Jurnal Ekonomi & Studi Pembangunan Volume 19, Nomor 2, Oktober 2018, hlm. 116-133 DOI: 10.18196/jesp.19.2.5003 THE IMPACT OF JAMKESMAS ON HEALTHCARE UTILIZATION IN EASTERN REGIONS OF INDONESIA: A PROPENSITY SCORE MATCHING METHOD Novat Pugo Sambodo Erasmus School of Health Policy and Management, Erasmus Universiteit Rotterdam Research Associate Pusat Kebijakan Pembiayaan dan Manajemen Asuransi Kesehatan, Medical Faculty Universitas Gadjah Mada Jl. Farmako, Sekip Utara, Yogyakarta 55281 Correspondence E-mail: pugosambodo@gmail.com Received: September 2018; Accepted: October 2018 Abstract: Underutilization of health care for the poor is one critical problem in Indonesia. Out of pocket share is dominant on overall health financing. Therefore, it is plausible that low demand of modern healthcare services mainly relates to financial aspect. In 2008, the government of Indonesia has introduced health insurance schemes for the poor to help them overcome the problem of medical costs barrier called Jamkesmas (Social Health Insurance). This paper examines the impact evaluation of Jamkesmas to health care utilization in Eastern Indonesia. Data are drawn from Indonesia Family Life Survey East (IFLS-East) that held in 2012. This data only covers the eastern regions of Indonesia that widely known has relatively lower performance in development and infrastructure. Moreover, this study employs Propensity Score Matching (PSM) approach to analyse the data. The results show that average treatment effect for treated group are positive for outpatient utilization. In addition, availability of the healthcare facility variables, travelling time and distance to district capital are fac- tors that determine Jamkemas coverage in Eastern Indonesia. Keywords: social health insurance, healthcare utilization, impact evaluation JEL Classification: I13, I15, H43 INTRODUCTION Underutilization of health care for the poor is one critical problem in Indonesia. Ac- cording to Somanathan (2008), out of pocket share during 1995 to 2004 was between 60-70% on overall health financing. Therefore, it is plausible that low demand of modern healthcare services mainly relates to financial aspect (Somanathan 2008, p. 1). Hence, Gov- ernment of Indonesia (GoI) tries to reform social safety nets in order to protect the most vulnera- ble family in the hardship situation, i.e. eco- nomics crises in 1997 and 2008. GoI has intro- duced various health insurance schemes for the poor to help them overcome the problem of medical costs barrier. Health insurance in Indonesia had been gone through several evolutions. It started with Dana Sehat in 1969, Jaminan Pemeliharaan Kesehatan Masyarakat (JPKM) in 1992, and Health Card in 1994. After that, it was followed by Social Safety Nets or Jaring Pengaman Sosial (JPS) which was introduced to mitigate the im- pact of Asian Financial Crisis in 1997-1998. Then, the GoI initiated Asuransi Kesehatan Untuk Masyarakat Miskin (Askeskin) in 2005-2007, and finally it is replaced by Jaminan Kesehatan Masyarakat (Jamkesmas)1 in 2008 (Vidyatama et al. 2014). Jamkesmas is a social assistance for healthcare that is provided for the poor and those who cannot afford the healthcare fee. GoI has allocated around 500 million USD or around 20% of all social assistance budget to funding Jamkesmas program. In addition, 1To avoid any confusion, there is also JAMKESDA which is a similar insurance but the regulation and coverage are under district or city local government responsibility. The Impact of Jamkesmas on Healthcare Utilization… (Novat Pugo Sambodo) 117 Ministry of Health appointed to implement this program starting from 2008 until early 2014. Currently, BPJS (Social Security Agency) pro- gram substitutes Jamkesmas with broader cover- age, i.e. not only for the poor. However, the lesson from Jamkesmas implementation remains relevant and valuable for policy analysis. There have been many studies evaluating health insurance program in Indonesia. The lat- est study by Vidyatama et al. (2014) finds that health insurance owner 8% more likely using healthcare service when falling sick and it be- comes 5% if people who are not sick are in- cluded in the estimation. Other study tries to contrast the effect of Askeskin and non-Askeskin (Aji et al. 2013). Their research finding supports the argument of financial barrier; both types of health insurance program can decrease out of pocket payment. Distance and location factors also have a significant influence on healthcare utilisation, especially for rural community. In contrast, people living in urban community are less sensitive to distance, but relatively more sensitive to medical fee (Erlyana et al. 2011). In brief, contributions of this paper have three points. First, this paper gives more atten- tion to eastern region of Indonesia than try to get national level studies. Most previous studies on the health insurance impact evaluation in Indonesia have a limitation on capturing geo- graphical aspect and eastern Indonesia focus. Nevertheless, this region is relatively lacking in many social development indicators as com- pared to the western regions. Furthermore, In- donesia Statistic Office reported that 70% of underdeveloped districts are located in eastern Indonesia. It hopes give more understanding of Jamkesmas implementation than get only general idea of national level. Second, this study also includes more var- iables such as travel time, distance and availa- bility of service variables. Unlike other datasets such as SUSENAS and RISKESDAS used by Vidayatama et.al (2014), and Sparrow et.al (2013), IFLS-East has a possibility to merge be- tween individual and household information with community or village data. IFLS-East data is the newest IFLS since the previous IFLS, IFLS 4 taken in 2007. Thus, this paper expect more update information as compared with other paper using previous IFLS data like IFLS 3 (Er- lyana et al. 2011) or IFLS 1 and IFLS 2 (Hidayat et al. 2010). This paper aims to analyse the impact of Jamkesmas on healthcare utilization in eastern part of Indonesia. With this objective, the study attempts to answer two research questions: (1) Does Jamkesmas significantly help the poor household to increase their health care utiliza- tion when falling ill? (2) Is there any difference of household choice preference between the public and the private health services given var- iables in the model? The following part of this essay briefly de- scribes Indonesian health insurance from re- form from 1998 (after economic crisis) with So- cial Safety Net (SSN) until recent implementa- tion of Social Security Agency (BPJS). Section 3 outlines some characteristics of data we use in this research. Empirical challenge and method- ology to deal with those challenges will be dis- cussed in section 4. Section 5 discusses the re- sult of this study and discussion. A final section highlights what this paper main finding and policy implication that we can make given the result from this paper. Reform in Indonesian Social Insurance Recently the Government of Indonesia (GoI) has set an ambition to have every citizen covered by insurance. GoI initiated Social Secu- rity Agency or Badan Penyelenggara Jaminan So- sial (BPJS) in 2014. It is a part of the implemen- tation of National Social Security System Law 2004 no. 40 and Social Security Agency Law 2011 no. 24. The law is introduced as a response of a rigid limitation in the insurance coverage that could only reach people with formal em- ployment status. These insurances include As- pen, Askes, Jamsostek and Asabri. Hence, the ul- timate goal of BPJS is to expand the coverage and improve the service to its beneficiaries. 