Remittances, school quality, and household education expenditures in Nepal

Bansak, Cynthia; Chezum, Brian; Giri, Animesh Article Remittances, school quality, and household education expenditures in Nepal IZA Journal of Migration Provided in Cooperation with: IZA – Institute of Labor Economics Suggested Citation: Bansak, Cynthia; Chezum, Brian; Giri, Animesh (2015) : Remittances, school quality, and household education expenditures in Nepal, IZA Journal of Migration, ISSN 2193-9039, Springer, Heidelberg, Vol. 4, pp. 1-19, https://doi.org/10.1186/s40176-015-0041-z This Version is available at: https://hdl.handle.net/10419/149439 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ ORIGINAL ARTICLE Open Access Remittances, school quality, and household education expenditures in Nepal Cynthia Bansak 1* , Brian Chezum 1 and Animesh Giri 2 * Correspondence: [email protected] 1 Department of Economics, St. Lawrence University, Canton, NY 13617, USA Full list of author information is available at the end of the article Abstract A heightened interest in understanding the remitting practices of immigrants and their impact on a variety of economic indicators has emerged as remittances to developing countries have risen substantially over the past decade. If remittances primarily enhance consumption, they may have no lasting impact on economic growth. However, through asset accumulation and human capital investment, remittances may serve as a vehicle for growth. In this paper, we use the 2010 Nepal Living Standards Survey III (NLSS III) to examine how remittances affect household expenditures on human capital investment. Overall, our findings suggest that at the margin, remittances do contribute to human capital investment, but this effect varies substantially by school quality within Nepal. In addition, our results indicate that internal remittances (remittances from household members migrating internally) have a greater impact on education than do external remittances. We posit that this may be due to a higher value placed on Nepali education by internal migrants as compared to the education needed for foreign job opportunities by migrants abroad. JEL codes: J61, I25, F22, F24, H52, O15 Keywords: Remittance; Education; Migration 1 Introduction A heightened interest in understanding the remitting practices of immigrants and their impact on a variety of economic indicators has emerged as remittances to developing countries have risen substantially over the past decade, in some cases surpassing development assistance flows to developing countries (Amuedo-Dorantes et al. 2005). In Nepal, for example, the World Bank reports that remittances amounted to $5.6 billion (US dollars) in 2013, or about 29% of GDP, while official development assistance and aid totaled approximately $870.6 million (World Bank 2015). Scholars have long been interested in how households use remittance income in developing countries (Lucas 1987). Certainly remittances received by a household relax the budget constraint and may lead to increased consumption. If remittances primarily enhance consumption, they may have no lasting impact on economic growth. However, remittances can foster growth if remittances increase household investment through acquiring more education, starting a small business or financing new agricultural technology (Woodruff, 2007; Nenova et al. 2009; Mendola 2008). Evidence of asset accumulation related to remittances has piqued economists’interest in remittances as a vehicle for economic growth and development. © 2015 Bansak et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http:// creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. Bansak et al. IZA Journal of Migration (2015) 4:16 DOI 10.1186/s40176-015-0041-z In this paper, we use the 2010 Nepal Living Standards Survey III (NLSS III) to examine how remittances affect household expenditures on education. As the direction and size of the effect of remittances on human capital investment is unclear a priori,we first examine the impact of remittance income on educational expenditures at the household level. We then allow for the possibility that remittance funds are more likely to be channeled to schooling where the return to education is