DP2010-31

Achieving eq uity in health th roughcomm unity bas ed h ealth insu rance:
Ind ia's ex per ienc e with a lar ge C BHI
progr amm e
Aradhna AGGAR WAL
November 10, 2010

Achieving equity in health through community based healthinsurance: India’s experience with a large CBHI programme*
Aradhna Aggarwal
Current affliation: Visiting Professor,
Research Institute of Economics and Business,
Kobe University, 2-1, Rokkodai-cho, Nada-ku, Kobe
657-8501 JAPAN
Tel: 81-78-803-7270; Fax: 81-78-803-7059

Parent affiliation: Associate Professor
Department of Business Economics
South Campus, University of Delhi
Benito Juarez Road, Delhi-110021
Telefax: 91-11-24111141
aradhna.aggarwal@gmail.com
Abstract: This paper analyses equity in one of the largest community based health insurance
programme in India: Yeshasvini, run by the Department of Cooperation in Karnataka, a statein India. The analysis is based on a primary survey of 4109 households in rural Karnataka.
The study covers various dimensions of vulnerability and assesses their relationship withenrolment, renewal of enrolment, and utilisation of health care services using logistic
regression techniques. The results demonstrate that inequities do exist. However, they are lesspronounced in the distribution of benefits than in enrolment and renewals. The study argues
that while CBHI may be used as a mechanism to reach the most disadvantaged populationgroups, they can not be considered as substitute for government created health infrastructure.
Key words: Community-based health insurance (CBHI) Karnataka state India, Equity,
Gender, vulnerability
JEL: I18* This paper is a part of a larger study assessing health and economic effects of Yeshasvini health careprogramme in rural Karnataka. I would like to thank the ‘Bill & Melinda Gates Foundation’ forfunding this study. I would take this opportunity to thank the Global Development Network (GDN)
for giving me an opportunity to carry out this study. I am indebted to Mr. Ravi Kumar and Mr.
S.R.Naik of the Department of Cooperation of Karnataka government and officials from various
Departments of the State Government for providing relevant data and information. An earlier versionof the paper was presented at the British Northern University, India Forum (BNUIF) seminar “India’sService Sector: Who does it serve?” Hyderabad 24-26 March, 2010. I would like to thank theparticipants and discussant for their useful coments and suggestions.

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Achieving equity in health through community based healthinsurance: India’s experience with a large CBHI programme
Abstract: This paper analyses equity in one of the largest community based health insurance
programme in India: Yeshasvini, run by the Department of Cooperation in Karnataka, a statein India. The analysis is based on a primary survey of 4109 households in rural Karnataka.
The study covers various dimensions of vulnerability and assesses their relationship withenrolment, renewal of enrolment, and utilisation of health care services using logistic
regression techniques. The results demonstrate that inequities do exist. However, they are lesspronounced in the distribution of benefits than in enrolment and renewals. The study argues
that while CBHI may be used as a mechanism to reach the most disadvantaged populationgroups, they can not be considered as substitute for government created health infrastructure.

1. The study
The new international thinking on health that emerged during the late 1990s underlines thenotion that the governments in developing countries should promote and strengthencommunity based health insrance (CBHI) schemes1 as a viable option in providing financial
protection to the poor (WHO 2000, 2001)2. As a result, governments in many developing
countries have taken initiatives to strengthen these schemes as part of their health policy. This
has led to proliferation of these programmes across developing countries (ILO 2005, Tabor2006 for survey). There is evidence albeir weak that such schemes help to improve financial
access, utilisation, and quality of health care services through cooperative and community
efforts (See, e.g. Aggarwal 2010 for a recent study). However, there is a gap in knowledge indetermining how equitable such schemes are. While examining the issue of equity, moststudies focus on the socio economic composition of the current members of these schemes.
Generally it is believed that for equity reasons, membership should not be biased towards the1

CBHI schemes are known by different names in different parts of the world, including: microinsurance, community health finance organisations, mutual health insurance schemes, pre-payment
insurance organisations, voluntary informal sector health insurance, mutual healthorganisations/associations, community health finance organisations, and community self-financing
health organisations.

2

The Commission on Macroeconomics and Health (WHO, 2001), for example, recommended that,“out-of-pocket expenditures by poor communities should increasingly be channeled into community
financing schemes to help cover the costs of community-based health delivery”(p 60).
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better off and be effectively open to economically vulnerable groups. Thus, most studies havelinked equity with income or wealth. However, equity aspects of a programme cannot be
assessed in terms of its ability to address economic vulnerability alone. In the recent
literature, multi dimensional measures of vulnerability are emerging. In a broader sense,
vulnerability is defined (Schmidt-Thomé and Jarva, 2003) as a set of conditions andprocesses resulting from physical, social, economic, locational and environmental factors,
which determine the susceptibility of an individual/community to the impact of catastrophic
events (deterioration of health in the present context). In the existing literature on equity inmicro health insurance, however, vulnerability arising out of economic factors occupies acentral place while other dimensions of vulnerabilities are relatively overlooked. Further,most studies have focused on the composition of current members by economc category; afew have examined the characteristics of those who have been renewing registration or haveactually been the claimants of the benefits. The present study addresses these gaps in theliterature. The main aim of the study is to examine, using a broad based concept ofvulnerability, equity in enrolment, renewal of membership, and utilisation of services in thecontext of one of the largest CBHI programmes in India: Yeshasvini Health Care programme.
The programme offers the rural poor an opportunity of taking benefits of advanced andhighly expensive surgical treatments which otherwise would be non accessible to them. This
study examines whether the programme reaches out to the most vulnerable sections of therural poor. The multi-dimensional concept of vulnerability used in the study covers:social vulnerability,

