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date: 22 August 2019

Microinsurance and Rural Health

Abstract and Keywords

Health microinsurance (HMI) has been used around the globe since the early 1990s for financial risk protection against health shocks in poverty-stricken rural populations in low-income countries. However, there is much debate in the literature on its impact on financial risk protection. There is also no clear answer to the critical policy question about whether HMI is a viable route to provide healthcare to the people of the informal economy, especially in the rural areas. Findings show that HMI schemes are concentrated widely in the low-income countries, especially in South Asia (about 43%) and East Africa (about 25.4%). India accounts for 30% of HMI schemes. Bangladesh and Kenya also possess a good number of schemes. There is some evidence that HMI increases access to healthcare or utilization of healthcare. One set of the literature shows that HMI provides financial protection against the costs of illness to its enrollees by reducing out-of-pocket payments and/or catastrophic spending. On the contrary, a large body of literature with strong methodological rigor shows that HMI fails to provide financial protection against health shocks to its clients. Some of the studies in the latter group rather find that HMI contributes to the decline of financial risk protection. These findings seem to be logical as there is a high copayment and a lack of continuum of care in most cases. The findings also show that scale and dependence on subsidy are the major concerns. Low enrollment and low renewal are common concerns of the voluntary HMI schemes in South Asian countries. In addition, the declining trend of donor subsidies makes the HMI schemes supported by external donors more vulnerable. These challenges and constraints restrict the scale and profitability of HMI initiatives, especially those that are voluntary. Consequently, the existing organizations may cease HMI activities.

Overall, although HMI can increase access to healthcare, it fails to provide financial risk protection against health shocks. The existing HMI practices in South Asia, especially in the HMIs owned by nongovernmental organizations and microfinance institutions, are not a viable route to provide healthcare to the rural population of the informal economy. However, HMI schemes may play some supportive role in implementation of a nationalized scheme, if there is one. There is also concern about the institutional viability of the HMI organizations (e.g., ownership and management efficiency). Future research may address this issue.

Keywords: microinsurance, health microinsurance, concentration, viability, scale, South Asia, rural health, health economics

Introduction

Low-income rural households in the developing countries are highly susceptible to financial shocks, such as those related to health, life, property, and livestock. Due to a lack of formal insurance, they essentially depend on informal insurance mechanisms to cope. Microinsurance was developed to provide these people with financial protection against adverse events. Evidence shows that 26% of households in low- and middle-income countries resort to borrowing and selling assets to cover healthcare expenses (Kruk, Goldmann, & Galea, 2009); thus illness, not job loss, is seen as the main cause of poverty (Asfaw, 2003; Dodd & Munck, 2001). Health microinsurance (HMI) emerged as a financial risk protection tool for coping with health shocks in low-income rural households in developing countries. In other words, HMI is a means to address the health-financing gap of these poverty-stricken people working in the informal sector. To qualify as an HMI, a health insurance mechanism must fulfill two fundamental criteria: (a) collection of premiums upfront and (b) a minimal copayment (not exceeding 25%). However, most of the HMI schemes around the globe do not fulfill the latter requirement. Nonetheless, these are considered health insurance in practice.

More than 100 HMI schemes are currently being practiced in 32 countries around the globe through four major types of delivery channels: the provider-driven model, the partner-agent model, the full-service model, and the community-based model. The major hub of HMI is in Asian and African countries, which account for 56.2% and 37.7%, respectively, of the total number of schemes. A few HMI schemes are present in North America (3.08%) and South America (3.08%). In the regional context, the main concentration is in South Asia (43%; International Labour Organization [ILO], 2013).

As an innovative health-financing tool targeting the informal sector, HMI initially showed some good prospects. A growing body of literature shows evidence of a positive impact of HMI on health and economic outcomes of the beneficiaries (Alkenbrack & Lindelow, 2013; Habib, Perveen, & Khuwaja, 2016; Hamid, Roberts, & Mosley, 2011a, 2011b; Saksena, Antunes, Xu, Musango, & Carrin, 2011; Savitha & Kiran, 2013; Wagstaff, 2007). However, there is strong evidence that HMI fails to protect the insured from financial risk (Prinja, Chauhan, Karan, Kaur, & Kumar, 2017; Selvaraj & Karan, 2012). The latter studies triggered the debate regarding the impact of HMI. In addition, some ask whether HMI schemes are even viable.

