When Characters All Sound the Same

The screenwriting books all say that one of the most common complaints around scripts (of all kinds) is that the characters sound the same, instead of each character having a distinct, unique voice.

But how can you establish distinct "voices" in a script's characters in the first 10 pages? (That's how long you've got, basically.) 

It helps, obviously, to have a vivid image, in your mind, of a real-life person (such as a certain actor who fits the part) for each character. As you write, imagine what kind of language that person would use.

The other more-or-less standard bit of advice you encounter a lot in books and articles on screenwriting: Go through and edit all of one character's dialog, whether it's the first 10 pages or the whole script, without looking at any other character's dialog. Then do that for the next character. And so on. This way, you have some hope of achieving consistency in dialect and clean separation of diction patterns from one character to the next.

But how can you achieve characterwise speech differentiation quickly, without resorting to patois or other gimmicks?

I'll tell you a trick I was able to use in the script I just finished. The trick is pretty simple: Have one character mock the other's diction. In my script, Greeners, the hero is a 29-year-old American grad student (think Mickey Rourke 1983). His future partner is a 23-year-old Hindustani woman from New Delhi, a post-doctoral fellow in his department. By page 7, she owes him, bigtime, for putting out a car fire. A few pages later they meet again, and she realizes she never properly thanked him. She tells him so. Then asks: "How shall I repay you?"

No American talks like that. No one says "shall I" in conversation. He says (mocking): "How shall you... 'repay me'? Come on, I didn't do anything."

It's a subtle reinforcement of the fact that they're from two different cultures. She learned British English as a second language. He's James Dean's lost brother. (When he suggests she "thank him" by letting him take her out to dinner, she doesn't nod. She does a little figure-eight head-wobble, Indian for okay/perhaps/why not.)

She's prim, well educated, polite to a fault, and therefore uses proper fully expanded English. Instead of "can't" she tends to say "cannot," for example. He can say "wouldn't've" (as in, "I wouldn't've done that..."); she has to say "would not have."

A few pages of this, and it's pretty clear who's the homie and who's here on a visa.

Another tip: People's true speech patterns always surface in a crisis. One person might shout "Bleedin' Christ!" whereas the next person might say "Oh dear Lord no," etc. So put a crisis on page 5 and differentiate the characters right then and there.

You were planning to do that anyway, though. Right?



If you'd like to see my (WGA-registered) screenplay, drop me a line. My hushmail dot com address is kasthomas. Inquiries held in strictest confidence.

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Lessons from the Script

The screenplay draft is complete. It ended up at 114 pages rather than 112. Boo hoo.

Here's some of what I've learned so far.

The books all say that screenwriting is hard. And it is. IMHO, it's harder than poetry. Makes writing a novel seem like a stroll on the beach.

Screenwriting is a lot like a stroll on the beach—Omaha Beach.

In his book On Writing (a fine book, BTW), Stephen King talks about how he develops stories. Basically, he creates interesting characters, puts them in a dramatic situation, and lets them figure it out. There is no conscious attempt to plot or create subplots; the characters do what they must, inevitably (given their proclivities and their situation) do. King merely transcribes. He gets all the way through a novel this way; following the headlights all the way to the destination, so to speak.

It works for King because he's a master at what he does, and the form (the novel) allows it.

Screenwriting is so constraint-intensive, there is precious little room (although there is some) for organic story growth. You almost have to come at it with a complete structure in mind. Screenwriter Matt Bird has a story structure checklist (encompassing character development and all sorts of other things) that's 100+ items long. You're not going to hit even a third of the points in that list using an unprepared "structure it as you go" approach. If you can, well, congratulations; maybe you're channeling Robert Towne; in which case please accept my unworthy bow as I exit stage-left.

Alien would work well as a silent movie. The dialog augments the story.

You'll run out of room. Guaranteed. Budget 32 pages for Act I and you'll go sailing through that mark like a drunk through a stop sign. Set an absolute page limit of 110 and you'll be at 120 before you know it.

Therefore the best advice I can give you is: Enlarge all margins and tab limits at the start, just a smidge, so you'll get fewer words per page. You'll gladly kill any number of grandmothers later to widen them out again.

Another tip that may keep you from busting your page count: One day when you're thoroughly blocked, write the ending. Write the final two scenes (more, if you can) of the script, complete. Behold: Your "active writing area" is now bookended by the already-written beginning and the already-in-place ending. You have less surface area in which to work, and the paint dries faster, forcing you to up the pace. Which you were wanting to do anyway, right?

