US Job Research Articles

Working for Jessica or Michael? Implications of gender stereotypes for job application intentions at technology startups

AbstractResearch SummaryWe examine a critical yet underexplored aspect of human resource management in nascent technology ventures: employee recruitment. Applying theories of gender stereotyping, we contend that female‐led technology startups face greater obstacles in attracting job applicants than their male counterparts. Evidence from a randomized online experiment conducted in 2020/2021 with 777 US job seekers substantiates this barrier, indicating that the disparities are partly rooted in gender‐stereotypical perceptions of female technology entrepreneurs as less competent, agentic, and warm, which contribute to less favorable assessments of their ventures' economic potential and employee empowerment potential. Startups with gender‐diverse leadership teams appear to overcome these biases. Confirmatory evidence comes from a 2024 replication study with 455 US job seekers, underscoring the need to address gender biases in the technological ecosystem.Managerial SummaryIn the competitive landscape of technology startups, attracting talent is key. Our study reveals that startups with female leaders face gender biases during recruitment, with job candidates perceiving female technology entrepreneurs as less competent, agentic, and warm—and their startup ventures as less likely to have what it takes to grow and to empower employees. Analysis from a randomized online experiment involving 777 US job seekers in 2020/2021 and a follow‐up study with 455 US job seekers in 2024 confirm such biases. Crucially, a gender‐balanced leadership team significantly counters such biases, enhancing the venture's appeal to potential hires. These insights highlight the need for technology startups to promote gender diversity within their leadership to dismantle stereotypes and attract a broader talent pool.

MH workers strike against Kaiser Permanente in Southern California

More than 2,000 unionized mental health workers in Southern California went on strike against Kaiser Permanente last week after the two sides failed to reach a new labor agreement, CNN reported Oct. 21. The National Union of Healthcare Workers (NUHW) — which represents 19,000 health care workers in California and Hawaii, including 4,700 mental health workers — picketed outside Kaiser facilities in Los Angeles, San Diego, Anaheim and Fontana, seeking an increase in salaries, restoration of pensions and increased staffing. When announcing the impending strike earlier this month, the union cited its 10‐week strike in Northern California in 2022, which resulted in higher wages, better working conditions to improve patient care and prevented rapid staff turnover (see “Kaiser agrees to historic settlement to overhaul its BH care system,” MHW, Oct. 25, 2023; https://doi.org/10.1002/mhw.33828). Union members on strike in Southern California have said their demands are in line in with what Kaiser has provided to the majority of its workforce. “Unless we strike, our coworkers are going to keep leaving and our patients are going to keep struggling in an underfunded, understaffed system that doesn't meet their needs,” Josh Garcia, a psychologist for Kaiser in San Diego, said in a press statement from the NUHW. The strike comes as employment in the mental health field — for psychiatrists, psychologists, therapists, counselors, psychiatric aides and social workers — is expected to grow three times faster than the average US job position, according to CNN's analysis of data released in September by the Bureau of Labor Statistics. A survey in 2022 from CNN in partnership with the Kaiser Family Foundation showed that nine out of 10 US adults believe the country has a mental health crisis.

Measuring the Total Number of US Job Seekers

Abstract I document a substantial rise in the proportion of job seekers who are classified as out of the labour force in the United States since 1980. I propose an adjusted unemployment rate to account for these searchers; the adjustment increases the unemployment rate by 5.2 percentage points, rids the unemployment rate of its downward trend and decreases volatility by 50%. I also construct a measure of total search effort in the economy, including employed job seekers. Finally, estimates of the Phillips curve using the adjusted unemployment rate or total searcher rate show no sign of a flattening output-inflation relationship in the post-2008 recession period.

