93259 Report On Infrastructure Supply Readiness In Indonesia – Achievements And Remaining Gaps Infrastructure Cencus 2011 Design & Layout Ardhi Yudho Publisher Coordinating Ministry for People’s Welfare in cooperation with TNP2K and PNPM Support Facility The view expressed in this paper are those of the authors alone and do not represent the views of the PNPM Support Facility or any of the many individuals or organizations acknowledged here. Report On Infrastructure Supply Readiness In Indonesia – Achievements And Remaining Gaps Abbreviations, Acronyms and Terms ii | BPS Statistics Indonesia (Badan Pusat Statistik) D3 Diploma 3 (Associate’s Degree) Dukun bayi Traditional Midwife GOI Government of Indonesia Kabupaten District Kecamatan Sub-district KDP Kecamatan Development Program NTB Nusa Tenggara Barat NTT Nusa Tenggara Timur OLS Ordinary Least Squares PCA Principal Components Analysis PAUD Pendidikan Anak Usia Dini (Pre-School Education Facility) PNPM Program Nasional Pemberdayaan Masyarakat (National Program for Community Empowerment) PODES Potensi Desa (Village Potential Statistics) Polindes Pondok Bersalin Desa (Community Maternity Clinic) Poskesdes Pos Kesehatan Desa (Community Health Post) Posyandu Pos Pelayanan Kesehatan Terpadu (Integrated Health Service Post) Puskesmas Pusat Kesehatan Masyarakat (Community Health Center) Puskesmas Pembantu Auxilliary Community Health Center S1 Sarjana 1 (Bachelor’s Degree) SD Sekolah Dasar (Elementary School) SLB Sekolah Luar Biasa (Special School for Diabled Students) SMA Sekolah Menengah Atas (Senior Secondary School) SMK Sekolah Menengah Kejuruan (Senior Secondary / Vocational School) SMP Sekolah Menengah Pertama (Junior Secondary School) Susenas Survei Sosial Ekonomi Nasional (National Socioeconomic Survey) TK Taman Kanak-Kanak (Kindergarten) WHO World Health Organization CONTENTS Contents iii Executive Summary iv I. Introduction 1 II. Data And Methodology 3 II.1. The 2011 Core Podes And The Infrastructure Census 4 II.2. Methodological Approach 7 III. Health Infrastructure 9 III.1. Selection of Supply Readiness Indicators 10 III.2. Description of the National Patterns of Infrastructure Availability 12 III.3. Composite Indices of Health Supply Readiness 18 III.4. Quantifying Needs for Investment 23 IV. Education Infrastructure 27 IV.1. Selection of Supply Readiness Indicators 28 IV.2. Description of the National Patterns of Infrastructure Availability 30 IV.3. A Composite Index of Education Supply Readiness 36 IV.4. Quantifying Needs for Investment 41 iii | V. Transportation Infrastructure 45 V.1. Selection of Supply Readiness Indicators 46 V.2. National Patterns of Infrastructure Availability 47 V.3. Quantifying Needs for Investment 49 V.4. Comparison with Health and Education Supply Readiness 50 VI. Summary Of Results And Policy Recommendations 51 VI.1. National Patterns of Infrastructure Supply Readiness 52 VI.2. Policy Recommendations 53 References 54 Appendix 55 EXECUTIVE SUMMARY At the request of the National Team for Poverty Reduction (TNP2K) and the Vice-President, a census of basic village infrastructure, including health and education, has been conducted using the 2011 round of PODES, the national village census. Based on the information from both the infrastructure census and the PODES core survey, the objective of this analysis is twofold. First, the in-depth information on the quantity and quality of existing infrastructure is used for a comprehensive assessment of the local-level availability of basic facilities and services. In particular, indicators that measure the supply readiness of health and education services are developed for all districts and sub-districts in Indonesia. Second, based on the analysis of local patterns of available infrastructure, this study aims to quantify the needs for investments in health, education and transportation infrastructure. The infrastructure census provides detailed facility-level information on public health and education facilities, covering a total of 166,506 health facilities and 164,561 schools all across the country. Along with the information on the physical availability of (public and private) health and education facilities from the PODES core survey, the data allow painting a nuanced picture of supply readiness of health and education services in Indonesia. To this end, seven indicators are selected for both the health and education sector, along three dimensions: (i) availability and accessibility of facilities; (ii) presence and qualification of personnel, and (iii) physical characteristics of facilities. All indicators represent a supply readiness norm or target, and are calculated at the sub-district level. Based on the indicators, existent supply gaps are quantified. For both health and education, the respective indicators are then combined into composite indices of supply readiness. While the data on transportation infrastructure in PODES is less inclusive than what is available for the health and education sectors, a number of supply readiness indicators are also provided for iv | transportation infrastructure. The main findings from the analysis are: • Overall, the results show a consistent picture of the quantity and quality of available basic infrastructure in Indonesia. For both health and education, we observe similar spatial patterns of supply readiness across the sectors’ different dimensions. Moreover, results are robust across sectors, with significantly positive correlations between the various indicators of health, education and transportation infrastructure. • In general, the largest gaps in infrastructure supply readiness are found for the Papua region, the Maluku Islands, NTT, as well as the remote areas of Kalimantan and Sulawesi. The urban-rural divide is thereby substantial, not only with respect to the accessibility, but also the quality of available services. • For health, the lowest average scores are found for the provinces of Kalimantan Barat (75 %), NTT (71 %), Maluku Utara (69 %), Maluku (66 %), Papua Barat (50 %), and Papua (39 %). The highest average levels of health supply readiness are observed for all Javanese provinces (ranging from 99 % for DI Yogyakarta to 92 % for Banten), Bali (99 %), Bangka Belitung (95 %), Sumatera Barat (92 %), and NTB (90 %). • Similar patterns are found for the ranking of average education supply readiness, with DKI Jakarta (98 %) and DI Yogyakarta (97 %) performing best, and Papua Barat (40 %) and Papua (26 %) showing the lowest average scores. These patterns are generally confirmed by the indicators of transportation infrastructure as well. • Despite these consistent overall trends, we observe substantial variations within regions and provinces. The availability of the indicators at sub-district level thereby allows for the identification of such local disparities. • Based on the indicators of supply readiness, the magnitude of existent gaps and resulting investment needs are quantified. This particularly includes, but is not limited to, the number of citizens without easy access to health and education facilities. Overall, it is estimated that more than 6 million people in Indonesia have no (easy) access to primary health care provision, v| and around 36 million people lack access to inpatient services offered at hospitals. For education, we find that more than 9 million people live in places without junior secondary schools readily available, with this number increasing to 16.6 million for early childhood education facilities. I. INTRODUCTION 1| INTRODUCTION Over the past decade, the Government of Indonesia has invested significant resources in community driven development approaches to poverty reduction and small scale infrastructure provision in rural areas. Initially targeted toward the poorest sub-districts, like the predecessor program KDP, PNPM-Rural has expanded to cover every rural sub-district and village in Indonesia. PNPM-Rural has spent the majority of it several-billion dollar budget on block grants to communities to build small-scale village infrastructure. A number of studies demonstrated positive returns and impacts (Olken et al., 2011; World Bank, 2011), but there is little understanding of the infrastructure deficit remaining, the cost of addressing such a deficit via a sustained PNPM or other approaches, and the most efficient means of doing so. To date, the GOI has developed less comprehensive and less evidence-based approaches on key issues such as whether and how much tertiary infrastructure contributes to poverty reduction; when and where maintenance is needed; and determining block grant allocation size. The primary reason for the current approach is a lack of complete and comprehensive data on existing infrastructure. Lacking good data on where and to what extent infrastructure gaps exist, a systematic and evidence-driven approach to addressing the gap via targeting of PNPM and other programs has not been feasible. At the request of the National Team for Poverty Reduction (TNP2K) and the Vice-President, the PNPM Support Facility (PSF) Monitoring and Evaluation team has implemented a census of basic infrastructure for all 76,000 villages in Indonesia through PODES 2011. The primary objective of the census is to quantify the gap of acceptable-quality working basic infrastructure for all villages in Indonesia (main road, bridges, schools and health clinics) as an input to developing better strategies for financing, timeframe, programming and management of national and international resources for all PNPM programs. The collected data and the results from this analysis will allow the government to create a mechanism to estimate and track the remaining gap of addressing the existing village infrastructure deficit at the national, regional and local levels. Moreover, 2| the data allows for a more systematic and evidence based approach to determining needs and priorities for PNPM moving forward (including targeting, maintenance and block grant size), assessing the impact of community-based programs on poverty reduction and determining local government allocation. This report provides a detailed overview of the analysis and its main results. Section II presents the data and the overall methodological approach. Sections III and IV describe the selection of indicators, their properties and distribution for the health and education sector, respectively. In section V, we turn to the results for transportation infrastructure, while section VI provides a summary with some concluding remarks and potential policy implications. II. DATA AND METHODOLOGY 3| II.1. The 2011 Core Podes And The Infrastructure Census In 2011, the PNPM Support Facility (PSF) conducted a The two data sources therefore allow for a comprehensive census of basic village infrastructure, including health assessment of both the quantity and quality of health and and education, through the 2011 wave of PODES (Village education infrastructure in Indonesia. As far as possible Potential Statistics Survey or Potensi Desa). Administered with the given data, we also evaluate the robustness of the by BPS, PODES is conducted three times per decade and survey. As the PODES core is based on responses of the collects socio-economic information from all Indonesian rural village heads, misreporting by local authorities is a major villages and urban neighborhoods.1 concern. If respondents expect their answers to affect the allocation of public funds to the village or, in general, have The core PODES survey includes a wide range of indicators, doubts about the purpose of the survey, the state of the ranging from population characteristics to infrastructure, community’s public services and facilities might not be economic activities, and social life. Using the available reported accurately. Further, relying on a single respondent information on existing health, education and transportation can be problematic when this person is not fully aware of the infrastructure, this analysis aims at providing an accurate and various aspects of village life. up-to-date picture of the local supply of basic infrastructure and services. The reliability of the data is therefore assessed in several ways. First, BPS and PSF implemented a range of quality For each village, the PODES core data provides information controls when collecting the data, including sending on (i) the type and number of existent education and health independent consultants to verify data, spot-checking facilities; (ii) the distance to the next facility in case a facility (also to remote areas) and going back to the field for is not present in the village;2 (iii) the number of physicians, some areas where high data error/inconsistency were 4| midwives and nurses; and (iv) the type and condition of found. Second, we evaluate the consistency of the survey existent roads and bridges. The information on the quantity information throughout the analysis (see section II.2 for the of available health and education facilities from the PODES methodological approach). In what follows, the available core is complemented by quality-related information on information on health and education infrastructure from these facilities from the infrastructure census. Drawing on both the PODES Core and Infrastructure Census dataset is the list of existent health and education facilities from the presented in more detail. PODES core survey, the infrastructure census was collected directly from the facilities and provides in-depth information on public health facilities (including the full sample of 9,212 Puskesmas, 22,883 Puskesmas Pembantu, 28,672 Poskesdes and 14,408 Polindes, as well as a sub-sample of 91,331 Posyandu) as well as public schools (including 134,517 primary (SD), 21,530 junior secondary (SMP), and senior secondary (6,224 SMA / 2,589 SMK) schools). 1 PODES 2011 includes 78,600 villages/neighborhoods. 2 For health facilities, PODES additionally provides information on how easily a certain facility type can be reached from the village. Information on Health Information on Education Infrastructure Infrastructure The information on existent health services available from The data on education supply and infrastructure from the the PODES data can be categorized along four dimensions: Core and the infrastructure census is also categorized along (i) physical availability and accessibility; (ii) health workforce; three dimensions: (i) physical availability; (ii) student numbers (iii) services and equipment; and (iv) building characteristics. and teacher characteristics; and (iii) available rooms and Table II.1 gives an overview of the variables at hand for each facility characteristics. Table II.2 provides an overview. of these dimensions. Information on existent public SD, SMP, SMA and SMK is The PODES core data provides information on the existence available from both the Core and the infrastructure census, of different types of health facilities in the village, including while the Core additionally provides information on early hospitals, maternal hospitals, polyclinics, Puskesmas, childhood education facilities (PAUD and Kindergarten/TK), Puskesmas Pembantu, Poskesdes, Polindes, and Posyandu, as well as the number of private facilities for all school types, as well as physician’s and obstetrician’s practices. In case including academies, special schools (SLB), Islamic boarding the respective facility is not available within the village/ schools, and Madrasah diniyah. Further, the core includes neighborhood, the core includes information on a) the information on the distance to the nearest school for each distance to and b) the ease of reaching the nearest facility. school type, if the respective facility is not present within the village or neighborhood. Both the core and the census include information on the number of physicians, dentists, midwives, nurses and other For all public schools the infrastructure census provides 5| health personnel working in the facilities and villages.3 information on the number of students (by sex and grade), Further, the infrastructure census contains information on the as well as the number of teachers, their type of contract availability of a range of services and equipment at the facility (permanent vs. temporary), and their level of education level. These variables are not available for those facilities that (S1 degree or higher versus D3 degree or lower). With this are not covered by the census (i.e. hospitals, polyclinics, information, the average number of students per class, physician’s and midwife’s practices). Aggregating this student-teacher ratios, and the share of permanent and/ information at village or sub-district level would hence only or teachers holding at least an S1 degree are calculated for be accurate for those sub-districts where no hospitals and each school. polyclinics are present (which applies to around 60 percent of the 6,771 sub-districts). Finally, the infrastructure census As for the health facilities, the school census provides provides information on a range of building characteristics, of information on a range of building characteristics. We which the availability of electricity and the supply of water, as focus on the availability of electricity and water within the well as indicators of roof and floor quality are most suited to facility and indicators of roof and floor materials and quality. assess the physical condition of facilities. Furthermore, the census contains information on available rooms, including the number of classrooms, laboratories, libraries, bathrooms, exercise fields, UKS rooms, and staffrooms. 3 In part, the numbers differ substantially between the two sources, which is due to the broader focus of the Core data (including hospitals, polyclinics, physician’s and midwife’s practices). Table II.1: Available Information on Health Infrastructure from PODES Dimension Indicator(S) 1. Physical Availability and Three indicators are available: Accessibility • Number of facilities per 10,000 population • Share of population that can easily reach the facility • Distance to the next facility For the following types of health facilities: • Hospitals • Polyclinics • Maternal Hospitals • Puskesmas • Puskesmas Pembantu • Poskesdes • Polindes • Physician’s practice • Midwife’s practice 2. Health Workforce • Physicians: number within the village & distance to / ease of reaching of the next practice • Midwives: number within the village & distance to / ease of reaching of the next practice • Dentists: number within the village • Nurses and other health personnel: number within the village 3. Services and Equipment The infrastructure census provides information on the availability of the following services (in the surveyed facilities): • Inpatient services • Dentist services 6| • Pregnancy check-up • Delivery by doctor/midwife • Immunization services • Family planning services • Laboratory • Weighing services • Provision of vitamin A • Provision of iron pills Incubator availability, Vaccine storage equipment 4. Building Characteristics Electrification, Water source, Type and condition of roof and wall Table II.2: Available Information on Education Infrastructure from PODES Dimension Indicator(S) Physical Availability • Number of Facilities per 10,000 population (public and private) • Distance to the next facility Students and Teachers • Student-Teacher Ratios (for public schools) • Number of Students per Class • Share of permanent / S1 teachers Available Rooms • Libraries and Facility Characteristics • Laboratories (for public schools) • Electrification • Water Source • Type and condition of roof and wall II.2. Methodological Approach This section provides a general overview of the main we rely on expert consultation and take into account official steps in the analysis, the methodological approach and government targets in order to identify those indicators most implementation. More detailed explanations are given in the reflective of local realities and policy priorities. As a general technical appendices, while the subsequent chapters only rule, the selected indicators of supply readiness take a value refer to the main results. between 0 and 1, reflecting the share of the population, facility or geographic area that meet a supply readiness The main goal and, at the same time, challenge of this norm or threshold. We choose at least two indicators for analysis is to use the immense amount of information from each dimension in health and education, which provides us the PODES data for a reliable and accessible description with seven indicators for both health and education supply. of the state of village infrastructure in Indonesia. On the The data on transportation infrastructure in PODES is less one hand, we aim for a comprehensive breakdown of inclusive than the in-depth information available for the the different aspects of local service supply; on the other health and education sectors, but still allow for deriving a hand, we intend to condense the available information number of supply readiness indicators. into a summary indicator that allows for an easy grasp of the overall situation. The analysis of infrastructure supply The analysis of the indicators’ statistical properties allows readiness therefore consists of three main phases: (i) –to a certain degree- for an assessment of the validity of identification of the main indicators and analysis of the the data. In particular, we evaluate the correlations between geographical distribution of these indicators; (ii) constructing indicators, both within and across the different sectors, to a composite index based on the selected indicators; and, (iii) identify common patterns in the data. This provides us with quantifying supply gaps. a measure of data consistency and, hence, an indicator of the reliability of the resulting relative rankings with respect 7| to village infrastructure across the country. Moreover, we relate the chosen indicators of supply readiness to actual Selection of the main outcomes of the health and education system, respectively, indicators as one (rather rough) way of testing the external validity of the PODES data. Still, a comparison of the PODES data with The data from both the PODES Core village survey and data on basic village infrastructure from other quantitative the infrastructure census are combined to make use of surveys or qualitative fieldwork would be desirable in order all the information available. Therefore, the facility-level to assess the accuracy of the reported absolute levels of information from the infrastructure census is first transformed supply readiness. While beyond the scope of this analysis, into village-level indicators, and then merged with the comparisons with data that are currently being collected by Core data into a single dataset. These indicators of local the GOI and others would constitute a valuable complement health, education and transportation infrastructure can to this study. be aggregated at sub-district, district and provincial level. Throughout this study the main level of analysis is the sub- district (kecamatan) for mainly three reasons: (i) a range of health and education institutions, such as Puskesmas or Construction of composite junior secondary schools, are provided at the sub-district