protecting the poor? the distributional impact of a bundled insurance scheme

World Development Vol. 35, No. 8, pp. 1404–1421, 2007� 2007 Elsevier Ltd. All rights reserved

0305-750X/$ - see front matter

www.elsevier.com/locate/worlddevdoi:10.1016/j.worlddev.2007.04.007

Protecting the Poor? The Distributional Impact

of a Bundled Insurance Scheme

TARA SINHAVimo SEWA, Ahmedabad, India

M. KENT RANSON and ANNE J. MILLS *

London School of Hygiene and Tropical Medicine (LSHTM), UK

Summary. — This study examines the distributional impact of a community-based insurancescheme which covers death, hospitalization, and asset loss benefits in a bundled package. It looksat the distribution of insurance benefits between urban and rural members and between the poorestand least-poor members. Urban members benefited much more from the scheme than rural mem-bers. While the scheme provided considerably higher benefit to the poor within urban areas, therural poor benefited far less than the better-off rural members. The results have implications forscheme administration and policy; changes need to be made at both levels to provide a more bal-anced distribution of scheme benefits.
� 2007 Elsevier Ltd. All rights reserved.
Key words — South Asia, India, insurance, benefit distribution, access, community-based insurance

* This research was carried out through collaboration

between Vimo SEWA and the Health Economics and

Financing Program at the London School of Hygiene

and Tropical Medicine. Financial support was provided

by the Wellcome Trust (United Kingdom) and the Ford

Foundation. The authors wish to thank members and

staff of the Self-Employed Women’s Association and

Vimo SEWA for their encouragement and support, and

Mirai Chatterjee in particular. We appreciate deeply the

incisive comments provided by Saul Morris. The study

would not have been possible without: research assis-

tance from Ami Bhavsar, Fenil Gandhi, Rupal Jayaswal,

Charumati Acharya, Kapila Chauhan, Dharmishtha

Kosthi, Bhagwati Parmar, Vanita Rathod, Shama She-

ikh, Dipti Vaghela, and Hetal Vyas; data entry assis-

tance from Bela Dubal and Smita Panchal; and

transportation from Jayanti Prajapati and Amrut Zala.

The opinions expressed in the paper are those of the

authors and do not necessarily represent the positions of

their respective institutions. We would also like to thank

the anonymous reviewers for providing valuable inputs

for strengthening the paper.

1. INTRODUCTION

Community-based insurance (CBI) is increas-ingly considered as a means of providing finan-cial protection to the poor (Preker & Carrin,2004). This raises the clear need to increaseour understanding of the distributional impact,namely, whether poor members benefit fromthe scheme as much as richer members, andwhether rural and urban members are alike.

The authors are aware of two previous stud-ies that examined the distributional impact of acommunity-based scheme and assess whetherpoorer members are able to benefit as muchas better-off scheme members (Dror, Koren,& Steinberg, 2006; Wang, Yip, Zhang, Wang,& Hsiao, 2005). The earlier study finds that richmembers benefit more than poor members,while Dror, Koren et al. find that that insuredhouseholds across all income groups use hospi-talization and consultations equitably, but thatpoorer members among the insured householdsreported a lower rate of hospital deliveries thanricher households.

This article adds to the study of the distribu-tional impact of CBI by analyzing the urban/rural and poorest/least-poor distribution of

140

benefits offered by Vimo SEWA’s bundledinsurance package.

4

PROTECTING THE POOR? 1405

Vimo SEWA (SEWA Insurance) is the insur-ance program launched in 1992 by SEWA,which provides bundled coverage for membersagainst several different risks. SEWA is a tradeunion of informal women workers, started byEla Bhatt in 1972. Headquartered in Ahmeda-bad (Gujarat, India), with members from 11of the state’s 25 districts, ‘‘it is an organizationof poor, self-employed women workers. . . whoearn a living through their own labour or smallbusinesses. . . (and who) do not obtain regularsalaried employment with welfare benefits likeworkers in the organized sector’’ (Self-Em-ployed Women’s Association, 1999). The orga-nization has two main goals: to organizewomen workers to achieve full employment,thereby enhancing work security, income secu-rity, food security, and social security; and tomake women individually and collectively self-reliant, economically independent, and capableof making their own decisions. In 2003, theyear of this study, the union’s membership inGujarat numbered 469,306.

(a) Risk protection through CBI

The strong link between poverty and the vul-nerability of poor households to unexpectedlosses is well recognized (Dercon, 2004; Gertler& Gruber, 2002; Holzmann & Jorgensen, 1999;Krishna, Kapila, Porwal, & Singh, 2003; WorldBank, 2001). In many low- and middle-incomecountries, the protection available to low-in-come households from the state and the marketis marginal (Dror & Jacquier, 1999). Informalmechanisms—such as gifts or loans providedby family, friends, or employers in times of cri-sis—offer most of the protection, which is nev-ertheless inadequate (Morduch, 1999; Roth,2002).

In this context, alternative risk-protectionsystems for the poor are being promoted. Theseschemes are characterized by their non-govern-mental, non-profit nature, where people, oftenpoor, voluntarily pool resources toward protec-tion against one or more types of risk (Bennett,2004). Two terms commonly used to describethese types of systems are community-basedinsurance (CBI) and micro-insurance. Thesesystems use the principles of insurance by pool-ing resources and sharing risks among a groupof people.

Available studies on the impact of CBIschemes in providing financial protection tothe poor are limited, and have mostly looked

at either the inclusion of the poor in suchschemes or the utilization of the scheme amongmembers and non-members. Findings on theinclusion of the poor are mixed. Older studies re-ported that the cost of joining deters the poorestfrom enrolling (Bennett, Creese, & Monasch,1998; Jakab & Krishnan, 2001). More recentstudies find that the poor are in fact includedin the membership of such schemes (Ransonet al., 2006; Schneider & Diop, 2004).

Utilization of community-based schemes hasbeen examined, almost exclusively, in the con-text of community-based health insurance(CBHI). There is some evidence that enrollmentin a CBHI scheme increases utilization of healthcare services among members versus non-mem-bers (Dror et al., 2005; Jutting, 2004). Butwithin the membership, CBHI schemes in vari-ous countries have been shown to have unequalpatterns of utilization, notably due to distancebetween the place of residence and health facil-ities: utilization of health care increases moreamong insured households located close to thehealth care facility (Bennett et al., 1998; Criel,Van der Stuyft, & Van Lerberghe, 1999). Thissuggests that people who live further away fromthe facility in effect cross-subsidize those wholive closer by, since the premium is the sameand does not take account of the transportationcost of access that is higher for people living fur-ther away from the healthcare facility.

This paper is organized as follows: In thenext section, we describe the bundled CBIscheme studied, which is run by the Self Em-ployed Women’s Association (SEWA) in Guja-rat. We then discuss the methodologicalapproach and data used to assess the distribu-tional impact of the scheme. The fourth sectionpresents the results of the analysis, followed bya discussion of the findings.

