Barriers to Entry: Decomposing the Gender Gap in Job Search in Urban Pakistan

Using matched data from three sources in Lahore, Pakistan, the paper finds that employers’ gender restrictions are a larger constraint on women’s job opportunities than supply-side decisions. At higher levels of education, demand-side barriers relax, allowing women to qualify for more jobs but at lower salaries. On the supply side, educated women become more selective in their search.


Appendix Tables
Note: Tertiary institute ranking is based on the ranking scores of universities by the Higher Education Commission (HEC).'High' ranking is assigned to all the universities that have a ranking score higher than the median score of 48.9 in our sample.'Medium' for institutes lying between 0 and 48.9.'Low' is for all those institutes that have not been assigned any score due to non-recognition by HEC.Source: Authors.Notes: This table reports the explanatory power (R-squared) of industry and occupation in gender restrictions.We regress an indicator for whether an ad is open to women on indicators for industry (column 1), occupation (column 2), or both (column 3).Each analysis drops singletons, i.e., industries with only 1 ad (column 1), occupations with only one ad (column 2), industry-occupation combinations with only 1 ad (column 3); hence the sample size varies between columns.Results are robust to dropping industries, occupations, and industry-occupation combinations with fewer than 5 ads.Standard errors are clustered by firm.* p < .1,** p < .05,*** p < .01.Source: Authors' estimates.Notes: This exhibit shows the swapping exercise for a CV pair.Three traits that were swapped in CV pairs were gender, secondary grade and university names.The traits to be swapped in any CV pair were determined randomly.Each of the traits was swapped with a probability of 50%.Disclaimer: These CVs contain fictional names; any similarity to an actual person is coincidental and unintentional.Source: Authors.

Figure A. 1 :
Figure A.1: Text Message Screenshot (translation of Urdu text)

Figure
Figure A.3: Qualify education/experience across salary quintiles, by education Panel A

Figure A. 4 :
Figure A.4: Firm gender composition and willingness to hire women

Figure A. 5 :
Figure A.5: Composition and gender restrictions of ads on platform by industry

Figure A. 7 :
Figure A.7: Vacancy characteristics and gender restrictions-with and without fixed effects

Table A .
1: Jobseeker selection into use of Job Talash platform Notes: Table compares the sample of individuals surveyed in the household listing exercise of this study (column 2) to an external benchmark: the area of Lahore where the study takes place (column 1).Lahore statistics are calculated from the Lahore subsample of the Pakistan Labor Force Survey (LFS) 2018.Standard deviations are shown in parentheses for continuous variables.Source: Job Talash platform.

Table A .
2: Firm selection into use of Job Talash platform

Table A
Note: Column 1 and 2 report average value of a CV trait for men and women.Column 3 reports p-values of the difference of means in column 1 and 2. 'Tertiary grades' range from 2-5 where 5 is A and 2 is D. 'Secondary grades' are coded the same as tertiary grades and apply to only those people who have higher than ten years of education.'3-5 years experience' is an indicator variable; years of experience for all CVs used in the IRR was less than five years.* p < .1,** p < .05,*** p < .01.Source: Authors' estimates.

Table A
,825 total firms surveyed.Firms who participate in the longer survey respond to questions about their employees, vacancies, gender composition and infrastructure along with an Incentivised Resume Rating module.A total of 87 firms agreed to participate in the IRR.

Table A .
6: Occupation lists provided to Jobseekers on Job Talash platform Source: Authors.

Table A
The unit of observation is a jobseeker-job dyad, assuming every individual surveyed signs up for the platform.We collect gender and education information for all individuals surveyed, and we use this to understand if these individuals would qualify for a given job along these two dimensions, had they signed up.The constant is the mean for males.

Table A
Notes: The unit of observation is a jobseeker-job dyad, for all jobseekers who sign up and all jobs posted on the platform, excluding the 41 vacancies for which the firm did not report gender composition.Zero female firm is the omitted category.Robust SEs in brackets, two-way clustered by jobseeker and vacancy.* p < .1,** p < .05,*** p < .01.Source: Authors' estimates.

Table A
Notes: The unit of observation is a jobseeker-job dyad, for all jobseekers who sign up and all jobs posted on the platform.Index refers to female friendly workspace index.Index includes indicators for if the firm has separate toilets and prayer spaces for women, and an indicator for if women work in a separate space (separate room/hall).This index is only computed for firms who answer questions about their infrastructure (53.9% of the sample).Robust SEs in brackets, two-way clustered by jobseeker and vacancy.* p < .1,** p < .05,*** p < .01.Source: Authors' estimates.

Table A .
10: Explanatory power of industry and occupation in predicting gender restrictions

Table A .
11: Relative value of CV attributes estimated from IRR choices This table displays results from an OLS regression of 'CV Chosen' (a binary indicator equal to 1 if CV was chosen) on different CV attributes.The unit of observation is a CV.The table includes CVs with at least a secondary education.The omitted category is primary education.'Experience' is a dummy.It is 0 for no experience at all and 1 for any experience greater than zero and up to five years.Robust standard errors in parentheses clustered by CV pairs.Tertiary institute ranking is based on the ranking scores of universities by the Higher Education Commission.'High' ranking is assigned to all the universities that have a ranking score higher than the median score of 48.7 in our sample.'Medium' for universities lying between 0 and 48.9.'Low' is the omitted category for all those universities that have not been assigned any score due to non-recognition by HEC.Regressions in Col 1 and 4 are run on mixed gender pair CVs, Col 2 and 5 on single gender pairs and Col 3 and 6 on all the CV pairs.* p < .1,** p < .05,*** p < .01.