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Droughts augment youth migration in Northern Latin America and the Caribbean


While evidence on the linkages between migration and climate is starting to emerge, the subject remains largely under-researched at regional scale. Knowledge on the matter is particularly important for Northern Latin America and the Caribbean, a region of the world characterized by exceptionally high migration rates and substantial exposure to natural hazards. We link individual-level information from multiple censuses for eight countries in the region with natural disaster indicators constructed from georeferenced climate data at the province level to measure the impact of droughts and hurricanes on internal mobility. We find that younger individuals are more likely to migrate in response to these disasters, especially when confronted with droughts. Youth exhibit a stronger inclination towards relocating to rural and small town settings, motivated possibly by opportunities for nearby off-farm employment and financing limitations for urban transport and living expenses. Migration decisions are mediated by national institutional arrangements. These findings highlight the importance of social protection and regional planning policies to reduce the vulnerability of youth to droughts in the future and secure their economic integration.

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  1. The literature establishes a strong precedence of migration for economic purposes in this region (Barham and Boucher 1998; Orrenius and Zavodny 2005; Halliday 2006; Acosta 2011).

  2. The trade-off in using national census data is that we are likely to underestimate the scale of environmental displacement due to the absence of reporting within-province and international moves. However, international migration is less common due to the mobility costs, language requirements, and uncertainty of receiving employment at the destination (Deshingkar and Grimm 2005). On the contrary, short-distance moves are often most responsive to changes in the environment (Fussell et al. 2014). Our estimates are therefore likely to provide a lower bound of the true effects of disasters on environmental migration.

  3. The censuses allow for measurement of migration over a 5-year window. They do not provide the exact year each person moved.

  4. We shorten the frame of reference of historical rainfall and temperature to 20 years, with the assumption that recent trends are more likely to influence the behavior of our relatively young sample.

  5. Daily rainfall during the hurricane is considered a significant predictor of hurricanes in the region (Palmieri et al. 2006). This is one motivation for using the TRMM data product which provides daily rainfall data relative to the CRUTS data which we use to measure historical precipitation trends and droughts (detailed later). Data on wind and storm surges at a fine resolution over time were unavailable, which precluded the computation of exposure measures using additional parameters (e.g., as in Logan and Xu 2015).

  6. The scale of exposure is limited to the province because the censuses report the origin of migrants at that level. All 132 provinces are included in our study, with the average size of 50,733 km2.

  7. The distribution of the hurricane intensity variable is provided in the Supplementary Material.

  8. The distribution of the drought intensity variable is provided in the Supplementary Material.

  9. The traditional difference-in-difference approach has been used in a variety of disciplines to measure the health consequences of disasters (Datar et al. 2013; Grabich et al. 2015). Disaster typically is a binary variable in which indicates whether the province is exposed to the event. We adopt a continuous version of the variable, by quantifying the intensity of the disaster in the provinces exposed to the event.

  10. Since the Disaster variable varies at the province level and the After variable varies by year, we do not explicitly need to control for the interacted variables on their own when including origin province and census year fixed effects. A simple double difference model shows positive migration effects attributable to both droughts and hurricanes (Supplementary Material).

  11. We estimate the regression results, varying the exposure reference to the migration recall period (5 years) and the migration recall period plus an additional year prior. Both produce similar migration impact estimates (Supplementary Material). Our inability to observe the precise timing of the move is a limitation of the study. For a fraction of the migrant sample, exposure might occur after they have moved. Conditional on the spatial and destination fixed effects and the time-varying historical weather averages, we would expect these moves to have zero correlation with the hurricane or drought intensity variables. Because we are measuring the average effect on the sample, we might expect this to put downward pressure on our positive effects. Thus, in the worst case scenario, our results are likely a lower bound estimate of the true impact.

  12. The preponderance of youth in the migrant population has been widely established in the 2007 World Development Report (World Bank. World Development Report 2007).

  13. Estimates of all coefficients in these regressions, save the province fixed effects, are presented in the Supplementary Material. Bootstrapping results indicate the results are robust when the sample size is reduced to 10% (Supplementary Material). We do not witness any obvious nonlinear effects when categorizing the hurricane intensity variables into binary variables that reflect whether the intensity value lies between 0 and 1, 1 and 2, and greater than 2 standard deviations (Supplementary Material). The impacts of droughts on youth migration are much larger for extreme values of intensity, however.

  14. To give perspective of the scope of environmental migration, the estimates in Specifications B and D suggest that (16,724,006 × 0.35 × 0.0071=) 41,559 and (16,724,006 × 0.35 × 0.0027=) 15,084 more 15–25-year olds in the region would migrate across provinces in response to a 1 standard deviation increase in drought and hurricane intensity, respectively. An additional (16,724,006 × 0.24 × 0.0029=)11,639 26–35-year olds would also move under the same change in hurricane exposure.

  15. Geolocation codes needed to define provincial and country capitals are not provided for Jamaica. We therefore omit all 14 provinces from Jamaica from the analysis in Table 4, as we are unable to identify the provincial capitals.


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The authors are grateful for constructive comments from Richard Akresh, Laura Chioda, and participants of the Carolina Population Center Interdisciplinary Research Seminar Series.

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Correspondence to Valerie Mueller.

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Baez, J., Caruso, G., Mueller, V. et al. Droughts augment youth migration in Northern Latin America and the Caribbean. Climatic Change 140, 423–435 (2017).

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  • Gross Domestic Product
  • Tropical Rainfall Measure Mission
  • Standard Deviation Increase
  • Drought Intensity
  • Hurricane Intensity