Do “Tiger Moms” shift equilibrium of parenting strategies?

Read “The Ivy Delusion – the real reason the good mothers are so rattled by Amy Chua” in The Atlantic, which discussed and tried to explain the reactions of many parents to the Yale Law Professor’s parenting memoir “Battle Hymn of the Tiger Mother.” Or rather, I would guess, their reactions to the provocative collection of quotes and passages published in the Wall Street Journal under her name (but which she herself claims misrepresents her work).

Anyway, the reason I mention this is that the explanation proposed for the backlash in The Atlantic seems particularly amenable to economic modeling (whether or not it is correct):

• Parents have preferences over their kids’ future material and professional success and over their current (and to some extent future) welfare, and there is trade-off between these. The more “Tiger Mom” you go on your kids (no playdates, piled on homework, extracurricular academic and artistic activities, etc) – the more you raise their chance of future success, but the lower their current welfare is (and to some extent, the higher the risk of future breakdowns).
• The (primarily Asian) “Tiger Moms” place a much lower emphasis on welfare relative to success according to the author.
• As a consequence, once discrimination became reduced, Asian kids came to dominate the merit-based slots at the top Universities. This involves an externality, in the sense that by driving their own kids hard, they raise the bar for other kids for getting into the top schools. (This is important – unless it is relative performance that matters to the parents, the mechanism breaks down)
• This reduces the success of the practices followed by so-called “good moms” of the article (used somewhat ironically or sarcastically, it seems to me), and pushes them to shift towards a stricter parenting style in order for their kids to not lose out in the Academic race – a change they dislike relative to the previous situation.
• The “good moms” then react by trying to get “everyone” to refrain from extreme Tiger Mom parenting – the goal: To reduce Tiger Mom behavior to the point where their own kids again are at the top of the performance distribution and get into the top schools.

In brief: As discrimination is reduced, Asian kids rise to the top, other middle-class parents see their kids left behind and consequently struggle to discredit and reduce the practices that make them lose out. The underlying mechanism: In a meritocracy where effort and time-consuming practice and discipline are a key determinant of your performance, the top spots go to those willing to pay the heaviest price – and those who are not willing to do so will want to change the game to rig it in their favor.

The thesis of the film [Race to Nowhere, which “good moms” arrange screenings of], echoed by an array of parents and experts, is that we can change the experience and reduce the stress and produce happier kids, so long as we all work together on the problem. This is the critical factor, it seems, the one thing on which all voices are in concert: no parent can do this alone; everyone has to agree to change. But of course parents can do this individually. By limiting the number of advanced courses and extracurricular classes a child takes, and by imposing bedtimes no matter what the effect on the GPA, they will immediately solve the problem of stress and exhaustion. It’s what I like to call the Rutgers Solution. If you make the decision—and tell your child about it early on—that you totally support her, you’re wildly engaged with her intellectual pursuits, but you will not pay for her to attend any college except Rutgers, everything will fall into place. She’ll take AP calculus if she’s excited by the challenge, max out at trig if not. It doesn’t matter, either way—Hello, New Brunswick!

But the good mothers will never do that, because when they talk about the soul-crushing race to nowhere, the “nowhere” they’re really talking about (more or less) is Rutgers. And more to the point, while you’re busily getting your child’s life back on track, Amy Chua and her daughters aren’t blinking.

Is economics a science? #1

I see that many have been discussing this question lately, but from the contributions I’ve seen it is difficult to know what they mean by the question.

My entrypoint to the discussion was Brad DeLong’s “highlights” post. Here’s some arguments from Adam Ozimek at Modeled Behavior:

if you’re going to hold economics research to an extremely high burden of proof, then you should be prepared to subject all of your beliefs to such standards. What this will leave you with is mostly weak beliefs about the world for a lot of stuff that matters to you, whether it be about medicine, history, biology, psychology, criminal justice, climate science, or economics. Maybe widespread weak beliefs are a better approximation of the truth, I don’t know, but I do know very few people do or are willing to reason like that consistently. Maybe they should. But even here the vast majority of humanity has more belief changing to do than economists.

I’m puzzled by this claim that economics research is held to an extremely high burden of proof. Is it? My impression is that the burden of proof required to make claims about reality differs strongly with the sub-discipline of economics you are looking at. Some of them are happy to whip up a stylized model consistent with “standard assumptions” (perfectly clearing markets, intertemporally optimizing rational consumers, etc), tweak it until it reproduces some “stylized facts” and explain that this has strong policy implications and that the phenomena has been satisfactorily explained. By saying that I find this type of economics about as convincing as homeopathy (which I find almost as convincing as their remaining concentration of active ingredients), am I holding economics research to an “extremely high burden of proof”? I would argue that nobody should find such work convincing, because it uses an inappropriate strategy for justifying the empirical claims they want to make. This is not a criticism of “economics,” it is a criticism of  silly methods. I would be just as sceptical of intricate social causal mechanisms from sociology that are based on high-flying conceptual distinctions and nuanced readings of past theorists with cursory mentions of some more or less representative facts about reality.

The reason I don’t see this as a criticism of economics is that there are many economists and parts of economics that are different: Researchers who are happy to get into long and serious discussions regarding data quality, alternative competing hypotheses, identification strategies for isolating causal effects, remaining weaknesses in their work, alternative interpretations – with more tentative and carefully worded policy recommendations coming out of it.

It can oftentimes be difficult to see the scientific process at work in economics, as in other fields. Sometimes we are stuck at impasses where we are left with little more than theory to guide us, and sometimes empiricism is limited to testing particular model parameters, and ultimately our confidence should be limited by this. And sometimes what looks like pointless or tautological theorizing is really theorists attempting to build tools and lay groundwork for empiricists. It’s easy to look at some of this and think it un-scientific, but not all steps of the scientific process look like science.

Yes, “ultimately our confidence should be limited by this.” But too often, in my experience, this is not the case: Economists do not temper their confidence in this way. That’s the problem, and that’s the “non-scientific” part of economics (in my opinion).

Another question is, if economics weren’t a science, then would previous paradigms so have been done in by empirical outcomes? The old Keynesian Phillips Curve held that there was a tradeoff between inflation and unemployment. When that relationship broke down during the stagflation of the 70s, the Phillips Curve was invalidated, and this helped shift macro away from old Keynesianism and towards the new classical paradigm. Real Business Cycle models of the 80s were also invalidated by reality: it was clear that money mattered, and in the real world it was hard to find technology shocks to explain actual recessions.

If the global federation of astrologists agreed that some rare astronomical event did not have the effect they believed it would have (because it occurred and not everyone born at that time became supersmart or had blue eyes or whatever) – that would not turn astrology into a science. Saying that one change of consensus due to empirical observations is sufficient to make something scientific is silly.

