Baumol’s earliest work on the subject, written with William Bowen, was published in 1965. Analyses like that of Messrs Helland and Tabarrok nonetheless feel novel, because the implications of cost disease remain so underappreciated in policy circles. For instance, the steadily rising expense of education and health care is almost universally deplored as an economic scourge, despite being caused by something indubitably good: rapid, if unevenly spread, productivity growth. Higher prices, if driven by cost disease, need not mean reduced affordability, since they reflect greater productive capacity elsewhere in the economy. The authors use an analogy: as a person’s salary increases, the cost of doing things other than work—like gardening, for example—rises, since each hour off the job means more forgone income. But that does not mean that time spent gardening has become less affordable.
It’s an implication of the Baumol effect that everyone ends up working in a low productivity industry!
The only true solution to cost disease is an economy-wide productivity slowdown—and one may be in the offing. Technological progress pushes employment into the sectors most resistant to productivity growth. Eventually, nearly everyone may have jobs that are valued for their inefficiency: as concert musicians, or artisanal cheesemakers, or members of the household staff of the very rich. If there is no high-productivity sector to lure such workers away, then the problem does not arise.
Misunderstanding the Baumol effect can lead to a cure worse than the “disease”:
These possibilities reveal the real threat from Baumol’s disease: not that work will flow toward less-productive industries, which is inevitable, but that gains from rising productivity are unevenly shared. When firms in highly productive industries crave highly credentialed workers, it is the pay of similar workers elsewhere in the economy—of doctors, say—that rises in response. That worsens inequality, as low-income workers must still pay higher prices for essential services like health care. Even so, the productivity growth that drives cost disease could make everyone better off. But governments often do too little to tax the winners and compensate the losers. And politicians who do not understand the Baumol effect sometimes cap spending on education and health. Unsurprisingly, since they misunderstand the diagnosis, the treatment they prescribe makes the ailment worse.
My only complaint is that the excellent reviewer has not followed our lead and called it the Baumol effect–cost disease is a misleading name!
Addendum: Other posts in this series.
The present work identifies a so-far overlooked bias in sequential impression formation. When the latent qualities of competitors are inferred from a cumulative sequence of observations (e.g., the sum of points collected by sports teams), impressions should be based solely on the most recent observation because all previous observations are redundant. Based on the well-documented human inability to adequately discount redundant information, we predicted the existence of a cumulative redundancy bias. Accordingly, perceivers’ impressions are systematically biased by the unfolding of a performance sequence when observations are cumulative. This bias favors leading competitors and persists even when the end result of the performance sequence is known. We demonstrated this cumulative redundancy bias in 8 experiments in which participants had to sequentially form impressions about the qualities of two competitors from different performance domains (i.e., computer algorithms, stocks, and soccer teams). We consistently found that perceivers’ impressions were biased by cumulative redundancy. Specifically, impressions about the winner and the loser of a sequence were more divergent when the winner took an early lead compared with a late lead. When the sequence ended in a draw, participants formed more favorable impressions about the competitor who was ahead during most observations. We tested and ruled out several alternative explanations related to primacy effects, counterfactual thinking, and heuristic beliefs. We discuss the wide-ranging implications of our findings for impression formation and performance evaluation.
It would be supremely ironic if the advance of the knowledge economy had the effect of devaluing knowledge. But that’s what I heard, recurrently, while reporting this story…If that’s the case, I asked John Sullivan, a prominent Silicon Valley talent adviser, why should anyone take the time to master anything at all? ‘You shouldn’t!’ he replied.
That is from a new Atlantic piece by Jerry Useem. In essence, the division of labor may be running in reverse in some endeavors. In Adam Smith’s argument, division of labor and specialization increase with the size of the market. But say a mix of Moore’s Law and globalization means that software (output and operations) expands rapidly, yet companies seek to shed labor costs due to competition. At the margin the new demand might be for generalists, who can step in whenever unforeseen problems arise which need fixing. Or in other words, you may not wish to specialize with your truly scarce factor, namely labor. In contrast, in Smith’s time, demographics were favorable and labor was pouring into cities from the countryside.
