Do Linguistic Styles in Hospitality Job Postings Matter for Job Application Behaviors? A People Analytics Approach

Online professional social networks such as LinkedIn serve as a marketplace, wherein job seekers can find right career opportunities and job providers can reach out to potential candidates. LinkedIn's job recommendations product is a key vehicle for efficient matching between potential candidates and job postings. However, we have observed in practice that a subset of job postings receive too many applications (due to several reasons such as the popularity of the company, nature of the job, etc.), while some other job postings receive too few applications. Both cases can result in job poster dissatisfaction and may lead to discontinuation of the associated job posting contracts. At the same time, if too many job seekers compete for the same job posting, each job seeker's chance of getting this job will be reduced. In the long term, this reduces the chance of users finding jobs that they really like on the site. Therefore, it becomes beneficial for the job recommendation system to consider values provided to both job seekers as well as job posters in the marketplace. In this paper, we propose the job application redistribution problem, with the goal of ensuring that job postings do not receive too many or too few applications, while still providing job recommendations to users with the same level of relevance. We present a dynamic forecasting model to estimate the expected number of applications at the job expiration date, and algorithms to either promote or penalize jobs based on the output of the forecasting model. We also describe the system design and architecture for LiJAR, LinkedIn's Job Applications Forecasting and Redistribution system, which we have implemented and deployed in production. We perform extensive evaluation of LiJAR through both offline and online A/B testing experiments. Our production deployment of this system as part of LinkedIn's job recommendation engine has resulted in significant increase in the engagement of users for underserved jobs (6.5%) without affecting the user engagement in terms of the total number of job applications, thereby addressing the needs of job seekers as well as job providers simultaneously.

Gender disparities in the labor market during COVID-19 lockdowns: Evidence from online job postings and applications in China

In the continuously changing labor market, understanding the dynamics of online job postings is crucial for economic and workforce development. With the increasing reliance on Online Job Portals, analyzing online job postings has become an essential tool for capturing real-time labor-market trends. This paper presents a comprehensive methodology for processing online job postings to generate labor-market intelligence. The proposed methodology encompasses data source selection, data extraction, cleansing, normalization, and deduplication procedures. The final step involves information extraction based on employer industry, occupation, workplace, skills, and required experience. We address the key challenges that emerge at each step and discuss how they can be resolved. Our methodology is applied to two use cases: the first focuses on the analysis of the Greek labor market in the tourism industry during the COVID-19 pandemic, revealing shifts in job demands, skill requirements, and employment types. In the second use case, a data-driven ontology is employed to extract skills from job postings using machine learning. The findings highlight that the proposed methodology, utilizing NLP and machine-learning techniques instead of LLMs, can be applied to different labor market-analysis use cases and offer valuable insights for businesses, job seekers, and policymakers.

Career opportunities in global health: A snapshot of the current employment landscape

The Radiology Job Market: Analysis of the ACR Jobs Board

We use novel high-frequency panel data on individuals’job applications from a job posting website to study how job seekers direct their applications over the course of job search. We …nd that at the beginning of search there is sorting of applicants across job postings by education. As search continues, education becomes a weaker predictor of which job a job seeker applies for, and an average job seeker applies for jobs that are a …rst-week choice of less educated job seekers. We interpret these …ndings to suggest that search is systematic, whereby a job seeker samples high wage opportunities (conditional on his belief about the probability of meeting the job requirements) …rst and lower wage opportunities later. The …ndings provide evidence in favor of theories of job seekers’learning and are consistent with the literature that documents declining reservation or desired wages.

Field experiments have proliferated throughout the social sciences and have become a mainstay for identifying racial discrimination during the hiring process. To date, field experiments of labor market discrimination have generally drawn their sample of job postings from limited sources, often from a single major online job posting website. While providing a large pool of job postings across labor markets, this narrow sampling procedure leaves open questions about the generalizability of the findings from field experiments of racial discrimination in the extant literature. In this paper, we present evidence from a field experiment examining racial discrimination in the hiring process that draws its sample from two sources: (1) a national online job posting website that aligns with previous research, and (2) a job aggregator service that scrapes the web daily in an effort to obtain all online job postings in the United States. While differing in the types of information they collect, we find the job postings drawn from the two sources result in similar estimates of discrimination against Black applicants. In other words, we do not find evidence that racial discrimination varies by the source of the job posting. We conclude by discussing the implications of these findings for studies of racial discrimination, discrimination along other axes of social difference, as well as field-experimental methods more broadly.

