Month: February 2018
2. Skepticism about Amazon in health care (NYT).
4. Justin Wolfers on women in economics (NYT).
5. Lasers uncover the complexity and density of Mayan civilization. Was it the most densely populated part of the world at the time?
David’s observation is that there are two technologies for producing automobiles in America.
One is to manufacture them in Detroit, and the other is to grow them in Iowa. Everybody
knows about the first technology; let me tell you about the second. First you plant seeds, which are the raw material from
which automobiles are constructed. You wait a few months until
wheat appears. Then you harvest the wheat, load it onto ships,
and sail the ships eastward into the Pacific Ocean. After a few months, the ships reappear with Toyotas on them.
I learned recently from Robert Allen’s Global Economic History that Friedman’s analysis was preceded by more than three hundred years by an unknown Micmac Indian who at the height of the fur trade observed:
In truth, my brother, the Beaver does everything to perfection. He makes for us kettles, axes, swords, knives and gives us drink and food without the trouble of cultivating the ground.
(8) Coarse Grades: Informing the Public by Withholding Information, by Rick Harbaugh and Eric Rasmusen
Certifiers of quality often report only coarse grades to the public despite having measured quality more finely, e.g., “Pass” or “Certified” instead of “73 out of 100.” Why? We show that coarse grades result in more information being provided to the public because the coarseness encourages those of middling quality to apply for certification. Dropping exact grading in favor of the best coarse grading scheme reduces public uncertainty because the extra participation outweighs the coarser reporting. In some circumstances, the coarsest meaningful grading scheme, pass-fail grading, results in the most information.
Here is the link to American Economic Journal: Microeconomics. Of course another mechanism favoring coarse grading is that corrupt grades are easier to spot. If too many one-star Michelin restaurants are slid up to three stars, it is obvious something is going on. But if on a scale of one hundred a restaurant that ought to be a 67 is given a 73, who is really to say what those numbers are supposed to mean? There are many market settings where the coarser grading scheme is preferred over the finer alternative.
Dan Hanson writes:
I wonder how many of the people making predictions about the future of truck drivers have ever ridden with one to see what they do?
One of the big failings of high-level analyses of future trends is that in general they either ignore or seriously underestimate the complexity of the job at a detailed level. Lots of jobs look simple or rote from a think tank or government office, but turn out to be quite complex when you dive into the details.
For example, truck drivers don’t just drive trucks. They also secure loads, including determining what to load first and last and how to tie it all down securely. They act as agents for the trunking company. They verify that what they are picking up is what is on the manifest. They are the early warning system for vehicle maintenance. They deal with the government and others at weighing stations. When sleeping in the cab, they act as security for the load. If the vehicle breaks down, they set up road flares and contact authorities. If the vehicle doesn’t handle correctly, the driver has to stop and analyze what’s wrong – blown tire, shifting load, whatever.
In addition, many truckers are sole proprietors who own their own trucks. This means they also do all the bookwork, preventative maintenance, taxes, etc. These people have local knowledge that is not easily transferable. They know the quirks of the routes, they have relationships with customers, they learn how best to navigate through certain areas, they understand how to optimize by splitting loads or arranging for return loads at their destination, etc. They also learn which customers pay promptly, which ones provide their loads in a way that’s easy to get on the truck, which ones generally have their paperwork in order, etc. Loading docks are not all equal. Some are very ad-hoc and require serious judgement to be able to manoever large trucks around them. Never underestimate the importance of local knowledge.
I’ve been working in automation for 20 years. When you see how hard it is to simply digitize a paper process inside a single plant (often a multi-year project), you start to roll your eyes at ivory tower claims of entire industries being totally transformed by automation in a few years. One thing I’ve learned is a fundamentally Hayekian insight: When it comes to large scale activities, nothing about change is easy, and top-down change generally fails. Just figuring out the requirements for computerizing a job is a laborious process full of potential errors. Many automation projects fail because the people at the high levels who plan them simply do not understand the needs of the people who have to live with the results.
Take factory automation. This is the simplest environment to automate, because factories are local, closed environments that can be modified to make things simpler. A lot of the activities that go on in a factory are extremely well defined and repetitive. Factory robots are readily available that can be trained to do just about anything physically a person can do. And yet, many factories have not automated simply because there are little details about how they work that are hard to define and automate, or because they aren’t organized enough in terms of information flow, paperwork, processes, etc. It can take a team of engineers many man years to just figure out exactly what a factory needs to do to make itself ready to be automated. Often that requires changes to the physical plant, digitization of manual processes, Statistical analysis of variance in output to determine where the process is not being defined correctly, etc.
A lot of pundits have a sense that automation is accelerating in replacing jobs. In fact, I predict it will slow down, because we have been picking the low hanging fruit first. That has given us an unrealistic idea of how hard it is to fully automate a job.
Do you have a process for note taking while you read? Like clipping important parts in books, etc? Or do you just read and that’s all?
