Abstract
We investigate gender disparities in the effect of COVID-19 on the labor market outcomes of skilled Ugandan workers. Leveraging a high-frequency panel dataset, we find that the lockdowns imposed in Uganda reduced employment by 69% for women and by 45% for men, generating a previously nonexistent gender gap of 20 p.p. Eighteen months after the onset of the pandemic, the gap persisted: while men quickly recovered their pre-pandemic career trajectories, 10% of the previously employed women remained jobless and another 35% remained occasionally employed. Additionally, the lockdowns shifted female workers from wage-employment to self-employment, relocated them into agriculture and other unskilled sectors misaligned with their skill sets, and widened the gender pay gap. Pre-pandemic sorting of women into economic sectors subject to the strongest restrictions and childcare responsibilities induced by schools’ prolonged closure only explain up to 65% of the employment gap.
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Notes
Adams-Prassl et al. (2020); Amuedo-Dorantes et al. (2023); Deshpande (2022); Farré et al. (2022); Heggeness (2020); Kristal and Yaish (2020); Andrew et al. (2022); Casale and Posel (2021); Dang and Viet Nguyen (2021); Kikuchi et al. (2021); Landivar et al. (2020); Reichelt et al. (2021); Kugler et al. (2023); Alon et al. (2022); and Casale and Shepherd (2022) find disproportionate effects of the economic restrictions on female workers. Torres et al. (2021); Gulesci et al. (2021); and Alfonsi et al. (2021) focus on entrepreneurs.
Authors’ elaboration of the latest Uganda National Household Survey from 2016/2017.
Exceptionally, schools reopened in October 2020 for students enrolled in the last year of their education cycle.
Like most Ugandan VTIs, none of these five tracked their graduates’ career developments nor kept their updated contacts. We therefore collected and digitized schools’ hard copies of registries for multiple cohorts of graduates, obtained contacts for 1368 alumni, and successfully contacted 52% of them. Our sample is not evidently selected with respect to the eligible population: due to the written nature and manual entry of the records, the digitization process was prone to error; additionally, the progressive implementation of the 2013 mandate of the Uganda Communication Commission to register all SIM-cards exogenously pushed many to change their phone numbers. Figure 9 shows an example of the digitized material.
This work was implemented in partnership with BRAC Uganda as a spin-off study of the Meet Your Future Project (Alfonsi et al., 2023), a randomized control trial connecting graduating vocational students with successful alumni of their schools to facilitate students’ transition into the labor market. The respondents of this study represent the pool of alumni from which we selected 154 young professionals who participated to the project as mentors for the students. To identify successful alumni who could provide quality mentorship to the students, we collected detailed information about their demographics, education, and work experience. Some of the variables we collected to make the selection are also primary outcomes in this study. There is no reason to believe that our respondents manipulated their answers to increase their chances to be selected. First, because the selection was based on merit but also on the goal to recruit mentors for each combination of school and course of study for which we had students, reducing the competition based on personal traits. Second, because the symbolic compensation and travel reimbursement we promised to respondents selected as mentors were likely insufficient to generate misreporting incentives, especially when weighted against the significant time and commitment that mentors put into preparation and actual implementation of the program. Third, because we elicited respondents’ broad interest in the project without informing them about the selection criteria. Hence, they were in practice unable to manipulate their score. Additionally, given our effort to find male and female mentors in similar fashion, there is no reason to believe that misreporting incentives differed by gender. Our findings are robust to excluding the respondents who served as mentors in the Meet Your Future Project from the sample.
