Emigration and wages in source countries:
Evidence from Mexico
Prachi Mishra*
International Monetary Fund, Washington, DC 20431, United States
Received 22 February 2004; received in revised form 4 August 2005; accepted 2 September 2005
Abstract
This paper examines empirically the effect of Mexican emigration to the United States on wages in
Mexico using data from the Mexican and US censuses from 1970 to 2000. The main result in the paper is
that emigration has a strong and positive effect on Mexican wages. There is also evidence for increasing
wage inequality in Mexico due to emigration. Simple welfare calculations based on a labor demand–supply
framework suggest that the aggregate welfare loss to Mexico due to emigration is small. However, there is a
significant distributional impact between labor and other factors.
D 2005 Elsevier B.V. All rights reserved.
JEL classification: F22; J31
Keywords: Emigration; Wages; Wage inequality
1. Introduction
A vast theoretical and empirical literature considers the labor-market impact of immigration.
In contrast, the literature on the labor-market impact of emigration or the outflow of workers is
almost exclusively theoretical.1
The absence of an empirical literature on the labor-market
impact of emigration is surprising because the shares of the labor force leaving many individual
0304-3878/$ - see front matter D 2005 Elsevier B.V. All rights reserved.
doi:10.1016/j.jdeveco.2005.09.002
* Tel.: +1 202 623 9409.
E-mail address: pmishra@imf.org.
1
See Borjas (1994, 1995) and Friedberg and Hunt (1995) for surveys of the empirical literature. The theoretical
literature on international movement of factors includes, for example, Bhagwati and Hamada (1974), Rivera-Batiz (1989)
and Quibria (1989).
Journal of Development Economics 82 (2007) 180–199
www.elsevier.com/locate/econbase
source countries is considerably higher than the proportionate changes in the labor force of many
receiving countries due to immigration.
To cite a few examples, the labor force in El Salvador, Jamaica, Barbados, Guyana and Belize
have been reduced by 15% or more due to emigration to the US between 1970 and 2000.2
In
comparison, immigrants constitute about 12% of the US labor force (Davis and Weinstein,
2002).
This paper is the first econometric study of the effect of emigration on the national wages in a
source country. I examine empirically the effect of Mexican emigration to the US on wages in
Mexico using data from the Mexican and the US censuses from 1970 to 2000. The questions
addressed in this paper are: how does emigration affect real wages in Mexico? What is the
direction and magnitude of the impact? What are the implications of emigration for (i) wage
inequality across schooling groups and (ii) national income distribution between labor and the
other factors in Mexico?
My identification strategy to estimate the impact of emigration on Mexican wages follows
Borjas (2003) in that I utilize variation across schooling and experience groups in the labor force,
and over time. Emigration is assumed to be exogenous in the basic specifications. However, I
also address concerns about endogeneity and self-selection biases in the estimates.
I find a strong and positive impact of the outflow of workers on wages in Mexico. A 10%
decrease in the number of Mexican workers due to emigration in a skill group (defined by
schooling and experience), increases the average wage in that skill group by about 4 percent. The
estimates suggest that the outflow of Mexican workers to the US between 1970 and 2000 has
increased the wage of an average Mexican worker by about 8%.
The impact on wages, moreover, differs dramatically across schooling groups, with the
greatest increase in wages being for the higher wage earners (those with 12–15 years of
schooling). Hence, the estimates in this paper suggest that emigration could serve as one
possible explanation for the increasing wage inequality in Mexico. Emigration as a channel to
explain the increasing wage inequality in developing countries has received little attention in
the literature.3
Simple welfare calculations in a competitive partial equilibrium framework show that the
emigration loss to Mexico due to the outflow of workers between 1970 and 2000 is 0.5% of
Mexico’s GDP in 2000.4
The estimated welfare loss is lower than the official worker remittances
to Mexico, which were about 1% of GDP in 2000 (IMF).5
The emigration loss would also be
easily outweighed by the big gains of the migrants themselves. However, there is a significant
distributional impact. The gain to the workers who have stayed behind is 5.9% of GDP and the
loss to the owners of fixed factors is 6.4% of GDP. Hence, the estimated distributional impact is
about 12–13 times the aggregate economic impact.
2
In several source countries, the reduction in the labor force due to emigration to the US is in the range of 7–27%. In
countries like Turkey and Algeria, the labor force has been reduced by about 10% due to emigration to Western Europe.
There is also anecdotal evidence of sizeable flows to the gulf countries for which no systematic data exists. (Sources: US
Census, OECD Migration Statistics, World Development Indicators).
3
Robbins (2002) and Zhu and Trefler (2005) provide evidence for increasing wage inequality in developing countries.
4
This is analogous to the estimates of immigration surplus that exists in the migration literature (Borjas, 1995). The
concept was first given by MacDougall (1960) in the context of capital flows. Based on the simple textbook model of
labor demand and supply, emigration leads to second order triangle losses for the aggregate economy (excluding the
migrants) but there are large redistribution effects between workers and other factors.
5
The figure, however, excludes large amounts of unrecorded remittances.
P. Mishra / Journal of Development Economics 82 (2007) 180–199 181
It is a very difficult problem to examine empirically the impact of emigration because source
countries, in general, do not record information on those who leave. Mexico offers an ideal case
to study because almost all emigration from Mexico is to the US. More than 95% of the
international migrants from Mexico go to the US (Woodruff and Zenteno, 2002). Hence it gives
an excellent opportunity to use the US data sources to study the effect on a source country’s
labor market. One important contribution of this paper is to merge two micro-level data sets to
examine empirically the impact of emigration on a source country’s labor market.6
The second
reason why Mexico offers an ideal case is that the outflow of workers is sizeable in proportion to
the Mexican labor force. In 2000, Mexican emigrants constituted about 16% of the Mexican
labor force or approximately one out of seven Mexican workers migrate to the US.
There are three types of studies on emigration that are related to this paper. First, there are a
few case studies by the International Labor Organization (Stalker, 1994). Second, there are a few
studies based on simulation exercises (e.g., O’Rourke and Williamson, 1999; Boyer et al., 1994).
