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Internal Migration, Selection Bias and Human Development: Evidence from Indonesia and Mexico

Author

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  • Deb, Partha
  • Seck, Papa
Abstract
The aim of this paper is to measure the returns to migration using non-experimental data taking both observed and unobserved characteristics into account. A significant challenge related to migration research and the issues of unobserved heterogeneity is that the standard 2stage least squares estimator (2SLS) is strictly only applicable to situations with linear and continuous treatment and outcomes, both of which are not appropriate for models of migration and many outcomes of interest. Furthermore, migration is not always a binary process given that people migrate to city or non-city locations and some migrants do return. Introducing these multinomial treatment effects means that one cannot rely on standard 2SLS methods. Using panel data from Indonesia (Indonesia Family Life Survey—IFLS) and Mexico (Mexican Family Life Survey— MxFLS) and applying non-linear instrumental variable (Heckman’s treatment effects model) and maximum simulated likelihood models, we measure the impacts of migration on a broad range of variables that include socio economic outcomes such as consumption, nutrition, health status and emotional well-being for adult household members and health and schooling outcomes for children. We find consistent results for both countries that point to significant trade-offs related to migration. We found that migration can greatly improve socio-economic status through increases in income or consumption but can also be detrimental to the health status and emotional well-being of migrants and/or their extended families.

Suggested Citation

  • Deb, Partha & Seck, Papa, 2009. "Internal Migration, Selection Bias and Human Development: Evidence from Indonesia and Mexico," MPRA Paper 19214, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:19214
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    References listed on IDEAS

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    30. Lucas, Robert E B & Stark, Oded, 1985. "Motivations to Remit: Evidence from Botswana," Journal of Political Economy, University of Chicago Press, vol. 93(5), pages 901-918, October.
    31. Sarah Bracking, 2003. "Sending money home: are remittances always beneficial to those who stay behind?," Journal of International Development, John Wiley & Sons, Ltd., vol. 15(5), pages 633-644.
    32. Partha Deb & Pravin K. Trivedi, 2006. "Specification and simulated likelihood estimation of a non-normal treatment-outcome model with selection: Application to health care utilization," Econometrics Journal, Royal Economic Society, vol. 9(2), pages 307-331, July.
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    36. Gustafsson, Bjorn & Makonnen, Negatu, 1993. "Poverty and Remittances in Lesotho," Journal of African Economies, Centre for the Study of African Economies, vol. 2(1), pages 49-73, May.
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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. James Ng, 2018. "Labour migration in Indonesia and the health of children left behind," WIDER Working Paper Series wp-2018-10, World Institute for Development Economic Research (UNU-WIDER).
    2. Mutenje, Munyaradzi & Kankwamba, Henry & Mangisonib, Julius & Kassie, Menale, 2016. "Agricultural innovations and food security in Malawi: Gender dynamics, institutions and market implications," Technological Forecasting and Social Change, Elsevier, vol. 103(C), pages 240-248.
    3. Lu, Yao, 2012. "Household migration, social support, and psychosocial health: The perspective from migrant-sending areas," Social Science & Medicine, Elsevier, vol. 74(2), pages 135-142.
    4. Calub, Renz Adrian, 2014. "Physician quality and payment schemes: A theoretical and empirical analysis," MPRA Paper 66038, University Library of Munich, Germany.
    5. Muhammad Zahid Naeem & Shama Arzu, 2017. "The Role of Remittances on Human Development: Evidence from Developing Countries," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 6(2), pages 74-91, June.
    6. Chan Bibi & Amjad Ali, 2021. "Do Remittances Impact Human Development in Developing Countries? A Panel Analysis of Selected Countries," Journal of Policy Research (JPR), Research Foundation for Humanity (RFH), vol. 7(2), pages 27-42, June.
    7. Kleemans, Marieke, 2015. "Migration Choice under Risk and Liquidity Constraints," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 200702, Agricultural and Applied Economics Association.
    8. Anas, Yulia & Alisjahbana, Armida & Purnagunawan, Rd. M. & Fahmi, Mohamad, 2022. "The Effect of Parental Internal Migration on Children’s Education: Evidence from Indonesia," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 56(2), pages 115-127.
    9. Christopher Harding & Zachary Patterson & Luis F Miranda-Moreno & Seyed Amir Zahabi, 2014. "A Spatial and Temporal Comparative Analysis of the Effects of Land-Use Clusters on Activity Spaces in Three Quebec Cities," Environment and Planning B, , vol. 41(6), pages 1044-1062, December.
    10. Andrew Hodge & Sonja Firth & Tiara Marthias & Eliana Jimenez-Soto, 2014. "Location Matters: Trends in Inequalities in Child Mortality in Indonesia. Evidence from Repeated Cross-Sectional Surveys," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-10, July.
    11. Aaron Gutiérrez & Daniel Miravet, 2016. "The Determinants of Tourist Use of Public Transport at the Destination," Sustainability, MDPI, vol. 8(9), pages 1-16, September.
    12. Cem Oyvat & Mwangi wa Gĩthĩnji, 2020. "Migration in Kenya: beyond Harris-Todaro," International Review of Applied Economics, Taylor & Francis Journals, vol. 34(1), pages 4-35, January.
    13. repec:lic:licosd:41119 is not listed on IDEAS
    14. James Ng, 2018. "Labour migration in Indonesia and the health of children left behind," WIDER Working Paper Series 010, World Institute for Development Economic Research (UNU-WIDER).
    15. Hussain, Nor Ermawati & Shaari, Mohd Shahidan & Mohamad Akhir, Noor Haslina & Chau, Diana Nabila Abdullah, 2018. "Macroeconomic Variables and In-Migration in Malaysia’s Developed States," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 52(1), pages 225-241.
    16. Zahabi, Seyed & Miranda-Moreno, Luis & Patterson, Zachary & Barla, Philippe, 2012. "Evaluating the effects of land use and strategies for parking and transit supply on mode choice of downtown commuters," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 5(2), pages 103-119.

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    More about this item

    Keywords

    The aim of this paper is to measure the returns to migration using non-experimental data taking both observed and unobserved characteristics into account. A significant challenge related to migration research and the issues of unobserved heterogeneity is that the standard 2stage least squares estimator (2SLS) is strictly only applicable to situations with linear and continuous treatment and outcomes; both of which are not appropriate for models of migration and many outcomes of interest. Furthermore; migration is not always a binary process given that people migrate to city or non-city locations and some migrants do return. Introducing these multinomial treatment effects means that one cannot rely on standard 2SLS methods. Using panel data from Indonesia (Indonesia Family Life Survey—IFLS) and Mexico (Mexican Family Life Survey— MxFLS) and applying non-linear instrumental variable (Heckman’s treatment effects model) and maximum simulated likelihood models; we measure the impacts of migration on a broad range of variables that include socio economic outcomes such as consumption; nutrition; health status and emotional well-being for adult household members and health and schooling outcomes for children. We find consistent results for both countries that point to significant trade-offs related to migration. We found that migration can greatly improve socio-economic status through increases in income or consumption but can also be detrimental to the health status and emotional well-being of migrants and/or their extended families.;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • O15 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Economic Development: Human Resources; Human Development; Income Distribution; Migration
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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