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Abstract
Despite a large growth in domestic and international migration and remittances in recent decades, there are limited works that systematically identify and establish interactions between internal and international migration. Using primary data from new urban areas of Nepal, we identify households that had migrated from rural to urban areas, explore their migration practices and educational investment behaviors, and analyze the effects of international migration and remittances on investment in education. The results show that, despite their lower income and consumption, migrant households that have members abroad have higher human capital investment measured by the level and budget share of expenditure on children’s education and the time their children spend for studying at home than do urban-native and other types of migrant households. Our findings suggest that searching for better education is one important motivation for migrating to urban areas among rural households having members abroad.
Acknowledgements
Ryuichi Tanaka, Yasuyuki Sawada and Ganesh Prasad Pandeya deserve special thanks for valuable suggestions and comments. We acknowledge the research assistance of Dhruba Ghimire and Nabin Acharya, enumerators and local facilitators as well as generous respondents of the survey area during field work.
Notes
1. Data are taken from the Department of Foreign Employment of Nepal (Citation2016). Under a reciprocal agreement between Nepal and India, the Nepalese can enter India through an open border and work there without a visa. Therefore, estimating the annual flow of the Nepalese people to India is difficult. It is estimated that more than 2 million Nepalese migrants live in India.
2. Figures are based on Nepal’s population censuses for 1991, 2001, and 2011.
3. The GoN’s declaration was not implemented immediately. Rather, in July 2014, the GoN declared 72 new municipalities, including 41 urban areas surveyed in this study. An additional 61 municipalities were declared in December 2014 and 26 municipalities in September 2015.
4. During the survey, CBS had released the preliminary results of the 2011 population censusonly up to the district level; therefore, we could not obtain data at the local administrative (village development committee or municipality) level. Instead, we used data from the 2001 population census to prepare the primary sampling frame.
5. We use the natural logarithm of one plus EDUEXP as a dependent variable to avoid missing values while taking the log.
6. Following Deaton and Zaidi (Citation2002), we calculated household income and consumption (see Appendix Table A1). We allowed for the nonlinearity of consumption by including its squared and cubed terms, but those did not generate statistically significant coefficients. Therefore, we proceeded with a linear Engel curve.
7. We assume that school-aged children (5–24 years) are less likely to engage in income-earning activities. Accordingly, their education level should not directly affect the household’s current income but may affect the household’s future income. Therefore, in calculating the years of schooling of the most educated adult member of the household, we excluded school-age children. The results using the years of schooling of the most educated member of the household aged 17 or above (not reported here) are robust to our main results shown in Table , but the R 2 is drastically smaller.
8. We used the natural logarithm of one plus CEDU as a dependent variable to avoid the problem of missing values while taking log.
9. Alternatively, we also estimated the model in the subsample of households with members abroad and not. The results, reported in the Appendix 2 (Table A2), are consistent with our main findings. Similarly, we also defined households having a returned or current international migrant member(s) as household with international migrants and estimated the model in the subsample of households with migrant members abroad and not. Again, the results were found to be consistent with our main findings.
10. According to Kennedy (Citation1981), it would not be correct to interpret the coefficient of the migration dummy (0.873) as migrant households spending 87.3% more expenditure on education than non-migrant households, other things being equal. Instead, following the formula proposed by Kennedy, it would be more accurate to state that the migrant households have 126% [i.e. 100(exp(0.873 − 0.5(0.342)) − 1) = 125.96%] higher expenditure on education than non-migrant households, other things being equal.
11. Three dummies – prior-conflict migrant households (those that arrived in the urban area between 1991 and 1995), in-conflict migrant households (those that arrived in the urban area between 1996 and 2006), and post-conflict migrant households (those that arrived in the urban area after 2006) – are used for migrant households with a dummy for urban-native households (including those arrived in the urban area before 1991) as the base category.