Emigration, Remittances, and Labor Force Participation in Mexico

May 2005

Gordon H. Hanson*
University of California, San Diego andNational Bureau of Economic Research
Abstract. In this paper, I examine emigration, remittances, and labor-force participationin Mexico during the 1990s. I use two samples of households for the analysis: (a) rural
households in Mexico in 2000, which vary according to whether they have sent migrants
to the United States or received remittances from the United States, and (b) individuals in
Mexico in 1990 and 2000 born in states with either high-exposure or low-exposure to
U.S. emigration. In the first sample, controlling for observable characteristics,
individuals are less likely to participate in the labor force if their household either has
sent migrants abroad or received remittances from abroad. Surprisingly, this result holds
for both women and men. These cross-sectional results are potentially contaminated byunobserved household characteristics that may be correlated with both household
migration behavior and household labor supply. In the second sample, which is lesssubject to concerns about self-selection into migration, I find that over the 1990s women(but not men) from high-migration states become less likely to work outside the home(relative to women from low-migration states). These results are consistent with
migration abroad, and the accompanying return flow of remittances, leading to greater
intra-household specialization. Results for labor hours are similar.
*

I thank Ernesto Lopez Cordoba, Chris Woodruff, and seminar participants at the InterAmerican Development Bank and National Bureau of Economic Research for helpfulcomments. Jeffrey Lin provided excellent research assistance.

1. Introduction
Migration to the United States is having dramatic consequences on the supply oflabor in Mexico. In 2000, 10% of individuals born in Mexico resided in the UnitedStates, up from 2% in 1970 (see Figure 1).1 As is the case in many countries, rates of
emigration are highest for young adults. During the 1990s alone, 9% of Mexicans 16 to
25 years old (based on age in 1990) migrated to the United States (see Table 1).
Migrant outflows of this magnitude affect labor markets in myriad ways. A
growing literature examines the impact of Mexico’s outmigration on labor-market
outcomes in the country.2 Chief among the effects, emigration appears to have put
upward pressure on wages in Mexico, particularly in regions that historically have sent
large numbers of migrants to the United States. Mishra (2004) estimates that over theperiod 1970-2000 emigration raised average wages in Mexico by 8%. Wage increases
are even larger in Mexico’s high-emigration states, which over the 1990s experienced 9%higher wage growth than low-emigration states (Hanson, 2005).
The outflow of labor from Mexico does more than raise incomes for migrants.
Once in the United States, migrants remit a portion of their income to family members
who remain in Mexico. In 2003, remittances from Mexican migrants in the United Statesequaled 2% of Mexico’s GDP (IADB, 2004). These inflows appear to considerablylarger than the loss in Mexico’s GDP due to emigration.3 Remittances also appear to1

I take the total population of Mexicans to be the sum of individuals born in Mexico who reside in either
Mexico or the United States (thus ignoring the small number of Mexicans residing in third countries).
2
Comparatively, the literature on the consequences of immigration in the United States is much larger. See
Borjas (2005) for a collection of recent work on the Mexican immigration and the U.S. economy.
3
Based on Mishra’s (2004) estimates, the emigration loss in Mexico for 2000 would be 0.5% of GDP (0.5
times the change in wages due to emigration of 8% times the loss in labor supply due to emigration of 16%
times a labor share of income of 0.7). In that year, remittances were 1% of Mexican GDP.

1

have encouraged capital accumulation in Mexico, contributing to higher investment insmall businesses (Woodruff and Zenteno, 2001).
An unexplored issue is whether remittances condition how emigration affects
labor-market outcomes. In the absence of remittances, emigration would be likely toincrease labor-force participation among adults, due both to the upward pressure thatlabor outflows put on wages and to the need to replace income lost to the exodus of wage
earners from households. With remittances, however, migrant families may feel lessneed to have non-migrating adults work outside the home. For women, in particular,
remittances may decrease incentives to spend time in the labor force and increase
incentives to invest in home production. Thus, emigration may increase intra-household
specialization, with migrants, who are often fathers, devoting more time to working
abroad, and non-migrants, who are often mothers, devoting time to working at home.4
In this paper, I examine the impact of emigration on labor-supply decisions in
Mexico. I use data from the 1990 and 2000 Mexico Census of Population and Housing to
examine differences in labor-force participation and labor hours worked acrossindividuals according to their exposure to opportunities to emigrate.
Complicating the empirical analysis, migration is not a random event.
Households choose to send migrants abroad based on the perceived gains from doing so.
Most emigrants from Mexico appear to enter the United States illegally.

Illegalimmigration is costly, given the need to hire the services of a smuggler to evade everstricter enforcement of the U.S.-Mexico border by U.S. immigration authorities. In thepresence of imperfect credit markets, migration costs may preclude the poorest
4

Of course, this outcome depends on households sending some but not all members abroad. If the entire
household migrates, then migration might reduce intra-household specialization (as would be the case if all
members of the household end up working outside the home).

