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Slides of the #AOM2025 #PatentPDW (and of previous Patent PDWs) are available at: patentpdw.wordpress.com/workshops/.
Thank you all for participating.@ThomasSchaper
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Mingtao Xu
2.2K posts
Mingtao Xu
@MtX9
A professor @Tsinghua_Uni @SEM_Tsinghua. #Strategy, #Innovation, #PropertyRights, #IPR #AI. All views are my own. [email protected]
Katılım Kasım 2012
698 Takip Edilen841 Takipçiler
Mingtao Xu retweetledi
📢 New Research with @SoyeonKang_PhD in @JAMA_current: Early-stage drug development worldwide has nearly doubled over the past decade and is shifting from a U.S.-dominated model to a dual-hub model centered in the United States and China.
JAMA@JAMA_current
From 2015 to 2024, the number of early-stage drug development programs worldwide increased by more than 80%, with pronounced growth in China and a relative decline in the US and other high-income countries. 🧵 ja.ma/4rULaL2
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Mingtao Xu retweetledi
Evidence that AI models can, indeed, learn "taste" in this paper where a small model, trained on citations, is able to predict which papers will be hits
Citations, upvotes & shares are signals that can teach AI judgment about quality, not just execution. arxiv.org/pdf/2603.14473
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Mingtao Xu retweetledi
This paper just came out in the American Economic Review.
One of my favorite findings was that people who experienced more economic growth while growing up had less "zero-sum" attitudes as adults.
Crémieux@cremieuxrecueil
A new paper on correlates of zero-sum thinking just came out and it has everything: race, sex, politics, class, mobility, and even deep roots! Here are my favorite graphs from the paper. But first: how was zero-sum thinking measured? The answer is as a set of four questions on whether things are zero-sum with respect to ethnicity ("If one ethnic group becomes richer, this generally comes at the expense of other groups in the country"), citizenship ("If those without American citizenship do better economically, this will generally come at the expense of American citizens"), trade ("In international trade, if one country makes more money, then it is generally the case that the other country makes less money"), and income ("If one group becomes wealthier, it is usually the case that this comes at the expense of other groups"). Endorsement of these ideas is considered zero-sum thinking. Because these can feel political, you might think that would compromise the results. And true! Measurement invariance wasn't tested, but removing mechanically-related questions didn't seem to change this paper's findings much. Onto the graphs! The first one I liked was on the demographics of zero-sum thinking. It's a young and middle-aged person's game, but it's also a game for Hispanics and Blacks, but not Asians, for Democrats and not Republicans, for urbanites, somewhat for ruralites, and not as much for suburbanites, and there are U-shaped relationships with income and education. There are lots of findings in the break-downs of these categories, like that Democrats who voted for Trump were often highly zero-sum thinkers, or that zero-sum thinking is simultaneously related to - The belief that luck trounces effort - The perception that mobility is high - Universalist values - A belief in the importance of tradition - Generalized trust A second finding I found extremely interesting was that people who experienced more growth in the first twenty years of their lives had less zero-sum values. Because of the correlation between growth and zero-sum thinking over time and compositional changes that covary with those changes, it's important to do some post-stratification to see if this result really holds up. If it does, it has fascinating implications. The paper is really chock-full of fun facts, like that, globally, right-wingedness is related to less zero-sum thinking, but in some countries, the relationship is nullified or reversed. Another finding was that being anti-immigrant and pro-redistribution was related to zero-sum thinking among Democrats, and even more strongly, among Republicans. Yet another finding was that parental, grandparental, and great-grandparental mobility was negatively related to zero-sum thinking. A more immigrant-focused finding was that later-generation immigrants are closer to non-immigrant levels of zero-sum thinking. That is, they become more zero-sum! More likely there's selection at play, but regardless, immigrants are less zero-sum and this held up in the 2nd and 3rd generations, too. It was also found that county foreigner shares were unrelated to zero-sum thinking in respondent's generation or their parent's generation, but they were negatively related in their grandparent's generation. Another intergenerational transmission of values question had to do respondents' self-identification of having ancestors who experienced different bouts of slavery. The descendants of African slaves, Holocaust survivors, indentured servants, interned Japanese Americans, and enslaved Amerindians were more likely to be zero-sum thinkers. The same was not true for the descendants of prisoners of war. Unlike with immigration, the zero-sum correlates of enslavement seem more robust. For example, a person's county enslaved share in 1860, their parent's county enslaved share in 1860, and their grandparent's county enslaved share in 1860 all correlated with zero-sum sentiment. This remained true for people, their parents, and their grandparents if they moved out of the American South! Additionally, these findings also held true for each level when it came to county Confederate culture. In other words, the transmission of values, even with controls for demographics, state, and race was robust! This study paints a vivid picture of the correlates of zero-sum thinking in the present day, internationally, and with respect to their roots in the deep past. I definitely recommend reading it! Go check it out: nber.org/papers/w31688
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Mingtao Xu retweetledi
Mingtao Xu retweetledi
That’s exactly what we find in our paper! Firms that adopt GenAI reduce the number of junior employees, and this effect operates entirely through a decline in hiring rather than increased separations.
