Amplification or suppression: Social networks and the climate change-migration association in rural Mexico

Relocation preference and settlement: Lessons from the Poverty Alleviation Relocation Program in China

We explored the linkages between socioeconomic and demographic factors, relocation preference, and settlement associated with China's Poverty Alleviation Relocation Program. Using multivariate ordinal logistic regression, panel data modeling, and multilevel methods, we found that outdated infrastructure at places of origin, such as long distances to paved roads or elementary schools, increased the probability of relocation, and the presence of left-behind household members at the origin compromised re-settlement. This study sheds light on the community- and household-level factors that influence relocation preference and settlement, offering valuable insights for future research and informing the design and implementation of relocation projects.

How do environmental stressors influence migration? A meta-regression analysis of environmental migration literature

BACKGROUND

The amount of literature on environmental migration is increasing. However, existing studies exhibit contradictory results. A systematic synthesis of the environment-migration relationship is much needed.

OBJECTIVE

This study summarizes research findings, calculates the effect sizes of environmental stressors, identifies publication bias, and investigates heterogeneous environmental effects on migration.

METHODS

We collected 3,380 estimates from 128 studies published between 2000 and 2020 to explore the environment-migration relationship and performed weighted instrumental variable regression to unveil the heterogeneous environmental effects on out- and net migration.

RESULTS

The majority of environmental stressors were not important predictors of out- and net migration. Among the results showing environmental impacts on migration, 58% and 68% reported that environmental stressors increased out- and net migration, respectively, while 58% reported that environmental stressors decreased in-migration. The overall environmental impact on migration was small; however, disaster-related stressors showed a medium effect, and rapid-onset stressors had a stronger impact than slow-onset ones. Multivariate meta-regression analyses demonstrated that environmental stressors were more likely to trigger internal migration than international migration and that developed countries were less likely to experience out-migration. Rapid-onset environmental stressors did not increase out-migration but played an important role in decreasing net migration toward environmentally stressed areas. Meanwhile, we also found a publication bias toward studies showing a positive relationship between environmental stressors and migration in the previous environmental migration literature.

CONCLUSIONS

Environmental stressors may affect migration; however, the environmental effect depends on migration measurements, environmental stressors' forces and rapidity, and the context in which migration takes place.

CONTRIBUTION

This study contributes to migration studies by synthesizing and validating the environment-migration relationship and enhancing our understanding of how and under what circumstances environmental stressors may affect migration.

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

Human migratory decisions are driven by a wide range of factors, including economic and environmental conditions, conflict, and evolving social dynamics. These factors are reflected in disparate data sources, including household surveys, satellite imagery, and even news and social media. Here, we present a deep learning-based data fusion technique integrating satellite and census data to estimate migratory flows from Mexico to the United States. We leverage a three-stage approach, in which we (1) construct a matrix-based representation of socioeconomic information for each municipality in Mexico, (2) implement a convolutional neural network with both satellite imagery and the constructed socioeconomic matrix, and (3) use the output vectors of information to estimate migratory flows. We find that this approach outperforms alternatives by approximately 10% (r 2), suggesting multi-modal data fusion provides a valuable pathway forward for modeling migratory processes.

The Childhood Origins of Climate-Induced Mobility and Immobility

The literature on climate exposures and human migration has focused largely on assessing short-term responses to temperature and precipitation shocks. In this paper, we suggest that this common coping strategies model can be extended to account for mechanisms that link environmental conditions to migration behavior over longer periods of time. We argue that early-life climate exposures may affect the likelihood of migration from childhood through early adulthood by influencing parental migration, community migration networks, human capital development, and decisions about household resource allocation, all of which are correlates of geographic mobility. After developing this conceptual framework, we evaluate the corresponding hypotheses using a big data approach, analyzing 20 million individual georeferenced records from 81 censuses implemented across 31 countries in tropical Africa, Latin America, and Southeast Asia. For each world region, we estimate regression models that predict lifetime migration (a change in residence between birth and ages 30-39) as a function of temperature and precipitation anomalies in early life, defined as the year prior to birth through age four. Results suggest that early-life climate is systematically associated with changes in the probability of lifetime migration in most regions of the tropics, with the largest effects observed in sub-Saharan Africa. In East and Southern Africa, the effects of temperature shocks vary by sex and educational attainment and in a manner that suggests women and those of lower socioeconomic status are most vulnerable. Finally, we compare our main results with models using alternative measures of climate exposures. This comparison suggests climate exposures during the prenatal period and first few years of life are particularly (but not exclusively) salient for lifetime migration, which is most consistent with the hypothesized human capital mechanism.

