Transitions and vocational rehabilitation success: Tracking outcomes for different types of youth

Abstract

BACKGROUND: Recent federal policy changes require vocational rehabilitation (VR) agencies to increase the share of spending they allocate to services for specific types of transition-age youth, though limited information exists about the outcomes for these different types.

OBJECTIVE: We seek to provide information about the range of VR outcomes across youth characteristics, with particular attention to differences by education and employment status at application.

METHODS: We tracked VR outcomes through 2013 for all transition-age youth with disabilities who applied and were eligible for VR services between 2004 and 2007.

RESULTS: High school dropouts had the lowest odds of receiving services and exiting with employment, and those still in high school at application had lower employment rates at closure than those who were working or in postsecondary school. Agency factors, such as timing and volume, were important influences in whether youth received services and exited with employment.

CONCLUSIONS: Sizeable increases in the youth populations served by VR agencies might have implications for an agency’s overall success, both with youth and with adults. Prioritizing one subgroup of youth, such as high school students, might come at the expense of services received by other subgroups of youth, such as high school dropouts.

Keywords

Youth with disabilities vocational rehabilitation agencies transition from school to adulthood Workforce Innovation and Opportunity Act

1 Introduction

The Workforce Innovation and Opportunity Act (WIOA) (public law 113–128) places an emphasis on improving services and outcomes for transition-age youth ages 14 to 24 years. WIOA, which replaces the Workforce Investment Act and amends the Rehabilitation Act of 1973, will require state vocational rehabilitation (VR) agencies to increase their focus on this population through shifts in agency funding allocations and prioritization of service provision to certain subpopulations of youth. These changes have left agency staff with a challenging mandate. VR agencies must decide how they should change their service approaches to respond to the law’s requirements and manage the effects the law might have on both the populations they serve and their outcomes.

However, limited information exists on the characteristics and differential outcomes of youth who apply for VR services. For example, the law targets students who are enrolled in secondary or postsecondary school at the time they are receiving VR services (requiring agencies to spend 15% of their total funding on pre-employment transition services for this population), yet baseline or pre-WIOA statistics on this and other targeted youth populations are not currently available. This lack of information potentially compromises administrators and policymakers who are planning for the WIOA expansion.

This study uses linked VR and Social Security Administration (SSA) administrative data to follow the VR experiences of transition-age VR customers eligible for services. Our sample includes youth ages 14 to 24 years who applied for VR services in fiscal years 2004 through 2007 and had a case closure through 2013. We present descriptive statistics on youths’ demographic and impairment characteristics. We then use descriptive and multivariate models to track VR service receipt and employment outcomes through 2013 for the entire sample. This long analysis period allows us to observe young adult outcomes for sample members 6 to 9 years after application. The findings provide an early glimpse into the transition patterns of youth who are eligible for VR services and might offer insights into the potential implications of WIOA as VR agencies begin to serve larger target populations in the future.

The VR outcomes of transition-age youth VR applicants differed across their education and employment characteristics at baseline. High school dropouts — youth who had no high school diploma and who were not working when they applied for services — had the lowest odds of both receiving services and exiting with employment. Youth who had a high school diploma and who were working — a relatively advantaged group — had among the highest odds of both receiving services and exiting with employment. This information may be useful for VR agency staff to consider additional outreach to specific youth populations and the supports they may need to ensure that specific subgroups of youth have the best outcomes possible, particularly for agencies that have not traditionally served large numbers of youth.

This paper is structured as follows. We summarize additional research literature on the employment of youth with disabilities and the importance of VR services for this population. We describe the sample and data used for this analysis and our methodologic techniques. In the final sections, we describe key results and limitations and discuss implications for policy and practice.

2 Background and literature review

Vocational rehabilitation agencies, funded jointly by the Department of Education and state governments, are one of the largest state–federal programs offering employment services and supports for people with disabilities, with nearly 600,000 annual applicants. In fiscal year 2013, expenditures for the program were $3 billion (Institute on Disability/UCED, 2016).

