arXiv subjects

Emil Palikot

Publications and source records attributed to Emil Palikot.

9 recordsLinked to original sources

arXiv2026

Fighting discrimination with reputation: The case of online platforms

On a large French ridesharing platform, new minority drivers earn 11.6% less revenue than otherwise similar nonminority drivers; the gap nearly vanishes as they accumulate reviews. Reviews drive the convergence: when a railway strike exogenously raised demand and sped up review accumulation, minority entrants gained the most. We explain the pattern with an estimated model of passenger choice and driver career concerns. Passengers hold overly pessimistic priors about minority entrants - expecting substantially lower quality before the ride than they report after it. As a result, minority drivers cut introductory prices and exert extra effort to overturn those beliefs quickly. Counterfactuals show the cost of incorrect priors is high, and the reputation system strictly benefits minority drivers.

econ.GN↗

arXiv2025

The Impact of Shared Telecom Infrastructure on Digital Connectivity and Inclusion

Nearly half the world remains offline, and capital scarcity stalls new network buildouts. Sharing existing mobile towers could accelerate connectivity. We assemble data on 107 tower-sharing deals in 28 low-income countries (2008-20) and estimate staggered difference-in-differences effects. Two years after a transaction covering over 1,000 towers, the PPP-adjusted mobile-price index falls USD 1.60 (s.e. 1.10) from a baseline of USD 3.16, while data prices drop USD 1.00 (0.29), baseline USD 3.41 per GB. The number of mobile connections increases. Rural internet access increases by 4.7 pp and female-headed households by 3.6 pp. Tower-sharing agreements increase product market competition as measured by Herfindahl-Hirschman Index.

econ.GN↗

arXiv2025

Better Together: Quantifying the Benefits of AI-Assisted Recruitment

Hiring algorithms have mostly scored the materials recruiters already see. Large language models (LLMs) can instead generate new information about candidates by conducting, at scale, structured interviews once reserved for a few finalists. We study this shift in two field experiments at a recruitment platform. The first experiment holds the candidate pool fixed and randomizes whether recruiters observe the AI Interview Report; the second embeds the AI interview as a requirement in a live hiring pipeline. In both, candidates shortlisted with AI interview information pass the final human interview (conducted blind to shortlisting condition) at rates 17.5 (SE 8.5) to 20 (SE 11.8) percentage points higher than candidates shortlisted from resumes alone. The gains concentrate where resumes are least informative: adding AI Interview Report ratings to conventional candidate features raises out-of-sample AUC by 0.18 for junior candidates, against 0.08 for non-junior candidates. The participation cost falls on applicants as 75 percent of invited candidates do not complete the interview. However, the attrition is itself a signal: completion is more consistent with job-search motivation than with predicted interview performance. AI interviews thus add information exactly where conventional signals fail, and they move the cost of screening from firms to applicants.

cs.CL↗

arXiv2024

The Value of Non-Traditional Credentials in the Labor Market

Workers without formal credentials experience substantially lower employment rates than their credentialed counterparts, but the extent to which information frictions contribute to these disparities remains unclear. We conducted a randomized experiment with over 800,000 online certificate earners from developing countries who lack college degrees, encouraging credential sharing on LinkedIn through reduced friction and reminders. We study credential visibility for the full sample and track employment outcomes for 40,000 learners. The intervention increased new employment by 6% (1.0 percentage point), with larger effects of 9% (1.2 percentage points) for jobs related to certificates. Treatment effects concentrate among learners with weak baseline employability: those in the bottom tercile experience employment gains of 12% while those in the top tercile see negligible effects. Those most responsive to the intervention benefit most from sharing, with a local average treatment effect of 11 percentage points for compliers. Counterfactual resume scoring shows that credentials improve perceived candidate quality by 8.5 points on average (on a scale from 1 to 100), with effects of 15-20 points for weak resumes but near-zero for strong resumes. Our findings demonstrate that information frictions substantially constrain employment for workers without traditional credentials, and that minimal-cost interventions targeting credential visibility can generate employment gains comparable to intensive training programs.