highest. Examining differences in school quality at the Nepali district level (a district is akin to a county in most U.S. states), we find that, at the margin, remittances do positively impact human capital investment, and more interestingly, the marginal impact is an increasing function of school quality. Therefore, the use of remittances to invest in human capital is dependent on the returns to education. Building on the work of Kandel and Kao (2000), we also examine the impact of the source of the remittance income on the decision to invest in human capital. In their work, they point out that education acquired in Mexico is more highly valued domestically compared to opportunities abroad. If this is also the case in Nepal, we may see a differential impact by the source of remittance income. The NLSS III dataset allows us to identify whether remittance income comes from a migrant within Nepal or from a migrant located in another country. We categorize the former as internal remittances and the latter as external remittances. Our results provide evidence that the impact on education spending is larger for internal remittances and this difference grows with school quality. 2 Conceptual Framework and Previous Literature The household decision to migrate for our purposes may conveniently be thought of in a cost-benefit framework; the costs incurred by the household include travel and search costs as a household member seeks employment in another community or country and the costs of lost home production contributed by the migrating member. The benefits primarily come in the form of increased income as remittances are returned to the household. Migration occurs if the benefits outweigh the costs. For our Nepali sample in the NLSSIII, the net benefit is positive for the vast majority of households as approximately seventy percent of households have at least one family member absent during the time of the survey. Migrants reveal that there are a number of reasons for remitting, such as consumption smoothing, target saving, altruism, and insurance purposes (Amuedo-Dorantes et al. 2005). Similarly, there are a number of uses for remittances ranging from consumption on daily living expenses, paying back loans, investing in education, paying for health expenses, funding a new business or building residential and nonresidential structures. In this paper, we focus on the decision to invest remittances into human capital and assess whether this choice is impacted by the quality of schools near the household receiving the remittances and whether the migrant sending the funds resides within Nepal or in another country. In terms of our research question of interest, the relationship between remittances and spending on education, there are potentially offsetting effects of migration and remittances on human capital investment. On the one hand, increasing income through remittances may increase investment in children’s schooling by relaxing household budget and capital constraints. Conversely household absenteeism pressures children to work in the home, reducing time for education. Bansak et al. IZA Journal of Migration (2015) 4:16 Page 2 of 19 The impact of remittances on domestic outcomes is important to policy makers as it may have an impact on economic well-being. If the income enhances domestic investment spending on both physical and human capital, it may serve as a vehicle for economic growth. In a macroeconomic growth accounting framework, growth occurs if there are increases in the stock of labor, capital, or an improvement in total factor productivity. If remittance income is invested in education, this may increase the quantity and quality of workers, increasing total factor productivity and enhancing economic growth. However, if remittances enable family members left behind to increase consumption and stop working or labor quality deteriorates through brain drain, remittances may act as a drag on growth. Thus, the net effect of remittances on economic growth is not necessarily positive. Not surprising, the empirical evidence on the impact of remittances on human capital expenditures is mixed. Adams et al. (2008) find that households in Ghana do not spend disproportionately from remittance income