A taxonomy of aggregate output (Actual, Forecast, Natural, Potential, Efficient)

Actual GDP:  Just that

Forecast GDP:  Actual + no further shocks

Natural GDP:  Forecast + full utilisation (i.e. no current or residual shocks, either)

Potential GDP:  Natural + fully flexible prices

Efficient GDP:  Potential + no market power

That then gives three different versions of an output gap:  Actual minus Natural, Potential or Efficient.

For some models, there is no difference between Natural GDP and Potential GDP.  I don’t like those models.

WTF?

I just got this email from the careers service here at LSE (emphasis mine):

A Conservative MP is looking for support in his role on the Public Accounts Select Committee.

The position is paid £7.85 p/h and will be for approx 15 hours per week.

The successful candidate must have excellent financial understanding in order to examine and analyse accounts.

The candidate should be inquisitive and have an interest in challenging public accounts.

The candidate should also be able to draft their findings into concise briefings and press releases.

To apply please send your CV and covering letter (1 page max) to XXXX by email XXXX@lse.ac.uk ASAP

£7.85 per hour?  Are they kidding?  They’re sending this to every economics Ph.D. candidate at the London School of EconomicsWhat the f*** are they thinking?  (the first person to say “non-monetary incentives” gets a clip ’round the ear)

Update 23 September 2010: Professor Frank Cowell, over on facebook, points us towards:

Gneezy, U. and Rustichini, A. (2000) “Pay Enough or Don’t Pay at All“, Quarterly Journal of Economics, 115, pp. 791-810.

Here’s the abstract:

Economists usually assume that monetary incentives improve performance, and psychologists claim that the opposite may happen. We present and discuss a set of experiments designed to test these contrasting claims. We found that the effect of monetary compensation on performance was not monotonic. In the treatments in which money was offered, a larger amount yielded a higher performance. However, offering money did not always produce an improvement: subjects who were offered monetary incentives performed more poorly than those who were offered no compensation. Several possible interpretations of the results are discussed.

People are not rational maximisers of von Neumann-Morgenstern utility

Here are two examples:

  • At every sandwich shop in Britain (Pret A Manger, Eat, etc), when you attempt to pay for your sandwich you will be asked if you will be eating in or taking the food out of the shop.  The reason is that, thanks to the complexities of the UK tax system, the shop is meant to pay VAT if you dine in, but they don’t have to if you take it out.

    The shop doesn’t care in the slightest whether you actually eat in or out. So long as they’ve asked you about your intentions, they’re legally covered.  The upshot is that for anybody actually intending to eat their sandwich in the shop, the rational thing to do is to say that you’re taking it out and then eat in the shop anyway.  If anybody asks why you chose to do so, simply explain that you changed your mind. Since the sandwich shop doesn’t care, the probability of being caught is zero; and since you can always say that you changed your mind even if you were, the cost of being caught is precisely none.  Hence, the rational von Neumann-Morgenstern expected utility maximiser should never pay more than the take-out price. But people do …

  • When you book cinema tickets online, you have the option of selecting a student discount.  Cinemas love online bookings because you then collect your ticket from a machine instead of a person.  That means that they’re free to either hire one less person, or put the person saved onto the candy counter.  It also means that they can have ticket collection take up less space and expand the candy counter (where all the fat profit margins are located).You do not need to show a student card when collecting your ticket from the machine at the cinema.

    You do not need to show a student card when entering the cinema with your ticket.  You can, in fact, claim a student discount without any risk of being asked to prove that you are actually a student.  But people don’t …

Both of these examples are of price discrimination by a monopolist.  In the second example, the Cinema is the discriminator, charging less to students because students, in general, have a lower willingness to spend than non-students.  In the first example, the UK government is the discriminator.  The people with the lower willingness to pay are those that are prepared to take their food out rather than dine in.

In standard economic theory, both examples should succeed only if a) people are risk averse — which, in general, they are — and b) there is a non-zero chance that a “cheater” will get caught and suffer some loss as a result.  Even then, the probability-weighted loss from being caught would need to exceed the probability-weighted gain from successfully “cheating”.

