The Impact of Economic Migration on Child Development:

Evidence from the Mexican Family Life Survey

By

Elizabeth T. Powers

Draft Date: November 30, 2009

Abstract: Data from the Mexican Family Life Survey are used to estimate the net impact of migration of a household member to the U.S. on the cognitive development of children remaining in Mexico. Single-equation estimates of a ‘value-added’ specification of early child development (ECD) suggest there may be adverse effects of migration on cognitive development when the parent migrates, but no effect when the migrant is a sibling. Effects tend to be larger in absolute magnitude for children who are younger and later-born. An instrumental variables strategy based on historical migration patterns is used to correct for potential biases due to omitted variables and endogeneity of migration with ECD. The IV coefficients of the migration effects are insignificant once the estimated standard errors are adjusted for clustering at the household level. The findings are robust with respect to a wide variety of specification changes.

Acknowledgments: This research was supported by a generous grant from the Inter-American Development Bank. The author benefitted greatly from the comments and suggestions of attendees at the first IDB discussion seminar on Improving Early Childhood Development in Latin American and the Caribbean. Seth Gitter very generously provided his instrumental variables data. I am grateful to Emilie Bagby for help with Spanish-English translation.

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INTRODUCTION

Migration is a critical option for enhancing income for many families in Latin America

and the Caribbean (LAC). The World Bank reports that the LAC is the top remittance-receiving

region in the world, with remittances topping $48.3 billion in 2005 (Fajnzylber & Lόpez, 2007).

In 2004, over 50 percent of Haiti’s GDP was remittances, while remittances to Jamaica,

Honduras, El Salvador, Guatemala, Nicaragua, and the Dominican Republic all surpassed 10

percent of GDP (Fajnzylber & Lόpez, 2007).

Of all countries in the LAC, Mexico is the absolute leader in remittance volume, with a

total of $21.8 billion received (Fajnzylber & Lόpez, 2007).1 Migration to the U.S. for

employment is an important income source for the Mexican economy, and migration is

pervasive. According to Hanson & Woodruff (2003), the U.S. Mexican immigrant population in

the United States equaled nearly 8 percent of the total population of Mexico in 2000. Thus,

many Mexican households are directly and indirectly affected by the opportunity to migrate to

the U.S.

Economic migration may affect early child development (ECD) through several

mechanisms. By increasing household income, remittances are hypothesized to benefit ECD

through enhanced consumption, including of costly education services. Time investment

patterns and allocations among household members must change to accommodate migration.

This imposes additional constraints on choices influencing ECD, which may have a detrimental

effect on child outcomes. However, increased income from migration opportunities also allows

families to specialize in activities, possibly enhancing ECD. Finally, when an adult leaves the

                                                            1 Remittances from the U.S. are believed to have fallen precipitously with the recent global economic crisis.

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household, it is possible that the identity of the de facto household decision maker changes in

ways that potentially benefit children.

Despite the widespread phenomenon of cycle migration in the LAC region and the

growing body of literature on ECD in the region, little is known about the effects of cycle

migration on ECD in the LAC region. Information enabling policies to be better tailored to the

fact of temporary economic migration is of immediate value. Understanding the extent to which

families and children are resilient in the face of major life changes such as migration is important

for better understanding child development and for crafting migration and family support

policies.

This project examines the impact of migration for economic opportunity to the United

States on the early cognitive development of Mexican children. Data from the Mexican Family

Life Survey (MXFLS) are employed to estimate the effect of sending a household member to the

U.S. on the cognitive development of children aged 5-12 who remain behind in Mexico.2

An immediate challenge to any analysis of this issue is that migration and child

investment decisions are jointly made. Endogeneity or simultaneity of the migration and

investment decisions, including the fact that migrant households and members self-select into

this status on the basis of both observed and unobserved characteristics, makes identification of

the causal effect of migration on child development difficult. The empirical strategy in this

paper exploits the unusually rich set of variables provided by the MXFLS and adds a distance-

based instrumental variables strategy. In theory the inclusion of parent cognitive scores goes a

long way towards addressing the problem of selection on unobservables, while an instrumental

                                                            2 The issues discussed in this paper also pertain to other areas of the globe. For example, Bryant (2002) estimates that 4.5 to 7.5 million children in the Philippines, Indonesia, and Thailand, and 10 to 20 percent of all children in the Philippines, have a parent working overseas.

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variables strategy addresses both unobserved heterogeneity and endogeneity/ simultaneity

problems.

The empirical approach is as follows. I first estimate the basic value-added model of

child development (Todd and Wolpin, 2003), modeling a child’s current (wave 2) cognitive

score as a function of her prior (wave 1) score, other ‘family background’ variables (including

parent cognitive scores), and interim shocks and changes hypothesized to affect development.

The latter include the incidence of household members’ migration to the U.S. Inclusion of child

and parent cognitive scores in the specification controls for spurious correlations of the child’s

subsequent cognitive ability with household migration status due to selective migration. In

addition, because historical migration patterns are hypothesized to effect migration but not other

decisions, an instrumental variables strategy based on historical migration patterns further

identifies the causal effect of migration on cognitive ability. Ideally the IV strategy eliminates

bias from residual influences of unobservables (i.e., ‘residual’ once child and parent cognitive

scores are controlled) and identifies variation in migration that is exogenous with respect to

cognitive development.

To preview the findings, single-equation estimates indicate that migration often has a

significant effect on child development, with the level of significance and magnitude of the

effect depending upon the identity of the migrant, and the age, sex, and birth order of the child.

These preliminary findings indicate that children whose parents migrate to the U.S. between

waves 1 and 2 of the MXFLS fall significantly behind their peers in cognitive attainment for age.

However, when the migrant is a sibling, young brothers and sisters develop at the same average

rate as children who do not experience any migration from their household. These migration

effects are usually estimated to be insignificant in the entire sample of children, but are

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significant and large for subsamples of children who are younger and later-born. To correct for

biases induced by unobserved heterogeneity and endogenous migration, instrumental variables

coefficients are also estimated. While the IV estimates are qualitatively similar to the single-

equation findings, the key coefficient estimates are never significant when errors are clustered at

the level of the household.

The paper proceeds as follows. The next section describes the present state of knowledge

about migration and ECD. A theoretical discussion follows that outlines a simple model in

which families make decisions about investments of adult time and purchased goods in children

(resulting in specific developmental attainments of children) and the economic migration of

adults. The key insights for empirical work are discussed. Next, a discussion of the data source

and a preliminary descriptive analysis are presented. After discussing the methodological

approach and empirical implementation, the main findings are presented. The final section

discusses the findings and draws conclusions.