Third, there are a few region/sector specific studies (see Hanson et al., 2002; Hanson, 2003;
Lucas, 1987). The present paper differs from the above studies in that I estimate the effect of
overall emigration (legal and illegal, temporary and permanent) on a source country’s national
(not regional) wages.7
The paper is organized as follows. Section 2 presents the empirical specifications, Section 3
describes the data and evidence, Section 4 discusses the results, Section 5 presents the
quantifiable implications of the results. Section 6 concludes.
2. Empirical framework
2.1. Empirical specification
Following Borjas (2003), I specify the regression equation as:
wijt ¼ dmijt þ si þ vj þ pt þ si4pt þ vj4pt þ si4vj þ eijt ð1Þ
where wijt is the mean value of monthly earnings (in logs) for workers in Mexico with education
level i, experience j and observed in year t.8
The measure of the emigrant supply shock in schooling-experience-time cell (i, j, t) is
denoted by mijtu
Mijt
Nijt
, where Mijt is the number of Mexican emigrants in the US in cell (i, j, t) and
Nijt is the national workforce in Mexico in cell (i, j, t). An emigrant from Mexico in the United
States is defined as a person whom the US Census records as being born in Mexico. Hence, by
this definition, an emigrant is a Mexican-born person in the US who may be a naturalized citizen
6
Chiquiar and Hanson (2005) also merge US and Mexican Census data for 1990 to examine who migrates from
Mexico to the United States. They find evidence of intermediate/positive self-selection in terms of observable skills,
which is consistent with the findings in this paper.
7
Robertson (2000a) looks at US–Mexico wage convergence in the border cities of Mexico and the US. He finds that
the convergence is stronger in Tijuana (the border city with the highest INS apprehensions) than in other border cities.
Based on the predictions of 3-good, 3-factor Heckscher Ohlin model, Mahmood (1991) conjectures that the rise in real
wages in Pakistan in the mid-1970s could be due to emigration.
8
The reduced form empirical specification is derived assuming a 3-level CES production function for the national
economy and perfectly competitive markets. The detailed derivation can be obtained upon request from the author. Using
emigrant share as a measure of the emigrant supply shock follows the immigration literature which uses the immigrant
share of the native population as a measure of the immigrant supply shock (e.g., Borjas, 2003, 1994; Friedberg, 2001;
Altonji and Card, 1991, for representative studies).
P. Mishra / Journal of Development Economics 82 (2007) 180–199182
or a non-citizen. Using the Census data, it is not possible to distinguish between legal and illegal
immigrants in the US. However, there exists evidence of an undercount of the illegal migrants in
the Census data, and hence also of emigration from Mexico (Costanzo et al., 2001). A worker in
Mexico is defined as a person who is counted by the Mexican Census as residing in Mexico in a
given year, and is a part of the labor force.
si, vj and pt are vectors of fixed effects indicating the group’s schooling, work experience and
time, respectively, which control for differences in wages across schooling, experience groups
and over time. The interaction terms (si * pt) and (vj * pt) are introduced to control for the
possibility that the returns to schooling and experience could change over time. The interaction
terms, (si*vj), control for the possibility that the experience profile for the wages could differ
across schooling groups. The impact of the economy-wide shocks would be captured through the
period fixed effects. The shocks, which have differential impact on schooling and age groups,
would be captured through the interaction of the period fixed effects with schooling and
experience, respectively.
The regressions are weighted by the number of workers in Mexico in cell (i, j, t). The
parameter d gives the percentage change in wages due to a 1% change in the number of Mexican
workers due to emigration. d is identified by within skill-group changes in emigrant shares over
time.
2.2. Using individual level data
It is also possible to estimate the effect of emigration on Mexican wages by estimating an
individual level wage regression, including the emigrant supply shock as one of the explanatory
variables. Friedberg (2001) also uses an individual level wage regression to estimate the impact
of immigration on the wages of natives.
The regression equation is specified as:
wI
ijt ¼ dmI
ijt þ bXI
ijt þ pt þ eI
ijt ð2Þ
where wI
ijt is the real wage (in logs) for individual I in schooling group i, experience j, counted in
year t. mI
ijt is the ratio of emigrants to the workforce in Mexico in the cell to which individual I
belongs, pt is a year dummy. mI
ijt is a group variable and is same for all individuals within a cell.
mI
ijt is the vector of standard control variables used in the labor literature like experience,
schooling, marital status, nativity, occupation, industry and the interactions of these variables
with the year dummy.
There are two advantages of using individual level regression specified in (2) as compared to
the group regressions specified in Eq. (1): (i) the individual level regression can control for many
individual-level factors affecting wages and (ii) greater efficiency because of more observations.
Since the emigrant supply shock is the same for all individuals in the same cell, the errors eI
ijt
may be correlated within the cell, which may lead to inefficient estimates. Therefore, the
standard errors have to be adjusted for clustering at the schooling-experience-time level. The
grouped regression nevertheless is important since the regressor of interest, i.e. the emigrant
supply shock is a group variable.
3. Data and evidence
Data on the Mexican born population in the US are drawn from the 1970, 1990 and 2000
Public Use Microdata Samples (PUMS) of the Decennial Census of US. Data on the population in
P. Mishra / Journal of Development Economics 82 (2007) 180–199 183
Mexico are drawn from the 1970, 1990 and 2000 PUMS of the Decennial Census of Mexico.9
The emigrant extracts form a 1% random sample in 1970 and 2000, and a 5% random sample in
1990. The extracts of the population in Mexico form a 1% random sample for 1970, 1990 and 2000.
The individuals in the sample are divided into schooling and experience groups. The workers
are divided into four schooling categories (i) high school dropouts (HSD)—less than 12 years of
schooling, (ii) high school graduates (HSG)—exactly 12 years of schooling, (iii) some college
(SC)—13 to 15 years of schooling, (iv) college graduates (CG)—16 years or more of schooling.
Since the largest proportion of Mexicans are high school dropouts, for the regressions, I use a
finer set of schooling categories where HSD are further divided into those with 0, 1–4, 5–8 and
9–11 years of schooling. The measure of work experience is defined as Age-AT, where AT is the
assumed age of entry into the labor market for the typical worker. AT is assumed to be 17, 19, 21
and 23 years for HSD, HSG, SC and CG, respectively. The assumed values of AT reflect the
assumption that individuals enter the labor force immediately after school. In order to focus on
individuals in the working age group, the sample is restricted to individuals with experience
ranging from 1 to 40 years (18–63 years of age). The data contains samples only of males due to
issues related to self-selection biases into the workforce. Also, the measure of work experience is
a more appropriate measure for males.