on education, food and other products. Similarly, Robles and Oropesa (2011) find that a higher risk of migration tends to have harmful effects on education for children remaining in the household using Peruvian data. Meanwhile, Edwards and Ureta (2003) find a significant impact of remittances on school retention in El Salvador. For the Philippines, Yang (2008) finds that positive exchange rate shocks affect remittances and lead to greater human capital investments. In a broader study, Acosta et al. (2007) examined 11 Latin American countries and found that the effect of remittances on education is often restricted to specific groups within a population. Some studies have examined remittance spending for boys and girls separately. The results from these studies also vary widely. Acosta 2006 finds that young girls and boys are more likely to be in school in households receiving remittances in El Salvador. Bansak and Chezum (2009) find that although remittances increase the probability the young are in school, girls benefit relatively less than boys, but suffer less harm from the absenteeism caused by a household member’s migration to fund remittances. In more recent work, Antman (2012) finds that parental emigration in Mexico significantly increases educational attainment for girls. Migration of parents, however, lowers the probability of boys completing junior high school and of boys and girls completing high school. Part of the differences in these findings may be directly related to the country under study and variation in the returns to education domestically and abroad. Understanding differences in local economic conditions for households and how these affect household decisions may shed light on the use of remittance funds. Rational economic agents will spend on those goods that, for a given price, provide the greatest household utility and invest where the rate of return is highest. Empirical evidence that suggests that households receiving remittances invest less may simply be indicating that these households are systematically located in areas with economic conditions that drive a low rate of return on human capital investment. Along these lines, recent research examines differences in spending patterns by the source of remittance income focusing on differences between internal and external remittances. If migrants return not only remittances but knowledge of new markets and/ or technologies, one may reasonably expect changes in spending patterns relative to non-migrants (Mendola 2008). If the knowledge returned home differs systematically Bansak et al. IZA Journal of Migration (2015) 4:16 Page 3 of 19 between internal and external migrants, then differences in spending patterns may arise. Empirical work that tests for different uses by source of funds generally finds a wide range of results. Costaldo and Reilly (2007) find that households receiving external remittances spend more at the margin on durable goods and utilities but less on food as compared to households that receive no remittances. Mendola (2008) finds that agricultural households engaging in international migration are more likely to invest in high-yield seed technology as compared to households with internal migrants or no migrants. She argues that high-yield seed, although producing higher average productivity also has a greater variance in output. International migration serves as a more effective insurance mechanism for these households, separating them from households who are not insured and therefore fall back on relatively low-yield, low variance seed. Adams et al. (2008) also look for differences based on the source of remittance income but find no differences in Ghana. In terms of investments in education specifically, empirical evidence suggests that households are more likely to invest in education when the funds are derived from internal sources. Kandel and Kao (2000) find that the migration of household individuals and families in Mexico positively impacts children’s aspirations to someday migrate to the U.S. for work. Aspirations to work in the U.S., in turn, actually negatively impact potential investment in education in Mexico because migrants abroad find that Mexican education is not highly valued