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The role of environmental perceptions in migration decision-making: evidence from both migrants and non-migrants in five developing countries

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Abstract

Research has demonstrated that, in a variety of settings, environmental factors influence migration. Yet much of the existing work examines objective indicators of environmental conditions as opposed to the environmental perceptions of potential migrants. This paper examines migration decision-making and individual perceptions of different types of environmental change (sudden vs. gradual environmental events) with a focus on five developing countries: Vietnam, Cambodia, Uganda, Nicaragua, and Peru. The survey data include both migrants and non-migrants, with the results suggesting that individual perceptions of long-term (gradual) environmental events, such as droughts, lower the likelihood of internal migration. However, sudden-onset events, such as floods, increase movement. These findings substantially improve our understanding of perceptions as related to internal migration and also suggest that a more differentiated perspective is needed on environmental migration as a form of adaptation.

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Notes

  1. Moreover, “displacement risk increases when populations that lack the resources for planned migration experience higher exposure to extreme weather events, such as floods and droughts…Changes in migration patterns can be responses to both extreme weather events and longer term climate variability and change, and migration can also be an effective adaptation strategy” (IPCC 2014: 73).

  2. We use the term “environmental migration” when referring to persons who are displaced primarily for environmental reasons (see Dun and Gemenne (2008) for a thorough discussion of the definition of “environmental migration”).

  3. For a more comprehensive and detailed review of the existing literature, see Adger et al. (2015), Hunter et al. (2015), McLeman (2014), Piguet (2010), or the Foresight Project (2011).

  4. We study voluntary migration, which occurs when environmental events lead to temporary disruption of livelihoods or to deterioration of environmental conditions, as opposed to forced migration, which occurs when environmental events threaten the physical safety of populations or lead to unfeasible livelihoods (see Renaud et al. 2011). Moreover, we focus on internal migration as there is strong consensus in the literature that most migration flows associated with environmental factors are of an internal nature (Hunter et al. 2015: 3; Foresight Project 2011; Adamo and Izazola 2010; Raleigh et al. 2008).

  5. Beegle et al. (2011), using micro-data from Tanzania, report that rainfall shocks increase the probability that people leave their villages. Similarly, Barrios et al. (2010) find that rainfall shortages raise rural out-migration in Sub-Saharan Africa. Lilleør and Van den Broeck (2011), on the other hand, provide a critical review of the existing theoretical and empirical research on how climate change and climate variability in less developed countries (LDCs) could affect migration via their effect on personal income.

  6. Burke and Emerick (forthcoming) and Dell et al. (2014) provide evidence for limited adaptation among farmers. Unfortunately, our survey data lack a temporal dimension, and we thus cannot say much about the effectiveness of the adaptation strategies our survey respondents implemented. We can report, however, that almost all respondents implemented at least one adaptation measure after they had experienced the environmental event mentioned in their questionnaire. In addition, existing literature does, in fact, provide evidence that perceptions of climatic changes such as changes in temperature are associated with a higher probability to implement some type of adaptation strategy, for example, Gourdji et al. (2015) for Nicaragua; Di Falco and Veronesi (2013) for Ethiopia; Bryan et al. (2013) for Kenya; Below et al. (2012) for Tanzania; Seo et al. (2010) for South America; Chinvanno et al. (2008) for Vietnam.

  7. Migration in the presence of short and sudden environmental events tends to be over a relatively short distance, temporary, and internal (Hunter et al. 2015; Gray and Mueller 2012a, b; Black et al. 2011b, c; Myers et al. 2008; Raleigh et al. 2008). Due to the lack of a temporal dimension in our survey, we are unable to determine whether migration is temporary or permanent, though. That said, our sampling procedure of migrants ensures, to some extent, that migration is a more permanent phenomenon in our data. See the discussion on the sampling procedure below.

  8. The Environmental Change and Forced Migration (EACH-FOR) project is the only other data on the environment–migration nexus with surveys carried out in 23 countries in six regions worldwide (Laczko and Aghazarm 2009; see also Warner 2011). Unlike our data, however, the EACH-FOR data focus on migrants only for most countries and comprise a relatively low number of cases (individuals) per state.

  9. While we sought to cover different regions of the world that may be particularly vulnerable to climate change, the selected countries are not representative of a particular region or continent.

