Economics

The genetic basis of social mobility

In 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.

A week of links

Links this week:

  1. Is your body mostly microbes? A nice take-down of  another trail of citations dead-ends. One thing I have learnt from my PhD research is that when it comes to citations, academics are lazy.
  2. A good piece on Peter Thiel. Curing death is on the agenda.
  3. Fixing gender bias in research subjects – because men and women are different. HT: John Durant
  4. Using data in determining punishment – some interesting implications. HT: Marginal Revolution
  5. Deadweight loss in pointless research.
  6. Three complexity MOOCs about to kick off from the Santa Fe Institute.
  7. A not-so-convincing attempt to take down the philosophy behind nudging.
  8. Don’t expect praise if you state your motivations behind hiring women.
  9. Predicting when bubbles will pop from MRIs.

Kahneman’s optimistic view of the mind

In the Gerd Gigerenzer versus Daniel Kahneman wars, most of the projectiles seem to fly one way. Gigerenzer attacks directly, Kahneman expends little effort in defence.

As one test of whether my impression was correct, I searched Kahneman’s Thinking, Fast and Slow for how many times Kahneman directly mentions Gigerenzer. The answer is six, once in the index and five times in the notes. Gigerenzer is only alluded to in the main text.

Of the notes, only one is substantive, but it is an interesting point. In a slight reversal of their usual roles, Kahneman defends the power of the human mind:

An alternative approach to judgment heuristics has been proposed by Gerd Gigerenzer, Peter M. Todd, and the ABC Research Group, in Simple Heuristics That Make Us Smart (New York: Oxford University Press, 1999). They describe “fast and frugal” formal procedures such as “Take the best [cue],” which under some circumstances generate quite accurate judgments on the basis of little information. As Gigerenzer has emphasized, his heuristics are different from those that Amos and I studied, and he has stressed their accuracy rather than the biases to which they inevitably lead. Much of the research that supports fast and frugal heuristic uses statistical simulations to show that they could work in some real-life situations, but the evidence for the psychological reality of these heuristics remains thin and contested. The most memorable discovery associated with this approach is the recognition heuristic, illustrated by an example that has become well-known: a subject who is asked which of two cities is larger and recognizes one of them should guess that the one she recognizes is larger. The recognition heuristic works fairly well if the subject knows that the city she recognizes is large; if she knows it to be small, however, she will quite reasonably guess that the unknown city is larger. Contrary to the theory, the subjects use more than the recognition cue: Daniel M. Oppenheimer, “Not So Fast! (and Not So Frugal!): Rethinking the Recognition Heuristic,” Cognition 90 (2003): B1–B9. A weakness of the theory is that, from what we know of the mind, there is no need for heuristics to be frugal. The brain processes vast amounts of information in parallel, and the mind can be fast and accurate without ignoring information. Furthermore, it has been known since the early days of research on chess masters that skill need not consist of learning to use less information. On the contrary, skill is more often an ability to deal with large amounts of information quickly and efficiently.

A week of links

Links this week:

  1. The Genetic Genealogist responds to Vox’s tabloid piece on genetic testing.
  2. Attempts to correct false claims often entrench them – the backfire effect. But telling politicians they will be fact checked still reduced their number of lies.
  3. Razib on heritability. Low heritability doesn’t make it easier to shape our children – once we’re above some minimum thresholds, we don’t really know what works.
  4. Tyler Cowen suggests the gender gap will close. I’m not so sure.
  5. Violence in chimps an evolutionary adaptation.
  6. Another study showing low social mobility. HT: Jayman
  7. A great collection of papers on altruism, reciprocity and the glucose model of self control.

Scarcity of time, money, friends and bandwidth

Sendhil Mullainathan and Eldar Shafir’s Scarcity: Why Having Too Little Means So Much is full of interesting insight and experimental results. It presents a novel way of looking at scarcity that extends beyond the typical analysis in economics, the original “science of scarcity”, and will certainly change the way I think about it.

But by the time I reached the end of the book, I was not entirely satisfied. I have plenty of new buzzwords and some interesting experiments to talk and think about, but I’m not convinced they have been presented with a coherent new perspective on how the world works. Scarcity reminded me of a Malcolm Gladwell book – I got presented with a lot of cool results, they were spun into an interesting narrative that has made me think, but I don’t buy the authors’ main message.

