market return to characteristic k estimated from model (3) and
cor(ln(Y ),Xk) is the correlation between the log of income and element k. Based
on a formula used to decompose inequality into different income factors, equation 4
quantifies the proportion of inequality explained by characteristic Xk. The advantage
of a specification like (4) is that we can express proportions sk as a function of both the
9Assume the functional form of equation 3 is given by ln(Y ) = + kXk + , define Yk = ^kXk
and substitute it into equation 2:
cov[ln(Y ),(^kXk)] ^k cov(ln(Y ),Xk)
sk = =
2[ln(Y )] 2[ln(Y )]
^k cor(ln(Y ),Xk) [ln(Y )] (Xk) ^k (Xk) cor(ln(Y ),Xk)
= =
2[ln(Y )] [ln(Y )]
12
distribution of characteristics X and their market reward ^. Fields (2003) shows that
this result applies for any inequality index, however it is only valid for decomposing
inequality of the log of incomes, which makes it a rather unattractive method.10 In a
closely related study, Morduch and Sicular (2002) integrate inequality decomposition
by factor components and population subgroups using a semi-parametric methodol-
ogy. The logic behind this method is similar to the one described by equation 4, with
two major modifications. First, the authors decompose the inequality of household
incomes--as opposed to the log of incomes as was done by Fields (2002). Second, with
the use of an axiomatic result defining the property of uniform additions, the authors
show that the regression-based decomposition formula will vary both with the inequal-
ity index used and the factor decomposition rule used.11 In the particular case where
the inequality measure that is used is the squared coefficient of variation (Entropy( =2)
in Table 1) the natural decomposition rule is equation 2, and therefore the regression-
based decomposition expression is equal to equation 4 but using the incomes in levels
instead of its logarithm (Morduch and Sicular 2002):
^k (Xk) cor(Y ,Xk)
sk = (5)
(Y )
Morduch and Sicular (2002) show that the coefficient of variation's decomposition rule
does not satisfy the property of uniform additions.12
The numerous decomposition rules found in the literature denote the lack of a unified
framework. This lack of consensus had led to several pitfalls in existing techniques.
For instance, the different decomposition rules will impose constraints on the inequality
10The variance of the log of incomes is an inequality index that violates the transfer principle
(Jenkins, 1991).
11Notice that this second aspect was first pointed out in Shorrocks (1982 and 1983). Shorrocks
(1982) shows that given the large range of possible decomposition rules, the contribution assigned to
any factor can be made to take any value from minus to plus infinity.
12Departing from the transfer and the scale invariant axioms, Morduch and Sicular (2002) define
the property of uniform additions as the reduction in an inequality index after a positive transfer of
equal size to each member of the population had occurred. The authors claim that a decomposition
rule will satisfy this property if the proportion of inequality attributable to factor k, sk, is negative
when Y is equally distributed. This property is not satisfied by Shorrocks' (1982) decomposition rule
k
(equation 2) because in such a case cov(Y ,Y ) = 0. However the desirability of such a condition is,
k
at least, debatable. The condition to have sk < 0 under equation 2 is cov(Y ,Y ) < 0. This will be
k
the case of, for instance, well-targeted government anti-poverty transfers, which should and will have
a negative effect upon overall inequality under equation 2. The discussion comes down to the welfare
economics' debate of evaluating the contribution of an income factor by eliminating it versus making
it an equally distributed component.
13
indexes that can be used. Another shortcoming of existing techniques is the unsatisfac-
tory way in which they deal with decompositions of factor components together with
population subgroups (Shorrocks, 1999). Driven by these concerns, Shorrocks (1999)
developed a unified framework for distributional analysis called the Shapley decompo-
sition. To explain the logic behind the Shapley decomposition, let us define f(·) as a
general function determining income distribution:
I(Y ) = f(X1,...,Xm,...,XM) (6)
Where {X,...,XM} is the set of explanatory variables determining income distribu-
tion. Notice that f(·) is a generally defined function that is able to incorporate an
income-generating model like equation 3 and even more sophisticated ones like the one
developed in Bourguignon, Fournier and Gurgand (2001). The amount of total income
inequality that is accounted for by Xm can be estimated via a counterfactual analy-
sis. The counterfactual capturing the contribution of Xm in total income inequality is
the following: what would income distribution look like had factor m been eliminated?
