Why transport chaos can be a good thing

People are creatures of habit. In behavioural economics, we refer to status quo bias. As an example, think about the route you take to get to work or school each day. You probably haven't rigorously evaluated all of the possible alternative routes - you probably tried a few different ones out, found one that appeared to work well, and have stuck to it ever since.

There's a good reason why you don't continue to improve your route selection. Herbert Simon (1978 Nobel Prize winner) suggested that humans do not optimise, but instead satisfice. That is, we look around at some of the options (not necessarily all of the options) and find one that is 'good enough'. A more rigorous interpretation of this is provided by search theory. Search theory (which among other things Peter Diamond, Dale Mortensen, and Christopher Pissarides won the 2010 Nobel Prize for) says that it is costly for us to search for things (e.g. buyers searching for sellers, commuters searching for the optimal route, etc.). We will continue to search for a better option only up to the point where the marginal benefit of continuing the search are equal to the marginal costs of searching. The marginal benefit is the benefit gained from finder a slightly better option. In the case of the commuter, it is the time saved from finding a faster route to work or school. Under search theory, the 'optimal' route is the one where any additional searching would make us worse off (marginal cost > marginal benefit).

If we were optimisers, we would (eventually through trial and error) find the fastest route to work or school, and there would be no gains from taking an alternative route. However, if we are satisficers or if we conform to search theory, then if our current preferred route is not available to us, we might experiment with other routes and find a new one that is even faster.

Which brings me to the point of this post. A recent paper by Shaun Larcom (University of Cambridge), Ferdinand Rauch (University of Oxford), and Tim Willems (University of Oxford) looks at exactly this question. The paper is summarised in a non-technical way here. Essentially they looked at public transport card data before and after the London tube strike in February 2014, and compared commuters who were affected by the strike with those who weren't.

They find:

...that those who were forced to explore alternative routes during the strike (‘the treated’) were significantly less likely to return to their pre-strike modal commute in the post-strike period, relative to the non-treated control group...

In terms of magnitude, the fraction of post-strike switchers is about five percentage points higher among the treated.

In other words, it's evidence that London commuters are not optimisers. So, are they satisficers, or is this search theory at work? The authors investigate:

Using conservative numbers for the estimated time saving and its monetary equivalent, we calculate that if commuters were adhering to the optimal search strategy, the cost of trying the most attractive untried alternative would have to be greater than £380. Given this implausibly large number, it seems that commuters in our dataset were experimenting less than what is described by the standard rational model. Instead, agents seem to satisfice in a way that is not straightforward to rationalise.

So, it appears that not only are London commuters not optimisers, they aren't optimising under search theory either.

In related news, Thomas Lumley over at Stats Chat pointed me to this Transport Blog post about the disruption to the Hutt Valley rail line in June 2013. The disruption allowed the Ministry of Transport to estimate the benefit of the rail line to commuters, at $330 million per year (in saved travel costs). Lumley quite rightly points out that if the rail line didn't exist, many people would live somewhere else instead (it might be better to live in downtown Wellington or in Porirua or Petone rather than commute from the Hutt Valley each day).

Note that in the Wellington case, that the commuters probably didn't continue driving into Wellington when the rail line was reopened. There was little to be gained from the alternative route (hence the large cost savings of the rail line).

[HT: Marginal Revolution for the former study]

Read more:

• My post on some previous research by Ferdinand Rauch on the persistence of the location of cities

Nobel Prize for Angus Deaton

The 2015 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel (aka Nobel Prize in Economics) has been awarded to Angus Deaton. I won't write too much here, but if you want to know a good deal about Deaton and his work see the links in this post by Tyler Cowen, this one by Alex Tabarrok, and additional links here.

Chris Blattman has an excellent piece, which I will largely echo. My own intersection with Deaton's work was during my PhD, where I undertook a fairly large (~680 households) household survey in Northeast Thailand. The Analysis of Household Surveys was one of many guidebooks that helped greatly with setting up the survey (along with several World Bank publications on the survey methods for the Living Standards Measurement Surveys which Deaton was also involved in), and the book was invaluable in the analysis phase (as you would expect from the title). In reading Deaton's work, I have come to realise just how much of his thinking had already been indirectly a part of my development economics training, even if my lecturers were not always explicit about their sources.

As others have mentioned, this is a very well deserved award for a wide body of work that has greatly enhanced our understanding of poverty, inequality, consumption, and development economics more generally. His contributions span both the theoretical, the empirical, and the analytical. Deaton's name had no doubt been on the shortlist for a number of years.

Two books on economics and romance

Economists aren't exactly known for their romantic tendencies. Which is why it is unusual to see several books recently on the topic of economics and love, romance, etc. I've read two of them in the last month or so.

The first book was "Everything I Ever Needed to Know about Economics I Learned from Online Dating" by Paul Oyer, a professor at Stanford. This book is a delightful treatment of how economics can apply to a wide range of activities, not just online dating (though obviously, that is the central theme of the book). The topics that Oyer covers are mostly unsurprising, including: search theory; cheap talk; network externalities; signalling; statistical discrimination (where Oyer takes a stand much closer to mine than do Gneezy and List in their book); thick vs. thin markets; adverse selection; assortative matching; and the returns to skills.

There are some highlights to this book, notably on the second page when Oyer remarks:

Match.com, eHarmony, and OkCupid, it turns out, are no different from eBay or Monster.com. On all these sites, people come together trying to find matches. Sure there are a lot of differences between someone selling a used bowling ball on eBay and someone signing up for Match.com, but the basic idea is the same. The bowler needs to think about how to present his bowling ball to get what he wants (money, presumably) just as the Match.com participant needs to present himself to get what he wants (a partner in most cases, casual sex in others). It's really not that different.

