Long-Run Effects of Public Sector Sponsored Training in West Germany

Abstract

We estimate the short‐, medium‐, and long‐term effects of different types of government‐sponsored training in West Germany using particularly rich data that allows us to control for selectivity by matching methods and to measure interesting outcome variables over eight years after a program's start. We use distance‐weighted radius matching together with a bias removal procedure based on weighted regressions in order to increase the precision and robustness of standard matching estimators. We find negative employment effects in the short term for all program types, effects whose magnitude and persistence is directly related to program duration. In the longer term, training seems to increase employment rates by 10–20 percentage points. For most programs the longer‐term positive effects seem to be sustainable over the eight‐year observation period.

Are the Children of Uneducated Farmers Doubly Doomed? Farm, Nonfarm and Intergenerational Educational Mobility in Rural China

“…The matching estimators are: (i) bias corrected nearest neighborhood matching due to Abadie and Imbens (2002) (henceforth called 'A-I Matching'), and (ii) bias corrected radius matching (henceforth called 'BC-RM') due to Lechner et al (2011). The two estimators based on propensity score weighting are: Normalized Inverse Propensity Score Weighted (NIPW) estimator due to Hirano and Imbens (2001) and Hirano et al (2003), and the Minimum Biased estimator (MB-NIPW) proposed by Millimet and Tchernis (2013).…”

“…20 There is substantial monte-carlo evidence in favor of these estimators for estimating causal effects with non-experimental data. Busso et al (forthcoming) provide evidence that NIPW performs best among a large set of matching and propensity score estimators in estimating a binary treatment effect, and Huber et al (2013) provide extensive evidence from empirical Monte-carlo that the BC-RM estimator due to Lechner et al (2011) performs very well among a wide set of estimators. While NIPW reduce biases in the estimates compared to the OLS estimates by using appropriate weighting, the MB-NIPW estimator is especially useful, because it minimizes the biases arising from selection on unobservables.…”

Training and search during unemployment

“…This seems consistent with the model's recommendation, as the human capital of these groups may be particularly low or have dropped due to their job loss. 21 …”

“…Both training and search e¤orts are costly for the agent. If training e¤orts increase the marginal cost of search (e.g., time spent on training reduces the time available for search), the required participation to training programs implies a negative lock-in e¤ect with low exit rates when programs are intensive (Lechner, Miquel and Wunsch 2011). I characterize the optimal unemployment insurance contract, specifying the consumption levels during unemployment and upon reemployment and the training 1 Spending on labor market programs, active and passive, averages about 3 percent of GDP in the OECD countries.…”

Education, unemployment and migration

“…When reinterpreting our distribution of education costs as distribution of success probabilities, we should expect that too many people participate in highly subsidized education and training programs. Thus, it is not astonishing that evaluation studies often …nd zero or small impacts of such programs on subsequent employment probabilities and earnings of the participants (see, e.g., Heckman et al, 1999;Bergemann et al, 2004;Lechner et al, 2005;Albrecht et al, 2005).…”