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.”
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.
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.
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.
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.
In standard microeconomics, the long-run demand is unimportant for the market price of a good. Firm competition, entry, and exit causes economic profits to be zero and the price to be equal to firms’ identical minimum average cost. This unreasonably assumes that they have constant technology. That is, they have a constant mix of productive inputs and practices.
Just so we’re clear: time is passing such that firms can enter, exit, and adjust the price – but no productive innovation occurs. For the modeling, we freeze time for technology, but not for other variables. The model ceases to reflect reality on the margin of scale-induced innovation. The standard model assumes an optimal quantity of production for each firm and the only way for total output to change is for there to be more or fewer firms. The model precludes adopting any different technology because firms are already producing at the minimum average cost – if they could produce more cheaply, then they would.
Enter Scale
One of my favorite details about production was taught to me by Robin Hanson.* Namely, that the scale of production isn’t merely with the aid of more raw materials, labor, and capital. There are perfectly well-known existing technologies and methods that reduce the average cost – if the firm could produce a large enough quantity. This helps to illustrate what counts are technology. A firm can achieve lower average costs without inventing anything, and merely by adopting a superficially different production method.
For-profit firms are well-oriented. The managers within firms may not make profit their only explicit priority, but it is pre-requisite to their other concerns. Without profits, firms eventually cease to exist. Non-profits are different. They might have revenues due to sales and operate much like a for-profit firm. But, they many times operate on revenue from donations and endowments. Because the success of non-profits is harder to measure, the signals of triumph and defeat do not orient the employees as clearly. The result can be that there is a lot of ruin in a non-profit. Plenty of tasks are done inefficiently, poorly, or not at all.
Mission-driven non-profits are able to attract enthusiastic, dedicated employees given the pay that they offer. But, supporting the mission of such an organization often acts as an implicit “belief test”, filtering out other would-be job applicants who self-select out of applying to open positions for which they are otherwise qualified. Indeed, part of the purpose of mission statements is to filter for the kind of employees that the organization managers or donors desire. While the employees may be enthusiastic and dedicated to the mission, that is mostly separate from whether they have the technical skills to flourish in their position and to effectively serve the organization.
I like a good lump sum tax. People *must* pay the tax without exception and the advantage over current progressive marginal income taxes is that the marginal wage received doesn’t fall with greater earnings. Employment rises and output rises. To the extent that college students fail to understand their student loans, the indebted graduates essentially pay a lump sum tax each period.
Of course, the exception is income based repayment (IBR) – especially with forgiveness after X years. IBR adjusts the incentives substantially. Under the standard system, your wages are garnished if you fail to make loan payments. Under IBR, lower earnings trigger lower monthly payments. Clearly, in contrast to the standard method, IBR incentivizes more leisure, less income, more black market activity, and higher loan balances. Indeed, all the more so if there is a forgiveness horizon. Someone just has to have low enough income for say 15 years, and their past debt is forgiven (with caveats & conditions).
My principal objection to IBR policy is the resulting malinvestment in human capital. Defaulting on loans is a sign that some investment was inadequately productive to repay the resources consumed by its endeavor. We call that a loss. Real resources of time, attention, and goods and services were consumed in order to produce capital that failed to serve others more than the opportunity cost of those resources.
Incentives matter. I’ve taught at both public and private universities, and students have given me both great course evaluations and less great student evaluations. The private university cared a lot more about them. Obviously, some parts of student evaluations of their instructors are beyond the instructor’s control. The instructor can’t control inalienables and may not be able to change their charisma. But what about the things that instructors can control? Regardless of your current evals, here are 5 policies that are guaranteed to improve your course evaluations.
1: Very Clear Expectations/Schedule
Have all deadlines determined by the time that the semester starts. Students are busy people and they appreciate the ability to optimally plan their time. Relatedly, students desire respect from their instructor. Having clear rubrics and deadlines helps students know your expectations and how to meet them – or at least understand how they failed to meet them. Students want to feel like they were told the rules of the game ahead of time. This means no arbitrary deductions or deadlines. The syllabus is a contract if you treat it like one.