Biases in science–a typology

The Mentaculus has a nice list with good explanations/definitions of different types of biases based on a recent article by David Chavalariasa and John Ioannidis. Haven’t read the underlying article, but the list provides a nice summary of potential biases of very different sorts that affect different parts of the scientific process and can help lead a field to report misleading results. Below, I’ve taken the items from the above list and merely shuffled them into a set of categories to reflect different parts of the research process:

• Deciding on design of study/statistical model
• confounding bias = when you think you are measuring the effect of variable X on variable Y, but in reality there is another variable Z that correlates with X and also affects Y, which you haven't considered. 
• Deciding on who to collect data from
• selection bias = when you think that all the various sub-groups of the population are proportionally just as likely to be in your sample, but in reality certain groups are more likely to be present than proportional, because of the way you collect your data.
• sampling bias = when you think your sample is representative of the population, but really it is not, because it is skewed in ethnicity, attractiveness, age, gender, and/or etc, casting doubt on your generalizations from the sample to the population. (this is actually a sub-category of selection bias, with the distinction of external vs internal validity that sounds cool but also troublesomely postmodern)
• Evaluating data quality
• response bias = when respondents answer your questions in the way they think you want them to answer, rather than according to their true beliefs; this could also happen in animal research if you reward animals for responding in a certain way outside of the main test.
• recall bias = when respondents are more likely to remember the content of your question if they hold a certain belief on it.
• Researcher’s beliefs
• attention bias = when you focus only on data that supports your hypothesis and ignore data that would make your hypothesis less likely.
• publication bias = when you are more likely to publish or tell others about your results if they 1) conform to what you expect, or 2) are what you think others would prefer to hear.

Seems to me there would be others as well that might be worth considering, such as (off the top of my head)

• Biased research questions – Let us say you are for or against some activity that (like most things) has positive and negative aspects (e.g. tobacco smoking, climate regulation, free trade, cannabis use, the internet). You narrow down and specify your research problem and outcome measure so as to only pick up on effects that go in your preferred direction, and present it as though it is a comprehensive or broad outcome measure or representation of the problem.
• Biased analysis – when you (due to presumably common psychological mechanisms) try new model specifications when you are “not satisfied” (e.g., don’t get the results you like) and keep on running new regressions, new models, new methods until you get significant effects in your desired direction.
• Publication bias type 2 – When editors and referees impose different burdens of proof depending on whether they agree with a piece of research or not (particularly if they do so even when there is a lack of consensus in the discipline, if everyone gets results in one direction and a new submission doesn’t, then it makes sense to ask for extraordinary evidence for extraordinary claims)
• Biased data  – If the data was collected for some other reason than research and then employed for research, this may have affected the incentives the ultimate source had for reporting truthfully. E.g., tax reports may underestimate sources of income that it is easy to shield from the IRS. some parts of administrative forms that are filled out are filled out because “they have to be filled out” even though no one uses the information much – making them of poor quality.

Does peer review ensure quality?

I’ve written (somewhat unsystematically) on peer review and academic journals lately, especially on the question of why we have a hierarchy of journals (top journals, mid-tier etc.). I suggested some possible justifications for this, but they all rest on the assumption that editors and referees are good at estimating the “quality” of research (in the sense of long term importance). A reader suggested that having top journals could motivate researchers to do better work than otherwise, but this too requires that it is a relevant sense of scientific quality that determines acceptance in top journals. (This benefit mainly comes about if this system identifies and “marks” quality better than alternative systems focused on citation rates etc. Otherwise, it is mostly an early (noisy) estimate of long term importance that allows you to reap an expected status earlier than otherwise.)

Anyway – here’s some stuff I’ve gathered lately on the quality of refereeing. Mostly, this comes from bloggers sceptical of the current system – please add more positive stuff if you know of it.

First, from Cameron Neylon at Science in the Open

what evidence we do have shows almost universally that peer review is a waste of time and resources and that it really doesn’t achieve very much at all. It doesn’t effectively guarantee accuracy, it fails dismally at predicting importance, and its not really supporting any effective filtering.  If I appeal to authority I’ll go for one with some domain credibility, lets say the Cochrane Reviews which conclude the summary of a study of peer review with “At present, little empirical evidence is available to support the use of editorial peer review as a mechanism to ensure quality of biomedical research.” Or perhaps Richard Smith, a previous editor of the British Medical Journal, who describes the quite terrifying ineffectiveness of referees in finding errors deliberately inserted into a paper. Smith’s article is a good entry into to the relevant literature as is a Research Information Network study that notably doesn’t address the issue of whether peer review of papers helps to maintain accuracy despite being broadly supportive of the use of peer review to award grants.

I (very briefly) looked at Neylon’s links (hope to go more in-depth another time), but the Cochrane Review mostly notes that there is no evidence that peer review raises quality. That is, it is more a lack of evidence either way than evidence in one direction. Smith’s article, on the other hand, is more aggressive:

If peer review is to be thought of primarily as a quality assurance method, then sadly we have lots of evidence of its failures. The pretentiously named medical literature is shot through with poor studies. John Ioannidis has shown how much of what is published is false [4]. The editors of ACP Journal Club search the 100 'top' medical journals for original scientific articles that are both scientifically sound and important for clinicians and find that it is less than 1% of the studies in most journals [5]. Many studies have shown that the standard of statistics in medical journals is very poor [6].

[…]

While Drummond Rennie writes in what might be the greatest sentence ever published in a medical journal: 'There seems to be no study too fragmented, no hypothesis too trivial, no literature citation too biased or too egotistical, no design too warped, no methodology too bungled, no presentation of results too inaccurate, too obscure, and too contradictory, no analysis too self-serving, no argument too circular, no conclusions too trifling or too unjustified, and no grammar and syntax too offensive for a paper to end up in print.'

(BTW: I would recommend this readable article that provides a nice introduction on Ionnides)

The usually interesting Robin Hanson noted a recent study that shows that not much of the variability in referees’ ratings is explained by a tendency to agree (which tells you that the “signal” of quality is (if it is there) very noisy):

The above is from their key figure, showing reliability estimates and confidence intervals for studies ordered by estimated reliability. The most accurate studies found the lowest reliabilities, clear evidence of a bias toward publishing studies that find high reliability. I recommend trusting only the most solid studies, which give the most pessimistic (<20%) estimates.

Some years ago, Hanson also uncovered a study from the “good-old days” when you were allowed to mislead study subjects if this was necessary to get valid results. This study showed that reviewers do not agree, and by manipulating the conclusions and creating studies that were equivalent methodologically but supported different conclusions, they also showed that referee opinions regarding manuscripts were

strongly biased against manuscripts which reported results contrary to their theoretical perspective.

