The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

Stochastic population projections on an uncertainty for the future Korea

미래의 불확실성에 대한 확률론적 인구추계

  • Oh, Jinho (Department Mathematical Sciences, HanBat National University)
  • 오진호 (한밭대학교 공과대학 기초과학부)
  • Received : 2019.11.21
  • Accepted : 2020.02.12
  • Published : 2020.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

Scenario population projection reflects the high probability of future realization and ease of statistical interpretation. Statistics Korea (2019) also presents the results of 30 combinations, including special scenarios, as official statistics. However, deterministic population projections provide limited information about future uncertainties with several limitations that are not probabilistic. The deterministic population projections are scenario-based estimates and show a perfect autocorrelation of three factors (birth, death, movement) of population variation over time. Therefore, international organizations UN, the Max Planck Population Research Institute (MPIDR) of Germany and the Vienna Population Research Institute (VID) of Austria have suggested stochastic based population estimates. In addition, some National Statistics Offices have also adopted this method to provide information along with the scenario results. This paper calculates the demographics of Korea based on a probabilistic or stochastic basis and then draws the pros and cons and show implications of the scenario (deterministic) population projections.

예전부터 시나리오 인구추계(scenario population projection)는 미래 실현개연성이 높은 상황 반영과 통계적 음해석 용이성으로 각광을 받아왔다. 통계청 (2019)도 특별 시나리오를 포함한 30가지 조합 결과를 공식통계로 제시하고 있다. 하지만, 이런 결정론적(determinant) 인구추계는 미래의 불확실성(uncertainty)에 대해 제한적으로 정보를 제공하고, 시나리오 기반 예측치이므로 확률적이지 않으며, 시간에 따라 인구변동 3요소(출산, 사망, 이동)들의 완벽한 자기상관을 보이는 등 여러 한계점이 있다. 따라서 국제기구 UN, 독일 막스플랑크 인구연구소(MPIDR), 오스트리아 비엔나인구연구소(VID) 등은 확률론적(stochastic) 기반 인구추계를 제시하고 있다. 더불어 해외 일부 국가 통계청에서도 이 방식을 도입해 시나리오 결과와 함께 정보를 제공하고 있다. 본 논문은 우리나라의 인구추계를 확률론적 기반으로 산출한 후, 시나리오(결정론적) 인구추계 결과와 비교해 장·단점과 시사점을 도출해본다.

