Economics makes you selfish

I was motivated to write this post by fellow Australian young economist Gabriela D’Souza

"After Taking Economics, Students Become More Selfish and Expect Worse of Others" Well that's just common sense.
— Gabriela D'Souza (@gabster0191) October 24, 2013

I disagree. Selfishness is not common sense. It all seems to have started with this article, part of the periodic publicity the sprouts up around new studies into the selfishness of economists and economics students.

There is now quite a deal of evidence that economists are ‘more selfish’ than other groups. Here is some research showing lower rates of donations by economics students. Here is research showing economics students lie more, and here is a good summary of other research. The evidence is overwhelming that economists act in ways which most people find unacceptably selfish.

To me this body of evidence reveals the massive disconnect between mainstream theory in economics, that rests on the fundamental notion that greed or selfishness is the driver of coordination in a market economy, and the reality that social cooperation rests fundamentally on trust.

I would certainly agree with Francis Amasa Walker’s 1879 interpretation of the apparent social “odor” of economists arising from their disregard of “…the customs and beliefs that tie individuals to their occupations and locations and lead them to act in ways contrary to the predictions of economic theory.”

As Frans de Waal explains “Economists are being indoctrinated into a cardboard version of human nature, which they hold true to such a degree that their own behavior has begun to resemble it… Exposure in class after class to the capitalist self-interest model apparently kills off whatever prosocial tendencies these students have to begin with. They give up trusting others, and conversely others give up trusting them. Hence the bad odor.”

Without justifying this behaviour, let me just make it clear that economic indoctrination teaches that this apparently selfish behaviour is both what everyone actually does (despite ample evidence to the contrary), and that through self interest we prosper. They have swallowed this iconic Adam Smith quote hook line and sinker.

It is not from the benevolence of the butcher, the brewer, or the baker that we expect our dinner, but from their regard to their own interest

I want to use this post to provide an example of how such a view, an economic way of thinking, can lead you astray in everyday life. I draw on ideas from my good friend Uwe Dulleck, whose expertise is credence goods.

Credence goods are those whose value or utility can never be known by the buyer due to information asymmetries. The classic examples are doctors, who can prescribe medication for a diagnosed illness which you will never know is what you are truly suffering from. Or car mechanics, who diagnose mechanical failures and sell repairs, without the customer being able to know whether such repairs were either needed or carried out.

As usual Uwe’s research centres on some important questions

Under which conditions do experts have an incentive to exploit the informational problems associated with markets for diagnosis and treatment? What types of fraud exist? What are the methods and institutions for dealing with these informational problems? Under which conditions does the market provide incentives to deter fraudulent behaviour? And what happens if all or some of those conditions are violated?

Uwe introduces a simple example of a behaviour that, by economic reasoning, is expected to reduce fraud in credence goods markets

For some of us a feasible solution might be… to ask the mechanic to put the replaced part in the back of the car and to inspect the defect of this part. 

Uwe is cautious about whether this advice is sound. As am I. But I reckon that most economists would be more than happy to take this advice based on the ‘economic intuition’.

But does the common sense of unselfish non-economists also support this behaviour? Or is this an example of how the economic model of self-interest can lead us astray? I suggest the latter. And as a peek at my conclusion, the behaviour I might advise is to buy the mechanic a six-pack of beer.

Imagine you are a mechanic. Occasionally you realise that a customer is a bit of a sucker with too much money, so you charge them a little extra for some repairs you didn’t do. Most of the time you are pretty straightforward and honest.

One day a new customer comes in. They don’t seem particularly knowledge about cars, and since this is their first visit there is nothing to suggest they will become a regular customer. You diagnose the problem with their car, which is a very typical problem in that model, and explain that the repair could involve replacing certain parts, but you won’t know till you start taking things apart. This new customer agrees to go ahead with the repair, but asks you to put the old parts in the boot when you are done. It’s an odd request.

You realise that by making this request the customer has revealed that they are less knowledgable about cars than you thought, have no trust in you, and are solely relying on seeing a bunch of parts in the boot to judge your service.

What do you do? I’ll tell you what I would do. I would grab a bunch of parts from around the workshop and stick them in the boot, then charge for parts and repairs I didn’t do.

By following the behaviour suggested by a model of selfish individuals you have inadvertently signalled you complete ignorance about cars and a complete lack of trust.

Now imagine you are the mechanic who dealt with this customer and they didn’t ask for you to put the old parts in the boot. Maybe you still fleeced them a little and replaced a couple of parts that really didn’t need replacing. When the customer comes to collect the car they bring you six-pack of beer and thank you for your good work as they are so dependent on having a reliable car.

