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Reemployment Services Evidence: A Collection of Briefs on RESEA Components (Compendium of Issue Briefs on Literature Review Findings)

Release Date

Jun 10, 2022

Publication Author(s)

  • Abt Associates
  • Andrew Clarkwest
  • Jacob Klerman
  • Zachary Epstein
  • The Urban Institute
  • Demetra Nightingale

Research Methodology

  • Qualitative Analysis
  • State of the Evidence Analysis

Populations

  • Adult
  • Dislocated Worker
  • Employers
  • Reemployment Services and Eligibility Assessments
  • Unemployment Insurance

States & Territories

  • All 50 states and three territories
  • Connecticut
  • Georgia
  • Louisiana
  • Maryland
  • Nevada
  • New York
  • North Carolina
  • Ohio
  • Washington
  • Wisconsin

Abstract

In 2018, amendments to Section 306(c) of the Social Security Act (SSA) permanently authorized the Reemployment Services and Eligibility Assessments (RESEA) program and introduced substantive changes, including formula-based funding to states and a series of requirements intended to increase the use and availability of evidence-based reemployment interventions and strategies. The Department of Labor provides funding to states to operate the RESEA program, which aims to help Unemployment Insurance (UI) claimants return to work quickly and meet eligibility requirements.

The evaluation of the RESEA program includes four major components: 1) an implementation report; 2) a brief on serving UI claimants during the COVID-19 pandemic; 3) a collection of evidence briefs about RESEA program components, including selecting claimants and meeting attendance, basic career services, and individualized services; and 4) a report on options for building evidence on RESEA programs. This collection of evidence briefs aims to inform states about the status of evidence on RESEA programs and strategies. The briefs examine the evidence and gaps in three subject areas: 1) program activities that precede the in-person RESEA meeting--claimant selection, scheduling, and attendance policies; 2) impacts of basic career services; and 3) impacts of individualized career services.

Nearly all completed impact evaluations estimate the effectiveness of whole RESEA programs, not components of those programs. Findings from the evidence briefs indicate that:

Available evidence on selection criteria comes from whole-program impact studies that test for differential impacts across subgroups of claimants. While programs often select claimants with statistical models that predict the likelihood of benefit exhaustion, evidence suggests that impacts usually do not vary with the scores from those models. Prior research suggests that other UI claimant characteristics, such as the weekly benefit amount, may better predict program impact.

One impact study finds that an emphasis on clear and concise communication may improve program attendance rates, but most analyses of approaches to improve program attendance are descriptive. Scheduling and communication strategies have rarely been tested rigorously, and additional research could increase the knowledge base of the effects of self-scheduling, alternative communication channels, and non-compliance policies on attendance.

Reemployment programs for UI claimants sometimes require program participation, and mandatory participation appears to be relevant to program impacts. Few studies have isolated the effect of reemployment services from the requirement to participate and these requirements may need further study.

RESEA programs include a combination of career development and employment-related services. States may be interested in the available evidence for specific services or groups of career services. The briefs review the evidence for basic and individualized career services separately; however, previous studies generally have not isolated the effects of these types of services.

Reemployment services, particularly when evaluated separately, can be expected to have small impacts when compared to a control group. As a result, state evaluations of low-intensity components will often require large samples.

Final Report

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