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I explained the Baumol effect in an earlier post based on Why Are the Prices So D*mn High?. In this post, I want to point out some special features of the Baumol effect that help to explain the data. Namely:
- The Baumol effect predicts that more spending will be accompanied by no increase in quality.
- The Baumol effect predicts that the increase in the relative price of the low productivity sector will be fastest when the economy is booming. i.e. the cost “disease” will be at its worst when the economy is most healthy!
- The Baumol effect cleanly resolves the mystery of higher prices accompanied by higher quantity demanded.
First, in the literature on rising prices it’s common to contrast massive increases in spending with little to no increases in quality, as for example, in contrasting education expenditures with mostly flat test scores (see at right). We have spent so much and gotten so little! Cui Bono? It must be teacher unions, administrators or the government!
All of that could be true but the Baumol effect predicts that more spending will be accompanied by no increase in quality. Go back to the classic example of the string quartet which becomes more expensive because labor in other industries increases in productivity over time. The price of the string quartet rises but does anyone expect that the the quality rises? Of course not. In the classic example the inputs to string quartet playing don’t change. The wages of the players rise because of productivity increases in other industries but we don’t invest any more real resources in string quartet playing and so we should not expect any increases in quality.
In just the same way, to the extent that greater spending on education, health care, or car repair is due to the rising opportunity costs of inputs we should not expect any increase in quality. (Note that increases in real resource use such as more teachers per student should result in increases in quality (and perhaps they do) but by eliminating the price increase portion of the higher spending we have eliminated a large portion of the mystery of higher spending with no increase in quality.)
Second, explanations of rising prices that focus on bad things such as monopoly power or rent seeking tend to imply that price increases should be largest when the economy is doing poorly. In contrast, the Baumol effect predicts that increases in relative prices will be largest when the economy is booming. Consider health care. From news reports you might think that health care costs have gotten more “out of control” over time. In fact, the fastest increases in health care costs were in the 1960s. The graph at left is on a ratio scale so slopes indicate rates of growth and what one sees is that the growth rate of health expenditures per person is slowing. That might seem good but remember, from the Baumol point of view, the decline in relative price growth reflects slowing growth elsewhere in the economy.
Third, holding all else equal, the only rational response to an ordinary cost increase is to substitute away from the good. But in many rising price sectors we see not only greater expenditures (driven by increased prices and inelastic demand) but also greater quantity demanded. As I showed earlier, for example, we have increased the number of doctors, nurses and teachers per capita even as prices have risen. John Cochrane correctly noted that this is puzzling but it’s a bigger puzzle for non-Baumol theories than for Baumol. For non-Baumol theories to explain increases in the quantity purchased, we need two theories. One theory to explain the increase in price (bloat/regulation etc.) and another theory to explain why, despite the increase in price, people are still purchasing more (e.g. income effect). The world is a messy place and maybe that is what is happening. But the Baumol effect offers a cleaner answer.
A Baumol increase in relative price is always accompanied by higher income so it’s much easier to explain how price increases can accompany increases in quantity as well as increases in expenditure. The Baumol story for increased purchase of medical care even as prices increase, for example, is no more mysterious than why people can take more leisure when wages increase–namely the higher wage means a higher income for any given hours and people choose to take some of this higher income in leisure. Similarly, higher productivity in say goods production increases income at any given production level and people choose to take some of this higher income in services.
Summing up, if we examine each sector–education, health care, the arts, etc.–on its own then there are always many possible explanations for why prices might be increasing. Many of these explanations have true premises–there are a lot of administrators in higher education, health care is highly regulated, lower education is government run. But, on closer inspection the arguments often don’t fit the data very well. Prices were increasing before administrators were important, health care is highly regulated but so is manufacturing, private education is also increasing in price, the arts are not highly regulated. It’s impossible to knock down each of these arguments in every industry, so there is always room for doubt. Indeed, the great difficult is that these factors often do result in higher costs and greater inefficiency but I believe those are predominantly level effects not effects that accumulate over time. Moreover, when one considers the rising price industries as a whole these explanations begin to look ad hoc. In contrast, the Baumol effect appears capable of explaining the pricing behavior of a wide variety of industries over a long period of time using a simple but powerful and unified theory.
