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In the last couple of decades, and in particular during the last couple of administrations, the Mexican government has implemented various social programs targeted specifically to women, such as PROGRESA/Oportunidades, a child care program, and a gender equality program (PROIGUALDAD). The impact that those programs may have on the work behavior of women largely depends on the form that the female labor supply takes, and in particular, on the labor supply elasticities with respect to own wages, and the husband’s wages. Despite this fact, the literature on female labor supply in Mexico is very scarce. To our knowledge, there is no estimate of the female labor supply elasticities at the national level. This paper fills in this gap in the literature. Using data from the 1990 and 2000 Mexican Census of Population, we estimate a structural model of labor supply through an application of Wooldridge’s (2002) threestep procedure. We …nd that the female labor supply elasticities had a rather sharp decrease between 1990 and 2000, which suggests that women are getting increasingly attached to the labor market. We also find evidence of heterogenous effects for women with young children and women of different cohorts. Even though female are now less responsive to changes in wages, the elasticities that we …nd are still large enough so that social programs aimed at modifying females´ work behavior through incentives might still be very successful.
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LABOR SUPPLY OF MARRIED WOMEN IN MEXICO: 1990-2000
Eva O. Arceo Gómez
Centro de Investigación y Docencia Económicas
Raymundo M. Campos-Vázquez
El Colegio de México
DOCUMENTO DE TRABAJO
Núm. XVI - 2010
.
Labor Supply of Married Women in Mexico:
1990-2000
Eva O. Arceo mez
y
Centro de Investigación y Docencia Económicas
Raymundo M. Campos Vázquez
z
El Colegio de México
December 2, 2010
Abstract
In the last couple of decades, and in particular during the last couple of adminis-
trations, the Mexican government has implemented various social programs targeted
speci…cally to women, such as PROGRESA/Oportunidades, a child care program, and
a gender equality program (PROIGUALDAD). The impact that those programs may
have on the work behavior of women largely depends on the form that the female labor
supply takes, and in particular, on the labor supply elasticities with respect to own
wages, and the husband’s wages. Despite this fact, the literature on female labor
supply in Mexico is very scarce. To our knowledge, there is no estimate of the female
labor supply elasticities at the national level. This paper lls in this gap in the litera-
ture. Using data from the 1990 and 2000 Mexican Census of Population, we estimate a
structural model of labor supply through an application of Wooldridge’s (2002) three-
step procedure. We nd that the female labor supply elasticities had a rather sharp
decrease between 1990 and 2000, which suggests that women are getting increasingly
attached to the labor market. We also nd evidence of heterogenous ects for women
with young children and women of di¤erent cohorts. Even though female are now
less responsive to changes in wages, the elasticities that we nd are still large enough
so that social programs aimed at modifying females’work behavior through incentives
might still be very successful.
We are indebted to David Card for his support and guidance during the development of this project.
We would also like to thank George Akerlof, Chang Tai-Hsieh, Edward Miguel, Steven Raphael, and Rafael
de Hoyos for their c omments at di¤erent stages of this project.
y
Eva’s contact information: eva.arceo@cide.edu
z
Raymundo’s contact information: rmcampos@colmex.mx. Mailing address: Camino al Ajusco 20, Pe-
dregal de Santa Teresa, México D.F., 10740.
1
1 Introduction
In the last couple of decades, and in particular during the last couple of administrations,
the Mexican government has implemented various social programs targeted speci…cally to
women, such as PROGRESA/Oportunidades, a child care program, and a gender equality
program (PROIGUALDAD). The impact that those programs may have on the work be-
havior of women largely depends on the form that the female labor supply takes, and in
particular, on the own-wage and husband’s-wage (or cross-wage) labor supply elasticities.
Despite this fact, the literature on female labor supply in Mexico is very scarce. To our
knowledge, there is no estimate of the female labor supply elasticities at the national level.
This paper lls in this gap in the literature. We estimate a structural model of labor sup-
ply using Wooldridge’s (2002) three-step procedure. We nd that the female labor supply
elasticities had a rather sharp decrease between 1990 and 2000, which suggests that women
are getting increasingly attached to the labor market. We also nd evidence of heteroge-
nous ects for women with young children and women of di¤erent cohorts. Even though
female are now less responsive to changes in wages, the elasticities that we nd are still large
enough so that social programs aimed at modifying married females’work behavior through
incentives might still be very successful.
Female labor supply has b een an active eld of research in labor economics since the
1960s.
1
The study of female labor supply behavior is important because of its profound social,
economic, and political consequences. The ability of women to earn their own income ects
their position relative to males, as female work enhances gender equality; their decisions
about education, marriage and fertility; their ability to inuence intrahousehold decision
making; their ability to organize politically and advance their own interests; and the gender
relations within a society (Killingsworth and Heckman, 1987; Duo, 2005; World Bank,
1
The literature in the United States and the United Kingdom is extremely abundant, and it is not my
objective to review it here. Killingsworth and Heckman (1987), and Blundell and Macurdy (1999) provide a
review of the literature. Two recent studies which take a long-term approach to analyze female labor supply
in the United States are Blau and Kahn (2007) an d Goldin (2006).
2
2001). In the context of developing countries these issues are much more important given
the inferior position of females relative to males on all dimensions of the social, economic and
political life. Moreover, the development process tends to favor females disproportionately
more than it favors males (Duo, 2005; World Bank, 2001). Since the development process
is itself interrelated to female participation in the labor market (Goldin, 1994; Cordourier
and Gómez Galvarriato, 2004), understanding the implications of female labor supply in a
developing country is especially signi…cant.
In a cross section of countries, Goldin (1994) found that there is a U-shaped relationship
between income per capita and female labor force participation. Apparently the income
ects dominate at low levels of development, whereas the substitution e¤ects do so at
higher income levels. Mexico is an interesting case because it shares some characteristics of
an industrialized and a developing country. Figure 1 shows that this U-shaped pattern is
present in Mexico over time: participation rates dropped until 1930, and they have been
growing ever since then. Moreover, Figure 2 shows that this U-shaped relationship is also
present in a cross-section; the gure presents the participation rates by municipality against
the log of the average income in the municipality. At very low income levels, the participation
rates are very high; then, as income in the municipality increases we can observe a gradual
drop in the percentage of females working, which reverses at very high income levels.
Despite the progress made over the last decades, Mexico’s female labor force participation
is still at a very low level, around 40% in 2005. Figure 3 compares Mexico with other Latin
American countries. There have been sharp increases in the participation rates of Mexican
females, but these look modest when compared countries in Latin America with similar levels
of development, like Argentina, Brazil, and Colombia (Panel A).
2
Mexico looks more similar
to Central American countries (Panel B). A striking feature in Mexicos plot is that it seems
to be the least steep among the Latin American countries, with the exception of Chile. In
Figure 4 Panel A, we observe that Mexico, the United States, Canada and Portugal share
2
Chile is perhaps the only country in Latin America (apart from Mexico) that exhibits this low partici-
pation rate.
3
a common feature of their female labor participation: the participation rate has leveled-
in the last 10 years; however, Mexico did so at a very low level. Ireland and Mexico
started in the 1990s with a similar participation rate, but Ireland has seen a massive
incorporation of females into the labor force, possibly boosted by its economic performance.
