iiinova — where your digital self keeps evolving

7/6 Also: I'm on the job market! If you're interested in the economics of (industrial policy for) open source or want to see a (very simple) example of PyBLP-powered micro BLP in action, check out my job market paper: bsky.app/profile/jeff...

6/6 All this to say: if you're doing methods research, there are some clear private benefits that can come jointly with doing open source work (impact, transparency, networking, etc.) but also others that could be more surprising.

GitHub - Mixtape-Sessions/Demand-Estimation: Demand Estimation taught by Jeff Gortmaker and Ariel Pakes

5/6 Having an open source package lets you show students exactly how the method works with real-time examples. I used PyBLP to teach one of @causalinf.bsky.social @kylefbutts.bsky.social's Mixtape sessions (github.com/Mixtape-Sess...), which would have been far less accessible without it.

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Demand Estimation taught by Jeff Gortmaker and Ariel Pakes - Mixtape-Sessions/Demand-Estimation

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4/6 When others rely on your code, you write more tests, which can benefit your (future) self. For every PyBLP version, I run ~2k tests across ~50 functions. These made it easy to broaden the scope of our papers by adding new simulations and empirical examples with confidence.

3/6 For micro BLP, I spent time iterating on PyBLP's interface to balance usability and flexibility. Feedback from users helped improve my design, which mapped directly into our standardized econometric framework, a key contribution of the 2nd paper.

2/6 I expected PyBLP's issue tracker (github.com/jeffgortmake...) to be mostly bug reports, but most are questions. Our 2nd paper was motivated by these interactions, and many of our explanations were informed by discussions on the tracker and emails from less vocal users.

Suproteem Sarkar @suproteem.cc · 20/11/2024

Happy to share that my and @cconlon.bsky.social's micro BLP paper was just accepted at the Journal of Econometrics! jeffgortmaker.com/files/Incorp... It's our 2nd tied to our PyBLP software, so here's a thread on surprising (to me) benefits of combining methods research with open source work. 1/6

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Reposted by Jeff Gortmaker

Economic valuations fluctuate in ways empirical research cannot fully explain What information are we missing? Economic theories emphasize the role of hard-to-quantify beliefs and perceptions My job market paper develops algorithms + measurement to quantify perceptions of firms

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Reposted by Jeff Gortmaker

Shani Cohen @shanicn.bsky.social · 20/11/2024

I am on the job market, which seems like a great opportunity for my first post on here! My job market paper is about failures of contingent thinking -- the act of reasoning about hypothetical events. 1/

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Jeff Gortmaker @jeffgortmaker.com · 20/11/2024

I’m curious whether DeFi legality affects contributions. And time zone would for sure be a useful predictor! (It’s for sure at least somewhat predictive of country on GitHub.) Useful to hear that there’s probably no low hanging fruit for wallet geocoding - thanks.

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Jeff Gortmaker @jeffgortmaker.com · 19/11/2024

Legality of geocoding wallets? Makes sense. Best public data I could find from a quick search is a coarse proxy involving web traffic (documents1.worldbank.org/curated/en/7...).

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Jeff Gortmaker @jeffgortmaker.com · 19/11/2024

Thanks, Gina! Given your research, I'm curious if you think some comparable spillover stats could be measured for crypto OSS at the country level? E.g., comparing crypto GitHub contributions by country to transaction volumes for geocode-able (?) wallets?

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GH Archive is a project to record the public GitHub timeline, archive it, and make it easily accessible for further analysis.

Probably a lot of the questions are the same! A big thing that's changed is the increasing availability of incredible datasets like www.gharchive.org and innovationgraph.github.com

gharchive.org

GH Archive

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Jeff Gortmaker, Ph.D. candidate in Business Economics at Harvard University, specializing in industrial organization, with secondary interestes in econometrics and finance.

18/18 This has been a joy to write and present (not only because I get to use PyBLP screenshots to illustrate US-China collaboration). I'm excited to keep studying the tech sector from an IO perspective, and building econometric tools on the way. Links: jeffgortmaker.com

jeffgortmaker.com

Jeff Gortmaker

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17/ Subsidies seem more effective and generate large innovation spillovers, especially if the US responds in kind. Subsidies can be cheap because OSS investment is rare (weak private benefits), but impactful because OSS use is widespread (strong public benefits).

16/ What if China tightened its restrictions? Benefits don't seem localized enough for a "tax" on foreign collaboration to boost domestic OSS investment. Instead, lost spillovers raise web development costs globally.

15/ These estimates are key inputs into my policy simulations. Weak private benefits may justify subsidies that reduce underprovision, while strongly localized benefits may justify policies that coordinate domestic investment.

14/ Subject to the production function, firms' cost-minimizing choices reveal web development costs. Firms prefer OSS with more, own, and domestic investment. The model translates these patterns into public, private, and localized benefits of OSS investment.

13/ I estimate website quality's production function using timing assumptions. A weak link between OSS use and traffic translates into a weak effect of OSS use on quality, limiting the effect of OSS on competition and consumers in policy simulations.

12/ Consumers choose websites based on country, language, and quality. I get to use my own OSS PyBLP to estimate demand! I find strong "home bias," limiting international competition in policy simulations.

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11/ Investment grows codebases, a form of knowledge capital. Key externality: all firms can benefit from OSS. But benefits are uneven: firms contribute features that primarily help themselves (private benefits) and features in Chinese especially help China (localized benefits).

10/ I build an empirical model of private incentives to provide and use web OSS. Firms choose labor and software to maximize profits: revenue from quality production minus web developer costs.

9/ Using survey data, I convert OSS activity and LinkedIn employment into hours of OSS and in-house investment. While OSS investment is rare and concentrated, web OSS usage is widespread and dispersed.

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8/ I construct a panel of firms operating the world's top websites. First, I identify OSS used to build archived homepages of the top 10,000 websites (2014-2022). Then, I link web OSS → 200K GitHub contributors → employers on LinkedIn → back to these websites.

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7/ I focus on web development: a large industry employing about 13% of software developers. Web development is ideal because it lets us observe software inputs - key for a revealed preference approach.

6/ What if China tightened restrictions, increased subsidies, or the US responded? I build an empirical model to study the economic effects of these scenarios.

5/ China has also restricted its domestic platform. In May 2022, Gitee had to freeze all OSS projects for manual review. I collect Gitee data to document a sharp drop in activity after this censorship event.

4/ Chinese developers use VPNs to bypass internet restrictions. I show a growing "VPN gap" between my measure of Chinese GitHub activity (based on self-reported locations) and GitHub's Innovation Graph data (based on IPs).

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3/ Along with subsidies, China appears willing to restrict OSS contributions. I use censorship watchdog data to show that in 2021, China made it harder for domestic developers to access GitHub directly.

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2/ I build an empirical model to quantify the global effects of China expanding its OSS policies (and a US response). Before the model, what's going on in China? Most OSS collaboration happens on GitHub. Less-known is its Chinese counterpart, Gitee, state-backed since 2020.

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