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Left home high and dry-reduced migration in response to repeated droughts in Thailand and Vietnam

Abstract

We investigate the extent to which droughts impact migration responses of rural households in Thailand and Vietnam, as well as the role of underlying mechanisms such as risk aversion and socioeconomic status that may affect the response. We combine longitudinal household data from the Thailand Vietnam Socio Economic Panel from 2007 to 2017 with monthly high-resolution (0.5°) rainfall and temperature data from the Global Historical Climatology Network Version 2 and the Climate Anomaly Monitoring System (respectively) to characterize droughts at the sub-district level. We find that exposure to two consecutive years of moderate drought decreases household participation in migration by 5.3 percentage points (11.1% of the mean). Analysis of underlying mechanisms highlights the role of socioeconomic status in shaping these reductions in migration. While drought exposure substantially erodes socioeconomic status and increases risk aversion, it is deteriorations in consumption and assets per capita that appear to shape the negative effect of droughts on migration. This pattern is consistent with the presence of an environmentally induced poverty trap, whereby exposure to climate shocks directly and indirectly reduces rural population mobility, particularly among poorer households.

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Notes

  1. In particular, many of these studies emphasize the role of the environment in migration decisions due to environmentally induced agricultural losses. Also, see Cai et al. (2016); Jessoe et al. (2018).

  2. Similarly, scholarship on subjective perceptions of environmental change and their role in shaping livelihoods is limited (Meïjer-Irons, 2015; Cullen & Anderson, 2017).

  3. Additionally, see Feng et al. (2010), Dillon et al. (2011), Gröger and Zylberberg (2016), Riosmena et al. (2018), Jessoe et al. (2018), and Quiñones (2019).

  4. For example, empirical studies that investigate the impact of natural disasters find evidence for increasing risk aversion (Cameron & Shah, 2015; Chantarat et al., 2015; Cassar et al., 2017; Liebenehm et al., 2018), decreasing risk aversion (Bchir & Willinger, 2013; Hanaoka et al., 2018; Kahsay & Osberghaus, 2018), or an inconsistent effect (Eckel et al., 2009; Willinger et al., 2013).

  5. Although it is possible that exogenous stochastic shocks also deteriorate human capital stocks, we assume that this is not the case, in the interest of simplicity. Therefore, we show that the shock-induced deteriorations in human capital are not necessary to influence risk attitudes and migration decisions.

  6. See Carter and Lybbert (2012) in Burkina Faso, Gröger and Zylberberg (2016) in Vietnam, and Henry et al. (2019) in Jamaica.

  7. See Liebenehm et al. (2018) in Thailand and Vietnam, Chantarat et al. (2015) in Cambodia, Said et al. (2015) in Pakistan, and Cameron and Shah (2015) in Indonesia.

  8. For example, see Hoang et al. (2001) and Nguyen et al. (2007) in Vietnam, and Munshi and Rosenzweig (2016) in India.

  9. The northeastern part of Thailand has enjoyed little of the development success that has characterized much of the country over the past three decades, where poverty rates decreased from 67% in 1986 to 7.6% in 2017. The majority of households in the northeast still depend on rain-fed agriculture, despite frequent exposure to droughts. Over this period, Thailand has experienced a sequence of political power struggles including two military coups, and it is currently ruled by an (unstable) pro-military government (World Bank, 2019). As a whole, Vietnam has also made impressive improvements in terms of poverty reduction, income diversification, and education. However, considerable differences in living standards and economic behavior persist between ethnic minority and majority groups, with minority groups relegated the lower end of the wealth scale (e.g., Baulch & Vu, 2010; Imai et al., 2011; Kang & Imai, 2012; Nguyen et al., 2017b). Despite numerous poverty-reduction programs for ethnic minorities, the gap between majority groups remains (e.g., Montalvo & Reynal-Querol, 2005; Tung & Waibel, 2014; Kozel, 2014).

  10. For details on the sampling procedure, see Hardeweg et al. (2013a).

  11. The survey also stipulates that the individual did not commute between locations.

  12. With respect to precipitation data, the GPCC applies climatological infilling for regions following the validated approach of Yamamoto (2000) when an entire 5 degree grid is without any station for the analysis month given. In the TVSEP study area and across the time from 1948 and 2016, the number of stations per 0.5 degree grid per month varies between 0 and 3, with an average of 0.643. After interpolation, there are no missing values in the processed GPCC data. With respect to temperature data, the GHCN + CAMS applies an anomaly interpolation approach that is described and tested by Fan and van den Dool (2008), which also does not result in any missing values in the processed data we use to calculate our SPEI measures.

