Maurice retweeted
Gideon Lewis-Kraus’s feature story “Sand Castles” in @NewYorker includes a remarkable statistic: in 2025, one in every 46 babies born in Taiwan was born to a family with a TSMC employee.
That sounded almost impossible, so I checked the math.
TSMC (VisEra included) had about 80,000 employees in Taiwan, roughly 0.54% of Taiwan’s population aged 20–64. Yet 2,331 of the 107,812 babies born in Taiwan, 2.16% of all births, were born to TSMC employee families.
So, mechanically, TSMC employees are overrepresented among the parents of newborns by a factor of 2.16/0.54, or about 4.
But this comparison is not quite right. TSMC has a very young workforce: the average employee is about 36, and almost 94% of employees are under 50. Comparing TSMC employees with everybody aged 20–64 is therefore not the right exercise.
Ideally, I would take the age and sex distribution of TSMC’s employees in Taiwan and apply Taiwan’s age- and sex-specific fertility rates. Unfortunately, the public corporate reports do not give me enough information to do this cleanly.
TSMC reports the sex composition of its Taiwan workforce, but the age-by-sex distribution I can find is for its worldwide workforce, not Taiwan alone. Moreover, the age categories are very coarse: 18–30, 31–50, and 51+. For fertility, whether an employee in the 31–50 group is 32 or 48 obviously matters enormously.
Thus, I do not think the data justify a precise age-adjusted estimate. But my best estimate is that TSMC employees in Taiwan have around 2.5 times more children than the average age-adjusted fertility, with a plausible range of roughly 2–3.5.
Of course, even a proper age adjustment would not identify a causal effect of working at TSMC. The selection problem is enormous. TSMC hires highly educated workers, pays them well, gives them unusually stable jobs, and provides generous family benefits. Finally, we have strong assortative mating in the marriage market.
In other words, the interesting exercise would condition not only on age and sex, but also on education, income, and marital status. My guess is that much of the remaining difference would disappear and that there is little causal effect of working for TSMC (in opposition to the large positive causal effect on fertility of having a high-income job with high education; yes, in most advanced economies those having more kids are the highly educated, high-income adults).
Still, the raw statistic is extraordinary: a company employing about half of one percent of Taiwan’s working-age population is connected to more than 2% of all the babies born in the country.
Maurice retweeted
I'm excited to share the dataset behind my book project: the Japanese Municipal Elections Dataset (JMED)!
👤 223,561 candidates
🗳️ 21,111 elections
🗾 2,027 municipalities
🗓️ 2005–2025
Explore the map or download the data: charlesmcclean.com/jmed/
Maurice retweeted
Recordings of the special lectures are now available on the official UTMD YouTube channel.
Special Lectures: “Lectures in Discrete Choice, Optimal Transport and Matching models” Professor Alfred Galichon (New York University)
youtube.com/playlist?list=PL…
Maurice retweeted
A badge of honor
The most intriguing part of the investigation readout on former Social Security Fund vice chairman Wang Wenling is the charge of privately bringing in and reading books with "serious political issues", rumored to be @DAcemogluMIT's "The Narrow Corridor".
Maurice retweeted
What does it mean for labor to become capital? Human capital in Becker’s sense is inalienable. A model trained on your expertise is nonrival and can be copied. More so, what the model learns from any one lawyer (or economist?) is really the knowledge of the whole profession. Each expert sells individually something the profession produced jointly — a huge collective-action problem.
This question came from Session 4 of our @UCBerkeley PhD reading group on the macro of AI: data ownership. We discussed "Nonrivalry and the Economics of Data" by @ChadJonesEcon and @ChrisTonetti, and "Labor as Capital: AI and the Ownership of Expertise" by @zoebcullen, @Danielle__Li and @ShengwuLi.
Today we kick off a PhD reading group at @UCBerkeley on Growth, Technology, and Innovation.
Sessions on innovation and automation, property rights, and knowledge in organizations — old classics and new work on AI, side by side, built around gaps where the next papers should be.
Here is the reading list for those who are interested.
Maurice retweeted
New working paper just dropped. #econtwitter
Maurice retweeted
X上の学振結果報告を調べると
学振:4599
学振 (採択 OR 採用):410
学振 (不採択 OR 不採用):279
であり、採用報告のほうが多い. ここから導き出される結論は「Xをやっている人は学振に採択されやすい」である.
