Abstract

By the middle of the 1990s, Indonesia had enjoyed over three decades of remarkable social, economic, and demographic change and was on the cusp of joining the middle-income countries. Per capita income had risen more than fifteenfold since the early 1960s, from around US$50 to more than US$800. Increases in educational attainment and decreases in fertility and infant mortality over the same period reflected impressive investments in infrastructure.

In the late 1990s the economic outlook began to change as Indonesia was gripped by the economic crisis that affected much of Asia. In 1998 the rupiah collapsed, the economy went into a tailspin, and gross domestic product contracted by an estimated 12-15%-a decline rivaling the magnitude of the Great Depression.

The general trend of several decades of economic progress followed by a few years of economic downturn masks considerable variation across the archipelago in the degree both of economic development and of economic setbacks related to the crisis. In part this heterogeneity reflects the great cultural and ethnic diversity of Indonesia, which in turn makes it a rich laboratory for research on a number of individual- and household-level behaviors and outcomes that interest social scientists.

The Indonesia Family Life Survey is designed to provide data for studying behaviors and outcomes. The survey contains a wealth of information collected at the individual and household levels, including multiple indicators of economic and non-economic well-being: consumption, income, assets, education, migration, labor market outcomes, marriage, fertility, contraceptive use, health status, use of health care and health insurance, relationships among co-resident and non- resident family members, processes underlying household decision-making, transfers among family members and participation in community activities. In addition to individual- and household-level information, the IFLS provides detailed information from the communities in which IFLS households are located and from the facilities that serve residents of those communities. These data cover aspects of the physical and social environment, infrastructure, employment opportunities, food prices, access to health and educational facilities, and the quality and prices of services available at those facilities. By linking data from IFLS households to data from their communities, users can address many important questions regarding the impact of policies on the lives of the respondents, as well as document the effects of social, economic, and environmental change on the population.

The Indonesia Family Life Survey complements and extends the existing survey data available for Indonesia, and for developing countries in general, in a number of ways.

First, relatively few large-scale longitudinal surveys are available for developing countries. IFLS is the only large-scale longitudinal survey available for Indonesia. Because data are available for the same individuals from multiple points in time, IFLS affords an opportunity to understand the dynamics of behavior, at the individual, household and family and community levels. In IFLS1 7,224 households were interviewed, and detailed individual-level data were collected from over 22,000 individuals. In IFLS2, 94.4% of IFLS1 households were re-contacted (interviewed or died). In IFLS3 the re-contact rate was 95.3% of IFLS1 households. Indeed nearly 91% of IFLS1 households are complete panel households in that they were interviewed in all three waves, IFLS1, 2 and 3. These re-contact rates are as high as or higher than most longitudinal surveys in the United States and Europe. High re-interview rates were obtained in part because we were committed to tracking and interviewing individuals who had moved or split off from the origin IFLS1 households. High re-interview rates contribute significantly to data quality in a longitudinal survey because they lessen the risk of bias due to nonrandom attrition in studies using the data.

Second, the multipurpose nature of IFLS instruments means that the data support analyses of interrelated issues not possible with single-purpose surveys. For example, the availability of data on household consumption together with detailed individual data on labor market outcomes, health outcomes and on health program availability and quality at the community level means that one can examine the impact of income on health outcomes, but also whether health in turn affects incomes.

Third, IFLS collected both current and retrospective information on most topics. With data from multiple points of time on current status and an extensive array of retrospective information about the lives of respondents, analysts can relate dynamics to events that occurred in the past. For example, changes in labor outcomes in recent years can be explored as a function of earlier decisions about schooling and work.

Fourth, IFLS collected extensive measures of health status, including self-reported measures of general health status, morbidity experience, and physical assessments conducted by a nurse (height, weight, head circumference, blood pressure, pulse, waist and hip circumference, hemoglobin level, lung capacity, and time required to repeatedly rise from a sitting position). These data provide a much richer picture of health status than is typically available in household surveys. For example, the data can be used to explore relationships between socioeconomic status and an array of health outcomes.

Fifth, in all waves of the survey, detailed data were collected about respondents¹ communities and public and private facilities available for their health care and schooling. The facility data can be combined with household and individual data to examine the relationship between, for example, access to health services (or changes in access) and various aspects of health care use and health status.