Ideally, we should have an external measure of quality that is reasonably independent of “bandwagon” effects (e.g., if everyone wants to cite American Economic Review because it is the top journal, then citation rates of articles from AER would tend to reflect the status of the journal rather than the quality of the articles). Seems to me like this would be easier to find in a more decisively empirical discipline than economics (or sociology for that matter), where long-term citation rates might more credibly reflect empirically valid and important results and theories.

Why should we have “top journals” – are there good reasons?

In a previous post I listed some functions of a system of academic publishing. The ones relevant to the “progress of science” were:

• Facilitate scientific progress, by

  • ensuring quality of published research by weeding out work that is riddled with errors, poor methodology etc. through anonymous peer-review by relevant experts
  • assessing/predicting importance of researchand thus how “high up” in the journal hierarchy it should be published,
  • making research results broadly accessible so that disciplines can build their way brick-by-brick to greater truths
  • promoting a convergence towards consensusby ensuring reproducibility of research and promoting academic dialogue and debate

This got me thinking: Are there any good reasons at all for having a hierarchy of journals of different “quality” or importance? In asking this, it may be good to have a clear idea of the alternative we are comparing it to. I’m thinking of mega-journals such as PLOS One, that have referees evaluating whether the method and arguments and evidence is sufficiently good to merit publication – and who then publish everything that  - in the referees’ eyes - clears this “minimal” bar of purely scientific criteria. In other words – importance or “originality” does not feature into it.

Compared to this – what do we get from a hierarchy of journals? My first guess would be that they help answer questions such as:

• “What is the key citation for this theory/hypothesis/claim?”– I would guess most researchers would prefer citing from a “top journal” than from a lower-tier journal (ceteris paribus). This may be useful provided the first and/or best justifications are generally found in articles from top-journals (i.e., if the quality sorting is good).
• “What should I read/pay attention to? What are the most important recent research results/theoretical innovations/topics?” Helping readers sort out the dross and focus on the choice bits of juicy, nutritious research. Again, may be useful if the quality sorting is good.”

Are there others? I realize top journals are important for other reasons (such as helping rank faculty), but are there other good and valid reasons such a system would help facilitate scientific progress?

Mistaking beauty for truth… again…

Olivier Blanchard has recently been quoted as saying that

"Before the crisis, we had converged on a beautiful construction" to explain how markets could protect themselves from harm, said Olivier Blanchard, an economics counselor at the International Monetary Fund. "But beauty is not synonymous with truth."

This isn’t that far from what Krugman said some years ago. “Mistaking beauty for truth” was the first section of his widely discussed 2009 essay “How did economists get it so wrong?” where he also wrote that

As I see it, the economics profession went astray because economists, as a group, mistook beauty, clad in impressive-looking mathematics, for truth. […] as memories of the Depression faded, economists fell back in love with the old, idealized vision of an economy in which rational individuals interact in perfect markets, this time gussied up with fancy equations. […] the central cause of the profession’s failure was the desire for an all-encompassing, intellectually elegant approach that also gave economists a chance to show off their mathematical prowess.

Is it just me or does this sound crazy? If this is true, isn’t it a damning judgment on the entire “scientific process” of economics? How can presumably sensible and unquestionably intelligent researchers searching for a reasonable way to examine the world converge on a beautiful construction that completely misleads them? Instead of leading economists stepping forth to profoundly state that “beauty is not truth,” couldn’t they instead put their weight behind something that would help avoid this happening again in the future? If physicists over time converged on (beautiful, elegant, rigorously formal) theories that made laboratories blow up and spew poisonous gas over entire cities, wouldn’t it make sense to question the kinds of evidence and arguments used to justify empirical claims in physics?

As “Zombie Economics” author John Quiggin has stated,

The prevailing emphasis on logical rigor has given economics an internal consistency that is missing in other social sciences. But there is little value in being consistently wrong. Economics must move on from the infinitely rational, farsighted and asocial beings whose decisions have been the central topic of analysis in recent decades.

[…]

Finally, with the collapse of yet another economic ‘New Era’ it is time for the economics profession to display some humility. More than two centuries after Adam Smith, economists have to admit the force of Socrates’ observation that ‘The wisest man is he who knows that he knows nothing’. While knowledge in the sense of absolute certainty may be unattainable, economists can still contribute to a better understanding of the strengths and weaknesses of markets, firms and other forms of economic organisation, and the possibilities for policy action to yield improved economic and social outcomes.

Are economists more “conservative” because they dislike complexity?

There was a recent buzz about liberal bias in academia, covered (amongst other places) on the Freakonomics blog. I noted that there was an interesting correlation from the online dating service OK Cupid that revealed that people’s stated preference for complex vs. simple people was a very strong predictor for whether they were Conservative or Democrat, and suggested that people who prefer complexity (e.g. Democrats/liberals) would also be more comfortable with and interested in research.

It turns out there are people who have been actively pursuing this hypothesis. Over on the Discover blog, Chris Mooney reports that his posting on the topic triggered a response from a researcher (Everett Young), that stated (amongst other things)

If the psychological profile that produces curiosity and the desire to learn both makes one liberal and makes one more likely an academic, then its making one a scientist is barely in need of explanation.

and that

if you look at the political science literature over the last few decades, the burden of proof has shifted dramatically onto those who would deny a psychology-ideology link. Even without Jost, the evidence has grown into somewhat of a mountain. And Alford, et al.’s findings on genetics are only controversial insofar as people don’t like them. The evidence for a genetics-ideology link is also overpowering, even if we haven’t mapped out exactly how it happens.

This, however, raises another interesting question: Can we go backwards from the degree of “liberal overrepresentation” in a research discipline to an inference about the discipline’s complexity? Ongoing research by Daniel Klein shows that the liberal “overrepresentation” is weakest in economics (the table is copied from this paper):

Some might balk at my insinuation that economics is not “complex” (“Hey – that math is hard!”), but I would suggest that maybe a lot of economic theory might be complicated but not complex. I’m thinking particularly of those working in theory building on standard rational choice discussions, the sentiment embodied in Stigler and Becker’s “we-should-try-to-explain-everything-within-our-present-framework” De Gustibus article. The focus and goal here is to protect an already established way of thinking – and fitting, squeezing and forcing everything into this mould, and to use only a small handful of principles that flow from the rational choice model to explain everything.

Returning to Mooney, he relates Young’s point to something several discussants of his previous post had claimed:

many discussants cited a “traditionalism vs. openness/progress axis, in which liberals/scientists were depicted as being in search of the different and new (new findings, new experiences) where as conservatives were painted as resistant to change and attracted to routines, stability, and long existing structures.”

