CAN EDUCATION EXPLAIN INCOME INEQUALITY CHANGES IN … · decomposition technique to explore the impact of participation decisions and occupational choice, returns and endowments

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CAN EDUCATION EXPLAIN INCOME INEQUALITYCHANGES IN MEXICO?

César Bouillon, Arianna Legovini and Nora Lustig

June 7, 1999

We study the microeconomic determinants of the large increase in inequality experienced by Mexico between1984 and 1994. Using the Mexican National Household Income-Expenditure Surveys, we estimate a fullyspecified model of the labor market including earnings and participation equations. We apply a regression-baseddecomposition technique to explore the impact of participation decisions and occupational choice, returns andendowments effects on the changes in income distribution. We find that education plays a pivotal role in thedetermination of changes in income inequality, and policy should address both the private incentives forinvesting in education and the availability and access to educational facilities. Furthermore, we confirmed ourprevious finding that rural conditions have seriously deteriorated relative to urban conditions, substantiallycontributing to increased income inequality.

JEL Classification D1: Household Behavior and Family Economics; I2: Education; I3: Welfare andPoverty; R2: Urban, Rural and Regional Economics, Household Analysis

This paper is part of the research project "The Microeconomics of Income Distribution Dynamics in East Asia andLatin America", jointly sponsored by the Inter-American Development Bank and the World Bank. We would liketo thank Francois Bourguignon for his invaluable advice and support. We would also like to thank Luis Tejerinafor his research assistance, and Janet Herrlinger for patiently working through the editorial changes.

Nora Lustig is Chief, Arianna Legovini is an economist, and César Bouillon is a consultant, respectively, of thePoverty and Inequality Advisory Unit of the Sustainable Development Department at the Inter-AmericanDevelopment Bank. The views expressed here are those of the authors and do not necessarily reflect those of theInter-American Development Bank or the World Bank. Send comments to: [email protected] [email protected].

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I. INTRODUCTION

In the 1984-94 period, Mexico experienced a sharp increase in both earnings and household

income inequality (Table 1). During the same period, the Mexican economy underwent a series of

structural reforms that may well have affected the observed trends. Among the most salient were

trade and financial liberalization. In mid-1985, more than 90 percent of domestic production was

protected by a system of import licenses with ten tariff levels and with a production-weighted

tariff of 24 percent. Five years later, import licenses had been revoked and the production-

weighted tariff was only slightly above 10 percent (Lustig 1998). Reforms affecting the financial

sector included liberalization of interest rates, the elimination of direct controls on credit, and the

reduction in high reserve requirements for commercial banks (Coorey 1992).

Mexico also experienced important demographic changes. Between 1984 and 1994, female

participation in the labor market grew from 33 to 41 percent, and the proportion of workers in

wage employment increased while that of self-employment declined (Table 2). Falling fertility

rates resulted in a reduction of the average family size (-9%) and dependency ratios (-17%). The

falls were somewhat more accentuated at the bottom of the distribution (Table 3).

This was a period of a significant expansion of the stock of education and marked changes in the

returns to education. Average years of schooling for working individuals rose from 5.6 to 6.9, and

the share of workers with secondary schooling and above rose from 25 to 40 percent (Table 4).

Returns to university-level education increased but those to lower education levels declined.

Indeed, the wage gap between skilled and unskilled workers rose considerably between 1984 and

1994 (Table 5).

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The widening gap in the returns to education is the result of demand and supply factors affecting

the labor market. On the demand side, the rising wage gap has been linked to trade liberalization

and technology. Hanson and Harrison (1995), for example, found that 23 percent of the increase

in relative wages for skilled workers during the period 1986-1990 could be attributed to the

reduction in tariffs and the elimination of import license requirements. Revenga’s (1995) analysis

suggests that employment and wages for unskilled labor are more responsive to reductions in

protection levels than are those for skilled labor, due to concentration in sectors more affected by

liberalization. Tan and Batra (1997) find that investments in technology and export-orientation

have a large impact on the size-wage distributions for skilled workers and a relatively smaller

effect on wages paid to the unskilled. Cragg and Eppelbaum (1996) present evidence on the effect

of skill-biased technical change on the wage premium for skilled workers. Trade liberalization and

technology absorption may thus explain, in part, the observed increases in income inequality via a

change in the rewards to skills as proxied by education. They may also have contributed to

increased segmentation between urban and rural labor markets.

In this paper we examine the link between higher earnings inequality and changing patterns in the

returns to skills as proxied by education and experience. In addition, the analysis looks into the

contribution to increased earnings inequality of supply-side factors such as labor force

participation and occupational choice [pending: as well as changes in the distribution of

endowments such as education and experience]. Finally, the paper examines whether demographic

changes and assortative matching explain the fact the household income inequality increased less

than earning inequality (Table 1).

The exercise is based on the application of a micro-simulation decomposition methodology first

proposed by Almeida dos Reis and Paes de Barros (1991) and Juhn, Murphy, and Pierce (1993) in

the context of earnings equations, and generalized to the household income model by

Bourguignon, Fournier, and Gurgand (1998). Briefly, it consists of estimating a labor market

model at different points in time, and simulating the effect of changes in participation rates, prices,

and endowments on the changes in the distribution of earnings and household income.

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The analysis presented here is an extension of our previous work where we decomposed changes

in distribution using a reduced-form household income regression model (Bouillon, Legovini, and

Lustig, henceforth BLL, 1999). The benefit of using a labor market model, as opposed to a

reduced-form equation, is that the endogenous labor market decisions such as labor force

participation and occupational choice are made explicit. We find that quite significant differences

emerge from the two approaches, in particular, regarding the impact of the returns to education

and regional fixed effects on household income inequality.

The first main result of the analysis points to the importance of education and, in particular,

structural shifts that affect the labor market returns to education, in the determination of income

distribution changes. The majority of earners experienced falling returns to their education, and

the increase in general education was not enough to offset the effect. The implicit fall in the

private incentives to invest in education poses the first policy dilemma: to temper against falling

returns more has to be invested in education not less.

The other result is that developments in the 1984-94 period have led to a decaying position of the

rural areas in absolute terms and relative to their urban counterpart. Here, while trade

liberalization and technological innovation may contribute to overall national growth, they may

have hurt the rural sector position. Maintaining social cohesion and avoiding larger influxes into

the urban areas suggest the need to pursue a pro-rural policy strategy.

II. DATA AND METHODOLOGY

DATA

The analysis relies on the Mexican "National household income and expenditure surveys"

(ENIGH) for the years 1984 and 1994, produced by the National Institute of Statistics,

Geography and Informatics (INEGI). The surveys have national coverage and the sample size is

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4,735 for 1984 and 12,815 for 1994 (households). Income data were adjusted to account for

regional differences in inflation using the consumer price indexes for each region estimated by the

Banco de Mexico.

METHOD

The method proceeds in three steps. First, we estimate a labor market model including labor

participation and occupational choice equations, earnings equations and education endowments

equations for the years 1984 and 1994, assuming market segmentation along gender, urban/rural

location, and wage- and self-employment. Second, we simulate the contribution of “participation

effects,” “price effects,” “population effects,” and “random effects” to the change in the

distribution of individual earnings in each category and overall.1 Third, we aggregate back to the

household level and perform the same decomposition on total household labor and non-labor

income. The decomposition is computed for four income inequality measures: the Gini, and three

measures from the generalized entropy class, i.e. the mean log deviation (E0), the Theil Index

(E1), and the transformed coefficient of variation (E2).