In the Panel B of that Figure, we compare Mexico to Mediterranean countries and we also
observe that Mexico has the least steep plot with the obvious exception of Turkey. Despite
the importance of the topic and the seemingly puzzling facts on the evolution of the female
participation rates in Mexico, the literature on female labor supply in Mexico is very scarce.
The existing literature on the constraints to female labor in Mexico focuses on the intra-
household division of labor. This literature hypothesizes that females’labor is constrained
by their socially determined household responsibilities as they relate to childbearing and do-
mestic reproduction. In particular, several researchers have analyzed the ect of household
structure on female labor force participation. Wong and Levine (1992) estimate reduced
form equations of the ect of household structure on motherswork decisions and fertility
decisions. They nd that having a mother substitute” increases participation in the la-
bor force and reduces fertility for non-working women (but they nd no ect on working
women). Gong and Soest (2002) estimate a structural model of female labor supply to shed
light on the ect of household structure on female labor decisions. Using data for Mexico
City from the National Survey of Urban Employment from the second quarter of 1992, they
nd that the own-wage elasticity is around 0.86 and the income elasticity is around -0.17.
3
In order to have an idea of the magnitude of these estimates, using US data for the period
1989-1991, Blau and Khan (2006) nd that the own-wage elasticity is around 0.6 and the
cross-wage elasticity with respect to the husband’s wage is around -0.25. Considering Mex-
icos context, the income elasticity seems to be rather low. Although the estimates are not
strictly comparable, married females do not seem to be much more responsive to changes in
the income of other family members than in the United States.
3
Non-labor income is measured as the income of the household less own income. The data they use has
no information on asset’s income or other type of bene…ts.
4
Cunningham (2001) estimates the response of female labor force participation to falling
family earnings. She hypothesizes that female labor responses to falling family earnings are
in‡uenced by the household role of the female. In this sense, women who act as caregivers
might be used as added workers when the family experiences economic hardship. However,
women who act as breadwinners must not respond in the same way. Her ndings conrm this
hypothesis on the household roles. Married females with children tend to respond more in
times of economic struggle than single females who are heads of household or single women
without children. Parker and Skou…as (2004, 2006) study the ect of the 1995 peso crisis
on female labor force participation. They also show evidence that there are signi…cant added
worker ects during the crisis period. That is, as the husbands lost their employment, more
wives were pushed into the labor force in order to smooth the ects of the crisis on family
earnings. There are two shortcomings in this literature. First, the literature focuses just on
the extensive margin, and it does not analyze the ects of the crisis on hours of work. And
second, Cunningham (2001) does not di¤erentiate the ect of the husband’s earnings on
female labor from the ect of the wages of other family members.
As such, there are many remaining questions unanswered in the literature on Mexico. An
example is that there is no empirical evidence on how female labor supply decisions respond
to their husband’s wages: what is the cross-wage elasticity of female labor supply in Mexico?
An estimate of the cross-wage elasticity would also allow us to draw some conclusions on
the importance that women place on their own work: are female labor decisions heavily
dependent upon the income-generating p ower of their husbands, or do they make their labor
decisions on a more independent fashion?
In this paper we will use data from the 1990 and 2000 Mexican Census of Population
to estimate a structural model of labor supply. The estimation of such a model imposes
several identi…cation challenges on our empirical strategy because of the presence of sample
selection and the endogeneity of wages. In order to correct for endogeneity, we will follow
Mroz (1987) and use two sets of instrumental variables: (1) the interactions between the
5
female’s education and age variables; and (2) these interactions and the husband’s age and
education. Moreover, we will also assume that the number of children and the husbands
wage are exogenous. We then proceed to estimate our model using Wooldridge’s (2002)
three-step procedure. The identi…cation of the labor supply parameters will come from the
assumption that the non-linearities of education and age do not ect wages.
Our main results suggest that the own-wage and cross-wage elasticities of females de-
creased between 1990 and 2000. We do not observe this drop in the elasticities of the labor
supply of males, so that this change in behavior seem to be exclusive of women. Our results
are similar to those found by Blau and Kahn (2007) for the United States, and they suggest
that Mexican women, too, are getting increasingly attached to the labor force. We estimated
the structural model for other subgroups of women: those with children under 5 years of
age and by age groups. We nd that women with young children are much more responsive
to changes in the wages than the average Mexican woman. This may be possibly due to
the fact that their time allocation is constrained by their caregiving responsibilities. Our
estimations by age groups suggest that younger women (i.e. those born between 1956 and
1965) are the ones who exhibit the largest drop in the elasticities. However, the female labor
supply of women of all age groups is growing increasingly inelastic over time. Nevertheless,
the elasticities we found are still large enough for social programs aimed to increase the labor
supply of married women to be successful.
The paper is organized as follows. Section 2 describes the data and some stylized
facts about the recent trends of the female labor supply in Mexico. Section 3 explains
the identi…cation problems when estimating the female labor supply and our estimation
strategy. In Section 4 we lay out our main results, some robustness tests, and we explore
the heterogeneity in work behavior across di¤erent groups of women. Section 5 presents
some concluding remarks and the policy implications of our estimates. In this last section
we also comment on future areas of research.
6
2 Data and Facts
We use a 10 percent random sample from the 1990 and 2000 Mexican Census of Population
provided by the IPUMS-International (Ruggles et al., 2008). Since our analysis consists on
estimating the own- and cross-wage elasticities of female labor supply, our sample consists
of married couples who are between 25 and 55 years of age.
4
In this way we are trying to
make sure that the decision to work is relevant: these people are not likely to b e retired nor
enrolled in school. Our analysis will be based on information about hours of work and the
husbands and wife’s wages, so we dropped the people who reported working without pay, or
which reported working, but did not report a wage. We are going to restrict our analysis to
individuals living in urban areas, which are de…ned as those locations with more than 2,500
inhabitants.
5
In order to make our case for this restriction, Table 1 presents a comparison
between the females in urban and rural areas. Women in rural areas are less educated
and have more children on average. These women represent a challenge to our estimation
because they have very low participation rates: only 6.5 of them worked in 1990, and only
20.4 percent did so in 2000. In contrast, women living in urban areas had participation
rates of 27.9 and 43.3 percent in 1990 and 2000, respectively.
We further restricted our data to those households with just one married couple, and
where both of the spouses were present. Table 2 presents the summary statistics of our nal
sample which consists of 537,109 couples in 1990, and 627,114 couples in 2000. Our sample
of urban households is slightly older and more educated than the average urban household.
The females in our sample participate less, and tend to work less hours than the average
urban married female. Between 1990 and 2000 we nd that the education gap between
husbands and wives closed from 1.22 years to just 0.72. The females in our sample also
increased their participation in the labor force from 17.94 percent to 33 percent, an increase
4
We decided to focus on married women because their decision to work is more of a choice as compared
to single women with children. Moreover, one of the goals of the paper is to estimate the elasticity with
respect to the husband’s wage.
5
This is the de…nition of urban areas used by the Instituto Nacional de Estadística y Geografía (INEGI),
the main statistical agency in Mexico.
7
of just over 15 percentage points.
