Internal and International Mobility as Adaptation to Climatic Variability in Contemporary Mexico: Evidence from the Integration of Census and Satellite Data

Abstract

Migration provides a strategy for rural Mexican households to cope with, or adapt to, weather events and climatic variability. Yet prior studies on “environmental migration” in this context have not examined the differences between choices of internal (domestic) or international movement. In addition, much of the prior work relied on very coarse spatial scales to operationalize the environmental variables such as rainfall patterns. To overcome these limitations, we use fine-grain rainfall estimates derived from NASA’s Tropical Rainfall Measuring Mission (TRMM) satellite. The rainfall estimates are combined with Population and Agricultural Census information to examine associations between environmental changes and municipal rates of internal and international migration 2005–2010. Our findings suggest that municipal-level rainfall deficits relative to historical levels are an important predictor of both international and internal migration, especially in areas dependent on seasonal rainfall for crop productivity. Although our findings do not contradict results of prior studies using coarse spatial resolution, they offer clearer results and a more spatially nuanced examination of migration as related to social and environmental vulnerability and thus higher degrees of confidence.

Climate Migration at the Height and End of the Great Mexican Emigration Era

“…Likewise, using 2010 census data and rainfall data estimated from satellite imagery, Leyk et al. () show that lower than expected rainfall is associated with higher international migration only in municipalities with lower irrigation coverage and thus more sensitive to climate shocks. We build on Nawrotzki et al.’s study by using finer‐resolution data on rainfall and temperature and by controlling for crop irrigation levels and municipality crop mix as a further measure of sensitivity to climate stress.…”

Population responses to the 1976 South Dakota drought: Insights for wider drought migration research

“…In most regions, the bulk of studies focuses on drought effects on migration patterns within countries, but there is a notable cluster of studies on international drought‐related migration between Mexico and the United States. These suggest that droughts in dryland areas of Mexico in the 1990s and early 2000s were associated with higher rates of rural–urban migration within Mexico and, to a lesser extent, increased migration to the United States along pre‐existing social networks (Chort & de la Rupelle, 2016; Hunter et al, 2013; Leyk et al, 2017; Nawrotzki et al, 2013; Nawrotzki et al, 2015). However, migration is not an immediate response to drought but instead emerges after a lag period when other adaptive responses are attempted first (Nawrotzki & DeWaard, 2016).…”

Deep learning fusion of satellite and social information to estimate human migratory flows

“…Efforts to mitigate challenges associated with extreme variations of migratory flows commonly depend on: (1) improving our ability to forecast migratory flows to better allocate resources during anticipated periods of high migration activity; and (2) reducing migratory outflows by improving living conditions at migrant origin locales. In this context, scholars and practitioners have conducted research into the drivers of migration (Hanson & Spilimbergo, 1999; Hunter et al, 2015; Lindstrom & Lauster, 2001; Massey & Zenteno, 2000; McKenzie & Rapoport, 2010; Nawrotzki et al, 2015b; Riosmena, 2010; Runfola et al, 2016; Sue et al, 2019), including early exploratory efforts on the potential of satellite imagery to advance our understanding of migration dynamics, and the ability to predict patterns of migratory flows (Leyk et al, 2017; Nawrotzki et al, 2015a; Runfola et al, 2016; Runfola & Napier, 2016). One particular challenge in pursuing this research agenda has been the fact that migration‐relevant information is conveyed across many disparate sources, ranging from tabular datasets (i.e., household surveys) to satellite imagery and even news and social media.…”