- Email: [email protected]
The impact of mandatory arrest laws on domestic violence in times of economic stress
Accepted Manuscript The impact of mandatory arrest laws on domestic violence in times of economic stress Jeremy A. Cook, Timothy W. Taylor
PII: DOI: Reference:
S0165-1765(19)30055-2 https://doi.org/10.1016/j.econlet.2019.02.013 ECOLET 8369
To appear in:
Economics Letters
Received date : 20 August 2018 Revised date : 5 February 2019 Accepted date : 14 February 2019 Please cite this article as: J.A. Cook and T.W. Taylor, The impact of mandatory arrest laws on domestic violence in times of economic stress. Economics Letters (2019), https://doi.org/10.1016/j.econlet.2019.02.013 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
*Highlights (for review)
Highlights Manuscript #EL45353: “The Impact of Mandatory Arrest Laws on Domestic Violence in Times of Economic Stress”
We categorize domestic violence arrest laws by state and level of offense. We test the relationship between local economic shocks and incidents of domestic violence. The effectiveness of arrest laws are tested with changes in unemployment rates. We find domestic violence increases with rising rates of unemployment. The study finds conditional effects for the mitigating role of mandatory arrest laws.
*Title Page
The Impact of Mandatory Arrest Laws on Domestic Violence in Times of Economic Stress⇤ Jeremy A. Cook Department of Business and Economics, Wheaton College Timothy W. Taylor Department of Politics and International Relations, Wheaton College February 2019
Abstract We test the e↵ectiveness of mandatory arrest laws to suppress domestic violence under changing levels of community-wide economic stress. While existing economic scholarship focuses upon the influence of arrest laws and financial strain independently of one another, we provide a meaningful bridge across these two factors to assess whether arrest laws are e↵ective when communities need them most. Using county-level monthly unemployment rates and national crime data for years 2000 to 2015, we examine changes in incidents of intimidation and assault between intimate partners across states with and without mandatory arrest laws. After controlling for baseline county characteristics, we document the subsequent increase in domestic violence from rising rates of unemployment. We find the efficacy of arrest laws to mitigate intimate partner violence is strongest when unemployment increases. However, these results do not hold for more severe forms of domestic violence. Our results suggest that while mandatory arrest laws are not a single solution to domestic violence, they lessen the adverse e↵ects of rising unemployment rates.
JEL Classification: J12, J11, J18 Keywords: Domestic violence; Intimate partner violence; Unemployment; Arrest laws; Economic stress
⇤
We thank James Drury and Ethan Jenkins for excellent research assistance and the Wheaton College Center for Faith, Politics, and Economics for research support. We appreciate the useful comments of participants at the Southern Economic Association Annual Conference and the Midwest Economic Association Annual Conference. Additional comments are welcome at [email protected] or [email protected].
*Manuscript Click here to view linked References
1
Introduction
Do domestic violence arrest laws matter when we need them most? E↵orts to abate intimate partner violence have led states to direct police to execute warrantless arrests for domestic violence. Indeed, thirty-one state legislatures have passed laws designed to mandate police officers arrest perpetrators of domestic violence compared to other types of violent crimes (Zeoli, Norris, and Brenner, 2011). While several studies have assessed the ability of mandatory arrest statutes to reduce violence against women (Eitle, 2005; Hirschel et al., 2007; Iyengar, 2009), little is known on the e↵ectiveness of these laws during times of financial stress. We test whether mandatory arrest laws are e↵ective when communities are under economic stress, as measured through changes in the local unemployment rate. Economic shocks can have substantial repercussions on the well-being of the household, even culminating in intimate partner violence (Renzetti, 2009; Harney, 2011). Partners who report feeling high levels of financial stress, for example, are three times more likely to su↵er domestic violence compared with couples feeling low levels of financial strain (Benson and Fox, 2002). The unemployment shocks of the Great Recession led media outlets to report the upsurge in domestic violence (Wardrop, 2008; Glaberson, 2009). Recent economic research has found household and community economic conditions to be significant determinants of domestic violence (Bowlus and Seitz, 2005; Aizer, 2010; Anderberg et al., 2016). Additionally, studies have shown rates of domestic violence are influenced by public policies that shape the legal and economic environment (Nou and Timmins, 2005; Aizer and Dal B´o, 2009; Hsu, 2017). Intersecting research on arrest laws and economic stress, we test whether mandatory arrest laws abate domestic violence with changing levels of unemployment. Using national crime data from 2000 – 2015, we find that mandatory arrest policies reduce the prevalence of domestic violence in the form of intimidation. However, we document these policies have no statistically significant e↵ect on lowering domestic assault. Moreover, we find rates of domestic violence increase with worsening local economic conditions. We provide not only a critical test for the efficacy of arrest laws, but also add to the burgeoning economic literature on determinants of domestic violence.