The WIOA is intended to increase access to vocational services, education, and training that are needed for employment success by aligning the various workforce-related agencies and programs funded through the Department of Labor and the Department of Education (Office of Special Education and Rehabilitation Services, 2015; public law 113–128; U.S. Government Accountability Office, 2015). The act requires each state to allocate 15% of its federal matching grant funds to provide pre-employment transition services to qualified youth with disabilities who are in high school or postsecondary school. These pre-employment services include transition counseling, job exploration, in-school or after-school work experiences, and job readiness training. WIOA also requires that 50% of each state’s supported employment grants be dedicated to services for youth with significant disabilities. Other important changes resulting from the law include allowing agencies to serve groups of youth and emphasizing service provision to youth after they leave high school.

About one-third of all people seeking VR services are transition-age youth, and many VR agencies have specialized services and programs to help ease the transition from school to work (The Study Group, Inc., 2007). For students receiving services under the auspices of the Individuals with Disabilities Education Act (IDEA), VR agency staff often participate in secondary school transition planning to provide vocational counseling support. Eligibility for VR services requires that (1) the applicant have a qualified disability that does not prevent employment and (2) the applicant can benefit from VR services to achieve an employment goal. Applicants who are determined to be eligible for services must sign a mutually agreed upon individual plan for employment (IPE) to receive ongoing VR services. The IPE is expected to facilitate placement in competitive employment (Parker, Szymanski, & Patterson, 2005). Roughly 8% of youth with disabilities, as identified in the American Community Survey, apply for VR services each year (Honeycutt, Thompkins, Bardos, & Stern, 2015).

Though it will be some time before WIOA is fully implemented, state VR agencies will need to make many changes in response to the law. For example, the pre-employment transition services, mentioned earlier, will require a significant shift in resources toward youth who are still in high school or postsecondary school. The magnitude of these shifts will vary across agencies for several reasons: agencies differ widely in the proportion of youth served (Honeycutt, Thompkins, Bardos, & Stern, 2016); their practices in service provision (such as when they begin serving youth) (Honeycutt, Bardos, & McLeod, 2015); and the programs they offer for youth (Swankoski, Hulette, & Sevak, 2016).

Many individual-level characteristics are associated with positive employment outcomes for youth with disabilities. Being White or male, having less severe disabilities, residing in higher-income households, and having higher levels of educational attainment (such as a regular high school diploma or postsecondary training) are typically, though not consistently, associated with a higher likelihood of being employed (Shandra & Hogan, 2008; Sima, Wehman, Chan, West, & Leucking, 2015; Wehman et al., 2015).

Research specific to youth VR applicants and the individual characteristics associated with positive employment outcomes is more limited. Many studies that have examined youth and VR have focused on subsets of youth with specific disabilities, such as youth with autism and learning disabilities (Dunham, 1998; Migliore, Timmons, Butterworth, & Lugas, 2012; Westbrook et al., 2012); youth involved in specific transition programs (such as Brewer et al., 2011); or youth in a specific state (Jun, Kortering, Osmanir, & Zhang, 2015). Studies that have tracked youths’ experiences in VR at the national level, such as the VR Study (The Study Group, Inc., 2007), have focused on agency-level practices rather than on youths’ individual characteristics, though Honeycutt et al. (2016) compared outcomes for youth with and without SSA benefits, finding that those with benefits have poorer employment outcomes than those without benefits.

The lack of current information on VR youth characteristics, particularly as they relate to long-term outcomes into early adulthood, potentially limits planning by policymakers and administrators in providing VR services to youth with disabilities under WIOA. Gaps in available research on vocational rehabilitation of youth include the number or proportion of youth in or out of high school; the proportion of system-involved youth (such as those involved in the juvenile justice system); the likelihood that different types of youth will receive services or exit with employment; the types and intensity of services that youth receive; and the demographic and disability characteristics and long-term outcomes of youth who apply at different points in their transition process.