So first, to the buzzwords, which added to the Gladwell-esque feeling. Scarcity is having less time, money, friends or packing space than you feel you need (Note the subjective element – scarcity could be caused by relative rather than absolute scarcity. And it can apply across a range of domains). Scarcity can have good effects, such as the focus dividend when scarcity captures the mind. It can make us experts in the area in which we are scarce, with poor people better able to judge the value of a dollar saved despite the context in which it appears. However, scarcity also causes us to tunnel, which is to focus single-mindedly on the scarcity at hand, potentially at the neglect of more important or less timely demands. Scarcity also reduces bandwidth, a combination of  cognitive function and executive control. Scarcity imposes a bandwidth tax. When people suffer from scarcity and tunnel, they are also forced to juggle, as they move from one pressing task to the next. The way to avoid scarcity is to have some slack, an untapped budget we  can turn to in times of need.

The most salient example of the negative effects of scarcity comes from a study in a New Jersey mall. Mullainathan, Shafir and Jiaying Zhao presented people with a scenario where they need to fund some car repairs. They then gave the participants tests for fluid intelligence and cognitive control. When the cost of the repairs was $150, high and low-income people scored similarly in the tests. But when the shortfall was $1,500, the performance of the poor plunged – the equivalent of losing 13 or so IQ points. The authors point out that this result has been replicated many times, suggesting it is a robust result. By causing the poor to focus on their lack of resources through a high and potentially unmanageable repair bill, the poor’s bandwidth for completing the tests was taxed. In another experiment, people were effectively made rich or poor by the flip of a coin, with the poor demonstrating greater present bias. In that case, the bandwidth tax was clearly not due to the inherent traits of the poor.

Mullainathan and Shafir take these results to mean that we don’t need talent or inherent trait-based explanations for differences between the rich and poor. Instead, scarcity makes the poor perform poorly, leaving them in a scarcity trap. They suggest that the effect of scarcity on bandwidth is a good explanation for differences as it can make sense of diverse empirical facts across behaviour, time and place. But it leaves open the question why they imply that scarcity induced and inherent trait-based explanations are mutually exclusive, or why inherently low bandwidth can’t explain an equally diverse set of facts.

Part of the difference between their and my interpretation is that they are observing a short-term dynamic and extrapolating that to the longer term. My view is that over the longer-term, inherent explanations play a larger role.

For example, when we look at twin studies, we find that IQ and other traits inherently differ between people. Adoption studies show long-term outcomes are representative of biological and not adoptive parents. Differences in scarcity of financial or other resources due to differences in adoptive parents have almost no effect on IQ, income, obesity or a range of other long-term outcomes.

Greg Clark’s work on social mobility shows the long-term persistence of status across many generations, despite short-term shocks that would affect scarcity. If Mullainathan and Shafir’s thesis were true, a shock one generation would carry through future generations, rather than seeing the next generation reverting to the underlying status (genotype). Under Mullainathan and Shafir’s explanation, we would also see immigrants’ IQ increase when they move country and ease their scarcity (although if scarcity is relative, perhaps their relative position may not have improved). We would see large increases in the IQ of previously poor lottery winners, who would experience a sudden surge in mental resources. In The Son Also Rises, Clark pulls together a few examples of windfalls of this nature and demonstrates the lack of long-term effect. In one case, a lottery of land parcels had no effect on the outcomes of the winners’ children.

As a result, I am not convinced that their arguments truly capture the differences between the poor and the rich, or the rushed and the relaxed. Perhaps someone might eat more from time to time due to the bandwidth tax. They might take an ill-advised payday loan when they are stretched for money. But despite this short-term effect, we see little trace of it in long-term outcomes. Something is allowing some people but not others to break the cycle of scarcity.

I also doubt that Mullainathan and Shafir’s description of the poor as suffering from scarcity is generally true. When it comes to time, the poor watch more television, invest less time in caring for their children, have plenty of free time to think about what they will eat, and yet are more likely to be obese. Their characterisation of the poor having a lot on their mind whereas the rich are relaxed despite their more complex employment does not seem particularly strong.

The book is least satisfying when Mullainathan and Shafir start pulling up examples of scarcity from other domains – the padding to turn an interesting idea into a book length argument. Many of them are banal and simply involve scarcity in the way economists might think of it, with no evidence that people were suffering anything more than a lack of time. For example, they talk of the Benihana restaurant, where the chef cooking in front of people sets a quick pace for the meal, allowing more customers to come through. It is a story of scarce time, but provides no interesting insight into their thesis. The fact the Benihana restaurant management took to the time to solve their problem suggests they were not overly taxed. Mullainathan and Shafir discuss the crash of NASA’s Mars Orbiter, which had to be launched by a certain date, leading to shortcuts. But what is the evidence that there was tunnelling or taxed bandwidth as opposed to a simple lack of time to do all the checks they would have liked to have done? They don’t present the evidence to distinguish.