gender-specific vulnerability,

health vunerability,

economic vulnerability, and

location specific vulnerability

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The rest of the study is planned as follows. Section 2 provides a theoretical discussion onequity dimensions of CBHI. Section 3 focuses on the Yeshasvini programme. It describes thecore characteristics of the programme and discusses their implications for equity. Section 4
provides a theoretical framework which Section 5 describes the research designs and database.
Section 6 discusses the empirical results. Finally, Section 7 concludes the analysis.

2. CBHI and health equity: Theoretical discussion

The theoretical explanations for the success of CBHI are set within the fold of the social
capital framework (Atim, 1999, Jakab and Preker 2001, Dror and Preker 2002, Criel and
Waelkens, 2003, Dror and Preker, 2002, 2004, Hsiao, 2001, Jowett, 2003, 2008, KiwanukaMukiibi et al.2005, Meessen et al, 2002, Ron, 1999, Schneider, 2004, Zhang et al., 2006)3.
Low-income households are generally benefited by informal risk protection through family
and relatives, community links, institutional links or societal links. These social andcommunity links, if institutionalised through insurance programmes, may serve as social
capital and can effectively play an important role in promoting social welfare (Dror andPreker 2002, p. 48). Social connections as well as trust and local community control overthese prorammes overcome the informational disadvantages and high transaction costs
involved in providing insurance to low income population. This in turn improves willingness
to pay even by the poorest of the poor. Further, communities are set within highly different
economic, cultural, demographic, political and epidemiological contexts. One majoradvantage of micro- insurance is that their technical designs are heavily influenced by thesefactors. This offers considerable flexibility to the parties negotiating a contract, reducing
3

Mladovsky and Mossialos (2008) however use the social capital framework to argue how the same
factors may affect the growth of CBHI adversely.
4transaction costs and scaling up participation (Churchill 2006). Thus, social bonding,solidarity, trust, intra- and extra-community networks, vertical civil society links and statesociety relations at the local level are important factors in explaining the success of the CBHIin reaching out to the poorest. Theoretically, therefore, CHI schemes may be an effective
mechanism for ensuring equitable distribution of health protection among the rural populationin the resource-poor settings where governments have limited financial and institutional
capacity.

The above arguments notwithstanding, evidence indicates that there have been wide
variations in the performance of these schemes. Some CBHIs have played a useful risk
protection role and complemented public health care efforts (see, Tabor 2006 among others)by extending social protection to the most vulnerable sections that would otherwise have nofinancial protection against the cost of illness. But, many others have failed. In their review ofliterature on equity, Preker et al (2002) and Jakab and Krishnan (2004) find that while CBHI
is effective in reaching the low income group people, the poorest and socially excluded
groups are not automatically included. This exclusion effect is observed by Ekman (2004)
also. The most important reason turns out to be the poor ability to pay followed by the
distance from the providers. Using cluster randomised controlled trial over 2003-05, Ranson
et al (2007) evaluated the impact of after-sales-service with supportive supervision andprospective reimbursement on the uptake of benefits of a community based health insurance
scheme (SEWA) in India by its poorest members. However, time and information
dissemination measures were not found to be sufficient to ensure the poorer members agreater share of the scheme benefits. In contrast, Onwujekwe et al. (2009) and Polonsky et al(2009) found strong evidence of equity in CBHI schemes in Nigeria and Armenia,
respectively. While analysing equity dimensions of two CBHI programmes in Nigeria,

5Onwujekwe et al. (2009) suggested that there was no inequity in enrolment, willingness torenew registration and utilisation of services. The analysis was based on the data collectedusing a questionnaire that was administered to 971 respondents in two communities, selectedby simple random sampling. Polonsky (2009) evaluates equity of Oxfam's CBHI schemes inrural Armenia. Members of a random sample of 506 households in villages operating
insurance schemes in rural Armenia were interviewed using a structured questionnaire.
Logistic and Poisson regression analyses were performed to identify the determinants ofhealth facility utilisation across socio-economic strata. Its findings demonstrate that the
distributionof benefits

amongmembersof

thiscommunity-financing

schemeis equitable. Apparently, the equity effects of CBHI scheme are expected to vary acrosscountries, and CBHI types and designs; and are subject to empirical analysis. In a qualitative
investigation of demand for health insurance in rural West Africa, De Allegri et al (2006)
suggest that the success of a CBHI programme largely depends on its technical design. The
present study thus focuses on the “Yeshasvini” health care programme in India and analyses
its equity dimensions.

