Hanging the curtains back up

There were not a lot of successful female writers and academics in the 1970’s. Maybe I underestimate how many there were, but obviously they would have been in the minority. I’m reading a chapter on the anthropologist Mary Douglas who somehow combined raising three children with remaining active in academia. I read a few pages while helping at the Cub Scout camping trip.

In one of her books, Douglas added an apology for professional duties eclipsing domestic ones: ‘All our things have fallen into neglect while I have been writing, floors unpolished, curtains falling off hooks. I am grateful to my family for their patience.’

page 130 of The Slain God by Timothy Larsen

It is irksome to hear this woman apologizing for working
what is essentially two jobs and performing so well at each one. (I wouldn’t want
to put anyone off reading Larsen, who admires her very much.)

I had planned to do this a year ago, but then I ended up
writing papers on artificial intelligence and doing a bunch of related speaking
engagements. (I love it – anyone who wants a speaker on ChatGPT should invite
me out.) Anyway, I’m going to try to do the equivalent of fixing the
“curtains falling off hooks.” The curtains really do fall down. You
could have a well-functioning household and drawers full of clothes that fit
your children… and then if someone is not engaged in constant warfare… it
will all fall apart in about 6 months.



Notes on ChatGPT from Sama with Lex

This is a transcript of Lex Fridman Podcast #419 with Sam Altman 2. Sam Altman is (once again) the CEO of OpenAI and a leading figure in artificial intelligence. Two parts of the conversation stood out to me, and I don’t mean the gossip or the AGI predictions. The links in the transcript will take you to a YouTube video of the interview.

(00:53:22) You mentioned this collaboration. I’m not sure where the magic is, if it’s in here or if it’s in there or if it’s somewhere in between. I’m not sure. But one of the things that concerns me for knowledge task when I start with GPT is I’ll usually have to do fact checking after, like check that it didn’t come up with fake stuff. How do you figure that out that GPT can come up with fake stuff that sounds really convincing? So how do you ground it in truth?

Sam Altman(00:53:55) That’s obviously an area of intense interest for us. I think it’s going to get a lot better with upcoming versions, but we’ll have to continue to work on it and we’re not going to have it all solved this year.

Lex Fridman(00:54:07) Well the scary thing is, as it gets better, you’ll start not doing the fact checking more and more, right?

Sam Altman(00:54:15) I’m of two minds about that. I think people are much more sophisticated users of technology than we often give them credit for.

Lex Fridman(00:54:15) Sure.

Sam Altman(00:54:21) And people seem to really understand that GPT, any of these models hallucinate some of the time. And if it’s mission-critical, you got to check it.

Lex Fridman(00:54:27) Except journalists don’t seem to understand that. I’ve seen journalists half-assedly just using GPT-4. It’s-

Sam Altman(00:54:34) Of the long list of things I’d like to dunk on journalists for, this is not my top criticism of them.

As EWED readers know, I have a paper about ChatGPT hallucinations and a paper about ChatGPT fact-checking. Lex is concerned that fact-checking will stop if the quality of ChatGPT goes up, even though no one really expects the hallucination rate to go to zero. Sam takes the optimistic view that humans will use the tool well. I suppose that Altman generally holds the view that his creation is going to be used for good, on net. Or maybe he is just being a salesman who does not want to publicly dwell on the negative aspects of ChatGPT.

I also have written about the tech pipeline and what makes people shy away from computer programming.

Lex Fridman(01:29:53) That’s a weird feeling. Even with a programming, when you’re programming and you say something, or just the completion that GPT might do, it’s just such a good feeling when it got you, what you’re thinking about. And I look forward to getting you even better. On the programming front, looking out into the future, how much programming do you think humans will be doing 5, 10 years from now?

Sam Altman(01:30:19) I mean, a lot, but I think it’ll be in a very different shape. Maybe some people will program entirely in natural language.

Someday, the skills of a computer programmer might morph to be closer to the skills of a manager of humans, since LLMs were trained on human writing.

In my 2023 talk, I suggested that programming will get more fun because LLMs will do the tedious parts. I also suggest that parents should teach their kids to read instead of “code.”

The tedious coding tasks previously done by humans did “create jobs.” I am not worried about mass unemployment yet. We have so many problems to solve (see my growing to-do list for intelligence). There are big transitions coming up. Sama says GPT-5 will be a major step up. He claimed that one reason OpenAI keeps releasing intermediate models is to give humanity a heads up on what is coming down the line.

