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A Composite Labor Market Conditions Index for Türkiye

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  • Demirhan Demir
  • Selcuk Gul

Abstract

[EN] This study aims to construct a Composite Labor Market Index (CLMI) to better understand the recent mixed signals from the labor market indicators. We follow the principal component analysis to construct the CLMI where the first component explains almost half of the common variation in a large dataset, consisting of 23 labor market indicators. While the unemployment rate and the broader unemployment rate may provide mixed signals regarding labor market conditions in some periods, our index offers a clearer assessment. Additionally, our index allows for the decomposition of the contributions from each set of labor market indicators to the overall labor market conditions over time. The results suggest that, in 2024, labor market conditions are stronger than what the broader unemployment indicators imply, yet weaker than what the main unemployment rate indicates. [TR] Bu calismanin amaci, son donemde isgucu piyasasi gostergelerinden gelen karisik sinyalleri daha iyi anlamak icin Bilesik Isgucu Piyasasi Endeksi'ni (BIPE) olusturmaktir. BIPE'yi olustururken, 23 isgucu piyasasi gostergesinden olusan genis bir veri setindeki ortak degiskenligin neredeyse yarisini aciklayan birinci bilesen kullanilmaktadir. Issizlik orani ve genis tanimli issizlik oranlari bazi donemlerde isgucu piyasasi kosullari hakkinda farkli sinyaller verse de endeksimiz daha net bir degerlendirme sunmaktadir. Ayrica endeksimiz, her bir isgucu piyasasi gostergesi grubunun zaman icinde genel isgucu piyasasi kosullarina katkilarinin ayristirilmasina da olanak saglamaktadir. Sonuclar, 2024 yilinda isgucu piyasasi kosullarinin, genis tanimli issizlik gostergelerinin isaret ettiginden daha guclu, ancak temel issizlik oraninin imâ ettiginden daha zayif oldugunu gostermektedir.

Suggested Citation

  • Demirhan Demir & Selcuk Gul, 2025. "A Composite Labor Market Conditions Index for Türkiye," CBT Research Notes in Economics 2517, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    • Handle: RePEc:tcb:econot:2517
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    References listed on IDEAS

    as
    1. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    2. Michelle L. Barnes & Ryan Chahrour & Giovanni P. Olivei & Gaoyan Tang, 2007. "A principal components approach to estimating labor market pressure and its implications for inflation," Public Policy Brief, Federal Reserve Bank of Boston.
    3. 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.).
    4. Burcu Gurcihan Yunculer & Gonul Sengul & Arzu Yavuz, 2014. "A Quest for Leading Indicators of the Turkish Unemployment Rate," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 14(1), pages 23-45.
    5. 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.
    6. Craig S. Hakkio & Jonathan L. Willis, 2013. "Assessing labor market conditions: the level of activity and the speed of improvement," Macro Bulletin, Federal Reserve Bank of Kansas City, issue july18, pages 1-2, July.
    7. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
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