Broadening Access to Micro Health Insurance. Evidence From a Randomized Field Experiment in Kolkata Slums. Draft Version Please do not cite

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1 Broadening Access to Micro Health Insurance. Evidence From a Randomized Field Experiment in Kolkata Slums Clara Delavallade 1 and Tilak Mukherjee Draft Version Please do not cite Abstract Micro health insurance faces challenges in expansion and sustainability. Low perceived or real value of the product seems to be a major factor of the low demand for micro health insurance. This study assesses the extent to which providing guaranteed benefits to micro health insurance clients (both claimants and non-claimants) may retain them in the scheme. Half of the clients of a micro-health insurance policy in Kolkata slums (India) were randomly offered a free health check-up as an additional benefit to the insurance scheme. We show that the demand for preventive health care was relatively high: take-up was at 40%. The check-up offer raised the demand for the MHI scheme (measured by reported willingness to pay) and this impact was driven by two mechanisms: the perception of a positive income shock despite the absence of a real income shock and an improvement in the satisfaction with the scheme and trust in the health service and insurance provider. There was no impact on the clients objective financial and health situation and, interestingly, check-up attendees did not substitute the free service for a consultation they would have paid for otherwise, suggesting that there are cash constraints in the poor access to quality health care. Most of the effects are concentrated on households in a poorer health condition at the date of enrolment. Lastly, the free medical consultation raised the reported demand for the MHI scheme provided by NGO offering the check-up and the scheme, not for MHI schemes provided by other insurance, yielding useful results for MHI companies willing to design products increasing the demand the face but not the whole market demand. Keywords: Demand Micro Health Insurance, Reported Willingness to Pay, Trust, Access to Health Care, Poverty, India JEL Classification: I13, I15, O15 1 School of Economics and SALDRU, University of Cape Town and Universite Paris 1 Pantheon-Sorbonne. UCT, Private Bag X3, Rondebosch 7701, South Africa. Tel: I am grateful to Thomas Bossuroy, Rachel Glennerster and Rebecca Thornton for very helpful suggestions and comments. I thank Calcutta Kids and especially Noah Levinson for numerous discussions and excellent implementation as well as Jonathan Bertscher, Tirtha Chatterjee and Alex Montgomery for dedicated research assistance. This project was supported by grants from the International Labour Organization, the Centre for Insurance and Risk Management and the University of Cape Town.

2 1 Introduction Health care microinsurance (HCM) has proven to be an effective means of protecting households from potentially crippling hospitalization expenses (Chankova et al. 2008; Preker et al. 2002; J. P. Jutting 2004). But micro health insurance faces challenges in expansion and sustainability. Low demand for health insurance translates into low enrolment rates and low renewal rates. This is a significant impediment to access to health care for the poor and to financial viability; with low renewal rates come higher administrative costs per head and adverse selection. It has been argued that the persistency of low renewal rates is attributable to the low perceived value of the product (Jehu-Appiah et al. 2011), especially for non-claimants (McCord 2001). Low demand for health care microinsurance might also result from the low quality of health care (Dror 2007) and the low access to quality health care. In this paper, we evaluate a randomized field experiment in an Indian slum designed to address these issues and especially whether free provision of good quality preventive health services may add value to a micro health insurance scheme as well as the extent to which this may retain clients into the health insurance scheme. Half of the clients of a health care microinsurance scheme, randomly chosen, have been offered an expansion of the scheme through ``wellness clinic check-ups''. Through this service, insurance clients in a slum in Howrah (West Bengal, India) were invited, during the remaining two months of their insurance coverage, to free wellness clinic check-ups with a certified doctor for all interested members of their families. The service was designed to add value to the insurance product for both claimants and non-claimants, improve the perception clients have of the quality of health care and in turn motivate them to renew their policy for the next insurance period. We measure demand for micro health insurance through the reported willingness to pay for the health insurance scheme. We find that offering clients a free health check-up as an additional benefit to the MHI scheme is efficient in raising the demand for this scheme. The raise in demand for the MHI scheme is not driven by an objective improvement either in the financial situation (through a real income shock) or in the health knowledge or health status of the household. There is no significant difference in the impact of the health check-up among claiming and non-claiming clients and no impact either on the amount of claims (not shown here). But the free check-up offer seems to have improved the perception households have of their financial situation (a perceived income shock) and their trust in the health care and insurance provider and satisfaction with the scheme. These two mechanisms may be driving the positive impact on demand for the MHI scheme. We also provide evidence of cash constraints in the access of the poor to good-quality health care: there is no significant impact on the share of health expenses, indicating no substitution of the free check-up for a payable consultation. The remainder of the paper is laid out as follows: In section 2, we review the determinants of low demand for micro health insurance among the poor. This is followed in Sections 3 and 4 by a description of the setting of the field experiment and the data. Section 5 presents and discusses the take-up results. We present the results on the impact of the free check-up offer in Section 6. Section 7 concludes.