Figure 5 presents the participation rates and the mean hours of work conditional on
employment across education and age groups. Panel A in the Figure shows that all females
increased their participation rates. The increase was the largest for females with less than
primary school, and for females aged 25-34 years in 1990 and turned 35-44 in 2000. If we
just look at the age groups, then females between 35 and 44 years old are the ones who
exhibit the largest increase, but as we said, this may be a cohort ect. Panel B in the
gure shows the mean weekly hours of work conditional on employment. Women do not
seem to have adjusted their supply of hours by much, and if anything, these decreased on
average. So females have mostly adjusted their work supply at the extensive margin (i.e.
participation), which is in contrast to the behavior of men who have adjusted their supply
mostly at the intensive margin (i.e. hours of work).
6
3 Estimation Strategy
Our objective is to establish whether females’work behavior has changed over time in Mexico.
Hence, we are interested on estimating an equation of the following form
h
f
i
=
f
log w
f
i
+
m
log w
m
i
+ X
i
+ u
i
; (1)
where h
f
i
are the female is hours of work; w
f
i
is her wage; w
m
i
is the wage of her husband;
and X
i
is a vector of covariates. In our case, X
i
will include age and education of the
female, indicator variables for numb er of kids less than ve years old in the household (one
kid, two kids and more than two kids) and indicator variables for region (5 variables).
7
The
estimation of this equation in a cross-section imposes several empirical challenges on the
6
Table B-1 in Appendix B presents these same work variables for married males in our sample.
7
It is imp ortant to mention that the labor supply regression does not include any information on the
husband. Even though Blau and Kahn (2007) includ e these variables into the main regression, most of the
literature does not follow that convention.
8
identi…cation strategy. First and foremost, we can only observe w
f
i
if the woman is working,
h
f
i
> 0; so that there are sample selection and simultaneity issues. Females with strong
preferences for work are going to have a higher participation regardless of their wage, and
their husband’s wages. Hence running a regression for only working women provides a
biased estimate of the labor supply parameters (Heckman, 1974 and 1979).
The second problem with the estimation of regression (1) is that w
f
i
is endogenous and
subject to division bias. For instance, females with a strong taste for work will tend to
invest more in their human capital, and thus increase the potential wage that they can earn
in the labor market. At the same time, females with a stronger preference for work will tend
to participate more in the labor market regardless of their own wage. As a consequence,
a spurious correlation between hours and work may arise given the preferences for work.
Hence, in order to correctly identify the own-wage elasticity one would require either a
demand shifter or an instrumental variable for the wage.
8
Finally, it is not clear that the husband’s wage and the number of children in the house-
hold are truly exogenous. For example, if there is positive assortative matching, then the
husbands characteristics are going to be positively correlated with the wife’s characteristics.
As a result, some unobservable characteristics that are both linked to the husband’s wage
and the female labor supply may bias our estimates. In the case of children, it is possible
that women with strong preferences for work will anticipate their intensive participation in
the labor market, and thus decide to have less children.
The literature in female labor supply has used di¤erent speci…cations to estimate the own-
wage and cross wage elasticities. In this paper we will implement Mroz’s (1987) specication
to estimate the elasticities of the labor supply of married females. After an extensive analysis
of female labor supply speci…cations, Mroz nds that the husbands income and the number
8
Several approaches have been undertaken in the literature to tackle this problem. Ransom (1987) ignores
it, and assumes wages are exogenous. Kooreman and Kapteyn (1987) use age and education as an instrument
for the wage. Mroz (1987) u ses interactions of the female’s age and education as instruments for the wage.
Juhn and Murphy (1997) use the wage decile as an instrumental variable. Devereux (2004) uses group
indicators as instruments. The group indicators are de…ned by the age and education of the husband and
wife.
9
of children can be considered exogenous in the regression. We follow this nding and assume
that husbands income and number of kids are exogenous in speci…cation (1). Moreover, in
order to correct for the endogeneity of wages, we will also use two sets of instruments: (IV1)
interactions of female’s age and education variables, and (IV2) those interactions and the
husbands age and education.
In order to estimate the female and male labor supply, we follow Wooldridge’s (2002)
three-step method to control for sample selection issues. This procedure is similar to that in
Heckman (1976, 1979). However, instead of running a probit in the rst stage, Wooldridge
proposes to estimate a Tobit model, and then to use the residuals of this regression as
a control function in the wage equation (which is analogous to the inverse Mills ratio in
Heckmans specication). Consider the following structural female labor supply model:
log
w
f
i
= log w
m
i
+ X
1i
+ "
i
h
f
i
= max
h
0;
f
log w
f
i
+
m
log w
m
i
+ X
2i
+ u
i
i
where w
f
i
is the female’s wage; w
m
i
is the male’s wage; X
1i
and X
2i
are vectors of exogenous
variables; h
f
i
is the female’s hours of work; and ("
i
; u
i
) are assumed to be bivariate normal
with zero mean. The problem with the estimation of this system is that we can only observe
w
f
i
if h
f
i
> 0; so w
f
i
is endogenous. Wooldridge’s three-step procedure is as follows:
1. Estimate a Tobit of h
f
i
on all the exogenous variables:
h
f
i
=
~
m
log w
m
i
+
~
X
2i
+
i
;
where the bounds are set to [0; 96] ;
9
and obtain the residuals ^
i
:
2. Estimate the wage equation controlling for the residuals obtained in (2) and restricting
9
The upper bound is set to 96 weekly hours because that is the amount of hours an individual with two
full-time jobs would work per week (assuming she takes a day per week).
10
the estimation to working individuals:
log w
f
i
=
~
log w
m
i
+
~
X
1i
+ ^
i
+ "
i
; if h
f
i
> 0 (2)
Then, we need to estimate the wages for everyone in the sample as follows:
\
log w
f
i
=
b
~
log w
m
i
+
b
~
X
1i
:
This step allows us to correct for the selection bias. If there is a selection problem,
then we would expect that 6= 0:
10
Hence, these estimated wages are going to be
purged of selection bias.
3. Using a Tobit model estimate the labor supply equation where the endogenous variable
is substituted with the predicted wage estimated in step 2:
h
f
i
=
f
\
log w
f
i
+
m
log w
m
i
+ X
2i
+ u
i
The identi…cation in this procedure is coming from the inclusion of at least one variable
in X
1i
that is not in X
2i
: As we have mentioned, we will use two sets of instruments:
interactions between own education and own age (IV1), and these interactions and spouse’s
age and spouse’s education (IV2). The identifying assumption implies that non-linearities
in the age-education prole ect wages but do not ect hours of work after including age
and education variables. It is hard to argue in favor of the excludability of these variables
from the hours equation, but this is the convention in the literature (Mroz, 1987) and we are
going to follow it in this regard. We estimate this model for the females whose husband’s
have a valid wage.
10
We will also add higher order terms of ^
i
to the equation above. In this case, we will test whether all
the co cients in the polynomial of ^
i
are equal to zero using an F-statistic.
11
4 Results
4.1 Main Speci…cation
Table 3 shows the estimates of the female supply wage coe¢ cients and the implied elasticities
using the rst set of instruments: interactions b etween own education and own age. This
table also shows an estimation of the male labor supply, which we will use as a benchmark to
analyze changes in the behavior of females.
11
Column (1) in Table 3 presents the estimates of
the female labor supply curve in 1990. According to these estimates, a ten percent increase
in female wages would induce a 28 percent increase in the hours of work. In contrast, a ten
percent increase in the husbands wage would lead females to decrease their labor supply by
9.5 percent.
