Vocational rehabilitation service receipt,service expenditures,and ruralness

Abstract

BACKGROUND:

An important factor embedded within Vocational Rehabilitation (VR) delivery capacity relates to geography, such as distance from the VR office and availability of service providers or community rehabilitation programs.

OBJECTIVE:

We explored receipt of VR job search and placement services based on distance to an urban center, demographics, and disability variables after controlling for local employment conditions.

METHODS:

Using 2015 RSA-911 case services data, we used probit to produce estimates for each combination of service and service source (agency and purchased), and Ordinary Least Squares (OLS) and semi-parametric regression to estimate log expenditures for each service category.

RESULTS:

Being Black or living at a long distance from a metro area increased the probability of receiving agency-based services but lowered the probability of receiving purchased services. Conversely, being older and having less education lowered the probability of receiving agency services but increased the probability of receiving purchased services. Females, Blacks, and those living at a distance greater than 50 miles from a metro area received significantly lower expenditures.

CONCLUSION:

Systematic differences in the types of services provided call for more in-depth analysis to ensure that policies and procedures are in place to minimize sociodemographic disparities in service delivery and outcomes.

Keywords

Disability vocational rehabilitation service delivery rural equity

1 Introduction

The U.S. state-federal Vocational Rehabilitation (VR) program offers a range of individualized services to assist people with disabilities obtain, advance in, or retain employment. Each year, the VR program closes approximately 325,000 consumers with disabilities (Lauer & Houtenville, 2017). Of those receiving services, approximately 55% achieve a competitive employment outcome (Department of Education, 2018; Lauer & Houtenville, 2017). There are 79 VR agencies across the country, with 23 states operating both blind and general agencies. The remaining 27 states, DC, and 5 territories operate combined agencies. Broadly, VR services include vocational assessment, counseling and guidance, training, job placement, and other services that support employment (U.S. Department of Labor, Employment and Training Administration, 2021), but delivery can vary across individual agencies (Lund & Cmar, 2019; Roux, Rast, & Shattuck, 2020).

Multiple studies highlight the value of VR services for specific disability groups and for people with disabilities generally (Alsaman & Lee, 2017; Chan, Hart, & Goodman, 2006; Dean et al., 2015, 2017, 2018, 2019; Dutta et al., 2008; Kaya, 2018; Kaya & Chan, 2017; Lund & Cmar, 2019; Roux et al., 2020; Schmidt et al., 2019). Dutta et al. (2008) found that receipt of job placement, on-the-job supports, maintenance, and other services were significant predictors of increased employment outcomes across disability groups. In addition, receipt of diagnosis and treatment of impairments and rehabilitation technology services were significant predictors of increased employment outcomes for individuals with sensory and physical disabilities, and intensive counseling was a significant predictor of increased employment outcomes for individuals with physical and mental impairments (Dutta et al., 2008). Kaya and Chan (2017) found that 43% of consumers with depression and other mood disorders became employed after receiving VR services, but those receiving more intensive services, coupled with reduced time in services, had significantly better employment outcome. Roux et al. (2020) found similar findings for transition-aged youth with Autism, where those receiving more intensive services over a shorter time-period had improved employment outcomes. Receipt of diagnosis and treatment services, job placement, on-the-job support, on-the-job training, maintenance, and information referral were also associated with competitive employment outcomes among transition-age youth with Autism (Roux et al., 2020). Across transition-aged youth more broadly, shorter time span to individualized plan of employment (IPE) development was also an important factor (Honeycutt et al., 2017; Roux et al., 2020).

This overview is just a sample of research attempting to better understand how the mix and intensity of VR services result in employment outcomes for a variety of subpopulations within the VR system. In a systematic review of outcomes for consumers with visual impairments, Lund & Char (2019) highlighted that state- or agency-level factors were missing from many analyses. Roux et al. (2020) reported that state-level variations accounted for significant variations in outcomes after adjusting for individual-level demographics and state-level unemployment. Similarly, Ipsen & Stern (2020) found that applications to receive VR services varied widely across states after controlling for individual and economic factors but also found that application rates were systematically lower in rural areas compared to urban areas.

An important factor embedded within delivery capacity relates to geography, such as distance from the VR office and the availability of related service providers or community rehabilitation programs (Ipsen et al., 2019). A few studies have highlighted how outcomes vary across places. For instance, Ipsen and Swicegood (2015) found that rural VR consumers had to wait longer to receive services, received fewer supports in integrated settings, and had higher closure rates to self-employment. Landon et al. (2019) reported several barriers to rural service delivery, such as few community providers and supports, mistrust of outsiders (including the VR agency), constrained employment opportunities, and limited transportation options. These types of delivery constraints may place rural people with disabilities at a double disadvantage. Not only do they live in locations with more limited economic opportunities, but they may also receive fewer VR services to help them overcome employment barriers.

The current study aims to explore receipt of VR services based on distance to service, demographic, and disability variables after controlling for local employment conditions. We hypothesize that greater distance to an urban center negatively impacts receipt of VR services, particularly for services that are often delivered via contractors, vendors, or community rehabilitation programs such as job placement and coaching services (Ipsen et al., 2019; Landon et al., 2019).

2 Methods

2.1 Data sources

For these analyses, we used 2015 Rehabilitation Services Administration Case Service Report (RSA-911) data. The 2015 RSA-911 dataset includes case level demographic, disability, VR services, and employment status information for consumers whose cases closed during the 2015 fiscal year. The 2015 RSA-911 dataset also includes county of residence, which we used to match with Federal Information Processing System (FIPS) codes. FIPS codes are used across multiple data sets and can be used to match case level data to a variety of county-level indicators.