118 Jurnal Ekonomi & Studi Pembangunan Vol. 19, No. 2, Oktober 2018: 116-133 Before Jamkesmas is implemented, Indone- sia has a long experience in providing insurance to its citizens, see Figure 1. In 1998 Indonesia in- troduced Jaring Pengaman Sosial or Social Safety Net as a response of economic crisis. The inten- tion of this program is to protect the poor from economic turbulence during this Asian Finan- cial Crisis 1997-1998. Shrinking indicators, like a massive decline of unemployment rate, high inflation and socio-politic crisis, make the poor more vulnerable. As part of JPS, a health card program is introduced to poor households to waive the fee to access the public healthcare provider, i.e. Public Health Centre (Puskesmas) and public hospital. In 2005 the GoI attempted to reform the social health insurance with broader benefi- ciaries. The government introduced Askeskin (health insurance for the poor) with the goal to expand the coverage to the informal sector workers that had not been covered by the ex- isting insurances. Afterwards, the GoI ap- pointed Ministry of Health to manage the fi- nancial aspect of Askeskin because there had been many requests for evaluation and im- provement. Then, it was renamed to Jamkesmas in 2008. In this program, the near poor group was included as eligible recipient. Furthermore, to standardize with the establishment of Na- tional Social Assistance, the GoI incorporated Jamkemas under National Health Insurance (JKN); Jamkesmas is managed by BPJS. With this merger, all Jamkesmas’s members automatically become member of National Health Insurance Program under BPJS. According to Harimurti et.al. (2013), there are several changes in Jamkesmas compared to Askeskin. First, the insurance fee is higher, it in- creases between IDR 5,000 to IDR 6,500 per in- dividual per month. Second, Jamkesmas only gives the limited basic package with some spe- cific exclusions of benefit and no cost-sharing. However, the member may get an extended package as add-in. Another benefit of Jamkesmas is that the medicine is covered with prescribed evidence. Jamkesmas holders can exercise the insurance in Puskesmas, Public Hospital and some registered private hospital (Harimurti et.al 2013, p.14). According to World Bank background pa- per (World Bank 2012), the official number of Jamkesmas recipients in 2010 approximately 74.6 million people. In term of budget, the average cost of health services utilized per card is Rp6,250, while the administrative cost itself is Rp9,362 (US$ 0.9). Moreover, this report also shows that Jamkesmas successfully cover around 41% of poor household. To manage the imple- mentation, Ministry of Health works together with public hospitals and local health centers as service providers and fee claims. BPJS regulates the eligibility and targeting. PT Askes handles the card production and distribution. Ministry of Finance is responsible for financing the dis- bursement. Local government also has a role to distribute Jamkesmas cards, provide sufficient socialization and undertake monitoring and evaluation. Source: Author‟s estimation based on Vidyatama et.al. (2014) Figure 1. Evolution of Health Insurance in Indonesia The Impact of Jamkesmas on Healthcare Utilization… (Novat Pugo Sambodo) 119 RESEARCH METHOD Data This paper utilizes the IFLS-East 2012 (Sikoki et al. 2013), which is the first survey that specifically covers the eastern provinces of In- donesia that have never been surveyed by 4 previous IFLS. It covers the information in indi- vidual, household and community level. There are seven provinces surveyed: Kalimantan Ti- mur, Nusa Tenggara Timur, Maluku, Maluku Utara, Papua, Papua Barat, and Sulawesi Tenggara. Moreover, IFLS-East data involves 99 villages consisting of 3,159 and 2,547 house- holds. Within these households, 10,887 individ- uals are interviewed (Satriawan et al. 2014). The richness of information presented in this dataset supports the analysis, thus leading to better es- timates in explaining the independent variables. IFLS-East data is accessible at this URL . This study exercises some dependent var- iables, including outpatient variables for total, public health centres and private health ser- vices. This paper also tries to capture the impact of Jamkesmas on inpatient utilization. Similar to outpatient outcome, it also classifies both public and private. Using the household expenditure dataset from IFLS, this paper constructs the out of pocket variables and the catastrophic health expenditure incident if the health expenditure of the household exceeds 15% of its total. The fundamental interest of this program evaluation study is to investigate the real im- pact of Jamkesmas on the main outcome. How- ever, we face some empirical challenges in the data. First, it is required to estimate the out- comes that capture the “true” difference be- tween the impact of Jamkesmas to the treated group and the untreated group. This cannot be done by simply estimating the outcome, like the outpatient and inpatient service utilization or health expenditure variable of people with and without Jamkesmas. That naive approach is not sufficient to capture the causal effect relation- ship between program and outcomes. Hence, the main challenge for this impact evaluation study is to get the counterfactual group in the data. Each household needs to get match com- parison with other household with same char- acteristic before get the program. Second, the allocation of Jamkesmas is based on the eligibility determined by Indonesian Ministry of Health, and certainly it is not selected randomly. Jamkesmas is only provided for the poor and the non-poor. Hence, measuring the outcome with simple Ordinary Least Square could produce a bias estimation. This is because there is also a possibility that some poor and near poor households who are eligible, but they do not receive the benefit of Jamkesmas. These eligible households have a tendency to have less utilization, even if they hold a health insurance. If the randomness of data is satisfied, we could make an estimation with other estimation model, such as randomized selection, regression discontinuity and difference-in-difference. However, since the randomness is not satisfied, the IFLS-East da- taset is a cross-sectional data. Lastly, we as- sumed that the eligibility of Jamkesmas are ob- servable in variables contained in