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Does health insurance program close the equality gap?: lesson learn from Indonesia Budi Hidayat* Hasbullah Thabrany Center for Health Economics and Policy Faculty of Public Health, University of Indonesia *Correspondence: Budi Hidayat Faculty of Public Health University of Indonesia Depok, Indonesia 16424 Email: b_hidayat@hotmail.com

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Abstract Background: Indonesia introduced a social health insurance scheme for civil servants (Askes) since 1968. In the early 1990, health financing reforms were initiated, e.g. the Social Security Act #3/1992 mandates all employers in the private sector to register their employees in the SHI for private employees (Jamsostek); the Insurance Act #2/1992 allows private insurance firms to offer a voluntary health insurance (Private insurance). Objective: To investigate the changes and inequality in health insurance coverage between 1993 and 1997 in the context of associated health financing reforms in Indonesia. Methods: Individuals age between 19 and 60 years from a panel of the Indonesian Family Life Survey data conducted in 1993 (n=16,166) and 1997 (n= 16,489) were analysed. The author compared the proportion of the insured over study period. The concentration indexes (CI) was utilized to quantify the degree of inequality. Robust standard errors for the CI were calculated using a New-West regression estimation. To show inequality empirically, the concentration curves (LI(p) was adopted.

Findings: Health insurance coverage increased from 9.4% in 1993 to 13.6% in 1997 (increase 43%), which was contributed by Jamsostek, Private insurance, and Duplicate (individuals with more than one scheme). Individuals covered by Askes declined by about 2.8%. The CI of the insured for 1993 and 1997 was 0.2676 (p<1%) and 0.3395 (p<1%) respectively, implying that the insured was pro-rich distributed. The finding is supported by the LI(p) which is below the equality line. The LI(p) for 1997 was closer to the equality lines than the LI(p) for 1993, suggesting that health financing reforms have a positive impact on reducing the equality gap. Conclusions: Current health insurance schemes cover only small fraction of the population and its distributions was concentrated among the rich. Proposing a National Social Health Insurance would foster the memberships and could ensure equality of the insured. The policy, however, must be supplemented with the demand-side subsidy program to pay premium for the poor. Keywords Social Health Insurance, equality, IFLS, Indonesia

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Introduction

As health insurance can be effective in increasing access to health services, a number of

countries have undertaken substantial expansions of their formal health insurance either

as a voluntary or mandatory program. The mandatory health insurance program (Social

Health Insurance – SHI) scheme has been introduced in many countries. Not only for

high-income countries but also in low- and middle-income ones have been implemented

the SHI (see, Barnighausen & Sauerborn 2002, for review).

In Indonesia, the SHI for civil servants has been introduced since 1968. Additionally,

health financing reforms were initiated in the early 1992 on which the Government

passed the Social Security Act #3/1992 and the Insurance Act #2/1992. The former Act

mandates (but optional) all employers in the private sector to register their employees in

the SHI for private employees, while the later allows a private insurance firm to offer a

voluntary health insurance products to unspecified populations. The two regulations were

being implemented between two rounds of a panel survey conducted in 1993 and 1997,

namely the Indonesia Family Life Survey (IFLS)

Notwithstanding the above insurance opportunities, current insurance scheme in

Indonesia only cover approximately 14% of the population (Thabrany 2001). Further, the

utilization of formal health care and health outcomes of the Indonesian population

declined during period 1997 and 1998 (Waters et al. 2003). These have motivated the

Government to expand the insured population toward universal coverage by proposing a

national SHI scheme. A National Social Security Act #40/2004 was passed in October

2004.

Research interest has frequently focused on the effects of health insurance on health care

demand (e.g., Waters 1999; Vera-Hernandez 1999; Trujillo 2002) and was mostly

conducted in well-establish economic countries such as in the US (Manning et al. 1987;

Kreider and Nicholson 1997), Australia (Cameron et al 1988), German (Geil et al. 1997),

Switzerland (Holly et al 1998), French (Chiappori et al 1998, Delattre and Dormont

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2003). There has been also a large body of researches interest in inequality which mostly

focused on health outcome measures (see, Wagstaff and Van Doorslaer (2000), for

review), and as such based upon single cross-section of dataset.

This article takes advantage of the unique opportunity presented by the availability of the

dataset-the IFLS- that brackets two policy interventions of health financing reform in

Indonesia to address two issues: first, it investigates the changes of health insurance

coverage between 1993 and 1997; second, it examines inequality in health insurance

coverage over study periods. This article therefore will provide an evidence base of

whether proposing a national SHI would be welfare enhancing in terms of fostering the

memberships and reducing the equality gap of the insured.

