The data source most central to our analysis is the RSA-911 file. The RSA-911 file contains a wealth of information about VR clients at the individual level. Using this data source, we can observe individual-level characteristics of transition-age SVRA clients, the services they received, and their case closure status. However, the RSA-911 file does not allow us to track the employment and earnings of VR clients before they apply for services, at the time of closure if they do not receive services, or at any time after they exit VR. By linking the RSA-911 file to data from the Master Earnings File (MEF), we can measure employment and earnings outcomes during these periods. The MEF is provided to SSA by the Internal Revenue Service, is updated annually, and contains earnings information for all individuals based on tax returns. We also will link RSA-911 and MEF data to the DAF. The DAF contains a longitudinal record for every person age 10 through the Social Security full retirement age (currently age 66) who received Social Security or SSI disability benefits at any time from 1996 on. By matching these data to RSA-911 records, we can identify which VR youth applicants received benefits when they applied for VR services or any time thereafter, as well as those who attempt to use SSA work supports, such as Ticket to Work.
These data sources will allow us to analyze several factors that may be influential for young adult transitions. The variables in the analysis will span five categories:
Individual-level characteristics include traits, such as age at application, gender, race/ethnicity, disability type, earnings before SVRA application, and education level, that are likely correlated with VR service receipt and employment outcomes.
Agency access variables capture the extent to which transition-age youth wait for and receive VR services. Examples include the percentage of youth receiving VR services and time to IPE of those receiving services.
Agency service variables describe the type and amount of services a client receives, such as cost of services, time from application to closure, and services received.
Agency-level characteristics capture information on SVRAs that may influence outcomes. Examples include an agency’s order of selection status, annual total expenditures, level of federal funding, wait time for services, percentage receiving postsecondary services, percentage applying for services before age 19, and cost of services.
Employment-related outcomes measure how VR clients fare when they exit SVRAs and afterward. We will consider the following types of outcomes:
- VR outcomes describe clients’ status when they exit VR. Key variables include exiting VR with employment status and exiting before service receipt but after eligibility status.
- Earnings outcomes, based on the MEF, measure median earnings before and after the VR application year. We also will examine the percentage of VR clients with any earnings in the years after application.
- SSA outcomes identify VR clients who received SSA disability supports when they applied for VR services, as well as the percentage who received SSA disability supports after exiting VR.
Read less >