Personal,environmental,and service-delivery determinants of employment quality for state vocational rehabilitation consumers: A multilevel analysis

Abstract

BACKGROUND: This study used the World Health Organization’s International Classification of Functioning, Disability and Health (ICF) framework to examine the individual and interactional impact of personal characteristics, state vocational rehabilitation (VR) services, and state environmental factors on employment quality for people with disabilities served by the state-federal VR system.

DESIGN: Quantitative descriptive research design using multilevel analysis.

PARTICIPANTS: Data extracted from the Rehabilitation Services Administration (RSA) case service report (RSA-911) database and other related databases from FY 2007 to 2009 were analyzed using multilevel (hierarchical) linear modeling to investigate patterns explaining variations among state VR agencies in achieving quality employment outcomes for consumers whose cases were closed successfully.

RESULTS: Consumer characteristics predominantly predicted employment quality among the personal, service, and environmental factors studied. Personal factors also explained a considerable amount of between-state differences in quality employment outcomes. Improvement in educational attainment was related to higher employment quality, whereas receiving supported employment and job placement-related services were associated with lower employment quality. Additionally, the proportion of consumers receiving social security benefits in a particular state and state per capita income factors were strong environmental predictors of employment quality.

CONCLUSION: Personal and environmental factors used in the present study were useful in predicting the quality of employment outcomes of VR consumers. However, developing a better measurement model for assessing quality employment and including additional factors in future research is warranted.

Keywords

Employment quality people with disabilities environmental factors vocational rehabilitation multilevel analysis

1 Introduction

Work is fundamental to the physical health and psychological well-being of people with and without disabilities (Chan et al., 1997; Chiu et al., 2015; Dutta, Gervey, Chan, Chou, & Ditchman, 2008). Compared to persons who are employed, thosewho are unemployed tend to experience more health and mental health problems, use alcohol more frequently, and report lower levels of self-esteem and quality of life (Dutta et al., 2008). Recognizing the importance of employment, vocational rehabilitation (VR) professionals have consistently advocated for work as a fundamental human right of people with disabilities (Chan et al., 1997).

However, the employment rate for individuals with disabilities continues to be low. A recent U.S. Bureau of Labor Statistics (BLS) Employment Situation report estimated the employment-population ratio for individuals with disabilities to be 19.7% comparedto 64.2% for people without disabilities (BLS, November, 2014). The Great Recession of 2007–2009 had a disproportionate impact on workers with disabilities, with the number of employed workers withdisabilities declining at a rate more than three timesthat of workers without disabilities and the unemployment rate rising dramatically to levels exceeding thatof other workers (Kaye, 2010). In addition to unemployment, people with disabilities who are working are at risk for underemployment. According to a report by the U.S. Census Bureau presenting data from 2010, adults with disabilities between the ages of 21 and 64 had median monthly earnings of only $1,961, compared to $2,724 for similarly-aged people without disabilities (Brault, 2012). Further, the number of individuals receiving Social Security Disability Insurance (SSDI) benefits dramatically rose from 2.86 million in 1980 to 8.20 million in 2010, an increase of 187 percent (Goss, 2013). Without a doubt, limited opportunities for good paying jobs exclude people with disabilities from full participation in community life. Moreover, unemployment and underemployment of people with disabilities can significantly impact their ability to join or remain in the middle class as well as their health-related quality of life.

