Post on 31-Jan-2021
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Policy Diffusion in a Redistributive Policy: Affordable Housing and State Housing Trust Funds
Carla Flink
Assistant Professor
American University
School of Public Affairs
Department of Public Administration and Policy
4400 Massachusetts Avenue, NW
Washington, DC 20016-8070
Office Phone: 202-885-3106
Fax: 202-885-2347
flink@american.edu
(Corresponding Author)
Rebecca J. Walter
Associate Professor
University of Washington
College of Built Environments
Runstad Department of Real Estate
3950 University Way NE 317
Gould Hall, Box 355727
Seattle, Washington 98195
Office Phone: 206-221-8560
rjwalter@uw.edu
Xiaoyang Xu
PhD Student
American University
School of Public Affairs
Department of Public Administration and Policy
4400 Massachusetts Avenue, NW
Washington, DC 20016-8070
Office Phone: 202-885-2375
xx0266a@student.american.edu
mailto:flink@american.edumailto:rjwalter@uw.edumailto:xx0266a@student.american.edu
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Policy Diffusion in a Redistributive Policy: Affordable Housing and State Housing Trust Funds
ABSTRACT
As one theory of the policy process, diffusion models state that policies will transfer across
governments. How does state policy adoption occur in a redistributive policy area coupled with
declining federal support? In this study, we focus on U.S. state level efforts in affordable
housing—a policy area where states have increasing responsibility. Drawing from policy
diffusion, social construction theory, federalism, and redistributive policy literatures, our
research examines the determinants of the creation of state level Housing Trust Funds (HTFs).
We utilize event history analysis with logit regressions and survival modeling to examine how
problem severity, economic standing, housing investment, elected leadership, neighbor adoption,
and demographics predict state HTF adoption. Results indicate that both problem severity and
elected leadership predict the adoption of HTFs. This work improves our understanding of when
states create redistributive policies in areas of declining federal support.
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Introduction
The determinants of when governments adopt policy has been an enduring area of study
for public policy scholars. As one theory of the policy process, diffusion models explore the
conditions under which policies transfer across governments. Throughout decades of work,
scholars have applied the diffusion mechanism in numerous policy contexts and national
contexts. Recent contributions have included policy areas such as energy policy (Nicholson-
Crotty and Carley 2018), tobacco policy (Pacheco 2017), medical marijuana laws (Hannah and
Mallinson 2018), and same-sex marriage (Fay 2018). Theories of policy diffusion also have been
applied to China, Germany, Latin American countries, and United States (Abel 2019; Heggelund
et al 2019; Shipan and Volden, 2014; Meseguer 2004). We continue in this line of policy
diffusion research in the context of U.S. states with application to housing affordability.
An enduring theme of policy diffusion research is that states must innovate and learn
from one another to solve problems. The housing policy arena has experienced a deepening
problem—housing affordability. The standard definition used to define affordable housing is
households that pay no more than 30 percent of their income toward housing costs (Stone 2016).
The literature on housing affordability has grown over the last several decades as the severity of
the problem has intensified, while the federal government has passed on more responsibility for
state and local governments to meet housing needs. Housing is a basic necessity that not only
provides physical safety but is a primary requirement for an individual’s overall well-being
(Bratt, Stone and Hartman 2006). Extant research has shown the impact of housing and
neighborhoods on economic, social, physical, and mental health outcomes (e.g., Burgard,
Seefeldt and Zelner 2012; Chetty, Hendren and Katz 2016; Steiner, Makarios and Travis 2011).
The private housing market is unable to provide housing for the lowest income households and
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factors such as housing discrimination and income inequality contribute to the lack of access to
decent affordable housing (Bratt, Stone and Hartman 2006). Government intervention is required
to address these issues that are at the root of the affordable housing crisis.
Housing affordability represents a policy area with a unique combination of
characteristics for state innovation. For one, housing policy innovation has a set of obstacles as a
redistributive policy area benefiting people of lower political power. Theories on the “race to the
bottom” and social construction of target populations outline the barriers to successful policy
creation and implementation. Additionally, this policy area has experienced worsening problems,
punctuated with a major negative event—the -2007-2008 financial crisis and housing collapse—
that disproportionately affected the poor and racial and ethnic minorities. Coupled with
increasing severity, federal burden shedding (Weaver 2020) has pushed states to fill the gap of
declining support; a trend seen in many other redistributive policy areas (Franko and Witko
2017).
There is little literature devoted to understanding the role states play in the area of
affordable housing production, although their involvement and importance in this arena is
increasing. Before 1980, states had minimal involvement in housing policy and finance (Goetz
1995). In the past couple decades, states have had to innovate to address the affordable housing
crisis. From 1990 through 2016, the national median rent and median home price has risen faster
than inflation (20 percent and 41 percent, respectively) (Joint Center for Housing Studies 2018).
The number of low-cost rentals has fallen since 2012 making up only 25 percent of the national
rental stock in 2017 (Joint Center for Housing Studies 2020). Rapidly increasing housing
expenses and stagnant incomes have widened affordability gaps. There are only 36 affordable
units available for every 100 extremely low-income renters, which leaves 71 percent of
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extremely low-income renters severely cost burdened (National Low Income Housing Coalition
2020). Due to the loss of privately and publicly federally funded affordable rental units,
diminishing federal support, rising housing costs, and the increasing number of rent burdened
households, the need to fill the gap between demand and supply for affordable housing remains
(Larsen 2004).
Extant research has started to assess the role states play and how they are innovating to
expand affordable housing during federal devolution of local housing expenditures (Basolo and
Scally 2008; Scally 2009). State housing finance agencies have stepped in to develop financing
mechanisms that address local housing needs (Scally 2009). As an example, state housing
finance agencies issue mortgage revenue and multifamily housing bonds to help finance
homeownership opportunities and rental housing for moderate- and lower-income households.
Another mechanism that gained popularity in the 1990s is the establishment of state housing trust
funds with a dedicated source of funding (Brooks 1995).
State housing trust funds do not typically secure funding through the formal state
budgetary appropriation process. They are usually established with a dedicated source of funding
to generate capital for affordable housing development. Examples of dedicate funding sources
include real estate transfer taxes, linkage fees, document recording fees, and unclaimed property
funds. The funds are typically distributed through a competitive process as a grant or loan for
affordable housing construction or rehabilitation. Trust funds may also provide support for
programs that assist with housing stability such as foreclosure prevention or rental subsidies, and
help households attain homeownership through down payment assistance and home buying
education and counseling.
