Working Paper: CEPR ID: DP8294
Authors: Michael Lechner; Conny Wunsch
Abstract: Based on new, exceptionally informative and large German linked employer-employee administrative data, we investigate the question whether the omission of important control variables in matching estimation leads to biased impact estimates of typical active labour market programs for the unemployed. Such biases would lead to false policy conclusions about the cost-effectiveness of these expensive policies. Using newly developed Empirical Monte Carlo Study methods, we find that besides standard personal characteristics, information on individual health and firm characteris-tics of the last employer are particularly important for selection correction. Moreover, it is important to account for past performance on the labour market in a very detailed and flexible way. Information on job search behaviour, timing of unemployment and program start, as well as detailed regional characteristics are also relevant.
Keywords: active labour market policies; job search assistance; matching estimation; training
JEL Codes: J68
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Omitting key control variables (C29) | Biased impact estimates of active labor market programs (J68) |
| Including health information (I10) | Alters estimated effects of program participation on labor market outcomes (J68) |
| Failure to account for health information (I10) | Considerable bias in impact estimates (C51) |
| Controlling for timing of unemployment and program start (C41) | Obtaining unbiased estimates (C51) |
| Neglecting caseworker assessments and regional information (I38) | Distorts evaluations of program effectiveness (H43) |
| Lack of important control variables (C29) | Biases impact estimates (C51) |
| Biases impact estimates (C51) | Misleads policymakers regarding cost-effectiveness (D61) |