Addendum: Other posts in this series.
After looking at education and health care and doing a statistical analysis covering 139 industries, Helland and I conclude that a big factor in price increases over time in the rising price of skilled labor. Many industries use skilled labor, however, and even so prices decline so that cannot be a full explanation. Moreover, why is the price of skilled labor increasing? The Baumol effect answers both of these questions. In this post, I’ll explain the effect drawing from Why Are the Prices so D*mn High.
The Baumol effect is easy to explain but difficult to grasp. In 1826, when Beethoven’s String Quartet No. 14 was first played, it took four people 40 minutes to produce a performance. In 2010, it still took four people 40 minutes to produce a performance. Stated differently, in the nearly 200 years between 1826 and 2010, there was no growth in string quartet labor productivity. In 1826 it took 2.66 labor hours to produce one unit of output, and it took 2.66 labor hours to produce one unit of output in 2010.
Fortunately, most other sectors of the economy have experienced substantial growth in labor productivity since 1826. We can measure growth in labor productivity in the economy as a whole by looking at the growth in real wages. In 1826 the average hourly wage for a production worker was $1.14. In 2010 the average hourly wage for a production worker was $26.44, approximately 23 times higher in real (inflation-adjusted) terms. Growth in average labor productivity has a surprising implication: it makes the output of slow productivity-growth sectors (relatively) more expensive. In 1826, the average wage of $1.14 meant that the 2.66 hours needed to produce a performance of Beethoven’s String Quartet No. 14 had an opportunity cost of just $3.02. At a wage of $26.44, the 2.66 hours of labor in music production had an opportunity cost of $70.33. Thus, in 2010 it was 23 times (70.33/3.02) more expensive to produce a performance of Beethoven’s String Quartet No. 14 than in 1826. In other words, one had to give up more other goods and services to produce a music performance in 2010 than one did in 1826. Why? Simply because in 2010, society was better at producing other goods and services than in 1826.
The 23 times increase in the relative price of the string quartet is the driving force of Baumol’s cost disease. The focus on relative prices tells us that the cost disease is misnamed. The cost disease is not a disease but a blessing. To be sure, it would be better if productivity increased in all industries, but that is just to say that more is better. There is nothing negative about productivity growth, even if it is unbalanced.
In this post, I will discuss some implications of the fact that productivity is unbalanced. See the book for more discussion and speculation about why productivity growth is systematically unbalanced.
The Baumol effect reminds us that all prices are relative prices. An implication is that over time prices have very little connection to affordability. If the price of the same can of soup is higher at Wegmans than at Walmart we understand that soup is more affordable at Walmart. But if the price of the same can of soup is higher today than in the past it doesn’t imply that soup was more affordable in the past, even if we have done all the right corrections for inflation.
We can see this in the diagram at right. We have a two-good economy, Cars and Education. The production possibilities frontier shows all the combinations of Cars and Education that we can afford given our technology and resources at time 1 (PPF 1). Now suppose society chooses to consume the bundle of goods denoted by point (a). The relative price of Cars and Education is given by the slope of the PPF at that point. That price/slope tells us if we give up some education how many more cars can we get? In a market economy the price has to be given by the slope of the PPF because that is the only price at which people will willing consume the bundle of goods at point (a), i.e. it’s the equilibrium price.
Now at time 2, productivity has increased which means that with the same resources we can now have more of both goods. Productivity of Car production has increased more than that of Education production, however, so the curve shifts out more towards Cars than towards Education. Suppose society continues to consume Cars and Education in the same proportions, i.e. at point (b). The price of education must increase–and all that means is that if we give up a unit of education at point b we will get more cars than before which is the same as saying that if we want more education at point b we must give up more cars than before, i.e. the price has increased.
Notice, however, that although the price of education has increased, education is not less affordable. Indeed, at point (b) we are consuming more of both goods–broadly speaking this is exactly what has happened–namely, the price of education has increased and we now consume more of it than ever before.
When we recognize that all prices are relative prices the following simple yet deep facts follow:
- If productivity increases in some industries more than others then, ceteris paribus, some prices must increase.
- Over time, all real prices cannot fall.
