Active Labour Market Policy Evaluation in Luxembourg (ALMPs)
Given the high cost of labour market programmes, the fundamental necessity to evaluate whether interventions have had or will have their intended effects, and the need for better tools to design future policies, evaluation is becoming a fundamental aspect of policy making. It should provide answers about what programmes work for whom and why, and how to design optimal future policies. OECD (2010, 2012) policy recommendations are clearly formulated “… job prospects amongst unemployed and cost effectiveness would benefit from a better design of labour market programmes in Luxembourg”. Few studies exist on the effectiveness of active labour market policies (ALMPs) in the country (e.g., Brosius and Zanardelli, 2012). They report a positive effect of the training on post-treatment employment in the short-term, but reduced in the long-run. However, a variety of heterogeneous LM sub-programmes took place in the country (e.g., trainings, public employment contracts, internships...etc), with a focus on different target groups, objectives, and duration as well as selection rules. This questions the validity of the classical (binary) treatment effect approach (that is, evaluation of being trained or not on sub-sequent labor market outcomes). In contrast, the type of the measure received should be acknowledged to uncover new type of insights on the effectiveness of alternative ALM schemes generally implemented by the local government. Finally, ESF supported activities related to the promotion of ALMPs will be also included in our studies.
The aim of this project is to contribute to this field of research by:
- building the relative administrative dataset, collecting information included in the global social security database on labour force in Luxembourg (IGSS) and the administrative data collected by the Employment Agency (ADEM);
- providing systematic studies of all active labour market programmes in Luxembourg (such as descriptive statistics and survival analyses);
- conducting impact evaluation studies of all active labour market programmes implemented in the country, based on advanced methodologies that guarantee the robustness and reliability of the results as well as the tractability and availability of tools for policy analysis.
- Blanco, G., Flores, C., Flores-Lagunes, A., (2014) “Bounds on Average and Quantile Treatment Effects of Job Corps Training on Wages”, The Journal of Human Resources, forthcoming.
- Brosius, J., Zanardelli, M (2012), "Evaluation de l'efficacité des mesures de formation destinée aux chômeurs, LISER, Technical Report, Ministry of Labour Market and Employment of Luxembourg.
- Flores, C., Flores-Lagunes, A., Gonzalez, A., and Neuman, T., (2012) "Estimating the Effects of Length of Exposure to Instruction in a Training Program: The Case of Job Corps", The Review of Economics and Statistics, 94, 153-171.
- Flores, C. and Mitnik, O., (2014) “Comparing Treatments across Labor Markets: An Assessment of Nonexperimental Multiple-Treatment Strategies”, The Review of Economics and Statistics, forthcoming.
- Fitzenberger, B. and Völter, R., (2007) “Long-run effects of training programmes for the unemployed in East Germany”, Labour Economics, 14, 730-755.
- Heckman, J.J., Lalonde, R.J., and Smith, J.A. (1999) “The economics and econometrics of active labor market programs”, Handbooks in Economics, 5, 1865-2085.
- Kluve, J., Schneider, H., Uhlendorff, A., and Zhao, Z., (2012) “Evaluating Continuous Training Programs Using the Generalized Propensity Score”, Journal of the Royal Statistical Society, Series A, 175, 587-617.
- Kluve, J., (2007) “The effectiveness of European ALMP’s”, in Kluve et al. ALMPs in Europe: Performance and Perspectives. Berlin and Heidelberg: Springer, 153-203.
- Jin, H., and Rubin, D.B., (2008) “Principal stratification for causal inference with extended partial Compliance”, Journal of the American Statistical Association, 103, 101-111.
- Lechner, M. and Wiehler, S., (2013) “Does the order and timing of Active Labour Market Programmes Matter?”, Oxford Bulletin of Economics and Statistics, 75, 180-212.
- Mealli F., Pacini B. (2013) “Using secondary outcomes to sharpen inference in randomized experiments with noncompliance”, Journal of the American Statistical Association, 108, 1120-1131.
- Mercatanti, A., LI, F. And Mealli, F., (2012) “Improving inference of Gaussian mixtures using auxiliary variables”, Discussion Paper, Dept. Stat. Science, Duke University, Durham, NC.