Abstract
Eviction is concentrated in poor communities of color, and studies indicate eviction may affect social processes. Community and crime literature demonstrates that structural factors, such as socio-economic status and racial composition, are linked with crime and that social processes protect neighborhoods from crime. Therefore, eviction is likely concentrated in neighborhoods vulnerable to crime, but the connection between eviction and neighborhood violent crime has not yet been examined. Using data from Princeton University’s Eviction Lab National Database, the National Neighborhood Crime Study 2, and the Boston Neighborhood Survey, this Boston-based study is a first step in filling this knowledge gap. Findings indicate that eviction is positively associated with crime and partially explains why crime is concentrated in disadvantaged communities of color.
Introduction
Eviction is a disruptive housing event that has harmful consequences, and new data collection efforts by Princeton University’s Eviction Lab demonstrate how prevalent eviction is in the United States (Desmond et al., 2018a). In 2016, on average, approximately 1 in every 43 renter-occupied households faced eviction across the country, and among big cities, the eviction rate reached as high as 16.5% (Desmond et al., 2018a). As well, approximately one of every seven children born in large United States cities between 1998 and 2000 experienced eviction by age 15 (Lundberg & Donnelly, 2019). Research indicates that forced displacement is associated with a variety of individual level harms, ranging from damage to credit history which reduces ability to secure future housing (Gold, 2016; Kleysteuber, 2006) to public health concerns such as suicide, drug relapse, and depression (Damon et al., 2019; Desmond & Kimbro, 2015; Fowler et al., 2015).
The effects of eviction may be felt beyond the individual level, and recent work suggests that eviction influences the community (Desmond, 2012a, 2016; Greenberg et al., 2016). Current studies indicate that eviction is associated with community social processes related to neighborhood violent crime and that eviction is linked to neighborhood structural factors that are connected to crime, such as socio-economic status and racial composition (Desmond, 2016; Matsueda, 2006; Sampson, 2012; Sampson et al., 1997; Shaw & McKay, 1942; Taylor et al., 1984; Warner, 2007). However, research has yet to establish whether there is a relationship between community-level eviction and neighborhood violent crime.
To unravel possible overlaps in these connections, this Boston-based study uses eviction data from the Eviction Lab National Database (Desmond et al., 2018a) to examine the relationships between eviction, community social processes and structural factors, and neighborhood violent crime. The study begins by drawing on theoretical and empirical work to explore how eviction may be linked to neighborhood violent crime. Next, in the current study section, the geographic context of the analysis and goals of the current study are presented. Then the study methods are reviewed, specifically the three data sources, the study measures, and the analytic strategy. The results section offers evidence that eviction has harmful neighborhood consequences. Finally, in the discussion and conclusion, I explore opportunities for future studies and offer policy recommendations.
Linking Eviction and Neighborhood Crime
The harm of eviction reverberates throughout the community such that “a single eviction could destabilize multiple city blocks, not only the block from which the family was evicted but also the block to which it begrudgingly relocated” (Desmond, 2016, p. 70). This community-level disruption may conceivably affect neighborhood violent crime, as eviction and crime are concentrated in similar communities and associated with similar community social processes (Desmond, 2012a, 2016; Greenberg et al., 2016; Sampson et al., 1997; Shaw & McKay, 1942). Two complementary mechanisms offer explanations for untangling how eviction is intertwined with various neighborhood factors that make communities vulnerable to violent crime.
Community Social Processes
Recent eviction literature suggests that eviction is linked with social processes, particularly social ties and collective efficacy (Bezdek, 1992; Desmond, 2012a, 2012b, 2016; Greenberg et al., 2016; Hartman & Robinson, 2003). Social ties are the connections between individuals and allows access to social capital, which are the resources that exist in the relationships between individuals (Coleman, 1988). For those experiencing eviction, they often develop “disposable ties,” or short-term connections that are activated in times of need to access means for survival (Desmond, 2012a). Collective efficacy, which is a community’s ability to access common norms and values to stimulate effective social control (Sampson et al., 1997), is another social process that may be affected by eviction. Neighborhoods experiencing high levels of eviction will have greater residential disruption, and the instability generated by eviction may impede cohesion among residents, impairing neighborhood collective efficacy, and civic activity (Greenberg et al., 2016).
