In this paper, we propose a new variance reduction method for quantile regressions with endogeneity problems, for alpha-mixing or m-dependent covariates and error terms. First, we derive the asymptotic distribution of two-stage quantile estimators based on the fitted-value approach under very general conditions. Second, we exhibit an inconsistency transmission property derived from the asymptotic representation of our estimator. Third, using a reformulation of the dependent variable, we improve the efficiency of the two-stage quantile estimators by exploiting a tradeoff between an inconsistency confined to the intercept estimator and a reduction of the variance of the slope estimator. Monte Carlo simulation results show the fine performance of our approach. In particular, by combining quantile regressions with first-stage trimmed least-squares estimators, we obtain more accurate slope estimates than 2SLS, 2SLAD and other estimators for a broad set of distributions. Finally, we apply our method to food demand equations in Egypt.

Heterogeneity in how some independent variables affect a dependent variable is pervasive in many phenomena. In this respect, this paper addresses the question of constant versus nonconstant effect through quantile regression modelling. For linear quantile regression under endogeneity, it is often believed that the fitted-value setting (i.e., replacing endogenous regressors with their exogenous fitted-values) implies constant effect (that is: the coefficients of the covariates do not depend on the considered quantile, except for the intercept). Here, it is shown that, under a weakened instrumental variable restriction, the fitted-value setting can allow for nonconstant effect, even though only the constant-effect coefficients of the model can be identified. An application to food demand estimation in 2012 Egypt shows the practical potential of this approach.

Training costs may hamper intra-firm human capital accumulation. As a consequence, firms may be tempted to have workers pay for their on-the-job training (OJT). In this paper, we analyse the links of OJT and worker remuneration in the suburb of Tunis, using case study data for eight firms. We find that the duration of former OJT negatively influences starting wages, while there is no anticipated effect of future training on wages at the firm entry. In contrast, current wages are positively affected by former OJT but negatively affected by ongoing OJT. These results provide very rare empirical support in Less Developed Countries (LDCs) for classical human capital theories and cost sharing theories applied to OJT.

We conduct a case study of the linkages of task organization, human capital accumulation and wages in Morocco, using matched worker-firm data for Electrical-mechanical and Textile-clothing industries. In order to integrate task organization into the interacting processes of workers’ training and remunerations, we assume a recursive model, which is not rejected by our estimates: task organization influences on-the-job training that affects wages. Beyond sector and gender determinants, assignment of workers to tasks and on-the-job training is found to depend on former education and work experience in a broad sense. Meanwhile, participation in on-the-job training is stimulated by being assigned to a team, especially of textile sector and for well-educated workers. Finally, task organization and on-the-job training are found to effect wages.