The price of COVID-19 risk in a public university.

We study the allocation of and compensation for occupational COVID-19 risk at Auburn University, a large public university in the U.S. In Spring 2021, approximately half of the face-to-face classes had enrollments above the legal capacity allowed by a public health order, which followed CDC's social distancing guidelines. We find lower-ranked graduate student teaching assistants and adjunct instructors were more likely to deliver riskier classes. Using an IV strategy in which teaching risk is shifted by classroom features (geometry and furniture), we show instructors who taught at least one risky class earned $7,400 more than those who did not.

Assessing the Impact of the Covid-19 on the US Airline Market Using an Instrumental Variable and a DID Empirical Strategy

In this paper, we examine the affects of COVID-19 and related policies on the aviation industry. Using archival data from the John Hopkins Coronavirus Resource Center, Department of Transportation Statistics, and the COVID-19 U.S. State Policy database, and an instrumental variable and a difference-in-differences empirical strategy, we find that COVID-19 severity is negatively correlated with both the mean ticket price and the number of passengers for the four major airlines in the US, and that the implementation of COVID-19 control policies is positively correlated with the mean ticket price, while negatively correlated with the number of passengers. © 2022 ACM.

Frontiers: The persuasive effect of FOX News: Noncompliance with social distancing during the COVID-19 pandemic

To what extent do mass media outlets influence viewers' trust in scientific evidence and compliance with behavior recommended by scientific experts? Exploiting the U.S. lockdown period of the COVID-19 pandemic in early 2020, we analyze a large longitudinal database that combines daily stay-at-home behavior from approximately 8 million mobile phones and local viewership of cable news networks. Early in the pandemic, several of Fox News' hosts downplayed the severity of the pandemic and the risks associated with the transmission of the virus. A combination of regression analysis and a natural experiment finds that a 10% increase in viewership of Fox News in a zip code causes a 0.76- percentage-point reduction in compliance with stay-at-home behavior. The results imply a media persuasion rate that is larger than typical advertising persuasion rates on consumer behavior. Similar analyses using viewership of MSNBC and CNN, which supported lockdown measures, were inconclusive but suggested a smaller, positive effect on compliance with social distancing regulations. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Non-pharmaceutical Interventions and COVID-19: Do County- and State-Level Policies Predict the Spread of COVID-19?

This study examines the impact of county- and state-level policies on the spread and severity of COVID-19 in communities in the USA during the first wave of the COVID-19 pandemic. We use county-level COVID-19 death and case data to examine the impact of county- and state-level mandates and non-pharmaceutical interventions (NPIs) on the spread and severity of COVID-19. Following previous work by Amuendo-Dorantes et al. (2020), we utilize a strategy that incorporates the duration of NPI implementation within a county. Specifically, we examine aggregated measures of mask mandates, daycare closures, stay-at-home orders, and restaurant and bar closures. In addition to the implementation and duration of NPI policy, we examine the role of pre-existing factors that contribute to social determinants of health in a locality. We incorporate information on the incidence of prior health conditions, socio-economic factors, and demographics including racial and ethnic composition, share of immigrant population of counties, and state governance in our estimations. To alleviate the possible endogeneity of COVID-19 outcomes and NPIs, we use instrumental variable estimation and our results show that collectively NPIs decreased the intensity of the pandemic by decreasing the total deaths and cases. Furthermore, we find the magnitude of the impact of NPIs increases the longer they are implemented. We also estimate a specification that allows for heterogeneity of NPI impact based on the racial and ethnic composition of counties. Our results suggest that NPIs have a non-uniform impact in counties with different racial and ethnic compositions.

Understanding Covid-19 Mobility Through Human Capital: A Unified Causal Framework.

This paper seeks to identify the causal impact of educational human capital on social distancing behavior at workplace in Turkey using district-level data for the period of April 2020 - February 2021. We adopt a unified causal framework, predicated on domain knowledge, theory-justified constraints anda data-driven causal structure discovery using causal graphs. We answer our causal query by employing machine learning prediction algorithms; instrumental variables in the presence of latent confounding and Heckman's model in the presence of selection bias. Results show that educated regions are able to distance-work and educational human capital is a key factor in reducing workplace mobility, possibly through its impact on employment. This pattern leads to higher workplace mobility for less educated regions and translates into higher Covid-19 infection rates. The future of the pandemic lies in less educated segments of developing countries and calls for public health action to decrease its unequal and pervasive impact.

