Saturday, 14 July 2018

The success of smiling football teams and scientists

Which of these two groups will win on Monday morning (New Zealand time)?

Can you tell just by the photos which team is more likely to win? For instance, if the players are smiling, does that indicate self-confidence and a higher likelihood of victory? If they're striking a more angry facial expression, does that demonstrate strength and determination?

In a new paper published in the Journal of Economic Psychology (ungated earlier version here), Astrid Hopfensitz (University of Toulouse Capitole) and Cesar Mantilla (Universidad del Rosario, Colombia) looked at data from player photos (from the Panini stickers collections) for every world cup from 1970 to 2014. First, they identified using automated software (FaceReader):
...the activation level of six basic emotions: anger, happiness, disgust, fear, sadness, and surprise, which are non-exclusive.
They then tested whether those emotions (averaged at the team level, rather than individually) were associated with team success in the World Cup, for the 304 teams that took part in those tournaments. They found that:
...display of anger as well as happiness is positively correlated with a favorable goal difference (i.e. more goals scored than conceded). This correlation is robust to the inclusion of our control variables... We also observe the standardized display of anger and happiness is negatively correlated with the overall ranking in the World Cup... That is, teams that display either more anger or happiness, reach an overall better position in the whole tournament...
We observe a clear difference with respect to the two emotions. While the display of happiness is linked to the scoring of goals... anger is linked to conceding fewer goals...
Interesting, when they separate the analyses for defensive and offensive players, they find that:
...the display of happiness is still predictive in each sub-group. By contrast, the display of anger remains predictive only for defensive players, and for one of the outcomes.
Teams with happy offensive players do well, and teams with happy (or to a lesser extent, angry) defensive players do well.

Now, I know you're scrolling back up to check the France and Croatia teams to see who is smiling more [*], but before you do you should know that the links between smiling and success are an example of correlation, not causation. There isn't anything in the study to suggest that smiling causes success, even though you can tell a plausible story about it.

However, you should also know that the correlation between smiling and success isn't limited to football. In another new paper, published in the Journal of Positive Psychology (ungated version here), Lukasz Kaczmarek (Adam Mickiewicz University, Poland) and co-authors looked at the correlation between smiling and success for scientists. Using data for 220 male and 220 female scientists taken from the research social networking site ResearchGate, Kaczmarek et al. first coded whether the researchers were smiling in their profile picture, and then looked at whether that was related to a range of research metrics. They found that:
As expected, smile intensity was significantly related to the number of citations, the number of citations per paper, and the number of followers after controlling for age and sex... Smile intensity was not significantly related to the number of publications produced by the author or the number of publication reads.
It is plausible that there is causality working in two directions here. More successful researchers (those whose papers are cited more often) are more likely to be smiling, happy people (explaining the correlation between citations and smiling), while smiling, happy researchers are more likely to entice other people to follow them on a social network (explaining the correlation between followers and smiling). However, more work would need to be done to establish whether those explanations hold for a larger sample.

Either way, both studies suggest a strong correlation between smiling and success. Go Croatia!

[HT: Marginal Revolution, for the Kaczmarek et al. paper]


[*] Please note that I take no responsibility for the outcomes of any bets you make as a result of your new knowledge about successful smiling footballers.

Thursday, 12 July 2018

Oh, the places you’ll go!

In economics, the cost-benefit principle is the idea that a rational decision-maker will take an action if, and only if, the incremental (extra) benefits from taking the action are at least as great as the incremental (extra) costs. We can apply the cost-benefit principle to find the optimal quantity of things (the quantity that maximises the difference between total benefits and total costs). When we do this, we refer to it as marginal analysis.

Marginal analysis challenges the idea that we are always better off with more of things. Yes, we might like there to be more white rhinos, but if there was one living in every front yard, we'd probably regret it. More is not always better.

The easiest way to understand marginal analysis is to see it in action. A recent article in The Economist provides us with a good example:
When it comes to habitat, human beings are creatures of habit. It has been known for a long time that, whether his habitat is a village, a city or, for real globe-trotters, the planet itself, an individual person generally visits the same places regularly. The details, though, have been surprisingly obscure. Now, thanks to an analysis of data collected from 40,000 smartphone users around the world, a new property of humanity’s locomotive habits has been revealed.
It turns out that someone’s “location capacity”, the number of places which he or she visits regularly, remains constant over periods of months and years. What constitutes a “place” depends on what distance between two places makes them separate. But analysing movement patterns helps illuminate the distinction and the researchers found that the average location capacity was 25. If a new location does make its way into the set of places an individual tends to visit, an old one drops out in response. People do not, in other words, gather places like collector cards. Rather, they cycle through them. Their geographical behaviour is limited and predictable, not footloose and fancy-free.
When it comes to the number of locations we visit, there appears to be an optimal number and that optimal number is 25. Why? Consider the costs and benefits of adding one more location to the number that you regularly visit. We can refer to those costs and benefits as marginal costs and marginal benefits. When economists refer to something as marginal, you can think of it as being associated with one more unit (in this case, associated with one more location that you regularly visit).

The marginal benefit of locations declines as you add more locations to your regular routine. Why is that? Not all locations provide you with the same benefit, and you probably go to the most beneficial places most often. So naturally, the next location you add to your regular routine is going to provide less additional benefit (less marginal benefit) than all of the other locations you already visit regularly. So, as shown in the diagram below, the marginal benefit (MB) decreases as you include more locations in your routine.

The marginal cost of locations increases as you add more locations to your regular routine. Why is that? Every location you choose to go to entails an opportunity cost - something else that you have given up in order to go there. When you add a new location to your routine, you are probably giving up spending some time at one of the other locations you were already going to, which provide you with a high benefit. The more locations you add, the more you need to cut into your time at high-benefit locations. So, as shown in the diagram below, the marginal benefit (MC) increases as you include more locations in your routine.

The optimal number of locations occurs at the quantity of locations where marginal benefit exactly meets marginal cost (at Q*). If you regularly visit more than Q* locations (e.g. at Q2), then the extra benefit (MB) of visiting those locations is less than the extra cost (MC), making you worse off. If you regularly visit fewer than Q* locations (e.g. at Q1), then the extra benefit (MB) of visiting those locations is more than the extra cost (MC), so visiting one more location would make you better off.

And, it turns out, the optimal number of locations (Q*) is limited to roughly 25.

Tuesday, 10 July 2018

Congratulations Dr Gemma Piercy-Cameron

Yesterday, my wife had her PhD oral examination. She passed with flying colours, giving a very impressive summary of her very in-depth research on baristas' work identity (the title of her thesis is "Baristas: The Artisan Precariat"), as well as a very impressive display of answering the examiners' questions. Her examination was a model of how an excellent student can demonstrate their deep topic knowledge, their understanding of the relevant literature and how it relates to their own work, and the advantage and limitations of their chosen research methods. It was clear throughout the examination that the examiners were very impressed, and I was not surprised to hear that she passed with only minor revisions required to her thesis (and even the minor revisions are very minor, and will take no more than a couple of hours to action.

Gemma has worked incredibly hard to get to this point, not helped by her ongoing chronic health issues. She should be very proud of her achievement, as I am of her.

