Improving behavioural economics

A neat new paper has appeared on SSRN from Owen Jones – Why Behavioral Economics Isn’t Better, and How it Could Be (HT: Emanuel Derman via Dennis Dittrich). My favourite part is below. As I have said many times before, giving a bias a name is not theory.

[S]aying that the endowment effect is caused by Loss Aversion, as a function of Prospect Theory, is like saying that human sexual behavior is caused by Abstinence Aversion, as a function of Lust Theory. The latter provides no intellectual or analytic purchase, none, on why sexual behavior exists. Similarly, Prospect Theory and Loss Aversion – as valuable as they may be in describing the endowment effect phenomena and their interrelationship to one another – provide no intellectual or analytic purchase, none at all, on why the endowment effect exists. …

[Y]ou can’t provide a satisfying causal explanation for a behavior by merely positing that it is caused by some psychological force that operates to cause it. That’s like saying that the orbits of planets around the sun are caused by the “orbit-causing force.” …

[L]oss aversion rests on no theoretical foundation. Nothing in it explains why, when people behave irrationally with respect to exchanges, they would deviate in a pattern, rather than randomly. Nor does it explain why, if any pattern emerges, it should have been loss aversion rather than gain aversion. Were those two outcomes equally likely? If not, why not?

Part of the solution provided by Jones, as reflected in much of his past work, rests in evolutionary theory.

An updated economics and evolutionary biology reading list and a collection of book reviews

I have updated my economics and evolutionary biology reading list, with a few new additions including John Coates’s The Hour Between Dog and Wolf, Gregory Clark’s new book on social mobility and Jonathan Haidt’s The Righteous Mind. As before, I have been selective, adding only the best books (or articles) in the area. That said, I am always open for suggestions or comment.

When updating the list, I realised I have written a lot of book reviews over the last few years. I have collected most of them together on one page, which you can find here. It includes a lot of good books that aren’t on the reading list as they are not on topic. It also contains a few books that are on topic but haven’t made the cut.

A week of links

Links this week:

  1. Cooperation in humans versus apes.
  2. In praise of pilots.
  3. Are women better decision makers? You can ask about some sex differences.
  4. Amazon is doing us a favour. Goodbye book publishers.
  5. The logic of failure.
  6. The Behavioural Insights Team has lunch with Walter Mischel. Mischel’s work is fantastic and his new book is on my reading list, but the mention of brain plasticity and epigenetics (in the same sentence!) has reduced my expectations.
  7. Charles Murray on Ayn Rand. HT: Alex Tabarrok

Finding taxis on rainy days

A classic story on the play-list of many behavioural economics presentations is why you can’t find taxis on rainy days. The story is based on the idea that taxi drivers work to an income target. If driver wages are high due to high demand for taxis, such as when it rains, they will reach their income target earlier and go home for the day. The result is you can’t find a taxi when you need one most.

The story is such a favourite as it conflicts with conventional economic wisdom that people are maximisers who respond positively to incentives such as higher wages. Instead, drivers are satisficers who quit work for the day once have hit their target, even though the high wages would allow them to earn more than normal.

This story originates from a 1997 article by Colin Camerer and friends (I suggest following Camerer on twitter). They analysed taxi trips in New York and found that as wages went up, labour supply (taxis on the street) goes down. Their preferred explanation, based on what some drivers said, was that taxi drivers work to a daily income target. Their article did not include the reference to the rain, but it has become the way the story is traditionally told.

But, a new study suggests this negative relationship between wages and supply might not generally be the case for New York taxi drivers. Using a much bigger dataset of New York taxi driver activities, Henry Farber has found that, as standard economic theory would suggest, taxi drivers drive more when they can earn more. There was no evidence of income targeting in the data.

As another blow to the rainy day story, Farber also found that taxi drivers didn’t earn more when it was raining. As traffic was worse and they travelled less distance, their earnings didn’t increase despite the higher demand. There were less taxis on the street when it was raining, but this must be due to causes such as drivers preferring not to work when traffic is bad.