Do more stringent policies reduce daily COVID-19 case counts? Evidence from Canadian provinces.

The enactment of COVID-19 policies in Canada falls under provincial jurisdiction. This study exploits time-series variation across four Canadian provinces to evaluate the effects of stricter COVID-19 policies on daily case counts. Employing data from this time-period allows an evaluation of the efficacy of policies independent of vaccine impacts. While both OLS and IV results offer evidence that more stringent Non-Pharmaceutical Interventions (NPIs) can reduce daily case counts within a short time-period, IV estimates are larger in magnitude. Hence, studies that fail to control for simultaneity bias might produce confounded estimates of the efficacy of NPIs. However, IV estimates should be treated as correlations given the possibility of other unobserved determinants of COVID-19 spread and mismeasurement of daily cases. With respect to specific policies, mandatory mask usage in indoor spaces and restrictions on business operations are significantly associated with lower daily cases. We also test the efficacy of different forecasting models. Our results suggest that Gradient Boosted Regression Trees (GBRT) and Seasonal Autoregressive-Integrated Moving Average (SARIMA) models produce more accurate short-run forecasts relative to Vector Auto Regressive (VAR), and Susceptible-Infected-Removed (SIR) epidemiology models. Forecasts from SIR models are also inferior to results from basic OLS regressions. However, predictions from models that are unable to correct for endogeneity bias should be treated with caution.

The impact of COVID-19 on route-level changes in transit demand an analysis of five transit agencies in Florida, USA

This study examined the impacts of COVID-19 on changes in route-level transit demand across five transit agencies in the state of Florida. Data for 120 routes from five transit agencies were used to develop two-stage instrumental variable models. Data from January of 2019 to December of 2020 were used in the analysis. Routes that served a greater mix of land-uses experienced a smaller decline in ridership. The impacts of several other land-use variables were, however, not consistent across the five transit agencies. Fare suspension was estimated to have a positive impact on ridership. In contrast, occupancy reduction measures (to promote social distancing within the transit vehicle) had a very strong negative impact on demand. The magnitude of the negative impact of occupancy reduction was larger than the positive impacts of fare suspension. Extending this analysis to a larger set of routes across more agencies would be useful in enhancing the robustness of the findings from our models. Extending our analysis to include data from 2021 and later to capture the recovery phase is also an important direction for future work. © 2022

COVID-19 transmission in cities.

Do cities accelerate COVID-19 transmission? Increased transmission arising from population density prompts spatial policies for financial support and containment, and poorer prospects for recovery. Using daily case counts from over 3,000 counties in the U.S. from February to September 2020, I estimate a compartmental transmission equation. Rational sheltering behavior plausibly varies by location, so I propose two instruments that exploit unanticipated variation in exposure to potential infection. In the first month of local infections, an additional log point of population density raises the expected transmission parameter estimate by around 3%. After the first month, the relation vanishes: density effects occur only in the outbreaks. Public transport, work-from-home jobs and income explain additional variation in transmission but do not account for the density effects. Consistent with location-varying optimal sheltering behavior, I document stronger mobility declines in denser areas, but only after the first month of infections. These results suggest that differences in transmission between cities and other places do not motivate spatial policies for recovery or containment, or poorer prospects after the pandemic.

The Impact of Internet Banking on Performance and Branches: Crisis or Change in Practice?

Internet Banking has become an important alternative to traditional banks. This article studies how Internet Banking's use influenced European banks' profitability and if the phenomenon of the branches' strong contraction (started in 2009 and consolidated from 2011 to 2016) depended on using these technologies or the economic crisis of the period. The exclusion of the COVID-19 crisis derived from the voluntariness to analyze the branches' reduction in your primitive form and not as a consequence of the event's extreme. The sample data comprises 3679 European banks from 2011 to 2016. The methodology used is the instrumental variables. This approach can control internal validity threats, such as confounding variables, measurement error, spuriousness, simultaneity and reverse causality. The results show that Internet Banking has positively influenced the net interest income and negatively affected the distribution of the branches, justifying the strong reduction. Surprisingly, the insignificance of GDP rates on the branch network, while the strong European prudential regulation had a negative impact. The study of Internet Banking's impact on the branches is a new aspect and important for further developments. As transactions are handled online, branch offices will become more like service lounges rather than rows of tellers handling daily transactions.

