The effect of ruralness on vocational rehabilitation applications
Principal Investigator(s): View help for Principal Investigator(s) Catherine Ipsen, Rural Institute for Inclusive Communities at the University of Montana; Steven Stern, Consultant
Version: View help for Version V2
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Project Citation:
Ipsen, Catherine, and Stern, Steven. The effect of ruralness on vocational rehabilitation applications. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2024-06-10. https://doi.org/10.3886/E205041V2
Project Description
Summary:
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This
study examines the impacts of ruralness on decisions to apply for Vocational
Rehabilitation (VR) services, by exploring the factors that influence VR
applications including demographic variables and geographic location. The
Rehabilitation Services Administration (RSA) plays a pivotal role in
disbursing formula-based grants to 80 Vocational Rehabilitation (VR) agencies
nationwide and in all U.S. territories. These agencies offer a wide array of
services tailored to individual needs, encompassing vocational assessment,
counseling, training, and job placement, with an annual investment of
approximately $4 billion. The study utilizes data sourced from the
Rehabilitation Services Administration Case Service Report (RSA-911) and the
American Community Survey (ACS) to model application decisions. Various
statistical methods were employed such as Ordinary Least Squares (OLS) and
Maximum Likelihood Estimation (MLE) to analyze the data. Demographic
variables such as race, age, education level, and proximity to metro areas
emerged as significant factors for individuals seeking VR services. The
findings indicate that individuals with disabilities residing in rural
counties exhibit a decreased likelihood of applying for VR services compared
to their urban counterparts. The analysis highlights potential challenges
faced by individuals in rural areas seeking VR services, including limited
transportation options and inconsistent outreach efforts by VR agencies.
Leveraging data from sources like the RSA-911 and ACS is crucial to
understanding the geographic disparities in VR application rates to inform
the development of targeted interventions and policies that enhance access to
VR services for all individuals, regardless of geographic location. The study
suggests a pressing need for more research at the state level to
comprehensively understand variations in VR application rates across
different regions and counties, and development of targeted policies and
strategies to address low application rates among underserved groups.
Funding Sources:
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NIDILRR (90RTCP0002)
Scope of Project
Subject Terms:
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Vocational Rehabilitation (VR);
Maximum Likelihood Estimation (MLE);
Ordinary Least Squares (OLS);
Disabilty
Geographic Coverage:
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United States
Time Period(s):
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1/2019 – 12/2019 (Data originates from 2015 RSA-911 records. The study was conducted in 2019. )
Collection Date(s):
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1/2015 – 12/2015 (Data originates from 2015 RSA-911 records. The study was conducted in 2019. )
Universe:
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Individuals
with disabilities eligible for Vocational Rehabilitation (VR) services in the
United States
Data Type(s):
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census/enumeration data;
survey data
Collection Notes:
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2015
Rehabilitation Services Administration Case Service Report (RSA-911) dataset
and the 2013–2017 American Community Survey (ACS) 5-year summary data
Methodology
Sampling:
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Non-probability
sampling of all closed cases within the specified time frame
Data Source:
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The
Rehabilitation Services Administration (RSA) provides block grant funding to
79 VR agencies across the country.
Annually, each VR program is required to upload a common set of
program indicators to RSA, which are compiled into the RSA-911 database.
Scales:
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Services received:
RSA-911
does not contain specific scales, but rather VR services delivered to
individual consumers. For our
analyses, we aggregated case services received into 9 categories (see table 2
in the attached manuscript).
County-level indicators: We matched counties from the RSA-911 with Federal Information Processing (FIPs) Codes and Office of Management and Budgeting (OMB) county classifications. OMB classifies counties as metropolitan (urban), micropolitan (rural), and noncore (rural). Metropolitan counties include at least one urban core of 50,000 or more people 1,167 counties* are classified as metropolitan. Micropolitan counties include an urban core of 10,000 to 50,000 people 658 counties* are classified as micropolitan. Noncore counties are counties with an urban core population of less than 10,000 people 1,317 counties* are classified as noncore.
Distance to the VR office: 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. 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. 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)
County-level indicators: We matched counties from the RSA-911 with Federal Information Processing (FIPs) Codes and Office of Management and Budgeting (OMB) county classifications. OMB classifies counties as metropolitan (urban), micropolitan (rural), and noncore (rural). Metropolitan counties include at least one urban core of 50,000 or more people 1,167 counties* are classified as metropolitan. Micropolitan counties include an urban core of 10,000 to 50,000 people 658 counties* are classified as micropolitan. Noncore counties are counties with an urban core population of less than 10,000 people 1,317 counties* are classified as noncore.
Distance to the VR office: 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. 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. 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)
Unit(s) of Observation:
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Individual VR cases within the RSA-911 dataset
Geographic Unit:
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United States
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