Sixth, because the waves of IFLS span the period from several years before the economic crisis hit Indonesia, to just prior to it hitting, to one year and then three years after, extensive research can be carried out regarding the living conditions of Indonesian households during this very tumultuous period. In sum, the breadth and depth of the longitudinal information on individuals, households, communities, and facilities make IFLS data a unique resource for scholars and policymakers interested in the processes of economic development.

Geographic coverage

National coverage

Analysis unit

• Communities
• Facilities
• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

Because it is a longitudinal survey, the IFLS3 drew its sample from IFLS1, IFLS2, IFLS2+. The IFLS1 sampling scheme stratified on provinces and urban/rural location, then randomly sampled within these strata (see Frankenberg and Karoly, 1995, for a detailed description). Provinces were selected to maximize representation of the population, capture the cultural and socioeconomic diversity of Indonesia, and be cost-effective to survey given the size and terrain of the country. For mainly costeffectiveness reasons, 14 of the then existing 27 provinces were excluded. The resulting sample included 13 of Indonesia's 27 provinces containing 83% of the population: four provinces on Sumatra (North Sumatra, West Sumatra, South Sumatra, and Lampung), all five of the Javanese provinces (DKI Jakarta, West Java, Central Java, DI Yogyakarta, and East Java), and four provinces covering the remaining major island groups (Bali, West Nusa Tenggara, South Kalimantan, and South Sulawesi).

Household Survey:

Within each of the 13 provinces, enumeration areas (EAs) were randomly chosen from a nationally representative sample frame used in the 1993 SUSENAS, a socioeconomic survey of about 60,000 households. The IFLS randomly selected 321 enumeration areas in the 13 provinces, over-sampling urban EAs and EAs in smaller provinces to facilitate urban-rural and Javanese-non-Javanese comparisons.

Within a selected EA, households were randomly selected based upon 1993 SUSENAS listings obtained from regional BPS office. A household was defined as a group of people whose members reside in the same dwelling and share food from the same cooking pot (the standard BPS definition). Twenty households were selected from each urban EA, and 30 households were selected from each rural EA.This strategy minimized expensive travel between rural EAs while balancing the costs of correlations among households. For IFLS1 a total of 7,730 households were sampled to obtain a final sample size goal of 7,000 completed households. This strategy was based on BPS experience of about 90% completion rates. In fact, IFLS1 exceeded that target and interviews were conducted with 7,224 households in late 1993 and early 1994.

IFLS3 Re-Contact Protocols The sampling approach in IFLS3 was to re-contact all original IFLS1 households having living members the last time they had been contacted, plus split-off households from both IFLS2 and IFLS2+, so-called target households (8,347 households-as shown in Table 2.1*) Main field work for IFLS3 went on from June through November, 2000. A total of 10,574 households were contacted in 2000; meaning that they were interviewed, had all members died since the last time they were contacted, or had joined another IFLS household which had been previously interviewed (Table 2.1*). Of these, 7,928 were IFLS3 target households and 2,646 were new split-off households. A 95.0% re-contact rate was thus achieved of all IFLS3 "target" households. The re-contacted households included 6,800 original 1993 households, or 95.3% of those. Of IFLS1 households, somewhat lower re-contact rates were achieved in Jakarta, 84.5%, and North Sumatra,

Abstract

The 1993 Indonesia Family Life Survey (IFLS) provides data at the individual and family level on fertility, health, education, migration, and employment. Extensive community and facility data accompany the household data. The survey was a collaborative effort of Lembaga Demografi of the University of Indonesia and RAND, with support from the National Institute of Child Health and Human Development, USAID, Ford Foundation, and the World Health Organization. In Indonesia, the 1993 IFLS is also referred to as SAKERTI 93 (Survai Aspek Kehidupan Rumah Tangga Indonesia). The IFLS covers a sample of 7,224 households spread across 13 provinces on the islands of Java, Sumatra, Bali, West Nusa Tenggara, Kalimantan, and Sulawesi. Together these provinces encompass approximately 83 percent of the Indonesian population and much of its heterogeneity. The survey brings an interdisciplinary perspective to four broad topic areas:

• Fertility, family planning, and contraception • Infant and child health and survival • Education, migration and employment • The social, economic, and health status of adults, young and old

Additionally, extensive community and facility data accompany the household data. Village leaders and heads of the village women's group provided information in each of the 321 enumeration areas from which households were drawn, and data were collected from 6,385 schools and health facilities serving community residents.

