IDEAS home Printed from https://ideas.repec.org/a/sae/sagope/v13y2023i3p21582440231196658.html
   My bibliography  Save this article

Modeling the Macroeconomic and Demographic Determinants of Life Insurance Demand in Ghana Using the Elastic Net Algorithm

Author

Listed:
  • Phyllis Asorh Oteng
  • Victor Curtis Lartey
  • Amos Kwasi Amofa

Abstract

The government of Ghana and the National Insurance Commission have shown concern over the low insurance patronage in Ghana. In order to take the necessary steps to increase insurance patronage, there is the need to, among other things, find the macroeconomic determinants of insurance demand in Ghana. The purpose of this study is to model the macroeconomic and demographic determinants of life insurance demand in Ghana. Data covering the period 1994 through 2020 are used for the study. Even though many studies have been done on determinants of insurance demand elsewhere (not in Ghana), almost all these studies use ordinary least square regression, stepwise regression, or similar regression methods. However, these methods are not robust enough to handle problems of multicollinearity, over-fitting, and inability to do out-of-sample prediction. This current study uses a regularization method known as elastic net regression algorithm which is more robust for handling the aforementioned problems, and more. The results of the study show that the dominating predictors (those with non-zero coefficients) of life insurance demand include old aged dependency ratio, life expectancy, urbanization, and financial development. The first three have positive relation with life insurance demand, while the last one has negative relation with life insurance demand. Insurance regulators and insurance companies are advised to design more innovative and attractive insurance policies for the aged and the old aged dependents as they have the highest tendency to affect insurance demand in Ghana.