So how do we reconcile these conflicting findings? A starting point is in the original study. In a show of humility, Camerer and colleagues were open to the idea that their result might not be robust. They close with the following paragraph:

Because evidence of negative labor supply responses to transitory wage changes is so much at odds with conventional economic wisdom, these results should be treated with caution. Further analyses need to be conducted with other data sets (as in Mulligan [1995]) before reaching the conclusion that negative wage elasticities are more than an artifact of measurement or the special circumstances of cabdrivers. If replicated in further analyses, however, evidence of negative wage elasticities calls into question the validity of the life-cycle approach to labor supply.

To use the cliché, more research is required. And there has been a lot more research since Camerer and friends’ had their study published. While I’ve pitched the story as a new paper tearing up an almost 20-year old favourite, there has been a sequence of papers over the years with both supporting and conflicting results, including by Farber.

Farber’s explanation for the result in his latest paper is that he had access to a larger dataset – five million shifts compared to a few thousand in Camerer and friends’ or Farber’s earlier studies. Technological progress in recording taxi data also allowed Farber’s work to be at a much finer level of detail than was possible at the time of the original study. Other studies also had small datasets or used less reliable data such as from surveys (such as this one from Singapore), but there have also been at least one involving similarly large sets of taxi data that did find a negative relationship (such as a second from Singapore, although in that case the negative relationship seemed of too low a magnitude to support income targeting).

Another explanation might lie in the methodological battle about how you should measure the relationship between wages and supply for taxi drivers. Farber’s 2005 paper picked apart the original methodology, particularly around their assumptions on wages, and he chose a different approach based on drivers deciding whether to continue or not at the end of each ride. When I previously invested some time to understand it, I found Farber’s critique reasonably persuasive. However, I haven’t taken the time to understand the finer points of Farber’s new analysis and to what extent methodology determines the result, so it will be interesting to see some responses to this latest salvo.

Another potential distinction is that Camerer and friends’ original study was able to distinguish between owner-operators and employee drivers, each of which face different incentives. Farber wasn’t able to tease the two apart. However, Camerer and friends found a negative relationship for both groups, so at a minimum, Farber’s work suggests that the finding would not hold across both. Farber did consider whether there might be many different types of driver, which there may be. But if the satisficers do exist, there are not many of them.

On a brighter note, there is some hope that we will be better able to catch a taxi on rainy days in the future. With current taxi regulation and fixed pricing, the inconvenience of driving in bad traffic results in less taxis on the road. But with new entrants such as Uber able to charge more and adjust pricing at times of high demand, we might actually get more taxis or other vehicles on the road when we need them most. And we can have some comfort that when those taxis are needed most, there will be plenty of maximisers around to fill our need.

A week of links

Links this week:

  1. Was the paper proposing that mice can pass their fears onto their offspring and grandchildren via epigenetic mechanisms too good to be true? Neuroskeptic comments (and read the comments to Neuroskeptic’s post).  And my favourite epigenetics statement of the week: “Women too can succeed in business. Because epigenetics.”
  2. What are agent based models?
  3. The Bell Curve 20 years on.
  4. Genetic engineering will create the smartest humans who have ever lived.
  5. Is low fertility really a problem?

The invisible hand of Jupiter

I’m note sure how I hadn’t come across this before (one need only read the Wikipedia entry “invisible hand”), but Adam Smith used the phrase “invisible hand” three times. It is used once in The Theory of Moral Sentiments (1759) and The Wealth of Nations (1776) – both of those I knew. The third time comes from a posthumously published (1795) essay The History of Astronomy, written before The Theory of Moral Sentiments. Smith wrote:

For it may be observed, that in all Polytheistic religions, among savages, as well as in the early ages of heathen antiquity, it is the irregular events of nature only that are ascribed to the agency and power of the gods. Fire burns, and water refreshes; heavy bodies descend, and lighter substances fly upwards, by the necessity of their own nature; nor was the invisible hand of Jupiter every apprehended to be employed in those matters. But thunder and lightning, storms and sunshine, those more irregular events, were ascribed to his favour, or his anger. Man, the only designing power with which they were acquainted, never acts but either to stop, or to alter the course, which natural events would take, if left to themselves. Those other intelligent beings, whom they imagined, but knew not, were naturally supposed to act in the same manner; not to employ themselves in supporting the ordinary course of things, which went on of its own accord, but to stop, to thwart, and to disturb it. And thus, in the first ages of the world, the lowest and most pusillanimous superstition supplied the place of philosophy.