3. Core Characteristics of Yeshasvini and Their Implications for Equity

Organisational Characteristics

The scheme was introduced in June 2003 in “Karnataka”, an Indian state by the Departmentof Cooperation (DOC) to provide insurance cover to members of cooperative societies inrural Karnataka (Kuruvilla 2005, Radermacher et al 2005, and ILO 2006). While theprogramme is being run under the auspices of a government department, it is governed by anindependent charitable trust: “Yeshasvini Cooperative Farmers’ Health Care Trust”. The

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Trust consists of 12 members and is chaired by the Principal Secretary of the Department for
Cooperation. Other members include employees of the Karnataka Department of
Cooperation; health professionals representing network hospitals; and the Director of the
Health Department4. The Trust has hired “Family Health Plan Limited” (FHPL), a TPA5
registered with “Insurance Regulatory and Development Authority” to administer thescheme. The vast administrative infrastructure of the DOC has facilitated the collection ofrevenue at no additional cost. Its involvement ensures government backing of the scheme andproves to be an important trust building factor for the programme. Further, access to thecooperative network assists the managers of the programme in mobilising membership andimplementing the scheme at the grassroots level with the help of cooperative societies.
Cooperative societies organise farmers and other informal sector workers in rural areas in an
institutional framework and act as a communication channel between the government and therural population. There were a total number of 32900 Co-operative societies in Karnataka in2007. Of them, 28099 co-operatives were actively working. These societies not only mobilise
membership but also extend help to members in seeking treatment from network hospitals.

The designated health care providers in the scheme are mainly private hospitals, althoughcharitable, public sector and cooperative sector hospitals are also participating in the scheme.
Some are super specialty hospitals. In 2008-09, the number of network hospitals was 349.
These were spread across 27 districts. The Trust has fixed a price for each surgery, including
nearly all connected costs. An important feature of the programme is that the price forsurgery paid to a network hospital is significantly below the normal market charges and awell specified list of exclusions is provided to all the hospitals.
45Included as the 12th trustee in November 2005.

Third Party Administrators (TPAs) are for-profit-companies that assume most of the administrationof an insurance scheme in exchange for a commission. The TPA’s main duty is to manage the contact
between the client, the health care provider and the insurance scheme.
7Thus, the Trust is benefited by the government administrative machinery, provisions ofprivate health care services, and values and institutional infrastructure of the cooperative
societies.

Technical design

Low contribution: The premium was fixed at Rs 120 per year per person. Recently, it has
been raised to Rs 130. A rebate of 15 % is offered on contribution for families of five or moremembers.

Long enrolment schedules: Enrolment schedules are extended to 5 months namely, January to
May. These months are also the harvesting time of cash crops such as cotton and sugarcane
which make it easier for farmers to make the payment.

Flexible mode of payment: The mode of payment is not fixed either. It is decided by the local
cooperative societies depending on the local conditions. Some societies accept monthly
payments during the enrolment period while others demand lump sum payment. Recognisingthat the subscription may be financial obstacle to membership, some societies accept
bimonthly payment also. Credit cooperative societies generally deduct the subscription
amount with the consent of the member while lending money.

Attractive sevice package: The benefit package is well defined and well designed focusing onhigh cost surgery events that could be catastrophic for the poor households. Free OPD
consultations and diagnostic lab tests at concessional rates are optional. The maximum8

coverage per person per year amounts to Rs. 200,000 with free OPD. The benefits arereviewed from time to time and appropriate changes are introduced in the package dependingon the demand. For instance, recently, normal deliveries and emergencies such as snake bite,bull gore and dog bites are also included in the package, keeping in view the growing demand
for such coverage.

Cashless transactions: An important feature of this scheme is that the procedures areminimised and transactions are cashless. Paients are not involved in any administrative
process.

Enrolment process for network hospitals: A hospital willing to join the Network can apply if
it has 25 or more beds. It is given a self assessment form to assess itself. Each medical facility
provided by the hospital is assigned specific marks in this form. A minimum of 88 marks is tobe scored to be part of the network. Hospitals claims are verified through inspections by theTrust before making the final decision of its enrolment.

Monitoring of network hospitals: The FHPL undertakes regular inspections of networkhospitals to monitor the quality of their services, and to ensure that the latter remain adhered
to their commitments made to the Trust. If they are found defaulting, strict actions are taken
against them including permanent ban on their yeshasvini membership.