This is what I’m asking: Could it be that a lot of mainstream economists are “resistant to change” in the standard theoretical framework, and that they are attracted to the long existing structures and stability and routine of explaining things within such models? (At times, such research seems almost like an exercise in re-labeling variables to make a model “apply” to a new field) And does this imply that there should be a stronger liberal “slant” to the researchers working in, say, behavioral economics, where the number of explanatory principles is larger and the ambiguity bigger? Could this also be relevant to understanding the rational-choice-ad-absurdum of those schools of macroeconomics which seem the most consistently “hard-core” neoclassical and simultaneously the most sceptical of government intervention?

Speculation on top of speculation sprinkled with speculation. I’m not claiming any expertise here.

Book “review”: Why everyone (else) is a hypocrite

“Why everyone (else) is a hypocrite” by Robert Kurzban is written in an extremely clear prose that made me envious of his ability to explain things in a simple, yet precise way. Lots of (to me) new ways of thinking about/interpreting, for instance, cognitive dissonance, morality, procrastination and preference “instability” and time inconsistency.

Basically, the book presents the modular view of the mind, that sees that mind as consisting of a number of specialized modules (specialized information-processing mechanisms)  linked together (or not) in various ways, and then proceeds to discuss a number of implications this view has for psychology, economics etc.

One idea I liked, was that when you add modules, linking them up to pass and receive information with other modules is work, and only undertaken if it is worth it. (All quotes out of “actual” order)

Evolution must act to connect modules, and it will only act to do so if the connection leads to better functioning.

As Nisbett and Wilson put it, “there may be little or no direct introspective access to higher order cognitive processes.” In other words, the cause of the decision in this case –whatever it was , and whether you want to call it “higher order” or anything else – is not available to the modules that are explaining the decision. The part of the mind that talks just doesn’t get the information from the decision-making modules.

The strong version of this argument is that some systems might be engineered specifically not to get information from (or send information to) other modules.

[…] an even more extreme version of this claim is that not only do some modules work better when they have less  information, some might work better when they have wrong information.’

As a result:

It’s a mistake to pay attention only to what comes out of the mouth when we’re trying to understand what’s in the mind, because there are many, many parts of the mind that can’t talk.

An example of this is

[…] “moral dumbfounding", [...]. At least for some kinds of moral judgments, people can’t give good reasons for their views, though they often try quite hard.

An important implication is that there is no singular, unitary “you,” your consciousness is just one amongst many modules, and lacks information about a number of causal reasons for your actions. Indeed, it may be “deliberately” misinformed:

[…] communication is obviously useful for manipulating what others think in a way that works to one’s advantage, and many modular systems in the mind seem to be designed for this purpose. Indeed, I think there is some sense in which the part of you that feels like “you” is, more or less, designed to serve this public relations function.

This is an important argument in the book, as it is our connections to other people, our need to have status and be accepted and valued by others, that provides us-as-organisms with strategic motives for not consciously being aware of our true motives. As an example of this, Kurzban discusses some interesting experiments where participants seem to actively refrain from getting information that would reveal whether benefitting themselves would hurt others (thus giving them plausible deniability).

Having information – especially information others know you have – changes how your choices – and, consequently, actions – are evaluated by others because there is the reasonable sense that you now have a duty to act on that information.

As Roy Baumeister put it, “Self-presentation is . . . the result of a trade-off between favorability and plausibility.”

As Sedikides and Gregg recently put it, “self-enhancement occurs within the constraints imposed by rationality and reality.”

Along the way Kurzban also explains how the serious and scientific business of evolutionary psychology is ridiculed and rejected by researchers with competing explanations from other disciplines who don’t even have any relevant expertise in ev-psych. He also spends some time explaining how ridiculous explanations from competing disciplines can be easily rejected by him (and us) even without expertise in those areas.

Kurzban also makes (justified) fun of economists and their view of the person as a unitary thing with one, consistent set of preferences.

It turns out that people might not have “real” preferences in the same way people don’t have “real” beliefs.

Instead, we have a number of modules that aim for various goals, are activated by various cues, and interact to determine action. The outcome of this system is not likely to be a consistent, simple and “rational” set of preferences. This seems to me somewhat inconsistent with Kurzban’s later praise of the free market:

Markets leave buyers and sellers better off. The sellers now have money, which they value more than the item they just sold, and the buyers have something that they value more than the money they just paid. Everyone’s better off.

Everyone is better off. The beauty of the markets is that – I really think it’s worth repeating – everyone is better off.

[…] Markets are a great way to ensure that the people who want something the most get it.

And so on. Yeah, I get it, and mostly agree with it: Markets are good in many cases. But it seems weird (hypocritical) to use this way of arguing about it after having written a whole book about how there is no unitary self whose interests can be easily identified, how evolution doesn’t care whether your consciousness is happy or not, how evolution doesn’t even care if the actions and thoughts caused by your modules make your consciousness happy or not, and so on.

Anyway. Good book. Well written. Neat, elegant theories consistent (not surprisingly) with the evidence he’s chosen to include. To me, quite persuasive and insightful (although I feel that I’m too easily swayed and drawn in by evolutionary-psychology theories. Evolution seems so self-evidently true, that it has shaped human brains seems so obvious, that I first become very enthusiastic and then kind of suspicious of my own enthusiasm). And, who knows, the instances of hypocrisy in the book may even have been added as a subtle joke?

Is it OK to disregard Macroeconomists? - Robin Hanson versus Robin Hanson

I don’t always agree with Robin Hanson on the Overcoming Bias blog, but I (very) often find him interesting.
Recently, Vladimir M  trashed macroeconomics for being a discipline dealing with ideologically charged questions and lacking clear research directions:

Even a casual inspection of the standards in this field shows clear symptoms of cargo-cult science: weaving complex and abstruse theories that can be made to predict everything and nothing, manipulating essentially meaningless numbers as if they were objectively measurable properties of the real world, experts with the most prestigious credentials dismissing each other as crackpots.

Hanson counters that

If ideology severely compromises others’ analysis on this subject, then most likely it severely comprises yours as well.  You should mostly just avoid having opinions on the subject.  But if you must have reliable opinions, average expert opinions are probably still your best bet.  (Unless of course you have a prediction market available. )

My first thought was that this sounds like a tiresome and difficult task. In a field as polarized as macroeconomics, estimating “average expert opinions” is not a simple matter (all economists? all AEA members? all Nobel Laureates? do we include both the DSGE models crowd, the Neo-Keynesians, Austrian Business Cycle theorists, New Keynesians, institutionalists? Only tenured professors – in the US only or globally?) And the choice of population is important when views are polarized: Changing the population you consider will have a big impact on the “average” opinion.
My second thought is that we might not need to bother. There is a different defence of Vladimir M from an earlier version of Robin Hanson.  Writing on the strong selection bias in economics, he noted that you can justify any conclusion or answer you want by selecting from the many assumptions and models that are available. He wrote that

we [economists] are so capable of choosing further assumptions to get the answers we want that outsiders can’t gain much policy advantage from our further insight.