In this case, the invisible hand of Jupiter is the explanation that “savages” and those in “the early ages of heathen antiquity” apply to otherwise unexplainable irregular events. It isn’t much help in interpreting the other two uses.

HT: Jag Bhalla

Lazy analysis – inequality edition

Over at WSJ Real Time Economics, Josh Zumbrun turns the following chart into a claim that “the SAT is just another area in American life where economic inequality results in much more than just disparate incomes.”

SAT- Student Affluence Test

But what does the chart actually tell us? In a perfect meritocracy, the smartest students will score the highest. But as intelligence is heritable, the smarter kids will tend to have smarter and higher income patterns, giving us the pattern we see in the chart. In an alternative world where parents pay for results, we end up with the same pattern. So that charts tell us nothing. It’s consistent with both worlds.

I’m not exactly Robinson Crusoe in criticising this article. See also Arnold Kling and James Pethokoukis – although the assortative mating Pethokoukis refers to isn’t necessary to get a graph that looks like this, even though it is almost certainly playing a role.

Having picked on this article, the use of a bivariate analysis (a natural result of using a graph) while ignoring other confounding variables is a disappointingly common feature in the increasingly popular “data journalism”.

A week of links

Links this week:

  1. Plenty of press and interesting articles sparked by Peter Thiel’s new book. First, he has a swipe at business schools. And some great one-liners. But is he wrong about the future?
  2. Another tech-billionaire – Elon Musk wants to put people of Mars. But he doesn’t need one million people to get enough genetic diversity.
  3. Eric Crampton has some great posts this week on public health. First, where should the money be going?  Some thoughts on soda taxes and fat taxes. And drinking when pregnant.
  4. Cameron Murray risks walking onto Steven Landsburg’s lawn.
  5. Rajiv Sethi defends agent based models from Chris House. House tends to overreach when he strolls into the unfamiliar and attacks the heterodox rather than his standard (and also not overly convincing) defense of the orthodox.
  6. Put your laptops away kids.
  7. The missing heritability puzzle is slowing being chipped away. But the genetic post-modernists continue their losing battle.
  8. The heritability of educational attainment reflects many genetically influenced traits, not just intelligence. A Science Daily summary. Plus, emotional intelligence is overrated (HT: Stuart Ritchie). Intelligence is important, and to the extent other traits matter, they are heritable too.
  9. Does evolutionary theory need a rethink? No.
  10. Doctor decision fatigue – more unnecessary antibiotics in the afternoon.

A week of links

Links this week:

  1. The workplace is needed to overcome our lack of self control.
  2. Resisting instant gratification – the FT explores Walter Mischel’s the Marshmallow Test.
  3. Most critiques of twin studies recycle the same discredited 40-year-old arguments. Here’s another paper pulling them apart.
  4. The college educated are still getting married, just later. The same can’t be said for everyone else.
  5. A critique of the 10,000 hour rule. But people should not feel obliged to follow every mention of the role of genetics with an apology for war, slavery and genocide (surprised eugenics didn’t get a mention).
  6. People respond to prices on medical procedures. Demand curves still slope down.
  7. Your baby looks like your ex. Slightly disturbing.
  8. A collection of W. Brian Arthur papers on complexity in economics is due out at the end of the month.
  9. Matt Ridley reviews Steven Johnson’s book How We Got To Now.
  10. Neuroskeptic skewers claims that “this will change your brain”.
  11. Young, middle-aged and old men all want women in their 20s. But they’re realistic about what they can get.
  12. Academics writing stinks.
  13. French regulators are mad. Or at least a touch madder than the rest.

The genetic basis of social mobility

The Son Also RisesIn 2007’s A Farewell to Alms: A Brief Economic History of the World, Gregory Clark argued that the higher fertility of the rich in pre-industrial England sowed the seeds for the Industrial Revolution. As children resemble their parents, the increased number of prudent, productive people made possible the modern economic era.

Part of the controversy underlying Clark’s argument – made stark by Clark in articles and speeches following A Farewell to Alm’s publication – was that he considered there may be a genetic basis to the transmitted traits. The higher fertility of the rich and changing character of the population was natural selection at work.