Methods

Data Sources

This study used a panel of the Indonesian Family Life Survey (IFLS) data conducted in

1993/94 (IFLS1) and 1997/98 (IFLS2). The IFLS is a large-scale integrated socio-

economic and health survey that collects extensive information on the lives of

respondents, their households, their families, and the communities in which they live. The

survey was undertaken by the RAND Corporation collaboration with the Indonesian

researchers and various international agencies. The IFLS was described more fully in

Frankenberg & Karoly (1995) and Frankenberg & Thomas (2000) for the IFLS1 and

IFLS2, respectively.

The IFLS sample is representative of about 83% of the Indonesian population and

contains over 30,000 individuals living in 13 provinces on the islands of Java, Sumatra,

Bali, West Nusa Tenggara, Kalimantan, and Sulawesi. Owing to the fact that the IFLS is

a panel design, the sampling scheme for the first wave is the primary determinant of the

sample in the following wave. The sample scheme stratified on provinces, then randomly

sampled within enumeration areas (EAs) in each of 13 provinces. A total of 321 EAs

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were selected from a nationally representative sample frame used in the 1993

SUSENAS1. Within each EA, households were randomly selected using the 1993

SUSENAS listings obtained from regional offices of the Bureau Pusat Statistik (BPS).

In the IFLS1, a total of 7,730 households were sampled to obtain a final sample size goal

of 7,000 completed households. The surveys, in fact, succeeded interviews with 7,224

households in the IFLS1. In the IFLS2, a total of 6,751 households (93.5% of the IFLS1

households) were relocated and re-interviewed

Indicators and Data Analysis

Health insurance status is measured by proportion of the sample covered by insurance. It

is defined into the following mutually exclusive classes: mandatory insurance for civil

servants (Askes), mandatory insurance for private employees (Jamsostek), and voluntary

insurance (Private). Individual with a possible combination of having such insurance

(Duplicate) is separated and also included in the analysis. The total insured was then

calculated by summing up of these insurance schemes. Details characteristics of the

insurance schemes used in this study are described elsewhere (Thabrany 2005).

For purposes of this analysis, we focus on the sub-sample of individuals between 19 and

60 years. This sample represents the working age-group people where such insurance are

commonly mandated to. With the exception of Askes, people above 60 years of age have

problems to be covered by private insurance since employers usually terminated their

eligibility. Also, if they buy private insurance, insurers will normally reject them.

Changes in health insurance

To evaluate changes in health insurance status, the sample proportion covered by such

insurance was calculated for both 1993 and 1997 data. The analysis took into account the

sampling weight used in the study, and therefore it yields estimations for the entire 1 Survey Social Economics National (SUSENAS), conducted annually, is a survey of about 60,000 households.

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population rather than sample estimators (StataCorp 2001). We then compared the results

derived from 1993 and 1997, by using 1993 as the base data. This comparison allows us

to measure the changes over study periods.

Inequality in health insurance coverage

To investigate inequality in health insurance, we used the concentration index (denote as

CI). We calculated the CI for all insurance attributes as well as the CI for the total insured.

Robust standard errors for the inequality measures were estimated by a Newey-West

regression estimator (Newey and West 1994). This estimator corrects for autocorrelation

and any form of heteroscedasticity.

We also drew the concentration curve (henceforth LI(p)) which shows the cumulative

proportion of the insured against the cumulative proportion of the sample population

ranked by income (Wagstaff and Van Doorslaer 2000). If everyone, irrespective of the

income level, has exactly the same value of the coverage, the LI(p) will then coincide

with the diagonal (equality line). If the LI(p) lies above (below) the equality line, it

indicates inequality in favor to the individuals in the poorest (richest). The further LI(p)

lies from the equality line, the greater degree of inequality in the coverage across income

groups.

Results

Changes in health insurance status

Table 1 gives the degree of health insurance status found by the surveys in 1993 and

1997, disaggregated by income quintiles, residence (urban/rural), gender and marital

status. The p-value is reported for each insurance scheme corresponding to the selected

exogenous variables being examined. The table shows that the proportion of the total

insured increased from 9.4% in 1993 to 13.6% in 1997 (increase 45%). This changes

were contributed from the three schemes (namely Jamsostek, Private, and Duplicate).

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The coverage of Askes, by contrast, decreased approximately 3%. This pattern is

graphically shown in Figure 1.

Figure 1. Change in health insurance coverage between 1993 and 1997 (white bars: 1993; black bar: 1997)

0

4

8

12

16

Askes Jamsostek Private Duplicate Total

Insurance types

Perc

ent i

nsur

ed (%

)

Table 1 also shows that the proportion of the insured rose steadily as one move up the

income levels, which was apparent in both 1993 and 1997 for all the schemes and was

highly significant (p<1%). Overall the proportion of the total insured went from 1.4% and

24% of those in the lowest-income groups up to 2.4% and 30.6% of those in the highest-

income ones for 1993 and 1997, respectively (Figure 2).