But since the probability of being caught in these examples is zero and, with the sandwich shop, at least, the loss from being caught is also zero, the theory breaks down here.

I suspect that even Loss Aversion, a consequence of Prospect Theory, would fail to explain people’s behaviour here because we are talking about zero-probability events.

I don’t think we can avoid including social norms and ethics to explain them.  People have a socially-conditioned aversion to lying (saying that you will take the food out when you really intend to eat in; saying that you’re a student when you’re not) and this is what offsets the gain from the deception.  It also pretty clearly depends on the size of the gain relative to some internal scale.  A non-student with a low income is more likely to pretend to be a student than someone with a high income.

Changing the typesetting margins in Scientific Workplace

At least half of the LSE economics department uses Scientific Workplace, but an absurdly large fraction of all PDFs they produce have two-inch margins so they end up wasting half the page.

I finally got sufficiently annoyed to discover how to change it:

  1. Open a SW tex file
  2. Under the ‘Typeset’ menu, choose ‘Options and Packages…’
  3. Under the ‘Packages’ tab, add the ‘geometry’ package
  4. Under the ‘Typeset’ menu, choose ‘Preamble…’
  5. Add a line at the end specifying the margins.

For example:

\geometry{left=1in,right=1in,top=1in,bottom=1in}

Units of measurement available are listed on the webpage where I got this:  http://www.mackichan.com/index.html?techtalk/370.htm

The short-long-run, the medium-long-run and the long-long-run

EC102 has once again finished for the year.  It occurs to me that my students (quite understandably) got a little confused about the timeframes over which various elements of macroeconomics occur.  I think the reason is that we use overlapping ideas of medium- and long-run timeframes.

In essense, there are four models that we use at an undergraduate level for thinking about aggregate demand and supply.  In increasing order of time-spans involved, they are:  Investment & Savings vs. Liquidity & Money (IS-LM),  Aggregate Supply – Aggregate Demand (AS-AD), Factor accumulation (Solow growth), and Endogenous Growth Theory.

It’s usually taught that following an exogenous shock, the IS-LM model reaches a new equilibrium very quickly (which means that the AD curve shifts very quickly), the goods market in the AS-AD world clears quite quickly and the economy returns to full-employment in “the long-run” once all firms have a chance to update their prices.

But when thinking about the Solow growth model of factor (i.e. capital) accumulation, we often refer to deviations from the steady-state being in the medium-run and that we reach the steady state in the long-run.  This is not the same “long-run” as in the AS-AD model.  The Solow growth model is a classical model, which among other things means that it assumes full employment all the time.  In other words, the medium-run in the world of Solow is longer than the long-run of AS-AD.  The Solow growth model is about shifting the steady-state of the AS-AD model.

Endogenous growth theory then does the same thing to the Solow growth model: endogenous growth is the shifting of the steady-state in a Solow framework.

What we end up with are three different ideas of the “long-run”:  one at business-cycle frequencies, one for catching up to industrialised nations and one for low-frequency stuff in the industrialised countries, or as I like to call them: the short-long-run, the medium-long-run and the long-long-run.

Negative productivity shocks are conceptually okay when applied idiosyncratically to labour

This is mostly a note to myself.

Way back in the dawn of the modern-macro era, the fresh-water Chicago kids came up with Real Business Cycle theory where they endogenised the labour supply and claimed that macro variation was explained by productivity shocks.

The salt-water gang then accepted the techniques of RBC but proposed a bunch of demand-side shocks instead.

The big criticism of productivity shocks has always been to ask how you can realistically get negative shocks to productivity.  Technological regress just doesn’t seem all that likely.

Now, models of credit cycles like Kiotaki (1998) show how a small and temporary negative shock to productivity can turn into a large and persistent downturn in the economy.  In short:  Credit constraints mean that some wealth remains in the hands of the unproductive instead of being lent to the productive sectors of the economy.  The share of wealth owned by the productive is therefore a factor in aggregate output.  A temporary negative shock to productivity keeps more of the wealth with the unproductive for production purposes and it takes time for the productive sector to accumulate its wealth back.  If some sort of physical capital (e.g. land) is used as collateral, the shock will also lower the price of the capital, thus decreasing the value of the collateral and so imposing tighter restrictions on credit.

