metadata: authors: 0: author: Mckenzie,David J.
1: author: Rapoport,Hillel
count: Mexico
docna: 0: docna: Can migration reduce educational attainment
            ? Evidence from Mexico
docty: Policy Research Working Paper
subtopic: Human Migrations & Resettlements,Private Sector Economics,Primary Education,Economics of Education,Educational Sciences,Private Sector Development Law,Marketing
teratopic: Culture and Development,Social Protections and Labor,Education
abstracts: cdata!: The authors examine the impact of
            migration on educational attainment in rural Mexico. Using
            historical migration rates by state to instrument for
            current migration, they find evidence of a significant
            negative effect of migration on schooling attendance and
            attainment of 12 to 18 year-old boys and 16 to 18 year-old
            girls. IV-Censored Ordered Probit results show that living
            in a migrant household lowers the chances of boys completing
            junior high school and of boys and girls completing high
            school. The negative effect of migration on schooling is
            somewhat mitigated for younger girls with low educated
            mothers, which is consistent with remittances relaxing
            credit constraints on education investment for the very
            poor. However, for the majority of rural Mexican children,
            family migration depresses educational attainment.
            Comparison of the marginal effects of migration on school
            attendance and on participation in other activities shows
            that the observed decrease in schooling of 16 to 18
            year-olds is accounted for by the current migration of boys
            and increased housework for girls.
geo_reg_key: 517191,119228
repnme: repnme: Can migration reduce educational attainment
            ? Evidence from Mexico
display_title: Can migration reduce educational
            attainment Evidence from Mexico
listing_relative_url: /research/2006/06/6875033/can-migration-reduce-educational-attainment-evidence-mexico
topicv3: Private Sector Development m1326060 1921,Primary Education m1326139 480,Education m1327048 470,Economics of Education m1327511 1540,Human Capital Flight m1504170 2845
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Culture and Development,Social Protections and Labor,Education
no_of_pages: 42
author_keys: 0: author_key: 437864
1: author_key: 857958
model_extractions: WPS3952

# **Can migration reduce educational attainment?**

# **Evidence from Mexico**<sup>*****</sup>

**David McKenzie**<sup>**a**</sup> **and Hillel Rapoport**<sup>**b**</sup>

**a Development Research Group, World Bank**

> **b Department of Economics, Bar-Ilan University, CADRE, University of Lille II,**

### **and Stanford Center for International Development**

### **Abstract:**

This paper examines the impact of migration on educational attainment in rural Mexico. Using historical migration rates by state to instrument for current migration, we find evidence of a significant negative effect of migration on schooling attendance and attainment of 12 to 18 year-old boys and 16 to 18 year-old girls. IV-Censored Ordered Probit results show that living in a migrant household lowers the chances of boys completing junior high school and of boys and girls completing high school. The negative effect of migration on schooling is somewhat mitigated for younger girls with low educated mothers, which is consistent with remittances relaxing credit constraints on education investment for the very poor. However, for the majority of rural Mexican children, family migration depresses educational attainment. Comparison of the marginal effects of migration on school attendance and on participation in other activities shows that the observed decrease in schooling of 16 to 18 year-olds is accounted for by the current migration of boys and increased housework for girls.

Keywords: Migration, migrant networks, education attainments, Mexico JEL codes: O15, J61, D31 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

## World Bank Policy Research Working Paper 3952, June 2006

> * Corresponding author: David McKenzie, MSN MC3-300, The World Bank, 1818 H Street N.W., Washington D.C., USA 20433. Email: dmckenzie@worldbank.org. We thank Thomas Bauer, Gordon Hanson, Frédéric Jouneau, Omar Licandro, Ernesto Lopez-Cordoba, François-Charles Wolff, and participants at the Inter-American Development Bank's "Economic Integration, Remittances and Development" conference in Washington D.C., February 2005, at the meeting of the European Society for Population Economics, Paris, June 2005, and at the Development Economics Workshop, Louvainla-Neuve, September 2005, for useful comments on an earlier draft.

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# **1. Introduction**

The rapid growth in migrants’ remittances has triggered considerable attention in policy circles (e.g. GCIM 2005, World Bank 2005) and has led to renewed research attention to the development impacts of remittances.<sup>1</sup> Several recent empirical studies have emphasized the potential for remittance transfers to alleviate credit constraints and thereby increase educational attainment of children in migrant families.<sup>2</sup> The recent theoretical literature on the “beneficial brain drain” or “brain gain” suggests a second channel through which migration can increase educational attainment. The basic idea of such theories is that education has a high return when migrating, and so the prospect of migrating in the future raises the expected return to education, inducing higher domestic enrollment in schools.<sup>3</sup> However, Chiquiar and Hanson (2005) have shown that the return to education is higher in Mexico than for Mexican migrants in the United States. This arises due to higher inequality in Mexico than in the U.S., and additionally from the fact that most first-time migration from rural Mexico is illegal, leading only to job opportunities with low formal educational requirements. As a result, the incentive effect of the prospect of future migration for children growing up in migrant households in Mexico may actually lower the incentive to invest in education, counteracting the remittances effect.

The implicit assumption in most of the existing studies of remittances is that migration only affects educational outcomes through remittances, and not through any

> 1 See the recent survey of Rapoport and Docquier (2006), which provides an extensive discussion of different motives for remitting.

> 2 See Cox Edwards and Ureta (2003) for Nicaragua, Lopez-Cordoba (2004) for Mexico and Yang (2004) for the Philippines.

> 3 See for example Commander, Kangasniemi and Winters (2004) for a review of the various channels through which a beneficial brain drain can be obtained.

2
other channel such as this incentive effect.<sup>4</sup> However, in addition to the potential incentive effect, migration of a family member may have a number of other effects on child schooling. For example, parental absence as a result of migration may translate into less parental inputs into education acquisition and may also require remaining children to undertake housework or work to help meeting short-term labor and cash shortages. If any of these other channels operate, studies which focus just on the effect of remittances will generally give biased estimates<sup>5</sup> .

This paper therefore focuses on identifying the overall impact of migration on educational attainment in Mexico, estimating the net impact of these various effects. Every fifth household in rural Mexico has at least one member with international migration experience,<sup>6</sup> so the impacts of migration on the next generation are potentially very large. We use historical migration networks formed by 1920 as an instrument for migration seven decades later in order to account for the potential endogeneity of household migration decisions. Our main finding is that living in a household with migration experience depresses the educational attainment of rural Mexican children, with a stronger effect on 16 to 18 year-olds.

We begin with bivariate probit models of school attendance, which reveal large negative effects of being in a migrant household on school attendance of 16 to 18 year-old males and females, and on 12 to 15 year-old males, with insignificant results for 12 to 15 year-old females. Estimation of two-stage least squares models of

> 4 See the appendix in McKenzie (2005) for a methodological discussion of this point.

> 5 Theoretically one could separate the effect of remittances from other effects of migration through the use of a valid instrument which predicts whether or not one migrant will send more remittances than another. Such instruments are uncommon in practice, with the exchange rate shocks used by Yang (2004) coming closest in this regard among the existing literature (although as he acknowledges, these shocks also affect migrant wealth holdings).

> 6 Source: own calculations from ENADID data (see Table 1).

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completed years of schooling then reveals negative effects of migration on 16 to 18 year-olds, and insignificant effects on 12 to 15 year-olds.

However, a high proportion of 12 to 15 year-olds are still in school. Mexico’s education system provides for nine grades of compulsory schooling, and so it is not until the age of 15 or 16 that children and their families begin making decisions about completion of non-compulsory grades. We allow for this censoring of attained education for children still in school, and for the potential nonlinearities in grade progression probabilities caused by natural stopping points such as the end of junior high (9<sup>th</sup> grade). This occurs through estimation of an instrumented censored ordered probit model. Doing this reveals a significant negative effect of migration on educational attainment of 12 to 15 year-old males, and increases the size of the estimated effects for 16 to 18 year-olds. Overall, living in a migrant household is estimated to lower the probability of completing high school by 13 percent for males and 14 percent for females.

We then allow for heterogeneity in the effects of migration by interacting household migration status with maternal education, a proxy for wealth. We find marginally significant evidence for less negative effects of migration on educational attainment for children in poorer households, which is consistent with remittances relaxing credit constraints. However, the overall effect of migration on education is still negative for 16 to 18 year-olds, even in poor households. When we explore the channels for this depressive effect of migration on schooling, we find the majority of the effect can be explained by young males in migrant households themselves migrating instead of attending school, and young females in migrant households dropping out of school to engage in housework.