2. SEWA’S BUNDLED INSURANCESCHEME

The evolution of the Vimo SEWA scheme,and its impact—in terms of coverage, accessto health services, financial protection andcost-recovery, among other things—have beendescribed in several documents (Chatterjee &Ranson, 2003; Garand, 2005; International La-bour Office (STEP Unit), 2001; McCord, Isern,& Hashemi, 2001; Sinha & The Vimo SEWATeam, 2002). The essential elements are re-peated below.

1406 WORLD DEVELOPMENT

Vimo SEWA was started in 1992 as an exten-sion of micro-finance loan services to self-em-ployed women (Chatterjee & Vyas, 1997).During its first two years of operation (1992–94), Vimo SEWA provided life insurance only,in partnership with the nationalized Life Insur-ance Corporation of India (LIC). A survey of500 SEWA Bank 1 loan defaulters in 1977 re-vealed that of these 500 women, 20 had died(15 at delivery); most of the others reportedthat they or a family member were ill causingfinancial hardship (Chatterjee & Vyas, 1997;McCord et al., 2001).

In 1994, hospitalization and asset protectionwere added to the life insurance. This coveragewas provided with active support from a youngwoman employed by United India InsuranceCompany (UIIC), which had no previous expe-rience with below poverty line clients. Prior to1994 and for years, officials from SEWA andSEWA Bank had intermittently met with repre-sentatives of insurance companies to seek‘‘non-life cover’’—that is, hospitalization andassets insurance—but the insurers were unwill-ing to insure clients that the insurers felt wouldbe unable to afford the premium, and that were‘‘risky,’’ that is, perceived always to be in crisisof one sort or another. Finally, the bundledproduct was launched in 1994, in part becauseof the determination of the UIIC officer toinnovate by launching such insurance. Whilecertain aspects of the scheme have evolved—including the amount of the benefits and pre-mium, the family members eligible for cover-age, and the administration—the basicstructure of the bundled package has not chan-ged since 1994.

Membership in Vimo SEWA is voluntary.Women affiliated with SEWA are the payingmembers, and can extend insurance coverageto husbands and children as their dependents.Most members pay an annual premium, whileothers use the fixed-deposit option. Under thelatter option, members can authorize deductionof the annual premium from the interest ac-crued on a one-time fixed deposit in SEWABank. Vimo SEWA is run by a team of full-timestaff and grassroots level workers called aage-wans, who are the link between members andscheme administrators. However, the insurancerisk is shouldered by the insurance companies;Vimo SEWA acting as agent; it transfers thepremium to the insurers.

Vimo SEWA’s life insurance component cov-ers natural and accidental death, with the ben-

efit being Rs. 3,000 for the former and Rs.40,000 for the latter. 2 The hospitalizationinsurance component covers inpatient expensesonly, to a maximum of Rs. 2,000 per memberper year. Insured members enjoy free choiceof health care provider, be it a private-for-prof-it, private-non-profit, or a public facility. Theasset insurance covers damage to the member’shome up to a maximum of Rs. 5,000 per houseper year. For all types of coverages, claimantsare paid the benefit after they have submitteda claim to Vimo SEWA, including the requiredcertificates (e.g., death certificate or police cer-tificate detailing the loss of assets) and receipts(e.g., for drugs and medical supplies).

The insurance product is ‘‘bundled’’ insofar asmembers must buy all three types of coveragesas a single contract, and cannot buy only onecomponent. Vimo SEWA has bundled the insur-ance coverage for the risks that are among themost financially burdensome to its target popu-lation. Vimo SEWA, in turn, un-bundles the pre-mium and purchases insurance policies fromdifferent insurance companies. Under Indianinsurance law, a single company cannot sell bothlife and non-life insurance (e.g., hospitalizationand asset insurance). Therefore, Vimo SEWApasses the risks of each category of loss to a dif-ferent underwriter, and bears very limited (orno) insurance risk itself, while nevertheless suc-ceeding to ‘‘bundle’’ a package that is otherwiseunavailable on the market. This bundled prod-uct has met with some degree of success.

For example in calendar year 2003, VimoSEWA had more than 100,000 members (some85,000 paying members and 18,000 adult mendependents); most clients lived in Gujarat state,of which approximately two-thirds (67,500)lived in rural areas and one-third (33,000) inAhmedabad City. Previous research has foundthat the scheme is inclusive of the poorest, with32% of rural members, and 40% of urban mem-bers, drawn from below the 30th percentile ofthe socio-economic status (SES) of the generalpopulation 3 (Ranson et al., 2006).

In that year (2003), most adult women paidan annual premium of Rs. 85 for the bundleddeath-hospitalization-and-asset insurance.Approximately 63% of that premium waspassed on to a number of insurance companiesand the other 37% was retained by VimoSEWA to cover administrative costs. However,it is difficult to estimate the costs of administer-ing Vimo SEWA, in part because other SEWAdepartments perform administrative activities


(e.g., enrolling new members). Prior to 2000,administrative costs were relatively modest; astudy by the International Labour Office foundthat basic administration costs accounted for10.2–22.9% per year of Vimo SEWA expenses(International Labour Office (STEP Unit),2001). These administrative expenses were fullycovered by interest earned on a grant of 100million rupees given in 1993 by the GermanDevelopment Cooperation (GTZ). Since 2001,Vimo SEWA has been receiving technical andfinancial support from a consortium of donors(which includes Ford Foundation, the Interna-tional Labour Office, and the GTZ) with thegoal of rapidly expanding access to micro-insurance. This scaling up has resulted in alarge increase in administrative expenses. In2003, for example, administrative costs repre-sented 97% of total premiums collected, androughly 50% of total expenditures by VimoSEWA (Garand, 2005). The shortfall in admin-istrative expenses has been fully covered by do-nors from 2001 to 2006. It is expected that theadministrative costs (as a percentage of totalscheme expenditures) will decrease over thenext five years (Garand, 2005).

3. METHODOLOGICAL APPROACH ANDDATA SOURCES

(a) Reimbursement process

The process of eligibility for reimbursementfor a loss suffered is described in Figure 1.The scheme is essentially indemnity insurance,with some social checks at various milestonesof the reimbursement process. First, a memberhaving experienced loss must seek compensa-tion. Second, the hospitalization benefit is dueonly for admissions lasting longer than 24hours. Thirdly, the client must submit docu-mentary evidence of the loss suffered, which isreviewed by a grassroots worker (aagewan).Then, a claims committee decides to approveor reject claims; the committee also decides onthe reimbursable amount for hospitalizationand asset benefits, and decisions on the amountare subject to the maximum; for life insuranceclaims, the reimbursement amount is fixed.