Would you fleece them again next time?

My point is that society deals with credence goods through the establishment of trust, either through non-market signals, like memberships of reputable societies, or ongoing social relationships. That mainstream economic theory ignores the fundamental role of trust and the cooperative behaviours that results from it, leaves their advice typically unsuited for many circumstances. As experimentalists know, in repeated games many forms of cooperation can become entrenched, yet most economic theory relies on the selfish response to a one-shot game.

Until economics courses around the world move beyond indoctrinating students into “cardboard version of human nature” we will continue to have selfish economists.

Economic models are plausible stories

‘Economists do it with models’ is one of the favourite insider jokes of the econ tribe. I recently tweeted that it would be nicer if economists did it with evidence. One of Australia’s most switched-on young economists responded and I elaborated my original point.

@ZacGross Models can't do that. Caus/corr problem needs knowledge of data generation. Model=assumed knowledge. Value is in predictive power
— Cameron Murray (@Rumplestatskin) October 12, 2013

It is a very common attitude in economics. Models, their solutions and any data correlations consistent with those solutions, are believed to constitute evidence that the assumptions embedded in the model accurately capture causal relations of some real life phenomena.

But of course that’s not the case. The key value of a scientific model is in its ability to predict outcomes in new situations, but also to generate new questions and directions for research. The model is not the answer, its a tool for discovery.

I have been reading Australian sociologist Duncan Watts’ book Everything is Obvious, which reminded me of the importance of evidence and the limitations of the model-building and correlation approach that almost defines economics.

Watts, a physicist turned sociologist whose work on networks is revolutionising the discipline, is completely frank about the near impossibility of determining causality in the one-shot experiment that is real life. In the section ‘Whoever tells the best story wins’, he concludes that 

Part of the problem is also that social scientists, like everyone else, participate in social life and so feel as if they can understand why people do what they do simply by thinking about it. It is not surprising, therefore, that many social scientific explanations suffer from the same weaknesses—ex post facto assertions of rationality, representative individuals, special people, and correlation substituting for causation—that pervade our commonsense explanations as well.

No matter how much your model appeals to your intuitive reasoning, or how well it fits the data, it cannot be shown to be of scientific value unless it offers useful predictions. For the economists out there just consider that models of constrained optimisation are simply a bunch of simultaneous equations, which read equally well in reverse (as do correlations). Moreover, micro-models of this persuasion almost always overlook methods of aggregation, leaving us to guess what sort of aggregate patterns should occur in the data. 

A discussion on the use of economic models would be incomplete without referring to Milton Friedman’s views that the reality of assumptions are unrelated to the usefulness of a model.

Consider the problem of predicting the shots made by an expert billiard player. It seems not at all unreasonable that excellent predictions would be yielded by the hypothesis that the billiard player made his shots as if he knew the complicated mathematical formulas that would give the optimum directions of travel, could estimate accurately by eye the angles, etc., describing the location of the balls, could make lightning calculations from the formulas, and could then make the balls travel in the direction indicated by the formulas. Our confidence in this hypothesis is not based on the belief that billiard players, even expert ones, can or do go through the process described; it derives rather from the belief that, unless in some way or other they were capable of reaching essentially the same result, they would not in fact be expert billiard players. 

My reading of this passage is that models should be judged on their predictive powers rather than their assumptions. Yet it also implies that if more plausible assumptions are possible that yield similar predictions, perhaps these generate more plausible models. 

If I were to propose a model of expert billiard play I wouldn’t start with the laws of physics but rather with a model of learning by trial and error. This simple model not only has more plausible assumptions, but predicts ‘expertness’ in billiards correlates with practice. It is also a general model applicable to such games as lawn bowls, where Friedman’s calculating-man model would require significant modifications to account for the weighted bowls. Friedman’s model is merely an assumption about the data-generating process. It translates to “if I know the data-generation process from the point when a ball is struck, I can use that knowledge to make a useful model that includes a prior point in time”. 

To reiterate, data can’t verify, support or prove (or even contradict) the causal assumptions in a model unless we have controlled part, or all, of the data generation process (either through experiment, natural, field or otherwise). 

Meanwhile, we have a whole field of econometrics that attempts to match models to data - refining the art of assumption-hiding and promoting the illusion of causality testing. For example, Angrist and Pischke’s book Mostly Harmless Econometrics: An Empiricist’s Companion is very loose with notions of causality. They say

Two things distinguish the discipline of econometrics from the older sister field of statistics. One is the lack of shyness about causality. Causal inference has always been the name of the game in applied econometrics. Statistician Paul Holland (1986) cautions that there can be “no causation without manipulation,” a maxim that would seem to rule out causal inference from nonexperimental data. Less thoughtful observers fall back on the truism that “correlation is not causality.” Like most people who work with data for a living, we believe that correlation can sometimes provide pretty good evidence of a causal relation, even when the variable of interest is not being manipulated by the researcher of experimenter.