Clark’s new book The Son Also Rises: Surnames and the History of Social Mobility, also makes a new and unique argument. And like A Farewell to Alms, there is a genetic underlay.

Clark’s primary argument is that across a range of societies and eras – from pre-Industrial to modern England, from pre- to post-revolution China, and across the centuries in the United States, Sweden and India – social mobility is low. The correlation in social status between one generation and the next is around 0.7 to 0.8, meaning we can find the echoes of high status 10 or more generations later. Status does “regress to the mean”, but it does so slowly.

To put this in context, Norman surnames are overrepresented at Cambridge and Oxford today by around 25 per cent, 950 years after the Norman conquest. The descendants of the samurai, who lost any legal privileges in 1871, are today several times overrepresented in high status occupations in Japan. The eighteenth century elite of egalitarian Sweden are still a privileged group.

What makes Clark’s finding particularly striking is that most studies of intergenerational mobility have found lower numbers – often an intergenerational correlation of around 0.2 to 0.4. A correlation of that order would erase the traces of social status in a few generations.

Clark suggests one reason for his finding of lower social mobility is that previous studies measured noise. Suppose status is a result of two components – a fixed factor transmitted between generations, and a part determined by luck. Measuring across a single generation, the luck disguises the underlying correlation. If measured across multiple generations, bad lack in one generation will typically be followed by reversion to the underlying status the next. Status across multiple generations provides a better measure of the persistence of social status and of the effect of the fixed factor transmitted between them.

Clark directly relates this point to the biological concepts of genotype and phenotype. The underlying base social status is the genotype. If you observe someone’s external characteristics, you observe the phenotype, which reflects genotype plus some degree of noise. While this might be seen as an analogy, Clark argues that a genetic explanation is the best explanation of what he observes.

A second reason for the difference between Clark’s results and the previous studies is that most studies of intergenerational transmission of status focus on a single measure of status such as correlation in income between parents and their children. But people often trade off one type of status for another. A political leader or leading academic typically receives income that would barely scrape them into the top one per cent. People take lower pay for more prestigious or interesting jobs. Looking a single measure of status will overestimate the change in status as it cannot capture the trade-offs across different domains.

So how does Clark avoid this problem? Clark’s trick is to use rare surnames – an idea suggested to him by the former New York Times science writer Nicholas Wade – and treat them as large families. By finding rare surnames with high or low status, Clark and a range of colleagues tracked the average status of these surnames across the generations through measures such as relative representation in legal practitioner and medical rolls or lists of the wealthy.

As a rare surname comprises many people, the noise and trade-offs across different types of status is averaged out across the “family”. Surname families have an underlying status genotype that can be tracked more faithfully that the individual phenotypes observed in typical studies.

The body of research Clark presents through the book is impressive, although it is not always the most exhilarating reading. As he works through one society to the next, looking at various measures of status, the story is usually the same. Status is persistent and surprisingly similar across times, countries and different measures of status. One interesting finding presented by Clark is over-representation of Norman surnames in the military – and this is not a finding that can be explained by persistence in status. Clark suggests that 10 generations after the Norman conquest, the descendants of the Norman conquerors still had a taste and facility for organised violence.

Clark’s results suggest that while 100 years of Swedish social democracy may have created a more economically equal society, it is no more mobile. The Scientific Revolution, Enlightenment and Industrial Revolution did little to change social mobility in England, and the persistence of status has been almost unaffected by massive changes in inheritance tax. China’s Cultural Revolution had little effect. And across all these countries, government interventions from universal education to progressive taxation have failed to budge social mobility. As Clark states:

Events that at the time seem crucial, powerful and critical determinants of the fate of societies leave astonishingly little imprint in the objective records of social mobility rates.

That social mobility is low but still occurring is a combination of some interesting factors. First, Clark argues that low mobility is affected by assortative mating. If people mate with people of similar status, their children will better reflect their status. However, assortative mating is not perfect. People do not precisely assort by status. And even if they did, observed status (phenotype) does not perfectly reflect the underlying genotype. Both of these factors mean that high or low status people will tend to partner with people closer to the mean, meaning that their children will similarly be closer to the mean.