2
2
State Arrest Laws
As domestic violence became increasingly salient in the 1980s, citizen advocates called for policy solutions to combat the battering of women. Much of the attention was due to the seemingly low response by police; intimate partner violence arrest rates during this period averaged less than 15% (Bayley, 1986; Dutton, 1984). Police officers are less likely to arrest a suspect when he is the intimate partner of the victim than when the suspect is unknown to the victim (Berk, Fenstermaker, and Loseke, 1980). Requiring strong arrest policies for domestic violence is meant to overcome the potential bias police have in arresting a “stranger” over an “intimate partner”. Consequently, the advent of arrest statutes led to an aggregate change in outcomes where the arrest rate increased to over 30% by 1990 (Bourg and Stock, 1994). Understanding the e↵ectiveness of arrest laws, however, requires a more thorough investigation of statutes. Indeed, several case studies have found mandatory arrest laws to be either ine↵ective or overstated in their ability to reduce domestic violence (Wanless, 1996; Ruttenberg, 1994). Not all arrest laws are equal in their constraint on police officers. One state may enact an arrest policy that is nearly undi↵erentiated from officer discretion. Consider the example of a 1992 Kansas arrest statute where police agencies are required to include “a statement directing that the officer shall make an arrest” in a domestic violence incident (Kansas Statute: §22-2307(b)(1) 1992). This indirect requirement may easily be considered “mandatory”, but its application would vary widely across Kansan police districts and be difficult to enforce. When compared to the Alaska 1996 law that requires officers “shall arrest” the identified perpetrator, the pro-arrest Kansas state law is indiscernible from officer discretion in execution (Alaska Statute: §18.65.530(A) 1996). Investigations in mandatory arrest efficacy, therefore, must be vigilant in their categorization of state statues. After careful review of state statutes on domestic violence, we identify the arrest policies for all 50 states. Table 1 reports whether a state (1) allows for officer discretion, or (2) obligates officers to make an arrest. Because six states have mandatory arrest policies that are enforced only for more violent domestic crimes, we categorize arrest policy according to the following incident types: (1) intimidation, defined as intentional behavior that would cause a person to fear harm, and (2) 3
assault, defined as intentional behavior resulting in bodily harm.
3
Analysis
We measure domestic violence using crime data from the National Incident-Based Reporting System (NIBRS) for the years 2000 – 2015. The NIBRS, the most comprehensive dataset on crime in the United States, records all criminal incidents reported by participating police agencies.1 Each incident report includes detailed data on the victim, the o↵ender, and the type of incident. We define domestic violence as an incident of intimidation or assault where the female victim is the spouse, common-law spouse, girlfriend, or ex-spouse of the male o↵ender. These individual crime incidents are aggregated to the agency-month and weighted by 100,000 residents to account for city size. We then construct two dependent variables for domestic violence: intimidation and assault. Our independent variable of interest is the unemployment rate, measured monthly for each county (Bureau of Labor Statistics). While other economic characteristics such as household income and poverty rate are used in demography research to approximate health of a community, unemployment rates have two strengths. Firstly, changes in unemployment rate better reflect the extensive-margin shock of sudden loss of employment. Secondly, unemployment rates are measured monthly whereas other indicators such as poverty rates are measured annually. Monthly-level variation allows us to better measure the dynamic demography of economic shocks in the community. Furthermore, we control for local characteristics that may predict rates of domestic violence. Table 2 depicts summary statistics for domestic violence rates, unemployment, and our community-level demographic controls.2 Because the dependent variable is monthly incidents, we employ negative binomial count models with standard errors clustered by police agency (Iyengar, 2009; Hsu, 2016). In order to test the e↵ects of arrest laws we estimate separate regression equations for agencies in states using officer discretion and agencies in states governed by mandatory arrest laws. Comparing the coefficients 1
In these data, “agency” is defined as a law enforcement agency authorized to report crime data to the Federal Bureau of Investigation. 2 Although other studies examine the e↵ects of gender-specific economic conditions (Aizer, 2010; Anderberg et al., 2015; Lindo et al., 2018), U.S. data limitations in reporting monthly unemployment rates do not allow us to test for gender di↵erences at the county level.
4
and the marginal e↵ects across the two sets of regressions, we test for di↵erences across the arrest statutes at varying levels of unemployment. To further control for confounding di↵erences across regions we employ two models. The first model includes county-level demographic variables. The second model employs county-level fixed e↵ects to directly control for baseline di↵erences in communities.3 All models include month fixed e↵ects and cubic year time trends.
4
Results
Table 3 reports estimates from four models with domestic violence at the level of intimidation (Panel A) and four models assessing domestic violence at the level of assault (Panel B). Focusing upon intimidation as reported in Panel A, we find that under officer discretion unemployment predicts an increasing incident rate of domestic violence, albeit at a diminishing rate. While the individual coefficients are not statistically significant under mandatory arrest, the cubic terms are jointly significant at the 1% level. The county demographics and county fixed e↵ects models present similar results and provide robustness to the finding. Given we control for county-level poverty through either the county demographics model or county fixed e↵ects model, we are confident in the robustness of the predicted relationship between unemployment and domestic violence. Turning toward domestic violence assaults, as reported in Panel B, we find that unemployment predicts an increasing incident rate in counties governed by either officer discretion or mandatory arrest statutes. Moreover, the direction and magnitude of coefficients are similar across the models. Whether using county demographics or county fixed e↵ects model specifications, we find similar robust findings.4 To better interpret the di↵ering e↵ects of state policies, we compare the regression-adjusted incident rates across the range of unemployment rates using the results from the county-level fixed e↵ects model. Figure 1a displays the mean predicted intimidation rate, along with the 95% confi3
A di↵erence-in-di↵erences approach would be the preferred identification strategy. Because most mandatory arrest laws were implemented in the 1980s and 1990s we do not have sufficient data to examines incidents both before and after policy implementations. 4 Further robustness tests find substantively and statistically similar results when monthly unemployment rates are implemented through either indicator variables or splines. Additionally, replacing year time trends with year fixed e↵ects yields similar results.