The existing review articles focusing on the impact of HMI schemes make some strong concluding remarks (Habib et al., 2016). Some literature (Hamid, 2016; Koven, Chandani, & Garand, 2013; Sanofi Espoir Foundation & Agence Française de Développement, 2015; Weilant, 2015) emphasizes the viability of HMI schemes in a few countries. However, there is no clear answer about the viability of HMI schemes, which is a gap in the literature. Moreover, there is no rigorous analysis on the concentration of HMI schemes, which was the impetus for this review. This article mainly focuses on the concentration of HMI and the viability issue of HMI schemes. The information analyzed in this article may be useful for policy discussion regarding HMI.

The next section explains the methods and materials used for the analysis. The third section describes the concentration of HMI around the globe, followed by the evidence on the impact of HMI schemes and the viability issues of HMI in the south Asian context. The last section offers discussions and concluding remarks.

Methods and Materials

The article is based on secondary information obtained from journal articles, published books, documents and guidelines, policy briefs, websites of various organizations, and other web-based resources. Keywords used while searching the Internet-based literature included concentration/penetration of health microinsurance, growth of health microinsurance, delivery channels of health microinsurance, community-based health microinsurance, partner agent model of health microinsurance, provider-based model of health microinsurance, full-service model of health microinsurance, community-based model of health microinsurance, impact of health microinsurance, viability of health microinsurance, and sustainability of health microinsurance.

The information about the concentration of HMI was obtained mainly from the ILO’s Impact Insurance Facility website. The systematic review (Habib et al., 2016; Prinja, Chauhan, Karan, Kaur, & Kumar, 2017) and analytical overview (Ahsan, Khalily, Hamid, Barua, & Barua, 2013) provided us with important clues for searching the materials on the impact of HMI. A quick screening was primarily conducted based on the title, abstract, main findings, and conclusions for selecting the materials for review. The materials on the viability issue of HMI was mainly found in some policy briefs, working papers, and case studies. An article in a peer-reviewed journal that addresses the viability issue of HMI is still lacking.

We defined the penetration or concentration of HMI in terms of the number of schemes. We also attempted to illustrate the concentration of HMI on the basis of number of beneficiaries. However, due to unavailability of data regarding beneficiaries of about half of the schemes, we could not show the concentration in terms of the number of beneficiaries.

Concentration of Health Microinsurance

The available information shows that globally there are 130 HMI schemes of which 14 (10.77%) are not currently in operation. As seen in Table 1, the concentration of HMI schemes is mainly in Asia (56.15%) and Africa (37.7%). In the regional context, HMI schemes are mainly concentrated in South Asia (about 43%), East Africa (about 25.4%), and West Africa (9.23%). If we consider country-specific concentration of HMI schemes, India alone accounts for 30% (39 out of 130) of the schemes, followed by Bangladesh (10%, or 13 out of 130) and Kenya (9.23%, or 12 out of 130; ILO, 2013).

Table 1. Concentration of Health Microinsurance Schemes by Continent

Continent

Number of HMI schemes

Percentage

Asia

73

56.15

Africa

49

37.7

North America

4

3.08

South America

4

3.08

Europe

0

0

Australia

0

0

Total

130

100

Note: HMI = health microinsurance.

Source: ILO (2013).

Most HMI schemes (about 87%, or 113 out of 130) are voluntary in nature. The remaining (about 13%, or 17 out of 130) schemes are compulsory (ILO, 2013). Seventeen countries have a single HMI scheme, in which all but one is voluntary. There are double schemes in three countries (China, Indonesia, and Peru) and triple schemes in three countries (Benin, Ghana, and Pakistan). These are all voluntary. More than two-thirds (98 out of 130) of the HMI schemes are concentrated in nine countries, in which about 89% are voluntary (Table 2). In addition, about half (64 out of 130) of the schemes are located in three countries (India, Bangladesh, and Kenya), and more than 90% (58 out of 64) of those are voluntary.

Most of the HMI schemes are owned by nongovernmental organizations (NGOs), microfinance institutions (MFIs), or other not-for-profit entities, while there are 13 state-sponsored HMI schemes globally, of which 10 schemes belong to the countries that have more than 3 schemes (Table 2).

