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      데이터마이닝을 활용한 인구집단 구성과 경제적 불평등 평가 = Data Mining Approach to Grouping Populations and the Analysis of Economic Inequalities

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      https://www.riss.kr/link?id=A103662727

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      다국어 초록 (Multilingual Abstract)

      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.
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      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...

      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.

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      참고문헌 (Reference)

      1 정창률, "한국 노후소득보장수준의 국제비교: 가설적 위험 인구 집단 추정 방식을 중심으로" 한국보건사회연구원 32 (32): 428-459, 2012

      2 여윤경, "최적 종신연금 계획의 수익률에 관한 실증분석 - IRR과 연금환급률을 중심으로 -" 한국응용경제학회 12 (12): 123-151, 2010

      3 송영남, "지역간 빈곤격차의 변화에 관한 연구" 한국산업경제학회 20 (20): 17-38, 2007

      4 전승훈, "은퇴 후 필요소득수준과 국민연금 및 퇴직연금의 자산충분성" 한국경제학회 57 (57): 67-100, 2009

      5 위경우, "우리나라 퇴직연금의 효율적 지급방안에 관한 연구" 대한경영학회 25 (25): 995-1018, 2012

      6 석상훈, "우리나라 중고령자의 은퇴경로 유형과 은퇴 후 소득 비교" 한국경제발전학회 16 (16): 59-82, 2010

      7 강성호, "생애기간을 고려한 공ㆍ사적 연금소득 추정" 한국보험학회 (88) : 51-87, 2011

      8 변루나, "데이터마이닝 기법을 이용한 도시가계소비성향 분석" 통계청 6 (6): 85-111, 2001

      9 전해정, "데이터 마이닝을 이용한 한국 가구 근로소득 분석에 관한 연구" 대한국토·도시계획학회 50 (50): 227-241, 2015

      10 문숙재, "노인가계와 비노인가계의 재정상태 비교분석" 한국가정관리학회 14 (14): 223-235, 1996

      1 정창률, "한국 노후소득보장수준의 국제비교: 가설적 위험 인구 집단 추정 방식을 중심으로" 한국보건사회연구원 32 (32): 428-459, 2012

      2 여윤경, "최적 종신연금 계획의 수익률에 관한 실증분석 - IRR과 연금환급률을 중심으로 -" 한국응용경제학회 12 (12): 123-151, 2010

      3 송영남, "지역간 빈곤격차의 변화에 관한 연구" 한국산업경제학회 20 (20): 17-38, 2007

      4 전승훈, "은퇴 후 필요소득수준과 국민연금 및 퇴직연금의 자산충분성" 한국경제학회 57 (57): 67-100, 2009

      5 위경우, "우리나라 퇴직연금의 효율적 지급방안에 관한 연구" 대한경영학회 25 (25): 995-1018, 2012

      6 석상훈, "우리나라 중고령자의 은퇴경로 유형과 은퇴 후 소득 비교" 한국경제발전학회 16 (16): 59-82, 2010

      7 강성호, "생애기간을 고려한 공ㆍ사적 연금소득 추정" 한국보험학회 (88) : 51-87, 2011

      8 변루나, "데이터마이닝 기법을 이용한 도시가계소비성향 분석" 통계청 6 (6): 85-111, 2001

      9 전해정, "데이터 마이닝을 이용한 한국 가구 근로소득 분석에 관한 연구" 대한국토·도시계획학회 50 (50): 227-241, 2015

      10 문숙재, "노인가계와 비노인가계의 재정상태 비교분석" 한국가정관리학회 14 (14): 223-235, 1996

      11 주소현, "기대수명 증가와 종신연금" 한국소비자학회 23 (23): 1-24, 2012

      12 권순원, "근로자 속성에 따른 퇴직연금 최적유형 선택에 관한 연구" 대한경영학회 24 (24): 549-568, 2011

      13 김경아, "국내 노년층의 빈곤실태와 공적연금의 빈곤완화 효과에 관한 실증연구" 한국산업경제학회 21 (21): 1503-1523, 2008

      14 Queisser, M., "The public-private pension mix in OECD countries" 38 (38): 542-568, 2007

      15 Ebbinghaus, B., "The Varieties of Pension Governance - Pension Privatization in Europe" Oxford University Press 2011

      16 Turner, J., "Social Security Development and Reform Around the World" AARP Public Policy Institute 2001

      17 Bridgen, P., "Social Right, Social Justice and Pension Outcomes in Four Multi-Pillar Systems" 25 (25): 129-137, 2009

      18 Palier, B., "Reforming The Bismarckian Welfare Systems" Blackwell Publishing 2008

      19 Bridgen, P., "Private Pensions Versus Social Inclusion?" Edward Elgar 3-45, 2007

      20 Price, M., "Pension Policy in China, Singapore, and South Korea : An Assessment of the Potential Value of the Notional Defined Contribution Model" 26 (26): 79-89, 2012

      21 Hair, J. F., "Multivariate Date Analysis" Prentice Hall 2005

      22 Ward, J., "Hierarchical Grouping to Optimize an Objective Function" 58 : 236-244, 1963

      23 Gordon C. L., "European Pensions and Global Finance: Continuity or Convergence?" 7 (7): 67-91, 2002

      24 Theil, H., "Economics and Information Theory"

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      연월일 이력구분 이력상세 등재구분
      2022 평가예정 재인증평가 신청대상 (재인증)
      2019-01-01 평가 등재학술지 유지 (계속평가) KCI등재
      2016-01-01 평가 등재학술지 유지 (계속평가) KCI등재
      2012-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      2011-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2009-01-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.55 0.55 0.47
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.47 0.46 0.727 0.13
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