Climate Mobility and Development Cooperation

Development cooperation actors have been addressing climate change as a cross-cutting issue and investing in climate adaptation projects since the early 2000s. More recently, as concern has risen about the potential impacts of climate variability and change on human mobility, development cooperation actors have begun to design projects that intentionally address the drivers of migration, including climate impacts on livelihoods. However, to date, we know little about the development cooperation's role and function in responding to climate related mobility and migration. As such, the main aim of this paper is to outline the policy frameworks and approaches shaping development cooperation actors' engagement and to identify areas for further exploration and investment. First, we frame the concept of climate mobility and migration and discuss some applicable policy frameworks that govern the issue from various perspectives; secondly, we review the toolbox of approaches that development cooperation actors bring to climate mobility; and third, we discuss the implications of the current Covid-19 pandemic and identify avenues for the way forward. We conclude that ensuring safe and orderly mobility and the decent reception and long-term inclusion of migrants and displaced persons under conditions of more severe climate hazards, and in the context of rising nationalism and xenophobia, poses significant challenges. Integrated approaches across multiple policy sectors and levels of governance are needed. In addition to resources, development cooperation actors can bring data to help empower the most affected communities and regions and leverage their convening power to foster more coordinated approaches within and across countries.

Predicting road quality using high resolution satellite imagery: A transfer learning approach

Recognizing the importance of road infrastructure to promote human health and economic development, actors around the globe are regularly investing in both new roads and road improvements. However, in many contexts there is a sparsity—or complete lack—of accurate information regarding existing road infrastructure, challenging the effective identification of where investments should be made. Previous literature has focused on overcoming this gap through the use of satellite imagery to detect and map roads. In this piece, we extend this literature by leveraging satellite imagery to estimate road quality and concomitant information about travel speed. We adopt a transfer learning approach in which a convolutional neural network architecture is first trained on data collected in the United States (where data is readily available), and then “fine-tuned” on an independent, smaller dataset collected from Nigeria. We test and compare eight different convolutional neural network architectures using a dataset of 53,686 images of 2,400 kilometers of roads in the United States, in which each road segment is measured as “low”, “middle”, or “high” quality using an open, cellphone-based measuring platform. Using satellite imagery to estimate these classes, we achieve an accuracy of 80.0%, with 99.4% of predictions falling within the actual or an adjacent class. The highest performing base model was applied to a preliminary case study in Nigeria, using a dataset of 1,000 images of paved and unpaved roads. By tailoring our US-model on the basis of this Nigeria-specific data, we were able to achieve an accuracy of 94.0% in predicting the quality of Nigerian roads. A continuous case estimate also showed the ability, on average, to predict road quality to within 0.32 on a 0 to 3 scale (with higher values indicating higher levels of quality).

