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Golub Capital Social Impact Lab @gsbsilab.bsky.social · 1d

New Paper Alert! Read the thread below for key takeaways from “Does Q&A Boost Engagement? Health Messaging Experiments in the United States and Ghana” by @erikakirgios.bsky.social @susanathey.bsky.social @angeladuckworth.bsky.social et al.

Erika Kirgios @erikakirgios.bsky.social · 19d

What happens if you *ask* instead of *tell*? Turns out, teasing people with a question before sharing a fact can shape whether people engage with critical health information. Read our new paper in Management Science to learn more: pubsonline.informs.org/doi/full/10....

Does Q&A Boost Engagement? Health Messaging Experiments in the United States and Ghana | Management Science

pubsonline.informs.org

Golub Capital Social Impact Lab @gsbsilab.bsky.social · 5d

During the @gsb.stanford.edu 100 Faculty Celebration of Scholarship @susanathey.bsky.social highlighted the GSB’s proud tradition of synergy between business and practice during her presentation. Listen to her thoughts and more in the podcast: stanford.io/3K8mcrz

A Century of Impact

In 1925, Herbert Hoover, a Stanford alum and future U.S. president, had an idea. “A graduate School of Business Administration is urgently needed upon the Pacific Coast,” he wrote.

stanford.io

Golub Capital Social Impact Lab @gsbsilab.bsky.social · 14d

Earlier this month @susanathey.bsky.social joined Stanford University President Levin, @siepr.bsky.social Director @nealemahoney.bsky.social and other distinguished faculty to connect over cutting-edge research developments at Stanford Open Minds New York.

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Golub Capital Social Impact Lab @gsbsilab.bsky.social · Aug 27

And learn more about surrogate indices in the papers by @susanathey.bsky.social, Raj Chetty, Guido Imbens, and Hyunseung Kang.

www.nber.org/papers/w26463

arxiv.org/abs/2006.09676

The Surrogate Index: Combining Short-Term Proxies to Estimate Long-Term Treatment Effects More Rapidly and Precisely

Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, an...

www.nber.org

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Golub Capital Social Impact Lab @gsbsilab.bsky.social · Aug 27

Listen to Raj Chetty talk about how surrogate indices make it possible to make decisions more quickly using multiple short-term outcomes to predict long-term effects with @nber.org

www.nber.org/research/vid...

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Golub Capital Social Impact Lab @gsbsilab.bsky.social · Aug 22

Learn more about how ratings, measurement, nudges, and dashboards are used to support service quality on online platforms in the paper by @susanathey.bsky.social, Juan Camilo Castillo, & Bharat Chandar

www.nber.org/papers/w33087

Service Quality on Online Platforms: Empirical Evidence about Driving Quality at Uber

Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, an...

www.nber.org

Golub Capital Social Impact Lab @gsbsilab.bsky.social · Aug 22

Check out @susanathey.bsky.social’s interview on how AI-powered after-the-fact quality checks boost driver performance at Uber—and what it means when AI can track compliance. Insights via @StanfordGSB.

www.gsb.stanford.edu/insights/how...

How Uber Steers Its Drivers Toward Better Performance

www.gsb.stanford.edu

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Golub Capital Social Impact Lab @gsbsilab.bsky.social · Jul 18

Watch @Susan_Athey’s talk on the implications of AI on the organisation of industry and work at #G20SouthAfrica

www.youtube.com/watch?v=okDG...

#G20SouthAfrica Side event — The implications of Al on the organisation of industry and work

YouTube video by SAReserveBank

www.youtube.com

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Golub Capital Social Impact Lab @gsbsilab.bsky.social · Jul 18

“Governments will play a key role…in whether we actually develop the technology that will help lower-skilled workers become more productive by using AI to augment them with expertise that previously was difficult to acquire.”

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Golub Capital Social Impact Lab @gsbsilab.bsky.social · Jul 15

The talk will cover:

✔️How AI is altering industry dynamics & structures

✔️How these shifts will impact public services such as health and education

✔️How AI market concentration could tax the global economy

✔️Why govt policy will be crucial in shaping AI competition and innovation

Golub Capital Social Impact Lab @gsbsilab.bsky.social · Jul 15

AI & digitisation are rapidly reshaping the way we work.

Policymakers need to understand how, and what to do about it.

