Spatial Inequalities in Disabled Livelihoods: an Empirical Study of U.S

Spatial Inequalities in Disabled Livelihoods: An Empirical Study of U.S. Counties

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the

Graduate School of The Ohio State University

Nicholas Britt Garcia, MS

Graduate Program in the School of Environment and Natural Resources

The Ohio State University

2019

Dissertation Committee:

Professor Linda Lobao, Adviser

Professor Jeff Sharp

Professor Cathy Rakowski

Nicholas B. Garcia

2019

Abstract

I consider contributions and limitations of traditional approaches to disability and

inequality, noting a lack of quantitative empirical studies to address persistent poverty and

underemployment since passage of the Americans with Disabilities Act (ADA). I find that the

majority of literature is predicated upon assumptions of interpersonal discrimination and

accessibility, without corresponding empirical study of how these factors influence the economic

well-being of people with disabilities. Using newly-available county prevalence data from the

American Community Survey (ACS), I present three studies to address areas of disability and inequality that have been neglected in sociological research.

In the first, I address rising disability prevalence across U.S. counties and test prominent explanations involving health behaviors against place-based deprivation measures. Increasing disability prevalence is often attributed to rising obesity rates in the United States. Poverty and inequality, although commonly explored in studies of health disparities, have not been well- studied in their relationship to disability. I examine differences in disability prevalence across

2,964 U.S. counties to compare these competing explanations. I find that poverty is consistent in explaining the prevalence of overall disability and four subcategories of disability, while health behaviors are only significant when explaining some specific categories of disability. I further find that industrial composition of places plays an overlooked role in shaping disability

prevalence, presumably from occupational hazards associated with extractive industrial activities

across counties.

In the second, I address the increasing gap in disabled employment that has persisted

since the implementation of the ADA. The ADA offered protections against discriminatory hiring and workplace accessibility, but did not address other place-based and individual determinants of disability employment. I examine how the socioeconomic composition of places and county-level indicators of mobility shape employment of people with disabilities, while also considering how each type of disability may have different employment prospects. I find that the socioeconomic composition of places and the types of disability most common in a county are significant in explaining employment differences across U.S. counties.

Finally, I address the support received by people with disabilities from the government.

Conventional explanations of disability welfare from Supplementary Supportive Income (SSI)

describe increasing enrollment in government programs as a product of insufficient work

incentives and fraudulent claims of disability. I compare self-reported disability rates of each county to SSI enrollment and find that differences in SSI coverage are related to two competing explanations. The first involves sociopolitical interests that shape flows of federal welfare dollars to districts based on racial and economic makeup of counties. The second involves the capacity of medical institutions and government to administer and process disability claims.

Although conventional accounts of disability welfare assume economic self-interests as the driver of SSI enrollment, I find that economic interests fail to be significant in explaining SSI coverage.

Taken together, these findings describe an account of inequality and disability that is

connected to the places in which people with disabilities live. I find that poverty and industrial

activities that contribute to disabilities in a county also shape employment outcomes of people

with disabilities. Places with high poverty have higher disability prevalence and worse

employment outcomes for people with disabilities. Programs offering economic support are also

limited in areas of economic hardship, as high income areas with substantial medical and

governmental resources have the most responsive government programs. Within the sociology

of disability, inequality and disability is often explained in terms of stigma, discrimination, and

marginalized social status. In my three empirical studies, I find that social and economic characteristics of counties play an overlooked role in explaining differences in employment and governmental support among people with disabilities.

iii

Para mi familia.

Acknowledgments

I did not do this alone. It's difficult to put a spotlight on how my academic career came

together without acknowledging the very long and winding road that took me here. If I don't

take the time to say my piece now, I'm afraid I'll always be too self-conscious to tell everyone how much they've done to get me here. (And if you think it's too long, I've got some bad news for you about the chapters that follow!)

On the homefront, I'm fortunate to have had a pal, a coconspirator, a cheerleader, a coach, and a literal lifesaver in my wife Evie. I would not have gone back to school if not for her. Now we have our own family, and my hope to contribute to a better world for them has driven me to seek work that I can be proud of. I grew up in a household of teachers, and I was surrounded by ravenous readers and curious minds. I was lucky to have patient parents that supported me as I pursued learning for the sake of learning. They've given me unconditional support even if they were unclear on what exactly I studied for a living. I grew up with a brother that was both friend and foil, always appreciating expertise but never satisfied. How could I have made it to this point without coming home to people like these?

I am humbled by the faculty at Ohio State that have helped to shape me into who I am today. I cannot express how deeply indebted I am to Linda Lobao. She is undoubtedly the most brilliant person I've ever met. This is not hyperbole. I've marveled at her ability to recall citations, draw from highly specialized literature outside of her own research, and still be incredibly in touch with world events. But more remarkable is the work that she's put in that has

v made her so knowledgeable. She is passionate about her work, and her light was often on during the late nights I spent at Kottman Hall. It would be intimidating if it wasn't downright inspirational. I'm grateful for the direct guidance she's given me throughout the years. She's pushed me to produce work beyond anything I thought myself capable of. But her own work has fundamentally shaped how I view the work of sociologists, and I'm so damn thankful to have worked with her over these years.

My experiences with our rural sociology faculty have been so rewarding, both in and out of the classroom. My first graduate teaching appointment was with Cathy Rakowski, and I am thankful for the experience. Cathy routinely asks sociologists, "Where are the people?!" when reading their work. She is a compassionate sociologist that cares deeply about the work we do in the world. But I found this compassion extends to her view of the classroom as well. Jeff Sharp has also been incredibly responsive in his role as director at our school. I've always appreciated the precision and focus of his scholarship, and I think SENR has benefitted greatly from his leadership. I am so thankful that Ohio State hired Kerry Ard during my graduate career. (I'm sure they're thankful, too!) Kerry has been so gracious with her time and energy. I've learned a lot about classroom instruction, job market preparation, career development- all from someone that wasn't my advisor. I've been very lucky. I've felt supported by the rural sociology faculty, and I've learned how rare that experience is.

My peers within rural sociology have helped me immensely. Sometimes having a shoulder to lean on or having a laugh together. It helped to know we were all going through things together. It helped not to feel alone. I relied on the experiences of Dani, Joe, Rebecca,

Vicki, Molly, and Cory to let me know what graduate life was like. It can be difficult to navigate

vi conferences or to confront the job market. They gave me hope. As the program grew and I came out of my shell, I was heartened by the character and drive of new rural sociologists. I was surrounded by truly inspiring people that did meaningful work in the community. It's no surprise that Jazz and Caitlyn are doing great things in the world. I've truly been surrounded by superstars with great hearts. Soon Anne and Sarah will be joining their ranks, and I'm eager to see them as powerful forces of good in the world. I would have benefited from having their maturity, passion, and vision when I was younger. I know I'll be citing the work of Paige Kelly throughout my career. It's a bizarre feeling to meet someone and know that they will be a titan in their field. I've already laughed when she's referred to me as a mentor. I have learned so much from Paige over the years, and I truly think this dissertation would not have been possible without the countless hours she spent talking over research questions and literature, teaching me quantitative methods, and most importantly by just being a friend. These have not been easy years for the world, and it's been good to have a friend that's equally pissed off and devastated. I know I'll look back one day and marvel at having met a young Paige Kelly before she had her graduate degree.

Before going to graduate school, I was supported and inspired by some truly great friends. How did I ever encounter people that wanted to start a science reading club? How did such a picky eater become part of a dinner club? Those circles of friends have meant the world to me, and it's hard to tell people so without offering laundry lists of names with “Thank you!” attached. I'm looking forward to celebrating my 20th friendiversary with Ana, and talking about all the ins and outs that led here. Ana and Jason have known me since I was a video store clerk still deciding on an undergraduate major. Since then they've been roommates, bandmates,

vii classmates, and shoulders to lean on throughout. They gave me an intimate look at what graduate school was like before I thought it was possible for me. They also gave me hope for the future when life was dire. I couldn't have asked for better friends, and I am fond of all the bad food and movies we've indulged in all those years ago.

I have a lot of people that I'm thankful for. And I think it's worth noting what the world was like when I encountered all of the help they've given me. It's 2018 when I write this. Just a few months ago I couldn't stop openly sobbing, having watched news coverage of children being separated from their parents and thrown into tent cities at the southern border of the United

States. A few months before that, I watched Nazi and white supremacist groups hold rallies at college campuses. And a few months before that, I saw a thrice bankrupt real estate mogul- turned reality TV star become a president. The world is an ugly place, and it's a lonely feeling to see ignorance and hate thrive. The people I thank in this dissertation gave me reason to believe

I'm not alone. And they gave me reason to believe in hope. I hope that in the future we can look at these dark days as a low point and not a standard.

viii

Vita

June 1998 Jackson Memorial High School

December 2002 B.A. The Ohio State University

June 2013 M.S. The Ohio State University

Publications

Ard, Kerry, Garcia, Nick, & Kelly, Paige. 2017. Another avenue of action: an examination of

climate change countermovement industries’ use of PAC donations and their relationship

to Congressional voting over time. Environmental Politics, 26, 6.

Garcia, Nick & Lobao, Linda M. “Rural Sociology.” In Oxford Bibliographies in Sociology.

Ed. Lynette Spillman. New York: Oxford University Press, August 2018.

Fields of Study

Major Field: Environment and Natural Resources

Table of Contents

Abstract ...... i Dedication ...... iv Acknowledgments ...... v Vita ...... ix List of Tables ...... xii List of Figures ...... xiv CHAPTER 1 DISABILITY AND THE EMPIRICAL RESEARCH GAP ...... 1 I. Literature and Background ...... 4 A. The Leeds School: Disability Sociology and Disability Studies ...... 5 B. Sociological Research and the Disability Void ...... 7 C. Disability Geography and Environmental Contexts of Disability ...... 9 D. Moving Forward: A Synthesis ...... 11 II. Organization of Chapters ...... 12 CHAPTER 2 : DISABILITY PATTERNS ACROSS U.S. COUNTIES: ASSESSING INFLUENCES OF SOCIAL INEQUALITY AND HEALTH PRACTICES ...... 15 I. Introduction ...... 15 II. Literature and Background...... 17 A. Health Disparity and Social Inequality ...... 17 B. Health Practices and the Disablement Process...... 24 C. Moving Forward: Reconnecting the Social System to the Disablement Process ...... 30 III. Summary and Research Questions ...... 33 IV. Data and Methods ...... 37 A. Data ...... 37 B. Methods...... 41 V. Results ...... 45 A. Global Moran’s I ...... 45 B. Local Moran’s I (LISA) ...... 46 C. Spatial Lag Models ...... 48 VI. Conclusion ...... 55

CHAPTER 3 : DISABILITY AND THE EMPLOYMENT GAP ...... 61 I Introduction ...... 61 II Background ...... 65 A. Disability as determinant of employment: Discrimination and Accessibility ...... 66 B. Place-based Dimensions of Inequality ...... 69 C. Individual-level Explanations of Employment Variation ...... 73 III. Summary and Research Questions ...... 75 A. Place-based Relationships ...... 75 B. Individual-based Relationships ...... 77 III. Data and Methods ...... 79 A. Data ...... 81 B. Methods...... 86 IV. Results ...... 87 V. Conclusion ...... 93 CHAPTER 4 : DISABILITY BENEFIT AND SSI DISTRIBUTION ...... 99 I. Introduction ...... 99 II. Literature Review ...... 101 A. Sociopolitical Interest...... 102 B. Capacity-oriented Explanations ...... 105 III. Summary and Research Questions ...... 109 IV. Data and Methods ...... 111 A. Data ...... 113 B. Methods...... 116 V. Results ...... 118 VI. Conclusion ...... 122 CHAPTER 5 : CONCLUSION...... 126 I. Review of Findings ...... 126 A. Socioeconomic Well-being, Health, and Disability ...... 126 B. Disability and Inequality in Employment ...... 127 C. Disability and the State ...... 128 II. Limitations and Future Research ...... 128 A. Individual-level Data...... 128 xi

B. Longitudinal Research ...... 129 C. Within-Group Differences among People with Disabilities ...... 130 D. Disability and Policy ...... 131 REFERENCES ...... 135 APPENDIX: ADDITIONAL TABLES AND FIGURES...... 155

xii

List of Tables

Table 1.1 Disability research in sociology: 1980-2017 ...... 2

Table 2.1 Summary of hypotheses ...... 37

Table 2.2 Descriptive statistics for selected variables ...... 38

Table 2.3 Global Moran's I analysis of spatial clustering ...... 45

Table 2.4 Disability prevalence by type ...... 45

Table 2.5 Summary of findings ...... 45

Table 3.1 Summary of hypotheses ...... 79

Table 3.2 Descriptive statistics for employment sample ...... 80

Table 3.3 SAR model of disabled and non-disabled employment difference ...... 88

Table 3.4 Summary of hypothesized relationships and results ...... 92

Table 4.1 Descriptive statistics for SSI coverage ...... 112

Table 4.2 Summary of predicted relationships ...... 112

Table 4.3 Gap in SSI coverage for people with disabilities ...... 117

Table 4.4 Summary of hypothesized relationships and results ...... 119

Table A.1 Summary of significant variables ...... 119

xiii

List of Figures

Figure.2.1 The Disablement Process, adapted from Verbrugge and Jette 1994 ...... 25

Figure 2.2 LISA plots of disability prevalence calculated by age group ...... 47

Figure 3.1 Poverty rate:1980-2013 ...... 62

Figure 3.2 Employment rate among working age adults: 1981-2014 ...... 62

Figure 3.3 ADA enforcement: 1997-2017 ...... 68

Figure 3.4 Financial awards for EEOC discrimination claims ...... 68

Figure A.1 Disability prevalence (1990-2014) as reported by CPS ...... 155

Figure A.2 Google Analytics analysis of disability news coverage: 2008-2018 ...... 155

Figure A.3 Disability prevalence by age group from ACS datasets ...... 156

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CHAPTER 1 DISABILITY AND THE EMPIRICAL RESEARCH GAP

Disability is a topic in which growing public concern has not been met with corresponding research among sociologists. Between 2008 and 2018, news coverage addressing disability and people with disabilities tripled (see Appendix: Google Analytics 2018). The public discussion of disability has involved three general areas. The first area addresses disability as a health issue, concentrating on the rise in disability prevalence, its causes, and implications. The public discussion situates the rise in disability prevalence as a consequence of increasing obesity rates (Schoeni 2008), and is often accompanied by speculation on how increasing disability rates may strain social security (Greeley 2016). The second area concerns people with disabilities as a social group facing economic hardship. People with disabilities have 20.2% higher rate of poverty and 59.7% lower employment rate than people without disabilities (CPS 2015), and persistent economic inequality has galvanized disability rights advocates and think tanks to promote reforms to address economic hardship (Cokley 2018;

ADAPT 2016). The most visible area of disability coverage involves the relationship between people with disabilities and their appeals for government responsiveness. When protections for preexisting conditions and funding for independent living were threatened by the American

Health Care Act of 2017 (AHCA), a large disability protest movement shaped the discussion on how healthcare reforms were coordinated. Disability activists were dumped from their wheelchairs by law enforcement in the U.S. Capitol Building, this vivid imagery drew attention to the legacy of protest and the history of disabled people struggling for government responsiveness (Gomez and Koronowski 2018).

Although the public discussion of disability has gained momentum to incorporate these health, employment, and governance concerns, there has not been a corresponding increase in attention towards disabilities within sociology. Among three of the most-cited sociology journals, a total of four research articles included some abstract mention of disability between

1980 and 2017.1 Although underemployment of marginalized social groups is discussed in some

sociological studies, the employment gap between people with and without disabilities is not

(van der Lippe and van Dijk 2002; Smith 2002). Since 1980 two of the most-cited rural sociology journals published a total of ten articles addressing people with disabilities.2 Nine of

those articles appeared before 2005. Research among rural sociologists that have addressed the

relationship between place and livelihoods of people with disabilities are largely found outside of

prominent journals.3

Table 1.1Disability research in sociology: 1980-2017 Disability research in sociology: 1980-2017 5

4 AJS 3 ASR

2 ARS RSS 1 SR

0 1980 1985 1990 1995 2000 2005 2010 2015

1 American Journal of Sociology, American Sociological Review, and Annual Review of Sociology 2 Rural Sociology and Sociologia Ruralis 3 The Journal of AgroMedicine has featured articles connecting disability prevalence to hazardous work in agriculture (Deboy et al 2008) and examining work reentry after disability onset (Friesen et al 2010). The edited volume Critical Issues in Rural Health (2004) features several prominent rural sociologists, examining disability patterning and health risks in rural environments. 2

I view disability as a subject for social science research that is without a clear disciplinary

home for developing theory and advancing empirical research. Concepts emerging from the activist community that have informed disability sociology have not been incorporated into

general sociology. Further, place-based considerations of disabled livelihoods provided by

disability geographers have not been considered in health and employment research. I

acknowledge foundations for social research on disability across disciplines, and see this

dissertation as an opportunity to develop a template for empirical research that is responsive to

the public discussion of health, employment, and government welfare.

In this dissertation, I address three central research questions that connect disability to

empirical research trajectories in inequality outside of disability sociology. First, I examine disability as a health outcome, considering the relationship between disability prevalence, social inequality, and health practices. The central question of Chapter Two asks to what extent is

disability prevalence explained by social inequality and health practices? Second, I consider

how employment trends among people with disabilities have changed since the Americans with

Disabilities Act (ADA) and examine relationships between lagging employment and social,

economic, and disability characteristics across U.S. counties. The central question of Chapter

Three asks to what extent do economic composition of place and individual characteristics of

disabled contribute to employment outcomes? Finally, I view people with disabilities as a social

group whose higher rates of poverty and unemployment have been met with limited government

support. I explore the unequal provision of Supplemental Security Income (SSI) and disability

benefits across U.S. counties in Chapter Four, asking to what extent do political interests and

capacities of state governments contribute to differences in disability benefits?

I. Literature and Background Social science research addressing disability is not well developed. Disability can be treated as a social group category or as a health status, and researchers across social science

disciplines tend to stress one dimension of disability to the exclusion of the other. Typically,

researchers who focus on disability as a social group category are rooted in the work of disability

rights activists. Often their works are not empirical, instead interpreting the meaning of

“disability” and how representations of people with disabilities reinforce social stigmas (Grue

2011). The Leeds School has been at the forefront of this approach since the early 1990s, and

scholarship from the Leeds School provided key concepts used in disability sociology and

disability studies (Kitchin 2000). In contrast, researchers concentrating on disability as a health

status use disability as an outcome variable without exploring how this health status relates to

broader social inequality (Barnes and Mercer 1997). Medical sociologists and health

demographers engage in disability research, but generally sociologists tend not to study people

with disabilities as a marginalized social group.

An emerging area of empirical research considers the environmental contexts of

disability (Imrie 2004). Researchers within geography and planning examine how people with

disabilities encounter difficulties with features of the built environment. Inaccessible

environments present obstacles to mobility that prevent people with disabilities from interacting

with their communities, and this limited mobility undermines a sense of personal autonomy. In

this way, the physical structures of the environment exacerbate differences between people with

and without disabilities, rather than social stigmas or a health status. By addressing

environmental obstacles to mobility, geographers and planners connect functional limitations of

disability to social isolation of people with disabilities.

A. The Leeds School: Disability Sociology and Disability Studies The Leeds School is the most direct descendent of the disability rights movements of the

1970s and 1980s. Activists were hired to create a disability studies program and subsequently

launched several journals dedicated to the study of disability in society. Mike Oliver, Tom

Shakespeare, Geof Mercer, and Colin Barnes emerged as foundational figures within disability

sociology and disability studies. Their work is largely conceptual in nature, taking an

interpretivist approach to understanding disabilities and their social construction.

The determination of what counts as a disability is understood as part of a larger social

process that plays upon norm formation and stigmatization of people with certain physical,

mental, or behavioral characteristics. Tracing the historical roots of how people with disabilities

have been treated, disability sociologists view institutionalization, abuse, and social exclusion of people with disabilities across societies over time and situate their analysis by referencing legacies of oppression. People with disabilities have been historically stigmatized, and determination of what counts as a disability reflects broader social values that accept some individual characteristics while excluding others. Polydactylism, insomnia, and stutters may each present physical, mental, or behavioral “abnormalities.” However, they do not necessarily result in social isolation, nor are people with these traits the targets of genocidal agendas from eugenics movements. Blindness, deafness, and skin lesions, however, have each been met with varying degrees of acceptance and rejection based upon the social and historical contexts in which people with these attributes lived.

Two conceptual models of disability were outlined by Michael Oliver (1990) in explaining how disability status can play off of larger social stigma. Oliver (1990) formulated a distinction between a medical model and a social model of disability. The medical model depicts

disabilities as physical or mental conditions which may be overcome if given effective medical

or physical rehabilitation. When using a medical model of disability, a disability is understood

as being connected to an individual through their physical body. In contrast, the social model of

disability depicts disability as being socially constructed. Disability status is not an objective

account of physical or mental characteristics, but is instead the result of social biases which

accept some features while rejecting others. Whereas the medical model problematizes

individual characteristics as the root cause of disabled people’s problems, the social model

problematizes institutions that reinforce stigmatization and promote social exclusion.

Researchers in the Leeds tradition tend to examine two features that perpetuate the

marginalization of people with disabilities. The first involves the use of the medical model in

policy, media, and in social science research. Disability sociologists engage in critical

interpretive analysis that connects how employment of the medical model participates in a

narrative of disabled people’s oppression. There has been recent growth in discourse analysis in

this area, explaining how rhetorical framing of disability shapes policy and public understanding

of disability (Grue 2011). As the medical model is referenced in policy discussions, the public

increasingly takes on an impression that people with disabilities are responsible for their own

underemployment, poverty, and disconnection from community. The second feature of Leeds-

influenced research focuses on the exclusion of disabled perspectives in academic research and policy formation (Lloyd et al. 1996). Without input from people with disabilities, academic and policy agendas can prioritize the interests of people without disabilities and overlook obstacles in the built environment that exclude people with disabilities. In assuming that marginalization of disabled people can be captured by those that have not shared their lived experience, disability sociologists view these projects as uninformed (Stone and Priestly 1996). 6

These critical and meta-analytical features of disability sociology have shaped how

disability sociologists conduct research. Generalizability of disability research has been the subject of intense scrutiny, and Leeds-influenced researchers are openly critical of the scientific method (Barnes and Mercer 1997). Alternative research methods that focus on the personal experience of disability are prominent among disability sociologists (Barnes and Mercer 1997;

Morris 1996). Because their research focuses on individual experiences and their relationship

to broader narratives of oppression, disability sociologists have not produced empirical research

that is responsive to employment or poverty trends among disabled people. Quantitative

research documenting national trends is instead left for mainstream sociologists to address.

B. Sociological Research and the Disability Void Apart from journals dedicated to disability sociology and disability studies, there is a void of research that specifically addresses people with disabilities as a disadvantaged social group.

Richard Jenkins (1991) provides the clearest explanation of how sociologists can extend inequality research to disabled people. Reviewing the study of disability within sociology,

Jenkins (1991) noted the neglect of disability research as demonstrating “marginal substantive specialism, of little apparent interest to the sociological mainstream…It is as if, in the eyes of sociology, people with physical or mental handicaps are not considered socially significant

actors” (p.560). The necessary step to treating people with disabilities as a disadvantaged social

group involves bringing “visibility” (p. 560) to two areas of their livelihood. The first area

considers the predicament of people with disabilities in the labor market. People with disabilities

encounter unique obstacles to employment that are connected to their disability, and employment

obstacles contribute to greater economic hardships that drive “their class disadvantage” (p.573)

The second area of study involves the formal legal recognition of disabled status as it relates to

“economic transfers from the state” (p. 560). Whereas employment studies can inform

sociologists of the economic class implications of disability, Jenkins views welfare provision as a

means by which sociologists can capture the social status of people with disabilities. The

combined study of employment and welfare would provide needed visibility to people with

disabilities, and would put sociological study of the disabled on par with other disadvantaged

social groups

In the context of economic inequality, people with disabilities have higher rates of poverty and encounter employment difficulties. Jenkins (1991) outlined how sociologists could

extend their research to the case of disabled people. There is a rich literature base within

sociology that accounts for interpersonal discrimination towards women and racial minorities in

the workplace (van der Lippe and van Dijk 2002; Smith 2002), and Jenkins saw this as a natural

inroad to connect disability and social stigma to address underemployment of disabled people.

Hiring decisions can reflect employer preferences for applicants that are more similar,

demographically, to the employer. But he further suggested that costs and perceived costs of

hiring people with disabilities might contribute to a larger structural problem for the hiring of

people with disabilities. The costs of accommodations for people with special workplace needs

would, he claimed, influence the perceived value of disabled applicants. This could apply to

wheelchair ramps and Braille signage in the office place, as well as higher insurance costs

incurred by employers. But Jenkins (1991) further explained that disabled applicants may lose

hours of work due to routine health maintenance needs. Similar to the motherhood penalty

experienced by women in the workforce, the conception of these applicants as people that might

require extended time off meant that people without disabilities would be preferred hires.

While Jenkins (1991) provides a bridge for sociologists to research disability, disability is treated by the American Sociological Association (ASA) as a subject for disability sociologists to address. The newly-formed section on Disability and Society (2009) emerged to connect disability researchers within the ASA. An examination of their paper awards, recommended

journals, and programs of academic study, however, do not reflect an extension of stratification

scholarship to people with disabilities. The section engages in the promotion of two areas of

disability research: (1) promotion of the Leeds School, participation in its journals, and

enrollment into its academic programs; (2) promotion of program evaluation and rehabilitative

services, as understood by social work, public administration, and their respective journals.