As for the Navy:
The LCS was the first class of Navy ship that, because of technological change and the high cost of personnel, turned away from specialists in favor of “hybrid sailors” who have the ability to acquire skills rapidly. It was designed to operate with a mere 40 souls on board—one-fifth the number aboard comparably sized “legacy” ships and a far cry from the 350 aboard a World War II destroyer. The small size of the crew means that each sailor must be like the ship itself: a jack of many trades and not, as 240 years of tradition have prescribed, a master of just one.
Minimal manning—and with it, the replacement of specialized workers with problem-solving generalists—isn’t a particularly nautical concept. Indeed, it will sound familiar to anyone in an organization who’s been asked to “do more with less”—which, these days, seems to be just about everyone. Ten years from now, the Deloitte consultant Erica Volini projects, 70 to 90 percent of workers will be in so-called hybrid jobs or superjobs—that is, positions combining tasks once performed by people in two or more traditional roles.
Skype and Zoom aren’t quite as good as meeting in the physical world. But why? Pioneer and Emergent Ventures are looking to fund research on exactly how and why video conferencing interactions are different. Apply at https://pioneer.app and mention this tweet…Given the rise of remote work, the economic impact of this research could be Nobel-worthy.
Using the University of British Columbia as a case study, we investigated whether the faculty at our institution who flew the most were also the most successful. We found that beyond a small threshold there was no relationship between scholarly output and how much an individual academic flies…
We certainly did find evidence that researchers fly more than is likely necessary. In the portion of our sample composed of only fulltime faculty, we categorized 10% of trips as “easily avoidable”. These were trips like going to your destination and flying back in the same day or flying a short distance trip that could have been replaced by ground travel. Interestingly, green academics (those studying subjects like climate change or sustainability) not only had the same level of emissions from air travel as their peers, but they were indistinguishable in the category of “easily avoidable” trips as well.
But success isn’t just measured by scholarly output, and so we also checked for relationships between how much academics flew and their annual salaries (which are publicly available). We did find a significant relationship: people who fly more, get paid more. Causation though, could lie in the other direction. Prestigious scholars with more grant money may have extra funds with which to book air travel, for instance.
Definitely recommended, talking to strangers is one of the most important things you do and it can even save your life. This book is the very best entry point for thinking about this topic. Here is a summary excerpt:
We have strategies for dealing with strangers that are deeply flawed, but they are also socially necessary. We need the criminal justice system and the hiring process and the selection of babysitters to be human. But the requirement of humanity means that we have to tolerate an enormous amount of error. That is the paradox of talking to strangers. We need to talk to them. But we’re terrible at it — and, as we’ll see in the next two chapters, we’re not always honest with each other about just how terrible at it we are.
One recurring theme is just how bad we are at spotting liars. On another note, I found this interesting:
…the heavy drinkers of today drink far more than the heavy drinkers of 50 years ago. “When you talk to students [today] about four drinks or five drinks, they just sort of go, “Pft, that’s just getting started,'” the alcohol researcher Kim Fromme says. She says that the heavy binge drinking category now routinely includes people who have had twenty drinks in a sitting. Blackouts, once rare, have become common. Aaron White recently surveyed more than 700 students at Duke University. Of the drinkers in the group, over half had suffered a blackout at some point in their lives, 40 percent had had a blackout in the previous year, and almost one in ten had had a blackout in the previous two weeks.
Poets die young. That is not just a cliche. The life expectancy of poets, as a group, trails playwrights, novelists, and non-fiction writers by a considerable margin. They have higher rates of “emotional disorders” than actors, musicians, composers, and novelists. And of every occupational category, they have far and away the highest suicide rates — as much as five times higher than the general population.
It also turns out that the immediate availability of particular methods of suicide significantly raises the suicide rate; it is not the case that an individual is committed to suicide regardless of the means available at hand.
Returning to the theme of talking with strangers, one approach I recommend is to apply a much higher degree of arbitrary specificity, when relating facts and details, than you would with someone you know.
In any case, self-recommending, this book shows that Malcolm Gladwell remains on an upward trajectory. You can pre-order it here.
Hal of course was in top form, here is the audio and transcript. Excerpt:
COWEN: Why doesn’t business use more prediction markets? They would seem to make sense, right? Bet on ideas. Aggregate information. We’ve all read Hayek.