2

households from sending migrants abroad. Individuals in richer households, in turn, may
feel working legally in Mexico is preferable to working illegally in the United States.
Thus, household income, wealth, and willingness to tolerate the vagaries of illegal
migration are all likely to influence the migration decision. Since these characteristics
are measured imperfectly (or not at all), households are likely to self-select into sending
migrants abroad based in part on their unobserved characteristics.
To gauge the consequences of self-selection into migration for the analysis, I
report results using two samples of individuals. The first sample contains individuals
living in rural areas of Mexico in 2000. The 2000 Mexico Census reports whether
households had sent a migrant to the United States in the last five years or received
remittances from migrants abroad in the last month. I see whether labor-supply decisions
vary across individuals, according to the migration status of their household. I focus on
rural households, since they appear to be the units most likely to send individual migrants
abroad. Urban dwellers, if they migrate, may be likely to move the entire household(since the cost of maintaining a household in Mexico is higher in urban areas than in rural
areas). Without a valid instrument for the household migration decision, the analysis
using this first sample is subject to concerns about the correlation between household
migration status and unobserved household characteristics.
The second sample I use includes individuals from high-migration states and lowmigration states in Mexico. Due partly to historical accident, central and western Mexico
have long had the country’s highest labor flows abroad (Cardoso, 1980; Durand, Massey,
and Zenteno, 2001). In Figure 2, which shows the fraction of households that sent
migrants to the United States over 1995-2000 by Mexican state, emigration rates are3relatively low in states along the U.S. border, sharply higher in states 600-1200
kilometers from the United States, and lowest in distant southern states. Most states thathave high current emigration rates also had relatively high emigration rates 50 or more
years ago (see Figure 3). I compare individual labor-supply decisions in 1990 and 2000
for two groups of states, those that had high emigration rates in the 1950s and those that
had low emigration rates in the 1950s. In this approach, I use historical migration rates asa reduced-form determinant of current migration opportunities.5 Since high emigration in
the past could have altered regions in a manner that affects current labor-market
conditions, a reduced-form approach is more appropriate than using past migration
behavior as an instrument for current migration.6 To control for internal migration, I use
the 1950s emigration rate in an individual’s birth state, rather than his or her current stateof residence. Historical migration rates in an individual’s birth state are thus meant tocapture current opportunities to migrate abroad.
An additional challenge for the estimation is that there may be other, unobserved
shocks that have affected high and low migration states differently. Candidate shocks
include the North American Free Trade Agreement, the privation and deregulation ofindustry, the reform of Mexico’s land-tenure system, and the 1994-1995 peso crisis.7

5This approach depends on the assumptions that (a) labor is sufficiently immobile across Mexican regions
for region-specific labor-supply shocks to affect regional earnings differentials (see Robertson 2000,
Chiquiar 2004, and Hanson 2004 for evidence consistent with this assumption), and (b) current
opportunities to migrate to the United States depend on regional historical migration patterns (see Munshi
2003 and Orrenius and Zavodny 2004 for recent work on migration networks).
6
For instance, states with higher emigration rates in the past could have attracted less investment over time,
affecting the demand for labor in these states today.
7
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).

4

Obviously, the potential for these shocks to contaminate the analysis is an importantconcern, which I address in discussing interpretations of the results.
In the next section, I document how migration behavior varies across regions of
Mexico and discuss criterion for selecting which Mexican households and states toinclude in my sample. In sections 3 and 4, I examine the impact of emigration on labor
supply and labor-force participation in Mexico. In section 4, I discuss interpretations of
the results and limitations of the estimation strategy.

2. Regional Patterns of Emigration in Mexico
2.1 Data Sources
Data for the analysis come from two sources. In 1990, I use the 1% microsampleof 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 about migration abroad:(i) whether in the last five years anyone from the household migrated to the United States
or another foreign country, and (ii) whether in the previous month anyone in thehousehold received remittances from migrants located abroad. These questions haveobvious shortcomings.

They provide no indication of migrant schooling, migration

histories, annual remittances, or in-kind transfers from migrants. Still, the 2000 census isuseful in that it is the only nationally representative sample available for Mexico that
contains information about migration to the United States.8

8

Many studies on Mexican migration to the U.S. use data from the Mexican Migration Project (MMP),a
household survey in several dozen rural Mexican communities over the period 1987-1997 where
emigration tends to be high (Durand et al., 1996; Massey et al., 1994). The MMP is not suitable for my5

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 1964, allowed U.S. employers to import workers
from Mexico (and the Caribbean) to fulfill short-term labor contracts (of less than a year).
The vast majority of temporary migrants admitted under the program worked inagriculture (Calavita, 1992). Woodruff and Zenteno (2001) also provide data on state
emigration rates in 1924, which I use in some exercises.

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. Railroad construction in the late 19th century linked interior Mexico to the U.S.Mexico border, giving U.S. employers improved access to Mexico labor (Cardoso, 1980).
Just after the turn of the last century, farmers in Texas began to recruit laborers in
Mexico. To find workers, recruiters followed the main rail line to the relatively densely
populated states in the central and western regions of the country. The early migrants
from Mexico came primarily from nine states in this region (Durand, Massey, andZenteno, 2001).9 Mexican migration to the United States increased in the 1920s, after the
U.S. Congress lowered quotas on U.S. legal immigration. Recruitment expanded again in

purposes. The MMP sample of migrants is selected both in terms of the communities included, which arepoor, rural, and in high-migration states (and so very far from being nationally representative), and in termsof its focus on return migrants (rather than on migrants currently residing in the United States).
9
These nine states are Aguascalientes, Colima, Durango, Guanajuato, Jalisco, Michoacán, Nayarit, San
Luis Potosí, and Zacatecas.