When Will the Fed Raise Rates?

Everyone else keeps asking when the Fed will cut rates, and yesterday Chair Powell said they will likely cut this year. Either they are all crazy or I am, because almost every indicator I see indicates we are still above the Fed’s inflation target of 2% and are likely to remain there without some change in policy. Ideally that change would be a tightening of fiscal policy, but since there’s no way Congress substantially cuts the deficit this year, responsibility falls to the Federal Reserve.

Source: https://fiscaldata.treasury.gov/americas-finance-guide/national-deficit/

Lets start with the direct measures of inflation: CPI is up 3.1% from a year ago. The Fed’s preferred measure, PCE, is up 2.4% from a year ago. Core PCE, which is more predictive of where inflation will be going forward, is up 2.8% over the past year. The TIPS spread indicates 2.4% annualized inflation over the next 5 years. The Fed’s own projections say that PCE and Core PCE won’t be back to 2.0% until 2026.

The labor market remains quite tight: the unemployment rate is 3.7%, payroll growth is strong (353,000 in January), and there are still substantially more job openings than there are unemployed workers. The chattering classes underrate this because they are in some of the few sectors, like software and journalism, where layoffs are actually rising. Real GDP growth is strong (3.2% last quarter), and nominal GDP growth is still well above its long-run trend, which is inflationary.

I do see a few contrary indicators: M2 is still down from a year ago (though only 1.4%, and it is up over the past 6 months). The Fed’s balance sheet continues to shrink, though it is still trillions above the pre-Covid level. Productivity rose 3.2% last quarter.

But overall I am still more worried about inflation than about a recession, as I was 6 months ago. Financial conditions have changed dramatically from a year ago, when the discussion was about bank runs and a near-certain recession. Today the financial headlines are about all time highs for Bitcoin, Gold, Japan, and US stocks, with an AI-fueled boom (bubble?) in tech pushing the valuation of a single company, Nvidia, above the combined valuation of the entire Chinese stock market. All of this screams inflation, though it could also indicate a recession in a year or so if the bubble pops.

At least over the past year I think fiscal policy is more responsible than monetary policy for persistent inflation. But I can’t see Congress doing a deficit-reducing grand bargain in an election year; the CBO projects the deficit will continue to run over 5% of GDP. That means our best chance for inflation to hit the target this year is for the Fed to tighten, or at least to not cut rates. If policy continues on its current inflationary path, our main hope is for a deus-ex-machina like a true tech-fueled productivity boom, or deflationary events abroad (recession in China?) lowering prices here.

How This Economist Cares for a Baby

I have four children, and all them were or are babies. As an economist, I know that becoming more productive includes contributions to labor, capital, and technology. Caring for and pacifying babies is no different. Here are some of my methods for pacifying and employing babies who are 4-18 months old.

Own a pacifier. You don’t need to use it or even force it into your baby’s mouth. But just have it around. Paul Romer said that we learn and innovate by interacting with capital. So, let’s get the capital.

Employ your baby’s labor. Children as small as 2 or 3 can go get the eggs from the hen house. But what about a smaller baby? Of course we need to stimulate, feed, water, change, and rest the baby. But sometimes, you just need them to be quiet. What to do? Babies respond to Pavlovian stimulus at a very early age. If they’re crying or even just somewhat bored, then place the pacifier in their hand and say, in a very low but normal voice, ‘pacifier’. Babies will instinctively put the pacifier in their mouth. If you have it clipped on, then eventually, they’ll be able to find it when they need it. Developing physical human capital takes work experience and time. I always insist that my older children place the pacifier in the baby’s hand rather than the baby’s mouth. Greater human capital will yield productivity gains.

There came a point when my baby would awaken at night. I wouldn’t even get out of bed. I’d just calmly, and dispassionately say ‘pacifier’. And our baby would pop the pacifier in their own mouth. Employ your baby’s labor. Innovation happens when you interact with capital.

In the same vein, I’d balance the baby bottle on my child’s front side, and place their hands on it. Next thing I knew, my baby was holding their own bottle earlier than the internet said that I should expect them to. Those little hands aren’t useless. They’re low marginal product labor just waiting to be employed. Given that home production is a team effort and labors have interaction effects, that small marginal product for the baby frees your labor to have a larger marginal product for the household. Take advantage of interaction effects, specialization, and comparative advantage.