(b) Methodological approach

This study compares equality of benefit pay-out to urban versus rural members and topoorer versus richer members; and in case of

inequality, what role the reimbursement pro-cess plays in that inequality. Due to the largedifferences in the social, economic, and infra-structural conditions between these two co-horts, urban versus rural place of residencewas selected as an explanatory variable forinequality of reimbursement in the context ofVimo SEWA. Within each of the rural and ur-ban membership, we examined the distributionof benefits across different socio-economicgroups, which captures both economic andnon-economic dimensions of inequality. Theprimary measure we used to assess distribu-tional impact was benefit per capita receivedby members, calculated by dividing total bene-fits by total members for rural versus urbanmembership, and for socio-economic groupswithin rural and urban areas. With a view torefining the understanding of the underlyingreasons for the distribution of benefits amongthe groups, we quantified differences in themember categories at the final three stages inthe reimbursement process, namely claim sub-mission, approval or rejection of claim, andadjudication of claim amount.

(c) Data

The data were collected in three cross-sec-tional household surveys of three different pop-ulations: (Survey I) the general populationfrom which Vimo SEWA draws its members;(Survey II) Vimo SEWA 2003 members; and(Survey III) those who submitted claims toVimo SEWA in calendar year 2003. The refer-ence year for all three surveys was 2003; thepopulation survey was carried out in 2003 andthe sample of members and claimants was ta-ken from Vimo SEWA’s 2003 membership. Atthe study’s inception, it was decided thatAhmedabad City and the rural areas servedby Vimo SEWA differ so significantly—forexample, in terms of types of housing and ame-nities, density of hospitals, and the nature ofservices provided by Vimo SEWA—thatthroughout the study, they would be dealt withseparately.

The questionnaire used in Survey I was thesame for rural and urban areas. It was basedlargely on a standardized survey tool developedby the Consultative Group to Assist the Poor-est (CGAP) and the International Food PolicyResearch Institute (IFPRI) to measure the pov-erty of micro-finance clients (Henry, Sharma,Lapenu, & Zeller, 2000). The instrument hadmodules on

Figure 1. Steps for receiving benefits under the insurance scheme.


—demographic characteristics of householdand members,—quality of housing,—household assets,—human capital,—food security and vulnerability,—household expenditures on clothing andfootwear, and—hospitalization.

For the survey of the general population (Survey I),sampling in both rural areas and Ahmedabad Citywas done using two-stage, random sampling. Atthe first stage in rural areas, 50 towns/villages wererandomly selected, with the sampling probabilityproportionate to the size (PPS) of the town/village.For villages with more than one Enumeration Block(EB) (blocks of roughly equal population that aredemarcated for conducting the national census), asingle EB was randomly selected per town/village.The only urban area taken was Ahmedabad City;50 EBs were randomly selected out of a total of10,385, using systematic random sampling.

In both rural and urban areas, 16 individual house-holds per EB were selected by ‘‘random walk’’ sam-pling. On the EB maps, each block of houses was

numbered and a ‘‘start point’’ was randomly se-lected. After the start point, every 2nd householdwas included in the sample, following structures inthe same order in which they were numbered onthe EB map. In this way 800 rural and 800 urbanhouseholds were randomly selected for the generalpopulation survey.

Shorter questionnaires were used for Surveys IIand III, one for urban and one for rural house-holds. These ‘‘rapid assessment’’ questionnairesincluded only a subset of the questions asked inSurvey I—the questions necessary to providedata for the indicators that most strongly distin-guished relative levels of SES, based on statisti-cal analysis (see Table 1).

In 2003, Vimo SEWA was actively working in11 districts, mostly rural, and in AhmedabadCity. Sample size estimation for Survey I wasbased on the premise that a SES score wouldbe developed for each surveyed household,and that the key statistic to be identified wasthe cut-off value of this score identifying thepoorest 30% of the population. 4

Surveys II and III, carried out with VimoSEWA members and claimants, respectively,

Table 1. Socio-economic indicators assessed in Surveys II and III

Domain Rural Urban

Humanresources

• The percentage of household adults whocan read and write (continuous)

• The percentage of household adults whose max. level of schoolingwas ‘‘attended college or university’’ (continuous)

• The percentage of household adults whose main occupation was reported‘‘unskilled work for daily wages’’ (continuous)

• The percentage of household adults whose max. level of schooling was‘‘attended college or university’’ (continuous)

• The percentage of household adults whose max. level of schooling was‘‘completed secondary’’ (continuous)

Dwelling • Number of rooms, excluding kitchen (continuous)• Whether the home’s walls are made of ‘‘brick or stone with

plaster’’ (dichotomous)• Whether the home’s walls are made of materials other than brick

or stone (dichotomous)• Whether the household has no electrical connection, shared

connection, or its own connection (categorical)

• Number of rooms, excluding kitchen (continuous)• Whether natural gas is the primary cooking fuel used (dichotomous)• Observed structural condition of the dwelling (categorical) [1 = seriously

dilapidated; 2 = needs major repair; 3 = needs minor repair; 4 = soundstructure]

Foodsecurity

• During the last year, when cooking oil stores were highest, whetherthere was sufficient stock to last one month (dichotomous)

• During the last year, when millet or millet flour stores were highest,whether there was sufficient stock to last 12 months (dichotomous)

• During the last year, when wheat or wheat flour stores were highest,whether there was sufficient stock to last one month (dichotomous)

• During the last year, when cooking oil stores were highest,the number of months for which the stores were sufficient (categorical)

Assets • Number of refrigerators (continuous)• Number of electric fans (continuous)• Number of mattresses (continuous)• Number of wrist watches (continuous)

• Number of refrigerators (continuous)• Number of wrist watches (continuous)• Number of televisions (continuous)• Number of video-cassette recorders (VCRs) or video

CD players (VCDs) (continuous)• Number of motorcycles or scooters (continuous)

PR

OT

EC

TIN

GT

HE

PO

OR

?1409

Table 2. Overview of Surveys I and II

Survey I: general population Survey II: Vimo SEWA members

Rural Urban Rural Urban

Questionnaire Same for all households: CGAP tool modified to local setting Rural rapid assessmentquestionnaire: 13 indicators

Urban rapid assessmentquestionnaire: 11 indicators

Samplinguniverse

Households in 11 ruraldistricts (2001

population = 19,298,638)

Households in AhmedabadCity (2001

population = 4,519,278)

36,837 adult women, 2003members who reside in 16

rural talukas

10,844 adult women, 2003members who reside in fourzones of Ahmedabad City

Sample size 800 800 1,200 300Sampling

methodologyTwo-stage random sampling Two-stage random sampling Two-stage random sampling Systematic random sampling

from listSampling at

first-stageTowns/villages sampled with

PPSaSystematic random sampling

of enumeration blocksVillages (or clusters of

villages) sampled with PPSNA

Sampling atsecond-stage

Random walk Random walk Systematic random samplingfrom list

NA

Fieldwork dates May 22–August 5, 2003 May 22–August 5, 2003 October 16–December 24,2003

January 2–February 12, 2004

Interviewers 10 Vimo SEWA interviewers 10 Vimo SEWA interviewers 10 Vimo SEWA interviewers 10 Vimo SEWA interviewersCriteria for

countinghousehold asabsent

No household memberpresent on the day of 1st

(and only) visit

No household membercontacted after three visits

No household membercontacted after two visits

No household membercontacted after three visits

Achievedsample size

780 (98%) 745 (94%) 967 (82%) 220 (75%)

Reasons for‘‘non-response’’

Refused int. (5%); allhousehold members absent

(95%)

Refused int. (51%);household moved/not found

(15%); all householdmembers absent (34%)

household moved/not found(86%): all household

members absent (14%)

Household moved/not found(88%): all household

members absent (12%)

a PPS—probability proportionate to size.

