S.h.e.l.t.e.r 0.08 Download
Research Summary We examine citizen decision‐making in the context of providing access to safe housing to different noncriminal and criminal populations. More than 4,000 national online survey respondents considered different "emergency housing policy" scenarios that would affect the housing conditions of one of five randomly assigned populations of varying stigma (three noncriminal, two criminal). We find that the criminal populations had the least support for helpful housing policies and the most support for harmful housing policies. Furthermore, compared with a "no cost" policy, average support levels decreased when it increased taxes for the respondent. Policy Implications Citizens seem more willing to subject criminal populations to poor and unsafe housing conditions compared with noncriminal populations. Thus, citizen support may be higher when policies are pitched in ways that do not imply specifically helping ex‐offenders, when they do not involve a personal sacrifice through increased taxes, and when they do not involve "in‐my‐backyard" proposals. For example, a housing policy pitched as aiding the area's homeless (ex‐offenders included) would likely see more support than one that identifies ex‐offenders (and particularly sex offenders) as the population being targeted for help, or that identifies a specific neighborhood as a potential housing facility location.
Discover the world's research
- 20+ million members
- 135+ million publications
- 700k+ research projects
Join for free
RESEARCH ARTICLE
EMERGENCY SHELTER HOUSING
INTERVENTIONS
Public Support for Emergency Shelter
Housing Interventions Concerning
Stigmatized Populations
Results From a Factorial Survey
Christopher P. Dum
Kent State University
Kelly M. Socia
Jason Rydberg
University of Massachusetts—Lowell
Research Summary
We examine citizen decision-making in the context of providing access to safe housing
to different noncriminal and criminal populations. More than 4,000 national online
survey respondents considered different "emergency housing policy" scenarios that would
affect the housing conditions of one of five randomly assigned populations of varying
stigma (three noncriminal, two criminal). We find that the criminal populations had
the least support for helpful housing policies and the most support for harmful housing
policies. Furthermore, compared with a "no cost" policy, average support levels decreased
when it increased taxes for the respondent.
Policy Implications
Citizens seem more willing to subject criminal populations to poor and unsafe hous-
ing conditions compared with noncriminal populations. Thus, citizen support may
The authors would like to thank Senior Guest Editor Eric Grommon, William Bales, Justin Pickett, Eric Baumer,
Greg Gibson, Brooke Long, Fritz Yarrison, and multiple anonymous reviewers for their helpful feedback on
earlier versions of this article. Direct correspondence to Christopher P. Dum, Department of Sociology, Kent
State University, 323 Merrill Hall, Kent, OH 44242–0001 (e-mail: cdum@kent.edu).
DOI:10.1111/1745-9133.12311 C 2017 American Society of Criminology 835
Criminology & Public Policy r Volume 16 r Issue 3
Research Article Emergency Shelter Housing Interventions
be higher when policies are pitched in ways that do not imply specifically helping
ex-offenders, when they do not involve a personal sacrifice through increased taxes,
and when they do not involve "in-my-backyard" proposals. For example, a housing
policy pitched as aiding the area's homeless (ex-offenders included) would likely see
more support than one that identifies ex-offenders (and particularly sex offenders) as
the population being targeted for help, or that identifies a specific neighborhood as a
potential housing facility location.
Keywords
housing, public opinion, sex offenders, reentry, stigma, NIMBY
Homelessness is a social problem that touches the lives of many individuals.
According to the U.S. Department of Housing and Urban Development, in
January 2015, there were 564,708 homeless people in America on a given
night (Henry, Shivji, de Sousa, and Cohen, 2015). Although polices that attempt to solve
homelessness operate in many political arenas, the relationship between homelessness and
the criminal justice system deserves special attention.
It is well documented that finding suitable housing is the key to successful prison reentry
(Petersilia, 2003) and that homelessness among ex-prisoners is a sizable problem. Indeed, the
Urban Institute estimates that a tenth of released prisoners experience homelessness (Roman
and Travis, 2004), which is similar to Metraux and Culhane's (2004) estimate that in the
first 2 years of their release, 11.4% of returning prisoners experience homelessness. Given
national estimates of homelessness in the general population of 18 per 10,000 (National
Alliance to End Homelessness, 2015), this suggests that returning prisoners are more
than 50 times as likely to experience homelessness compared with the general population.
Furthermore, there is considerable overlap between shelter use and criminal justice system
contact (Metraux and Culhane, 2006), and shelter use during reentry is associated with
recidivism (Metraux and Culhane, 2004).
Although many factors limit an ex-offender's ability to find suitable housing (e.g., lack
of friends and family and parole conditions), citizen pushback can play a significant role
in limiting these housing options (see Garland, Wodahl, and Saxon, 2014; Stojkovic and
Farkas, 2013). This pushback, also known as not-in-my-backyard (NIMBY), is fueled by
the stigma applied to ex-offenders.
Stigma has been conceptualized in a variety of ways, but one of the most common
definitions came from Erving Goffman, who wrote that stigma is "an attribute that is
deeply discrediting" (1963: 3). Sociologists Link and Phelan (2001) argued that those
who experience stigma suffer status loss and discrimination to the point where their life
chances (e.g., employment, social capital, well-being, and access to housing) are affected in
important ways.
836 Criminology & Public Policy
Dum, Socia, and Rydberg
The results of criminological research demonstrate that stigma affects the housing
opportunities of many types of offenders. For example, citizen opposition has frequently
forced sex offenders (SOs) to live in neighborhoods considered to be socially disorganized
and with few social supports (e.g., Hughes and Burchfield, 2008; Hughes and Kadleck,
2008; Socia and Stamatel, 2011; Tewksbury and Mustaine, 2006, 2008). Researchers have
found that citizens also strongly oppose housing drug and violent offenders in their cities
and neighborhoods (Garland, Wodahl, and Schuhmann, 2013). Furthermore, landlords
are hesitant to rent to those with criminal records (Israelsen-Hartley, 2008). Because of the
public's NIMBY attitude, criminal justice and social welfare agencies often place ex-prisoners
in housing facilities or shelters (Roman and Travis, 2004).
Nevertheless, these housing solutions are far from ideal. The results of ethnographic
research reveal that ex-prisoners are placed in shelters and buildings that suffer from
dangerous illegal building code violations (Dum, 2016). The experience of living in
these conditions negatively impacts ex-prisoner's feelings of self-worth, builds mistrust in
government, and creates intense psychological stress (Dum, 2016). These effects have the
potential to increase the risk of recidivism and endanger public safety. Additionally, unsta-
ble and temporary housing has been linked by researchers to higher recidivism rates for
ex-prisoners (Roman and Travis, 2004).
Despite these outcomes from research on public attitudes toward housing ex-offenders,
it is unclear how the public would feel about policies that expose (or do not expose)
different types of ex-offenders to particular housing conditions that endanger their mental
and physical well-being. This issue is important to explore, given that the media exposure
of the dangerous conditions that returning prisoners live in (e.g., Zou and Miller, 2015)
could drive local policy makers to address them. Nevertheless, the willingness to act may be
constrained by negative public perceptions. Researchers have shown that public opinion can
have significant impact on public policy (Burstein, 2003) and that government responds
to the desires of its citizens concerning criminal justice issues (Nicholson-Crotty, Peterson,
and Ramirez, 2009). If citizens were asked to weigh in on policies that could improve the
housing situations of ex-offenders, how would the stigma applied to this population affect
their decision making?
In the current study, we examine this question of criminal justice stigma and life chances
by using national public opinion data gathered from an online panel survey. We then exam-
ine responses to a series of experimental vignettes that were used to assess citizen support for
policies that affect the living conditions of a motel serving as a social services emergency shel-
ter. By manipulating the population of the shelter to represent social groups of varying stigma
(e.g., homeless families, drug offenders, and SOs), we also examine how the stigma attached
to different types of offenders and nonoffenders affects their life chances and public support
for policy implementations. Next, we address the public opinion research on ex-offenders
and nonoffenders, our data and methods, and the findings of this study. We conclude with
a discussion of the implications for research, public policy, and community safety.
Volume 16 r Issue 3 837
Research Article Emergency Shelter Housing Interventions
Theoretical and Empirical Background
Stigma and Public Attitudes Toward Ex-Offenders
Sociologists Link and Phelan argued that, "a core concern of sociology is to understand the
distribution of life chances, whether those refer to careers, earnings, social ties, housing,
criminal involvement, health, or life itself. . . . stigma processes have a dramatic and
probably a highly underestimated impact on such life chances" (2001: 381). In the criminal
justice arena, the stigma that the public applies to returning ex-offenders affects their life
chances in a variety of ways.
In their study of public support for reentry initiatives, Garland et al. (2013) found that
the public is strongly opposed both to helping ex-offenders achieve middle-class status and
to providing them with health care. Citizen opposition was particularly strong when this
assistance was tied to an increase in taxes. Garland et al. (2013) attributed these opinions
to values oriented around social welfare, which includes concern for the safety and welfare
of society, retribution (i.e., belief that sufficient punishment is deserved for breaking the
law), and self-interest (i.e., concern for the individual's immediate interests). Their findings
demonstrate that although there is public support for assisting ex-offenders, this support
decreases as the social welfare value conflicts with retribution and self-interest. Additionally,
Garland and colleagues' (2014) findings suggest that public support for reentry initiatives
varies depending on the offenders being serviced (e.g., drug offenders, violent offenders,
and sex offenders).
Sex offenders are perhaps the most stigmatized class of offender, and as a result, they are
subjected to specific punitive punishments that affect their life chances, such as residence
restrictions and public registration. The results of research demonstrate that several processes
drive public punitive attitudes toward sex offenders. In their attempt to tease out the forces
behind punitive attitudes toward SOs, Pickett, Mancini, and Mears (2013) examined
three models of thought. The victim-oriented concerns model states that public punitiveness
is driven by outrage over the perceived innocence and vulnerability of victims. The sex
offender stereotypes model focuses on stereotypes of SOs as monsters who are incapable of
being reformed. Finally, the risk-management model is concerned with rising rates of sex
crimes and the difficulties of protecting citizens from sexual victimization. After testing
the models, Pickett and colleagues (2013) found partial support for all three models, with
the strongest findings pointing toward views of sex offenders as unreformable as driving
punitive attitudes.
The findings from other research show that attitudes are largely driven by the concerns
about child victims. Citizens strongly support incarceration, rather than community-based
sanctions, for SOs who offended against children, and are most likely to support registration
for SOs with child victims (Kernsmith, Craun, and Foster, 2009; Mears, Mancini, Gertz,
and Bratton, 2008). King and Roberts (2015) used vignettes to examine how sex offense
characteristics affected citizen's attitudes toward incarceration and registration. They found
that the most punitive attitudes involved situations with serious offenses, male offenders,
838 Criminology & Public Policy
Dum, Socia, and Rydberg
and young victims (King and Roberts, 2015). The research results also show that many
members of the public tend to hold misperceptions regarding both sex offenders and sex
crimes (Craun and Theriot, 2009; Mancini and Budd, 2015), with such misconceptions
promoted in the media (Galeste, Fradella, and Vogel, 2012; Socia and Harris, 2016). This
combination of fear and misperception may explain why citizens strongly support punitive
policies that affect the life chances of sex offenders.
Stigma and Public Attitudes Toward the Poor
Stigma also has important impacts on nonoffenders who live in poverty. The dominant
cultural response to the poor can be described as "separation, exclusion, devaluation, dis-
counting and designation as the 'other'" (Lott, 2002: 100). Both the United States and
England historically had laws that required those on public assistance to wear badges and
clothing that identified their status (Feagin, 1975; Spicker, 1984). Modern attitudes toward
the poor are dependent on how poverty is framed and attributed. Contemporary research
results show a multitude of public explanations for poverty, such as "having too many
children," as well as "living in broken families, being born inferior, being born with low IQ,
being forced to attend bad schools" (Lepianka, Van Oorschot, and Gelissen, 2009: 425).
Nevertheless, there are stable and enduring negative perceptions of the poor, particularly
when poverty is perceived as a result of lack of effort (Bullock, Williams, and Limbert,
2003; Chafel, 1997; Shaw and Shapiro, 2002). Will (1993) examined who citizens con-
sidered to be the "deserving poor" by asking survey respondents to award weekly income
to hypothetical families. The results of the survey suggested compassion toward children
in poverty, compared with harsh views toward the unemployed who were not looking for
work.
These results help explain the public's particular disdain for "welfare" policies. The
stigma attached to "welfare" can be tied to suspicions about actual levels of need (e.g.,
people receiving benefits but not actively seeking employment). Survey responses show that
most Americans believe that welfare rosters are filled with those who could be working or
who are dishonest about their welfare needs (Feagin, 1975; Gilens, 1999; Kluegel, 1987).
This label is particularly powerful, and simply changing the wording of a question from "the
poor" to "welfare" creates more negative responses (Smith, 1987), which is a finding that
has been consistent for more than 30 years (Smith, 2015). This welfare stigma discourages
participation in welfare programs, particularly among African Americans, who have long
been perceived as welfare dependent (Shaw and Shapiro, 2002; Stuber and Schlesinger,
2006). The racial aspect of attitudes toward the poor is hard to ignore. Associating African
Americans with entrenched welfare dependencies leads many White respondents to oppose
programs for the poor, even if they support spending on programs such as education and
health care (Gilens, 1995). Conversely, African Americans, along with females and those of
lower socioeconomic status, are supportive of programs to help the poor (Roff, Klemmack,
McCallum, and Conaway, 2002).
Volume 16 r Issue 3 839
Research Article Emergency Shelter Housing Interventions
Substantial stigma is also attached to poor individuals who have the added character-
istic of being homeless. Respondents create more social distance from the homeless poor
compared with the housed poor (Phelan, Link, Moore, and Stueve, 1997). If poverty is
the result of personal flaws, then to be homeless is to be even more flawed (Phelan et al.,
1997). This view, or the "sin talk" attribution of homelessness, places the responsibility
with the individual's character, rather than with some sort of treatable disorder (sick talk) or
structural issue (system talk) (Gowan, 2010). Citizens also stigmatize the homeless because
they are viewed as no longer contributing to the capitalist system (Belcher and DeForge,
2012).
Attitudes toward the homeless can be nuanced. Personal contact with the homeless
reduces the likelihood that individuals see homelessness as the result of individual failure,
but it does not have much impact on policy opinions (Knecht and Martinez, 2009). Citizen
characteristics also influence views toward the homeless. Women, liberals, the poor, and
younger respondents are more likely to view homelessness as a result of structural problems,
and these views have remained stable over time (Benedict, Shaw, and Rivlin, 1988; Tompsett,
Toro, Guzicki, Manrique, and Zatakia, 2006; Toro and McDonnell, 1992). There have been
mixed results in regard to the influence of race on attitudes toward the homeless. One study
found that African Americans are more likely than Whites are to support spending on the
homeless, even though they are more likely to endorse stereotyped views of the homeless
(Tompsett et al., 2006), whereas other research results indicate that African Americans and
Hispanics as less likely to support the homeless but may change their views after exposure
(Lee, Farrell, and Link, 2004).
Stigma and Housing
Of particular concern are the ways in which stigma affects the life chances of ex-offenders and
nonoffenders when it comes to housing. In attempting to house homeless individuals (both
involved and not involved with the criminal justice system), social service agencies have
partnered with low-budget motels, using them as emergency shelters. Jurisdictions such as
Vermont, Massachusetts, California, New Jersey, Maine, and the District of Columbia have
placed or have considered placing the homeless in such motels (Billings, 2013; Eckholm,
2009; Rose, 2014; Rosenberg, 2014; Samuels, 2014; Weiss-Tisman, 2015). In 2013, Mas-
sachusetts spent $48.1 million to house nearly 2,000 families in low-cost motels (Shenoy
and The New England Center for Investigative Reporting, 2014). Portland, Maine, placed
families at motels for 198 nights in 2013, which was an increase of 191% compared with
68 nights in 2012 (Billings, 2013).