The state-federal vocational rehabilitation (VR) program, which serves approximately 1,000,000 individuals a year and spends more than $2.5 billion annually, plays a large and instrumental role in helping persons with disabilities achieve their independent living and employment goals (Martin, West-Evans, & Connelly, 2010; U.S. Government Accountability Office [GAO], 2005). However, state VR agencies vary in their ability to produce successful employment outcomes and also in their ability to help people with disabilities find good paying jobs with benefits. In fact, the mean hourly wage of all VR customers whose cases were closed successfully in competitive employment is only 52% that of the general workforce (Rehabilitation Services Administration [RSA], 2008). This discrepancy in state VR outcomes has led researchers in the Government Accountability Office to examine person-environment factors affecting earnings of social security beneficiaries after receiving state VR services (GAO, 2007). They found that differences in earnings after VR could be explained in part by state economic conditions and the characteristics of consumers served by these agencies. Together, state unemployment rates and per capita income levels accounted for approximately one-third of the differences among state VR agencies in the proportion of social security beneficiaries who had employment-based earnings during the year after VR services had been discontinued. These findings underscore the importance of recognizing the impact that economic indicators and agency characteristics can have on the quality of employment outcomes (GAO, 2005; GAO, 2007). Clearly, the persistent unemployment and underemployment problems of people with disabilities cannot be reduced to a single factor (i.e., disability). They are the result of a set of adverse internal (personal) and external (environmental) factors interacting with each other in a complex manner. The World Health Organization’s (WHO) International Classification of Functioning, Disability and Health (ICF) conceptualizes disability as a range of functional capability rather than a dichotomous concept, positing that all people function at different levels due to contextual factors (WHO, 2001). In particular, the ICF model’s focus on the personal (P) and environmental (E) contextual factors as mediators and moderators of the relationship between functional disability and participation can be used to help VR researchers and clinicians better understand the dynamic relationship between disability and work so that effective VR interventions can be developed to improve the employment outcomes and employment quality for people with disabilities (Chan, Tarvydas, Blalock, Strauser, & Atkins, 2009). This emphasis is consistent with the Workforce Innovation and Opportunity Act (WIOA) of 2014 and its accompanying Amendments to the Rehabilitation Act of 1973, which formalizes a commitment to helping people with disabilities find good jobs with benefits to facilitate entry to the middle class (U.S. Department of Education, 2014).

The purpose of this study was to use the ICF model as a research framework to examine the effect of personal characteristics, VR services, and environmental factors on employment quality (i.e., hours worked, earnings, and employer-based health insurance) of people with disabilities after successful closure from the state-federal VR program. Data extracted from the Rehabilitation Services Administration (RSA) Case Service Report (RSA-911) database and other related national databases were analyzed using multilevel analysis (hierarchical linear modeling) to capture the nested structure of individual characteristics (Level-1), VR service patterns (Level-1), and state-level environmental factors (Level-2) that can be used to explain variations in employment quality achieved by people with disabilities served by state VRagencies.

2 Method

2.1 Data sources

The RSA-911 datasets for fiscal year (FY) 2007, FY2008, and FY2009 were used to investigate any patterns underlying variations among state VR agencies in achieving higher employment quality for VR customers with successful closures. In addition, information used to construct state-level economic indicators and VR agency characteristics data were extracted from the national databases provided by RSA, the U.S. Department of Labor, and the U.S. Department of Commerce. The criteria for case selection were that the VR consumers (a) received services at general and combined state VR agencies in the United States (excluding the U.S. territories) and not state VR agencies for the blind; (b) had successful closures with competitive employment as an outcome after receiving VR services; (c) were of working age (between 16 and 64) at the time of application; and (d) had records with no missing data on all variables of interest.

2.2 Outcome variable

Employment quality was the outcome variable for this study and was operationalized based on work hours, earnings, and benefits. Specifically, the variable was constructed based on three standardized quality indicators: (a) hours worked per week (up to 40 hours), (b) weekly earnings, and (c) employer-based health insurance. To have an unbiased estimate of the outcome variable, weekly earnings were adjusted with the weight of state average salary to control for between-state differences in economic conditions and living expenses. Also, hours worked per week were limited to 40 hours to represent the general expectation of full-time employment. Employer-based health insurance was a ditchomous (Yes/No) variable. The value of employment quality was calculated by summing the three Z-scores of the adjusted number of working hours per week, adjusted weekly earnings, and employer-based health insurance. A higher score of employment quality refers to a better quality job that a consumer had after receiving VR services. Specifically, a positive value of employment quality indicates that someone has better employment quality than the average VR consumer with a successful closure, whereas a negative value indicates employment quality lower than the VR national average based on the three quality indicators used in this study. The employment quality scores in this study ranged from –5.2 to 41.4 with M = 0 and SD = 2.4 for all three fiscal years. Less than 1% of the participants had an employment quality score above 6.0. In terms of state variations in employment quality, the range of state average scores was from –1.1 to 1.5. The distributions ofmeans and standard deviations in state average employment quality are presented in Appendix A.

2.3 Predictor variables

This study used the ICF framework and assumed that the employment quality outcome variable can be attributed to two (or more) levels of predictor variables: individual (Level-1 or micro) and environment (Level-2 or macro). Individual factors consist of variables related to consumer characteristics and types of VR services received. Environmental factors include variables pertaining to state-level economic conditions or VR policy and agency characteristics. All Level-1 and Level-2 variables of interest were collected from 2007 to 2009 and describedbelow.

