 Willingness to Pay for Health InsuranceChapter 5 in Financing Micro Health Insurance:Theory, Methods and Evidence, 2018, pp 113-116 from World Scientific Publishing Co. Pte. Ltd. Abstract: Following are the consolidated main messages from Chapters 5.1–5.3:Estimating Willingness to Pay (WTP) for health insurance (HI) coverage is the only way to estimate the expected income of voluntary and contributory HI schemes. This estimate is needed to ensure that the cost of benefits packages remain within available resources, so as to minimize the risk of bankruptcy.Data on WTP are necessary to inform the design of customized HI benefits packages by consumers, notably by groups or communities.One approach to estimate individual WTP is the Revealed Preferences method (RP), introduced first by Samuelson, which involves studying actual purchasing behavior of products which are related to the product we are interested in. The assumption is that revealed preferences can identify the value people place on a product for which purchasing information does not exist.Another approach to estimate WTP is the Stated Preferences method (SP), which involves asking people what they would be willing to pay for insurance coverage that they do not yet have, and that may not be available in the market. SP has been refined into several techniques, and the one used most often to value non-market goods is called Contingent Valuation (CV). It consists of asking respondents the maximum amount they would be willing to pay for an intervention under evaluation (e.g. insurance).Most of the rural population in low-income countries is not familiar with the concept of insurance. Hence, it is challenged to reveal the price they are willing to pay for (insurance coverage) a product they have never before been offered or purchased.Field evidence of experiments with choice of package and price by groups of rural poor people in India points that they tend to converge toward consensus on the components of the benefit package and the price per person per year (PPPY), namely on the generally accepted declared WTP.It must be recognized that the actual level of their WTP could differ from the declared level. A first estimate of the gap between declared and actual WTP in community-based health insurance (CBHI) has been obtained from the evidence that households modulate WTP levels not by negotiating a different price, but by limiting the number of household members enrolled (at the agreed price PPPY) to reduce the total cost per household for HI. Initial evidence suggests that households enrolled only half their members in the first and second year. Longitudinal studies could verify whether the gap between the declared and actual WTP would diminish over time.Dror and Koren (2012) conducted a review of 14 experimental field studies eliciting WTP for health insurance among low-income persons in developing countries. They observed that the large methodological diversity reported in these studies made it impossible to identify a single “gold standard” method to elicit and estimate WTP.However, there was implied consensus that WTP must be explored locally, because there is no single “one-size-fits-all” option to determine the income side of different HMI schemes operating in different settings. Thus, estimates of WTP were based on household surveys. Collecting data with such surveys is both time-consuming and expensive.Several studies explored faster and cheaper methods to estimate WTP than surveys.Binnendijk et al. (2013) examined whether the relation between WTP for HI and income might be similar to Engel’s Law, an observation in economics stating that the proportion of income spent on food decreases as income increases, even if actual expenditure on food rises. These authors used data from six locations in India to check WTP expressed as a percentage of three anchors: overall income, discretionary income, and food expenditures, by calculating the Coefficient of Variation (for inter-community variation) and Concentration indices (for intra-community variation). They found that food expenditures had the most consistent relationship with WTP within each location, and across the six locations. This suggests that, just like food, HI is considered a necessity good even for people with very low income and no prior experience with health insurance. Thus, it is possible to estimate the WTP level based on each community’s food expenditures.On an average, WTP for CBHI was around 4.5% of food expenditures in the studied locations. Food expenditure information can be obtained through cheap and fast research methods, such as focus group discussions with target communities or from data published routinely and in the public domain.Nosratnejad et al. (2016) estimated WTP for HI on the basis of readily available data pertaining to GDP per capita, by using vote counting to identify factors which were consistently correlated with higher WTP for insurance: family size, education level, income, past hospitalization, and perceived poor health status. Their meta-analysis revealed that the WTP for HI among rural households in low and middle-income countries (LMICs) was just below 2% of the GDP per capita per household per year. Keywords: Microinsurance; Micro Health Insurance; Health Insurance; Community-Based Health Insurance; CBHI; Demand for Health Insurance; Willingness to Pay for Health Insurance; Microinsurance Package Design; Implementation of CBHI; Business Case of Microinsurance (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:wsi:wschap:9789813238480_0005 Ordering information: This item can be ordered from Access Statistics for this chapter More chapters in World Scientific Book Chapters from World Scientific Publishing Co. Pte. Ltd. |
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