MOVING BEYOND RACE, SEX, AND EDUCATION: EXPLORING THE RELATIONSHIP
BETWEEN DISABILITY AND LONG-TERM WELFARE RECEIPT
By
Mellissa K. Wright

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Sociology - Master of Arts
2015

i

ABSTRACT
MOVING BEYOND RACE, SEX, AND EDUCATION: EXPLORING THE RELATIONSHIP
BETWEEN DISABILITY AND LONG-TERM WELFARE RECEIPT
By
Mellissa K. Wright
PSID data reveals that there is a distinct subpopulation of individuals that are at a
significantly greater risk of being both chronically poor and accessing public assistance for
extended periods of time. Although many researchers have examined the demographic
characteristics of individuals who are the most likely to be persistently poor, the emphasis has
been on race, sex, and education as predictive variables. Very little attention has been paid to the
role that disability plays in long-term poverty and benefit access. Therefore, the objective of this
paper is to utilize data from the longitudinal ADD Health Study in order to explore whether or
not the presence of certain types of disabilities might also affect an individual's likelihood for
accessing long-term public support such as food stamps, cash assistance, or housing subsidies.
Results of odds ratio regressions indicate that disability is a strong predictor of whether or not an
individual is likely to access welfare benefits across both Wave 1 and Wave 3. However, the type
of disability does matter in making these predictions. Those with learning disabilities and mental
health disabilities, such as depression, are more likely than individuals who are physically
disabled to access welfare benefits. The implications of this study are significant, especially
considering that one of the central features of the 1996 welfare reforms included lifetime limits
for assistance benefits. Consequently, lifetime limits on assistance could mean that individuals
who already face numerous barriers to self sufficiency, including disability, may face greater
personal and economic hardships once they reach the limit on their lifetime eligibility.

ii

To my advisor Cliff, my partner George, and to my family.
I would not have finished this thesis without your encouragement and support.

iii

ACKNOWLEDGMENTS

I would like to thank the members of my committee, Dr. Clifford L. Broman, Ph.D., and Dr.
Tobias Teneyck, Ph.D. and Dr. Carl Taylor, Ph.D., for all of their critical feedback and support.

iv

TABLE OF CONTENTS

LIST OF TABLES .........................................................................................................

vi

Introduction ....................................................................................................................

1

Literature Review...........................................................................................................
Overview of TANF Reforms ......................................................................................
Income Mobility Trends in the United States ............................................................

2
2
5

Methods..........................................................................................................................
Methods and Measures .............................................................................................

10
11

Results ............................................................................................................................

15

Discussion ......................................................................................................................
Implications...............................................................................................................

28
36

WORKS CITED ............................................................................................................

38

v

LIST OF TABLES

Table 1: Descriptive Statistics .......................................................................................

15

Table 2: Odds Ratios Predicting Independent Variables: Wave 1 Yes Welfare Receipt
& Wave 3 Yes Welfare Receipt ....................................................................... 17

Table 3: Odds Ratios Predicting Independent Variables: Wave 1 No Welfare Receipt
& Wave 3 Yes Welfare Receipt .......................................................................

19

Table 4: Odds Ratios Predicting Independent Variables: Wave 1 Yes Welfare Receipt
& Wave 3 No Welfare Receipt ........................................................................ 21

Table 5: Odds Ratios Predicting Independent Variables: Wave 1 No Welfare Receipt
& Wave 3 No Welfare Receipt ........................................................................

23

Table 6: Odds Ratios Predicting All Independent Variables & Assistance Status
Wave 1 & 3 ......................................................................................................

25

vi

Introduction
PSID data reveals that there is a distinct subpopulation of individuals that are at a
significantly greater risk of being both chronically poor and accessing public assistance for
extended periods of time. Although many researchers have examined the demographic
characteristics of individuals who are the most likely to be persistently poor, the emphasis has
been on race, sex, and education. Very little attention has been paid to the role disability plays in
long-term poverty and benefit access. Therefore, the objective of this paper is to utilize
longitudinal ADD Health data in order to explore whether or not the presence of a disability
might also affect an individual's likelihood for accessing long-term public support such as food
stamps, cash assistance, or housing subsidies. The implications of this study could be significant,
especially considering that one of the central features of the 1996 welfare reforms included
lifetime limits for assistance benefits. Consequently, lifetime limits on assistance could mean that
individuals who already face numerous barriers to self sufficiency, including disability, may face
greater personal and economic hardships once they reach the limit on their lifetime eligibility.

1

Literature Review
Overview of TANF Reforms
In 1996, President Bill Clinton signed legislation designed to, as he said, “…end welfare
as we know it.” That year’s decision ended the traditional entitlement program known as Aid to
Families with Dependent Children (AFDC) and replaced it with a time-limited block grant
program called Personal Opportunity and Work Reconciliation Act (POWRA) which also
included strict participation requirements in order for beneficiaries to access cash assistance
called Temporary Assistance to Needy Families (TANF). Under AFDC, families accessed cash
benefits to support their children for an indefinite period of time, or so long as their income level
qualified them based on federal poverty guidelines. Although some states had already amended
their welfare programs to include a variety of work requirements and lifetime limits prior to the
passing of POWRA, the new federal standard for the disbursement of cash assistance overrode
the states' established programs (Neubeck & Cazenave, 2001; Nadasen, 2007; Orloff, 2002).
Under TANF, in order to receive cash assistance, all beneficiary families must participate in job
search or work-related activities, also known by its colloquialism "workfare". Failure to meet
these participation requirements could result in sanctions being imposed against recipients, and
consequently, against their dependent children. If a case is sanctioned, a family experiences
partial or complete suspension of benefits until compliance with the program requirements is reestablished (Soss, Fording, & Schram, 2011). Additionally, POWRA imposes a five year lifetime
limit on cash assistance benefits for all eligible families, regardless of program compliance or
economic stability at the time of benefit termination (Soss, et. al., 2011).
Lifetime limits on cash assistance have had a negative effect on the economic certainty of
persistently poor families. Some families will leave TANF for employment (Osborne & Knab,

2

2007; Hoefferth, Smith, McLoyd, & Finkelstein, 2000; Morris, Bloom, Kemple, & Hendra,
2003; Coley, Lohman, Votruba-Drzal, Pittman, & Chase-Lansdale, 2007), however, many will
remain at or near poverty regardless of their employment status after benefits cease (ChaseLandale, Cherlin, Guttmannova, Fomby, Ribar, Levine-Cooley, 2011; Schram, et. al., 2008;
Soss, et. al., 2011; Bennett, Lu, & Song, 2004; Morris, et. al. 2003; Chase et. al., 2007; Kalil &
Dunifon; 2007; Osborne & Knab, 2007). Therefore, when examining TANF reform outcomes
within a broad and long-term economic context, the results of these initiatives appear to be less
effective than initially believed by the original policy makers. Unfortunately, the implementation
of lifetime limits on cash assistance will cause millions of chronically poor households to no
longer have access to TANF assistance during periods of increased hardship (Bilter, Hoynes,
Jencks, & Meyer, 2010). As a result, many of these households are and will continue to
experience even greater hardship in the post-reform era (Bilter, et. al., 2010; Greiger, Lloyd, &
Wyse, 2013). This result is significant because both cross-sectional and national PSID (Panel
Study of Income Dynamics) 1 data reveals that there is a subpopulation of persistently poor
households who frequently oscillate between welfare and low wage employment throughout
their lives. Most researchers that explore the social characteristics of these households have
focused mainly on demographic information such as education (Neblett, 2007; Rank & Hirschl,
2009a, Acs, 2008; Grieger & Danziger, 2011), race (Sharkey, 2008; Acs, 2008; Timberlake,
2007; Grieger, Lloyd & Wyse, 2013; Sandoval, et. al., 2009ab; Grieger, Lloyd, & Wyse, 2008;
Ryan et. al., 2006; Soss, et. al., 2011; Neubeck & Cazenave, 2001), gender (Acs, 2008;
Sandoval, et. al., 2009ab; Grodner, Bishop, & Kniesner, 2006; Nadesen, 2007), and their
relationship with employment, welfare receipt, and other socioeconomic outcomes. However,
1

The Panel Study of Income Dynamics is a federally funded study that has followed thousands of families nationally
since 1965. The objective of this study is to tract the intergenerational mobility of families in the United States.