5
dence interval, for both officer discretion and mandatory arrest states. Notably, after controlling for county-specific unobserved baseline characteristics, there remains a strong positive relationship between unemployment and intimidation. Figure 2a displays the di↵erences in the mean outcomes between officer discretion and mandatory arrest across the range of unemployment as well as the 95% confidence interval of this di↵erence. At low levels of unemployment there is no significant difference in the intimidation rate across state policies. However, we find a clear separation between these two policies’ predicted incident rates as unemployment increases. Specifically, mandatory arrest tends to abate domestic acts of violent intimidation as unemployment worsens. Starting at around 4.75% unemployment, mandatory arrest begins to suppress intimidation incidents with an increasing e↵ect. This statistically significant di↵erence remains as the unemployment rate grows. However, as unemployment exceeds 8.5% the two arrest policies become undi↵erentiated. We interpret this finding in that arrest laws cannot overcome all levels of shocks. If economic strain heightens violent behavior, then at some point the deterrence of mandatory arrest policies will be fully negated by rising levels of unemployment. Surprisingly, mandatory arrest laws do not predict an independent e↵ect in reducing intimidation at low levels of unemployment. In other words, when the local community is at or below the natural rate of unemployment, mandatory arrest laws lose their efficacy compared with officer discretion policies. Turning toward incidents of assault as shown in Figure 1b, we document the positive relationship between the unemployment rate and incidents of assault. As unemployment worsens, we find the incident rate of assault substantively increases both in states with officer discretion and in states with mandatory arrest laws. Figure 2b displays the mean di↵erences between officer discretion and mandatory arrest along with the associated 95% confidence interval. Unlike intimidation, there is no statistically discernible di↵erence between types of arrest laws. In this regard, the efficacy of mandatory arrest is weakened with respect to assault. Mandatory arrest policies may lose their e↵ectiveness if deterrence requires rational decision-making (Sherman and Berk, 1984). Given assault is a more severe form of domestic violence, the heightened emotional environment may result in the o↵ender ignoring the consequences of his actions.
6
5
Conclusion
In assessing arrest laws conditional upon unemployment, this paper provides a first quantitative test intersecting a public policy response to domestic violence with the local economic environment. This study is the first to systematically document the positive relationship between local unemployment rates and domestic violence in the United States. Addressing whether arrest laws reduce domestic violence in times of economic stress, we find mixed evidence. While arrest laws tend to reduce the onset of intimidation as unemployment begins to worsen, the mitigating e↵ects of mandatory arrest disappear under severe economic crisis. However, we fail to find evidence that mandatory arrest laws reduce the incident rate of assault, across the range of unemployment. Despite not being a universal cure for domestic violence, mandatory arrest statutes tend to reduce the rates of domestic violence when communities are most sensitive to economic shocks, namely for o↵enses of intimidation. Our findings promote future research to investigate the hitherto unknown inefficacies of mandatory arrest in suppressing domestic violence at the ends of the unemployment distribution. Additional scholarship should examine the conditional e↵ects of arrest law across unemployment rates when disaggregated by gender. Future studies that progress our understanding of the mechanisms of arrest laws would be beneficial for families and communities in economic distress.
7
6
References
Aizer, A. (2010). The gender wage gap and domestic violence. American Economic Review. 100(4), 1847-1859. Aizer, A., and Dal B´o, P. (2009). Love, hate and murder: Commitment devices in violent relationships. Journal of Public Economics. 93(3), 412-428. Anderberg, D., Rainer, H., Wadsworth, J., and Wilson, T. (2016). Unemployment and domestic violence: Theory and evidence. The Economic Journal, 126(597), 1947-1979. Bayley, D. H. (1986). The tactical choices of police patrol officers. Journal of Criminal Justice, 14(4),329-348. Benson, M., and Fox, G. L. (2002). Economic distress, community context and intimate violence: An application and extension of social disorganization theory, final report. Washington, DC: US Department of Justice, National Institute of Justice. Berk, S. F. and Loseke, D. R. (1980). ‘Handling’ family violence: Situational determinants of police arrest in domestic disturbances. Law and Society Review 15(2), 317-346. Bourg, S., and Stock, H. V. (1994). A review of domestic violence arrest statistics in a police department using a pro-arrest policy: Are pro-arrest policies enough? Journal of Family Violence. 9(2), 177-189. Bowlus, A. J., and Seitz, S. (2006). Domestic violence, employment, and divorce. International Economic Review. 47(4), 1113-1149. Dutton, D. G. (1987). The criminal justice response to wife assault. Law and Human Behavior. 11(3), 189-206. Eitle, D. (2005). The influence of mandatory arrest policies, police organizational characteristics, and situational variables on the probability of arrest in domestic violence cases. Crime & Delinquency. 51(4), 573-597. Farmer, A. and Tiefenthaler, J. (1997). An economic analysis of domestic violence. Review of Social Economy. 55(3), 337-358. Glaberson, W. (2009, December 27). The recession begins flooding into the courts. The New York Times. Retrieved from http://www.nytimes.com/ Harney, C. (2011). The impact of the recession on domestic violence against women and support services in Ireland: an exploratory study. Critical Social Thinking: Policy and Practice. 3, 26-39.