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In related work, Hanson and Woodruff (2003) also estimate the overall impact of migration on education in Mexico. They use the 2000 Mexican census, and look at the impact on number of school grades completed of 10 to 15 year-olds. Their main finding is that migration to the U.S. is associated with more years of completed education for 13 to 15 year-old girls, but only for those whose mothers have three years or less of education. We employ a large demographic survey instead of the Census, allowing us to consider a broader measure of household migration experience. We obtain an insignificant effect of migration on education for 12 to 15 year-olds girls with poorly educated mothers, and can not reject positive effects of similar magnitudes to those they find. However, our work builds on their findings in three important respects. Most fundamentally, we consider 16 to 18 year-olds, who are at the age when migration for work starts to become a possibility, especially for males, and who are also at the age when they may be entrusted with household responsibilities which take the place of schooling. That is, this is precisely the age range at which many of the other channels through which migration affects education start to manifest themselves.

Secondly, Hanson and Woodruff (2003) note that school attendance is high amongst their sample, with 82.5 percent of 10-15 year-olds attending school. Nevertheless, they use two-stage least squares for estimation, which does not account for this high rate of right-censoring. Once we do this, insignificant 2SLS results for 12 to 15 year-old males become significant. Finally, the survey we use enables examination of what children are doing when they are not in school, enabling investigation of the channels through which migration is affecting schooling.

The remainder of this paper is organized as follows. Section 2 presents the demographic survey data used for the empirical analysis and contrasts it to the 2000 5
Mexican Census. Section 3 discusses our identification strategy and other econometric issues such as censoring and the presence of nonlinearities in education decisions. Section 4 provides a broad theoretical framework that outlines how the main effects of migration on the feasible and desired amounts of education balance out at different wealth levels. The results on school attendance and education attainments are presented in Section 5. Section 6 then asks what children in migrant households are doing instead of going to school and Section 7 concludes.

# **2. Data**

This paper uses data from the 1997 Encuesta Nacional de la Dinámica Demográfica (ENADID) (National Survey of Demographic Dynamics) conducted by Mexico’s national statistical agency (INEGI) in the last quarter of 1997.<sup>7</sup> The ENADID is a large nationally representative demographic survey, with approximately 2000 households surveyed in each state, resulting in a total sample of 73,412 households. We restrict our analysis to rural communities, defined here to be municipalities which are outside of cities of population 50,000 or more. Our main results are robust to lowering this threshold to cities with population below 15,000. Within these communities we have a sample of 20,388 children aged 12 to 18 years, living in 12,980 households.

The key variables of interest are migration and schooling. The ENADID asks several questions concerning migration, including whether household members have _ever_ been to the United States in search of work. This question is asked of all household members who normally live in the household, even if they are temporarily studying or working elsewhere. Additional questions ask whether any household 7 Survey methodology, summary tables, and questionnaires are contained in INEGI (1999).

6
members have gone to live in another country in the past five years, capturing migration for study or other non-work purposes in addition to work related migration. We define a child as living in a _migrant household_ if the household has a member aged 19 and over who has ever been to the U.S. to work, or who has moved to the U.S. in the last five years for any other reason. The migrant member or members may have returned to Mexico or still be in the U.S. at the time of the survey.

Table 1 provides summary statistics for the key variables used in this study. Twenty two percent of all households in our sample with a child aged 12 to 18 have an adult member who has ever migrated to the U.S. Several recent studies of migration and schooling in Mexico have used the 2000 Mexican Census (Hanson and Woodruff, 2003; Lopez-Cordoba, 2004). The Mexican Census only asks about migration within the last five years. The ENADID questions on migration within the last five years are identically worded to the Census, and so for comparison we also calculate the proportion of households with migrants according to the Census definition. Table 1 shows that relying on the Census questions to define migrant status understates the proportion of households with migrant experience by almost fifty percent.

Examining migration within the last five years is likely not to be unduly restrictive for certain types of analysis. However, there are number of reasons to prefer looking at whether household members have ever migrated in examining the impact of migration on education. Schooling is a cumulative process, with each year building on the year before. Any impact of migration on schooling during the years of primary education may therefore affect schooling six to ten years later. A portion of this effect may be at the extensive margin: 10 percent of 12 year-olds in our sample are not currently attending school, which makes it likely they will not be attending 7
school at age 18. There are also likely to be effects at the intensive margin, whereby household resources and effort devoted to schooling during primary school affect the ability of children to continue schooling in later years. In addition to these direct effects through prior schooling, migration by household members six or more years ago may still result in higher household wealth today, influencing the ability to pay for schooling later on. Furthermore, schooling decisions may depend on the expectation of migration in the future. This expectation will depend in part on previous household migration experience, whether or not the migration episodes occurred within the last five years. For these reasons we prefer the ENADID to the Census for examining the effects of migration on education.

The ENADID asks migrants who have ever been to the U.S. for work a set of additional questions about their migrant experience, including the number of trips they have ever made, and whether they had legal documentation to work. Approximately 50 percent of all migrants have made more than one trip, with a mean of 2.8 trips per migrant. The vast majority of migrants in our sample had no legal documentation to work, especially on their first trip. Over 91 percent of first-time migrants who went to work in the U.S. had no legal documentation to do so. This is important to note, as it indicates that the majority of Mexicans in our sample contemplating migration are likely to end up working without documentation in the United States. Kossoudji and Cobb-Clark (2002) find evidence from an amnesty program that the returns to human capital are higher for legal workers than for illegal workers in the United States. This corroborates our conjecture that migration lowers the incentives to acquire education for prospective Mexican immigrants.<sup>8</sup>

> 8 See also Rivera-Batiz (1999).

8
Our main measure of education is based on years of schooling attained by children and adults. Elementary education (grades 1 to 6) is compulsory in Mexico and is normally provided to children aged 6 to 14. Lower secondary education (grades 7 to 9) became compulsory in 1993 and is generally given to children aged 12 to 16 years who have completed elementary education. This is followed by three years of upper secondary schooling (grades 10 to 12) and higher studies. Despite education being compulsory, there is still far from complete compliance and a lack of infrastructure in some remote rural areas (SEP, 1999). Approximately half of all 15 year-olds with less than 9 years of attained schooling were not attending school in 1997. We focus our study on children aged 12 to 18, the ages at which children will be receiving the majority of their post-primary education, and the age range at which children start leaving school. 90 percent of 12 year-old males and 83 percent of 12 year-old females in our sample were attending school in 1997, compared to 51 and 47 percent of 15 year-old males and females, and 20 and 16 percent of 18 year-olds.

Figure 1 plots the proportion of females and males attending school by age and the migrant status of their households, along with mean years of schooling attained by age. The raw data show school attendance is higher in migrant households among young children of five or six years, and similar in migrant and non-migrant households in the early teenage years. However, school attendance drops among boys in migrant households relative to non-migrant households from age 14 onward. The result of this is that mean schooling levels attained for boys are very similar in migrant and non-migrant households, while girls in migrant households have higher mean schooling levels as they age than girls in non-migrant households. However, these are unconditional differences, and do not take account of other differences 9
between migrant and non-migrant households which also affect schooling. We turn to this issue next.

# **3. Empirical Methodology and Identification Strategy**

# **3.1. Identification**

The first challenge in estimating the causal impact of migration on education outcomes is the possibility of unobserved characteristics of households which influence their decision to migrate also playing a role in their schooling decisions. For example, parents who care more strongly about the education of their children may migrate in order to earn income that can be used to pay for schooling expenses, and will also devote more attention and non-income resources to improving schooling outcomes of their children. A simple comparison of migrants and non-migrants would in this case overstate the education gains from migration. Alternatively, Hanson and Woodruff (2003) note that negative labor market shocks experienced by parents may both induce migration and require children to work instead of spending time in school, leading to a spurious negative relationship between migration and years of schooling. As such the direction of any selectivity bias is theoretically uncertain.

We therefore follow Woodruff and Zenteno (2001) and a number of subsequent studies<sup>9</sup> in using historic state-level migration rates as an instrument for current migration stocks. In particular, we use the U.S. migration rate from 1924 for the state in which the household is located, taken from Foerster (1925)<sup>10</sup> . Since this instrument only varies at the state level, we cluster our standard errors at the state

> 9 Hanson and Woodruff (2003); McKenzie and Rapoport (2004); López-Córdoba (2004); and Hildebrandt and McKenzie (2004) all employ historic migration rates as instruments for current migration.

> 10 Thanks to Chris Woodruff for supplying these historic rates.

10
level to allow for arbitrary correlation in the error structure of individuals within a state. These historic rates can be argued to be the result of the pattern of arrival of the railroad system in Mexico coupled with changes in U.S. demand conditions for agricultural labor. As migration networks lower the cost of migration for future migrants, they become self-perpetuating, and as a result, continue to influence the migration decisions of households today.

Our identifying assumption is then that historic state migration rates do not affect education outcomes over 70 years later, apart from their influence through current migration. Instrumental variables estimation relies on this exogeneity assumption, and so it is important to consider and counteract potential threats to its validity. One potential threat is that historic levels of inequality and historic schooling levels helped determine migration rates in response to the railroad expansion, and also influence current levels of schooling due to intergenerational transmission of schooling. To allow for this possibility we control for a number of historic variables at around the same time period as our historic migration measure. The controls are the proportion of rural households owning land by state in 1910 taken from McBride (1923)<sup>11</sup> ; and the number of schools per 1000 population by state in 1930, and male and female school attendance for 6 to 10 year-olds by state for 1930, both taken from DGE (1941).