Journals established by the Leeds School, those within disability studies, and those within social

work comprise the overwhelming majority of recommended journals for publication in this

section. Neither the American Sociological Review nor the American Journal of Sociology are listed as journals for potential publication. This produces a predicament for sociologists that want to address disability and inequality through quantitative research: While the Leeds School leaves quantitative research on disability and inequality for mainstream sociologists to address, the flagship organization within mainstream sociology directs its members to the Leeds School for research direction and publication. There is no academic home for the quantitative study of disability and inequality that can address the research agenda outlined by Jenkins (1991).

C. Disability Geography and Environmental Contexts of Disability In the absence of quantitative sociological study of disability and inequality, a growing body of research has approached the subject by concentrating on environmental contexts of disability. Emerging from the work of human geographer Reginald Golledge (1993), disability

geography connects people with disabilities to their environments, accounting for obstacles to

9 mobility in the built environment that restrict the ability of disabled people to participate in society. Disability geographers put the disabled experience at the forefront of research by considering how specific types of disability entail different mobility needs. But rather than problematize the disability, disability geographers focus on how social planners and designers are not responsive to universal accessibility of public transportation or building design.

Disability geographers acknowledge physical implications of disability while viewing mobility difficulties as being imposed upon people with disabilities.

Disability geographers have provided needed empirical research that documents how and where environmental factors contribute to inequality. Golledge’s original survey (1993) considered obstacles for the blind in pedestrian infrastructure, but the scope of disability geography has widened. In a study of wheelchair mobility across U.S. cities, Meyer et al. (2002) documented difficulties that go beyond work commuting, finding that the ability to see family and attend church were impeded by inaccessibility of the built environment. Parr (1997) offers an analysis of the relationship between people with mental health difficulties and public spaces, emphasizing differences in how disability may make some places more important to their livelihood. Anderson (2001) takes on the discipline of geography itself, observing how the inaccessibility of classrooms, lack of disability considerations in conference planning, and inaccessible educational materials create a space that excludes disabled people within the academy. Disability geographers have provided a productive area for producing research that addresses disability and inequality.

While disability geographers have produced needed empirical research, their focus on accessibility as the mechanism for inequality limits the kinds of research questions that they can explore. People with disabilities are a marginalized social group, and their social status, 10

susceptibility to employer discrimination, and lack of consideration from government are not

captured by surveys of accessibility. The dynamic social relationships between disability and

inequality are not reducible to place accessibility, but disability geographers demonstrate that

place considerations can shape day-to-day livelihoods of disabled people and their access to resources.

D. Moving Forward: A Synthesis There is a growing public interest in disability prevalence, persistent economic hardship of disabled people, and in government responsiveness to the disabled, and sociologists have not been part of the public discussion. Disability sociologists provide concepts that can inform how disability is framed in political discourse, but treating disability as a social construct hinders their ability to discuss employment trends and health patterning. In the 26 years since Jenkins (1991) appealed for more research on disability among sociologists, there has been a decline in disability research in leading journals (see Table 1.1). And while disability geographers have provided empirical tools to report inaccessible infrastructure within communities, mobility of disabled people is restricted to neighborhood case studies that are not extended to regional or national analysis. There is a need for sociological research that can inform the public discussion on disability.

Despite their absence from the public discussion on disability, health, employment, and

welfare literatures can contribute insights into how we address prominent areas of concern.

Disability sociologists understand people with disabilities as a marginalized social group and

highlight how disabled status involves different treatment by employers and government.

Sociologists who study inequality are accustomed to researching marginalized social groups and

stratification processes. Studies of racial and gender inequality can be extended to explain how

people with disabilities encounter greater hardship in employment and in receiving assistance

from the government. Disability geographers connect place-based obstacles to disabled livelihoods, moving research beyond accounts of interpersonal discrimination to incorporate accessibility into the discussion of inequality.

Rural sociologists may be well-equipped to engage in interdisciplinary study of disability that incorporates place considerations. Rural sociologists are accustomed to researching the degree to which marginalized social groups experience inequality across places. Poverty of place research among rural sociologists has incorporated historical legacies of discrimination against marginalized groups into analysis of poverty and employment (Tickamyer and Wornell 2017).

Rural sociologists have also connected place considerations to health outcomes. Schulman and

Slesinger (2004) examined disablement risks from hazardous industries, while Hummer (2004) has explored the implications of limited healthcare provision in rural places.

In the following chapters I address prevalence of disability, underemployment of disabled people, and relationship between disability and government welfare by incorporating rural sociological foundations for my research. I use cross-county comparisons of disability prevalence, employment differences, and disability welfare receipt from over rural and metropolitan counties. I consider how the public discussion of disability issues are represented in social scientific research, but incorporate subnational consideration of environmental conditions and disability composition to provide new insights.

II. Organization of Chapters

This dissertation is organized in five chapters. This chapter provides a background on how topics in disability are addressed within disability sociology, mainstream sociology, and

12 disability geography. I highlight contributions and limitations from each of these literatures, addressing how each can contribute to our understanding of disability prevalence, employment, and government responsiveness. I acknowledge foundations for place-based considerations of inequality and health research within rural sociology.

The remainder of this dissertation is organized as follows. In Chapter Two I examine disability as a health status, analyzing the spatial patterning of disability prevalence by connecting social inequality explanations in health disparities research to health practices research from public health and social epidemiology. My central research question in Chapter

Two asks to what extent is disability prevalence explained by social inequality and health practices?

In Chapter Three I examine people with disabilities as a social group that has experienced worse employment rates compared to people without disabilities. I consider how characteristics of places and of disabled individuals explain differences in employment between disabled and non-disabled adults. My central research question in Chapter Three asks to what extent do social inequality, industry composition, and disability types contribute to differences in employment between people with and without disabilities across counties?

In Chapter Four I compare self-reported rates of disability to rates of receipt for disability benefits across counties to address differences in government provision of disability welfare. I consider how the distribution of disability benefits might parallel the distribution of other social welfare programs, and incorporate social, political, and organizational capabilities involved in their provision. My central research question in Chapter Four asks to what extent do sociopolitical interests and the capacity of medical and governmental entities shape the provision of disability benefits? 13

In Chapter Five I review findings from these three empirical research projects, discussing their limitations and identifying future directions for study. I review the underlying relationships between disability and inequality, and offer several avenues for further study of this topic. I also examine how my consideration of differences across disability types brings to light inequalities within this social group.

CHAPTER 2 : DISABILITY PATTERNS ACROSS U.S. COUNTIES: ASSESSING INFLUENCES OF SOCIAL INEQUALITY AND HEALTH PRACTICES I. Introduction

Disability prevalence in the United States has been on the rise, as 2016 levels are now 0.9% higher than 2010 levels. Working age adults have fueled growth in disability prevalence, as disability rates have experienced an overall decline among adults over 65 for the previous two decades (Lakdalla et al. 2004). The magnitude and rate of these changes have prompted renewed interest in disability as a health outcome within sociological research. Yet despite a surge in published research on the topic, explanations of disability prevalence have not adopted research practices or theoretical insights from other fronts in health inequality research.

Within the sociology of health, disability has been out of sync with broader paradigmatic changes experienced throughout the field. The relationship between health trends and race, economic inequality, and place disadvantage are widely explored in what Cockerham (2014) describes as a paradigm shift “toward middle-range theories with a structural or macro orientation” (p. 1032). These changes have taken on a spatial dimension as health inequalities across gender and racial categories now consider neighborhood, regional, and cross-national aspects of social composition in their study of health patterning. Rather than examine health patterning strictly as an individuated process of health practices, place-based differences within the social system play a crucial role in explaining where and how health impacts are manifested.

Yet explanations of disability prevalence in the United States have focused on individual health behaviors as the driving force in disability trends, invoking Verbrugge and Jette’s (1994)

Disablement Process to explain how health practices influence physical impairment. Research

in this tradition examines disability as part of an individuated pathology, connecting micro-level

considerations of individuals to national-level data on obesity in explaining the rise in disability

prevalence. This approach leaves analysis of the social system in the background, instead

focusing exclusively on health dynamics as they relate to behaviors and the healthcare delivery

system.

Both approaches to disability prevalence have been hobbled by a lack of spatial data that

could connect disability prevalence, social composition, and health behaviors at a subnational

level. Before 2000, only Social Security Disability Insurance (SSDI) coverage rates could

estimate disability prevalence at the subnational level. But because SSDI approval involves

overreporting incentives from potential economic benefit and underreporting obstacles from

legal processing of disability claims, SSDI coverage did not provide a reliable means of

estimating disability prevalence. The introduction of disability questions in the 2008 American

Community Survey (ACS) provided the first opportunity for an ongoing national dataset that

reported disability prevalence across all U.S. counties. The availability of county-level data now

allows economic and demographic data to be connected to health practices across place,

providing an opportunity for both approaches to be mobilized in researching disability

patterning.

In this chapter I ask whether there is a spatial patterning to disabilities in the United States.

To what degree do socioeconomic composition and health practices explain differences in

disability prevalence across place? In this chapter I use county-level analysis of disability prevalence to apply spatial and mid-level approaches to health inequality in disability research.

II. Literature and Background

Two trajectories of research approach questions of disability as a health outcome. The first considers socioeconomic composition, stressing the relationship between economic, gender, and racial disparities to health outcomes. Finding its roots in conflict theory, this approach explores how inequality and resource deprivation have privileged some social groups while introducing hardship to others. There are persistent patterns of worse health outcomes among those with lower socioeconomic status (SES), women, and racial and ethnic minorities. Access to resources, connection to services, and social hardship are analyzed to explain health disparities.

The second approach concentrates on health practices of individuals and their connection to healthcare resources. This explanation is concerned with practical assessments that concentrate on direct health processes instead of the broader social system. Dietary practices and physical activities are directly related to cardiovascular health, mobility, resiliency to diseases and other physical processes that contribute to disability outcomes. But access to the healthcare system also plays a vital role in preventative and rehabilitative interventions that can ward off disabling conditions. Researching current trends in disability prevalence provides the opportunity to explore the explanatory capabilities of both approaches.

A. Health Disparity and Social Inequality

Social inequality plays a role in producing negative health outcomes among disadvantaged populations. Understanding disability as a product of social inequality builds from a rich tradition that examines economic, gender, and racial disparities as a reflection of broader inequalities across the social system. Socially disadvantaged groups experience more frequent ailments, higher mortality risks, and stressors associated with poorer health throughout the

lifecourse. Research in social inequality and health disparities has progressed from a focus on

access to material resources to now advance more complex explanations of place-based

considerations of social composition (Adler and Stewart 2010). This body of research identifies

the role of underlying social inequality in determining health outcomes, viewing variation in

health outcomes in relation to social arrangements in places.

Place-based health considerations are a part of a rich sociological tradition, with Durkeim’s

(1951) classical analysis of suicide providing a bridge between individual and societal interactions. While Durkheim situated health in place and across social groups by viewing private individual actions as a product of social integration, a conflict-oriented perspective has since emerged to analyze how social groups experience health disparities as a result of resource deprivation or social marginalization. From this view, class, gender, and racial disparities don’t reflect “excessive individuation” in a social setting, but instead groups have differential access to resources and stressors that culminate in worse health outcomes. The relationship between health and inequality is now broadly applied by sociologists to explain differences across these groups in terms of resource deprivation, stratification, and biosocial processes related to hardship.

1. Class, gender, and race

The relationship between class and health disparities can be categorized into three varieties of explanation. The first arose in the mid-1800s with a focus on poverty, identifying the poverty threshold as the point at which population health outcomes diverged (Engels and Wischnewetzky

2010; Scrambler 2012). Population studies noted significantly higher mortality and disease rates among people in poverty, citing a lack of resources corresponding with harsh living conditions,

unsafe work environments, and public neglect in health services. The second explanation used a

more linear approach to show a broader correlation between income and health across all

economic classes. The resulting “Deprivation Hypothesis” (DH) advanced the idea that there

was no absolute threshold in which negative health outcomes were introduced, but that instead

health differences existed even among higher economic classes (Jones and Wildman 2008;

Scrambler 2012). Finally, Fundamental Cause Theory (FCT) incorporated a spectrum of

socioeconomic status (SES) characteristics to explain how the degree of inequality influences

health benefits and risks across a social system (Link and Phelan 1995). As SES stratification

increases, so are connections to health resources that impact health literacy, referral networks,

and health behaviors. Independent of income measures and poverty rates, the degree of

inequality creates obstacles to health benefits and results in a clustering of resources among

higher classes. I understand disability prevalence as a health disparity that can have a parallel

relationship to economic inequality and view poverty, income, and income inequality measures

as important variables for study.

Current research assessing differences across social classes incorporates biosocial processes

that underlie health outcomes. The relationship between depression and stress from social and economic hardship was detailed by Pearlin’s Stress Process (Pearlin et al. 1981). There, he connected depression to social contexts, explaining how contributing factors (workplace difficulties, inadequate earnings) and social resources (supportive acquaintance networks, wellness resources) influence risk of depression. In explaining how social and economic well-

being influenced a specific health outcome, Pearlin laid the groundwork for biosocial research

that could connect inequality with specific bodily processes and health outcomes. McEwan and

Stellar (1993) described allostatic loads as the body’s heightened stress response to 19

environmental and social hardship, connecting social stressors with specific processes related to

cardiovascular health, regenerative capability of muscle tissue, and health of the immune system.

The accumulation of these allostatic loads contributed to specific health penalties to populations

undergoing economic and social stress in the form of diabetes, cardiac arrest, obesity, and more.

This approach to health inequality views being at the bottom of the social ladder as itself a health

liability regardless of health resources. I recognize that these biosocial processes can extend

across types of social disadvantage to inform explanations of disability prevalence.

Gendered health disparities are studied among medical sociologists as a function of social inequality, but take an intersectional approach that considers economic inequality and biosocial stressors as they are experienced by women versus men (Annandale 2010). Vebrugge’s Gender

Paradox (1989), which described greater longevity of women in spite of more health risks in

their life course, explained that a number of preventable conditions resulted from inequality in

the social system. With lower comparative wages and limited job offerings with fewer benefits,

women were not afforded the same health resources that were available to men. But the

comparatively lower social status of women also involved greater stress in the workplace and

emotional burdens within the home (Warren et al. 2004). Consequently, women faced economic and social burdens associated with more chronic conditions in the lifecourse (Mirowsky and

Ross 2003), while men engaged in more dangerous health behaviors that resulted in earlier deaths. I view this relationship between gender inequality and health disparities as a neglected consideration in explaining disability prevalence.

Health disparities across racial groups are studied from an intersectional approach that considers economic hardship and biosocial stressors. Racial minorities have disproportionately high mortality rates and negative health indicators across the United States. Explanations for 20 these health disparities build from economic and status accounts referenced above. Sociologists and public health scientists incorporate higher rates of poverty and unemployment among black populations in explaining worse health outcomes compared to whites (Hatzenbuehler et al.

2013). But racial minority status also brings more sources of social stress to produce higher allostatic loads than whites (Geronimous et al. 2006). Black Americans also find different treatment within the healthcare system, where they are less likely to receive needed procedures and they are less informed of health interventions by medical professionals (Peterson et al. 2002;

Popescu et al. 2007; Geronimous et al. 2006). In light of this relationship between racial composition and health disparities, I acknowledge the role of race as a possible influence on disability prevalence and see its inclusion as important for analysis.

2. Empirical precedent of health disparities and social inequality research Despite a wealth of research connecting inequality to health disparities, there have been limited applications of these explanations to address disability prevalence across the United

States. Nevertheless, empirical studies have connected measures of inequality to a variety of health disparities with some degree of spatial analysis. And while inequality has not been the focus of studies in the spatial patterning of disabilities, there is an empirical precedent for connecting social inequality to disability outcomes.

Although not specific to the contexts of disability, McLaughlin et al. (2007) connect mortality risks to place, analyzing the spatial patterning of mortality outcomes across counties and modeling the influence of social inequality on mortality rates. Using Compressed Mortality

Files from 1996-2000 to capture mortality rates of 3,062 U.S. counties, they used a threefold analysis to connect mortality patterning and rates to indicators of socioeconomic inequality.

First, they provided a Global Moran’s I measure to compare expected distribution with actual

county distribution of mortality. They found clustering that was significant at the 0.001 level.

Second, they provided an Anselin’s local Moran’s I (LISA) clustering analysis to demonstrate regional patterning in mortality rates. They found Appalachian clustering extending into the

Ozark region. Both of these findings supported initial hypothesized relationships between greater income inequality and structural disadvantage associated with places in these regions.

Using a spatial linear regression model, they then modeled the influence of income inequality, income, and education to find a positive relationship between economic inequality and mortality rates. Incorporating structural disadvantage indicators of unemployment and race, they found a positive relationship with mortality rates. This approach, identifying whether spatial clustering

exists, and connecting health outcomes to measures of economic development, disadvantage, and

demographic composition across places, provides a model for rural sociological study of

disability. Parallel study of disability should take into account how disability status may differ

in its potential to vary throughout the lifecourse. Whereas mortality rates capture a definitive

end of life, disability status can appear and disappear throughout the lifecourse. And because

characteristics of place can influence health risks and assets, the potential for migration from

place-to-place can complicate a simple replication of models.

Additional work from Keene and Li’s (2005) study of health differences between men and

women across age cohorts connects divergent health outcomes to socioeconomic differences that

underlie differences in use of health services. Their study of individual use of community health,

mental health, and social services across counties found pronounced age and gender differences,

whereby higher use of services among older cohorts and women was limited to community services. In contrast, men made higher use of rehabilitative services than women across age cohorts, providing additional protections against potentially disabling conditions. Their study 22

concludes by pointing to underlying socioeconomic differences that implicate women and older

populations. By connecting gender inequality directly to health maintenance and use of health

services, this study explains how gender inequality may play a role in shaping divergent health

outcomes.

Geronimus, Bound, and Ro (2014) offer needed empirical research that addresses the effect

of residential mobility in shaping spatial patterning of disability. Comparing 20 focal areas from

economically similar urban and rural places, they use a logistical regression model to compare

characteristics of place inequality (poverty, racial composition, and educational attainment) with

migration flows to determine the degree to which each shapes disability clustering. Restricting

their analysis to working age populations (16-64), they find that socioeconomic characteristics of

place remained significant determinants of disability clustering and that residential migration

effects yielded no statistically significant influence on disability prevalence. Comparisons of

disability prevalence between 1995 and 2000 showed that poverty, median income, race, and

urban/non-urban designation were statistically significant determinants of disability prevalence

when controlling for age and education. In restricting the scope of disability prevalence to

working age adults, this study provides an avenue for researching disability prevalence across

place without distorting place effects with mobility flows in older age. But this study also

supports economic, demographic, and rurality indicators as important variables in assessing

disability prevalence across place. Both McGlaughlin et al. (2007) and Geronimus, Bound, and

Ro (2014) lay a framework for repeated study of age-restricted disability across urban and rural

counties in the U.S.. There is an empirical precedent provided here that connects economic, race, and gender inequality as determinants of divergent health outcomes. These should be applied to

the case of disability, where inequality is likely to have a positive relationship with disability

prevalence.

B. Health Practices and the Disablement Process

Medical sociologists have joined public health researchers and healthcare administrators to study disability prevalence with a focus on health practices within the healthcare system and among individuals. These researchers attempt to pinpoint disability onset with reference to risks and assets that influence an individual’s likelihood of acquiring a disability. Rather than evaluate disability prevalence as it relates to social inequality, the scope of this research is restricted to elements that directly impact health outcomes.

These researchers connect health practices to disability onset through a disablement process, a series of steps whereby accumulated health risks result in some functional disability (see Figure

2.1). The disablement process was first introduced by Verbrugge and Jette (1994) to capture how disabilities emerge during the lifecourse. According to their four stage model, biochemical and physiological conditions (pathologies) disrupt the function of bodily systems (impairment) to restrict physical and mental activities (functional limitation). When these restrictions result in difficulties in expected daily activities, a condition moves from being an impairment or limitation and becomes a disability. Verbrugge and Jette (1994) articulate a linear process where each stage can be conditioned by the introduction of risk factors or preventative interruptions.

The nature of risk and recovery factors can involve material considerations of changes to medical technology and genetics or the influence of social conditions like poverty and health behaviors.

By broadening the scope of consideration for disability-influencing factors, the disablement process allowed social and medical sciences to offer complementary explanations for disease

prevalence that could incorporate data from across specialized fields. Since its introduction,

researchers have applied NLTCS, NHIS, and ACS data to interpret trends in disability

prevalence with respect to explanations involving risk and recovery factors encountered in the

lifecourse.

Figure.2.1The Disablement Process, adapted from Verbrugge and Jette 1994 The disablement process has two categories of factors that shape the likelihood of becoming

disabled. The first, extra-individual factors, constitutes structures outside of an individual that

can influence the progression from impairment to disability. These factors can include medical

services (treatment, rehabilitation, or medication) or the environment itself. While built,

physical, and social environments can be identified to incorporate broader analysis of the social

system, aspects of the healthcare delivery system are often the exclusive focus of extra-

individual analysis. The second, intra-individual factors, emanate from individuals themselves to

influence progression from impairment to disability. Lifestyles, behaviors, and coping

mechanisms initiated by the individual can alter health trajectories to prevent or recover from disabling conditions. The extra/intra-individual distinction is meant to sort individual actions

25 from assessments of the larger healthcare delivery system in the promotion of health maintenance and rehabilitation.

1. Extra-individual factors in the health system

Extra-individual considerations in disablement process research involve collaborative work between medical sociologists, healthcare administrators, and public health researchers. Their work aims at finding risks and assets within the healthcare delivery system that shape disability onset. While these extra-individual considerations generally do not extend to the broader social system, the focus on delivery of healthcare services provides analysis that is readily translated into policy initiatives. Guiding this approach are two key assumptions: First, routine health maintenance deters disability onset; And second, healthcare services can recover functionality of the body to prevent long term disability. Together, these represent the extent of extra-individual factors currently examined in disablement process research.

In the context of disabilities, a relationship between Primary Care Providers (PCPs) and patients can influence the risk of disability onset and recovery in a multiple ways. Empirical research demonstrates disability prevention and recovery associated with PCP access, with varying degrees of effectiveness based on duration of the PCP/patient relationship (Radosevich et al. 2001). From a preventative perspective, PCPs can promote health activities and foster health literacies wherein patients learn how to alter personal habits to ward off potentially disabling conditions (Stevens, Mistry, and Halfon 2006). The promotion of healthy diets, exercise, and avoidance of harmful substances removes risks to the body that threaten longterm disability. But PCPs also have the ability to detect and respond to ailments or malformities, providing medical or technological interventions that can restore hearing loss (Cohen, Labadie,

and Haynes 2005) or prevent mobility impairment from scoliosis (Larson 2011). Consistent

across this research is the claim that having a PCP itself makes the possibility of health

interventions more likely than intermittent care which responds to health impacts as they happen.

With regard to cognitive and mental health, routine discussions with patients is itself a tool to offset disabilities, regardless of whether treatment is provided. PCPs are able to suggest changes in individual behaviors that can work around functional limitations of some disabilities to help patients integrate into their communities (Radosevich et al. 2001; Messinger-Rapport and

Rapport 1997). But a number of anxiety and depressive disorders are made worse by feelings of futility regarding self-care. New research demonstrates that by having a PCP patients are more likely to view the responsiveness of PCPs as an imperative to value their own health, actually preventing the escalation of anxiety and depressive disorders into disabling conditions (Van der

Leuw et al. 2015).

Insurance coverage is understood as a key mechanism to provide regular access to the healthcare system. Glaucoma, cancer, and preventable diseases are higher among uninsured, as

incremental care and costs make routine checkups and screenings less likely (Woolhandler and

Himmelstein 1988). A lack of insurance further undermines the effectiveness of treatments, as

costs associated with followup care can cause infections, disrupt pain management, or leave

deteriorating conditions untreated by healthcare providers (Roetzheim et al. 2000). In the event

that healthcare services are provided, a lack of insurance corresponds with diminished quality of

care. Training of health professionals attached to patient care and use of health information

systems are shown to be worse for patients without insurance (Porterfield and Kinsinger 2002).

Intermittent health visits and medicine pricing concerns associated with being uninsured further

complicate how healthcare professionals can respond to patients. This can cause delays in the 27

administration of medication or management of prescribed treatment (Ferris et al. 2002).

Interactions with the healthcare system are made worse by a lack of insurance. Interactions

between individuals and the healthcare system provide benefits that ward off disability onset

(Miller et al. 2014), and I acknowledge PCP and insurance coverage as important variables to

consider when explaining variation in disability prevalence.

2. Intra-individual factors: Health behaviors and attributes

Research assessing intra-individual considerations in disablement process involves

collaborative work between medical sociologists and public health researchers. Their work aims

at finding risks and assets shaped by individual health behaviors that influence disability onset.

These intra-individual considerations do not extend to the broader social system, but often aim to

inform the public on how personal practices can improve an overall health profile. While extra-

individual factors in the healthcare system can recover functionality, intra-individual factors

often mitigate risks associated with disability onset.

Health risk behaviors comprise one category of intra-individual factors, including all practices initiated by individuals with potential to create lasting harm to physical or mental function. The use of harmful substances, some sexual behaviors, and dietary practices are typically studied in relation to disability prevalence. Alcohol consumption introduces multiple pathways to disability. Alcohol consumption relates to physical injury from impaired driving, cognitive impairments from prenatal exposure, and exacerbation of depressive states. Tobacco consumption erodes respiratory capabilities to impede mobility and cause debilitating cancers.

Risky sexual practices can promote the transmission of sexually transmitted infections that can impair bodily function. Research in disabilities and these health risk behaviors is often clinical,

typically offered without application to overall disability prevalence trends. Obesity is treated

differently.