Small work pleasures and two types of well‐being

AbstractDoes wage labor contribute to well‐being beyond providing an income? Well‐being can be understood in eudaimonic terms as the happiness derived from a socially valued life or in hedonic terms as the experience of pleasure. The eudaimonic–hedonic divide is replicated in competing progressive visions of the place of work in a good life. Laborist theories stress the centrality of paid work for a meaningful life. By contrast, for post‐work theories, pleasure is important for well‐being, and work is generally not expected to be pleasurable. Surprisingly, many of the participants in my study of diverse US job seekers described one or more of the jobs they had held as “fun.” Fun connotes enjoyment without deeper meaning, a hedonic rather than eudaimonic account of nonfinancial work rewards. What made a job fun were small work pleasures: enjoyment of the tasks and feeling competent at them, enjoyment of the physical work environment, or enjoyment of social relations on the job. These small pleasures could be found in both standard and nonstandard, precarious jobs. This study indicates the need for a labor politics that improves hedonic well‐being on the job. It also expands an “anthropology of the good” to include ordinary enjoyment.

Artificial Intelligence: A Blessing or a Threat for Language Service Providers in Portugal

According to a recent study by OpenAI, Open Research, and the University of Pennsylvania, large language models (LLMs) based on artificial intelligence (AI), such as generative pretrained transformers (GPTs), may have potential implications for the job market, specifically regarding occupations that demand writing or programming skills. This research points out that interpreters and translators are one of the main occupations with greater exposure to AI in the US job market (76.5%), in a trend that is expected to affect other regions of the globe. This article, following a mixed-methods survey-based research approach, provides insights into the awareness and knowledge about AI among Portuguese language service providers (LSPs), specifically regarding neural machine translation (NMT) and large language models (LLM), their actual use and usefulness, as well as their potential influence on work performance and the labour market. The results show that most professionals are unable to identify whether AI and/or automation technologies support the tools that are most used in the profession. The usefulness of AI is essentially low to moderate and the professionals who are less familiar with it and less knowledgeable also demonstrate a lack of trust in it. Two thirds of the sample estimate negative or very negative effects of AI in their profession, expressing the devaluation and replacement of experts, the reduction of income, and the reconfiguration of the career of translator to mere post-editors as major concerns.

Firms’ Use of Roles with General Purpose Skills: Evidence from US Job Postings 2010-20

Firms’ Use of Roles with General Purpose Skills: Evidence from US Job Postings 2010-20

Essential competencies for organizational learning professionals according to job ads in the Israeli employment market

PurposeThis study aims to examine core professional competencies required by organizational learning (OL) field, using the main knowledge, skills and abilities (KSA) theoretical framework for job candidates in Israel.Design/methodology/approachAn analysis was conducted on 100 job postings from two online platforms using content analysis techniques. The job offers were evaluated according to criteria established by prior research conducted in the USA.FindingsThe findings indicate that job announcements appear for three main professions in the field of learning in organizations in Israel: learning designer, learning developer and instructional designer. Most of the offers are for full-time jobs, without requiring a relevant academic degree or previous experience. In comparison to the US employment market, in Israel the demand for OL professionals necessitates communication abilities in English, macro development skills, knowing how to manage professional training and mastery of learning through innovative technology such as augmented reality/virtual reality. The findings also indicated which competencies are most recently required in the OL branch in Israel and the significant differences in KSA necessary for OL professionals in each of the three identified professions.Practical implicationsThe study highlighted critical elements of the OL professional field and has implications for OL professionals seeking employment and human resources (HR) recruiters seeking them. Job seekers need to know the current job market requirements in the OL field, and HR recruiters need to know what is happening in the current job market. This can be done by following updated job offers in the OL field and responding quickly to changes. The findings also have implications for the educational/academic aspect of the OL teachers in various settings and inform them to refine the content of their syllabus and course content in accordance with the current requirements of the job market in the field of OL.Originality/valueThe study is based on the KSA theoretical framework and analysis of the OL US job market according to Wang et al.’s (2021) work. This study presents the Israel OL job market and discuss the authors’ critical view on Wang et al.’s work.

