Is economics looking at itself?

Patricia Cowen recently wrote a piece for the New York Times:  “Ivory Tower Unswayed by Crashing Economy

The article contains precisely what you might expect from a title like that.  This snippet gives you the idea:

The financial crash happened very quickly while “things in academia change very, very slowly,” said David Card, a leading labor economist at the University of California, Berkeley. During the 1960s, he recalled, nearly all economists believed in what was known as the Phillips curve, which posited that unemployment and inflation were like the two ends of a seesaw: as one went up, the other went down. Then in the 1970s stagflation — high unemployment and high inflation — hit. But it took 10 years before academia let go of the Phillips curve.

James K. Galbraith, an economist at the Lyndon B. Johnson School of Public Affairs at the University of Texas, who has frequently been at odds with free marketers, said, “I don’t detect any change at all.” Academic economists are “like an ostrich with its head in the sand.”

“It’s business as usual,” he said. “I’m not conscious that there is a fundamental re-examination going on in journals.”

Unquestioning loyalty to a particular idea is what Robert J. Shiller, an economist at Yale, says is the reason the profession failed to foresee the financial collapse. He blames “groupthink,” the tendency to agree with the consensus. People don’t deviate from the conventional wisdom for fear they won’t be taken seriously, Mr. Shiller maintains. Wander too far and you find yourself on the fringe. The pattern is self-replicating. Graduate students who stray too far from the dominant theory and methods seriously reduce their chances of getting an academic job.

My reaction is to say “Yes.  And No.”  Here, for example, is a small list of prominent economists thinking about economics (the position is that author’s ranking according to ideas.repec.org):

There are plenty more. The point is that there is internal reflection occurring in economics, it’s just not at the level of the journals.  That’s for a simple enough reason – there is an average two-year lead time for getting an article in a journal.  You can pretty safely bet a dollar that the American Economic Review is planning a special on questioning the direction and methodology of economics.  Since it takes so long to get anything into journals, the discussion, where it is being made public at all, is occurring on the internet.  This is a reason to love blogs.

Another important point is that we are mostly talking about macroeconomics.  As I’ve mentioned previously, I pretty firmly believe that if you were to stop an average person on the street – hell, even an educated and well-read person – to ask them what economics is, they’d supply a list of topics that encompass Macroeconomics and Finance.

The swathes of stuff on microeconomics – contract theory, auction theory, all the stuff on game theory, behavioural economics – and all the stuff in development (90% of development economics for the last 10 years has been applied micro), not to mention the work in econometrics; none of that would get a mention.  The closest that the person on the street might get to recognising it would be to remember hearing about (or possibly reading) Freakonomics a couple of years ago.

From marriage to trade with China

In another great example of bouncing topics around in the often-academic blogs, we have this:

Betsey Stevenson and Justin Wolfers wrote an article for Cato Unbound: “Marriage and the Market“. Here is a brief summary of their idea (the exact snippet chosen is stolen directly from Arnold Kling):

So what drives modern marriage? We believe that the answer lies in a shift from the family as a forum for shared production, to shared consumption…the key today is consumption complementarities – activities that are not only enjoyable, but are more enjoyable when shared with a spouse. We call this new model of sharing our lives “hedonic marriage”.

…Hedonic marriage is different from productive marriage. In a world of specialization, the old adage was that “opposites attract,” and it made sense for husband and wife to have different interests in different spheres of life. Today, it is more important that we share similar values, enjoy similar activities, and find each other intellectually stimulating. Hedonic marriage leads people to be more likely to marry someone of their similar age, educational background, and even occupation. As likes are increasingly marrying likes, it isn’t surprising that we see increasing political pressure to expand marriage to same-sex couples.

…the high divorce rates among those marrying in the 1970s reflected a transition, as many married the right partner for the old specialization model of marriage, only to find that pairing hopelessly inadequate in the modern hedonic marriage.

It produced a flurry of responses and reactions, but the chain I want to follow is this one:

Which finally brings me to why I wrote this entry. I love this sentence from Tyler:

Symbolic goods usually have marginal values higher than their marginal costs of production; Americans for instance love the idea of their flags but the cloth is pretty cheap, especially if it comes from China.

Brilliant. 🙂

New Hampshire and the prediction markets

Plenty of people, Paul Krugman among them, are pointing out that just like the polls (which, on average, had Obama ahead of Clinton by over 8 points), the prediction markets were plainly wrong in forecasting the outcome of the Democratic New Hampshire primary. They’ve got a point.

These are the daily closing prices on the Clinton and Obama contracts to win the New Hampshire primary from InTrade:

dem_nh_clinton.png

dem_nh_obama.png

Up until Iowa, they were fairly steady at ~60% for Clinton and ~40% for Obama, but from the 3rd of January onwards, there was a clear movement towards Obama. On the day before the primary, the markets had Obama 97.8% likely to win. On the day, Clinton won with 39.07% of the vote, while Obama received 36.47%. So why did the market get it wrong?