8
Hirschel, D., Buzawa, E., Pattavina, A., and Faggiani, D. (2007). Domestic violence and mandatory arrest laws: To what extent do they influence police arrest decisions? Journal of Criminal Law and Criminology. 98(1), 255-298. Hsu, L. (2016). The timing of welfare payments and intimate partner violence. Economic Inquiry. 55(2), 1017-1031. Iyengar, R. (2009). Does the certainty of arrest reduce domestic violence? Evidence from mandatory and recommended arrest laws. Journal of Public Economics 93(1), 85-98. Nou, J., and Timmins, C. (2005). How do changes in welfare law a↵ect domestic violence? An analysis of Connecticut towns, 1990-2000. The Journal of Legal Studies. 34(2), 445-470. Renzetti, C. M. (2009). Economic Stress and Domestic Violence. Center for Research on Violence Against Women Faculty Research Reports and Papers. 1, 1-15. Ruttenberg, M. H. (1994). A feminist critique of mandatory arrest: An analysis of race and gender in domestic violence policy. American University Journal of Gender and the Law. 2, 171-200. Sherman, L. W., and Berk, R. A. (1984). The specific deterrent e↵ects of arrest for domestic assault. American Sociological Review. 49(2), 261-272. Wanless, M. (1996). Mandatory arrest: A step toward eradicating domestic violence, but is it enough. University of Illinois Law Review. 2, 533-586. Wardrop, M. (2008, December 20). Recession Will Prompt Rise in Domestic Violence. The Telegraph. Retrieved from http://www.telegraph.co.uk/ Zeoli, A. M., Norris, A., and Brenner, H. (2011). A summary and analysis of warrantless arrest statutes for domestic violence in the United States. Journal of Interpersonal Violence. 26(14), 2811-2833.
9
Table 1: State Arrest Policies by O↵ense Officer Discretion
Mandatory Arrest
Intimidation
Alabama, Arizona, Idaho, Indiana, Iowa, Kentucky, Michigan, Nebraska, New Hampshire, Ohio, Oklahoma, Pennsylvania, South Carolina, Texas, Vermont, West Virginia
Arkansas, Colorado, Connecticut, Illinois, Kansas, Louisiana, Maine, Massachusetts, Mississippi, Missouri, Montana, North Dakota, Oregon, Rhode Island, South Dakota, Tennessee, Utah, Virginia,Washington, Wisconsin
Assault
Alabama, Arizona, Idaho, Indiana, Iowa, Kentucky, Michigan, Nebraska, New Hampshire, Oklahoma, Pennsylvania, South Carolina, Texas, Vermont, West Virginia
Arkansas, Colorado, Connecticut, Illinois, Kansas, Louisiana, Maine, Massachusetts, Mississippi, Missouri, Montana, North Dakota, Ohio, Oregon, Rhode Island, South Dakota, Tennessee, Utah, Virginia, Washington, Wisconsin
Table 2: Summary Statistics Variable
Mean
S.D.
Intimidation rate (monthly incidents per 100,000) Assault rate (monthly incidents per 100,000) Unemployment rate (monthly county-level) Median household income (thousands) Renter-occupied housing (thousands) Percent white Percent African American Percent Hispanic Rural-urban continuum* Percent high school degree Percent bachelors degree
2.57 13.50 6.39 45.92 26.69 83.08 7.30 5.35 4.05 33.38 22.23
9.04 21.55 2.77 12.29 51.68 14.28 11.76 7.05 2.50 6.92 10.06
*Nine-category classification created by the United States Department of Agriculture to classify counties by population and urbanization.
10
Table 3: Model Coefficients
Panel A: Intimidation Incident Rate (monthly per 100k) Officer Discretion Unemployment Unemployment2 Unemployment3
County demographics County fixed e↵ects Observations
Mandatory Arrest
Officer Discretion
Mandatory Arrest
0.221*** (0.070) -0.016*** (0.003) 0.000*** (0.000)
0.071 (0.178) 0.003 (0.021) -0.000 (0.001)
0.227*** (0.084) -0.020** (0.008) 0.001** (0.000)
0.083 (0.116) -0.005 (0.013) 0.000 (0.000)
Yes No 339,366
Yes No 330,771
No Yes 340,704
No Yes 331,356
Officer Discretion
Mandatory Arrest
Panel B: Assault Incident Rate (monthly per 100k) Officer Discretion Unemployment Unemployment2 Unemployment3
County demographics County fixed e↵ects Observations
Mandatory Arrest
0.178*** (0.068) -0.019*** (0.007) 0.001** (0.000)
0.199*** (0.073) -0.019** (0.009) 0.001* 0.000
0.263*** (0.068) -0.021*** (0.005) 0.001*** (0.000)
0.154** (0.071) -0.013* (0.007) 0.000 (0.000)
Yes No 281,914
Yes No 382,017
No Yes 283,007
No Yes 382,810
Standard errors clustered at the state level. All models include month fixed e↵ects and cubic year time trends. *** significant at 1% level, ** significant at 5% level, * significant at 10% level.