Fig. 1 shows the measure of the emigrant supply shock—i.e. the ratio of emigrants to the
workforce in Mexico, in aggregate and by schooling groups. In 1970, the emigrants constituted
around 3% of the Mexican labor force and it increased to 11% in 1990 and 16% in 2000.
The aggregate migration rates, however, mask a lot of variation by schooling. It is a wellknown
fact that immigration has increased by the greatest proportion the supply of high school
dropouts in the US (Borjas et al., 1997). However, there is a sharp contrast when we look at
migration from the perspective of the source country. As shown in Fig. 1, the bigger
proportionate losses are amongst the high school graduates and those with some college
education. In 2000, Mexican emigrants with 12 years of schooling constituted 47% while
emigrants with 13–15 years of schooling constituted about 52% of the corresponding labor force
9
The data used in this paper is publicly available at www.ipums.org. The earthquake of 1985 destroyed the Mexican
Census agency computing facilities before a national microdata sample could be constructed from the 1980 enumeration.
Fig. 1. HSD=High School Dropouts (less than 12 years of schooling), HSG=High School Graduates (12 years of
schooling), SC=those with some college (13–15 years of schooling), CG=College Graduates (16 or more years of
schooling). All the figures are for males in the labor force, who were not enrolled in school at the time of the Census, and
have experience in the range of 1–40 years. An emigrant is defined as a person counted by the US census as being born in
Mexico in a given year. Aworker in Mexico is defined as person counted by the Mexican Census as residing in Mexico in
the same year and is a part of the labor force in Mexico.
P. Mishra / Journal of Development Economics 82 (2007) 180–199184
in Mexico in the same schooling groups. In contrast, Mexican emigrants who are high school
dropouts constituted only 16% of the corresponding labor force in Mexico. The reason is, though
Mexican migrants are predominantly high school dropouts in absolute numbers, Mexico is so
abundant in high school dropouts that the emigrant supply shock in the high school dropout
category is relatively small. Around 80% of Mexican workers are high school dropouts whereas
only about 12% have 12–15 years of schooling.10
An important issue in measuring emigration by schooling categories is where the emigrant
acquired the education, in the US or in Mexico. The US census does not provide information on
this. Even if this information were available, it is not clear what the counterfactual level of
educational attainment of the migrant would have been, had he not migrated and whether his
emigration could constitute shocks to the same schooling groups in Mexico. The strategy I follow
in this paper is to exclude from the migrant sample those who are more likely to have got their
schooling in the US. The literature on Mexican migration to the US (e.g., Grogger and Trejo, 2002;
Gonzalez, 2002; Chiquiar and Hanson, 2005; Clark and Jaeger, 2002) provides strong evidence
that those who arrive in late teens or later are less likely to complete high school in the US.
Following Chiquiar and Hanson (2005), I exclude from the migrant sample individuals who
were less than 17 years of age at the time of entry into the US.11
In the restricted migrant sample,
there is a higher probability that these individuals got their schooling in Mexico. The emigrant
supply shock (not shown) in the 12–15 years of schooling category remains high. In 2000,
Mexican emigrants who are high school graduates constituted 31% while emigrants with some
college education constituted 33% of the corresponding labor force in Mexico.
Appendix Fig. 1 shows the emigrant supply shocks by schooling and experience. The
emigrant sample excludes those who migrated as children, as defined above. The emigrant
supply shocks are not balanced across experience groups within particular schooling categories.
The central message is that there is variation in the emigrant supply shock across schooling and
experience groups and over time that we can utilize to estimate the impact on Mexican wages.
Fig. 2 shows the average real monthly earnings (nominal earnings deflated by Consumer
Price Index from the International Financial Statistics) in Mexico. Between 1970 and 2000, the
real wage in Mexico decreased both on average, as well as for each schooling group.
4. Results and interpretation
4.1. Basic regressions
Table 1 shows the estimates of d, i.e. the effect of emigration on the wages in Mexico. There
are 840 schooling-experience-time cells (7 schooling groups, 40 experience groups and 3 time
periods). Specification I includes fixed effects for schooling (si), experience (vj) and time (pt).
The estimate shown in column 1 is 0.33 (SE=0.06). Specification II includes the interactions of
period fixed effects with the education and experience fixed effects, respectively. The interaction
terms control for the changing wage structure for different schooling and age groups respectively
over time. The estimate of d is 0.42 (SE=0.08) in Specification II. Specification III shows the
10
It is surprising that the emigrant supply shock in the college graduate category is very small. One possible reason
could be that the highly educated Mexicans want to live in Mexico and consume the cheap non-traded goods and have a
high standard of living.
11
The estimates of the effect of emigration on Mexican wages are robust to assuming higher cutoffs like 18, 19 or 21
years.
P. Mishra / Journal of Development Economics 82 (2007) 180–199 185
estimates from the fully specified regression model with both the individual fixed effects as well
as the pair-wise interactions between the vectors of fixed effects. The estimate of the impact of
emigration, shown in the third column of Table 1, is 0.44 (SE=0.10).12
To summarize, the basic
regressions indicate that there exists a positive, strong and statistically significant (at 1% level)
relationship between emigration and Mexican wages. A 10% decrease in the supply of workers
in any skill group due to emigration (where skill is defined by schooling and experience)
increases the average wage in that skill group by about 4%.13
4.2. Using individual level data
The estimates from the individual wage regressions are shown in Table 2. The explanatory
variables include additional individual level controls that can affect wages—marital status,
nativity (whether the person is born in Mexico or outside Mexico), occupation, industry, state
and the interaction of these with the year dummy. The estimated coefficient is 0.31 (SE=0.06).
This estimate is similar to that of the group level regressions in Table 1. The individual wage
regression with additional regressors supports the result that emigration has a positive and strong
effect on wages in Mexico.14
4.3. Self selection issues
The estimates of impact of emigration on Mexican wages may be biased if the workers in
Mexico constitute a non-random sample from the population. Selectivity bias may arise because
12
When four schooling categories are used instead of seven, the coefficient estimate in Specification III, Table 1 is 0.38
(SE=0.16).