3 2 Theoretical Considerations on the Demand for Micro Health Insurance Many developing countries face a major obstacle in their attempt to create a health insurance system: the poor in developing countries typically exhibit a low demand for health care microinsurance (HCM) 2, otherwise known as community-based health insurance (CBHI). This section investigates various mechanisms that can explain why uptake and renewal rates of HCM are so low among the poor. 2.1 Credibility and quality of the health care system One of the explanations for the weak demand for preventive health care suggests a particular challenge for HCM. That is the issue of trust in the formal health care system and preferences for traditional medical care. Someone who does not trust the health care system through which the insurance scheme operates will not trust the scheme to deliver value. They will perceive purchasing insurance to be more risky as a result. When the poor complain that the insurance product is too expensive, they do so in relation to the perceived benefits, which are negatively affected by low levels of trust in the formal health care system. Credibility of the health care system might thus strongly influence two important factors that make all forms of microinsurance undesirable to the poor levels of trust relating to insurance benefits and the perceived value of the product. What makes health care microinsurance different from other insurance products is thus the quality of the formal health care services available to the poor (Dercon et al. 2008). If these perceptions are wrong then it also affects another driver of low microinsurance demand poor understanding of the benefits. However, to the extent that people are correct in their expectations of the value that the formal health care system offers, the scheme might simply not deliver value for money in which case there would be no mystery as to why they do not demand it. After an extensive review of the research to date, Magnoni and Zimmerman (2011) conclude that at this stage there is not enough evidence that proves that microinsurance offers better value to the poor than traditional risk strategies. Following comprehensive reviews of the literature, similar conclusions have been reached by Levine et al. (2010) and Devadasan et al. (2006). If academics and insurance organisations cannot agree on the efficacy of health care microinsurance then it is only natural for people to be sceptical whether poor or not. Where they have been effective, their contributions have been modest. However, many of these schemes are still relatively young and their benefits may become evident in time (Carrin et al. 2005). There are some findings that are suggestive of the potential of HCM. For instance, insurance was found to be an efficient and equitable means of financing immunization if included in a programme providing health care to the entire population. There is evidence that health microinsurance is an effective means of protecting households from potentially crippling hospitalisation expenses (Chankova et al. 2008; Preker et al. 2002; J\"utting 2004). But the same studies found no evidence for a lower out-of-pocket spending for outpatient care. Studies have found health microinsurance to significantly increase healthcare utilisation in Africa and Asia (Hamid et al. 2011; J\"utting 2004; Preker et al. 2002; Smith and Sulzbach 2008) but no significant effects on 2 The term microinsurance is used in this paper in reference to contractual insurance products targeted at the poor and offered by registered NGOs, companies or government agencies.

4 health care status or quality of health care received (Ranson and John 2002). More research is needed to determine whether perceptions of value are accurate. 2.2 Access to formal health care Apart from the quality of service, ease of access to formal health care is another factor unique to this form of microinsurance. Chankova et al. (2008) found a higher likelihood of enrolment among those with greater access to health facilities. People who have difficulty obtaining treatment within the formal health care system have no need for microinsurance that covers costs associated with it (Noble and M. McCord J. 2007). People with less convenient access to health facilities will be more prone to procrastination due to hyperbolic discounting since the costs associated with visiting them will be greater. These higher costs, in terms of time spent travelling and subjective discomfort on the way, are not compensated for and thus also decrease the relative benefits of insurance. 2.3 Scheme coverage Some HCM products may not provide adequate cover (Banerjee and Duflo 2011). This may be due to a lack of market research and failure to tailor the product to the target population. Some papers have stressed the importance of context in designing microinsurance schemes (Dercon et al. 2008; M. Cohen and Sebstad 2005; Sebstad et al. 2006). This requires an appreciation for the specific difficulties faced by the poor in particular countries or regions and their particular traditions, cultures and practices. It also means understanding the most important risks that the poor need to manage. The appropriateness of the coverage will again impact the perceived value of the product. This is particularly important for health insurance because of the strong reliance on traditional medicine among the poor and the mistrust with which they view the formal healthcare system. Demand might suffer from schemes refusing to compensate expenses associated with traditional health care. 2.4 High costs due to lack of risk data In the previous section We discuss how perceived high costs and credit constraints severely restrict demand. A particular difficulty faced by HCM providers is achieving the right balance between cost and coverage (M. J. McCord 2001). Breadth of coverage is an important determinant of demand. However, more cover makes providing the product more expensive, which often necessitates increasing prices in order to remain sustainable. In addition, there is a lack of risk data related to the health of the poor. This creates uncertainty, which drives up costs and these are typically passed on to clients (Noble and M. McCord J. 2007). 2.5 Improving the demand for health care microinsurance The literature suggests several ways to boost demand for HCM schemes. We note some of them here briefly. It is important to keep in mind that different strategies will be appropriate in different situations and their impacts will be context-specific. The effect that one has also relies on what other strategies that are implemented at the same time (Meng et al. 2011). The quality of the health care system that treats the scheme s clients should be improved and access expanded (Noble and M. McCord J. 2007; Meng et al. 2011; Sinha et al. 2006). This should improve

