Meta loss aversion

Back in August, this article in The Conversation pointed me to this article by David Gal (University of Illinois at Chicago) and Derek Rucker (Northwestern), published in the Journal of Consumer Psychology (sorry I don't see an ungated version anywhere). Loss aversion is the idea that people value losses much more than equivalent gains (in other words, we like to avoid losses much more than we like to capture equivalent gains). It is a central and defining idea in behavioural economics. In their article, Gal and Rucker present evidence that loss aversion is not real:

In sum, an evaluation of the literature suggests little evidence to support loss aversion as a general principle. This appears true regardless of whether one represents loss aversion in the strong or weak forms presented here. That is, a strong form suggests that, for loss aversion to be taken as a general principle, one should observe losses to generally outweigh gains and for gains to never outweigh losses. The weak form, as we have represented it, might simply be that, on balance, it is more common for losses to loom larger than gains than vice versa. This is not to say that losses never loom larger than gains. Yes, contexts exist for which losses might have more psychological impact than gains. But, so do contexts and stimuli exist where gains have more impact than losses, and where losses and gains have similar impact.

From what I can see Gal and Rucker's argument rests on a fairly selective review of the literature, and in some cases I'm not convinced. A key aspect of loss aversion is that people make decisions in relation to a reference point, and it is comparison with that reference point that determines whether they are facing a loss or a gain. Gal and Rucker even recognise this:

For example, one individual who obtained $5 might view the $5 as a gain, whereas another individual that expected to obtain $10, but only obtained $5, might view the $5 obtained as a loss of $5 relative to his expectation... 

It isn't clear to me that the following evidence actually takes account of the reference point:

The retention paradigm involves a comparison of participants’ [willingness-to-pay] to obtain an object (WTP-Obtain) to a condition where participants are asked their maximum willingness-to-pay to retain an object they own (WTP-Retain). The retention paradigm and its core feature—the WTP-retain condition— proposes to offer a less confounded test of the relative impact of losses versus gains...

...in the discrete variant of the retention paradigm, both the decision to retain one’s endowed option and the decision to exchange the endowed option for an alternative are framed as action alternatives. In particular, participants in one condition are informed that they own one good and are asked which of two options they would prefer, either (a) receiving $0 [i.e., the retention option], or (b) exchanging their endowed good for an alternative good. Participants in a second condition are offered the same choice except the endowed good and the alternative good are swapped.

It seems to me that in both the endowed option (where the research participant is paying to retain something they have been given) and the alternative option (where the research participant is paying to obtain that same item), the reference point is not having the item. So, it isn't a surprised when Gal and Rucker report that, in some of their earlier research:

Gal and Rucker (2017a) find across multiple experiments with a wide range of objects (e.g., mugs and notebooks; mobile phones; high-speed Internet, and train services) that WTP-Retain did not typically exceed WTP-Obtain. In fact, in most cases, little difference between WTP-Retain and WTP-Obtain was observed, and for mundane goods, WTP-Obtain exceeded WTP-Retain more often than not.

An interesting aspect of Gal and Rucker's paper is that they try to explain why, in the face of the competing evidence they have accumulated, loss aversion is so widely accepted across many disciplines (including economics, psychology, law, marketing, finance, etc.). They adopt a Kuhnian argument:

Kuhn (1962) argues that as a paradigm becomes entrenched, it increasingly resists change. When an anomaly is ultimately identified that cannot easily be ignored, scientists will try to tweak their models rather than upend the paradigm. They “will devise numerous articulations and ad hoc modifications of their theory in order to eliminate any apparent conflict” (Kuhn, 1970, p. 78).

I want to suggest an alternative (similar to an earlier post I wrote about ideology). Perhaps academics are willing to retain their belief in loss aversion because, if they gave up on it, they would keenly feel its loss? Are academics so loss averse that they are unwilling to give up on loss aversion? If that's the case, is that evidence in favour of loss aversion? It's all getting rather circular, isn't it?

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