6the 1940s, after Congress enacted the Bracero Program (Calavita, 1992). From the 1920s
to the 1960s, the nine west-central states accounted for 44% to 56% of Mexican
migration to the United States (but only 27% to 32% of Mexico’s total population)(Durand, Massey, and Zenteno, 2001).
After a stint working in the United States, migrants often return to Mexico andhelp later generations go abroad. Migrants staying in the United States have createdhome-town associations that assist other individuals from their home communities in
Mexico (Cano, 2004). There are also many informal networks through which current
migrants help prospective migrants settle in the United States. These networks tend to berelated to family or community of birth, giving them a strong regional component. They
also appear to affect migrant outcomes. Munshi (2003) finds that Mexican migrants in
the United States are more likely to be employed the larger is the U.S. population ofresidents from their home towns in Mexico.
Figure 3 gives evidence of strong persistence in regional migration behavior. The
states that had high migration rates in the 1950s, during the height of the Bracero
Program, continue to be high migration states today. The correlation between state
emigration rates in the 1995-2000 and the 1955-59 is 0.73. The correlation between statemigration rates in the 1995-2000 and 1924 is 0.48 (Woodruff and Zenteno, 2001). Figure
4 shows a similar strong positive correlation between the share of remittances in state
GDP in 1995 and the 1955-59 state emigration rate.
Table 2 reports regressions using as a dependent variable either the fraction ofhouseholds sending migrants abroad or of the fraction of households receiving
remittances from abroad over the 1995-2000 period. High migration states are not simply

7

the poorest states or those closest to the United States. In columns 1 and 5, there is anegative correlation between emigration rates or remittances and state per capita income,but the explanatory power of income is not very strong. Adding distance to the UnitedStates, in columns 2 and 6, doubles the R2 of the regressions.

Adding the state

emigration rate in 1924, in columns 3 and 7, also raises the R-squared substantially.
There appears, however, to be little covariation between 1995-2000 and 1924 state
emigration rates that is independent of the 1950s state emigration rate. In columns 4 and8, once the 1955-59 emigration rate is added as a regressor the R2 rises further and the1924 migration rate becomes statistically insignificant.
If states with relatively high emigration rates are also more exposed to otheraspects of globalization, the empirical analysis might confound the effects of migration
with the effects of trade or capital flows. During the 1980s and 1990s, Mexico lowered
its barriers to foreign trade and investment. See Chiquiar (2004) and Hanson (2004,
2005) for evidence that high-emigration states are not those that have benefited
disproportionately from investment and trade reform (not surprisingly, the states thathave benefited disproportionately are those close to the U.S.-Mexico border).

2.3 Sample Design
The goal of this paper is to examine the consequences of emigration on labor
supply in Mexico. The first approach I take is to utilize data on migration to the UnitedStates in Mexico’s 2000 population census and to compare labor-market outcomes inhouseholds with emigrants to outcomes in households without emigrants. One concern
with this approach is that household migration behavior is endogenous. The unobserved

8characteristics of households that affect labor supply are also likely to affect whether
households choose to send migrants abroad.
One might consider historical state emigration rates as potential instruments for
current migration opportunities. Based on data from the 2000 census, the likelihood ahousehold either has sent a migrant to the United States in the last five years or hasreceived remittances from abroad in the last month is strongly positively correlated withthe 1955-59 emigration rate in the household head’s birth state (Hanson, 2005).
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 have affected
an individual’s labor market experience or the quality of education the individual
obtained. Thus, past emigration opportunities are likely to affect current labor-market
outcomes both directly, through their impact on current emigration, and indirectly,
through their impact on an individual’s human capital.
Given these concerns, I also employ a second approach in which I comparechanges in cross-section labor-market outcomes, where I categorize individuals according
to the emigration rate in their birth state. In so doing, I capture both the direct andindirect effects of historical emigration opportunities on current labor-market outcomes.
This strategy is thus to compare labor-market outcomes in regions that have been more orless exposed to opportunities to migrate to the United States.
Table 3 describes the sample of states. I drop the six border states from thesample, since these states appear to have benefited disproportionately from trade andinvestment liberalization. Most border states had above average emigration rates in the
1950s and including them in the sample would potentially confound the effects of9

emigration with those of other aspects of globalization. To help isolate the effects ofemigration, I limit high-migration states to those with emigration rates in the top threedeciles of non-border states and low-migration states to those with emigration rates in thebottom three deciles of non-border states.10 In 2000, 11% of households in the six highmigration states had sent a migrant to the United States in the previous five years,
compared with only 2% of households in the six low-migration states.

3. Summary Statistics on Wages and Labor-Force Participation
The most direct effect of foreign labor outflows has been to reduce the populationof young adults born in high-migration states. Figures 5 and 6 show cohort sizes basedon age in 2000 for males and females born in high-migration or low-migration states. In
the absence 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. Population declines are largest for 20-29 year-old men (men born between
1971 and 1980) in high-migration states, whose number declines by a whopping 37 log
points. For those born in low-migration states, the number of 20-29 year-old men dropsby the smaller-yet-still-impressive value of 14 log points, such that the relative decline of
the 20-29 year-old male population in high-migration states over 1990-2000 is 23 log
points. Overall, the population of 20-59 year-old men declines by 9 log points for highmigration relative to low-migration birth states.

10

From this group, I drop the Federal District (Mexico City) from low-migration states (since this state has
the highest per capita income in Mexico and is highly exposed to globalization; other low-migration states
are poor, located in southern Mexico, and little exposed to globalization); and I drop Jalisco from highmigration states (since this state has Mexico’s second largest city, which has also been highly exposed to
globalization). The results are unchanged when these two states are added to the sample.

