# **4. Inequality in labor market impacts**

## **Methodology**

To shed light on how different groups were affected in terms of employment, we test whether changes in the early-phase and long-run labor market outcomes described above differ across types of workers and households. First, we regress the relevant work indicator on the respondent’s gender, age, education level, family composition,<sup>11</sup> urban vs rural location, and income diversification (whether the household had income from more than one type of source). We then additionally control for pre-COVID-19 industry of work, which is taken from the most recent face-to-face LSMS-ISA survey.

As described in the previous section, the samples are restricted to respondents who were working before the COVID-19 outbreak. To assess common patterns, we run OLS regressions on the pooled sample of respondents across the four countries, controlling for country fixed effects and with standard errors clustered by enumeration area. Estimation results for country-specific and rural and urban sub-samples are reported in Appendix Tables A6 to A11. In what follows, we first present and discuss results for earlyphase labor market impacts, and then turn to the one-year labor market impacts.

## **Results: early-phase impacts**

Estimation results for the early-phase labor market outcomes are reported in Table 1. In the early phase of the pandemic, women were significantly more likely to lose their job than men (column 1). This finding is in line with the gender difference in employment trends across 40 developing countries documented by Kugler et al. (2021), although the disadvantage for women is more pronounced in our sample.<sup>12</sup> Conditional on industry (column 2), the gender gap is slightly larger, suggesting it is driven by gender differences in job loss within (aggregate) industries. Again, this is in line with findings for other developing countries (Kugler et al. 2021).<sup>13</sup> The gender gap in job loss is driven by urban areas (Table A6), while there is no significant gender difference in the rural sample.<sup>14</sup> Looking at the interaction terms of gender and children in column 1, we find no evidence that higher job loss among women is related to the presence of children. If anything, the gender gap in job loss is smaller among respondents with school-age children, suggesting job loss among women was not primarily driven by increased child-care demands related to school closures. In the urban sub-sample (Table A6), however, women with children age 0-4 were about 11 percentage points more likely to lose their job than men with young children. This seems to be driven mainly by the Nigerian sample. It is possible that care for very young children was most difficult to combine with work in urban areas, while formal or informal childcare was limited during lockdowns. It could also be that urban women were concentrated in specific activities that were more affected by lock-downs, such as hair-dressing and hospitality, which are not captured by our aggregate industries.

The disadvantage of female respondents in terms of job loss was further aggravated by a lower probability of re-employment (Table 1, columns 3-4). By October 2020, women were 14 to 20 percentage points less likely than men to be re-employed after job loss. Again, this gender gap is smaller among respondents

> 11 The presence of children age 0-4 and age 5-18, as well as interaction terms with gender

> 12 Other studies have also found disproportionate impacts on female workers. For example, Dang and Nguyen (2021) for China, Italy, UK and US; Farré et al. (2021) for Spain; and Casale and Posel (2021) for South Africa. Bluedorn et al. (2021) find that women’s employment rates declined faster than men’s in many advanced and emerging economies, although the gender gaps typically disappeared after three to six months. Likewise, Lee et al. (2021) show that in the US, the negative impact on employment was larger for women, but the gender gap had disappeared by November 2020.

> 13 Alfonsi et al. (2022) show that differential impacts across detailed sectors (such as hairdressing, tailoring, and plumbing) account for a large share of the gender gap in job loss among highly skilled young urban workers in Uganda.

> 14 In additional analyses, we found no effect of employment status (unpaid or self-employed worker versus employee) on job loss once we control for pre-COVID-19 industry. Including employment status does not affect the estimated gender gap.

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