Identifying effect heterogeneity to improve the efficiency of job creation schemes in Germany

Abstract

Previous empirical studies of job creation schemes (JCS) in Germany have shown that the average effects for the participating individuals are negative. However, we find that this is not true for all strata of the population. Identifying individual characteristics that are responsible for the effect heterogeneity and using this information for a better allocation of individuals therefore bears some scope for improving programme efficiency. We present several stratification strategies and discuss the occurring effect heterogeneity. Our findings show that JCS do neither harm nor improve the labour market chances for most of the groups. Exceptions are long-term unemployed men in West and long-term unemployed women in East and West Germany who benefit from participation in terms of higher employment rates.

Acknowledgements

The authors thank Richard Blundell and Jeff Smith for valuable comments. The article has also benefited from fruitful discussions at the annual meeting of the Italian Association of Labour Economists (AIEL) in 2004. Financial support of the Institute for Employment Research (IAB) within the project ‘Effects of Job Creation and Structural Adjustment Schemes’ is gratefully acknowledged. An earlier version of this article has circulated as ‘Effect Heterogeneity, Profiling and Targeting – How can we improve the Efficiency of Labour Market Policies?’ All remaining errors are our own. A supplementary appendix to this paper is available on request from the authors and can also be downloaded from http://www.caliendo.de/papers/identifying_supplement.pdf

Notes

¹ Other purposes of JCS, for example the relief of the stock of unemployed in regions with great imbalances of the labour market, are secondary only and will not be evaluated here.

² This is also a common finding in the recent evaluation literature of ALMP programmes in Europe. Whereas ALMP were seen as a reasonable opportunity to reduce and avoid unemployment for a long time, the international experiences with the implemented programmes show a mixed picture. The majority of programmes seem to be ineffective in terms of their goals. As the overviews by Martin and Grubb (2001) for OECD countries and Calmfors et al. (2002) for Sweden clarify, ALMP in their present design and implementation are not able to achieve a lasting reduction of unemployment.

³ The legal basis for JCS is §§ 260–271, 416 Social Code III. They have been the second most important instrument of ALMP in Germany in respect of the fiscal volume and the number of promoted individuals. For 2002 the number of promoted individuals in JCS amounts to 112 462 in East and 52 229 in West Germany. These figures correspond to spendings from 1639.5 million euro in East and 693.5 million euro in West Germany.

⁴ With the 2002 amendment, unemployed individuals whose only occupation opportunity is participation in JCS can be placed in programmes independently of the preceding unemployment duration. In addition, the 5%-Quota was augmented up to 10%.

⁵ The final version of the MTG includes information on all ALMP programmes of the FEA.

⁶ The value of good data is an essential building block for a valid evaluation. As for example Heckman,et al., (1998) mention, having access to a geographicallymatched comparison group administered the same questionnaire as programme participants matters in devising effective nonexperimental estimators of programme impacts.

⁷ Only the first programme participation is evaluated, any participation in later programmes is viewed as an outcome of the first treatment and is defined as a failure.

⁸ The special situation of the labour market in the capital city requires a separate evaluation of the integration effects of JCS into regular employment. The small number of participants aggravates the interpretation of the results.

⁹ See for example Heckman et al. (1999), Angrist and Krueger (1999) or Blundell and Costa-Dias (2002).

¹⁰ See Imbens (2004) or Smith and Todd (2005) for a recent review regarding matching methods.

¹¹ Relevant variables are all those covariates that jointly determine assignment to treatment and the potential outcomes.

¹² We have also estimated the propensity scores for the two regions using dummy variables for gender. However, using the results of the two estimations ignores possible gender-specific interaction effects and the fact, that the coefficients in the estimation differ in their significance and magnitude. This leads to a worse matching quality in the sense that the balancing of covariates after matching is reduced, i.e. the standardized bias (see subsequenttext) is higher.

¹³ All estimations are done using the PSMATCH2 Stata package by Leuven and Sianesi (2003).

¹⁴ See Caliendo and Kopeinig (2008) for an overview regarding such specification tests and other issues concerning the implementation of matching estimators.

¹⁵ The results of these estimations and the standardized biases before and after matching are available on request by the authors.

¹⁶ This are especially persons who are no more able to work in their profession due to health restrictions and therefore should receive a promotion for vocational rehabilitation.

¹⁷ Due to the large number of observations in our samples, using the whole range of the propensity scores of participants and nonparticipants leads to a skewed stratification. Hence, we refer to the propensity scores of the participants only to reduce this skewness. The choice of twenty strata for each of the four groups emerged from balancing tests of the propensity score among treated and comparison persons using a smaller number of blocks.

¹⁸ We also checked the balancing property of stratification by comparing the means of the incorporated variables in the logit models for participants and nonparticipants within each stratum as suggested by Rosenbaum and Rubin (1983). The results for selected variables are available on request by the authors.