Evaluating the Long-Term Earnings Outcomes of Vocational Rehabilitation Participants using Administrative Data Sources Presented at the: Cornell StatsRRTC 2006 State-of-the-Science Conference The Future of Disability Statistics: What We Know and Need to Know Arlington, Virginia October 6, 2006 Presented by: Dr. David H. Dean & Dr. Robert M. Schmidt Bureau of Disability Economics Research University of Richmond Richmond, VA 23229

In 2002 the VR purchased $1.7 billion in various job-training services For over 500,000 eligible individuals with disabilities More than 40% ending up in some form of competitive employment

Evaluations of VR efficacy in enhancing employment have used data from the “RSA-911” Compiled nationally by the Department of Education’s Rehabilitation Services Administration This nationwide data file uses a standardized format by all state VR agencies For all program “closures” occurring within the given federal fiscal year

Information include a person’s: 1) demographic and socio-economic attributes 2) disabling condition(s) 3) VR service provision 4) employment status at application and closure from the program

Unfortunately, there are numerous shortcomings in using the RSA-911 closure file: 1) obtaining information at closure rather than at time of application 2) a lack of longitudinal employment data for both the pre- and post-VR application period 3) a lack of longitudinal data on the costs and specific types of VR service provision 4) insufficient information on the severity of the functional limitation 5) no information on the local labor market and the nature of the job training “environment”

The “enhanced” sample frame is from: Administrative records of all individuals making an application for services to the Virginia Department of Rehabilitative Services (DRS) During state Fiscal Year 1988 (July 1, June 30, 1988) A total of 11,595 “valid” applications

Enhancements of this data set: 1) Information on a Cohort of Applicants 2) Longitudinal Employment Data 3) Service-Specific Cost Data 4) Better Measures of Disability Severity and Functional Limitations 5) Measures of the External Factors Influencing Vocational Outcomes

Analysis incorporates these data enhancements to estimate the impact of VR service provision on the earnings of various treatment cohorts, stratified by: 1) gender 2) impairment (cognitive, mental illness, musculo-skeletal, and “internal”)

Following the approach of Hotz (1992) Incorporate three well-established econometric techniques to adjust for the presence of selection bias in estimating treatment impacts: 1) “control function” estimator techniques 2) longitudinal data “fixed effects” estimators 3) “propensity score” statistical matching techniques

Compare these results with those obtained using: 1) Difference-in-means approach 2) Multi-variate regression approach based solely on observed variables