Emigration, Labor Supply, and Earnings in Mexico

April 2005

Gordon H. Hanson*
University of California, San Diego andNational Bureau of Economic Research

Abstract. In this paper, I examine changes in labor supply and earnings across regions of
Mexico during the 1990s. I focus the analysis on individuals born in states with either
high-exposure or low-exposure to emigration, as measured by historical data on statemigration to the United States. During the 1990s, rates of external migration and interval
migration were higher among individuals born in high-migration states. Consistent withpositive selection of emigrants in terms of observable skill, emigration rates appear to behighest among individuals with earnings in the top half of the wage distribution.
Controlling for regional differences in observable characteristics and for initial regional
differences in earnings, the distribution of male earnings in high-migration states shiftedto the right relative to low-migration states. Over the decade, average hourly earnings inhigh-migration states rose relative to low-migration states by 6-9%.

*

I thank David Autor, Jose Ernesto Lopez Cordoba, Chris Woodruff, and participants in
the NBER conference on Mexican Immigration for helpful comments. Jeffrey Lin
provided excellent research assistance.

1. Introduction
Over the last several decades, migration to the United States has profoundly
affected the Mexican economy. The most obvious change has been to Mexico’s labor
supply. Between 1970 and 2000, the share of the Mexican population (individuals bornin Mexico) residing in the United States increased from 1.7% to 8.6% (Figure 1).1
Emigration rates have been rising steadily over time and are highest for young adults.
Between 1990 and 2000, 10.0% of males and 7.7% of females born in Mexico between
1965 and 1974 migrated to the United States, raising the share of this age cohort living in
the U.S. to 17.5% for males and 12.6% for females (Table 1).
Not surprisingly, the outmigration of labor appears to have put upward pressure
on wages in Mexico. Mishra (2004) estimates that in Mexico over the period 1970-2000
the elasticity of wages with respect to the outflow of migrant labor was 0.4 and thatemigration raised average wages in the country by 8.0%. Upward pressure on wages has
been strongest for young adults with above-average education levels (those with 9 to 15
years of schooling), who in the 1990s were the individuals most likely to migrate to theUnited States (Chiquiar and Hanson, 2005). Increased labor flows between Mexico andthe United States appear to be one factor contributing to labor-market integration between
the two countries. For the 1990s, Robertson (2000) finds that a shock that raises U.S.
wages by 10% raises wages in Mexico by 1.8% to 2.5%.
Were the only effect of emigration to raise wages for migrants and for nonmigrating workers who substitute for migrant labor, the labor outflow would yield static

1

In this calculation, the numerator is the population of individuals born in Mexico, as enumerated in the
U.S. population census, and the denominator is the sum of this figure and the population of individuals
born in Mexico, as enumerated in the Mexican population census. This calculation ignores the small
number of individuals born in Mexico who have migrated to third countries.

1

welfare losses in Mexico. However, an additional consequence of Mexican emigration
has been an increase in the return flow of remittances.

In 2003, remittances from

Mexican immigrants in the United States equaled 2.0% of Mexican GDP (IADB, 2004).
These appear to more than offset the loss in GDP due to emigration.2
An important aspect of migrant behavior in Mexico is that the propensity toemigrate varies greatly across regions of the country. Due partly to historical accident,
central and western Mexico have long had the country’s highest labor flows abroad. In
Figure 2, which shows the fraction of households that sent migrants to the United Statesover 1995-2000 by Mexican state, emigration rates are relatively low in states along the
U.S. border, sharply higher in states 600-1200 kilometers from the United States, andlowest in distant southern states. Regional variation in migration behavior suggests thatthe labor-market consequences of migrant outflows may be concentrated in specificareas. If this is true, estimates of the impact of emigration at the national level may
understate its impact on the most affected regions. While the importance of specificsending regions in Mexican migration to the United States has long been recognized(Cardoso, 1980), there is relatively little empirical work that assesses the regional
economic effects of emigration in Mexico (Durand, Massey, and Zenteno, 2001).
In this paper I examine the regional impacts of emigration on labor supply andlabor-market earnings in Mexico. I compare changes in labor-market outcomes acrossindividuals between 1990 and 2000 in two groups of states, states that had highemigration rates in the 1950s and states that had low emigration rates in the 1950s. There

2

Based on Mishra’s (2004) estimates, the emigration loss in Mexico for 2000 would be 0.45% of GDP (0.5
times change in wages due to emigration of 8.0% times loss in labor supply due to emigration of 16.0%
times labor share of income of 0.70). In that year, remittances were 1.1% of Mexican GDP. See Borjas(1999a) for estimates of the immigration surplus for the United States.

2

are two key identifying assumptions in my analysis. One is that labor is sufficiently
immobile across Mexican regions for region-specific labor-supply shocks to affectregional earnings differentials. Robertson (2000), Chiquiar (2004), and Hanson (2004)
provide evidence of region-specific labor-market shocks having affected Mexico’s
regional wage structure, which is consistent with some degree of regional labor
immobility. The second identifying assumption is that current opportunities to migrate to
the United States depend on regional historical migration patterns. One reason this may
be the case is that migration networks are regionally organized and historicallydependent. Munshi (2003) and Orrenius and Zavodny (2004) are recent contributions toa large literature that finds that in Mexico access to family or community networks helps
migrants enter and succeed in the United States.3
In the estimation, I use migration rates in the 1950s as a reduced-form
determinant of current migration opportunities. Since high emigration in the past could
have altered regions in a manner that affects current labor-market conditions, a reducedform approach is more appropriate than using past migration behavior as an instrument
for current migration. To control for internal migration, I use the 1950s emigration rate
in an individual’s birth state, rather than his current state of residence.

Historicalmigration rates in an individual’s birth state are thus meant to capture current access tomigration networks, and so current opportunities to emigrate, in the Mexican regional
labor market in which an individual is located. The persistence in regional differences inmigration behavior (Figure 3) is roughly consistent with my identifying assumptions.

3

An implicit third identifying assumption is that emigration incentives for Mexicans were stronger in the
1990s than in previous decades, which in combination with the second assumption would imply that any
negative labor supply shock associated with emigration would be larger in states with a longer history of
U.S. migration. Data presented in section 3 are consistent with this assumption.

3

The challenges to identifying the regional consequences of emigration in Mexico
are analogous to those in identifying the regional consequences of immigration in the
United States. Many studies have found that across U.S. cities and states immigrant
inflows are only weakly negatively correlated with wage changes for U.S. native
workers, suggesting that immigration has had little impact on the U.S. wage structure (seeLaLonde and Topel, 1997; Smith and Edmonston, 1997; Borjas, 1999; Card; 2001).
Borjas, Freeman, and Katz (1997) argue that cross-area wage regressions of this typeidentify the wage impact of immigration only under restrictive assumptions.

Thetendency for immigrants to settle in regions with high wage growth makes estimates of
the immigration wage impact based on cross-area regressions susceptible to upward bias.
The standard practice of using the preceding decade’s regional immigrant stock toinstrument for current regional immigrant inflows may not be valid if regional labormarket shocks persist over time. Borjas (2003) examines age and education cohorts atthe national level and finds larger wage effects from immigration. He estimates that over
1980-2000 the elasticity of U.S. native wages with respect to immigrant inflows was 0.30.4 and that immigration contributed to a decrease in U.S. average wages of 3%.
Similar to the cross-area regression approach, I distinguish between Mexican
states based on historical migration behavior. However, distinct from this approach I am
able to use much longer lags on regional migration rates and to measure historicalmigration rates in an individual’s birth state. These features help address the concernsthat (i) regional labor-market shocks may persist for more than a decade, and (ii) an
individual’s current state of residence may be affected by current regional migration4

rates. The assumptions underlying my approach are thus perhaps less restrictive thanthose underlying the standard cross-area approach in literature on U.S. immigration.
An obvious challenge for the estimation is that there may be other, unobserved
differences between high and low migration states that may affect current labor-market
outcomes.

By examining regional differences in changes in outcomes, rather thanregional differences in outcome levels, I am able to control for time-invariant regionspecific characteristics. Still, there may have been other shocks in the 1990s that had
differential effects on regions with high versus low opportunities to migrate to the UnitedStates.

Candidate shocks include the North American Free Trade Agreement, theprivation and deregulation of industry, the reform of Mexico’s land-tenure system, andthe 1994-1995 peso crisis.4 The potential for these shocks to contaminate the analysis is
an important concern, which I address in discussing qualifications to my results.
In the next section, I document further how migration behavior varies acrossregions of Mexico and discuss the criterion I use for selecting which Mexican states toinclude in my sample. In section 3, I describe how changes in labor supply vary acrosshigh and low-migration states in Mexico and compare mean earnings and the distributionof earnings in high and low-migration states. In section 4, I use standard parametrictechniques and non-parametric techniques developed by DiNardo, Fortin, and Lemieux(1996) and Leibbrandt, Levinsohn, and McCrary (2004) to examine how earnings havechanged over time in high and low migration states. By wage of conclusion in section 5,
I discuss limitations of the estimation strategy and ideas for extending the analysis.