Fig. 2. The wages shown above are the average of the logarithm of real monthly earnings. Monthly earnings are deflated
by the CPI from the IMF. The figures are for males, in the labor force, who are not enrolled in school, and have work
experience of 1–40 years, and report positive monthly earnings. HSD=High School Dropouts, HSG=High School
Graduates, SC=those with some college, CG=College Graduates.
13
I also estimate the model in differences. Regressing the difference in wages on difference in the emigrant supply
shocks, controlling for schooling, experience and period effects gives an estimate of 0.37 (SE=0.09).
14
One concern is that of unemployment in Mexico. Surprisingly, the national unemployment rate in Mexico for males is
about 1% (Source: Mexican Census, 2000. Unemployed are defined as those who are out of work and actively seeking
work), much lower than that in the US. One reason for the low unemployment could be that there is no unemployment
insurance program in the country. However, there could also be measurement errors or underemployment in Mexico that
is not measured.
P. Mishra / Journal of Development Economics 82 (2007) 180–199186
wages in Mexico are observed only for the selected workers who decide not to migrate. If the
blowest abilityQ individuals are the most likely to migrate from every skill group, then the
positive correlation between emigration and wages might not be due to the favorable impact of
emigration on Mexican wages. Instead it could be due to the blowest abilityQ individuals leaving
and raising the average wage in that skill group.
It is difficult to ascertain who emigrates in terms of the unobserved characteristics from
each skill group; hence it is difficult to predict the direction of the sample-selection bias. I
follow one approach taken to address the classical sample selection problem encountered in
estimating wage equations in the labor literature where the issue is that the wages are observed
only when the individual decides to participate in the labor force. It is argued that the sample
selection bias is less severe in a sub-sample of male workers whose labor force participation
rates are very high, so that in this restricted sample, almost everyone participates in the labor
force (Pencavel, 1986).
Applying the same logic to the case of emigration, the analysis is undertaken for a subsample
of Mexican workers who are least likely to emigrate implying that we would observe
ballQ the individuals and not just the ones who decide not to migrate. In this sub-sample, it
cannot be the case that those left behind in schooling-experience cells are different from those
who emigrate from these cells simply because almost no one (or very few) emigrate from
these cells.
The literature on Mexican migration provides plenty of evidence that some states send
more migrants than others due to long-standing regional networks. (The US census does not
ask the migrant which state in Mexico does he/she comes from). Also there has been
continuity in the state migration patterns over time (e.g., Woodruff and Zenteno, 2002; Massey
and Espinoza, 1997). Woodruff and Zenteno (2002) estimate that the states in central-west
Mexico have very high migration rates, with 10–15% of the households having an external
migrant. There are eight states mostly in southeast Mexico, where 2% or less of the
households have an external migrant.
I estimate the impact of national emigrant supply shocks on wages of workers in the low
migration states of Mexico. The assumption here is that the Mexican labor market is integrated
at the margin, i.e. that the wage impact of emigration gets transmitted to the economy as a
Table 1
Estimated effect of emigration on Mexican wages (1970–2000)
Dependent variable: average real monthly earnings (in logs) in schooling-experience-time cell (i, j, t) in Mexico
I II III
Ratio of the number of emigrants to the workforce in
Mexico in cell (i, j, t)
0.33*** (0.06) 0.42*** (0.08) 0.44*** (0.10)
p-value 0.00 0.00 0.01
Schooling, experience and time fixed effects Yes Yes Yes
Interaction between schooling and time fixed effects No Yes Yes
Interaction between experience and time fixed effects No Yes Yes
Interaction between schooling and experience fixed effects No No Yes
There are 840 observations. The total number of schooling-experience-time cells is 7*40*3=840. The standard errors
reported have been corrected for heteroskedasticity using White’s correction. The regressions are weighted by the number
of workers in Mexico in schooling-experience-time cell. *** Indicates statistical significance at 1% level. An emigrant
from Mexico in the United States is defined as a person whom the US Census records as being born in Mexico. A worker
in Mexico is defined as a person who is counted by the Mexican Census as residing in Mexico in a given year and is a
part of the labor force.
P. Mishra / Journal of Development Economics 82 (2007) 180–199 187
whole.15
The regression model in (1) is estimated using the wages from the sample of workers
from the low-migration states. The emigrant supply shocks are national shocks, as measured
before. The results for the fully specified regression model are shown in Table 3. The estimates
of the coefficient of emigration are positive and statistically significant (at 1% level) and similar
to that in Table 1 when we consider the wages of the entire sample of workers in Mexico. The
fact that the impact of emigrant supply shocks gets transmitted to the country as whole provides
evidence that the results are not driven by sample selection but rather through supply effects that
are transmitted throughout the integrated Mexican labor market.16
4.4. Endogeneity issues
Another potential concern about the empirical strategy used for the estimations is the issue of
endogeneity, i.e. the emigrant supply shock may be correlated with the unobserved component
of wages. There is evidence that migration increases when wages decline in Mexico (Hanson and
Spilimbergo, 1999; Hanson et al., 2001). If lower wages lead to increased emigration then the
estimated coefficient on emigration would be an underestimate of the impact of emigration on
Mexican wages.
To check the robustness of the results, I use an instrumental variable strategy. An ideal
instrument should be exogenous to the wages in Mexico and should be strongly correlated with
emigration. The sociology literature argues that an important factor driving US–Mexico
migration is social capital i.e. social connections to the US migrants (Massey and Espinoza,
Table 2
Estimated effect of emigration on Mexican wages—individual wage regressions (1970–2000)
Dependent variable: log real monthly earnings of individual workers in Mexico
Ratio of the number of emigrants to the workforce in Mexico in the individual
worker’s schooling-experience-time cell
0.31*** (0.06)
p-value 0.00
Experience, experience squared, dummies for schooling, year, interaction of year
dummy with schooling dummy and experience
Yes
Dummies for marital status, nativity, occupation, industry, state, interaction of year
dummy with marital status, nativity, occupation, industry, state
Yes
Number of observations 321,346
R2
0.46
The standard errors are adjusted for clustering at schooling-experience-time level. *** Indicates statistical significance at
1% level. The principal regressor ratio of emigrants to workforce in Mexico measures the emigrant supply shock in the
individual worker’s schooling-experience-time cell. This is a group variable and is the same for all the individuals in the
same schooling-experience-time cell. The data on Mexican wages are from a 1% random sample of the Mexican
population in 1970, 1990 and 2000.