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In this study, we utilize the policy diffusion framework to examine the factors that lead to
the creation of state HTFs. We use panel data on 50 states from 1980 to 2016, collected from the
state Housing Trust Fund and Housing Finance Agency websites, Census data, and American
Community Survey. This study builds on the foundational work of Scally (2012) in the housing
literature by updating the last 15 years of HTF adoption—which covers the end of the housing
bubble, the collapse of the housing market and subsequent recession, and the last decade of
recovery and economic growth. We analyze HTF adoption through event history analysis
utilizing logit and survival analyses to identify factors that influence the likelihood of policy
adoption. Results indicate that both problem severity and elected leadership predict the adoption
of HTFs.
This study can inform not only housing policy researchers, but the broader political
science and policy process scholars on how housing policy dynamics give insights to theories of
policy adoption. For the housing field, this study contributes to the knowledge on when states are
primed to adopt beneficial policy solutions that expand the affordable housing stock. Political
scientists and policy process scholars have done little work in the housing policy arena (Myers
2004; Johnson et al 2018) compared to the extensive and growing research in the fields of
education, welfare, criminal justice, environmental, and health policy to name a few. This work
advances the policy process literature on diffusion by expanding to a new policy area – housing
affordability. Theoretically, this study adds to the diffusion literature by examining another
redistributive policy area that has gone through a devolution of responsibility from federal to
state governmentsi. Moreover, applications of policy diffusion on affordable housing are
important across national contexts. Our findings contribute to the understanding of housing
policy diffusions.
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Literature: policy diffusion and housing policy
Policy diffusion
Research in policy innovation and diffusion has been rapidly growing since Walker
(1969). At the most basic definition, policy innovation occurs when governments adopt new
policies while policy diffusion is the transfer of policies, programs, and ideas from one
government to another (Shipan and Volden 2008; Graham, Shipan and Volden 2013; Shipan and
Volden 2014). Studies of policy diffusion have been applied to a wide range of policy fields,
including state lotteries (Berry and Berry 1990), education (McLendon, Hearn and Deaton 2006),
abortion regulation (Medoff and Dennis 2011), tax and expenditure limits (Seljan and Weller
2011), and criminal justice (Hoyman and Weinberg 2006). Recent work includes policy areas
such as energy policy (Nicholson-Crotty and Carley 2018), health policies (Pacheco 2017),
marijuana legalization (Hannah and Mallinson 2018), public budgeting (Krenjova and Raudla
2017), policy networks (Yi, Berry and Chen 2018), same-sex marriage (Fay 2018), and
antismoking restrictions (Shipan and Volden 2014). There is also emerging research looking at
the diffusion mechanism outside of the U.S. context to expand the external validity of the theory
across countries (Beer and Cruz-Aceves 2018; Heggelund et al 2019). In addition to the vast
field of policies studied over the decades, the literature has expanded to studying numerous
policy areas at once, to create more generalizable findings on diffusion dynamics (Boehmke and
Skinner 2012). The literature has been critiqued for a pro-innovation bias (Karach et al 2016),
missing the other ways policies can transfer across governments even without legislation. Smith
(2020) for example, shows how policy diffusion occurs through the budgetary process and
bureaucracy, developing common standards in the context of early childhood education.
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The policy innovation and diffusion literature stipulate that a combination of internal
features (Berry 1994a; Berry and Berry 2014) and diffusion mechanisms lead to policy adoption.
The theory of policy diffusion suggests that governmental policy decisions are affected by not
only neighboring governments (a definition that has expanded beyond a simple geographical
understanding of neighbor) for horizontal diffusion (Berry and Berry 1990; Graham, Shipan and
Volden, 2013), but also governments at other levels in vertical diffusion (Allen et al. 2004;
Karch and Rosenthal 2016). Diffusion literature theorizes that federalism allows for innovation,
learning, and trial-and-error across jurisdictions. Decentralization will lead to innovation by the
state governments through the greater number of jurisdictions, variation in ideological ranges,
and smallness of government and scale to enact policies (Adams 2020).
Policy theory and housing policy
Lowi (1964)’s categorization of policies by distributive, regulative, constituent, and
redistributive has been useful to decipher how mechanisms of diffusion vary by policy area.
Additionally, Hollander and Patapan (2017) outline the ways that federalism can enable but also
at times stifle policy innovation and diffusion in morality policies. The context of housing policy
highlights policy adoption in a redistributive policy area, leading to its own unique obstacles to
innovation and diffusion of policies (Shipan and Volden 2012). Scholars have theorized that
national governments are best able to address redistributive policy (Oates 1968; Musgrave 1969;
Peterson 1995). Viewing federalism as a way for states to be competitors, states can adopt
policy as reactive, strategic, anticipatory, or preemptive to other states’ actions (Baybeck, Berry,
and Siegel 2011; Berry and Berry 1990). Applying the competition logic to redistributive
policies at the state level has led to differing viewpoints among scholars on the “race to the
bottom” in policy adoption (Allard and Danziger 2000; Berry, Fording, and Hanson 2003; Figlio,
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Koplin, and Reid 2000). The concern is that competition would lead to less optimal outcomes for
society as states negotiate serving the less fortunate while simultaneously not becoming “welfare
magnets” (Peterson and Rom 1990). The federal government, however, has made efforts at
separating itself from many redistributive type policies. There are also many examples of state
and local governments successfully implementing redistributive policies (Martin 2001;
Swanstrom 1988). In housing, for example, the federal government has applied such policy
dynamics as coercion and burden shedding (Weaver 2020). To generalize, the federal
government has invested less money and slashed major programs in housing over time, leading
states to address the growing affordability crisis. Decentralizing housing affordability support
has spurred states to innovate on their own to address their populations’ needs (Adams 2020).
Drawing from the social construction framework (Schneider, Ingram and DeLeon 2014),
scholars have shown how political power and positive/negative social construction of target
groups can dictate the public policy process. It can be much easier to generate support from
legislators and/or the population for policy beneficiaries that have political power and are
positively constructed as deserving of government support. Redistributive policies typically
benefit those people who have low political power and vary in society’s judgement of their
“deservingness” for help. This social construction orientation coupled with a competition
mentality can make policy innovation and diffusion in redistributive policies a challenge.