How do you produce sleep in a baby? Let’s examine the production function. It typically includes: warmth, a clean diaper, darkness, a full belly, maybe some motion, and a lack of disruptive noise. Once the baby is asleep, you really only need the warmth, darkness, and peaceful noise. Leverage your capital to make yourself more productive. Capital may not be able to replace you in helping your baby fall asleep. But it can replace you to help keep them asleep. Repurpose your current stock of capital. If only there was a warm, dark, white noise chamber in your house already. There is. It’s called a bathroom. Get your infant to fall asleep, then put them in the dark bathroom with the fan on. Now you can grade your papers, clean the house, or write your articles.

Addendum on diaper changing:

When it comes to changing a diaper, you should act like you have a low discount rate. That is, you should bear the cost of preparing a changing space so that your future self is thankful. This means preparing the changing pad, opening the new diaper, unfolding the wipes, preparing for diaper disposal, and preparing any new clothes. This makes the diaper changing process much easier and mitigates stochastic costs like leaks, mid-change accidents, etc. Further, your MPL is lower when you have to mind a baby who’s on an elevated surface. Employ your labor when it’s more productive – before you lay them down.

Do you have a baby who fights or cries during diaper changes? Take a hint from the Fed and engage in forward guidance. Did you know that if you blow in a baby’s face, that they instinctively close their eyes and mouth and stop flailing? Early on this can act as a reset and interrupt crying. As a baby gets older, they’ll learn to anticipate the blown air. But only if you build your reputation.

When my 12 month old would start to fight, I’d audibly inhale. My baby would immediately stop fighting and clothes her eyes and mouth, and stop flailing in preparation of me blowing in her face. That’s called forward guidance. Building a reputation of action means that signaling action is often just as good as the act itself. But be careful, if you always blow in their face, they grow accustomed to it due to expectations augmented responses. So, I introduce stochastic bluffs wherein I audibly inhale, but then neglect to blow in their face. Stimulus only works repeatedly if you can violate their expectations.

Stay tuned for more economist parenting tips.

Pistol Squats Complete the Home Workout

A good strength workout includes a push, a pull, and legs. When I can get to the gym I like to alternate bench press and incline press for the push; rows and pulldowns for the pull; and squats and deadlifts for the legs. But with a baby to take care of at home, its been hard to find time for the gym. Between driving, waiting for equipment, and the actual lifts, the gym takes an hour. Doing a similar workout at home can take just 10 minutes, and has the advantage that you can watch a baby while doing it.

But the big challenge with home workouts was finding a good leg exercise. Pushes are easy: just do pushups. Pulls are pretty easy: just buy a $15 pullup bar to hang over a door. But how to do a good leg workout without costly barbells and plates that take up lots of space? Enter the pistol squat.

The idea is simply to start from a stand and lower yourself down almost to the ground on a single leg, then come back up on one leg, with the other leg out front for balance:

Source: Snapshot from this video, which shows how to do the standard pistol plus many variations

I find this to be about as difficult as doing a traditional two-legged barbell squat with 1x bodyweight on the bar. The traditional squat has two legs lifting 2x bodyweight (your body itself, plus 1x bodyweight on the bar); the pistol squat has one leg lifting 1x bodyweight (just your body itself), which is about equal. This was perfect for me because I was doing about 3 sets of 5 reps of squats with 1x bodyweight on the bar, so I just do the same number of pistol squats. But what if you’re not exactly at that weight?

Going lighter is easy– just put one hand on something sturdy nearby like a table and lean on it until it takes enough of your weight that you can do the squat. This helps with balance too if that is an issue. Going heavier is harder, but you could carry something heavy in your hands, turn the rise into more of an explosive jump, or just do more reps.

I’d still rather be at the gym, but the complete home workout seems like a good application of the Pareto Principle– you get most of the benefits of the gym while paying only a small fraction of its time and money costs.

Supply & Demand, With gifs

I’ve discussed the ways to teach supply and demand in the past. Regardless, almost all principles of economics classes require a book. But even digital books are often just intangible versions of the hard copy. Supply and demand are illustrated as static pictures, using arrows and labels to do the leg-work of introducing exogenous changes. There’s often a text block with further explanation, but it lacks the kind of multi-sensory explanation that one gets while in a class.

In a class, the instructor can gesticulate and vary their speech explain the model, all while drawing a graph. That’s fundamentally different from reading a book. Studying a book requires the student to repeatedly glance between the words and the graph and to identify the appropriate part of the graph that is relevant to the explanation. For new or confused students, connected the words to one of many parts of a graph is the point of failure.

