[논문]미래의 불확실성에 대한 확률론적 인구추계

오진호

doi:10.5351/kjas.2020.33.2.185

미래의 불확실성에 대한 확률론적 인구추계
Stochastic population projections on an uncertainty for the future Korea 원문보기

응용통계연구 = The Korean journal of applied statistics, v.33 no.2, 2020년, pp.185 - 201

오진호 (한밭대학교 공과대학 기초과학부)

초록
AI-Helper

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.

Abstract ▼ AI-Helper

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.

주제어

표/그림 (7)

그림 Figure 2.1. Comparions with deterministic and stochastic population projections (Dunstan and Ball, 2016).
그림 Figure 2.2. Trend on population structure for upper and lower 5-Scenarios.
표 Table 2.1. Rank of High and Low ﬁve scenarios by population group
그림 Figure 3.1. Stochastic projection on 4 parameters of generalized log gamma model.
그림 Figure 3.2. Stochastic projections on total fertility rate.
그림 Figure 3.3. The result of stochastic projection on life expectancy, net migration, population by gender.
그림 Figure 3.4. Population, trend of population structure for three ages-groups, and Dependency ratio.

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