1410W

OR

LD

DE

VE

LO

PM

EN

T

Table 3. Overview of the claimant surveys (Survey III)

Life insurance claimants Hospitalization insurance claimants Asset insurance claimants

Rural Urban Rural Urban Rural Urban

Sampling

universe

143 life claims,

January–September,

2003

79 life claims, January–

September, 2003

653 hospitalization

claims, January–

September, 2003

468 hospitalization

claims, January–

September, 2003

291 assets claims,

January–September,

2003

1,280 assets claims,

January–September, 2003

Sample size 143 79 653 468 291 250

Sampling

methodology

NA (all claimants

interviewed)

NA (all claimants

interviewed)

NA (all claimants

interviewed)

NA (all claimants

interviewed)

NA (all claimants

interviewed)

Simple random sampling

from lists

Criteria for

counting

household

as absent

No household member

contacted after two

visits

No household member

contacted after three

visits

No household member

contacted after two

visits

No household member

contacted after three

visits

No household member

contacted after two

visits

No household member

contacted after three visits

Fieldwork dates March 1, 2004–June 10,

2004

March 1, 2004–May 25,

2004

December 16, 2003–

February 25, 2004

30 January–25

February, 2004

March 1, 2004–June

2004

March 1, 2004–May 25, 2004

Interviewers 20 district-based, Gram

Vikas interviewers

10 Vimo SEWA

interviewers

20 district-based, Gram

Vikas interviewers

10 Vimo SEWA

interviewers

20 district-based, Gram

Vikas interviewers

10 Vimo SEWA interviewers

Achieved

sample size

117 (85%) 66 (84%) 621 (95%) 427 (91%) 268 (92%) 222 (89%)

Reasons for

‘‘non-response’’Total non-response: 26

Refused int. (0)

Household moved/not

found/death (12)

Household members

absent (12)

Incomplete information

(2)

Total non-response: 13

Refused int. (0)

Household moved/not

found/death (10)

Household members

absent (3)


(0)


Household moved/not

found (24)

Household members

absent (6)


(2)


Household moved/not

found/death (34)

Household members

absent (6)


(1)


Household moved/not

found (16)

Household members

absent (4)


(3)

Total non-response:

28

Household moved/

not found (21)

Household members

absent (5)

Incomplete

information (2)

PR

OT

EC

TIN

GT

HE

PO

OR

?1411


were restricted to 16 rural sub-districts andfour urban zones. 5 The 16 rural sub-districtswere those with the highest number of adult,women Vimo SEWA members. Each of thefour urban zones selected for Surveys II andIII comprised two wards of Ahmedabad City.A ward is a municipal sub-division of the citymade for census purposes and for electing localgovernment representatives; each ward is fur-ther divided into EBs referred to above. Theseeight wards had the highest number of VimoSEWA members, each with more than 1,000adult women members. Approximately 64%of the rural 2003 Vimo SEWA members livedin the 16 selected sub-districts and 42% of ur-ban members lived in the four selected urbanzones.

The sampling universe for Survey II was the36,837 adult women members in the 16 ruralsub-districts and the 10,844 members in thefour urban zones. Both rural and urban surveyswere sufficiently large to estimate the propor-tion of members drawn from the poorest 30%of the general population to within 3% pointson either side of the true value. The rural sur-vey was larger for reasons related to a subse-quent intervention trial. Table 2 providessampling and survey information for SurveysI and II.

For Survey III, Table 3 provides samplingand survey information for the six discretesub-populations surveyed: life, hospitalization,and assets claimants, in both urban and ruralareas. 6 Survey III did not involve sampling(except for urban asset loss claimants), and in-cluded all claimants from the 16 rural sub-dis-tricts and four urban zones who sufferedlosses (or were discharged from hospital), be-tween January 1, 2003 and September 30,2003. This nine-month window was defined soas to obtain P900 hospitalization insuranceclaims (rural and urban areas combined) andto avoid any distortion in the claims data as aresult of a seasonal peak or trough. 7 Inthe case of urban asset loss claimants, we ran-domly selected 250 from a total of 1,280 claim-ants.

(d) Analyses

All survey data were double entered into cus-tomized EpiInfo databases. The socio-eco-nomic data from Survey I (generalpopulation) were analyzed using the widely ap-plied approach of Principal Components Anal-ysis (PCA) and Stata 7.0 (Stata Corporation,

College Station, TX) to produce an index whichcould act as a proxy for household SES.

PCA involves breaking down assets (e.g.,radio, wrist watch) or household service access(e.g., water, electricity) into categorical or inter-val variables. The variables are then processedin order to obtain weights and principal com-ponents. The results obtained from the firstprincipal component (explaining the most vari-ability) are used to develop an index based onthe formula (Filmer & Pritchett, 2001):

Aj ¼ f 1 � ða1j� �a1Þ=s1þ � � �þ fN � ðaNj� �aNÞ=sN ;

where j indicates households, f1 is the scoringfactor or weights for the first asset, a1 to aNare the various assets, �a indicates the meanvalue of an asset (over all households), and sthe standard deviation. Based on this equation,SES of households was assigned to the residentsof those households.

The variable weights derived from Survey Iwere then applied to members (respondents inSurvey II) and claimants (respondents in Sur-vey III). In order to assess the distribution ofclaims by SES of members, the member popu-lation was divided into deciles. The 1st–10thdeciles were assigned in the continuum of poor-est and least poor. The index scores that demar-cate deciles among members were then appliedthe claimants.

To summarize the distribution between thepoorest and least-poor members, we used therich:poor ratio, a commonly used indicatorfor measuring equality, and compared the ben-efits received by claimants falling in the threeleast-poor deciles with those in the three poor-est deciles, given that 34.7% of the Indian pop-ulation lives below $1 a day (World Bank,2004). While comparable state-level figures arenot available, we assumed that roughly 30%of Gujarat’s population falls below this interna-tional poverty line, given that Gujarat tends toperform slightly better than all-India based onother measures of poverty.

4. RESULTS

(a) Benefit per capita for urban and ruralmembers

Urban members of Vimo SEWA benefited farmore from the scheme than did their ruralcounterparts, the former receiving an average


of Rs. 107.1 per member in calendar year 2003(95% CI = Rs. 96.9–117.2), against a premiumof Rs. 85 per member, compared to only Rs.42.5 among rural members (95% CI = Rs.39.4–45.6; Figure 2), an urban:rural ratio of2.5. The difference was greatest for asset insur-ance—a 5.4-fold difference, but the differencewas also high for hospitalization (2.2-fold dif-ference) and life insurance (1.6-fold difference)components (Appendix A).

