본 연구는 중국의 11개 4F급 공항을 대상으로 변이할당분석을 통해 공항의 경쟁 력을 비교한다. 분석기간은 2001년-2021년으로 설정하고 2001년-2007년, 2008년-201 8년 그리고 2019년-2021년 세 구간을 설정하고 있다. 변이효과를 권역별로 보면 화 북지역의 공항 경쟁력은 감소하고 있으며, 화동지역, 화남지역과 화중지역의 공항 경 쟁력은 모두 상승하고 있다. 반대로 서북지역과 서남지역은 공항 경쟁력이 감소하고 있다. 할당효과를 보면 북경수도국제공항과 상해포동국제공항, 광주백운국제공항, 그 리고 심천보안국제공항의 할당효과가 가장 크다. 그리고 권역별로 절대적 성장치와 할당효과의 분석결과를 종합해 보면 화동지역, 화남지역 그리고 화중지역의 공항 경 쟁력이 상대적으로 높으며, 화북지역과 서북지역 그리고 서남지역의 공항 경쟁력은 상대적으로 낮다고 해석할 수 있다.

This study, in order to complement instability of analysis result stemming from the choice between reference point and comparison point which is pointed out as the defect of shift-share analysis, conducted shift-share analysis using Gross Regional Domestic Product (GRDP) trend of Geumgang area, Chungcheongnam-do for the period from 2000 to 2011. As a result of the analysis, ➀ industries that had both the positive Regional Share Effect (RSE) and Industrial Mixed Effect (IME) were service industries such as manufacturing industry, electricity gas, transportation industry, art, etc., which are positively influencing the regional industry. ➁ industries that had both the negative RSE and IME were other service industries such as wholesale and retail businesses, lodging industry, food industry, real estate business and leasing service, business service industry, public administration, etc., which provide basic livelihood services for the residents. ➂ industries that had the positive RSE and negative IME were agriculture, forestry and fishery industry, mining industry, construction industry, and educational service industry. ➃ industries that had the negative RSE and positive IME were info-communications industry, financial and insurance businesses, health industry, etc.

The purpose of this study is to develop rural population model adapting cohort survival method with sift-share effects. Administrative district in this study is below Myun: about 2,000 population. Population data of rural area in 1990, 1995, and 2000 by age cohort were selected for applying developed model. Damping coefficient from population data was calculated as 7% and results applying this coefficient in rural population data below the error from 12% to 1.06%. In detail, most of cohorts fitted with developed model except from 15 to 29 age groups. Application result of small population area; DaesulMyun revealed that main factor of population change is not natural change but migration.