10

Absolute and relative changes for female cohorts are smaller. The cohort of 2029 year-old women declines by 18 log points in high-migration states and 5 log points in
low-migration states. Overall, the population of 20-59 year-old women declines by 7 log
points in high-migration relative to log-migration states. Larger declines in the malepopulation are consistent with the relatively high emigration rates for men in Table 1.
Since we lack data on households with migrants abroad in 1990, we cannot perform thesame analysis, comparing 1990 and 2000, at the household level.
One might expect the educational profile of individuals to differ according to themigration status of their households. Chiquiar and Hanson (2005) show that in Mexico
individuals with moderately high education levels are those most likely to migrate to theUnited States. If schooling is correlated across individuals within households, thenindividuals living in migrant households may tend to have relatively high educationlevels. On the other hand, if households tend to send their more-educated members
abroad, those remaining in migrant households may tend to have lower education levels
than individuals in households without foreign migrants.
Table 4 shows the distribution of schooling by age cohort in 2000 for individuals
in rural areas, who live in households either with or without migrants in the UnitedStates. Individuals who live in households that receive remittances from abroad aremoderately over-represented in the 1-to-5-year-of-schooling group and under-represented
in higher-schooling other groups.

Among 30-39 year-old men in 2000, 34% hadcompleted nine or more years of schooling in households without migrants abroad, versus
31% in households with migrants abroad. For women, these figures are 26% and 23%,11

respectively. A similar pattern is revealed when separating households by whether they
have sent a migrant to the United States in the last five years.
Repeating the analysis for high-migration and low-migration states, Table 5shows the distribution of schooling by age cohort in 2000 for individuals born in these
two groups of Mexican states. For men, average schooling is similar in low-migrationand high-migration states. Among 30-39 year-old men in 2000, 47% had completed nineor more years of schooling in low-migration states, versus 46% in high-migration states.
For women, these figures are 40% and 41%, respectively.
Despite comparable education levels in high and low-migration states, wages
appear to be higher in high-migration states.11 Figure 7 shows kernel densities for log
average hourly wages. In 1990, wages have lower dispersion and a higher mean in highmigration states when compared to low-migration states. In 2000, these features are
more pronounced. Relative to low-migration states, the wage density in high-migration
states shows a distinct rightward shift, indicating positive relative wage growth.
The reported wage densities do not control for differences in the distribution ofcharacteristics across regions. Hanson (2005) finds that after controlling for differencesin age, education, and other observable characteristics, wages are higher in highmigration states and over the 1990s increase in high-migration states relative to lowmigration states. Between 1990 and 2000, wages grow by 9% more on average for men
in high-migration states, relative to low-migration states. Relative wage gains are evenlarger among male workers with higher education levels (9 to 15 years of schooling).
11

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.

12The increase in relative wages in high-migration states is consistent with the decrease in
the supply of labor in high-migration states relative to low-migration states, which isevident in Figures 5 and 6.
Table 6 shows the fraction of 30-49 year old men and women with positive labor
earnings by schooling group in rural households with and without migrants in the UnitedStates. Individuals have uniformly lower labor-force participation rates in households
that either receive remittances (relative to households that don’t) or that have sent
migrants to the United States (relative to households that haven’t). This is suggestive
evidence that incentives to participate in the labor force are lower in households that havesent migrants to the United States, which would be consistent with greater intrahousehold specialization in migrant-sending households. However, this interpretation is
subject to the caveat that differences in unobserved characteristics between householdswith and without migrants may contribute to differences in their labor-supply behavior.
Table 7 shows the fraction of 30-49 year old men and women with positive labor
earnings by schooling group in high-migration and low-migration states.

Amongindividuals with low schooling levels, men tend to have slightly lower labor-force
participation in high-migration states. This pattern is evident in both 1990 and 2000.
Among individuals with high schooling levels, labor-force participation is very similar in
the two groups of states. For women, labor-force participation is much lower overall.
Labor-force participation appears to be modestly lower in high-migration states in 2000,
but not in 1990. Given sharply higher wages in high-migration states, similar labor-force
participation rates in high-migration and low-migration states are perhaps surprising.
This is additional suggestive evidence that households in migrant-sending regions may

13

tend to be more specialized in home production than households in other regions. In thenext section, we examine differences in labor-force participation across households andbetween regions in more detail.

4. Emigration and Labor-Force Participation
4.1 Rural Households in 2000
Emigration may affect the labor-supply decision through several channels. For
men and women, higher emigration may put upward pressure on wages, making it more
likely that they supply labor outside the home. Within the household, sending migrants
abroad may increase remittances to the household, raising incomes among family
members in Mexico. Previous research has shown that higher household income is often
associated with lower female labor supply (Altonji and Blank, 1999). For women, then,the impact of emigration on the labor-supply decision is ambiguous, depending in part onwhether they substitute for emigrant labor in the Mexican labor market and on whether
emigration increases household income through remittances.
To examine these channels, I estimate the labor-force participation and the laborhours decisions as functions of individual characteristics, household characteristics, andregional characteristics. Following a large academic literature, I model the labor-supply
decision in reduced-form. Coefficients indicate which types of individual, household, orregional characteristics tend to be associated with higher returns in the labor-market(which would tend to increase labor supply, as long as substitution effects dominate
income effects in the labor-leisure choice) or higher family income (which would tend to
decrease labor supply, particularly for women).

