Data Mining Approach to Grouping Populations and the Analysis of Economic Inequalities
Authors:   Asia-Europe Perspective Association, Min Daiki 
Published: June 2018

The importance of public pension system recently has been growing because of an ageing population in Korea. One of the major purposes of public pension system is to reduce economic inequalities across population groups that have been mainly defined by a single variable such as age, income, sex, etc in the literature. This paper aims to evaluate how well the population groups are practically and clearly designed in the literature so as to specify economic inequalities across different groups.
For this purpose, we investigate the retired household sample obtained from KLIPS (Korean Labor and Income Panel Study) and conduct the clustering analysis to organize population groups. The clustering analysis consists of four steps: variable selection, hierarchical clustering analysis to determine the number of clusters and centroid, non-hierarchical clustering analysis (we use K-means clustering analysis) and define population groups, and describing the characteristics of population groups. The clustering analysis divides the population into six subgroups that have similar characteristics.
We compare the characteristics of the population groups obtained from the clustering analysis with those designed in the literature with respect to household, income, consumption and asset. From the comparison, we observe that the assumptions on population groups in the literature are somewhat different from the findings from the clustering analysis. In particular, the period of employment and the full-time period are significantly different from the literature. The clustering analysis reveals that the period of employment is approximately 31-45 years, which is shorter than the period of employment assumed in the literature. Furthermore, few research has clearly considered the full-time period, which accounts for 14%-45% of the total period of employment. We believe that the findings in this study provides meaningful information on how to practically organize population groups as a part of designing public pension system. For example, researchers are encouraged to consider the employment periods and full-time periods obtained from the data analysis when considering the significant impact of working periods on pension payment and benefits.
Population should be well grouped so as to specify the difference in economic inequalities across groups. In general, various variables are jointly involved in determining the economic inequalities, but populations have been simply grouped by a single variable such as age, gender, education level, household size, etc. in the literature. This paper employs the Generalized Entropy (GE) measure of economic inequality and its decomposition to evaluate the contributions of several variables on the overall economic inequality. For this analysis, populations are first grouped by several variables such as clusters obtained from the analysis, sex, education level, period of employment and household size. Then, we measure the inequaltiy index and its decomposition for monthly income, monthly consumption and asset.
Grouping populations using the clustering analysis specifies the economic inequality more precisely provides better measure of similarity within a group and difference between groups. This result supports the effectiveness of the clustering analysis in grouping populations.