Specification of the labor choice and earnings equations

Labor choice. It is assumed that individuals can choose one of J options: not to participate in the

labor market or to select between wage work, self-employment or multiple occupation

employment. We assume that the decision to participate and the choice of occupation are made in

one step; that the structure of the decision is different for heads of household, spouses, and other

members of the family; and that the decision of rural dwellers will be different from that of urban

ones. We also posit that the reservation wage depends on household potential income, household

size and composition, education level of other household members and household asset

characteristics. To estimate the probability of each labor choice j, we then estimate multinomial

logit equations for each category (i.e. heads of household, spouses, and other members of the

1 We define the “labor choice effect” as the effect of changes in the participation and occupational choice decisions;

“price effect” as the effect of changes in the returns to individual characteristics such as education;“population effect” as the effect of changes in the distribution of individual characteristics; and “randomeffect” as the effect of changes in the distribution of the random component in our earnings equations.

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family, in the urban and rural areas) and in each period (1984 and 1994). Individual i in period t

will select option j whenever the utility of option j is greater than the utility of any other option k,

including inactivity.

jkallforZZobuuobP itktkitkitjtjitjitkitjitj ≠>−−+=>= ..).....0(Pr)(Pr νλνλ (1)

Earnings equations. The assumption made here is that the labor market is segmented along 12

categories of gender, urban/rural location, and wage, self-employment and mixed activity

employment. Because individuals self-select into each type of employment, we estimate Mincer

equations both by OLS and via a Heckman procedure. Due to the instability of the self-selection

parameter ‘?’, only the OLS results are presented here.2

εγββββαεβ ++++++=+= RExpExpEduEduXy .)log( 42

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1 (2)

where:

y

Edu

Exp

= individual monthly income

= years of education

= work experience (age-edu-6)

R = {R1, R2, R3, R5, R9, Rso} regional dummies (excluded category: R4 – Center-West:

Aguascalientes, Colima, Guanajuato, Jalisco, Michoacán)R1 = North-West (Baja California, Baja California Norte, Sinaloa, Sonora, Nayarit)

R2 = North-East (Tamaulipas, Nuevo León)

R3 = North (Coahuila, Chihuahua, San Luís Potosí, Zacatecas, Durango)

R5 = Center (Hidalgo, Querétaro, Tlaxcala, México, Morelos, Puebla)

R9 = Federal District

Rso = Southern region dummy includes South (Tabasco, Veracruz); South-East (Chiapas, Guerrero, Oaxaca); and

South-West (Campeche, Quintana Roo, Yucatán)

2 The ?’s had wide fluctuations in magnitude and even reversed signs from one year to the next depending on the

category

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Decomposition method

This paper applies the simulation methodology that was first proposed by Almeida dos Reis and

Paes de Barros (1991) and Juhn, Murphy and Pierce (1993), and subsequently generalized to

household income data by Bourguignon, Fournier, and Gurgand (1998). We use this methodology

to address the following question: what would have been the distribution of income in 1984 if the

conditions were those prevailing in 1994, and vice versa?

Let the inequality indicator be described by:

},,;;|{ tttltttt ZXyDD ελβ= (3)

where yt is defined as in equation (2). The decomposition consists in estimating the effects on Dt

of changing one or more arguments of D{.}. The labor choice effect is estimated by simulating the

vector of participation by replacing λt (the estimated parameters in the labor choice equations);

the price effect by changing βt (the estimated returns to education, experience, and location in the

earnings equations); the population effect by modifying the structure of endowments (such as

education levels); and the random effect by simulating the distribution of residuals.

Briefly, after estimating the labor choice, earnings and education equations for 1984 and 1994, we

first substitute one by one the parameters of one year into the structure of the other year, and

recompute each time the earnings vectors for all working people, for each of the 12 category of

interest.3 This allows us to estimate the contribution of these parameters to income inequality

within each category. Second, we stack all categories to estimate the contributions of these

parameters to total earnings inequality. Finally, we aggregate individuals back to the household

level and add the household’s non-earnings to estimate the contribution of the parameters to the

household total income inequality.

3 The 12 categories are: wage urban males and females, mixed-employment urban males and females, self-

employed urban males and females, wage rural males and females, mixed-employment rural males andfemales, and self-employed rural males and females.

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Modifying the distribution of the residual terms can be done in several ways: for example, by

randomly drawing error terms for each observation given an assumed distribution, or modifying

the original estimated error terms by assuming a different variance. Juhn et al. (1993) compute

residuals based on the actual income percentile of a household in a particular year and the average

cumulative distribution over time. If we assume a normal distribution, then this is equivalent to

scaling the error terms in one year by the standard deviation of the other year. The latter

procedure was used here.

Simplifying the notation, let D(y) be the income distribution measure of interest, where y = Xβ +

ε, where β are prices, X endowments and ε the error terms. Let λ be the probability of

participating in the labor market. We can then rewrite D(y) as D(β,X,ε, λ). Let y be income in

year 0 and y’ income in year 1. We are interested in explaining the change in income distribution

between year 0 and year 1:

∆ = D(y’) - D(y) = D(β’,X’,ε’, λ’) - D(β,X,ε, λ)

This can be decomposed into:

∆ = [D(β’,X’,ε’ , λ’) - D(β,X’,ε’ , λ’)] + [D(β,X’,ε’, λ’) - D(β,X’,ε, λ’)] + [D(β,X’,ε, λ’) – D(β,X,ε,

λ’)] + [D(β,X,ε, λ’) – D(β,X,ε,, λ)]

which can be expressed as:

∆ = β(X’,ε’, λ’) + ε (β,X’, λ’) + X(β,ε, λ’)+ λ(β,X, ε)

= λ(β’,X’,ε ’)+ β(X’,ε’ ,λ’)+ ε(β’,X’,λ’)+ X(β,ε, λ’)+ [ε (β,X’) - ε (β’,X’)]+ [λ(β,X,ε)- λ(β’,X’,ε ’)]

Or alternatively:

∆ = λ(β,X,ε)]+ β(X,ε ,λ)+ ε(β,X,λ)+ X(β’,ε ’, λ)+ [ε (β’,X) - ε (β,X)]+ [λ(β’,X’,ε ’)- λ(β,X,ε)]

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This says that the total change in the distribution of y can be expressed as the sum of the effect

due to participation changes, price changes, and change in the distribution of the errors – given

final (initial) conditions – and the effect of changes in endowments –given initial (final) conditions.

The remainder term in square brackets accounts for some differences in the error and participation

effects due to the weights used to evaluate them. Figure 1 traces the steps of the decomposition

exercise.

III. LABOR CHOICE AND EARNINGS EQUATIONS

LABOR CHOICE EQUATIONS

The results of the labor choice equations reveal that education tends to increase the probability of

choosing the wage sector, and experience that of choosing self-employment (see Tables A1 in

Annex). This is not surprising since the wage sector generally requires formal studies while self-

employment may reward equally learning-by-doing. Living in the South tends to increase the

probability of choosing self-employment, and living in Mexico City, that of choosing the wage

sector. This is probably due to the correlation with income levels and with the relative availability

of formal employment in the two areas.

The main change between 1984 and 1994 is an increase in the probability of choosing the wage

sector over self-employment.

EARNINGS EQUATIONS

The estimates for the earnings equations are presented in Tables A2 in the Annex.

