IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v5y2017i1p17-d93048.html
   My bibliography  Save this article

The Impact of Changes to the Unemployment Rate on Australian Disability Income Insurance Claim Incidence

Author

Listed:
  • Gaurav Khemka

    (Research School of Finance, Actuarial Studies and Statistics, Building 26C, Kingsley Street, Australian National University, Canberra, ACT 2601, Australia)

  • Steven Roberts

    (Research School of Finance, Actuarial Studies and Statistics, Building 26C, Kingsley Street, Australian National University, Canberra, ACT 2601, Australia)

  • Timothy Higgins

    (Research School of Finance, Actuarial Studies and Statistics, Building 26C, Kingsley Street, Australian National University, Canberra, ACT 2601, Australia)

Abstract

We explore the extent to which claim incidence in Disability Income Insurance (DII) is affected by changes in the unemployment rate in Australia. Using data from 1986 to 2001, we fit a hurdle model to explore the presence and magnitude of the effect of changes in unemployment rate on the incidence of DII claims, controlling for policy holder characteristics and seasonality. We find a clear positive association between unemployment and claim incidence, and we explore this further by gender, age, deferment period, and occupation. A multinomial logistic regression model is fitted to cause of claim data in order to explore the relationship further, and it is shown that the proportion of claims due to accident increases markedly with rising unemployment. The results suggest that during periods of rising unemployment, insurers may face increased claims from policy holders with shorter deferment periods for white-collar workers and for medium and heavy manual workers. Our findings indicate that moral hazard may have a material impact on DII claim incidence and insurer business in periods of declining economic conditions.

Suggested Citation

  • Gaurav Khemka & Steven Roberts & Timothy Higgins, 2017. "The Impact of Changes to the Unemployment Rate on Australian Disability Income Insurance Claim Incidence," Risks, MDPI, vol. 5(1), pages 1-18, March.
  • Handle: RePEc:gam:jrisks:v:5:y:2017:i:1:p:17-:d:93048
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/5/1/17/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-9091/5/1/17/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. HJ Smoluk & Bruce H. Andrews, 2009. "Group Long-Term Disability Insurance Claims and the Business Cycle," Journal of Insurance Issues, Western Risk and Insurance Association, vol. 32(2), pages 154-172.
    2. Brooker, Ann-Sylvia & Frank, John W. & Tarasuk, Valerie S., 1997. "Back pain claim rates and the business cycle," Social Science & Medicine, Elsevier, vol. 45(3), pages 429-439, August.
    3. Antonio, Katrien & Frees, Edward W. & Valdez, Emiliano A., 2010. "A Multilevel Analysis of Intercompany Claim Counts," ASTIN Bulletin, Cambridge University Press, vol. 40(1), pages 151-177, May.
    4. Jean-Philippe Boucher & Michel Denuit & Montserrat Guillén, 2007. "Risk Classification for Claim Counts," North American Actuarial Journal, Taylor & Francis Journals, vol. 11(4), pages 110-131.
    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. Benjamin D. Blair & John Hughes & William B. Allshouse & Lisa M. McKenzie & John L. Adgate, 2018. "Truck and Multivehicle Truck Accidents with Injuries Near Colorado Oil and Gas Operations," IJERPH, MDPI, vol. 15(9), pages 1-8, August.
    2. Phi-Hung Nguyen & Jung-Fa Tsai & Ihsan Erdem Kayral & Ming-Hua Lin, 2021. "Unemployment Rates Forecasting with Grey-Based Models in the Post-COVID-19 Period: A Case Study from Vietnam," Sustainability, MDPI, vol. 13(14), pages 1-27, July.

