Measuring Development II – Living Standards and GDP per capita

In the first blog entry I wrote about the meaning of the word “development” and it led us to an interesting discussion of whether our definitions of development are normative or positive. In the second part of the “Measuring Development” series I would like to talk a bit about an indicator of development: living standards.

Living standards are obviously of utmost importance to economics. Sen (1988, p.11), for instance, points out that “the enhancement of living conditions must clearly be an essential – if not the essential object of the entire economic exercise”. Clearly, we can mean different things when we talk about living standards. To start with a rather simple differentiation, I distinguish between material living standards (MLS) and a broader notion of living conditions (LC), which includes aspects of life that go beyond the material component (i.e. education, health, job satisfaction,… ). The following paragraphs discuss whether GDP per capita successfully measures MLS.

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QE is the Fed’s Way to Target Unemployment

During my recent research I thought it would be interesting to look at what different central banks say they target and what they really target. To anyone that does not study economics and reads this, you might think about skipping it. It’s probably going to get somewhat technical. Also, this is as much to find out if what I’m doing makes sense as it is to actually present some of the stuff I’ve stumbled upon.

As many papers have showed, different forms of Taylor rules have worked pretty well in the past in describing particularly the behavior of the Fed in an ex-post fashion. Taylor rules, in term, are essentially nothing else than a formula that describes a central bank’s attempt to minimize deviations from its targets. Tinkering around a bit, the following form of the Taylor rule seems to be able to explain around 95% of Fed behavior (which seems too good to be true, but combing through econometrics books has so far not forced me to assume I did something coming up with my regression results):

TaylorThis isn’t too different from the basic Taylor rule. The only thing it adds is an interest rate lag, essentially the simplest way to take interest smoothing behavior into account, which has been shown many times to be important in describing Fed policy decisions. As a side note, this interest rate smoothing behavior makes it so the Taylor rule no longer obeys the Taylor principle: a rise in inflation of 1% is no longer countered by a rise in the interest rate of more than 1%. The new rule also replaces the output gap with the unemployment gap, in line with the Fed’s official mandate. Let me throw out some graphs for you below the fold. Excuse the somewhat unaesthetic presentation, I’m still coming to grips with R.

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Monetary Policy Should not Care About Fiscal Policy

For some reason a fairly extensive part of the literature on monetary economics is dedicated on the “interaction between monetary and fiscal policy”. I am not sure what this means, nor why it matters at all. As a core idea, and again resorting to Friedman’s notion that Inflation is always and everywhere a monetary phenomenon in the sense that it is and can be produced only by a more rapid increase in the quantity of money than in output”, the level of inflation is 100% under the control of and thus determined by the central bank. The only additional assumptions required to make this hold is central bank independence, the importance of which has been long established, and at least some degree of central bank competence, without which we can essentially stop talking about monetary policy in general.

Let’s start from a point where a central bank is achieving its target, however defined. Now let’s bring in fiscal policy, and let’s say it tries to increase spending. Clearly this increase in spending would, momentarily, move the economy away from what the central bank is targeting, which would require central bank intervention to bring it back on track. In the end, for the macroeconomic indicators that are generally included in the objective function of central banks, the fiscal policy does not do much after the initial short-lived shock. The whole concept is essentially summarized by the concept of “monetary offset”, basically explained in this paper by Scott Sumner (.pdf).  This inability of fiscal policy to do much on a macroeconomic level in a world where the central bank actually does its job is a feature, not a bug. Fiscal policy should concentrate on achieving democratically determined goals, and as such it can legitimate alter e.g. the income and wealth distribution in an economy in order to bring them in line with this consensus view on what it should be, as well as give the central bank a different target if it so desires. The optimal target a central bank should be given very much depends on what is possible on a fiscal level, particularly in terms of the ability of governments to tax different things. Yet once that is determined, the central banks job is to make sure that whatever the fiscal authorities do does not move the economy away from the target it’s job it is to achieve. Apart from the target setting itself this is essentially a strictly objective task.

In other words the central bank will always dominate the fiscal authorities given the two basic assumptions I made. It should not be the job of fiscal policy to achieve these targets in the first place. If a situation arises where someone has to give in, it is always the fiscal authority that will unless at least one of the two assumption is violated. In terms of final macroeconomic variables targeted by the central bank (once its target is set) like the level of inflation or the level of NGDP, what fiscal policy does, or even its very existence, is irrelevant.

Bubble Detection: The Challenge

Damned! It seems Flo and I will never truly disagree about anything.

Let me summarize our agreement: A fully rigorous definition of fundamental value is “the expected value, conditional on the information available today, of the discounted sum of future cash flows”. Fundamental value is equal to the equilibrium price we would get in an efficient market. That’s the efficient market hypothesis (which is a misnomer, because it’s not just a hypothesis but a true theorem). Hence what we mean, conceptually, when talking about a bubble is the deviation of the actual price from this hypothetical equilibrium price. And these deviations can come from a) people being stupid in the sense of not taking into account all the available information and/or b) market imperfections like transaction costs or costs of information gathering.

