The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

Scenario Analysis of Fertility in Korea using the Fertility Rate Prediction Model

출산율 예측모형을 이용한 한국의 출산력 시나리오 분석

  • Kim, Keewhan (Department of Applied Statistics, Korea University) ;
  • Jeon, Saebom (Department of Applied Statistics, Korea University)
  • 김기환 (고려대학교 응용통계학과) ;
  • 전새봄 (고려대학교 응용통계학과)
  • Received : 2015.06.29
  • Accepted : 2015.07.06
  • Published : 2015.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

The low fertility rate and the unprecedented rapid pace of population aging is a significant factor degrading the national competitiveness and the social security system of Korea. The government has implemented various maternity incentives to alleviate the low birth problem; however, the policy seems in effective to solve the problem of low fertility. This study proposes a conditional birth-order specific fertility rate and investigates the policy effects of fertility transition in Korea to provide a basis for more effective policy development. The use of a conditional birth-order specific fertility rate allows for an effective calculation of the change and the effect in total fertility rate than a birth-order specific fertility rate. We compare the effects of the total fertility rate according to various scenarios that enables us to calculate how the total fertility rate can achieve the current multi-child childbirth support policy of the government and estimate how the total fertility rate can be achieved when focusing on the first or second childbirth support policy. We also summarize the research results on policy development for a practical increase in the childbirth that considers the rapid decrease in women of childbearing age (15-49 years) due to continued low fertility and present the number of childbirths in accordance with the total fertility rate.

한국의 지속적인 저출산은 세계적으로 유례가 없을 정도의 급격한 고령화 속도와 맞물려 국가경쟁력 및 사회보장 시스템을 약화시키는 요인이 되었다. 저출산 문제를 해결하기 위하여 정부에서는 각종 출산장려정책을 실시하고 있으나, 현재까지 저출산에서 벗어나지 못하고 있어 정책이 효과적이지 못하였던 것으로 평가된다. 그러므로 본 연구에서는 보다 효과적인 정책개발의 근간을 마련하기 위하여 조건부 순위별 출산율을 제안하고, 이를 이용하여 한국의 출산정책 효과를 파악하였다. 조건부 순위별 출산율을 사용하면 순위별 출산율을 사용하는 것보다 합계출산율의 변화와 효과를 명확히 산출할 수 있으므로, 다양한 순위별 출산율의 시나리오에 따라 합계출산율의 변화를 비교하였다. 이를 통하여 현재 정부의 셋째 아 출산지원 정책으로 도달할 수 있는 합계출산율 및 둘째 아 또는 첫째 아의 출산지원을 하였을 때 도달할 수 있는 합계출산율을 산출할 수 있었다. 또한 지속적인 저출산으로 빠르게 감소하고 있는 가임여성(15-49세)을 고려하여 합계출산율에 따른 출생아수를 함께 제시하여 실질적인 출생아수의 증가를 유도하는 정책개발에 도움이 될 수 있도록 연구결과를 정리하였다.

Keywords

References

  1. 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. https://doi.org/10.1007/s13524-011-0040-5
  2. Brass W. (1964). Uses of census or survey data for the estimation of vital rates. Paper prepared for the African Seminar on Vital Statistics, Addis Ababa 14-19 December 1964. Document No. E/CN.14/CAS.4/V57. United Nations, New York.
  3. Brass, W. (1985). Advances in methods for estimating fertility and mortality from limited and defective data.
  4. Choi, K. (2004). Causes of the recent fertility drop in Korea, Korean Journal of Population Studies, 27, 35-59.
  5. Coale, A. J. and McNeil, D. R. (1972). The distribution by age of the frequency of first marriage in a female cohort, Journal of the American Statistical Association, 67, 743-749. https://doi.org/10.1080/01621459.1972.10481287
  6. Eom, J. M. and Kim, K.-W. (2013). A study on forecasting total fertility rate using female first marriage rate, Journal of the Korean Data Analysis Society, 15, 1261-1272.
  7. Eun, K.-S. (2001). Age at marriage, marriage cohort and first birth interval, Korean Journal of Sociology, 35, 105-113.
  8. Goldstein, J. R., Sobotka, T. and Jasilioniene, A. (2009). The end of "Lowest-Low" fertility?, Population and Development Review, 35, 663-699. https://doi.org/10.1111/j.1728-4457.2009.00304.x
  9. Greville, T. N. E. (1981). Moving-weighted-average smoothing extended to the extremities of the data. I. Theory, Theory, Scandinavian Actuarial Journal, 1981, 39-55. https://doi.org/10.1080/03461238.1981.10413730
  10. Jun, K.-H. (2006). Development of fertility assumptions for the future population projection, Korean Journal of Population Studies, 29, 53-88.
  11. Kaneko, R. (2003). Elaboration of the Coale-McNeil nuptiality model as the generalized log gamma distribution: A new identity and empirical enhancements, Demographic Research, 9, 223-262. https://doi.org/10.4054/DemRes.2003.9.10
  12. Kim, C.-S. (2007). Parity specific approach to the plan of having an additional child, Korean Journal of Population Studies, 30, 97-116.
  13. Kim, T.-H., Lee, S.-S. and Kim, D.-H. (2006). Fertility differentials by demographic and socioeconomic characteristics: Analysis of Korean population census data, Korean Journal of Population Studies, 29, 1-23.
  14. Kohler, H. P., Billari, F. C. and Ortega, J. A. (2002). The emergence of lowest-low fertility in Europe during the 1990s, Population and Development Review, 28, 641-680. https://doi.org/10.1111/j.1728-4457.2002.00641.x
  15. Kohler, H. P., Billari, F. C. and Ortega, J. A. (2006). Low fertility in Europe: Causes, implications and policy options. The baby bust: Who will do the work, 48-109.
  16. Lutz, W., Scherbov, S., Cao, G. Y., Ren, Q. and Zheng, X. (2007). China's uncertain demographic present and future, Vienna Yearbook of Population Research, 37-59.
  17. 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, 167-192.
  18. Ministry of Health and Welfare (2010). Critical research projects over the next five years in low fertility aging society.
  19. Park, S. H. and Chun, D. W. (2004). Trends of child birth in older maternal age(1985-2003), Journal of Reproductive Medicine and Population, 17, 101-105.
  20. Park, Y.-S., Park, S.-K., Choi, B.-S. and Kim, K.-W. (2009). A comparison of probability of death result using Beers's interpolation coefficient and the Greville's formula, Journal of the Korean Data Analysis Society, 11, 97-110.
  21. Preston, S. H., Heuveline, P. and Guillot, M. (2001). Demography: measuring and modeling population processes, Population and Development Review, 27, 365. https://doi.org/10.1111/j.1728-4457.2001.00365.x
  22. Shiu, E. S. W. (1984). Minimum-Rz Moving-Weighted-Average Formulas, ARCH, Society of Actuaries, Chicago, 36, 489-500.
  23. Sloggett, A, Brass, W., Eldridge, S. M., Timæus, I. M., Ward, P. and Zaba, B. (1994). Estimation of Demo- graphic Parameters from Census Data, Statistical Institute for Asia and the Pacific, Tokyo.
  24. UN Population Division (1983). Manual X: Indirect Techniques for Demographic Estimation, United Nations, Department of Economic and Social Affairs, ST/ESA/SER.A/81, New York.
  25. Yoon, J. Y. and Kim, K.-W. (2011). A comparison study for forecasting fertility rate by models and data type, Journal of the Korean Data Analysis Society, 13, 2901-2914.