5 the perceived and actual value of formal health care and subsequently that of the insurance scheme. Coverage should also be expanded to include out-patient care and take into account the cultures and particular risks that the target population faces (M. J. McCord 2008). Clients should be educated on how the product works and why it provides an advantage over informal insurance mechanisms (J\"utting 2003). Improving the health care system should help to improve perceptions but informing the poor about the specific risks and benefits associated with modern medicine should improve the way in which it is viewed by them. It is particularly important that the poor are given reason to trust the institution providing the insurance (Jehu-Appiah et al. 2011). This can be achieved by engaging the population through transparent lines of communication (Meng et al. 2011). Staff should be trained in how to communicate with clients and potential clients, especially with regard to cultural sensitivity (Banerjee et al. 2004; Banerjee and Duflo 2011). The process of enrolment and renewal should be simplified and made more accessible to those with lower levels of education (Meng et al. 2011). Premiums should be decreased to make insurance more affordable to the poor (Banerjee and Duflo 2011; Dercon et al. 2008). Suggestions of how to do this include reducing costs, offering subsidies and implementing sliding scales. Credit constraints can be eased by making timing of payments more flexible (Jutting 2003). 3. Background on the Field Experiment 3.1 Setting The experiment we evaluate was designed to address some of these potential drivers of the low demand for micro health insurance. It is aimed at identifying the extent to which increasing the real value of the scheme and trust in the health care system might retain micro health insurance clients. The experiment consisted in offering randomly chosen MHI clients a preventive medical check-up as an additional benefit to the insurance product. Companies usually try to increase the {\it perceived value} of their insurance product by developing marketing campaigns which highlight the benefits claimants may get. The campaign evaluated here is different in that (i) it proposes to increase the {\it real} value of the product, directly by providing an additional service to clients and indirectly by building trust in the services covered by the product, namely the health care system, (ii) it is adding value for both claimants and non-claimants. The program was run by the NGO Calcutta Kids in Fakir Bagan, an urban slum in Howrah (West Bengal, India) between December 2010 and January The NGO Calcutta Kids has been operating a voluntary micro health insurance scheme in Fakir Bagan since March The scheme covered expenses associated with inpatient care for all members of a given household. It was done in partnership with the United India Insurance Company. Calcutta Kids served as an intermediary between the insurance company and the population. The NGO's involvement in the insurance process aimed at improving enrolment and renewal of the scheme for the slum population through three different channels: (i) facilitating access of low-educated people to the scheme by facilitating the paperwork and procedure (Meng et al. 2011), (ii) easing credit

6 constraints by making timing of payments more flexible (Jutting 2004), (iii) building trust in the institution providing the insurance product (Jehu-Appiah 2011) as Calcutta Kids has a long-lasting history of providing services to the poor in the slum. A total of 831 households enrolled themselves and their families in the one-year insurance scheme -- or renewed it -- between March 2010 and February 2011 in three waves: 367 households enrolled between March and May 2010, 274 between July and September 2010 and 190 between December 2010 and February They were all surveyed in baseline. 3.2 Project Design In December 2010, an expansion of the scheme was introduced in the form of a free medical checkup with a registered physician in Calcutta Kids wellness clinic. MHI clients were randomly split into two equal-size groups. The 416 households in the treatment group received access to and encouragement to use the wellness clinic for a free check-up of any or all members of the insured family under the policy while the 415 households in the control group were not offered the free check-up. The check-up was offered within the last two months of their policy. Households in the treatment group were invited through door to door personal invitations by Calcutta Kids health workers. A coupon indicating details of the offer was then handed over to the household. The coupon stated that the family had been selected by lottery among MHI clients to benefit from a free health checkup worth around Rs.400 (\$8) for the entire family as part of the insurance scheme. The coupon also mentioned the opening hours of the clinic (9:30am to 5:30pm). Visits were allowed for about two months after the coupon was handed out. In most cases, all the household members went to the check-up. On a typical visit, Calcutta Kids health workers welcomed the patients, recorded their name, checked their height, weight and blood pressure and directed them to the doctor's waiting lounge. The doctor did a thorough check-up of each patient (ear, throat, chest, etc...), enquired about allergies, potential symptoms of usual types of contamination, the medical history of the patient, her diet. Where medical problems were identified, diet, medicine or follow-up investigations were prescribed and the information registered in the client's medical record. The consultation with the doctor usually lasted around 20 minutes. The experiment evaluates the various impacts of providing the free medical check-up to micro health insurance clients on renewal rates, perceptions of the quality of the health care system and of the value of the scheme and knowledge of health status as well as amount of claims. Random selection of beneficiaries and non-beneficiaries of the free medical check-up allows isolating the causal effect of the program from confounding factors. 4 Presentation of the Data 4.1 Sampling and Origin of the Data The sample comprises of all households who subscribed to the MHI scheme managed through Calcutta Kids between March and December 2010 (since data are only available for the first two

7 waves). The composition of the treatment and control groups was randomized with stratification based on the pre-existence of a relationship between clients and Calcutta Kids through one of their other programs (MYCHI, OPCS program or MHI). The empirical analysis derives from two original surveys, a baseline and an endline survey, both of them carried out in three waves in order to follow the pattern of enrolment of clients into the micro health insurance scheme. We also use compliance data collected by Calcutta Kids at the time of check-up. The baseline survey was conducted between March 2010 and February 2011 by Calcutta Kids project manager when enrolling households in the insurance scheme, approximately 9 to 10 months before the invitation to the medical check-up. The endline survey was conducted by a survey company about 15 months after enrolment. The endline questionnaire comprises 8 sections: ``household composition'', ``assets and expenditure'', ``loan and borrowing'', ``health care seeking behaviour, health care expenditure and accessibility'', ``satisfaction with health insurance'', ``health status and knowledge'', willingness to pay'', ``attitude towards risk''. XXX households were surveyed at baseline and included in the experiment sample (189 in wave 1, 132 in wave 2, XXX in wave 3). However, endline data are only available for the first two waves temporarily reducing our sample to households enrolling in the first two waves (321 households). 4.2 Descriptive Sample Characteristics and Randomization Check A baseline survey was administered at households' homes at the time of micro-health insurance purchase between March 2010 and February The baseline assessed socio-economic status, demographics and insurance coverage. Table 1a presents summary statistics of household characteristics at baseline for all households in the sample as well as the p-value of a test of the null hypothesis that the means in the treatment and control groups cannot be distinguished from each other. Some of the socio-demographic characteristics are shown for the primary holder of the health insurance policy who is usually the household head. Overall, the two groups display very similar characteristics. Out of the 22 characteristics shown in Table 1a, only one exposure an outpatient care consultation provided by Calcutta Kids within the past nine months shows significantly different means in the treatment and control groups, as one would expect. A large majority of the invitees are Hindu (94 percent). Almost half of the primary holders have not attended secondary education (52 percent). Half of them are in a precarious working situation, either daily wage workers (33 percent) or unemployed (7 percent). 16 percent of the households benefited from the MYCHI program previously offered by Calcutta Kids. 20 percent of them subscribed the previous year to the same health insurance scheme as the one offered that year and only 3 percent had benefited from the OPCS program.

