The Income and Consumption Effects of Covid-19 and the Role of Public Policy

Piyapromdee, Suphanit; Spittal, Peter

doi:10.2139/ssrn.3642977

Cited by 11 publications

(11 citation statements)

References 5 publications

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“…One early study used differential telework ability and essential worker distribution to identify negative labor shocks by occupation, 31 while another examined the same outcome using physical proximity predictors from O*NET 32 . Other studies have used O*NET data to create indices reflecting the ease of remote work, and concluded that the economic burden of COVID‐19 falls disproportionately on low income workers, 33,34 women, and workers with low educational attainment 35 . These economic outcomes represent variable social determinants of health that are crucial in understanding the differential impact of COVID‐19 on individuals and populations 36 and may lend support to social insurance as a means to reduce hardship in particularly vulnerable workers 37 …”

Section: Discussionmentioning

confidence: 99%

Estimation of differential occupational risk of COVID‐19 by comparing risk factors with case data by occupational group

Zhang

2020

American J Industrial Med

122

118

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Background: The disease burden of coronavirus disease 2019 (COVID-19) is not uniform across occupations. Although healthcare workers are well-known to be at increased risk, data for other occupations are lacking. In lieu of this, models have been used to forecast occupational risk using various predictors, but no model heretofore has used data from actual case numbers. This study assesses the differential risk of COVID-19 by occupation using predictors from the Occupational Information Network (O*NET) database and correlating them with case counts published by the Washington State Department of Health to identify workers in individual occupations at highest risk of COVID-19 infection. Methods: The O*NET database was screened for potential predictors of differential COVID-19 risk by occupation. Case counts delineated by occupational group were obtained from public sources. Prevalence by occupation was estimated and correlated with O*NET data to build a regression model to predict individual occupations at greatest risk. Results: Two variables correlate with case prevalence: disease exposure (r = 0.66; p = 0.001) and physical proximity (r = 0.64; p = 0.002), and predict 47.5% of prevalence variance (p = 0.003) on multiple linear regression analysis. The highest risk occupations are in healthcare, particularly dental, but many nonhealthcare occupations are also vulnerable. Conclusions: Models can be used to identify workers vulnerable to COVID-19, but predictions are tempered by methodological limitations. Comprehensive data across many states must be collected to adequately guide implementation of occupationspecific interventions in the battle against COVID-19.

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Section: Discussionmentioning

confidence: 99%

Estimation of differential occupational risk of COVID‐19 by comparing risk factors with case data by occupational group

Zhang

2020

American J Industrial Med

122

118

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“…Dynarski and Sheffrin (1987) and Stephens (2001) argue that blue collar workers have shorter unemployment spells and their consumption is less sensitive to a change in employment status. Similarly, other factors such as age, gender and educational level are being linked to differing individual consumption behaviours during the pandemic (Minguez et al 2020 andPiyapromdee andSpittal (2020)). In Sect.…”

Section: Related Literaturementioning

confidence: 99%

Living with Reduced Income: An Analysis of Household Financial Vulnerability Under COVID-19

Midões

Seré

2021

Soc Indic Res

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The COVID-19 crisis has led to substantial reductions in earnings. We propose a new measure of financial vulnerability, computable through survey data, to determine whether households can withstand a certain income shock for a defined period of time. Using data from the ECB Household Finance and Consumption Survey (HFCS) we analyse financial vulnerability in seven EU countries. We find that, out of the 243 million individuals considered, 47 million are vulnerable to a three month long income shock (the average length of the first wave COVID-19 lockdown), i.e., they cannot afford food and housing expenses for three months without privately earned income. Differences across countries are stark. Individuals born outside the EU are especially likely to be vulnerable. Being younger, a single parent, and a woman are also statistically significant risk factors. Through a tax-benefit microsimulation exercise, we look into the COVID-19 employment protection benefits, the largest income support measure in the countries considered. Considering as our sample individuals in households where someone receives a salary, we derive household net income when employees are laid-off and awarded the COVID-19 employment protection benefits enacted. Our findings suggest that the employment protection schemes are extremely effective in reducing the number of vulnerable individuals. The relative importance of rent and mortgage suspensions, (likewise, widespread COVID-19 policies), in alleviating vulnerability, is highly country dependent.

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“…Of significance, the economic impact of the COVID‐19 pandemic has had a disproportionately negative impact on workers who have traditionally experienced vulnerability (e.g., certain racialized communities, low‐wage workers, and immigrants) 210‐216 . For instance, data from Canada highlighted that in some regions over two‐thirds of COVID‐19 infections have been experienced by racialized groups 212 .…”

Section: Resultsmentioning

confidence: 99%

Fragmentation in the future of work: A horizon scan examining the impact of the changing nature of work on workers experiencing vulnerability

Jetha

Shamaee

Bonaccio

et al. 2021

American J Industrial Med

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Introduction: The future of work is characterized by changes that could disrupt all aspects of the nature and availability of work. Our study aims to understand how the future of work could result in conditions, which contribute to vulnerability for different groups of workers.Methods: A horizon scan was conducted to systematically identify and synthesize diverse sources of evidence, including academic and gray literature and resources shared over social media. Evidence was synthesized, and trend categories were developed through iterative discussions among the research team.Results: Nine trend categories were uncovered, which included the digital transformation of the economy, artificial intelligence (AI)/machine learning-enhanced automation, AI-enabled human resource management systems, skill requirements for the future of work; globalization 4.0, climate change and the green economy, Gen Zs and the work environment; populism and the future of work, and external shocks to accelerate the changing nature of work. The scan highlighted that some groups of workers may be more likely to experience conditions that contribute to vulnerability, including greater exposure to job displacement or wage depression.The future of work could also create opportunities for labor market engagement. Conclusion:The future of work represents an emerging public health concern. Exclusion from the future of work has the potential to widen existing social and health inequities. Thus, tailored supports that are resilient to changes in the nature and availability of work are required for workers facing vulnerability.

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The Income and Consumption Effects of Covid-19 and the Role of Public Policy

Cited by 11 publications

References 5 publications

Estimation of differential occupational risk of COVID‐19 by comparing risk factors with case data by occupational group

Estimation of differential occupational risk of COVID‐19 by comparing risk factors with case data by occupational group

Living with Reduced Income: An Analysis of Household Financial Vulnerability Under COVID-19

Fragmentation in the future of work: A horizon scan examining the impact of the changing nature of work on workers experiencing vulnerability

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