For the record, here's the abstract from her thesis (noting that it is a thesis in labour studies, not economics):
My research in the work identit(ies) of baristas demonstrates that different workplaces, in conjunction with individual biographies, produce different kinds of work identities. Connected to these differences are the actual and perceived levels of skill and/or social status ascribed to workers within the service work triadic relationship (customers, co-workers/managers and workers). The higher the level of skill or social status, the greater the capacity of workers to experience more autonomy in their work and/or access improved working conditions. These findings are informed by my research approach, which incorporates three key methods: key informant interviews; observation/participant observation; life history interviews; all of which is underpinned by the mystory approach and autoethnography.
My findings from the case study research of the barista work identity are expressed in the following. (1) The different work identities within a specific occupation contribute to the heterogeneity of service workers and service work. This heterogeneity, in turn, obscures the range of skills utilised in the technical and presentational labour mobilised in service work. The skills are obscured by the social and practice-based nature of knowledge transmission in service work like that of baristas, as well as by the dynamic and shifting alliances that may occur in the triadic relationship of customer, workers and employer/manager. (2) Interactive service workers are involved in providing labour or work that is more complex than is socially understood and recognised. This complexity stems from the ways in which presentational labour is commodified, appropriated and mobilised in the workplace within the spaces of the organisational context, internal practices, and the service encounter. (3) I further argue that service workers are also dehumanised as part of the service encounter through the structure of capitalism, specifically the application of commodity fetishism to workers by customers, colleagues, managers, capital and at times themselves. Commodity fetishism dehumanises workers, creating an empathy gap between customers/managers and workers. As such, the commodity fetishisation of service workers also reinforces and promotes compliance with the insecure and precarious employment practices common to occupations in the service sector. (4) As the conditions of precarious work continue to spread, the employment relationship is being altered in relation to consumption practices. Based on this shift in employment relations, I argue that we are moving towards a labour market and society shaped by the practices of the consumer society as well as the traditional production-based economy. However, the increasing influence of consumption practices stems from neoliberal inspired changes in employment relationships rather than the consumer society emphasis on agentic identity projects. As such, the self-determining identity projects highlighted by researchers engaged in aspects of service work and consumption-based research also need to be accompanied by an understanding of the political economy and structural forces which shape the labour process. 
 Congratulations Dr Piercy-Cameron!

Sunday, 8 July 2018

Auckland Airport passport control's accidental nudge fail

I just got back from Europe, where I was attending the EduLearn 2018 conference. On arriving back at Auckland Airport, we were confronted as usual with rows of SmartGate (or eGate) machines, which scan your e-passport and take a photo of you, rather than having to have your passport physically checked by an officer. These SmartGates at Auckland Airport are now usable by many different passport holders (which caused me some disquiet when we arrived in London to find that we couldn't use the same facilities there and yet UK citizens can do so in New Zealand - whatever happened to reciprocity?). Anyway, I digress.

Nudge theory was brought to prominence by Richard Thaler and Cass Sunstein's excellent 2008 book Nudge. The idea is that relatively subtle changes to the decision environment can have significant effects on behaviour. If I remember correctly, one of their examples was the difference between opt-in and opt-out retirement savings schemes, where opt-out schemes have much higher enrolment rates compared with otherwise-identical opt-in schemes. One of the most important insights of behavioural economics and nudge theory is the idea that how a decision is framed can make a difference to our decisions. Governments are making increasing use of nudges to modify our behaviour, including the Behavioural Insights Team in the UK, and similar efforts in the U.S. and Australia.

Not all nudges are intentional or helpful though, as we discovered at Auckland Airport passport control. Above the SmartGate machines was a helpful row of the flags representing all the nations whose passport holders could use SmartGate. However, these flags were lined up in groups of three or four (or two, in the case of Australia and New Zealand), with each group of flags located above a corresponding group of SmartGate machines (I'm really sad I can't share a photo, because of laws prohibiting photography in this area, so you'll have to make do with my description). Unsurprisingly, this gave a strong impression to arriving passengers that they should go to the machines corresponding to the flag of their passport. Passport control framed our decision about which machine to choose by making it seem that the flags mattered. In actuality, all SmartGate machines could handle any of the e-passports.

So, when my wife and I arrived at passport control, there was a huge line for the machines with the New Zealand and Australian flags, and virtually no lines at all for the machines with the European flags. We weren't caught out by this unintentional nudge (because we knew that all of the machines worked the same, and we were willing to buck the trend and not line up in the 'New Zealand and Australia' line), and managed to substantially jump the queue.

I wonder how long it will take for Auckland Airport (or Customs or whoever controls that area) to realise their error and correct it? I'm off to Ireland in August for another conference, so I guess I will see then.

Thursday, 5 July 2018

This couldn't backfire, could it?... Paying farmers not to grow coca edition

One of my favourite topics in my ECONS102 class is the first topic, in part because we spend a bit of time considering the unintended consequences of (often) otherwise well-meaning policies. And there are so many examples to choose from, I just can't fit them all into that class. Here's a new one from The Economist last week:
The increased cultivation of coca in Colombia defied expectations that the government’s peace deal with the FARC guerrillas, who relied financially on drug trafficking, would curtail the cocaine trade. One explanation is a textbook example of the law of unintended consequences. The peace agreement required the government to make payments to coca farmers who switched to growing other crops. This wound up creating a perverse incentive for people to start planting coca, so they could receive compensation later on for giving it up.
As we discuss in my ECONS102 class, unintended consequences arise because of the incentives that a policy creates. The incentives to change behaviour arise because the benefits and/or costs have changed. In this case, the benefits of planting coca increased, because the farmers could then switch to another crop and claim the payment from the government. If they weren't growing coca, then they couldn't claim the payment, so an incentive was created to plant more coca. So the payments that were supposed to encourage farmers to plant less coca, actually encouraged farmers to plant more coca.

Of course, this has then led to an increase in the supply of coca, and a consequent increase in the supply of cocaine. Which is exactly the opposite of what was intended.

However, the effect here could be limited to the short run, because farmers can only switch away from planting coca once. So, the incentive to plant coca only occurs until the farmers have switched to something else. Supply should eventually fall. Although it wouldn't surprise me to learn that the incentive payment has been structured in such a way that clever farmers can claim it more than once!
As Steven Levitt and Stephen Dubner noted in their book Think Like a Freak (which I reviewed here), no individual or government will ever be as smart as all the people out there scheming to take advantage of an incentive plan.

Sunday, 1 July 2018

New results questioning the beauty premium should be treated with caution

The beauty premium - the empirical finding that people who are more attractive earn higher incomes - seems to be a fairly robust result in labour economics. Daniel Hamermesh's book Beauty Pays: Why Attractive People are More Successful (which I reviewed here) does a great job of summarising the literature, much of which Hamermesh himself has extensively contributed to.

However, in a new paper published in the Journal of Business and Psychology (ungated version here), Satoshi Kanazawa (LSE) and Mary Still (University of Massachusetts - Boston) question the existence of the beauty premium. Using data from several waves of the Add Health survey, and based on a sample size of around 15,000, they look at how attractiveness (as rated by interviewers at ages 16, 17, 22, and 29) is associated with earnings at age 29. Attractiveness is rated on a five-point scale (very unattractive, unattractive, average, attractive, or very attractive). Most previous studies have grouped the bottom two groups (very unattractive and unattractive) into a single category for analysis, due to the small number in the very unattractive category. Kanazawa and Still keep these two groups separate, and it is here where their results differ strikingly from the previous literature: is clear that the association between physical attractiveness and earnings was not at all monotonic, as predicted by the discrimination hypothesis. In fact, while there is some evidence of the beauty premium in Table 4, where attractive and very attractive Add Health respondents earn slightly more than the average-looking respondents, there is no clear evidence for the ugliness penalty, as very unattractive respondents at every age earn more than either unattractive or average-looking respondents.
In other words, there is some evidence for a 'very-unattractive premium' in their data. The results are not driven by outliers, because the same pattern holds for median and mean earnings. They explain this apparent very-unattractive premium as being due to very unattractive Add Health respondents at age 16 being significantly more intelligent, and obtaining more education, than unattractive or average-looking respondents. Once they control in their analysis for education, intelligence, and personality traits, the beauty premium (compared with the very unattractive group) becomes statistically insignificant.

