Willingness to Pay for Health Insurance amongst the Urban Poor: Evidence from a Slum in Mumbai, India

Transcription

1 Artha Vijnana Vol. LIII, No. 2, June 2011, pp Willingness to Pay for Health Insurance amongst the Urban Poor: Evidence from a Slum in Mumbai, India Soumitra Ghosh and Shinjini Mondal The study aims to assess the demand for health insurance, gather evidence on willingness to pay for it and its determining factors amongst the urban poor in Mumbai. This was ascertained through dichotomous bidding process on 300 households. Results of the multivariate regression model suggest that households with presence of morbid conditions and prior experience of inpatient admissions were more likely to join and pay for health insurance. They indicate the need for a state-subsidized insurance scheme as more than 50 per cent of the people willing to pay expressed their inability to pay more than one per cent of their annual income. I Introduction In India, health insurance is increasingly being looked at as a way to provide quality health care to the people and protect them financially against the risk of illness. At present, it has not made much headway with only a small proportion (about 10 per cent) of the population covered by any form of health insurance. Importantly, the private health insurance plans offered by both public and private insurance companies are mainly targeted to the urban middle and upper classes and settled job holders with regular flow of income and higher ability to pay. Of late, the government has introduced a publicly funded health insurance scheme namely Rashtriya Swasthya Bima Yojana (RSBY) to cover the Below Poverty Line (BPL) households working in the informal sector. But none of the health insurance companies has put out any scheme for the population just above the poverty line and engaged in informal sector or self-employed people with the assumption that they lack the capacity to pay. These groups have remained outside the ambit of any social security scheme or government financed health scheme despite the growing evidence of higher incidence of catastrophic health expenditure and impoverishment due to out of pocket health payments amongst them (IIPS-WHO 2006). Soumitra Ghosh, Assistant Professor, Centre for Health Policy, Planning and Management, School of Health System Studies, Tata Institute of Social Sciences, Deonar, Mumbai , India, soumitra@tiss.edu. Shinjini Mondal, Programme Associate, ICICI Foundation-Centre for Child Health and Nutrition, Mumbai , India, mshinjini@gmail.com.

2 118 Soumitra Ghosh and Shinjini Mondal The population under study, urban poor forms a similar group as they share a different profile from non-poor urban. The interaction of social, economic and environment together forms the vulnerable conditions for urban poor. Economic crisis arises because of irregular employment and poor access to fair credit. Quality of living environment is affected by poor access to safe water supply and sanitation facilities. Over-crowding, poor housing and insecure land tenure make them more prone to water-borne and communicable diseases. With all these vulnerabilities and no special schemes and consideration for this population, coping with their living conditions becomes difficult (NUHM 2008). Their financial condition is further vulnerable during illness when households often do not have the resources to seek health care and this leads to catastrophic expenditure and impoverishment. Surprisingly, the willingness of urban poor to participate in health insurance schemes in the Indian context has not attracted much scholarly attention. The literature on Willingness to Pay (WTP) in India has been scanty and the past studies mainly include examination of the WTP for a viable health insurance for the poor and rural population (Mathiyazhagan 1998, Dror et al. 2007). In view of the above context, in this paper we explore whether the urban poor really lack the capacity to pay for health insurance, assess the demand for health insurance, willingness of urban poor in joining a health insurance scheme and the factors that determine the willingness to avail of such schemes. Data and Methods Data The area chosen for the study is a slum called Turbhe store in Navi Mumbai. To draw the sample from the area multi stage-sampling was used. First, clusters were identified and then proportionate sample according to the population of a cluster was drawn by systematic random sampling. Estimation of the number of households was taken from urban health post in Turbhe store where every two to three years a census is conducted to know the population of the area. There were 6,638 households in the area, according to the census done by the urban health department in the year The area could be divided into seven clusters leaving the red light area. Population of each cluster was obtained from the health post. Out of the eight clusters, five were randomly chosen. A sample size of 300 households was decided and accordingly the sample was drawn proportionately from five clusters, depending upon the population size of the cluster. Further, selection was based on drawing random number for each row of houses and then selecting every tenth household after that. The same process was repeated for subsequent row of houses. Though the sample size was 300 households, information was collected from each member of the family which accounted to 1502 individuals.