Table 2. Concentration of Voluntary and Compulsory HMI Schemes

Country*

Nature of Enrollment

Total

Ownership Status

Voluntary HMI

Compulsory HMI

State-Sponsored HMI

NGOs/MFIs/Others Owned HMI

Bangladesh

12

1

13

0

13

Cambodia

4

0

4

0

4

Ethiopia

4

0

4

1

3

India

34

5

39

2

37

Kenya

12

0

12

2

10

Nigeria

3

2

5

1

4

Philippines

5

0

5

1

4

Tanzania

5

2

7

2

5

Uganda

8

1

9

1

8

Total

87

11

98

10

88

Percentage

89

11

100

10.2

89.8

Note: HMI = health microinsurance; NGOs = nongovernmental organizations; MFIs = microfinance institutions.

* Countries with more than three schemes.

Sources: Ahmed, Islam, and Quashem (2005); ILO (2005, 2013); Smith, Chamberlain, Smit, and Ncube (2010); Smith and Chamberlin (2010); Women’s World Banking (2012); Marwa (2016).

HMI is offered through a number of delivery channels: partner-agent model, provider-based model, community-based/mutual/cooperatives, and full-service model. Other models include the commercial insurance based model and the social insurance based model.

In the partner-agent model, a regulated insurance company underwrites a microinsurance product, while other parties are responsible for provision or delivery of the product. A wide range of organizations, MFIs, can act as intermediaries. The insurers generally understand little about low-income clients, and the clients have a general distrust toward the insurers, which lead to the failure of HMI targeted toward this population. Collaboration with the organizations that already work with the targeted population facilitates trust and understanding. Affinity groups and MFIs also have the benefit of introducing their clients to the insurance facilities without the trouble of risk-bearing functions. Both can thus focus on their core functions and better serve clients (Churchill, 2006; Microinsurance Network, 2018).

In the provider-based model, the healthcare provider is also the insurer and, hence, is responsible for all operations, design, and service delivery. The provider cum insurer has all the control, but that can often come at the expense of the quality of the services. The provider is able to increase access to, and demand for, these services and, at the same time as the supplier, has control over the quality of the service provided. Service quality is a crucial element in client satisfaction and retention and can deteriorate when the service provider has to bear the necessary risk or perform other functions required for an insurer. Because the healthcare provider must perform activities apart from its core functions, quality of service can be compromised; this is a drawback of this model (Microinsurance Network, 2018).

Communities initiate and manage the community-based HMI schemes, and usually they are run by nonprofessional voluntary staff. Members are owners and beneficiaries at the same time. These insurance schemes are not for profit, promote solidarity and social cohesion, and often restrict their operations to a small geographic area. The community can either hire healthcare providers or contract with them (Churchill, 2006).

In the mutual model, the insurer is owned by clients or members, who share benefits and costs of the insurance operations; often the members’ liability is limited to paying premiums. These organizations are based on a risk-sharing and resource-pooling mechanism like other insurance systems. But these organizations do not select their members on the basis of their individual risk profile as they are not for profit. The main objective of these organizations is increasing access to healthcare through solidarity (Churchill, 2006; Microinsurance Network, 2018).

Cooperative insurers may or may not be owned by clients. These models have characteristics similar to the mutual model, such as involving insurance clients in management and serving pre-existing groups of clients (such as borrowers from a credit and savings cooperative or MFIs or residents of a limited geographic area; Microinsurance Network, 2018). Lending organizations often offer insurance contracts to their clients that cover the balance of a loan to be paid back. They offer life insurance and rarely provide housing, funeral, invalidity, and accident policies as well. These products come with the mainstream credit and savings services (Microinsurance Network, 2018).

In the full-service model, NGOs, MFIs, insurance companies, or other organizations can sell their policies directly to the poor through agents who are paid by salary, sales commission, or both. In this model, the same entity bears all costs and risks associated with the product and also performs all distribution and servicing functions. In short, the microinsurance scheme is in charge of everything: both the design and delivery of the products to the clients and working with external healthcare workers to provide the services. The entity (e.g., an NGO) is the insurer and also provides the healthcare services. This model has the advantage of offering microinsurance schemes with full control but the disadvantage of higher risks (Microinsurance Network, 2018).