Throughout the paper, the focus is on estimating the net impact of migration activity on

cognitive development, as measured by age-adjusted Raven scores. The particular channels by

which migration may affect ECD (e.g., increased remittances, reduced time spent with children)

are not identified.3

PRIOR LITERATURE ON MIGRATION AND ECD

Little research has been done on the topic of migration and ECD. Fajnzylber and Lόpez

(2007) review the literature and present their own original work on several topics (none of the

                                                            3 The analysis presented below can be readily extended to provide evidence on the impact of migration on other potential ECD measures and potential inputs to ECD such as household income, ‘quality’ time spent with children, and child care.

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studies discussed treat migration as endogenous, with a single exception noted below). They

find that remittances positively affect anthropomorphic measures for Nicaraguan children and

provide some cross-country LAC evidence of increased school enrollment of 10-15-year olds

receiving remittances. Yang and Martinez (2006) find that greater remittances in the Philippines

increase school attendance and reduce child labor. However other research, also on the

Philippines, fails to find a beneficial effect of remittances on schooling (Bryant, 2002). Some

research for Ghana (Guzman, et al., 2007) suggests that migration affects child consumption

patterns in ways that may be beneficial for ECD.

Bryant (2002) draws conclusions from a survey of evidence from the Philippines,

Indonesia and Thailand.

[I]t appears that (i) migration of parents improves the material conditions of the

children left behind, which probably flows through to children’s health and

schooling, and (ii) the social costs are strongly mitigated by the involvement of

the extended family. In the Philippines, but less so in Indonesia and Thailand,

governmental and non-governmental organizations already provide a range of

services for children and migrants.

A handful of studies also provide evidence that children of migrants have better physical

abilities, no worse or better mental health, and are no more likely to engage in risky behavior as

older teenagers (Bryant, 2002).

Evidence on the school attainment of migrants’ children is mixed; while children of

migrants are more likely to attend private school, they are equally likely to be out of school and

there is little effect of migration on achievement as measured by grades (Bryant, 2002). Hanson

& Woodruff (2005), using an instrumental variables approach to address the potential

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endogeneity of migration, estimate the effect of having any household-member migrant to the

U.S. on years of completed schooling of Mexican children ages 10-15. Children (particularly

girls) in migrant households where parents have low education levels complete significantly

more years of schooling. They argue that “the results are consistent with emigration helping

relax household credit constraints on the financing of education.”

There is some evidence that the extended family steps up its caregiving in response to

migration, as the hypothesis that migration is endogenous with child investment suggests.

Changes in caregiving and household arrangements have been documented in a number of

studies (see the citations in Bryant, 2002). Bryant (2002) also finds that “children of migrants

are more likely to have relatives from outside the nuclear family (i.e., cousin, aunt, uncle, or

grandparent) living in the same household, especially if both parents are overseas.”

Finally, there is little direct research on how migrants’ family roles moderate the effects

of migration on children. Surveys conducted in the Philippines indicate that children believe that

the migration of mothers would have a more detrimental impact on them (Bryant, 2002).

A THEORETICAL MODEL AND HYPOTHESES

The centerpiece of the basic model of human capital development is a human capital

production function that specifies the relationships of inputs to outputs (e.g., Behrman, Pollack,

Taubman, 1982). Families optimize with respect to consumption and human capital investments

in their child or children. The issue of migration can be analyzed in a straightforward way by

permitting adults to work outside the home. For simplicity’s sake, adult earnings are assumed to

be the sole income source. Note that the migration decision is not modeled discretely to avoid

complicating the analysis, nor are the financial and psychic costs of migration to the adults made

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explicit. The latter, however, are readily handled by defining the wage to include earnings

opportunities abroad, net of migration costs.

Behrman (1998) lays out the human capital investment problem in the case of multiple

children with heterogeneous characteristics. Without loss of generality, suppose there are k=1,2

children and j=1,2 adults in the family. The family maximizes a welfare function whose

arguments are consumption (c) and the human capital attainment of the children (Hk), W(c, H1,

H2).4 For simplicity, there is no utility from leisure. A human capital production function for

each child is specified Hk=h(t1k, t2

k, ck;ak). The number of units of time investment of adult j in

child k is denoted tjk and ck is consumption of child k. The parameter vector ak summarizes key

characteristics of the child that affect its human capital production, including observed (e.g., age,

sex) and unobserved (‘teach-ability’) characteristics, as well as important characteristics of the

two adults (again, both observed and unobserved) that moderate the transformation of time and

consumption inputs into the realized human capital of child k. The time constraint for each adult

is tj1+ tj

2 + lj<=16, j=1,2, and the household budget constraint is c1+c2<=l1w1+l2w2.

For simplicity, assume the family utility function is separable in consumption. The

problem is to maximize V(H1, H2) subject to the technological, time, and budget constraints. The

first-order conditions from this optimization problem can be manipulated to reveal the

relationship, for each child k, between the substitutability of adult time in the production function

and relative wages, or

.

                                                            4 The possibility that migration itself affects the allocation of consumption to the child (e.g., through intrafamily bargaining) is not permitted.

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For each child k, the optimal time investment contributions of heterogeneous adults are governed

by the substitutability of adult 1 and 2’s time investment in the human capital production

function (valued in utility terms), balanced against their relative wages. In the special case

where time contributions from adults 1 and 2 are perfect substitutes in the human capital

production function, the optimum requires specialization; the higher-wage adult makes no time

investment while the other adult provides all the time investment. In the special case where the

time investments of two adults are perfect complements, both adults contribute time investment,

regardless of their relative earnings power. Therefore, adult 1 is more likely to migrate to the

extent that he or she earns a sufficiently high wage abroad and/or to the extent that the time

investment of adult 2 is sufficiently substitutable for adult 1’s time investment.

For each parent, the distribution of his or her total time investment across the two

children is governed by

.

If the children are homogenous and parental preferences are “equal concern,” then each parent

divides his investment contribution equally among the children. In general, this will not be the

case. When children are heterogeneous, parents allocate their total contribution so as to equalize

the marginal benefit of an additional hour spent with the child. Heterogeneous child and adult

characteristics imply that an adult’s time investment is more productive for some children than

others. Thus, there could well be differential investment across the family’s children by the

same adult. Similarly, the relative value of time versus consumption investments in children

may vary, so that the consumption allocation is unequal across children.5

                                                            5 The relevant first-order condition is   .

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The model indicates that the ECD impacts of migration depend heavily on the family role

of the migrant. In turn, optimizing families take child and adult characteristics into account

when choosing who will migrate for economic opportunity. Migration may have little effect on

ECD if families can undertake compensatory adjustments, either by sending someone whose role

is not critical for ECD (e.g., an extended family member who does not normally reside with the

children) or if other household members are good substitutes for the migrant in the human capital

production functions. Families lacking a rich household roster of adults are predicted to be less

likely to choose economic migration, ceteris paribus. However, there are plausible

circumstances when families with sparse household rosters may also find it optimal to send an

economic migrant and reduce time investment. For instance, in very poor families with

extremely low consumption, the marginal value of an additional unit of consumption investment

may outweigh the developmental loss from reduced time investment in the child.

