IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2403.19563.html
   My bibliography  Save this paper

Flexible Analysis of Individual Heterogeneity in Event Studies: Application to the Child Penalty

Author

Listed:
  • Dmitry Arkhangelsky
  • Kazuharu Yanagimoto
  • Tom Zohar

Abstract

We provide a practical toolkit for analyzing effect heterogeneity in event studies. We develop an estimation algorithm and adapt existing econometric results to provide its theoretical justification. We apply these tools to Dutch administrative data to study individual heterogeneity in the child-penalty (CP) context in three ways. First, we document significant heterogeneity in the individual-level CP trajectories, emphasizing the importance of going beyond the average CP. Second, we use individual-level estimates to examine the impact of childcare supply expansion policies. Our approach uncovers nonlinear treatment effects, challenging the conventional policy evaluation methods constrained to less flexible specifications. Third, we use the individual-level estimates as a regressor on the right-hand side to study the intergenerational elasticity of the CP between mothers and daughters. After adjusting for the measurement error bias, we find the elasticity of 24\%. Our methodological framework contributes to empirical practice by offering a flexible approach tailored to specific research questions and contexts. We provide an open-source package ('unitdid') to facilitate widespread adoption.

Suggested Citation

  • Dmitry Arkhangelsky & Kazuharu Yanagimoto & Tom Zohar, 2024. "Flexible Analysis of Individual Heterogeneity in Event Studies: Application to the Child Penalty," Papers 2403.19563, arXiv.org.
  • Handle: RePEc:arx:papers:2403.19563
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2403.19563
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Manuel Arellano & Stéphane Bonhomme, 2012. "Identifying Distributional Characteristics in Random Coefficients Panel Data Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 79(3), pages 987-1020.
    2. Victor Chernozhukov & Mert Demirer & Esther Duflo & Iván Fernández-Val, 2018. "Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Immunization in India," NBER Working Papers 24678, National Bureau of Economic Research, Inc.
    3. V Chernozhukov & W K Newey & R Singh, 2023. "A simple and general debiased machine learning theorem with finite-sample guarantees," Biometrika, Biometrika Trust, vol. 110(1), pages 257-264.
    4. Martin Eckhoff Andresen & Emily Nix, 2022. "Can the child penalty be reduced?. Evaluating multiple policy interventions," Discussion Papers 983, Statistics Norway, Research Department.
    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. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-dimensional econometrics and regularized GMM," CeMMAP working papers CWP35/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.
    3. Committee, Nobel Prize, 2023. "Scientific Background to the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2023," Nobel Prize in Economics documents 2023-2, Nobel Prize Committee.
    4. Hoderlein, Stefan & White, Halbert, 2012. "Nonparametric identification in nonseparable panel data models with generalized fixed effects," Journal of Econometrics, Elsevier, vol. 168(2), pages 300-314.
    5. M. Adam & O. Bonnet & E. Fize & T. Loisel & M. Rault & L. Wilner, 2023. "How does fuel demand respond to price changes? Quasi-experimental evidence based on high-frequency data," Documents de Travail de l'Insee - INSEE Working Papers 2023-17, Institut National de la Statistique et des Etudes Economiques.
    6. Abhijit Banerjee & Emily Breza & Esther Duflo & Cynthia Kinnan, 2019. "Can Microfinance Unlock a Poverty Trap for Some Entrepreneurs?," NBER Working Papers 26346, National Bureau of Economic Research, Inc.
    7. Geert Dhaene & Koen Jochmans, 2015. "Split-panel Jackknife Estimation of Fixed-effect Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(3), pages 991-1030.
    8. Stefan Hoderlein & Hajo Holzmann & Alexander Meister, 2015. "The triangular model with random coefficients," CeMMAP working papers 33/15, Institute for Fiscal Studies.
    9. Albanesi, Stefania & Olivetti, Claudia & Petrongolo, Barbara, 2022. "Families, labor markets and policy," LSE Research Online Documents on Economics 118038, London School of Economics and Political Science, LSE Library.
    10. Piasenti, Stefano & Valente, Marica & Van Veldhuizen, Roel & Pfeifer, Gregor, 2023. "Does Unfairness Hurt Women? The Effects of Losing Unfair Competitions," Working Papers 2023:7, Lund University, Department of Economics.
    11. Elek, Péter & Bíró, Anikó, 2021. "Regional differences in diabetes across Europe – regression and causal forest analyses," Economics & Human Biology, Elsevier, vol. 40(C).
    12. Patrick Bajari & Zhihao Cen & Victor Chernozhukov & Manoj Manukonda & Jin Wang & Ramon Huerta & Junbo Li & Ling Leng & George Monokroussos & Suhas Vijaykunar & Shan Wan, 2023. "Hedonic prices and quality adjusted price indices powered by AI," CeMMAP working papers 08/23, Institute for Fiscal Studies.
    13. Petru Crudu, 2023. "Long-term effects of early adverse labour market conditions: A Causal Machine Learning approach," Working Papers 2023:21, Department of Economics, University of Venice "Ca' Foscari".
    14. Nayoung Lee & Hyungsik Roger Moon, 2021. "Heterogeneous Income Profiles Model with Fixed Effects: Incorporating Labour Income Shocks," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1377-1407, December.
    15. Jan-Emmanuel De Neve & Clément Imbert & Johannes Spinnewijn & Teodora Tsankova & Maarten Luts, 2021. "How to Improve Tax Compliance? Evidence from Population-Wide Experiments in Belgium," Journal of Political Economy, University of Chicago Press, vol. 129(5), pages 1425-1463.
    16. repec:gnv:wpaper:unige:76321 is not listed on IDEAS
    17. Ben-Moshe, Dan, 2018. "Identification Of Joint Distributions In Dependent Factor Models," Econometric Theory, Cambridge University Press, vol. 34(1), pages 134-165, February.
    18. Xavier d'Haultfoeuille & Stefan Hoderlein & Yuya Sasaki, 2013. "Nonlinear difference-in-differences in repeated cross sections with continuous treatments," CeMMAP working papers CWP40/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    19. Juan Carlos Escanciano, 2020. "Irregular Identification of Structural Models with Nonparametric Unobserved Heterogeneity," Papers 2005.08611, arXiv.org.
    20. Stefano Caria & Grant Gordon & Maximilian Kasy & Simon Quinn & Soha Shami & Alexander Teytelboym, 2020. "An Adaptive Targeted Field Experiment: Job Search Assistance for Refugees in Jordan," CESifo Working Paper Series 8535, CESifo.
    21. Giovanni Compiani & Yuichi Kitamura, 2016. "Using mixtures in econometric models: a brief review and some new results," Econometrics Journal, Royal Economic Society, vol. 19(3), pages 95-127, October.

    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:arx:papers:2403.19563. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.