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International Migration and the Education of Children: Evidence from Lima, Peru


The issue of whether emigration has consequences for the education of children who remain behind in the country of origin occupies an increasingly prominent place in the agendas of both scholars and policy makers. The conventional wisdom is that the emigration of family members may benefit children by relaxing budget constraints through remittances that can be used to cover educational expenses. However, the empirical evidence on the overall effect of migration is inconclusive. This is due in part to a substantive emphasis on remittances in the literature, as well as the inability of some studies to deal satisfactorily with the endogeneity of household migration decisions in comparing outcomes across migrant and non-migrant households. Using Peruvian data from the Latin American Migration Project (LAMP), we apply an innovative instrumental variable technique to evaluate the overall effect of migration on educational attainment and schooling disruption among the children of immigrants. In contrast to conventional wisdom, our results suggest that a higher household risk of immigration has deleterious consequences for the education of children who remain behind.

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  1. Acosta et al. (2007) estimate per capita household income for migrant households in the counterfactual scenario of no remittances and no migration. Since they lack of data on the household’s income before any migrant left, the authors obtain a predicted pre-migration income level based on a reduced-form specification for the determinants of income estimated on the sample of non-migrant households.

  2. The LAMP is a collaborative research project based at Princeton University and the University of Guadalajara, supported by the National Institute of Child Health and Human Development (see

  3. The final sample of 288 households was arrived at by dropping 525 households with no children in the desired age range, 6 households with one person in the household roster, and 3 because of missing data. In addition, 37 deceased individuals from 31 households were removed from the sample (with no additional loss of households).

  4. Both groups are included because migration may have an impact through monetary and non-monetary channels. All children living in the household, irrespective of their relationship to the head, may be exposed to both monetary and non-monetary consequences of household migration (perhaps with different intensities). Moreover, the head’s children who did not live in the household at the time of the survey may also have been influenced by information flows, networks, or other migration-related factors.

  5. For example, in Peru there are 6 years of primary school and 5 years of secondary school (unlike the United States). The minimum ages for enrollment in the fourth and fifth years of secondary school are normally 15 and 16, respectively. The observed ages of children whose highest completed education is the fourth year of secondary school is 16-20. Given that the completion of four years of secondary school falls within the range of normal progress for 16 year olds who might have been currently enrolled in the fifth year, just 17–20 olds whose highest level of education is the fourth year would be considered as experiencing some form of educational disruption.

  6. The date of household creation is measured as the date of the most recent marriage or union of the household head. If the head is single and children live in the household, the age of the oldest child is used. For single-headed households with no children (57 cases), we used the age of the youngest member living in the household.

  7. In evaluating the study in light of these limitations, one should not lose sight of the fact that household-level migration data for Peru is scarce. The LAMP data remains the best available to achieve our primary objective. Thus, our results provide an important “first analysis” of the welfare effects of migration in Peru. Future data collection efforts should be designed to overcome these limitations.

  8. For example, imagine that households with authoritarian heads tend to send more migrants abroad and have more educated children. If we do not observe the authoritarianism of the head and fail to control for it, we will erroneously infer that there is a positive correlation between migration and child’s educational attainment when looking at the coefficient for migration in a reduced-form equation. Additional omitted variable problems are introduced if one tries to measure the impact of migration using household remittance receipts as the only treatment variable. If migration has effects on children’s education through channels besides remittances, estimates from an education equation with remittances as the treatment variable will be biased.

  9. For migrant households, the cumulative hazard is estimated at the time of first migration. For non-migrant households, the cumulative hazard is calculated at the last period of observation (i.e. at the time of the survey).

  10. Multiple observations are available from about one-third of the households who supplied two or more children in the age ranges that we examined.

  11. The survey does not allow us to link children to their father or mother unless they are a child of the head. Only 16.5% of the children in our sample are not offspring of the head.

