米国会議員を対象とした研究でも、娘をもつと支持する政策に変化が出ることが知られている。このテーマでは外せない文献。 Washington, Ebonya L. 2008. "Female Socialization: How Daughters Affect Their Legislator Fathers' Voting on Women's Issues." American Economic Review 98 (1): 311–32.

A recent video presentation of "AI and Our Economic Future" from the Stanford GSB Alumni Reunion (i.e. for a broad audience): youtube.com/watch?v=xBpGn3BD…

In 2001, Thailand's government unexpectedly gave every village 1 Million Baht (about $25,000) to form a village bank. The results were puzzling. The price of credit didn't change, and neither did investment. Instead, consumption rose dollar for dollar. What? Why? How? 1/

ยืนขายก๋วยเตี๋ยวอยู่ดีๆก็มีสามี 😭😭 ยินดีด้วยค่ะ

I often get asked by parents, what does my kid need to achieve to earn a place at UC Berkeley? The answer is, I don’t know. There used to be an implicit social contract that if a California resident excels in school and is a very high achiever, there would be a predictable path to one of the leading campuses of the University of California system. Until the early-2000s this was explicit in that between 50-75% of incoming classes had to be admitted on the basis of academic criteria alone (GPA, SATs, APs). A high school student basically knew what they had to hit to get admitted to Cal. UCs no longer considers SAT/ACT scores, and letters of recommendation are generally not part of the application. The remaining academic record is heavily based on grades, course-taking, "school context", supplemented by personal insight questions (in the AI era!) and activities. But in an era of substantial grade inflation, transcripts have become a noisy signal. True academic achievement matters much less than it used to. The result is, while not literally random, highly unpredictable and illegible admissions. For California families, it is no longer clear what level of achievement is enough for their kids, or even what kind of achievement the system is trying to reward. I suspect that is not what most families in California want.

🔥🔥 คนที่เดินทางมาเที่ยวอเมริกาแล้วอยากมาเที่ยวอุทยานแห่งชาติหรือ National Park ของที่นี่พึงรู่ไว้ว่าตอนนี้ค่าเข้ามันไม่เหมือนเดิมแล้วนะครับ ในอุทยานแห่งชาติที่มีชื่อเสียงดังในลิสต์ 11 แห่ง นักท่องเที่ยวจะถูกตรวจบัตรตอนเข้า ถ้าเป็นชาวต่างชาติอายุเกิน 16 ปี จะโดนเก็บค่าเข้าเพิ่มจากค่าเข้าปกติอีกคนละ 100 USD ย้ำนะครับว่าต่อคน เช่นถ้าค่าเข้ารถต่อคันคือ 35 USD แล้วมีคนไทยมาเที่ยวในรถ 4 คน (อายุเกิน 16 ปี) เราจะต้องจ่ายทันที 435 USD หรือ 14,300 บาท ถ้าอยากประหยัดหน่อยต้องซื้อ America the Beautiful Annual Park ราคา 250 USD ที่รวมค่ารถที่ขับเข้าชมและชาวต่างชาติที่อายุมากกว่า 16 ปี 4 คน (อายุน้อยกว่า 16 ปี ไม่ต้องเสีย) และบัตรมีอายุ 1 ปี ราคาค่าเข้าโหดมาก วางแผนกันให้ดีนะครับ โดยเฉพาะถ้าคนจะไป Grand Canyon หรือ YellowStone หลายๆคน โดนหนักกันแน่นอน #USnationalpark

刚看了一段伯克利工程学院的毕业典礼演讲，是今年看过最打动人的一段视频 演讲者收到伯克利博士录取通知的时候，96岁的奶奶跟她说："我很为你骄傲，但我不想让你走。因为我不知道我还能不能再等五年，看到你博士毕业。" 她说从那一刻开始，她一直在想一个问题：一个PhD的重量到底是什么？ 不只是论文和答辩。是导师的指导，是实验室里无数个孤独的夜晚，是家人替你承担的那些你看不到的代价 她的奶奶成长在动荡年代，连小学都没读完。但今天她坐在台下，看着孙女成为博士 演讲里有一段话我反复听了几遍：工程是人类最古老的职业之一，从石器到蒸汽机到今天的AI。我们有建造和定义时代的力量，但每个系统背后都是真实的人生，每个决定都有重量 现在AI这么热，大家都在聊模型多强、效率多高。但偶尔需要有人提醒一下：技术最终是服务于人的，不是反过来 她最后说，她设计传感器的时候，总会想象奶奶生活在这个技术世界里