econ.GN↗

arXiv2024

CAREER: A Foundation Model for Labor Sequence Data

Labor economists regularly analyze employment data by fitting predictive models to small, carefully constructed longitudinal survey datasets. Although machine learning methods offer promise for such problems, these survey datasets are too small to take advantage of them. In recent years large datasets of online resumes have also become available, providing data about the career trajectories of millions of individuals. However, standard econometric models cannot take advantage of their scale or incorporate them into the analysis of survey data. To this end we develop CAREER, a foundation model for job sequences. CAREER is first fit to large, passively-collected resume data and then fine-tuned to smaller, better-curated datasets for economic inferences. We fit CAREER to a dataset of 24 million job sequences from resumes, and adjust it on small longitudinal survey datasets. We find that CAREER forms accurate predictions of job sequences, outperforming econometric baselines on three widely-used economics datasets. We further find that CAREER can be used to form good predictions of other downstream variables. For example, incorporating CAREER into a wage model provides better predictions than the econometric models currently in use.

cs.LG↗

arXiv2024

Digital interventions and habit formation in educational technology

As online educational technology products have become increasingly prevalent, rich evidence indicates that learners often find it challenging to establish regular learning habits and complete their programs. Concurrently, online products geared towards entertainment and social interactions are sometimes so effective in increasing user engagement and creating frequent usage habits that they inadvertently lead to digital addiction, especially among youth. In this project, we carry out a contest-based intervention, common in the entertainment context, on an educational app for Indian children learning English. Approximately ten thousand randomly selected learners entered a 100-day reading contest. They would win a set of physical books if they ranked sufficiently high on a leaderboard based on the amount of educational content consumed. Twelve weeks after the end of the contest, when the treatment group had no additional incentives to use the app, they continued their engagement with it at a rate 75\% higher than the control group, indicating a successful formation of a reading habit. In addition, we observed a 6\% increase in retention within the treatment group. These results underscore the potential of digital interventions in fostering positive engagement habits with educational technology products, ultimately enhancing users' long-term learning outcomes.

econ.GN↗

arXiv2022

Personalized Recommendations in EdTech: Evidence from a Randomized Controlled Trial

We study the impact of personalized content recommendations on the usage of an educational app for children. In a randomized controlled trial, we show that the introduction of personalized recommendations increases the consumption of content in the personalized section of the app by approximately 60%. We further show that the overall app usage increases by 14%, compared to the baseline system where human content editors select stories for all students at a given grade level. The magnitude of individual gains from personalized content increases with the amount of data available about a student and with preferences for niche content: heavy users with long histories of content interactions who prefer niche content benefit more than infrequent, newer users who like popular content. To facilitate the move to personalized recommendation systems from a simpler system, we describe how we make important design decisions, such as comparing alternative models using offline metrics and choosing the right target audience.

econ.GN↗

arXiv2022

Effective and Scalable Programs to Facilitate Labor Market Transitions for Women in Technology

We evaluate two interventions facilitating technology-sector transitions for women in Poland: Mentoring, focused on expanding professional networks, and Challenges, focused on building credible skill signals. Randomizing oversubscribed admissions, we find both programs substantially increase technology employment at twelve months - by 15 percentage points for Mentoring and 11 p.p. for Challenges. The distinct mechanisms through which the programs operate translate to heterogeneous treatment effects across geography, career stage, and baseline credentials. These differential effects create scope for improved allocation: algorithmic targeting across programs outperforms random assignment by 86% and experts' selection into Mentoring by 11%.

econ.GN↗

arXiv2022

Smiles in Profiles: Improving Efficiency While Reducing Disparities in Online Marketplaces

Online platforms often have conflicting goals: they face tradeoffs between increasing efficiency and reducing disparities, where the latter may relate to objectives such as the longer-term health of the marketplace or the organization's mission. We examine how participants' profile pictures shape this trade-off in the context of a peer-to-peer lending platform. We develop and apply an approach to estimate marketplace participants' preferences for different profile features, distinguishing between (i) "type" (e.g., gender, age) and (ii) "style" (e.g., smiling in the photo). Relative to type, style features are easier to change, and platforms may be more willing to encourage such changes. Our approach starts by using causal inference methods together with computer vision algorithms applied to observational data to identify type and style features of profiles that appear to affect demand for transactions. We further decompose type-based disparities into a component driven by demand for certain types and a component that arises because different types have different distributions of style features; we find that style differences often exacerbate type-based disparities. To improve internal validity, we then carry out two randomized survey experiments using generative models to create multiple versions of profile images that differ in one feature at a time. We then evaluate counterfactual platform policies based on the changeable profile features and identify approaches that can ameliorate the disparity-efficiency tension.

econ.GN↗