4

See Chiquiar (2003) on recent policy changes in Mexico. For work on the labor-market implications ofglobalization in Mexico, see Cragg and Epelbaum (1996), Feenstra and Hanson (1997), Revenga (1997),
Hanson and Harrison (1999), Robertson (2000, 2004), Feliciano (2001), Farris (2003), Ariola and Juhn
(2003), Chiquiar (2004), and Hanson (2004).

5

2. Regional Patterns of Emigration in Mexico
2.1 Data Sources
Data for the analysis come from two Mexican sources. In 1990, I use the 1%
microsample of the XII Censo General de Poblacion y Vivienda, 1990, and in 2000 I use
a 10% random sample of the 10% microsample of the XIII Censo General de Poblacion y
Vivienda, 2000. Unfortunately, the 1990 census contains no information about household
emigration behavior. The 2000 census includes two questions related to emigration: (i)whether anyone from the household migrated to the United States (or another foreigncountry) in the last five years (and the number, age, and gender of these individuals), and(ii) whether anyone in the household received income in the previous month in the formof remittances from migrants located abroad (and the quantity received). These questions
have obvious shortcomings. They provide no indication of the education of migrants,
return or round-trip migration, migration before 1995, annual receipts of remittances, ortransfers from migrants in kind rather than in cash. Still, the 2000 census is useful in that
it is the only nationally representative sample available for Mexico that contains
information about migration to the United States.
For data on historical migration patterns, I use estimates of state emigration rates
from Woodruff and Zenteno (2001). They calculate the fraction of each Mexican state’s
population that migrated to the United States over 1955-59 by combining data on
Mexican state populations with data on annual U.S. immigration of temporary legal
workers from each Mexican state under the U.S. Bracero Program.

The Bracero

Program, which lasted from 1942 to 1965, allowed U.S. employers to import workers
from Mexico (and the Caribbean) to fulfill short-term labor contracts. Most braceros

6

worked in agriculture (Calavita, 1992). Woodruff and Zenteno (2001) also provide dataon state emigration rates in 1924, which I use in some empirical exercises.
For the analysis of earnings, I focus on men, since their labor-force participation
rates are relatively stable over time, rising modestly from 73% in 1990 to 74% in 2000
(and are quite similar in high and low migration states). Labor-force participation rates
for women are low and variable over time, rising from 21% in 1990 to 32% in 2000. For
women, this creates issues of sample selection associated with who supplies labor outside
the home that complicates examining changes in the distribution of earnings.

2.2 Regional Patterns in Mexican Migration to the United States
Large scale migration from Mexico to the United States began in the early 20th
century. The construction of railroads in the late 19th century linked interior Mexico to
the U.S.-Mexico border, which gave U.S. employers improved access to Mexico labor(Cardoso, 1980). In the early 1900s, growers in Texas began to recruit farm laborers in
Mexico.

At the time, the population on the Texas-Mexico border was small anddispersed. To find workers, recruiters followed the main rail line into Mexico, which ran
southwest through relatively densely populated states in the west-central region of thecountry. Early migrants came primarily from nine states in this region (Durand, Massey,
and Zenteno, 2001).5 The recruitment efforts of U.S. employers intensified in the 1920s,
after the U.S. Congress imposed stringent quotas on U.S. legal immigration, which
sharply reduced immigration of low-skilled labor from southern and eastern Europe.
Recruitment intensified further in the 1940s, after Congress passed legislation allowing

5

These nine states are Aguascalientes, Colima, Durango, Guanajuato, Jalisco, Michoacán, Nayarit, San
Luis Potosí, and Zacatecas.

7large-scale temporary legal immigration from Mexico under the Bracero Program(Calavita, 1992). From the 1920s to the 1960s, the nine west-central states accounted for
44.0% to 56.1% of Mexican migration to the United States (but only 27.1% to 31.5% of
Mexico’s total population) (Durand, Massey, and Zenteno, 2001).
After working in the United States, many migrants return to Mexico where theyoften assist later generations in emigrating. Migrants remaining in the United States havecreated home-town associations that help members of their communities in Mexico makethe transition to living north of the border (Cano, 2004). In addition to home-town
associations, there appear to be many informal networks through which current migrants
help prospective migrants enter the United States, find housing in U.S. cities, and obtainjobs with U.S. employers. These networks are often embedded in relationships involving
family, kin, or community of birth, which gives them a strong regional component. Of
218 home-town associations formed by Mexican immigrants enumerated in 2002 surveyof such organizations in southern California, 86.6% were associated with one of the ninewest-central states (Cano, 2004). Networks appear to be important for migrant outcomesin the receiving country. Munshi (2003) finds that Mexican immigrants in the UnitedStates are more likely to be employed the larger is the U.S. population of residents fromtheir home community in Mexico (where he instruments for the size of the homecommunity population using time-series data on regional rainfall in Mexico).

The

importance of migrant networks for migration behavior and their strong regional
character may help explain regional persistence in migration patterns.
Figure 3 provides graphical evidence of persistence in regional migration
behavior. The states that had high migration rates in the 1950s, during the height of the8Bracero Program, continue to be high migration states. The correlation between state
emigration rates in the 1995-2000 and the 1955-59 periods is 0.73. The correlation
between state migration rates in the 1995-2000 and 1924 periods is 0.48.
As Figure 2 illustrates, high migration states are not those closest to the UnitedStates. Nor does income appear to be the sole determinant of emigration. Table 2 reports
regressions of state emigration rates in 1995-2000 on income and other statecharacteristics. In column 1, there is a negative correlation between state emigration
rates and state per capita GDP, but the explanatory power of income isn’t all that high. In
column 2, adding distance to the United States (and distance squared) more than doubles
the R-squared of the regression. The relation between emigration and proximity to the
U.S. is nonlinear, with emigration initially rising with distance (reflecting low emigrationin states on the U.S. border) and then declining with distance (reflecting high emigration
for central states and low emigration for distant southern states). In column 3, adding thestate emigration rate in 1924 as an independent variable raises the R-squared of theregression from 0.25 to 0.46. However, there appears to be little covariation between the
1995-2000 and 1924 emigration rates that is independent of the 1950s emigration rate. In
column 4, once the 1955-59 emigration rate is added the R-squared rises further to 0.67
and the 1924 migration rate becomes statistically insignificant, reflecting the strong
historical persistence in state emigration patterns. Columns 5-8 repeat the exercise using
the fraction of households in 2000 receiving remittances from migrants abroad as thedependent variable, with similar results.
If states with relatively high emigration rates are also states that are more exposed
to other aspects of globalization, then the empirical analysis might confound the effects

9

of emigration with the effects of trade or capital flows. During the 1980s and 1990s, theMexican government lowered barriers to international trade and foreign investment.
Chiquiar (2004) and Hanson (2004) find that since 1985 Mexican states more engaged ininternational trade have enjoyed faster growth in average income and labor earnings.
However, high emigration states do not appear to have benefited disproportionately fromtrade and investment reform. As expected, trade liberalization has affected states on the
U.S.-Mexico border most strongly, and, as Figure 2 shows, border states are not highemigration states. Most high emigration states appear to have relatively low exposure toforeign trade and investment. This is seen in Figures 4 and 5, which plot the fraction of
the state population migrating to the United States over 1995-2000 against the share offoreign direct investment in state GDP and the share of imports in state GDP. Table 3
shows that across Mexico states in the 1990s emigration rates are weakly negatively
correlated with exposure to trade and foreign investment. It appears high exposure toemigration is not associated with high exposure to globalization. I discuss variation instate exposure to these and other shocks again in Section 5.

2.3 Sample Design
The goal of this paper is to examine the regional labor-market consequences of
emigration in Mexico. One approach would be to utilize data on migration to the UnitedStates in Mexico’s 2000 population census. Using the 2000 data, I could compare labormarket outcomes in households with emigrants to outcomes in households without
emigrants.

Or, combining the household cross-sections in 1990 and 2000, I could

examine the covariation between the 1990-2000 change in household outcomes with the
1995-2000 state emigration rate. The obvious concern with either of these approaches is10that household migration behavior is endogenous. The unobserved characteristics ofhouseholds that affect their earnings and labor supply are also likely to affect whether
households send migrants to the United States.
One way to address the endogeneity problem would be to use historical state
emigration rates as an instrument for current opportunities to migrate abroad.

The

discussion in section 2.2 suggests that the 1950s emigration rate in an individual’s birthstate would be a good indicator of an individual’s access to migration networks and so of
an individual’s relative opportunity to migrate to the United States. Using data from the2000 census, unreported probit regressions show that the likelihood a household either
has sent a migrant to the United States in the last five years or has received remittancesfrom abroad in the last month is strongly positively correlated with the 1955-59
emigration rate in the household head’s birth state.6
However, historical state emigration rates are unlikely to be a valid instrument for
current migration rates. Emigration opportunities in an individual’s birth state may haveaffected an individual’s accumulation of human capital, either by influencing theindividual’s early employment prospects (if local emigration rates affect local wage
levels) or the quality of education the individual received as a youth (if remittances or
local income levels affect the quality of local schools). Past emigration opportunities arethus likely to affect current labor-market outcomes both directly, through their impact on

6Additional controls in this regression are a cubic in age of the household head, dummies for theeducational attainment of the household head, the sex of the household head, and dummy variables for thestate of residence. Evaluated at mean values for the other regressors, individuals born in high-migration
states are 24.3% more likely to have had someone in their household migrate to the United States in the last
five years and 21.7% more likely to have received remittances from migrants located abroad in the lastmonth (with both of these effects very precisely estimated).