An additional limitation in our understanding of VR agencies is the relationship between agency policies and outcomes for youth. VR agency policies and characteristics can be potential barriers to or facilitators of youths’ success. One such policy that may interfere with service provision is an agency being in order of selection (OOS); when an agency has limited funding to serve all applicants, it enters into OOS and serves only those with the most significant disabilities. Other VR agency-level factors might influence both the likelihood of receiving VR services and the outcomes of those services, such as wait time for services, though the research on this topic (Honeycutt & Stapleton, 2013; Schimmel Hyde, Honeycutt, & Stapleton, 2014) has examined only adults’ experiences. Processes specific to youth include the variation in the proportion of youth applicants who received services; agencies that served higher proportions of youth applicants or that had higher proportions of youth who exited with employment had fewer such applicants who later received SSA disability benefits (Honeycutt et al., 2016).

The current study addresses two gaps in the literature: differential receipt of services and differential employment outcomes. To do this, we compare and contrast the experiences of a cohort of youth determined to be eligible for VR across their various demographic and agency characteristics. We examine two types of VR outcomes: how likely a youth applicant is to receive services (vs. dropping out before receiving services) and how likely a youth who receives services is to exit with employment (vs. receiving services and exiting without employment). We focus our analysis on differences across youth education and employment statuses at application. This focus provides insight into the outcomes of different populations that VR agencies may target in response to WIOA, particularly as the need for services might vary across groups, and we also provide baseline information to assess changes that result from WIOA.

3 Methods

3.1 Participants and data sources

This study uses data from Rehabilitation Services Administration (RSA)-911 case closure files — specifically, the Disability Analysis Files (DAF)-RSA matched files residing at SSA — to identify youth ages 14 to 24 years who applied for VR services between fiscal years 2004 and 2007. RSA-911 files contain administrative data compiled annually for all individuals who exit from a VR agency in a given fiscal year. The data include demographic characteristics, disability information, service descriptions, and, for those who receive services, employment outcomes.

We restrict our analysis sample to the 570,146 VR applicants who, subsequent to their application, were deemed eligible for VR services and closed within seven fiscal years of their application. To identify this sample, we use RSA-911 data from 2004 through 2013. Because the RSA-911 files include individual closures, applications in a given year are unknown until those applicants’ VR files are closed, necessitating the use of multiple years of data to identify a given year’s applicants. Although the RSA-911 data cover a longer period, we limit our analyses to applicants through 2007 because of the time lags between application and VR closure. We made some additional restrictions in sample selection. First, we excluded from our analyses VR agencies in the U.S. territories. We also excluded all consumers whose reason for closure was death (less than 1%) or who were found ineligible for VR services (15% of all youth applicants). We made the latter cut because of our interest in focusing on those who were potentially likely to receive services. Finally, for the current analysis, we include results only for general and combined agencies; we will present similar results for agencies serving the blind separately in an online issue brief available at http://vrpracticesandyouth.org/.

We matched the RSA-911 records to SSA’s 2013 DAF to obtain disability benefit information at the time of VR application. The DAF includes a record for every individual ages 18 to 65 years who received a Supplemental Security Income (SSI) or Social Security Disability Insurance (SSDI) benefit in at least one month since 1996, as well as all individuals ages 10 to 17 years who received benefits in any month since 2005. This age restriction for the DAF means that we are unable to identify a very small number of 2004 VR youth applicants who had SSA benefits in 2004 but not in subsequent years. Among other data, the DAF includes variables about monthly benefit receipt and amounts (Bronnikov et al., 2016), and the information is more accurate than the SSA benefit variables included in the RSA-911 files.

3.2 Outcome measures

From the RSA-911 closure status variable, we examined two stages of VR outcomes. First, we identified who did or did not receive services. One-third (33%) of the sample did not receive services, meaning that they exited from VR before receiving a signed IPE despite being eligible for services. The remainder (67%) did receive services. Second, of the group that received services, we examined who was employed when their case was closed. Less than one-third of all eligible youth applicants (30% of the sample) received VR services but exited VR without becoming employed. The final group included youth who received an IPE, received services, and were employed for at least 90 days when they exited from VR. This group represented 37% of the entire sample.

