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Ghana’s National Health Insurance Scheme: An Ordinal Probit Valuation of Willingness to Pay Higher Premiums for Improved Services

Received: 18 November 2019     Accepted: 17 April 2020     Published: 27 May 2020
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Abstract

The importance of improved healthcare services under Ghana’s National Health Insurance Scheme (NHIS) is nationwide admitted. However, service improvement for insurance schemes comes with extra cost. To fill the funding gap, insurance schemes typically charge enhanced premiums. This requires clients’ approval and cooperation to implement. For this reason, this study was conducted to assess Ghana’s NHIS subscribers’ willingness to pay (WTP) enhanced premiums for improved services. Some socio-economic and demographic factors were used as covariates. WTP, being the dependent variable, was categorized into high WTP, moderate WTP, low WTP, and no WTP enhanced premiums. The likelihood of a client falling in a particular WTP category was examined using the Cumulative Ordinal Probit (COP) regression model. A likelihood ratio chi-square of 58.82 with p < 0.000 shows that the model was statistically significant, and fit for prediction. Results showed that age-groups 18-30, 30–45, unemployed, tertiary education, and level of income significantly influenced WTP. Predictions showed that for any average national health insurance user, the probability of being in high WTP, moderate WTP, low WTP and no WTP premium are respectively 0.51, 0.27, 0.11 and 0.12. Based on the results of this study, we recommend that Ghana’s NHIS should institute a progressive premium regime in order to cater for the different needs and financial abilities of clients, thus helping to fill the funding gap.

Published in American Journal of Theoretical and Applied Statistics (Volume 9, Issue 3)
DOI 10.11648/j.ajtas.20200903.15
Page(s) 57-62
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

WTP, Premiums, MLE, Cumulative Ordinal Probit Regression Model, NHIS

References
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[2] P. Alagidede, W. Baah-Boateng, E. Nketiah-Amponsah (2013). The Ghanaian Economy: An Overview, Ghanaian Journal of Economics, 1: 1-33.
[3] J. Okoroh, S. Essoun, A. Seddoh, H. Harris, J. S. Weissman, L. Dsane-Selby, R. Riviello (2018). Evaluating the impact of the national health insurance scheme of Ghana on out of pocket expenditures: a systematic review, BMC Health Serv. Res. 18: 1-35.
[4] Ahmed S., Hoque M. E., Sarker A. R., Sultana M., Islam Z., Gazi R., Jahangir A. M. (2016). Willingness-to-Pay for Community-Based Health Insurance among Informal Workers in Urban Bangladesh, 11 (2): 148-211.
[5] Tundui, C., & Macha, R. (2014). Social Capital and Willingness to Pay for Community BasedHealth Insurance: Empirical Evidence from Rural Tanzania; Journal of Finance and Economics, 2 (4): 50-67.
[6] Bolyle K. J. (2003). Contingent Valuation in Practice. In Champ, P. A., K. J. Boyle, and T. C. Brown, eds. A Primer on Nonmarket Valuation. Dordrecht, The Netherlands: Kluwer Academic Publishers: 111-158.
[7] Carson R., Flores N. (2000). Contingent valuation: controversies and evidence, Environmental and Resource Economics, 19 (2): 173-210.
[8] Agresti, A. (2019). An Introduction to Categorical Data Analysis. USA. John Wiley and Sons, Inc.,: 159-187.
[9] D. Bonato, S. Nocera, H. Telser (2001). The Contingent Valuation Method in Health Care: An Economic Evaluation of Alzheimer’ s Disease, Switzerland, Universität Bern.
[10] Brant, R. (1990). Assessing proportionality in the proportional odds model for ordinal logistic regression. Biometric, 46: 1171-1178.
[11] Puurbalanta, R., & Adebanji, A. O. (2016). Household Poverty-Risk Analysis and Prediction Using Bayesian Ordinal Probit Models. International Journal of Statistics and Applications, 6 (6): 399–407.
[12] Greene W. H., Hensher D. A. (2009). Modeling Ordered Choices, New York University, New York: 88-95.
[13] Clogg C., Shihadeh E. S. (1994). Statistical models for ordinal variables. Thousand Oaks, California.
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  • APA Style

    Richard Puurbalanta, Mark Adjei, Vida Afosaa. (2020). Ghana’s National Health Insurance Scheme: An Ordinal Probit Valuation of Willingness to Pay Higher Premiums for Improved Services. American Journal of Theoretical and Applied Statistics, 9(3), 57-62. https://doi.org/10.11648/j.ajtas.20200903.15

