A New Claims-Based Unemployment Dataset: Application to Postwar Recoveries Across U.S. States

By Andrew Fieldhouse, Sean Howard, Christoffer Koch, David Munro

June 10, 2022

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Format: Chicago

Andrew Fieldhouse, Sean Howard, Christoffer Koch, and David Munro. A New Claims-Based Unemployment Dataset: Application to Postwar Recoveries Across U.S. States, (USA: International Monetary Fund, 2022) accessed December 21, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

Using newly digitized unemployment insurance claims data we construct a historical monthly unemployment series for U.S. states going back to January 1947. The constructed series are highly correlated with the Bureau of Labor Statics' state-level unemployment data, which are only available from January 1976 onwards, and capture consistent patterns in the business cycle. We use our claims-based unemployment series to examine the evolving pace of post-war unemployment recoveries at the state level. We find that faster recoveries are associated with greater heterogeneity in the recovery rate of unemployment and slower recoveries tend to be more uniformly paced across states. In addition, we find that the pace of unemployment recoveries is strongly correlated with a states' manufacturing share of output.

Subject: Business cycles, Economic growth, Economic recession, Labor, Labor markets, Unemployment, Unemployment rate

Keywords: Business cycles, Economic recession, Economic Recoveries, Labor markets, Recession date, Regional Business Cycles, State-Level Unemployment Rates, Unemployment, Unemployment dataset, Unemployment Insurance, Unemployment rate, Unemployment rate series, Unemployment recovery, Unemployment series

Publication Details

Pages:

56
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2022/117
Stock No:

WPIEA2022117
ISBN:

9798400212604
ISSN:

1018-5941

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