The possibility that our respondents suffered from recollection bias is the main risk from using retrospective information. If true, we could overstate the autocorrelation between outcomes over time (Godlonton et al., 2018), and the existence of a gender gap in our outcomes at the time of measurement may lead us to overestimate the gap in recollected periods. To explain this point, suppose that the employment rate is lower for women than for men at time T, when there is no reporting bias. Then, women would be more likely, due to recollection bias, to say they were not employed in T-1; the opposite would be true for men, and we would overstate the employment gender gap in T-1. There are, however, several reasons why recollection bias is likely limited in our context. First, recollection bias is more pronounced among poor individuals (Das et al., 2012), while our respondents belong to the top tail of the education and income distribution in Uganda. Second, salient events are less subject to recollection bias (Beegle et al., 2012; Das et al., 2012). We structured our questionnaire to clearly identify moments before, during, or after the two nationwide lockdowns, which were disruptive events with tremendous consequences on the lives of our respondents and far beyond. We thus believe that our respondents accurately tracked their labor market outcomes around the lockdowns. Additionally, even if the recollected data points were considered unreliable and dropped from our analysis, all our conclusions would still apply.
In our data we cannot distinguish unemployed and not economically active individuals.
This dynamic is consistent with the positive association between employment and age found for vocational graduates of both genders in the UNHS (panel [a] of Fig. 11).
We reweight the female sample so that the average of Hit Sectori matches the male sample average. Hit Sectori is an indicator equal to one for respondents that pre-pandemic were employed (or trained, if non-employed) in a sector in which more than 50% of our respondents’ pre-pandemic businesses were closed during the first lockdown: motor-mechanics, food and hospitality, tailoring, hairdressing, teaching, secretary, and retail. Weights are equal to one for men.
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Acknowledgements
We thank Gaia Dossi, Andrew Foster, Supreet Kaur, Eliana La Ferrara, John Friedman, Selim Gulesci, Jeremy Magruder, Ted Miguel, Jonathan Roth, Elisabeth Sadoulet, Bryce Steinberg, Matthew Suandi, Diego Ubfal, Christopher Walters, seminar participants at the Berkeley Development Lunch and Development Therapy, the Brown University Development Tea, Applied Microeconomics Breakfast and Applied Microeconomics Lunch, the LEAP Alumni conference, the NEUDC conference, and the PacDev conference, and two anonymous referees for very insightful conversations and suggestions. Marco Lovato and Irina Vlasache provided excellent research assistance. We received IRB approval from UC Berkeley. A previous version of this paper circulated under the title “The Gendered Impacts of Covid-19: Evidence from the Ugandan Shecession”. All errors are our own.
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This work is supported by the International Development Research Centre via the BRAC-CEGA Learning Collaborative Secretariat; the IZA and the UK Foreign, Commonwealth & Development Office via the IZA/FCDO Gender, Growth and Labour Markets in Low Income Countries Programme (G2LIC∣IZA) [grant agreement GA-5-696]; the Watson Institute for International and Public Affairs at Brown University; the Orlando Bravo Center for Economic Research at Brown University; and the Institute for Research on Labor Economics at UC Berkeley. Our sponsors had no role in the study design, collection, analysis and interpretation of data, in the writing of the report and in the decision to submit the article for publication.
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M.N. was employed at BRAC Uganda and S.S. was supported by the James M. and Cathleen D. Stone Wealth and Income Inequality Project Fellowship (Spring 2022) and from the Graduate Program in Development Fellowship through the Watson Institute for International and Public Affairs (Fall 2020 and Spring 2021) during the writing of this paper. The authors declare that they have no relevant or material financial interests that relate to the research described in this paper.
