IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v69y2024ipas1544612324011930.html
   My bibliography  Save this article

Public debt and welfare with machine learning

Author

Listed:
  • Zhu, Jingjing
  • Huang, Tianyuan

Abstract

The issuance of public debt affects asset returns in the market, which in turn affects macroeconomic equilibrium and wealth distribution. We use a two-period overlapping generations model with idiosyncratic investment risk to solve the general equilibrium problem of public debt and welfare, using machine learning techniques to obtain stable distributions and comparative static analysis to derive four channels that affect welfare. We find that the income channel has the largest impact on welfare from changes in public debt and the investment ratio channel has the smallest impact on welfare, where the setting of the model parameters does not affect the results of the channel decomposition.

Suggested Citation

  • Zhu, Jingjing & Huang, Tianyuan, 2024. "Public debt and welfare with machine learning," Finance Research Letters, Elsevier, vol. 69(PA).
    • Handle: RePEc:eee:finlet:v:69:y:2024:i:pa:s1544612324011930
    DOI: 10.1016/j.frl.2024.106164
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612324011930
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2024.106164?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ziad Obermeyer, 2015. "Prediction Policy Problems," American Economic Review, American Economic Association, vol. 105(5), pages 491-495, May.
    2. Piero Gottardi & Atsushi Kajii & Tomoyuki Nakajima, 2015. "Optimal Taxation and Debt with Uninsurable Risks to Human Capital Accumulation," American Economic Review, American Economic Association, vol. 105(11), pages 3443-3470, November.
    3. Nakajima, Tomoyuki & Takahashi, Shuhei, 2017. "The optimum quantity of debt for Japan," Journal of the Japanese and International Economies, Elsevier, vol. 46(C), pages 17-26.
    4. Aiyagari, S. Rao & McGrattan, Ellen R., 1998. "The optimum quantity of debt," Journal of Monetary Economics, Elsevier, vol. 42(3), pages 447-469, October.
    5. Laurence Ball & N Gregory Mankiw, 2023. "Market Power in Neoclassical Growth Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(2), pages 572-596.
    6. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    7. Bellocchi, Alessandro & Marin, Giovanni & Travaglini, Giuseppe, 2023. "The labor share puzzle: Empirical evidence for European countries," Economic Modelling, Elsevier, vol. 124(C).
    8. William A. Brock & Leonard J. Mirman, 2001. "Optimal Economic Growth And Uncertainty: The Discounted Case," Chapters, in: W. D. Dechert (ed.), Growth Theory, Nonlinear Dynamics and Economic Modelling, chapter 1, pages 3-37, Edward Elgar Publishing.
    9. Floden, Martin, 2001. "The effectiveness of government debt and transfers as insurance," Journal of Monetary Economics, Elsevier, vol. 48(1), pages 81-108, August.
    10. Javier Diaz-Gimenez & Andrew Glover & José-Víctor Ríos-Rull, 2011. "Facts on the distributions of earnings, income, and wealth in the United States: 2007 update," Quarterly Review, Federal Reserve Bank of Minneapolis.
    11. Jing Wan & Shenghao Zhu, 2019. "Bequests, estate taxes, and wealth distributions," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 67(1), pages 179-210, February.
    12. Mariacristina De Nardi, 2004. "Wealth Inequality and Intergenerational Links," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 71(3), pages 743-768.
    13. Yikai Wang & Hans Holter & Marcus Hagedorn, 2015. "The Optimum Quantity of Capital and Debt," 2015 Meeting Papers 1220, Society for Economic Dynamics.
    14. Acikgoz, Omer, 2015. "Transitional Dynamics and Long-run Optimal Taxation Under Incomplete Markets," MPRA Paper 73380, University Library of Munich, Germany.
    15. Sebastian Dyrda & Marcelo Pedroni, 2023. "Optimal Fiscal Policy in a Model with Uninsurable Idiosyncratic Income Risk," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(2), pages 744-780.