IFLS-East da- taset. In this non-ideal condition, there is one method that can solve the counterfactual group problem. It is by looking the counterfactual group within dataset that has a similar or exact characteristic of the treated group, except the fact that they get the insurance. This can be done by using the exact match Propensity Score Matching (PSM).According to Rosenbaum & Rubin (1983), propensity score which is also known as balancing score, represent the condi- tional probability of observation that will be given a treatment based on the definite pre- treatment specification. Furthermore, the fun- damental reason of PSM is the absence of experimental framework of program and allo- cation of program in non-random setting. Then, the difference of treatment group and control group is not only in their status in program as a receiver, but also on the other characteristics 120 Jurnal Ekonomi & Studi Pembangunan Vol. 19, No. 2, Oktober 2018: 116-133 that might impact on the outcome. This bias can be avoided if we can get the corresponding sim- ilar households or individuals. After estimating the outcome of both groups, we then compare those outcomes. The average difference out- come of treated and untreated groups allows us to get impact of the program on beneficiaries. PSM approach has tree steps in order to get the average impact of the treatment. First, we need to estimate the probability of house- holds in datasets who are receiving Jamkesmas. This is based on several selected control varia- bles, which are observable. In this step, we can utilize Logit or Probit estimation. Both esti- mates only have minor difference, and the se- lection is based on the researcher‟s adjustment. In this study, the Logit method is used. The next step is to limit our analysis only for house- holds that have a range of common supports. Then, after obtaining the range of common support for each treatment group, we pair them with the untreated household having the same or the closest balancing score. Finally, in the last step we produce the average treatment effect on the treated group (ATT) by acquiring the aver- age difference of expected outcome (outpatient, inpatient, health spending) from people with and without Jamkesmas. Based on Jamkesmas and datasets charac- teristics, this research prefer to use PSM model that also used by Sparrow et al. (2013) and Pra- dhan et al. (2004) for Askeskin and Health Card program, respectively. As an extension of their work, this paper is to add more specific infor- mation data on the community infrastructure, travel time or distance, and availability of healthcare facility characteristic both public and private healthcare provider. The matching model using Logit estimation is shown as fol- low: ( ) (1) Equation (1) is the matching model, where Yi is an outcome of household probability that is covered by Jamkesmas (Pr (Yi=1)) i.e Y=1 if yes and Y=0 if no. In this logit estimation (equation 1) there are some variables that are included in the con- trol variables. The variables in the category αind represent factors attached to person in demo- graphic categories such as age, sex, years of ed- ucation, education level, marital status, while the category αhh represents the household level characteristics, such as education of household head, whether of household head is female and household expenditure (food, non-food and medical expenditure). Variables in the category αfas include the availability of the supply sides, such as the availability of health center facilities, tools availability and number of staff. The cate- gory αcomm comprises of community character- istics, such as geographical and infrastructure variables. This research also gives more atten- tion in this aspect as the sample relatively lacks in infrastructure. Furthermore, self-reported illness is not included in these covariates. It is because the inclusion of self-reported illness could lead us to a selection bias because the probability for people who are sick and actively looking for Jamkesmas is relatively high. This is also related that rich people has more tendency to report their illness rather than the poor. This research employs the five nearest- neighbours matching approach to match the treated group with the control group. The matching is based on the propensity score. Af- ter this process, the difference between those two groups is possible to calculate. To estimate the average impact of a treatment for a house- hold that get Jamkesmas in notation 𝑝𝑠𝑚, we determine the disparity between the expected outcome of the treatment group and the ex- pected outcome of the non-treated group as mentioned earlier. In mathematical notation, this can be expressed as follow (see Sparrow et.al 2013): 𝑝𝑠𝑚=𝐸 (𝑦𝑖𝐴=1, S=1) −𝐸 (𝑊𝑖𝑦𝑖𝐴=0, S=1) (2) In equation (2), (𝑦𝑖𝐴=1, S=1) is the expected out- come of household groups who receive The Impact of Jamkesmas on Healthcare Utilization… (Novat Pugo Sambodo) 121 Jamkesmas (A=1) and having a common support (S=1) as conditional requirement. Then, E (𝑊𝑖𝑦𝑖𝐴=0, =1) shows the potential outcome of „artificial‟ control groups based on the propen- sity score that do not have Jamkesmas (A=0) and have common support (S=1). We denote the weight estimated balancing score. RESULT AND DISCUSSION Jamkesmas Coverage Table 3 shows the experiment result of Jamkesmas coverage that has been classified into rural and urban groups, quartiles as well as gender. It is to be noted that this table is in in- dividual level. Even though the allocation might not be entirely received by the targeted groups, quartile 1 and quartile 2 still have the highest percentage of people holding the insur- ance, i.e. 52.61% and 43.21%, respectively. This pattern indicates that Jamkesmas has reached the target that is the poor and the near poor group. However, there is an indication that Jamkesmas is utilized by unintended groups, i.e. quartile 3 and quartile 4. This means that there is leakage of Jamkesmas allocation in eastern region of In- donesia. This finding is similar with a study done by Sparrow et al. (2013) and Vidyatama et.al (2014) in the national level case. In addi- tion, more people in the rural area take the ben- efit of Jamkesmas rather than the urban counter- parts. Around 44.71% of people in the rural area who receive Jamkesmas, while only 22.86% of urban people who receive Jamkesmas. Another finding is that there is no significant difference of allocation for male or female groups. They are equally likely to receive Jamkesmas. Source: Author‟s calculation based on IFLS-East 2012 Figure 2. Targeting of Jamkesmas Coverage in 2012 Table 1. Utilization and Health Spending for Household with or without Jamkesmas Holder Household with no Jamkesmas holder Household with Jamkesmas holder Total Outpatient 0.163 0.176 0.168 Public 0.086 0.122 0.101 Private 0.068 0.050 0.061 Inpatient 0.044 0.035 0.040 Public 0.037 0.034 0.036 Private 0.015 0.007 0.012 Out of pocket health expenditure (%) 1.539 0.861 1.267 Catastrophic health spending (more than 15% of