11
Figure 1: Predicted Outcomes (a) Intimidation Incidents per 100k
(b) Assault Incidents per 100k 18 Assault per 100k (mean)
Intimidation per 100k (mean)
3.5
3
2.5
2
16
14
12
10
1.5
8 3
4
5
6 7 8 9 Unemployment rate (county level) Officer discretion
10
11
12
3
4
5
Mandatory arrest
6 7 8 9 Unemployment rate (county level) Officer discretion
10
11
12
11
12
Mandatory arrest
Figure 2: Mean Di↵erences (a) Intimidation Incidents per 100k
(b) Assault Incidents per 100k
Difference in assault per 100k (mean)
Difference in intimidation per 100k (mean)
1
.5
0
-.5
-1
4
2
0
-2
-4 3
4
5
6 7 8 9 Unemployment rate (county level)
10
11
12
Difference: Officer discretion minus mandatory arrest
3
4
5
6 7 8 9 Unemployment rate (county level)
10
Difference: Officer discretion minus mandatory arrest
12
PII: DOI: Reference:
S0165-1765(19)30055-2 https://doi.org/10.1016/j.econlet.2019.02.013 ECOLET 8369
To appear in:
Economics Letters
Received date : 20 August 2018 Revised date : 5 February 2019 Accepted date : 14 February 2019 Please cite this article as: J.A. Cook and T.W. Taylor, The impact of mandatory arrest laws on domestic violence in times of economic stress. Economics Letters (2019), https://doi.org/10.1016/j.econlet.2019.02.013 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
*Highlights (for review)
Highlights Manuscript #EL45353: “The Impact of Mandatory Arrest Laws on Domestic Violence in Times of Economic Stress”
We categorize domestic violence arrest laws by state and level of offense. We test the relationship between local economic shocks and incidents of domestic violence. The effectiveness of arrest laws are tested with changes in unemployment rates. We find domestic violence increases with rising rates of unemployment. The study finds conditional effects for the mitigating role of mandatory arrest laws.
*Title Page
The Impact of Mandatory Arrest Laws on Domestic Violence in Times of Economic Stress⇤ Jeremy A. Cook Department of Business and Economics, Wheaton College Timothy W. Taylor Department of Politics and International Relations, Wheaton College February 2019
Abstract We test the e↵ectiveness of mandatory arrest laws to suppress domestic violence under changing levels of community-wide economic stress. While existing economic scholarship focuses upon the influence of arrest laws and financial strain independently of one another, we provide a meaningful bridge across these two factors to assess whether arrest laws are e↵ective when communities need them most. Using county-level monthly unemployment rates and national crime data for years 2000 to 2015, we examine changes in incidents of intimidation and assault between intimate partners across states with and without mandatory arrest laws. After controlling for baseline county characteristics, we document the subsequent increase in domestic violence from rising rates of unemployment. We find the efficacy of arrest laws to mitigate intimate partner violence is strongest when unemployment increases. However, these results do not hold for more severe forms of domestic violence. Our results suggest that while mandatory arrest laws are not a single solution to domestic violence, they lessen the adverse e↵ects of rising unemployment rates.
JEL Classification: J12, J11, J18 Keywords: Domestic violence; Intimate partner violence; Unemployment; Arrest laws; Economic stress
⇤
We thank James Drury and Ethan Jenkins for excellent research assistance and the Wheaton College Center for Faith, Politics, and Economics for research support. We appreciate the useful comments of participants at the Southern Economic Association Annual Conference and the Midwest Economic Association Annual Conference. Additional comments are welcome at [email protected] or [email protected].
*Manuscript Click here to view linked References
1
Introduction
Do domestic violence arrest laws matter when we need them most? E↵orts to abate intimate partner violence have led states to direct police to execute warrantless arrests for domestic violence. Indeed, thirty-one state legislatures have passed laws designed to mandate police officers arrest perpetrators of domestic violence compared to other types of violent crimes (Zeoli, Norris, and Brenner, 2011). While several studies have assessed the ability of mandatory arrest statutes to reduce violence against women (Eitle, 2005; Hirschel et al., 2007; Iyengar, 2009), little is known on the e↵ectiveness of these laws during times of financial stress. We test whether mandatory arrest laws are e↵ective when communities are under economic stress, as measured through changes in the local unemployment rate. Economic shocks can have substantial repercussions on the well-being of the household, even culminating in intimate partner violence (Renzetti, 2009; Harney, 2011). Partners who report feeling high levels of financial stress, for example, are three times more likely to su↵er domestic violence compared with couples feeling low levels of financial strain (Benson and Fox, 2002). The unemployment shocks of the Great Recession led media outlets to report the upsurge in domestic violence (Wardrop, 2008; Glaberson, 2009). Recent economic research has found household and community economic conditions to be significant determinants of domestic violence (Bowlus and Seitz, 2005; Aizer, 2010; Anderberg et al., 2016). Additionally, studies have shown rates of domestic violence are influenced by public policies that shape the legal and economic environment (Nou and Timmins, 2005; Aizer and Dal B´o, 2009; Hsu, 2017). Intersecting research on arrest laws and economic stress, we test whether mandatory arrest laws abate domestic violence with changing levels of unemployment. Using national crime data from 2000 – 2015, we find that mandatory arrest policies reduce the prevalence of domestic violence in the form of intimidation. However, we document these policies have no statistically significant e↵ect on lowering domestic assault. Moreover, we find rates of domestic violence increase with worsening local economic conditions. We provide not only a critical test for the efficacy of arrest laws, but also add to the burgeoning economic literature on determinants of domestic violence.