3

only two scholars within this literature review have included even a limited reference to some
type of disability in their analysis (Acs, 2008; Neblett, 2007). This is an important gap within the
literature because disability has been shown to have a negative impact on lifetime earning
potential (Hall & Thomas, 2014; Yin, Shaewitz, & Megra, 2014; Erickson, Lee, & von Schrader,
2013; Parish, Grinstein-Weiss, Yeo, Rose, & Rimmerman, 2010; Mamun, Wittenburg, &
Gregory, 2011; Census, 2014) and, therefore, households dealing with disability may be at an
increased risk of joining the subpopulation of chronically poor, long-term welfare recipients.
For this reason, the focus of this paper is to utilize longitudinal data from the Adolescent
to Adult Health Study (ADD Health) in order to examine the relationship between disability and
long-term welfare receipt. Using this dataset, I explore (1) whether or not disability is a factor
that influences the probability of long-term welfare receipt, (2) whether or not having a disability
increases the likelihood of receiving welfare as a young adult (even if the individual did not
receive it as a child), and (3) if the presence of a disability reduces the likelihood that an
individual would never receive assistance at any point in their life course. In order to examine
the relationship between disability and long-term welfare receipt, I use learning disabilities,
physical disabilities, and mental health disabilities as categorical constructs to evaluate whether
or not disabilities have adverse effects on an individual's economic status.

4

Income Mobility Trends in the United States
Scholarly research which has used PSID data to evaluate patterns of social mobility in the
United States has found that almost half of all United States citizens will experience an episode
of poverty in their lifetimes (Sandoval, Rank, Hirschl, 2009; Bilter, et. al., 2010). These
experiences are often temporary events caused by specific, destabilizing events such as
unemployment (Broman, et. al., 2001; Valletta, 2006; Leadbeater & Way, 2001) or
macroeconomic changes (Sandoval, et. al, 2009; Bilter, et. al, 2006; Pilkauskas, Currie, &
Garfinkel, 2012; Hofferth, Stanhope, & Harris, 2005; Slack & Meyers, 2014). During these
periods of economic instability, many of these individuals and their families will access a variety
of welfare related supports designed to ameliorate some of the hardships faced during these
periods (Bilter et. al., 2006; Grieger, et. al., 2013; Rank et. al., 2009; Pilkaukas, et. al., 2012;
Sandoval, et. al., 2009; Hoefferth, et. al., 2005; Slack & Meyers, 2014). Since most of these
families regain financial stability, long-term welfare receipt is unlikely and children in these
households are no more likely than the general population to experience long-term poverty as
adults (Sandoval, et. al., 2009).
However, researchers using PSID data have also found that within the United States’
class structure, there is a small group of persistently poor individuals (Worts, Sacker, &
McDonough, 2010; Acs, 2008; Islam, 2013; Sharkey & Elwart, 2011) who are unlikely to regain
economic stability. Furthermore, the individuals who are most likely to be experience persistent
poverty are also the most likely to transfer their poverty to the next generation (Sharkey, 2008;
Islam, 2013; Sharkey & Elwart, 2011; Greiger, et. al., 2013). Although many of these families
will leave welfare for work, they often return to state assistance within two to three years (Worts,
et. al., 2010; Grieger, et. al., 2013; Rank, et. al., 2009; Hoefferth et. al, 2005).

5

The findings presented in PSID literature is reinforced similar findings from crosssectional literature that examines the socioeconomic status (SES) and employment patterns of
current and former TANF recipients. Researchers have found that while some TANF recipients
leave welfare for long-term employment (Osborne & Knab, 2007; Hoefferth, et. al., 2000;
Morris, et. al., 2003; Coley, Lohman, et. al., 2007), the families who are at the greatest risk of
long-term receipt tend to remain poor even if they are employed (Chase-Landale, et. al., 2011;
Soss, et. al., 2011; Bennett, et. al., 2004; Morris, et. al. 2003; Chase et. al., 2007; Kalil &
Dunifon; 2007; Osborne & Knab, 2007). Cross-sectional data is consistent with PSID data, in
that it shows that former welfare beneficiaries often secure employment that is limited in tenure
(Bilter, et. al., 2010; Hofferth, et. al., 2005; Grieger, et. al., 2013; Soss, et. al., 2011; Schram et.
al., 2008) and both low skill (Soss, et. al, 2011; Schram, Fording, et. al., 2008) and low wage
(Grieger, et. al., 2013; Bilter, et. al., 2010; Morris, et. al., 2003; Chase-Lansdale, 2010; Hoefferth
et. al., 2000; Soss, et. al., 2011; Schram et. al., 2008; Neubeck & Cazenave, 2001; Nadesan,
2007). For these reasons, employment status often results in little actual change to a household’s
overall SES (Bitler, et. al., 2010; Grieger, et. al., 2013; Grieger et. al., 2008; Neblett, 2007;
Vartanian & McNamara, 2004). Consequently, regardless of their employment or benefit status,
persistently poor households tend to have fewer resources overall to absorb income shocks or
prevent the return to assistance during periods of economic instability. This may at least partly
explain the observations made by numerous PSID researchers regarding the persistence of
poverty in the United States (Huggett, Ventrua, & Yaron, 2007; Worts, et. al., 2010; Greiger, et.
al, 2013; Valletta, 2006; Grieger et. al., 2008; Acs, 2008).
Oscillation between employment and welfare has been found in a variety of longitudinal
data, and is likely the result of several overlapping socioeconomic factors. For example, heads of

6

poor households that are the most at risk of being chronically poor and accessing welfare for
long periods of time often face numerous barriers to self-sufficiency such as lower educational
attainment (Neblett, 2007; Greiger & Danziger, 2011; Rank & Hirschl, 2009), limited vocational
skills, lack of transportation, and possibly physical or mental difficulties (Acs, 2008; Neblett,
2007).
Although using disability as a factor has received little attention during previous research
of persistent poverty and long-term public assistance receipt, it is arguable that disability could
be a critical factor influencing some of the long-term trends found within both the PSID and
TANF data. A variety of research, outside of TANF, has consistently found that persons with a
disability face numerous economic barriers since they tend to have lower educational attainment
(Bridges, 2008; Yin, Shaewitz, & Megra, 2014; Erickson, Lee, & von Schrader, 2013), lower
lifetime earnings (Hall & Thomas, 2014; Yin, Shaewitz, & Megra, 2014; Erickson, Lee, & von
Schrader, 2013; Parish, Grinstein-Weiss, Yeo, Rose, & Rimmerman, 2010; Mamun, A.,
Wittenburg, & Gregory, 2011; Census, 2014), and higher rates of poverty (Hall & Thomas,
2014; Yin, Shaewitz, & Megra, 2014; Erickson, Lee, & von Schrader, 2013; Parish, GrinsteinWeiss, Yeo, Rose, & Rimmerman, 2010; Census, 2014). Many of these barriers remain even
during periods of employment being the main source of household income. Therefore, similar to
many TANF recipients, it appears that disabled individuals who do become employed, often
secure marginal employment and are therefore paid less by employers (Yin, Shaewitz, & Megra,
2014).
Studies that have examined individual and societal level costs incurred by mental health
disabilities have found similar negative economic outcomes to those found within the non-mental
health disability literature. While some people who experience depression are likely to be

7

employed, their workplace performance is often adversely affected by their symptoms (Stewart,
Ricci, Chee, Hahn, & Morganstein, 2003; Thomas & Morris, 2003; Adler, McLaughlin, Chang,
Lapitsky, & Lerner, 2006). Lower productivity in the workplace might, at least in part, explain
the higher rates of job turnover amongst individuals who exhibit depressive symptoms (Lerner,
Adler, Chang, Lapitsky, Hood, Perissinotto, & Rogers, 2004). Additionally, it was also found
that youth who struggle with depression are less likely to graduate from high school (Fletcher,
2008; Needham, 2009; Wilcox-Gok, Marcotte, Farahati, & Borkoski, 2004) compared to their
peers. Additionally, adults with mental health disabilities are more likely to have lower lifetime
earnings than their non-disabled counterparts in the workforce (Stewart et. al., 2003; Kessler,
Heeringa, Lakoma, Petukhova, Rupp, Schoenbaum, & Zaslavsky, 2008; Wilcox-Gok, et. al.,
2004).
When combined, this research demonstrates that there is common relationship between
depression as a mental disability and poverty. However, the polarity of this relationship seems to
vary based on the models used in each study. Most research indicates that poverty increases the
likelihood of depression for an individual (Simmons, Braun, Charnigo, Havens, & Wright, 2008;
Galea, Ahern, Nandi, Tracy, Beard, & Vlahov, 2007; Belle Doucet, 2003). On the other hand,
some research has found that depression increases the likelihood of an individual being poor
(Frazer, 2011; Caspi, Wright, Moffitt, & Silva, 1998). The correlation between depression and
poverty may be, at least indirectly, attributed to depressed individuals' lower lifetime earnings
and educational attainment. Inconsistent results from these studies may be the result of using of
cross-sectional data in order to evaluate the relationship between disability and economic
stability rather than longitudinal data. The advantage of using longitudinal data, like the National
Longitudinal Study of Adolescent Health (Add Health) dataset used in this study, assessment of

8

the temporal relationship between disability and poverty (MacInnes & Broman, 2013) be
addressed.