(b) Benefit distribution across deciles of memberswithin rural and urban areas

Overall, within both rural and urban areas,benefits were fairly equally distributed bySES. The top, left graphs in Figures 3 and 4show that members in each of the deciles ineach area received approximately 10% of thetotal benefits. However, when we disaggregatedthe distribution of benefits by type of loss suf-fered, the picture changed.

In rural areas, life and assets insurance ben-efits flowed disproportionately to the poorest,while hospitalization insurance benefits floweddisproportionately to the least-poor (Figure 3).The least-poor 30% of the membership in ruralareas received only 0.3 times the benefit re-ceived by the poorest 30% under the life insur-ance component, and only 0.5 times the benefitunder the assets insurance component (Appen-dix A). But they received 2.4 times the benefit

10.0

26.8

5.7

0.0

20.0

40.0

60.0

80.0

100.0

120.0

Rural

Asset

Hospitalization

Life

Figure 2. Benefit per capita by insurance component (12 mo

areas (benefit

under the hospitalization insurance compo-nent.

In urban areas, as in rural areas, the benefitsof the life and assets insurance flowed dispro-portionately to the poorest. But unlike in ruralareas, benefits of the hospitalization insurancecomponent in the city were distributed rela-tively equally across the socio-economic spec-trum (Figure 4). The least-poor:poor ratiowas less than 1 for all three insurance compo-nents: 0.8 for hospitalization insurance, 0.3for life insurance, and 0.2 for assets insurance.

The results show that the distribution of ben-efits was unequal on two counts. Inequalities inthe scheme occurred between rural and urbanmembers, and between the poorest and least-poor rural members for hospitalization claims.In order to explore further, differences wereexamined between rural and urban members,and between the poorest and least-poor ruralmembers, at the stages of (i) claim submission,(ii) claim acceptance or rejection, and (iii) reim-bursement (benefit) amount received.

(c) Claims submission

In 2003, Vimo SEWA members in urbanareas submitted 186.3 claims (of all kinds) per1,000 members, compared with only 34.1claims per 1,000 members in rural areas—a5.5-fold difference (Figure 5 and Appendix A).The difference was by far the greatest for assets

16.2

60.4

30.5

Urban

nths, 2003) for rural areas (benefit Rs. 42.5) versus urban

Rs. 107.1).

0.1

.20

.1.2

1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

All (N=1,006) Assets (N=268)

Hospitalization (N=621) Life (N=117)

Frac

tion

of to

tal R

s re

imbu

rsed

(9 m

os)

Claimants by decile of SES

Figure 3. Fraction of total rupees received by rural members grouped by SES decile (16 rural sub-districts; nine months,

2003).

0.1

.2.3

0.1

.2.3

1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10

All (N=715) Assets (N=222)

Hospitalization (N=427) Life (N=66)

Frac

tion

of to

tal R

s re

imbu

rsed

(9 m

os)

Claimants by decile of SES

Figure 4. Fraction of total rupees received by urban members grouped by SES decile of SES (eight urban zones; nine

months, 2003).


insurance claims (14.4-fold), and less for hospi-talization (2.3-fold) and life (1.8-fold) insuranceclaims. The rural–urban difference in benefitsreceived was thus determined largely by differ-ences in rates of claims submission.

Figure 6 shows the relative frequency of hos-pitalization insurance claims submission bydecile of SES for rural Vimo SEWA members.The distribution was almost identical to the dis-

tribution of benefits for the hospitalizationinsurance component for rural members, inFigure 3. The least-poor:poor ratio was 2.3(Appendix A).

(d) Rejection of claims

Rejection rates for submitted claims were sig-nificantly higher in urban areas at 19.4% than

4.5 8.120.5

47.79.1

130.5

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

180.0

200.0

Rural

AssetHospitalizationLife

Urban

Figure 5. Insurance claims per 1,000 members per annum (2003).

050

100

150

Num

ber o

f cla

ims

(9 m

os)

1 2 3 4 5 6 7 8 9 10Claimants by decile of ses

Figure 6. Frequency distribution of rural hospitalization insurance claims submitted by socio-economic status

(N = 621; 16 rural sub-districts; nine months, 2003).


rural areas at 9.5% (p < 0.001 using Fisher’s ex-act). The lower benefits to rural members weretherefore not due to a differential in rejectionrates of submitted claims; in fact, the lowerrejection rate for rural claims went some waytoward ameliorating the difference. In ruralareas, the rate of rejection was highest for hos-pitalization (13.9%), followed by life (8.6%) and

assets claims (0.0%). In urban areas, the rejec-tion rate was highest for assets (36.9), followedby life (19.7) and then hospitalization (10.3).

Within rural areas, there was no associationbetween SES and rate of claim rejection (seeAppendix A—all least-poor:poor ratios wereclose to 1). So the differential frequency ofclaim acceptance or rejection does not help to


explain inequalities in hospitalization insurancebenefit distribution within rural areas.

(e) Mean amount paid, per accepted claim

The mean amount paid (per accepted claim)was similar in urban and rural areas for hospi-talization and life insurance claims. For assetinsurance, the mean amount paid in urbanareas was only 0.5 times that paid in rural areas(Appendix A), presumably due to a larger num-ber of homes being damaged to a relativelyminor extent. The higher average claim amountfor rural asset claims did not reduce theinequality between urban and rural membersbecause the claims rate for urban memberswas far greater.

Within rural areas, there was a significantassociation between SES and the mean amountreimbursed per claim, but this association wassignificant for hospitalization insurance claimsonly. For rural hospitalization insuranceclaims, a one unit increase in SES was associ-ated with an increase in the reimbursed amountof Rs. 56 (p = 0.026). The corresponding least-poor:poor ratio was 1.1 (Appendix A), suggest-ing that this differential (while significant) wasnot much responsible for the unequal distribu-tion of net benefits for rural hospitalizationinsurance claims by member SES.

(f) Relative importance of hospitalizationinsurance claims

The study revealed that hospitalization insur-ance claim benefits constituted 63% and 58% oftotal benefits received in rural and urban areas,respectively. The reasons for this high propor-tion of hospitalization claim benefits differedfor rural and urban areas. In rural areas, hospi-talization claims constituted 60% of all theclaims submitted. The mean amount paid outper claim, in rural areas, was Rs. 1,606 (95%CI = Rs. 1,562–1,651) for hospitalizationclaims, Rs. 3,000 (95% CI = Rs. 3,000–3,000)for life claims and Rs. 684 (95% CI = Rs.656–713) for asset claims. In urban areas, whilehospitalization claims accounted for only 25%of the total, and asset claims for 70%, hospital-ization insurance benefits were higher becausethe mean hospitalization claim of Rs. 1,547(95% CI = Rs. 1,487–1,607) was more thanfour times the mean urban asset claim of Rs.370 (95% CI = Rs. 337–403). As in rural areas,

the mean amount reimbursed per life insuranceclaim was Rs. 3,000 (95% CI = Rs. 3,000–3,000).