3.3 Applicant and agency characteristics

Table 1 includes information on the individual and agency characteristics of our sample. We show the proportion of all youth with each characteristic (column 1) and disaggregate the sample across those who did and did not receive services (columns 2 and 3), and (for those who received services) those who did and did not exit with employment (columns 4 and 5).

Given WIOA’s emphasis on outreach to youth in and out of secondary school, an important focus of this analysis is the youth’s education and employment status at the time of VR application. We used two RSA-911 data elements (level of education attained at application and employment status at application) to assign youth to the following categories: (1) enrolled in high school and without a high school diploma; (2) had no high school diploma or special education certificate and were not working or in school; (3) had no high school diploma or special education certificate and were either working or in training or postsecondary school; (4) had at least a high school diploma or special education certificate and were working; (5) had at least a high school diploma or special education certificate and were enrolled in postsecondary school or training; (6) had at least a high school diploma or special education certificate and were neither working nor in postsecondary school or training; and (7) had missing or inconsistent information on school and/or work status. As shown in Table 1, youth predominantly either had a high school diploma but were not working (29%) or were enrolled in high school (28%). (A table detailing the education and employment at application codes used for this typology can be obtained from the corresponding author.)

Education and employment status at application appear related to closure outcomes. First, relative to the other VR closure groups, those who were employed at closure included fewer youth who at the time of application did not have a high school diploma and were not employed. The group employed at closure also included more youth who had a high school diploma and were working at the time of application.

Our approach to using education and employment status at application variables makes two key assumptions. First, the RSA-911 data do not contain an explicit measure of either secondary or postsecondary school enrollment. For the employment variable, a person is first identified as employed; if he or she is not employed, the person is then identified as either in school or not otherwise working. A youth who is both working and enrolled in high school or postsecondary school might therefore likely be identified as working, but not as enrolled in school. Because of this issue, many of those in the third listed group (those who did not have a high school diploma and were either working or in school), as well as some in the other categories, might be high school students with jobs. Second, the special education certificate category for educational attainment at application (14% of the analysis sample) does not distinguish individuals who had completed that certificate from those who were currently attending school on a special education certificate track. We cannot therefore identify whether a youth had obtained the certificate or was still in secondary school working toward a certificate. When a special education certificate youth was identified as not working because he or she was a student, we classified the individual as enrolled in high school; otherwise, we classified the youth as having obtained a high school diploma or certificate, along with his or her employment status variable (working; in postsecondary school; or neither working nor in postsecondary school).

Other individual-level variables included in the analysis are shown in the middle portion of Table 1. Transition-age youth who were eligible for VR services tended to be male, White, and younger than 19 years. The most common disability types were learning/cognitive (39%) or developmental/intellectual (24%). Fewer than 1 in 10 youth received some type of public support at application, and about 1 in 4 received some type of SSA disability benefit (predominantly SSI). A roughly equal proportion of youth applied across the 4 fiscal years. In comparing youth across the VR outcomes, we observe the largest differences for mental health disabilities (with fewer youth in the successful closure category) and developmental/intellectual disability (with fewer youth who did not receive services). In addition, youth with SSI benefits were overrepresented in the unsuccessful closure category.

We used or created several measures indicative of agency operations at the time of a youth’s application. From RSA’s quarterly cumulative caseload report (RSA-113) data, we identified whether each agency was in OOS at the time of a youth’s application. More than half of youth applied to agencies in OOS during the period of observation. From the RSA-911 data, we created agency-level variables to describe, for each applicant fiscal year, the proportion of youth applicants of all VR applicants (mean = 33%) and the proportion of youth who received services (mean = 58%). We also calculated the mean time between VR application and eligibility for VR services (1.34 months), the mean time from VR eligibility until a signed IPE was reported, among those with an IPE (4.23 months), and the average cost of purchased VR services ($2,543, indexed to 2014 dollars). With the latter variable, it is important to note that agencies differ in their levels of purchased services, as some agencies might rely more on purchased services than on providing some services directly.

