No, I’m not in Iowa, but I’ve never covered it before, and today seems like as good a day as any. Here goes:
1. Painter: Grant Wood. Here is an interpretative take on American Gothic. It’s not by the way man and wife in the picture, but rather Wood’s sister standing next to the local dentist.
2. Novelist: I draw a blank, sorry people…Does it count that Joe Haldeman (The Forever War) was a product of the Iowa Writer’s Workshop? There must be other examples as well.
3. Hero: Norman Borlaug.
4. Actor: John Wayne is from Iowa, but I can’t call him a favorite. I guess he is my favorite version of…John Wayne. If that. Can one call Johnny Carson an actor? I never took to him either.
5. Jazz musician: Yes there is one, Bix Beiderbecke. Art Farmer too, and also Charlie Haden. Yet how rarely one hears of the “Iowa jazz tradition.”
6. Guitarist: Dick Dale, don’t by the way forget his Lebanese background, which you can hear in his riffs.
7. Movie, set in: What’s Eating Gilbert Grape? Honorable mention to the more obvious Field of Dreams, an OK but not great film in my view.
The bottom line: Who would have thought “jazz musician” would be the strongest category here? Those Iowans are so busy with their jazz, it is amazing they have time to lobby for their ethanol subsidies.
That is a new paper by Ali Faraji-Rad and Michel Tuan Pham, here is the abstract:
Uncertainty is an unavoidable part of human life. How do states of uncertainty influence the way people make decisions? We advance the proposition that states of uncertainty increase the reliance on affective inputs in judgments and decisions. In accord with this proposition, results from six studies show that the priming of uncertainty (vs. certainty) consistently increases the effects of a variety of affective inputs on consumers’ judgments and decisions. Primed uncertainty is shown to amplify the effects of the pleasantness of a musical soundtrack (study 1), the attractiveness of a picture (study 2), the appeal of affective attributes (studies 3 and 4), incidental mood states (study 6), and even incidental states of disgust (study 5). Moreover, both negative and positive uncertainty increase the influence of affect in decisions (study 4). The results additionally show that the increased reliance on affective inputs under uncertainty does not necessarily come at the expense of a reliance on descriptive attribute information (studies 2 and 5), and that the increased reliance on affect under uncertainty is distinct from a general reliance on heuristic or peripheral cues (study 6).
The pointer is from Cass Sunstein on Twitter. File under “The culture that is Iowa”?
Every year since 2006 more democracies have experienced erosion in political rights and civil liberties than have registered gains, as we find in our annual Freedom in the World report. In all, 110 countries, more than half the world’s total, have suffered some loss in freedom during the past 10 years.
That is from Mark P. Lagon and Arch Puddington at the WSJ. I would like to see a good theory of how liberty, democracy, and liberalism — or however we wish to characterize that bundle — comove across the globe, in both positive and negative times.
“Unemployment is really hard to handle,” said U Saw Tha Pyae, whose six elephants have been jobless for the past two years. “There is no logging because there are no more trees.”
Myanmar’s leading elephant expert, Daw Khyne U Mar, estimates that there are now 2,500 jobless elephants, many of them here in the jungles of eastern Myanmar, about two and a half hours from the Thai border. That number would put the elephant unemployment rate at around 40 percent, compared with about 4 percent for Myanmar’s people.
“Most of these elephants don’t know what to do,” Ms. Khyne U Mar said. “The owners have a great burden. It’s expensive to keep them.”
Adult elephants, which each weigh about 10,000 pounds, eat 400 pounds of food a day and, other than circuses and logging, have limited job opportunities.
Logging is arduous. But elephant experts say hard work is one reason Myanmar’s elephants have remained relatively healthy. A 2008 study calculated that Myanmar’s logging elephants, which have a strict regimen of work and play, live twice as long as elephants kept in European zoos, a median age of 42 years compared with 19 for zoo animals.
Here is the full NYT story, via Michelle Dawson and Otis Reid. The story is interesting throughout, you will note the elephants had strong labor law protections:
The military governments adhered to a strict labor code for elephants drawn up in British colonial times: eight-hour work days and five-day weeks, retirement at 55, mandatory maternity leave, summer vacations and good medical care. There are still elephant maternity camps and retirement communities run by the government. In a country where the most basic social protections were absent during the years of dictatorship, elephant labor laws were largely respected.
Interesting throughout — I wonder what is the natural rate of unemployment for elephants in a freer labor market…?
We match individual-level survey data with information on the historical lifeways of ancestors, focusing on Africa, where the transition away from such modes of production began only recently. Within enumeration areas and occupational groups, we find that individuals from ethnicities that derived a larger share of subsistence from agriculture in the pre-colonial era are today more educated and wealthy. A tentative exploration of channels suggests that differences in attitudes and beliefs as well as differential treatment by others, including less political power, may contribute to these divergent outcomes.
That is from a recent paper by Michalopoulos, Putterman, and Weil. Here are video and ungated versions.
Although Stockfish and Komodo have differences in their evaluation scales—happily less pronounced than they were 1 and 2 years ago—they agree that the world’s elite made six times more large errors when on the lower side of equality.
We don’t know how general this phenomenon is, but interestingly it seems to hold much more strongly for top players than for weak players. That is from chess of course.
Here is much more detail from Ken Regan, along with some suggested hypotheses and resolutions.
From Foreign Affairs. Here is my bottom line:
His latest entry into this debate [over stagnation], The Rise and Fall of American Growth, is likely to be the most interesting and important economics book of the year. It provides a splendid analytic take on the potency of past economic growth, which transformed the world from the end of the nineteenth century onward.