A second possible threat to validity is that the development of the railroads in certain states and communities ushered in the subsequent development of other infrastructure, such as school facilities, and led to changes in the income distribution which themselves influenced the incentives and ability to invest in schooling. We include the following state-level controls for this possibility, all calculated from the

> 11 Land ownership data were kindly provided by Ernesto López-Córdoba.

11
public use sample of the 1960 Mexican Census: the Gini of household income, the Gini of years of schooling accumulated for males and females aged 15-20, and the average levels of years of schooling accumulated for males and females aged 15-20. Spearman rank-order correlation tests do indeed indicate some significant correlations between the 1924 migration rates and some of these controls: states with higher historic migration rates had higher average rates of schooling and lower inequality in schooling in 1960. This might represent the influence of migration over the 1924-60 period, or the effects of concomitant trends, and so we prefer to include these 1960 education inequality and levels as controls. Even after controlling for these variables, historic migration rates remain a powerful predictor of current community migration prevalence, with a first-stage F-statistic of 28.

A final threat to the validity of this instrument is the possibility that the historic community migration network has a direct effect on educational attainment through changing the incentives to acquire education. We argue that the incentive effects should be much stronger if children have a household member who has previously migrated than if they merely have someone in their community who has migrated, so that the direct effect of the community network is likely to be secondorder in the education decision. As a check on this assumption, we split states into those above and below the median migration rate in 1924, and then regress years of schooling on a dummy variable for being in a high migration state for children in nonmigrant households. Table 2 shows the effect of the community network is insignificant for three out of the four groups, and has a small _positive_ effect on school attainment of 12 to 15 year-old females. This provides us with further confidence in our instrument and suggests that a finding of migration lowering education rates is not a result of the community network directly lowering education rates.

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# **3.2. Estimation techniques**

The first outcome of interest that we study is whether children are currently attending school. As this is a binary outcome, we use maximum-likelihood to estimate a bivariate probit model, following Newey (1987), which we will follow common practice in referring to as the IV-Probit model.<sup>12</sup> The marginal effects of this model will then be compared to marginal effects from standard probit estimation. However, current school attendance does not allow for possible delays in starting schooling, catch-up and grade repetition. As seen in Figure 1, it appears that children in migrant households are slightly more likely to start school at age 5 than children in nonmigrant households. Therefore greater attendance in non-migrant households at older ages may just be the result of these late starters catching up.

Instead of school attendance, we therefore focus most of our attention on educational attainment, measured by the grade-years of schooling attained by children. Following Hanson and Woodruff (2003) we begin with two-stage least squares (2SLS) of the following equation: _Schoolingi =_ α _+_ β _*Migranti+_ δ _’Xi +_ ε _i_ (1), where _Schoolingi_ is the years of schooling attained by child _i, Migranti_ is a dummy variable taking the value one if a household has a migrant member and zero otherwise, and _Xi_ is a set of individual and community controls.

However, there are several features of education data that render OLS and 2SLS analysis inappropriate. Ideally one would like to observe the final level of schooling completed by individuals and relate it to the migrant status of the household in which they grew up. Here we face the problem that while schooling is complete for

> 12 Estimation was carried out using the IVProbit command in STATA version 9.

13
adults, we have no information on the households in which they lived during their childhood, or on the migration status of their parents. We must therefore restrict our sample to children of school age. However, while we have information on the migration status of the households in which these children are living, many of them are still attending school and so we do not observe their completed level of schooling.

The data are thus right-censored for children who are attending school. OLS and 2SLS estimation ignores this censoring, treating the educational attainment of children still in school as identical to those who have finished schooling. This results in biased estimates of the impact of migration on final schooling attainment. Censoring is likely to be especially important for schooling outcomes of 12 to 15 year-olds, since many in this age group will not have finished schooling.

A second issue is that OLS and 2SLS assume a continuous distribution for the dependent variable, years of schooling attained. However, as seen in Figure 2, the observed schooling distribution is characterized by large spikes at 6 years and 9 years, representing the completion of primary and lower secondary school. As noted in the education literature (see e.g., Glick and Sahn, 2000), grade attainment is the outcome of a series of ordered discrete choices. The choice to continue onto junior secondary school or onto high school for one year is thus likely to be different from the choice to continue for an extra year once one has started junior high or high school.

As a result of these features of education grade data, King and Lillard (1987) and subsequent studies in the economics of education literature (e.g. Glick and Sahn (2000), Holmes (2003), Maitra (2003)) have adopted a censored ordered probit framework when examining the impact of household characteristics on schooling. In this framework, an individual’s desired latent propensity for schooling, _yi_ is determined by a linear relationship analogous to equation (1): 14
_yi =_ α _+_ β _*Migranti+_ δ _’Xi +_ ε _i_ (2) However, _yi_ is unobserved. For individuals who have finished their schooling, we observe schooling level _S_ if the value of _yi_ falls between two cut-off points, corresponding to grades _S_ and _S+1:_ For individuals with no schooling, we only know that the index falls below the lowest threshold, normalized to zero, and for individuals with the maximum level of schooling, we know only that _yi_ ≥μ _max_ . We classify education grade attainment into seven ordered categories for the purposes of this analysis: no schooling, 1 to 5 years, 6 years (complete primary), 7 to 8 years, 9 years (completed junior high), 10 to 11 years, and 12 and above years (completed high school). Assuming normality of the error terms, ε i, one can then write down the likelihood function, and via this ordered probit model, estimate these cutoff points along with the coefficients of interest (see Greene, 2000). For children who are still in school, we know that they will attain at least their current grade, and hence that for an individual currently in school with _J_ grades of schooling attained, _yi_ ≥μ _J._ One can therefore modify the likelihood function to allow for this censoring, and estimate the censored ordered probit model via maximum-likelihood.<sup>13</sup> To allow for the potential endogeneity of household migration status within the ordered probit and censored ordered probit model we follow the methodology of Rivers and Vuong (1988). In the first stage, a household migrant status is regressed on the instrument and exogenous regressors. The fitted values and residuals from this first stage are then both included in the censored ordered probit model estimated in

> 13 See Appendix A of Glick and Sahn (2000) for specification of the likelihood function. Estimation was carried by programming the likelihood in STATA version 9.

15
the second stage.<sup>14</sup> We will refer to the estimates from this process as IV-Ordered Probit and IV-Censored Ordered Probit estimates.

# **4. Theoretical framework**

We now turn to an examination of the theoretical impact of migration on the schooling of children. Let _ri,s_ denote the present discounted value of the additional returns to child _i_ of completing schooling year _s_ , _ci,s_ denote the additional financial costs of the child completing this additional year of schooling, and _ki,s_ denote the additional non-financial costs of the child completing this additional schooling year, such as foregone income and the disutility of school effort. Costs are realized at the moment of schooling whereas returns are not realized until the future. Financial costs of schooling must therefore be met out of the household’s current resources. The household’s schooling decision is then to choose _s_ ∈ _{0,1,2,...,N}_ to maximize the net present discounted value of schooling, subject to the condition that total financial schooling costs must be met out of current household resources net of subsistence needs, _Ai_ . That is, Let _siU_<sup>denote the unconstrained optimal level of education for child</sup><sup>_i_, which</sup> occurs when the financing constraint does not bind. We expect this to be weakly increasing in mother’s education and household resources due to the possibility of more educated mothers lowering the disutility and non-financial costs of schooling by placing higher emphasis on education, helping with schoolwork, and perhaps due to a

> 14 Such an approach is also carried out by Maitra (2003).

16
genetic ability component. The returns to schooling may also be higher for richer households due to peer effects and the ability to enter occupations with high start-up _P_ costs. Denote by _si_ the maximum possible years of schooling the household can afford under its budget constraint. This is clearly increasing in wealth, and is likely to be increasing in maternal education since household resources are likely to be correlated with mother’s schooling. Then: _*_ Figure 3 then illustrates the relationship between _si_ and household wealth levels or maternal education. Child schooling is predicted to increase with household resources, both due to relaxing of credit constraints and to the possible higher desired levels of education for children in richer households with more educated mothers.

Now consider the potential impacts of migration on a household’s optimal education. Remittances and potentially higher earnings after migration (such as from entrepreneurship, see Woodruff and Zenteno, 2001, or farm investments, see Taylor and Wyatt, 1996) increase the value of household resources _Ai_ , increasing the maximum years of schooling the household can afford, _siP._ The relaxation of creditconstraints allows households to move to or towards their unconstrained optimal level of education, resulting in higher education for their children. In contrast, if credit constraints are not binding, then remittances will have no effect on their schooling.

In addition to remittances, migration can have a number of other effects on child schooling. Hanson and Woodruff (2003) note that one potential negative effect is that migration may disrupt household structure, removing children from the presence of guardians and role models, and require older children to take on additional household responsibilities. In our model this can be thought of as 17
increasing the non-financial costs of schooling, _ki,s,_ leading households to lower _siU_ , their unconstrained level of education.