2.3.1 Individual variables (Level 1 predictors)

Individual level variables include two subsets, demographics and receipt of specific VR services. Demographic covariates included age, gender, race/ethnicity, pre-service education level, types of disability (sensory, physical, cognitive, and mental health impairments), severity of disability (significant vs. not significant), number of cash benefits receivedat application (including Supplemental Security Income [SSI], Social Security Disability Insurance [SSDI], Temporary Assistance for Needy Families [TANF], general assistance, veterans’ disability benefits, workers’ compensation, and other public financial benefits), number of medical benefits received at application (including Medicaid, Medicare, and public insurance from other sources).

VR service variables included supported employment (receiving supported employment services as part of the individualized plan for employment) and improvement in educational attainment (a dichotomous variable indicating the pre- and post-service change in highest educational level attained). Also, in the RSA-911 database, there are 22 categories of VR services. For the multilevel analysis, these categories were organized into four continuous variables to make the analysis manageable.

2.3.1.1. Counseling and assessment. This variable with a range of 0-3 indicates how many counseling and assessment services a consumer received, including assessment, diagnosis and treatment, and VR counseling and guidance.

2.3.1.2. Training. This variable has a range of 0–7 indicating how many training services a consumer received from the state VR agency. These training services include: college/university training, occupational/vocational training, on-the-job training, basic academic remedial/literacy training, job readiness training, disability related augmentative skills training, and miscellaneous training.

2.3.1.3. Job placement. This variable has a range of 0–3 and indicates how many job placement related services a consumer received, including job search assistance, job placement assistance, and on-the-job supports.

2.3.1.4. Support services. There are nine supportive services available for state VR consumers: transportation, maintenance, rehabilitation technology, reader, interpreter, personal attendant, technical assistance, information and referral, and other services. This variable had a range of 0–9 to indicate how many of these supportive services a consumer received through the state VR agency.

2.3.2 Environmental variables (Level 2 predictors)

Environmental variables included three factors related to each state’s economic environment and eight state VR system factors for each state. A brief description of the state environmental variables is presented below.

2.3.2.1. State economic environment. State unemployment rate is defined as the percentage of the state labor force that was ever unemployed within a given year. Per capita income or personal income per capita is the numerical quotient of all sources of income divided by the state population, in monetary units (data analysis unit: $1,000). Urban population rate is the percentage of the total state population living in urban areas.

2.3.2.2. State VR system. Social Security beneficiary rate is aggregated from all selected participants in each state to determine the percentage of VR consumers who were social security beneficiaries. Significant disability rate reports the percentage of people having significant/severe disabilities among all state participants selected. Supported employment rate indicates the percentage of supported employment service recipients in each state VR agency sample. Average case expenditure and average service time refer to the average amounts of money ($1,000) and time (month) cost by a state VR agency for those with successful closures from the beginning of service application to the end. Average caseload presents the average number of consumers that state VR counselors served annually. The values were calculated from the RSA Annual Review Report (available from http://rsa.ed.gov/choose.cfm?menu=mb_reports_arr), which provides annual state data on total numbers of individuals served and total numbers of counselors. However, this indicator could underestimate average caseload because information about inactive cases (i.e., the cases that were open but received no services in a given fiscal year) was not available. Administration cost rate and purchased service cost rate were also calculated from the data retrieved from the RSA Annual Review Report. The former refers to the percentage of state VR funds used in administration and the latter indicates the percentage of non-administration funds used to purchase services provided by external vendors.

2.4 Data analysis

Having successful VR closure resulting in quality employment not only depends on an individual’s efforts but may also be affected by the macro environment (e.g., national economic development, VR policies, and societal attitudes toward disability). Therefore, people with disabilities will share the same organizational and environmental benefits and risks if they are served by the same state VR agency or live in the same state. Multilevel analysis or hierarchical linear modeling (HLM) is appropriate and suitable for research designs where the data for participants are organized (nested) at more than one level (Tabachnick & Fidell, 2007). With multilevel linear modeling, each of the levels in the structure is formally represented by its own sub-model. These sub-models express relationships among variables within a given level, and specify how variables at one level influence relations occurring at another (Raudenbush & Bryk, 2002). In this study, employment quality was measured for VR consumers at the individual level (Level-1), and they (VR consumers), in turn, were grouped within states (Level-2). Three stages of multilevel modeling were utilized to investigate how personal characteristics, VR services, and environmental factors, either separately or interactively, predict employment quality of VR consumers with successful closure (see Table 1).