In Figure 22 the economy moves from point (a) to point (b). If we graph the same transition over time it will look something like Figure 23.
Looking at such graphs, our attention naturally is drawn to the rising cost of education. Why are costs rising so quickly? Entranced by such graphs, we may enter into a detailed analysis of the special factors of education—regulation, unionization, government purchases, insurance, international trade, and so forth—to try to explain the dramatic increase in costs. Yet the rising costs in the education sector are simply a reflection of increased productivity in the car sector. Thus, another deep lesson of the Baumol effect is that to understand why costs in the stagnant sector are rising, we must look away from the stagnating sector and toward the progressive sector.
Finally, there is one other addition to the Baumol effect which is not often recognized but worth drawing attention to. In Figure 22, I assumed that preferences were such that people wanted to consume the same ratio of goods over time so we moved from point (a) to point (b). But suppose that as we get wealthier we get tired of more cars and would like relatively more education so we move towards point (d). As we move from point (b) to point (d) we are taking resources away from car production, resources which were probably well-suited to making cars, and instead moving them towards education where they are probably less well suited. As a result as we move from point (b) to point (d) we are driving up the price of education as we try to turn auto workers into teachers. In this case, the Baumol effect gets magnified. We could alternatively move from point (b) to point (c) which would turn teachers into less productive auto workers thus driving down the price of education (i.e. increasing the price of cars). Thus, depending on preferences, the Baumol effect can be magnified or ameliorated.
As a society it appears that with greater wealth we have wanted to consume more of the goods like education and health care that have relatively slow productivity growth. Thus, preferences have magnified the Baumol effect.
Next week, I will wrap up the discussion by explaining some features of the data that the Baumol effect fits much better than do other theories.
Addendum: Other posts in this series.
He was one of the greatest of living economists, with contributions in numerous areas, including productivity, economics of the arts, contestable markets, environmental economics, and entrepreneurship theory. Here are previous MR entries on Baumol and his ideas.
We estimate three models of cost per student using data from Carnegie I and II public research universities. There are 841 usable observations covering the period from 1987 to 2008. We find that staffing ratios are individually and collectively significant in each model. Further, we find evidence that shared governance lowers cost and that the optimal staffing ratio is approximately three tenure track faculty members for every one full time administrator. Costs are higher if the ratio is higher or lower than three to one. As of 2008 the number of full time administrators is almost double the number of tenure track faculty. Using the differential method and the coefficients estimated in the three models, we deconstruct the real cost changes per student between 1987 and 2008 into Baumol and Bowen effects. This analysis reveals that for every $1 in Baumol cost effects there are over $2 in Bowen cost effects. Taken together, these results suggest two thirds of the real cost changes between 1987 and 2008 are due to weak shared governance and serious agency problems among administrators and boards.
For the pointer I thank Michael Tamada (who does not necessarily endorse the argument).
It is self-recommending, here are a few points of relevance:
1. There has been a clear cost disease in most kinds of education and many kinds of medicine, but I blame institutions and laws as much as the intrinsic nature of the product.
2. I do not see the arts as subject to the cost disease very much at all. As for the “live performing arts,” the disease seems to afflict the older and less innovative sectors, such as opera and the symphony. There is plenty of live music these days, it is offered in innovative ways, and much of it is free.
3. Even “the live performing arts” can be broken down into underlying characteristics, many of which show a great deal of recent innovation. For instance the supply of “musical immediacy” has been non-stagnant through YouTube, which often gives you a better glimpse of the performer than you get through nosebleed seats and giant screens. YouTube isn’t “live,” but there is no particular reason to break down the analysis at that level and certainly it is not a sacred category for consumers.
4. In many sectors of the arts, especially music, consumers demand constant turnover of product. Old music becomes “obsolete” — for whatever sociological reasons — and in this sense the sector is creating lots of new value every year. From an “objectivist” point of view they are still strumming guitars with the same speed, but from a subjectivist point of view — the relevant one for the economist – they are remarkably innovative all the time in the battle against obsolescence. A lot of the cost disease argument is actually an aesthetic objection that the art forms which have already peaked — such as Mozart — sometimes have a hard time holding their ground in terms of cost and innovation.