Define income inequality under this counterfactual as I(Y |Xm ř). The contribution
made by factor m to total income inequality is defined as (Shorrocks 1999):
Ck = I(Y ) - I(Y |Xm ř) (7)
The problem with this type of decomposition is that normally M
m=1 Cm = I(Y ),
i.e. the sum of the contributions won't necessarily give an exact decomposition. A
second possibility is to eliminate the M factors in sequences however the contribution
of factor m, Cm, depends on the remaining factors in the counterfactual therefore there
is a `path dependency' problem. In other words, Cm might be different if element
m had been eliminated or not. Shorrocks (1999) deals with this `path dependency'
problem by eliminating Xm in all possible sequences, computing the contribution made
by factor m in each round and finally averaging over all rounds. Define the set of
all possible scenarios given by the eliminating sequences as L = {1,...,M!}. All
possible contributions of factor m are then defined as {Cm,...,Cm }. The Shapley
1 M!
value contribution is computed as:
1 M!
Cm =
Cm (8)
M!
m=1
Equation 8 can be interpreted as the expected contribution of factor m in total in-
equality over all possible elimination paths (Shorrocks 1999). The decomposition rule
14
specified by equation 8 is exact (the sum of the contributions is equal to total inequality)
and treats factors symmetrically.13
The Shapley decomposition can handle satisfactorily decompositions by income factors
and population subgroups given that the underlying model, f(·), had been appropri-
ately specified. In the context of income factor decompositions, equation 8 can be
further simplified. Taking the square of the coefficient of variation (CV ) as our pre-
2
ferred inequality index and defining the underlying model, f(·), simply as the sum
of inequality shares attributed to the different income sources (similar to the model
defined by equation 1), then Shorrocks (1999) shows that the Shapley contribution of
income factor k is equal to:
cov(Y ,Xk)
Ck =
(9)
µ2
And the proportion of total inequality explained by factor k is equal to:
sk = Ck cov(Y ,Y )
= k (10)
CV 2 2(Y )
Equation 10 is exactly the unique decomposition rule proposed in Shorrocks (1982).
This is quite a powerful result because it allows us to interpret the unique decomposition
results (equation 2) as the expected value of the factor's contribution over all possible
scenarios, given that CV is chosen as the income inequality index and equation 1 is
2
the underlying income distribution model.
4 Decomposition Results
4.1 Non-Parametric Approach
Given the advantages and disadvantages of the different decomposition methods, in this
paper we will undertake conventional non-parametric decompositions and complement
them with the semi-parametric approach just described.14
13A point that it is not clear from the Shapley decomposition rule is the way in which interactions
ought to be treated. The basic idea is to compute the average marginal impact of factor k over
all possible eliminating sequences. However, the elimination of, say, Xm will capture part of the
contribution made by, say, Xm if model 6 includes interaction term XmXm .
14The method developed by Bourguignon, Fournier and Gurgand (2001) goes beyond the scope of
this paper. For two recent applications of this kind using Mexican data see: De Hoyos (2005b) and
15
To capture the distributional effects of the sectoral asymmetries described in section
2, let us define household income as the sum of incomes derived from manufacturing
earnings (Y ), other earnings (Y ), incomes from informal activities (Y ), agricul-
m s i
tural incomes (Y ) and other incomes (Y ).15 An alternative approach would have
a o
been to classify heads of households in the different sectors as forming differen popula-
tion subgroups and then undertaking between and within subgroups inequality type of
analysis. Although useful, this approach cannot be combined with the semi-parametric
techniques described in the last section. Therefore in this subsection we treat incomes
derived from the different sectors as different household income sources.16
In Table 2 we show the proportion of inequality explained by each element of household
per capita incomes using Shorrocks' (1982) decomposition rule (equation 2). Or else,
if we are willing to take CV as our preferred inequality measure, we can interpret the
2
results in Table 2 as the Shapley contribution of each factor to total income inequality
within a more general and integrated framework.
