What Do First-Year Economics Students Know About Inequality?

I dedicate one of the lectures in the first year Micro-economics course I teach to talking about inequality. It starts out in the context of the labour market, covering things like wage differentials, the education premium and discrimination in the labour market, but subsequently I also talk about more general aspects of income and wealth inequality like the Gini coefficient and Lorenz Curves.

For the last few years I have been using Norton and Ariely (2011) as an exercise to get the students to think about (wealth) inequality. It's a very simple experiment with fun results. I ask the students to guess the wealth distribution (for the UK, where we are) by getting them to say what share of the total wealth they think is owned by the poorest 20%, by the second poorest 20% etc. In a similar fashion I also ask what their ideal distribution would be.

Typically, the results are very similar to those in the original Norton and Ariely paper (and my experience in this respect is pretty much the same as for Barnes, Easton and Leupp Hanig (2018) who write about how they have also used this experiment in the classroom). Students seem to underestimate the existing inequality by a considerable margin. Below I've created Lorenz Curves (using this website) for the average distribution as guessed by the students (left) and the actual distribution (right, based on data from the Equality Trust). The ideal distribution from the students is even more equal.

I really like this exercises but there are a couple of problems with it. For one, my students seem to have some trouble working with quintiles in this context. I often got back distributions where the share for a lower quintile was higher than that for a higher quintile. Following Norton and Ariely I usually reordered the percentages so that it made logical sense. But still, it suggests that students are not necessarily thinking about the question correctly.

Eriksson and Simpson (2011) (pdf) have another point of criticism with regards to the original paper. Their hypothesis is that big effect that is found using the Norton and Ariely method, is largely caused by an anchoring effect. By asking participants to describe the distribution in five percentages, that need to add up to 100, the equal division of 20% each might function like an anchor and steer answers into a more equal direction. Eriksson and Simpson run a version where they simply ask for average amounts: 'What is [should be] the average household wealth, in dollars, among the 20% richest households in the United States?' (and similarly for the other quintiles). The effect of this small change in how to phrase the question is startling. The distributions are much closer to the actual ones.

Inspired by this I decided to try and run the Eriksson and Simpson version this year in my class. I was a bit worried that by asking for amounts, instead of percentage shares, I would get lots of nonsensical answers. And my students indeed seemed to have strange ideas with regards to typical household wealth. Guesses of the total average wealth run from £3100 to £42.000.000. But if you look at the distribution of this wealth my students, on average at least, seem to have a very good grasp of the wealth inequality in the uk. The figure below compares the average distribution as guessed by the students using the Eriksson and Simpson method (left) with the actual distribution (right, same as above). And they are remarkably similar.

I guess there is no point going back to Norton and Ariely now. As suggested by Eriksson and Simpson, their finding seems pretty much driven by how the question is asked. A bit of a shame, 'students vastly underestimate how unequal the UK is' is a much more interesting starting point in the classroom than 'students pretty acurately guess how unequal the UK is'.