  12. At first glance, this percentage may appear rather high (given national-level figures reported above). However, our estimates reflect all past migration experiences, including those of members who no longer live abroad. Our estimates may also be influenced by Lima’s prominence as a port of exit for migrants and a sampling frame that excluded areas that were likely to have negligible numbers of migrants.

  13. This can also be seen in survival curves (not shown, available on request) that plot the percentage of households with heads, spouses, or other members who are non-migrants by age of the household. Each “survival” to migration curve is constructed using a non-parametric estimation of the Kaplan–Meier survivorship function. The survivor curves show that less than 5% of the households experience migration during the first 20 years after the household is created. As the household grows older, survival curves for the head and spouse remain flat. In turn, migration sharply increases for children and other members in the household: by the 50th year of life of the household, the probability of a household surviving to migration of members other than the head or the spouse is only 0.57.

  14. Multilevel models that control for the clustering of observations within households and communities are estimated using the command gllamm in STATA. Details about the estimation procedures can be found in Rabe-Hesketh et al. (2004) and

  15. We also tested for interactions between migration risk and various covariates (e.g. sex, parents’ education) in the preliminary analysis but they were not significant.

  16. We replicated the models estimated in the previous section and defined the first migration episode as the first time that either the head or her/his spouse moved abroad. In this case, there is no significant effect of migration on educational outcomes.


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Appendix: Survival Analysis

Appendix: Survival Analysis

The survivorship function (S) is defined as follows:

$$ S\left( t \right) = { \Pr }(T_{m} > t) $$

where t denotes time and T m denotes the time at which first migration occurs. Moreover, the hazard function or rate, denoted as λ, is defined as the rate at which migration occurs at time t conditional on survival until or later than time t:

$$ \lambda \left( t \right) = \mathop {\lim }\limits_{{{{\Updelta}}t \to 0}} {\frac{{{ \Pr }\left( {t < T_{m} < t + dt|T_{m} > t} \right)}}{{{{\Updelta}}t}}} = {\frac{f(t)}{S(t)}} = {\frac{{S^{\prime } (t)}}{S(t)}} $$

where f(t) is T m ’s probability density function. Related to the hazard rate is the cumulative hazard function, Λ, which is obtained by integrating λ(t) over time:

$$ \Uplambda \left( t \right) = - \ln S(t) $$

Parametric proportional hazard models assert that the hazard rate for the jth subject is:

$$ \lambda \left( {t |x_{j} } \right) = \lambda_{0} \left( t \right)e^{{\left( {x_{j} \beta_{j} } \right)}} $$

where λ0(t), the baseline hazard function, is parameterized, x j is a vector containing k characteristics of individual j, and the coefficients β j are obtained from the data. To parameterize λ 0(t), we need to assign a specific probability distribution to the survival function in Eq. 3. To get a better idea of how the survival function for first migration behaves in the our sample, we arrived at a non-parametric maximum-likelihood estimate of S(t) using the Kaplan–Meier estimator, \( \hat{S}(t) \):

$$ \hat{S}\left( t \right) = \mathop \prod \limits_{{t_{i} < t}} {\frac{{n_{i} - d_{i} }}{{n_{i} }}} $$

where n i is just the number of survivors just prior to time t i and d i is the number of deaths at time t i . After analyzing the Kaplan–Meier migration survivorship function for the household (figure available on request), the Weibull distribution was best suited to describe the pattern of first migration occurrence with the survivorship function S(t) = exp(−t p) and a hazard function which allows the hazard to grow as the household ages:

$$ \lambda \left( t \right) = pt^{p - 1} $$

Our parameterization yields \( \lambda_{0} (t) = pt^{p - 1} \exp (a) \), where a is an extra parameter to estimate and p is the shape parameter for the Weibull distribution. The exponentiated coefficients β x in Eq. 4 are the hazard ratios, which give us the relative hazard faced by a subject who has a certain characteristic denoted by x k .

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Frisancho Robles, V., Oropesa, R.S. International Migration and the Education of Children: Evidence from Lima, Peru. Popul Res Policy Rev 30, 591–618 (2011).

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