8 5 Demand for Free Preventive Health Care XXX clients and their families were invited to the free check-ups in three waves between March 2010 and February 2011, in accordance to the three waves of enrolment in the MHI scheme 9 to 10 months after enrolment (93 in wave 1, 66 in wave 2, XXX in wave 3). The results below relate to clients enrolling in the first two waves. Figure X shows the sampling and attendance statistics: 159 households were invited to the free health check-up. Among those, 64 attended. The attendance rate was high overall (40%) suggesting that slum dwellers value preventive health care. But this rate was much lower in the second wave (6%) than in the first wave (65%). Anecdotal evidence suggests that this loss of enthusiasm towards the check-up might reflect the rapid spread of disappointment for not getting free medicine at the check-up as well as disillusionment for the insurance scheme being discontinued at the end of the period. Lastly, the invitation was done by CK health workers, locally hired, in the first wave and by Calcutta Kids project manager in the second wave. The larger proximity and trust towards the former might have favoured uptake in the first wave. As Table 2 shows, after controlling for household characteristics, households invited to the check-up were 43.6% more likely to attend the check-up than households in the control group (column (1)). Interestingly, households whom members had experienced longer periods of illness at baseline were significantly less likely to attend the check-up upon invitation (column (2)). This might be due to a substitution effect: households experiencing more illness might have consulted more frequently with a health provider making the free consultation less beneficial to the household. It might also be the result of the illness itself making the cost of going to the wellness clinic for the check-up higher. To study patterns of attendance, we estimate the correlates of the demand for the free health check-up. We run the following regression on the sample comprised of households invited to the free health check-up: = where is a dummy indicating that at least one household member attended the free health check-up, is the household total income (primary and secondary incomes). indicates that the primary holder of the insurance scheme is a male. is a vector of dummies for Hindu, Christian and Muslim, a vector of dummies indicating that the primary holder of the health insurance product has received some primary education, some secondary education, some tertiary education or completed post-graduate studies. is a dummy indicating whether any household member has consulted any kind of health provider over the three months preceding the baseline survey (between 10 and 13 months before the check-up). is a dummy indicating that any household member has experienced who suffered from any illness and was unable to perform his/her normal activities for two or more days. is an interaction term between the two previous dummies. is a dummy indicating whether the household holds a Below Poverty Line (BPL) card. Lastly, is a vector of dummies indicating whether the household had a pre-existing relationship with the NGO Calcutta Kids either because they subscribed to the insurance product provided by CK the previous year already

9 (Renewal i ) or because they benefited from the MYCHI program (MYCHI i ) or because they benefited from the OPCS program (OPCS i ). Table 7 shows the results of the linear probability model regression run on the sample comprised of the 159 households invited to the free health check-up (treatment group). Limited levels of education are significantly correlated with higher attendance rates: MHI clients who received some primary school education are 19 percent more likely to attend the check-up at the wellness clinic than those without any education. This suggests that better information about the benefits one can get through preventive health care favours uptake. Female are more likely to attend the check-up although this effect is not significant. The coefficients associated with religion and income, including BPL card holding, are not significant at the 10 percent level either. On the contrary, past illness is a strong predictor of attendance: having at least one household member experiencing any type of illness in the three months preceding the baseline, i.e. between 13 and 10 months before the free health check-up, raised the probability that any household member would attend the check-up of 67% (Table 3, column (4)). This effect is significant at the 5 percent level. Assuming that the person is still ill at the time of check-up, this might just reflect that the demand for curative care is significantly higher than the demand for preventive care. Assuming that the person is cured at the time of check-up, this interestingly suggests that the poor generally tend to overestimate their health condition, hence the low demand for preventive care and that experiencing illness lead them to readjust their perception of their own health and value more preventive health care. One could use the medical record data collected by the doctor at check-up to rule out one of the two hypotheses. Unfortunately, these data are not available yet. Interestingly, the interaction term between illness and consultation is significant at the 10 percent level. For those experiencing illness in the household, consulting with any type of doctor (public or private, registered or unregistered) reduces the probability of attending the preventive check-up of 47%. Most of the consultations take place with an unregistered private doctor. Dissatisfaction with the doctor, diagnosis and/or treatment might nurture distrust in the medical institution in general, making free health care less valuable. The extent to which trust plays a role in explaining the low demand for preventive health care is also visible in the significant and positive impact of a previous exposure to Calcutta Kids health program. Households having benefited from the pre- and post-natal care program (MYCHI) provided by the same NGO Calcutta Kids are significantly more likely to attend the check-up. Benefiting from this previous program might have reinforced the trust slum dwellers place in Calcutta Kids and their workers and in health care more generally. However, the MYCHI program is offered to all pregnant women and young mothers in Calcutta Kids catchment area. Therefore, instead of a trust effect, one might capture another type of readjustment of own health status perception whereby pregnancy and infancy are perceived as vulnerable health conditions which make preventive care more valuable. Previous exposure to another of Calcutta Kids programs, Out Patient Counselling Services (OPCS), does not significantly affect attendance rates. Although this is meant to capture any effect of the

