2007SSRN Electronic JournalOpen access

Why 'One-Size-Fits-All' Health Insurance Products are Unsuitable for Low-Income Persons in the Informal Economy in India

David M. Dror

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Abstract

Limited funding dictates that health insurance for low-income persons would compensate only part of healthcare needs. Existing health insurance products in India are too restrictive to be attractive to low-income & rural populations. We hypothesize that attractive health insurance must represent an optimum match between clients' needs for health care, demand for health insurance, and available supply of health care. Based on data from a household survey among rural poor and urban slum dwellers in seven locations in India collected in 2005, we provide evidence of marked differences across locations in all three parameters: solvent demand for health insurance (proxy: willingness to pay), medical needs (proxy: frequency of illness episodes and the number of days of illness per HH), and the supply of healthcare (proxy: type of healthcare provider and out-of-pocket expenditure on health care). We also show that aggregated expenses of consultations and drugs exceed those of hospitalizations in all locations. We conclude that because the variations in clients' needs, cost of healthcare, availability of services and clients' demand for health insurance across locations cannot be optimized in a single partial benefit package, a context-specific solution is needed to be relevant in each location.

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What this paper is about

Limited funding dictates that health insurance for low-income persons would compensate only part of healthcare needs. Existing health insurance products in India are too restrictive to be attractive to low-income & rural populations. We hypothesize that attractive health insurance must represent an optimum match between clients' needs for health care, demand for health insurance, and available supply of health care. Based on data from a household survey among rural poor and urban slum dwellers in seven locations in India collected in 2005, we provide evidence of marked differences across locations in all three parameters: solvent demand for health insurance (proxy: willingness to pay), medical needs (proxy: frequency of illness episodes and the number of days of illness per HH), and the supply of healthcare (proxy: type of healthcare provider and out-of-pocket expenditure on health care). We also show that aggregated expenses of consultations and drugs exceed those of hospitalizations in all locations. We conclude that because the variations in clients' needs, cost of healthcare, availability of services and clients' demand for health insurance across locations cannot be optimized in a single partial benefit package, a context-specific solution is needed to be relevant in each location.

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Available abstract

Limited funding dictates that health insurance for low-income persons would compensate only part of healthcare needs. Existing health insurance products in India are too restrictive to be attractive to low-income & rural populations. We hypothesize that attractive health insurance must represent an optimum match between clients' needs for health care, demand for health insurance, and available supply of health care. Based on data from a household survey among rural poor and urban slum dwellers in seven locations in India collected in 2005, we provide evidence of marked differences across locations in all three parameters: solvent demand for health insurance (proxy: willingness to pay), medical needs (proxy: frequency of illness episodes and the number of days of illness per HH), and the supply of healthcare (proxy: type of healthcare provider and out-of-pocket expenditure on health care). We also show that aggregated expenses of consultations and drugs exceed those of hospitalizations in all locations. We conclude that because the variations in clients' needs, cost of healthcare, availability of services and clients' demand for health insurance across locations cannot be optimized in a single partial benefit package, a context-specific solution is needed to be relevant in each location.

Key concepts: Proxy (statistics), Health care, Business, Context (archaeology), Health insurance, Environmental health, Actuarial science, Economic growth

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