Bay Area Tech Economics Seminars
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Intended audience: Graduate-trained economists and other quantitative social scientists from academia, policy, or the private sector who are interested in the tech economy and methods for analyzing it, and in making connections with others in this broadly-defined space.
Thursday, October 9 , 6:30-8:30 PM Simonyi Conference Center, CoDa Stanford | Susan Athey |
November 13, 6:30-8:30 PM USF | Emma Brunskill |
Ground rules:
Doors will open at 6PM, a reception with light refreshments will follow the talk to facilitate ongoing conversations. Location details for each session will be provided to registered participants.
Organizers:
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In-person events
2025
September 25, 2025 Xavier Auditorium, Fromm Hall, USF | Mike Bailey, Senior Data Scientist, Meta Social Capital Lab Social capital—the strength of an individual’s social network and community—has been identified as a potential determinant of outcomes ranging from education to health, but there is very limited measurement of social capital worldwide. We construct and analyze several global measures of social capital including economic connectedness—the extent to which individuals form friendships across socioeconomic lines—using data on 550 billion social connections between 1.7 billion Facebook users across nearly 200 countries and territories. We find that economic connectedness varies substantially both across and within countries. Economic connectedness is associated with higher intergenerational economic mobility, greater trust in others and in democratic institutions, and more pro-social norms. We also identify several factors--population density, language homophily, and inequality--that together predict two-thirds of the cross-region variation in homophily by socioeconomic status. |
Thursday, March 27 , 6:30-8:30 PM Harney Science Center 136, USF | Steve McBride, Head of Economy and Ads Science at Roblox The Economics of Virtual Economies: Roblox Roblox is a leading virtual economy designed to connect game players, developers, shoppers, and UGC creators across community-generated experiences (games). Roblox advances this mission by architecting and enabling a diverse, competitive, and participatory virtual economy using its virtual currency Robux. In this talk you will learn how Roblox uses economic science to achieve these goals in addition to economic efficiency across initiatives in four product areas: 1) a virtual cosmetic goods marketplace; 2) game economy optimization; 3) ads measurement in 3D immersive worlds.; and 4) in-game currency design The emphasis is on the use of science, economic theory, and experimental methods to advance knowledge and influence product development. |
Thursday, April 24, 6:30-8:30 PM Xavier Auditorium, Fromm Hall, USF | Daryl Fairweather, Chief Economics at Redfin Hate the Game: Economics Cheat Codes for Life, Love, and Work Signup link coming soon. |
Thursday, May 8, 6:30-8:30 Stanford |
2024
Tuesday, March 18, 6:30-8:30 PM Harney Science Center 136, USF | Angela Zhang, Professor of Law, University of Southern California America’s Legal Gambit to Curb China’s Technological Rise Register |
Date, Jan. 16, 6:30-8:30 PM Stanford University | Reflections of an Economist Who Manages People Speaker: Jonathan Hall, Uber |
Thursday, Dec 5, 6:30-8:30 PM USF | Does Sponsored Search Advertising Augment Organic Search? Evidence from an E-commerce Platform. Speaker: Sarah Moshary, UC Berkeley |
Thursday, Nov 7, 6:30-8:30 PM Stanford University | GDP-B: Accounting for the Value of New and Free Goods in the Digital Economy Speaker: Erik Brynjolfsson |
Thursday May 9. 6:30-8:30 PM Stanford University | The Interplay of User Behavior and Algorithm Design in Digital Ecosystems Speaker: Hannah Li, Columbia University The interactions between user behavior and algorithms employed on online platforms present challenges that conventional data science tools like A/B testing and recommender systems often overlook. For example, recommender systems can create feedback loops that affect both user and content creator retention. Moreover, users may strategically engage with or avoid certain content to shape their future recommendations. This talk highlights several ways these dynamics can influence platform learning and outcomes and suggests modifications to mitigate these challenges. |
Thursday March 28, 6:30-8:30pm, University of San Francisco | The Impact of Generative AI on Jobs and Skills Speaker: Karin Kimbrough, Chief Economist, LinkedIn Generative AI (GAI) is beginning to shape the world of work. Our data of 1B members worldwide allows us to track this transformative journey of enthusiasm for these technologies, through adoption of GAI skills by professionals and a 70% increase in demand by companies for specific GAI skills. With skills at the forefront, we examine our data of 1B members for where progress is being made and where challenges are still evident. |
Thursday February 8th, 6:30-8:30pm, Stanford University | Is Generative AI Disrupting the Digital Economy? Early Evidence from Card Spending Data Suggests Not Speaker: Kenneth Wilbur, Professor of Marketing and Analytics, UC San Diego Generative artificial intelligence raises concern about human jobs, but what about other products and services? If customers “hire” products to “do jobs,” is generative AI threatening the services that perform those jobs? We investigate a large card spending panel to understand how early ChatGPT-4 adopters changed their spending on other digital services. We use later cohorts of ChatGPT-4 adopters to predict early adopters’ counterfactual spending, and apply a triple-difference identification strategy with Coarsened Exact Matching. We find that ChatGPT-4 adoption increased consumer spending on other AI products. Our estimates rule out market share changes of 1% for the large majority of brands, with a few exceptions. We will present more results during the talk. |
2023