However, there is good reason to treat these results with caution. The sample size was around 15,000 in each year, but only around 200 of those respondents were rated as very unattractive. Kanazawa and Still rightly point out that the small numbers will make us fairly uncertain about the average earnings of that group:
Just like earlier surveys of physical attractiveness, very few Add Health respondents were in the very unattractive category (ranging from 0.9% at 17 to 2.7% at 29). As a result, the standard error of earnings among the very unattractive workers tended to be very large, which prompted earlier researchers in this field to collapse very unattractive and unattractive categories into a below average category. However, the very small number of very unattractive respondents and their large standard errors actually strengthened, rather than weakened, our conclusion because standard errors figured into all the significant tests in the pairwise comparisons. Very unattractive workers earned statistically significantly more than unattractive and average-looking workers despite the large standard errors.
That last point is correct, but you can't have it both ways here. Very unattractive workers may earn statistically significantly more on average than unattractive or average-looking workers in spite of the large standard errors, but one of their other key results is the statistical insignificance of the beauty premium (compared with very unattractive workers) once you control for intelligence, education and personality traits. That null result could be driven by the large standard errors on the very unattractive group. In general, null results are much more difficult to justify because, as in this case, they can be driven purely by a lack of statistical power.

One other result from the paper concerned me slightly. Kanazawa and Still test for whether choice of occupation matters for the beauty premium by including occupation in their regression models. That is fine, but all it does is control for differences in mean earnings between different occupations, but assumes that the beauty premium is the same in all occupations. If you actually wanted to test for self-selection into occupations by attractiveness, you would probably expect that attractive people would self-select into occupations where the beauty premium is largest (Hamermesh makes this point in his book). So, to test for self-selection you really need to allow the beauty premium to be different across occupations, which Kanazawa and Still didn't do.

So, the results of the Kanazawa and Still paper are interesting, but I don't think they overturn the many previous papers that find a robust beauty premium.

[HT: Marginal Revolution]

Read more:

Saturday, 30 June 2018

What the Blitz can tell us about the cost of land use restrictions

Land use regulations are frequently cited as impediments to urban development, economic growth or employment growth (and they are a frequent topic on Eric Crampton's Offsetting Behaviour blog - see here for his latest post on the topic). The problem with land use regulations is that it can be very difficult to work out what would have happened if the regulations were not in place. Comparing areas with and areas without land use restrictions isn't helpful, since land use restrictions are not random, and may be affected by urban development, economic growth or employment growth - exactly the things you want to test the effect of land use restrictions on!

So, in order to test the impact of land use restrictions, ultimately you need some natural experiment where land use change was more permissive in some neighbourhoods than others, and where that assignment to permissive land use change was applied randomly. In a new working paper, Gerard Dericks (Oxford) and Hans Koster (Vrije Universiteit Amsterdam) use the World War II bombing of London ('the Blitz') as a tool that provides the necessary randomisation (they also have a non-technical summary of their paper here). The buildings that were hit by bombs during the Blitz were effectively random, and after controlling for the distance to the Thames and areas that were specifically targeted by Germans during the Blitz, Dericks and Koster show that the density of bombs was also effectively random. If you doubt this, consider that Battersea Power Station (probably the most important target in London) suffered only one very minor hit, and no bridge over the Thames was struck in the whole of the Blitz. If bombing was non-random, then these high-profile targets would have suffered much worse.

How does this relate to land use regulations? Buildings and neighbourhoods that suffered more bombing were more able to be rebuilt with less restrictive land use. That means that areas with higher density of bomb strikes were randomly assigned to less restrictive land use, and so Deriks and Koster use that to test the effect on land rents and employment density. They find:
...a strong and negative net effect of density frictions on office rents: a one standard deviation increase in bomb density leads to an increase in rents of about 8.5%... Back-of-the-envelope calculations highlight the importance of density frictions: if the Blitz bombings had not taken place, employment density would be about 50% lower and the resulting loss in agglomeration economies would lower total revenues from office space in Greater London by about $4.5 billion per year, equivalent to 1.2% of Greater London's GDP or 39% of its average annual growth rate.
Areas with greater density of bomb strikes, and hence less restrictive land regulation, have taller buildings, higher land rents, and greater employment density. None of this is terribly surprising, but the size of the effects are very large.

These results should be of broader interest, especially in other cities where land use restrictions appear to be holding back development, like Auckland. If you think that Auckland and London are too dissimilar in restrictive land use, consider this small example: Auckland protects view shafts to volcanic cones; London protects view shafts to St Paul's Cathedral.

Some land use restrictions are good and necessary. However, they don't come without cost, and this cost in terms of lost employment and output needs to be taken into consideration.

Thursday, 28 June 2018

The new fuel tax will be regressive, provided you understand what regressive means

I really despair over the economic literacy failures of our government and media. The latest example, reported in the New Zealand Herald this morning:
By late 2020, new fuel taxes will mean Aucklanders are paying an average $5.77 more a week for petrol, according to figures to be released by Government ministers today.
And in a startling revelation, the ministers claim that the wealthier a household is, the more it is likely to pay for petrol. They say the wealthiest 10 per cent of households will pay $7.71 per week more for petrol. Those with the lowest incomes will pay $3.64 a week more.
That's all good so far. Higher income households spend more on most goods (what economists term normal goods) including fuel, and so it makes sense that they would end up paying more of the fuel taxes. It's what comes next that's problematic:
This is a complete reversal of the most common complaint about fuel taxes, which is that they are "regressive". That means, the critics say, they affect poor people more than wealthy people.
Finance Minister Grant Robertson will join Transport Minister Phil Twyford and Associate Transport Minister Julie-Anne Genter this afternoon in Auckland to reveal the details of the new excise levies on fuel...
The MoT figures break the population into 10 segments, or deciles, from poorest (decile 1) to wealthiest (decile 10). They show that the wealthier a household is, the more money it is likely to spend on fuel.
In the first year, the average increase for Aucklanders, who will pay both taxes, is $3.80 per week. Decile 1 Aucklanders will pay on average $2.40, for decile 5 the average will be $3.75 and for decile 10 it will be $5.08.
"It's simply not true that fuel taxes cost low-income families more," Twyford said. "The figures show that the lowest-income families will be paying only a half or even a third as much as those on the highest incomes."
It's hard to tell here if Phil Twyford is being economically illiterate, or deliberately misleading. While it is true that, according to his figures, higher income households will pay more of the tax, that doesn't mean that the tax is not regressive. A regressive tax is one where lower income people pay a higher proportion of their income on the tax than higher income people.

So, you need to compare the tax paid with income to determine if the tax is regressive or not. It isn't enough to simply look at the tax paid by each group, and conclude that the tax is not regressive because higher income people pay more. This will be true of every excise tax on a normal good.

The latest data from the Household Economic Survey I could easily find using my slow hotel internet connection was this data for June 2016 (side note: trying to search for data on the new Statistics NZ website may actually be more difficult than for the old site - that's quite an accomplishment!). It doesn't give average incomes for each decile, but it does tell us the ranges. It also isn't limited to Auckland, but I don't think that will make much difference.