Stage 1. To examine the “pure” state environmental effect (i.e., the general state effect), only the outcome variable was entered in Model 1 and all selected participants were clustered by their state identification to explore the within-state as well as between-state difference (or similarity) in employment quality. Between-state difference indicates the possibility of having different employment quality for an “average/typical” consumer receiving the same VR services but in different states. The intraclass coefficient ρ was computed to determine the amount of variance in employment quality that could be accounted for by between-state differences. For this estimate, the equation ρ=τ00²/(σ² +τ00²) was used, where σ² is the value for Level-1 (within-state) variance and τ00² is the value for Level-2 (between-state) variance. A strong pure environmental effect would suggest that VR employment quality can be largely determined by the state from which a person receives VR services.

Stage 2. At this stage, the impact of personal factors and state VR service factors on employment quality were sequentially tested. Model 2 estimated the extent to which individual characteristics (i.e., age, gender, race/ethnicity, types of disability, and pre-service education level) determine employment quality based on the decrease or increase in the percentage of Level-1 variance σ². To what extent personal characteristics influenced between-state variation can also be assessed (i.e., the decrease/increase in the percentage of Level-2 variance τ00²). Next, state VR service variables were added to Model 3 to test the influence of state VR services on employment quality after controlling for personal factors.

Stage 3. At this stage, state-level environmental variables pertaining to state economic environment and state VR agencies were separately added to the models to examine their ability to predict employment quality after controlling for the personal and VR service (Level-1) factors. The magnitude of the decrease in the percentage of Level-2 variance (τ00²) was used to evaluate how strongly a specific environmental factor explained between-state variation in employment quality. Before adding to the models, all Level-2 variables were centered with grand means to have better interpretability of results.

3 Results

3.1 Sample characteristics

The total numbers of participants were 180,641 in FY2007; 179,692 in FY2008; and 157,330 in FY2009 (see Table 2).

Data show a substantial drop in the number of successful closures in 2009, whereas the numbers of applicants from 2007 to 2009 remained relatively unchanged. The three annual samples had similar distributions of sample characteristics, supporting the homogeneity assumption needed to make fair cross-year comparisons. The majority of participants were male, Caucasian, had a high school degree or higher, and had a significant disability. The sensory disability group was smaller than the other three disability groups. Participants aged 16–20 years old represented the largest age group (24–25%), followed by those 41–50 years old (20–23%); consumers 51–64 years old constituted the smallest of the age groups (16–18%). About two-thirds of participants did not receive any public monetary or medical benefits. In terms of VR services, the service patterns were generally stable (see Table 3); participants were likely to receive approximately two counseling/assessment-related services and one service from each of the other three service categories (i.e., training, job placement, and support services). The rehabilitation process took VR consumers on average two years and cost about $4,800 to achieve successful competitive employment.

3.2 Between-state differences

In Model 1 (see Table 4), the estimated pure state-environmental effect (i.e., between-state difference) accounts for 7% of the variance in employment quality [ρ=τ₀₀²/(σ²+τ₀₀²) ₌ 6.7% in 2007; 7.2% for 2008; and 7.1% for 2009]. Results suggest thatreceiving VR services in different states explains about 7% of the variation in employment quality for the average person with a disability.

3.3 Personal and service (Level-1) factors

Personal factors and VR service factors were sequentially added to Models 2 and 3. As can be observed from Table 4, all results across the threeyears were quite similar. Compared to Model 1, the inclusion of personal factors (Model 2) not only substantially decreased the within-state/individual variation (20% of Level-1 variance) but also decreased the between-state/environmental difference in employment quality (50% of Level-2 variance). These results suggest that the compositions of consumers with successful employment outcomes among all states varied in personal characteristics, which, in turn, strongly influenced their likelihood of having quality employment. After controlling for personal factors, VR service factors (Model 3) contributed to another 5% decrease in the within-state variation of employment quality but were unable to decrease any between-state variation. Therefore, the effectiveness of VR services has been supported at the individual level. The inability of VR services to influence between-state variation suggests that different state VR agencies are providing similar and tailored VR services to consumers with the same demographic and disability characteristics (Chiu et al., 2014; Strauser et al., 2010).

Specifically, disability characteristics and disability benefits were dominant predictors of employment quality among personal factors for Model 2 in Table 5. The significant predictors included gender (in favor of men), education, disability type, significance of disability (in favor of non-significant disability), and the receipt of cash or medical benefits. Rehabilitation consumers were more likely to find good jobs with benefits if they had higher educational levels. People with sensory impairments had the highest employment quality, followed by those with physical impairments and mental health impairments, whereas individuals with cognitive impairments had the lowest employment quality scores. Moreover, age was a statistically but not clinically significant predictor of employment quality due to the negligible effect size. African Americans and Asians had slightly lower employment quality outcomes compared to Caucasians; nonetheless, race/ethnicity, in general, was not a sufficient predictor of employment quality.