  13. The SPEI is a standardized variable; the average value is 0, and the standard deviation is 1. This enables comparisons of SPEI values over time and space.

  14. Mediation should not be conflated with moderation, which refers to changes in the direction or strength of relationships and is typically tested via treatment exposure and control variable interactions. In the interests of assessing the heterogeneity of results, we also explore the variation in impacts associated with baseline values of socioeconomic status and willingness to take risk. This analysis should be regarded as complementary and secondary to the analysis of direct and indirect effects.

  15. We restrict our modeling exercises to migration outcomes and mediators beginning in 2008 because measures of willingness to take risk are not available in the 2007 round of the survey.

  16. Thailand and Vietnam have programs that provide financial aid to households to cope with natural disasters. We acknowledge that transfers made under these could influence our analysis. In our sample, on average (taken over six survey waves) 16.7% and 25.3% of households in Thailand and Vietnam, respectively, report having received assistance through programs such as the Social Relief for disaster and Contingency fund for pre-harvest starvation. However, the mean values of transfers are only 205 USD and 114 USD, annually for Thailand and Vietnam, respectively. These are very low in comparison to their annual incomes; therefore, we believe that transfers would not affect our core findings.

  17. Alternatively, we consider households’ baseline characteristics measured during the 2007 wave (\({X}_{it = 2007}\)) as part of our robustness checks (see Sect. 6, specifically column 5 of Table 15 in the Appendix). These characteristics include household size, average age, maximum education, dependency ratio, female ratio, share of workforce engaged in agriculture, ethnic minority, and home country. By using baseline characteristics, we attempt to reduce the possibility that time-varying characteristic trends related to drought exposure bias the results, though we also test the sensitivity of this approach to the inclusion of time-variant controls (see Sect. 6, specifically columns 6 and 7 in Table 15 in the Appendix).

  18. Beyond the conditional exogeneity that we assume in estimating the direct effects of drought, we additionally assume sequential unconfoundedness in the course of exploring the mediating indirect impacts. The identifying assumption is sequential unconfoundedness, which requires that any selection inherent to the mediator of interest be limited to observable characteristics (as opposed to selection on unobservables), as well as the absence of omitted variables delineating the relationship between drought and migration decisions, conditional on controls.

  19. The proportional change is computed as the \(\mathrm{Total Effect}/\mathrm{Mean}\), which is \(- 0.034/0.478=- 0.071\) in this case.

  20. Note that the analysis uses the analytical sample of 17,365 observations (or 3473 households from 2008 to 2017). Hence, the summary statistics provided in Table 5 are different than those provided in Table 2.

  21. Note that a natural log transformation is applied to the consumption, food, income, and productive asset outcome measures. As a result, the coefficients are interpreted in percent terms (as opposed to percentage point units).

  22. Note that, for ease of interpretation, the drought exposure variable here is a binary indicator of two consecutive droughts that is equal to one when the household was exposed to droughts in t and in t−1, and is zero otherwise. The results presented here are consistent with those using the triple interaction between droughts in t, droughts in t-1, and the mechanism measure in Table 10 in the Appendix.

References

  • Acharya, A., Blackwell, M., & Sen, M. (2016). Explaining causal findings without bias: Detecting and assessing direct effects. American Political Science Review, 110(3), 512–529.

    Google Scholar 

  • Akgüç, M., Liu, X., Tani, M., & Zimmermann, K. F. (2016). Risk attitudes and migration. China Economic Review, 37, 166–176.

    Google Scholar 

  • Angrist, J. D., & Pischke, J. S. (2009). Mostly harmless econometrics: An empiricist’s companion. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Barrett, C. B., & Carter, M. R. (2013). The economics of poverty traps & persistent poverty: Empirical & policy implications. Journal of Development Studies, 49(7), 976–990.

    Google Scholar 

  • Barrett, C. B., Carter, M. R., & Chavas, J. P. (2019). Introduction. In Barrett, C. B., Carter, M. R., & Chavas, J. P. (Eds.) The economics of poverty traps. National Bureau of Economic Research. Chicago: University of Chicago Press.

  • Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182.