An obvious and important extension of this model is to multiple periods, as in Cunha and

Heckman (2007). In a dynamic model, human capital investment may be complementary over

time and ‘self-productive’ in the sense that increases in one period make investment more

productive in the next, giving rise to what Cunha and Heckman (2007) term “critical” and

“sensitive” periods of child development. An obvious implication of this extension is that the

timing and length of migration relative to the child’s developmental stage could greatly influence

ECD. In addition, in a dynamic model the relative substitutability of adults with respect to a

child could vary over time. However, the basic notion that having ‘substitute’ adults on the

household roster mitigates detrimental effects of economic migration on ECD, although made

more complex by consideration of the temporal dimension, continues to be a key hypothesis.

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DATA AND DESCRIPTIVE ANALSYIS

Data from the first two waves of the Mexican Family Life Surveys (MXFLS) are used to

evaluate the impact of migration on early child development (ECD). The MXFLS is an ongoing,

longitudinal, nationally representative and comprehensive survey of Mexican households. Two

waves are currently available. The first wave consists of 8,440 households in 150 communities

surveyed in 2002. Follow-up interviews were conducted in 2006 and a third wave of surveying

is planned for 2010. The MXFLS contains detailed data on individuals and households,

including measures of cognitive development, remittances, and temporary migration experiences.

The MXFLS also provides information on the communities where the respondents live.

The MXFLS is an excellent resource for this study for several reasons. Flows of

temporary migration between Mexico and the U.S. are very large, so a substantial number of

households with workers abroad appear in the survey. The MXFLS has an excellent measure of

cognitive development, the Raven figure test. The identities of wave 1 household members who

reside in the U.S. in wave 2 are provided, as is complete information on family relationships that

can be used to infer the household role of the migrant.6

The major insight of the simple theoretical model is that the household roster is a major

determinant of migration decisions in the presence of children, as well as of migration’s potential

impact on ECD. Ceteris paribus, the presence of close-substitute caregivers on the household

roster increases both specialization in human capital investment among the adult household

members and the likelihood of migration of adult roster members with the best overseas earning

potentials. Thus, it is important to characterize the household roster of potential caregivers and

                                                            6 The analysis presented below can be broadened to exploit information contained in the MXFLS on remittance amounts, program participation, community characteristics, other child outcomes, and time investments in children.

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workers in empirical work. Ideally this characterization extends beyond the current household

membership to include those whose time investments and earnings contributions are potentially

available to the household, rather than just observed, but this is impractical given typical data

limitations. Plausible initial assumptions are that men and women are less substitutable in the

child development function than adults of the same sex, that the human capital development

function may vary with the child’s age and sex, and that men in the middle of the domestic

earnings distribution have the greatest relative earning opportunities in the US.

In this section, the construction of the sample is explained and descriptive information is

presented on cognitive scores and sample characteristics according to migration activity.

Identifying Children with Migrating Relatives

The sample consists of children interviewed in wave 1 of the MXFLS who remain at

home in wave 2, but who have a wave 1 household member residing in the US in wave 2. A

wave 2 follow-up module tracks wave 1 household members who are in the U.S. in wave 2.

While this is the best way to identify all household members’ movements to the U.S., a

disadvantage is that families may benefit from non-household members’ migration. This sample

selection likely leads to understated estimates of the overall benefits of migration to ECD. On

the other hand, household member migration is arguably the type of migration likely to have the

greatest impact on the household’s children via time investment, so this type of migration is of

greater policy interest. A drawback is that remittance information is not provided.7

Using this module to identify those affected by migration to the U.S., there are 2,018

individuals in the wave 1 sample (of 34,674 total individuals) who are ‘left behind’ in Mexico by

a wave 1 household member who has migrated to the U.S. by wave 2. Of children with reported                                                             7 There are at least two other sources of information on temporary migration in the MXFLS. It may be possible to combine information from several sources to gain a more detailed picture of migration.

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wave 1 cognitive (Raven) scores ‘left behind’ by a U.S.-migrating family member, in 35 percent

of cases the migrating family member is their parent, their sibling in 67 percent of cases, and

another household member in 7 percent of cases. 11 percent of these children experience

migration by more than one type of household member.

Raven Scores

The indicator of cognitive progress used in this study is the Raven colored progressive

matrices instrument, designed to measure visual reasoning ability. test consists of a series of 18

figures that measure visual reasoning ability. A child is shown a series of two related two-figure

panels. They are then asked to select a figure so as to complete the third panel by composing the

pattern that is most consistent with the first two panels. For children age 5-12, this process is

repeated 18 times (I construct the score as the percentage of correct answers). A chief advantage

of the instrument is that it is designed to be “culture free;” it does not require knowledge of a

particular language or formal schooling on the part of the respondent. It is possible to study

some very young children (ages 5-8) in each wave, and it is also possible to track progress over

time for the same children between waves. An adult version of the test consists of 12 questions

and is administered to persons older than 12.

In the first wave, over 6,300 children are tested, along with over 19,800 adults. In the

second wave, over 5,500 children are tested, along with over 14,800 adults. Thus, it is also

possible to control for parents’ cognitive abilities when predicting children’s ECD. There are

474 sample children ages 5-12 in the ‘left behind’ group with reported wave 1 Raven scores.

246 of these children are tested with the ‘child’ test version in wave 2, while an additional 205

are re-tested with the adult version.

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Figure 1 shows that Raven scores rise steadily with age, from a low of 49 percent correct

answers at the age-5 sample mean to 71 percent correct at age 12 (see the y-axis scale on the

right). The figure also shows (see the y-axis scale on the left) that the inter-wave correlation of

scores rises with age in wave 1, consistent with cognitive ability plateauing and coalescing at

older ages.8 At young ages, the inter-wave correlation is quite low, but it roughly triples by age

9. Differences in Raven scores by sex are modest, with boys slightly dominating girls

throughout the distribution, with the exception of the extreme left tail. There are no obvious sex

differences in the age pattern of inter-wave correlations (not shown).

Figures 2 and 3 present kernel density function plots of wave 2 Raven scores. In an

attempt to remove the strong trend of scores with age, each individual’s score is benchmarked

against the sample average score for the corresponding year of age. In particular, the two waves’

worth of Raven score observations are pooled and the mean determined for each calendar year of

age in the sample. Each observed Raven score is transformed by dividing through by the age-

specific sample mean for the appropriate in-sample calendar year of age. The transformed

‘relative’ scores are interpreted as “cognitive achievement for age.” Scores in excess of one

indicate above-average achievement.

Figure 2 presents age-adjusted Raven score distributions according to migration status.