The housing policy area holds these characteristics. Many of the issues around housing
affordability illustrate the politics of the poor and minority communities. The incidence of
housing affordability issues has historically fell disproportionately on racial and ethnic
minorities. Finding legislative ways to support housing initiatives relied on Congressional
coalitions centered on addressing poverty plus innovative funding mechanisms. However,
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contemporary research in this area shows how many households across socio-economic status,
racial groups, and ethnicities face housing affordability issues today, especially in high-cost
cities. This has expanded the target groups in need. Of the housing support that is offered,
research has shown that federal government support favors owners over renters and it targets
higher income households (Fischer and Sard 2017; Krueckeberg 1999; McCabe 2018). This
illustrates how efforts at redistribution are not always completely effective at reaching the most
vulnerable groups. Furthermore, federal funding for some housing programs, such as Low
Income Housing Tax Credits, are distributed to states based on population and not need
(Keightley 2017). Below the case of HTFs is explained. We then offer a theoretical section on
determinants of state HTF adoption.
Policy context: the need for affordable housing
History of state housing trust funds
Decades of weakening federal leadership and diminishing funds for housing have left
states and local governments with the responsibility to increase the supply of affordable housing
(Ammann 1999; Basolo 1999; Connerly 1990; Orlebeke 2000). The cuts to funding for housing
that started during the later years of the Carter Administration in the late 1970s were expanded
under the Reagan Administration in the 1980s (Connerly 1993; Goetz 1995). These cuts occurred
while affordability issues and homelessness were on the rise (Connerly 1993). The federal
government shifted some responsibility to states in the form of block grants through the
Community Development Block Grant (CBDG) and HOME Investment Partnerships programs.
States also authorize their own priorities through Qualified Allocation Plans to distribute tax
credits for the federal Low Income Housing Tax Credit Program (Basolo 1999; Goetz 1995).
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However, prior to the 1980s, there were few state governments that were involved in housing
policy (Goetz 1995).
The development community, local housing providers, social service agencies, low-
income communities, and advocacy coalitions responded to the growing demand for affordable
housing by seeking additional financing mechanisms to produce housing (Brooks 1992).
Housing trust funds are one of many tools used to raise capital for affordable housing
development (Ammann 1999). Housing trust funds are used to provide a dedicated source of
funding for affordable housing and are a tool to allow for local flexibility and attract private
sector partners (Brooks 1995; Larsen 2007). In response to federal devolution of affordable
housing, states have used housing trust funds to increase their spending on housing (Scally
2009).
The origin of housing trust funds can be traced to 1968 when Delaware was the first state
to create one (Larsen 2007). Momentum emerged in the 1980s when California and Maryland
initiated housing trust funds (Center for Community Change 2016; Connerly 1990). This was
during a time when many new state housing programs were established (Goetz 1995). Figure 1
highlights state HTF adoptions over time. The map reveals that coastal states in both the East and
West, and several Midwest states, were among the first to adopt HTFs. Many of these states like
California and New York have urban areas that were the first to experience housing supply
shortages, rising housing costs, severe rent burdens, and homelessness. HTFs gained popularity
across the rest of the U.S. by the 1990s with 29 states initiating housing trust funds as federal
support for low-income housing continued to diminish (Brooks 2007). By the mid-2000s, 40
states had housing trust funds (Larsen 2009) and by 2016 nearly all states had at least one
housing trust fund (Center for Community Change 2016). Although housing trust funds can now
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be found across America, they were originally concentrated in the West and Northeast (Connerly
1993). By 2015, states reported leveraging on average $7 for every $1 that is dedicated to
housing trust fund activities and generated more than $790 million, with five states –
Connecticut, Florida, New Jersey, New York, and Washington D.C. – generating more than $50
million annually (Center for Community Change 2016).
[Insert Figure 1 about here]
State housing trust funds adoption and administration
Elected governments create housing trust funds and the money generated is used pursuant
to the enabling legislation without having to go through an appropriation process (Center for
Community Change 2016). Establishing a housing trust fund often requires public outreach and
education, political negotiations, and support from interest groups and the local community
(Brooks 1992). At the time of creation, how the funds are distributed and what entity is
responsible for such dispersal is incorporated into the legislation, which is permanent; equivalent
legislative action is needed to eradicate a housing trust fund (Brooks 1992). The funds are
typically managed by the authorizing legislative body or state housing finance agencies (Brooks
1995). A board or advisory committee comprised of governmental staff, industry experts, elected
officials, developers, housing providers, social service agencies, community stakeholders, and
low-income residents often oversee the operations and are charged with creating the guidelines
for identifying need, distributing the funds, and program reporting mechanisms (Brooks 1995).
Many housing trust funds have a dedicated source of funding when created although a
few have been established without one. The first housing trust funds generated revenue through
real estate transfer taxes and linkage fees (charged to developers on new commercial or industrial
construction to balance the additional housing needed for employment growth) (Ammann 1999;
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Brooks 1995). The range of options available to fund housing trust funds has expanded to almost
forty distinctive sources (Brooks 1995). The most common revenue sources include real estate
transfer taxes, followed by appropriations from the state general fund, document recording fees,
revenues from state housing finance agencies, and interest from real estate escrow accounts.
Other sources include bond proceeds, contractor’s excise tax, foreclosure filings, interest on title
escrow accounts, state income tax, tobacco tax, unclaimed property fund, and utility charges
(Center for Community Change 2016).
Housing trust funds are typically distributed through a competitive process as grants or
loans for both new construction and rehabilitation. Multiple sources of funding such as
Community Development Block Grants or HOME are often used in conjunction with housing
trust funds as the amounts awarded from trust funds are not enough to support the entire project
(Brooks 1995). The projects primarily provide housing for low-income households, which are
households that earn below 80 percent of the area median income. Some funds are specifically
used to serve very low-income (50 percent of the area median income) or extremely low-income
households (30 percent of the area median income). However, one criticism of housing trust
funds is that they lack deep subsidies and often do not serve extremely low-income households
(Connerly 1993; Larsen 2004). Affordable developments are often required to remain affordable
for a specified timeframe (Brooks 1992). Some housing trust funds provide support for specific
populations such as the homeless or individuals reentering society (Center for Community
Change 2016; Connerly 1990). Non-construction related activities such as foreclosure
prevention, down payment assistance, home education and counseling, or rental subsides are also
supported by some state housing trust funds (Brooks 1995; Center for Community Change
2016).
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To the authors’ knowledge, there has only been one study conducted that has examined
factors that influence state adoption of housing trust funds. Scally (2012) examined the factors
that influence U.S. state housing policy innovation, specifically housing trust funds over a 20-
year period using a state policy innovation framework. The framework included indicators of
internal organizational characteristics of agencies administering trust funds, environmental
determinants (social, economic, and political), and policy diffusion. Scally (2012) found that
states with a higher proportion of Black citizens, previous housing expenditures, higher rates of
single-family construction, more politically leaning liberal citizens, and severe affordability
issues were more likely to adopt housing trust fund policy innovation.