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Appendix
Appendix
Sample Construction - Records Digitization
Educational Attainment of Ugandan Youths from UNHS and Study Sample. This figure shows the cumulative distribution function of years of education for the population of Ugandan adults aged 18–39 from the Uganda National Household Survey 2016/2017 (UNHS). The UNHS sample of young adults is reweighted so that its age and gender distribution matches that of the study sample. The four dashed lines indicate the number of years of education corresponding to completing primary education (7), completing lower secondary education (11), completing upper secondary education (13) and completing the National Certificate program at a Vocational Training Institute (15). The latter corresponds to the minimum education level attained by the respondents in our sample
Vocational Graduates’ Careers in the UNHS. This figure shows average employment rate (panel a) and monthly earnings in USD conditional on employment (panel b) by age and a fitted line separately for female and male respondents who completed post-secondary vocational education from the Uganda National Household Survey 2016/2017 (UNHS). The UNHS sample is restricted to respondents aged 18–39 and then reweighted so that its age and gender distribution matches that of the study sample. In panel (a), the slopes and standard errors of the fitted lines are 0.014 (0.01) for males and 0.012 (0.01) for females. In panel (b), they are 6.74 (1.28) for males and 2.72 (1.34) for females
The Emergence of Gender Disparities in Unskilled Employment After the Lockdowns. The figure illustrates the average share of respondents employed in agriculture (panel a) and in non-agricultural unskilled sectors (panel b) conditional on employment over time and by gender. Non-agricultural unskilled sectors include retail, and “Other Unskilled” occupations. For details on this residual category, see the notes to Table 1. The first data point refers to the respondents’ first activity after completing vocational education. It may coincide with the activity in January 2020 and its start and end date may be different for each respondent. 95% robust confidence intervals are reported
Robustness of Gender Gaps in Employment and Occupation Type in the Balanced Panel. The figure illustrates the average share of respondents that are employed (panel a), wage-employed (panel b), self-employed (panel c), enrolled in educational programs (panel d), and engaged in casual occupations (panel e) over time and by gender in the balanced panel of respondents. This sample includes the 456 respondents we successfully interviewed in all the four survey rounds. See the notes to Table 1 for details on how the variables are constructed. 95% robust confidence intervals are reported
Robustness of Gender Gaps in Employment Quality in the Balanced Panel. The figure illustrates the average employment rate in the training sector, unconditional (panel a) and conditional on employment (panel b), employment rate in skilled sectors, unconditional (panel c) and conditional on employment (panel d), and monthly earnings in USD, unconditional (pannel e) and conditional on employment (panel f), over time and by gender in the balanced panel of respondents. This sample includes the 456 respondents we successfully interviewed in all the four survey rounds. See the notes to Table 1 for details on how the variables are constructed. 95% robust confidence intervals are reported
The Evolution of Female Employment Rate for Different Cohorts of Vocational Graduates. The figure illustrates average employment rates for female respondents from different cohorts (i.e., who completed vocational graduation in different years) over time. Young, middle, and old cohorts refer to female respondents who graduated in 2019+, 2017–2018, and 2016− respectively. The young cohort includes 98 respondents. The middle cohort includes 113 respondents. The old cohort includes 81 respondents. 95% robust confidence intervals are reported
Heterogeneities in Effect of Lockdowns on Employment by Socio-Demographics. The figure illustrates average employment rates over time for respondents aged below and above the sample median (panel a), single and married (panel b), with and without children (panel c), from rural and urban households (panel d), with caretaker educated below and above the sample median (panel e), from agricultural and non-agricultural households (panel f), with household’s and own asset indexes above and below the sample medians (panels g and h), anxious about covid above and below median (panel i). At each point in time, a respondent is coded as employed if her main activity is either wage-employment or self-employment. The first data point refers to the respondents’ first job after completing vocational education. It may coincide with the job in January 2020 and its start and end date may be different for each respondent. 95% robust confidence intervals are reported
Gendered Effect of Lockdowns on Employment, Leaving Out one Training Sector at a Time
