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1. Skilled Workers, Knowledge Transfer, & AI.

   Collier, C. (January 2023, HCI International, Developing Culturally Competent Autonomous Systems through Human-Centered Design Workshop)
2. Black and Latinx Workers Reap Lower Rewards than White Workers from Careers in Big Prosperous Cities

   https://doi.org/10.31235/osf.io/24cvs  

   Buchholz, Maximilian; Storper, Michael (May 2024, SocArXiv; forthcoming in the Proceedings of the National Academy of Sciences of the United States of America)

   The large labor markets of big cities offer greater possibilities for workers to gain skills and experience through successively better employment opportunities. This ”experience effect” contributes to the higher average wages that are found in big cities compared to the economy as a whole. Racial wage inequality is also higher in bigger cities than in the economy on average. We offer an explanation for this pattern, demonstrating that there is substantial racial inequality in the economic returns to work experience acquired in big cities. Using data from the National Longitudinal Survey of Youth, 1979 we find that each year of work experience in a big city increases Black and Latinx workers’ wages by about one quarter to half as much as White workers’ wages. A substantial amount of this inequality can be explained by further racial disparities in the benefits of high-skill work experience. This research identifies a heretofore unknown source of inequality that is distinctly urban in nature, and expands our knowledge of the challenges to reaching inter-racial wage equality. 

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3. Crisis Response and Suicidal Behaviors of Essential Workers and Children of Essential Workers During the COVID-19 Pandemic

   https://doi.org/10.1177/00333549221148177  

   Sugg, Margaret_M; Runkle, Jennifer_D; Ryan, Sophia_C; Singh, Devyani; Green, Shannon; Thompson, Martie (January 2023, Public Health Reports®)

   Objective:The COVID-19 pandemic has put unprecedented stress on essential workers and their children. Limited cross-sectional research has found increases in mental health conditions from workload, reduced income, and isolation among essential workers. Less research has been conducted on children of essential workers. We examined trends in the crisis response of essential workers and their children from April 2020 through August 2021. Methods:We investigated the impact during 3 periods of the pandemic on workers and their children using anonymized data from the Crisis Text Line on crisis help-seeking texts for thoughts of suicide or active suicidal ideation (desire, intent, capability, time frame), abuse (emotional, physical, sexual, unspecified), anxiety/stress, grief, depression, isolation, bullying, eating or body image, gender/sexual identity, self-harm, and substance use. We used generalized estimating equations to study the longitudinal change in crisis response across the later stages of the pandemic using adjusted odds ratios (aORs) for worker status and crisis outcomes. Results:Results demonstrated higher odds of crisis outcomes for thoughts of suicide (aOR = 1.06; 95% CI, 1.00-1.12) and suicide capability (aOR = 1.14; 95% CI, 1.02-1.27) among essential workers than among nonessential workers. Children of essential workers had higher odds of substance use than children of nonessential workers (aOR = 1.33; 95% CI, 1.08-1.65), particularly for Indigenous American children (aOR = 2.76; 95% CI, 1.35-5.36). Essential workers (aOR = 1.17; 95% CI, 1.07-1.27) and their children (aOR = 1.18; 95% CI, 1.07-1.30) had higher odds of grief than nonessential workers and their children. Conclusion:Essential workers and their children had elevated crisis outcomes. Immediate and low-cost psychologically supportive interventions are needed to mitigate the mental health impacts of the COVID-19 pandemic on these populations. 

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4. Research Methods to Study & Empower Crowd Workers

   Savage, S; Toxtli, C; Betanzos, E. (January 2021, Oxford)

   null (Ed.)

   The artificial intelligence (AI) industry has created new jobs that are essential to the real world deployment of intelligent systems. Part of the job focuses on labeling data for machine learning models or having workers complete tasks that AI alone cannot do. These workers are usually known as ‘crowd workers’—they are part of a large distributed crowd that is jointly (but separately) working on the tasks although they are often invisible to end-users, leading to workers often being paid below minimum wage and having limited career growth. In this chapter, we draw upon the field of human–computer interaction to provide research methods for studying and empowering crowd workers. We present our Computational Worker Leagues which enable workers to work towards their desired professional goals and also supply quantitative information about crowdsourcing markets. This chapter demonstrates the benefits of this approach and highlights important factors to consider when researching the experiences of crowd workers. 

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5. Platform-mediated Markets, Online Freelance Workers and Deconstructed Identities

   https://doi.org/10.1145/3555092  

   Munoz, Isabel; Dunn, Michael; Sawyer, Steve; Michaels, Emily (November 2022, Proceedings of the ACM on Human-Computer Interaction)

   We advance the concept of deconstructed identity to explain how online workers' identities are being reshaped, diminished and controlled by digital labor platforms. We focus on online freelance workers and contribute to contemporary conceptualizations regarding worker's self-presentation. The empirical basis for our analysis and theorizing build from two rounds of a longitudinal panel study of online freelance workers and their interactions with online labor platforms. Findings illuminate how online freelancer's identity presentation is constrained by the structuring of their profile, the ratings and client feedback, the algorithms used by the digital platform, and platform's terms of use. Data demonstrate that workers' profiles are focused on skills, reflecting the realities of competing for work in under-regulated labor markets. Study participants report the centrality of client and platform ratings of their work, and the need to manage client feedback and ratings as a core part of their online identity presentation. These findings suggest that, far from a subjective and personal story, a freelancer's identity on a digital labor platform is better understood as a standardized depiction of skills, ratings, and metrics controlled by platform algorithms. Coupled with use policies and evolving platform designs, this platform control creates what seems to be a form of indentured servitude. We further note online freelancers both recognize this control and resist their deconstructed identity. 

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