5. DISCUSSION AND CONCLUSIONS

(a) Summary of results

The study has examined the distributionalimpact of a bundled CBI scheme which offersprotection on several fronts to poor members.Overall, within rural and urban areas and con-sidering all insurance components together, thedistribution of benefits was relatively equalacross poor and less-poor members. However,comparing the data for rural and urban areas,and for different components of the scheme,two main inequalities became apparent. Thedistribution of benefits was skewed in favor ofurban members, who received a far higher pro-portion of benefits compared to rural members.Further, in rural areas, the poorest memberswere disadvantaged compared to better-offmembers with regard to hospitalization bene-fits; under this component, the least-poor decileof rural members received almost four times thebenefit received by the poorest decile. The un-equal distribution of benefits was primarilydue to different rates of claim submission, andnot to unequal rates of claim rejection or un-equal amounts paid out per approved claim.

(b) Strengths and weaknesses of the study

The study adds significantly to understandingof the distributional impact of CBI schemes.The authors are aware of only two other studiesthat examine this issue for CBHI schemes(Dror et al., 2006; Wang et al., 2005), and nonethat does so for a bundled scheme. Given thenumber of bundled insurance schemes func-tioning in India (International Labour Office(STEP Unit), 2005), and perhaps elsewhere,and their aim of protecting the poor fromfinancial risk, it is important to know how wellthe poor are in fact served by such schemes.

The methods used to determine SES werestrong, relative to other studies of distribu-tion/equality, insofar as they were based on re-cent household data, collected expressly forassessing SES among the target populations;and used an SES index that had strong concep-tual grounding. Creators of the underlying


CGAP tool have carefully justified all of theirrecommendations on domains to be includedin their index and methods of data reduction.The tool (and corresponding methodology) islocally appropriate—incorporating variablesthat might be important indicators of SES ina particular study setting—but it is also quitegeneralizable and field-tested in four differentcountries (Henry et al., 2000). The data wereeasy to collect, less prone to manipulation (par-ticularly under-reporting) than income orexpenditure data, partially verifiable, and arethought to have minimal measurement error.

The limitations in the study are 4-fold.Firstly, the study could examine only the distri-bution of benefits per member in each group,not in relation to relative ‘‘need.’’ With respectto hospitalization benefit, there may have beengreater inequality if rural and poorer membershad greater need relative to urban and less-poormembers, but we are unable to examine thiswith the available data.

Secondly, this study was carried out as part ofa larger, earlier study which had a hospitaliza-tion insurance focus. The time window of ninemonths for submitted claims was selected be-cause it yielded an adequate number of the hos-pitalization insurance claims required by theprimary study. The number of life insuranceclaims submitted in Ahmedabad City during thisperiod was relatively small, and the final analysiswas based on only 66 urban death claims.

Thirdly, the pattern of benefit distributionpresented in the study may be specific to thatyear—it should not be assumed that the find-ings are representative of the many years thatVimo SEWA has been functioning. Events dur-ing the period may have impacted on the typeor volume of claims submitted; for example,heavy rainfall and subsequent flooding inAhmedabad City resulted in a high number ofurban asset claims. The flooding may also havecontributed to higher rates of illnesses, and sub-sequent hospitalization and death, in Ahmeda-bad City. The membership in 2003 may alsohave differed from other years; for example,the 2003 membership included a significantproportion of rural members who were enrolledthrough income-generating groups instead ofdirect sales to individuals or households—approximately 22,500 of the 67,500 rural mem-bers. Members enrolled through income-gener-ating groups may have been provided with lessknowledge regarding the scheme, their mem-bership in it, or processes of submitting a claim

(Garand, 2005). If these members were amongthe poorest members, then this could explainthe urban–rural differentials (asset, hospitaliza-tion, and life) or the fact that hospitalizationinsurance benefits in rural areas flowed dispro-portionately to the least-poor. The fact that thescheme is dynamic—in terms of benefits, premi-ums and the systems for enrolling members,and processing claims—and that externalevents can have a major impact on the typeand volume of claims, highlights the impor-tance of longitudinal monitoring of the distri-bution of scheme benefits.

On the other hand, several factors suggest thatthe findings for 2003 were fairly representative.Data from the Vimo SEWA management infor-mation system (MIS) show that for the fouryears 2002–05, the rates of life and hospitaliza-tion claim submission were consistently higherfor urban relative to rural members. Thisinequality appears, however, to be decreasingwith time. The rate of asset claims, on the otherhand, was higher in urban areas for some years(2002–03) and in rural areas for others (2004–05). The MIS does not provide information onthe rate of claims by SES of members. A surveyconducted in 2005, identical to the one describedin this paper, found that the SES of hospitaliza-tion claimants (relative to members in the samesub-district) had not changed significantly from2003—the unequal pattern of hospitalizationclaim submission remained.

Lastly, multiple regression analysis wouldhave been useful to examine the relative impor-tance of a variety of different explanatory vari-ables—in addition to urban/rural residence andSES, these might have included gender, age,caste, literacy, etc.—on the primary outcomevariable, total benefits received from VimoSEWA. Regression analyses were not possibleas our member and claimant surveys sampleddifferent groups of households, and would onlyhave been possible if we had been able to sam-ple a member population that was large enoughto include a representative sample of claimants.This would not have been feasible logistically,as it would have required a huge sample size, gi-ven that rates of claims per annum vary fromonly 0.45% of members (rural life) to 13% ofmembers (urban assets) (see Figure 5).

(c) Possible explanations for study findings

This study did not examine the factors thatunderlie differential rates of claims submission.


However, a review of available data related tothe subject offers possible explanations. Onereason for fewer hospitalization claims fromthe poorest rural members may be the scarcityof hospitals and the poor transportation sys-tems in rural India (Ramani & Mavalankar,2005). While these barriers are faced by all ruralmembers, the poorest are most constrained bythem due to cost factors. Figure 7 comparesour results with the best available hospitaliza-tion data for Gujarat state (National SampleSurvey Organization, 1995–96). In rural areas,the rate of claims by quintile of SES runs paral-lel to—but fairly consistently higher than—therate of hospitalization, suggesting that the un-equal pattern of Vimo SEWA utilization inrural areas by SES is due largely to an underly-ing, unequal pattern of hospital utilization. Inurban areas, the rate of claims to Vimo SEWAis relatively flat, while the rate of hospitaliza-tion in the general population rises with levelof wealth. These data suggest that the VimoSEWA scheme may help to overcome barriersto hospitalization among poor urban members.Like the rate of hospitalization claim submis-sion to Vimo SEWA (urban:rural ratio of2.3), the rate of hospitalizations among the gen-eral population is higher in urban areas (2,128/100,000 persons) than in rural areas (1,497/100,000) by a ratio of 1.4. Thus, it is possiblethat barriers to seeking hospitalization accountfor all of the inequality in claim submission bySES (in rural areas) and for much, but not all,of the urban–rural difference.

