[논문]초저출산율에 따른 시도별 출산율 변동을 반영한 예측 연구

오진호

doi:10.5351/kjas.2021.34.1.075

초저출산율에 따른 시도별 출산율 변동을 반영한 예측 연구
A study on prediction for reflecting variation of fertility rate by province under ultra-low fertility in Korea 원문보기

응용통계연구 = The Korean journal of applied statistics, v.34 no.1, 2021년, pp.75 - 98

오진호 (한밭대학교 공과대학 수리과학과)

초록
AI-Helper

본 논문은 전국과 시도별 출산율의 관계를 규명하는 세 가지 통계적 모형을 비교한다. 세 모형은 10년간 평균 연령별 누적출산율의 Gompit변환 자료를 대입한 회귀모형, 연령별 출산율 자료 변환 없이 원자료를 적용한 회귀모형, 그리고 확률과정 관점에서 불안정한 연령별 출산율 시계열을 적합할 경우 고려할 수 있는 공적분 모형이다. 본 논문은 전국과 지역간 비정상성 출산율의 관계를 도출하고자 할 때 다음을 제안한다. 전국과 지역 출산율의 공적분 관계식를 선행적으로 도출한다. 더 나아가 이 관계가 유의하지 않으면 변환 없는 원자료를 활용한 회귀모형 접근으로 전국과 시도별 출산율 관계를 살펴보는 것을 제안한다. 또한 Gompit 변환 자료를 대입한 회귀모형 방법은 출산율이 다른 방식과 비교해 과대추정되는 결과가 도출되었다. 끝으로 서울, 부산, 대구, 인천, 광주, 대전, 경기는 2025-2030년까지 타 지역과 다르게 합계출산율이 1.0명 이하로 예측되므로 시급하고 효율성 있는 출산율 제고정책이 필요하다고 판단된다.

Abstract ▼ AI-Helper

This paper compares three statistical models that examine the relationship between national and provincespecific fertility rates. The three models are two of the regression models and a cointegration model. The regression model is by substituting Gompit transformation for the cumulative fertility rate by the average for ten years, and this model applies the raw data without transformation of the fertility data. A cointegration model can be considered when fitting the unstable time series of fertility rate in probability process. This paper proposes the following when it is intended to derive the relation of non-stationary fertility rate between the national and provinces. The cointegrated relationship between national and regional fertility rates is first derived. Furthermore, if this relationship is not significant, it is proposed to look at the national and regional fertility rate relationships with a regression model approach using raw data without transformation. Also, the regression model method of substituting Gompit transformation data resulted in an overestimation of fertility rates compared to other methods. Finally, Seoul, Busan, Daegu, Incheon, Gwangju, Daejeon and Gyeonggi province are expected to show a total fertility rate of 1.0 or less from 2025 to 2030, so an urgent and efficient policy to raise this level is needed.

주제어

표/그림 (18)

그림 Figure 1: Trend of TFR of provinces and whole country (Source: Kosis.kr).
그림 Figure 2: Log-Log and Gompit transformation on ASCFR of provinces and whole country (Source: Kosis.kr).
그림 Figure 3: Gompit, probit, logit, and log transformation for ASCFR by provinces and whole country (Source: Kosis.kr)
그림 Figure 4: Trend of TFR for 2016~2047 on province by Gompit, probit, logit transformation.
표 Table 1: TFR by province on three types transformations from Seoul to Sejong
표 Table 2: TFR by province on three types transformations from Gyeonggi to Jeju
표 Table 3: Trend of Group-ASFR for province-specific by Gompit transformation 1
표 Table 4: Trend of Group-ASFR for province-specific by Gompit transformation 2
표 Table 5: Trend of Group-ASFR for province-specific by Gompit transformation 3
표 Table 6: Trend of Group-ASFR for province-specific by raw fertility data (1)
표 Table 7: Trend of Group-ASFR for province-specific by raw fertility data (2)
표 Table 8: Trend of Group-ASFR for province-specific by raw fertility data (3)
그림 Figure 5: 95% confidence interval of TFR by regression for raw data.
표 Table 9: Cointegration test and model by province and TFR and ASFR from 2020 to 2047
표 Table 10: Cointegration test and model by province and TFR and ASFR from 2020 to 2047
표 Table 11: Cointegration test and model by province and TFR and ASFR from 2020 to 2047
표 Table 12: Cointegration test and model by province and TFR and ASFR from 2020 to 2047
그림 Figure 6: Trend of TFR and comparison for three methods.

참고문헌 (35)

Alkema, L., Raftery, A. E., Gerland, P., Clark, S. J., Pelletier, F., Buettner, T., and Heilig, G. K. (2011). Probabilistic projections of the total fertility rate for all countries, Demography, 48, 815-839.