Financial sustainability: At the current level of premium, financial sustainability is notachievable even with a vast membership base. This is because it covers high end medical
treatment. In order to augment its resources, the Trust welcomes donations from private andgovernment bodies. It has also been decided to collect 3 percent of the profits from the9profitable societies as donations. Furthermore, the management has created a contingency
fund which is invested in interest yielding assets to earn interest income. Finally, the scheme
receives government subsidy. A part of this is added to the contingency fund. The capacity ofmanagers to manage funds has thus ensured the financial sustainability of the scheme.

Achievements
Large membership base: Yeshasvini Health insurance programme has effectively created alarge membership base. In the first year of the scheme itself 1.6 million cooperative members
enrolled with the scheme. In 2008-09, enrolment increased to 3.0 million which marked 29.3
percent increase over the increased base of the last year.
Broad based use of services: There has been a rapid increase in the number of surgery cases.
In absolute terms, in 2007-08, a total number of 60,668 surgeries took place. In 2009, during
April-October alone 23,883 sureries had been perromed under the programme. A wide rangeof surgical processes are being offered by the scheme. Heart, gynae, general, Uro, Optho,
ENT and Ortho surgeries accounted for 85 percent of the total surgeries performed over theperiod of three years, 2003-04 to 2006-07. Heart surgeries accounted for almost 20 percent of
total surgeries followed by general and OBG surgeries.
Spatial dispersion of service providers: There has been a considerable change in the
distribution of beneficiaries by location within a short span of time (Table 1). In 2004-05,
five of the 27 districts: Mandya, Kolar, Hassan, Bangalore and Davangere accounted foralmost half the beneficiaries. The level of concentration declined sharply within the next two
years. The HH index of concentration of claimants was 670 in 2004-05, it fell to 323.3 in2006-07.

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INSERT TABLE 1 HERE
In 2004-05, 148 hospitals registered 17,407 surgeries under Yeshasvini. Of this, 1700 (9.81
percent) were performed in Narayana Hrudayala hospital alone. In 2006-07, 290 hospitals
registered Yeshasvini patients and the HH ratio declined from 254 (in 2004-05) to 230.
Spatial distribution of hospitals performing surgeries under the programme also showed aremarkable change. In 2006-07, hospitals across 79 cities participated in the process and the
HH index declined steeply.
In nutshell, the programme has developed mechanisms to tap the government for its vastadministrative machinery to mobilise members and monitor the scheme without creating
additional administrative infrastructure; the private sector for quality health care services; andthe cooperative sector networks for the societal capital generated over the years throughbusiness linkages between these societies and their members. The synergy developed atvarious levels is directed to finance high cost medical treatment of the poor who otherwise
have no health security provided by the State. It is designed and managed by professionals
who have developed well specified procedures for enrolment, networking of hospitals,
treatment and claim settlement. There are mechanisms to monitor the operations of various
actors and punish the defaulters. As a result, Yeshasvini Health insurance programme has
effectively created a large membership base and has successfully been providing services
across the state. The fixed rates of surgery, tight monitoring and the possibility of punitive
actions discourage over-utilisation of health care services, both on the demand and supply
side. The institutional, organisational and technical features of the programme, and its
success in creating a wide membership and effective service provisions are expected toensure that the scheme is equitable.

114. Determinants of enrolement, renewals and claims: Theoretical Model

People purchase health insurance if the utility of the expected benefits of coverage, in the form of
expected covered expenditures plus the value of protection from financial risk, exceed thepremium. Algebraically, let UN be the utility from not obtaining insurance, such that

UN = UN (YN, M; Tw) + eN

where, YN is income in the uninsured state, M are out-of-pocket medical expenses whenuninsured (assumed to be zero when insured), Tw is a shift variable capturing the addition toutility from non-participation for individuals with weak preferences for coverage, and eN is astochastic error term. Similarly, let Ui be the utility from insurance

UI= UI(YI,P; Ts) + eI

It depends upon income in the insured state, premium P, a shift factor, Ts, accounting for thegain in utility from enrolling in health insurance, and error term eI . Out-of-pocket medical
expenses are assumed to be zero in the insured state.

An individual will enroll if UI(YI,P; Ts) - UN (YN, M; Tw) > eN – eI. The difference is a linear
function of Y, P, M and the difference in tastes for coverage, Ts, and TW. Thus

Enrolment = f ( Y,P,M, Pf)

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Where Pf = Ts – TW and denotes individual preference. Assuming (eN – eI) to be logistically
distributed, coefficient estimates will be derived from logit estimates of the likelihood ofenrollment.

