Category Archives: Economic education and methods

Interventionism

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Noah Smith writes,

economists were more likely than the public to support the U.S. auto bailouts, by 58.6 percent to 52 percent. They were also more likely to support President Barack Obama’s economic stimulus bill, by 52.8 percent to 43.4 percent. More economists — over 97 percent — were in favor of tax hikes, and fewer supported school-voucher programs.

He cites a paper by Sapienza and Zingales.

On a related note, Barry Eichengreen praises capital controls.

It’s fair to say that the vast majority of economists are deeply skeptical about (if not downright hostile toward) their imposition. Yet it is not hard to find evidence in international financial markets of the kind of distortions that are likely to lead to imperfect information and, as a result, to economically inefficient and socially undesirable outcomes.

Pointer from Mark Thoma.

In a related essay, Smith argues that the current debate in economics is between the center-left and the radical left.

The New Center-Left Consensus is attractive to academics and policy wonks. It draws on an eclectic mix of mainstream economic theory, empirical studies and historical experience. It refuses to assume, as many conservatives and libertarians do, that free markets are always the best unless there is a glaring case for government intervention. It’s more willing to entertain all kinds of ways that government can improve the economy, from welfare to infrastructure spending to regulation, but it also recognizes that these won’t always work. . .

But there’s a second strain of progressive economic thinking that is gaining attention and strength. This alternative could be called the New Heterodox Explosion. It’s basically a movement to purge mainstream economics from progressive policy-making and thought.

Smith and the left dismiss those of us who favor free markets as outmoded and simple-minded. So the real debate is between economists who believe that elite mainstream economists know best how to fix the economy and others who believe that complexity theorists or evolutionary economists know best how to fix the economy.

I think that he accurately portrays the state of the discussion. I cannot think of a period in my life when market-oriented economists had less respect, unless it was the early 1960s when “fine tuning” had yet to be discredited.

Factor-price Non-equalization?

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A commenter wrote,

Given this definition of ‘discipline’, under what condition do you stop believing your intuition? What would you observe that would cause you to drop your belief in price-factor equalization, or, assortative mating?

Coincidentally, Josh Zumbrun writes,

Why would a company pay someone $80,000 if most people with an identical background—clones, in the paper’s parlance—earn $40,000? Conversely, why would someone with that background stay in the job earning $40,000 if another company will pay $80,000 for the same work?

The puzzle is that worker pay increases are highly correlated with the rate at which profit increases at the firms that they work.

My thoughts:

1. I tend to distrust the ability of economists to know more than a firm what the firm’s workers ought to be paid. Imagine an economist trying to tell Google that, based on your regression equation, it is paying its programmers more than the market wage. Google might reply that its programmers earn more because Google has selected better programmers.

2. The article offers the theory that firms with monopoly power will pay workers more. Perhaps, but a monopolist still has an incentive to avoid paying an above-market wage to its workers. In any case, if Google pays more because it has monopoly power, how pervasive is this? Do they pay above-market wages to janitorial staff? Above-market prices for office supplies? When Google employees travel, does Google pay more than the asking prices for hotel rooms and airline tickets?

3. Suppose that we grant that two workers who appear to be identical to someone running a regression equation are in fact identical in practice. It could be that each worker made a long-term commitment to a firm, and one chose a firm that happened to succeed and the other chose a firm that happened to fail. That might lead to factor-price non-equalization.

As you can see, I am very reluctant to let go of factor-price equalization. But if more evidence against it accumulates, I will be willing to change my mind.

However, in Specialization and Trade, I make the point at length that economic propositions are not falsifiable. If you want to stay committed to a proposition, you can. I say that economics deals with very few falsifiable hypotheses and many non-falsifiable frameworks of interpretation.

I can tell from the comments on previous posts that my position bothers some people enormously. They strongly oppose the situation as I describe it. Perhaps it will help to say that I have nothing against rigorous falsifiability in science, I just do not think it can be carried out in economics.

I hope you can appreciate that this is the situation with history. I doubt that we will ever be able to prove that wars are caused by X or that revolutions are caused by Y. Still, there are useful interpretive frameworks that tell us something about these issues.

