Tim Harford quite often writes about trade. I particularly liked some of the things he wrote in this post last week, since they echo ideas that I discuss in both ECON100 and ECON110:
Economists disagree about most things, but for a couple of centuries they’ve agreed about the merits of free trade, basically for the reasons outlined above. But some readers may be faintly aware of cracks in that consensus — haven’t economists realised that free trade is sometimes bad?
Broadly, the answer is “no” — economists remain thoroughly persuaded of the merits of international trade. But there are cautionary notes. First, modern trade agreements tend to be loaded with rules — food safety, financial regulation, intellectual property — that are not about tariffs. Some of these rules are closely connected with trade itself: long arguments at customs can restrict trade just as surely as a border tariff. But others have little to do with trade, and sometimes the rules are simply bad. So you can favour free trade yet oppose some “free-trade” agreements, as many economists do.
I encourage you to read all of Harford's post, if you have any doubts about why free trade is a good idea. However, 'free trade' is not the same as 'free trade agreements' since most free trade agreements aren't about making trade more free at all, in the sense of reducing tariffs and other trade barriers. The Trans-Pacific Partnership is a case in point. While New Zealand may have gained from reduced tariffs for our exports into the U.S. and Japan, the neo-colonial provisions on intellectual property and investor-state dispute resolution to me made it a hard sell that the net effect was positive. And when you consider the distributional impacts of the agreement (where the gains would be concentrated among farmers and other exporters, with the potential costs borne by everyone), it becomes an even harder sell. And it is this last point (the distributional consequences of trade) that has arguably contributed to Brexit, Trumpism, and other populist movements.
Harford also covers a similar point:
But deep down, trade is just another kind of productive technology — a technology that turns Minis into camembert [MC: To understand that point, you need to read Harford's whole post]. Like any productive technology, it makes us richer. But it creates winners and losers, and the winners may take their good fortune for granted while the losers are acutely aware of what they’ve lost. The losers have votes too. And if they’re frustrated about China, let’s see what happens if self-driving vehicles put several million truckers and taxi drivers out of work.
It's important for us not to lose sight of the fact that there are winners and losers in trade, and as this post notes, those who lose may face long-term consequences that governments have not traditionally allowed adequate compensation for.
When Pokemon Go came out last year, many people lauded the possibility of the augmented reality game to increase physical activity and health. I even wrote a semi-serious piece advocating that the government subsidise the game. However, the bubble has burst, and the game is pretty much dead now (at least, compared to where it began).
Aside from the overall decline in player numbers though, it seems that any benefits in terms of increased physical activity have been grossly overestimated. In its annual Christmas issue, the British Medical Journal had an (open access) article by Katherine Howe (Harvard) and others, on the effect of Pokemon Go on physical activity. The authors conducted a survey of 1182 iPhone 6 users in the U.S., and used screen captures of the number of steps the Pokemon Go players and non-players took. They compared players (before and after starting to play Pokemon Go) with non-players (before and after the median start date for the players), in a difference-in-difference analysis. Here's what they found:
Playing Pokémon GO was common across various subgroups of the population... players, however, tended to be younger, have a lower education and household income, and be obese, and were more likely to be single and less likely to be black compared with non-players... In the four weeks before installation of Pokémon GO, participants who played the game took on average 4256 (SD 2697) steps daily. The corresponding number for non-players in the four weeks preceding 8 July (median date of Pokémon GO installation among the players) was 4126 (SD 2930). After installation of the game, the daily steps among players increased sharply before gradually returning to the pre-installation levels, whereas the number of daily steps for non-players remained at similar levels throughout the study period... The difference in difference analysis confirmed the pattern: Pokémon GO was associated with an increase in daily steps of 955 (95% confidence interval 697 to 1213) during the first week, the effect was gradually attenuated over the subsequent weeks, and by week 6 it was not significant...
In other words, Pokemon Go had a very small effect on physical activity - the authors note that an extra 1000 steps is about 11 minutes of walking, which is much less than any guidelines recommend. And that effect had more than halved within four weeks, and was essentially gone within six weeks. Hardly cause to hail the game as a solution to obesity. Back to the treadmill, I guess.
In a post last month, Tim Harford perceptively characterised the posturing between Britain and the European Union over Brexit as a game of chicken:
First: to be an effective negotiator often means accepting some risk of disaster. The simplest model of this is the game of “Chicken”, in which two leather-clad rebels get into their cars, and drive towards each other at a furious pace. The first one to veer off the road loses his dignity, unless neither of them swerve, in which case both of them will lose a lot more than that.
Chicken is an idiotic game, whose players have little to gain and much to lose. But Chicken teaches us that you can gain an advantage by limiting your own options. Imagine detaching your steering wheel and flamboyantly discarding it as you race headlong towards your opponent. Victory would be guaranteed. Nobody would drive straight at a car that cannot steer out of the way. But here’s a worrisome prospect: what if, as you hurl your own steering wheel out of the window, you notice that your rival has done exactly the same thing?
All this matters because both the UK and the EU are doing their best to give the impression that they’ve thrown their steering wheels away. Control of immigration is non-negotiable, says Theresa May. Fine, says the EU — in that case membership of the single market is out of the question. Fine, says May: we’re out. Don’t let the door hit you as you leave, says the EU.
It’s easy to see why both sides are behaving like this — it’s the logic of Chicken. But the eventual result may be something no sane person wants: a car crash. In May’s recent speech, she set out her willingness to risk such a crash by saying she might walk away without a deal. That does make some sense: it’s how you act if you want to win a game of Chicken. But there are games of Chicken that nobody wins.
