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Estimating the Health Effects of Expansions in Health Expenditure in Indonesia: A Dynamic Panel Data Approach

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Abstract

Background

The marginal productivity of a country’s healthcare system refers to the health gains produced per unit change in the level of spending. In budget-constrained settings, this metric reflects the opportunity cost, in terms of health gains forgone, of committing additional or existing resources to alternative uses within the healthcare system. It can therefore assist in evidence-based decisions on whether different interventions represent good value for money.

Objective

The aim of this paper was to estimate the marginal productivity of the Indonesian healthcare system using subnational data, and to use this to inform health opportunity costs in the country.

Methods

We define a dynamic health production function to model the stream of effects of current and prior public health spending decisions on population under-five mortality. To estimate the model, we use data from the 33 Indonesian provinces for the 2004–2012 period. The estimated elasticity is then translated into gains in terms of cost per DALY (disability-adjusted life-year) averted. We use dynamic panel data methods to address potential endogeneity issues in the model.

Results

Our base-case estimates suggest that a 1% expansion in the level of health spending reduces under-five mortality by 0.38% (95% CI 0.00–0.76), which translates into a cost of averting one DALY of $235 (2019 US$).

Conclusion

With Indonesia aiming for universal health coverage, our results support these efforts by highlighting the associated benefits resulting from increases in public health expenditure and have the potential to inform the decision-making process about a suitable locally relevant cost-effectiveness threshold.

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Notes

  1. Given the changing nature of administrative units in Indonesia (with forming districts and provinces over the study period), we took efforts to adequately link districts to provinces in a given year, using information from a repository of administrative units in Indonesia from several sources (from the INDO-DAPOER as well as the Statoids website [45]).

  2. First, wherever there was a maximum of two consecutive missing periods, we imputed the missing data by fitting smooth polynomials on the logarithm of the district level expenditure time series. Where three or more district-level data points were missing, we imputed them using the mean expenditure of the province in the given year. For province-level missing budgets and other province-level variables for a given period, we used linear interpolation (7.6% of the observations of the health expenditure variable were missing, and on average 5.9% of observations for the other variables, and never for more than one period).

  3. The 2011 Health Facility Census (Rifaskes) reported availability issues of BCG, measles, polio and DPT vaccines in the provinces including Papua and Maluku.

  4. The elasticity can be derived using a simple transformation and it informs of the percentage change in mortality following a 1% increase in the level of expenditure.

  5. Unlike Acemoglu et al. [38], we approximate the importance of oil in the economy using the volume of exports, which was derived using information from the INDO-DAPOER and CEIC databases [46].

  6. To ensure the conditions for instrument validity are met while avoiding the risk of instrument proliferation, we use only the first three lagged values and the log of own-source revenue as instrumental variables.

  7. Using morbidity burden of disease allows us to capture both morbidity related to mortality (i.e., from children who would have died) and morbidity from surviving children.

  8. We assume that this ratio stays constant with the levels of mortality.

  9. The long-term effect of expenditure could be derived as \({\sum }_{s=0}^{k}{{{\lambda }^{s}\beta }_{0}x}_{i,\mathrm{t}-k}\). However, the coefficients of lagged mortality rates, used for this calculation, are not statistically significant at conventional levels. Our results therefore are not informative of the cumulative effect spending decisions on the current mortality rate.

  10. Filmer and Pritchett [5] argue that the lower elasticity of IMR with respect to health spending compared to U5MR could be explained by the genetic component of neonatal deaths, which account for a large proportion of IMR.

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Acknowledgements

We gratefully acknowledge the extremely valuable input from Budi Hidayat, excellent research assistance by Lydia Jowitt, as well as fruitful discussions with Karl Claxton.

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Authors and Affiliations

  1. Department of Health Services Research and Policy, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, WC1H 9SH, UK

    Silvia Moler-Zapata

  2. Centre for Health Economics, University of York, Heslington, York, YO10 5DD, UK

    Noémi Kreif, Jessica Ochalek, Andrew J. Mirelman & Marc Suhrcke

  3. Department of Health Policy and Administration, Faculty of Public Health, University of Indonesia, Depok, Indonesia

    Mardiati Nadjib

  4. Luxembourg Institute of Socio-economic Research, 4366, Esch-sur-Alzette, Luxembourg

    Marc Suhrcke

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Correspondence to Silvia Moler-Zapata.

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Funding

This work was supported by the Bill and Melinda Gates Foundation through the International Decision Support Initiative (iDSI) and by the National Institute for Health Research (NIHR) [16/137/90] using UK aid from the UK Government to support global health research. The views expressed in this publication are those of the authors.

Conflict of interest

The authors declare no conflicts of interest.

Ethics approval

No ethics approval was required for this study.

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Not applicable.

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Not applicable.

Availability of data and material

All data used in this study are available in the public domain.

Code availability

The data-cleaning process and all descriptive and statistical analyses were conducted in STATA 17. Access to code can be provided upon request.

Authors contributions

SMZ, NK, JO, AM, and MS were involved in the design of the study. NK, AM, and MS were involved in the acquisition of the funding for the study. SMZ, NK, JO, and AM conducted the main analysis. NK, MN, and MS provided senior reviews of the study. All authors were involved in the interpretation of the results and writing-up of the manuscript. All authors have given their approval for the final version to be published.

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Moler-Zapata, S., Kreif, N., Ochalek, J. et al. Estimating the Health Effects of Expansions in Health Expenditure in Indonesia: A Dynamic Panel Data Approach. Appl Health Econ Health Policy 20, 881–891 (2022). https://doi.org/10.1007/s40258-022-00752-x

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