Keywords

References

  1. Alho, J. M. (1997). Scenario, uncertainty and conditional forecasts of the world population, Journal of Royal Statistical Society, 160, 71-85. https://doi.org/10.1111/1467-985X.00046
  2. Alho, J. M. (2005). Remarks on the use of probabilities in demography and forecasting. In N. Keilman (Ed), Perspectives on Mortality Forecasting (Vol 2. Probabilistic Models, 27-38), Swedish Social Insurance Agency, Stockholm.
  3. Alho, J. M. and Spencer, B. D. (1985). Uncertain population forecasting, Journal of the American Statistical Association, 80, 306-314. https://doi.org/10.1080/01621459.1985.10478113
  4. Alders, M. and De Beer, J. (2004). Assumptions on fertility in stochastic population forecasts, International Statistical Review, 72, 65-79. https://doi.org/10.1111/j.1751-5823.2004.tb00224.x
  5. Alkema, L., Gerpand, P., Raftery, A., and Wilmoth, J. (2015). The United Nations Probabilistic Population Projections: an introduction to demographic forecasting with uncertainty, Foresight (Colch), 37, 19-24.
  6. Billari, F. C., Graziani, R., and Melilli, E. (2012). Stochastic population forecasts based on conditional expert opinions, Journal of the Royal Statistical Society, 175, 491-511. https://doi.org/10.1111/j.1467-985X.2011.01015.x
  7. Booth, H. (2006). Demographic forecasting: 1980 to 2005 in review, International Journal of Forecasting, 22, 547-581. https://doi.org/10.1016/j.ijforecast.2006.04.001
  8. Booth, H. and Tickle, L. (2008). Mortality modelling and forecasting: a review of methods, Annals of Actuarial Science, 3, 3-43. https://doi.org/10.1017/S1748499500000440
  9. Box, G. E. P. and Cox, D. R. (1964). An analysis of transformations, Journal of the Royal Statistical Society, Series B, 26, 211-252.
  10. De Beer, J. (2000). Dealing with uncertainty in population forecasting, Statistics Netherlands. Available from: www.cbs.nl/nr/rdonlyres/7dc466f9-fe4c-48dc-be90-30216b697548/0/dealingwithuncertainty.pdf
  11. Dunstan, D. and Ball, C. (2016). Demographic projections: user and producer experiences of adoption a stochastic approach, Journal of Official Statistics, 32, 947-962. https://doi.org/10.1515/jos-2016-0050
  12. Gerland, P., Raftery, A. E., Sevcikova, H., et al. (2014). World population stabilization unlikely this century, Science, 346, 234-237. https://doi.org/10.1126/science.1257469
  13. Hoem, J. M., Madsen, D., Nielsen, J. L., Ohlsen, E. M., Hansen, H. O., and Rennermalm, B. (1981). Experiments in modelling recent Danish fertility curves, Demography, 18, 231-244. https://doi.org/10.2307/2061095
  14. Horiuchi, S. and Wilmoth, J. R. (1995). Aging of Mortality Decline, Rockefeller University, New York.
  15. 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. https://doi.org/10.1016/j.ijforecast.2008.02.009
  16. 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. https://doi.org/10.1007/s13524-012-0145-5
  17. Hyndman, R. J. and Ullah, M. S. (2007). Robust forecasting of mortality and fertility rates: a functional data approach, Computational Statistics & Data Analysis, 51, 4942-4956. https://doi.org/10.1016/j.csda.2006.07.028
  18. 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
  19. Keilman, N. (2005). Erroneous population forecasts. In Perspectives on Mortality Forecasting: Vol II Probabilistic Models, N. Keilman (Ed), Swedish National Social Insurance Board, Stockholm.
  20. Keilman, N., Pham, D. Q., and Hetland, A. (2002). Why population forecasts should be probabilistic - illustrated by the case of Norway, Demographic Research, 6, 409-454. https://doi.org/10.4054/DemRes.2002.6.15
  21. Keyfitz, N. (1981). The limits of population forecasting, Population and Development Review, 7, 579-593. https://doi.org/10.2307/1972799
  22. Kim, S. Y. and Oh, J. H. (2017). A study comparison of mortality projection using parametric and nonparametric model, The Korean Journal of Applied Statistics, 30, 701-717. https://doi.org/10.5351/KJAS.2017.30.5.701
  23. KOSTAT (2016). Population projection 2015-2065.
  24. KOSTAT (2019). The Special population projection 2017-2067.
  25. Lee, R. D. (1998). Probabilistic approaches to population forecasting, Population and Development Review, 24, 156-190. https://doi.org/10.2307/2808055
  26. Lee, R. D. (2004). Quantifying our ignorance: stochastic forecasts of population and public budgets, Population and Development Review, 30, 153-175.
  27. Lee, R. D. and Carter, L. R. (1992). Modeling and forecasting U.S. mortality, Journal of the American Statistical Association, 87, 659-671. https://doi.org/10.2307/2290201