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Returns to Education

As observed in other economies, the Mexican wage skill-gap, as measured by the returns to

education, has widened in the 1984-94 period. The curvature of the returns to education functions

has increased (i.e. the function is more convex) and there has been a decrease in the returns to low

and medium levels of education and a rise in the returns to high education4.

The change in the returns’ structure only benefits highly educated individuals. In the urban areas,

depending on the category, only individuals with at least 12 and as many as 19 years of education

benefit from the change. In the rural areas, where education levels are lower, the cut-off points

range between 4 and 16 years of education. These results confirm those of Cragg and Epelbaum

(1996) and are consistent with the hypothesis that labor demand may have become more skill-

biased.

Returns to Experience

The returns to experience do not show a clear pattern. The returns to urban male and female

experience tend to fall for all experience levels and sectors of employment. In the rural areas,

returns of males decrease in the wage sector, and increase among the self-employed. Those of

females increase in the wage sector, and decrease among the self-employed.

The results may be somewhat spurious since experience is not measured directly, but imputed as

age minus education minus six. This tends to overstate experience particularly for females and

thus underestimate the returns to it, which in part explains the higher returns to experience for

males. The measurement problem may also differ in the two years of reference, due to changes in

the patterns of participation behavior.

Regional Effects

Our results confirm the finding that the Southern region has substantially fallen behind the rest of

the country (BLL 1999). In all 12 categories the fixed effect associated with the South has

4 Indeed, the quadratic term is highly significant in 1994 in almost all regressions whereas it was hardly so in

1984.

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deteriorated. Indeed, in 1984, the South fixed effect was not statistically significant at any

reasonable confidence level. To the contrary, in 1994, in all but 2 categories the negative effects

are significant at the 95% level or above.

IV. DECOMPOSING CHANGES IN INCOME INEQUALITY

As measured by per capita income, income inequality increased substantially in the 1984-94

period. The Gini coefficient, the mean log deviation (E0), the Theil Index (E1), and the

transformed coefficient of variation (E2) increase, respectively by 12, 26, 32 and 49 percent

(Table 1). While these increases are already quite large, they somewhat underestimate the “true”

change in inequality. This is because, given the larger size of poor families, per capita income

measures tend to underestimate income at the bottom of the distribution where the economies of

scale are greater. This overestimates inequality levels and underestimates inequality changes5.

As for their relative size, because E0 weighs more the observations at the bottom of the

distribution, E1 weighs each observation equally, and E2 weighs the top observations more heavily,

we can conjecture that it is the top of the distribution which is responsible, to a greater extent, for

the increase in inequality.6 Given the correlation between income and education, this is consistent

with the changes in the distribution of returns to high levels of education we have observed.

Also, the drop in fertility has affected larger and poorer families somewhat more than smaller and

richer families7, which implies that income per capita measures may underestimate income of the

5 This is clear when a common upper bound exists such that the two measures must converge to it. Such is the case

for the Gini coefficient.6 The degree of inequality aversion η equals 1-θ, where θ is the entropy parameter. When η=0 individuals are

awarded equal weights. When η>0 individuals are awarded weights that are decreasing in income (1%increase in income implies η% decrease in the weight). When η<0 individuals are awarded weights whichare increasing in income. This implies that E0 weighs more the observations at the bottom of the distribution,E1 weighs each observation equally, and E2 weighs more the observations at the top of the distribution.

7 Average family size declined by 10 percent in the bottom 3 deciles and by 6 percent in the top 3 deciles.

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bottom of the distribution less in 1994 than they did in 1984 (Table 3). By itself, this movement

would contribute to increased equality from the bottom of the distribution. In other words, the

fact that the bottom contributes less to higher income inequality may in part reflect the use of per

capita rather than adult equivalent unit measures.

Next we turn to reporting our results on the decomposition of income inequality changes in each

labor category, overall for all earners and for households. The decompositions are computed

across the whole distribution of incomes by isolating the effects of labor choice, returns to specific

endowments, endowments overall and specifically for education, and residuals. The

decomposition at the individual level is computed by simulating changes in each labor category

and aggregating back to the overall labor market. The decomposition at the household level is

obtained simulating changes at the individual level and then aggregating individuals back to the

household level. This procedure is quite distinct from the one used in BLL (1999) where the

simulation was done directly on total household income in per capita terms. Because of the

aggregation, it should be recalled that while the entropy class measures are decomposable, the

Gini coefficient is not, in the sense that although inequality may increase in every subgroup, the

Gini may still report a decrease overall and vice versa.8

In each case, we obtain three sets of results: one which consist in simulating the contributions of

different factors using 1984 as the base year; the second by using 1994 as the base year; and the

third by averaging the two previous results. The first two represent the upper and lower bounds of

the contributions of changes in various factors to changes in overall inequality, assuming

monotonicity of the decomposition relative to changes in those factors.9 We present here average

results for simplicity of exposition.

DECOMPOSITION OF THE CHANGES IN EARNINGS INEQUALITY BY CATEGORY

In this section, we briefly investigate some of the characteristics of changes in income distribution

8 The generalized entropy measures are the only ones that satisfy simultaneously the properties of weak and strong

principle of transfers, decomposability, scale independence and the population principle. The Gini coefficient,does not satisfy decomposability, meaning that, although inequality may increase in every subgroup, the Ginimay still report a decrease overall and viceversa.

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within each of the 12 labor market categories in which we have divided our sample, i.e., by

gender, urban/rural location and wage, self-employment and mixed sectors of activity. Within

earnings inequality explains the bulk of total earnings inequality. We estimate that it accounts for

83 to 91 percent of overall earnings inequality in 1984 and for 76 to 93 percent in 1994

(depending of the entropy measure used).

The first result of this exercise is that inequality has increased in almost every category (Table 7).

It increased the most among wage urban males and females, while it declined for wage rural males

and females. Increases are consistently more marked for measures giving extra weight to the top

of the distribution, suggesting that it is the higher quantile that is taking distance from the middle

and lower ones.

Second, returns to education substantially contribute to increased inequality in the urban

categories while they tend to be equalizing in the rural categories. Indeed, return to education in

the urban categories are markedly more convex and the change tend to benefit highly educated

individuals only (which is consistent with the top getting further ahead) (Table 8). The rising

urban skill premium reflects the fact that the demand for high skill individuals may have outpaced

supply.

Third, endowments have greater unequalizing effects for female categories than for males (Table

8). One reason behind this may be that women have less of an opportunity to relocate

geographically than men have. Also, due to the maturing of female participation in the workforce,

the variance of female experience may have gone up in the period.

DECOMPOSITION OF CHANGES IN OVERALL EARNINGS INEQUALITY

To address the question of what lies behind the observed increase in overall earnings inequality we

combine earners in the twelve categories and observe the overall effect of the different factors

across the various measures of inequality. The effect of labor participation and occupational

choice is ambiguous (-1 to 27% depending on the measure), the effect of all returns combined is

9 We have tested monotonicity by calculating the so-called endowment effect using average 1984-1994 returns.

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unequalizing (31-43%), and the endowment effect is strong and unequalizing (49-61%). The

error terms effect, which is small and ambiguous (-4 to 6%) measures the effect of all the

unobservables in terms of choice, returns and endowments combined (Table 9).

The decomposition revealed once more the powerful impact of education in explaining rising

inequality. The changes in the returns to education are highly unequalizing. This effect reflects the

increased convexity of returns to education we found for almost every category: returns to lower

education fell while returns to higher education increased. This convexification –together with the

fact that the lowest decile lagged severely behind in the gains in education (5% increase in years

of schooling) and that the top decile (28%) did better than the average (24%) (Table 10)–

explains why the first decile suffered the largest loss in real labor earnings (-23%) and the top

decile secured the largest gain (53%) (Table 11).