2.2 Data cleaning

Data cleaning included several steps. First, we removed all cases where we were unable to link FIPS codes. This resulted in the removal of all observations from Alaska and U.S. territories plus additional individual cases that did not collect accurate county indicators. Second, we removed individual observations with missing data on key demographic variables including gender, race, and age. Finally, we removed any observations that fell outside the 18–65 age range. Table 1 reports on our data cleaning process and the final number of observations included in the study. The remaining data represented approximately 86% of all cases.

Table 1
Data cleaning description

Cleaning steps Observations

Initial number of observations 555,075

Observations without FIPS codes 20,005

Observations with missing gender 288

Observations with missing race 154

Observations of individuals <18 or >65 59,868

Final study sample 474,760

Cleaning steps	Observations
Initial number of observations	555,075
Observations without FIPS codes	20,005
Observations with missing gender	288
Observations with missing race	154
Observations of individuals <18 or >65	59,868
Final study sample	474,760

2.3 Measures

We used 2015 RSA-911 data for sociodemographic controls and received services. The advantages of the RSA-911 national data are that it covers all states in one data set and it is readily available to researchers. Alternatively, we could have collected individual state VR agency service data. With state agency data, one can get RSA-911 data elements along with some extra variables (see Dean et al., 2015, 2017, 2018, 2019; Schmidt et al., 2019). However, securing data at the state level requires much administrative, data cleaning, and data-merging effort.

Gender. Coded as dummy variable for female.

Race/ethnicity. Coded as dummy variables for White, Black or African American, American Indian or Alaska Native, Asian, Native Hawaiian or other Pacific Islander, and Hispanic or Latino.

Age. Calculated as 2015 minus birth year.

Education level at application. Classified into four groups: less than high school graduation, special education certificate of completion, high school graduate/GED, and post-secondary education.

Disability type. Counselors report disability in terms of impairment codes and cause or source codes. These data were used to collapse disability types into 10 categories including intellectual disability, learning disability, attention deficit hyperactivity disorder (ADHD), traumatic brain injury (TBI), Autism, mental illness, substance abuse, physical impairment, vision impairment, and hearing impairment.

Disability severity. VR counselors classify severity of disability as 0 = no significant disability, 1 = significant disability, and 2 = most significant disability. Those with the most significant disabilities have the highest barriers to employment and require multiple VR services over time. Also, when agencies are required to go into order of selection (where VR state budgets do not allow for service delivery to all eligible applicants), those with the most significant disabilities are served first.

VR services. The 2015 RSA 911 collected data on 27 service categories. For each service, there is an indicator variable: service was not received (= 0), was provided by the VR agency (= 1), was purchased by the VR agency (= 2), was provided by comparable services and benefits providers (= 3), or was provided by a combination of agency and/or purchased and/or comparable services (= 4). There are follow-up questions about purchased services in terms of provider type and costs of purchased services from Title 1 and Title 4, Part B funds.

For our analyses, we collapsed service categories into nine groups based on past return on investment research (Dean et al., 2015, 2017, 2018, 2019; Schmidt et al., 2019; Stern et al., 2019). Table 2 provides this categorization.

Table 2
Aggregation of services

Service category Services included

Assessment •Assessment

Disability Treatment •Diagnosis and treatment of impairments

•VR counseling and guidance

Education •Graduate college or university training

•Four-year college or university training

•Junior or community college training

•Basic academic remedial or literacy training

•Miscellaneous training

Job Training •Occupational or vocational training

•On-the-job training

•Apprenticeship training

•Job readiness training

•Disability-related skills training

Job Search and Placement •Job search assistance

•Job placement assistance

•On-the-job supports – short term

•Technical assistance services

Supported Employment •On-the-job supports – supported employment

•Customized employment services

Other Supports •Transportation

•Maintenance

•Information and referral services

Disability Accommodations •Rehabilitation technology

•Reader services

•Interpreter services

•Personal attendant services

Benefits •Benefits counseling

Service category	Services included
Assessment	•Assessment
Disability Treatment	•Diagnosis and treatment of impairments
	•VR counseling and guidance
Education	•Graduate college or university training
	•Four-year college or university training
	•Junior or community college training
	•Basic academic remedial or literacy training
	•Miscellaneous training
Job Training	•Occupational or vocational training
	•On-the-job training
	•Apprenticeship training
	•Job readiness training
	•Disability-related skills training
Job Search and Placement	•Job search assistance
	•Job placement assistance
	•On-the-job supports – short term
	•Technical assistance services
Supported Employment	•On-the-job supports – supported employment
	•Customized employment services
Other Supports	•Transportation
	•Maintenance
	•Information and referral services
Disability Accommodations	•Rehabilitation technology
	•Reader services
	•Interpreter services
	•Personal attendant services
Benefits	•Benefits counseling

For each service category, we created three dependent variables:

Agency services = 1 if the consumer received only agency-provided services in the category (as opposed to any purchased services from an outside vendor).

Purchased services = 1 if the consumer received purchased services in the category.

Log expenditures = the log of aggregated purchased services for each service category, conditional on receipt of services.

Distance. The distance variable captures how far a rural (micropolitan or noncore) county is from the nearest metropolitan county, based on Office of Management and Budget (OMB) county designations. 1 The distance measure was constructed using county-level population centroids from the 2010 U.S. Census and the Haversine formula, which is commonly used to calculate the shortest distance between two points. 2 Within each state, we calculated the county’s distance from every other county, and retained the minimum distance of a rural county from an urban county. For metro counties, the minimum distance is naturally zero. We added 1 to each distance measure for mathematical reasons. Using these data, we classified VR cases into three groups: (1) live under 20 miles from a metro office, (2) live 20 to 50 miles from a metro office, and (3) live greater than 50 miles from a metro office.