indices for health and education level; (ii) most existent community driven development programs in Indonesia target sub-districts; and (iii) the sub- The composite index of supply readiness for each sector district level allows for both sufficiently accurate and detailed basically reflects a weighted average of the selected information. indicators, and will therefore also be bounded by 0 and 1. A larger value indicates a higher degree of supply readiness, Based on the sub-district dataset, the information available although interpretation of the value itself is not always from the two surveys is explored in order to identify the most straightforward, as this depends on the weights attached suitable indicators of health and education supply readiness. to each of the underlying indicators. The composite index The selection of indicators is partly built on statistical is therefore better suited for comparing relative rather than properties, such as the nationwide variation of indicators absolute performance of districts. Note that we do not or the correlation between different indicators. Moreover, construct a composite index for transportation infrastructure, due to the limited number of indicators. The choice of method for assigning weights is a crucial, ii. Weights are based on the supply indicators’ contribution yet admittedly arbitrary, step in constructing the composite to inequality in health or education outcome variables. We index of infrastructure readiness in Indonesia. It is crucial measure inequality by means of a concentration index, because the weights determine the relative influence of each which we decompose into the individual contributions of of the underlying indicators of the composite index. It is also the seven supply indicators. These individual contributions arbitrary because the choice of weights inevitably involves a are the product of (i) the responsiveness (or elasticity) value judgement. It is therefore important to be transparent of the outcome variables with respect to the supply in both the arguments for the choice of weight, and the indicators, and (ii) the inequality in the distribution of method for constructing the weight. We opt for assessing supply indicators across districts. For details on the three different methods for constructing weights, each with inequality decomposition see Appendix 1. different implicit choices, argumentation and intuition, while aiming to keep the methods as straightforward as possible. First, we base the weights on explicit policy preferences. Quantifying existing gaps Although such a weighting scheme is clearly very arbitrary, the advantage is that the choices explicitly reflect different Existing shortcomings in infrastructure supply readiness, policy priorities and are open to scrutiny and debate. Here, and the corresponding supply needs, are then quantified we propose three in principle arbitrary weighting schemes: based on the main indicators, with the existing supply gap expressed as the distance to the maximum value of 1.. We i. Relatively larger weights to indicators in the physical provide different scenarios using different assumptions and availability dimension, which would emphasize the benchmarks. In particular, we distinguish between absolute important role of availability of facilities for delivering health and relative levels of deprivation in terms of access to basic care and education services. infrastructure. ii. Equal weights across all dimensions of accessibility. As the number of indicators may vary across dimensions, this In general, two different approaches are possible to identify could imply that the weights across indicators will not be targeting priorities. First, policy interventions can focus equal. on those regions where the largest share of population, iii. Equal weights across the seven indicators of supply facilities, or villages is lacking certain infrastructure. A 8| readiness. potential policy target with this approach would be to increase supply readiness to a value of, for example, 0.75 Second, weights are derived by means of so-called Principal for all sub-districts in Indonesia. As the sub-districts lagging Components Analysis (PCA), a statistical method used to behind the most are mostly rural areas with a low population summarize the information from a large number of related, density, a relatively small number of people would benefit or correlated, variables.4 We derive the first principal from infrastructure improvements in these areas. component, the linear combination of the selected indicators that best captures the variation in the data, and use the Alternatively, investment priorities can be determined based eigenvectors of the first component as relative weights on the absolute number of citizens that lack access to basic for the composite index. The advantage of PCA is that it services. With this second approach, the focus would, at seems less arbitrary in that we let the covariance in the data least partly, shift from remote, sparsely populated areas with determine the policy priorities. However, PCA based weights very little infrastructure available, to more urban, densely are also difficult to interpret and to relate to policy priorities. populated areas with an overall higher level of supply readiness, but larger numbers of citizens without access Third, we relate the weighting scheme for the supply to certain services. We will identify the magnitude of the readiness indicators to explicit policy objectives in terms of gaps as well those areas most eligible for infrastructure actual outcomes of the health and education systems, such investments based on both approaches. as health care utilization by potential patients or average test scores from the National Exam (Ujian Nasional, UN). Two methods are used to assess the relative importance of the different supply indicators for health and education outcomes: i. Weights are based on the supply indicators’ contribution to the absolute level of the health or education outcomes, by means of OLS regressions of the selected indicators on district-level outcome variables. The estimated coefficients are then used to construct the weights. 4 A well-known application of the PCA is the asset index, where information on the ownership of a large number of items is reduced into a single index. III. HEALTH INFRASTRUCTURE 9| III.1. Selection of Supply Readiness Indicators The PODES Core and the Infrastructure Census allow We group the nine facility types into three indicators in order for categorizing the available information along four main to capture different functions of the health care system: dimensions. We derive a total of seven indicators in order to reflect the various aspects of health care supply. In what • Access to Primary Care: share of the population that follows, the choice of the different indicators is motivated. can easily reach a polyclinic, Puskesmas, Puskesmas Pembantu, or physician’s practice. • Access to Secondary Care: share of the population that 1. Physical Availability and can easily reach a hospital Accessibility • Access to Delivery Facilities: share of the population that can easily reach a hospital, maternity hospital, The three types of indicators at hand (number of facilities Puskesmas, Polindes or midwife’s practice. per capita (“population-based”), distance-based, access- based) provide different pictures of the availability of health The first indicator is intended to measure access to basic facilities. The population-based indicators tend to be lower health services, which requires a choice on the health in densely populated areas and higher in sparsely populated facilities to be included. For comparison, we do provide areas, and hence do not necessarily reflect actual availability alternative definitions of primary care, in particular using a of services. The correlations of these indicators with other broader definition which includes all facility types other than indicators of infrastructure readiness are usually low or even hospitals (provision of secondary care) and Posyandu (no negative, which is largely driven by the substantial impact provision of core health services). of the population denominator on the indicator. As this 10 | would lead to a biased mapping of available infrastructure, no population-based indicators are included neither for the 2. Health Workforce health nor for the education sector. However, we do account for population density when assessing the magnitude of We have information on the number of physicians, midwives existing infrastructure gaps. and nurses in each village and by facility type. We propose two indicators that reflect targets set by the GOI: A more reliable measure of health care accessibility is the “distance to the next facility” indicator. However, these • Physician at Puskesmas: In each Puskesmas, at least one indicators show a relatively high number of missing values physician should be present. We measure the share of (no information for up to 1,000 sub-districts). Therefore, a Puskesmas in a sub-district that fulfill this condition. “ease of reaching” indicator is constructed, which is based • Midwife in the Village: The presence of midwives is crucial on the assessment of the village head on how easy a certain for maternity care and attended delivery. We measure the health facility can be reached from the village.5 The “ease share of the sub-district population living in villages where of reaching” dummy at village level equals 1 if a facility is a midwife is present. a) found within the village or b) “very easy” or “easy” to reach (according to the village head/the core respondent). Measuring the share of the sub-district population that can easily reach a certain facility, these indicators indirectly account for distance and transport infrastructure. The correlation with the distance-based indicators is generally high, around 0.60, which confirms the reliability of this class of indicators. 5 For all nine health facility types, the village head/respondent of the PODES core reports on whether it is “very easy”, “easy”, “difficult”, or “very difficult” to reach the next facility(if no such facility is available in the village). The World Health Organization proposes an indicator of 4. Building Characteristics health professionals per 10,000 population to measure health workforce density (WHO, 2011). However, population- Instead, the quality of health facilities is measured with two based indicators are problematic for the above stated indicators of basic amenities. reasons. Indeed, the WHO indicator performs poorly, with (i) negative or very low correlations with all other indicators • Water Supply Puskesmas: An official target for Puskesmas of supply readiness; and (ii) no explanatory power when facilities is to have access to water either at the facility or assessing the determinants of health care utilization. An within 500 meters from the building. As no information on indicator that performs slightly better is based on the number the distance to the next water source is available, we use of physicians per 10,000 population, which is used as an a dummy that equals one if the next water facility can be alternative indicator of health workforce (see Appendix 2 for reached in 10 minutes or less. a more detailed description of population-based indicators of • Electrification: The second indicator measures the share health workforce). of health facilities in the sub-district (excluding Posyandu) with electricity. 3. Services and Equipment We do not use indicators of building material, as these indicators are likely to also reflect regional differences in The information on services and equipment available from building styles, and, hence, not necessarily the quality of the infrastructure census is problematic for three reasons. infrastructure. Table III.1 provides an overview of the selected First, only facilities covered by the infrastructure census indicators of health supply readiness. are included, hence the indicators miss out on services offered at hospitals and polyclinics as well as at physician’s and midwife’s practices. Second, in case a service is not available within a village, no information on the location of the nearest facility that offers the service is available. Third, the service categories and the information on available equipment are relatively broadly defined and therefore not well suited for the assessment of supply quality (for instance, 11 | the impact of a laboratory crucially depends on equipment and tests available). We therefore do not use the information on services and equipment for the index. Table III.1: Overview of Selected Health Indicators Indicator Description Access to Primary Care Share of Population that can easily reach a polyclinic, Puskesmas, Puskesmas Pembantu, or physi- cian’s practice Access to Secondary Care Share of Population that can easily reach a hospital Access to Delivery Facility Share of Population that can easily reach a hospital, maternity hospital, Puskesmas, Polindes or mid- wife’s practice Physician at Puskesmas Share of Puskesmas with at least one physician present Midwife in the village Share of Population living in villages with a midwife present Water Supply Puskesmas Share of Puskesmas with water installation within facility or 10 min walk Electrification Share of health facilities with electricity (excluding Posyandu) III.2. Description of the National Patterns of Infrastructure Availability Descriptive statistics for the seven indicators are presented Access to secondary care is more restricted, with an average in Table III.2, where all indicators are bounded between 0 of only two thirds of the sub-district’s population living and 1, and larger values indicate a higher degree of supply in villages from where a hospital can easily be reached. readiness. On average, 92.6 percent of population in the Delivery facilities are, on average, difficult to reach for about 6,771 sub-districts has access to primary health services as ten percent of the sub-district population. The indicators of defined in Table III.1. When in addition access to Polindes, health personnel and building characteristics show similar Poskesdes and midwife’s practices is considered, this sub-district averages, ranging between 0.81 for the share of average increases to 95.5 percent. Overall, basic health health facilities with power supply and 0.86 for the share of care is hence readily available in many parts of Indonesia. Puskesmas with a physician present. However, regional differences are still substantial and are discussed in greater detail below. Table III.2: Health Indicators: Descriptive Statistics Descriptive Statistics Obs. Mean SD Min Max Access to Primary Care 6771 0.926 0.173 0 1 Access to Secondary Care 6771 0.673 0.407 0 1 12 | Access to Delivery Facility 6771 0.899 0.220 0 1 Physician at Puskesmas 6771 0.858 0.339 0 1 Midwife in the village 6771 0.848 0.251 0 1 Water Supply Puskesmas 6771 0.848 0.345 0 1 Electrification 6771 0.814 0.267 0 1 Table III.3 reports the correlations between indicators, which range between 0.30 and 0.62 (with the exception of access to delivery facilities and access to primary care: 0.78). These significantly and uniformly positive correlations point to similar patterns of supply readiness across different dimensions and, moreover, confirm the consistency of the chosen indicators. Along with the, in part, substantial variations of the indicators across sub-districts, these statistical properties suggest a reasonably robust assessment of the local availability of basic health infrastructure across Indonesia. Table III.3: Health Indicators: Correlations Correlations Primary Secondary Delivery Physician Midwife Water Access to Secondary Care 0.54 1.00 Access to Delivery Facility 0.78 0.62 1.00 Physician at Puskesmas 0.42 0.36 0.47 1.00 Midwife in the village 0.60 0.53 0.65 0.50 1.00 Water Supply Puskesmas 0.37 0.30 0.40 0.49 0.43 1.00 Electrification 0.47 0.45 0.51 0.44 0.54 0.38 Before turning to the spatial patterns of health care supply, Figure III.1 provides a graphical representation of the seven indicators’ distribution. While primary health care services are almost universally available, access to hospital treatment is severely limited in about 20 percent of all sub-districts. In more than 80 percent of the sub-districts, Puskesmas are staffed with at least one physician. However, large variations are observed for the village-level availability of midwives, with a total of 1,136 sub-districts in which a midwife is present in less than 50 percent of the villages. A somewhat similar picture emerges for the two indicators of basic amenities: Water supply is a given for most Puskesmas, while electrification of health facilities is less prevalent, with universal access to electricity found in only about 45 percent of the sub-districts. Figure III.1: Distribution of Health Supply Readiness Indicators 80 80 80 60 60 60 40 40 40 Percent 20 20 20 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Ind. 1: Access to Primary Care Ind. 2: Access to Secondary Care Ind. 3: Access to Delivery Facility 80 80 60 60 40 40 Percent 20 20 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Ind. 4: Share of Puskesmas with Doctor Ind. 5: Midwife in Village 13 | 80 80 60 60 40 40 Percent 20 20 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Ind. 6: Water Available in Puskesmas Ind. 7: Electrification In what follows, maps for all seven indicators present the regional patterns of infrastructure supply readiness. The same classification is used for all indicator maps (as well as for the maps of the composite indices in the next section) in order to simplify comparisons across the different aspects of health care supply.6 6 The data from PODES 2011 is not (yet) completely compatible with the administrative coding that underlies the most recent sub-district maps. Therefore, a total of 38 sub-districts cannot be represented by the maps. Despite this minor incompatibilities between the PODES codes and the mapping tools, the whole set of indicators is available for all sub-districts covered by PODES 2011. Dimension 1: Physical Availability And Accessibility Figure III.2 confirms widespread access to primary health care in most of Java (access given for an average of 98 percent of the sub-district population), Bali (100%) and NTB (98%). Availability of health services is more limited in rural areas7 of Kalimantan, Sumatra and Sulawesi, with a respective average of 10, 7, and 7 percent of the sub-district population lacking easy access to primary care. Severe gaps in basic access to health care are observed for the rural sub-districts of Papua (average sub-district access rate of 62 %) and, less dramatic, in rural Papua Barat (77%) and Maluku (87%). Appendix 3 provides province and district-level overviews of all indicators. Figure III.2: Map – Share of the Population with Access to Primary Care 14 | Figure III.3: Map – Share of the Population with Access to Secondary Care In contrast to the overall good access to primary health services, secondary care at hospitals is less easily available in large parts of the country. Besides Papua and Papua Barat (average sub-district access rate of 18 percent) and the Moluccas (37%), low access rates are also observed across NTB and NTT (51%), Kalimantan (53%), Sulawesi (62%) and Sumatra (71%). Urban-rural differences are substantial: While an average of 91 percent of the population in urban sub-districts has easy access to hospitals, this holds true for an average of only 51 percent of the population in rural sub-districts across the country. 7 A sub-district is classified as urban when at least one village/neighborhood in the sub-district is coded as urban (2,763 sub-districts in total). Accordingly, a sub-district is classified as (exclusively) rural when all villages in the sub-district are coded rural (4,008 sub-districts). The availability of delivery facilities by and large follows similar patterns as observed for access to primary health care. However, especially in rural areas off Java a large share of the population has limited access to delivery facilities: In the 3,377 rural sub-districts outside Java, an average of 19 percent of the population is lacking easy access, as compared to only 2 percent of the population in the 631 rural sub-districts in Java. Figure III.4: Map – Share of the Population with Access to Delivery Facilities Dimension 2: Health Workforce Given the existence of only one Puskesmas in most sub-districts, the share of Puskesmas with a physician is almost a binary indicator. Again, gaps are most prevalent in Papua, where in many sub-districts a Puskesmas is not available at all. Overall, 15 | one quarter of rural sub-districts off Java does not provide a Puskesmas staffed with a physician, with this share increasing to 40 percent for the Moluccas and 69 percent for Papua / Papua Barat. Figure III.5: Map – Share of Puskesmas with at least one Physician In line with the overall patterns of health care availability, the presence of midwives is particularly limited in rural and remote areas. Overall, midwives are present in 96 percent of urban neighborhoods, but only in 78 percent of rural villages. Lowest rural access rates are observed for the provinces of Sulawesi Utara (61%), Maluku (54%), Kalimantan Timur (51%), Maluku Utara (50%), Papua (30%) and Papua Barat (27%). It is important to note that our definition of midwives does not include traditional midwives (dukun bayi). Accounting for the presence of dukun bayi, the share of villages without any midwife is reduced to an average of 11 percent for all rural areas, with this indicator substantially above 10 percent only for Papua / Papua Barat (47 percent). Figure III.6: Map – Share of the Population Living in a Village with a Midwife 16 | Dimension 3: Building Characteristics Similar to the indicator of the presence of a physician at Puskesmas, the indicator of the share of Puskesmas with water supply either within the facility or within 10 minute walk has almost a binary distribution. Outside Java and Bali, and with the exception of Papua / Papua Barat, a relatively uniform picture evolves: around 10 percent of the urban sub-districts and 20 percent of the rural sub-districts do not provide a Puskesmas with water installation. In Papua / Papua Barat, a similar 12 percent of the urban sub-districts do not provide a Puskesmas with water supply, but this figure increases to 61 percent for the province’s rural sub-districts. Figure III.7: Map – Share of Puskesmas with Water Installation The availability of electricity in health facilities varies greatly across both regions and facility types. Overall, health facilities in Papua / Papua Barat (52 percent), the Moluccas (66 percent), and NTT / NTB (70 percent) are least likely to have access to electricity, while almost universal supply is given in Java (97 percent) and Bali (96 percent). In Table III.4, these figures are disaggregated by type of health facility. With the exception of Papua / Papua Barat, average electrification rates for Puskesmas are above 90 percent across the country. Puskesmas Pembantu, Poskesdes, and Polindes have significantly less often access to electricity, with