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Assessing labor market conditions in Greece: a note

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  • Salamaliki, Paraskevi

Abstract

This note uses 18 labor market variables and a dynamic factor model to construct labor market conditions indicators (LMCI) for Greece. The indicators capture common movements among the labor market series and assess improvement of the labor market across a number of dimensions. LMCI changes indicator was deteriorated during the crisis, yet it rebounded back to positive values in late 2013, with speed of improvement being on average much higher compared to the pre-2009 period. Speed of improvement was weakened in early 2015, a period associated with increased political and economic uncertainty. Level LMCI indicator re-exceeded its long-run average 7 years after beginning of the crisis, while its current level is far below levels for the entire sample until 2008. The unemployment rate is found to understate the deterioration and the improvement in labor market conditions in the pre-crisis and the post-crisis period, respectively.

Suggested Citation

  • Salamaliki, Paraskevi, 2019. "Assessing labor market conditions in Greece: a note," MPRA Paper 97559, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:97559
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    References listed on IDEAS

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    1. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    2. Michael W. McCracken & Serena Ng, 2016. "FRED-MD: A Monthly Database for Macroeconomic Research," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 574-589, October.
    3. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1014-1024, November.
    4. Congressional Budget Office, 2014. "The Slow Recovery of the Labor Market," Reports 45011, Congressional Budget Office.
    5. Stock, James H & Watson, Mark W, 1993. "A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems," Econometrica, Econometric Society, vol. 61(4), pages 783-820, July.
    6. Congressional Budget Office, 2014. "The Slow Recovery of the Labor Market," Reports 45011, Congressional Budget Office.
    7. Hess T. Chung & Bruce Fallick & Christopher J. Nekarda & David Ratner, 2014. "Assessing the Change in Labor Market Conditions," FEDS Notes 2014-05-22, Board of Governors of the Federal Reserve System (U.S.).
    8. Craig S. Hakkio & Jonathan L. Willis, 2014. "Kansas City Fed's Labor Market Conditions Indicators (LMCI)," Macro Bulletin, Federal Reserve Bank of Kansas City, pages 1-2, August.
    9. Congressional Budget Office, 2014. "The Slow Recovery of the Labor Market," Reports 45011, Congressional Budget Office.
    10. Bodnár, Katalin, 2018. "Recent developments in part-time employment," Economic Bulletin Boxes, European Central Bank, vol. 2.
    11. Riccardo (Jack) Lucchetti & Ioannis A. Venetis, 2019. "Dynamic Factor Models in gretl. The DFM package," gretl working papers 7, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    12. Konrad Zmitrowicz & Mikael Khan, 2014. "Beyond the Unemployment Rate: Assessing Canadian and U.S. Labour Markets Since the Great Recession," Bank of Canada Review, Bank of Canada, vol. 2014(Spring), pages 42-53.
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    More about this item

    Keywords

    Labor market conditions index; dynamic factor model; unemployment rate; factors;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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