12
In 2000, these female labor supply elasticities are much smaller.
These changes in the own- and cross-wage elasticities are the main focus of our analy-
sis. According to our estimates the own-wage elasticity of female labor supply in Mexico
decreased by over 2 percentage p oints between 1990 and 2000. We also nd that the cross-
wage elasticity of female labor decreased by over half a percentage point. In 2000, a ten
percent increase in female wages induces a 6.1 percent increase in the hours of work; whereas,
a ten percent increase in male wages induces just a 2.7 percent decrease in the females’hours
of work. Hence, the female labor supply got more inelastic to changes in both own and hus-
band’s wages. Our estimates are more or less robust to the addition of the spouse’s age and
education as instrumental variables, as Table 4 presents. The results are also robust to the
addition of higher order terms to the control function in equation (2) (not shown).
13
11
The procedure we use d to estimate the labor supply of males is described in Appendix A. The complete
female and male labor supply estimations are shown in Tables B-1 and B-3 in Appendix B. The estimations
in Tables B-2 and B-4 included higher order terms of ^v
i
in the wage regression (2) : The estimates are robust
to the addition of these terms in the control function.
12
Our estimate of the own-wage elasticity is not entirely comparable to that in Gong and Soest (2002),
since they used data just for Mexico City. Mexico City is the most developed city in Mexico, and it has the
highest female labor force participation rates in the country. So it is natural to expect that the own-wage
female labor supply elasticity is lower in Mexico City as compared to Mexico overall. However, we still
think that Gong and van Soest’s (2002) estimates are low, even f or Mexico City’s context.
13
The co cient on ^
i
in equation (2) is statistically di¤erent from zero in our speci…cations (not shown).
Whenever we controlled for a polynomial on ^
i
; the F-statistic rejected the hypothesis that all the coe¢ cients
in the polynomial were equal to zero. These two tests suggest that there is a selection problem.
12
As compared to the female labor supply, we do not nd such large decreases in the
elasticities of males (see Columns 3 and 4 in Table 3 and Table 4). The literature on labor
supply has commonly found that the male labor supply is much less elastic than the female
labor supply, possibly due to the fact that bread-winning responsibilities lie on the men.
Our ndings suggest that over time womens work behavior has started to look more like
their mens counterpart. This is a similar result to that in Blau and Kahn (2007) and
Goldin (2006) in the United States. It is possible that the di¤erence between the males and
female’s roles within the household is attenuating over time, so that females are now more
attached to the labor market than in the past. As a result, females’labor supply becomes
less responsive to changes in both their own wages and the wages of their husbands.
4.2 Heterogeneity
We would like to explore whether females with di¤erent characteristics exhibit a di¤erent
work behavior. For instance, if married females with young children are more constrained
by their caregiving responsibilities, then they should exhibit less attachment to the labor
market. Table 5 presents the estimated elasticities for married females with young children
in 1990 and 2000. The IV1 results suggest that the labor supply of females with young
children is much more responsive to changes in own and husband’s wages. In addition, the
relative change between 1990 and 2000 is not as large as that we observed in the estimates for
all married women. The results are robust to the use of the second set of instruments, and
to the addition of higher order terms to the control function in equation (2) (not shown).
These estimates indicate that mothers with children under 5 years of age are much less
attached to the labor force than the average married woman. This lack of attachment is
possibly due to their children imposing important constraints to their allocation of time.
We are concerned with the possibility that our results are driven by cohort ects.
Younger cohorts may be much more attached to the labor force due to cultural changes.
In the case of the United States, Goldin (2006) pointed out that younger women are more
13
career oriented than their older counterparts. As a result, the increases in the labor force
participation that the United States experienced were mostly driven by the incorporation
of these women into the labor force. It is very possible that Mexico is undergoing a simi-
lar transformation. The education levels of women have grown constantly in the last two
decades, and the education gap between men and women is closing. In order to explore these
issues and following Blau and Kahn (2007), we estimated our model by age groups. Table
6 presents these labor supply elasticities for the two sets of instruments that we have been
using although we will focus on the IV1 implied elasticities. In 1990 women aged between
45 and 54 seemed to have the lowest own-wage and cross-wage elasticities, so they exhibited
the strongest attachment to the labor force when compared to the other two groups. How-
ever, by 2000, it is the youngest group of females the one that is the most attached to the
labor force. This is suggestive that there could be large di¤erences across cohorts.
Our results suggest that women of all ages have experienced a drop in the elasticities.
However, if we follow the cohort over time, we will see that this drop is the largest for females
between 25 to 34 years of age in 1990 (those born between 1956 and 1965). Taking the IV1
estimates, we nd that their own-wage elasticity was 3.3294 in 1990, and it decreased to
0.6487 in 2000 when they are 35-44 years old this is over an 80 percent drop. We observe
something similar in the cross-wage elasticity: in 1990 this is -1.039, but in 2000 it dropped
to -0.2913 over a 70 percent decrease. Females born between 1946 and 1955, or those
between 35 and 44 years old in 1990, also experience a decrease in both elasticities, but it is
not as large as the one experienced by the next ten year cohort. The ndings suggest that
the overall trend in the elasticities is dominated by the females born between 1956 and 1965.
5 Conclusions
Our goal in this paper was to study the changes in the labor supply of Mexican married
women. We nd that between 1990 and 2000 married women in Mexico experienced a rather
14
substantial increase in their participation in the labor force of around 10 percentage points.
After estimating a structural model of labor supply, we found that the own- and cross-wage
elasticities decreased during this period suggesting that Mexican women are getting more
attached to the labor market. When we compare our results to those reported in Blau and
Kahn (2007), we nd however that in 1990 the labor supply elasticities were considerably
higher than the levels experienced by American women in 1979-1980. However, in 2000 we
found that the elasticities of Mexican females are around the same level of those American
married women in 1989-1990, which suggests a very sharp change in the behavior of Mexican
females. Given that most of the adjustment in the female labor supply has been at the
extensive margin, it may be pertinent to perform a similar analysis as the one we presented
for the decision to participate in the labor force. We leave this analysis for our future
research agenda.
Our results have important policy implications. First, the urban components of social
programs, such as PROGRESA/Oportunidades in Mexico may have adverse ects on the
women’s incentive for work by providing them with another income source. Our ndings
though suggest that these adverse ects are attenuating over time. Hence, it is important
to perform a study of the impact of this type of programs in the work behavior of the adult
bene…ciaries. This is a research topic that we will leave for future work.
Our results also suggest that programs that promote gender equality at work, such as
the Programa Nacional de Igualdad entre Mujeres y Hombres (PROIGUALDAD National
Program for the Equality between Men and Women), can have important ects on the labor
force participation of females. For instance, according to the 2004 Human Development
Report (UNDP, 2005), the income of females in Mexico represented only 36 percent of that
of men. Suppose PROIGUALDAD were able to reduce the gap such that women could earn
half as much as men leaving mens wages constant. Then our estimates for 2000 suggest
that such an increase in the wages would increase female labor supply by 23 percent. This
high own-wage labor supply elasticity represents a unique opportunity for the policymakers
15
to provide incentives for women’s work.