Throughout the period, the element explaining the highest proportion of inequality is
Y , accounting for more than 50 per cent of total household income inequality. This
o
is not surprising given the way in which we define household income components. Y o
is basically composed of incomes derived from entrepreneurial activities and the re-
turns to financial assets, where inequality is concentrated. These two elements are
highly correlated with household incomes, specially at the upper tail of the income
distribution. On the other hand, incomes from agricultural and informal activities,
Y and Y , jointly account for at most 4 percent of total income inequality. This
a i
is due to the poor correlation between these income components and total household
income. Between 1989 and 1994, within a growing economy context, the proportion of
inequality attributable to Y decreased and this came together with an increase in the
o
earning components, Y and Y . These changes are the result of the macroeconomic
m s
stability observed during those years, with earnings increasing its covariance with total
household income. This trend was reversed during the post-crisis year of 1996 but then
resumed after 1998. It is important to notice that incomes from agricultural activi-
ties made a negative contribution to total household income inequality during years
1994 and 1996. This shows how households dependent upon incomes from agricultural
Legovini, A., Bouill´on, C. and Lustig, N. (2005)
15 We classify workers in the informal sector when they are non-professional self-employed workers.
We exclude family workers that get no monetary remuneration (See Maloney 1999).
16 This approach is based on a definition of total household income as the sum of earnings, in-
come from self employment activities and an exogenous income. We further divide earnings and
self-employed incomes into earnings in the tradeable sector (manufacturing) and non-tradable sector
(services) and agricultural and informal incomes, respectively.
16
activities were falling down in overall income distribution before the peso crisis.
Table 2: Proportion of Inequality Explained by Factor Components
1989 1992 1994 1996 1998 2000
sm 2.43 4.77 7.35 7.38 4.66 8.58
ss 10.57 16.84 36.84 24.44 25.02 31.76
si 3.15 0.92 3.89 2.74 1.38 3.81
sa 0.33 0.14 -0.13 -0.04 1.21 0.17
so 83.49 77.31 52.04 65.49 67.72 55.65
Source: Own calculations with data from ENIGH
The results in Table 2 can be used to address our main question, i.e. what propor-
tion of total household income inequality can be accounted for by incomes derived in
the different sectors? Incomes derived from service, manufacturing, agricultural and
informal sectors can account for almost half of total household income inequality.17
The proportion of inequality that is attributable to incomes generated in these sectors,
increases in periods of stabilization. Despite the significant sectoral redistribution doc-
umented in section 2, the proportion of inequality that is explained by earnings in the
manufacturing and service sectors, reduced between 1994 and 2000.
The results from Table 2 made apparent how little we actually know about the causes
behind the high levels of Mexican income inequality. Even using complex enough mod-
els able to fully parameterize and account for total variation within income components
Y , Y , Y and Y , we still wouldn't be able to account for more than half of to-
m s i a
tal household income inequality in Mexico during the 1990s.18 Our results show that
much of the income variation is actually concentrated in the highest income cohorts,
where very little is known about their income generating processes. To the best of our
knowledge, there are no models trying to estimate structural relationships capturing
the income generating process occurring at the very top of the distribution. This leaves
more than 50 percent of total income disparities without any structural interpretation,
opening a mandatory line for future research on the causes behind Mexican high levels
of inequality.
17Taking CV as the inequality index, we can assert that this proportion of total inequality explained
2
by sectoral differences is indeed the expected value of their contribution (the Shapley value).
18For example, De Hoyos (2005b) estimates a model that parameterizes income components Y , m
Y , Y incorporating behavior and labor market restrictions. This model would be able to account
s i
for less than half of total income inequality between 1994 and 2000.