9

Methods
This study uses data from the Add Health dataset, which is a nationally representative
sample of adolescents, ranging from grades 7 to 12 in the United States (Bearman, Jones, &
Udry, 1997). The first wave of data was collected in 1994-1995 and follow up interviews were
completed in 1996, 2001, and 2007-2008. The study includes a series of questions regarding
adolescent health, sexual behaviors, relationships, substance use, income, and other topics. The
data collection process includes a school based questionnaire that approximately 90,000 public
school students participated in. A portion of the students who completed the in-school
questionnaires were also selected to complete an at-home interview. In the first round of data
collection (Wave 1) 20,745 students were interviewed, with 12,105 of the participants being the
core sample and the remainder comprising a special sample.
There are two Add Health data sets available, one for public use and another for
restricted use. In this study, the restricted data set was utilized, which required a contractual
agreement and evidence of Internal Review Board (IRB) training as prerequisites to access the
contents. The benefits of using the restricted data, compared to the public use set, include
increased access to comprehensive and detailed descriptions of the respondents, expanded
questionnaires, variables added to correct design issues with the instruments, and the frequencies
of responses to questions on each questionnaires. More information about the sample selection
process, terms of agreement for access, and the Add Health data set itself is available from a
variety of other sources (see Bearman et al., 1997 or Add Health Homepage:
http://www.cpc.unc.edu/projects/addhealth/data).

10

Methods and Measures
STATA, a statistical software program, was used to perform logistic regressions in order
to examine the relationship between the persistence of poverty across Wave 1, Wave 3, and a
variety of demographic variables. The variables of race, sex, and educational attainment were
variables that were controlled for, since the research literature indicates that they all play an
important role in the likelihood that an individual will be persistently poor and access public
assistance. Since the respondents were still in secondary school during Wave 1, Wave 3 data was
used to evaluate the role of educational attainment in the connection between disability and longterm welfare access. The continuous variables for education were collapsed into five categories,
with one indicating less than high school, two indicating attendance, but not completion of high
school, three reflecting high school completion, and four and five reflecting participation in, or
completion of, a post-secondary degree, respectively.
Three variables were used to indicate whether or not an individual has ever accessed
public assistance. These variables include a combination of TANF resources such as cash
assistance and food stamps, and also non-TANF resources such as housing subsidies. Since both
TANF benefits and housing subsidies are both means tested services, this ensures that the
income of individuals, and/or their households, must have been within the specific parameters
established by the federal poverty guidelines.
Supplemental Security Income (SSI) is a federally awarded cash benefit received by
individuals who are disabled but for the purposes of this study, however, SSI was not considered
welfare. Furthermore, individuals receiving SSI were not included within this study because the
qualifying terms of SSI receipt and the range of benefits received varies widely. Due to this

11

variance, even if a person qualifies for SSI, it would be difficult to determine their actual
economic status and would therefore pollute the results.
Respondents self-identified their sex and race in each of the waves of data collection.
During Wave 3, the respondents reported their age and educational attainment in yearly intervals.
To complete the analysis, male sex was dummy coded as '1'. Dummy codes were also assigned
to the Asian, Black, Hispanic, White, and Other racial categories.
In Wave 1, parents were asked two questions during the at-home interviews regarding the
presence or absence of a learning disability in the adolescent respondent. First, parents were
asked if their child showed signs of having a learning disability such as "...difficulty with
attention, dyslexia, or some other math, reading, or spelling disability?" Afterward, parents were
then asked if their child received any type of special education services within the previous
twelve months. If the answer to either of those questions was 'yes', then the response was coded
as a '1', otherwise, a '0' was recorded. Additionally, an answer of 'no' to both questions was also
coded as '0'.
Table 1 shows that approximately 15.8% of Add Health data respondents had a learning
disability, according to parent responses which appears to be somewhat higher than the national
average reported in the National Health Interview Study (NHIS). The NHIS reported that only
9.9% of children between the ages of 12 and 17 years had a learning disability (Pastor & Rueben,
2008), compared to the Add Health findings of 15.8% in the same age range. The NHIS survey
also relied on parent reported information for their data collection. However, since the NHIS data
required that the disability had to be officially diagnosed by a doctor to qualify, that survey had
narrower criteria for declaring a child as learning disabled. It is possible that the narrow

12

definition of learning disability, as used by the NHIS data, resulted in an underestimate of
disability within the sample (MacInnes & Broman, 2013).
Although there are differences between the ADD Health and NHIS findings, it is still
likely that Add Health data provides an accurate measure of the prevalence of children with
learning disabilities which may be true for multiple reasons. Firstly, Further, research by Stone
(1997) suggests that parents' evaluation of their children's learning disability status is often
accurate. Additionally, Stone (1997) found that the teacher's assessment of the child's learning
disability status was also typically accurate. Therefore, since the Add Health survey asked the
parents if their child had a learning disability and if their child had received any special
educational services in the last twelve months, the combination of these two questions should
provide a reliable indicator of a child's learning disability status.
In the Wave 1 in-home interviews, the teenage participants were asked about whether or
not they had a physical disability. This section of the instrument included a long series of
questions regarding specific types of disabilities and corresponding levels of severity including
heart conditions and specific limitations of appendages. For the purposes of this study, a question
which asks generally if the respondent has any "...difficulty using your hands, arms, legs, or feet
because of a permanent physical condition?", was used to capture the range of potential physical
limitations and qualify respondents as physically disabled.
Finally, in both Wave 1 and Wave 3, respondents were asked a series of questions
measuring symptoms of depression. The measurement instrument was very similar to a well
known CES-D depression scale that is still supported by the American Psychological Association
(APA) as an accurate instrument for such measurements (Center for Epidemiological Studies,
2015). The original scale was developed in the 1970's by Radloff (1977), however, it is still

13

currently used by a variety of scholars (Bares, Delva, & Andrade, 2015; Sims, Thorpe, Gamaldo,
Aiken-Morgan, Hill, Allaire, & Whitfield, 2014; Whittington, Qin, Zhou, Michael, Liu, Cohen,
& Xie, 2015). For the purposes of this study, severe symptoms of depression are considered a
mental health disability. As previously mentioned, mental health problems such as depression
have been found to have adverse effects on an individual’s socioeconomic outcomes. For this
reason, a scale evaluating the severity of depressive symptoms was used as a proxy for mental
health disability in this study.

14

Results

Table 1: Descriptive Statistics
Variable

Mean

Range

Age (W 1)

16.1

12-29

Sex (1 = male)

0.50

0-1

White

0.52

0-1

Black

0.21

0-1

Hispanic

0.17

0-1

Asian

0.07

0-1

Other race

0.03

0-1

Education (W 3)

2.61

1-5

Always welfare (W 1)

0.02

0-1

Never welfare (W 1)

0.82

0-1

W1 no – w3 yes

0.05

0-1

W1 yes – w3 no

0.12

0-1

Physical Disability (W1)

0.03

0-1

Learning Disability (W1)

0.14

0-1

Mental Health Disability

1.52

0-7

(mean) (W1)(W3)

Table 1 displays the descriptive statistics for all variables included in this study. The age
range of respondents during the Wave 1 interviews was 12-29 years old with a mean age of 16.1
years old. Half of all respondents were male and the racial composition of the sample included
52% White non-Hispanics, 21% Black non-Hispanics, .07% Asians, and .3% other races.
Education was collapsed into a scale that ranged from 1-5. The mean level of educational
attainment during the Wave 3 interviews was 2.61, or completion of secondary education.

15

Mental Health disability was measured on a scale of 0-7. Mental Health disability was the
most common form of disability present in this sample of respondents with a mean of 1.52.
Learning disability was the next most common form of disability with a mean of .14. Finally,
physical disability was the least common form of disability with a mean of .03.
For the dependent variable, welfare receipt, only a small minority of individuals had ever
accessed welfare benefits in either wave of data collection and even fewer had been long-term
welfare beneficiaries. Instead, 82% of respondents have never accessed welfare (the ‘no-no’
category), whereas only 2% accessed welfare during both waves of data collection (the ‘yes-yes’
category). Furthermore, only 5% of respondents who did not access welfare as children, accessed
it for the first time as young adults (the ‘no-yes’ category). However, 12% of respondents who
accessed welfare benefits as children did not access welfare benefits as young adults (the ‘yesno’ category).

