Thursday, 22 June 2017

Why researchers need to name their teaspoons

The annual Christmas issue of the British Medical Journal always has at least one 'interesting' paper included. For example, I recently blogged about the study on Pokemon Go and obesity. I recently read this 2005 paper (open access) by Megan Lim, Margaret Hellard, and Campbell Aitken (all from the Burnet Institute in Melbourne) about the Institute's mysteriously disappearing teaspoons. The paper explains:
In January 2004 the authors found their tearoom bereft of teaspoons. Although a flunky (MSCL) was rapidly dispatched to purchase a new batch, these replacements in turn disappeared within a few months. Exasperated by our consequent inability to stir in our sugar and to accurately dispense instant coffee, we decided to respond in time honoured epidemiologists' fashion and measure the phenomenon.
Here's what they did:
At the completion of the pilot study we carried out a longitudinal cohort study. We purchased and numbered a further 54 stainless steel teaspoons. In addition we purchased and discreetly numbered 16 teaspoons of higher quality. The teaspoons were distributed (stratified by spoon type) throughout the eight tearooms, with a higher proportion allocated to those tearooms with the highest teaspoon losses in the pilot study.
We carried out counts of the teaspoons weekly for two months then fortnightly for a further three months.
They then essentially conducted a very simple survival analysis of the teaspoons. They found that:
After five months, 56 (80%) of 70 teaspoons had disappeared. The half life of the teaspoons was 81 days (that is, half had disappeared permanently after that time) compared with 63 days in the pilot study...
If you think this study is inconsequential, think again:
If we assume that the annual rate of teaspoon loss per employee can be applied to the entire workforce of the city of Melbourne (about 2.5 million), an estimated 18 million teaspoons are going missing in Melbourne each year. Laid end to end, these lost teaspoons would cover over 2700 km—the length of the entire coastline of Mozambique—and weigh over 360 metric tons—the approximate weight of four adult blue whales. 
There is an economics aspect to the study. Teaspoons in a common area are subject to the 'tragedy of the commons', as the authors explain:
The tragedy of the commons applies equally well to teaspoons. In the Burnet Institute the commons consists of a communally owned set of teaspoons; teaspoon users (consciously or otherwise) make decisions that their own utility is improved by removing a teaspoon for personal use, whereas everyone else's utility is reduced by only a fraction per head (“after all, there are plenty more spoons…”). As more and more teaspoon users make the same decision, the teaspoon commons is eventually destroyed. The fact that teaspoons were lost significantly more rapidly from the Burnet Institute's communal tearooms (the “commons”) compared with programme linked rooms, correlates neatly with Hardin's principle.
The tragedy of the commons arises because the resource (teaspoons) is rival (one person taking a teaspoon reduces the amount of teaspoons left available for everyone else) and non-excludable (it isn't easy to prevent someone taking a teaspoon). One solution to the tragedy of the commons is to create property rights, which would make the teaspoons excludable. In this case, everyone in the Institute would have their own named teaspoons, with a rule that no one can use others' teaspoons without some suitably dire punishment befalling them.

As with most of these BMJ papers, this one was an interesting diversion. Also, if you haven't ever heard of counterphenomenological resistentialism, I recommend you read the paper and be enlightened.

Wednesday, 21 June 2017

Are men more likely to cheat if working with more women, or do cheating men prefer to work with more women?

I recently read this 2013 paper by Masanori Kuroki (Occidental College), published in the journal Economics Letters (sorry I don't see an ungated version anywhere). In the paper, Kuroki looks at whether the sex ratio in the workplace affects the likelihood of marital infidelity for men and for women. She used data from the 1998 General Social Survey (as an aside, you can get the data for yourself here - not just for 1998 but for every wave of the survey, which is a pretty cool resource).