14

In one exercise, I use cross-section data on household migration behavior fromthe 2000 population census to estimate the following regression for labor hours,
y hs = α s + X hs β1 + β 2 Re mitt hs + ε hst(15)where y is labor hours worked last week, X is a vector of observed characteristics, andRemit is a dummy variable indicating whether or not the household receives remittancesfrom migrants in the United States (which is replaced in some regressions with thevariable Has Migrant in U.S., to indicate whether a household has sent a migrant abroadin the last five years). The regression includes controls for state-of-birth fixed effects andadjusts standard errors for correlation across observations associated with the same birthstate. I estimate equation (15) as a tobit, given that hours worked are zero for many
individuals. In a second exercise, I estimate an analogous probit regression in which thedependent variable is a dichotomous outcome on whether or not an individual participatesin the labor force. The sample is individuals living in rural households in 2000.
Table 8 shows regressions for whether an individual supplies labor outside thehome. Labor-force participation is measured by whether the individual earns positive
labor income; labor hours are measured by the reported number of hours an individual
supplied for remunerated work in the previous week. The regressors are a quartic in age,dummy variables for schooling, dummy variables for the number of children in thehousehold, dummy variables for state of birth, and an indicator of whether the household
has sent a migrant to the United States in the last five years or whether the household hasreceived remittances from migrants abroad in the last month.
In households that have sent migrants to the United States, the coefficientestimates indicate that men are 11% less likely and women are 2.5% less likely to supply

15

labor outside the home, with both effects precisely estimated.12 It is surprising that theestimated effects are so much larger for men than for women. For women, the results are
consistent with sending migrants abroad leading to higher family income and lower
female labor supply. Since a relatively large fraction of the migrants households sendabroad are men, the results for them are harder to interpret. It could be that higher
household income associated with remittances lowers male labor supply. It could also bethat the members households choose to finance to go abroad or those most likely to workoutside the home. The men remaining in Mexico could be individuals whose unobserved
characteristics make them less likely to work for a wage (where this effect may operate
for women, as well). Given these concerns, one may suspect the results are telling us
more about the correlation between unobservables and labor supply than about the impactof migration behavior on labor supply.
The results for remittances are quite similar, indicating that in households thatreceive remittances from abroad men are 11% less likely to work outside the home andwomen are 2.2% less likely to work outside the home, with both effects precisely
estimated. Again, it is surprising that the effects are so much larger for men.
Turning to labor hours, the second panel of Table 8 shows tobit regression resultsfor hours worked in the previous week. Individuals in households that either have sent
migrants abroad or received remittances from migrants abroad tend to supply fewer labor
hours than individuals in non-migrant households. These effects are precisely estimatedboth for the Has Migrant in U.S. variable and for the Received Remittances variable. The
coefficient magnitudes are larger for women, consistent with previous literature.

12

These probabilities are based on mean values for the other regressors.

16One might be concerned that the results are driven by unobserved differences inhousehold wealth.

In rural households, wealth is likely to be correlated with theeducation of household members. To continue beyond primary school (six years ofeducation), individuals in many rural areas may have to relocate to a small or medium
size town in order to gain access to a secondary school. In the second four columns ofTable 8, I restrict the sample to individuals with 6 or fewer years of education. For bothlabor-force participation and labor hours, these results are very similar to those in the first
four columns. One might also be concerned that the migration variables are picking up
unobserved differences in household access to migration networks, rather than with theimpact of migration itself. In the third four columns of Table 8, I restrict the sample torural households in high-migration states. Presumably, there is less variation acrosshouseholds in access to migration networks in this sample. Again, the results are very
similar to those in the first four columns of Table 8.
To return to the earlier discussion, the cross-section results in Table 8 are subject
to concerns about the unobserved determinants of household migration behavior. The
problem is that the unobserved characteristics of households that are correlated with labor
supply may also be correlated with the household decision to send migrants abroad. For
instance, households with higher unobserved sources of income may be more likely tohave sent migrants to the United States (since higher income makes it easier to financemigration costs) and less likely to have mothers in the household work outside the home(since higher income may lower the shadow value of female earnings for the household).
As an attempt to deal with selection into migration by households, I redo the analysis for17

individuals from high-migration or low-migration birth states, where birth state serves asa reduced-form determinant of access to migration networks.

4.2 High-Migration and Low-Migration States in 1990 and 2000
In my alternative estimation strategy, I pool data across households in 1990 and2000 and limit the sample to individuals living in rural areas who were born in either ahigh-migration state or a low-migration state. I then see whether labor-supply behavior
changes differentially between high-migration and low-migration states over the 1990s.
For hours worked, I estimate the following regression,
y 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 y is hours worked last week, 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. I also estimate an analogous probit
regression in which the dependent variable is a dichotomous outcome on whether or not
an individual participates in the labor force. Standard errors are adjusted for correlation
across observations associated with the same birth state.
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 trade reform more generally. Other shocks in the 1990’s

18

include a currency crisis in 1995, a reform of the land-tenure system, privatization ofstate-owned enterprises, and industry deregulation. The existence of these shocks leaves
the results subject to the caveat that factors other than emigration may have contributed todifferential regional changes in earnings.
Table 9 reports the results. The results for women are quite similar to those in
Table 8 but the results for men differ sharply. For women, there is an increase in the
differential between high-migration and low-migration states in labor-force participation
over the 1990-2000 period. The coefficient estimates indicate (based on mean values forthe other regressors) that over the 1990s women in high-migration states become 2.2%
less likely to work outside the home, where this change is precisely estimated. For men,
there is no statistically significant change in their labor-force participation behavior
between 1990 and 2000 in high-migration versus low-migration states. Limiting thesample to individuals with six or fewer years of education, the results are unchanged.
This is additional evidence that, despite wage increases in high-migration states, womenin these states became less likely to work outside the home. Consistent with the crosssectional results for 2000, it appears migration abroad contributes to greater withinhousehold specialization, with women devoting less time to work outside the home.
For labor hours, the results for women in Table 9 are also consistent with those in
Table 8. Over the 1990s, women born in high-migration states reduce labor hours
relative to women born in low-migration states, with this effect precisely estimated bothin the full sample and in the sample of individuals with a primary education or less. For
men, there is also a reduction in labor supply in high-migration states relative to lowmigration states, but the effect is much smaller than that for women. As with the 2000

