scholarly journals UI Generosity and Job Acceptance: Effects of the 2020 CARES Act

2021 ◽  
pp. 1.000-34.000
Author(s):  
Nicolas Petrosky-Nadeau ◽  
◽  
Robert G. Valletta
Keyword(s):  
Labor Market ◽  
Unemployment Insurance ◽  
Decision Problem ◽  
Annual Earning ◽  
Wide Range ◽  
Job Offers ◽  
Simple Statistic ◽  
Job Finding ◽  
The U.S ◽  
Job Acceptance

To provide relief to the U.S. labor market following the onset of the COVID-19 pandemic, the CARES Act granted an extra $600 per week in UI benefit payments from late March through July 2020. This unprecedented increase in UI generosity raised concern that UI recipients would be largely unwilling to accept job offers, slowing the labor market recovery. Job acceptance decisions weigh the value of a job against remaining unemployed. A reservation level of benefit payments exists in this dynamic decision problem at which an individual is indifferent between accepting and refusing an offer. This reservation benefit is a simple statistic summarizing the decision problem conditional on the perceived state of the labor market and the weeks of Unemployment Insurance (UI) compensation remaining. Estimating the reservation benefit for a wide range of US workers suggests few would turn down an offer to return to work at the previous wage under the CARES Act expanded UI payments. Direct empirical analysis of labor force transitions using matched Current Population Survey (CPS) data, linked to annual earning records from the CPS income supplement to form UI replacement rates, shows moderate disincentive effects of $600 supplemental payments on job finding rates; this empirical framework also suggests small effects of the $300 weekly UI supplement available during 2021.

scholarly journals Unemployment Effects of Stay-at-Home Orders: Evidence from High Frequency Claims Data

2020 ◽  
pp. 1-72 ◽  
Author(s):  
ChaeWon Baek ◽  
Peter B. McCrory ◽  
Todd Messer ◽  
Preston Mui
Keyword(s):  
Labor Market ◽  
Unemployment Insurance ◽  
High Frequency ◽  
Claims Data ◽  
Currency Union ◽  
Employment Level ◽  
Market Effects ◽  
Union Model ◽  
The U.S ◽  
At Home

We use the high-frequency, decentralized implementation of Stay-at-Home orders in the U.S. to disentangle the labor market effects of SAH orders from the general economic disruption wrought by the COVID-19 pandemic. We find that each week of SAH exposure increased a state's weekly initial unemployment insurance (UI) claims by 1.9% of its employment level relative to other states. A back-of-the-envelope calculation implies that, of the 17 million UI claims between March 14 and April 4, only 4 million were attributable to SAH orders. We present a currency union model to provide conditions for mapping this estimate to aggregate employment losses.

scholarly journals Market Externalities of Large Unemployment Insurance Extension Programs

2015 ◽  
Vol 105 (12) ◽  
pp. 3564-3596 ◽  
Author(s):  
Rafael Lalive ◽  
Camille Landais ◽  
Josef Zweimüller
Keyword(s):  
Labor Market ◽  
Unemployment Insurance ◽  
Lower Risk ◽  
Extension Programs ◽  
Equilibrium Conditions ◽  
Large Group ◽  
Job Finding

We provide evidence that unemployment insurance affects equilibrium conditions in the labor market, which creates significant “market externalities.” We provide a framework for identification of such equilibrium effects and implement it using the Regional Extension Benefit Program (REBP) in Austria which extended the duration of UI benefits for a large group of eligible workers in selected regions of Austria. We show that non-eligible workers in REBP regions have higher job finding rates, lower unemployment durations, and a lower risk of long-term unemployment. We discuss the implications of our results for optimal UI policy. (JEL E24, J64, J65, R23)

scholarly journals Immigration and economic mobility

2021 ◽  
Author(s):  
Maria F. Hoen ◽  
Simen Markussen ◽  
Knut Røed
Keyword(s):  
Labor Market ◽  
Low Income ◽  
Labor Market Outcomes ◽  
Lower Class ◽  
Low Income Countries ◽  
Market Outcomes ◽  
Wide Range ◽  
Class Background ◽  
The Rich ◽  
Relative Prime

