Publication: Educational inequalities during COVID-19: Results from longitudinal surveys in Sub-Saharan Africa
Loading...
Date
2025-01-21
ISSN
0738-0593
Published
2025-01-21
Author(s)
Editor(s)
Abstract
While the literature on the COVID-19 pandemic is growing, there are few studies on learning inequalities in a lower-income, multi-country context. Analyzing a rich database consisting of 34 longitudinal household and phone survey rounds from Burkina Faso, Ethiopia, Malawi, Mali, Nigeria, Tanzania, and Uganda with a rigorous linear mixed model framework, we find lower school enrolment rates during the pandemic. But countries exhibit heterogeneity. Our variance decomposition analysis suggests that policies targeting individual household members are most effective for improving learning activities, followed by those targeting households, communities, and regions. Households with higher education levels or living standards or those in urban residences are more likely to engage their children in learning activities and more diverse types of learning activities. Furthermore, we find some evidence for a strong and positive relationship between public transfers and household head employment with learning activities for almost all the countries.
Link to Data Set
Associated URLs
Associated content
Other publications in this report series
Journal
Journal Volume
Journal Issue
Collections
Related items
Showing items related by metadata.
Publication Impact of COVID-19 on Learning(World Bank, Washington, DC, 2021-05)The COVID-19 pandemic has wreaked havoc upon global learning, with many countries facing severe school disruptions and closures. An emerging literature based on household survey data points to the pandemic as having exacerbated inequalities in education and learning in countries from Italy to Denmark, the United Kingdom, and the United States. This brief offers new analysis on the impacts of the COVID-19 pandemic on learning outcomes for six sub-Saharan African countries. The authors analyze detailed household level data from several rounds of panel phone surveys collected by the World Bank in Burkina Faso, Ethiopia, Malawi, Mali, Nigeria, and Uganda. These surveys were first implemented between late April and early June 2020, after school closures due to the pandemic. In each survey round, the surveyed households were asked a set of core questions on topics such as knowledge of COVID and mitigation measures, access to educational activities during school closures, dynamics of employment, household income and livelihood, income loss and coping strategies, and received assistance.OPublication Imputing Poverty Indicators without Consumption Data(Washington, DC: World Bank, 2024-08-19)Accurate poverty measurement relies on household consumption data, but such data are often inadequate, outdated, or display inconsistencies over time in poorer countries. To address these data challenges, this paper employs survey-to-survey imputation to produce estimates for several poverty indicators, including headcount poverty, extreme poverty, poverty gap, near-poverty rates, as well as mean consumption levels and the entire consumption distribution. Analysis of 22 multi-topic household surveys conducted over the past decade in Bangladesh, Ethiopia, Malawi, Nigeria, Tanzania, and Viet Nam yields encouraging results. Adding household utility expenditures or food expenditures to basic imputation models with household-level demographic, employment, and asset variables could improve the probability of imputation accuracy by 0.1 to 0.4. Adding predictors from geospatial data could further increase imputation accuracy. The analysis also shows that a larger time interval between surveys is associated with a lower probability of predicting some poverty indicators, and that a better imputation model goodness-of-fit (R2) does not necessarily help. The results offer cost-saving inputs for future survey design.Publication Inequalities in Job Loss and Income Loss in Sub-Saharan Africa during the COVID-19 Crisis(World Bank, Washington, DC, 2022-08)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.Publication Using Survey-to-Survey Imputation to Fill Poverty Data Gaps at a Low Cost(Washington, DC: World Bank, 2024-03-26)Survey data on household consumption are often unavailable or incomparable over time in many low- and middle-income countries. Based on a unique randomized survey experiment implemented in Tanzania, this study offers new and rigorous evidence demonstrating that survey-to-survey imputation can fill consumption data gaps and provide low-cost and reliable poverty estimates. Basic imputation models featuring utility expenditures, together with a modest set of predictors on demographics, employment, household assets, and housing, yield accurate predictions. Imputation accuracy is robust to varying the survey questionnaire length, the choice of base surveys for estimating the imputation model, different poverty lines, and alternative (quarterly or monthly) Consumer Price Index deflators. The proposed approach to imputation also performs better than multiple imputation and a range of machine learning techniques. In the case of a target survey with modified (shortened or aggregated) food or non-food consumption modules, imputation models including food or non-food consumption as predictors do well only if the distributions of the predictors are standardized vis-Ã -vis the base survey. For the best-performing models to reach acceptable levels of accuracy, the minimum required sample size should be 1,000 for both the base and target surveys. The discussion expands on the implications of the findings for the design of future surveys.Publication Inequality and Welfare Dynamics in the Russian Federation during 1994-2015(World Bank, Washington, DC, 2018-10)The Russian Federation offers the unique example of a leading centrally planned economy swiftly transforming itself into a market-oriented economy. This paper offers a comprehensive study of inequality and mobility patterns for Russia, using multiple rounds of the Russian Longitudinal Monitoring Surveys over the past two decades spanning this transition. The findings show rising income levels and decreasing inequality, with the latter being mostly caused by pro-poor growth rather than redistribution. The poorest tercile experienced a growth rate that was more than 10 times that of the richest tercile, leading to less long-term inequality than short-term inequality. The analysis also finds that switching from a part-time job to a full-time job, from a lower-skill job to a higher-skill job, or staying in the formal sector is statistically significantly associated with reduced downward mobility and increased income growth. However, a similar transition from the private sector to the public sector is negatively associated with income growth.
Users also downloaded
Showing related downloaded files
No results found.