IDEAS home Printed from https://ideas.repec.org/p/nim/nimawp/40-2010.html

Measuring match quality using subjective data

Author

Abstract

We examine whether data routinely collected in household surveys and surveys of workers can be used to construct a measure of underlying match quality between worker and firm which helps test matching models and predict subsequent labour market outcomes of workers. We use subjective data from employees both on reported levels of job satisfaction with various aspects of the current job and on whether they would like a new job with a new employer to construct a measure of underlying match quality. We then use this to test several implications of matching models relating to wage-tenure profiles, wages, and separations.

Suggested Citation

  • Priscila Ferreira & Mark Taylor, 2010. "Measuring match quality using subjective data," NIMA Working Papers 40, Núcleo de Investigação em Microeconomia Aplicada (NIMA), Universidade do Minho.
    • Handle: RePEc:nim:nimawp:40/2010
    as

    Download full text from publisher

    File URL: http://www3.eeg.uminho.pt/publications/NIMAwp40.pdf
    File Function: full text
    Download Restriction: none
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. is not listed on IDEAS
    2. Larissa Fuchs & Matthias Heinz & Pia Pinger & Max Thon, 2025. "How to Attract Talent? Field-Experimental Evidence on Emphasizing Flexibility and Career Opportunities in Job Advertisements," CESifo Working Paper Series 12331, CESifo.
    3. Eike Emrich & Christian Pierdzioch, 2016. "Volunteering, Match Quality, and Internet Use," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 136(2), pages 199-226.
    4. Barmby, Tim & Bryson, Alex & Eberth, Barbara, 2012. "Human capital, matching and job satisfaction," Economics Letters, Elsevier, vol. 117(3), pages 548-551.
    5. Suguru Otani & Tohya Sugano, 2024. "A Note on Identification of Match Fixed Effects as Interpretable Unobserved Match Affinity," Papers 2406.18913, arXiv.org, revised Aug 2024.
    6. Gaetano Lisi, 2018. "Job satisfaction, time allocation and labour supply," Working Papers 2018-04, Universita' di Cassino, Dipartimento di Economia e Giurisprudenza.
    7. Constantin Mang, 2016. "Market Consequences of ICT Innovations," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 70, July.
    8. Yi Zhang & Martin Salm & Arthur Soest, 2021. "The effect of training on workers’ perceived job match quality," Empirical Economics, Springer, vol. 60(5), pages 2477-2498, May.
    9. Gaetano Lisi, 2018. "Job satisfaction, job match quality and labour supply decisions," International Review of Economics, Springer;Happiness Economics and Interpersonal Relations (HEIRS), vol. 65(4), pages 489-505, December.
    10. Larissa Fuchs & Matthias Heinz & Pia Pinger & Max Thon, 2025. "How to Attract Talent? Field-Experimental Evidence on Emphasizing Flexibility and Career Opportunities in Job Advertisements," CRC TR 224 Discussion Paper Series crctr224_2025_683v2, University of Bonn and University of Mannheim, Germany, revised Jan 2026.
    11. Marta Silva & Luis Filipe Martins & Helena Lopes, 2015. "Asymmetric labour market reforms and wage growth with fixed-term contracts: does learning about match quality matter?," Working Papers Series 2 15-04, ISCTE-IUL, Business Research Unit (BRU-IUL).
    12. Constantin Mang, 2012. "Online Job Search and Matching Quality," ifo Working Paper Series 147, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    13. Kampkötter, Patrick & Petters, Lea M. & Sliwka, Dirk, 2021. "Employee identification and wages – on the economics of “Affective Commitment”," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 608-626.
    14. David C. Ribar & Mark Wooden, 2020. "Four Dimensions of Quality in Australian Jobs," The Economic Record, The Economic Society of Australia, vol. 96(S1), pages 26-49, June.
    15. Yang LIU, 2019. "Relative Wages and Job Satisfaction of Migrant Workers: An Economic Perspective Using Data from Japan," Discussion papers 19033, Research Institute of Economy, Trade and Industry (RIETI).
    16. Zubanov, Nick & Shakina, Elena, 2023. "Performance Costs and Benefits of Collective Turnover: A Theory-Driven Measurement Framework and Applications," IZA Discussion Papers 16413, Institute of Labor Economics (IZA).
    17. Antonia Asenjo & Verónica Escudero & Hannah Liepmann, 2024. "Why Should we Integrate Income and Employment Support? A Conceptual and Empirical Investigation," Journal of Development Studies, Taylor & Francis Journals, vol. 60(1), pages 1-29, January.
    18. Adam Ayaita & Christian Grund & Lisa Pütz, 2022. "Job Placement via Private vs. Public Employment Agencies: Investigating Selection Effects and Job Match Quality in Germany," Schmalenbach Journal of Business Research, Springer, vol. 74(2), pages 137-162, June.
    19. Berlingieri, Francesco & Erdsiek, Daniel, 2012. "How relevant is job mismatch for German graduates?," ZEW Discussion Papers 12-075, ZEW - Leibniz Centre for European Economic Research.
    20. Yang, Yanlin & Shao, Xu, 2018. "Understanding industrialization and employment quality changes in China: Development of a qualitative measurement," China Economic Review, Elsevier, vol. 47(C), pages 274-281.
    21. Barmby, Tim & Bryson, Alex & Eberth, Barbara, 2012. "Human capital, matching and job satisfaction," Economics Letters, Elsevier, vol. 117(3), pages 548-551.
    22. Belot, Michèle & Liu, Xiaoying & Triantafyllou, Vaios, 2024. "Measuring the quality of a match," Labour Economics, Elsevier, vol. 89(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • J28 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Safety; Job Satisfaction; Related Public Policy
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J62 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Job, Occupational and Intergenerational Mobility; Promotion
    • J63 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Turnover; Vacancies; Layoffs

    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:nim:nimawp:40/2010. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: NIMA (email available below). General contact details of provider: http://nima.eeg.uminho.pt/ .

    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.