2
2
State Arrest Laws
As domestic violence became increasingly salient in the 1980s, citizen advocates called for policy solutions to combat the battering of women. Much of the attention was due to the seemingly low response by police; intimate partner violence arrest rates during this period averaged less than 15% (Bayley, 1986; Dutton, 1984). Police officers are less likely to arrest a suspect when he is the intimate partner of the victim than when the suspect is unknown to the victim (Berk, Fenstermaker, and Loseke, 1980). Requiring strong arrest policies for domestic violence is meant to overcome the potential bias police have in arresting a “stranger” over an “intimate partner”. Consequently, the advent of arrest statutes led to an aggregate change in outcomes where the arrest rate increased to over 30% by 1990 (Bourg and Stock, 1994). Understanding the e↵ectiveness of arrest laws, however, requires a more thorough investigation of statutes. Indeed, several case studies have found mandatory arrest laws to be either ine↵ective or overstated in their ability to reduce domestic violence (Wanless, 1996; Ruttenberg, 1994). Not all arrest laws are equal in their constraint on police officers. One state may enact an arrest policy that is nearly undi↵erentiated from officer discretion. Consider the example of a 1992 Kansas arrest statute where police agencies are required to include “a statement directing that the officer shall make an arrest” in a domestic violence incident (Kansas Statute: §22-2307(b)(1) 1992). This indirect requirement may easily be considered “mandatory”, but its application would vary widely across Kansan police districts and be difficult to enforce. When compared to the Alaska 1996 law that requires officers “shall arrest” the identified perpetrator, the pro-arrest Kansas state law is indiscernible from officer discretion in execution (Alaska Statute: §18.65.530(A) 1996). Investigations in mandatory arrest efficacy, therefore, must be vigilant in their categorization of state statues. After careful review of state statutes on domestic violence, we identify the arrest policies for all 50 states. Table 1 reports whether a state (1) allows for officer discretion, or (2) obligates officers to make an arrest. Because six states have mandatory arrest policies that are enforced only for more violent domestic crimes, we categorize arrest policy according to the following incident types: (1) intimidation, defined as intentional behavior that would cause a person to fear harm, and (2) 3
assault, defined as intentional behavior resulting in bodily harm.
3
Analysis
We measure domestic violence using crime data from the National Incident-Based Reporting System (NIBRS) for the years 2000 – 2015. The NIBRS, the most comprehensive dataset on crime in the United States, records all criminal incidents reported by participating police agencies.1 Each incident report includes detailed data on the victim, the o↵ender, and the type of incident. We define domestic violence as an incident of intimidation or assault where the female victim is the spouse, common-law spouse, girlfriend, or ex-spouse of the male o↵ender. These individual crime incidents are aggregated to the agency-month and weighted by 100,000 residents to account for city size. We then construct two dependent variables for domestic violence: intimidation and assault. Our independent variable of interest is the unemployment rate, measured monthly for each county (Bureau of Labor Statistics). While other economic characteristics such as household income and poverty rate are used in demography research to approximate health of a community, unemployment rates have two strengths. Firstly, changes in unemployment rate better reflect the extensive-margin shock of sudden loss of employment. Secondly, unemployment rates are measured monthly whereas other indicators such as poverty rates are measured annually. Monthly-level variation allows us to better measure the dynamic demography of economic shocks in the community. Furthermore, we control for local characteristics that may predict rates of domestic violence. Table 2 depicts summary statistics for domestic violence rates, unemployment, and our community-level demographic controls.2 Because the dependent variable is monthly incidents, we employ negative binomial count models with standard errors clustered by police agency (Iyengar, 2009; Hsu, 2016). In order to test the e↵ects of arrest laws we estimate separate regression equations for agencies in states using officer discretion and agencies in states governed by mandatory arrest laws. Comparing the coefficients 1
In these data, “agency” is defined as a law enforcement agency authorized to report crime data to the Federal Bureau of Investigation. 2 Although other studies examine the e↵ects of gender-specific economic conditions (Aizer, 2010; Anderberg et al., 2015; Lindo et al., 2018), U.S. data limitations in reporting monthly unemployment rates do not allow us to test for gender di↵erences at the county level.
4
and the marginal e↵ects across the two sets of regressions, we test for di↵erences across the arrest statutes at varying levels of unemployment. To further control for confounding di↵erences across regions we employ two models. The first model includes county-level demographic variables. The second model employs county-level fixed e↵ects to directly control for baseline di↵erences in communities.3 All models include month fixed e↵ects and cubic year time trends.