Social processes, including social ties and collective efficacy, have also been investigated by criminologists to explain higher levels of crime in poor communities of color. Rather than impacting crime directly, neighborhood structural factors (i.e., socio-economic factors, residential stability, heterogeneity) influence social processes, which then affects crime. Social ties help enforce norms and serve as means for positive interactions with individuals with a community, and changes in social ties impact the community’s capacity to prevent crime (Matsueda, 2006; Taylor et al., 1984; Warner, 2007); however, neighborhood structural factors may influence activities associated with social ties, including reciprocal exchange which is the trade of resources and/or advice among community members (Sampson et al., 1999). Also, collective efficacy, which is composed of the community’s cohesion and informal control, is a widely accepted explanation for the relationship between community structural factors and neighborhood crime (Sampson et al., 1997).
Therefore, eviction possibly influences neighborhood violent crime through its effect on social ties and collective efficacy. The disruptive element of eviction may impact the quality of social ties, affecting the community’s ability to share resources through reciprocal exchange. As well, the upheaval caused by eviction may reduce the community’s level of social cohesion and diminish the likelihood of informal control, thus influencing community collective efficacy and creating an environment more conducive to crime. Since existing studies suggest that eviction influences neighborhood social processes (Desmond, 2012a, 2016; Greenberg et al., 2016), a conceivable next step in this chain of community effects instigated by eviction is neighborhood violent crime.
Neighborhood Structural Factors
Eviction does not evenly occur across communities; it disproportionately befalls disadvantaged, single-parent African American households (Bezdek, 1992; Desmond, 2012b; Engler, 2010; Hartman & Robinson, 2003). In a Baltimore study investigating rent court, the defendants are described as primarily poor, African American women (Bezdek, 1992), and a Milwaukee-based study demonstrates that although African American women constitute only 9.5% of the population, they account for 30% of the evictions (Desmond, 2012b). Although some characteristics among housing courts vary, a review of housing court reports indicates that the tenants’ demographics remain fairly consistent; they are primarily poor women of color (Engler, 2010). Notably, community structural factors associated with eviction, specifically disadvantage and racial composition, are also linked to neighborhood violent crime.
Social disorganization theory, a well-established line of theory in communities and crime literature, argues that socio-economic status, residential instability, and heterogeneity contribute to community disorganization and facilitate neighborhood crime (Shaw & McKay, 1942). As theory developed, measures of socio-economic status came to incorporate other factors that tap into the concept of disadvantage. Sampson (1987) found strong relationships between socio-economic factors, including male joblessness and family disruption, suggesting that single-parent households should be included in the measure of disadvantage. As eviction disproportionately occurs in poor communities of color, particularly among single-parent households, it is possible that the relationship between neighborhood structural factors and violent crime may be partially explained by community-level eviction.
Concentration effects, a concept associated with social disorganization theory, offers a rationale for suspecting a relationship between eviction, neighborhood structural factors, and crime. In advancing this concept, Wilson (1987) suggests that the concentration of structural social disorganization factors (e.g., poverty, joblessness) makes some neighborhoods more vulnerable to crime, and empirical research offers evidence. For example, in researching single-parent families, which disproportionately occur in African American households, Sampson (1987) suggests that the relationship between race and crime stems from the concentration of factors associated with disadvantage (e.g., unemployment, family disruption) that are present in African American communities. Another study, that examines the relationship between race, employment, and crime, finds that labor instability, which is concentrated in poor communities of color, mediates the relationship between race and crime (Crutchfield et al., 2006). Similarly, eviction disproportionally occurs in disadvantaged African American communities, and it may partially explain the relationship between community structural factors, specifically concentrated disadvantage and race, and neighborhood violent crime.