Her measure of the sex ratio of the workplace is based on a self-reported measure in response to this question:
"Are the people who work at this location mostly men or women?" Individuals respond on a 7-point scale: (1) all women, (2) almost all women (e.g. 95%), (3) mostly women (e.g. 70%), (4) about half men and half women, (5) mostly men (e.g. 70%), (6) almost all men (e.g. 95%), and (7) all men.
She then converted the categorical measure to a numerical measure (which just screams out "measurement error", but we'll put that to one side as there is a more important issue with the paper). Her measure of marital infidelity is based on this question:
"Have you ever had sex with someone other than your husband or wife while you were married?"
I'm sure you can immediately see a problem here. The interpretation of the results is not straightforward, since the cross-sectional correlation that results from her analysis will be between current workplace sex ratio and whether the person has ever been unfaithful. Here's what Kuroki finds:
An increase in one standard deviation in a fraction of coworkers of the opposite sex is predicted to increase the likelihood of an extramarital affair by 2.9 percentage points. Considering that 22% of people have committed infidelity in the sample, this magnitude is not trivial...
Next I run separate regressions for men and women... The coefficient on the fraction of coworkers of the opposite sex continues to be positive and statistically significant for men but not for women. An increase in one standard deviation in a fraction of female coworkers is predicted to increase the likelihood of an extramarital affair by 4.6 percentage points for men. 
The coefficient may imply a result that is not trivial, but it is correlation not causation, and as noted above the interpretation is not straightforward. Does it mean that men are more likely to be unfaithful if their current workplace has a high proportion of women, or that men who have a history of infidelity are more likely to choose to work in a workplace with a high proportion of women? The answer is not clear.

Sunday, 18 June 2017

Book Review: You are Not So Smart

I recently read a 2011 book by David McRaney, "You are Not So Smart". The book has 48 short chapters, each of which is devoted to a different cognitive bias, heuristic, or logical fallacy, all of which demonstrates how all of us are not so smart. As McRaney puts it in the introduction:
These are components of your mind, like organs in your body, which under the best conditions serve you well. Life, unfortunately, isn't always lived under the best conditions. Their predictability and dependability have kept confidence men, magicians, advertisers, psychics, and peddlers of all manner of pseudoscientific remedies in business for centuries. It wasn't until psychology applied rigorous scientific method to human behavior that these self-deceptions became categorized and quantified...
You will soon realize you are not so smart, and thanks to a plethora of cognitive biases, faulty heuristics, and common fallacies of thought, you are probably deluding yourself minute by minute just to cope with reality.
Many of the heuristics and biases are ones that I cover in the behavioural economics topic in my ECON110 class, such as framing, the Dunning-Kruger effect, procrastination and present bias. All are supported by appropriate citations to research and interesting anecdotes. And some of the bits are priceless, such as this (on introspection):
Is there a certain song you love, or a photograph? Perhaps there is a movie you keep returning to over the years or a book. Go ahead and imagine one of those favorite things. Now, in one sentence, try to explain why you like it. Chances are, you will find it difficult to put into words, but if pressed you will probably be able to come up with something.
The problem is, according to research, your explanation is probably going to be total bullshit.
The only problem with this book is that, while it presents a lot of problems with our cognitive processes, it is very light on solutions. And when solutions are presented, they can sometimes be inconsistent. For instance, the solution to normalcy bias (where you pretend everything is normal, even in the wake of a major crisis) is repetition of warnings. However, in the next paragraph McRaney points out the cases of Y2K, swine flu, and SARS, where media over-hyping has led people to become complacent!

Overall, I found this book to be an easy and interesting read, and recommended for anyone who wants to understand why we are not so smart. If you're looking for more, McRaney has a website/blog, and a follow-up book, "You are Now Less Dumb", which I look forward to reviewing in the future.

Wednesday, 14 June 2017

How segregated is Auckland, and New Zealand?