Thirty pages seems like a lot in the beginning. But when you've got just thirty pages left to write, it's never enough.

Let actions and situations do the talking whenever possible. Let characters speak only if and when they must, and even then, let subtext, not the actual words, tell the story.

Never forget one thing. Watching a movie is an act of voyeurism. Hence, all dialog is overheard. Hence, no conversation needs to begin at the beginning. Think of the times you've overheard a conversation (whether at a bar, a social function, standing in line, or whatever) in real life. Did you ever hear the actual start of the conversation? Chances are you came along in the middle. Yet within a few seconds, you figured out the essentials of the conversation (who, what, where, when, why) from context. That's how all movie dialog works. The film viewer is an eavesdropper, not a court stenographer.

Take a scene, any scene. Cut the first line of dialog. Does the scene still work? Yes? Then why did you ever think you needed that line of dialog?

More often than not, dialog is about subtext, backstory, misdirection, and/or foreshadowing, not just what's going on at the moment. We can see what's going on at the moment.

Dialog, properly done, wears a hairpiece. It's never bald.

It helps to know something about stage magic and its crazy stepsister, comedy. Both are all about misdirection.

Would your script still make for a good moviegoing experience with the sound off? Alien would play damn well as a silent movie. My Dinner with Andre would not. Look at your script with dialog turned off (in your head, at least). Which did you write? Alien, or My Dinner with Andre?

I was fortunate, with the script I just wrote, to have a multinational story, with lots of non-English-speaking characters (but zero subtitles, thanks). Try it sometime. Write a scene that involves foreign nationals, speaking their native tongues, using no subtitles, with the idea that the scene has to be understandable to anyone, of any language. See how far you can take it. Can you still make the scene work? Why or why not?

There's more to tell. Suffice it to say, the screenplay-writing process has taught me a lot. And I thought I already knew a thing or two. I was right, as it turns out. I knew a thing. Maybe two at the most.


If you'd like to see my (WGA-registered) screenplay, drop me a line. My hushmail dot com address is kasthomas. Inquiries held in strictest confidence. Please mention your Twitter handle (if applicable).

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Eugene Fama explained. Kind of. Part 2: Asset pricing

Following up on my post on Fama’s corporate governance contributions, let’s turn to a mildly technical explanation of Fama’s asset pricing work, for those who haven’t read finance papers since 1990 or so. A lot of this was joint work with Ken French, who is great—don’t believe everything you read on the Internet.* If I have time and interest, I might do a third post on miscellaneous Famacana.

You might think that a field called “asset pricing” would explain the prices of assets. You would be wrong. Instead, it is mostly about asset returns and expected returns.

They’re not that different because a return is simply one price divided by previous one. To actually get to prices, you need to estimate cash flows (or earnings or dividends), and academics, with some exceptions, hate doing that. So we’re left with these weird ratios of prices known as “returns”.

In my post on Lars Peter Hansen, I wrote down an asset pricing model based on a representative consumer utility maximization problem. Using slightly different assumptions, we can justify the so-called Capital Asset Pricing Model or CAPM, which is an older model that holds that returns can be described by \begin{equation} \label{capm} E_t[r_{i,t+1} - r_{f,t+1}] = \beta_i \lambda, \end{equation} where \( r_{f,t+1} \) is the risk-free rate and \( \lambda \) is a number known as the risk premium and \( \beta_i \) is the regression coefficient from a regression of asset returns \( r_{i, t+1} \) on the market return, \( r_{m, t+1} \). (That’s assuming there is a risk free asset. If not, the result changes slightly.) This implies that assets that are more correlated with the market have a higher expected (excess) return, while assets that are uncorrelated or even negatively correlated with the market have a lower expected return, because they provide more insurance against fluctuations in the market.

These days almost every stock picking site lists “beta”, usually the slope of a regression of returns on the S&P 500 index or perhaps a broader market index. An implication of CAPM in the form of equation \( \eqref{capm} \) is that the intercept of that regression, known as Jensen’s alpha or just alpha or abnormal return, is zero.

Ever since this stuff was first proposed, it has been well known that alpha is not zero when you actually run those regressions. (Of course there are ton of econometric disputes in this area. Nobody does empirical research because it is easy and fun.) It’s not zero for individual stocks, but individual stocks are weird and maybe that’s because of noise or other shenanigans.