Motels that are used as emergency shelters can house a variety of ex-offenders for a
variety of reasons (Dum, 2016). For instance, the increased housing instability caused by
residence restrictions, combined with a lack of adequate transitional facilities, have led many
sex offenders to become homeless (Levenson, 2008; Levenson, Ackerman, Socia, and Harris,
2013; Rydberg, Grommon, Huebner, and Bynum, 2014). This is exacerbated by homeless
840 Criminology & Public Policy
Dum, Socia, and Rydberg
shelter policies that ban sex offenders (Rolfe, Tewksbury, and Schroeder, 2016). One solution
is to place these homeless SOs in emergency boarding houses or motels (Crawford, 2014;
Swaner, 2014). In other instances, the inability to afford a security deposit or monthly
rent, combined with poor employment options as a result of a criminal record, may lead
ex-offenders (SOs and otherwise) to seek these emergency housing options as an alternative
to being homeless (e.g., Dum, 2016; Kras, Pleggenkuhle, and Huebner, 2016; Roman and
Travis, 2004).
Yet the conditions of these emergency housing options may pose significant threats to
residents' health and well-being. In Brooklyn, many SOs and other parolees are sent to an
illegal boarding house that has not paid city fines for violations such as broken sewage pipes
and mold (Zou and Miller, 2015). SOs and other ex-offenders seeking refuge in low-budget
motels have encountered similar code violations in New York (Dum, 2016), New Jersey
(Rose, 2014), Florida (Longa, Fernandez, and Gallas, 2014), Wisconsin (Comp, 2008),
and Alabama (Harris and Britzius, 2015). This increased reliance on low-budget motels to
house a variety of homeless populations, ex-offender and otherwise, has fueled increased
public discourse about their use.
For instance, the living conditions of low-budget motels housing the homeless have
drawn particular attention from both citizens and local government. In Chelmsford, Mas-
sachusetts, state officials grew concerned with the conditions facing homeless families in
motels, calling it "warehousing families," and moved to amend regulations to improve the
living conditions of the motels through increased inspections (Melanson, 2015). Residents
of the town of Danvers, Massachusetts, crowded a local meeting to confront government
officials about the living conditions of local motels (Forman, 2014). Other government
entities have made similar efforts, some going as far as shutting down motels with over-
whelming code violations (Harris and Britzius, 2015), even though in one case that meant
forcing residents to locate alternative housing options in the middle of winter with less than
a day's notice (Dum, 2016).
Given that citizens and government officials are concerned about the conditions facing
homeless families, it makes sense that citizens would support policies that improve motel
living conditions for such families. The findings reported in the literature would suggest
that these families are viewed as victims of structural problems and that the presence of
children increases compassion from society (Will, 1993). Nevertheless, what is less clear is
how citizens would respond to policies that address the living conditions of motels housing
more stigmatized populations whose life circumstances are attributed to individual character
flaws (sin talk; see Gowan, 2010), such as ex-offenders, or those on welfare. Even if citizens
were made aware of unsuitable or dangerous conditions affecting these populations, and
asked to act, the stigma attached to different groups may influence their support of policies
that deal with poor living conditions.
The results of previous research into public support for reentry housing paints a bleak
picture for ex-offenders. This may be because the public underestimates the role of housing
Volume 16 r Issue 3 841
Research Article Emergency Shelter Housing Interventions
in successful reentry. In 2006, the National Council on Crime and Delinquency conducted
a national poll on American attitudes toward rehabilitation and prison reentry in their
communities. Less than half of the 1,039 respondents rated access to public housing as very
important. When compared with job training, drug treatment, mental health services, help
for families, and mentoring, access to housing in general was the least likely of the group to
be rated as very important (Krisberg and Marchionna, 2006).
Laws concerning public housing make it difficult for ex-prisoners to obtain federally
subsidized housing, and landlords conducting criminal background checks often discrimi-
nate against those with histories of incarceration (Roman and Travis, 2004). Without access
to affordable housing, many offenders seek or are placed in transitional housing facili-
ties, such as halfway houses (Roman and Travis, 2004). Nevertheless, there is substantial
NIMBY resistance to transitional and supportive housing (Visher and Farrell, 2005). Not
surprisingly, a Harris poll found that half of U.S. residents oppose having a halfway house
in their neighborhood (Abadinsky, 1987).
NIMBY attitudes have limited the number of supportive housing programs available
for returning ex-offenders (John Jay College of Criminal Justice, 2011). A study of seven
communities that had experienced NIMBY battles over housing for ex-offenders found that
community members understood the need for housing but were incredibly fearful of having
ex-offenders in their neighborhood and encountering them during their daily lives (Doble
and Lindsay, 2003).
Study findings indicate that public support for housing facilities varies depending on
the population served by the housing facility, with criminal offenders receiving less support
than groups such as the mentally and physically handicapped (Dear, Gaber, Takahashi,
and Wilton, 1997; Solomon, 1983). Resistance can be particularly strong to offenders
with substance abuse issues (Roman and Travis, 2004). When asked about their support
for transitional housing programs for offenders in their cities and neighborhoods, 50% of
respondents supported programs in their city, but only 25% supported housing programs
in their neighborhood. The support for both city and neighborhood policies dropped when
the housing programs specifically addressed drug offenders (34% and 18%, respectively),
and it was even lower when the programs dealt with violent offenders (24% and 10%,
respectively) (Garland et al., 2013).
These differences in support for offenders can also be observed when sex offenders are
introduced as a population. When citizens indicated their support for transitional housing
in their city and neighborhood for violent offenders, drug offenders, and sex offenders,
support was the lowest when the housing facility would serve sex offenders, followed by
violent offenders, and then drug offenders. Furthermore, support was influenced by the
respondent's age and education level, as well as by having a close family member imprisoned
(Garland et al., 2014).
The public also tends to be unsupportive for housing poor and homeless nonoffenders,
especially in their neighborhoods (Benedict et al., 1988; Dum, 2016). NIMBY opposition
842 Criminology & Public Policy
Dum, Socia, and Rydberg
to affordable housing is linked to ideological views about welfare policy and to negative
views about future residents (Tighe, 2010). Residents often oppose affordable housing as a
result of concerns over property values and community character, whereas local politicians
focus on safety and value of their jurisdictions (Scally, 2012). Local governments and
citizens have employed zoning and permit codes in an attempt to displace the homeless and
force emergency shelters to close (Brinegar, 2010). In one instance, a newspaper campaign
portraying the homeless negatively was influential in closing a shelter (Forte, 2002). As a
result of displacement, shelters are often moved to the most impoverished neighborhoods
of the inner city (Brinegar, 2003).
Current Study
In this study, we build on previous research by using an experimental factorial survey design
to explore citizen decision making in the context of the conditions that ex-offenders face
when housed by social services. Despite strong research that has been conducted on citizen
support for housing policies for ex-offenders, we are not aware of any research that has
involved experimental methods to examine the predictors of citizen support. Because post-
reentry housing issues are important for criminal justice policy and public safety, through
this study, we provide important insight into how stigma affects the life chances of ex-
offenders in real-world situations. Specifically, we examine whether citizens view unsanitary
conditions and code violations in an emergency shelter as something worthy of various
policy interventions, regardless of the population living there. Alternatively, Garland et al.'s
(2013) research results on value conflict suggest that when compared with less stigmatized
groups (e.g., minimum wage workers), citizens may be less likely to support improving
the living conditions of ex-offenders. Furthermore, this support may depend on the type
of offender being considered. For instance, nonviolent drug offenders may receive more
policy support than sex offenders but less policy support than nonoffenders. In the next
section, we discuss the specific research questions being explored, as well as the associated
hypotheses.
Research Questions and Hypotheses
In this study, we examine four main research questions, each with a related hypothesis based
on the existing literature:
Research question 1. Does policy support for fixing the substandard conditions
of emergency housing situations vary based on the population being considered?
Hypothesis 1: Policy support for fixing the substandard conditions of emergency
housing situations will vary by the population being considered in a particular
vignette. The most support will be found for homeless families with children
who have been displaced from their homes as a result of natural disaster or
Volume 16 r Issue 3 843
Research Article Emergency Shelter Housing Interventions
events beyond their control, and the least support will be found for sex offenders
(SOs).1
Research question 2. Compared with a policy involving no personal sacrifice,
is policy support lower, on average, when it involves a personal sacrifice of
increased taxes?
Hypothesis 2: Compared with a policy involving no personal sacrifice, average
policy support will be lower when it comes with a personal sacrifice.
Research question 3. When considering the relocation of individuals living
in an emergency housing situation to an "in-my-backyard" (IMBY) location
for the respondent, will policy support vary based on the population being
considered?
Hypothesis 3: Policy support for an IMBY policy will vary by the population
being considered. The most support will be found for homeless families with
children, and the least support will be found for sex offenders.
Research question 4. When considering the relocation of individuals living
in an emergency housing situation to a substandard NIMBY location for the
respondent, will policy support vary based on the population being considered?
Hypothesis 4: Policy support for a NIMBY policy will vary by the population
being considered. The least support for a NIMBY policy will be found for
homeless families with children, and the most support will be found for sex
offenders.
Research Method
Web-Based Data Collection
The data for this study come from a large Web-based survey conducted in the summer of
2015 with a nonprobability sample of adults from the United States who were 18 years
and older. For this study, respondents were sampled from an online panel from Survey
Sampling International (SSI). Individuals are recruited to join SSI's panels through a variety
1. Research results suggest that the public stigmatizes the homeless more than they do the poor (Phelan
et al., 1997). Nevertheless, we believe that for the purposes of this study, this group of homeless families
with children will elicit the most support for two reasons. First, it involves children in potentially
hazardous living conditions, and research results suggest that respondents sympathize with children in
poverty (Stuber and Schlesinger, 2006; Will, 1993). Second, the vignettes specify that these families with
children are homeless as a result of disasters out of their control (see the Appendix). Therefore, in
accordance with the findings reported in the literature on stigma and attributions for poverty,
respondents are not likely to attribute the condition of homeless families with children to any sort of
personal failure. As a result, this group represents a group of low or no stigma, and it will likely elicit the
most compassionate response.
844 Criminology & Public Policy
Dum, Socia, and Rydberg
of Internet means (e.g., banners and invitations) and are rewarded with points that can
be redeemed for cash or gift cards at retailers, as well as prize drawings and donations to
charity. SSI's online panels draw on more than 11.5 million respondents from greater than
100 countries. SSI employs several processes to ensure the quality of their respondents, such
as digital fingerprinting to prevent the same individual from taking a survey more than once
and pattern recognition to identify and exclude fraudulent respondents.
SSI uses a three-stage randomization process to match respondents to surveys that they
are likely to complete. The first stage randomly invites respondents from the panel to take
a survey. The second stage employs a random set of profiling questions. After completing
these questions, respondents are then matched with a random survey that they will be likely
to complete. The invitation to respondents as well as the introduction/consent portion of
the survey used the terminology "Housing and Neighborhood Attitudes Survey," so it is
unlikely that the decision to take the survey was influenced by any feelings on ex-offender
housing policies.
In matching respondents to surveys, SSI only samples respondents who match the
criteria specified by the researcher. For this study, we requested that respondents be at least
18 years old and residing in the United States. Because many elements of the survey focused
on respondents' attitudes toward their neighborhood, the survey design also excluded any
students living in on-campus housing. As a result of a survey launch error with SSI, we
were able to oversample younger respondents (35 or younger), while doubling our initial
expected sample size (from 2,000 to 4,000). This oversampling of younger respondents was
beneficial because online samples often underrepresent younger individuals (Chang and
Krosnick, 2009). SSI was directed to recruit respondents until we had 1,000 completed
surveys from each of the following groups: non-Hispanic Whites, non-Hispanic Blacks,
non-Hispanic Asians, and Hispanics. Each group of 1,000 consisted of 500 individuals
recruited from ages 18 to 35 and of 500 individuals who were at least 18. In total, we had
4,043 completed surveys from unique individuals.
Although we used a nonprobability sample in this study, online samples such as ours
have important benefits to research, especially studies involving public opinion. Because our
study involves public opinions on sensitive topics, we sought to minimize social desirability
bias. Other findings suggest that Web surveys have less social desirability bias compared
with surveys administered over the phone and in person (Baker et al., 2010; Chang and
Krosnick, 2009; Kreuter, Presser, and Tourangeau, 2008; Link and Mokdad, 2005). Web
surveys also allow researchers to present complex scenarios in visual format, while giving
respondents the ability to take the survey at their own pace without interference from an
interviewer (Baker et al., 2010). This was especially beneficial given that our study relies
heavily on detailed vignettes. The findings of other studies also suggest that nonprobability
Web samples have a high degree of reliability (Burhmester, Kwang, and Gosling, 2011),
may yield better data quality than population-based Web samples (Weinberg, Freese, and
McElhattan, 2014) and random digit dialing samples (Chang and Krosnick, 2009), and
Volume 16 r Issue 3 845
Research Article Emergency Shelter Housing Interventions
obtain similar experimental results to studies that use probability samples (Simmons and
Bobo, 2015).
Nonprobability Internet samples have also been used by researchers to explore attitudes
in other fields, such as cognitive processes on religion (Gervais and Norenzayan, 2012), and
perceptions of change (Quoidbach, Gilbert, and Wilson, 2013). Furthermore, nonproba-
bility samples have been used by researchers in many high-quality criminological studies
(e.g., Broidy, 2001; Hay, 2001; Van Gelder and De Vries, 2012), including studies on pub-
lic attitudes toward sex offenders (e.g., Pickett et al., 2013), and experimental work (e.g.,
McGloin and Rowan, 2015; Van Gelder, Luciano, Weulen Kranenbarg, and Hershfield,
2015).
Survey Design
To examine citizen decision making regarding housing options for various populations,
we constructed a factorial survey. Factorial surveys are useful for understanding how peo-
ple evaluate social objects, such as "actions, objects, other persons, other groups, institu-
tions, ideas, and so on" (Rossi and Anderson, 1982: 15). In a factorial survey, researchers
use vignettes that manipulate particular characteristics of the social object under study
(Wallander, 2009). These surveys are particularly useful when researchers want to investi-
gate "normative judgements" or views about how something "ought to be" (Jasso, 2006:
336). The judgments under investigation are then measured by a rating task, which creates
the dependent variable (Jasso, 2006). Factorial surveys have been used in many instances to
study public opinions on the poor, the homeless, and ex-offender populations (e.g., Homant
and Kennedy, 1982; Phelan et al., 1997; Will, 1993).
In addition to collecting relevant demographic variables, respondents were asked to
consider a vignette concerning unsafe housing conditions at a budget residential motel
where a given population was residing. Respondents were then presented with a series of
four rating task scenarios that each presented a unique policy option that would address the
unsafe housing conditions, and respondents indicated their level of support for each given
policy.2 The vignette and rating tasks are provided in the Appendix. Of note is that the
first and second rating tasks were always the "no cost" and "sacrifice" scenarios, respectively.
Next, the ordering of the two remaining rating tasks, IMBY and NIMBY, were randomly
assigned for each respondent.
Another advantage of this type of factorial survey design is that it can be transformed
into an experiment by using random assignment. The most common form of survey
experiment involves randomly assigning subgroups of respondents to alternative versions of
the same question/vignette, where the alternatively worded questions/vignettes function as
the independent variable and the responses are the dependent variable (Gilens, 2002). This
2. We use the terms "rating task" and "scenario" interchangeably.
846 Criminology & Public Policy
Dum, Socia, and Rydberg
random assignment of the independent variable creates subgroups that are identical, and
therefore, the differences observed across the subgroups can be interpreted as caused by the
difference in the question wording (Gilens, 2002).
For our experimental design, respondents were randomly assigned one of five popu-
lations to consider for the set of four rating tasks: (1) homeless families with children, (2)
individuals working minimum wage jobs, (3) homeless individuals receiving government
welfare, (4) homeless individuals on parole after serving sentences for nonviolent drug of-
fenses, and (5) homeless sex offenders currently on parole. These populations were chosen
to vary degrees of stigma and criminal justice system involvement under consideration by
the participants. Because we used random assignment to expose respondents to one of five
different populations, this design allows us to establish causal inference about the effects of
each given population on respondents' normative judgments.