Previous qualitative research has identifiedbarriers to utilization of Vimo SEWA’s hospi-talization insurance that may be particularlyburdensome to the rural poor (Sinha, Ranson,

Hospitalizations

Rural

0500

1000150020002500300035004000

I II III IV VQuintile

Rat

e (e

piso

des/

100,

000/

year

)

Figure 7. Rate of hospitalization among the population of G

claims to Vimo SEWA (among the insured) by SES quintiles

urba

Chatterjee, Acharya, & Mills, 2006). These bar-riers included

—lack of funds to pay for hospitalization ortransportation;—distance from residence to hospital;—lack of cooperation from the doctor (orhospital) in getting the required documents;—member or aagewan unclear about termsand conditions of the policy (e.g., diseaseexclusions);—member unclear about the documentsrequired;—opportunity costs—for example, of travelor time—of getting the documents and thensubmitting a claim;—fear of the claim being rejected;—weak linkages between the member andthe local aagewan.

The above discussion suggests that bothmacro-level factors (such as poor physicalinfrastructure and too few hospitals in ruralareas), and features of scheme design andadministration affect the distribution of benefitsfrom a CBI scheme. Initiatives are therefore re-quired at both levels to improve the scheme’sbenefit distribution.

(d) Implications of study findings for policy andCBI schemes

Governments and donors are increasinglylooking to CBHI to provide financial protec-tion to households and supplement health carefinancing in the face of limited state resources(Preker & Carrin, 2004) . To ensure that the po-tential of such schemes to provide financial pro-tection to the poor is in fact realized, it is

Claims to Vimo SEWA

Urban

0

1000

2000

3000

4000

5000

6000

7000

I II III IV VQuintile

Rat

e (e

piso

des/

100,

000/

year

)

ujarat by expenditure quintiles and rate of hospitalization

(both rates are per 100,000 per year). (a) Rural and (b)

n.


important to carefully design and monitor theseschemes.

The higher submission of death and asset lossclaims from poorer members, relative to hospi-talization claims, highlights the importance ofthe design of the insurance product. Lower lifeexpectancy among the poor and an upper agelimit of 60 years for life insurance probably ex-plain higher life insurance claims from poormembers. Scheme administrators should movecautiously in increasing the upper age limit oflife insurance coverage—increasing coverageto 70 years of age, for example, may result ina disproportionate increase in claims fromless-poor members. The asset loss insurancecovers damage to the house and the member’slivelihood related assets within it. Poorer qual-ity housing structures among poor members—for example, walls and roofs that are less resis-tant to damage during heavy rains—are themost likely explanation for their higher ratesof asset loss claims.

Similarly, instead of a uniform scheme for allcategories of members, product design featurescan be adapted to enhance the equality of thebenefits and compensate for inequalities at themacro-level. For instance, the scheme couldhave differential premiums for rural and urbanmembers. A study of the distribution of healthinsurance benefits for formal-sector Indianinsurance programs found that uniform premi-ums are regressive because policy holders fromsmall towns subsidize those in major urban cen-ters (Gupta, 2004). Another possible design fea-ture to increase equality of utilization amongmembers could be to include transportationcosts to hospital for rural members to compen-sate for the greater distances that they are re-quired to travel to reach hospitals.

Poor households face multiple risks (Cohen& Sebstad, 2003), and a bundled insuranceproduct can be expected to provide more com-prehensive protection to the poor. However, itis more complex to monitor such a scheme thanone that covers a single risk. Such monitoring,however, is critical to ensure that the benefitsfrom the different components are flowing

equally to all members. As this study suggests,a clear picture emerges only through disaggre-gating the benefits from the different compo-nents of the scheme and examining theseseparately for different categories of members.

Vimo SEWA is already taking steps towardimproving the distribution of hospitalizationinsurance benefits. One pilot intervention aimsto increase the rate of submission of (appropri-ate) claims, particularly among Vimo SEWA’spoorest members, primarily by improving theknowledge, capacity, and motivation of Vimoaagewans, and enhancing two-way communica-tions between aagewans and members. It ishoped that these activities will make it easierfor members to compile and submit a claim,and ultimately, result in increased financial pro-tection among members.

A second pilot intervention aims at reducingthe financial barriers for poorer members. On apilot basis, in eight rural sub-districts (coveringmore than 10,000 women members, and theirinsured spouses), Vimo SEWA is reimbursingits members, cash-in-hand, prior to their dis-charge from hospital as long as they use se-lected ‘‘empanelled’’ hospitals, of which thereare two in each sub-district. These hospitalsare (with only one exception) public and pri-vate-non-profit facilities, selected because theycharge much less than private-for-profit facili-ties (historically, much more popular amongmembers, as they are perceived to give faster,more courteous service), and provide morecomprehensive and higher quality services.

The high payout to urban members in claimsreimbursement compared to the premium col-lected is of concern not only from an equalityperspective but also in terms of the long termsustainability of the CBI scheme. Thus far,the scheme has focused on improving the utili-zation by rural, and especially the poorestrural, members. In the interest of providinginsurance coverage to poor members over thelong term, it would be useful for scheme admin-istrators to look deeper into factors contribut-ing to the higher costs of urban claims andaddress these where appropriate.

NOTES

1. SEWA Bank is a cooperative bank started by thesame parent organization that started Vimo SEWA.

2. One USD is equal to approximately Rs. 44.

3. Thirty percentage was chosen because the latestpoverty statistics for India suggest that 34.7% of thetotal population lives below $1 per day (World Bank,2004).


4. A household was defined as a group of peopleregularly eating from the same kitchen; members hadeither to have (1) been present in the household four ofthe last seven nights or (2) lived in the household six ofthe last 12 months and intended to return within twomonths, to spend at least half of their time living in thehouse. More details about the sampling methodol-ogy are discussed in an earlier paper (Ranson et al.,2006).

5. Districts are divided into sub-district areas calledtalukas, each centered around a major town (taluka

place) and with a population of 50,000–250,000. Urbanareas are divided into wards.

6. Accidental death claims were excluded from theanalysis as they skewed the results: the number of claimswas very small (three of the 82 urban death claims andfour of the 147 rural death claims were for accidentaldeaths, and they were reimbursed a much higheramount: Rs. 40,000 for an accidental death vs only Rs.3,000 for a natural death).

7. The summer/monsoon season—June–September—isthe peak claims period.

REFERENCES

Bennett, S. (2004). The role of community-based healthinsurance within the health care financing system: Aframework for analysis. Health Policy and Planning,19(3), 147–158.

Bennett, S., Creese, A., & Monasch, R. (1998). Healthinsurance schemes for people outside formal sectoremployment. Geneva: Division of Analysis, Researchand Assessment, World Health Organization.

Chatterjee, M., & Ranson, M. K. (2003). Livelihoodsecurity through community-based health insurancein India. In L. Chen, J. Leaning, & V. Narasimhan(Eds.), Health and human security (pp. 277–300).Cambridge, MA, Harvard: Harvard UniversityPress.

