Steffen Reinhold is an assistant professor at the University of Mannheim, Fellow at the Munich Center for the Economics of Aging (MEA), and a research fellow at the Danish National Centre for Social Research. Kevin Thom is an assistant professor at New York University. This paper draws on and replaces the MEA Discussion Paper 182–2009 “Temporary Migration and Skill Upgrading: Evidence from Mexican Migrants.” The code for replicating the data and results is available upon request. The current correspond-ing e- mail is kevin.thom@nyu.edu. The authors thank Robert Moffi tt, Tiemen Woutersen, Paul Romer, Jennifer Hunt, Martin Salm, Govert Bijwaard, and Christina Gathmann for helpful comments on this and the previous version of this paper. Also, they are grateful for the feedback offered by seminar participants at the Institute for Research on Poverty Summer Research Workshop, the CUNY Graduate Center, Oregon State University, the University of Mannheim, CERGE- EI, the meetings of the International Economic Association, the meetings of the Society of Labor Economists, and the meetings of the Verein für Socialpo-litik. Finally, they would like to acknowledge the excellent research assistance of Jacob Bastian and Nicole Hildebrandt.
[Submitted August 2011; accepted September 2012]
SSN 022 166X E ISSN 1548 8004 8 2013 2 by the Board of Regents of the University of Wisconsin System
T H E J O U R N A L O F H U M A N R E S O U R C E S • 48 • 3
the Mexican Labor Market
Steffen Reinhold
Kevin Thom
A B S T R A C T
I. Introduction
This paper investigates the relationship between migration experience and earnings among return migrants in the Mexican labor market. Our analysis com-plements a growing literature assessing the effects of out- migration on the economies of migrant- sending countries. Two main concerns dominate research on this topic: the consequences of skilled migration (the “brain drain”) and the effects of remittances1. However, if migration is temporary and migrants eventually return home, then out- migration might have an additional effect by upgrading the skills and raising the earn-ings of return migrants. We develop a theoretical model of return migration and skill upgrading to guide the interpretation of our empirical results. In particular, the model provides conditions under which OLS regression estimates provide a lower bound on the average causal effect of migration experience for migrants. We further explore the nature of skill upgrading by considering several mechanisms that might mediate this relationship including occupation- specifi c learning, knowledge spillovers in large urban areas, the acquisition of English skills, and legal status during migration.
We use data from the Mexican Migration Project (MMP) and the Population and Dwelling Count (1995) to document the relationship between past U.S. migration experience and the labor market earnings of return migrants in Mexico. Our baseline specifi cation suggests that each additional year of migration experience is associated with an increase in earnings of approximately 2.2 percent, everything else equal. We fi nd that the return to migration experience is at least twice as high as the return to age in the Mexican labor market at nearly every point in the life cycle.
A natural concern is that the observed relationship between earnings and migra-tion experience refl ects the self- selection of high- ability or high- skill individuals into return migration, or the endogeneity of length- of- stay in the United States. To assess the likelihood and consequences of such biases, we develop a model of temporary mi-gration with heterogeneity in ability or skill. Under plausible assumptions, the model suggests that our empirical estimates may understate the true effect of migration ex-perience on earnings. Furthermore, the model shows that extensive margin selection (where return migrants are placed in the skill distribution) does not bias OLS results once a dummy for any migration experience is included. Rather, the OLS estimates suffer from bias only if unobserved components of skill are correlated with accumu-lated migration experience within the group of return migrants.
Instrumental variables techniques and fi xed effects methods are two common strat-egies for addressing this kind of endogeneity. In an earlier working paper, we used macroeconomic shocks in the United States to instrument for accumulated migration experience, and estimated a very large return (around 9 percent) to migration experi-ence. However, the instruments were weak in many specifi cations and quite sensitive to the inclusion or exclusion of age controls. Furthermore, one might be concerned that U.S. macro shocks are correlated with Mexican macro shocks, and thus cannot be credibly excluded from the earnings equation. In the absence of credible instruments, one could address this question with panel data. Unfortunately, existing Mexican panel
data sets do not contain suffi cient information on return migrants to be useful.2 While panel data with earnings observations before and after migration are not available, we can observe wage data from the United States for the migrants in our sample to con-struct a control for ability and unobserved skill. When we add this control to our basic specifi cation, our estimates are very close to the baseline. We argue that this exercise provides evidence against the proposition that the relationship between migration ex-perience and earnings is being driven by correlation between migration exex-perience and permanent unobserved skill.
Another contribution of the paper is to compare results on the returns to migra-tion experience across both the MMP and the Populamigra-tion and Dwelling Count Survey (PDC). While the MMP provides superior information on migration histories, the MMP only includes data on Mexican income (not wages). However, the PDC reports hours data that allow us to construct wage rates. When using comparable regressors and samples, we fi nd very similar results in both the MMP and the PDC. Furthermore, the analysis using the PDC wage data reveals that our results primarily refl ect a rela-tionship between migration experience and wages, not experience and hours worked. The paper also contributes to the literature on migration and human capital accu-mulation by suggesting and empirically testing some possible explanations for the ex-istence of a return to migration experience. We fi nd evidence that occupation- specifi c job experience accounts for much of this return. The return to migration experience is largest for migrants who worked in occupations in the United States that match their current occupation in Mexico. Indeed, the return to a year of this kind of job- relevant migration experience is estimated to be a little less than 4.8 percent in the whole sample, and as high as 8.7 percent when restricting the sample to unskilled manufacturing workers. It is noteworthy that our basic estimate of the return to a year of job- relevant migration experience is nearly as large as our estimate of the return to education in our baseline specifi cations.
Another mechanism that could explain our results is the effect of exposure to urban labor markets. As documented by Glaeser and Mare (2001) and others, it could be the case that experience in large urban labor markets is more valuable than experience elsewhere because of greater knowledge spillovers that might occur in cities. The rich migration information available in the MMP allows us to test whether the return to migration experience is actually a return to urban labor market experience in the United States. However, we do not fi nd evidence that urban migration experience is more valuable than nonurban experience.
We also test whether the return to migration experience is related to the acquisition of English skills. Data limitations prevent us from drawing fi rm conclusions here, but controlling for English ability does not substantially reduce the return to migration experience. This suggests that English language acquisition does not explain much of the relationship between migration experience and earnings. However, we do fi nd that the return to migration experience is higher for those with some English skills. This is consistent with the idea that individuals who can more easily communicate while abroad might be better able to absorb skills while working.
Just as English skills might allow migrants to interact more freely in U.S. labor mar-kets, one’s legal status while a migrant might also shape the opportunities that one has to accumulate skills while abroad. We fi nd evidence of a greater return to documented migration experience, and a greater propensity for documented migrants to acquire job- relevant work experience appears to drive this result.
II. Literature on Return Migration
and Skill Upgrading
There is a large literature on the determinants and effects of migration, and Mexican migration in particular. Hanson (2006) offers a comprehensive survey of the work on Mexico. Much of this literature addresses the selection of migrants observed in the United States. While our empirical work does not directly engage this issue, later we will place our results in the context of that literature (see the appendix). Rather, here we restrict our attention to the theoretical and empirical work on return migration and skill upgrading.
