The Social Security Information System for Alleviation of Welfare Blind Spots was established to preemptively discover the vulnerable by using ICT and big data analysis. System operation proceeds in the stages of collecting data, analyzing big data, deriving high-risk group, providing a list of risk group to local governments, and consulting and supporting the vulnerable. In this process, issues such as the data accuracy, the development of various models, privacy and personal information problems, and the non- profit resources gap may occur. Therefore, there is a need to address these issue.
A social security administration data system is under development based on the Framework Act on Social Security revised in 2021. An integrated source of data, the social security administration data system is expected to help better understand, and improve the effectiveness of, the growing social security system. There are many challenges to address for effective and efficient use of social security administration data. First, it is necessary to make reasonable decisions on the scope of information collected. Second, governance for data cooperation should be strengthened. Third, there is a need to improve the accuracy of data-linkage keys. Fourth, new research topics and methods such as changes in the analysis unit should be developed. Finally, policy improvement methods should be developed that reflect the results of data analysis.
The Social Security Information System for Alleviation of Welfare Blind Spots was established to preemptively discover the vulnerable by using ICT and big data analysis. System operation proceeds in the stages of collecting data, analyzing big data, deriving high-risk group, providing a list of risk group to local governments, and consulting and supporting the vulnerable. In this process, issues such as the data accuracy, the development of various models, privacy and personal information problems, and the non- profit resources gap may occur. Therefore, there is a need to address these issue.
The Ministry of Health and Welfare (MOHW) establishes a national statistical development plan every five years. The new 5-year plan is to be implemented for the years 2023~2027. In order for the new plan to present the right direction, considerable analysis must be preceded. According to the analysis of national approval statistics, the MOHW lacks the designated statistics that are the basis of national policy design. And the statistical verification of the national indicators that monitor the current state of national policy is partially insufficient, so this should be improved. In addition, efforts should be made to strengthen the work network between related agencies and to produce and provide accurate and reliable statistical information in a position to integrate and coordinate statistics in the health and welfare sector.
The demographic transition in Korea, characterized by the decrease in births and population aging, has been extremely rapid. Reliable demographic data are needed to analyze demographic changes in Korea. Furthermore, to understand the values, attitudes, and behaviors related to fertility, it is beneficial to explore low-fertility situations in other countries. This study examines the Generations & Gender Survey (GGS), a demographic survey that enables international comparisons. It should be considered adding longitudinal elements in ‘The National Survey on Fertility and Family Health and Welfare’ or taking part in GGS, to implement and evaluate policies to respond to population change.
Survey data are usually constructed through sampling by extracting some subjects from the population of interest. Since we make inferences about the entire population with the survey data, there will be a difference between the sample estimate and the true population value. The difference between the sample estimate and the true population value is defined as an error, which can occur from various causes and situations.
This study analyzes the measurement errors that occurr in sample survey data, and proposes two methods(measurement error correction in the linear regression models with continuous outcomes and kernel density estimation for heaped data) for correcting them. In addition, this study proposes a survey data management method that allows production of high-quality data.
The 「Children with Disabilities Welfare Support Act」 was enacted in 2011 to help children with disabilities grow up healthy and actively participate in society in stable family life and to alleviate the burden on families of children with disabilities. However, there had been lack of regional-level service delivery to realize the goals of this Act. Accordingly, in December 2020, the 「Welfare Support Act for Disabled Children」 and its enforcement ordinance were amended, laying the groundwork for establishing local level child support centers for children with disabilities. This is a situation that requires the realization of national policies for children with disabilities and their families. This paper intends to discuss the considerations of establishing a local support center for children with disabilities and their families, and the core functions that the local support centers should be created in the future.