Chatterjee, M., & Vyas, J. (1997). Organizing insurancefor women workers: The SEWA experience. SelfEmployed Women’s Association, Ahmedabad(Unpublished paper).

Cohen, M., & Sebstad, J. (2003). Reducing vulnerability:The demand for microinsurance. MicroSave-Africa:An Initiative of Austria/CGAP/DFID/UNDP, Mi-cro-insurance Center, Nairobi.

Criel, B., Van der Stuyft, P., & Van Lerberghe, W.(1999). The Bwamanda hospital insurance scheme:Effective for whom? A study of its impact on hospitalutilization patterns. Social Science and Medicine,48(7), 897–911.

Dercon, S. (Ed.) (2004). Insurance against poverty.Oxford: Oxford University Press.

Dror, D. M., & Jacquier, C. (1999). Extending healthinsurance to the excluded. International Social Secu-rity Review, 52(1), 71–97.

Dror, D. M., Koren, R., & Steinberg, D. M. (2006). Theimpact of Filipino micro health-insurance units onincome-related equality of access to healthcare.Health Policy, 77(3), 304–317.

Dror, D. M., Soriano, E., Sarol, J., Azcuma, R.,Lorenzo, M., & Koren, R. (2005). Field basedevidence of enhanced healthcare utilization amongpersons insured by Micro Health Insurance UnitsonIncome-related Equality of Access to Healthcare.Health Policy, 73(3), 71–97.

Filmer, D., & Pritchett, L. H. (2001). Estimating wealtheffects without expenditure data—or tears: An appli-

cation to educational enrollments in states of India.Demography, 38(1), 115–132.

Garand, D. (2005). Vimo SEWA, India (Case study No.16). CGap Working Group on Microinsurance,World Bank, Washington, DC.

Gertler, P., & Gruber, J. (2002). Insuring consumptionagainst illness. American Economic Review, 92(1),51–70.

Gupta, A. (2004). Wealth through health insurance: Theway to sustainable development. IRDA Journal,II(11), 24–25.

Henry, C., Sharma, M., Lapenu, C., & Zeller, M. (2000).Assessing the relative poverty of microfinance clients:A CGAP operational tool. International Food PolicyResearch Institute: Washington, DC.

Holzmann, R., & Jorgensen, S. (1999). Social protectionas social risk management: Conceptual underpin-nings for the social protection sector strategy paper.Social Protection Discussion Paper Series, No. 9904,Human Development Network, The World Bank.

International Labour Office (STEP Unit). (2001).Women organizing for social protection: The Self-employed Women’s Association’s Integrated Insur-ance Scheme, India. ILO-STEP, Geneva.

International Labour Office (STEP Unit). (2005). Com-munity based schemes—India: An inventory ofmicro insurance-schemes. Working Paper No. 2.ILO-STEP, Geneva.

Jakab, M., & Krishnan, C. (2001). Community involve-ment in health care financing: A survey of theliterature on the impacts, strengths and weaknesses.Washington, DC: HNP Network, World Bank.

Jutting, J. (2004). Do community-based health insuranceschemes improve people’s access to health care:Evidence from rural Senegal. World Development,32(2), 273–283.

Krishna, A., Kapila, M., Porwal, M., & Singh, V.(2003). Falling into poverty in a high growth state:Escaping poverty and becoming poor in Gujaratvillages. Economic and Political Weekly, 5171–5179.

McCord, M. J., Isern, J., & Hashemi, S. (2001).Microinsurance: A case study of an example of thefull service model of microinsurance provision—Self-


Employed Women’s Association (SEWA—India).Nairobi: MicroSave-Africa.

Morduch, J. (1999). Between the state and the market:Can informal insurance patch the safety net? TheWorld Bank Research Observer, 14(2), 187–207.

Preker, A., & Carrin, G. (Eds.) (2004). Health financingfor poor people: Resource mobilization and risksharing. Washington, DC: The World Bank, WHO,ILO.

Ramani, K. V., & Mavalankar, D. (2005). Healthsystems in India: Opportunities and challenges forimprovements. Ahmedabad: Indian Institute of Man-agement, India.

Ranson, M. K., Sinha, T., Chatterjee, M., Acharya, A.,Bhavsar, A., Morris, S. S., et al. (2006). Makinghealth insurance work for the poor: Learning fromSEWA’s community-based health insurance scheme.Social Science and Medicine, 62(3), 707–720.

Roth, J. (2002). Informal microinsurance schemes: Thecase of funeral insurance in South Africa. SmallEnterprise Development, 12(1), 39–50.

Schneider, P., & Diop, F. (2004). Community-basedhealth insurance in Rwanda. In A. S. Prekin, & G.Carrin (Eds.), Health financing for poor people:

Least-poor:poor ratio

R

Hospitalizati

Number of claims 2.3Frequency of claim acceptance 1.0Mean amount per accepted claim 1.1Net benefit (i.e., total

rupees received)2.4

Urban:rural ratio

Frequency of submitting claimFrequency of claim acceptanceMean amount per accepted claimNet benefit (i.e., total rupees received)

Note: >1 suggests trend toward inequality favoring the lefavoring the poor or rural; 1 is an equal distribution.

APPENDIX A. LEAST-POOR:POOR

Resource mobilization and risk sharing. Washington,DC: World Bank.

Self-Employed Women’s Association. (1999). Self-Em-ployed Women’s Association: Annual Report 1999.SEWA, Ahmedabad.

Sinha, S., & The Vimo SEWA Team (2002). Strength insolidarity: Insurance for women workers in the infor-mal economy. Ahmedabad: Self-Employed Women’sAssociation.

Sinha, T., Ranson, M. K., Chatterjee, M., Acharya, A.,& Mills, A. J. (2006). Barriers faced by members of acommunity-based insurance scheme in accessingbenefits: Lessons learnt from SEWA Insurance,Gujarat. Health Policy and Planning, 21(2), 132–142.

Wang, H., Yip, W., Zhang, L., Wang, L., & Hsiao, W.(2005). Community-based health insurance in poorrural China: The distribution of net benefits. HealthPolicy and Planning, 20(6), 366–374.

World Bank (2001). World Development Report 2000/2001: Attacking poverty. Oxford: Oxford UniversityPress.

World Bank (2004). World development indicators.Washington, DC: The World Bank.

ural Urban

on Life Assets Hospitalization Life Assets

0.3 0.4 0.7 0.4 0.21.1 1.0 1.1 0.8 0.91.0 1.1 1.1 1.0 1.00.3 0.5 0.8 0.3 0.2

Hospitalization Life Assets

2.3 1.8 14.41.0 0.9 0.61.0 1.0 0.52.2 1.6 5.4

ast-poor or urban; <1 suggests trend toward inequality

AND URBAN:RURAL RATIOS

protecting the poor? the distributional impact of a bundled insurance scheme

Documents