A number of studies, including Borjas and Bratsberg (1996), Dustmann and Weiss (2007), and Dustmann, Fadlon, and Weiss (2011), develop theoretical models of tem-porary migration in which migrants acquire additional skills while working abroad that are rewarded in the home country. If the return to such skill is suffi ciently high, then this mechanism provides an incentive for individuals to return home. As Domingues Dos Santos and Postel- Vinay (2003) argue, this effect of temporary migration may help to expand a source country’s human capital stock and increase its rate of eco-nomic growth. Mayr and Peri (2009) further link this mechanism to the literature on the “brain gain” by developing a model of return migration, skill upgrading, and endogenous schooling to analyze the conditions under which temporary migration opportunities can raise the education level of a sending country.
While there is an existing empirical literature on temporary migration and skill up-grading, it tends to focus on the European experience. De Coulon and Piracha (2005) analyze data from Albanian workers and fi nd that the return migrants in their sample are negatively selected on the basis of premigration earnings, but experience a wage premium as a result of temporary migration. Using Hungarian data, Co, Gang, and Yun (2000) conclude that time spent abroad improves the labor market performance of female migrants, but not the performance of male migrants. Barrett and O’Connell (2001) and Barrett and Goggin (2010) also fi nd a premium for return migrants in Ireland.
data contain very rich information about migration experience over the life cycle, which is valuable in exploring the return to migration experience. Zahniser and Green-wood (1998) use an early version of the MMP with fewer observations dominated by high- migration communities and fi nd a large positive return to migration experience. Alternately, using data from the 2000 Mexican census, Lacuesta (2010) fi nds that migrants tend to earn about 7–10 percent higher wages than nonmigrants upon return-ing. However, Lacuesta does not fi nd that wages grow with experience, and interprets this as evidence that the return migrant premium refl ects self- selection rather than skill upgrading. While the Census data has the advantage of being nationally representative, it only provides data on the duration of the last trip to the United States taken within the last fi ve years. We fi nd that these data limitations have substantial consequences for the estimation of returns to migration experience when we censor the MMP data in a similar way.
III. A Simple Model of Temporary Migration
Our empirical analysis centers on OLS regressions of Mexican earn-ings on accumulated migration experience. A natural concern is that the self- selection of return migrants and the endogeneity of accumulated migration experience bias the OLS estimates of a return to migration experience. Here we present a simple theoreti-cal model to help guide the interpretation of the observed relationship between earn-ings and migration experience given this concern. We argue below that the primary challenge to interpreting the results of such a regression comes not from extensive margin selection (how return migrants compare to nonmigrants in terms of unobserved skill), but rather from intensive margin selection (how migration experience is cor-related with unobserved skill within the group of return migrants). Under plausible parameterizations, the model predicts that total accumulated migration experience should be negatively correlated with skill conditional on migration. This implies that OLS regression coeffi cients will represent a lower bound on the true causal effect of migration experience on income. The model considered here builds on the temporary migration models of Dustmann (2003) and Dustmann and Weiss (2007).
Consider the following environment. There are two countries: the home country (h), and the foreign country (f). Individuals are endowed with a unit of continuous time, and they start life in the home country. An individual can only migrate at time t = 0, and chooses some fraction of time τ to spend in the foreign country. If the individual chooses τ = 1, then the individual is a permanent migrant. If the individual chooses 0 < τ < 1, the individual is a temporary migrant and returns home after τ units of time. Note that this model restricts immigrants to taking at most one migratory trip to the United States. In reality, many Mexican migrants engage in patterns of repeated circular migration involving multiple trips to the United States (see Massey, Durand, and Malone 2003; Thom 2010; Rendon and Cuecuecha 2010). While the assumption of a one- trip structure is needed to keep the theoretical analysis tractable, the model presented here can be thought of as an approximation to a more general multiple- trip model in which individuals choose the overall fraction of time that they spend abroad.
workers including innate ability and skills accumulated before migration. For the pur-poses of the model, si can be thought of as subsuming several components of skill, some of which will be observed in the data (such as schooling), and some of which will be unobserved. Skill is randomly distributed in the population, and we will allow some of the model parameters to depend on skill. We assume that wage rates in the home and foreign countries depend both on skill and on the amount of migration ex-perience that has been accumulated up to time t: wc = wc(si,τt) for c = h, f. There is an international wage gap, so wf – wh > 0. Furthermore, we assume that wages are in-creasing in both skill and migration experience in both countries: (∂wc/∂s) > 0, (∂wc /∂τ) > 0.
We allow the relationship between wages and migration experience to depend on baseline skill, and the nature of this dependence is theoretically ambiguous: (∂2w
c /∂τ∂s)P0. For example, consider the return to migration experience in the home country. It could be the case that less- skilled workers gain more from such ex-perience. This could occur if the job opportunities for migrants in the foreign country are skewed toward low- skilled occupations. Experience as a cab driver is unlikely to be useful for a migrant who is qualifi ed to work as an engineer in his home country. Additionally, work experience in the foreign country might serve a remedial function if it provides low- skilled workers with opportunities to gain skills that only more- skilled workers can gain at home. For example, if more advanced capital equipment in construction or manufacturing is scarce at home and only more- skilled workers are able to gain experience using it, then low- skilled workers might have more chances to gain such experience by working abroad. Alternately, the return to migration experi-ence might be higher for more- skilled workers if jobs in the foreign country provide learning opportunities that are only useful for the skilled. A physician, engineer, or Ph.D. trained academic from a developing country might profi t tremendously from the interactions with similar workers in a high- income country.
Given this wage structure, individuals choose an optimal duration of stay in the foreign country and a life- time consumption profi le, where we assume that the instan-taneous utility from consumption is given by u(ct) = log(ct). Migration enables higher consumption over the life cycle, but there exists a tradeoff because living abroad in-curs disutility of η per unit of time. In addition, if an individual migrates, he or she must pay a fi xed migration cost of λ(si). As argued by Chiquiar and Hanson (2005), the costs of migration might be lower for high- ability people if such individuals are more capable or ambitious. If there is a correlation between baseline skill and the cost of migration, we would thus expect it to be negative: (∂λ /∂s) < 0.
Given these assumptions, the individual’s decision problem becomes:
τ,ct
max V =
0 1
∫
log(ct)e−δtdt−0
τ
∫
ηe−δtdts.t. 0
1
∫
cte−rtdt =0
τ
∫
wfe−rtdt+τ
1
∫
whe−rtdt− λ1(τ> 0) 0≤ τ ≤1rate, c, over the life cycle. Furthermore, we also assume that wages in both countries have the following log- linear form:
(1) log(wc(si,τ)) = γ0c(s
with respect to migration experience as the return to migration experience. This is given by γ1c+ γ
2
cs for the wages of someone with baseline skill level s in country c. Under these particular assumptions, the individual’s decision problem becomes: (2) max differentiation of this condition allows us to derive the relationship between optimal migration duration and baseline skill:
(3) ∂τ*
A set of plausible assumptions on the model parameters allows us to predict that the sign of this partial derivative should be negative. First note that the derivative of con-sumption with respect to baseline skill is positive for everyone ((∂c/∂s) > 0) as long as migration costs do not increase with skill. Also, lifetime consumption should be increasing in τ for anyone who migrates ((∂c/∂τ) > 0 evaluated at τ*), or otherwise τ* is not optimal. These conditions allow us to sign the terms involving η (disutility of staying in the foreign country) as negative.