Employment ratio. The employment ratio was constructed using employment data from the U.S. Census, Bureau of County Business Patterns and county-level population estimates from the U.S. Census, Population Division. The employment ratio for each county is defined as (county employment of nearest metro area/county population of nearest metro area) * (1/distance) (Dean et al., 2015, 2017, 2018, 2019; Schmidt et al., 2019). This measure assesses economic opportunity based on the employment rate and distance from the nearest metro area. Note that this variable declines as distance increases.

2.4 Data analyses

We were interested in learning how receipt of services was influenced by distance to a metro area while controlling for sociodemographic characteristics. We hypothesized that some services, particularly those that rely on purchased services, would be sensitive to distance due to the unavailability of vendors or community rehabilitation programs in more rural locations (Ipsen et al., 2019). For each service category, we explored this question with two dependent variables including receipt of services and log expenditure of purchased services, conditional on service receipt.

Methodology for services receipt. We used probit to produce estimates for each combination of service and service source (agency services and purchased services); for a discussion of probit, see Maddala (1993). We felt comfortable using probit (with normally distributed errors) instead of some other binary choice error assumption because of results in Stern (1996). For each estimation exercise associated with agency services, the choice was either receipt of agency services or no receipt of agency services. There was a similar specification for purchased services. This resulted in 18 different estimation exercises based on the 9 different service categories for both agency services and purchased services. We used the same set of explanatory variables in each of the estimation procedures.

Methodology for conditional log expenditure of purchased services. RSA 911 collected expenditure data only on purchased services. Services provided by the agency or as a comparable benefit were not assigned a monetary value. For this reason, our analyses of expenditure data were confined to services that were purchased. We used log expenditures to reduce the influence of large outliers and because log expenditure data appear to have a normal distribution. The way to interpret coefficient estimates associated with the log expenditure equations is the percentage increase in expenditure as the variable of interest increases by one. For each explanatory variable that increases log expenditure, it also increases expenditure, and for each explanatory variable that decreases log expenditure, it also decreases expenditure.

We explored both Ordinary Least Squares (OLS) and semi-parametric regression models to estimate log expenditures for each service category. For many services, including assessment, job training, job search and placement, supported employment, and other supports, the linear specification imposed by OLS resulted in similar estimates to the semi-parametric estimates. For other categories including disability treatment, education, disability accommodations, and benefits, the OLS estimates were not proportional to the semi-parametric estimates, and therefore OLS was not a good specification of the model.

Estimation results. Although all probit, OLS, and non-parametric model results are available in supplementary tables (see https://sites.google.com/site/stevensterneconomics/research-interests/vocational-rehabilitation/ipsen-jain-stern-estimates), we focused our discussion on receipt of job search and placement services. Past research indicated that receipt of VR job search and placement services strongly predicts employment outcomes across disability types (Dutta et al., 2008; Leahy et al., 2018; Nord & Hepperlen, 2016). Our results included three models with identical explanatory variables listed in Table 3. Model 1 used probit to estimate the effects of explanatory variables on receiving agency-based job search and placement services. Model 2 used probit to estimate the effects of explanatory variables on receiving purchased job search and placement services. Model 3 used OLS to estimate the effects of explanatory variables on purchased job search and placement services, conditional on receipt of the service. Because OLS and semi-parametric models were proportional or comparable for purchased job search and placement services, we reported only OLS estimates to streamline discussing results.

Table 3
Descriptive statistics for explanatory variables

Person-level Percentage or M(SD)

Explanatory variables

Female 44.5%

White 70.4%

Black 27.1%

Hispanic 10.6%

Age 36.3% (14)

Education

Special education 8.3%

High school diploma 40.3%

>High school diploma 30.3%

Disability type

Mental illness 38.1%

Physical disability 16.6%

Learning disability 12.2%

Substance abuse 10.0%

Intellectual disability 8.6%

Hearing impairment 7.3%

ADHD 7.2%

Vision 5.0%

Autism 3.5%

TBI 1.9%

Significant disability 44.5%

Most significant disability 41.6%

Distance from metro

< 20 miles 82.6%

20–50 miles 9.1%

> 50 miles 8.3%

Person-level	Percentage or M(SD)
Explanatory variables
Female	44.5%
White	70.4%
Black	27.1%
Hispanic	10.6%
Age	36.3% (14)
Education
Special education	8.3%
High school diploma	40.3%
>High school diploma	30.3%
Disability type
Mental illness	38.1%
Physical disability	16.6%
Learning disability	12.2%
Substance abuse	10.0%
Intellectual disability	8.6%
Hearing impairment	7.3%
ADHD	7.2%
Vision	5.0%
Autism	3.5%
TBI	1.9%
Significant disability	44.5%
Most significant disability	41.6%
Distance from metro
< 20 miles	82.6%
20–50 miles	9.1%
> 50 miles	8.3%

2.5 Study sample

The sample was predominantly male (55%), White (70%), and with a high school diploma or less (70%). The most common disability types were mental illness (38%), followed by physical impairment (17%). Table 3 shows descriptive statistics for the study sample. The reference groups were male, not-White or Black, not-Hispanic, and education less than a high school diploma.

2.6 Descriptive statistics for receipt of services

Table 4 shows the percent of cases that received services for each service category by source. The most common services received include assessment services (55%), benefits counseling (49%), other supports (34%), and job search and placement services (33%). While assessment, benefits, and other supports help individuals get linked with additional resources and address barriers to employment, job search and placement services are related to finding employment.