16

Table 2: Odds Ratios Predicting Independent Variables:
Wave 1 Yes Welfare Receipt & Wave 3 Yes Welfare Receipt

Variable

Coefficient

Age W1

1.03

Sex W1

.17***

White Non Hispanic

0.63

Black Non Hispanic

3.06***

Hispanic

1.02

Asian

0.46

Education W3

.27***

Physical Disability W1

0.79

Learning Disability W1

1.12

Mental Health Disability W1

1.15***

Constant

0.25

R2

0.23

N= 12,286

Table 2 shows the regression for the dependent variable, welfare access at Wave 1 and
Wave 3, and all the independent variables evaluated in this model (age, sex, race, education, and
disability type). This table examines the consistency welfare access over time. During Wave 1,
the respondents were in middle and high school. By Wave 3, respondents were entering young
adulthood. Answers confirming the access of benefits as a child and as a young adult are
displayed on this table to predict what individuals are most likely to be long-term welfare
beneficiaries. It may be important to clarify that odds ratios are calculated in the results tables.
Odds ratios are interpreted somewhat differently than traditional regressions. With odds ratios, a
coefficient of 1.00 means that there is no significant relationship between variables, a coefficient

17

of less than 1.00 means there is a negative relationship (so the inverse is calculated), and one that
is greater than 1.00 means there is a positive relationship.
Consistent with findings from the PSID and cross-sectional welfare literature, a
significant relationship appears between race, sex, education, and an individual's long-term
benefit receipt. In Table 2, the results show that sex is highly significant. With a coefficient of
.17 and a p value of .00, it appears that women are more likely to consistently access welfare
compared to men. In other words, they are more likely than men to be accessing welfare during
both Wave 1 and Wave 3 (known as the 'yes-yes' category). Black non-Hispanic was the only
racial category to have significance on this table. The coefficient for Black non-Hispanic was
3.06 with a p value of .00. As predicted by the literature, those who are Black non-Hispanic are
both more likely to be persistently poor and access welfare during both Wave 1 and Wave 3.
Another predictive variable is individuals with lower educational attainment, as they also appear
to be more likely to access welfare during both waves. The coefficient for lower educational
attainment was .27 with a p value of .00.
Table 2 demonstrates that, of the three categories of disability, only mental health
disability appears to have any important relationship to an individual's long-term benefit status.
However, the relationship is very significant. While the other two variables had positive
coefficients, there was no significance between welfare access and the other disabilities used in
the regression. Mental health disability, on the other hand, had both a positive coefficient of 1.15
and a p value of .00. This suggests that individuals with mental health disabilities are 1.15 times
more likely to access welfare during both Wave 1 and Wave 3.

18

Table 3: Odds Ratios Predicting Independent Variables:
Wave 1 No Welfare Receipt & Wave 3 Yes Welfare Receipt

Variable

Coefficient

Age W1

1.14***

Sex W1

.27***

White Non Hispanic

0.92

Black Non Hispanic

1.39

Hispanic

0.73

Asian

0.72

Education W3

.61***

Physical Disability W1

1.15

Learning Disability W1

1.67***

Mental Health Disability W1

1.10***

Constant

0.03

R2

0.09

N= 12,286

Table 3 shows the results from the regression of the dependent variable, negative welfare
receipt during Wave 1 and positive welfare access during Wave 3 (the 'no - yes' category). All
the same independent variables used in Table 2 were also used in Table 3. As previously
mentioned, respondents of the Wave 3 interviews were entering young adulthood. Therefore,
this table reveals which individuals are more likely to begin receiving public assistance for the
first time, as a young adult. In this table, it appears that while sex, educational attainment, and
mental health disability continue to remain major factors impacting whether or not an individual
will begin accessing assistance during young adulthood, race no longer seems to be significant.
Instead, age and learning disability emerge as important factors for predicting benefit status.

19

In Table 3, sex increases in significance as the coefficient shifts from .17 with a p value
of .00 in Table 2, to a coefficient of .27 and a p value of .00. Still, the negative relationship
between sex and benefit receipt continues to lend support to popular findings which indicate that
women are more likely than men to utilize public assistance at some point in their lives.
Similarly, education's coefficient increases from a coefficient of .27 in Table 2, to a coefficient of
.61 in Table 3; the figure on both tables has a p value of .00. Age's coefficient also increases
from 1.03 in Table 2, to 1.14 in Table 3, however, it is only significant on Table 3 with a p value
of .00. This difference in the tables suggests that as women increase in age, especially as they
reach child bearing years, the likelihood that they will access public assistance increases.
Although mental health disability shows a slight decrease in its coefficient from Table 2
(1.15) to its coefficient on Table 3 (1.10), it continues to play a highly significant role in
predicting the likelihood that an individual will become a first time welfare beneficiary as a
young adult. The CESD maintains significance in both tables with a p value of .00.
Learning disability was not significant in Table 2, however, its coefficient increases
substantially in Table 3 (1.12 to 1.67 respectively) where it also acquires a p value of .00. As a
child ages, this p value increase suggests that the effects of a learning disability on economic
stability may be also increase over the life course of the child. As young adults, those individuals
with learning disabilities are more likely than those without them to access welfare benefits for
the first time, regardless of whether or not they received them as a child.
Overall, since the respondents were older at Wave 3, and age is positively correlated with
benefit receipt, the result from Table 3 suggest that those who are disabled, either learning or
mental health, are more likely to begin accessing assistance during young adulthood than
individuals without such disabilities.

20

Table 4: Odds Ratios Predicting Independent Variables:
Wave 1 Yes Welfare Receipt & Wave 3 No Welfare Receipt

Variable

Coefficient

Age W1

1.01

Sex W1

1.02

White Non Hispanic

.55***

Black Non Hispanic

2.02***

Hispanic

1.26

Asian

.37***

Education W3

.54***

Physical Disability W1

0.98

Learning Disability W1

1.33***

Mental Health Disability W1

1.03

Constant

0.49

R2

.10

N= 12,286

Table 4 discusses which individuals grew up on welfare but who are not receiving it as
young adults, as indicated by the yes W1(Wave 1) and no W3 (Wave 3) heading (the 'yes-no'
category). Wave 3 indicates that age is no longer a predictor of benefit receipt, however, race
remains persistently significant in predicting adult outcomes. Asians are the least likely to access
public assistance as young adults, having a coefficient of .37 and a p value of .00. Non-Hispanic
Whites are the next least likely to access benefits, with a coefficient of .55 and a p value of .00.
Hispanics and non-Hispanic Blacks have a positive coefficient in Wave 3, however, compared to
Hispanics, only non-Hispanic Blacks show any significance in these results. Consistent with the
findings across the previous two tables, these results indicate that since Black non-Hispanics are

21

more likely to access welfare as children, they are also more likely to access welfare as young
adults compared to other races.
Also consistent with the general literature, education continues to be a very significant
predictor of long-term benefit receipt status (Acs, 2008; Carnevale, Rose, & Cheah, 2011). With
a coefficient of .54 and a p value of .00, individuals with lower educational attainment are the
least likely to not access welfare as adults, especially if they accessed it as children. In contrast to
Table 3, the results of disability status change. It now appears that mental health disability is no
longer a relevant predictor of who is most likely to not access welfare as a young adult; learning
disability is. Although learning disability decreases somewhat in significance, from a coefficient
of 1.67 on Table 3 to 1.33 in Table 4, it still remains an important factor with a p value of .00.
The significance of learning disability on Table 4 suggests that in addition to race, those with
lower educational attainment or a learning disability are more likely than their counterparts to not
escape poverty as adults.

