Monday, 17 June 2019

Book review: Nudge Theory in Action

Richard Thaler and Cass Sunstein's book Nudge set policymakers on a path to taking advantage of the insights of behavioural economics to modify our behaviour, in areas such as retirement planning, nutrition, tax payments, and so on. It spawned the Behavioural Insights Team (otherwise known as the 'Nudge Unit') in the U.K., and similar policy units in other countries. However, it also caused a lot of controversy, particularly from libertarian groups that would prefer less government intervention into private decision-making.

I recently finished reading the book Nudge Theory in Action, a volume edited by Sherzod Abdukadirov. I have to say it was not at all what I expected. I thought I was going to get a lot of examples of nudges applied by governments and the private sector, and hopefully with some explanations of the underlying rationales and maybe some evaluations of their impact. The book does contain some examples, but mostly they are examples that have already been widely reported, and not all of them would necessarily qualify as 'nudges', under the definition originally proposed by Thaler and Sunstein.

Essentially, most of the chapters in this book are libertarian critiques of nudges in theory and in practice. Richard Williams sums up the underlying premise of book well in the concluding chapter:
The purpose of this book is to demonstrate that there is a strong private sector that helps people's decision making and that stringent criteria ought to be met before governments attempt to improve on private decision making, whether through structuring information to "nudge" people into making the government-preferred decision or using more stringent measures to achieve the same thing. Where people have difficulty matching their inherent preferences into real life decisions that satisfy those preferences, a private market will almost always arise that can help to match decisions with preferences.
Thaler and Sunstein defined a nudge as "any aspect of the choice architecture that alters people's behavior in a predictable way without forbidding any options or significantly changing their economic incentives". The second part of that definition is important, and most of the chapters pay lip service to it, while at the same time ignoring it in favour of critiquing almost any government policy proposal that would restrict decision-making. The most cited example is the failed attempt by former New York mayor Michael Bloomberg to ban sales of large sodas. Given that it involves a ban, and therefore does forbid an option, under the original Thaler and Sunstein definition it is not a nudge.

However, policy makers do themselves no favours in this case by referring to policies like the New York large soda ban as a "nudge", and invoking behavioural economics principles in favour of all sorts of policies that are not, in fact, nudges. So, a more reasonable critique would be directed at policy makers' incorrect usage of the term 'nudge', rather than damning all nudges using examples that are not even nudges.

Having said that, there are some good and thought-provoking chapters. Mario Rizzo has an excellent theoretical chapter, and while I don't buy into the arguments he made, it definitely made me think more deeply about what we mean when we refer to rational behaviour. Jodi Beggs (from Economists Do It With Models fame) presents a great framework that differentiates private sector nudges into those that improve welfare for consumers (which she terms 'Pareto nudges', invoking the idea of a Pareto improvement from welfare economics), and those that make consumers worse off to the benefit of firms (which she terms 'rent seeking nudges'). Beggs also notes the subversion of the term 'nudge' to mean almost any policy change that aims to change behaviour. Several chapters raised the (very valid) point that not only are consumers (or savers or whoever) subject to the behavioural biases that behavioural economics identifies, but government decision makers are also likely to be subject to those same biases. In that case, we should be cautious about the ability of government to create the 'right' choice architecture to achieve their goals.

However, there are also some notable misses among the chapters. In his chapter, Mark White critiques government attempts to alter the choice architecture to favour some option over others, but never engages with the fact that there will always be some choice architecture in place. In many cases, there simply isn't a way to avoid presenting decision makers with options, and in those cases there has to be a choice architecture of some type in place. Why not attempt to make it one that will steer people to making decisions that improve their long-term wellbeing? Similarly, Adam Thierer argues that nudges prevent 'learning by doing' or learning through making mistakes. That is good in theory, but how many opportunities do we have to make mistakes in our own retirement planning, for instance? He also fails to acknowledge that governments can also learn from nudges that don't work as intended. As Steve Wendel notes in his chapter:
We should be skeptical that behavioral nudges will work the same (or at all) once they are translated from an academic environment into consumer products, because of core lessons in the behavioral literature itself - that the details of implementation and decision-making context matter immensely.
That isn't an argument not to attempt nudges. However, it is an argument that applies equally to government nudges - they may not work as intended, so they should be rigorously evaluated.

Ultimately, if you are looking for ammunition to mount a libertarian counter-attack against nudge theory applied by government, you will find a lot of suitable material in this book. However, as a general guide to 'nudge theory in action', I believe this book falls short.

