응용통계연구 (The Korean Journal of Applied Statistics)

  • 제32권4호
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  • Pages.503-515
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  • 2019
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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국회 법안 검토 기간의 생존함수 추정: 제 17, 18, 19대 국회의 사례를 바탕으로

Estimation of the survival function of the legislative process in Korea: based on the experiences of the 17th, 18th, and 19th National Assembly of Korea

  • 윤영규 (서울대학교 경제학부) ;
  • 조윤수 (인사혁신처 국가공무원인재개발원) ;
  • 정혜영 (서울대학교 기초교육원)
  • Yun, Yeonggyu (Department of Economics, Seoul National University) ;
  • Cho, Yunsoo (National Human Resources Development Institute, Ministry of Personnel Management) ;
  • Jung, Hye-Young (Faculty of Liberal Education, Seoul National University)
  • 투고 : 2019.02.11
  • 심사 : 2019.07.26
  • 발행 : 2019.08.31
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초록

본 연구는 제 17, 18, 19대 국회에 제출된 법안의 검토 기간의 생존함수를 추정하고, 정치상황적 요인들이 법안 검토 기간에 미치는 영향을 분석했다. 본 연구는 입법 데이터에 존재하는 절단과 사건 종료의 종속성 문제를 완화하고자 새로운 관점에서 입법 과정 종료를 정의했다. 또한 비례위험 가정이 분석 대상 데이터에 대해 성립하지 않는다는 것을 보이고, 이에 따라 로그정규분포 가정 하의 가속종료시간모형을 통해 정치상황 상의 요인들이 법안 검토 기간에 미치는 영향을 분석했다. 분석 결과 정책 분야별로 법안 검토 기간이 상이하게 나타났고, 여소야대 시기에 발의된 법안이 그렇지 않은 시기에 발의된 법안보다 신속하게 검토된 것으로 나타났다.

In this study we estimate the survival function of duration of the legislative processes in the 17th, 18th, and 19th National Assembly of Korea, and further analyze effects of the political situation variables on the legislative process. We define the termination of legislative process from a novel perspective to alleviate issues of dependency between censoring and failure in the data. We also show that the proportional hazards assumption does not hold for the data, and analyze data employing a log-normal accelerated failure time model. The policy areas of law agendas are shown to affect the speed of legislative process in different ways and legislative process tends to be prompt in times of divided governments.

키워드

참고문헌

  1. Anderson-Bergman, C. (2017). An efficient implementation of the EMICM algorithm for the interval censored NPMLE, Journal of Computational and Graphical Statistics, 26, 463-467. https://doi.org/10.1080/10618600.2016.1208616
  2. Hanson, T. and Johnson, W. O. (2004). A Bayesian semiparametric AFT model for interval-censored data, Journal of Computational and Graphical Statistics, 13, 341-361. https://doi.org/10.1198/1061860043489
  3. Hess, K. R. (1995). Graphical methods for assessing violations of the proportional hazards assumption in Cox regression, Statistics in Medicine, 14, 1707-1723. https://doi.org/10.1002/sim.4780141510
  4. Hughes, T. and Carlson, D. (2015). Divided government and delay in the legislative process: evidence from important bills, 1949-2010, American Politics Research, 43, 771-792. https://doi.org/10.1177/1532673X15574594
  5. Kim, J. (2012). Passing through the chamber: analyzing legislative time and outcomes in the 18th Korean National Assembly, Journal of Korean Politics, 21, 71-98.
  6. Klein, J. P. and Moeschberger, M. L. (2003). Survival Analysis: Techniques for Censored and Truncated Data (2nd ed), Springer, New York.
  7. Konig, T. (2008). Analysing the process of EU legislative decision-making: To make a long story short..., European Union Politics, 9, 145-165. https://doi.org/10.1177/1465116507085960
  8. Lee, H. and Kim, J. (2012). Taking legislative time and legislative outcomes seriously: A competing risk analysis of legislative process in the 17th Korean National Assembly, Korean Political Science Review, 46, 121-144. https://doi.org/10.18854/kpsr.2012.46.5.006
  9. Mok, J. (2009). A study on decision factors for legislative bill passage time, Korean Public Administration Quarterly, 21, 821-839.
  10. Park, Y. and Jang, S. (2016). An analysis on the legislative activity's effect by the revision of National Assembly Reform Act: Focusing on the pass time and approval of bill during 18th and 19th Assembly, Korean Party Studies Review, 15, 5-37.
  11. Royston, P. and Parmar, M. K. B. (2002). Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects, Statistics in Medicine, 21, 2175-2197. https://doi.org/10.1002/sim.1203
  12. Seo, H. and Park, K. (2009). An analysis on passage of the legislators' bills, Korean Political Science Review, 43, 89-111. https://doi.org/10.18854/kpsr.2009.43.2.004
  13. Turnbull, B. W. (1976). The empirical distribution function with arbitrarily grouped, censored and truncated data, Journal of the Royal Statistical Society: Series B (Methodological), 38, 290-295. https://doi.org/10.1111/j.2517-6161.1976.tb01597.x
  14. Xue, Y. and Schifano, E. D. (2017). Diagnostics for the Cox model, Communications for Statistical Applications and Methods, 24, 583-604. https://doi.org/10.29220/CSAM.2017.24.6.583
  15. Yano, T., Smith, N. A., and Wilkerson, J. D. (2012). Textual predictors of bill survival in congressional committees, Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 793-802.
  16. Zhang, J. and Peng, Y. (2007). An alternative estimation method for the accelerated failure time frailty model, Computational Statistics & Data Analysis, 51, 4413-4423. https://doi.org/10.1016/j.csda.2006.06.017