Why all students need to understand adverse selection and signalling

Of the economic concepts we cover in ECON100 and ECON110, adverse selection is one of the most deceptively difficult problems to explain. It is easy to understand that some people simply know some things that others don't (economists call that private information, and when there is private information we also say that there is information asymmetry). However, in order for there to be an adverse selection problem, the private information needs to lead to market failure in some way, and explaining the market failure is more difficult than explaining the private information. Not all private information leads to market failure, and the market failure is the reason why we need to have ways of dealing with the adverse selection problem. Since the problem stems from private information, solving an adverse selection problem involves revealing the private information to the uninformed party. When the informed party (the one that knows the private information) tries to credibly reveal that information, economists call that signalling.

Students are engaging in a sophisticated array of signals, on multiple levels. It's not possible to avoid signalling in this case, since trying not to provide a signal is itself a signal. The problem that this signalling is trying to avoid stems from private information about the quality of the student - students know whether they are high quality (intelligent, hard working, etc.), but employers don't. Employers want to hire high-quality applicants, but they can't easily tell them apart from the low-quality applicants. This presents a problem for the high-quality applicants too, since they want to distinguish themselves from the low-quality applicants, to ensure that they get the job. In theory, this could lead the market to fail, but in reality the market has developed ways for this private information to be revealed.

One way this problem has been overcome is through job applicants credibly revealing their quality to prospective employers - that is, by job applicants providing a signal of their quality. In order for a signal to be effective, it must be costly (otherwise everyone, even those who are lower quality applicants, would provide the signal), and it must be costly in a way that makes it unattractive for the lower quality applicants to do so (such as being more costly for them to engage in).

Qualifications (degrees, diplomas, etc.) provide an effective signal (they are costly, and more costly for lower quality applicants who may have to attempt papers multiple times in order to pass, or work much harder in order to pass). So by engaging in university-level study, students are providing a signal of their quality to future employers. The qualification signals to the employer that the student is high quality, since a low-quality applicant wouldn't have put in the hard work required to get the qualification. Qualifications confer what we call a sheepskin effect - they have value to the graduate over and above the explicit learning and the skills that the student has developed during their study.

However, there are actually multiple levels of signalling associated with university study. Employers are faced with many applicants that have similar qualifications, and it is difficult to distinguish who, among those with the qualification, is the better applicant. So, the choice of major provides an additional signal to employers. Some majors are clearly more difficult than others - students who can complete a degree while majoring in more difficult majors are signalling to employers that they higher-quality employees than students who complete a degree with easier majors. I leave it up to you to determine which majors might be easier, and which might be more difficult.

Within majors there is a further signal, which is the student's choice of papers. Taking economics as an example, econometrics is likely to be the most difficult paper that students will take. So, students who complete an economics major without completing an econometrics paper are signalling to employers that (among economics graduates) they are the lower-quality economics graduates. [*] I'm sure there are certain papers within other majors that are perceived as difficult and provide a similar signal for students taking those majors.

Within papers there is a yet another signal, which is the grade the student receives. It is harder to get an A than to get a C, so the grade a student receives in any paper also provides a signal of student quality to employers. The saying goes that "C's get degrees", which may be true, but students with C's don't get their first choice of jobs (or at least, they have a lesser chance of getting the good jobs).

But there is still one more signal that students engage in, which isn't a signal to employers but a signal to their lecturers. Just like employers, lecturers don't know who the high quality students are (remember this is private information). So, how students engage in class, and how they perform in assessments, is a way for the students to signal their quality to lecturers. Students who don't complete some assessment items, or who don't attend lectures or tutorials, or who don't complete online tests, and so on, are providing a signal to their lecturers and it's not a signal of their high quality. Even avoiding a small piece of assessment, or an optional task in class, is a signal to the lecturer. And that signal may make the difference between an A and B grade, or between a pass and fail. Which in turn becomes a signal to employers, as noted above.

So, students are generating many signals (by completing a qualification, by their choice of major and individual papers to include in their qualification, by their grades in those papers, and by how they engage and perform within each paper). All of which means that every student needs to understand adverse selection and signalling. Otherwise, they might just end up providing the wrong signals.

