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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