A further effect which we wish to consider is the possibility that due to information and network effects, having a migrant parent increases the likelihood that the children themselves will become migrants. This may have an _immediate substitution effect_ , whereby as a result of the opportunity cost of staying in school increasing due to higher potential earnings abroad, children drop out of school in order to migrate to work. Again this can be viewed as increasing _ki,s,_ leading _U_ households to lower _si_ .

Even if children do not migrate at the age when they would be attending school, the possibility they may migrate in the future can influence the expected returns to education, changing _ri,s_ in our framework. As returns to schooling appear to be higher in Mexico than in the United States, the possibility of migration in the future will _lower_ the expected returns from migration. Since children of migrants are more likely to migrate in the future than children of non-migrants, we would therefore _U_ 15 expect this _incentive effect_ to lower _si_ in migrant households.

Each of these three additional channels through which migration may affect child schooling (disruption of household structure, direct substitution of schooling today for migration today, and the change in expected future returns to education) all _U_ act to lower _si_ . Assume first that this reduction in the unconstrained desired level of schooling occurs equally across wealth and maternal education levels. Coupling this _P_ with the increase in _si_ arising from remittances gives an overall effect of migration as seen in Figure 4. Two possibilities arise. Figure 4a shows the case where the effect of

> 15 For example, in a survey of students in Zacatecas, Kandel and Kao (2001) find that living in a migrant household is negatively associated with directly elicited university aspirations.

18
alleviating credit constraints outweighs the reduction in desired schooling levels for the poor, so that child schooling increases in poor households. In contrast, unconstrained households only experience the effects of reductions in desired schooling, and so schooling falls. Figure 4b shows the case in which the fall in desired schooling is sufficiently large that no household would have been credit-constrained, even in the absence of remittances. In this case, schooling falls for all wealth levels after migration, but still should fall by more for richer households.

Nevertheless, under some circumstances one might anticipate seeing more of a reduction in schooling among poorer households following migration, and a corresponding increase in education inequality. Figure 4c outlines one such scenario. Basic education is provided free by the state along with free textbooks (SEP, 1999). Along with a number of targeted programs towards the poor, it is likely that even the poorest Mexican households have sufficient household resources to afford some years of post-primary schooling. It is therefore possible that desired schooling levels lie _U_ below possible schooling. Migration may then lower _si_ by more for poorer households than for richer households. Therefore in addition to the overall impact of migration on education being uncertain, it is also uncertain as to how the size of the effect will vary with wealth.

# **5. Results**

# **5.1. School Attendance**

Table 3 presents the probit and iv-probit estimates of the impact of being in a migrant household on school attendance. The probit results show a significant, but small, negative impact of being in a migrant household on school attendance of boys, and an insignificant effect on school attendance of girls. Once we instrument for migration 19
however, these effects become larger, and being in a migrant household is estimated to significantly lower the probability of attending school by 16 percentage points for 12 to 15 year-old males, 21 percentage points for 16 to 18 year-old males, and 20 percentage points for 16 to 18 year-old females. The coefficient on migration for females aged 12 to 15 is also negative, but is insignificant. All specifications also show a strong positive effect of mother’s years of education on school attendance, and that children in areas which have historically had more schools are currently more likely to attend school.

# **5.2. Years of Schooling Attained**

Tables 4 to 7 present the results of estimating the impact of being in a migrant household on grade years of schooling attained for each sex-age group. In each table, Columns 1 and 2 first present the OLS and 2SLS estimates of equation (1). Column 3 gives the ordered probit estimates, and Column 4 gives the iv-ordered probit estimates. Column 5 gives the censored ordered probit estimates, while column 6 adjusts these for endogeneity of migration.

The OLS results show a positive overall association of migration with attained years of schooling, which is significant for females and for males aged 12 to 15. However, once we control for the endogeneity of migration, the 2SLS results all show a negative impact for migration on schooling, with this effect being significant for 16 to 18 year-old males and females. Comparison of the OLS and 2SLS results therefore suggests that children in migrant households have unobserved characteristics which make them _more_ likely to receive schooling than observationally similar children in 20
non-migrant households. This would be consistent with migration of parents who care a lot about the education of their children.

The iv-ordered probit and iv-censored order probit results show the importance of allowing for these more complex specifications. The negative impact of migration on education of males aged 12 to 15 is significant in these two specifications, compared to the insignificant 2SLS specification. The significance level also increases for the negative impact on males and females aged 16 to 18. Comparison of the iv-ordered probit and iv-censored ordered probit results shows a stronger negative impact of migration after allowing for censoring, with this difference greater amongst 12 to 15 year-olds than amongst 16 to 18 year-olds. This concurs with our a priori view that censoring was particularly likely to be a problem for estimation of schooling at younger ages. The impact of migration on schooling is still insignificant for females aged 12 to 15 after allowing for censoring and differential schooling level effects.

The size of the effects can not be easily seen from the coefficients in Tables 4 to 7. Interpretation of the coefficients of an ordered probit model is complicated by the fact that the direction of the effect is only unambiguous for the lowest and highest category (see Greene, 2000, p. 878). As a result, we calculate the marginal effect of a change in household migrant status on the probability of having schooling of each one of our seven categories. Marginal effects can be further complicated in a censored model, since a change in a variable of interest will affect both schooling attained at the time of observation, and the probability of the observation being censored by the child continuing to attend school. We report marginal effects for the change in the latent index, which captures both of these effects, and therefore provides an estimate of the impact of migrant status on final schooling attainment.

21
Table 8 reports the marginal coefficients from the iv-ordered probit and ivcensored ordered probit models. These effects demonstrate that living in a migrant household has different effects at different levels of schooling, and it is not the case that the effects are linear. For example, for 12 to 15 year-old males, the instrumented censored order probit model shows that living in a migrant household lowers the probability of having 9 years of completed education by 22.5 percent, and lowers the probability of having 10 or 11 years of education (or more) by 12 percent. These effects are substantially larger than in the ordered probit model which does not allow for censoring, reflecting the fact that many 12 to 15 year-olds are still in school. Similarly for 15 to 18 year-old males and females, allowing for censoring shows a much larger effect of migration on lowering the probability of completing 12 years or more education. Overall these marginal effects show migration having very little effect on the probability of completing 7 to 8 years of education, while lowering the probabilities of receiving more years than this, and increasing the probability of receiving less years than this.

# **5.3. Allowing for heterogeneous effects**

As discussed in Section 4, the impact of migration on education may possibly vary with household wealth, since households with lower wealth may be more likely to be liquidity constrained when making their education decisions. In such cases, the remittance effect of migration is likely to relax these liquidity constraints and therefore potentially increase education, or at least not reduce it as much as one sees for those with higher wealth levels. Unfortunately the ENADID contains only limited information on household wealth, in the form of current asset indicators, which are themselves affected by a household’s migration decision. We therefore instead use 22
maternal education as a proxy for household wealth: mother’s years of schooling has a 0.46 correlation with an asset index formed as the first principal component of a number of asset indicators in our sample.

Table 9 then reports the results of 2SLS estimation of the impact of living in a migrant household on child schooling, interacted with mother’s education. We employ two methods of carrying out this interaction. The first is a straight interaction with the number of years of schooling. Secondly, we concentrate on the poorest segment by interacting with whether or not the child’s mother has two years or less of education. We use 2SLS rather than the censored ordered probit for ease of interpreting the coefficients on the interactions, and because the likelihood functions did not always converge in ordered probit estimation with interactions.

Table 9 offers only limited evidence for heterogeneous effects of migration on child’s schooling. The interaction effect with total years of schooling of the mother is always negative, indicating that children in households which are likely to be richer have even more of a negative impact from migration. However, none of these interaction terms are significant. When we interact with the mother having two years or less of schooling, we find positive interaction effects, showing that children in poorer households are less likely to face a reduction in years of schooling from migration. These interaction effects are significant at the 10 percent level for females 16 to 18 and at the 11 percent level for females 12 to 15. For the 12 to 15 year females, the size of the coefficient is almost enough to take the overall effect of migration for children with less-educated mothers to zero. Moreover, the standard error is such that we can’t reject a positive effect for this group of magnitudes similar to the 0.7 years estimated by Hanson and Woodruff (2003). However, for the 16 to 18 23
year-old females, the overall effect of migration is still negative, just less negative than for females of this age with more educated mothers.

# **6. What are they doing instead of school?**

The above analysis has found that children in migrant households are less likely to be attending school and complete less total years of schooling than children in nonmigrant households. In this section we explore what these children are doing instead of schooling. It is possible that the absence of a migrant parent may require the child to undertake tasks normally carried out by that migrant, such as working in a family business or doing housework. Since it can take a while for migrants to start earning money and remitting, children may also need to work to cover short-term household liquidity constraints.<sup>16</sup> Any of these activities are also consistent with the child (or the parents) no longer valuing schooling due to future migration plans. Finally we can also examine whether or not the child has migrated during this age range where they could be still engaged in schooling.