Kernel methods for causal functions: dose, heterogeneous and incremental response curves

Abstract We propose estimators based on kernel ridge regression for nonparametric causal functions such as dose, heterogeneous and incremental response curves. The treatment and covariates may be discrete or continuous in general spaces. Because of a decomposition property specific to the reproducing kernel Hilbert space, our estimators have simple closed-form solutions. We prove uniform consistency with finite sample rates via an original analysis of generalized kernel ridge regression. We extend our main results to counterfactual distributions and to causal functions identified by front and back door criteria. We achieve state-of-the-art performance in nonlinear simulations with many covariates, and conduct a policy evaluation of the US Job Corps training programme for disadvantaged youths.

Job Stress and Sleep Disturbances Among Career Firefighters in Northern California.

This study examined the association between job stress and sleep disturbance among career firefighters. A cross-sectional survey study was conducted with 154 career firefighters working in Northern California, US job stress was measured using the short form of the Effort-Reward Imbalance questionnaire and sleep was measured with the Patient-Reported Outcomes Measurement Information System Sleep Disturbance. Approximately 75% experienced sleep disturbance. For firefighters' job stress, high effort (odds ratio [OR] = 3.68; 95% confidence interval [CI]: 1.25-10.80), high effort-reward ratio (OR = 3.55; 95% CI: 1.23-10.23), and high overcommitment (OR = 9.09; 95% CI: 2.30-35.85) were significantly associated with increased likelihood of sleep disturbance, after adjustment for other factors. Job stress significantly affected firefighters' sleep health, suggesting the need to design effective health promotion interventions to reduce job stress and improve sleep quality for these public service workers.

COVID-19, jobs and skills-Exploratory analysis of the job postings in the US and UK healthcare job market.

The COVID-19 pandemic has significantly affected all spheres of life, including the healthcare workforce. While the COVID-19 pandemic has started driving organizational and societal shifts, it is vital for healthcare organizations and decision-makers to analyze patterns in the changing workforce. In this study, we aim to identify patterns in healthcare job postings during the pandemic to understand which jobs and associated skills are trending after the advent of COVID-19. Content analysis of job postings was conducted using data-driven approaches over two-time intervals in the pandemic. The proposed framework utilizes Latent Dirichlet Allocation (LDA) for topic modeling to evaluate the patterns in job postings in the US and the UK. The most demanded jobs, skills and tasks for the US job postings are presented based on job posting data from popular job posting websites. This is obtained by mapping the job postings to the jobs, skills and tasks defined in the O*NET database for the healthcare occupations in the US. The topic modeling results clearly show increased hiring for telehealth services in both the US and UK. This study also presents an increase in demand for specific occupations and skills in the USA healthcare industry. The results and methods used in the study can help monitor rapid changes in the job market due to pandemics and guide decision-makers to make organizational shifts in a timely manner.

Two Stage Job Title Identification System for Online Job Advertisements

Data science techniques are powerful tools for extracting knowledge from large datasets. Analyzing the job market by classifying online job advertisements (ads) has recently received much attention. Various approaches for multi-label classification (e.g., self-supervised learning and clustering) have been developed to identify the occupation from a job advertisement and have achieved a satisfying performance. However, these approaches require labeled datasets with hundreds of thousands of examples and focus on specific databases such as the Occupational Information Network (O*NET) that are more adapted to the US job market. In this paper, we present a two-stage job title identification methodology to address the case of small datasets. We use Bidirectional Encoder Representations from Transformers (BERT) to first classify the job ads according to their corresponding sector (e.g., Information Technology, Agriculture). Then, we use unsupervised machine learning algorithms and some similarity measures to find the closest matching job title from the list of occupations within the predicted sector. We also propose a novel document embedding strategy to address the issues of processing and classifying job ads. Our experimental results show that the proposed two-stage approach improves the job title identification accuracy by 14% to achieve more than 85% in some sectors. Moreover, we found that incorporating document embedding-based approaches such as weighting strategies and noise removal improves the classification accuracy by 23.5% compared to approaches based on the Bag of words model. Further evaluations verify that the proposed methodology either outperforms or performs at least as well as the state-of-the-art methods. Applying the proposed methodology to Moroccan job market data has helped identify emerging and high-demand occupations in Morocco.

