Measuring Labor Supply and Demand Shocks during COVID-19.
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ABSTRACT:
SUBMITTER: Chaar T
PROVIDER: S-EPMC8739705 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:We measure labor demand and supply shocks at the sector level around the COVID-19 outbreak by estimating a Bayesian structural vector autoregression on monthly statistics of hours worked and real wages. Most sectors were subject to large negative labor supply and demand shocks in March and April 2020, with substantial heterogeneity in the size of shocks across sectors. Our estimates suggest that two-thirds of the drop in the aggregate growth rate of hours in March and April 2020 are attributable to labor supply. We validate our estimates of supply shocks by showing that they are correlated with sectoral measures of telework.
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Project description:The impacts of COVID-19 on labor in the food supply chain and on workers' decisions to accept essential jobs are discussed. We then analyze surveys administered to low-skilled domestic workers before and during the pandemic to assess respondents' attitudes toward food production, guest workers, immigration policy, and the government's response to COVID-19. Results suggest the outbreak resulted in respondents, on average, shifting their view toward food being a national security issue and a higher degree of empathy for H-2A workers. Regression analysis shows that gender, current agricultural workers, and information on COVID-19 and agricultural field workers influenced respondents' answers.
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Project description:The availability of an adequate blood supply is a critical public health need. An influenza epidemic or another crisis affecting population mobility could create a critical donor shortage, which could profoundly impact blood availability. We developed a simulation model for the blood supply environment in the United States to assess the likely impact on blood availability of factors such as an epidemic. We developed a simulator of a multi-state model with transitions among states. Weekly numbers of blood units donated and needed were generated by negative binomial stochastic processes. The simulator allows exploration of the blood system under certain conditions of supply and demand rates, and can be used for planning purposes to prepare for sudden changes in the public's health. The simulator incorporates three donor groups (first-time, sporadic, and regular), immigration and emigration, deferral period, and adjustment factors for recruitment. We illustrate possible uses of the simulator by specifying input values for an 8-week flu epidemic, resulting in a moderate supply shock and demand spike (for example, from postponed elective surgeries), and different recruitment strategies. The input values are based in part on data from a regional blood center of the American Red Cross during 1996-2005. Our results from these scenarios suggest that the key to alleviating deficit effects of a system shock may be appropriate timing and duration of recruitment efforts, in turn depending critically on anticipating shocks and rapidly implementing recruitment efforts.
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Project description:This paper tries to examine how the COVID-19 shock affects different countries through their regional integration and their exposure to Global Value Chains (GVCs). Using input-output tables from the EORA dataset, our contribution is threefold. First, we conceptually revise the approache to analyse input-output relationships and underline the difference between the bilateral flow of value added and trade and distinguish between the producers and consumers of value-added. Second, we distinguish between the supply and demand channels through which these countries can be affected by the disruptions in GVCs. Third, we apply this empirical exercise on an understudied region, namely the Mediterranean region that is characterised by its involvement in several trade agreements that might boost their integration into GVCs. Our main findings show that, first, most of the countries have relatively larger backward GVC linkages than forward ones. Second, in the Northern shore of the Mediterranean, Italy and France are net suppliers of value added since they produce more value-added absorbed abroad than the foreign value-added they consume. Third, our results highlight also the limited integration between Southern shore partners, whose integration is almost completely driven by linkages with Southern European developed countries.
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Project description:We use job vacancy data collected in real time by Burning Glass Technologies, as well as unemployment insurance (UI) initial claims and the more traditional Bureau of Labor Statistics (BLS) employment data to study the impact of COVID-19 on the labor market. Our job vacancy data allow us to track the economy at disaggregated geography and by detailed occupation and industry. We find that job vacancies collapsed in the second half of March. By late April, they had fallen by over 40%. To a first approximation, this collapse was broad based, hitting all U.S. states, regardless of the timing of stay-at-home policies. UI claims and BLS employment data also largely match these patterns. Nearly all industries and occupations saw contraction in postings and spikes in UI claims, with little difference depending on whether they are deemed essential and whether they have work-from-home capability. Essential retail, the "front line" job most in-demand during the current crisis, took a much smaller hit, while leisure and hospitality services and non-essential retail saw the biggest collapses. This set of facts suggests the economic collapse was not caused solely by the stay-at-home orders, and is therefore unlikely to be undone simply by lifting them.
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Project description:BackgroundCOVID-19 Convalescent Plasma (CCP) is a promising treatment for COVID-19. Blood collectors have rapidly scaled up collection and distribution programs.MethodsWe developed a detailed simulation model of CCP donor recruitment, collection, production, and distribution processes. We ran our model using varying epidemic trajectories from 11 U.S. states and with key input parameters drawn from wide ranges of plausible values to identify key drivers of ability to scale collections capacity and meet demand for CCP.ResultsUtilization of available CCP collections capacity followed increases in COVID-19 hospital discharges with a lag. Utilization never exceeded 75% of available capacity in most simulations. Demand was met for most of the simulation period in most simulations, but a substantial portion of demand went unmet during early, sharp increases in hospitalizations. For epidemic trajectories that included multiple epidemic peaks, second wave demand could generally be met due to stockpiles established during the decline from an earlier peak. Apheresis machine capacity (number of machines) and probability that COVID-19 recovered individuals are willing to donate were the most important supply-side drivers of ability to meet demand. Recruitment capacity was important in states with early peaks.ConclusionsEpidemic trajectory was the most important determinant of ability to meet demand for CCP, although our simulations revealed several contributing operational drivers of CCP program success.
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Project description:This paper examines sectoral productivity shocks of the COVID-19 pandemic, their aggregate impact, and the possible compensatory effects of improving productivity in infrastructure-related sectors. We employ the KLEMS annual dataset for a group of OECD and Latin America and the Caribbean countries, complemented with high-frequency data for 2020. First, we estimate a panel vector autoregression of growth rates in sector level labor productivity to specify the nature and size of sectoral shocks using the historical data. We then run impulse-response simulations of one standard deviation shocks in the sectors that were most affected by COVID-19. We estimate that the pandemic cut economy-wide labor productivity by 4.9% in Latin America, and by 3.5% for the entire sample. Finally, by modeling the long-run relationship between productivity shocks in the sectors most affected by COVID-19, we find that large productivity improvements in infrastructure-equivalent to at least three times the historical rates of productivity gains-may be needed to fully compensate for the negative productivity losses traceable to COVID-19.Supplementary informationThe online version contains supplementary material available at 10.1007/s41885-021-00098-z.
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Project description:As demands on agriculture increase, food producers will need to employ management strategies that not only increase yields but reduce environmental impacts. Modeling is a powerful tool for informing decision-making about current and future practices. We present a model to evaluate the effects of crop diversification on the robustness of simulated farms under labor shocks. We use an example inspired by the Florida production system of high-value, labor-intensive fruits. We find that crop diversification to high-value crops is a robust strategy when labor shocks are mild, and that crop diversification becomes less valuable as more simulated farms practice it. Based on our results, we suggest that crop diversification is a useful management strategy under specific conditions, but that policies designed to encourage crop diversification must consider broad effects as well as farm-level benefits.
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