Another important policy implication of our results relates to the mothers of young
children. Our estimates suggest that women with children under 5 years of age are much
less attached to the labor force, and although they also exhibit a decrease in their labor supply
elasticities, the decrease is not as large as that of the average married woman. These results
suggest that young children impose a constraint on the time their mothers can supply to the
labor market. Hence, the provision of child care may encourage these women to increase
their labor supply and, hence, to be more attached to the labor force. In order to know
whether this conjecture is correct, we require a more systematic study of the ect of children
on female labor supply in Mexico.
As we have mentioned, our results suggest that Mexican females are also converging to
the males’ work behavior as it has been happening in the United States during the last
decades (Blau and Kahn, 2007). It would be very interesting to study whether this is a
generalized trend in Latin America. Much more interesting would be to nd what the
driving forces of this process are. So far, the literature has only hypothesized that women
are more career-oriented. However, this attitude towards work may be another result in the
process of incorporation of females into the labor force.
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laboral de las mujeres en la industria: una visión de largo plazo,” Economía Mexicana,
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(2006): The Quiet Revolution That Transformed Women’s Employment, Educa-
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Gong, X., and A. v. Soest (2002): Family Structure and Female Labor Supply in Mexico
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Juhn, C., and K. M. Murphy (1997): Wage Inequality and Family Labor Supply,”
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of Labor Economics, chap. 2, pp. 103–204. Elsevier.
Kooreman, P., and A. Kapteyn (1987): A Disaggregated Analysis of the Allocation of
Time within the Household,The Journal of Political Economy, 95(2), 223249.
Lundberg, S., and E. Rose (2002): The ects Of Sons And Daughters On Men’S Labor
Supply And Wages,”The Review of Economics and Statistics, 84(2), 251–268.
Mroz, T. A. (1987): The Sensitivity of an Empirical Model of Married Women’s Hours of
Work to Economic and Statistical Assumptions,”Econometrica, 55(4), 765–99.
Parker, S. W., and E. Skoufias (2004): The added worker ect over the business
cycle: evidence from urban Mexico,”Applied Economics Letters, 11(10), 625–630.
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sarrollo Humano México 2004. PNUD, Distrito Federal, Mex.
Ransom, M. R. (1987): An Empirical Model of Discrete and Continuous Choice in Family
Labor Supply,”The Review of Economics and Statistics, 69(3), 465–72.
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M. King, and C. Ronnander (2008): Integrated Public Use Microdata Series: Version
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181.
18
The World Bank (2001): Engendering Development: Through Gender Equality in
Rights, Resources, and Voice, World Bank Policy Research Report 21776, The World
Bank.
Wong, R., and R. E. Levine (1992): The ect of Household Structure on Women’s Eco-
nomic Activity and Fertility: Evidence from Recent Mothers in Urban Mexico,”Economic
Development and Cultural Change, 41(1), 89–102.
Wooldridge, J. M. (2002): Econometric Analysis of Cross Section and Panel Data. The
MIT Press, Cambridge, Mass.
19
Figure 1: Historical Female Participation in Mexico
0 10 20 30 40
Female Participation Rate
1900 1920 1940 1960 1980 2000
Year
Notes: The graph was constructed using Encuesta Nacional de Empleo
Urbano (ENEU) and Census data.
20
Figure 2: Female Participation Rates by Municipality
0 .1 .2 .3 .4 .5 .6 .7 .8
Female Participation Rate
9 10 11 12 13 14
Income in 1990 (log)
1990 2000
Notes: Author’s estimation using 1990 and 2000 Census data.
21
Figure 3: Mexico vs. other Latin American Countries
A.
20 30 40 50 60
Female Participation Rate
1940 1960 1980 2000
Year
Argentina Brazil Chile
Colombia Mexico
B.
10 20 30 40 50
Female Participation Rate
1940 1960 1980 2000
Year
Costa Rica El Salvador Guatemala
Panama Mexico
Notes: The graph uses data from the International Labor Organization.
22
Figure 4: Mexico vs. other OECD Countries
A.
30 40 50 60 70
Female Participation Rate
1990 1995 2000 2005
Year
United States Mexico Canada
Portugal Ireland
B.
25 30 35 40 45 50
Female Participation Rate
1990 1995 2000 2005
Year
Turkey Mexico Italy
Greece Spain
Notes: The graph uses data from the Organization of Economic Cooperation
and Development.
23
Figure 5: Married Females Work Behavior
A.
1990
2000
1990
2000
1990
2000
1990
2000
1990
2000
1990
2000
1990
2000
0 .2 .4 .6 .8
Female Participation Rate
< Primary PrimarySecondary College Age 25-34 Age 35-44 Age 45-54
B.
1990
2000
1990
2000
1990
2000
1990
2000
1990
2000
1990
2000
1990
2000
0 10 20 30 40
Mean Hours (cond. employed)
< Primary PrimarySecondary College Age 25-34 Age 35-44 Age 45-54
Notes: Author’s estimations using 1990 and 2000 Census data.
24
Table 1: Urban vs. Rural Females
1990 2000
Urban Rural Urban Rural
Socio-demographic characteristics:
Age 36.33 36.87 36.8 37.02
Education 6.518 2.861 8.442 4.418
< Primary 0.3459 0.7851 0.2168 0.5874
Primary 0.4666 0.1928 0.4942 0.3647
Secondary 0.1283 0.01486 0.184 0.03409
College 0.05921 0.007172 0.105 0.01374
Num. children 2.464 3.296 2.029 2.804
Num. children<5 0.4452 0.6669 0.3516 0.5055
% w/ Children<5 0.3334 0.4369 0.2842 0.3681
Family Size 5.207 5.894 4.799 5.6
% Married 0.7758 0.8516 0.7384 0.8195
Work behavior:
Hrs. of work 11.28 2.656 17.42 7.928
[19.77] [11.01] [23.01] [18.17]
% Working 0.2793 0.06555 0.4332 0.204
[0.4486] [0.2475] [0.4955] [0.403]
Income 4913 3169 3456 1976
[20954] [16815] [11089] [11992]
Observations 998673 317210 1165373 632150
Notes: Author’s calculations from the 1990 and 2000 Mexican Census .
Standard deviations are in brackets.
25
Table 2: Summary Statistics of the Final Sample
1990 2000
Male Female Male Female
Socio-demographic characteristics:
Age 38.22 35.21 38.52 35.95
Education 7.789 6.569 9.257 8.538
< Primary 0.2805 0.3253 0.1685 0.193
Primary 0.4699 0.4989 0.495 0.5225
Secondary 0.1242 0.1238 0.1752 0.187
College 0.1254 0.05205 0.1613 0.09746
Num. children 3.007 2.433
Num. children<5 0.6016 0.4689
% w/ Children<5 0.4514 0.3774
Family Size 5.192 4.63
Work behavior:
Hrs. of work 43.75 6.816 47.76 12.3
[19.23] [15.82] [19.41] [20.21]
% Working 0.9243 0.1794 0.9447 0.3299
[0.2645] [0.3837] [0.2285] [0.4702]
Income 6973 5344 5099 3635
[24716] [22156] [13506] [10581]
Observations 537109 537109 627114 627114
Notes: Author’s calculations from the 1990 and 2000 Mexican Census .
Standard deviations are in brackets.