total expenditure) (%) 0.020 0.007 0.015 Source: Author‟s estimation based on IFLS-East 2012 122 Jurnal Ekonomi & Studi Pembangunan Vol. 19, No. 2, Oktober 2018: 116-133 Table 1 exhibits a naïve comparison be- tween household with and without Jamkesmas with regards to the utilization of healthcare ser- vice, out of pocket expenditure and catastrophic health incidence. This table is based on the household level data. Jamkesmas’s holder has a slightly higher average of visitation than house- hold with no Jamkesmas. The value of 0.176 means that 17.6% of household with Jamkesmas is reported to access modern healthcare (either public or private) in the past 4 weeks. The dif- ference gets bigger in public healthcare pro- vider, which is 0.122 for Jamkesmas holder and only 0.086 for non- Jamkesmas household. This pattern differs from the case of outpatient pri- vate healthcare; the average number of people go to private healthcare provider is larger for non- Jamkesmas household. In terms of spend- ing, out of pocket health expenditure for non- Jamkesmas household is relatively higher, and that is almost double. Similarly, catastrophic health incidence spending is also higher for non- Jamkesmas household, though the value is very small. In general, it can be inferred that with this naïve analysis the utilization of healthcare is higher for the Jamkesmas holder and they pay less health spending. In Propensity Score Matching analysis, there are two properties that must be satisfied. First, there should be enough common support in balancing the treated and the untreated group. Second, the balancing properties are sat- isfied. Estimation on the propensity score shown in the table 6 on the appendix consists of 54 propensity score estimated for each variable. Using Logit estimation, the probability of household getting Jamkesmas coverage is calculated. Some variables show a positive coeffi- cient, which means that it has higher probabil- ity to receive Jamkesmas. For example, Uncondi- tional Cash Transfer (BBM BLT) is introduced as the compensation of subsidy cut on fuel; this might be the same eligibility requirement be- tween Jamkesmas and BLT. Other variables that also indicate a positive coefficient are the size of household, the accessibility to clean water, the accessibility to piped water, the private clinic‟s accessibility to water, and the residency of household in rural area Unexpected positive sign appears from group that has far proximity with hospital. This means that the longer travel time might positively correlates with the proba- bility to get Jamkesmas. There are also positive sign variables, although they are not statisti- cally significant, that are interesting to note. There are private clinics that provide health check-up examination services. Many villages have public transport facilities, and their main road is made from asphalt. We expect that im- proving availability and infrastructure might broaden the allocation of Jamkesmas. In contrast, there are variables that can significantly reduce the probability of Jamkesmas coverage. Variables, like Askes, Jamsostek and company insurance, have a negative sign and they are significant. This shows that households having other kind of insurance are less likely to receive Jamkesmas. Moreover, variables related to household assets, such asthe size of house (m2) and the vehicle ownership also reduce the probability of Jamkesmas coverage. This is desir- able because the richer households should have less probability to be covered by Jamkesmas. Interestingly, if one of the household members working in the government office, their propen- sity score is significantly lower. This could be because they are automatically covered by Ask- es. Moreover, the variable of the distance of vil- lage capital to district capital in kilometres has a negative value. This result is expected. Other distance and travel time related variables also have a negative sign, but not significant. The availability of private clinics is deter- mined by many variables. It is predicted that these variables have a positive sign. The accessi- bility of clean water is positive and significant. However, there is a variable that has a negative sign, i.e. the availability of dental service in pri- vate clinic. In the first property of balancing common support, PSM analysis does not obtain lack of The Impact of Jamkesmas on Healthcare Utilization… (Novat Pugo Sambodo) 123 common support. Table 9 in the appendices re- veals range of common support based on the number of observation whether it is off support or on support. In this table there are 36 out of 1953 are off support. It means 36 observation of treated group does not have match comparison group and dropped as a consequences. Meanwhile in the Figure 2 Distribution of the propensity score for treatment and control group, it shows the overlap pattern and also present how each group of treated are com- pared with some group of control (untreated). Furthermore in this matching step, 5 Nearest Neighborhood matching technique is em- ployed. In the balancing properties in table 10 in the Appendices, we can see that there are some variables do not satisfy balancing property. It means some of the differences between treated and control groups are large in those variables indicated by t-test show significant result. The author try to make some changes in the covari- ates by make some interaction variable but the significant feature in the t-test are unchanged. As a consequence, we need to get the new set of covariates that satisfied balancing properties. Due to the time constraint, author will limit the analysis here and will update with the newest balanced set of controls. Impact of Jamkesmas on Healthcare Utiliza- tion and Healthcare Expenditure Table 2 shows the result of the estimated impact of Jamkesmas on healthcare utilization using Propensity Score Matching method. In general, Jamkesmas’ holders has a higher proba- bility of using modern healthcare outpatient service than those without Jamkesmas. For total level, there is 2.9% of difference between the treated groups with the controlled groups. The probability of Jamkesmas’ holders using public healthcare facility is slightly higher, that is 3.6% difference. Hence, this shows how Jamkesmas could significantly impact the outpatient service usage. Table 2. Estimated Impact of Jamkesmas on Healthcare Utilization and Health Expenditure (PSM) Outpatient Inpatient Out of pock- et ex- penditure Catastrophic health spending (more than 15% of total expenditure) VARIABLES All Public Private All Public Private Total 0.0290* 0.0359*** -0.0053 0.0127* 0.0103 0.0036 -0.0395 0.0000 0.0154 0.0130 0.0103 0.0076 0.0085 0.0044 0.2416 0.0090 Quartile 1 0.0217 0.0177 0.0008 -0.0031 -0.0042 0.0043 -0.2009 0.0083 0.02748 0.02306 0.01779 0.01279 0.01376 0.00429 0.33174 0.00583 Quartile 2 0.0039 0.0067 -0.0041 0.0274 0.0301* -0.0001 -0.1645 -0.0156 0.0318 0.0266 0.0220 0.0137 0.0173 0.0061 0.3936 0.0173 Quartile 3 0.0505 0.0545** 0.0105 0.0038 