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Figure 2. Change in total insured between 1993 and 1997 by income quintiles (white bar: 1993; black bars: 1997)

0

5

10

15

20

25

30

35

Lowest 2 3 4 HighestIncome quintiles

Perc

ent i

nsur

ed (%

)

In all insurance types, urban dwellers were more likely to be insured (p<1%). This

finding was true for both 1993 and 1997. Although female were less likely to by covered

by Askes in both 1993 and 1993, the finding was not significant. By contrast, Female had

higher chance to be covered by the other three insurance schemes. With regard to marital

status, ever-married people were more likely to be covered

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Table 1. Health insurance status (%) in 1993 (n= 16,166) and 1997 (n= 16,489) Askes Jamsostek Private Duplicate Total % Se % Se % Se % se % Se Year 1993 National coverage 7.3 0.6 0.7 0.1 1.0 0.2 0.4 0.1 9.3 0.7 By income quintiles

Quintile 1 1.2 0.3 0.1 0.1 0.1 0.1 0.0 0.0 1.4 0.3Quintile 2 2.9 0.6 0.6 0.2 0.6 0.3 0.1 0.1 4.2 0.7Quintile 3 4.9 0.7 1.1 0.4 0.3 0.1 0.1 0.1 6.5 0.8Quintile 4 12.1 1.3 0.7 0.2 1.7 0.3 0.7 0.2 15.2 1.4Quintile 5 19.1 1.5 1.0 0.3 3.1 0.7 1.0 0.2 24.1 1.7

F( 4, 310) 48.7*** 4.4*** 9.2*** 7.7*** 58.4*** By urban

Rural 4.0 0.5 0.2 0.1 0.3 0.1 0.1 0.0 4.6 0.5Urban 13.6 1.1 1.6 0.3 2.4 0.4 0.8 0.1 18.4 1.3

F( 1, 313) 67.7*** 15.7*** 23.8*** 18.5*** 102.5*** By gender

Male 7.8 0.6 0.5 0.1 0.7 0.2 0.6 0.1 9.7 0.7Female 6.6 0.5 0.8 0.2 1.5 0.2 0.1 0.0 9.0 0.7

F( 1, 313) 14.1*** 7.8*** 14.6*** 23.3*** 3.5** By marital status

Never-married 8.6 1.8 0.4 0.2 1.5 0.8 0.0 0.0 10.5 2.0Ever-married 7.2 0.6 0.7 0.2 1.0 0.2 0.4 0.1 9.3 0.7

F( 1, 313) 0.6 1.3 0.5 31.8*** 0.4 Year 1997 National coverage 7.1 0.5 2.0 0.3 1.6 0.2 2.8 0.4 13.5 1.0 By income quintiles

Quintile 1 1.3 0.3 0.7 0.4 0.3 0.1 0.1 0.1 2.4 0.6Quintile 2 2.9 0.5 1.1 0.4 0.9 0.5 1.0 0.3 5.9 1.0Quintile 3 6.6 1.1 2.6 0.6 1.0 0.3 2.5 0.5 12.7 1.5Quintile 4 10.5 1.2 2.5 0.6 2.4 0.6 3.7 0.8 19.1 1.7Quintile 5 15.6 1.4 3.5 0.7 3.9 0.6 7.6 1.1 30.6 2.0

F( 4, 372) 36.9*** 5.2*** 12.8*** 16.9*** 63.2*** By urban

Rural 4.8 0.6 0.8 0.3 0.9 0.2 0.9 0.2 7.4 0.8Urban 11.4 1.0 4.5 0.6 2.9 0.4 6.6 0.8 25.4 1.7

F( 1, 375) 31.9*** 29.5*** 17.7*** 52.4*** 94.0*** By gender

Male 7.2 0.6 1.9 0.3 1.9 0.3 4.1 0.6 15.2 1.1Female 7.0 0.6 2.1 0.4 1.4 0.3 1.8 0.3 12.3 1.0

F( 1, 375) 0.2 0.4 2.4 21.4*** 12.5*** By marital status

Never-married 5.9 1.6 3.3 1.4 3.1 1.2 3.9 1.4 16.2 2.7Ever-married 7.2 0.6 2.0 0.3 1.5 0.2 2.7 0.4 13.4 1.0

F( 1, 375) 0.7 1.0 1.8 0.7 1.1 Note: *** significant at the 1% level; ** significant at the 5% level.

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Inequality in health insurance status

The estimated concentration indexes (CI) and their t-statistics are presented in Table 2.

The significantly positive CI indicates that the higher income groups the more likely to be

insured. This was also apparent for all insurance types and for both 1993 and 1997

samples. This pattern corresponds with the LI(p) depicted in Figure 3 showing that the

curve lies far below the equality line2. It is important to note that the LI(p) for 1997 are

closer to the equality line than the LI(p) for 1993.