16
One potential concern is that lower ability individuals may be migrating internally. However, there is not much
evidence for internal migration within Mexico. For 80% of the individuals in the sample, the state of current residence is
the state of residence 5 years ago (see also Hanson, 2003). The wage impact could be transmitted to the country as a
whole through movement of goods, capital or labor. It is not necessary that there should be perfect mobility of labor. How
are wage impacts transmitted throughout the country, is an interesting question. However, given the dataset, addressing
this question is beyond the scope of the paper and would be an interesting area for future research.
15
It is important to note that I am looking at wage adjustments over very long periods of time (10 or 20 years span).
Robertson (2000a) also provides evidence for co-movement of wages between the interior and border regions of Mexico.
Chiquiar (2003) finds that for the period 1970–1985, there was convergence in per capita state GDP levels across US states.
P. Mishra / Journal of Development Economics 82 (2007) 180–199188
1997). Each migrant creates additional social capital capable of sustaining further migration. I
apply this idea and use a measure of networks as an instrument for the emigrant supply shock.
Networks are usually based on relatives and friends where schooling and occupation are similar.
I use lagged migration rates by schooling as a measure of social networks.17
The correlation
between the past and current emigrant supply measures is high, around 0.8.
The results of the instrumental variable regressions are shown in Table 4. The IV regressions
are implemented using the individual wage specification. Specification I includes wages from
the full sample of Mexican workers as the dependent variable. Specification II includes the
wages of only workers from low migration states. In Specification II, both the concerns—
sample-selection and endogeneity, are addressed together. The IV regressions do not include
dummies for schooling. This is a necessary identification assumption to use past migration rates
by schooling as instrument.
The first stage regression (not shown) indicates a significant and positive relationship
between the past and current emigrant supply shocks. In the first stage regression, the coefficient
of the lagged emigrant supply shock is 1.0 (SE=0.0) and the R2
=0.7. The second stage IV
estimate (shown in Table 4) is about 0.3–0.4 (SE=0.1). The IV estimate of 0.42 in Table 4
(Specification I) is higher than the non-IV regression estimate of 0.31 (see Table 2). To
summarize, the instrumental variable regression results support that emigration has a large and
positive effect of on Mexican wages.
4.5. How long do the labor supply shocks last?
In the empirical exercise above, the emigrants are grouped into schooling-experience cells
irrespective of how long ago they left Mexico. The emigrant supply shocks are assumed to be
Table 3
Estimated effect of emigration on Mexican wages—sample selection (1970–2000)
Dependent variable: average real monthly earnings (in logs) in schooling-experience-time cell (i, j, t) of workers
in Mexico from low-migration states
I II III
Ratio of the number of emigrants to the workforce
in Mexico in cell (i, j, t)
0.32*** (0.10) 0.54*** (0.11) 0.54*** (0.13)
p-value 0.00 0.00 0.00
Schooling, experience and time fixed effects Yes Yes Yes
Interaction between schooling and time fixed effects No Yes Yes
Interaction between experience and time fixed effects No Yes Yes
Interaction between schooling and experience fixed effects No No Yes
There are 840 observations. The total number of schooling-experience-time cells is 7*40*3=840. The standard errors
reported have been corrected for heteroskedasticity using White’s correction. The regressions are weighted by the number
of workers in Mexico in schooling-experience time cell (i, j, t). *** Indicates statistical significance at 1% level. The
wages are only for workers in eight low migration states of Baja California Sur, Campeche, Chiapas, Distrito Federal,
Quintana Roo, Sonora, Tabasco and Yucatan. The emigrant supply shocks are national emigrant supply shocks for
Mexico as a whole.
17
Network connections based on experience may not be very plausible. However, IVestimate is higher if past migration
rate by schooling and experience is used as instrument. One potential concern with using lagged explanatory variable as
instrument is that if the dependent variable is correlated over time, then the instrument may not be exogenous. However,
this is less of a concern here because I am using data over very long periods of time. The concern would be more severe if
I were using high frequency data.
P. Mishra / Journal of Development Economics 82 (2007) 180–199 189
permanent. One possibility is that the shocks persist for a shorter duration. The question that
follows is how long do the shocks last, and whether the entire stock of emigrants should affect
wages in Mexico, even when some of them have been out of the country for two or three
decades.
In order to address this concern, I break up the emigrant supply shock into two parts. The
emigrant sample is divided into (i) recent emigrants defined as those who emigrated in the last
decade—the 1990 emigrant sample is restricted to those who emigrated between 1980 and 1990,
and the 2000 emigrant sample is restricted to those who emigrated between 1990 and 2000, and
(ii) those whose emigrated more than a decade back—the 1990 emigrant sample is restricted to
those who emigrated before 1980, and the 2000 emigrant sample is restricted to those who
emigrated before 1990.
I run an alternative empirical specification given by:
wijt ¼ dSRmSR
ijt þ dLRmLR
ijt þ si þ vj þ pt þ si4pt þ si4vj þ ej þ eijt ð3Þ
where mSR
ijt u
MSR
ijt
Nijt
; mLR
ijt u
MLR
ijt
Nijt
M SR
ijt =number of emigrants who migrated in the last decade,
M lR
ijt =number of emigrants who migrated more than a decade back. dSR can be termed as the
bshort-runQ effect and dlR can be termed as the blong-runQ effect of emigration on wages.
The results are shown in Table 5, column I. Since the sample of emigrants in 1970 is small, I
do not include 1970 in the analysis. The estimated short-run effect is 0.34, and it is statistically
significant at 1% level ( p-value=0.00). The estimated long-run effect is 0.53 ( p-value=0.00).
The significant long run effect suggests that the effect of emigration on wages tends to persist
and does not die out over time.
