Health insurance and hospitalisation duration: empirical evidence from Ghana’s national health insurance scheme

Abstract

The study aims to explore the causal effect of Ghana’s National Health Insurance Scheme (NHIS) on hospitalisation duration. The analysis was based on the Ghana Socioeconomic Panel Survey datasets, comprising the second wave (2014/2015) and the third wave (2018/2019). The study employed the endogenous switching regression model for count data (ESRC) to control selection bias and unobserved heterogeneity. The ESRC estimates reveal that NHIS membership significantly reduces the length of stay in the hospital, indicating that health insurance has a negative association with hospitalisation. On average, insured people spend nearly five fewer days in the hospital than their uninsured counterparts. The findings further revealed common and heterogeneous determinants of hospitalisation care for both insured and uninsured individuals. Age, household expenditure, and self-assessed health were the main predictors of hospitalisation duration for insured and uninsured persons. Heterogeneously, gender, education, and physical inactivity are significant determinants of NHIS members’ hospitalisation care, while chronic illness affects the length of stay in the hospital of the uninsured. The paper concludes with a discussion of the policy options for increasing NHIS enrolment to reduce the length of stay in the hospital and improve individuals’ well-being.

Keywords:

• Endogenous switching regression model
• health insurance
• hospitalisation
• Ghana

Reviewing Editor:

• Joe Thomas, Institute of Health and Management, Australia

Subjects:

• Economics and Development
• Health & Development
• Economics

Acknowledgements

Data was sourced from the Ghana Socioeconomic Panel Survey (GSPS). This dataset was collected through the shared effort of the University of Ghana’s Institute of Statistical, Social, and Economic Research and Yale University’s Economic Growth Centre. The authors are grateful to both the Economic Growth Centre at Yale University and the Institute of Statistical, Social, and Economic Research (ISSER) at the University of Ghana for using the Ghana Socioeconomic Panel Survey dataset.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are openly available in Harvard Dataverse at https://doi.org/10.7910/DVN/E5QP0F,referencenumberV1,UNF:6:JLtXxepgNXfzyX0ThGLDiw==[fileUNF]

Notes

1 Computed from the estimations in Table 2 as coefficient of age divided by -2(age squared).

Additional information

Notes on contributors

Samuel Sekyi

Samuel Sekyi graduated from the University of South Africa with a PhD in Economics. He is an Associate Professor of Applied Economics at the Department of Economics, Simon Diedong Dombo University of Business and Integrated Development Studies, Ghana. His main areas of expertise and interest are microeconometrics, health economics, development economics, and agricultural economics.

James Dickson Fiagborlo

James Dickson Fiagborlo is a Lecturer at the Department of Multidisciplinary Studies, Ho Technical University, Ghana. He obtained his PhD in Economics from the University of Cape Coast, Ghana. His research interests span areas such as applied econometrics, transportation economics, agriculture technologies, education studies, and health economics.

Gloria Essilfie

Gloria Essilfie is a Lecturer at the Department of Applied Economics, University of Cape Coast. Gloria is a graduate of the University of Cape Coast, where she obtained her BA, MPhil, and PhD in Economics. Gloria Essilfie’s academic background covers the fields of applied microeconomics and development economics.