Table 10 reports the percentage of 12 to 18 year-olds by sub-age group and gender who are in school, working, working and not in school, migrated, doing housework, and working in family businesses. Children in migrant households are more likely to have migrated themselves than children in non-migrant households, especially among males. 3.4 percent of 12 to 15 year-old males and 8.2 percent of 16 to 18 year-old males with older migrant members in the household have themselves migrated, compared to only 0.2 percent of males in non-migrant households. Males also are more likely to be working, especially as unpaid workers in family businesses,

> 16 The ENADID asks whether or not you have worked in the past week, regardless of whether or not you are also attending school, which we define as _working_ . Another possible activity in the last week for individuals who were not students and who were not working was doing _housework._ Among the individuals who are working, we also look more closely to see whether or not they are working as unpaid workers in a family enterprise, defined as _unpaid family workers_ .

24
if they are in migrant households. In contrast, women in migrant households are not that likely to migrate themselves, and instead are more likely to be engaged in housework than women in non-migrant households.

In Table 11 we examine whether the differences observed in Table 10 are significant once we control for observable differences between children in migrant and non-migrant households and control for the endogeneity of migration. We present probit results for whether or not the child has migrated, since the historic migrant network instrument is clearly not excludable from this model. For the other outcomes, we again instrument living in a migrant household with historic migration rates, using iv-probit models. The results show children living in migrant households to be significantly more likely to migrate themselves than observationally similar children living in non-migrant households. This effect is largest for 16 to 18 year-old males, who are 7.3 percent more likely to migrate when living in migrant households. Migrant and non-migrant males do not exhibit significantly different probabilities of working or engaging in housework, and the higher likelihood of working in family businesses observed for males in migrant households in Table 10 is not significant. Among females, we see a strong significant effect of living in a migrant household on doing housework.

How much then do these other activities explain the lower school participation of children in migrant households? Comparing the marginal effects of migration on school attendance in Table 3 to those on participation in these other activities in Table 11 shows that current migration can more than account for the lower likelihood of school participation for 16 to 18 year-old males, and can account for 60 percent of the lower likelihood of school participation for 12 to 15 year-old males. For 12 to 15 year-old females, there was no significant effect of migration on school attendance, 25
but housework can account for the size of the point estimate. For 16 to 18 year-old females, the increase in housework as the main activity more than accounts for the decrease in schooling, with a decrease in non-school work also needed to account for the large increase in housework.

# **7. Conclusion**

This paper examined the overall impact of migration on educational attainment in rural Mexico. This impact is the sum of three main effects: the effect of remittances on the feasible amount of education investment, which is likely to be positive where liquidity constraints are binding; the effect of having parents absent from the household as a result of migration, which may translate into less parental inputs into education acquisition and maybe into more house and farm work by remaining household members, including children; and the effect of migration prospects on the desired amount of education, which is likely to be negative, as we argued, in the face of lower returns to schooling in the U.S. than in Mexico, especially in a context of illegal immigration.

Our results are in line with these predictions. Using historical migration rates by state to instrument for current migration, we find evidence of a significant negative effect migration on schooling attendance and attainments of 12 to 18 year-old boys and of 16 to 18 year-old girls. IV-Censored Ordered Probit results show that living in a migrant household lowers the chances of boys completing junior high school (by 22 percent) and of boys and girls completing high school (by 13 to 15 percent). This is consistent with migration increasing the opportunity cost, and lowering the expected return to education. However, the negative effect of migration on schooling is somewhat mitigated for younger girls with low educated mothers, which is consistent 26
with remittances allowing to relax credit constraints on education investment at the lower end of the wealth and income distribution.

We also examine what children are doing instead of going to school and find that living in a migrant household significantly increases the chances of boys migrating themselves at all school ages and of older (16 to 18 year-old) girls doing housework. Comparison of the marginal effects of migration on school attendance and on participation to other activities shows that the observed decrease in schooling of 16 to 18 year-olds is more than accounted for by current migration of boys and increases in housework for girls. This is at an age where work is also an important form of human capital accumulation, so it appears that Mexican females in migrant households are losing out on both schooling and work.

To the extent that this reduction in education is a conscious choice of individuals in the face of better opportunities abroad, it should be less of a policy concern than a restriction on schooling due to liquidity constraints. However, given the large literature on positive externalities of education, there may still be some concern at this effect of potential migration on schooling incentives. One possible policy solution would be to take measures to increase the return to schooling in the U.S., which is likely to occur if migrants have better access to legal jobs.

27
# **7. References**

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- Commander, Simon, Maria Kangasniemi and Allan L. Winters (2004): The brain drain: curse or boon? A survey of the literature, in R. Badlwin and L.A. Winters, eds.: Challenges to Globalization, The University of Chicago Press, Chapter 7.

- Cox Edwards, A. and M. Ureta (2003): Internation migration, remittances and schooling: evidence from El Salvador, _Journal of Development Economics_ , 72, 2: 429-61.

- Dirección General de Estadística (DGE) (1941) _Anuario Estadístico de los Estados Unidos Mexicanos 1939._ Secretaría de la Economía Nacional, DGE: Mexico City.

- Foerster, Robert F. (1925) _The Racial Problems Involved in Immigration from Latin America and the West Indies to the United States._ Washington D.C.: The United States Department of Labor.

- Glick, Peter and David E. Sahn (2000) “Schooling of girls and boys in a West African country: the effects of parental education, income, and household structure”, _Economics of Education Review_ 19: 63-87.

- Global Commission on International Migration (GCIM) (2005) _Migration in an interconnected world: New directions for action_ , Report of the Global Commission on International Migration, www.gcim.org.

- Greene, William H. (2000) _Econometric Analysis: Fourth Edition_ , Prentice-Hall: Upper Saddle River, New Jersey.

- Hanson, Gordon H. and Christopher Woodruff (2003): Emigration and educational attainment in Mexico, Mimeo., University of California at San Diego.

- Hildebrandt, Nicole and David J. McKenzie (2004) “The Effects of Migration on Child health in Mexico”, forthcoming, _Economia._

- Holmes, Jessica (2003) “Measuring the determinants of school completion in Pakistan: analysis of censoring and selection bias”, _Economics of Education Review_ 22: 249-64.

- Instituto Nacional de Estadística, Geografía e Informática (INEGI) (1999). _ENADID: Encuesta Nacional de la Dínamica Demográfica 1997._ INEGI: Aguascalientes.

- Kandel, William and Grace Kao (2001): The impact of temporary labor migration on Mexican children’s educational aspirations and performance, _International Migration Review_ , 35, 4: 1205-31.

King, Elizabeth M. and Lee A. Lillard (1987) “Education policy and schooling attainment in Malaysia and the Philippines”, _Economics of Education Review_ 6(2): 167-81.

- Kossoudji, Sherrie A. and Deborah A. Cobb-Clark (2002) “Coming out of the shadows: learning about legal status and wages from the legalized population”, _Journal of Labor Economics_ 20(3): 598-628.

López-Córdoba, Ernesto (2004) “Globalization, Migration, and Development: The Role of Mexican Migrant Remittances”, forthcoming, _Economia_ .

Maitra, Pushkar (2003) “Schooling and Educational Attainment: Evidence from Bangladesh”, _Education Economics_ 11(2): 129-53.

- McBride, George M. (1923) _The Land Systems of Mexico._ New York: American Geographical Society.

- McKenzie, David (2005): Beyond remittances: the effects of migration on Mexican households, in Caglar Ozden and Maurice Schiff, eds.: _International migration, remittances and the brain drain_ , McMillan and Palgrave. Chapter 4, pp. 123-47.

- McKenzie, David and Hillel Rapoport (2004): Network effects and the dynamics of migration and inequality: theory and evidence from Mexico, BREAD Working Paper No 063.

- Newey, Whitney K. 1987. “Efficient Estimation of Limited Dependent Variable Models with Endogenous Explanatory Variables.” _The Journal of Econometrics_ November _36_ (3): 231250.

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- Rapoport, Hillel and Frederic Docquier (2006): The Economics of Migrants’ Remittances, in S.-C. Kolm and J. Mercier Ythier, eds.: _Handbook of the Economics of Reciprocity, Giving and Altruism_ , Amsterdam: North Holland, forthcoming.

- Rivera-Batiz, Francisco (1999) “Undocumented workers in the labor market: An analysis of the earnings of legal and illegal Mexican immigrants in the United States”, _Journal of Population Economics_ 12:91-116.

- Rivers, Douglas and Quang H. Vuong (1988) “Limited Information Estimators and Exogeneity Tests for Simultaneous Probit Models”, _Journal of Econometrics_ 39: 347-66.

- Secretaría de Educación Pública (SEP) (1999) _Profile of Education in Mexico._ Ministry of Public Education, http://www.sep.gob.mx.

