ABSTRACT
During the 2020 COVID-19 epidemic, the U.S. Congress passed the CARES Act that (among other measures) provides direct payments to households. Using a large debit cards database, we analyze consumer expenditures following the stimulus payments. We observe zip code level daily transactions (approximately 12 million cards) before and immediately following the disbursements of stimulus checks. Empirical analysis exploits geographical variation in timing of federal deposits to identify marginal propensity to consume (MPC) for stimulus payments. We estimate between 0.29 (excluding banking) and 0.51 (all spend) of the rebate is spent within a few days of receipt. We find large cross-sectional heterogeneity with MPC estimates that are three times higher in magnitude in the most densely populated urban areas with higher cost-of-living. In areas with more restricted movement during the pandemic (as measured by Google workplace mobility), MPC estimates are approximately 60% higher. We reanalyze data from previous fiscal initiatives (2001 tax rebates and the 2008 fiscal stimulus) and find similar geographical differences. Collectively our results highlight an important shortcoming in fiscal policies that ignore local environment, particularly cross-sectional differences in cost-of-living across the United States. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
ABSTRACT
Health surveillance and assessment are considered essential components of a functional public health system. The recent ubiquity of mobile devices and social media have created a wealth of behavioral data, and bring into existence new forms of population health monitoring. These new digital sources can provide direct and passive data for more detailed and nuanced health factors, and have expanded the human, spatial, and temporal scales at which these factors can be measured. In this project, I leverage digital trace data from tweets and mobile device location pings to explore population scale sleep loss, and nature exposure through park visitations in the United States. Both sleep and nature exposure are essential contributors to well-being, and have historically relied on either survey data or direct observation of individuals to measure. I begin by demonstrating the ability of Twitter data to passively reflect population-scale sleep loss at the state level. This is followed by an exploration of park visitation measured through mobile device GPS data. Changes in county-scale park visitation behavior at the onset of the COVID-19 pandemic are analyzed and comparisons are made using population density, employment sector, income, and voting records. In the final chapter I investigate the viability of predicting park visitation using demographic information from the surrounding neighborhood. I conclude with a brief discussion of the significance of measuring these behaviors, and the potential for health policy improvement. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
ABSTRACT
Health surveillance and assessment are considered essential components of a functional public health system. The recent ubiquity of mobile devices and social media have created a wealth of behavioral data, and bring into existence new forms of population health monitoring. These new digital sources can provide direct and passive data for more detailed and nuanced health factors, and have expanded the human, spatial, and temporal scales at which these factors can be measured. In this project, I leverage digital trace data from tweets and mobile device location pings to explore population scale sleep loss, and nature exposure through park visitations in the United States. Both sleep and nature exposure are essential contributors to well-being, and have historically relied on either survey data or direct observation of individuals to measure. I begin by demonstrating the ability of Twitter data to passively reflect population-scale sleep loss at the state level. This is followed by an exploration of park visitation measured through mobile device GPS data. Changes in county-scale park visitation behavior at the onset of the COVID-19 pandemic are analyzed and comparisons are made using population density, employment sector, income, and voting records. In the final chapter I investigate the viability of predicting park visitation using demographic information from the surrounding neighborhood. I conclude with a brief discussion of the significance of measuring these behaviors, and the potential for health policy improvement. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
ABSTRACT
Vaccination yields the direct individual benefit of protecting recipients from infectious diseases and also the indirect social benefit of reducing the transmission of infections to others, often referred to as herd immunity This research examines how prosocial concern for vaccination, defined as people's preoccupation with infecting others if they do not vaccinate themselves, motivates vaccination in more and less populated regions of the United States. A nationally representative, longitudinal survey of 2,490 Americans showed that prosocial concern had a larger positive influence on vaccination against influenza in sparser regions, as judged by a region's nonmetropolitan status, lesser population density, and lower proportion of urban land area. Two experiments (total n = 800), one preregistered, provide causal evidence that drawing attention to prosocial (vs. individual) concerns interacted with social density to affect vaccination intentions. Specifically, prosocial concern led to stronger intentions to vaccinate against influenza and COVID-19 but only when social density was low (vs. high). Moderated mediation analyses show that, in low-density conditions, the benefits of inducing prosocial concern were due to greater perceived impact of one's vaccination on others. In this light, public health communications may reap more benefits from emphasizing the prosocial aspects of vaccination in sparser environments.