22

Table 5: Odds Ratios Predicting Independent Variables:
Wave 1 No Welfare Receipt & Wave 3 No Welfare Receipt

Variable

Coefficient

Age W1

.95***

Sex W1

1.82***

White Non Hispanic

1.65***

Black Non Hispanic

.43***

Hispanic

0.92

Asian

2.32***

Education W3

2.20***

Physical Disability W1

1.01*

Learning Disability W1

.67***

Mental Health Disability W1

.92***

Constant

1.43

R2

0.14

N= 12,286

Table 5 shows the results for individuals who are the least likely to have ever accessed
welfare in either wave of data collection as indicated by the W1 (Wave 1) no and W3 (Wave 3)
no heading (the 'no-no' category). This table contains the highest frequency of significant
indicators of all the tables. Beginning with age, which has a coefficient of .95 and a p value of
.00, it appears to have a slightly negative association with welfare beneficiary status over time.
This negative association suggests that the younger an individual is, the less likely they are to
have accessed benefits. This may be attributable to their decreased likelihood of having
dependents at the time of the survey.
Consistent with most of the findings presented in this study, sex continues to have a
significant relationship to benefit status. With a coefficient of 1.82 and a p value of .00, men are
23

82% more likely than women to have never accessed welfare. Again, both Whites and Asians are
significantly more likely to never have accessed benefits, with coefficients of 1.65 and 2.32,
respectively. Hispanics were the only racial category to found insignificant. Non- Hispanic
Blacks are the least likely to never have accessed welfare benefits, with the lowest coefficient of
.43 and a p value of .00. Together, it is clear that race maintains a strong influence on an
individual's long-term benefit receipt status. Additionally, education continues to be a strong
predictor of welfare access, with a positive coefficient of 2.20 and a p value of .00, those with
more education are much less likely to ever access public assistance in either wave compared to
that of their lesser educated peers.
Those who are disabled, are the least likely to have never have accessed welfare over the
course of their lives, however, the type of disability does matter. Although the coefficient for
physical disability is 1.01, which would make it negligible, it does have at least a marginal
relationship to welfare access with a p value of .05. Those with a physical disability are
somewhat less likely than those without similar disabilities to access welfare benefits as either
children or young adults. However, individuals with mental health or learning disabilities are
more likely to access welfare at some point in their lives that peers without disabilities. With
coefficients of .67 and .95 respectively, and p values of .00, there is a distinct negative
relationship between disability status and never accessing welfare. Finally, amongst those
individuals who are learning disabled rather than mental health disabled, they are least likely to
not access welfare over their life course.

24

Table 6: Odds Ratios Predicting All Independent Variables & Assistance Status Wave 1 & 3

W1 Yes

W1 No

W1 Yes

W1 No

Wave & Benefit Status

W3 Yes

W3 Yes

W3 No

W3 No

Variable

Coefficient

Coefficient

Coefficient

Coefficient

Age W1

1.03

1.14***

1.01

.95***

Sex W1 (Male=1)

.17***

.27***

1.02

1.82***

White Non Hispanic

0.63

0.92

.55***

1.65***

Black Non Hispanic

3.06***

1.39

2.02***

.43***

Hispanic

1.02

0.73

1.26

0.92

Asian

0.46

0.72

.37***

2.32***

Education W3

.27***

.61***

.54***

2.20***

Physical Disability W1

0.79

1.15

0.98

1.01*

Learning Disability W1

1.12

1.67***

1.33***

.67***

Mental Health Disability W1 1.15***

1.10***

1.03

.92***

Constant

0.25

0.03

0.49

1.43

R2

0.23

0.09

0.1

0.14

N= 12,286

Table 6 collectively shows the results of each previous tables' regressions. It is important
to note that Table 6 does not show any new analysis, but rather it concatenates the results from
Tables 2-5. Similar to most cross-sectional and longitudinal data sources, the most consistent
variables having a significant impact on benefit receipt status across each of the tables included
sex, race, and education. Low educational attainment certainly exerts a powerful influence over
the probability that any individual will access public assistance during their lifetime. However,
the coefficients for sex in each table strongly indicate that, overall, women are much more likely
than men to access public assistance. While not every table yielded highly significant results for
race, non-Hispanic Blacks were clearly disproportionately more likely to access welfare as
25

children compared to all other racial groups. They were also much more likely to access welfare
as adults, compared to their non-Hispanic White counterparts.
Where disability has been largely overlooked in the existing literature, the results from
this analysis do indicate that certain types of disability exert a strong influence on the probability
that individuals will access welfare benefits at some point their life. Mental health disability was
the most significant predictor of long-term welfare access, as those individuals with a higher
prevalence of depressive symptoms are much more likely to require assistance. Learning
disability also played a strong role in predicting beneficiary.
There was a consistent positive relationship between respondents' age and their benefit
receipt status. In particular, as respondents entered their young adult years, they were more likely
to access benefits even if they did not receive them as children. This finding parallels PSID data
which found that individuals in their early adulthood are at higher risk for accessing welfare than
middle aged and older adults. (Sandoval et. al., 2009; Greiger & Danziger, 2011).
While these studies did not consider the role of disability in this relationship, other
researchers have found disability to have a negative impact on lifetime earnings (Hall & Thomas,
2014; Yin, et. al., 2014; Erickson, et. al., 2013; Parish, et. al., 2010; Mamun, et. al., 2011;
Census, 2014). Therefore, the positive relationship between learning disability and public
assistance receipt in young adulthood is not surprising. However, the lack of significance for
physical disability as a predictive factor in welfare outcomes was unexpected. None of the tables
indicated any strong significance to the relationship between accessing welfare and physical
disability. In fact, it was only found to be marginally significant for predicting the likelihood that
individuals would have never accessed welfare during their lifetime. While disability in general
was found to be an important factor for predicting long-term welfare status, it is of equal

26

importance to specify which type of disability is being referenced by the results, as there was
such disparity between physical disability and both mental health disability and learning
disability.

27

Discussion
Recent PSID findings have found that while half of U.S. population will experience
poverty in their lifetimes (Sandoval, et.al., 2009; Bilter, et. al., 2010), mostly this will only be a
temporary experience (Grieger & Wyse, 2013; Sandoval et. al., 2009). Episodes of poverty, in
these cases, seem to be the result of broader economic cycles which thrust people into poverty
during macro-economic downturns (Sandoval, et. al, 2009; Bilter, et. al, 2006; Pilkauskas, et. al.,
2012; Hofferth, Stanhope, & Harris, 2005; Slack & Meyers, 2014). During economic upturns and
periods of stability, many affected households regain footing. However, PSID data reveals that
there is a distinct subpopulation of individuals who are more likely than the general population to
be chronically poor (Worts, et. al., 2010; Acs, 2008; Islam, 2013; Sharkey & Elwart, 2011),
regardless of the economy's condition. These individuals and their households are at the highest
risk of being long-term welfare beneficiaries and are the most likely to oscillate between low
wage employment and public assistance (Worts, et. al., 2010; Grieger, et. al., 2013; Rank, et. al.,
2009; Hoefferth et. al, 2005).
The characteristics of individuals who are most likely to be at risk of being both
persistently poor and long-term TANF recipients is consistent across both longitudinal and crosssectional studies. Descriptive data shows that these same individuals are disproportionately more
likely to be non-white women (Sharkey, 2008; Acs, 2008; Timberlake, 2007; Grieger &
Danziger, 2011; Sandoval, et. al., 2009ab; Grieger, Lloyd, & Wyse, 2008; Ryan, Manlove &
Hofferth, 2006 ) who are less educated (Neblett, 2007; Rank & Hirschl, 2009a, Acs, 2008;
Grieger & Danziger, 2011), often unemployed or marginally employed (Greiger, et. al., 2013;
Acs, 2008; Bilter, et. al., 2010; Neblett, 2007; Hofferth, et. al., 2005) and are the heads of
household (Acs, 2008; Sandoval, et. al., 2009ab; Grodner, Bishop, & Kniesner, 2006).