3 Willingness to Pay for Health Insurance 119 Morbidities had a reference period of 15 days. Cases of hospitalization were also taken into consideration with a reference period of one year. Method In this study, the method employed to estimate the willingness to pay for health insurance is double bounded dichotomous choice elicitation. Here, the subject is asked whether he is willing to pay the first chosen bid and if the answer is affirmative, it is followed by a higher bid. Conversely, if the answer is negative, it is followed by a lower bid. Using this sequence of questions to narrow down the range within each respondent leads to generation of true WTP and also reduces inefficiency. Compared with other elicitation method, the process has significant statistical efficiency (Hanemann W et al. 1996, Smith 2000, Yoo and Yang 2001). The comprehensive medical benefit package offered to the respondents in the process was adapted from RSBY scheme (Government of India 2008). The objective behind using the RSBY scheme was that it is especially for BPL population and the population under study is also a poor population though not necessarily BPL. Hence, considering their above poverty status, an attempt has been made to find out whether they are willing and able to afford such health benefit packages at normal value without subsidization. A growing body of evidence suggests that a household is a better unit for enrolment in terms of protecting other household members (non-household heads) such as women, elders and children (Dong et al. 2004). Hence, it was taken as a unit of analysis in this study to determine the WTP for health insurance. We employed logit regression model on the households willingness to pay. II Findings WTP among Households The data reveal that awareness about availability of health insurance schemes was low in the population as only 25 per cent of the households knew about them. After the respondents were explained the benefits of the hypothetical health insurance scheme, 35 per cent households expressed their willingness to be a part of it, while 59 per cent declined to be associated with any such scheme (Table 1). Also, a small proportion (six per cent) of households was unable to decide whether they would join the hypothetical health insurance scheme.

4 120 Soumitra Ghosh and Shinjini Mondal Table 1: Proportion of Urban Poor who Expressed Willingness vis-a-vis those Not Willing to be Part of Health Insurance Plan, Mumbai Slum, 2010 Per cent Willing to be part of health insurance plan 35 Not willing to be part of health insurance plan 59 Unable to decide 6 It was also found that 60 per cent of the people expressing need for prepayment schemes said that they had the ability to purchase such schemes, while the remaining 40 per cent expressed their inability to pay as they did not have the purchasing power for such schemes or were unable to estimate the exact amount they would be able to pay, due to the informal nature of occupation and absence of fixed monthly income. Amongst the 60 per cent able to pay for health insurance scheme, it was found that 50 per cent people were able to pay only less than one per cent of their annual income (Fig. 1). This may not be a substantial amount to sustain a premium for health insurance at the market rates. Figure 1: WTP as a Percentage of Household Consumption Expenditure The reasons for non-willingness to be a part of any health insurance plan are analyzed (Table 2). One third of the households unwilling to join a health insurance scheme perceived their family health as good and relatively free from morbidities. The other 10 per cent of the families felt that they were susceptible to small illnesses which did not account for large expenses and hence there was no need for insurance. People availing public facilities also did not express their

5 Willingness to Pay for Health Insurance 121 willingness to join the scheme as they tended to pay a relatively small amount while availing the treatment. Around 18 per cent people said they do not wish to join the scheme due to either previous experience of non-usefulness of other insurance policies or lack of trust in investing money in such schemes. The other six per cent people felt that these schemes are not poor-friendly as they were already unable to bear the burden of meeting the daily requirements. Table 2: Reasons for Unwillingness to be Part of Health Insurance Plan, Mumbai Slum, 2010 Per cent Perceived good health of family members 33 Availability of inexpensive treatment at public health facilities 33 Ailment not considered serious requiring large expenditure 10 Bad experience in the past 18 Schemes being not pro-poor 06 Findings of Multivariate Analysis Binary logistic regression model was used to ascertain statistically significant relation between WTP for health insurance and its determinants. The results are presented in Table 3 which shows the impact of variables on WTP at different levels of significance. After controlling the effect of other factors, the analysis indicated that age of the household head, caste and educational attainment were not having any statistically significant association with WTP for health insurance. The negative signs of educational attainment indicate that willingness to pay for health insurance was inversely related with education though not statistically significant. When the possible effect of a recent experience of a health-related high-cost event (hospitalization in last one year) on WTP was assessed, the analysis demonstrated a clear and significant (p<0.001) positive impact of incidence of inpatient episodes (OR=28.7). It was evident from the multivariate analysis that households which experienced a hospitalization episode in the past one year were willing to pay more (about 28 times higher) than those households which did not require hospitalization. Another significant health-related factor, namely, the presence of morbidities in the household also acted as a positive factor in influencing the household s favourable decision for joining a health insurance scheme. Households which had an episode of morbidity in last fifteen days were more than thrice (OR=3.47) willing to pay than their counterparts. A household s Monthly Per Capita Consumption Expenditure (MPCE) was used as an alternative proxy indicator for household economic status. Statistically significant relation (p<0.1)) was observed between richest MPCE group and willingness to pay for the scheme. The richest were twice (OR=2.1) more likely to pay for health insurance compared with the poorest group.