Multiple Imputation
There were some missing data in the original survey results for some of our independent
variables. To correct this, we used multiple imputation to estimate plausible values for the
missing cases. We also used initial listwise deletion for cases missing on any of the four
dependent variables or on one of the variables used for poststratification weighting (age,
sex, and race/ethnicity). This process resulted in removing only an estimated 3.3% of the
full sample (132 cases), and it resulted in 3,911 retained cases. Approximately 21.0% of the
remaining cases had missing data for at least one other variable: 4.5% of Parent , 10.0% of
Liberalism , 11.5% of Political Party,1.0%ofEducation, and 7.5% of Income .Basedonthis
fraction of missing information, a total of 24 imputed data sets were generated across the
entire sample with the Amelia package in R, which uses an expectation maximization with
bootstrapping algorithm to produce plausible values for missing cases (Graham, Olchowski,
and Gilreath, 2007; Honaker, King, and Blackwell, 2014; R Core Team, 2015).
Several steps were taken to improve the quality of the imputations and the validity of
the subsequent analyses. Specifically, we included several auxiliary predictors to contribute
to the imputation models (e.g., whether the respondents own their current homes, grew
up in the neighborhoods they currently live in, and how long they have resided at their
current addresses). Second, we used a ridge prior of 1% of the total cases to correct
for correlations between predictors in the imputation algorithm (Honaker et al., 2014).
Third, multiple imputation can add a degree of random error into the estimates, raising
the question of whether repeating the imputation procedure with the same data would
produce different results (White, Royston, and Wood, 2011). We checked both within and
between imputation variance and Monte Carlo error to determine whether the random
component was problematic in terms of influencing coefficients, test statistics, and p values.
These checks suggested that any potential added error across imputations did not approach
any problematic threshold (White et al., 2011). Furthermore, all analyses reported in the
following discussion were conducted with and without multiple imputation and substantive
Volume 16 r Issue 3 847
Research Article Emergency Shelter Housing Interventions
conclusions were not altered. As such, we feel confident that we used a sufficient number of
imputations and that the results reported here are not artifacts of the imputation process.
Poststratification Survey Weights
Although the initial survey was comprised of a nonprobability online panel of respondents,
for the results to be more generalizable to the U.S. population, we incorporated a post-
stratification weighting technique to the data after the imputation procedure. Specifically,
poststratification weights were estimated with the survey package in R on the basis of
the joint distribution of age, race/ethnicity, and sex in the 2010 American Community
Survey (Lumley, 2014; R Core Team, 2015). Because no cases were missing data on the
stratification variables, each imputed data set has the same stratification weights. To avoid
extreme weights, the weight distribution was trimmed and redistributed to a maximum
value of 10.3 This procedure succeeded in approximating the joint population distribution
of age, race/ethnicity, and sex. Estimates with the weighted sample data should more closely
reflect national estimates of the American adult population compared with those with the
unweighted sample data.
Dependent Variables
Four unique dependent variables were each examined in a separate model. Each measure the
level of support for a given housing policy, described in one of the four rating tasks, meant
to address the unsafe living conditions of the population's current motel. All dependent
variables were measured as 5-point Likert scales for support of the measure, which ranged
from "Not at all likely" to "Very likely." The scale itself for each support question was 1.
Not at all likely; 2. Somewhat unlikely; 3. Neutral; 4. Somewhat likely; 5. Very likely.
No cost. The first dependent variable measures a respondent's level of support for the
housing policy that would force the motel owner to fix the (unsafe) living conditions of
the motel before the next monthly inspection or face fines. In essence, this reflects whether
the respondent wants the motel residents' living conditions to be improved at all (at no
cost to the respondent), or instead, he or she is willing to subject the population being
considered to continued unsafe living conditions. As noted in the hypotheses, it is expected
that homeless families with kids should have the most policy support, whereas sex offenders
should have the least policy support.
Sacrifice. The second dependent variable measures a respondent's level of support for
the housing policy that would raise taxes by $100 per household to help cover the cost
3. Only one age-race/ethnicity/sex group was affected by this trimming procedure as White males 45 to
49 years of age were underrepresented in the initial panel. As a result of constraining the weights, this
group remains underrepresented relative to the joint population distribution, but it avoids extreme
weighting of a small number of cases. After trimming, the Kish approximation of the design effect (Kish,
1965) was 2.97, reflecting the impact of the poststratification weighting on the variance of the
subsequent estimates over that of a simple random sample.
848 Criminology & Public Policy
Dum, Socia, and Rydberg
of emergency repairs to fix the living conditions of the motel. This reflects whether the
respondent is willing to sacrifice his or her resources to improve motel residents' subpar
living conditions. Similar to the first rating task, it is expected that homeless families with
kids should have the most policy support, whereas sex offenders should have the least policy
support. Nevertheless, as noted in the hypotheses, given this involves a potential sacrifice
(in terms of increased taxes), it is also expected that average support for this policy would
overall be lower than the average support for the "no cost" policy option.
Nearby motel (IMBY). The third dependent variable measures a respondent's level of
support for the housing policy that would relocate motel residents to a nearby motel with
improved living conditions (e.g., no code violations, a nearby bus stop, and nearby services).
The nearby motel would be located within a 15-minute walk of the respondent's home,
and thus, it may be thought of as being located in the respondent's neighborhood (see
Sampson and Groves, 1989). This measure reflects whether the respondent is willing to
improve the living conditions of the motel's population, while keeping this population
within the respondent's neighborhood. In essence, this is an IMBY policy, as respondents
would be supporting a policy that would keep the motel's population in their metaphorical
"backyard." As noted in the hypotheses, it is expected that the most support will be
found for homeless families with children, and the least support will be found for sex
offenders.
Far point motel (NIMBY). The fourth dependent variable measures a respondent's level
of support for the housing policy that would relocate motel residents to another motel
that is 10 miles away from the respondent, and has no code violations, but is known to
have poor living conditions (e.g., isolated, poor access to public transportation, and lack
of services). Thus, respondents must weigh the benefits of moving motel residents out of
a motel with existing code violations with the costs of potentially decreasing their living
conditions in terms of isolation and access to services. Furthermore, the respondent may
personally benefit by moving the motel residents out of their neighborhood. Compared
with the Nearby Motel policy, this is more of a direct NIMBY policy. Unlike the prior
questions, as noted in the hypotheses, it is expected that the most support for this NIMBY
policy will be found for sex offenders and that the least support will be found for homeless
families with children.
Independent Variables
Population condition. As noted, the results from existing research suggest that support for
housing may depend on the type of population being considered. Therefore, we define our
main variable of interest as the type of population being considered in the vignette's rating
tasks. Respondents were randomly assigned one of five unique populations to consider.
Three of these populations were noncriminal in nature (homeless families with children,
individuals working minimum wage jobs [Min. Wage Workers], and individuals receiving
Volume 16 r Issue 3 849
Research Article Emergency Shelter Housing Interventions
government welfare [Welfare Recipients]), and two were criminal in nature (individuals on
parole after serving sentences for nonviolent drug offenses [Drug Offenders] and homeless
sex offenders currently on parole [Sex Offenders]). Respondents were asked about this
population for each of the vignette's four policy rating tasks.
In a general sense, we assume that the amount of stigma potentially attached to each
population would increase in the following order: homeless families with children (least
stigma), minimum wage workers, welfare recipients, drug offenders, and sex offenders
(most stigma). Furthermore, as sex offenders face particularly strong stigma compared with
other groups, as a result of their violent criminal histories, we use the sex offender population
as the comparison group.4
We also measured several demographic characteristics about respondents.This included
ameasureofRace/Ethnicity, which we measured using four categories: White non-Hispanic
(comparison group), Black non-Hispanic, Asian non-Hispanic, and Hispanic.5 We also
asked whether respondents were Female ,theirAge in years, and whether they were a Parent
(by birth, adoption, marriage). Household Income was an ordinal response with 13 categories
(< $9,999; $10,000–$14,999; ...; $55,000–$59,999; $60,000–$74,999; and higher than
$75,000). Education was an ordinal response, coded as 1: High-school diploma, GED
equivalent, or less (comparison group); 2: Some College or Technical education; 3: a
College Degree; and 4: a Graduate (e.g., M.A. or Ph.D.) or Professional Degree (e.g.,
M.D. or J.D.). Political ideology was measured as an ordinal response ranging from Very
Conservative (1) to Very Liberal (5) and, thus, measures Liberalism .Political Party was
measured as Republican (comparison group), Independent/Other, and Democrat.
Analytical Models
To estimate the impact of the assigned population condition on policy support, we initially
used an analysis of variance (ANOVA) framework to examine the distribution of policy
support across population conditions and then compared support for each condition with
one another. After this analysis, we used multivariate modeling to consider whether respon-
dent demographics drive policy support, over and above the contribution of the population
conditions. We first examined whether the models met the proportional odds assumption
by using a parallel slopes test. The Brant test rejected the null hypothesis that the slopes
were equivalent across sets. As such, we used multinomial logistic regression to compare
differences between the Neutral response and the (combined) Unlikely responses, referred
4. Unfortunately, we did not ask respondents to rank-order the different populations by their stigma
feelings toward them, and thus, we are making an assumption about how respondents generally feel
about each population in terms of stigma. The ordering of these populations in terms of stigma feelings
would be useful to measure in future research.
5. Although respondents were asked their race and ethnicity in separate questions, as a result of the
sampling frame specified by race
and
ethnicity, respondents who were Hispanic were grouped into a
single category regardless of their racial choice.
850 Criminology & Public Policy
Dum, Socia, and Rydberg
TAB L E 1
Descriptive Statistics
Unweighted, Nonimputed Data Weighted, Nonimputed Weighted, Imputed Data
Variable % Missing N Mean SD N Mean SD N Mean SE
Support for No Cost Policy 0.0 3,911 3.71 1.16 3,911 3.78 1.15 3,911 3.78 .03
Support for Sacrice Policy 0.0 3,911 3.35 1.29 3,911 3.28 1.36 3,911 3.28 .04
Support for Nearby Policy 0.0 3,911 3.48 1.18 3,911 3.52 1.19 3,911 3.52 .03
Support for Far Point Polic y 0.0 3,911 2.84 1.24 3,911 2.77 1.25 3,911 2.77 .03
Population Condition 0.0 3,911 — — 3,911 — — 3,911 — —
Homeless families with kids — 782 .20 .40 786 .20 .40 786 .20 .01
Min. wage workers — 773 .20 .40 784.3 .20 .40 784.3 .20 .01
Welfare recipients — 799 .20 .40 814.3 .21 .41 814.3 .21 .01
Drug oenders — 784 .20 .40 762.4 .19 .41 762.4 .19 .01
Sex oenders — 773 .20 .40 764 .20 .41 764 .20 .01
Race/Ethnicity 0.0 3,911 — — 3,911 — — 3,911 — —
White non-Hispanic — 1,006 .26 .44 2,659.2 .68 .47 2,659.2 .68 .01
Black non-Hispanic — 973 .25 .43 483.9 .12 .33 483.9 .12 .01
Asian non-Hispanic — 948 .24 .43 210.5 .05 .23 210.5 .05 < .01
Hispanic — 984 .25 .43 557.4 .14 .35 557.4 .14 .01
Female 0.0 3,911 .52 .50 3,911 .52 .50 3,911 .52 .01
Age (years) 0.0 3,911 37.57 15.98 3,911 45.92 17.18 3,911 45.92 .40
Parent 4.5 3,735 .50 .50 3,783.2 .60 .49 3,911 .59 .01
Household Income 7.5 3,616 8.21 4.08 3,661.4 8.24 4.13 3,911 8.20 .12
Education 1.0 3,871 — — 3,872 — — 3,911 — —
High-school degree or less — 737 .19 .39 787 .20 .40 798.2 .20 .01
Some college/tech — 1,207 .31 .46 1,250.4 .32 .47 1,261.9 .32 .01
College degree — 1246 .32 .47 1,189.8 .31 .46 1,200.2 .31 .01
Graduate/prof degree — 681 .18 .38 644.9 .17 .37 650.6 .17 .01
Liberalism 10.0 3,520 3.05 1.17 3,611.5 2.90 1.19 3,911 2.91 .03
Political Party 11.5 3,461 — — 3,601.7 — — 3,911 — —
Republican — 614 .18 .38 921.8 .26 .44 1,000.0 .26 .01
Independent/Other — 1,138 .33 .47 1,259.1 .35 .48 1,379.4 .35 .01
Democrat — 1,709 .49 .50 1,420.8 .40 .49 1,534.6 .39 .01
Note. Reports weighted estimates for N, means, standard deviations (SDs), and standard errors (SEs).
to as "oppose" positions, and the Neutral category and the (combined) Likely responses,
referred to as "support" positions.
Results
Descriptive statistics for our sample, both before and after imputation, are presented in
Table 1. Of note is that this table presents weighted estimates for N ,means,standard
deviations, and standard errors. Although our original sample (not shown) included almost
equal percentages of each racial/ethnic category, and an oversampling based on respondents
Volume 16 r Issue 3 851
Research Article Emergency Shelter Housing Interventions
being younger than 35 years of age, the weighted data are more reflective of the adult U.S.
population according to the 2010 Census.
Validating Success of the Randomization Procedure
A balance check helped determine whether the random assignment procedure elimi-
nated selection bias across the five population conditions on the observed covariates (see
Table 2). We used the standard difference to compare the pairwise balance between cat-
egories (see Austin, 2011, for calculation), with 0.10 representing a negligible difference
and 0.20 reflecting the threshold for covariate imbalance between groups (Austin, 2011;
Loughran, Wilson, Nagin, and Piquero, 2015). The average pairwise standard difference for
every covariate was below the threshold for imbalance, and of the 160 pairwise comparisons
(i.e., 10 comparisons across 16 variables), only 2 (1.3%) were above this threshold. The
largest difference was observed for the "college degree" variable between the "homeless fam-
ilies with children" and "welfare recipients" conditions (d= 0.25). As a result of this degree
of covariate balance, we are confident that the randomization procedure was successful and
that selection bias does not present a plausible threat to internal validity for subsequent
analysis.
Policy Support Across Population Conditions
Pursuant to Research Question 2, we first compared the unconditional average policy
support across the policy rating tasks (see Table 3). Consistent with Hypothesis 2, the
average support for the No Cost policy was significantly higher than it was for any other
scenario, including a similar policy that came at a financial cost to the respondent (Sacrifice
Policy). The Far Point Motel policy received significantly lower support than did any
other scenario. We then used an ANOVA framework to consider whether support varied
significantly across the population conditions (see Table 4).
First, a multivariate ANOVA (MANOVA) indicated significant between-group differ-
ences in the population conditions across each policy rating task, (16,11,924) =0.95,
p<.001. This variation is displayed in Figure 1, representing the entire weighted distribu-
tion of policy support across the rating tasks and population conditions. Subsequent univari-
ate ANOVAs were then used to compare average support across the population conditions
(Table 4). Consistent with Hypotheses 1 and 3, the conditional mean policy support de-
creased linearly from homeless families with children (highest) to sex offenders (lowest).
Consistent with Hypothesis 4, the pattern of support for the Far Point Motel policy demon-
strated the opposite pattern. A series of post hoc mean difference tests were then employed
to compare average support for groups within each policy scenario (see Table 4).
