The re-eruption of the war of words between E.O. Wilson and Richard Dawkins has occurred just as I have come around to reading Wilson’s 2012 book The Social Conquest of Earth. In an interview on BBC2 (watch it at the bottom of this post), Wilson stated:

There is no dispute between me and Richard Dawkins and there never has been, because he’s a journalist, and journalists are people that report what the scientists have found and the arguments I’ve had have actually been with scientists doing research.

It is an interesting call to authority that Wilson himself challenged in his reply to Dawkins’s stinging review of the book in Prospect magazine (Wilson’s reply is at the bottom of the Dawkins piece) – or even Wilson’s statement at the end of the book that:

Science belongs to everybody. Its constituent parts can be challenged by anybody in the world who has sufficient information to do so.

Regardless, the debate between Wilson and Dawkins is a continuation of the group selection debate that has been running since the 1960s, with Wilson now on the group selection side, and Dawkins on that of the selfish gene. But despite this framing of the debate as a confrontation between two apparently diametrically opposed views, The Social Conquest of Earth suggests that Wilson’s view is somewhat more complicated, and possibly confused.

The old and new group selection

As background, it is worth defining three concepts: group selection, a newer conception of group selection called multilevel selection, and inclusive fitness.

The older form of group selection is a process where the differential survival of groups leads to the evolution of traits that benefit the group. This type of group selection, pushed in the 1960s by V.C Wynne-Edwards in particular, might involve members of a group restraining reproduction during times of scarcity so that the group does not experience resource shortages.

This concept received many harsh critiques, most famously by George Williams and in popular form by Dawkins in The Selfish Gene. The basic problem is that if someone cheats and does not restrain reproduction when others do, they will have more offspring and come to dominate the group. The altruistic trait will only emerge if groups with more altruists have a large enough advantage over other groups to compensate for their disadvantage within their groups. These conditions are generally considered to be met in limited circumstances, and most evolutionary biologists would say that the evolution of group adaptations in this way is a theoretical possibility, occurs in some circumstances, but is a practical rarity.

Group selection was somewhat reinvigorated in the late 1970s by David Sloan Wilson and friends under a reworking that is commonly called multilevel selection. The first distinguishing feature of multilevel selection is that the definition of “group” can include transitory groupings that regularly remix. You could consider two individuals who briefly trade to be a group. The second feature of multilevel selection is that selection is decomposed across multiple levels. The analysis would look at the fitness of the two trading individuals with respect to each other, which is the individual level selection, and of the fitness of their group relative to other groups.

Multilevel selection has received a largely muted response, with inclusive fitness the alternative framework preferred by Dawkins and friends – not to mention the dominant paradigm in evolutionary biology. Inclusive fitness combines the direct effects of a trait on an individual with the indirect effects of the trait on other individuals who possess that trait. Kin selection, a strategy of favouring relatives, maximises inclusive fitness.

Inclusive fitness is famously captured by Hamilton’s rule, which states that an altruistic trait will spread if rb>c. c is the cost to the altruist of the trait, b the benefits to others, and r the relatedness between the altruist and beneficiaries. A trait to favour your brother will spread if the benefits to the brother, who is 0.5 related to you, are double the costs to you. Or as J.B.S. Haldane put it, he would give his life for two brothers or eight cousins.

While apparently opposing perspectives, inclusive fitness and multilevel selection are two sides of the same coin. If you can describe an evolutionary dynamic in terms of multilevel selection, you can also give an inclusive fitness story (many suggest the two approaches are mathematically equivalent, although this is debated). They are simply different accounting methods, or languages. The intuitive explanation for the link is that higher levels of selection (the level of groups) can favour the spread of a trait because the members of that group have a degree of relatedness.

Wilson’s critique of kin selection

Wilson’s core argument through The Social Conquest of Earth is that the concept of inclusive fitness has been discredited. This claim stems from the infamous 2010 Nature paper by Martin Nowak, Corina Tarnita and Wilson on eusociality.

Eusociality involves a division of reproductive labour, such as that which occurs in the bees, ants and wasps. Eusociality and kin selection are closely linked as the higher relatedness between sisters in the bees, ants and wasps has been used to explain the willingness of most females to forgo their reproductive success for one of their sisters, the queen.