Most (about 37%, or 48 out of 130) HMI schemes around the globe follow the partner-agent model, followed by community-based or mutual model (about 22%, or 29 out of 130). The full-service model and provider-based model comprise about 15% (20 out of 130) and about 9% (12 out of 130) of HMI schemes, respectively. There are also examples of the social insurance model (about 10%, or 13 out of 130) and commercial insurance model (6%, or 8 out of 130; ILO, 2013).

Evidence on the Impact of HMI Schemes

In this section, we present some evidence on the impact of HMI schemes around the world. The literature mainly focuses on the following categories of outcomes: access to healthcare, financial risk protection by reducing out-of-pocket (OOP) expenditure and/or catastrophic health expenditures, and economic factors (poverty, household savings, and household borrowing).

There is a debate regarding the impact of HMI, especially on its worthiness as a means of financial risk protection. Some studies show that HMI protects its beneficiaries from the exposure of financial risks by reducing OOP health expenditure and/or catastrophic health expenditure; others show that HMI contributes to reduced borrowing as well as lower incidence of falling into poverty by the enrollees. HMI has also been shown to have a positive safeguarding effect on household savings, assets, and consumption patterns (Table 3).

Table 3. Evidence on Impact of HMI by Health and Economic Outcomes

Type of Indicator

Impact

Country

Reference

Access to healthcare and/or utilization of care

• Sick individuals with HMI were 15% more likely to get treatment than individuals in non-member households

Tanzania

Msuya, Jütting, & Asfaw, 2004

• People with HMI were more likely than the uninsured to seek malaria treatment

Blanchard-Horan, 2007

• Individuals with HMI were less likely to self-diagnose and self-manage illness

Vietnam

Wagstaff & Pradhan, 2005

• Utilization of healthcare among the insured compared to the non-insured increased by 12% to 244% in different HMI schemes in India

Prinja, Chauhan, Karan, Kaur, & Kumar, 2017

Out-of-pocket expenditure

• Members of the HMI schemes incurred much less OOP outlays, at the point of service, when seeking healthcare

Tanzania, Uganda, and Benin

Dekker & Wilms, 2010

• 67% of insured households at ACCORD HMI scheme and 34% of insured households at SEWA HMI scheme were protected from making OOP payments for healthcare

India

Devadasan, Criel, Van Damme, Ranson, & Van der Stuyft, 2007

• Health Care Fund for the Poor made a significant reduction in OOP expenditure among the insured, through a price reduction effect

Vietnam

Wagstaff, 2007; Pham & Pham, 2012

Catastrophic health expenditures

• HMI was proven to be providing financial protection to the member households against CHE during episodes of illness, where CHE had been defined as OOP spending on healthcare that exceeds 25% of the total household budget

Tanzania

Kihaule, 2015

• In the absence of community-based health insurance, 8% of the households insured with a ACCORD HMI scheme and 49% with a SEWA HMI scheme would have faced CHE

India

Devadasan, Criel, Van Damme, Ranson, & Van der Stuyft, 2007

• HCFP HMI program helped reduce the incidence of CHE when dealing with adverse health events, by increasing the overall health awareness among the beneficiaries that encouraged them to go for more regular medical check-ups

Vietnam

Wagstaff, 2007

Poverty

• Grameen Kalyan HMI had some role in headcount poverty reduction

Bangladesh

Hamid, Roberts, & Mosley, 2011a

• A HMI scheme called RMHC reduced poverty headcount by 8.3% to 13.1% while a government-led NCMS reduced headcount poverty by 3.5% to 3.9%

China

Yip & Hsiao, 2009

Household savings

• “Yeshaswini” CBHI scheme made the use of household savings to pay for healthcare up to 74% less among the insured than the uninsured

India

Aggarwal, 2010

Household borrowing

• The Yeshasvini CBHI scheme made the total borrowings 30% to 36% less than the non-enrollees

India

Aggarwal, 2010

Note: HMI = health microinsurance.

Source: Reconstructed from Habib et al. (2016) and Leatherman, Christensen, and Holtz, (2010).

On the contrary, 9 out of 13 studies in the Indian context reviewed by Prinja et al. (2017) found no financial risk protection (i.e., no decrease in OOP expenses), and 5 showed an increase in the exposure of financial risk through increased OOP outlays. Three-fourths of the studies using catastrophic health expenditure as a measure of financial risk protection showed a higher incidence of catastrophic health counts. The studies evaluating state -sponsored HMI schemes including Rashtriya Swasthya Bima Yojna (RSBY), showed similar results.