Theories of optimal risk taking behavior suggest that households which are more likely toface vulnerability exhibit a higher coefficient of risk aversion (Lin 2009) and hence greater
preference for insurance. In the above model, Y represents economic vulnerabilities (income,wealth, income sources) while M is a function of health related vulnerabilities (age andfamily health background, sanitaition conditoions, natural calamities). Individual preference
are determined by socio-cultural and location specific factors. Socio-cultural factors cover,social vulnerabilities (such as social exclusion, lack of education, poor access to information,
gender bias); and family size. Location specific factors on the other hand cover locational
vulnerabilities (health and transport infrastructure); presence of cooperative societies; and thequality of governance. Thus, our model is

Enrolement = f (E (.), H (.),G (.), S (.), L(.), Z(.))….......(1)where E, H, G, S, and L represent economic, health, gender specific and locational
vulnerabilities, respectively; and Z represents other variables represnting individual
preferences. The same set of factors is also likely to influence renewals and the utilisation ofhealth care services. Besley (1989) argues that just as demand for health services is derived
from demand for health, demand for health insurance is derived from demand for healthservices. Thus the same set of factors is expected to affect the utilisation of health care andhealth insurance.

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Commercial health insurance cannot result in socially optimal distribution of insurance and isfound to be negatively related with vulnerabilities. Our major hypothesis here is that micro
insurance removes obstacles to obtaining health insurance coverage for the vulnerable
population and hence is positively related with vulnerabilities. The most basic vulnerability is
the existence of health vulnerability i.e. ill-health, disease, and disability. Commercial
insurance imposes restrictions on insurance policies in terms of waiting periods, pre-existing
condition exclusions, and cost sharing requirements that impede consumers in poor healthfrom obtaining insurance. CBHI programmes however do not pose any such conditions. A
second vulnerability arises from economic disadvantage. People with low incomes areespecially price-sensitive in their purchase of health coverage. Small premiums in CBHI
programmes however make insurance affordable for the poor. A third type of vulnerability
arises from race or ethnicity. In part, the disadvantages associated with race or ethnicity are
due to poverty and low income. Yet, potential discrimination based on race/ethnicity can alsopose complex barriers to insurance coverage. Socially vulnerable groups such as women, andpeople with low education and poor access to information may also face important barriers toeffective and informed decision-making. CBHI in particular Yeshasini operates at thegrassroot level and involves cashless transctions to remove such obstacles. Finally, locational
vulnerabilities such as poor health and transport infrastructure make people vulnerable tocatastrophic shocks. A sparse supply causes inequity in access to services but at the sametime insufficient supply of health services may well be an inefficient reaction to insufficientdemand. Weak demand may not be linked with slight ability to pay but due to the prevalenceof barter transactions in a predominantly monetised economy. Microinsurance has thepotential of breaking this vicious circle by generating demand for health care, which, in turn,
could improve supply (Dror and Jacquier, 1999). As seen above, Yeshasvini has large
membership base with vast network of health service providers spread across the state.

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Finally, the programme’s success in building the atmosphere of trust and transparency is
expected to have contributed to the willing to pay by the poor. Thus, we hypothesise that alarge community based insurance programme offers a viable insurance option for thevunerable population.

5. Model Estimation and database

In an attempt to estimate Model (1), we express both dependent and independent variables interms of quantifiable variables. These are as follows.

Dependent:

Enrolment = 1, if a atleast one memberof the household is enrolled with the programme= 0, otherwise;
Renewal = 1 if at leastone member of the household has been member of the programme forthe pat three years= 0 otherwise;
Claimant/Beneficiary= 1 if at least one member of the household has claimed benefit in thepast three years.
=0.

Independent:

Economic vulnerabilities: Economic vulnerabilities are captured by the following income
related variables:15

Annual income (annual_inc) : Annual household income

Concen_inc : HH index of concentration of income sources

Household wealth indices (hh_wealth) : Wealth indices based on ownership of assets weregenerated using principal components analysis.

Health vulnerabilities: The likelihood of a health shock may vary systematically with observablecharacteristics, such as age, prevalence of chronic health problems, and ecological and sanitation
conditions in the surroundings in which households live. Age is captured by two variables:
Headage: Age of the head of the family
Age_dividend: proportion of household members in the working age group.

The prevalence of chronic health problem is represented by a dummy,
chron_health: =1 if at least one member is suffering from chronic health problem.
=0, otherwise.
Ecological and sanitation conditions are proxied by,

V_wat_san: Index of living conditions (measured by principle component analysis) ,and

V_Naturalcdn: No. of beneficiaries of natural disaster as percentage of total population

Gender vulnerability Sh_female: Share of female members in the household

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Social vulnerability:

These are captured by the following variables

SC_group: =1 if belongs to SC/ST=0, otherwise
Aveduyears: average education years of the household
Access_TV: regularity of watching TV on likert scale of 1 to 4
Access_Paper : regularity of reading newspaper on likert scale of 1 to 4.
Membershg: =1 if member of Self help groups (SHGs)=0 otherwise

Locational vulnerabilities: Village specific, and district specific health attributes along withhousehold specific characteristics are used in the study to capture locational vulnerability.
They include health infrastructure, distance from the nearest health facility, and distance fromthe nearest yeshasvini facility, prevalence of natural calamities, and water and sanitation
conditions.
V-hlthinfra : Index of health facilities created using the principal component analysis (PCA)
V-hlthdistance: Distance from the nearest health facility
Y-dist : distance of the nearest Yeshasvini facility
D_ healthinfra : Index of the quality of district level health infrastructure
D-tpt: PCA based index of district level transport facilities

