The research delves into the underexplored area of how production network structures influence the severity of economic downturns, particularly during the last financial crisis. Utilizing the RSTAN database from the OECD, we meticulously derived critical measures from the input-output matrices for 61 economies. Our methodology entailed a panel analysis spanning from 2008 to 2010, which is a period marked by significant recessionary pressures. This analysis aimed to correlate economic performance with various production network metrics, taking into account control factors such as interest rates and the prevalence of service sectors. The findings reveal a noteworthy positive correlation between the density of production networks and economic resilience during the crisis, which remained consistent across multiple model specifications. Conversely, as anticipated, higher interest rates were linked to poorer economic performance, highlighting the critical interplay between monetary policy and economic outcomes during periods of financial instability. Given these insights, we propose a policy recommendation emphasizing the strategic enhancement of production network density as a potential buffer against economic downturns. This approach suggests that policymakers should consider the structural aspects of production networks in designing economic stability and growth strategies, thus potentially mitigating the impacts of future financial crises.

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.

This study attempts to explore the impact of external debt ($Debt), foreign reserves ($Reserves), and political stability & absence of violence/terrorism (PS&AVT) on the current financial crisis in Sri Lanka. Using data from 1996 to 2022 obtained from the World Bank (WB) and the Central Bank of Sri Lanka (CBSL), a regression analysis is conducted, with a composite variable named "CRISIS," which accounts for interest rate, inflation, currency devaluation adjusted to GDP growth, as the dependent variable. The findings indicate that, collectively, these predictors significantly contribute to explaining the variance in the financial crisis, although their impact is relatively minor. While the direct influence of PS&AVT on the financial crisis is not statistically significant, it indirectly affects the crisis through its considerable impact on debt and reserves. Granger causality tests showed predictive value for $Debt and $Reserve in relation to CRISIS, but the reverse relationship was not significant. Regression analysis using the error term and scatter plots supports the absence of endogeneity issues in the model. These findings suggest that while external debt and foreign reserves are more directly related to financial crises, political stability and the absence of violence/terrorism can influence the crisis indirectly through their effects on debt accumulation and reserve levels. This study represents a pioneering effort in investigating the impact of external debt, foreign reserves, and political stability on the financial crises in Sri Lanka. By utilizing a comprehensive dataset and applying a regression analysis, it sheds light on the complex interactions between these variables and their influence on the country’s financial stability.

This study attempts to explore the impact of external debt ($Debt), foreign reserves ($Reserves), and political stability & absence of violence/terrorism (PS&AVT) on the current financial crisis in Sri Lanka. Using data from 1996 to 2022 obtained from the World Bank (WB) and the Central Bank of Sri Lanka (CBSL), a regression analysis is conducted, with a composite variable named "CRISIS," which accounts for interest rate, inflation, currency devaluation adjusted to GDP growth, as the dependent variable. The findings indicate that, collectively, these predictors significantly contribute to explaining the variance in the financial crisis, although their impact is relatively minor. While the direct influence of PS&AVT on the financial crisis is not statistically significant, it indirectly affects the crisis through its considerable impact on debt and reserves. Granger causality tests showed predictive value for $Debt and $Reserve in relation to CRISIS, but the reverse relationship was not significant. Regression analysis using the error term and scatter plots supports the absence of endogeneity issues in the model. These findings suggest that while external debt and foreign reserves are more directly related to financial crises, political stability and the absence of violence/terrorism can influence the crisis indirectly through their effects on debt accumulation and reserve levels. This study represents a pioneering effort in investigating the impact of external debt, foreign reserves, and political stability on the financial crises in Sri Lanka. By utilizing a comprehensive dataset and applying a regression analysis, it sheds light on the complex interactions between these variables and their influence on the country’s financial stability.