Beer prices and STIs

Risky sexual behaviour is, by definition, sexual behaviour that increases a person's risk of contracting a sexually transmitted infection (STI). Risky sex is more likely to happen when the participants are affected by alcohol. So, if there is less alcohol consumption, it seems reasonable to assume that there will be less risky sex. And if alcohol is more expensive, people will drink less (economists refer to that as the Law of Demand). Putting those four sentences together, we have a causal chain that suggests that when alcohol prices are higher, the incidence of sexually transmitted infections should be lower. But how much lower? And, could we argue that alcohol taxes are a good intervention to reduce STI incidence?

A 2008 article by Anindya Sen (University of Waterloo, in Canada) and May Luong (Statistics Canada), published in the journal Contemporary Economic Policy (ungated here) provides some useful evidence. Sen and Luong used provincial data from Canada for the period from 1981 to 1999, and looked at the relationship between beer prices and gonorrhea and chlamydia rates. Interestingly, over that time beer prices had increased by 10%, while gonorrhea incidence had decreased by 93% and chlamydia incidence had decreased by 28%.  They find that:

...higher beer prices are significantly correlated with lower gonorrhea and chlamydia rates and beer price elasticities within a range of -0.7 to -0.9.

In other words, a one percent increase in beer prices is associated with a 0.7 to 0.9 percent decrease in gonorrhea and chlamydia rates. So, if the excise tax on beer increased, then the incidence rate of STIs would decrease. However, it is worth noting that the effect of a tax change will be much less than that implied by the elasticities above. According to Beer Canada, about half of the cost of a beer is excise tax (although that calculation is disputed, I'll use it because it is simple). So, a 1% increase in beer tax would increase the price of beer by 0.5%, halving the effect on STIs to a decrease of 0.35 to 0.45 percent. Still substantial.

Of course, that assumes that Sen and Luong's results are causal, which they aren't (although they do include some analysis based on an instrumental variables approach, which supports their results and has an interpretation that is closer to causality). However, in weighing up the optimal tax on alcohol, the impact on STI incidence is a valid consideration.

The economic impact of the 2010 World Cup in South Africa

The empirical lack of economic impact of mega sports events is reasonably well established. Andrew Zimbalist has a whole book on the topic, titled Circus Maximus: The Economic Gamble behind Hosting the Olympics and the World Cup (which I reviewed here; see also this 2016 post). So, I was interested to read a new study on the 2010 FIFA World Cup in South Africa that purported to find significant impacts.

This new article, by Gregor Pfeifer, Fabian Wahl, and Martyna Marczak (all University of Hohenheim, in Germany) was published in the Journal of Regional Science (ungated earlier version here). World-Cup-related infrastructure spending in South Africa between 2004 (when their hosting rights were announced) and 2010 was:

...estimated to have totaled about $14 billion (roughly 3.7% of South Africa’s GDP in 2010) out of which $11.4 billion have been spent on transportation...

Unsurprisingly, the spending was concentrated in particular cities, which were to host the football matches. To measure economic impact, Pfeifer et al. use night lights as a proxy. They explain that:

...[d]ata on night lights are collected by satellites and are available for the whole globe at a high level of geographical precision. The economic literature using high‐precision satellite data, also on other outcomes than night lights, is growing... The usefulness of high‐precision night light data as an economic proxy is of particular relevance in the case of developing countries, where administrative data on GDP or other economic indicators are often of bad quality, not given for a longer time span, and/or not provided at a desired subnational level.

They find that:

Based on the average World Cup venue on municipality level, we find a significant and positive short‐run impact between 2004 and 2009, that is equivalent to a 1.3 percentage points decrease in the unemployment rate or an increase of around $335 GDP per capita. Taking the costs of the investments into account, we derive a net benefit of $217 GDP per capita. Starting in 2010, the average effect becomes insignificant...

That is pretty well demonstrated in the following figure. Notice that the bold line (the treated municipalities) sits above the dashed line (the synthetic control, see below) only from 2004 up to 2010, where they come back together.