What’s was more worrying is that the return on somewhat mechanical trading strategies did not have zero alpha: A portfolio of stocks with a high ratio of book value to market value (“value stocks”) has a higher alpha than one with a low such ratio (“growth stocks”). A portfolio of stocks of small companies has a higher alpha than one a portfolio of large company stocks. There are other examples like this, and they suggest that CAPM does not provide a good explanation of the cross section of expected stock returns, or why different stocks have different expected returns.

That’s worrying for the efficient markets hypothesis if CAPM is The True Model. The results of Fama and French (1992) and (1993) suggest that it may not be. Based on the empirical evidence, they propose that expected stock returns are related not just to the stock’s exposure to market risk, but also to two additional factors: The return on a portfolio that is long value stocks and short growth stocks (“HML” or high minus low), and the return on a portfolio that is long small stocks and short big stocks (“SMB” or small minus big). If you do a multivariate regression \begin{equation} r_{i,t+1} = \alpha + \beta_m r_{m,t+1} + \beta_{\mathit{HML}} r_{\mathit{HML},t+1} + \beta_{\mathit{SMB}} r_{\mathit{SMB}, t+1} + \varepsilon_{t+1}, \end{equation} you have an alpha against what is now called the Fama–French 3-factor model. When you let \( r_{i,t+1} \) be returns on portfolios of stocks sorted by either value, size, or both, the resulting 3-factor alphas are a lot closer to zero. Here are the t-statistics:

It’s not perfect, but it’s better than people were able to do before. If Fama–French is the correct model, EMH is in slightly better shape.

Since then a ton of researchers have tried to add factors to the model to better explain the cross section of expected returns, the most widely used being the Carhart momentum factor, to form a 4-factor model. The 3-factor and 4-factor models are the most widely used models in finance for almost any setting where expected and abnormal returns are studied.

There have been many attempts to explain why the value and size factors exist and what explains the risk premia associated with them, i.e. the size of the premium those stocks command. Most of them revolve around hypotheses that the market index does not fully capture the systematic, undiversifiable risk that investors are exposed to. For example, one explanation is that both value and size factors are related to distress risk, the risk of being exposed extra costs associated with financial distress that are not fully captured in the market return measure.

Most papers that propose new factors—someone once claimed that there are 50 factors in the literature explaining returns, but I find that figure quite low—propose some kind of explanation. Some of the arguments build on consumption based asset pricing. One example is a factor relating to takeover risk: the hypothesis is that for companies that are likely to be bought, much of the expected return comes from a potential takeover premium. But takeovers are cyclical and come in waves in a way that you can’t diversify away, so investors have to be rewarded for that risk in addition to market risk (and whatever value and small stock premia represent).

* The photos on that piece are now lost to posterity. The first one is reproduced above, courtesy of mahalanobis. The last one was French with some blonde models. Also, I think an MBA from Rochester costs a lot more than $19.95.

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112 Pages of "What If?"

I haven't blogged a heck of a lot over the past month or so, because I'm in the middle of a Quixotic quest to finish a screenplay. It's been heck of a journey; and the journey is (in many ways) just beginning.

I can't say a lot about the project just yet (although I'll definitely be saying more in future posts, so watch this space). Here's what I can say.

First, writing a screenlay is hard. It's harder than a novel. By quite a bit, I'm afraid.

Secondly, it's difficult writing something with the knowledge that maybe only a handful of people will ever read it. I keep telling myself: It's good exercise. Even if it goes nowhere, it leaves me with all the scaffolding for a novel: well-defined characters with character arcs, a story, backstories, plot points, subplots, reversals, key dialog, key visuals.

Thirdly, having said all this, I wouldn't waste my own (nor anybody else's) time on a project I didn't think was truly epic, relevant, commercial, original, and flat-out un-putdownable. The project is based on an original idea I had about two years ago. It's been germinating and blossoming ever since.

It's a high-concept story involving world hunger, with a Philip K. Dick slant. The concept proceeds from the idea: What if you (or someone you knew) stumbled onto a tool, technique, technology, or idea that really, truly could make a dent in world hunger? What then?

"What then" is 112 pages of screenplay.