26
Table 3: Female and Male Labor Supply Elasticities (IV1)
Female
a
Male
a
1990 2000 1990 2000
(1) (2) (3) (4)
Regression coe¢ cients
Wife’s wage 85.0469** 19.6218** -5.1108** -5.3030**
[3.4656] [1.9074] [0.7366] [0.7041]
Husband’s wage -28.6287** -8.5358** 7.6115** 8.6003**
[1.1558] [0.7119] [0.5661] [1.0593]
Implied elasticities
Wife’s wage 2.8272** 0.6160** -0.1149** -0.1093**
[0.1157] [0.0599] [0.0166] [0.0145]
Husband’s wage -0.9517** -0.2680** 0.1711** 0.1772**
[0.0386] [0.0223] [0.0127] [0.0218]
Instrumental variables
Interact. Own Age*Educ Y Y Y Y
Spouse Age and Educ N N N N
Observations 463759 557171 537109 627114
Notes:
a
Control variables: own age, own education, dummies for region; dummies for
one, two, or three or more children; and family size.
Robust standard errors in brackets
** p<0.01, * p<0.05
27
Table 4: Female and Male Labor Supply Elasticities (IV2)
Female
a
Male
a
1990 2000 1990 2000
(1) (2) (3) (4)
Regression coe¢ cients
Wife’s wage 55.5235** 20.4784** -3.1329** -3.3795**
[3.1806] [1.8970] [0.7217] [0.6747]
Husband’s wage -18.8668** -8.8530** 6.0787** 5.6895**
[1.0626] [0.7083] [0.5473] [1.0123]
Implied elasticities
Wife’s wage 1.8441** 0.6430** -0.0704** -0.0696**
[0.1059] [0.0596] [0.0162] [0.0139]
Husband’s wage -0.6266** -0.2779** 0.1367** 0.1172**
[0.0354] [0.0222] [0.0123] [0.0208]
Instrumental variables
Interact. Own Age*Educ Y Y Y Y
Spouse Age and Educ Y Y Y Y
Observations 463759 557171 537109 627114
Robust standard errors in brackets
** p<0.01, * p<0.05
Table 5: Labor Supply Elasticities of Women with Young Children
With Kids
1990 2000
Implied elasticities IV1
Wife’s wage 3.4514** 1.5053**
[0.1980] [0.1204]
Husband’s wage -1.085** -0.5705**
[0.0626] [0.0441]
Implied elasticities IV2
Wife’s wage 2.4534** 1.4298**
[0.1815] [0.1196]
Husband’s wage -0.7719** -0.5432**
[0.0575] [0.0438]
Observations 212297 221656
Robust standard errors in brackets
** p<0.01, * p<0.05
28
Table 6: Labor Supply Elasticities by Age Groups
Age 25-34 Age 35-44 Age 45-54
Cohort of birth 1956-65 1965-75 1946-55 1956-65 1936-45 1946-55
Census year 1990 2000 1990 2000 1990 2000
Implied elasticities IV1
Wife’s wage 3.3294** 0.6561** 2.3225** 0.6487** 1.5236** 1.1899**
[0.1820] [0.1011] [0.1625] [0.1015] [0.3559] [0.1984]
Husband’s wage -1.0390** -0.2597** -0.8364** -0.2913** -0.5942** -0.5217**
[0.0573] [0.037] [0.0567] [0.0372] [0.1283] [0.0773]
Implied elasticities IV2
Wife’s wage 2.7374** 0.5604** 0.8840** 0.5901** -1.4032** 0.2274
[0.1762] [0.0982] [0.1396] [0.1004] [0.2411] [0.1823]
Husband’s wage -0.8535** -0.2249** -0.3398** -0.2701** 0.4493** -0.1496*
[0.0556] [0.0361] [0.0488] [0.0368] [0.0874] [0.0712]
Observations 245211 274270 164927 210773 53621 72128
Robust standard errors in brackets
** p<0.01, * p<0.05
29
A Male Labor Supply
The male labor supply was estimated using more or less the same procedure than the female
labor supply. Consider the following structural male labor supply model
log (w
m
i
) = log w
f
i
+ X
1i
+ "
i
(3)
h
m
i
= max
h
0;
f
log w
f
i
+
m
log w
m
i
+ X
2i
+ u
i
i
(4)
where w
m
i
is the male’s wage; w
f
i
is the female’s wage; X
i
is a vector of exogenous variables
which includes age, age squared, education, region dummies, and dummies for the number
of children less than 5 and family size;
14
and h
f
i
is the female’s hours of work. In the case
of men, however, we cannot restrict the estimation to the sample of husbands whose wives
have a valid wage (as we did for females). This is due to the fact that only 30 percent of the
females work, so we would be limiting our estimates to a very selected sample of males. So
we had to impute the wage for all females using the rst two steps of Wooldridge’s (2002)
procedure. In this way we obtain a measure of the wage,
\
log w
f
i
; for all females that has
been corrected for selection into work. We substitute this imputed wage into equations (3)
and (4), and then using Wooldridge’s three-step procedure estimate the following structural
model
log (w
m
i
) =
\
log w
f
i
+ X
1i
+ "
i
h
m
i
= max
0;
f
\
log w
f
i
+
m
log w
m
i
+ X
2i
+ u
i
:
B Appendix of Tables and Figures
14
We kept the children and family variables because the literature has found that they are signi…cant in
the male labor supply. See for example Lundberg and Rose (2002).
30
Figure B-1: Married Males Work Behavior
A.
1990
2000
1990
2000
1990
2000
1990
2000
1990
2000
1990
2000
1990
2000
0 .2 .4 .6 .8 1
Male Participation Rate
< Primary PrimarySecondary College Age 25-34 Age 35-44 Age 45-54
B.
1990
2000
1990
2000
1990
2000
1990
2000
1990
2000
1990
2000
1990
2000
0 10 20 30 40 50
Mean Hours (cond. employed)
< Primary PrimarySecondary College Age 25-34 Age 35-44 Age 45-54
Notes: Author’s estimations using 1990 and 2000 Census data.
31
Table B-1: Female Labor Supply
Dependent variable: IV1 IV2
Weekly hrs. of work 1990 2000 1990 2000
(1) (2) (3) (4)
Wife’s wage (log) 85.0469** 19.6218** 55.5235** 20.4784**
[3.4656] [1.9074] [3.1806] [1.8970]
Husband’s wage (log) -28.6287** -8.5358** -18.8668** -8.8530**
[1.1558] [0.7119] [1.0626] [0.7083]
Age 6.7405** 4.7614** 6.6889** 4.7494**
[0.1822] [0.1375] [0.1824] [0.1375]
Age
2
-0.0973** -0.0637** -0.0943** -0.0636**
[0.0025] [0.0018] [0.0025] [0.0018]
Primary 8.3393** 5.4985** 12.7286** 5.3481**
[0.6062] [0.4511] [0.5720] [0.4494]
Secondary 32.3094** 19.4254** 38.0464** 19.0506**
[0.7798] [0.9149] [0.7331] [0.9102]
University 39.7959** 30.4631** 49.2510** 29.8869**
[1.1967] [1.3506] [1.1110] [1.3432]
Children under 5:
One -8.5214** -10.5071** -7.5652** -10.5635**
[0.3079] [0.2510] [0.3054] [0.2508]
Two -18.7518** -19.2278** -16.4863** -19.3356**
[0.5265] [0.4656] [0.5176] [0.4652]
Three or more -24.6526** -25.6131** -22.3970** -25.7315**
[1.2531] [1.4158] [1.2505] [1.4149]
Family size -3.2999** -2.2893** -3.4314** -2.2802**
[0.0880] [0.0764] [0.0878] [0.0763]
Other controls:
Region dummies Y Y Y Y
Constant -330.0086** -128.1070** -273.6059** -129.1217**
[7.3463] [3.3483] [6.8924] [3.3408]
Observations 463759 557171 463759 557171
Notes: The omitted educ ation category is "Less than Primary". The regions are:
Northern border, North-Central, Paci…c, Central, South, and Mexico City.
