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Evaluating the Impact of Health Insurance for the Poor: Evidence from Indonesia. Pandu Harimurti World Bank Jakarta Office. Health risk and role of health Insurance. Of all the risks, health risk is probably pose the biggest threat to the lives of the poor
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Evaluating the Impact of Health Insurance for the Poor: Evidence from Indonesia PanduHarimurti World Bank Jakarta Office
Health risk and role of health Insurance • Of all the risks, health risk is probably pose the biggest threat to the lives of the poor • A health shock may cause financial catastrophe that, if uninsured, may lead to long-term poverty • Through debt, sale of assets and removal of children from school • Lower access to health care for those hit by the health shock may lower the quality of human capital • Health insurance may help individuals to deal with financial catastrophe by overcoming financial barrier to access the health care • Indonesia launched health insurance for the poor in 2005, accompanied fuel subsidy cut, as first step to universal health insurance coverage in 2019
The Indonesia Insurance Program for the Poor: ASkeskin • The program budget started with 3 trillion rupiah in 2005/2006, and increased to 4.5 trillion rupiah (about US$ 480 million) in 2007 and afterward. • 90% was allocated to hospitals and 10% to local health centers. • This constituted one-third of MoH total annual budget, but it still translates into relatively low monthly premium per beneficiary 5,000 rupiahs (about US$ 0.55) • Provides free basic outpatient care, third-class inpatient care and obstetric service package. • For remote areas also includes: mobile health services and some other special services • It targeted 76.4 million poor and near-poor individuals • Using Socio-Economic Survey ‘05 (PSE ‘05) in which beneficiary data was obtained through village head and then was verified by BPS
An Empirical Model • Our aim is to capture the effect of insurance on some outcomes. Consider the model: • We want to interpret the coefficient, a, as the effect of insurance • But of course there are several identification problems when evaluating targeted program like Askeskin: • Access to insurance is likely correlated with unobserved heterogeneity: community, household or individuals. • Case study works in Indonesia suggests that individuals might request or receive the Askeskin Insurance card upon becoming ill which is unobserved and may also reflect preference toward health-seeking behavior.
Common Non-Experimental Approaches • PSM: matching insured households to uninsured households with similar characteristics, and compare outcomes of the insured to those of the uninsured with similar profiles [Wagstaff and Pradhan (2003), Trujillo et al (2005)] • Present of unobserved heterogeneity may still bias the parameter • Sparrow and co-authors: use Susenas HH panel and control for fixed household unobservables to estimate program impact. • Askeskin is individual level insurance. If unbiased, estimated program impact only provide lower bound impact. • HH fixed effect will not address individual level time-invariant unobserved heterogeneity potentially correlated with outcomes of interest as well as access to Askeskin
Empirical Strategy • Thus we estimate following model: • where we add into previous specification, μiis individuals fixed-effect to control for time-invariant unobserved heterogeneity to remove main source of bias which is arguably preference toward health seeking behavior. • Also include vjis district dummies which may capture heterogeneity across districtssuch as local health policy, differences in health supply side, etc. • The caveat for this strategy is that αwill remain bias if there is time varying unobservables at individual correlated with access to insurance such the presence of health shocks or other anti-poverty program correlated with Askeskin. • If any, such unobservables may cause upward bias
indonesia family life survey • A panel data of individual and household covering 15 provinces with total sample about 60,000 individuals and 14,000 HHs • representative of about 83% of Indonesia population • Use Information from the 2000 & 2007 surveys • A very low attrition panel survey –can maintain re-contact rate at about 92% • Sample is panel individuals in the two survey waves • The program variable is individual access to Askeskinprogram. • The outcome variables are outpatient and inpatient visit as well as per-capita health related expenditure • Covariates: age (years), own education (years), characteristics of head of HH (age, education, female), PCE (log), # of children in HH, # of male and female adult in HH, and observed community characteristics (asphalt road, piped water, and proportion of HH with electricity).
Impact of Askeskin on Percapita Health-Related Expenditure, IFLS 2000 & 2007
Discussions: negligible impact on outpatient care • While the impact for inpatient and percapita health-related expenditure robust, such significant impact disappear for outpatient when we control for unobserved heterogeneity. • Some factors might explain the change in outpatient visit: • the already low access fee for outpatient care at local public clinic • high case rate for self-medication (OTC medication) • Targeting performance might also explain the result: • while Askeskin targeting is pro-poor, both inclusion and exclusion error remain high • previous experience on accessing health care services matter in explaining utilization after receiving Askeskin card.
Efficiency issue in the USE of Askeskin resource • While we find evidence that the Askeskinessentially reduced health-related expenditures for the people who were already using the health care system, the fiscal cost of reducing medical expenditures seems far exceeded the benefit received by beneficiaries. • Annual cost for each beneficiary was 60 thousands rupiah (USD 0.65), but impact on percapita health-related expenditure (-0.14) implies that each beneficiary benefited by only about 41 thousands rupiah indicating potential ‘inefficiency’ in managing Askeskin public resources about 30%. • Although could such resources can also be used for health services-related improvement purposes or else.
Conclusions • We find that the impact of Askeskin mixed: • Non-significant for outpatient visit • Significant for inpatient visit and percapita health-related expenditure • Impact of Askeskin for the poor: on inpatient visit was slightly larger while for percapita health-related expenditure tend to be similar. • Overall impact of the program seems, if any, still small and this implying rooms to improve for Askeksin effectiveness if government can do: • Better targeting • Increase efficiency Askeskin resources • Improved supply side as well as removing other obstacles