The top panel shows the distribution for children ages 5-12 in wave 2, while the bottom panel

provides comparable estimates for the sample of adults from the same households. The

distribution of scores in the children’s samples appears to be shifted to the left for migrant-

sending households, indicating lower overall cognitive ability of children in households with a

migrant. The pattern for adults (Figure 2) is similar. Since one doesn’t expect adult’s cognitive                                                             8 This analysis uses only observations of children who have taken the child version of the Raven figure test in wave 2.

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development to be much affected by short-term migration, this suggests that the pattern for

children may simply be a product of adult self-selection for migration and heritability.

Figure 3 presents similar kernel density estimates, using further detail on the identity of

the migrant from the wave 1 household. The top panel contrasts the distributions of children in

migrant households whose sibling or parent migrates. Except at the extremes of the distribution,

the distribution of Raven scores for children appears less favorable when a parent migrates. In

the case of adults (bottom panel), the ‘parent’ group exhibits less density in the lower range of

scores. In contrast to Figure 2, the densities for children are not very similar to the adults.’ It is

not obvious that the pattern for children is simply due to selective migration.

Sample Characteristics by Migration Activity

Table 1 presents characteristics of migrants and non-migrants from wave 1 households

with children. Migrants tend to be much younger (nearly a decade on average), more often male,

and most often the child of the wave 1 household head, rather than the head themselves. They

are very unlikely to be the spouse of the head. Migrants are also less likely to be married and

tend to be better educated, having more often attended or graduated high school. Relatively

more migrants report working in the past 12 months, but migrants are no more likely to have

worked for pay than other sample members. As measured by the relative Raven score, migrants

have lower average cognitive ability than non-migrants.

Table 2 presents select characteristics of the households in which the sample children

reside, according to the household’s migration status. The categories examined are “household

has no migrant”, “household has any migrant,” and the subcategories of the latter group,

“household sends parent,” and “household sends sibling.” The latter three groups are not

mutually exclusive, as households may have multiple migrants.

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Households that send a migrant to the U.S. are large and growing rapidly. Households

with a migrant are larger by around 1.4 members in wave 1 and gain around 0.40 additional

members (relative to other households) between waves 1 and 2. The largest migrant households

are those sending a sibling of a sample child, but households sending parents are also quite large.

Migrant households also tend to be well supplied with adults.9 Over 70 percent of migrant-

sending households have more than two adults in the household in wave 1. Despite the loss of a

migrant, households experiencing migration gain an entire adult member, on average. Thus, it

does not appear that migration leads to a ‘shortage’ of adult household members. Households

with migrants are also relatively ‘rich’ in male family members, which is expected given the

greater propensity of males to migrate. The ratio of male to female adults in wave 1 is 0.86 for

households with a migrant versus 0.64 for those without. In households sending a sibling, the

initial male-female ratio is nearly one. Finally, migrant households live in states with historically

high migration rates. The state-average 1950s migration rate for households sending a migrant is

just over 2.0, in contrast with a rate of just 1.7 for households without migrants.

METHODOLOGICAL APPROACH

The empirical strategy is to implement a “value-added” specification of child

development, augmenting this approach with parent Raven scores and instrumental variables in

order to correct for estimation biases due to unobserved selection on migration and the

simultaneous determination of migration and ECD investment choices.

                                                            9 Throughout the paper, the term ‘adult’ refers to individuals aged 15 or more.

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Todd and Wolpin (2003) argue the value-added model is a relatively reasonable approach

to estimating ECD when choosing among imperfect alternatives. The basic specification is

, , , ,

Child i’s wave 2 relative Raven score (RKit) is modeled as a function of the wave 1 score

(RKi,t-τ) and other observed factors (reduced forms for investment and changes/shocks/events)

that influence the child’s development in the intervening period between two ability measures,

denoted Xi,t-τ. Migration of a household member in the intervening period is interpreted here as

one of these factors. The specification also indicates that intervening unobserved influences at

the child (a) and household (h) level may influence development.

An advantage of the ‘value added’ specification is that the impact of any systematic

unobserved differences between children in migrant and non-migrant households that occur up

through period ‘t-τ+j’ (including any time-invariant, ‘permanent’ household or child

heterogeneity) are reflected in RKt-τ. Remaining concerns about unobservables are thus limited

to non-permanent ‘shocks’ occurring in the intervening period between waves, a roughly three-

to-four year period.

The value-added specification directly addresses concerns about estimation bias due to

selection of migrant households with regard to child cognitive ability. However, other biases are

legitimately concerning, and these problems and potential solutions are now discussed in turn.

A practical concern of implementation of the value-added model is that children’s Raven

scores may be quite noisy. In particular, the earliest observed score, which plays the crucial role

in controlling for selection, can be a quite noisy indicator of the true score for very young

children. Therefore, the initial distribution of child cognitive ability with respect to migration

status may not be well characterized, and this problem may worsen when examining younger

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subsamples. Maternal Raven scores have been shown to be better predictors of children’s later

cognitive attainment than the child’s own early scores, so a straightforward remedy is to include

parent Raven scores as ‘state’ variables in addition to RKt-τ.10

Many variables that should plausibly be included in ‘X’, such as family structure

changes, are endogenously determined with migration, leading to potentially inconsistent

estimates of all the parameters. In addition, other standard ‘inputs’ to cognitive development,

such as schooling, are also likely endogenous. Using instruments for migration that are

uncorrelated with the Xs, do not directly influence ECD, but that directly influence migration,

yields a consistent coefficient estimate for migration. An appropriate IV strategy also addresses

the problem of ‘transitory’ unobserved influences on ECD.

Inlcuding migration as an ‘X’ variable is an ad hoc extension of the value added model

because, as the theoretical discussion indicates, migration, child investments, and ECD are all

jointly determined. The theoretical model indicates that factors that directly influence ECD may

also influence migration. It may therefore be difficult to identify the migration coefficient when

these factors are also included along with migration in an ECD specification. An IV strategy

addresses the potential identification problem that arises from extending the value-added model

to encompass migration. An exogenous shifter of migration aids in the identification of its

coefficient.

                                                            10According to Cunha, Lochner, and Masterov (2005), measures like the Raven score, which they would characterize as “pure cognitive ability” do not predict adult IQ well. They argue that prior to age 5, maternal IQ is a better predictor of age-15 IQ than any available test score and that after age 10, “IQ becomes stable within the constraints of psychometric measurement error.” The scores of the younger children taking the Raven test in this sample could be subject to this problem.