Neglecting the second order terms for a moment, ∂τ/∂s will be negative at the optimum if these two (suffi cient) conditions are satisfi ed. The fi rst condition is that [(∂wf /∂s) – (∂wh/∂s)] < 0 at the optimal duration, making the numerator of
Equa-tion 3 negative. Since s includes both observed and unobserved components of an in-dividual’s skill, it is diffi cult to verify that this condition holds on the basis of existing empirical evidence. However, the condition does seems to hold for the case of Mexico if one looks at important observable components of skill, such as schooling.4 For ex-ample, hourly wage calculations presented in Hanson (2006) suggest that the US- Mexico wage gap is generally declining with education, at least for individuals with less than 16 years of schooling. Evidence for an international earnings gap that de-clines with skill is also found by Ambrosini and Peri (2012) .
The second condition is that the denominator of Equation 3 must be negative. Under the assumption that the optimal migration duration is small (approximately zero), the following condition ensures that the denominator of Equation 3 will be negative for an individual of skill s:
(4) (γ1f +γ This condition essentially requires that the return to migration experience in the United States not be too much larger than the return to migration experience in Mexico. In-deed, for plausible parameter values, this will only be violated for extremely high rates of return to migration experience in the United States.5
In the model presented here, optimal migration duration is negatively associated with skill. The primary reason for this is that more- skilled individuals are assumed to face a smaller international wage gap. The opportunity cost of spending time abroad is higher for these individuals, and so they choose shorter durations. Under some al-ternate assumptions, this result becomes ambiguous. For example, if the disutility of spending time abroad is lower for more- skilled individuals ((∂η/∂s) < 0), then it will not be possible to sign ∂τ* /∂s with the assumptions made here.
We have also neglected the second- order terms in Equation 3. The prediction that our model makes about the sign of ∂τ* /∂s would become ambiguous if there are large increasing returns to U.S. labor market experience in Mexico ((∂2w
h/∂τ
2) > 0 and large). In addition, we have to consider the cross- derivative (∂2w
h/∂s∂τ*) =
hs)). If there are large complementarities between skill and U.S. labor experience (if γ2h > 0 and large), then this could make sign of ∂τ* /∂s ambiguous as more- skilled workers would have an extra incentive to extend their stay. Note that this also implies that return migrants will be positively selected from the set of migrants, a pattern consistent with the empirical results of Ambrosini and Peri (2012) and Biavaschi (2010).
We proceed by assuming that wages are log- linear in their arguments, and that the parameters are such that (∂τ* /∂s) < 0 for an interior solution. In the population indi-viduals will choose to stay home, become permanent migrants, or become temporary migrants depending on their initial skill level. Figure 1 provides some numerical ex-amples that highlight how the optimal migration duration varies with baseline skill, which is drawn from the interval [0,1]. Panel A assumes some reasonable parameters that generate a pattern of behavior that is broadly consistent with the features of Mex-ican migration. Nonmigrants are drawn from both the lower and upper ends of the skill distribution. At the lower end, there is some group of individuals that either cannot afford to migrate (so lifetime resources under migration are negative), or who can af-ford to migrate but are better off not paying the cost of migration. At the upper end, there is a group of individuals that do not have a wage incentive to migrate since the international wage gap is declining in skill. For those who migrate temporarily, the 5. To give some sense of the strictness of this condition, consider males aged 23-27 with fi ve to eight years of education, a group with one of the highest migration rates in Mexico. The calculations in Hanson (2006) indicate that the US-Mexico wage ratio for this group is equal to θ = (8.19 / 1.80) = 4.55. In this case, the condition in Equation 4 requires γf(s) < 1.2γh(s) + 2.77. Thus, this condition requires that the return to
migra-tion experience in the United States not be very much greater than the returns in Mexico. Suppose that the return to migration experience is 2 percent per year in Mexico. Since τ represents a fraction of a working lifetime, a hypothetical 2 percent return in Mexico per year of migration experience corresponds to γh = 1
assuming a 50 year working life. In order for the condition in Equation 4 to hold in this scenario, it would need to be true that γf < 3.97, or that the return to migration experience be less than 8 percent per year in the
amount of time spent abroad will vary negatively with skill as long as our maintained assumptions hold. There will generally be skill bounds S and S such that only indi-viduals with baseline skill levels in the interval s∈[S,S] will fi nd it optimal to mi-grate. The skill threshold S* further separates the group of migrants who permanently migrate from those that temporarily migrate. Panel B shows what happens to behavior if the returns to migration experience are shut down in both countries. Although this change in parameters induces fewer individuals to migrate, the same basic qualitative patterns present in Panel A continue to hold.
Panels C and D further highlight scenarios in which violations of our assumptions can result in a positive relationship between migration durations and baseline skill.
0 0.5 1
Baseline Skill
Optimal Migration Duration
0 0.5 1
(C) γh > 0 and large 2
(A) Baseline Scenario (B) No Returns to Experience
(D) η is declining in skill
0 0.5 1
Baseline Skill
Optimal Migration Duration
0 0.5 1
S S S 0 0.5 1
Baseline Skill
Optimal Migration Duration
0 0.5 1
S S S
0 0.5 1
Baseline Skill
Optimal Migration Duration
0 0.5 1
Figure 1
Numerical Examples of the Model Solution
Panel C describes a scenario in which the return to migration experience at home rises steeply with baseline skill. In this scenario, more- skilled individuals have an incentive to make longer temporary trips abroad. Likewise, if the disutility of spending time abroad is negatively related to skill, then the more- skilled will be observed completing longer stays abroad. Such a scenario is depicted in Panel D.
IV. Implications for Relationships in the Data
Consider a cohort of individuals who behave according to the model presented here, and suppose that we have cross- sectional data on this cohort at age t back in the home country. In this cross- section, we observe nonmigrants with skill levels in the set: S
nm = {si|si < Sorsi> S}. We also observe the temporary migrants who have returned by age t. Let s(⋅) represent the inverse of the optimal duration func-tion. Then the return migrants that we observe in the data have skill levels that fall on the interval S = [s(⋅),S], and optimal migration durations that fall on the interval T = [τ*(S),t]. Let M refer to the set of migrants returning by age t.