17
4.2 Semi-parametric approach
The trade versus relative wages literature and some of the broader decomposition stud-
ies had emphasized the role played by the distribution of skills (education) in reshaping
income distribution. To quantify how much inequality can be accounted for by the dis-
tribution of education (and other personal and household characteristics) we will use
the regression-based decomposition approach explained in Section 3. Taking CV as 2
our inequality measure and hence using equation 5 as our regression-based decomposi-
tion method, let us define the following human capital regression model:
Y = X + (11)
Where X is a (N ×K) matrix containing (K -1) personal and household characteris-
tics plus a constant for N heads of household; is a (K ×1) vector with the `prices' of
those characteristics, and is a vector of random components assumed to be normally
distributed with zero mean.19 The elements forming X include three personal char-
acteristics: years of schooling, years of schooling interacting with a dummy variable
for higher education, experience, experience squared and gender; and three household
characteristics: household size and the ratio of dependent to total household members
and a regional dummy variable for households located in the north of Mexico.20 Equa-
tion 11 is a rather rigid specification imposing a constant `price' of characteristics X
and the same functional form across all sectors of the economy. It also assumes no
labor supply effects on Y . Bearing these constraints in mind, the regression results are
shown in the top part of Table 3. All the variables included in our regression are highly
significant and show the expected sign with the unexpected exception being the dummy
for the gender of the head of household, which turned out to be not-significant. Heads
of household with more education, more than high school level and/or more experience
tend to have higher incomes. At some points in time, particularly during the post-1994
recovery years, households located in the north of Mexico had, on average, higher per
capita household incomes. On the other hand, larger households and especially those
with a high dependency ratio have significantly less per capita incomes compared with
smaller households.
19Notice that since we are relating total household income inequality with personal characteristics
such as education, we are constraint to use only the heads of the household. Nevertheless, the orig-
inal sampling weights are multiply by the household size to preserve a representation of the entire
population.
20In this context, each element Xkk and can be interpreted as an income factor Yk within
Shorrcoks' (1982) framework.
18
Table 3: Regression-Based Decomposition Results
1989 1992 1994 1996 1998 2000
Schooling 158.0 263.0 305.0 168.0 187.0 237.0
Schooling*(H) 100.0 153.0 149.0 114.0 130.0 154.0
Experience 73.0 174.0 131.0 49.0 78.0 128.0
Experience Sq. -0.7 -2.0 -1.2 -0.3 -0.7 -1.2
Gender 1.6 462.5 116.9 206.4 98.3 -86.1
HH size -157.0 -255.0 -245.0 -214.0 -247.0 -324.0
Dep. Ratio -2289.0 -2922.0 -3179.0 -1893.0 -2096.0 -2548.0
Region 256.0 77.0 185.0 170.0 301.0 598.0
Intercept 1630.0 103.0 850.0 1396.0 1159.0 830.0
R-squared 0.102 0.231 0.306 0.280 0.295 0.291
N 10,005 9,181 11,123 12,220 9,430 8,593
% Contribution to total inequality
Schooling 7.2 14.8 24.5 21.1 18.2 21.7
Experience -1.2 -2 -2.9 -2.2 -2 -1.9
Gender 0 0.2 0 0.1 0 0
HH charact. 2.9 7.2 7.8 8.3 6.4 7.4
Region 0.1 0 0 0.1 0.2 0.5
Residual 91 79.8 70.7 72.6 77 72.3
Dependent variable: per capita household incomes
*,** significant at the 5% and 1% level respectively
In the bottom part of Table 3 we show the percentage contribution to total income
inequality (using equation 5 as the decomposition rule) of each element included in
the income equation 11. A story similar to the one told by the Shorrocks (1982) or-
thodox decomposition, or the more general Shapley procedure with inequality index
CV , is shown by the regression-based method. The amount of inequality explained
2
by our regression model increases during the stable period 1989-1994, mainly driven by
the proportion of inequality accounted for by education and household characteristics,
and then it marginally reduces during the crisis years.21 The parallelism between the
non-parametric and semi-parametric results suggest that at least part of the covariance
21The contribution of elements that enter more than one time in the regression equation is simply
the sum of their individual contribution as computed by equation 5.