Expanding branding: effects of multiple brand types on organizational attractiveness

PurposeThis study aims to examine the relative effects of three organizational brand types (product, employment and corporate social responsibility brands) on organizational attractiveness. The potential differences in the impacts exerted by each brand on organizational attractiveness between the US and Chinese job seekers are also examined.Design/methodology/approachA policy-capturing design was used among both US and Chinese participants to test the hypothesized relationships using multilevel modeling.FindingsResults suggest that each brand type independently contributes to the prediction of attractiveness, with the employment brand a significantly stronger predictor than the other two. Besides, the strength of relationships between brands and organizational attractiveness varies among job seekers from different national contexts.Originality/valueThe findings contribute to the limited understanding of how different types of brands together influence organizational attractiveness among job seekers, and the role national context plays in it.

Review on new spending of United States Bipartisan Infrastructure Bill

The US Infrastructure Investment and Job Act (IIJA), also commonly referred to as the Bipartisan Infrastructure Bill, passed in 2021, has drawn international attention. It aims to help to rebuild US infrastructure, including transportation networks, broadband, water, power and energy, environmental protection and public works projects. An estimated $1.2 trillion in total funding over ten years will be allocated. The Bipartisan Infrastructure Bill is the largest funding bill for US infrastructure in the recent history of the United States. This review article will specifically discuss funding allocations for roads and bridges, power and grids, broadband, water infrastructure, airports, environmental protection, ports, Western water infrastructure, electric vehicle charging stations and electric school buses in the new spending of the Infrastructure Investment and Job Act and why these investments are urgently necessary. This article will also briefly discuss the views of think tank experts, the public policy perspectives, the impact on domestic and global arenas of the new spending in the IIJA, and the public policy implications.

Forecast for the US CRM Industry and Job Market, 2022–2031

ABSTRACTIn the next 10 years, the US cultural resource management (CRM) industry will grow in terms of monies spent on CRM activities and the size of the CRM labor force. Between US fiscal years 2022 and 2031, annual spending on CRM will increase from about $1.46 to $1.85 billion, due in part to growth in the US economy but also to an added $1 billion of CRM activities conducted in response to the newly passed infrastructure bill. The increased spending will lead to the creation of about 11,000 new full-time positions in all CRM fields. Archaeologists will be required to fill more than 8,000 positions, and of these, about 70% will require advanced degrees. Based on current graduation rates, there will be a significant MA/PhD-level job deficit. Accordingly, there is a compelling need to (a) stop the trend to close or decrease the size of current graduate programs, (b) reorient academic programs to give a greater emphasis to the skills needed to be successful in CRM, and (c) better integrate academic and applied archaeology to leverage the vast amount of data that will be generated in the next decade to best benefit the public.

RCT evidence on differential impact of US job training programmes by pre-training employment status

While the recent evidence around the effectiveness of job training programmes has been generally positive, there has been little evidence regarding the differential impact in the U.S. by pre-training employment status. The city of New Orleans implemented a job training programme funded by the U.S. Department of labour Workforce Innovation Fund. They screened potential trainees for suitability and likelihood of completing and benefiting from training. They selected and developed curriculum with input from local firms. The city implemented the training as a randomized controlled trial. In this paper, we evaluate the impact of the training on employment, earnings, and industry of employment, both overall and separately by baseline employment status. We find overall positive effects for earnings, driven by those who did not have a job at the time of training enrolment. We also find positive employment effects for short-term unemployed workers. Individuals in the treatment group were more likely to work in the target industries, suggesting that the gains were happening through the gains in industry-specific human capital.