    Google Scholar 

  • Baulch, B., & Vu, H. (2010). Poverty dynamics in Vietnam, 2002–2006. Washington, DC: World Bank.

    Google Scholar 

  • Bchir, M. A., & Willinger, M. (2013). Does the exposure to natural hazards affect risk and time preferences: Some insights from a field experiment in Perú. Working Paper 13–04. LAMETA, University of Montpellier.

  • Beine, M. A., & Jeusette, L. (2018). A meta-analysis of the literature on climate change and migration. CESifo Working Paper 7417.

  • Beine, M., & Parsons, C. (2017). Climatic factors as determinants of international migration: redux. CESifo Economic Studies, 63(4), 386–402.

    Google Scholar 

  • Benonnier, T., Millock, K., & Taraz, V. (2019). Climate change, migration and irrigation. Paris School of Economics Working Paper 2019–21 (halsh-0210798).

  • Black, R., Bennett, S. R. G., Thomas, S. M., & Beddington, J. R. (2011). Migration as adaptation. Nature, 478, 447–449.

    Google Scholar 

  • Black, R., & Collyer, M. (2014). Populations ‘trapped’ at times of crisis. Forced Migration Review 45, 52–56. https://www.fmreview.org/crisis/black-collyer.

  • Bohra-Mishra, P., Oppenheimer, M., Cai, R., Feng, S., & Licker, R. (2017). Climate variability and migration in the Philippines. Population and Environment, 38(3), 286–308.

  • Botzen, W. J. W., Aerts, J. C. J. H., & van den Bergh, J. C. J. M. (2009). Dependence of flood risk perceptions on socioeconomic and objective risk factors: Individual perceptions of climate change. Water Resources Research, 45(10).

  • Brown, P., Daigneault, A. J., Tjernström, E., & Zou, W. (2018). Natural disasters, social protection, and risk perceptions. World Development, 104, 310–325.

    Google Scholar 

  • Cai, R., Feng, S., Oppenheimer, M., & Pytlikova, M. (2016). Climate variability and internal migration: The importance of the agricultural linkage. Journal of Environmental Economics and Management, 79, 35–151.

    Google Scholar 

  • Cameron, L., & Shah, M. (2015). Risk-taking behavior in the wake of natural disasters. Journal of Human Resources, 50(2), 484–515.

    Google Scholar 

  • Carrico, A. R., & Donato, K. (2019). Extreme weather and migration: evidence from Bangladesh. Population and Environment, 41(1), 1–31.

    Google Scholar 

  • Carter, M. R., & Barrett, C. B. (2006). The economics of poverty traps & persistent poverty: An asset-based approach. Journal of Development Studies, 42(2), 178–199.

    Google Scholar 

  • Carter, M. R., & Lybbert, T. (2012). Consumption versus asset smoothing: Testing the implications of poverty trap theory in Burkina Faso. Journal of Development Economics, 99(2), 255–264.

    Google Scholar 

  • Cassar, A., Healy, A., & von Kessler, C. (2017). Trust, risk and time preferences after a natural disaster: Experimental evidence from Thailand. World Development, 94(2017), 90–105.

    Google Scholar 

  • Cattaneo, C., & Peri, G. (2016). The migration repsonse to increasing temperatures. Journal of Development Economics, 122, 127–146.

    Google Scholar 

  • Chantarat, S., Cheng, K., Minea, K., Oum, S., Samphantharak, K., & Sann, V. (2015). The effects of natural disasters on households’ preferences and behaviours: Evidence from Cambodian rice farmers after the 2011 mega flood. ERIA Research Project Report, 2013–34, 85–130.

    Google Scholar 

  • Chuang, Y., & Schechter, L. (2015). Stability of experimental and survey measures of risk, time, and social preferences: a review and some new results. Journal of Development Economics, 117, 151–170.

    Google Scholar 

  • Cullen, A. C., & Anderson, C. L. (2017). Perception of climate risk among rural farmers in Vietnam: Consistency within households and with the empirical record. Risk Analysis, 37(3), 531–545.

    Google Scholar 

  • Dang, N. A., Tackle, C., & Hoang, X. T. (2003). Migration in Vietnam: A review of information on current trends and patterns, and their policy implications. Paper presented at the Regional Conference on Migration, Development and Pro-Poor Policy Choices in Asia, on 22–24 June 2003 in Dhaka, Bangladesh.