Watch @Susan_Athey speak to G20 leaders about these issues tomorrow 16 July @ 13:30 CET. #G20SouthAfrica

bit.ly/3GyMFgm or bit.ly/44PTXFP

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Golub Capital Social Impact Lab @gsbsilab.bsky.social · Jun 30

Beyond predictions, @keyonv.bsky.social also worked with @gsbsilab.bsky.social to show how these models can be used to make better estimations of important problems, such as the gender wage gap among men and women with the same career histories. Learn more here: bsky.app/profile/gsbs...

Golub Capital Social Impact Lab @gsbsilab.bsky.social · Jun 30

Foundation models make great predictions. How should we use them for estimation problems in social science?

New PNAS paper @susanathey.bsky.social & @keyonv.bsky.social & @Blei Lab:
Bad news: Good predictions ≠ good estimates.
Good news: Good estimates possible by fine-tuning models differently 🧵

Golub Capital Social Impact Lab @gsbsilab.bsky.social · Jun 30

If we know someone’s career history, how well can we predict which jobs they’ll have next? Read our profile of @keyonv.bsky.social to learn how ML models can be used to predict workers’ career trajectories & better understand labor markets.

medium.com/@gsb_silab/k...

Keyon Vafa: Predicting Workers’ Career Trajectories to Better Understand Labor Markets

If we know someone’s career history, how well can we predict which job they’ll have next?

medium.com

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Golub Capital Social Impact Lab @gsbsilab.bsky.social · Jun 30

Paper: arxiv.org/abs/2409.09894

Estimating Wage Disparities Using Foundation Models

The rise of foundation models marks a paradigm shift in machine learning: instead of training specialized models from scratch, foundation models are first trained on massive datasets before being adap...

arxiv.org

Golub Capital Social Impact Lab @gsbsilab.bsky.social · Jun 30

Analyzing representations tells us where history explains the gap.

Ex: there are two kinds of managers: those who used to be engineers and those who didn’t. The first group gets paid more and has more males than the second.

Models that don’t use history omit this distinction.

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Golub Capital Social Impact Lab @gsbsilab.bsky.social · Jun 30

We use these methods to estimate wage gaps adjusted for full job history, following the literature on gender wage gaps.

History explains a substantial fraction of the remaining wage gap when compared to simpler methods. But there’s still a lot that history can’t account for.

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Golub Capital Social Impact Lab @gsbsilab.bsky.social · Jun 30

This result motivates new fine-tuning strategies.

We consider 3 strategies similar to methods from the causal estimation literature. E.g. optimize representations to predict the *difference* in male-female wages instead of individual wages.

All perform well on synthetic data.

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Golub Capital Social Impact Lab @gsbsilab.bsky.social · Jun 30

Two extremes:

A representation that's just the identity function meets condition (1) trivially but not (2).

A representation that uses a very simple summary of history (e.g. # of years worked) should meet (2) but fails (1)

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Golub Capital Social Impact Lab @gsbsilab.bsky.social · Jun 30

New result: Fast + consistent estimates are possible even if a representation drops info

Two main fine-tuning conditions:
1. Representation only drops info that isn't correlated w/ both wage & gender
2. Representation is simple enough that it’s easy to model wage & gender from it

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Golub Capital Social Impact Lab @gsbsilab.bsky.social · Jun 30

Intuition: If working in job X at some point has a small effect on wages, but men are much likelier to have worked in job X than women, it may be omitted by a model optimized to predict wage.

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Golub Capital Social Impact Lab @gsbsilab.bsky.social · Jun 30

Foundation models are usually fine-tuned to make predictions (like wages).

But representations fine-tuned this way can induce omitted variable bias: the gap adjusted for full history can be different from the gap adjusted for the representation of job history.

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Golub Capital Social Impact Lab @gsbsilab.bsky.social · Jun 30

We use CAREER, a foundation model of job histories. It’s pretrained on resumes but its representations can be fine-tuned on the smaller datasets used to estimate wage gaps.

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Golub Capital Social Impact Lab @gsbsilab.bsky.social · Jun 30

But this discards information that’s relevant to the wage gap.

In contrast, foundation models learn *representations*: lower-dimensional variables that summarize information.

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Golub Capital Social Impact Lab @gsbsilab.bsky.social · Jun 30

Consider estimating the wage gap explained by differences in job history.

Job history is high-dimensional since there are many possible sequences of jobs. So most economic models describe histories using hand-selected summary stats (e.g., # of years worked).

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Golub Capital Social Impact Lab @gsbsilab.bsky.social · Jun 30

Decompositions can inform policy: a large explained gender wage gap can suggest differences in choices or opportunities earlier in a worker’s career, while an unexplained gap may arise due to differences in factors such as skill, care responsibilities, or bargaining.

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