But Kiyotaki’s model still requires some productive regress …

Looking at Aiyagari (1994) and Castaneda, Diaz-Gimenez and Rios-Rull (2003) today (lecture 3 by Michaelides in EC442), I realise that small negative productivity shocks are conceptually okay if they’re applied idiosyncratically (i.e. individually) to labour.

Let s_{t} be your efficiency state in period ts is a Markov process with transition matrix \Gamma_{ss}e\left(s\right) is the efficiency of somebody in state s.  Castaneda, Diaz-Gimenez and Rios-Rull use this calibration, taken from the data:

State s=1 s=2 s=3 s=4
e(s) 1.00 3.15 9.78 1,061.00
Share of population 61.1% 22.35% 16.50% 0.05%

The transition matrix would be such that the population-shares for each state are stationary.

A household’s labour income is then given by e(s)wl.

A movement from s=3 to s=2, say, is therefore a negative labour productivity shock for the household.

The trick is to think of the efficiency states as job positions. Somebody moving from s=3 to s=1 is losing their job as an engineer and getting a job as an office cleaner.  They will probably increase l to partially compensate for the lose in hourly wage (e\left(s\right)w).

Remember that in the (Neo/New) Classical models, there’s an assumption of zero unemployment.  However much you want to work, that’s how much you work.  [That might sound silly to a casual reader, but it’s okay as a first approximation.  There are (i.e. search-and-matching) models out there that look at unemployment and can be fitted into this framework.]

If everybody is equally good at every job position (as we have here) and all the idiosyncratic shocks balance out so the population shares are constant, then – I believe – there shouldn’t be any change in observed aggregate productivity.

However, if you introduced imperfect transfer of ability across positions so that efficiency becomes e\left(s,\theta\left(s\right)\right) where \theta\left(s\right) is your private type per job position, then idiosyncratic shocks could therefore show up in aggregate numbers.

This is essentially an idea of mismatching.  A senior engineering job is destroyed and a draftsman job is created both in Detroit, while the opposite occurs in Washington state.  Since the engineer in Detroit can’t easily move to Washington, he takes the lower-productivity job and a sub-optimal person gets promoted in Washington.

Is economics looking at itself?

Patricia Cowen recently wrote a piece for the New York Times:  “Ivory Tower Unswayed by Crashing Economy

The article contains precisely what you might expect from a title like that.  This snippet gives you the idea:

The financial crash happened very quickly while “things in academia change very, very slowly,” said David Card, a leading labor economist at the University of California, Berkeley. During the 1960s, he recalled, nearly all economists believed in what was known as the Phillips curve, which posited that unemployment and inflation were like the two ends of a seesaw: as one went up, the other went down. Then in the 1970s stagflation — high unemployment and high inflation — hit. But it took 10 years before academia let go of the Phillips curve.

James K. Galbraith, an economist at the Lyndon B. Johnson School of Public Affairs at the University of Texas, who has frequently been at odds with free marketers, said, “I don’t detect any change at all.” Academic economists are “like an ostrich with its head in the sand.”

“It’s business as usual,” he said. “I’m not conscious that there is a fundamental re-examination going on in journals.”

Unquestioning loyalty to a particular idea is what Robert J. Shiller, an economist at Yale, says is the reason the profession failed to foresee the financial collapse. He blames “groupthink,” the tendency to agree with the consensus. People don’t deviate from the conventional wisdom for fear they won’t be taken seriously, Mr. Shiller maintains. Wander too far and you find yourself on the fringe. The pattern is self-replicating. Graduate students who stray too far from the dominant theory and methods seriously reduce their chances of getting an academic job.