Model 3 demonstrates the predictive strengths of VR service variables (see Table 5). All six VR service-related variables were significant predictors of employment quality but only supported employment, change in educational attainment, and job placement services had effect sizes large enough to influence employment quality. Findings indicate that people receiving supported employment, job placement services, or both are more likely to have lower employment quality. In contrast, people with post-service higher levels of educational attainment tend to have better quality jobs. Additionally, the inclusion of VR service variables successfully decreased most of the effect sizes of those significant disability and benefits related predictors (through the comparison of the results in Model 2 and Model 3). VR services interacted with most disability types, some race/ethnic groups (i.e., Asian/Pacific Islanders), and the recipients of cash or medical benefits to affect employment quality.

3.4 State environmental (Level-2) factors

To find potentially useful environmental factors, each of the 11 state environmental variables was added to a new model to evaluate the change in the value of between-state variance (τ₀₀²), after controlling for both personal and VR service (Level-1) factors. As can be seen in Table 4, Social Security beneficiary rate was identified as the best environmental factor, with an additional 38% drop in between-state (Level-2) variance (τ₀₀²) in FY 2007, 19% for FY 2008, and 30% for FY 2009 (Model 7 vs. Model 3). Among other environmental factors, per capita income decreased the between-state variance in employment quality by 15–19% (Model 6 vs. Model 3) across FY2007–2009. Urban population rate was also able to explain about 10% of the between-state variation. State unemployment, significant disability, and administration cost rates also decreased the state-level variation in employment quality but the magnitudes were small.

Overall, the three state economic factors included in the study all helped to explain the between-state differences in employment quality across the three years, but some effects were small. Of the VR system factors, only Social Security beneficiary rate, significant disability rate, and administration cost contributed to the decreased between-state difference in employment quality.

3.5 Person-environment interaction model of employment quality: An example

To present the interaction of Level-1 and Level-2 factors on employment quality, Model 6 was used to demonstrate how state per capita income moderates the relationship between personal/VR service factors and employment quality. Across the three years, the state per capita income variable changed the extentto which employment quality was associated with a number of individual Level-1 variables, including: gender, pre-service educational level, Latino/Hispanic, mental illness, significant disability, cash benefits, support services, and educational change.

Specifically, people living in states with higher average personal income tend to experience less gender and educational disparity in employment quality and vice versa. The correlation coefficient of gender on employment quality would drop 0.01 value for every $1,000 increase in state’s average personal income above the national average. Likewise, the discrepancy in employment quality between physical impairments and mental impairments becomes smaller as a state’s average personal income exceeds the national average. Also, public cash beneficiaries living in states with better economic conditions were found to score higher on the employment quality indicator compared to those who living in poorer states. Lastly, all the χ² tests of the models in this study reached statistical significance, suggesting there is need to explore of other additional Level-1 andLevel-2 factors, especially the former.

4 Discussion

In the present study, we conducted an extensive investigation of the multilevel and multifacetedfactors contributing to the employment quality of VR consumers with successful case closures. Based on the ICF framework, the present study examined the interactions among personal, VR service, and environmental factors and their effects on employment quality. As hypothesized, consumer characteristics were found to be associated with employment quality. This finding augments what we know from existing rehabilitation literature— namely, that individual characteristics (e.g., gender, educational attainment, types of disability, significant disability, and receipt of public cash/medical benefits) are strong predictors of employment outcomes (Dutta et al., 2008). In addition, these personal characteristics explain a considerable portion of the between-state differences found in employment quality, suggesting that the heterogeneous compositions of VR consumers with successful closures from state VR agencies might account for the between-state differences found in average employment quality. Order of selection might partially explain the effect of the heterogeneous consumer characteristics between states (Ditchman et al., 2013). States without order of selection might accept more applicants with less significant disabilities and therefore may be more likely to have higher average VR employment quality scores. People with significant disabilities have more functional limitations that could prevent them from working full-time or finding good paying jobs with benefits (Lusting, Strauser, & Donnell, 2003). Also, a higher percentage of people with severe disabilities receive Social Security benefits and could be encouraged to look for jobs that would allow them to work part-time in order to avoid losing benefits.

