The Impact of Out-patient Health Insurance and Preventive & Promotive Products in a Randomized Controlled Trial experiment

Transcription

1 The Impact of Out-patient Health Insurance and Preventive & Promotive Products in a Randomized Controlled Trial experiment Ajay Mahal, * Karuna Krishnaswamy, Rupalee Ruchismita March 2012 PRELIMINARY AND INCOMPLETE Abstract Low income rural Indians have poor access to and utilization of healthcare services in their neighbourhoods, a paucity of health financing schemes, and insufficient usage of preventive & promotive products, leading to high out-of-pocket health expenditure and poor health outcomes. To test a model to potentially address these shortcomings, a pilot was designed by CARE Foundation and the Center for Insurance and Risk Management in which low-skilled resident Village Health Champions were trained and deployed in 50 villages in rural Maharashtra in 2009, to offer first-level primary and preventive-promotive care consultations. VHCs use a telephone to consult a doctor and make referrals to the doctor as needed. The VHC further offers an out-patient health insurance product and a range of preventive & promotive products. The insurance does not cover hospitalization. In a randomized controlled trial experiment, we study the impact of having out-patient insurance in villages with this health service on the patients. We also study the impact of a combination of the insurance and the preventive & promotive products to inform the case for bundling insurance products with value added services. Our key finding is that being assigned to the discounted out-patient insurance group increases the number of visits to the covered provider, and decreases hospitalization expenses. Our explanation is that insurance incentivizes more frequent meetings with the VHC, leading to earlier identification of sicknesses before they get exacerbated and more timely referrals to a hospital, where the patient can get treated at an earlier stage and hence at a lower cost. We find some heterogeneous treatment effects on morbidity, out-patient health expenses and health status. We conclude that insurers as well as governments deploying hospitalization insurance schemes could benefit if they provide primary care as a value-added service through a decrease in their overall total expense ratios and future premiums and hence an increase in future enrolment rates. We thank CARE Foundation for supporting the study, and ILO s Micro Insurance Facility for funding the intervention and the research. * Monash University, ajay.mahal@monash.edu Center for Insurance and Risk Management, karuna.krishnaswamy@cirm.in, contacting author Center for Insurance and Risk Management, rupalee.ruchismita@cirm.in

2 I - Introduction There are three major problems with the healthcare sector in India which result in poor health outcomes and high out-of-pocket expenditure. Poor utilization and access to healthcare: Despite higher morbidity rates in rural compared to urban areas, the quality and access to treatment is much poorer in rural areas (Shariff et al, 1999). On an average 11% of the episodes reported ill do not resort to treatment. Several studies show that people in the rural areas are dissatisfied with the services. Distance to a facility is several kilometres by foot, the presence of a doctor is not guaranteed at a public facility, they might have to wait for a long time for treatment, and medicines might not be available on premises. Medicines, especially antibiotics, are often in short supply. Emergency equipment and life saving drugs such as oxygen and steroids are not available in a large number of Primary Health Centres. Hence, there is preference for private practitioners, vaids, hakims (traditional healers) and other traditional system of medicine like homeopathy, unani, ayurvedic and siddha. Berman (1997) shows that 82 percent of illness episodes in rural areas sought consultation at private providers. Inequitable financing of health: The poor spend less on medical care than commensurate with their morbidity rates (Duggal and Amin 1989). However, they spend 7 to 8 percent of their annual household income on health care while the rich spend only about 2 to 3 percent of their incomes on health care (Sharriff, 1995). Sanyal (1996) shows that about 20% of health expenditure is for incidental expenses like travel costs, food, stay, or bribes. World Bank (1995) shows that most of the expenditure by poor households goes towards curative services. Even for primary services (which are free in widespread government facilities) 82 percent of the expenditure is out-of-pocket. Berman (1997) estimates that 65 percent of the out-of-pocket expenditure in rural areas goes toward nonhospital treatment out of which out-of-pocket spending for private non-hospital treatment in rural areas is 56 percent. Insufficient focus on Primary Health and Preventive-promotive interventions: Of the 9.7 million under five deaths, 25 percent occurs in India. Nearly one million children die within one month of their birth. Of the 26.3 million un-immunized children, million (43 per cent) reside in India. Sixty percent of the global measles deaths occur in India. According to the 2007 data, 60 percent of all polio cases (756 of 1181 cases) occurred in India. Out of 536,000 maternal deaths, (22 percent were in India. It also accounts for almost 40 percent of all underweight children under five in the world (54 out of 143 million children). ** ** World Health Report, 2007