Of the same order of magnitude is the effect of the deteriorating conditions in the rural areas on

earnings inequality (Table 9a). To investigate this effect, we estimate earnings equations for urban

and rural earners together (but separated by gender and activity) yet leaving all rural coefficients

unrestricted. Although this is identical to regressing rural and urban data separately with the

exception of the common error term, it does allow us to separate the marginal effect of returns to

rural endowments from the overall returns’ effect in the decomposition. The results confirm the

importance of the deterioration of conditions in the rural areas relative to those in urban areas in

explaining increased income inequality. In particular, the fixed effect of living in rural areas,

adjusted to keep mean income unchanged, has a large unequalizing effect (30-41%), which is only

partially counterbalanced by the convergence of urban/rural returns to education and experience.

The result is consistent with the fact that income inequality between rural and urban areas has

increased.

The labor market choice effect is slightly equalizing at the bottom of the distribution, and highly

unequalizing at the top of the distribution (Table 9). This means that those who lose because of

the fall in returns to lower education work more to make up for the downfall, and those who

gained because of increased returns to higher education respond to the increased incentives by

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also working more.10 The result should be treated with care, because our multinomial logit has

small predictive power. While we estimate participation at the top accurately, we tend to

overestimate participation at the bottom of the distribution (Figure 5). For this reason, we may be

overstating the contribution of the participation effect at the bottom.

When the participation effect is decomposed by gender (Table 9), we find that the male

component is equalizing (except when we weigh more the top of the distribution) while the female

one unequalizing. The equalizing effect of male labor market choices derives from the shift

towards wage labor and away from self-employment and the mixed sector (Table 2). The shift is

equalizing because the average wage-sector premium (over other sectors) fell during this period

(Table 12). This equalizing effect occurred despite the fact that within-group inequality for wage-

earners rose substantially. For females, the increased participation in the labor force was not

uniform across the distribution. Particularly, the relatively larger increase in participation in the

top two deciles has an unequalizing effect (Table 13), which is consistent with the fact that it is

E2, the indicator most sensitive to changes at the top, that records the largest unequalizing effect

of female labor choice (Table 7).

Finally, the endowment effect is large and unequalizing. Changes in the distribution of education

endowments cannot explain this result since the overall distribution of education has improved in

the period. For example, the Gini coefficient for education at the national level fell from 42 to 37

percent between 1984 and 1994.11

The distribution of experience, on the other hand, has become more unequal and can partly

explain the unequalizing effect of endowments. For example, the Gini of years of experience

increased from 39 to 41 percent. Remembering that we do not measure experience directly but

calculate it as the difference between age and education, increased inequality in the distribution of

experience and decreased inequality in the distribution of education imply that the correlation

10 Working more here may mean either increased labor force participation, engage in a second occupation or

switching between wage and self-employment to increase earnings.11 Other measures of inequality follow the same trend.

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between age cohorts and education levels is becoming more negative. This implies that, in 1994,

younger cohorts, in terms of experience, resemble less older ones than was the case in 1984, thus

tending to increase income inequality. The result is particularly strong in the rural areas where

because of the greater economic hardship and falling real incomes, there may be other forces at

work. For example, women with little or no experience may have been induced to enter the

workforce and older people with many years of experience may have been induced to continue

working, thus increasing the spread of years of experience.

Together with experience, regional effects may also have contributed to the unequalizing

endowment effect through interregional migration. This could be the case, for example, if

individuals with better earnings potential were moving from the regions that have fared badly (e.g.

the Southern part of the country) in terms of returns to skills to the regions that have done

relatively well, (e.g. the northern border areas) thus increasing further their earnings. Migration

patterns seem to confirm this supposition. Attracted by greater opportunities and lower

unemployment rates, people migrated from the center and south of the country to the faster

growing northern regions. Thirty percent of the population in the northern border regions were

estimated to be immigrants from other regions in the 1990 census, and urbanization rates in the

northern cities far outpaced that of other major urban centers (Anguiano 1998).

DECOMPOSITION OF THE CHANGES IN HOUSEHOLD INCOME INEQUALITY

The decomposition of the change in household income inequality in the 1984-94 period is robust

to the choice of indicator, and is consistent overall with the decomposition of earnings inequality

(Table 14). Labor participation and occupational choice have a small equalizing effect (-1 to -9%

depending on the inequality measure), returns to the endowments a strong unequalizing effect

(30-41%) particularly at the bottom of the distribution; endowments an even stronger

unequalizing effect (56-83%); and errors a mild equalizing effect (0 to -7%).

The first result to underscore is that labor market participation and occupational choice have a

greater equalizing effect on household income than on individual earnings. This suggests that the

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labor decisions of family members are not independent, but respond to changes in the incomes of

others in the household. In particular, female participation is less unequalizing for household

income than for earnings. This implies that, while, in general, female labor market decisions have

increased income inequality, these decisions have not been made independently of household

conditions. For example, when the head of household’s earnings have increased more than the

average, spouses have decreased their participation. Indeed, for the top decile, the correlation

between the head’s earnings and spouse’s participation has become more negative in 1994 than in

1984, meaning that spouses are even less likely to work in 1994 as the head’s income increases

(Table 15). This movement partially offsets the higher returns in the top decile and lowers overall

household income inequality relative to earnings inequality.

Endowments become somewhat more important in explaining changes in household income

inequality than earnings inequality. This may be for various reasons. One is that the endowment

effect in the case of the household includes the distributional effects of non-earnings (not modeled

here). These are unequalizing because the correlation between labor and non-labor per capita

income almost doubles from 16 percent in 1984 to 27 percent in 1994.12 Since labor inequality has

increased, so has non-labor income inequality, leading to more household income inequality.

Second, we observe an increase in the matching behavior of couples. For example, the correlation

between the labor earnings of husbands and their wives increased significantly between 1984 and

1994. This suggests that the matching of endowments has also increased reinforcing the

endowment effect.

Finally, at the household level, unobservables turn equalizing. This means that the households at

the bottom may have been able to respond more to changing market conditions than those at the

top. This is consistent with the observation that the dependency ratio composition has fallen more

for low-income than upper-income families.

12 Author’s calculations.

18

A comparison with the reduced household income specification model

The differences with the results reported in BLL (1999) point to the importance of model

specification. When comparing the decomposition based on the reduced-form household income

model with that obtained from the one presented here, we find three significant differences. First,

with the fully specified model, endowments gain in importance in explaining changes in inequality.

Second, the returns to education become less unequalizing. Third, the regional effect,

unequalizing in the reduced form model, turns equalizing (Table 16).

That endowments now explain more of the change in inequality is, at least in part, due to the fact

that endowments do not mean the same thing in the two estimations. As already mentioned, the

endowment component here includes the effect of changes in the distribution of non-labor income,

which is found to be unequalizing because of its increased correlation with earnings. In the

reduced-form decomposition that effect is spread across both endowments and returns.

Education returns explain substantially less of the change in inequality than they did in the

reduced-form household income decomposition. This may simply reflect the restrictions imposed

in the reduced-form model, in particular that returns to education are the same for urban and

rural, and wage and non-wage workers. There, by construction, the returns to education capture

the overall unequalizing rural/urban effects.