Control variables:
Size: Household size
D_R_f_rat : rural female literacy rates
D_f_m_gp: female membership gram panchayats as ratio of total members

17V_copop : cooperative societies per capita
D_pcy : per capita income at the district level

In general, the model is specified as:
Z=βX+U
where Ui is a stochastic error term. The models estimated are of discrete choice type, which
explain the probability that a person chooses to enroll, renew membership and utilize theprogramme benefits in alternative specifications, in terms of the variables representing
household specific characteristics, and the socio-economic-health environment in which the
population lives. Logit regression methods were employed for estimation of the specified
functions. The binary logistic regression model is stated in terms of the probability that Y=l given X.:

The probability that the household is member/claimant is given by:

Probi = Pr [P0i = 1\Xi] =Pr [Ui<β1Xi] = θ [1-β1Xi
Where X = a vector containing all the independent variables.

Secondary and primary data sources were used to create a database for the study. The
database contained three levels of hierarchy: district level; village level and the household
level.

Household data: Primary data were collected through fully structured household
questionnaires in 82 villages drawn from a sample of 16 districts in Karnataka. A total of4109 households were interviewed during the months of December 2007 to May 2008. A
multi-stage stratified design was adopted for the survey for deriving the coverage.
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Village level data: The village level information was collected through primary andsecondary sources. The ‘Department of Rural Development and Panchayat Raj’ maintains
vast information system on the basic amenities available in the villages of Karnataka. We
compiled this information for 82 sample villages. This was supplemented with the primarydata collected from 82 sample villages.

District level information: At the district level, information was collected from various
government departments. These included, the Directorate of Economics and Statistics, the‘Planning and Statistics Department’ and the office of the Registrar of Cooperative Societiesof the Government of Karnataka.

6. Empirical findings

Our household sample comprised of two broad groups of households: cooperative households(CH); and non cooperative households (NCH). A pertinent question was whether the
representative sample should comprise of only the eligible (cooperative) households or thefull sample. The argument in favour of the former is that the programme covers only
cooperative households and therefore the impact of the factors affecting membership can be
more meaningfully captured by the regression based on the censored sample. However, theparticipation in cooperative societies itself is voluntary, the possibility of unobservable self
selection bias could not therefore be ruled out. Any regression based only on the sample ofcooperative members will not capture the systematic difference between the two groups. If
the regression is based only on the cooperative households then it is necessary to adjust it forthe selection bias. Since we do not have any idea of the bias involved, we used both, the19censored and the full sample in alternative specifications. The logit model estimations show
a relatively good fit of the model, expressed by Chi-squared statistics in both thespecifications (Table 2). The results are also found to be consistent and stable. In what
follows, we discuss the results.
Gender equity: Our logistic regressions reveal that the probability of enrolling into the CBHI
scheme was gender neutral. The variable Sh_Female turns insignificant in all the
specifications of enrollment (including renewals). This indicates that the programme offers
equal access to health care services to all irrespective of the gender. Interestingly however,the variable turns positive and significant in the estimation of beneficiaries’ function
indicating that the women are likely to be the major beneficiaries of the programme. The
programme has gender positive effects in terms of the distribution of benefits.
Evidence suggests that in developing countries females are more susceptible to illness thanmen. Nutritional status of women and girls is compromised by unequal access to food, by
heavy work demands, and by special nutritional needs (such as for iron). Furthermore, they
are often trapped in a cycle of ill health exacerbated by childbearing, exposure to heavy
smoke from kitchen fires, and hard physical labor, especially in agricultural areas. Despite
their serious health problems they do not get adequate levels of preventive care. The
consequences of women's unfavorable status in inherently inequitable social system prevalent
in most developing countries include discrimination in the allocation of household resources,
such as food, health care and education. Women's health and nutritional status is thus
inextricably bound up with social, cultural, and economic factors that influence all aspects of
their lives. Therefore, women comprise the key stakeholders in health policy debates and are
the most vulnerable section of the society. The focus of any social health insurance scheme
should therefore be to ensure that the scheme provided is just as attractive to women as it is to20the men and that it is effective in attracting their participation and providing health access.
The Yeshasvini health care programme is an opportunity for the rural women to accessquality health care services at low contribution.
Social equity: While the programme is equitable in terms of female participation, it isinequitable among different social groups. After controlling for the socio economic
characteristics of households, H_SC turned significant with a negative sign in all the
specifications of enrolment, renewal of enrolment and utilisation after controlling for otherhousehold and location specific characteristics. This implies that there is a problem of social
exclusion in enrollment and utilisation. The programme enrollment is biased in favour of theempowered classes of the society. Since independence, there has been political and social
mobilisation of SCs in India. However, this mobilisation has failed to translate into positivechange in the social sector for this excluded population. This is reflected in the enrollment
process of the programme.
Education, access to information (access_tv, access-paper) and membership of self help
groups, are found to be empowering factors that increase the likelihood of joining the
programme and renewing membership. Thus the population groups that suffer from social
vulnerabilities in terms of lack of education, access to information and community groups
have lower probability of joining the programme. The programme is socially inequitable inthat sense.

