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Economic vulnerability in US metropolitan areas
https://orcid.org/0000-0003-0485-0144
Abstract This paper examines various aspects of recent employment vulnerability in US metropolitan areas. Based on the three decades preceding COVID-19, an estimate is made of the volatility (sensitivity) in each area’s unemployment rate, relative to the national rate, and this reflects the area’s overall employment vulnerability to external events. Using the Brechling–Thirlwall time-series approach, the monthly change in each area’s unemployment rate is first compared to the monthly change in the nation’s unemployment rate. Regression analysis is then used to tie the volatility seen in those metropolitan unemployment rates to various initial conditions: degree of specialization in primary (+), manufacturing (+), and government (−) activities; initial unemployment (+); human-created (−) and natural amenities (+); real wages (−); self-employment (−); and the presence of major colleges or universities (−). An alternative specification reassesses these estimates after including the volatility of unemployment rates across the nation’s various states. A short discussion then addresses the issue of vulnerability in specific activities. Selecting four industries that were identified “at risk” during early COVID events, ranked employment specialization indices (LQs) are correlated with ranked volatility estimates of unemployment rates. In the more advanced economies, metropolitan areas typically specialize in, and trade across, different industries, but this specialization can create overall employment vulnerability.
Economic vulnerability in US metropolitan areas
https://orcid.org/0000-0003-0485-0144
Abstract This paper examines various aspects of recent employment vulnerability in US metropolitan areas. Based on the three decades preceding COVID-19, an estimate is made of the volatility (sensitivity) in each area’s unemployment rate, relative to the national rate, and this reflects the area’s overall employment vulnerability to external events. Using the Brechling–Thirlwall time-series approach, the monthly change in each area’s unemployment rate is first compared to the monthly change in the nation’s unemployment rate. Regression analysis is then used to tie the volatility seen in those metropolitan unemployment rates to various initial conditions: degree of specialization in primary (+), manufacturing (+), and government (−) activities; initial unemployment (+); human-created (−) and natural amenities (+); real wages (−); self-employment (−); and the presence of major colleges or universities (−). An alternative specification reassesses these estimates after including the volatility of unemployment rates across the nation’s various states. A short discussion then addresses the issue of vulnerability in specific activities. Selecting four industries that were identified “at risk” during early COVID events, ranked employment specialization indices (LQs) are correlated with ranked volatility estimates of unemployment rates. In the more advanced economies, metropolitan areas typically specialize in, and trade across, different industries, but this specialization can create overall employment vulnerability.
Economic vulnerability in US metropolitan areas
https://orcid.org/0000-0003-0485-0144
Abstract This paper examines various aspects of recent employment vulnerability in US metropolitan areas. Based on the three decades preceding COVID-19, an estimate is made of the volatility (sensitivity) in each area’s unemployment rate, relative to the national rate, and this reflects the area’s overall employment vulnerability to external events. Using the Brechling–Thirlwall time-series approach, the monthly change in each area’s unemployment rate is first compared to the monthly change in the nation’s unemployment rate. Regression analysis is then used to tie the volatility seen in those metropolitan unemployment rates to various initial conditions: degree of specialization in primary (+), manufacturing (+), and government (−) activities; initial unemployment (+); human-created (−) and natural amenities (+); real wages (−); self-employment (−); and the presence of major colleges or universities (−). An alternative specification reassesses these estimates after including the volatility of unemployment rates across the nation’s various states. A short discussion then addresses the issue of vulnerability in specific activities. Selecting four industries that were identified “at risk” during early COVID events, ranked employment specialization indices (LQs) are correlated with ranked volatility estimates of unemployment rates. In the more advanced economies, metropolitan areas typically specialize in, and trade across, different industries, but this specialization can create overall employment vulnerability.
Economic vulnerability in US metropolitan areas
Mulligan, Gordon F. (author)
2022
Article (Journal)
Electronic Resource
English
BKL:
83.64$jRegionalwirtschaft
/
74.12
Stadtgeographie, Siedlungsgeographie
/
38.00$jGeowissenschaften: Allgemeines
/
38.00
Geowissenschaften: Allgemeines
/
83.64
Regionalwirtschaft
/
74.12$jStadtgeographie$jSiedlungsgeographie
RVK:
ELIB39
/
ELIB18
/
ELIB45
Local classification FBW:
oek 4450
Economic vulnerability in US metropolitan areas
| Springer Verlag | 2023
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