They go on in the quoted chapter to discuss the use of instrumental variables methods address part of the causality problem. But recall the requirements of a useful instrument 

a variable (the instrument, which we’ll call Zi), that is correlated with the causal variable of interest Si, but uncorrelated with any other determinants of the dependent variable.

If you are thinking a little here you would realise we have simply introduced a second layer of model assumptions about the true data-generation process. You may believe there is a valid reason to do this, but again, the model can’t say whether this reason is sound or not. You are simply deferring one assumption about the nature of the world to an alternative, and perhaps more plausible assumption. 

What is more interesting is that founders of the instrumental variables method where challenged in the 1920s by the problem of causal inference in a model of markets with supply and demand curves. Since price is the simultaneous solution to supply and demand in the model there was no way to differentiate relative movements of the curves. Such problems persist to this day when applying demand/supply models to market analysis. 

Models aren't quite the scientific tools economics often believe them to be. At best they offer plausible stories about a particular phenomena and provide some predictive power. The religious attachment of the economics discipline to its core models is at times quite astounding.

It is genuinely challenging for social scientists to make gains in knowledge under the uncontrollable conditions of real life, and I can only hope that the future of research involves far more experimentation, either in the lab or in the field. In the mean time I hope the profession can be far more honest about the limits to knowledge, more humble in its policy recommendations, and more open to competing views of the world whose claims often stand on equal scientific footing.

Quote of the day

I've started Truman F. Bewley's book Why Wages Don't Fall During a Recession.  The book reports Bewley's research program that involved interviewing "more 300 businesspeople, labor leaders, counselors of the unemployed and business consultants in the Northeast of the United States during the recession of the early 1990s".

Here's the quote.

From the interviews, I conclude that wage rigidity stems from a desire to encourage loyalty, a motive that superficially seems incompatible with layoffs. My findings support none of the existing economic theories of wage rigidity, except those emphasizing the impact of pay cuts on morale. Other theories fail in part because they are based on the unrealistic psychological assumptions that people's abilities do not depend on their state of mind and that they are rational in the simplistic sense that they maximize a utility that depends only on their own consumption and working conditions, not the welfare of others.

To me this type of research is an avenue too rarely employed in economic research (a notable exception being Alan Blinder's work). It suggests that the an economic toolbox that contains only rationality, and is unable to incorporate other crucial elements of human behaviour such as loyalty, is destined to poorly explain real patterns of social organisation.

Top young economists

Believing that the next generation of economists will be for more rigorous and honest about their research keeps me happy. Today I’m not so happy. 

Here’s a list I came across last year, which recently came across my screen again. It compiles the views of 8 top young economists on where economics is going. And it’s bloody frustrating.

It’s frustrating because the more I read of economic research these days, the more it seems to neatly fall into either ‘the optimal control’ game, the ‘label the residual game, or the “correlation game”. And these young guns are simply rehashing these same games.

The ‘optimal control game’ is about mathematising an idea so that it fits into the structure of the calculus of optimal control. Often it doesn’t matter how you squeeze your idea into this mould; simply pluck a term out of thin air, call it optimism, certainty, talent, or some such unmeasurable or knowable thing and stick it in an equation. You then you solve the equations, finding the optimal point, which you can compare to the solutions to the equations when you ‘shock the model’ by changing a parameter - maybe a magical bout of increased optimism.

This method tells us very little about anything, especially when the terms are often vague unmeasurable concepts, and when there is not clue about how the optimal point can be obtained in reality if you don’t actually start there. 

‘Label the residual’ is the game of writing a model that has a term which captures all the unmeasured variation from the other terms. Noah Smith described it here. 

The ‘correlation game’ is about mining data for relationships that suit your audience. 

There is no prediction game, despite this being the true test of a theory. 

There is also no reward for exploring new methods of analysis, and much criticism when it is tried. And to my genuine concern, outcomes of the unscientific games being played are taken seriously not only within the profession, but often in the political arena.

With such self-inflicted constraints the discipline struggles to adequately answer even the most basic questions it is tasked with - why are some people and countries wealthier than others, why is wealth distributed the way it is, what is money, why is there a business cycle, and more. 

So it is of interest that we examine what the insiders, the next leaders of the ‘tribes of econ’, see as the direction the field is heading. Are they ready to break free from these silly games, testing new models of cooperation and interaction, bringing dynamic mathematical tools to the economics toolbox, and gaining qualitative input from the real decision-makers in the sectors they analyse? Will they incorporate political elements, experimental regularities, and the mechanics of real institutions into their research? 