Data and Methods
We have relied on the literatures in policy innovation and diffusion, housing
affordability, federalism, and policy types to develop a model of state HTF adoption. Our model
consists of six components: problem severity, economic standing, housing investment, elected
leadership, neighbor adoption, and demographics. Below, each component is discussed in regard
to variables and theory.
Problem severity
Policy adoption is advanced by the extent of the problem (Walker 1969; Gray 1973). The
description above illustrates how the housing affordability crisis has been exacerbated in states.
Drawing from the diffusion literature, we expect the severity of the housing crisis to increase the
likelihood of HTF adoption—more severe housing concerns likely pressure policymakers to
adopt initiatives that support housing production. However, redistributive policy and social
construction theory tell us that it is harder to generate new policy given the housing policy
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context and target group. In this light, we may expect problem severity to have little to no impact
on the rate of HTF adoption. Our empirical models will give some clarity to this puzzle.
In our empirical models, severity of the housing crisis is measured by three variables: the
cost burden of renters, the cost burden of homeowners, and gross vacancy rate. These variables
were collected from two sources: Decennial Census and the American Community Survey
(ACS). The Decennial Survey provided data from 1980, 1990, 2000, and 2010. The ACS was
available annually from 2011 to 2016. To fill in the missing data, the mean value was used for
the first and last year in the missing range. For example, years 1981-1989 consisted of the mean
of 1980 and 1990 in each state. Years 1991-1999 was the mean of 1990 and 2000, and so on.
The two cost burden variables determine how many renters and homeowners are paying
35 percent or more of their income toward housing costs. There has been a long-standing percent
of income standard that was originally 25-percent, and now 30-percent, to measure housing
affordability (Herbert, Hermann and McCue 2018). Households paying more than the 30-percent
standard are considered cost-burdened since this leaves them with little to spend on other
necessities such as food, clothing, and medical expenses. As expected, the percent share of cost
burden renters is higher than homeowners. On average, cost burden owners account for 13
percent of all households, with the lowest proportion at 8.4 percent and the highest share at 23.5
percent. Cost burden renters account for 28.8 percent, on average for all households, with a range
from 3.4 percent to 46.6 percent.
Vacancy rates can be used as another indicator of the severity of the affordable housing
crisis. Vacancy rates are often used to assess demand in housing markets. Low vacancy rates can
be an indicator of a strong housing market with a shortage of housing supply that increases
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housing costs (Joint Center for Housing Studies 2019). Vacancy rates range from 6.2 percent to
21.2 percent, with an average gross vacancy rate of 11 percent.
Economic standing
The overall economic climate in a state should also influence housing policy
development. The economic well-being of the state is measured by the poverty rate and
unemployment rate in our models. The state poverty rate range is from 2.9 percent to 27.2
percent with an average poverty rate of 13.95 percent. The average state unemployment rate is
6.34 percent with a range of 2.3 to 17.8 percent. The poverty rate is available annually from the
Census Bureau and the unemployment rate is available from the Bureau of Labor Statistics.
The economic standing variables also give divergent predictions on HTF adoption. Given
the relationship between socioeconomic status and housing, we expect that an increased poverty
rate and unemployment rate should raise the likelihood of HTF adoption, as informed by
diffusion theory. On the other side, poor economic standing could cause policymakers to act, but
in the interests of those with higher social standing and power-like businesses and economic
development—and not towards helping low-income households. This could lead to little-to-no
impact of economic standing on HTF adoption.
Housing investment
More direct to the policy area, we examine how state monetary investment in housing
influences HTF adoption. It is measured by the amount of state general funds dedicated to
housing and community development per capita. This includes housing and community
development expenditures in state programs, including trust funds, that are used for activities
such as but not limited to: planning, construction, and operation of affordable housing projects;
rent subsidies, homeownership and renovation programs; urban renewal and slum clearance; and
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programs that encourage housing production in the private sector. This variable was collected
from the Census Bureau State and Local Finance Data and was available annually. The total state
housing and community development spending per capital ranges from 0.01 to 0.2 with the mean
of 0.05. Our expectation is that more state monetary investment should lead to faster adoption of
a HTF. The more money invested signals that state budget officers and policymakers are
interested in the policy area. More money should mean more awareness and interest in
developing and/or being an innovator on that policy front.
Elected leadership
Elected leadership can also influence the speed of policy adoption. We explore this
connection in two ways: the partisanship of the state legislature and the proportion of female
state representatives. To assess the effect of partisanship, we use two measures. The first
measure is for government ideology from Berry, Ringquist, Fording, and Hanson (1998). In this
measure, a higher score means a more liberal ideology. In our sample it ranges from 18.6 to 72.6,
with a mean at 48.8. The second measure accounts for institutional control—it is a dummy
variable for Democratic party unified government. To our knowledge the academic literature has
not addressed how partisanship guides housing policy efforts. Given this lack of theory,
however, we are unsure whether elected official partisanship predicts HTF adoption. In general,
we may expect the Democratic party to be associated with supporting those groups in poverty,
thus a greater presence of Democrats in government can increase the probability of policy
adoption.
We also included the percentage of female state legislators as a control variable in the
models. This data was made available by Center for American Women in Politics at Rutgers and
Stateminder from Georgetown University. In our sample, the percentage ranges from 1.1 to 41,
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with the mean of 15.51. Female state legislators and male state legislators have different policy
focuses (MacDonald and O’Brien 2011; Osborn and Mendez 2010). Female members of
congress, across party ideologies, represent women beyond their district lines (Carroll 2002).
Female representatives, including city managers and legislators, tend to focus more on feminine
policies such as social welfare, health, and civil rights (Atkinson 2020; Funk and Philips 2019;
Holman 2014). Housing policies, as one of the feminized policy areas, is a top priority topic for
female state legislators (Atkinson 2020). We predict that those states with a higher percentage of
female state legislators are more likely to adopt a Housing Trust Fund.
Neighbor adoption
An essential variable in the diffusion literature is the number of neighbor states that have
adopted the policy of interest. In our analyses, we use the lagged percent of geographic
neighbors (Mallinson 2019; Mintrom 1997) to adopt a HTF to measure policy diffusion. This
measure, geographic neighbors, makes the most sense given our context as opposed to other
measures of neighbors (like political ideology). Neighboring states often face similar housing
market dynamics and housing stock challenges. For example, Walter et al. (2020) documents the
different types of housing stock challenges housing organizations face in high cost western states
with rental subsidies compared to aging housing stocks in the northeastern portion of the
country. Also, given the race-to-the-bottom theory, the geographical neighbor is an important
benchmark in incentivizing/deterring groups of people.