Another important finding is that race/ethnicity was not a substantial personal characteristic determinant of employment quality as compared to other demographic and disability variables. Only African Americans and Asians were found to have slightly lower employment quality than Caucasians, but the discrepancies were relatively small and could be further diminished by the effect of VR services. This finding suggests that disability had a more negative impact than race/ethnicity on employment quality.

Although demographics and disability-related variables (non-modifiable factors) predominantly predicted employment quality, VR services (modifiable factors) also contributed substantially to employment quality for consumers. Supported employment and job placement services were negatively associated with employment quality, whereas change in educational attainment was positively associated. It is not surprising that consumers receiving supported employment had jobs with lower employment quality because supported employment services are usually provided to people with severe mental illness and people with severe intellectual and developmental disabilities who are Social Security beneficiaries and frequently placed into unskilled to semi-skilled part-time employment with no employee benefits (Bond, Campbell, & Drake, 2013; Lustig et al., 2003). Conversely, consumers who received postsecondary education interventions were more likely to obtain higher paying jobs with more working hours and better employee benefits. With regard to the negative association between job placement services and employment quality, there are two plausible explanations: lack of job skills and vendor-related effects. Rehabilitation counselors might be more inclined to provide or purchase job placement services for consumers with lower levels of employability or limited placeability skills. Therefore, it is less likely for consumers who lack these skills to find high paying jobs with good benefits. In addition, vendors who provide job placement services for state VR agencies may have incentives to place as many consumers as quickly as possible, regardless of the quality of the employment.

In terms of environmental effect, between-state differences account for only 7% of variance in employment quality. This small effect may be partially attributed to the relatively strong predictive power of personal factors and VR services. However, several other reasons could potentially help to explain the small environmental effect. First of all, the present study defined employment quality as having a full-time job with good wages and health insurance. However, within the context of state VR services, a high percentage of VR consumers were placed in unskilled or semi-skilled jobs with flexible working hours and no employee benefits. In comparison to the general population, VR consumers therefore would have smaller variations in types of occupations, skills requirements and earnings potential, resulting in smaller between-state and within-state variance in employment quality. It is also possible that the universal successful closure criteria and similar service modalities provided by state VR agencies suppressed the between-state variance in employment quality. In recent years, rehabilitation researchers have argued that it is important to measure VR outcomes using both objective indicators and subjective measures of quality of life (Rubin, Chan, & Thomas, 2003). Using other indicators of employment quality, such as job satisfaction, may result in a different set of significant P x E predictors of employment quality.

Finally, state VR agencies with a higher percentage of SSI/SSDI recipients tended to have lower average employment quality scores. As previously mentioned, at both the individual and state level, employment quality was strongly influenced by Social Security beneficiaries’ propensity to seek part-time employment with earnings below substantial gainful activity. Other state VR agency factors (e.g., caseload size, case expenditure, and purchased cost rate) did not have any notable effects on employment quality outcomes. In contrast, state economic factors are generally more useful to explain the between-state variation in employment quality (GAO, 2007). In the present study, after adjusting for weekly earnings by taking into consideration each state’s average salary, state per capita income was still a strong state environmental factor and moderated the relationships between personal and VR service factors and employment quality. Results suggest that economy-related outcome indicators, like employment quality used in the present study, are mainly determined by economic factors, whereas state VR agency factors may determine other kinds of VR outcomes, such as job satisfaction and life satisfaction. This is consistent with existing research, which advocated the use of multidimensional measurements of employment outcomes (Bond et al., 2012, Lustig et al., 2002; Rubin et al., 2003). Therefore, further research focusing on refining P x E factors and a better measurement model of quality employment is warranted.

4.1 Implications for VR practice

According to the WHO (1995), the greatest cause of ill-health, suffering, and mortality in the world is poverty. Since the Great Recession, the gulf between the poor and the rich of the world is widening. In the United States, incomes for the highest-earning one percent of Americans skyrocketed 31 percent between 2009 and 2012, while the rest of the country grew an average of just 0.4 percent (Saez, 2013). Poverty and income inequality have direct and indirect effects on the social, mental, and physical well-being of individuals with and without disabilities. The WIOA is a bipartisan, bicameral bill to improve America’s workforce development system and has an emphasis on job-driven training and helping Americans find good paying jobs with benefits as a pathway back to the middle class.