Viability of Health Microinsurance Schemes

The viability of a HMI scheme depends on its institutional strength, including financial sustainability, scaling up, and replication. However, information about the latter two factors is not available in all the cases under this review. Hence, the focus is on the dimensions of viability where the information is available. The discussion is restricted to South Asia as most of the existing literature regarding the viability of HMI focuses on this context.

We found three studies in this group. A report, prepared for the Health Finance and Governance Project of the United States Agency for International Development, identified the major challenges faced by HMI schemes in Bangladesh (Hamid, 2016). The report included a number of HMI schemes, including Gonoshasthaya Kendra (GK), Sajida Foundation, Dhaka Community Hospital, Grameen Kalyan, Amader Shasthya, Diabetic Association of Bangladesh, Developing Inclusive Insurance Sector Project and Niramoy. All but that of the Sajida Foundation are voluntary schemes. The latter two were pilot schemes and are no longer in operation. Neither of the existing schemes apart from the Sajida Foundation was able to recover its operating costs. GK, the oldest HMI in Bangladesh, was able to recover only 35% of its operating costs after about 50 years of operation. The schemes mainly received subsidies either from their mother organization or sister concerns. The study also identified that all the schemes had low enrollment rates as well as severely low renewal rates. These schemes were neither scaled up, as per expectation, nor replicated. A detailed list of factors, as depicted in Table 4, including demand side, supply side, provider side, and regulatory aspects, were considered as responsible.

Table 4. Challenges of HMI Schemes in Bangladesh

Aspect

Challenges

Demand

Lack of confidence or trust on prepayment mode

Low enrollment and low renewal

Negative perception and lack of insurance awareness

Lack of affordability to pay premium and more weight attached to present consumption

Supply

Voluntary nature of the schemes

Low level of benefits

Lack of continuum of care

High co-payment charged

Provider

Lack of full-fledged private hospitals

High price of services of private hospitals

Lack of fiscal autonomy of the public hospitals to be empaneled into insurance scheme

Lack of effective referral mechanism

Regulatory

Regulatory ambiguity between Microfinance Regulatory Authority and Insurance Development and Regulatory Authority

Note: HMI = health microinsurance.

Source: Reconstructed from Hamid (2016).

Hamid (2016) concludes that MFIs have not yet been able to leverage their size and skills to offer effective HMI in Bangladesh. Currently, Bangladesh does not have an insurance culture, and hence the lack of trust in insurance institutions is high. Additionally, MFIs lack the knowledge and the necessary skilled manpower to offer insurance. Existing schemes offer unattractive benefit packages, high copayments, and poor claim settlement; refusal or slow payments; and partial reimbursements. Finally, the lack of network of healthcare providers implies local coverage and weak referral linkages (there is no continuum of care).

Weilant (2015) conducted a comprehensive study on the sustainability of five schemes (Nirapotta–Sajida Foundation, Bangladesh; Shasthayabima–GK, Bangladesh; Naya Jeevan, Pakistan; Tata AIG–RSBY, India; and ARY, India) in three countries of South Asia. Tata AIG–RSBY is different from the other traditional HMI schemes, as both central and state governments of India fund it.

Weilant (2015) viewed sustainability from a financial arrangements and outcomes perspective. Sustainability was classified into one of five categories: (a) unprofitable, unsubsidized, and unsustainable; (b) unprofitable with losses subsidized; (c) profitable with explicit subsidy; (d) profitable with implicit subsidy; and (e) profitable, unsubsidized, and self-sustaining. The framework used to assess HMI sustainability in this study is based on four drivers: achieving scale, controlling claims costs, managing expenses, and using subsidies. This article focuses on the scaling and subsidy issues. A detailed review of the study is depicted in Annex Table A1.

The study found that none of the schemes were profitable, unsubsidized, and self- sustaining. On the other hand, none of the schemes was unprofitable, unsubsidized, and unsustainable. The Bangladeshi and Pakistani schemes were declared unprofitable with losses subsidized while the Indian schemes were classified as profitable—one with explicit subsidy (Tata AIG–RSBY) and the other with implicit subsidy (ARY, India).