    16. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    17. Röhrs, Sigrid & Winter, Christoph, 2017. "Reducing government debt in the presence of inequality," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 1-20.
    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. Bayer, Christian & Born, Benjamin & Luetticke, Ralph, 2023. "The liquidity channel of fiscal policy," Journal of Monetary Economics, Elsevier, vol. 134(C), pages 86-117.
    2. Chien, YiLi & Wen, Yi, 2024. "The Ramsey steady-state conundrum in heterogeneous-agent economies," Journal of Economic Theory, Elsevier, vol. 220(C).
    3. Röhrs, Sigrid & Winter, Christoph, 2015. "Public versus private provision of liquidity: Is there a trade-off?," Journal of Economic Dynamics and Control, Elsevier, vol. 53(C), pages 314-339.
    4. Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
    5. Sebastian Dyrda & Marcelo Pedroni, 2015. "Optimal Fiscal Policy in a Model with Uninsurable Idiosyncratic Shocks," Working Papers tecipa-550, University of Toronto, Department of Economics.
    6. Röhrs, Sigrid & Winter, Christoph, 2017. "Reducing government debt in the presence of inequality," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 1-20.
    7. Hannes Wallimann & Silvio Sticher, 2023. "On suspicious tracks: machine-learning based approaches to detect cartels in railway-infrastructure procurement," Papers 2304.11888, arXiv.org.
    8. Clemens, Christiane & Heinemann, Maik, 2015. "Endogenous growth and wealth inequality under incomplete markets and idiosyncratic risk," Journal of Macroeconomics, Elsevier, vol. 45(C), pages 300-317.
    9. Akira Okamoto, 2025. "The optimum quantity of debt for an aging Japan: welfare and demographic dynamics," The Japanese Economic Review, Springer, vol. 76(1), pages 1-52, January.
    10. Martin Floden & Jesper Lindé, 2001. "Idiosyncratic Risk in the United States and Sweden: Is There a Role for Government Insurance?," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 4(2), pages 406-437, July.
    11. Frédéric Dufourt & Lisa Kerdelhué & Océane Piétri, 2022. "Budget-Neutral Capital Tax Cuts," Annals of Economics and Statistics, GENES, issue 146, pages 93-121.
    12. Urmat Dzhunkeev, 2022. "Forecasting Unemployment in Russia Using Machine Learning Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 81(1), pages 73-87, March.
    13. Filmer,Deon P. & Nahata,Vatsal & Sabarwal,Shwetlena, 2021. "Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness," Policy Research Working Paper Series 9847, The World Bank.
    14. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Machine-Learning Approach," Economics working papers 2021-08, Department of Economics, Johannes Kepler University Linz, Austria.
    15. Falco J. Bargagli Stoffi & Kenneth De Beckker & Joana E. Maldonado & Kristof De Witte, 2021. "Assessing Sensitivity of Machine Learning Predictions.A Novel Toolbox with an Application to Financial Literacy," Papers 2102.04382, arXiv.org.
    16. Gallego, Jorge & Rivero, Gonzalo & Martínez, Juan, 2021. "Preventing rather than punishing: An early warning model of malfeasance in public procurement," International Journal of Forecasting, Elsevier, vol. 37(1), pages 360-377.
    17. Hannes Wallimann & David Imhof & Martin Huber, 2023. "A Machine Learning Approach for Flagging Incomplete Bid-Rigging Cartels," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1669-1720, December.
    18. Yikai Wang & Hans Holter & Marcus Hagedorn, 2015. "The Optimum Quantity of Capital and Debt," 2015 Meeting Papers 1220, Society for Economic Dynamics.
    19. Susan Athey & Herman Brunborg & Tianyu Du & Ayush Kanodia & Keyon Vafa, 2024. "LABOR-LLM: Language-Based Occupational Representations with Large Language Models," Papers 2406.17972, arXiv.org, revised Feb 2025.
    20. de Blasio, Guido & D'Ignazio, Alessio & Letta, Marco, 2022. "Gotham city. Predicting ‘corrupted’ municipalities with machine learning," Technological Forecasting and Social Change, Elsevier, vol. 184(C).

    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:eee:finlet:v:69:y:2024:i:pa:s1544612324011930. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    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.