19

cross-section results, this finding is surprising. Relative to low-migration states, wewould expect to see male relative labor supply increase in high-migration states in
response to the observed increase in high-migration state relative wages.
Overall, there is some evidence that emigration is associated with lower
household labor supply. Women are less likely to work outside the home in householdswith migrants abroad and show a decrease in the likelihood of working outside the homein high-migration states relative to low-migration states. If remittances raise householdincome, then these results are to be expected. In effect, households may be using their
extra income to buy back labor time of women remaining in Mexico. However, we also
see indication that emigration is associated with lower male labor supply.

This issurprising and counterintuitive. Since male wages are rising in relative terms in highmigration states, we would expect to see increases rather than decreases in relative malelabor supply. The results for men may indicate that neither regression approach I employ
sufficiently accounts for correlation between unobservables and migration behavior.

5. Discussion
In this paper, I examine how emigration may have affected adult labor force
participation in Mexico. To investigate the issue, I employ two estimation strategies, oneof which exploits data on household migration behavior in Mexico’s 2000 population
census and the other of which exploits strong persistence of regional differences within
Mexico in the likelihood of emigrating to the United States. A key concern in theempirical analysis is how to account for unobserved characteristics of households that
may influence both labor-supply and migration behavior.

20

As in earlier decades, during the 1990s individuals born in Mexico’s highmigration states appear to have a high propensity to migrate abroad. Between 1990 and
2000, the population of 20-59 year-old men born in high-migration states declined by 9log points relative to similarly aged men born in low-migration states. For women, thecorresponding relative regional change in population was 7 log points. Most of these
population changes appear due to emigration. Mishra (2004) and Hanson (2005) find thatemigration-induced reductions in labor supply have raised wages in Mexico.
In cross-section results for 2000, women in households either with migrants
abroad or receiving remittances from abroad are less likely to work outside the home andsupply fewer labor hours overall.

Comparing 1990 and 2000, women from high-migration states in Mexico become less likely to work outside the home and reduce their
total labor hours (relative to women from low-migration states). This pattern of behavior
is consistent with households choosing to reduce female labor supply in response toemigration-induced increases in family income or regional income. That is, emigration
appears to be associated with greater intra-household specialization.
What casts some doubt on this interpretation is that, at least in the cross-section
regressions for 2000, the results for men and women are similar. This is surprising sinceprevious literature would suggest that higher wages associated with emigration would
increase male labor supply. One interpretation of the results for men (and possibly for
women) is that they reveal information about self-selection into migration. In households
that send migrants abroad, the men that remain in Mexico may be individuals whose
unobserved characteristics make them less able or less willing to work outside the home.
Similarly, in states that send large numbers of migrants to the United States, the men that21

choose not to migrate abroad may be those whose labor-market opportunities, either in
Mexico or the United States, are relatively unattractive.
Concerns about self-selection into migration make it necessary to qualify theempirical results in this paper.

We can say that I find suggestive evidence of a

relationship between emigration and within-household specialization. To remove thequalification, we would need longitudinal data on households that would allow us to
examine changes in within-household migration behavior over time.

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IL: University of Chicago Press): 67-92.
LaLonde, Robert and Robert Topel. 1997. “Economic Impact of International Migrationand Migrants.” In Mark R. Rosenzweig and Oded Stark, eds., Handbook of Population
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24Munshi, Kaivan. 2003. “Networks in the Modern Economy: Mexican Migrants in the
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in Mexico.” Mimeo, UC San Diego.

25

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

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).

26

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)

Households Receiving Remittances 2000
(5)
(6)
(7)
(8)
0.069
0.064
0.082
0.071
(0.032)(0.031) (0.035) (0.028)-0.025(0.011)-0.007(0.004)-0.036(0.011)-0.03(0.011)-0.017(0.009)-0.012(0.004)-0.010(0.004)-0.006(0.003)

Log Distanceto U.S.

0.070(0.027)0.006(0.029)-0.025(0.026)0.024(0.009)-0.003(0.010)-0.013(0.009)Log Distanceto U.S.2-0.007(0.003)0.000(0.003)0.003(0.003)-0.003(0.001)0.000(0.001)0.001(0.001)

13.835(3.435)4.653(4.116)Migration Rate
1924

32.813
4.295(10.210) (10.210)

Migration Rate
1955-59

1.919(0.386)

Adjusted R2
N

0.116
32

0.252
32

0.456
32

0.667
32

0.618
(0.128)
0.072
32

0.228
32

0.504
32

0.670
32

The sample is the 31 states of Mexico plus the Federal District. In columns 1-4, the dependent
variable is the average share of households in a state that had sent a migrant to the United Statesin the 1995-2000 period; in columns 5-8, the dependent variable is the share households in astate in 2000 that had reported receiving remittances from migrants located abroad in theprevious month. Standard errors are in parentheses.

Table 3: Ranking Mexican States by Historical Emigration Rates

State

Migration Rate
1995-2000 1955-1959

Per Capita
GDP 1995

Pop. 2000('000s)High
Aguascalientes
Migration Durango
Guanajuato
Michoacán
San Luis Potosí
Zacatecas

0.090
0.093
0.114
0.130
0.087
0.151

0.032
0.055
0.041
0.031
0.025
0.059

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

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

Mean

0.114

0.038

1,077

2,438

0.011
0.009
0.009
0.007
0.037
0.013

0.000
0.000
0.000
0.002
0.000
0.002

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

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

0.021

0.001

1,030

2,652

0.049
0.032

0.007
0.020

1,096
2,054

2,925
2,759

Low
CampecheMigration Chiapas
Quintana Roo
TabascoVeracruz
Yucatán
Mean
Other Non-Border States
Border States

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.