I believe that all of the disciplines that deal with human society are going to have to live with this. If someone is really attached to their interpretive framework, it will be difficult or impossible to dissuade them. The best that one can hope is that people will not be so unreasonable as to assign 100 percent credibility to any information that supports their view and 0 percent credibility to any information that opposes it.

I think that we naturally try to fight back when one of our views is threatened, as mine are by the research cited above. The ideal would be for us to fight back when a study supports our views and be more accepting of studies that threaten our view, but it is difficult to live up to that ideal.

Ed Glaeser on Science and Economics

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He writes,

Science is ultimately about method, not the degree of certainty. Economics is a science whenever economists use the scientific method, which I understand to mean Karl Popper’s process of starting with particular facts, producing refutable hypotheses and then seeing whether the data reject those hypotheses. Yet the public unfortunately takes the word science to mean “certitude,” and economists (including myself) have too often been guilty of wrapping ourselves in our scientific mantles to make ideological pronouncements seem more compelling. Messrs. Offer and Söderberg suggest that “policy requires more humility” and that economists should face “some downgrading of authority, but not all the way.” I agree with the need for humility but would point out that politicians, pundits and ideologues of all stripes regularly make statements with far less factual basis than most economists.

I think that the public has a sort of binary classification. If it’s “science,” then an expert knows more than the average Joe. If it’s not a science, then anyone’s opinion is as good as anyone else’s. I strongly favor an in-between category, called a discipline. Think of economics as a discipline, where it is possible for avid students to know more than ordinary individuals, but without the full use of the scientific method.

Insight, Proof, and Knowledge

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A commenter writes,

So in your opinion intuition is sufficient. As long as we can tell an intuitive story about something, that is as good as proving it?

I think that “proof” is too high a standard to use in economics. If our knowledge is limited to what we can prove, then we do not know anything. I think that we have frameworks of interpretation which give us insights. This is knowledge, even if it is not as definitive or reliable as knowledge in physics or chemistry.

As an example, take factor-price equalization. The insight is that the easier it is to trade across countries, the more that factor prices will tend to converge. I think that this is an important insight. It is one of what I call the Four Forces driving social and economic trends in recent decades. (The other three are assortative mating, the shift away from manufacturing toward health care and education, and the Internet.)

Paul Samuelson proved a “factor-price equalization theorem” for a special case of two factors, two goods and two countries. However, it is very difficult, if not impossible, to extend that theorem to make it realistic, including the fact that not all industries are subject to diminishing returns. In my view, Samuelson’s theorem per se offers no insight, because it is so narrow in scope. The unprovable broader insight is what is useful.

Incidentally, I also think that factor-price equalization is hard to prove statistically. Too many other things are happening at once to be able to say definitively that factor-price equalization is having an effect, say, on unskilled workers’ wages in the U.S. and China. I believe that it is having an effect, and there are studies that support my view, but it is not provable.

In order to prove something mathematically, you have to make narrow assumptions. In physics or engineering, this often works out well. When you roll a ball down an inclined plane, ignoring friction causes only a small error in the calculation.

In economics, the factors that you leave out in order to build a mathematical model tend to be more important. As a result, the requirement to express ideas in the form of mathematical models is harmful in two ways. We waste time proving false theorems and we miss out on useful insights.

The narrow assumptions lead you to prove something which is false in the real world.. For example, the central insight of the “market for lemons” proof is that a used car market cannot work. However, once we expand the assumptions to allow for warranties, dealer reputations, mechanics’ inspections, and so on, the original theorem does not hold.

Meanwhile, there are insights that are missed because they cannot be represented in an elegant mathematical way. A lot of the insights that I offer in Specialization and Trade fall in that category.

Our goal should be to acquire knowledge. The demand for proof hurts rather than helps with that process.

Two Pointed Questions Posed as Tweets

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1. From Josh Hendrickson:

I don’t get it. Everyone has a model; whether they use math/graphs/words. Why are only models w/math denigrated?