As per the equalizing regional effect, this seems to contradict the finding in BLL (1999) where a

rising regional gap accounted for a large portion of increased household income inequality. There,

however, it is the rural component of the regional effect that explains the largest portion of the

change, meaning that between 1984 and 1994, Mexico experienced a polarization between the

conditions in urban and rural areas. When the urban/rural effect is explicitly captured (Table 14a)

the difference disappears. Indeed, rural coefficients in the fully specified model explain about 18

percent of the changes in the Gini similar to the 17 percent rural effect captured in BLL (1999).

19

V. CONCLUSION

Education has clearly played a major role in determining the increase in both earnings and

household income inequality. As returns fell, only the substantial increase in average education

could stave off falls in real income. In the bottom decile, where average education did not

increase, real incomes plummeted. At the same time, the drop in returns directly implies a fall in

private incentives to invest in education. This consideration presents some important policy

questions. First, why was the bottom decile excluded from average gains in education? Is this a

cohort effect or is access to education denied to the poorer and, possibly, more isolated sectors of

the population? Second, are the private returns to education underestimating the true social value

of education, and should policy aim at increasing the incentives for private investment in

education? As for the first question, there are a number of isolated and very poor communities

that have little or no access to educational facilities. Some effort should go into increasing the

supply of educational services. As for the second, the benefits of more education in terms of, for

example, poverty reduction, lower fertility, and social cohesion, have been recorded elsewhere

(Behrman ?). The need for generating adequate private incentives for families to invest in their

children’s education, particularly for low-income families, cannot be overstated. In this context,

programs like PROGRESA, which provides monetary incentives to poor families to invest in

health and education, appear to be well focused in addressing the problem. More effort, though,

should be expended on the supply side to ensure the availability of schooling facilities.

The divergence of conditions in the rural areas relative to the urban areas and the absolute fall in

rural real incomes are an important source of inequality. Rural poverty should be addressed

directly with policies aimed at expanding opportunities, particularly in non-agricultural activities.

Components of such policy may be the need to resolve coordination failures in the production and

export of local manufacturing and food processing, development of incentives for, and

partnerships with, the private sector for investing in rural areas, and technical training of rural

populations to respond to private sector demands. The failure to address rural poverty will only

increase the incentives to migration and urbanization, and worsen the already pressing problems

20

of overcrowding and urban violence. The process of economic integration with the United States,

which is indeed contributing to the “industrialization” of rural areas in the northern regions, may

well be part of such policy.

21

References

Anguiano Téllez, María Eugenia. 1998. Migración a la frontera norte de México y su relación conel mercado de trabajo regional. Working paper, El Colegio de la Frontera Norte, Mexico.

Almeida dos Reis, J. and R. Paes de Barros. 1991. Wage Inequality and the Distribution ofEducation: A Study of the Evolution of Regional Differences in Inequality in Metropolitan Brazil.Journal of Development Economics, Vol. 36, pp. 117-43.

Bouillon, César, Arianna Legovini and Nora Lustig. 1999. Rising Inequality in Mexico: Returnsto Household Characteristics and the “Chiapas Effect”. Mimeo, The Inter-American DevelopmentBank.

Bourguignon, François, Francisco Ferreira and Nora Lustig. 1998. The Microeconomics ofIncome Distribution Dynamics in East Asia and Latin America. IDB-World Bank ResearchProposal.

Bourguignon, François, Martin Fournier, and Marc Gurgand. 1998. Labor Incomes and LaborSupply in the Course of Taiwan’s Development, 1979-1994. Mimeo.

Coorey. S. 1992. Financial Liberalization and Reform in Mexico, in Mexico: The Strategy toAchieve Sustainable Economic Growth, C. Loser, and E. Kalter (eds.), International MonetaryFund, Washington D.C.

Cragg, Michael Ian and Mario Epelbaum. 1996. Why has wage dispersion grown in Mexico? Is itthe incidence of reforms or the growing demand for skills?. Journal of Development Economics,Vol. 51, pp. 99-116.

Cruz Piñeiro, Rodolfo. 1997. "Situación demográfica de la frontera norte". El Colegio de laFrontera Norte, Tijuana, mimeo.

Gomez de León, José. 1999. Correlative Dimensions of Poverty in Mexico: Elements forTargeting Social Programs. Latin American and Caribbean Economic Association. Mimeo

Hanson, G., and Harrison A 1995. Trade, Technology and Wage Inequality in Mexico. NationalBureau of Economic Research (NBER) Working Paper No. 5110, May.

Juhn, Chihui, Kevin Murphy and Brooks Pierce. 1993. Wage inequality and the rise in returns toskill. Journal of Political Economy, Vol. 3 no. 3, pp. 410-448.

Lustig, Nora 1998. Mexico: The Remaking of an Economy. Brookings Institution Press,Washington, D.C.

Lustig, Nora and Miguel Székely. 1998. Economic Trends, Poverty and Inequality in Mexico.Mimeo, The Inter-American Development Bank, Washington, D.C..

22

Revenga, A. (1995). Employment and Wage Effects of Trade Liberalization: The case of MexicanManufacturing. Mimeo, The World Bank, Washington, D.C.

Tan H. and Batra G. (1997). Technology and Firm Size Wage Differentials in Colombia, Mexico,and Taiwan (China). World Bank Economic Review 11 59-83.

World Bank. 1996. Mexico: Rural Poverty. Report 15058-ME, September 30.

23

VII. TABLES

24

Table 1

1984 1994Percentage

changeLabor earnings inequality

Gini 49.14 57.26 16.53Mean log deviation (E0) 52.92 71.37 34.85Theil (E1) 45.13 68.75 52.33Modified coefficient of variation (E2) 76.15 192.94 153.36

Household per capita income inequalityGini 49.14 54.91 11.74Mean log deviation (E0) 42.83 54.05 26.21Theil (E1) 45.57 60.17 32.04Modified coefficient of variation (E2) 87.47 130.09 48.73

Inequality

Source: Authors' calculations using the Household Income and Expenditure Surveys for 1984 and 1994 (INEGI).

Table 2Composition of Employment

1984 1994 1984 1994 1984 1994

Participation (*) 92.88 92.56 33.28 41.27 62.12 66.11

OccupationWage 54.46 63.01 58.10 60.05 55.47 62.05Mixed 11.45 5.96 2.74 2.12 9.04 4.72Self Employed 34.09 31.04 39.17 37.83 35.50 33.22


Male Female Total

* Participation is defined as the ratio of working individuals to working-age individuals (15-65 years of age). The unemployed are counted as non-participants for the purpose of our analysis.

25

Table 3Demographics of the Family (by decile of household per capita income)

Decile 1984 1994Percentage

Change 1984 1994Percentage

Change

1 7.04 6.47 -8.13 5.48 4.75 -13.252 6.51 5.78 -11.10 4.99 4.18 -16.213 6.08 5.33 -12.23 4.47 3.58 -19.954 5.38 5.05 -6.07 3.88 3.34 -13.915 5.26 4.63 -12.08 3.81 2.96 -22.276 4.86 4.34 -10.80 3.39 2.71 -20.277 4.67 4.22 -9.73 3.07 2.49 -19.068 4.07 3.76 -7.68 2.57 2.14 -16.909 3.80 3.45 -9.24 2.38 1.96 -17.83

10 3.05 2.98 -2.17 1.82 1.67 -7.97

Total 5.07 4.60 -9.30 3.59 2.97 -17.05* Dependents are defined as individuals with no earnings.


