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Macroeconomic shocks and the probability of being employed

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  • Tom Kornstad
  • Ragnar Nymoen
  • Terje Skjerpen

    ()
    (Statistics Norway)

Abstract

Macroeconomic theories take polar views on the importance of choice versus chance. At the micro level, it seems realistic to assume that both dimensions play a role for individual employment outcomes, although it might be difficult to separate these two effects. Nevertheless the choice and chance dimension are seldom treated symmetrically in models that use micro data. We estimate a logistic model of the probability of being employed among married or cohabitating women that are in the labor force. Besides variables that measure individual characteristics (choice), we allow a full set of indicator variables for observation periods that represent potential effects of aggregate shocks (chance) on job probabilities. To reduce the number of redundant indicator variables as far as possible and in a systematic way, an automatic model selection is used, and we assess the economic interpretation of the statistically significant indicator variables with reference to a theoretical framework that allows for friction in the Norwegian labor market. In addition, we also estimate models that use the aggregate female and male unemployment rates as ‘sufficient’ variables for the chance element in individual employment outcomes. Data are for Norway and span the period 1988q2-2008q4.

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Bibliographic Info

Paper provided by Research Department of Statistics Norway in its series Discussion Papers with number 675.

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Date of creation: Jan 2012
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Handle: RePEc:ssb:dispap:675

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Keywords: Job probability; Automatic model selection; Random utility modeling;

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  1. David Hendry & Hans-Martin Krolzig, 2003. "The Properties of Automatic Gets Modelling," Economics Series Working Papers 2003-W14, University of Oxford, Department of Economics.
  2. Kevin D. Hoover & Stephen J. Perez, 1999. "Data mining reconsidered: encompassing and the general-to-specific approach to specification search," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 167-191.
  3. Dale T. Mortensen & Christopher A. Pissarides, 1993. "Job Creation and Job Destruction in the Theory of Unemployment," CEP Discussion Papers dp0110, Centre for Economic Performance, LSE.
  4. David F. Hendry & Hans-Martin Krolzig, 1999. "Improving on 'Data mining reconsidered' by K.D. Hoover and S.J. Perez," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 202-219.
  5. Per Krusell & Toshihiko Mukoyama & Richard Rogerson & Ayşegül Şahin, 2010. "Aggregate labor market outcomes: The roles of choice and chance," Quantitative Economics, Econometric Society, vol. 1(1), pages 97-127, 07.
  6. Robert E. Hall, 2005. "Job Loss, Job Finding, and Unemployment in the U.S. Economy Over the Past Fifty Years," NBER Working Papers 11678, National Bureau of Economic Research, Inc.
  7. Kydland, Finn E & Prescott, Edward C, 1982. "Time to Build and Aggregate Fluctuations," Econometrica, Econometric Society, vol. 50(6), pages 1345-70, November.
  8. John Dagsvik & Tom Kornstad & Terje Skjerpen, 2013. "Labor force participation and the discouraged worker effect," Empirical Economics, Springer, vol. 45(1), pages 401-433, August.
  9. Lovell, Michael C, 1983. "Data Mining," The Review of Economics and Statistics, MIT Press, vol. 65(1), pages 1-12, February.
  10. Jennifer Castle & David Hendry & Jurgen A. Doornik, 2010. "Evaluating Automatic Model Selection," Economics Series Working Papers 474, University of Oxford, Department of Economics.
  11. Mortensen, Dale T. & Pissarides, Christopher A., 2011. "Job Matching, Wage Dispersion, and Unemployment," OUP Catalogue, Oxford University Press, number 9780199233786 edited by Tatsiramos, Konstantinos & Zimmermann, Klaus F..
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