A Mater of Truthiness

I’ve been doing some research on lying lately. There’s a wealth of really interesting literature out there, but most of it is (IMHO) flawed. The main method of collecting data is based on surveys. Basically, this means the researchers are asking people to honestly tell them about instances of dishonest behaviour. I’d be counting on you to tell me the truth about lying.

I’ve been using a different technique. I give subjects an opportunity to lie to another subject for money. Depending on the situation, a little more than half the people I had as subjects (about 200) lied. Not all that surprising. What surprised me is the types of things that seem to influence a person’s likelihood of lying.

I designed the experiment to see if there was a difference in lying based on if the subject faced a potential gain or a potential loss. An asymmetric value function, like the one I’ve written about here before, predicts that people are more likely to lie when facing a loss. This is actually what I discover. So pay extra attention when dealing with someone who has something to lose.

The common perception is that women are more trustworthy than men. Well, not in this experiment. Men and women were equally likely to lie. Some other researchers using similar environments found that women were actually less likely to lie for monetary gain. These experiments were done in Sweden and the US. Canadian women,it seems, are more like males than their Swedish or American sisters. Score one for Canadian equality?

Having divorced parents is known to mess kids up. I found that people who’s parents are divorced are more likely to lie than others. What I found really interesting though was that people who reported being raised by a single parent were a lot less likely to lie. I’m not sure why that might be. Keep in mind that I’ve got a sample selection problem in that the people I’m talking about are all university students. So it might be that only the most well adjusted/honest kids raised by a single parent make it that far.

One of my favourite results of this work is that the faculty of the student seems to make a difference. In particular, business students lie a lot more than others. There are a couple of interesting possibilities. First, business students may be more sensitive to monetary reward and are therefore more likely to lie for cash. It could also be that business students have a lower “cost” of lying and are more likely to do it. These are similar, but not quite the same thing. It could also be that business students are more competitive. Lying meant you ended up with more money than the other person in this environment. That could have been a key motivating factor.

In short, we don’t really have a handle on why and when people lie. We’re getting better at catching them in the act, but predicting when it will happen? Not really sure.

Building silos

This is about something I've noticed on and off over my years in the alternate universe that is the academy.  A great many faculty and other people bemoan the formation of discipline silos, and yet nobody seems to have a grasp on how and why they form.  I'm starting to think they are the inevitable result of a number of forces that aren't going to disappear soon.

Let's start with one that's near and dear to every economist's heart; specialization.  There are huge gains to be made in almost every field of human endeavor by having different people focus on different things.  Knowledge creation is no different than any other human endeavor in this regard.  We can delve more deeply into things through specialization.  As our body of knowledge increases, the number of things someone needs to know to be able to make a meaningful contribution increases.  The number of academic articles published across disciplines every month is more than a life time's reading, never mind an entire discipline's worth of background material.  So the silos are in part a natural part of the growing body of knowledge.

Specialized methods of analysis:  An increasing number of disciplines have moved away from the ancient philosophy mode of figuring out how things work, namely words and thought experiments.  The use of formal mathematic models can create a barrier to entry that is hard for many to overcome.  This division between disciplines, even when studying the same problems, makes cross understanding difficult.  A small, but growing, number of academics are able to translate complex mathematical concepts into something more accessible.  It's a long between theoretical physics and postmodern art.

Resources:  This is the dirty little secret of universities in developed countries.  A growing share of a university's budget is not devoted to academic staff.  Instead it is being spent on a variety of other things, some legitimate, others ...  let's just say I'm not convinced yet.  The resulting fights for resources tend to make interdisciplinary cooperation difficult.  In order get new positions or often to even replace retiring faculty, a remarkable battle with highly uncertain rules and potential outcomes ensues.  Often, but not consistently enough to make it truly workable, resource allocations come down to the number of students who have declared a discipline as their major.

Confirmation bias:  Now we focus on students.  People choose disciplines and view disciplines based on their own preconceptions.  How could they not?  Which discipline will someone who believes poverty is the result of an oppressive system choose?  It likely isn't evolutionary biology.  It isn't likely to be economics, though it does happen.  By the same token someone who believes in self determination isn't likely to pursue a degree in sociology. This self selection reenforces the things that make the disciplines different.  Thus a difference between disciplines that starts out small will be continually reenforced until the gulf between the disciplines becomes almost impossible to cross.  