  • DeWaard, J., Hunter, L. M., Mathews, M., Quiñones, E. J., Riosmena, F., & Simon, D. H. (2020). Operationalizing and empirially identifying trapped populations. Mimeo.

  • Dillon, A., Mueller, V., & Salau, S. (2011). Migratory responses to agricultural risk in northern Nigeria. American Journal of Agricultural Economics, 93(4), 1048–1061.

    Google Scholar 

  • Dohmen, T., Falk, A., Golsteyn, B., Huffman, D., & Sunde, U. (2017). Risk attitudes across the life course. Economic Journal, 127(605), F95–F116.

    Google Scholar 

  • Dohmen, T., Falk, A., Huffman, D., & Sunde, U. (2010). Are risk aversion and impatience related to cognitive ability? American Economic Review, 100(2010), 1238–1260.

    Google Scholar 

  • Dohmen, T., Falk, A., Huffman, D., Sunde, U., Schupp, J., & Wagner, G. G. (2011). Individual risk attitudes: measurement, determinants, and behavioral consequences. Journal of the European Economic Association, 9(3), 522–550.

    Google Scholar 

  • Du, Y., Park, A., & Wang, S. (2005). Migration and rural poverty in China. Journal of Comparative Economics, 33(4), 688–709.

    Google Scholar 

  • Dustmann, C., Fasani, F., Meng, X. & Minale, L. (2017). Risk attitudes and household migration decisions. Discussion Paper Series No. 10603. IZA Institute of Labor Economics.

  • Eckel, C. C., El-Gamal, M. A., & Wilson, R. K. (2009). Risk loving after the storm: A Bayesian-network study of Hurricane Katrina evacuees. Journal of Economic Behavior and Organization, 69(2), 110–124.

    Google Scholar 

  • Entwisle, B. (2007). Putting people in place. Demography, 44(4), 687–703.

    Google Scholar 

  • Entwisle, B., Williams, N. E., Verdery, A. M., et al. (2016). Climate shocks and migration: An agent-based modeling approach. Population and Environment, 38(1), 47–41.

    Google Scholar 

  • Falk, A., Becker, A., Dohmen, T., Enke, B., Huffman, D., & Sunde, U. (2018). Global evidence on economic preferences. Quarterly Journal of Economics, 133(4), 1645–1692.

    Google Scholar 

  • Falk, A., Becker, A., Dohmen, T., Huffman, D., & Sunde, U. (2016). The preference survey module: A validated instrument for measuring risk, time, and social preferences. IZA Discussion Paper 9674.

  • Fan, Y., & van den Dool, H. (2008). A global monthly land surface air temperature analysis for 1948–present. Journal of Geophysical Research, 113(D1), 1–18. https://doi.org/10.1029/2007JD008470

    Article  Google Scholar 

  • FAO. (2016). “El Nino” event in Viet Nam. Agriculture, food security and livelihood needs assessment in response to drought and salt water intrusion. Ha Noi, Viet Nam: Food and Agriculture Organization of the United Nations.

  • Feng, S., Krueger, A. B., & Oppenheimer, M. (2010). Linkages among climate change, crop yields and Mexico–US cross-border migration. Proceedings of the National Academy of Sciences USA, 107, 14257–14262.

    Google Scholar 

  • Foresight: Migration and global environmental change. (2011). Final project report The Government Office for Science London, UK

  • Goldbach C., & Schlüter, A. (2018). Risk aversion, time preferences, and out-migration. Experimental evidence from Ghana and Indonesia. Journal of Economic Behavior and Organization, 150(2018), 132–148.

  • Gray, C., & Bilsborrow, R. (2013). Environmental influences on human migration in rural Ecuador. Demography, 50(4), 1217–1241.

    Google Scholar 

  • Gray, C. L., & Mueller, V. (2012). Natural disasters and population mobility in Bangladesh. Proceedings of the National Academy of Sciences USA, 109(16), 6000–6005.

    Google Scholar 

  • Gröger, A., & Zylberberg, Y. (2016). Internal labor migration as a shock coping strategy: Evidence from a typhoon. American Economic Journal: Applied Economics, 8(2), 123–153.

    Google Scholar 

  • Guiso, L., Sapienza, P., & Zingales, L. (2018). Time varying risk aversion. Journal of Financial Economics, 128(3), 403–421.