Current Study
The present study is based in Boston, which has had a stable eviction rate from 2008 to 2016, ranging from a low of 1.01% to a high of 1.55% (Desmond et al., 2018a). Boston has a relatively low eviction rate; the 2016 eviction rate for Boston was 1.3% versus the 2016 national eviction rate of 2.3% (Desmond et al., 2018a). However, the availability of a unique data set allows this analysis to explore several mechanisms that may link eviction and crime. This study draws on the Boston Neighborhood Survey (BNS), which allows the analysis to test whether social processes may explain the relationship between eviction and neighborhood crime (Injury Control Research Center & Boston Area Research Initiative, 2019). Specifically, the BNS contains measures of reciprocal exchange and collective efficacy.
This study investigates three questions examining the relationship between eviction and neighborhood violent crime. First, the study tests whether eviction influences two community social processes associated with neighborhood violent crime: reciprocal exchange and collective efficacy. Next, the analysis examines whether eviction is associated with neighborhood violent crime. Third and finally, the study explores two mechanisms through which eviction may be associated with neighborhood violent crime. Specifically, the analysis investigates: (1) whether the eviction undermines community social processes, allowing the neighborhood to become vulnerable to neighborhood violent crime; and (2) whether eviction is so embedded in disadvantaged communities of color that eviction partially mediates the relationship between community structural factors (i.e., percent Black and concentrated disadvantage) and neighborhood violent crime.
Methods
Data
The study employs three data sources, the first of which is the Eviction Lab National Database (Desmond et al., 2018a). This database contains eviction data that were gathered from formal eviction notices in 48 states and the District of Columbia. The Eviction Lab National Database also includes values for the number of renter-occupied households, and that measure is used to calculate an eviction rate, which is the number of evictions per 100 renter-occupied households. The data are aggregated at the 2010 U.S. census tract-level, and for Boston, the Eviction Lab National Database contains census tract-level data on evictions for 2001 to 2002 and 2007 to 2016.
The second data source is the National Neighborhood Crime Study 2 (NNCS2), which contains a variety of census tract-level data for cities across the United States, including demographic, socioeconomic, and crime data (Krivo et al., 2019). The data in the NNCS2 are gathered from multiple sources, including the FBI Uniform Crime Reporting (UCR) Program and the 2008 to 2012 American Community Survey (ACS). From the NNCS2, this study employs tract-level crime data and control variables. The NNCS2 contains data on 164 census tracts in Boston. Comparing data from the NNCS2 with the Eviction Lab National Database, there is an overlap of 163 census tracts, which constitute the tracts explored in the analysis.
The third and final data source is the Boston Neighborhood Survey (BNS). Through this survey, a sample of Boston residents, age 18 or older, were asked to respond to a telephone survey; participants were randomly selected from a list-assisted sampling frame with separate random probability samples that are proportional to neighborhood size (Azrael et al., 2009). Residents were asked about a variety of topics, including situations associated with reciprocal exchange and collective efficacy. The 2010 BNS data is aggregated at the census tract-level, using 2000 U.S. census tracts. For purposes of this study, the data on reciprocal exchange and collective efficacy are converted to 2010 U.S census tracts using the Longitudinal Tract Data Base (Logan et al., 2012).
Measures
Eviction rate
The study’s independent variable is eviction rate, and the analysis employs data on eviction and renter-occupied units from 2008 to 2010 from the Eviction Lab National Database to calculate an eviction rate. Three years of eviction data are used to account for possible 1-year aberrations, and the selected 3-year period ensures temporal order with the dependent variables. The average 2008 to 2010 annual eviction rate for Boston is 1.38, which indicates that 1.38 of every 100 renter households in Boston face eviction each year; the range for eviction rates in Boston is 0 to 4.44 evictions for every 100 renter housholds. 1 For comparison, the average 2010 national eviction rate is just over double the average rate of Boston, at 2.95 evictions per 100 renter households.
Social processes
From the 2010 BNS data, the study includes reciprocal exchange and collective efficacy, which have been used in prior communities and crime literature as dependent variables (Sampson et al., 1997, 1999). The individual-level responses are entered into HLM, and the model controls for individual-level demographic characteristics to generate tract-level empirical Bayes residuals (EBRs), which are employed as the tract-level measure for the variable scales (Injury Control Research Center & Boston Area Research Initiative, 2019).