3.4 Analytical approaches

We used multivariate methods to predict two binary outcomes. The first outcome is the receipt of VR services. We estimated the likelihood of receiving VR services relative to exiting VR without receiving services. These results provide insight into which types of youth are more likely to drop out of VR at an early stage, along with the characteristics of VR agencies that might present barriers or facilitators to service receipt. The second outcome is exiting with employment, conditional on receiving services.

Both models use logistic regression to estimate the outcome, and we present odds ratios for the estimates. Odds ratio values that are greater than 1.0 (and whose confidence interval does not include 1.0) indicate increased odds for the outcome, whereas odds ratio values that are less than 1.0 (and whose confidence interval does not include 1.0) indicate decreased odds. All models include state and year fixed effects.

4 Results

4.1 Receipt of VR services among customers eligible for services

As seen in Table 2, the odds for receiving services for all of the education and employment categories differed significantly from the odds for high school enrollees, in varying directions. To calculate the odds of the outcome, we used one category – in this case, youth enrolled in high school – as the basis for comparison (that is, the reference group). Youth who had a high school diploma and were either working or in postsecondary school at the time of application were most likely to receive services. The odds of receiving VR services among youth in postsecondary school were 14% greater than the odds for youth enrolled in high school, whereas the odds for youth with a high school diploma who were working were 7% greater. Youth who were not working and did not have a high school diploma were least likely to receive services, with odds that were 74% as likely as youth enrolled in high school. Youth who did not have a high school diploma and were working or who had a high school diploma and were not working also had slightly lower odds of receiving services (with odds 95% as likely). The final category of youth – those from whom we were missing education and employment information – had slightly higher odds.

Other individual characteristics were associated with VR service receipt. Odds for receiving VR services were higher when youth were female, White, older, and not receiving public benefits or SSI disability benefits. The converse is that the odds for exiting before receiving VR services were higher for youth who were male, non-White, younger, receiving public benefits, or receiving SSI disability benefits.

The analysis uncovered significant differences in service receipt by youth’s disability status. The comparison group in the logistic regression model was youth with learning or cognitive disabilities. Relative to this group, youth with developmental/intellectual, other physical, or unknown disabilities had higher odds of receiving services, whereas youth with mental health or substance abuse disabilities had lower odds of receiving services.

Several agency-level characteristics were significantly associated with VR service receipt. Youth who applied to agencies in OOS were 83% as likely to receive services as youth who applied to agencies not in OOS. This finding is congruent with expectations; an agency in OOS will place more youth on waiting lists for services. As the time between application and eligibility or the time between eligibility and IPE increased, the odds of receiving services decreased, suggesting the importance of timely connections to services. Finally, as the average cost of purchased services increased, the odds of receiving services decreased. The proportion of an agency’s applicants who were youth was not significantly associated with a youth’s likelihood of receiving services. We did not include the proportion of youth who received services in this model because it was highly correlated with the outcome variable.

4.2 Employment at closure among customers who received VR services

Next, we examine whether youth who received services exited from VR with employment. It is important to note that the analysis sample declines from 570,146 youth (all youth applicants eligible for services) to 382,941 youth (all youth applicants who were eligible and received services). Table 3 shows the odds of exiting with employment versus exiting without employment, and in each instance, we use a reference category as the basis for comparison.

Compared with youth who were enrolled in high school, youth who were working when they applied for VR services had greater odds of having a successful closure, regardless of whether they had a high school diploma. The odds of exiting with employment for a working youth with a high school diploma were 74% greater than for a youth enrolled in high school. For working youth without a high school diploma, odds were 12% greater. On the other end of the spectrum, youth who did not have a high school diploma and who were not working at the time of application had the lowest odds; such youth were 77% as likely to be employed as a result of their involvement with VR. The odds of successful closure among youth who had a high school diploma and who were either not working or were enrolled in postsecondary school were no different from the odds for youth enrolled in high school.