3 Primary care acts as a gatekeeper to secondary care. If more people are passed from primary care to secondary care, the multiplier effect on total health care costs could potentially be much higher. CARE Foundation and Centre for Insurance and Risk Management (CIRM) partnered to conduct an action research programme to test the viability and impact of a model to address these issues. The intervention is a primary care facility offered in the village by a Village Health Champion who is trained in first-level primary and preventive care. She collects basic information, consults a doctor over phone, sells over-the-counter (OTC) drugs and preventive products or refers the patient to the clinic in Yavatmal district. The doctor in the district may in turn refer the patient to a specialist or to a hospital. This study measures the impact of offering out-patient insurance (which covers visits to the VHC and the doctor). Since micro-insurance take-up is often low among low income segments, we also study the impact of a composite insurance plus preventive-promotive products on health outcomes to gain insights into the additional benefits of throwing in the preventive products as a value-added service to the insurance. This paper makes a contribution to the literature as follows: it studies the impact of an out-patient insurance product in a developing country setting on out-of-pocket out-patient and in-patient expenses on health, health seeking behavior and morbidity. It further seeks to make the link between out-patient insurance and preventive practices and hospitalization. This paper is organized as follows. Section II summarizes the literature on the impact of health insurance. Section III describes the intervention. Section IV presents the experiment design and methodology. Section V discusses the results. Section VI concludes. II - Brief lit review of impact of outpatient insurance and preventive practices The literature is largely comprised of studies of inpatient schemes with cover for primary and preventive care in some cases. While most focus on health seeking behaviour and out-of-pocket health expenses, few studies reported health measures. The results are not always consistent, including those studying the same scheme (such as in Vietnam s state scheme) Thornton et al. (2010) study a government-run voluntary health insurance program offered to informal workers through Micro Finance Institutions. They find that while insured individuals substituted towards services at covered facilities, there was no evidence of an increase in health care utilization among the newly insured. Total out-of-pocket (OOP) expenditures dropped less than the premium amount. They find no impact on health status. Add reference

4 Aggarwal (2010) evaluates India s Yeshasvini community-based health insurance programme which covers highly catastrophic and less discretionary in-patient surgical procedures and offers free outpatient diagnostics and lab tests at discounted rates. Insurance increased the usage of the health facility, largely the use of out-patient services including out-patient surgery but not hospitalisation. Overall, medical expenses were actually higher for the insured, with the poor experiencing no change. Hospitalization expenditure is significantly lower for the insured as can be expected. Miller et al. (2009) studies a fully-funded pro-poor Columbian government scheme that covers primary and in-patient care. The study finds no effect on average out-patient expenditure although inpatient expenditure and lowers incidence of high-end expenditure among the insured is observed. The insured also use preventive services more. Wagstaff (2007 & 2010) and Axelson et al. (2008) studied Vietnam s pro-poor subsidized Vietnam Health Care for the Poor (VHCFP), which covers in-patient and out-patient care only at public providers and some preventive care. However the three studies over time showed inconclusive effects. Lei and Lin (2009) and Wagstaff et al. (2009) study China s New cooperative medical system (NCMS), a government subsidized rural scheme. While all the counties covered in-patient care, only some covered out-patient care. Lei and Lin (2009) find no difference in the level of OOP expenditure or health status of the insured while Wagstaff et al. (2009) find weak evidence of lower expenditure. In an earlier community insurance scheme in China, Wagstaff and Lindelow (2008) report that insurance yielded higher levels of catastrophic payments. Wang et al. (2009) using EQ-5D instruments to assess health, report from a community-based health insurance in China that the scheme had positive effects on health status for all insured and also the poor under the scheme. Measuring regional changes, Dow and Schmeer (2003) find no correlation in changes in infant mortality as regional insurance uptake improved in Costa Rica s National Insurance Expansion programme. This study seeks to add to the literature on the impact of insurance on health seeking behavior, morbidity, health expenses and further link out-patient insurance to hospitalization outcomes. III - The intervention EQ-5D is a standardised instrument for use as a measure of health outcome. Applicable to a wide range of health conditions and treatments, it provides a simple descriptive profile and a single index value for health status.