Overall, our results show that when compared with sex offenders, the three nonof-
fender populations (homeless families, minimum wage workers, and welfare recipients) had
significantly more support for policies that would positively affect their housing situation,
even when these policies came at a personal cost to the respondent. When predicted levels
852 Criminology & Public Policy
Dum, Socia, and Rydberg
TAB L E 2
Covariate Balance Check: Weighted Variable Means Across Population Conditions
Population Condition
Variable Homeless Families w/ Kids Minimum Wage Workers Welfare Recipients Drug Oenders Sex Oenders
Pairwis e Standard
Dierence [Range]
Race/Ethnicity
White non-Hispanic 0.674 0.693 0.675 0.672 0.685 0.023 [0.002–0.047]
Black non-Hispanic 0.130 0.121 0.123 0.126 0.118 0.018 [0.004–0.037]
Asian non-Hispanic 0.049 0.051 0.051 0.058 0.060 0.026 [0.003–0.049]
Hispanic 0.146 0.135 0.150 0.144 0.137 0.024 [0.007–0.045]
Female 0.527 0.511 0.549 0.498 0.511 0.048 [0.000–0.104]
Age (years) 45.510 44.674 46.870 46.385 46.178 0.061 [0.012–0.128]
Parent 0.581 0.623 0.596 0.594 0.585 0.039 [0.005–0.086]
Household Income 8.474 8.207 8.322 8.004 8.088 0.057 [0.020–0.116]
Education
High-school degree or less 0.166 0.227 0.182 0.244 0.203 0.100 [0.038–0.192]
Some college/tech 0.313 0.308 0.359 0.328 0.295 0.063 [0.010–0.138]
College degree 0.386 0.297 0.269 0.277 0.318 0.118 [0.018–0.252]
Graduate/profdegree 0.134 0.167 0.190 0.151 0.184 0.078 [0.014–0.152]
(Continued )
Volume 16 r Issue 3 853
Research Article Emergency Shelter Housing Interventions
TAB L E 2
Continued
Population Condition
Variable Homeless Families w/ Kids Minimum Wage Workers Welfare Recipients Drug Oenders Sex Oenders
Pairwis e Standard
Dierence [Range]
Liberalism 2.955 3.013 2.853 2.876 2.923 0.066 [0.020–0.132]
Political Party
Republican 0.258 0.224 0.266 0.251 0.280 0.059 [0.017–0.129]
Independent/other 0.371 0.334 0.368 0.354 0.328 0.050 [0.009–0.154]
Democrat 0.371 0.441 0.366 0.396 0.392 0.072 [0.009–0.139]
Notes. Variable means reect multiple imputation and poststratication weights. A standard dierence below 0.2 is considered to be indicative of covariate balance across treatment categories.
854 Criminology & Public Policy
Dum, Socia, and Rydberg
TAB L E 3
Unconditional Policy Support
Measure / Comparison No Cost Policy Sacrice Policy Nearby Motel Policy FarPoint Motel Policy
Mean Support (SE) 3.78 (0.03) 3.28 (0.04) 3.52 (0.03) 2.77 (0.03)
vs. No Cost Policy — 0.51 (0.04)*** 0.27 (0.04)*** 1.01 (0.05)***
vs. Sacrice Policy — –0.24 (0.04)*** 0.50 (0.05)***
vs. Nearby Motel Policy — 0.74 (0.06)***
vs. Far Point Motel Policy —
Note. Mean dierences estimated via survey weighted pairwise ttests, using Holm corrected p values.
***pࣘ.001.
TAB L E 4
Conditional Policy Support: Univariate ANOVA and Post Hoc Comparisons
No Cost Policy Sacrice Policy Nearby Motel Far Point Motel
Conditional Means Mean (SD) Mean (SD) Mean(SD) Mean (SD)
Homeless Fam. w/ Kids (HFwK) 3.93 (1.13) 3.41 (1.35) 3.73 (1.11) 2.53 (1.21)
Min. Wage Workers (MWW) 3.91 (1.07) 3.38 (1.33) 3.80 (1.10) 2.49 (1.19)
Welfare Recipients (WR) 3.88 (1.14) 3.35 (1.31) 3.62 (1.14) 2.86 (1.28)
Drug Oenders (DO) 3.65 (1.21) 3.17 (1.41) 3.24 (1.28) 2.90 (1.24)
Sex Oenders (SO) 3.54 (1.17) 3.05 (1.36) 3.17 (1.18) 3.09 (1.22)
FValue (4, 3,906) 12.88*** 7.99*** 34.31*** 14.95***
Post Hoc Group Comparisons No Cost Policy Sacrice Policy Nearby Motel Far Point Motel
MWW vs. HFwK –0.11 –0.10 0.00 –0.08
WR vs. HFwK –0.12 –0.11 –0.22** 0.03
DO vs. HFwK –0.17* –0.19*–0.39*** 0.18*
SO vs. HFwK –0.39*** –0.33*** –0.54*** 0.34***
WR vs. MWW –0.01 –0.01 –0.23*** 0.11
DO vs. MWW –0.06 –0.09 –0.39*** 0.26***
SO vs. MWW –0.28*** –0.23** –0.54*** 0.42***
DO vs. WR –0.05 –0.08 –0.16* 0.15
SO vs. WR –0.27*** –0.22** –0.31*** 0.31***
SO vs. DO –0.22** –0.14 –0.15 0.16
Notes. Analyses weighted by poststratication weights. Post hoc signicance based on Tukey's Honest Signicant Dierence Test.
*p< 0.05. **p< 0.01. *** p< 0.001.
of support are inspected in Figure 1, although drug offenders had more policy support
than did SOs in each of these rating tasks, these differences were always nonsignificant,
which suggests a generally lower level of support for the offender populations than for the
nonoffender populations. When considering a NIMBY policy that would harm the pop-
ulation's well-being, but benefit the respondent, there was significantly less policy support
Volume 16 r Issue 3 855
Research Article Emergency Shelter Housing Interventions
FIGURE 1
Weighted Level of Support for Policy Questions
for homeless families and minimum wage workers, as compared with SOs (see Table 4 and
Figure 1). The level of NIMBY policy support was lower for welfare recipients and drug
offenders but not significantly different from that of sex offenders.
Predicting Opposition or Support for the Policy Scenarios
The prior analyses have established that policy support varies based on both the population
and the type of policy being considered. As support generally hovered around the "Neutral"
response even when taking the population conditions into account, we now consider
whether individual respondent characteristics are associated with support or opposition
to the policies. Because a Brant test of parallel slopes failed, we used multinomial logistic
regression models to examine which respondent characteristics are associated with movement
856 Criminology & Public Policy
Dum, Socia, and Rydberg
away from neutrality for a given policy, and toward either opposition or support. These
results are presented in Table 5.
These results can be interpreted as a one-unit increase in the independent variable being
associated with a b -sized change in the relative log odds of having an Oppose response (Not
at all likely; Somewhat unlikely) versus a Neutral response, or having a Support response
(Somewhat likely; Very likely) versus a Neutral response, for the given policy option.6 All
four models were significant at the .001 level. As support across population conditions has
been examined previously (Tables 3 and 4), we focus here on the contribution of respondent
characteristics over and above these conditions. For the No Cost policy, no respondent
characteristics were significantly associated with being in an opposed category (vs. neutral).
Nevertheless, we find that individuals who are older or wealthier are significantly more likely
to support the policy (vs. being neutral).
For the Sacrifice policy, older individuals are significantly more likely to oppose the
policy (vs. being neutral). Wealthier individuals are significantly more likely to support the
policy (vs. being neutral), whereas Asians are significantly less likely to support the policy
(vs. being neutral).
For the Nearby Model (IMBY) policy, Asians are significantly more likely to be opposed
to the policy (vs. being neutral) and significantly less likely to be supportive (vs. being
neutral). Yet, individuals who are female, older, or the most educated were all significantly
more likely to support the policy (vs. being neutral).
For the Far Point Motel (NIMBY) policy, individuals who are female, older, or more
liberal are significantly more likely to be opposed to the policy (vs. being neutral). Wealthier
individuals are significantly more likely to support the policy (vs. being neutral).
Overall, there is little consistency in terms of respondent characteristics that signifi-
cantly predict either opposition or support of the policy options. Combined with the high
likelihood of a neutral response across the policies, these results suggest that consistent
policy support or opposition cannot be predicted based on the type of respondent. Thus,
the characteristics of the respondent considering the policy are less important in predicting
support/opposition than are the specific details of the policy being considered.
Discussion and Policy Implications
To contribute to the existing literature concerning public attitudes toward emergency
housing policies, we sought to examine citizens' decision making as it relates to different
populations. Specifically, we asked citizens to assess their level of support for policies that
would affect the housing conditions of five unique populations. In an experimental factorial
6. Because of space limitations, we do not show the relative risk ratios (RRRs) for these coefficients, but
they can be easily calculated by taking the exponent of the coefficient. The RRRs can then be
interpreted as the change in odds of being Opposed (or Supportive) versus Neutral given a one-unit
increase in the independent variable.
Volume 16 r Issue 3 857
Research Article Emergency Shelter Housing Interventions
TAB L E 5
Multinomial Logistic Regression Models Predicting Policy Support
No Cost Policy Sacrice Policy Nearby Motel (IMBY) Policy Far Point Motel (NIMBY) Policy
Oppose vs.
Neutral
Support vs.
Neutral
Oppose vs.
Neutral
Support vs.
Neutral
Oppose vs.
Neutral
Support vs.
Neutral
Oppose vs.
Neutral
Support vs.
Neutral
Variable B SE BSE BSE BSE B SE B SE BSE BSE
Population Condition
Min. wage workers –.17 .34 –.11 .21 .19 .27 .06 .21 –.05 .31 .09 .21 .12 .21 –.01 .23
Welfare recipients .28 .34 < –.01 .22 –.03 .26 –.05 .21 .03 .30 –.16 .21 –.14 .21 .56 .23
Drug oenders .30 .32 –.41 .21 .30 .25 –.09 .21 .62 .27 –.38 .20 –.28 .21 .38 .22
Sex oenders .20 .31 –.61 .21 .47 .25 –.10 .21 .47 .27 –.64 .21 –.62 .22 .48 .22
Race/Ethnicity
Black non-Hispanic .24 .21 .04 .15 .01 .17 .02 .15 –.03 .18 –.12 .14 .05 .14 .19 .16
Asian non-Hispanic .22 .21 –.13 .15 –.26 .18 –.49 .15 .32 .18 –.44 .15 –.15 .15 .12 .16
Hispanic .28 .20 .15 .14 –.22 .16 –.05 .14 .06 .17 –.12 .14 .08 .14 .11 .15
Female –.22 .20 –.06 .14 .10 .16 .08 .14 .03 .17 .30 .13 .32 .14 –.17 .14
Age (years) .01 .01 .02 < .01 .01 < .01 < .01 < .01 .01 < .01 .01 < .01 .01 < .01 –.01 <.01
Parent .38 .20 .04 .15 –.12 .17 .12 .15 .04 .19 .10 .15 > –.01 .15 .28 .16
Household Income .02 .03 .05 .02 .03 .02 .04 .02 .03 .02 .02 .02 .03 .02 .07 .02
Education
Some college/tech –.36 .26 .09 .19 –.17 .21 –.11 .19 –.14 .24 .33 .19 –.04 .19 –.02 .21
College degree –.24 .29 .02 .21 .18 .23 .25 .21 –.07 .25 .32 .20 –.03 .21 –.13 .22
Graduate/profdegree .13 .32 .39 .26 –.29 .28 .29 .25 –.08 .28 .64 .23 –.12 .24 .05 .24
Liberalism .04 .10 .10 .06 –.09 .08 .10 .06 –.08 .09 .05 .07 .16 .07 –.03 .07
(Continued )
858 Criminology & Public Policy
Dum, Socia, and Rydberg
TAB L E 5
Continued
No Cost Policy SacricePolic y Nearby Motel (IMBY ) Policy Far Point Motel (NIMBY) Policy
Oppose vs.
Neutral
Support vs.
Neutral
Oppose vs.
Neutral
Support vs.
Neutral
Oppose vs.
Neutral
Support vs.
Neutral
Oppose vs.
Neutral
Support vs.
Neutral
Variable B SE B SE BSE BSE BSE BSE BSE BSE
Political Party
Independent/other –.01 .27 –.11 .21 .06 .23 –.28 .22 –.06 .24 .03 .20 .07 .21 .04 .22
Democrat .06 .30 .08 .22 –.15 .25 –.20 .21 –.39 .26 –.03 .21 –.09 .21 .04 .22
Constant –1.44 .51 –.63 .35 –.51 .42 –.04 .35 –1.00 .48 –.22 .37 –1.02 .37 –.68 .40
N3,911 3,911 3,911 3,911
df (average) 3,364.07 3,200.79 3,189.91 3,215.95
F F(34, 3896.4) =3.25 F(34, 3892.7) =2.68 F(34, 3892.7) =3.73 F(34, 3894.0) =3.65
Notes. Reports weighted estimates after missing data imputation. Oppose includes "Not at all likely" and "Somewhat unlikely" responses. Support includes "Somewhat likely" and "Very likely" responses.
Population comparison group is homeless families with children. Race/Ethnicity comparison group is White non-Hispanic. Education comparison group is High-School Degree or less. Political Party comparison
group is Republican. Bold coecients are signicant at pࣘ .05.
Volume 16 r Issue 3 859
Research Article Emergency Shelter Housing Interventions
design, online survey respondents were randomly assigned to a population of varying
stigma (homeless families with children, individuals working minimum wage jobs, homeless
individuals receiving government welfare, homeless paroled nonviolent drug offenders, and
homeless paroled sex offenders), and then they were asked to respond to four different policy
rating tasks that concerned their assigned population. The sample was then weighted to
approximate the U.S. population in regard to age, sex, and racial/ethnic joint distributions.
The outcomes from the study on value conflict by Garland and colleagues (2013)
shed light on the processes at work in our study. In the present study, each scenario dealt
with a concern about social welfare, specifically the housing conditions facing a particular
population. Although citizens reported policy attitudes that respected the social welfare
of nonoffenders, this social welfare support dropped significantly when criminal justice
populations were involved. The IMBY and NIMBY results indicate that respondents were
likely concerned with their self-interests (e.g., protecting their neighborhood from ex-
offenders) when deciding where ex-offenders should live. In sum, when faced with the
choice of addressing the living conditions for different populations, citizen concerns over
social welfare are strongest for nonoffenders, whereas concerns about self-interest become
more salient when the welfare of ex-offenders is at stake.
The public's reluctance to improve the living conditions of ex-offenders can also be
viewed as a value conflict between social welfare and retribution (Garland et al., 2013). If
living in poor conditions is considered additional punishment for committing a crime, then
citizens can further express their desire for retribution by keeping ex-offenders in dangerous
conditions. This desire for further punishment beyond legally proscribed measures can be
observed in instances of citizens enacting vigilante justice against sex offenders (Fenton,
2014; Pandell, 2013). Our study results demonstrate that citizens may put concerns for
social welfare of criminal populations aside when they can legally act retributively toward
ex-offenders in their community.
The results of research on welfare and attributions for poverty can further illuminate
our findings. Citizens are likely to act compassionately toward children in poverty, as well
as toward those whose poverty is out of their control (Will, 1993). By contrast, the public
has particular disdain for those whose poverty is considered a result of inaction or personal
flaws (Bullock et al., 2003; Chafel, 1997; Shaw and Shapiro, 2002). Respondents in this
study reflected those views when they acted most compassionately toward groups who were
the least at fault for their poverty. They acted less compassionately toward those on welfare
and ex-offenders. The poverty of these latter groups can be attributed to perceptions of
unwillingness to remove oneself from welfare and character flaws, respectively. Therefore,
ex-offender populations in poverty face particular difficulty garnering public support because
their poverty is attributed to their personal behavior (e.g., criminal offenses).
As noted by Wallander (2009: 514), "there has been very little interest in modelling
between-subject variation in judgements" in prior factorial surveys. Although the main
analyses involved the differences in support across policy options and population conditions,
860 Criminology & Public Policy
Dum, Socia, and Rydberg
we also briefly examined whether there was any consistent or otherwise important between-
subject variation in judgments about these policies (controlling for the population being
considered).
Even though no respondent characteristics consistently predicted support or opposition
across the policy options, we find that some characteristics are important in certain instances.
For instance, we find that compared with non-Hispanic Whites, Asians seem to have less
support (and more opposition) toward certain types of policies involving personal sacrifice
or continued exposure to nearby undesirable populations (IMBY). As this was not found
for either non-Hispanic Black or Hispanic respondents, this finding provides some nuance
to recent similar research, which has been restricted to using dichotomous racial/ethnic
indicators (e.g., Garland et al., 2014; Pickett et al., 2013).
Age had some significant effects across the policy options, but this was not consistently
in a supportive or oppositional direction. Although not consistent, it suggests that age
sometimes does influence these judgments, which is a finding that contrasts with recent
research observing null effects for age on punitive attitudes or treatment support, at least
when considering sex offender populations (Mancini and Budd, 2015; Pickett et al., 2013).
