Partial Idendification of Wage Effects of Training Programs

In an evaluation of a job-training program, the influence of the program on the in-dividual wages is important, because it reflects the program effect on human capital. Esti-mating these effects is complicated because we observe wages only for employed individuals, and employment is itself an outcome of the program. Only usually implausible assumptions allow identifying these treatment effects. Therefore, we suggest weaker and more credible assumptions that bound various average and quantile effects. For these bounds, consistent, nonparametric estimators are proposed. In a reevaluation of a German training program, we find that a considerable improvement of the long-run potential wages of its participants.Bounds; treatment effects; causal effects; program evaluation

Quantile Treatment Effects in the Regression Discontinuity Design: Process Results and Gini Coefficient

This paper shows nonparametric identification of quantile treatment effects (QTE) in the regression discontinuity design. The distributional impacts of social programs such as welfare, education, training programs and unemployment insurance are of large interest to economists. QTE are an intuitive tool to characterize the effects of these interventions on the outcome distribution. We propose uniformly consistent estimators for both potential outcome distributions (treated and non-treated) for the population of interest as well as other function-valued effects of the policy including in particular the QTE process. The estimators are straightforward to implement and attain the optimal rate of convergence for one-dimensional nonparametric regression. We apply the proposed estimators to estimate the effects of summer school on the distribution of school grades, complementing the results of Jacob and Lefgren (2004).quantile treatment effect, causal effect, endogeneity, regression discontinuity

Unconditional Quantile Treatment Effects under Endogeneity

This paper develops IV estimators for unconditional quantile treatment effects (QTE) when the treatment selection is endogenous. In contrast to conditional QTE, i.e. the effects conditional on a large number of covariates X, the unconditional QTE summarize the effects of a treatment for the entire population. They are usually of most interest in policy evaluations because the results can easily be conveyed and summarized. Last but not least, unconditional QTE can be estimated at √n rate without any parametric assumption, which is obviously impossible for conditional QTE (unless all X are discrete). In this paper we extend the identification of unconditional QTE to endogenous treatments. Identification is based on a monotonicity assumption in the treatment choice equation and is achieved without any functional form restriction. Several types of estimators are proposed: regression, propensity score and weighting estimators. Root n consistency, asymptotic normality and attainment of the semiparametric efficiency bound are shown for our weighting estimator, which is extremely simple to implement. We also show that including covariates in the estimation is not only necessary for consistency when the instrumental variable is itself confounded but also for efficiency when the instrument is valid unconditionally. Monte Carlo simulations and two empirical applications illustrate the use of the proposed estimators.instrumental variables, quantile treatment effects, nonparametric regression

Public Sector Pay Gap in France: New Evidence Using Panel Data

We estimate the public wage gap in France for the period 1990-2002, both at the mean and at different quantiles of the wage distribution, for men and women separately. We account for unobserved heterogeneity by using fixed effects estimations on panel data and, departing from usual practice, allow the public wage markup to vary over time. We also provide one of the very first applications of fixed effects quantile regressions. Contrary to common belief, results convey that monetary returns are not fundamentally different in the public sector. Firstly, public wage ‘premia’ (for women) or ‘penalties’ (for men) are essentially the result of selection. After controlling for unobserved heterogeneity, only small pay differences between sectors remain over time, reflecting fluctuations due to specific public policies and to the pro-cyclicality of private sector wages. The long-term difference is essentially zero. Secondly, the relative compression of the wage distribution by the public sector is also partly due to unobserved characteristics. The most natural explanation for these results is that the civil sector manages to attract better workers in the lower part of the distribution, in part because of non-monetary gains (including job protection), but fails to retain the most productive ones at the top.wage gap, public sector, selection, fixed effects, quantile regression

Quantile Regression in the Presence of Sample Selection

Most sample selection models assume that the errors are independent of the regressors. Under this assumption, all quantile and mean functions are parallel, which implies that quantile estimators cannot reveal any (per definition non-existing) heterogeneity. However, quantile estimators are useful for testing the independence assumption, because they are consistent under the null hypothesis. We propose tests for this crucial restriction that are based on the entire conditional quantile regression process after correcting for sample selection bias. Monte Carlo simulations demonstrate that they are powerful and two empirical illustrations indicate that violations of this assumption are likely to be ubiquitous in labor economics.Sample selection, quantile regression, independence, test

