IDEAS home Printed from https://ideas.repec.org/p/tse/wpaper/126574.html
   My bibliography  Save this paper

Lifecycle Wages and Human Capital Investments: Selection and Missing Data

Author

Listed:
  • Gobillon, Laurent
  • Magnac, Thierry
  • Roux, Sébastien

Abstract

We derive wage equations with individual specic coe¢ cients from a structural model of human capital investments over the life-cycle. This model allows for interruptions in labor market participation, and addresses missing data and attrition issues. We further control for selection in a exible way by using interactive e¤ects. Estimation is based on long administrative panel data of male wages in the private sector in France. A structural function approach shows that interruptions negatively a¤ect average wages. More surprisingly, they also negatively a¤ect the inter-decile range of wages after twenty years, and this is due to interruptions being endogeneous. These results question the popular Missing At Random assumption that is made when assessing the building up of wage inequalities over the life cycle.

Suggested Citation

  • Gobillon, Laurent & Magnac, Thierry & Roux, Sébastien, 2022. "Lifecycle Wages and Human Capital Investments: Selection and Missing Data," TSE Working Papers 22-1299, Toulouse School of Economics (TSE).
    • Handle: RePEc:tse:wpaper:126574
    as

    Download full text from publisher

    File URL: https://www.tse-fr.eu/sites/default/files/TSE/documents/doc/wp/2022/wp_tse_1299.pdf
    File Function: Full Text
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Jochmans, Koen & Weidner, Martin, 2024. "Inference On A Distribution From Noisy Draws," Econometric Theory, Cambridge University Press, vol. 40(1), pages 60-97, February.
    2. Iván Fernández-Val & Martin Weidner, 2018. "Fixed Effects Estimation of Large-TPanel Data Models," Annual Review of Economics, Annual Reviews, vol. 10(1), pages 109-138, August.
    3. Iván Fernández-Val & Martin Weidner, 2018. "Fixed Effects Estimation of Large-TPanel Data Models," Annual Review of Economics, Annual Reviews, vol. 10(1), pages 109-138, August.
    4. Huang, Wenxin & Jin, Sainan & Su, Liangjun, 2020. "Identifying Latent Grouped Patterns In Cointegrated Panels," Econometric Theory, Cambridge University Press, vol. 36(3), pages 410-456, June.
    5. Su, Liangjun & Ju, Gaosheng, 2018. "Identifying latent grouped patterns in panel data models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 206(2), pages 554-573.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Thierry Magnac, 2024. "Human Capital and Search Models: A Happy Match," Revue économique, Presses de Sciences-Po, vol. 75(1), pages 11-29.
    2. Magnac, Thierry, 2023. "Capital humain et recherche d'emploi: un mariage heureux - Human Capital and Search Models: A Happy Match," TSE Working Papers 23-1489, Toulouse School of Economics (TSE).
    3. Alain-Philippe Fortin & Patrick Gagliardini & Olivier Scaillet, 2023. "Latent Factor Analysis in Short Panels," Swiss Finance Institute Research Paper Series 23-44, Swiss Finance Institute.