博士に進学する人はXをやるべきである。
#データから導かれる間違った解釈
X上のポストを「科研費」で検索してみると,昨日からのポスト数が,
科研費:1836
科研費 採択:664
科研費 (不採択 OR 落ちた):306
なので,X上では採択の報告のほうが多い.
ここから導き出される結論は「Xをやっている人は科研費に採択されやすい」である.
#データから導かれる間違った解釈
chatgpt sucks at building econ model and estimation strategy, but somehow incredibly good at solving model numerically 🤔.
Maurice retweeted
I want to briefly explain why I joined Abhijit Banerjee, Peter Diamond, Esther Duflo, Paul Krugman and Joe Stiglitz in signing the letter on the California billionaire tax: ft.com/content/94ec0fee-53ab…
In principle, I am not convinced that permanent wealth taxes would be necessary if the tax-transfer system were designed optimally.
But the US tax system is very far from optimal, and has been for decades. As I have documented in my research (for example, here: mitsloan.mit.edu/shared/ods/…), labor income is taxed much more heavily than capital income.
This asymmetry creates two distinct problems.
First, it makes the tax system highly regressive at the top. The very rich, who receive much of their income from capital or can use accounting tricks to reclassify their income as capital income, pay remarkably little in taxes. For example, a business owner who runs their own company should receive a significant part of their income as labor earnings for their work as CEO. Instead, they can take their compensation in stock and borrow against those holdings to finance whatever consumption they desire, minimizing their tax obligations. Even their heirs may avoid paying these taxes.
Second, the asymmetry distorts automation decisions: it effectively subsidizes machinery and AI relative to hiring workers (for example, here: brookings.edu/articles/does-…).
These distortions have allowed a small number of people to amass vast fortunes without paying their fair share of taxes, and have fueled excessive automation.
The resulting inequality is a problem in its own right. It is all the more dangerous today because our institutions have become fragile, allowing the very wealthy to exert growing control over the political process.
A temporary wealth tax can therefore be justified on three grounds: (1) it partially reverses the effects of more than two decades of tax avoidance by the very wealthy; (2) it acts as a brake on their growing dominance over the political process; and (3) it may pave the way for more comprehensive tax reform at the federal level.
The California billionaire tax is not perfect. For example, a federal tax would lessen risks related to capital flight, and removing the rigid earmarking of the revenues for specific purposes would enable the proceeds to reduce the national debt. Nevertheless, with few other options on the table, I believe the California proposal deserves support.
Maurice retweeted
Fui ler o paper do NBER que tá sacudindo Singapura. Pesquisadores de Stanford e Columbia usaram IA pra varrer TODAS as transações imobiliárias do país entre 1995 e 2019 e cruzaram com o cadastro de 141 mil servidores públicos.
Funcionários compravam imóveis perto de futuras estações de metrô 1-2 anos ANTES do anúncio oficial. 60% acima da taxa normal. Não pagavam mais caro na compra, mas revendiam com retorno de 12% ao ano. Os parentes faziam a mesma coisa. S$270 milhões no total.
Pra descartar que fossem só mais espertos, compararam com corretores de imóveis e diretores de empresa. Nenhum dos dois mostrou o mesmo padrão. Rodaram 1.000 simulações aleatórias e o efeito real ficou totalmente fora da distribuição.
Quem mais operava? Gerentes médios do planejamento de transporte. Cargo suficiente pra saber, baixo o bastante pra ninguém olhar.
Em 2012, Singapura apertou a fiscalização com novas leis e punição de casos de alto escalão. O padrão sumiu, dos funcionários e dos parentes.
Singapura é top 5 em transparência no mundo. Norma social sozinha não segurou.
fonte: NBER WP 35756
Maurice retweeted
Deep learning can make high-dimensional dynamic equilibrium models tractable by learning useful state-space representations. The paper explains the methods and illustrates them in a standard growth model.
econometricsociety.org/publi…
瀧本さんおめでとう👏 – at Kyoto-shi Kamigyo-ku, Kyoto
定年間近の58歳、独学で京大合格 「進路気にせず勉強は楽しい」
mainichi.jp/articles/2026091…
役職定年を機に受験勉強を始め、58歳で京大生になった元銀行員の男性がいます。生活費をできるだけ切り詰めるため、学生寮の「吉田寮」に入寮し、卒業後は大学院に進みました。









