  28. Lee, R. D. and Tuljapurkar, S. (1994). Stochastic population forecasts for the United States: beyond high, medium, and low, Journal of the American Statistical Association, 89, 1175-1189. https://doi.org/10.1080/01621459.1994.10476857
  29. Leslie, P. H. (1945). The use of matrices in certain population mathematics, Biometrika, 33, 183-212. https://doi.org/10.1093/biomet/33.3.183
  30. Leslie, P. H. (1948). Some further notes on the use of matrices in population mathematics, Biometrika, 35, 213-245. https://doi.org/10.1093/biomet/35.3-4.213
  31. Li, N. and Gerland, P. (2011). Modifying the Lee-Carter Method to Project Mortality Changes up to 2100, the Population Association of America 2011 Annual meeting-Washington, DC, Session 125, formal Demography I: Mathematical Models and Methods.
  32. Li, N. and Lee, R. (2005). Coherent mortality forecasts for a group of populations: an extension of the Lee-Carter method, Demography, 42, 575-594. https://doi.org/10.1353/dem.2005.0021
  33. Li, N., Lee, R., and Gerland, P. (2013). Extending the Lee-Cater method to model the rotation of age patterns of mortality decline for long-term projections, Demography, 50, 2037-2051. https://doi.org/10.1007/s13524-013-0232-2
  34. Li, Q., Reuser, M., Kraus, C., and Alho, J. (2007). Aging of a giant: a stochastic population forecast for China, 2001-2050, MPIDR Working Papers WP-2007-032, Max Planck Institute for Demographic Research, Rostock, Germany.
  35. Lutz, W. (2009). Toward a Systematic, Argument-Based Approach to Defining Assumptions for Population Projections (Interim Report IR-09-037), International Institute for Applied Systems Analysis, Austria. Available from: http://pure.iiasa.ac.at/9115/1/IR-09-037.pdf (accessed 19 May 2016).
  36. Lutz, W., Butz, W. P., and KC, S. (2014). World Population & Human Capital in the Twenty-first Century: Executive Summary, IIASA, Laxenburg, Austria.
  37. Lutz, W. and Goldstein, J. R. (2004). Introduction: How to deal with uncertainty in population forecasting?, International Statistical Review, 72, 1-4. https://doi.org/10.1111/j.1751-5823.2004.tb00219.x
  38. MPIDR (2006). Stochastic forecast of the population of Poland, 2005-2050. MPIDR working paper WP-2006-26.
  39. MPIDR (2007). Aging of a giant: a stochastic population forecast for Poland, 2005-2050. MPIDR working paper WP-2007-32.
  40. Oh, J. H. (2018). A comparison between the real and synthetic cohort of mortality for Korea, The Korean Journal of Applied Statistics, 31, 427-446. https://doi.org/10.5351/KJAS.2018.31.4.427
  41. ONS (2009). progress report on developing stochastic population forecasts for the United Kingdom.
  42. Ramsay, J. O. and Silverman, B. W. (2005). Functional Data Analysis (2nd ed), Springer-Verlag, New York.
  43. Raftery, A. E., Li, N., Sevcikova, H., Gerland, P., and Heilig, G. K. (2012). Bayesian probabilistic population projections for all countries, Proceedings of the National Academy of Sciences, 109, 13915-13921. https://doi.org/10.1073/pnas.1211452109
  44. Raftery, A. E., Chunn, J. L., Gerland, P., and Sevcikova, H. (2013). Bayesian probabilistic projections of life expectancy for all countries, Demography, 50, 777-801. https://doi.org/10.1007/s13524-012-0193-x
  45. SCB (2006). Stochastic Population Projections for Sweden, Research and Development, Methodology reports from Statistics Sweden.
  46. Smith, D. P. and Keyfitz, N. (1977). Mathematical Demography: Selected Papers, Springer Verlag, Berlin.
  47. Smith, S. K., Tayman, J., and Swanson, D. A. (2001). State and Local Population Projections: Methodology and Analysis, Kluwer Academic/Plenum Publishers, New York.
  48. Stoto, M. (1983). The accuracy of population projections, Journal of the American Statistical Association, 78, 13-20. https://doi.org/10.1080/01621459.1983.10477916
  49. Statistics Canada (2015). Population Projections for Canada (2013 to 2063), Provinces and Territories (2013 to 2038): Technical Report on Methodology and Assumptions.
  50. Statistics NZ (2016). National Population Projections: 2016(base)-2068. Available from: http://www.stats.govt.nz/browse for stats/population/estimates and projections/NationalPopulationProjections HOTP2016.aspx (accessed 25 October 2016)
  51. UN (2017). World Population Prospects 2017.
  52. UN (2019). World Population Prospects 2019.
  53. UNICEF (2017). Level & Trends in Child Mortality.
  54. VID (2016). 40 years of the Vienna Institute of Demography 1975-2015.
  55. Woo, H. B. (2010). Stochastic demographic and population forecasting, Korea Journal of Population Studies, 33, 161-189.