    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. Pechon, Florian & Denuit, Michel & Trufin, Julien, 2019. "Home and Motor insurance joined at a household level using multivariate credibility," LIDAM Discussion Papers ISBA 2019013, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Mihaela DAVID, 2014. "Modeling The Frequency Of Claims In Auto Insurance With Application To A French Case," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 13, pages 69-85, June.
    3. Katrien Antonio & Emiliano Valdez, 2012. "Statistical concepts of a priori and a posteriori risk classification in insurance," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(2), pages 187-224, June.
    4. Sergi Jiménez-Martín & Arnau Juanmarti Mestres & Judit Vall Castelló, 2019. "Great Recession and disability insurance in Spain," Empirical Economics, Springer, vol. 56(5), pages 1623-1645, May.
    5. Deprez, Laurens & Antonio, Katrien & Boute, Robert, 2021. "Pricing service maintenance contracts using predictive analytics," European Journal of Operational Research, Elsevier, vol. 290(2), pages 530-545.
    6. Martin Branda, 2014. "Optimization Approaches to Multiplicative Tariff of Rates Estimation in Non-Life Insurance," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 31(05), pages 1-17.
    7. Boone, Jan & van Ours, Jan C. & Wuellrich, Jean-Philippe & Zweimüller, Josef, 2011. "Recessions are bad for workplace safety," Journal of Health Economics, Elsevier, vol. 30(4), pages 764-773, July.
    8. Catalina Bolancé & Raluca Vernic, 2017. "“Multivariate count data generalized linear models: Three approaches based on the Sarmanov distribution”," IREA Working Papers 201718, University of Barcelona, Research Institute of Applied Economics, revised Oct 2017.
    9. Jolien Ponnet & Robin Van Oirbeek & Tim Verdonck, 2021. "Concordance Probability for Insurance Pricing Models," Risks, MDPI, vol. 9(10), pages 1-26, October.
    10. Payandeh Najafabadi Amir T. & MohammadPour Saeed, 2018. "A k-Inflated Negative Binomial Mixture Regression Model: Application to Rate–Making Systems," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 12(2), pages 1-31, July.
    11. Hugo Benítez-Silva & Richard Disney & Sergi Jiménez-Martín, 2010. "Disability, capacity for work and the business cycle: an international perspective [Has the boom in incapacity benefit claimant numbers passed its peak?]," Economic Policy, CEPR;CES;MSH, vol. 25(63), pages 483-536.
    12. Lluís Bermúdez & Dimitris Karlis & Isabel Morillo, 2020. "Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models," Risks, MDPI, vol. 8(1), pages 1-13, January.
    13. Christensen, Bent Jesper & Parra-Alvarez, Juan Carlos & Serrano, Rafael, 2021. "Optimal control of investment, premium and deductible for a non-life insurance company," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 384-405.
    14. Denuit, Michel & Guillen, Montserrat & Trufin, Julien, 2018. "Multivariate credibility modeling for usage-based motor insurance pricing with behavioral data," LIDAM Discussion Papers ISBA 2018032, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    15. Anna Szymańska, 2017. "The Application Of Bűhlmann-Straub Model To The Estimation Of Net Premium Rates Depending On The Age Of The Insured In The Motor Third Liability Insurance," Statistics in Transition New Series, Polish Statistical Association, vol. 18(1), pages 151-165, March.
    16. Adland, Roar & Jia, Haiying & Lode, Tønnes & Skontorp, Jørgen, 2021. "The value of meteorological data in marine risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    17. Zhao, Xiao Bing & Zhou, Xian & Wang, Jing Long, 2009. "Semiparametric model for prediction of individual claim loss reserving," Insurance: Mathematics and Economics, Elsevier, vol. 45(1), pages 1-8, August.
    18. Michele Campolieti, 2002. "Moral Hazard and Disability Insurance: On the Incidence of Hard-to-Diagnose Medical Conditions in the Canada/Quebec Pension Plan Disability Program," Canadian Public Policy, University of Toronto Press, vol. 28(3), pages 419-441, September.
    19. Michele Campolieti & Harry A. Krashinsky, 2003. "Substitution Between Disability Support Programs in Canada," Canadian Public Policy, University of Toronto Press, vol. 29(4), pages 417-429, December.
    20. Baumgartner, Carolin & Gruber, Lutz F. & Czado, Claudia, 2015. "Bayesian total loss estimation using shared random effects," Insurance: Mathematics and Economics, Elsevier, vol. 62(C), pages 194-201.

    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:gam:jrisks:v:5:y:2017:i:1:p:17-:d:93048. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.