0.0029 -0.0014 -0.1454 0.0063 0.0318 0.0277 0.0208 0.0173 0.0207 0.0114 0.5257 0.0213 Quartile 4 0.0647 0.0251 0.0310 0.0338 0.0258 0.0080 0.8784 0.0253 0.0400 0.0339 0.0297 0.0259 0.0269 0.0108 0.7853 0.0245 Rural 0.0298* 0.0183 0.0119 0.0139* 0.0133 0.0024 -0.1030 -0.0024 0.0173 0.0144 0.0115 0.0079 0.0088 0.0029 0.2691 0.0085 Urban 0.0221 0.0576** -0.0272 0.0130 0.0136 0.0033 -0.2923 -0.0034 0.0290 0.0286 0.0183 0.0200 0.0221 0.0131 0.4442 0.0181 Robust standard errors in parentheses *** p<0.01, ** p<0.05, *p<0.1 Source: Author‟s calculation based on IFLS-East 2012 124 Jurnal Ekonomi & Studi Pembangunan Vol. 19, No. 2, Oktober 2018: 116-133 As we can see in table 3, outcome of inpa- tient service utilization affected only in total level. The coefficient means that Jamkesmas’s holder has a bigger probability with around 1.3 higher, but it is not statistically significant for public and private categories. Decomposition in the quartile groups shows no considerable dif- ference. It is expected that the two lowest quar- tiles get the most of impact. However, the result does not meet this expectation. Moreover, the out of pocket health expenditure has a negative difference, although it is not statistically signifi- cant across the groups. Similar average treat- ment effect pattern also happens for the cata- strophic health spending incidence. This find- ing is similar with the result from Suryanto et.al (2013) using previous IFLS 3, IFLS 4, Susenas 2009 and 2010 that health cost assistance to the poor has no significant influence on reducing catastrophic health expenditure. The one reason to explain is because the informal sector and who poor reducing their health related ex- penses and decide to use traditional or even inappropriate method. Furthermore, the rural households who receive Jamkesmas have a higher probability to use the healthcare service in total level, both outpatient and inpatient service. However, this finding is different with the urban household receive Jamkesmas. The impact only occurs in the public outpatient service, but it has a bigger magnitude with 5.6% ATT. CONCLUSION The aim of this study is to investigate the impact of Jamkesmas on health care utilization of in eastern Indonesia using IFLS-east data. The prior knowledge of about eastern Indonesia is they are relatively less developed than western part of Indonesia. Thus, they need more atten- tion given their lack of infrastructure and health facilities and staff. We expect that Jamkesmas could reduce those barrier to access health ser- vices, with better targeting with better impact. Moreover, allocation of Jamkesmas is more likely goes to quantile 1 and 2 of income group. It reflects that Jamkesmas program that are re- ceived by people targeted as eligibility criteria that Jamkesmas for the poor and near poor. However, there is still some leakage with peo- ple in quartile 3 and 4 still get this health insur- ance. In addition, propensity score evaluation shows that people with longer distance and travelling time between village capital and dis- trict capital and health facilities like Puskesmas and private health provider has a less probabil- ity to get covered by Jamkesmas. In contrast with distance, if the availability of the Public Health Centre in that village is better, the higher probability of household participates in Jamkesmas program. As a main purpose of this study, results show that in general utilization in general In general, Jamkesmas’s holder has a bigger proba- bility to utilize in healthcare service especially for public health center but only in outpatient. Inpatient is not statistically significant impacted by Jamkesmas in public or private groups but in total level. Furthermore, Jamkesmas has no sig- nificant impact on health spending both out of pocket expenditure and the probability of cata- strophic health spending incidence. Within those findings, however, we need to note some point that some factors might af- fect utilization of Jamkesmas which are not cap- tured in the model. For example, the shock of when people is get chronic illness which will increase possibility for household to looking for Jamkesmas after get chronic condition. This study finds distance and travelling time varia- bles are significant variables to reduce Jamkesmas coverage in Eastern region of Indone- sia. Thus, improving more infrastructure or provision of transportation will help household participation in health insurance and health care utilization to get less time in travelling. The Impact of Jamkesmas on Healthcare Utilization… (Novat Pugo Sambodo) 125 REFERENCES Aji, B., De Allegri, M.D., Souares, A., and Sauer- born, R. (2013). The Impact of Health In- surance Programs on Out-of-Pocket Ex- penditures in Indonesia: An Increase or a Decrease?. International Journal of Envi- ronmental Research and Public Health, 10(7), 2995-3013. Erlyana, E., Damrongplasit, K.K., and Melnick, G. (2011). Expandinghealth Insurance To Increase Health Care Utilization: Will It Have Different Effects In Rural Vs Urban Areas?. Health Policy, 100, 273–81. Government Regulation Number 40 Year 2004 Concerning National Social Security System (Republic of Indonesia). Government Regulation Number 24 Year 2011 Concerning National Social Security Agency (Republic of Indonesia). Harimurti, P., Pambudi, E., Pigazzini, A and Tandon, A. (2013). The nuts and bolts of Jamkesmas, Indonesia‟s government-fi- nanced health coverage program for the poor and near-poor, The World Bank, UNICO Studies Series 8, Washington. Viewed 4 June 2015, . Hidayat, B., and Pokhrel, S. (2010). The Selec- tion of an Appropriate Count Data Model for Modelling Health Insurance and Health Care Demand: Case of In- donesia, International Journal of Environ- mental Research and Public Health, 7(1), 9- 27. Satriawan, E., Priebe, J., Prima, R.A., and How- ell, F. (2014). An introduction into the IFLS-East 2012: Sampling, question- naires, maps and socio-economic back- ground characteristics, TNP2K (Tim Nasional Percepatan Penanggulangan Kemiskinan). Viewed 16 March 2015, . Sikoki, B., Witoelar, F., Strauss J., Meijer, E., and Suriastini N.W. (2013). Indonesia Family Life Survey East 2012: User's guide and field report. Yogyakarta: Survey ME- TER. Somanathan, A. (2008). The impact of price sub- sidies on child health care use: evalua- tion of the Indonesian healthcard. Viewed 20 March 2015, . Sparrow, R., Suryahadi A., and Widyanti, W. Social Health Insurance for the Poor: Targeting and Impact of Indonesia‟s Askeskin Programme. Social Science & Medicine, 96, 264–71. Suryanto, B.A., Mukti, A.G., Kusnanto, H., and Satriawan, E. (2015). The Role of Health Insurance, Borrowing and Aids to Pay for Health Care on Reducing Cata- strophic Health Expenditure in Indone- sia. Viewed 4 June 2015, http://papers.ssrn.com/sol3/papers.cf m?abstract_id=2586648. Vidyatama, Y., Miranti, R., and Resosudarmo, B.P. (2014). The role ofhealth insurance membership in health service utilization in Indonesia. Bulletin of Indonesian Economic Studies 50(3), 