28

While race, sex, and education are the typical characteristics used to identify the
chronically poor, very little attention has been given to the role of disability in analyzing the
probability that an individual might be persistently poor and at risk of being long-term welfare
beneficiaries. In fact, only two studies were found which made any reference to this potential
demographic characteristic (Acs, 2008; Neblett, 2007). Therefore, the purpose of this study was
to utilize longitudinal Add Health data to examine the relationship between disability status and
the receipt of long-term public assistance.
Overall, the results of this study were consistent with most other findings across a range
of sources. Primarily, race, sex, and education were found to be significant predictors of benefit
receipt status across Waves 1 and 3. In particular, individuals who are most likely to being
welfare beneficiaries across both waves were those who were African American, female, and had
lower educational attainment. These same individuals were also disproportionately more likely
to have accessed welfare at some point in their lifetimes (Table 3) and the least likely to have
never accessed assistance in their lifetimes (Table 5). This could be attributed to the patterns of
gender stratification which burden women with greater care-giving responsibilities compared to
their male counterparts. These disproportionate care giving burdens further penalize women by
reducing their comparative earnings in labor markets (Dwyer, 2013). The combination of lower
earnings and greater care giving responsibilities would predictably make women, rather than
men, more likely to access public assistance in order to support their households.
In addition to the gendered stratification factors discussed previously, this likelihood for
women with lower education levels to access benefits may also be attributable to the generally
higher fertility rates (Martinez, McDaniels, & Chandra, 2012) and lower incomes of young
adults (Sandoval, et. al., 2009; Greiger & Danziger, 2011). During this period of the life course,

29

individuals are at greatest risk of being poor, while at the same time are starting families, which
require additional financial resources to support.
African Americans, of any sex, face greater rates of discrimination within the labor force
(Pager, Western, Bonikowski, Manza, & Sauder, 2009) compared to any other racial group.
Additionally, African Americans have the highest rate of unemployment (Bureau Labor
Statistics, 2014), likely due to the effects of economic restructuring (Wilson, 2009; Soss, et. al.,
2011). Therefore, it is not surprising that African Americans are more likely to experience
chronic poverty (Rank et. al., 2009; Greiger & Wyse, 2008; Sharkey, 2008; Timerlake, 2007)
and long-term welfare beneficiaries (Greiger & Danziger, 2011; Neubeck & Casevane, 2001;
Soss et. al., 2011) compared to other racial groups.
Educational attainment is significantly correlated with lifetime earning potential (Acs,
2008; Carnevale, Rose, & Cheah, 2011), therefore, it is also predictable that individuals with
lower educational attainment are more likely to be poor and subsequently access public
assistance at some point during their lives as compared to their more educated peers.
Although age was not significant in predicting the likelihood of welfare access at both
Wave 1 and Wave 3 (the 'yes-yes' category), it became significant in Table 3 when predicting
benefit access for the first time in young adulthood (the 'no-yes' category). In Table 3, race was
no longer significant, however, both gender and education remained important variables with
less educated women being the most likely to access benefits.
The data resulting from using disability as a predictor of benefit receipt status does add to
the findings of the PSID literature, as well as the cross-sectional disability and welfare literature.
While many researchers have previously argued that disability may not predict poverty
(Simmons, et. al., 2008; Galea, et. al., 2007; Belle Doucet, 2003), there is strong support

30

indicating that disability does indeed have an negative effect on lifetime earnings (Hall &
Thomas, 2014; Yin, et. al., 2014; Erickson, et. al., 2013; Parish, et. al., 2010; Mamun, et. al.,
2011; Census, 2014). It is also clear, from numerous studies, that those who are disabled are
much more likely to be poor than those who are not disabled (Hall & Thomas, 2014; Yin, et. al.,
2014; Erickson, et. al., 2013; Parish, et. al., 2010; Census, 2014). In some findings, the type of
disability was specified but, in others, a more general reference to disability was central to the
argument presented.
In this analysis, three types of disability were examined to determine if a relationship
between certain types of disability and the probability of accessing public assistance existed.
Similar to age, learning disability was not significant in predicting if an individual would have
accessed assistance at both Wave 1 and Wave 3. However, it was a significant predictor of
benefit receipt status for the 'no-yes' category in Table 3 where respondents did not access
assistance as children (Wave 1), but they did as young adults (Wave 3). Parallel to age, it appears
that the effects of a learning disability on both earnings and welfare receipt may not appear until
respondents age enough to begin establishing their own independent lifestyles and households.
During this period of the life course, individuals are not only beginning families but are also
often moving into the job market where their own earnings are the primary source of their
household’s income. Since disability has been found to decrease lifetime earnings (Hall &
Thomas, 2014; Yin, et. al., 2014; Erickson, et. al., 2013; Parish, et. al., 2010; Mamun, et. al.,
2011; Census, 2014), then it is predictable that individuals with a learning disability are also
likely to be poor and access public assistance to support themselves.
Alternatively, the presence of a learning disability in childhood acts as a mediator in the
relationship between educational attainment and adulthood poverty. Research regarding learning

31

disabilities has found that having a learning disability as a child does have a negative effect on
educational attainment (Fletcher, 2008; Needham, 2009; Wilcox-Gok, et. al., 2004). Lower
educational attainment has been found to have a positive relationship to adulthood poverty (Acs,
2008; Mayer, 2010), therefore, the presence of a disability may be an important factor to
consider when explaining this correlation. Since this potential relationship was not be teased out
within this study so this might be an avenue for future research.
Mental health disability, especially the presence of depressive symptoms, was a strong
predictor of benefit receipt status. In almost every table, the CESD scale variable yielded
significant results. Those who exhibited mental health challenges were the most likely to access
public assistance at both waves of data collection (Table 2) and were more likely to access
assistance as young adults, even if they did not access it as children (Table 3). Additionally, they
were least likely to never access assistance over their lifetimes (Table 5). Although the
coefficients decreased somewhat in significance when all variables were taken into
consideration, the presence of mental health issues remained consistently significant. These
results provide some positive indicators that more than just race, sex, and education powerfully
influence the likelihood that an individual will be in poverty and at higher risk for becoming
long-term welfare beneficiaries.
These results are also consistent with a variety of existing research that has found
depression to have adverse effects on employment tenure (Lerner, et. al., 2004), productivity in
the work place (Stewart, et. al., 2003; Thomas & Morris, 2003; Adler, McLaughlin, Chang,
Lapitsky, & Lerner, 2006), and lifetime earnings (Stewart et. al., 2003; Kessler, et. al., 2008;
Wilcox-Gok, et. al., 2004). While the literature certainly indicates that depression could be
correlated with poverty, none of the literature explored the potential relationship between

32

depression and long-term welfare receipt. Furthermore, neither the PSID data nor the welfare
literature made a direct connection between mental health specific disabilities, chronic poverty,
or welfare receipt.
Consequently, this analysis provides a new opportunity for further expansion to the
existing literature. If race, education, sex, and mental health disability all appear to be strong
indicators of public assistance status, then perhaps there is an underlying relationship that could
to be researched more deeply. Although it was beyond the scope of this study, it might be
advantageous to examine the direction in the relationship between these variables in order to
tease out whether depression is a causal, or at least a mediating factor, in the long-term poverty
status of individuals of certain races, sexes, and educational statuses. For instance, are women
more likely than men to be depressed? Does educational attainment predict rates of depression,
or vice versa? Some researchers have found that poverty predicts depression (Simmons, et. al.,
2008; Galea, et. al., 2007; Belle Doucet, 2003), while others have made the case that depression
can also predict poverty (Frazer, 2011; Caspi, et. al., 1998). It would be illuminating to see if
these outcomes vary by sex, race, or educational attainment.
Finally, what was most unexpected regarding disability, was the marginal significance of
physical disability in predicting long-term welfare receipt. As previously mentioned, several
scholars have found that disability has a negative impact on lifetime earnings. So it was
surprising that the presence of a physical disability at Wave 1 appeared to have no impact on
benefit status at Wave 3. There was only marginal significance in predicting which individuals
were most likely to have never received welfare at both waves. Table 5 shows that those with a
physical disability were only slightly less likely than their non-disabled counterparts to never
access welfare during their lifetimes.

33

Perhaps, the deviation of physical disability from mental health and learning disability is
because individuals with physical disabilities are more likely to receive federal Supplemental
Social Security (SSI) benefits and these benefits were not included in the variable for public
assistance. However, Food Stamps (FS) were included and given the low benefit amount of SSI,
many people receiving SSI do qualify for FS. To be specific, minimum net SSI benefits in 2015
were $773 per month (Social Security, 2015). The net income threshold for single individuals to
receive FS in 2015 is $973 per month (Supplemental Nutrition Assistance Program, 2014).
Therefore, individuals accessing the minimum SSI benefits should eligible to receive FS and
there are no lifetime limits on FS. Gathering a more detailed understanding of how physical
disability does not significantly impact an individual's assistance status would also be another
possible area for future research.
Table 6 displays each sets of regressions concurrently, which allows for the evaluation of
consistency across each set of data. It is clear from the data that race, sex, education, and the
presence of specific disabilities have a significant impact on the probability of long-term welfare
receipt. To this point, this analysis has emphasized who is most likely to be assistance
beneficiaries. However, column 4 of Table 6 also makes the inverse clearly visible as well. From
the data, older white and Asian men, with higher levels of education, and no disabilities of any
kind, are most likely to have never accessed assistance. The significance of these results across
each regression shows a specific trend for how both social benefits and privileges are
differentially stratified across social groups. Not all barriers to self sufficiency are equally
distributed and neither are the assets that allow some people to escape the cycle of poverty.
One limitation to this data set is that the Wave 4 instrument did not include a reliable
question regarding welfare receipt. As a result, it was not possible to evaluate the relationship

34

between key independent variables and assistance status when respondents were entering their
late twenties and early thirties. In Wave 4, respondents were asked a series of very specific
questions regarding potential experiences with financial hardship. Some of these questions
included: "In the past 12 months, was there a time when you/your household was without phone
service because you didn't have enough money?", "... were evicted from your house or apartment
for not paying the rent or mortgage?", and " ... you/your household didn't pay the full amount of
a gas, electricity, or oil bill because you didn't have enough money?" Unfortunately, none of
these questions included a direct reference to welfare receipt. Nevertheless, they do indirectly
indicate the likelihood that an individual is financially distressed and experiencing some
dimension of poverty at this time of their life. It is possible that the relationships between longterm welfare receipt and disability may have continued into this period of their adult lives,
however, specific results are currently unavailable. Still, if Wave 5 data does include a reliable
question(s) regarding welfare status, it could certainly be valuable to explore whether or not
disability continues to affect an individual's assistance status into their adulthood.