 That sounds about right to me, given the current state of macro.

Economists - Drawing opposite conclusions from the same evidence

Christina Romer tried to explain recently why people have opposing views on the expected inflationary effects of monetary expansion. She argued that it was a distinction between theorists and empiricists. Krugman disagreed by saying she was too kind – the other guys are just crazy:

I mean, yes, there are theoretical models in which monetary expansion translates immediately into sudden inflation. But these same models also say, essentially, that what we’re experiencing now — a prolonged period of high unemployment in which wage growth has slowed, but wages haven’t plunged — couldn’t happen. So to believe the inflation scare stories you have to be not just a theorist but a theorist who believes his theories, not his own lying eyes.

This seems wrong. No theory will be right in every detail across the board, particularly in economics, and there has to be some judgment as to what evidence is relevant and what evidence is not relevant. I’m often frustrated as well by how economists cling to (what I feel are) insane theories, but still - I’m sure Thomas Sargent could have sounded sensible defending the theories Krugman dismisses (see here, for instance), even though I also feel (?) pretty certain Sargent is wrong.

However, this reminds me of an interesting study which showed that exposing people to imperfect and non-conclusive evidence  (which is almost all evidence in economics, particularly macro-economics) can lead people with opposing views to move in different directions: An (old) experiment done on psychology undergraduates exposed people opposed and in favor of the death penalty to (fake) studies (emphasis added below):

[…] subjects supporting and opposing capital punishment were exposed to two purported studies, one seemingly  confirming and one seemingly disconfirming their existing beliefs about the deterrent efficacy of the death penalty. As predicted, both proponents and opponents of capital punishment rated those results and procedures that  confirmed their own beliefs to be the more convincing and probative ones, and they reported corresponding  shifts in their beliefs as the various results and procedures were presented. The net  effect of such evaluations and opinion  shifts was [an] increase in  attitude polarization.

Fighting publication bias #1

Short version: By having a hierarchy of journals that accept work partly based on a prediction of how important/novel the work seems to be to a few referees and an editor, researchers will

• try too hard to find results that will seem to be novel/important
• try too hard to reproduce new results and show that they too have found this new novel/important thing
• shelve their work (because it seems flawed or because will at best be publishable only in less interesting lower-tier journals) if they fail to reproduce the new novel/important things

The current academic publishing system with peer-reviewed journals is an attempt to achieve a lot of different goals at the same time:

• Facilitate scientific progress, by
• ensuring quality of published research by weeding out work that is riddled with errors, poor methodology etc. through anonymous peer-review by relevant experts
• assessing/predicting importance of research and thus how “high up” in the journal hierarchy it should be published,
• making research results broadly accessible so that disciplines can build their way brick-by-brick to greater truths
• promoting a convergence towards consensus by ensuring reproducibility of research and promoting academic dialogue and debate
• Simplify the evaluation of individual researchers (given the above, the number of articles weighted by journal type is a proxy for the importance and quality of your research)
• Generate huge profits for publishing houses (To quote an article from Journal of Economic Perspectives, “

The six most-cited economics journals listed in the Social Science Citation Index are all nonprofit journals, and their library subscription prices average about $180 per year. Only five of the 20 most-cited journals are owned by commercial publishers, and the average price of these five
journals is about $1660 per year.

Now, clearly, not all of these goals are compatible – most obviously, it is hard to square rocketing subscription costs with the goal of making research results more accessible. However, the ranking of academics based on where in a hierarchy of journals they have published seems likely to lead to issues as well.

If you want to get ahead as a researcher, you need to be published, preferably in good journals. If you want to be published in a good journal you need to do something surprising and interesting. You need to either show that something  people think is smart is stupid, or that something people think is stupid is smart. As a result, you get a kind of publication bias that can be illustrated by a simple thought experiment:

Imagine that the world is exactly as we think it is. If you drew a number of random samples, the estimates for various parameters of interest would tend to be distributed rather nicely around the true values. Only the researchers “lucky” enough to draw the outlier samples whose estimated parameters were surprising would be able to write rigorously done research that supported new (and false) models of the world that were in line with these (non-representative) results. This is actually not a very subtle point: One out of twenty samples will by definition have results that reject a true null hypothesis at 5% significance level.

OK, so let us say ideological bias, fashions and trends in modeling approaches etc. are irrelevant, so the result is published. Right away, this becomes a hot new topic, and anyone else able to reproduce it (read: anyone else drawing random but non-representative samples) get published. And then, gradually, the pendulum shifts – and the interesting and novel thing is to disprove the new result.

Now, clearly the above thought model is too simple. For one thing, we don’t know the truth. But the recent New Yorker essay on “The decline effect” sounds like this might be part of what’s going on:

all sorts of well-established, multiply confirmed findings have started to look increasingly uncertain. It’s as if our facts were losing their truth: claims that have been enshrined in textbooks are suddenly unprovable. This phenomenon doesn’t yet have an official name, but it’s occurring across a wide range of fields, from psychology to ecology. In the field of medicine, the phenomenon seems extremely widespread, affecting not only antipsychotics but also therapies ranging from cardiac stents to Vitamin E and antidepressants

The essay discusses a number of explanations (some of them sort of mystical and new-agish), but also notes the explanation above. When biologist Leigh Simmons failed to replicate a new interesting result, he failed to replicate it:

“But the worst part was that when I submitted these null results I had difficulty getting them published. The journals only wanted confirming data. It was too exciting an idea to disprove, at least back then.” For Simmons, the steep rise and slow fall of fluctuating asymmetry is a clear example of a scientific paradigm, one of those intellectual fads that both guide and constrain research: after a new paradigm is proposed, the peer-review process is tilted toward positive results. But then, after a few years, the academic incentives shift—the paradigm has become entrenched—so that the most notable results are now those that disprove the theory.

It seems to me that this is an almost unavoidable result of the current journal system, but not an unavoidable result of peer-reviewed journals as such. The problem seems to me to stem from the hierarchy of journals, and from the two tasks we give to referees (assess quality and assess importance/interest). The new open-access mega-journals (PLOS One, Sage Open, etc) that aim to publish all competently done research independently of how “important” it seems should at least mitigate the problem. Not necessarily by making it less important to have a “breakthrough” paper with a seemingly important result, but by making it easier to publish null-results.