The author also found that while ARY and Naaya Jeevan had limited scale, Tata AIG–RSBY and GK achieved good scale. As the Nirapotta product is compulsory for all of Sajida’s microcredit borrowers, it experienced several constraints to scale up after its initial growth. Sajida’s limited ability to increase its loan base in a competitive microcredit market in Bangladesh may restrict its further growth to a large extent. The study raises a grave concern about the small-scale private HMI initiatives as they may frustrate the overall risk pool within a given geographic region. As a remedial measure the study wished to coordinate the smaller private schemes with and complement or supplement larger government schemes.

The study further found that all of the schemes used some forms of subsidy, such as cross-subsidization of losses from external or related entities, cross-subsidization of premiums between classes of the insured, explicit premium subsidies, and implicit subsidies. The author predicted that the HMI schemes would not be sustainable without using some form of subsidy.

The viability issue was also discussed in a research brief conducted by Koven et al. (2013) on 10 HMI schemes in India, including two terminated products. The functional schemes were UPLIFT India Association, Yeshasvini Cooperative Farmers Health Care Trust, Self Help Promotion for Health and Rural Development, ICICI Lombard General Insurance, MicroEnsure, VimoSewa, BASIX, and SAS Poorna Arogya Healthcare while the terminated ones were Swasth India and Bajaj Allianz.

The brief analyzed the schemes in terms of program age, scale, business model, product design, distribution, subsidy, competition, and enrollment mode. The study found no correlation with program age and scalability or financial sustainability. It also found that the government subsidy drove up the scale of government-supported HMI schemes but scaled down the stand-alone ones. The stand-alone HMI schemes spent a large proportion of premiums for administration compared to those that were subsidized. The government-subsidized HMI schemes offered higher benefits and attractive packages. No scheme was found to achieve profitability without subsidy. Voluntary enrollment in the stand-alone HMI schemes did not ensure significant as well as sustained participation, especially those tied up with MFIs. The problem would be further intensified with the ongoing declining trend of the drying up of donor subsidy. There is also evidence that those who participated in the competitive public bidding to serve the RSBY beneficiaries were bidding below costs, which may challenge the viability of the government-supported HMI schemes in the future.

The author concluded that, there was no workable business model for Indian HMI schemes with subsidy or without subsidy. They added that HMI was not a viable strategy, as individual voluntary enrollment lacked significant enrollment and renewal, especially for the schemes targeting relatively small populations.

A roundtable discussion organized by Sanofi Espoir Foundation and Agence Française de Développement in October 2015 also highlighted the viability issue of HMI schemes. This meeting came to a similar conclusion that HMI schemes are neither scalable nor viable without government subsidy (Sanofi Espoir Foundation & Agence Française de Développement, 2015).

Discussions and Conclusions

Analyzing viability of HMI was the key focus of this article. It analyzed the concentration as well as the impact of HMI schemes. As the article is based on secondary information, it could not provide the analysis in some crucial categorizations (e.g., pilot vs. post-pilot; government sponsored vs. NGOs/MFIs owned) due to lack of adequate information. Despite these shortcomings, the findings are useful for policy discussions, especially for finding the answer to the critical question of whether HMI is a viable route to provide primary healthcare to the rural population working in the informal sector.

The findings show that acceptance of HMI, an innovative health financing mechanism for the informal rural economy, has grown widely in low-income countries, especially in South Asia and East Africa. India is the main hub of HMI schemes. Bangladesh and Kenya also possess a good number of schemes. As discussed, HMI offers its products mainly through four delivery channels, including partner-agent, community-based, provider-driven, and full service. About 60% of HMI products are delivered through partner-agent and community-based models. Most of the schemes accept voluntary enrollment of beneficiaries. Irrespective of the nature of enrollment and types of delivery channels, HMI schemes demonstrate their potentiality by yielding significant impact on health and economic outcomes of the beneficiaries. However, there is strong evidence that HMI fails to provide financial risk protection to its enrollees.

The findings also show that scale is a major concern, especially in the stand-alone initiatives enrolling clients through a voluntary basis. Small scale led by low enrollment and few renewals are common concerns of the voluntary HMI schemes in India, Bangladesh, and Pakistan. Unattractive benefit packages that feature mainly primary care, high copayments, a lack of continuum of care, a low level of awareness, and a negative perception about prepaid mechanisms are largely responsible for low and unsustained participation in these countries. Further scaling up by the mandatory scheme tied up with micro lending (e.g., Sajida’s Nirapotta in Bangladesh) is difficult in a competitive microfinance market like Bangladesh.