Suggested Citation

  • Phyllis Asorh Oteng & Victor Curtis Lartey & Amos Kwasi Amofa, 2023. "Modeling the Macroeconomic and Demographic Determinants of Life Insurance Demand in Ghana Using the Elastic Net Algorithm," SAGE Open, , vol. 13(3), pages 21582440231, September.
    • Handle: RePEc:sae:sagope:v:13:y:2023:i:3:p:21582440231196658
    DOI: 10.1177/21582440231196658
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/21582440231196658
    Download Restriction: no
    File URL: https://libkey.io/10.1177/21582440231196658?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. Subir Sen, 2008. "An Analysis Of Life Insurance Demand Determinants For Selected Asian Economies And India," Working Papers 2008-036, Madras School of Economics,Chennai,India.
    2. repec:eme:mfppss:03074350310768779 is not listed on IDEAS
    3. Park, Sojung Carol & Lemaire, Jean, 2012. "The Impact of Culture on the Demand for Non-Life Insurance," ASTIN Bulletin, Cambridge University Press, vol. 42(2), pages 501-527, November.
    4. Waheed Akhter & Saad Ullah Khan, 2017. "Determinants of Takāful and conventional insurance demand: A regional analysis," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1291150-129, January.
    5. Samuel Ampaw & Edward Nketiah-Amponsah & Nkechi Srodah Owoo, 2018. "Gender perspective on life insurance demand in Ghana," International Journal of Social Economics, Emerald Group Publishing Limited, vol. 45(12), pages 1631-1646, August.
    6. Gloria A. Fofie, 2016. "What Influence Customer Patronage of Insurance Policies: An Empirical Assessment of Socio-Economic and Demographic Determinants of Insurance Patronage in Ghana," International Review of Management and Marketing, Econjournals, vol. 6(1), pages 81-88.
    7. Simona Laura Dragos, 2014. "Life and non-life insurance demand: the different effects of influence factors in emerging countries from Europe and Asia," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 27(1), pages 169-180, January.
    8. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    9. Liam J. A. Lenten & David N. Rulli, 2006. "A Time-Series Analysis of the Demand for Life Insurance Companies in Australia: An Unobserved Components Approach," Australian Journal of Management, Australian School of Business, vol. 31(1), pages 41-66, June.
    10. Donghui Li & Fariborz Moshirian & Pascal Nguyen & Timothy Wee, 2007. "The Demand for Life Insurance in OECD Countries," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 74(3), pages 637-652, September.
    11. Alan J. Auerbach & Laurence J. Kotlikoff, 1989. "How Rational Is the Purchase of Life Insurance?," NBER Working Papers 3063, National Bureau of Economic Research, Inc.
    12. Cheng Yuan & Yu Jiang, 2015. "Factors affecting the demand for insurance in China," Applied Economics, Taylor & Francis Journals, vol. 47(45), pages 4855-4867, September.
    13. 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.
    14. Mouna Zerriaa & Mohamed Marouen Amiri & Hedi Noubbigh & Kamel Naoui, 2017. "Determinants of Life Insurance Demand in Tunisia," African Development Review, African Development Bank, vol. 29(1), pages 69-80, March.
    15. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    16. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    17. Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ashesh Rambachan, 2018. "Algorithmic Fairness," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 22-27, May.
    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. J. François Outreville, 2011. "The relationship between insurance growth and economic development - 80 empirical papers for a review of the literature," ICER Working Papers 12-2011, ICER - International Centre for Economic Research.
    2. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2020. "lassopack: Model selection and prediction with regularized regression in Stata," Stata Journal, StataCorp LP, vol. 20(1), pages 176-235, March.
    3. Francesco Bloise & Paolo Brunori & Patrizio Piraino, 2021. "Estimating intergenerational income mobility on sub-optimal data: a machine learning approach," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 19(4), pages 643-665, December.
    4. Elena Ivona DUMITRESCU & Sullivan HUE & Christophe HURLIN & Sessi TOKPAVI, 2020. "Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds," LEO Working Papers / DR LEO 2839, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    5. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    6. Waheed Akhter & Saad Ullah Khan, 2017. "Determinants of Takāful and conventional insurance demand: A regional analysis," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1291150-129, January.
    7. Huck, Nicolas, 2019. "Large data sets and machine learning: Applications to statistical arbitrage," European Journal of Operational Research, Elsevier, vol. 278(1), pages 330-342.
    8. J. François Outreville, 2013. "The Relationship Between Insurance and Economic Development: 85 Empirical Papers for a Review of the Literature," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 16(1), pages 71-122, March.
    9. Halko, Marja-Liisa & Lappalainen, Olli & Sääksvuori, Lauri, 2021. "Do non-choice data reveal economic preferences? Evidence from biometric data and compensation-scheme choice," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 87-104.
    10. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
    11. Felipe Leal & Carlos Molina & Eduardo Zilberman, 2020. "Proyección de la Inflación en Chile con Métodos de Machine Learning," Working Papers Central Bank of Chile 860, Central Bank of Chile.
    12. Suriyan Jomthanachai & Wai Peng Wong & Khai Wah Khaw, 2024. "An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 741-792, February.
    13. Edward I. Altman & Marco Balzano & Alessandro Giannozzi & Stjepan Srhoj, 2023. "Revisiting SME default predictors: The Omega Score," Journal of Small Business Management, Taylor & Francis Journals, vol. 61(6), pages 2383-2417, November.
    14. Blazquez, Desamparados & Domenech, Josep, 2018. "Big Data sources and methods for social and economic analyses," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 99-113.
    15. Holger Breinlich & Valentina Corradi & Nadia Rocha & Michele Ruta & Joao M.C. Santos Silva & Tom Zylkin, 2021. "Machine Learning in International Trade Research ?- Evaluating the Impact of Trade Agreements," School of Economics Discussion Papers 0521, School of Economics, University of Surrey.
    16. Thiago Christiano Silva & Benjamin Miranda Tabak & Idamar Magalhães Ferreira, 2019. "Modeling Investor Behavior Using Machine Learning: Mean-Reversion and Momentum Trading Strategies," Complexity, Hindawi, vol. 2019, pages 1-14, December.
    17. Chen, Ya & Tsionas, Mike G. & Zelenyuk, Valentin, 2021. "LASSO+DEA for small and big wide data," Omega, Elsevier, vol. 102(C).
    18. Jin-Kyu Jung & Manasa Patnam & Anna Ter-Martirosyan, 2018. "An Algorithmic Crystal Ball: Forecasts-based on Machine Learning," IMF Working Papers 2018/230, International Monetary Fund.
    19. Dimitris Korobilis, 2018. "Machine Learning Macroeconometrics: A Primer," Working Paper series 18-30, Rimini Centre for Economic Analysis.
    20. Lionel Roger, 2018. "Blinded by the light? Heterogeneity in the luminosity-growth nexus and the African growth miracle," Discussion Papers 2018-04, University of Nottingham, CREDIT.

    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:sae:sagope:v:13:y:2023:i:3:p:21582440231196658. 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: SAGE Publications (email available below). General contact details of provider: .

    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.