|Accountant|0.41|0.69|1.68|0.28|0.00|
|---|---|---|---|---|---|
|Office administrative|0.01|0.86|59.6|0.85|0.00|
|Call center operator|0.49|0.71|1.45|0.22|0.03|
|**Male dominatedjobs**|**0.25**|**0.14**|**0.54**|**-0.11**|**0.00**|
|**Male domjobspre-cov**|**0.40**|**0.22**|**0.55**|**-0.18**|**0.20**|
|**Male domjobspost cov**|**0.24**|**0.13**|**0.53**|**-0.11**|**0.01**|
|IT specialist|0.40|0.22|0.56|-0.18|0.20|
|Welder|0.27|0.18|0.65|-0.09|0.12|
|Driver|0.20|0.07|0.37|-0.13|0.01|
|Discrimination differ?|Chi2( 5) =|77.95; Prob > c|hi2 = 0.0000|||
|Observations|771|||||

<mark>Note: To test whether gender discrimination differs significantly by job, we run the probit regression. Response(0,1)= α +βI</mark><sup><mark>JobType</mark></sup> <mark>+ μI</mark><sup><mark>Female</mark></sup> <mark>+ π(I</mark><sup><mark>JobType</mark></sup> <mark>×I</mark><sup><mark>Female</mark></sup> <mark>)+ε. The dependent variable is a dummy for receiving an interview, while I</mark><sup>JobType</sup> <mark>and I</mark><sup>Female</sup> <mark>are, respectively, indicators for each of six job types (omitting accountant) and being a female applicant. The Chi</mark><sup>2</sup> <mark>test above is a test for the joint significance of the five coefficients. The test is significant regardless of which of the four job type indicators are omitted.</mark> We observe significant heterogeneity across job types. For accountant, office manager, call center operator, driver positions, gender differences in callback rates were large, while for IT specialist positions and welder they were insignificant (due in part to lower overall callback rates and statistical power). For example, a man wishing to be employed as an accountant would have to submit 68 percent more applications to receive the same number of callbacks, and a man who aims to be employed as call center operator would have to submit 45 percent more applications. A man wishing to work as an office manager has a very low probability of a callback. Actual callbacks were so low that the magnitude of the difference is difficult to estimate, but the point estimates roughly suggest a man would have to submit 5,800 percent more applications to receive the same number of callbacks as a woman applying for the same position with the same experience. A woman wishing to work as a driver would have to submit 171 percent more applications. Our results document the extent to which women are preferred for female dominated positions, whereas men for male dominated positions. As such, they appear to be driving the results that women are discriminated against in receiving callbacks in male dominated occupations and men in female dominated occupations.

As an additional test of robustness, we split the estimation between pre- and post-COVID-outbreak phases of the study. The results (reported in Table 4) are qualitatively similar for the two periods, and in the expected direction. However, the results may be affected by difference in occupations (and the expected gender dominance of each) during the pre-pandemic period (pre-nationwide lockdown) and period after the outbreak in Uzbekistan. More detailed disaggregated results are provided in Annex 4. According to the Annex 4 the probability of female’ callback rates in both male- and female dominated jobs after the outbreak period decreased.

These findings are consistent with a considerable literature, including the empirical findings of Riach and Rich (2006), Booth and Leigh (2010), Carlsson (2011), Zhou et al. (2013). It is apparent that gender stereotyping in both male and female dominated occupations is widespread in Uzbekistan, as is the case in some other countries. Further confirming the underlying cause, we received several emails in which denials were explicitly based on gender. For instance, we recorded numerous cases for which an employer indicated that an office management or call center operator position is not suitable for men and driver position is not suitable for women.

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