WSJ: “Politicians and the press mislead voters and readers when they claim that tax cuts for the rich don’t benefit other economic classes. We all gain from new, improved products made possible by innovative startups funded by the wealthy. Excessive taxation, doubtless a feature of a “middle-out” plan, could deplete the funds that entrepreneurs use to start and sustain useful ventures. Americans shouldn’t worry so much about wealth distribution. Instead, we should be grateful for how the wealthy enable entrepreneurial ideas to come to life, allowing everyone to prosper.”
Noah Smith: “[Here is] a theory of how totalitarianism might naturally triumph. The basic idea is that when information is costly, liberal democracy wins because it gathers more and better information than closed societies, but when information is cheap, negative-sum information tournaments sap an increasingly large portion of a liberal society’s resources. Remember that I don’t believe this theory; I’m merely trying to formulate it.”
Ryan Bourne: “Price controls are making a comeback across the United States. Federal, state, and local governments increasingly control rents, minimum wages, interest rates on short-term loans, healthcare prices and premiums, credit card late fees and even food delivery service charges. The sharpest inflation burst since the early 1980s has also seen Democrats demand a federal anti-price gouging law, anti-”junk fees” rules, and even efforts to prevent companies engaging in “shrinkflation” or algorithmic dynamic pricing. I call all this The War on Prices, the title of a new Cato Institute book. And when it comes to this war, economists are typically pacifists. The long sweep of history, from ancient Egypt to modern America, shows us that price controls can’t quell inflation, because they don’t fundamentally change the money supply or aggregate production. What controls on market prices guarantee is inefficiency. They squelch the delicate coordination mechanism that prices and their movements provide to encourage economical action. Price ceilings, the most common incarnation, are thus a tried-and-tested recipe for shortages, declines in product quality, and black markets.”
Anirban Mahapatra: “A study published in Frontiers in Psychology recently found that students who took handwritten notes had higher levels of brain activity across the regions responsible for movement, vision, sensory processing, and memory. In contrast, typing led to minimal activity in these areas. Researchers Audrey van der Meer and Ruud van der Weel suggest handwriting forces students to process information more deeply. When you’re writing by hand, you can’t copy everything down verbatim, so you have to think as you write, unlike typing, where the temptation to transcribe lectures word-for-word is high. In addition, handwriting reinforces memory and learning pathways. The action of forming letters creates a feedback loop with our visual and sensory systems, embedding information more deeply in our brains. This process is like drawing or building something. In all these cases, the process helps strengthen the concept and makes it stick in our memory.”
VentureBeat: “Researchers from the University of Chicago have demonstrated that large language models (LLMs) can conduct financial statement analysis with accuracy rivaling and even surpassing that of professional analysts. The findings, published in a working paper titled “Financial Statement Analysis with Large Language Models,” could have major implications for the future of financial analysis and decision-making. The researchers tested the performance of GPT-4, a state-of-the-art LLM developed by OpenAI, on the task of analyzing corporate financial statements to predict future earnings growth. Remarkably, even when provided only with standardized, anonymized balance sheets, and income statements devoid of any textual context, GPT-4 was able to outperform human analysts. “We find that the prediction accuracy of the LLM is on par with the performance of a narrowly trained state-of-the-art ML model,” the authors write. “LLM prediction does not stem from its training memory. Instead, we find that the LLM generates useful narrative insights about a company’s future performance.””