Other things equal, it is harder to understand what is going on in a math presentation. Other things equal, insisting on math restricts the sort of assumptions you can work with to those that you find tractable.

That would suggest that verbal arguments dominate mathematical arguments. I am not going to insist that this is always the case, but I think it does create a presumption in favor of verbal arguments. Yes verbal arguments can be vague. But a lot of hand-waving goes on in mathematical papers as well.

So the way I would put it is that today there is a strong presumption in favor of expressing models (or, to use my preferred term, interpretive frameworks) in mathematical terms. I would like to see the presumption go the other way.

Pete Boettke has an essay/post that is pertinent and aligns with my views. Strongly recommended.

2. From someone with the Twitter handle “representative agent’:

I’m thinking about PSST as a business cycle theory. what are its most distinctive implications?

One important implication is that unemployed workers will not be hired back into the same jobs they had before. I believe that in the 1950s, there were recessions that were primarily inventory corrections, so that after you went through a couple of quarters with automobile manufacturers and their suppliers laying off workers, those workers got recalled. Those examples run counter to PSST.

A related implication is that just “boosting demand” in general will not do much to deal with unemployment. The adjustments that are needed are specific to workers located in specific parts of geographic/industry/skill space. It predicts that just throwing money at, say, the green energy industry, will not necessarily increase employment.

Another implication is that shocks to sectors that are closely connected to other sectors (as might be shown by a network graph) will have more effects than shocks to sectors that are more isolated. That may explain why the crash of the dotcom bubble did very little, but shocks to the energy sector in the 1970s and to the banking sector at other times have had severe impacts.

Second Thoughts on This Year’s Economics Nobel Prize

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There is something that I find troubling about the Nobel Prize for Hart and Holmstrom, and I want to try to articulate what it is.

Think of their work as consisting of three steps.

1. Identifying some real-world complexities that affect how businesses operate. For example, output may result from both effort and luck. Output may be joint. A worker’s job description may include more than one objective.

2. Construct a mathematical optimization model that incorporates such complexities.

3. Offer insights into designing appropriate compensation systems, including when to outsource an activity altogether.

A big question is: how important is step 2?

In the eyes of the mainstream economics profession, it is extremely important. Without it, you either do not get to step 3, or your claims in step 3 lack reliability and credibility. Step 2 is why Hart and Holmstrom earned the Nobel Prize.

In my view, step 2 is unnecessary. If anything, it tends to get in the way, often creating a barrier to doing step 1 properly, because economists limit themselves to what is mathematically tractable. I think that Hart and Holmstrom sometimes (often?) made good choices in step 1, and that is what accounts for the value of where they arrived at in step 3.

In Specialization and Trade, I offer a number of asides that go from step 1 to step 3 directly (I will put some examples below the fold). In these asides, I am looking at Hart-Holmstrom issues. But I do not think in terms of mathematical optimization. Instead, I think in terms of a dynamic process of trial and error. A manager tries an approach to compensation. As long as it seems to work, it persists. Once it gets gamed too much by the employees, something happens–the manager makes changes, the manager gets fired, or the firm goes out of business.

Another point is that I believe that managers closer to the problem do a better job of solving it. Writing the problem down in mathematical terms makes it seem as though you can solve the problem remotely. It leads a David Cutler to believe that the government can design a compensation system for doctors that will correctly incent “quality health care.” It ignores what I call the “regulator’s calculation problem.”

I have seen several George Mason economists, including Tabarrok, Cowen, and Boettke, praise the Nobel for Hart and Holmstrom. I certainly think that the Nobel committee could have done worse. But in the end, I think Hart and Holmstrom represent a way of doing economics that is too constrained by the arbitrary requirement to use math, too focused on optimization relative to a given problem rather than the dynamics of trial and error, and too inclined to suggest that decisions can be made effectively by remote algorithms (and potentially by regulators who might use such algorithms) when in fact local decision-makers have important information that is not available remotely.
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The Three Iron Laws, Illustrated

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Tyler Cowen writes,

I find few people are willing to embrace the more consistent statistical preference plus agnosticism, rather they play the game of “statistical noise for thee but not for me.”