Employers increasingly reach job seekers through online job postings, particularly for jobs requiring a higher education qualification. Job postings available online provide a rich source of real-time and detailed data on the qualifications and skills sought by employers across industries, occupations and locations. Using a sample of over 9 million job postings in four US states (Ohio, Texas, Virginia and Washington), this paper explores three questions. How does employer demand for graduate skills vary geographically, within and among occupations? For graduates in a general study field without a dedicated career vocational pathway, like sociology, what occupational clusters show evidence of employer demand, and what skills are sought? Given the high demand in the field of information and communications technology (ICT), are employers looking for ICT specialists open to hiring graduates from study fields other than ICT? We find evidence of variation in occupational demand, and to some extent in skill demand, within occupational clusters across the four states. We identify three occupational clusters where sociology graduates are in most demand, with distinct skill profiles. We also find that, when filling ICT positions, a notable share of employers considers recruiting graduates from other fields of study while requiring those graduates have the right technical transferable skills. Job posting data, we conclude, hold promise to complement existing labour market information systems and aid educators and policy makers in aligning labour demand and educational offerings. If analysed and disseminated effectively, such data could also assist students and workers in making learning and career decisions, for instance by identifying opportunities to build their own non-traditional path into high-demand, high-paying ICT occupations.

Gender segregation remains a significant problem in many occupations and organizations. To solve this problem, many U.S. employers now seek to craft gender-neutral job postings. In this article, we investigate whether such employer recruitment efforts are successful in encouraging women and men to apply equally for jobs. Specifically, we move beyond the well-studied effects of the gender typing of occupations, organizations, and industries to study the extent to which the recruiting language used in job postings influences the actual preapplication behavior of job seekers of different genders. Using unique data from both a large-sample observational field study (Study 1) and a field experiment study (Study 2) of real online job postings, we first assess the gendered language mechanism by testing whether stereotypical femininity in the wording that recruiters use to advertise otherwise identical jobs differently influences female and male job seekers’ interest in applying. We then assess the recruiter gendering mechanism by testing whether the gender of the recruiter and the femininity in the wording recruiters use when presenting themselves to job seekers further contribute to gender job search disparities. Our analyses ultimately show negligible effects for both the gendering of job postings or of the job poster, and we therefore conclude that, in practice, employers’ efforts to simply tweak the language of recruitment messages do not matter much for gender equality and diversity. This paper was accepted by Olav Sorenson, organizations. Funding: The authors received financial support from the James S. Hardigg (1945) Work and Employment Fund and the Massachusetts Institute of Technology Sloan School of Management. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4674 .

The labor market refers to the market between job seekers and employers. As much of job seeking and talent hiring activities are now performed online, a large amount of job posting and application data have been collected and can be re-purposed for labor market analysis. In the labor market, both supply and demand are the key factors in determining an appropriate salary for both job applicants and employers in the market. However, it is challenging to discover the supply and demand for any labor market. In this paper, we propose a novel framework to built a labor market model using a large amount of job post and applicant data. For each labor market, the supply and demand of the labor market are constructed by using offer salaries of job posts and the response of applicants. The equilibrium salary and the equilibrium job quantity are calculated by considering the supply and demand. This labor market modeling framework is then applied to a large job repository dataset containing job post and applicant data of Singapore, a developed economy in Southeast Asia. Several issues are discussed thoroughly in the paper including developing and evaluate salary prediction models to predict missing offer salaries and estimate reserved salaries. Moreover, we propose a way to empirically evaluate of equilibrium salary of the proposed model. The constructed labor market models are then used to explain the job seeker and employer specific challenges in various market segments. We also report gender and age biases that exist in labor markets. Finally, we present a wage dashboard system that yields interesting salary insights using the model.