5. In general “cost disease” sectors do not remain constant over time. Agriculture has been unusually stagnant for the last twenty or so years, but it is hardly obvious that this trend will continue for the next century to come and it certainly was not the case for the period 1948-1990, quite the contrary.
6. The stagnancy of one sector may depend on the stagnancy of other sectors in non-transparent ways. “Live music” may seem like it doesn’t change much, but lifting the embargo on Cuba would boost the quantity and quality of my consumption of spectacular concert experiences, as would a non-stop flight to Haiti.
You can buy the book here.
Addendum: Matt Yglesias comments.
1. An alternative to the Baumol cost-disease hypothesis (but is it really, isn’t worker allocation across sectors endogenous to, among other things, Baumol-like factors?)
4. “…mass shootings are more likely after anniversaries of the most deadly historical mass shootings. Taken together, these results lend support to a behavioral contagion mechanism following the public salience of mass shootings.” Link here.
A nice, well-reasoned piece from Harold Lee pushing back on the idea that we should buy experiences not goods:
While I appreciate the Stoic-style appraisal of what really brings happiness, economically, this analysis seems precisely backward. It amounts to saying that in an age of industrialization and globalism, when material goods are cheaper than ever, we should avoid partaking of this abundance. Instead, we should consume services afflicted by Baumol’s cost disease, taking long vacations and getting expensive haircuts which are just as hard to produce as ever.
Put that way, the focus on minimalism sounds like a new form of conspicuous consumption. Now that even the poor can afford material goods, let’s denigrate goods while highlighting the remaining luxuries that only the affluent can enjoy and show off to their friends.
[The distinction is too tightly drawn]…tools and possessions enable new experiences. A well-appointed kitchen allows you to cook healthy meals for yourself rather than ordering delivery night after night. A toolbox lets you fix things around the house and in the process learn to appreciate how our modern world was made. A spacious living room makes it easy for your friends to come over and catch up on one another’s lives. A hunting rifle can produce not only meat, but also camaraderie and a sense of connection with the natural world of our forefathers. In truth, there is no real boundary between things and experiences. There are experience-like things; like a basement carpentry workshop or a fine collection of loose-leaf tea. And there are thing-like experiences, like an Instagrammable vacation that collects a bunch of likes but soon fades from memory.
Indeed, much of what is wrong with our modern lifestyles is, in a sense, a matter of overconsuming experiences. The sectors of the economy that are becoming more expensive every year – which are preventing people from building durable wealth – include real estate and education, both items that are sold by the promise of irreplaceable “experiences.” Healthcare, too, is a modern experience that is best avoided. As a percent of GDP, these are the growing expenditures that are eating up people’s wallets, not durable goods. If we really want to live a minimalist life, then forget about throwing away boxes of stuff, and focus on downsizing education, real estate, and healthcare.
Hat tip: The Browser.
Photo Credit: MaxPixel.
4. Soros on Xi (WSJ).
5. Did you expect the Spanish Inquisition (to have long-run, persistent effects)?
1. I had a fun and wide-ranging conversation with Jonah Goldberg on the Remnant. We covered the economy, immigration, cyborgs and the Baumol effect among other topics.
2. Tim Harford covers fractional dosing at the FT:
The concept of a standard or full dose is fuzzier than one might imagine. These vaccines were developed at great speed, with a focus on effectiveness that meant erring towards high doses. Melissa Moore, a chief scientific officer at Moderna, has acknowledged this. It is plausible that we will come to regard the current doses as needlessly high.
3. The Brunswick Group interviews me:
Act like you’re in a crisis. That has been economist Alex Tabarrok’s advice since the start of the COVID-19 pandemic. Tabarrok was among the earliest and loudest voices arguing for urgency and risk-taking when it came to increasing rapid testing, investing in vaccine capacity, and employing flexible vaccine dosing. In hindsight, he has been proven regularly right when most health experts were wrong.
The excellent Random Critical Analysis has a long blog post, really a short book, on why the conventional wisdom about health care, especially in the United States, is wrong. It’s a tour-de-force. Difficult to summarize but, as I see it, the key points are the following. (I also drawn on It’s still not the health care prices.)