relatively similar average electrification rates across facility types within regions. Figure III.8: Map – Share of Health Facilities with Electricity (excl. Posyandu) Table III.4: Share of Health Facilities with Electricity – by Region and Facility Type Region Puskesmas Pustu Poskesdes Polindes 17 | Sumatra 97.4 83.3 82.2 85.5 Java & Bali 100.0 96.4 95.3 97.4 NTT & NTB 94.2 69.1 70.5 61.4 Kalimantan 98.1 75.0 74.8 73.1 Sulawesi 94.7 80.4 69.8 68.8 Maluku & North Maluku 90.5 64.3 60.6 53.6 Papua & Papua Barat 72.3 50.3 30.0 39.0 III.3. Composite Indices of Health Supply Readiness In a next step, the information from the seven indicators is below 75 percent.8 Before having a closer look at the spatial 18 | aggregated into (i) sub-indices for each dimension, as well as patterns of the supply of basic health services, this section (ii) composite indices based on all indicators. The provision presents the construction of the various composite indices. of such condensed information thereby allows assessing overall supply readiness at local levels and identifying priority To begin with, Table III.5 shows the mean values and regions for future policy interventions. In general, the island pairwise correlations of the sub-indices for the three major of Java and the province of Bali perform best, while the dimensions physical availability, health workforce, and largest gaps in infrastructure supply readiness are found for building characteristics. The sub-indices are calculated the Papua region, the Maluku islands, NTT, as well as for the as simple averages of the respective indicators in each interior of Kalimantan. Overall, 19 percent of the Indonesian dimension. Similar mean values and positive correlations sub-districts can be considered supply ready with a between 0.55 and 0.65 endorse the impression of fairly maximum score of 100 percent, while substantial gaps are consistent patterns of supply readiness across different observed for one quarter of the sub-districts with a score of dimensions of health infrastructure. Table III.5: Sub-Indices Health – Mean Values and Correlations Sub-Index Mean Correlations Availability Workforce Building Physical Availability 0.833 1.00 Health Workforce 0.853 0.63 1.00 Building Characteristics 0.831 0.55 0.65 1.00 8 These statistics are based on version A of the composite health index, where particular weight is given to the indicators of physical availability. Going a step further, we combine the information from all seven indicators into one global index of health supply readiness. As discussed in section II.2., we propose six different weighting schemes for the composite index for comparison and robustness purposes. First, the weights are determined based on policy preferences, giving (i) a total weight of 60 percent to the three indicators of physical availability; (ii) equal weights across the three dimensions of accessibility, personnel, and building characteristics; and (iii) equal weights across the seven indicators of supply readiness. Second, the Principal Components Analysis (PCA) is employed to derive weights for the seven indicators. Table III.6 presents the respective eigenvectors and weights for each indicator from the PCA analysis, which results in fairly equal weights across all seven indicators of health supply readiness. Table III.6: Principal Component Analysis Health Indicators Indicators Eigenvector Weight Access to Primary Care 0.408 0.155 Access to Secondary Care 0.366 0.139 Access to Delivery Facility 0.432 0.164 Physician at Puskesmas 0.345 0.131 Midwife in the village 0.410 0.156 Puskesmas Water Supply 0.310 0.118 Electrification 0.361 0.137 2.631 1.000 Third, we link the supply readiness indicators to actual outcomes of the health system, namely health care utilization by potential patients. Outpatient utilization rates, the dependent variable in our regression model, measure the share of the 19 | population that used outpatient services in the last month – out of those respondents reported sick. As this variable, which is derived from the 2010 Susenas, is only available at district-level, we aggregate the seven supply readiness indicators accordingly. Table III.7 presents the correlations between outpatient utilization rates and the seven indicators, as well as the OLS regression estimates and resulting weights for the composite index. Table III.7: OLS Regression Results: Determinants of Outpatient Utilization Rates Indicator 1. Correlation 2. OLS I 3. OLS II 4. Weights Access to Primary Care 0.47 0.02 0.02 0.031 (0.867) (0.854) Access to Secondary Care 0.51 0.09*** 0.09*** 0.169 (0.002) (0.002) Access to Delivery Facility 0.52 0.24** 0.24** 0.438 (0.015) (0.016) Physician at Puskesmas 0.37 Midwife in Village 0.41 -0.00 -0.09* (0.917) (0.053) Personnel: Score Physicians 0.13 -0.09* -0.00 (0.061) (0.949) Puskesmas Water Supply 0.43 0.09* 0.09* 0.158 (0.093) (0.093) Electrification 0.49 0.11*** 0.11*** 0.204 (0.006) (0.005) Observations: 497 497 R2: 0.319 0.319 P-values in parentheses. Statistical significance: * at 10%; ** at 5%; *** at 1%. Constant included. On table III.7, Column 1 (correlation) shows that outpatient With all positive concentration indices, the contribution utilization rates are strongly and positively correlated with all of each covariate to the overall inequality of utilization is supply readiness indicators, hence providing some evidence determined by the sign of the regression coefficient and the for the external validity of the chosen indicators. To assess subsequent elasticity. The residual component is very large, these correlations further, we run simple OLS regressions indicating that the supply indices only explain a limited part on outpatient utilization rates and obtain positive regression of inequality in utilization. However, this is not unexpected coefficients for the three access indicators as well as for given the relatively low R-squared of the OLS regression. the two indicators of building characteristics (column 2). For As to translate these results to weights, the indicators with comparison, we replace the ‘Physician at Puskesmas’ with a negative contribution are given a weight of zero and the the ‘Physicians Score’ indicator (see Appendix 2 for details). other contributions are rescaled so they sum to 1. As the regression results do not improve (column 3), we stick to our seven core indicators. Based on the regression This leaves us with a total of six alternative weighting coefficients from OLS I, the weights for the composite index schemes for the composite index of health infrastructure are derived, where indicators with negative coefficients are supply readiness. Table III.9 summarizes the weights of given zero weight and the weights for the remaining five the seven indicators for each of the six alternative indices. indicators are rescaled as to sum to 1 (column 4). While this While the composite indices A to D use the full set of seven is a simple way of assessing the determinants of health care indicators, versions E and F are based on the regressions on utilization, the results provide an alternative approach to the outpatient utilization rates and result in the exclusion of the determination of the indicator’s weights. health personnel indicators. A second alternative to determine weights with the help of health outcome variables is to assess the supply indicators’ contribution to inequality in health care utilization using the concentration index (see Appendix 1 for a more detailed description of the method). Table III.8 presents the results from this approach. We start from the OLS I regression of the seven supply indicators on outpatient utilization rates. The concentration index for the outpatient utilization rates equals 20 | 0.029, which indicates a pro-rich distribution of outpatient utilization for those reported ill. The concentration indices for all the covariates result in all positive values (column 2), likewise pointing to a relatively more abundant health care supply in wealthier districts. Table III.8: Health Indicators: Decomposition of the Concentration Index Indicator 1. Coefficients 2. CI 3. Contribution 4. Percent 5. Weights Access to Primary Care 0.017 0.028 0.001 3.8 0.019 Access to Secondary Care 0.094 0.099 0.017 56.9 0.286 Access to Delivery Facility 0.242 0.038 0.021 71.3 0.360 Puskesmas with Physician -0.004 0.042 0.000 -1.4 Midwife in Village -0.087 0.053 -0.010 -33.4 Water Installation 0.087 0.030 0.006 19.6 0.098 Electrification 0.113 0.061 0.014 46.8 0.236 Residual -0.048 -163.6 Total 0.029 100.0 1.000 Table III.9: Overview of Weights for the Composite Health Indices Index Primary Secondary Delivery Physician Midwife Water Electr. A. Focus on Access 0.200 0.200 0.200 0.100 0.100 0.100 0.100 B. Equal Dimension 0.111 0.111 0.111 0.166 0.166 0.166 0.166 C. Equal Indicator 0.143 0.143 0.143 0.143 0.143 0.143 0.143 D. PCA 0.155 0.139 0.164 0.131 0.156 0.118 0.137 E. Utilization OLS 0.031 0.169 0.438 0.158 0.204 F. Utilization CI 0.019 0.286 0.360 0.098 0.236 Tables III.10 and III.11 provide descriptive statistics and pairwise correlations for the six composite indices, respectively. Like the underlying indicators, the composite indices are bounded between 0 and 1, with higher values indicating higher supply readiness. The average Indonesian sub-district achieves a score of around 0.84 or 84 percent, dependent on the weighting scheme used. Using composite index A as reference, both the highest possible score of 1 (1,291 sub-districts) and the lowest possible score of 0 (35 sub-districts) are observed. Table III.10: Composite Health Indices: Descriptive Statistics Descriptives n Mean SD Min Max Index A: Focus on Access 6771 0.836 0.212 0 1 Index B: Equal Weights Dimensions 6771 0.839 0.214 0 1 Index C: Equal Weights Indicators 6771 0.838 0.212 0 1 Index D: PCA 6771 0.841 0.209 0 1 21 | Index E: Utilization OLS 6771 0.836 0.218 0 1 Index F: Utilization CI 6771 0.809 0.235 0 1.00 Electrification 6771 0.814 0.267 0 1 Interestingly enough, the alternative weighting schemes have little impact on the distribution of the composite indices. This is confirmed by extremely high correlations between the different versions of the composite indices. Versions A to D are almost identical, due to similar weights and the positive correlations between the seven sub-indicators. Even when the health personnel indicators are excluded for the regression-based weighting schemes (versions E and F), correlations are still above 0.95 (with the exception of version B and E). Table III.11: Composite Health Indices: Correlations Correlations A B C D E Index B: Equal Weights Dimensions 0.97 1.00 Index C: Equal Weights Indicators 0.99 1.00 1.00 Index D: PCA 0.99 0.99 1.00 1.00 Index E: Utilization OLS 0.98 0.95 0.96 0.97 1.00 Index F: Utilization CI 0.97 0.92 0.95 0.95 0.99 Finally, the similarity of the different composite indicators is confirmed by their almost identical distribution (Figure III.9). While the potential user of the indices can decide on his or her preferred weighting scheme, this choice will actually not alter the results substantially. Figure III.9: Distribution of Alternative Composite Indices of Health Supply Readiness 40 40 40 30 30 30 20 20 20 Percent 10 10 10 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 A: Focus Access B: Equal Weights Dimensions C: Equal Weights Indicators 40 40 40 30 30 30 20 20 20 Percent 10 10 10 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 D: PCA E: Utilization OLS F: Utilization CI The similarity of the different composite indices leads to accordingly similar spatial patterns. Representative of all composite indices, Figure III.10 maps the spatial distribution of index A. Sub-districts in Bali (0.99) and Java (0.96) have achieved very high levels of health supply readiness, average scores are observed for Sumatra (0.87), Sulawesi (0.82), Kalimantan (0.80), and NTT & NTB (0.77), while the Moluccas (0.68) and in particular Papua / Papua Barat (0.42) still lag behind. The overall gap between urban (0.96) and rural (0.75) sub-districts is substantial and particularly pronounced in regions with an overall low 22 | level of infrastructure supply readiness. Figure III.10: Map – Composite Index of Health Supply Readiness (Index A) III.4. Quantifying Needs for Investment Based on the seven indicators of health supply readiness, the investment needed to achieve basic levels of infrastructure throughout Indonesia is estimated. The distance of each indicator to its maximum value of 1 is thereby calculated for each sub-district and interpreted as gap. Table III.12 gives an overview of total national gaps for each indicator, derived from the sum of sub-district gaps. 1. Physical Availability and Accessibility For the three indicators of physical availability, the number equally across rural areas outside Java. While indicative of citizen without easy access to the respective services is of an overall low level of health care supply, it is likely that calculated. An estimated 6.2 million people in Indonesia lack many of these sub-districts without Puskesmas emerged easy access to primary health services, of which 80 percent only recently from the process of pemekaran, the formation live in rural sub-districts. A total of 383 or 6 percent of the of new districts and sub-districts during the decentralization sub-districts do not provide a Puskesmas, with about 60 process. percent of these sub-districts located in Papua or Papua Barat, and the remaining 40 percent distributed relatively 23 | Table III.12: Overall Gaps in Health Supply Readiness, by Indicator Indicator Type of Gap Total National Gap Access Primary Care Number of citizens without access 6.23 Mio. Number of sub-districts without Puskesmas 383 (population: 1.41 Mio) Access Secondary Care Number of citizens without access 35.97 Mio. Number of districts without hospital 42 (population: 4.73 Mio) Access Delivery Facility Number of citizens without access 6.77 Mio. Number of sub-districts w/o delivery facility 222 Physician at Puskesmas Number of Puskesmas without physician 732 (8 %) Midwife in the Village Number of villages without midwife 14,842 (population: 11.82 Mio) Puskesmas Water Supply Number of Puskesmas without water installation 852 (9 %) Electrification Number of health facilities without electricity 10,629 (14 %) • Puskesmas 305 (3 %) • Puskesmas Pembantu 3,855 (17 %) • Poskesdes 4,229 (15 %) • Polindes 2,198 (15 %) Relative gaps in brackets for facility-level gaps. In 694 sub-districts, less than 75 percent of the population Map III.11 describes the absolute number of people without can easily reach primary health care providers. A policy goal access to primary care by sub-district. In Papua, for instance, aiming at minimum access rates to primary health care of more than 5,000 citizens without access are found in a total of 75 percent across all sub-districts would require providing 42 sub-districts (which equals 7 percent of the sub-districts in access to a total of 1.31 million people. Table III.13 gives an the province). 46 such sub-districts are located in Jawa Barat, overview of the regional distribution of (i) the sub-districts 35 in Jawa Tengah, 29 in Sumatera Utara, 25 in Jawa Timur, with access rates below 75 percent; and (ii) the number of 27 in Kalimantan Barat, and 20 in NTT and Banten each. people without easy access to primary care, secondary care and delivery services, respectively. The figures reveal the different distributions of relative and absolute gaps. Out of the 694 sub-districts with access rates below 75 percent, 42 percent are located in Papua and 17 percent in Sumatra. However, out of the 6.23 million people without access to primary care, ‘only’ 15 percent live in Papua or Papua Barat, while 29 and 27 percent of the citizens without access are found in Java and Sumatra, respectively. Table III.13: Access to Health Services – Absolute and Relative Gaps Region Primary Care Secondary Care Delivery Facilities Share Kec. below Share People Share Kec. below Share People Share Kec. below Share People 0.75 0.75 0.75 Sumatra 16.7 25.6 25.5 28.5 14.4 25.9 Java & Bali 5.2 28.5 11.2 29.5 1.8 8.7 NTT & NTB 9.2 7.9 9.3 8.8 8.7 10.5 24 | Kalimantan 10.2 10.4 12.7 11.7 12.8 15.3 Sulawesi 10.8 9.3 16.5 12.3 12.6 16.9 Maluku & North Maluku 5.8 3.4 5.4 3.2 6.5 5.0 Papua & Papua Barat 42.1 14.9 19.5 5.7 40.2 17.7 Absolute Numbers 694 6.23 Mio. 2,578 35.97 Mio. 956 6.77 Mio. ‘Share Kec. below 0.75’ reports the regional distribution of the 694 sub-districts with an indicator score below 0.75 (e.g. 16.7 % of the 694 sub- districts are found in Sumatra). ‘Share People’ reports the regional share in the total number of people without access. Figure III.11: Map – Number of Citizens without Access to Primary Health Care Similar differences between relative and absolute gaps are Significantly more people lack easy access to hospital observed for the two other indicators of physical availability. services. As revealed by Table III.13, large numbers of In particular, the availability of delivery services follows similar sub-districts with access rates of less than 75 percent are patterns than the indicator of access to primary care. Some found across all regions of the country. 30 percent of the 40 percent of the sub-districts with access rates below 75 overall 36 million people without access live in Java, 29 percent are located in Papua / Papua Barat, while only 18 percent in Sumatra. The provinces with highest absolute percent or 1.20 million out of the 6.77 people without access gaps are thereby Jawa Barat (5.0 million people), Jawa Timur live in this region. The largest number of people without (2.6 Mio.), Sumatera Utara (2.3 Mio.), NTT (2.2 Mio.), and access is observed for Sumatra, which accounts for one Kalimantan Barat (2.0 Mio.). Figure III.13 provides a graphical quarter of the citizens without access. Figure III.12 presents representation of these gaps at sub-district level. the spatial distribution of absolute gaps with respect to access to delivery facilities. Figure III.12: Map – Number of Citizens without Access to Delivery Services 25 | Figure III.13: Map – Number of Citizens without Access to Secondary Health Care 2. Health Workforce 3. Building Characteristics Turning to the indicators of health personnel, we find The assessment of existing gaps with respect to building that 732 of the existing Puskesmas are not staffed with characteristics is comparably straightforward. We observe a physician. Most of the Puskesmas without a physician 852 Puskesmas without water supply within the facility or present are thereby found in Papua (109), NTT (67), Papua 10 min walk, and a total of 10,629 health facilities without Barat (55), Maluku (52), and Sulawesi Tenggara (51). If the electricity.9 Most important should be to provide electricity for 383 sub-districts currently without a Puskesmas were to the 305 Puskesmas without power supply, of which 93 are be equipped with such a community health center, a total located in Papua, 34 in Sulawesi Tenggara, 27 in NTT, and of 1,049 physicians would need to be hired to achieve the 20 in Sumatera Utara. policy goal of staffing each Puskesmas with at least one physician. The GOI target of having one midwife per village is not (yet) fulfilled in 14,148 or 27 percent of the Indonesian villages. A total of 11.82 million people live in these villages, with the highest numbers of people without access to a midwife in the village found for Papa (1.33 Mio.), NTT (0.99 Mio.), NAD (0.79 Mio.), Sumatera Utara (0.72 Mio.), Jawa Barat (0.65 Mio.), Kalimantan Barat (0.59 Mio.), and Sulawesi Utara (0.59 Mio.). Figure III.14 illustrates these spatial patterns. Figure III.14: Map – Number of Citizens Living in Villages without Midwife 26 | 9 Included are Puskesmas, Puskesmas Pembantu, Poskesdes, Polindes. IV. EDUCATION INFRASTRUCTURE 27 | IV.1. Selection of Supply Readiness Indicators Similar to the analysis of health care supply readiness, the available information on education infrastructure is categorized along three dimensions, and seven indicators are derived to capture the different aspects of the school system. 1. Physical Availability and 2. Quality of Teaching Accessibility The two types of indicators available (population-based, The infrastructure census provides detailed data at school distance-based) offer different pictures of the availability level, including information on the number of students of education facilities. As discussed before, per capita and the number and qualification of teachers. While this measures tend to be largely driven by the population allows for the calculation of the frequently used indicators denominator and do not necessarily reflect the density of of student-teacher ratios (STR) and average numbers of supply. Indicators of the distance to the next facility are more students per class, we do not include these variables in reliable measures of education accessibility, and we focus the index for the following reasons. The average number on two indicators:10 (i) access to early childhood education of students per class is strongly correlated with population (ECED) facilities and (ii) access to junior secondary schools density and constitutes a weak proxy for education quality. (SMP): In fact, we find positive correlations between average class 28 | size and all other indicators of supply readiness used for 1. Access to ECED. This indicator is composed out of two the index. Likewise, low student-teacher ratios especially in variables at village level: (i) the existence of an Early small schools often indicate an over-hiring of teachers rather Childhood Education Post (PAUD) in the village; and (ii) than excellent learning conditions (World Bank, 2010). For the existence of a Kindergarten/TK in the village or within instance, the MSS target for SD schools is to have at least 1 km from the village. The “Access to ECED” indicator one teacher per 32 students, which is fulfilled in 97 percent measures the share of the sub-district population that lives of the sub-districts. Such an indicator would hence possess in a village for which at least one of the two conditions is neither sufficient explanatory nor statistical power. fulfilled. 2. Access to SMP: share of the sub-district population that We focus instead on the qualification of teachers, measured lives in a village with a SMP within 6 km, which is an by the share of teachers holding a bachelor’s (S1) degree. official MSS (Minimum Service Standard) target for remote Based on the MSS for elementary and junior secondary areas. For comparison, the same indicator is constructed schools, we construct the following two indicators: for a maximum distance of 3 km from the village.11 3. Teacher Qualification SD: According to the MSS, each We do not use the distance to elementary schools (SD) as a elementary school (SD) should employ at least two national indicator of supply readiness, since the data show teachers with S1 qualification. We therefore calculate the almost universal physical access to primary education for share of SD in a sub-district fulfilling this condition. most regions. However, substantial shares of the populations 4. Teacher Qualification SMP: Another MSS target states that in sub-districts in Aceh (11 percent on average), Papua Barat 70 percent of the teachers at junior secondary schools (16 percent), and Papua (41 percent) have no SD available (SMP) should hold an S1 degree. The indicator measures within 1 km from the village, and this indicator could be the average share of teachers with an S1 degree at SMP employed for local targeting. schools in a given sub-district.12 10 As no information on the ease of reaching education facilities is available from the PODES core data, and as the data on distances to the nearest schools are available from all sub-districts, the distance indicators are used to assess the physical availability of education facilities. 