We evaluate how changes in weather patterns affected rural-urban migration across 41 sub-Saharan African countries, by age and sex, over the 1980–2015 period. We combine recent age- and sex-specific estimates of net rural-urban migration with historical data on rainfall and temperature from the Climate Research Unit (CRU). We also compare standard unweighted estimates of rainfall and temperature to estimates weighted by the proportion of the country’s total rural population in the CRU grid. Results show that rural out-migration of young adults is the most sensitive to shifts in weather patterns, with lower rainfall, lower variability in rainfall, and higher temperatures increasing subsequent rural out-migration—though the last of these is not observed in weighted models. The strength of these effects has grown stronger over time for 20–24 year olds, though weaker above age 30. In contrast, increasing temperature variability is associated with a higher rural in-migration of children (0–9) and older adults (55–64). Gender differences in these effects are minimal and concentrated in areas which experienced heavy reductions in rainfall.
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Male and female differences in migration throughout this paper are framed in relation to “gender,” since mechanisms linking climate change to migration focus on how male/female differences are expressed in social relations. In discussing our data, however, or specific aspects of measurement, we refer to “sex.” This difference in linguistic register reflects a basic limitation in our data and arguably in all extant demographic data.
Our “rural” and “urban” categories are based on national administrative definitions. These are not always comparable or valid (Dorélien et al. 2013)—based on measures of population density, they are particularly prone to miscategorize semi-rural areas. However, they are the standard type of data used in country-level analyses and the standard type made available by research arms of multilateral organizations, from the UNFPA and FAO to the European Commission, which produces the Gridded Population data that we describe below. Our assumption here is that in a sample of countries and time periods as large as this one, these categories are sufficiently accurate to capture changes in patterns of movement, especially between unambiguously rural and urban sectors.
The temporal coefficients indicate variability in trends with an initial rise in migration in the late 1980s, followed by declines that lasted into the first decade of the twenty-first century, and ending with a slow rise back to the early 1980s level by 2015. There is clear evidence of the moderating effects of national wealth measured by GDP per capita, which tends to be higher in more urbanized countries in Africa (UN data; own calculations). Of particular interest is the significant positive coefficient of the TFR 15 years prior to migration estimate, which highlights the impact of past fertility levels on current pressures for rural-urban migration. Reductions in fertility reduce the number of adolescents entering the labor market 15–20 years later, pointing to parallels between rural-urban migration in SSA and undocumented migration from Mexico into the USA (Villareal 2014).
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Appendix 1. The CSRM approach to estimating age- and sex-specific rural-to-urban migration rates
Our estimates of net rural-to-urban migration by age and sex are published in full in Menashe-Oren and Stecklov (2017). Here we summarize the method.
An early version of the census survival ratio method (CSRM) was developed by Hamilton and Henderson (1944) and then extended by Preston (1979). It has been repeatedly applied in United Nations Population Division (UN 1980; UN 2001) analyses, including in their estimates of the share of urban growth that is driven by net rural to urban migration.
The key feature underlying the CSRM is that age- and sex-specific net migration rates can be estimated in contexts with relatively little or no migration data using a minimum of two points in time as long as the rural and urban sector populations are separately measured. Given these minimum data requirements, Preston (1979) showed how net rural to urban migration rates could be calculated for each age group and by sex. In addition, he carefully described the assumptions that make this approach possible.
Underlying the CSRM is the notion that a broad measure of survival for the population can be estimated across time using data from national level cohort survival calculations. These survivorship estimates are then adjusted based on assumptions about rural and urban mortality level differentials. Given the estimated survivorship predictions, differences between the predicted cohort size in each sector and the actual measured size can be calculated. This difference is the net rural to urban flow.
CSRM has two key limitations, widely acknowledged by those who use it. The main one is that the net migration estimates also include reclassification as areas are rezoned over time (Preston 1979; Moultrie et al. 2013). Estimates of reclassification have indicated that it may have accounted for as much as 25% of urban growth in parts of sub-Saharan Africa in the 1980s, though it is unclear whether this figure will have changed in the period we examine here (Beauchemin and Bocquier 2004; Chen et al. 1998).
The second limitation of the CSRM is that it typically ignores international migration, since there is little if any data available for the types of societies where CSRM is most useful. In contexts with high levels of net international migration and strong differentials by age, the estimated survivorship measures that underlie the CSRM therefore yields some bias in the net rural to urban migration estimates.
Here, following Menashe-Oren and Stecklov (2017), we use country-level assessments of urban and rural populations by age and sex (URPAS) across seven 5-year intervals over the 1980–2015 period (United Nations 2014b) for 41 countries in SSA.
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Weinreb, A., Stecklov, G. & Arslan, A. Effects of changes in rainfall and temperature on age- and sex-specific patterns of rural-urban migration in sub-Saharan Africa. Popul Environ 42, 219–254 (2020). https://doi.org/10.1007/s11111-020-00359-1
- Internal migration
- Rural-urban migration
- Climate variability
- Age-specific migration