Group-Motivated Sampling: From Skewed Experiences to Biased Evaluations

From planning daily activities with close others to passively overhearing a stranger’s conversation on the bus, humans spend roughly half of their waking life in the presence of other humans (US Bureau of Labor Statistics, 2003-2016). These social interactions constitute the source of a large proportion of the information that individuals gather on a daily basis. At the same time, people are often limited to only a small portion of information, or samples, in relation to all the possible information made available during a social experience (Ambady & Rosenthal, 1993; Fiedler, 2000). The fundamental question guiding this chapter is how group-motivated sampling of social information facilitates biased evaluations.

Group-Motivated Sampling: From Skewed Experiences to Biased Evaluations

From planning daily activities with close others to passively overhearing a stranger’s conversation on the bus, humans spend roughly half of their waking life in the presence of other humans (US Bureau of Labor Statistics, 2003-2016). These social interactions constitute the source of a large proportion of the information that individuals gather on a daily basis. At the same time, people are often limited to only a small portion of information, or samples, in relation to all the possible information made available during a social experience (Ambady & Rosenthal, 1993; Fiedler, 2000). The fundamental question guiding this chapter is how group-motivated sampling of social information facilitates biased evaluations.

An Examination of the Employment Recovery in the United States in the Wake of the COVID-19 Pandemic

The “shock” of the onset of the COVID-19 pandemic set about wholesale changes across American life, impacting the way we socialized, shopped, and yes, worked. There were significant disruptions across the business landscape in the United States, accompanied by significant job losses, starting in March 2020. In this article, we examine the size and scope of the changes in employment that took place in the U.S. at the start of the COVID-19 pandemic and during the recovery period up through April 2021. Utilizing official government data from the Bureau of Labor Statistics, the author examines how the pandemic caused both widespread job losses and record-setting levels of unemployment but had differential effects across a variety of industries. Then, the author looks at the recovery period, examining the recovery in employment has been unequally distributed across the United States, with differentiations between both urban and rural areas and between metropolitan areas across the county. The article concludes with a look at what this means for the future of work and for management moving forward - hopefully - into the post-pandemic period.

Connecting O*Net® Database to Cybersecurity Workforce Professional Certifications

The Occupational Information Network O*NET is considered the primary source of occupational information in the U.S. I explore here possible uses of O*NET data to inform cybersecurity workforce readiness certification programs. The O*NET database is used to map out education requirements and how they relate to professional certifications as required by employers and job designers in accordance with the National Initiative for Cybersecurity Careers and Studies (NICCS). The search focuses on the “Information Security Analysts” occupation as listed on O*NET, Careeronestop, U.S. Bureau of Labor Statistics (BLS), and finally tied back to NICCS source work role to identify certifications requirements. I found that no site has listed any certification as required, desirable or mandatory. NICCS offered general guidance to potential topics and areas of certification. Careeronestop site provided the ultimate guidance for this role certification. Professional certifications are still not integrated in the Cybersecurity Workforce Framework official guidance.

Consumer Expenditure Survey Methods Symposium and Microdata Users’ Workshop, July 21–24, 2020

The Consumer Expenditure Surveys (CE) program collects expenditure, demographic, and income data from families and households. The CE program held its annual Survey Methods Symposium and Microdata Users’ Workshop from July 21 to 24, 2020, to address CE-related topics in survey methods research, to provide free training in the structure and uses of the CE microdata, and to explore possibilities for collaboration. Economists from the CE program, staff from other U.S. Bureau of Labor Statistics offices, and research experts in a variety of fields—including academia, government, and private industry—gathered virtually to explore better ways to collect CE data and to learn how to use the microdata once they are produced. The experience was unique for presenters and attendees alike in that this was the first time either event was held online, in whole or in part.

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

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.

Maintaining and Enhancing Labor Statistics during the COVID-19 Pandemic

The US Bureau of Labor Statistics (BLS) continued to produce key employment statistics while reengineering operations as a result of the COVID-19 pandemic. In-person data collection moved to electronic, deadlines were adjusted, and flexibilities were implemented to get the job done, all while maintaining confidentiality and data quality. Opportunities arose, such as collection of new information from households and businesses on changes that resulted from the pandemic. This is a story that continues to unfold and will transform the way BLS moves forward. This paper focuses on changes in BLS operations and in the economy as reflected in BLS data.