The past two decades of research in disability prevalence have put a spotlight on how obesity

has shaped national trends. There is a divergence in disability trends among age cohorts, as

rising disability rates among working age adults are contrasted with falling rates among older

Americans. By referencing rising obesity trends in working age adults, researchers have

repeatedly pointed to obesity as the primary explanation for this difference (Lakdawalla et al.

2004; Freedman et al. 2006; Ogden 2006; Ringel and Sturm 2004; Schoeni 2008).

Complementing this national snapshot of disability and obesity, longitudinal data have been extensive in exploring how obesity introduces disability risks. Obesity appears to operate as an early and midlife risk factor, disrupting cardiac, kidney, and cognitive function in adults, thereby contributing to limitations in physical activities, diabetes, and related restrictions to ambulatory and visual function (Ringel and Sturm 2004).

Current research in disability prevalence and the disablement process has not incorporated spatial processes and meso-level considerations of place that influence health outcomes. The

sociology of health and medical sociology are currently undergoing a paradigm shift away from

“methodological individualism” and “toward middle-range theories with a structural or macro orientation” (Cockerham 2014, p. 1032). Whereas research in health disparities in the social system has explored negative health outcomes in relation to broader social processes which mediate risk exposure and health assets, the disablement process has been used to explain disability outcomes with reference to individual encounters in the lifecourse.

Advancing the lifecourse perspective without considering the social environment is an incomplete approach to the disablement process that is not responsive to research showing a 29 relationship between health disparities and social inequality. While explanations of internal health processes are important in demonstrating how diseases manifest, social inequality has been a greater predictor of health conditions like arthritis and high blood pressure than genetic attributes and individual processes (Cobb and Kasl 1966; Higgenbottom 2006). Moreover, discussions of prevalence extend beyond individuals to instead account for larger population dynamics in a given area. The aspatial applications of disablement process scholarship have fallen behind other local and regional analyses which have captured effects of economic hardship and stressors in a social environment. Given that consideration of the social environment was part of Verbrugge and Jette’s (1994) original conception of the disablement process, it seems that the absence of social environments from current research has left the disablement process only partially explored.

C. Moving Forward: Reconnecting the Social System to the Disablement Process

Approaches that connect social inequality to health disparities could benefit from considering the health system and health behaviors as a characteristic of social inequality. While there is work connecting some hospital practices and clinic placement to health disparities, PCP shortages and insurance coverage are not typical in inequality research. Insurance coverage represents the financial bridge to receiving healthcare, and its absence leaves the relationship between healthcare access and economic hardship unexplored. Moreover, spatial research on food deserts and the marketing of tobacco and alcohol are informed by broader inequality in the social system. Obesity, tobacco consumption, and alcohol could be incorporated into a social systems analysis of health disparities as a function of inequality.

Research addressing rural health disparities offers a template for such meso-level analysis, incorporating place composition into explanations of health disparities. Metro and nonmetro comparisons of disability prevalence show some distinctions in the types of disabilities, activities of daily living, and use of assistive devices (Johnson 2004). A rural health disadvantage extends to mortality patterning, as metropolitan counties have had declines in every major disease category through the mid-200s while non-metro counties have experienced increases in diabetes and cancer (Wright Morton 2004). Rural sociologists have examined both the characteristics of the social system and health practices alike in explaining these disparities. The experience of poverty in rural areas is made worse by an historical lack of healthcare associated with extractive industries, where the industrial makeup of rural counties finds rural workers 10.4 percentage points less likely to be offered insurance compared to urban workers (Larson and Hill 2005).

Schulman and Slesinger (2004) further describe risks to disablement from fatal and nonfatal injuries associated with forestry, agriculture, and mining that are inherent in the industry, regardless of efficiency improvements in resource extraction. Health disparities have also been linked to high rates of uninsured in nonmetropolitan counties, presumably from seasonal employment and limited benefits associated with these industries (Hummer et al. 2004). Rural research in health disparities offer a template for county-level analyses that encourages researchers to move beyond considerations of population composition and health practices to examine social contexts that include measures of inequality and industry within counties.

New data provide opportunity to revisit disability with these aims in mind, with improved sampling of all U.S. counties and reporting of disability. With the introduction of county-level disability data in the 2008 American Community Survey (ACS), researchers are presented with an opportunity to examine prevalence and its spatial variation across every county. Considering 31

that disability prevalence varies from 3%-32% across counties (U.S. Census 2016), researchers can connect aspects of the social system (poverty, racial composition, gender inequality) with the health practices (PCPs, insurance coverage, obesity) to find what explains changes in disability prevalence across place.

The ACS data provide added benefits from reporting prevalence of different types of disabilities, allowing assumptions of uniform processes in current approaches to account for a spectrum of mental and physical conditions. The current ACS survey poses questions to identify disabilities related to difficulties related to hearing, vision, cognitive function, ambulation, self- care, and independent living. The questions are asked using the following language in the 2008

ACS Disability Questionnaire:

1. Is this person deaf or does he/she have serious difficulty hearing? 2. Is this person blind or does he/she have serious difficulty seeing even when wearing glasses? 3. Because of a physical, mental, or emotional condition, does this person have serious difficulty concentrating, remembering, or making decisions? 4. Does this person have serious difficulty walking or climbing stairs? 5. Does this person have difficulty dressing or bathing? 6. Because of a physical, mental, or emotional condition, does this person have difficulty doing errands alone such as visiting a doctor’s office or shopping?

These questions allow a more nuanced consideration of health to capture whether social and health dynamics hold different influence.

Trends in disability prevalence, SES, healthcare services, and health behaviors have shown significant variation across places. With the paradigm shift toward middle-range and macro theory, the availability of place-data provides new opportunities to explore disability. Is there a spatial patterning of disability prevalence in the United States? To what extent do inequality, health behaviors, and healthcare explain variations in disability prevalence? And do our explanations of disability prevalence change according to what type of disability is being 32

examined? To answer these questions, I connect spatial analysis of U.S. counties to regression models that test the explanatory power of our theories.

III. Summary and Research Questions

My central research question asks to what extent county disability prevalence is influenced by social inequality and health practices. I acknowledge Cockerham’s (2014) appeal to a paradigmatic transition that incorporates meso-level considerations of place influence, and I incorporate rural-urban factors that may inform differences in disability prevalence. I further extend this research to examine how these factors may influence the prevalence of specific types of disabilities, including blind, deaf, cognitive, and mobility-related disabilities.

Social inequality has played a role in explaining health disparities, where economic, racial, and gender inequality have each contributed to health burdens. The health burden associated with economic hardship is described by the deprivation hypothesis, where disease onset and mortality rates are worsened as poverty levels increase. But the relationship between health disparities and economic inequality is not merely a consideration of the poverty threshold. The fundamental cause hypothesis suggests that health gains are not evenly distributed across populations, and greater inequality prevents health gains from being realized throughout society.

Coupled with these theories on inequality and health disparities, Pearlin’s Stress Process describes heightened allostatic loads experienced by racial minorities and women that make the body susceptible to illness. From these considerations I develop the following hypotheses:

H1: Based on the deprivation hypothesis, I expect poverty will be related to higher disability

rates.

H2: Based on the fundamental cause hypothesis, I expect greater inequality to have a positive

relationship with disability prevalence. Higher inequality as measured by the Gini

coefficient should be related to higher disability rates.

H3: Based on Pearlin’s stress process, I expect that marginalized status of racial and ethnic

minorities will contribute to disabled health outcomes. I expect areas with greater nonwhite

composition will be related to higher disability rates.

H4: Based on Pearlin’s stress process, I expect that gender inequality will correspond with

worse health outcomes among women. When measuring gender inequality by the gap in

earnings between men and women, I expect greater earnings differences to have a positive

relationship with disability prevalence.

Taking into account place dimensions of inequality, rural-urban differences also involve inequalities related to industrial composition. Greater exposure to extractive industries in rural areas involves work hazards and limited healthcare benefits that introduce greater risk of disablement to rural people. Although military service provides healthcare services, injury and fatality rates among military personnel are also likely to introduce greater risks of disablement.

From these considerations, I develop the following hypotheses:

H5: Greater shares of extractive employment will be related to higher disability rates.

H5a: Greater shares of farming employment will be related to higher disability rates.

H5b: Greater shares of logging employment will be related to higher disability rates.

H5c: Greater shares of coal employment will be related to higher disability rates.

H5d: Greater shares of oil and gas employment will be related to higher disability rates.

H6: Greater shares of military employment will be related to higher disability rates.

Health practices consider aspects of the healthcare delivery system and personal health

behaviors that contribute to disability risks. The healthcare delivery system is dependent upon a

pool of qualified practitioners that can provide preventative medicine, routine health

maintenance, and rehabilitative services that can prevent disability. Limited availability of primary care providers reduces the health resources available to prevent disability. Access to these services, however, involves financial costs that can be offset by insurance. As the share of a county’s uninsured population increases, its access to healthcare resources is also likely to be limited. From these considerations I develop the following hypotheses:

H7: Higher county PCP rates will be related to lower disability rates.

H8: The size of a county’s uninsured population will be related to higher disability rates.

Risky and harmful health behaviors pose both direct and indirect determinants of disability.

Obesity, smoking, and binge drinking each present direct risks to bodily health that can result in a permanent condition of disability. But engaging in these behaviors is also used as an indirect indicator of whether individuals value their personal health. These behaviors can suggest neglect of personal health and a willingness to engage in risky behaviors that can result in a disability.

From these considerations, I develop the following hypotheses:

H9: Higher rates of unhealthy behaviors in counties will be related to higher disability rates.

H9a: Higher rates of obesity will be related to higher disability rates.

H9b: Higher rates of smoking will be related to higher disability rates.

H9c: Higher rates of heavy drinking will be related to higher disability rates.

Education has played a dynamic role in explaining health patterning. Literature addressing inequality and health disparities tends to treat educational attainment as a measure of social class, while literatures in health practices view education as a proxy for health literacy and an ability to 35

Table 2.1 Summary of hypotheses H1: Based on the Deprivation Hypothesis, I expect poverty will be related to higher disability rates.

H2: Based on the Fundamental Cause Hypothesis, I expect greater inequality to have a positive relationship with disability prevalence. Higher Gini values should correspond with greater disability prevalence. H3: Based on Pearlin’s Stress Process, I expect that the marginalized status of racial and ethnic minorities will contribute to disabled health outcomes. I expect areas with greater nonwhite composition will be related to higher disability rates.

Social Inequality Social H4: Based on Pearlin’s Stress Process, I expect that evidence of gender inequality will correspond with worse health outcomes among women. I expect greater earnings differences to have a positive relationship with disability prevalence.

H5: Greater shares of extractive industry employment will be related to higher disability rates.

H5a: Greater shares of farming employment will be related to higher disability rates.

H5a: Greater shares of logging employment will be related to higher disability rates.

H5a: Greater shares of coal employment will be related to higher disability rates. Industry H5a: Greater shares of oil and gas employment will be related to higher disability rates.

H6: Greater shares of military employment will be related to higher disability rates.

H7: Higher county PCP rates will be related to higher disability rates.

H8: The size of a county’s uninsured population will be related to higher disability rates. Healthcare System

H9: Counties with higher rates of unhealthy behaviors will be related to higher disability rates.

H9a: Higher rates of obesity will be related to higher disability rates.

H9b: Higher rates of smoking will be related to higher disability rates.

Health Behaviors H9c: Higher rates of heavy drinking will be related to higher disability rates.

Education H10: Higher rates of college degree holders in a county will be related to lower disability rates.

36 pursue healthy lifestyles. Both approaches treat educational attainment as an asset that is linked to better health outcomes, and higher educational attainment is likely to have a negative relationship to disability prevalence. From this consideration, I develop the following hypothesis:

H10: Higher rates of college degree holders in a county will be related to lower disability

rates.

Table 2.1Summary of hypotheses IV. Data and Methods

I use counties as units of analysis when evaluating disability prevalence. I sample from

48 states, excluding Hawaii and Alaska. Because I use a spatial regression for examining differences in disability prevalence, Hawaii and Alaska present non-contiguous areas (or

“islands”) that are incompatible with distance weighting and shared boundary considerations used in the construction of spatial weight matrices. Accounting for data suppression in reporting of income, insurance, and PCP rates, I use a 2,964 county sample in this model. Descriptive statistics for this sample are shown in Table 2.2 variables below.

A. Data

1. Dependent Variables.

The dependent variable, disability prevalence, is the share of the non-institutionalized population with a self-reported disability. This variable is derived from S1810 data provided by the 2016 American Community Survey. I restrict my analysis to adults aged 18-64. Removing seniors limits migratory effects of disability prevalence and concentrates on the period in the lifecourse when health disparities are most pronounced (Geronimus et al. 2014). I also removed children from the sample after preliminary analysis demonstrated no significant patterning of

disabilities across U.S. counties. On average, 13.5% of each county’s population reports having

some disability. I use the same S1810 dataset and age restrictions when reporting blind, deaf,

cognitive, and mobility-related disabilities within each county. Mobility-related disabilities are,

on average, the most prevalent type of disability in each county with a rate of 7.1%.

Table 2.2Descriptive statistics for selected variables Table 2.2: Descriptive statistics for selected variables (n=2,964) Variable Mean Std. Dev. Min Max Disability compositiona (%) Disability 13.51 4.66 1.77 39.99 Deaf 3.03 1.37 0 12.9 Blind 2.52 1.47 0 13.7 Cognitive 5.51 2.28 0 19.7 Mobility 7.08 3.15 0 27.6 Social composition Gini 0.43 0.04 0.32 0.67 Poverty (%) 15.79 6.2 0 48.4 Race/Ethnicity Hispanic (%) 7.96 12.67 0 98.45 Black (%) 9.12 14.57 0 85.67 Asian (%) 1.11 2.11 0 33.5 Other (%) 1.5 5.45 0 86.36 Sex earnings gapb 15.64 10.81 -37.41 132.77 Industry (% share) Military 1.65 0.12 0 84.14 Farming 4.65 0.07 0 75.29 Logging 0.31 0.01 0 11.25 Coal 0.24 0.02 0 30.73 Oil and gas 0.25 0.01 0 19.67 Health services PCP rate 55.1 33.44 0 508.31 Uninsured (%) 17.83 5.36 3.14 38.85 Health behaviors (%) Obesity 30.55 4.25 13.1 47.9 Smoking 18.36 3.63 6.9 38.2 Binge drinking 16.54 3.34 8.4 27.3 Bachelors (%) 12.78 5.35 2.4 39.6 Median age 40.05 4.87 21.9 62.2 aDisability composition among adults between ages of 18-64. bMedian earnings among those with a bachelor’s degree or higher, reported in $1,000USD

2. Independent Variables

Social inequality variables. Studies addressing health disparities as an effect of social inequality reference economic hardship, economic inequality, racial composition, and gender inequality. I use 2011 ACS data to derive county poverty rate, Gini measures of inequality, and 38 the racial and ethnic composition of each county. The average county in 2011 had a poverty rate of 15.8%. The Gini index of inequality is situated on a scale from 0 to 1, where 0 indicates absolute equality in wealth distribution and 1 indicates absolute inequality. The mean county score in this sample is 0.43. The racial and ethnic composition of U.S. counties in this sample is overwhelmingly white, with only 99 counties reporting nonwhite majorities. The average county in 2011 had a racial composition that was 78% white, 9.1% black, 1.1% Asian, and 1.5% as an uncategorized “other” racial group.4 The average county in the sample had an ethnic composition that was 8% Hispanic. To capture gender inequality, I examine earnings gaps provided by the S24011 Sex Earnings dataset of the ACS. To account for gender differences in occupational self-selection and informal work, I restrict the earnings gap to bachelor’s degree holders. The average difference in earnings among bachelor’s degree holders was approximately

$15,564, where 2,813 of the sampled counties demonstrated greater earnings among men. I anticipate greater social inequality, as determined by poverty, income inequality, racial composition, and gender pay gaps, to be positively related to disability prevalence.

Industry variables. Detailed breakdowns of employment across industries is provided by

2012 Economic Modeling Specialists, International (EMSI) data. This proprietary dataset improves upon one-digit sector-level industry data of the BEA to provide two-digit sectoral details that can capture nuances within industrial categories. Based on occupational hazards and limited benefits associated with extractive industry, I report county shares of work in coal, mining, farming, farming, logging, coal, and oil and gas. Due to high correlations between military service and disability, I also include shares of military employment in the model. I

4 Here “white” reflects respondents reporting a white racial category and a non-Hispanic ethnicity. 39

anticipate greater shares of these industries to be positively related to disability prevalence across

counties.

Health services variables. I understand health services as providing resources necessary to

prevent disability onset and promote rehabilitation. I use the 2011 Area Health Resource File

(AHRF) of the American Medical Association (AMA) to report Primary Care Provider (PCP)

rate and rate of uninsured across the 2,965 counties sampled here. AHRF and AMA report PCP

rate using the following formula: PCP Rate=(Number of PCPs/county population)*100,000. On

average, 17.8% of each county’s population was uninsured in 2011. I expect PCP rate to have a

negative relationship with disability prevalence and number of uninsured to have a positive

relationship with disability prevalence.

Health behaviors. I use the 2010 Center for Disease Control (CDC) dataset on obesity

prevalence and use the 2014 Behavioral Risk Factor Surveillance System (BRFSS) core

questionnaire for tobacco and alcohol use. BRFSS data reflect estimates from the 2006-2012 surveillance period. Each of these health behaviors are connected either directly to disability risks (diabetes-induced amputations and blindness, intoxicated driving and debilitating accidents, etc) or serve as indirect indicators of health maintenance. Higher rates of tobacco use, binge drinking, and obesity should correspond with higher rates of disability in the model.

Education. I use the S1501 Educational Attainment data from the 2007-2011 five year estimates of the ACS. I examine the share of each county’s population that holds a bachelor’s degree or higher as my indicator of educational attainment. Education is used in health research as an indicator of social class and health literacy. In this way, it does not uniquely represent theories in social inequality nor health behaviors. Of the counties used in this sample, an average

of 12.8% of each county’s population holds a bachelor’s degree or higher. I expect more

educated counties to have lower disability prevalence.

Metropolitan status. I incorporate 2013 USDA Rural Urban Continuum (RUC) codes in

reporting each county’s degree of rurality and adjacency to metropolitan amenities. I recode

nine categories of the rural urban continuum into three categories of Metropolitan, Rural

Metropolitan-Adjacent, and Rural Metropolitan-Non-Adjacent. Here, all counties with RUC values between 1 and 3 are coded as metropolitan, RUC 4, 6, 8 counties are coded as Rural

Metropolitan-Adjacent, and RUC 5, 7, 9 are Rural Metropolitan-Non-Adjacent. 1,140 of the counties in this sample are categorized as Metropolitan, 978 of the rural counties are adjacent to metropolitan counties, and the remaining 846 counties are rural counties that are non-adjacent to metropolitan amenities. I treat Metropolitan as the reference category in analysis.

Age. I restrict my analysis to adults aged 18-64 to focus on the influence of social inequality and health practices on disability prevalence. Because age introduces its own disability risks, I add median age to the model as a control for aging effects. The median age of counties in this sample is 40. I expect counties with higher median ages to have greater prevalence of disability.

B. Methods

My aim is to identify whether there is spatial patterning in disability prevalence and identify whether such clustering effects correspond to social inequality and health practices across counties. Do indicators of social inequality and healthcare access explain changes in disability prevalence? Are there hotspots and coldspots that prioritize areas of health concern? And how well do our explanations of health disparities describe disability across U.S. counties? These questions involve a combination of spatial analytic methods and linear regression modeling.

Typically spatial processes involve two-staged analyses which test for a global tendency of spatial randomness (global Moran’s I) with subsequent examination of spatial covariance with variables of interest. I apply Anselin’s local Moran’s I (or LISA) to test for high/low clustering between points and offer Getis-Ord Gi hotspot mapping for simple comparisons. The result of these processes should verify whether there is spatial patterning to disability. Through this analysis, I also provide maps that can demonstrate where prevalence rates are distinctly high or low. Because new data allow exploration of different types of disability, I am able to examine

whether the proposed variables influence hearing, vision, cognitive, and mobility-related disabilities.

As noted, counties within non-contiguous states are excluded, as their distance effects would likely add distortion to the national model without providing accurate information about the composition of Alaska or Hawaii themselves. The same processes used in Moran’s I calculations can be applied to both states individually, but the relatively small number of cases within each state (n=27 for Alaska, n=7 for Hawaii) would not provide statistically reliable results.

1. Global Moran’s I Statistical measures of dispersion involve calculations of variance from mean values. The global Moran’s I serves as a spatial test of dispersion, where deviations from mean values of a particular feature are calculated across all locations in the sample (Moran 1950). Spatial weights are incorporated in this process to consider proximity and related decay effects from increased distances. There is a strong precedent for applying global Moran’s I in capturing clustering trends in county-level mortality data (McGlauglin et al. 2007), with some emerging applications to assess disability clusters in Southeastern counties of the United States (Hollar 2017). For this

measure, the null hypothesis assumes complete spatial randomness without patterning, indicated

when I is equal to zero. Positive values indicate a tendency toward clustering, while negative

values suggest dispersion in the variable of interest.

2. Anselin’s Local Moran’s I (LISA) When quantifying differences between populations, cutoff points between values can

artificially suggest distinct patterns or smooth variations so no distinctions are visible. Anselin’s

local Moran’s I (LISA) calculates differences between a variable in one location and surrounding neighbors or distances to identify whether there is a statistically significant difference within a particular location relative to surrounding areas (Anselin 1995). Inverse distance weighting is used to prioritize the values in nearest locations for comparison instead of those further away.

In this study I calculate the LISA statistic corresponding to ACS “disability prevalence for all

ages” by comparing the prevalence value with surrounding counties. These maps indicate

whether patterns of high/high, high/low, low/high, low/low, or insignificant results define a

county’s disability prevalence compared to nearby locations. For this analysis, there is an

assumption of spatial “smoothness” where prevalence is not expected to be significantly higher

or lower from one county to its surrounding counties. Significant clustering results represent a

deviation from this assumption of even distribution. I anticipate that spatial patterning of

independent variables in the spatial regression will likely result in clustering of disabilities across

counties.

3. Spatial Lag Model I constructed a spatial autoregression (SAR) model, incorporating spatial relationships

between economic well-being, social inequality, health services, and health behaviors to

disability prevalence. A conventional regression model could examine how county disability

prevalence relates to changes in health practices and economic composition of counties, but

would not account for spatial relationships between adjacent counties. This can lead to a

misidentification of variable relationships made under the assumption that each county unit is

spatially independent. To account for spatial autocorrelation, or the spatial dependency between

a variable and nearby units, I use a spatial lag model that incorporates influence of neighboring

counties.5 In my model, I identify neighboring counties as any contiguous counties that share a

point of intersection.6

I constructed the SAR model after performing two tests for global spatial dependence

(Moran’s I and Geary’s C), which rejected spatial independence and identical distribution in

residuals of a standard OLS model. Residuals of a standard OLS model show correlation with nearby residuals and positive spatial autocorrelation in the dependent variables. To account for spatial dependence, I constructed a spatial lag model that employs a maximum likelihood (ML) fit on 2016 disability prevalence rates. To check for multicollinearity, I examined the variance inflation factors. The highest was for poverty at 4.3, indicating no high levels of multicollinearity in the model. The SAR model accounts for disability prevalence for each disability type across 2,964 U.S. counties.

5 This is distinct from a spatial error model, which addresses correlation in the error terms of nearby places but does not address variable influence from other places. A spatial lag model captures the effect of independent variables from nearby locations. 6 The construction of spatial weights based on shared vertices is referred to as a “queen’s case,” as it resembles adjacency in any direction in a manner that resembles a queen’s movement on a chess board. I selected the queen’s case over the alternative “rook’s case” approach because a rook’s case defines contiguity of neighboring units through a shared border (rather than vertex), and U.S. counties do not adequately conform to rigid polygons. 44

V. Results

A. Global Moran’s I

Table 2.3 displays results from the global Moran’s I analysis. All values generated for I were positive, suggesting a clustering direction instead of complete spatial randomness. Combined disability prevalence among all ages demonstrates spatial clustering with a Moran’s I of 0.04 that is statistically significant beyond the 99.9% level. As the age groupings of disability prevalence increased, both the Moran’s I and corresponding z-scores increased, suggesting increased clustered patterning of disabilities as the data captured older populations. Disability prevalence among populations under five did not demonstrate statistically significant clustering at the 95% level.

Table 2.3Global Moran's I analysis of spatial clustering Table 2.3 Global Moran’s I analysis of spatial clustering Variable Moran's Index z-score Nonwhite (%) 0.07*** 18.45 Median household income 0.50*** 120.04 Poverty (%) 0.50*** 120.04 Disabled 0.04*** 9.98 Under 5 0.01 1.44 Age 5-17 0.04*** 9.61 Age 18-64 0.05*** 12.72 Age 65+. 0.05*** 14.18 *p<.05; **p<.01; ***p<.001

While these results confirm clustering patterns of disabilities that are statistically significant,

it is important to identify what values like .04 and .05 actually mean. For comparison, I’ve

provided Moran’s I, z-score, and significance levels from several other variables from the 2016

ACS dataset. The clustering patterning of disability prevalence across counties is not nearly as

pronounced as poverty, but is roughly similar to levels of clustering that we would see in the

racial composition of counties. 45

B. Local Moran’s I (LISA)

While clustering does exist at the national level, Figure 2.1 identifies where areas with high

and low clustering occur across age groups. Consistent with global Moran’s I results, there is

little variation with statistical significance among populations under five. More clustering

patterns emerge as the population of consideration is older. Some counties have high rates only

for a particular age group, but most counties find increased high/high or low/low clustering

across each disability type as the age cohort increases.