Table 4
Aggregated service proportions by source

Service category Agency Purchased Comparable benefit Combination of sources Total

Assessment .159 .285 .007 .100 .551

Disability treatment .014 .122 .032 .086 .254

Education .007 .065 .008 .028 .108

Job training .036 .090 .005 .038 .169

Job search and placement .104 .140 .006 .078 .328

Supported employment .014 .128 .003 .065 .210

Other supports .146 .133 .003 .058 .340

Disability accommodations .007 .045 .002 .017 .071

Benefits .421 .014 .003 .051 .489

Service category	Agency	Purchased	Comparable benefit	Combination of sources	Total
Assessment	.159	.285	.007	.100	.551
Disability treatment	.014	.122	.032	.086	.254
Education	.007	.065	.008	.028	.108
Job training	.036	.090	.005	.038	.169
Job search and placement	.104	.140	.006	.078	.328
Supported employment	.014	.128	.003	.065	.210
Other supports	.146	.133	.003	.058	.340
Disability accommodations	.007	.045	.002	.017	.071
Benefits	.421	.014	.003	.051	.489

Table 5 reports on the ratio of cases that received agency services vs purchased services by distance from metro (i.e., < 20 miles, 20–50 miles, or > 50 miles). As distance to a metro area increased, ratios of agency service receipt to purchased service receipt increased substantially for assessment, job training, and job search and placement service categories.

Table 5

Ratio of cases receiving agency services vs purchased services by distance

Service category	< 20 miles	20–50 miles	> 50 miles
Assessment	0.517	0.732	0.814
Disability treatment	0.120	0.111	0.087
Education	0.117	0.098	0.113
Job training	0.376	0.536	0.635
Job search and placement	0.689	1.076	1.062
Supported employment	0.108	0.070	0.120
Other supports	1.090	0.952	1.280
Disability accommodations	0.153	0.164	0.212
Benefits	30.305	25.557	42.445

Table 6 shows the mean value of purchased services for each category. The mean expenditures presented in Table 6 are likely to be a misleading estimate of costs due to indications of kurtosis. This implies that the data were not normally distributed, and a lognormal distribution was more appropriate for analyses of purchased services conditional on purchased service receipt. We compared the empirical distribution function to that of an appropriately chosen log normal distribution and found excellent fits. These are reported in the supplementary tables.

Table 6

Moments of purchased expenditure data conditional on purchased service receipt

Service category	# Obs	Mean	St. dev.	Kurtosis
Assessment	134,392	$792	$1,731	664.6
Disability treatment	57,756	$1,189	$4,677	1341.6
Education	30,686	$3,953	$6,970	60.2
Job training	42,568	$2,509	$3,689	75.0
Job search and placement	66,286	$2,378	$2,753	299.1
Supported employment	60,358	$659	$1,844	694.9
Other supports	42,167	$886	$4,284	221.6
Disability accommodations	20,218	$3,260	$6,321	527.1
Benefits	4,292	$141	$496	247.2

Note: We observed very large values of kurtosis telling us that the right tail of the density is much fatter than we would expect under normality. Kurtosis is a measure of the ratio of the fourth centered moment and the variance squared. Lognormal densities exhibit much more kurtosis than normal densities. The amount of excess kurtosis for the lognormal density depends upon the variance parameter in the density and is unbounded.

3 Results

This paper reports on receipt of job search and placement services. Table 7 reports probit estimates for receipt of agency job search and placement services (Model 1) and receipt of purchased job search and placement services (Model 2). Highlighted rows reflect the explanatory variables that change directions for receipt of agency services compared to purchased services and were statistically significant. For instance, being Black, having a substance abuse or physical disability, or living a long distance from a metro area increased the probability of receiving agency services but lowered the probability of receiving purchased services. Conversely, being older, having less education, and having an intellectual disability, TBI, or Autism disability lowered the probability of receiving agency services but increased the probability of receiving purchased services. Although these patterns vary across different service categories (see supplementary materials), the findings provide valuable information to VR agencies regarding equity across groups.

Table 7
Probit estimates for receipt of agency and purchased job search and placement services (N = 474,760)