11

current emigration opportunities, and indirectly, through their impact on an individual’s
stock of human capital (which is only partially observed).
Given these concerns, I take a reduced-form approach by comparing changes incross-section labor-market outcomes, where I categorize individuals according to theemigration rate in their birth state. In so doing, I capture both the direct and indirect
effects of historical emigration opportunities on current labor-market outcomes.

In

presenting the empirical results, I will discuss whether the reduced-form effect ofhistorical emigration rates on labor-market outcomes is likely to under or over-state theeffect attributable solely to current emigration opportunities.
My empirical strategy is thus to compare labor-market outcomes in regions thathave been more or less exposed to opportunities to migrate to the United States. Table 4
describes the sample of states. I drop the six border states from the sample, since thesestates have benefited disproportionately from trade and investment liberalization. Most
border states had above average emigration rates in the 1950s and including them in thesample could confound the effects of emigration with those of other aspects ofglobalization. To help isolate the effects of emigration, I limit high-migration states tothose with emigration rates in the top three deciles of non-border states and lowmigration states to those with emigration rates in the bottom three deciles of non-borderstates. In 2000, 10.4% of households in the seven high-migration states had sent a
migrant to the United States in the previous five years, compared with only 2.1% ofhouseholds in the seven low-migration states.
With the exception of the Federal District, in which part of Mexico City is
located, all the low-migration states are in southern Mexico. Per capita income in the12Federal District is over three times that in the southern low-migration states. And, as
Figures 4 and 5 show, the Federal District has much higher exposure to international
trade than the southern low-migration states. There is also heterogeneity among highmigration states. Jalisco, in which Guadalajara (the country’s second largest city) is
located, has high relatively high exposure to international trade. By way of checking therobustness of the results, I will perform the analysis with and without individuals born in
the Federal District or Jalisco included in the sample.

3. Preliminary Analysis
3.1 Population Changes in High and Low Migration States
The most direct effect of emigration has been to reduce the relative population ofyoung adults born in high-migration states. Figures 6 and 7 show cohort sizes based onage in 2000 for males and females born in high-migration or low-migration states. In theabsence of measurement error, changes in population size are due to either net migration
abroad or to death. Cohort sizes decline for all age-sex groups, except 10-19 year olds.7
Population declines are largest for 20-29 year-old men (men born between 1971 and1980) from high-migration states, whose number declines by 33.4 log points. In lowmigration states, the number of 20-29 year-old men drops by only 9.4 log points, suchthat the relative decline of the 20-29 year-old male population in high-migration statesover 1990-2000 is 24.0 log points.

Overall, the population of 20-59 year-old men

declines by 9.8 log points in high-migration relative to low-migration states.8

7

One explanation for the increases in cohort size for 10-19 year olds is greater measurement error in the1990 census (in which case Figures 7 and 8 may understate reductions in cohort sizes over the decade).
8
One might imagine that internal migration in Mexico could have partly reversed the change in relative
regional labor supplies due to emigration. The large exodus of individuals born in high-migration statesmight have given individuals from other states an incentive to move in. But data on population by state of13

Absolute and relative changes in female cohorts are smaller. The cohort of 20-29
year-old women declines by 16.8 log points in high-migration states and 2.0 log points in
low-migration states. Overall, the population of 20-59 year-old women declines by 8.4
log points in high-migration relative to log-migration states.9 Figure 8 shows that as aresult of higher emigration rates for males, the share of men in the population of 20-29
year olds from high-migration states falls from 49% to 45% during the 1990s. In lowmigration states the change is more modest, with a drop of 50% to 48%.
It appears men and women born in high-migration states in Mexico have become
more likely to migrate abroad. One might also wonder whether they have become more
likely to migrate internally. Table 5 reports probit regressions using data from 1990 and2000 on whether individuals born in high-migration or low-migration states havechanged their state of residence since birth. The regressors are (a) a cubic in age, dummy
variables for five categories of educational attainment (1-5 years, 6-8 years, 9-11 years,
12-15 years, 16+ years), a dummy variable for marital status, dummy variables forpresence of children in the household (ages 0-5, 6-12, 13-18), dummy variables for thestate of birth, and a dummy variable for 2000; (b) interactions between the age,education, marital status, and children variables and the year 2000 dummy; (c)interactions between the age, education, marital status, and children variables and adummy variable for whether the individual was born in a high-migration state; and (d) theinteraction between the year 2000 dummy and the dummy for whether an individual wasborn in a high-migration state. I report results only for this last variable, which captures
residence (rather than state of birth) suggest that this is not the case. During the 1990s, high-migration
states experienced the largest net decrease in resident population, followed by low-migration states. Borderstates had the largest net increase in resident population.
9
Dropping the Federal District and Jalisco, the relative population of 20-59 year olds in high-migration
states declines by 9.4 log points for men and 7.3 log points for women.

14the change in the likelihood of having migrated internally over 1990-2000 for individuals
born in a high-migration state relative to those born in a low-migration state.
Between 1990 and 2000, men from high-migration states become 3.4% more
likely to live in a state different than their birth state, relative to men from low-migration
states. Excluding the Federal District and Jalisco the estimate falls to 1.6%. Between
1990 and 2000, women from high-migration states become 4.1% more likely to live in astate different than their birth state, relative to women from low-migration states.
Dropping the Federal District and Jalisco the estimate falls to 2.1% (and remains
precisely estimated). It appears that during the 1990s individuals from high-migration
states have become more likely to migrate either externally or internally.

3.2 Education and Earnings in High and Low Migration States
The educational profile of individuals by birth state varies between high- and lowmigration states. Table 6 shows the distribution of schooling by age cohort in 2000 for
the sample of Mexican states. For men, average schooling is higher in low-migration
states. Among 30-39 year-old men in 2000, 62.6% had completed nine or more years ofschooling in low-migration states, versus 47.7% in high-migration states. For women,
these figures are 57.5% and 42.7%, respectively. These differences, however, depend on
including among low-migration sates the Federal District, which has the most educatedwork force in the country. Once the Federal District and Jalisco are dropped from thesample, educational attainment is relatively similar in the two groups of states, with
46.9% of men and 40.1% of women in the 30-39 age cohort having completed nine ormore years of education in low-migration states and 45.9% of men and 40.6% of womenin the 30-39 age cohort doing so in high-migration states.
15

Despite comparable or higher education levels in low-migration states, wages
appear to be higher in high-migration states. Table 7 shows average hourly wages by age
and schooling cohort in 1990 and 2000.10 For the full sample of states, wages are higher
in high-migration states for most cohorts in 1990 and for all cohorts in 2000. In 1990, formen with 6-8 years of education, which spans mean schooling levels in either year,
average hourly wages are $0.06 to $0.44 higher in high-migration states, depending on
the age cohort (based on age in 2000). In 2000, these wage differentials widen to $0.25
to $0.74.

Wages in high-migration states increase relative to wages in low-migration

states in 15 of the 18 age-schooling cohorts. Dropping the Federal District and Jalisco,wages remain higher in high-migration states in most cohorts for both years.
Figure 9, which shows kernel densities for log average hourly wages, gives
another perspective on wages in high and low-migration states. In 1990, wages havelower dispersion and a higher mean in high-migration states when compared to lowmigration states.

In 2000, these features are more pronounced.

Relative to high-migration states, wages in low-migration states show an increase in relative dispersion
and in relative mass in the lower tail.

In Figure 10, which shows wage densities

excluding the Federal District and Jalisco, the relative rightward shift in the wage
distribution for high-migration is more evident.
Both in terms of average wages and wage densities, it appears that unconditional
wages in high-migration states are higher than those in low-migration states and that thisdifferential increases over the 1990s. This is seen clearly in Figure 11, which shows the10

Average hourly wages are calculated as monthly labor income/(4.5*hours worked last week). I need to assume
individuals work all weeks of a month, which could bias wage estimates downwards. To avoid measurement error
associated with implausibly low wage values or with top coding of earnings, I restrict the sample to be individuals withhourly wages between $0.05 and $20 in Mexico (in 2000 U.S. dollars). This restriction is nearly identical to dropping
the largest and smallest 0.5% of wage values.

16

double difference in wage densities for high-migration and low-migration states (i.e., the2000 difference in wage densities for high-migration and low-migration states minus the1990 difference in wage densities). Relative to low-migration states, over time highmigration states gain mass in the upper half of the wage distribution.