Suppose that we observe the log of earnings, yi = log(wh,i), where the true data generating process is given by the following log- linear specifi cation:
(5) yi =γ0h+γ
0s hs
i+ γ1
hτ i+γ2
hs iτi
For simplicity, we ignore covariates and suppose that all skill is unobserved. Then such a data generating process will leave us with observations of log- earnings, and migration durations for those individuals that migrate. A natural empirical strategy to learn about the relationship between migration experience and earnings is to regress log- earnings on a dummy for any migration experience and a measure of migration experience (which is zero for nonmigrants):
(6) yi =δ0+δ
11(τi> 0)+δ2τi+ui
Where τi is the optimal migration duration chosen by individual i (we suppress the star notation here), and ui is an error term. What do the OLS estimates identify in this case? The point estimates of the parameters in Equation 6 can be decomposed into the fol-lowing summary statistics of observables and unobservables (See the appendix for a derivation):6
(7) δˆ0 = (yi|τ i=0)
(8) δˆ1= (yi|i∈M)−(yi|τi=0)−(τi|i∈M)ˆδ2
(9) δˆ2= γ1h+ γ
2
h(s i|i∈M)
Avg. effect for return migs.
.
+γ
0s
h Cov(si,τi|i∈M)) Var(τi |i∈M)
Negative
+γ
2
h(τi|i∈M)Cov(si,τi|i∈M) Var(τi|i∈M)
Sign depends on γ2h
We can now give an interpretation to the OLS coeffi cients in this simple regression model. The coeffi cient on U.S. migration experience, δˆ2, has three parts. The fi rst part refl ects the true average causal effect of U.S. labor market experience on wages in Mexico for return migrants, our main parameter of interest. The average effect on re-turn migrants could be higher or lower than the average effect for the whole population, depending on the sign of γ2h and the pattern of extensive margin selection into return migration. If there is negative to intermediate selection into return migration relative to the whole population (which is consistent with our fi ndings and those of the previous literature), and if γ2h > 0, then the true average return for the migrants in the sample will be less than the population average. If this pattern of selection holds and γ2h < 0, then the average return for migrants will be greater than the population average.
The second and third terms in Equation 9 introduce bias in the OLS estimate of the average return to migration experience. Rearranging, we see that the bias- inducing terms equal (γ0hs+ γ
2
hτ
i|i∈M)(Cov(si,τi|i∈M) /Var(τi |i∈M)). Given our assump-tions on the parameters, (Cov(si,τi|i∈M) /Var(τi |i∈M)) < 0 so the sign of the bias will depend on the sign of (γ0hs+ γ
2
hτ
i| i∈M). This represents the average partial
derivative of log- earnings with respect to skill among return migrants, holding migra-tion duramigra-tions fi xed. Intuitively, the relationship between skill and wages should be positive (even fi xing migration durations), making the OLS bias negative. However, if there is a suffi ciently large degree of negative complementarity between skill and mi-gration experience (γ2h < 0 and large), then this average could be negative and the OLS bias could be positive. Suppose that skill in the model were perfectly measured by observed years of education. Then we could get positive bias only if the coeffi cient on the interaction between education and migration experience were greater in magnitude than (1 / τ) times the coeffi cient on schooling itself. In later empirical results, we will fi nd that the estimated coeffi cient on the interaction is far lower than this threshold value.
Furthermore, if we make the assumption that there is no heterogeneity in the return to migration experience, we get:
(10) δˆ2= γ1h+γ
0s
h Cov(si,τi|i∈M) Var(τi|i∈M)
which indicates a negative bias. Our conclusion is that the endogeneity of τ is likely to bias the OLS estimate of δ2 downward relative to γ1h. Under the assumptions made here, the model predicts that δˆ2 will provide a lower bound for the true average effect of migration experience on the earnings of return migrants.
does not result from the selectivity of return migrants, but from the endogeneity of accumulated migration experience.
V. Data and Descriptive Statistics
The Mexican Migration Project (MMP) is a collaborative research project based at the Princeton University and the University of Guadalajara.7 The data collected by the MMP present researchers with the unique opportunity to observe earnings for particular individuals along with detailed migration histories. Each year, the MMP selects a group of Mexican communities and surveys a random sample of the households in each location. The MMP survey collects demographic and economic data on households and individuals, with a particular emphasis on migration experi-ence. The survey also requests a detailed, self- reported life history from household heads recording some economic, demographic, and migration variables for every year in their lives. These life histories record whether an individual migrated in a given year, how many months an individual spent in the United States, and what documents, if any, were used to migrate.
Our sample consists of male household heads aged 18–65 who were surveyed during the years 1987–2009, and who were in Mexico at the time of the interview. The MMP asks each household head to report his or her current occupation, and their income in Mexico. Although the income variable is reported at different rates (weekly, biweekly, monthly), we convert all income measures to monthly values throughout the paper. In order to interpret the income variable as a measure of earn-ings we only consider those individuals who are currently employed, and who are not self- employed and do not own a business. After imposing these restrictions, and dropping those individuals with missing values for important regressors, we also trim the data by dropping individuals in the top and bottom 1 percent of the monthly earnings distribution, and those return migrants in the top 1 percent of the migra-tion experience distribumigra-tion (where migramigra-tion experience is measured in years). After making these restrictions, our full sample consists of 6,200 men. For the descriptive statistics and throughout the paper, we defl ate earnings using CPI indices for Mexico and the United States (2000 base year) taken from the IMF’s International Financial Statistics series. The appendix provides a more thorough description of the data and the sample selection criteria.
We present summary statistics of log earnings and important characteristics of migrants and nonmigrants in Table 1. The fi rst pair of columns displays statistics for the full sample of individuals interviewed while in Mexico. This includes non-migrants and return non-migrants, but excludes individuals who have migrated but have not returned to Mexico. The average age for the full Mexican sample is about 40, and the average level of education is about 6.8 years. Most individuals are married (89 percent). A substantial fraction of individuals have some experience migrating to the United States (28.0 percent). The average log of real monthly earnings is about 8, while the average real monthly earnings in levels is roughly 3,596 pesos per month.
The second pair of columns reports summary statistics for nonmigrants, while the
last pair of columns reports summary statistics for return migrants. Return migrants tend to be less educated, with an average number of years of education (5.54) that is more than a year and a half lower than the average education level of nonmigrants (7.29). This pattern is consistent the idea that return migrants are negatively selected compared to nonmigrants. The average earnings of return migrants is about 5 percent lower than the average earnings of nonmigrants.
Table 1 also presents some summary statistics related to the migration experience of return migrants. About 91 percent of return migrants have some experience as an undocumented migrant, while only 20 percent have any documented migration experi-ence. The migrants in our sample are thus predominantly engaging in undocumented migration. The average return migrant has accumulated about 2.63 years of experience in the United States, with about 2.18 years of undocumented migration experience, and about 0.45 years of documented experience.