    Google Scholar 

  • Hanaoka, C., Shigeoka, H., & Watanabe, Y. (2018). Do risk preferences change? Evidence from the Great East Japan earthquake. American Economic Journal: Applied Economics, 10(2), 298–330.

    Google Scholar 

  • Hardeweg, B., Klasen, S., & Waibel, H. (2013a). Establishing a database for vulnerability assessment. In S. Klasen & Waibel, H. (Eds.), Vulnerability to poverty. Theory, measurement and determinants, with case studies from Thailand and Vietnam. Basingstoke, England; New York: Palgrave Macmillan.

  • Hardeweg, B., Menkhoff, L., & Waibel, H. (2013b). Experimentally validated survey evidence on individual risk attitudes in rural Thailand. Economic Development and Cultural Change, 61(4), 859–888.

  • Heintze, H.J., Kirch, L., Küppers, B., Mann, H., Mischo, F., Mucke, P., et al. (2018). World risk report 2018. Focus: child protection and children’s rights. https://reliefweb.int/report/world/world-risk-report-2018-focus-child-protection-and-childrens-rights.

  • Henry, M., Spencer, N., & Strobl, E. (2019). The impact of tropical storms on households: Evidence from panel data on consumption. Oxford Bulletin of Economics and Statistics, 0305–9049.

  • Hoang, K., Baulch, B., Le, D., Nguyen, D., Ngo, G., & Nguyen, K. (2001). Determinants of earned income. In: J. Haughton, Haughton, D., & Nguyen, P. (Eds.), Living standards during an economic boom: the case of Vietnam. UNDP and Statistical Publishing House.

  • Hornbeck, R. (2012). The enduring impact of the American Dust Bowl: Short- and long-run adjustments to environmental catastrophe. American Economic Review, 102(4), 1477–1507.

  • Huget, J., & Chamratrithirong, A. (2011). Thai migration report 2011: Migration for development in Thailand—overview and tools for policy makers (Rep.). Bangkok: International Organization for Migration.

  • Hunter, L. M., Luna, J. K., & Norton, R. M. (2015). The environmental dimensions of migration. Annual Review of Sociology, 41, 377–397.

    Google Scholar 

  • Imai, K. S., Gaiha, R., & Kang, W. (2011). Poverty, inequality and ethnic minorities in Vietnam. International Review of Applied Economics, 25(3), 249–282.

    Google Scholar 

  • Jaeger, D. A., Dohmen, T., Falk, A., Huffman, D., Sunde, U., & Bonin, H. (2010). Direct evidence on risk attitudes and migration. Review of Economics and Statistics, 92(3), 684–689.

    Google Scholar 

  • Jessoe, K., Manning, D. T., & Taylor, J. E. (2018). Climate change and labour allocation in rural Mexico: Evidence from annual fluctuations in weather. Economic Journal, 128(608), 230–261.

    Google Scholar 

  • Kahsay, G. A., & Osberghaus, D. (2018). Storm damage and risk preferences: Panel evidence from Germany. Environmental and Resource Economics, 71(1), 301–318.

    Google Scholar 

  • Kang, W., & Imai, K. S. (2012). Pro-poor growth, poverty and inequality in rural Vietnam. Journal of Asian Economics, 23(5), 527–539.

    Google Scholar 

  • Kelley, L. C., Peluso, N. L., Carlson, K. M., & Afiff, S. (2020). Circular labor migration and land-livelihood dynamics in Southeast Asia’s concession landscapes. Journal of Rural Studies, 73, 21–33.

    Google Scholar 

  • Kozel, V. (2014). Well begun but not yet done. Progress and emerging challenges for poverty reduction in Vietnam. Washington, DC: The World Bank.

    Google Scholar 

  • Kumari, R. K., de Sherbinin, A., Jones, B., Bergmann, J., Clement, V., Ober, K., et al. (2018). Groundswell: Preparing for internal climate migration. Washington, DC: The World Bank.

    Google Scholar 

  • Liebenehm, S., Degener, N., & Strobl, E. (2018). Rainfall shocks and risk aversion: Evidence from Southeast Asia. TVSEP Working Paper 006.

  • Lönnqvist, J.-E., Verkasalo, M., Walkowitz, G., & Wichardt, P. C. (2015). Measuring individual risk attitudes in the lab: Task or ask? An empirical comparison. Journal of Economic Behavior and Organization, 119(2015), 254–266.