5 In 2009, CARE Foundation and CIRM collaborated to provide an innovative micro-insurance product coupled with healthcare services delivered through telemedicine by a Village Health Champion (VHC). This service was launched in the villages in Yavatmal, a poor and drought-prone district of Maharashtra. A qualitative study was conducted to gauge the morbidity rates, common diseases, medical infrastructure, sanitary practices and needs of the villagers. The delivery model of this pilot has a hub and spoke structure. CARE Foundation established an out-patient clinic at Yavatmal town and health care is delivered through the Village Health Champions (VHC) in 50 villages surrounding the town. CARE Foundation educates and trains VHCs who help people access medical advice from specialists through a mobile phone-enabled telemedicine service, and maintain a health database of their village. This model seeks to provide proximity services in the village while minimizing use of the scarcest resource, the doctor. Three services are offered: Out-Patient (OP) primary health care services: The VHC offers first-level consulting services in the villages. They conduct diagnostics on the spot, sell generic drugs and do follow ups. They further educate people, do preventive health checkups, advocate for public health programs, promote hygiene and assist in referrals and transportation to the clinic and the hospital in case of an emergency. The VHC network is supported by CARE s Primary Health Center in the town with a Doctor, Assistant, Pharmacist and Laboratory technician for minor procedures, drugs and diagnostic services. Of importance is the referral service. When the patient visits the VHC, she conducts a basic examination, gathers symptoms and consults the doctor for further course of action. The patient may then be referred to the doctor in Yavatmal town, and/or further to specialist doctors in Yavatmal or to the CARE hospital in Nagpur. Fee for consultation (for the uninsured) with the CARE doctor is Rs.12 ($0.24) per visit. All patients are offered a 25% discount on bed charges and in-house investigations at the hospital. Insuring Non-Hospital Care: The out-patient insurance product, called the Arogya (Health) Card is sold by the VHC. The Arogya Card costs Rs. 300 ($6) and covers four family members (two parents and two children between the ages of 6 months and 65 years) for one year for unlimited visits in the network for a sum assured of Rs. 2,500 ($50). The card entitles holders to cashless out-patient consulting services at the VHC and the CARE clinic in Yavatmal, diagnostics, and medicines. If referred to a hospital in Nagpur for secondary or tertiary care, a transport allowance of Rs. 200 ($ 4) is offered. However, the product offers no in-patient cover of any kind. The Arogya Card enrollment campaign is supported by an insurance literacy program and free distribution of bed-nets and/or water

6 filters whenever people purchase membership so that they see some value for money spent on the premium. From the baseline survey we know that the average household spends Rs 340 per month on outpatient expenses (1 month recall period question) or Rs. 4,080 per year. The average household size is This translates to Rs3,690 for a household with 4 members. So this insurance product should be attractive for a large number of households. Preventive and promotive services The VHC sells preventive and promotive products to the insured clients. The products on offer include handkerchiefs, soap, water purifiers, mosquito repellents, and sanitary napkins. VHC Characteristics The VHC is a resident native married woman in the village. Her average age is 29 and has an average of ten years of schooling. The modal self-reported number of days of training for the job is seven. Only 3 out of 26 VHCs have worked in the medical field before. Their typical household income is Rs.3730 ($75) per month. For a third of them, this is their only occupation, while the remainder mainly works in agriculture. Each VHC handles 23 card holders on average. The VHC is paid a commission of Rs 10 ($0.2) for the sale of a one year insurance card and a fixed Re.1 ($0.02) per card per month. She gets Rs. 5 ($0.10) as consultation fees for each uninsured patient visit, Rs. 2 ($0.04) for BP/weight/height checks and no consultation fees for treating an insured patient to prevent collusion. Common reasons for visits are Malaria, flu and cold symptoms, diarrhea, and infections. The Command area Yavatmal was chosen because of the estimated demand and utility of this service. The key features of the study area are summarized below from the baseline survey. The villages are small at around 200 households and 1000 residents in total approximately. Respondents have low economic indicators: The annual average household income in the sample households was Rs. 32,000 - less than $2 per day. Fifty percent have had no schooling, or have only reached primary school (5 years of schooling). Access to water, power, cooking fuels, mosquito nets and sanitation practices are poor: Only 24% have tap water on premises. Sixteen percent need to travel more than 15 minutes to access drinking water. Only 22% of the respondents have access to a toilet at their homes

7 the remainder use open spaces. Ninety-one percent use wood for cooking with 90% of them cooking inside the kitchen. Only 31% use mosquito nets or an electronic mosquito repellent. There is low awareness of good health practices: Only 61% responded that mosquito nets and electronic repellents are useful to prevent malaria. As regards preventing diarrhoea, 57% thought that boiling water was a good measure, and 25% cited using chlorine. Among those that wash hands, only 54% use soap. Half of the parents reported not immunizing their children. These responses suggest that improvements in preventive practices could potentially improve health outcomes as well as decrease incidences of preventable ailments. Informal practitioners are commonly used: The use of informal service providers (quacks), comprising Registered Medical Practitioners (by 93%) and unqualified doctors (by 49%) is very high. Less than 60% feel that government work on diarrhoea and malaria prevention is enough. Private hospitals and doctors are more commonly used by 65% for all kinds of medical need put together. Morbidity: A quarter of the household members had visited a formal health provider in the one month recall period prior to the survey, fairly evenly spread across income quintiles. The top ailments that the household members of the respondents had in the one month recall period were fever or malaria and diarrhoea followed by persistent cough and joint pain. Six percent of all household members of the respondents reported being hospitalized, at an average of 3.7 days in hospital. Hospitalization is expensive but rare, while non-hospital expenses account for 80% of expenses: Share of health spending out of monthly household expenditure is 11% among those who incurred any health expenses, with the ratio rising to 15% for the bottom income quintile. We analyzed the break-up of health expenses by different heads. Less than 3% reported expenses related to hospitalization. However, the top three categories by amount of expense are critical illnesses, drugs and consumables when hospitalized and hospitalization charges. These expenses averaging from Rs ($28) to Rs.3400 ($76) are significant compared to average household income of Rs ($72) per month. Non-hospital expenses borne by 97% of those who had incurred any medical expenses however account for 80% of all medical expenses, with a low average expense of Rs. 275 ($6) per episode.