Model 1 Model 2

Receipt of agency services Receipt of purchased services

Beta SE p AMP□ Beta SE P AMP□

Constant – 1.96 – 2.31

Female –.011 .005 .02 –.019 –.048 .005 ≤.001 –.081

White .076 .015 ≤.001 .136 .002 .012 .464 .002

Black .177 .015 ≤.001 .316 –.129 .012 ≤.001 –.218

Hispanic –.153 .009 ≤.001 –.272 –.063 .008 ≤.001 –.106

Age/100 –.197 .022 ≤.001 –.352 .191 .020 ≤.001 .320

Special education –.127 .011 ≤.001 –.226 .121 .009 ≤.001 .204

High school diploma –.055 .007 ≤.001 –.097 .100 .007 ≤.001 .168

>HS diploma .049 .008 ≤.001 .087 .088 .007 ≤.001 .147

Intellectual disability –.023 .010 .01 –.042 .438 .008 ≤.001 .735

Learning disability .131 .009 ≤.001 .233 .207 .008 ≤.001 .348

ADHD .013 .010 .105 .022 .039 .009 ≤.001 .065

TBI –.048 .019 .005 –.086 .181 .016 ≤.001 .303

Autism –.087 .015 ≤.001 –.155 .413 .012 ≤.001 .693

Mental illness .017 .006 .002 .031 .118 .006 ≤.001 .197

Substance abuse .263 .008 ≤.001 .468 –.143 .009 ≤.001 –.240

Physical impairment .063 .007 ≤.001 .112 –.015 .007 .018 –.025

Vision impairment –.170 .013 ≤.001 –.302 –.198 .012 ≤.001 –.333

Hearing impairment .017 .011 .053 .031 .157 .010 ≤.001 –.267

Significant disability .601 .011 ≤.001 1.068 1.099 .013 ≤.001 1.84

Most significant disability .727 .011 ≤.001 1.291 1.024 .013 ≤.001 1.72

20 mi < Distance<50 mi .214 .011 ≤.001 .383 –.040 .011 ≤.001 –.067

Distance≥50 .158 .012 ≤.001 .283 –.086 .012 ≤.001 –.145

Employment ratio .029 .018 .051 .051 .167 .015 ≤.001 .279

	Model 1	Model 2
Constant	– 1.96				– 2.31
Female	–.011	.005	.02	–.019	–.048	.005	≤.001	–.081
White	.076	.015	≤.001	.136	.002	.012	.464	.002
Black	.177	.015	≤.001	.316	–.129	.012	≤.001	–.218
Hispanic	–.153	.009	≤.001	–.272	–.063	.008	≤.001	–.106
Age/100	–.197	.022	≤.001	–.352	.191	.020	≤.001	.320
Special education	–.127	.011	≤.001	–.226	.121	.009	≤.001	.204
High school diploma	–.055	.007	≤.001	–.097	.100	.007	≤.001	.168
>HS diploma	.049	.008	≤.001	.087	.088	.007	≤.001	.147
Intellectual disability	–.023	.010	.01	–.042	.438	.008	≤.001	.735
Learning disability	.131	.009	≤.001	.233	.207	.008	≤.001	.348
ADHD	.013	.010	.105	.022	.039	.009	≤.001	.065
TBI	–.048	.019	.005	–.086	.181	.016	≤.001	.303
Autism	–.087	.015	≤.001	–.155	.413	.012	≤.001	.693
Mental illness	.017	.006	.002	.031	.118	.006	≤.001	.197
Substance abuse	.263	.008	≤.001	.468	–.143	.009	≤.001	–.240
Physical impairment	.063	.007	≤.001	.112	–.015	.007	.018	–.025
Vision impairment	–.170	.013	≤.001	–.302	–.198	.012	≤.001	–.333
Hearing impairment	.017	.011	.053	.031	.157	.010	≤.001	–.267
Significant disability	.601	.011	≤.001	1.068	1.099	.013	≤.001	1.84
Most significant disability	.727	.011	≤.001	1.291	1.024	.013	≤.001	1.72
20 mi < Distance<50 mi	.214	.011	≤.001	.383	–.040	.011	≤.001	–.067
Distance≥50	.158	.012	≤.001	.283	–.086	.012	≤.001	–.145
Employment ratio	.029	.018	.051	.051	.167	.015	≤.001	.279

□ Average marginal probability of using the service. Note: Light grey shading indicates significant beta coefficients that work in opposite directions across models for receipt of agency services and purchased services.

As highlighted in the methods section, OLS and semi-parametric models provided results that were proportional for both purchased job search and placement services. For this reason and to simplify the discussion, Table 8 includes OLS estimates to predict the log expenditures of purchased job search and placement services, conditional on receipt of the purchased service. Semi-parametric estimates for each service category are included in the supplementary tables.

Table 8

OLS regression estimates for log expenditures on purchased job search and placement services conditional on service receipt (N = 66,286)

	Beta	SE	t-score	p
Constant	7.225
Female	– 0.019	0.010	– 1.90	0.03
White	– 0.032	0.024	– 1.33	0.093
Black	– 0.069	0.025	– 2.76	0.003
Hispanic	– 0.023	0.016	– 1.44	0.083
Age/100	– 0.422	0.042	– 10.05	≤.001
Special education	– 0.067	0.018	– 3.72	≤.001
High school diploma	0.014	0.014	1.00	0.156
>HS diploma	0.079	0.015	5.27	≤.001
Intellectual disability	0.023	0.016	1.44	0.068
Learning disability	0.058	0.015	3.87	≤.001
ADHD	– 0.016	0.018	– 0.89	0.196
TBI	0.097	0.032	3.03	0.001
Autism	0.144	0.021	6.86	≤.001
Mental illness	– 0.013	0.011	– 1.18	0.128
Substance abuse	– 0.209	0.019	– 11.00	≤.001
Physical impairment	– 0.041	0.014	– 2.93	0.002
Vision impairment	0.012	0.028	0.43	0.331
Hearing impairment	0.053	0.023	2.30	0.01
Significant disability	0.278	0.038	7.32	≤.001
Most significant disability	0.053	0.039	1.36	0.083
20 mi < Distance < 50 mi	0.062	0.021	2.95	≤.001
Distance≥50	– 0.135	0.014	– 9.64	≤.001
Employment ratio	0.127	0.030	4.23	≤.001

R² = .016. Note: Light grey shading indicates significantly lower log expenditures; dark grey shading indicates significantly higher log expenditures.

Significant explanatory variables associated with reductions in expenditures on job search and placement services are shaded in light gray, and variables associated with increases in expenditures are shaded in dark grey. In terms of sociodemographic variables, being female, Black, older, having a special education (relative to receiving less than a high school diploma), and living more than 50 miles from a metro area were associated with reductions in expenditures. In terms of disability, individuals with substance abuse or physical impairment received fewer purchased services, and individuals with learning disability, TBI, Autism, hearing impairment, and significant disability (relative to least significant disability) received more purchased services.

Despite almost all estimates being statistically significant, the R² statistic is very small indicating that we are explaining only 1.6% of the variation in the log expenditure data. It is not clear what the missing explanatory variables are and whether they are observed, but the R² statistics are similar to other RSA-911 findings (for example, Schimmel, Honeycutt, & Stapleton, 2014).