1. Health care spending is well predicted, indeed caused, by income.
Notice that the United States doesn’t look unusual when income is measured at the household level, i.e. Actual Individual Consumption, which measures the value of the bundle consumed by households whether the bundle items are bought in the market or provided by governments or non-profits. (AIC also avoids some issues with GDP per capita when a country has lots of intellectual property and exports, e.g. Ireland).
2. The price of health care increases with income but at a slower rate than income.
As a result of the above:
3. The price of health care relative to income is lower in rich countries, including the United States.
Let that sink in, health care prices are lower relative to income in richer countries. Health care in the United States is cheaper relative to income than in Greece, for example.
Since spending is going up faster than income but prices are not it must be the case that quantities are also increasing with income.
4. The density of health care workers (number of workers) and intensity (what the workers do) increases with income.
RCA: Rich countries consume much more cutting edge health care technology (innovations). For every 1% increase in real income, we find a 1-3% increase in organ transplant operations, a 1-2% increase in pacemaker and ICD implants, a 1-2% increase in the density of medical imaging/diagnostic technology, and likely similar patterns for all manner of other new technologies (e.g., insulin pumps, ADHD prescriptions, etc.). Obviously, these indicators are just the tip of the iceberg. Still, where data of this sort are available, they tend to be highly consistent with extreme income elasticity (particularly newer, more expensive forms of health care). In the main, costs rise because this technological change tends to requires a lot more people in hospitals and providers’ offices to deliver this increasingly complicated array of health care (surgical procedures, diagnostics, drugs, therapies, etc.).
A bottom line is that health care spending in the United States is not exceptional once we take US income into account.
RCA’s analysis is consistent with the Baumol effect and my analysis with Helland in Why Are the Prices So Damn High (we have some minor differences with RCA on physician incomes but neither of our analyses depend on that point). A big point is that RCA and Helland and I argue that the rising price sectors are not crowding out consumption of other goods. We can and are buying more of other goods even as we spend more on health care and education. Or, as RCA puts it:
…these trends indicate that the rising health share is robustly linked with a generally constant long-term increase in real consumption across essentially all other major consumption categories.
It is true that the United States has a convoluted payment system which results in absurd and enraging bills. Fixing the pricing system could generate more equity and efficiency but RCA’s analysis tells us that billions are at stake, not trillions. A corollary is that as other countries reach current US levels of income their health care spending will look more like the United States does today.
See RCA for much more.
Here are the top MR posts for 2019, as measured by landing pages. The most popular post was Tyler’s
Alas, I don’t think that will help to create more Tylers. Coming in at number two was my post:
Other posts in the top five were 3. Pretty stunning data on dating from Tyler and my posts, 4. One of the Greatest Environmental Crimes of the 20th Century,and 5. The NYTimes is Woke.
My post on The Baumol Effect which introduced my new book Why are the Prices So Damned High (one of Mercatus’s most downloaded items ever) was number 6 and rounding out the top ten were a bunch from Tyler, including 7. Has anyone said this yet?, 8. What is wrong with social justice warriors?, 9. Reading and rabbit holes and my post Is Elon Musk Prepping for State Failure?.
Other big hits from me included
- Air Pollution Reduces IQ, a Lot (Mostly a Patrick Collison post)
- The Nobel Prize in Economic Science Goes to Banerjee, Duflo, and Kremer
- Bitcoin is Less Secure than Most People Think
- Active Learning Works But Students Don’t Like It
- Sex Differences in Personality are Large and Important
Tyler had some truly great posts in the last few days of 2019 including what I thought was the post of the year (and not just on MR!) Work on these things.
Also important were:
- “What will you do to stay weird?”
- Amazon and Taxes a Simple Primer
- Best Non-fiction books of 2019.
Happy holidays everyone!
The Nobel Prize goes to Abhijit Banerjee, Esther Duflo and Michael Kremer (links to home pages) for field experiments in development economics. Esther Duflo was a John Bates Clark Medal winner, a MacArthur “genius” award winner, and is now the second woman to win the economics Nobel and by far the youngest person to ever win the economics Nobel (Arrow was the previous youngest winner!). Duflo and Banerjee are married so these are also the first spouses to win the economics Nobel although not the first spouses to win Nobel prizes–there was even one member of a Nobel prize winning spouse-couple who won the Nobel prize in economics. Can you name the spouses?