The model presented in Section II suggests that if skill is negatively correlated with the Mexico- US wage gap but unrelated to other model parameters, then un-der quite general conditions there should be a negative relationship between skill and the accumulated migration experience of return migrants. Such a negative correlation would induce a downward bias in the OLS estimate of the return to a year of migration experience. To test this implication of the theory, we examine the relationship between education (an observable component of skill) and the accumulated migration experience of return migrants in Table 2. We are implicitly assuming that the pattern of correlation between observables and migration expe-rience is informative about the correlation between unobservables and migration experience. In Column 1, we regress migration experience on education and other controls. The point estimate suggests a weak negative relationship between educa-Table 1
Summary statistics
Entire Sample Nonmigrants Return Migrants
Mean
Standard
Deviation Mean
Standard
Deviation Mean
Standard Deviation
Age 40.44 11.53 40.30 11.60 40.80 11.32
Education 6.81 4.77 7.29 4.92 5.54 4.10
Married 0.89 0.31 0.87 0.33 0.93 0.25
Log earnings 8.00 0.59 8.02 0.58 7.97 0.62
Ever migrant 0.28 0.45
Ever undocumented 0.91 0.28
Ever documented 0.20 0.40
USExp 2.63 3.18
Undocumented experience 2.18 2.69
Documented experience 0.45 1.60
tion and migration experience. A one- year increase in education is associated with a decline in accumulated migration experience of –0.024 years. This relationship is not statistically signifi cant. To explore this a bit further, we add a quadratic term in education as a regressor in Column 2. Neither education coeffi cient is precisely estimated, but the point estimates suggest a nonmonotonic relationship, with migration experience rising with education up to about fi ve years, and there-after declining. For very low levels of education it thus appears that accumulated migration experience is positively associated with migration experience. However, we emphasize that this appears to hold only at the lowest levels of education. In Column 3 of Table 2, we restrict the sample to those with at least three years of ed-ucation, a group containing about 75 percent of the migrants in our main sample. In this subpopulation, there is a statistically signifi cant, negative relationship between education and accumulated migration experience. An additional year of education here is associated with a reduction in accumulated migration experience of about 0.0562 years.
The prediction of a negative relationship between migration experience and edu-cation weakly holds in the entire sample, and holds with much greater strength for the mass of individuals with three or more years of education. At the very least, we certainly do not fi nd clear evidence of a positive relationship between education and migration experience. Even the negative correlations that we fi nd are rather small in magnitude, suggesting that the endogeneity of accumulated migration experience probably explains little of any correlation between migration experience and Mexican income.
Table 2
Migration Experience and Education
(1) (2) (3)
Age 0.202*** 0.210*** 0.224***
(0.055) (0.055) (0.069)
Age2 –0.226*** –0.229*** –0.243***
(0.068) (0.068) (0.089)
Educ –0.024 0.082 –0.056*
(0.032) (0.102) (0.031)
Educ2 –0.007
(0.005)
Married –0.352 –0.360 –0.817
(0.619) (0.609) (0.759)
Sample Complete Complete Educ ≥ 3
Observations 1,673 1,673 1,262
R2 0.128 0.130 0.165
VI. Empirical Results
A. Basic PatternsTable 3 presents OLS estimates of the relationship between the log of monthly earn-ings in Mexico and U.S. migration experience. Each column adds an additional set of controls. Unless otherwise specifi ed, all regressions throughout the paper will include MMP community fi xed effects that subsume calendar year effects. All regressions also make use of the sampling weights provided by the MMP. The main regressor of inter-est measures the number of years of migration experience (USExp). A dummy for any migration experience (US) is also included, and this absorbs the average difference in earnings between migrants and nonmigrants that is not accounted for by observables including the years of migration experience. Column 1 presents results when the only additional controls are Age and Age2 / 100. The estimate of the coef
fi cient on US sug-gests that the average migrant earns about 9.6 percent less in Mexico than the average nonmigrant. As demonstrated in Section I, the coeffi cient on this variable partially refl ects the difference in the average skill level of return migrants and nonmigrants,
Table 3
Basic Regressions (Monthly Earnings)
(1) (2) (3) (4) (5)
USExp 0.020*** 0.022*** 0.022*** 0.024*** 0.038***
(0.008) (0.007) (0.007) (0.007) (0.013)
US –0.096*** –0.053** –0.055** –0.043* –0.082**
(0.028) (0.026) (0.026) (0.026) (0.042)
Age 0.045*** 0.040*** 0.039*** 0.032*** 0.039***
(0.005) (0.005) (0.005) (0.005) (0.005)
Age2 / 100 –0.058*** –0.044*** –0.043*** –0.036*** –0.043***
(0.006) (0.006) (0.006) (0.006) (0.006)
Educ 0.054*** 0.054*** 0.032*** 0.054***
(0.002) (0.002) (0.003) (0.002)
Married 0.049* 0.046* 0.048*
(0.026) (0.025) (0.026)
EducXUS 0.006
(0.005)
EducXUSExp –0.003*
(0.002)
Occupation dummies N N N Y Y
Observations 6,200 6,200 6,200 6,200 6,200
R2 0.238 0.372 0.372 0.436 0.373
and thus the selectivity of return migrants. The coeffi cient on years of migration ex-perience, USExp, is about 0.020 and highly signifi cant, suggesting that the return to a year in the United States (above any beyond the increase in age or general experience), is about 2 percent. This implies that the total return to a year of migration experience is more than double the return to age for all but the youngest individuals. Since ac-cumulated migration experience is negatively correlated with education within our group of return migrants, we would expect this OLS estimate to be biased downward if experience is also negatively correlated with unobserved skill. Given the assumptions made earlier, our theoretical model also predicts this pattern of bias, and thus suggests interpreting 2 percent as a lower bound.
The successive columns of Table 3 introduce additional explanatory variables to the basic regression that are likely to be correlated with unobserved skill. Column 2 adds years of education, which we expect to be highly correlated with unobserved skill. After introducing education, the coeffi cient on the US dummy is cut nearly in half, refl ecting (on average) negative selection of return migrants relative to nonmigrants. The coeffi cient on USExp rises slightly to 0.022, which is consistent with the theo-retical prediction of a negative correlation between optimal migration duration and unobserved skill. Overall, our results suggests patterns of selection into migration and return migration that are quite consistent with previous studies, especially Kaestner and Malamud (2010) and Fernández- Huertas Moraga (2011). The appendix offers a detailed discussion of how the patterns of selection that we fi nd in the data compare to those found in the existing literature.
Columns 3–4 add further controls for marriage and occupation (dummies), respec-tively. Looking across the fi rst four columns of Table 3, one notices that the coeffi cient on the US dummy in general falls as we add more and more controls that might be correlated with unobserved skill. This is expected, since in theory the coeffi cient on US should be directly related to the average skill difference between return migrants and nonmigrants. However, the estimate coeffi cient on USExp tends to rise as we add more controls. This is consistent with negative correlation between accumulated migration ex-perience and unobserved components of skill. When occupation fi xed effects are added in Column 4, we estimate a return to migration experience of about 2.4 percent per year.
Looking across the specifi cations reported in Columns 1–4, the stability of the estimated coeffi cient on USExp is noteworthy. The coeffi cient rises, but not drasti-cally, with the inclusion of important controls that are likely to be correlated with the unobserved determinants of earnings. Thus, while USExp is endogenous, the results presented here suggest that the effects of endogeneity bias on our basic estimates may be limited. Since occupation is in part an outcome of education and work experience, we prefer a specifi cation that does not include occupational dummies. We thus take 0.022 as our baseline estimate of the return to migration experience above and beyond the return to age. There appears to be an economically substantial relationship between migration experience and earnings in the Mexican labor market.
with more education.8 This is an interesting pattern in its own right, but it also suggests that it is reasonable to assume, as we did earlier, that the return to migration experience does not signifi cantly increase with skill.