Paul Krugman contends that the prediction markets were just reflecting the polls and talking heads, presumably because that was all the information there was to be had. This naturally raises the question of why they were wrong (e.g. did we just witness the Bradley effect in action?), but that is not the point here. A prediction market, according to the theory, is meant to be superior to the polls in predicting outcomes because it combines information contained in the polls with information from other sources. So perhaps Krugman is right. But if he is, why did the market go so far towards Obama?

My guesses:

  • Perhaps Krugman is partially right, but the talking heads provided a positive feedback loop. The polls predicted Obama, which the markets saw. The talking heads saw the polls too (perhaps in more detail) and then spoke about it on television, but added effectively no extra information. The markets saw the talking heads and believed it to be extra information in support of the polls.
  • Like any market, the prediction markets are susceptible to bubbles. Perhaps we saw one here in the days between Iowa and New Hampshire.
  • A lack of “true” liquidity. There was plenty of nominal liquidity in these markets leading up to and during the counting, but how much of the trading was arbitrage, how much was momentum (i.e. bubble) trading and how much was “true,” changing-belief-based trading? As the counting occurred, I was watching both the leaked figures on the Drudge Report and the movement on InTrade. It seemed that the prediction market was moving steadily towards Clinton, but nowhere near as quickly as one would have expected. For example, at 9:40pm, with 46% of the vote counted, Clinton was leading 49,719 (40%) to 45,383 (36%), from which one would conclude with extremely high confidence that Clinton would win, but the market was still only putting her at 65%.
  • Perhaps – and I’m by no means certain of this last point – in order for a prediction market to work perfectly, we also need people to set the size of their position in proportion to their confidence in that prediction. So perhaps there were traders who, looking at Drudge or some other source were extremely confident that Clinton would win from quite early in the counting, but since they did not take large enough positions, they did not move the market. In other words, liquidity requirements for a successful prediction market are not just on the number of trades, but on the volume traded.

Update: Justin Wolfers, a long-time researcher in prediction markets, has an article in the WSJ highlighting how surprising the result was given the market predictions.

We were led to this research by an age-old racetrack puzzle economists call the “favorite-long shot bias“: Horse bettors historically have overbet long shots, and they win less often than their odds suggest. Our research suggests that similar biases hold in political prediction markets.

As such, Sen. Clinton’s comeback is even more stunning, as political underdogs have historically won even less often than suggested by their prediction market odds.

Historical comparisons are already being drawn between the New Hampshire primary and the famous 1948 presidential race in which President Harry S. Truman beat Republican challenger Thomas Dewey, despite the infamous “Dewey Defeats Truman” headline in the Chicago Tribune.

Yet the magnitude of the Clinton surprise is arguably even greater. Indeed, historical research by Professors Paul Rhode of the University of Arizona and Koleman Strumpf of Kansas University has shown that in the Truman-Dewey race, prediction markets had seen hope for President Truman despite his dreadful polling numbers, and he was rated an 11% chance of winning the election by election-eve. Thus, Sen. Clinton’s victory on Tuesday was more surprising than President Truman’s in 1948.

Personally, I seem to be thinking of this the other way around. Assuming that prediction markets are generally better than other forms of forecasting, I find it surprising that they got it so wrong on this occasion. Rather than thinking of the result as the equivalent of a 6-sigma event given the prediction market, I wonder what was different this time that so disturbed the market’s ability to predict?

Update 2: Okay, okay a 3-sigma event 🙂  Justin in an email:

For the polls, this was about a 3-sigma event.  For the market, which had Hillary priced at about a 7% chance [JB: Justin is referring to the WSJ market], it is about a 1.7 sigma event.  They aren’t that unusual.  Indeed, they probably happen about 7% of the time

Justifying my continued existance

… as a blogger [*], that is.

Via Alex Tabarrok (with two r’s), I note that the National Library of Medicine (part of the NIH) is now providing guidelines on how to cite a blog.

There are the ongoing calls for more academic bloggers and, while there are certainly questions over incentives and the impact on research productivity, academia continues to dip the odd toe in the water. Justin Wolfers just did a week of it at Marginal Revolution and now I see this brief post by Joshua Gans:

As more evidence that blogging is going mainstream, a bunch of faculty at Harvard Business School are now in on the act (including economist Pankaj Ghemawat)

[*] I didn’t think it was possible for me to dislike any word more than I do “blog,” but it turns out that I do. To call myself “blogger” required a supression of my own gag reflex.

Richard Freeman, WorkChoices and the dead hand of government

Richard Freeman is continuing his assault on WorkChoices:

[T]he new Australian labour code is such a massive break with Western labour traditions that it merits [global] attention. It was enacted in the midst of prosperity, without union or management excesses that endangered the economy, or public support. From the perspective of social science, we cannot get much closer to the ideal random assignment experiment at the national level than WorkChoices – an extreme change in law with no economic rationale or cause.