Perhaps unsurprisingly, being female, more liberal, or more educated was significantly
associated with more "helpful" positions regarding the IMBY or NIMBY policies. Con-
versely, being wealthier was associated with more support for the policies that would fix the
living conditions of the present motel, but it was also associated with more support for the
NIMBY policy that would move the population out of the respondent's neighborhood.
Overall, and in line with the existing literature, our results support the view that
the stigma of a criminal conviction, and particularly a sexually based one, can influence
community members' attitudes and policy support. Specifically, the predicted response
patterns in Figure 1 suggest that when compared with less-stigmatized groups, both drug
and sex offenders received less support for policies that would increase their quality of life
and more support for NIMBY policies that would harm their quality of life and relocate
them far away from the respondents' community. Although our results show that SOs have
less policy support than do drug offenders, the difference between these two populations
is too close to reach statistical significance. Thus, although a (violent) sexual conviction
seems to reduce policy support compared with a (nonviolent) drug conviction, the more
pertinent differences in policy support involve comparisons between populations with and
without any criminal conviction. These findings can help explain how NIMBY policies like
residence restrictions have spread quickly and with great public support (e.g., Goodrich,
2015; Velmetti, 2015), whereas other proposals, like halfway houses and homeless shelters
targeting ex-offenders, face protests and pushback from residents (Hobbs, 2015; Kim, 2015;
Martyn, 2015; Mavity, 2015; Talarico, 2016).
Our results have important implications for policy and future research. The findings
from many previous studies demonstrate how the stigma of a criminal conviction impacts
self-esteem, employment, and access to housing (e.g., LeBel, 2012; Pager, 2003; Stoll and
Volume 16 r Issue 3 861
Research Article Emergency Shelter Housing Interventions
Bushway, 2008; Western, 2006; Western and Pettit, 2010), particularly for sex offenders
(e.g., Burchfield and Mingus, 2008; Huebner et al., 2014; Levenson, 2008; Tewksbury,
2000, 2005). Although the criminal justice and social welfare systems should be concerned
with providing housing for homeless ex-offenders, it is just as important to consider the
housing conditions that ex-offenders endure. Previous research aimed at housing has largely
overlooked this policy consideration, instead focusing on the types of neighborhoods ex-
offenders live in, and the geographical proximity to members of the public and places where
children gather.
Many local governments are struggling with finding housing for not just homeless sex
offenders but other less stigmatized populations as well. The findings of this study indicate
that the stigma attached to criminal populations can have significant impacts beyond
simple access to housing. We argue that stigma affects not only the locations where criminal
populations may live but also the conditions of these residences. Our results support Link and
Phelan's claim that stigma processes have "probably a highly underestimated impact" on all
manner of life chances of stigmatized groups (2001: 381). Specifically, citizens are much less
likely to support policies that address dangerous housing conditions when ex-offenders are at
risk compared with nonoffender populations. Citizens are also much more likely to support
policies that push ex-offenders, and particularly sex offenders, out of their neighborhoods,
even if it means exposing these individuals to substandard housing conditions. Finally, our
findings suggest that although there is overall more support for "no cost" policies compared
with those that raise taxes, when increased taxes are involved, citizens would much rather
have their tax dollars spent on helping nonoffenders than ex-offenders.
As safe shelter for the homeless grows harder to come by (Gross, 2014; Samuels,
2014), our findings suggest that citizens view ex-offenders as some of the least deserving
of safe housing. This holds true regardless of whether the housing policy would come
at a personal cost to the citizens themselves, although average support was less for all
populations when it came at a personal cost. These sentiments could make it difficult for
local governments to enact specific policies that address the plight of ex-offenders returning
to the community. Elected officials could face considerable pushback from citizens in the
form of town meetings or decreased support in local elections. Indeed, this citizen pushback
against local government actions concerning SOs has already occurred in communities
across the country (e.g., Curtis, 2013; Kilgannon, 2007; Shih, 2010). It is no surprise that
housing ex-offenders, and SOs in particular, is a politically divisive issue. Indeed, our results
demonstrate its contentious power.
Although these results are not necessarily surprising, they have important implications
for public safety, as well as for the criminal justice system. For instance, the U.S. Department
of Justice recently requested $91.3 million to assist reentry initiatives and prevent recidivism
(U.S. Department of Justice, 2016). Nevertheless, to use this money effectively, the criminal
justice system needs to fund more programs and services that help those with criminal
records. The results of this study suggest that implementing such initiatives may be difficult
862 Criminology & Public Policy
Dum, Socia, and Rydberg
as citizens would much prefer to aid nonoffenders rather than ex-offenders, at least when it
comes to housing support. Even though this is certainly understandable, directing resources
away from ex-offenders can increase reentry difficulties; leave them more vulnerable to
recidivism; and as a result, can make citizens less safe.
This dilemma is something that policy makers need to consider carefully when working
to address the housing problems of ex-offenders. Although policy makers want to be attentive
to the views of their constituents, in this regard, going against a community's wishes may
improve its overall safety. The challenge facing policy makers is how to "package" particular
policy proposals in a way that reduces citizen pushback and allows resources to be used to
aid offender reentry.
Our results, then, suggest that housing policies would have more support if they (1)
were not pitched as specifically helping ex-offender populations and (2) came at little
personal cost to the public (e.g., no increased taxes). For example, a housing policy pitched
as helping to house the homeless in general (ex-offenders included) would likely have much
higher public support than one that identifies ex-offenders (and particularly sex offenders)
as the population being helped.
Additionally, policy makers should consider how the use of labels might influence
public support, particularly as it relates to stigma surrounding a population. For example,
research findings show that the use of the label "sex offender" increases support for punitive
policies when compared with a more neutral term (Harris and Socia, 2014). Although not
specifically examined in this study, the label used to describe the population being helped
by the housing policy may influence public support. For example, a proposal for a halfway
house pitched as helping "recovering substance users" may experience more support than
one pitched as helping "paroled drug offenders" or "convicted drug addicts." This should
be explored in future research.
Finally, our results raise an additional concern about the government's responsibility
to care for ex-offenders in need of safe housing. If citizens would prefer to relegate ex-
offenders to dangerous housing conditions because of a desire for retribution or attributions
for poverty, local governments may adopt similar attitudes that could negatively affect the
well-being of ex-offenders. For example, at one emergency shelter motel that was notorious
for housing parolees and sex offenders, an inspection report was faked by government
building inspectors (see Dum, 2016). A few months after the falsified inspection report, a
resident fell through the floor, which prompted an investigation into code violations that
led to the motel's shutdown (see Dum, 2016). Even though citizens' attitudes toward ex-
offenders as being "more deserving" of dangerous housing conditions are understandable,
policy makers and other government representatives must ensure that they do not adopt
similar viewpoints. To do so would endanger the lives of individuals who are under the
care of government agencies, and this could be detrimental (not to mention scandalous) if
ex-offenders are injured or killed by illegal housing conditions. Policy makers must make
Volume 16 r Issue 3 863
Research Article Emergency Shelter Housing Interventions
sure that their decisions address reentry, recidivism, and public safety concerns, rather than
reflecting retributive attitudes.
Limitations and Directions for Future Research
This study is not without limitations. First, our sample was drawn from an opt-in online
panel, and it was oversampled based on race and age. We attempted to address this limitation
by using poststratification weighting. Nevertheless, this does not guarantee that our findings
fully generalize to the U.S. population. Although there was a fair amount of missing
data addressed with multiple imputations, the results of postimputation analyses suggest
the imputation procedure did not substantially alter results, and imputing such data was
only relevant when considering the influence of demographic characteristics (Table 5). Of
note is that results support the same overall substantive conclusions regardless of whether
models were run with the weighted imputed data, weighted nonimputed data, or imputed
nonweighted data (results not shown). Despite this, future research should attempt to
replicate these scenarios by using a sample from a population that is more generalizable
from the outset, to the population of adults in the United States.
It is also possible that the ordering of the scenarios in the survey could have influenced
our findings. Although the Near Point (IMBY) and Far Point (NIMBY) scenarios were
randomized in order, the No Cost scenario always preceded the Sacrifice scenario. Ques-
tion ordering can have large implications for the findings in factorial survey experiments,
particularly when earlier questions may prime respondents to view subsequent items differ-
ently (Tourangeau, Rips, and Rasinski, 2000). In the case of the current research, responses
to the Sacrifice scenario may be better presented as support for emergency repairs at a cost
to the respondent, even after being presented with a no-cost alternative, rather than support
for such a policy on its face. This priming effect may explain some of the difference in
support between the No Cost and Sacrifice scenarios. Future research should be aimed at
randomly presenting these scenarios, or randomly assigning a cost amount, to control for
any potential order effects.
We did not directly assess respondents' attribution of stigma to each of the five popu-
lation types, but we assumed that it would agree with our rough ordering of "least to most"
stigmatized populations: homeless families, minimum wage workers, welfare recipients,
paroled drug offenders, and paroled sex offenders. The discriminant effects for these vari-
ables among the four scenarios provide supportive evidence for this general interpretation.
Nevertheless, there were no significant differences between drug offenders and SOs in any
scenario. Thus, it seems that the most important distinction for policy support involved
the presence of any prior criminal record rather than the distinction between a nonviolent
and a violent criminal record. Future studies should ask respondents to rank all of the given
populations in terms of their perceived stigma, regardless of the population assigned for
scenario consideration.
864 Criminology & Public Policy
Dum, Socia, and Rydberg
Finally, as noted, it seems likely that the label used to describe the population(s)
could influence public support. Thus, future research may be aimed at examining how
describing the same population in different ways (e.g., drug offenders vs. drug addicts vs.
substance users) could influence public support for housing and other community-based
policies.
Conclusion
Convicted offenders face well-documented challenges upon reentry into society. The stigma
attached to a criminal conviction affects the ability to find housing, secure employment,
and establish social ties. The outcomes of this study show that the stigma attached to
ex-offenders is so powerful that it may relegate them into housing situations that are
both unsafe and unproductive for successful reentry. Citizens seem more content with
subjecting paroled drug and sex offenders to poor and unsafe housing conditions com-
pared with less stigmatized groups of nonoffenders, such as minimum wage workers or
homeless families with children. These findings show another way in which individuals
with criminal records, both nonviolent and violent, are being pushed to the margins of
society.
From a criminal justice standpoint, it makes sense that sending paroled offenders to
unsafe living situations is not conducive to reintegration. Rather, it is likely to exacerbate
emotional stress and, possibly, to increase the likelihood of recidivism or parole violations
(California Sex Offender Management Board, 2008; Clark, 2015; Metraux and Culhane,
2004). Policy makers and scholars should consider the implications of this study as they
continue to shape policies that affect the housing opportunities available to ex-offenders,
and particularly to sex offenders, in the community.
Appendix: Vignette and Scenario Details
Respondents were provided with a vignette, with each respondent randomly assigned one
of five populations to consider within the vignette. These populations included homeless
families with children who had been displaced from their homes as a result of disasters such
as fires, floods, and so on; individuals working minimum wage jobs; homeless individuals
receiving government welfare; homeless individuals on parole after serving sentences for
nonviolent drug offenses; and homeless sex offenders currently on parole. The wording of
the vignette was largely similar among the five populations, with some slight changes, as
noted in the subsequent discussion.
Once the respondent had read the vignette, three follow-up scenarios were presented,
involving four policy options that would address various housing problems for the given
population. Respondents were asked their level of support for each of the four proposed
policy options. Although the first two scenarios were presented in order, the third scenario
randomized the ordering of the third and fourth questions by respondent.
Volume 16 r Issue 3 865
Research Article Emergency Shelter Housing Interventions
Vignette
For the following scenarios, please imagine that the 30-room Budget Motel is located within
a 15-minute walk from your home. Daily rates are $25 and weekly rates are $125.
The majority of motel residents are [POPULATION]. These individuals may live/The
county Department of Social Services may place these individuals] in the motel for several
days to six months, depending on how long it takes for them to [find/save for] permanent
housing. For these residents, the alternative to the motel would be living on the streets,
staying in a shelter, mission, single room occupancy facilities, abandoned building, or
vehicle.
Scenario 1 (No Cost Policy)
For this scenario, please imagine that an inspection of the motel by county building
inspectors uncovered the following room conditions: "structural damage to roof, peeling
paint, insufficient lighting, interior water damage, missing bathroom tiles, bug infestation,
debris piled outside rooms, cracked toilets."
Although inspectors viewed the conditions as "unpleasant," the conditions did not
violate any laws or building codes. The county is proposing a measure that will force the
owner of the motel to improve the living conditions by the time of the next monthly
inspection, or face fines. There are no alternative housing options available for the motel's
residents for the next several months because all other shelters are full. If the measure fails,
the conditions of the motel will remain as they are until the next monthly inspection. If
the measure passes, the motel's owner will be directed to improve living conditions in time
for the next monthly inspection. Before enacting this request, the county is seeking citizen
feedback on this issue.
No Cost Question. How likely is it that you would support this measure? [5-Point
Likert scale response]
Scenario 2 (Sacrifice Policy)
Please imagine a different scenario, in which an inspection of the motel by county building
inspectors uncovered room conditions that were, in fact, illegal code violations: "raw sewage,
no up-to-date fire alarm certifications, exposed wiring, gas leak, rooms missing smoke
detectors, electric socket falling from wall, spliced wiring."
Unfortunately, there are no alternative housing options available for the motel's residents
for the next several months because all other shelters are full. The county orders the motel
owner to fix the violations, but the owner does not have the finances to do so. Because
of this, the county is proposing an emergency repair measure that will use taxpayer funds
to immediately improve the conditions of the motel. If the measure passes, the motels
conditions will be fixed immediately, while city taxes for the next fiscal year will increase by
an average of $100 per household. If the measure fails, the conditions will remain unfixed
866 Criminology & Public Policy
Dum, Socia, and Rydberg
and the motel residents will remain there until alternative housing becomes available or the
owner has the funds to fix the motel.
Sacrifice Question. How likely is it that you would support this measure? [5-Point
Likert scale response]
Scenario 3 (Nearby Motel/IMBY and Far Point Motel/NIMBY Policies)
Please imagine a different scenario, in which the county has decided to relocate residents
of the Budget Motel. There are two alternative housing locations available. The county is
asking for citizen input on where to house the former residents of the Budget Motel.
Nearby Motel is located in your neighborhood and is similarly situated within a 15
minute walk of your home. This motel has no documented code violations, and has a bus
stop out front, and is within walking distance of grocery stores, and other services that are
used by the motel's residents.
Far Point Motel is located 10 miles away from your residence, and while it does not
have any code violations, it is known to have poor living conditions. This motel is quite
isolated, with the nearest bus stop being a 10-minute walk away. The nearest grocery store,
laundromat, and other services are a 45-minute bus ride away from the motel.
[Randomized presentation order of these questions]
Nearby Motel (IMBY) Question. How likely is it that you would support sending
residents to Nearby Motel? [5-Point Likert scale response]
Far Point Motel (NIMBY) Question. How likely is it that you would support sending
residents to Far Point Motel? [5-Point Likert scale response]
References
Abadinsky, Howard. 1987. Probation and Parole: Theory and Practice, 3rd Edition. Engle-
wood Cliffs, NJ: Prentice-Hall.
Austin, Peter C. 2011. An introduction to propensity score methods for reducing the effects
of confounding in observational studies. Multivariate Behavioral Research, 46: 399–424.
Baker, Reg, Stephen J. Blumberg, J. Michael Brick, Mick P. Couper, Melanie Courtright, J.
Michael Dennis, . . . Dan Zahs. 2010. Research synthesis: AAPOR report on online
panels. Public Opinion Quarterly , 74: 1–71. doi:10.1093/poq/nfq048.
Belcher, John R. and Bruce R. DeForge. 2012. Social stigma and homelessness: The limits
of social change. Journal of Human Behavior in the Social Environment, 22: 929–946.
doi:10.1080/10911359.2012.707941.
Benedict, Annette, Jeffrey S. Shaw, and Leanne G. Rivlin. 1988. Attitudes toward the
homeless in two New York city metropolitan samples. Journal of Voluntary Action
Research, 17: 90–98.