Our disagreement is that, while Flo thinks there could be reliable ways to detect bubbles in the real world ex ante, I don’t. There’s only one way to find out. I can “predict” 50% of all bubbles merely by chance (e.g. by flipping a coin). So this must be the benchmark for any bubble spotting strategy. Let’s see whether credit growth can successfully predict the next 5 bubbles. Under the null hypothesis that credit growth has no better predictive power than a coin, the probability of guessing 5 out of 5 bubbles right is 3,125%. If Flo can do it, we can reject the null at a reasonable level of significance. Or, if you prefer, we can also reject the null if credit growth is able to predict at least 15 out of the next 20 bubbles (p-value 2%).

Fancy a bet, Flo?

On the impossibility of spotting bubbles

I finally found something where I really disagree with Florian. I think there is no reliable indicator to spot bubbles in advance. And I don’t that we will ever get one.

Like Katharina, I’m a big fan of clear definitions. The reason is that, particularly in discussions of economic issues, words often appear to have a clear meaning, but when you look at them more carefully you realize that they are either completely meaningless or their meaning is very different from what everyone thinks.

So what does Florian, or anybody else, mean when they talk about a bubble? Well, the standard definition is something like this: An asset bubble is a prolonged period of time in which the market price of an asset exceeds the asset’s fundamental value. And the fundamental value is the present value of future cash flows which the asset is expected to generate. Fair enough. So how can we spot bubbles?

According to Flo, “bubbles are generally characterized by building up exponentially”. Hence one should be able to detect bubbles by comparing current asset prices to their long-run (linear) trend. Let’s think about this. I see a stock steadily rising from $1 in December 2010 to $2 in December 2012. Suddenly in January 2013 the stock goes to $4 deviating from the 2010-2012 trend. Would you seriously infer from this fact alone, that there’s a bubble? Come on. In an efficient market, if investors receive new information in January which they didn’t have in December, the price should react immediately to reflect that new information. If fundamentals have an exponential trend, so should prices. Where is the theory that suggests that fundamentals always change linearly with time?

But what really puzzled me is Flo’s remark further down in his post where he writes “…the price of a stock, which by definition is not much more than present value of (expected) future incomes…”. If you believe that stock prices are always equal to the present value of expected future earnings, the whole talk about bubbles becomes meaningless. In other words, the concept of a bubble only makes sense if you believe in some kind of market inefficiency that allows persistent deviations of asset prices from fundamentals.

The fact of the matter is that we can’t observe fundamental value, so there is no straightforward way to spot bubbles. And everyone who says otherwise should be regarded with suspicion. If there would be an easy and reliable bubble detection recipe (“just look at deviations from a linear trend”, “just look at price/earnings ratios”, etc.), you should be able to make lots of money following that recipe. And if a significantly large group of investors follow it, there would be no bubbles to be detected according to this recipe in the first place.

The recent experience with Robert Shiller’s cyclically adjusted price-to-earnings ratio (CAPE) nicely illustrates this problem. In 1996, Campbell and Shiller showed in a paper that the CAPE could predict the stock market returns in the past fairly well. They were able to spot both the dotcom bubble of the late 90s and the subprime bubble of the 00s before they burst by comparing the current CAPE to its long-run average of 16. Now, during the past two years, the CAPE was well above that value which apparently led a lot of people to stay out of stocks during this time. And guess what, they missed a pretty nice rally. So just because some indicator successfully predicted bubbles in the past does not mean it is of much use in the future.

On the Fears of Bubbles

Katharina recently posted some of her thoughts on the developments in asset markets since the start of the FED’s different unconventional monetary policy programs. I honestly do not know how to go about answering the post, as I personally am unable to get much out of the FT report. But let me give it a try. A couple of months back I posted something dubbed “How destructive are asset bubbles really?”, which I find myself still mostly agreeing with. Of course a lot of that argument is based on monetary policy being able to be effective, which might be somewhat limited given we’re up against the ZLB, but right now is not the place to go into that discussion again. When it comes to identifying whether a bubble is indeed forming or not, I shared some of my thoughts in this post on “The Science of Bubble Spotting”, which probably provides a decent starting point to build on. So let’s get into it.

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A Financial Times Special Report and Some Thoughts on the Ineffectiveness of Monetary Policy

Let me start this by saying that I am reluctant to post about monetary policy. I read Florian’s posts with great interest and some reverence, due to his enormous knowledge on the issue. However, at the moment there is an interesting special report series going on in the Financial Times Video section. John Authers is interviewing a number of interesting people and showing different charts that support his hypothesis that the current US “recovery” is the direct result of monetary policy and, more importantly, completely unstable. I found his figures so interesting, that I decided to share them with you. Unfortunately the ft content is restricted to subscribers (you could get a four weeks for 4€ digital test-subscription though), which is why I’ll give you a brief summary of what he is showing. [Edit: I just saw that the ft has made the videos available on youtube. I added the links below]

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