My first stab at this was a manuscript for a young adult novel that I got 100 pages into (a year ago) before making the unforgivable mistake of sending off a few queries to agents, on a lark. To my shock and horror, I got an immediate request for a full read. And of course, there I sat with 25,000 words written and 45,000 unwritten. I never got back to the agent.

Instead, I put the project away for a while and thought about it some more. And I realized that the story would play out a lot better if the hero was significantly older. Rather than have the hero be a 17-year-old Indian-American whiz kid, I decided it would be better to make him a 29-year-old white-upper-middle-class American grad student with a female Hindustani lab partner (a post-doc) who, as his intellectual equal (and cultural superior, in many ways), can keep our hero honest, point up his faults, challenge him, goad him, infuriate him, and eventually (of course) fall in love with him—when he finally slays the (figurative) dragon.

But wait, you're saying, 29 is awfully old for a grad student, isn't it? Actually, I've met the type before. They get a master's degree right off the bat, but when it comes time to finish the Ph.D., the lab work just never seems to add up to a thesis. Nature takes its time handing out dissertation-worthy scientific discoveries. You can't force a discovery. Laboratory science is an art. Luck is involved. In my grad student days at U.C. Davis, I knew several "older" students who had labored valiantly for six years (seven being the max allowed by many universities) to achieve a coherent set of results for a thesis, without ever actually getting there. If you're one of these people, it can be frustrating to watch a younger grad student move into the lab, work on something for a year, stumble onto a great scientific result, and have a Ph.D. in 24 months. The two-year science Ph.D. is a very real phenomenon. So is the seven-year Ph.D., unfortunately.

My "what if" story follows the pair of young scientists on a journey that takes them from New Haven, Conneticut to Madhya Pradesh, India. It's epic in scale, global in relevance (and multi-ethnic in cast), has all the elements of a good suspense drama, and forces the viewer (or reader) to confront hard questions. What if X? What if Y? What if Z? What if all three?

I'll be able to reveal more specifics soon. I need to nail Act III first. And that could take a while. But I'm working on a self-imposed deadline. In two weeks, the screenplay will be done—or I'll stop eating until it is done. Hopefully it won't come to that.

On the theory that it is never too early to attach talent, I invite any actors or film people who might be interested in seeing the WGA-registered script when it's available (which is soon—days, not months) to drop me an e-mail. My hushmail dot com address is kasthomas. Inquiries will be kept in confidence.

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Eugene Fama explained. Kind of. Part 1: Corporate governance

Nobel Fever has broken out here at Not Quite Noahpinion, so let’s continue with some overlooked corners of the laureates’ œuvres. According to Google Scholar, Fama and Jensen (1983) is Fama’s third most cited paper, yet it isn’t mentioned at all in the scientific summary put out by the Economic Sciences Prize Committee of the Royal Swedish Academy of Sciences, which, if I remember correctly, sometimes does mention work that is not directly related to what the prize is being awarded for. In this case it is particularly tragic because Fama’s corporate governance papers are very good, and considered by some* to be much better than his asset pricing work. (A phrase which rhymes with “feta pining” is sometimes whispered in connection with the latter.)

I am talking about a pair of papers published back to back in the same issue of the Journal of Law and Economics in 1983; this is the other one. They are joint work with Michael Jensen, who is well known for his later corporate governance work and who is also the Jensen of “Jensen’s alpha” (these guys were generalists, of sorts). Go read them right now. They are very good, there is no math, I have linked to ungated versions, and their explanation is probably clearer than mine.

Both problems are about the separation of ownership and control, a feature seen in most organizations, both for-profit and non-profit. The academic study of organizations dates back to at least 1889, or even 1776, but who still reads Adam Smith?

If the owner of an enterprise exercises full control over its operations, as is the case in a sole proprietorship with few or no employees, there is no agency problem: the manager–owner can be trusted to make whatever decisions will maximize his profit (or whatever he is maximizing, when it is not a for-profit firm), and will even make appropriate tradeoffs between the short term and the long term.

In the real world, that’s not practical. Despite what Adam Smith might have preferred, modern firms (both for-profit and non-profit)—or even the commercial partnerships in the Middle Ages that Weber studied—are too large to be owned by one person or even a family and too complicated to be managed by a large group of stakeholders. If contracts could be perfectly specified and made contingent on every eventuality, there wouldn’t be a problem: the management contract would specify what the manager has to do no matter what happens, and the contract would be written to maximize profits. Of course this is even more unrealistic than thousands or millions of shareholders collectively making all the decisions of a firm.