11 The distance to the next SMP is missing for 84 sub-districts (1 in Sumatera Utara, 7 in Papua Barat, 76 in Papua), where no SMP is available at all. The “Access to SMP” indicator is coded zero for these sub-districts. 12 Alternatively, the share of SMP schools with at least 70 percent of the school’s teachers holding an S1 degree could be used. Given the generally low number of SMPs per sub-district, this would result in a categorical indicator with few different values. The share of S1 teachers among SMP teachers in the sub-district therefore provides a more continuous indicator, which better reflects the entire distribution of teacher qualification. 3. Available Rooms and Facility Characteristics The school census provides information on existent rooms and facilities in each school, and we include one indicator of available school facilities: 5. Laboratory in SMP: According to the MSS, each SMP should provide a natural science lab for its students. We proxy this target through the share of SMP in the sub-district that provide a laboratory. Finally, the characteristics of the school buildings are assessed through two indicators of electricity and water supply: 6. Electrification: Share of schools with electricity. 7. Water Supply: Share of schools with water available in the student’s bathroom. It is important to note that the indicators of teacher qualification and facility characteristics are derived from the infrastructure census and hence based on information from public schools only, while the indicators of accessibility derived from the PODES core incorporate both public and private school facilities. Table IV.1 provides an overview of the seven selected indicators of education supply readiness. Table IV.1: Overview of Selected Education Indicators Indicator Description Access to ECED Share of the population living in villages with an early childhood education post (PAUD) in the village or a Kindergarten/TK within 1 km of the village Access to SMP Share of the population living in villages with a SMP within 6 (3) km Teacher Qualification SD Share of SD with at least 2 teachers holding an S1 degree 29 | Teacher Qualification SMP Average Share of SMP teachers holding an S1 degree Laboratory in SMP Share of SMP with laboratory Electrification Share of schools with electricity Water Supply Share of schools with water available in the student’s bathroom IV.2. Description of the National Patterns of Infrastructure Availability Consistent with the indicators of health (and transportation) characteristics indicators show similar sub-district mean infrastructure, all seven education indicators are bounded values (0.73 to 0.80), while laboratories are available in only between 0 and 1, with larger values indicating larger supply 62 percent of the SMP in the average sub-district. readiness. The descriptive statistics in Table IV.2 reveal that, on average, about one fifth of the sub-district population has All indicators are thereby positively correlated (Table IV.3), no access to ECED facilities in the immediate vicinity. Junior with correlations ranging between 0.44 and 0.68. Similar to secondary schools are available within 6 km from the village the results for the health sector, we find consistent patterns for an average of 89 percent of the sub-district population, of infrastructure readiness across different dimensions of while an average of only 79 percent lives in places with education supply: areas with a high density of education a SMP facility within 3 km distance. The sub-district facilities are hence likely to provide more educated teaching averages observed for the teacher qualification and building staff and better equipped schools as well. Table IV.2: Education Indicators: Descriptive Statistics Descriptive Statistics Obs. Mean SD Min Max Access to ECED 6771 0.810 0.297 0 1 Access to SMP (6 km) 6771 0.887 0.216 0 1 30 | Access to SMP (3 km) 6771 0.789 0.242 0 1 Teacher Qualification SD 6771 0.728 0.333 0 1 Teacher Qualification SMP 6771 0.756 0.250 0 1 Laboratory in SMP 6771 0.621 0.365 0 1 Electrification 6771 0.795 0.295 0 1 Water in Bathroom 6771 0.751 0.265 0 1 Table IV.3: Education Indicators: Correlations Correlations ECED SMP 6 km SMP 3 km S1 SD S1 SMP Lab SMP Electr. Access to SMP (6 km) 0.68 1.00 Access to SMP (3 km) 0.68 0.87 1.00 Teacher Qualification SD 0.66 0.57 0.56 1.00 Teacher Qualification SMP 0.57 0.58 0.52 0.59 1.00 Laboratory in SMP 0.50 0.44 0.42 0.55 0.54 1.00 Electrification 0.65 0.58 0.55 0.68 0.55 0.54 1.00 Water in Bathroom 0.67 0.57 0.55 0.60 0.53 0.52 0.63 Figure IV.1 provides a graphical overview of the distribution with at least two teachers holding an S1 degree. Large of the education indicators. In 49 percent of the sub- disparities are also observed for the share of SMP teachers districts universal access to ECED facilities is given, while with an S1 degree: In 877 or 13 percent of the sub-districts, in a total of 1,057 sub-districts less than 50 percent of the an average of less than 50 percent of the teaching force at population has access to these services. Junior secondary public junior secondary schools has an S1 degree, while schools are available within 3 km in 96 percent of the urban this rate is found above 90 percent in about one third of the neighborhoods, but only in 71 percent of the rural villages. sub-districts. The MSS goal of a SMP facility within 6 km of villages in remote areas is fulfilled for 86 percent of the rural villages. Along similar lines, 16 percent of the sub-districts do not However, we observe 173 sub-districts in which no village provide a single SMP school equipped with a laboratory, has such access to junior secondary education. while in 37 percent of the sub-districts all SMP facilities provide a laboratory. The availability of electricity and of The variations in average teacher qualification prove to be water in the student’s bathroom is also distributed unequally even more substantial. On the one hand, in 32 percent of across the country. In 44 percent of the sub-districts all the sub-districts, every SD school fulfills the target of having public schools have access to electricity; at the same time, at least two teachers with an S1 degree; on the other hand, electricity is available in less than 50 percent of all public 577 sub-districts do not have a single elementary school schools in a total of 998 sub-districts. Figure IV.1: Distribution of Education Supply Readiness Indicators 50 50 50 40 40 40 30 30 30 20 20 20 31 | Percent 10 10 10 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share Villages with ECED Facility Share Villages with SMP within 6 km Share Villages with SMP within 3 km 50 50 40 40 30 30 20 20 Percent 10 10 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share SD with at least 2 S1 Teachers Share S1 Teachers in SMP 50 50 50 40 40 40 30 30 30 20 20 20 Percent 10 10 10 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share Villages with ECED Facility Share Villages with SMP within 6 km Share Villages with SMP within 3 km In what follows, maps of all seven indicators present the regional patterns of infrastructure supply readiness. The same classification as for the health indicators is used in order to simplify comparison. Dimension 1: Physical Availability And Accessibility While early childhood education facilities are readily available As for early childhood education facilities, access to junior in urban areas across the country (access given in 98 secondary schools is almost universally given in urban areas. percent of urban neighborhoods), their prevalence in rural In 99 percent of the 13,361 urban neighborhoods across areas is less common and characterized by huge regional Indonesia, a SMP is available within 6 km distance, in 96 differences. High availability in Java and Bali (95 percent percent within 3 km. The MSS goal of a SMP within 6 km of rural villages) is contrasted by substantially lower figures in remote areas is not fulfilled in 14 percent of Indonesia’s for Sumatra (64%), Kalimantan (64%), NTT (63%), Maluku rural villages, with the largest gaps in the Maluku provinces / Maluku Utara (49%), and Papua / Papua Barat (16%). (20%), Kalimantan (26%), and Papua / Papua Barat (52%). Moreover, substantial variations are present within regions Comparably comprehensive supply is found for rural villages and provinces. For instance, striking differences in the in Sulawesi (gap: 9%) and Sumatra (11%), with notable availability of ECED facilities are found between rural villages differences across provinces in these regions. While the in neighboring Sumatera Utara (48%) and Sumatera Barat average gap is even smaller for the rural areas of Java (4%) (96%). Likewise substantial variation is observed within the and Bali (5%), the picture changes when the 3km distance province of Sulawesi Selatan, where universal availability of threshold is applied: only 81 percent of rural villages in Java ECED facilities is given in half of the rural sub-districts, but and 75 percent in Bali provide access to SMP education 13 percent of the rural sub-districts still report easy access facilities within 3 km. Interestingly, a relatively high supply to ECED facilities for less than 50 percent of the population. density in this sense is observed for the rural villages of Sumatera Barat (84%), Sulawesi Utara (85%), Gorontalo (86%), and NTB (92%). Figure IV.2: Map – Share of the Population with Access to ECED Facilities 32 | Figure IV.3: Map – Share of the Population with SMP within 6 km from the Village Dimension 2: Teacher Qualification Overall, 84 percent of the 134,290 public SD schools In three out of four SMP schools, at least 70 percent of covered by the infrastructure census employ at least two the teachers hold an S1 degree, which is the MSS target. teachers with an S1 degree. This MSS target is often fulfilled When aggregating this information to the sub-district level, in Bali (99%), the provinces of Java (96%), Sulawesi Selatan the average share of SMP teachers with an S1 degree is (94%), and NTB (92%). Lowest rates of SD schools with at particularly high in urban sub-districts (85 percent vs. 61 least two teachers holding a bachelor’s degree are found for percent in rural areas). While the overall mean value for Kalimantan Barat (47%), Maluku (41%), NTT (32%), Papua Javanese sub-districts is at a high 91 percent, an average Barat (30%), Maluku Utara (30%), and Papua (29%). Urban- of only 86 and 73 percent of the SMP teachers holds an S1 rural differences are thereby substantial, with 97 percent of degree in Java Barat and Banten, respectively. Comparably the urban SD schools meeting the MSS target, but only 78 high(er) sub-district averages are reported for Kalimantan percent of the schools in rural villages. Selatan (88%) and Sulawesi Selatan (85%); lowest sub- district averages from NTT (45%), Papua Barat (40%), Kalimantan Barat (38%), Maluku (24%), and Papua (16%). Figure IV.4: Map – Share of SD Schools with at least 2 ‘S1 Teachers’ 33 | Figure IV.3: Figure IV.5: Map – Average Share of SMP Teachers holding an S1 degree Dimension 3: Facility Characteristics The indicator for SMP school laboratories shows the lowest The availability of electricity in schools varies greatly across average score of all education supply readiness indicators, regions, with the overall spatial distribution being similar to as only 64 percent of all 21,486 public SMP schools provide the patterns observed for the other indicators of education a laboratory for their students. SMP schools in urban areas supply readiness. Schools in urban areas usually have are quite well equipped (90 percent with laboratory), while access to electricity (99%), while power supply is given for facilities in rural villages lag behind substantially (55 percent only 82 percent of the schools in rural areas. Across regions, with laboratory). The variation within regions thereby tends to elementary schools show the lowest, and senior secondary be larger than the variation across island groups. In Java, for schools the highest rates of electrification. Table IV.4 instance, the relatively good provision of SMP schools with provides an overview of the share of schools that provide laboratories in Jawa Tengah (86 percent) is contrasted by a electricity and water in the student’s bathrooms, respectively, low 54 percent of SMP schools with laboratories in Banten. by region and type of school. Figure IV.6: Map – Share of SMP with Laboratory 34 | Figure IV.7: Map – Share of Schools with Electricity The second indicator of building characteristics shows a similar distribution across regions and school types: Water in the student’s bathroom is provided in 82 percent of the country’s 164,561 public schools, with an overall availability of 95 percent in urban areas and 76 percent in rural areas. Water supply is generally less predominant than electrification, though this trend is reversed for the SD level in some regions (in particular Kalimantan). Looking at differences across provinces, comparably low levels of water supply at public schools are observed for rural areas in Aceh (67%), Sumatera Barat (60%), Banten (66%), Sulawesi Tengah (67%), and Sulawesi Barat (63%), as well as for schools in the Moluccas (53%) and Papua / Papua Barat (38%). Table IV.4: Share of Schools with Electricity and Water Supply – by Region and School Type Electricity Water in Student’s Bathroom Region SD SMP SMA SMK SD SMP SMA SMK Sumatra 80.7 86.8 95.1 90.7 73.7 79.6 88.7 84.1 Java & Bali 98.3 99.3 100 99.8 87.9 95.3 98.6 96.9 NTT & NTB 70.6 78.4 90.5 92.5 73.9 79.7 86.1 85.0 Kalimantan 68.7 82.7 92.4 93.0 80.7 86.0 92.9 93.8 Sulawesi 71.7 83.5 91.7 91.4 75.3 80.6 84.0 83.1 Maluku & North Maluku 58.7 64.2 77.3 76.0 56.2 60.0 59.9 61.0 Papua & Papua Barat 47.3 64.1 78.9 84.0 39.7 51.5 61.3 62.7 Figure IV.8: Map – Share of Schools with Water in the Student’s Bathroom 35 | IV.3. A Composite Index of Education Supply Readiness In order to summarize the information from the seven fairly consistent patterns of supply readiness across different indicators, we aggregate them into sub-indices for each dimensions of the school system. 36 | dimension as well as composite indices based on all indicators. In general, the regional patterns of the supply Similar to the composite indices of health supply readiness of basic education services in Indonesia are in line with the (see section III.3), we combine the information from results observed for the health sector. With the islands of the seven education indicators into one global index of Java and Bali – on average – performing best and the region education supply readiness. We use the same three methods of Papua lacking behind most, 25 percent of the sub-district as for the health sector to derive the seven indicators’ achieve a supply readiness score of 95 percent or better, weights for the composite index: First, the weights are while 30 percent obtain a score of below 75 percent.13 determined based on policy preferences with three in Before discussing the composite indices of education supply principle arbitrary weighting schemes: (i) a particularly readiness in more detail, a brief description of the underlying focus on facility availability, with the two access indicators calculations is given. accounting for a total of 50 percent of the composite index and the five remaining indicators accounting for 10 percent Table IV.5 presents the mean values and pairwise each; and (ii) equal weights across the three dimensions correlations of the sub-indices for the three dimensions accessibility, teacher qualification, and facility characteristics. physical availability, teacher qualification, and facility For comparison, an alternative indicator of SMP availability is characteristics. The sub-indices are calculated as simple used, with the threshold of accessibility being reduced from averages of the respective indicators in each dimension. The 6 km to 3 km. strong positive correlations among the sub-indices confirm Table IV.5: Sub-Indices Education – Mean Values and Correlations Sub-Index Mean Correlations Availability Workforce Building Physical Availability 0.823 1.00 Health Workforce 0.742 0.74 1.00 Building Characteristics 0.722 0.73 0.77 1.00 13 These statistics are based on version A of the composite health index, where particular weight is given to the indicators of physical availability. Second, weights are derived from a principal components the relative importance of the different supply indicators for analysis of the seven indicators. Table IV.6 presents the the SMP students’ performance in the national exam: (i) eigenvectors from the first principal component and the OLS regressions of the seven indicators are run on average resulting weights. As the weights are fairly similar across test scores at district level and the estimated coefficients indicators, we do not include an additional composite index are used as weights; and (ii) based on the these OLS with equal weights across indicators (as is done for health). regressions, concentration indices are used to account for the indicator’s contribution to inequality in education Finally, we also link the supply readiness indicators to actual outcomes. Table IV.7 presents correlations between the outcomes of the education system; here using the average test scores and the seven indicators, as well as the OLS SMP-level test scores from the 2010 national exam (UN) at regression estimates and the resulting weights for the district level. Again, two methods are employed to assess composite index. Table IV.6: Principal Component Analysis Education Indicators Indicators Eigenvector Weight Access to ECED 0.401 0.152 Access to SMP (6 km) 0.372 0.141 Teacher Qualification SD 0.394 0.149 Teacher Qualification SMP 0.365 0.138 Laboratory in SMP 0.339 0.128 Electrification 0.391 0.148 Water in Bathroom 0.381 0.144 2.824 1.000 37 | Table IV.7: OLS Regression Results: Determinants of Average UN Test Scores (SMP) Indicator 1. Correlation 2. OLS I 3. OLS II 4. Weights Access to ECED 0.29 -5.58*** -5.86*** (0.001) (0.000) Access to SMP (6 km) 0.37 7.66*** 7.18*** 0.302 (0.000) (0.000) Teacher Qualification SD 0.47 6.96*** 6.53*** 0.275 (0.000) (0.000) Teacher Qualification SMP 0.35 -2.17 (0.267) Laboratories in SMP 0.46 6.29*** 6.08*** 0.256 (0.000) (0.000) Electrification 0.43 3.98** 3.95** 0.166 (0.010) (0.011) Water in Bathroom 0.27 -7.61*** -7.74*** (0.000) (0.000) Observations: 479 479 R2: 0.320 0.319 P-values in parentheses. Statistical significance: * at 10%; ** at 5%; *** at 1%. Constant included. Column 1 shows significantly positive correlations between this approach. We start from the OLS II regression of the the indicators of education supply readiness and the average seven supply indicators on average SMP test scores. The UN score, hence confirming the choice of indicators. To concentration index for the outpatient utilization rates equals assess these positive correlations further, we run simple 0.0064, which indicates a slightly pro-rich distribution of OLS regressions on test score averages at district level and (higher) test scores. Decomposing the concentration indices obtain positive regression coefficients for four out of the for all covariates results in all positive values (column 2), seven control variables / indicators (column 2). In column which points to relatively higher average test scores in 3, the insignificant indicator of SMP teacher qualification wealthier districts. is excluded and the weights for the composite index are derived from the estimated coefficients (column 4)14. While With all positive concentration indices, the contribution this is a simple way of assessing the determinants of of each covariate to the overall inequality in test scores is education outcomes, the results provide one alternative determined by the sign of the regression coefficient and the approach to the determination of weights. subsequent elasticity. The residual component is relatively small, indicating that the supply indices explain a substantial The second alternative to determine weights based on part of inequality in education outcomes. As to translate regression estimates uses the concept of the concentration these results to weights, the indicators with a negative index to assess the indicator’s contribution to inequality contribution are given a weight of zero and the other in test scores (a more detailed description of the method contributions are rescaled so they sum to 1. is given in Appendix 1). Table IV.8 presents the results for Table IV.8: Education Indicators: Decomposition of the Concentration Index Indicator 1. Coefficients 2. CI 3. Contribution 4. Percent 5. Weights Access to ECED -5.86 0.0833 -0.0055 -86.4 Access to SMP (6 km) 7.18 0.0425 0.0038 59.1 0.194 Teacher Qualification SD 6.53 0.1088 0.0072 112.3 0.367 38 | Teacher Qualification SMP Laboratory in SMP 6.08 0.1026 0.0054 84.4 0.276 Electrification 3.95 0.0733 0.0032 50.2 0.163 Water in Bathroom -7.74 0.0858 -0.0070 -109.6 Residual -0.0007 -10.0 Total 0.0064 100.0 1.000 14 Similar to the approach for the health index, indicators with negative regression coefficient is given zero weight, and the remaining coefficients are rescaled in order to sum to 1. This leaves us with a total of six alternative weighting composite index A as reference, both the highest possible schemes for the composite index of education infrastructure score of 1 (36 sub-districts) and the lowest possible score of supply readiness. Table IV.9 summarizes the weights of 0 (99 sub-districts) are observed. the seven indicators for each of the six alternative indices. While the composite indices A to C use the full set of seven The alternative weighting schemes have little impact on the indicators, versions D and E are based on the regressions on distribution of the composite indices. This is confirmed by UN test scores and result in the exclusion of three indicators. extremely high correlations between the different versions of the composite indices. Versions A to C are almost identical, Tables IV.10 and IV.11 provide descriptive statistics which is due to similar weights and the positive correlations and pairwise correlations for the six composite indices, between the seven sub-indicators. Even with the reduced respectively. Like the underlying indicators, the composite set of underlying indicators (versions D and E), correlations indices are bounded between 0 and 1, with higher values are still above 0.92. Finally, the similarity of the different indicating higher supply readiness. The average Indonesian composite indicators is confirmed by their