Decile 1 (the lowest income households) goes up to an annual income of $23,800. At that income level, the tax paid ($3.80 per week) would be 0.8% of their annual income (and would be a higher percentage for households with income below $23,800). For decile 10 (the highest income households), the minimum income is $180,200. At that income level, the tax paid ($5.08 per week) would be 0.1% of their annual income (and would be a lower percentage for households with income above $180,200).

Clearly, lower income households will be paying a higher proportion of their income on the tax than higher income households. The fuel tax is a regressive tax. Which just leaves the question: Is Phil Twyford being economically illiterate here, or wilfully misleading us in the hopes we wouldn't notice?

Wednesday, 27 June 2018

Why study economics? Uber data scientist edition...

There is a common misconception that the eventual job title that economics students are studying towards is 'economist', in the same way that engineering students become engineers, or accounting students become accountants. But actually, the vast majority of economics graduates don't get jobs with the title of economist. In my experience, the most common job title is some flavour of 'analyst' (market analyst, business analyst, financial analyst, risk analyst, etc.). However, a growing job title for economics graduates is 'data scientist', as for example in this new advertisement for jobs at Uber. The job description is interesting, and demonstrates a wide range of skills and attributes that economics graduates typically obtain:
Depending on your background and interests, you could focus your work in one of two areas:
  • Economics: Conduct research to understand our business and driver-partners in the context of the economies in which Uber operates. For example: We know that the flexible work model is very valuable to Uber drivers (see Chen et al. Opens a New Window. , Angrist et al. Opens a New Window. ) and that dynamic pricing is vital in protecting the health and efficiency of the dispatch market (see Castillo et al., “Surge Pricing Solves the Wild Goose Chase”); however, it’s likely that consistency (e.g., of pricing or earnings) also carries some value for riders and drivers.  What values should we put on these opposing virtues?
  • Cities and Urban Mobility: Study Uber's impact on riders and cities around the world with a special focus on different facets of urban mobility.  For example: What is the relationship between on-demand transportation and existing public transport systems. Do they complement or compete with each other? Or, does this relationship change depending on external factors? What could these external factors be and how do they change rider behavior?
Somewhat surprisingly, these jobs only require a "bachelor’s degree in Economics, Statistics, Math, Engineering, Computer Science, Transportation Planning, or another quantitative field", rather than a PhD (which has more often been the case for tech jobs for economists). However, the one or more years of quantitative or data science experience that is required suggests that picking up a job as a research assistant while studying, and doing some quality research at honours or Masters level is a pre-requisite.

In any case, this demonstrates that some of the coolest jobs for economics graduates are not as 'economists'.

Read more:

Tuesday, 26 June 2018

Tim Harford on opportunity cost

One of the first concepts I cover in my ECONS101 and ECONS102 classes is opportunity cost. It is also one of the most misunderstood concepts in economics. The inability to recognise that every choice comes with an associated cost (economists are fond of the phrase, "there is no free lunch") plagues public policy and business decision-making. And yet, the idea that when you choose something you are giving up something else that you could have chosen instead, should be intuitively obvious to anyone who has ever made a decision.

Tim Harford recently covered opportunity costs, in his usual easy-to-read style:
The principle of an opportunity cost does not at first glance seem hard to understand. If you spend half an hour noodling around on Twitter, when you would otherwise have been reading a book, the lost book-reading time is the opportunity cost of the tweeting. If you decide to buy a fancy belt for £100 instead of a cheaper one for £20, the opportunity cost is the £80 shirt you could otherwise have bought. Everything has a cost: whatever you were going to do instead, but couldn’t.
We should weigh opportunity costs with some care, mentally balancing any expenditure of time or money against what we might do or buy instead. However, observation suggests that this is not how we really behave. Ponder the agonised indecision of a customer in a stereo shop, unable to decide between a $1,000 Pioneer and a $700 Sony. The salesman asks, “Would you rather have the Pioneer, or the Sony and $300 worth of CDs?”, and the indecision evaporates. The Sony it is.
And Harford also explains why understanding opportunity costs is consequential:
Drawing our attention to opportunity costs, no matter how obvious, may change our decisions. The notorious falsehood on the campaign bus used by Vote Leave during the 2016 referendum campaign was well-crafted in this respect: not only could the UK save money by leaving the EU, we were told, but that money could then be spent on the National Health Service.
One could certainly debate the premise — indeed, the referendum campaign sometimes seemed to debate little else — but the conclusion was rock solid: if you have more money to spend, you can indeed spend more money on the NHS. (Just another way in which that bus was a display of marketing genius.)
We would make better decisions if we reminded ourselves about opportunity costs more often and more explicitly. Nowhere is this more true than in the case of time. Many of us have to deal with frequent claims on our time — “Can we meet for coffee so that I can pick your brains?” — and find it hard to say no. Explicitly considering the opportunity cost can help: if I meet for coffee I’ll have to work an hour later, and that means I won’t be able to read my son a story before bedtime.
Notice that in the latter example, the opportunity cost cannot easily be measured in monetary terms (how much is reading your son a story before bedtime worth?). However, in terms of impact on our satisfaction or happiness (what economists term 'utility'), we can make a comparison between these different options. You might also want to consider the costs you are imposing on others (whether monetary or otherwise) - economists refer to this as having social preferences (altruism is one example of social preferences). If your decisions affect others (which many decisions do), then others may face opportunity costs from your choices.

The next time you are making a decision, whether small or large, consider what is being given up to get the option you choose. It might not be measured in monetary terms, but there will always be a cost. You'll then make better decisions, or at least decisions that leave you happier overall.

Monday, 18 June 2018

The optimal queue

One of the biggest headaches that shoppers have to deal with is queues. Shoppers don't like having to wait, and if the queue is too long, some may simply give up without completing their purchase. But for store owners to reduce the length of queues (or eliminate them), they would face higher costs (at the least, they would need to hire more staff). So, eliminating queues is unlikely to be a good plan for stores. To see why, consider this recent article in The Conversation by Gary Mortimer (Queensland University of Technology) and Louise Grimmer (University of Tasmania):
Businesses face the challenge of identifying the optimum point where the costs of providing the service equal the costs of waiting. People in queues behave in ways that create direct and indirect costs for businesses. Sometimes customers will baulk and simply refuse to join the queue. Or they join the queue but renege, leaving because wait times are too long.
This behaviour leads to measurable costs. These costs are both direct, like abandoned carts, and indirect, like perceptions of poor service quality, increased dissatisfaction and low levels of customer loyalty.
There's lots of interesting points made in Mortimer and Grimmer's article (and for more on queueing, see my 2014 blog post on the topic). However, I want to highlight this diagram:

The diagram illustrates the optimal level of service. The y-axis shows costs to the store, and the x-axis shows the service level (where a higher service level is associated with shorter queues). The store is trying to minimise their total costs, but there are two marginal costs that they are trying to balance. The first cost is the marginal cost of providing service. This is upward sloping as we move from left to right, because each additional 'unit' of service level costs the store more than the last. To see why, consider your local supermarket. If it only had one checkout, adding another checkout would be fairly low-cost - the store would give up a little shelf space (which would entail an opportunity cost of some lost sales of items that would have been displayed there), and have to pay another worker to man the second checkout. A third checkout would entail more cost, as would a fourth, and so on.