    Google Scholar 

  • Lucas, R., & Stark, O. (1985). Motivations to remit: Evidence from Botswana. The Journal of Political Economy, 93, 901–918.

    Google Scholar 

  • Malanson, G. P., Verdery, A. M., Walsh, S. J., Sawangdee, Y., Heumann, B. W., McDaniel, P. M., & Frizzelle, B. G. (2014). Changing crops in response to climate: Virtual Nang Rong, Thailand in an agent based simulation. Applied Geography, 53, 202–212.

    Google Scholar 

  • Mckenzie, D., & Rapoport, H. (2007). Network effects and the dynamics of migration and inequality: Theory and evidence from Mexico. Journal of Development Economics, 84(1), 1–24.

    Google Scholar 

  • McLeman, R. (2016). Conclusion: migration as adaptation: conceptual origins, recent developments, and future directions. In: A. Milan, Schraven B., Warner K., & Cascone N. (Eds.), Migration, risk management and climate change: Evidence and policy responses. Global Migration Issues, vol 6. Springer.

  • Meïjer-Irons, J. (2015). Institutions, risk perceptions, and adaptation: Exploring behavioral response to climate change in Thailand. Mimeo.

  • Montalvo, J. G., & Reynal-Querol, M. (2005). Ethnic diversity and economic development. Journal of Development Economics, 76(2), 293–323.

    Google Scholar 

  • Munshi, K., & Rosenzweig, M. (2016). Networks and misallocation: insurance, migration, and the rural-urban wage gap. American Economic Review, 106(1), 46–98.

  • Nawrotzki, R. J., & DeWaard, J. (2016). Climate shocks and the timing of migration from Mexico. Population and Environment, 38(1), 72–100.

    Google Scholar 

  • Nguyen, B. T., Albrecht, J. W., Vroman, S. B., & Westbrook, M. D. (2007). A quantile regression decomposition of urbanrural inequality in Vietnam. Journal of Development Economics, 83(2), 466–490. Papers from a symposium: The social dimensions of microeconomic behaviour in low-income communities.

  • Nguyen, L. D., Raabe, K., & Grote, U. (2015). Rural–urban migration, household vulnerability, and welfare in Vietnam. World Development, 71(C), 79–93.

  • Nguyen, L. D., Grote, U., & Sharma, R. (2017). Staying in the cities or returning home? An analysis of the rural-urban migration behavior in Vietnam. IZA Journal of Development and Migration, 7(1), 3.

    Google Scholar 

  • Nguyen, V. C., Tran, T. Q., & Vu, H. V. (2017). Ethnic minorities in northern mountains of Vietnam: Employment, poverty and income. Social Indicators Research, 134(1), 93–115.

    Google Scholar 

  • Noy, I. (2017). To leave or not to leave? Climate change, exit, and voice on a Pacific Island. CESifo Economic Studies, 63(4), 403–420.

    Google Scholar 

  • Phan, D., & Coxhead, I. (2010). Inter-provincial migration and inequality during Vietnam’s transition. Journal of Development Economics, 91(1), 100–112.

    Google Scholar 

  • Quiñones, E. J. (2019). Anticipatory migration and local labor responses to rural climate shocks. Mimeo.

  • Rigaud, K. K., de Sherbenin, A., Jones, B., Bergmann, J., Clement, V., Ober, K., et al. (2018). Groundswell: Preparing for internal climate migration. Washington, DC: World Bank.

    Google Scholar 

  • Riosmena, F., Nawrotzki, R., & Hunter, L. (2018). Climate migration at the height and end of the great Mexican emigration era. Population and Development Review, 44(3), 455–488.

    Google Scholar 

  • Rosenzweig, M. R., & Stark, O. (1989). Consumption smoothing, migration, and marriage: Evidence from rural India. Journal of Political Economy, 97(4), 905–926.

    Google Scholar 

  • Said, F., Afzal, U., & Turner, G. (2015). Risk taking and risk learning after a rare event: Evidence from a field experiment in Pakistan. Journal of Economic Behavior & Organization, 118(October 2015), 167–183.

  • Schildberg-Hörisch, H. (2018). Are risk preferences stable? Journal of Economic Perspectives, 32(2), 135–154.