Utilisation of the programme however is influenced only by the levels of education; othervariables are not significant. Thus, social inequities are less skewed in terms of the
distribution of benefits.

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Economic equity: Enrollment into the programme and renewal of enrolment, both are foundto be inequitable in economic terms as well. The variable hholdasset is significant with apositive sign in all the specification of the logit model indicating that enrolment remains
disproportionately in favour of the wealthier classes. Income turns insignificant though.
Interestingly, both income and wealth related factors turned insignificant in the beneficiaries’equation. This means that there are no significant differences in the income levels of
yeshasvini beneficiaries and non beneficiaries. Apparently, while a well off household ismore likely to become a member, a beneficiary is not necessarily economically better off than
his non Y-member counterpart seeking surgery. Most studies while analysing financial
protection aspect of CBHI focus on membership and its relationship with income, and on
finding a positive relationship between income and the probability of becoming a member
conclude that the scheme does not reach the vulnerable. Our analysis also finds a positive
relationship between income and the probability of enrolment. However, it reveals at thesame time that benefits, which represent an outcome of membership, do not necessarily go to
wealthier households.

Health equity: chron_health also turns significant in all the equations. This implies that
poorer the health status of household members, the greater is the likelihood of the householdto join the programme. Technically, this is termed as ‘adverse selection’. But in practice, theobjective of the scheme is to provide equitable access to health care on the basis of need; if
people with the greatest needs are joining the scheme and increasing their use of services thatis consistent with this objective of equity. However, this raises concerns about the futuresustainability of the programme. Health service utilisation increases with age, a commonly
reported finding. As age increases, there is increasing likelihood of experiencing an episode22

of ill-health, and this greater health need accounts for the increased levels of utilisation
among individuals aged over 60.

INSERT TABLE 2 HERE
Locational equity: Location- specific vulnerabilities are expected to increase risk aversion.
Insurance therefore can promote locational equity. Our analysis shows that both, themembership and the renewal of membership, are positively related with locational
vulnerabilities as represented by D-hlthinfra and V_naturalcdn. Thus, the households locatedin districts with poor government health infrastructure are more likely to join the programme.
Location specific natural disadvantages also affect individual preferences to enroll with theprogramme. This implies that the programme is doing well in reaching the areas where
government health facilities are poor and the probability of natural calamity is higher. The
programme is thus covering the most vulnerable groups of the rural population and to that
extent, it is equitable. However, enrolment into the programme ( current and renewals) isfound to be negatively related with poverty, poor living conditions, and distances from thegovernment health care facilities and is positively related with transport facilities. By
disproportionately favouring the people in better off villages and districts it exacerbates
inequities. Thus the net effects of the enrolment into the programme on locational
vulnerabilities remain uncertain. When viewed from the perspective of beneficiaries however,the programme is neutral with regard to locational vulnerabilities. It does not
disproportionately favour the people in well off districts. Clearly the programme is more
equitable in terms of the distribution of actual benefits.

7. Conclusion
This study analyses whether micro health insurance programmes can serve as an effective
instrument in providing health security to the most vulnerable sections of the society. Morespecifically it examines how equitable is enrolment, renewal, and utilisation of communitybased health insurance? While doing so, it focuses on one of the largest and the mostinnovative CBHI programmes in India: Yeshasvini health care programme. Data wascollected using a questionnaire that was administered to 4109 respondents in rural Karnataka
selected by stratified random sampling. Our results based on logit models reveal that while23

enrolment and renewal of enrolment are inequitable, there is little inequity in utilisation ofservices.
Yeshasvini is one of the most innovative and successful CBHI programmes in India. It has
the potential to increase access of the most vulnerable groups of the population to health careand provide them financial protection (Aggarwal 2010). But nevertheless, it could not fully
resolve the issue of equality. In general, one can suggest that efforts need to be made toincrease the number of enrolees, so as to increase the pool of funds and risks. Information
dissemination is an important aspect of the scheme. A well designed marketing package may
be employed to promote enrolment and renewal of enrolment. Besides, strategies should be
adopted to increase levels of trust by improving


quality of care (through strategic purchasing);transparency and accountability among those managing the scheme;

recourse to justice to punish fraud; andcommunity participation in the programme management.

More importantly however, it indicates that CBHI programmes are useful in themselves, but
no substitute for hospitals and hospital networks that can respond to the needs of the mostvulnerable sections of the population. The National Rural Health Mission (NRHM) was
launched in 2005 by the Government in India to improve the availability of and access toquality healthcare by people, especially for the vulnerable population. An early impact
evaluation of the programme (Gill,2009) however indicates that there are many problems in
its implementation, so that delivery is far from what is ought to be. These problems need to
be overcome with the necessary political will and commitment at all levels.