in the U.S. Taylor and Mora (2006) also use data from Mexico and find that households who receive internal remittances invest relatively more in education compared to those who receive external remittances. Thus, for our study of Nepali migration, we might also expect systematic differences in education if remittances are generated internally versus externally. In particular, if Nepali education is valued differentially abroad as compared to domestically, then we should see corresponding differences in education spending across remittance sources. By examining pass rates of the “School Leaving Exam”and receipt of the “School Leaving Certificate”at the district level, we find suggestive evidence that there is a differential in spending remittance income on education based on the source of the remittances. (Nepal’s school leaving exam is akin to New York State's Regents Exam, where those high school students passing the exam receive a Regents Degree.) 1 Table 1 examines the relationship between where remittances come from and the SLC Pass Rate. The top panel gives the top ten districts ranked by frequency of receiving internal remittances in our data. The final column shows the district rank by SLC passing rate. The average rank for these districts was 16.6. The bottom panel shows similar data for the top ten external remittance-receiving districts and their associated rank in SLC pass rate. Here we see an average rank of 37.8, which is affected substantially by one outlier (Gulmi). These results suggest that internal remittances go to households in districts having higher SLC passing rates or a better (lower) SLC pass rate ranking. Further note that seven of the top ten internal remittance districts share a border with Kathmandu (as indicated by the * in the table) with only Udayapur being located more than one district away from Kathmandu. Taken together, internal remittances go to households near the country’s capital and to districts with higher quality schools as measured by achievement on the SLC exam. Bansak et al. IZA Journal of Migration (2015) 4:16 Page 4 of 19 For those receiving funds from abroad, shown in the bottom panel of Table 2, we do not observe of similar pattern of funds and SLC rankings. In essence, external funds are not disproportionately going to households in districts in Nepal with relatively higher SLC pass rates. Specifically, no top external remittance receiving districts share a border with Kathmandu, and all have more than three districts to cross in reaching Kathmandu. (Districts are not of uniform size, we offer this only as an approximation to the distance from Kathmandu.) Ultimately, it appears that internal migrants in Nepal are from districts where the SLC pass rate (or returns to education) is relatively high, while external migrants are from districts where the SLC pass rate (or returns to education) is relatively low. 3 Methodology We explore how remittances affect human capital formation, treating the level of remittances first as exogenous and then allowing for the possibility that remittance amounts may be endogenous to the investment decision. Specifically, we estimate the relationship between household human capital expenditures and the level of remittance and report results estimated via ordinary least squares (OLS) and instrumental variables (IV). We control for the quality of the educational infrastructure through the performance of students in the district on the Nepal School Leaving Exam; individuals who pass the exam are awarded a “School Leaving Certificate”(SLC throughout the remainder of the Table 1 Remittance source and school performance District Internal remittance rank SLC pass rate rank Makwanpur* 1 32 Kavrepalanchok* 2 10 Dolakha* 3 27 Kathmandu* 4 2 Sindhupalchok* 5 11 Lalitpur* 6 4 Bhaktapur* 7 5 Udayapur 8 7 Chitwan 9 18 Bara 10 50 Average Rank 16.6 District External Remittance Rank SLC Pass Rate Rank Syangja 1 41 Gulmi 2 1 Doti 3 59 Morang 4 35 Kailali 5 48 Arghakhanchi 6 30 Mahottari 7 61 Dailekh 8 55 Kaski 9 12 Rupandehi 10 36 Average Rank 37.8 *Indicates a border with Kathmandu Bansak et al. IZA Journal of Migration (2015) 4:16 Page 5 of 19 Table 2 Tests of difference in means for subsamples Remit (n= 3178) Noremit (n= 