Sentence examples for wage coefficient from inspiring English sources
The negative wage coefficient effect (|β m |>|β f |) suggests that women are less likely to quit in the face of a wage decrease than men.
The wage coefficient associated with the share of women is also significantly negative and three times higher compared to the wage discrimination against non-EU workers.
These choices do have effects on the coefficient effects for these variables but not to the wage coefficient of interest here.
As the focus is on the relationship of the wage coefficient to the overall coefficient effect, normalization concerns are not expected to affect this relationship.
By contrast, the wage coefficient for non-EU men equals −0.03 and is significantly lower compared to the penalty against EU women.
where β e is the ln(wage) coefficient from the hazard model in Eq. (10) for job separations to employment, β ne is the ln(wage) coefficient from the hazard model in Eq. (10) for job separations to non-employment, and θ is the proportion of separations that result in other employment.
The sign and statistical significance of wage coefficients and gender differences are robust across various hazard models.
The productivity coefficients are 4.97 versus −0.76, and the wage coefficients are 2.25 versus −0.33, for small and large firms respectively.
First, knowing the reason for job separations is critical, as elasticity estimates based on hazard models of quits vs. layoffs produce very different wage coefficients.
Second, I find that the sign of wage coefficients in the hazard model of voluntary separations is robust to various distributions.
The negative coefficient effect is due to lower wage coefficients for males (left (beta ^{m}_{text {vs}}=-0.810 }=-0.810 ^{f}_{text {vs.} = -0.460right)), a difference which serves to lower the gap in quit rates.