2: Mid-Semester Evaluations
One of the absolute best ways to improve your evaluation is to ask your evaluators for a performance update. Make a copy of your end-of-semester course evaluation and issue it about halfway through the semester. Then, summarize the feedback and review it with your class. This achieves three goals. (1) It is an opportunity to clarify policy if there are misplaced complaints. You may also wish to explain why policy is what it is. Knowing a good reason makes students more amenable to policies that they otherwise don’t prefer. (2) It provides voice to students who have things to say. Often, students want to be heard and acknowledged. It’s better that a student vents during the informal mid-semester survey than on the important one at the conclusion of the course. (3) If there are widespread issues with your course, then make changes. If you’re on the fence about something, then take a poll. And if you decide to make changes, then be graciously upfront about it. Unexplained or covert changes violate policy #1.
Lot’s of economists use FRED – that’s Federal Reserve Economic Data for the uninitiated. It’s super easy to use for basic queries, data transformations, graphs, and even maps. Downloading a single data series or even the same series for multiple geographic locations is also easy. But downloading distinct data series can be a hassle.
I’ve written previously about how the Excel add-on makes getting data more convenient. One of the problems with the Excel add-on is that locating the appropriate series can be difficult – I recommend using the FRED website to query data and then use the Excel add-on to obtain it. One major flaw is how the data is formatted in excel. A separate column of dates is downloaded for each series and the same dates aren’t aligned with one another. Further, re-downloading the data with small changes is almost impossible.
Only recently have I realized that there is an alternative that is better still! Stata has access to the FRED API and can import data sets directly in to its memory. There are no redundant date variables and the observations are all aligned by date.
At a Chinese restaurant, I got a fortune that said, “Success is in starting a new project at work.” It struck me as very funny, and it resonates with other people on Twitter.
Starting a new project at work does not translate to success in academia. The danger is usually in starting too many projects and finishing too few.
Starting a new research project, whether alone or with coauthors, is exciting. You fall in love with a new idea.
The hard part is sticking with that idea until the very end of the publication process. This is more comparable to staying married. The project will see you at your worst, and you will discover that the project is not as wonderful as it seemed initially. You might end up re-writing the manuscript several times, years after the initial infatuation has worn off.
Academics do need to start projects. It is important to start the right projects. A reason to not start too many projects is to preserve time for the best work. A downside to being overloaded is that you might have to say no to a new project when an actual good opportunity comes along.
In my post on the Beatles documentary Get Back, I observed the way that the bandmates start new songs together. It reminded me of coauthors convincing each other to start a new project.
Their creative process resembles co-authoring a research paper. When Paul is working out a song and humming through places he hasn’t worked the lyrics out yet, that reminds me of the early drafts of a paper. You don’t have to have the whole Introduction written. The hook of a song is a bit like the main result of a research paper. Persuading yourself and your coauthor that you have a project worth finishing is the first step. Coauthors have unspoken agreements on how the project is going to proceed. The tacit knowledge of the collaborative process is one of the most important things you can learn in graduate school.
This quote from Rules of Thumb was surprising: “None of this is part of a grand plan. At any moment, I work on whatever then interests me most. Coming up with ideas is the hardest and least controllable part of the research process. It is somewhat easier if you have broad interests.” He goes on to say: “I sometimes fear that because I work in so many different areas, each line of work is more superficial than it otherwise would be. Careful choice of co-authors can solve this problem to some extent, but not completely.”
He really refutes my fortune cookie with this line, “Deciding which research projects to pursue is the most difficult problem I face in allocating my time.” Success is about starting the right projects and no others.
I just found out I’ll be receiving a Course Buyout Grant from the Institute for Humane Studies. It will allow me to teach less next year in order to focus on my research on how Certificate of Need laws affect health care workers.
I’m happy about this because I think this research is valuable and time is my main constraint on doing it (especially doing it quickly enough to inform ongoing policy debates in several states). But I’m also happy because I finally got what I consider to be a “true” grant after many rejections.
I’ve received research funding many times before (e.g. Center for Open Science funding for replications), but it was always relatively small amounts that went directly to me. True grants tend to be larger and to be paid directly to the university. That’s the case with the course buyout grant, which essentially pays the university enough that they can hire someone else to teach my class.