Link to draft: papers.ssrn.com/sol3/papers.cf…
Alex Imas@alexolegimas
My hypothesis is that AI will lead to less dramatic layoffs (e.g., the Block memo) and more a slower drip: companies will continue to fire at similar rates, but will hire at much slower rates. Those who are left will be expected to use AI tools--to figure it out--in order to pick up the slack from those who were let go. This will naturally lead to AI models being adopted effectively throughout organizations. But the labor market implications for the economy are in some ways more dire than the dramatic scenario: exactly the type of slow drip of increasing unemployment and lower labor force participation that policy has the hardest time dealing with (policy is much better when there is a clear demarcated disaster).
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Mingtao Xu retweetledi
If you don't have time to read the 113 page paper, you should at least read this thread by @ccatalini
Christian Catalini@ccatalini
1/ Some Simple Economics of AGI—🔥🧵 Right now, there is a low-grade panic running through the economy. Everyone is asking the same anxious question: what exactly is AI going to automate, and what will be left for us?
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Mingtao Xu retweetledi
Recently accepted by #QJE: “The Macroeconomic Impact of Climate Change: Global Versus Local Temperature,” by Bilal (@AdrienBilal) and Känzig (@drkaenzig): doi.org/10.1093/qje/qj…
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Mingtao Xu retweetledi
🤖 Can LLMs replicate expert text coding?
➡️ Using interest group documents, @dchoiresearch @DenisPeskoff & @b_m_stewart show that fine tuned LLMs outperform trained coders and zero shot models in identifying informative vs associative signals cambridge.org/core/journals/… #FirstView
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Mingtao Xu retweetledi
A lot of the AI productivity data either comes from controlled "micro" studies or noisy aggregate data.
A new paper presents data from huge survey of *firms*, i.e., CEO and CFOs. This is exactly the type of data many of us have been waiting for. Lots of important results both on current adoption/employment consequences of AI, and future forecasts.
Currently:
1. AI has some adoption across 70% of firms.
2. Some cross-country differences. US adoption towards top end (78%), Australia towards bottom (59%).
3. ~70% of executives use AI, but only around 1.5 hours a week.
4. Large majority of execs report essentially zero productivity boost from AI. Perhaps not super surprising given how recently it's been adopted.
5. Essentially zero impact on employment.
Forecasts (large effects):
1. Execs predict large productivity gains over next three years, more than 2% in US, closer to 1% in Germany, Australia.
2. Execs predict negative employment effects, eg -1.19% in the US.
3. Interestingly, Accommodations and Food/ Wholesale and Retail are expected to have largest drops in employment (2%)
4. Employment forecasts are becoming *more* negative over time.
Lots of great stuff in the paper, kudos to the team.
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Mingtao Xu retweetledi
Mingtao Xu retweetledi
Claude can make blue1brown animations in minutes.
Education is about to explode.
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Mingtao Xu retweetledi
The simplest way to visualize the set of numbers.
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Mingtao Xu retweetledi
Here is the geolocation of people using GitHub to code. Paper here: science.org/doi/10.1126/sc…
This research project (@johannes_wachs, @FrankNeffke, @simone_daniotti, @xiangnan_feng), is expanding in various directions. Geography is one of them. Data is 30 million+ GitHub commits
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Mingtao Xu retweetledi
Mingtao Xu retweetledi
Recently accepted by #QJE: “How Do You Identify a Good Manager?” by Weidmann, Vecci, Said, Bhalotra, Adhvaryu, Nyshadham, Tamayo, and Deming: doi.org/10.1093/qje/qj…
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Mingtao Xu retweetledi
🚨 We just released a new version of the professor salary benchmarking tool:
👉 prof-salary.com
Thanks to the amazing work of Chelsea Che (@ChelseaChe0203), this update includes major upgrades, outlined below.
#econtwitter #AcademicTwitter
🧵👇
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Mingtao Xu retweetledi
Mingtao Xu retweetledi
A meta-analysis of 168 studies covering more than 11 million people found no reliable link between economic inequality and well-being or mental health.
go.nature.com/49h47R4
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Mingtao Xu retweetledi
Scientists Who Use AI Become Stunningly More Productive
In a new paper that just appeared, the authors found that researchers who use large language models (LLMs) become stunningly more productive. An analysis of more than 2 million papers revealed that those who adopt AI increase their paper output on average by 40%. For non-native English speakers it’s even more, up to 80%.
The scary part is that this is data only until July 2024, when scientists used LLMs mostly for writing. It is foreseeable that in the next few years the AI-adoption rate will go to nearly 100% across all scientific disciplines, resulting in more papers than ever and putting significant strain on peer review.
Source: Kusumegi et al, Science 390, 6779, 1240 (2025)
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Mingtao Xu retweetledi
Remarkable (and consistent across time and countries) decrease in inequality in Latin America
(after-tax income per capita); @lisdata
Brazil from 62 in 1991 to 48 now (-14 Gini points)
Mexico from 55 in 1992 to 44 now (-11 Gini points)
Peru from 55 in 2005 to 44 now (-11 Gini points).
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