10 quality of the relationship between slum dwellers and Calcutta Kids, this does not rule out the trust effect described above with regards to the MYCHI program. Indeed, the intensity of the MYCHI program is much higher than that of the OPCS program. The MYCHI program consists of regular and frequent visits (with approximately one visit to a given household every other month during pregnancy and about three visits during the first year after birth). This gives space and time for building a trust and care relationship between the beneficiary household and the dedicated Calcutta Kids health worker. On the other hand, the OPCS program consisted of a single consultation with a registered doctor whom services had been contracted temporarily by Calcutta Kids to conduct this program. Therefore, MYCHI appears as a better proxy for the quality of the relationship and the extent of trust between slum dwellers and Calcutta Kids than OPCS. Lastly, check-up attendance was significantly higher for households subscribing the insurance scheme through Calcutta Kids for the first time than for those renewing a scheme they already adhered to the previous year. Two alternative explanations might account for this behaviour. Firsttime subscribers may be more enthusiastic about a scheme that is new to them and willing to take full benefit of it. More interestingly, there might be an adverse selection effect at stake whereby households who perceive themselves as vulnerable in the recent past were more likely to both enrol in the insurance scheme and attend the check-up. 6 Impacts of Access to Quality Health Care Services 6.1 Model Estimates We estimate the effect of getting a free health check-up as part of a micro health insurance scheme on various outcomes: reported willingness to pay to renew the MHI scheme, health behaviour, financial situation, risk aversion, satisfaction with the health insurance scheme and provider, health status and knowledge. For all outcomes, we estimate two kinds of impact. First, we estimate the impact of being assigned to the treatment group, that is offered the free health check-up 3, this is the intention-to-treat (ITT) estimate. Second, we estimate the impact of actually attending the free check-up, this is the treatment-on-the-treated (ToT) estimate. The ITT estimate is obtained by running the following regression for each outcome: = (1) where Y i is the outcome of interest for household i, Invit i is a dummy variable equal to 1 if the household was offered a free health check-up and X i is a vector of household characteristics. The ToT effect is obtained by estimating the following model: = (2) This yields the average treatment effect of attending the free health check-up on our set of outcomes. Because the randomization only affected the probability that clients families are exposed to the check-up (invitations were distributed randomly), rather than the check-up attendance itself, 3 There were no cross-overs.

11 we use treatment assignment (invitation) as an instrument for check-up attendance. For each outcome, Model (2) is therefore estimated by an instrumental variable regression. In addition, clients whose family members were more ill at baseline were likely to benefit more from the free check-up. We therefore allow for such heterogeneous treatment effects by estimating the following models: = (1 ) = (2 ) where DaysIll i is the total number of days family members were ill during the last 90 days before enrolment into the MHI scheme. The results are presented in Tables 2 to 6. In each of these tables, Panel A reports estimates of Models (1) and (1 ) and Panel B reports estimates of Models (2) and (2 ). Reported Willingness to Pay to Renew the MHI Scheme Estimation results of the impact of the free check-up offer on reported willingness to pay to renew the micro health insurance scheme are presented in Table 2 (columns (3) to (8)). The check-up offer had a large and positive impact on reported willingness to pay, significant at the 5% level. Households invited to the check-up are willing to pay Rs. 45 ($ 0.8) more than non-invited households for the scheme premium, 21 percent more than the average premium cost households in the control group are willing to pay, Rs. 217 ($ 3.9). The ToT estimation (Panel B, column 3) yields a more than double effect: clients who actually visited the wellness clinic and got the check-up report being willing to pay Rs. 96 ($ 1.71) more than those who did not get the check-up, 44 percent more. This suggests that clients valued the check-up offer and the medical consultation itself even more. Unsurprisingly though, the value clients attached to the check-up was lower than the nominal value mentioned on the invitation voucher (Rs. 351 ($ 6.24) on average). Figure 1 shows the Kernel density of distribution of the premium households were willing to pay to renew the MHI scheme separately for the treatment group and the control group. Figure 2 shows the same densities of distribution but the restricted sample fo households willing to pay any premium. A lower share of households in the control group is willing to pay any premium but among households willing to pay any premium, the distribution is skewed to the right: treated households are more likely to pay any premium but they are more likely to pay a premium below approximately Rs This suggests the check-up raised demand for the MHI scheme among poorer households. Columns (6) and (8) in Table 2 further show that the more family members have been ill during the three months preceding enrolment, the more likely check-up offer and attendance is to increase the premium they are to willing to pay for an MHI scheme including a free health check-up and for an MHI scheme including outpatient care. In other words, getting the check-up not only raised the value attached to the MHI scheme but increased the value that clients may attach to the check-up itself and more generally to outpatient care, especially for those with the poorest initial health condition.

