Moral hazard

Moral hazard – it’s one of those theoretical concepts that you learn at some point in an intermediate microeconomics course, but that gets increasingly fuzzy in your memory as time passes. The authors of a really nice recent paper on the history of moral hazard make the point that the use of the term actually varies greatly across professional and academic disciplines. In economics, moral hazard is the financial risk to the insurer that arises from (rational) agents doing less to avoid losses when insurance is present. It is often contrasted with adverse selection, which is the financial risk to the insurer arising from loss-prone types of agent buying more insurance than their more fortunate peers. Moral hazard is about the incentives under insurance, not about any characteristics of the insured themselves.

The paper shows that moral hazard is used differently in the insurance literature, and how the ‘moral’ component has come to capture any kind of behaviour that can be interpreted as taking advantage of the insurance provider. For moral hazard in the economics definition, the canonical example is torching an insured building; for adverse selection, the canonical example is a risk-loving or unhealthy person buying life insurance. I think these examples clearly show how the term moral hazard can be used as a powerful economics buzzword to express indignation at immoral behaviour. But where do these moral values come from? Well, the paper also points out that insurance companies have incentives to take part in the public debate that shapes our attitudes towards those people that ‘take advantage’ of insurers. In this debate, the threat of higher insurance premiums is a powerful argument. Remind you of anything?

I think that the economics distinction between moral hazard and adverse selection is theoretically convenient (it sharpens your thinking) but the practice can be quite fuzzy (the paper makes this point, too). In empirical applications, it is hard to find out which of the two effects is at play (although there is a literature on identification strategies). Consider a car rental company that introduces new insurance allowing for greater excess reduction and subsequently finds that damage is more common for customers who buy the extra cover. If you don’t know anything about the drivers’ abilities, it’s hard to say whether the extra accidents are caused by average drivers being more careless or bad drivers buying more insurance. From the point of the rental company, there is a simple solution – the insurance premium for the extra excess reduction will be adjusted to match the damage frequency (insurance that matches expected values is called actuarially fair). The alternative action – screening customers on their safety record – is used by some companies to exclude drivers but is not used (as far as I know) to set the price of insurance.

Anyway, I digress. The paper is well worth a read.

An economist walks into a restaurant… on tipping in groups

If there’s one thing economists are good at, it’s taking the fun out of everyday things. Many times have I seen the exasperation on people’s faces after responding to their stories with a matter-of-factly statement, firmly grounded in expected utility. As a result, dinner party invites can be hard to come by - economic analysis is the kind of work that’s hard not to take home, as its subject matter is everywhere around us.

But economists, like other people, also have to eat (cue ‘free lunch’ joke). Some even like food so much that they write books about it. And when nobody invites them to dinner, economists do the rational thing and go out for food themselves. Sometimes, they hunt in packs and book a table at a restaurant together – it has been proven that such arrangements minimise social welfare loss measured in conversation utility. For these reasons (and perhaps some social motivation of minor importance), a group of us (mostly PhD students at UoN) are planning to go out for a meal this Friday. We do this about once a term, and don’t worry – we’re easy to spot in case you want to make a timely exit from the restaurant.

The tipping issue

There is a slight concern among my office mates that we will repeat last time’s painful conclusion to the dinner. Thing is, we came up short. After the bill had been passed around the table, calculations made and cash and cards gathered in a big heap… the restaurant was still owed some money (cue grumpy waiters). Some of us eventually made up the difference – insisting they had now paid for their mail, factored in a lavish tip AND paid for somebody else’s food. As a matter of fact, everyone of us seemed to be convinced that they had paid at least their share and that the shortfall was due to others. Unsurprisingly, the waiters did not receive a tip.

It appears that economists have yet again ruined the fun for everyone – many restaurants now automatically add a ‘service charge’ to the bill of any group of at least 6 diners. Whilst the fairness of this practice is disputed by some (you can sign an e-petition against it on a government website and have the honour of being the 3rd person in the UK to officially complain about it), I believe it is quite common. Not for a second do believe that the service charge compensates the restaurant staff for increasing marginal effort expended per guest. I remember well from my job as a waiter in my undergrad days that a table of 12 is a lot easier to serve than 6 couples (which, come to think of it, also explains why my former boss only required my skills when there was a group reservation). So it must be about the money.

Whether the restaurant owner keeps all the tips for himself or shares them with the staff is not important, seeing as higher tips are always better for the restaurant owner. Anyway, that’s not the point – the point is that groups have to be ‘encouraged’ to tip. I think the restaurant owners’ association might at some point have added this 1975 paper with the great title ‘Cheaper by the Bunch‘ to its reading list. The study presents evidence that the average tip in groups is lower, and the authors claim this is due to a diffusion of responsibility effect (the with remarkable foresight aptly titled best-seller The Tipping Point calls this the bystander effect). So the more bystanders, the less the urgency to tip – makes sense, right? A more recent empirical study suggests that there is a counterbalancing force – the social penalty imposed on the person that doesn’t tip – and that these effects cancel each other out.

Unfortunately neither of these studies prevents evidence on groups of economists failing to pay even the required amount minus tips at the first time of asking. Perhaps that’s just a reflection of academic funding these days…

EOS cheatsheet screenshot

Improve your writing with The Elements of Style – downloadable cheatsheet

I don’t know a better book on writing than the Elements of Style. It’s clear, it’s timeless, and best of all: it’s only 84 pages long. Nevertheless, I sometimes wish I could be reminded of the Elements as I write – flipping through the pages every time I write a blog post, report, or paper just isn’t practical. Hence my quest for a cheatsheet. For the uninitiated: a cheatsheet is a one or two-page set of notes for quick reference; they are typically used for technical topics such as programming.