*****

[*] Which is why I recommend to economics majors that they include econometrics in their programme of study, even though it is not compulsory.

This couldn't backfire, could it?... Possum bounty edition

Geoff Thomas wrote in the New Zealand Herald earlier this week:

We are going to get rid of all rats by 2050. Really? After killing all the rats in the mountains and forests and farms, how do they propose getting rid of the rats that live under most of the houses in New Zealand? Pet cats kill rats. They do a great job. But as long as you have pet cats, you will also have wild cats.

There is a partial solution which would tick a lot of boxes. Make the problem worth money.

When wild deer were being caught to stock the burgeoning deer farming industry in the 1960' and 70's you couldn't find a deer. A hind was bringing up to $2000 straight out of the bush and Kiwis came up with all sorts of ideas for live capture. They used helicopters, net guns, foot traps, fenced traps and all sorts of innovations, some of which didn't work very well. But the financial incentive ensured that deer were hard to find.

It has been reported that it costs something like $60 to kill a possum using aerial-spread 1080 poison. Whether it is $60 or less, the principle remains the same. If that was paid to hunters for every possum tail they produced, it would create employment in job-poor rural areas, encouraging youngsters to set traps and go out at night with a spotlight and a .22 rifle. Many do that now anyway.

Which should remind us of a famous story about cobras in Delhi that I wrote about earlier here:

The government was concerned about the number of snakes running wild (er... slithering wild) in the streets of Delhi. So, they struck on a plan to rid the city of snakes. By paying a bounty for every cobra killed, the ordinary people would kill the cobras and the rampant snakes would be less of a problem. And so it proved. Except, some enterprising locals realised that it was pretty dangerous to catch and kill wild cobras, and a lot safer and more profitable to simply breed their own cobras and kill their more docile ones to claim the bounty. Naturally, the government eventually became aware of this practice, and stopped paying the bounty. The local cobra breeders, now without a reason to keep their cobras, released them. Which made the problem of wild cobras even worse.

Replace cobras in the above story with possums, and Delhi with New Zealand, and you have Geoff Thomas's solution. Any entrepreneurial person would quickly realise that it is cheaper and easier to farm possums than to hunt them, and therefore much more profitable. This time really won't be different.

Read more:

• This couldn't backfire, could it?... Ragweed edition

Is inequality increasing?

My post a couple of days ago pointed out that people are more concerned about unfairness, than about inequality. However, in case any of you are concerned about inequality, it pays to know a little more about it.

The Australian Economic Review has an excellent section in each issue titled "For the student". In a 2015 issue, Richard Pomfret (University of Adelaide) wrote an excellent article for that section with the same title as this post. He didn't really answer the question with data, but nevertheless it is an excellent review that outlines two main theories about the sources of inequality: (1) Thomas Piketty's assertion that inequality arises because the returns on capital are exceeding the growth rate of the economy; and (2) as a result of technological change and globalisation. I encourage you to read it (if you have access - I can't see an ungated version anywhere). I found it the most accessible summary of Piketty's work I have read (noting that I haven't read Piketty's Capital in the Twenty-First Century myself).

Pomfret might not have answered his question for Australia, but in a recent paper published in the journal New Zealand Economic Papers (also unfortunately no ungated version I can see), Christopher Ball (Treasury) and John Creedy (Treasury, and Victoria University) do so for New Zealand. They use the Gini coefficient as their measure of inequality, and measure inequality based on market incomes (before taxes and transfers), disposable incomes (after taxes and transfers), and consumption expenditure. They find:

The Gini measure of market incomes saw a steady rise through the second half of the 1980s to the early 1990s, from about 0.4 to around 0.5. Subsequently, there has been a steady though less marked decline, with the exception of ‘spikes’ around 2001 and 2011. For disposable incomes, the systematic increase in the Gini measure does not appear to have started until the late 1980s, rising from about 0.27 to about 0.33 in the mid- 1990s.