4. Decomposing Changes in Earnings
During the 1990s, the earnings gap appeared to increase between men born inhigh-migration states and men born in low-migration states. At face value, this change isdifficult to interpret. It is possible that the large exodus of individuals from highmigration states may have increased the wages of non-migrating individuals from thesestates relative to wages for non-migrating individuals from low-migration states. In thiscase, the national wage changes associated with emigration reported by Mishra (2004)
would also be evident at the regional level.
However, other interpretations of the observed wage changes are plausible.
Borjas (1987) suggests that in countries with high skill premia and high earnings
inequality, such as Mexico, the less-skilled are likely to have the highest propensity to
migrate to countries with low skill premia and low earnings inequality, such as the U.S.
In Mexico, if low-skill, low-wage individuals are more likely to migrate abroad (migrants
are negatively selected in terms of skill), the apparent increase in wages in high-migration
states may be due partly to shifts in labor-force composition.
To describe wages changes in high-migration and low-migration states morethoroughly, I apply non-parametric techniques for constructing counterfactual wage
densities developed by DiNardo, Fortin, and Lemieux (1996) and Leibbrandt, Levinsohn,and McCrary (2004). In the first exercise, I compare the 1990-2000 change in the17

distribution of earnings between high-migration and low-migration states, holding thereturns to observable characteristics constant. By fixing the returns to characteristics butallowing the distribution of characteristics to vary over time and across regions, I isolate
how regional differences in the composition of the labor force have changed. This will
help reveal whether it is low-wage or high-wage individuals from high-migration states
who are more likely to migrate abroad. In the second exercise, I compare the 1990-2000
change in the distribution of earnings between high-migration and low-migration states,
holding the distribution of individual characteristics constant. By fixing the distributionof characteristics, but allowing the returns to characteristics to vary, I examine whether
non-migrating individuals in high-migration states have enjoyed wage gains relative to
non-migrating individuals in low-migration states.11
It is important to recognize that neither non-parametric exercise I perform
amounts to a truly valid counterfactual. This is because emigration is likely to havechanged both the distribution of worker characteristics and the returns to thesecharacteristics. By looking at each change in isolation, the counterfactual differences inwage densities I construct represent only partial decompositions of the change in thewage distribution.12

Nevertheless, the non-parametric analysis will be helpful forassessing the plausibility of the parametric results.
Following the non-parametric estimation, I consider a parametric regression ofdifferential wage changes in high-migration and low-migration states on differential
emigration opportunities (as summarized by historical emigration rates). The parametric
11

DiNardo, Fortin and Lemieux is not the only approach to non-parametrically decompose changes in wage
distributions. See Machado and Mata (2005) (and Autor and Katz, 2004) for an alternative methodology.
12
A complete decomposition would separate wage changes into components due to changes in returns forgiven characteristics, changes in characteristics for given returns, and the interaction of changes in returns
and changes in characteristics. The non-parametric analysis in effect ignores the third component.

18

approach will provide an estimate of the differential in wage growth between highmigration and low-migration states that is associated with emigration. There are several
reasons why we might be reluctant to assign a causal interpretation to the parametricresults, which I discuss in the concluding section.
Finally, the analysis doesn’t address changes in the distribution of unobservables.
If, holding observed characteristics constant, Mexican emigrants have low (high)unobserved ability relative to non-migrants in Mexico, I would tend to understate theextent to which migrants are negatively (positively) selected in terms of skill.

4.1 Estimating Counterfactual Earnings Densities
Let f(w|x,i,t) be the density of hourly labor earnings, w, conditional on a set ofobserved characteristics, x, in region i and time t. Define h(x|i,t) as the density ofobserved characteristics among wage earners in region i and time t. For regions, i=H
indicates high-migration states and i=L indicates low-migration states; for time periods,
t=00 indicates the year 2000 and t=90 indicates the year 1990. The observed density oflabor earnings for individuals in region i at time t is,g( w | i, t ) = ∫ f ( w | x , i, t )h ( x | i, t )dx
Differences in f ( w | x, H, t ) and f ( w | x, L, t ) reflect differences in returns to observables
in high and low-migration states; differences in h(x|H,t) and h(x|L,t) reflect differences inthe distribution of observables in high and low-migration states. The empirical analysis
examines how regional differences in these two sets of densities changed over the 1990s.
In the first exercise, I compare the composition of the labor force across regions.
I ask how the difference in earnings densities between high and low-migration states

19changes over time, holding constant returns to observables such that only the distributionof observables varies across regions and years. The first decomposition I consider is how
the wage density differs between high-migration and low-migration states in 1990 for acommon set of returns to observable characteristics:∫ f ( w | x, L,90)h ( x | H,90)dx − ∫ f ( w | x, L,90)h ( x | L,90)dx .

(1)

The density difference in equation (1) evaluates the difference in the earnings distribution
in high and low-migration states in 1990, fixing the returns to observables to be that in
low-migration states in 1990. This density difference characterizes the initial differencein the distribution of observables between high and low-migration states. Applying
DiNardo, Fortin, and Lemieux (DFL), I rewrite (1) as∫ [θL90 → H90

− 1]f ( w | x, L,90)h ( x | L,90)dx ,(2)h ( x | H,90).
h ( x | L,90)

(3)

where
θ L90 → H90 =Equation (2) is simply the observed marginal earnings density in low-migration states in
1990, adjusted by a weighting function. Given an estimate of the weighting function in(3), it would be straightforward to apply a kernel density estimator to equation (2).
Following DFL, I estimate the weighting function in (3) by running a logit on theprobability a Mexican male is from a low-migration state in 1990 for the sample of
Mexican males from high-migration and low-migration states in 1990.
Consider the analogue to equation (2) for 2000. The 2000 difference in theearnings distribution in high and low-migration states that is associated with differencesin the distribution of observable characteristics can be written as20

∫ f ( w | x, L,90)h ( x | H,00)dx − ∫ f ( w | x, L,90)h ( x | L,00)dx .

(4)

Using weighting functions analogous to (3), I rewrite equation (4) as∫ [θL90 → H 00

− θ L90 → L00 ]f ( w | x , L,90)h ( x | L,90)dx .

(5)

Putting (2) together with (5), we have the 1990-to-2000 change in the earnings
distribution in high-migration versus low-migration states that is associated with changes
in the distribution of observables:(∫ f (w | x, L,90)h(x | H,00)dx − ∫ f (w | x, L,90)h(x | L,00)dx) −(∫ f (w | x, L,90)h(x | H,90)dx − ∫ f (w | x, L,90)h(x | L,90)dx) =( L90→H00 − θL90→L00 )− (θL90→H90 − 1) f (w | x, L,90)h(x | L,90)dx

.

(6)

∫ θ

Equation (6) shows the difference in the earnings distribution in high-migration versus
low-migration states in 2000, relative to that in 1990, holding the returns to observables
constant. Since an individual’s birth state is fixed, I can use (6) to evaluate changes inlabor-force composition in high-migration versus low-migration states, where I evaluate
workers based on their place in the 1990 earnings distribution in low-migration states.
To perform this exercise, I estimate a series of logit regressions to construct theweighting functions and then apply the weights to a kernel density estimator to obtain
estimates for the densities described by (2), (5), and (6). The first two of these are for asingle difference in densities and the third is for a double difference in densities.

The second exercise I perform is to examine how the returns to observablecharacteristics have changed in high and low-migration states, holding the distribution ofcharacteristics constant. For 1990 the difference in earnings densities we’d like to see is∫ f ( w | x, H,90)h ( x | L,90)dx − ∫ f ( w | x, L,90)h ( x | L,90)dx .

(7)

21

which evaluates the difference in earnings distributions in high and low-migration statesin 1990, fixing the marginal density of observables to be that in low-migration states in1990. Following the logic of DFL, I rewrite equation (7) as
L90 → H90

∫ [λ− 1]f ( w | x, L,90)h ( x | L,90)dx ,(8)f ( w | x, H,90).
f ( w | x , L,90)

(9)

where
λL90 → H90 =The corresponding difference in densities for 2000 is∫ f ( w | x, H,00)h ( x | L,90)dx − ∫ f ( w | x, L,00)h ( x | L,90)dx ,(10)which evaluates the difference in earnings distribution between high and low-migration
states in 2000, again fixing the marginal density of observables to be that in lowmigration states in 1990. Using the weights,
λL90 → H 00 =f ( w | x, H,00)
f ( w | x, L,90)

and λL90 → L00 =f ( w | x, L,00),
f ( w | x, L,90)(11)

I rewrite equation (10) as
L90 → H 00

∫ [λ− λL90 → L00 ]f ( w | x, L,90)h ( x | L,90)dx .

(12)

Putting equations (8) and (12) together,(∫ f (w | x, H,00)h(x | L,90)dx − ∫ f (w | x, L,00)h(x | L,90)dx) −(∫ f (w | x, H,90)h(x | L,90)dx − ∫ f (w | x, L,90)h(x | L,90)dx) =( L90→H00 − λL90→L00 )− (λL90→H90 − 1) f (w | x, L,90)h(x | L,90)dx

.