Rewards and incentives can be OK

There’s a result from behavioral economics that increasing rewards and incentives for a behavior (e.g. to get kids to read more books) “crowds out” intrinsic motivation and leaves them less interested in books than before once the rewards dry out. Barking up the wrong tree notes a study that fails to find this when it comes to getting kids to eat vegetables. Good to know for those of us with kids.

Liking and intake of the vegetable were assessed in a free-choice consumption task at preintervention, postintervention, 1 month after intervention, and 3 months after intervention. Liking increased more in the three intervention conditions than in the control condition, and there were no significant differences between the intervention conditions. These effects were maintained at follow-up. Children in both reward conditions increased consumption, and these effects were maintained for 3 months; however, the effects of exposure with no reward became nonsignificant by 3 months. These results indicate that external rewards do not necessarily produce negative effects and may be useful in promoting healthful eating.

Why support free trade?

Economists are usually in favor of free trade. I myself am both an economist and usually in favor of free trade. But I thought this post on “Kids prefer Cheese” which Mark Thoma recently re-blogged had a good and valid point that economists do well to remember: Even if trade benefits the trading partners, that does not mean that a large number of people in a country may not be hurt by allowing free trade. And even if the monetary gains of the winners are bigger in sum than the monetary losses of the losers, that isn’t always a big help for the losers since there is no redistribution automatically triggered making everyone at least as well off as before.

Economists usually defend their stance on such issues by talking about Pareto efficiency, saying that making someone better off is always good provided someone else isn’t made worse off by it. Then they switch from talking about Pareto improvements (probably rare in actual policy) to talking about potential Pareto improvements, where the winners could compensate the losers and achieve a true Pareto improvement. Of course, they won’t do so in actuality, which makes the policy also have a redistributive element. A common reply is that redistribution should not be solved through trade measures, but through redistributive policies. But, guess what, most of those aren’t that popular amongst economists either: They distort incentives and reduce efficiency and involve moral hazard problems and, besides, inequality isn’t that horrible anyway. I may be completely wrong, but my guess is many of the economists most adamant about the glories of completely free trade are also amongst those staunchest in opposition to redistributive taxation and public welfare schemes. Though, being a guess, that may be just based on stereotypes and shouldn’t be given too much weight.

Anyway, here’s an excerpt:

People, the United States is not a person! Only in DSGE models do we assume that all individuals are identical! There is no "our" to which general statements can be attached.
Yes, going from autarky to free trade will raise the GDPs of both nations, but that is a very far cry from saying that a large number of individuals will not be made worse off in the process. I figure that NGM is familiar with the Stolper-Samuelson theorem, so I guess he is assuming the political process always provides adequate compensation for the losers??

ROFLMAO, anyone?
Here's a case for free trade:

Individuals should be allowed to contract with whoever they wish, without government interference based solely on geography.

Now, that is not much of an economic argument, but, to tell the ugly truth, THERE ISN'T MUCH OF AN ECONOMIC ARGUMENT.

Once you factor in agent heterogeneity, imperfect competition, increasing returns, and an arbitrarily large number of traded goods, the welfare economics of free trade is murky at best.

More good stuff making the same point here

Manipulating maths for whose amusement?

Amplify’d from www.technologyreview.com

Q&A: The Experimenter

Gary Loveman, the CEO of Caesars Entertainment, says there are three ways to get fired from the hotel and casino company: theft, sexual harassment, and running an experiment without a control group.

Loveman, who has a PhD in economics from MIT and was a professor at Harvard Business School, has impressed the importance of data analysis on his employees, who are expected to quickly scale small tests into company-wide initiatives. For example, they might test which is likelier to get customers to spend more: a free meal or a free night in a hotel.

When you got your economics PhD from MIT in 1989, subdisciplines like behavioral economics and experimental economics had a mixed reputation. Now—a couple of Nobel Prizes in the field later—they seem to be cornerstones of how many businesses and industries try to innovate.

My impression is that when I got my PhD, we were really manipulating mathematics for our own amusement, and we weren't producing all that much to help real people make real decisions. That was dissatisfying to me and, frankly, frustrating. The notion that we could do experiments based on the central tenets of economics and have that make a real-world difference was exciting. Of course, with Freakonomics and Predictably Irrational these themes have become more popularized and accessible. It's a very heartening development, and it's increased my enthusiasm for my own discipline enormously.

What do you like to tell your academic colleagues about the challenges of real-world experimentation and innovation?

Honestly, my only surprise is that it is easier than I would have thought. I remember back in school how difficult it was to find rich data sets to work on. In our world, where we measure virtually everything we do, what has struck me is how easy it is to do this. I'm a little surprised more people don't do this. Read more at www.technologyreview.com

 

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Experimental evidence on infinitely repeated games??? Infinity is a loooong time!

Surely an ongoing study by definition, reporting on some results from a work in progress. And from the most prestigious economics journal - the American Economic Review - no less.

My own criticism of the predictions from the theory of infinitely repeated games would be more directed towards their lack of applicability in my (AFAIK) finite life.

However, if I could gain immortality only by agreeing to spend it sitting in a laboratory playing prisoner's dilemma for ever - then I think I would pass. My guess is they have a sample selection problem.

And yes, I know I'm being dumb.

And no, I'm not being serious.

Amplify’d from www.ingentaconnect.com

The Evolution of Cooperation in Infinitely Repeated Games: Experimental Evidence

Authors: Bó, Pedro Dal; Fréchette, Guillaume R.

Source: The American Economic Review, Volume 101, Number 1, February 2011, pp. 411-429(19)

Abstract: A usual criticism of the theory of infinitely repeated games is that it does not provide sharp predictions since there may be a multiplicity of equilibria. To address this issue, we present experimental evidence on the evolution of cooperation in infinitely repeated prisoner's dilemma games as subjects gain experience. We show that cooperation may prevail in infinitely repeated games, but the conditions under which this occurs are more stringent than the subgame perfect conditions usually considered or even a condition based on risk dominance. Read more at www.ingentaconnect.com

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Economists should not be unduly concerned with reality?

It’s “Quotes out of context” day today. Here’s a couple of interesting quotes by prominent economists that I came across in a blog-post I stumbled onto. None of them really say anything factually wrong, but they seem (out of context, at least) indicative of an attitude valuing logically correct, sophisticated and elegant mathematical systems over pragmatically useful and informative, well-supported theories about the world. One danger of this is that if we use the word “economic theories” about both logical systems and theories-of-the-world, and if we also say that logical systems are correct or true when they are logically consistent and valued by economists, then it is only a small slip of the mind before we allow our views of the world to be colored and influenced by the logical systems that have yet to be related to reality.