He is writing about what is now a seemingly ancient question about the stock market’s reaction to the ups and downs of Mr. Trump. I want to say that this issue illustrates Merle Kling’s three laws of social science.

1. Sometimes it’s this way and sometimes it’s that way. In this case, sometimes one can find an affect of a change in Mr. Trump’s prospects and the market, and sometimes one cannot.

2. The data are insufficient. In this case, there is not enough data to make a definitive judgment.

3. The methodology is flawed. In this case, one can argue that the analyst is basing a conclusion on statistical noise.

Maybe the quote from Tyler suggests a fourth iron law: if the issue is emotionally salient, given that the first three iron laws hold, motivated reasoning and confirmation bias take over.

My Review of Erwin Dekker

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is now available. I write,

he identifies a number of tensions in their thought: the economist as detached observer versus the economist as political participant; progress versus decline; liberty versus restraint; individualism versus culture; modernity versus tradition; and what Jacob T. Levy would call rationalist versus pluralist.

The book, The Viennese Students of Civilization, is published by Cambridge University Press, about which I have several complaints.

1. After several tries, I still have not found it on their web site. Here it is on Amazon.

2. The price is terribly high, particularly for the Kindle version.

3. To add insult to injury, it appears to me that CUP did not spend a dime (or should I say a pence?) on editorial assistance. The book is filled with errors of English usage.

It is an important book, and shame on CUP for making it hard to afford and difficult to read.

Dueling Samuelson’s Ghost

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Peter J. Boettke, Christopher J. Coyne and Peter T. Leeson write,

the teachings of economics are necessary for understanding the complexities of social reality. Perhaps its two most important public roles are: (1) to explain how within a specific set of institutional arrangements the power of self-interest can spontaneously generate patterns of social order that simultaneously achieve individual autonomy, generalized prosperity and social peace, and (2) through means-ends analysis, to provide parameters on people’s utopian notions of economic policy. The first captures the didactic role of the economist in teaching the nuances of Adam Smith’s ‘invisible hand’ and the second captures the contribution that economics as a technical discipline can offer to public policy discourse. When we move beyond these roles and instead try to employ economics as the primary tool for social control, we run afoul and distort the teachings of the discipline.

Read the entire essay, which is a plea for economists to desist from acting as high priests of social engineering. However, I would place a (sic?) next to the word “parameters” in that paragraph. Perhaps the authors are talking about perimeters. In any case, it is the final sentence that is most important.

Specialization and Trade makes a similar case.

Andrew Gelman on the Replication Crisis

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He writes,

2011: Joseph Simmons, Leif Nelson, and Uri Simonsohn publish a paper, “False-positive psychology,” in Psychological Science introducing the useful term “researcher degrees of freedom.” Later they come up with the term p-hacking, and Eric Loken and I speak of the garden of forking paths to describe the processes by which researcher degrees of freedom are employed to attain statistical significance. The paper by Simmons et al. is also notable in its punning title, not just questioning the claims of the subfield of positive psychology but also mocking it.

Pointer from Alex Tabarrok.

I am pretty sure that at some point prior to 2011 when I criticizes macro-econometrics I said that the degrees of freedom belong to the researcher rather than to the data. That is a minor note.

More important, I think that John Ioannidis deserves a mention. Yes, Gelman is focused on research in the field of psychology and Ioannidis focused primarily on epidemiology, but his paper Why Most Published Research Findings are False strikes me as a milestone worth including in the timeline.

Gelman’s post is mostly about the tension between insiders and outsiders in the academic world. The insiders’ chief weapon is the peer-reviewed journal article. The outsiders’ chief weapon is the blog post. If, like me, your heart is with the outsiders, you will find Gelman’s post bracing.

I should note that in my high school statistics class last year, I had an autodidact student who, among other things, was very familiar with the term p-hacking and the related literature. This gives me hope that as the generations turn over in academia, things might improve. As Max Planck is said to have remarked, science advances one funeral at a time.