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Benchmarking regions: Estimating the counterfactual distribution of labor market outcomes

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  • Fitzenberger, Bernd
  • Furdas, Marina

Abstract

This paper develops and implements a new benchmarking approach for labor market regions. Based on panel data for regions, we use nonparametric matching techniques to account for observed labor market characteristics and for spatial proximity. As the benchmark, we estimate the counterfactual distribution of labor market outcomes for a region based on outcomes of similar regions. This allows to measure both the rank (relative performance) and the absolute performance based on the actual outcome for a region. Our outcome variable of interest is the hiring rate among the unemployed. We implement different similarity measures to account for differences in labor market conditions and spatial proximity, and we choose the tuning parameters in our matching approach based on a cross-validation procedure. The results show that both observed labor market characteristics and spatial proximity are important features to successfully match regions. Specifically, the modified Zhao (2004) distance measure and geographic distance in logs work best in our applications. Our estimated performance measures remain quite stable over time. --

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Bibliographic Info

Paper provided by ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research in its series ZEW Discussion Papers with number 12-023.

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Date of creation: 2012
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Handle: RePEc:zbw:zewdip:12023

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Keywords: matching function; regional employment offices; performance measurement; nonparametric matching; conditional quantile positions;

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  1. Aomar Ibourk & Bénédicte Maillard & Sergio Perelman & Henri Sneessens, 2004. "Aggregate Matching Efficiency: A Stochastic Production Frontier Approach, France 1990–1994," Empirica, Springer, vol. 31(1), pages 1-25, March.
  2. Andrew Worthington, 2001. "An Empirical Survey of Frontier Efficiency Measurement Techniques in Education," Education Economics, Taylor & Francis Journals, vol. 9(3), pages 245-268.
  3. Aomar IBOURK & Béatrice MAILLARD & Serge PERELMAN & Henri R. SNEESSENS, 2001. "Aggregate Matching Efficiency : A Stochastic Production Frontier Approach, France 1990-1994," Discussion Papers (IRES - Institut de Recherches Economiques et Sociales) 2001034, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
  4. Barbara Petrongolo & Christopher A. Pissarides, 2000. "Looking Into the Black Box: A Survey of the Matching Function," CEP Discussion Papers dp0470, Centre for Economic Performance, LSE.
  5. Fahr, René & Sunde, Uwe, 2005. "Regional Dependencies in Job Creation: An Efficiency Analysis for Western Germany," IZA Discussion Papers 1660, Institute for the Study of Labor (IZA).
  6. Gong, Byeong-Ho & Sickles, Robin C., 1992. "Finite sample evidence on the performance of stochastic frontiers and data envelopment analysis using panel data," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 259-284.
  7. Pekka Ilmakunnas & Hanna Pesola, 2003. "Regional Labour Market Matching Functions and Efficiency Analysis," LABOUR, CEIS, vol. 17(3), pages 413-437, 09.
  8. Zhong Zhao, 2004. "Using Matching to Estimate Treatment Effects: Data Requirements, Matching Metrics, and Monte Carlo Evidence," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 91-107, February.
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