According to a study by the U.S. Census Bureau, the impact of education levels on work-life earnings surpasses that of all other demographic factors (Julian & Kominski, 2011). In addition, the median yearly earnings (in constant 2012 dollars) was $46,900 for young adults with a bachelor’s degree, $30,000 for those with a high school credential, and $22,900 for those lacking a high school credential (Kena et al., 2014). Given the demonstrated effect of postsecondary educational intervention in the present study, counselors in state VR agencies must pay attention to the benefits of education on improving the employment quality of their consumers who have the preparation and potential for higher education. However, postsecondary education represents an expensive investment. To assure return on this investment, counselors must make appropriate arrangement to provide postsecondary education interventions to help consumers with college life adjustment and college persistence. VR services were found to decrease the disadvantage experienced by consumers with mental illness and cognitive impairments. Counselors should continue to integrate evidence-based practices (e.g., transition services, supported employment, and postsecondary education) that could improve the employment outcomes and employment quality for these subpopulations of people with disabilities who had the lowest levels of work participation. Additionally, men were found to have higher employment quality outcomes after receiving VR services than their female counterparts. This finding may reflect the larger societal issue that women in general still make less than men after controlling for age, race, hours and education (Goldin, 2014). However, counselors may want to provide job search/job readiness interventions that can help improve women consumers’ confidence and negotiation skills in the job placement process. Finally, counselors, researchers, and policy makers should be aware of the potential impact macro-level variables may have on the relationships between employment quality and consumer and VR service characteristics. For example, findings from this study suggest that higher state per capita income can mitigate gender and disability disparities, whereas these disparities may be exacerbated in states with poorer overall economic conditions.

4.2 Limitations

There are several limitations of the present study that should be considered. First, missing data threaten the internal and external validity of results. The RSA-911 datasets are generated from information recorded by counselors, and it is possible for some unexpected manual or memory errors (i.e., recall bias) to occur during the retrospective data-entry procedure. The RSA has developed 18 cross-checks to reduce potential errors. However, some states (e.g., New Jersey, Arizona) still had large amounts of missing data for specific variables, such as employer-based health insurance, or Social Security disability beneficiary status. To guard against threats to internal validity, the missing data in employer-based health insurance were replaced with 0 (no insurance). Therefore, caution is required when interpreting the state-level comparisons reported in the results. Moreover, owing to the eligibility determination criteria used by state VR agencies and the cross-sectional nature of RSA-911 datasets, the findings cannot be generalized to all people with disabilities and causality cannot be inferred.

Another limitation of this study is that VR services were grouped into four broad categories in the present study and did not consider the association of individual services with employment quality. Therefore, further research is needed to identify evidence-based VR services that directly increase employment quality for VR consumers. In addition, this study was limited to the personal and environmental variables included in the datasets. Future research is needed to more comprehensively evaluate the impact of additional ICF-defined envirionmental variables, such as those relate to the local environment (e.g., county level) and counselor/service provider characteristics.

Finally, a rudimentary measurement model of employment quality was used for this study. The focus on full-time employment, earnings, and employer-based health insurance may ignore other health and mental health benefits of employment in integrated settings. This measure may not be optimal for all VR consumers. Therefore, future research should focus on developing and validating measurement models of employment quality integrating both objective indicators and subjective measures that can be tailored to different disability groups. Despite these limitations, the preliminary results provided by the present study provide insightful information to guide future study designs that may better capture the interaction of different combinations of Level-1 and Level-2 factors.

5 Conclusion

The present study represents an attempt to better understand the main effect of individual characteristics, rehabilitation services, and environmental factors and their interactive effects on employment quality of consumers who received VR services. We identified the important contribution of individual characteristic factors, VR services and state economic indicators to quality of employment outcomes. However, the effect of VR system factors was less clear. Although this study calls attention to the importance of evaluating employment quality among VR case closures, the predictors in the current study were not able to account for a large amount of within-state and between-state variations. Therefore, the inclusion of additional person-environment factors in future multilevel analysis research is warranted.

Conflict of interest

The contents of this chapter were developed with support from the Rehabilitation Research and Training Center on Effective Vocational Rehabilitation Service Delivery Practices at the University of Wisconsin-Madison and the University of Wisconsin-Stout and with funding provided by the U.S. Department of Health and Human Services, National Institute on Disability, Independent Living, and Rehabilitation Research (Grant H133B100034). The ideas, opinions, and conclusions expressed, however, are those of the authors and do not represent recommendations, endorsements, or policies of the U.S. Department of Health and Human Services.