393-413. World Bank. (2012), Jamkesmas health service fee waiver social assistance program and public expenditure review, World Bank Background Paper, viewed 4 June 2015,http://www.wds.worldbank.org /external/default/WDSContentServer/ WDSP/IB/2012/03/06/000356161_2012 0306010803/Rendered/PDF/673120WP 00PUBL0Background0Paper0040.pdf. 126 Jurnal Ekonomi & Studi Pembangunan Vol. 19, No. 2, Oktober 2018: 116-133 APPENDICES Table 3. Utilization of Outpatient and Inpatient at Public and Private Health Facility, IFLS East 2012 Outpatient Inpatient All Public Private All Public Private Quartile 1 (poorest) 0.137 0.090 0.041 0.023 0.023 0.003 Quartile 2 0.170 0.108 0.061 0.038 0.038 0.006 Quartile 3 0.180 0.106 0.056 0.042 0.034 0.014 Quartile 4 (richest) 0.191 0.089 0.098 0.068 0.052 0.029 Urban 0.170 0.106 0.055 0.062 0.049 0.022 Rural 0.165 0.094 0.064 0.025 0.025 0.004 Male 0.139 0.084 0.047 0.035 0.029 0.010 Female 0.194 0.113 0.074 0.046 0.041 0.013 Non-Papua Island 0.167 0.094 0.063 0.036 0.03 0.012 Papua Island 0.166 0.114 0.052 0.055 0.053 0.011 Total 0.167 0.099 0.061 0.040 0.035 0.012 Source: Author‟s estimation based on IFLS-East 2012 Table 4. Distribution of Out-of-Pocket Health Expenditure, Non-Food Spending Share and Inci- dence of Catastrophic Spending Occurence (Percentages) Out of pocket expenditure Share of non-food spending Catastrophic health spend- ing (more than 15% of total expenditure) Quartile 1 (poorest) 0.807 33.171 0.005 Quartile 2 1.208 38.100 0.015 Quartile 3 1.350 40.913 0.016 Quartile 4 (richest) 1.945 46.421 0.026 Urban 1.837 47.403 0.024 Rural 0.844 32.848 0.008 Male 1.297 38.803 0.016 Female 1.227 39.114 0.013 Non-Papua Island 1.242 39.844 0.012 Papua Island 1.328 35.927 0.023 Total 1.261 38.962 0.015 Source: Author‟s estimation based on IFLS-East 2012 Table 5. Health Expenditure Regression, 2012, Ordinary Least Square VARIABLES Coefficient Standard Error JAMKESMAS -339.617 (3,324.383) ASKES 9,486.302 (6,865.709) JAMSOSTEK -10,329.332 (8,109.217) Company insurance 799.733 (8,378.626) Company clinic -368.546 (7,594.197) Private Insurance 17,963.538 (18,190.075) Unconditional Cash Transfer (BBMBLT) -5,251.233* (2,147.330) Female household head -9,737.538+ (5,203.506) Household head education 24.536 (691.828) Household size -4,677.177** (1,367.664) Share under 6 female -18,317.522 (16,172.800) Share under 6 male -6,671.307 (13,702.742) Share 6 to 17male -10,869.672 (11,613.777) Share 18 to 60 female 6,338.932 (18,026.549) Share 60 up female -16,677.414 (11,078.186) Share 60 up male -5,552.574 (15,899.435) Owned House -5,484.773 (5,955.024) House size (m2) 90.276+ (49.385) Own water access -842.489 (3,132.538) The Impact of Jamkesmas on Healthcare Utilization… (Novat Pugo Sambodo) 127 Own vehicle 1,593.262 (6,333.295) Own piped water -9,784.529 (6,973.282) Self employed 9,808.427* (4,991.176) Self Employed with permanent workers 4,161.914 (16,331.479) Self Employed with permanent workers 6,710.701 (6,209.129) Working part-time 5,266.362 (5,049.198) Government official -915.305 (6,811.227) Casual worker in agriculture -3,825.328 (4,564.503) Casual worker non in agriculture -7,978.930 (7,309.612) Puskesmas has a water access 6,487.737 (5,506.652) Puskesmas offer check-up/health examination 6,404.672 (4,008.677) Puskesmas offer inpatient service -3,947.382 (4,974.984) Puskesmas offer dental service -3,719.917 (6,357.939) Puskesmas has a pharmacy 5,957.999+ (3,070.323) Private clinic has an electricity 7,731.782* (3,715.223) Private clinic has an access to water -756.747 (6,328.137) Private clinic provides an inpatient services -10,592.019 (17,199.239) Private clinic provides dental services 17,211.214+ (10,207.628) Private clinic has more than 1 medical staff 19,429.780 (19,735.290) Private clinic‟s medical staff number 6,933.733 (13,742.041) Private clinic provide check-up/health examination services -14,558.457* (6,050.481) Village has public transport facilities 4,328.199 (3,890.562) Village main road from asphalt -1,000.469 (2,721.279) Distance of district capital from village office (km) 30.379 (33.255) Distance of bus station from village office (km) 47.645 (77.010) Travel time to nearest PUSKESMAS from village office (hours) -20,912.816** (6,869.707) Travel time to nearest private clinic from village office (hours) 14,211.392** (5,373.004) Travel time to nearest traditional clinic from village office (hours) -18,367.031 (29,020.811) Travel time to nearest hospital from village office (hours) 917.153 (646.347) rural -14,109.628+ (7,360.844) Constant 15,267.004 (14,286.709) Observations 2,009 R-squared 0.122 Robust standard errors in parentheses ** p<0.01, * p<0.05, + p<0.1 Table 6. Propensity Score Function, Probability of Jamkesmas Coverage (Logit Estimates) VARIABLES Coefficient Standard Error P>|z| ASKES -0.8039761*** 0.250713 0.001 JAMSOSTEK -0.6501821** 0.2969173 0.029 Company insurance -1.140431* 0.6489512 0.079 Company clinic -0.1234484 0.5685474 0.828 Private Insurance -1.020746 0.7798305 0.191 Unconditional Cash Transfer (BBMBLT) 0.9906677*** 0.1352175 0 Female household head -0.0704081 0.1917069 0.713 Household head education -0.0012435 0.0158683 0.938 Household size 0.2013327*** 0.0348588 0 Share under 6 female -0.7868103 0.5262906 0.135 Share under 6 male -0.2807972 0.5155342 0.586 Share 6 to 17male 0.6789076 0.418534 0.105 Share 18 to 60 female 0.1915376 0.3982037 0.631 Share 60 up female 1.020724 0.4501642 0.023 Share 60 up male -0.3541693 0.5264139 0.501 Owned House 0.1857353 0.1565389 0.235 House size (m2) -0.003937*** 0.0015075 0.009 Own water access 0.256806** 0.1448193 0.076 Own vehicle -0.0985058** 0.1461105 0.5 128 Jurnal Ekonomi & Studi Pembangunan Vol. 19, No. 2, Oktober 2018: 116-133 Own piped water 0.3635692* 0.2124169 0.087 Self employed 0.2033447 0.1463234 0.165 Self Employed with permanent workers 0.2259828 0.5190333 0.663 Self Employed with permanent workers -0.0912295 0.1488595 0.54 Working part-time 0.0218014 0.1466572 0.882 Government official -0.3719803* 0.2193433 0.09 Casual worker in agriculture -0.1483717 0.3833932 0.699 Casual worker non in agriculture -0.0438928 0.3062193 0.886 Puskesmas has a water access -0.1455417 0.1941079 0.453 Puskesmas offer check-up/health examination 0.5217562 0.188935 0.006 Puskesmas offer inpatient service 0.2094606 0.1876386 0.264 Puskesmas offer dental service -0.2494966 0.2128469 0.241 Puskesmas has a pharmacy -0.4318904 0.2567635 0.093 Private clinic has an electricity 0.2716368 0.3095453 0.38 Private clinic has an access to water 0.4141801** 0.2117421 0.05 Private clinic provides an inpatient services -0.7895023 0.6733281 0.241 Private clinic provides dental services -2.863848*** 0.6773531 0 Private clinic has more than 1 medical staff -0.0716691 0.5759863 0.901 Private clinic‟s medical staff number -0.7292938 0.4800033 0.129 Private clinic provide check-up/health examination services 0.817454 0.302973 0.007 Village has public transport facilities 0.4014857 0.2259131 0.076 Village main road from asphalt 0.2893342 0.2040933 0.156 Distance of