Effet du mobile money sur la résilience des ménages exerçant des activités génératrices de revenus au Cameroun

Resume Partant d'une enquete realisee en mai 2020 aupres de 1136 menages au Cameroun, cet article evalue l'effet du mobile money sur les menages exercant des activites generatrices de revenus. Les resultats obtenus, par l'estimation d'un modele de probabilite lineaire a variables instrumentales, montrent que le mobile money permet aux menages, pour ce qui est des chocs economiques, de faire face aux tensions de tresorerie liees a leur activite tout en payant les factures et a la pandemie COVID-19. De plus, concernant les chocs sociaux, le mobile money permet aux menages d'etre resilients face a la mort d'un membre important de la famille et aux differentes sollicitations de la famille. Sur la base de cette embellie de l'effet du mobile money, les offreurs du produit gagneraient a intensifier sa diffusion surtout dans les zones rurales oU les couts de transaction sont encore importants.

Comparative analysis on the three popular causality modeling methodologies

The idea of causality has lasted for over thousands of years. Unlike the idea of statistical correlation and regression, performing causal modeling and prediction is an even more challenging job. Under the intervention framework of causality, causal modeling is gaining popularity given the advances of big data and computational ability in recent years. In different scientific research areas, there exist three powerful causal modeling methodologies, namely, the potential outcomes method in statistics, the instrumental variables method in economics and Judea Pearl's causal diagram method (do-calculus) in computer science and artificial intelligence. In this paper, by linear causal modeling assumption, we prove that the above three causal methodologies are equivalent. That is, given a causal problem, all of the three modeling methods will generate the same causal relationship conclusion, despite that they own different causal inference processes. During the past one-and-half years, the global economy suffers severe impacts from the COVID-19 pandemic. To fight the deadly pandemic, various social distancing measures and actions, taken by the countries, are effective in curbing the impact of the pandemic over the population. However, such social distancing policy has an adverse effect over the global economy growth;if more stringent measures were taken, then there would be suffering in the forms of much slower economic growth and higher unemployment. In this paper, we study the causal relationships between social distancing, fatality rate and economy growth. This work provides a useful tool for the governments to keep balance between controlling the pandemic and maintaining economic growth.

Socio-economic and environmental factors in the global spread of COVID-19 outbreak.

COVID-19 is a virus with a very fast spread rate in the world. Therefore, knowledge of factors that may explain such spread is paramount. The main objective of this research was to analyze the determinants of the virus spread worldwide. Unlike previous studies that were limited to traditional factors, this research extends the analysis to government measures (quarantine, containment, and response budget) against the spread of the virus. Thus, an econometric model relating the variable of interest to a number of variables was carried out using the Ordinary Least Squares (OLS) and the Two Steps Least Squares (2SLS) methods on a sample of 163 countries. The main findings indicate that economic factors such as the level of development, the degree of trade openness and the response budget to the COVID-19 pandemic, have a positive effect on the spread of the virus. With regard to social factors, the population density and confinement are major causes of the spread of the virus. Finally, temperature contributes to reduce the spread of the virus. These findings are robust to the estimation technique and to the measurement of the spread of the virus considered. In the light to these findings, implications for economic policies have been drawn.

Too Healthy to Fall Sick? Longevity Expectations and Protective Health Behaviours during the First Wave of COVID-19.

Longevity expectations (LE) are subjective assessments of future health status that can influence a number of individual health protective decisions. This is especially true during a pandemic such as COVID-19, as the risk of ill health depends more than ever on such protective decisions. This paper examines the causal effect of LE on some protective health behaviors and a number of decisions regarding forgoing health care using individual differences in LE. We use data from the Survey of Health Ageing and Retirement in Europe, and we draw on an instrumental variable strategy exploiting individual level information on parental age at death. Consistent with the too healthy to be sick hypothesis, we find that individuals, exhibiting higher expected longevity, are more likely to engage in protective behaviours, and are less likely to forgo medical treatment. We estimate that a one standard deviation increase in LE increases the probability to comply always with social distancing by 0.6%, to meet people less often by 0.4% and decreases the probability to forgo any medical treatment by 0.6%. Our estimates vary depending on supply side restrictions influencing the availability of health care, as well as individual characteristics such as their gender and the presence of pre-existing health conditions.