(13)

∫ λ

Equation (13) shows the 1990-to-2000 change in earnings distribution in high-migration
states relative to low-migration states, holding the distribution of observables constant.

22

This is the component of the change in relative regional earnings densities associatedwith changes in relative regional returns to observable characteristics alone.
To estimate the weighting functions in (9) and (11), I use Leibbrandt, Levinsohn,and McCrary’s (2004) extension of DFL. As they show, applying Bayes’ Axiom yields
λL90 → L00 =f ( w | x , L,00)
Pr( t = 00, i = L) | w , x ) 1 − Pr( t = 00, i = L) | x )=f ( w | x, L,90) 1 − Pr( t = 00, i = L) | w , x ) Pr( t = 00, i = L) | x )λL90 → H 00 =f ( w | x, H,00)
Pr( t = 00, i = H) | w , x ) 1 − Pr( t = 00, i = H) | x )=.
f ( w | x , L,90) 1 − Pr( t = 00, i = H) | w , x ) Pr( t = 00, i = H) | x )λL90 → H90 =f ( w | x, H,90)
Pr( t = 90, i = H) | w , x ) 1 − Pr( t = 90, i = H) | x )=f ( w | x, L,90) 1 − Pr( t = 90, i = H) | w , x ) Pr( t = 90, i = H) | x )(14)Each weighting function in (14) is the product of odds ratios. In the first weight, the firstratio is the odds an individual is from a low-migration state in 2000 (based on a sample ofindividuals from low-migration states in 1990 and 2000), conditional on observables, x,
and earnings, w; and the second ratio is the (inverse) odds an individual is from a lowmigration state in 2000, conditional on just on x. To estimate the odds ratios, I estimate
two logit models. In each case, the regressand is a 0-1 variable on the outcome i=L andt=00 (based on a sample of (i=L, t=00) and (i=L, t=90)). For the first logit, the regressors
are x and w; for the second, the regressor is x, alone. Other weights can be estimatedanalogously. After constructing the weights, I estimate (8), (12), and (13).

4.2 A Parametric ApproachTo evaluate the association between emigration and earnings parametrically, I
pool data on working age men in 1990 and 2000 from high-migration or low-migration
states and estimate the following difference-in-difference wage regression,23

ln w hst = α s + X hst (β1 + β 2 Y 2000 ht + β 3 High hs ) + φ * Y 2000 ht * High hs + ε hst(15)where w is average hourly earnings, X is a vector of observed characteristics, Y2000 is adummy variable for the year 2000, and High is a dummy variable for whether anindividual was born in a high-migration state. The regression includes controls for stateof-birth fixed effects and allows returns to observable characteristics to vary acrossregions and time. The coefficient, φ, captures the mean differential 1990-to-2000 change
in earnings between high and low-migration states.13
One important estimation issue is that shocks other than emigration may have had
differential impacts on high and low-migration states. I’ve already discussed the shockassociated with NAFTA and other aspects of trade liberalization. Another shock was thepeso crisis of 1995. After a bungled devaluation of the peso in 1994, Mexico chose tofloat its currency, which proceeded to plummet in value relative to the dollar. The
ensuing increase in the peso value of dollar-denominated liabilities contributed to abanking collapse and a severe economic contraction. Low-migration states (excluding
Mexico City) are modestly less industrialized than high-migration states and so may havebeen less hurt by the credit crunch. Also, low-migration states tend to have larger tourist
industries, which may have benefited from the devaluation. Other shocks in the 1990’s
included a reform of Mexico’s land tenure system in 1992, the privatization of stateowned enterprises, and industry deregulation. The existence of these shocks leaves the13

Equation (15) is a standard difference-in-difference specification, which implies I estimate the meandifferential in wage growth between high and low-migration states. This approach ignores the possibility
that the wage effect of being in a high-migration state may not be uniform throughout the wage
distribution. A more elegant approach would be to estimate the regional differential in wage changes nonparametrically, as in the framework derived by Athey and Imbens (2003).

24

results subject to the caveat that factors other than emigration may have contributed todifferential regional changes in earnings. I return to this issue in section 5.

4.3 Empirical Results

The sample for the analysis is the cohort of Mexican men aged 20 to 49 years in1990 or 30 to 59 years in 2000 who were born in one of the seven high-migration states
or one of the seven low-migration states. By restricting the analysis to a single cohort, I
limit possible contamination of the sample associated with more-educated younger
workers entering the labor force and less-educated older workers exiting the labor force.
The dependent variable is log average hourly labor earnings (see note 10).
Figure 12 shows kernel density estimates for the density differences in equations(2) and (5), which characterize the difference in earning distributions between high andlow-migration states holding constant the return to observable characteristics. In 1990
and 2000, the density difference has negative mass above the mean and positive mass
below the sample mean (where the mean over the entire sample of states is normalized tozero). This implies that in either year there are relatively few men from high-migration
states with above-average earnings and relatively many men from high-migration stateswith below-average earnings. Whatever the source of this initial difference, it becomes
modestly more pronounced during the 1990s. Between 1990 and 2000, the density
difference loses mass above the mean and gains mass below the mean. Compared to lowmigration states, it appears that men with above-average earnings from high-migration
states disappear from the sample in larger numbers.
The change in the composition of the labor force is perhaps seen more clearly in
Figure 13, which shows the 1990-to-2000 change in the difference in earnings densities
25between high-migration and low-migration states (for constant returns to observables).
This (partial) double difference shows negative mass above the mean and positive mass
below the mean, indicating that over time the relative scarcity of high-wage workers has
increased in high-migration states relative to low-migration states.
Comparing units on the vertical axes in Figures 11 and 13, it is apparent that thecounterfactual double difference in wage densities is small, but it is still informative
about the nature of migrant selection on observables. Figure 7 shows that between 1990
and 2000 there was a relatively large loss in the population of working-age men born inhigh-migration states, which is consistent with individuals from high-migration states
having a relatively high propensity to migrate abroad. What Figures 12 and 13 suggest isthat the men most likely to migrate abroad are those in the top half of the earnings
distribution. This finding is inconsistent with negative selection of emigrants in terms ofobservable skills and suggests that emigrants exhibit intermediate or positive selection interms of observable skills. Using data from Mexican and U.S. population censuses,
Chiquiar and Hanson (2005) also find evidence against negative selection.14
One might also be concerned that including the relatively rich and globalized
regions of the Federal District and Jalisco in the sample of birth states affects the results.
In Figure 14, I show the double difference in counterfactual wage densities reported in
Figure 13 (with returns to observables fixed at those for low-migration states in 1990) fora sample that excludes the two states. Comparing Figures 13 and 14 shows that results
are similar with or without these states in the sample. The results are also robust to
dropping any one of the other states from the sample.

14

Results are similar if I evaluate change in earnings densities between high-migration and low-migration
states for returns to observables fixed at those for high (rather than low) migration states in 1990.

26

Over time, it appears that men born in high-migration states are emigrating from
Mexico in relatively large numbers and that the emigrants include a disproportionately
large number of individuals with relatively high earnings potential. In a simple laborsupply, labor-demand framework, a decrease in the relative supply of more-skilledworkers in high-migration states would put upward pressure on relative wages in thesestates (as long as labor was not perfectly mobile between regions of Mexico). Next, we
examine how relative regional returns to observables have changed over time.
Figure 15 shows kernel density estimates for the density differences in (8) and(12), which characterize the difference in earning distributions between high and lowmigration states holding constant the distribution of observable characteristics. In 1990
and 2000, the density difference has positive mass above the mean and negative mass
below the mean. In either year, returns to observables appear to be higher in highmigration states relative to low-migration states. Although one cannot identify from
Figure 15 the source of the initial difference in relative regional earnings, relatively highreturns to observables in high-migration states is consistent with the relative scarcity ofhigh-wage workers in high-migration states evident in Figure 12.
Over time, the difference in returns to observables between high and lowmigration states appears to have become more pronounced. Figure 15 shows that from1990 to 2000 the difference in wage densities between high-migration and low-migration
states gains mass above the mean and loses mass below the mean. This is seen moreclearly in Figure 16, which shows the 1990-to-2000 change in the difference in earnings
densities between high-migration and low-migration states, holding constant the
distribution of observables. This double difference shows positive mass above the mean

27

and negative mass below the mean, indicating that during the 1990’s the wage premium
for above-average wage earners increased for men born in high-migration states relative
to men born in low-migration states. Though the partial double difference in wage
densities is again small (compare to Figure 11),15 the increase in the relative wage formen born in high-migration states evident in Figure 17 is consistent with the decrease in
the relative supply of men born in high-migration states evident in Figure 13.

Inunreported density estimates, I obtain similar results when I drop men born in the Federal
District or Jalisco from the sample.
The non-parametric results suggest there has been an increase in relative wages
for men born in high-migration states in Mexico. To evaluate the change in regional
relative wages parametrically, Table 8 shows estimation results for equation (15). The
dependent variable is log average hourly earnings. The regressors are dummy variables
for educational attainment, a quadratic in age, a dummy variable for the year 2000 and its
interaction with the age and education variables, a dummy variable for having been bornin a high-migration state and its interaction with the age and education variables, dummy
variables for birth state, and the interaction of the year 2000 and high-migration dummy
variables. This last variable captures the differential change in wage growth in highmigration states relative to low-migration states.

