Female labor force participation under the pandemic: evidence from the 2020 Uganda High-Frequency Phone survey on COVID-19

Figures & data

Figure 1. Trends in Female Force Participation in Uganda Compared to other developing East African Counties and the Sub-Saharan African (SSA) Region in general.

Source: Authors compilation from World Development Indicators (WDI), World Bank.

Figure 2. Female Labor Force Participation and Economic Growth, the U-shaped function.

Source: Gandhi (2020), computed from WDI and ILOSTAT.

Figure 3. Predictive Margins of COVID-19 threat with a 95% Confidence Interval (CIs).

Table 1. Summary of the descriptive statistics.

Category	Age	Frequency	Percent	Worked last week
Boys	<18 years	516	22.88
Girls	<18 years	528	23.41
Men	>18 years	574	25.45	No = 179 Yes = 395
Women	>18 years	637	28.25	No = 210 Yes = 427
Total		2,255	100.00	1,211

Figure 4. Predictive Margins of COVID-19 threat with a 95% Confidence Interval (CIs).

Table 2. The impact of COVID-19 lockdown on female labor force participation.

Panel	Variables	FLFP	No-FLFP	Run a business
A1	Female parent locked down (PLD_female)	0.0712* (0.039)	−0.136*** (0.090)	0.135* (0.076)
A2	Male parent locked down (PLD_male)	0.204*** 0.060	−0.517*** (0.144)	−0.034 (0.105)
A3	Both parents locked down (PLD_both)	−0.243** (0.098)	0.565*** (0.206)	0.063 (0.187)
A4	COVID-19 Pose high threat	−0.324*** 0.086	0.744*** (0.286)	−0.339* (0.180)
A5	COVID-19 Pose moderate threat	−.253** (0.120)	0.538* (0.294)	−0.358* (0.186)
Number of Observations		427	210	1209
Mean dependent variable		0.670	0.174	0.580
SD dependent variable		0.478	0.379	0.494
Regional fixed effects		No	No	No
Date or interview control		No	No	No
Pre-COVID-19 employment status		Yes	Yes	Yes

Notes: The dependent variable is female labor force participation (FLFP) which is Dichotomous, taking on a value = 1 if a woman was working before the interview during March 2020 when the pandemic broke out and zero otherwise. The sample includes all adult women above 18 years of age who were working or not working during the lockdown. With this analysis, the pre-COVID-19 employment status is controlled for in all subsequent models. Robust standard errors are captured in the parentheses as ***p < 0.01, **p < 0.05, *p < 0.1.

Table 3. The impact of COVID-19 Lockdown on Female Labor Force Participation: with subgroups.

	By Children present		By age of the women			By highest level of education
Female labor force participation	Children	No children	18–30	31–50	51+	No school	Primary	Post primary
Female locked	0.517*** (0.146	0.471* (0.261)	0.522** (0.213	0.407* (0.219)	0.753*** (0.247)	0.000	0.374** (0.172)	0.364* (0.208)
Male locked	0.665*** (0.229	0.317 (0.411)	0.181 (0.377)	1.006** (0.351)	0.174 (0.287)	0.041 (0.750)	0.627** (0.259)	0.722** (0.365)
Both locked	−1.122*** (0.363)	0.218 (0.534)	−0.357 (0.529	−1.341** (0.518)	−0.787* (0.440)	−1.852* (0.996)	−0.430 (0.423)	−1.034** (0.523)
High threat	−1.184*** (0.407)	−0.627 (0.631)	−1.112** (0.459)	−0.474 (0.547)	−5.372*** (0.407)	−6.395*** (0.783)	−0.835** (0.391)	−1.469* (0.845)
Moderate threat	−1.019** (0.410)	0.081 (0.654)	−1.068** (0.470)	−0.085 (0.545)	−4.821*** (0.445)	−3.341*** (1.024)	−0.626 (0.413)	−1.234 (0.853)
Own business	0.513*** (0.140)	1.080*** (0.278)	0.448** (0.194)	0.697*** (0.208)	0.838*** (0.294)	−0.166 (0.963)	0.629*** (0.181)	0.722*** (0.188)
Home-schooling	0.037 (0.151)	0.259 (0.243)	−0.222 (0.225)	0.305 (0.228)	0.072 (0.231)	−1.620** (0.810)	0.204 (0.185)	0.151 (0.199)
Behavior change Hand washing	0.680*** (0.248)	1.527*** (0.516)	0.822** (0.342)	0.970** (0.387)	0.736* (0.455)	−0.471 (0.628)	0.865*** (0.269)	1.481*** (0.460)
Masks	0.094 (0.259)	1.153** (0.559)	0.379 (0.378)	0.143 (0.409)	0.235 (0.467)	0.000	0.133 (0.289)	1.052** (0.488)
Marital status  Married	0.160 (0.244)	−0.786** (0.345)	−0.061 (0.347)	0.507 (0.367)	−0.225 (0.320)	1.818** (0.703)	−0.206 (0.273)	0.623* (0.357)
Others	0.452* (0.261)	−0.502 (0.344)	0.226 (0.365)	0.702* (0.394)	0.374 (0.356)	2.698*** (0.948)	0.201 (0.298)	0.753** (0.363)
Received assistance	0.182 (0.420)	−0.074 (0.767)	0.525 (0.495)	−0.843 (0.726)	0.889 (0.707)	0.000	1.248** (0.516)	−0.338 (0.446)
Number of Obs	459	172	216	214	205	50.	345	221
Mean dept variable	0.669	0.663	0.639	0.682	0.688	0.700	0.719	0.593
SD dept variable	0.471	0.474	0.481	0.467	0.465	0.463	0.450	0.492
Days of interview	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Worked pre-COVID	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Regional fixed effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
HH & women x-tics	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes

Notes: FLFP is a dummy variable representing the female labor force participation, this was captured in the question of whether the women worked the previous week before the survey, it is a dichotomous (dummy) variable taking one 1 if YES and 0 if NO work. Working before the outcome is controlled for as a dummy variable for those who were working and 0 otherwise. We control for lockdown as a dummy variable captured by unemployed at home or working from home. The self-reported COVID-19 concern or threat captures financial or income stress to the individual. Figures in the buckets represent Robust standard errors and Level of significance are displayed as ***p < 0.01, **p < 0.05, *p < 0.1.

Gandhi, D. (2020). Figure of the week: Economic gains from increasing female labor force participation in Africa. | Brookings. Retrieved February 15, 2024, from https://www.brookings.edu/articles/figure-of-the-week-economic-gains-from-increasing-female-labor-force-participation-in-africa/.

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Availability of data and materials

Data about the conclusions made in this study is available and it was downloaded from the World Bank website. Other additional datasets that support our results can be availed at the request of the corresponding author.