The employability of ex-offenders: a field experiment in the Swedish labor market

Table 6 Positive employer response probabilities for female applicants as a function job categories

	Model iii	Model iv	Δ in marginal effects for an ex-offender
	Marginal effect for an ex-offender	Marginal effects for a non-offender	Δ in marginal effects for an ex-offender
Ex-offender	−0.134*** (0.019)
Accounting clerk		Baseline category	−0.183*** (0.029)
Auto mechanic		0.111 (0.131)	−0.036 (0.038)
Cleaner		−0.004 (0.081)	−0.117*** (0.034)
Enrolled nurse		0.126 (0.108)	−0.079 (0.056)
Preschool teacher		0.396*** (0.107)	−0.161*** (0.024)
Restaurant worker		0.084 (0.078)	−0.093*** (0.023)
Salesperson		0.239* (0.137)	−0.137*** (0.036)
Software developer		0.380*** (0.131)	−0.130*** (0.038)
Truck driver		−0.118* (0.061)	0.057 (0.062)
Observations	980		980
Pseudo R²	0.101		0.172

Notes: The table presents the marginal changes in the probability of receiving a positive response from an employer estimated using a probit regression model. Corresponding linear probability models and fixed-effects models generate similar results. The dependent variable is a positive response dummy. Also included in each regression are a dummy for full-time positions, a dummy for tenure, county dummies, season dummies, an application template dummy, and a dummy for order of application. Descriptions of all variables are provided in Table 2. Reported standard errors (in parentheses) are corrected for clustering of the observations at the employer level
***p < 0.01, *p < 0.10