The general expectation from diffusion studies is that neighbor adoption will make it
more likely for a state to adopt. Literature identifies three major mechanisms of how neighboring
states affect policy adoption: officials noting the positive policy outcomes of neighboring states’
policy, catching up with the economic advantages of neighboring states, and support from
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constituents (Berry, Fording, and Hanson 2003; Pacheco 2012; Volden 2006). Drawing from
theory in redistributive policies though, states may be less likely to innovate. The three major
mechanisms noted above may not hold for redistributive policies—positive policy outcomes
could deem them as “welfare magnets,” there are other policy areas more suited to lead to
economic advantages, and constituent support may not be from those most politically powerful.
Thus, theory gives us reasons to expect that the percent of geographic neighbors to adopt a HTF
may have little to no impact on states to adopt a HTF.
Demographics
Lastly, we account for a number of state demographic characteristics. Housing policy has
historically disadvantaged racial and ethnic minorities (Massey & Denton 1993; Rothstein 2017).
To account for this, we include the percent of non-white residents in a state. The average
proportion of the population that are racial and ethnic minorities in each state is 15.8 percent
with the smallest proportion at 14.5 percent and largest at 49.1 percent. We expect that greater
percentages of racial and ethnic minorities will increase the probability of HTF adoption, as
historically, these are the groups that need the housing support. However, we do recognize that
minority population presence may not lead to quicker adoption, as supported by social
construction theories.
Similar to our discussion of government ideology, citizen ideology is another guiding
factor in elected official decision-making. We use the Berry, Ringquist, Fording, and Hanson
(1998) measure for citizen ideology (similar to the measure for government ideology). This
measure averages 46.1, with a low of 9.3 and high of 93.9. It is a larger range than the
institutional ideology. Like before, we do not have academic literature on housing and
partisanship. We can deduce though, that lower income people are more likely to need housing
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assistance (that a HTF can provide) and lower income people are more likely to identify with the
Democratic party (McCarty, Poole, and Rosenthal 2003). This can give some support to the
expectation that a more liberal citizenry ideology can make HTF adoption more likely to occur.
Sudden growth in state populations can lead to housing shortages. We control for the
annual percent change in total population. On average, population increases by 1 percent
annually at the state level while the largest decline seen was 3.8 percent and the largest increase
experienced was 7 percent. Greater population numbers enhance the demand for housing and
contributes to housing affordability issues. If demand outpaces supply, it can increase the need to
develop a HTF. Thus, we expect population growth to speed the adoption of a HTF.
Methods
The dataset consists of 46 U.S. states from 1980 to 2016. The unit of analysis is state-
year. Table 1 has the variable descriptive statistics. We use event history analysis for two types
of empirical tests. We conduct three logistic regressions and a cox proportional hazard survival
model to test the conditions that make policy adoption more likely. These are common methods
of analysis within the policy diffusion literature (Berry and Berry 1990; Box, Steffensmeier and
Jones 2004; Fay 2018; Hannah and Mallinson 2018). In our context, we are testing how a host of
variables contribute to the likelihood of HTF adoption in a state.
[Insert Table 1 about here]
Our dependent variable for both models is HTF adoptionii. In each state, the variable is 0
until the year the state adopts a HTF. On the year the HTF is adopted, the variable is coded as 1
and then subsequent observations for that state are dropped from analyses. Additionally, a count
variable (starting in 1980) of years until HTF adoption in each state is used for the survival
models. If a HTF is never created, as is the case with three of the states in our study, then the
21
count continues until the end of the study period. Information on the year of HTF adoption was
based on the work of Scally (2012) and updated with the Center for Community Change Housing
Trust Fund Survey Report (2016) and each state’s HTF or Housing Finance Agency or similar
state government housing agency website.
Four states were dropped from all analyses. Alaska and Hawaii were dropped from
analyses since they do not have geographical neighbors, an essential element for our study of
policy diffusion in the redistributive policy context. Delaware was also dropped from analyses
since it was the first state to adopt a HTF (and did so more than ten years before any other state).
Nebraska was dropped from the analyses because of its atypical legislative structure. Of the 46
states in the empirical model, 43 have adopted a HTF. The fixed effects model drops the three
non-adopters (Wyoming, Utah, and Mississippi) since they have a constant dependent variable,
meaning the conditional fixed-effects logit is estimated. We report two random effects logistic
models, one for the full sample of 43 states and another of the 40 states to match the sample of
the fixed effects model.
Results
Table 2 shows the results of the logistic regressions and Table 3 shows the results of the
cox proportional hazard survival model. While none of the models give the exact same results,
they do illustrate some themes for each of the concepts expected to influence the rate of HTF
adoption in a stateiii.
[Insert Table 2 and Table 3 about here]
The problem severity variables give some support to the diffusion model. The cost
burdened renter variable is positive and statistically significant in the logit models. This means
that as a larger share of renters pay more of their income towards rent, states become more likely
22
to introduce HTFs as a way to fund housing construction and programs. Cost burden renter
households are often the most vulnerable to housing insecurity and are at the greatest risk of
becoming homeless. For this population, state housing trust fund revenues have supported
permanent homeless housing, transitional housing, and emergency and permanent rental
assistance programs (Center for Community Change 2016). The cost burden to homeowners and
the gross vacancy rates are not statistically significant in any of the models. Problem severity
represented by cost burdened renters is in line with diffusion theory that shows problem severity
can be a reason to adopt new policy, even for a redistributive policy area.
The economic variables did not have strong results. The poverty rate and unemployment
rate are not statistically significant. This did not support the expectation that economic standing
will influence HTF adoption. It supports our other prediction that the allocation of resources may
be directed to jobs and/or economic development rather than housing for low-income groups.
Additionally, housing may be more affordable in states that are struggling economically and/or
may not be seen as an essential part of economic recovery efforts.
For state housing monetary investment, there is one model with a statistically significant
finding. The logistic fixed effects model (Model 3 in Table 2) shows a strong, positive
relationship between the amount of money spent on state housing and community development
per capita and the adoption of a HTF. While we expected housing investment to be a stronger
predicter across all models, we can identify a couple of reasons why this relationship may not
hold. For one, states may view more money for housing as a substitution for other efforts (like a
HTF). Putting more money into existing programs may slow policy creation to new endeavors.
