These papers are working drafts of research which often appear in final form in academic journals. The published versions may differ from the working versions provided here.
SSRN Research Paper Series
The Social Science Research Network’s Research Paper Series includes working papers produced by Stanford GSB the Rock Center.
You may search for authors and topics and download copies of the work there.
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 “…
Market Design for Surface Water
Many proposed surface water transfers undergo a series of regulatory reviews designed to mitigate hydrological and economic externalities. While these reviews help limit externalities, they impose substantial transaction costs that also limit…
Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization
In many applications, e.g. in healthcare and e-commerce, the goal of a contextual bandit may be to learn an optimal treatment assignment policy at the end of the experiment. That is, to minimize simple regret. However, this objective remains…
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…
The Zero-Beta Rate
We use equity returns to construct a time-varying measure of the interest rate that we call the zero-beta rate: the expected return of a stock portfolio orthogonal to the stochastic discount factor. The zero-beta rate is high and volatile. In…
Targeting, Personalization, and Engagement in an Agricultural Advisory Service
ICT is increasingly used to deliver customized information in developing countries. We examine whether individually targeting the timing of automated voice calls meaningfully increases engagement in an agricultural advisory service. We define,…
Decomposing Changes in the Gender Wage Gap over Worker Careers
A large literature in labor economics seeks to decompose observed gender wage gaps (GWGs) into different sources, including portions explained by cross-gender differences in education, occupation, and experience. This paper provides new methods…
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…
Torch-Choice: A PyTorch Package for Large-Scale Choice Modelling with Python
The torch-choice is an open-source library for flexible, fast choice modeling with Python and PyTorch. torch-choice provides a ChoiceDataset data structure to manage databases flexibly and memory-efficiently. The paper demonstrates constructing a…
Online Appendix for Scaling Auctions as Insurance: A Case Study in Infrastructure Procurement
Online Appendix for Scaling Auctions as Insurance: A Case Study in Infrastructure Procurement
Flexible and Efficient Contextual Bandits with Heterogeneous Treatment Effect Oracles
Contextual bandit algorithms often estimate reward models to inform decision-making. However, true rewards can contain action-independent redundancies that are not relevant for decision-making. We show it is more data-efficient to estimate any…
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…
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 Contribution of High-Skilled Immigrants to Innovation in the United States
We characterize the contribution of immigrants to U.S. innovation, both through their direct productivity as well as through their indirect spillover effects on their native collaborators. To do so, we link patent records to a database containing…
Effective and Scalable Programs to Facilitate Labor Market Transitions for Women in Technology
We describe the design, implementation, and evaluation of a low-cost and scalable program that supports women in Poland in transitioning into jobs in the information technology sector. This program, called “Challenges,” helps…
Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learning
We design and implement an adaptive experiment (a “contextual bandit”) to learn a targeted treatment assignment policy, where the goal is to use a participant’s survey responses to determine which charity to expose them to in a donation…
Emotion- Versus Reasoning-Based Drivers of Misinformation Sharing: A Field Experiment Using Text Message Courses in Kenya
Two leading hypotheses for why individuals unintentionally share misinformation are that 1) they are unable to recognize that a post contains misinformation, and 2) they make impulsive, emotional sharing decisions without thinking about whether a…
Policy Learning with Adaptively Collected Data
Learning optimal policies from historical data enables the gains from personalization to be realized in a wide variety of applications. The growing policy learning literature focuses on a setting where the treatment assignment policy does not…
Smiles in Profiles: Improving Fairness and Efficiency Using Estimates of User Preferences in Online Marketplaces
Online platforms often face challenges being both fair (i.e., non-discriminatory) and efficient (i.e., maximizing revenue). Using computer vision algorithms and observational data from a microlending marketplace, we find that choices made by…
CAREER: Transfer Learning for Economic Prediction of Labor Sequence Data
Labor economists regularly analyze employment data by fitting predictive models to small, carefully constructed longitudinal survey datasets. Although modern machine learning methods offer promise for such problems, these survey datasets are too…