Using Difference-in-Differences to Identify Causal Effects of COVID-19 Policies

Goodman-Bacon, Andrew; Marcus, Jan

doi:10.2139/ssrn.3603970

Cited by 135 publications

(158 citation statements)

References 10 publications

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“…As with all difference-in-differences analyses, particularly those conducted in the rapidly changing policy context of COVID-19, 27 our study has clear limitations. Unemployment insurance, stimulus payments, and SNAP were often delivered in close temporal proximity to each other, making it difficult to fully distinguish the effects of each, even after covariate adjustment.…”

Section: Discussionmentioning

confidence: 90%

Unemployment insurance and food insecurity among people who lost employment in the wake of COVID-19

Raifman

Bor

Venkataramani

2020

Preprint

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Forty million U.S. residents lost their jobs in the first two months of the coronavirus disease 2019 (COVID-19) pandemic. In response, the Federal Government expanded unemployment insurance benefits in both size ($600/week supplement) and scope (to include caregivers and self-employed workers). We assessed the relationship between unemployment insurance and food insecurity among people who lost their jobs during the COVID-19 pandemic in the period when the federal unemployment insurance supplement was in place. We analyzed data from the Understanding Coronavirus in America (UAC) cohort, a longitudinal survey collected by the University of Southern California Center for Economic and Social Research (CESR) every two weeks between April 1 and July 8, 2020. We limited the sample to individuals living in households earning less than $75,000 in February 2020 who lost their jobs during COVID-19. Using difference-in-differences and event study regression models, we evaluated the association between receipt of unemployment insurance and self-reported food insecurity and eating less due to financial constraints. We found that 40.5% of those living in households earning less than $75,000 and employed in February 2020 experienced unemployment during the COVID-19 pandemic. Of those who lost their jobs, 31% reported food insecurity and 33% reported eating less due to financial constraints. Food insecurity peaked in April 2020 and declined over time, but began to increase again among people receiving unemployment insurance during the final wave of the survey ahead of the federal supplement to unemployment insurance ending. Food insecurity and eating less were more common among people who were non-White, lived in lower-income households, younger, and who were sexual or gender minorities. Receipt of unemployment insurance was associated with a 4.4 percentage point (95% CI: -7.8 to -0.9 percentage points) decline in food insecurity (a 30.3% relative decline compared to the average level of food insecurity during the study period). Receipt of unemployment insurance was also associated with a 6.1 percentage point (95% CI: -9.6 to -2.7 percentage point) decline in eating less due to financial constraints (a 42% relative decline). Estimates from event study specifications revealed that reductions in food insecurity and eating less were greatest in the four-week period immediately following receipt of unemployment insurance, with no evidence of differential pre-existing trends in either outcome. We conclude that receiving unemployment insurance benefits during the period when the $600/week federal supplement was in place was associated with large reductions in food insecurity.

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Section: Discussionmentioning

confidence: 90%

Unemployment insurance and food insecurity among people who lost employment in the wake of COVID-19

Raifman

Bor

Venkataramani

2020

Preprint

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“…This novel study provides a comparatively rich set of NPI variables, and observations that allows for ongoing analysis. The data collected supports a variety of study methods (case-control, survival analysis, and difference in differences) with extensive institutional and time/event variables [2,14]. The variation in timing and alignment with public health declarations show the value in a national dataset.…”

Section: Discussionmentioning

confidence: 73%

“…Understanding how NPIs can contain the pandemic is crucial for balancing public health, economic, and social costs [2]. The CDC pandemic mitigation framework indicates that NPIs are most effective when instituted in an early, targeted, and layered fashion [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…Research has focused on state and local government implementations as well as individual behaviors, however, limited literature is available focusing on institutional level adoption of NPIs in higher education [5,6]. Since non-pharmaceutical COVID-19 interventions are, by nature, not randomized, robust data is required to support institutional level study methods [2].…”

Section: Introductionmentioning

confidence: 99%

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COVID-19 observations and accompanying dataset of non-pharmaceutical interventions across U.S. universities, March 2020

et al. 2020

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Background The Centers for Disease Control and Prevention (CDC) publishes COVID-19 non-pharmaceutical intervention (NPI) guidance for specific institutional audiences to limit community spread. Audiences include: business, clinical, public health, education, community, and state/local government. The swift, severe, and global nature of COVID-19 offers an opportunity to systematically obtain a national view of how larger institutions of higher education adopted NPI guidance at the onset of the pandemic. Method An original database of COVID-19-related university NPI policy changes was compiled. Survey team members manually combed university websites and official statements capturing implementation decisions and dates for five NPI variables from 575 U.S. universities, across 50 states and the District of Columbia, during March of 2020. The universities included in this study were selected from the Department of Education Integrated Postsecondary Education Data System (IPEDS), which provides a set of university explanatory variables. Using IPEDS as the basis for the organizational data allows consistent mapping to event-time and institutional characteristic variables including public health announcements, geospatial, census, and political affiliation. Results The dataset enables event-time analysis and offers a variety of variables to support institutional level study and identification of underlying biases like educational attainment. A descriptive analysis of the dataset reveals that there was substantial heterogeneity in the decisions that were made and the timing of these decisions as they temporally related to

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“…It is thus extremely urgent for us to investigate a data-driven approach that can real-time and dynamically estimate time-dependent R0 and other parameters in facilitating the construction of an SEIR model for each state in the US. As a result, effective and proactive rather than reactive policy responses can be enacted and enforced promptly [15,16]. Undoubtedly, an interactive tool can help policymakers well understand the quantified rather than qualified consequences of different state-level mitigation policies in a scientific and real-time manner [2,17].…”

Section: Promoting and Developing Real-time Data-driven Policy Responmentioning

confidence: 99%

Data-driven Public Health and Social Intervention Modeling to Promote Effective Policy Responses to Curtailing the COVID-19 Pandemic

Qiu¹

2020

Preprint

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We might have to live with COVID-19 until 2025 according to a recent report published in Science! But if we act smartly, the adverse consequence of living with the virus could be minimized. Currently, many states in the United States are seeing spiking cases on a daily basis due to their inadequate policy responses to COVID-19. This paper promotes more research on improving SEIR modeling as it will play a critical role in facilitating the decision-making on promoting and implementing appropriate public health and social interventions. Hopefully, policymakers will listen to science and enact and implement adequate policy responses in combating the COVID-19 pandemic in each of the states across the United States so that we can win this “war” and be well prepared for the promising future.

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Using Difference-in-Differences to Identify Causal Effects of COVID-19 Policies

Cited by 135 publications

References 10 publications

Unemployment insurance and food insecurity among people who lost employment in the wake of COVID-19

Unemployment insurance and food insecurity among people who lost employment in the wake of COVID-19

COVID-19 observations and accompanying dataset of non-pharmaceutical interventions across U.S. universities, March 2020

Data-driven Public Health and Social Intervention Modeling to Promote Effective Policy Responses to Curtailing the COVID-19 Pandemic

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