4 Discussion

4.1 Receipt of services

These data highlight differences in service provision based on demographic, disability, and environmental characteristics. While some rationale might be provided for service delivery differences based on age, education, disability type, and distance to metro, it is more difficult to describe why gender and race were associated with differences. Each probit parameter estimate should be interpreted as the effect of the associated explanatory variable on the value of the service choice (agency or purchased) relative to any alternative choice and, therefore, the probability of choosing that choice. Models for receipt of agency services and purchased services showed that certain demographic groups consistently received different rates of job search and placement services regardless of delivery type (agency and/or purchased). For instance, (1) relative to males, females received significantly lower rates of both agency and purchased services, (2) relative to other race, Whites received significantly higher rates of agency services, and (3) relative to non-Hispanics, Hispanics received significantly lower rates of both agency and purchased services. Additionally, there were groups that received services in different ways. For instance, relative to other race, Blacks received significantly more agency services and significantly fewer purchased services. After controlling for other sociodemographic and geographic characteristics, differences in service provision based on race and gender suggest that the VR agencies may not have provided services equitably.

Differences in service provision also occurred because of differences in individual characteristics such as education level, disability type, and disability severity. At an intuitive level, these differences make logical sense. For instance, maybe those with significant and most significant disabilities received significantly higher rates of both agency services and purchased services, relative to those with no significant disabilities, because they required more services to achieve an employment outcome. Differences may also be explained by the degree or type of services needed to achieve an employment outcome. For instance, those with TBI, intellectual disability, or Autism received significantly fewer agency services but significantly more purchased services. These differences might show the niche that purchased services fill in terms of more intensive employment services that agency personnel are unable or unequipped to provide. 3

Geography also played a role in the type or mix of services received. Specifically, after controlling for sociodemographic characteristics and disability type, consumers living at a distance from the VR office received significantly higher rates of agency services and significantly lower rates of purchased services. Ipsen, Goe, and Bliss (2019) reported gaps in provider availability in rural communities due to increased economic, transportation, and delivery barriers. Specifically, fewer economic opportunities in rural communities make placements harder to secure; drive time to reach rural consumers adds to the costs of providing services; and it is difficult to support full-time staff in locations with limited numbers of consumers to serve. They posited that providers are less willing to serve rural consumers because it is more expensive to do so and placement success is not as favorable. They suggested that VR payment models do not adequately address these barriers or the added costs of doing business in rural locations (Ipsen et al., 2019). When vendors are unavailable, VR agency personnel are responsible for providing services.

4.2 Purchased services

For the subset of consumers who received purchased services, we conducted a linear regression of log expenditures of purchased job search and placement services on the same set of explanatory variables in the probit models to see which characteristics caused the differences in expenditures. We know that females and Blacks received fewer agency services and purchased services overall (as shown in the probit models). In addition, females and Blacks received significantly lower expenditures for purchased services (see Table 8: OLS log expenditure model beta estimates). For example, the t-statistic associated with the differences in estimates for Blacks and Whites was 3.058, indicating a statistically significant difference. Though there are other possible explanations for this difference, a possible cause of the result is racial discrimination.

In terms of disability, those with a learning disability, TBI, Autism, a hearing impairment, and significant disability received significantly higher levels of purchased services, while those with substance abuse or physical impairment received significantly lower levels of purchased services. As mentioned previously, this may relate to the intensity of services required for certain disability types.

The two “distance to metro” variables provided conflicting evidence. Those living between 20 and 50 miles from a metro office received significantly higher expenditures of purchased services relative to those living at most 20 miles from the VR agency, while those at a distance greater than 50 miles received significantly lower expenditures. In part, this may relate to differences in economic opportunity. Very rural locations may have such limited economic opportunity that costs for job search and placement services are limited, while places that are closer to an economic center may have more job opportunities to explore. This is similar to the employment ratio variable, which indicated employment opportunities had a positive impact on the amount of purchased services received.

Ipsen and Stern (2020) found that people with disabilities from rural counties were less likely to apply for VR services based on population estimates of the population with disabilities. One hypothesis for this lower-than-expected application rate was focused on expectations for positive employment outcomes. In part, this analysis builds evidence that this might be the case insomuch as rural consumers received fewer purchased services overall, and cases that were most rural received less expenditure.

4.3 Limitations

These analyses were limited in many ways. First, we used RSA 911 data from 2015, which provided a dated picture of VR service delivery. This choice was made for two reasons. First, these data reflect a timespan where the RSA 911 included geographic indicators and were collected annually on case closures versus quarterly as cases were progressing through the VR system. Second, we were interested in using data that allowed us to make time-relevant comparisons with other papers focused on rural VR service delivery (see Ipsen & Stern, 2020; Ipsen et al., 2019).

Another limitation relates to RSA 911 data collection protocols related to services received. While we know whether a consumer received agency services, we do not know how much agency services were received. Presently, many state VR agencies do not collect information on time or monetary expenditure for agency services. Information on agency expenditure would provide each agency a better understanding of how staff resources are used. This lack of agency expenditure data is quite different for purchased services. Having a common indicator of both agency services and purchased services received, such as dollars or time, would aid in (1) understanding parity across consumers, (2) controlling for regional differences in VR-related costs, and (3) providing a measure of service intensity required to achieve employment outcomes for specific groups.

5 Conclusions

This manuscript builds on limited past research about delivery of VR services across geography and provides an innovative way of assessing level of rurality. Based on past research, we know that (1) rural people tend to apply for VR services at lower rates, (2) the VR case mix looks different in rural communities, and (3) agencies report provider shortages in rural locations (Ipsen et al., 2019; Ipsen & Stern, 2020; Ipsen & Swicegood, 2015). This manuscript builds on these findings and offers a strategy to understand VR service delivery at the county, state, and national levels for rural and urban consumers. At a basic level, agency personnel can examine the ratios of consumers receiving agency services vs purchased services by distance (see Table 5) to examine how service delivery varies across places. Where differences exist, this may call attention to the need for alternate funding mechanisms (for example, hourly and compensated drive time vs performance-based funding or a state and/or federal subsidy to make up the difference for service providers) and provider arrangements (for example, independent contractors vs community rehabilitation programs) to develop a provider pool (Ipsen et al., 2019). Additionally, these types of analyses allow agencies to objectively measure some imperfect signs of inequity across consumer groups. Systematic differences in the types of services provided call for more in-depth analysis to ensure that policies and procedures are in place to minimize sociodemographic disparities in service delivery and outcomes.

