EvolvingSolutions

Annotated Bibliography

Justin Maynard

Feburary 23rd

Problem Statement

Climate change related migration is inherently linked to human development as climate change often disproportionately affects those in developing countries and Sub-Saharan Africa, given their the high dependence on agriculture along with the lack of resources and opportunities for adaptation. Voluntary migration is more easily achievable for wealthier and educated individuals as a response to climate change. The inability to migrate results in trapped populations, which relates to Amartya Sen’s definition of human development. These populations lack the freedom of protective security, as they are unable to migrate out of climate impacted regions.

Sources

1. Gray, C., & Bilsborrow, R. (2013). Environmental Influences on Human Migration in Rural Ecuador. Demography, 50(4), 1217–1241. doi: 10.1007/s13524-012-0192-y
2. Gray, C., & Wise, E. (2016). Country-specific effects of climate variability on human migration. Climatic Change, 135(3-4), 555–568. doi: 10.1007/s10584-015-1592-y
3. Mcleman, R. (2012). Developments in modelling of climate change-related migration. Climatic Change, 117(3), 599–611. doi: 10.1007/s10584-012-0578-2
4. Nawrotzki, R. J., Riosmena, F., & Hunter, L. M. (2012). Do Rainfall Deficits Predict U.S.-Bound Migration from Rural Mexico? Evidence from the Mexican Census. Population Research and Policy Review, 32(1), 129–158. doi: 10.1007/s11113-012-9251-8
5. Lu, X., Wrathall, D. J., Sundsøy, P. R., Nadiruzzaman, M., Wetter, E., Iqbal, A., … Bengtsson, L. (2016). Unveiling hidden migration and mobility patterns in climate stressed regions: A longitudinal study of six million anonymous mobile phone users in Bangladesh. Global Environmental Change, 38, 1–7. doi: 10.1016/j.gloenvcha.2016.02.002

1. Environmental Influences on Human Migration in Rural Ecuador

   ​ This article answers the question regarding whether environmental conditions influence human migration, and does so by using various datasets combined with GIS software. A retrospective migration survey was conducted in rural Ecuador to address the issue that few quantitative studies have investigated the environmental effects on migration. There are two environmental influences on population mobility, fast and slow. The fast pathway is defined as rapid environmental changes, like weather shocks and natural disasters. This pathway has the ability to harm development, as natural disasters can lead to the loss of assets or income, and can overwhelm local insurance mechanisms like informal lending, which are crucial in increasing development in developing areas. These rapid environmental changes also result in loss of ability to be part of the labor force, relating to Sen’s definition of human development, as not being able to work is an unfreedom. During slow environmental change, environmental characteristics, like land quality and climate norms, are viewed as assets that affect the productivity of agriculture based jobs. These slow forms of environmental degradation are expected to lead to out-migration, and affect more people than fast change.

   ​ To collect data, population based household surveys were conducted in three study areas in rural Ecuador. Areas were selected to be environmentally and demographically diverse, and a flexible sampling strategy was used to oversample rare migrant types. The areas on the study were selected based on census data, various environmental data, and contained a representative 7% of Ecuador’s rural population. Households were selected using a stratified, three-stage cluster sampling method, which produced data representative of the rural population at risk of migration. A questionnaire was created for individuals, households, and communities that collected evidence on place of residence, economic activity, demographic characteristics, and retrospective relative information about the community.

   ​ To compare with the household survey data, GPS points were collected for community centers, dwellings, and agricultural plots. Values were extracted from three existing data sets using GIS software. A Digital Elevation Model was used to extract mean land slope. Secondly, the global WorldClim data set containing historical climate information was used. Lastly, GIS was used to link communities to the closest rainfall station of which data was available. The findings were different from expected, showing that both fast and slow climate change are likely to influence human migration, but it is possible that potential migrants will be trapped in place rather than displaced. Instead of identifying and assisting “climate refugees,” climate assistance policies that focus on real time monitoring of environmental conditions in vulnerable areas should be used.

   ​ Instead of focusing on “climate refugees,” resources should be used to identify populations that are at risk of becoming trapped in place, or being forced to move locally. When families move locally, they are unable to escape the conditions brought on by climate change, meaning they are still susceptible to negative development effects. These effects include the inability to provide labor, and decline in the well being of the population impacted by climate driven disasters, which counters the UN development goal of improved well being.

2. Country-specific effects of climate variability on human migration

   ​ According to authors Clark Gray and Erika Wise, “involuntary human migration” is one of the most threatening social outcomes in this current era of global climate change. Numerous studies have been conducted investigating the consequences of short and medium term climate change on human migration, but these studies have “lacked expertise in the use of climate data,” and lack access to cross-national data on migration. This study was completed to address these limitations, and evaluated linked data on internal and international migration across a six year period. Data was collected from 9812 households in Kenya, Uganda, Nigeria, Burkina Faso and Senegal, and linked with high resolution gridded climate data from station and satellite sources.