Standard errors are adjusted forcorrelation across observations associated with the same birth state.
Panel (a) of Table 8 shows that during the 1990’s the cohort of men born in highmigration states enjoyed labor earnings growth that was 6.3 log points higher than15

Since both counterfactual double differences in densities are small, it appears that the interaction betweenchanges in worker characteristics and changes in returns to characteristics accounts for a large portion of
the total change in regional relative wages. However, the double differences in wage densities still appear
to be informative about the direction of these changes. Relative regional wage changes appear to be larger
where relative regional labor-supply changes are larger.

28

earnings growth for individuals born in low-migration states. These coefficients are
precisely estimated. This is consistent with the non-parametric estimates and again
suggests that men born in high-migration states enjoyed higher growth in labor earnings
than men born in low-migration states. The second two columns of Table 8 show results
where the year2000/high-migration interaction is interacted with an indicator for anindividual having 9 to 15 years of education (roughly, workers with above meanschooling years but with less than a college education).

This term allows relative

earnings growth to be larger for more-educated workers. The education interaction term
is positive, consistent with Figure 17 (while the variable appears imprecisely estimatedthe two reported interaction terms are jointly highly statistically significant).16
Panel (b) of Table 8 redoes the estimation, dropping observations for the Federal
District and Jalisco. Estimated relative wage growth for high-migration states is higher
for this sample, with men born in high-migration states enjoying labor earnings growth
8.6 to 8.9 log points higher than for men born in low-migration states. In the second twocolumns, the interaction between the year2000/high-migration interaction and the dummy
variable for secondary education is again positive (and the two interaction terms are again
jointly highly statistically significant).
Since emigration rates are highest for individuals in their twenties, one might
expect that wage changes between high-migration and low-migration states would havebeen largest for men who are more educated and young. In unreported results, I included
additional interactions between the year 2000 dummy, secondary education, and age, butthese proved to be imprecisely estimated in most regressions.

16

Introducing interaction terms for more disaggregated schooling categories yields similar results.

29

Based on the coefficient estimates, it is possible to construct an elasticity of therelative wage for high-migration and low-migration states with respect to the relativelabor supply in high-migration and low-migration states. From Figure 6, the supply ofworking-age men in high-migration states fell by 9.8 log points relative to the supply ofworking-age men in the same cohort in low-migration states. This implies a wage
elasticity of 0.64. Excluding the Federal District and Jalisco the wage elasticity is 0.91.
Either elasticity is larger than the value of 0.4 that Mishra (2004) estimates using data onchanges in wages and labor supply for age-schooling cohorts at the national level.
Recall, however, that my estimates are reduced form. They include the direct effect ofemigration on wages (through changes in the labor supply), and any indirect effectassociated with differential labor-demand growth in high-migration states that is
associated with historical emigration patterns.

Comparing my results to Mishra’s

suggests that the indirect effects of emigration on regional wages are positive.

5. Discussion

In this paper, I examine how emigration may have affected regional labor supply
and regional earnings in Mexico. Mexico has a long history of sending migrants to theUnited States. Since the early 1900s, emigration rates have varied widely across regionsof the country, with individuals from west-central states having the highest propensity to
migrate abroad. I exploit regional persistence in emigration behavior by focusing theanalysis on individuals born in states with a history of either high-migration or lowmigration to the United States, as measured by state emigration rates in the 1950s.
As in earlier decades, during the 1990s individuals born in Mexico’s highmigration states appeared to have a relatively high propensity to migrate abroad.
30

Between 1990 and 2000, the population of 20-59 year-old men born in high-migration
states declined by 10 log points relative to similarly aged men born in low-migration
states. For women, the corresponding relative regional change in population was 8 log
points.

The relatively large exodus of individuals from high-migration states isconcentrated among individuals with above-average earnings potential. This suggeststhat in terms of observable skills emigrants are positively selected. Controlling for
observables, wages in high-migration states rose relative to low-migration states by 69%. This implies an elasticity of wages with respect to the labor supply of 0.7-0.8. Thischange reflects both the direct effects of emigration on the labor supply and any indirect
effects of historical emigration patterns on current regional wage growth.
There are several possible interpretations of these results. One is that emigration
raises wages in Mexico, with the effects being most pronounced in states that have welldeveloped networks for sending migrants to the United States. This interpretation isconsistent with the findings in Munshi (2003), Hanson (2004), and Mishra (2004).
However, emigration was by no means the only shock to the Mexican economy
during the 1990’s. Other shocks may have also contributed to changes in regional
relative wages.

A large literature documents how NAFTA and other aspects ofglobalization appear to have increased regional wage differentials in Mexico. It is notclear how globalization interacts with emigration. States more exposed to globalization
appear to have lower migration rates to the United States, suggesting that emigration andglobalization may be complementary mechanisms for integrating Mexico into the North
American labor market. Another important shock was the Mexican peso crisis in 1995.
This may have hurt high-migration states more than low-migration states (since high-31

migration states have larger industrial bases and smaller tourist industries), suggesting myestimates may understate the true effect of emigration on regional wages.
Other policy changes, such as the privatization and deregulation of Mexican
industry or the reform of Mexico’s land-tenure system, may also have had differential
regional impacts. Privatization and deregulation appeared to lower union wage premiumsin these sectors (Fairris, 2003). Since more heavily unionized industries are concentrated
in Mexico’s north and center and relatively absent in Mexico’s south (Chiquiar, 2003),
we might expect a loss in union power to lower relative wages in Mexico’s highmigration states, in which case my results would tend to understate the true effect ofemigration. The reform of Mexico’s land-tenure system allowed the sale of agriculturalland that had previously been held in cooperative ownership. We might expect thischange to have raised relative incomes in southern Mexico, which specializes inagriculture. Since low-migration states are concentrated in southern Mexico, this is
another reason my results may tend to understate the true effect of emigration.
A brief review of Mexico’s other policy reforms during the 1990’s does notsuggest any obvious reason why they should account for the observed increase in relative
earnings in high-migration states. Still, in an environment where multiple shocks haveaffected Mexico’s labor market it is important to be cautious about ascribing shifts inrelative regional earnings to any specific event. In the end, we can only say that I findsuggestive evidence that emigration has increased relative earnings in Mexican states thathave stronger migration networks vis-à-vis the United States.

32

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in Wage Distributions Using Quantile Regression.” Journal of Applied Econometrics,
forthcoming.
Massey, Douglas S., L. Goldring, and Jorge Durand. 1994. “Continuities inTransnational Migration: An Analysis of Nineteen Mexican Communities.” American
Journal of Sociology 99 (March): 1492-533.
Mishra, Prachi. 2004. “Emigration and Wages in Source Countries: Evidence from
Mexico.” Mimeo, Columbia University.
Munshi, Kaivan. 2003. “Networks in the Modern Economy: Mexican Migrants in the
US Labor Market.” Quarterly Journal of Economics 118 (May): 549-97.
Orrenius, Pia M. and Madeline Zavodny. 2004. “Self-Selection among Undocumented
Immigrants from Mexico.” Journal of Development Economics, forthcoming.
Revenga, Anna L. 1997. “Employment and Wage Effects of Trade Liberalization: The
Case of Mexican Manufacturing.” Journal of Labor Economics 15(3): S20-43.
Robertson, Raymond. 2000. “Wage Shocks and North American Labor Market
Integration.” American Economic Review 90(4): 742-764.
Robertson, Raymond. 2004. “Relative Prices and Wage Inequality: Evidence from
Mexico” Journal of International Economics v 64, n2 (December): 387-409.
Smith, James and Barry Edmonston. 1997. The New Americans: Economic, Demographic
and Fiscal Effects of Immigration. National Academy Press: Washington, D.C.
Woodruff, Christopher and Rene M. Zenteno. 2001. “Remittances and Microenterprises
in Mexico.” Mimeo, UC San Diego.

35

Table 1: Share of U.S. Immigrants from Mexico in the Population of Mexico(percent)

Age Cohort
Age in 1990 Age in 2000

Males
% Residing in U.S.
1990
2000

Females% Residing in U.S.
Change1990
2000
Change--16 to 25

--

11.99

--

--

7.68

--

16 to 25

26 to 35

7.57

17.53

9.96

4.89

12.62

7.73

26 to 35

36 to 45

10.87

15.49

4.62

7.69

11.90

4.21

36 to45

46 to 55

9.18

12.21

3.03

7.47

10.44

2.97

46 to 55

56 to 65

7.00

8.64

1.64

6.44

8.36

1.92

56 to 65

--

5.70

--

--

5.84

--

--

This table shows Mexican immigrants in the U.S. as a percentage of the population ofindividuals born in Mexico (equal to the sum of the Mexico-born population residing in
Mexico and the Mexico-born population residing in the United States) by age and sex
categories. The sample is individuals 16-65 years old (in the U.S., excluding those ingroup quarters; in Mexico, excluding those not born in the country). Residents of Mexico
in 1990 are the 1% microsample of the XII Censo General de Poblacion y Vivienda,
1990, and in 2000 are a 10% random sample of the 10% microsample of the XIII Censo
General de Poblacion y Vivienda, 2000. Mexican immigrants are from the 1990 and2000 5% U.S. Public Use Microsample. Source: Chiquiar and Hanson (2005).