In a nutshell, Gordon is probably right about the past, but wrong about the future. My greatest reservation about the work is that Gordon thinks he can predict future rates of productivity growth with considerable accuracy:
…although Gordon focuses on the demographic challenges the United States faces, he never considers that today, thanks to greater political and economic freedom all over the world, more individual geniuses have the potential to contribute to global innovation than ever before.
…many past advances came as complete surprises. Although the advents of automobiles, spaceships, and robots were widely anticipated, few foretold the arrival of x-rays, radio, lasers, superconductors, nuclear energy, quantum mechanics, or transistors. No one knows what the transistor of the future will be, but we should be careful not to infer too much from our own limited imaginations.
And here is the “oops” aspect of the book:
What Gordon neglects to mention, however, is that he is also the author of a 2003 Brookings essay titled “Exploding Productivity Growth,” in which he optimistically predicted that productivity in the United States would grow by 2.2 to 2.8 percent for the next two decades, most likely averaging 2.5 percent a year; he even suggested that a three percent rate was possible.
…Gordon offers a brief history of the evolution of his views on productivity. Yet he does not mention the 2003 essay, nor does he explain why he has changed his mind so dramatically. He also fails to cite other proponents of the stagnation thesis, even though…their work predates his book.
Nonetheless this is a tract well worth reading. Again, here is my entire review.
The link is here, the contents include:
A Unit Root in Postwar U.S. Real GDP Still Cannot Be Rejected, and Yes, It Matters: David Cushman examines whether shocks are transitory, permanent, or some of each.
The political ideology of Industrial Relations: Using three metrics, Mitchell Langbert shows the left orientation of the field.
Eli Heckscher’s Ideological Migration Toward Market Liberalism: Benny Carlson explores the intellectual evolution of a great Swedish economist.
Glimpses of Adam Smith: Excerpts from the biography by Ian Simpson Ross.
Symposium:
Classical Liberalism in Econ, by Country: Authors from around the world tell us about their country’s culture of political economy, in particular the vitality of liberalism in the original political sense, historically and currently, with special attention to professional economics as practiced in academia, think tanks, and intellectual networks.
New contributions:
Young Back Choi and Yong Yoon:
Liberalism in Korea
Pavel Kuchař:
Liberalism in Mexican Economic Thought, Past and Present
(All of the papers from this symposium, which has carried across multiple issues of EJW, are collected at this page.)
EJW Audio
David Cushman on Transitory and Permanent Shocks to GDP
Hugo Faria on Venezuela and Liberalism
Fortune: Hiring a new employee, for instance, now takes 63 days, up from 42 in 2010, according to a 2015 study we did with 400 corporate recruiters. Meanwhile the average time to deliver an office IT project increased by more than a month from 2010 to 2015, and now stands at over 10 months from start to delivery—this particular nugget coming from a study we conducted with 2,000 project managers at more than 60 global organizations.
And when companies need to mesh processes, things get even slower. Multiple surveys we did with several thousand stakeholders in the realm of business-to-business sales revealed some striking evidence of institutional delay. The time required for one company to sell something to another, for example, has risen 22% in the past five years, as gaining consensus from one or two buyers has turned into five or more.
More here.
It certainly feels like more people are required to sign off on something than ever before and that fact is slowing things down. The time-series is short, however, and lots of other things are going on. Maybe firms take longer to hire when the growth rate is low. File under speculative.
The excellent Susan Athey addresses that question on Quora, here is one excerpt:
Machine learning is a broad term; I’m going to use it fairly narrowly here. Within machine learning, there are two branches, supervised and unsupervised machine learning. Supervised machine learning typically entails using a set of “features” or “covariates” (x’s) to predict an outcome (y). There are a variety of ML methods, such as LASSO (see Victor Chernozhukov (MIT) and coauthors who have brought this into economics), random forest, regression trees, support vector machines, etc. One common feature of many ML methods is that they use cross-validation to select model complexity; that is, they repeatedly estimate a model on part of the data and then test it on another part, and they find the “complexity penalty term” that fits the data best in terms of mean-squared error of the prediction (the squared difference between the model prediction and the actual outcome). In much of cross-sectional econometrics, the tradition has been that the researcher specifies one model and then checks “robustness” by looking at 2 or 3 alternatives. I believe that regularization and systematic model selection will become a standard part of empirical practice in economics as we more frequently encounter datasets with many covariates, and also as we see the advantages of being systematic about model selection.
…in general ML prediction models are built on a premise that is fundamentally at odds with a lot of social science work on causal inference. The foundation of supervised ML methods is that model selection (cross-validation) is carried out to optimize goodness of fit on a test sample. A model is good if and only if it predicts well. Yet, a cornerstone of introductory econometrics is that prediction is not causal inference, and indeed a classic economic example is that in many economic datasets, price and quantity are positively correlated. Firms set prices higher in high-income cities where consumers buy more; they raise prices in anticipation of times of peak demand. A large body of econometric research seeks to REDUCE the goodness of fit of a model in order to estimate the causal effect of, say, changing prices. If prices and quantities are positively correlated in the data, any model that estimates the true causal effect (quantity goes down if you change price) will not do as good a job fitting the data. The place where the econometric model with a causal estimate would do better is at fitting what happens if the firm actually changes prices at a given point in time—at doing counterfactual predictions when the world changes. Techniques like instrumental variables seek to use only some of the information that is in the data – the “clean” or “exogenous” or “experiment-like” variation in price—sacrificing predictive accuracy in the current environment to learn about a more fundamental relationship that will help make decisions about changing price. This type of model has not received almost any attention in ML.
The answer is interesting, though difficult, throughout. Here are various Susan Athey writings, on machine learning. Here are other Susan Athey answers on Quora, recommended. Here is her answer on whether machine learning is “just prediction.”
