IDEAS home Printed from https://ideas.repec.org/p/tcb/econot/2517.html

A Composite Labor Market Conditions Index for Türkiye

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.tcmb.gov.tr/wps/wcm/connect/cd4cc009-4eea-4e5a-bce5-ff0471bb9c3a/en202517.pdf?MOD=AJPERES&CACHEID=ROOTWORKSPACE-cd4cc009-4eea-4e5a-bce5-ff0471bb9c3a-pzQHu1G
    Download Restriction: no
    ---><---

    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. 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.).
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sirot, Guilhem & Unal, Umut & Maialeh, Robin, 2024. "Inflationary dynamics of labour market activity: Evidence from the Czech Republic," Economic Analysis and Policy, Elsevier, vol. 84(C), pages 1309-1327.
    2. Jed Armstrong & Günes Kamber & Özer Karagedikli, 2016. "Developing a labour utilisation composite index for New Zealand," Reserve Bank of New Zealand Analytical Notes series AN2016/04, Reserve Bank of New Zealand.
    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. Deicy J. Cristiano-Botia & Manuel Dario Hernandez-Bejarano & Mario A. Ramos-Veloza, 2021. "Labor Market Indicator for Colombia (LMI)," Borradores de Economia 1152, Banco de la Republica de Colombia.
    5. David Havrlant & Peter Tóth & Julia Wörz, 2016. "On the optimal number of indicators – nowcasting GDP growth in CESEE," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue 4, pages 54-72.
    6. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Economics Working Papers ECO2013/02, European University Institute.
    7. Luke Hartigan & James Morley, 2020. "A Factor Model Analysis of the Australian Economy and the Effects of Inflation Targeting," The Economic Record, The Economic Society of Australia, vol. 96(314), pages 271-293, September.
    8. Cahan, Ercument & Bai, Jushan & Ng, Serena, 2023. "Factor-based imputation of missing values and covariances in panel data of large dimensions," Journal of Econometrics, Elsevier, vol. 233(1), pages 113-131.
    9. Trucíos, Carlos & Mazzeu, João H.G. & Hotta, Luiz K. & Valls Pereira, Pedro L. & Hallin, Marc, 2021. "Robustness and the general dynamic factor model with infinite-dimensional space: Identification, estimation, and forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1520-1534.
    10. Samuele Centorrino & Antonia Diakantoni & Alexander Keck & Michele Ruta & Monika Sztajerowska & Yuting Wei, 2025. "Measuring Global Trade Policy Activity," IMF Working Papers 2025/220, International Monetary Fund.
    11. Zhang, Yixiao & Yu, Cindy L. & Li, Haitao, 2022. "Nowcasting GDP Using Dynamic Factor Model with Unknown Number of Factors and Stochastic Volatility: A Bayesian Approach," Econometrics and Statistics, Elsevier, vol. 24(C), pages 75-93.
    12. Salamaliki, Paraskevi, 2019. "Assessing labor market conditions in Greece: a note," MPRA Paper 97559, University Library of Munich, Germany.
    13. Chien-jung Ting & Yi-Long Hsiao, 2022. "Nowcasting the GDP in Taiwan and the Real-Time Tourism Data," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 12(3), pages 1-2.
    14. Xiong, Ruoxuan & Pelger, Markus, 2023. "Large dimensional latent factor modeling with missing observations and applications to causal inference," Journal of Econometrics, Elsevier, vol. 233(1), pages 271-301.
    15. Ma, Tao & Zhou, Zhou & Antoniou, Constantinos, 2018. "Dynamic factor model for network traffic state forecast," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 281-317.
    16. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil price realized volatility using information channels from other asset classes," Journal of International Money and Finance, Elsevier, vol. 76(C), pages 28-49.
    17. Jonas Krampe & Luca Margaritella, 2021. "Factor Models with Sparse VAR Idiosyncratic Components," Papers 2112.07149, arXiv.org, revised May 2022.
    18. Krist'of N'emeth & D'aniel Hadh'azi, 2024. "Generating density nowcasts for U.S. GDP growth with deep learning: Bayes by Backprop and Monte Carlo dropout," Papers 2405.15579, arXiv.org.
    19. Bennedsen, Mikkel & Hillebrand, Eric & Koopman, Siem Jan, 2021. "Modeling, forecasting, and nowcasting U.S. CO2 emissions using many macroeconomic predictors," Energy Economics, Elsevier, vol. 96(C).
    20. Rua, António, 2017. "A wavelet-based multivariate multiscale approach for forecasting," International Journal of Forecasting, Elsevier, vol. 33(3), pages 581-590.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:tcb:econot:2517. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge or the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/tcmgvtr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.