In order to measure how demand for pets affects in-home domestic violence, we need to link violence across Dallas with the reporting of Dallas Animal Services. The Dallas Police Department collects data regarding police incidents according to beat maps, while Dallas Animal Services collects data regarding animal intake by census tract. We overlay these two maps in Figure 7. Because all data is from the county of Dallas, this matching strategy helps us specify connections between assaults and animals. Dallas county is one single jurisdiction and our data cannot be aggregated any higher.
We define domestic violence as simple assault, aggravated assault, or intimidation between men and women in the household (single family, condominium/town home and apartment resi- dences). We use this definition as it closely follows the criminology and economics literature on how to measure the causes of domestic violence (Card and Dahl, 2011; Cardazzi et al., 2020).
Our study is subject to some limitations inherent to crime data. We assume laws and enforce- ment are constant across precincts, as the county of Dallas is a single jurisdiction. Criminologists use police reports as well as victim surveys to approximate crime. Though we rely ultimately on reports of assault recorded daily, our data aligns with the CDC’s National Intimate Partner and Sexual Violence Survey, which is reported annually. We assume that a decrease in reports is indicative of a decrease in assaults. In an extension of this paper, we will incorporate data from Dallas Women’s Shelters for more accurate measures of assault. This may be wishful thinking, as data on women’s shelters is limited to protect the anonymity of victims.
5.1 Regression Framework
We begin our analysis by measuring how the shelter in place order has affected in-home domestic violence. yct represents a count variable of daily reported in-home assaults per beat. First, we investigate changes to in-home vs. outside the home assaults, and then we look specifically at assaults against women and children. We estimate the following equation in Table 3.
yct = β0 + β1SIPOt + σwy + µc + ϵct.
To measure violence against women, we interact the number of female assault victims with our shelter in place order variable. We estimate how the demand for Dogs during the shelter in place order affects in-home domestic violence using a difference-in-differences framework. In the following equation, yct represents a count variable of daily reported in-home assaults per council districts.
yct = β0 + β1SIPOt + β2DogConfiscatedit + β3DogFosteredit
+β4DogStrayt + β5DogSurrenderedit
+β6DogTreatmentit + +β7CatConfiscatedit + β8CatFosteredit
+β9CatStrayt + β10CatSurrenderedit
+β11CatTreatmentit + +β12DogConfiscatedit × SIPOt+ β13DogFosteredit × SIPOt
+β14DogStrayt × SIPOt+ β15DogSurrenderedit × SIPOt
+β16DogTreatmentit × SIPOt+ +β17CatConfiscatedit × SIPOt+ β18CatFosteredit × SIPOt
+β19CatStrayt × SIPOt+ β20CatSurrenderedit × SIPOt
+β21CatTreatmentit × SIPOt+ +σwy + µc + ϵct.
We estimate the differences in assaults given the intake of dogs and cats to the Dallas Animal Services during the shelter in place order using an indicator variable, SIPOt, for the 52 days that the order is in place. Next, we include indicators for the daily number of dog and cat brought into a shelter because it was confiscated from owner, surrendered by owner, returned after fostering, received medical treatment, or is a stray animal. We then interact these variables with our indicator
for the shelter in place order to estimate differences in assaults given the outflow of pets from the household during the SIPO. σw and µd represent week and day of the week fixed effects, respectively. We also include month by beat fixed effects, λit. The day of the week fixed effects account for any differences in weekday vs. weekend in-home domestic violence and beat by month fixed effects account for any time-invariant factors that may affect in-home domestic violence at the beat level. ϵidt is our error term.