The associations of individual characteristics with employment, controlling for who received services, were in many ways consistent with findings reported in the broader literature on employment outcomes for youth with disabilities. The odds for female youth were 84% as likely as the odds for male youth to be employed; the odds for youth in all race and ethnic categories were 73% to 93% of the odds as Whites (with Asian being the only category where the findings were not significant); and the odds for older youth were 17% to 38% percent greater than youth ages 14 to 18 years. Whereas the odds of employment for youth with developmental/intellectual disabilities were 11% greater than the odds for youth with learning/cognitive disabilities (the reference group), the odds for youth in all other disability categories were lower. Finally, the odds for youth who were receiving public support or SSA benefits were all lower than the odds for youth not receiving such benefits. Youth who were receiving both SSI and SSDI benefits had the lowest such odds; taking the inverse of their odds ratio, these youth were more than twice as likely to close without employment than to close with employment.

Four agency characteristics were significantly related to the odds of a youth being employed at closure. Youth who applied to agencies that were in OOS, had higher proportions of youth applicants, or had higher costs of purchased services had higher odds of a successful closure. This finding is contrary to the pattern observed with OOS and service receipt. One reason might be that youth served while the agency is in OOS receive more or higher quality services, despite the potential constraint of services, because the agency is serving fewer individuals overall. Conversely, youth who applied to agencies that had higher proportions of youth who received services had lower odds of a successful closure. One reason for this latter result might be that serving fewer youth among those who apply (that is, being more selective) might result in better employment outcomes among those served, perhaps because of increased service intensity or differences in the characteristics of youth served. The two measures of time to services were not significantly associated with closure status; this pattern likely reflects that they are measures of obtaining services, but not related to processes after services begin.

5 Discussion

Our findings provide new information on the outcomes of a recent cohort of youth applicants across their education and employment characteristics at application, information that is relevant for upcoming changes that VR agencies are pursuing owing to WIOA. Of particular importance is that VR outcomes differed across youth who had varying education and employment experiences at the time they applied for VR services, with high school dropouts who were not working having the worst outcomes. Targeting employment services for this group might require supports to overcome significant barriers, both in receiving services and in achieving employment. High school youth, a group highly emphasized by WIOA, tended to rank somewhat in the middle of the other education and employment categories for both service receipt and exiting with employment. This finding is perhaps not surprising, given that high school youth might have very different needs from other subgroups of youth, in part because they are likely to be receiving school-based services or might not receive employment services until after they leave high school.

One pattern of particular importance is the characteristics – male, non-White, younger age, receipt of public benefits, and receipt of SSI benefits – associated with not receiving VR services. Most of these characteristics were also linked to exiting without employment. The fact that so many youth were eligible for services but did not receive them raises questions as to why youth leave before signing an IPE and what barriers they encounter during the assessment and IPE development process. Understanding why youth leave before completing an IPE – either because of delayed timing of services (as suggested by the current results) or because of poor customer/counselor fit (Rigles, Ipsen, Arnold, & Seekins, 2011) – could help identify ways to ensure that youth stay in contact with counselors during the assessment process. In addition to serving more at-risk youth, decreasing the number of youth who leave before receiving services could also help VR agencies meet their WIOA requirements by increasing the number of youth served.

Aggregate agency measures on the timing of initial agency processes and the volume of youth customers were strongly associated with the VR outcomes of individual youth. Longer average times to reach agency milestones – times that reflect the agency’s overall processes, not the agency’s work with an individual youth – increased a youth’s odds of dropping out before obtaining an IPE. In addition, the volume of youth an agency served mattered, though in contradictory ways. Youth were more likely to have successful VR outcomes at agencies that had a higher proportion of youth applicants, but were less likely to have successful outcomes when the agency accepted more of their youth applicants for services. One possible reason for these divergent results is that agencies that serve more raw numbers of youth might have more programs for youth and counselors who are trained on transition issues; such agencies – and perhaps their state environment as a whole – might consider serving youth a higher priority. Conversely, actually serving a greater proportion of youth among those who apply might somewhat dilute service provision or the attention that youth receive from the agency. Another potential factor influencing these relationships might be that youth applying to agencies that serve fewer youth overall might receive services that are more “adult” oriented than “youth” oriented, potentially complicating potential VR success for such youth.