Assessing Livelihood Vulnerability of Minority Ethnic Groups to Climate Change: A Case Study from the Northwest Mountainous Regions of Vietnam

Climate variability, climate change, and extreme events can compound the vulnerability of people heavily reliant on agriculture. Those with intersecting disadvantages, such as women, the poor, and ethnic minority groups, may be particularly affected. Understanding and assessing diverse vulnerabilities, especially those related to ethnicity, are therefore potentially important to the development of policies and programs aimed at enabling adaptation in such groups. This study uses a livelihood vulnerability index (LVI) method, along with qualitative data analysis, to compare the vulnerability of different smallholder farmers in Son La province, one of the poorest provinces in Vietnam. Data were collected from 240 households, representing four minority ethnic groups. The results indicated that household vulnerability is influenced by factors such as income diversity, debt, organizational membership, support from and awareness by local authorities, access to health services, water resources, and location. Results revealed that two of the ethnic groups’ households were, on average, more vulnerable, particularly regarding livelihood strategies, health, water, housing and productive land, and social network items when compared to the other two ethnic groups. The study shows the need for targeted interventions to reduce the vulnerability of these and similarly placed small ethnic communities.

Land Cover Mapping and Ecological Risk Assessment in the Context of Recent Ecological Migration

In order to protect the ecological environment and solve the poverty problem in the western region, China has established an ecological migration (EM) policy. This policy aims to relocate populations from poverty-stricken areas with fragile ecological environments, which inevitably leads to changes in land cover and the ecological environment. The objective of this study was to identify the effects of EM in a typical region (Wuwei), including changes in the land cover and ecological risk (ER). A land cover change monitoring method was implemented for the 2010–2019 period for six land cover classes using random forest, which is an effective supervised machine learning method. The land cover change patterns were analyzed by determining the area changes of the six classes and applying a land use transition matrix, and a landscape ecological risk model based on landscape disturbance and fragility was used. Our results demonstrate that the increase and decrease in the area of cultivated land, unused land, and construction land can be divided into two stages (2010–2015 and 2015–2019). The area of water and perennial snow doubled during the study periods. The major land cover transitions were between unused land and construction land and between unused land and crop land. In addition, the ER value for the Qilian Mountain National Nature Reserve decreased because of the implementation of EM in the study area, indicating that the ecological environment was effectively improved. The results demonstrate the advantage of the proposed approach in understanding the impact of EM on regional land cover changes and the ecological environment so as to provide guidance for follow-up planning and development.

Migration towards Bangladesh coastlines projected to increase with sea-level rise through 2100

To date, projections of human migration induced by sea-level change (SLC) largely suggest large-scale displacement away from vulnerable coastlines. However, results from our model of Bangladesh suggest counterintuitively that people will continue to migrate toward the vulnerable coastline irrespective of the flooding amplified by future SLC under all emissions scenarios until the end of this century. We developed an empirically calibrated agent-based model of household migration decision-making that captures the multi-faceted push, pull and mooring influences on migration at a household scale. We then exposed ~4800 000 simulated migrants to 871 scenarios of projected 21st-century coastal flooding under future emissions pathways. Our model does not predict flooding impacts great enough to drive populations away from coastlines in any of the scenarios. One reason is that while flooding does accelerate a transition from agricultural to non-agricultural income opportunities, livelihood alternatives are most abundant in coastal cities. At the same time, some coastal populations are unable to migrate, as flood losses accumulate and reduce the set of livelihood alternatives (so-called 'trapped' populations). However, even when we increased access to credit, a commonly-proposed policy lever for incentivizing migration in the face of climate risk, we found that the number of immobile agents actually rose. These findings imply that instead of a straightforward relationship between displacement and migration, projections need to consider the multiple constraints on, and preferences for, mobility. Our model demonstrates that decision-makers seeking to affect migration outcomes around SLC would do well to consider individual-level adaptive behaviors and motivations that evolve through time, as well as the potential for unintended behavioral responses.