district capital from village office (km) -0.0023017* 0.0012272 0.061 Distance of bus station from village office (km) -0.0012068 0.0038828 0.756 Travel time to nearest PUSKESMAS from village office (hours) -0.4524845 0.5309834 0.394 Travel time to nearest private clinic from village office (hours) -0.1529145 0.484605 0.752 Travel time to nearest traditional clinic from village office (hours) -0.5236731 0.9445133 0.579 Travel time to nearest hospital from village office (hours) 0.1859342*** 0.0477327 0 rural 1.021743*** 0.2392876 0 Kalimantan Timur -1.393772*** 0.3369993 0 Sulawesi Tenggara -1.053196*** 0.2440458 0 Maluku -1.330475*** 0.317391 0 Maluku Utara -1.978016*** 0.2771026 0 Papua Barat -0.3076135 0.2586118 0.234 Papua 0.0107798 0.2345287 0.963 Constant -1.249778 0.7074271 0.077 Number of obs = 1953 LR chi2(54) = 678.37 Prob> chi2 = 0.0000 Log likelihood = -948.49491 Pseudo R2 = 0.2634 Source: Author‟s estimation based on IFLS-East 2012 Table 7. Impact of Jamkesmas on Healthcare Utilization (OLS) VARIABLES outpa- tient outpub- lic outpri- vate inpa- tient inpub- lic inpri- vate wmedi- cal ch_oop 10 ch_oop 15 Quartile 1 (poor- est) 0.027 0.024 -0.004 -0.001 -0.003 0.004 -0.119 0.001 0.007 (0.028) (0.025) (0.014) (0.011) (0.012) (0.003) (0.219) (0.014) (0.005) Quartile 2 -0.003 -0.012 -0.005 0.012 0.002 0.010 -0.144 -0.009 -0.017 (0.034) (0.031) (0.018) (0.014) (0.016) (0.010) (0.348) (0.016) (0.015) Quartile 3 0.024 0.046 -0.000 0.013 0.031 -0.008 -0.622* -0.032* -0.013 (0.034) (0.034) (0.016) (0.015) (0.025) (0.008) (0.312) (0.016) (0.012) Quartile 4 (Rich- est) 0.067 0.058 0.002 0.038 0.043 -0.019 0.502 0.037 0.011 (0.046) (0.037) (0.033) (0.033) (0.032) (0.015) (0.752) (0.040) (0.030) Rural 0.029 0.016 0.013 0.016* 0.013+ 0.003+ 0.000 -0.002 -0.001 (0.020) (0.016) (0.012) (0.006) (0.008) (0.002) (0.178) (0.009) (0.007) Urban 0.007 0.027 -0.017 0.014 0.021 -0.004 -0.227 -0.004 -0.009 (0.030) (0.031) (0.020) (0.024) (0.027) (0.013) (0.521) (0.027) (0.023) The Impact of Jamkesmas on Healthcare Utilization… (Novat Pugo Sambodo) 129 Papua 0.071* 0.059* 0.023 0.019 0.046+ -0.011 -0.218 -0.003 -0.023 (0.034) (0.028) (0.025) (0.021) (0.028) (0.008) (0.541) (0.030) (0.022) Non Papua 0.018 0.009 0.002 0.010 0.003 0.004 -0.057 -0.000 0.000 (0.019) (0.018) (0.011) (0.010) (0.010) (0.006) (0.240) (0.013) (0.010) Total 0.028+ 0.028+ -0.004 0.015+ 0.015 0.001 -0.234 -0.013 -0.010 (0.017) (0.015) (0.010) (0.008) (0.010) (0.004) (0.200) (0.011) (0.008) Source: Author‟s estimation based on IFLS-East 2012 Table 8. Descriptive Statistics Variables Observation mea n Standar Devia- tion min max Outpatient total 2,411 0.167 0.243 0 1 Outpatient public 2,401 0.098 7 0.200 0 1 Outpatient private 2,401 0.062 8 0.165 0 1 Inpatient total 2,411 0.038 6 0.119 0 1 Inpatient public 2,357 0.035 5 0.125 0 1 Inpatient private 2,357 0.011 0 0.0743 0 1 Out of pocket health expenditure Share 2,411 1.291 3.550 0 73.6 7 Catastrophic health spending 10% 2,411 0.028 2 0.166 0 1 Catastrophic health spending 15% 2,411 0.013 7 0.116 0 1 illness 2,411 0.725 0.296 0 1 JAMKESMAS 2,411 0.361 0.480 0 1 ASKES 2,411 0.129 0.335 0 1 JAMSOSTEK 2,411 0.056 8 0.232 0 1 Company insurance 2,411 0.018 7 0.135 0 1 Private insurance 2,411 0.014 9 0.121 0 1 Company clinic 2,411 0.013 7 0.116 0 1 Household head female 2,411 0.161 0.367 0 1 HH head education 2,411 7.737 4.569 0 18 Household size 2,411 4.288 2.057 1 16 Share under 6 female 2,411 0.066 8 0.119 0 0.66 7 Share under 6 male 2,411 0.070 5 0.122 0 0.60 0 Share 6 to 17 female 2,411 0.117 0.161 0 1 Share 6 to 17 male 2,411 0.119 0.155 0 1 Share 18 to 60 female 2,411 0.290 0.186 0 1 Share 18 to 60male 2,411 0.261 0.205 0 1 Share 60 up female 2,411 0.046 5 0.151 0 1 Share 60 up male 2,411 0.039 2 0.119 0 1 Household own BBM BLT card 2,400 0.229 0.420 0 1 Owns house 2,411 0.763 0.425 0 1 House size (m2) 2,410 62.25 49.92 4 800 Owns water access 2,411 0.307 0.461 0 1 130 Jurnal Ekonomi & Studi Pembangunan Vol. 19, No. 2, Oktober 2018: 116-133 Household has a vehicle 2,411 0.316 0.465 0 1 self employed 2,411 0.287 0.453 0 1 Working Part Time 2,411 0.484 0.500 0 1 Self-employed with permanent workers 2,411 0.015 3 0.123 0 1 Government Official 2,411 0.155 0.362 0 1 Private Worker 2,411 0.202 0.402 0 1 Unpaid family worker 2,411 0.388 0.487 0 1 Casual worker in agriculture 2,411 0.020 7 0.143 0 1 Casual worker not in agriculture 2,411 0.037 7 0.191 0 1 Puskesmas has an electricity 2,411 0.847 0.360 0 1 Puskesmas has a water access 2,411 0.320 0.467 0 1 Puskesmas has a pharmacy 2,411 0.895 0.306 0 1 Puskesmas offer inpatient service 2,384 0.305 0.461 0 1 Puskesmas offer inpatient service other than birth 2,384 0.263 0.441 0 1 Puskesmas offer check-up/health examination 2,384 0.570 0.495 0 1 Puskesmas offer dental service 2,384 0.613 0.487 0 1 Private clinic has an electricity 2,411 0.858 0.349 0 1 Private clinic has an access to water 2,411 0.226 0.419 0 1 Private clinic provides an inpatient services 2,276 0.027 7 0.164 0 1 Private clinic provide check-up/health examination services 2,276 0.055 8 0.230 0 1 Private clinic provides dental services 2,276 0.026 4 0.160 0 1 Private clinic has more than 1 medical staff 2,411 0.078 8 0.269 0 1 Private clinic's number of medical staff 2,411 1.102 0.432 1 4 Village has public transport facilities 2,411 0.809 0.393 0 1 Village main road from asphalt 2,411 0.687 0.464 0 1 Distance of bus station from village office (km) 2,323 9.728 26.69 0.01000 200 Distance of district capital from village office (km) 2,213 56.03 83.42 0.500 450 Travel time to nearest PUSKESMAS from village office (hours) 2,411 0.450 1.898 0 16 Travel time to nearest private clinic from village of- fice (hours) 2,411 0.254 0.801 0 6 Travel time to nearest traditional clinic from village office (hours) 2,411 0.081 3 0.0752 0 0.50 0 Travel time to nearest Hospital from village office (hours) 2,411 0.697 2.828 0 24 Travel time to nearest POSYANDU from village of- fice (hours) 2,411 0.118 0.345 0 3 rural 2,411 0.706 0.456 0 1 HH size square 2,411 22.62 22.43 1 256 Papua 2,411 0.285 0.451 0 1 Source: Author‟s estimation based on IFLS-East 2012 Table 9. Common Support by Number of Observations using 5 Nearest Neighborhood Treatment Assignment Common Support Off support On Support Total Untreated 0 1229 1229 Treated 36 688 724 Total 36 1917 1953 The Impact of Jamkesmas on Healthcare Utilization… (Novat Pugo Sambodo) 131 Figure 2 Distribution of the propensity score for treatment and control group using five nearest neighbourhood Source: Author‟s estimation based on IFLS-East 2012 Table 10. Balancing Properties of the Matched Samples using 5 Nearest Neighborhood Variable Unmatched Treatment Bias t-test V_e[T]/ V_e[C] Matched Treatment Control % bias Reduce %|bias| t p>t ASKES Unmatched 0.06215 0.18308 -37.5 -7.58 0 0.38** Matched 0.06541 0.05581 3 92.1 0.75 0.456 1.2 JAMSOSTEK Unmatched 0.03315 0.08706 -22.8 -4.62 0 0.41** Matched 0.03488 0.04273 -3.3 85.4 -0.75 0.451 0.82 Company insurance Unmatched 0.00414 0.03255 -21.3 -4.15 0 0.13** Matched 