Nowak, Tarnita and Wilson’s argument was that the evolution of eusociality could be explained through simple individual selection and did not require the framework of inclusive fitness. They presented a model in which eusociality evolved without any reference to relatedness.

The model itself was interesting, but it was sandwiched between a not particularly well thought-out or supported claim that “the production of inclusive fitness theory must be considered meagre” and that it “does not provide additional insight or information” to standard natural selection theory. I will let the many responses to the paper speak for themselves, including the main response (with the 130 odd signatures – ungated version here), which contains a table indicating the contributions of inclusive fitness. But if I were to single one paper out, it is this one by Garnder, West and Wild, which addresses many of the mathematical arguments. Its main point, in short, is that Nowak, Tarnita and Wilson fail to distinguish between general kin selection theory and the kin selection methodology used to address specific problems. Their criticisms do not apply to the general theory.

Coming back to Wilson’s book, however, Wilson seems to take an even stronger stance than in the paper. For example, he states that:

Martin Nowak, Corina Tarnita, and I demonstrated that inclusive-fitness theory, often called kin selection theory, is both mathematically and biologically incorrect.

Through the book, Wilson’s characterisation of the paper’s reception has to be described as either deceptive or oblivious. Gems such as “The beautiful theory [inclusive fitness] never worked anyway, and now it has collapsed” contrasts with what even a cursory glance at the responses suggest. Nowak, Tarnita and Wilson’s critique has not generally been accepted, although reading the book gives no impression of the slightest opposition to Wilson’s position. The interview that triggered this latest spat suggests Wilson is still singing a deceptive tune. He states:

I have abandoned it [the notion of the selfish gene] and I think most serious scientists working on it have abandoned it. Some defenders may be out there, but they have been relatively or almost totally silenced since our major paper came out.

Given the paper, it is no surprise that Wilson argues throughout The Social Conquest of Earth that individual level and group selection is all that is required to explain the evolution of eusociality in insects. Wilson argues that, after the emergence of eusociality in a single colony through individual level selection, “between-colony selection” leads to the wider spread of the eusocial trait. Its selection at the individual and group levels without a multi-level selection framework. As Wilson states:

But multilevel selection, in which colonial evolution is regarded as the interests of the individual worker pitted against the interests of its colony, may no longer be a useful concept on which to build models of genetic evolution is social insects.

I have no idea why the preferred model isn’t simply a multilevel selection framework with alternative assumptions, and the confusion only increases from here.

Eusociality in humans

Where things get truly confusing is Wilson’s consideration of humans. Try as I could, I could not conceive of a sympathetic reading that would allow Wilson’s position to be seen as coherent.

First, his branding of humans as eusocial is a stretch under any definition, although he is not alone in attempting that.

But more confusingly, his argument that eusociality arose in humans due to multilevel selection is hard to understand because I have no clear idea of what he actually means. As a start, its not multilevel selection in the traditional sense, as Wilson has rejected the other side of the multilevel selection coin, inclusive fitness. Initially, I put it down to his error, but when I hit the last chapter, I realised he was using the term “multilevel selection” to mean something different. When Wilson speaks of multilevel selection, he is generally referring to individual level and group selection occurring in tandem, the “groups” being as we would traditionally define them. But then why isn’t his dynamic in eusocial insects multilevel selection under his definition?

Part of my confusion (and initial assumption) also stemmed from the contrast between Wilson’s past statements and what he wrote in the book. Compare these two paragraphs – the first from a 2007 paper co-authored with David Sloan Wilson, and the second from the last chapter of the book.

The theories that were originally regarded as alternatives, such that one might be right and another wrong, are now seen as equivalent in the sense that they all correctly predict what evolves in the total population. They differ, however, in how they partition selection into component vectors along the way.

Theorists of inclusive fitness themselves have argued that kin selection can be translated into group selection, even though that belief has now been disproven mathematically.

Based on this, it seems that E.O. Wilson is no longer on the same page as the number one champion of multi-level selection, David Sloan Wilson. It is particularly strange in that the two Wilsons characterise what multilevel selection means for humans in almost the same way. As E.O. Wilson writes, and I expect David Sloan Wilson would agree:

Selection at the individual level tends to create competitiveness and selfish behaviour among group members – in status, mating, and the securing of resources. In opposition, selection between groups tends to create selfless behavior, expressed in greater generosity and altruism, which in turn promote stronger cohesion and strength of the group as a whole.

