Mixed effects of remittances on child education

During the latter part of the 1990s, the Ecuadorian economy suffered one of its most severe crises with a staggering real GDP contraction of 28% for 1999, when measured in US dollars. Roughly one third of the population fell below the poverty line, and people living in extreme poverty doubled (Acosta et al. 2006). The crisis was characterized by business failures, increased unemployment, the official dollarization of the economy, the freezing of bank deposits, increasing public debt, and a large drop in health and education indicators.

This period of economic turmoil had deep social repercussions, especially in the form of the unprecedented emigration of millions of Ecuadorians to the USA and Europe. Close to 20% of the economically active population left the country in the early 2000s, and almost a quarter of the households had one or both parents migrate (Camacho and Hernández 2008). Camacho and Hernández (2008) also report that, on average, every migrant left behind two children under the age of 18. As a result, many homes became mono parental, children were left under the care of extended kin, or older siblings became the head of household.

The massive migration to North America and Europe produced a large flow of remittances back to Ecuador. Between 1990 and 2002, the volume of remittances entering the country increased by 30% annually. After a short slowdown in 2003, the rate recovered to an annual increase of 22% until 2007. Since 1999, the volume of remittances has been higher than that of foreign direct investment (FDI), and in 2007 alone, remittances were 16 times higher than FDI (Quintana et al. 2014). Between 2005 and 2007, remittances as a share of GDP neared 7%, surpassing the share of GDP generated by the value-added tax, and roughly equal to the share of petroleum, Ecuador’s number one export.

Similar to income, the distribution of remittances across Ecuador is highly unequal. Olivié et al. (2009) indicate that in 2006, the bottom two income quintiles of the population received less than 6% of the total volume of remittances, while the wealthiest 20% received over 34%. Further, the poorest 40% received remittances for an average period of 4.4 years, while the wealthiest 20% received them for almost seven years. Stated differently, poor children who start receiving remittances during the first grade of primary school may stop receiving them before they are able to complete the fifth grade.

In terms of how remittances are being used by recipients, the United Nations Population Fund reported that roughly 90% of remittances received are used for current expenditures (UNFPA and FLACSO 2008). Education and human capital formation expenditures account for 18% of the total.

The sign and magnitude of the change in education with a change in remittances is ambiguous, as it depends on the trade-off between the benefits of education and the household’s consumption. Also, if member a of the household migrates, then w _a =0, but r potentially increases. Thus, the trade-off between lost local parental wages vs. increased remittances makes the sign of Eq. (8) unclear. In addition, the trade-offs may vary across urban and rural settings, as imperfect labor markets, usually more pervasive in rural settings, may affect demand for child labor differently. Further, the relationship may also differ by income levels, age, and gender of the child.

Given the above discussion, we remain agnostic about the expected signs in our empirical results, as they may vary by population subgroups.

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.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.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.

where the subscripts c and k indicate child and household, respectively. α is the counterpart of Eq. (8). The vector z _r includes observable instrumental variables for r _k such that E(ε _eck |z _r =0). We could estimate this model through an IV-probit model by endogenizing remittances, r _k , as a continuous variable. This would approximate (11) with a linear probability model and (10) with a standard probit. However, the IV-probit framework does not yield consistent estimates as it does not respect the non-linearity of the first stage, a procedure that has been called a “forbidden regression” by Wooldridge (2010). A more appropriate method respects the binary nature of r _k to guarantee consistent and efficient parameters (Arendt and Larsen 2006). Thus, the preferred model to estimate (10) and (11) is a bivariate probit model that estimates remittances and education with ε _e and ε _r jointly normally distributed. We treat r _k as a predetermined regressor in a SUR framework as the maximization of the likelihood function still generates consistent parameters (Wooldridge 2010).

Table 8 in the Appendix reports the bivariate probit first-stage results for the effect of our instruments on remittances. The instrument coefficients are consistently significant and positive across all specifications. We confirm the existence of an endogenous relationship between remittance reception and schooling via Wald’s tests for ρ=0, where we reject the null hypothesis of no error correlation. We use specification three for all two-stage specifications, as it includes all instruments and controls, and its estimates provide a lower bound for the effect of remittances on school enrollment.

While the bivariate probit model is preferred, Table 2 also reports the estimates obtained through the standard probit and IV-probit models for comparative purposes.⁴ We present marginal effects, clustered standard errors, and number of observations for each model. The first row presents the effects of remittance receipt on child education for the whole sample. According to our bivariate probit results, the overall effect of receiving remittances increases the probability of school enrollment by 2.6 percentage points, relative to children that do not receive remittances. Notice that when we do not correct for the endogeneity of remittances in the standard probit model, all results are downward biased. When we split the sample by gender, we see that the benefits tend to be higher for boys than for girls. While males’ probability of being enrolled in secondary school increases by 3.4 percentage points, females’ probability increases by only 1.3%, less than half of the probability for boys. In absolute terms though, this translates into an additional 26,400 males and 10,000 females attending secondary school. This positive effect may be explained by a reduced need for recipient households to send children to work, as remittances relax their budget constraints. As we further divide the population, we will find larger and more interesting effects and differences across groups.