19
between elements Y , Y , Y , Y and per capita household income Y is given via
m s i a
X. The distribution of education and household characteristics together with their
respective `prices' account for as much as 25 percent and 8 percent of total household
income inequality, respectively. Experience, on the other hand, has a negative, though
small contribution to inequality. Therefore higher rewards to experience help amelio-
rating income disparities. This result is driven by the fact that experience, measured
as age minus years of schooling, is an endowment that is evenly distributed among
the population. As we would have expected from the regression results, given their
lack of significance, the gender and regional dummies do not help to explain much of
the differences in incomes. Indeed, the significance of the elements included in X is
related to their regression-based decomposition rule (equation 5) in a way such that
total inequality explained by elements in equation 11excluding the residualis equal
to the proportion of variance of Y explained by the variance of X, i.e. R2 in Table 3
(Fields, 2003).
From equation 5 we know that the contributions shown in the bottom part of Table 3
are the outcome of the distribution of characteristics X (more precisely, their standard
deviation and correlation with total household income) and their respective market re-
wards, ^. Therefore the documented pattern followed by the contribution to inequality
made by schooling and household characteristics between 1989 and 2000, could be the
outcome of shifts in market returns to those characteristics. From the upper part of
Table 3 we can corroborate that, regarding education endowments, this is indeed the
case. Market returns to schooling (and the premium for higher education) showed a
positive trend between 1989 and 1994, decreased during the crisis and then recovered
between 1996 and 2000. This was exactly the same patterned followed by the pro-
portion of inequality accounted for by education endowments. Therefore much of the
changes in the proportion of inequality that is attributable to education is, indeed, ex-
plained by changes in returns to schooling. These results show that increases in returns
to schooling are inequality-increasing, implying that skill (education) endowments are
a unequally distributed asset.
So far we have shown that differences in education, household characteristics and their
respective market `prices' can account for at most 25 percent of total income inequality
during the 1990s. This is a significant proportion of total income disparities, however
the importance of these characteristics in determining within Y could be different
k
across k. For instance, it might be the case that the distribution of education endow-
ments plays a much more important role in explaining earnings disparities than do
income differences in the informal sector. More important, given the tradeable/non-
tradeable asymmetries occurring during the period, returns to education could have
20
had an unequal effect within the different sectors. To quantify the proportion of total
within-sector inequality that is attributable to differences in education endowments and
other characteristics, let us define income components Y , where k = {m,s,i,a,o}, as
k
a function of Xk, in the following way:
Y = Xkk + k
k (12)
Equation 12 allows for full flexibility in the functional form used in each sector k,
i.e. explanatory variables as well as the value of the parameters can differ across
income factor components. The estimation of equation 12 still involves some restrictive
assumptions though. The conventional human capital equation regresses the log of
hourly wages (or self-employed incomes) against vector X; in our specification, the
dependent variables are the incomes from factor k in levels, Y . Hence each Y
k k
includes a labor supply component.22 Furthermore, while regressing incomes from
agricultural and informal activities (Y , Y ) we are implicitly assuming that their
a i
labor markets are complete and they are free of other production inputs (i.e. we are
assuming separability in their production function). In the case of urban informal
activities, the assumption is justified by empirical evidence supporting the existence of
complete labor markets in the Mexican informal sector (see Maloney 1999). Bearing
these assumptions in mind, we regress income factor components Y against matrix
k
Xk where Xk is defined as in our previous regression estimates (equation 11).
22In a fully parameterized income generating model, ln(Y ) = (kXk+k)Lk|Lk> , where Lk|Lk>
k 0 0
is a labor supply function and (kXk + k) is a function estimating the log of hourly wages. See De
Hoyos (2005b) for an estimation of this kind.