   ​ Survey data was retrieved from the World Bank’s African Migration and Remittances Surveys (AMRS), which provides standardized retrospective data on international and internal migration for each country in Africa. In all countries, disproportionate sampling was used to oversample areas where there were more drivers of migration. AMRS was used to create a household dataset containing information on the number of migrants, gender, and reported migration motivation. To compare against the AMRS, the Climatic Research Unit’s (CRU) time series containing high resolution monthly precipitation along with temperature data from the NASA Modern Era-Retrospective Analysis for Research and Applications (MERRA) was used.

   ​ The results reveal direct effects of temperature on voluntary migration but inconsistent effects of precipitation on voluntary migration. The nonlinear effects of temperature are jointly significant in Kenya, Uganda, Nigeria, and Burkina Faso, and the nonlinear effects of precipitation are only significant in Uganda and Nigeria. Migration increases the most with the highest observed temperatures in Uganda versus the lowest observed temperature in Nigeria and Burkina Faso, and increases at both ends of the temperate spectrum in Kenya.

   ​ Climate change related migration is inherently linked to human development as climate change often disproportionately affects those in developing countries and Sub-Saharan Africa, given their the high dependence on agriculture along with the lack of resources and opportunities for adaptation. Voluntary migration is more easily achievable for wealthier and educated individuals as a response to climate change. The inability to migrate results in trapped populations, which relates to Amartya Sen’s definition of human development. These populations lack the freedom of protective security, as they are unable to migrate out of climate impacted regions.

3. Developments in modelling of climate change-related migration

   ​ The two main ideas presented in this article are linking climate change and migration and modeling climate change using GIS systems. In linking climate change and migration, McLeman first mentions that “any number of potential factors” may contribute to the decision making process in migration, and isolating and interpretation the relative influence of climatic variables compared to other concurrent variables is not always possible. The author then discusses climate-related migration in the terms of “environmental refugees,” a term used to describe people who are involuntarily displaced as a result of environmental conditions. The author then describes the human impacts of climate change as being described in terms of vulnerability, which is a function of the sensitivity of a population to climatic disturbances and of the population’s ability to adapt. Developing regions that tend to rely on agriculture and natural resources, and landless laborers and tenant farmers are more sensitive, and most easily displaced by climate change related events. Furthermore, little access to economic and social capital may limit a population’s potential to engage in adaptive migration.

   ​ This is a human development problem as the key issue surrounding climate change migration is the lack of choices among populations in developing countries when faced with climate change related disasters. This lack of choice originates from the two un-freedoms, political and economic. Individuals may be unable to migrate due to political or social structures which prevent necessary migration from occurring, or may be unable to migrate because of their socioeconomic status. This relates to Amartya Sen’s definition of human development as the lack of choices and opportunities among populations reduces quality of life and development of individuals as a result of climate change. This issue relates to sustainable development goals of good health and well being as well as climate action, because general health and well being are diminished during the events of climate change forced migration, and well being is also diminished when individuals do not have the opportunity to migrate.

   ​ As of the writing of this article (2012), existing models had yet to incorporate climate-migration linkages with reliable population data. Global data on population movements is “coarse in nature,” and there are paucity of datasets that link population change to environmental stimuli. However, some solutions exist in combining data from a number of sources. The UN Office for the Coordination of Humanitarian Affairs and the International Displacement Monitoring Centre suggests a methodology for measuring displacement but combining datasets from CRED’s EM-DAT database, the Dartmouth Flood Observatory, Red Cross/Red Crescent Disaster Management Information Systems, and OCHA’s ReliefWeb. These datasets provide crude proxy figures in which global estimates of “involuntary climate change-related migration” can be measured, however it is impossible to measure if these affected by disasters become migrants.

   ​ There has been more success at the local levels, where data sets are able to link environmental information on population with migration data over specific time periods, which has been used in Burkina Faso, Nepal, and Amazonian Brazil among other areas. Another development in modeling is spatial vulnerability modelling, which uses models to identify populations potentially exposed to impacts of climate change by using GIS to combine outputs from general circulation models (GCMs) and regional climate models (RCMs) with various population, agro-economic, and resource data. These modelling techniques make assumptions about the potential for population displacement and migration, and identify sites where political instability or resource scarcity may lead to worsened effects of climate change.

   ​ This article shows that modelling the relationship between climate change and migration is a developing analytic methodology, and existing models face some issues, but also offer some hope.