Billings, Randy. 2013. Homelessness hits record high in Portland. Press Herald .
Retrieved from pressherald.com/news/Homelessness_hits_record_high_in_Portland_
.html?pagenum= full.
Volume 16 r Issue 3 867
Research Article Emergency Shelter Housing Interventions
Brinegar, Sarah J. 2003. The social construction of homeless shelters in the Phoenix area.
Urban Geography, 24: 61–74. doi:10.2747/0272-3638.24.1.61.
Brinegar, Sarah J. 2010. Response to homelessness in Tempe, Arizona: Public opinion and
government policy. Urban Geography , 21: 497–513. doi:10.2747/0272-3638.21.6.497.
Broidy, Lisa M. 2001. A test of general strain theory. Criminology , 39: 9–36.
doi:10.1111/j.1745-9125.2001.tb00915.x.
Bullock, Heather E., Wendy R. Williams, and Wendy M. Limbert. 2003. Predicting support
for welfare policies: The impact of attributions and beliefs about inequality. Journal of
Poverty , 7: 35–56. doi:10.1300/J134v07n03_03.
Burchfield, Keri B. and William Mingus. 2008. Not in my neighborhood: Assessing reg-
istered sex offenders' experiences with local social capital and social control. Criminal
Justice and Behavior, 35: 356–374. doi:10.1177/0093854807311375.
Burhmester, Michael, Tracy Kwang, and Samuel D. Gosling. 2011. Amazon's Mechanical
Turk: A new source of inexpensive, yet high-quality, data? Perspectives on Psychological
Science, 6: 3–5.
Burstein, Paul. 2003. The impact of public opinion on public policy: A review and an
agenda. Political Research Quarterly, 56: 29–40. doi:10.1177/106591290305600103.
California Sex Offender Management Board. 2008. Homelessness Among Registered Sex
Offenders in California: The Numbers, the Risks, and the Response. Sacramento, CA:
Author.
Chafel, Judith A. 1997. Societal images of poverty: Child and adult beliefs. Youth & Society,
28: 432–463.
Chang, Linchiat and Jon A. Krosnick. 2009. National surveys via RDD telephone inter-
viewing versus the Internet: Comparing sample representativeness and response quality.
Public Opinion Quarterly, 73: 641–678. doi:10.1093/poq/nfp075.
Clark, Valerie A. 2015. The Effect of Community Context and Post-Release Housing Place-
ments on Recidivism. St. Paul: Minnesota Department of Corrections. Retrieved from
doc.state.mn.us/pages/files/4814/3318/8378/Ecology_Study_-_April_2015.pdf.
Comp, Nathan J. 2008. Heartbreak motels. Isthmus . July 11. Retrieved March 9, 2016 from
isthmus.com/news/cover-story/heartbreak-motels/.
Craun, Sarah W. and Matthew T. Theriot. 2009. Misperceptions of sex offender perpe-
tration: Considering the impact of sex offender registration. Journal of Interpersonal
Violence, 24: 2057–2072. doi:10.1177/0886260508327706.
Crawford, Heather. 2014. Sex offenders in the place you may not expect. First
Coast News. Apr. 25. Retrieved December 9, 2015 from firstcoastnews.com/
story/news/community/2014/04/24/sex-offender-motel-room-next-door/8112427/.
Curtis, Brent. 2013. Officials: Sex offender moving to California. Times Ar-
gus. July 27. Retrieved January 1, 2015 from timesargus.com/article/
20130727/THISJUSTIN/707279927.
Dear, Michael, Sharon L. Gaber, Lois Takahashi, and Robert Wilton. 1997. Seeing people
differently: Changing constructs of disability and difference. Environment and Planning
D: Society and Space, 15: 455–480.
868 Criminology & Public Policy
Dum, Socia, and Rydberg
Doble, John and Margot Lindsay. 2003. Is NIMBY Inevitable? Washington, DC: Center
for Community Corrections.
Dum, Christopher P. 2016. Exiled in America: Life on the Margins in a Residential Motel.
New York: Columbia University Press.
Eckholm, Erik. 2009. As jobs vanish, motel rooms become home. The New York Times,
p. A1. Retrieved from nytimes.com/2009/03/11/us/11motel.html?pagewanted= all.
Feagin, Joe R. 1975. Subordinating the Poor. Englewood Cliffs, NJ: Prentice-Hall.
Fenton, Justin. 2014. "Vigilante justice" leads to prison time in fatal beating
of sex offender. The Baltimore Sun. Oct. 30. Retrieved March 22, 2016
from baltimoresun.com/news/maryland/crime/bs-md-ci-vigilante-justice-sentencing-
20141030-story.html.
Forman, Ethan. 2014. No quick solutions to homeless families living in Dan-
vers motels. The Salem News . Sept. 23. Retrieved November 30, 2015 from
salemnews.com/news/local_news/no-quick-solutions-to-homeless-families-living-in-
danvers-motels/article_8e25dd75–0fe4-561c-bbfa-78ec5f905717.html.
Forte, James A. 2002. Not in my social world: A cultural analysis of me-
dia representations, contested spaces, and sympathy for the homeless.
Journal of Sociology and Social Welfare, 29: 131–157. Retrieved from
search.ebscohost.com/login.aspx?direct= trueanddb = a9handAN = 8627335andsite =
ehost-live.
Galeste, Marcus A., Henry F. Fradella, and Brenda Vogel. 2012. Sex offender myths in print
media: Separating fact from fiction in U.S. newspapers. Western Criminology Review,
13: 4–24.
Garland, Brett, Eric Wodahl, and Caryn Saxon. 2014. What influences public support
of transitional housing facilities for offenders during reentry? Criminal Justice Policy
Review, 1–23. doi:10.1177/0887403414564866.
Garland, Brett, Eric Wodahl, and Robert Schuhmann. 2013. Value conflict and public
opinion toward prisoner reentry initiatives. Criminal Justice Policy Review, 24: 27–48.
doi:10.1177/0887403411424081.
Gervais, Will M. and Ara Norenzayan. 2012. Analytic thinking pro-
motes religious disbelief. Science , 336: 493–496. Retrieved from sci-
ence.sciencemag.org/content/336/6080/493.abstract.
Gilens, Martin. 1995. Racial attitudes and opposition to welfare. The Journal of Politics, 57:
994–1014. doi:10.2307/2960399.
Gilens, Martin. 1999. Why Americans Hate Welfare. Chicago, IL: University of Chicago
Press.
Gilens, Martin. 2002. An anatomy of survey-based experiments. In (Jeff Manza, Fay L.
Cook, and Benjamin I. Page, eds.), Navigating Public Opinion: Polls, Policy, and the
Future of American Democracy. New York: Oxford University Press.
Goffman, Erving. 1963. Stigma: Notes on the Management of Spoiled Identity. Englewood
Cliffs, NJ: Prentice-Hall.
Volume 16 r Issue 3 869
Research Article Emergency Shelter Housing Interventions
Goodrich, Kristine. 2015. Another city adopts sex offender residency re-
strictions. White Bear Press . Dec. 9. Retrieved January 1, 2015
from presspubs.com/white_bear/news/article_421644e4-9e03-11e5-8a0f-
636b50dabcf2.html.
Gowan, Teresa. 2010. Hobos, Hustlers, and Backsliders: Homeless in San Francisco.Min-
neapolis: University of Minnesota Press.
Graham, John W., Allison E. Olchowski, and Tamika D. Gilreath. 2007. How many
imputations are really needed? Some practical clarifications of multiple imputation
theory. Prevention Science , 8: 206–213.
Gross, Ashley. 2014. Seattle faces additional shortage of homeless shelter space this fall,
winter. KNKX 88.5 FM. Aug. 5. Retrieved from knkx.org/post/seattle-faces-additional-
shortage-homeless-shelter-space-fall-winter.
Harris, Andrew J. and Kelly M. Socia. 2014. What's in a name? Evaluating the effects of the
"sex offender" label on public opinions and beliefs. Sexual Abuse: A Journal of Research
and Treatment, 28. doi:10.1177/1079063214564391.
Harris, Sherea and Brianne Britzius. 2015. Sheriff: Shutdown of Anniston motel
makes tracking sex offenders. WBRC Fox 6 News . Feb. 6. Retrieved December
9, 2015 from wbrc.com/story/28047118/sheriff-shutdown-of-anniston-vann-thomas-
motel-makes-tracking-sex-offenders-more-difficult.
Hay, Carter. 2001. Parenting, self-control, and delinquency: A test of self-control theory.
Criminology, 39: 707–736. doi:10.1111/j.1745-9125.2001.tb00938.x.
Henry, Meghan, Azim Shivji, Tanya de Sousa, and Rebecca Cohen. 2015. The 2015
Annual Homeless Assessment Report (AHAR) to Congress: Part 1 Point-in-Time Estimates of
Homelessness. Washington, DC: U.S. Department of Housing and Urban Development.
Hobbs, Allegra. 2015. Shelter neighbors mapping neighborhood life to keep sex
offenders out. Brooklyn Paper . Oct. 21. Retrieved January 1, 2015 from
brooklynpaper.com/stories/38/43/dtg-shelter-sex-offender-map-williamsburg-2015-
10-23-bk.html.
Homant, Robert J. and Daniel B. Kennedy. 1982. Attitudes toward ex-offenders: A com-
parison of social stigmas. Journal of Criminal Justice, 10: 383–391.
Honaker, James, Gary King, and Matthew Blackwell. 2014. Amelia II: A program for
missing data. Gary King Website. Retrieved from gking.harvard.edu/amelia.
Huebner, Beth M., Kimberly R. Kras, Jason Rydberg, Timothy S. Bynum, Eric
Grommon, and Breanne Pleggenkuhle. 2014. The effect and implications of
sex offender residence restrictions. Criminology & Public Policy , 13: 139–168.
doi:10.1111/1745-9133.12066.
Hughes, Lorine A. and Keri B. Burchfield. 2008. Sex offender residence restric-
tions in Chicago: An environmental injustice? Justice Quarterly , 25: 647–673.
doi:10.1080/07418820802119976.
Hughes, Lorine A. and Colleen Kadleck. 2008. Sex offender community
notification and community stratification. Justice Quarterly , 25: 469–495.
doi:10.1080/07418820701710941.
870 Criminology & Public Policy
Dum, Socia, and Rydberg
Israelsen-Hartley, Sara. 2008. Housing is a hurdle for felons. Deseret News.Jan.7.Retrieved
March 21, 2016 from deseretnews.com/article/695241948/Housing-is-a-hurdle-for-
felons.html?pg= all.
Jasso, Guillermina. 2006. Factorial survey methods for studying beliefs and judgments.
Sociological Methods and Research, 34: 334–423. doi:10.1177/0049124105283121.
John Jay College of Criminal Justice. 2011. In Our Backyard: Overcoming Community
Resistance to Reentry Housing (a NIMBY Toolkit). New York: Author.
Kernsmith, Poco D., Sarah W. Craun, and Jonathan Foster. 2009. Public attitudes toward
sexual offenders and sex offender registration. Journal of Child Sexual Abuse, 18: 290–
301. doi:10.1080/10538710902901663.
Kilgannon, Corey. 2007. Suffolk County to keep sex offenders on the move. The New York
Times. Feb. 17. Retrieved from nytimes.com/2007/02/17/nyregion/17sex.html.
Kim, Bora. 2015. Neighbors outraged over sex offender house in east Charlotte. USA
Tod ay . Jan. 16. Retrieved from usatoday.com/story/news/local/2015/01/16/neighbors-
outraged-sex-offender-program-house-charlotte-hickory-grove-library/21860117/.
King, Laura L. and Jennifer J. Roberts. 2015. The complexity of public attitudes toward
sex crimes. Victims & Offenders , 1–19. doi:10.1080/15564886.2015.1005266.
Kish, Leslie. 1965. Survey Sampling . Hoboken, NJ: Wiley.
Kluegel, James R. 1987. Macro-economic problems, beliefs about the poor and attitudes
toward welfare spending. Social Problems , 34: 82–99. doi:10.2307/800731.
Knecht, Tom and Lisa M. Martinez. 2009. Humanizing the homeless:
Does contact erode stereotypes? Social Science Research , 38: 521–534.
doi:10.1016/j.ssresearch.2009.01.009.
Kras, Kimberly R., Breanne Pleggenkuhle, and Beth M. Huebner. 2016. A new way of
doing time on the outside: Sex offenders' pathways in and out of a transitional housing
facility. International Journal of Offender Therapy and Comparative Criminology, 60:
512–534. doi:10.1177/0306624X14554194.
Kreuter, Frauke, Stanley Presser, and Roger Tourangeau. 2008. Social desirability bias in
CATI, IVR, and web surveys: The effects of mode and question sensitivity. Public
Opinion Quarterly, 72: 847–865. doi:10.1093/poq/nfn063.
Krisberg, Barry and Susan Marchionna. 2006. Attitudes of US Voters toward Prisoner Rehabili-
tation and Reentry Policies. Oakland, CA: National Council on Crime and Delinquency.
LeBel, Thomas P. 2012. Invisible stripes? Formerly incarcerated persons' perceptions of
stigma. Deviant Behavior , 33: 89–107.
Lee, Barrett A., Chad R. Farrell, and Bruce G. Link. 2004. Revisiting the contact hypothesis:
The case of public exposure to homelessness. American Sociological Review , 69: 40–63.
Lepianka, Dorota, Wim Van Oorschot, and John Gelissen. 2009. Popular explanations of
poverty: A critical discussion of empirical research. JournalofSocialPolicy , 38: 421–438.
doi:10.1017/S0047279409003092.
Levenson, Jill S. 2008. Collateral consequences of sex offender residence restrictions. Crim-
inal Justice Studies, 21: 153–166. doi:10.1080/14786010802159822.
Volume 16 r Issue 3 871
Research Article Emergency Shelter Housing Interventions
Levenson, Jill S., Alissa R. Ackerman, Kelly M. Socia, and Andrew J. Harris. 2013. Where
for art thou? Transient sex offenders and residence restrictions. Criminal Justice Policy
Review, 26: 319–344. doi:10.1177/0887403413512326.
Link, Bruce G. and Jo C. Phelan. 2001. Conceptualizing stigma. Annual Review of Sociology ,
27: 363–385.
Link, Michael W. and Ali H. Mokdad. 2005. Alternative modes for health
surveillance surveys: An experiment with web, mail, and telephone.
Epidemiology, 16: 701–704. Retrieved from journals.lww.com/epidem/
Fulltext/2005/09000/Alternative_Modes_for_Health_Surveillance_Surveys_.19.aspx.
Longa, Lyda, Frank Fernandez, and John Gallas. 2014. 2 local mo-
tels have niche clientele: Sex offenders and predators. The Daytona
Beach News Journal. Oct. 5. Retrieved December 9, 2015 from news-
journalonline.com/article/20141005/news/141009683?p=6&tc= pg.
Lott, Bernice. 2002. Cognitive and behavioral distancing from the poor. American Psychol-
ogist, 57: 100–110. doi:10.1037/0003-066X.57.2.100.
Loughran, Thomas A., Theodore Wilson, Daniel S. Nagin, and Alex R. Piquero. 2015.
Evolutionary regression? Assessing the problem of hidden biases in criminal justice
applications using propensity scores. Journal of Experimental Criminology , 11: 631–
652. doi:10.1007/s11292-015-9242-y.
Lumley, Thomas. 2014. Survey: Analysis of complex survey samples. Survey Analysis in R
Website . Retrieved from r-survey.r-forge.r-project.org/survey/.
Mancini, Christina and Kristen M. Budd. 2015. Is the public convinced that
"nothing works?": Predictors of treatment support for sex offenders among amer-
icans. Crime & Delinquency , 62: 777–799. doi:10.1177/0011128715597693.
Retrieved from journals.sagepub.com/doi/pdf/10.1177/0011128715597693?related-
urls=yes&legid= spcad%3B0011128715597693v1&legid= spcad%
3B0011128715597693v1&legid=spcad%3B0011128715597693v1&cited-by= yes.
