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In ridge regression the estimation of the ridge parameter is an important issue. This paper generalizes some methods for estimating the ridge parameter for probit ridge regression (PRR) model based on the work of Kibria et al. (2011). The performance of these new estimators are judged by calculating the mean square error (MSE) using Monte Carlo simulations. In the design of the experiment we chose to vary the sample size and the number of regressors. Furthermore, we generate explanatory variables that are linear combinations of other regressors, which is a common situation in economics. In an empirical application regarding Swedish job search data we also illustrate the benefits of the new method.

The effects of wage dispersion on productive efficiency is a topic rich in theoretical conjecture, a common object of Scandinavian polemical debate and at the same time an issue almost barren of systematic econometric evidence. The Swedish record of enormous compression of relative wages under the institutional regime of centralized solidarity bargaining, followed by substantial de-compression of wages after central bargaining broke down, supplies observations well suited for empirical testing of theories and assertions about the response of productive efficiency to shifts in wage distribution. Results presented in this paper obtained from regression experiments based on distribution-augmented production and labor productivity functions yield no support of &#39fairness, morale and cohesiveness&#39 theories implying that wage leveling within workplaces and industries may enhance productivity. We do find substantial evidence, however, that reduction of inter-industry wage differentials contributed positively to aggregate output and productivity growth, most likely for the structural reasons first emphasized by leading Swedish trade union economists almost a half century ago.