Our data comes from the 2010 Ecuadorian Population and Housing Census conducted by Ecuador’s National Institute of Statistics and Census (INEC). The census collected information on schooling, remittances, household, and demographic characteristics for Ecuador’s entire population. Household remittance reception was determined by whether any member received money from relatives or friends living abroad during 2010. Since the census was conducted in November 2010, there was a period of 11 months in which respondents could have received remittances intermittently, regularly, or just once. The data does not include information on frequency or amount of reception; thus, the results are interpreted as the average effect on all households that received remittances. To identify households with migrant family members, the survey asked whether any individuals who resided in the household during the 2001 census had moved to another country and had not returned permanently. Follow-up questions inquired about migrants’ age, destination, and purpose, as well as year of emigration.
While there is variation in the effect of remittances across gender and income groups, the largest differences appear to be on the rural/urban dimension. Figure 1 presents the schooling rates for urban and rural regions by remittance reception status and age. Note that before the age of 11, there is little difference in the schooling rates across groups and enrollment rates are close to 100%. However, starting at age 12, the schooling rates rapidly diverge, where urban children that receive remittances have the highest schooling rates, and rural children that do not receive remittances are the ones least likely to attend school. Even when Ecuadorian laws make it mandatory for children to attend school until the age of 14, Fig. 1 shows that this requirement has little impact on the rate at which enrollment rates decrease. Rather, it is the primary/secondary school jump that creates the discontinuity. While the slope of enrollment rates with changes in age is practically horizontal in primary school, it is clearly negative between the ages of 12 and 17 across groups. About 95% of 12-year-olds attend secondary school, but this proportion quickly drops and diverges to around 85% for urban remittance receivers and 60% among rural children who who do not receive remittances. At least in part, these differences may be explained by the increasing opportunity costs that arise from delaying the child’s entry into the labor force. As children age, they become more capable of contributing to their households’ income and taking over domestic responsibilities that often times make them drop out of school. The rapid decline in enrollment rates may be also explained by availability of secondary schools, especially in rural areas. While primary schools are common across Ecuador, the density of secondary schools is significantly lower, and secondary schools are often located in urban centers. Additional transportation costs added to an increased opportunity cost may push the marginal cost of education above its marginal benefit and force children out of school.
Due to the above discussion, our study focuses on the effect of remittances on the school enrollment of children who are between 12 and 17 years old. Although some children may graduate from school after the age of 17, restricting the bound ensures exclusion of non-schooling effects. Our final sample includes 1.7 million individuals.
The endogenous variable of interest, remittances, is instrumented via four variables. First, in line with the literature (Acosta 2011; Coon 2016; Davis and Brazil 2016), we connect an individual’s likelihood of receiving remittances to historical migration networks. The expectation is that children who live in areas more prone to international migration are more likely to receive remittances. Note that migration of a nuclear family member is not a necessary condition to remittance reception, as relatives and friends commonly send income from abroad to one or more nuclear family units. In fact, Table 1 reports that only 32.5% of children who receive remittances have an immediate relative living overseas. To estimate historical migration networks, we use the 2001 census and calculate the proportion of migrants out of the total canton population.Footnote 2 This ensures that the historical migration patterns are not affected by our 2010 migration variable.
Second, we use migrants’ characteristics to determine the probability of remitting without being directly related to the likelihood of education. We use migrants’ age as an instrumental variable because it is potentially exogenous to socio-economic conditions in Ecuador, thus not affecting schooling decisions but having an effect on the probability to remit. We use a dummy variable as the instrument that indicates whether the migrants’ ages at the time of survey were between 20 and 50 years old. In this way, we account for the higher probability of a migrant working and sending remittances if they are part of the working-age population. The identification strategy requires that variation in remittance reception as a result of the migrants’ age is not directly related to education. As a matter of fact, our data shows that children with a migrant in this age group were 20 points more likely to receive remittances, while their probability of education only increased marginally.
Third, following Antman (2011), we capture the main destination countries for Ecuadorian migrants by including dummies that control for migration to either the USA and Canada or Europe. These variables capture the economic conditions by destination and consequently the differing probabilities of remitting. The rationale is that general economic conditions in destination countries determine the likelihood of remittances without directly affecting school enrollment rates at the origin. More detailed information on migrants’ destination would have allowed to include time-varying instruments such as unemployment and GDP per capita (see, e.g., Böhme 2015). However, Table 1 shows that individuals who receive remittances are over 10 points more likely to have a migrant relative in the USA, Canada, or Europe than those who do not receive remittances. Thus, we use destination to explain in part the different probabilities of remitting.
To further validate our instruments, Table 6 in the Appendix presents the results of a correlation analysis between the instruments and the dependent variables. The coefficients indicate a very weak association between school enrollment and the instruments and a much stronger relationship with remittances. We also run a simple probit model to assess the joint likelihood of the instruments in predicting schooling and remittance reception, and report the coefficients and model summaries in Appendix: Table 7. We find that our instruments have a low predictive power for education and a high predictive power for remittances. The likelihood ratio χ2 test is significant in both cases, but the value for the remittances model is 30 times that of the school enrollment model. Similarly, McFadden’s pseudo R2 for the remittances regression is 0.171 while it equals 0.003 for school enrollment. In line with McFadden (1977), who describes a pseudo R2 value between 0.2 and 0.4 as an “excellent fit,” we conclude that the inclusion of instruments offers a considerable larger improvement for remittances than for school enrollment over their individual intercept models.
Our empirical model specifications control for province fixed effects to net out any potential local unobserved externalities that affect both recipient and non-recipient households, like quality of education and availability of schools. We also control for the following child, parent, and household characteristics: age, gender, number of children younger than five in the household, parents’ highest level of education, location (urban/rural), ethnicity, presence of a disability, number of migrants, and wealth. As the the census does not collect information on actual income figures, nor its subgroup remittances, wealth is proxied by an index of 20 equally weighted variables that contain information on access to basic services and technologies, as well as materials, services, and housing conditions.Footnote 3 Although the use of a wealth index is a common control in studying the effect of remittances on household outcomes (e.g.,Acosta 2011; Antón 2010; Dustmann and Okatenko 2014), there is a potential for endogeneity if wealth is not assessed through pre-remittance reception data. Households who have received remittances for some time may be more likely to have moved up in the wealth distribution. To minimize this risk, we use variables that capture long-term socioeconomic status, like access to utilities and dwelling quality, rather than short-term measures, like income. The use of construction quality and asset ownership also allows us to use variables that are more responsive to past wealth than current flows of remittances. Furthermore, when we partition the sample to analyze effects within wealth groups, we use terciles as it is less likely that remittances would have caused households to cross the 33 and 66% thresholds to reach the next group.
Table 1 presents the descriptive statistics for the children, parent, and household variables by remittance reception status. Secondary school enrollment rate is 83%, with the remittance receiving group being 5.7% higher. We see that 7.5% of individuals between 12 and 17 reside in a remittance receiving household, meaning that over 130 thousand secondary school-aged children receive income from abroad. Not surprisingly, individuals who receive remittances are more likely to live in a migrant household and to have more relatives living outside the country. In terms of the migrants’ main destinations, 40% of children who receive remittances see their relatives moving to the USA or Canada, while over 50% migrate to Europe. Table 1 additionally shows that families in the remittances group are relatively wealthier and more educated, with a lower proportion of recipients living in urban areas. In terms of ethnicity, mestizos and whites are over-represented among remittance recipients whereas individuals who identify as Afro-descendants, Montubios, or Indigenous are under-represented. The remainder of this paper estimates remittance marginal effects on schooling across sub-groups while addressing the potential endogeneity of remittances.