35

Implications
Even without reliable Wave 4 data, the Add Health data available from Waves 1 and 3
further reinforce the existing longitudinal literature which indicates that there is a subpopulation
of chronically poor individuals present within the U.S. population. Many of these individuals
were born into poverty and will remain poor for the majority of their lives (Worts, et. al., 2010;
Acs, 2008; Islam, 2013; Sharkey & Elwart, 2011). Facing numerous barriers to establishing self
sufficiency, including the presence of a possible disability, these individuals are least likely to
permanently climb out of poverty and to never require any form of public assistance (Acs, 2008;
Neblett, 2007).
This a critical issue considering that lifetime limits on cash assistance were a central
feature to the 1996 welfare reforms. At the federal level, income eligible families will be
permanently terminated from the program and will cease receiving cash assistance after reaching
a cumulative period of 48 benefit months (five years total) over the course of their lives (Soss,
Fording, & Schram, 2011). This may not adversely affect the majority of the U.S. population
who will only experience a short period of poverty in their lives. However, those families at
greatest risk of being chronically poor may be hit the hardest by reforms when they are unable to
return to assistance during future periods of serious financial instability (Bilter, Hoynes, Jencks,
& Meyer, 2010). As a result, lifetime limits on assistance will likely exacerbate the negative
effects of chronic poverty to the well being of both the individual and the individual's family
(Bilter, et. al., 2010; Greiger, Lloyd, & Wyse, 2013).
The next question then becomes, how are people coping with chronic poverty in the postreform era? Only one PSID articles makes any reference to this question in its analysis. Greiger
& Wyse (2013) suggested that more families are cohabitating with benefit eligible relatives.

36

Greiger & Wyse also suggested that another possible coping strategy might include applying for
other sources of public aid which are not time limited, such as SSI. If this is true, then the
relationship between certain types of disability and long-term public assistance receipt may
become more evident if other dependent variables are examined. It would certainly be worth
exploring whether or not there has been a simultaneous rise in SSI disability claims and a
decrease in TANF caseloads.

37

WORKS CITED

38

WORKS CITED

Acs, G. (2008). US Intragenerational Economic Mobility from 1984 to 2004: Trends and
Implications.

Adler, D. A., McLaughlin, T. J., Rogers, W. H., Chang, H., Lapitsky, L., & Lerner, D. (2006).
Job performance deficits due to depression.

Bares, C. B., Delva, J., & Andrade, F. H. (2015). Pathways to Adolescent Depression and
Cigarette Smoking: A Longitudinal Investigation of Chilean Mothers and Their Children.
Social Work Research, svu029.

Bearman, P., Jones, J., & Udry, J. (1997). The national longitudinal study of adolescent health:
Research design. Chapel Hill, NC: Carolina Population Center.

Belle Doucet, D. (2003). Poverty, inequality, and discrimination as sources of depression among
US women. Psychology of Women Quarterly, 27(2), 101-113.

Bennett, N. G., Lu, H. H., & Song, Y. (2004). Welfare reform and changes in the economic wellbeing of children*. Population Research and Policy Review, 23(5-6), 671-699.

Bitler, M. P., Hoynes, H. W., Jencks, C. and Meyer, B. D. (2010). The state of the social safety
net in the post-welfare reform era (with comments and discussion). Brookings Papers on
Economic Activity: 71-147.

Caspi, A., Wright, B. R. E., Moffitt, T. E., & Silva, P. A. (1998). Early failure in the labor
market: Childhood and adolescent predictors of unemployment in the transition to
adulthood. American Sociological Review, 424-451.

Carnevale, A. P., Rose, S. J., & Cheah, B. (2011). The college payoff: Education, occupations,
lifetime earnings. Georgetown University Library. Retrieved February 20, 2015, from
https://repository.library.georgetown.edu/bitstream/handle/10822/559300/collegepayoffcomplete.pdf?sequence=1&isAllowed=y.

39

Center for Epidemiological Studies Depression (CESD). (2015, January 1). Retrieved January
14, 2015, from http://www.apa.org/pi/about/publications/caregivers/practicesettings/assessment/tools/depression-scale.aspx

Chase-Lansdale, P. L., Cherlin, A. J., Guttmannova, K., Fomby, P., Ribar, D. C., & Coley, R. L.
(2011). Long-term implications of welfare reform for the development of adolescents and
young adults. Children and youth services review, 33(5), 678-688.

Coley, R. L., Lohman, B. J., Votruba-Drzal, E., Pittman, L. D., & Chase-Lansdale, P. L. (2007).
Maternal functioning, time, and money: The world of work and welfare. Children and
Youth Services Review, 29(6), 721-741.

Dwyer, R. E. (2013). The Care Economy? Gender, Economic Restructuring, and Job Polarization
in the US Labor Market. American Sociological Review, 78(3), 390-416.

E-16. Unemployment rates by age, sex, race, and Hispanic or Latino ethnicity. United States
Bureau of Labor Statistics (2015). Retrieved February 21, 2015, from
http://www.bls.gov/web/empsit/cpsee_e16.htm

Erickson, W., Lee, C., & von Schrader, S.(2013). Disability Statistics from the 2011 American
Community Survey (ACS). Ithaca, NY: Cornell University Employment and Disability
Institute (EDI). Retrieved Oct 22, 2013 from www.disabilitystatistics.org

Fletcher, J. M. (2008). Adolescent depression: diagnosis, treatment, and educational attainment.
Health Economics, 17(11), 1215-1235.

Frazer, M. S. (2011). Poverty Measurement and Depression Symptomology in the Context of
Welfare Reform (Doctoral dissertation, University of Minnesota).

Galea, S., Ahern, J., Nandi, A., Tracy, M., Beard, J., & Vlahov, D. (2007). Urban neighborhood
poverty and the incidence of depression in a population-based cohort study. Annals of
epidemiology, 17(3), 171-179.

Grieger, L.D. & Danziger, S.H. (2011). Who receives food stamps during adulthood? Analyzing
repeatable events with incomplete life histories. Demography, 48(4), 1601-1614.

40

Grieger, L. D., & Wyse, J. (2013). The impacts of welfare reform on federal assistance to
persistently poor children. Journal of Children and Poverty, 19(2), 71-89.

Grodner, A., Bishop, J. A., & Kniesner, T. J. (2006). County characteristics and poverty spell
length.

Hall, J. P., & Thomas, K. C. (2014). The Medicaid Medically Improved Group: Losing
Disability Status and Growing Earnings.

Hofferth, S. L., Smith, J., McLoyd, V. C., & Finkelstein, J. (2000). Achievement and Behavior
among Children of Welfare Recipients, Welfare Leavers, and Low‐Income Single
Mothers. Journal of Social Issues, 56(4), 747-774.

Hofferth, S. L., Stanhope, S., & Harris, K. M. (2005). Remaining off welfare in the 1990s: The
influence of public policy and economic conditions. Social Science Research, 34(2), 426453.
Horn, K., & Tyler, S. Michigan House of Representatives, (2011). Legislative analysis: Family
independence program revisions (HB 4409 & HB4410). Retrieved from House Fiscal
Agency website:
http://www.legislature.mi.gov/(S(j2smrs55mt41nu55nbdglh45))/mileg.aspx?page=GetOb
ject&objectname=2011-HB-4409.

