Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/3743
Title: Preference-Based Pricing of the Indonesian JKN-KIS Health Insurance for Urban Middle-Income Self-Funders
Authors: Pratikto, Fransiscus Rian
Keywords: Preference-based Pricing
JKN-KIS Health Insurance
Discrete Choice Experiment
Mixed Multinomial Logit
Bayesian
Issue Date: 2021
Abstract: We determine optimal premiums of the Indonesian JKN-KIS health insurance plans for urban middle-income self-funders based on their preferences. The demand function was derived using the discrete choice experiment assuming a mixed multinomial logit model. The choice data were collected using questionnaires containing choice tasks that are randomly generated such that they are balanced, orthogonal, and have minimal overlap. The population is middle-income people living in the urban area that pay for health insurance with their own money. An online survey with a simple random sampling method taken time from February until March 2020. As many as 228 questionnaires were completed and collected. Individual utilities were estimated from choice data using the Bayesian method and subsequently used for deriving price-response functions. We found that more than 90% of respondents prefer first-class and second-class plans. Accordingly, we set up a pricing optimization formulation for those two plans to maximize total contribution while maintaining the price difference between them and setting the price of the third-class plan as it was. We came up with monthly premiums of Rp290,000 and Rp240,000 for the first-class and second-class plan, respectively, with an estimated monthly total contribution of Rp1.191 trillion, a 150% increase compared to that of the current pricing. It reveals the opportunity for increasing revenue by implementing finer price differentiation without sacrificing the mission of serving the underprivileged with the third-class plan.
URI: http://localhost:8080/xmlui/handle/123456789/3743
ISSN: 2355-3596
Appears in Collections:VOL 17 NO 1 2021

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