2810) Difference Standard error Education spending 14.22 19.53 −5.314*** −1.528 Remittance income 104.39 SLC pass rate 57.96 60.88 −2.929*** −0.397 Number remitters 1.62 Urban 0.279 0.402 −0.123*** −0.0121 Share ages 4-7 0.0831 0.0767 0.00642* −0.00323 Share ages 8-15 0.193 0.183 0.01 −0.00515 Share ages 16-64 0.576 0.61 −0.0341*** −0.00651 Share elderly 0.0776 0.0664 0.0113* −0.00461 Farm income 1.916 2.935 −1.019 −1.236 Business income 60.62 80.52 −19.89 −19.54 Wage income 36.52 75.90 −39.37*** −4.10 Married couples 0.77 1.082 −0.312*** −0.0175 Household production 5.581 5.086 0.495** −0.19 External (n= 1718) Internal (n= 1989) Difference Standard Error Education spending 14.69 14.07 0.625 −1.029 Remittance income 129.9 54.61 75.26** −28.05 SLC pass rate 57.8 57.82 −0.0191 −0.471 Number remitters 1.732 1.817 −0.0853* −0.0356 Urban 0.274 0.265 0.00862 −0.0146 Share ages 4-7 0.0921 0.0771 0.0150*** −0.0043 Share ages 8-15 0.205 0.182 0.0227*** −0.00675 Share ages 16-64 0.557 0.586 −0.0290*** −0.00842 Share elderly 0.0697 0.0844 −0.0146* −0.00601 Farm income 2.984 1.436 1.548 −2.239 Business income 36.17 74.59 −38.42 −28.95 Wage income 30.31 39.35 −9.04** −3.06 Married couples 0.692 0.814 −0.123*** −0.0239 Household production 5.487 5.771 −0.285 −0.25 Noremit (n= 2810) Internal (n= 1989) Difference Standard Error Education spending 19.53 14.07 5.460** −1.897 Remittance income SLC pass rate 60.88 57.82 3.062*** −0.451 Number remitters 0 1.817 −1.817*** −0.0209 Urban 0.402 0.265 0.137*** −0.0138 Share ages 4-7 0.0767 0.0771 −0.000401 −0.00353 Share ages 8-15 0.183 0.182 0.00112 −0.0057 Share ages 16-64 0.61 0.586 0.0238** −0.00735 Share elderly 0.0664 0.0844 −0.0180*** −0.00534 Farm income 2.935 1.436 1.499 −1.304 Business income 80.52 74.59 5.926 −24.54 Wage income 75.89 39.35 36.54*** −5.02 Married couples 1.082 0.814 0.268*** −0.0195 Household production 5.086 5.771 −0.685** −0.223 Bansak et al. IZA Journal of Migration (2015) 4:16 Page 6 of 19 paper.) Using this variable we are able to estimate the impact of remittances on education spending and also at the same time show how the impact of remittances are dependent on the returns to education. Our main specification is: Educationid ¼β0þβ1Remiti ðÞþβ2SLC Pass Rated ðÞþβ3SLC Pass RatedRemiti ðÞ þXiβþεi; where the dependent variable measures the spending on Education for a given household (in 1000s of rupees); Remit measures the total level of remittance income for households that reported receiving any remittances and includes in-kind transfers (in 1000s of rupees); SLC Pass Rate is the district level (denoted by subscript d) passing rate on the school leaving exam for 2006 (in percentages); SLC Pass Rate*Remit is the interaction between the passing rate and the level of remittances; and finally Xrepresents a vector of covariates. In this framework, β 1 and β 3 are our parameters of interest and allow for the marginal impact of remittances to vary with school quality at the district level. It is likely the impact of remittances may be biased because of endogeneity and the direction of this bias is uncertain. First, high ability individuals may have better prospects when migrating (either internally or externally). If these high ability individuals are more likely to send their children to school, then we should expect an upward bias in the estimated impact of remittances on education spending. On the other hand, if negative job shocks not accounted for in the model push individuals to migrate, the estimated impact of remittances on human capital choices will be biased downward. A negative job shock likely increases the probability of migrating to remit while at the same time the absenteeism induces more household members into home production and out of school. To account for the potential endogeneity, we estimate the model via instrumental variables methods. Table 2 Tests of difference in means for subsamples (Continued) Noremit (n= 2810) External (n= 1718) Difference Standard Error Education spending 19.53 14.69 4.835* −2.035 Remittance income SLC pass rate 60.88 57.8 3.081*** −0.482 Number remitters 0 1.732 −1.732*** −0.0198 Urban 0.402 0.274 0.129*** −0.0145 Share ages 4-7 0.0767 0.0921 −0.0154*** −0.00383 Share ages 8-15 0.183 0.205 −0.0216*** −0.00608 Share ages 16-64 0.61 0.557 0.0528*** −0.00755 Share elderly 0.0664 0.0697 −0.00339 −0.00511 Farm income 2.935 2.984 −0.0487 −1.613 Business income 80.52 36.17 44.35*** −11.8 Wage income 75.89 30.31 45.58*** −53.284 Married couples 1.082 0.692 0.391*** −0.0203 Household production 5.086 5.487 −0.4 −0.225 *p< 0.05, ** p< 0.01, *** p< 0.001 Bansak et al. IZA Journal of Migration (2015) 4:16 Page 7 of 19 4 Data Using data from the NLSS III, we construct an initial sample of 5,988 households who responded to the survey. The NLSS III contains information on the extent, nature and determinants of poverty in Nepal, covering different aspects of household welfare including education and remittances. The survey asks each household to provide information on the amount spent on education for each family member currently in school. The variable Education Spending is the sum of all local education expenditures identified in the survey. As shown in the top panel of Table 2, education spending averages about 14,200 rupees (orabout140U.S.dollars)forthosewho receive remittances. This sample of 3,178 household is our main sample that we use in our regressions and computations of marginal effects, which are later presented in Tables 3 and 4. For remittances, each household is asked if they have received a remittance from any individual in the last twelve months and the origin of the funds. We define respectively Remittance, Internal Remittance and External Remittance Income as the total of all cash and in-kind remittances from all sources, internal sources and external sources respectively (all stated in 1000s of rupees). In the sample, among 5,988 households, 3,178 (roughly 53%) received some remittance income. Of the 3,178 receiving a remittance, 1,989 (47%) received funds from at least one internal source, 1,718 (41%) received funds from an external (outside Nepal) source and 529 received funds from both an internal and external source. Remittances have the impact of relaxing household budget constraints, but at the cost of removing a household member and thereby lowering household production. The NLSSIII identifies each individual who provides remittances to a household and allows us to calculate the total number of remitters (Number Remitters), which we use as a control for absenteeism. For our sample of remitters, the average number of remitters is 1.62 individuals per household. Additionally, we include the variable Household Production, defined as the monetary value of goods produced for home consumption within the household, to control for the importance of home production to the household. The average value produced for households receiving remittances, our primary sample, is just over 5,500 rupees per year. To estimate the impact of remittance income on education expenditures, we include variables controlling for household income from three sources and household age structure and family structure. For each household we define Farm Income,Business Income and Wage Income as the total family net income from each of the three sources mentioned (each divided by 1,000). Presumably, income proxies for worker productivity in the household and therefore is an indicator of ability. In the NLSSIII, farm income averages 1,900 rupees, business income average 60,600 rupees and wage income generates about 36,500 rupees per year for households in our sample of remittance recipients. Thus, business and wage income are predominant sources of income and dwarf household production. Age composition of the household is captured through the variables Share Ages 4–7, Share Ages 8–15,Share Ages 16–64 and Share Elderly. Individuals between 4 and 15 are school aged, with the younger group (ages 4 to 7) attending early education, while those between 8 and 15 are eligible for secondary education in Nepal. Individuals aged Bansak et al. IZA Journal of Migration (2015) 4:16 Page 8 of 19 Table 5 Education spending as a function of remittance income and school quality, internal vs. external remittance (1) (2) (3) (4) Variables OLS IV OLS IV Internal remittance −0.119* −0.619*** (−1.95) (−3.04) SLC pass rate* 0.003** 0.013*** Internal remittance (1.97) (2.99) External remittance 0.002 0.139 (0.09) (0.88) SLC