Footnotes

Appendix A

State average score in employment quality

State	FY2007	FY2008	FY2009
	M(SD)	M(SD)	M(SD)
Alabama	0.00(2.09)	-0.01(2.03)	0.03(2.05)
Alaska	0.30(2.56)	0.50(2.48)	0.48(2.53)
Arizona	0.26(2.29)	0.39(2.37)	0.30(2.41)
Arkansas	0.80(2.18)	0.71(2.12)	0.67(2.16)
California	-0.23(2.14)	-0.21(2.14)	-0.22(2.15)
Colorado	-0.48(2.44)	-0.40(2.45)	-0.16(2.56)
Connecticut	0.39(2.88)	0.77(2.89)	0.71(3.07)
Delaware	-0.17(1.93)	-0.20(1.90)	-0.31(1.99)
Washington DC	0.13(1.51)	-0.02(1.50)	-0.36(1.39)
Florida	0.46(2.32)	0.51(2.39)	0.12(2.23)
Georgia	-0.06(2.00)	0.02(1.92)	0.07(1.96)
Hawaii	0.84(2.63)	0.99(2.54)	1.18(2.75)
Idaho	0.16(2.62)	0.16(2.50)	0.15(2.52)
Illinois	-0.74(2.23)	-0.78(2.15)	-0.70(2.21)
Indiana	0.06(2.98)	-0.15(2.77)	0.02(2.82)
Iowa	0.67(2.72)	0.82(2.54)	0.70(2.47)
Kansas	-0.35(2.44)	-0.23(2.38)	-0.16(2.42)
Kentucky	0.61(2.47)	0.58(2.42)	0.66(2.59)
Louisiana	1.35(2.75)	1.61(3.02)	1.15(2.89)
Maine	-0.39(2.90)	-0.58(2.80)	-0.62(2.93)
Maryland	-0.65(2.22)	-0.68(2.24)	-0.76(2.16)
Massachusetts	-0.97(2.40)	-0.77(2.40)	-0.84(2.39)
Michigan	0.04(2.39)	0.12(2.41)	0.39(2.63)
Minnesota	-0.49(2.36)	-0.53(2.36)	-0.58(2.32)
Mississippi	1.06(2.47)	1.03(2.42)	1.19(2.36)
Missouri	-0.23(2.48)	-0.36(2.41)	-0.38(2.35)
Montana	-0.08(2.60)	-0.33(2.66)	-0.07(2.63)
Nebraska	0.33(2.54)	0.41(2.30)	0.32(2.43)
Nevada	0.21(2.24)	0.30(2.24)	0.32(2.24)
New Hampshire	-0.57(2.56)	-0.51(2.52)	-0.46(2.57)
New Jersey	-1.07(1.91)	-0.47(2.19)	-0.24(2.35)
New Mexico	0.07(2.49)	0.08(2.49)	0.24(2.54)
New York	-0.52(2.05)	-0.50(2.05)	-0.47(2.05)
North Carolina	-0.56(2.24)	-0.65(2.18)	-0.78(2.12)
North Dakota	0.80(2.49)	0.75(2.59)	0.83(2.66)
Ohio	0.71(2.68)	0.99(2.70)	1.08(2.86)
Oklahoma	0.90(2.23)	0.78(2.22)	0.82(2.57)
Oregon	-0.26(2.45)	-0.25(2.45)	-0.20(2.51)
Pennsylvania	0.17(2.32)	-0.21(2.05)	-0.19(1.89)
Rhode Island	-1.17(2.18)	-1.24(2.09)	-1.17(2.07)
South Carolina	0.29(1.98)	0.19(1.80)	0.13(1.81)
South Dakota	-0.78(2.34)	-0.51(2.31)	-0.48(2.31)
Tennessee	-0.13(2.72)	-0.15(2.62)	-0.39(2.74)
Texas	-0.13(2.03)	-0.15(2.02)	-0.01(2.12)
Utah	0.60(2.21)	0.71(2.30)	0.67(2.18)
Vermont	-0.71(2.59)	-0.70(2.60)	-0.74(2.60)
Virginia	-0.55(2.03)	-0.83(1.87)	-0.68(2.08)
Washington	-0.96(2.36)	-0.96(2.35)	-0.92(2.30)
West Virginia	1.49(2.30)	1.44(2.29)	1.64(2.33)
Wisconsin	-0.51(2.53)	-0.50(2.87)	-0.51(2.90)
Wyoming	0.15(2.61)	-0.05(2.55)	0.16(2.45)

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