VARIAN: Right. And we had a prediction market. I’ll tell you the problem with it. The problem is, the things that we really wanted to get a probability assessment on were things that were so sensitive that we thought we would violate the SEC rules on insider knowledge because, if a small group of people knows about some acquisition or something like that, there is a secret among this small group.
You might like to have a probability assessment of whether that would go through. But then, anybody who looks at the auction is now an insider. So there’s a problem in you have to find things that (a) are of interest to the company but (b) do not reveal financially critical information. That’s not so easy to do.
COWEN: But there are plenty of times when insider trading is either illegal or not enforced. Plenty of countries where it’s been legal, and there we don’t see many prediction markets in companies, if any. So it seems like it ought to have to be some more general explanation, or no?
VARIAN: Well, I’m just referring to our particular case. There was another example at the same time: Ford was running a market, and Ford would have futures markets on the price of gasoline, which was very relevant to them. It was an external price and so on. And it extended beyond the usual futures market.
That’s the other thing. You’re not going to get anywhere if you’re just duplicating a market that already exists. You have to add something to it to make it attractive to insiders.
So we ran a number of cases internally. We found some interesting behavior. There’s an article by Bo Cowgill on our experience with this auction. But ultimately, we ran into this problem that I described. The most valuable predictions would be the most sensitive predictions, and you didn’t want to do that in public.
COWEN: But then you must think we’re not doing enough theory today. Or do you think it’s simply exhausted for a while?
VARIAN: Well, one area of theory that I’ve found very exciting is algorithmic mechanism design. With algorithmic mechanism design, it’s a combination of computer science and economics.
The idea is, you take the economic model, and you bring in computational costs, or show me an algorithm that actually solves that maximization problem. Then on the other side, the computer side, you build incentives into the algorithms. So if multiple people are using, let’s say, some communications protocol, you want them all to have the right incentives to have the efficient use of that protocol.
So that’s a case where it really has very strong real-world applications to doing this — everything from telecommunications to AdWords auctions.
VARIAN: Yeah. I would like to separate the blockchain from just cryptographic protocols in general. There’s a huge demand for various kinds of cryptography.
Blockchain seems to be, by its nature, relatively inefficient. As an economist, I don’t like this proof of work that this is. I don’t like the fact that there’s one version of the blockchain that has to keep being updated. I don’t like the fact that it’s so slow. There are lots of things that you could fix, and I expect to see them fixed in the future, but I would say, crypto in general — big deal. Blockchain — not so much.
COWEN: Now, users seem to like them both, but if I just look at the critics, why does it seem to me that Facebook is more hated than Google?
VARIAN: Well, you know, I actually don’t use Facebook. I don’t have any moral objection to it. I just don’t have the time to do it. [laughs] There are other things of this sort that can end up soaking up a substantial amount of time.
I think that one of the reasons — and this is, of course, quite speculative — I think that one of the reasons people are most worried about Facebook is they don’t really understand the limits of what can be done at Facebook. Whereas at Google, I think we’re pretty clear that we’re showing you ads. We’re showing you ads that are targeted to one thing or another, but that’s how the information’s used.
So, you’ve got this specific application in our case. In Facebook’s case, it’s more amorphous, I think.
There is much, much more at the link.
What You Do Is Who You Are: How to Create Your Own Business Culture. It is the best book on business culture in recent memory, here is one bit:
When Tom Coughlin coached the New York Giants, from 2004 to 2015, the media went crazy over a shocking rule he set: “If you are on time, you are late.” He started every meeting five minutes early and fined players one thousand dollars if they were late. I mean on time…”Players ought to be there on time, period,” he said. “If they’re on time, they’re on time. Meetings start five minutes early.”
Two lessons for leaders jump out from Senghor’s experience:
Your own perspective on the culture is not that relevant. Your view or your executive team’s view of your culture is rarely what your employees experience.
You can pre-order the book here, due out in October.
Worse than you think, I enjoyed the discussion of dictionaries most of all:
One of the most unreasonably difficult things about learning Chinese is that merely learning how to look up a word in the dictionary is about the equivalent of an entire semester of secretarial school. When I was in Taiwan, I heard that they sometimes held dictionary look-up contests in the junior high schools. Imagine a language where simply looking a word up in the dictionary is considered a skill like debate or volleyball! Chinese is not exactly what you would call a user-friendly language, but a Chinese dictionary is positively user-hostile.