4. Do Rainfall Deficits Predict U.S.-Bound Migration from Rural Mexico? Evidence from the Mexican Census

   ​ Rural Mexico is heavily dependent on primary sector activities, like agriculture, which is influenced by rainfall and other environmental issues. Mexico also contains well established transnational migrant networks, leading to the investigation between rainfall patterns and United States bound migration from rural areas. Migration has historically been used as a strategy to mitigate the consequences of weather events and a changing climate. Individuals may relocate temporarily or permanently, seeking to escape rapid disasters, or longer-duration events such as droughts. The connection between climate and migration is also dependent on factors such as socioeconomic status and the existence of networks in communities that send migrants. In this context, migration is used as an informal insurance system to decrease the negative effects of unemployment due to market fluctuations or crop failure.

   ​ In the negative effects of unemployment, the main development harm is found. As described by Amartya Sen’s Development as Freedom, the ability to provide work is a freedom that contributes to human development, and being unable to be part of a labor force is an unfreedom that restricts and contracts development. The inability of choice in being part of a labor force is a lack of economic freedom. Another unfreedom is the lack of social programs, such as crop insurance programs, that could be used in Mexico to alleviate the effects of climate change on rural residents. This unfreedom of being unable to work and lack of protective securities leads to a loss in income necessary to provide for a family, leading to those in rural areas to sending migrants to the US in search of jobs.

   ​ As an alternative to the neoclassical migration framework, the new economics of labor migration (NELM) theory was developed, which emphasizes that migration is a response to interregional or international differences in labor supply and demand. Migration decisions are made within social units, notably the family, and migration is not used to maximize expected gain, but to minimize risk associated with market failures, in this case drought. Households in less developed countries have limited access to formal risk, such as crop insurance, forcing them to rely on migrant payments as to supplement income.

   ​ Despite the large amount of evidence between internal migration and its connection to climate change related events, there are few studies relating international migration to climate change related events. This study addresses this gap, and household level representative data from the 2000 Mexican census was used in conjunction with rainfall and economic information to observe the association between rainfall deficits and international out migration from rural Mexico to the United States. The census data was made available by the Integrated Public Use Microdata Series, which provides individual and household characteristics coupled with retrospective migration information. Rainfall information was available from the Mexican Migration Project, which contains monthly precipitation data. The findings showed that rainfall deficits in dry states was associated with international out migration, and there was no association in wet states. In wet states, migration is unrelated to precipitation, and social networks are instead the driving factor of a move. In dry states, a decline in rainfall provided a strong motivation for a move, regardless of social networks. This paper and the findings support the NELM framework that individuals’ risk management due to climate change was a primary factor in migration.

5. Unveiling hidden migration and mobility patterns in climate stressed regions: A longitudinal study of six million anonymous mobile phone users in Bangladesh

   ​ The most important idea presented in this article is the promising use of mobile network operator call detail records (CDR) as a supplementary data source used to monitor and respond to migration as a result of climate change. The article specifically looked at Bangladesh where Cyclone Mahasen struck in May 2013. Bangladesh is prone to climate change related weather, as their sea-level rise is occurring faster than global averages, which exposes 11 thousand square kilometers of land and 20.5 million people to greater risk by 2050. Bangladesh is also a suitable candidate for this study because of the increase in mobile phone ownership, with the proportion of households with at least one phone rising from 78% to 89% between 2011 and 2014. The study succeeded in quantifying various elements of migratory episodes in Bangladesh using CDR, such as incidence, direction, duration, and seasonality.

   ​ The significant harms being addressed in the article are migration as a response to climate change, and disaster management. One harm comes in the form of decreased economic opportunities in affected areas, which is an un-freedom that prevents development according to Amartya Sen. When disasters affect regions, less people are likely to move into disaster affected areas, decreasing opportunities for work, which according to Sen, decreases quality of life. The impact of climate change on migration reflects on the UN development goals number three, decent work, and eight, good health and well being.

   ​ To measure the impact of migratory episodes, two de-identified data sets from the largest mobile network operator in Bangladesh, Grameephone (GP), was used. The first data set (D1), covered April 30th to June 2013, which includes the period before and after the Cyclone Mahasen hit in May 2013. The second dataset (D2) comprised of a simple random sample of one million mobile phones drawn from the entire national set of phones on the GP network. One use of the data was in measuring human mobility, in which mobility was assessed as a response to evacuation messages across a large area, allowing more contextualized responses. Another use was in determining specific neighborhoods receiving migrants, which could be identified to steer intervention resources. The authors found correlations between incidence and duration of migratory episodes, and between in and out migration per district. The result of comparing incidence and duration of migratory episodes is a strong linear correlation, in which an increase in the proportion of persons migrating increases the time migrants spend away from their district. The negative correlation between in and out migration is related to labor opportunities. Increasing out migration decreases labor opportunities in the departed area, which would negatively affect the probability that people from outside will migrate into the departed area in search of labor opportunities. Overall, this CDR data and its relationships prove the viability of CDR in studying the effects of climate change in migration.