Thursday December 7th, 6:30-8:30pm, Stanford University | Causal Adaptive Learning for Recommendations Speaker: Maria Dimakopoulou, Spotify Sequential decision making and accurate model estimation from adaptively collected data lie at the heart of personalized recommendations. If we want to have reliable decision making in practical recommender systems which adapt to users’ feedback via contextual bandit or reinforcement learning algorithms, we have to get model estimation right. In this talk, we will cover how to incorporate state-of-the-art methods from the causal inference literature in the model estimation of recommender systems and how to pair them with efficient exploration strategies such as Thompson Sampling. Further, we will discuss the personalization performance gains that this approach unlocks in the presence of real-world challenges such as selection bias, covariate shift, model-misspecification and bias due to adaptive data collection. |
Thursday October 12, 6:30-8:30pm, University of San Francisco | Treatment Effects in Market Equilibrium Speaker: Stefan Wager, Stanford University When running randomized trials in a marketplace where prices equilibrate supply and demand, one needs to account for spillovers due to price effects. I'll show how to capture -- and correct for -- such spillovers within the Neyman-Rubin potential outcomes model for causal inference. I'll also discuss methods for spillover-aware optimal targeting. |
Thursday September 28, 6:30-8:30pm, University of San Francisco | The U.S. 2020 Facebook/Instagram Election Study Speaker: Matthew Gentzkow Overview: This project is a novel academic-private sector collaboration designed to study the impact of Facebook and Instagram on key political attitudes and behaviors during the U.S. 2020 elections. Key outcomes include (1) dis/mis/information, knowledge, and (mis)perceptions; (2) political polarization; (3) political participation, both online and offline, including vote choice and turnout; and (4) attitudes and beliefs about democratic norms and the legitimacy of democratic institutions. We will discuss results from recently published papers as well as not-yet-published findings. |
Thursday June 1, 7-8:15pm Stanford | Panelists: Erik Brynjolfsson, Tyna Eloundou, Katya Klinova Moderator: Noah Smith A 2 hour interactive panel discussing various economic aspects of generative AI: effects on productivity, employment, and inequality; effects on competition; effects on communication and entertainment; and appropriate norms and regulation. |
Thursday May 18, 7-8:15pm | Jennifer Pan (Stanford) With 900 million social media users, China's market for social media is larger than that of any other country, and also dramatically different from all other countries. No Facebook. No YouTube, No Instagram. China's market for social media is dominated by domestic Chinese companies. This dominance of Chinese platforms has allowed the Chinese government to influence the digital information environment to an unparalleled extent. This talk will lay out key features of the Chinese social media landscape and discuss what we know about how the Chinese government intervenes in it. |
Thursday April 20, 7-8:15 PM Google San Francisco | Ignacio Martinez Staff Economist & Manager, The Chief Economist's Team at Google Speaking on data's behalf: A Bayesian framework for business decision making Abstract: In this talk I will discuss why a Bayesian framework can be very useful to inform business decisions. First, I will discuss the types of questions that business leaders may want to answer using data. Then, I will argue that the traditional frequentist framework cannot answer these questions directly and that this failure in the traditional analytic approach is a big problem and can result in the wrong decisions being taken. Finally, I will outline a Bayesian alternative to answer these questions. I will argue that we should not shy away from using informative priors, bound ourselves to how we would present results in a frequentist way, and that we should encourage decision-makers to think in bets. |
Thursday April 13, 7-8:15pm University of San Francisco | Carl Shapiro (UC Berkeley) Professor Shapiro will discuss the promise and peril of regulating large digital platforms. He will first comment on the track record of antitrust enforcement in this area, assessing claims that antitrust enforcement relating to digital platforms has been inadequate. He will then consider the pros and cons of sector-specific regulation, drawing lessons from our experience regulating other industries subject to rapid technological change. |
Thursday March 2, 7-8:15pm | Qing Feng (Meta) A/B tests are the gold-standard method for internet firms to evaluate the performance of system changes, yet these experiments are generally limited to evaluating the effects of only one or two variants. In many cases, however, we are interested in evaluating the effects of thousands or a potentially infinite number of possible interventions, such as treatments parametrized by continuous variables. Adaptive experimentation (e.g., Bayesian optimization, bandit optimization) is the machine-learning guided process of iteratively exploring a large action space in order to identify optimal configurations in a resource-efficient manner. In this talk, I will first give an overview of adaptive experimentation at Meta and show how this ML-assisted process allows experimenters to explore more effectively and intelligently. Furthermore, I will discuss our recent advancements in methodology, including the handling of multiple objectives, noisy and non-stationary measurements, and data from different experimentation modalities, and explain how these developments can further improve the efficiency and effectiveness of experimentation. Related open source libraries: |
2022
Thursday Sept 15, 7-8:15pm | University of San Francisco, Hilltop Campus | . |
Thursday Oct 13, 7-8:15pm | University of San Francisco, Hilltop Campus | |
Thursday Oct 27, 7-8:15pm | Stanford | Elizabeth Stone (Netflix) |
Thursday Dec 1, 7-8:15pm | Stanford |