The Influence of Internal Migration on Exposure to Extreme Weather Events in Mexico

Runfola, Daniel Miller; Romero‐Lankao, Patricia; Jiang, Leiwen; Hunter, Lori M.; Nawrotzki, Raphael J.; Sánchez, L. Andrés

doi:10.1080/08941920.2015.1076918

Cited by 11 publications

(6 citation statements)

References 7 publications

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“…These studies have documented evidence of a climate signal, with warming temperatures and declines in precipitation contributing to international out-migration (Barrios Puente et al 2015; Feng and Oppenheimer 2012; Hunter et al 2013; Nawrotzki et al 2015b). However, most contemporary migration in Mexico is internal (versus international) (Aguayo-Tellez and Martinez-Navarro 2013; Runfola et al 2016), and we presently lack comparable information on the relationship between climate shocks and internal mobility in Mexico. These internal migration flows are of particular interest to policy makers and local authorities given the potential impacts on population (e.g., size, density, and composition), infrastructure, and the availability and provision of resources.…”

Section: Introductionmentioning

confidence: 99%

Climate shocks and rural-urban migration in Mexico: exploring nonlinearities and thresholds

et al. 2016

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Adverse climatic conditions may differentially drive human migration patterns between rural and urban areas, with implications for changes in population composition and density, access to infrastructure and resources, and the delivery of essential goods and services. However, there is little empirical evidence to support this notion. In this study, we investigate the relationship between climate shocks and migration between rural and urban areas within Mexico. We combine individual records from the 2000 and 2010 Mexican censuses (n=683,518) with high-resolution climate data from Terra Populus that are linked to census data at the municipality level (n=2,321). We measure climate shocks as monthly deviation from a 30-year (1961-1990) long-term climate normal period, and uncover important nonlinearities using quadratic and cubic specifications. Satellite-based measures of urban extents allow us to classify migrant-sending and migrant-receiving municipalities as rural or urban to examine four internal migration patterns: rural-urban, rural-rural, urban-urban, and urban-rural. Among our key findings, results from multilevel models reveal that each additional drought month increases the odds of rural-urban migration by 3.6%. In contrast, the relationship between heat months and rural-urban migration is nonlinear. After a threshold of ~34 heat months is surpassed, the relationship between heat months and rural-urban migration becomes positive and progressively increases in strength. Policy and programmatic interventions may therefore reduce climate induced rural-urban migration in Mexico through rural climate change adaptation initiatives, while also assisting rural migrants in finding employment and housing in urban areas to offset population impacts.

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Section: Introductionmentioning

confidence: 99%

Climate shocks and rural-urban migration in Mexico: exploring nonlinearities and thresholds

et al. 2016

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“…The strength of our climate vulnerability index is its independence from direct climate-hazard information (Fekete 2009). Our index captures vulnerability to climate change that would not be identified by a pure climate hazard assessments (c.f., Runfola et al 2016). Yet it is important to note that vulnerability is context and scale specific (Vincent 2007) and researchers should consider these dimensions when constructing similar indices for other countries.…”

Section: Discussionmentioning

confidence: 99%

“…To validate our climate vulnerability index, we make use of the DesInventar Sendai data collection, which provides georeferenced information on disaster occurrence and associated losses for Costa Rica (UNDRR 2019). DesInventar Sendai is sponsored by the United Nations Office for Disaster Risk Reduction (UNDRR) and the United Nations Development Programme (UNDP) and has been widely used for research on natural hazards (Marulanda et al 2010;Runfola et al 2016;Panwar and Sen 2020;Quesada-Román et al 2021).…”

Section: Data Sourcesmentioning

confidence: 99%

Climate change vulnerability hotspots in Costa Rica: constructing a sub-national index

et al. 2023

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For policies and programs aiming at reducing climate risk, it is important to obtain vulnerability information at the sub-national level to identify hotspots. For the case of Costa Rica, no sub-national climate vulnerability index exists to date. To fill this gap, we constructed a climate vulnerability index at the canton level. We ground our work in the conceptual framework that vulnerability is a function of exposure, sensitivity, and adaptive capacity. Making extensive use of geographic information systems and publicly available data, we constructed 13 spatial layers to reflect the multi-dimensionality of vulnerability. Layers reflect for example, changes in climatic extremes, flood risk, vegetation cover, access to infrastructure (road density) and health services (distance to hospitals), as well as various socioeconomic (wealth level, employment rates, remittances, literacy rate) and demographic (infant mortality) characteristics. Following normalization, we constructed an inverse variance weighted index of canton-level climate vulnerability. We confirmed the validity of our climate vulnerability index through correlation with disaster damage data. We find the strongest climate vulnerability not only in the rural, agricultural producing border cantons (Los Chiles, Matina, Talamanca, Buenos Aires), but also for a few central urban cantons (Tibas, San Jose). Projects and interventions in these hot spot cantons may reduce sensitivity through strengthening hydrological infrastructure and economic development, while adaptive capacity may be improved through addressing barriers of remittance transfer, and via public health programs.

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“…Given that the total population is growing, the shift from a farming population to non-farming is evident in Mexico. Much of the migration (~65%) in Mexico from 2005 to 2010 was to urban destinations [57]. Mexico is a highly urbanized country with a rapidly rising urban population, whereas Ethiopia remains a predominantly agricultural country with a slowly increasing urban population.…”

Section: Case Study Countriesmentioning

confidence: 99%

The Influence of Environmental Change (Crops and Water) on Population Redistribution in Mexico and Ethiopia

et al. 2019

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This paper discusses the effects of long-term environmental change (represented by the abundance or scarcity relative to the long-term average level of crop yield/river flow) and short-term environmental shock (represented by the maximum number of consecutive years below the median crop yield/river flow per decade) on population redistribution in Mexico and Ethiopia. Crop production and water resources, which are affected by climate change and influence human survival and activities, were selected as research variables. Two developing countries, namely, Mexico and Ethiopia, were selected as comparison cases. The results showed that short-term environmental shocks had no correlation with population redistribution. Short-term environmental shocks might fail to influence migration decisions or cause only temporary displacements that cannot be detected by demographic statistics. Among the long-term environmental change factors, only crop yield deviation was found to have a significant positive correlation with population redistribution. Based on two different datasets and two different decades, crop yield deviation is positively correlated with population redistribution; the correlation coefficients between crop yield deviation and population redistribution were 0.134 to 0.162 in Mexico and 0.102 to 0.235 in Ethiopia. When urbanization was considered as the control variable, the correlation coefficient between crop yield deviation and population redistribution in Mexico dropped by half, while it was almost the same in Ethiopia. However, Ethiopia's population redistribution was more clearly influenced by the population itself. Crop yield deviation relative to water flow deviation meant changes in livelihoods. Population redistribution is a possible means of adapting to changes in livelihood. Mexico exhibited high resilience to changes in livelihoods caused by long-term environmental change, especially in its densely populated areas. In contrast, Ethiopia was characterized mainly by high population growth and low population migration. People in some areas of Ethiopia were forced to endure hardship of livelihood deterioration or to stay where they were due to the difficulty of obtaining sufficient resources to afford the cost of migration.

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The Influence of Internal Migration on Exposure to Extreme Weather Events in Mexico

Cited by 11 publications

References 7 publications

Climate shocks and rural-urban migration in Mexico: exploring nonlinearities and thresholds

Climate shocks and rural-urban migration in Mexico: exploring nonlinearities and thresholds

Climate change vulnerability hotspots in Costa Rica: constructing a sub-national index

The Influence of Environmental Change (Crops and Water) on Population Redistribution in Mexico and Ethiopia

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