8 Proximity service will provide value: Transportation, wages lost and food expenses accounted for 32% of health expenses. Seventy percent of those who were ill needed someone else to accompany them to the medical facility for an average of 2.6 days losing an average of Rs. 320 ($7) days of income. IV - Experiment design and data The theory of change The first goal of the intervention is to offer a quality proximate primary care service. However, this study focuses on the benefits of the insurance component and the use of preventive & promotive products on the patients. Impact of Insurance Insurance is expected to improve health seeking behavior, decrease the duration of sickness, and improve overall well-being. It is expected that having insurance with zero co-pay, as well as the proximity, and hence low marginal cost of an additional visit, will encourage patients to visit the VHC as soon as they feel ill and not delay consultancy. In fact, it is expected that they would over-utilize the VHC even if the symptoms do not call for a visit. The referral system will ensure that the patient gets appropriately referred to the CARE clinic in town or to a specialist or hospital. Hence, ailments are expected to be identified sooner and if serious, would be referred by a suitable health provider in time before it escalates into a more serious condition requiring hospitalization. Hence, we expect that the severity and duration of sickness and hence hospitalization expenses are likely to be lower. This may also show up in increased number of hours worked in a week, increased business investments, number of school or work days lost due to sickness or accompaniment of a sick family member. Effect on out-of-pocket (OOP) expenses on out-patient care, is an empirical question. that depends on the cost & frequency of visits by patients as well as outcomes of treatment by other medical providers available (in comparison to the VHC) and used by the villagers. While average OOP expenses may be expected to decrease by reducing the right tail and reducing the average cost per consultancy, it may increase because the new access may increase health seeking and preventive behavior and hence the number of visits and expenses in the short term. Impact of Preventive-Promotive products The VHC sells individual preventive & promotive (PP) items at a discount to one treatment group. This is expected to incentivize villagers to adopt better preventive and sanitary practices. This also has a secondary benefit of increased number of interactions between client and VHC permitting more

9 medical consultation and awareness messages. This discounted service is expected to lead to more purchases of the PP product leading to fewer incidences of easily preventable air & water borne diseases and malaria. Randomization Design We randomize by individual in each village (across multiple villages) using discounts to encourage higher take-up in the treatment groups compared to the control group which is in the same village. This has the benefit of higher power and hence smaller sample size and lower cost of data collection and permits us to study intent-to-treat effects without excluding anyone from the health service. CARE Foundation has launched the VHC and insurance service on a pilot basis in 50 villages in 2 batches of 30 first and 20 subsequently. We selected the first 30 out of the 50 pilot villages for this study. We conducted a listing in each village and randomly drew 30 households. We then randomly assigned the 30 households into 3 equal sized groups. All groups have access to the VHC s primary care service. The control group, C, received a one-time free visit to the VHC so that they are familiar with the service, its quality and operational details. This group may chose to purchase the insurance in future or simply visit the VHC without insurance. Treatment group T1 received an 80% discount voucher on the premium to the insurance to use the VHC and another 80% discount voucher on the preventive-promotive (PP) package. Treatment group T2 received an 80% discount voucher on the premium of the insurance to use the VHC. To summarize: Treatment group T1: VHC and insurance discount and PP discount Treatment group T2: VHC and insurance discount Control group: VHC only These non-transferrable vouchers were given to the subjects immediately after the completion of the baseline interview. The baseline was conducted between November 2010 and February We expected that take-up rates would be much lower among the control group than the treatment groups We exploit the fact that the groups with the discounts will have more purchases than the one without. Hence the treatment group is more treated than the control group which has access to the VHC and the PP products at full price. In this study, the impact is observed through three comparisons: 1. T1 vs. C: measures the impact of the bundled OP insurance plus PP package 2. T2 vs. C: measures the impact of OP insurance only