When restricting spatial analysis to adult cohorts, consistent patterns of high clustering are seen across age cohorts and disability types. Ohio and North Carolina demonstrate high clustering throughout each state, with Appalachian clustering shown throughout the South. West

Texas, Northern Louisiana, and Arkansas also demonstrate significant clustering across disability types. Coastal regions of Louisiana, Alabama, and Mississippi demonstrate significantly low

prevalence compared to surrounding areas. Central Florida, Minnesota, North Dakota, Northern

Wisconsin, and the Upper Peninsula of Michigan show low prevalence, as well. These trends are

seen across age cohorts and disability types.

Evidence of clustering in LISA analysis is consistent with the assumption of spatial

relationships between independent variables and disability prevalence. Given higher poverty

rates, more extractive industrial activity, higher median ages, and more risky health behaviors in

Appalachia, higher clustering of disability is expected. But while LISA provides a tool for

Figure 2.2: LISA plots of disability prevalence calculated by age group using 2016 ACS data. Figure 2.2LISA plots of disability prevalence calculated by age group

visualizing spatial patterns, it does not disentangle which county indicators are related to higher

disability prevalence. To establish what variables are significant in influencing disability

prevalence, the SAR model is necessary. 7

C. Spatial Lag Models When considering place-based influences on overall disability prevalence, I anticipated spatial models to remain consistent with hypothesized relationships outlined in Table 2.1.

Global Moran’s I and LISA analysis each indicate spatial dependence of the variables used in the model, and results from each of the spatial lag models demonstrate significant variation in disability prevalence across counties (rho ranges from 4%-7%, p<.001). Indicators of social inequality, hazardous industry, and harmful health behaviors should increase disability prevalence, while greater access to healthcare resources and educational attainment should decrease disability prevalence. These variables capture considerable variation in the dependent variable whether applied to prevalence of overall disability (69.5%), deaf (41.8%), blind

(44.8%), cognitive (54.9%), or mobility-related disability (69.5%). I find mixed results within each variable group, as summarized in Table 2.5. Because literatures on health disparities and disablement do not distinguish between disability types, I expected those relationships to be consistent across each disability type. I find differences in which variables are significant when explaining prevalence of each disability type, as reported in Table 2.4.

Social inequality. The model shows mixed support for a relationship between overall disability prevalence and poverty. As shown in Table 2.4, counties with higher poverty rates have higher disability prevalence than counties with lower poverty rates (beta 0.149, p<.001).

7 Clustering analysis for each independent variable is provided in the Appendix.

Table 2.4: Disability prevalence (%) by type Model 1: Disability Model 2: Deaf Model 3: Blind Model 4: Cognitive Model 5: Mobility Coeff. (S.E.) Coeff. (S.E.) Coeff. (S.E.) Coeff. (S.E.) Coeff. (S.E.) Socioeconomic composition Gini -2.447(1.909) -0.077(0.774) 0.496(0.783) -0.820(1.134) -2.249(1.241) Poverty (%) 0.149(0.017)*** 0.023(0.007)*** 0.032(0.007)*** 0.090(0.010)*** 0.086(0.011)*** Hispanic (%) -0.009(0.008) 0.001(0.003) 0.006(0.003) -0.013(0.005)* -0.003(0.005) Black (%) -0.010(0.008) -0.020(0.003)*** 0.000(0.003) -0.005(0.005) 0.003(0.005) Asian (%) -0.022(0.036) -0.001(0.015) -0.006(0.015) -0.002(0.021) 0.018(0.023) Other (%) -0.022(0.012) -0.001(0.005) -0.007(0.005) -0.036(0.007)*** 0.004(0.008) Gender earnings gap -0.015(0.005)** 0.000(0.002) -0.004(0.002)* -0.009(0.003)*** -0.009(0.003)** Industry Military (%) 0.016(0.004)*** 0.007(0.002)*** 0.003(0.002)* 0.006(0.002)** 0.008(0.003)*** Farming (%) -0.050(0.009)*** -0.007(0.004)* -0.007(0.004) -0.033(0.005)*** -0.033(0.006)*** Logging (%) 0.386(0.074)*** 0.132(0.030)*** 0.075(0.030)* 0.127(0.044)** 0.233(0.048)*** Coal (%) 0.101(0.034)** 0.061(0.014)*** 0.054(0.014)*** 0.037(0.020) 0.091(0.022)*** Oil and gas (%) 0.012(0.053) -0.013(0.021) -0.039(0.022) -0.053(0.031) 0.024(0.034) Health services Primary Care Provider rate -0.001(0.002) -0.001(0.001) 0.000(0.001) 0.001(0.001) -0.002(0.001)* Uninsured (% -0.082(0.022)*** -0.017(0.009)* -0.005(0.009) -0.060(0.013)*** -0.040(0.014)** Health behavior Obese (%) 0.046(0.020)* 0.018(0.008)* 0.011(0.008) 0.012(0.012) 0.005(0.013) Smoking (%) 0.273(0.034)*** 0.041(0.014)** 0.057(0.014)*** 0.133(0.020)*** 0.191(0.022)*** Heavy drinking (%) -0.148(0.032)*** -0.011(0.013) -0.056(0.013)*** -0.057(0.019)** -0.109(0.021)*** Metropolitan statusa Non-metro, metro-adjacent -0.120(0.128) 0.052(0.052) 0.036(0.053) -0.070(0.076) -0.157(0.084) Non-metro, non-adjacent -0.267(0.192) 0.017(0.078) 0.064(0.079) -0.161(0.114) -0.275(0.125)* Bachelor’s degree (%) -0.205(0.017)*** -0.045(0.007)*** -0.031(0.007)*** -0.086(0.010)*** -0.137(0.011)*** Median Age 0.228(0.015)*** 0.071(0.006)*** 0.030(0.006)*** 0.074(0.009)*** 0.162(0.010)*** Intercept 3.573(1.212)** -0.245(0.494) 0.666(0.551) 2.183(0.719)** 0.988(0.802) Lambda 5.647(0.180) 0.936(0.030) 0.995(0.032) 1.981(0.064) 2.421(0.076) Rho 0.040(18.950)*** 0.054(12.150)*** 0.070(8.990)*** 0.048(14.520)*** 0.043(17.090)*** Pseudo R2 0.695 0.418 0.445 0.549 0.695 aMetropolitan is the reference category *p<.05; **p<.01; ***p<.001

This relationship follows previous studies addressing the deprivation hypothesis (Jones and

Wildman 2008; Scrambler 2012) in that economic hardship contributes to health burden. Every

single percent increase in a county’s poverty rate corresponds with a 0.15% higher prevalence in

disability. Gender inequality, as measured by earnings differences, does not align with the

hypothesized positive relationship assumed in Pearlin’s stress process (beta -0.015, p<.001).

Instead, every $1,000 increase in men’s earnings over women’s earnings corresponds with a

0.015 decrease in disability prevalence. Economic inequality (Gini) and racial composition fail to be significant in the model.

While the deprivation hypothesis and Pearlin’s stress process each explain health disparities with reference to inequality, these findings suggest that material resources may play a larger role in disability outcomes than stress and status. This could reflect deficits in healthcare infrastructure and an inability of poorer populations to afford routine health maintenance, but can also relate to greater exposure to environmental hazards. These results are consistent with this precedent in research. Although I found no evidence of multicollinearity between the gender earnings gap and poverty rates, the two are negatively correlated. Given the explanatory power of poverty in explaining each type of disability prevalence, it’s possible that the relationship between the gender earnings gap and disability prevalence may be related to broader economic processes in areas with higher or lower poverty. Future research would benefit from examining other measures of gender inequality that can capture social stress without being directly connected to economic processes.

Extending the model to deaf, blind, cognitive, and mobility-related disabilities, the significance and magnitude of social inequality variables differ from overall disability prevalence. Poverty demonstrates a significant and positive relationship with deaf, blind,

50 cognitive, and mobility-related disability prevalence. The size of the beta coefficients is smaller when describing prevalence of each disability type, however. Every single percent increase in a county’s poverty rate (beta 0.023, p<.001) corresponds with a 0.02% higher deaf prevalence.

Gender inequality demonstrates a significant and negative relationship to blind, cognitive, and mobility-related disability prevalence. Again, the size of the beta coefficients is smaller when describing prevalence of these disabilities. Racial and ethnic composition become significant when accounting for specific disability types. Black composition demonstrates a negative relationship with deaf prevalence, where each single percent increase in black composition corresponds with a 0.02 increase in deaf prevalence. Hispanic and Other composition demonstrate significant negative relationships, where each single percent increase in their composition corresponds with a 0.01 and 0.04 increase in cognitive prevalence. These results are not consistent with Pearlin’s stress process.

Again, when comparing material deprivation to social inequality, each type of disability prevalence is better explained through the deprivation hypothesis than by Pearlin’s stress process. Poverty matters when explaining the prevalence of each type of disability, and this relationship is consistent at the 0.001 level. Significance of the gender earnings gap and racial composition fluctuates between disability types, and it is difficult to explain why mobility- related disabilities have a significant relationship with inequality in gender earnings and not deaf prevalence.

Industry. The model shows mixed support for a relationship between extractive industry and disability prevalence. Among extractive industries, logging and coal employment demonstrate significant positive relationships with disability prevalence. Farming, however, shows a significant negative relationship with disability prevalence, where every single percent increase

in farming employment corresponds with 0.05% lower disability prevalence (beta -0.050,

p<.001). Oil and gas employment fails to be significant. Military employment has a positive

relationship with disability prevalence that is significant at the .001 level. The positive

relationship between disability prevalence and extractive industries is, for the most part,

consistent with hypothesized relationships. Hazardous work associated with these industries, as

described by Schulman and Slesinger (2004) may play a role in explaining higher disability rates

in counties with greater extractive activity. And although oil and gas industries involve

hazardous work, they largely rely on a mobile workforce (Jacquet 2014; Schafft et al. 2014) that

may not contribute to the disability rates of host counties. The negative relationship between

farming and disability prevalence may be complicated by the nature of farming operations within each county. Some farms may utilize migrant workers, and county prevalence measures may fail to capture health outcomes of a mobile laborforce (Frank et al. 2013). But the financial well- being of farmers relative to non-farmers may also contribute to lower prevalence rates.

Assessments of income and employment diversity have found little differences between farm and nonfarm households, but a substantial wealth advantage among farmers (Mishra et al. 2002).

Health advantages within farming areas may come from wealth advantages, and future research may benefit from more detailed data in comparing prevalence rates between farming communities to find whether wealth or laborforce mobility are driving outcomes.

Extending the model to specific disability types, deaf and mobility-related disability prevalence parallel relationships shown in the overall model. Blind prevalence, however, only shows significant relationships with logging and coal employment. Cognitive disabilities are the only disability type with a significant relationship to oil and gas employment, where each percent increase in employment corresponds with a 0.05% lower disability prevalence.

Healthcare system. The model does not support the expected relationship between access

to healthcare services and disability prevalence. PCPs, hypothesized to be an important health

asset in preventing disability onset and promoting rehabilitation, are only found significant in

their relationship to mobility-related disability prevalence. The number of uninsured shows a

negative relationship to disability prevalence that is significant at the .001 level. The assumed

positive relationship is based on access to healthcare resources. The negative relationship shown

here is unlikely to describe health benefits associated with costs of healthcare. According to

Baker et al. 2001, perceptions of good health are related to delays in insurance enrollment, and

this individual-level process may explain the positive relationship between uninsured populations

and disability prevalence.

Health behaviors. The model offers mixed support for the relationship between risky

health behaviors and disability prevalence. Smoking demonstrates a positive relationship with

disability prevalence, which is consistent with disablement process explanations that link

cardiopulmonary and respiratory damage to disability onset. Binge drinking, however,

demonstrates a significant and negative relationship to disability prevalence. There is no precedent for excessive drinking contributing to improved health in epidemiological research, but this finding may reflect a growing concern about use of binge drinking in public health research. Drinking intensity, frequency of intoxication, and distinctions between liquor and beer are not accounted for in BRFSS data, and binge drinking has been shown to produce inconsistent effects on self-reported health measures (Stranges et al. 2006). The relationship between county- level binge drinking behaviors and disability prevalence reported here should be given more scrutiny. Obesity holds a significant and positive relationship with disability prevalence.

Extending the model to specific disability types, only smoking was consistent in its

relationship to each category of disability. Heavy drinking failed to be significant in explaining

deaf prevalence. Obesity failed to be significant in explaining blind, cognitive, and mobility-

related disability prevalence. This finding is surprising, given the strong precedent for obesity as

the most prominent explanatory variable in explaining disability prevalence- particularly since the proposed link between obesity and disability is related to difficulties with mobility. Given

that other variables in this model are significant in explaining disability prevalence, it’s possible

that previous studies of obesity were not accounting for how obesity may be related to other

indicators of social inequality that drive disability outcomes.

Education. The model offered support for the negative relationship between education and total disability prevalence. Each single percent increase in Bachelor’s degree attainment corresponds with a 0.2% decrease in disability prevalence. Extending the model to specific disability types, every type of disability showed a significant and negative relationship between education and disability prevalence. While my analysis cannot determine whether the benefits of

educational attainment are related to social class or health literacy, these findings are consistent

with my hypothesized relationships.

Metropolitan status. As compared to populations in metropolitan counties, those in nonmetropolitan counties demonstrated lower mobility-related disability prevalence. Distinctions between metropolitan and rural counties are not significant in explaining overall disability, deaf, blind, and cognitive disability prevalence. As discussed, rural-urban health differences have been studied in terms of economic characteristics, industrial hazards, and limited healthcare resources. Because the model already accounts for these differences in county composition, it is unclear how to interpret these findings.

Age. Finally, results show a significant positive relationship between median county age and each category of disability prevalence. These results are consistent with the assumption of declining health that accompanies older age. Counties with younger populations are expected to have lower disability prevalence.

Table 2.5 Summary of findings

Overall Deaf Blind Cognitive Mobility H1: Poverty (+) + + + + + H2: Gini (+) NS NS NS NS NS

Social H3: Race (+) NS - NS - NS Inequality H4: Gender (+) - NS - - - H5: Extractive (+) X X X X X H5a:Farming (+) - - NS - - H5a: Logging (+) + + + + + H5a: Coal (+) + + + NS + Industry H5a: Oil & Gas (+) NS NS NS NS NS H6: Military (+) + + + + +

H7: PCP rate (-) NS NS NS NS -

System H8: Uninsured (+) - - NS - - Healthcare Healthcare

H9: Behavior (+) X X X X X H9a: Obesity (+) + + NS NS NS

Health H9b: Smoking (+) + + + + + Behaviors H9c: Drinking (+) - NS - - - Education H10: Education(-) - - - - -

VI. Conclusion

This research provides a template by which research in health disparities and health practices can be brought in jointly to address spatial implications of disability. While health disparities research has undergone a “paradigm shift” to address meso-level inequality and spatial patterning of health outcomes, disability research has largely been treated as an additional health

measure used to contrast health profiles of marginalized social groups. Although health

disparities research examines patterning of health outcomes, nationally-representative disability

prevalence may have precluded meaningful spatial research from emerging. At the same time,

aspatial national assessments of disability prevalence have proliferated, concentrating on how

health practices are likely shaping disability prevalence. As a result, major research on disability

prevalence neglects regional hotspots of disability and their relationship to social and economic

conditions, instead connecting overall disability prevalence to a national obesity epidemic. This

research, however, acknowledges obesity and other health practices, putting those factors

alongside traditional socioeconomic variables in health disparities research, to situate how those

variables contribute to the wide-ranging disability rates across U.S. counties. There are three major implications of this research.

First, this research demonstrates that there is a spatial patterning of disability that is not accounted for in previous disability research. Clustering analysis from the Global Moran’s I, coupled with LISA-generated choropleth maps reporting disability prevalence, indicate non-

randomized patterning of disability where some regions have distinctly higher rates than others.

The Gulf and Plains regions show low prevalence rates of disability that are in sharp contrast to

high clustering of disabilities in regions surrounding Ohio and Texas. Reviewing SAR models,

these spatial patterns involve subnational processes that influence each county’s disability

prevalence. Direct in-county impacts and indirect spillover effects each contribute to disability

rates across counties, and changing economic, social, and industrial composition across place

influence disability prevalence.

Both health-centered and socioeconomic explanations each play a role in describing the regional patterning of disabilities. The patterns of high disability prevalence around Ohio and

within Gulf states parallel previous regional analysis from Brownless et al. (2012) in

documenting hospital service areas and healthcare utilization. In analyzing local and regional

differences in healthcare and prevalence of selected ailments, they stressed the importance of

hospital service areas within hospital referral regions8. Within these Ohio and Gulf states

regions, they report low rates of medical interventions in response to selected ailments (i.e. low

knee and hip replacement rates) (Brownless et al. 2012). These added regional hurdles to health

maintenance (resulting from distance to hospitals and lower healthcare utilization rates) can

explain some of the regional phenomenon that my county-level analysis does not capture.

Poverty and industrial composition of these regions also explain some of the disability prevalence patterning shown in Figure 2.2. High disability prevalence across Texas, Gulf states, and Appalachian counties is consistent with the patterning of high poverty regions (Gann,

Bowers, and Walton 2018). Deprivation explanations of health disparities are entirely consistent with these regional patterns. National mining activity closely corresponds with regional disability prevalence across Texas, Appalachia, and Northern Nevada. Counties within Texas,

West Virginia, and Nevada offer the highest shares of mining employment in the nation9. When

comparing poverty rates (Gann, Bowers, and Walton 2018) to mining employment (Perry and

Visher 2017; Kramer et al. 2005) in these regions, mining employment in Northern Nevada and

Texas are more consistent with high disability prevalence than poverty. While some differences

may be better explained by some socioeconomic variables than others, the regional patterns are

consistent with expected relationships.

8 A hospital referral region (HRR) was created by the Dartmouth Atlas of Healthcare to group areas based on health care market referral patterns. A hospital service area (HSA) are more localized areas where resident hospitalizations are connected to zip codes. Ohio offers the largest HRR in the Great Lakes Region, with all central and southern counties sharing an HRR with parts of Kentucky and West Virginia. 9 According to 1996-2014 employment data from EMSI, 20 of the 25 counties in the U.S. with the highest shares of mining employment are located within these three states.

From a research perspective, these subnational differences in disability prevalence involve social inequality and health practices, and researchers would benefit from considering how each contribute to health patterning. Socioeconomic factors involved in health disparity research have been absent from discussions of the disablement process. Given strong relationships between poverty, industry, education, and disability reported in these models, disablement process researchers should supplement analysis of health practices with these variables. The tendency among disablement process researchers to focus on health practices without incorporating social inequality has allowed obesity to go unchecked as the standard explanation of disability in public health research. Conversely, health disparities research could benefit from considering insurance

and health behaviors. In the case of disability prevalence, both influence disability rates across

counties. Incorporating these measures into health disparity research provides consideration of

important omitted variables, but can also serve as an avenue by which theory-oriented health

sociologists and public health officials can interact.

Finally, this research greatly benefited from acknowledging that there is a meaningful

difference in types of disability. Other research has addressed rising disability rates as some

monolithic measure, but often connect disability prevalence to specific biophysical processes

related to one type of disability. In connecting obesity to the disablement process, researchers

attempted to address a rise in overall disability by explaining mobility difficulties associated with

pain, exhaustion, and cardiovascular health effects from increased body mass. This conflation of

disability in the aggregate with mobility-related disability persists without use of any data

describing how much of the nation’s disability rates are influenced by mobility. In applying

spatial models to overall disability prevalence and specific types of disability, I find that there are

significant differences in the variables that influence prevalence and how spatial processes differ.

Of the 17 variables used in the SAR model, deaf and mobility-related disability prevalence differed in significance of 6 variables. While some generalizations can be made about disabilities, it is important that researchers note the justification for how and why generalizations are possible.

In spite of these contributions this study faced several limitations that should be addressed to

move future research forward. First, I lack longitudinal data and could not study change in

disability over time. The ACS provides the only national sample of disability prevalence at the

county level, but only reports remote rural county composition in five year estimates. The

current five year window includes 2012-2016, overlapping with the first 2008-2012 estimates.

Because much of the disability prevalence research is responding to increases in disability

prevalence, future research should incorporate longitudinal data to account for change.

Second, as this study is an area analysis addressing disability prevalence across place, it

introduces two complications for interpreting disability. In terms of migration, I am unable to

detect whether migration effects are involved in regional patterns of disability. Previous research

from Geronimo et al. (2014) suggests that migration effects are only significant when

considering people with disabilities over 65 years old, but absent longitudinal data attached to

individuals I cannot evaluate whether clusters of disabilities shown in LISA analysis result from

migration effects.

Finally, future research could benefit from considering gender, race, class, and age

cohorts of people with disabilities. The importance of social inequality is stressed in health

disparities literature, and recognizing the impact of social strain on allostatic loads involves

details about the social status of individuals relative to their surroundings. While this research

considers general racial composition and a measure of gender inequality, these measures do not

account for the means by which race and gender discrimination is experienced by individuals

with disabilities. The lack of these identifiers in disability data also prevent group comparisons

which can examine how socioeconomic characteristics of place might differ in their influence.

Rising disability prevalence in working age adults involves a multitude of factors that span the social system and health practices. The increasing interest in disability prevalence research has been limited by these two explanations being pursued in isolation. Research in social inequality has been attentive to how aspects of the social system influence health outcomes across places. But extending this place-based approach to health behaviors and the

healthcare delivery system allows researchers to move beyond individual-level assessments

throughout the lifecourse to consider broader population trends. Given the pronounced spatial

patterning and results from a county-level analysis of disability prevalence, this research

provides a basis for incorporating both perspectives through population analysis across place.

CHAPTER 3 : DISABILITY AND THE EMPLOYMENT GAP I Introduction

Rural sociological research addressing economic inequality has undergone a transition from individual-level explanations to a more spatially-oriented structural account that describes a poverty of place. This research has been extended across social groups, explaining how race and gender have varied in economic well-being with reference to the demographic composition, industry, and historical legacies of discrimination in an area. But despite representing approximately 11% of working age adults in the United States (Erickson et al. 2017) and demonstrating high prevalence in rural areas (see: Chapter 2), rural sociologists have not extended such analysis to people with disabilities. People with disabilities represent a historically disadvantaged group with higher rates of poverty and worse employment rates than non-disabled. National level statistics report persistence of an employment gap over time that has not improved with Americans with Disabilities Act (ADA) of 1990 (Figure 3.1& Figure 3.2).

These trends present a puzzle to disability sociologists, as provisions of the ADA specifically addressed issues of employment bias and accessibility that are commonly used to explain this disadvantage.

Not reflected in these national-level assessments, however, is consideration of place- variation in economic livelihoods of people with disabilities. At the county level, 2017 poverty rates among people with disabilities range from less than 1% to 67%, while the employment rate ranges from 0.4% to 86.8%. The degree to which people with disabilities experience economic hardship varies wildly from place-to-place and rural sociological analysis of these subnational

phenomena can provide insights about the spatial elements of disadvantage that may be obscured

in national-level assessments.

Figure 3.1Poverty rate:1980-2013

0 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 With Disability Without Disability

Figure 3.2Employment rate among working age adults: 1981-2014 90 80 70 60 50 40 30 20 10 0 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

With Disability Without Disability

Poverty of place approaches, which consider socioeconomic composition and legacies of

discrimination, have tied industry makeup to hiring and firing trends among disadvantaged social

groups (Tickamyer and Wornell 2017; Trotter 2015). Rural sociologists have studied how women’s entry into the workforce was precluded in areas with limited work opportunities and

62 benefit structures (Sachs et al. 2014; Tallichet 2006). They’ve also examined the role of segregated work environments and legacies of discrimination in exacerbating racial inequality across regions (Strauss 2016; Snipp 1996). Research addressing disability employment has tended to take an aspatial approach, concentrating on discrimination and workplace accessibility at an individual level (Gleeson 1999). I extend consideration of industrial composition and discrimination across place to disability employment. As a disadvantaged social group with limited entry into the workforce, people with disabilities may undergo similar experiences as women and racial minorities.

Research into employment of people with disabilities also provides the opportunity to explore neglected aspects of human capital related to health and work limitations. Poverty of people approaches consider personal attributes of potential workers (Tickamyer and Wornell

2017). Researchers taking this approach can incorporate job skills and work capability of individuals when explaining employment outcomes (Handel 2005). But they can also incorporate work incentives that may drive individual decisions to take up work (Krueger and

Meyer 2002; Moffitt 2015). In the contexts of disabilities, there are personal attributes related to work capability and work incentive. Ergonomic research that captures hearing, vision, mobility, and cognitive disabilities can connect these individual attributes to work capability and employment outcomes (Chi 2004). There is also precedent for studying the relationship between disability welfare programs and incentive to work, where SSI compensation can be examined as a disincentive to seek work (Autor and Duggan 2007).

Explanations of disability and inequality have traditionally focused on discrimination and accessibility as the two primary mechanisms of disadvantage (Withers 2012). Hiring and firing decisions may be influenced by social norms that treat disability as a pitiable condition, an

aberration, or as a potential liability to employers (Barnes, Mercer, and Shakespeare 1999;

Withers 2012). But the built environment also presents its own accessibility obstacles. Mobility

throughout a community or within a workplace is dependent upon whether planning and design

have considered wheelchair use, hearing difficulty, or limited vision (Gleeson 1999; Golledge

1993). Neither discrimination nor accessibility have been applied to explain place-variation in

employment trends of people with disabilities in the United States.

In this chapter I examine place-based and individual-based explanations of unequal employment between people with and without disabilities. I use newly available county-level data on disability employment. I examine employment differences between people with disabilities and people without disabilities across counties. I consider place-based influences, including the socioeconomic composition, industry, and work access amenities of each county, as they relate to differences in employment outcomes. I also consider individual-based influences, including the work-limitations associated with specific disabilities and disincentive effects of SSI compensation, in describing county employment. In uniting these approaches, this research provides new directions for disability sociologists to address inequality while also addressing the deficit in disability research within rural sociology.