I may have lost count but I’m pretty sure this was the 13th “true grant” I have applied for, and the 1st I will actually receive. Academics have to get used to rejection, since we need to publish and decent journals tend to reject 80%+ of the articles they receive. But for some reason I’ve found grants much harder even than that. From some combination of skill, luck, and targeting lower-tier journals than perhaps I could/should, my acceptance rate for journal articles is probably nearing 50%. I expected this to translate over to grants but it absolutely did not, they seem to be a much different ballgame, one I’m still figuring out.
I’d like to share some of those past misses, both to let junior people see the bumpy road behind success (like a CV of failures), and to try to extract lessons from an admittedly small sample. These proposals were not funded, and probably weren’t even close:
Peterson Foundation US 2050
MacArthur Foundation 100 & Change
RI INBRE (2x)
National Institute for Health Care Management (1x, waiting to hear but probably about to be 2x)
What did these failures of mine all have in common? Me, of course. This is not just a truism; in most of these cases I was applying for major grants solo as an assistant professor without previous funding. The usual advice is to work your way up with smaller grants or, preferably, as the collaborator of a senior professor with lots of previous funding who knows how things work. I knew that would be smart but I’ve tended to be at institutions without senior people in similar fields; almost all my research has either been solo or coauthored with students or assistant professors. Even my PhD advisor was a brand-new assistant professor when we started working together. I had good reasons for ignoring the usual advice to work with well-known seniors, and it has mostly served me well, but grants seem to be the exception.
Twice, I think I did come close on grant proposals, and both times it involved help from seniors at other institutions who had lots of previous funding. At one foundation that funds a lot of social science, my senior coauthor and I got glowing external reviews, but the internal committee rejected us on the grounds that we could do the project without their funding. They were right in the sense that we did do project anyway with no funding; it got published and even won a best paper award. But with their funding we would have done it faster and better and they would have gotten credit for it.
I do think it is smart for funders to consider whether the research would happen anyway without them, or whether their funding really improves things. But I think it is rare for funders to actually do this, and taking this rejection as advice probably led me to more rejections. I tried to propose bigger, more ambitious projects that needed expensive data so it was clear that I really needed the funding; but for most funders this probably made things worse. I have since heard several times that people who get lots of funding from major funders like NIH tend to submit proposals for research they have essentially already finished; that is why their proposals can look so thorough, credible, and polished. They then use the funding to work on their next project (and next proposal) instead of what they said it was for. That seems sketchy to me, but it’s certainly ethical to turn the proposal dial back somewhat toward “obviously achievable for me” from “ambitious and expensive”, and that’s what I’ve done more recently.
The other time I came close was with an R03 proposal to the Agency for Healthcare Research and Quality. First I got a not-close rejection, as I mentioned in the big list, where my proposal was “not discussed”. But AHRQ allows resubmission. At the prompting of my (excellent) grants office, I got feedback on the proposal from two kind seniors at other schools who get lots of funding. I rewrote the proposal based on their comments plus the rejection comments (which were actually quite detailed despite it being “not discussed”) and resubmitted it. This went way better- the resubmission got discussed with an impact score of 30 and a percentile of 17. Lower scores are better for AHRQ/NIH so this was pretty good, good enough that it might have been funded in a normal year, but 2019 was a bad year for government funding (though through some weird quirk I never actually got rejected; 4 years later their system still says “pending council review”). Again, the key to getting close was getting detailed feedback from people who know what they are talking about.
Of course, it also helps to get to know people at the funders and to become more senior yourself. It’s not surprising that my first major grant is coming from IHS given that I’ve been involved with them in all sorts of ways since going to a Liberty & Society seminar way back in 2009. Most funding goes to more senior people who have more connections, knowledge, and proven experience. This is extreme at perhaps the largest funder of research, the National Institutes of Health, where less than 2% of funded principal researchers are under age 36.
This may be the real secret for winning grants- just get older. My 12 rejections all came when I was younger than 36, while my first acceptance came less than a month after my 36th birthday.
In all seriousness, thanks to the Institute for Humane Studies, and I hope that a year from now I’ll be writing here about the great work that came out of this. For everyone with a growing pile of rejections, maybe the 13th time will be the charm for you too.
Economist Writing Every Day 