    Google Scholar 

  • Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., & Ziese, M. (2018). GPCC full data monthly product version 2018 at 0.5°: Monthly land-surface precipitation from rain-gauges built on GTS-based and historical data. https://doi.org/10.5676/DWD_GPCC/FD_M_V2018_050

  • Stark, O., & Bloom, D. E. (1985). The new economics of labor migration. The American Economic Review, 75(2), 173–178. http://www.jstor.org/stable/1805591.

  • Tse, C. (2011). Do natural disasters lead to more migration? Mimeo: Evidence from Indonesia.

    Google Scholar 

  • Tung, P. D., & Waibel, H. (2014). The poverty and welfare effects of the 2008 food price crisis in Vietnam: A decomposition analysis. World Food Policy, 1(2), 67–88. https://doi.org/10.18278/wfp.1.2.4.

    Article  Google Scholar 

  • TVSEP. (2019). Thailand Vietnam Socioeconomic Panel. http://www.tvsep.

  • Vicente-Serrano, S. M., Beguería, S., & López-Moreno, J. I. (2010). A multi-scalar drought index sensitive to global warming: The Standardized Precipitation Evapotranspiration Index – SPEI. Journal of Climate, 23, 1696. https://doi.org/10.1175/2009JCLI2909.1.

    Article  Google Scholar 

  • Vieider, F., Lefebvre, M., Bouchouicha, R., Chmura, T., Hakimov, R., Krawczyk, M., & Martinsson, P. (2015). Common components of risk and uncertainty attitudes across contexts and domains: Evidence from 30 countries. Journal of the European Economic Association, 13(3), 421–452.

    Google Scholar 

  • Willinger, M., Bchir, M. A., & Heitz, C. (2013). Risk and time preferences under the threat of background risk: A case-study of Lahars risk in Central Java. Working Paper 13–08. University of Montpellier: LAMETA.

  • Wooldridge, J. M. (2001). Econometric analysis of cross section and panel data. Cambridge, MA: MIT Press.

    Google Scholar 

  • World Bank. (2019). Country profile Thailand. https://www.worldbank.org/en/country/thailand/overview.

  • Yamamoto, J. K. (2000). An alternative measure of the reliability of ordinary Kriging estimates. Mathematical Geology, 32, 489–509.

    Google Scholar 

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Acknowledgements

Earlier versions of this paper were presented at the 2019 conference on Demographic Responses to Changes in the Natural Environment held at the University of Wisconsin-Madison, as well as the virtual 2020 Association for Public Policy Analysis and Management (APPAM) International Conference and Fall Research Conferences, the Annual Meeting of the Southern Economics Association (SEA), and the International Union for the Scientific Study of Population’s (IUSSP) Population, Poverty and Inequality Research Conference (PopPov). This research is supported, in part, by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R13 HD096853). We would particularly like to thank Lori Hunter (CU-Boulder) and participants at the conference on Demographic Responses to Changes in the Natural Environment for their feedback and encouragement. Furthermore, we are thankful to Niels Wendt (Leibniz University Hannover) for his continuous support and advice in using spatially referenced data, as well as Jenna Nobles (UW-Madison) and Brad Barham (UW-Madison) for their support and encouragement. Finally, we are grateful to the editors and anonymous referees at Population and Environment. All remaining errors are our own.

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Appendix

Appendix

See Tables 8, 9, 10, 11, 12, 13, 14, 15, 16, and 17 here.

Table 8 Descriptive statistics of household characteristics (2007–2017 averages)
Table 9 Impact of potential mechanisms on migration outcomes
Table 10 Heterogeneous impact of exposure to two consecutive droughts on migration outcomes by potential mechanisms using triple interaction specification
Table 11 Household (HH) attrition over survey years
Table 12 Attrition bias analysis-only treatment variables
Table 13 Attrition bias analysis-treatment and mediator variables including consumption per capita
Table 14 Attrition bias analysis – treatment and mediator variables including food consumption per capita
Table 15 Alternative model specifications
Table 16 Regression of indicators of households’ risk behavior on survey-based measure of willingness to take risk
Table 17 Impact of droughts on indicators of households’ risk behavior

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Quiñones, E.J., Liebenehm, S. & Sharma, R. Left home high and dry-reduced migration in response to repeated droughts in Thailand and Vietnam. Popul Environ 42, 579–621 (2021). https://doi.org/10.1007/s11111-021-00374-w

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  • DOI: https://doi.org/10.1007/s11111-021-00374-w