One way to preserve a group’s status is to have marital endogamy – people only marry within the group – preventing mistakes in marrying low status genotypes that had good luck. This is observed is the highest status castes in India. High status individuals within that group will regress to the mean of that group, but it is regression to a higher mean than that of the rest of the population. The corollary of this point is that to maximise social mobility, you would encourage marriage across groups and status levels.

The inability to observe your potential partner’s status perfectly suggests that if you want to achieve high status, you should not look not just at your potential partner but also at their relatives. Since luck may have affected your potential partner’s status, you gain more information from the status of their relatives. However, these relatives will not contribute anything to the success of your child – transfers of wealth do not make status more persistent. But the status of relatives provides evidence of the social status underlying your potential mate. There are high rewards to this choice. Through appropriate choice of mates, a lineage can avoid downward mobility forever.

In addition to marital endogamy, another apparent exception to regression to the mean is through the loss of low or high status members from high or low status groups respectively. In Ireland, high status Catholics are more likely to change to become Protestant than Catholics of low status, and low status Protestants tend to drift the other way. The expected regression to the mean of these groups does not occur, although if you track Irish surnames, there is still the typical low level of social mobility. Gypsies likely maintain their low status through similar dynamics.

One of Clark’s obvious in hindsight findings is that the social mobility he describes works both ways. In the same way that regression to the mean is slow, the path to the top or bottom follows a similar process. Increases in status are largely driven by random shuffling of genes and good luck in marrying people of better genotype than phenotype. Rags to riches stories and vice versa are rare for whole groups of surnames. The super rich tend to be children of moderately rich, and the poorest children are the children of the moderately poor. This does not mean that you can predict which families will rise to the top – but you can predict the long and slow process.

So why does Clark feel that genetics must be involved, and not transmission of resources or other advantages that accrue to those with high status? As a start, the genetic story is consistent with the mountain of twin and adoption studies that demonstrate a strong role for genetics and a limited role for family environment.

One of his more interesting arguments is that genetics is required to explain regression to the mean. In modern societies, high status people typically have lower fertility and are able to make much larger investments in each child. If this transfer to children mattered, status should persist or these groups should move even further above the mean.

A weakness with A Farewell to Alms was that Clark did not seem ready to bite the population genetic bullet. The tools of population genetics could have helped Clark nail his points and put to bed many of the criticisms that were made of his work. Since then Clark has spent a lot more time in the company of geneticists, and this is reflected in his arguments. His use of genetic data in interpreting the social mobility in Ashkenazi Jews helped his argument cut through. He still does not use population genetic tools in the way he could, but it seems he is much more prepared to fight on the genetic front.

So what do Clark’s arguments mean for how we should think about inequality or social mobility? Clark points out that a genetic basis to social mobility means that people do not achieve what they do because of family background. Instead, it is their ability, their propensity to work hard, and their resilience to failure that leads to success. We can predict success based on family background, but family background is not the cause.

Clark suggests (and I agree) that world is actually fairer than we believe if there is a genetic basis to social outcomes. Large investments by the upper class are doomed to failure. Do not worry that you cannot afford that expensive preparatory school for your kids. People still need to struggle and put in effort to succeed. Genetics just suggests which people will be most likely to struggle and invest that effort.

Also on the optimistic front, the lower fertility of high status people means that social mobility in the modern world is predominantly upward. Groups tend to move up to fill the space at the top – which is the opposite of the dynamic that existed in pre-Industrial England. (Although I am not convinced that lower fertility of the rich is either a general or a long-term dynamic)

Having slashed through the idea that government policy might promote social mobility, Clark is still relatively progressive in his policy recommendations. As he states, why do we want to multiply the awards to the genetic lottery winners? He prefers a Nordic model where, even though social mobility is low, the gap between those of high and low status is not as large. Clark argues the persistence of status, despite the range of taxation and other measures people have been subject to, suggests reward is not required to stimulate achievement.

I disagree with Clark on this point. Reward is important, but the reward just happens to be status itself. A world with no difference in economic outcomes as opposed to reduced difference could see marked changes in effort. Clark also spends little time discussing the other trade-offs involved in the Nordic model, such as the effect on overall wealth.

Finally, Clark does not ask whether the Nordic countries have lower underlying (genetic) variation in their status. It may be that the Nordic institutions reflect the characteristics of the population, rather than being the cause.