All of these processes combine to make the gulf between disciplines hard to transcend.  Maybe, just maybe, the gains of these silos out weigh the costs.

Failing for Freedom

Freedom may cost a little more and in a different way than we generally think. In order to be free our actions have to have meaningful consequences. These consequences have to include at least some possibility of failure. Without the possibility of failure, choices are meaningless and no one can be free.

What do I really mean by freedom in this context? I mean the ability to determine where we end up; I'm talking about outcomes. Freedom is the ability to make choices that you care about.

The best way I can think of to make this clear is an analogy. Pretend you're a contestant on the classic game show "Let's Make a Deal". You've made it to the end of the show and have to choose 1 of 3 doors. If all 3 doors have "zonk" (joke prizes like a toy car instead of a real car) prizes behind them, it won't matter which one you choose. The outcome will always be the same. In this deterministic setting, you can't really say you exercised any meaningful freedom as nothing you could have done would have changed the outcome. The same logic applies if all the doors had a major prize behind them. Once again your actions have no influence on the outcome, therefore, you weren't free.

In order for your actions to matter, in order to be free, there must be a variety of possible outcomes. Once different outcomes are possible , choices you make can have an impact on outcomes and you can reasonably be said to enjoy some degree of freedom.

The unfortunate reality of freedom is that sometimes "zonk" prizes will be chosen.

Economists do it with models

This is for RY.

In a comment left here about a month or so ago – I’ve been swamped with my real job – someone complained about the use of models in economics. I get these complaints all the time. Economics uses too many models, the models in economics are unrealistic, the models in economics are too complicated (this amounts to being too realistic), and so on.

All of these complaints have some merit. The models that students encounter in their first economics courses are painfully simple. It’s hard to get into a meaningful discussion about the ideas behind these stripped down models.

Many of the models used, even at higher levels, are unrealistic. There are entire classes of models (rational expectations, perfect information, etc) that I really, really don’t like. We often make heroic assumptions about people that don’t have a lot to do with real live human beings. I cringe whenever I here a job candidate defend a particular model by saying, “this assumption was made for tractability”. That just means they assumed something to make the math easier to solve.

Finally, many of the models used in economics (particularly at the higher levels) are quite complex. It’s easy to lose track of what’s going on if you’re dealing with a system of 6 or 7, never mind 20 equations. Some of this problem is the way the models are sometimes presented. All the equations can usually be reduced to just one or two, but all are shown with the idea that it’s important to know where they come from. The other source of complexity is the math required to solve the models, but more on that later.

So, on its face the indictment has merit. Now we turn to the defense.

A model is just a simplification of the real world so that we can focus on a small part of what’s going on. The models that first year students see are extremely stripped down versions of more robust models. Why strip them down? To allow students to focus on what many in the discipline feel are the fundamental relationships. Everybody knows that price isn’t the only thing that influences how much of something people want to buy, but it is almost always a factor. We ignore all the other stuff in an effort to make sure we get that part right. More complex relationships are added later, not as quickly as I would like, but you can’t have everything.

The unrealism has very little defense in my mind. Assuming that people are always right, except for some small error term bugs the crap out of me. The only defense to unrealism I can buy into so far is we’re slowly working in the direction of realism. So the models maybe unrealistic now, but they’re more realistic than they were. This is progress, of a sort.

Finally we turn to complexity. I get this at all levels, right from intro to graduate. At the lower levels, I’m sorry to say the blame has to fall on the public education system (this is the more on that later issue). A frightening number of students arrive at university illiterate and even more are innumerate. At the undergrad level the models we’re talking about aren’t that complicated (they’re even linear), but many students lack the background preparation to simple algebra. At the upper levels, most economists (including me) aren’t trained in enough mathematics. We all need more. There are few economists who have the ability to invent the math required to solve a problem and translate the math into something accessible. Hell, there aren’t many in physics either. The advantage of using mathematics is precision and the fact that if you’ve done the math correctly, you can’t have made an error in logic.

So given all of these issues, why use models at all? To further our understanding of real world phenomena! Every discipline uses models. All a model is, at its core, as a way of organizing facts and ideas. A model is anything that describes the interconnectedness of two or more things. All the humanities use models and just the sciences and pretenders to science. Economists do it with models, and so does everybody else. Economics is just a little more explicit about it than others.

