Food insufficiency and Twitter emotions during a pandemic.

The COVID-19 pandemic initially caused worldwide concerns about food insecurity. Tweets analyzed in real-time may help food assistance providers target food supplies to where they are most urgently needed. In this exploratory study, we use natural language processing to extract sentiments and emotions expressed in food security-related tweets early in the pandemic in U.S. states. The emotion joy dominated in these tweets nationally, but only anger, disgust, and fear were also statistically correlated with contemporaneous food insufficiency rates reported in the Household Pulse Survey; more nuanced and statistically stronger correlations are detected within states, including a negative correlation with joy.

The Role of Community Food Services in Reducing US Food Insufficiency in the COVID-19 Pandemic

(p. 2 2016) suggest that "both public and private food assistance programs serve as important mechanisms to tackle the problem of hunger and food insecurity in the United States." Using the HPS data, Bauer (2020) shows that low-income households with children are more likely to suffer food insufficiency and enroll in food assistance programs (e.g., SNAP, WIC, and Pandemic Electronic Benefit Transfer) during the pandemic. Instead of using the free food access variables from the HPS, we therefore draw on the 2019 County Business Patterns data (US Census Bureau, 2019) to shed light on the role of preexisting Community Food Services (CFS) in mitigating food vulnerability in the states during the current pandemic. [...]while the number of such establishments per 10,000 persons may have changed between 2019 (the most recent year for which data are available at the time of this writing) and March 2020, we suggest that once we control for the main driving forces, such as the spread of the disease and unemployment, which can affect both food insufficiency and CFS capacity, the 2019 CFS establishments per 10,000 persons variable is a reasonable proxy for the amount of experience a given state has with CFS and related establishments and its capacity to deliver free food through such a venue.

An early assessment of COVID-19’s impact on tourism in U.S. counties

We use county-level data to examine how the COVID-19 pandemic affected the tourism and hospitality sector, which was by far the most impacted of all sectors, focusing on employment and wage changes. Results support our hypothesis that rural counties experienced fewer negative impacts or even benefited from the COVID-19 pandemic in terms of job growth. We present maps showing the pandemic?s effects on leisure and hospitality (L&H) employment across the nation, identifying the communities both hardest hit and least impacted. A linear regression model is developed to explore independent factors that influenced the pandemic?s local impact. Results are robust across different measures of the key variable (rurality), including rural-urban continuum codes, distance from metropolitan areas, and population density. We also consider the impacts of social capital, income, and local economic diversification, among other factors. Our results suggest that remote, less-populated counties were more likely to experience stable employment in the L&H sector relative to pre-pandemic levels, and in some cases even experienced employment growth.JEL Classification: J2, J3, R1

Visualizing Spatial Economic Supply Chains to Enhance Sustainability and Resilience

This article presents a spatial supply network model for estimating and visualizing spatial commodity flows that used data on firm location and employment, an input–output table of inter-industry transactions, and material balance-type equations. Building on earlier work, we proposed a general method for visualizing detailed supply chains across geographic space, applying the preferential attachment rule to gravity equations in the network context;we then provided illustrations for U.S. extractive, manufacturing, and service industries, also highlighting differences in rural–urban interdependencies across these sectors. The resulting visualizations may be helpful for better understanding supply chain geographies, as well as business interconnections and interdependencies, and to anticipate and potentially address vulnerabilities to different types of shocks.