Geographic coverage

National

Analysis unit

Households

Universe

Household Survey data were collected for household members through direct interviews (for adults) and proxy interviews (for children, infants and temporarily absent household members). The IFLS-1 conducted detailed interviews with the following household members:

• The household head and their spouse
• Two randomly selected children of the head and spouse aged 0 to 14 (interviewed by proxy)
• An individual age 50 and above and their spouse, randomly selected from remaining members
• For a randomly selected 25 percent of the households, an individual age 15 to 49 and their spouse, randomly selected from remaining members.

The Community and Facility Survey collected data from a variety of respondents including: the village leader and his staff and the leader of the village women's group; Ministry of Health clinics and subclinics; private practices of doctors, midwives, nurses, and paramedics; community-based health posts and contraceptive distribution centers; public, private, and religious elementary schools; public, private, and religious junior high schools; public, private, and religious senior high schools. Unlike many other surveys, the sample frame for the survey of facilities was drawn from the list of facilities used by household survey respondents in the area.

Kind of data

Sample survey data [ssd]

Sampling procedure

1. HOUSEHOLD SELECTION

(a) SAMPLING

The IFLS sampling scheme stratified on provinces, then randomly sampled within provinces. Provinces were selected to maximize representation of the population, capture the cultural and socioeconomic diversity of Indonesia, and be cost effective given the size and terrain of the country. The far eastern provinces of East Nusa Tenggara, East Timor, Maluku and Irian Jaya were readily excluded due to the high costs of preparing for and conducting fieldwork in these more remote provinces. Aceh, Sumatra's most northern province, was deleted out of concern for the area's political violence and the potential risk to interviewers. Finally, due to their relatively higher survey costs, we omitted three provinces on each of the major islands of Sumatra (Riau, Jambi, and Bengkulu), Kalimantan (West, Central, East), and Sulawesi (North, Central, Southeast). The resulting sample consists of 13 of Indonesia's 27 provinces: four on Sumatra (North Sumatra, West Sumatra, South Sumatra, and Lampung), all five of the Javanese provinces (DKI Jakarta, West Java, Central Java, DI Yogyakarta, and East Java), and four provinces covering the remaining major island groups (Bali, West Nusa Tenggara, South Kalimantan, and South Sulawesi). The resulting sample represents 83 percent of the Indonesian population. (see Figure 1.1 of the Overview and Field Report in External Documents). Table 2.1 of the same document shows the distribution of Indonesia's population across the 27 provinces, highlighting the 13 provinces included in the IFLS sample.

The IFLS randomly selected enumeration areas (EAs) within each of the 13 provinces. The EAs were chosen from a nationally representative sample frame used in the 1993 SUSENAS, a socioeconomic survey of about 60,000 households. The SUSENAS frame, designed by the Indonesian Central Bureau of Statistics (BPS), is based on the 1990 census. The IFLS was based on the SUSENAS sample because the BPS had recently listed and mapped each of the SUSENAS EAs (saving us time and money) and because supplementary EA-level information from the resulting 1993 SUSENAS sample could be matched to the IFLS-1 sample areas. Table 2.1 summarizes the distribution of the approximately 9,000 SUSENAS EAs included in the 13 provinces covered by the IFLS. The SUSENAS EAs each contain some 200 to 300 hundred households, although only a smaller area of about 60 to 70 households was listed by the BPS for purposes of the annual survey. Using the SUSENAS frame, the IFLS randomly selected 321 enumeration areas in the 13 provinces, over-sampling urban EAs and EAs in smaller provinces to facilitate urban rural and Javanese-non-Javanese comparisons. A straight proportional sample would likely be dominated by Javanese, who comprise more than 50 percent of the population. A total of 7,730 households were sampled to obtain a final sample size goal of 7,000 completed households. Table 2.1 shows the sampling rates that applied to each province and the resulting distribution of EAs in total, and separately by urban and rural status. Within a selected EA, households were randomly selected by field teams based upon the 1993 SUSENAS listings obtained from regional offices of the BPS. A household was defined as a group of people whose members reside in the same dwelling and share food from the same cooking pot (the standard BPS definition). Twenty households were selected from each urban EA, while thirty households were selected from each rural EA. This strategy minimizes expensive travel between rural EAs and reduces intra-cluster correlation across urban households, which tend to be more similar to one another than do rural households. Table 2.2 (Overview and Field Report) shows the resulting sample of IFLS households by province, separately by completion status.