There’s one by Samuelson:

Nobel Prizewinner Paul Samuelson's conclusion in his famous 1939 article on "The Gains from International Trade":

"In pointing out the consequences of a set of abstract assumptions, one need not be committed unduly as to the relation between reality and these assumptions."[3]

This attitude did not deter him from drawing policy conclusions affecting the material world in which real people live.

And one from

the textbook Microeconomics by William Vickery, winner of the 1997 Nobel Economics Prize:

"Economic theory proper, indeed, is nothing more than a system of logical relations between certain sets of assumptions and the conclusions derived from them... The validity of a theory proper does not depend on the correspondence or lack of it between the assumptions of the theory or its conclusions and observations in the real world. A theory as an internally consistent system is valid if the conclusions follow logically from its premises, and the fact that neither the premises nor the conclusions correspond to reality may show that the theory is not very useful, but does not invalidate it. In any pure theory, all propositions are essentially tautological, in the sense that the results are implicit in the assumptions made."[4]

Should we see it coming?

Michael Lewis has a wonderfully engaging, well-written (long) article about the Irish economic catastrophe in Vanity Fair. Worth reading for all sorts of reasons.

Here, I just want to point out the simple arguments and observations used by an economics professor during the boom to argue that there was a housing bubble. It’s puzzling how something that seems obvious in retrospect, based on simple, big-picture statistics that were easily googled at the time, could be so ignored or downplayed or rejected by economists and others alike at the time. The sense that “this time is different,” “past cases don’t apply,” and that all sorts of more or less good “small” arguments are enough to (psychologically?) weaken the impact of the big-picture items. Sometimes, the difficult thing is to just keep pounding on the big, strong, clear argument instead of allowing yourself to get derailed into lots of smaller-scale discussions of all sorts of details that don’t really count for much in the big picture. (It seems to me, for instance on the basis of this graph, that Norwegian house prices are grossly inflated today (the red curve is Norway, the blue US, both in real terms and normalized to 1890 levels)– but when I present this graph to others I constantly get derailed into side-tracks like “building standards are more stringent now than in the past, which might have increased costs”)

Morgan Kelly is a professor of economics at University College Dublin, […] Kelly saw house prices rising madly and heard young men in Irish finance to whom he had recently taught economics try to explain why the boom didn’t trouble them. And they troubled him. “Around the middle of 2006 all these former students of ours working for the banks started to appear on TV!” he says. “They were now all bank economists, and they were nice guys and all that. And they were all saying the same thing: ‘We’re going to have a soft landing.’ ”

The statement struck him as absurd: real-estate bubbles never end with soft landings. A bubble is inflated by nothing firmer than expectations. The moment people cease to believe that house prices will rise forever, they will notice what a terrible long-term investment real estate has become and flee the market, and the market will crash. It was in the nature of real-estate booms to end with crashes—just as it was perhaps in Morgan Kelly’s nature to assume that, if his former students were cast on Irish TV as financial experts, something was amiss. “I just started Googling things,” he says.

Googling things, Kelly learned that more than a fifth of the Irish workforce was employed building houses. The Irish construction industry had swollen to become nearly a quarter of the country’s G.D.P.—compared with less than 10 percent in a normal economy—and Ireland was building half as many new houses a year as the United Kingdom, which had almost 15 times as many people to house. He learned that since 1994 the average price for a Dublin home had risen more than 500 percent. In parts of the city, rents had fallen to less than 1 percent of the purchase price—that is, you could rent a million-dollar home for less than $833 a month. The investment returns on Irish land were ridiculously low: it made no sense for capital to flow into Ireland to develop more of it. Irish home prices implied an economic growth rate that would leave Ireland, in 25 years, three times as rich as the United States. (“A price/earning ratio above Google’s,” as Kelly put it.) Where would this growth come from? Since 2000, Irish exports had stalled, and the economy had been consumed with building houses and offices and hotels. “Competitiveness didn’t matter,” says Kelly. “From now on we were going to get rich building houses for each other.”

The endless flow of cheap foreign money had teased a new trait out of a nation. “We are sort of a hard, pessimistic people,” says Kelly. “We don’t look on the bright side.” Yet, since the year 2000, a lot of people had behaved as if each day would be sunnier than the last. The Irish had discovered optimism.

Their real-estate boom had the flavor of a family lie: it was sustainable so long as it went unquestioned, and it went unquestioned so long as it appeared sustainable. After all, once the value of Irish real estate came untethered from rents there was no value for it that couldn’t be justified. The 35 million euros Irish entrepreneur Denis O’Brien paid for an impressive manor house on Dublin’s Shrewsbury Road sounded like a lot until a trust controlled by the real-estate developer Sean Dunne’s wife reportedly paid 58 million euros for a 4,000-square-foot fixer-upper just down the street. But the minute you compared the rise in prices to real-estate booms elsewhere and at other times, you re-anchored the conversation; you biffed the narrative. The comparisons that sprung to Morgan Kelly’s mind were with the housing bubbles in the Netherlands in the 1970s and Finland in the 1980s, but it almost didn’t matter which examples he picked: the mere idea that Ireland was not sui generis was the panic-making thought. “There is an iron law of house prices,” he wrote. “The more house prices rise relative to income and rents, the more they subsequently fall.”

Why scientists are liberals – some speculative comments

The Freakonomics blog discusses political bias in sciences, and quotes a social psychologist who estimated that 80% of attendees at a conference were liberals (he asked for a show of hands). Dubner seems worried that political views will shape research conclusions, and writes:

How can it be that an academic field is so politically homogeneous? What kind of biases does such homogeneity produce? What sort of ideas get crowded out? And how homogeneous are other disciplines?

I have to say that I was surprised at the overt political (leftward) bias exhibited by several prominent economists at the recent American Economics Association meetings, although my sample set was quite small.

It is interesting — and sobering — that two fields, psychology and economics, that we rely upon to describe and amend bias in the world are themselves so susceptible to bias within the ranks of their practitioners.

Krugman disagrees, implying that research conclusions probably push attitudes towards the liberal side, saying

Biologists, physicists, and chemists are all predominantly liberal; does this reflect discrimination, or the tendency of people who actually know science to reject a political tendency that denies climate change and is broadly hostile to the theory of evolution?

Now, I don’t mean to say that political bias in the academy is absent, although it’s not consistent: I can well imagine that it’s hard to be a conservative in some social sciences, but in economics, the obvious bias in things like acceptance of papers at major journals is towards, not against, a doctrinaire free-market view. But the point is that doing head counts is a terrible way to assess that bias.