So it is easy to see why the total cost of providing service increases, but why does the marginal cost (the cost of providing one additional checkout) increase? It's because each additional checkout is not as productive as the previous one. If you only have one or two checkouts in your supermarket, the workers on those checkouts are going to be going flat out all day. But if you add a seventeenth checkout, that checkout might stand idle during quiet periods (or days, or weeks), and that added cost is going to be spread over fewer customers, so the cost per customer for that checkout (the marginal cost of that checkout) is higher than the first ones. So, that's why the marginal cost of providing service is upward sloping. It is low when providing a low level of service, but increases as you provide higher levels of service.

The second cost is the marginal cost of waiting time. This cost increases when you provide lower levels of service, because the lower service levels, the more frustrated your customers will be. Perhaps they decide to leave the store without completing their purchase, or perhaps they complete their purchase but don't return in future. Either way, that is a cost for the store (an opportunity cost of foregone sales), and the cost is greater the lower the service level. An alternative way of thinking about this is that it is the marginal benefit of providing better service.

The optimal level of service is the level of service where the marginal benefit exactly meets the marginal cost (or, in this case, where the marginal cost of providing service is equal to the marginal cost of waiting time). That's the optimal service level, because if you moved in either direction, the cost to the store would be greater.

To see why, consider a point just to the left of the optimum on the diagram above. The store is offering a slightly lower level of service than optimal. It saves on the cost of providing a checkout, but that cost saving is less than the extra waiting cost it incurs (this is easy to see on the diagram - notice that the marginal waiting cost is above the marginal cost of providing service). That makes the store worse off.

Now consider a point just to the right of the optimum on the diagram above. The store is offering a slightly higher level of service than optimal. It encourages more customers to stay and purchase, saving on waiting cost, but that is less than the amount it saves on the cost of providing better service (again, this is easy to see on the diagram - notice that the marginal cost of providing service is above the marginal waiting cost). That also makes the store worse off.

The optimal service level is probably not to ensure that no customer ever queues. It is to keep the queues just long enough that it balances the marginal cost of providing better service against the marginal cost of lost custom.

Read more:

Friday, 15 June 2018

The future of education may be more blended learning, but I'm still not convinced it should be

Long-time readers of this blog will recognise that I am a skeptic when it comes to online education, massive open online courses (MOOCs), as well as blended learning (for example see here or here). Back in 2016, I argued that MOOCs were approaching that 'trough of disillusionment' section of the hype cycle. The key issue for me isn't that online learning doesn't work for some students - it is that online learning works well for self-directed and highly engaged students, while actually making less self-directed students feel isolated, leading to disengagement with learning.

So, I was really interested to read this April article in The Atlantic by Jeffrey Selingo on the future of college education:
As online learning extends its reach, though, it is starting to run into a major obstacle: There are undeniable advantages, as traditional colleges have long known, to learning in a shared physical space. Recognizing this, some online programs are gradually incorporating elements of the old-school, brick-and-mortar model—just as online retailers such as Bonobos and Warby Parker use relatively small physical outlets to spark sales on their websites and increase customer loyalty. Perhaps the future of higher education sits somewhere between the physical and the digital.
A recent move by the online-degree provider 2U exemplifies this hybrid strategy. The company partnered with WeWork, the co-working firm, to let 2U students enrolled in its programs at universities, such as Georgetown and USC, to use space at any WeWork location to take tests or meet with study groups. “Many of our students have young families,” said Chip Paucek, the CEO and co-founder of 2U. “They can’t pick up and move to a campus, yet often need the facilities of one.”...
As the economy continues to ask more and more of workers, it is unlikely that most campuses will be able to afford to expand their physical facilities to keep up with demand. At the same time, online degrees haven’t been able to gain the market share, or in some cases the legitimacy, that their proponents expected. Perhaps a blending of the physical and the digital is the way forward for both.
So, it seems that the limits of purely online learning are being reached, and (some) students are wanting something different. But reading Selingo's article, it still seems to me that it's the self-directed students that are arguing for something more than purely online learning. Again, those are the students who thrive in this model, but they are not necessarily the students that we should be focused on as teachers. And it we are trying to extend the reach of higher education to more non-traditional students, then a move to more blended learning is even more unconvincing to me. I'm still yet to see an online approach that incorporates a meaningful (and effective) way of engaging students below the median of the grade distribution, and keeping them engaged through to course completion.

Read more:

    Tuesday, 12 June 2018

    Immigrant restrictions and wages for locals

    A simple economic model of demand and supply tells us that, if there are two substitute goods and the supply of one of them decreases, then demand for the other substitute will increase. This leads the price to increase for both goods. If the two 'goods' here are the labour of immigrants and the labour of locals, then a decrease in the supply of immigrant workers should lead to an increase in the demand for local workers, and higher wages for both groups. However, that simple analysis ignores that there is often another substitute for labour - mechanisation (or capital). So, it is by no means certain that restricting the number of immigrant workers will raise the wages of local workers, because employers might substitute from immigrant workers to technology, rather than from immigrant workers to local workers.

    Which brings me to this new paper (ungated earlier version here) by Michael Clemens (Center for Global Development), Ethan Lewis (Dartmouth College), and Hannah Postel (Princeton), published in the journal American Economic Review. In the paper, Clemens et al. look at the effect of the 1964 exclusion of Mexican braceros from the U.S. farm labour market. At the time, the exclusion was argued for because it would lift wages for domestic farm labourers. However, looking at data from 1948 to 1971, Clemens et al. find that it had no effect. That's no effect on wages and no effect on employment of domestic farm workers.

    They argue that the reason for the null effects is that farmers shifted to greater use of mechanisation (which they had not adopted in great numbers up to that point). They provide some empirical support for this. Crops where there was an existing technology that was not in wide use (e.g. tomatoes, where expensive harvesting machines were available that could double worker productivity) didn't suffer a drop in production after the bracero exclusion, because farmers simply adopted the available technology. In contrast, crops where there was no new technology available (e.g. asparagus) suffered a large drop in production (because farmers couldn't substitute to new technology, and fewer workers were employed).

    The lesson here is that when prompted to change, producers will usually adopt the cheapest available production technology (as I have noted before). But that isn't necessarily the production technology that policy makers want them to adopt. In this case, instead of a production technology that made use of more local workers, the farmers opted for a production technology that made greater use of mechanisation. So, even if as a policy maker you believed that reducing immigration would improve wages for local workers, it isn't certain that would be the result of polices that reduce immigration (more on the effect of immigrants on local wages in a future post).

    [HT: Eric Crampton at Offsetting Behaviour]

    Sunday, 10 June 2018

    More on the Oregon marijuana market shake-out

    A few weeks ago, I wrote about the ongoing shake-out of the marijuana market in Oregon. Last week, the New Zealand Herald ran another story on this issue:
    When Oregon lawmakers created the state's legal marijuana program, they had one goal in mind above all else: to convince illicit pot growers to leave the black market.
    That meant low barriers for entering the industry that also targeted long-standing medical marijuana growers, whose product is not taxed. As a result, weed production boomed — with a bitter consequence.
    Now, marijuana prices here are in freefall, and the craft cannabis farmers who put Oregon on the map decades before broad legalization say they are in peril of losing their now-legal businesses as the market adjusts...
    The key issue there is that the profit opportunities for new growers attracted a lot of additional supply, leading to decreased profits for all. Usually, we think of barriers to market entry as being a bad thing, and indeed they are from the consumer's perspective - they decrease competition and lead to higher prices. However, from the perspective of the sellers, barriers to entry are a great thing because they provide the sellers with some amount of market power - that is, some power to raise the price above their costs.