The first social process variable, reciprocal exchange, is measured by responses to five questions related to resource sharing activities. The questions ask how often people in their neighborhood: (1) do favors for each other; (2) have parties or other get-togethers where neighbors are invited; (3) visit with each other in homes or on the street; (4) ask each other advice about personal things such as childrearing or job openings; and (5) watch over neighbor’s property when they are not home. Possible responses are: (1) never; (2) rarely; (3) sometimes; and (4) often. Higher valued responses reflect greater reciprocal exchange. The Cronbach’s alpha, which tests for internal consistency, is .81, and the average level of reciprocal exchange is 2.86.
The second social process variable is collective efficacy, and it is based on responses to ten questions measuring social cohesion and informal social control. The first five questions address social cohesion, asking level of agreement to the following statements about the resident’s neighborhood: (1) people can be trusted; (2) people are willing to help their neighbors; (3) people know and like each other; (4) people get along with each other; and (5) people share the same beliefs about what is right and wrong. Possible responses are: (1) strongly disagree; (2) disagree; (3) agree; and (4) strongly agree. Informal social control is likewise measured using responses from five questions; questions ask how likely neighbors would: (1) organize together to keep a fire station open that was going to close; (2) do something about neighborhood children skipping school and hanging out on a street corner; (3) do something about a child showing disrespect to an adult; (4) do something about a child spray-painting graffiti on a local building; and (5) do something if there was a fight in your neighborhood and someone was being beaten or threatened. Possible responses are: (1) very unlikely; (2) unlikely; (3) likely; and (4) very likely. Higher-valued responses of strongly agree for social cohesion and very likely for informal social control reflect greater collective efficacy. The Cronbach’s alpha for the 10 collective efficacy variables is .87, and the average level of collective efficacy is 3.51.
Neighborhood violent crime
The third and final dependent variable is violent crime rate, and it is measured as the number of violent crimes per 1,000 residents. The NNCS2 includes a measure for neighborhood violent crime rate over a 4-year period, from 2010 to 2013. For Boston, the average violent crime rate is 8.6 crimes per 1,000 residents per year. For comparison, the average violent crime rate for all tracts in the NNCS2 is slightly below that of Boston, at 7.9 crimes per 1,000 residents per year.
Control variables
The study controls for two index variables available in the NNCS2: concentrated disadvantage and residential instability. Both index variables are determined using data from the American Community Survey 2008 to 2012. Concentrated disadvantage is calculated using four variables: (1) percent secondary sector low-wage jobs, (2) jobless rate for working age population, (3) percent female-headed households, and (4) poverty rate. In unrotated factor analysis, the four variables load on the first factor ranging 0.57 to 0.88, and the first extracted component explains 98.0% of the total variance (eigenvalue = 2.15). The average interitem correlation is .52, and the Cronbach’s alpha is .81. The second index variable, residential instability, is an index of the standardized scores of two variables: (1) percent renters and (2) percent recent movers (i.e., percent of population age 5 and older who lived in a different house in 2005). These variables are significantly correlated at .43 (p < .001).
The study controls for several other neighborhood factors: percent Black (i.e., non-Latino Black), percent Asian, percent Hispanic, percent foreign born, percent males age 15 to 24, and central business district (CBD). All measures, except CBD, come from the NNCS2 and are calculated using data in the American Community Survey 2008 to 2012. Distinct race/ethnicity variables are used, rather than a heterogeneity index, to understand whether eviction has a unique relationship with the different race/ethnicity measures. Concentrated disadvantage often includes percent Black, but to capture any direct effect of race, percent Black is held separate. The final control variable, CBD, is a dummy variable that is associated with high non-resident crime rates (Crutchfield, 1989). Three census tracts that encompass the downtown business section and tourist-trafficked areas are identified as CBD. It is appropriate to control for crimes related to individuals working in or visiting the area, since this study is examining the violent crime consequences of eviction, a measure associated with residents. Table 1 presents descriptive statistics for the variables employed in the study.
Neighborhood-Level Variable Descriptive Statistics (n = 163).