10 3. T1 vs. T2: compares combined intervention (OP insurance + PP package) with those with only OP insurance. However, this is an approximate assessment, since we cannot control for the interaction between OP insurance and the PP product usage. We note that spill-over effects are likely since the control and treatment groups are in the same village and since the insurance and PP is available to the control group, this will lead to understating the difference between the two groups. Data Collection The baseline survey was conducted in November, 2010 on 889 households. We tried to track these households down in the endline survey conducted between November and December 2011, approximately a year after the insurance product was launched. However, four out of the original 30 villages (a total of 129 households) did not have the service since the VHCs dropped out due to lack of support from the local community. We acknowledge that the results of this study are applicable to those locations where this service is viable and hence not necessarily generalizable to all similar villages. However given that 4 out of 30 is only 13%, the results are applicable to a large potential population in India. We administered the endline in these 4 villages as well but we dropped these observations in the difference-in-difference estimates. A total of 27 households migrated permanently and hence could not be interviewed in the endline. This leaves us with 743 endline observations. The surveys are almost identical except for additional questions regarding the intervention in the endline. A different survey company was hired for each round. The dropouts may have been problematic if predominantly from one group since it might have made that group dissimilar to the other groups. The attrition though considerable, is evenly spread across the groups as we see below. We further verified that the 27 dropouts were not significantly different from the 743 (Table 1). Table 1: T-test Baseline Control vars - Mean Endline Participants vs. Mean Dropouts Mean of Endline Participants Mean of Drop Outs P value of difference Annual HHD Expenses Avg. HHD age % HHD male HHD size Was immunized? Has tap water Has pucca house Has ration card

11 In sum, we have in the 26 treatment villages, 770 observations in the baseline and 743 in the endline after attrition. Table 2: Sample size in the treatment villages Baseline (with Voucher Code 26 villages) Endline Baseline Insurance plus PP Insurance only Control Total Questions in the surveys covered sicknesses and details of health seeking behavior in a 30 day recall period (for better recollection) and also details of major sicknesses (malaria, diarrhea and infant sicknesses) in a 6 month recall period. Hospitalization incidences and entailing expenses are recorded in a 6 month recall period to allow for enough incidences. Health expenses are recorded for the illnesses identified in the 1 month recall period (by type of expense) as also in the 6 month recall period questions. Finally we also report annual expenses on health as a share of total annual expenses for corroboration. Randomization checks The premise of comparing control and treatment groups in a Randomized Controlled Trial experiment is that since treatments were randomly assigned, the groups are likely to be similar in any characteristic that might influence outcomes and hence any difference in outcomes can be attributed to the intervention. As we see in Table 3, the Insurance only group had significantly better indicators for immunization and access to tap water. It may be argued that these two factors would contribute to better health outcomes in addition (or in interaction) to receiving the voucher and hence over-state the effect of the voucher. We add these variables as controls in the DiD estimation model. Moreover, we find no correlation between take-up rates and these two variables, nor indeed correlation to any variables except for the discount voucher code. Table 3: Descriptive statistics, Control variables Baseline Insurance plus PP Insurance only Control Total Difference T1 and control Difference T2 and control C_annual_total _exp ( ) ( ) ( ) ( ) (-0.07) (0.84) C_age (13.21) (12.98) (12.75) (12.98) (0.79) (0.98) C_male (0.179) (0.218) (0.277) (0.228) (0.63) (0.38) C_hhd_size (1.754) (1.750) (1.714) (1.740) (1.59) (1.11) C_was_immuni zed_hhd *

12 (0.454) (0.464) (0.420) (0.447) (-1.56) (-2.10) C_has_tap_wat er C_has_pucca_h ouse * (0.491) (0.499) (0.477) (0.490) (-1.24) (-2.43) (0.413) (0.401) (0.404) (0.406) (-0.35) (0.09) C_has_ration_c ard (0.368) (0.385) (0.335) (0.363) (1.04) (1.61) Observations We present the other baseline outcome variables of interest (Table 4). We note that while the difference in the total hospitalization expenses is large, it is not significant. Share of health expenditure is higher in the Insurance only group, while health seeking behavior, as reported by incidence of seeking a formal health provider when sick is lower in the Insurance plus PP. As such it appears that the Insurance only group is less healthy compared to the control group. Table 4: Baseline randomization checks Insurance plus PP Total hospitalization expenses in past 6 mths (Rs.) Insurance only Control Total Difference: T1 and control Difference: T2 and control (2387.9) (3295.1) (2052.8) (2622.6) (-0.76) (-1.15) No. of days in hospital in past 6 mths OP expenses in past 1 mth (Rs.) (3.184) (3.304) (4.144) (3.569) (-0.42) (-0.86) (6589.8) (787.9) (831.2) (3874.7) (-1.36) (-1.13) % health exp in past 1 year * (0.263) (0.0885) (0.121) (0.176) (-1.65) (2.32) Sought health provider when sick in past 1 mth? * (0.456) (0.467) (0.489) (0.472) (-2.33) (-1.68) No. times sick in past 6 mths (0.645) (0.680) (0.610) (0.646) (-1.09) (-1.94) Self reported health ranking out of 5 (summed for the household) (7.787) (8.171) (8.363) (8.120) (1.92) (0.83) Observations Take-up and usage of vouchers The voucher design has gone according to plan - take up rates of insurance are close to 63% in the treatment groups (Table 6). However, it is 35% in the control group which is considerably higher than expected. This means that the differences between the control and treatment groups would be much lower than expected. Usage of the VHC is correlated to the percentage of take-up of insurance, as