almost identical sub-district achieves a score of around 0.77 or 77 percent, distribution (Figure IV.9). dependent on the weighting scheme used.15 Using Table IV.9: Overview of Weights for the Composite Education Indices Index ECED SMP 6 km S1 SD S1 SMP Lab SMP Electr. Bathroom A. Focus Access 0.250 0.250 0.100 0.100 0.100 0.100 0.100 A1. SMP Distance 3 km 0.250 0.250 (3km) 0.100 0.100 0.100 0.100 0.100 B. Equal Dimension 0.166 0.166 0.166 0.166 0.111 0.111 0.111 C. PCA 0.152 0.141 0.149 0.138 0.128 0.148 0.144 D. Utilization OLS 0.302 0.275 0.256 0.166 E. Utilization CI 0.194 0.367 0.276 0.163 39 | Table IV.10: Composite Education Indices: Descriptive Statistics Descriptives Obs. Mean SD Min Max A. Focus Access 6771 0.789 0.227 0 1 A1: SMP Distance 3 km 6771 0.765 0.231 0 1 B. Equal Dimension 6771 0.771 0.230 0 1 C. PCA 6771 0.766 0.231 0 1 D. Utilization OLS 6771 0.759 0.245 0 1.00 E. Utilization CI 6771 0.740 0.260 0 1 Table IV.11: Composite Education Indices: Correlations Correlations A A1 B C D A1: SMP Distance 3 km 0.99 1.00 B: B: Equal Dimension 0.99 0.98 1.00 C: C: PCA 0.99 0.98 1.00 1.00 D: D: Utilization OLS 0.95 0.94 0.97 0.97 1.00 E: E: Utilization CI 0.93 0.92 0.96 0.96 1.00 15 Index E provides a slightly lower average score due to the relatively large weight given to the indices of teacher qualification in SD and laboratories in SMP, which both show relatively low averages themselves. Figure IV.9: Distribution of Alternative Composite Indices of Education Supply Readiness 25 25 25 20 20 20 15 15 15 10 10 10 Percent 5 5 5 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 A: Focus Availability A1: Index A with SMP 3 km B: Equal Weights Dimensions 25 25 25 20 20 20 15 15 15 10 10 10 Percent 5 5 5 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 C: PCA D: Utilization OLS E: Utilization CI Figure IV.10: Map – Composite Index of Education Supply Readiness (Index A) 40 | The similarity of the different composite indices translates On average, sub-districts in Bali (0.96) and Java (0.94) into accordingly similar spatial patterns. Representative of all achieve the highest levels of education supply readiness, composite indices, Figure IV.10 maps the spatial distribution average scores are observed for Sulawesi (0.81), Sumatra of index A. (0.80), Kalimantan (0.74), and NTT & NTB (0.72), while the Moluccas (0.60) and in particular Papua / Papua Barat (0.30) lag behind substantially. The overall gap between urban (0.93) and rural (0.70) sub-districts is likewise significant. IV.4. Quantifying Needs for Investment We use the seven indicators of education supply readiness to provide estimates of the existing gaps in the supply of education services. As for the health sector, we interpret the distance between the sub-district score and the maximum value of 1 as gap and calculate the total national gap for each indicator. Table IV.12 gives an overview of the results. Table IV.12: Overall Gaps in Education Supply Readiness, by Indicator Indicator Type of Gap Total National Gap Access to ECED Number of citizens without access 16.64 Mio Number of villages w/o ECED facility 19,052 Access to SMP Number of citizens without access 9.46 Mio (school within 6 km) Number of Kecamatan w/o SMP 230 (population: 2.39 Mio) Teacher Qualification SD Number of additional ‘S1’ teachers needed in SD 32,586 (to reach 2 S1 teachers in every school) Teacher Qualification SMP Number of additional ‘S1’ teachers needed in SMP 26,086 to reach 70% in every school 41 | Number of additional ‘S1’ teachers needed in SMP 14,675 to reach 70% among permanent staff Laboratory in SMP Number of SMP without laboratory 7,796 (36 %) Electrification Total Number of Public Schools without electricity 21,653 (13 %) SD 18,610 (14 %) SMP 2,537 (12 %) SMA 338 (5 %) SMK 168 (7 %) Water in Bathroom Number of Public Schools without water 30,207 (18 %) installation in student’s WC SD 25,896 (19 %) SMP 3,355 (16 %) SMA 642 (10 %) SMK 314 (12 %) Relative gaps in brackets for facility-level gaps. 1. Physical Availability and Accessibility For the two indicators of physical availability, the number Table IV.13 provides an overview of the regional distribution of citizen without easy access to the respective services is of (i) the sub-districts with access rates below 75 percent; calculated. As no village-level information on the number and (ii) the number of people without easy access to ECED of children at different ages is available, an estimate of the facilities and SMP, respectively. Most sub-districts with number of ‘eligible’ children without easy access cannot be access rates to ECED facilities below 75 percent are found provided. Drawing on total population numbers instead, we in Sumatra (34 % of the 1,770 sub-districts) and Papua (28 find 16.6 million people with no immediate access to early %). When looking at the absolute numbers of people without childhood development facilities. Out of the 19,052 villages easy access (hence accounting for population densities), the with no ECED facility in the village or within 1 km distance, picture changes, with 41 percent of these citizens living in 99 percent are in rural areas. A similar urban-rural divide is Sumatra, but only 12 percent in the Papua provinces. observed for access to junior secondary schools, with an SMP available within 6 km (3 km) in 99 percent (96 percent) of all urban neighborhoods. Table IV.13: Access to Education Facilities – Absolute and Relative Gaps Access to ECED SMP within 6 km Region Share Kec. Share People Share Kec. Share People below 0.75 below 0.75 Sumatra 34.2 41.2 16.3 27.7 Java & Bali 3.1 13.7 3.6 26.4 NTT & NTB 7.6 8.4 7.5 6.6 42 | Kalimantan 11.4 12.4 18.8 14.7 Sulawesi 9.6 8.2 6.3 7.6 Maluku & North Maluku 6.2 4.1 4.1 2.3 Papua & Papua Barat 28.1 12.0 43.5 14.6 Absolute Numbers 1,77 16.64 Mio. 978 9.46 Mio. ‘Share Kec. below 0.75’ reports the regional distribution of the 694 sub-districts with an indicator score below 0.75. ‘Share People’ reports the regional share in the total number of people without access. Figure IV.11 shows the absolute number of people without Overall similar patterns are observed for the availability of a access to ECED facilities by sub-district. Most sub-districts SMP facility within 6 km from the village. While 44 percent with more than 10,000 people living in villages without of the sub-districts with access rates below 75 percent are access to ECED facilities are found for Sumatera Utara (83), located in the Papua provinces (Table IV.13), this region Sumatera Selatan (52), Kalimantan Barat (51), Papua (41), accounts for 15 percent of the 9.46 Mio. citizens without Banten (30), as well as NAD (28) and NTT (28). Again, we access. Most people without junior secondary schools within observe particularly significant differences between relative 6 km distance live in Java and Sumatra, with the highest and absolute gaps for Papua, driven by at the same time low absolute numbers found for the provinces of Papua (1.23 relative scores and low population densities. Mio.), Sumatera Utara (0.84 Mio.), Jawa Barat (0.77 Mio.), Jawa Tengah (0.76 Mio.), Jawa Barat (0.71 Mio.), Kalimantan Barat (0.68 Mio.), and Sumatera Selatan (0.60 Mio.). Figure IV.11: Map – Number of Citizens without Access to ECED Facilities 43 | Figure IV.12: Map – Number of Citizens without Access to SMP within 6km 2. Teacher Qualification Turning to the indicators of teacher qualification, we Sumatera Utara, 10.4 percent in Kalimantan Barat, 9.6 calculate the number of additional teachers with an S1 percent in NTT, as well as 7.5 and 6.4 percent in the Maluku degree needed to reach the targets of (i) at least two ‘S1’ and Papua provinces, respectively. At SMP level, the target teachers in each SD school and (ii) at least 70 percent of of having at least 70 percent of the teaching force holding teachers with S1 degree in each SMP school, respectively. an S1 degree would require the qualification/replacement of Under the assumption of a constant total teaching force, 2,180 ‘non-S1 teachers’ in the province of Sumatera Utara, 32,586 SD teachers and 26,086 SMP teachers would 2,094 in NAD, 2,012 in Jawa Barat, 1,832 in Kalimantan need to obtain a bachelor’s degree or be replaced by new Barat, 1,739 in NTT, and 1,713 in Maluku. teachers with this qualification. However, these numbers may be seen as upper-bound estimates for at least two reasons. First, we also consider temporarily hired teachers here. For 3. Building Characteristics instance, the number of additionally needed SMP teachers with an S1 degree decreases to 14,675 when the 70 percent The assessment of existing gaps with respect to building goal is to be achieved among permanent staff only. Second, characteristics reveals that a total of 21,653 public schools recent studies point to an over-supply of teachers especially lack electricity and 30,207 schools do not provide water in small schools in rural areas (World Bank, 2010), which in the student’s bathroom. Most of these schools are would call into question the underlying assumption of a elementary schools, which account for 86 percent of the constant teaching force. schools both without electricity and water in the student’s bathroom. Most of the schools without electricity are thereby Nevertheless, the numbers provide insight into the found in Sumatra (7,337), Sulawesi (4,701) and Kalimantan spatial distribution of teacher (under)qualification. Out (4,010). Water in the student’s bathroom is not available in of the estimated 32,856 additional SD teachers with S1 10,256 public schools in Sumatra, 8,276 in Java, 4,379 in qualification, 11.1 percent would need to be hired in Sulawesi, and 2,574 in Kalimantan 44 | V. TRANSPORTATION INFRASTRUCTURE 45 | V.1. Selection of Supply Readiness Indicators The analysis of transportation infrastructure draws on the 2. Characteristics of Bridges transportation module of the PODES core, which provides some information on the physical availability and accessibility The village census asks village heads about the condition of of infrastructure. Within this dimension we discern three the existing bridges in the village and the requirements for categories and six indicators. new bridges in the villages, providing the following indicators: • Condition of the Bridges: share of the villages for which 1. Characteristics of Main Roads the bridges are considered to be in “good” condition or to suffer from just minor damage (in contrast to moderate or The information available on road characteristics is based heavy damages).16 on subjective assessment of the type and quality of the main • Need for New Bridges: share of the villages that indicate road in a village. that additional bridges are required. • Surface of the Main Road: This indicator reflects the share of villages for which the main road has a hardened 3. Availability of Public Transport surface, made of either asphalt/concrete or gravel/stone. The main road is defined as the widest road heading to Finally, we use information on the availability of public the highway that leads to the nearest district head’s office. transport from the village head’s office to (i) the offices of • Condition of the Main Road: share of the villages for which the sub-district head; and (ii) the district regent or major. We the main road is considered to be in “good” condition, focus only on public transport that follows a fixed route. 46 | which implies that the road suffers from no or just minor damages (in contrast to damage along most or all of the • Public Transport to Sub-district Head: share of the villages road). where public transport to the office of the sub-district head is available and follows a fixed route. • Public Transport to Regent/Major: share of the villages where public transport to the office of the regent or major is available and follows a fixed route. The six indicators of infrastructure supply readiness are listed in Table V.1 below. Table V.1: Available Information on Transportation Infrastructure from PODES Dimension Indicator Description Surface of Main Road Share of villages with main road surface being either asphalt/ Characteristics Main Road concrete or gravel/stone etc. Condition of Main Road Share of villages with main road with no or minor damages Condition of Bridges Share of villages with bridges with no or minor damages Characteristics Bridges Need for New Bridges Share of villages that report no need for new bridge(s) Public Transport to Share of villages with public transport with fixed route to the Sub-district Head office of the sub-district head Availability of Public Transport Public Transport to Share of Villages with public transport with fixed route to the Regent/Mayor office of the regent/major 16 Only villages with at least one bridge are considered for this indicator, which prevents us from calculating this indicator for a total of 301 sub-districts that lack any bridges. V.2. National Patterns of Infrastructure Availability Descriptive statistics are presented in Table V.2, where all indicators are again bounded by 0 and 1. On average, 86 percent of villages per sub-district have a main road that is paved by asphalt or concrete, or has a stone/gravel surface. In just over half of the villages the main roads are, on average, considered to be in good condition. The average condition of bridges scores better, with 83 percent of villages (that have bridges) reporting that their bridges are in good condition and suffer at most minor damage. About a quarter of villages indicate that new bridges are required in the village. Public transport, with a fixed route, to the nearest sub-district head’s office and regent/majors office is available in 34 and 60 percent of the villages of the average sub-district, respectively. Table V.2: Transportation Indicators: Descriptive Statistics Descriptive Statistics Obs. Mean SD Min Max Main Road: Paved or Gravel/Stone 6703 0.858 0.277 0 1 Good Condition of Main Road 6671 0.577 0.318 0 1 Good Condition of Bridges 6470 0.832 0.207 0 1 No Need for New Bridges 6771 0.226 0.262 0 1 Public Transport to Sub-district Head 6771 0.344 0.365 0 1 Public Transport to Regent/Mayor 6767 0.600 0.402 0 1 47 | The geographical profile of road and bridge characteristics, and public transport availability, is portrayed in figures V.1 to V.3. Figure V.1 shows that across sub-districts in Java almost all villages have main roads with asphalt or gravel/stone surfaces. On Sumatra and Sulawesi, sub-districts also score predominantly above 90 percent, but still a non-trivial number of sub- districts fall in the range between 0.5 and 0.9, and incidentally we see sub-districts where less than half of the villages have main roads with some form of hardened surface. For Kalimantan, NTB and NTB we observe a large degree of variation across sub-districts within regions, while sub-districts on Maluku and in particular Papua score very low, predominantly below 0.5 and often below 0.25. Figure V.1: Map – Share of Villages with Asphalt or Gravel/Stone Main Road The sub-districts shares of villages where no new bridges With regard to the availability of public transport, Figure are required, is shown in Figure V.2. On Java, villages V.3 portrays large geographic differences, both across and predominantly report that no new bridges are needed, within regions. On Java, the availability of public transport although there is quite some variation across sub-districts. with fixed routes is the norm. However, on Kalimantan and On Sumatra and Papua, we see a much greater degree of Papua the village shares are predominantly below 0.25. variation across sub-districts than on Java, but still ranging Sumatra, NTB, NTT, and Maluku also score low on average, along the full scale from 0 and 1. A large variation is also but still show some variation within the larger regions. observed for Kalimantan, NTB and NTB, and Maluku, but average levels are lower, often below 0.5. Figure V.2: Map – Share of Villages with NO need for New Bridges 48 | Figure V.3: Map – Share of Villages with Public Transport to Regent/Mayor’s Office V.3. Quantifying Needs for Investment The gap in access to transportation infrastructure is given in Table V.3, expressing the gap in terms of the number of villages that report a lack of infrastructure. Out of 78,600 villages included in the census, 9,735 villages report having a main road that does not have a hardened surface. However, a much larger number of villages, 31,309, report substantial damage to the main village road. A comparable number of villages, 35,048, reports substantial damages to bridges along the main road, while 17,450 villages need additional bridges to be built. Access to public transport with a fixed route to the sub-districts head’s and the regent/mayor’s office is lacking in 51,316 and 31,026 villages, respectively. Table V.3: Overall Gaps in Transportation Infrastructure Dimension Type of Gap Total Gap Number of villages with main road not asphalted 9,735 Characteristics of the Village’s Main Road or with graveled surface Number of villages with main road substantially 31,309 damaged Characteristics of Bridges Bridges with substantial damages 35,048 Number of villages with need for additional 17,45 bridge(s) 49 | Number of villages without public transport with 51,316 Availability of Public Transport fixed route to the sub-district head’s office Number of citizens without public transport with 31,026 fixed route to the regent/mayor’s office V.4. Comparison with Health and Education Supply Readiness There is an unambiguously positive correlation between transportation and indicators for health and education availability, as shown in Table V.4, suggesting the presence of common determinants for investments in local infrastructure across sectors. This correlation is particularly strong for hardened roads, with correlation coefficients ranging from 0.54 to 0.63. For the quality of roads and bridges, and adequacy of bridges, the correlation coefficients are also positive yet slightly smaller, ranging from 0.20 to 0.41. A similar degree of positive correlation with health and education indicators is also observed for the local availability of public transport. Table V.4: Correlations between Transportation and Health/Education Availability Indicators Indicators of Physical Main Road Bridges Public Transport Availability: Health and Asphalt or In Good In Good No extra To Sub-D. To Regent/ Education Gravel/Stone condition Condition bridges head’s office Mayor’s needed office Access to Primary Care 0.55 0.20 0.32 0.21 0.22 0.29 Access to Secondary Care 0.54 0.41 0.38 0.36 0.42 0.30 Access to Delivery Facility 0.63 0.31 0.36 0.29 0.30 0.33 Access to ECED 0.61 0.29 0.37 0.27 0.33 0.34 50 | Access to SMP 0.61 0.25 0.34 0.24 0.27 0.33 VI. SUMMARY OF RESULTS AND POLICY RECOMMENDATIONS 51 | VI.1. National Patterns of Infrastructure Supply Readiness The analysis of the 2011 PODES data provides a consistent As to the health sector, the lowest average scores are found picture of the supply of basic services in Indonesia. Across for the provinces of Kalimantan Barat (75 %),18 NTT (71 the health, education and transportation infrastructure %), Maluku Utara (69 %), Maluku (66 %), Papua Barat (50 indicators, similar regional patterns of supply readiness are %), and Papua (39 %). The highest average levels of health revealed. For instance, the composite indices for health and supply readiness are observed for all Javanese provinces education show high correlations of around 0.80 to 0.85, (ranging from 99 % for DI Yogyakarta to 92 % for Banten), dependent on the weighting schemes used. Figure VI.1 Bali (99 %), Bangka Belitung (95 %), Sumatera Barat (92 %), combines the composite indices for health and education and NTB (90%). into one ‘meta index’,17 in order to illustrate the general spatial distribution of (gaps in) the provision of social Similar patterns are found for the ranking of average services. education supply readiness, with DKI Jakarta (98 %), DI Yogyakarta (97 %), Jawa Tengah (96 %), and Bali ( While the interpretation of the absolute values of this ‘meta 96 %) performing best, and Kalimantan Barat (64 %), Maluku index’, which combines a total of 14 sub-indicators, is Utara (61 %), Maluku (60 %), Papua Barat (40 %) and Papua not straightforward, the map does summarize the main (26 %) showing the lowest average sub-district scores. To results of the analysis at a glance, reflecting the relative a large extent, these patterns are also observed for the geographical differences in overall infrastructure supply indicators of transportation infrastructure. However, despite readiness. In general, the island of Java and the province the consistent overall trends, we also observe substantial 52 | of Bali perform best with respect to the quantity and quality variations within regions and provinces. One explanation of available infrastructure. However, and despite the good for these local disparities is a stark urban-rural divide, not average results for these regions, local needs for investment only with respect to the accessibility, but also the quality of still exist, in particular in the provinces of Jawa Barat and available services. Banten. Overall, the largest gaps in infrastructure supply readiness are found for the Papua region, the Maluku islands, NTT, as well as for the interior of Kalimantan. Figure VI.1: Map – Combined Health and Education Index 17 We use the composite indices for health and education with a particular focus on physical availability (versions A), respectively, and calculate the average of these two indices for this ‘meta index’. 18 The reported scores represent the average sub-district score by province (based on the composite indices with a particular focus on physical availability). Tables A.3 and A.4 alternatively present the scores calculated at the provincial level. VI.2. Policy Recommendations The 2011 PODES infrastructure census provides detailed and up-to-date information on the availability and quality of basic infrastructure in Indonesia. The various indicators developed through this analysis may, therefore, constitute a valuable tool for informing national and local governments, international organizations, and NGOs alike, on regional variation in infrastructure investment needs. Potential applications of these data include: • Improve targeting of PNPM and other government programs: Based on a complete and comprehensive picture of remaining deficits in the provision of basic infrastructure, the indicators can contribute to an improved targeting of PNPM and other government programs. • Contribute to the provision, assessment, and improvement of social services in Indonesia: Actively disseminating the data can foster its usage by the various public and private stakeholders that are engaged in the provision of social services in Indonesia. This awareness of the data would thereby also help avoid the costly collection of already existent information. • Support efforts to improve transparency and accountability at local level: Regional and local inequities can be identified given the implementation of the analysis at sub-district level. The public dissemination of the indicators may contribute to increased transparency, and, hence, political accountability at local level. • Provide the basis for follow-up analyses and the continuous monitoring of infrastructure supply readiness: The present assessment of the local supply of basic services offers various opportunities for follow-up analyses. –– The results from the PODES data should be compared with related surveys of local health and education infrastructure in order to further evaluate the reliability of the data and to bring together available information, where possible. The assessment of the quality of the provided indicators may also be complemented by qualitative fieldwork. 