Figuring out all the radicals and their variants, plus dealing with the ambiguous characters with no obvious radical at all is a stupid, time-consuming chore that slows the learning process down by a factor of ten as compared to other languages with a sensible alphabet or the equivalent. I’d say it took me a good year before I could reliably find in the dictionary any character I might encounter. And to this day, I will very occasionally stumble onto a character that I simply can’t find at all, even after ten minutes of searching. At such times I raise my hands to the sky, Job-like, and consider going into telemarketing.
Chinese must also be one of the most dictionary-intensive languages on earth. I currently have more than twenty Chinese dictionaries of various kinds on my desk, and they all have a specific and distinct use. There are dictionaries with simplified characters used on the mainland, dictionaries with the traditional characters used in Taiwan and Hong Kong, and dictionaries with both. There are dictionaries that use the Wade-Giles romanization, dictionaries that use pinyin, and dictionaries that use other more surrealistic romanization methods. There are dictionaries of classical Chinese particles, dictionaries of Beijing dialect, dictionaries of chéngyǔ (four-character idioms), dictionaries of xiēhòuyǔ(special allegorical two-part sayings), dictionaries of yànyǔ (proverbs), dictionaries of Chinese communist terms, dictionaries of Buddhist terms, reverse dictionaries… on and on. An exhaustive hunt for some elusive or problematic lexical item can leave one’s desk “strewn with dictionaries as numerous as dead soldiers on a battlefield.”
There is however much more, by David Moser, via someone on Twitter, sorry I forgot.
That is the topic of my latest Bloomberg column and my answer is no, here is one excerpt:
One core reason to have unions is to boost the real wages of needy workers. But graduate students are not employees in the traditional sense. They are receiving training, often on very favorable terms. Typically a university is investing large sums of money to make those students employable and successful, usually on the academic market; the University of Chicago says it invests more than $500,000 per doctoral student. If those students demanded and received higher wages for their teaching, the university would not necessarily increase its investment in them at all; it could simply reallocate existing funds. Thus it is misleading to think there is a real bargaining situation here.
Think of a university as an investor in these students, and toward that end it must choose between boosting their academic quality through better training, or paying them higher stipends and teaching wages to ease their immediate financial concerns. The incentive for the university, which cares about its broader and longer-term reputation, is to invest in the quality of those students but pay them smaller amounts (though enough to live on). In contrast, the incentive for a graduate student union would be to push for higher wages, given that the other university investments are less visible and hard to monitor.
At the margin, society is better off if the focus is on the training, which enhances productivity in the long term, rather than on higher wages and stipends for students in the short term.
In general, when considering this issue, ask yourself a question: When it comes to bringing about change, do today’s universities have too many veto points or too few?
Some researchers have pointed out that graduate student unions don’t seem to have harmed the public universities that allow them (such unions, which are permissible in many states, would not be affected by the federal government’s decision). The evidence may be compelling in the short run, but the real costs are likely to come later — by slowing down or even preventing beneficial changes to the U.S. system of higher education. Furthermore, state labor laws dramatically limit what public employees can negotiate for. Unionized graduate students at private universities unions would not face similar restrictions.
Recommended, do read the whole thing.
SlateStarCodex, whose 2017 post on the cost disease was one of the motivations for our investigation, says Why Are the Prices so D*mn High (now available in print, ePub, and PDF) is “the best thing I’ve heard all year. It restores my faith in humanity.” I wouldn’t go that far.
SSC does have some lingering doubts and points to certain areas where the data isn’t clear and where we could have been clearer. I think this is inevitable. A lot has happened in the post World War II era. In dealing with very long run trends so much else is going on that answers will never be conclusive. It’s hard to see the signal in the noise. I think of the Baumol effect as something analogous to global warming. The tides come and go but the sea level is slowly rising.