상세보기
Banerjee, A., Dolado, J., Galbraith, J. W., and Hendry, D. F. (1993). Co-Integration, Error-Correction, and the Econometric Analysis of Non-Stationary Data. Advanced Texts in Econometrics. Oxford University Press, Oxford.
BBC (2019). South Korea's population paradox, https://www.bbc.com/worklife/article/20191010-south-koreaspopulation-paradox.
Bernhard Pfaff (2016). urcs R package: Unit Root and Cointegration Tests for Time Series Data, http://https://cran.rproject.org/web/packages/urca/urca.pdf.
Bongaarts, J. (1978). A framework for analyzing the proximate determinants of fertility, Population and Development Review, 4, 105-132.

상세보기
GEFIRA (2018). The collapse of the South Korean population: the countdown has begun, https://gefira.org/en/2018/01/11/childless-south-korea.
Goldstein, J. R. (2010).A Behavioral Gompertz Model for Cohort Fertility Schedules in Low and Moderate Fertility Populations, MPIDR Working Paper WP 2010-021 February 2010.
Hyndman, R. J. and Booth H. (2008). Stochastic population forecasts using functional data models for mortality, fertility and migration, International Journal of Forecasting, 24, 323-342.

상세보기
Hyndman, R. J., Booth, H., and Yasmeen, F. (2013). Coherent mortality forecasting: The product-ration method with functional time series models, Demography, 50, 261-283.
Hyndman, R. J. and Ullah, S. (2007). Robust forecasting of mortality and fertility rates: a functional data approach, Computational Statistics and Data Analysis, 51, 4942-4956.

상세보기
Jeon, K. H. (1997). A mechanism of fertility transition, Understanding of Korea fertility Phase, Ilshin Press.
Jeon, K. H., Kim, D. S., Park, S. T., and Eun, K. S. (2002). Fertility, Population for Korea, Statistics Korea.
Jo, S. S., Son, Y. S., and Seong, B. C. (2019). Time Series Analysis, Yul Gok Press, Seoul.
Johansen, S. (1988). Statistical analysis of cointegration vectors, Journal of Economic Dynamics and Control, 12, 231-254

상세보기
Kim, K. W. and Jeon, S. B. (2015). Scenario analysis of fertility in Korea using the fertility rate prediction model, The Korean Journal of Applied Statistics, 28, 685-701.
Kim, H. S., Gye, B. O., and Kim, H. T. (2016). A study on policy and improvement of projection method on fertility in 2016, Statistics Korea.
Kim, S. Y. and Oh, J. H. (2019). A study on forecasting provinces-specific fertility for Korea, The Korean Journal of Applied Statistics, 32, 229-263.
KOSIS (2017). province population projection 2015-2045.
KOSIS (2019). population projection 2017-2067.
KOSIS (2019). province population projection 2017-2047.
Lee, C. H. (2012). A Decomposition of Decline in total fertility rate in Korea: Effects of changes in marriage and marital fertility, The Korean Journal of Population Studies, 35, 117-144.
Lee, S. S. and Choi, H. J. (2012), Development of model estimating fertility rate for Korea, The Korean Journal of Population Studies, 35, 109-134.
Lutz, W., Skirbekk, V., and Testa, M. R. (2006). The low-fertility trap hypothesis: forces that may lead to further postponement and fewer births in Europe, Vienna Yearbook of Population Research, 4, 167-192.

상세보기
McDonald, P. (2007). Low fertility and policy, Ageing Horizons, 7, 22-27.
Na, J. H. (2017). R Applied Regression Analysis, Freeaca Press, Seoul.
Oh, J. H. (2014). Review of the latest trends in Fertility model for the population projections, Statistical Research Institute.
Oh, J. H. (2018). A study on forecasting cohort incomplete fertility for Korea, The Korean Journal of Population Studies, 41, 109-134.
Oh, J. H. (2019). Forecast and identifying factors on a double dip fertility rate for Korea, The Korean Journal of Applied Statistics, 32, 463-483.
Park, Y. S., Kim, M. R., and Kim, S. Y. (2013). Probabilistic fertility models and the future population structure of Korea, The Korean Association for Survey Research, 14, 49-78.
Ramsay, J. O. and Silverman, B. W. (2005). Functional Data Analysis (2nd ed), Springer, New York.
Raftery, A. E., Li, N., Chunn, J. L., Sevcikova, H., Gerland, P., and Heilig, G. K. (2012). Bayesian probabilistic population projection for all countries. In Proceedings of the National Academy of Sciences of the United States of America, 109, 13915-13921.
Raftery, A. E., Alkema, L., and Gerland, P. (2014). Bayesian population projections for the United Nations, Statistical Science, 29, 58-68.
Sevcikova, H., Alkema, L., and Raftery, A. E. (2011). bayesTFR: an R Package for Probabilistic Projections of the Total Fertility Rate, Journal of Statistical Software, 43, 1-29.
Sobotka, T. and Lutz, W. (2011). Misleading policy messages derived from the period TFR: Should we stop using it?, Comparative Population Studies, 35, 637-664.
Yoo, S. H. and Sobotka, T. (2018). Ultra-low fertility in south Korea: The role of the tempo effect, Demographic Research, 38, 549-576.

상세보기

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