An Imputation Model for Dropouts in Unemployment Data

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Abstract

Incomplete unemployment data is a fundamental problem when evaluating labour market policies in several countries. Many unemployment spells end for unknown reasons; in the Swedish Public Employment Service’s register as many as 20 percent. This leads to an ambiguity regarding destination states (employment, unemployment, retired, etc.). According to complete combined administrative data, the employment rate among dropouts was close to 50 for the years 1992 to 2006, but from 2007 the employment rate has dropped to 40 or less. This article explores an imputation approach. We investigate imputation models estimated both on survey data from 2005/2006 and on complete combined administrative data from 2005/2006 and 2011/2012. The models are evaluated in terms of their ability to make correct predictions. The models have relatively high predictive power.

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  • Arntz M. S. Lo and R. Wilke. 2007. “Bounds Analysis of Competing Risks: A Nonparametric Evaluation of the Effect of Unemployment Benefits on Migration in Germany.” ZEW - Centre for European Economic Research Discussion Paper No. 07-049. Doi: http://dx.doi.org/10.2139/ssrn.1010286.

  • Bennmarker H. K. Carling and A. Forslund. 2007. Vem blir långtidsarbetslös? Report 2007:29. Uppsala: Institute for Labour Market Policy Evaluation (IFAU). Available at: http://www.ifau.se/globalassets/pdf/se/2007/r07-20.pdf (accessed June 1 2016).

  • Bound J. C. Brown and N. Mathiowetz. 2001. “Measurement error in survey data.” In Handbook of Econometrics vol. 5 edited by J. Heckman and E. Leamer 3705–3833. Amsterdam: Elsevier. Available at: http://www.psc.isr.umich.edu/pubs/pdf/rr00-450.pdf (accessed June 1 2016).

  • Bring J. and K. Carling. 2000. “Attrition and Misclassification of Drop-outs in the Analysis of Unemployment Duration.” Journal of Official Statistics 16: 321–330. Available at: http://www.jos.nu/Articles/abstract.asp?article=164321 (accessed June 1 2016).

  • Heckman J. and B. Singer. 1982. “Population Heterogeneity in Demographic Models.” In Multidimensional Mathematical Demography edited by K. Land and A. Rogers 567–599. New York: Academic Press. Available at: http://www.popline.org/node/410098 (accessed June 1 2016).

  • Lancaster T. 1979. “Econometric Methods for the Duration of Unemployment.” Econometrica 47: 939–956. Doi: http://dx.doi.org/10.2307/1914140.

  • Lundström S. and C-E. Särndal. 2001. Estimation in the Presence of Nonresponse and Frame Imperfections. Örebro: SCB-Tryck.

  • Pyy-Martikainen M. and U. Rendtel. 2009. “Measurement Errors in Retrospective Reports of Event Histories. A Validation Study with Finnish Register Data.” Survey Research Methods 3: 139–155. Doi: http://dx.doi.org/10.18148/srm/2009.v3i3.2372.

  • Rubin D.B. 1987. Multiple Imputation for Nonresponse in Surveys. New York: John Wiley.

  • Rubin D.B. 1996. “Multiple Imputation After 18+ Years (with discussion).” Journal of the American Statistical Association 91: 473–489. Doi: http://dx.doi.org/10.1080/01621459.1996.10476908.

  • Wilke R. 2009. “Unemployment Duration in the United Kingdom: An Incomplete Data Approach.” Doi: http://dx.doi.org/10.2139/ssrn.1348019.

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