    So, how did Oregon get into this situation? The Herald story explains:
    The oversupply can be traced largely to state lawmakers' and regulators' earliest decisions to shape the industry.
    They were acutely aware of Oregon's entrenched history of providing top-drawer pot to the black market nationwide, as well as a concentration of small farmers who had years of cultivation experience in the legal, but largely unregulated, medical pot program.
    Getting those growers into the system was critical if a legitimate industry was to flourish, said Sen. Ginny Burdick, a Portland Democrat who co-chaired a committee created to implement the voter-approved legalization measure.
    Lawmakers decided not to cap licenses; to allow businesses to apply for multiple licenses; and to implement relatively inexpensive licensing fees.
    Limiting the number of licences would create an effective barrier to entry into the market. By not limiting licences, Oregon's legislators set up a situation where marijuana sellers have to compete with many others. Note that, for now, this is only a problem for the sellers, who end up with low profits as a result of the competitive market. However, if the coming shake-out results in a smaller number of large firms being the only ones left, and Oregon goes on to crack down on the issue of new licences (which is a possibility), then we could end up in a situation where not only is there market power, but where it is concentrated in the hands of a few large sellers. Of course, that will be highly profitable for the sellers, but marijuana buyers will be much worse off.

    Read more:

    Friday, 8 June 2018

    Employers offset minimum wage increases with decreases in fringe benefits

    Employment compensation is made up of the wage that employees are paid, plus other fringe benefits that employers provide. Those fringe benefits might include training opportunities, discounted (or free) goods and services, use of a vehicle, travel and accommodation, health insurance, superannuation contributions, and so on.

    If we consider a very simple model of labour demand, employers will employ any worker where the value of the marginal product of labour (essentially, the amount of profit contribution that the worker makes for the employer) is greater than the total compensation paid to that worker. If wages rise, then a bunch of workers will not be making enough profit contribution for the firm any more, and they will be laid off. This is the basis for the downward sloping demand curve for labour.

    However, employers mostly don't want to lay off workers when wages rise. So, rather than laying workers off, employers could seek to reduce the other fringe benefits they provide, keeping total compensation low even though wages have increased.

    Now consider workers on the minimum wage. Realistically, they don't receive all of the fringe benefits I listed in the first paragraph. However, they may receive discounted goods or services, training opportunities, or (in the U.S. at least) health insurance. So, is there evidence to support the assertion that employers reduce fringe benefits for low-wage workers when the minimum wage increases? There is an old literature on the effects on training, but a new NBER Working Paper (ungated version here) by Jeffrey Clemens (UC San Diego), Lisa Kahn (Yale), and Jonathan Meer (Texas A&M) looks at the impact on employer-provided health insurance.

    Clemens et al. use data from the American Community Survey over the period 2011-2016. They can't observe actual wages paid to the survey respondents, but they can look at what happens to those in different occupations. To this end, they separate occupations into those that are Very Low paying, Low paying, Moderate paying, Middle paying, and High paying. Bigger effects are expected for the Very Low paying occupations, where changes in the minimum wage are more likely to affect respondents.

    Unsurprisingly, they find that increases in the minimum wage are associated with higher wages:
    We find that a $1 minimum wage increase generates significant wage increases for workers in low-to-modest paying occupations. At the 10th percentile, increases are on the order of 12% and 9% for Very Low and Low paying occupations, respectively, and even 3% for Modest paying occupations.
    However, those wage increases are offset by decreases in health insurance coverage:
    For those in Very Low and Low paying occupations, we find that a $1 minimum wage increase is associated with a 1 to 2 percentage point (2 to 4%) reduction in the probability of coverage. We also estimate a 1 percentage point (1.5%) loss in coverage for those in Modest paying occupations, suggesting a non-trivial role for spillovers. Losses in employer coverage manifest largely among employed workers, rather than through impacts of the minimum wage on employment. 
    It's not all bad news though, because the lost value of employer contributions doesn't fully offset the increase in wages:
    When we compare wage changes to changes in employer coverage, we find that coverage declines offset a modest 9% of wage gains for Very Low wage occupations and a larger fraction for the Low and Modest groups (16% and 57%, respectively). The offsets we estimate are, unsurprisingly, much larger for the latter groups that experienced relatively small wage gains following minimum wage hikes.
    However, because they can't observe whether employers reduce the extent of insurance coverage (such as by choosing plans for their employees that have higher co-pays), the extent of offset could be worse than these results suggest.

    Finally, in the simple discussion of the minimum wage that we engage in during ECONS101 and ECONS102, we tend to suggest that at least those workers who receive a higher minimum wage and keep their job (that is, they aren't made unemployed by the decrease in quantity of labour demanded) are made better off. That might not be true. Say that employers are able to buy health insurance at a discount to the general public (perhaps because of risk pooling across their workforce, or quantity discounts, or kickbacks from the insurance provider). When the employer reduces spending on health insurance to offset the higher minimum wage and restore the original level of total compensation for a worker, the cost to the worker of the health insurance they have lost could well be much greater than the gain in wage earnings, because it would cost them more than it costs the employer to restore the same level of health insurance coverage.

    Overall, these results need to be read alongside the literature on the employment effects of the minimum wage. They also have interesting implications for the living wage movement, although I haven't seen any living wage advocates engaging with total compensation as a concept at all, when they definitely should.

    [HT: Marginal Revolution, followed by Offsetting Behaviour]

    Read more:

    Wednesday, 6 June 2018

    Price discrimination and the Great Walks

    In today's New Zealand Herald, Brian Rudman argued against charging different prices to tourists and locals for access to New Zealand's "Great Walks":
    Last weekend, Green MP and Conservation Minister Eugenie Sage, followed through with the previous National Government's pledge to up fees to cover costs. But she managed to retain the existing subsidy for the New Zealanders who make up about 40 per cent of users. Kiwi trampers will now bludge off their overseas fellow travellers, whose hut fees will double to $140 per night on the Milford Track, $130 per night on the Kepler and Routeburn Tracks and $75 per night on the Abel Tasman Coastal Track. Kiwi trampers fees will remain unchanged at half this rate. In addition, international children under 18 will now pay the full fee, while New Zealand kids will pay nothing.
    Eugenie Sage says the free ride for Kiwi kids is "to encourage our tamariki to engage with their natural heritage." Fair enough, but why are they and their parents, doing their "engaging," at the expense of overseas visitors and their children? They certainly wouldn't get half-rates at a beach motel or bach over the same period...
    It now seems "fleece the tourist" has become the new game of the day.
    Indeed, and as I have argued before, so it should. Price discrimination in tourism (where locals pay different prices to tourists) is the norm internationally. New Zealand is out of line with global practice with our insistence that locals have to pay the same jacked-up prices that cash-cow tourists pay.

    The first problem here is that the "Great Walks" cost more to service than they attract in fees (another point I've made before, when the Great Walks were free). So, realistically the government has to increase fees to cover those costs (or else be subsidising trampers at the expense of hospitals or schools or something else - no subsidy comes 'free' of opportunity costs). There is no rule that says there has to be one price for all, and in fact it makes more sense to charge higher prices to tourists.

    Consider the difference in price elasticity of demand. Tourists have relatively inelastic demand for the Great Walks. They've come a long way to New Zealand, incurring costs of flights and so on. The cost of going on the Great Walks is small in the context of the total cost of their holiday in New Zealand. So, an increase in the price of the Great Walks is unlikely to deter many of them from paying (so, their demand is relatively price inelastic - relatively less responsive to a change in price).

    In contrast, for locals the price that DoC would charge for access to the Great Walks makes up the majority of the total cost of going on the Great Walks. So, a change in the price is much more significant in context for locals (so, their demand is relatively price elastic - relatively more responsive to a change in price).