It might be that the most recent amusing statistical post on the blog for dating service OK Cupid has the answer. The post analyzes its database to identify the most unthreatening, innocent questions that best predict characteristics that you may not want to ask about directly (whether they’re religious, would have sex on a first date, their political ideology etc.). Based on their national US data they write that the question identifying politics is

• Do you prefer the people in your life to be simple or complex?

Because...

We were very surprised to find that this one question very strongly predicts a person's ideas on these divisive issues:

Should burning your country's flag be illegal?

Should the death penalty be abolished?

Should gay marriage be legal?

Should Evolution and Creationism be taught side-by-side in schools?

In each case, complexity-preferrers are 65-70% likely to give the Liberal answer. And those who prefer simplicity in others are 65-70% likely to give the Conservative one.

Seems to me that this is pretty consistent with the “bias” in academia. Academia is often very much concerned with complexity – finding nuances in interpretations and methods, considering alternative explanations for patterns in data, etc. If you prefer simplicity as a general trait in people and thoughts you would probably be pretty frustrated as an academic. And a 2:1 ratio is roughly 66%, which isn’t that far away from the estimate of 80% that we started with.

Sidenote: Interesting that Dubner is so worried by the left-wing attitudes of economists he encountered, given that his freakonomics podcast on how the world would look if it was driven by the gloriously rational economists basically said they would implement Milton Friedman’s pretty libertarian proposals. His (presumably unbiased and representative?) economist picked to answer on the behalf of the profession was Russ Roberts at George Mason University, who answered that his policy program would

start with some obvious things. I would get rid of the Department of Commerce. The Department of Commerce doesn’t do anything except subsidize exports, which is just a way of saying it makes certain companies rich at the expense of the rest of us. So I don’t think the Department of Commerce does anything particularly useful, I would get rid of that. I’d get rid of the Department of Education. I don’t think that the Federal Government has any productive role to play in the school system. I’d get rid of all tariffs. I’d let people be free to buy whatever they wanted from all around the world. What else? I would get rid of the minimum wage law, which I think makes it hard for low-skilled people to find work; it makes them artificially expensive. I’d change the Federal Reserve. We spend a lot of time trying to find the right interest rate. That’s a fool’s game that has contributed to the current crisis. So I would change the Federal Reserve. I would certainly at a minimum require it to only care about price stability. Right now it cares about price stability, unemployment, the health of the stock market, Wall Street salaries, evidently. So I would get all of those things out. It’s going to be hard to do legislatively, so I would probably replace the the Fed with a Friedmanite fixed growth and money supply or just abolish it entirely and let private money emerge. I’m getting out of control here.

You don’t say…

Update: More links and discussion here from McArdle in the Atlantic. Her take seems to be that there is a bias, that it is amusing to see conservatives (usually dismissive of bias accusations) believe it and liberals (usually sympathetic to bias accusations) dismissive, that it is unsolvable, and that we should all just try harder to get along and see each other's point of view.

Why intuitive stories are important and dangerous

Below are some excerpts from a blogpost on the importance of "simple" stories/models that Paul Krugman praised on his NYT blog. I fail to find a simple moral to the story as it seems (to me) to involve a lot of different views on this issue, such as (in my formulations):



"Simple case-stories/thought-experiments are a necessary adjunct to sophisticated models/theories - because we cannot reason using models but need simplified versions that our brains can grasp"

"Simple case-stories/thought-experiments are rhetorically convincing in discussions/debates"

"Simple case-stories/thought-experiments trigger the psychological feeling of understanding/insight which is a better signal of truth than other kinds of evidence"

"Formal/standard models in economic theory are accepted because other economists accept them (emperor's new clothes) but people who accept them don't really understand the mechanisms they involve"

"Economists are confused concerning what it takes to evaluate claims about the real world"



Here's part of Krugman's comment on the same post (http://krugman.blogs.nytimes.com/2011/02/02/models-plain-and-fancy/ ):



"I have nothing against mathematical models and econometrics. But my experience is that many misunderstandings in economics come about because people don’t have in their minds any intuitive notion of what it is they’re supposed to be modeling. The whole notion of an economy-wide shortfall in demand is just hard to grasp — by famous economists as well as the lay public; quite a lot of our hopeless public debate reflects the fact that many people, some of them imagining themselves to be sophisticated about the issue, just can’t visualize what Keynesian ideas are about. But the baby-sitting coop offers a human-scale example, and makes the whole thing clear."

Amplify’d from modeledbehavior.com

more than any other analysis the baby-sitting coop story made me a confident Keynesian. Before then I could parrot the New Keynesian models and understood that this was more or less what a smart economist was supposed to say.

However, I didn’t know how to counter the logic of Laizze Faire except to say, “well there are sticky prices and an Euler equation and so the household will adjust consumption . . . “  This is compelling to virtually no one – not even, on a deep level, to myself.

When it really came down to it, I would have been left with “Great Depression! Want it to happen again? No? Then we need to spend more money or cut taxes! Why? Because I am very smart and I have a whiteboard. Do you have a whiteboard?”

However, a simple story about baby-sitting and it all fell into place Read more at modeledbehavior.com

 

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Ethical economists again...

Alex Tabarrok at marginal revolution applauds Glaeser's take on the ethical basis of economics and quotes a text-book he has co-authored with Tyler Cowen which he claims makes a similar point (see below).



In this case, I'd make the point that their "take" is only superficially similar. It presupposes more. Glaeser's point was that you make an assumption when you jump from "the person chose A over B" to "A is better for the person than B," and that "preferences" before you make this jump refer to nothing more than what you would observe the person choosing. Cowen and Tabarrok, on the other hand, write as though they've already made this jump.More specifically, they seem to beg the question when they state that economists don't second-guess people's "preferences" and do "not regard some preferences as better than others" and don't mind it if people "like" wrestling better than opera. Choice, here, is already taken as (always??) an expression of what serves the choosing person's actual tastes and judgments best.

Amplify’d from www.marginalrevolution.com via Google Reader

Even though the predictions of economics are independent of any ethical theory, there are ethical ideas behind normative economic reasoning. An economist who rejects the idea of exploitation in kidney purchases, for example, is treating the seller of kidneys with respect—as a person who is capable of choosing for himself or herself even in difficult circumstances.

Similarly, economists don’t second-guess people’s preferences very much. If people like wrestling more than opera, then so be it; the economist, acting as economist, does not regard some preferences as better than others. In normative terms, economists once again tend to respect people’s choices.

None of this it to say that economists are always right in their ethical assumptions. As we warned you in the beginning, this chapter has more questions than answers. But the ethical views of economists—respect for individual choice and preference, support for voluntary trade, and equality of treatment—are all ethical views with considerable grounding and support in a wide variety of ethical and religious traditions. Read more at www.marginalrevolution.com

 

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