4
Results
Table 3 reports estimates from four models with domestic violence at the level of intimidation (Panel A) and four models assessing domestic violence at the level of assault (Panel B). Focusing upon intimidation as reported in Panel A, we find that under officer discretion unemployment predicts an increasing incident rate of domestic violence, albeit at a diminishing rate. While the individual coefficients are not statistically significant under mandatory arrest, the cubic terms are jointly significant at the 1% level. The county demographics and county fixed e↵ects models present similar results and provide robustness to the finding. Given we control for county-level poverty through either the county demographics model or county fixed e↵ects model, we are confident in the robustness of the predicted relationship between unemployment and domestic violence. Turning toward domestic violence assaults, as reported in Panel B, we find that unemployment predicts an increasing incident rate in counties governed by either officer discretion or mandatory arrest statutes. Moreover, the direction and magnitude of coefficients are similar across the models. Whether using county demographics or county fixed e↵ects model specifications, we find similar robust findings.4 To better interpret the di↵ering e↵ects of state policies, we compare the regression-adjusted incident rates across the range of unemployment rates using the results from the county-level fixed e↵ects model. Figure 1a displays the mean predicted intimidation rate, along with the 95% confi3
A di↵erence-in-di↵erences approach would be the preferred identification strategy. Because most mandatory arrest laws were implemented in the 1980s and 1990s we do not have sufficient data to examines incidents both before and after policy implementations. 4 Further robustness tests find substantively and statistically similar results when monthly unemployment rates are implemented through either indicator variables or splines. Additionally, replacing year time trends with year fixed e↵ects yields similar results.
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dence interval, for both officer discretion and mandatory arrest states. Notably, after controlling for county-specific unobserved baseline characteristics, there remains a strong positive relationship between unemployment and intimidation. Figure 2a displays the di↵erences in the mean outcomes between officer discretion and mandatory arrest across the range of unemployment as well as the 95% confidence interval of this di↵erence. At low levels of unemployment there is no significant difference in the intimidation rate across state policies. However, we find a clear separation between these two policies’ predicted incident rates as unemployment increases. Specifically, mandatory arrest tends to abate domestic acts of violent intimidation as unemployment worsens. Starting at around 4.75% unemployment, mandatory arrest begins to suppress intimidation incidents with an increasing e↵ect. This statistically significant di↵erence remains as the unemployment rate grows. However, as unemployment exceeds 8.5% the two arrest policies become undi↵erentiated. We interpret this finding in that arrest laws cannot overcome all levels of shocks. If economic strain heightens violent behavior, then at some point the deterrence of mandatory arrest policies will be fully negated by rising levels of unemployment. Surprisingly, mandatory arrest laws do not predict an independent e↵ect in reducing intimidation at low levels of unemployment. In other words, when the local community is at or below the natural rate of unemployment, mandatory arrest laws lose their efficacy compared with officer discretion policies. Turning toward incidents of assault as shown in Figure 1b, we document the positive relationship between the unemployment rate and incidents of assault. As unemployment worsens, we find the incident rate of assault substantively increases both in states with officer discretion and in states with mandatory arrest laws. Figure 2b displays the mean di↵erences between officer discretion and mandatory arrest along with the associated 95% confidence interval. Unlike intimidation, there is no statistically discernible di↵erence between types of arrest laws. In this regard, the efficacy of mandatory arrest is weakened with respect to assault. Mandatory arrest policies may lose their e↵ectiveness if deterrence requires rational decision-making (Sherman and Berk, 1984). Given assault is a more severe form of domestic violence, the heightened emotional environment may result in the o↵ender ignoring the consequences of his actions.
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5
Conclusion
In assessing arrest laws conditional upon unemployment, this paper provides a first quantitative test intersecting a public policy response to domestic violence with the local economic environment. This study is the first to systematically document the positive relationship between local unemployment rates and domestic violence in the United States. Addressing whether arrest laws reduce domestic violence in times of economic stress, we find mixed evidence. While arrest laws tend to reduce the onset of intimidation as unemployment begins to worsen, the mitigating e↵ects of mandatory arrest disappear under severe economic crisis. However, we fail to find evidence that mandatory arrest laws reduce the incident rate of assault, across the range of unemployment. Despite not being a universal cure for domestic violence, mandatory arrest statutes tend to reduce the rates of domestic violence when communities are most sensitive to economic shocks, namely for o↵enses of intimidation. Our findings promote future research to investigate the hitherto unknown inefficacies of mandatory arrest in suppressing domestic violence at the ends of the unemployment distribution. Additional scholarship should examine the conditional e↵ects of arrest law across unemployment rates when disaggregated by gender. Future studies that progress our understanding of the mechanisms of arrest laws would be beneficial for families and communities in economic distress.
