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Service Quality in the Gig Economy: Empirical Evidence about Driving Quality at Uber
The rise of marketplaces for goods and services has led to changes in the mechanisms used to ensure high quality. We analyze this phenomenon in the Uber market, where the system of pre-screening that prevailed in the taxi industry has been…
Policy Learning with Adaptively Collected Data
In a wide variety of applications, including healthcare, bidding in first price auctions, digital recommendations, and online education, it can be beneficial to learn a policy that assigns treatments to individuals based on their characteristics…
LABOR-LLM: Language-Based Occupational Representations with Large Language Models
Many empirical studies of labor market questions rely on estimating relatively simple predictive models using small, carefully constructed longitudinal survey datasets based on hand-engineered features. Large Language Models (LLMs), trained on…
The Heterogeneous Impact of Changes in Default Gift Amounts on Fundraising
When choosing whether and how much to donate, potential donors often observe a set of default donation amounts known as an “ask string.” In an experiment with more than 400,000 PayPal users, we replace a relatively unused donation amount ($75) on…
The Value of Non-traditional Credentials in the Labor Market
This study investigates the labor market value of credentials obtained from Massive Open Online Courses (MOOCs) and shared on business networking platforms. We conducted a randomized experiment involving more than 800,000 learners, primarily from…
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…
Digital Interventions and Habit Formation in Educational Technology
We evaluate a contest-based intervention intended to increase the usage of an educational app that helps children in India learn to read English. The evaluation included approximately 10,000 children, of whom about half were randomly selected to…
Impact Matters for Giving at Checkout
We conducted two experiments on PayPal’s Give at Checkout feature to learn about the effect of 1) information about charity outcomes on donations, and 2) exposure to these point-of-sale microgiving requests on subsequent giving. In this “…
Optimal Experimental Design for Staggered Rollouts
In this paper, we study the design and analysis of experiments conducted on a set of units over multiple time periods where the starting time of the treatment may vary by unit. The design problem involves selecting an initial treatment time for…
Low-Intensity Fires Mitigate the Risk of High-Intensity Wildfires in California’s Forests
The increasing frequency of severe wildfires demands a shift in landscape management to mitigate their consequences. The role of managed, low-intensity fire as a driver of beneficial fuel treatment in fire-adapted ecosystems has drawn interest in…
Preparing for Generative AI in the 2024 Election: Recommendations and Best Practices Based on Academic Research
The rapid development of generative AI technology is transforming the political landscape, presenting both challenges and opportunities for the 2024 US election. This document provides a research-based overview of the potential impact of…
What Kinds of Incentives Encourage Participation in Democracy? Evidence from a Massive Online Governance Experiment
How can we democratically govern the AI, social media, and online platforms of the future? Today, low participation is a major barrier to community governance online. We leverage a digital quasi-experiment that allows us to study the links…
Can Personalized Digital Counseling Improve Consumer Search for Modern Contraceptive Methods?
This paper analyzes a randomized controlled trial of a personalized digital counseling intervention addressing informational constraints and choice architecture, cross-randomized with discounts for long-acting reversible contraceptives (LARCs),…
Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal
In many settings, interventions may be more effective for some individuals than others, so that targeting interventions may be beneficial. We analyze the value of targeting in the context of a large-scale field experiment with over 53,000 college…
Federated Causal Inference in Heterogeneous Observational Data
We are interested in estimating the effect of a treatment applied to individuals at multiple sites, where data is stored locally for each site. Due to privacy constraints, individual-level data cannot be shared across sites; the sites may also…
Machine-Learning-Based High-Benefit Approach versus Conventional High-Risk Approach in Blood Pressure Management
In medicine, clinicians treat individuals under an implicit assumption that high-risk patients would benefit most from the treatment (‘high-risk approach’). However, treating individuals with the highest estimated benefit using a novel machine-…
The Heterogeneous Earnings Impact of Job Loss Across Workers, Establishments, and Markets
Using generalized random forests and rich Swedish administrative data, we show that the earnings effects of job displacement due to establishment closures are extremely heterogeneous across workers, establishments, and markets. The decile of…
Market Re-Design of Framework Agreements in Chile Reduces Government Procurement Spending
Framework agreements (FAs) are procurement mechanisms used in private and public organizations by which a central procurement agency selects an assortment of products, typically through auctions, and then affiliated organizations can purchase…
On Frequentist Regret of Linear Thompson Sampling
This paper studies the stochastic linear bandit problem, where a decision-maker chooses actions from possibly time-dependent sets of vectors in ℝd and receives noisy rewards. The objective is to minimize regret, the difference between the…
The Evolving Battlefronts of Shareholder Activism
In this Closer Look, we consider current trends in shareholder activism and their potential impact. We examine the introduction of universal proxies, the increase in “activism experience” among directors, and the changing strategies of activists…
The Design of Optimal Pay-as-Bid Procurement Mechanisms
Problem definition: We consider the mechanism design problem of finding an optimal pay-as-bid mechanism in which the platform chooses an assortment of suppliers in order to balance the trade-off between two objectives: providing enough variety to…
Analytics Saves Lives During the COVID-19 Crisis in Chile
Franz Edelman Award 2022, Winning Project
During the COVID-19 crisis, the Chilean Ministry of Health and the Ministry of Sciences, Technology, Knowledge and Innovation partnered with the Instituto Sistemas Complejos de Ingeniería…
Battling the Coronavirus Infodemic Among Social Media Users in Africa
During a global pandemic, how can we best prompt social media users to demonstrate discernment in sharing information online? We ran a contextual adaptive experiment on Facebook Messenger with users in Kenya and Nigeria and tested 40 combinations…
Digital Public Health Interventions at Scale: The Impact of Social Media Advertising on Beliefs and Outcomes Related to COVID Vaccines
Public health organizations increasingly use social media advertising campaigns in pursuit of public health goals. In this paper, we evaluate the impact of about $40 million of social media advertisements that were run and experimentally tested…