The COVID-19 pandemic may have spurred automation, especially in critical occupations. This article explores the potential of each detailed Standard Occupational Classification System (SOC) occupation being automated due to COVID-19. The authors explore two key elements of each occupation: its exposure to diseases such as COVID-19 and the probability of that occupation being automated. The results reveal that food preparation, service, and cleaning-related occupations have a higher chance of pandemic-induced automation. Using monthly U.S. job postings from 2016 to 2021, the estimates show that the potential pandemic-induced automation is associated with a statistically significant decrease in job postings. A higher Automation Index is associated with fewer job postings since the pandemic. Such trends remain robust after accounting for posting duration and excluding health-related occupations. These findings contribute to the early assessment of the impact of COVID-19 on the potential integration of automation in the labor force and offer insights into building a resilient and labor-centric post-pandemic labor market.

We are starting to understand the magnitude of economic damage from the COVID-19 pandemic. Current estimates cover the national or state level, but tell us little about how massive business closures may be affecting urban vitality at the intrametropolitan level. A particular concern is whether urban areas with high poor or minority populations are more deeply affected. This paper combines InfoGroup Historic Business Data and Google Map API to analyze business closures at the neighborhood level in Franklin County, Ohio, encompassing the Columbus Metropolitan Area. As expected, retail and restaurant sectors had the highest number of closures, but closure rates were higher in other sectors. Descriptive and multivariate analyzes reveal that downtown Columbus has been severely affected, but no disadvantaging effect for communities of color or neighborhoods with concentrations of poor residents is found to be statistically significant.

Minimum wage opponents often argue that businesses owned by marginalized communities, which include woman-owned, Black-owned, and immigrant-owned businesses, are exceptionally vulnerable to minimum wage increases. Little research has investigated this claim. Using a unique survey of Seattle businesses that includes owners' nativity status and was administered while the city began to phase in its $15 minimum wage ordinance, the authors find that immigrant-owned businesses respond to the higher minimum wage in ways that largely conform to the responses of other businesses. Nevertheless, immigrant-owned are less likely than other franchises to fire employees, reduce employees' hours, or lower the wages of employees earning more than $15 per hour. Evidence suggests that immigrant franchisees have a lower likelihood of passing the increased labor costs onto employees because they use fewer employees and rely more heavily on family labor compared to other franchisees. The authors' findings suggest that firms owned by marginalized and nonmarginalized groups respond to municipal-level minimum wage increases in comparable ways. Nevertheless, marginalized status may matter more in certain sectors of the economy than in others.