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References
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Hirschel, D., Buzawa, E., Pattavina, A., and Faggiani, D. (2007). Domestic violence and mandatory arrest laws: To what extent do they influence police arrest decisions? Journal of Criminal Law and Criminology. 98(1), 255-298. Hsu, L. (2016). The timing of welfare payments and intimate partner violence. Economic Inquiry. 55(2), 1017-1031. Iyengar, R. (2009). Does the certainty of arrest reduce domestic violence? Evidence from mandatory and recommended arrest laws. Journal of Public Economics 93(1), 85-98. Nou, J., and Timmins, C. (2005). How do changes in welfare law a↵ect domestic violence? An analysis of Connecticut towns, 1990-2000. The Journal of Legal Studies. 34(2), 445-470. Renzetti, C. M. (2009). Economic Stress and Domestic Violence. Center for Research on Violence Against Women Faculty Research Reports and Papers. 1, 1-15. Ruttenberg, M. H. (1994). A feminist critique of mandatory arrest: An analysis of race and gender in domestic violence policy. American University Journal of Gender and the Law. 2, 171-200. Sherman, L. W., and Berk, R. A. (1984). The specific deterrent e↵ects of arrest for domestic assault. American Sociological Review. 49(2), 261-272. Wanless, M. (1996). Mandatory arrest: A step toward eradicating domestic violence, but is it enough. University of Illinois Law Review. 2, 533-586. Wardrop, M. (2008, December 20). Recession Will Prompt Rise in Domestic Violence. The Telegraph. Retrieved from http://www.telegraph.co.uk/ Zeoli, A. M., Norris, A., and Brenner, H. (2011). A summary and analysis of warrantless arrest statutes for domestic violence in the United States. Journal of Interpersonal Violence. 26(14), 2811-2833.
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Table 1: State Arrest Policies by O↵ense Officer Discretion
Mandatory Arrest
Intimidation
Alabama, Arizona, Idaho, Indiana, Iowa, Kentucky, Michigan, Nebraska, New Hampshire, Ohio, Oklahoma, Pennsylvania, South Carolina, Texas, Vermont, West Virginia
Arkansas, Colorado, Connecticut, Illinois, Kansas, Louisiana, Maine, Massachusetts, Mississippi, Missouri, Montana, North Dakota, Oregon, Rhode Island, South Dakota, Tennessee, Utah, Virginia,Washington, Wisconsin
Assault
Alabama, Arizona, Idaho, Indiana, Iowa, Kentucky, Michigan, Nebraska, New Hampshire, Oklahoma, Pennsylvania, South Carolina, Texas, Vermont, West Virginia
Arkansas, Colorado, Connecticut, Illinois, Kansas, Louisiana, Maine, Massachusetts, Mississippi, Missouri, Montana, North Dakota, Ohio, Oregon, Rhode Island, South Dakota, Tennessee, Utah, Virginia, Washington, Wisconsin
Table 2: Summary Statistics Variable
Mean
S.D.
Intimidation rate (monthly incidents per 100,000) Assault rate (monthly incidents per 100,000) Unemployment rate (monthly county-level) Median household income (thousands) Renter-occupied housing (thousands) Percent white Percent African American Percent Hispanic Rural-urban continuum* Percent high school degree Percent bachelors degree
2.57 13.50 6.39 45.92 26.69 83.08 7.30 5.35 4.05 33.38 22.23
9.04 21.55 2.77 12.29 51.68 14.28 11.76 7.05 2.50 6.92 10.06
*Nine-category classification created by the United States Department of Agriculture to classify counties by population and urbanization.
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Table 3: Model Coefficients
Panel A: Intimidation Incident Rate (monthly per 100k) Officer Discretion Unemployment Unemployment2 Unemployment3
County demographics County fixed e↵ects Observations
Mandatory Arrest
Officer Discretion
Mandatory Arrest
0.221*** (0.070) -0.016*** (0.003) 0.000*** (0.000)
0.071 (0.178) 0.003 (0.021) -0.000 (0.001)
0.227*** (0.084) -0.020** (0.008) 0.001** (0.000)
0.083 (0.116) -0.005 (0.013) 0.000 (0.000)
Yes No 339,366
Yes No 330,771
No Yes 340,704
No Yes 331,356
Officer Discretion
Mandatory Arrest
Panel B: Assault Incident Rate (monthly per 100k) Officer Discretion Unemployment Unemployment2 Unemployment3
County demographics County fixed e↵ects Observations
Mandatory Arrest
0.178*** (0.068) -0.019*** (0.007) 0.001** (0.000)
0.199*** (0.073) -0.019** (0.009) 0.001* 0.000
0.263*** (0.068) -0.021*** (0.005) 0.001*** (0.000)
0.154** (0.071) -0.013* (0.007) 0.000 (0.000)
Yes No 281,914
Yes No 382,017
No Yes 283,007
No Yes 382,810
Standard errors clustered at the state level. All models include month fixed e↵ects and cubic year time trends. *** significant at 1% level, ** significant at 5% level, * significant at 10% level.
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Figure 1: Predicted Outcomes (a) Intimidation Incidents per 100k
(b) Assault Incidents per 100k 18 Assault per 100k (mean)
Intimidation per 100k (mean)
3.5
3
2.5
2
16
14
12
10
1.5
8 3
4
5
6 7 8 9 Unemployment rate (county level) Officer discretion
10
11
12
3
4
5
Mandatory arrest
6 7 8 9 Unemployment rate (county level) Officer discretion
10
11
12
11
12
Mandatory arrest
Figure 2: Mean Di↵erences (a) Intimidation Incidents per 100k
(b) Assault Incidents per 100k
Difference in assault per 100k (mean)
Difference in intimidation per 100k (mean)
1
.5
0
-.5
-1
4
2
0
-2
-4 3
4
5
6 7 8 9 Unemployment rate (county level)
10
11
12
Difference: Officer discretion minus mandatory arrest
3
4
5
6 7 8 9 Unemployment rate (county level)
10
Difference: Officer discretion minus mandatory arrest
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