Michael Kremer wrote two of my favorite papers ever. The first is Patent Buyouts which you can find in my book Entrepreneurial Economics: Bright Ideas from the Dismal Science. The idea of a patent buyout is for the government to buy a patent and rip it up, opening the idea to the public domain. How much should the government pay? To decide this they can hold an auction. Anyone can bid in the auction but the winner receives the patent only say 10% of the time–the other 90% of the time the patent is bought by the government at the market price. The value of this procedure is that 90% of the time we get all the incentive properties of the patent without any of the monopoly costs. Thus, we eliminate the innovation tradeoff. Indeed, the government can even top the market price up by say 15% in order to increase the incentive to innovate. You might think the patent buyout idea is unrealistic. But in fact, Kremer went on to pioneer an important version of the idea, the Advance Market Commitment for Vaccines which was used to guarantee a market for the pneumococcal vaccine which has now been given to some 143 million children. Bill Gates was involved with governments in supporting the project.
My second Kremer paper is Population Growth and Technological Change: One Million B.C. to 1990. An economist examining one million years of the economy! I like to say that there are two views of humanity, people are stomachs or people are brains. In the people are stomachs view, more people means more eaters, more takers, less for everyone else. In the people are brains view, more people means more brains, more ideas, more for everyone else. The people are brains view is my view and Paul Romer’s view (ideas are nonrivalrous). Kremer tests the two views. He shows that over the long run economic growth increased with population growth. People are brains.
The work for which the Nobel was given is for field experiments in development economics. Kremer began this area of research with randomized trials of educational policies in Kenya. Duflo and Banerjee then deepened and broadened the use of field experiments and in 2003 established the Poverty Action Lab which has been the nexus for field experiments in development economics carried on by hundreds of researchers around the world.
Much has been learned in field experiments about what does and also doesn’t work. In Incentives Work, Dufflo, Hanna and Ryan created a successful program to monitor and reduce teacher absenteeism in India, a problem that Michael Kremer had shown in Missing in Action was very serious with some 30% of teachers not showing up on a typical day. But when they tried to institute a similar program for nurses in Putting a Band-Aid on A Corpse the program was soon undermined by local politicians and “Eighteen months after its inception, the program had become completely ineffective.” Similarly, Banerjee, Duflo, Glennerster and Kinnan find that Microfinance is ok but no miracle (sorry fellow laureate Muhammad Yunus). A frustrating lesson has been the context dependent nature of results and the difficult of finding external validity. (Lant Pritchett in a critique of the “randomistas” argues that real development is based on macro-policy rather than micro-experiment. See also Bill Easterly on the success of the Washington Consensus.)
Duflo, Kremer and Robinson study How High Are Rates of Return to Fertilizer? Evidence from Field Experiments in Kenya. This is an especially interest piece of research because they find that rates of return are very high but that farmers don’t use much fertilizer. Why not? The reasons seem to have much more to do with behavioral biases than rationality. Some interventions help:
Our findings suggest that simple interventions that affect neither the cost of, nor the payoff to, fertilizer can substantially increase fertilizer use. In particular, offering farmers the option to buy fertilizer (at the full market price, but with free delivery) immediately after the harvest leads to an increase of at least 33 percent in the proportion of farmers using fertilizer, an effect comparable to that of a 50 percent reduction in the price of fertilizer (in contrast, there is no impact on fertilizer adoption of offering free delivery at the time fertilizer is actually needed for top dressing). This finding seems inconsistent with the idea that low adoption is due to low returns or credit constraints, and suggests there may be a role for non–fully rational behavior in explaining production decisions.
This is reminiscent of people in developed countries who don’t adjust their retirement savings rates to take advantage of employer matches. (A connection to Thaler’s work).
Duflo and Banerjee have conducted many of their field experiments in India and have looked at not just conventional questions of development economics but also at politics. In 1993, India introduced a constitutional rule that said that each state had to reserve a third of all positions as chair of village councils for women. In a series of papers, Duflo studies this natural experiment which involved randomization of villages with women chairs. In Women as Policy Makers (with Chattopadhyay) she finds that female politicians change the allocation of resources towards infrastructure of relevance to women. In Powerful Women (Beaman et al.) she finds that having once had a female village leader increases the prospects of future female leaders, i.e. exposure reduces bias.