Effect of border policy on exposure and vulnerability to climate change

Migration may be increasingly used as adaptation strategy to reduce populations' exposure and vulnerability to climate change impacts. Conversely, either through lack of information about risks at destinations or as outcome of balancing those risks, people might move to locations where they are more exposed to climatic risk than at their origin locations. Climate damages, whose quantification informs understanding of societal exposure and vulnerability, are typically computed by integrated assessment models (IAMs). Yet migration is hardly included in commonly used IAMs. In this paper, we investigate how border policy, a key influence on international migration flows, affects exposure and vulnerability to climate change impacts. To this aim, we include international migration and remittance dynamics explicitly in a widely used IAM employing a gravity model and compare four scenarios of border policy. We then quantify effects of border policy on population distribution, income, exposure, and vulnerability and of CO2 emissions and temperature increase for the period 2015 to 2100 along five scenarios of future development and climate change. We find that most migrants tend to move to areas where they are less exposed and vulnerable than where they came from. Our results confirm that migration and remittances can positively contribute to climate change adaptation. Crucially, our findings imply that restrictive border policy can increase exposure and vulnerability, by trapping people in areas where they are more exposed and vulnerable than where they would otherwise migrate. These results suggest that the consequences of migration policy should play a greater part in deliberations about international climate policy.

Climate Change and Migration: New Insights from a Dynamic Model of Out-Migration and Return Migration

In popular accounts, stories of environmental refugees convey a bleak picture of the impacts of climate change on migration. Scholarly research is less conclusive, with studies finding varying effects. This paper uses an agent-based model (ABM) of land use, social networks, and household dynamics to examine how extreme floods and droughts affect migration in Northeast Thailand. The ABM explicitly models the dynamic and interactive pathways through which climate-migration relationships might operate, including coupled out and return streams. Results suggest minimal effects on out-migration but marked negative effects on return. Social networks play a pivotal role in producing these patterns. In all, the portrait of climate change and migration painted by focusing only on environmental refugees is too simple. Climate change operates on already established migration processes that are part and parcel of the life course, embedded in dynamic social networks, and incorporated in larger interactive systems where out- and return migration are integrally connected.

The changing climate-migration relationship in China, 1989-2011

A persistent concern about the social consequences of climate change is that large, vulnerable populations will be involuntarily displaced. Existing evidence suggests that changes in precipitation and temperature can increase migration in particular contexts, but the potential for this relationship to evolve over time alongside processes of adaptation and development has not been widely explored. To address this issue, we link longitudinal data from 20 thousand Chinese adults from 1989-2011 to external data on climate anomalies, and use this linked dataset to explore how climatic effects on internal migration have changed over time while controlling for potential spatial and temporal confounders. We find that temperature anomalies initially displaced permanent migrants at the beginning of our study period, but that this effect had reversed by the end of the study period. A parallel analysis of income shares suggests that the explanation might lie in climate vulnerability shifting from agricultural to non-agricultural livelihood activities. Taken together with evidence from previous case studies, our results open the door to a potential future in which development and in-situ adaptation allow climate-induced migration to decline over time, even as climate change unfolds.

Do Social Protection Programs Foster Short-term and Long-term Migration Adaptation Strategies?

We examine how migration is influenced by temperature and precipitation variability, and the extent to which the receipt of a cash transfer affects the use of migration as an adaptation strategy. Climate data is merged with georeferenced panel data (2010-2014) on individual migration collected from the Zambian Child Grant Program (CGP) sites. We use the person-year dataset to identify the direct and heterogeneous causal effects of the CGP on mobility. Having access to cash transfers doubles the rate of male, short-distance moves during cool periods irrespective of wealth. Receipt of cash transfers (among wealthier households) during extreme heat causes an additional retention of males. Cash transfers positively spur long-distance migration under normal climate conditions in the long term. They also facilitate short-distance responses to climate, but not long-distance responses that might be demanded by future climate change.

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

Previous research has linked increasing climate-change-related variability to Mexico-US migration, but only under particular climatic/social conditions and periods of high irregular migration. Using the 2000 and 2010 Mexican censuses, we examine this environment-migration nexus across a broader set of socioecological contexts and during periods of both increasing (1995-1999) and declining (2005-2009) migration. Consistent with the notion that climate can "trap" populations in place, we find that frequent/severe bouts of hot or dry conditions are associated with lower US-bound migration from most of rural Mexico. However, we do find higher climate outmigration during episodes hot and dry climate, or out of places with lower vulnerability. Our comparisons across periods suggest that climate migration is affected by conditions in the U.S. in a similar or slightly weaker manner as other forms of migration are. Altogether, our findings suggest that rural Mexico is unlikely to push large numbers of international "climate refugees" and that climate migration is indeed sensitive to conditions in sending and destination areas.