In contrast, my friend Bryan Caplan is not happy. Bryan’s basic point is to argue, ‘look around at all the stupid ways in which the government prevents health care and education prices from falling. Of course, government is the explanation for higher prices.’ In point of fact, I agree with many of Bryan’s points. Bryan says, for example, that immigration would lower health care prices. Indeed it would. (Aside: it does seem odd for Bryan to argue that if K-12 education were privately funded schools would not continue their insane practice of requiring primary school teachers to have B.A.s when in fact, as Bryan knows, credentialism has occurred throughout the economy)
The problem with Bryan’s critiques is that they miss what we are trying to explain which is why some prices have risen while others have fallen. Immigration would indeed lower health care prices but it would also lower the price of automobiles leaving the net difference unexplained. Bryan, the armchair economist, has a simple syllogism, regulation increases prices, education is regulated, therefore regulation explains higher education prices. The problem is that most industries are regulated. Think about the regulations that govern the manufacture of automobiles. Why do all modern automobiles look the same? As Car and Driver puts it:
In our hyperregulated modern world, the government dictates nearly every aspect of car design, from the size and color of the exterior lighting elements to how sharp the creases stamped into sheet metal can be.
(See Jeffrey Tucker for more). And that’s just design regulation. There are also environmental regulations (e.g. ethanol, catalytic converters, CAFE etc.), engine regulations, made in America regulations, not to mention all the regulations on the inputs like steel and coal. The government even regulates how cars can be sold, preventing Tesla from selling direct to the public! When you put all these regulations together it’s not at all obvious that there is more regulation in education than in auto manufacturing. Indeed, since the major increase in regulation since the 1970s has been in environmental regulation, which impacts manufacturing more than services, it seems plausible that regulation has increased more for auto manufacturing.
As an empirical economist, I am interested in testable hypotheses. A testable hypothesis is that the industries with the biggest increases in regulation have seen the biggest increases in prices over time. Yet, when we test that hypothesis as best we can it appears to be false. Remember, this does not mean that regulation doesn’t increase prices! It can and probably does it’s just that regulation is not the explanation for the differences in prices we see across industries. (Note also that Bryan argues that you don’t need increasing regulation to explain increasing prices, which is true, but I still need a testable hypotheses not an unfalsifiable claim.)
So by all means let’s deregulate, but don’t expect 70+ year price trends to reverse until robots and AI start improving productivity in services faster than in manufacturing.
Let me close with this. What I found most convincing about the Baumol effect is consilience. Here, for example, are two figures which did not make the book. The first shows car prices versus car repair prices. The second shows shoe and clothing prices versus shoe repair, tailors, dry cleaners and hair styling. In both cases, the goods price is way down and the service price is up. The Baumol effect offers a unifying account of trends such as this across many different industries. Other theories tend to be ad hoc, false, or unfalsifiable.
Addendum: Other posts in this series.
In addition to being a great economist, Marty was an institution builder. He was the early driving force behind the rise of the NBER, he led the development of empirical public finance as a respected field, and also very early on he pushed health care economics, both through his leadership at the NBER and through his own work and mentorship. He always was reaching out to help others, and Larry Summers, Jim Poterba, David Cutler, Raj Chetty, and Jason Furman were some of those he mentored. The economics of art museums was yet another topic he had a real interest in, and stimulated research in.
Marty also was one of my oral examiners at Harvard, and he asked only excellent questions. I thank him for judging my answers to be good enough.
…we suggest that this division of innovative labor has not, perhaps, lived up to its promise. The translation of scientific knowledge generated in universities to productivity enhancing technical progress has proved to be more difficult to accomplish in practice than expected. Spinoffs, startups, and university licensing offices have not fully filled the gap left by the decline of the corporate lab. Corporate research has a number of characteristics that make it very valuable for science-based innovation and growth. Large corporations have access to significant resources, can more easily integrate multiple knowledge streams, and direct their research toward solving specific practical problems, which makes it more likely for them to produce commercial applications. University research has tended to be curiosity-driven rather than mission-focused. It has favored insight rather than solutions to specific problems, and partly as a consequence, university research has required additional integration and transformation to become economically useful. This is not to deny the important contributions that universities and small firms make to American innovation. Rather, our point is that large corporate labs may have distinct capabilities which have proved to be difficult to replace.
That is from Ashish Arora, Sharon Belenzon, Andrea Patacconi, and Jungkyu Suh, “The Changing Structure of American Innovation: Some Cautionary Remarks for Economic Growth,” recommended, an excellent paper spanning several disciplines. I would myself note this is further reason not to split up the major tech companies.