Figure 3. Concentration curves of the total insured, 1993 and 1997

0

20

40

60

80

100

0 100

Cumulative % sample, ranked by income quintiles

Cum

ulat

ive

% in

sure

d

Line of equality Insured_97 Insured_93

2 Figure 3 only present the LI(p) for the total insured. The LI(p) for each schemes are available for request, however.

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Table 2. Concentration index (CI) for 1993 and 1997 1993 1997 Insurance types CI

a (se)b

t-testc CIa

(se)b t-testc

Askes 0.2146 27.29* 0.1704 23.4* (0.0079) (0.0073) Jamsostek 0.0107 5.17* 0.0516 11.9* (0.0021) (0.0043) Private 0.0294 9.50* 0.0425 10.6* (0.0031) (0.0040) Duplicate 0.0128 6.49* 0.0749 15.3* (0.0020) (0.0049) Total insured 0.2676 31.16* 0.3395 35.9* (0.0086) (0.0095) aConcentration index and b(robust standard error) were calculated by a Newey-west regression. ct-test (a/b) for testing the null hypothesis of equality in the distribution of insurance coverage * indicates significant at the 1% level

Discussion

Over study period the total insured increased, yet the coverage was relatively low.

Possible explanation is because of low prices of health care services due to the extensive

subsidization of medical care cost, particularly at the public facilities. People currently

pay a little for the services and do not face the risk of incurring substantial costs when

using public health facilities. Hence, there is little incentive for those who are currently

not covered by insurance to join insurance which covers the costs of public facilities.

Evidence from other country supports this argument. Low costs for hospitals services in

Kenya, for example, reduce the incentive for potential contributors to enrol in the

compulsory National Health Insurance Fund (Kraushaar 1997).

Even though the coverage of Jamsostek increased, compared with the eligible members

the increasing rate is far behind than the expected. In 1997, for instance, there were

approximately 30 millions eligible members should be covered by the Jamsostek, but

only 1.2 millions (4%) were enrolled in (Thabrany 2001). The most plausible explanation

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is that the Act mandates health benefits of Jamsostek as ‘optional mandatory’, that is if

employers have in place better health benefits for their employees then they are exempted

from mandatory enrolment for the Jamsostek. Many employers choose to opt out, for

instance, by providing health benefits by themselves (self-insured) or purchasing private

insurance. Not surprisingly this study detects that health insurance coverage by voluntary

scheme (Private) increased by approximately 60%. This finding does imply that

‘conditional nature’ mandatory in Jamsostek would not be very effective to foster the

memberships.

Previous literatures have documented that health insurance increased access to formal

health care (e.g., Waters 1999; Vera-Hernandez 1999; Trujillo 2002), and the effects of

health insurance on health care access was more pronounce among the poorest (Hidayat

et al 2004). Meanwhile the estimate of the concentration index in this study indicates that

the distributions of the insured are unequally distributed. Individuals who are less likely

to be able to cover the costs of health care were also less likely to have health insurance.

This finding suggests that most of low income people are still facing the most barriers in

consuming health care.

What is the impact of the health financing reforms on reducing the equality gap? As

depicted in Figure 3, the concentration curves (LI(p)) of the total insured for 1997 dataset

was closer to the equality line than the LI(p) for 1993 dataset, implying that the health

financing reforms initiated in the early 1990 have positive impact on reducing the

equality gap.

If the priority of the Indonesian government is to speed up health insurance coverage and

ensuring equality of the insured, proposing a national social health insurance (SHI) do

work. The SHI has its ability to force all individuals to have insurance regardless of their

risks. To mandate all population, however, the government must provide demand side

subsidy program to pay the premiums for the poor. The SHI can also be seen as

mechanisms for improving efficiency of health care resource use, controlling the growth

rate of health care expenditure (Kutzin 1998) and increasing access to formal health care

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(Hidayat 2004). Further, McIntyre et al (2003) pointed out that the SHI could be a

powerful mechanism for enhancing health system equity and financial sustainability as

long as it is designed appropriately. Given that recent health insurance schemes in the

country tend to be fragmented and some parts of the SHI already existed (e.g., Askes and

Jamsostek schemes), further reforms are urgently needed. Details reforms agenda of the

National SHI which is beyond the scope of this study is described elsewhere (Hidayat et

al 2005).

Conclusions Between 1993 and 1997 the total insured of the Indonesian population increased by about

43%, but its coverage is still a low. Although over study period the distributions of the

insured are pro-rich, there is evidence that health financing reforms initiated in the early

1990 reduce this equality gap. Mandating population through a National Social Health

Insurance program could improve the coverage, and would ensure equality in health

insurance coverage. The government have to provide subsidy program to pay premium

for the poor (demand side subsidy), however.

Acknowledgments We thank the RAND Corporation, Los Angeles, USA for providing us with the IFLS

dataset. All views expressed and errors encountered are the author’ own.

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