And in the same week William D. writes to me:
…How do annotate, or mark up books that you read? This question is prompted by a lively discussion between a professor of mine (who argued that the text should be kept clean to ensure the integrity of re-readings) and myself (I find that my comprehension and ability to navigate the text is increased by annotations). Do you think that re-readings are harmed or benefitted by the presence of past annotations on the text? Personally I am not sure. Does it depend on the text?
My approach is simple, though not sophisticated. If I own the book, and there is something interesting on the page, I fold over the page corner. (If it is a library book I simply write down the page numbers.) The mere act of folding makes the fact or point easier to remember, and in fact that is my main purpose, namely to turn the piece of information into a claim about visual space. That said, the folds also make the source easier to find again if needed. I agree that marking up the page “ruins” your next read of the source. I find that by having to search again on the page I find other significant ideas as well.
That said, if I teach a book I have to mark it up to find particular passages more easily on the spur of the moment before the students in class. Then I stop learning from my rereads of the book, but instead learn from the teaching of it.
I pretend no universality for those procedures, but they work for me. Do you do something different?
New technological breakthroughs in biomedicine should have made it easier for countries to improve life expectancy at birth (LEB). This paper measures the pace of improvement in the decadal gains of LEB, for the last 60-years adjusting for each country’s starting point of LEB.
LEB increases over the next 10-years for 139 countries between 1950 and 2009 were regressed on LEB, GDP, total fertility rate, population density, CO2 emissions, and HIV prevalence using country-specific fixed effects and time-dummies. Analysis grouped countries into one-of-four strata: LEB < 51, 51 ≤ LEB < 61, 61 ≤ LEB < 71, and LEB ≥ 71.
The rate of increase of LEB has fallen consistently since 1950 across all strata. Results hold in unadjusted analysis and in the regression-adjusted analysis. LEB decadal gains fell from 4.80 (IQR: 2.98–6.20) years in the 1950s to 2.39 (IQR:1.80–2.80) years in the 2000s for the healthiest countries (LEB ≥ 71). For countries with the lowest LEB (LEB < 51),
decadal gains fell from 7.38 (IQR:4.83–9.25) years in the 1950s to negative 6.82 (IQR: -12.95–1.05) years in the 2000s. Multivariate analysis controlling for HIV prevalence, GDP, and other covariates shows a negative effect of time on LEB decadal gains among all strata.
Contrary to the expectation that advances in health technology and spending would hasten improvements in LEB, we found that the pace-of-growth of LEB has slowed around the world.
Of course in many United States counties, life expectancy is moving backwards these days.
For the pointer I thank the eternal Kevin Lewis.
For that reason, Woodrow says that he saw their version of self-driving trucks as complementing humans, not replacing them. To make their case, Uber created a model of the industry’s labor market based on Bureau of Labor Statistics data. Then, they created scenarios that looked at a range of self-driving-truck adoption rates and how often those autonomous trucks would be on the road in comparison to human-driven vehicles.
Their numbers for autonomous-truck adoption are intentionally very aggressive, Woodrow says, corresponding to 25, 50, and 70 percent of today’s trucks being self-driven. These do not reflect an Uber prediction that between 500,000 and 1.5 million self-driving trucks will be on the road by 2028, but rather they allow the model to show the dynamics in the labor market that might result from widespread adoption. “Imagine that self-driving trucks are incredibly successful and impactful,” he says. “What would that mean?”
The other set of numbers in the model—the utilization rate of the self-driving trucks—is the component that leads Uber to a different analysis of the effect that these vehicles will have on truckers. Basically, if the self-driving trucks are used far more efficiently, it would drive down the cost of freight, which would stimulate demand, leading to more business. And, if more freight is out on the roads, and humans are required to run it around local areas, then there will be a greater, not lesser, need for truck drivers.
Skip the first fifteen (!) minutes of introduction:
It is easy to develop a better understanding of Renaissance Venice or Florence by simply visiting the cities, as much of their past remains to be seen. Ancient Greece of course is much tougher, though still there are shards of significance. I am pleased that I can read Shakespeare without a translation, although I suspect this won’t be true for most educated Americans a century from now.
To maximize the total joy from understanding and consuming the past, how close to that past do you wish to be? One thousand years from now, assuming things are still up and running, you will have another thousand years of history to consume, enjoy, and perhaps grieve over. But many important eras will seem strange or incomprehensible to you, beyond your intellectual grasp. You might not know what “colonial America” really was, whereas today I know actual people who live in colonial homes, for instance in Alexandria, Virginia.
Is having a longer past to look back upon always more rewarding? Or would you rather have a shorter past to ponder, but be closer in time and sympathy to some of the most foundational developments?
Would you prefer to see them inaugurate those space colonies, or instead have some partial grasp of what “The Enlightenment” really was about?
Is there a worry that human history could become like one of those never-ending, exhausting series of fantasy novels, where only the diehards care about volume 27 and the ongoing saga of the Mrithythambs and their struggle against the Kohnipoors? One of the advantages of living in the current day is that you can have a pretty good and internally coherent narrative for what has happened from the ancient Greeks (or earlier?) up through the current day.