Dependence on external (e.g., government or donor) or internal subsidy is another common concern of HMI schemes. For example, GK, the oldest HMI scheme in Bangladesh, hardly recovered 35% of the operating costs from its HMI activities. Its business enterprises, such as a medical college hospital, a private university, and a pharmaceutical company, usually support the HMI activities. The declining trend of donor subsidy makes the HMI schemes subsidized by externals donor more vulnerable. There is also concern about the dependence on government subsidy. Bidding below the cost price for winning the competitive bids leads to unattractive benefit packages, which ultimately affects the scale of the schemes.

The challenges and constraints are more critical in the country-specific context. For example, in Bangladesh, there is an absolute lack of full-fledged private facilities to serve the rural people through HMI mechanisms. Due to lack of fiscal autonomy, government facilities also cannot take part in any insurance-based mechanism. Hence, there is a huge crisis in providing services through HMI in rural areas. There is also a severe regulatory dilemma to run HMI activities in Bangladesh.

All of these challenges and constraints restrict the scale and profitability of HMI initiatives, especially the voluntary ones owned by NGOs and MFIs, in South Asian countries. This may lead the existing organizations to quit HMI activities. As evidenced, 14 out of 130 HMI schemes have already ceased HMI operations. Thus the existing HMI practices, especially those owned by NGOs and MFIs, are not a viable route to provide healthcare to the rural population of the informal economy. However, HMI schemes may play some supportive roles to implement a nationalized scheme, if any. There is also concern about the institutional viability of the organizations (e.g., ownership and management efficiency). Future research may address this issue.

Further Reading

Bergkvist, S., Wagstaff, A., Katyal, A., Singh, P., Samarth, A., & Rao, M. (2014). What a difference a state makes: Health reform in Andhra Pradesh. World Bank Policy Research Working Paper. Washington, DC: World Bank.Find this resource:

Dhanaraj, S. (2014). Health shocks and coping strategies: State health insurance scheme of Andhra Pradesh, India. WIDER Working Paper. Helsinki, Finland: WIDER.Find this resource:

Fan, V. Y., Karan, A., & Mahal, A. (2012). State health insurance and out-of-pocket health expenditures in Andhra Pradesh, India. International Journal of Health Care Finance and Economics, 12(3), 189–215.Find this resource:

Katyal, A., Singh, P. V., Bergkvist, S., Samarth, A., & Rao, M. (2015). Private sector participation in delivering tertiary health care: A dichotomy of access and affordability across two Indian states. Health Policy and Planning, 30(1), 23–31.Find this resource:

Philip, N. E., Kannan, S., & Sarma, S. P. (2012). Utilization of comprehensive health insurance scheme, Kerala: A comparative study of insured and uninsured BPL households. BMC Proceedings, 6(Suppl. 5).Find this resource:

Rao, M., Katyal, A., Singh, P. V., Samart, A., Bergkvist, S., & Kancharla, M. (2014). Changes in addressing inequalities in access to hospital care in Andhra Pradesh and Maharashtra states of India: A difference-in-differences study using repeated cross-sectional surveys. BMJ Open, 4(6).Find this resource:

Sood, N., Bendavid, E., Mukherji, A., Wagner, Z., Nagpal, S., & Mullen, P. (2014). Government health insurance for people below poverty line in India: Quasi-experimental evaluation of insurance and health outcomes. BMJ, 13(349).Find this resource:

References

Aggarwal, A. (2010). Impact evaluation of India’s “Yeshasvini” community‐based health insurance programme. Health Economics, 19(Suppl. 1), 5–35.Find this resource:

Ahmed, M., Islam, S. K., & Quashem, M. A. (2005). Health microinsurance: A comparative study of three examples in Bangladesh. CGAP Working Group on Microinsurance, Good and Bad Practices, Case Study No. 13. Munich, Germany: International Labour Organization.Find this resource:

Ahsan, S., Khalily, M., Hamid, S., Barua, S., & Barua, S. (2013). The microinsurance market in Bangladesh: An analytical overview. The Bangladesh Development Studies, 36(1), 1–54.Find this resource:

Alkenbrack, S., & Lindelow, M. (2013). The impact of community‐based health insurance on utilization and out‐of‐pocket expenditures in Lao People’s Democratic Republic. Health Economics, 24(4), 379–399.Find this resource:

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Appendix

Table A1. Driving Factors of Viability for Five South Asian Schemes

ARY (India)

Tata AIG–RSBY (India)

Shasthayabima–GK (Bangladesh)

Nirapotta–Sajida (Bangladesh)

Naya Jeevan (Pakistan)

Achieving scale

  • Launched in 2006

  • 60,000 members as of 2008

  • CAGR = –54%

  • Achieved the largest scale of any scheme in this study

  • CAGR = +91%

  • By the end of 2010, RSBY had been insuring approximately 63 million individuals

  • Extreme scale is driven by the explicit central/state government premium subsidies

  • Has roots in the organization’s origins in 1972

  • Achieved relatively large scale

  • CAGR= +8%

  • GK currently insures over 500,000 lives, a reasonably large “scale” by HMI standards

  • Achieves relatively large scale, with over 500,000 lives currently insured

  • CAGR= +6%

  • Obstacle to achieving even larger scale is that growth in this product is directly tied to Sajida’s ability to increase its loan base

  • Launched in June 2009

  • 11,000 members as of June 2011

  • CAGR = +136%

Controlling claims costs

  • Claims ratio increased from 2009 (93%) to 2010 (139%)

  • Claims ratio has fallen to 40% in 2012

  • Claims ratio is the lowest of any of the claims ratios in the study

  • Claims ratio decreased from 2010 (41%) to 2012 (27%)

  • Reasons behind this favorable claims ratios compared to other insurers participating in the scheme are unknown

  • GK does not report “claims paid”

  • Claims ratio increased over the study period

  • Claims ratio increased from 2009 (58%) to 2012 (83%)

  • Claims ratio increased dramatically between 2010 (50%) and 2012 (96%) although it is reported to have declined in 2013 (71%)

Managing expenses

  • Uses technology to make cashless claim settlements which reduces administrative costs and fraud

  • Expense ratio started falling from 2009 (12%) to 2011 (1%) then increased in 2012 (4%)

  • Has a materially lower expense ratio than the other two schemes Tata AIG-RSBY and Nirapotta

  • This is driven by the implicit subsidy from Biocon Foundation as well as the free enrollment services, health clinics, and other discounted items at no cost

  • Uses technology to make cashless claim settlements

  • Expense ratio decreased from 2010 (23%) to 2012 (18%)

  • Enrollment costs for the program are kept low through mass enrolments that use portable enrollment technology

  • Given the unique structure, expense information was not included

  • Has the highest expense ratio (21% in 2009 and 28% in 2012) of any scheme in this study due to lack of appropriate technology for a pure cashless system

  • Efficiency was improved dramatically in 2012 through allowing reimbursements to be paid at the insured’s local MFI branch instead of a central location

  • This expense saving is reflected in decreasing expense ratio from 2011 (36%) to 2012 (28%)

  • Given the unique structure, expense information was not included

Using subsidies

  • Profitable with implicit subsidy from partnership with Biocon Foundation

  • Premiums paid by subscribers without any explicit subsidies

  • Profitable with explicit premium subsidy from state/central government

  • Subsidy varies from 10% to 25% for state government and for central government it varies from 75% to 90%

  • Unprofitable with losses in each calendar year which are subsidized by gains from GKs commercial ventures

  • Unprofitable with losses which are subsidized by gains from Sajida’s MFI loan portfolio

  • Unprofitable with losses subsidized by grants from donor organizations

Profit ratio

  • Changes in the premiums and product’s benefit design quickly moved from a negative profit ratio to instant and rapidly increasing positive profit ratios, though to the detriment of the number of insured lives covered

  • Profits ratio started increasing from 2009 (–5%) to 2012 (56%)

  • The only scheme that experienced positive profit ratios in each year of the study

  • Profits ratio increased from 2010 (36%) to 2012 (55%)

  • Loss in each calendar year

  • Profits ratio was –52% in 2009 and –33% in 2012

  • Profits ratio started decreasing from 2009 (21%) to 2012 (–11%) for increasing claims ratio over the study period

  • It was not possible to determine the aggregate profitability for the carriers that participate in this scheme

Note: CAGR = compound annual growth rate.

Profit ratio = (Profit/loss) ÷ (Total revenue); Expense ratio = Expenses ÷ Earned premium.

Source: Prepared based on Weilant (2015).