B. Results with U.S. Wage Data
If we had access to panel data on wages or income in Mexico before and after migra-tion to the United States, we could use tradimigra-tional fi xed- effects methods to control for permanent unobserved components of skill. While the MMP data does not contain repeated observations of earnings in Mexico, we do observe data on the hourly wage rates that migrants earn on their last trip to the United States.9 We assume that wages refl ect worker skill plus shocks to productivity in the United States. One can therefore use the wage in the United States as a noisy measure of skill, and include this in Mexi-can income regressions as a control for unobserved skill.
Although we could directly include the U.S. wage in the Mexican income regres-sions, we would like to try to reduce some of the noise in this measure of skill by elim-inating variation from observable shocks. To do this, we fi rst regress an individual’s last U.S. wage on a set of controls, and then add the U.S. wage residual as a regressor to our basic Mexican earnings specifi cation. For a subsample of return migrants, we observe complete data on the hourly wage rate and other control variables related to the last trip to the United States. We discuss the construction of this sample in the appendix. After dropping the top and bottom 1 percent of U.S. wage observations, we are left with 1,240 return migrants with U.S. wage data. In the fi rst column of Table 4, we report the results of regressing the real hourly wage rate in the United States on age, the square of age, education, accumulated migration experience, as well as dum-mies for year, U.S. state, interactions between year and U.S. state, and an individual’s community of origin in Mexico. All of these variables refl ect characteristics at the time of the last migration. While the coeffi cient estimates from this regression are not of interest by themselves, we extract the residual from this regression as a measure of unobserved skill. In the next column of Table 4, we reestimate our basic Mexican earnings regression using only the 1,240 return migrants with U.S. wage data. Since we are restricting ourselves to migrants in this subsample, we drop the US dummy variable. With this smaller subsample of migrants, we estimate a return to U.S. migra-tion experience of about 2.3 percent, which is very close to the estimate obtained in the baseline specifi cation and reported in Column 3 of Table 3. In Column 3 of Table 4, we directly add the log of the real wage from the last migratory trip as a control. The coeffi cient on the log wage is small and imprecisely estimated, consistent with the idea 8. Earlier, we found that the negative relationship between education and migration experience was strongest for educated migrants. Indeed, for those with less than three years of education, there appears to be a positive relationship between education and migration experience. Thus, an alternate interpretation of the results in Column 5 is that the interaction term captures not heterogeneity in returns, but rather changes in the correla-tion between USExp and the unobserved components of income across education groups. However, when we restrict the sample to those with three years of education or more, we get very similar point estimates and again fi nd a decline in the return to migration experience as education rises.
that the log wage by itself is a very noisy measure of underlying skill. In Column 4, we add the normalized U.S. wage residual (residual divided by the standard deviation of residuals) to the Mexican earnings specifi cation. The coeffi cient on the U.S. wage residual suggests that a one standard deviation increase in the U.S. wage residual is associated with a 3.8 percent increase in earnings back in Mexico, and this estimate is signifi cant at the 0.10 level. This relationship suggests that the U.S. wage does contain information about an individual’s level of skill that is not captured by our observables. Crucially, when the U.S. wage residual is added, the coeffi cient on U.S. migration experience is virtually identical to the estimate in the previous column. If endogeneity were driving our results, we would expect the coeffi cient on years of migration experi-ence to drop substantially with the inclusion of a the U.S. wage residual as a control. This exercise provides some evidence against the claim that selection and endogeneity bias are driving our main results.
C. Comparison with the Population and Dwelling Count
Two further concerns naturally present themselves related to the empirical specifi ca-tions in Tables 3–4. First, it might be the case that the communities selected by the Table 4
Regressions with U.S. Wage Residual
Dependent variable
(1) Wage (U.S.)
(2) Earnings (Mexico)
(3) Earnings (Mexico)
(4) Earnings (Mexico)
USExp 0.015** 0.023*** 0.023*** 0.023***
(0.007) (0.009) (0.009) (0.009)
Age –0.024* 0.048*** 0.048*** 0.047***
(0.013) (0.014) (0.014) (0.014)
Age2 0.033* –0.054*** –0.054*** –0.054***
(0.018) (0.016) (0.016) (0.016)
Educ 0.022*** 0.046*** 0.045*** 0.046***
(0.006) (0.006) (0.006) (0.006)
Married 0.099* 0.153** 0.153** 0.156**
(0.056) (0.073) (0.074) (0.074)
U.S. wage (log) 0.012
(0.051)
U.S. wage residual 0.038*
(0.022)
Observations 1,240 1,240 1,240 1,240
R2 0.538 0.350 0.350 0.352
MMP are not generally representative of Mexico, or even of the states in which they are located. Secondly, the MMP is only able to provide data on monthly labor market earnings and not hourly wage rates. Thus, we might be concerned that the estimates presented in Table 3 do not reveal much about the effect of migration experience on productivity (as measured through wages), but rather refl ect a combination of the effect of migration on wages and hours worked. To assess these concerns, and more generally test the robustness of the fi ndings, we compare the patterns found here with those present in the 1995 Population and Dwelling Count.
The 1995 Population and Dwelling Count (hereafter abbreviated PDC) is a nation-ally representative survey that contains some questions on the amount of time that return migrants spent abroad before returning to Mexico.10 We defi ne our main PDC sample by applying the same sample selection criteria used for the MMP sample. Specifi cally, we restrict our attention to male household heads, aged 18–65, who are employed and do not own a business. We would like to restrict the PDC sample so that it covers similar communities as those in the MMP. To do this, we initially restrict the PDC sample to only include those individuals that live in the same State- Community Size cells as those represented in the MMP.11
Table 5 presents summary statistics related to the PDC sample. While the detailed life- histories in the MMP allow us to construct a measure of total lifetime migration experience, the PDC contains a more limited set of migration questions. The PDC survey asks respondents if they have previously resided in a different location, and then asks individuals how long they resided in that previous location. We count as return migrants those individuals that indicate previously residing in the U.S., and we measure USExp in the PDC as the duration of stay in the previous location. It is note-worthy that there are substantially fewer return migrants in the PDC than in the MMP. While about 28 percent of the MMP sample has some past migration experience, only about 5 percent of individuals in the PDC are classifi ed as return migrants. Some of this gap is due to the fact that the PDC asks about the place of last residence, which could be a domestic location. Thus, if people move around in Mexico after returning from a trip to the United States, they will not be counted as return migrants. In the MMP sample, we can observe whether such internal migration takes place. Classifying post- return internal movers as nonmigrants reduces the fraction of return migrants in the MMP sample to about 22 percent.
