Pandemic response policies' democratizing effects on online learning.

The COVID-19 pandemic has changed peoples' lives in unexpected ways, especially how they allocate their time between work and other activities. Demand for online learning has surged during a period of mass layoffs and transition to remote work and schooling. Can this uptake in online learning help close longstanding skills gaps in the US workforce in a sustainable and equitable manner? We answer this question by analyzing individual engagement data of DataCamp users between October 2019 and September 2020 (n = 277,425). Exploiting the staggered adoption of actions to mitigate the spread of COVID-19 across states, we identify the causal effect at the neighborhood level. The adoption of nonessential business closures led to a 38% increase in new users and a 6% increase in engagement among existing users. We find that these increases are proportional across higher- and lower-income neighborhoods and neighborhoods with a high or low share of Black residents. This demonstrates the potential for online platforms to democratize access to knowledge and skills that are in high demand, which supports job security and facilitates social mobility.

Did the paycheck protection program hit the target?

This paper provides a comprehensive assessment of financial intermediation and the economic effects of the Paycheck Protection Program (PPP), a large and novel small business support program that was part of the initial policy response to the COVID-19 pandemic in the US. We use loan-level microdata for all PPP loans and high-frequency administrative employment data to present three main findings. First, banks played an important role in mediating program targeting, which helps explain why some funds initially flowed to regions that were less adversely affected by the pandemic. Second, we exploit regional heterogeneity in lending relationships and individual firm-loan matched data to study the role of banks in explaining the employment effects of the PPP. We find the short- and medium-term employment effects of the program were small compared to the program's size. Third, many firms used the loans to make non-payroll fixed payments and build up savings buffers, which can account for small employment effects and likely reflects precautionary motives in the face of heightened uncertainty. Limited targeting in terms of who was eligible likely also led to many inframarginal firms receiving funds and to a low correlation between regional PPP funding and shock severity. Our findings illustrate how business liquidity support programs affect firm behavior and local economic activity, and how policy transmission depends on the agents delegated to deploy it.

Sectoral digital intensity and GDP growth after a large employment shock: A simple extrapolation exercise.

We introduce a state-dependent algorithm with minimal data requirements for predicting output dynamics as a function of employment across industries and locations. The method generalizes insights of Okun (1963) by leveraging measures of industry heterogeneity. We use the algorithm to examine gross domestic product (GDP) dynamics following the COVID-19 pandemic of 2020, delivering informative projections of aggregate and sectoral output. Because the pandemic curtailed the ability to perform certain tasks at work, our application examines whether greater reliance on digital technologies can mediate employment and productivity losses. We use industry-level indices of digital task intensity and ability to work from home, together with publicly available data on employment and GDP for Canada, to document that: (i) employment responses after the shock's onset are milder in digitally intensive sectors and (ii) conditional on the size of employment changes, GDP responses are less extreme in digitally intensive sectors. Our projections indicate a return to pre-crisis aggregate output within eight quarters of the initial shock with significant heterogeneity in recovery patterns across sectors.

Intensité numérique sectorielle et croissance du PIB après un important choc sur le plan de l'emploi : un simple exercice d'extrapolation. Nous utilisons un algorithme dépendant de l'état avec des exigences minimales en matière de données pour prédire les dynamiques de production comme fonction de l'emploi dans plusieurs industries et lieux. La méthode généralise les observations de Okun (1963) en mettant à profit les mesures d'hétérogénéité de l'industrie. Nous utilisons l'algorithme pour examiner les dynamiques du produit intérieur brut (PIB) après la pandémie de COVID­19 de 2020, produisant ainsi des projections informatives sur la production globale et sectorielle. Puisque la pandémie a réduit la capacité d'exécuter certaines tâches au travail, notre application examine si le fait de miser davantage sur les technologies numériques peut atténuer les pertes d'emplois et de productivité. Nous avons recours à des indices à l'échelon de l'industrie sur l'intensité des tâches numériques et la capacité de travailler à la maison, de concert avec les données disponibles au public sur l'emploi et le PIB au Canada, en vue de documenter que : i) les réponses de l'emploi après le début du choc sont plus tempérées dans les secteurs à intensité numérique et ii) de façon conditionnelle à l'ampleur des changements sur l'emploi, les réponses du PIB sont moins extrêmes dans les secteurs à intensité numérique. Nos projections indiquent un retour à la production globale antérieure à la crise dans les huit trimestres suivant le choc initial ainsi qu'une hétérogénéité significative dans les modèles de reprise dans l'ensemble des secteurs.