This paper investigates how economic downturns affect the flow of human capital to startups. Using proprietary data from AngelList Talent, we study how individuals’ online job searches and applications changed during the emergence of the COVID-19 crisis. We find that job seekers shifted their searches toward larger firms and away from early-stage ventures, even within the same individual over time. Simultaneously, job seekers broadened their other search parameters, considering lower salaries and a wider variety of job types, roles, markets, and locations. Relative to larger firms, early-stage ventures experienced a decline in the number of applications per job posting, a decline driven by higher quality and more experienced job seekers. This led to a deterioration in the quality of the human capital pool available to early-stage ventures during the downturn. These declines hold within a firm as well as within a job posting over time. Our findings uncover a flight to safety channel in the labor market, which may amplify the pro-cyclical nature of entrepreneurial activities.

Timely and accurate statistics on the labour market enable policymakers to rapidly respond to changing economic conditions. Estimates of job vacancies by national statistical agencies are highly accurate but reported infrequently and with time lags. In contrast, online job postings provide a high-frequency indicator of vacancies with less accuracy. In this study we develop a robust signal averaging algorithm to measure job vacancies using online job postings data. We apply the algorithm using data on Australian job postings and show that it accurately predicts changes in job vacancies over a 4.5-year period. We also show that the algorithm is significantly more accurate than using raw counts of job postings to predict vacancies. The algorithm therefore offers a promising approach to the timely and reliable measurement of changes in vacancies.

Job postings online have become popular these days due to connecting to job seekers around the world. There are also instances where the fraudulent employer posts a job online and expects people to apply to these postings. These fraudulent employers impend job seekers' privacy, spawns fake job offers, and wanes. We perceived that most of the Online Recruitment Fraud (ORF) has matching features. Though the user cannot categorize them, we propose using various predictive models like Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest, Naïve Bayes, or Logistics Regression to detect them effortlessly. Dataset with 17780 job postings was downloaded from Kaggle to identify which proposed model best predicts the fraudulent job posting. The dataset includes 14 features to determine whether online job posting is fraudulent or non-fraudulent. 70% of these job postings train the model, and the remaining 30% test the model's efficiency. The outcomes of each model are predicted using four evaluation metrics – Classification Accuracy (CA), Precision, Recall and F-1 score. The research found its suitability from two sides: the websites can identify fake jobs before being published, and job seekers are sheltered from fraudulent job postings.

This research investigates whether and when a job applicant’s unemployment status (i.e., employed, short-term unemployed, or long-term unemployed) affects the probability of receiving an interview request by examining interview request rates in the presence of versus absence of unemployment status antidiscrimination legislation. In response to 3,335 fictitious resumes sent to 1,237 online job postings in Los Angeles and New York City, we received an overall interview request rate of 10.37. Long-term unemployed applicants were less likely to receive an interview request than short-term unemployed applicants in Los Angeles but not in New York City, which has unemployment status antidiscrimination legislation. These findings are supplemented with self-report survey data about perceptions of the unemployed from 200 hiring personnel in New York City and Los Angeles. Practical and theoretical implications are discussed for the unemployment, job search, and selection literatures.

Labor market information is an important input to labor, workforce, education, and macroeconomic policy. However, granular and real-time data on labor market trends are lacking; publicly available data from survey samples are released with significant lags and miss critical information such as skills and benefits. We use generative Artificial Intelligence to automatically extract structured labor market information from unstructured online job postings for the entire U.S. labor market. To demonstrate our methodology, we construct a sample of 6,800 job postings stratified by 68 major occupational groups, extract structured information on educational requirements, remote-work flexibility, full-time availability, and benefits, and show how these job characteristics vary across occupations. As a validation, we compare frequencies of educational requirements by occupation from our sample to survey data and find no statistically significant difference. Finally, we discuss the scalability to collections of millions of job postings. Our results establish the feasibility of measuring labor market trends at scale from online job postings thanks to advances in generative AI techniques. Improved access to such insights at scale and in real-time could transform the ability of policy leaders, including federal and state agencies and education providers, to make data-informed decisions that better support the American workforce.