I feel so seen

最近在带入组的本科实习生，发现怎么读论文其实是科研训练里最容易被忽略的一步。 推荐一篇每个科研新人都该读的经典短文：S. Keshav 的 How to Read a Paper。 文章提出了非常实用的“三遍读论文法”： 第一遍，5 到 10 分钟快速扫读：标题、摘要、引言、章节标题、结论和参考文献。 目标是回答 5C： Category, Context, Correctness, Contributions, Clarity。 也就是判断这篇论文是什么、和谁相关、假设是否合理、贡献是什么、写得清不清楚。 第二遍，认真读论文主线，但先跳过证明细节。重点看图表、实验设置、结果是否清楚、引用了哪些关键工作。 第三遍才进入深度理解：尝试像复现一样重建作者的思路，检查假设、方法、创新点和潜在漏洞。 放在今天看，这个方法和 AI 辅助读论文其实很契合。 第一遍可以让 AI 帮忙快速总结论文的研究问题、核心贡献和主要结论，但自己一定要判断这篇文章是否真的值得继续读。 第二遍可以让 AI 帮忙解释方法、实验设置、图表和不熟悉的概念，但不能只看 AI 总结。关键图表、实验设计和结果数字一定要回到原文核对。 第三遍可以让 AI 扮演 reviewer，帮你追问：这篇文章的假设是否成立？实验是否支持结论？有没有 missing baseline？有没有潜在的数据泄漏、评价偏差或过度 claim？ 读论文不是“读完”就行。真正重要的是知道什么时候快速跳过，什么时候认真理解。 尤其在 AI 工具越来越强的情况下，科研新人更需要训练自己的判断力。 AI 可以帮你压缩信息，但不能替你决定一篇论文是否重要、是否可信、是否值得借鉴。 web.stanford.edu/class/ee384…

Very disappointed to see that @AramHur's latest paper in @koreaobserver has six completely fabricated citations. Academic integrity matters, people. A short 🧵

Recently accepted by #QJE: “A Cognitive Theory of Reasoning and Choice,” by Bordalo, Gennaioli, Lanzani, and Shleifer: doi.org/10.1093/qje/qjag023

I will begin as a co-editor at Econometrica, beginning in July 2027. I'm so honored to take on the job and commit to do the best possible !

I'm delighted that this short survey, a labor of love, is forthcoming in Notices of the AMS later this month. I'll post a few threads over the next few weeks. Today: why do eigenvectors keep showing up in models of networks and influence?! 1/

This is cool. I had the same question, but I could not find a county-level measure, so I used the state-level Google Search Volume Index (GSVI) instead.

Pleased to announce that Eitan Sapiro-Gheiler and I have created the Comprehensive Courts of Appeals Database, which extends the Songer Database to the universe of published Courts of Appeals cases between 1892 and 2025. coadata.org/data/

Had the pleasure of attending the "Recent Advances in Money & Macro" workshop in Paris. My paper: "Clash of the Titans: Strategic Deindustrialization and Industrial Policy Games" joint with @roozbeh52. Thanks to Mariana, Regis and @ecovibi for organizing!

This is not synthetic controls it is SDID (left) with cluster bootstrap standard errors. It should not suffer from the classic synthetic control problem of “misleadingly beautiful” graphs even for placebos. Coverage is correct under relevant assumptions.

Is it the phones? A new paper uses AT&T's 2007–2011 iPhone monopoly as a natural experiment and finds that iPhone access sharply reduced births, especially among younger women. Smartphones may explain 33–52% of the post-2007 decline in U.S. fertility.

A beautiful example of an "optimal stopping problem" – Feynman's restaurant problem – with a great backstory behind it. This is a fun, well written article, and a fun math problem too. doi.org/10.1073/pnas.2509612…

One of my favourites