13 seen in row 2, satisfying us that people who purchased insurance also used it. As regards purchase of PP products, 39% of the Insurance plus PP treatment group purchased at least 1 product from the VHC compared to 17% in the insurance group and finally 6% in the control group. Table 5: Take-up and Usage Insurance plus PP Group Insurance only Group Control Group Bought CARE health insurance Avg. no. of visits to VHC Bought any CARE PP product Total Bought CARE health insurance and any PP product Observations 743 Table 6 shows the different kinds of products purchased by group. Table 6: Take up of the Preventive-Promotive Products Insurance plus PP Insurance VHC Only group Total Group Only Group Soap (1049) (330) (115) (1494) Gent's Hankies (203) (40) (20) (263) Women's Hankies (206) (47) (17) (270) Water purifier drops (69) (10) (6) (85) Mosquito coil (49) (4) (4) (57) Mosquito Net (66) (12) (6) (84) Mask (49) (0) (0) (49) mean coefficients; sum in parentheses * p < 0.05, ** p < 0.01, *** p < The top 3 reasons for purchasing insurance was the promise of saving money in future, low cost of VHC, and because a friend of relative recommend it. The main reason to buy the PP product from the VHC was low cost. The main reasons for not purchasing insurance were the lack of understanding of the scheme and lack of money. The top reason for not purchasing any PP product was lack of awareness of what products the VHC had on offer and lack of affordability. Awareness and attitude towards the scheme

14 In this sub-section, we summarize self-reported awareness and attitudes towards the scheme. We see that close to 74% of the respondents are aware of the VHC s name, while only 17% have been to the health camp (Table 7). Table 7: Scheme Usage Insurance plus PP Group Insurance only Group Control Group Knows VHC's name (0.424) (0.429) (0.467) (0.442) Total No. times went to Health Camp (0.654) (0.613) (0.556) (0.608) Total no. of visits to VHC (1.839) (1.472) (1.002) (1.518) Will renew Health Card (0.490) (0.485) (0.484) (0.499) Will buy Health Card (0.404) (0.443) (0.499) (0.462) Observations 743 The average willingness to pay for the health card is Rs For those willing to renew their insurance, 50% preferred a one-time payment of premium while 40% preferred 2 payments per year. The common reason for wanting to buy a health card next year was that they would save money (65%), good service (26%) while 9% reported that did not have access to any other service options. For those not wanting to buy insurance, the reasons were poor service (32%), anticipation of little usage (31%), price (13%) while the lack of discounts beyond the study period dissuaded only 4%. As regards the PP product, there is very little seasonal patterns in demand. Mosquito products are most sought after in the summer (15-20%), water drops in the rainy season (20%), handkerchiefs in summer (11%) while there are no other seasonal preferences. Overall 62% of all the respondents cited private clinics followed by government clinics (18%) as their preferred health provider. Seventeen % of the households preferred the VHC as a medical provider. This number rises to roughly 20% in the treatment groups compared to 10% in the control group. The top reasons for this preference as shown below are lower price, better quality of doctor and better service overall.

15 Lower price Near my house Better Service Better quality of doctor Better tests, lab etc Got cured Other 32 Figure 1: Reasons for preferring their favoured medical provider V Empirical Strategy We focus on the intent-to-treat effect, i.e., the average effect of the product on those who were offered the discount regardless of whether they purchased the insurance or the preventive products or indeed even used them or not after purchase. For the outcome variables available in both baseline and endline, we use the difference-in-difference (DiD) estimator (Equation 1). Since there are large variations between groups in the outcome variables of interest, this method improves the likelihood that we can detect an effect of the treatment in our sample at a high confidence level. A DiD estimate for an outcome variable of interest tells us the difference in the change (from baseline to endline) between the control and treatment groups. For example, we see that hospitalization expenses (in a six month recall period) have dropped from baseline to endline on average for most respondents. The DiD estimate tells us by how much the drop in average hospitalization expenses from baseline to endline of the treatment group is higher than the average drop in hospitalization expenses of the control group. So this estimate is primarily useful in telling us whether the intervention had any effect or not. Interpretation is less obvious for quantifying the magnitude of this effect since both the control and treatment groups have enrolments but to varying extents and hence we are in fact comparing a group with a higher insurance take-up rate with