The Economic Costs of Democratic Backsliding? Backsliding and State Location Preferences of US Job Seekers

The Economic Costs of Democratic Backsliding? Backsliding and State Location Preferences of US Job Seekers

The rise of remote work and its implications on US organizations and boards of directors

This study investigated the decision of international and non-international students when it comes to remote work preference at a southern private American university located in the Southeast United States and the implications of those decisions on the US educational system, job market, and board of directors’ strategic plans. For this purpose, a database that included 3001 full-time undergraduates at this southern American university was used. A chi-square test and a logit regression were used to analyze the data. The results of the study showed that there was no significant difference between international and non-international students regarding being decided on the remote work model. This study can help US colleges and universities, educational leaders, boards of directors, policymakers, and government officials, understand the needs and preferences of both American and international students, at the undergraduate level, which is a major pillar of the US labour force. An improved understanding of the students’ Work Model Preference will help the governing boards of American colleges and universities, and various US institutions to better allocate their human, financial, and physical resources in order to attract highly qualified candidates. Organizational leaders and boards of directors know full well that colleges and universities are a major source of candidates for their businesses. Organizations that provide those candidates with the work flexibility and work-life balance they demand will increase the likelihood of their competitive advantage and success

Professionalization and Corporate Social Responsibility: A Comparative Study on German and US Job Requirements in CSR

Given the steady interest in corporate social responsibility (CSR), this study explores the process of professionalization of CSR. Drawing upon the literature on ‘organizational’ professionals, explicit and implicit CSR, as well as varieties of capitalism, professionalization of CSR is explored in order to trace processes of explicitization and potential cross-national differences between the United States and Germany. In a comparative longitudinal study, we analyse job announcements in the field of CSR and find that although the hybridity of explicit and implicit CSR between the US and Germany is starting to unfold, job characteristics and job requirements in CSR in Germany and the US are still not the same. Our results suggest there is a more distinct trend in professionalization in the US than in Germany in terms of the manifestation of explicit CSR and that the institutional context is linked to how employers drive professionalization processes in non-traditional professions.

Asymmetries in Online Job-Seeking: A Case Study of Muslim-American Women

As job-seeking and recruiting processes transition into digital spaces, concerns about hiring discrimination in online spaces have developed. Historically, women of color, particularly those with marginalized religious identities, have more challenges in securing employment. We conducted 20 semi-structured interviews with Muslim-American women of color who had used online job platforms in the past two years to understand how they perceive digital hiring tools to be used in practice, how they navigate the US job market, and how hiring discrimination as a phenomenon is thought to relate to their intersecting social identities. Our findings allowed us to identify three major categories of asymmetries (i.e., the relationship between the computing algorithms' structures and their users' experiences): (1) process asymmetries, which is the lack of transparency in data collection processes of job applications; (2) information asymmetries, which refers to the asymmetry in data availability during online job-seeking; and (3) legacy asymmetries, which explains the cultural and historical factors impacting marginalized job applicants. We discuss design implications to support job seekers in identifying and securing positive employment outcomes.

Altruism and Inequality: How Pro-social Pay Discounts Offset the College Pay Premium

In an era of substantial labor market inequality, employers increasingly invoke pro-social mission, corporate responsibility and organizational purpose. Many commentators criticize these prosocial commitments as ineffectual and insincere. In this paper, we argue on the contrary that these employer commitments inadvertently reduce earnings inequality. Building on research on job values, we hypothesize that college graduates are more willing to sacrifice pay for pro-social impact. So employers’ increased emphasis on pro-social job amenity will lower pay disproportionately for higher skill workers. We test this theory with data on the near-universe of online US job postings. We find that pro-social jobs which require a college degree post 10 percent lower wages than standard postings with exactly the same job requirements. Wages at pro-social, but low-education jobs, are no different from comparable regular jobs. This gap reduces the aggregate college wage premium by around 5 percent. We present a variety of supplementary evidence using resumes, survey data and an online experiment with hiring managers. The findings show that employers' embrace of pro-social messages can offset macro-level inequality.