Saturday, 15 June 2019

Corruption and quid pro quo behaviour in professional soccer

In their book Freakonomics, Steven Levitt and Stephen Dubner described research (from this article by Levitt and Mark Duggan - ungated earlier version here) that showed evidence of rigged matches in sumo wrestling. Specifically, wrestlers who were approaching their eighth win (which comes with much greater earnings and ranking) were more likely to win against those who already had eight or more wins, than would be expected based on their relative ability. And that was in Japan - a country not known for widespread corruption (Transparency International ranks Japan 18th out of 180 countries in its Corruption Perceptions Index). How bad could things be in other, less honest or trustworthy, countries?

A 2018 article by Guy Elaad (Ariel University), Alex Krumer (University of St. Gallen), and Jeffrey Kantor (Ariel University), published in the Journal of Law, Economics, and Organization (ungated version here), provides a window to widespread corruption in domestic soccer. [*] They look at games in the final round of the season, in domestic soccer leagues, where one of the teams needed to win (or draw) in order to avoid relegation, and where the match was inconsequential for the other team. They have a database of 1723 such matches in 75 countries over the period from 2001 to 2013. Most interestingly, they look at how the win probability (controlling for a range of factors, including the strength of the opposition and home advantage) varies by the level of corruption of the country. They find that:
...the more corrupt the country is according to the Corruption Perceptions Index (CPI), the higher is the probability of a team (Team A) to achieve the desired result to avoid relegation to a lower division relative to achieving this result in non-decisive games against the same team (Team B)... This finding is robust when controlling for possible confounders such as differences in abilities, home advantage, and countries’ specific economic, demographic and political features.
Then, there is evidence that the winning team in that game returns the favour (quid pro quo) in the following season, since they find that: more corrupt countries the probability of Team A to reciprocate by losing in the later stages of the following year to Team B is significantly higher than losing to a team that is on average better (stronger) than Team B. This result strengthens the suspicious of corrupt norms, since in the absence of any unethical behavior, we would expect the opposite result, since naturally the probability of losing increases with the strength of the opponent.
There's clearly a lot of mutual back scratching going on in professional soccer. It is worth noting, though, that the top divisions in Europe (Premier League, Ligue 1, Bundesliga, etc.) were not included in the analysis, which focused on the second-tier leagues in Europe, and top leagues outside of Europe.

An interesting follow-up to this study would be to look at betting odds. Do the betting markets price this corruption into their expectations (so that the team needing to avoid relegation has betting odds that suggest a higher probability of winning than would be expected based on home advantage, strength of opponents, etc.)? If not, then there may be opportunities of positive expected gains available from betting on those games.

[HT: Marginal Revolution, last year]


[*] Or football, if you prefer. To me, football involves shoulder pads and helmets.

Wednesday, 12 June 2019

The alcohol made them do it

Alcohol has well-documented effects on a range of harms such as drunk driving (almost by definition), violence, poor health, and mortality. However, the causal evidence for alcohol's effect on a range of less serious harms is less clear - things like risky sexual activity and other substance use. A new article by Jason Fletcher (University of Wisconsin-Madison), published in the journal Contemporary Economic Policy (ungated earlier version here), aims to fill that gap.

It is trivial to show that access to alcohol is correlated with measures of harm. The challenge with any study like this is to show that access to alcohol has a causal effect on the harm. That is, that the observed correlation represents a causal effect, and is not the result of some other factor. Fletcher does this by exploiting the Minimum Legal Drinking Age in the U.S. (of 21 years of age), and using a regression discontinuity approach. Essentially, that involves looking at the measures of harm and how they track with age up to age 21, and then after age 21. If there is a big jump upwards between the time before, and the time after, age 21, then plausibly you could conclude that the sudden jump upwards is due to the onset of access to alcohol at age 21. This approach has previously been used to show the impact of access to alcohol on arrests and on mortality. In this paper, Fletcher instead focuses on:
...drinking outcomes, such as any alcohol use, binge use, and frequency of use as well as drinking-related risky behaviors, such as being drunk at work; drunk driving; having problems with friends, dates, and others while drinking; being hung over; and other outcomes.
He uses data from the third wave of the Add Health survey in the U.S., which occurred when the research participants were aged from 18-26 years old. He analyses the results all together, and separately by gender, and finds that:
...on average, access increases binge drinking but has few other consequences. However, the effects vary considerably by gender; where females (but not males) are more likely to initiate alcohol use at age 21, males substantially increase binge drinking at age 21. In addition, males (but not females) face an increased risk of problems with friends and risky sexual activity at age 21. There is also some evidence of an increase in drunk driving and violence.
Interesting results, but not particularly surprising. Fletcher then tries to draw some policy implications on what would happen if the MLDA was reduced, by looking at differences between young people living with their parents and those not living with their parents. He finds that: harm reduction associated with binge drinking for those individuals living with their parents around age 21; in fact, individuals living with their parents (regardless of whether they are in school) have larger increases in alcohol-related risky behaviors than individuals living away from their parents.
He uses that result to suggest that parents are not good at socialising their children into safer drinking behaviours (and the results, on the surface, suggest this because those living at home engage in more risky behaviour after they attain age 21. However, there is another interpretation that Fletcher doesn't consider. Those who are not living at home might be more likely to be drinking alcohol before age 21, and so experiencing some of the negative impacts earlier. So, those living at home may be simply catching up to their peers, when they are 'allowed' to drink. Maybe that strengthens his other results.

