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Motu researchers on the utter uselessness of job interviews, the ethical challenge facing AI, twelve questions for 2017, putting a price tag on diversity, how globalisation affected manufacturing - plus lawyer, lawyer your pants are on fire and more

Motu researchers on the utter uselessness of job interviews, the ethical challenge facing AI, twelve questions for 2017, putting a price tag on diversity, how globalisation affected manufacturing - plus lawyer, lawyer your pants are on fire and more

Today's Top 10 (and an extra one for fun) is a guest post from Kate Preston, Sally OwenWilbur Townsend, Loïc Henry, Edmund Lou and Nathan Chappell at economic and public policy research house Motu

As always, we welcome your additions in the comments below or via email to

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See all previous Top 10s here.

1. The Utter Uselessness of Job Interviews

Employers use interviews to learn more about job applicants. But what if the apparent facts gleaned are actually fiction? The author of this article has conducted research showing that interviews can be worse than useless – they can undercut more objective measures of an applicant because of the narratives interviewers create in their minds. One experiment gave subjects the task of predicting the future grade point average (GPA) of other students, based on past GPA and students’ planned courses. Predictions were less accurate for subjects who also interviewed the students. Furthermore, subjects didn’t notice and even reported that they learned more about students who gave random answers to yes/no questions. The author warns against unstructured interview formats; they may be counterproductive if humans form a coherent narrative from any pattern. But perhaps we should be cautious drawing broad conclusions from a study on students’ GPAs – research in other contexts would be valuable.

2. The ethical challenge facing artificial intelligence

This article outlines AI progress and key issues, reporting the results of a panel discussion of technologists from Google, Harvard Law, Microsoft, MIT and Duke University. Their responses centre around the ethical issues of the values that will be written into AI decision-making in industries as varied as medicine, justice and recruitment.

3. Does welfare inhibit success? The long-term effects of removing low-income youth from the disability rolls

Some people devote their political energy to creating a more just society, protecting the vulnerable and, generally, making things better. Others steal money from the poor. Bizarrely, this latter group claims that they are the real heroes – they claim that if poor people had even less money, they would work harder and thus be better off. In this article, an economist at the University of Chicago asks whether a 1996 American law cutting welfare helped those who had money taken from them. Specifically, the author studies the stricter medical requirements introduced for certain recipients of a disability benefit. The requirements only applied to those who turned 18 after the law’s passage, and so the author can compare those who turned 18 just before the law to those who turned 18 just after. While those subject to the stricter requirements did earn a little more, overall they were worse off. Across the 16 years following the law’s passage, after discounting and accounting for their increased earnings, the law’s victims had US$76 000 less than they would’ve had they been born a few months earlier.

4. A way to fight bank runs—and regulatory complexity

The Financial CHOICE Act, introduced by US Republican Jeb Hensarling to replace the Obama-regime’s Dodd-Frank Act, has an advantage. It appeals for a simple leverage ratio rather than conforming to complicated Basel rules. This is something that Andy Haldane, chief economist at the Bank of England, would die for. However, the usage of the age-long leverage ratio cannot prevent regulatory arbitrage; it needs to be improved (at a minimum). Chicago Booth’s John Cochrane offers his viewpoint on how to make such improvement.

5. Twelve questions we need to prioritise in 2017

The BBC’s Future Now project asked scientists, technologists, business leaders and entrepreneurs to name what they saw as the most important issues of the 21st Century. They come up with quite a range, from overpopulation, safe use of gene-editing in humans, resistance to antibiotics and natural disaster communications.

6. Studying advanced mathematics: the potential boost to women’s career prospects

Using data from Denmark, this study shows that women are no less good at maths than men, nor do they receive lower payoffs to studying advanced maths. However, the way maths is promoted and taught in high schools is not encouraging young women to pursue study in these areas! This means a lot of potential mathematical talent is not being put to use. Changing the high school system could see more women making it to the top in the areas of science, finance and mathematics.

7. Putting a price tag on biodiversity

This study used plant, soil, and ecosystem carbon storage data from two long-term grassland biodiversity experiments to show that greater species richness increases economic value on the carbon market, by enhancing carbon storage. The ecologists and economists involved in the research pinned down the monetary value of carbon storage as a single practical service that biodiversity provides to people. This demonstration of the economic value of biodiversity provides a foundation for assessing its value in decisions about land management.

8. Kenneth Arrow and the golden age of economic theory

Steven Durlauf outlines some of the massive contributions of Kenneth Arrow, who died last February. The Nobel Memorial Prize in Economic recipient shifted economics towards a formal approach, built on robust mathematics and providing the field with logical foundations. Five of Arrow’s PhD students have also received the Nobel Memorial Prize in Economic and his legacy will probably enlighten economics for ages.

9. How globalisation affected manufacturing around the world

This article looks at how globalisation over the last thirty years has caused structural changes that are relatively faithful to trade theory predictions. In skill-abundant, developed regions, manufacturing became more skill-intensive, while in skill-scarce Asia, labour-intensive manufacturing expanded. According to Adrian Wood from Oxford, the current political backlash against globalisation maybe due to the failure of governments to pay enough attention to predictable social problems that have resulted from the process.

10. Is it Time for a Journal of Insignificant Results?

One of the drawbacks for the way academia values journal publication and citations is the resulting publication bias and the bad incentives faced by researchers. Many disciplines have tried to combat this by publishing a journal comprised of articles submitted to other publications, but rejected because of insignificant or unsurprising results. Andrea Menclova from the University of Canterbury proposes the idea of a journal for economists which accepts these articles.

11. Miami lawyer’s pants erupt in flames during arson trial in court

Not a joke (but still kinda funny). Poor guy had an e-cigarette in his pocket.

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The ethical AI article has a critical paragraph pointing out that people need meaningful relationships, work and passionate interests otherwise depression, suicide and substance abuse goes up. We already have major problems with that in NZ without automation being brought in. If we put effort into solving those problems then automation wouldn't be such a large risk.


The AI article has a delicious, if totally irrelevant blockquote:

The challenge now is to make sure everyone benefits from this technology

It is irrelevant because, as with every other major advance, it is driven by military research. Prime example: drone swarms - or if'n y'all like the Erudite version (MIT) here:…

So I bravely predict that the talkfest will, as is always the case, be overtaken by events in a far-off country, pour encourager les autres....

It is doubly irrelevant because, as with the example of emergent behaviour in machine learning cited by Danny Hillis, recounted in the seminal book 'Emergence' -… - Hillis confessed that having grown sorting software using his Massively Parallel Machine, and looked at the code, he simply could not understand how it functioned -

So 'controlling' and directing such efforts to noble social goals is futile......because we cannot comprehend the methods involved, let alone the results. Emergence is like that.....