IDEAS home Printed from https://ideas.repec.org/a/wly/quante/v9y2018i3p1195-1241.html

Estimation of dynastic life‐cycle discrete choice models

Author

Listed:
  • George‐Levi Gayle
  • Limor Golan
  • Mehmet A. Soytas

Abstract

This paper explores the estimation of a class of life‐cycle discrete choice dynastic models. It provides a new representation of the value function for these class of models. It compare a multistage conditional choice probability (CCP) estimator based on the new value function representation with a modified version of the full solution maximum likelihood estimator (MLE) in a Monte Carlo study. The modified CCP estimator performs comparably to the MLE in a finite sample but greatly reduces the computational cost. Using the proposed estimator, we estimate a dynastic model and use the estimated model to conduct counterfactual simulations to investigate the role Nature versus Nurture in intergenerational mobility. We find that Nature accounts for 20 percent of the observed intergenerational immobility at the bottom of income distribution. That means that 80 percent of mobility at the bottom of the income distribution is explained by economic decision and economic/institutional constraints.

Suggested Citation

  • George‐Levi Gayle & Limor Golan & Mehmet A. Soytas, 2018. "Estimation of dynastic life‐cycle discrete choice models," Quantitative Economics, Econometric Society, vol. 9(3), pages 1195-1241, November.
    • Handle: RePEc:wly:quante:v:9:y:2018:i:3:p:1195-1241
    DOI: 10.3982/QE771
    as

    Download full text from publisher

    File URL: https://doi.org/10.3982/QE771
    Download Restriction: no
    File URL: https://libkey.io/10.3982/QE771?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Musab Kurnaz & Mehmet Soytas, 2019. "Early Childhood Investment and Income Taxation," 2019 Meeting Papers 290, Society for Economic Dynamics.
    2. Naijia Guo & Charles Ka Yui Leung, 2021. "Do elite colleges matter? The impact on entrepreneurship decisions and career dynamics," Quantitative Economics, Econometric Society, vol. 12(4), pages 1347-1397, November.
    3. Sebastian Galiani & Juan Pantano, 2021. "Structural Models: Inception and Frontier," NBER Working Papers 28698, National Bureau of Economic Research, Inc.
    4. Gayle, George-Levi & Golan, Limor & Soytas, Mehmet A., 2022. "What is the source of the intergenerational correlation in earnings?," Journal of Monetary Economics, Elsevier, vol. 129(C), pages 24-45.
    5. Mehmet Soytas & Limor Golan & George-Levi Gayle, 2014. "What Accounts for the Racial Gap in Time Allocation and Intergenerational Transmission of Human Capital?," 2014 Meeting Papers 83, Society for Economic Dynamics.
    6. Kurnaz, Musab & Soytas, Mehmet A., 2025. "Intergenerational income mobility and income taxation," Journal of Economic Dynamics and Control, Elsevier, vol. 176(C).

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply
    • J62 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Job, Occupational and Intergenerational Mobility; Promotion

    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:wly:quante:v:9:y:2018:i:3:p:1195-1241. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/essssea.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.