For this post I am indebted to a lunchtime conversation with S.
Professional Ethics 101: In the Journal of Economic Literature, Anne Krueger reviewed The Oxford Handbook of Professional Economic Ethics. The volume’s editors George DeMartino and Deirdre McCloskey reply, suggesting that Krueger’s review emblematizes the very concern of the book, the ethical competence of the economics profession.
The Progressive Legacy Rolls On: Also in the Journal of Economic Literature, Marshall Steinbaum and Bernard Weisberger reviewed Thomas Leonard’s Illiberal Reformers: Race, Eugenics, and American Economics in the Progressive Era. Phillip Magness arguesthat Steinbaum and Weisberger treat Leonard unjustly and that they fail in their attempt to excuse the progressive legacy of some of its disgraces.
Will the Real Specification Please Stand Up? Alex Young reports on mysteries in how specification description varies between a working paper and its published form in The Accounting Review, mysteries that data release would resolve. The authors Andrew Bird and Stephen Karolyi respond.
Guns and crime: The right-to-carry debate carries on, with emphasis on the handling of state trends and the crack-cocaine period, with Carlisle Moody and Thomas Marvell criticizing recent work, and lead author John Donohue firing back.
New entries extend the Classical Liberalism in Econ, by Country series to 17 articles:
- Ukraine: Mykola Bunyk and Leonid Krasnozhon treat liberalism in the country’s intellectual and political history and its scene today.
- Ecuador: Pedro Romero, Fergus Hodgson, and María Paz Gómez describe the country’s political fortunes and the role of liberal ideas, notably in dollarization.
EJW moves to the Fraser Institute.
EJW thanks its referees and others who contribute to its mission.
That is the topic of my latest Bloomberg column, here is one excerpt:
Using land value capture for New York City subway improvements makes sense because other funding methods have failed politically. Earmarking some of the state income tax to the subway might be better, but people who don’t use the subway — the majority in New York State — just don’t want to pay. So the state must look elsewhere.
In the meantime, new subway lines are rare, even though the population and economic output of the city have grown substantially. The new Second Avenue line opened only last year, though construction started in 1972 and had to overcome numerous fiscal and political obstacles. On the older lines, delays are frequent and the system lacks modern technology. It is not unusual for signal switches to date from the 1930s. By one estimate, a much-needed revamp of the New York City subway system would cost more than $100 billion.
It is also good practice to consider when one’s argument doesn’t hold:
My own locality, Fairfax County in northern Virginia, treats landowners and real estate developers pretty favorably. They have been a dominant special interest group with many state and local politicians. That might not sound ideal, but those individuals have strongly supported the building out of the community, creating jobs and keeping down home prices. If landowners had been asked to foot more of the bill, the local political pressures for pro-growth policies probably would have been less strong and a NIMBY mentality would have prevailed. Unlike with the New York City subway, here the local interests have much greater sway, and thus land value capture could clog up politics rather than inducing new construction.
Recently I spent a day at a conference discussing Henry George’s “Progress and Poverty,” a late 19th century work that is perhaps the best-selling economics book in U.S. history. George spent much of his life campaigning for a relatively high tax on land and thus landlords, developing the fairness and efficiency arguments I mentioned above. By the end of the conference, I concluded that George had some good economic arguments, but also that he was politically naive. At the margin we should move in George’s direction, but ultimately landowners have to be part of the building coalitions rather than pure victims.
Do read the whole thing.
4. Russ Roberts video, The Paradox of Household Income.
Democrats’ trust in government data has shrunk over time; Republicans’ trust has grown. Today, with their party in unified control of government, Republicans are slightly more likely than Democrats to believe official government economic stats; 58 percent of Republicans completely or somewhat trust these numbers, compared with 52 percent of Democrats.
That is from Catherine Rampell.
What would make more sense to me is that, having first built an interface for its employees, and then a standardized infrastructure for its health care suppliers, is that Amazon converts the latter into a marketplace where PBMs, insurance administrators, distributors, and pharmacies have to compete to serve employees. And then, once that marketplace is functioning, Amazon will open the floodgates on the demand side, offering that standard interface to every large employer in America…
This is certainly ambitious enough — basically intermediating U.S. employers and the U.S. healthcare industry — but in fact this only sets the stage for the wholesale disruption of American healthcare. First, Amazon could not only open up its standard interface to other large employers, but small-and-medium sized businesses, and even individuals; in this way the Amazon Health Marketplace could aggregate by far the most demand for healthcare.
And to close the piece:
My expectation, then, is not that the Internet methodically disrupts industry after industry in some sort of chronological order, but rather that the entire edifice lasts far longer than technologists think, only to one day collapse far quicker than anyone expected.
The ultimate winners of this shakeout, then, are not only companies that are building businesses predicated on the Internet, but just as importantly, are willing and able to build those businesses with the patience that will be necessary to wait for the old order to collapse, particularly if that collapse happens years or decades after the underlying business models are rotten.