A novel penalized inverse-variance weighted estimator for Mendelian randomization with applications to COVID-19 outcomes.

Mendelian randomization utilizes genetic variants as instrumental variables (IVs) to estimate the causal effect of an exposure variable on an outcome of interest even in the presence of unmeasured confounders. However, the popular inverse-variance weighted (IVW) estimator could be biased in the presence of weak IVs, a common challenge in MR studies. In this article, we develop a novel penalized inverse-variance weighted (pIVW) estimator, which adjusts the original IVW estimator to account for the weak IV issue by using a penalization approach to prevent the denominator of the pIVW estimator from being close to zero. Moreover, we adjust the variance estimation of the pIVW estimator to account for the presence of balanced horizontal pleiotropy. We show that the recently proposed debiased IVW (dIVW) estimator is a special case of our proposed pIVW estimator. We further prove that the pIVW estimator has smaller bias and variance than the dIVW estimator under some regularity conditions. We also conduct extensive simulation studies to demonstrate the performance of the proposed pIVW estimator. Furthermore, we apply the pIVW estimator to estimate the causal effects of five obesity-related exposures on three coronavirus disease 2019 (COVID-19) outcomes. Notably, we find that hypertensive disease is associated with an increased risk of hospitalized COVID-19; and peripheral vascular disease and higher body mass index are associated with increased risks of COVID-19 infection, hospitalized COVID-19, and critically ill COVID-19.

The Moderating Effect of COVID-19 on the Relationship between Spot Market Prices and Capital Investment in the Motor-Carrier Sector

Capital investment by motor carriers in new trucks and trailers is essential for the effective and efficient operation of supply chains. Strong evidence exists that motor carriers' capital investment is positively correlated with truckload spot prices. This article explores whether the onset of the COVID-19 pandemic moderated the relationship between spot prices and capital investment by motor carriers. We theorize that the onset of the COVID-19 pandemic muted the relationship between spot prices and investment in trucks to a greater degree than it muted the relationship between spot prices and investment in trailers. We attribute this to the COVID-19 pandemic creating tremendous uncertainty about the availability of truck drivers, which made investing in trucks less attractive than investing in trailers that do not require additional labor. To test our theory, we assemble a time series dataset that combines proprietary and public data from several sources. Econometric estimates corroborate our predictions and are robust to instrumental variable estimation to assuage concerns about endogeneity of spot prices. We explain the implications of our findings for theory and practice. Copyright © 2022 The Pennsylvania State University, University Park, PA

Social learning in the COVID-19 pandemic: Community establishments' closure decisions follow those of nearby chain establishment

As conveners that bring various stakeholders into the same physical space, firms can powerfully influence the course of pandemics such as coronavirus disease 2019. Even when operating under government orders and health guidelines, firms have considerable discretion to keep their establishments open or closed during a pandemic. We examine the role of social learning in the exercise of this discretion at the establishment level. In particular, we evaluate how the closure decisions of chain establishments, which are associated with national brands, affect those of proximate, same-industry community establishments, which are independently owned or managed. We conduct these analyses using cell phone location tracking data on daily visits to 230,403 U.S.-based community establishments that are colocated with chain establishments affiliated with 319 national brands. We disentangle the effects of social learning from confounding factors by using an instrumental variables strategy that relies on local variation in community establishments' exposure to closure decisions made by brands at the national level. Our results suggest that closing decisions of community establishments are affected by the decisions made by chain establishments;a community establishment is 3.5% more likely to be open on a given day if the proportion of nearby open chain establishments increases by one standard deviation. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

Frontiers: The Persuasive Effect of Fox News: Noncompliance with Social Distancing During the COVID-19 Pandemic