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

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 shocks and rural-urban migration in Mexico: Exploring nonlinearities and thresholds

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.

Domestic and International Climate Migration from Rural Mexico

Evidence is increasing that climate change and variability may influence human migration patterns. However, there is less agreement regarding the type of migration streams most strongly impacted. This study tests whether climate change more strongly impacted international compared to domestic migration from rural Mexico during 1986-99. We employ eight temperature and precipitation-based climate change indices linked to detailed migration histories obtained from the Mexican Migration Project. Results from multilevel discrete-time event-history models challenge the assumption that climate-related migration will be predominantly short distance and domestic, but instead show that climate change more strongly impacted international moves from rural Mexico. The stronger climate impact on international migration may be explained by the self-insurance function of international migration, the presence of strong migrant networks, and climate-related changes in wage difference. While a warming in temperature increased international outmigration, higher levels of precipitation declined the odds of an international move.

Climate, migration, and the local food security context: Introducing Terra Populus

Studies investigating the connection between environmental factors and migration are difficult to execute because they require the integration of microdata and spatial information. In this article, we introduce the novel, publically available data extraction system Terra Populus (TerraPop), which was designed to facilitate population-environment studies. We showcase the use of TerraPop by exploring variations in the climate-migration association in Burkina Faso and Senegal based on differences in the local food security context. Food security was approximated using anthropometric indicators of child stunting and wasting derived from Demographic and Health Surveys (DHS) and linked to the TerraPop extract of climate and migration information. We find that an increase in heat waves was associated with a decrease in international migration from Burkina Faso, while excessive precipitation increased international moves from Senegal. Significant interactions reveal that the adverse effects of heat waves and droughts are strongly amplified in highly food insecure Senegalese departments.

Climate Variability and Inter-Provincial Migration in South America, 1970-2011

We examine the effect of climate variability on human migration in South America. Our analyses draw on over 21 million observations of adults aged 15-40 from 25 censuses conducted in eight South American countries. Addressing limitations associated with methodological diversity among prior studies, we apply a common analytic approach and uniform definitions of migration and climate across all countries. We estimate the effects of climate variability on migration overall and also investigate heterogeneity across sex, age, and socioeconomic groups, across countries, and across historical climate conditions. We also disaggregate migration by the rural/urban status of destination. We find that exposure to monthly temperature shocks has the most consistent effects on migration relative to monthly rainfall shocks and gradual changes in climate over multi-year periods. We also find evidence of heterogeneity across demographic groups and countries. Analyses that disaggregate migration by the rural/urban status of destination suggest that much of the climate-related inter-province migration is directed toward urban areas. Overall, our results underscore the complexity of environment-migration linkages and challenge simplistic narratives that envision a linear and monolithic migratory response to changing climates.

Climate Shocks and the Timing of Migration from Mexico

Although evidence is increasing that climate shocks influence human migration, it is unclear exactly when people migrate after a climate shock. A climate shock might be followed by an immediate migration response. Alternatively, migration, as an adaptive strategy of last resort, might be delayed and employed only after available in-situ (in-place) adaptive strategies are exhausted. In this paper, we explore the temporally lagged association between a climate shock and future migration. Using multilevel event-history models, we analyze the risk of Mexico-U.S. migration over a seven-year period after a climate shock. Consistent with a delayed response pattern, we find that the risk of migration is low immediately after a climate shock and increases as households pursue and cycle through in-situ adaptive strategies available to them. However, about three years after the climate shock, the risk of migration decreases, suggesting that households are eventually successful in adapting in-situ.