Table 2 includes correlations of study’s key variables: eviction, the two social process variables (i.e., reciprocal exchange and collective efficacy), violent crime, and structural factors that previous literature suggests are associated with eviction and crime (i.e., percent Black and concentrated disadvantage). The correlation table shows that eviction is strongly correlated with violent crime, concentrated disadvantage, and percent Black, in the anticipated positive direction with correlation rates of 0.71, 0.70, and 0.88, respectively. As well, eviction is significantly negatively correlated with collective efficacy but at a lower value (−0.22); the correlation rate between eviction and reciprocal exchange is not statistically significant. Given the strong correlation with concentrated disadvantage, a follow-up correlation matrix (not shown) examines the relationship between eviction and the four elements that constitute the concentrated disadvantage index. Eviction is most strongly correlated with female-headed households (.84), which supports the eviction literature that links evictions with single-parent households (Desmond, 2012b, 2016).
Correlation Table (n = 163).
p < .05. **p < .01. ***p < .001.
Analysis Strategy
The analysis employs three steps to explore the relationship between community-level eviction, community social processes and structural factors, and neighborhood violent crime. First, linear regression is employed to test whether eviction associated with two social processes: reciprocal exchange and collective efficacy. Next, a series of linear regression models are used to examine the relationship between eviction and violent crime. In the third and final step, structural equation modeling is employed to test whether the relationship between eviction and neighborhood violent crime is explained by either of the proposed theoretical mechanisms: (1) social processes mediate the relationship between eviction and neighborhood violent crime; or (2) eviction mediates the relationship between neighborhood structural factors and neighborhood violent crime.
Results
Predicting Social Processes
To explore whether eviction is related with neighborhood social processes, the analysis starts with a bivariate model. This is followed by a full model that incorporates neighborhood structural factors. Beginning with reciprocal exchange, results of the bivariate model demonstrate that eviction does not have a significant relationship with reciprocal exchange. As well, in the full model, the relationship between eviction and reciprocal exchange is not significant.
In examining the relationship between eviction and collective efficacy, the bivariate regression model indicates that eviction has a statistically significant negative relationship with collective efficacy with a coefficient of −0.03 (p < .01). For the full model, neighborhood structural factors are added to the model predicting collective efficacy (Table 3). Results indicate that only two variables have a statistically significant relationship with collective efficacy. Eviction remains significant, and findings suggest that an increase of 1 eviction per 100 renter-occupied households is associated with a decrease of 0.05 standardized units of collective efficacy (p < .05). 2 As well, residential stability has a significant relationship with collective efficacy with a coefficient of −0.08 (p < .001).
Predicting Collective Efficacy (n = 163).
Note. SE = standard error.
p < .05. ***p < .001.
Predicting Violent Crime
Next, the analysis examines the relationship between eviction and neighborhood violent crime, employing three linear regression models (Table 4). Model 1 is a baseline model that uses neighborhood variables rooted in social disorganization theory to predict violent crime. Results from Model 1 demonstrate that concentrated disadvantage, residential instability, percent Black, percent Hispanic, and CBD have a positive relationship with violent crime, while percent foreign born and males age 15 to 24 both have a negative relationship with violent crime. All of the relationships, with the exception of males age 15 to 24, are in the predicted direction; one explanation for this anomaly is that Boston has a large college and university population which may influence the relationship between population of young males and crime. 3 Variance inflation factors (VIF) are examined to explore the concern of multicollinearity. The mean VIF is 3.10, and concentrated disadvantage has the highest individual VIF at 5.10, followed by percent Hispanic (4.46) and percent Black (3.63).
Predicting Violent Crime (n = 163).
Note. SE = standard error.
p < .05. **p < .01. ***p < .001.
In Model 2, eviction is added to the regression. Results demonstrate that eviction has a statistically significant relationship with violent crime. In fact, of all covariates, the analysis finds that eviction and CBD have the strongest relationships with violent crime (p < .001). An increase of 1 eviction per 100 renter-occupied households is associated with an increase of 3.40 violent crimes per 1,000 residents (p < .001). In examining the changes from Model 1 to Model 2, all covariates, except percent Black, retain a significant relationship with violent crime; concentrated disadvantage, residential instability, percent Hispanic, and CBD have a positive relationship with violent crime, and percent foreign born and percent males age 15 to 24 have a negative relationship with violent crime. The mean VIF increases in this model to 3.75, and percent Black has the highest individual VIF at 5.94, followed by concentrated disadvantage (5.94), eviction rate (5.64), and percent Hispanic (4.46). An examination of standard errors does not produce significant evidence of multicollinearity. A likelihood-ratio test demonstrates that including eviction significantly improves the model (26.43, p < .001).