E.O. Wilson’s varying use of these terms points to one of the problems group selection has in popular discourse. The term group selection has been used so inconsistently and used to refer to so many different dynamics, it is often hard to know what someone means when they refer to it. This article (ungated pdf) points to four different uses of the term “group selection”, although I have seen some suggestions that there are six different uses in the literature. When people like Wilson present their arguments in such a confusing manner, it is no surprise that others with less expertise are similarly confused. Look at Jonathan Haidt’s confusion of old group and multilevel selection as a prime example.

The other bits

Beyond Wilson’s take on group selection, there are some interesting parts to the book.

One is Wilson’s argument that many examples of kin selection can be explained as pure self interest. For example, he describes how some bird and mammal offspring remain at their parents’ nest. This has been interpreted as an example of kin selection – it helps the bird or mammal’s parents and siblings. However, Wilson suggests direct self interest is at play. In cases of resource or territory scarcity, they remain with the parents to inherit the parents’ nest when the parents fall off the perch. Wilson provides several examples of this type, suggesting that the focus on kin selection clouds the assessment of what is actually occurring.

Funnily enough, these arguments mirror an argument I often make about apparently altruistic acts sought to be explained by multilevel or group selection. Many apparently altruistic acts are self interested, such as the trade that characterises our economies. If you classed two people trading with each other as a group, as you might in a multilevel selection framework, you could class the person who gained the least surplus from the trade as an “altruist”. But the simplest explanation is that they seek to gain from trade.

The final sections of the book seek to explain “who we are”. I can only say that there are better places to read about the evolutionary origins of religion, art or language. While the last chapter of Sociobiology was revolutionary in its application of evolutionary theory to humans, the short snapshots Wilson provides in The Social Conquest of Earth do not do justice to the work that has occurred in the last 30 years. But that large body of work is, of course, one of Wilson’s great legacies. As Dawkins noted, despite The Social Conquest of Earth, Wilson’s place in history is assured.

Links this week:

1. W. Brian Arthur on economic complexity.
2. A great article on humans as imitators.
3. Higher latitudes have colder weather which leads to larger people which causes lower population and higher investment in children which triggers economic growth.
4. An epidemic of over-diagnosis.
5. Financial price data are converted into music, the music is played to a rat, then the rat guesses whether the price will fall or rise.
6. Is being good at science a matter of nature?
7. Women earn less even when they set the pay.
8. Social and cognitive skills are complements.

In the Pacific Standard, David Dunning of the Dunning-Kruger effect writes:

A whole battery of studies conducted by myself and others have confirmed that people who don’t know much about a given set of cognitive, technical, or social skills tend to grossly overestimate their prowess and performance, whether it’s grammar, emotional intelligence, logical reasoning, firearm care and safety, debating, or financial knowledge. College students who hand in exams that will earn them Ds and Fs tend to think their efforts will be worthy of far higher grades; low-performing chess players, bridge players, and medical students, and elderly people applying for a renewed driver’s license, similarly overestimate their competence by a long shot.

But education is not always the answer:

While educating people about evolution can indeed lead them from being uninformed to being well informed, in some stubborn instances it also moves them into the confidently misinformed category. In 2014, Tony Yates and Edmund Marek published a study that tracked the effect of high school biology classes on 536 Oklahoma high school students’ understanding of evolutionary theory. The students were rigorously quizzed on their knowledge of evolution before taking introductory biology, and then again just afterward. Not surprisingly, the students’ confidence in their knowledge of evolutionary theory shot up after instruction, and they endorsed a greater number of accurate statements. So far, so good.

The trouble is that the number of misconceptions the group endorsed also shot up. For example, instruction caused the percentage of students strongly agreeing with the true statement “Evolution cannot cause an organism’s traits to change during its lifetime” to rise from 17 to 20 percent—but it also caused those strongly disagreeing to rise from 16 to 19 percent. In response to the likewise true statement “Variation among individuals is important for evolution to occur,” exposure to instruction produced an increase in strong agreement from 11 to 22 percent, but strong disagreement also rose from nine to 12 percent. Tellingly, the only response that uniformly went down after instruction was “I don’t know.”