The impact of COVID-19 on the U.S. child care market: Evidence from stay-at-home orders.

Stay-at-home orders (SAHOs) were implemented in most U.S. states to mitigate the spread of COVID-19. This paper quantifies the impact of these containment policies on a measure of the supply of child care. The supply of such services may be particularly vulnerable to a SAHO-type policy shock, given that many providers are liquidity-constrained. Using plausibly exogenous variation from the staggered adoption of SAHOs across states, we find that online job postings for early care and education teachers declined by 16% after enactment. This effect is driven exclusively by private-sector services. Indeed, hiring by public programs like Head Start and pre-kindergarten has not been influenced by SAHOs. We also find that ECE job postings increased dramatically after SAHOs were lifted, although the number of such postings remains 4% lower than that during the pre-pandemic period. There is little evidence that child care search behavior among households was altered by SAHOs. Because forced supply-side changes appear to be at play, our results suggest that households may not be well-equipped to insure against the rapid transition to the production of child care. We discuss the implications of these results for child development and parental employment decisions.

Understanding the impact of COVID-19 intervention policies on the hospitality labor market.

Using new high-frequency data that covers a representative sample of small businesses in the United States, this study investigates the effects of the COVID-19 pandemic and the resulting state policies on the hospitality industry. First, business closure policies are associated with a 20-30% reduction of non-salaried workers in the food/drink and leisure/entertainment sectors during March-April of 2020. Second, business reopening policies play a statistically significant role in slowly reviving the labor market. Third, considerable differences exist in the impact of policies on the labor market by state. Fourth, the rise of new COVID-19 cases on a daily basis is associated with the continued deterioration of the labor market. Lastly, managerial, practical, and economic implications are described.

Converging technologies: a critical analysis of cognitive enhancement for public policy application.

This paper investigates cognitive enhancement, specifically biological cognitive enhancement (BCE), as a converging technology, and its implications for public policy. With an increasing rate of technological advancements, the legal, social, and economic frameworks lag behind the scientific advancements that they support. This lag poses significant challenges for policymakers if it is not dealt with sufficiently within the right analytical context. Therefore, the driving question behind this paper is, "What contingencies inform the advancement of biological cognitive enhancement, and what would society look like under this set of assumptions?" The paper is divided into five components: (1) defining the current policy context for BCEs, (2) analyzing the current social and economic outcomes to BCEs, (3) investigating the context of cost-benefit arguments in relation to BCEs, (4) proposing an analytical model for evaluating contingencies for BCE development, and (5) evaluating a simulated policy, social, technological, and economic context given the contingencies. In order to manage the risk and uncertainty inherent in technological change, BCEs' drivers must be scrutinized and evaluated.

Information, partisanship, and preferences in a pandemic.

We investigate the role of information exposure in shaping attitudes and behaviors related to the SARS-CoV-2 (COVID-19) pandemic and whether baseline political affiliation and news diet mediate effects. In December 2020, we randomly assigned 5,009 U.S. adults to nine brief text-based segments related to the dynamics of the pandemic and the safety of various behaviors, estimating the effects on 15 binary outcomes related to COVID-19 policy preferences, expected consumer behavior, and beliefs about safety. Average effects reach significance (95% CI) in 47 out of 120 models and equal 7.4 ppt. The baseline effects are large for all outcomes except beliefs. By contrast, interaction effects by political party and media diet are significant for beliefs but rarely significant for policy and behavioral attitudes. These findings suggest partisan policy and behavioral gaps are driven, at least in part, by exposure to different information and that equalizing information sources would lead to partisan convergence in beliefs.

The effects of department of Veterans Affairs medical centers on socio-economic outcomes: Evidence from the Paycheck Protection Program.