Also, we do not know the exact use of those state funds across all states over time except for the
broad title of “housing and community development.” The efforts of those funds may not be
23
fully devoted to housing affordability per se, thus would not be a full signal to prior efforts on
housing insecurity.
The effect of elected leadership shows some significant results. Institutional ideology is a
statistically significant predictor for HTF adoption in the survival analysis. The variable as a
hazard ratio is above one, and positive as a coefficient. It means that HTF adoption becomes
more likely with liberal leaning governments. The variable noting a full Democratic government,
however, is not statistically significant in any of the models. Of statistical significance across all
of the models is the presence of female legislators; a greater number of female legislators is a
positive indicator of adopting a HTF. This is in line with the literature on policy areas female
legislators promote. Taken together, this does provide some support on a type of “poverty
coalition” building as observed in the academic literature and in practice. Our findings suggest
that more liberal governments and female legislators support policy development in our
redistributive policy context of housing affordability.
In contrast to much of the policy diffusion literature, adoption of a HTF is not influenced
by the previous policy adoption by geographical neighbor states. Perhaps other types of
“neighbors” are influencing state adoption, such as the sustained organizational influence
(Collingwood et al 2019). These findings, though, give some support to the literature on
redistributive policies—states do not always deem it in their best interests to be as generous as
their neighbors.
Lastly, the citizen demographics in a state has little influence on the HTF adoption. The
only statistically significant result for citizen ideology is in Model 3. It is in line with the
institutional ideology results showing that more liberal ideology increases the likelihood of state
adoption. The presence of non-whiteiv citizens do not have statistically significant effects in our
24
models. Given the historical discrimination of non-white citizens in housing, it is surprising that
their presence in a state does not lead to HTF creation. This finding is in line with other work
(Flink and Molina 2017) that shows it is not just citizen demand that changes policy, but the need
of the target population. The presence of non-white population does not perfectly predict housing
needs. The annual total population change is statistically significant in only the full sample
random effects model. For population growth, it does not always strain housing supply, so
perhaps that is why population does not predict HTF adoption.
Discussion and conclusion
What influences the creation of HTFs for state governments? We revisit this question and
confirm and expand on previous findings. We use six categories of variables to predict the
adoption of a state level policy: problem severity, economic standing, housing investment,
elected leadership, neighbor adoption, and population demographics. Overall, our results give
some support that the proportion of cost burden renters, monetary investment in housing and
community development, liberal leaning governments, female legislators, and liberal leaning
citizens all make HTF adoption more likely to occur.
Our work has similarities to earlier work on the adoption of state HTFs by Scally (2012).
For example, she also finds that state housing expenditures and more liberal citizen ideology
increases HTF adoption. It is also not surprising, as Scally (2012) suggests, that the severity of
the affordability crisis and the proportion of cost-burdened households is linked to state HTF
adoption. On the other hand, Scally (2012) finds that a higher proportion of Black citizens
increase adoptions, while we do not find any racial impacts with the additional 15 years of HTF
adoption data added to the analysis. Even though the severity of the affordable housing crisis
disproportionately impacts racial and ethnic minorities, in more recent years housing
25
affordability has become a mainstream concern for many households, especially those living in
high-cost cities. Where political pressure to innovative in the housing arena may have been
driven by communities and organizations that historically have been most impacted by
residential segregation in the past, political pressure is increasing in all segments of the
population to address rising housing costs.
Our contributions extend beyond the housing literature by bringing a new policy area for
consideration to political science and policy process scholars. Basing our work in diffusion
theory, we draw from literatures on policy type, social construction, and federalism to develop an
understanding of policy development in housing affordability. For the diffusion literature, we
add to the body of work on how policy creation works (or does not work) in a redistributive
policy area (Shipan and Volden 2012, Franko and Witko 2017). This also gives contributions for
social construction theory by seeing how less politically powerful groups can gain policy action
to their benefit (Schneider, Ingram, DeLeon 2014). For federalism, we examine a policy area that
demanded state innovation due to increasing problem severity and declining federal support. In
tightening financial times, the federal government can limit its contributions to help state and
local governments. Our work adds to the literature on how financial situations shape state policy
development (Allen, Pettus, and Haider-Markel 2004; Stone 1997; Welch and Thompson 1980).
Future work can examine a number of questions on housing affordability generally, and
the creation of HTFs more specifically. In housing affordability, research can ask what role
female legislators play in the adoption of housing policy and programs at the state level. With a
renewed energy for female voices and representation in government, state legislatures’ policy
agenda could be reshaped. Research has shown that female legislators tend to focus on social
welfare policy areas (Atkinson 2020; MacDonald and O’Brien 2011; Osborn and Mendez 2010)
26
generally, while ours has shown women’s potential to prioritize housing. Female legislators may
participate and contribute more in the process of forming legislation and programs on housing
policies. This is a new line of inquiry that merges the political science and housing fields that has
been understudied but may have substantial implications for housing policy innovation.
Furthermore, policy diffusion models can be applied to other state housing policies and programs
such as the requirements and scoring criteria that is adopted in state Qualified Allocation Plans
for the distribution of Low Income Housing Tax Credits. Moreover, the diffusion models can be
explored in other national context and study how housing policies diffuse. Factors such as
economic development and culture might also have effects on housing policy adoptions.
In direct relationship to this study, there are avenues for future research. For example, of
the states that created a HTF, what leads to the fund being administered by the state Housing
Finance Agency or another agency? This type of study falls in line with newer work on
administrative agency or process diffusion. Another question is what influences the decision to
create a dedicated revenue source? Funding is a new aspect to assess in diffusion studies. State
and local governments must think strategically about financing mechanisms to fund new policy
work and how much revenue these mechanisms will create. The creation of a HTF is a larger
financial endeavor that needs consideration for fiscal sustainability. Creating a stable revenue
source is essential for trust fund success. There are also potential avenues of research to examine
the role of vertical diffusion in HTFs—many local governments have created their own trust
funds independent of state efforts. Does municipal HTF creation prompt the county or state to
create a HTF or adopt/expand dedicated revenue sources? Do states who create HTFs see a rise
in local HTF initiations? This research can highlight how multiple government actors work in
single policy spaces with similar goals.
27
Future work should evaluate the success of the HTFs. We’ve examined what leads to
their creation in a state. However, are the HTFs successful in their work? How does
organizational performance vary in this field? HTFs vary in the activities they do to support
housing. Do some HTFs do only a small amount of activities well? Do they expand the scope of
their work over time? What do HTFs see as the obstacles to their success? There is much work to
be done to understand the work of states in the housing policy area.
