Hit Harder, Recover Slower? Unequal Employment Effects of the Covid-19 Shock

Lee, Sang Yoon; Park, Minsung; Shin, Yongseok

doi:10.3386/w28354

Cited by 73 publications

(44 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unemployed workers with jobs returned to employment much faster than "jobless" unemployed workers who had no job to return to. Bick and Blandin (2020), Cajner et al (2020) and Lee et al (2021) provide further evidence on the importance of recall unemployment in 2020.…”

Section: Introductionmentioning

confidence: 81%

The Economic Ripple Effects of COVID-19

Buera¹,

Jaef²,

Neumeyer³

et al. 2021

Self Cite

View full text Add to dashboard Cite

VirtualMacro Seminar (VMACS), and the World Bank for helpful comments and suggestions. The views expressed herein are those of the authors and do not necessarily represent the views of the World Bank. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

show abstract

Section: Introductionmentioning

confidence: 81%

The Economic Ripple Effects of COVID-19

Buera¹,

Jaef²,

Neumeyer³

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Examples of the concerns raised by this pandemic include whether initial supply shocks can lead to deficits in demand (Brinca et al [13]), how the magnitudes of supply and demand shocks compare across industries (Guerrieri et al [14]), and what socioeconomic implications of a global pandemic might persist in the future (Nicola et al [15]). Other research focused on the impact to employment caused by the health crisis; Lee et al [16] analyzed the impact on employment rates and their subsequent recoveries across demographics, Blustein et al [17] presented avenues for research on strategies for dealing with the large unemployment rates, and identifying vulnerable workers, Milani [18] found that the response in unemployment varied across countries and that the U.S. employment rate was particularly susceptible to health shocks, and Farrell et al [19] investigated the relationship between unemployment insurance and aggregate consumption. Barrot et al [20] used a general equilibrium model with Cobb-Douglas technologies to determine the economic impact of social distancing and the resulting labor reduction in France, Carvalho and Tahbaz-Salehi [21] presented a literature review of these production network models.…”

Section: Literature Reviewmentioning

confidence: 99%

Socio-Economic Impact of the Covid-19 Pandemic in the U.S.

Barlow

Vodenska

2021

Entropy

View full text Add to dashboard Cite

This paper proposes a dynamic cascade model to investigate the systemic risk posed by sector-level industries within the U.S. inter-industry network. We then use this model to study the effect of the disruptions presented by Covid-19 on the U.S. economy. We construct a weighted digraph G = (V,E,W) using the industry-by-industry total requirements table for 2018, provided by the Bureau of Economic Analysis (BEA). We impose an initial shock that disrupts the production capacity of one or more industries, and we calculate the propagation of production shortages with a modified Cobb–Douglas production function. For the Covid-19 case, we model the initial shock based on the loss of labor between March and April 2020 as reported by the Bureau of Labor Statistics (BLS). The industries within the network are assigned a resilience that determines the ability of an industry to absorb input losses, such that if the rate of input loss exceeds the resilience, the industry fails, and its outputs go to zero. We observed a critical resilience, such that, below this critical value, the network experienced a catastrophic cascade resulting in total network collapse. Lastly, we model the economic recovery from June 2020 through March 2021 using BLS data.

show abstract

“…These actions, together with voluntary social distancing, appear to have reduced the rates of new COVID-19 cases, deaths, 2 and hospitalizations 3 but were also associated with substantial increases in unemployment and other economic hardships. [4][5][6] To alleviate financial harms, several states started reopening at the end of April 2020. By the middle of May 2020, most nonessential businesses had resumed at least some activities nationwide.…”

Section: Introductionmentioning

confidence: 99%

US Trends in COVID-19–Associated Hospitalization and Mortality Rates Before and After Reopening Economies

Gupta

Georgiou²,

Sen

et al. 2021

JAMA Health Forum

View full text Add to dashboard Cite

IMPORTANCE After abrupt closures of businesses and public gatherings in the US in late spring 2020 due to the COVID-19 pandemic, by mid-May 2020, most states reopened their economies.Owing in part to a lack of earlier data, there was little evidence on whether state reopening policies influenced important pandemic outcomes-COVID-19-related hospitalizations and mortality-to guide future decision-making in the remainder of this and future pandemics. OBJECTIVE To investigate changes in COVID-19-related hospitalizations and mortality trends after reopening of US state economies. DESIGN, SETTING, AND PARTICIPANTS Using an interrupted time series approach, this crosssectional study examined trends in per-capita COVID-19-related hospitalizations and deaths before and after state reopenings between April 16 and July 31, 2020. Daily state-level data from the University of Minnesota COVID-19 Hospitalization Tracking Project on COVID-19-related hospitalizations and deaths across 47 states were used in the analysis. EXPOSURES Dates that states reopened their economies. MAIN OUTCOMES AND MEASURES State-day observations of COVID-19-related hospitalizations and COVID-19-related new deaths per 100 000 people. RESULTSThe study included 3686 state-day observations of hospitalizations and 3945 state-day observations of deaths. On the day of reopening, the mean number of hospitalizations per 100 000 people was 17.69 (95% CI, 12.54-22.84) and the mean number of daily new deaths per 100 000 people was 0.395 (95% CI, 0.255-0.536). Both outcomes displayed flat trends before reopening, but they started trending upward thereafter. Relative to the hospitalizations trend in the period before state reopenings, the postperiod trend was higher by 1.607 per 100 000 people (95% CI, 0.203-3.011; P = .03). This estimate implied that nationwide reopenings were associated with 5319 additional people hospitalized for COVID-19 each day. The trend in new deaths after reopening was also positive (0.0376 per 100 000 people; 95% CI, 0.0038-0.0715; P = .03), but the change in mortality trend was not significant (0.0443; 95% CI, −0.0048 to 0.0933; P = .08). CONCLUSIONS AND RELEVANCEIn this cross-sectional study conducted over a 3.5-month period across 47 US states, data on the association of hospitalizations and mortality with state reopening policies may provide input to state projections of the pandemic as policy makers continue to balance public health protections with sustaining economic activity.

show abstract

Hit Harder, Recover Slower? Unequal Employment Effects of the Covid-19 Shock

Cited by 73 publications

References 10 publications

The Economic Ripple Effects of COVID-19

The Economic Ripple Effects of COVID-19

Socio-Economic Impact of the Covid-19 Pandemic in the U.S.

US Trends in COVID-19–Associated Hospitalization and Mortality Rates Before and After Reopening Economies

Contact Info

Product

Resources

About