(b) SELECTION OF RESPONDENTS WITHIN HOUSEHOLDS For each household selected, a representative member provided household-level demographic and economic information. In addition, several household members were randomly selected and asked to provide detailed individual information.

1. THE COMMUNITY SURVEY SAMPLING PROCEDURE

(a) SAMPLING

The goal of the CFS was to collect information about the communities of respondents to the household questionnaire. The information was solicited in two ways. First, the village leader of each community was interviewed about a variety of aspects of village life (the content of this questionnaire is described in the next section). Information from the village leader was supplemented by interviewing the head of the village women's group, who was asked questions regarding the availability of health facilities and schools in the area, as well as more general questions about family health in the community. In addition to the information on community characteristics provided by the two representatives of the village leadership, we visited a sample of schools and health facilities, in which we conducted detailed interviews regarding the institution's activities. A priori we wanted data on the major sources of outpatient health care, public and private, and on elementary, junior secondary, and senior secondary schools. We defined eight strata of facilities/institutions from which we wanted data. Different types of health providers make up five of the strata, while schools account for the other three. The five strata of health care providers are: government health centers and subcenters (puskesmas, puskesmas pembantu); private doctors and clinics (praktek umum/klinik); the private practices of midwives, nurses, and paramedics (perawats, bidans, paramedis, mantri); traditional practitioners (dukun, sinshe, tabib, orang pintar); and community health posts (posyandu, PPKBD).The three strata of schools are elementary, junior secondary, and senior secondary. Private, public, religious, vocational, and general schools are all eligible as long as they provide schooling at one of the three levels. Our protocol for selecting specific schools and health facilities for detailed interview reflects our desire that selected facilities represent the facilities available to members of the communities from which household survey respondents were drawn. For that reason, we were hesitant to select facilities based solely either on information from the village leader or on proximity to the village center. The option we selected instead was to sample schools and health care providers from lists provided by respondents to the household survey. For each enumeration area lists of facilities in each of the eight strata were constructed by compiling information provided by the household regarding the names and locations of facilities the household respondent either knew about or used. To generate lists of relevant health and family planning facilities, the CFS drew on two pieces of information from the household survey. The IFLS

Prevalence of hypertension by age, sex and socio-economic development in Indonesian men and women, IFLS 2007.

This release of the 1993 Indonesian Family Life Survey (IFLS-1-PR) is a revised and restructured version of the Wave 1 data. This data collection provides a broad range of economic, demographic, and health information at both the household and community levels across 13 provinces on the islands of Java, Sumatra, Bali, West Nusa Tenggara, Kalimantan, and Sulawesi. A sample of 7,224 households was interviewed during August 1993 through January 1994. Household-level data cover topics such as household characteristics, income, education of both adults and children, marriage histories, inter-household transfers, pregnancy history, and knowledge and use of contraceptives. At the community-facility level, information was gathered from village leaders and heads of village women's groups in each of the 321 enumeration areas (EAs) where the households were located. Questions were asked regarding community characteristics (transportation, water and sanitation, history of schools, and availability of health facilities), nurses, midwives, and paramedics (facility management and family planning history, vignettes on types of care), and traditional health practitioners (buying or making herbal medicines or using services of traditional practitioners, rituals, and incantations). When the household data are combined with the community-facility data, the 1993 Indonesian Family Life Survey provides a unique look at areas of fertility, family planning, infant and child health, education, migration, employment, and the social, economic, and health status of over 7,000 households in a diverse setting during a period of rapid demographic and socioeconomic change.As of June 2015, there are four waves of data for the IFLS. However, a fifth wave of data collection has begun. Please see the IFLS Web site for more information on how to obtain these data.

Stepwise logistic regression analysis of factors associated with uncontrolled hypertension.

Abstrak:

Kesehatan masyarakat belum banyak dihubungkan dengan properti fisik lingkungan. Penelitian ini bertujuan untuk mencari hubungan antara data kesehatan masyarakat dengan data fisik infrastruktur dan kualitas sumber air dari IFLS dan data kualitas air tanah sebagai salah satu sumber air terbesar yang digunakan oleh masyrakat Indonesia, khususnya Bandung.