21
Table 4: % Contribution (sk) to Total Within Y Inequality
k
1989 1992 1994 1996 1998 2000
Ym
Education 25.6 25.2 26.1 27.8 22.4 31.4
Experience -4.2 -3.1 -4.1 -1.0 -2.4 -3.3
Gender 0.0 0.1 0.0 0.1 0.2 0.0
HH charact 11.3 9.2 8.4 7.3 9.3 7.6
Region 0.0 0.0 0.3 0.1 0.2 0.0
Residual 67.2 68.6 69.3 65.7 70.3 64.2
Ys
Education 17.1 19.6 23.6 19.5 19.8 14.1
Experience -2.8 -2.1 -2.8 -1.9 -1.7 -1.0
Gender 0.0 0.6 0.0 0.0 0.1 0.0
HH charact 14.8 13.9 9.9 12.1 9.7 8.2
Region 0.0 0.0 0.0 0.1 0.3 0.1
Residual 70.8 68.0 69.4 70.1 71.9 78.7
Yi
Education 2.5 11.3 10.3 7.1 9.0 7.1
Experience 0.8 -1.5 -1.1 -0.2 -0.8 -0.5
Gender 0.2 0.0 0.0 0.2 0.0 0.4
HH charact 7.1 12.6 11.4 11.7 15.9 11.3
Region 0.2 0.0 0.1 0.3 0.8 0.2
Residual 89.3 77.6 79.4 80.9 75.1 81.5
Ya
Education 3.1 1.8 3.2 6.8 27.9 1.6
Experience -0.3 -0.4 -0.5 -0.3 -0.9 0.0
Gender 1.1 0.1 0.1 0.0 2.2 0.0
HH charact 7.9 7.4 12.1 9.0 3.8 6.8
Region 1.9 2.5 2.5 5.1 0.4 3.5
Residual 86.3 88.6 82.6 79.4 66.6 88.1
Yo
Education 2.3 5.1 7.5 6.6 6.0 9.1
Experience -0.4 -0.5 -0.9 -0.7 -0.4 -0.2
Gender 0.0 0.1 0.4 0.3 0.0 0.0
HH charact 0.7 2.5 3.5 3.9 2.7 3.5
Region 0.1 0.0 0.0 0.0 0.0 0.5
Residual 97.3 92.8 89.6 89.9 91.6 87.1
22
The regression results are presented in Tables 1 to 5 in Appendix A. Although there are
some interesting results in the within sector income regressions, given the objective of
this paper, we will center our discussion in their contribution to within-sector inequal-
ity. These results are shown in Table 4. As the human capital theory would predict,
differences in years of formal education are much more closely related to differences
in earnings (both the manufacturing and non-manufacturing earnings sectors, Y and m
Y respectively) than they are to differences in other sources of income. Distribution
s
of education endowments account for around 20 to 25 percent of total earnings in-
equality, whereas the same factor accounts for only around 6 percent of total inequality
within other sectors (Y ). On the other hand, notice how the distribution of household
o
characteristics are relatively more important to determine income differences in the in-
formal and agricultural sectors (Y and Y ). This result suggests that the variables
i a
included as household characteristics represent important factors of production in the
urban informal and in the agricultural sectors. A second interesting result is found in
the regional impact to distribution. Despite the fact that regional differences account
for less than 1 percent of total within-sector inequality in all urban sectors, regional
differences can account for up to 5 percent of total income dispersion within the agri-
cultural sector. This is not a surprising result given the huge differences between rural
areas in the north of Mexico (basically large cattle fields) compared with the south of
the country (small parcels of ejidos or communal agricultural production).
Are the contributions made by the different characteristics to within sector inequalities
explained by their market returns? From Table 4 we can see that the proportion of
within sector inequality explained by education endowments increased between 1989
and 1994 for all sectors. These results are, at least partly, explained by an increase in
the returns to schooling during the same period (see Tables 1 to 5 in Appendix A).
Hence an increase in the returns to schooling have an adverse within-sector distributive
impact in all sectors of the economy. Hence, as it is the case for the whole population,
skill endowments are unevenly distributed assets within sectors.
In 1996, as a result of the economic crisis and NAFTA, the returns to schooling were
lower in all non-tradable sectors, i.e. formal services (Y ), informal sectors (Y ) and
s i
other urban (Y ), hence educational endowments accounted for less inequality in these
o
sectors during that year. However, quite a different pattern is shown in the tradable
sectors: manufacturing (Y ) and agricultural (Y ). Although average returns to
m a
schooling also decreased between 1994 and 1996, the premium for higher education in
these two sectors showed a important increase (see Tables 1 and 4 in Appendix A).