My central research question asks what influences differences in disability employment across places? I consider two perspectives in my research. The first perspective considers place characteristics as influencing employment outcomes. Place-based determinants of disability employment incorporate socioeconomic composition, industry makeup, and social inequality to explain how places disadvantage disabled populations. The second perspective considers individual characteristics as influencing employment outcomes. Individual-based determinants of disability employment incorporate ergonomic research on work capability and the relationship

between disability welfare and individual work incentives. These perspectives deviate from

aspatial explanations of inequality among disability sociologists. Whereas disability sociologists

tend to treat disability as a social construct that generally involves exclusion and inequality, I

treat disability as an individual predicament that is situated across places that differ in their

degree of disadvantage.

II Background The Americans with Disabilities Act (ADA) of 1990 is the product of a long history of

disability activism and advocacy. Its policy roots can be found in the passage of Section 504 of

the 1973 Rehabilitation Act, which banned discrimination on the basis of disability from any

entity receiving federal operating funds (Anderson 1996). Exclusion of people with disabilities

from educational settings and places of work was, for the first time in U.S. policy,

conceptualized as a function of discrimination (Colker 2005). People with disabilities were recognized as a minority group, facing discrimination and unequal access to education and employment opportunities. Language of Section 504 reflected statements among disability activists from decades prior, and the formalization of that language into policy bolstered greater organization (and the formation of new groups like the American Coalition of Citizens with

Disabilities) to push for expansion of protective measures beyond federally funded entities. In

the decade that followed, mass sit-ins and protests effectively kept disability provisions in policy

discussions.

In 1988 the National Council on the Handicapped report “On the Threshold of

Independence” provided an official assessment to Congress of discrimination and exclusion of

people with disabilities. Central to the document was language addressing discriminatory

behaviors that prevented employment of people with disabilities and a lack of accessibility

provisions that created exclusive settings which contributed to “segregation, and relegation to

lesser services, programs, activities, benefits, jobs, or other opportunities” (42 U.S.C. § 12101).

The ADA acted on these considerations, enforcing anti-discrimination provisions and promoting

accessibility as central functions of the Act (Young and NCD 1997).

A. Disability as determinant of employment: Discrimination and Accessibility

People with disabilities are an economically disadvantaged group. Poverty estimates from the past four decades reveal persistently high rates of poverty among people with, ranging from 26% to 32% at the national level (CPS 2017)(see Figure 3.1). Employment among people with disabilities has also lagged, as people with disabilities have experienced a peak employment rate of 28.8% between 1981 and 2016 (CPS 2017) (see Figure 3.2). Economic hardships associated with poor employment outcomes have traditionally been linked to two explanatory mechanisms.

The first considers people with disabilities as subject to discriminatory practices. A foundational concept among disability sociologists is that disability status is socially-constructed, reflecting stigmatization of bodies and behaviors that are excluded from social norms (Oliver

1990). From this perspective, poor employment outcomes result from the discriminatory attitudes that accompany disability status. Hiring and firing decisions are part of an exclusionary practice that segregates people with disabilities from those without. Stigma attached to disabilities carries over to work environments, where negative attitudes towards people with disabilities vary by disability type and by industry (Johnson 1988; Unger 2002). These discriminatory attitudes play upon “uneasiness” around disability, but also involve perceived costs to employers and an assumption of lower productivity (Robert and Harlan 2006). From this perspective, people with disabilities are likely to have worse employment outcomes in areas with higher degrees of discrimination.

The second explanation considers how the manifestation of disabilities (i.e. blindness, deafness, etc.) relates to accessibility of work. Geographers and ergonomic researchers consider how mobility and interaction with the built environment present unique obstacles for people with different types of disabilities. Disability geographer Reginald Golledge (1993) understood employment as a function of workplace inaccessibility, where people with disabilities could not commute to places of employment, nor enter actual buildings because of mobility obstacles.

Audible crosswalk alerts, shuttle services, Braille signage, and door width are each understood as obstacles to employment among people with disabilities (May, Leake, and Berrett 1991).

Accessibility also extends to tasks within the workplace, as the ability to use equipment and communicate with coworkers is dependent upon how specific disability features interact with these tasks. Desk height, machinery features, and keyboard tools serve as examples of how wide and industry-specific accessibility problems can be for people with disabilities (Watson and

Lightfoot 2003; Zwerling et al. 2003). This perspective would predict worse employment outcomes in areas with obstacles to workplace accessibility.

The ADA was introduced in 1990 and implemented to specifically address both mechanisms of inequality. In terms of discriminatory hiring and firing, Title I of the ADA explicitly prohibits discriminatory hiring, firing, and determination of wage rates on the basis of a disability. Although prior research has addressed early difficulties in pursuing ADA discrimination suites, records from 1997-2017 demonstrate 431,569 charges filed through the

Equal Employment Opportunity Commission (EEOC) resulting in $1.6 billion in monetary benefits (EEOC 2018). The volume of charges filed, resolutions to cases, and growth in monetary awards during this period also demonstrate that this has not been a hollow policy without enforcement (see Figures 3.3 and 3.4). Effective implementation of antidiscrimination

67 provisions of the ADA challenge the idea that discriminatory hiring and firing practices can explain the degree of lagging employment among people with disabilities.

Figure 3.3ADA enforcement: 1997-2017

ADA Enforcement Charges: 1997-2017 35,000

30,000

25,000

20,000

15,000

10,000

5,000

0 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 RECEIPTS RESOLUTIONS

Figure 3.4 Financial awards for EEOC discrimination claims EEOC-Awarded Benefits(Millions): 1997-2017 $160.00

$140.00

$120.00

$100.00

$80.00

$60.00

$40.00

$20.00

$0.00

Furthermore, accessibility provisions of the ADA reshaped the transportation landscape while requiring reasonable accommodations for accessibility in the workplace. Sixty-two federal programs have funded an estimated $2.4 billion in transportation services for people with disabilities since the implementation of the ADA (Siggerud 2003). Transportation landscape in the United States has been transformed to provide accessible infrastructure and supplementary

transit services to facilitate mobility throughout the community. Moreover, “reasonable

accommodation” provisions within Title I have reshaped accessibility within the workplace,

requiring “modifications or adjustments to the work environment…that enable a qualified

individual with a disability to perform the essential functions of that position” (Americans With

Disabilities Act of 1990, § 12112a). Funding levels for transportation and enforcement of workplace accessibility challenge the idea that accessibility explains the degree of lagging employment among people with disabilities.

Despite the ADA’s significant intervention to address discriminatory practices and accessibility, disability sociologists are faced with a puzzle: Why haven’t economic livelihoods of people with disabilities improved in the 28 years since its implementation? Here, rural sociological insight may prove helpful. There is a 67% range in disabled and non-disabled employment differences across counties (Figure 3.3), and subnational variation in employment outcomes can inform what improves economic livelihoods for the disabled. Revisiting explanations of economic inequality from a rural sociological lens, place-based assessments which consider socioeconomic composition, industry, discriminatory legacies, and place-based transit amenities can move our understanding of employment inequality forward. By incorporating place-based dimensions of inequality and people-based considerations of work capability, this research provides an opportunity to move disability sociology beyond discrimination and accessibility assessments.

B. Place-based Dimensions of Inequality

Place-based research questions are a defining feature of rural sociology. Poverty of place literature within the rural sociological tradition gives consideration to economic and social processes at the subnational level that can influence how inequality manifests (Tickamyer and

Wornell 2017). Measures of inequality follow distinct spatial patterns where wealth, health, and political representation demonstrate metropolitan county advantages and disadvantages (Lobao,

Hooks, and Tickamyer 2007). Subnational analyses of industrial activities and social

composition play an important role in explaining these patterns. Recent trends in this tradition

have extended analysis to specific social groups, contextualizing their disadvantage with

reference to economic development and embedded discriminatory legacies. In accounting for

variation in employment outcomes among people with disabilities, this framing can inform how

these place dynamics shape differences in employment success across U.S. counties.

Economic development has played a central role in explaining place-variation in

economic well-being across U.S. counties. Historically, rural areas in the United States have not

experienced the same levels of economic growth as their metropolitan counterparts, with

extractive industries providing major sources of employment. Rural areas were historically

considered “left behind” by the 1960s from economic gains experienced by the rest of the nation

(Breathitt 1967). Proponents of modernization contended that growth could serve as a catalyst

for economic diversification to draw more industrial activity, employment opportunities, and

higher wages (Galston and Baehler 1995). Increases in productivity have not produced these

results, however. Despite exponential growth in yields (mineral, and timber) a “resource curse”

has been used to describe lagging wage growth and employment in areas typified by extractive

industries (Partridge, Betz, and Lobao 2013 ). And while some rural areas experienced a surge

in manufacturing from the 1980’s-early 2000’s, the industry’s movement was accompanied by

anti-union policies, lower wages, and limited benefit structures (Eckes 2005). The extractive

foundations of these rural areas serve as a long-term detriment to economic improvement that is

used to explain place-based variation in employment trends. But remoteness is also studied as a

driver of economic difference that can shape industrial composition of rural areas. Areas lacking

communication and transportation infrastructure are poor environments for high-tech and

financial industries that are correlated high wages and stable employment (Falk et al. 2003).

Small population densities and distance further worsen the funding of social services and

prospects of policy interventions to promote economic well-being (Smith and Tickamyer 2011).

When analyzing economic livelihoods, subnational variation is informed by these place-based features of development. Remote places with high levels of extractive activity and limited infrastructure are likely to demonstrate worse employment outcomes for people with disabilities.

The poverty of place approach has more recently made an “intersectional turn” that examines how economic development, industrial practices, and historical legacies of discrimination magnify the degree to which disadvantaged social groups experience inequality across places

(Tickamyer and Wornell 2017). This approach puts disadvantaged groups at the forefront of research, examining how women and racial minorities vary in employment outcomes across locations. The economic and industrial composition of a place is understood as the arena wherein resources are unevenly distributed among social groups. Labor queues, the rank-order preferences among employers which privilege applicants from some social groups over others, can be more exclusionary in rural areas with less industrial activity and smaller labor pools. That is, applicants from more marginalized social groups have even fewer options for work.

Extractive industries in rural areas have demonstrated discriminatory patterns of denying employment, offering lower wages, and providing hostile workplaces to women and people of color (Tallichet 2006; Trotter 2015). Tallichet (2006) describes a mining culture where men capitalize on remote settings and unsafe conditions to intimidate women from keeping mining jobs. This characterization is in sharp contrast to the more impersonal and bureaucratic

characterizations of white collar jobs in finance and government (DuGay 2000; Ramsay and

Parker 1991). The bureaucratic organization of finance and government is instead associated with a protective effect on socially marginalized groups, where legal protections and a muting of personal identity provide cover against discrimination in the hiring and firing process (Crewson

1995; Dobbin et al. 1993). White collar industry and government employment should be correlated with fewer differences in employment rates between people with and people without disabilities. Because labor queues reflect the larger social hierarchy, it is likely that places with greater gender and racial discrimination are also likely to have worse employment outcomes for people with disabilities.

The racial composition of place can also influence employment opportunities for people with disabilities. Kain’s (1968) spatial mismatch hypothesis emerged in the 1960s to describe a spatial process where predominantly black areas suffered from lower wages, poorer quality of jobs, and fewer work opportunities because of a shift in economic development to wealthier and whiter areas. In the following decades, a rich empirical research history has traced the spatial separation of black populations from areas experiencing job growth (Jencks and Mayer 1990;

Wheeler 1993; Ihlanfeldt and Sjoquist 1998; Gobillon et al. 2007). The spatial separation from higher quality jobs further saddled black populations with additional transit costs and lower likelihoods of encountering job-acquisition networks (Arnott 1998; Holzer et al. 1994). People with disabilities are less likely to find employment in areas with limited work opportunities

(Barnes, Mercer, and Shakespeare2000; Nietupski et al. 1996), and additional transit burdens have a disproportionately higher effect on people with visual and mobility-related disabilities

(Gleeson 1999; Golledge 1993). Because nonwhite composition is related to limited

employment opportunities and additional transit burdens, greater nonwhite composition is likely

to correspond with worse employment outcomes for people with disabilities.

C. Individual-level Explanations of Employment Variation

Poverty of people explanations focus on individual attributes as they contribute to poor employment outcomes. Dimensions of human capital and personal incentive are used to describe

participation in the workforce (Tickamyer and Wornell 2017). Human capital considerations are

at the forefront of this approach, connecting skilling and educational attainment of the labor pool

to demands of employers. Places that have undergone changes to their industrial composition

have experienced higher unemployment that is tied to lags in education and training. In

assessing lags in regional employment, education is a standard proxy for job skills (Handel

2005). This research tends to neglect functional limitations to work that are frequently

encountered by people with disabilities (Autor 2013). Economic researchers addressing work

capability have tended to concentrate on education without incorporating ergonomic research

that connects specific types of disability to limitations in work capability across work

environments. Bragman and Cole’s (1984) typology of disability considerations in job demands

has been a foundation for current ergonomic research addressing disability and job mismatch.

This typology has been used to explain employment outcomes of people with disabilities by

connecting job tasks and industry profiles to work-limitations that correspond to specific types of

disabilities. Chi (1999) examined 1285 occupations from Employment and Vocational Training

Administration profiles form 1987-1995. Occupational hazards, communication tasks, visual

capability, body agility, and manual ability each provided disability-specific limitations that

corresponded to job tasks across industries. Chi et al. (2004) use these disability-specific limitations for an empirical analysis of successful job placement of disabled workers across

industries. Using successful job placement of 1,257 workers with disabilities, this analysis

considered job placement as an indicator of accommodating industry. People with sensory- related disabilities were most commonly placed in administrative jobs (18%), while people with cognitive-related disabilities were most commonly placed in low-skilled services (34%). More physically-demanding sectors (agriculture, manufacturing) tended to have lower levels of job placement. Schur’s (2002) analysis of disability employment highlighted differences in employment rates by disability type. People with sensory-related disabilities had the highest rates of employment (41.1%), followed by those with mobility-related disabilities (36.3%), and people with cognitive disabilities (33.6%). This descriptive analysis of difference in disability employment by type complements industrial ergonomic research, suggesting that county disability composition influences employment outcomes. Counties with a higher composition of sensory-related disabilities are likely to have higher rates of employment than counties with higher composition of mobility and cognitive disabilities.

The second component of the poverty of people approach views employment as part of a rational decision-making process, viewing employment as an individual’s choice given other benefit opportunities. Potential employees decide to seek or refrain from employment based on whether compensation is sufficient to perform work. Opting out of work was historically inked to cultural critiques that portrayed unemployment as the result of personal laziness (Tickamyer and Wornell 2017; Katz 2013). More recent accounts consider the rise of precarious work, where opting out of work is a decision meant to preserve one’s health and well-being (Kalleberg

2009). In the context of disability, government assistance in the form of Social Security

Disability Insurance (SSDI) has been described as a possible disincentive to work. Autor and

Duggan (2003) analyzed changes in SSDI enrollment between 1984-2001 as they related to labor

force exits among working-age recipients. Combining state-level SSA data on recipient characteristics with benefit:wage ratios calculated from BLS earnings reports, they report two important findings. First, increased enrollment in SSDI during this period corresponded with exits from the labor force. Liberalization in SSDI enrollment qualifications corresponded with a

35% higher likelihood of labor force exit. Second, decreased wages also corresponded with increased SSDI enrollment across states, but this effect failed to be significant when accounting for growth in SSDI benefits. As the economic value of disability benefits grow relative to working wages, people with disabilities have less incentive to pursue employment. Taking this into consideration, places with higher disability benefits relative to wages are likely to have lower rates of employment than those with lower disability benefits.

III. Summary and Research Questions

In this study I concentrate on the difference in employment between people with and without disabilities. There is a disability employment gap (see Figure 3.2) where employment rates among people with disabilities are consistently lower than those of people without disabilities.

Rather than explore determinants of employment rates among people with disabilities, I focus on how this gap varies across U.S. counties. In this study, I examine how functional limitations of disability types and employment opportunities within counties may influence the size of the disability employment gap.

A. Place-based Relationships I examine place characteristics related to socioeconomic composition, industry makeup, work access amenities, and inequality. Economic development and job growth increase the amount of work opportunities available to people with disabilities. People with disabilities have functional limitations to work, where the pool of jobs available to people with disabilities is narrower than what is available to those without disabilities. Poverty and educational attainment play a role in

75 shaping employment opportunities, where areas of low poverty and higher educational attainment attract more diverse industrial activities and act as a magnet for job growth (Gottlieb and Fogarty 2003). But the industrial makeup of place further shapes employment for people with disabilities. Extractive industries are associated with physically-demanding work and lagging job growth. Each of these presents an obstacle to employment for people with disabilities. From these considerations, I develop the following hypotheses:

H1: Counties with higher poverty rates will have higher disability employment gaps than

places with lower poverty rates.

H2: Counties with higher rates of college degree holders will have lower disability

employment gaps than places with lower rates of college degree holders.

H3: Counties with greater shares of extractive industries will higher disability employment

gaps than those with fewer shares of extractive industries.

Place-based obstacles to mobility also shape how people with disabilities reach work opportunities in their area. Deficits in pedestrian infrastructure, reliance on shuttle services, and remoteness of the workplace are each addressed by disability geographers as obstacles in the build environment that impede work (Gleeson 1999; Golledge 1993). Telecommuting has been proposed as an opportunity for improving employment outcomes for people with disabilities, as the option to work from home provides a means of avoiding transit difficulties (Anderson et al.

2001). From these considerations of work access, I develop the following hypotheses:

H4: Counties with greater commuting difficulties will have higher disability employment

gaps than those with fewer commuting difficulties.

H5: Counties with more options to work from home will have lower disability employment

gaps than those with fewer options to work from home.

H5a: Internet access provides greater potential for telecommuting work opportunities for

people with disabilities. Places with higher rates of internet inaccessibility will have higher

disability employment gaps than places with less internet inaccessibility.

Marginalized social groups can experience inequality differently across place. The

“intersectional turn” in poverty of place literature describes greater hardship among marginalized groups that can be tied to segregation and discriminatory legacies. In work contexts, women and racial minorities have been subject to lower wages, intimidation at the workplace, or passed over in favor of white male applicants. As a marginalized social group, people with disabilities are also likely to encounter hardship in places with high social inequality.

H6: Counties with higher gender inequality will have higher disability employment gaps than

counties with less gender inequality.

H7: Counties with a higher nonwhite population will have higher disability employment gaps

than places with a lower nonwhite population.

B. Individual-based Relationships I examine individual-based determinants of disability employment by considering ergonomic implications of work capability and the effect of disability welfare as a work incentive. Ergonomic research on disability and work capability connect blind, deaf, mobility, and cognitive disabilities to employment (Bragman and Cole 1984; Chi 1999; Chi et al. 2004).

Descriptive analysis of employment rates by disability type (Schur 2002) found individuals with sensory-related disabilities as having greater rates of employment than people with mobility and cognitive disabilities. Based on ergonomic research on work capability and disability-specific employment outcomes, I develop the following hypotheses:

H8: Counties with a larger share of the population who are deaf will have lower disability

employment gaps.

H9: Counties with a higher share of the population who are blind will have lower disability

employment gaps.

H10: Counties with a larger share of the population with cognitive disabilities will have

higher disability employment gaps.

H11: Counties with a larger share of the population with mobility-related disabilities will

have higher disability employment gaps.

Individuals with disabilities may negotiate entry into the workforce by considering whether there is sufficient incentive to pursue work over SSI compensation. Earnings typically offer an incentive to work, where higher earnings correspond with workforce participation. But

SSI offers a competing disincentive to work, where higher SSI compensation relative to earnings can motivate individuals to opt out of working (Autor and Duggan 2003; Autor and Duggan

2007). I consider compensation and work incentive to develop the following hypotheses:

H12: Places with higher per capita SSI payments will have higher disability employment

gaps than places with lower per capita SSI payments.

H13: Places with higher median earnings will have lower disability employment gaps

than places with lower median earnings.

Table 3.1Summary of hypotheses Table 3.1 Summary of hypotheses Predicted Hypothesized relationships Relationship H1: Higher poverty is related to a higher disability employment gap. + Socioeconomic H2:Higher college attainment is related to a lower disability - composition employment gap. H3: Higher shares of extractive industry are related to a higher disability employment gap. + H4: Greater commuting difficulties are related to a higher disability employment gap. +

Place H5: Greater work at home options are related to a lower Work access disability employment gap. - H5a: Higher degrees of internet inaccessibility are related to a higher disability employment gap. + H6: Higher gender inequality is related to a higher disability employment gap. + Inequality H7: Higher nonwhite composition is related to a higher disability employment gap. + H8: Higher deaf composition is related to a lower disability employment gap. - H9: Higher blind composition is related to a lower disability - Disability employment gap. composition H10: Higher cognitive disability composition is related to a higher disability employment gap. + H11: Higher mobility-related disability composition is related + Individual to a higher disability employment gap. H12: Higher per capita SSI payments are related to a higher disability employment gap. + Incentive H13: Higher incomes are related to a lower disability employment gap. -

III. Data and Methods

I use counties as units of analysis. Because I use a spatial regression for examining differences in employment, Hawaii and Alaska are incompatible with distance weighting and shared boundary considerations used in the construction of spatial weight matrices. Accounting

79 for data suppression in reporting of earnings, broadband access, and SSI payment, I use 3,060 counties in this analysis. Descriptive statistics are shown in Table 3.2 below:

Table 3.2Descriptive statistics for employment sample Table 3.2 Descriptive statistics for employment sample (n=3,060) Std. Variable Mean Min Max Dev. 2016 Employment (%) Disabled 34.69 11.00 0.42 86.84 Non-disabled 74.65 6.81 43.98 91.82 Disabled/Non-disabled difference 39.96 8.16 -5.80 72.85 2011 Socioeconomic composition % Poverty 15.93 6.34 3.00 48.40 % Bachelor's degree 12.64 5.31 2.40 39.60 Men's median earningsa 50.92 12.07 10.00 177.89 Women's median earningsa 35.47 6.72 2.50 68.75 Gender earnings differencea 15.46 11.04 -37.41 13.28 % Hispanic 7.96 12.67 0 98.45 % Asian 1.11 2.11 0 33.50 % Black 9.12 14.57 0 85.67 % Other 1.50 5.45 0 86.36 2012 Industry (% share) Manufacturing 10.92 8.90 0 67.12 FIRE 3.81 1.84 0.12 3.22 Management 0.60 1.17 0 30.11 Logging 0.32 0.85 0 13.37 Coal 0.24 1.68 0 30.73 Oil & gas 0.27 1.12 0 19.67 Farming 4.76 6.88 0 75.29 Government 21.67 9.43 3.86 93.26 2012 Disability composition (%) Deaf 3.15 1.45 0.20 14.50 Blind 2.37 1.54 0.00 19.00 Cognitive 5.25 2.29 0.70 19.00 Mobility 7.17 3.21 0.80 23.60 Work access amenities % Without broadband access 2013 10.55 12.75 0.00 98.43 Commute time (minutes) 2011 23.03 5.32 9.70 42.50 % Work from home 2011 4.77 3.08 0.00 33.40 2011 Work incentive SSI paymenta (monthly) 0.49 0.05 0.19 0.83 Median earningsa 26.25 5.07 4.42 61.88 SSI:Median earnings ratio 0.02 0.00 0.01 0.12 2000 Disability employment (%) 54.93 9.39 15.78 88.03 aReported in $1,000 USD

A. Data 1. Dependent Variable

The dependent variable is the difference in employment between people with disabilities and

people without disabilities, referred to here as the “disability employment gap.” I construct this variable by subtracting the percent of employed people with disabilities from the percent of employed people without disabilities in each county. I use data from five year estimates from

S1810 data of the 2016 ACS in measuring prevalence of disability types, employment of people with disabilities, and employment of people without disabilities. I restrict my analysis to working age adults, aged 18-6410. Of the 3,060 counties in sample, only five counties reported a

higher percentage of employed people with disabilities.11 On average, there is a 39.96% gap in

employment between people with disabilities and people without disabilities.

2. Independent Variables

a. Place Variables Socioeconomic Composition. I consider three aspects of socioeconomic composition

when assessing place-based influence on employment. I use 2007-2011 ACS five year estimates

for the poverty rate and bachelor’s degree attainment. Higher poverty should be negatively

related to the employment of people with disabilities, while greater educational attainment

should be related to greater employment. I examine the industrial makeup of each county using

2012 Economic Modeling Specialists, International (EMSI) data. I report the county share of employment of selected industries from this dataset. I use eight industries in the model.

Extractive industries have limited health, retirement, and promotional benefits and are associated

10 Six U.S. counties contain military reservations and report 100% government employment. I’ve excluded those counties from this analysis. 11 The five counties were remote rural counties in Colorado, Kansas, South Dakota, and Texas. These counties had lower disability prevalence among working age adults (mean of 8.14%)

with lagging wage growth and employment in areas where they make up a large share of county

industry. Coupled with the physically-demanding work tasks that are common in extractive

sectors, a large share of those industries is likely to present worse employment opportunities for

people with disabilities. I include logging, coal, oil and gas, and farming shares of employment

in my analysis. I include manufacturing shares of employment for comparison, as work in this

sector is physically-demanding, but health and retirement benefits and wage growth differ from

extractive industry. I include two white-collar industries shares, finance and real estate (FIRE)

and management, as high paying sectors with less physically-demanding work and greater

bureaucratic protections for marginalized populations.12 Finally, government employment is

assumed to include bureaucratic protections for marginalized populations and is likely to

demonstrate greatest compliance with ADA provisions. I anticipate a higher disability

employment gap in places with higher share of extractive industry. I expect to find lower

disability employment gaps in counties that have higher shares of white-collar and government

employment.