  10. Note that there is no variation on the presence of environmental stressors, i.e., everyone experiences environmental stress. However, since we are not interested in objectively present environmental stress, but rather perceptions of and attitudes toward environmental stress, our research design is appropriate. In essence, only with an environmental stressor present, people can perceive it as a reason for migration (or not). We return to this issue in the robustness section.

  11. Recent literature suggests that a better understanding of environmental migration requires distinguishing between three possible outcomes of environmental events: migration, displacement, and immobility (see Black et al. 2013; Black and Collyer 2014). This distinction clearly matters, and we incorporate immobility in the dependent variable (Migration = 0). Due to the lack of data, however, we leave a thorough examination of the displacement outcome to future research, but we also believe that we consider this at least partly since democracy and economic development should condition displacement as well. See also footnote 4 above on this.

  12. In terms of education and poverty, the results are virtually identical when using the original ordinal variable instead of the binary items and when both original ordinal variables are included in the same model.

  13. The “Appendix” reports the corresponding descriptive statistics.

  14. Our results are robust across different specifications of the structure of the covariance matrix for the random effects, including when allowing all variances and covariances to be distinct. The findings also remain unchanged when employing a linear probability model (LPM) with regional fixed effects. We thank an anonymous reviewer for this suggestion.

  15. One could further argue that migratory decisions are correlated within villages. To control for this possibility, we run models with bootstrapped standard errors clustered on villages. The main results of our analysis stay the same. Results are available upon request.

  16. Examining the results by country suggests that a positive and significant effect is only found in Nicaragua and Peru, while we observe a negative coefficient for the other countries (although only significantly different from 0 in Uganda). There is also little evidence that women following their husbands, i.e., dependent migration, can explain the positive effect of female migration. Hence, better job opportunities in cities are likely to be the driver behind women migration in Nicaragua and Peru.

  17. We do not explicitly examine interactive relationships between the determinants of migration at different levels, since Hunter et al. (2015: 9) describe these relationships as rather “additive.” However, preliminary analyses suggest no clear pattern between the political system and individual reactions to the type of environmental event. Since the low number of cases at the country level (five) implies that it is difficult to calculate models with multiplicative specifications, we divide our sample along the interacted variables. Hence, we run individual regression models by countries to better understand how democracy affects the decision to engage in environmental migration. We provide the corresponding results in the “Appendix”.

  18. We thank an anonymous reviewer for highlighting this issue.

  19. Moreover, this assessment is further supported by the more formal test introduced in Oster (2015). However, this applies more to Gradual Events than to Sudden Events.

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Acknowledgments

The authors thank two anonymous reviewers and the journal editor for their helpful comments. This research is part of the project “Environmental Change and Migration” funded by the Swiss Network for International Studies (SNIS).

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Authors and Affiliations

  1. Center for Comparative and International Studies (CIS), ETH Zurich, IFW C 43.2, Haldeneggsteig 4, 8092, Zurich, Switzerland

    Vally Koubi & Tobias Böhmelt

  2. Department of Economics, University of Bern, Bern, Switzerland

    Vally Koubi

  3. Department of Political Science and Sociology, University of Salzburg, Rudolfskai 42, 5020, Salzburg, Austria

    Gabriele Spilker

  4. Department of Politics and Public Administration, University of Konstanz, Box 86, Universitätsstr. 10, 78457, Konstanz, Germany

    Lena Schaffer

  5. Department of Government, University of Essex, Winenhoe Park, Colchester, CO4 3SQ, UK

    Tobias Böhmelt

  6. Institute for Environmental Decisions (IED), ETH Zurich, Zurich, Switzerland

    Tobias Böhmelt

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  1. Vally Koubi
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Correspondence to Vally Koubi.

Appendix

Appendix

See Tables 4, 5, and 6.

Table 4 Overview of surveys
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Table 5 Individual country data overviews
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Table 6 Individual country regressions: baseline models by country
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Koubi, V., Spilker, G., Schaffer, L. et al. The role of environmental perceptions in migration decision-making: evidence from both migrants and non-migrants in five developing countries. Popul Environ 38, 134–163 (2016). https://doi.org/10.1007/s11111-016-0258-7

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  • DOI: https://doi.org/10.1007/s11111-016-0258-7

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