동태적 변이할당분석을 통한 지역고용 회복력 분석
Classifying Regional Employment Resilience through Dynamic Shift-Share Analysis
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초록

· Research topics: This study classifies regional employment resilience types during the COVID-19 pandemic and empirically analyzes structural heterogeneity within each type. · Research background: The pandemic revealed disparities in employment shocks and recovery across regions, highlighting the need for a detailed analysis of resilience. Prior studies have primarily focused on differences between types, overlooking intra-type variation. · Differences from prior research: This research emphasizes heterogeneity within resilience types and uses dynamic shift-share analysis to trace yearly recovery paths. · Research method: Four resilience types—Declining, Recovering, Resilient, and Vulnerable—were identified through dynamic shift-share analysis. Cluster analysis was conducted using six variables (three for industrial capacity and three for employment vulnerability) within each type. · Research results: Clear differences emerged not only between types but also within them. The Vulnerable type exhibited the weakest structural conditions, characterized by high non-standard employment and low industrial diversity. · Contribution points and expected effects: The study demonstrates that long-term industrial and employment structures shape recovery paths. It also validates the application of evolutionary resilience and supports the need for targeted regional policies.

키워드

지역고용 회복력동태적 변이할당분석지역 유형화코로나 19 팬데믹Regional Employment ResilienceDynamic Shift-Share AnalysisRegional TypologyCOVID-19 pandemic
제목
동태적 변이할당분석을 통한 지역고용 회복력 분석
제목 (타언어)
Classifying Regional Employment Resilience through Dynamic Shift-Share Analysis
저자
고윤숙이영민
DOI
10.22924/jhss.33.3.202508.002
발행일
2025-08
유형
Y
저널명
인문사회과학연구
33
3
페이지
27 ~ 55