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Table 1: Patterns of claimants by district and hospital (2004-07)
District

share of top
top
three

District
2004-05 14.87
2005-06 13.13
2006-07 11.54
Hospital city
2004-05 27.12
2005-06 24.76
2006-07 23.87
Hosp Name
2004-05 9.81
2005-06 12.59
2006-07 10.74

top 5

HH
index

No.

33.38
29.76
25.43

47.92
42.53
37.35

670.5

27
27
27

50.80
40.43
37.34

69.70
53.98
48.07

1417.7

17.94
22.13
19.48

24.78
28.30
26.03

254.1

323.2

829.5

230.3

60
60
79
148
160
290

Source: Based on FHPL data

Table 2: Logit estimations of the probability of enrolling, renewing and utilizing the
programme
Sample of Cooperative households
Variable
Chron_healthAge-dividend

Enrolment

Renewal of Utilisation
membership
0.205351a
0.28371 a
0.240782(2.69)(3.5)(1.26)
0.002079(-1.08)-0.00029(-0.12)

Full sample
Enrolment
0.213358 a(3.1)

Renewal ofmembership
0.290039 a(3.68)

0.001(0.57)

0.000356(0.16)

0.035462 a(2.92)

headageUtilisation
0.286547(1.55)

0.036535 a(3.27)

sh_female-0.14818(-0.57)0.203141(0.69)

1.398251 c(1.74)-0.28732(-1.27)

0.093576(0.34)

1.277904 c(1.71)

sc_grp-0.7004 a(-6.11)-0.61464 a(-3.99)-1.13899 a(-2.53)-0.75175 a(-7.24)-0.69427 a(-4.66)-1.18738 a(-2.68)0.141982 a(-3.72)0.037442 b(2.28)

0.035622(0.88)

0.0823 a(6.11)

0.049656 a(2.99)

0.068074 c(1.7)

aveduyears

27access_paper-0.08404 b(-2.26)-0.09165 b(-2.02)-0.10225 a(-3.08)-0.10597 b(-2.41)access_tv-0.0767 c(-1.76)-0.06488(-1.18)-0.11038 a(-2.81)-0.09833 c(-1.84)membershg

0.157691 b(2.39)

0.065381(0.88)-0.16761(-0.95)0.171098 a
2.89

0.087044(1.22)-0.15119(-0.84)

Annual_inc

0.092432
1.23

0.018317
0.21-0.01091-0.06

0.18792.7

0.152372 c
1.82

0.046913
0.24

hh_wealth0.23584 a(3.14)

0.263417 a(3.32)

0.336971 a(5.00)

0.32368 a(4.29)

Concen_income-2.70E-05(-1.17)-1.70E-05(-0.63)-3.50E-05 c(-1.71)-2.60E-05(-1.00)V_wat_san-5.66E-06(-0.49)2.45E-05 c(1.78)-0.26624(-0.99)-6.83E-06(-0.67)

2.12E-05 c(1.67)-0.32946(-1.29)V_hlthdistance-0.03196(-0.55)-0.12809 c(-1.87)-0.25207(-1.39)-0.15594 a(-3.08)-0.2102 a(-3.26)-0.3198 c(-1.82)

V_hlthinfra
V_naturalcdn-0.08953(-0.77)0.041919(0.97)

0.082117 c(1.88)

Yesh_dis

0.06901 c(1.77)

0.103093 c(2.5)-0.00782 c(-1.87)

D_health_infra-0.05231 c(-1.71)-0.06052 c(-1.67)

D_tpt

0.152201 a(3.11)

0.134603 a(2.55)

hsize

0.024448(-1.35)0.037321 c(1.88)

D_pcy

4.78E-05 a(3.66)

D_f_mem_gp

0.091721(0.6)

4.65E-05(0.96)-0.00699 c(-1.74)-0.08136 a(-2.84)-0.08004 b(-2.23)0.020483(0.49)

0.067954(1.35)

0.02011.15

0.036624 c
1.82-5.84E-06(-0.67)

4.25E-05 a(3.61)

0.182434 c(1.83)

D_panchay_-0.25307(-0.37)-0.26049(-0.33)

V_copop5.778508(0.28)

102.317 a(4.49)_cons-0.69997(-0.92)229.562756

LR statistics
NOB-0.10007(-0.89)0.123493(0.86)

5.11E-05(1.13)
0.203465 b(2.14)

1.049763 c(1.68)

0.541192(0.7)

116.0558 c(1.69)

30.74835 c(1.67)

114.3783 a(5.21)

167.355 a(2.62)-2.28857 a(-2.6)-4.0531 c(-1.89)-2.69936 a(-3.98)-3.51732 a(-4.17)-5.28689 b(-2.47)

177.78
2756

36.14
395

444.163772

296.01
3772

49.57
483

28