Martyn, Amy. 2015. At Dallas' once stylish cabana motor hotel, ex-prisoners
struggle to re-enter the world. Dallas Observer. Apr. 15. Retrieved March
21, 2016 from dallasobserver.com/news/at-dallas-once-stylish-cabana-motor-hotel-ex-
prisoners-struggle-to-re-enter-the-world-7182173.
Mavity, Ryan. 2015. No decision on West Rehoboth shelter. Cape Gazette.Dec.29.
Retrieved January 1, 2015 from capegazette.villagesoup.com/p/no-decision-on-west-
rehoboth-shelter/1462391.
McGloin, Jean Marie and Zachary R. Rowan. 2015. A threshold model of collective crime.
Criminology, 53: 484–512. doi:10.1111/1745-9125.12077.
Mears, Daniel P., Christina Mancini, Marc Gertz, and Jake Bratton. 2008. Sex crimes,
children, and pornography: Public views and public policy. Crime & Delinquency , 54:
532–559. doi:10.1177/0011128707308160.
Melanson, Alana. 2015. Chelmsford board seeks limits on hotel stays by
homeless. Lowell Sun . Nov. 29. Retrieved November 30, 2015 from
lowellsun.com/todaysheadlines/ci_29179283/chelmsford-board-seeks-limits-hotel-
stays-by-homeless.
872 Criminology & Public Policy
Dum, Socia, and Rydberg
Metraux, Stephen and Dennis P. Culhane. 2004. Homeless shelter use and rein-
carceration following prison release. Criminology & Public Policy , 3: 139–160.
doi:10.1111/j.1745-9133.2004.tb00031.x.
Metraux, Stephen and Dennis P. Culhane. 2006. Recent incarceration history
among a sheltered homeless population. Crime & Delinquency , 52: 504–517.
doi:10.1177/0011128705283565.
National Alliance to End Homelessness. 2015. The State of Homelessness in America.Wash-
ington, DC: Author.
Nicholson-Crotty, Sean, David A.M. Peterson, and Mark D. Ramirez. 2009. Dynamic
representation(s): Federal criminal justice policy and an alternative dimension of public
mood. Political Behavior, 31: 629–655. doi:10.1007/s11109-009-9085-1.
Pager, Devah. 2003. The mark of a criminal record. American Journal of Sociology, 108:
937–975.
Pandell, Lexi. 2013. The vigilante of Clallam County. The Atlantic .Dec.4.Re-
trieved March 22, 2016 from theatlantic.com/national/archive/2013/12/the-vigilante-
of-clallam-county/281968/.
Petersilia, Joan. 2003. When Prisoners Come Home: Parole and Prisoner Reentry .NewYork:
Oxford University Press.
Phelan, Jo C., Bruce G. Link, Robert E. Moore, and Ann Stueve. 1997. The stigma of
homelessness: The impact of the label "homeless" on attitudes toward poor persons.
Social Psychology Quarterly, 60: 323–337.
Pickett, Justin T., Christina Mancini, and Daniel P. Mears. 2013. Vulnerable victims,
monstrous offenders, and unmanageable risk: Explaining public opinion on the
social control of sex crime. Criminology , 51: 729–759. doi:10.1111/1745-9125.
12018.
Quoidbach, Jordi, Daniel T. Gilbert, and Timothy D. Wilson. 2013.
The end of history illusion. Science , 339: 96–98. Retrieved from sci-
ence.sciencemag.org/content/339/6115/96.abstract.
R Core Team. 2015. R: A language and environment for statistical computing. Vienna. R
Project Website. Retrieved from r-project.org.
Roff, Lucinda L., David L. Klemmack, Debera M. McCallum, and Michael B. Conaway.
2002. Assessing public support for state government social welfare expenditures. The
Social Policy Journal, 1: 55–72.
Rolfe, Shawn M., Richard Tewksbury, and Ryan D. Schroeder. 2016. Homeless shelters'
policies on sex offenders: Is this another collateral consequence? International Journal
of Offender Therapy and Comparative Criminology. doi:10.1177/0306624X16638463.
Retrieved from journals.sagepub.com/doi/pdf/10.1177/0306624X16638463?related-
urls=yes&legid= spijo%3B0306624X16638463v1&legid=spijo%
3B0306624X16638463v1&legid=spijo%3B0306624X16638463v1&cited-
by= yes&patientinform-links= yes.
Roman, Caterina G. and Jeremy Travis. 2004. Taking Stock: Housing, Homelessness, and
Prisoner Reentry. Washington, DC: Fannie Mae Foundation.
Volume 16 r Issue 3 873
Research Article Emergency Shelter Housing Interventions
Rose, Lisa. 2014. The sex offender in room 13: Behind the doors of Seaside
Heights' welfare motels. NJ.com. Dec. 29. Retrieved from nj.com/ocean/index.ssf/
2014/12/the_dark_side_of_seaside_heights_emerges_from_the_shadows_as_winter_
takes_hold.html#incart_hbx.
Rosenberg, Steven A. 2014. Motels becomes kids' homes in Danvers, Waltham,
Weymouth. Boston Globe. Nov. 16. Retrieved June 11, 2015 from
bostonglobe.com/metro/regionals/north/2014/11/16/motels-becomes-kids-homes-
danvers-waltham-weymouth/gIYha2mkhZY2cZaZoy3k1O/story.html.
Rossi, Peter H. and Andy B. Anderson. 1982. An introduction. In (Steven L. Nock and
PeterH.Rossi,eds.),Measuring Social Judgments: The Factorial Survey Approach. Beverly
Hills, CA: Sage.
Rydberg, Jason, Eric Grommon, Beth M. Huebner, and Timothy Bynum. 2014. The effect
of statewide residency restrictions on sex offender post-release housing mobility. Justice
Quarterly, 31: 421–444. doi:10.1080/07418825.2012.667141.
Sampson, Robert J. and W. Byron Groves. 1989. Community structure and crime:
Testing social-disorganization theory. American Journal of Sociology, 94: 774.
doi:10.1086/229068.
Samuels, Robert. 2014. DC seeks motels to house families this win-
ter amid expected homeless surge. The Washington Post.Nov.5.Re-
trieved from washingtonpost.com/local/dc-seeks-motels-to-house-families-this-
winter-amid-expected-homeless-surge/2014/11/05/7f47e8c8-6505-11e4-836c-
83bc4f26eb67_story.html.
Scally, Corianne P. 2012. The nuances of NIMBY: Context and perceptions of
affordable rental housing development. Urban Affairs Review , 49: 718–747.
doi:10.1177/1078087412469341.
Shaw, Greg M. and Robert Y. Shapiro. 2002. Trends: Poverty and public assistance. The
Public Opinion Quarterly, 66: 105–128.
Shenoy, Rupa and The New England Center for Investigative Reporting. 2014. State
seeks to move homeless from hotel rooms to group homes. WGBH News . Feb. 23.
Retrieved March 28, 2014 from wgbhnews.org/post/state-seeks-move-homeless-hotel-
rooms-group-homes.
Shih, Gerry. 2010. Officials allow sex offender to live near school. The New York Times.
Mar. 6. Retrieved from nytimes.com/2010/03/07/us/07sfoffender.html?_r= 0.
Simmons, Alicia D. and Lawrence D. Bobo. 2015. Can non-full-probability Internet sur-
veys yield useful data? A comparison with full-probability face-to-face surveys in the
domain of race and social inequality attitudes. Sociological Methodology , 45: 357–387.
doi:10.1177/0081175015570096.
Smith, Tom W. 1987. That which we call welfare by any other name would smell sweeter:
An analysis of the impact of question wording on response patterns. The Public Opinion
Quarterly, 51: 75–83.
Smith, Tom W. 2015. Trends in National Spending Priorities, 1973-2014. Gen-
eral Social Survey Final Report. Chicago, IL: National Opinion Research
874 Criminology & Public Policy
Dum, Socia, and Rydberg
Center, University of Chicago. Retrieved from norc.org/PDFs/publications/
SmithT_Trends_Nat_Spend_2008.pdf.
Socia, Kelly M. and Andrew J. Harris. 2016. Evaluating public perceptions of the risk
presented by registered sex offenders: Evidence of crime control theater? Psycholog y,
Public Policy, and Law, 22: 362–374.
Socia, Kelly M. and Janet P. Stamatel. 2011. Neighborhood characteristics and the
social control of registered sex offenders. Crime & Delinquency , 58: 565–587.
doi:10.1177/0011128711420111.
Solomon, Phyllis. 1983. Analyzing opposition to community residential facilities for trou-
bled adolescents. Child Welfare: Journal of Policy, Practice, and Program, 62: 361–366.
Spicker, Paul. 1984. Stigma and Social Welfare. London, U.K.: Croom Helm.
Stojkovic, Stan and Mary A. Farkas. 2013. So you want to find a transitional house for
sexually violent persons: An account of political failure. Criminal Justice Policy Review,
1–24. doi:10.1177/0887403413511633.
Stoll, Michael A. and Shawn D. Bushway. 2008. The effect of criminal back-
ground checks on hiring ex-offenders. Criminology & Public Policy , 7: 371–404.
doi:10.1111/j.1745-9133.2008.00516.x.
Stuber, Jennifer and Mark Schlesinger. 2006. Sources of stigma for means-
tested government programs. Social Science and Medicine , 63: 933–945.
doi:10.1016/j.socscimed.2006.01.012.
Swaner, Jon. 2014. Motel has DOC contract to house sex offenders. WTHITV 10 .Apr.4.
Retrieved December 9, 2015 from wthitv.com/2014/04/04/motel-has-doc-contract-
to-house-sex-offenders/.
Talarico, Lauren. 2016. Parents protest ex-cons moving next to De Zavala
Elementary. KHOU.com. Jan. 22. Retrieved March 21, 2016 from
khou.com/story/news/local/2016/01/22/parents-protest-ex-cons-moving-next-
de-zavala-elementary/79199272/.
Tewksbury, Richard. 2000. Experiences and attitudes of registered female sex offenders.
Federal Probation, 68: 30–34.
Tewksbury, Richard. 2005. Collateral consequences of sex offender registration. Journal of
Contemporary Criminal Justice, 21: 67–81. doi:10.1177/1043986204271704.
Tewksbury, Richard and Elizabeth E. Mustaine. 2006. Where to find sex offenders: An
examination of residential locations and neighborhood conditions. Criminal Justice
Studies, 19: 61–75. doi:10.1080/14786010600615991.
Tewksbury, Richard and Elizabeth E. Mustaine. 2008. Where registered sex offenders live:
Community characteristics and proximity to possible victims. Victims & Offenders ,3:
86–98. doi:10.1080/15564880701752371.
Tighe, J. Rosie. 2010. Public opinion and affordable housing: A review of the literature.
Journal of Planning Literature, 25: 3–17. doi:10.1177/0885412210379974.
Tompsett, Carolyn J., Paul A. Toro, Melissa Guzicki, Manuel Manrique, and Jigna
Zatakia. 2006. Homelessness in the United States: Assessing changes in prevalence
Volume 16 r Issue 3 875
Research Article Emergency Shelter Housing Interventions
and public opinion, 1993-2001. American Journal of Community Psychology , 37: 47–
61. doi:10.1007/s10464-005-9007-2.
Toro, Paul A. and Dennis M. McDonnell. 1992. Beliefs, attitudes, and knowledge about
homelessness: A survey of the general public. American Journal of Community Psychology,
20: 53–80.
Tourangeau, Roger, Lance J. Rips, and Kenneth Rasinski. 2000. The Psychology of Survey
Response. Cambridge, U.K.: Cambridge University Press.
U.S. Department of Justice. 2016. FY 2017 Budget Request Smart on Crime. Washington,
DC: Author. Retrieved from justice.gov/jmd/file/820791/download.
Van Gelder, Jean-Louis and Reinout E. De Vries. 2012. Traits and states: Integrating
personality and affect into a model of criminal decision making. Criminology , 50:
637–671. doi:10.1111/j.1745-9125.2012.00276.x.
Van Gelder, Jean-Louis, Eva C. Luciano, Marleen Weulen Kranenbarg, and Hal E. Hersh-
field. 2015. Friends with my future self: Longitudinal vividness intervention reduces
delinquency. Criminology , 53: 158–179. doi:10.1111/1745-9125.12064.
Velmetti, Bruce. 2015. Judge casts cloud over web of sex offender residency restric-
tions. Milwaukee, Wisconsin Journal Sentinel . Apr. 1. Retrieved January 1, 2015
from jsonline.com/news/crime/judge-casts-cloud-over-web-of-sex-offender-residency-
restrictions-b99472821z1-298378831.html.
Visher, Christy A. and Jill Farrell. 2005. Chicago Communities and Prisoner Reentry .Wash-
ington, DC: Urban Institute.
Wallander, Lisa. 2009. 25 years of factorial surveys in sociology: A review. Social Science
Research, 38: 505–520. doi:10.1016/j.ssresearch.2009.03.004.
Weinberg, Jill, Jeremy Freese, and David McElhattan. 2014. Comparing data characteristics
and results of an online factorial survey between a population-based and a crowdsource-
recruited sample. Sociological Science , 1: 292–310. doi:10.15195/v1.a19.
Weiss-Tisman, Howard. 2015. State looking to change homeless motel voucher
program. Brattleboro Reformer. Jan. 5. Retrieved January 10, 2016 from
reformer.com/latestnews/ci_28500944/state-looking-change-homeless-motel-
voucher-program.
Western, Bruce. 2006. Punishment and Inequality in America. New York: Russell Sage
Foundation.
Western, Bruce and Becky Pettit. 2010. Collateral Costs: Incarceration's Effect on Economic
Mobility. Washington, DC: Pew Charitable Trusts.
White, Ian R., Patrick Royston, and Angela M. Wood. 2011. Multiple imputation using
chained equations: Issues and guidance for practice. Statistics in Medicine , 30: 377–399.
Will, Jeffry A. 1993. The dimensions of poverty—Public perceptions of the deserving poor.
Social Science Research, 22: 312–332.
Zou, Jie J. and Roger Miller. 2015. Housing the unwanted. The New York World.Mar.
18. Retrieved March 19, 2015 from thenewyorkworld.com/2015/03/18/housing-
unwanted/.
876 Criminology & Public Policy
Dum, Socia, and Rydberg
Christopher P. Dum is an assistant professor in the Department of Sociology at Kent State
University. His research focuses on inequality, criminal justice policy, and homelessness. His
work has appeared in Justice Quarterly , and he is author of the book, Exiled in America: Life
on the Margins in a Residential Motel (2016, Columbia University Press).
Kelly M. Socia is an assistant professor in the School of Criminology and Justice Studies at
the University of Massachusetts, Lowell (UML), where he is also a fellow at the Center for
Public Opinion at UML. His research interests include sex offender reentry and recidivism,
public perceptions, public policy making, violent crime, and spatial analyses.
Jason Rydberg is an assistant professor in the School of Criminology and Justice Studies at
the University of Massachusetts Lowell, where he is also an associate with the Center for Pro-
gram Evaluation. His research interests include prisoner reentry, sex offender management
in the community, and the evaluation of criminal justice programs.
Volume 16 r Issue 3 877
... The data for this study come from a larger project administered via a national Webbased survey in the summer of 2015 (see Dum, Socia, & Rydberg, 2017) and approved by the Institutional Review Board of the first author's university. This nonprobability sample consists of adults from the United States that were 18 years and older, gathered from an online panel from Survey Sampling International (SSI). ...
... Online nonprobability samples may also provide data equivalent to or higher quality than random-digit dialing samples (Chang & Krosnick, 2009). In the criminological field, researchers have used nonprobability online samples to great success to examine public opinion (Dum et al., 2017;Pickett et al., 2013). For more details about the general survey procedure of the current study, please see the work by Dum et al. (2017). ...
... In the criminological field, researchers have used nonprobability online samples to great success to examine public opinion (Dum et al., 2017;Pickett et al., 2013). For more details about the general survey procedure of the current study, please see the work by Dum et al. (2017). ...