Footnotes

Acknowledgments

The authors have no acknowledgements.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethics statement

This is not applicable as the data was de-identified and from a secondary data source.

Funding

A portion of this research was supported by grant #90RTCP0002 (Research and Training Center on Disability in Rural Communities) from the National Institute on Disability and Rehabilitation Research within the Administration on Community Living, U.S. Department of Health and Human Services. The contents and opinions expressed reflect those of the authors and should not be considered an endorsement by the funding agency or the Federal Government.

Informed consent

This is not applicable as the data was de-identified and from a secondary data source.

The OMB designates counties as metropolitan, micropolitan, or noncore. A metro area contains a core urban area of 50,000 or more population, and a micro area contains an urban core of at least 10,000 (but less than 50,000) population. In this paper, all counties that are not metropolitan are considered rural.

The ‘haversine’ formula is commonly used to calculate the great-circle distance between two points – that is, the shortest distance over the earth’s surface. The formula is ${a = \sin}^{2} (\otimes) / 2) + \cos φ_{1}^{*} \cos φ_{2}^{*} {a = \sin}^{2} (\otimes ⌊ / 2)$ ,

c = 2 * atan(²(√ a, √(1– a))),

d = R * c

where φ is latitude, λ is longitude, ⊗ denotes the difference, and R is the earth’s radius (mean radius = 6,371 km).

Note that angles are denoted in radians.

Differences suggest that it may be necessary to control for the endogeneity of service expenditure on labor market outcomes, but some distance variables are available to do that.

References

Alsaman,

M. A.

, & Lee,

C.-L.

(2017). Employment outcomes of youth with disabilities in Vocational Rehabilitation: A multilevel analysis of RSA- data. Rehabilitation Counseling Bulletin60(2), 98–107. https://doi.org/10.1177/0034355216632363

Chan,

, Hart,

L. G.

, & Goodman,

D. C.

(2006). Geographic access to health care for rural Medicare beneficiaries, The Journal of Rural Health22(2), 140–146.

Dean,

D. H.

, Pepper,

J. V.

, Schmidt,

R. M.

, & Stern,

S. N.

(2015). The effects of Vocational Rehabilitation for people with cognitive impairments (SSRN Scholarly Paper No. 2600298). https://doi.org/10.1111/iere.12109

Dean,

, Pepper,

, Schmidt,

, & Stern,

(2019). The effects of youth transition programs on labor market outcomes of youth with disabilities, Economics of Education Review68(C), 68–88.

Dean,

, Pepper,

J. V.

, Schmidt,

, & Stern,

(2017). The effects of Vocational Rehabilitation services for people with mental illness, Journal of Human Resources52(3), 826–858.

Dean,

, Schmidt,

, Pepper,

, & Stern,

(2018). The effects of Vocational Rehabilitation for people with physical disabilities, Journal of Human Capital12(1), 1–37. https://doi.org/10.1086/696098

Department of Education. (2018). Rehabilitation Services: Fiscal year 2019 budget request. (p. 76). Department of Education. https://www.ed.gov/about/overview/budget/budget/justifications/i-rehab.pdf

Dutta,

, Gervey,

, Chan,

, Chou,

C.-C.

, & Ditchman,

(2008). Vocational Rehabilitation services and employment outcomes for people with disabilities: A United States study. Journal of Occupational Rehabilitation18(4), 326–334. https://doi.org/10.1007/s10926-008-9154-z

Honeycutt,

T. C.

, Thompkins,

A. V.

, Bardos,

M. E.

, & Stern,

S. N.

(2017). Youth with disabilities at the crossroads: The intersection of Vocational Rehabilitation and disability benefits for youth with disabilities, Rehabilitation Counseling Bulletin60(3), 131–144. https://doi.org/10.1177/0034355215621897

10.

Ipsen,

, Goe,

, & Bliss,

(2019). Vocational Rehabilitation (VR) funding of job development and placement services: Implications for rural reach. Journal of Vocational Rehabilitation51(3), 313–324. https://doi.org/10.3233/JVR-191048

11.

Ipsen,

, & Stern,

(2020). The effect of ruralness on Vocational Rehabilitation applications, Journal of Vocational Rehabilitation53(1), 89–104. https://doi.org/10.3233/JVR-201088

12.

Ipsen,

, & Swicegood,

(2015). Rural and urban differences in Vocational Rehabilitation case mix, delivery practices, and employment outcomes, Rehabilitation Research, Policy, and Education29(4), 349–370. https://doi.org/10.1891/2168-6653.29.4.349

13.

Kaya,

(2018). Demographic variables, Vocational Rehabilitation services, and employment outcomes for transition-age youth with intellectual disabilities, Journal of Policy and Practice in Intellectual Disabilities15(3), 226–236. https://doi.org/10.1111/jppi.12249

14.

Kaya,

, & Chan,

(2017). Vocational Rehabilitation services and outcomes for working age people with depression and other mood disorders, Journal of Rehabilitation83(3), 44–52.

15.

Landon,

, Lizotte,

, Peña,

, & Connor,

(2019). Rehabilitation counseling in rural settings: A phenomenological study on barriers and supports, Journal of Rehabilitation85(2), 47–57.

16.

Lauer,

, & Houtenville,

(2017). Annual Disability Statistics Compendium: 2016. University of New Hampshire – Institute on Disability. https://disabilitycompendium.org/compendium/annual-statistics-2016?page=15

17.