This is part of why the Marginal Revolution University videos do well. They’re well produced, with context and audio-overlaid video of graphs. It’s pretty close to the in-person experience sans the ability to ask questions, but includes the additional ability to rewind, repeat, adjust the speed, display captions, and share.

Continue reading

National Health Expenditure Accounts Historical State Data: Cleaned, Merged, Inflation Adjusted

The government continues to be great at collecting data but not so good at sharing it in easy-to-use ways. That’s why I’ve been on a quest to highlight when independent researchers clean up government datasets and make them easier to use, and to clean up such datasets myself when I see no one else doing it; see previous posts on State Life Expectancy Data and the Behavioral Risk Factor Surveillance System.

Today I want to share an improved version of the National Health Expenditure Accounts Historical State Data.

National Health Expenditure Accounts Historical State Data: The original data from the Centers for Medicare and Medicaid Services on health spending by state and type of provider are actually pretty good as government datasets go: they offer all years (1980-2020) together in a reasonable format (CSV). But it comes in separate files for overall spending, Medicare spending, and Medicaid spending; I merge the variables from all 3 into a single file, transform it from a “wide format” to a “long format” that is easier to analyze in Stata, and in the “enhanced” version I offer inflation-adjusted versions of all spending variables. Excel and Stata versions of these files, together with the code I used to generate them, are here.

A warning to everyone using the data, since it messed me up for a while: in the documentation provided by CMMS, Table 3 provides incorrect codes for most variables. I emailed them about this but who knows when it will get fixed. My version of the data should be correct now, but please let me know if you find otherwise. You can find several other improved datasets, from myself and others, on my data page.

Joy’s Fashion Globalization Article with Cato

I am published by Cato this week:

Fast Fashion, Global Trade, and Sustainable Abundance

This is part of a 10-part series called “Defending Globalization: Society and Culture

Imagine trying to explain the world today to a person who time traveled forward from 300 years ago. How could someone who lived in France in the year 1600 understand our modern problems?

Person from the Past: So, how is it with 8 billion people?

Me Today: It’s bad. We have too many clothes.

PftP: Right. With 8 billion you wouldn’t have enough clothes for everyone.

MT: Too many.

PftP: Not enough?

MT: I said we have TOO MANY clothes. Not even the poorest people in the world want them. Shirts pile up on the beaches and pollute the ocean.

PftP: …

My article highlights the fact that we live in an era of unprecedented clothing abundance. First, that was not always true.

Most of human history has been characterized by privation and low‐​productivity toil. As one American sharecropper exclaimed in John Steinbeck’s Depression‐​era novel The Grapes of Wrath, “We got no clothes, torn an’ ragged. If all the neighbors weren’t the same, we’d be ashamed to go to meeting.”

https://www.cato.org/publications/globalization-fashion

Secondly, not everyone is celebrating.

The United Nations Economic Commission for Europe called the fashion industry an “environmental and social emergency” because clothing production has roughly doubled since the year 2000. Their main concerns are fast fashion’s environmental impact and working conditions. 

Some of my article is a response to the critics of modern low-cost mass production.

Thirdly, I explain how we could keep most of the benefits of cheap clothes with less litter in the environment. The item I am most optimistic about is using our new artificial intelligence tools to re-sort the world’s junk. We would produce and throw away fewer clothes if we had a better system for rearranging the stock of goods that we already have. The problem I see today is that I have “perfectly good” clothes in my house that I don’t really want; however, attention and time are so scarce that no one will pay me for them. Even if I donate them, I worry that half will end up in the trash. Someone on this earth could use them but identifying that someone and making the trade still has high prohibitively high transaction costs. Very smart AI could come to my house and scan my stuff and pay me for it because very smart AI could get it to someone with a positive value for it.

If you’d like to see a trail of blogs that I wrote while in the research phase for this article, use https://economistwritingeveryday.com/?s=fashion

Lastly, we thank Tyler for the Marginal Revolution link.

Malinvestment Produces Knowledge

Austrian economists rightfully have some gripes about mainstream macroeconomics – specifically about aggregation. The conventional wisdom says that a fall in output can be prevented or remedied in the short-run by an expansion of total spending (via increasing the money supply). Total output is stabilized and the crisis is averted. Even if rising spending preceded the output decline, the standard prescription is the same.