… Downloading the Workchoices legislation, I found a 687-page law with 565 pages of accompanying memorandum, all amending [i.e. not replacing] the government’s previous 861-page labour act …

… Parts of the law made so little economic sense that it seemed as if the Howard government had found a new band of whigged judges and labour lawyers to write it, on behalf of management. Which, in fact, I learned, was more or less how the law was developed. Writing the law was outsourced to the major Australian law firms that represented management …

… If re-elected this fall, the government will stay the course with Workchoices and we will see the results of this extraordinary effort to destroy collective action by workers. For the sake of social science, it would be great to see the experiment carried through to completion. For the sake of Australia, it would be great to see the election end the experiment.

He has managed to attract the attention of Justin Wolfers, guest-blogging on Marginal Revolution:

This is what happens when conservative governments confuse decentralization and deregulation.

Professor Freeman visited Australia back in September, speaking at the University of Sydney (I can’t seem to find a transcript online; only the event details and the press release) and on the ABC. He is not without his critics on the topic, but I think his points are valid. Even if you hate the unions, you’ve got to oppose Workchoices for the sheer weight of it. Where are the small-government Liberals in Australia?

Cam Riley wrote on this a while back:

When I read through the Workchoices legislation a while ago it was a brain dulling experience. The bill was long, boring and complex. It recently received a one hundred and eleven page amendment to add to the Workplace Relations Amendment Act, the Workplace Relations Amendment Bill, the Explanatory Memorandum, the Supplementary Explanatory Memorandum and the Second Reading Speech. Human Resources just got job security in the same way accountants do with the complex tax system.

Have a look at the graphs on Cam’s page. Make sure you take note of the scale on the vertical axes.

Meanwhile, John Quiggin has a suggestion for the Labor party in their campaign:

If I were running Labor’s campaign, I’d take the government’s total ad spending this term (around $750 million, IIRC) and convert that into around $5 million per electorate. Then find, for each electorate, $5 million of spending effectively foregone (two extra teachers at X High School, a local road project etc). Finally, promise to create a fund for worthwhile local projects like these, to be funded by a cessation of large-scale government propaganda.

Moving the mainstream (some notes)

I’ve been wanting to write an essay on this for ages, but every time I think or talk to someone about it, I get hit with more ideas and different approaches. In the interests of not forgetting them, I thought it might be worthwhile formalising, if not my opinions, then at least the topics that I want to write on. I’m very interested in people’s opinions on these, so if you have a particular view, please leave some comments.

  1. Economics as an expression of ideology
  2. Language choice as:
    1. (+ve) a means of aiding communication in a specialised field
    2. (+ve) a means of enforcing definitional and therefore intellectual rigour [e.g. arguments over the meaning of “market failure”]
    3. (~) a shaper of methodology
    4. (~) a signal of author competence or paper quality [e.g. “the market for lemmas” or the comment made by a French philosopher, mentioned by Daniel Dennett in a footnote of his book “Breaking the spell”]
    5. (-ve) an embodiment of ideology or bias [e.g. 95% of the work in feminism interpretting literature seems to be in highlighting this sort of stuff]
    6. (-ve) a barrier to outside comment or involvement
  3. The fact that mathematics in general and modelling in particular are each a choice of language
  4. “All models are wrong; some are useful” — George Box
  5. The different purposes of models:
    1. to explore the implications of particular assumptions [moving forwards]
    2. to illustrate the possibility (or plausibility) of a particular outcome [moving backwards]
    3. to explain an observed outcome, or a collection of observed outcomes [moving backwards]
  6. Closed-form (i.e. analytically solvable) modelling versus simulation modelling
  7. Empirical work: justifying assumptions versus confirming outcomes (or challenging either)
  8. Simplifying assumptions versus substantive assumptions
  9. The reasonableness of assumptions:
    1. Representative assumptions [e.g. Friedman’s billiards player]
    2. Direct behaviour versus emergent behaviour
    3. The importance of context [e.g. what is valid at the individual level may not be at the aggregate level]
  10. Fashions and fads in academia. The conflict between:
    1. The need to tackle “the big issues”
    2. The desire to stand out (do something different)
    3. The impulse to follow-the-leader/jump-on-the-bandwagon
    4. The (incentive driven ?) need to publish rapidly, frequently and consistently [i.e. the mantra of “publish or perish“]
    5. The desire to influence real-world policy or public opinion
  11. Heuristics in academia. Rules-of-thumb or a preference for particular techniques. Is it “better” to learn a few types of model extremely well than several models reasonably well? It does allow researchers to jump onto a new topic and produce a few papers very quickly … [e.g. this]
  12. Mainstream conclusions (or opinions) versus mainstream methodology
  13. How to move the mainstream:
    1. Stay in and push or jump out and call to those still in? [e.g. See, in particular, all the discussion on the topic of heterodoxy vs. orthodoxy and Keynesianism vs. Neoclassicalism around the blogosphere before, during and after this comment by Brad DeLong]
    2. The importance of data
    3. The importance of tone and language
    4. The importance of location (both institution and country) [e.g. Justin Wolfers: “I could do the same work I’m doing now for an Australian institution, and the truth is, no one would listen“]
    5. The importance of academic standing
    6. The risk versus the reward