Leahy,

M. J.

, Del Valle,

R. J.

, Landon,

T. J.

, Iwanaga,

, Sherman,

S. G.

, Reyes,

, & Chan,

(2018). Promising and evidence-based practices in vocational rehabilitation: Results of a national Delphi study. Journal of Vocational Rehabilitation48(1), 37–48. https://doi.org/10.3233/JVR-170914

18.

Lund,

E. M.

, & Cmar,

J. L.

(2019). Factors related to employment outcomes for Vocational Rehabilitation consumers with visual impairments: A systematic review. Journal of Visual Impairment & Blindness113(6), 518–537. https://doi.org/10.1177/0145482X19885277

19.

Nord,

, & Hepperlen,

(2016). More job services— better employment outcomes: Increasing job attainment for people With IDD, Intellectual and Developmental Disabilities54(6), 402–411. https://doi.org/10.1352/1934-9556-54.6.402

20.

Roux,

A. M.

, Rast,

J. E.

, & Shattuck,

P. T.

(2020). State-Level variation in Vocational Rehabilitation service use and related outcomes among transition-age youth on the Autism spectrum. Journal of Autism and Developmental Disorders50(7), 2449–2461. https://doi.org/10.1007/s10803-018-3793-5

21.

Schimmel Hyde,

, Honeycutt,

, & Stapleton,

(2014). The relationship between timely delivery of vocational rehabilitation services and subsequent federal disability benefit application and receipt. IZA Journal of Labor Policy3(1), 15. https://doi.org/10.1186/2193-9004-3-15

22.

Schmidt,

R. M.

, Clapp,

C. M.

, Pepper,

J. V.

, & Stern,

S. N.

(2019). Applications of the VR-ROI Project: ROI estimates for Virginia and Maryland (SSRN Scholarly Paper No. 3532014). Social Science Research Network. https://papers.ssrn.com/abstract=3532014

23.

Stern,

(1996). Semiparametric estimates of the supply and demand effects of disability on labor force participation, Journal of Econometrics71(1), 49–70. https://doi.org/10.1016/0304-4076(94)01694-1

24.

Stern,

S. N.

, Clapp,

C. M.

, Pepper,

J. V.

, & Schmidt,

R. M.

(2019). Data issues in developing valid ROI estimates. (SSRN Scholarly Paper No. 3531990). Social Science Research Network. https://papers.ssrn.com/abstract=3531990

25.

U.S. Department of Labor, Employment and Training Administration. (2021). CareerOneStop: Vocational Rehabilitation. CareerOneStop. //www.careeronestoorg

	Model 1				Model 2
	Receipt of agency services				Receipt of purchased services
	Beta	SE	p	AMP□	Beta	SE	P	AMP□
Constant	– 1.96				– 2.31
Female	–.011	.005	.02	–.019	–.048	.005	≤.001	–.081
White	.076	.015	≤.001	.136	.002	.012	.464	.002
Black	.177	.015	≤.001	.316	–.129	.012	≤.001	–.218
Hispanic	–.153	.009	≤.001	–.272	–.063	.008	≤.001	–.106
Age/100	–.197	.022	≤.001	–.352	.191	.020	≤.001	.320
Special education	–.127	.011	≤.001	–.226	.121	.009	≤.001	.204
High school diploma	–.055	.007	≤.001	–.097	.100	.007	≤.001	.168
>HS diploma	.049	.008	≤.001	.087	.088	.007	≤.001	.147
Intellectual disability	–.023	.010	.01	–.042	.438	.008	≤.001	.735
Learning disability	.131	.009	≤.001	.233	.207	.008	≤.001	.348
ADHD	.013	.010	.105	.022	.039	.009	≤.001	.065
TBI	–.048	.019	.005	–.086	.181	.016	≤.001	.303
Autism	–.087	.015	≤.001	–.155	.413	.012	≤.001	.693
Mental illness	.017	.006	.002	.031	.118	.006	≤.001	.197
Substance abuse	.263	.008	≤.001	.468	–.143	.009	≤.001	–.240
Physical impairment	.063	.007	≤.001	.112	–.015	.007	.018	–.025
Vision impairment	–.170	.013	≤.001	–.302	–.198	.012	≤.001	–.333
Hearing impairment	.017	.011	.053	.031	.157	.010	≤.001	–.267
Significant disability	.601	.011	≤.001	1.068	1.099	.013	≤.001	1.84
Most significant disability	.727	.011	≤.001	1.291	1.024	.013	≤.001	1.72
20 mi < Distance<50 mi	.214	.011	≤.001	.383	–.040	.011	≤.001	–.067
Distance≥50	.158	.012	≤.001	.283	–.086	.012	≤.001	–.145
Employment ratio	.029	.018	.051	.051	.167	.015	≤.001	.279

Vocational rehabilitation service receipt,service expenditures,and ruralness

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1 Introduction

2 Methods

2.1 Data sources

2.2 Data cleaning

Table 1 Data cleaning description Cleaning steps Observations Initial number of observations 555,075 Observations without FIPS codes 20,005 Observations with missing gender 288 Observations with missing race 154 Observations of individuals <18 or >65 59,868 Final study sample 474,760

2.6 Descriptive statistics for receipt of services

4.1 Receipt of services

4.2 Purchased services

4.3 Limitations

5 Conclusions

Footnotes

Acknowledgments

Conflict of interest

Ethics statement

Funding

Informed consent

References

Table 1
Data cleaning description

Cleaning steps Observations

Initial number of observations 555,075

Observations without FIPS codes 20,005

Observations with missing gender 288

Observations with missing race 154

Observations of individuals <18 or >65 59,868

Final study sample 474,760