    When you have two sub-markets, one with relatively more elastic demand and one with relatively less elastic demand, and you can separate people by sub-market, then price discrimination is an easy way to increase profits. Of course, the government isn't trying to profit from the Great Walks. It is trying to raise money to cover the costs while keeping access open to the maximum number of people. And that's exactly what price discrimination would allow. Charging a higher price to tourists raises the bulk of the money from tourists without deterring too many of them from going on the Great Walks, while simultaneously keeping the price low enough that locals would also want to go on the Great Walks.

    Of course, you could argue, as Rudman does, that tourists are losing out on the deal. Which of course is true - their consumer surplus (the difference between the maximum they would be willing to pay and what they actually pay for access to the Great Walks) does decrease. However, I can't see why it is government's role to protect the consumer surplus of people who aren't New Zealand taxpayers (except to the extent that we don't want to overly deter tourists from coming to the country at all).

    Raise the price of access to the Great Walks, and raise it even more for tourists. They can afford to pay, and would be happy to do so, having come all the way here to see the sights.

    Read more:

    Tuesday, 5 June 2018

    Mark Kleiman on the economics of fentanyl

    On the Reality Based Community blog, Mark Kleiman has an excellent post on the fentanyl epidemic in the U.S. It is difficult to excerpt, as it is pretty thoroughly written. There are lots of excellent bits on the economics of fentanyl, so is well worth your time reading. It especially explains why the epidemic of fentanyl use is a recent problem, even though fentanyl has been around for over 60 years. The short version of the story can be summarised as:

    • Fentanyl in the 1980s was good from the dealer's perspective, as it was high value-to-bulk, so it could be transported cheaply, but from the buyer's perspective it was 'Russian roulette', because diluting accurately into a form that wouldn't potentially kill the user was very difficult;
    • Besides which, heroin was really cheap so users preferred it as a cheaper substitute, which kept demand for illicit fentanyl low;
    • Then in the 1990s, oxycodone (and hydrocodone) became increasingly available, and didn't require buyers to interact with dodgy dealers since they could buy the pills from "their favorite script-happy M.D. or “pill mill” pharmacy", so demand for these drugs increased;
    • The continuing fall in the price of heroin, alongside cracking down on diverted oxycodone and hydrocodone, encouraged buyers to switch to heroin as a cheaper substitute;
    • Chinese sellers entered the market for fentanyl, using the Internet and delivering via the standard mail service, and this increase in supply greatly lowered the price of fentanyl to direct buyers, and the wholesale cost of fentanyl for dealers;
    • This led to fentanyl becoming a cheaper substitute for dealers to sell; and
    • Some new innovation allowed dealers to dilute fentanyl in a way that was much less likely to kill users.
    All of which led to:
    And for a retail heroin dealer, the financial savings from buying fentanyl (or an analogue) rather than heroin, and the convenience of having the material delivered directly by parcel post rather than having to worry about maintaining an illegal “connection,” constituted an enormous temptation.
    This lends itself well to using a supply-and-demand model to show what is going on in the market for fentanyl, as per the diagram below. There are effects on both the demand side and the supply side. On the demand side, there has been an increase in demand (from D0 to D1). This isn't because of the change in price of fentanyl (since that would simply mean a movement along the demand curve). It is because there is less risk (of death) to buyers because the sellers are better able to dilute fentanyl (though I will come back to this point later in the post). On the supply side, there has been a large increase in supply (from S0 to S1), because of the reducing costs of production and distribution of fentanyl (cheaper sources of supply from China, along with more sellers). The combination of the increase in demand and greater increase in supply have led to a decrease in the price of fentanyl (from P0 to P1), and an increase in the quantity of fentanyl consumed (from Q0 to Q1).

    But if fentanyl is now safer because, as Kleiman wrote:
    Somewhere in here someone figured out a technique for diluting the stuff with enough accuracy to reduce the consumer’s risk of a fatal overdose: far from perfectly, but enough to create a thriving market. (I don’t know what that technique is, though I can think of at least one way to do the trick.)
    Then why have the number of overdoses greatly increased? It's probably because the number of uses (and number of doses) has greatly increased (as per the supply-and-demand diagram above). A larger number of doses consumed multiplied by a lower per-dose risk of overdose could easily lead to more total deaths than a smaller number of doses consumed multiplied by a high per-dose risk. Which has led to this:

    [HT: Marginal Revolution]

    Monday, 4 June 2018

    Book Review: WTF?! An Economic Tour of the Weird

    I just finished reading Peter Leeson's new book, WTF?! An Economic Tour of the Weird. I thoroughly enjoyed his earlier book "The Invisible Hook: The Hidden Economics of Pirates", so I had high expectations for this one. The Invisible Hook explained a number of the interesting practices of pirates (e.g. why did they fly the skull and crossbones flag, and why did they adhere to the 'pirate code') using the tools of economics. WTF does a similar job for a broader collection of interesting practices such as medieval ordeals, wife auctions in England, the use of oracles to settle disputes, vermin trials, and trial by battle, among several others. Once Leeson describes some of these practices to you, they genuinely will make you think "WTF", so the book it aptly titled. The reasoning behind each practice is clearly explained and shown to be consistent with rational economic behaviour, or at least to be consistent with prevailing incentives.

    Leeson's own research is embedded throughout the book, as you might expect. Many of the topics covered have relevance to modern times as well, but Leeson surprisingly avoids drawing the reader's attention to this until almost the end of the book:
    Was Norman England's judicial system, which decided property disputes by having litigants hire legal representatives to fight one another physically, less sensible than contemporary England's judicial system, which decides property disputes by having litigants hire legal representatives to fight one another verbally?
    Is contemporary Liberia's criminal justice system, which sometime uses evidence based on a  defendant's reaction to imbibing a magical potion, less sensible than contemporary California's criminal justice system, which sometimes uses evidence based on a defendant's reaction to being hooked up to a magical machine that makes squiggly lines when her pulse races?
    How about Renaissance-era ecclesiastics' tithe compliance program, which used rats and crickets to persuade citizens to pay their tithes? Any less sensible than World War II-era US Treasury Department officials' tax compliance program, which used Donald Duck to persuade citizens to pay their taxes?
    The book makes use of an interesting narrative device - it is presented as a tour through a museum of the weird. Some readers might find that approach a little distracting. I found it quirky and an interesting way to present the material. It wasn't necessary though - based on Leeson's earlier book I'm sure he could have written an equally interesting book without resorting to creating fictional tour characters to pose questions.

    Overall, the book possibly isn't going to teach you a lot of economic principles, but it will show you how economics can be used to explain some seemingly weird practices (some contemporary, many historical). If you are into the quirky or weird, or just want to see economics pop up in unusual situations, then like The Invisible Hook this book is a must-read.

    Sunday, 3 June 2018

    What's in a (first) name?

    Following on from yesterday's post on middle initials, it is interesting to wonder how important names are more generally. In a 2010 paper published in the journal Economic Inquiry (ungated and much earlier version here), Saku Aura (University of Missouri) and Gregory Hess (Claremont McKenna College) looked at the effect of first names on a number of different outcomes. They used data on over 5500 people from the 1994 and 2002 waves of the U.S. General Social Survey (and as a reminder, apart from the names data, most of the data for the GSS is available for free online).

    Unlike the other studies I've been referring to this week (see here and here and here), the Aura and Hess paper wasn't about academic outcomes, but instead about broader life outcomes. It is related to earlier work on name-based discrimination (ungated here) by Marianne Bertrand and Sendhil Mullainathan. Bertrand and Mullainathan used a field experiment (they sent out CVs with African American and non-African-American names and compared the number that were invited to interviews), whereas Aura and Hess's paper is based on survey data. Aura and Hess also look at a broader range of features of names, rather than just how much more likely they are to be names of African Americans.