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Finally, a potentially important problem remains. Migration may be correlated over time,

so that families experiencing migration in the past are more likely to have migrants in the

present. If so, causality may run from the ‘current’ migration variable to the initial (‘baseline’)

child cognitive score. Such a relationship makes it difficult to identify the separate influences of

initial cognitive ability and intervening household migration status on current cognitive

development. It is not evident that the particular IV strategy taken in this paper can successfully

address this problem. Because the instruments are based on historical migration patterns, the

instrumented migration variable may well be correlated with baseline ECD, since baseline ECD

has presumably been influenced by past migration. Under the assumption that migration during

the child’s lifetime does not affect adults’ cognitive development, an alternative approach is to

replace the child’s initial Raven score with those of his parents.

IV Strategy

The IV strategy is developed in Woodruff and Zenteno (2001) and also implemented in

Gitter, Gitter, and Southgate (2008). Historical migration rates from the 1950s, by edo (state)

serve as instruments for current migration. Following Gitter, Gitter, and Southgate (2008), the

migration rates are also interacted with the region of Mexico. (These region dummies are always

included in the main ECD equation). The intuition behind the IV is that early migration patterns

were established by geographic barriers, transportation advances established and located prior to

the phenomenon of widespread migration (railroads), and the Federal Bracero program that

brought large numbers of Mexican migrants to the U.S. for the first time. The underlying

hypothesis is that migration networks (knowledge about and practical help with migration)

sprang up in communities that were initially advantaged in migration, and that this explains why

contemporary patterns of migration still strongly mimic earlier historical patterns.

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FINDINGS

Single-Equation Estimates

Single-equation estimates of the value-added specification for children’s Raven scores

are presented in Tables 3, 4, and 5. Tables 3 and 4 present findings on how the key coefficients

evolve as sets of explanators are sequentially added to the specification.

Table 3 presents estimates for the entire sample of children (i.e., 5-12-year-olds in wave

1). All specifications include two dummy variables indicating migration status—whether there

is any migrant from the household and whether a sibling is a migrant from the household.

Column 1 presents the findings from the value-added specification without any additional

explanatory variables aside from the child’s wave 1 Raven score. The effect of migration is

negative at a confidence level exceeding 95 percent, but the estimated effect of sibling migration

is insignificant. As additional explanators are added to the model, the effect of having a migrant

from the household diminishes and becomes insignificant. The effect of a sibling migrant is

insignificant at standard confidence levels for every specification. The child’s Wave 1 Raven

score is always highly significant (at confidence levels exceeding 99 percent). Its effect tends to

decline as additional explanators are introduced to the model. The estimated magnitude of its

coefficient drops by almost one-third when moving from the least to most ‘saturated’

specifications. The (unadjusted) R-squared doubles as explanators are added. The only groups

of explanators that are not found to be jointly insignificantly different from zero at standard

levels of confidence are detailed family structure variables and interim shock and change

variables (i.e., variables in ‘X’).

The blocks of explanatory variables are as follows. “Dwelling characteristics” originally

included indicators of an indoor toilet, tap water access, and whether the home was paid for. The

20  

other variables turn out to be redundant with the presence of an indoor toilet, so only the latter is

included as a dwelling characteristic. The effect of an indoor toilet on ECD is positive.11

The set of child characteristics includes whether an indigenous language is spoken at

home, the grade of school currently attended, the birth order of the child (expressed as dummy

variables indicating first born, second born, etc… up to fifth or higher-born), twin status, only-

child status, dummy variables indicating the presence of one, two, or three or more younger

siblings, whether the mother is reported to be the ‘caregiver’, sex, and a full set of dummies for

child age.12 Cognitive development is significantly slower for children who speak an indigenous

language, the later-born, and those with a greater number of younger siblings. The findings

suggest a declining effect of age on later achievement, although several individual age dummies

have large standard errors.13

Maternal and paternal characteristics consist of age, education, and work status variables,

and parent Raven scores. Parental factors that significantly increase ECD are parent Raven

scores and higher maternal and paternal educational attainment. Parental factors diminishing

ECD are maternal age and whether the father worked in the past year.

Family structure variables consist of the ratio of males to females, the number of

individuals in the household, the number of adults in the household, and a dummy variable

indicating the presence of three or more adults. None of these variables has a significant effect

and the variables are insignificant as a group. Geographic information includes the region of the

                                                            11 Throughout this discussion findings are reported as significant from the ‘saturated’ specification in column (7), which excludes potentially endogenous variables.

12 In practice, the variable reporting ‘mother is caregiver’ appears to capture whether the father is involved with the family.

13 Since the Raven scores are age-normed, this finding suggests that the likelihood of improving cognition for age is declining with age, consistent with cognitive ability coalescing in later childhood.

21  

country and the size of the municipality. Central location has a negative effect on ECD, but

these variables are also insignificant as a group.

Table 4 presents the same specifications, but restricts the sample to young children (those

ages 5-8 in wave 1). In contrast to the first set of findings, the effect of sending a migrant is

estimated to be negative at standard confidence levels across the first seven specifications, and

the coefficient of the sibling migration variable is typically significantly positive. The findings

consistently indicate that non-sibling migration has a negative effect on ECD, while sibling

migration has no effect. The effect of the prior Raven score on the current Raven score is

smaller for the younger sample, as one might expect (i.e., the scores of younger children have

less predictive value), and the coefficient of the Raven score declines by about 40 percent from

the first to the seventh specifications. The effects of other variables are as described above.

To place the magnitude of these changes in perspective, the mean inter-wave change in

relative Raven scores for the young child sample is 0.106. According to the preferred

specification (7), sending a parent to migrate reduces the wave 2 Raven score by almost 0.058,

implying that more than half of expected average cognitive gains are lost.

Table 5 presents single-equation findings for other subsamples and specifications,

including interacting the migration variables with the sex of the child. Columns (1) and (2)

repeat the findings in tables 3 and 4 (column 7) for reference purposes. In column 3, the

specification is extended to include interactions of migration status with child sex for the sample

of young children. The F-tests reported below the coefficient estimates indicate that there are no

significant migration effects for boys or girls, but this is because all the migration coefficients are

now estimated very imprecisely.

22  

Another characteristic of children that may plausibly influence the impact of migration is

birth order. There is evidence from the U.S. that first-born children receive more parental time

investment (e.g., see Price, 2008). Whatever mechanism produces this outcome may also be

protective against the adverse consequences of migration for first-borns. Column (4) applies the

preferred specification to the subsample of those children who are not first-born. Without

additional age restrictions, the effects of migration have large estimated standard errors and are

insignificant. However, when the sample is restricted to young children who are later-born

(column 5), migration by family members other than the sibling is estimated to have a negative

effect due to estimated coefficients for the age-restricted subsample. Finally, sex is interacted

with migration variables for the subsample of younger, later-born children. In this case, F-tests

indicate that there is a negative effect of migration on later-born boys unless the migrant is a

sibling. While migration effects are estimated to be insignificantly different from zero for late-

born girls, the coefficient values are comparable to the young child sample, but the standard

errors are large.