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    ACS Style

    Richard Puurbalanta; Mark Adjei; Vida Afosaa. Ghana’s National Health Insurance Scheme: An Ordinal Probit Valuation of Willingness to Pay Higher Premiums for Improved Services. Am. J. Theor. Appl. Stat. 2020, 9(3), 57-62. doi: 10.11648/j.ajtas.20200903.15

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    AMA Style

    Richard Puurbalanta, Mark Adjei, Vida Afosaa. Ghana’s National Health Insurance Scheme: An Ordinal Probit Valuation of Willingness to Pay Higher Premiums for Improved Services. Am J Theor Appl Stat. 2020;9(3):57-62. doi: 10.11648/j.ajtas.20200903.15

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  • @article{10.11648/j.ajtas.20200903.15,
      author = {Richard Puurbalanta and Mark Adjei and Vida Afosaa},
      title = {Ghana’s National Health Insurance Scheme: An Ordinal Probit Valuation of Willingness to Pay Higher Premiums for Improved Services},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {9},
      number = {3},
      pages = {57-62},
      doi = {10.11648/j.ajtas.20200903.15},
      url = {https://doi.org/10.11648/j.ajtas.20200903.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20200903.15},
      abstract = {The importance of improved healthcare services under Ghana’s National Health Insurance Scheme (NHIS) is nationwide admitted. However, service improvement for insurance schemes comes with extra cost. To fill the funding gap, insurance schemes typically charge enhanced premiums. This requires clients’ approval and cooperation to implement. For this reason, this study was conducted to assess Ghana’s NHIS subscribers’ willingness to pay (WTP) enhanced premiums for improved services. Some socio-economic and demographic factors were used as covariates. WTP, being the dependent variable, was categorized into high WTP, moderate WTP, low WTP, and no WTP enhanced premiums. The likelihood of a client falling in a particular WTP category was examined using the Cumulative Ordinal Probit (COP) regression model. A likelihood ratio chi-square of 58.82 with p < 0.000 shows that the model was statistically significant, and fit for prediction. Results showed that age-groups 18-30, 30–45, unemployed, tertiary education, and level of income significantly influenced WTP. Predictions showed that for any average national health insurance user, the probability of being in high WTP, moderate WTP, low WTP and no WTP premium are respectively 0.51, 0.27, 0.11 and 0.12. Based on the results of this study, we recommend that Ghana’s NHIS should institute a progressive premium regime in order to cater for the different needs and financial abilities of clients, thus helping to fill the funding gap.},
     year = {2020}
    }
    

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    T1  - Ghana’s National Health Insurance Scheme: An Ordinal Probit Valuation of Willingness to Pay Higher Premiums for Improved Services
    AU  - Richard Puurbalanta
    AU  - Mark Adjei
    AU  - Vida Afosaa
    Y1  - 2020/05/27
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    DO  - 10.11648/j.ajtas.20200903.15
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    JF  - American Journal of Theoretical and Applied Statistics
    JO  - American Journal of Theoretical and Applied Statistics
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    EP  - 62
    PB  - Science Publishing Group
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    UR  - https://doi.org/10.11648/j.ajtas.20200903.15
    AB  - The importance of improved healthcare services under Ghana’s National Health Insurance Scheme (NHIS) is nationwide admitted. However, service improvement for insurance schemes comes with extra cost. To fill the funding gap, insurance schemes typically charge enhanced premiums. This requires clients’ approval and cooperation to implement. For this reason, this study was conducted to assess Ghana’s NHIS subscribers’ willingness to pay (WTP) enhanced premiums for improved services. Some socio-economic and demographic factors were used as covariates. WTP, being the dependent variable, was categorized into high WTP, moderate WTP, low WTP, and no WTP enhanced premiums. The likelihood of a client falling in a particular WTP category was examined using the Cumulative Ordinal Probit (COP) regression model. A likelihood ratio chi-square of 58.82 with p < 0.000 shows that the model was statistically significant, and fit for prediction. Results showed that age-groups 18-30, 30–45, unemployed, tertiary education, and level of income significantly influenced WTP. Predictions showed that for any average national health insurance user, the probability of being in high WTP, moderate WTP, low WTP and no WTP premium are respectively 0.51, 0.27, 0.11 and 0.12. Based on the results of this study, we recommend that Ghana’s NHIS should institute a progressive premium regime in order to cater for the different needs and financial abilities of clients, thus helping to fill the funding gap.
    VL  - 9
    IS  - 3
    ER  - 

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Author Information
  • Department of Statistics, Faculty of Mathematical Sciences, University for Development Studies, Navrongo Campus, Tamale, Ghana

  • Department of Statistics, Faculty of Mathematical Sciences, University for Development Studies, Navrongo Campus, Tamale, Ghana

  • Department of Mathematics and Statistics, Faculty of Applied Sciences, Sunyani Technical University, Sunyani, Ghana

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