    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. Magnac, Thierry & Roux, Sébastien, 2021. "Heterogeneity and wage inequalities over the life cycle," European Economic Review, Elsevier, vol. 134(C).
    2. Hou, Yanxi & Leng, Xuan & Peng, Liang & Zhou, Yinggang, 2024. "Panel quantile regression for extreme risk," Journal of Econometrics, Elsevier, vol. 240(1).
    3. Xuan Leng & Jiaming Mao & Yutao Sun, 2023. "Debiased Inference for Dynamic Nonlinear Panels with Multi-dimensional Heterogeneities," Papers 2305.03134, arXiv.org, revised May 2025.
    4. Chen, Mingli & Fernández-Val, Iván & Weidner, Martin, 2021. "Nonlinear factor models for network and panel data," Journal of Econometrics, Elsevier, vol. 220(2), pages 296-324.
    5. Weidner, Martin & Zylkin, Thomas, 2021. "Bias and consistency in three-way gravity models," Journal of International Economics, Elsevier, vol. 132(C).
    6. Fernández-Val, Iván & Gao, Wayne Yuan & Liao, Yuan & Vella, Francis, 2022. "Dynamic Heterogeneous Distribution Regression Panel Models, with an Application to Labor Income Processes," IZA Discussion Papers 15236, Institute of Labor Economics (IZA).
    7. Crozet, Matthieu & Hinz, Julian & Stammann, Amrei & Wanner, Joschka, 2021. "Worth the pain? Firms’ exporting behaviour to countries under sanctions," European Economic Review, Elsevier, vol. 134(C).
    8. Chernozhukov, Victor & Fernández-Val, Iván & Weidner, Martin, 2024. "Network and panel quantile effects via distribution regression," Journal of Econometrics, Elsevier, vol. 240(2).
    9. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    10. Zhang, Huiming & Huang, Jiying & Wu, Kai & Wang, Shouyang & Nygaard, Christian & Qiu, Yueming, 2022. "Do political connections affect corporate poverty alleviation decisions? Evidence from China," China Economic Review, Elsevier, vol. 73(C).
    11. Carneiro, Anabela & Portugal, Pedro & Raposo, Pedro & Rodrigues, Paulo M.M., 2023. "The persistence of wages," Journal of Econometrics, Elsevier, vol. 233(2), pages 596-611.
    12. Matias D Cattaneo & Michael Jansson & Xinwei Ma, 2019. "Two-Step Estimation and Inference with Possibly Many Included Covariates," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 86(3), pages 1095-1122.
    13. Bonaccolto-Töpfer, Marina & Castagnetti, Carolina & Prümer, Stephanie, 2022. "Understanding the public-private sector wage gap in Germany: New evidence from a Fixed Effects quantile Approach∗," Economic Modelling, Elsevier, vol. 116(C).
    14. Camarero, Mariam & Moliner, Sergi & Tamarit, Cecilio, 2024. "A Fresh Assessment of the Depth of the “Euro Effect" on US FDI," Single Market Economics Papers WP2024/18, Directorate-General for Internal Market, Industry, Entrepreneurship and SMEs (European Commission), Chief Economist Team.
    15. Hatem Altaee, 2018. "Trade Openness and Economic Growth in the GCC Countries: A Panel Data Analysis Approach," International Journal of Business and Economic Sciences Applied Research (IJBESAR), Democritus University of Thrace (DUTH), Kavala Campus, Greece, vol. 11(3), pages 57-64, December.
    16. Camarero, Mariam & Moliner, Sergi & Tamarit, Cecilio, 2021. "Japan's FDI drivers in a time of financial uncertainty. New evidence based on Bayesian Model Averaging," Japan and the World Economy, Elsevier, vol. 57(C).
    17. Gao, Jiti & Liu, Fei & Peng, Bin & Yan, Yayi, 2023. "Binary response models for heterogeneous panel data with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 235(2), pages 1654-1679.
    18. Liyuan Cui & Guanhao Feng & Yongmiao Hong, 2024. "Regularized Gmm For Time‐Varying Models With Applications To Asset Pricing," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 65(2), pages 851-883, May.
    19. Mariam Camarero & Sergi Moliner & Cecilio Tamarit, 2021. "Is there a euro effect in the drivers of US FDI? New evidence using Bayesian model averaging techniques," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 157(4), pages 881-926, November.
    20. Irene Botosaru & Chris Muris, 2022. "Identification of time-varying counterfactual parameters in nonlinear panel models," Papers 2212.09193, arXiv.org, revised Nov 2023.

    More about this item

    Keywords

    Human capital investment; wage inequalities; factor models; missing data;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

    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:tse:wpaper:126574. 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 (email available below). General contact details of provider: https://edirc.repec.org/data/tsetofr.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.