As you read through the book, Oyer may just convince you of this. Although he couldn't convince me that online dating isn't adverse selection personified (literally!), and my ECON110 students will continue to laugh their way through the tutorial example on online dating. But, [potential spoiler alert!], at least there is a happy ending to this book.

The second book is "The Romantic Economist - A Story of Love and Market Forces" by William Nicolson. While Oyer (understandably) takes a fairly research-based approach in his book, Nicolson's book is much more a narrative. It's the story of Will's (unsuccessful) attempts to apply rational economic thinking to his love life. This book was a lot of fun, even if you won't necessarily learn as much about the applications of economics in it.

The book does cover a similar set of topics to Oyer's: supply and demand; the efficient market hypothesis; market power; game theory; signalling; bargaining power; investment; credible threats; sunk costs and opportunity costs; and backwards induction. The book also has some highlights, like a model of sweethearts and dickheads (where sweethearts find it difficult to signal that they are sweethearts rather than dickheads), the game theory of toilet seats (I'll be using this example in ECON100 next year for sure!), and the opportunity costs of being in a relationship. Unfortunately, the book doesn't have nearly as happy an ending.

One small criticism of The Romantic Economist is that it is written from the perspective of a man, and does come across in a lot of places as pretty sexist (although if you can suspend your indignation for the duration of the read, it is pretty funny too). Nicolson does provide a disclaimer early in the book, and rightly I think it invites an opportunity for a similar book from a woman's perspective!

Both books are recommended - Oyer's if you want to learn some real-world applications of economics (backed by robust research in most cases), and Nicolson's if you're looking for some light reading related to economics.

Are teaching and research substitutes or complements?

A couple of weeks ago I wrote a post on adjuncts being better teachers than tenured or tenure-track professors. My argument there was that the results were not particularly surprising:

Teaching and research both require investment in terms of time and effort. While some may argue that teaching and research are complementary, I'm not convinced. I think they're substitutes for most (but not all) faculty (I'll cover more on that in a post in the next week). Faculty who do teaching but little or no research can be expected to put more effort into teaching than faculty who do a lot of (particularly high quality, time intensive) research. So, contingent teaching faculty should do a better job of teaching on average. These results seem to support that.

However, the question of whether teaching and research are substitutes or complements remains a fairly open question. The theoretical framework that underlies a lot of the work in this area dates from this excellent William Becker paper from 1975 (gated).

Which brings me to this paper in Applied Economics (ungated earlier version here) by Aurora Garcia-Gallego (Universitat Jaume I), Nikolaos Georgantizis (Universidad de Granada and Universitat Jaume I), Joan Martin-Montaner (Universitat Jaume I), and Teodosio Perez-Amaral (Universidad Complutense). In the paper, the authors use yearly panel data from Universitate Jaume I in Spain over the period 2002-2006 to investigate the question of whether better researchers are also better teachers or not. They also look at administrative work as well, but I want to focus instead on the teaching/research data and results.

I've been in contact with the authors, as I was sorely tempted to write a comment on their paper for publication in the journal [*]. The authors were kind enough to provide me with some additional analysis that doesn't appear in their paper, that I will share with you below.

Anyway, I'll first explain what my issue is with the paper. Here's what the authors found:

Summarizing our results, we find that professors with a typical research output are somewhat better teachers than professors with less research. Moreover, nonresearchers are 5 times more likely than researchers to be poor teachers. In general, the quality of university-level teaching is positively related with published research across most levels of research output.

Those seem like fairly strong results, but of course that depends on how things are measured. The authors' measure of research is based on an internal measure of research quality, while teaching quality "is obtained from students' responses to an overall satisfaction survey question using a 0-9 Likert scale", and the teaching quality measure is calculated for each professor as the average evaluation across all of their courses. I know what you're thinking but no, my issue isn't with conflating teaching quality with popularity. However, I do think the teaching measure is a problem. Here's a density plot of the measure of teaching quality (provided to me by the authors):

So, what you have there is a reasonably normal distribution, centred on five. Which is what you would expect from Likert scale data, especially if you are averaging the scores across many courses. But wait! What's that huge bar at zero? Are there really a large number of teachers with zero teaching quality? That seems very implausible. I highly suspect that missing data has been treated as zeroes - not necessarily by the authors themselves, but probably in the administrative database they are using.

When the authors separate their data into researchers and non-researchers, this is what the histograms look like:

So, that large spike at zero is a feature of the non-researchers sub-sample, but not so much in the researchers sub-sample. Which supports my argument that this is a missing data problem - it's more likely that you would have missing data for fixed-term or part-time (adjunct) staff, who are also more likely to be non-researchers. Unfortunately, that probably drives the results that the authors get.

In fact, the authors were kind enough to re-run their analysis for me, excluding the 85 observations where teaching quality was less than one. Here's their results (the first regression shows the original results, and the second regression excludes the observations with teaching quality less than one):

For those of you who lack a keen eye for econometrics, I'll summarise. The key result from the first regression is that the variable research1 has a large and statistically significant and positive relationship with the dependent variable (teaching quality). This shows that better researchers have higher teaching quality. However, when you remove the hokey data (in the second regression), the coefficient on research1 halves in size and becomes statistically insignificant. Which doesn't necessarily mean there is no effect - the regression might simply be underpowered to identify what could be a very small effect (although with nearly 2,000 observations and lots of degrees of freedom you'd expect it to have reasonable statistical power).

All of which means that this paper doesn't provide strong evidence for research and teaching being complements. Neither does it provide evidence for research and teaching being substitutes (for which there would have had to have been with a significant negative relationship between research and teaching in the regression model).

I've invited the authors to respond - I'll keep you posted.

*****

[*] The editors of the journal didn't respond to my query as to whether they would consider publishing a comment, so instead I summarised much of what I would have written in this post.