I wish I could be so optimistic. 

* I disclose that I don’t personally know any of these economists and haven’t read their work for this article (I lied, I have poked around their work a little). 

Nicholas Bloom 
Why are developing countries poor? It’s a good question and a fundamental one to economics for a long time. But poor Bloom seems to be playing a new version of the ‘label the residual game’. He says

I think the answer is complex and linked to a combination of factors around history, geography, luck, etc. I am personally working on management practices: people in developing countries are poor because wages are low, and wages are low because firms are very unproductive, and firms seem to be unproductive in large part because of bad management.

The one thing I give him credit for is that he is involved in randomised experiments, avoiding falling into the “correlation game”. But do we believe that poor countries are poor mainly because there are bit disorganised? Doesn’t that beg the question of why they are disorganised? Or is it actually about uncertainty fairy?

Ray Chetty

How can we increase the rate of economic growth and overall well-being, and how can we reduce the rate of poverty? Good questions. Not new at all, but Chetty seems not overly certain about where to look, naming just about every policy there is as a potential mechanism for growth. This seems like a very broad program of labelling the residual.

Gauti Eggertsson 

How to use monetary and fiscal policy to eliminate unemployment and control inflation? These fundamental questions of economics are still on the table after centuries of research and policy experimentation. Why is that?

We seem to know that credit creation is the real inflation driver, and we seem pretty sure it is closely related to capital investment and employment. But if questioning banking and money remains taboo in economics, I can’t see change happening fast, especially when the top young guns seem to think introducing financial frictions into models is the way forward. Back to the 'optimal control game' then.

Xavier Gabaix

How to model realistic economic agents? Now there’s a better question. But Gabaix seems very certain that some form of bounded rationality is the answer here, which is of course to say, that no one wants to drop their foundation methods, prefer to play a more sophisticated version of the ‘optimal control game'.

 
Gita Gopinath

How does one accomplish sustainable growth without large boom-bust cycles? Again, another classic problem of economics still waiting for a decent answer. Gopinath at least raises some important issues routinely ignored in economic research - global imbalances, currency wars, capital controls etc. But again, we here about “understanding the propagation of shocks across economies”, which implies we are playing the “optimal control game” once again and don’t really want to think about the mechanics of economic interactions, investment and so forth. 

Peter Leeson

What is the status of the rationality postulate?

If we view economics as an “engine” for understanding the world, the rationality postulate was that engine in nearly all of economics until quite recently. The rise of behavioral economics has challenged the usefulness and, in a more subtle but radical way, the legitimacy of the rationality engine.

A very vague and non committed response by Leeson. He suggests that beliefs about rationality drive economists views and their interpretations of events. But still, what sort of solution to rationality’s status would Leeson be happy with? 

Glen Weyl

Glen seems to think that the rise of digital information, and the outsourcing of decisions we are making to automated computer systems, is threatening to overwhelm Hayek’s argument that “free markets were necessary in order to allow the incorporation of information held by dispersed individuals into social decisions”.

Which is strange, because every economic model of markets requires that all the information is available to every decision maker before any trade occurs. AS I have said before “if prices can convey all information, then there must also exist an alternative non-price method to convey that information which governments could use for an alternative allocation system.”

Justin Wolfers 

What will economists do with more data? Well, Justin thinks economists will continue expanding their social science empire in the spirit of Gary Becker. Of course, any statistician will tell you that uncontrolled data alone can’t generate any useful scientific findings.  “Economic theory will become the tool we use to structure our investigation of the data”. I hope not. Since economic theory doesn’t usually tell us what to expect in the data at large, given the general failure to consider problems of aggregation.

This is really a sad story. The econ tribes wield so much political influence, yet have so little to offer.  These same questions could have been asked 30 years ago and would have seemed cutting edge.  So much has happened, in the world, and very little it seems in economics [1].

If I was to answer I would say the big question for economics is finding new tools that are able to incorporate dynamic elements and evolving strategies of cooperation, addressing all the aggregation problems that arise from the 'optimal control game'.  I would say that on the policy side the big questions are about the mechanics of real institutions, for example the banking system and credit creation, and of the political games that arise when new institutions are formed.  For me the future of economics includes power, rents, and conflict. 

But today I doubt we will see the future I imagine. 

[1] Many researchers will deny that little has changed.  We do know that the economics community is more empirical now than ever. But without theory, uncontrolled data can’t reveal much of scientific value.  And theory seems stuck in a time warp.