Robust standard errors in brackets
** p<0.01, * p<0.05
32
Table B-2: Female Labor Supply (w/ higher-order terms of residuals)
Dependent variable: IV1 IV2
Weekly hrs. of work 1990 2000 1990 2000
(1) (2) (3) (4)
Wife’s wage (log) 77.2389** 19.0454** 54.8365** 20.2197**
[3.1885] [1.8685] [2.9758] [1.8562]
Husband’s wage (log) -25.8348** -8.3937** -18.4926** -8.8333**
[1.0560] [0.7046] [0.9875] [0.7003]
Age 6.5781** 4.7601** 6.5776** 4.7434**
[0.1822] [0.1375] [0.1824] [0.1375]
Age
2
-0.0944** -0.0637** -0.0927** -0.0635**
[0.0025] [0.0018] [0.0025] [0.0018]
Primary 7.3605** 5.5592** 11.3156** 5.3490**
[0.6455] [0.4490] [0.6143] [0.4471]
Secondary 31.7535** 19.4255** 36.7141** 18.8951**
[0.8063] [0.9225] [0.7660] [0.9168]
University 42.7753** 30.6740** 49.8147** 29.8725**
[1.0961] [1.3424] [1.0342] [1.3335]
Children under 5:
One -8.4174** -10.5088** -7.6520** -10.5876**
[0.3068] [0.2516] [0.3050] [0.2513]
Two -18.2029** -19.2529** -16.4750** -19.4045**
[0.5170] [0.4678] [0.5110] [0.4672]
Three or more -24.3351** -25.4751** -22.5509** -25.6346**
[1.2513] [1.4136] [1.2495] [1.4125]
Family size -3.1287** -2.2738** -3.2911** -2.2596**
[0.0895] [0.0769] [0.0891] [0.0768]
Other controls:
Region dummies Y Y Y Y
Constant -310.3183** -126.0821** -268.7869** -127.4022**
[6.7140] [3.2341] [6.3960] [3.2259]
Observations 463759 557171 463759 557171
Notes: The omitted educ ation category is "Less than Primary". The regions are:
Northern border, North-Central, Paci…c, Central, South, and Mexico City.
Robust standard errors in brackets
** p<0.01, * p<0.05
33
Table B-3: Male Labor Supply
Dependent variable: IV1 IV2
Weekly hrs. of work 1990 2000 1990 2000
(1) (2) (3) (4)
Wife’s wage (log) -5.1108** -5.3030** -3.1329** -3.3795**
[0.7366] [0.7041] [0.7217] [0.6747]
Husband’s wage (log) 7.6115** 8.6003** 6.0787** 5.6895**
[0.5661] [1.0593] [0.5473] [1.0123]
Age 0.6212** 0.5858** 0.6491** 0.6120**
[0.0428] [0.0451] [0.0428] [0.0452]
Age
2
-0.0112** -0.0113** -0.0115** -0.0114**
[0.0005] [0.0006] [0.0005] [0.0006]
Primary 1.2542** 2.0150** 1.1856** 2.1955**
[0.1077] [0.1254] [0.1090] [0.1233]
Secondary -1.2199** -0.9587** -1.0353** -0.0757
[0.1899] [0.3527] [0.1887] [0.3386]
University -2.9099** -5.1559** -2.3868** -3.1428**
[0.3160] [0.7597] [0.3101] [0.7267]
Children under 5:
One 0.4821** 0.7118** 0.4175** 0.5908**
[0.0740] [0.0873] [0.0744] [0.0865]
Two 0.7472** 0.9589** 0.6038** 0.7467**
[0.1180] [0.1497] [0.1185] [0.1483]
Three or more 0.2415 1.4613** 0.0898 1.1705**
[0.2516] [0.3812] [0.2520] [0.3797]
Family size 0.2141** 0.1531** 0.2179** 0.1556**
[0.0201] [0.0247] [0.0202] [0.0247]
Other controls:
Region dummies Y Y Y Y
Constant 28.3368** 30.8080** 26.1905** 32.2739**
[1.3412] [1.0709] [1.3407] [1.0500]
Observations 537109 627114 537109 627114
Notes: The omitted educ ation category is "Less than Primary". The regions are:
Northern border, North-Central, Paci…c, Central, South, and Mexico City.
Robust standard errors in brackets
** p<0.01, * p<0.05
34
Table B-4: Male Labor Supply (w/ higher-order terms of residuals)
Dependent variable: IV1 IV2
Weekly hrs. of work 1990 2000 1990 2000
(1) (2) (3) (4)
Wife’s wage (log) -19.5325** -5.3577** -16.6828** -3.8893**
[1.2389] [0.6792] [1.1880] [0.6617]
Husband’s wage (log) 21.6822** 9.3992** 18.8950** 6.9841**
[1.1144] [1.1091] [1.0528] [1.0757]
Age 0.2889** 0.5481** 0.2751** 0.5727**
[0.0476] [0.0458] [0.0487] [0.0460]
Age
2
-0.0076** -0.0108** -0.0072** -0.0109**
[0.0006] [0.0006] [0.0006] [0.0006]
Primary 0.7773** 1.9870** 0.8774** 2.1346**
[0.1082] [0.1254] [0.1086] [0.1236]
Secondary -3.5589** -1.3199** -3.0243** -0.5511
[0.2487] [0.3801] [0.2375] [0.3683]
University -7.9061** -5.4512** -6.7759** -3.8401**
[0.4685] [0.7649] [0.4422] [0.7406]
Children under 5:
One 1.0760** 0.7818** 1.0060** 0.6758**
[0.0852] [0.0907] [0.0854] [0.0903]
Two 1.9386** 0.8618** 1.7718** 0.7327**
[0.1448] [0.1419] [0.1446] [0.1419]
Three or more 1.1732** 1.6543** 1.0533** 1.3880**
[0.2606] [0.3867] [0.2613] [0.3853]
Family size 0.4279** 0.2119** 0.3910** 0.1981**
[0.0226] [0.0256] [0.0221] [0.0255]
Other controls:
Region dummies Y Y Y Y
Constant 37.7613** 29.5838** 36.9428** 31.1086**
[1.4849] [1.1567] [1.5293] [1.1292]
Observations 537109 627114 537109 627114
Notes: The omitted educ ation category is "Less than Primary". The regions are:
Northern border, North-Central, Paci…c, Central, South, and Mexico City.
Robust standard errors in brackets
** p<0.01, * p<0.05
35
... This could represent unpaid help in domestic chores and household care, and would result in an increase in the number of hours available to supply to the labour market. Secondly, the presence of children has a negative effect on hours worked, because caring for them can put major demands on women's time (Arceo-Gómez and Campos-Vázquez, 2010). Consequently, household structure is an important determinant of the number of hours women have available for paid work. ...
