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Table 6 presents coefficient estimates from the propensity score models for males and females.
These results indicate that the IV-Probit model is appropriate to estimate the models for males, and the regular Probit model is appropriate for females.
To test the plausibility of wage elasticity differences as an explanation of earnings gaps, I estimate separation models for males and females separately, allowing for two tests of the results.
On the grounds that relationships may vary according to gender, we re-estimated the overeducation models for males and females separately (Table 3).9 The overall migrant variable and those controlling for time since arrival and country of origin were again insignificant.
We ran two separate models for males and females.
Based on the empty models for males and females.
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Furthermore, market segmentation with respect to the gender was performed, developing separate models for male and female respondents.
Comparison of Tables 5 and 6 and the separate models for male and female applicants in Table 7 suggest that the marginal effects of being an ex-offender for female applicants are somewhat larger than for male applicants.
All models for male tail length performed better than the random model (Table 1).
The top panel of Figure 5 compares the MFP and linear-quadratic BMI models for male and female never smokers, respectively, at age 50.
This methodology was used to propose models for male cats (n = 24), female cats (n = 24), for the two gender together (n = 48), and for cats before and after weight loss (n = 16).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com