53 | –– If data on cost values are available, the results can also be used to estimates the financing gap of addressing the existing infrastructure deficit at the national, regional and local levels –– Combining the PODES indicators with other socio-economic datasets will facilitate research into the determinants of local service supply, demand, and outcomes. –– To this end, it is not only desirable to regularly conduct the PODES core survey, but also to repeat the infrastructure census in the future, thus enabling continuous monitoring of the quantity and quality of village infrastructure. Keeping track of changes over time will enable a more rigorous evaluation of social programs. If the infrastructure census is conducted again in the future, we suggest the following changes and amendments: • The health survey covers a subset of public health facilities. Subject to budget restrictions, other public and private facilities, such as hospitals or polyclinics, should be included for completeness. • The information on the services offered by health facilities could be expanded and, in part, be rendered more precisely. In particular, further information on available medical equipment, supplies, and treatments would provide an interesting supplement to assess the quality of services. • The education survey is confined to public facilities. Again subject to budgetary constraints, the inclusion of private facilities would be particularly useful in the area of secondary schooling, where private schools account for a substantial share of the existent facilities. Moreover, no facility-level information is collected on early childhood education facilities. REFERENCES • Olken B., J. Onishi and S. Wong (2011), ‘ Indonesia’s PNPM Generasi Program: Final Impact Evaluation Report’, Unpublished Draft. • World Bank (2011), ‘ Program Keluarga Harapan: Impact Evaluation Report of Indonesia’s Household Conditional Cash Transfer Program’, World Bank Office Jakarta, Unpublished Draft. • World Bank (2010), ‘Transforming Indonesia’s Teaching Force, Volume 1: Executive Summary’, Report No. 53732-ID, The World Bank Office Jakarta. • World Health Organization (2011), ‘Measuring Service Availability and Readiness: Service Availability Indicators’, Geneva. 54 | APPENDIX Appendix 1: Decomposition of Concentration Indices An alternative method for determining weights is to assess the policy priority of the various indices in terms of their contribution to inequality in access to health care and education services, respectively. In what follows, we describe the method for the determination of weights for the composite health index. The approach for the composite education index follows the methodology, using test scores from the national exams instead of utilization rates as outcome variable. Inequality in health care utilization can be expressed in terms of a concentration index: where h is health care utilization, μ is the mean of h, and r is the district fractional rank in the national distribution of some welfare measure. The determinants of health care utilization can be assessed through a linear regression: 55 | Inequality in health care utilization can then be decomposition into individual contributions of these determinants: While the regression coefficients describe what determines average health care utilization, the decomposed CI describes to what extent these determinants contribute to inequality in health care utilization across districts. This contribution is the product of (1) the responsiveness (or elasticity) of health care utilization with respect to various types of health care supply, and (2) inequality in the distribution of this health care supply across districts. For example, variable x may be a good predictor of health care utilization, but it will only contribute to inequality of health care utilization if it is itself unequally distributed. That is, if x is equally distributed across districts, then the effect of changing x will be similar across districts. Appendix 2: Alternative Indicators of Health Personnel For comparison, the following four population-based indicators of health workforce are briefly described: • Number of Physicians per 10,000 Population [Target: 1 Physician per 10,000] • Number of Midwives per 10,000 Population • Number of Nurses per 10,000 Population • Number of Core Medical Professionals (Physicians, Midwives, Nurses) per 10,000 Population [WHO Target: 23] Table A.1 provides descriptive statistics for the six indicators. In the average sub-district, 1.6 physicians, 7.2 midwives, and 9.5 nurses are available per 10,000 inhabitants, which results in 18.3 core health professionals per 10,000 population. We calculate score indicators for the availability of physicians and core health professionals, using 1 physician and 23 core health professionals per 10,000 population as target, respectively. Table A.2 reports the correlations between the population-based indicators and the seven core indicators selected for the index. The consistently insignificant or negative correlations point to a structurally different picture of health service supply that is obtained from population-based indicators, as compared to the results from all seven core indicators of health supply readiness. Table A.1: Alternative Health Personnel Indicators: Descriptive Statistics 56 | Descriptives n Mean SD Min Max Physicians per 10,000 Population 6771 1.6 2.7 0 52.1 Midwives per 10,000 Population 6771 7.2 7.0 0 208.5 Nurses per 10,000 Population 6771 9.5 13.1 0 300.8 Core Medical Professionals per 6771 18.3 17.9 0 433.2 10,000 Pop. Core Medical Professionals Score 6771 0.621 0.289 0 1 (23=100) Physicians Score (max=1)* 6771 0.637 0.415 0 1 The Physicians Score takes on the value 1 for sub-districts that fulfill the target of 1 physician per 10,000 population. In this special case, the indicator represents a truncated version of the Physicians per 10,000 population indicator. Table A.2: Alternative Health Personnel Indicators: Correlations of with Core Indicators Correlation with the other Primary Secondary Delivery Physician Midwife Water Electr. Index-Variables Physicians per 10,000 Pop. 0.07 0.11 0.07 0.16 0.02 0.08 0.10 Midwives per 10,000 Pop. 0.03 -0.13 -0.04 -0.03 0.05 -0.05 -0.04 Nurses per 10,000 Pop. -0.08 -0.31 -0.21 -0.13 -0.30 -0.09 -0.17 Core Medical Pro’s per 10,000 -0.04 -0.26 -0.16 -0.09 -0.20 -0.08 -0.12 Core Medical Pro’s; Score 0.06 -0.23 -0.08 -0.02 -0.16 -0.02 -0.12 Physicians; Score 0.26 0.24 0.27 0.42 0.24 0.27 0.24 This impression is confirmed by the graphical representation of the population-based indicators. Figures A.1 and A.2 describe the spatial distribution of the two score indicators of health personnel. The maps reveal that the population-based indicators are heavily driven by the population denominator, resulting in higher scores especially in Papua and Kalimantan, while densely populated areas in Java perform relatively poorly. Similar geographical patterns are generally observed for indicators which are based on per capita measures. We therefore do not use this type of indicator as to avoid a biased assessment of available basic infrastructure. Figure A.1: Map – Core Medical Professionals per 10,000 Population – Score (Target: 23) 57 | Figure A.2: Map – Physicians per 10,000 Population – Score (Target: 1) 58 | Appendix 3: Province- and District-level Overviews Table A.3: Health Indicators and Composite Indices – Province-level Scores19 Province Physical Availability Health Personnel Building Characteristics Composite Indices Primary Secondary Delivery SubIndex GP Pusk. Midwife SubIndex Water P. Electricity SubIndex Access Equal D. Equal I. PCA OLS CI Nanggroe Aceh Darussalam 0.97 0.74 0.96 0.89 0.97 0.83 0.90 0.85 0.91 0.88 0.89 0.89 0.89 0.89 0.90 0.88 Sumatera Utara 0.96 0.83 0.95 0.91 0.92 0.95 0.93 0.89 0.85 0.87 0.91 0.90 0.91 0.91 0.90 0.88 Sumatera Barat 0.99 0.87 0.99 0.95 0.98 0.99 0.99 0.91 0.90 0.90 0.95 0.95 0.95 0.95 0.94 0.92 Riau 0.98 0.77 0.96 0.90 0.98 0.98 0.98 0.86 0.80 0.83 0.90 0.90 0.90 0.91 0.88 0.86 Jambi 0.98 0.77 0.97 0.91 0.94 0.95 0.94 0.87 0.80 0.83 0.90 0.89 0.90 0.90 0.89 0.86 Sumatera Selatan 0.95 0.78 0.96 0.90 0.89 0.96 0.92 0.90 0.82 0.86 0.89 0.89 0.89 0.90 0.89 0.87 Bengkulu 0.97 0.82 0.97 0.92 0.92 0.88 0.90 0.78 0.82 0.80 0.89 0.87 0.88 0.89 0.88 0.87 Lampung 0.97 0.77 0.98 0.91 0.97 0.97 0.97 0.92 0.77 0.84 0.91 0.91 0.91 0.91 0.89 0.86 Kepulauan Bangka Belitung 1.00 0.92 1.00 0.97 1.00 0.97 0.99 0.95 0.93 0.94 0.97 0.97 0.97 0.97 0.96 0.95 Kepulauan Riau 1.00 0.89 0.99 0.96 0.97 0.98 0.98 0.96 0.88 0.92 0.95 0.95 0.95 0.95 0.94 0.93 DKI Jakarta 1.00 1.00 1.00 1.00 0.98 0.99 0.99 0.98 1.00 0.99 1.00 0.99 0.99 0.99 1.00 1.00 Jawa Barat 0.99 0.88 0.99 0.96 0.98 0.99 0.98 0.94 0.96 0.95 0.96 0.96 0.96 0.96 0.96 0.95 Jawa Tengah 0.98 0.95 1.00 0.98 0.99 0.99 0.99 0.98 0.95 0.96 0.98 0.98 0.98 0.98 0.97 0.97 DI Yogyakarta 1.00 0.99 1.00 1.00 1.00 0.97 0.99 0.96 0.98 0.97 0.99 0.98 0.99 0.99 0.99 0.99 Jawa Timur 0.99 0.93 1.00 0.97 0.98 1.00 0.99 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.97 Banten 0.98 0.88 0.98 0.95 0.97 0.97 0.97 0.97 0.97 0.97 0.96 0.96 0.96 0.96 0.96 0.95 Bali 1.00 0.98 1.00 0.99 0.99 0.99 0.99 0.97 0.96 0.97 0.99 0.98 0.99 0.99 0.99 0.98 Nusa Tenggara Barat 0.99 0.80 0.97 0.92 0.97 0.92 0.95 0.97 0.92 0.94 0.93 0.94 0.94 0.94 0.93 0.91 Nusa Tenggara Timur 0.91 0.53 0.88 0.77 0.80 0.79 0.80 0.83 0.59 0.71 0.76 0.76 0.76 0.76 0.75 0.71 Kalimantan Barat 0.92 0.58 0.90 0.80 0.83 0.87 0.85 0.87 0.67 0.77 0.80 0.81 0.81 0.81 0.79 0.75 Kalimantan Tengah 0.96 0.64 0.89 0.83 0.86 0.86 0.86 0.88 0.73 0.80 0.83 0.83 0.83 0.83 0.82 0.78 Kalimantan Selatan 0.97 0.81 0.97 0.92 0.98 0.92 0.95 0.87 0.91 0.89 0.92 0.92 0.92 0.92 0.91 0.90 Kalimantan Timur 0.98 0.80 0.94 0.90 0.96 0.90 0.93 0.95 0.83 0.89 0.91 0.91 0.91 0.91 0.89 0.87 Sulawesi Utara 0.98 0.83 0.96 0.92 0.95 0.75 0.85 0.95 0.87 0.91 0.91 0.90 0.90 0.90 0.92 0.90 Sulawesi Tengah 0.96 0.66 0.92 0.85 0.81 0.86 0.83 0.91 0.73 0.82 0.84 0.83 0.84 0.84 0.84 0.80 Sulawesi Selatan 0.97 0.81 0.94 0.91 0.94 0.95 0.94 0.93 0.84 0.88 0.91 0.91 0.91 0.91 0.89 0.87 Sulawesi Tenggara 0.96 0.62 0.94 0.84 0.79 0.80 0.79 0.86 0.59 0.72 0.81 0.79 0.79 0.80 0.80 0.76 Gorontalo 0.97 0.75 0.95 0.89 0.96 0.79 0.88 0.84 0.82 0.83 0.88 0.87 0.87 0.87 0.88 0.85 Sulawesi Barat 0.92 0.66 0.87 0.81 0.89 0.90 0.90 0.75 0.75 0.75 0.82 0.82 0.82 0.82 0.79 0.77 Maluku 0.93 0.57 0.87 0.79 0.68 0.83 0.76 0.74 0.63 0.68 0.76 0.74 0.75 0.76 0.75 0.72 Maluku Utara 0.90 0.55 0.87 0.77 0.72 0.68 0.70 0.87 0.71 0.79 0.76 0.75 0.76 0.76 0.78 0.74 Papua Barat 0.91 0.62 0.85 0.79 0.56 0.69 0.62 0.77 0.69 0.73 0.75 0.72 0.73 0.73 0.77 0.74 Papua 0.71 0.40 0.63 0.58 0.60 0.54 0.57 0.68 0.45 0.57 0.57 0.57 0.57 0.57 0.56 0.53 19 Thereported indicator scores are calculated at provincial level, e.g. measuring the share of the provincial population with easy access to primary health care. These scores differ from average sub-district scores by province, where equal weight is given to each sub-district irrespective of population size. Table A.4: Education Indicators and Composite Indices – Province-level Scores20 Physical Availability Teacher Qualification Facility Characteristics Composite Indices Province ECED SMP (6) SubIndex SD S1 SMP S1 SubIndex Lab SMP Electricity Water SubIndex Access SMP 3km Equal D. PCA OLS CI Nanggroe Aceh Darussalam 0.73 0.96 0.85 0.74 0.72 0.73 0.62 0.96 0.71 0.76 0.80 0.77 0.78 0.78 0.81 0.78 Sumatera Utara 0.82 0.94 0.88 0.72 0.75 0.73 0.59 0.82 0.69 0.70 0.79 0.77 0.77 0.76 0.77 0.74 Sumatera Barat 0.99 0.98 0.98 0.89 0.83 0.86 0.65 0.93 0.77 0.78 0.90 0.89 0.88 0.87 0.86 0.85 Riau 0.92 0.95 0.94 0.80 0.73 0.76 0.59 0.72 0.79 0.70 0.83 0.82 0.80 0.79 0.78 0.76 Jambi 0.93 0.96 0.94 0.76 0.78 0.77 0.61 0.74 0.73 0.69 0.83 0.82 0.80 0.79 0.78 0.75 Sumatera Selatan 0.82 0.92 0.87 0.71 0.78 0.75 0.63 0.75 0.82 0.73 0.80 0.78 0.78 0.78 0.76 0.74 Bengkulu 0.82 0.95 0.88 0.86 0.80 0.83 0.56 0.79 0.70 0.69 0.81 0.78 0.80 0.79 0.80 0.78 Lampung 0.93 0.97 0.95 0.81 0.72 0.77 0.67 0.79 0.81 0.76 0.85 0.83 0.82 0.82 0.82 0.80 Kepulauan Bangka Belitung 0.94 0.92 0.93 0.73 0.81 0.77 0.72 0.95 0.94 0.87 0.88 0.85 0.86 0.86 0.82 0.80 Kepulauan Riau 0.96 0.98 0.97 0.72 0.74 0.73 0.56 0.92 0.87 0.78 0.87 0.85 0.83 0.83 0.79 0.76 DKI Jakarta 1.00 1.00 1.00 1.00 0.90 0.95 0.96 1.00 0.99 0.98 0.98 0.98 0.98 0.98 0.99 0.99 Jawa Barat 0.98 0.98 0.98 0.96 0.86 0.91 0.76 0.98 0.84 0.86 0.93 0.92 0.92 0.91 0.92 0.91 Jawa Tengah 0.99 0.98 0.99 0.94 0.91 0.93 0.86 1.00 0.96 0.94 0.96 0.94 0.95 0.95 0.94 0.94 DI Yogyakarta 1.00 1.00 1.00 0.99 0.86 0.92 0.97 1.00 0.99 0.99 0.98 0.97 0.97 0.97 0.99 0.99 Jawa Timur 0.99 0.98 0.98 0.98 0.93 0.96 0.82 0.97 0.87 0.89 0.95 0.93 0.94 0.94 0.94 0.93 Banten 0.93 0.99 0.96 0.96 0.79 0.87 0.54 0.98 0.76 0.76 0.88 0.87 0.86 0.86 0.86 0.85 Bali 0.99 0.97 0.98 0.99 0.89 0.94 0.81 1.00 0.96 0.92 0.95 0.93 0.95 0.95 0.94 0.94 Nusa Tenggara Barat 0.96 0.99 0.98 0.92 0.85 0.89 0.58 0.90 0.78 0.75 0.89 0.89 0.87 0.86 0.85 0.84 Nusa Tenggara Timur 0.74 0.88 0.81 0.32 0.66 0.49 0.36 0.57 0.73 0.55 0.67 0.64 0.62 0.61 0.54 0.48 Kalimantan Barat 0.71 0.85 0.78 0.47 0.60 0.53 0.46 0.62 0.82 0.63 0.69 0.67 0.65 0.65 0.61 0.57 Kalimantan Tengah 0.86 0.87 0.86 0.73 0.78 0.76 0.48 0.58 0.75 0.61 0.76 0.75 0.74 0.73 0.68 0.66 Kalimantan Selatan 0.95 0.95 0.95 0.81 0.86 0.84 0.68 0.91 0.87 0.82 0.89 0.86 0.87 0.87 0.83 0.82 Kalimantan Timur 0.95 0.94 0.94 0.73 0.80 0.77 0.49 0.83 0.85 0.72 0.84 0.83 0.81 0.80 0.75 0.72 Sulawesi Utara 0.96 0.98 0.97 0.69 0.72 0.71 0.62 0.92 0.81 0.78 0.86 0.85 0.82 0.82 0.80 0.76 Sulawesi Tengah 0.92 0.93 0.93 0.57 0.76 0.67 0.50 0.70 0.69 0.63 0.79 0.77 0.74 0.73 0.68 0.64 Sulawesi Selatan 0.93 0.96 0.95 0.94 0.88 0.91 0.65 0.85 0.81 0.77 0.89 0.86 0.88 0.86 0.86 0.85 Sulawesi Tenggara 0.86 0.96 0.91 0.58 0.77 0.68 0.52 0.53 0.74 0.60 0.77 0.75 0.73 0.71 0.67 0.63 Gorontalo 0.97 0.99 0.98 0.75 0.68 0.72 0.49 0.88 0.84 0.73 0.85 0.84 0.81 0.80 0.77 0.74 Sulawesi Barat 0.88 0.93 0.90 0.73 0.83 0.78 0.44 0.51 0.66 0.54 0.77 0.75 0.74 0.72 0.68 0.66 Maluku 0.76 0.92 0.84 0.41 0.48 0.45 0.37 0.64 0.59 0.53 0.67 0.66 0.61 0.60 0.59 0.54 Maluku Utara 0.72 0.91 0.81 0.30 0.65 0.47 0.37 0.60 0.56 0.51 0.65 0.64 0.60 0.59 0.55 0.48 Papua Barat 0.72 0.81 0.76 0.29 0.70 0.50 0.43 0.65 0.51 0.53 0.64 0.62 0.60 0.59 0.54 0.49 Papua 0.39 0.58 0.48 0.30 0.55 0.42 0.35 0.48 0.41 0.41 0.45 0.43 0.44 0.44 0.43 0.40 20 Thereported indicator scores are calculated at provincial level, e.g. measuring the share of the provincial population with easy access to primary health care. These scores differ from average sub-district scores by province, where equal weight is given to each sub-district irrespective of population size. 59 | 60 | Table A.5: Health Indicators and Composite Indices –District-level Scores District BPS Code Physical Availability Health Personnel Building Characteristics Composite Indices Primary Secondary Delivery SubIndex GP Pusk. Midwife SubIndex Water P. Electricity SubIndex Access Equal D. Equal I. PCA OLS CI Nanggroe Aceh Derussalam 0.97 0.74 0.96 0.89 0.97 0.83 0.90 0.85 0.91 0.88 0.89 0.89 0.89 0.89 0.90 0.88 Simeulue 1101 0.99 0.53 0.74 0.75 0.75 0.80 0.78 0.75 0.88 0.81 0.77 0.78 0.78 0.78 0.74 0.72 Aceh Singkil 1102 0.96 0.56 0.91 0.81 0.91 0.86 0.88 0.82 0.80 0.81 0.82 0.83 0.83 0.83 0.81 0.77 Aceh Selatan 1103 0.97 0.71 0.95 0.88 0.95 0.77 0.86 0.67 0.74 0.71 0.84 0.81 0.82 0.83 0.82 0.80 Aceh Tenggara 1104 0.98 0.75 0.98 0.91 0.89 0.74 0.82 0.79 0.86 0.83 0.87 0.85 0.86 0.86 0.89 0.87 Aceh Timur 1105 0.93 0.53 0.94 0.80 0.92 0.86 0.89 0.85 0.95 0.90 0.84 0.86 0.85 0.86 0.86 0.81 Aceh Tengah 1106 0.93 0.62 0.98 0.84 1.00 0.88 0.94 0.79 0.92 0.85 0.86 0.88 0.87 0.88 0.87 0.84 Aceh Barat 1107 0.95 0.74 0.93 0.87 1.00 0.72 0.86 0.77 0.77 0.77 0.85 0.83 0.84 0.84 0.84 0.82 Aceh Besar 1108 0.99 0.85 0.99 0.94 1.00 0.86 0.93 0.92 0.96 0.94 0.94 0.94 0.94 0.94 0.95 0.94 Pidie 1109 0.99 0.85 0.99 0.94 1.00 0.67 0.83 0.88 0.97 0.93 0.92 0.90 0.91 0.90 0.94 0.93 Bireuen 1110 0.95 0.77 0.97 0.90 1.00 0.90 0.95 0.94 0.98 0.96 0.92 0.93 0.93 0.93 0.93 0.91 Aceh Utara 1111 0.97 0.46 0.95 0.80 1.00 0.69 0.84 0.79 0.91 0.85 0.82 0.83 0.82 0.83 0.83 0.78 Aceh Barat Daya 1112 1.00 0.86 0.99 0.95 0.92 0.88 0.90 0.77 0.94 0.85 0.92 0.90 0.91 0.91 0.92 0.92 Gayo Lues 1113 0.94 0.57 0.90 0.80 1.00 0.93 0.97 1.00 0.89 0.94 0.86 0.90 0.89 0.89 0.86 0.81 Aceh Tamiang 1114 0.96 0.91 0.99 0.95 1.00 0.97 0.99 1.00 0.98 0.99 0.97 0.98 0.97 0.97 0.98 0.96 Nagan Raya 1115 0.97 0.79 0.98 0.91 1.00 0.73 0.86 0.85 0.94 0.89 0.90 0.89 0.89 0.89 0.92 0.90 Aceh Jaya 1116 0.98 0.78 0.99 0.92 1.00 0.84 0.92 0.88 0.65 0.76 0.89 0.87 0.87 0.88 0.87 0.84 Bener Meriah 1117 0.95 0.68 0.96 0.86 1.00 0.88 0.94 0.90 0.97 0.93 0.89 0.91 0.90 0.91 0.90 0.87 Pidie jaya 1118 0.99 0.94 0.99 0.97 1.00 0.86 0.93 0.90 0.95 0.93 0.96 0.94 0.95 0.95 0.96 0.96 Banda Aceh 1171 1.00 1.00 1.00 1.00 1.00 0.97 0.98 0.91 1.00 0.95 0.99 0.98 0.98 0.98 0.99 0.99 Sabang 1172 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Langsa 1173 1.00 0.98 1.00 0.99 1.00 0.96 0.98 1.00 1.00 1.00 0.99 0.99 0.99 0.99 1.00 0.99 Lhokseumawe 1174 1.00 1.00 1.00 1.00 1.00 0.98 0.99 0.83 0.99 0.91 0.98 0.97 0.97 0.98 0.97 0.98 Subulussalam 1175 0.88 0.39 0.89 0.72 1.00 0.97 0.98 0.80 0.82 0.81 0.79 0.84 0.82 0.83 0.78 0.72 Sumatera Utara 0.96 0.83 0.95 0.91 0.92 0.95 0.93 0.89 0.85 0.87 0.91 0.90 0.91 0.91 0.90 0.88 Nias 1201 0.69 0.31 0.53 0.51 0.63 0.36 0.49 0.63 0.39 0.51 0.51 0.50 0.50 0.50 0.48 0.45 Mandailing natal 1202 0.88 0.68 0.94 0.83 0.88 0.93 0.90 0.85 0.90 0.87 0.85 0.87 0.86 0.87 0.87 0.84 Tapanuli Selatan 1203 0.89 0.67 0.95 0.84 0.88 0.93 0.90 0.88 0.89 0.88 0.86 0.87 0.87 0.87 0.88 0.85 Tapanuli Tengah 1204 0.96 0.73 0.98 0.89 0.90 0.97 0.94 0.81 0.87 0.84 0.89 0.89 0.89 0.89 0.89 0.86 Tapanuli Utara 1205 0.90 0.63 0.92 0.82 1.00 0.96 0.98 0.89 0.94 0.92 0.87 0.91 0.89 0.89 0.87 0.84 Toba Samosir 1206 0.87 0.74 0.90 0.84 1.00 0.98 0.99 0.89 0.97 0.93 0.89 0.92 0.91 0.91 0.88 0.87 Labuhan Batu 1207 0.98 0.82 0.97 0.92 0.92 0.99 0.96 0.85 0.52 0.68 0.88 0.85 0.86 0.87 0.83 0.81 Asahan 1208 0.97 0.87 0.94 0.93 1.00 0.99 0.99 1.00 0.90 0.95 0.95 0.96 0.95 0.95 0.93 0.92 Simalungun 1209 0.98 0.87 0.98 0.94 1.00 0.99 0.99 0.91 0.81 0.86 0.94 0.93 0.93 0.94 0.92 0.90 Dairi 1210 0.97 0.64 0.91 0.84 0.89 0.97 0.93 0.83 0.76 0.80 0.85 0.86 0.85 0.86 0.82 0.79 District BPS Code Physical Availability Health Personnel Building Characteristics Composite Indices Primary Secondary Delivery SubIndex GP Pusk. Midwife SubIndex Water P. Electricity SubIndex Access Equal D. Equal I. PCA OLS CI Karo 1211 0.99 0.92 0.97 0.96 0.95 1.00 0.97 0.95 0.98 0.96 0.96 0.97 0.96 0.97 0.96 0.96 Deli Serdang 1212 1.00 0.97 1.00 0.99 1.00 1.00 1.00 0.97 0.92 0.94 0.98 0.98 0.98 0.98 0.97 0.97 Langkat 1213 0.98 0.86 0.97 0.94 1.00 0.99 1.00 0.93 0.88 0.91 0.94 0.95 0.95 0.95 0.93 0.92 Nias Selatan 1214 0.75 0.33 0.64 0.57 0.28 0.34 0.31 0.52 0.24 0.38 0.48 0.42 0.44 0.45 0.49 0.44 Humbang Hasundutan 1215 0.86 0.46 0.76 0.69 1.00 1.00 1.00 0.83 0.97 0.90 0.80 0.86 0.84 0.84 0.77 0.73 Pakpak Bharat 1216 0.96 0.63 0.84 0.81 0.88 0.96 0.92 1.00 0.77 0.89 0.85 0.87 0.86 0.86 0.82 0.78 Samosir 1217 0.79 0.50 0.83 0.71 1.00 0.98 0.99 0.75 0.98 0.86 0.80 0.85 0.83 0.84 0.79 0.76 Serdang Bedagai 1218 1.00 0.90 0.99 0.96 1.00 0.99 0.99 0.85 0.93 0.89 0.96 0.95 0.95 0.96 0.94 0.94 Batu Bara 1219 1.00 0.73 0.99 0.91 1.00 1.00 1.00 1.00 0.88 0.94 0.93 0.95 0.94 0.95 0.93 0.89 Padang Lawas Utara 1220 0.79 0.43 0.82 0.68 0.93 0.82 0.88 0.87 0.72 0.79 0.74 0.78 0.77 0.77 0.74 0.69 Padang Lawas 1221 0.94 0.78 0.94 0.89 0.91 0.80 0.86 0.82 0.93 0.87 0.88 0.87 0.87 0.88 0.89 0.88 Labuhan Batu Selatan 1222 0.97 0.74 0.98 0.90 1.00 0.99 0.99 1.00 0.63 0.82 0.90 0.90 0.90 0.90 0.87 0.83 Labuhan Batu Utara 1223 0.99 0.65 0.96 0.86 1.00 1.00 1.00 0.94 0.56 0.75 0.87 0.87 0.87 0.87 0.82 0.77 Nias Utara 1224 0.86 0.20 0.70 0.59 0.82 0.48 0.65 0.82 0.45 0.63 0.61 0.62 0.62 0.62 0.59 0.51 Nias Barat 1225 0.66 0.13 0.49 0.43 0.83 0.31 0.57 0.67 0.56 0.61 0.49 0.54 0.52 0.51 0.48 0.42 Sibolga 1271 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Tanjung Balai 1272 1.00 1.00 1.00 1.00 1.00 0.99 0.99 0.88 0.84 0.86 0.97 0.95 0.96 0.96 0.95 0.95 Pematang Siantar 1273 1.00 0.98 1.00 0.99 1.00 1.00 1.00 1.00 0.98 0.99 0.99 0.99 0.99 0.99 0.99 0.99 Tebing Tinggi 1274 1.00 1.00 1.00 1.00 0.89 1.00 0.94 1.00 0.98 0.99 0.99 0.98 0.98 0.98 1.00 0.99 Medan 1275 1.00 1.00 1.00 1.00 1.00 0.99 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Binjai 1276 1.00 1.00 1.00 1.00 1.00 0.97 0.99 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Padangsidimpuan 1277 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.90 0.95 0.99 0.98 0.98 0.99 0.98 0.97 Gunungsitoli 1278 0.83 0.76 0.79 0.80 0.25 0.69 0.47 0.75 0.42 0.59 0.69 0.62 0.64 0.65 0.71 0.69 Sumatera Barat 0.99 0.87 0.99 0.95 0.98 0.99 0.99 0.91 0.90 0.90 0.95 0.95 0.95 0.95 0.94 0.92 Kepulauan Mentawai 1301 0.72 0.10 0.61 0.48 0.57 0.81 0.69 0.86 0.67 0.76 0.58 0.64 0.62 0.62 0.58 0.50 Pesisir Selatan 1302 0.99 0.63 1.00 0.88 1.00 1.00 1.00 1.00 0.88 0.94 0.91 0.94 0.93 0.93 0.91 0.87 Solok 1303 0.98 0.90 0.97 0.95 1.00 1.00 1.00 0.83 0.88 0.85 0.94 0.93 0.94 0.94 0.92 0.91 Sijunjung 1304 0.99 0.76 0.98 0.91 1.00 1.00 1.00 0.92 0.90 0.91 0.93 0.94 0.94 0.94 0.92 0.89 Tanah Datar 1305 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.83 0.95 0.89 0.98 0.96 0.97 0.97 0.96 0.97 Padang Pariaman 1306 0.99 0.95 1.00 0.98 1.00 1.00 1.00 0.83 0.97 0.90 0.97 0.96 0.96 0.97 0.96 0.96 Agam 1307 1.00 0.88 0.98 0.95 1.00 1.00 1.00 0.95 0.96 0.96 0.96 0.97 0.97 0.97 0.95 0.94 Lima Puluh Kota 1308 1.00 0.85 1.00 0.95 0.95 1.00 0.98 0.95 0.82 0.89 0.94 0.94 0.94 0.94 0.93 0.91 Pasaman 1309 1.00 0.83 1.00 0.94 1.00 1.00 1.00 0.81 0.85 0.83 0.93 0.93 0.93 0.93 0.91 0.90 Solok Selatan 1310 1.00 0.74 1.00 0.91 1.00 1.00 1.00 1.00 0.90 0.95 0.94 0.95 0.95 0.95 0.93 0.90 Dharmasraya 1311 0.98 0.80 0.95 0.91 1.00 1.00 1.00 0.92 0.93 0.92 0.93 0.94 0.94 0.94 0.92 0.90 61 | 62 | District BPS Code Physical Availability Health Personnel Building Characteristics Composite Indices Primary Secondary Delivery SubIndex GP Pusk. Midwife SubIndex Water P. Electricity SubIndex Access Equal D. Equal I. PCA OLS CI Pasaman Barat 1312 0.99 0.85 0.99 0.94 0.94 1.00 0.97 0.88 0.86 0.87 0.93 0.93 0.93 0.94 0.92 0.91 Padang 1371 1.00 0.99 1.00 1.00 1.00 0.99 1.00 0.95 0.91 0.93 0.98 0.97 0.98 0.98 0.97 0.97 Solok 1372 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.75 0.94 0.85 0.97 0.95 0.96 0.96 0.95 0.96 Sawah Lunto 1373 1.00 1.00 1.00 1.00 1.00 0.98 0.99 1.00 0.95 0.97 0.99 0.99 0.99 0.99 0.99 0.99 Padang Panjang 1374 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.93 