Instrumental Variables Estimates

The appendix table presents first-stage regression coefficient estimates for the

instrumental variables, along with F-tests for the joint exclusion of the instruments from the first-

stage specification. All the other model variables are also included in these specification (i.e.,

dwelling characteristics, child and parent characteristics, detailed family structure, and

geographic information), but their coefficients are not reported. For the entire sample of

children, variables that increase the chance that a household sends any migrant to the U.S. are a

toilet in the house, higher birth order, maternal age, the ratio of adult males to females, and living

in a non-rural area. Factors that reduce the probability of sending any migrant are mother is the

23  

caregiver, mother worked in the past year, and father’s age. Neither cognitive scores for children

nor parents are significant predictors of migration from the household, nor is parents’ education.

The findings reported in the appendix table indicate that the strength of the instrument

set varies widely across subsamples. The instruments are strongest (as indicated by an F-test for

their joint significant in the first stage) for the entire sample and the sample of later-born

children. The instruments’ predictive power is substantially weaker when the sample is

restricted by age.

Table 6 presents the IV estimates. The scale has changed because the instrumental

variables model is estimated as a linear probability model in the first stage. Therefore, the right-

hand-side migration variables explaining ECD are now migration probabilities, not binary

outcomes. In all cases, standard errors of the IV coefficients are quite large when errors are

clustered at the household level, and none of the effects is estimated to be significantly different

from zero. (The migration coefficients are significant at standard confidence levels in the

absence of clustering).

Robustness of the Findings

As mentioned in the methodology discussion, child Raven scores may be subject to error,

particularly at young ages. Therefore, the robustness of the main findings is explored with

respect to dropping the child Raven score and letting parent Raven scores proxy for the child’s

‘baseline’ cognitive ability. Single-equation estimates yield significant migration coefficients

(with a negative effect of migration and no effect if the sibling is the migrant, as usual) for the

young child subsample and the subsample of later-born younger children. IV estimates continue

to be insignificant when errors are clustered.

24  

An additional consideration is the interim ‘X’ variables, which have been excluded from

the main analysis to this point. While these variables may obviously be endogenous with

migration and their inclusion problematic, their exclusion may also introduce problematic biases.

Arguably, the instrumental variables strategy helps to identify the migration coefficients,

although other coefficients may be inconsistently estimated. Inclusion of these variables in the

IV estimation results in coefficients for the migration variables that are larger in absolute

magnitude but still insignificant at standard confidence levels.

Finally, the inclusion of interim ‘shock’ or ‘change’ variables may be problematic, since

these are doubtless endogenously determined with migration. However, the IV strategy may

permit unbiased estimation of the migration coefficients. Column 7 of Tables 3 and 4 indicate

that this group of variables (which consists of indicators for death of a household member,

serious illness of a household member, unemployment of a household member, experience of a

natural disaster or crop failure, an increase in the number of younger siblings, the change in total

household size, the change in the number of household adults, and a decrease in the number of

older siblings in residence) is jointly insignificant in single-equation specifications. Their

inclusion in the model has little effect on the IV estimates.

CONCLUSIONS

Data from the MXFLS are used to estimate the net impact of migration of household

member(s) to the US on the cognitive development of children remaining in Mexico. A simple

theoretical model indicates that migration and ECD are jointly determined. Optimizing families

with children are generally predicted to send household members to the U.S. who have good

potential relative earnings gains and who are either not influential for child development or who

25  

are good substitutes for remaining family members in the household roster in the ECD

production function. Descriptive statistics and first-stage estimation of migration indicate that

households sending migrants are large, relatively more endowed in males and adults, and that

children higher in the birth order are more likely to experience migration from their families.

Single-equation estimates of children’s cognitive development indicate that the identity

of the migrant matters for ECD, as do the characteristics of the child. Generally, where

significant effects of migration are found, children experiencing migration from the household

have lower cognitive gains over the interwave period. However, when it is a sibling who

migrates, there is no difference in developmental gains according to migration status. Adverse

effects of non-sibling migration are largest for younger and later-born children. Further analysis

does not provide strong evidence for differences in the effect of migration according to the sex of

the child.

The value-added specification controls for the distribution of ECD in a way that arguably

addresses the issue of selective migration based on ‘permanent’ child and household

characteristics that also influence ECD. Inclusion of parent Raven scores also controls for the

unreliability of very young children’s scores and further controls for selective migration, since

parent cognitive ability is presumably determined prior to migration from the household.

However, potentially important problems of selection on intervening characteristics, reverse

causality from ECD to migration, endogeneity with intervening observed variables with

migration, and potential endogeneity of the initial measure of ECD with migration remain.

To that end, an instrumental variables strategy that uses historical migration patterns

interacted with region is applied in a two-stage-least squares framework. The IVs are good

predictors of migration from the household so long as the sample is not restricted to the youngest

26  

children. The IV findings are qualitatively similar to the single-equation findings, in that the

estimated coefficient of ‘any migrant’ has a negative sign, and the effect of a sibling migrant is

of opposite sign and of roughly the same absolute magnitude. However, the significance levels

of the IV estimates are not robust with respect to clustering of errors at the household level.

The findings in this paper suggest several directions for additional research. The analysis

can be extended to consider other child development indicators, such as school progress, or

physical health. Further investigation of the exact mechanisms by which migration may effect

ECD (e.g., remittances, time use, consumption allocated to children) appears merited based on

the evidence presented here.

27  

REFERENCES

Behrman, Jere R., "Intrahousehold Distribution and the Family," 1998. Chapter 4 in Volume 1A, Handbook of Population and Family Economics, Rosenzweig and Stark, editors.

Behrman, Jere R., Robert A. Pollack, and Paul Taubman, 1982. "Parental Preferences and Provision for Progeny," Journal of Political Economy 90(1, Feb.), 52-73.

Bryant, John, 2002. Children of International Migrants in Indonesia, Thailand and the Philippines: A Review of Evidence and Policies. Cunha, Flavio and Heckman, James, 2007. “The Technology of Skill Formation.” American Economic Review 97(2,May), pp. 31‐47. Cunha. F.. J. Heckman. L. Lochner, D. Masterov. 2005. "Interpreting the Evidence on Life Cycle Skill Formation." NBER Working Paper 11331. Cambridge. Mass Fajnzylber, Pablo, and J. Humberto Lόpez, 2007. “Close to Home: The Development Impact of Remittances in Latin America.” The International Bank for Reconstruction and Development/ The World Bank. Washington, DC. 89 pp. Gitter, Seth R., Gitter, Robert J., and Southgate, Douglas, 2008. “The Impact of Return Migration to Mexico.” Estudios Economicos 23(1, enero-junio), pp. 3-23. Guzmán, Juan Carlos, Andrew R. Morrison and Mirja Sjöblom, 2007. “The impact of remittances and gender on household expenditure patterns : evidence from Ghana ” in The international migration of women, Andrew R. Morrison, Maurice Schiff and Mirja Sjöblom, editors. Washington, DC : World Bank. Hanson, G. H. and C. Woodruff (2003): “Emigration and Educational Attainment in Mexico” mimeo, University of California, San Diego, California. Price, Joseph, 2008. “Parent-Child Quality Time: Does Birth Order Matter?” Journal of Human Resources 43(1): 240-265.