Given that higher education endowments (Schooling*H) are very unequally distributed
among the population, a rise in its market premium was enough to compensate for
23
the reduction in returns to schooling and end up increasing the contribution made
by education to within-tradeable sectors' inequality. The same pattern is somehow
shown in the years following NAFTA and the crisis, with the proportion of inequality
attributable to educational endowments decreasing in the non-tradable sectors and
increasing the tradable ones. This evidence is consistent with the results found by the
trade-relative wages literature and also by other studies undertaking decomposition
analysis.23 Our results show that the sectoral redistribution caused by the combination
of the peso devaluation and the enactment of NAFTA had as a consequence an increase
in the proportion of inequality accounted for by skill endowments in the tradable sector
(manufacturing and agricultural).
5 Summary and Conclusions
The present study analyzes the underlying causes behind the high levels of Mexican
household income inequality during the 1990s. In order to capture the sectoral dis-
parities observed during this period, total household income was divided into income
derived from activities taking place in different sectors of the economy. Our discussion
focused in the importance played by sectoral disparities, skill endowments, and their
market returns to account for total income inequality.
The results from conventional income factor decomposition `a la Shorrocks (1982) shows
how little we actually know about the causes behind the high levels of income inequal-
ity in Mexico. More than 50 percent of total income dispersion is accounted for by
income derived from entrepreneurial activities and returns to financial assets. These
are income sources with few structural interpretations and that nevertheless account
for a large income proportion of household situated at the very top of the income
distribution.
Trying to add more economic structure to the conventional non-parametric decomposi-
tions, we decompose income inequality using the semi-parametric approach developed
by Morduch and Sicular (2002) and Fields (2003). We find that skill endowments
accounted for, at most, 25 percent of total household income distribution in Mexico
during the 1990s. Our results show that the proportion of inequality attributable to
skill endowments increased during periods of macroeconomic stability and economic
growth and reduced during the crisis. This pattern is, to a great extent, explained by
23A literature review on the trade versus relative wages studies can be found in Hanson (2003).
Lopez-Acevedo (2000), De Hoyos (2005b) and Legovini (2005) undertake decompose household income
distribution to analyse the importance of returns to schooling in the trade-inequality debate.
24
shifts in returns to schooling rather than changes in the distribution of skills. Positive
shifts in the market returns to schooling are associated with increases in the propor-
tion of inequality accounted for by skill endowments, hence indicating that skills are
an unevenly distributed asset.
Applying the same semi-parametric model to decompose within-sector income inequal-
ity, we show that skill endowments are also unevenly distributed within sectors, hence
increases in the market results to schooling, have an adverse within-sector distribu-
tional impact. Difference in educational endowments and their market returns account
for as much as 1/4 of total income inequality in the earnings sectors but as little as
5 percent in the non-earnings sectors. The currency crisis of December 1994 together
with the enactment of NAFTA caused a sectoral redistribution favoring the tradable
sectors. This redistribution was associated with lower returns to schooling in the non-
tradable sectors and a higher premium for well-educated workers in the tradable sector.
Given the positive relationship between returns to schooling and within-sector income
inequality, after 1994, skill endowments had a lower contribution to income inequality
in the non-tradable sectors and and a higher in the tradable sectors. These results cor-
roborate the results found by the trade versus relative wages literature, supporting the
view of a skill-biased shift in labor demand brought about by the Mexican liberalizing
reforms of the 1990s.