Fama and Jensen’s analysis starts with the role of residual claims. Some claimants on a firm are promised fixed amounts that they will receive when the cash flows are paid out. For example, a bank or a group of bondholders that makes a loan to a business will only receive the amount that was actually borrowed, plus interest. The firm might go bankrupt, but hopefully in all probability the bank will simply be paid what it was promised. This means that by and large, they won’t care too much about the firm’s management decisions. Only the residual claimants, who in a typical corporation are the shareholders, need to fret about the firm’s management from day to day. In turn, they will bear most of the risk associated with the firm’s operations, and also receive the rewards if the firm is better managed than expected.

At a granular level, the agency problem is about making decisions. Fama and Jensen split up the decision process into decision management, which involves initiating decisions (coming up with the idea) and implementing decisions (actually doing the work), and decision control, which involves ratifying or approving decisions and monitoring that they are carried out faithfully. We now end up with three roles: residual claimant, decision management and decision control.

Some undertakings are “noncomplex”: the necessary information can be concentrated in a few people. Examples include small firms, as well as large firms with relatively simple decisions such as mutual funds and other mutual financial firms. In noncomplex organizations it can be optimal to combine decision management and decision control to economize on decision costs, but then you would have the foxes watching the henhouse. Who looks after the interests of residual claimants? The answer is that when decision management and decision control are combined, you would often restrict residual claims to a small group of people who either are managers or trust them, for example family members and close business associates. Partnerships and small corporations with stock transfer restrictions examples of what Fama and Jensen call “closed corporations”. The tradeoff is that you reduce the possibility of risk sharing and some efficiency is lost that way.

In more complex organizations, decision management would be separated from decision control. The information necessary to make decisions may be diffused among a lot of people, who would each be responsible for initiating and implementing decisions in their little area, but a few people—the managers, who would also be the residual claimants or close family members or business associates—could handle decision control, ratifying and monitoring the decision managers.

In large corporations with a lot of assets, you need more residual claimants to share risk, which in turn makes it impractical for all the residual claimants to participate in monitoring, and the agency problems associated with combining decision management and decision control are worsened. For such organizations, Fama and Jensen hypothesize, decision management and decision control will tend to be separated. In the largest corporations, this separation is complete, and residual claimants have almost zero participation in decision control.

Fama and Jensen also survey a variety of organizational forms that have different tradeoffs between the three roles and ways in which separation is achieved, for example with expert boards and through the market for takeovers. In financial mutuals, a large body of customers are also owners, and decision control is delegated to a professional board of directors, but an additional control function exists in the ability of each residual claimant to quickly and easily terminate his or her claims by redeeming the claim.

In nonprofit organizations, there are no residual claimants as such, which Fama and Jensen justify as a means of reducing the conflict between donors and residual claimants. Who would want to give a donation what will ultimately end up in the pockets of residual claimants? The solution: eliminate the latter. In US nonprofits, decision control is typically exercised by a board of directors that consists of large donors.

In the Roman Catholic Church, control is not exercised by donors (parishioners), but by the church hierarchy itself, and ultimately the Pope, with almost no separation between decision management and decision control. The solution is vows of chastity and obedience that bind the hierarchy to the organization, in exchange for lifetime employment. Fama and Jensen go on to claim that Protestantism is a response to the breakdown of this contract, and that “the evolution of Protestantism is therefore an example of competition of among alternative contract structures to resolve an activity’s major agency problem—in this case monitoring important agents to limit expropriation of donations.”

* weasel words, I know. Sorry.

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Rationality: The Issue Is Not the Issue

The latest round of sort-of-Nobels, awarded to Fama, Hansen and Schiller for work on (or in Hansen’s case related to) the dynamics of asset markets, has brought up once again the question of whether economic models should assume that individuals are rational.  Mark Thoma makes the case for rationality as a reference assumption which we can vary to see how the real world might differ from the “ideal” one.  Undoubtedly, the politics of the Fama-Schiller divide map into intuitions about rationality and the role of the state.  In the Fama/freshwater world people are rational, and the government can succeed only by duping them, which it can do only temporarily at long-term cost.  In the Schiller/saltwater world people are less than fully rational, and careful government action can improve market outcomes over some potential time horizons. Thus the intensity with which otherwise rational people debate rationality.