Before Banerjee became a randomistas he was a theorist. His A Simple Model of Herd Behavior is also a favorite. The essence of the model can be explained in a simple example (from the paper). Suppose there are two restaurants A and B. The prior probability is that A is slightly more likely to be a better restaurant than B but in fact B is the better restaurant. People arrive at the restaurants in sequence and as they do they get a signal of which restaurant is better and they also see what choice the person in front of them made. Suppose the first person in line gets a signal that the better restaurant is A (contrary to fact). They choose A. The second person then gets a signal that the better restaurant is B. The second person in line also sees that the first person chose A, so they now know one signal is for A and one is for B and the prior is A so the weight of the evidence is for A—the second person also chooses restaurant A. The next person in line also gets the B signal but for the same reasons they also choose A. In fact, everyone chooses A even if 99 out of 100 signals are B. We get a herd. The sequential information structure means that the information is wasted. Thus, how information is distributed can make a huge difference to what happens. A lot of lessons here for tweeting and Facebook!
Banerjee is also the author of some original and key pieces on Indian economic history, most notably History, Institutions, and Economic Performance: The Legacy of Colonial Land Tenure Systems in India (with Iyer).
Before last year’s Nobel announcement Tyler wrote:
I’ve never once gotten it right, at least not for exact timing, so my apologies to anyone I pick (sorry Bill Baumol!). Nonetheless this year I am in for Esther Duflo and Abihijit Banerjee, possibly with Michael Kremer, for randomized control trials in development economics.
As Tyler predicted he was wrong and also right. Thus, this years win is well-timed and well-deserved. Congratulations to all.
The graph at right made the twitter rounds a few days ago (1.3k RTs and 2.7k likes for Noah). The graph looked off to me immediately. Between approximately 1992 and 1994 the number of administrators went up by a factor of 4? (Or, if something goes from a 500% growth since 1970 to a 2000% growth rate since 1970, is that a factor of 3? Confusing! Anyway, a big jump.) Big jumps are often a sign that definitions, not reality, have changed. Indeed, what is an administrator?
There’s another problem with this type of graph which shows not absolute numbers but percent growth since 1970. Everything in this graph depends on getting one number, the number of administrators in 1970, exactly correct! But the first number is the one that is the farthest in the past, often the hardest to find and the most suspect. But if that first number is underestimated then every other number in the chart is overestimated.
People send me this kind of thing all the time. “See,” they say, “Why are the Prices So D*mn High is wrong! It isn’t Baumol!”–and I am always reluctant to follow-up because tracking down the underlying data, figuring out what it means, if there are mistakes etc. is a huge time sink. It was the excellent Conversable Economist who go the ball rolling on the latest iteration of this graph, however, and he cites the graph to noted health economist Uwe Reinhardt’s last book, Priced Out so I thought it could be worthwhile to go deeper.
Unfortunately, Reinhardt simply calls this a “famous graph” and it’s clear that he just found it on the internet like everyone else! Oh dear. Following up further, I did find the original Woolhandler-Himmelstein analysis written in 1991 and taking the data up to 1987. WH cite the Statistical Abstract of the United States (Table 64-2, 109th edition). You can find the SA 109th edition here but it doesn’t have the data. At least, I couldn’t find it. Ok, several hours wasted.
Finally, however, I did find a number for health administrators in an earlier edition of the SA. In the 1980 edition in Table 165, Employed Persons in Selected Health Occupations, there is a number for “Health administrators,” which says 118 thousand in 1972. Aha! Now things are beginning to make sense because from that same table there were at least 3.5 million workers (physicians, nurses, technicians and others) in health occupations and 118 thousand administrators is clearly far too low. Indeed, in a later paper Woolhandler, Campbell and Himmelstein estimate that in 1969, 18.2% of health care workers were in administration which would imply a figure of 639 thousand health administrators circa 1970, a much more plausible number.
