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Building a Policy-Relevant Research Agenda on Environmental Migration in Africa

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Part of the book series: International Handbooks of Population ((IHOP,volume 10))

Abstract

Periodic drought or heat stress during the main growing season, torrential flooding during harvesting periods, and soil erosion are just a few factors that have historically jeopardized household income. These sources of income variability incline households to adapt through informal channels, such as engaging in seasonal or permanent migration. Scarcity of wage employment opportunities, poor transportation infrastructure, and weak endowments in financial capital pose strong barriers to use of migration as an adaptation strategy in Africa. Within this unique context, I synthesize the main findings of the literature on environmental migration in Africa. Inconsistencies in migration definitions, the absence of evaluations of slow onset events, and limited evidence to support the mechanisms underlying migratory responses to environmental change are identified as major knowledge gaps. I discuss how new methods and data may be used to address the above gaps, while informing the types of polices and interventions being considered by development agencies and governments.

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Notes

  1. 1.

    Nigeria experienced a major flooding in 2012 which affected 7 million people in the south-central part of the country.

  2. 2.

    Nawrotzki and Bakhtsiyarava (2017) confirm the relationship between heat and international migration in Burkina Faso with an alternative data source.

  3. 3.

    Nawrotzki and Bakhtsiyarava (2017) indicate that excessive precipitation—the percentage of times in which rainfall was above one standard deviation above the long-term average over the 6-year period leading up to the interview year—induces individuals to migrate abroad from Senegal. The cross-sectional analysis may be underlying the inconsistency across studies. In this case, the authors are unable to generalize their results beyond the year in which the census was collected due to their inability to control for the cyclical trends that coincide with climate anomalies as well as affect mobility within the country.

  4. 4.

    There are a number of articles that discuss the advantages of various types of in situ and satellite data available globally to measure climate variability (Auffhammer et al., 2013; Dell et al., 2014; Donaldson & Storeygard, 2016).

  5. 5.

    The use of self-reported information on drought exposure comes with the additional concern that its inclusion in an empirical model of migration may be subject to parameter bias. This is because household perceptions of risk are correlated with other characteristics unobserved by the researcher, such as level of risk aversion, farming ability of the household, and so on.

  6. 6.

    Although individual migration data is available over multiple rounds of the study, soil samples were only collected at baseline. The implements needed to collect soil samples at the household level, as well as the technical expertise required to analyze the data render performing the procedure cost-prohibitive for all survey rounds.

  7. 7.

    Gray (2011) notes one of the main limitations of the index is that soil properties depend on the agricultural activities underlying the household rendering the explanatory variable endogenous. To address this concern, he performs a robustness check replacing the index with a measure of soil carbon which is less reliant on the farming techniques applied on the parcel.

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Mueller, V. (2022). Building a Policy-Relevant Research Agenda on Environmental Migration in Africa. In: Hunter, L.M., Gray, C., Véron, J. (eds) International Handbook of Population and Environment. International Handbooks of Population, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-76433-3_8

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