When we turn to solutions, it is interesting that many of them do not depend on Mullainathan and Shafir’s argument being true. They are solutions that would be equally useful in dealing with inherently untalented or impatient people. Savings reminders could be useful regardless of the cause of the lack of foresight. Presenting pay day loans in terms of dollars rather than interest rates is standard behavioural economics fare and may work for people lacking bandwidth regardless the cause.

The short-term fluctuation of bandwidth does present some interesting possibilities. They suggest that if education fails due to low bandwidth, we should time education when people can best learn. They also give an example of Kenyan farmers failing to use fertiliser and missing out on large gains to their yields. By getting farmers to pre-purchase the fertiliser when they were flush with cash after harvest, more benefited from the yield gains. (Although again, are their decisions because they are inherently shortsighted or taxed?)

Ultimately, the long-term solution to the costs of scarcity is creating bandwidth and a buffer stock of slack. This makes sense, but this point drew me back to an example early in their book. They describe an experiment involving Indian vendors who were provided with money to allow them to escape loans with exorbitant interest rates that consumed much of their income. But despite having their debts cleared, bit by bit they fell back into the scarcity trap. Mullainathan and Shafir accredit their return to the trap to shocks. But why did they not save what they would otherwise have been paying in interest to create a buffer from shocks. Why did the new bandwidth not allow at least some of them to escape? Their income had effectively doubled.

One interesting idea Mullainathan and Shafir leave lying around is whether the bandwidth of whole economies can fluctuate through good times and bad. As a random idea of my own, is the Flynn effect due to the easing of scarcity in the modern world? [No]

Having said the above, the ideas in the book are well worth considering, especially for contemplating how short-term mental capacity might be affected by the environment. But extrapolating the results to the long-term despite evidence from twin studies, adoption studies and social mobility analysis needs far more. You cannot simply throw the effect of inherent talent and traits out of the window.

*As a postscript, after writing most of this review I searched for other reviews of the book and came across this piece by Tim Harford. Many of the same points – Harford notes the buzzwords and provides a Gladwell reference too. I should note that, as it seems for Harford, the Gladwell comparison is at least part praise.

A week of links

Links this week:

  1. An excellent Econtalk podcast with Jonathan Haidt. Just don’t buy his lines about group selection – my reasons here.
  2. Steven Pinker’s amusing article on the Ivy League. Pinker also pointed out this oldie but goodie – Bell Curve Liberals.
  3. Greg Clark applies his work on social mobility to immigration. Reihan Salam comments.
  4. A great swipe at “talent deniers”.
  5. Tracking supercentenarians.
  6. The agricultural origins of time preference – I’ll blog about this once I digest. HT: Tyler Cowen
  7. The cognitive gains from Head Start fade out by elementary school.
  8. I’m back into my habit of linking to Andrew Gelman articles every week – this time a great rant about expected utility titled “It’s as if you went into a bathroom in a bar and saw a guy pissing on his shoes, and instead of thinking he has some problem with his aim, you suppose he has a positive utility for getting his shoes wet

Nudging citizens to be risk savvy

I should start this review of Gerd Gigerenzer’s least satisfactory but still interesting book, Risk Savvy: How to Make Good Decisions, by saying that I am a huge Gigerenzer fan and that this book is still worth reading. But there was something about this book that grated at times, especially against the backdrop of his other fantastic work.

In part, I continue to be perplexed by Gigerenzer’s ongoing war against nudges (as I have posted about before), despite his recommendations falling into the nudge category themselves. Nudges are all about presenting information and choices in different ways – which is the staple of Gigerenzer’s proposal to make citizens “risk savvy”. Gigerenzer’s use of evidence and examples throughout the book also fall well short of his other work, and this is ultimately the element of the book that left me somewhat disappointed.

The need to make citizens risk savvy comes from Gigerenzer’s observation (which matches that of most of Gigerenzer’s faux adversaries – the behavioural scientists) that people misinterpret risks when they are presented in certain ways. If I say that screening reduces the risk of dying from breast cancer by 20 per cent, most people will interpret it to mean that 200 of every 1,000 people will be saved, rather than understanding that it means screening reduces the risk of death from 6 in 1,000 to 5 in 1,000 – effectively saving one out of 1,000.