- Christopher P. Dum
-
![Kelly Michael Socia]()
- Brooke L. Long
- Fritz Yarrison
Previous research has explored the impact of faith and religion on recidivism. However, it focused primarily on violent offenders, drug users, tax evaders, and so on. Missing is an examination of registered sex offenders (RSOs) and the role religion and religiosity play in facilitating reentry. Religiosity and religious organizations may play a role in increasing social bonds and reducing isolation in RSOs. In addition, being surrounded by a faith-based community could act as a catalyst for identity transformation from a RSO to a community member. Using a national online sample of U.S. adults, this research investigates individual's support of policies controlling sex offenders in religious communities and how demographic characteristics affect these views. Results suggest that Protestants and Other (non-Catholic) Christians are the most accepting of RSOs in places of worship. In addition, the stronger an individual's faith, the less accepting they are of RSOs. Older, liberal, and educated respondents are more accepting of RSOs.
... A Qualtrics program randomly assigned participants to one of two conditions. Participants read a vignette describing a classmate who either (1) stated that they spent time in prison for a felony conviction or (2) said nothing about time in prison or a felony conviction (for a discussion of factorial and vignette design, see Dum et al., 2017). In the Campbell and Stanley (1973) classification, the present study is a "posttest only" experiment. ...
-
![Jon Overton]()
- Michelle D. Fretwell
- Christopher P. Dum
Objectives This brief communication tests how an undergraduate student's incarceration history (i.e., previous incarceration vs. no previous incarceration) affects evaluations by their peers on several scales (e.g., desired social distance, warmth, competence, expected immoral behaviors).Methods The experimental conditions were presented in a survey delivered to a sample of MTurk respondents currently enrolled in undergraduate classes (N=400). OLS regression was used to estimate the impact of the experimental manipulation on respondents' feelings toward formerly incarcerated peers.ResultsFormerly incarcerated students were rated as less warm and less moral by respondents, and incarceration history led to increased desired social distance among respondents. Meditation analysis indicates perceived warmth is the main cause of desired social distance.Conclusions The results show that formerly incarcerated undergraduate students are stigmatized by their peers in significant ways. However, concerns about morality and competence do not affect desired social distance. The behavioral penalties assessed against students with incarceration histories are driven by concerns about warmth. Further research on the mechanisms that create (as well as reduce) stigma for formerly incarcerated college students is necessary.
... Nonprobability samples recruited online via crowdsourcing and opt-in panels have begun to appear in leading criminology journals (e.g., Enns and Ramirez 2018;Denver et al. 2017;Dum et al. 2017;Gottlieb 2017;Pickett et al. 2013;Vaughan et al. 2019). These samples tend to overrepresent certain population groups-whites, liberals, females, the college educated, and the young (Levay et al. 2016;Ross et al. 2010;Weinberg et al. 2014). ...
Objectives Similar to researchers in other disciplines, criminologists increasingly are using online crowdsourcing and opt-in panels for sampling, because of their low cost and convenience. However, online non-probability samples' "fitness for use" will depend on the inference type and outcome variables of interest. Many studies use these samples to analyze relationships between variables. We explain how selection bias—when selection is a collider variable—and effect heterogeneity may undermine, respectively, the internal and external validity of relational inferences from crowdsourced and opt-in samples. We then examine whether such samples yield generalizable inferences about the correlates of criminal justice attitudes specifically. Methods We compare multivariate regression results from five online non-probability samples drawn either from Amazon Mechanical Turk or an opt-in panel to those from the General Social Survey (GSS). The online samples include more than 4500 respondents nationally and four outcome variables measuring criminal justice attitudes. We estimate identical models for the online non-probability and GSS samples. Results Regression coefficients in the online samples are normally in the same direction as the GSS coefficients, especially when they are statistically significant, but they differ considerably in magnitude; more than half (54%) fall outside the GSS's 95% confidence interval. Conclusions Online non-probability samples appear useful for estimating the direction but not the magnitude of relationships between variables, at least absent effective model-based adjustments. However, adjusting only for demographics, either through weighting or statistical control, is insufficient. We recommend that researchers conduct both a provisional generalizability check and a model-specification test before using these samples to make relational inferences.
... Our data come from an anonymous, nationwide survey administered in 2016 to American adults aged 18 years and older. Similar to many previous studies examining public attitudes (e.g., Dum, Socia, & Rydberg, 2017;Enns & Ramirez, 2018;Pickett et al., 2013), we sampled respondents from an online opt-in panel. Such samples have greater "fit for purpose" for examining relationships between variables than for identifying univariate prevalence estimates (Baker et al., 2013, p. 98). ...
Much prior research has examined the sources of individuals' attitudes toward the application of punishment via the justice system. Some findings from this literature suggest that punitive attitudes are expressive, retributive, and closely connected to racial resentment. Other research, however, emphasizes that these sentiments are instrumental, utilitarian, and associated with the management of perceived risk. To date, little research has explored public attitudes regarding employment as a reentry barrier, and it is unclear which of these perspectives is more salient for understanding support for employers' use of criminal records in hiring decision-making. Using survey data on a national sample of American adults (N = 1,202), the current study finds stronger support for an instrumental model than an expressive model.
... The populist punitiveness perspective (Bottoms, 1995;Roberts, Stalans, Indermaur, & Hough, 2003) contends that political actors leverage or adjust to what they perceive as the public's preoccupation with "sex offenders" for their own advantage. There is ample evidence of the public's concern with persons convicted of sexual crimes, ranging from widespread endorsement of punitive policy responses (Mancini et al., 2010;, to lower support for supportive reentry programs for such individuals (Dum, Socia, & Rydberg, 2017), and endorsement of popular myths surrounding "sexual predators" (Mancini & Pickett, 2016;Pickett et al., 2013). ...
This study examines effects of court and community contextual factors on sentencing outcomes for individuals convicted of sexual crimes using indicators from two perspectives—focal concerns and populist punitiveness. Sourced from the Pennsylvania Commission on Sentencing, the sample includes 9,431 persons convicted of sexual crimes and a precision-matched sample of persons convicted of non-sexual violent crimes for comparison. Based on multilevel hurdle regression models for both incarceration and sentence length decisions, results indicate that individuals convicted of sexual crimes face enhanced sentence severity in judicial districts with smaller courts, increased jail capacity, stronger political competition, and higher religious homogeneity. The results also suggest statistically significant differences between effects for persons convicted of sexual crimes and a matched sample of persons convicted of violent crimes. Overall, results suggest that specific contextual factors have a distinguishable impact on sentencing of individuals convicted of sexual crimes.
This study examines the post-incarceration housing experiences of 33 women. Using Residential Timeline Followback methodology, participants were asked to report where they lived at arrest and every location since their release. Follow-up questions asked women to describe these locations, who they lived with, how much they paid, and whether or not they felt safe. Demographic information and criminal justice history were recorded. The data paint a complicated picture of social and community resources, persistence, and struggle. Housing assets lost at incarceration were difficult to recover. Most women bounced between various locations, relying heavily on short-term subsidized congregate housing programs and rarely securing independent housing. Participants described the family, friends, and acquaintances who housed them during reentry as overextended and vulnerable. Implications for policy and practice are explored.
The current study examined attitudes about the homeless among a range of social service and healthcare employees using both self-report and an experimental approach. Ninety-six respondents were recruited from drop-in shelters, medical facilities and social service agencies. After completing an initial measure of homelessness stigmatization, participants were randomly assigned into one of two experimental conditions in which they were given a description of a fictional 20-year-old client described as either homeless or domiciled. It was hypothesized that prior to the manipulation there will be no differences between the two groups and that the manipulation would induce those who read about the homeless client to subsequently endorse more stereotyped beliefs than those who read about a domiciled counterpart. The results revealed no pre-manipulation differences between the groups, while the manipulation invoked beliefs that the homeless client was dangerous, needed help with reading and financial literacy, and needed advice on personal hygiene, compared to the control group. These results highlight the work that still needs to be done in training service providers in terms of providing a bias-free environment for potential clients. Future studies should investigate whether proper training and education reduce preexisting assumptions about homeless clients.
- Kyle McLean
In an effort to provide a theoretical framework for understanding citizens' decisions to complain about the police, this paper suggests that citizen complaints can be viewed as a justice-restoring response and tests six hypotheses using a factorial vignette experiment. The findings indicate that individuals are more likely to complain when they perceive the interaction as procedurally unfair, distributively unfair, and when the outcome is unfavourable. Positive pre-existing attitudes towards the police result in an increased likelihood of engaging in a justice-restoring response. Despite drawing on Tyler's legitimacy theory, these findings differentiate justice-restoring responses from legitimacy by the comparatively greater impact of outcome favourability and the differing direction of the effect of pre-existing attitudes towards the police.
The goal of the current study was to describe the experiences of discrimination based on homelessness among youth between the ages of 18 and 24 using both quantitative and qualitative data. Pilot data were collected on 85 homeless emerging adults in New York City recruited at drop-in centers and residential shelters. Quantitative results show the majority of the sample (81%) experienced at least one instance of homelessness discrimination in the past year. Over half the sample felt like others treated them as mentally inferior because of their homelessness, labeling them as crazy, irresponsible, and lazy. Additionally, nearly a third felt like they were ignored and/or treated unfairly by police officers and service providers. Qualitative data are used to supplement these results by detailing specific discriminatory experiences. Written responses about specific incidents reveal that youth feel dehumanized by friends and family as well as service providers and random people based on their homelessness. Results are discussed in the context of the unique challenges and universal developmental changes faced by this vulnerable population.
Survey research suggests that many members of the public ascribe to myths about sex offenders. These "mythic narratives" relate to the perceived homogeneity of the sex offender population and the extent and nature of reoffense risk. The prominence of such belief systems in media and policy discourse may contribute to adoption of public policies that carry significant symbolic value, yet may fall short of their ostensible goals of protecting children and preventing sexual victimization—a condition framed by some as crime control theater. This study surveyed a nationally representative Internet sample of 1,000 U.S. adults to examine mythic narrative beliefs regarding the risk presented by registered sex offenders (RSOs) who are on the public Internet registry. Respondents estimated the proportion of RSOs who were pedophiles, sexual predators, strangers to their victims, and who were at a high risk of committing 6 types of sexual and nonsexual offenses. Factor analysis revealed high levels of convergence in respondent ratings across these 9 variables, and relatively high estimates of RSO risk, affirming that the public generally ascribes to the mythic narratives underlying crime control theater. Higher estimates of RSO risk were associated with respondents who were female, Hispanic, less educated, more conservative, and less politically knowledgeable. Further, higher estimates of RSO risk were associated with never having used the registry, believing the registry is effective and warrants increased funding, believing sex crimes are increasing, and maintaining that research evidence would not change their views about registry effectiveness. Implications for policy and practice are discussed.
This report, "Taking Stock: Housing, Homelessness, and Prisoner Reentry," examines how those who have spent time in prison or jail fare in securing safe and affordable housing following their release and discusses housing programming and practice designed to assist them. Every prisoner facing discharge from a correctional institution must answer this question: "Where will I sleep tonight?" For many returning prisoners, the family home provides an answer to that question. But reunions with families are not always possible—or are only temporary—sometimes due to the dictates of criminal justice or housing policies, or sometimes due to family dynamics. For those who cannot return to the homes of families or friends, the question of housing becomes considerably more complex. For some, the final answer to the question "Where will I sleep tonight?" is a homeless shelter or the street. Many are finding that the difficulties in securing affordable and appropriate housing complicate the reentry process, further reducing already limited chances for successful community reintegration. The report is the culmination and synthesis of three tasks designed to inform the state of knowledge around housing, homelessness, and prisoner reentry: (1) a descriptive report on the barriers and challenges facing returning prisoners, as well as potential opportunities for serving or supporting the housing-related needs of returning prisoners, (2) a scan of promising housing and other housing-related service programs for returning prisoners and ex-offenders, and (3) a roundtable discussion by experts in the field held in Washington, D.C., on October 30, 2003. The goal of the roundtable was to bring together prominent practitioners, researchers, and community leaders to identify the most pressing housing issues and the most promising strategies for resolving these issues. The report and scan of practice were developed to serve as background materials to help frame the discussion, already underway in many communities, about the extent of the housing challenges faced by returning prisoners. The roundtable participants were provided a copy of the draft report and scan of practice. After the roundtable, the report was revised to include a synthesis of the roundtable discussion. Our ultimate aim is to sharpen the nation's thinking on the issue of housing and prisoner reintegration, and to foster policy innovations that will improve outcomes for individuals, families, and communities. In this executive summary, we provide brief background information on the issues surrounding housing and prisoner reentry to lay the foundation for a presentation of the highlights from the day-long roundtable discussion.
-
![Shawn M. Rolfe]()
- Richard Tewksbury
- Ryan D. Schroeder
The primary focus of sex offender research has been on the efficacy and collateral consequences of sex offender registration and notification (SORN) and residence restrictions. Past scholarship has found these laws to cause numerous re-entry barriers for sex offenders. Such barriers have affected sex offenders' ability to find and maintain housing, employment, and social support. Moreover, registered sex offenders (RSOs) have become homeless due to such laws. Although previous scholarship has highlighted the collateral consequences of SORN, there is a lack of scholarship addressing homeless sex offenders. Specifically, the current study assesses policies regarding RSO access to homeless shelters in a four-state region, focusing on the effect of structural, procedural, and geographic factors, as well as a shelter's proximity to children. Drawing on the loose coupling organizational framework, the findings suggest that a small maximum occupancy, unwritten policies for RSOs, being in Kentucky or Tennessee, being located near a school, and being near a higher proportion of homes with children all decrease the odds that a homeless shelter allows RSOs. Furthermore, although unwilling to make exceptions to the policies regarding RSOs, shelters were generally willing to make exceptions to other policies governing shelter accessibility.
- Annette Benedict
- Jeffrey S. Shaw
- Leanne G. Rivlin
Attitude questionnaires were administered to a sample of New York City residents and a suburban sample who worked in New York City (n = 112 for each). While overall attitudes toward the homeless were sympathetic, feelings about a shelter for the homeless in one's neighborhood were not favorable. Feelings toward a shelter were unfavorable regardless of whether the shelter was to serve "over 20" or "up to 10" homeless persons. Despite demographic differences on income, age, time living in the New York City area and education, the two samples differed significantly on only two responses related to attitudes or to experiences with the homeless. New York City residents rated their attitudes toward the elderly as more sympathetic than did suburban residents (p <. 05), though both samples reported very favorable attitudes. Also, a greater proportion of the New York City residents, 76.7%, as opposed to 52.8% for suburban residents, stated that the situation of the homeless had gotten worse in the past few years (p <. 001). To examine the relationships between attitude responses and other variables, factor analyses were carried out for each sample on those variables that correlated significantly with the attitude measures. Composite variables based on these factors revealed that, for both New York City and suburban residents, significantly more favorable attitudes were obtained for those respondents who had given money to the homeless and who had used the media and their own reading in forming an opinion about the homeless.
- R Immarigeon
-
![Joan Petersilia]()
Every year, hundreds of thousands of jailed Americans leave prison and return to society. Largely uneducated, unskilled, often without family support, and with the stigma of a prison record hanging over them, many, if not most, will experience serious social and psychological problems after release. Fewer than one in three prisoners receive substance abuse or mental health treatment while incarcerated, and each year fewer and fewer participate in the dwindling number of vocational or educational pre-release programs, leaving many all but unemployable. Not surprisingly, the great majority is rearrested, most within six months of their release. As long as there have been prisons, society has struggled with how best to help prisoners reintegrate once released. But the current situation is unprecedented. As a result of the quadrupling of the American prison population in the last quarter century, the number of returning offenders dwarfs anything in America's history. A crisis looms, and the criminal justice and social welfare system is wholly unprepared to confront it. Drawing on dozens of interviews with inmates, former prisoners, and prison officials, the book shows us how the current system is failing, and failing badly. Unwilling merely to sound the alarm, it explores the harsh realities of prisoner re-entry and offers specific solutions to prepare inmates for release, reduce recidivism, and restore them to full citizenship, while never losing sight of the demands of public safety. As the number of ex-convicts in America continues to grow, their systemic marginalization threatens the very society their imprisonment was meant to protect.
Posted by: choosewidget.blogspot.com
Source: https://www.researchgate.net/publication/319013457_Public_Support_for_Emergency_Shelter_Housing_Interventions_Concerning_Stigmatized_Populations