The Brexit negotiations chicken game is laid out in the table below. The EU and the UK can choose to 'make concessions', or to 'play hardball'. If both make concessions, the outcome is essentially pretty neutral (a payoff of zero for both of them). However, if either the EU or the UK plays hardball while the other makes concessions, whichever of them plays hardball comes out better off (positive payoff) at the expense of the other (negative payoff). Finally, if both play hardball, both will be much worse off (very negative payoffs).
Where are the Nash equilibriums in this game? To identify them, we can use the 'best response' method. To do this, we track: for each player, for each strategy, what is the best response of the other player. Where both players are selecting a best response, they are doing the best they can, given the choice of the other player (this is the definition of Nash equilibrium).
For our game outlined above:
If the UK makes concessions, the EU's best response is to play hardball (since + is better than 0) [we track the best responses with ticks, and not-best-responses with crosses; Note: I'm also tracking which payoffs I am comparing with numbers corresponding to the numbers in this list];
If the UK plays hardball, the EU's best response is to make concessions (since - is better than --);
If the EU makes concessions, the UK's best response is to play hardball (since + is better than 0); and
If the EU plays hardball, the UK's best response is to make concessions (since - is better than --).
Note that there are two Nash equilibriums, where one of the EU or the UK plays hardball, and the other makes concessions. However, both of them want to be the one playing hardball. This is a type of coordination game, and it is likely that both the EU and the UK will try to play hardball (but leading both to incur big losses!).
The solution to getting your preferred equilibrium outcome in the chicken game is to make a credible commitment (such as removing the steering wheel that Harford suggests for the classic chicken game). In the case of Brexit though, it isn't clear how either side can make a credible commitment to the hardball strategy, and both are already moving their feet towards the accelerator. But if neither are willing to make concessions, the outcome is clear.
One of the challenges of economics research is that you can't set up experiments. For instance, you can't easily randomly assign people to different treatments (e.g. gender) to see if or how their behaviour (or outcomes) are affected. Sure, you can run laboratory or field experiments, but then you wonder if the laboratory conditions affect the results and whether they hold in the 'real world'. Sports data provides an interesting solution to some of these issues, since they involve known rules and the incentives are usually pretty clear (most sports competitors are trying to win).
Which brings me to this IZA discussion paper from last year by René Böheim, Christoph Freudenthaler, and Mario Lackner (all from Johannes Kepler University Linz). In the paper, the authors use data from the NBA and WNBA playoffs from 2002-03 to 2013-14 to investigate whether there are differences in risk taking behaviour between men and women. This is an important question to investigate, since it may go some of the way towards explaining the gender wage gap, for instance. Risk-taking behaviour is notoriously difficult to observe, but in sports data it is readily apparent to the knowledgeable fan when a player is undertaking a high-risk strategy.
Specifically, Böheim et al. look at the probability that a player attempts a three-point shot in the closing minutes of a playoff game. A three-point shot is more risky than a standard two-point shot - it carries a higher reward (greater probability of winning, particularly if your team is down by two points), but a higher risk (since the probability of successfully sinking a three-pointer is less than for a two-pointer). They find that:
male teams increase their risk-taking towards the end of matches when they are trailing by a small amount and a successful risky strategy could secure the winning of the match. Our key finding shows that female teams, in contrast, reduce their risk-taking in these situations. The less time left in a match, the larger is the gap. A detailed investigation shows however that this difference is the result of risk-taking in matches where the costs of an unsuccessful risky strategy are relatively lower. In situations where the costs of an unsuccessful risky strategy are large - losing the match and, in consequence, the round in the elimination tournament - we find no difference in risk-taking between male and female teams. One potential explanation for our results might be male overcon fidence. If this were the reason for our findings, we would expect to observe a lower probability to throw successfully or to win the match. We do not find such evidence.
The results are interesting, but I don't fully buy their interpretation. To get more specific, they found that male teams increase their risk-taking (i.e. they attempt more three-pointers) when they are behind in the game (by up to two points), but this effect is only present when the team is ahead or tied in the playoff series overall. A team that is trailing in the playoff series (e.g. down 1-2 in a best-of-seven NBA finals series) does not engage in significantly more risk taking when down by one or two points.
My prior here would be that teams that are trailing in the series would be more likely to throw caution to the wind in an attempt to catch up, but Böheim et al. find the opposite (but only for men). The authors put this finding down to the costs of an unsuccessful risky strategy being lower (for those teams that are leading or tied in the series, compared with those who are trailing). However, I would expect that the benefits of the risky strategy are also lower (for teams that are leading or tied in the series), so there isn't a clear theoretical case for their interpretation (although the empirical case is that the risk-reward is positive for teams that are ahead in the series).
An alternative explanation may be to do with the difference in the cost-benefit calculation for the individual player versus the team. If your team is ahead in the series, then the series MVP is more likely to come from your team, so running up your personal score carries a high individual benefit (in addition to the team's benefit), whereas the cost is borne mostly by the team since no one will remember the three-pointers you missed when your team was ahead in the series. On the other hand, if your team is behind in the series and you undertake the risky strategy of attempting three-pointers, both the benefits (the fans remember you as the guy who got them back into the series) and the costs (the fans may remember you as the guy who wasted all those scoring opportunities by putting up crazy three-point attempts) fall on the individual. So, overall the benefit-cost calculation tends towards more risky strategies by the individual player if the team is ahead in the series.
But only for men. Is that because women are more team-oriented, so the difference in individual vs. team costs and benefits is less important? Or perhaps the monetary and status rewards for being a series MVP are greater in the NBA than the WNBA? Men may take more risks, but the key question of why is hardly settled.
Bonus video clip (this sort of shot wasn't included in the dataset, as there is little in the way of strategy when it comes to buzzer beaters; and yes, I know it's not pro basketball, but it is cool nonetheless):