In Column 2 of Table 6, we estimate our basic specifi cation with MMP data, but now we impose the two kinds of measurement error that are present in the PDC’s USExp variable on our preferred measure in the MMP. That is, we only consider experience form the last trip, and we code internal movers as nonmigrants. When we do this, we get results that are quite consistent with those from the PDC data. The estimated coeffi cient on USExp is smaller and insignifi cant (0.011), and the coeffi cient on US is very close to zero. The measurement error present in the PDC’s measure of migration experience thus seems to decrease the precision of estimated coeffi cient on experience, and perhaps induce a downward bias. The lack of precision could also be due to the smaller number of return migrants in the PDC sample relative to the MMP sample. If the MMP data is any guide, then the 0.015 estimate is probably a lower bound of the return to migration experience in the PDC data.
In Column 3 of Table 6, we expand our PDC sample to include all of Mexico. In this sample, we estimate a return to migration experience of 2.3 percent which is sta-tistically signifi cant at the 0.05 level. Again, this may be a lower bound on the return to migration experience given the presence of measurement error described above. Nevertheless, it is interesting that we get a return to migration experience here that is very similar in magnitude to our basic MMP estimate of 2.2 percent. In Column 4, we try to assess whether this relationship between migration experience and earnings is still present when we look at the log hourly wage as the dependent variable. Here we estimate a return of about 1.9 percent that is imprecisely estimated. Some of the im-precision in the wage specifi cation could be due to noisy hours and income measures for those individual who are more marginal participants in the labor force. In Columns 5–6, we repeat this exercise but now restrict the PDC sample to those working at least 20 hours. Here we estimate a return to migration experience of 2.5 percent when income is the dependent variable, and 2.1 percent when the wage is the dependent variable. Both coeffi cient estimates are signifi cant at the 0.10 level or higher. Thus, it appears that most of the relationship between income and migration experience in the PDC data is coming through wages.
Table 5
Summary statistics—Population and Dwelling Count Survey
Entire Sample Return Migrants
Mean
Standard
Deviation Mean
Standard Deviation
Age 37.10 10.53 35.98 9.74
Education 7.30 4.81 6.72 4.32
Married 0.80 0.40 0.84 0.36
Monthly earnings 3232.95 2860.90 3252.16 3009.78
Return migrant 0.05 0.22
USExp (last trip) 1.82 2.36
T
he
J
ourna
l of H
um
an Re
sourc
es
Table 6
Comparison with the Population and Dwelling Count
(1) (2) (3) (4) (5) (6)
Dependent variable Earnings Earnings Earnings Hourly Wage Earnings Hourly Wage
Data source PDC MMP PDC PDC PDC PDC
Regions MMP MMP All All All All
Hours restriction None None None None ≥ 20 ≥ 20
USExp 0.015 0.011 0.023** 0.019 0.025** 0.021*
(0.011) (0.009) (0.011) (0.012) (0.011) (0.013)
US 0.000 0.007 –0.025 –0.011 –0.033 –0.005
(0.036) (0.027) (0.037) (0.040) (0.038) (0.039)
Age 0.038*** 0.040*** 0.037*** 0.039*** 0.037*** 0.039***
(0.004) (0.005) (0.003) (0.004) (0.003) (0.004)
Age2 –0.039*** –0.044*** –0.037*** –0.037*** –0.037*** –0.037***
(0.005) (0.006) (0.004) (0.005) (0.004) (0.005)
Educ 0.076*** 0.054*** 0.080*** 0.088*** 0.080*** 0.089***
(0.002) (0.002) (0.001) (0.002) (0.001) (0.002)
Married 0.051*** 0.046* 0.061*** 0.049*** 0.055*** 0.055***
(0.017) (0.026) (0.013) (0.015) (0.013) (0.014)
Observations 10,570 6,200 25,280 25,190 24,709 24,619
R2 0.526 0.369 0.500 0.464 0.506 0.488
One caveat to this exercise is that the PDC and the MMP samples cover differ-ent time periods. This raises the possibility that the results from the MMP and the PDC may not be comparable because of changes over time in the relationship be-tween migration experience and earnings. Whereas the PDC data represent a single cross- section from 1995, the MMP data come from survey waves throughout the in-terval 1987–2009. One could imagine restricting the comparison to MMP data from 1995, but since only a small number of communities are surveyed in each year, and community characteristics change over time, the resulting sample would be quite small and would be representative of very few communities. Without richer data, it is diffi cult to learn more about such time trends.
Although the different sampling frames and migration questions in the MMP and PDC make precise comparisons diffi cult, the exercises performed here suggest that the two data sets provide reasonably consistent evidence on the relationship between migration experience and income. Measuring experience only in terms of the last trip seems to decrease the precision of estimates and lower the estimated returns to migra-tion experience. In the larger namigra-tionally representative PDC sample, we get a return to migration experience that is comparable to our baseline MMP estimate, and in the presence of measurement error, this may be a lower bound on the average return.
D. Mechanisms
1. Relevant Experience
Our hypothesis is that the return to migration experience observed in the data refl ects a causal effect of migration experience on earnings, and not primarily a correlation between migration and unobserved characteristics that infl uence earnings. In the fol-lowing sections, we propose some mechanisms that might be generating or mediating such an effect, and test for empirical evidence on their presence. There are several possible reasons why a causal relationship might exist between migration experience and Mexican earnings. One of the most basic explanations is that individuals learn skills while working in the United States that are useful in the Mexican labor market. These skills could be occupation- specifi c (such as working with occupation- specifi c machinery), or they could be more general, such as English skills that might be useful in a wide variety of occupations and industries.12 Alternately, the process of success-fully migrating and returning might make an individual more confi dent and motivated, increasing their productivity in a wide variety of tasks. Other stories, such as the role that migration might play in signaling quality to potential employers, could also be operative.
cupation that matches the occupation that they currently hold in Mexico. Since there are a large number of occupations, we group occupations into nine categories: agricul-ture, manufacturing (skilled), manufacturing (unskilled), manufacturing (operatives), manufacturing (supervisory), transportation, sales workers, service workers, and other. To construct the relevant experience measure, we identify which of these nine occupa-tion groups individual worked in during the survey year, and we add up the number of years spent in the United States working in an occupation that falls into that group.
Table A2 in the appendix provides some summary statistics related to occupation groups and relevant experience. About 25 percent of individuals in the MMP sample are engaged in agricultural occupations, while about 37 percent are employed in some kind of occupation related to manufacturing. Smaller fractions of workers are engaged in Transportation, Service, and Sales occupations. About 21 percent of the individuals are employed in an occupation that does not fall into one of these other categories (other). For the migrants in the MMP sample in nonother occupations, Table A2 also provides summary statistics on relevant experience and the difference between total migration experience and relevant experience (USExp − RelevantExp). The distinction between relevant experience and total experience is statistically meaningful. That is, many individuals experience a signifi cant gap between their observed level of overall migration experience and their level of job- relevant migration experience. The vast majority of return migrants, about 74 percent, have a strictly higher level of total mi-gration experience than relevant experience. The average gap between total experience and relevant experience is about 1.79 years, which is actually larger than the average number of years of relevant experience (0.94 years).