The study’s findings should be considered in light of three caveats. First, this study uses RSA-911 data that are collected for administrative purposes. Such databases have the potential for errors due to processing delays, staff input, or limitations in the measures they contain. With this study, as noted, we lacked a specific variable on school enrollment, as we cannot identify youth who are both employed and enrolled in school in the RSA-911 data. Many of the youth identified as working and without a high school diploma likely were also enrolled in high school, given that their characteristics were so similar to high school enrollees. Second, our findings are correlational in nature; we provide no causal evidence on the relationship between individual characteristics or agency factors and VR outcomes. Third, we examined the patterns in aggregate for all VR-eligible youth, rather than for specific subsets (such as by sex or disability). The relationships between characteristics and outcomes might differ for those specific subgroups, which might lead to tailored interventions. Despite these caveats, the findings, which provide important insight into youth outcomes across their individual characteristics, offer guidance both for additional research paths and for program development.

Our results, particularly the important differences we find by education and employment categories, could help inform what might happen in response to WIOA. WIOA emphasizes increased VR service provision for transition-age youth broadly, particularly for youth still in secondary or postsecondary school. Targeting this subgroup of youth, which currently represents less than one-third of all youth, with pre-employment transition services and other school-based activities risks limiting access to services for other types of youth, such as high school dropouts and recent high school graduates. An immediate effect of this emphasis will be that services for in-school youth (representing about 10% of the overall VR applicant pool) will be relatively overfunded (receiving, at a minimum, 15% of funding on pre-employment transition services alone). Although WIOA also targets youth not attached to the workplace, school, or services, the requirements for serving these youth are less specific, thereby offering no guarantee that these youth will receive services. Importantly, both of these types of youth had among the lowest rates of exiting VR with employment. If agencies successfully serve more of these types of youth, one implication of our findings is that – all else being equal – the proportion of youth who have successful closures might decrease, particularly if the increase in these types of students squeezes out youth who are working at the time of application.

An important consideration to the above is that WIOA-required changes will result in current youth applicants not encountering the same service environment as this study’s youth applicants. Many, if not all, agencies will be increasing the pre-employment transition and other services they provide to high school and postsecondary school youth, with potential spillover effects for other youth. Another implication of WIOA is that, if agencies expand their outreach to serve more unattached youth, those who have graduated from high school might be easier to serve – and have better outcomes – than high school dropouts.

Although they are unsurprising, sizeable changes in the populations served by VR agencies might have implications for an agency’s overall success, as measured by its rehabilitation rate, both for youth and adults. Policymakers could emphasize other measures of success, such as changes in the numbers or proportions of specific types of youth served or the number of youth enrolled in specific programs. Taking a broader view of VR agencies’ roles in their communities, we might consider whether service access is more important for youth in helping with the transition process than rehabilitation rates at closure, particularly given the poorer rates of accessing services among those most at risk, such as minority youth and high school dropouts.

Conflict of interest

The authors have no conflict of interest to report.

Footnotes

Acknowledgments

Funding for this study was provided by the Rehabilitation Research and Training Center on Vocational Rehabilitation Practices for Youth at TransCen, Inc., which is funded by the U.S. Department of Health and Human Services, Administration for Community Living, National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR) (grant no. 90RT5034-02-00). The contents do not necessarily represent the policy of the U.S. Department of Health and Human Services and endorsement by the federal government should not be assumed (Edgar, 75.620 [b]). We appreciate the insights and comments provided by Jeffrey Hemmeter, Purvi Sevak, and an anonymous reviewer, as well as the programming and technical support of Miaomiao Shen, Xiao Berry, and Connie Qian.

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