Access to Work. I use three measures of work access that incorporate considerations of

commuting difficulties for people with disabilities. The first is an indicator of commuting

difficulty that reports the average time (in minutes) commuting to work for each county. Given

specific transit needs of people with disabilities (i.e. shuttle services, accessible pedestrian

infrastructure, etc.), commuting time presents a general indicator of commuting difficulty for a

county. I use commuting time data from 2007-2011 five year estimates of the ACS. I anticipate

higher commuting times will be related a higher disability employment gap, as the commuting

12 NAICS Industry Group Codes include “scientific and technical consulting services” within the management category.

burden is likely to disproportionately fall on people with disabilities.13 The second measure

reports the degree to which people can avoid commuting difficulty by instead working from

home. I use the “percent working from home” commuting category from the ACS. I anticipate

greater work-at-home options will relate to a lower the difference in employment between people with disabilities and those without. The last measure considers internet service provision, a prerequisite for telecommuting, by using the 2013 National Broadband Map database. I examine whether households have access to at least one broadband internet provider as an indicator of internet access.14 I anticipate that a lack of broadband Internet service will relate to a higher

disability employment gap.

Social Inequality. Greater hardship among marginalized groups across places can be tied

to segregation and discriminatory legacies. I expect that people with disabilities are also likely to

encounter hardship in places with high social inequality. To establish a measure of inequality in

the context of work, I examine earnings differences by sex and race provided by the 2007-2011

ACS using S24011 and B20017 tables. I interpret counties with higher wage gaps as

demonstrating greater social group inequality. To account for gender differences in

occupational self-selection and informal work, I restrict the earnings gap to bachelor’s degree

holders. The average difference in gender earnings among bachelor’s degree holders was

approximately $15,457, where 2,895 of the counties demonstrated greater earnings among men. I

anticipate that employment differences for people with disabilities will be greater in areas with

greater differences in pay by gender. Breakdowns of wages and employment by race in the

B20017 dataset excluded reporting in remote rural counties, so I do not report wage differences

13 DP03 Selected Economic Characteristics 14 Access to broadband is distinct from Internet service. Although Internet service can be provided through cellphone service, broadband connections are a standard requirement for telecommuting. See: Orazem et al. 2006.

by race. I include variables for racial composition, accounting for the share of nonwhite adults

across Asian, Black, and Other categories. I also account for Hispanic composition. Given the

spatial relationship between nonwhite composition and limited job opportunity (see: Kain 2004),

I anticipate greater differences in disability employment in areas with higher nonwhite

composition.

b. Individual Variables Disability Composition. Ergonomic research on deaf, blind, cognitive, and mobility-

related disabilities identifies meaningful differences in work capability. I use 2008-2012 ACS

estimates (S1810 disability characteristics variables) to report the county share of the non-

institutionalized population reporting a deaf, blind, cognitive, or mobility-related disability. I use data from working age adults 18-64 in the sample. Mobility-related disabilities are, on average, the most prevalent type of disability in each county with approximately 7.2% of adults reporting this disability. The ACS dataset reflects self-reported disabilities, where respondents answer a series of questions about “serious difficulty” performing activities of daily living. These variables are used here to reflect each county’s disability composition, but ACS data do not address whether respondents report having multiple disabilities. Counties with a larger share of the disabled population who are deaf or blind will have lower disability employment gaps

Counties with a larger share of the disabled population who have cognitive or mobility-related disabilities will have higher disability employment gaps.

Work Incentives. I use two variables to address personal incentives to work and disability employment. Previous research has examined size of SSI payments in relation to median earnings in markets to explain the rates in which people with disabilities opt-out of seeking employment (Autor and Duggan 2003, 2004, and 2007). Whereas higher median earnings in an area present an incentive to seek employment, higher SSI payments provide an incentive to

withdraw from employment altogether. I use per capita SSI payments reported by the Social

Security Administration’s 2011 dataset on recipients and payments by county (SSA 2018). I

anticipate higher SSI payments will serve as an incentive for people with disabilities to withdraw

from employment, and higher SSI payments will relate to a higher disability employment gap. I use ACS median earnings to capture the incentive to seek employment. I anticipate higher median earnings in a county will serve as an incentive for people with disabilities to seek employment, and higher median earnings will relate to a lower disability employment gap. c. Additional variables Metropolitan status. I incorporate 2013 USDA Rural Urban Continuum (RUC) codes in reporting each county’s degree of rurality and adjacency to metropolitan amenities. I recode nine categories of the rural urban continuum into three categories of metropolitan, rural metropolitan-adjacent, and rural metropolitan-non-adjacent. Here, all counties with RUC values between 1 and 3 are coded as metropolitan, RUC 4, 6, 8 counties are coded as Rural

Metropolitan-Adjacent, and RUC 5, 7, 9 are Rural Metropolitan-Non-Adjacent. Of the counties in this sample, 1,140 are categorized as metropolitan, 978 are rural counties that are adjacent to metropolitan counties, and 846 counties are rural counties that are non-adjacent to metropolitan amenities. I treat metropolitan as the reference category in analysis.

Employment Rates. I use two control variables to capture effects of the general climate of employment and difference in 2016 disability employment: employment rates among non- disabled and past employment rate of people with disabilities in each county. These variables are meant to address concerns that cross-county variation in employment may reflect broader employment trends. Higher employment rates among people with disabilities could reflect higher employment among the general population, where disability employment parallels the overall employment rate of a county. I control for this effect by including the employment rate

among non-disabled from the 2012-2016 non-disabled employment rate provided by the ACS.15

A second consideration involves changes in the employment rate of people with disabilities over

time. I consider limited improvements in employment since the ADA, and include the disability

employment rate from 2000 in the model to incorporate some degree of change. Longitudinal

analysis of disability employment is complicated in that disability questions were only recently

added to the ACS in 2008. To construct estimates for remote rural counties, the ACS reports in

five year estimation windows. Presently, the first estimates (2008-2013) and current estimates

(2012-2016) overlap in their sampling windows. Although the 2000 Census does not use identical questions in determining disability, it is the only prior year where employment data among people with disabilities were reported. I include the 2000 disability employment rate as a control in this analysis.

B. Methods I construct a spatial autoregression (SAR) model to examine determinants of employment differences between people with and without disabilities across the United States, incorporating spatial relationships between socioeconomic composition and disability composition of counties.

A conventional regression model could examine how county employment relates to industry and population makeup of counties, but would not account for spatial relationships between adjacent counties. This can lead to a misidentification of variable relationships made under the assumption that each county unit is spatially independent. To account for spatial autocorrelation, or the spatial dependency between a variable and nearby units, I use a spatial lag model that

15 C18120 Employment Status five year estimates from 2012-2016

incorporates influence of neighboring counties.16 In my model, I identify neighboring counties

as any contiguous counties that share a point of intersection.17

I use a spatial regression model after first verifying spatial autocorrelation, where

employment outcomes may be influenced by values in neighboring counties. I ran two tests for

global spatial dependence (Moran’s I and Geary’s C), which rejected independence and identical

distribution in residuals from a preliminary OLS regression and demonstrated spatial

dependence.18 To account for spatial influence, I constructed a spatial lag model (SAR) that

employs a maximum likelihood (ML) fit on 2016 employment rates of people with disabilities

and the 2016 gap in employment between people with and without disabilities. Variance

inflation factors showed little evidence of multicollinearity, with the proportion of mobility-

related disabilities having the highest score of 5.4. The SAR model used here is based on a sample of 3,066 counties in the continental U.S..

IV. Results

The SAR model explains approximately 37% of the variation (pseudo R2=0.365) in

employment difference in the sample. I report results in Table 3.3, with a comparison to

summarized relationship in Table 3.4. Based on the SAR model, I find support for place-based and individual-level explanations of employment differences across counties, explaining approximately 37% (R2=0.37) of the variation in employment difference in the sample wish

16 This is distinct from a spatial error model, which addresses correlation in the error terms of nearby places but does not address variable influence from other places. A spatial lag model captures the effect of independent variables from nearby locations. 17 The construction of spatial weights based on shared vertices is referred to as a “queen’s case,” as it resembles adjacency in any direction in a manner that resembles a queen’s movement on a chess board. I selected the queen’s case over the alternative “rook’s case” approach because a rook’s case defines contiguity of neighboring units through a shared border (rather than vertex), and U.S. counties do not adequately conform to rigid polygons. 18 See appendix for model comparisons with preliminary OLS, OLS with state-fixed effects, and diagnostic tests.

87 significant variation in employment across counties (rho=0.07, p<.001). Below I provide detailed interpretations of results within each variable grouping.

Table 3.3SAR model of disabled and non-disabled employment difference Table 3.3: Spatial lag model of disabled and non-disabled employment difference (%) 2011 Socioeconomic Composition Coeff. (S.E.) Bachelors degree (%) -0.401(0.045)*** Poverty (%) 0.100(0.042)* Gender earnings gapa 0.000(0.000) Asian (%) -0.052(0.092) Black (%) 0.059(0.019)** Other (%) 0.063(0.029)* Hispanic (%) -0.086(0.020)*** 2012 Industry (% share) Manufacturing -0.112(0.018) FIRE 0.003(0.075) Management 0.180(0.108) Logging 0.147(0.172) Coal 0.081(0.087) Oil and gas -0.449(0.132)*** Farming -0.050(0.023)* Government 0.002(0.009) 2012 Disability composition (%) Deaf -0.791(0.133)*** Blind -0.342(0.135)* Cognitive 0.380(0.104)*** Mobility 0.371(0.092)*** Work access amenities Without internet (%) in 2013b 0.009(0.012) Commute time 2011c 0.102(0.038)** Work from home (%) 2011 -0.142(0.056)* 2011 Work incentives Monthly SSI pay 5.479(2.924) Median earnings 0.022(0.048) 2000 Disability employment rate -0.110(0.024)*** 2016 Non-disabled employment rate 0.629(0.035)*** Metropolitan Statusd Non-metro, metro-adjacent 0.207(0.340) Non-metro, non-adjacent -0.209(0.519) Intercept -3.820(3.333) ** Lambda 38.622(1.242)*** Rho 0.069(7.56)*** Pseudo R2 0.375 N 3,066 aReported in $1000USD b Refers to broadband service availability by percent of households in a coverage area c Reported in minutes dMetropolitan is the reference category *p<.05; **p<.01; ***p<.001

Socioeconomic composition. The model shows support for a relationship between socioeconomic composition and the employment difference between people with and without disabilities. As shown in results from Table 3.3, counties with higher poverty rates have greater differences in employment than counties with lower poverty rates (beta 0.10, p<.01). This result is consistent with the predicted relationship between poverty and employment, where places with higher poverty rates and fewer amenities have a negative relationship to employment growth

(Anderson 1994). For each single percent increase in poverty within a county, the gap in disabled employment increases by 0.1. Examining educational attainment, I find support for the hypothesized negative relationship (beta -.401, p<.001), where a 1% increase in bachelor’s attainment corresponds with a 0.4 decrease in the disability employment gap.

Industry composition. The expected relationship between industry composition and the disability employment gap is not supported based on my analysis. Farming and oil and gas employment shares each demonstrate a negative relationship with the employment gap, where increases in their share of employment corresponds with a decrease in the disability employment gap. A single percent increase in farming as a county’s share of employment corresponds with a

0.05 (beta -0.050, p<.05) decrease in the disability employment gap, while each percent increase in oil and gas employment decreases the gap by 0.45 (beta -0.449, p<.001). The remaining extractive industries failed to demonstrate significant relationships with the gap in employment between people with disabilities and those without.

These results are inconsistent with ergonomic literature, and may be complicated by the mobility of the laborforce in these sectors. Oil and gas relies on a highly mobile workforce that is unlikely to takeup permanent residency in the county of employment (Jacquet 2014; Schafft et al. 2014). And although the agricultural laborforce can demonstrate patterns of return, they are

still a largely mobile workforce (Frank et al. 2013). I am not suggesting that the mobility of

laborers in these industries is simply producing unreliable results. Instead, the mobility of this

workforce may be more closely-connected to the physical fitness of the labor pool in these

industries relative to others. Although physical demands may harm the chances of employment

among the disabled, these sectors each rely on a mobile laborforce that migrates to locations

where they are eligible workers. This “salmon bias”19 has been used to describe health

advantages in areas with high migrant composition (Chiswick, Lee, & Miller 2008; Gushulak

2007), as the most physically fit are best able to relocate. The composition of this mobile

laborforce may consist of highly employable disabled people with limited restrictions to their

work capability. Because the only industries in Table 3.3 with high rates of laborforce mobility

also show fewer disabled/non-disabled employment differences, I believe the results involve

associated sorting effects.

Work access amenities. I found support for the hypothesized relationship between work

access and the disability employment gap. Commuting difficulty results supported the expected

relationship, where each one minute increase in commute time corresponds with a 0.1 increase in

the disability employment gap. Working from home also matched expected relationship, where

a 1% increase in the share of people working from home corresponds with a 0.14 decrease in the

disability employment gap. Internet access is not statistically significant. Previous research has

suggested that broadband access, although a prerequisite for telecommuting, is not a reliable

predictor of working from home (Kolko 2012). People with disabilities face disproportionate

burdens in transit, and these results suggest that places with faster commutes and work at home

options have lower disability employment gaps.

19 Also known as the Salmon Effect, referring to the ability of the most physically fit salmon to travel to the upper reaches of rivers to spawn, while less fit cannot.

Social Inequality. Gender inequality was not statistically significant in explaining

variation in disability employment gap. Given the precedent for understanding disability

underemployment as a result of underlying discriminatory attitudes, I expected to find some

evidence of gender inequality as a predictor of the disability employment gap. I did find that racial composition was statistically significant in explaining variation in the disability employment gap, where greater Black and Other composition corresponded with a higher gap.

Every 1% increase in Black and Other composition each corresponded with a 0.06 increase in the disability employment gap. The concentration of black populations has historically been linked to limited job opportunities, and the greater disability employment gap is consistent with that relationship. Hispanic composition is negatively related to the disability employment gap,

however, where each 1% increase in Hispanic composition corresponds with a 0.09 decrease in

disabled/non-disabled employment difference. Recent studies in the underemployment of

Hispanic populations have revealed that they face a persistent disadvantage in the labor market

as compared to other social groups (Young and Mattingly 2016). The lower disability

employment gap in areas of high Hispanic composition may reflect gains of people with

disabilities relative to Hispanic laborers.

Disability composition. Results from the model show support for predicted relationships, where greater sensory-related disability composition (blind and deaf) correspond with a lower

disability employment gap. Every 1% increase in deaf composition is related to a 0.79 lower

disability employment gap. Each 1% increase in blind composition corresponds with a 0.34

lower disability employment gap. Deaf and blind groups are described as more employable and

work capable in previous research (Chi 2001; Schur 2004), and these results were consistent with those findings. Both cognitive and mobility-related disabilities show a positive relationship to

the disability employment gap, where greater in their share of a county’s disabled population correspond with a higher disability employment gap. For every 1% increase in cognitive or mobility-related disabilities, there is a 0.3 higher disability employment gap.

Work incentive. SSI is hypothesized to have an opt-out influence on disability

employment, where higher rates of SSI compensation correlate with a widening gap in employment between people with and without disabilities. I do not find support for that

relationship in the results, as the level of SSI compensation and median earnings fail to be

significant in explaining employment differences. The average county’s monthly per capita SSI payment is $493 and the maximum payment in the counties sampled is $827 It is possible that

these payments are too small to provide sufficient incentive for potential workers to opt out of

employment. Although median earnings range from $4,000 to over $60,000 across counties in

the sample, county differences in earnings have not provided sufficient incentives to result in

changes in employment.

Table 3.4Summary of hypothesized relationship Metropolitan status. I do not find metropolitan status to be significant in explaining

changes in the disability employment gap. The assumption of a higher disability employment

gap in rural areas is rooted in limited employment opportunities, greater extractive shares of

industry, and the contribution of rural remoteness to transit difficulties among people with

disabilities. In my analysis, I’ve included metropolitan status as a distinct variable in the event

that rurality contributes to employment differences in a way that industry composition and

commuting variables did not capture. It is likely that metropolitan status is not significant in

these results because these variables are accounted for.

V. Conclusion

This study is important in moving explanations of inequality and disability forward.

Discrimination and accessibility have been the steadfast explanatory mechanisms used in studies of employment among people with disabilities. But these explanations have been static, unresponsive to cultural changes that may have shaped social values or significant state intervention in the form of the ADA. Although this research does not find support for a climate of discrimination in determining employment outcomes, it connects aspects of accessibility to industry composition and community mobility while accounting for several disability types. The social model of disability, in portraying disabilities as a social construct resulting from

s and results 3.4 Summary of hypothesized relationships and results Predicted Results Relationship H1: Poverty + + Socioeconomic H2:Education composition - - H3: Extractive industry + Mixed H4: Commuting difficulty + +

Place Work access H5: Work at home - - H5b: Inaccessible Internet + NS H6: Gender inequality + NS Inequality H7: Nonwhite + NS H8: Deaf - -

Disability H9: Blind - - composition H10: Cognitive + + H11: Mobility + + Individual H12: Per capita SSI + NS Incentive H13: Earnings - NS

exclusionary social practices, has been unable to treat differences in visual, auditory, or mobility considerations as unique or meaningful to explanations of inequality among the disabled. My study, however, connects these forms of disability to community mobility, work capability, and

the industrial makeup of a place to explain employment outcomes. This provides an opportunity

for new directions in research and theory-building.

Support for place-based explanations of disability employment found in my study mark a

departure from discrimination and workplace accessibility approaches common in disability

sociology. The economic well-being, industrial profile, and level of community mobility within

a place influence employment outcomes for people with disabilities. Discrimination has been

understood as an interpersonal facet of inequality that involves personal values and judgement.

That approach to capturing inequality, using individual-level assessments and qualitative study of ableist thinking, is limited in the types of research questions that it can address. When employment among people with disabilities varies from 41% to 98% between counties, this presents a challenge for theories that reference discrimination and accessible design to address why inequality is worse in some places than others. Moreover, addressing work accessibility as a community mobility issue instead of a feature of the workplace provides a needed addition to design-centric conceptions of accessibility. Accessibility is often understood at an individual

level, using the experience of disability as the means by which researchers understand obstacles

to mobility. As a result, specific fixtures within the workplace and in the pedestrian landscape

become the focus of accessibility research. The approach in my research, however, considers

county-level indicators of work access by incorporating broader indicators of community mobility (commute time) and options for employment that bypass obstacles in the built environment. Given county-level patterns describing disability prevalence and employment

outcomes, these indicators provide a better means of comparing inequality across places.

In addressing the degree to which economic inequality varies across places, poverty of

place variables in this research show that economic well-being and industrial composition can

inform our understanding of disability employment. What kinds of industries provide the basis

for employment in the county? Is there evidence of educational resources and college degree

attainment? These kinds of questions are distinct from considerations of discrimination and

accessibility, but play an important role in explaining why some counties have better

employment outcomes than others. I find that the socioeconomic composition of place plays an

important role in explaining the disability employment gap and its variation across counties.

Disabled employment is most similar to non-disabled employment in places with higher educational attainment and lower poverty rates. But my research is important in demonstrating that these explanations are not mutually exclusive. Rather, the inclusion of social contexts alongside measures of discrimination can inform researchers of the degree to which each explains disability employment.

This research also highlights how individual attributes can still be an important facet of inequality between disabled and non-disabled populations. Functional limitations to work vary by disability type, and this research shows a relationship between disability composition and employment outcomes. For example, I found that counties with more sensory-related disabilities

(blind and deaf) relative to cognitive or mobility-related disabilities have a lower disability employment gap. This finding provides opportunity for new directions in research and theory- building. One such direction may analyze dimensions of inequality that exist among people with disabilities. Disability sociology has suffered from either treating disability as a single monolithic status or from delving into analysis of a single disability type. But as my research indicates employment outcomes vary according to disability composition, it begs the question of whether aspects of discrimination and accessibility apply differently across disability types. Job training programs, state policymaking, and public advocacy may benefit some disability types

more than others. Within Ohio, for instance, job training and placement programs are shaped by the Division of Employer and Innovation Services (EIS) in partnership with the Ohio Business

Leadership Network. Rather than consider the disability composition of each Ohio county when developing training or placement programs, EIS is informed by private business groups of

desirable employee characteristics (OOD 2018). Because different disability types demonstrate greater employability than others, I suspect that his kind of arrangement would disproportionately improve employment among blind and deaf populations relative to other disabled groups. My research highlights different employment outcomes by disability type, and

programs meant to foster greater disability employment might benefit from considering the

disability composition of each county through targeted training and placement programs by

disability type.

There are three aspects of this study that should be revisited as data allows. First, the

new data that made this research possible is still limited in its coverage. Because ACS disability

data is new, I am unable to perform needed analysis addressing changes in employment. The

2012-2016 estimates used to report disability prevalence in this study overlaps with the earliest

available ACS sample. As a result, this research was limited to cross-sectional questions that

compare employment outcomes across places. Future research would benefit from considering

the types of places that are improving or worsening in their employment of people with

disabilities. New questions could consider the Great Recession and post-recession recovery, potential impacts from national healthcare reforms, and also revisit aspects of stigma that may accompany the recent rise of extremist (eugenics) ideologies. Second, the quantitative approach of this research cannot resolve questions of how SSI compensation influences disability employment. When interpreting work motivation, county SSI compensation (relative to median

96 earnings) fails to be significant in explaining employment of people with disabilities. SSI compensation does not appear to suppress employment of people with disabilities, but without qualitative data to analyze how a decision is made readers should be cautioned against inferring motivation. Regardless of these findings, the lingering question of how SSI causally impacts employment is beyond the scope of area data used here.

Finally, while my research demonstrates that a relationship between industry composition and disability employment, it does not explain why this relationship exists. My research was conducted under the assumption that employment was influenced either as an aspect of economic development or some feature of human capital related to job tasks. Unfortunately, it does not resolve whether benefit structures, firm size, human resources training, or industry norms may influence the employment of disabled workers. Nor does this research consider how each type of disability may uniquely fit into a given industry. Some disabilities have been an asset to employers, as noisy shop floors have been environments where hearing difficulty can act as an asset (Morata et al. 2005). Without dedicated research on this relationship, it is difficult to determine whether the relationship is ergonomic or sociological.

Research addressing people with disabilities as a marginalized social group has been limited by reliance on discrimination and accessibility as the determinants of inequality. My analysis of employment differences makes two contributions to move our understanding of disability inequality forward. First, by studying subnational variation in employment difference I connect place characteristics to livelihood outcomes of people with disabilities. And second, by considering differences between disability types as meaningful and involving corresponding differences in work capability, I incorporate inequalities within the disabled population. In

97 identifying place-based aspects of inequality that correspond with disability types, future studies in disability inequality can move beyond discrimination and accessibility considerations.

CHAPTER 4 : DISABILITY BENEFIT AND SSI DISTRIBUTION

I. Introduction

Government responsiveness to people with disabilities has been an area of limited

sociological study. Often responsiveness to people with disabilities is approached at a national

level, using Americans with Disabilities Act (ADA) funding and Supplementary Security Income

(SSI) coverage as subjects for understanding the relationship between people with disabilities

and the state. But these national-level approaches to government responsiveness do not take into

account subnational variation in how programs serve the needs of people with disabilities.

Studying SSI coverage across counties provides the opportunity to examine how a disability-

specific government program varies across place. In this study I ask to what extent differences

in SSI provision are shaped by sociopolitical interests and the capacity of institutions to provide

welfare.

There is need for studying subnational variation in government support for people with

disabilities. SSI is the only disability-specific government program that has devolved to all U.S.

states.20 Since 1972 amendments to the Social Security Act, SSI has been completely federally- funded without requiring state matching funds, with complete state control over in-state funding decisions, staffing, and in locating Disability Determination Services (DDS) offices (CIDDP

2007; U.S. House Committee on Ways and Means 1997).21 $4.5 billion in SSI payments were

20 Note: Supplemental Security Income (SSI) can sometimes be confused with Social Security Disability Insurance (SSDI) when addressing federal assistance and disability benefits. SSI is a need-based or “means-tested” assistance program that provides supplemental income for people with disabilities with either limited income or no income. SSDI is a form assistance that is tied to the applicant’s work history, with the size of SSDI payments reflecting the applicant’s income level and number of years paying into SSDI. 21 SSA provides no medical standards, certification requirements, or training. Its primary responsibility is the distribution of funds to state programs (CIDD 2007).

distributed in 2016, with an average of $1.5 million in cash assistance reaching each county.

Distribution of these benefits is dependent upon SSI determination processing in each state,

which is shaped by county disability prevalence and availability of in-state resources.

To analyze gaps in SSI coverage, I borrow from two literatures addressing government

responsiveness in provision of social welfare. The first approach is conflict-oriented, examining

how distribution of resources is coordinated on the basis of sociopolitical interest rather than need. Wealth, inequality, and racial composition provide the basis for how financial flows to social welfare programs vary. The second is a capacity-oriented approach that considers the institutions that administer social welfare programs. This involves the capacity of state legislatures to implement and support social welfare programs within states, but also considers the immediate capacities of medical and governmental institutions to directly provide services.

Applied to county-level SSI provision, I examine how income, income inequality, and racial composition of a county may explain gaps in SSI coverage. In this study, I also account for each county’s share of state employment, medical care, and composition of state legislatures as they relate to SSI provision.