... Nonetheless, a decision to undertake paid work in the home could also reflect the shortage of schools and child-care centres with suitable hours to give mothers more options in terms of paid formal employment. In this connection, a study by Arceo-Gómez and Campos-Vázquez (2010) shows that the number of hours of paid work done by Mexican women with children under five years of age is much more sensitive to variations in the wage than those of the average Mexican woman. This could be explained by the fact that traditional female roles in the household put constraints on how women choose to distribute their time. ...
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The hours worked by Mexican women depend not only on wages and individual characteristics, but also on factors related to household structure, which generate incentives for women to restrict their hours of paid work. This study uses a pseudo-panel containing five million observations from the National Survey on Occupation and Employment, for 2005-2010. Different age cohorts of the female working population are analysed along with a pseudopanel model that measures the sensitivity of women’s hours worked to wage variations and factors related to household structure, such as the availability of help in the home and the presence of children. It is found that women’s hours worked increase when the household contains another adult woman, but decrease in the presence of children or a male adult.
... En lo relativo a factores económicos, se pueden mencionar aquellos relacionados con las peculiaridades de las estructuras económicas locales o regionales, las tasas de desempleo, los niveles salariales y el ciclo económico (Abramo, 2004y Serrano, Gas parini, Marchionni y Glüzmann, 2019. Dentro de los elementos cultu rales, destacan los asociados a las costumbres o roles de género que son asignados a hombres y mujeres en las actividades productivas y repro ductivas, las cuales constituyen una limitante o condicionante para la inserción de las mujeres en el mercado laboral (Miller, 2004;Encinas y Martínez, 2009;Arceo y Campos, 2010;Sánchez, Herrera y Perrotini, 2015, Tomaselli, 2017Rodríguez y Muñoz, 2018). Finalmente, se pueden señalar los factores que tienen que ver con la normatividad institucio nal, las leyes, los reglamentos, las políticas públicas, los programas, las acciones, etcétera, que favorecen u obstruyen su actividad en el mer cado de trabajo (Miller, 2004;Encinas y Martínez, 2009, y Kaplan y Pi ras, 2019. ...
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En el capítulo 3, David Castro Lugo y Reyna Elizabeth Rodríguez Pérez, analizan la presencia de las mujeres en el mercado laboral y los factores relevantes en su participación en la ciudad de Oaxaca. En este estudio se consideran variables sociodemográficas, económicas y cul- turales. Los resultados, a partir de indicadores estadísticos, revelan que las mujeres de la capital oaxaqueña tienen una tasa de participación la- boral superior al promedio nacional, aunque con mayor presencia en trabajos por cuenta propia relacionados con el comercio y los servi- cios diversos. Los factores más relevantes que inciden positivamente son los relacionados con los niveles educativos, grupo de edad de 25 a 45 años, condición de jefatura de hogar y apoyos económicos por parte del gobierno –no así para apoyos recibidos por familiares–. En aspectos culturales, se identifica que las actividades domésticas, en especial el cuidado de menores y adultos mayores, constituyen una limitante para la presencia más activa en labores remuneradas. Por otra parte, las percepciones sobre algunas afirmaciones con connotaciones cultura- les permiten determinar que éstas tienen una incidencia negativa sobre la participación laboral de las mujeres; este hecho destaca en los hogares con hablantes de alguna lengua indígena.
... En lo relativo a factores económicos, se pueden mencionar aquellos relacionados con las peculiaridades de las estructuras económicas locales o regionales, las tasas de desempleo, los niveles salariales y el ciclo económico (Abramo, 2004y Serrano, Gas parini, Marchionni y Glüzmann, 2019. Dentro de los elementos cultu rales, destacan los asociados a las costumbres o roles de género que son asignados a hombres y mujeres en las actividades productivas y repro ductivas, las cuales constituyen una limitante o condicionante para la inserción de las mujeres en el mercado laboral Encinas y Martínez, 2009;Arceo y Campos, 2010;Sánchez, Herrera y Perrotini, 2015, Tomaselli, 2017Rodríguez y Muñoz, 2018). Finalmente, se pueden señalar los factores que tienen que ver con la normatividad institucio nal, las leyes, los reglamentos, las políticas públicas, los programas, las acciones, etcétera, que favorecen u obstruyen su actividad en el mer cado de trabajo Encinas y Martínez, 2009, y Kaplan y Pi ras, 2019). ...
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Pobreza, participación laboral y empoderamiento económico de las mujeres en Oaxaca, México, busca responder las siguientes preguntas: ¿Cuáles son los factores que inhiben la participación de las mujeres en el mercado laboral de Oaxaca?¿Las mujeres en condición de pobreza tienen menos posibilidades de incorporarse al mercado laboral? ¿La cultura y la adaptación de los roles de género en la sociedad oaxaqueña restringen la participación laboral de ellas? ¿Qué factores contribuyen a su empoderamiento económico y social ¿Cómo influye la experiencia de vida de las mujeres en la realización del trabajo remunerado? para responder estas preguntas, el documento se conforma de seis capítulos en los cuáles se abordan aspectos teóricos y empíricos sobre los factores que determinan la participación laboral femenina, características sociodemográficas de los hogares, así como análisis cuantitativo y cualitativo sobre la participación laboral y empoderamiento de las mujeres en la Zona Metropolitana de Oaxaca. Los resultados aquí ofrecidos podrán ser de ayuda para los tomadores de decisiones y quienes formulan políticas públicas que tengan por objetivo disminuir o terminar con la pobreza, así como contribuir a la igualdad entre hombres y mujeres en todos los aspectos, sobre todo los económicos y sociales.
... Tradicionalmente, el rol de la mujer ha estado condicionado a las labores domésticas, de maternidad y de la crianza de los hijos, lo cual guarda una estrecha relación con la participación en el mercado laboral (Blau et al., 2013;Mateo y Rodríguez, 2013;Arceo y Campos, 2010). De acuerdo con Gasparini y Marchioni (2015), a nivel internacional, solo dos de cada diez mujeres trabajaban en la década de los sesenta; sin embargo, en la última década, la oferta de trabajo femenino se ha triplicado. ...
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... Second, women in Mexico have lower labor force participation than in highincome countries. Historically, women in middle and low-income countries have had lower labor supply (Goldin, 1994), which is at least partially due to cultural norms against women working, especially in Latin America (Arceo-Gomez and Campos-Vazquez, 2010). Today, just under 50% of women participate in the labor force in Mexico (Bustelo et al., 2019, Novta andWong, 2017), with higher labor supply from single, younger, and more educated women (Bustelo et al., 2019, Hoehn-Velasco and Penglase, 2021, Novta and Wong, 2017. ...
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... Our results show that married women of childbearing age have demand-side barriers to jobs from two different sources: the usual discrimination in hiring, and the double discrimination from explicitly discriminatory employers. Married women in Mexico have unusually low levels of female labor force participation (Arceo-Gómez and Campos-Vázquez 2010). Our evidence of married women's double discrimination may be an additional barrier to female work. ...
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... The extension of intrahousehold models to a framework that considers the presence of children and the participation decision simultaneously is particularly relevant in Mexico. As in other developing countries, Mexico's female labor force participation is still at a very low level (Arceo and Campos, 2010). However, the low participation rate does not imply that women's preferences are not taken into account in household resource allocations. ...
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