36

Table 2: Emigration and Characteristics of Mexican States

Constant

Log Per Capita
GDP in 1995

Migration to U.S. 1995-2000
(1)
(2)
(3)
(4)
0.231
0.169
0.211
0.175
(0.085)(0.085)(0.098)(0.077)-0.025(0.011)-0.036(0.011)-0.03(0.011)-0.017(0.009)Log Distanceto U.S.

0.070(0.027)0.006(0.029)-0.025(0.026)

Log Distanceto U.S.2-0.007(0.003)0.000(0.003)0.003(0.003)32.813(10.210)4.295(10.210)Migration Rate
1924
Migration Rate
1955-59
Adjusted R2
N

1.919(0.386)
0.116
32

0.252
32

0.456
32

0.667
32

The sample is the 31 states of Mexico plus the Federal District. The dependent variable is theaverage share of households in a state that had sent a migrant to the United States in the 19952000 period. Standard errors are in parentheses.

Table 3: Correlation in Measures of Exposures to Globalization across Mexican States

Maquiladora
Value Added/
State GDP

Foreign Direct
Investment/
State GDP

Imports/
State GDP

Foreign Direct Investment/
State GDP

0.391
(0.027)

Imports/
State GDP

-0.007(0.968)0.571(0.001)

Share of State Population
Migrating to US, 1995-2000-0.128(0.484)-0.368(0.038)-0.253(0.162)

Share of State Population
Migrating to US, 1955-1959

0.188(0.303)-0.123(0.502)-0.133(0.468)

Share of State
Population
Migrating to US,
1995-2000

0.725(0.000)The sample is the 31 states of Mexico plus the Federal District. Shares of state GDP(maquiladora value added, foreign direct investment, imports) are averages over the period 19931999. Correlations are weighted by state share of the national population (averaged over 1990 to2000). P-values are in parentheses.

38

Table 4: Ranking Mexican States by Historical Emigration Rates

State
High
Aguascalientes
Migration Durango
Guanajuato
Michoacán
San Luis Potosí
Zacatecas
JaliscoMigration Rate
1995-2000 1955-1959

Per Capita
GDP 1995

Pop. 2000('000s)

0.090
0.093
0.114
0.130
0.087
0.151
0.082

0.032
0.055
0.041
0.031
0.025
0.059
0.020

1,728
1,329
1,062
901
1,094
878
1,479

952
1,440
4,604
3,921
2,362
1,348
6,272

0.104
0.114

0.033
0.038

1,197
1,077

2,986
2,438

0.011
0.009
0.009
0.007
0.037
0.013
0.021

0.000
0.000
0.000
0.002
0.000
0.002
0.001

2,341
678
2,437
951
912
1,159
3,823

680
3,877
876
1,911
6,923
1,646
8,544

0.021
0.021

0.001
0.001

2,006
1,030

3,494
2,652

Other Non-Border States (12)

0.049

0.007

1,096

2,925

Border States (6)

0.032

0.020

2,054

2,759

Mean
Mean w/o Jalisco
Low
CampecheMigration Chiapas
Quintana Roo
TabascoVeracruz
Yucatán
Federal District
Mean
Mean w/o Fed. Dis.

This table shows rates of migration to the United States, per capita GDP, and population for
Mexican states. Means are weighted by the 2000 population of the subgroup.

39

Table 5: Probability of Internal Migration(a) All High-Migration and Low-Migration States
Moved since Birth
Men
Women
(1)
(2)
Year 2000*High Migration
0.034
0.041
(0.014)(0.130)RN

0.068
159,0670.060
174,052(b) Excluding the Federal District and Jalisco
Moved since Birth
Men
Women
(3)
(4)
Year 2000*High Migration
0.016
0.021
(0.010)(0.007)RN

0.077
107,3100.066
116,864

This table reports results for probit regressions in which the dependent variable equals one if an
individual resides in a different state than his/her birth state and zero otherwise. The sample ismen and women in Mexico aged 20-49 in 1990 or 30-59 in 2000 born in a high-migration or alow-migration Mexican state. The other regressors are: (a) a cubic in age, dummy variables forfive categories of educational attainment (1-5 years, 6-8 years, 9-11 years, 12-15 years, or 16+
years), a dummy variable for marital status, dummy variables for presence of children in thehousehold (ages 0-5, 6-12, or 13-18 years), dummy variables for the state of birth, and a dummy
variable for the year 2000; (b) interactions between the age, education, marital status, andchildren variables and the year 2000 dummy; and (c) interactions between the age, education,
marital status, and children variables and a dummy variable for whether the individual was bornin a high-migration state. The coefficients show the change in the probability of internalmigration associated with an individual being from a high-migration state in 2000 versus that in1990 (evaluated at mean values for other regressors). Standard errors (corrected for correlationin the errors within birth states) are in parentheses.

40

Table 6: Schooling by Age Cohort in High-Migration and Low-Migration States, 2000

Sex
Men

ExcludingFederal District& Jalisco

Women

ExcludingFederal District& JaliscoState
Migration
Rate

2000
Age
Cohort

0

Low
Low
Low

30-39
40-49
50-59

High
High
High

Years of Schooling
1-5
6-8
9-11

12-15

16+

0.042
0.064
0.119

0.131
0.192
0.289

0.201
0.241
0.240

0.262
0.174
0.124

0.200
0.145
0.097

0.164
0.184
0.132

30-39
40-49
50-59

0.046
0.084
0.169

0.200
0.283
0.377

0.277
0.290
0.236

0.238
0.142
0.089

0.135
0.084
0.054

0.104
0.118
0.074

Low
Low
Low

30-39
40-49
50-59

0.072
0.108
0.182

0.220
0.307
0.404

0.238
0.253
0.213

0.218
0.127
0.075

0.147
0.089
0.056

0.104
0.116
0.070

High
High
High

30-39
40-49
50-59

0.052
0.090
0.174

0.215
0.292
0.386

0.274
0.288
0.235

0.233
0.142
0.089

0.129
0.082
0.050

0.097
0.106
0.065

Low
Low
Low

30-39
40-49
50-59

0.064
0.105
0.197

0.155
0.227
0.278

0.205
0.255
0.238

0.237
0.162
0.125

0.210
0.156
0.113

0.128
0.095
0.050

High
High
High

30-39
40-49
50-59

0.052
0.103
0.203

0.220
0.350
0.407

0.302
0.292
0.232

0.217
0.122
0.086

0.141
0.083
0.054

0.069
0.050
0.019

Low
Low
Low

30-39
40-49
50-59

0.113
0.177
0.301

0.261
0.353
0.367

0.225
0.231
0.195

0.186
0.105
0.067

0.131
0.076
0.048

0.084
0.057
0.022

High
High
High

30-39
40-49
50-59

0.060
0.113
0.218

0.236
0.364
0.414

0.298
0.283
0.216

0.205
0.116
0.083

0.135
0.079
0.052

0.066
0.044
0.017

This table shows the distribution of educational attainment by age cohort for individuals 30-59 years old
in 2000 born in high-migration or low-migration Mexican states (based on 1955-1959 emigration rates).

41

Table 7: Average Hourly Wages by Age and Schooling Cohort, 1990 and 2000
State
2000
Migration
Age
Years of Schooling
Year
Rate
Cohort
0
1-5
6-8
9-11
12-15
16+

1990
1990
1990

Low
Low
Low

30-39
40-49
50-59

0.92
1.21
1.27

1.62
1.31
1.83

1.56
2.56
2.49

2.14
2.97
3.88

2.76
4.25
6.10

4.61
6.30
8.10

1990
1990
1990

High
High
High

30-39
40-49
50-59

1.41
1.58
1.53

1.77
2.87
1.93

1.76
3.00
2.55

2.77
3.00
3.80

2.80
3.67
4.76

5.00
5.55
7.13

2000
2000
2000

Low
Low
Low

30-39
40-49
50-59

0.61
0.54
0.60

1.06
0.70
0.85

1.19
1.31
1.57

1.50
1.84
1.89

2.59
3.25
3.56

5.11
6.19
6.97

2000
2000
2000

High
High
High

30-39
40-49
50-59

1.18
1.21
0.98

2.63
1.22
2.56

1.44
2.05
1.97

2.39
2.02
2.65

2.72
3.51
3.69

4.39
5.12
6.50

1990
1990
1990

Low
Low
Low

30-39
40-49
50-59

Excluding the Federal District & Jalisco
0.83
1.05
1.26
1.96
2.34
3.27
1.14
1.25
1.71
2.01
3.21
4.22
1.22
1.60
2.41
3.11
4.86
5.70

1990
1990
1990

High
High
High

30-39
40-49
50-59

1.31
1.41
1.49

1.74
2.96
1.64

1.68
3.22
2.43

1.75
3.00
3.96

2.80
3.44
4.47

4.36
4.85
6.71

2000
2000
2000

Low
Low
Low

30-39
40-49
50-59

0.56
0.51
0.56

1.05
0.63
0.79

1.06
1.11
1.29

1.23
1.70
1.75

2.28
2.64
3.20

3.79
5.54
5.88

2000
2000
2000

High
High
High

30-39
40-49
50-59

1.19
1.10
0.82

2.98
1.11
2.47

1.39
2.19
1.62

2.55
1.86
2.47

2.58
3.13
3.54

4.30
4.96
6.66

This table shows average hourly wages by age and schooling cohort for individuals aged 20-49 in 1990 or
30-59 in 2000 born in a high-migration or a low-migration state. Wage levels are in 2000 U.S. dollars formen with average hourly earnings between $0.05 and $20. See note 10 on how wages are constructed.