Here I would like to propose that the rationality dispute, while important, is not that important (an obscure reference to Lewis Mumford on Wilhelm Reich).  In fact, for most macroeconomic questions whether or not you assume individuals are rational has almost no bearing at all.  Here is an off-the-top-of-my-head list of modeling assumptions modern macroeconomists make that are not about rationality but actually drive their results.

1. Market-clearing equilibria are stable everywhere.  This is the default assumption.  The main departure that gets invoked to yield “Keynesian” outcomes is delayed adjustment to clearing, as in a two period model where adjustment occurs with a lag.  Another is the use of steady-state outcomes in matching models of the labor market, in which wages adjust to achieve a constant ratio of unemployed workers to job vacancies.  But stability concerns run deeper.  (a) Many markets appear to adjust to a permanent state of excess supply; in fact this is one of the hallmarks of a capitalist economy, just as permanent shortages result from central planning.  We don’t have a good understanding at the present how this works, and this includes not having a good understanding of the adjustment process.  (b) Expectations feed back into the adjustment process of markets through changes in the valuations people give to surpluses and shortages, as Keynes noted in The General Theory.  The difficulty in drawing up realistic models of expectation formation with heterogeneous agents is not just a matter of identifying the location of an equilibrium but also its stability properties.  (c) The Sonnenschein-Debreu-Mantel result is about the stability of multi-market equilibrium.  It says: in general they aren’t.  Rather, out-of-equilibrium trading generates path dependence.

2. Firms are long run profit-maximizers.  It is convenient to be able to treat households and firms symmetrically, but firms are not individuals.  (And households are not individuals either, but that may or may not wash out at the macro level.)  The structure of incentives that translates individual choice into the collective choice of organizations can hardly be assumed to achieve perfect correspondence between one and the other.  As we’ve seen, this issue is especially acute when financial institutions are involved.

3. Default does not occur in equilibrium.  This is a corner solution and therefore intrinsically implausible.  Rational decision-makers will usually accept some positive default risk, which means that defaults can and will happen; they are not disequilbrium phenomena.  Gradually we are beginning to see models that consider the systemic implications of ubiquitous default risk.  In the future, I hope that no macro model without default will be taken seriously.

4. All uncertainty is statistical.  No it’s not.  And any model in which, for instance, individuals fine-tune today’s consumption as part of a maximization that includes their consumption decades later, based on a calculable lifetime expected budget constraint, is grossly implausible.  Similarly with long-lived investments.  This is the point of Keynes’ animal spirits, and it strikes at the heart of conventional intertemporal modeling.

5. There is no interdependence among individuals or firms other than what is channeled through the market.  Everything else is a matter of simply adding up: me plus you plus her plus Microsoft plus the local taco truck.  We live in isolated worlds, only connecting through the effects of our choices on the prices others face in the market.  This assumption denies all the other ways we affect one another; it is very eighteenth century.  In all other social sciences it has disappeared, but in economics it rules.  Yet non-market interconnections matter mightily at all levels.  There are discourses in asset markets, for instance: different narratives that compete and draw or lose strength from the choices made by market participants and the reasons they give for them.  Consumption choices are profoundly affected by the consumption of others—this is what consumption norms are about.  The list of interactions, of what makes us members of a society and not just isolated individuals, is as long as you care to make it.  Technically, the result is indeterminacy---multiple equilibria and path dependence—as well as the inability to interpret collective outcomes normatively (“social optima”).  Also, such models quickly become intractable.

6. Rational decision-making takes the form of maximizing utility.  Utility maximization is a representation of rational decision-making in a world in which only outcomes count and all outcomes are commensurable.  This is another eighteenth century touch, this time at the level of individual psychology.  Today almost no one believes this except economists.  It is not a matter of whether people are rational, by the way, but what the meaning and role of rationality is.  It is not irrational to care about processes as well as outcomes, or to recognize that some outcomes do not trade off against others to produce only a net result.  (This is what it means to be torn.)  The utilitarian framework is a big problem in a lot of applied micro areas; it is less clear what damage it does in macro.  I suspect it plays a role in wage-setting and perhaps price setting in contexts where long-term supplier-customer relationships are established.  We have a few models from the likes of George Akerlof that begin to get at these mechanisms, but their utilitarian scaffolding remains a constraint.