For comparison, the estimates obtained when all emigrants are included in one measure, are
shown in column II. The coefficient is 0.42 ( p-value=0.00), it lies in between the short-run and
the long run effect, and can be termed as the baverage effectQ of emigration on wages. The basic
empirical specifications used in the paper essentially capture this baverage effectQ.
Table 4
Estimated effect of emigration on Mexican wages—instrumental variable (1990–2000)
Dependent variable: Log real monthly earnings of individual workers in Mexico in 1990 and 2000
I II
Full Sample of
workers in Mexico
Restricted sub-sample
of workers from low
migration states in Mexico
Emigrant supply shock in 1990 and 2000 in the
worker’s schooling-experience group instrumented
with emigrant supply shock in 1970, 1990 in
the worker’s schooling group
0.42*** (0.09) 0.32*** (0.09)
p-value 0.00 0.00
Number of observations 261,246 59,764
R2
0.36 0.42
The standard errors are adjusted for clustering at schooling-experience-time level. *** Indicates statistical significance at
1% level. In the first stage regression, emigrant supply shock in 1990 and 2000 in a worker’s skill group is regressed on
lagged emigrant supply shocks in 1970 and 1990 in the worker’s schooling group. The coefficient of lagged emigration is
1.00 and 0.97 (SE=0.0) in specifications I and II, respectively, and the R2
of the first stage regressions in both the
specifications is 0.7. The first and second stage regressions include experience, experience squared, dummies for year,
nativity, marital status, occupation, industry, state, and interactions of the year dummies with experience, nativity, marital
status, occupation, industry and state.
P. Mishra / Journal of Development Economics 82 (2007) 180–199190
4.6. Additional robustness checks
4.6.1. Alternative categories of experience
To check for the robustness of the results I also allow for alternative experience categories.
The workers are divided into eight instead of forty categories as used in the earlier analysis
(Table 1). The idea behind having broader experience groups is to allow for the possibility of
non-independence of earnings in adjacent schooling-experience cells, which could be a source of
bias in the standard errors. The results are shown in Appendix Table 1 (column I). The estimate
when broader experience groups are used is much higher than the basic regressions in Table 1,
when work experience was divided into finer categories. One possible reason could be that there
is measurement error in the emigrant supply shock when using finer experience categories,
which leads to attenuation bias in the estimates. Using broader experience categories reduces the
measurement error.
4.6.2. Labor demand shocks
The aim of the paper is to isolate the impact of emigration, controlling for other variables
that are correlated with emigration and affect wages. If there are labor demand shocks that
are correlated with emigration and drive up the wages, then the estimates will be biased.
Two possible sources of labor demand shocks are technology shocks and capital flows.
However, based on the identification strategy used in the paper, shocks correlated with both
emigration and wages, which are not controlled for in the framework must be skill
(schooling and experience) specific, and must vary at the schooling-experience-time level.
Other shocks are controlled for by the fixed effects and the two-way interactions specified in
Eq. (1).
Positive technological shocks and capital flows to particular schooling-experience cells would
increase labor demand. This would increase wages and thus discourage emigration implying that
emigration is negatively correlated with the unobserved component of wages. Hence the positive
estimates would be biased downwards. In this case, the true effect of emigration on wages would
be bigger in magnitude than the estimated effect.
However, if there exist technology shocks, which are positively correlated with emigration,
then this would imply that the impact of emigration on wages is overestimated and raise serious
Table 5
Estimated effect of emigration on Mexican wages (1990–2000): alternative definition of emigrant supply shocks
I II
Emigrant supply shock with recent migrants
(who came within the last 10 years)
0.34*** (0.08)
p-value 0.00
Emigrant supply shock with those migrants who
have been out of the country for more than a decade
0.53*** (0.10)
p-value 0.00
Emigrant supply shock with all emigrants included 0.42*** (0.08)
p-value 0.00
Number of observations 560 560
The standard errors reported have been corrected for heteroskedasticity using White’s correction. The regressions are
weighted by the number of workers in Mexico in schooling-experience time cell (i, j, t). *** Indicates statistical
significance at 1% level. The regressions include fixed effects for schooling, experience, time and all the two-way
interactions.
P. Mishra / Journal of Development Economics 82 (2007) 180–199 191
concerns. For example, there could be positively correlated labor demand shocks in the US and
Mexican labor markets that raise labor demand and wages in Mexico but also attract Mexican
migrants to the US. To address this concern, I control for average wages in the US in the
schooling-experience-time cell in Appendix Table 1, column II. The estimate of the coefficient
of emigration is similar to those in the basic specifications. I also control for the average wage of
Mexican migrants in the US in the schooling-experience-time cell. Wages of migrants can be
considered as a proxy for remittances as well. The coefficient on emigration (not shown) is
similar to that shown in Appendix Table 1, column II.
4.6.3. Undercount of illegal migrants
Another issue is that of undercount of illegal migrants in the US census. To correct for the
undercount, I make adjustments to the measure of emigrant supply shock making the following
two assumptions: (i) all illegal migrants are high school dropouts (ii) that the Census
undercounts by 15% or 30% the number of emigrants in each of the schooling-experience cells
with high school dropouts in the age group of 26–45 (or 9–28 years of experience) (Costanzo et
al., 2001; Jorda´n and Tera´n, 1996). The basic regression model in (1) is estimated by revising the
number of emigrants by the estimates of the undercount. The estimates (shown in Appendix
Table 1, columns III and IV) are similar to the estimates in Table 1. Thus, the results are robust to
the undercount of illegal migrants.
4.6.4. Cross skill effects
The empirical specification in (1) does not control for emigration in other skill categories. To
allow for cross-effects, I augment the empirical specification in (1) to allow for emigration in
other schooling categories (in the same experience group). The coefficient of emigration in the
own schooling-experience cell is constrained to be the same for all schooling groups. The
regression model is specified as:
wijt ¼ dmijt þ
X
s p1
dsmsjt þ si þ vj þ pt þ si4pt þ vj4pt þ si4vj þ eijt ð4Þ
where msjt is the ratio of emigrants to workers in Mexico in the schooling group s, s p i,
experience cell j, at time t. The estimates (shown in Appendix Table 1, column V) of the
owneffect (d) are positive and statistically significant (at least at 5% level), and the magnitude is
similar to those in Table 1. Additionally, I also control for emigration in two closest experience
groups in the same schooling groups, using the individual wage specification. The estimate of
the own-effect (not shown) is positive, statistically significant and similar to the estimates in
Table 2. To summarize, controlling for emigration in other schooling groups and experience
cells, reinforces the strong and positive own-effect of emigration in a particular skill group on
wages in that skill group.
