동태적 변이할당분석을 통한 지역고용 회복력 분석

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.

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