12 Which benefits did the clients get from the free check-up that led them to value it as well as the MHI scheme it was taking part in? Health Behaviour, Knowledge and Status Table 3 reports estimates of the impact of the health check-up offer on health outcomes: knowledge, self-assessment and behaviour. The treatment did not significantly affect the knowledge households have of their own health. Although all households were weighed at check-up and had their blood pressure assessed, there is no additional knowledge on those basic assessments for households attending the check-up (Table 3, columns (1) to (4)). The more ill the household members were when they enrolled into the MHI scheme, the more likely they are to have improved their assessment of their own health after the check-up. While the health status has not been affected by the check-up (these results are not shown here), the perception clients in a family with a poor health condition have of their own health has been improved by the check-up. This is in line with the view that the poor tend to overestimate the quality of their health: although their objective health condition has not significantly improved, they went out of the check-up with the feeling that it had. This might be a corollary of the overall satisfaction treated households got from the consultation in an environment where access to high quality health care services is severely constrained. The stronger impact among households with a poorer health condition might result from the reduction in uncertainty the check-up provided them. Self-assessment of health status is based on real knowledge but also on perceptions and uncertainty about one s health. By providing preventive information and discussing symptoms but not testing major diseases, the check-up might have reduced uncertainty rather than disclosed evidence about one s own health condition. Households reporting a poorer health condition at baseline were more likely to benefit from this uncertainty mitigation. In addition, the check-up seems to have increased the attention households pay to their own health and their health cautiousness. Households who were offered the medical consultation, and among those households who attended it, are respectively 6 and 14 percent more likely to later prefer less risky health situation than those who did not (columns (7) and (8)). Impact on awareness is limited though: there is no significant effect of the treatment on the probability of visiting a health provider quickly when one feels ill. One interpretation is that the free check-up has not succeeded in improving the overall trust households have in the health care system. Financial Situation We then estimate the impact of the check-up offer and attendance on various outcomes measuring the households financial situation. We examine objective outcomes such as percentage of health expenses, amount of cash assets, savings and debt as well as a subjective measure through the household s self-assessment of its own financial situation. These results are presented in Table 4. While the treatment had no impact on objective financial outcomes, the perception households in a poor health condition have of their own financial situation improved in response to the treatment (columns (9) and (10)). Although the income shock did not translate into standard indicators such as the amount of savings, debt and cash assets as we expand on below, the check-up was perceived as a positive income shock by low-health status households who attended the check-up (Panel B) but

13 also by low-health condition households who were invited to the check-up (irrespective of whether or not they attended it (Panel A). This perceived income effect may have driven the rise in the reported willingness-to-pay for both households who were offered the check-up and for those who attended the check-up. The additional premium treated households (relative to control households) are willing to pay for an MHI scheme that would include a free check-up (Rs. 97) is equivalent to a 2- percent variation in income, this gives a rough estimate of the low intensity of the perceived income shock. There was no significant impact of the check-up on the percentage of health expenses (columns (1) and (2)). The absence of a significant drop in the share of income households spend on health care services and medication indicates that treated households have not substituted the check-up to other consultations (that they would have paid for otherwise) or other types of medical expenses. The free check-up therefore came as an in-kind complement of income for treated households and resulted in an increase in their absolute consumption of health services (this should be checked when more data on health care behaviour becomes available). This supports the view that the poor are constrained in their access to quality health care. The free check-up slightly released these constraints. However, check-up offer (Panel A) and attendance (Panel B) did not significantly alter the amount of cash assets held by the households (columns (3) and (4)), their savings (columns (5) and (6)) or the level of their debt (columns (7) and (8)). This suggests that the positive income shock households seem to have perceived does not reflect into tangible indicators. An income shock would indeed have led to either an increase in the amount of saving or a decrease in the debt amount, and the variation in savings or debt should have been higher than the income shock to smooth consumption (Townsend, 1994). Satisfaction with MHI scheme, doctor and free health check-up Table 5 reports estimates of the impact of the check-up offer and visit on the clients satisfaction with the micro health insurance scheme and with the health check-up. Both the invitation (Panel A) and of the medical consultation (Panel B) significantly raised the satisfaction towards the MHI scheme of clients with a poor health condition at the date of enrolment (column (2)). While there is a raise in satisfaction for all households (column (1)), it is not significant at the 10% level. As discussed above, clients with a poor health condition were more likely to benefit from the reduction in uncertainty about their health provided by the check-up. Attendees were 12 percentage points more likely to report that the free check-up was helpful as the main reason for their satisfaction with the health insurance scheme and this effect is significant at the 10% level (column (3)). In parallel, they were 14 percentage points less likely to report that the free check-up was not helpful as the main reason for dissatisfaction, this effect is significant at the 5% level (column (5)). This seems to show that check-up was valued in itself by both invitees and attendees adding to the perceived income shock in driving the rise in reported willingness to pay. Additional evidence that the check-up was valued in itself and not only as a perceived positive income shock comes from the impact on the probability of reporting a free check-up as a desired benefit attached to a health insurance scheme (column (9)). Interestingly, invitation and attendance to the check-up raised the probability of reporting wanting free medication from an MHI scheme of 5 and 11 percentage points respectively (column (7)). This suggests that invitation to the check-up

14 (and later going to the medical consultation) set high expectations in terms of in-kind benefits, especially as regards free medication. This is corroborated by anecdotal evidence of some disappointment among attendees for not receiving free medication. As one should expect, there is no significant impact of the check-up offer on the probability of reporting free hospitalization as a benefit from an MHI scheme as hospitalization expenses were covered by the scheme for both treatment and control groups (columns (11) and (12)). Trust in Service Provider To better understand the mechanisms through which the check-up added value to the MHI scheme, we report results on the impact on trust in Table 6. Interestingly, clients who attended the check-up were almost 6 percentage points more likely to report preferring visiting a charitable or an NGO doctor when seeking health care (column (1)). This suggests that the check-up was successful at building trust into the health care service provider an NGO doctor. As expected, trust was raised by exposure to the health provider, not by the invitation to the check-up: although positive, this effect is not significant in the ITT estimation (Panel A). In addition, households with a poorer health condition were significantly more likely to declare being willing to renew the MHI scheme with Calcutta Kids, the NGO providing the check-up which served as an intermediary for the MHI scheme, after being invited (Panel A) and exposed (Panel B) to the check-up (column (4)). Because it served as an intermediary for the MHI scheme, Calcutta Kids was also seen as the MHI scheme provider. Therefore, the free check-up raised trust in the perceived MHI scheme provider as well. However, the impact on the reported willingness to renew the scheme with United Insurance India, the MHI actual provider, although positive is not significant. Nor is the impact on the reported willingness to renew the scheme with another insurance company. The offer of a high quality health care service raised trust in the health and insurance service provider, Calcutta Kids, which translated into an increase in the demand for micro health insurance products from this very provider not in the demand for micro health insurance products more generally. This last set of results suggests that the improvement in trust resulting from the check-up offer has been key in raising satisfaction towards the MHI scheme and therefore the reported willingness to pay to renew the scheme. It also suggests that, from the insurance provider standpoint, offering a free health check-up is all the more beneficial as it raises the premium households are reportedly willing to pay (the next sub-section will examine whether or not this is a cost-effective strategy), in other words the demand they face without raising the overall demand for health insurance (which would benefit their competitors as well). 6.2 Cost-Effectiveness 7 Conclusion This paper presents very preliminary results based on a sample of 321 clients of a micro-health insurance scheme provided by the NGO Calcutta Kids living in a slum close to Kolkata (India). Half of these households were randomly invited to a free health check-up with a certified private doctor as an additional benefit to their MHI scheme. Investigating the determinants of check-up attendance suggests that the demand for preventive health care may be raised by improving the perception