16 a group with lower take-up. Hence the DiD values represent a lower bound to the intent-to-treat effect that we would witness in a hypothetical experiment where the control group has no take-up. Y HVT = α + VP HV (β 11 + β 12 T) + VI HV (β 21 + β 22 T) + β 3 T + β 4 X HV + V V + ε HVT (1) where the subscript H represents household H, V represents the village and T indicates whether it is the baseline or the endline. Y is an outcome variable of interest such as number of doctor visits, number of days gap between sickness onset and visiting doctor (any doctor including VHC), rupees spent on outof-pocket health expenditure, number of days of sickness in each spell, school attendance, labour supplied, use of quacks, percentage share of health out of total household expenditure etc. Health outcomes include sickness incidence rates, and self-reported measure of healthfulness. X is a vector of baseline individual or household level control variables including variables that are not balanced between the different groups such as household size, income, immunization rate, availability of tap water, average age and percentage female. VP = 1 if given a discount voucher code 1 (for insurance and PP) and 0 otherwise, VI = 1 if given a discount voucher code 2 (for insurance only) and 0 otherwise. T = 1 if the observation is from the endline and T = 0 if from baseline V V is a village level fixed effect, which is important since take-up rates vary considerably by village. All variables are summed across all members of the household (regardless of whether a member is insured or not). Standard errors are clustered at the village level to account for correlation between characteristics and behaviour of people in the same village. The coefficients of interest are β 12 and β 22. They measure the difference-in-difference estimates. We also find out heterogeneous effects for the above. This estimate enables us to find out whether the impact of the intervention on a given outcome was different for different kinds of people. We focus on three such groups: richer versus poorer, people who self-reported to be sicker in the baseline versus those who were not and people with better earlier access versus those with less access. We use responses to household expenditure, sickness in a one month recall period, and whether they sought a formal medical provider if yes to the previous question. The groups were created as follows. The median of the pooled observations in the baseline was the cut-off. Each group was then divided into two parts observations with responses higher than the

17 median and lower to form the two heterogeneous groups. However, we caution that finding an effect, say in an outcome that is higher for the richer than the poorer respondents does not necessarily imply that this result is replicable, since it is quite possible that the outcome difference is actually governed by some other household attribute which is correlated to wealth. Y HVT = α 1 + VP HV (β 11 + β 12 T) + VI HV (β 21 + β 12 T) + β 3 T + H HV (α 2 + VP HV (β 41 + β 42 T) + VI HV (β 51 + β 52 T)) + β 6 X HV + V V + ε HVT (2) H is a heterogeneous treatment effect term = 1 if the household is above the median, and 0 if below in income, past morbidity, or access to medical providers as reported in the baseline. The coefficients of interest here are β 42 and β 52. They measure the triple difference: the difference in outcome trend rates between the two heterogeneous parts of the group. Similarly, we run a specification which compares outcomes between the Insurance plus PP group and the Insurance only group. These effects should be viewed as an approximate effect of the preventivepromotive products since the combination of access to insurance and of the PP products may lead to some interaction effects from which we are unable to isolate the independent effect of only the PP products. Local Average Treatment Effect Possibly of greater importance in this specific intervention is the effect of the product on those who use it, not just those who were offered a discount voucher, called the Local Average Treatment Effect. Bloom (1995) suggests that this may be computed as below, (3) where π T and π C is the take up rates in the control and treatment groups. We calculate these values for those values that are significant in the intent-to-treat effects. Inference and alternate methods We have only 26 villages. This is considered too small by Cameron, Miller, and Gelbach (2007) and many others for the standard cluster robust standard errors generated by STATA to be reliable for inference. We correct for this in two ways. We use their suggestion to use critical values for T tests of significance from a T distribution with 24 degrees of freedom. The critical values for the 1%, 5%, and 10% significance levels are thus 2.797, 2.064, and respectively. The results are robust to cluster bootstrapping. We then conduct randomization inference. (To be done).