Notably, introducing eviction into the model also influences the relationship of two covariates, concentrated disadvantage and percent Black, with neighborhood violent crime. For concentrated disadvantage, in Model 1, a one standardized unit increase in concentrated disadvantage is associated with an increase of 3.27 violent crimes per 1,000 residents, but this relationship is reduced to 1.65 violent crimes per 1,000 residents in Model 2. In Model 1, a 1% increase in percent Black is associated with an increase of 0.12 violent crimes per 1,000 residents, but the relationship between percent Black and violent crime is no longer significant in Model 2. A t-test demonstrates that adding eviction to the model significantly reduces the relationship between concentrated disadvantage and crime (p < .01), and percent Black and crime (p < .001). This finding suggests that eviction may partially explain the relationship between percent Black and violent crime, and concentrated disadvantage and violent crime.
Finally, in Model 3, reciprocal exchange and collective efficacy are introduced into the model. Findings do not indicate that reciprocal exchange or collective efficacy is associated with violent crime. As well, the model is not strengthened with the addition of reciprocal exchange and collective efficacy. 4
Linking Eviction and Crime
This study proposes that two mechanisms may explain a positive relationship between community-level eviction and neighborhood violent crime. The first is that the disruption of eviction undermines beneficial community social processes, such as reciprocal exchange and collective efficacy, which then influences neighborhood violent crime. Results from the regressions predicting violent crime demonstrate that neither reciprocal exchange nor collective efficacy has a statistically significant relationship with neighborhood violent crime. Therefore, findings do not offer evidence that the relationship between eviction and neighborhood violent crime is mediated by either of the two social processes tested in this study.
The second explanation for the relationship between eviction and neighborhood violent crime is rooted in concentration effects and suggests that eviction mediates the relationship between neighborhood structural factors and violent crime. The analysis predicting violent crime suggests that eviction may mediate the relationship between percent Black and violent crime, and the relationship between concentrated disadvantage and violent crime. To formally test for mediation, structural equation modeling is employed in Stata; the model predicts violent crime with all neighborhood covariates, and the test of mediation by eviction is run concurrently for percent Black and concentrated disadvantage.
Results suggest that eviction mediates both relationships (Figure 1). For the percent Black, the total effect on violent crime is 0.12 (SE = 0.02, p < .001), and the indirect effect of percent Black through eviction is statistically significant at 0.09 (SE = 0.02, p < .001). When including mediation, the direct relationship between percent Black and violent crime is no longer statistically significant, suggesting the relationship between percent Black and violent crime is fully mediated through eviction. For concentrated disadvantage, the total effect of concentrated disadvantage on violent crime is 2.57 (SE = 0.78, p < .01). Once adjusting for mediation, the direct effect of concentrated disadvantage on violent crime is 1.59 (SE = 0.80, p < .05), and the indirect effect of concentrated disadvantage through eviction is significant at 0.98 (SE = 0.26, p < .001). Therefore, 38% of the relationship between concentrated disadvantage and violent crime is mediated through eviction.
Mediation by eviction (n = 163).
Discussion and Conclusion
This study offers three main insights into the harmful neighborhood effects of eviction. First, community-level eviction is positively associated with neighborhood violent crime, even when controlling for neighborhood structural factors that typically predict crime. Second, eviction is strongly associated with neighborhood structural factors associated with crime; eviction is so intertwined with race and disadvantage that the concentration of eviction within poor communities of color helps explain the level of neighborhood violent crime. Finally, results demonstrate that eviction harms collective efficacy, although this relationship does not explain the mechanism through which eviction influences neighborhood violent crime.