4.6.5. Additional specification checks
Appendix Table 2 shows some additional specification checks. Column I shows the result
when the regressions in Table 1 are not weighted by the number of Mexican workers in the skill
group. Column II shows the results when both males and females are included in the
measurement of the emigrant supply shock. Column III controls for the number of Mexican
workers in the schooling-experience-time cell to address the concern that changes in the size of
the Mexican workforce due to reasons other than emigration could be driving the results.
P. Mishra / Journal of Development Economics 82 (2007) 180–199192
Column IV shows the result when the migrant sample excludes those who migrated at less than
21 years of age to increase the likelihood that the migrants got their schooling back in Mexico.
The estimates in all these specifications remain positive and statistically significant and
comparable to the estimates in Table 1.
5. Implications of the results
There are at least two key quantifiable implications of the impact of emigration on real wages.
First, the variation across schooling groups has implications for wage inequality within Mexico.
Secondly, the estimated elasticity can be used to compute simple welfare measures based on a
standard partial equilibrium model of labor demand and labor supply.
5.1. Implications for wage inequality
Since the mid-1980s, Mexico has experienced a widening wage inequality and this has been
attributed mainly to an increase in relative demand for skill owing to trade and investment
liberalization in Mexico (Hanson, 2003; Robertson, 2000b; Hanson and Harrison, 1999;
Feenstra and Hanson, 1997; Cragg and Epelbaum, 1996). This section focuses on an alternative
channel—the impact of emigration on wage inequality.
The estimates of the effect of emigration on wages (Table 1) can be used to calculate the
wage impact in different schooling groups. The estimate of 0.4 implies that a 10% decrease in
the supply of workers owing to emigration in a particular schooling-experience group
increases the wages in that group by 4%. To estimate the wage impact of emigration in a
particular schooling category, we have to aggregate the impact in all experience cells within
that schooling category.
The Ideal Log Change Index Number measures the percentage change in unit labor costs
between two time periods. Following Sato (1976), the bIdeal Log Change Index NumberQ, or the
change in log wage for schooling group S between 1970 and 2000, can be expressed as:
DlnwS;2000À70 ¼
X
j
/jSDlnwSj;2000À70 ð5Þ
Where /jS is the weight for experience cell ( j) within a schooling group (S), consistent with the
CES production function and can be expressed as:
/jS ¼
DEjS=DEjS
P
j DEjS=DEjS
À Á ð6Þ
Where EjS is the earning share of experience group j in total earnings in schooling group S,
DEjS =EjS,2000 ÀEjS,1970, DEjS =logEjS,2000 ÀlogEjS,1970. Using the estimated impact of
emigration on wages (Specification III in Table 1), and using (5) and (6), emigration between
1970 and 2000 increased the wages of high school dropouts by 5%, of high school graduates by
15%, those with some college education by 13%, and of college graduates by around 2%.
Aggregating the wage impact over the schooling groups, the emigrant outflow between 1970 and
2000 increased the wage of the average Mexican worker by 8%.
It is useful to look at the estimated increase in wages due to emigration between 1990 and 2000
because the Mexican Census data shows increasing wage inequality during this period. As shown
in Fig. 3, between 1990 and 2000, the relative wages of high school graduates increased by 11%, of
P. Mishra / Journal of Development Economics 82 (2007) 180–199 193
those with some college education by 21% and of college graduates by 8% (relative to high school
dropouts). Using (5) and (6), the outflow of workers between 1990 and 2000 increased the
relative wages of high school graduates by 4% and those with some college education by 3%
(relative to high school dropouts). Thus, the estimated impact of emigration accounts for
approximately 37% of the increase in relative wages of high school graduates and 14% of the
increase in relative wages of those with some college education. The greater impact of
emigration on wages of workers with 12–15 years of schooling is driven by the fact that this
group exhibits the highest emigration rates given that we assume constant labor demand
elasticity across schooling groups.18
Emigration does not explain the increase in relative wages of college graduates. The
magnitude of the positive effect of emigration on the wages of high school dropouts is higher
than that on the wages of college graduates; hence emigration leads to a decrease in the relative
wage of college graduates. Since emigration does not explain the entire rise in wage inequality, it
should be considered as a complementary explanation for the rising wage inequality in Mexico.
Emigration as a channel to explain increasing wage inequality in developing countries has
received little attention in the literature (Robbins, 2002). The estimates in the case of Mexico
suggest that it could potentially be an important factor.
5.2. Welfare implications
In this section, I use the estimated impact of emigration on Mexican wages to compute simple
welfare measures based on the standard textbook model of labor demand and supply. The simple
economic model of labor demand and supply is an important starting point to quantify the
welfare implications and has been used in the literature in the context of immigration and capital
flows (Borjas, 1995; MacDougall, 1960). The calculations in this paper differ in one important
Fig. 3. The figures are for males, in the labor force, who are not enrolled in school, and have work experience of 1–40
years, and report positive monthly earnings. HSD=High School Dropouts, HSG=High School Graduates, SC=those
with some college, CG=College Graduates. Relative wages are measured by the ratio of the real monthly earnings in
schooling category s (s =HSG, SC, CG) to the real monthly earnings of HSD. Monthly earnings are deflated by the CPI
from the IMF.
18
I also allow for different coefficients across schooling categories. However, only experience and period fixed effects
can be included in these regressions (and not their interactions). The estimates are imprecise but the effect is much higher
in the higher schooling categories. One possible reason for the small effect in the lower schooling category could be
unemployment.
P. Mishra / Journal of Development Economics 82 (2007) 180–199194
aspect from that of the gains from immigration that exist in the literature. Prior work calculates
the impact based on the estimates of labor demand elasticity drawn from studies unrelated to
migration. This paper uses the elasticity of wages with respect to emigration estimated from the
Mexican data to do the calculations.
Welfare is measured by GDP accruing to those who have stayed behind (TSB) in Mexico.