- Taylor, J. Edward and T.J. Wyatt (1996) “The Shadow Value of Migrant Remittances, Income and Inequality in a Household-farm Economy”, _Journal of Development Studies_ 32(6): 899-912.

- Woodruff, Christopher, and Rene M. Zenteno (2001): Remittances and micro-enterprises in Mexico, Mimeo, UC San Diego.

- World Bank (2005) _Global Economic Prospects 2006: Economic Implications of Remittances and Migration_ , The World Bank, Washington D.C.

- Yang, Dean (2004) “International Migration, Human Capital, and Entrepreneurship: Evidence from Philippine Migrants’ Exchange Rate Shocks”, Mimeo. Ford School of Public Policy, University of Michigan.

29
<!-- Start of picture text --> School Attendance - Boys School Attendance - Girls<br>5 10 15 20 5 10 15 20<br>Age Age<br>Migrant Household Non-migrant Household Migrant Household Non-migrant Household<br>Mean Years of Schooling - Boys Mean Years of Schooling - Girls<br>5 10 15 20 5 10 15 20<br>Age Age<br>Migrant Household Non-migrant Household Migrant Household Non-migrant Household<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br><!-- End of picture text -->

**Figure 1: School attendance and attainment by age and sex**

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# **Figure 2: Distribution of Years of Schooling Attained by 16 to 18 year olds**

<!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br>0 1 2 3 4 5 6 7 8 9 10 11 12 13<br>Years of Schooling<br>Males Females<br>Frequency (%)<br><!-- End of picture text --> 31
# **FIGURE 3: REMITTANCE EFFECT ON CHILD SCHOOLING**

<!-- Start of picture text --> Education s P (new)<br>s P (old)<br>s u<br>Household Wealth<br><!-- End of picture text --> Remittances shift the possible schooling line upwards, increasing education for poorer households. Bold line shows the new education choice, bold dashed line shows the old education choice.

# **FIGURE 4: OVERALL EFFECT OF MIGRATION ON CHILD SCHOOLING Figure 4a: Equal but small reduction in unconstrained schooling**

<!-- Start of picture text --> Education s P (new)<br>s P (old)<br>s u (old)<br>s u (new)<br>Household Wealth<br><!-- End of picture text --> Bold dotted line shows the original schooling decision, bold solid line shows the new schooling decision after migration.

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# **Figure 4b: Equal but larger reduction in unconstrained schooling**

<!-- Start of picture text --> s P (new)<br>Education<br>s P (old)<br>s u (old)<br>s u (new)<br>Household Wealth<br><!-- End of picture text --> Bold dotted line shows the original schooling decision, bold solid line shows the new schooling decision after migration, which lies entirely below the original levels.

# **Figure 4c: More of a reduction in unconstrained schooling for the poor**

<!-- Start of picture text --> s P<br>s u (old)<br>s u (new)<br>Household Wealth<br><!-- End of picture text --> <!-- Start of picture text --> Education<br><!-- End of picture text --> Bold dotted line shows the original schooling decision, bold solid line shows the new schooling decision after migration, which lies entirely below the original levels, but more so for the poor.

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# **TABLE 1: SUMMARY STATISTICS OF KEY VARIABLES**

||Number of<br>Observations|Mean<br>All hou|Std. Dev.<br>seholds|Mean<br>Migrant H|Std. Dev.<br>ouseholds|Mean<br>Non-migran|Std. Dev.<br>t households|
|---|---|---|---|---|---|---|---|
|_Household Variables (for households with a child aged 12 to 18)_||||||||
|Proportion of Households with a migrant|12980|0.22||1||0||
|Proportion of Households with a migrant by census definition<br>|12980|0.13||0.60||0.002||
|Proportion receiving remittances<sup>1</sup><br>|12301|0.06||0.22||0.02||
|Percentage share of income from remittances<sup>1</sup>|12301|3.83|16.79|14.91|30.87|0.80|7.43|
|_Individual Variables_||||||||
|Years of Schooling of Mother for children aged 12 to 18|20388|3.32|3.20|3.47|2.98|3.28|3.27|
|Years of Schooling of Males 12 to 15|6537|5.79|2.02|5.94|1.82|5.74|2.08|
|Years of Schooling of Males 16 to 18|4159|7.10|2.85|7.10|2.73|7.10|2.88|
|Years of Schooling of Females 12 to 15|6196|5.92|2.05|6.23|1.79|5.83|2.11|
|Years of Schooling of Females 16 to 18|3489|7.32|2.87|7.52|2.58|7.26|2.96|
|_State level variables_||||||||
|State migration rate in 1924|20388|0.006|0.008|0.011|0.008|0.004|0.007|
|Percentage of rural households owning land in 1910|20388|2.283|1.705|2.698|1.514|2.159|1.739|
|Male School Attendance in 1930 (% of 6 to 10 year olds)|20388|39.39|9.00|39.902|8.153|39.242|9.226|
|Female School Attendance in 1930 (% of 6 to 10 year olds)|20388|36.84|10.00|38.944|9.264|36.211|10.121|
|Gini of Household Income in 1960|20388|0.783|0.069|0.778|0.068|0.784|0.069|
|Number of Schools per 1000 population in 1930|20388|1.100|0.319|0.993|0.305|1.131|0.315|
|Gini of Years of Schooling for Males 15-20 in 1960|20388|0.554|0.080|0.551|0.087|0.555|0.078|
|Gini of Years of Schooling for Females 15-20 in 1960|20388|0.573|0.099|0.556|0.100|0.578|0.098|
|Average Male Years of Schooling in 1960|20388|2.614|0.638|2.671|0.647|2.597|0.635|
|Average Female Years of Schoolingin 1960|20388|2.437|0.745|2.561|0.756|2.400|0.738|

Source: own calculation from ENADID 1997 communities with population <100,000 and 50 or more households sampled. Education Ginis are only reported for communities with 20 or more children in the given age category

# **TABLE 2: DOES THE HISTORIC NETWORK AFFECT EDUCATION LEVELS IN NON-MIGRANT HOUSEHOLDS?**

Dependent Variable: Years of education attained (non-migrant households only)

||Males|Males|Females|Females|
|---|---|---|---|---|
||12 to 15|16 to 18|12 to 15|16 to 18|
|Living in state with migration rate above median in 1924|0.14|-0.07|0.18|0.27|
||(1.35)|(0.33)|(2.24*)|(1.50)|

T-statistics are in parentheses with standard errors clustered at the state level.

* significant at the 5% level.

Coefficients from OLS regressions which also include age of child, age of child squared, maternal education, proportion of rural households owning land in 1910, school attendance in 1930, income gini in 1960, number of schools per 1000 population in 1930, gini of years of schooling in 1960, and mean years of schooling in 1960 as other controls.

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# **Table 3: The Impact of Migration on School Attendance**

||Probit<br>Males|IV-Probit<br>12 to 15|Probit<br>Males 1|IV-Probit<br>6 to 18|Probit<br>Females|IV-Probit<br>12 to 15|Probit<br>Females|IV-Probit<br>16 to 18|
|---|---|---|---|---|---|---|---|---|
|Child is in a Migrant Household|-0.036|-0.162|-0.042|-0.217|0.011|-0.090|-0.020|-0.205|
||(2.04)**|(1.69)*|(1.67)*|(2.21)**|(0.44)|(1.11)|(0.75)|(2.49)**|
|Age of Child|-0.108|-0.080|0.383|0.389|-0.348|-0.333|0.431|0.524|
||(0.66)|(0.52)|(0.64)|(0.63)|(1.70)*|(1.60)|(0.48)|(0.55)|
|Age of Child Squared|-0.001|-0.002|-0.014|-0.015|0.008|0.008|-0.016|-0.018|
||(0.13)|(0.32)|(0.82)|(0.81)|(1.10)|(1.01)|(0.59)|(0.65)|
|Mother's Years of Education|0.031|0.031|0.034|0.033|0.045|0.046|0.053|0.051|
||(14.52)***|(13.66)***|(10.67)***|(9.53)***|(11.76)***|(12.47)***|(20.25)***|(12.29)***|
|Proportion of rural households owning land in 1910|-0.015|-0.010|0.008|0.017|-0.007|-0.004|-0.008|0.001|
||(2.31)**|(1.55)|(0.86)|(1.64)|(1.26)|(0.67)|(0.87)|(0.08)|
|School Attendance in 1930 (6 to 10 year olds)<sup>1</sup>|-0.003|-0.002|-0.002|-0.001|-0.002|-0.001|-0.003|-0.001|
||(1.56)|(0.83)|(0.89)|(0.42)|(1.00)|(0.53)|(1.03)|(0.28)|
|Gini of Income in 1960|-0.267|-0.222|-0.278|-0.180|-0.088|-0.028|-0.719|-0.587|
||(1.60)|(1.34)|(1.31)|(1.10)|(0.38)|(0.11)|(2.68)***|(2.05)**|
|Number of Schools per 1000 population in 1930|0.149|0.099|0.148|0.082|0.135|0.097|0.096|0.016|
||(4.12)***|(1.76)*|(3.77)***|(1.41)|(4.72)***|(2.41)**|(2.58)**|(0.35)|
|Gini of Years of Schooling for 15-20 year olds in 1960<sup>1</sup>|0.068|0.085|0.375|0.438|0.005|0.020|-0.844|-0.754|
||(0.18)|(0.23)|(0.84)|(0.98)|(0.02)|(0.05)|(1.83)*|(1.50)|
|Average Years of Schooling in 1960<sup>1</sup>|-0.004|-0.007|-0.016|-0.008|0.028|0.027|-0.123|-0.118|
||(0.10)|(0.18)|(0.29)|(0.15)|(0.56)|(0.50)|(2.28)**|(1.88)*|
|Observations|6454|6454|4094|4094|6108|6108|3430|3430|

