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Abstract
This report presents an actuarial framework for examining the costs of unemployment compensation (UC) programs. The framework emphasizes three factors: (1) the unemployment rate (unemployment as a percent of the labor force), (2) the recipiency rate (the share of the unemployed who collect UC benefits) and (3) the replacement rate (weekly UC benefits as a ratio to average weekly wages). The framework is derived in Section 1. Sections 2 and 3 then examine replacement rates in a sample of 20 high income countries from the Organization for Economic Cooperation and Development (OECD). Section 2 focuses upon the determinants of replacement rates while Section 3 compares the empirical estimates from Section 2 with estimates published by the OECD. The two estimates are found to differ widely in analysis both time series and cross section data. More analysis of the cause(s) of the differences is recommended.
Introduction
The cost of unemployment compensation (UC) support payments can be examined within an actuarial framework that recognizes three factors: 1) the economy’s underlying unemployment rate, 2) the recipiency rate (the share of the unemployed who receive benefits) and 3) the replacement rate (the ratio of periodic (weekly or monthly) benefit payments to average earnings for the same time period. The present paper operates within this actuarial framework but focuses primarily upon replacement rates.
Part 1 derives the framework for examining UC benefit costs. Parts 2 and 3 then undertake two empirical investigations of replacement rates. Part 2 fits time series regressions to determine the effects of unemployment and UC statutory provisions on replacement rates in 20 OECD member countries. Replacement rates are found to be strongly connected to the maximum UC benefit payment. Part 3 examines the association between the replacement rates derived from aggregate program data and replacement rates published by the OECD. Because the two sets of replacement rates are derived using different methodologies, they are not necessarily closely linked. This is found to be the case in regressions fitted to time series and cross section data.
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The nonpartisan Urban Institute publishes studies, reports, and books on timely topics worthy of public consideration. The views expressed are those of the authors and should not be attributed to the Urban Institute, its trustees, or its funders.
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