IDEAS home Printed from https://ideas.repec.org/a/wly/soecon/v87y2021i3p753-768.html
   My bibliography  Save this article

In sickness and in health, until death do us part: A case for theory

Author

Listed:
  • Donna B. Gilleskie

Abstract

No abstract is available for this item.

Suggested Citation

  • Donna B. Gilleskie, 2021. "In sickness and in health, until death do us part: A case for theory," Southern Economic Journal, John Wiley & Sons, vol. 87(3), pages 753-768, January.
    • Handle: RePEc:wly:soecon:v:87:y:2021:i:3:p:753-768
    DOI: 10.1002/soej.12474
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/soej.12474
    Download Restriction: no
    File URL: https://libkey.io/10.1002/soej.12474?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
    ---><---

    References listed on IDEAS

    as
    1. Claudia R. Sahm, 2012. "How Much Does Risk Tolerance Change?," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 2(04), pages 1-38.
    2. Donna Gilleskie & Denise Hoffman, 2014. "Health Capital and Human Capital as Explanations for Health-Related Wage Disparities," Journal of Human Capital, University of Chicago Press, vol. 8(3), pages 235-279.
    3. Christopher J. Cronin, 2019. "Insurance‐Induced Moral Hazard: A Dynamic Model Of Within‐Year Medical Care Decision Making Under Uncertainty," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 60(1), pages 187-218, February.
    4. Matthew C. Harris, 2019. "The Impact Of Body Weight On Occupational Mobility And Career Development," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 60(2), pages 631-660, May.
    5. Michael Darden, 2017. "Smoking, Expectations, and Health: A Dynamic Stochastic Model of Lifetime Smoking Behavior," Journal of Political Economy, University of Chicago Press, vol. 125(5), pages 1465-1522.
    6. Donna B. Gilleskie, 1998. "A Dynamic Stochastic Model of Medical Care Use and Work Absence," Econometrica, Econometric Society, vol. 66(1), pages 1-46, January.
    7. Donna Gilleskie & Euna Han & Edward Norton, 2017. "Disentangling the Contemporaneous and Dynamic Effects of Human and Health Capital on Wages over the Life Cycle," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 25, pages 350-383, April.
    8. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    9. Yang Wang, 2014. "Dynamic Implications of Subjective Expectations: Evidence from Adult Smokers," American Economic Journal: Applied Economics, American Economic Association, vol. 6(1), pages 1-37, January.
    10. Liran Einav & Amy Finkelstein & Iuliana Pascu & Mark R. Cullen, 2012. "How General Are Risk Preferences? Choices under Uncertainty in Different Domains," American Economic Review, American Economic Association, vol. 102(6), pages 2606-2638, October.
    11. 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.
    12. Betty Tao Fout & Donna B. Gilleskie, 2015. "Does Health Insurance Encourage or Crowd Out Beneficial Nonmedical Care? A Dynamic Analysis of Insurance, Health Inputs, and Health Production," American Journal of Health Economics, MIT Press, vol. 1(2), pages 125-164, Spring.
    13. Grossman, Michael, 1972. "On the Concept of Health Capital and the Demand for Health," Journal of Political Economy, University of Chicago Press, vol. 80(2), pages 223-255, March-Apr.
    14. Matthew C. Harris & Jennifer L. Kohn, 2018. "Reference Health and the Demand for Medical Care," Economic Journal, Royal Economic Society, vol. 128(615), pages 2812-2842, November.
    15. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    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. Amitabh Chandra & Courtney Coile & Corina Mommaerts, 2023. "What Can Economics Say about Alzheimer's Disease?," Journal of Economic Literature, American Economic Association, vol. 61(2), pages 428-470, June.
    2. Marcela PARADA‐CONTZEN, 2023. "Gender, family status and health characteristics: Understanding retirement inequalities in the Chilean pension model," International Labour Review, International Labour Organization, vol. 162(2), pages 271-303, June.
    3. Barton Hamilton & Andrés Hincapié & Emma C. Kalish & Nicholas W. Papageorge, 2021. "Medical Innovation and Health Disparities," NBER Working Papers 28864, National Bureau of Economic Research, Inc.
    4. Tsang, Andrew, 2021. "Uncovering Heterogeneous Regional Impacts of Chinese Monetary Policy," MPRA Paper 110703, University Library of Munich, Germany.
    5. Blankenship, Brian & Aklin, Michaël & Urpelainen, Johannes & Nandan, Vagisha, 2022. "Jobs for a just transition: Evidence on coal job preferences from India," Energy Policy, Elsevier, vol. 165(C).
    6. Andrei Dubovik & Adam Elbourne & Bram Hendriks & Mark Kattenberg, 2022. "Forecasting World Trade Using Big Data and Machine Learning Techniques," CPB Discussion Paper 441, CPB Netherlands Bureau for Economic Policy Analysis.
    7. Askitas, Nikos, 2024. "A Hands-on Machine Learning Primer for Social Scientists: Math, Algorithms and Code," IZA Discussion Papers 17014, Institute of Labor Economics (IZA).
    8. Arthur Charpentier & Romuald Élie & Carl Remlinger, 2023. "Reinforcement Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 425-462, June.
    9. Mona Aghdaee & Bonny Parkinson & Kompal Sinha & Yuanyuan Gu & Rajan Sharma & Emma Olin & Henry Cutler, 2022. "An examination of machine learning to map non‐preference based patient reported outcome measures to health state utility values," Health Economics, John Wiley & Sons, Ltd., vol. 31(8), pages 1525-1557, August.
    10. 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.
    11. Marcela Parada-Contzen, 2020. "Crowding-out in savings decisions, portfolio default adoption and home ownership: evidence from the Chilean retirement system," Review of Economics of the Household, Springer, vol. 18(2), pages 543-569, June.
    12. Mehmet Güney Celbiş & Pui-Hang Wong & Karima Kourtit & Peter Nijkamp, 2021. "Innovativeness, Work Flexibility, and Place Characteristics: A Spatial Econometric and Machine Learning Approach," Sustainability, MDPI, vol. 13(23), pages 1-29, December.
    13. Ahlfeldt, Gabriel M. & Heblich, Stephan & Seidel, Tobias, 2023. "Micro-geographic property price and rent indices," Regional Science and Urban Economics, Elsevier, vol. 98(C).
    14. Nir Chemaya & Daniel Martin, 2023. "Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals," Papers 2311.14720, arXiv.org, revised Jan 2024.
    15. Dang,Hai-Anh H. & Kilic,Talip & Carletto,Calogero & Abanokova,Kseniya, 2021. "Poverty Imputation in Contexts without Consumption Data : A Revisit with Further Refinements," Policy Research Working Paper Series 9838, The World Bank.
    16. Harold D. Chiang & Kengo Kato & Yukun Ma & Yuya Sasaki, 2022. "Multiway Cluster Robust Double/Debiased Machine Learning," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1046-1056, June.
    17. Dario Sansone & Anna Zhu, 2023. "Using Machine Learning to Create an Early Warning System for Welfare Recipients," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(5), pages 959-992, October.
    18. Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2022. "Urban economics in a historical perspective: Recovering data with machine learning," Regional Science and Urban Economics, Elsevier, vol. 94(C).
    19. Yongtong Shao & Tao Xiong & Minghao Li & Dermot Hayes & Wendong Zhang & Wei Xie, 2021. "China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1082-1098, May.
    20. 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.

    More about this item

    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:soecon:v:87:y:2021:i:3:p:753-768. 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: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)2325-8012 .

    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.