Female Concentration in Severely Impacted Economic Sectors Over Time. The figure displays the economic sectors in which our workers were employed pre-pandemic by the share of female workers hosted before the pandemic and the share of businesses that were closed in May 2020, July 2020, May 2021, and July 2021. A linear fit was added for each period. In May 2021 and July 2021, the share of business closed is approximated by the share of non-employed respondents. This measure has been validated by comparing the share of business closed and the share of non-employed workers in previous periods, when both variables are available. The slope and standard errors (in parenthesis) of the fitted lines are: 0.55 (0.12) in May 2020; 0.59 (0.19) in July 2020; 0.02 (0.09) in May 2021; and 0.24 (0.09) in July 2021
The Emergence and Persistence of a Gender Gap in Employment for Respondents in Mixed- and Single-Gender Sectors. The figure illustrates average employment rates separately for male and female respondents who received training in mixed- or single-gender sectors over time. Single-gender sectors are sectors in which more than 95% of the trainees have the same gender, as measured in our sample. Using this definition, motor-mechanics, welding and carpentry are fully-male sectors; tailoring ad teaching are fully-female sectors. Mixed-gender sectors include plumbing, food service and hospitality, hairdressing, construction, electrical work, secretary and accounting, agriculture, and machining and fitting. There are 194 women and 285 men in mixed-gender sectors and 101 women and 134 men in single-gender sectors. At each point in time, a respondent is coded as employed if her main activity is either wage- or self-employment. The first data point refers to the respondents’ first job after completing vocational education. It may coincide with the job in January 2020 and its start and end date may be different for each respondent. 95% robust confidence intervals are reported
Gender Gap in Impact of School Closure on Employment and Household Childcare Support. The figure displays the average employment rate for female and male respondents with different ratios of school-age children to adults in the households in periods in which schools were open (January and March 2020) and periods in which schools were closed (May, July and December 2020, May, July and September 2021). The higher the ratio, the heavier are childcare responsibilities. Respondents with a ratio equal to zero have no school-age children in the household. Respondents with a ratio between zero and one have more adults than school-age children in the household. Respondent with a ratio greater than one have multiple school-age children per adult in the household. There are 89 female and 229 male respondents with a ratio equal to zero; 98 female and 90 male respondents with a ratio between zero and one; and 28 female and 19 male respondents with a ratio greater than one. School-age children are children aged 3 or more. 95% robust confidence intervals are reported
Orthogonality of Employment Sectors and Childcare Responsibilities for Women. Panel (a) illustrates the distribution of the number of school-age children in the household in the original female sample and in the female sample reweighted so that that the first moment of Hit Sectori, an indicator for whether pre-pandemic the respondent was employed in a severely hit sector, matches that in the male sample. Weights are equal to one for male workers. School-age children are children aged three or more. Severely hit sectors are sectors in which more than 50% of the businesses in which our workers were employed pre-pandemic were closed during the first lockdown in May 2020: motor-mechanics, food and hotel, tailoring, hairdressing, teaching, secretary, and retail. The dashed and the dotted lines indicate the average number of school-age children in the original female sample and in the reweighted female sample respectively. Panel (b) illustrates the distribution of Hit Sectori in the original female sample and in the female sample reweighted so that the proportions of respondent with zero, one, and two or more school-age children in the household in the female sample match those in the male sample. The dashed and the dotted lines indicate the average of Hit Sectori in the original female sample and in the reweighted female sample respectively
Heterogeneities in Gendered Effect of Lockdowns on Employment by Socio-Economic Characteristics. The figure illustrates average employment rates over time for respondents with different gender and: aged below and above the sample median (panel a), single and married (panel b), with and without children (panel c), from rural and urban households (panel d), with caretaker educated below and above the sample median (panel e), from agricultural and non-agricultural households (panel f), with household’s and own asset indexes above and below the sample medians (panels g and h), anxious about covid above and below median (panel i). At each point in time, a respondent is coded as employed if her main activity is either wage- or self-employment. 95% robust confidence intervals are reported
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Alfonsi, L., Namubiru, M. & Spaziani, S. Gender gaps: back and here to stay? Evidence from skilled Ugandan workers during COVID-19. Rev Econ Household 22, 999–1046 (2024). https://doi.org/10.1007/s11150-023-09681-7
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DOI: https://doi.org/10.1007/s11150-023-09681-7