Alternatives to the BoC’s inflation target

This post comes from something a former student shared with me. The link they shared is here
The Bank of Canada’s policy target (currently 2% inflation as measured by the CPI) is up for review/renewal this year. This is a good time to look at other options. Let’s examine a few.

1% - this is just silly, the BoJ and Fed have been engaged in a huge experiment in monetary policy with zero or near zero interest rates. The results from the US are not in yet, but I’m not optimistic and there isn’t a lot to suggest it’s been successful in the Japan. I’m more likely to side with raising targets (a la Olivier Blanchard) rather than reducing them.

NGDP targeting – this is a little more interesting. The idea is the central bank targets a combination of price level and output (essentially P*Y), this is sort of what the Fed does in the US. A reasonable target for this might be something like 5 or so, given past growth rates and inflation.

Problem: what happens if you have a wildly good year? Deflation? Ouch. Say we get a year of growth 6% in real GDP, in order to meet the target we’d have to have deflation. If this were only a one way target, it’d be pretty pointless. So I don’t really see this working out.

Status Quo - We’ve got the U.K. which has overshot its inflation target yet again, despite poor performance in terms of output. Canada, on the other hand, has done quite well in terms of meeting its inflation target and having the needs of meeting the inflation target matching the needs of the real economy. In short when inflation has been below its target the real economy has generally been in need of stimulus.

Why is Canada different? I’m pretty sure it has to do with the mix of what we produce and what we consume, and the resulting impact on the exchange rate. Given that we import finished goods a drop in the exchange rate means an increase in the CPI. We tend to export raw materials and import finished goods. The U.K., on the other hand tends to export services (particularly financial services) and import finished goods. When the demand for the financial services they produce fell dramatically output followed suit. The drop in demand for the financial services (their key export) also caused the value of the pound to fall and inflation, as measured by changes in the CPI, rose. Result: The needs of inflation targeting are contrary to the needs of the real economy, increasing the interest rate would likely increase the value of the pound and decrease the inflation rate – but this would hurt exports and output (or at least not help). This makes sense; the U.K.’s export industry is exceptionally pro-cyclical with respect to the current economic crisis.

Canada is in a slightly different situation. While on the surface it looks a lot like what’s going on in the U.K., there might be something a little different about what drives Canada’s exchange rate. Global demand for the kind of raw materials Canada exports tends to be pretty stable. The stability of demand for our raw materials isn’t the only the reason why Canada’s different. When the price of raw materials falls, say due to a lack of demand, the price of finished products tends to fall too. Thus, a drop in the demand for Canadian exports tends to be matched with a drop in the price of the finished products we import. This means that unless the types of goods that are considered key inputs changes radically (this could happen if hydrogen or other alternate transportation fuels work out) Inflation targeting is a good match for Canada.

Of course this is based on a Keynesian interpretation of monetary policy, if you're a follower of Hayek, things are a little different.

A Dangerous Narrative

In my research I work mostly with raw statistics. That means data (generally secondary) based on observations of hundreds if not thousands of people. Thanks to desktop computing I have a myriad of ways analyzing, reducing, organizing, and presenting this data, all in the name of trying to find some consistent relationships between variables. All of these fancy techniques are designed to make sure I identify relationships between things that actually exist and can be used to predict what relationships will emerge when I’m using a different data set. Get this right and, ideally, we can predict what is going to happen before we collect the data. This is the whole point.

When I’m getting ready to lecture in a class, I’m engaged in an entirely different exercise. Most of the students in lower level classes that I run aren’t ready or able to deal with the kind of statistical arguments that the theories require. The symbolic logic that underpins the theories does no better. Instead, I go looking for stories – narratives. Students at lower levels (ie before they’re indoctrin… – I mean properly educated) tend to find narratives more convincing anyways.
So what’s the problem – students become convinced of the “right” things and I don’t have to figure out how to explain a probit or fixed effects panel model to first year students, wins all around, right?

Well, things get a little more complicated when you start to think about how people form expectations. From experimental work in both economics and behavioural psychology we can identify some of the consistent mistakes people make when forming their beliefs about how likely something is. Let’s start with why narratives work.
The effectiveness of a narrative is based on the fact that most people are able to project themselves into someone else’s position if they invest a little energy. This is easier, the more like you the person is seen to be. As a result the best narratives are those that feature people as close to how the audience sees themselves as possible. For those who want to explore this further consult Adam Smith’s Theory of Moral Sentiments.