I found a cheatsheet on the web but I felt that it could be improved. So I created my own: a two-page PDF [click to download] of the Elements of Style, clearly laid out in compact tables with examples. I plan to print a double-sided copy and laminate it for everyday use.

Disclaimer: having to compress the whole book into a two-page sheet meant I had to sacrifice some of the rules and finer points. I hope I have succeeded in leaving out only those parts that are either (too) obvious or no longer necessary in a world of auto-correcting text editors and pop-up dictionaries. I also moved some of the examples from the Words and Expressions Commonly Misused section to an earlier section. I take full responsibility for any remaining errors and poor judgement.

N.B.: if you like the cheatsheet, buy the book. You won’t regret it.

Pay day loans experiment

Loans with a typical APR exceeding 500% – surely that’s impossible? Or even if they do exist, no one in their right mind would sign up for these terms, right? Well, ask the people walking into one of these:

Pay day loan store shop front

Because of the risk involved in making pay day loans, the cost to the borrower of obtaining such ‘alternative finance’ is higher than regular credit. The problem is that pay day borrowers usually don’t have access to regular credit. As the Economist eloquently puts it: “For someone who is truly hard-up, the only thing worse than borrowing $200 at 600% APR may be not borrowing $200 at all.”

So pay day loans are expensive. But are they harmful, and should they be regulated? The arguments for and against pay day lending are familiar and well-rehearsed economic justice arguments:

  • AGAINST: “Offering pay day loans is taking advantage of vulnerable people.”
  • FOR: “But no one is forcing the borrowers – it’s their own choice.”
  • AGAINST: “But poor people make poor choices when tempted by shrewd lenders – somebody do something!”
  • FOR: “Who? The government? Who says they know what’s best for people?”

Obviously, this discussion is going nowhere – it’s unavoidably political. But don’t despair, there is a third way: the behavioural intervention. A behavioural intervention is Nudge speak for giving people better structured information so that they can make better choices. This is what a pair of economists from the University of Chicago investigated in a well designed field experiment. The researchers manipulated the print on the cash envelopes used in 77 branches of a US pay day lending firm over a two-week period. They tried three print versions as experimental treatments:

  1. A comparison of pay day loan APR to other forms of credit
  2. A list of the accumulated cost of pay day loans in dollars, compared to credit card borrowing fees
  3. Information on how quickly people repay pay day loans

Compared to a control group who receive non-manipulated, company branded envelopes, the second envelope manipulation reduced the likelihood of the recipient re-applying for a pay day loan in the next pay cycle by 11%. Furthermore, individuals in all three envelope treatments reduce their average borrowing over the next pay cycle by more than the control group.

So, assuming that at least some pay day borrowers would rather not use pay day loans to make ends meet, the behavioural intervention is a success. Whilst I’m not sure that the conclusion “information disclosure that is inspired by, and responds to, cognitive biases or limitations that surround the payday borrowing decision has a significant effect” is warranted (the treatment is actually less effective for those who spend the loan money on eating out and holidays), the reduction in loans across the whole sample is encouraging. Perhaps this is one for the Behavioural Insights Team?

Greed’s been good for us

I was skimming through a working paper on the role of behavioural biases in the Irish banking crisis, the objective of which is to inject some science into the explanations of a crisis that has often been said to result from ‘greed’ or ‘mania’. Now, the problem with greed and mania is that they are easy to recognise after the bubble has burst (of course the rise in house prices/bank profit growth was unsustainable), but not so much when the bubble is still growing. Without evidence from a parallel universe, empirical economists can only turn to historical data. Interestingly enough, the track record of greed is quite good (I’m not so sure about mania), said the late Milton Friedman:

Getting started with Stata 11 on Ubuntu Linux

I recently had to re-install Stata 11 for Unix after upgrading my operating system to Ubuntu 11.04 Natty. I followed Stata’s installation instructions and they worked fine. If you want Stata to ‘just work’, then I recommend installing the statically linked version instead of the dynamically linked version. But if you’re familiar with Linux package dependencies you might disagree, and check out the blog posts by Andrew Dyck and Jonas Ranstam for helpful post-installation instructions for the dynamically linked version.

Now, assuming you’ve got Stata installed properly, you will probably want to update your version and set and save your preferences. Because these tasks are not as easy as you would like, I’ve documented them here.

Updating Stata version

Why would you want to update your newly installed Stata software? Because it’s probably not the latest version, and therefore contains bugs. Stata 11.0 is rumoured to contain a bug that affects its calculations (I can’t actually find a online bug report for it at the moment) which means your statistics and therefore your conclusions could be way off the mark! So protect your academic credibility and enter:

update query

If the outcome is ‘all files up to date’ then you’re safe. If not, you want to type:

update all

and when that’s all done, restart Stata like this:

update swap

Setting your preferences

After you have applied the update, you can change your settings through the Preferences screen (I changed the default font to ‘DejaVu Sans Mono Book’ because the default font is awful). For some reason Stata wouldn’t save my preferences before the update, but the update to Stata 11.2 seems to have resolved this.

New evidence on the tech bubble

A new empirical paper on the dot-com bubble of the late nineties presents some serious challenges to the efficient market hypothesis. The authors report that 'institutions trade in the direction of clear mispricing in a small sample of equity carve-outs', the mispricing being a parent firm's share in a dot-com venture that exceeds the value of the parent firm (yes, that means that part of company A is worth more than company A altogether!). This should seriously worry anyone who believes that experts' use of arbitrage is a rationalising force in financial markets.

Furthermore, the paper takes some of the bubble blame away from individual investors by identifying big institutions (such as hedge funds) as the main drivers behind the bubble. This finding again (remember this guy?) contradicts the idea that the dot-com bubble was fuelled by personal investors, which seems like a textbook case of mistaking correlation for causality.