…

The way we traditionally conceive of ignorance—as an absence of knowledge—leads us to think of education as its natural antidote. But education, even when done skillfully, can produce illusory confidence. Here’s a particularly frightful example: Driver’s education courses, particularly those aimed at handling emergency maneuvers, tend to increase, rather than decrease, accident rates. They do so because training people to handle, say, snow and ice leaves them with the lasting impression that they’re permanent experts on the subject. In fact, their skills usually erode rapidly after they leave the course. And so, months or even decades later, they have confidence but little leftover competence when their wheels begin to spin.

In cases like this, the most enlightened approach, as proposed by Swedish researcher Nils Petter Gregersen, may be to avoid teaching such skills at all. Instead of training drivers how to negotiate icy conditions, Gregersen suggests, perhaps classes should just convey their inherent danger—they should scare inexperienced students away from driving in winter conditions in the first place, and leave it at that.

The full article is worth reading.

Links this week:

1. The freedom to pursue informed self-harm has a long and noble tradition.
2. What happens when behavioural economics is used to explain rational behaviour.
3. A great summary of some of Gordon Tullock’s work. HT: Garett Jones
4. Another study on the limited effect of parenting on IQ. HT: Billare via Stuart Ritchie
5. What Hayek might say to Republicans.
6. The long shadow of history on the distribution of human capital in Europe. HT: Ben Southwood
7. Opposition to urban development by “environmentalists” is among my bigger gripes. Left-leaning cities are less affordable.
8. I have only just come across Dominic Cummings. Some interesting thoughts. Check out his blog.
9. Affirmative action to overcome liberal bias.
10. How your brain decides without you.

A few months back, I posted about a Conference on Genetics and Behaviour held by the Human Capital and Economic Opportunity Global Working Group at the University of Chicago. In that post, I linked to a series of videos from the first session on the effect of genes on socioeconomic aggregates.

Over the last couple of days, I watched the videos from the session on Genome Wide Association Studies (GWAS). As for the first set of videos, they are technical (as you might expect for a bunch of academics) – particularly the questions – but cover some important points.

In early studies linking genetic factors to behaviour and socioeconomic outcomes, candidate gene studies were the dominant method. In a candidate gene study, a gene is hypothesised to have an effect, and that hypothesis is tested directly. However, there are some major problems with candidate gene studies, with the literature littered with claims of the “gene for X” that simply can’t be replicated.

David Cesarini opened the session by pointing to this low level of replication of candidate gene studies. He suggests three problems might be causing this failure to replicate. These are multiple hypothesis testing coupled with publication bias, population stratification, and the low power of the small samples typically used.

Multiple hypothesis testing in candidate gene studies arises because more than one gene tends to be tested. In that case, the significance level of the tests should be adjusted to account for the multiple tests. But the reality is that the many negative tests never see the light of day, with the successful ones presented as successfully meeting a threshold appropriate for a single test. Publication bias exacerbates that problem as negative results tend not the be published and you don’t know how many tests have been conducted.

In contrast, GWAS is a hypothesis free approach. All SNPs in a sample (single nucleotide polymorphisms – DNA sequence variations in which a single nucleotide varies in the population) are tested for association with a trait. As there are as many hypotheses being tested as there are SNPs, very high significance thresholds are applied to avoid false positives. But as the number of SNPs in an array is known from the start, there is no doubt about the appropriate threshold.

Cesarini’s talk focused on the second problem, population stratification. This occurs where allele (variants of a gene) frequencies correlate with confounding variables. A classic example is analysing a mixed population of Asians and Caucasians and discovering the chopsticks gene. This can be overcome in GWAS by a technique called principal components analysis, which can be used to model the ancestry of the population and correct for stratification before conducting the analysis.

The next speaker, Daniel Benjamin, spoke on the third problem – the low power of candidate gene studies. Power is the ability to statistically demonstrate an association when that association exists. A test with low power will miss the associations most of the time.

The low power of candidate gene studies is partly due to their typically low sample size, usually between 50 and 3,000 people. Benjamin points out that there may not be any genes in social science with effects large enough to be detected in samples of this size.

