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231006 ||| eng |
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1 |
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|a Contreras-Gonzalez, Ivette
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| 245 |
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|a Inequalities in Job Loss and Income Loss in Sub-Saharan Africa during the COVID-19 Crisis
|h Elektronische Ressource
|c Ivette Contreras-Gonzalez
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| 260 |
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|a Washington, D.C
|b The World Bank
|c 2022
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| 300 |
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|a 40 pages
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| 653 |
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|a Job Loss by Age
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| 653 |
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|a Gender and Poverty
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| 653 |
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|a Household Survey Data
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| 653 |
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|a Economic Shock
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| 653 |
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|a Vulnerability to Poverty
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| 653 |
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|a Social Protections and Labor
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| 653 |
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|a Jobs
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| 653 |
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|a Labor Markets
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| 653 |
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|a Inequaliy
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| 653 |
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|a Inequality
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| 653 |
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|a Coronavirus (COVID-19)
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| 653 |
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|a Poverty Reduction
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| 653 |
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|a Gender and Social Policy
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| 653 |
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|a Employment and Unemployment
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| 653 |
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|a COVID-19 Impact
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| 653 |
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|a Job Loss
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| 653 |
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|a Gender and Employment
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| 653 |
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|a Gender
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| 700 |
1 |
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|a Palacios-Lopez, Amparo
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| 700 |
1 |
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|a Oseni, Gbemisola
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| 700 |
1 |
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|a Weber, Michael
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b WOBA
|a World Bank E-Library Archive
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| 028 |
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|a 10.1596/1813-9450-10143
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| 856 |
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|u http://elibrary.worldbank.org/doi/book/10.1596/1813-9450-10143
|x Verlag
|3 Volltext
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| 082 |
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|a 330
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| 520 |
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|a This paper uses high-frequency phone survey data from Ethiopia, Malawi, Nigeria, and Uganda to analyze the impacts of the COVID-19 crisis on work (including wage employment, self-employment, and farm work) and income, as well as heterogeneity by gender, family composition, education, age, pre-COVID19 industry of work, and between the rural and urban sectors. The paper links phone survey data collected throughout the pandemic to pre-COVID-19 face-to-face survey data to track the employment of respondents who were working before the pandemic and analyze individual-level indicators of job loss and re-employment. Finally, it analyzes both immediate impacts, during the first few months of the pandemic, as well as longer run impacts through February/March 2021. The findings show that in the early phase of the pandemic, women, young, and urban workers were significantly more likely to lose their jobs. A year after the onset of the pandemic, these inequalities disappeared and education became the main predictor of joblessness. The analysis finds significant rural/urban, age, and education gradients in household-level income loss. Households with income from nonfarm enterprises were the most likely to report income loss, in the short run as well as the longer run
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