15 individuals have of their own health or that of members of their household and by building trust and improving reliability in the health service provider. We then assess the impact of the health check-up offer and attendance on a variety of indicators in two directions: impact on the demand for micro-health insurance and impact on health behaviour. We find that both the offer and actual medical consultation have a strong positive impact on the reported willingness to pay for the micro health insurance scheme. The increase in the demand for this specific MHI scheme seems to be driven by two mechanisms: a perceived positive income shock and an improved trust in and satisfaction with the NGO providing both the MHI scheme and the medical check-up. Check-up attendance did not improve the knowledge households have of their own health. But it improved the perception households have of their own financial situation, indicating that the additional scheme benefit through the free check-up was perceived as an income shock. This income shock was only perceived ant not real though: there is no impact on the amount of cash assets, savings and debt held by the household and the free check-up was not substituted to payable health expenses but came as an additional health service for the household suggesting that cashconstraints are a barrier to the access of good-quality health care for the poor and that the free check-up helped release these constraints. The raise in trust and satisfaction towards this particular micro-health insurance scheme and provider translated into a positive impact on the demand for this MHI scheme (and an increase in the reported premium one is willing to pay) with this scheme (and check-up) provider, not with any micro-health insurance provider. This yields useful results for insurance companies seeking to design products that are likely to increase the demand for their own micro-health insurance products, not the demand for any health insurance product that would benefit their competitors as well. References Ahuja, R. and Jutting, J., Are the poor too poor to demand health insurance? Ashraf, N., Berry, J. and Shapiro, J.M., Can higher prices stimulate product use? Evidence from a field experiment in Zambia, National Bureau of Economic Research. Banerjee, A. and Duflo, E., Poor Economics: A Radical Rethinking of the Way to Fight Global Poverty, PublicAffairs,U.S. Banerjee, A., Deaton, A. and Duflo, E., HEALTH, HEALTH CARE, AND ECONOMIC DEVELOPMENT. The American economic review, 94(2), pp Bhat, R. and Jain, N., A Study of Factors Affecting the Renewal of Health Insurance Policy. IIMA Working Papers. Bleakley, H., Malaria eradication in the Americas: A retrospective analysis of childhood exposure. American Economic Journal: Applied Economics, 2(2), pp Cai, H. et al., Microinsurance, Trust and Economic Development: Evidence from a Randomized Natural Field Experiment, National Bureau of Economic Research.

16 Carrin, G., Waelkens, M.P. and Criel, B., Community-based health insurance in developing countries: a study of its contribution to the performance of health financing systems. Tropical Medicine and International Health, 10(8), pp Chankova, S., Sulzbach, S. and Diop, F., Impact of mutual health organizations: evidence from West Africa. Health Policy and Planning, 23(4), pp Chaudhury, N. et al., Missing in action: teacher and health worker absence in developing countries. The Journal of Economic Perspectives, 20(1), pp Cohen, J. and Dupas, P., Free distribution or cost-sharing? evidence from a malaria prevention experiment, National Bureau of Economic Research. Cohen, M. and Sebstad, J., Reducing vulnerability: the demand for microinsurance. Journal of International Development, 17(3), pp Cole, S. et al., Barriers to Household Risk Management: Evidence from India. Working Papers. Cutler, D. et al., Early-life malaria exposure and adult outcomes: Evidence from malaria eradication in India. American Economic Journal: Applied Economics, 2(2), pp Das, J. and Hammer, J., Which doctor? Combining vignettes and item response to measure clinical competence. Journal of Development Economics, 78(2), pp Van Der Gaag, J., Health Care for the World s Poorest: Is Voluntary (Private) Health Insurance an Option? About IFPRI and the 2020 Vision Initiative, p.329. Dercon, S., Gunning, J.W. and Zeitlin, A., The demand for insurance under limited credibility: evidence from Kenya. In International Development Conference, DIAL. Dercon, S. et al., Microinsurance paper No. 1: Literature review on Microinsurance. Review Literature And Arts Of The Americas, (1), pp Devadasan, N. et al., The landscape of community health insurance in India: An overview based on 10 case studies. Health Policy, 78(2), pp Dror, Iddo, Social Capital and Microinsurance - Insights from Field Evidence in India (December 03, 2007). Microfinance Insights, Vol. 5, December 2007 Duflo, E. and Banerjee, A., Poor Economics: A Radical Rethinking of the Way to Fight Global Poverty, PublicAffairs. Dupas, P., 2009a. Do teenagers respond to HIV risk information? Evidence from a field experiment in Kenya, National Bureau of Economic Research. Dupas, P., 2009b. What Matters (and What Does Not) in Households Decision to Invest in Malaria Prevention. American Economic Review, 99(2), pp Fischer, C., Read this paper even later: Procrastination with time-inconsistent preferences. Discussion Papers.