28
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35
Table 1: Descriptive Statistics N =757
Variable Mean Std.Dev. Min Max
Cost burden of renters (lagged) 28.60 3.26 21.77 46.60
Cost burden of owners (lagged) 12.95 2.77 8.40 23.54
Gross Vacancy Rate (lagged) 10.69 2.67 5.63 21.22
Poverty rate 13.95 4.394 2.90 27.20
Unemployment rate 6.34 2.31 2.30 17.80
State housing and community
development spending per capita 0.05 0.03 0.01 0.20
Institutional Ideology 48.76 12.18 18.62 72.56
Democratic party unified government .28 .45 0 1
Percent Female Legislators 15.51 7.00 1.10 41.00
HTF Diffusion (lagged) 30.75 32.61 0 100
Percent Non-White 15.78 10.85 1.45 49.11
Citizen Ideology 46.06 14.63 9.25 93.91
Annual Percent Population Change 0.86 1.12 -3.77 6.70
36
Table 2: Logistic Regression Models
Variables
Model 1
Random Effects
Full Sample
Model 2
Random Effects
Adopters Only
Model 3
Fixed Effects
Adopters Only
Problem Severity
Cost burden of renters (lagged) 0.17 (0.09)* 0.32 (0.13) ** 1.91 (0.77) **
Cost burden of owners (lagged) -0.12 (0.12) -0.21 (0.14) 1.59 (1.05)
Gross Vacancy Rate (lagged) 0.002 (0.09) 0.01 (0.10) -0.58 (1.32)
Economic and Fiscal Standing
Poverty Rate 0.12 (0.10) 0.16 (0.11) -0.75 (0.47)
Unemployment Rate -0.18 (0.15) -0.24 (0.16) 0.34 (0.52)
Housing Investment
State Housing and Community
Development Spending Per Capita 8.85 (8.39) 10.57 (8.73) 168.91 (63.94) ***
Elected Leadership
Institutional Ideology 0.04 (0.03) 0.03 (0.03) 0.06 (0.11)
Democratic party unified
government -0.17 (0.55) 0.03 (0.57)
0.58 (1.83)
Percent Female Legislators 0.11 (0.06)* 0.11 (0.06)* 2.61 (0.94) ***
Neighbor Adoption
Geographical Neighbor Diffusion 0.01 (0.01) 0.01 (0.01) 1.36 (242.56)
Demographics
Percent Nonwhite Population 0.01 (0.03) 0.02 (0.03) 0.41 (0.28)
Citizen Ideology 0.02 (0.02) 0.02 (0.02) 0.17 (0.09)*
Annual Percent Population Change 0.35 (0.19)* 0.30 (0.20) 0.16 (0.78)
Constant -12.80 (3.95)*** -15.64 (4.89)*** -
N 757 649 649
Log odds coefficients reported. Standard errors in parentheses. *** p
37
Table 3: Cox Proportional Hazard Survival Models
Model 4
Variables Hazard Ratio Coefficients
Problem Severity
Cost burden of renters (lagged) 1.16 (0.11) 0.15 (0.10)
Cost burden of owners (lagged) 0.83 (0.09) -0.18 (.11)
Gross Vacancy Rate (lagged) 1.02 (0.07) 0.02 (0.07)
Economic Standing
Poverty Rate 1.09 (0.07) 0.09 (0.07)
Unemployment Rate 0.76 (0.13) -0.28 (0.18)
Housing Investment
State Housing and Community Development
Spending Per Capita
12338.87
(101336.9)
9.42 (8.21)
Elected Leadership
Institutional Ideology 1.04 (0.02)* 0.04 (0.02)*
Democratic party unified government 0.78 (0.38) -0.25 (0.49)
Percent Female Legislators 1.06 (0.03)* 0.06 (0.03)*
Neighbor Adoption
Geographical Neighbor Diffusion 1.01 (0.01) 0.01 (0.01)
Demographics
Percent Nonwhite Population 1.01 (0.02) 0.01 (0.02)
Citizen Ideology 1.00 (0.02) -0.01 (0.2)
Annual Percent Population Change 1.29 (0.25) 0.26 (0.19)
N= 757. Standard errors in parentheses
*** p
38
Figure 1. Housing Trust Fund State Adoption by Year
39
Supplementary Materials (Not for publication)
Table 2: Correlation Table
Variables (1) (2) (3) (4) (5) (6)
(1) Cost burden of renters
(lagged)
1.000
(2) Cost burden of owners
(lagged)
0.671 1.000
(3) Gross Vacancy Rate
(lagged)
0.100 0.078 1.000
(4) Institutional Ideology 0.024 -0.009 -0.205 1.000
(5) Democratic party unified
government
-0.007 0.023 -0.152 0.663 1.000
(6) Percent Female Legislators 0.176 0.235 0.242 -0.196 -0.268 1.000
(7) Poverty rate 0.067 -0.049 0.158 0.231 0.276 -0.473
(8) Unemployment rate 0.175 -0.079 -0.068 0.339 0.275 -0.364
(9) State housing and
community development
spending per capita
0.367 0.521 -0.187 -0.107 -0.118 0.284
(10) HTF Diffusion (lagged) 0.324 0.372 -0.018 -0.281 -0.236 0.273
(11) Percent Non-White 0.296 0.317 -0.076 0.188 0.249 -0.421
(12) Citizen Ideology 0.066 -0.004 -0.230 0.414 0.042 0.215
(13) Annual Percent
Population Change
0.168 0.333 0.127 -0.118 0.022 0.200
Variables (7) (8) (9) (10) (11) (12) (13)
(7) Poverty rate 1.000
(8) Unemployment rate 0.565 1.000
(9) State housing and
community development
spending per capita
-0.292 -0.344 1.000
(10) HTF Diffusion
(lagged)
-0.254 -0.454 0.619 1.000
(11) Percent Non-White 0.482 0.247 -0.019 -0.029 1.000
(12) Citizen Ideology -0.259 -0.049 0.314 0.096 -0.266 1.000
(13) Annual Percent
Population Change
-0.198 -0.233 0.006 0.070 0.120 -0.272 1.000
40
Table 1: Build Up Logistic Regressions, Random Effects
Variables
Model 1
Problem
Severity
Model 2
Economic
and
Fiscal
Standing
Model 3
Housing
Investment
Model 4
Elected
