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

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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.

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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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Authors and Affiliations

  1. International Division, Mathematica, Chicago, IL, USA

    Esteban J. Quiñones

  2. Department of Agricultural and Resource Economics, University of Saskatchewan, Saskatoon, Canada

    Sabine Liebenehm

  3. Institute for Environmental Economics and World Trade, Leibniz University Hannover, Hanover, Germany

    Rasadhika Sharma

Authors
  1. Esteban J. Quiñones
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  2. Sabine Liebenehm
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  3. Rasadhika Sharma
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Correspondence to Esteban J. Quiñones.

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