0.96 0.99 0.99 0.99 0.99 0.98 0.98 Bukittinggi 1375 1.00 1.00 1.00 1.00 0.88 1.00 0.94 1.00 0.85 0.92 0.97 0.95 0.96 0.96 0.97 0.96 Payakumbuh 1376 1.00 1.00 1.00 1.00 1.00 0.96 0.98 1.00 0.90 0.95 0.99 0.98 0.98 0.98 0.98 0.98 Pariaman 1377 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Riau 0.98 0.77 0.96 0.90 0.98 0.98 0.98 0.86 0.80 0.83 0.90 0.90 0.90 0.91 0.88 0.86 Kuantan Singingi 1401 0.99 0.78 0.96 0.91 0.91 0.97 0.94 0.91 0.79 0.85 0.90 0.90 0.90 0.91 0.89 0.87 Indragiri Hulu 1402 0.96 0.67 0.96 0.86 1.00 0.97 0.99 0.79 0.84 0.81 0.88 0.89 0.88 0.89 0.86 0.83 Indragiri Hilir 1403 0.97 0.47 0.88 0.78 0.92 0.98 0.95 0.80 0.75 0.77 0.81 0.83 0.83 0.83 0.78 0.73 Pelalawan 1404 0.96 0.72 0.92 0.87 1.00 0.99 1.00 0.83 0.70 0.77 0.87 0.88 0.88 0.88 0.83 0.80 Siak 1405 0.98 0.92 0.98 0.96 1.00 0.99 0.99 0.93 0.92 0.93 0.96 0.96 0.96 0.96 0.95 0.95 Kampar 1406 0.99 0.80 0.98 0.92 1.00 0.98 0.99 0.89 0.73 0.81 0.91 0.91 0.91 0.91 0.88 0.86 Rokan Hulu 1407 0.99 0.91 0.98 0.96 1.00 0.99 1.00 0.90 0.79 0.85 0.94 0.93 0.94 0.94 0.92 0.91 Bengkalis 1408 0.99 0.84 0.96 0.93 1.00 0.97 0.99 0.91 0.82 0.87 0.93 0.93 0.93 0.93 0.90 0.89 Rokan Hilir 1409 0.98 0.62 0.99 0.86 0.94 0.98 0.96 0.88 0.86 0.87 0.88 0.90 0.89 0.90 0.88 0.84 Kepulauan Meranti 1410 0.89 0.38 0.89 0.72 1.00 0.94 0.97 0.63 0.72 0.67 0.76 0.79 0.78 0.79 0.73 0.68 Pekanbaru 1471 1.00 0.98 1.00 0.99 1.00 1.00 1.00 1.00 0.88 0.94 0.98 0.98 0.98 0.98 0.97 0.96 Dumai 1473 0.99 0.99 0.99 0.99 1.00 1.00 1.00 0.67 0.92 0.79 0.95 0.93 0.94 0.95 0.93 0.94 Jambi 0.98 0.77 0.97 0.91 0.94 0.95 0.94 0.87 0.80 0.83 0.90 0.89 0.90 0.90 0.89 0.86 Kerinci 1501 0.98 0.80 0.98 0.92 1.00 0.71 0.86 1.00 0.84 0.92 0.91 0.90 0.90 0.90 0.92 0.90 Merangin 1502 0.96 0.74 0.98 0.89 0.95 0.97 0.96 0.79 0.80 0.80 0.89 0.88 0.88 0.89 0.87 0.85 Sarolangun 1503 0.92 0.63 0.97 0.84 0.92 0.95 0.93 0.92 0.76 0.84 0.86 0.87 0.87 0.87 0.86 0.82 Batang Hari 1504 1.00 0.64 0.94 0.86 1.00 0.99 0.99 1.00 0.71 0.86 0.89 0.90 0.90 0.90 0.85 0.81 Muaro Jambi 1505 0.98 0.76 0.97 0.90 1.00 0.97 0.99 1.00 0.94 0.97 0.93 0.95 0.94 0.94 0.93 0.90 Tanjung Jabung Timur 1506 0.98 0.44 0.91 0.78 0.71 0.99 0.85 0.65 0.65 0.65 0.76 0.76 0.76 0.77 0.74 0.69 Tanjung Jabung Barat 1507 1.00 0.57 0.92 0.83 0.94 0.99 0.97 0.75 0.66 0.70 0.83 0.83 0.83 0.84 0.78 0.74 Tebo 1508 0.99 0.76 0.99 0.91 0.93 1.00 0.96 0.86 0.91 0.88 0.92 0.92 0.92 0.92 0.91 0.89 Bungo 1509 1.00 0.98 0.99 0.99 0.94 0.97 0.96 0.78 0.75 0.76 0.94 0.90 0.92 0.92 0.91 0.91 Jambi 1571 1.00 1.00 1.00 1.00 0.95 0.98 0.97 0.95 1.00 0.98 0.99 0.98 0.98 0.98 0.99 0.99 Sungai Penuh 1572 0.99 0.98 0.99 0.99 1.00 0.62 0.81 0.83 0.93 0.88 0.93 0.89 0.91 0.91 0.95 0.96 Sumatera Selatan 0.95 0.78 0.96 0.90 0.89 0.96 0.92 0.90 0.82 0.86 0.89 0.89 0.89 0.90 0.89 0.87 Ogan Komering Ulu 1601 0.99 0.81 0.96 0.92 0.87 1.00 0.93 0.93 0.85 0.89 0.92 0.91 0.92 0.92 0.91 0.89 District BPS Code Physical Availability Health Personnel Building Characteristics Composite Indices Primary Secondary Delivery SubIndex GP Pusk. Midwife SubIndex Water P. Electricity SubIndex Access Equal D. Equal I. PCA OLS CI Ogan Komering Ilir 1602 0.83 0.56 0.87 0.75 0.84 0.95 0.90 0.96 0.78 0.87 0.81 0.84 0.83 0.83 0.81 0.77 Muara Enim 1603 0.95 0.84 0.98 0.92 0.91 0.98 0.94 0.77 0.91 0.84 0.91 0.90 0.91 0.91 0.91 0.90 Lahat 1604 0.97 0.88 0.98 0.94 0.84 0.81 0.83 0.84 0.81 0.82 0.90 0.86 0.88 0.88 0.90 0.90 Musi Rawas 1605 0.97 0.78 0.97 0.91 0.96 0.97 0.97 0.89 0.83 0.86 0.91 0.91 0.91 0.91 0.90 0.87 Musi Banyuasin 1606 0.95 0.79 0.94 0.89 1.00 0.98 0.99 0.80 0.83 0.81 0.90 0.90 0.90 0.90 0.87 0.85 Banyu Asin 1607 0.92 0.60 0.94 0.82 0.97 0.99 0.98 0.97 0.85 0.91 0.87 0.90 0.89 0.89 0.87 0.82 Ogan Kom. Ulu Selatan 1608 0.86 0.47 0.88 0.74 0.33 0.82 0.58 0.87 0.70 0.78 0.71 0.70 0.70 0.71 0.77 0.72 Ogan Kom. Ulu Timur 1609 0.93 0.63 0.97 0.85 0.86 1.00 0.93 1.00 0.78 0.89 0.87 0.89 0.88 0.88 0.88 0.83 Ogan Ilir 1610 0.93 0.77 0.98 0.89 0.83 0.96 0.90 1.00 0.87 0.93 0.90 0.91 0.91 0.91 0.92 0.89 Empat Lawang 1611 0.99 0.78 0.99 0.92 1.00 0.65 0.83 0.75 0.57 0.66 0.85 0.80 0.82 0.82 0.83 0.81 Palembang 1671 1.00 0.99 1.00 1.00 1.00 0.99 0.99 0.85 0.95 0.90 0.98 0.96 0.97 0.97 0.96 0.97 Prabumulih 1672 1.00 1.00 1.00 1.00 0.86 1.00 0.93 0.86 0.85 0.85 0.96 0.93 0.94 0.94 0.94 0.95 Pagar Alam 1673 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.96 0.98 1.00 0.99 0.99 0.99 0.99 0.99 Lubuklinggau 1674 1.00 0.99 1.00 1.00 1.00 0.97 0.98 1.00 0.89 0.94 0.98 0.97 0.98 0.98 0.98 0.97 Bengkulu 0.97 0.82 0.97 0.92 0.92 0.88 0.90 0.78 0.82 0.80 0.89 0.87 0.88 0.89 0.88 0.87 Bengkulu Selatan 1701 1.00 0.82 0.98 0.93 0.93 0.96 0.95 1.00 0.80 0.90 0.93 0.93 0.93 0.93 0.92 0.89 Rejang Lebong 1702 0.99 0.98 0.99 0.99 1.00 0.93 0.96 0.76 0.92 0.84 0.95 0.93 0.94 0.94 0.94 0.95 Bengkulu Utara 1703 0.96 0.68 0.92 0.86 0.90 0.88 0.89 0.76 0.70 0.73 0.84 0.83 0.83 0.84 0.81 0.79 Kaur 1704 0.97 0.68 0.95 0.87 0.94 0.64 0.79 0.63 0.77 0.70 0.82 0.78 0.80 0.80 0.82 0.80 Seluma 1705 0.93 0.69 0.95 0.86 0.90 0.78 0.84 0.95 0.68 0.82 0.85 0.84 0.84 0.84 0.85 0.81 Mukomuko 1706 0.97 0.60 0.98 0.85 1.00 0.93 0.96 0.88 0.87 0.87 0.87 0.89 0.89 0.89 0.87 0.83 Lebong 1707 0.95 0.91 0.95 0.93 0.71 0.77 0.74 0.79 0.98 0.88 0.88 0.85 0.86 0.87 0.92 0.93 Kepahiang 1708 0.94 0.92 0.95 0.94 0.86 0.82 0.84 0.71 0.90 0.81 0.89 0.86 0.87 0.88 0.90 0.90 Bengkulu Tengah 1709 0.94 0.79 0.98 0.90 0.95 0.91 0.93 0.60 0.81 0.71 0.87 0.85 0.85 0.86 0.85 0.84 Bengkulu 1771 1.00 1.00 1.00 1.00 0.95 0.99 0.97 0.76 0.95 0.86 0.97 0.94 0.95 0.96 0.95 0.96 Lampung 0.97 0.77 0.98 0.91 0.97 0.97 0.97 0.92 0.77 0.84 0.91 0.91 0.91 0.91 0.89 0.86 Lampung Barat 1801 0.91 0.47 0.90 0.76 0.89 0.79 0.84 0.79 0.70 0.74 0.77 0.78 0.78 0.78 0.77 0.72 Tanggamus 1802 0.96 0.77 0.95 0.89 1.00 0.93 0.96 0.86 0.72 0.79 0.89 0.88 0.88 0.89 0.86 0.83 Lampung Selatan 1803 0.99 0.83 0.99 0.94 0.96 0.99 0.97 1.00 0.92 0.96 0.95 0.96 0.95 0.96 0.95 0.93 Lampung Timur 1804 0.99 0.79 1.00 0.93 0.97 0.99 0.98 0.81 0.79 0.80 0.91 0.90 0.90 0.91 0.89 0.87 Lampung Tengah 1805 0.98 0.77 1.00 0.91 0.97 0.99 0.98 1.00 0.86 0.93 0.93 0.94 0.94 0.94 0.93 0.90 Lampung Utara 1806 0.95 0.74 0.95 0.88 0.96 0.97 0.97 0.92 0.72 0.82 0.89 0.89 0.89 0.89 0.86 0.83 Way Kanan 1807 0.95 0.64 0.95 0.85 1.00 0.93 0.97 1.00 0.70 0.85 0.87 0.89 0.88 0.88 0.86 0.81 Tulangbawang 1808 0.98 0.64 0.98 0.87 1.00 0.98 0.99 0.94 0.74 0.84 0.89 0.90 0.90 0.90 0.87 0.82 Pesawaran 1809 0.97 0.82 0.98 0.92 1.00 0.94 0.97 0.83 0.80 0.81 0.91 0.90 0.91 0.91 0.89 0.88 63 | 64 | District BPS Code Physical Availability Health Personnel Building Characteristics Composite Indices Primary Secondary Delivery SubIndex GP Pusk. Midwife SubIndex Water P. Electricity SubIndex Access Equal D. Equal I. PCA OLS CI Pringsewu 1810 1.00 0.97 1.00 0.99 1.00 0.98 0.99 1.00 0.66 0.83 0.96 0.94 0.94 0.95 0.92 0.91 Mesuji 1811 0.92 0.25 0.98 0.72 1.00 0.98 0.99 0.56 0.71 0.63 0.75 0.78 0.77 0.79 0.73 0.66 Tulang Bawang Barat 1812 0.96 0.67 0.99 0.87 0.80 0.99 0.90 1.00 0.65 0.83 0.87 0.86 0.87 0.87 0.87 0.82 Bandar Lampung 1871 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.96 0.87 0.91 0.98 0.97 0.98 0.98 0.97 0.96 Metro 1872 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.90 0.95 0.99 0.98 0.99 0.99 0.98 0.97 Kepulauan Bangka Belitung 1.00 0.92 1.00 0.97 1.00 0.97 0.99 0.95 0.93 0.94 0.97 0.97 0.97 0.97 0.96 0.95 Bangka 1901 0.99 0.94 1.00 0.98 1.00 0.99 1.00 1.00 0.93 0.97 0.98 0.98 0.98 0.98 0.97 0.96 Belitung 1902 1.00 0.95 1.00 0.98 1.00 1.00 1.00 0.89 0.94 0.91 0.97 0.97 0.97 0.97 0.96 0.96 Bangka Barat 1903 0.99 0.70 0.99 0.89 1.00 0.95 0.97 1.00 0.95 0.97 0.92 0.95 0.94 0.94 0.93 0.90 Bangka Tengah 1904 1.00 0.98 1.00 0.99 1.00 1.00 1.00 1.00 0.91 0.96 0.99 0.98 0.99 0.99 0.98 0.97 Bangka Selatan 1905 1.00 0.89 1.00 0.96 1.00 0.97 0.99 0.88 0.95 0.91 0.96 0.95 0.95 0.96 0.95 0.94 Belitung Timur 1906 1.00 0.99 1.00 1.00 1.00 0.94 0.97 1.00 0.91 0.96 0.98 0.97 0.98 0.98 0.98 0.98 Pangkal Pinang 1971 1.00 1.00 1.00 1.00 1.00 0.94 0.97 0.89 0.90 0.89 0.97 0.95 0.96 0.96 0.96 0.96 Kepulauan Riau 1.00 0.89 0.99 0.96 0.97 0.98 0.98 0.96 0.88 0.92 0.95 0.95 0.95 0.95 0.94 0.93 Karimun 2101 1.00 0.84 0.98 0.94 1.00 1.00 1.00 1.00 0.83 0.91 0.95 0.95 0.95 0.95 0.93 0.91 Bintan 2102 0.99 0.88 1.00 0.96 1.00 0.99 0.99 1.00 0.88 0.94 0.96 0.96 0.96 0.96 0.95 0.94 Natuna 2103 0.98 0.44 0.95 0.79 0.92 0.78 0.85 0.85 0.84 0.84 0.81 0.83 0.82 0.83 0.83 0.77 Lingga 2104 1.00 0.58 0.99 0.86 0.86 0.86 0.86 0.86 0.87 0.86 0.86 0.86 0.86 0.86 0.87 0.83 Kepulauan Anambas 2105 0.97 0.24 0.88 0.70 1.00 0.93 0.97 1.00 0.88 0.94 0.80 0.87 0.84 0.84 0.79 0.71 Batam 2171 1.00 0.97 1.00 0.99 1.00 1.00 1.00 1.00 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 Tanjung Pinang 2172 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.81 0.91 0.98 0.97 0.97 0.97 0.96 0.95 DKI Jakarta 1.00 1.00 1.00 1.00 0.98 0.99 0.99 0.98 1.00 0.99 1.00 0.99 0.99 0.99 1.00 1.00 Kepulauan Seribu 3101 1.00 0.24 1.00 0.75 1.00 1.00 1.00 0.83 1.00 0.92 0.83 0.89 0.87 0.88 0.85 0.77 Jakarta Selatan 3171 1.00 1.00 1.00 1.00 0.99 1.00 0.99 0.99 1.00 0.99 1.00 1.00 1.00 1.00 1.00 1.00 Jakarta Timur 3172 1.00 1.00 1.00 1.00 0.98 1.00 0.99 0.98 1.00 0.99 1.00 0.99 0.99 0.99 1.00 1.00 Jakarta Pusat 3173 1.00 1.00 1.00 1.00 1.00 0.96 0.98 1.00 1.00 1.00 1.00 0.99 0.99 0.99 1.00 1.00 Jakarta Barat 3174 1.00 1.00 1.00 1.00 0.95 0.99 0.97 1.00 1.00 1.00 0.99 0.99 0.99 0.99 1.00 1.00 Jakarta Utara 3175 1.00 1.00 1.00 1.00 0.98 1.00 0.99 0.96 1.00 0.98 0.99 0.99 0.99 0.99 0.99 1.00 Jawa Barat 0.99 0.88 0.99 0.96 0.98 0.99 0.98 0.94 0.96 0.95 0.96 0.96 0.96 0.96 0.96 0.95 Bogor 3201 0.99 0.91 1.00 0.97 1.00 0.99 0.99 0.93 0.98 0.95 0.97 0.97 0.97 0.97 0.97 0.96 Sukabumi 3202 0.96 0.74 0.98 0.89 0.93 0.98 0.96 0.97 0.91 0.94 0.91 0.93 0.92 0.93 0.92 0.89 Cianjur 3203 0.95 0.67 0.97 0.86 0.98 0.97 0.98 0.98 0.93 0.95 0.90 0.93 0.92 0.92 0.91 0.87 Bandung 3204 1.00 0.95 0.99 0.98 0.98 0.98 0.98 0.92 0.96 0.94 0.97 0.97 0.97 0.97 0.97 0.97 Garut 3205 0.98 0.68 0.99 0.89 0.78 0.97 0.87 0.91 0.92 0.91 0.89 0.89 0.89 0.90 0.91 0.88 Tasikmalaya 3206 0.98 0.73 0.99 0.90 1.00 1.00 1.00 0.90 0.98 0.94 0.93 0.95 0.94 0.94 0.93 0.90 District BPS Code Physical Availability Health Personnel Building Characteristics Composite Indices Primary Secondary Delivery SubIndex GP Pusk. Midwife SubIndex Water P. Electricity SubIndex Access Equal D. Equal I. PCA OLS CI Ciamis 3207 0.99 0.78 0.99 0.92 0.98 1.00 0.99 0.90 0.98 0.94 0.94 0.95 0.95 0.95 0.94 0.92 Kuningan 3208 0.99 0.93 0.99 0.97 0.95 1.00 0.97 0.97 0.96 0.96 0.97 0.97 0.97 0.97 0.97 0.96 Cirebon 3209 1.00 0.96 1.00 0.99 0.98 0.99 0.99 0.96 0.99 0.98 0.98 0.98 0.98 0.98 0.98 0.98 Majalengka 3210 0.99 0.95 1.00 0.98 1.00 1.00 1.00 0.97 0.97 0.97 0.98 0.98 0.98 0.98 0.98 0.97 Sumedang 3211 0.99 0.94 1.00 0.98 1.00 1.00 1.00 0.94 0.92 0.93 0.97 0.97 0.97 0.97 0.96 0.96 Indramayu 3212 1.00 0.92 1.00 0.97 0.98 0.99 0.98 0.94 0.91 0.93 0.97 0.96 0.96 0.97 0.96 0.95 Subang 3213 0.98 0.85 1.00 0.94 1.00 1.00 1.00 0.98 0.98 0.98 0.96 0.97 0.97 0.97 0.96 0.95 Purwakarta 3214 0.97 0.85 0.98 0.94 1.00 0.98 0.99 0.95 0.96 0.96 0.95 0.96 0.96 0.96 0.95 0.94 Karawang 3215 0.99 0.97 1.00 0.99 1.00 1.00 1.00 1.00 0.99 1.00 0.99 0.99 0.99 0.99 0.99 0.99 Bekasi 3216 0.98 0.93 1.00 0.97 1.00 0.99 1.00 0.92 0.98 0.95 0.97 0.97 0.97 0.97 0.97 0.96 Bandung Barat 3217 0.97 0.78 1.00 0.92 1.00 0.99 1.00 0.81 0.95 0.88 0.92 0.93 0.93 0.93 0.92 0.90 Bogor 3271 1.00 0.99 1.00 1.00 1.00 0.91 0.95 1.00 1.00 1.00 0.99 0.98 0.99 0.98 1.00 1.00 Sukabumi 3272 1.00 1.00 1.00 1.00 1.00 0.96 0.98 1.00 1.00 1.00 1.00 0.99 0.99 0.99 1.00 1.00 Bandung 3273 1.00 1.00 1.00 1.00 1.00 0.94 0.97 0.89 1.00 0.94 0.98 0.97 0.98 0.98 0.98 0.99 Cirebon 3274 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Bekasi 3275 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Depok 3276 1.00 0.99 1.00 1.00 1.00 0.99 0.99 0.97 1.00 0.98 0.99 0.99 0.99 0.99 0.99 0.99 Cimahi 3277 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.94 0.97 0.99 0.99 0.99 0.99 0.99 0.99 Tasikmalaya 3278 1.00 0.94 1.00 0.98 1.00 1.00 1.00 0.90 0.96 0.93 0.97 0.97 0.97 0.97 0.97 0.96 Banjar 3279 1.00 1.00 1.00 1.00 0.90 1.00 0.95 1.00 1.00 1.00 0.99 0.98 0.99 0.99 1.00 1.00 Jawa Tengah 0.98 0.95 1.00 0.98 0.99 0.99 0.99 0.98 0.95 0.96 0.98 0.98 0.98 0.98 0.97 0.97 Cilacap 3301 0.97 0.87 1.00 0.94 1.00 1.00 1.00 1.00 0.97 0.99 0.96 0.98 0.97 0.97 0.97 0.95 Banyumas 3302 0.98 0.98 1.00 0.99 1.00 0.99 0.99 0.92 0.97 0.95 0.98 0.98 0.98 0.98 0.98 0.98 Purbalingga 3303 0.99 0.93 1.00 0.97 0.95 1.00 0.98 1.00 0.97 0.99 0.98 0.98 0.98 0.98 0.98 0.97 Banjarnegara 3304 0.98 0.94 1.00 0.97 1.00 1.00 1.00 0.91 0.97 0.94 0.97 0.97 0.97 0.97 0.97 0.96 Kebumen 3305 0.96 0.94 0.98 0.96 1.00 0.99 0.99 0.97 0.95 0.96 0.97 0.97 0.97 0.97 0.97 0.96 Purworejo 3306 0.96 0.90 1.00 0.95 1.00 0.89 0.95 0.96 0.94 0.95 0.95 0.95 0.95 0.95 0.96 0.95 Wonosobo 3307 0.96 0.81 0.98 0.92 1.00 1.00 1.00 1.00 0.92 0.96 0.94 0.96 0.95 0.95 0.94 0.92 Magelang 3308 0.99 0.99 1.00 0.99 0.97 0.99 0.98 1.00 0.97 0.99 0.99 0.99 0.99 0.99 0.99 0.99 Boyolali 3309 0.97 0.91 0.99 0.96 1.00 0.99 0.99 0.97 0.94 0.95 0.96 0.97 0.97 0.97 0.96 0.95 Klaten 3310 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.97 0.94 0.95 0.99 0.98 0.99 0.99 0.98 0.98 Sukoharjo 3311 1.00 0.98 1.00 0.99 1.00 1.00 1.00 1.00 0.97 0.98 0.99 0.99 0.99 0.99 0.99 0.98 Wonogiri 3312 1.00 0.94 0.99 0.98 0.97 1.00 0.99 0.94 0.90 0.92 0.97 0.96 0.96 0.96 0.95 0.95 Karanganyar 3313 0.99 0.97 0.99 0.98 1.00 1.00 1.00 1.00 0.95 0.98 0.98 0.99 0.99 0.99 0.98 0.98 Sragen 3314 1.00 0.98 1.00 0.99 1.00 1.00 1.00 1.00 0.97 0.99 0.99 0.99 0.99 0.99 0.99 0.99 65 | 66 | District BPS Code Physical Availability Health Personnel Building Characteristics Composite Indices Primary Secondary Delivery SubIndex GP Pusk. Midwife SubIndex Water P. Electricity SubIndex Access Equal D. Equal I. PCA OLS CI Grobogan 3315 0.97 0.92 1.00 0.96 1.00 1.00 1.00 1.00 0.91 0.96 0.97 0.97 0.97 0.97 0.97 0.95 Blora 3316 0.97 0.89 0.99 0.95 1.00 0.98 0.99 0.96 1.00 0.98 0.96 0.97 0.97 0.97 0.97 0.96 Rembang 3317 0.98 0.93 0.97 0.96 1.00 0.96 0.98 0.94 0.99 0.96 0.96 0.97 0.97 0.97 0.96 0.96 Pati 3318 0.99 0.98 1.00 0.99 1.00 1.00 1.00 0.97 0.96 0.96 0.99 0.98 0.98 0.98 0.98 0.98 Kudus 3319 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Jepara 3320 1.00 0.98 1.00 0.99 1.00 1.00 1.00 1.00 0.91 0.95 0.99 0.98 0.98 0.98 0.98 0.97 Demak 3321 0.97 0.92 1.00 0.96 1.00 1.00 1.00 1.00 0.87 0.93 0.97 0.97 0.97 0.97 0.96 0.94 Semarang 3322 1.00 0.97 1.00 0.99 1.00 1.00 1.00 1.00 0.98 0.99 0.99 0.99 0.99 0.99 0.99 0.99 Temanggung 3323 0.98 0.93 1.00 0.97 0.91 0.99 0.95 1.00 0.98 0.99 0.97 0.97 0.97 0.97 0.98 0.97 Kendal 3324 0.98 0.90 1.00 0.96 0.97 0.99 0.98 1.00 0.95 0.97 0.97 0.97 0.97 0.97 0.97 0.96 Batang 3325 0.99 0.97 1.00 0.99 1.00 1.00 1.00 1.00 0.93 0.97 0.98 0.98 0.98 0.98 0.98 0.97 Pekalongan 3326 0.96 0.91 1.00 0.96 1.00 0.99 0.99 0.96 0.94 0.95 0.96 0.97 0.97 0.97 0.96 0.95 Pemalang 3327 0.99 0.94 1.00 0.98 1.00 0.99 1.00 1.00 0.95 0.98 0.98 0.98 0.98 0.98 0.98 0.97 Tegal 3328 0.99 0.98 1.00 0.99 0.93 1.00 0.96 0.93 0.95 0.94 0.97 0.96 0.97 0.97 0.97 0.97 Brebes 3329 0.98 0.92 1.00 0.97 0.97 1.00 0.99 1.00 0.86 0.93 0.96 0.96 0.96 0.96 0.96 0.94 Magelang 3371 1.00 0.97 1.00 0.99 1.00 0.95 0.98 1.00 0.94 0.97 0.98 0.98 0.98 0.98 0.98 0.98 Surakarta 3372 1.00 1.00 1.00 1.00 1.00 0.91 0.95 1.00 1.00 1.00 0.99 0.98 0.99 0.99 1.00 1.00 Salatiga 3373 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Semarang 3374 1.00 0.99 1.00 1.00 1.00 0.87 0.93 0.97 1.00 0.99 0.98 0.97 0.98 0.98 0.99 0.99 Pekalongan 3375 1.00 1.00 1.00 1.00 1.00 0.95 0.98 1.00 1.00 1.00 1.00 0.99 0.99 0.99 1.00 1.00 Tegal 3376 1.00 1.00 1.00 1.00 0.86 1.00 0.93 1.00 1.00 1.00 0.99 0.98 0.98 0.98 1.00 1.00 DI Yogyakarta 1.00 0.99 1.00 1.00 1.00 0.97 0.99 0.96 0.98 0.97 0.99 0.98 0.99 0.99 0.99 0.99 Kulon Progo 3401 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.86 0.98 0.92 0.98 0.97 0.98 0.98 0.97 0.98 Bantul 3402 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.96 0.99 0.98 1.00 0.99 0.99 0.99 0.99 0.99 Gunung Kidul 3403 1.00 0.97 0.99 0.99 1.00 0.98 0.99 1.00 0.95 0.98 0.99 0.99 0.99 0.99 0.98 0.98 Sleman 3404 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Yogyakarta 3471 1.00 1.00 1.00 1.00 1.00 0.78 0.89 0.94 1.00 0.97 0.97 0.95 0.96 0.96 0.99 0.99 Jawa Timur 0.99 0.93 1.00 0.97 0.98 1.00 0.99 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.97 Pacitan 3501 0.97 0.77 1.00 0.91 1.00 0.98 0.99 1.00 0.99 1.00 0.95 0.97 0.96 0.96 0.96 0.93 Ponorogo 3502 0.99 0.96 1.00 0.99 1.00 0.99 1.00 1.00 0.98 0.99 0.99 0.99 0.99 0.99 0.99 0.98 Trenggalek 3503 0.97 0.78 0.99 0.92 1.00 1.00 1.00 1.00 0.97 0.99 0.95 0.97 0.96 0.96 0.95 0.93 Tulungagung 3504 1.00 0.99 1.00 0.99 1.00 0.99 1.00 1.00 0.98 0.99 0.99 0.99 0.99 0.99 0.99 0.99 Blitar 3505 1.00 0.96 1.00 0.99 0.92 1.00 0.96 0.96 0.99 0.97 0.98 0.97 0.97 0.98 0.98 0.98 Kediri 3506 1.00 0.99 1.00 0.99 1.00 1.00 1.00 0.89 0.97 0.93 0.98 0.97 0.98 0.98 0.97 0.98 Malang 3507 0.99 0.94 1.00 0.98 0.95 0.99 0.97 1.00 0.97 0.99 0.98 0.98 0.98 0.98 0.98 0.97 District BPS Code Physical Availability Health Personnel Building Characteristics Composite Indices Primary Secondary Delivery SubIndex GP Pusk. Midwife SubIndex Water P. Electricity SubIndex Access Equal D. Equal I. PCA OLS CI Lumajang 3508 1.00 0.98 1.00 0.99 1.00 1.00 1.00 1.00 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 Jember 3509 1.00 0.99 1.00 1.00 0.98 1.00 0.99 0.98 0.96 0.97 0.99 0.99 0.99 0.99 0.99 0.98 Banyuwangi 3510 1.00 0.94 1.00 0.98 1.00 1.00 1.00 1.00 0.96 0.98 0.98 0.99 0.98 0.99 0.98 0.97 Bondowoso 3511 0.99 0.94 0.97 0.97 0.92 1.00 0.96 1.00 0.99 0.99 0.97 0.97 0.97 0.97 0.97 0.97 Situbondo 3512 0.98 0.93 1.00 0.97 1.00 1.00 1.00 1.00 0.98 0.99 0.98 0.99 0.98 0.98 0.98 0.97 Probolinggo 3513 0.98 0.94 1.00 0.97 0.91 0.98 0.94 0.94 0.99 0.96 0.97 0.96 0.96 0.96 0.98 0.97 Pasuruan 3514 0.99 0.95 1.00 0.98 1.00 1.00 1.00 1.00 0.98 0.99 0.99 0.99 0.99 0.99 0.99 0.98 Sidoarjo 3515 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Mojokerto 3516 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Jombang 3517 1.00 0.98 1.00 0.99 0.97 0.99 0.98 0.97 0.98 0.98 0.99 0.98 0.98 0.99 0.99 0.99 Nganjuk 3518 1.00 0.97 1.00 0.99 1.00 1.00 1.00 0.95 0.99 0.97 0.99 0.99 0.99 0.99 0.99 0.98 Madiun 3519 1.00 0.97 1.00 0.99 1.00 1.00 1.00 0.92 0.98 0.95 0.98 0.98 0.98 0.98 0.98 0.98 Magetan 3520 1.00 1.00 1.00 1.00 1.00 0.99 1.00 1.00 0.98 0.99 1.00 1.00 1.00 1.00 1.00 1.00 Ngawi 3521 1.00 0.97 1.00 0.99 0.96 1.00 0.98 1.00 0.98 0.99 0.99 0.99 0.99 0.99 0.99 0.99 Bojonegoro 3522 0.99 0.92 1.00 0.97 1.00 1.00 1.00 1.00 0.98 0.99 0.98 0.99 0.98 0.98 0.98 0.97 Tuban 3523 1.00 0.98 1.00 0.99 0.88 0.99 0.93 0.97 0.99 0.98 0.98 0.97 0.97 0.97 0.99 0.99 Lamongan 3524 0.98 0.90 0.99 0.96 0.97 1.00 0.98 1.00 0.99 0.99 0.97 0.98 0.98 0.98 0.98 0.96 Gresik 3525 1.00 0.85 1.00 0.95 0.97 0.99 0.98 0.97 1.00 0.98 0.96 0.97 0.97 0.97 0.97 0.95 Bangkalan 3526 0.96 0.76 0.99 0.91 0.95 0.99 0.97 0.91 0.96 0.93 0.93 0.94 0.93 0.94 0.93 0.91 Sampang 3527 0.90 0.59 0.99 0.83 0.95 0.99 0.97 0.95 0.98 0.97 0.88 0.92 0.91 0.91 0.91 0.87 Pamekasan 3528 0.96 0.84 1.00 0.93 0.95 0.99 0.97 0.95 0.99 0.97 0.95 0.96 0.95 0.96 0.96 0.95 Sumenep 3529 0.95 0.58 0.98 0.84 1.00 1.00 1.00 0.97 0.92 0.94 0.89 0.93 0.91 0.91 0.90 0.85 Kediri 3571 1.00 1.00 1.00 1.00 1.00 0.99 1.00 1.00 0.98 0.99 1.00 1.00 1.00 1.00 1.00 0.99 Blitar 3572 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Malang 3573 1.00 1.00 1.00 1.00 1.00 0.99 0.99 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Probolinggo 3574 1.00 1.00 1.00 1.00 1.00 0.99 0.99 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Pasuruan 3575 1.00 1.00 1.00 1.00 1.00 0.99 0.99 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Mojokerto 3576 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.97 0.99 1.00 1.00 1.00 1.00 0.99 0.99 Madiun 3577 1.00 1.00 1.00 1.00 1.00 0.99 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Surabaya 3578 1.00 1.00 1.00 1.00 0.98 1.00 0.99 0.96 0.99 0.98 0.99 0.99 0.99 0.99 0.99 0.99 Batu 3579 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Banten 0.98 0.88 0.98 0.95 0.97 0.97 0.97 0.97 0.97 0.97 0.96 0.96 0.96 0.96 0.96 0.95 Pandeglang 3601 0.90 0.55 0.94 0.79 0.92 0.91 0.91 0.94 0.98 0.96 0.85 0.89 0.88 0.88 0.88 0.84 Lebak 3602 0.92 0.64 0.93 0.83 1.00 0.90 0.95 0.93 0.99 0.96 0.88 0.91 0.90 0.90 0.89 0.86 Tangerang 3603 0.99 0.95 1.00 0.98 0.98 0.99 0.98 0.98 0.94 0.96 0.98 0.97 0.98 0.98 0.98 0.97 67 | 68 | District BPS Code Physical Availability Health Personnel Building Characteristics Composite Indices Primary Secondary Delivery SubIndex GP Pusk. Midwife SubIndex Water P. Electricity SubIndex Access Equal D. Equal I. PCA OLS CI Serang 3604 0.98 0.90 1.00 0.96 0.97 0.95 0.96 1.00 0.94 0.97 0.96 0.96 0.96 0.96 0.97 0.95 Tangerang 3671 1.00 1.00 1.00 1.00 1.00 0.99 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Cilegon 3672 1.00 1.00 1.00 1.00 0.88 1.00 0.94 1.00 0.92 0.96 0.98 0.97 0.97 0.97 0.98 0.98 Serang 3673 1.00 1.00 1.00 1.00 1.00 0.96 0.98 1.00 1.00 1.00 1.00 0.99 0.99 0.99 1.00 1.00 Tangerang Selatan 3674 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Bali 1.00 0.98 1.00 0.99 0.99 0.99 0.99 0.97 0.96 0.97 0.99 0.98 0.99 0.99 0.99 0.98 Jembrana 5101 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.98 0.99 1.00 1.00 1.00 1.00 1.00 1.00 Tabanan 5102 1.00 1.00 1.00 1.00 1.00 0.99 1.00 0.95 1.00 0.98 0.99 0.99 0.99 0.99 0.99 0.99 Badung 5103 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.99 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Gianyar 5104 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.99 0.99 1.00 1.00 1.00 1.00 1.00 1.00 Klungkung 5105 1.00 0.76 1.00 0.92 1.00 1.00 1.00 1.00 0.92 0.96 0.94 0.96 0.95 0.95 0.94 0.91 Bangli 5106 1.00 1.00 1.00 1.00 1.00 0.96 0.98 0.91 0.95 0.93 0.98 0.97 0.98 0.98 0.98 0.98 Karang Asem 5107 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.84 0.92 0.98 0.97 0.98 0.98 0.97 0.96 Buleleng 5108 1.00 0.96 1.00 0.98 1.00 0.97 0.98 0.95 0.98 0.97 0.98 0.98 0.98 0.98 0.98 0.98 Denpasar 5171 1.00 1.00 1.00 1.00 0.91 0.99 0.95 1.00 1.00 1.00 0.99 0.98 0.99 0.99 1.00 1.00 Nusa Tenggara Barat 0.99 0.80 0.97 0.92 0.97 0.92 0.95 0.97 0.92 0.94 0.93 0.94 0.94 0.94 0.93 0.91 Lombok Barat 5201 1.00 0.91 0.99 0.96 1.00 0.90 0.95 0.93 0.92 0.93 0.95 0.95 0.95 0.95 0.95 0.94 Lombok Tengah 5202 0.99 0.77 0.97 0.91 1.00 0.95 0.97 1.00 0.94 0.97 0.94 0.95 0.95 0.95 0.93 0.91 Lombok Timur 5203 0.99 0.92 0.98 0.97 1.00 0.84 0.92 1.00 0.97 0.98 0.96 0.96 0.96 0.96 0.97 0.96 Sumbawa 5204 0.98 0.69 0.93 0.87 0.88 0.96 0.92 0.92 0.91 0.91 0.89 0.90 0.89 0.90 0.88 0.85 Dompu 5205 0.97 0.70 0.95 0.87 1.00 0.97 0.98 0.89 0.84 0.87 0.89 0.91 0.90 0.91 0.88 0.85 Bima 5206 0.99 0.59 0.94 0.84 0.95 0.98 0.97 1.00 0.87 0.93 0.88 0.91 0.90 0.90 0.88 0.83 Sumbawa Barat 5207 0.98 0.28 0.95 0.74 1.00 0.97 0.98 0.89 0.93 0.91 0.82 0.88 0.86 0.86 0.83 0.75 Lombok Utara 5208 0.97 0.57 1.00 0.85 1.00 0.99 0.99 1.00 0.98 0.99 0.91 0.94 0.93 0.93 0.92 0.87 Mataram 5271 1.00 0.98 1.00 0.99 1.00 0.95 0.97 1.00 0.98 0.99 0.99 0.99 0.99 0.99 0.99 0.99 Bima 5272 1.00 0.91 0.95 0.95 1.00 1.00 1.00 1.00 0.98 0.99 0.97 0.98 0.98 0.98 0.96 0.95 Nusa Tenggara Timur 0.91 0.53 0.88 0.77 0.80 0.79 0.80 0.83 0.59 0.71 0.76 0.76 0.76 