pass rate* 0.0001 0.00005 External remittance (0.45) (0.03) SLC pass rate 0.214*** −0.231 0.292*** 0.005 (3.81) (−1.54) (5.27) (0.03) Number remitter 1.466** 17.838*** 1.018 −8.200 (2.51) (2.83) (1.35) (−0.99) Urban household 15.243*** 11.444*** 16.691*** 9.016* (9.75) (4.92) (8.53) (1.69) Share 4 to 7 27.072*** 35.276*** 22.444*** 26.393** (5.97) (3.97) (5.37) (2.16) Share 8 to 15 32.390*** 43.753*** 33.341*** 33.816*** (11.21) (5.07) (9.95) (3.21) Share 16 to 64 23.257*** 23.181*** 25.114*** 19.994* (5.65) (3.13) (4.71) (1.95) Share elderly 9.658*** 11.659 10.309*** 21.662** (3.45) (1.51) (2.97) (1.99) Farm income −0.005 −0.007 0.002 0.006 (−1.01) (−0.95) (0.51) (0.88) Enterprise income 0.000 0.001 0.009** 0.004 (0.71) (0.87) (1.97) (0.70) Wage income 0.000** 0.000*** 0.000** 0.016 (2.07) (2.70) (2.24) (0.92) Married couples 1.752* 4.904*** 1.032 1.504 (1.93) (2.77) (1.06) (0.67) Home production −0.126** −0.244* −0.177*** 0.014 (−2.39) (−1.93) (−2.65) (0.07) Constant −30.310*** −42.524*** −34.292*** −16.082 (−5.41) (−2.97) (−5.21) (−1.11) Observations 1,989 1,989 1,718 1,718 Hansen’s J 0.216 0.0101 p-value 0.642 0.920 First stage F Remittance income 0.302 2.153 Number remitter 1.054 2.326 SLC*Remittance 7.056 4.339 Robust t-statistics in parentheses *** p< 0.01, ** p< 0.05, * p< 0.1 Bansak et al. IZA Journal of Migration (2015) 4:16 Page 15 of 19 household. This is reinforced by the finding that internal remittances have a greater impact on education as compared to external remittances.Althoughwearenotabletodirectlytest the proposition, if households who receive remittances from internal migrants are more likely to migrate internally then we should expect a greater investment in education if domestic employers value a Nepali education more than foreign employers. Our results appear to be most consistent with the findings of Kandel and Kao (2000) and Taylor and Mora (2006), who find that internal remittances have a greater impact on investment in education. However, other studies, for example Adams et al. (2008), Mendola (2008) and Costaldo and Reilly (2007) find either no difference or that external remittances have a greater impact. Acosta et al. (2007) conclude that the findings for any one country may not be generalized outside that country or specific region. These mixed conclusions force researchers to ask what local characteristics cause differences in the use of remittance income. Ultimately, our results show us that a lack of homogeneity in economic conditions both within and across developing countries creates an additional challenge in understanding the role of remittances on household choices and therefore development outcomes. Endnotes 1 Nepal’s Ministry of Education periodically collect records on the number of individuals who take the exam and the number that pass each year. We obtained 2006 data on the passing rate at the district level. For each district we are able to measure the SLC Pass Rate as the percentage of individuals taking the SLC exam who passed. There is wide variation in performance across the districts. While the mean is roughly 57%, performance ranges from a low 18% to a high of 84%. 2 Table 3 also presents the first stage F-values for our endogenous variables and other diagnostics such as Hansen’s J and the corresponding p-value. Table 6 Marginal effect of remittances on education by SLC pass rate percentiles Internal External Percentile SLC pass rate OLS IV OLS IV Minimum 18.00 −0.073** −0.393*** 0.004 0.140 Jajarkot (0.04) (0.13) (0.01) (0.13) 10 41.79 −0.014** −0.095** 0.007 0.141 Mahottari (0.01) (0.04) (0.01) (0.09) 25 47.49 0.001** −0.023 0.008 0.141* Kailali (0.00007) (0.03) (0.01) (0.08) 50 55.81 0.022** 0.081** 0.009** 0.142** Dhanusa (0.01) (0.05) (0.00) (0.07) 75 72.32 0.063** 0.288*** 0.011** 0.143** Kavre (0.03) (0.11) (0.01) (0.06) 90 83.71 0.092** 0.430*** 0.013 0.143** Kathmandu (0.05) (0.15) (0.01) (0.06) Maximum 84.18 0.093** 0.436*** 0.013 0.143** (Gulmi) (0.05) (0.16) (0.01) (0.06) Standard errors in parentheses *** p< 0.01, ** p< 0.05, * p< 0.1 Bansak et al. IZA Journal of Migration (2015) 4:16 Page 16 of 19 Appendix 1 Table 7 presents results for the specifications presented in Table 2 using the full sample of 5,988 households. Table 