Number of dependents (*)Family size

Distribution of Working Individuals by School Achievement

1984 1994Percentage

Change

No education 15.2 11.9 -21.5Primary incomplete 32.4 21.9 -32.5Primary 22.6 21.0 -6.9Secondary Incomplete 4.7 5.6 19.8Secondary 11.8 19.0 60.6High School Incomplete 2.0 3.2 59.4High School 4.2 6.5 57.1Superior Incomplete 2.7 4.3 56.1Superior 4.2 5.9 41.5Graduate 0.1 0.6 305.1

Total 100.0 100.0

Table 4


26

Table 6 Between and Within Decomposition of Inequality Measures

E0 E1 E2 E0 E1 E2 E0 E1 E2

By labor categories: gender, occupation and location

Between group 16.91 17.04 9.26 23.76 20.74 6.98 40.51 21.73 -24.59Within group 83.09 82.96 90.74 76.24 79.26 93.02 -8.25 -4.46 2.51

By occupation Between group 2.82 3.23 1.88 2.64 2.59 0.88 -6.27 -19.84 -53.31Within group 97.18 96.77 98.12 97.36 97.41 99.12 0.18 0.66 1.02

By location: Urban and RuralBetween group 10.45 11.08 6.05 17.47 15.62 5.03 67.20 41.00 -16.95Within group 89.55 88.92 93.95 82.53 84.38 94.97 -7.84 -5.11 1.09

By Gender: Male and FemaleBetween group 2.66 2.91 1.63 2.59 2.52 0.85 -2.70 -13.47 -47.68Within group 97.34 97.09 98.37 97.41 97.48 99.15 0.07 0.40 0.79

Source: Authors' calculations using 1984, and 1994 Households Surveys.

(Percentage Contribution)Percentage Change1984 1994

Table 5Skill Premium

Total Males Females Total Males Females Total Males Females

Wage earnersAverage hourly real earnings (Pesos) 5.94 6.03 5.70 6.85 6.95 6.65 15.39 15.14 16.53Earnings Premia

Average to low skill earnings 1.58 1.50 1.99 2.01 1.94 2.38 27.27 29.21 19.73High to low skill earnings 3.88 3.81 4.40 6.12 6.70 5.49 57.78 75.85 24.95

MixedAverage earnings 3.89 3.88 4.00 4.90 4.88 4.97 25.80 25.77 24.35Earnings Premia

Average to low skill earnings 1.38 1.41 1.01 2.32 2.36 1.91 68.81 68.05 89.95High to low skill earnings 9.59 10.01 3.91 10.73 11.89 4.65 11.86 18.85 18.91

Self EmployedAverage earnings 4.66 4.54 4.97 4.92 5.47 3.97 5.68 20.50 -20.05Earnings Premia

Average to low skill earnings 1.41 1.29 1.77 1.49 1.52 1.45 5.56 17.16 -18.13High to low skill earnings 5.32 5.34 0.93 7.86 7.82 6.53 47.68 46.44 603.27


1984 1994 Percentage Change

27

Table7 Within Group Inequality

GINI E0 E1 E2

1984 1994 Percentage

Change 1984 1994 Percentage



Change

UrbanMale

Wage 38.27 51.04 33.37 27.72 45.84 65.37 27.49 54.35 97.71 41.52 118.34 185.01Mixed 51.63 55.48 7.46 46.76 55.89 19.54 55.88 60.51 8.29 120.21 113.78 -5.35Self Employed 57.65 59.50 3.20 67.23 69.09 2.78 62.76 80.82 28.77 111.37 312.32 180.43

FemaleWage 34.81 44.65 28.27 29.72 37.06 24.70 21.60 35.56 64.63 23.07 52.15 126.06Mixed 40.18 41.10 2.29 30.62 34.99 14.29 27.97 27.64 -1.16 33.33 27.48 -17.54Self Employed 57.15 58.77 2.84 70.84 73.32 3.51 62.18 75.06 20.73 112.85 188.25 66.81

RuralMale

Wage 41.23 39.66 -3.81 34.60 31.30 -9.52 29.26 27.60 -5.67 36.25 35.68 -1.55Mixed 39.05 47.14 20.70 26.39 40.88 54.88 28.61 43.00 50.30 52.22 81.17 55.44Self Employed 53.86 61.83 14.81 59.40 86.99 46.44 54.71 71.37 30.46 132.35 127.30 -3.81

FemaleWage 47.48 43.57 -8.22 55.16 38.52 -30.18 37.71 32.90 -12.76 38.31 41.03 7.10Mixed 39.00 47.09 20.73 29.94 43.07 43.86 26.78 41.40 54.60 30.94 61.16 97.66Self Employed 63.51 66.56 4.80 87.11 100.91 15.84 80.73 92.96 15.15 165.57 256.72 55.05


28

Table 8 Decomposition of Changes in Within Group Earnings Inequality (Average % Contribution)

Gini E0 E1 E2 Gini E0 E1 E2

Wage Urban Returns 29.17 29.73 27.95 24.37 20.60 25.52 22.91 23.13

Education 31.52 33.38 29.46 24.46 24.34 37.58 27.34 26.78Experience -4.02 -6.09 -3.65 -3.55 -2.05 -6.62 -2.09 -1.41Regions 1.44 2.05 1.73 2.50 -1.67 -6.09 -2.43 -2.27Remainder term 0.23 0.39 0.42 0.95 -0.02 0.65 0.09 0.03

Endowments * 63.32 60.46 63.80 65.58 88.15 99.61 87.50 86.73Errors 7.39 9.87 8.23 9.89 -8.43 -24.99 -10.41 -10.23Remainder term 0.12 -0.06 0.03 0.16 -0.31 -0.14 0.00 0.37

Mixed Urban Returns 98.22 81.59 177.20 -250.19 -15.04 55.72 -301.09 32.25

Education 21.54 18.58 70.84 -195.26 -320.10 -95.69 1144.58 93.46Experience 34.37 31.66 32.64 6.69 331.21 140.54 -1538.21 -93.22Regions 59.81 48.21 116.98 -156.21 -179.58 -45.07 587.09 46.32Remainder term -17.49 -16.85 -43.25 94.59 153.42 55.94 -494.54 -14.31

Endowments * -14.05 3.80 -109.18 414.09 107.03 40.41 440.03 70.77Errors 15.73 15.70 36.75 -89.35 8.93 3.71 -37.19 -2.53Remainder term 0.09 -1.08 -4.77 25.45 -0.92 0.17 -1.75 -0.49

Self UrbanReturns -39.61 -60.84 -1.97 23.77 -63.74 -60.45 -20.85 -11.88

Education 4.17 -18.66 11.34 15.31 86.92 113.11 33.43 17.73Experience 44.70 138.58 11.55 7.17 -43.27 -63.67 -2.39 10.06Regions -79.90 -163.82 -18.54 12.65 -99.01 -122.02 -51.33 -46.33Remainder term -8.57 -16.94 -6.32 -11.36 -8.39 12.13 -0.55 6.66

Endowments * 245.51 457.32 135.40 89.24 378.88 591.27 198.87 163.87Errors -107.96 -305.69 -37.14 -22.86 -206.68 -436.93 -81.84 -58.07Remainder term 2.07 9.21 3.71 9.85 -8.46 6.12 3.83 6.09

Wage Rural Returns 75.63 63.17 52.26 -255.51 44.74 26.52 63.28 -175.42

Education -27.94 -16.26 -83.11 -814.48 -13.44 3.46 -24.98 144.45Experience 77.92 68.14 102.17 429.01 0.06 -0.05 2.21 -13.45Regions 33.04 13.87 47.05 271.40 54.69 24.04 85.74 -316.97Remainder term -7.39 -2.58 -13.85 -141.44 3.44 -0.93 0.30 10.54