They also find that:

...the average picture obscures heterogeneity related to the sources of economic activity and the locations within the treated municipalities. More specifically, we demonstrate that around and after 2010, there has been a positive, longer‐run economic effect stemming from new and upgraded transport infrastructure. These positive gains are particularly evident for smaller towns, which can be explained with a regional catch‐up towards bigger cities... Contrarily, the effect of stadiums is generally less significant and no longer‐lasting economic benefits are attributed to the construction or upgrade of the football arenas. Those are merely evident throughout the pre‐2010 period. Taken together, our findings underline the importance of investments in transport infrastructure, particularly in rural areas, for longer‐run economic prosperity and regional catch‐up processes.

In other words, the core expenditure on the tournament itself, such as stadiums, had no economic impact after construction ended (which is consistent with the broader literature), while the expenditure on transport infrastructure did. South Africa would have gotten the same effect by simply building the transport infrastructure without the stadiums.

There were a couple of elements of the study that troubled me. They used a synthetic control method. You want to compare the 'treated' municipalities (i.e. those where new transport infrastructure or stadiums were built) with 'control' municipalities (where no infrastructure was built, but which are otherwise identical to the treatment municipalities). The problem is that control municipalities that are identical to the treatment municipalities is all-but-impossible. So, instead you construct a 'synthetic control' as a weighted average of several other municipalities, so that the weighted synthetic control looks very similar to the treated municipality. This is an approach that is increasingly being used in economics.

However, in this case basically all of the large cities in South Africa were treated in some way. So, the synthetic control is made up of much smaller municipalities. In fact, the synthetic control is 80.8% weighted to uMhlathuze municipality (which is essentially the town of Richards Bay, northeast of Durban). So, effectively they were comparing the change in night lights in areas with infrastructure development with the change in night lights for Richards Bay (and the surrounding municipality).

Second, they drill down to look at the impacts of individual projects, and find that some of the projects have significant positive effects that last beyond 2010 (unlike the overall analysis, which finds nothing after 2010). Given the overall null effect after 2010, that suggests that there must be some other projects that had negative economic impacts after 2010. Those negative projects are never identified.

The economic non-impact of mega sports events is not under threat from this study. The best you could say is that hosting the FIFA World Cup induced South Africa to invest in transport infrastructure that might not have otherwise happened. Of course, we will never know.

How many zombies are there in New Zealand?

Let's say there is some rare group of people and that you want to know how many people there are in the group. Say, people who own fifteen or more cats, or avid fans of curling. Conducting a population survey isn't going to help much, because if you survey 10,000 people and three belong to the group that doesn't tell you very much. Now, let's say that not only is the group rare, but people don't want to admit (even in a survey) that they belong to the group. Say, people who enjoyed the movie Green Lantern, or secret agents, or aliens, or vampires, or zombies. How do you get a measure of the size of those populations?

One way that you might be able to achieve this is an indirect method. If you survey a random sample of people, and you know how many people they know (that is, how many people are in their social network), you could simply ask each person in your survey how many Green Lantern lovers, or how many zombies, they know. You could then extrapolate from that how many there are in the population as a whole, if you make some assumptions about the overlaps between the networks of the people you surveyed.

It's not a totally crazy idea, but is sufficiently lampooned by Andrew Gelman (Columbia University) in this article published on ArXiv:

Zombies are believed to have very low rates of telephone usage and in any case may be reluctant to identify themselves as such to a researcher. Face-to-face surveying involves too much risk to the interviewers, and internet surveys, although they originally were believed to have much promise, have recently had to be abandoned in this area because of the potential for zombie infection via computer virus...

Zheng, Salganik, and Gelman (2006) discuss how to learn about groups that are not directly sampled in a survey. The basic idea is to ask respondents questions such as, "How many people do you know named Stephen/Margaret/etc." to learn the sizes of their social networks, questions such as "How many lawyers/teachers/police officers/etc. do you know," to learn about the properties of these networks, and questions such as "How many prisoners do you know" to learn about groups that are hard to reach in a sample survey. Zheng et al. report that, on average, each respondent knows 750 people; thus, a survey of 1500 Americans can give us indirect information on about a million people.

If you're interested, the Zheng et al. paper is open access and available here. So, how many zombies are there in New Zealand? To find out, someone first needs to do a random survey asking people how many zombies they know.