I examine subnational variation in government support for people with disabilities by analyzing county-level differences in SSI coverage relative to each county’s self-reported disability rate. I use a spatial autoregressive analysis (SAR) to empirically test the degree to which sociopolitical interest and capacity-oriented explanations address gaps in SSI coverage across 2,330 U.S. counties. This research is unique in addressing governmental responsiveness to people with disabilities at the subnational level. By examining a national program that is exclusive to people with disabilities, I provide needed empirical analysis for understanding how a social welfare program for people with disabilities is coordinated.

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II. Literature Review

SSI is an important program for understanding government responsiveness to people with disabilities. A number of federal programs for people with disabilities exist, but have patchwork coverage across U.S. regions and often involve overlap with other groups like veterans and aged populations. There are approximately 6.6 million adults currently enrolled, or 16.8% of working age people with disabilities. The program is a valued resource among people with disabilities, providing approximately $750 monthly to individuals and granting automatic eligibility for

Medicaid (SSA 2018). SSI constitutes a disability-specific government program servicing working age adults across the county. SSI therefore provides an ideal case for studying government responsiveness to people with disabilities.

SSI enrollment is widely studied as an area of concern for livelihoods of people with disabilities. SSI research commonly considers rising national enrollment as an area of concern, threatening its long-term fiscal solvency (Autor and Duggan 2006; Autor and Duggan 2007).

These studies tend to treat enrollment as a problem, speculating on fraudulent disability claims and lax standards for admission as drivers of SSI growth. While SSI is considered an invaluable asset to people with disabilities, these authors tend to see greater enrollment as a problem for long-term sustainability of the program. Other research takes a different approach, examining county-level determinants of enrollment in comparison to the overall size of SSI. Income, race, and rurality are used to explain how aspects of hardship drive higher SSI enrollment in some areas than others (Wong 2016). Both varieties of study in SSI enrollment consider SSI size independently of the needs of people with disabilities.

Responsiveness, here understood as measuring to what extent welfare programs serve the actual population, is not captured by studies that focus on the size of SSI enrollment. While

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these studies can capture program size and resources, they offer no consideration of how SSI

enrollment relates to the actual disabled population. New ACS data which account for self-

reported disabilities provide the opportunity to compare how SSI enrollment corresponds with

their numbers. When comparing self-reported rates of disability to SSI coverage rates across

U.S. counties, the gap in SSI coverage ranges from 1.6% to 30%, with an average difference of

11.1%. There is a significant difference in means and distribution of SSI coverage from self-

reported disability rates (see Table 4.1), and analysis that focuses solely on SSI enrollment by

county misses a fundamental question addressing whether SSI is responsive to the county’s

population with disabilities. A need-based explanation of the SSI gap would consider

employment of people with disabilities as the most reliable predictor of SSI provision, as

enrollment is available only to people with disabilities that cannot find stable employment. In the following sections, I consider how conflict-oriented and capacity-oriented explanations that can be extended to explain county differences in the provision of SSI.

A. Sociopolitical Interest When examining social welfare appropriation as an indicator of government responsiveness, some approaches view the distribution of public resources as based on sociopolitical interest rather than need. The provision of social welfare instead represents the political struggle over distribution decisions that reflect larger inequalities and social cleavages. This explanation of welfare distribution often involves accounts of elite capture, whereby public funds are directed towards affluent areas where powerful interests are served (Bardhan and Mookherjee 2000). But coordination of public programs may also reflect racial and ethnic division, where welfare restriction is part of a coordinated strategy to protect the social position of whites against gains among minority groups (Bobo and Hutchings 1996). Rather than approach SSI as a form of

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welfare meant to reach those in need, the provision of welfare is part of a broader system that

privileges wealthy and white interests.

1. Economic inequality Marxian foundations within this approach consider interplay between working class,

capitalist, and state manager interests as they shape public policy. Working class interests, tend

to support a stronger social safety net and the use of public funds for upward mobility among this

group (Bjorklund 2017). State managers tend to cater to capitalist interests that promote

economic growth, thereby securing tax revenues that add to state finances (Block 1987; Manza

and McCarthy 2011). The alignment of capitalist and state manager interests has the effect of

directing social welfare funds to already affluent areas that are typified by high incomes.

Empirical research on distribution of Temporary Assistance for Needy Families (TANF) funds

(Bruch, Ferre, and Soss 2010) and Aid to Families Dependent Children (AFDC) programs (Kail

and Dixon 2011) each has found positive relationships between distribution levels and incomes

of residents in recipient counties. I expect SSI provision to follow a similar pattern, where

counties with residents reporting higher median incomes will have greater SSI coverage than

those with lower median incomes.

2. Race The struggle over resource distribution also involves racial differences. The racial threat

hypothesis describes protection of white social position against gains from other minority groups

(Blumer 1958; Bobo and Hutchings 1996). Mobilization by whites and political protection of whites by state actors block upward mobility among nonwhites. While this has included

measures against affirmative action and support for residential segregation (Massey, Rothwell, &

Domina 2009), control of welfare funds has also been used as a tool to maintain the dominant

white position in racial hierarchy. Both the size of welfare compensation and restrictions to

103 welfare access are linked to racial composition of recipient areas. In their study of in-state

TANF restrictions, Keiser, Mueser, and Choi (2004) compared white and nonwhite sanctions to

TANF funding. They found that nonwhites were subject to more sanctions, even when comparing financial need and availability of state employees to administer TANF funds.

Johnson’s (2001) study of per capita AFDC distribution found differences based on racial diversity, where greater nonwhite population shares were correlated with lower per capita AFDC dollars. These findings were persistent even when accounting for racial attitudes across places.

When considering cross-county differences in the provision of social welfare programs, nonwhite composition plays an important role in determining the degree of government responsiveness. I expect SSI provision to follow a similar pattern, where counties with higher nonwhite racial composition will have lower SSI coverage than those with lower nonwhite racial composition.

3. Conflict and SSI distribution: Fiscal federalism and “colorblind” welfare Two aspects of SSI administration disrupt the assumption of wealthy and white preference in program allocations. First, distinct federal and state responsibilities in SSI provision involve competing priorities in distribution of funds. Oates’ (1972) foundational work on “the political economy of fiscal federalism” examined how fiscal dimensions of policy administration can reflect distinct powers of federal and state agencies. In the mid1990s, a “second generation” of fiscal federalism literature began to form around concerns that “[g]overnment officials may not need to seek the common good as assumed in the first generation theory; rather, they may not act to maximize the welfare of their constituencies” (Vo, 2010, p. 673). Whereas the federal government may prioritize efficient distribution strategies to reach constituents in need, subnational governments are often more susceptible to elite influence at the local level (Vo

2010). At the federal level agents of the state often demonstrate dedication to their role as a

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public agents and may avoid influence of wealthy and white political interests (Skocpol an

Amenta 1986; Glasmeier and Wood 2005; Amenta et al. 1998). Nevertheless, states can still

send funds to high income areas if there is also evidence of hardship in those counties (Claw

2010). Given (1) federal priorities to distribute welfare funds based on need and (2) conflicting

subnational priorities to distribute funds to wealthy areas, places with high incomes and high

inequality are likely to have greater SSI coverage.

Second, explanations of white preference in welfare distribution may not apply to the case of

SSI. White opposition to welfare is built from decades of racially-coded messages which established stereotypes of “freeloading” minorities (Reese 2007). Housing subsidies and TANF spending have been opposed by whites that see them as handouts designed for the benefit of minorities, but social security has remained overwhelmingly supported by whites as a necessary social safety net that whites might use (Kail and Dixon 2011; Tucker 2013). The link between race and disability is not widely known among the public, and administration of funding through the SSA avoids racial scrutiny that other forms of cash assistance are subject to (Pokempner and

Roberts 2001). Race may influence SSI coverage in an altogether different way. Rather than present a racial threat, nonwhite composition can be a source of political power that advocates for social welfare spending. Higher nonwhite composition is generally associated with greater advocacy for social welfare programs (Kinder and Mendelberg 1995). Because SSI is not perceived as racially-polarizing, greater nonwhite composition may promote higher SSI coverage across counties.

B. Capacity-oriented Explanations When interpreting the provision of social welfare services as an indicator of governmental responsiveness, assessments of institutional capacity focus on whether services can effectively be delivered. The provision of welfare has been tied to institutional capacity in two ways. The first

105 considers the capacity of state legislatures to establish and oversee social welfare programs. The ability of state legislatures to coordinate social welfare programs is influenced by the political composition of House and Senate, and interparty conflict can shape the degree to which legislatures fund and support welfare services. The second considers the capacity of institutions to directly provide program services. Because receiving SSI services requires a medical evaluation and state determination of disability status, the capacities of medical and state institutions are each factors that can influence SSI provision.

1. Political legislative capacity The ability of state legislatures to pursue social welfare programs is dependent on the political composition of House and Senate. There is a political legislative capacity that involves the coordination of welfare programs by state legislatures to best serve the public interest, where capacity for responsive programs is dependent upon party alignment, interparty competition, and unified party control over House and Senate (Brace and Mucciaron 1990). These three elements influence the degree to which social welfare programs are responsive to public needs. Political coordination within state legislatures are important in SSI administration, as they determine location of DDS offices, staffing, training, and support across districts (Committee on Improving the Disability Decision Process 2007).

With respect to political alignment, the two major political parties have ideological differences on social spending and distribution of public funds. GOP agendas tend to advocate cuts to social welfare spending and DNC agendas have historically supported the maintenance of a social safety net (Rose and Baumgartner 2013). But the degree to which these party agendas are pursued depends largely on whether there is meaningful competition between political parties. Interparty competition spurs greater advocacy for programs that serve the public interest. Competition for seats in state legislature prompt stronger advocacy for public programs,

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regardless of party ideology (Amenta 1998). Competitive districts are also more accountable to

“have-nots” that may otherwise be taken for granted in political campaigns (Fording 2003). But the ability for state legislatures to act on behalf of public needs is shaped by whether House and

Senate are under control of a single unified party. Interparty competition can benefit the public,

but split state legislatures are susceptible to legislative paralysis that is exploited by elite special

interests. This phenomenon is described in Key’s (1949) political disorganization theory, which

explains that divided legislatures are more susceptible to elite capture than unified legislatures

that cater to broad interests. These in-state political dynamics are complex, but have important

implications for SSI responsiveness. Competitive state legislatures under unified party control

are likely to promote effective SSI programs when compared to noncompetitive and divided state

legislatures.

2. Service capacity State and medical institutions are responsible for providing SSI enrollment, and their

capacity to provide essential services can influence the degree to which SSI coverage is

coordinated. The process of receiving government-recognized disability status through SSI requires individuals to obtain a medical examination from a licensed physician to evaluate whether an impairment to activities of daily living is likely to last more than one year. Claims are then reviewed by DDS field offices for an evaluation process that lasts, on average, between

49 and 157 days (SSA 2015). This is a selective process with only 39% of applicants having their disability verified by DDS (Dahl 2010). Variation in disability determination decisions, processing times, and backlogs have each been linked to differences in each state’s SSI administration (Dahl 2010).

By medical capacity, I mean the availability of medical professionals and the economic means to pay for services. Primary care providers (PCPs) play an important gatekeeping role in

107 establishing disability claims. While PCPs may not have expertise in every area of disability,

PCPs are positioned to interact with networks of experts for referral purposes (Cunningham

2009). Unlike emergency room and convenience clinics, PCPs are also afforded the time to develop relationships with patients necessary to evaluate long term health conditions that are likely to contribute to functional impairments. The supply of PCPs for an area can further influence the conditions by which patients have access to care. PCP shortages put constraints on the amount of time spent with patients, add additional waiting periods for scheduling evaluations, and increase reliance on aides for medical services (Bodenheimer and Pham 2010).

Because PCP availability influences the quality of, timing of, and ability to obtain a disability evaluation, PCP rates provide an indicator of medical capacity for SSI processing. Places with lower PCP rates are likely to have compromised medical capacities and higher gaps in SSI coverage.

Backlogs in disability determination are also a function of limited government capacity to process claims. Since 2009, SSA has cited shortages in manpower necessary to evaluate intake claims and perform reviews to determine whether claimants demonstrated continued disability

(Swank 2012). As a direct measure of service capacity, sufficient manpower is important. But the size of state employment is also correlated more generally with effective social service delivery. State employees have pools of specialized knowledge that can be coordinated across agencies to effectively respond to public needs (Chernick 1999). This dimension of state size as a determinant of effective service provision has been used in the past to explain higher AFDC benefits (Chernick 1999) and policy efforts in support of social service provision (Lobao, Adua, and Hooks 2014). Given the relationship between state size and social service provision, SSI gaps should be negatively related to state size.

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III. Summary and Research Questions

My central question asks to what extent gaps in SSI coverage are influenced by conflict and

capacity-oriented attributes across places. A conflict-oriented approach considers sociopolitical interests involved in distribution differences according to winners and losers in a struggle for resources. Typically, more affluent areas are expected to receive more support for social services. But the context of SSI as a devolved federal program tempers distribution by preserving some degree of public need in distribution. Because the federal government plays a limited role in disability determination and actual distribution decisions, the heightened role of state and local governments in welfare provision is susceptible to sociopolitical influence

(Bardhan and Mookherejee 2000). This suggests that while places with high incomes may have lower SSI gaps, places demonstrating need will also be included in flows of federal welfare dollars. Counties with high levels of inequality (as measured by Gini) present likely candidates for higher flows, as they contain both the wealthier population that provide tax base and include poorer populations that can be targeted with federal (rather than local) funds. From these considerations I develop the following hypotheses:

H1: Higher incomes will be negatively related to county SSI gaps.

H2: Higher inequality will be negatively related to county SSI gaps

Race-based differences in social welfare provision are thought to follow distribution

patterns based on racial threat, where whites prevent expansion of welfare dollars to programs

that might benefit racial minorities. Although an objection to social welfare spending might

normally predict worse governmental provisions in areas with higher nonwhite composition, SSI

is more likely to be perceived as a “colorblind” form of welfare. Welfare programs coordinated

by the SSA are more likely to receive support among whites. Moreover, SSI may receive

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additional support from nonwhite composition, as social welfare spending is advocated by

nonwhites. From these considerations, I develop the following hypothesis:

H3: Higher nonwhite racial composition will be negatively related to county SSI gaps.

The capacity-oriented approach considers how social welfare programs are administered at the institutional level. Political legislative capacity, which captures the degree to which state legislatures can coordinate social welfare programs for the public interest, involves consideration of interparty competition and avoidance of split state legislatures. Ideological differences between Democrats and Republicans align Democrats with support in social welfare spending and Republican support for welfare retrenchment. Legislatures typified by changing party rule provide competitive environments where either party is more likely to court the public by effectively expanding on social services. Additionally, split House and Senate rule promotes political disorganization that is more likely to cater to special interests than the public. From these considerations, I develop the following hypotheses:

H4: Places where state legislatures are typified by competitive interparty changes will

have lower county SSI gaps than noncompetitive legislatures.

H5: Places where House and Senate are not split between parties will have lower SSI

gaps relative to split legislatures.

H6: Places with interparty competition that have single-party rule between House and

Senate will have lower SSI gaps relative to non-competitive split legislatures.

The capacity of medical institutions to directly provide services is also important when considering variation in SSI gaps. The ability to provide medical diagnoses necessary for DDS evaluation is determined by accessible medical professionals that can perform these duties. PCP rates capture whether there are greater or lesser personnel available to perform these tasks.

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Economic obstacles to receiving medical care should also be considered, as securing a medical

evaluation can involve significant costs. Insurance coverage can address cost barriers to care,

where a lack of insurance is likely to produce significant economic costs for medical evaluations.

From these considerations, I develop the following hypotheses:

H7: Higher PCP rates will be negatively related to county SSI gaps.

H8: Higher rates of uninsured in a county will be positively related to SSI gaps.

State governments are tasked with providing disability-determination evaluations. Their

capacity for these services are conditioned by the number of state employees and the size and

remoteness of populations they are meant to serve. Fewer state employees may suggest

increased strain on administration of welfare programs, where more state employees can provide

necessary manpower for effective service delivery. Metropolitan amenities and higher

population density are also understood as attributes for more efficient delivery of public services.

This suggests that counties with smaller and more remote populations are likely to have worse

delivery of social welfare services (Johnson et al. 1999). From these considerations, I develop the following hypotheses:

H9: Greater state employment in a county will be negatively related to SSI gaps.

H10: Population size will be negatively related to county SSI gaps.

H11: Compared to metropolitan counties, rural counties will have larger SSI gaps.

IV. Data and Methods

I use 2,290 counties as units of analysis when evaluating differences in SSI coverage. This

excludes Nebraska, Hawaii, and Alaska in accounting for SSI gaps across 46 states. I exclude

Nebraska in my analysis, as its unicameral state legislature prevents interpretation of political

legislative capacity in shaping SSI coverage. Because I use a spatial regression for examining

111 differences in SSI coverage, Hawaii and Alaska are incompatible with distance weighting and shared boundary considerations used in the construction of spatial weight matrices. Accounting for data suppression in reporting of income, insurance, and PCP rates, I provide descriptive statistics for this sample in Table 4.1 below.

Table 4.1Descriptive statistics for SSI coverage Table 4.2Descriptive statistics for SSI coverage Table 4.1 Descriptive statistics for SSI coverage (n=2,290 county sample) Variable Count Mean Std. Dev. Min Max SSI coverage gap (%SSI enrolled / % self-reported) 11.12 3.646 1.61 29.99 SSI enrollment 3.37 2.079 0 19.02 Self-reported disability 13.92 4.677 3.26 39.99 Sociopolitical interest variables Disability employment rate (%) 33.16 9.962 5.66 74.61 Median incomea 44.98 11.909 19.34 120.10 Gini index 0.44 0.034 0.32 0.67 Racial and ethnic composition (%) Asian 1.20 2.203 0 33.50 Black 10.44 15.125 0 84.94 Other 1.58 5.664 0 86.36 Hispanic 8.73 13.498 0 98.45 Nonwhite composition (%) 23.48 19.458 0.44 98.73 Political legislative capacity Interparty competition 0.49 0.500 0 1 House and Senate rule Split 461 Democrat 500 Republican 1369 Interaction: Competitive unified party rule Competitive split 433 Competitive Democrat 94 Competitive Republican 603 Medical capacity PCP rate Uninsured (%) 18.23 5.404 3.14 38.85 Government capacity State government employment 3.99 5.070 0 41.30 Population 10.62 1.314 5.61 16.12 Metropolitan status Metropolitan 980 Non-metro, metro-adjacent 802 Non-metro, non-adjacent 548 Welfare generosity 4.57 3.434 0 13 aHousehold median income reported in $1,000US

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A. Data

1. Dependent Variable

The dependent variable, the SSI gap, represents the difference between self-reported

disability prevalence and SSI enrollment in each county. Self-reported disability rates are

derived from S1810 data provided by the 2016 American Community Survey. I restrict my

analysis to working age adults, aged 18-64. Using the Social Security Administration “SSI

Recipients by State and County 2016” dataset, I use the blind and disabled category of SSI recipients to identify disability enrollment in each county. The dependent variable represents the

SSI gap, which indicates the difference between SSI-enrolled people with disabilities and people with self-reported disabilities in that age group. On average, each county has approximately an

11.1% difference in self-reported disabilities and SSI-coverage. Coverage gaps vary between

1.6% and 30%, with substantial variation in coverage within each state.

2. Independent Variables

Sociopolitical interest. Studies addressing variation in government responsiveness as a function of social conflict focus on economic resources, inequality, and racial composition. I use

2011 ACS data to derive median household income, the Gini coefficient for income inequality,

and racial composition in each county. The average county has a household median income of

$44,984. The Gini index of inequality is situated on a scale from 0 to 1, where 0 indicates absolute equality in income distribution and 1 indicates absolute inequality. The mean county

score in this sample is 0.43. Among nonwhite racial categories, the average county population

is 1.2% Asian, 10.44% black, and 1.58% fall within the Other racial category within the ACS.

The average county’s ethnic composition is 8.73% Hispanic.

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Political legislative capacity variables. The in-state political factors that shape SSI enrollment include party composition, competition, and unified party rule over both House and

Senate. I gather information on party composition of state legislatures by using the National

Conference of State Legislatures (NCSL) Partisan Composition dataset. I construct a dummy variable for political competitiveness among state legislatures by averaging scores reflecting party rule from 2004-2012 in each state legislature. This variable indicates whether or not mean scores are concentrated toward party extremes, where “0” indicates persistent one-party rule over time and “1” indicates either tendencies toward changes in party rule or split party rule during that time period. 48.5% of the state legislatures in this sample demonstrated tendencies towards interparty competition. I constructed a categorical variable that identifies whether each legislature had split party rule, unified Democratic rule, or unified Republican rule across House and Senate in 2012. In the model, I use additional comparisons between Republican and

Democratic rule in competitive and non-competitive settings.

Service capacity variables. I employ two aspects of institutional capacity related to SSI processing in disability recognition: medical and governmental. To address medical capacity, I use two variables that capture both the ability to offer a medical diagnosis and economic barriers to obtaining medical service. I use the 2011 Area Health Resource File (AHRF) of the American

Medical Association (AMA) to report data on the PCP rate and the percent of the population that is uninsured across the 2,290 counties sampled here. The average county has 18% of its population without insurance. To address government capacity I focus on the size of the state, population, and rural categorization. I use 2012 Economic Modeling Specialists, International

(EMSI) data to report the share of employment in each county that is comprised of state government employees. The average county in this sample has approximately 4% of its

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workforce employed by the state. I use 2011 population estimates, where I include only

noninstitutionalized adults. The population variable used here is measured by the logged

population. Finally, I incorporate 2013 USDA Rural Urban Continuum (RUC) codes in

reporting each county’s degree of rurality and adjacency to metropolitan amenities. I recode

nine categories of the rural urban continuum into three categories of metropolitan, rural

metropolitan-adjacent, and rural metropolitan-non-adjacent. Here, all counties with RUC values between 1 and 3 are coded as metropolitan, RUC 4, 6, 8 counties are coded as rural metropolitan- adjacent, and RUC 5, 7, 9 are rural metropolitan-non-adjacent. I treat metropolitan as the reference category in analysis.

Welfare generosity. I add another variable to capture the level of non-medical poverty assistance to address whether the SSI gap is distinct from some broader generosity in welfare. I use Rose and Baumgartner’s (2013) Government Generosity Index (GGI) from their study of government responsiveness to poverty. In their analysis, Rose and Baumgartner constructed a state-level GGI by comparing government spending on poverty to the level of severity in poverty, establishing whether the governments demonstrated a tendency toward “stinginess” or

“generosity.” In my analysis, I apply their GGI indicator to the county-level to capture whether the SSI gap is distinct from general welfare generosity behaviors.

Disability and economic need. Because SSI represents cash assistance for people with disabilities with restricted work capability, I also include S1810 ACS data reporting the employment rate among working age people with disabilities in each county. By including this variable, I provide a measure of economic need among people with disabilities so I can isolate the degree to which conflict and capacity-oriented variables explain variation in SSI coverage.

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B. Methods

I constructed a spatial autoregression (SAR) model, incorporating spatial relationships to describe differences in SSI coverage across counties, taking into account sociopolitical interests

and capacity-oriented variables. A conventional regression model could examine how each

county’s provision of SSI benefits relates to sociopolitical interests and capacity to deliver

services within counties, but would not account for spatial relationships between adjacent

counties. This can lead to a misidentification of variable relationships made under the

assumption that each county unit is spatially independent. To account for spatial autocorrelation,

or the spatial dependency between a variable and nearby units, I use a spatial lag model that

incorporates influence of neighboring counties.22 I constructed two spatial weight matrices to

account for contiguity and distance in the samples, finding that accounting for contiguity

explained the majority of variation in error terms. In my model, I identify neighboring counties

as any contiguous counties that share a point of intersection.23

I developed the SAR after testing preliminary OLS regression models for evidence of spatial

autocorrelation and finding evidence of spatial dependence. Results from the Moran’s I test of spatial dependence report a chi2 of 463.61 that is significant at the 0.001 level. Residuals of a standard OLS model show correlation with nearby residuals and positive spatial autocorrelation in the dependent variables. To account for spatial dependence, I constructed a spatial lag model that employs a maximum likelihood (ML) fit on 2016 SSI provision across 2,290 counties. To check for multicollinearity, I examined the variance inflation factors. The final model was

22 This is distinct from a spatial error model, which addresses correlation in the error terms of nearby places but does not address variable influence from other places. A spatial lag model captures the effect of independent variables from nearby locations. 23 The construction of spatial weights based on shared vertices is referred to as a “queen’s case,” as it resembles adjacency in any direction in a manner that resembles a queen’s movement on a chess board. I selected the queen’s case over the alternative “rook’s case” approach because a rook’s case defines contiguity of neighboring units through a shared border (rather than vertex), and U.S. counties do not adequately conform to rigid polygons.

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revised to exclude poverty and size of SSI payments due to multicollinearity with other

variables. With those variables excluded, the highest variance inflation factor (3.24) among

variables in the model is population size. The mean variance inflation factor is 1.96.

Table 4.2 Summary of predicted relationships Hypotheses Predicted Relationship H1: Higher incomes will be related to lower SSI -

gaps H2: Higher inequality will be related to lower SSI - gaps Interest H3: Higher nonwhite composition will be related to

Sociopolitical Sociopolitical - lower SSI gaps H4: Party competition will be related to lower SSI - gaps H5: Unified party rule will be related to lower SSI - gaps Political H6: Competitive unified party rule will be related - to lower SSI gaps

H7: Higher PCP rates will be related to lower SSI - gaps H8: Higher rates of uninsured will be related to Capacity Medical + higher SSI gaps H9: Higher rates of state employment will be - related to lower SSI gaps H10: Population size will be related to lower SSI - gaps Government H11: Rurality will be related to higher SSI gaps + Hypotheses explained in the Summary and Research Questions section are displayed in

Table 4.2. I anticipate income, inequality, and nonwhite composition to be negatively related to

the SSI gap as a function of sociopolitical interest. I anticipate party competition and unified party rule of state legislatures to be negatively related to the SSI gap, as political disorganization

is unlikely to result in responsive social welfare programs. I expect the availability of Primary

Care Providers (PCPs) to increase SSI enrollment, while the number of uninsured will present an

economic barrier to acquiring a medical evaluation. I further anticipate that the share of state

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employees will reduce the gap in SSI coverage, particularly in populous areas with metropolitan

amenities. Because SSI is a form of welfare, I expect that greater welfare generosity will carry

over to SSI to reduce gaps in SSI coverage. I also anticipate that greater employment among

people with disabilities is likely to suppress SSI enrollment, both because it indicates that there

are potential alternatives to SSI income and because employment disqualifies people with disabilities from enrolling in SSI.