Endowments * 62.37 72.93 103.52 639.02 12.76 39.25 -28.83 459.10Errors -37.61 -35.27 -52.92 -247.26 35.07 31.04 50.35 -111.81Remainder term -0.39 -0.84 -2.86 -36.25 7.43 3.19 15.20 -71.87

Mixed Rural Returns -14.39 -12.93 -23.73 -51.85 -353.50 -1477.12 -1061.98 -3602.43

Education -23.67 -21.13 -33.09 -65.10 -336.63 -1367.42 -1194.33 -6438.12Experience 7.03 6.88 7.91 12.79 8.83 -19.99 12.59 20.58Regions 1.12 -0.17 -1.64 -8.33 -57.47 -76.84 -116.00 -199.04Remainder term 1.13 1.48 3.09 8.79 31.76 -12.87 235.76 3014.15

Endowments * 57.54 57.41 66.13 67.10 357.04 1465.26 1137.71 4719.37Errors 56.78 56.58 59.22 94.65 108.65 128.85 78.84 36.93Remainder term 0.08 -1.06 -1.62 -9.91 -12.19 -16.99 -54.57 -1053.87

Self Rural Simulated Income

Returns 24.87 20.16 24.25 63.49 -19.54 -20.71 -37.55 -36.36Education 21.99 18.58 33.27 -660.03 -26.12 -9.07 -11.72 -10.33Experience 14.35 10.59 25.42 -590.83 6.85 3.22 3.98 3.15Regions 5.35 2.66 -2.86 450.69 1.09 -8.84 -21.92 -20.97Remainder term -16.82 -11.67 -31.58 863.66 -1.35 -6.01 -7.89 -8.22

Endowments * -10.45 -1.18 -46.05 2367.36 109.28 98.74 122.96 128.84Errors 82.14 78.10 115.16 -2378.66 10.61 5.08 5.39 4.09Remainder term 3.44 2.92 6.63 47.80 -0.34 16.89 9.19 3.43

Source: Authors' calculations based on OLS Earnings Equations Results (See Table A2)

Male Female

29

Table 9Decomposition of Changes in Earnings Inequality (Average % Contribution)

Gini E0 E1 E2

I. Participation effect -0.52 -0.81 5.30 26.52Male -7.94 -9.36 -1.44 15.04Female 7.44 8.76 6.62 11.12

II. Returns 37.10 42.79 33.53 31.29Education 23.01 18.66 22.51 19.64Experience 0.89 -0.12 1.58 4.38Regions -5.18 -5.02 -4.22 3.84Constant 20.85 31.38 17.55 14.00Remainder -2.47 -2.11 -3.89 -10.58

III. Endowments 58.82 48.88 61.29 61.43IV. Error term 2.81 5.74 0.91 -3.80V. Remainder 1.80 3.41 -1.04 -15.44Source: Authors' calculations based on OLS Participation and Earnings Equations Results (See Table A1-A2)

Table 9a Decomposition of Rural Returns Effects on Earnings Inequality (Average % Contribution)

Gini E0 E1 E2

Returns (*) 37.1 42.86 33.58 31.33Average Returns 17.71 9.32 19.92 25.12

Education 19.77 13.58 19.17 15.46 Experience 1.14 0.44 1.8 4.9 Regions 0.08 1.21 0.94 9.09 Constant -2.45 -5.46 -0.46 1.27 Interaction -0.83 -0.45 -1.54 -5.59

Rural Returns 20.02 35.02 14.78 8.25 Fixed effects 31.69 29.93 35.21 40.84 Education -5.2 -5.84 -6.52 -11.5 Experience -8.89 1.71 -16.04 -36.69 Regions 3.35 4.66 1.84 -0.89 Constant 8.61 8.69 12.45 20.84 Interaction -9.55 -4.12 -12.16 -4.34

Source: Authors' calculations based on OLS Earnings Equations results

* Total returns effect differ from those reported in Table 9 because they are based on regressions combining urban and rural data.

30

Table 10Years of Schooling by Decile of Earnings

Total Male Female




Change

1 3.22 3.37 4.56 3.67 3.55 -3.24 2.84 3.19 12.282 3.31 4.49 35.79 3.32 4.29 29.19 3.29 4.83 46.633 3.68 4.73 28.76 3.80 4.63 22.09 3.36 4.92 46.554 4.05 5.65 39.39 3.91 5.34 36.38 4.54 6.22 36.995 4.42 5.88 32.97 4.17 5.44 30.63 5.42 6.75 24.606 5.64 6.53 15.68 5.19 6.15 18.43 6.90 7.55 9.427 5.88 7.45 26.65 5.47 6.96 27.33 6.98 8.88 27.268 7.16 7.99 11.58 6.56 7.47 13.77 8.73 9.62 10.249 8.48 10.09 19.00 8.12 9.31 14.69 9.67 11.80 22.0210 10.11 12.94 27.95 9.91 12.76 28.76 11.35 13.61 19.96

TOTAL 5.59 6.91 23.54 5.57 6.83 22.63 5.67 7.09 25.12Source: Authors' calculations using the Household Income and Expenditure Surveys for 1984 and 1994 (INEGI).

31

Table 11Real Labor Monthly Earnings by Decile of Earnings


Change

1 57.98 44.92 -22.522 176.56 163.81 -7.223 302.12 310.15 2.664 443.26 445.38 0.485 611.12 586.02 -4.116 775.72 737.86 -4.887 955.00 911.70 -4.538 1169.25 1198.48 2.509 1536.37 1748.60 13.8110 3274.69 5007.74 52.92

Total 929.68 1115.14 19.95Source: Authors' calculations using the Household Income and Expenditure Surveys for 1984 and 1994 (INEGI).

Table 12.Real Labor Monthly Earnings by Category

1984 1994 Percentage ChangeTotal Male Female Total Male Female Total Male Female

UrbanWage 1181.88 1269.88 980.83 1526.41 1711.92 1174.94 29.15 34.81 19.79Mix 1124.22 1168.14 832.07 2513.04 2849.52 1441.96 123.54 143.94 73.30Self 1063.38 1323.02 484.56 1405.90 1801.04 766.16 32.21 36.13 58.11

RuralWage 664.29 699.73 546.17 634.39 653.42 570.44 -4.50 -6.62 4.44Mix 578.24 591.38 368.24 586.51 602.81 450.30 1.43 1.93 22.28Self 484.01 573.02 274.15 400.00 499.65 219.13 -17.36 -12.80 -20.07

Total 929.68 1020.10 696.44 1115.14 1254.35 821.92 19.95 22.96 18.02Source: Authors' calculations using the Household Income and Expenditure Surveys for 1984 and 1994 (INEGI).