0.00436 0.00552 -0.9 95.9 -0.31 0.759 0.78* Company clinic Unmatched 0.00829 0.02116 -10.7 -2.17 0.031 0.40** Matched 0.00872 0.00581 2.4 77.4 0.63 0.526 1.49* Private insurance Unmatched 0.00276 0.02766 -20.4 -3.97 0 0.10** Matched 0.00291 0.00465 -1.4 93 -0.53 0.598 0.64* Unconditional Cash Transfer (BBMBLT) Unmatched 0.14917 0.16029 -3.1 -0.65 0.513 0.94 Matched 0.15262 0.1532 -0.2 94.8 -0.03 0.976 1 Female household head Unmatched 7.0359 8.5248 -33.5 -7.02 0 0.74* Matched 7.0959 7.093 0.1 99.8 0.01 0.99 0.89 Household head education Unmatched 4.6878 4.1676 25.2 5.41 0 1.08 Matched 4.5974 4.4544 6.9 72.5 1.26 0.209 0.9 Household size Unmatched 0.06516 0.06661 -1.2 -0.26 0.796 0.81 Matched 0.06485 0.0621 2.3 -90 0.45 0.656 0.94 Share under6female Unmatched 0.07192 0.0724 -0.4 -0.08 0.933 0.89 Matched 0.07173 0.07363 -1.6 -294.1 -0.29 0.769 0.96 Share under6male Unmatched 0.1314 0.11267 11.6 2.47 0.014 1 Matched 0.13085 0.11297 11 4.5 2.13 0.034 1.25 Share 6to17female Unmatched 0.13496 0.1082 17.3 3.72 0 1.12 Matched 0.13182 0.13491 -2 88.5 -0.35 0.724 0.93 Share 6to17male Unmatched 0.26636 0.2973 -17.9 -3.74 0 0.71* Matched 0.27021 0.26832 1.1 93.9 0.21 0.833 0.85 Share 18to60female Unmatched 0.05554 0.03612 13.3 2.9 0.004 1.47* Matched 0.0544 0.05847 -2.8 79.1 -0.46 0.649 0.86 Share 60upfemale Unmatched 0.04246 0.03652 5 1.07 0.285 1.03 Matched 0.0425 0.05068 -6.9 -37.7 -1.18 0.239 0.81 Share 60upmale Unmatched 0.81768 0.71359 24.7 5.18 0 0.70* Matched 0.80959 0.8125 -0.7 97.2 -0.14 0.891 1.02 Owned house Unmatched 55.021 68.533 -27.7 -5.62 0 0.40** Matched 55.83 55.465 0.7 97.3 0.18 0.854 1.18 Size of house (M2) Unmatched 0.34116 0.28478 12.2 2.62 0.009 1.14 Matched 0.33866 0.34419 -1.2 90.2 -0.22 0.829 1 Own water access Unmatched 0.28591 0.38405 -20.9 -4.42 0 0.86 Matched 0.2907 0.29157 -0.2 99.1 -0.04 0.972 1 House hold has a vehicle Unmatched 0.31354 0.25386 13.3 2.85 0.004 1.15 Matched 0.31686 0.31919 -0.5 96.1 -0.09 0.926 0.99 self employed Unmatched 0.56215 0.42718 27.2 5.81 0 0.97 0 .2 .4 .6 .8 1 Propensity Score Untreated Treated: On support Treated: Off support 132 Jurnal Ekonomi & Studi Pembangunan Vol. 19, No. 2, Oktober 2018: 116-133 Matched 0.54651 0.54273 0.8 97.2 0.14 0.888 0.99 Working Part Time Unmatched 0.00967 0.02116 -9.3 -1.9 0.057 0.46** Matched 0.01017 0.01395 -3.1 67.1 -0.64 0.521 0.74* Self-employed with permanent workers Unmatched 0.10221 0.20423 -28.6 -5.9 0 0.57* Matched 0.10756 0.09099 4.6 83.8 1.03 0.304 1.24 Government Official Unmatched 0.19751 0.2441 -11.2 -2.38 0.018 0.85 Matched 0.20058 0.20581 -1.3 88.8 -0.24 0.81 0.97 Private Worker Unmatched 0.4779 0.34093 28.1 6.04 0 1.06 Matched 0.4593 0.43779 4.4 84.3 0.8 0.423 0.98 Unpaid family worker Unmatched 0.0221 0.02034 1.2 0.26 0.794 1.09 Matched 0.0218 0.0218 0 100 0 1 1 Casual worker in agriculture Unmatched 0.04144 0.03173 5.2 1.12 0.262 1.30* Matched 0.0436 0.05174 -4.3 16.1 -0.71 0.479 0.85 Casual worker not in agriculture Unmatched 0.8895 0.90724 -5.9 -1.27 0.206 1.15 Matched 0.89099 0.92762 -12.1 -106.5 -2.37 0.018 1.36* Puskesmas has an electricity Unmatched 0.22928 0.41904 -41.4 -8.66 0 0.69* Matched 0.23983 0.22791 2.6 93.7 0.52 0.602 1.06 Puskesmas has a water access Unmatched 0.8453 0.91456 -21.4 -4.73 0 1.66* Matched 0.85029 0.83983 3.2 84.9 0.54 0.592 0.98 Puskesmas has a pharmacy Unmatched 0.43232 0.26444 35.8 7.75 0 1.23 Matched 0.41424 0.44244 -6 83.2 -1.06 0.291 0.98 Puskesmas offer inpatient service Unmatched 0.33149 0.22295 24.4 5.3 0 1.27* Matched 0.32122 0.31366 1.7 93 0.3 0.764 1.02 Puskesmas offer inpatient service other than birth Unmatched 0.58149 0.60862 -5.5 -1.18 0.238 1.04 Matched 0.57994 0.55727 4.6 16.4 0.85 0.396 0.99 Puskesmas offer check-up/health examination Unmatched 0.6105 0.65419 -9.1 -1.94 0.052 1.05 Matched 0.60174 0.5936 1.7 81.4 0.31 0.758 1.01 Puskesmas offer dental service Unmatched 0.90746 0.93653 -10.9 -2.37 0.018 1.40* Matched 0.90988 0.91628 -2.4 78 -0.42 0.674 1.04 Private clinic has an electricity Unmatched 0.16022 0.29455 -32.5 -6.74 0 0.65* Matched 0.1657 0.15203 3.3 89.8 0.69 0.489 1.09 Private clinic has an access to water Unmatched 0.00552 0.04638 -25.9 -5.04 0 0.18** Matched 0.00581 0.00436 0.9 96.4 0.38 0.705 1.34* Private clinic provides an inpatient services Unmatched 0.08011 0.0537 10.6 2.31 0.021 1.45* Matched 0.0843 0.06105 9.3 11.9 1.66 0.097 1.24 Private clinic provide check-up/health examination services Unmatched 0.00691 0.04394 -23.7 -4.64 0 0.22** Matched 0.00727 0.00698 0.2 99.2 0.06 0.949 1.04 Private clinic provides dental services Unmatched 0.01796 0.13588 -45.4 -8.87 0 0.19** Matched 0.0189 0.02267 -1.5 96.8 -0.49 0.624 0.83 Private clinic has more than 1 medical staff Unmatched 1.0166 1.1798 -39.2 -7.61 0 0.13** Matched 1.0174 1.0227 -1.3 96.8 -0.54 0.587 1.18 Private clinic‟s medical staff number Unmatched 0.88398 0.80716 21.4 4.44 0 0.69* Matched 0.87936 0.86744 3.3 84.5 0.66 0.507 0.93 Village has public transport facilities Unmatched 0.73757 0.74044 -0.7 -0.14 0.889 1.03 Matched 0.73401 0.74128 -1.7 -153.2 -0.31 0.76 1 Village main road from asphalt Unmatched 10.58 10.986 -1.4 -0.3 0.763 0.76* Matched 10.845 11.625 -2.8 -91.9 -0.52 0.6 0.92 Distance of bus station from village office (km) Unmatched 51.431 58.606 -8.8 -1.8 0.072 0.47** Matched 51.517 49.744 2.2 75.3 0.49 0.622 0.96 Distance of district capital from village office (km) Unmatched 0.16867 0.33233 -21.2 -4.31 0 0.45** Matched 0.17265 0.18242 -1.3 94 -0.29 0.775 0.86 Travel time to nearest Puskesmas from village office (hours) Unmatched 0.16664 0.27796 -14.4 -2.93 0.003 0.43** Matched 0.16771 0.18823 -2.7 81.6 -0.59 0.555 0.87 Travel time to nearest private clinic from village office (hours) Unmatched 0.0788 0.09231 -17.8 -3.69 0 0.64* Matched 0.07863 0.07695 2.2 87.5 0.47 0.642 1.08 Travel time to nearest traditional clinic from village office (hours) Unmatched 0.87396 0.81623 1.9 0.4 0.692 0.63* Matched 0.87936 1.0341 -5.1 -168 -1.02 0.306 0.87 Travel time to nearest Hospital from village office (hours) Unmatched 0.06209 0.14582 -25.3 -4.94 0 0.14** Matched 0.06347 0.06841 -1.5 94.1 -0.57 0.566 1.32* Travel time to nearest Posyandu from village office (hours) Unmatched 0.83149 0.59072 55.1 11.37 0 0.49** Matched 0.82267 0.81105 2.7 95.2 0.56 0.577 0.91 The Impact of Jamkesmas on Healthcare Utilization… (Novat Pugo Sambodo) 133 Kalimantan Timur Unmatched 0.03867 0.18552 -47.8 -9.49 0 0.30** Matched 0.0407 0.03924 0.5 99 0.14 0.891 1.04 Sulawesi Tenggara Unmatched 0.14641 0.16599 -5.4 -1.14 0.253 0.9 Matched 0.15262 0.1314 5.8 -8.4 1.13 0.26 1.14 Maluku Unmatched 0.12845 0.17331 -12.6 -2.64 0.008 0.75* Matched 0.13517 0.11424 5.9 53.3 1.17 0.24 1.12 Maluku Utara Unmatched 0.06906 0.1546 -27.4 -5.6 0 0.44** Matched 0.07267 0.06831 1.4 94.9 0.32 0.752 1.08 Papua Barat Unmatched 0.16575 0.11229 15.5 3.38 0.001 1.43* Matched 0.17151 0.19157 -5.8 62.5 -0.96 0.335 0.91 Papua Unmatched 0.16851 0.12205 13.2 2.87 0.004 1.30* Matched 0.17151 0.1561 4.4 66.8 0.77 0.44 1.07 Source: Author‟s estimation based on IFLS-East 2012