Todd, Petra E., and Kenneth I. Wolpin. 2003. “On the Specification and Estimation of the Production Function for Cognitive Achievement.” Economic Journal (113, February): pp. F3-33.

Woodruff, C. and R. Zenteno. 2001. Remittances and Microenterprises in Mexico, University of California San Diego (mimeo). Yang, Dean, and Claudia A. Martínez. 2006. “Remittances and Poverty in Migrants’ Home Areas: Evidence from the Philippines.”In International Migration, Remittances, and the Brain Drain, ed. Çag˘lar Özden and Maurice Schiff, 81–121. New York: Palgrave Macmillan.

28  

Figure 1: Average Raven scores and variation by age

Source: Author’s computations from the MXFLS data.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

5 7 9 11

Age

Corr(W1,W2) scores

mean W1 Score

29  

Figure 2a: W2 Raven scores of children, by household ‘sending’ status

Figure 2b: W2 Raven scores of adults, by household ‘sending’ status

0.5

11.

5D

ensi

ty

0 .5 1 1.5 2Wave 2 Raven score

Non-migrant householdMigrant household

Author's computations from 4,911 and 451 observations of children in the MXFLS.

Kernel density estimate0

.2.4

.6.8

1D

ensi

ty

0 1 2 3Wave 2 Raven score

Nonmigrant householdMigrant household

Author's computations from 12,211 and 900 adults in the MXFLS.

Kernel density estimate

30  

Figures 3a: W2 Raven scores of children, by detailed household ‘sending’ status

Figure 3b: W2 Raven scores of adults, by detailed household ‘sending’ status

0.5

11.

5D

ensi

ty

0 .5 1 1.5 2rRaven_W2

Sibling migrant householdParent migrant household

Author's computations from 304 and 149 observations of children in the MXFLS.

Kernel density estimate0

.2.4

.6.8

1D

ensi

ty

0 .5 1 1.5 2rRaven_W2

Sibling migrant householdParent migrant household

Author's computations from 411 and 161 observations of adults in MXFLS.

Kernel density estimate

31  

Table 1: Characteristics of adult migrants and non-migrants Non-migrant adults Migrant adults Age 28544

32.23 (20.20)

747 23.11

(12.20) Male 28593

0.474 (0.499)

748 0.607

(0.489) Child of head 28593

0.404 (0.491)

748 0.644

(0.479) Household head 28593

0.290 (0.454)

748 0.184

(0.388) Spouse of head 28593

0.216 (0.412)

748 0.064

(0.245) Married 28593

0.491 (0.500)

748 0.330

(0.471) No education 28593

0.108 (0.310)

748 0.040

(0.196) Elementary education 28593

0.349 (0.477)

748 0.362

(0.481) Secondary education 28593

0.251 (0.434)

748 0.3

(0.484) High school education 28593

0.109 (0.311)

748 0.131

(0.338) Worked in past 12 months 28593

0.442 (0.497)

748 0.508

(0.500) Any income in past 12 months 28593

0.360 (0.480)

748 0.382

(0.486) Raven score, W1 19242

0.956 (0.475)

580 0.878

(0.444) Notes: Each entry is number of observations with mean and standard deviation (in parentheses).

32  

Table 2: Characteristics of ‘sending’ households

(1) (2) (3) (4) Non-sending

households

Sending householdsHousehold

sends sibling Household

sends parent Total household members, W1

3465 5.28

(1.79)

246 6.70 (2.11)

158 6.91

( 2.18)

90 6.52

(2.05)

Change in total household members, W1 to W2

3465 -0.046 (1.70)

246 0.434

(0.931)

158 0.361

(0.966)

90 0.322

(0.934)

Total number of adults, W1

3465 2.77

(1.28)

246 3.57

(1.50)

158 3.68

(1.54)

90 3.39

(1.50)

More than 2 adults in HH, W1

3465 0.440

(0.497)

246 0.720

(0.450)

158 0.760

(0.423)

90 0.633

(0.484)

Change in number adults, W1 to W2

3465 0.454

( 1.270)

246 1.08

(0.920)

158 1.18

(0.873)

90 0.900

(0.900)

Ratio of male to female adult members, W1

3413 0.638

(0.375)

246 0.839

(0.495)

158 0.900

(0.522)

87 0.810

(0.446)

Historical state migration rate

3463 1.51

(1.62)

246 2.12

(1.97)

158 2.21

(2.02)

90 1.95

(1.87) Notes: Sample in column (1) consists of households that report a children’s relative Raven score for some member in wave 1. Columns (2)-(4) are further restricted as indicated.

33  

Table 3: OLS Estimates of children’s Raven scores

(1) (2) (3) (4) (5) (6) (7) (8) Sending household

-.067** (0.028)

-.064** (0.027)

-.064** (0.028)

-0.060** (0.028)

-0.054*** (0.028)

-.050*** (0.028)

-0.041 (0.028)

-0.039 (0.030)

Sibling is the migrant

0.027 (0.033)

0.031 (0.033)

0.054 (0.034)

0.062*** (0.034)

0.056*** (0.034)

0.054 (0.034)

0.047 (0.034)

0.040 (0.035)

Wave 1 Raven score

0.275* (0.015)

.254* (0.015)

0.235* (0.015)

0.201* (0.015)

0.193* (0.015)

0.193* (0.015)

0.189* (0.016)

0.187* (0.016)

Dwelling characteristics

YES YES YES YES YES YES YES

Child Characteristics

YES YES YES YES YES YES

Maternal characteristics

YES YES YES YES YES

Paternal characteristics

YES YES YES YES

Detailed family structure

YES* YES* YES*

Geographic information

YES* YES*

Shocks & change variables

YES*

R-squared 0.0739 0.0848 0.1024 0.1203 0.1276 0.1294 0.1331 0.1352

Observations 5362 5362 5347 5347 5347 5294 5294 5189

Cases with migration

451 451 450 450 450 448 448 435

Notes: Coefficient estimates are reported with standard errors in parentheses beneath. (*,**,**) indicates significance at the (99th, 95th, 90th) confidence level, respectively. Errors are clustered at the household level. See text for description of variables. All specifications include a constant. Samples consist of children with a child Raven score in wave 1 and a Raven score (child or adult test version) in wave 2. *The group of variables is insignificantly different from zero at standard confidence levels based on F-Test statistic.