AbstractWe examine how immigration affects natives’ relative prime-age labor market outcomes by economic class background, with class background established on the basis of parents’ earnings rank. Exploiting alternative sources of variation in immigration patterns across time and space, we find that immigration from low-income countries reduces intergenerational mobility and thus steepens the social gradient in natives’ labor market outcomes, whereas immigration from high-income countries levels it. These findings are robust with respect to a wide range of identifying assumptions. The analysis is based on high-quality population-wide administrative data from Norway, which is one of the rich-world countries with the most rapid rise in the immigrant population share over the past two decades. Our findings suggest that immigration can explain a considerable part of the observed relative decline in economic performance among natives with a lower-class background.

scholarly journals Race and Networks in the Job Search Process

2019 ◽  
Vol 84 (6) ◽  
pp. 983-1012 ◽  
Author(s):  
David S. Pedulla ◽  
Devah Pager
Keyword(s):  
Social Networks ◽  
Labor Market ◽  
African Americans ◽  
Racial Disparities ◽  
Job Search ◽  
Search Process ◽  
Job Seekers ◽  
Employment Opportunities ◽  
Black And White ◽  
Job Offers

Racial disparities persist throughout the employment process, with African Americans experiencing significant barriers compared to whites. This article advances the understanding of racial labor market stratification by bringing new theoretical insights and original data to bear on the ways social networks shape racial disparities in employment opportunities. We develop and articulate two pathways through which networks may perpetuate racial inequality in the labor market: network access and network returns. In the first case, African American job seekers may receive fewer job leads through their social networks than white job seekers, limiting their access to employment opportunities. In the second case, black and white job seekers may utilize their social networks at similar rates, but their networks may differ in effectiveness. Our data, with detailed information about both job applications and job offers, provide the unique ability to adjudicate between these processes. We find evidence that black and white job seekers utilize their networks at similar rates, but network-based methods are less likely to lead to job offers for African Americans. We then theoretically develop and empirically test two mechanisms that may explain these differential returns: network placement and network mobilization. We conclude by discussing the implications of these findings for scholarship on racial stratification and social networks in the job search process.

scholarly journals Corporate Governance of Artificial Intelligence in the Public Interest

Information ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 275
Author(s):  
Peter Cihon ◽  
Jonas Schuett ◽  
Seth D. Baum
Keyword(s):  
Artificial Intelligence ◽  
Corporate Governance ◽  
Law Enforcement ◽  
Nonprofit Organizations ◽  
Public Interest ◽  
Department Of Defense ◽  
The Public ◽  
Wide Range ◽  
The Media ◽  
The U.S

Corporations play a major role in artificial intelligence (AI) research, development, and deployment, with profound consequences for society. This paper surveys opportunities to improve how corporations govern their AI activities so as to better advance the public interest. The paper focuses on the roles of and opportunities for a wide range of actors inside the corporation—managers, workers, and investors—and outside the corporation—corporate partners and competitors, industry consortia, nonprofit organizations, the public, the media, and governments. Whereas prior work on multistakeholder AI governance has proposed dedicated institutions to bring together diverse actors and stakeholders, this paper explores the opportunities they have even in the absence of dedicated multistakeholder institutions. The paper illustrates these opportunities with many cases, including the participation of Google in the U.S. Department of Defense Project Maven; the publication of potentially harmful AI research by OpenAI, with input from the Partnership on AI; and the sale of facial recognition technology to law enforcement by corporations including Amazon, IBM, and Microsoft. These and other cases demonstrate the wide range of mechanisms to advance AI corporate governance in the public interest, especially when diverse actors work together.

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