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1. Elucidating Skills for Job Seekers: Insights and Critical Concerns from a Field Deployment in Switzerland

   https://doi.org/10.1145/3461778.3462049  

   Cherubini, Mauro; Lu, Alex Jiahong; Hsiao, Joey Chiao-Yin; Zhao, Muhan; Aggarwal, Anandita; Dillahunt, Tawanna R (June 2021, In Designing Interactive Systems Conference 2021)

   null (Ed.)

   This article contributes results of a longitudinal field study of SkillsIdentifier, an employment tool originally designed and assessed in the United States (U.S.), to support “underrepresented” job seekers in identifying and articulating their employment skills. To understand whether the tool could support the needs of job seekers outside the U.S., we assessed it among 16 job seekers with limited education and language resources in Switzerland. While many of our results mirrored those of the U.S., we found that the tool was especially beneficial for non-French speaking immigrants who needed support describing their skills outside of their native language. We also found that listing skills like “active listening” without important context was insufficient and risked hiding key skills and meaning behind those skills to employers. Taking these factors into account, we illustrate the design implications of our findings and directions for practitioners who wish to design employment tools in support of job seekers, especially those who have traditionally been excluded from the labor market. We then provide insight into the potential for unintended consequences as a result of focusing solely on skills in a post-COVID labor market and contribute ways to mitigate them. 

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2. AI exposure predicts unemployment risk: A new approach to technology-driven job loss

   https://doi.org/10.1093/pnasnexus/pgaf107  

   Frank, Morgan_R; Ahn, Yong-Yeol; Moro, Esteban; Kimbrough, ed., Erik (April 2025, PNAS Nexus)

   Abstract Is AI disrupting jobs and creating unemployment? This question has stirred public concern for job stability and motivated studies assessing occupations’ automation risk. These studies used readily available employment and wage statistics to quantify occupational changes for employed workers. However, they did not directly examine unemployment dynamics primarily due to the lack of data across occupations, geography, and time. Here, we overcome this barrier using monthly occupation-level unemployment data from each US state’s unemployment insurance office from 2010 to 2020 to assess AI exposure models, job separations, and unemployment through a new measure called unemployment risk. We demonstrate that standard employment statistics are inadequate proxies for occupations’ unemployment risk and find that individual AI exposure models are poor predictors of occupations’ unemployment risk states’ total unemployment rates, and states’ total job separation rates. However, an ensemble approach exhibits substantial predictive power, accounting for an additional 18% of variation in unemployment risk across occupations, states, and time compared to a baseline model that controls for education, occupations’ skills, seasonality, and regional effects. These results suggest that competing models may capture different aspects of AI exposure and that automation shapes US unemployment. Our results demonstrate the power of occupation-specific job disruption data and that efforts using only one AI exposure score will misrepresent AI’s impact on the future of work. 

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3. Gender Wage Gap in Online Gig Economy and Gender Differences in Job Preferences

   Liang, Chen; Hong, Yili; Gu, Bin; Peng, Jing (October 2018, International Conference on Information Systems (ICIS) 2018 Proceedings)

   We explore whether there exists gender wage gap in the gig economy and examine to what degree gender differences in job application strategy could account for the gap. With a large-scale dataset from a leading online labor market, we show that females only earn around 81.4% of the hourly wage of their male counterparts. We further investigate three main aspects of job application strategy, namely bid timing, job selection, and avoidance of monitoring. After matching males with females using the propensity score matching method, we find that females tend to bid later and prefer jobs with a lower budget. In particular, the observed gender difference in bid timing can explain 7.6% of the difference in hourly wage, which could account for 41% of the gender wage gap (i.e. 18.6%) observed by us. Moreover, taking advantage of a natural experiment wherein the platform rolled out the monitoring system, we find that females are less willing to bid for monitored jobs than males. To further quantify the economic value of the gender difference in avoidance of monitoring, we run a field experiment on Amazon Mechanical Turk (AMT), which suggests that females tend to have a higher willingness to pay (WTP) for the avoidance of monitoring. The gender difference in WTP for the avoidance of monitoring can explain 8.1% of the difference in hourly wage, namely, 44% of the observed gender wage gap. Overall, our study reveals the important role of job application strategies in the persistent gender wage gap. 

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4. Positive Feedback and Self-Reflection: Features to Support Self-efficacy among Underrepresented Job Seekers

   https://doi.org/10.1145/3313831.3376717  

   Dillahunt, Tawanna R.; Hsiao, Joey Chiao-Yin (January 2020, Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems)

   Technologies play a key role in finding employment in today's job market. However, the majority of those who are unemployed, e.g., individuals who have limited education or who are racial and ethnic minorities, are not well supported by existing digital employment tools. Therefore, we conducted an 8-month randomized field experiment to evaluate two tools-Review-Me and Interview4-designed to address these job seekers' key employment needs. We used the Theory of Planned Behavior to examine the tools' effects on three factors influencing job seekers' job search intention: job search self-efficacy, subjective norms, and job search attitudes. Our interview data suggested that the tools positively affected all factors, but our survey results were mixed. Interview results suggest that these trends were caused by positive feedback and self-reflection. We contribute ways to integrate these two features into future tools for, and techniques to increase study retention among, underrepresented job seekers. 

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5. Reference-Dependent Job Search: Evidence from Hungary*

   https://doi.org/10.1093/qje/qjx015  

   DellaVigna, Stefano; Lindner, Attila; Reizer, Balázs; Schmieder, Johannes F. (May 2017, The Quarterly Journal of Economics)

   Abstract We propose a model of job search with reference-dependent preferences, with loss aversion relative to recent income (the reference point). In this model, newly unemployed individuals search hard since consumption is below their reference point. Over time, though, they get used to lower income and thus reduce their search effort. In anticipation of a benefit cut, their search effort rises again, then declines once they get accustomed to the lower postcut benefit level. The model fits the typical pattern of exit from unemployment, even with no unobserved heterogeneity. To distinguish between this and other models, we use a unique reform in the unemployment insurance (UI) benefit path. In 2005, Hungary switched from a single-step UI system to a two-step system, with overall generosity unchanged. The system generated increased hazard rates in anticipation of, and especially following, benefit cuts in ways the standard model has a hard time explaining. We estimate a model with optimal consumption, endogenous search effort, and unobserved heterogeneity. The reference-dependent model fits the hazard rates substantially better than plausible versions of the standard model, including habit formation. Our estimates indicate a slow-adjusting reference point and substantial impatience, likely reflecting present-bias. 

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