V. Results Sociopolitical interest. Results from my analysis show support for a relationship between

the size in SSI gap and sociopolitical interest. As reported in Table 4.3, counties with higher

median incomes have lower SSI gaps than counties with lower median incomes (beta -0.14,

p<.001). This relationship follows previous studies on TANF and AFDC (Bruch, Ferre, and

Soss 2010) allocations, in that government responsiveness is greater in resource-rich areas with higher income bases. For every $1,000 increase in median income there is a corresponding 0.14 lower SSI gap. The other variables addressing economic inequality and racial composition also align with predicted relationships. Economic inequality demonstrates a negative relationship to

SSI gaps (beta -11.27, p<.001) where higher Gini values correspond with smaller gaps in SSI

coverage. These results are consistent with expected patterns, as high income and high

inequality areas have greater provision of SSI. Each racial category demonstrates a statistically

significant and negative relationship with SSI gaps, where a single percent increase in Asian,

Black, and Other populations correspond with 0.09, 0.04, and 0.03 lower SSI gaps respectively.

When considering a single percent higher Hispanic composition, SSI gaps are 0.04 lower. These

results are inconsistent with a racial threat hypothesis, as areas with higher nonwhite composition

receive higher SSI coverage. Instead, I find that greater provision of SSI is consistent with

Pokempner and Roberts (2001) SSI may benefit from advocacy for social spending

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Table 4.3Gap in SSI coverage for people with disabilities Table 4.3: Gap in SSI coverage for people with disabilities Coeff. (S.E.) Sociopolitical interest Median incomea -0.135(0.009)*** Gini index -11.273(2.094)*** Racial and ethnic composition (%) Asian -0.092(0.037)* Black -0.036(0.007)*** Other -0.032(0.011)** Hispanic -0.044(0.008)*** Political legislative capacity Interparty competition 2.643(0.635)*** House and Senate rule Democrat 3.929(0.654)*** Republican 2.317(0.626)*** Interaction: Competitive unified party Competitive Democrat -3.183(0.829)*** Competitive Republican -1.789(0.673)** Medical capacity PCP rate -0.009(0.002)*** Uninsured (%) -0.024(0.024) Government capacity State government employment -0.023(0.011)* Population -0.387(0.081)*** Metropolitan statusb Non-metro, metro-adjacent 0.084(0.160) Non-metro, non-adjacent -0.409(0.206)* Welfare generosity -0.205(0.040)*** Disability employment rate (%) 0.004(0.003) Intercept 25.835(1.490)*** Lambda 5.492(0.187) Rho 0.084(-1.280)*** Pseudo R2 0.451 N 2,290 aMedian household income reported in thousands bMetropolitan is the reference category *p<.05; **p<.01; ***p<.001

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among nonwhite groups (Kinder and Mendelberg 1995) while being supported by whites as a

legitimate social welfare program (Pokempner and Roberts 2001).

Political legislative capacity. The model shows support for a relationship between the

SSI gap and the political environment. Places with competitive political environments have a

2.64 (beta 2.643, p<.001) higher SSI gap than places with consistent rule by either Democrats or

Republicans. Additionally, single-party rule of both State House and Senate in 2012 (-even

Democratic rule!) is indicative of larger gaps than split legislatures. As compared to places with split party rule between House and Senate, there are higher SSI gaps in states with unified

Democrat (beta 3.929, p<.001) or Republican (beta 2.317, p<.001) legislatures. Two of

Amenta’s (1998) predictors of welfare policy support, interparty competition and “left-wing”

(p.172) party rule, are not borne out by these results.

But looking at the interaction between interparty competition and unified party rule,

Table 4.4 displays lower SSI gaps in places with histories of political competition that had unified party rule in 2012. As compared to places with non-competitive state legislatures, places with histories of interparty competition have lower SSI gaps when there is unified party rule under Democrats (beta -3.183, p<.001) or Republicans (beta -1.789, p<.001). When rule of state legislatures is unified, but uncertain, it appears that there is greater provision of social welfare.

Key’s (1949) description of political disorganization can be seen in this outcome, as SSI provision appears to suffer when legislatures are divided and political futures are unlikely to change.

Service capacity. Service capacity variables present mixed support for both medical and

governmental processing capabilities as a determinant of county SSI gap. Among the medical

capacity variables, only the county PCP rate demonstrated a significant relationship to SSI

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coverage. This was consistent with the literature, which described the availability of medical

professionals as an important part of recording health conditions and processing disability claims

(Bodenheimer and Pham 2010). Counties with a smaller share of PCPs have lower SSI coverage.

While I anticipated a lack of insurance to provide an economic obstacle to receiving medical

service, this failed to be significant in explaining variation in SSI gaps.

The number of state government workers has a significant negative relationship to SSI

coverage. The gap in SSI coverage is lower where the county share of state employees is higher.

Given the speculation that manpower shortages contributed to backlogs in processing and an

inability to review continued disability claims, this finding is consistent with those assumptions

(Swank 2012). Compared to metropolitan areas, only remote rural areas demonstrated a

significant difference in SSI coverage gaps, where they are 0.41 lower than metropolitan counties. This finding is inconsistent with the assumptions of efficiency in metropolitan service provision. It’s possible that PCP shortages and population size, variables that are also included in the model, mute the effects we might otherwise see in rural places.24 This relationship deserves further scrutiny to determine how rurality actually interacts with SSI, since it is a function distinct from population size. Population size, on the other hand, demonstrated a significant and negative relationship with SSI gaps, where larger populations were more likely to have their disabled populations receive SSI coverage.

Controls. Finally, results show a significant negative relationship between welfare generosity and the size of a county’s SSI gap. Counties which have a greater responsiveness to poverty via cash assistance have greater government-recognition of disability status through SSI provision. This is consistent with Rose and Baumgartner (2013), as places already framed as

24 When population size is left out of the analysis, remote rural counties are not statistically significant in their SSI gap differences from metropolitan counties.

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having significant hardship and demonstrating worthiness of assistance reap continued support

from the state in welfare dollars. However, the level of employment among people with disabilities is not significant in its relationship with the gap in SSI coverage. Because employment disqualifies people with disabilities from receiving SSI benefits, I expected a need-

based relationship between SSI provision and economic need among the disabled. SSI gaps are

not lower in counties with higher disabled employment, however. Given the significance of

sociopolitical interest and capacity-oriented variables, it appears that SSI provision is not

explained based on need of the disabled.

Table 4.4 Summary of hypothesized relationships and results Hypotheses Predicted Conclusion Relationship H1: Higher incomes will be related to lower SSI - -

gaps H2: Higher inequality will be related to lower SSI - - gaps Interest H3: Higher nonwhite composition will be related to

Sociopolitical Sociopolitical - - lower SSI gaps H4: Party competition will be related to lower SSI - + gaps H5: Unified party rule will be related to lower SSI - + gaps Political H6: Competitive unified party rule will be related - - to lower SSI gaps

H7: Higher PCP rates will be related to lower SSI - - gaps H8: Higher rates of uninsured will be related to Capacity Medical + NS higher SSI gaps H9: Higher rates of state employment will be - - related to lower SSI gaps H10: Population size will be related to lower SSI - - gaps Government H11: Rurality will be related to higher SSI gaps + Mixed VI. Conclusion Differences in self-reported disability rates and government-recognized disability

demonstrate subnational variation that is connected to broader features of place. By approaching

122 this gap as a function of state-responsiveness, I find that the conflict and capacity considerations add a new dimension to debates involving growing disability rolls. Rather than approach the size of SSI enrollment by speculating on national levels fraud in the pursuit of government assistance,

I find that predictors of conflict-based distribution and practical considerations of welfare service delivery explain 46.8% of the variation in how SSI coverage is appropriated to people with disabilities across U.S. counties.

The context of fiscal federalism in the evaluation of SSI distribution was important in understanding the relationship between wealth, inequality, and welfare distribution. The relationship between these variables is easy to oversimplify, as there is ample precedent to assume elite interest in determining resource distribution. Approaching SSI distribution without accounting for intergovernmental dynamics between federal and state priorities would have difficulties in explaining how income, poverty, and inequality explain variation in SSI coverage.

Income and deprivation measures do not have a zero-sum relationship wen explaining resource distribution, and fiscal federalism can explain how federal and state priorities differ in coordinating welfare distribution.

This research also benefitted from understanding the racialized connotations of welfare.

Racial threat is predicated on perceptions of minority benefit and mobilization to control the size of welfare benefits and their distribution. By paying attention to whether SSI is part of a rich history of racial stereotyping or whether it represents a more colorblind for of welfare, I was able to anticipate how nonwhite composition could actually attract SSI distributions. Future studies in government responsiveness should be wary of how particular forms of social assistance are historically treated, as this can determine whether racial threat explanations are likely to be relevant in analysis.

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Future study of government responsiveness to people with disabilities could benefit from

additional data, comparative methods, and qualitative research. My study faced a significant

hurdle to analysis in that self-reported disability rates are relatively new. Because self-reported disability rates across counties began with the 2008 disability questionnaire and relies on five year estimates, there is not yet sufficient data to examine how self-reported disabilities have changed over time. Because so much research compares rising disability rates to rising SSI enrollment in their analysis (Wong 2016, Autor and Duggan 2003; Autor and Duggan 2006;

Autor and Duggan 2007), it is difficult to compare cross-sectional 2016 analysis of self-reported disability with long-term trends in disability prevalence. As new data become available, researchers should incorporate change over time to best interact with their findings.

Additionally, while this research used the largest government program for people with disabilities as an indicator of government responsiveness, there are potential insights from comparative research that contrasts that cash-based assistance of SSI with other forms of disability support. Provisions for disability voting access, transportation accessibility funding, and enforcement of ADA anti-discrimination provisions each provide opportunities for studying how the state responds to different needs of people with disabilities. Although the patchwork implementation of disability assistance programs may prevent county-by-county analysis, these are still facets of disability responsiveness that are worthy of consideration. And finally, this research rested on a large dataset that incorporated demographic, economic, and political data from 2,330 counties to determine differences in SSI enrollment relative to the number of self- reported disabilities in a county. Although I can identify some factors that shape differences in

SSI coverage, I cannot address personal motivations or whether choice is involved in SSI enrollment. Cultural stigmas associated with welfare and personal needs for the security of SSI

124 support are unexamined in the research I’ve provided. Qualitative work is needed to address this aspect of SSI coverage.

SSI provides an important form of government support that is not distributed equally among people with disabilities. Although SSI is meant to provide an economic livelihood for people with disabilities that cannot find meaningful employment, I do not find support for a need-based explanation of SSI provision. But by incorporating sociopolitical interest and capacity-oriented explanations of government responsiveness, I find that both of these literatures provide meaningful insights to understand to what extent government responsiveness to people with disabilities is determined. Support for people with disabilities is subject to sociopolitical interests and varying capabilities in providing services. Literature addressing disability and inequality would benefit from incorporating these dimensions of support.

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CHAPTER 5 : CONCLUSION

Disability and inequality are often researched with a focus on how individual-level characteristics shape the livelihoods of the disabled, overlooking place-based factors that can contribute to different outcomes. I address the shortage of empirical place-based research by analyzing disability prevalence, employment, and government support for people with disabilities across U.S. counties. Rather than focusing on health behaviors and individual work incentives, I find that poverty and educational attainment across counties play a role in determining livelihood differences among people with disabilities. The degree of social inequality, industrial composition, and degree of rurality in place play overlapping roles in determining differences in disabled livelihoods across places. Chapters Two, Three, and Four provide a template for incorporating spatial analysis into the study of disability and inequality.

I. Review of Findings

A. Socioeconomic Well-being, Health, and Disability

In Chapter Two I address disability as a health outcome and outline connections between disabled status and socioeconomic well-being. I review research on the disablement process prominent in public health literature, noting that the treatment of disability as part of an individual pathology often neglects broader considerations of social inequality and spatial processes. Modeling disability prevalence by incorporating place-based dimensions of economic well-being, I find that poverty and education are consistent predictors of disability prevalence across different types of disability. While obesity is often linked to disability prevalence in disablement process literature, it is not a statistically significant indicator when explaining why some county populations have more disabilities than others. Although individual health

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behaviors like smoking and drinking are significant in explaining variation in some types of

disabilities, broader consideration of social inequality (a focus of my study) are often excluded

from analysis of disability prevalence. By including social inequality indicators across places,

my research puts the disability literature more in line with “meso-level analysis” that Cockerham

(2014) describes in health disparities research. Future research would benefit from incorporating subnational differences in disability as they relate to social inequality.

B. Disability and Inequality in Employment

In Chapter Three I address people with disabilities as a social group, demonstrating a gap in employment outcomes between people with and without disabilities that’s persisted in spite of policy intervention. I review aspects of discrimination, but find that industry makeup and affluence combine with considerations of individual disability types in work capability to account for some of the employment gap. Although the Americans with Disabilities Act (ADA) may meaningfully address discrimination and accessibility in the workplace, the economic, industrial, and demographic composition of counties pose obstacles to disability employment that are beyond its scope. Counties with greater employment of people with disabilities tend to be places with lower poverty, higher educational attainments, and shorter work commutes.

Research on disability employment has tended to concentrate on obstacles to ADA implementation, stressing the roles of discrimination and accessibility as the established mechanisms for perpetuating the employment gap. I find that incorporating place-based variables that capture inequality and economic well-being, alongside ergonomic considerations of disability type, offer a new avenue for research that can account for lagging employment outcomes even in light of implementation of the ADA.

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C. Disability and the State

In Chapter Four I address support for people with disabilities, comparing the number of people with self-reported disabilities with the number of people actually receiving government assistance for their disability. Variation in government assistance for people with disabilities is often depicted as an issue of welfare fraud, describing higher SSI enrollment as a product of economic interest among people in areas with low pay. Rather than examine characteristics of people that are receiving SSI, I examine SSI enrollment by considering the proportion of people with self-reported disabilities as a population that could potentially enroll in SSI. I address differences in government assistance by combining two literatures that describe determinants of welfare distribution. The first, rooted in conflict theory, explains that welfare provision is influenced by sociopolitical interest rather than need. The second describes welfare provision as conditioned by the capacity for institutions to administer and process welfare allotments. I find that, even after accounting for employment of people with disabilities in a county, places with high incomes and economic inequality have greater SSI enrollment. I also find that there is greater SSI enrollment in places with more primary care physicians and government workers.

Rather than approach SSI enrollment as a demand-side issue of fraudulent claims, I see the significance of these findings as a competing supply-side explanation that accounts for provision of welfare and how distribution is coordinated.

II. Limitations and Future Research

A. Individual-level Data

While area data provide a lens to view how counties fare in health outcomes, employment, and government support, these models cannot account for individual processes which may shape these outcomes. Although obesity fails to be significant in explaining

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disability prevalence, individual health pathologies can still be referenced in explaining disability

onset. Future research could benefit from examining how individuals with health risks (i.e.

diabetes, cardiac history, obesity, etc.) vary in disability onset across different environments.

Are people with diabetes more likely to become disabled in impoverished areas? Are there rural/urban differences in disability onset among individuals that are morbidly obese? Public health researchers could ask these kinds of questions, connecting individual-level considerations of disability onset with place-based determinants of disability prevalence outlined in my research.

In the case of employment and SSI enrollment, again individual-level data could provide more detailed analysis of outcomes. Given the relationship between disability composition, industry makeup, and employment outcomes at the county-level, further study could trace how individuals among each disability type compare in their employment across different industries.

Do deaf people have better employment in areas with more manufacturing jobs? Does a high school diploma improve employment for people with cognitive disabilities in coal counties?

Researchers could address these more detailed questions of employment by connecting individual-level data to industrial activities in each county.

B. Longitudinal Research

Additional research is needed to trace the change in disability prevalence, employment, and SSI enrollment over time. My research is cross-sectional, limiting my ability to capture changes in health behaviors, the effects of the Affordable Care Act (ACA), changes in ADA enforcement, and changes in political coordination of SSI over time. Between 2000 and 2015, there has been an increase in cancer, diabetes, and obesity in the United States as smoking has decreased and exercise has increased (CDC 2016). At the same time, costs of healthcare

129 expenditures have doubled and the number of uninsured has declined (NCHS 2017). The cross- sectional analysis of health practices in my research is not sensitive to these national changes in the healthcare delivery system nor in health behaviors. As the scope of disability prevalence data widens to allow for longitudinal analysis, future researchers could examine how changes in health practices shape disability prevalence within counties.

Several studies have examined changes in ADA enforcement over time as they relate to employment of people with disabilities. Maroto and Pettinicchio’s (2014) study of disability employment found differences in employment rates across states according to whether states had implemented their own ADA-like provisions prior to 1991. They found 5.5% higher disability employment rates among states with earlier implementation of ADA-like provisions, suggesting that lagging ADA implementation may account for some differences in employment. Other research by Donahue et al. (2011), however, found negligible differences in hours worked among people with disabilities before and after ADA implementation. While my research focuses on how economic well-being and industry makeup shape employment outcomes, it does not account for the degree to which the ADA has been implemented in each county over time. As more data become available, future researchers could examine changes in ADA enforcement alongside county economic well-being and industrial composition.

C. Within-Group Differences among People with Disabilities

While Chapters Two and Three each highlight the role of disability type in explaining cross-county variation in outcomes, my research is limited in accounting for the diversity of disabilities encountered by individuals. I consider four broad categories (blind, deaf, cognitive, and mobility-related) of disability without accounting for the diversity of experiences within each category. Question Four of the ACS Disability Questionnaire asks whether respondents

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have “serious difficulty seeing” as a determinant of blindness. But within this blindness category

are people with difficulties involving light sensitivity (achromatopsia), blurred vision in a single

eye (amblyopia or “lazy eye”), low light vision (choroideremia and “night blindness”), and

complete blindness (AFB 2017). A myriad of behavioral and mental disabilities, many of which

are associated with social stigmas and requiring workplace accommodations, are also

unaccounted for when using the ACS Disability Questionnaire. Although the ACS dataset

provides the most comprehensive data on disability prevalence across U.S. counties, the broad

categories of disability that it provides are not comprehensive.

My research is also limited in that it does not account for whether individuals have more

than one disability. I find that counties with more blind and deaf composition have better

disabled employment than counties with more cognitive and mobility-related disability

composition. I cannot determine what proportion of each county’s disabled population has more

than one disability. Chi’s (1999) analysis of work limitations indicate that each disability type

involves a different set of challenges to performing work tasks. This suggests that people with

multiple disabilities may encounter more challenges to work than people with a single disability

type. As data improves to account for multiple disabilities, researchers should account for

people with more than one disability in their sample.

D. Disability and Policy

The ADA represents the landmark policy addressing disability and inequality, despite worsening poverty and employment rates relative to the non-disabled population. Although it

has provided avenues to combat two underlying mechanisms of inequality (accessibility and

discrimination), I find substantial differences in employment inequality across U.S. counties and

connect some of these differences to the disability composition of each place. Policy discussions

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would benefit from incorporating spatial considerations that connect how disabled populations

and local employment structures vary from county-to-county. Unfortunately, the approaches to

disability policies at the state level have been limited in their offerings.

Since 2012, the most commonly adopted state strategy to address disability inequality has

involved Employment First policies25. The National Conference of State Legislatures now

showcases Employment First policies as model legislation for reducing the gap in disability

employment, and ten states have now implemented some form of Employment First (Rall et al.

2016). These policies involve the creation of task forces, coordination of agencies, or creation of

statewide initiatives to improve employment of people with disabilities. While Employment

First policies center employment as a crucial component of disability services, they offer no

specific hiring incentives for people with disabilities, resources for potential workers, nor

mandates that require action from the private sector (APSE 2014). Adoption of these policies

does represent a form of state advocacy for the disabled and a commitment to their economic

well-being. This is important, as disability advocates view policy treatment of people with

disabilities as influencing broader perceptions of the disabled as competitive in the workforce

(Dejean 2017). If state advocacy matters, we should see improvements in employment

inequality among Employment First adopters. My analysis of disability employment, however,

suggests that prevalence of specific disability types influences employment outcomes.

Employment First policies show no evidence of moving beyond broad categorizations of

25 The emergence of Employment First policies is likely a response to the 2009 announcement that the Department of Justice would aggressively enforce the 1999 Olmstead decision, which granted people with disabilities the right to receive state funded supports and services in the community rather than institutions (US Department of Justice 2017). Employment First policies are a means for states to demonstrate commitment to employment of people with disabilities, rather than institutionalization.

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“disabled” to consider the diversity of disabilities and their prevalence within each state and

county.

In contrast to these general resolutions, subnational actors have historically adopted

workshop models to directly provide training and job placement for people with disabilities.

Approximately 7,000 workshops operate across the United States, often involving public-private partnerships with local industry, where state or county actors arrange employment of people with disabilities as part of a broader job training program (Braddock et al. 2008). Since their emergence in the 1960’s, workshops have often focused on employment for people with cognitive disabilities. While workshop approaches demonstrate capability of the state to coordinate employment and productivity of disabled workers for private companies, sheltered workshops are an undesirable option to reduce inequality. First, a growing body of research has shown wage penalties associated with sheltered workshops as compared to more general worker supports (Cimera et al. 2011; Kregel and Dean 2002). Sheltered workshops evade wage, promotion, and benefits standards, and have come under fire for perpetuating employment inequality26 (Campbell 2014; Cimera et al. 2011; Danielson 2017). But workshop programs also

involve public-private partnership arrangements that find the state acting to cater to the needs of

local businesses instead of actual residents with disabilities (Castellini 2005). This presents an

industry-driven strategy, rather than one which is informed by demographic needs within an

area.

Rather than pursue policies that gift industries with wage evasion and training for a specific

company, subnational actors should craft policies that are responsive to the disability

composition of their residents. Disability employment outcomes across counties are highly

26 The Department of Labor regularly issues waivers for workshops to pay at rates below the federal minimum wage as part of a 1938 provision to the Fair Labor Standards Act (National Council on Disability 2012).

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dependent on the types of disabilities that makeup each county’s disability profile (Table 3.3).

Cognitive disabilities are the leading disability type in only 355 of the 2,290 counties in the

continental U.S., but state-supported employment and workshop strategies have focused almost

exclusively on this population (Braddock et al. 2008). States and counties would benefit from

engaging in training, placement, and recruiting policies that acknowledged the actual composition of disabilities within districts in place of industry-driven national strategies that

focus on people with cognitive disabilities. Programs could target specific types of vocational

rehabilitation, assistive technologies, and job-placement opportunities that are responsive to the

disabled population first. Given that mobility-related disabilities are the most prevalent in U.S.

counties and that commuting difficulties are also significant in exacerbating employment

inequality (Table 3.3), transit assistance might provide a disability-specific mechanism to

improve employment outcomes. Currently, only Washington offers paratransit grants for

transportation investment, shuttle services, and procurement of accessible fleets for people with

mobility-related disabilities (Rall et al. 2016). Such an initiative addresses tangible problems to

employment and obstacles that are specific to the most prevalent disabilities in an area. If

disability inequality is to be reduced, policies should reflect local needs, incorporating

demographic composition of disability while prioritizing the interests of disabled populations

over business interests.

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APPENDIX: ADDITIONAL TABLES AND FIGURES

Figure A.1Disability prevalence (1990-2014) as reported by CPS

Disability Prevalence: 1990-2014 8.5 8.25 8 7.75 7.5 7.25 7 6.75

Figure A.2Google Analytics analysis of disability news coverage: 2008-2018

Disability News Trends: 2008-2018 90 80 70 60 50 40 30 20 10 0 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

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Figure A.3Disability prevalence by age group from the ACS dataset

Age 5-15 Age 16-20 Age 21-64 5.6 6.4 11 5.5 6.2 10.9 10.8 5.4 6 10.7 5.3 5.8 10.6 10.5 5.2 5.6 10.4 5.1 5.4 10.3 5 5.2 10.2 10.1 4.9 5 10 4.8 4.8 9.9 2008 2009 2010 2011 2012 2013 2014 2015 2016 2008 2009 2010 2011 2012 2013 2014 2015 2016 2008 2009 2010 2011 2012 2013 2014 2015 2016

Age 65-74 Age >75 All ages 27 52 13 12.8 26.5 51.5 51 12.6 26 12.4 50.5 25.5 12.2 50 12 25 49.5 11.8 24.5 49 11.6 24 48.5 11.4 2008 2009 2010 2011 2012 2013 2014 2015 2016 2008 2009 2010 2011 2012 2013 2014 2015 2016 2008 2009 2010 2011 2012 2013 2014 2015 2016

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Table A.1 Summary of significant variables Disability Disability SSI prevalence employment coverage Economic Well-Being Poverty X X Income X Earnings Education X X Inequality Gini X Race X X Employment Extractive Industry X X State employment X Healthcare Resources PCP rate X X Uninsured (%) X Health Behaviors Obesity Smoking X Drinking X Work Amenities Internet Work commuting X Disability Composition Deaf X Blind X Cognitive X Mobility X SSI Welfare generosity X Political composition X Population size X Rurality X X

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