0.76 0.75 0.71 Sumba Barat 5301 0.87 0.61 0.92 0.80 1.00 0.75 0.87 1.00 0.52 0.76 0.81 0.81 0.81 0.81 0.80 0.74 Sumba Timur 5302 0.91 0.43 0.85 0.73 0.80 0.82 0.81 0.85 0.57 0.71 0.74 0.75 0.75 0.75 0.73 0.67 Kupang 5303 0.96 0.47 0.77 0.73 0.70 0.83 0.76 0.78 0.46 0.62 0.72 0.70 0.71 0.71 0.66 0.61 Timor Tengah Selatan 5304 0.81 0.33 0.84 0.66 0.67 0.70 0.68 0.74 0.47 0.60 0.65 0.65 0.65 0.66 0.66 0.60 Timor Tengah Utara 5305 0.93 0.73 0.98 0.88 0.52 0.89 0.71 0.80 0.66 0.73 0.82 0.77 0.79 0.80 0.84 0.81 Belu 5306 0.91 0.56 0.94 0.80 0.48 0.89 0.69 0.78 0.54 0.66 0.75 0.72 0.73 0.74 0.77 0.72 Alor 5307 0.83 0.46 0.77 0.69 0.73 0.65 0.69 0.82 0.43 0.62 0.67 0.67 0.67 0.67 0.66 0.61 Lembata 5308 0.92 0.74 0.91 0.86 1.00 0.82 0.91 0.89 0.77 0.83 0.86 0.87 0.86 0.86 0.85 0.83 District BPS Code Physical Availability Health Personnel Building Characteristics Composite Indices Primary Secondary Delivery SubIndex GP Pusk. Midwife SubIndex Water P. Electricity SubIndex Access Equal D. Equal I. PCA OLS CI Flores Timur 5309 0.96 0.52 0.98 0.82 0.89 0.68 0.79 0.79 0.69 0.74 0.80 0.78 0.79 0.79 0.81 0.76 Sikka 5310 0.94 0.72 0.96 0.87 0.95 0.98 0.97 0.86 0.68 0.77 0.87 0.87 0.87 0.88 0.85 0.82 Ende 5311 0.87 0.62 0.90 0.80 0.96 0.65 0.81 1.00 0.46 0.73 0.79 0.78 0.78 0.78 0.78 0.72 Ngada 5312 0.94 0.80 0.95 0.90 0.80 0.77 0.78 0.80 0.77 0.79 0.85 0.82 0.83 0.84 0.86 0.85 Manggarai 5313 0.93 0.62 0.87 0.80 1.00 0.86 0.93 0.82 0.58 0.70 0.81 0.81 0.81 0.81 0.76 0.72 Rote Ndao 5314 0.94 0.31 0.67 0.64 0.92 0.69 0.80 0.92 0.89 0.90 0.72 0.78 0.76 0.76 0.70 0.65 Manggarai Barat 5315 0.78 0.19 0.69 0.55 0.83 0.77 0.80 0.83 0.48 0.66 0.62 0.67 0.65 0.65 0.59 0.51 Sumba Tengah 5316 0.94 0.30 0.95 0.73 0.83 0.66 0.75 0.67 0.60 0.63 0.71 0.70 0.71 0.72 0.72 0.65 Sumba Barat Daya 5317 0.92 0.38 0.89 0.73 0.80 0.86 0.83 0.60 0.51 0.56 0.72 0.71 0.71 0.72 0.68 0.63 Nagekeo 5318 0.97 0.48 0.95 0.80 1.00 0.79 0.89 1.00 0.85 0.93 0.84 0.87 0.86 0.86 0.86 0.80 Manggarai Timur 5319 0.86 0.32 0.82 0.67 0.90 0.59 0.75 0.85 0.47 0.66 0.68 0.69 0.69 0.69 0.67 0.60 Sabu Raijua 5320 0.89 0.29 0.82 0.67 0.83 0.57 0.70 0.67 0.30 0.48 0.64 0.62 0.63 0.63 0.60 0.53 Kupang 5371 1.00 1.00 1.00 1.00 1.00 0.97 0.99 0.90 0.98 0.94 0.99 0.98 0.98 0.98 0.98 0.98 Kalimantan Barat 0.92 0.58 0.90 0.80 0.83 0.87 0.85 0.87 0.67 0.77 0.80 0.81 0.81 0.81 0.79 0.75 Sambas 6101 0.98 0.53 0.97 0.83 0.89 0.96 0.93 0.96 0.89 0.93 0.87 0.89 0.88 0.89 0.88 0.83 Bengkayang 6102 0.91 0.71 0.89 0.84 0.53 0.86 0.70 0.71 0.56 0.63 0.77 0.72 0.74 0.75 0.77 0.74 Landak 6103 0.93 0.54 0.92 0.79 0.81 0.84 0.83 0.81 0.47 0.64 0.77 0.75 0.76 0.77 0.75 0.69 Pontianak 6104 0.99 0.81 0.99 0.93 0.86 0.98 0.92 0.71 0.87 0.79 0.90 0.88 0.89 0.90 0.89 0.88 Sanggau 6105 0.92 0.50 0.86 0.76 1.00 1.00 1.00 0.94 0.57 0.76 0.81 0.84 0.83 0.83 0.76 0.70 Ketapang 6106 0.88 0.37 0.82 0.69 0.77 0.77 0.77 0.81 0.73 0.77 0.72 0.74 0.74 0.74 0.73 0.67 Sintang 6107 0.78 0.34 0.84 0.65 0.81 0.61 0.71 0.76 0.63 0.69 0.67 0.68 0.68 0.68 0.70 0.63 Kapuas Hulu 6108 0.82 0.36 0.77 0.65 0.74 0.70 0.72 1.00 0.56 0.78 0.69 0.72 0.71 0.70 0.70 0.63 Sekadau 6109 0.93 0.39 0.90 0.74 0.92 0.82 0.87 0.83 0.59 0.71 0.76 0.77 0.77 0.77 0.74 0.67 Melawi 6110 0.72 0.30 0.68 0.57 1.00 0.58 0.79 0.70 0.58 0.64 0.63 0.67 0.65 0.65 0.60 0.55 Kayong Utara 6111 0.92 0.43 0.94 0.77 0.86 0.93 0.89 0.86 0.76 0.81 0.80 0.82 0.81 0.82 0.80 0.74 Kubu Raya 6112 0.96 0.59 0.91 0.82 0.80 0.99 0.89 0.90 0.80 0.85 0.84 0.85 0.85 0.85 0.83 0.79 Pontianak 6171 1.00 1.00 1.00 1.00 0.91 0.98 0.95 1.00 0.97 0.99 0.99 0.98 0.98 0.98 0.99 0.99 Singkawang 6172 1.00 1.00 1.00 1.00 1.00 0.92 0.96 1.00 0.64 0.82 0.96 0.93 0.94 0.94 0.93 0.91 Kalimantan Tengah 0.96 0.64 0.89 0.83 0.86 0.86 0.86 0.88 0.73 0.80 0.83 0.83 0.83 0.83 0.82 0.78 Kotawaringin Barat 6201 0.98 0.74 0.96 0.90 1.00 0.97 0.99 0.93 0.91 0.92 0.92 0.93 0.93 0.93 0.91 0.88 Kotawaringin Timur 6202 0.96 0.69 0.90 0.85 0.85 0.81 0.83 1.00 0.77 0.88 0.85 0.85 0.85 0.85 0.86 0.82 Kapuas 6203 0.91 0.58 0.85 0.78 0.83 0.84 0.83 0.74 0.76 0.75 0.79 0.79 0.79 0.79 0.77 0.74 Barito Selatan 6204 0.97 0.69 0.92 0.86 0.80 0.90 0.85 0.80 0.60 0.70 0.83 0.80 0.81 0.82 0.80 0.77 Barito Utara 6205 0.99 0.56 0.87 0.81 0.67 0.96 0.81 1.00 0.76 0.88 0.82 0.83 0.83 0.83 0.82 0.77 Sukamara 6206 1.00 0.60 0.97 0.86 1.00 0.89 0.95 1.00 0.80 0.90 0.88 0.90 0.90 0.90 0.88 0.83 69 | 70 | District BPS Code Physical Availability Health Personnel Building Characteristics Composite Indices Primary Secondary Delivery SubIndex GP Pusk. Midwife SubIndex Water P. Electricity SubIndex Access Equal D. Equal I. PCA OLS CI Lamandau 6207 1.00 0.63 0.92 0.85 0.89 0.87 0.88 0.78 0.79 0.78 0.84 0.84 0.84 0.84 0.82 0.79 Seruyan 6208 0.95 0.71 0.90 0.85 0.91 0.73 0.82 0.73 0.71 0.72 0.82 0.80 0.80 0.81 0.80 0.78 Katingan 6209 0.98 0.30 0.87 0.71 0.80 0.83 0.81 0.80 0.67 0.74 0.74 0.75 0.75 0.76 0.72 0.65 Pulang Pisau 6210 0.93 0.32 0.87 0.71 0.82 0.87 0.84 0.91 0.84 0.87 0.77 0.81 0.79 0.80 0.78 0.71 Gunung Mas 6211 0.87 0.50 0.75 0.71 0.92 0.74 0.83 0.92 0.53 0.73 0.74 0.75 0.75 0.74 0.69 0.64 Barito Timur 6212 0.99 0.85 0.94 0.93 1.00 0.82 0.91 0.91 0.63 0.77 0.89 0.87 0.88 0.88 0.86 0.84 Murung raya 6213 0.97 0.50 0.68 0.72 0.75 0.81 0.78 0.83 0.57 0.70 0.73 0.73 0.73 0.73 0.66 0.62 Palangka Raya 6271 1.00 0.99 1.00 0.99 0.90 0.99 0.95 1.00 0.90 0.95 0.98 0.96 0.97 0.97 0.98 0.97 Kalimantan Selatan 0.97 0.81 0.97 0.92 0.98 0.92 0.95 0.87 0.91 0.89 0.92 0.92 0.92 0.92 0.91 0.90 Tanah Laut 6301 1.00 0.71 0.99 0.90 1.00 0.99 1.00 1.00 0.95 0.97 0.93 0.96 0.95 0.95 0.94 0.90 Kota Baru 6302 0.94 0.45 0.92 0.77 1.00 0.79 0.89 0.77 0.80 0.78 0.80 0.82 0.81 0.81 0.79 0.74 Banjar 6303 0.95 0.83 0.96 0.91 1.00 0.90 0.95 0.96 0.85 0.90 0.92 0.92 0.92 0.92 0.91 0.90 Barito Kuala 6304 0.96 0.57 0.96 0.83 1.00 0.92 0.96 0.84 0.92 0.88 0.87 0.89 0.88 0.89 0.87 0.83 Tapin 6305 0.91 0.87 0.93 0.90 1.00 0.94 0.97 0.54 0.90 0.72 0.88 0.86 0.87 0.88 0.85 0.86 Hulu Sungai Selatan 6306 0.99 0.92 0.98 0.96 1.00 0.95 0.98 0.85 0.89 0.87 0.95 0.94 0.94 0.94 0.93 0.93 Hulu Sungai Tengah 6307 0.99 0.96 0.99 0.98 0.74 0.96 0.85 0.79 0.94 0.87 0.93 0.90 0.91 0.92 0.95 0.95 Hulu Sungai Utara 6308 0.99 0.78 0.98 0.92 1.00 0.72 0.86 0.92 0.97 0.94 0.91 0.91 0.91 0.91 0.93 0.91 Tabalong 6309 0.97 0.76 0.99 0.91 1.00 0.95 0.98 0.87 0.92 0.89 0.92 0.93 0.93 0.93 0.92 0.90 Tanah Bumbu 6310 0.92 0.71 0.95 0.86 1.00 0.86 0.93 0.86 0.85 0.86 0.87 0.88 0.88 0.88 0.87 0.85 Balangan 6311 0.95 0.83 0.98 0.92 1.00 0.77 0.89 0.91 0.97 0.94 0.92 0.92 0.92 0.92 0.94 0.93 Banjarmasin 6371 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.98 0.99 1.00 1.00 1.00 1.00 1.00 0.99 Banjar Baru 6372 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.88 0.92 0.90 0.98 0.97 0.97 0.97 0.96 0.97 Kalimantan Timur 0.98 0.80 0.94 0.90 0.96 0.90 0.93 0.95 0.83 0.89 0.91 0.91 0.91 0.91 0.89 0.87 Paser 6401 0.94 0.66 0.91 0.84 1.00 0.91 0.95 0.88 0.83 0.86 0.87 0.88 0.88 0.88 0.85 0.82 Kutai Barat 6402 0.94 0.52 0.77 0.74 0.83 0.55 0.69 0.88 0.66 0.77 0.74 0.73 0.74 0.73 0.73 0.69 Kutai Kartanegara 6403 0.98 0.70 0.90 0.86 1.00 0.91 0.95 1.00 0.90 0.95 0.90 0.92 0.91 0.91 0.89 0.85 Kutai Timur 6404 0.95 0.45 0.84 0.74 0.95 0.72 0.83 0.89 0.87 0.88 0.79 0.82 0.81 0.81 0.79 0.74 Berau 6405 1.00 0.67 0.87 0.84 1.00 0.95 0.98 0.94 0.81 0.88 0.88 0.90 0.89 0.89 0.84 0.80 Malinau 6406 0.93 0.64 0.80 0.79 0.77 0.64 0.70 1.00 0.81 0.91 0.80 0.80 0.80 0.79 0.81 0.78 Bulungan 6407 0.99 0.65 0.97 0.87 1.00 0.92 0.96 1.00 0.72 0.86 0.89 0.90 0.89 0.89 0.87 0.82 Nunukan 6408 0.93 0.48 0.86 0.75 1.00 0.61 0.81 0.92 0.70 0.81 0.78 0.79 0.78 0.78 0.77 0.72 Penajam Paser Utara 6409 1.00 0.99 0.99 1.00 1.00 0.97 0.99 0.91 0.89 0.90 0.97 0.96 0.96 0.97 0.96 0.96 Tana Tidung 6410 1.00 0.00 0.76 0.59 1.00 0.89 0.94 0.67 0.90 0.78 0.70 0.77 0.75 0.75 0.65 0.57 Balikpapan 6471 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.98 0.99 1.00 1.00 1.00 1.00 1.00 0.99 Samarinda 6472 1.00 0.99 1.00 1.00 1.00 1.00 1.00 1.00 0.92 0.96 0.99 0.99 0.99 0.99 0.98 0.98 District BPS Code Physical Availability Health Personnel Building Characteristics Composite Indices Primary Secondary Delivery SubIndex GP Pusk. Midwife SubIndex Water P. Electricity SubIndex Access Equal D. Equal I. PCA OLS CI Tarakan 6473 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Bontang 6474 1.00 1.00 1.00 1.00 0.67 0.86 0.77 1.00 1.00 1.00 0.95 0.92 0.93 0.94 1.00 1.00 Sulawesi Utara 0.98 0.83 0.96 0.92 0.95 0.75 0.85 0.95 0.87 0.91 0.91 0.90 0.90 0.90 0.92 0.90 Bolaang Mongondow 7101 0.98 0.76 0.97 0.90 1.00 0.72 0.86 1.00 0.91 0.96 0.91 0.91 0.91 0.90 0.93 0.90 Minahasa 7102 0.98 0.94 0.98 0.97 1.00 0.70 0.85 1.00 0.85 0.93 0.94 0.91 0.92 0.92 0.95 0.94 Kepulauan Sangihe 7103 0.93 0.59 0.89 0.80 0.94 0.57 0.75 1.00 0.86 0.93 0.82 0.83 0.82 0.82 0.85 0.81 Kepulauan Talaud 7104 0.94 0.67 0.86 0.82 0.68 0.62 0.65 0.84 0.79 0.82 0.79 0.76 0.77 0.77 0.81 0.79 Minahasa Selatan 7105 0.99 0.91 1.00 0.97 1.00 0.73 0.87 1.00 0.85 0.93 0.94 0.92 0.93 0.92 0.95 0.94 Minahasa Utara 7106 0.97 0.81 0.95 0.91 1.00 0.84 0.92 0.90 0.82 0.86 0.90 0.89 0.90 0.90 0.89 0.87 Bolaang Mong. Utara 7107 0.98 0.54 0.98 0.83 1.00 0.70 0.85 0.88 0.89 0.88 0.84 0.85 0.85 0.85 0.87 0.82 Siau Tagulandang Biaro 7108 0.92 0.49 0.87 0.76 1.00 0.62 0.81 0.75 0.92 0.83 0.78 0.80 0.80 0.80 0.80 0.76 Minahasa Tenggara 7109 0.99 0.88 0.96 0.94 1.00 0.57 0.78 1.00 0.92 0.96 0.92 0.90 0.90 0.90 0.95 0.93 Bolaang Mong. Selatan 7110 0.96 0.28 0.91 0.72 0.80 0.64 0.72 1.00 0.95 0.98 0.77 0.80 0.79 0.79 0.83 0.75 Bolaang Mong. Timur 7111 0.98 0.66 0.96 0.87 1.00 0.56 0.78 1.00 0.97 0.98 0.87 0.88 0.88 0.87 0.92 0.88 Manado 7171 1.00 0.99 1.00 0.99 1.00 0.83 0.91 1.00 0.85 0.92 0.96 0.94 0.95 0.95 0.97 0.96 Bitung 7172 0.96 0.83 0.95 0.91 1.00 0.85 0.92 1.00 0.86 0.93 0.92 0.92 0.92 0.92 0.92 0.90 Tomohon 7173 1.00 1.00 1.00 1.00 1.00 0.96 0.98 1.00 0.93 0.96 0.99 0.98 0.98 0.98 0.98 0.98 Kotamobagu 7174 1.00 1.00 1.00 1.00 1.00 0.99 0.99 1.00 0.93 0.96 0.99 0.99 0.99 0.99 0.99 0.98 Sulawesi Tengah 0.96 0.66 0.92 0.85 0.81 0.86 0.83 0.91 0.73 0.82 0.84 0.83 0.84 0.84 0.84 0.80 Banggai Kepulauan 7201 0.90 0.27 0.77 0.65 0.80 0.55 0.68 0.87 0.56 0.71 0.67 0.68 0.67 0.67 0.66 0.59 Banggai 7202 0.99 0.74 0.98 0.90 0.71 0.81 0.76 0.86 0.64 0.75 0.84 0.80 0.82 0.83 0.85 0.82 Morowali 7203 0.95 0.56 0.84 0.78 0.65 0.84 0.74 0.88 0.63 0.76 0.77 0.76 0.76 0.77 0.76 0.72 Poso 7204 1.00 0.85 0.98 0.94 0.85 0.98 0.91 0.95 0.80 0.87 0.92 0.91 0.92 0.92 0.92 0.90 Donggala 7205 0.97 0.61 0.88 0.82 0.86 0.90 0.88 1.00 0.85 0.93 0.85 0.87 0.87 0.86 0.85 0.81 Toli-Toli 7206 0.98 0.68 0.96 0.87 0.86 0.90 0.88 0.79 0.63 0.71 0.84 0.82 0.83 0.84 0.82 0.79 Buol 7207 0.97 0.80 0.95 0.91 0.82 0.77 0.79 0.73 0.69 0.71 0.85 0.81 0.82 0.83 0.84 0.83 Parigi Moutong 7208 0.95 0.36 0.95 0.76 0.79 0.87 0.83 1.00 0.86 0.93 0.80 0.84 0.83 0.83 0.84 0.76 Tojo Una-Una 7209 0.88 0.60 0.72 0.73 0.69 0.69 0.69 1.00 0.72 0.86 0.75 0.76 0.76 0.75 0.75 0.71 Sigi 7210 0.94 0.82 0.94 0.90 0.93 0.89 0.91 0.93 0.81 0.87 0.90 0.89 0.90 0.90 0.89 0.87 Palu 7271 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.88 0.94 0.99 0.98 0.98 0.98 0.98 0.97 71 | 72 | District BPS Code Physical Availability Health Personnel Building Characteristics Composite Indices Primary Secondary Delivery SubIndex GP Pusk. Midwife SubIndex Water P. Electricity SubIndex Access Equal D. Equal I. PCA OLS CI Sulawesi Selatan 0.97 0.81 0.94 0.91 0.94 0.95 0.94 0.93 0.84 0.88 0.91 0.91 0.91 0.91 0.89 0.87 Selayar 7301 0.99 0.50 0.81 0.76 0.77 0.83 0.80 1.00 0.74 0.87 0.79 0.81 0.80 0.80 0.78 0.72 Bulukumba 7302 1.00 0.71 0.98 0.89 0.94 0.99 0.97 0.83 0.78 0.81 0.89 0.89 0.89 0.90 0.87 0.84 Bantaeng 7303 0.98 0.87 0.98 0.94 1.00 0.98 0.99 0.75 0.93 0.84 0.93 0.93 0.93 0.93 0.92 0.92 Jeneponto 7304 1.00 0.90 0.99 0.96 0.83 0.97 0.90 0.94 0.76 0.85 0.93 0.91 0.91 0.92 0.92 0.90 Takalar 7305 1.00 0.97 0.97 0.98 1.00 0.97 0.99 1.00 0.91 0.95 0.98 0.97 0.98 0.98 0.97 0.96 Gowa 7306 1.00 0.81 0.97 0.93 0.91 0.98 0.95 0.91 0.73 0.82 0.91 0.90 0.90 0.91 0.89 0.86 Sinjai 7307 1.00 0.76 0.92 0.89 0.80 1.00 0.90 0.93 0.73 0.83 0.88 0.88 0.88 0.88 0.86 0.83 Maros 7308 0.97 0.82 0.90 0.90 1.00 0.98 0.99 0.79 0.77 0.78 0.89 0.89 0.89 0.90 0.85 0.84 Pangkajene Dan Kep. 7309 0.95 0.70 0.86 0.83 0.95 0.98 0.97 1.00 0.93 0.97 0.89 0.92 0.91 0.91 0.87 0.84 Barru 7310 1.00 0.86 0.99 0.95 0.90 0.98 0.94 1.00 0.84 0.92 0.94 0.94 0.94 0.94 0.94 0.92 Bone 7311 0.96 0.67 0.90 0.84 0.83 0.86 0.85 0.92 0.72 0.82 0.84 0.84 0.84 0.84 0.83 0.79 Soppeng 7312 1.00 0.94 0.99 0.98 1.00 1.00 1.00 0.94 0.93 0.93 0.97 0.97 0.97 0.97 0.96 0.96 Wajo 7313 0.98 0.78 0.95 0.90 0.96 0.92 0.94 0.96 0.95 0.95 0.92 0.93 0.93 0.93 0.92 0.90 Sidenreng Rappang 7314 0.99 0.87 0.99 0.95 1.00 1.00 1.00 1.00 0.95 0.97 0.96 0.97 0.97 0.97 0.96 0.95 Pinrang 7315 0.97 0.88 0.95 0.93 1.00 0.99 0.99 1.00 0.93 0.96 0.95 0.96 0.96 0.96 0.94 0.93 Enrekang 7316 0.99 0.49 0.87 0.78 1.00 0.93 0.96 0.92 0.96 0.94 0.85 0.90 0.88 0.88 0.83 0.79 Luwu 7317 0.90 0.67 0.82 0.80 0.95 0.99 0.97 0.90 0.80 0.85 0.84 0.87 0.86 0.86 0.81 0.78 Tana Toraja 7318 0.83 0.58 0.80 0.74 0.84 0.85 0.85 0.89 0.73 0.81 0.77 0.80 0.79 0.79 0.76 0.73 Luwu Utara 7322 0.94 0.76 0.91 0.87 1.00 0.95 0.98 0.92 0.84 0.88 0.89 0.91 0.90 0.90 0.87 0.85 Luwu Timur 7325 1.00 0.78 0.95 0.91 1.00 0.99 1.00 1.00 0.83 0.91 0.93 0.94 0.94 0.94 0.91 0.88 Toraja Utara 7326 0.85 0.55 0.79 0.73 0.91 0.86 0.89 0.91 0.83 0.87 0.79 0.83 0.81 0.81 0.78 0.74 Makassar 7371 1.00 0.98 0.99 0.99 1.00 0.93 0.96 0.97 0.92 0.95 0.98 0.97 0.97 0.97 0.97 0.97 Pare-Pare 7372 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.92 0.96 0.99 0.99 0.99 0.99 0.98 0.98 Palopo 7373 0.99 0.97 0.99 0.98 1.00 0.97 0.99 0.90 0.89 0.89 0.97 0.95 0.96 0.96 0.95 0.95 Sulawesi Tenggara 0.96 0.62 0.94 0.84 0.79 0.80 0.79 0.86 0.59 0.72 0.81 0.79 0.79 0.80 0.80 0.76 Buton 7401 0.99 0.46 0.95 0.80 0.97 0.86 0.91 0.94 0.55 0.74 0.81 0.82 0.82 0.82 0.78 0.71 Muna 7402 0.96 0.59 0.90 0.81 0.59 0.79 0.69 0.71 0.59 0.65 0.76 0.72 0.73 0.74 0.75 0.72 Konawe 7403 0.93 0.68 0.92 0.85 0.80 0.62 0.71 0.80 0.62 0.71 0.79 0.75 0.77 0.77 0.80 0.77 Kolaka 7404 0.98 0.67 0.95 0.87 0.86 0.92 0.89 1.00 0.62 0.81 0.86 0.85 0.86 0.86 0.85 0.80 Konawe Selatan 7405 0.96 0.63 0.95 0.85 0.82 0.52 0.67 0.86 0.67 0.77 0.79 0.76 0.77 0.77 0.82 0.78 Bombana 7406 0.85 0.21 0.83 0.63 0.81 0.70 0.76 0.81 0.49 0.65 0.66 0.68 0.67 0.67 0.65 0.57 Wakatobi 7407 1.00 0.52 0.98 0.83 0.53 0.96 0.74 0.88 0.35 0.62 0.77 0.73 0.75 0.76 0.76 0.69 Kolaka Utara 7408 0.96 0.33 0.93 0.74 0.75 0.92 0.84 1.00 0.82 0.91 0.79 0.83 0.82 0.82 0.82 0.74 Buton Utara 7409 0.93 0.00 0.87 0.60 0.78 0.69 0.73 0.78 0.35 0.57 0.62 0.63 0.63 0.64 0.60 0.49 District BPS Code Physical Availability Health Personnel Building Characteristics Composite Indices Primary Secondary Delivery SubIndex GP Pusk. Midwife SubIndex Water P. Electricity SubIndex Access Equal D. Equal I. PCA OLS CI Konawe Utara 7410 0.89 0.43 0.89 0.74 1.00 0.46 0.73 0.83 0.60 0.72 0.73 0.73 0.73 0.73 0.74 0.68 Kendari 7471 1.00 0.99 1.00 1.00 1.00 0.98 0.99 0.93 0.66 0.79 0.96 0.93 0.94 0.94 0.92 0.91 Bau-Bau 7472 1.00 1.00 1.00 1.00 0.69 0.97 0.83 0.81 0.60 0.71 0.91 0.85 0.87 0.88 0.89 0.89 Gorontalo 0.97 0.75 0.95 0.89 0.96 0.79 0.88 0.84 0.82 0.83 0.88 0.87 0.87 0.87 0.88 0.85 Boalemo 7501 0.99 0.50 0.94 0.81 1.00 0.80 0.90 0.90 0.79 0.85 0.83 0.85 0.85 0.85 0.83 0.77 Gorontalo 7502 0.97 0.78 0.93 0.89 1.00 0.85 0.92 0.90 0.83 0.87 0.89 0.89 0.89 0.89 0.88 0.86 Pohuwato 7503 0.97 0.72 0.97 0.88 0.94 0.71 0.82 0.69 0.78 0.73 0.84 0.81 0.82 0.83 0.84 0.82 Bone Bolango 7504 0.94 0.80 0.96 0.90 0.94 0.71 0.83 0.72 0.73 0.73 0.85 0.82 0.83 0.83 0.85 0.83 Gorontalo Utara 7505 0.98 0.56 0.96 0.83 0.92 0.51 0.72 1.00 0.83 0.92 0.83 0.82 0.82 0.82 0.87 0.82 Gorontalo 7571 1.00 1.00 1.00 1.00 1.00 0.95 0.98 1.00 0.97 0.99 0.99 0.99 0.99 0.99 0.99 0.99 Sulawesi Barat 0.92 0.66 0.87 0.81 0.89 0.90 0.90 0.75 0.75 0.75 0.82 0.82 0.82 0.82 0.79 0.77 Majene 7601 0.97 0.93 0.96 0.96 0.78 0.94 0.86 1.00 0.83 0.92 0.93 0.91 0.92 0.92 0.94 0.93 Polewali Mandar 7602 0.91 0.81 0.87 0.86 0.95 0.94 0.95 0.80 0.80 0.80 0.87 0.87 0.87 0.87 0.83 0.83 Mamasa 7603 0.85 0.26 0.61 0.57 0.88 0.68 0.78 0.69 0.74 0.71 0.64 0.69 0.67 0.67 0.60 0.55 Mamuju 7604 0.90 0.58 0.91 0.80 0.86 0.90 0.88 0.72 0.67 0.69 0.79 0.79 0.79 0.80 0.78 0.74 Mamuju Utara 7605 0.99 0.51 0.92 0.81 1.00 0.93 0.96 0.64 0.76 0.70 0.82 0.82 0.82 0.83 0.78 0.74 Maluku 0.93 0.57 0.87 0.79 0.68 0.83 0.76 0.74 0.63 0.68 0.76 0.74 0.75 0.76 0.75 0.72 Maluku Tenggara Barat 8101 0.94 0.59 0.82 0.78 0.45 0.70 0.58 0.91 0.43 0.67 0.72 0.68 0.69 0.70 0.72 0.67 Maluku Tenggara 8102 0.96 0.52 0.86 0.78 0.69 0.89 0.79 0.69 0.44 0.56 0.74 0.71 0.72 0.73 0.70 0.65 Maluku Tengah 8103 0.97 0.59 0.93 0.83 0.78 0.93 0.86 0.72 0.74 0.73 0.82 0.81 0.81 0.82 0.80 0.77 Buru 8104 0.94 0.67 0.88 0.83 0.89 0.84 0.87 1.00 0.85 0.93 0.86 0.87 0.87 0.87 0.86 0.83 Kepulauan Aru 8105 0.87 0.46 0.78 0.70 0.70 0.59 0.65 0.45 0.47 0.46 0.64 0.60 0.62 0.63 0.61 0.58 Seram Bagian Barat 8106 0.97 0.27 0.96 0.73 0.50 0.93 0.71 0.88 0.66 0.77 0.74 0.74 0.74 0.75 0.77 0.68 Seram Bagian Timur 8107 0.83 0.14 0.66 0.54 0.36 0.51 0.43 0.64 0.57 0.61 0.53 0.53 0.53 0.54 0.56 0.49 Maluku Barat Daya 8108 0.68 0.04 0.50 0.40 0.78 0.58 0.68 0.67 0.35 0.51 0.48 0.53 0.51 0.51 0.42 0.35 Buru Selatan 8109 0.66 0.22 0.53 0.47 0.75 0.41 0.58 0.63 0.40 0.51 0.50 0.52 0.51 0.51 0.47 0.42 Ambon 8171 1.00 1.00 1.00 1.00 0.82 0.97 0.89 0.91 0.92 0.92 0.96 0.94 0.95 0.95 0.97 0.97 Tual 8172 0.99 0.82 0.94 0.91 0.60 0.91 0.76 0.80 0.46 0.63 0.83 0.77 0.79 0.80 0.80 0.78 73 | 74 | District BPS Code Physical Availability Health Personnel Building Characteristics Composite Indices Primary Secondary Delivery SubIndex GP Pusk. Midwife SubIndex Water P. Electricity SubIndex Access Equal D. Equal I. PCA OLS CI Maluku Utara 0.90 0.55 0.87 0.77 0.72 0.68 0.70 0.87 0.71 0.79 0.76 0.75 0.76 0.76 0.78 0.74 Halmahera Barat 8201 0.92 0.58 0.93 0.81 1.00 0.70 0.85 0.90 0.65 0.77 0.81 0.81 0.81 0.81 0.81 0.76 Halmahera Tengah 8202 1.00 0.21 0.78 0.66 0.88 0.92 0.90 1.00 0.79 0.89 0.76 0.82 0.80 0.80 0.73 0.64 Kepulauan Sula 8203 0.81 0.32 0.69 0.60 0.62 0.53 0.57 0.77 0.60 0.68 0.61 0.62 0.62 0.62 0.62 0.57 Halmahera Selatan 8204 0.81 0.42 0.89 0.71 0.30 0.51 0.40 0.83 0.51 0.67 0.64 0.59 0.61 0.62 0.72 0.66 Halmahera Utara 8205 0.92 0.73 0.86 0.84 1.00 0.68 0.84 0.82 0.75 0.79 0.83 0.82 0.82 0.82 0.81 0.79 Halmahera Timur 8206 0.88 0.09 0.81 0.60 0.86 0.67 0.77 1.00 0.75 0.87 0.69 0.74 0.72 0.72 0.71 0.61 Pulau Morotai 8207 0.82 0.32 0.67 0.60 1.00 0.62 0.81 0.80 0.85 0.82 0.69 0.75 0.73 0.72 0.67 0.63 Ternate 8271 0.99 0.95 0.99 0.98 1.00 0.79 0.90 0.88 0.95 0.91 0.95 0.93 0.93 0.93 0.96 0.96 Tidore Kepulauan 8272 1.00 0.64 1.00 0.88 0.71 0.87 0.79 0.86 0.94 0.90 0.87 0.86 0.86 0.87 0.90 0.87 Papua Barat 0.91 0.62 0.85 0.79 0.56 0.69 0.62 0.77 0.69 0.73 0.75 0.72 0.73 0.73 0.77 0.74 Fakfak 9101 0.95 0.73 0.87 0.85 0.89 0.79 0.84 0.89 0.62 0.75 0.83 0.81 0.82 0.82 0.80 0.77 Kaimana 9102 0.81 0.07 0.63 0.50 0.57 0.64 0.60 0.86 0.79 0.83 0.59 0.64 0.62 0.62 0.61 0.53 Teluk Wondama 9103 0.97 0.02 0.84 0.61 0.67 0.44 0.55 0.83 0.68 0.76 0.63 0.64 0.64 0.64 0.67 0.57 Teluk Bintuni 9104 0.90 0.19 0.79 0.63 0.61 0.65 0.63 0.83 0.65 0.74 0.65 0.67 0.66 0.66 0.67 0.59 Manokwari 9105 0.90 0.78 0.88 0.86 0.59 0.71 0.65 0.77 0.59 0.68 0.78 0.73 0.75 0.76 0.79 0.78 Sorong Selatan 9106 0.84 0.46 0.80 0.70 0.27 0.35 0.31 0.55 0.46 0.50 0.58 0.50 0.53 0.54 0.63 0.59 Sorong 9107 0.92 0.67 0.85 0.81 0.35 0.75 0.55 0.76 0.73 0.75 0.75 0.70 0.72 0.73 0.78 0.76 Raja Ampat 9108 0.83 0.25 0.66 0.58 0.50 0.45 0.48 0.72 0.79 0.76 0.60 0.61 0.60 0.60 0.64 0.58 Tambrauw 9109 0.55 0.00 0.51 0.35 0.50 0.28 0.39 0.75 0.63 0.69 0.43 0.48 0.46 0.45 0.49 0.41 Maybrat 9110 0.75 0.08 0.58 0.47 0.50 0.21 0.36 0.50 0.67 0.58 0.47 0.47 0.47 0.47 0.51 0.45 Sorong 9171 1.00 0.99 0.99 0.99 1.00 0.91 0.96 1.00 0.97 0.99 0.98 0.98 0.98 0.98 0.99 0.98 Papua 0.71 0.40 0.63 0.58 0.60 0.54 0.57 0.68 0.45 0.57 0.57 0.57 0.57 0.57 0.56 0.53 Merauke 9401 0.98 0.68 0.80 0.82 0.75 0.97 0.86 0.88 0.54 0.71 0.81 0.80 0.80 0.80 0.74 0.71 Jayawijaya 9402 0.72 0.44 0.73 0.63 1.00 0.43 0.72 0.83 0.17 0.50 0.62 0.62 0.62 0.61 0.58 0.52 Jayapura 9403 0.93 0.56 0.90 0.80 0.70 0.66 0.68 0.75 0.86 0.81 0.78 0.76 0.77 0.77 0.81 0.78 Nabire 9404 0.95 0.82 0.88 0.88 0.44 0.87 0.66 0.89 0.56 0.72 0.81 0.76 0.77 0.78 0.81 0.79 Kepulauan Yapen 9408 0.93 0.57 0.82 0.77 0.75 0.66 0.70 0.63 0.26 0.44 0.69 0.64 0.66 0.67 0.64 0.60 Biak Numfor 9409 0.92 0.71 0.87 0.83 0.59 0.75 0.67 0.76 0.82 0.79 0.79 0.76 0.77 0.78 0.82 0.80 Paniai 9410 0.37 0.22 0.44 0.34 0.40 0.29 0.34 0.50 0.18 0.34 0.34 0.34 0.34 0.34 0.36 0.32 Puncak Jaya 9411 0.29 0.14 0.27 0.23 0.38 0.23 0.30 0.38 0.20 0.29 0.26 0.28 0.27 0.27 0.25 0.23 Mimika 9412 0.91 0.85 0.89 0.88 0.54 0.77 0.65 0.92 0.53 0.73 0.81 0.75 0.77 0.78 0.81 0.79 Boven Digoel 9413 0.78 0.18 0.75 0.57 0.38 0.65 0.52 0.54 0.59 0.56 0.56 0.55 0.55 0.57 0.59 0.53 Mappi 9414 0.59 0.29 0.47 0.45 0.60 0.34 0.47 1.00 0.65 0.83 0.53 0.58 0.56 0.55 0.56 0.51 Asmat 9415 0.46 0.13 0.33 0.31 1.00 0.62 0.81 0.89 0.71 0.80 0.51 0.64 0.59 0.57 0.47 0.42 District BPS Code Physical Availability Health Personnel Building Characteristics Composite Indices Primary Secondary Delivery SubIndex GP Pusk. Midwife SubIndex Water P. Electricity SubIndex Access Equal D. Equal I. PCA OLS CI Yahukimo 9416 0.53 0.01 0.24 0.26 0.27 0.12 0.19 0.45 0.09 0.27 0.25 0.24 0.24 0.24 0.21 0.16 Pegunungan Bintang 9417 0.18 0.09 0.15 0.14 0.75 0.10 0.43 0.50 0.19 0.35 0.24 0.30 0.28 0.26 0.20 0.18 Tolikara 9418 0.48 0.21 0.41 0.37 0.44 0.18 0.31 0.22 0.19 0.21 0.32 0.30 0.31 0.31 0.30 0.28 Sarmi 9419 0.85 0.12 0.78 0.58 0.57 0.47 0.52 0.71 0.71 0.71 0.60 0.61 0.60 0.61 0.64 0.57 Keerom 9420 0.94 0.75 0.88 0.86 0.88 0.79 0.83 1.00 0.88 0.94 0.87 0.88 0.88 0.87 0.88 0.86 Waropen 9426 0.69 0.02 0.60 0.44 0.25 0.39 0.32 0.50 0.65 0.57 0.44 0.44 0.44 0.45 0.50 0.44 Supiori 9427 0.95 0.70 0.91 0.85 0.60 0.39 0.50 1.00 0.72 0.86 0.78 0.74 0.75 0.75 0.85 0.81 Mamberamo Raya 9428 0.59 0.00 0.30 0.30 0.50 0.20 0.35 0.75 0.20 0.47 0.34 0.37 0.36 0.35 0.31 0.24 Nduga 9429 0.90 0.00 0.51 0.47 0.50 0.21 0.36 0.63 0.22 0.42 0.44 0.42 0.42 0.43 0.39 0.31 Lanny Jaya 9430 0.50 0.01 0.39 0.30 0.40 0.53 0.47 0.30 0.24 0.27 0.33 0.35 0.34 0.35 0.29 0.24 Mamberamo Tengah 9431 0.54 0.00 0.66 0.40 1.00 0.71 0.86 0.60 0.07 0.34 0.48 0.53 0.51 0.51 0.41 0.32 Yalimo 9432 0.89 0.20 0.48 0.52 1.00 0.67 0.83 0.67 0.35 0.51 0.58 0.62 0.61 0.61 0.45 0.39 Puncak 9433 0.24 0.03 0.23 0.17 0.57 0.41 0.49 0.29 0.25 0.27 0.25 0.31 0.29 0.29 0.21 0.18 Dogiyai 9434 0.65 0.00 0.53 0.39 0.29 0.27 0.28 0.57 0.21 0.39 0.37 0.35 0.36 0.36 0.39 0.31 Intan Jaya 9435 0.20 0.00 0.19 0.13 0.60 0.18 0.39 0.40 0.33 0.37 0.23 0.30 0.27 0.26 0.22 0.19 Deiyai 9436 0.79 0.00 0.81 0.53 0.57 0.78 0.68 0.71 0.14 0.43 0.54 0.55 0.54 0.56 0.52 0.41 Jayapura 9471 1.00 1.00 1.00 1.00 0.92 0.85 0.89 1.00 0.86 0.93 0.96 0.94 0.95 0.95 0.97 0.97 75 | Kementerian Koordinator Kesejahteraan Rakyat PNPM Support Facility Jl. Medan Merdeka Barat No. 3 Jl. Diponegoro No. 72 Menteng Jakarta Pusat 10110 Indonesia Jakarta Pusat 10310 Indonesia Phone (62-21) 3459077 Phone (62-21) 3148175 Fax (62-21) 3459077 Fax (62-21) 3190209 The original had problem with text extraction. pdftotext Unable to extract text.