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27
Appendix
A Regression Results for Household Income Com-
ponents Y k
Table 1: Earnings from Manufacturing, Y m
1989 1992 1994 1996 1998 2000
Schooling 103.6 158.3 203.8 92.5 86.5 159.8
Schooling*(H) 57.6 99.8 80.8 91.7 88.8 124.8
Experience 32.4 51.2 57.2 -19.1 15.7 64.4
Experience Sq. -0.3 -0.4 -0.4 0.5 -0.0 -0.5
Gender -3.5 -246.3 -61.7 -90.5 -165.0 97.0
HH size -121.2 -200.5 -122.5 -111.5 -157.1 -207.9
Dep. Ratio -1213.0 -899.4 -1894.4 -735.9 -871.6 -855.4
Region 147.8 0.1 -238.0 43.1 120.4 -28.4
Intercept 1214.1 1014.6 739.8 1464.1 1441.8 146.2
R-squared 0.324 0.309 0.305 0.342 0.291 0.347
N 1,779 1,525 1,831 2,134 1,681 1,524
*,** significant at the 5% and 1% level respectively
28
Table 2: Earnings from Other Sectors, Y s
1989 1992 1994 1996 1998 2000
Schooling 94.2 142.7 171.2 113.8 106.3 123.5
Schooling*(H) 60.4 74.6 132.5 66.8 91.4 91.5
Experience 34.6 56.2 83.2 12.9 33.9 11.5
Experience Sq. -0.3 -0.7 -0.8 0.0 -0.3 0.1
Gender -50.8 440.9 -235.0 14.2 90.5 -66.7
HH size -131.2 -114.7 -196.7 -128.7 -174.2 -177.6
Dep. Ratio -2711.9 -2537.4 -3298.2 -2269.2 -1872.1 -2868.9
Region -39.8 -61.0 133.7 132.0 210.5 209.7
Intercept 2537.0 1099.9 2049.3 1906.8 1514.2 2575.2
R-squared 0.288 0.313 0.300 0.294 0.281 0.216
N 5,167 4,704 5,602 6,257 4,786 4,462
*,** significant at the 5% and 1% level respectively
Table 3: Income from Informal Activities, Y i
1989 1992 1994 1996 1998 2000
Schooling 65.1 104.5 122.1 57.3 63.4 121.5
Schooling*(H) -21.4 -4.5 30.5 43.0 3.7 9.6
Experience 0.3 15.9 39.8 60.1 14.8 69.8
Experience Sq. -0.1 -0.1 -0.3 -0.8 -0.1 -0.7
Gender -201.1 -106.7 5.0 89.6 1.3 -448.7
HH size -102.9 -127.7 -185.1 -118.6 -109.1 -261.6
Dep. Ratio -1324.0 -596.7 -906.8 -1027.8 -733.7 -950.6
Region 143.0 28.9 95.8 129.2 156.3 157.9
Intercept 2460.9 1127.1 934.6 440.5 962.7 1086.0
R-squared 0.104 0.217 0.202 0.186 0.245 0.173
N 2,034 1,973 3,028 3,190 2,467 1,859
*,** significant at the 5% and 1% level respectively
29
Table 4: Income from Agricultural Activities, Ya
1989 1992 1994 1996 1998 2000
Schooling 50.4 52.8 76.2 17.1 107.1 31.7
Schooling*(H) 38.8 33.0 -1.1 73.8 313.9 42.6
Experience 13.1 20.1 42.0 12.7 33.7 22.6
Experience Sq. -0.2 -0.3 -0.6 -0.1 -0.3 -0.3
Gender 451.9 -196.5 -242.5 0.3 817.7 -19.5
HH size -46.8 -69.0 -67.1 -43.7 -51.2 -74.6
Dep. Ratio -844.5 -610.3 -1372.6 -352.5 -575.0 -651.1
Region 263.1 333.6 381.1 276.7 106.9 470.2
Intercept 496.5 1009.8 1154.6 488.3 -803.6 684.0
R-squared 0.137 0.114 0.174 0.206 0.334 0.119
N 1,940 1,703 2,094 2,138 1,552 1,569
*,** significant at the 5% and 1% level respectively
Table 5: Other Income, Y o
1989 1992 1994 1996 1998 2000
Schooling 80.6 121.7 123.9 73.3 79.0 107.8
Schooling*(H) 87.7 104.0 57.3 46.5 70.8 91.9
Experience 61.5 122.0 50.5 31.0 53.0 102.2
Experience Sq. -0.6 -1.4 -0.3 -0.2 -0.4 -1.0
Gender 19.4 278.6 313.2 203.2 29.7 -9.4
HH size -145.3 -202.9 -181.6 -164.0 -193.4 -257.9
Dep. Ratio 609.4 -745.2 -60.9 385.5 -227.8 199.4
Region 266.6 35.0 25.2 17.7 72.1 450.8
Intercept -843.7 -824.6 -479.6 -259.8 11.4 -1093.6
R-squared 0.030 0.086 0.117 0.102 0.117 0.140
N 6,841 8,774 10,575 11,744 8,955 8,229
*,** significant at the 5% and 1% level respectively
30