18 We further conduct an ANCOVA analysis following McKenzie (2011). Y HV T=1 = α + β 1 *V1 HV + β 2 *V2 HV + β 3 * Y HV T=0 + β 4 *X HV + β 5 *V HV + ε HVT (4) where Y HVT1 is the outcome variable at endline while Y HVT0 is the outcome variable at the baseline. There being low correlation between baseline and endline values for many outcome variables (0.1 to 0.4), we would expect the ANCOVA estimates to have better power. However, we do not find better results with ANCOVA. We also attempted to do a cross-sectional endline comparison of outcomes at the household member level between control and treatment. The results though of the right sign, are not significant. Power calculations confirmed that ex-post the sample size is not large enough to conduct a cross-sectional analysis. Finally as regards heterogeneous treatment effects, we run an alternate measure. We divide each group into two groups, above and below the median in terms of baseline income, morbidity rate, and rates of seeking a medical provider when sick. We then ran the DiD regressions on the sub-populations of interest such as low income, high morbidity and lower usage of a medical provider. The results show more significance with this approach (not shown in this version). VI Results Intent-to-treat effect of insurance plus preventive-promotive products and of insurance only We report the DiD estimates of the intent-to-treat effects. The tables on the effects first list the DiD effect (coefficients β 12 and β 22 from equation 1) on the entire treatment group, followed by the impact of the treatment on the upper half of the treatment group by income, morbidity and usage of a formal medical provider (a proxy for access) as reported in the baseline respectively (coefficients β 42 and β 52 from equation 2). Table A (in the appendix) summarizes the average outcome variables of interest in the endline per household. Households spend close to 5% of total annual household expenditure on health. In a one month recall period, average expenses on out-patient care is higher at Rs. 546 compared to Rs. 85 for hospitalization expenses. There are 1.45 incidences of sickness per family in a month, lasting almost 5 days per episode, although they wait almost 4 days before seeking a health provider. While a provider is being sought 1.36 times (rather than do nothing or opt for a home remedy), there is some usage of quacks at 1%. Morbidity

19 We look for differences in the number of episodes of sickness per household in both the one month and six month recall periods, total number of days sick in a month and self reported health measures (Table 13). While there are no base effects, we observe some heterogeneous treatment effects. People in the insurance plus PP group that reported morbidity rates above the median in the baseline had fewer days of sickness due to the intervention compared to those below the median baseline morbidity rates. We find no effects on self reported sickness in a six month recall period or even a negative sign consistently. We also report finding (with 94.4% confidence) of the impact of having insurance and PP on households with lower access to a health provider, which work 8.3 hours more per week compared to the higher access half in the endline, compared to the baseline. Table 13: Morbidity and health status DiD Estimates (1) (2) (3) (4) (5) Total no. of days sick in past 1 mth Hours worked last week No. of episodes of sickness in No. times sick in past 6 mths Self reported health ranking Base effect - Insurance plus PP vs. VHC only group past 1 mth out of (0.172) (0.787) (0.468) (0.985) (0.114) Base effect Insurance vs. VHC only High income - Insurance plus PP vs. VHC only group High income Insurance group vs. VHC only High morbidity - Insurance plus PP vs. VHC only group High morbidity Insurance vs. VHC only group High access - Insurance plus PP vs. VHC only group (0.641) (0.923) (0.885) (0.327) (0.915) (0.153) (0.250) (0.224) (0.584) (0.834) (0.431) (0.118) (0.241) (0.359) (0.251) ** (0.002) (0.910) (0.191) (0.722) (0.528) (0.094) (0.671) (0.907) (0.335) (0.207) (0.592) (0.056) (0.829) (0.093) (0.512) High access Insurance vs. VHC only group (0.878) (0.995) (0.278) (0.337) (0.327) Observations p-values in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001

20 Health seeking behaviour As expected, having insurance increases usage of the VHC since the marginal cost of a VHC visit is close to zero for the insured (Table 8). We do not have much reason to think that there are too many incidences of needless visits to the VHC (just because it is free). Only 5 out of 26 VHCs interviewed, report that they think some card holders visit them even when the symptoms should not compel a visit to a health provider. We would also expect that insurance would incentivize the insured to visit their medical provider sooner after the onset of sickness than those without insurance (even though the consulting fee for the uninsured is low). We find this effect on high income households in the insurance and PP group only. We also find that the insurance plus PP group went to any type of medical provider (when sick) fewer times than the control group (Table 9). It may reflect a needlessly high number of follow up visits that other (non-insured) providers recommend. Table 8: VHC Usage: Endline comparison (2) (3) No. of times visited VHC in past 1 mth % of VHC to total provider visits Base effect - Insurance plus PP ** ** vs. VHC only group (0.001) (0.005) Base effect Insurance vs. VHC only High income - Insurance plus PP vs. VHC only group High income Insurance group vs. VHC only High morbidity - Insurance plus PP vs. VHC only group High morbidity Insurance vs. VHC only group High access - Insurance plus PP vs. VHC only group ** *** (0.001) (0.001) (0.661) (0.776) (0.304) (0.241) (0.565) (0.799) (0.975) (0.673) (0.258) (0.521) High access Insurance vs VHC only group (0.303) (0.259) Observations p-values in parentheses p < 0.05, ** p < 0.01, *** p < Table 9: Health Seeking Behaviour DiD Estimates (1) (2) (3)