# Notes:

1. These variables are for historic male schooling in columns 1-3, and historic female schooling in columns 4-6. T-statistics are in parentheses with standard errors clustered at the state level. Instruments are 1924 state-level migration rate

* significant at 10%; ** significant at 5%; *** significant at 1%

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**Table 4: The Impact of Migration on the Education of Males Aged 12 to 15**

||(1)<br>OLS|(2)<br>2SLS|(3)<br>Ordered<br>Probit|(4)<br>IV-Ordered<br>Probit|(5)<br>Censored <br>Ordered<br>Probit|(6)<br> IV-Censored<br>Ordered<br>Probit|
|---|---|---|---|---|---|---|
|Child is in a Migrant Household|0.151|-0.438|0.013|-0.362|-0.037|-0.512|
||(2.13)*|(1.41)|(0.30)|(2.24)*|(0.55)|(2.20)**|
|Age of Child|3.655|3.779|2.094|2.176|0.203|0.376|
||(6.37)**|(6.62)**|(5.92)**|(6.07)**|(0.21)|(0.39)|
|Age of Child Squared|-0.113|-0.118|-0.061|-0.064|-0.008|-0.014|
||(5.26)**|(5.49)**|(4.64)**|(4.80)**|(0.22)|(0.40)|
|Mother's Years of Schooling|0.205|0.207|0.128|0.129|0.161|0.163|
||(18.56)**|(18.96)**|(22.48)**|(23.39)**|(11.51)**|(11.54)**|
|Proportion of rural households owning land in 1910|-0.066|-0.044|-0.039|-0.024|-0.091|-0.070|
||(1.68)|(1.15)|(1.70)|(1.18)|(3.99)**|(3.11)**|
|Male School Attendance in 1930 (6 to 10 year olds)|0.002|0.008|0.001|0.004|-0.005|0.000|
||(0.42)|(1.27)|(0.15)|(1.26)|(1.09)|(0.10)|
|Gini of Income in 1960|-1.305|-1.155|-0.766|-0.673|-1.560|-1.365|
||(1.76)|(1.43)|(1.65)|(1.46)|(2.90)**|(2.61)**|
|Number of Schools per 1000 population in 1930|-0.154|-0.383|-0.092|-0.239|0.240|0.026|
||(0.96)|(1.84)|(0.99)|(2.12)*|(2.01)**|(0.17)|
|Gini of Male Years of Schooling for 15-20 year olds in 1960|-4.336|-4.251|-2.536|-2.486|-2.625|-2.517|
||(2.92)**|(3.19)**|(3.05)**|(3.19)**|(3.08)**|(2.99)**|
|Average Male Years of Schooling in 1960 for 15-20 year olds|-0.451|-0.471|-0.249|-0.261|-0.300|-0.313|
||(2.90)**|(2.79)**|(3.00)**|(3.31)**|(2.73)**|(2.83)**|
|Observations|6451|6451|6451|6451|3226|3226|

# Robust t-statistics in parentheses clustered at the state level. Instrument is the 1924 state-level migration rate.

Censored ordered probit regressions are carried out on a 50% random sample, and use school attendance as censoring variable. * significant at 5%; ** significant at 1% 36
**Table 5: The Impact of Migration on the Education of Males Aged 16 to 18**

||(1)<br>OLS|(2)<br>2SLS|(3)<br>Ordered<br>Probit|(4)<br>IV-Ordered<br>Probit|(5)<br>Censored <br>Ordered<br>Probit|(6)<br> IV-Censored<br>Ordered<br>Probit|
|---|---|---|---|---|---|---|
|Child is in a Migrant Household|0.151|-1.366|0.041|-0.613|-0.012|-0.653|
||(1.34)|(1.99)*|(0.87)|(2.92)**|(0.16)|(2.81)**|
|Age of Child|-3.945|-3.921|-1.716|-1.709|-1.137|-1.096|
||(0.84)|(0.82)|(0.76)|(0.75)|(9.41)**|(9.11)**|
|Age of Child Squared|0.120|0.119|0.053|0.053|0.033|0.032|
||(0.87)|(0.84)|(0.80)|(0.78)|(8.15)**|(7.70)**|
|Mother's Years of Schooling|0.340|0.337|0.139|0.138|0.149|0.149|
||(12.98)**|(12.52)**|(15.88)**|(15.09)**|(12.38)**|(12.34)**|
|Proportion of rural households owning land in 1910|-0.107|-0.043|-0.039|-0.011|-0.022|0.006|
||(1.98)|(0.71)|(1.71)|(0.55)|(1.05)|(0.31)|
|Male School Attendance in 1930 (6 to 10 year olds)|-0.003|0.007|-0.002|0.002|-0.005|-0.001|
||(0.25)|(0.48)|(0.40)|(0.51)|(1.23)|(0.26)|
|Gini of Income in 1960|-2.173|-1.475|-0.793|-0.495|-0.167|0.142|
||(1.56)|(1.04)|(1.43)|(0.99)|(0.35)|(0.30)|
|Number of Schools per 1000 population in 1930|0.483|0.005|0.209|0.004|0.355|0.149|
||(1.90)|(0.01)|(2.01)*|(0.03)|(2.91)**|(1.08)|
|Gini of Male Years of Schooling for 15-20 year olds in 1960|-5.168|-4.690|-1.878|-1.681|-2.295|-2.117|
||(1.76)|(2.13)*|(1.58)|(2.02)*|(2.84)**|(2.64)**|
|Average Male Years of Schooling in 1960 for 15-20 year olds|-0.574|-0.528|-0.204|-0.185|-0.218|-0.198|
||(1.98)|(1.95)|(1.67)|(1.97)*|(0.034)|(1.92)*|
|Observations|4094|4094|4094|4094|2047|2047|

# Robust t-statistics in parentheses clustered at the state level. Instrument is the 1924 state-level migration rate.

Censored ordered probit regressions are carried out on a 50% random sample, and use school attendance as censoring variable. * significant at 5%; ** significant at 1% 37
**Table 6: The Impact of Migration on the Education of Females Aged 12 to 15**

||(1)<br>OLS|(2)<br>2SLS|(3)<br>Ordered <br>Probit|(4)<br> IV-Ordered <br>Probit|(5)<br>Censored <br> Ordered<br>Probit|(6)<br> IV-Censored<br>Ordered<br>Probit|
|---|---|---|---|---|---|---|
|Child is in a Migrant Household|0.272|-0.225|0.125|-0.205|0.126|-0.307|
||(3.36)**|(0.77)|(2.78)**|(1.34)|(1.53)|(1.04)|
|Age of Child|1.613|1.671|0.748|0.787|0.475|0.477|
||(2.18)*|(2.23)*|(1.81)|(1.90)|(0.21)|(0.39)|
|Age of Child Squared|-0.036|-0.038|-0.010|-0.012|-0.016|-0.016|
||(1.31)|(1.36)|(0.66)|(0.74)|(0.19)|(0.35)|
|Mother's Years of Schooling|0.216|0.217|0.144|0.145|0.196|0.198|
||(25.93)**|(24.32)**|(23.28)**|(23.29)**|(13.30)**|(13.37)**|
|Proportion of rural households owning land in 1910|-0.094|-0.082|-0.060|-0.052|-0.031|-0.020|
||(3.98)**|(2.82)**|(3.75)**|(3.37)**|(1.13)|(0.73)|
|Female School Attendance in 1930 (6 to 10 year olds)|0.002|0.007|-0.000|0.003|-0.005|-0.001|
||(0.34)|(1.00)|(0.13)|(0.90)|(1.09)|(0.11)|
|Gini of Income in 1960|-1.143|-0.887|-0.665|-0.496|0.323|0.561|
||(1.61)|(1.06)|(1.41)|(0.98)|(0.50)|(0.94)|
|Number of Schools per 1000 population in 1930|0.066|-0.112|0.086|-0.032|0.256|0.098|
||(0.53)|(0.62)|(0.95)|(0.29)|(2.42)**|(0.67)|
|Gini of Female Years of Schooling for 15-20 year olds in 1960|-4.061|-3.969|-2.295|-2.236|-0.798|-0.690|
||(4.83)**|(5.11)**|(4.64)**|(4.67)**|(0.76)|(0.68)|
|Average Female Years of Schooling in 1960 for 15-20 year olds|-0.304|-0.305|-0.150|-0.150|0.088|0.087|
||(2.94)**|(2.64)**|(2.57)*|(2.24)*|(0.61)|(0.64)|
|Observations|6107|6107|6107|6107|3053|3053|