6 122 Soumitra Ghosh and Shinjini Mondal Self-rated health status of the family s health demonstrated an association with WTP as people who perceived that their families had good and fair health, and were relatively free from morbidities were more likely not to pay for health insurance (OR=.259) than others who perceived their health status as bad. Table 3: Multivariate Analysis of Determinants of WTP Characteristics Coefficient (β) Significance Exp (B) Education of household head Illiterate and literate without formal education Primary and Secondary Higher secondary and above Age of household head Less than 35 years More than 35 years Caste Others SC/ST/OBC Hospitalisation Present *** 28.7 Absent Morbidities Present *** Absent MPCE Richest group * Middle group Poorest group Self rated health status Others * Bad -Log like hood Notes: ***p<0.001, **p<0.05, *p<0.1 MPCE for three groups are Poorest group ( ), Middle group ( ), Richest group ( ) in ` Others in self rated health status comprise of good and fair health status as quoted by households. III Conclusion The study makes a concerted effort to present a comprehensive assessment of evidence on willingness to pay for health insurance scheme among the urban poor. Healthcare which is an esssential commodity and a human right often becomes unavailable to this population, especially as it does not have protection mechanisms to prevent calamitous expenditure. A little over one-third of the households have expressed the need for health insurance. However, it would be wrong to infer that the demand for health insurance is low in this population as the low response could be attributed to factors such as insufficient awareness about health insurance, relatively low health expectation and poor financial conditions of the people living in slums. Those households which expressed WTP for health insurance scheme found that half of them were able to pay only less than one per cent of their annual income.

7 Willingness to Pay for Health Insurance 123 The study establishes a positive association between household wealth status and WTP for health insurance, implying that there are differentials by the economic status amongst slum dwellers in Mumbai. The greater willingness found in high income groups reinforces the fact that poorest households which are needier and share a higher burden of disease are left out because of their inability to benefit from these schemes. The need for health insurance is not felt in households which perceive their health status as good and fair. They did not incur catastrophic expenditure in the past, and so failed to identify the sudden and unpredictable illness which could affect anyone. On the other hand, people who had fallen prey to severe illnesses that required high expenditure were more willing to pay for the scheme. In the absence of sensitization to high cost expenditure, people failed to identify the usefulness of these schemes. A section of the population was unable to decide whether it would be willing to join the scheme or not. This can be taken as methodological limitation of the study and can be attributed to the structure of the tool. As under contingent valuation method a hypothetical good is described and its value is ascertained and at times it creates a difficult situation for the respondents to think, judge and act considering the fact that the population had a low awareness about insurance schemes. Further refining of the tool is required as it would lead to more accurate results for the future studies. Apart from this, the study has tried to capture the whole scenario for willingness and its determinants. It suggests that the difficulties expressed by the population to pay are similar to a BPL population. But this group does not find a place in the list of officially declared poor. They are entrapped between non-poor urban and BPL poor as they do not fit into any of the definitions. Their vulnerability is further supported by results obtained in the study, as only 60 per cent of the people expressing their willingness to purchase such pre-payment scheme, the remaining 40 per cent of the population was willing to join but unable to pay. It was also found that only 53 per cent of the households willing to pay were able to pay the starting bidding amount and more; and the remaining population couldn t afford the first bid. Thus, a large number of households were willing to join but could not do so because of financial crunch. Therefore, it becomes essential to protect this population from slipping into the depths of poverty. A social health insurance scheme in line with RSBY covering both outpatient and inpatient care can be floated for this population to provide them access to quality health care.

8 124 Soumitra Ghosh and Shinjini Mondal Figure 2: Willingness to Pay across Income Groups References Dong, Hengjin, Bocar Kouyate, John Cairns and Rainer Sauerborn (2004), Health Policy and Planning, 19(2): Dror, D.M., R. Radermacher and R. Koren (2007), Willingness to Pay for Health Insurance among Rural and Poor Persons: Field Evidence from Seven Micro Health Insurance Units in India, Health Policy, 82: a Government of India (2008), Swasthya Bima Yojana, Ministry of Labour and Employment, New Delhi. Hanemann W., J. Loomis, B. Kanninen (1991), Statistical Efficiency of Double Bounded Dichotomous Choice Contingent Valuation, American Journal of Agricultural Economics, 73: International Institute for Population Sciences (IIPS) and World Health Organisation (2006), Health System Performance Assessment: World Health Survey, 2003, India, IIPS, Mumbai, India. Mathiyazhagan, K. (1998), Willingness to Pay for Rural Health Insurance through Community Participation in India, International Journal of Health Planning Management, 13: National Urban Health Mission (2008), Meeting the Health Challenges of Urban Population Especially the Urban Poor, Urban Health Division, Ministry of Health and Family Welfare, Government of India. Smith, Richard D. (2000), The Discrete-choice Willingness-to-pay Question Format in Health Economics: Should We Adopt, Medical Decision-Making, 20: 194. Yoo, S.H. and H.J. Yang (2001), Application of Sample Selection Model to Double Bounded Ichotomous Choice Contingent Valuation Studies, Environmental and Resources Economics, 20: