Abstract
Using a unique dataset covering 178,794 households with 460,654 individuals spanning Africa, Asia, and Latin America, we explore employment of youths across rural zones (peri-urban, intermediate, hinterland) and urban areas. Using full-time equivalents (FTEs), we compare own-farming versus farm-wage labour, and nonfarm wage- and self-employment. Nonfarm includes: (a) agrifood system (AFS) jobs post-farmgate, in food processing, wholesale, logistics, retail, and food service; (b) non-AFS. Key findings are noted in order by Africa, Asia, and Latin America (whose youth employment rates are 61%, 39%, and 48%). (1) AFS shares in FTEs of employed rural youths are substantial (21%, 21%, and 23%). Wage employment share of AFS is lower in Africa (25%) versus Asia and Latin America (75%). (2) Own-farming in FTEs of employed rural youths are higher in Africa (51%, 19%, and 12%). The share for adults in Africa is 36%. Regressions show youths’ being in school does not reduce employment in own-farming (they are compatible), but reduces nonfarm labour. (3) Farm-wage employment shares in FTEs are small (4%, 13%, and 16%). (4) Regressions show that rural youths’ being in a peri-urban area sharply increases AFS and non-AFS employment compared with hinterland youths who depend more on own-farming.
Acknowledgements
The authors are thankful to all the participants of the multiple workshops organized at IFAD for the Rural Development Report 2019, which provided the basis (and the data) for the research in this paper. The authors also thank Margherita Squarcina, Eva-Maria Egger and Fabian Löw for excellent research assistance.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1. Given the 12 month recall period of FTEs, unemployment is not defined.
2. National shares of the data: Bangladesh 16%, Cambodia 1%, Ethiopia 9%, Indonesia 28%, Malawi 1%, Mexico 14%, Nepal 3%, Nicaragua 1%, Niger 1%, Nigeria 14%, Peru 4%, Tanzania 4%, and Uganda 4%.
3. 1.1. Shares of data by population density: hinterland 19%, intermediate rural 21%, peri-urban 32%, and urban 29%. Population density thresholds (1,000 people per sqkm): hinterland < = 0.16, intermediate rural > 0.16 & < = 0.58, peri-urban > 0.58 & < = 2.39, and urban > 2.39.
5. DIVA-GIS is a free computer program for mapping and geographic data analysis (a geographic information system (GIS)). For more information see: https://www.diva-gis.org/.
6. We argue that our limited set of household characteristics (dependency ratio, receive remittances and own land) affect an individual’s employment decisions and not merely their consumption decisions. Therefore the inclusion of these household characteristics would be consistent with a fully separable model as suggested by the Citation2016 paper by LaFave and Thomas. Dependency ratio affects an individual’s ability to provide labour away from their home. Receiving remittances affects an individual’s reservation wage. Owning land affects an individual’s capacity to earn an income from own farming.
7. We use the MODIS Enhanced Vegetation Index (EVI) as a proxy for agricultural potential to facilitate global comparisons (Jaafar & Ahmad, Citation2015).
8. Regional levels of households receiving remittances are: Africa 15%; Asia 45%; Latin America 13%. In Asia, the high share of households with remittances is driven mainly by Indonesia: Indonesia, 58%; Cambodia, 34%, Nepal, 33%; Bangladesh, 21%.
9. For the three regions together, the youth FTE share of own-farm and farm-wage labour combined was 29%.