Kami mengambil data kualitas air di sebanyak 70 titik sumur warga dengan kedalaman bervariasi antara 6 hingga 20 m. Konsentrasi tujuh ion utama dianalisis dengan tambahan pengukuran temperatur, TDS, dan pH di lapangan menggunakan peralatan jinjing yang ringan. Khusus di kawasan bantaran Sungai Cikapundung, kami melakukan identifikasi kandungan bakteriologi, dalam hal ini bakteri E coli. Kemudian di beberapa lokasi, kami dibantu mahasiswa S1, juga melakukan pengukuran berulang (time series) sebanyak empat kali dalam sehari, untuk melihat fluktuasi parameter temperatur, TDS, dan pH pada air sungai.

Hasilnya cukup menarik, saat data IFLS dan data penderita diare di Puskesmas, menunjukkan adanya korelasi positif antara kualitas infrastruktur sumber air dengan jumlah penderita diare. Hal ini didukung dengan peningkatan kandungan E coli di air S. CIkapundung sebesar hampir 2 kali lipat antara kawasan hulu dengan kawasan hilir. Kondisi ini berimbas kepada air tanah, karena pada daerah selatan (dari Viaduct ke Dayeuhkolot), air sungai meresap ke dalam akuifer. Dari sini, kami berpendapat bahwa akan terjadi interaksi yang sangat intensif antara air sumur warga di sekitar sungai (khususnya yang menggunakan pompa) dengan air sungai.

Untuk meningkatkan kontribusi kepada ITB dan Kota Bandung pada umumnya, kami mengusulkan adanya portal data hidrologi yang dikelola bersama antara Pemkot Bandung dan ITB agar data didapatkan secara rutin dan dapat dianalisis kapanpun. Hal ini kami usulkan karena seringkali analisis tidak dapat dilakukan secara instan, karena perlu diawali dengan tahapan mencari data.

Selain itu, riset ini juga menambah contoh riset terbuka kepada para dosen/peneliti dengan cara: membuat repositori data dan repositori riset yang terbuka (selain Simlitabmas yang masih tertutup), membuat artikel blog yang diperbarui mengikuti perkembangan riset, serta membudayakan konsep open access, yang mana seluruh luaran riset ini bebas untuk dibaca, digunakan/dianalisis ulang, dan dipadukan dengan hasil yang lain (free to read, reuse, remix), untuk berbagai keperluan pembaca.

Abstract:

Public health has not been connected closely to physical environment. This research looks for the connections between public health using sum of diarrhoea case in Bandung area (using kecamatan puskesmas data) with water quality, water source quality (especially groundwater quality) and sanitation system quality. As we know, groundwater is the main water supply in Indonesia.

We took 70 samples from community and private dug wells with depth varied from six to 20 m underground. We measured field parameters using portable equipments: groundwater depth, temperature, TDS, pH, to support lab analysis on seven major ions. In the Cikapundung riverbank, the team had sampled bacteriology content, E coli. Next, at some points, we also brought some undergraduate and master students to measure temperature, TDS, and pH readings in the river water, four times a day for 5 months to see the fluctuation and daily, weekly, and monthly variations.

The initial results is interesting, when IFLS data matches with data from puskesmas in the sum of diarrhoea case. We also note a possitive correlation between water supply infrastructure with the diarrhoea case. The concentration of E coli in the river stream increases nearly two times between upstream and downstream. This condition contributes to the groundwater quality given the close connection between both unconfined groundwater and surface water, especially in the southern part with losing stream type. We could expect an intensive mixing between dug wells in the riverbank with river water.

To extend our contribution to ITB and Bandung, we also initiate a open data portal for hydrological records that can be managed both my ITB and Bandung government to provide a routine data collecting. We convince this step could solve data sharing problem between governmental unit with data users. We also need to showcase a model of open research implementation among researchers by providing open data repository, project blog, aside to only use close-system Simlitabmas portal. The blog project is the one that take the most of our interest with the increasing number of readers. This research shows another open access way to run a research program, where all of the research outputs are free to read, reuse, and remix by the readers.

Reported are marginal effects (standard error).Sig.: †significant at 5% or less; ‡significant at 1% or less.

Socio-demographic characteristics of respondents in rural and urban areas, 2014–2015.

Summary statistics for cognitive function and its contributors using complete case analysis, 2000 and 2007.

Comparison between complete case analysis and multiple imputation analysis.

Decomposition of inequality in children's cognitive function ranked by contribution in 2000.