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Climate change, migration and voice

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Abstract

Climate change is frequently predicted to result in dramatic increases in international migration, yet current research has largely failed to identify such movements in practice. This paper sheds light on this apparent paradox. Drawing on Hirschman’s treatise on Exit, Voice and Loyalty, we provide empirical evidence that voicing about climatic change, as captured through media reports, is associated with greater exposure to climate risks and lower emigration rates. Our finding is consistent with individuals’ variously responding to climatic change either by emigrating or remaining and voicing about climatic change, with the aim of influencing current mitigation, adaptation or compensation policies. Our results, in turn, have implications for policies aimed at shaping international migration patterns and the ability of governments and residents to voice their concerns about climate change.

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Notes

  1. For an elaboration on this distinction, see Otto et al. (2012).

  2. Please refer to Boas et al. (2019), Berlemann and Steinhardt (2017), Millock (2015) and Piguet et al. (2011) for recent literature reviews as well as the meta-analysis of Beine and Jeusette (2018).

  3. See among others Black et al. (2011), Black et al. (2013), Penning-Rowsell et al. (2013), Adger et al. (2015), Gray and Wise (2016), Cattaneo and Peri (2016) and Nawrotzki and DeWaard (2018). Interested readers are also directed to Ayeb-Karlsson et al. (2018) for a discursive review of the literature on ‘trapped populations’.

  4. See for instance (Dallmann and Millock 2017) or Mastrorillo et al. (2016) for recent evidence in India and South Africa.

  5. In the specific case of Tuvalu, the most important adaptation ‘funding’ they can receive is a change in the international maritime law, which will allow them to retain possession of their exclusive economic zone around their islands even if these disappear or are abandoned. For another example of this voicing from the Pacific Island atoll nations, see CANCC (2016).

  6. This literature is growing rapidly, resulting in more than 100 papers on the subject. Interested readers are directed to the surveys of Berlemann and Steinhardt (2017), Millock (2015) and Piguet et al. (2011).

  7. See among others the papers quoted before, Fankhauser (2017) and Mueller et al. (2020) among others. Bennonier et al. (2019) looks for instance at the adaptation effect of irrigation on the mobility adjustment mechanism. They show that access to irrigation can ameliorate the adverse effects of temperature on agricultural productivity. In turn, this might alleviate the negative impact of temperatures on emigration in low income countries, an effect that had been documented by Cattaneo and Peri (2016).

  8. For a recent literature review on the subject, readers are referred to Baudassé et al. (2018). Our paper contributes to the voicing literature by showing that the exit-voice trade-off contributes to the understanding of the climate-migration nexus.

  9. This is true even in countries without representative democratic regimes. Even in autocracies, the government is responsive to its citizens, though maybe to a more limited extent.

  10. Notably, though, our non-instrumented results are robust to the inclusion of country fixed effects.

  11. The results of these dynamic estimations are not reported for the sake of brevity but are available upon request to the authors.

  12. This index has been computed by the firm Maplecroft and the variables on which it is based do not change much over short horizons. See https://maplecroft.com/about/news/ccvi.html.

  13. These countries are: Cape Verde, Comoros, Dominica, Fiji, Grenada, Jamaica, Kiribati, Maldives, Marshall Islands, Mauritius, Micronesia Fed. States, Nauru, Palau, Samoa, Sao Tome and Principe, Seychelles, Solomon Islands, Timor Leste, Tonga, Tuvalu and Vanuatu.

  14. Given the fact that Factiva relies not only on a select set of newspapers but also other sources like information agencies, an article published in a local newspaper is likely to be captured through referencing by other sources.

  15. In our samples, out of the 87 countries, 15 (resp. 19) have Spanish (resp. French) as their official language.

  16. Google Trends is unsuitable for our purposes due to the patchy coverage in developing countries.

  17. The Appendix provides an example of news retrieved from foreign newspapers written in another language than English (the most widespread language in Bangladesh).

  18. Note that a given press report can be classified at the same time as a report on Official and Domestic voicing. This explains why the average filtered voicing reports are not the sum of Official and Domestic voicing reports.