A story published on the Newsroom site last month discussed diversity and segregation in Auckland:
How often do we hear that Auckland is this wonderfully diverse city where immigration has produced an exciting multicultural mix and made it a truly dynamic city to live in?
The portrayal of Auckland as a place where different ethnicities live side by side and share the fruits of its booming economy suits many narratives, but is it a myth?
Associate Professor of Pacific Studies at the University of Auckland Damon Salesa says the residential segregation in Auckland is remarkably high in Auckland, and not far behind what you would find in South Africa or parts of the American South.
“The most segregated population is actually European New Zealanders in Auckland. These people have no window or vision on the rest of Auckland…. the city many European New Zealanders live in is not diverse at all."
Now, I've been working for the last couple of years on developing new projections of future ethnic diversity as part of the MBIE-funded CADDANZ (Capturing the Diversity Dividend of Aotearoa New Zealand) project. Those projections make use of some fairly sophisticated population projections and microsimulation methods, but I won't be talking about those here just yet. And given continuing record rates of net migration, understanding these changes is pretty important.

Before thinking about future diversity, it pays to have a good understanding of current and past diversity. So, my very able research assistant Tobias has been working on estimating measures of segregation and isolation for New Zealand (and Auckland) recently. And it seems that Salesa has things quite wrong, based on the data (or he's reading the data quite differently to us).

There are several ways of measuring the extent of segregation of a population group (by which I mean how separated a given population group, such as Europeans, is from other population groups). Two widely used measures of residential segregation (for groups) are:

  1. The Index of Segregation (IS), which measures the proportion of people in a population subgroup that would have to relocate in order to make their distribution identical to that of all other groups (on average); and
  2. The Modified Isolation Index (MII), which measures the extent to which members of a population subgroup are disproportionately located in the same area as other members of their group. 
Both measures range from 0 (low segregation or isolation) to 1 (high segregation or isolation). I'll focus on the Index of Segregation in this post (but the MII results are similar [*]). To calculate the measures we use data from the New Zealand Census 2001-2013 based on the number of people belonging to each of five ethnic groups (European, Asian, Maori, Pacific, and Other). [**]

First, here's the picture for the country as a whole:

Clearly, the most segregated ethnic group is the Pacific group, and segregation of that group has been declining since 2001. The Asian group is the next most segregated, followed by European and Maori (which are about even). The "Other" ethnic group is the least segregated of all. So, that doesn't seem to support Salesa's assertion that Europeans are the most segregated ethnic group. Also, they haven't been becoming more segregated over time. But his statement was about Auckland, so here's the same picture for Auckland:

Again, the Pacific group is the most segregated. Asians are clearly less segregated in Auckland than in the rest of the country, and in Auckland there is little to choose between that group and Europeans. Maori are also less segregated in Auckland than in the country as a whole, but notice the trend towards less segregation for Maori is the same for both Auckland and the whole country. Again, there is little support for Salesa in these data.

Those first two charts are based on data at the area unit level (an area unit is a geographical unit that in urban areas equates roughly to the size of a suburb). You might worry that using these arbitrary boundaries matters, but it doesn't much. Here's the picture for the whole country based on 2014 electoral boundaries:

Notice that it doesn't look much different from the country as a whole using area unit boundaries (the first chart in this post), in terms of the ranking of the different ethnicities' levels of segregation (and if we look at electorates in Auckland only, it looks similar to the second chart in this post). Overall, we can probably conclude that the European ethnic group is not the most segregated (in Auckland, or in the country as a whole). If we are concerned about segregation, we should be looking at the Pacific and Asian groups (and particularly the Pacific group in Auckland).

As a bonus, it's interesting to note that New Zealand is much more segregated by ethnicity than by political affiliation. Here's segregation by vote share from the last five national elections (2002-2014) plotted on the same scale (and with five groups, to make it most comparable to the ethnic segregation data above):

New Zealand is much less segregated politically than ethnically, and none of the political parties' supporters seem vastly more segregated than any other (though I wonder if Labour and NZ First will continue their previous trajectories through this year's election)?


[*] The MII results accentuate some of the differences, and some of the rankings are slightly different, but the overall picture is the same in that the Pacific group is the most isolated.

[**] These calculations are based on the 'total response' ethnicity for each area. In the Census, people can report that they belong to more than one ethnic group, and the 'total response' counts these people once for each group they belong to. So, the total number of reported people by ethnicity in an area will always be greater than the total number of people in an area. I can't see that this would bias the statistics in any serious way, however.