The IFLS Study Design
By the middle of the 1990s, Indonesia had enjoyed over three decades of remarkable social, economic, and demographic change. Per capita income had risen since the early 1960s, from around US$50 to more than US$1,100 in 1997. Massive improvements occurred in many dimensions of living standards of the Indonesian population. The poverty headcount measure as measured by the World Bank declined from over 40% in 1976 to just 18% in 1996. Infant mortality fell from 118 per thousand live births in 1970 to 46 in 1997. Primary school enrollments rose from 75% in 1970 to universal enrollment in 1995 and secondary schooling rates from 13% to 55% over the same period. The total fertility rate fell from 5.6 in 1971 to 2.8 in 1997.
In the late 1990s the economic outlook began to change as Indonesia was gripped by the economic crisis that affected much of Asia. At the beginning of 1998 the rupiah collapsed and gross domestic product contracted by an estimated 13%. Afterwards, gross domestic product was flat in 1999. Between 2003 and 2014 GDP growth fluctuated between 5% and 6% per year and recovery ensued.
Different parts of the economy were affected quite differently by the 1998 crisis, for example the national accounts measure of personal consumption showed little decline, while gross domestic investment declined 35%. Across Indonesia there was considerable variation in the impacts of the crisis, as there had been of the earlier economic success. The different waves of the Indonesia Family Life Survey can be used to document changes before, during and 3,10 and 17 years after the economic crisis for the same communities, households and individuals.
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, 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.
IFLS is an ongoing longitudinal survey. The first wave, IFLS1, was conducted in 1993–1994. The survey sample represented about 83% of the Indonesian population living in 13 of the country’s 26 provinces. IFLS2 followed up with the same sample four years later, in 1997–1998. One year after IFLS2, a 25% subsample was surveyed to provide information about the impact of Indonesia’s economic crisis. IFLS3 was fielded on the full sample in 2000, IFLS4 in 2007-2008 and IFLS5 in 2014-2015.
Contributions of the IFLS
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 population-based longitudinal surveys are available for developing countries and very few are available for an extended period of time; 21 years now for IFLS. 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 dynasty households (any part of the original IFLS1 households). In IFLS4 the recontact rate of original IFLS1 dynasties was 93.6% (of course the period between waves was 7 years, not 3). In IFLS5 the dynasty recontact rate was 92%. For the individual target households (including splitoff households as separate) the re-contact rate was a little lower, 90.5%. Among IFLS1 dynasties, 87.8% were either interviewed in all 5 waves, or died, some 6,341 households, of which 6,275, or 86.9% are actually interviewed in all 5 waves. 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, labor outcomes of young adults can be related to their conditions 21 years earlier as very young children, or in infancy.
Fourth, IFLS collected extensive measures of health status, including self-reported measures of general health status, symptoms, pain, doctor diagnosed chronic conditions, time spent on different physical activities and biomarker measurements (height, weight, leg length, blood pressure, pulse, waist and hip circumference, hemoglobin level, grip strength, lung capacity, and time required to repeatedly rise from a sitting position). In addition dried blood spot data were collected in waves 4 and 5 and assayed for hs C-reactive protein (CRP) in both waves 4 and 5 and for HbA1c in wave 5. 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 respondents’ 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. Although the facility data are not designed to be a panel, in fact they are for many facilities.
Sixth, because the waves of IFLS span the period from several years before the 1998 financial crisis hit Indonesia, to just prior to it hitting, to one year, three years, ten years and now 17 years after, extensive research can be carried out regarding the living conditions of Indonesian households during this very tumultuous period and its long-run aftermath.
In sum, the breadth and depth of the longitudinal information over 21 years on individuals, households, communities, and facilities make IFLS data a unique resource for scholars and policymakers interested in the processes of economic development. However, the data are complex. In this and other volumes of the IFLS documentation, we seek to provide scholars and policymakers interested in using the data with the information necessary to do so efficiently.