Gigerenzer’s contribution to this area is to show that if presented in natural frequencies (i.e. tell people about the statistics as proportions of, say, 1,000 people), people are better able to understand the actual risks. This includes doctors, who are equally confused by statistics as everyone else, and who Gigerenzer suggests need training to communicate risks in ways that their patients can understand.

This ability to make citizens and experts risk savvy leads Gigerenzer to argue that people do not always need to be at the mercy of their biases. People can be educated to understand risks and experts can present them in ways that others understand. He advocates risk literacy programs in school, showing that simple decision tools can dramatically increase understanding of probability and statistics, although he spends little time discussing how well this education sticks. In making his point, Gigerenzer takes aim at the behavioural science crowd by claiming that natural frequencies won’t help if people are subject to cognitive illusions – a strawman argument. As he does at semi-regular intervals through the book, Gigerenzer clouds an interesting argument with an attempt to engage in a battle that doesn’t really exist.

That said, I did enjoy this part of the book and have found myself quoting a lot of the examples. His arguments about how to present risk are compelling. Further, it is enjoyable to read Gigerenzer’s evisceration of the presentation of risk by various high-profile cancer organisations.

There are parts of the book where Gigerenzer is more pessimistic about the ability to educate the masses, such as when he channels Nassim Taleb and berates the finance industry for not understanding the difference between risk and uncertainty. In a world of uncertainty – where we do not know the probability of events – simple rules often outperform more complex models that are overfitted to past data. This provides a natural entry point to Gigerenzer’s well-established work (and subject of some of his better books) on the accuracy of heuristics. Risk Savvy has plenty of additional advocacy for their use with Gigerenzer arguing that we can be trained to use useful heuristics in making better decisions. Gigerenzer covers areas from marriage (set your aspiration level and choose the first person who meets it) to business to the stability of financial institutions, building on decades of evidence he has accumulated on the accuracy of simple rules.

Gigerenzer’s heuristics don’t always match up with his optimism that we can make people risk savvy. One heuristic he suggests is: “If reason conflicts with a strong emotion, don’t try to argue. Enlist a conflicting and stronger emotion.” He also recognises the limits to education, with heuristics such as “don’t buy financial products you don’t understand.” But given that a lot of people don’t understand compound interest, we might need to rely on the Dunning-Kruger effect to allow people to follow this rule and still make any investments.

One interesting point made by Gigerenzer is that there is still a role for experts (and even consultants) in a world where we use simple heuristics. Suppose we replace our complex asset allocation models with a 1/N rule – allocate our assets equally across N choices. This still leaves questions such as the size of N, what we will include in N, or when you should rebalance. For many heuristics, there may be more complex underlying choices – although I imagine heuristics could be developed for many of these too.

Gigerenzer is also a stout defender of gut instinct – again, as covered in his other books. Gigerenzer suggests (and I agree) that data is often gathered due to a culture of defensive decision-making and not because data is the major reason in the decision. This is, however, the weakest area of the book, as Gigerenzer’s stories reek of survivorship bias. Gigerenzer notes that leading figures in business reveal in surveys that they rely on gut instinct and not data in making major decisions. But how many corpses who relied on gut instinct are strewn along the road of entrepreneurship?

As another example, Gigerenzer talks of a corporate headhunter who had put a thousand senior managers and CEOs into their positions. The headhunter said that nearly all the time he based his selection on a gut decision. He was now being replaced by tests by psychologists. Gigerenzer puts this down to a negative error culture, with the procedures designed to protect the decision makers. But what is the evidence that the headhunter has been good at their job and could outperform the psychologists armed with tests?  Similarly, Gigerenzer suggests listening to those with good track records in business. Again, survivorship bias could make this a useless exercise. When talking of predictions of exchange rates in other parts of the book, Gigerenzer effectively makes this very same point – the successful people you see in front of you could simply be the lucky survivors.

However, the evidence that Gigerenzer has developed in the past would make it folly for anyone in business to throw gut instinct out the window – or to completely discard Gigerenzer’s arguments. But the way he makes the case through Risk Savvy feels built on anecdote and weak examples.

There is one rule I am going to take away from the book – an extension of my usual habit of flipping a coin for decisions about which I’m indifferent. Gigerenzer suggests flipping a coin and as it spins, considering what side you don’t want to come up. He used this example in the context of choosing a partner, but it’s not a bad way to elicit that gut instinct that you can’t otherwise hear.