OK, so we can make a story more compelling by choosing someone as like the audience as possible – big deal. This introduces to proximity. This is the idea that events you can relate to are seen as more likely than they actually are. If your friend’s house is robbed you’re more likely to worry about your house, even if you live across the city. Somebody you don’t know in the next neighborhood – not much effect. The more like you the person in the story seems, the bigger the impact on probability.

Here’s another way things can get weird, some of the some things that make for an interesting story generally screw with our perceptions. For example, more extreme events are more interesting and a lot easier to recall. This taps into what is called availability; the easier it is for you to recall an event the more likely you believe it to. A good narrative will increase availability in both these ways, even when the event is incredibly unlikely.

We also have to worry about issues like representativeness and conservatism. Representativeness just means we assign probabilities based on a prior belief and how well any new data represents the conditional event. If 85% of cars are blue and somebody who is wrong 20% of the time tells you a car is blue, you’ll generally go with an 80% chance the car was blue. This, for those who have studied stats, is dead wrong, the correct answer is 41%. Conservatism means you start with a prior and resist updating your beliefs by giving little weight to new data. So a good narrative can entrench an incorrect belief very easily.

We also have to deal with the so called, law of small numbers. The idea here is that a remarkably small sample should be representative of the population that generated the sample. This really comes out when you ask people to generate a small set of random numbers. The numbers they come up with tend to have negative autocorrelation (a big number is followed by a small one), which means the series isn’t random. What this means is that a small number of narratives are often assumed to be representative of an entire population, particularly if those few narrative agree. This just isn’t the case.

Don’t get me wrong, I’m not saying there is no place for narrative in research or teaching, they’re a great place to start, but a horrible place to stop. If all we consider are narratives, however, we’re going to get it wrong.

Relative Poverty or Consumerism: Choose your evil.

I recently spent a day in a room with public school teachers, education experts, and humanities types. I was the only one there with any experience in empirical data or the kind of formal logic commonly used in empirical disciplines. My time with this group (I get to do it again for 2 days in May) will form the basis of a number of posts, but here’s the first.

One of the things that gets this group worked is consumerism (they often use the label capitalism but consumerism is more accurate). “Consumerism is the capitalist system trying to keep the worker down.” “Consumerism is destroying the planet.” “Consumerism is nothing but lies designed to keep us all unhappy.” If you’re reading this I’m sure you are familiar with this type of statement. I’m not going to discuss the veracity of these statements here (there is some truth here). I want to focus on a glaring hypocrisy.

Shortly after the outpourings affirmation of anti-consumerist ideology the discussion moved onto relative poverty, specifically decrying the unequal distribution of income for people identified as indigenous. (To be honest there was some actual discussion education related topics in between, but nobody want to hear about that).

I want to be clear – there are some people in this country living in deplorable conditions, many of them on reserves. In many cases, these living conditions count as absolute poverty and need to be addressed. Not tomorrow, but now! (Yes, I do have a suggestion, but it definitely isn’t politically correct and would likely piss off a number of people who were in that room to no end). Absolute poverty isn’t what they were talking about so I won’t either.

The concern was that some people are getting richer faster than others, as it always is with relative poverty. It isn’t about the fact that some people don’t have enough to meet a standard of living we would consider basic in this country (living high off the hog in most of the world).

So what does an increase in relative poverty without an increase in absolute poverty mean? It means that some are getting richer faster than others. So what does becoming richer really mean? It means you have more consumption opportunities than before. Consider your stereotypical working class joe. They are in no danger of starving to death, generally have a decent roof over their head, and can even afford some luxuries. So why would anyone be worried that other people are becoming relatively richer? The only possible reason is that consumption and *gasp* consumerism yields benefits. For the argument to make any sense it must be that the group getting richer is gaining happiness and those not getting richer are not. Remember that the only meaningful difference is consumption opportunities. If consumer is so “bad” we should be celebrating any reduction in consumption opportunities of any group. The worry about relative poverty is the worst form of keeping up with the Joneses.

So which is it, is consumerism bad or is relative poverty bad?