Consider a single good which is the numeraire and its production function is given as:
Q ¼ F K; Lð Þ ð7Þ
Where K is the fixed factor assumed to be internationally immobile, L is the labor employed in
production and Q is the gross domestic product. Fig. 4 shows the simple model of labor demand
and supply. The initial equilibrium wage is w0. A large emigration flow of a magnitude M of
workers reduces the labor force from (N +M) to N. Wage rate as a result increases from w0 to w1.
The workers who have stayed behind gain an area equal to w0w1ab (rectangle region A), owners
of the fixed factors in the economy lose an area equal to w0w1ac (rectangle region A +triangle
region B) and the country as a whole loses the triangle abc (region B). The triangle abc (region
B) can be termed as the bemigration lossQ.
As a fraction of the post-emigration GDP:
Emigration loss ðtriangle B in Fig: 4Þ ¼ 1=2ð Þsem2
ð8Þ
Gain to workers who have stayed behind ðrectangle A in Fig: 4Þ ¼ sem ð9Þ
Loss of the owners of fixed factors ðA þ B in Fig: 4Þ ¼
1
2
sem2
þ sem ð10Þ
Where e ¼ eLLjL¼N ¼ Dw
DL
N
w, s ¼ wN
wNþrK, m ¼ M
N , s is the share of labor in GDP, e is the percent
change in wages due to a 1% change in the labor force, where the elasticity is measured at the
post-emigration labor-force and m is the ratio of emigrants to the workforce in Mexico. The
expressions in (8)–(10) are analogous to Borjas (1995) study of immigration.
The emigration loss and distributive impact as a result of the flow of workers between 1970
and 2000 can be estimated using (8)–(10). From Section 5.1, the estimated change in wages of a
Fig. 4. Effect of immigration on wages—labor demand–supply model.
P. Mishra / Journal of Development Economics 82 (2007) 180–199 195
typical worker in Mexico due to flow of emigrants between 1970 and 2000 (e*m) is around 8%.
The assumed share of labor income in GDP (s) is 0.7 (Borjas, 1995, 2003; Hall and Jones,
1999).19
The emigrant share of the Mexican workforce (m) estimated from the 2000 Mexican
and US censuses is about 16% (including men and women).20
The estimated emigration loss to Mexico is about 0.5% of Mexico’s GDP in 2000. The
economic loss from emigration in a $580 billion economy is about $3 billion per year. The gain
to the workers who have stayed behind is about 5.9% of GDP and the loss to the owners of the
fixed factors is about 6.4% of GDP, the difference being the estimated aggregate economic loss
to Mexico. Thus, the estimated distributive impact is about 12–13 times the aggregate economic
loss to Mexico.
6. Conclusion
This paper presents the first attempt in the literature to estimate the effect of emigration on the
national wages in a source country. The main result in the paper is that there is a strong and
positive relationship between emigration and wages in Mexico. The impact of emigration on
wages has important implications for wage inequality across schooling groups and for national
income distribution between labor and other factors (which are assumed to be immobile in this
paper).
The measured welfare impact on who have stayed behind in the sending country is based
on the very simple static partial equilibrium framework. There may be other counteracting
forces like the impact on human capital formation, positive external effects through networks
and diaspora, remittances, etc., that can outweigh this loss and result in a net benefit to the
source country. Also, if Mexico has some market power in the world market, emigration may
affect terms of trade of Mexico. Quantifying these additional channels through which
emigration can affect the welfare of Mexican workers who have stayed behind, is a subject for
further research.
Acknowledgements
This paper is a part of my PhD dissertation at Columbia University. I am deeply indebted to
Donald Davis and David Weinstein for their guidance and support. I am also grateful to Ronald
Findlay, Jagdish Bhagwati, Rajeev Dehejia, Francisco Rivera-Batiz, Gordon Hanson, George
Borjas, Stanley Fischer, David Autor, Joseph Tracy, Albert Fishlow, Sanjay Reddy, Miguel
Urquiola, Cristian Pop-Eleches, Till Von Wachter, Lena Edlund, Cordelia Reimers, Rohini
Pande, Ratna Sahay, David O. Robinson, Paul Cashin, two anonymous referees and seminar
participants at Columbia, Yale, World Bank, IADB, FRB Boston and the IMF for comments and
discussions. I also wish to thank the University of Minnesota Historical Census Projects team,
Steven Ruggles and Matthew Sobek et al. (Integrated Public Use Microdata team Series: Version
3.0 University of Minnesota, 2003) for helping me with the data from the US and Mexican
Censuses. All errors are mine.
20
The elasticity estimate used in the welfare calculations is from Table 1 (when only men are included in the analysis).
However, the estimate is very similar when women are included as well (Appendix Table 2, column II).
19
According to Mexico’s national accounts, labor share of GDP is about 0.35; this figure seems to be highly
underestimated. However, the welfare results are qualitatively unchanged under the alternative estimate of the labor share.
P. Mishra / Journal of Development Economics 82 (2007) 180–199196
Appendix
Appendix Fig. 1. Emigrant supply shock—by schooling and experience. The emigrant supply shock is defined as the
ratio of Mexican-born emigrants in the US to the workforce in Mexico. The estimates are for males, in the labor force,
who are not enrolled in school, and have work experience of 1–40 years. The emigrant sample excludes those who
migrated at an age of less than 17 years.
Appendix Table 1
Robustness checks
I II III IV V
8 experience
groups
Controlling for
average wages in the
US in schooling
experience time cell
15% undercount
of illegal migrants
in the Census
30% undercount of
illegal migrants in
the Census
Controlling for
cross-schooling
effects
Ratio of emigrants
to the workforce
in Mexico
1.1** (0.48) 0.38** (0.16) 0.42*** (0.15) 0.45*** (0.15) 0.31** (0.16)
p-value 0.03 0.02 0.01 0.00 0.05
The standard errors reported have been corrected for heteroskedasticity using White’s correction. The regressions are
weighted by the number of workers in Mexico in schooling-experience time cell (i, j, t). ***, ** and * indicate statistical
significance at 1%, 5% and 10% levels, respectively. All regressions include fixed effects for schooling, experience, time
and all the two-way interactions.
P. Mishra / Journal of Development Economics 82 (2007) 180–199 197
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