  19. See: https://www.un.org/en/development/desa/population/migration/data/estimates2/estimates15.asp.

  20. See, for example the DEMIG C2C dataset: https://www.imi-n.org/data/demig-data/demig-c2c-data.

  21. See: www.sediac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2. This measure has been used, for example by Burznski et al. (2019) to compute the share of the population that would be forced to leave under several scenarios of sea level rise in a quantitative model, which predicts the total displacement of populations affected by climate change. We are grateful to Michal Burzynski for providing access to these data.

  22. More details are available at: https://datahelpdesk.worldbank.org/knowledgebase/articles/902061-climate-data-api.

  23. This proxy has been used especially in samples involving developing countries for which internal migration data are often unavailable. See for instance (Barrios et al. 2006).

  24. Related to that point, our IV estimations can be regarded as the estimation of the underlying 2-equation system of Section 3, with exogenous variations in the emigration rates.

  25. The visa restrictions have been collected by the DEMIG project at Oxford University. They are drawn manually from the International Air Transport Association manuals capturing each year the bilateral requirements in terms of tourist visas within any pair of countries in the World. The data span the period 1995–2013. These data have been recently used by Czaika and de Haas (2017) and Czaika and Neumayer (2017). An earlier version of the data specific to the year 2004 has been also used by Neumayer (2006) and (Bertoli and Fernández-Huertas Moraga 2015) among others. Since the original data are annual, we average the restrictions over 5 years. For the last sub-period (2010–2015), we assume no changes between 2013 and 2015.

  26. Given missing data, this index computes the proportion of visa requirements with respect to all destinations for which data are available. In other terms, the proportion is adjusted for missing data and we treat missing data agnostically.

  27. As an illustration, in 2013, about 40% of all destinations request a visa for people from Luxembourg to be admitted. This proportion was more than double for countries like Tuvalu (78%) and Kiribati (81%).

  28. The literature typically lacks some clear agreement about how to capture slow onset climatic change, such as gradual warming.

  29. Note that in the PPML IV estimations, income dummies are no longer significant such that they are omitted in the final specification (col. 5 of Table 3). In contrast, the island dummy becomes significant and is included in the final specification. Also an interesting feature of the PPML IV first stage regressions is the negative and highly significant coefficient of internal mobility. This suggests that our estimations capture the usual trade-off between internal mobility and international emigration.

  30. It should be emphasized that this relationship would likely be stronger in a larger sample of countries. Our selection of countries based on the climate change index lead to a sample of mostly developing countries. Most of our included countries are therefore subject to higher visa restrictions compared to developed countries. The coefficients of visa restriction in the first stage regressions of Table 3 are therefore estimated on a sample with limited variability along this dimension.

  31. Neither do we attempt to proxy bilateral migrant flows based on the differences in bilateral migrant stocks, due to the high proportion of negative values that result.

  32. If for a given dyad and a given period there is no positive values but well reported zero values, we ascribe a zero value to this observation.

  33. See: https://devpolicy.org/new-zealands-climate-refugee-visas-lessons-for-the-rest-of-the-world-20200131

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Acknowledgements

We thank the participants of the GLO conference on Climate Change and Human Responses (Hong Kong, 2018), the Migration and Development conference (Madrid, 2019), the Conference on the Economics of Global Interactions (Bari, 2019), and audiences at DIAL (Paris), Galway, Hawaii, LISER (Luxembourg), Trier, and Waseda for valuable comments. The paper benefited from insightful comments from Nicola Coniglio, Frederic Docquier, Arnaud Dupuy, Christina Gathmann, Laszlo Goerke, Xenia Maschke, Katrin Millock, Kevin Shih and Jackie Wahba among others. We are grateful to Mathias Czaika and Michal Burzynski for sharing their data, and to Lionel Jeusette and Jacob Pastor-Paz for outstanding research assistance. The usual disclaimers apply

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  1. CREA, University of Luxemburg, CES Ifo and IZA, 6, rue Coudenhove-Kalergi, L-1359, Luxembourg City, Luxembourg

    Michel Beine

  2. Victoria University of Wellington, Wellington, New Zealand

    Ilan Noy

  3. University of Western Australia, Perth, Australia

    Christopher Parsons

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Correspondence to Michel Beine.

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IN conceptualised the choice, while MB and CP conceptualised the research approach. All authors were involved in the conduct of the research and the writing of the paper.

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Beine, M., Noy, I. & Parsons, C. Climate change, migration and voice. Climatic Change 167, 8 (2021). https://doi.org/10.1007/s10584-021-03157-2

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