Nothing too surprising there for anyone who is also a reader of Eric Crampton's excellent blog (see here and here and here) - inequality increased during the reform period in the late 1980s and early 1990s, and then has remained fairly steady or declined since then. Here's their Figure 1, which shows the results over time in more detail:

The changes in taxes and benefits have interesting incentive effects, as Ball and Creedy note:

It appears that the 1980s reforms involving cuts in the top income tax rate along with benefit cuts and the ending of centralised wage setting are associated with increasing inequality. The spikes in the market and disposable income profiles from 2000 may also be associated with changes in top income tax rates. In the first case of an increase from 33 to 39 per announced in 2000 but effective in 2001, the anticipation of the rate increase could have led to a certain amount of income shifting into the year before the increase. Much of the shifting is likely to have been by those in higher income groups, and hence this contributes to the sudden increase in inequality, followed by a reduction. In the case of the 2010 reduction in the top rate, the opposite incentive effect operated.

The changes in inequality over time in New Zealand are interesting, and you can tell a plausible story about the relationship between those and changes in taxes and benefits based on the results of this paper (and especially the figure above). However, Ball and Creedy don't do so, instead concluding that:

...interesting questions about the precise causes of those changes remain a challenge for future research.

I'd say they have gotten us about 90% of the way there already.

People want fairness, not equality

I was interested to read this new paper by Christina Starmans, Mark Sheskin, and Paul Bloom (all from Yale), which reviews a lot of recent research that demonstrates that people want fairness, not equality. This actually relates to a post I made last year on this topic, which funnily enough linked to this 2015 article in The Atlantic, by Paul Bloom. So, clearly this idea has been around for a while and yet, most commentators still seem to focus on inequality as a great evil. Anyway, coming back to the Starmans et al. paper, here's a little of what they say (I do recommend reading the whole article though, as it is very readable):

...when people are asked to distribute resources among a small number of people in a lab study, they insist on an exactly equal distribution. But when people are asked to distribute resources among a large group of people in the actual world, they reject an equal distribution, and prefer a certain extent of inequality. How can the strong preference for equality found in public policy discussion and laboratory studies coincide with the preference for societal inequality found in political and behavioural economic research?

We argue here that these two sets of findings can be reconciled through a surprising empirical claim: when the data are examined closely, it turns out that there is no evidence that people are actually concerned with economic inequality at all. Rather, they are bothered by something that is often confounded with inequality: economic unfairness.

One bit of that bears repeating (and in bold): it turns out that there is no evidence that people are actually concerned with economic inequality at all. What people really care about is ensuring that there is no unfairness. And that would explain the unusual differences between laboratory experiments and real-world observations, which Starmans et al. do a good job of explaining in their paper. I also like this bit in the conclusion:

Worries about inequality are conflated with worries about poverty, an erosion of basic rights, and—as we have focused on here—unfairness. If it’s true that inequality in itself isn’t really what is bothering people, then we might be better off by more carefully pulling apart these concerns, and shifting the focus to the problems that matter to us more.

This is a point I have made before - most of the arguments I have heard about why inequality is bad and should be addressed, are really arguments about why poverty is bad and should be addressed. People conflate inequality and poverty, when they are not the same thing at all. To see why, consider this thought experiment I do with my ECON110 students every year: Think about a problem that you associate with inequality. Would the problem be reduced by burning 10% of the wealth of all of the richest people? If the answer is yes, then the problem probably stems primarily from inequality. Otherwise it is more likely to be primarily a problem of poverty.

Now, if you are concerned about fairness you are very likely to be concerned about poverty. In most circumstances, it would be hard to argue that poverty is a fair outcome for the poor person. But that doesn't mean that all inequality is also unfair. In fact, equality may be seen as unfair, if it means that some people work harder than others to achieve the same outcome. And that was one of the points that Starmans et al. were making.

[HT: Berk Ozler at Development Impact, and Marginal Revolution]

Read more:

• Inequality, redistribution, and fairness
• Changes over time in attitudes towards redistribution in the U.S.
• Who cares about income inequality?