https://orcid.org/0000-0002-9052-5868
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Abstract
We combine nationally representative data from Nigeria with spatiotemporal data from remote sensing and other sources to study how young migrants respond to observable characteristics of potential destinations, both in absolute terms and relative to origin locations. Migrants prefer destinations with better welfare, land availability and intensity of economic activity. We also find that migrants prefer shorter distances and those destinations with better urban amenities and infrastructure. However, responses vary by type of migrant and migration. For example, rural-rural migrants are more responsive to land availability and agricultural potential, while rural-urban and urban-urban migrants are more responsive to welfare and economic vibrancy (measured by nightlight intensity) in destinations. Distance induces varying impact on migration choices of poor and non-poor migrants as well as across more educated and less educated migrants. Longer distances discourage migration for female migrants, poorer migrants, and less educated migrant while the implication for the non-poor and more educated migrants appears to be negligible. This is intuitive because poorer and less educated migrants have liquidity constraints to finance high migration costs. Our results suggest potential scope for predicting how labour mobility responds to alternative regional development policies.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1 For example, Amare et al. (Citation2021) investigate whether urban growth and associated spatial and temporal differences in urban growth can attract youth migration.
2 Although the aggregate welfare impacts of such moves can be larger than those accruing to aggregate moves to large urban centers, because of the differing magnitudes of such flows (Christiaensen et al., Citation2019).
3 Similarly, De Weerdt et al. (Citation2021) employ a similar approach for understanding the implication of distance to potential destinations. As noted elsewhere in this paper, Fafchamps and Shilpi (Citation2013) take a similar approach to analyzing migration destination choices in Nepal.
4 We adopt this commonly used definition in part to facilitate comparison with other studies of youth. We acknowledge, however, that any discrete age-based classification criterion is arbitrarily defined and may not always map cleanly onto societal definitions of youth or functional attributes of young people (Ripoll et al., Citation2017).
5 For example, rural-urban migrants are those individuals who were present in round t in rural areas but reported to have moved to an urban area in round t + 1. We use the rural/urban classification of the enumeration areas in the LSMS-ISA dataset, as defined by the National Bureau of Statistics in Nigeria. Criteria used for such classification generally include population size, population density, type of economic activity, physical characteristics and level of infrastructure.
6 The Operational Linescan System (OLS) sensors of the Defense Meteorological Satellite Program (DMSP) of the United States Airforce are the source of these data. Night light intensity data measured at a resolution of approximately 1 km2 from all over the planet are captured on a daily basis by these satellite-based remote sensors and are then processed by the National Oceanic and Atmospheric Administration (NOAA). Long-term rainfall data are extracted from the daily Africa Rainfall Climatology Version 2 (ARC2) of the National Oceanic and Atmospheric Administration’s Climate Prediction Center (NOAA-CPC) (Novella & Thiaw, Citation2013); the ARC2 data are available at a spatial resolution of approximately 11 km2.
7 Nigeria has 36 states and one federal territory (the Federal Capital Territory). For simplicity, we refer to these as 37 states.
8 We employ spatial differences in urban intensity as proxied by night light intensity between migrants’ destinations and their origins of migration. Night light intensity data have several advantages in measuring the dynamics of urbanization and related human activities (Abay & Amare, Citation2018; Amare, Arndt, Abay, & Benson, Citation2020; Elvidge et al., Citation1997; Henderson, Yeh, Gong, Elvidge, & Baugh, Citation2003). They are argued to be valid markers (proxies) for urbanization, especially compared to census-based rural-urban binary indicators. Spatial differences in night light intensity between these two locations can capture wage differentials or other differences in infrastructural and social services.