Read more:

• Zombies, vampires and population modelling

The law and economics (and game theory) of Survivor

As I mentioned in a post last year, I really love the reality show Survivor. One day I might even collate some of the cool economics-related examples from the show - comparative advantage, asymmetric information, risk and uncertainty, public goods, common resources, and lots and lots of game theory (coalitions, prisoners' dilemmas, repeated games, coordination games, and so on).

I recently ran across this 2000 working paper by Kimberley Mason and Maxwell Stearns (both George Mason University) on the law and economics of Survivor. It would probably be more correct if the title said it was about the game theory of Survivor, which is what it is. It was written soon after the conclusion of the first season of Survivor (which is currently showing its 37th season - David vs. Goliath). The paper is interesting in that it traces out all the strategic decision-making in the first season, and relates it to game theory and rational decision-making. Mason and Stearns also highlight the masterstroke of the eventual winner, Richard Hatch, in bowing out of the last immunity challenge:

The strenuous nature of the competition helped Richard to justify a decision that was ultimately a well disguised defection from his suballiance with Rudy. Recall that Richard withdrew from the competition, claiming that he knew he would not win. If one construes the Richard/Rudy suballiance as a commitment to do whatever they can to ensure that they emerge as the finalists... then by withdrawing, Richard defected. To see why consider how the game was necessarily played as a result of Richard’s decision. Had Rudy won the competition, he would have voted to keep Richard on as a finalist, consistent with his commitment to the suballiance. Because Kelly preferred Rudy to Richard (as shown in her first vote in cycle 13), this would have risked a 4 to 3 vote for Rudy by the jury. (This assumes that the remaining six jurors vote as they did.). But if Kelly won the game, then she would choose between Rudy and Richard. She knew that either of them would vote for the other as a juror. The only question from her perspective was who was more popular with the remaining jurors. As Richard likely knew, Rudy was more popular, meaning that if Kelly won, Richard would still be selected as a finalist. In contrast, if Richard stayed in the immunity contest and won, he faced another Catch-22. If he voted to keep Rudy, then Kelly would vote for Rudy as a juror, and as a result, Richard would lose (again assuming the other jurors voted as they did). And if he voted for Kelly, then he would violate the express terms of the suballiance with Rudy, and risk Rudy’s retribution. If Rudy also defected, then Kelly would win. The only way that Richard could reduce the likelihood of this result was to withdraw from the game. While he would remain a finalist regardless of whether Rudy or Kelly won, he hoped that Kelly would win because she would eliminate his toughest final competitor.

Kelly won the challenge, and Richard duly won Survivor by a vote of 4-3. Mason and Stearns conclude:

At the beginning of this essay, we posited that Survivor was played in a manner that was consistent with the predictions of rational choice theory. We certainly do not suggest that every player played in a manner that optimized his or her prospects for winning. Indeed, that is largely the point. At each step in the game, those who best positioned themselves to win were the ones who played in a rational and strategic manner.

Interestingly, the paper also contains a discussion of the optimal size of an alliance, based on theory from Gordon Tullock and Nobel Prize winner James Buchanan, which should be familiar to my ECONS102 students:

Professors Buchanan and Tullock present an optimal size legislature as a function of two costs, agency costs, which are negatively correlated with the number of representatives, and decision costs, which are positively correlated with the number of representatives... The optimum point, according to Buchanan and Tullock, is that which minimizes the sum of agency and decision costs...

These two conflicting costs, which are both a function of coalition size, pit the benefits of safety in numbers against the risks of disclosure to non-alliance members... As the size of the coalition increases, the members are increasingly protected against the risk that a member will defect in favor of an alternative coalition. Conversely, as coalition size increases, the members face an increased risk of disclosure, which could lead to a coalition breakdown.

The optimal size of an alliance is one that correctly balances the benefits of being large enough to be safe from the non-allied players voting you off (the marginal benefit of adding one more person to the alliance decreases as the alliance gets larger), against the costs of the alliance being revealed to all players (the marginal cost of adding one more person to the alliance increases as the alliance gets larger). The cost of having a large alliance also relates to the chance of defection - the chance that one or more members of the alliance switch sides and blindside someone. It is easier to maintain trust and cohesion in a smaller alliance.

Survivor is a great example of economics in action. If you aren't already a fan, you should start watching it!

Read more:

• The economics of Survivor