# **IV.B. Regression result: Existence of gender preference**

The goals of our regression analyses in section _V.A.a_ . are (1) to determine whether gender-bias in hiring varies by occupations after controlling for attributes influencing hiring preferences, (2) to see if the hypothesis of negative skills relationship in general, that is professional and high-skilled jobs projecting less gender-preference, holds after controlling for other ad characteristics.

Coefficients, standard errors associated with coefficients, and odds ratios of logistic estimation are presented in Table 2. In all three regressions, the dependent variable Yi equals 1 if the ad specifies either male or female as preferred and 0 otherwise. We use city-level fixed effect (20 cities) to control for any unobserved city-level heterogeneity. Model A shows the estimates only controlling for occupational category. We add additional employer and ad characteristics as well as independent variables of our interest as we move across columns. The subsequently added variables in Model B are type of employer, number of vacancies under each ad, whether the job is part-time, whether the job requires work at night shift, natural log of offered salary, preferred experience measured in years, and year dummy variables to capture period effect. The column for Model C presents the estimates for the full model with city fixed effect.

Table2: Binary logistic estimation of Gender-Targeting in job ads

|||Model A||Model<br>job &|B (With em<br>time varia|ployer,<br>bles)|Model C|(With additio<br>fixed effect)|nal city|
|---|---|---|---|---|---|---|---|---|---|
||Coef.|(S.E.)|OR|Coef.|(S.E.)|OR|Coef.|(S.E.)|OR|
|Occupation category|(base=other)<sup>6</sup>|||||||||
|Professional|0.077***|(0.021)|1.08|-0.05**|(0.02)|0.95|-0.05**|(0.02)|0.95|
|Teaching|0.36***|(0.024)|1.43|0.02|(0.02)|1.02|0.02|(0.02)|1.02|
|BPO|-0.69***|(0.021)|0.50|-0.57***|(0.02)|0.57|-0.56***|(0.02)|0.57|
|Service|1.07***|(0.021)|2.91|0.72***|(0.02)|2.06|0.72***|(0.02)|2.06|
|Clerical|0.65***|(0.021)|1.92|0.49***|(0.02)|1.63|0.49***|(0.02)|1.63|
|Elementary|1.43***|(0.021)|4.17|1.15***|(0.02)|3.17|1.15***|(0.02)|3.17|
|Machine-related|1.25***|(0.021)|3.48|0.92***|(0.02)|2.52|0.93***|(0.02)|2.52|
|Sales|0.76***|(0.021)|2.13|0.67***|(0.02)|1.95|0.66***|(0.02)|1.95|
|Type of employer (b|ase= Staffing|company)||||||||
|HR enterprise||||0.23***|(0.01)|1.26|0.23***|(0.01)|1.26|
|Household||||0.29***|(0.01)|1.34|0.30***|(0.01)|1.35|
|Unknown||||-0.04***|(0.01)|0.96|-0.04***|(0.01)|0.97|
|SME||||0.29***|(0.01)|1.34|0.29***|(0.01)|1.34|
|Job characteristics||||||||||
|No. of vacancies||||-0.01***|(0.00)|0.99|-0.01***|(0.00)|0.99|
|Shift: Part-time||||0.31***|<br>(0.01)|1.36|0.31***|<br>(0.01)|1.37|
|Shift: Night||||-0.02|(0.03)|0.98|-0.03|(0.03)|0.97|
|Log (Real offered sa|lary)|||-0.36***|(0.01)|0.70|-0.36***|(0.01)|0.70|
|Experience (years)||||0.21***|(0.00)|1.23|0.21***|(0.00)|1.23|
|Period Dummy||||||||||
|Year 2012||||-0.05***|(0.01)|0.95|-0.05***|(0.01)|0.95|
|Year 2013||||0.03**|(0.01)|1.03|0.03**|(0.01)|1.03|
|Year 2014||||-0.06***|(0.01)|0.94|-0.06***|(0.01)|0.94|
|Year2015||||-0.27***|<br>(0.01)|0.76|-0.26***|<br>(0.01)|0.77|
|Year2016||||0.08***|(0.01)|1.08|0.08***|(0.01)|1.09|
|Year 2017||||-0.49***|<br>(0.02)|0.61|-0.49***|<br>(0.02)|0.61|
|City fixed effect|||||||YES|||
|<br>Constant|-1.147***|-0.02|0.32|2.06***|(0.06)|7.86|2.03***|(0.06)|7.58|
|Pseudo R2||0.075|||0.110|||0.111||
|Observation||830,929|||830,929|||830,929||

*** p<0.01, ** p<0.05, * p<0.1

6 ISCO1 classification has been used to categorize ads by occupation except for teaching and BPO which have been kept separate because of their sheer volume and importance as occupations.

9