34  

Table 4: OLS Estimates of young children’s Raven scores (1) (2) (3) (4) (5) (6) (7) (8) Sending household

-0.80* (0.029)

-0.079* (0.028)

-0.080* (0.028)

-0.073** (0.029)

-0.067** (0.028)

-0.066** (0.029)

-0.058** (0.029)

-0.050*** (0.030)

Sibling is migrant

0.061 (0.038)

0.066*** (0.038)

0.089** (0.038)

0.099** (0.038)

0.090** (0.038)

0.089** (0.038)

0.086** (0.038)

0.071*** (0.039)

Initial Raven score

0.177* (0.015)

0.160* (0.015)

0.147* (0.015)

0.117* (0.015)

0.111* (0.015)

0.108* (0.015)

0.104* (0.015)

0.105* (0.015)

Dwelling characteristics

YES YES YES YES YES YES YES

Other child characteristics

YES YES YES YES YES YES

Maternal characteristics

YES YES YES YES YES

Paternal characteristics

YES YES YES YES

Family structure

YES* YES* YES*

Geographic controls

YES* YES*

Shocks and interim changes

YES*

R-squared 0.0529 0.0677 0.1006 0.1275 0.1397 0.1420 0.1456 0. 1465 Observations 3130 3130 3126 3126 3126 3105 3105 3063 Cases with migration

246 246 246 246 246 245 245 235

Notes: Coefficient estimates are reported with standard errors in parentheses beneath. (*,**,**) indicates significance at the (99th, 95th, 90th) confidence level, respectively. Errors are clustered at the household level. See text for description of variables. All specifications include a constant. Samples consist of children with a child Raven score in wave 1 and a child Raven score in wave 2. *The group of variables is insignificantly different from zero at standard confidence levels based on F-Test statistic.

35  

Table 5: Single-equation Estimates of children’s Raven scores, subsamples and interactions

(1)

(2)

(3)

(4) (5) (6)

All children

Young children

Young children

Later born

children

Later born young

children

Later born young

children Sending household -0.043

(0.029) -0.058** (0.029)

-0.060 (0.388)

-0.063*** (0.037)

-0.076** (0.036)

-0.054 (0.049)

Sending household x male

0.050 (0.054)

-0.045 (0.062)

Sibling is migrant 0.048

(0.034) 0.086** (0.038)

0.086*** (0.049)

0.066 (0.042)

0.109** (0.044)

0.087 (0.057)

Sibling migrant x male

-0.001 (0.068)

0.046 (0.075)

W1 Raven score 0.190*

(0.016) 0.104* (0.015)

0.104* (0.015)

0.175* (0.018)

0.100* (0.018)

0.100 (0.018)

H0: Any migrant + male x any migrant = 0

F(1, 2346) = 1.92

Prob > F = 0.1655

F(1, 1765) = 4.77

Prob > F = 0.0290

H0: Sibling migrant + Sibling migrant x male =0

F(1, 2346) = 2.54 Prob > F

= 0.1111

F(1, 1765) = 5.08

Prob > F = 0.0244

Mean interwave change in relative Raven score

0.078 (0.395)

0.106 (0.397)

0.106 (0.397)

0.082 (0.4023)

0.110 (0.400)

0.110 (0.400)

R-squared 0.1323 0.1450 0.1450 0.1294 0.1508 0.1510 Observations 5294 3105 3105 3739 2254 2254 Notes: Coefficient estimates are reported with standard errors in parentheses beneath. (*,**,**) indicates significance at the (99th, 95th, 90th) confidence level, respectively. Errors are clustered at the household level. All specifications include a constant, child’s initial Raven score, dwelling characteristics, other child characteristics, maternal and paternal characteristics, family structure variables, and geographic controls.

36  

Table 6: Instrumental Variables Estimates of children’s Raven scores, subsamples and interactions

(1) (2) (3) (4) All

children Young

children Later born children

Later born young children

Sending household

-1.71 (1.37)

-1.72 (1.06)

-2.59 (2.18)

-1.48 (1.08)

Sibling is migrant 1.61 (1.75)

1.88 (1.41)

2.66 (2.62)

1.59 (1.34)

W1 Raven score 0.195* (0.022)

0.111* (0.024)

0.190* (0.034)

0.106* (0.026)

Observations 5294 3105 3738 2254 Notes: Coefficient estimates are reported with standard errors in parentheses beneath. (*,**,**) indicates significance at the (99th, 95th, 90th) confidence level, respectively. Errors are clustered at the household level. All specifications include a constant, child’s initial Raven score, dwelling characteristics, other child characteristics, maternal and paternal characteristics, family structure variables, and geographic controls.

 

   

37  

APPENDIX TABLE: First-stage estimates of household ‘sending’ status

(1)

(2)

(3)

(4)

(5) (6)

(7) (8)

All children Young children Later born children Later born young children

Any migrant

Sibling migrant

Any migrant

Sibling migrant

Any migrant

Sibling migrant

Any migrant

Sibling migrant

1950 migration rate

0.030* (0.004)

0.024* (0.003)

0.097 (0.076)

0.005 (0.059)

0.038* (0.005)

0.032* (0.004)

0.035* (0.006)

0.028* (0.005)

1950 migration rate x Border state

-0.022* (0.008)

-0.025* (0.007)

0.104 (0.076)

-0.009 (0.060)

-0.035* (0.011)

-0.036* (0.010)

-0.031** (0.014)

-0.035* (0.012)

1950 migration rate x North state

omitted omitted 0.125*** (0.076)

omitted omitted omitted omitted omitted

1950 migration rate x Center state

-0.010** (0.005)

-0.008*** (0.004)

0.116 (0.758)

0.016 (0.059)

-0.015** (0.007)

-0.014** (0.006)

-0.013 (0.008)

-0.012*** (0.007)

1950 migration rate x Capital

-0.016* (0.062)

-0.080 (0.051)

omitted omitted -0.199** (0.089)

-0.110 (0.078)

-0.160 (0.107)

-0.008 (0.089)

F-statistic for IVs

F(4, 5244)

= 24.76

F(4, 5244) = 21.67

F(4, 3055) = 13.09

F(4, 3055) = 11.82

F(4, 3691) = 22.35

F(4, 3691) = 19.40

F(4, 2206) = 12.20

F(4, 2206) = 10.94

P-value for F-statistic (Prob > F = )

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Adjusted R-squared

0.1106 0.1328 0.1063 0.1486 0.1151 0.1287 0.1120 0.1490

Observations 5294 5294 3105 3105 3739 3739 2254 2254 Notes: Coefficient estimates are reported with standard errors in parentheses beneath. (*,**,**) indicates significance at the (99th, 95th, 90th) confidence level, respectively. Errors are clustered at the household level. All specifications include a constant, child’s initial Raven score, dwelling characteristics, other child characteristics, maternal and paternal characteristics, family structure variables, and geographic controls.