7 Education spending as a function of remittance income and school quality (1) (2) (3) (4) (5) (6) Variables OLS IV OLS IV OLS IV Remittance income 0.001 0.048 0.001 0.127*** −0.020 −0.450*** (1.33) (1.13) (1.42) (2.60) (−1.49) (−2.76) SLC pass rate* 0.0004 0.009*** Remittance (1.54) (3.30) SLC pass rate 0.406*** 0.226*** 0.381*** −0.093 (6.23) (4.29) (5.70) (−0.81) Number remitter 0.902* 2.809 1.069** −19.902*** 0.840* −3.070 (1.89) (0.59) (2.23) (−3.52) (1.87) (−0.40) Urban household 20.350*** 20.437*** 15.611*** 9.040*** 15.510*** 13.659*** (9.99) (7.46) (9.81) (4.39) (9.74) (4.77) Share 4 to 7 28.213*** 32.199*** 27.438*** 16.064*** 27.514*** 28.912*** (6.11) (6.29) (6.00) (2.88) (6.01) (3.02) Share 8 to 15 47.142*** 48.835*** 46.433*** 32.191*** 46.689*** 54.098*** (6.08) (6.33) (6.07) (4.46) (6.11) (4.71) Share 16 to 64 35.611*** 36.803*** 32.240*** 22.153*** 32.312*** 34.391*** (7.08) (6.98) (6.98) (4.47) (7.00) (4.28) Share elderly 21.080*** 20.452*** 17.932*** 18.389*** 18.372*** 28.238*** (4.72) (4.20) (4.34) (3.10) (4.46) (3.39) Farm income −0.014 −0.010 −0.012 −0.004 −0.012 −0.014 (−1.43) (−1.17) (−1.42) (−0.41) (−1.41) (−1.31) Enterprise income 0.007 0.007 0.007 0.009 0.007 0.008 (1.31) (1.21) (1.33) (1.63) (1.33) (1.45) Wage income 0.023** 0.022** 0.019** 0.021** 0.020** 0.023** (2.54) (2.57) (2.19) (2.41) (2.20) (2.35) Married couples 8.647 10.540** 8.776 0.457 8.852 12.208** (1.60) (2.02) (1.62) (0.10) (1.64) (2.02) Home production −0.271*** −0.348*** −0.213** 0.006 −0.212** −0.212* (−2.83) (−3.35) (−2.36) (0.07) (−2.35) (−1.71) Constant −32.949*** −39.680*** −53.410*** −13.469 −52.171*** −31.640** (−3.65) (−3.66) (−4.53) (−1.35) (−4.41) (−2.44) Observations 5,988 5,988 5,988 5,988 5,988 5,988 Hansen’s J 0.927 7.954 0.799 p-value 0.629 0.0187 0.371 First stage F: Remittance income 7.751 6.741 6.741 Number remitter 16.54 20.14 20.14 SLC*Remittance 7.028 Robust t-statistics in parentheses *** p< 0.01, ** p< 0.05, * p< 0.1 Bansak et al. IZA Journal of Migration (2015) 4:16 Page 17 of 19 Appendix 2 Table 8 provides the first stage estimates of the instrumental variable regressions Table 8 First stage estimates for IV estimation (2) (3) (4) Variables Remittance SLC Pass Rate * Remit IV SLC pass rate 0.710 168.156*** −0.002* (0.61) (2.65) (−1.82) Urban household 25.933 1945.060 −0.091** (1.17) (1.54) (−1.96) Share 4 to 7 −40.249 −2673.915 −0.688*** (−0.74) (−0.66) (−3.48) Share 8 to 15 60.421 1820.742 −0.532*** (0.49) (0.29) (−3.16) Share16 to 64 29.739 1164.112 −0.340*** (0.44) (0.30) (−2.10) Share elderly 82.661 2436.116 0.088 (0.47) (0.28) (0.46) Farm income −0.149 −7.627 0.0003 (−1.15) (−1.21) (−1.01) Enterprise income 0.002 0.097 0.00001 (0.47) (0.38) (0.25) Wage income 0.213 10.224 0.0003 (0.86) (0.83) (−1.48) Married couples 13.685 544.111 0.014 (0.50) (0.40) (0.50) Home production 1.999 86.977 0.009** (0.88) (0.76) (1.95) Class A −49.823 −600.459 −0.354*** (−0.83) (−0.21) (−4.74) Class B −65.691** −3744.811** −0.074 (−1.81) (−2.13) (−1.41) Class C −41.605 −2338.092 −0.131*** (−1.40) (−1.60) (−2.91) Migration rate −83.973 −4244.088 −0.036 (−0.81) (−0.86) (−0.31) Constant 26.553 −4581.797 2.130 (0.82) (−2.04) (11.86) Observations 5,988 5,988 5,988 Robust t-statistics in parentheses *** p< 0.01, ** p< 0.05, * p< 0.1 Bansak et al. IZA Journal of Migration (2015) 4:16 Page 18 of 19 Competing interests The IZA Journal of Migration is committed to the IZA Guiding Principles of Research Integrity. The authors declare that they have observed these principles. Acknowledgement The authors would like to thank the anonymous referee. Responsible editor Amelie F Constant. Author details 1 Department of Economics, St. Lawrence University, Canton, NY 13617, USA. 2 Department of Economics, Berry College, 2277 Mount Berry Hwy, NW Mount Berry, GA 30149, USA. Received: 5 January 2015 Accepted: 17 June 2015 References Acosta P (2006) Labor Supply, School Attendance, and Remittances from International Migration: The Case of El Salvador. In: World Bank Policy Research Working Paper No. 3903 Acosta PA, Fajnzylber PR, Lopez H (2007) The Impact of Remittances on Poverty and Human Capital: Evidence from Latin American Household Surveys. 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