The low power of a study has an important implication beyond the inability to find any effects that exist. If real results are rare, they will be swamped by the false positives, which would occur for 1 in 20 tests using the typical significance level. Benjamin runs through some numerical examples and shows that given the expected effect sizes of genes on social science outcomes, you simply shouldn’t trust most candidate gene study results. False positives will drown the real findings. This contrasts with GWAS. Once you get to decent sample sizes in the order of 100,000, you can be relatively confident that what you do find (even though you miss a lot) will be true.

Benjamin also talks about the Social Science Genetic Association Consortium (SSGAC), which is an attempt to build datasets large enough to apply GWAS to social outcomes such as IQ and risk aversion. The proof of concept was on educational attainment, which the next speaker covers in more detail.

Philipp Koellinger opens by asking why there are so many null results in the search for genetic influences. Is it because the effects are small? Because they are non-linear? Or there are gene-environment interactions? Maybe the results of twin studies showing most social outcomes are heritable are wrong?

Part of the answer was given by a study of educational attainment in which Koellinger and the previous two speakers were involved. They used a GWAS to search for SNPs that affected educational attainment in an initial sample of 100,000 people. They then replicated the result in another sample of 25,000 people. All three SNPs found in the discovery stage were replicated.

Importantly, the effect sizes were smaller than expected, with those three SNPs explaining 0.02% of the variation in educational attainment. If you added up the effects of all the SNPs in their sample, you could explain around 2 to 2.5% of the variation.

While this sounds low, it provides a basis for hope. Based on projections for larger sample size, it should be possible to explain 20% of the variation in education attainment through genetic factors.

Jason Fletcher was next, and he asked two main questions. First, how much should we believe GWAS results given how differently GWAS is done compared to normal science procedure. Second, what use are GWAS results? He spends more time on the second question and points out the usual possibilities, such as providing measures for latent variables. For example, if you don’t know the IQ of your sample but have their genomes and know how this affects intelligence, the genetic information could be used to attempt to determine the effect of IQ on a certain outcome.

Fletcher also points to the potential for exploration of gene-environment effects. He gives the example of people responding differently to tobacco taxation based on having different alleles. His paper on this topic is here.

Within his talk, Fletcher asks an interesting question about whether the SSGAC will become a natural monopoly in GWAS. Do we need a second SSGAC to enable people to check the results, and is it feasible for one to emerge? Others may be more viable as genetic testing becomes cheaper, but the tendency for one to dominate may still remain.

In the questions to Fletcher’s presentation, Benjamin makes the important point that the use of GWAS results as control variables could give much more precision to the estimates of the effect that a social science experiment is designed to measure. He gives the example of the Perry pre-school project – expensive educational interventions with a small sample, in which any added precision as to their effects would be of great value.

The last speaker, Dalton Conley, returned to the population stratification problem. His argument is that it may not be as easy to solve as it seems. Conley refers mainly to a technique called Genomic-relatedness-matrix restricted maximum likelihood (GREML) or Genome-wide complex trait analysis (GCTA) (which I have posted about before). This technique seeks to determine the contribution of all the sampled SNPs combined to variation in a trait. The output is a lower bound estimate of heritability. This technique relies, however, on an assumption that among those who are less related than second cousins (higher degrees of relatedness are removed), they share alleles in a way that is uncorrelated with any similarity in environment.

Conley argues that this assumption is false, and shows that using GREML, he can obtain a finding that birth in an urban or rural environment is heritable, in direct violation of the assumption. This result does not disappear after controlling for population stratification.

To deal with this problem, consideration should be given to testing for variation within families – any differences in genes between siblings will truly be random. The problem with this is that most massive datasets for which GWAS is performed don’t have pedigree data of that nature. The good news, however, is that the violation of the assumption does not seem to puncture the GWAS results. It is violated but the consequences are trivial. A paper by Conley and friends on this paper can be found here.

Links this week:

1. A good Jared Diamond interview.
2. The 10,000 hours rule – the best you can do is find the peak of your own ability.
3. Tinder works because a picture is “worth that fabled thousand words, but your actual words are worth… almost nothing”. (HT: Razib)
4. Dumb incentives, although economists would be the first to point out a lot of the unintended consequences.
5. No evidence for the benefits of expertise for fund managers.
6. Drunks are more utilitarian. And maybe you should do that drinking on an empty stomach.
7. Are social psychologists biased against Republicans?

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.

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.

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

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.