My reaction is to say “Yes.  And No.”  Here, for example, is a small list of prominent economists thinking about economics (the position is that author’s ranking according to ideas.repec.org):

There are plenty more. The point is that there is internal reflection occurring in economics, it’s just not at the level of the journals.  That’s for a simple enough reason – there is an average two-year lead time for getting an article in a journal.  You can pretty safely bet a dollar that the American Economic Review is planning a special on questioning the direction and methodology of economics.  Since it takes so long to get anything into journals, the discussion, where it is being made public at all, is occurring on the internet.  This is a reason to love blogs.

Another important point is that we are mostly talking about macroeconomics.  As I’ve mentioned previously, I pretty firmly believe that if you were to stop an average person on the street – hell, even an educated and well-read person – to ask them what economics is, they’d supply a list of topics that encompass Macroeconomics and Finance.

The swathes of stuff on microeconomics – contract theory, auction theory, all the stuff on game theory, behavioural economics – and all the stuff in development (90% of development economics for the last 10 years has been applied micro), not to mention the work in econometrics; none of that would get a mention.  The closest that the person on the street might get to recognising it would be to remember hearing about (or possibly reading) Freakonomics a couple of years ago.

Moving the mainstream (some notes)

I’ve been wanting to write an essay on this for ages, but every time I think or talk to someone about it, I get hit with more ideas and different approaches. In the interests of not forgetting them, I thought it might be worthwhile formalising, if not my opinions, then at least the topics that I want to write on. I’m very interested in people’s opinions on these, so if you have a particular view, please leave some comments.

  1. Economics as an expression of ideology
  2. Language choice as:
    1. (+ve) a means of aiding communication in a specialised field
    2. (+ve) a means of enforcing definitional and therefore intellectual rigour [e.g. arguments over the meaning of “market failure”]
    3. (~) a shaper of methodology
    4. (~) a signal of author competence or paper quality [e.g. “the market for lemmas” or the comment made by a French philosopher, mentioned by Daniel Dennett in a footnote of his book “Breaking the spell”]
    5. (-ve) an embodiment of ideology or bias [e.g. 95% of the work in feminism interpretting literature seems to be in highlighting this sort of stuff]
    6. (-ve) a barrier to outside comment or involvement
  3. The fact that mathematics in general and modelling in particular are each a choice of language
  4. “All models are wrong; some are useful” — George Box
  5. The different purposes of models:
    1. to explore the implications of particular assumptions [moving forwards]
    2. to illustrate the possibility (or plausibility) of a particular outcome [moving backwards]
    3. to explain an observed outcome, or a collection of observed outcomes [moving backwards]
  6. Closed-form (i.e. analytically solvable) modelling versus simulation modelling
  7. Empirical work: justifying assumptions versus confirming outcomes (or challenging either)
  8. Simplifying assumptions versus substantive assumptions
  9. The reasonableness of assumptions:
    1. Representative assumptions [e.g. Friedman’s billiards player]
    2. Direct behaviour versus emergent behaviour
    3. The importance of context [e.g. what is valid at the individual level may not be at the aggregate level]
  10. Fashions and fads in academia. The conflict between:
    1. The need to tackle “the big issues”
    2. The desire to stand out (do something different)
    3. The impulse to follow-the-leader/jump-on-the-bandwagon
    4. The (incentive driven ?) need to publish rapidly, frequently and consistently [i.e. the mantra of “publish or perish“]
    5. The desire to influence real-world policy or public opinion
  11. Heuristics in academia. Rules-of-thumb or a preference for particular techniques. Is it “better” to learn a few types of model extremely well than several models reasonably well? It does allow researchers to jump onto a new topic and produce a few papers very quickly … [e.g. this]
  12. Mainstream conclusions (or opinions) versus mainstream methodology
  13. How to move the mainstream:
    1. Stay in and push or jump out and call to those still in? [e.g. See, in particular, all the discussion on the topic of heterodoxy vs. orthodoxy and Keynesianism vs. Neoclassicalism around the blogosphere before, during and after this comment by Brad DeLong]
    2. The importance of data
    3. The importance of tone and language
    4. The importance of location (both institution and country) [e.g. Justin Wolfers: “I could do the same work I’m doing now for an Australian institution, and the truth is, no one would listen“]
    5. The importance of academic standing
    6. The risk versus the reward