Huggett, M., Ventura, G., & Yaron, A. (2007). Sources of lifetime inequality (No. w13224).
National Bureau of Economic Research.

Islam, T. M. (2013). Childhood neighborhood conditions and the persistence of adult income.
Regional Science and Urban Economics, 43(4), 684-693.

Kalil, A., & Dunifon, R. (2007). Maternal work and welfare use and child well-being: Evidence
from 6 years of data from the women's employment study. Children and Youth Services
Review, 29(6), 742-761.

Kessler, R. C., Heeringa, S., Lakoma, M. D., Petukhova, M., Rupp, A. E., Schoenbaum, M., ... &

Zaslavsky, A. M. (2008). Individual and societal effects of mental disorders on earnings in the
United States: results from the national comorbidity survey replication.
41

Leadbeater, B. J. R., & Way, N. (2001). Growing up fast: Transitions to early adulthood of
inner-city adolescent mothers. Psychology Press.

Lerner, D., Adler, D. A., Chang, H., Lapitsky, L., Hood, M. Y., Perissinotto, C., ... & Rogers, W.
H. (2004). Unemployment, job retention, and productivity loss among employees with
depression. Psychiatric Services, 55(12), 1371-1378.

Luppa, M., Heinrich, S., Angermeyer, M. C., König, H. H., & Riedel-Heller, S. G. (2007). Costof-illness studies of depression: a systematic review. Journal of affective disorders, 98(1),
29-43.

MacInnes, M., & Broman, C. L. (2013). Learning disability and depression in young adulthood.
Learning Disabilities: A Multidisciplinary Journal, 19(1), 38-46.

Martinez, G., McDaniels, K., & Chandra, A. (2012, April 1). Fertility of Men and Women Aged
15–44 Years in the United States: National Survey of Family Growth, 2006–2010.
Retrieved February 21, 2015, from http://www.cdc.gov/nchs/data/nhsr/nhsr051.pdf
Mamun, A., O’Leary, P., Wittenburg, D. C., & Gregory, J.(2011). Employment among social
security disability program beneficiaries, 1996–1997. Socia l Security Bulletin, 71(3),
12–34
Radolff, L. (1977). The CESD scale: A self –report depression scale for research in the general
population. Applied Psychological Measurement. 1, 385-401.

Ryan, S., Manlove, J., & Hofferth, S. L. (2006). State-level welfare policies and nonmarital
subsequent childbearing. Population Research and Policy Review, 25(1), 103-126.

Mayer, S.E. (2010) The relationship between income inequality and inequality in schooling.
Theory and Research in Education, 8(1), 5-20.

Morris, P., Bloom, D., Kemple, J., & Hendra, R. (2003). The effects of a time‐limited welfare
program on children: The moderating role of parents' risk of welfare dependency. Child
development, 74(3), 851-874.
Nadasen, P. (2007). From widow to "welfare queen". Black women, gender and family, 1(2), 5277.
42

Needham, B. L. (2009). Adolescent depressive symptomatology and young adult educational
attainment: An examination of gender differences. Journal of Adolescent Health, 45(2),
179-186.

Neblett, N. G. (2007). Patterns of single mothers' work and welfare use: What matters for
children's well-being? Journal of Family Issues, 28(8), 1083-1112.

Neubeck, K., & Cazenave, N. (2001). Welfare racism: Playing the race card against America's
poor. (pp. 1-294). New York, NY: Routledge.

Orloff, A. (2002). Explaining us welfare reform: power, gender, race and the US policy legacy.
Critical Social Policy, 22(1), 96-118.

Osborne, C., & Knab, J. (2007). Work, welfare, and young children's health and behavior in the
Fragile Families and Child Wellbeing Study. Children and Youth Services Review, 29(6),
762-781.

Pager, D., Western, B., Bonikowski, B., Manza, J., & Sauder, M. (2009). Race at Work: The
Realities of Race and Criminal Record in the NYC Job Market. Inequality and Society:
Social Science Perspectives on Social Stratification.

Parish, S. L., Grinstein-Weiss, M., Yeo, Y. H., Rose, R. A., & Rimmerman, A.(2010). Assets
and income: disability-based disparities in the United States. Social Work Research,
34(2), 71–82. http://dx.doi.org/10.1093/swr/34.2.71

Pilkauskas, N. V., Currie, J., & Garfinkel, I. (2012). The great recession, public transfers, and
material hardship. The Social service review, 86(3), 401.

Rank, M. R., & Hirschl, T. A. (2009). Estimating the risk of food stamp use and impoverishment
during childhood. Archives of pediatrics & adolescent medicine, 163(11), 994-999.

Sandoval, D. A., Rank, M. R., & Hirschl, T. A. (2009). The increasing risk of poverty across the
American life course. Demography, 46(4), 717-737.

43

Schram, S., Fording, R., & Soss, J. (2008). Neo-liberal governance: race, place, and the punitive
turn in US welfare policy. Cambridge Journal of Regions, Economy and Society, 1(17),
36.

Sharkey, P. (2008). The intergenerational transmission of context1. American Journal of
Sociology, 113(4), 931-969.

Sharkey, P., & Elwert, F. (2011). The legacy of disadvantage: Multigenerational neighborhood
effects on cognitive ability. AJS; American journal of sociology, 116(6), 1934.

Simmons, L. A., Braun, B., Charnigo, R., Havens, J. R., & Wright, D. W. (2008). Depression
and poverty among rural women: A relationship of social causation or social selection?.
The Journal of Rural Health, 24(3), 292-298.

Sims, R. C., Thorpe, R. J., Gamaldo, A. A., Aiken-Morgan, A. T., Hill, L. K., Allaire, J. C., &
Whitfield, K. E. (2014). Cognition and Health in African American Men. Journal of
aging and health, 0898264314543474.

Slack, T., & Myers, C. A. (2014). The great recession and the changing geography of food stamp
receipt. Population Research and Policy Review, 33(1), 63-79.

Social Security. (2015, January 1). Retrieved March 23, 2015, from
http://www.ssa.gov/oact/cola/SSI.html

Soss, J., Fording, R., & Schram, S. (2011). Disciplining the Poor: Neoliberal Paternalism and the
Persistent Power of Race. (1st ed, pp vii-351). Chicago, IL: The University of Chicago
Press.

Stone, C.A. (1997). Correspondences among parent, teacher, and student perceptions of
adolescent’s learning disabilities. 39, 660-669.

Supplemental Nutrition Assistance Program (SNAP). (2014, October 1). Retrieved January 1,
2015, from http://www.fns.usda.gov/snap/eligibility#Income

Stewart, W. F., Ricci, J. A., Chee, E., Hahn, S. R., & Morganstein, D. (2003). Cost of lost
productive work time among US workers with depression. Jama, 289(23), 3135-3144.
44

Timberlake, J. M. (2007). Racial and ethnic inequality in the duration of children's exposure to
neighborhood poverty and affluence.

Thomas, C. M., & Morris, S. (2003). Cost of depression among adults in England in 2000. The
British Journal of Psychiatry, 183(6), 514-519.

U.S. Census Bureau(2014). Survey of Income and Program Participation. Retrieved from http
://www.census.gov/sipp/

Vartanian, T. P., & Buck, P. W. (2005). Childhood and Adolescent Neighborhood Effects on
Adult Income: Using Siblings to Examine Differences in Ordinary Least Squares and
Fixed‐Effect Models. Social Service Review, 79(1), 60-94.

Valletta, R. G. (2006). The ins and outs of poverty in advanced economies: Government policy
and poverty dynamics in Canada, Germany, Great Britain, and the United States. Review
of Income and Wealth, 52(2), 261-284.

Whittington, C. J., Qin, B., Zhou, X., Michael, K. D., Liu, Y., Cohen, D., ... & Xie, P. (2015).
Psychotherapy for Depression in Children and Adolescents: Study Protocol for a
Systematic Review and Network Meta-Analysis. BMJ Open.

Wilcox-Gok, V., Marcotte, D. E., Farahati, F., & Borkoski, C. (2004). Early onset depression
and high school dropout. Research in Human Capital and Development, 15, 27-52.

Wilson, W. J. (2009). More than just race: Being black and poor in the inner city (issues of our
time). WW Norton & Company.

Worts, D., Sacker, A., & McDonough, P. (2010). Falling short of the promise: Poverty
vulnerability in the United States and Britain, 1993–20031. American Journal of
Sociology, 116(1), 232-271.

Yin, M., Shaewitz, D., & Megra, M. (2014). An Uneven Playing Field: The Lack of Equal Pay
for People With Disabilities.

45