These are enormous problem areas for economic theorizing, and none of them are about whether or not people are “irrational”.  Rather, taking in the picture as a whole, debates over rationality seem narrow and misplaced.  Alas, the current Riksbank award will tend to focus attention on the status of the rationality assumption, as if it were the fault line on which economic theory sits.

I don’t want to be a complete curmudgeon, however.  I think microeconomics is steadily emerging from the muck, thanks to an ever-increasing role for open-minded empirical methods.  Not only is the percentage of empirical papers increasing at the expense of theory-only exercises, but the theoretical priors within empirical work are becoming less constraining as techniques improve for deriving better results from fewer assumptions.  Macro is another story, though.  Here the problem is simply lack of data: there are too many relevant variables and too few observations; traction requires lots of theory-derived structure.  If the theory is poor, so is the “evidence”.  How do we escape?

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If Fama were Newton, would Shiller be Einstein?

OK OK, another quick break from my blogging break. An Econ Nobel for behavioral finance is just too juicy to resist commenting on.

Before the prize was announced, I said that it would be funny if Fama and Shiller shared the prize. But I also think it's perfectly appropriate and right that they did. Many people have called this prize self-contradictory, but I don't think that's the case at all. If Newton and Einstein had lived at the same time, and received prizes in the same year, would people say that was a contradiction? I hope not! And although it's probably true that no economist is on the same intellectual plane as those legendary physicists, Fama's Efficient Markets Hypothesis (which was actually conceived earlier, in different forms, by Bachelier and Samuelson and probably others) seems to me to be the closest thing finance has to Newton's laws, and behavioral finance - of which Shiller is one of the main inventors - seems like an update to the basic EMH theory, sort of like relativity and quantum mechanics were updates to Newton.

People criticize the Efficient Markets Hypothesis a lot, because it fails to account for a lot of the things we see in real financial markets - momentum, mean reversion, etc. But that's like criticizing Newton's laws of motion because they fail to explain atoms. Atoms are obviously important. But the fact is, Newton's laws explain a heck of a lot of stuff really well, from cannonballs to moon rockets. And the EMH explains a heck of a lot of real-world stuff really well, including:

* Why most mutual funds can't consistently beat the market without taking a lot of risk.

* Why almost all of you should invest in an index fund instead of trying to pick stocks.

* Why "technical analysis", or chartism, doesn't work (except perhaps in the highest-frequency domains).

* Why many measures of risk are correlated with high average returns.

These are important empirical, real-world victories for the EMH. Just like Newton's Laws will help you land on the moon, the EMH will help you make more money with less effort. I don't know what more you could ask of a scientific theory. If macroeconomics had a baseline theory as good as the EMH, it would be in much better shape than it is! (And if you think I'm being contentious by saying that, see what Gene Fama has to say about our understanding of recessions...)

Update: EMH-haters should also realize that the EMH implies that there are large market failures in the finance industry, because of the continued popularity of active management. So informational efficiency implies economic inefficiency in finance.

Anyway, sticking with the physics analogy, Shiller is really more like Michelson & Morley than Einstein. He found an anomaly - mean reversion in stock prices - that provoked a paradigm shift. In other words, Shiller really discovered behavioral finance rather than inventing it (actually, Fama, whose work is mainly empirical, isn't really like Newton either). The theorizers who came up with the reasons why markets couldn't be completely efficient were people like Joseph Stiglitz, Sanford Grossman, Paul Milgrom, and Nancy Stokey. I hope those people win Econ Nobels as well (Stiglitz, of course, already has one). But that paradigm shift built on the EMH, it didn't knock it down.

I think Mark Thoma puts it very well. The EMH is a simple model that works well in some cases, not so well in others. It's a great guide for most investors. It's not a great guide for policy, because bubbles are real, and they hurt the economy even if you can't reliably predict when they'll pop. And it's not a great guide for executive compensation, because stock prices fluctuate more than the value of firms.

But that's how science should work. You find a useful model, and then you take it as far as you can. When you hit the limits of its usefulness, you look for new stuff to add. And, by and large, that is what finance theory has done over the last half-century.

Anyway, for more info and opinions on this year's Econ Nobelists, check out Mark Thoma's link roundup. I especially like this one by Daniel of Crooked Timber. Also, see the various posts by my co-bloggers. Now back to work for yours truly...

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