Do medical facilities also help advance improvements in socio-economic outcomes? We focus on Veterans, a vulnerable group over the COVID-19 pandemic who have access to a comprehensive healthcare network, and the receipt of funds from the Paycheck Protection Program (PPP) between April and June as a source of variation. First, we find that Veterans received 3.5% more loans and 6.8% larger loans than their counterparts (p < 0.01), controlling for a wide array of zipcode characteristics. Second, we develop models to predict the number of PPP loans awarded to Veterans, finding that the inclusion of local VA medical center characteristics adds almost as much explanatory power as the industry and occupational composition in an area and even more than the education, race, and age distribution combined. Our results suggest that VA medical centers can play an important role in helping Veterans thrive even beyond addressing their direct medical needs.

Developing and Implementing Predictive Models in a Learning Healthcare System: Traditional and Artificial Intelligence Approaches in the Veterans Health Administration.

Predicting clinical risk is an important part of healthcare and can inform decisions about treatments, preventive interventions, and provision of extra services. The field of predictive models has been revolutionized over the past two decades by electronic health record data; the ability to link such data with other demographic, socioeconomic, and geographic information; the availability of high-capacity computing; and new machine learning and artificial intelligence methods for extracting insights from complex datasets. These advances have produced a new generation of computerized predictive models, but debate continues about their development, reporting, validation, evaluation, and implementation. In this review we reflect on more than 10 years of experience at the Veterans Health Administration, the largest integrated healthcare system in the United States, in developing, testing, and implementing such models at scale. We report lessons from the implementation of national risk prediction models and suggest an agenda for research.

Correction: How social capital helps communities weather the COVID-19 pandemic.

[This corrects the article DOI: 10.1371/journal.pone.0245135.].

How social capital helps communities weather the COVID-19 pandemic.

Why have the effects of COVID-19 been so unevenly geographically distributed in the United States? This paper investigates the role of social capital as a mediating factor for the spread of the virus. Because social capital is associated with greater trust and relationships within a community, it could endow individuals with a greater concern for others, thereby leading to more hygienic practices and social distancing. Using data for over 2,700 US counties, we investigate how social capital explains the level and growth rate of infections. We find that moving a county from the 25th to the 75th percentile of the distribution of social capital would lead to a 18% and 5.7% decline in the cumulative number of infections and deaths, as well as suggestive evidence of a lower spread of the virus. Our results are robust to many demographic characteristics, controls, and alternative measures of social capital.

Does marriage protect mental health? Evidence from the COVID-19 pandemic.

Using weekly variation from April 23 to June 23 2020, we exploit the surge in unemployment over the coronavirus pandemic to identify the effects on mental health outcomes and the role of marital status as a protective factor for households. We find that married respondents are 1-2 percentage points less likely, relative to their unmarried counterparts, to experience mental health problems following declines in work-related income since the start of the pandemic. Our results suggest that the combination of intrafamily substitution and the psychological benefits of marriage helps insure against unanticipated fluctuations in job and income loss.

Ethical Applications of Artificial Intelligence: Evidence From Health Research on Veterans.

BACKGROUND: Despite widespread agreement that artificial intelligence (AI) offers significant benefits for individuals and society at large, there are also serious challenges to overcome with respect to its governance. Recent policymaking has focused on establishing principles for the trustworthy use of AI. Adhering to these principles is especially important for ensuring that the development and application of AI raises economic and social welfare, including among vulnerable groups and veterans. OBJECTIVE: We explore the newly developed principles around trustworthy AI and how they can be readily applied at scale to vulnerable groups that are potentially less likely to benefit from technological advances. METHODS: Using the US Department of Veterans Affairs as a case study, we explore the principles of trustworthy AI that are of particular interest for vulnerable groups and veterans. RESULTS: We focus on three principles: (1) designing, developing, acquiring, and using AI so that the benefits of its use significantly outweigh the risks and the risks are assessed and managed; (2) ensuring that the application of AI occurs in well-defined domains and is accurate, effective, and fit for the intended purposes; and (3) ensuring that the operations and outcomes of AI applications are sufficiently interpretable and understandable by all subject matter experts, users, and others. CONCLUSIONS: These principles and applications apply more generally to vulnerable groups, and adherence to them can allow the VA and other organizations to continue modernizing their technology governance, leveraging the gains of AI while simultaneously managing its risks.