Providing a first step in linking community-level eviction with neighborhood violent crime, results point to several future pathways for research. First, the eviction-crime relationship should be explored in other settings and across multiple locations to address concerns of generalizability and provide a more informed interpretation of the relationship between eviction and crime. As well, this study indicates that additional studies are needed to inform as to how eviction impacts crime. Although eviction predicts collective efficacy in this study, eviction does explain the relationship between eviction and violent crime. Also, results do not offer evidence that eviction is associated with reciprocal exchange. However, Desmond (2012a, 2016) suggests that resource sharing occurs among those experiencing eviction, so long as those connections can be activated; in fact, ties and resource exchange may be required for survival for those facing eviction. Therefore, the measures for reciprocal exchange in this study may not significantly differentiate ties of those experiencing eviction to those who do not, and other means that capture different dimensions of social ties associated to crime may offer different results.
In addition, future work could examine other community social processes that may explain the eviction-crime relationship. The process of eviction may undermine individuals trust in formal control, motivated by troubling interactions with housing agencies or institutions, such as tenant court. That distrust may transfer to other formal control entities, such as the police. Therefore, in neighborhoods were eviction is more prevalent, community members may be less likely to reach out to entities of formal control, creating an environment susceptible to crime. To examine the role of agencies and institutions in explaining the relationship between eviction and neighborhood crime, future studies may investigate formal social control mechanisms, such as legal cynicism (Sampson & Bartusch, 1998) or legal estrangement (Bell, 2016).
Findings also suggest eviction-related policies may have beneficial consequences at the community-level, including addressing neighborhood violent crime. The potential onslaught of eviction filings related to financial hardships associated with the COVID-19 pandemic makes such policy initiatives particularly important. If the threat of eviction is not addressed through policy efforts, a recent analysis suggests that 20 million renters, or one in five of those living in renter households, could face eviction due to the COVID-19 crisis (McKay et al., 2020). One way of addressing eviction is through landlord tenant rights laws, which encapsulate a series of laws that can influence eviction (Hatch, 2017). Some landlord tenant rights laws, such as right to counsel and just cause laws, reduce eviction (Desmond & Gershenson, 2017; Hartman & Robinson, 2003), while others, like nuisance laws, may contribute to situations conducive for eviction (Desmond & Valdez, 2013). Therefore, strengthening housing policies that protect renters and minimizing the harms associated with nuisance laws may not only benefit those facing eviction, but also reduce likelihood of crime in the neighborhoods in which they live.
A main challenge to eviction prevention is simply the availability of affordable housing. In the United States, 72% of renter households with income less than $15,000 spend greater than 50% of their income on housing (Joint Center for Housing Studies of Harvard University, 2019), and if an unexpected expense arises, eviction may be hard to avoid. One suggestion to level the playing field for maintaining housing is put forth by Desmond (2016): a universal housing voucher program, which provides a voucher to families below a certain income. A universal housing voucher would allow families to maintain secure housing and increase likelihood that sufficient resources remain to pay for other needs, such as food, education, and healthcare. Reshaping the financial pie in this way has significant benefits. A recent study demonstrates that Medicaid expansion is associated with a reduction of evictions, suggesting that “decreased financial exposure to health care costs and medical expenses may increase timely rent payments” (Zewde et al., 2019, p. 1383). In the case of that study, heath care policy had an unintended benefit for housing, and policies addressing eviction may likewise have unintended positive consequences, particularly for those communities in which crime is concentrated.
Therefore, the benefits of eviction-related housing policy, such as the adoption of a universal housing voucher program and the strengthening of tenant rights laws, may be felt by others beyond just the individuals who are at risk of eviction. As illustrated by the concept of concentration effects, eviction piles on to the structural social disorganization factors that may harm a community, making a neighborhood more vulnerable to crime. By linking eviction and crime, the study provides a new argument for strengthening housing policy associated with eviction, one that proposes that housing policy may be associated with beneficial neighborhood outcomes, including a reduction of violent crime.
Footnotes
Acknowledgements
The author is grateful to Dr. Lauren Krivo, Dr. María Vélez, and Dr. Christopher Lyons for generously providing access to the National Neighborhood Crime Study 2 for this study. Also, the author would like to thank Dr. Kevin Drakulich for his advice and recommendations during the development of this article.
Declaration of Conflicting Interests
The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author received no financial support for the research, authorship, and/or publication of this article.