32

Table 13Decile of Earnings Composition of Working Individuals by Gender (%)

Decile Male Female Male Female Male Female

1 6.31 19.73 7.16 16.00 13.55 -18.942 9.16 12.13 9.09 11.92 -0.74 -1.803 10.21 10.01 9.64 10.77 -5.63 7.524 10.65 7.76 9.59 10.85 -9.96 39.875 11.06 7.46 9.80 10.42 -11.41 39.646 10.14 9.43 10.88 8.38 7.29 -11.127 10.02 9.90 10.93 7.83 9.04 -20.948 10.02 9.98 11.18 7.54 11.61 -24.409 10.57 8.49 10.09 9.78 -4.54 15.1210 11.86 5.10 11.65 6.52 -1.83 27.92

1984 1994 Change


33

Table 14Decomposition of Changes in Household Income Inequality (Average % Contribution)Average Gini E0 E1 E2

I.Participation effect -4.95 -0.50 -3.86 -8.66Male -5.52 -0.44 -4.44 -9.15Female 0.45 1.66 -0.13 -4.76

II. Returns 37.51 41.25 33.22 30.47Education 22.48 20.89 23.40 24.74Experience 3.93 4.73 5.26 10.40Regions -5.82 -5.77 -6.37 -9.86Constant 17.51 22.29 15.76 17.34Remainder -0.58 -0.89 -4.85 -12.14

III. Endowments 65.00 55.70 70.56 83.01IV. Error term -1.57 -0.44 -1.84 -7.13V. Remainder 4.00 3.99 1.92 2.30Source: Authors' calculations based on OLS Labor Choice and Earnings Equations results (Tables A1-A2)

Table 14a Decomposition of Rural Returns Effects onhousehold Income Inequality (Average % Contribution)

Gini E0 E1 E2

Returns * 37.34 40.92 33.08 30.40Average Coefficients 18.75 16.98 19.57 20.76

Education 19.61 17.25 20.29 20.04Experience 1.58 2.22 3.26 8.84Regions -1.80 -0.55 -2.16 -3.40Constant -2.99 -3.89 -0.76 1.32Interaction 2.34 1.94 -1.06 -6.03

Rural Coefficients 17.97 23.84 14.08 11.74Fixed effects 18.92 21.88 23.71 63.55Education -3.50 -4.14 -3.79 -10.66Experience -3.02 -2.29 -7.54 -26.60Regions 3.51 4.47 3.24 2.12Constant -6.20 -10.44 0.38 20.95Interaction 8.26 14.37 -1.93 -37.62

Source: Authors' calculations based on OLS Earnings Equations results* Total returns effect differ somewhat from those reported in Table 14 because they are based on regressions combining urban and rural data.

34

Table 15 Assortative Matching

Decile 1984 1994 1984 1994

1 -0.02 -0.18 0.48 0.542 -0.18 -0.16 0.46 0.503 -0.08 -0.19 0.49 0.594 -0.17 -0.10 0.57 0.585 -0.20 -0.15 0.60 0.636 -0.24 -0.13 0.61 0.607 -0.24 -0.18 0.68 0.638 -0.07 -0.17 0.70 0.509 -0.23 -0.15 0.63 0.6710 -0.13 -0.17 0.65 0.64

Total -0.0223 -0.0078 0.7324 0.7435Source: Authors' calculations using the Household Income and Expenditure Surveys for 1984 and 1994 (INEGI).

Correlation between Head Labor Earnings and Spouse Working

Correlation between Husband and Wife Years of Schooling

35

Table 16Decomposition of the Sources of Rising of Household Income Inequality on Per Capita Income (1984-1994)

(in percentage)1984 1994 Average 84/94Contribution to Contribution to Contribution to

Sources Gini Returneffectsonly

Actualchangein theGini

Gini Returneffectsonly


Returneffectsonly


ORIGINAL INCOME 49.14 54.91ESTIMATED INCOME 49.14 54.84SIMULATED INCOME :TOTAL (I + II + III + V) 100.00 100.00 100.00I. Return Effects P(X,ε) (a + b + c) 52.77 100.00 62.99 50.03 100.00 83.38 100.00 73.18

a. Household Characteristics 51.52 65.57 41.30 51.61 67.18 56.01 66.49 48.66Demographics 49.19 1.62 1.02 55.44 -12.44 -10.37 -6.39 -4.67

Education 52.05 80.24 50.54 51.30 73.73 61.47 76.53 56.01Working age 52.09 81.27 51.19 51.18 76.10 63.45 78.33 57.32

Male 51.78 72.80 45.85 51.93 60.65 50.57 65.88 48.21Female 49.36 6.14 3.86 53.97 18.09 15.08 12.94 9.47

Older than working age 49.10 -1.01 -0.64 54.98 -2.87 -2.39 -2.07 -1.52Male 48.89 -6.83 -4.30 55.13 -5.88 -4.91 -6.29 -4.60Female 49.32 5.15 3.24 54.73 2.45 2.05 3.61 2.64

Assets 49.03 -2.98 -1.87 54.85 -0.20 -0.16 -1.39 -1.02Absent Head 49.08 -1.48 -0.93 54.89 -0.88 -0.73 -1.14 -0.83Interaction * -11.83 -7.45 6.96 5.81 -1.12 -0.82

b. Regional Effects 49.76 17.08 10.76 54.47 7.88 6.57 11.84 8.67Urban 49.26 3.36 2.12 54.80 0.90 0.75 1.96 1.43Rural 49.64 13.74 8.65 54.51 6.96 5.80 9.87 7.23

c. South 49.77 17.47 11.00 53.72 23.42 19.53 20.86 15.27Fixed effects 49.49 9.83 6.19 54.12 14.97 12.48 12.76 9.34

Urban 49.14 0.10 0.06 54.85 -0.20 -0.17 -0.07 -0.05Rural 49.49 9.77 6.16 54.12 15.14 12.62 12.83 9.39

Household Characteristics 49.40 7.26 4.57 54.41 9.06 7.56 8.29 6.07

II. Error Terms Effect ε(P,X) 49.35 3.77 54.62 3.84 3.81III. Endowment Effect ** X(P',ε') 33.75 12.28 23.01IV. Residual Effect ε(P’,X) - ε(P,X) -0.51 0.50 -0.01Note: Based on the decomposition presented in the Technical Appendix* Calculated as a difference, other interaction terms are not shown since they are small (see appendix for a complete decomposition)** Calculated as the difference between total change and other components.

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VIII. FIGURES

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FIGURE 1

SIMULATION PROCEDURE

?I(?ß , ?λ, ?e, ?X) (1)

?I(?ß, ?λ, ?e) (2) ?I( ?X) (3) ?I(corr[(?ß, ?e), ?X]) (4)

?I(?ß ?λ) (5) ?I(?e) (6) ?I(corr[(?ß ?λ) , ?e]) (7)

(5) = Returns and participation effect

(6) = Error terms effect

(3) + (4) = Endowment effect = (1) – (2)

(7) = Remainder Term = (2) – (5) – (6)

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Figure 5: Participation of labor force (1984 base year)

40

45

50

55

60

65

70

75

80

85

3 8 13 18 23 28 33 38 43 48 53 58 63 68 73 78 83 88 93 98

Percentiles of per capita household income

Observed 1984

Simulated 1994

Observed 1994

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Figure 6a: Lorenz Curve of Individual Education (Working Individuals only)

0

10

20

30

40

50

60

70

80

90

100

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96

Percentiles of Labor Earnings

1984

1994

Figure 6b: Lorenz Curve of Total Household Education of Working Individuals

0

10

20

30

40

50

60

70

80

90

100

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96

Percentiles of Household per Capita Income

1984

1994

40

Figure 7: Percentage Change in per Capita Household Income due to Changes in Returns to Education 1984 - 1994

-4

-2

0

2

4

6

8

101 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91

Percentiles of per capita household income

1994 endowments

1984 endowments

Documents

CAN EDUCATION EXPLAIN INCOME INEQUALITY CHANGES IN … · decomposition technique to explore the impact of participation decisions and occupational choice, returns and endowments