Overall, the paper doesn't tell us much that wasn't already known, although the causal aspect of the study is a nice touch. The differences by gender were a bit more surprising, and hopefully there are other studies that can work further in this area to test them further.

Monday, 10 June 2019

Why Uber drivers will make no money in the long run

This is the third post in as many days about Uber (see here and here for the earlier installments), all based on this New York Times article. Today, I'm going to focus on this bit of the article:

Drivers, on the other hand, are quite sensitive to prices — that is, their wages — largely because there are so many people who are ready to start driving at any time. If prices change, people enter or exit the market, pushing the average wage to what is known as the “market rate.”
The article is partially right here. It isn't just the price elasticity of supply that is at fault - it is the lack of barriers to entry into (and exit from) the market that create a real problem for drivers. A 'barrier to entry' is something that makes it difficult for other potential suppliers to get into the market. A taxi medallion is one example, if a medallion is required before you can drive a taxi. However, there is nothing special required in order to be an Uber driver, and most people could do it. Similarly, a 'barrier to exit' is something that makes it difficult for suppliers to get out of the market once they are in it, such as a long-term contract. Barriers to exit can create a barrier to entry, because potential suppliers might not want to get into a market in the first place, if it is difficult to get out of later if things go wrong. Again, Uber has no barriers to exit for drivers. These low barriers (to entry and exit) ensure that, in the long run, drivers can't make any more money from driving than they could from their next best alternative.

To see why, consider the diagrams below. The diagram on the left represents the market for Uber rides. For simplicity, I've ignored the 'Uber tax' (that I discussed in yesterday's post). The diagram on the right tracks the profits of Uber drivers over time. The market starts in equilibrium, where demand is D0, supply is S0, and the price of an Uber ride is P0. This is associated with a level of profits for Uber drivers of π0. For reasons we will get to, this is the same earnings that an Uber driver would get in their next best alternative (maybe that's driving for Domino's, or as a taxi driver, or working as a stripper).

Now, say there is a big increase in demand for ride-sharing, from D0 to D1. The price of an Uber ride increases to P1, and the profits for driving increase to π1. Now profits from being an Uber driver are high, but they won't last. That's because many other potential Uber drivers can see these profits, and they enter the market (there are no barriers to entry, remember?). Let's say that lots of drivers enter the market. The supply of Uber drivers increases (from S1 to S2), and as a result the price decreases to P2, and profits for Uber drivers decrease to π2.

Now the profits for Uber drivers are really low. There's no barriers to exit, so some drivers decide they would be better off doing something else (driving for Domino's, etc.). Let's say that a lot of drivers choose to leave, but not all of those who entered the market previously. The supply of Uber drivers decreases (from S2 to S3), and as a result the price increases to P3, and profits for Uber drivers increase to π3.

Now the profits for Uber drivers are high again (but not as high as immediately after the demand increase). Drivers start to enter the market again, and so on, until we end up back at long-run equilibrium, where the price of a ride is back at P0, and driver profits are back at π0. At that point, every driver who is driving makes the same low profit as before. So, in the long run, even if demand for ride-sharing is increasing over time, the drivers are destined not to profit in the long run. [*]


[*] You might have noticed that the producer surplus is higher after supply increases, which implies that drivers (as a group) are earning higher profits after the market has settled back to long-run equilibrium. However, remember that supply is higher than before - those higher profits are shared among many more drivers, so the profit for each driver individually is the same as before.

[HT: The Dangerous Economist]

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