Unconditional quantile treatment effects under endogeneity

This paper develops IV estimators for unconditional quantile treatment effects (QTE) when the treatment selection is endogenous. In contrast to conditional QTE, i.e. the effects conditional on a large number of covariates X, the unconditional QTE summarize the effects of a treatment for the entire population. They are usually of most interest in policy evaluations because the results can easily be conveyed and summarized. Last but not least, unconditional QTE can be estimated at pn rate without any parametric assumption, which is obviously impossible for conditional QTE (unless all X are discrete). In this paper we extend the Identification of unconditional QTE to endogenous treatments. Identification is based on a monotonicity assumption in the treatment choice equation and is achieved without any functional form restriction. Several types of estimators are proposed: regression, propensity score and weighting estimators. Root n consistency, asymptotic normality and attainment of the semiparametric efficiency bound are shown for our weighting estimator, which is extremely simple to implement. We also show that including covariates in the estimation is not only necessary for consistency when the instrumental variable is itself confounded but also for efficiency when the instrument is valid unconditionally. Monte Carlo simulations and two empirical applications illustrate the use of the proposed estimators.

Quantile Treatment Effects in the Regression Discontinuity Design

This paper shows nonparametric identification of quantile treatment effects (QTE) in the regression discontinuity design (RDD) and proposes simple estimators. Quantile treatment effects are a very helpful tool to characterize the effects of certain interventions on the outcome distribution. The distributional impacts of social programs such as welfare, education, training programs and unemployment insurance are of large interest to economists.quantile treatment effect, causal effect, endogeneity, regression discontinuity

Earnings Effects of Training Programs

In an evaluation of a job-training program, the influence of the program on the individual earnings capacity is important, because it reflects the program effect on human capital. Estimating these effects is complicated because earnings are observed for employed individuals only, and employment is itself an outcome of the program. Point identification of these effects can only be achieved by usually implausible assumptions. Therefore, weaker and more credible assumptions are suggested that bound various average and quantile effects. For these bounds, consistent, nonparametric estimators are proposed. In a reevaluation of Germany's training programs of 1993 and 1994, we find that the programs considerably improve the long-run earnings capacity of its participants.Bounds, treatment effects, causal effects, program evaluation

Public-private sector wage differentials in Germany: Evidence from quantile regression

This paper measures and decomposes the differences in earnings distributions between public sector and private sector employees in Germany for the years 1984-2001. Oaxaca decomposition results suggest that conditional wages are higher in the public sector for women but lower for men. Using the quantile regression decomposition technique proposed by Machado and Mata (2004), we find that the conditional distribution of wages is more compressed in the public sector. At the low end of wages, differences in characteristics explain less than the raw wage gap when it is the opposite at high wages. Separate analyses by work experience and educational groups reveal that the most experienced employees and those with basic schooling do best in the public sector. All these results are stable over the 80s and 90

Privatization and Changes in the Wage Structure: Evidence from Firm Personnel Records

We investigate the wage effects of privatization using person-level firm-based panel datasets from one privatized and one nonprivatized public sector firm in the same country for the years immediately before and after privatization. Thus, we can analyze the before-after effects of privatization while controlling for individual and time fixed effects and allowing for firm-specific trends. Because the change in wage regime coincides with substantial losses in the market share of the privatized but not the nonprivatized firm, the situation approximates a natural experiment in switching workers from the public to the private sector. We find significant changes in the wage structure of the privatized but not the nonprivatized firm. Specifically, wage and wage growth distributions widened significantly after privatization. Conditioning on worker characteristics, we find that younger employees and those with shorter tenure gained from privatization, while high-skilled workers gained relative to medium-skilled workers. Surprisingly, low-skilled workers also gained, although seemingly in the form of temporary compensation intended to increase acceptance of privatization.privatization, liberalization, competition, labor markets, wage distributions