42

Table 8: Regression Results

All
Workers

Workers w/
20-80 Hour
Work Week

All
Workers

Workers w/
20-80 Hour
Work Week(a) Full Sample of Workers
Year 2000*High Migration

0.063(0.027)0.063(0.026)

Year 2000*High Migration*
9-15 Years of Education
R
N

0.308
110,8370.349
103,232

0.045(0.034)0.049(0.033)0.057(0.030)0.043(0.030)0.308
110,8370.349
103,232(b) Excluding the Federal District and Jalisco
Year 2000*High Migration

0.089(0.032)0.086(0.032)

Year 2000*High Migration*
9-15 Years of Education
R
N

0.261
71,5570.302
66,1520.066(0.042)0.066(0.042)0.084(0.046)0.065(0.048)0.261
71,5570.303
66,152The dependent variable is log average hourly labor earnings. In columns 1 and 3, the sample ismales born in a high-migration state or a low-migration state; in columns 2 and 4, the sample
includes males who report working 20-80 hours a week. Other regressors (quadratic in age,
dummies for year of education, and their interactions with year 2000 dummy and with HighMigration dummy; year 2000 dummy variable; state dummy variables) are not shown. Standard
errors are in parentheses and are adjusted for correlation across observations within birth states.
In panel (a), the sample is working males in all high and low-migration states and time periods;
in panel (b), I drop observations for the Federal District and Jalisco from the sample.

43

.1
0

Share of Mexican Population in US
.02
.04
.06
.08

1960

1970

1980
Year

1990

2000

Figure 1: Share of Population Born in Mexico Residing in the U.S.

44

Zacateca.15

Househlds w/ Migrant in US, 2000

Michoaca

Guanajua.1
DurangoAguascalMorelos
San Luis
HidalgoNayaritJalisco
GuerreroColimaQueretar
Oaxaca.05

Chihuahu

PueblaSinaloa
Veracruz
MexicoTlaxcala

Tamaulip
Coahuila
Baja Cal Nuevo Le

DF

SonoraBaja Cal
TabascoYucatanChiapas Campeche
Quintana

0
0

500

1000
1500
Kilometers to US Border

2000

2500

Figure 2: Rate of Migration to the U.S. 1995-2000 by Mexican State
Zacateca.15

Share of Households Sending
Migrants to the US, 1995-2000

Michoaca

Guanajua.1
Hidalgo Morelos
Nayarit

San Luis

AguascalDurangoJalisco
GuerreroColimaQueretar

Oaxaca.05

PueblaSinaloa
Veracruz
Tamaulip
MexicoTlaxcala
Baja CalDF Sonora
Baja Cal
YucatanCampeche
ChiapasQuintana
TabascoChihuahu

Coahuila
Nuevo Le

0
0

.02
.04
Share of Residents Migrating to US, 1955-1959.06

Figure 3: State Rates of Migration to the U.S. in 1990s versus 1950s

45

Zacateca.15

H ousehlds w/ Migrant in US, 2000

Michoaca

Guanajua.1

DurangoHidalgoAguascalNayaritMorelos LuisSan

Jalis co

GuerreroColimaQueretar
Oaxaca.05

Chihuahu

PueblaSinaloa
Veracruz
Coahuila

T amaulip
MexicoNuevo LeT laxcalaBaja CalDF

SonoraBaja CalYuc atanCampecChiapashe
Quintana
T abasco

0
.025
.05
FDI Share of GDP, 1994-99

0

.075

Figure 4: State Exposure to Emigration and Foreign Direct Investment

Zacateca.15

H ousehlds w/ Migrant in US, 2000

Mi cho aca

Guanajua.1

Dur ango
Aguascal
San Luis
HidalgoMorelos
NayaritJalisco

GuerreroColimaQueretar
Oaxaca.05

Chi huah uPueb laSinaloa
Veracruz
T amaulip
T laxcalaCo ahuila
MexicoBaja Cal
Baja CalCampeche
ChiapasQuintana

Nuevo Le
SonoraDF

Yuca tan
T abasco

0
0

.02
.04
Import Share of GDP, 1993-99.06

Figure 5: State Exposure to Emigration and International Trade46

3
Population in millions
1
1.5
2
2.5
.5

Low

High

Low

10-19

High

Low

20-29

30-39
2000

.5

Population in millions
1
1.5
2

2.5

3

1990

High

Low

High

40-49

Low

High

Low

50-59
1990

High

60-69
2000

Figure 6:
Cohort Sizes for Men Born in High and Low-Migration States (Based on Age in 2000)

47

3
Population in millions
1
1.5
2
2.5
.5

Low

High

Low

10-19

High

Low

20-29

30-39
2000

.5

Population in millions
1
1.5
2

2.5

3

1990

High

Low

High

40-49

Low

High

Low

50-59
1990

High

60-69
2000

Figure 7:
Cohort Sizes for Women Born in High and Low-Migration States (Based on Age in 2000)

48

Male Share of Population.1 .15 .2 .25 .3 .35 .4 .45 .5

Low

High

Low

10-19

High

Low

20-29

30-39
2000

Male Share of Population.1 .15 .2 .25 .3 .35 .4 .45 .5

1990

High

Low

High

40-49

Low

High

Low

50-59
1990

High

60-69
2000

Figure 8:
Share of Men in the Population by Age Cohort in High and Low-Migration States

49

.6
.4
.2
0
-4

-2

0
lnw

4

Low Migration, 1990

0

.2

.4

.6

High Migration, 1990

2

-4

-2

0
lnw

High Migration, 2000

2

4

Low Migration, 2000

Figure 9: Kernel Densities for Average Log Hourly Wages, 1990 and 2000

50

.6
.4
.2
0
-4

-2

0
lnw

4

Low Migration, 1990

0

.2

.4

.6

High Migration, 1990

2

-4

-2

0
lnw

High Migration, 2000

2

4

Low Migration, 2000

Figure 10: Kernel Densities for Log Wages, Excluding Federal District and Jalisco

51[Hi-Lo 2000]-[Hi-Lo 1990], Actual Densities
.1
.05
0
-.05
-4

-2

0
lnw

2

4

2

4

-.2

[Hi-Lo 2000]-[Hi-Lo 1990], Actual Densities
.1
0
-.1

.2

(a) Full Sample

-4

-2

0
lnw

(b) Excluding the Federal District and Jalisco
Figure 11: 1990 to 2000 Change in Wage Densities
for High-Migration States relative to Low-Migration States

52

.1
.05
0
-.05
-.1
-4

-2

0
lnw

Density Diff: [90Hi-90Lo]2

4

Density Diff: [00Hi-00Lo]

Density Diff: [00Hi-90Hi]-[00Lo-90Lo], 90Lo base case
-.01
-.005
0
.005
.01

Figure 12: Differences in Counterfactual Wage Densities
between High-Migration and Low-Migration States(with returns to observable characteristics evaluated for low-migration states in 1990)

-4

-2

0
lnw

2

4

Figure 13: Double Difference in Counterfactual Wage Densities(with returns to observable characteristics evaluated for low-migration states in 1990)

53

Density Diff: [00Hi-90Hi]-[00Lo-90Lo], 90Lo base case
-.01
-.005
0
.005
-4

-2

0
lnw

2

4

Figure 14: Double Difference in Wage Densities, Excluding Federal District and Jalisco(with returns to observable characteristics evaluated for low-migration states in 1990)

54

.04
.02
0
-.02
-.04
-.06
-4

-2

0
lnw

Density Diff: [90Hi-90Lo]2

4

Density Diff: [00Hi-00Lo]

Density Diff: [00Hi-90Hi]-[00Lo-90Lo], 90Lo base case
-.005
0
.005
.01

Figure 15: Differences in Counterfactual Wage Densities
between High-Migration and Low-Migration States(with distribution of observable characteristics evaluated for low-migration states in 1990)

-4

-2

0
lnw

2

4

Figure 16: Double Difference in Counterfactual Wage Densities(with distribution of observable characteristics evaluated for low-migration states in 1990)

55