Effects of COVID-19 Shutdowns on Domestic Violence in US Cities

Miller, Amalia R.; Segal, Carmit; Spencer, Melissa K.

doi:10.3386/w29429

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…Given that the national emergency declaration occurred on March 13, 2020, we collected data from October 1, 2019, to August 31, 2020 (treatment year), including 23 weeks as the pretreatment period and 24 weeks as the post-treatment period, as our treatment group. Further, we obtained data a year ago during the same calendar date, from October 1, 2018, to August 31, 2019 (control year), as the control group following the Miller et al (2021)'s and Sim et al (2022)'s approach. We relied on data between October 1, 2018, to August 31, 2019, to provide a control year that was unaffected by the pandemic but captured the seasonal trend in social media and define "pre" and "post" periods based on a counterfactual treatment date -the same calendar date (month and day) of the national emergency declaration in 2019.…”

Section: Natural Experiments Setting and Datamentioning

confidence: 99%

Coupling Neural Networks Between Clusters for Better Personalized Care

Kraus,

Hambauer,

Müller

et al. 2024

Proceedings of the Annual Hawaii International Conference on System Sciences

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Many companies are utilizing social media as the primary avenue for customer service during the pandemic. However, how customers' behaviors and interactions with customer service agents on social media are impacted by the lockdowns has not been well understood. In this study, we examine the impact of lockdowns and physical distancing on changes in customers' behaviors, such as emotional expressions in tweets and customers' satisfaction with social media customer service. Using a difference-indifferences research design, we find that with the lockdowns and physical distancing, customers expressed more negative emotions when tweeting the company they were having issues with. Surprisingly, compared to before the pandemic period, customers' emotional expressions became more positive and they were more likely to express their satisfaction after interacting with customer service agents. Interestingly, our findings reveal that gender differences exist in these scenarios. We also discuss the theoretical and practical implications of these findings.

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Section: Natural Experiments Setting and Datamentioning

confidence: 99%

Coupling Neural Networks Between Clusters for Better Personalized Care

Kraus,

Hambauer,

Müller

et al. 2024

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…International empirical studies exist that estimate the effect of suppression on movement, gatherings, and SAH orders on domestic violence. Miller et al (2021) and Bullinger et al (2021) estimate the impact of SAH orders on domestic assaults using a standard DiD model for the United States with both identifying declines in the number of assaults during these periods. Alternatively, Ravindran and Shah (2020), Leslie and Wilson (2020) and Berniell and Facchini (2021) implement a standard DiD model for data on India, the United States and 11 countries within South America, respectively.…”

Section: Introductionmentioning

confidence: 99%

The impact of COVID‐19 stay‐at‐home orders on domestic violence assaults: A study of New South Wales

Graceffo

2024

Australian Economic Papers

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Introduction of stay‐at‐home orders during the COVID 19 pandemic led to immediate disruption of individuals' daily routines with confinement to their households and changes in risk factors of domestic violence. Utilising data from the Bureau of Crime Statistics and Research (BOCSAR), I estimate the causal impact of stay‐at‐home orders on domestic violence assaults in New South Wales. Since adoption dates for these restrictions varied across LGAs, I implement a staggered difference‐in‐difference (DiD) specification. I find a 21% decline in daily domestic violence assault incidents during stay‐at‐home order periods compared to pre‐restriction incidence levels. These results are robust under weekly frequency and extrapolated fixed effects models. I further utilise google search term data as a proxy of consistent reporting rates and unemployment data to identify household power dynamics. Analysis of both data sets support estimates of declines in domestic violence during stay‐at‐home order periods.

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Effects of COVID-19 Shutdowns on Domestic Violence in US Cities

Cited by 2 publications

References 19 publications

Coupling Neural Networks Between Clusters for Better Personalized Care

Coupling Neural Networks Between Clusters for Better Personalized Care

The impact of COVID‐19 stay‐at‐home orders on domestic violence assaults: A study of New South Wales

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