Research
Experimental methodology
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An Adaptive Experiment to Boost Online Skill Signaling and Visibility
Digital matching platforms promise to reduce frictions on the labor market by providing low-cost information on available positions and candidates. As such, they may form a welcome addition to the toolbox available to Public Employment Services to bridge labor supply and demand. However, there are certain challenges associated with their adoption. For instance, vulnerable populations may face difficulties in utilizing digital tools effectively. In this study, we evaluate the impact of a communication campaign by email designed to encourage the use of an online matching platform maintained by the French Public Employment Service, Pôle emploi. We designed several email templates that combined information, support or motivational content to encourage jobseekers to engage with their profiles on the platform. In order to discover email effectiveness we implement an adaptive experiment (contextual bandit) where the goal is to use past jobseekers take-up responses and characteristics to determine email allocation in the future, reducing gradually the allocation of less promising templates. Additionally, we built an optimal personalization allocation strategy based on collected data and test its effectiveness. Emails had a positive impact on the usage of the platform, as measured by a wide range of outcomes. However, attempts at learning a personalized emailing strategy did not manage to significantly improve on a random allocation of email templates.
Draft
Labour Markets
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From Hidden Skills to Opportunities : The Impact of Job Portals use on Labor Market Outcomes
In this paper, we propose to investigate the benefits of the use of digital matching platform. In partnership with the French Public Employment Service, Pôle emploi, we encourage randomly selected registered jobseekers to fill-in and publish their profile on the PES platform making them more visible to recruiters. We are able to track the effects of the intervention on a large variety of outcomes, including employment, through rich administrative data and web logs provided by the French Employment Service
Work In Progress -
Signaling in Recruitment: Impact on Applications and Recruiters Work Conditions
n a shifting labor market paradigm, jobseekers traditionally held the role of signaling, but recruiters are increasingly competing for talent. This paper analyzes an innovative initiative where firms are randomly asked to provide an “inversed CV” containing detailed information on work conditions, values, and direct contact details. Our analysis focuses on assessing the intervention’s impact on both the quantity and quality of job hirings, as well as its influence on work conditions proposed by firms.
Work In Progress
Recommender systems & AI Ethics
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Toward Job Recommendation for All
IJCAI (AI And Social Good Track), 2023. Also presented at ECML PKDD, AI4HR Workshop, 2023
This paper presents a job recommendation algorithm designed and validated in the context of the French Public Employment Service. The challenges, owing to the confidential data policy, are related with the extreme sparsity of the interaction matrix and the mandatory scalability of the algorithm, aimed to deliver recommendations to millions of job seekers in quasi real- time, considering hundreds of thousands of job ads. The experimental validation of the approach shows similar or better performances than the state of the art in terms of recall, with a gain in inference time of 2 orders of magnitude.
Proceedings
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Fairness in job recommendations: estimating, explaining, and reducing gender gaps
Algorithmic recommendations of job ads have the potential to reduce frictional unemployment, but raise concerns about fairness due to biases in past data. Our research investigates the issue of algorithmic fairness with a specific focus on gender in a hybrid job recommendation system developed in partnership with the French Public Employment Service (PES), which is trained on past hires. First, by viewing job ads as a set of characteristics (such as wage and contract type), we document how the algorithm treats job seekers differently based on gender, both unconditionally and conditionally on their search parameters and qualifications. Second, we discuss the notion(s) of algorithmic fairness applicable in this context and the trade-offs involved. We show that the considered system reflects some existing differences in hiring or applications but does not exacerbate them. Finally, we consider adversarial de-biasing technique as a practical tool to demonstrate the trade-offs between recall and reduced differentiated treatment.
Proceedings