How Much Does the (Social) Environment Matter? Using Artificial Intelligence to Predict COVID-19 Outcomes with Socio-demographic Data.

While the coronavirus pandemic has affected all demographic brackets and geographies, certain areas have been more adversely affected than others. This paper focuses on Veterans as a potentially vulnerable group that might be systematically more exposed to infection than others because of their co-morbidities, i.e., greater incidence of physical and mental health challenges. Using data on 122 Veteran Healthcare Systems (HCS), this paper tests three machine learning models for predictive analysis. The combined LASSO and ridge regression with five-fold cross validation performs the best. We find that socio-demographic features are highly predictive of both cases and deaths-even more important than any hospital-specific characteristics. These results suggest that socio-demographic and social capital characteristics are important determinants of public health outcomes, especially for vulnerable groups, like Veterans, and they should be investigated further.

Leveraging machine learning to characterize the role of socio-economic determinants on physical health and well-being among veterans.

INTRODUCTION: We investigate the contribution of demographic, socio-economic, and geographic characteristics as determinants of physical health and well-being to guide public health policies and preventative behavior interventions (e.g., countering coronavirus). METHODS: We use machine learning to build predictive models of overall well-being and physical health among veterans as a function of these three sets of characteristics. We link Gallup's U.S. Daily Poll between 2014 and 2017 over a range of demographic and socio-economic characteristics with zipcode characteristics from the Census Bureau to build predictive models of overall and physical well-being. RESULTS: Although the predictive models of overall well-being have weak performance, our classification of low levels of physical well-being performed better. Gradient boosting delivered the best results (80.2% precision, 82.4% recall, and 80.4% AUROC) with perceptions of purpose in the workplace and financial anxiety as the most predictive features. Our results suggest that additional measures of socio-economic characteristics are required to better predict physical well-being, particularly among vulnerable groups, like veterans. CONCLUSION: Socio-economic characteristics explain large differences in physical and overall well-being. Effective predictive models that incorporate socio-economic data will provide opportunities to create real-time and personalized feedback to help individuals improve their quality of life.

Designing COVID-19 mortality predictions to advance clinical outcomes: Evidence from the Department of Veterans Affairs.

Using administrative data on all Veterans who enter Department of Veterans Affairs (VA) medical centres throughout the USA, this paper uses artificial intelligence (AI) to predict mortality rates for patients with COVID-19 between March and August 2020. First, using comprehensive data on over 10 000 Veterans' medical history, demographics and lab results, we estimate five AI models. Our XGBoost model performs the best, producing an area under the receive operator characteristics curve (AUROC) and area under the precision-recall curve of 0.87 and 0.41, respectively. We show how focusing on the performance of the AUROC alone can lead to unreliable models. Second, through a unique collaboration with the Washington D.C. VA medical centre, we develop a dashboard that incorporates these risk factors and the contributing sources of risk, which we deploy across local VA medical centres throughout the country. Our results provide a concrete example of how AI recommendations can be made explainable and practical for clinicians and their interactions with patients.

Human flourishing and religious liberty: Evidence from over 150 countries.

This paper studies the spatial and time series patterns of religious liberty across countries and estimates its effect on measures of human flourishing. First, while there are significant cross-country differences in religious liberty, it has declined in the past decade across countries, particularly among countries that rank higher in economic freedom. Second, countries with greater religious liberty nonetheless exhibit greater levels of economic freedom, particularly property rights. Third, using micro-data across over 150 countries in the world between 2006 and 2018, increases in religious freedom are associated with robust increases in measures of human flourishing even after controlling for time-invariant characteristics across space and time and a wide array of time-varying country-specific factors, such as economic activity and institutional quality. Fourth, these improvements in well-being are primarily driven by improvements in civil liberties, such as women empowerment and freedom of expression.