The Austrian Business Cycle theory says that, actually, the prior expansion in spending resulted in yet-to-be-realized poor investments due to easy credit. The decline in output is self-inflicted by unsustainable endeavors, and the money supply expansion response prevents the correction. The consequence is more malinvestment. The Austrians say that the focus on gross investment is a misleading aggregation and commits the fallacy of composition that all investment is the same or the same on relevant margins.

Both schools of thought are on firm ground. I don’t see them as conflicting. They both make valid points and are correct about the world. The conventional wisdom is able to paper-over short-run hiccups, and the Austrians recognize that resources are suboptimally allocated. The two sides are talking past each other to some extent.

The market process of seeking profits and satisfying consumer demands is a messy process. Prices and profits (and losses) incentivize firms with information that they use to adjust their behavior. They innovate and reallocate resources from bad projects and toward money-making projects. When firms earn negative profits (a loss) they learn that their understanding of the world was wrong and that they malinvested their scarce resources. Therefore, malinvestment is a standard and *necessary* part of the market process of identifying and serving the changing and unknown demands of individuals. Without malinvestment we lack the necessary information to distinguish success from failure.

Mal-investment is harmful insofar as it represents resources that were invested such that future output did not rise as it could have otherwise. So, while malinvestment is necessary to the market process, a preponderance of it makes us poorer in the future. Luckily, firms have incentives and finite resources such that mal-investment remains somewhat tamed. Indeed, malinvestment is the cost that we bear for innovation and identifying what works.

The issue is that the above discussion is oriented to the long-run. The conventional wisdom is oriented toward resolving the short-run threats. The two meet one another when malinvestment realizations occur in a correlated manner. It’s not that policy causes malinvestment. Rather, depressed interest rates and easy credit prevent firms from identifying which of their projects turned out to be more or less productive. Firms persist in bad investments because they can’t discriminate between the failed and successful projects ex ante.

So, when interest rates suddenly rise, low or negative productivity projects are identified and resources are reallocated. The discovery and reallocation process takes time. And if many projects are found to be failures at once, then the result is a drop in economic activity that is detectable at the aggregate level. The problem is not that malinvestment exists. The problem is that malinvestment was permitted to persist and grow such that the eventual realization of losses is correlated and has macroeconomic effects. We observe spending, output, and employment declines. That’s the ‘business cycle’ part of the Austrian Business Cycle. Interest rates rising helps to identify the bad projects. That’s good. But policy that increases the popularity of bad projects is bad. It makes us poorer in the long-run and more vulnerable to declines in the short-run.

Video for new ChatGPT users

Have you not gotten around to trying ChatGPT for yourself yet?

Ethan and Lilach Mollick have released a series of YouTube videos that encapsulate some current insights, aimed at beginners, posted on Aug. 1, 2023. It covers ChatGPT, Bing, and Bard. Everyday free users are using these tools.

Practical AI for Instructors and Students Part 2: Large Language Models (LLMs)

If you are already using ChatGPT, then this video will probably feel too slow. However, they do have some tips that amateurs could learn from even if they have already experimented. E. Mollick says of LLMs “they are not sentient,” but it might be helpful to treat them as if they are. He also recommends thinking of ChatGPT like an “intern” which is also how Mike formulated his suggestion back in April.

  • I used GPT-3.5 a few times this week for routine work tasks. I am not a heavy user, but if any of our readers are still on the fence, I’d encourage you to watch this video and give it a try. Be a “complement” to ChatGPT.
  • I’ll be posting new updates about my own ChatGPT research soon – the errors paper and also a new survey on trust in AI.
  • I hear regular complaints from my colleagues all over the country about poor attempts by college students to get GPT to do their course work. The experiment is being run.
  • Ethan Mollick has been a good Twitter(X) follow for the past year, if you want to keep up with the evolution and study of Large Language Models. https://twitter.com/emollick/status/1709379365883019525
  • Scott wrote this great recent tutorial on the theory behind the tools: Generative AI Nano-Tutorial
  • It was only back in December 2023 that I did a live ChatGPT demonstration in class, and figured that I was giving my students there first ever look at LLMs. Today, I’d assume that all my students have tried it for themselves.
  • In my paper on who will train for tech jobs, I conclude that the labor supply of programmers would increase if more people enjoyed the work. LLMs might make tech jobs less tedious and therefore more fun. If labor supply shifts out, then quantity should increase and wages should fall – good news for innovative businesses.