17 Giné, X., Townsend, R. and Vickery, J., Patterns of rainfall insurance participation in rural India. The World Bank Economic Review, 22(3), pp Hamid, S.A., Roberts, J. and Mosley, P., Evaluating the Health Effects of Micro Health Insurance Placement: Evidence from Bangladesh. World Development, 39(3), pp Ito, S. and Kono, H., WHY IS THE TAKE-UP OF MICROINSURANCE SO LOW? EVIDENCE FROM A HEALTH INSURANCE SCHEME IN INDIA. The Developing Economies, 48(1), pp Jehu-Appiah, C. et al., Household perceptions and their implications for enrolment in the National Health Insurance Scheme in Ghana. Health Policy and Planning. Jutting, J., Health insurance for the poor? Determinants of participation in community-based health insurance schemes in rural Senegal. Jutting, J.P., Do community-based health insurance schemes improve poor people s access to health care? Evidence from rural Senegal. World Development, 32(2), pp Kent Ranson, M. and John, K., Quality of hysterectomy care in rural Gujarat: the role of community-based health insurance. Reproductive Health Matters, 10(20), pp Kremer, M. and Miguel, E., The illusion of sustainability. Quarterly Journal of Economics, 122(3). Larsen, B., Hygiene and health in developing countries: defining priorities through cost?-? benefit assessments. International Journal of Environmental Health Research, 13(S1), pp Lengeler, C., Insecticide-treated bed nets and curtains for preventing malaria. Cochrane Database Syst Rev, 2(2). Levine, D.I. et al., Assessing the Effects of Health Insurance: The SKY Micro-Insurance Program in Rural Cambodia. Lim, Y. and Townsend, R.M., General equilibrium models of financial systems: theory and measurement in village economies. Review of Economic Dynamics, 1(1), pp Lucas, A.M., Malaria eradication and educational attainment: Evidence from Paraguay and Sri Lanka. American Economic Journal: Applied Economics, 2(2), pp Magnoni, B. and Zimmerman, E., Do clients get value from microinsurance. A systematic review of recent and current research. Appleton, WI: The MicroInsurance Centre, LLC. McCord, M.J., Health care microinsurance-case studies from Uganda, Tanzania, India and Cambodia. Small Enterprise Development, 12(1), pp McCord, M.J., Visions of the future of Microinsurance, and thoughts on getting there, Microinsurance note.

18 McIntyre, D. et al., What are the economic consequences for households of illness and of paying for health care in low- and middle-income country contexts? Social Science and Medicine, 62(4), pp Meng, Q. et al., Expanding health insurance coverage in vulnerable groups: a systematic review of options. Health Policy and Planning, 26(2), p.93. Meredith, J. et al., Keeping the Doctor Away: Experimental Evidence on Investment in Preventative Health Products. Miguel, E. and Kremer, M., Worms: identifying impacts on education and health in the presence of treatment externalities. Econometrica, 72(1), pp Noble, J. and McCord, M., J., Microinsurance Notes \#6: Health Microinsurance, USAID MicroLinks USA. Onwujekwe, O., Hanson, K. and Fox-Rushby, J., Inequalities in purchase of mosquito nets and willingness to pay for insecticide-treated nets in Nigeria: challenges for malaria control interventions. Malaria journal, 3(1), p.6. Preker, A.S. et al., Effectiveness of community health financing in meeting the cost of illness. Bulletin of the World Health Organization, 80(2), pp Rabin, M., A perspective on psychology and economics. European economic review, 46(4-5), pp Rosenzweig, M.R., Risk, implicit contracts and the family in rural areas of low-income countries. The Economic Journal, 98(393), pp Rosenzweig, M.R. and Wolpin, K.I., Credit market constraints, consumption smoothing, and the accumulation of durable production assets in low-income countries: Investments in bullocks in India. Journal of Political Economy, pp Schneider, P., Trust in micro-health insurance: an exploratory study in Rwanda. Social Science and Medicine, 61(7), pp Sebstad, J., Cohen, M. and McGuinness, E., Guidelines for market research on the demand for microinsurance, United States Agency for International Development. Sijbesma, C. and Christoffers, T., The value of hygiene promotion: cost-effectiveness analysis of interventions in developing countries. Health Policy and Planning, 24(6), pp Sinha, T. et al., Barriers to accessing benefits in a community-based insurance scheme: lessons learnt from SEWA Insurance, Gujarat. Health policy and planning, 21(2), p.132. Sinha, T. et al., Why have the members gone? Explanations for dropout from a communitybased insurance scheme. Journal of International Development, 19(5), pp

19 Smith, K.V. and Sulzbach, S., Community-based health insurance and access to maternal health services: evidence from three West African countries. Social Science and Medicine, 66(12), pp Sunstein, C. and Thaler, R., Nudge. New Haven, CT: Yale University Press. Ten Examples of Early Tortoise-Shell Inscriptions. Harvard Journal of Asiatic Studies, 11, pp.1 2. Townsend, R.M., Risk and insurance in village India. Econometrica: Journal of the Econometric Society, pp

20 Table 1a Variable Obs Mean Std. Dev. Min Max P-value Joint Test Household Demographics and Economic Status Sex of primary holder HH Size Number of Children (-13) Total Monthly Income Health Condition and Links with Calcutta Kids Days Ill Round Renewal OPCS MYCHI Education Illiterate Upto Primary School (Upto Std. V) Upto High School (Upto Std. X) Graduation Higher education beyond graduation Occupation Self-employed Regular salaried employee Daily wage worker Trade Unemployed Religion Hindu Muslim Christian

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