28

Table 4: Schooling by Age, Gender, and Household Migration Status, 2000

Sex

Age

Receives
Remittances

Men

3039
4049
5059

No
No
No

0.082
0.145
0.254

0.284
0.411
0.488

0.294
0.267
0.174

0.207
0.091
0.048

0.087
0.047
0.019

0.047
0.039
0.018

3039
4049
5059

Yes
Yes
Yes

0.106
0.138
0.254

0.322
0.534
0.558

0.263
0.209
0.141

0.22
0.075
0.033

0.062
0.032
0.011

0.026
0.012
0.004

3039
4049
5059

No
No
No

0.119
0.22
0.356

0.32
0.442
0.461

0.298
0.225
0.13

0.156
0.067
0.033

0.074
0.032
0.013

0.034
0.015
0.006

3039
4049
5059

Yes
Yes
Yes

0.065
0.14
0.243

0.38
0.554
0.571

0.326
0.25
0.154

0.165
0.035
0.017

0.052
0.016
0.005

0.012
0.005
0.01

Sex

Age

Has US
Migrant

0

1-5

Years of Schooling
6-8
9-11

12-15

16+

Men

3039
4049
5059

No
No
No

0.083
0.145
0.256

0.283
0.406
0.486

0.293
0.267
0.173

0.206
0.094
0.048

0.086
0.049
0.018

0.048
0.041
0.019

3039
4049
5059

Yes
Yes
Yes

0.069
0.139
0.235

0.306
0.514
0.534

0.3
0.256
0.159

0.212
0.053
0.041

0.091
0.024
0.024

0.022
0.014
0.007

3039
4049
5059

No
No
No

0.12
0.222
0.362

0.318
0.435
0.455

0.297
0.225
0.129

0.158
0.068
0.033

0.075
0.033
0.014

0.033
0.016
0.006

3039
4049
5059

Yes
Yes
Yes

0.074
0.173
0.265

0.385
0.534
0.552

0.322
0.23
0.148

0.137
0.047
0.025

0.056
0.011
0.005

0.026
0.006
0.005

Women

Women

0

1-5

Years of Schooling
6-8
9-11

12-15

16+

This table shows the distribution of educational attainment for individuals in rural Mexico in 2000 by age
and by whether an individual’s household received remittances from the United States in the last month orsent a migrant to the United States in the last five years.

29

Table 5: Schooling by Age Cohort in High-Migration and Low-Migration States, 2000

Sex
Men

Women

State
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+

0.072
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

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

0.113
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).

30

Table 6: Labor Force Participation by Household Migration Status
Men
Years of
Schooling

Remittances
Yes
Diff.

No

0

0.558

0.558

1-5

0.615

6-8

Women
Has Migrant in US
No
Yes
Diff.

Remittances
No
Yes
Diff.

Has Migrant in US
No
Yes
Diff.

0.000

0.561

0.508

-0.053

0.127

0.091

-0.036

0.13

0.082

-0.0480.519-0.096

0.62

0.522

-0.098

0.144

0.118

-0.026

0.146

0.119

-0.027

0.676

0.414

-0.262

0.679

0.553

-0.126

0.184

0.172

-0.012

0.186

0.164

-0.022

9-11

0.752

0.698

-0.054

0.757

0.635

-0.122

0.278

0.235

-0.043

0.277

0.273

-0.004

12-15

0.817

0.714

-0.103

0.827

0.594

-0.233

0.501

0.359

-0.142

0.503

0.418

-0.085

16+

0.88

0.901

0.021

0.882

0.81

-0.072

0.762

0.743

-0.019

0.765

0.715

-0.050

Total

0.674

0.537

-0.137

0.679

0.552

-0.127

0.205

0.157

-0.048

0.207

0.16

-0.047This table shows the fraction of 30-49 year olds with positive labor earnings by sex, years of schooling, and whether an individual’shousehold received remittances from the United States in the last month or sent a migrant to the United States in the last five years.

Table 7: Labor Force Participation in High-Migration and Low-Migration States

1990
Migration State
Low
High
Diff.

2000
Migration State
Low
High
Diff.

0
1-5
6-8
9-11
12-15
16+

0.663
0.713
0.748
0.757
0.666
0.784

0.612
0.677
0.741
0.761
0.686
0.823

-0.052-0.036-0.007
0.004
0.020
0.039

0.625
0.684
0.777
0.837
0.862
0.892

0.596
0.668
0.775
0.831
0.859
0.886

-0.029-0.016-0.002-0.006-0.003-0.005

Total

0.722

0.714

-0.008

0.774

0.767

-0.007

Men
Years of
Schooling

1990
Migration State
Low
High
Diff.

2000
Migration State
Low
High
Diff.

0
1-5
6-8
9-11
12-15
16+

0.058
0.079
0.156
0.289
0.427
0.589

0.092
0.095
0.150
0.284
0.450
0.594

0.033
0.016
-0.006-0.005
0.022
0.005

0.167
0.214
0.268
0.357
0.538
0.733

0.172
0.171
0.254
0.358
0.522
0.720

0.004
-0.044-0.0150.001-0.015-0.013

Total

0.183

0.199

0.016

0.315

0.297

-0.018Women
Years of
Schooling

This table shows the fraction of 30-49 year olds (based on age in 2000) that have positive labor
earnings by year, sex, years of schooling completed, and whether an individual’s birth state ishigh-migration or a low-migration.