Leadership
Model 5
Neighbor
Adoption
Model 6
Demographics
Problem Severity
Cost burden of
renters (lagged)
0.181**
(0.0747)
0.278***
(0.0845)
0.273***
(0.0859)
0.317***
(0.112)
0.338***
(0.128)
0.317**
(0.127)
Cost burden of
owners
(lagged)
0.0101
(0.0799)
-0.0725
(0.0873)
-0.127
(0.0968)
-0.167
(0.118)
-0.169
(0.130)
-0.214
(0.136)
Gross Vacancy
Rate (lagged)
0.0250
(0.0570)
0.00124
(0.0630)
0.0342
(0.0677)
-0.00119
(0.0830)
-0.00533
(0.0944)
0.0123
(0.0975)
Economic and Fiscal Standing
Poverty Rate 0.0374
(0.0516)
0.0380
(0.0529)
0.123
(0.0962)
0.154
(0.106)
0.162
(0.111)
Unemployment
Rate
-0.277**
(0.112)
-0.224*
(0.116)
-0.306*
(0.156)
-0.301*
(0.165)
-0.236
(0.163)
Housing Investment
State Housing
and
Community
Development
Spending Per
Capita
7.542
(5.473)
9.716
(7.391)
8.934
(8.503)
10.57
(8.734)
Elected Leadership
Institutional
Ideology
0.0258
(0.0217)
0.0302
(0.0241)
0.0254
(0.0266)
Democratic
party unified
government
0.0253
(0.515)
0.0577
(0.548)
0.0269
(0.565)
41
Percent Female
Legislators
0.0915*
(0.0552)
0.114*
(0.0613)
0.114*
(0.0632)
Neighbor Adoption
Geographical
Neighbor
Diffusion
0.0104
(0.0105)
0.0115
(0.0104)
Demographics
Percent
Nonwhite
Population
0.0177
(0.0333)
Citizen
Ideology
0.0173
(0.0228)
Annual Percent
Population
Change
0.304
(0.204)
Constant -
8.349***
(1.613)
-8.660***
(1.686)
-8.921***
(1.726)
-12.92***
(3.725)
-
14.82***
(4.476)
-15.64***
(4.892)
N = 649. Log odds coefficients reported. Standard errors in parentheses
*** p
42
Table 2: Build Up Logistic Regressions, Fixed Effects
Variables
Model 1
Problem
Severity
Model 2
Economic
and
Fiscal
Standing
Model 3
Housing
Investment
Model 4
Elected
Leadership
Model 5
Neighbor
Adoption
Model 6
Demographics
Problem Severity
Cost burden of
renters (lagged)
0.812***
(0.275)
0.925***
(0.321)
0.982***
(0.316)
1.495***
(0.402)
1.504**
(0.585)
1.905**
(0.770)
Cost burden of
owners
(lagged)
1.883***
(0.493)
2.008***
(0.467)
1.621***
(0.550)
1.468**
(0.588)
1.158
(0.852)
1.585
(1.047)
Gross Vacancy
Rate (lagged)
-0.513
(0.539)
-0.702
(0.578)
-0.810
(0.659)
-0.613
(0.770)
-0.711
(0.919)
-0.584
(1.322)
Economic and Fiscal Standing
Poverty Rate 0.196
(0.140)
0.189
(0.145)
-0.0381
(0.237)
-0.389
(0.367)
-0.751
(0.465)
Unemployment
Rate
-1.045***
(0.244)
-0.833***
(0.256)
-0.687**
(0.309)
-0.276
(0.408)
0.339
(0.517)
Housing Investment
State Housing
and
Community
Development
Spending Per
Capita
92.75***
(26.11)
77.98***
(28.44)
130.7***
(48.69)
168.9***
(63.94)
Elected Leadership
Institutional
Ideology
0.0142
(0.0821)
0.0228
(0.0993)
0.0565
(0.111)
Democratic
party unified
government
1.547
(1.506)
1.165
(1.566)
0.575
(1.832)
43
Percent Female
Legislators
1.699***
(0.503)
2.031***
(0.677)
2.611***
(0.944)
Neighbor Adoption
Geographical
Neighbor
Diffusion
1.345
(257.3)
1.357
(242.6)
Demographics
Percent
Nonwhite
Population
0.413
(0.277)
Citizen
Ideology
0.173*
(0.0926)
Annual Percent
Population
Change
0.160
(0.780)
N = 649. Log odds coefficients reported. Standard errors in parentheses
*** p
44
Table 3: Build Up Cox Proportional Hazard Survival Models (Coefficient Reported Below)
Variables
Model 1
Problem
Severity
Model 2
Economic
and
Fiscal
Standing
Model 3
Housing
Investment
Model 4
Elected
Leadership
Model 5
Neighbor
Adoption
Model 6
Demographics
Problem Severity
Cost burden of
renters (lagged)
0.0859
(0.0720)
0.187**
(0.0877)
0.191**
(0.0893)
0.166*
(0.0919)
0.156*
(0.0920)
0.146
(0.0972)
Cost burden of
owners (lagged)
0.0195
(0.0840)
-0.0300
(0.0866)
-0.0743
(0.0933)
-0.106
(0.0996)
-0.123
(0.100)
-0.183
(0.113)
Gross Vacancy
Rate (lagged)
0.00371
(0.0628)
-0.00156
(0.0626)
0.0337
(0.0669)
0.0107
(0.0683)
0.0138
(0.0686)
0.0228
(0.0728)
Economic and Fiscal Standing
Poverty Rate 0.0241
(0.0516)
0.0305
(0.0514)
0.0702
(0.0607)
0.0930
(0.0633)
0.0885
(0.0675)
Unemployment
Rate
-0.305*
(0.158)
-0.286*
(0.155)
-0.335**
(0.157)
-0.336**
(0.158)
-0.281
(0.175)
Housing Investment
State Housing
and Community
Development
Spending Per
Capita
8.655
(6.315)
5.208
(6.914)
5.196
(6.923)
9.421
(8.213)
Elected Leadership
Institutional
Ideology
0.0346*
(0.0203)
0.0305
(0.0207)
0.0394*
(0.0237)
Democratic
party unified
government
-0.203
(0.476)
-0.107
(0.480)
-0.248
(0.489)
Percent Female
Legislators
0.0472*
(0.0254)
0.0566**
(0.0266)
0.0556*
(0.0294)
45
Neighbor Adoption
Geographical
Neighbor
Diffusion
0.0118
(0.00856)
0.0125
(0.00907)
Demographics
Percent
Nonwhite
Population
0.00695
(0.0210)
Citizen
Ideology
-0.00496
(0.0196)
Annual Percent
Population
Change
0.256
(0.193)
N = 757. Log odds coefficients reported. Standard errors in parentheses
*** p