# Robust t-statistics in parentheses clustered at the state level. Instrument is the 1924 state-level migration rate.

Censored ordered probit regressions are carried out on a 50% random sample, and use school attendance as censoring variable. * significant at 5%; ** significant at 1% 38
**Table 7: The Impact of Migration on the Education of Females Aged 16 to 18**

||(1)<br>OLS|(2)<br>2SLS|(3)<br>Ordered <br>Probit|(4)<br> IV-Ordered <br>Probit|(5)<br>Censored<br> Ordered<br>Probit|(6)<br>IV-Censored<br>Ordered<br>Probit|
|---|---|---|---|---|---|---|
|Child is in a Migrant Household|0.338|-1.443|0.098|-0.663|0.037|-0.824|
||(2.75)*|(2.21)*|(1.92)|(2.83)**|(0.48)|(3.26)**|
|Age of Child|0.695|1.354|0.291|0.581|-0.236|0.128|
||(0.19)|(0.33)|(0.18)|(0.35)|(1.62)|(0.02)|
|Age of Child Squared|-0.015|-0.034|-0.006|-0.014|0.005|-0.005|
||(0.14)|(0.28)|(0.12)|(0.29)|(1.08)|(0.02)|
|Mother's Years of Schooling|0.418|0.414|0.176|0.176|0.215|0.213|
||(16.33)**|(15.07)**|(18.32)**|(17.76)**|(14.72)**|(14.77)**|
|Proportion of rural households owning land in 1910|-0.140|-0.069|-0.052|-0.021|-0.058|-0.023|
||(2.07)*|(0.83)|(1.95)|(0.89)|(2.27)**|(0.91)|
|Female School Attendance in 1930 (6 to 10 year olds)|-0.027|-0.006|-0.012|-0.003|-0.008|0.002|
||(1.75)|(0.39)|(1.93)|(0.56)|(1.72)|(0.41)|
|Gini of Income in 1960|-3.478|-2.477|-1.477|-1.054|-1.30|-0.751|
||(1.94)|(1.21)|(2.10)*|(1.51)|(2.38)**|(1.38)|
|Number of Schools per 1000 population in 1930|0.396|-0.270|0.184|-0.101|0.153|-0.176|
||(1.23)|(0.59)|(1.45)|(0.70)|(1.54)|(1.34)|
|Gini of Female Years of Schooling for 15-20 year olds in 1960|-7.445|-6.971|-2.987|-2.801|-1.846|-1.638|
||(2.79)**|(2.78)**|(2.85)**|(3.09)**|(2.06)**|(1.85)|
|Average Female Years of Schooling in 1960 for 15-20 year olds|-0.534|-0.537|-0.218|-0.221|-0.153|-0.152|
||(1.76)|(1.56)|(1.86)|(1.76)|(1.37)|(1.33)|
|Observations|3431|3431|3431|3431|1716|1716|

# Robust t-statistics in parentheses clustered at the state level. Instrument is the 1924 state-level migration rate.

Censored ordered probit regressions are carried out on a 50% random sample, and use school attendance as censoring variable. * significant at 5%; ** significant at 1% 39
# **Table 8: Marginal Effects from Ordered Probit Models**

||IV-Ordered<br>Probit<br>Males 1|IV-Censored<br>Ordered<br>Probit<br>2 to 15|IV-Ordered<br>Probit<br>Males 1|IV-Censored<br>Ordered<br>Probit<br>6 to 18|IV-Ordered<br>Probit<br>Females 1|IV-Censored<br>Ordered<br>Probit<br>2 to 15|IV-Ordered<br>Probit<br>Females|IV-Censored<br>Ordered<br>Probit<br>16 to 18|
|---|---|---|---|---|---|---|---|---|
|No Schooling|0.009<br>(0.004)*|0.043|0.038<br>(0.014)**|0.044|0.005<br>(0.004)|0.024|0.030<br>(0.012)**|0.053|
|1 to 5 years|0.121<br>(0.053)*|0.159|0.127<br>(0.041)**|0.123|0.063<br>(0.046)|0.106|0.129<br>(0.042)**|0.144|
|6 years|0.001<br>(0.004)|0.137|0.078<br>(0.030)**|0.080|0.006<br>(0.006)|0.117|0.103<br>(0.041)*|0.115|
|7 to 8 years|-0.100<br>(0.045)*|0.006|-0.002<br>(0.003)|0.001|-0.052<br>(0.039)|-0.002|0.002<br>(0.002)|-0.001|
|9 years|-0.029<br>(0.013)*|-0.225|-0.110<br>(0.038)**|-0.079|-0.021<br>(0.015)|-0.145|-0.106<br>(0.036)**|-0.122|
|10 to 11 years|-0.002<br>(0.001)*|-0.120|-0.094<br>(0.034)**|-0.039|-0.001<br>(0.001)|-0.100|-0.110<br>(0.042)**|-0.046|
|12 years or more|n.a.|n.a.|-0.036<br>(0.013)**|-0.130|n.a.|n.a.|-0.047<br>(0.017)**|-0.145|

n.a. denotes not applicable, as 12 to 15 year olds have not reached 12 years of schooling.

* significant at 5%; ** significant at 1%

40
# **Table 9: Interaction of Migration with Mother's Schooling**

||Males<br>12 to 15|Males<br>12 to 15|Males<br>16 to 18|Males<br>16 to 18|Females<br>12 to 15|Females<br>12 to 15|Females<br>16 to 18|Females<br>16 to 18|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||2SLS|2SLS|2SLS|2SLS|2SLS|2SLS|2SLS|2SLS|
|Child is in a Migrant Household|-0.201|-0.538|-1.244|-1.741|0.138|-0.520|-0.817|-2.211|
||(0.52)|(1.48)|(1.51)|(2.16)*|(0.31)|(1.44)|(0.94)|(2.53)*|
|in a Migrant Household *Mother's Years Schooling|-0.072||-0.042||-0.098||-0.187||
||(1.43)||(0.34)||(1.34)||(1.34)||
|in a Migrant Household * Low Mother's education||0.355||0.796||0.489||1.090|
|||(1.03)||(1.43)||(1.60)||(1.82)|
|Observations|6451|6451|4093|4093|6107|6107|3431|3431|

Robust z statistics in parentheses * significant at 5%; ** significant at 1% Regressions also include all controls in Tables 3-6.

41
**Table 10: Percentage of 12 to 18 year olds doing other activities than schooling**

||Migrant<br>Household<br>Males|Non-migrant <br>Household<br>12 to 15|Migrant<br>Household<br>Males|Non-migran<br>Household<br>16 to 18|t Migrant<br>Household <br>Female|Non-migran<br> Household<br>s 12 to 15|t Migrant<br>Household<br>Female|Non-migrant<br>Household<br>s 16 to 18|
|---|---|---|---|---|---|---|---|---|
|Percent attending school|70.7|74.8|26.9|32.4|67.0|66.9|24.4|26.8|
|Percent who have migrated|3.4|0.1|8.2|0.2|2.4|0.1|3.1|0.3|
|Percent working|39.3|36.8|70.9|69.0|16.5|17.5|33.2|35.4|
|Percent working and not in school|20.6|19.2|61.3|58.7|9.6|11.2|29.0|30.9|
|Percent housework as main activity|1.8|1.7|1.2|1.1|23.2|21.8|45.2|40.7|
|Percent unpaid familyworkers|25.4|21.7|26.3|21.3|9.0|9.0|8.8|10.2|

**Table 11: Marginal Effect of being in a Migrant Household on Other Activities of 12 to 18 year olds**

||Coefficient<br>Males 1|Z-statistic<br>2 to 15|Coefficient<br>Males 1|Z-statistic<br>6 to 18|Coefficient<br>Females|Z-statistic<br>12 to 15|Coefficient<br>Female|Z-statistic<br>s 16 to 18|
|---|---|---|---|---|---|---|---|---|
|Has Migrated themselves|0.022|6.50**|0.073|11.53**|0.018|6.95**|0.023|7.54**|
|Works|0.040|0.22|-0.087|0.47|-0.095|0.87|-0.203|1.59|
|Works and is not in school|0.059|0.82|0.002|0.03|-0.058|0.94|-0.149|1.20|
|Does housework as main activity|0.011|0.62|-0.004|0.31|0.093|2.23*|0.346|3.74**|
|Is an unpaid familyworker|0.010|0.06|0.015|0.08|-0.090|1.11|-0.076|0.85|

Coefficients are marginal effects from probit (Migration) and iv-probit (other activities) Probits contain all controls in Tables 3 through 6 * significant at 5%; ** significant at 1% 42