To what extent do mass media outlets influence viewers' trust in scientific evidence and compliance with behavior recommended by scientific experts? Exploiting the U.S. lockdown period of the COVID-19 pandemic in early 2020, we analyze a large longitudinal database that combines daily stay-at-home behavior from approximately 8 million mobile phones and local viewership of cable news networks. Early in the pandemic, several of Fox News' hosts downplayed the severity of the pandemic and the risks associated with the transmission of the virus. A combination of regression analysis and a natural experiment finds that a 10% increase in viewership of Fox News in a zip code causes a 0.76 percentage-point reduction in compliance with stay-at-home behavior. The results imply a media persuasion rate that is larger than typical advertising persuasion rates on consumer behavior. Similar analyses using viewership of MSNBC and CNN, which supported lock down measures, were inconclusive but suggested a smaller, positive effect on compliance with social distancing regulations.

SNAP participation and the health and health care utilisation of low-income adults and children.

OBJECTIVE: This article examined whether participation in the Supplemental Nutrition Assistance Program (SNAP) produced changes to adult and child health and health care utilisation during a period of economic recession. DESIGN: Instrumental variables analysis relying on variation in state SNAP policies to isolate exogenous variation in household SNAP participation. SETTING: Nationally representative data on child and adult health from the 2008 to 2013 National Health Interview Survey. PARTICIPANTS: Participants were 92 237 adults and 45 469 children who were either eligible for SNAP based on household income and state eligibility rules or were low income but not eligible for SNAP benefits. RESULTS: For adults, SNAP participation increased the probability of reporting very good or excellent health, and for both adults and children, reduced needing but having to go without dental care or eyeglasses. The size of these benefits was especially pronounced for children. However, SNAP participation increased the probability of needing but not being able to afford prescription medicine, and increased psychological distress for adults and behavioural problems for children under age 10. CONCLUSIONS: SNAP's benefits for adult health and improved access to dental and vision care for adults and children suggest benefits from the program's expansions during the current COVID-induced crisis. Predicted negative effects of SNAP participation suggest the need for attention to program and benefit structure to avoid harm and the need for continued research to explore the causal effects of program participation.

The impact of COVID-19 on the ride-sharing industry and its recovery: Causal evidence from China.

The COVID-19 pandemic has brought unprecedented disruptions to many industries, and the transportation industry is among the most disrupted ones. We seek to address, in the context of a ride-sharing platform, the response of drivers to the pandemic and the post-pandemic recovery. We collected comprehensive trip data from one of the leading ride-sharing companies in China from September 2019 to August 2020, which cover pre-, during-, and post-pandemic phases in three major Chinese cities, and investigate the causal effect of the COVID-19 pandemic on driver behavior. We find that drivers only slightly reduce their number of shifts in response to increased COVID-19 cases, likely because they have to make a living from providing ride-sharing services. Nevertheless, conditional on working, drivers exhibit strong risk aversion: As the number of new cases increases, drivers strategically adjust the scope of their search for passengers, complete fewer trips, and as a result, make lower daily earnings. Finally, our heterogeneity analyses indicate that the effects appear to vary both across drivers and over time, with generally stronger effects on drivers who are older, more experienced, more active before the pandemic, and higher-status within the firm. Our findings have strong policy implications: These drivers tend to contribute more to the focal company, and also rely more on providing ride-sharing services to make a living. Therefore, they should be prioritized in stimulus plans offered by the government or the ride-sharing company.

Incarceration and mortality in the United States.

The ongoing COVID-19 pandemic has spotlighted the role of America's overcrowded prisons as vectors of ill health, but robust analyses of the degree to which high rates of incarceration impact population-level health outcomes remain scarce. In this paper, we use county-level panel data from 2927 counties across 43 states between 1983 and 2014 and a novel instrumental variable technique to study the causal effect of penal expansion on age-standardised cause-specific and all-cause mortality rates. We find that higher rates of incarceration have substantively large effects on deaths from communicable, maternal, neonatal, and nutritional diseases in the short and medium term, whilst deaths from non-communicable disease and from all causes combined are impacted in the short, medium, and long run. These findings are further corroborated by a between-unit analysis using coarsened exact matching and a simulation-based regression approach to predicting geographically anchored mortality differences.