The Woolhandler, Campbell and Himmelstein piece also finds that between 1989 and 1994 the share of health care administrators as a percent of the health care workforce increased from 25.5% to….wait for it….25.7%. In other words, no big jump and inconsistent with the huge jump seen in the graph.
It was at this point that I found Kevin Drum’s excellent analysis. Drum was also suspicious of the graph and after a lot of work he concludes that the graph exaggerates by at least a factor of 3 and probably more. Drum estimates an increase in administrators of 831%; using my initial number and Drum’s end number, I estimate an increase of 682%. All numbers to be taken with a grain of salt. Is that a big increase? Compared to what? Drum gives his best takeaway of the data as this graph, administration costs as a percent of health care costs :
I agree with Drum–this way of presenting the data looks plausible, sensible and much less sensationalist than the original graph. Note that there has been an increase in administrative costs. Why? Here’s Drum’s bottom line:
Bottom line: the health care system has grown tremendously over the past 50 years, but that’s mostly not because we have a lot more doctors. It’s because we have MRI techs and ultrasound specialists and more kinds of nurses and more kinds of pills and enormous proton beams to cure cancer. (Those proton beams are massively expensive and require large staffs, but that doesn’t mean you need any more oncologists per patient.) To manage all this new stuff, we need bigger admin and support staffs. As a result, admin and support have grown about 50-100 percent on a relative basis. That’s the real number.
I believe the original graph uses a number for administrators that is too low in 1970 and includes what I suspect was a change in definitions around 1992 (project the 1970 to 1990 line forward or the 1994 to 2009 line backward and you will get a more accurate graph.) More generally, the graph is misleading because it suggests that “administrators” are to blame for high health care costs and if only we could focus on the “real producers” of medicine, the physicians, costs would be much lower. Blaming administrators for high prices is a lot like blaming “the middlemen.” It’s easy to say and easy to tweet but blaming the middlemen reflects a naive perspective on how goods and services are actually produced in a modern economy.
Administrative costs in the United States are high compared to other countries like Canada. (Helland and I discuss this in Why are the Prices So D*mn High.) We might even be able to lower administrative costs by moving to a single-payer, universal system. But there is no free lunch and there is no returning to an administrative free Eden.
Erik Torenberg, co-founder of the VC firm Village Global, interviews me in a wide-ranging podcast. Here is one bit from a series of questions on what do you disagree about with ____. In this case, Paul Krugman.
AT: …Krugman and I are almost in perfect agreement. Only marginally different. Paul says ‘Republicans are corrupt, incompetent, unprincipled and dangerous to a civil society’. I agree with that entirely. I would only change one word. I would change the word Republicans to the word politicians. If Paul could only be convinced of doing that, coming over to the libertarian side, we would be in complete agreement. But he is much more partisan than I am and even though I worry about Republicans more than Democrats at this particular point in time I think the larger incentive is that we all need to be worried about politicians rather than any one particular party.
Although I agree with Paul a lot of the time, sometimes he does just drive me absolutely batty. He just says things which I think are so wrong. In his latest column which to be fair was written as a column fifty years in the future so maybe it was a bit tongue in cheek. The column was pretending that Elon Musk and Peter Thiel were a hundred years of age and fit and fiddle and still major players in society. And Krugman wrote:
Life extension for a privileged few is by its nature a socially destructive technology and the time has come to ban it.
Now to me this is just evil. This is like something out of Ayn Rand’s Anthem, that it is evil to live longer than your brothers and all must be sentenced to death so that none live more than their allotted time. I think it is evil if we accept even the premise of his argument that these technologies are very expensive. Even on that ground it’s evil to kill people just so that they don’t live longer than average. But perhaps even a bigger point is that I think these technologies of life extension are some of the most important things that people are working on today. And the billionaires are doing an incredible service to humanity by investing in these radical ideas and pushing the frontier and that is going to have spillover effects on everyone. If we are to reach the singularity it will because the billionaires are getting us there earlier and faster and they are the ones pushing us to the singularity and everyone will benefit from these life extension technologies.
So I agree with Paul quite a bit, more than you might expect, but sometimes he just says things which are absolutely evil.
We cover open borders, whether capitalism and democracy are compatible, the Baumol effect and more. Listen to the whole thing.
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.