    Aura and Hess find that:
    ...more popular names are generally associated with better lifetime outcomes: that is, more education, occupational prestige and income, and a reduced likelihood of having a child before 25. Also, broadly speaking, names starting with vowels and ending in either an ‘‘ah’’ or ‘‘oh’’ sound are related to poorer lifetime outcomes.
    Interestingly though, when they look at male and female names separately, the effects of names are not apparent for males, only for females (and even then, only for some of the outcome variables). Some of the problem there is the reduced statistical power from splitting the sample into males and females, but I think there's a robustness issue there as well.

    Aura and Hess conclude that their research doesn't support the discrimination argument (although, it doesn't not support it either), because the features of names are also correlated with other person-specific variables such as race and age. However, I'd argue that's exactly what we would expect if discrimination did explain the results.

    Anyway, as with yesterday's post on middle initials, if we want to look at the effects of names on academic outcomes such as the number of citations or other measures of research quality, we could easily use a similar method to those in the previous papers I blogged about this week (see here and here). That might be an interesting exercise, alongside looking at the effect of middle initials.

    So, to summarise: what have we learned this week about getting research published in top journals and cited a lot? It's possibly better for research papers to have a short title, and to use data on the U.S. Maybe middle initials matter (still an open question in terms of research quality), and maybe first name could matter (it seems there is no specific research on this in an academic context, that I can find). And, thinking back to a much earlier post of mine, having a surname early in the alphabet is a good idea if you have academic career aspirations.

    Read more:

    Saturday, 2 June 2018

    Middle initials and perceptions of intellectual ability

    Continuing the theme from this week, I just read this article by Wijnand van Tilburg (university of Southampton) and Eric Igou (University of Limerick), published in the European Journal of Social Psychology (ungated earlier version here). The authors use survey data drawn from seven studies undertaken with different samples (most were University of Limerick students, but other samples were online samples from the U.S. and continental Europe) to investigate whether the use of middle initials affects people's perceptions of intellectual performance. They find that:
    Authors with middle initials compared with authors with no (or less) middle initials were perceived to be better writers (Studies 1 and 5). In addition, people with names that included middle initials were expected to perform better in an intellectual — but not athletic — competition (Studies 3 and 6) and were anticipated to be more knowledgeable and to have a higher level of education (Study 7). In addition, a similar pattern of results was obtained on perceived status (Studies 2 and 4), which was identified to mediate the middle initials effects (Studies 5-7)...
    Additional support for the robustness of the middle initials effect is evident from the use of both male and female name variations and by observing the phenomenon across samples of Western Europeans as well as North Americans.
    In other words, using your middle initial(s) increases people's perceptions of your intellectual ability. However, I wouldn't read too much into it. The effect seemed to not be apparent when there was some other signal of intellectual ability - for instance, the effect of middle initials for people who were identified to research participants as 'professors' was in the opposite direction (but not statistically significant). So, using a middle initial only may actually not be valuable for authors of research papers.

    However, it is worth noting that this was purely a stated preference study. It would be interesting to follow this up by looking at actual research papers, comparing those with authors using their middle initials with those without, in terms of subsequent citations or other measures of research quality. Of special interest would be cases where the same author uses their middle initials in some papers and not in others, and cases where middle initials are required (or prohibited) by the journal's style policy, especially if that policy has changed over time. Definitely doable, using a similar method to the papers I wrote about earlier in the week (see here and here).

    In my own case, I do usually use my middle initial. However, this is for purely practical reasons. When you have a common name, it is easy to confuse readers about who you are. For instance, there was another Michael Cameron at the Ministry of Health at the time I started writing on public health issues, and I'm sure there are many others (although interestingly, I'm the only one with a Google Scholar profile, there are at least 13 Michael Camerons with profiles on Research Gate). Hence, I am "Michael P. Cameron" in almost all of my authorship credits.

    [HT: Marginal Revolution]

    Wednesday, 30 May 2018

    It seems it's better to publish economics papers with U.S. data

    This post follows on from yesterday's post about the length of article titles, which showed a strong negative correlation between title length and research quality (so papers with shorter titles were more likely to be published in better journals, and attracted more citations). There are, of course, a lot of other factors that affect where journal articles get published. One gripe for many researchers outside the U.S. or the U.K. is how hard it is to get published in top journals using data from outside the U.S. or the U.K. Until relatively recently, that gripe was based on purely anecdotal evidence. However, a 2013 article by Jishnu Das, Quy-Toan Do, Karen Shaines (all from the World Bank), and Sowmya Srikant (FI Consulting), published in the Journal of Development Economics (ungated version here) provides some empirical evidence on this.

    Das et al. use data on over 76,000 papers published in 202 economics journals over the period from 1985 to 2005, and the disparity in data sources for published economics papers is clear:
    Over the 20 year span of the data, there were 4 empirical economics papers on Burundi, 9 on Cambodia and 27 on Mali. This compares to the 37,000 or so empirical economics papers published on the U.S. over the same time-period. This variation is also reflected among the highly selective top-tier general interest journals (henceforth top-tier journals) of the economics profession (American Economic Review, Econometrica, The Journal of Political Economy, The Quarterly Journal of Economics and The Review of Economic Studies). American Economic Review has published one paper on India (on average) every 2 years and one paper on Thailand every 20 years. The first-tier journals together published 39 papers on India, 65 papers on China, and 34 papers on all of Sub-Saharan Africa. This compares to 2383 papers on the U.S. over the same time period.
    They then go on to show about 75 percent of the cross-country variation in the geographical focus of research is explained by GDP per capita and by population. Countries that have higher GDP are more likely to be the focus of research. This is a disappointing result if you are interested in developing countries (as the authors of the paper clearly are). Surprisingly though:
    ...the U.S. is not an outlier in the volume of research that is produced on it... the volume of research for the U.S. lies well within the predicted confidence interval and excluding the U.S. leads to the same coefficient estimates as its inclusion. In other words, a lot more is produced on the U.S. because it is rich and it is big; the natural comparator for the U.S. would be all of Europe and here, the volume of research is very similar.
    However, when it comes to the elite journals, the U.S. is a clear outlier:
    The difference between the U.S. and the rest of the world is substantial — 6.5% of all papers published on the U.S. are in the top tier journals relative to 1.8% of papers from other countries.
    For comparative purposes, over their 20-year period there were 2,383 publications on the U.S. published in the top five economics journals, and one on New Zealand (yes, you read that right, just one - I don't know which article it was, sorry).

    So, is this discrimination against non-U.S. research? Perhaps. Or, it could be as simple as the U.S. having a greater density of top-quality researchers, who are more likely to publish in top-quality journals, and who, because they are located in the U.S., have readier access to U.S. data. Or, perhaps the quality of U.S. data is higher. Das et al. point out that data quality has a superstar effect to it (similar to the superstar effects in the labour market that I have written about before):
    Researchers converge on the “best” dataset even if it is 1% better than other data available, and the initial work creates a focal point for further research with the same data.
    Again, like the paper I discussed yesterday, there isn't necessarily a causal interpretation to these results (doing research on the U.S. doesn't necessarily cause papers to be accepted into top journals). But it is disappointing, particularly given the quality of linked administrative data that we have in New Zealand through the Integrated Data Infrastructure, which (I think) should be attractive for publication in top journals.