Table 8: Labor Supply and Household Migration Behavior

Sent Migrant to US
in Last 5 Years

Full Rural Sample
Men
Women
-0.292-0.103(0.020)(0.019)Received Remittancesfrom US in Last MonthR
N

Sent Migrant to US
in Last 5 Years-0.281(0.038)0.064
117,585-0.092(0.040)0.103
126,0170.103
126,017

Full Rural Sample
Men
Women
-4.999-5.827(0.336)(0.762)

Received Remittancesfrom US in Last Month
R
N

0.062
117,585-6.633(0.598)0.006
117,585

0.006
117,585

Labor Force Participation
Primary Education or Less
Men
Women
-0.271-0.091(0.019)(0.031)0.024
126,0170.05
82,7730.042
94,5400.042
94,540

Hours Worked
Primary Education or Less
Men
Women
-4.171-6.709(0.392)(1.024)-8.008(1.236)0.024
126,017-0.076(0.041)-0.266(0.043)0.051
82,773-6.025(0.652)
0.006
82,7730.006
82,773High-Migration States
Men
Women
-0.344-0.108(0.027)(0.030)0.066
25,9860.013
94,5400.061
25,9860.119
30,2680.118
30,268High-Migration States
Men
Women
-6.943-6.480(0.570)(1.262)-8.154(1.606)0.013
94,540-0.065(0.049)-0.223(0.053)-7.700(0.927)0.007
25,9860.006
25,986-9.278(1.968)0.032
30,2680.032
30,268The sample is individuals living in rural areas of Mexico aged 20-59 in 2000. The first table shows a probit on whether an individual has positive labor
earnings. The second table shows a tobit on hours worked. “Sent Migrant to US” indicates whether the individual’s household sent a migrant to theUnited States in the last five years. “Receives Remittances” indicates whether the individual’s household received remittances from migrants living
abroad in the last month. Other regressors are a quartic in age, dummy variables for schooling, dummy variables for the number of children in thehousehold, and dummy variables for state of birth. Standard errors (in parentheses) are adjusted for correlation in the errors within birth states.

Table 9: Labor Supply in High-Migration and Low-Migration States, 1990-2000

Labor-Force Participation
Full Rural Sample
Primary Education or Less
Men
Women
Men
Women

Hours Worked
Full Rural Sample
Primary Education or Less
Men
Women
Men
Women

Year 2000*
High Migration

0.062(0.091)-0.145(0.065)0.065(0.106)-0.172(0.069)-2.400(0.488)-8.484(1.787)-2.729(0.536)-11.546(2.304)RN

0.049
59,201

0.139
62,271

0.039
48,4690.067
54,1630.006
59,201

0.049
62,271

0.006
48,4690.034
54,163This table reports results for probit (tobit) regressions in which the dependent variable equals one if an individual has positive labor earnings(hours worked). The sample is men and women living in rural communities in Mexico aged 20-49 in 1990 or 30-59 in 2000 born in a highmigration or a low-migration Mexican state. The other regressors are: (a) a quartic in age, dummy variables for five 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 the household (ages 0-5, 6-12, or 13-18 years), dummy variables for the state of birth, and a dummy variable forthe year 2000; (b) interactions between the age, education, marital status, and children 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 born in ahigh-migration state. Coefficients show the change in the probability of labor-force participation (change in hours worked) associated withan individual being from a high-migration state in 2000 versus that in 1990 (evaluated at means for other regressors in probit regressions).
Standard errors (corrected for correlation in the errors within birth states) are in parentheses.

34

.1
0

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

1960

1970

1980
Year

1990

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

2000

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 State37

Zacateca.15

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

Michoaca

Guanajua.1
Hidalgo Morelos
NayaritDurangoAguascal

San Luis
Jalisco
GuerreroColimaQueretar

Oaxaca.05

Chihuahu

PueblaSinaloa
Veracruz
Tamaulip
MexicoTlaxcala
Baja CalDF Sonora
Baja Cal
YucatanCampeche
ChiapasQuintana
TabascoCoahuila
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.06
Households Receiving Remittances, 2000

Zacateca

Michoaca
Durango.04

Nayarit
Guanajua
GuerreroColimaJaliscoSan Luis
AguascalMorelos
HidalgoSinaloa
Baja Cal.02

Chihuahu

OaxacaTamaulip Queretar
SonoraPueblaVeracruz
Tlaxcala
MexicoDF
Yucatan
Baja CalCampeche
Quintana
ChiapasTabascoCoahuila
Nuevo Le

0
0

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

Figure 4: Remittances and Historical Migration Rates to the U.S.

38

2
Population in millions
1
1.5
.5

Low

High

Low

10-19

High

Low

20-29

30-39
2000

.5

Population in millions
1
1.5

2

1990

High

Low

High

40-49

Low

High

Low

50-59
1990

High

60-69
2000

Figure 5:
Cohort Sizes for Men Born in High and Low-Migration States (Based on Age in 2000)
39

2
Population in millions
1
1.5
.5

Low

High

Low

10-19

High

Low

20-29

30-39
2000

.5

Population in millions
1
1.5

2

1990

High

Low

High

40-49

Low

High

Low

50-59
1990

High

60-69
2000

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

40

.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 7: Kernel Densities for Log Wages in High-Migration and Low-Migration States

41