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Statistical Assistance for Programme Selection - For a Better Targeting of Active Labour Market Policies in Switzerland

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  • Stefanie Behncke
  • Markus Frölich
  • Michael Lechner

Abstract

In this paper the motivation and various concepts of statistical systems for assisting case workers in assigning unemployed persons to active labour market programmes (ALMP) are examined and the particular implementation of such a statistical system in Switzerland, which was introduced in the form of a randomized pilot study, is discussed.

Suggested Citation

  • Stefanie Behncke & Markus Frölich & Michael Lechner, 2006. "Statistical Assistance for Programme Selection - For a Better Targeting of Active Labour Market Policies in Switzerland," University of St. Gallen Department of Economics working paper series 2006 2006-09, Department of Economics, University of St. Gallen.
  • Handle: RePEc:usg:dp2006:2006-09
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    References listed on IDEAS

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    1. Marco Caliendo & Reinhard Hujer & Stephan Thomsen, 2008. "Identifying effect heterogeneity to improve the efficiency of job creation schemes in Germany," Applied Economics, Taylor & Francis Journals, vol. 40(9), pages 1101-1122.
    2. Markus Frölich & Michael Lechner & Heidi Steiger, 2003. "Statistically Assisted Programme Selection - International Experiences and Potential Benefits for Switzerland," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 139(III), pages 311-331, September.
    3. Randall W. Eberts, 2005. "After the doors close: assisting laid-off workers to find jobs," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 29(Q II), pages 75-85.
    4. Michael Gerfin & Michael Lechner, 2002. "A Microeconometric Evaluation of the Active Labour Market Policy in Switzerland," Economic Journal, Royal Economic Society, vol. 112(482), pages 854-893, October.
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    Cited by:

    1. Staghøj, Jonas & Svarer, Michael & Rosholm, Michael, 2007. "A Statistical Programme Assignment Model," IZA Discussion Papers 3165, Institute of Labor Economics (IZA).
    2. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    3. Awotide, Bola Amoke & Awoyemi, Taiwo Timothy & Diagne, Aliou & Ojehomon, Vivian E.T., 2012. "Impact of Seed Voucher System on Rice Farmers’ Welfare in Nigeria: A Randomized Control Trial Approach," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 124995, International Association of Agricultural Economists.
    4. Philip J. O’Connell & Seamus Mcguinness & Elish Kelly, 2012. "The Transition from Short- to Long-Term Unemployment: A Statistical Profiling Model for Ireland," The Economic and Social Review, Economic and Social Studies, vol. 43(1), pages 135-164.
    5. Bas van der Klaauw & Sandra Vriend, 2015. "A Nonparametric Method for Predicting Survival Probabilities," Tinbergen Institute Discussion Papers 15-126/V, Tinbergen Institute.
    6. Jonas Staghøj & Michael Svarer & Michael Rosholm, 2010. "Choosing the Best Training Programme: Is there a Case for Statistical Treatment Rules?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(2), pages 172-201, April.
    7. Loxha, Artan & Morgandi, Matteo, 2014. "Profiling the unemployed : a review of OECD experiences and implications for emerging economics," Social Protection Discussion Papers and Notes 91051, The World Bank.

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    More about this item

    Keywords

    Statistical treatment rules; treatment effect heterogeneity;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • J68 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Public Policy

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