Table 7 presents estimation results for earnings regressions that include relevant experience as a regressor. Since the “other” category applies to a variety of dissimilar occupations, it does not make sense to construct the relevant experience of individuals in this group. Thus, these individuals are excluded in all of the regressions presented in Table 7. First, Column 1 presents results of the baseline regression including only total experience, or USExp as a measure of migration experience. Since we are exclud-ing the individuals workexclud-ing in the “Other” occupation category, the point estimate for the return to migration experience changes compared to the past results, and is now estimated to be about 0.027. In Column 2, we add both total migration experience and relevant experience as regressors. An extra year of relevant experience also adds an extra year of generic migration experience, so the coeffi cient on the relevant experi-ence regressor should be interpreted as an extra return, above and beyond the return to a generic year of experience. Strikingly, when relevant experience is added as a regressor, the return to a year of generic migration experience is cut almost in half (reduced to 0.015), while we estimate the extra return to a year of relevant experience to be 0.031 and signifi cant. A year of relevant experience is thus associated with a 4.6 percent increase in earnings.
Re
inhol
d a
nd
T
hom
791
Occupations
(1) All
(2) All
(3) Agriculture
(4) Skilled Manufacturing
(5) Unskilled Manufacturing
(6) All Manufacturing
USExp 0.027*** 0.015** 0.013 0.000 0.017 0.013
(0.008) (0.007) (0.013) (0.010) (0.014) (0.009)
RelevantExp 0.031** 0.001 0.036 0.070** 0.065***
(0.013) (0.016) (0.028) (0.033) (0.024)
US –0.053* –0.049* 0.002 –0.024 –0.064 –0.067
(0.028) (0.028) (0.044) (0.054) (0.082) (0.042)
Age 0.028*** 0.028*** 0.022*** 0.043*** 0.029** 0.038***
(0.005) (0.005) (0.009) (0.011) (0.013) (0.008)
Age2 –0.031*** –0.031*** –0.025*** –0.049*** - 0.032* –0.042***
(0.006) (0.006) (0.009) (0.014) (0.017) (0.010)
Educ 0.027*** 0.027*** 0.030*** 0.020*** 0.034*** 0.026***
(0.003) (0.003) (0.005) (0.007) (0.007) (0.004)
Married 0.045 0.043 0.089* 0.084 0.050 0.030
(0.028) (0.028) (0.045) (0.060) (0.064) (0.040)
Observations 4,999 4,999 1,939 1,209 730 2,147
R2 0.354 0.357 0.435 0.296 0.407 0.295
extra return to relevant experience in agriculture. For those in skilled manufacturing, we estimate an extra return to a year of relevant experience of about 3.6 percent, although this is imprecisely estimated. However, for those in unskilled manufacturing occupations, one year of relevant experience is associated with a very large 7 percent extra return. This estimate is signifi cant at the 0.05 level. In Column 6, we pool all of the manufacturing occupations together, and there estimate an extra return to relevant experience of 6.5 percent which is highly signifi cant.
The estimation results presented in Table 7 provide evidence that the correlation be-tween migration experience and earnings appears to be driven by occupation- specifi c skill acquisition in the United States. When relevant experience is added as a regres-sor, the estimated coeffi cient on total or generic U.S. experience is always small and insignifi cant. This is noteworthy because if correlation between migration experience and unobserved components of skill were driving the relationship between migration experience and earnings, we would still expect generic experience to matter. Endo-geneity or selection bias might certainly drive these results. Occupational choice is surely endogenous, and it could be the case that more- skilled individuals are more likely to get occupations in Mexico that match the occupation they held in Mexico. However, the selection and endogeneity story required to explain away the results seems much more specifi c and fragile than a baseline scenario of correlation between migration experience and unobservables.
If the acquisition of job- specifi c skills explains much of the return to U.S. migra-tion experience, this raises the quesmigra-tion of why such skills are more easily acquired in the United States relative to Mexico. It could be the case that that fi rms in the United States have access to better capital equipment or technology, and that it is easier to learn more advanced production techniques under such circumstances. It is also pos-sible that U.S. fi rms are better organized and managed, and workers learn more in such environments. Existing empirical evidence suggests that the quality of fi rm man-agement varies more widely in developing countries than in the United States, and that improved management practices can substantially raise fi rm productivity. Indeed, Bloom et al. (2012) provide evidence on the gains in fi rm productivity estimated from a randomized management consulting experiment in India. In a country like Mexico, where there is a substantial informal sector, it is possible that less effective manage-ment likewise hinders labor productivity and on- the- job skill formation.
2. Urban Experience
large urban area to be any city with a population of 1 million or more. The MMP data indicates the Metropolitan Statistical Area that a migrant visited in a given year, and we match these MSAs with cities in the Census Bureau’s County and City Data Book for the year 2000.13
Table 8 presents the results of adding urban migration experience as an extra regres-sor in our basic specifi cations. In Column 1, we add the urban migration experience variable (USUrbanExp), where an urban area is considered to be any city with a popu-lation greater than 500,000. Since we also include the standard U.S. migration experi-ence variable, the coeffi cient on USUrbanExp should be interpreted as the difference in the return to a year of urban migration experience relative to a year of nonurban mi-gration experience. Somewhat surprisingly, the results in Column 1 suggest that there is actually a smaller return to urban experience than to nonurban experience, although the difference is not statistically signifi cant. Whereas a year of nonurban experience is associated with an increase in log- earnings of about 0.029, a year of urban experience is associated with a smaller increase of about 0.017. In Column 2, we use the stricter defi nition of an urban area, and we get very similar results.
To explore these patterns a bit more, we reestimate the specifi cation in Column 1 using different subsamples. In Column 3 of Table 8, we restrict the sample to those individuals employed in agricultural occupations in Mexico. Here, the difference between urban and nonurban experience is even more pronounced. For agricultural workers, a year of generic experience is estimated to increase log earnings by 0.022, and this relationship is marginally signifi cant (p- value=0.106). However, for these agricultural workers, a year of urban experience is associated with a much smaller increase of about 0.006. The estimated coeffi cients on generic U.S. experience and urban experience nearly cancel out. This contrasts sharply with the patterns that we observe for nonagricultural workers in Mexico. In Column 4, we restrict the sample to those working in nonagricultural occupations. Here the estimates indicate the return to a year of U.S. experience to be about 3.3 percent, with a return of about 2.6 percent for a year of urban experience. These estimates are much closer than the corresponding estimates for agricultural workers. We interpret this as being consistent with our hy-pothesis that skill acquisition and the accumulation of occupation- relevant experience is what explains the correlation between earnings and migration experience. Since urban work experience is less likely to be job- relevant for agricultural workers, it is unsurprising that there is a much smaller correlation between urban migration experi-ence and earnings for agricultural workers.
3. English Skills
Next, we investigate the role that English language ability may play in explaining the relationship between migration experience and earnings in Mexico. We might think of at least two channels by which English ability may be related to the relationship between migration experience and earnings back in Mexico. First, it could be the case that individuals actively improve their English language ability while in the United States, and these improved language skills might be rewarded in the Mexican labor market (for example, in service sector or hospitality jobs that require interacting with
