Monthly Archive for August, 2007


Afghan opium production (Updated)

The UN Office on Drugs and Crime has just released their latest report, including details on opium production in Afghanistan. Here are a few of the statistics:

2006 Year-on-year difference 2007
Net opium poppy cultivation 165,000 ha +17% 193,000 ha
In per cent of agricultural land 3.65% 4.27%
Eradication 15,300 ha +24% 19,047 ha
Weighted average opium yield 37.0 kg/ha +15% 42.5 kg/ha
Potential production of opium 6,100 mt +34% 8,200 mt
In percent of global production 92% 93%
Number of households involved in opium cultivation 448,000 +14% 509,000
Afghanistan GDP US$ 6.7 billion +12% US$ 7.5 billion
Afghanistan GDP per capita US$ 290 +7% US$ 310
Total farm-gate value of opium in per cent of GDP 11% 13%
Indicative gross income from opium per ha US$ 4,600 +13% US$ 5,200
Indicative gross income from wheat per ha US$ 530 +3% US$ 546

Make sure you read that correctly: Area under cultivation is up 17% and area eradicated is up 24%. That might make you think (as it did me originally) that the total area producing is down, but you’d be wrong … the 17% was on a huge area and the 24% was on a tiny area. Net productive area went from 149,700 ha to 173,953 ha — an increase of 16%. On top of that, yield per hectare is up 15%, so overall output is up 34%.

GDP/capita is US$310 and — assuming that your crop isn’t eradicated by the US — income from growing opium poppy is US$5,200 per hectare (or $US2,100 per acre). That’s an awfully big incentive. On top of all that, I understand that one of the big appeals of growing poppy is that opium has an extremely high value per unit volume and per unit weight, which are important considerations for a farmer living in a country with very low-quality and high-cost transportation infrastructure.

I wonder how they got the yield up so quickly? Was it improved infrastructure (watering systems), the dedication of better quality land to poppy cultivation, better inputs (fertilizer) or something else? Whatever the answer, that looks to me to be fairly serious evidence of planned investment. These farmers are not just doing this on the side.

This would be a good time to point readers to this opinion piece by Willem Buiter (blog, NBER, LSE) . The discussion by other FT contributors is also well worth reading. Here’s a taster from the first paragraph so you know what he’s saying:

As an economist with a strong commitment to personal liberty and responsibility, my preference would be to see all illegal drugs legalised. The only exception would be substances whose consumption leads to behaviour likely to cause material harm to others.

Further update (30 Aug):

The Economist has produced this graph to illustrate world opium production since 1990:

[ Image removed because it was messing with my site. Click on the Economist link to see it ]


Two questions for the punters

1) Does the large gap in the implied probabilities of a Labor win in the upcoming Australian Federal Election between overall-result betting (62% at the latest update) versus seat-by-seat betting (23% at the latest update) imply some sort of arbitrage opportunity? Is the overround really that large?

2) Why the gap in the first place? I assume it’s got something to do with the particulars of just how marginal each seat is (see below), but why do the two markets disagree so much? Is one of them massively incorrect?

For those that don’t know, I’d recommend looking at the tabular and graphical representations of the marginal seats here. Here are the guts of it (there are 150 seats in the House of Representatives):

Uniform swing Labor gains Labor total Coalition total Result
2.92% (+13) 73 75 Clear Coalition win
3.27% (+14) 74 74 Sort-of-hung parliment (depending on the two independents)
3.27% (+15) 75 73 Sort-of-hung parliment (depending on the two independents)
4.85% (+16) 76 72 Clear Labor win

The first 13 seats for Labor only need a 2.92% swing, or 0.22% per seat. The 14th seat will take an extra 0.35%, the 15th a further 0.90% again and the 16th another 0.71% on top. Remember that each extra percent of swing to Labor takes an increasing amount of goodwill from the electorate (i.e. increasing your swing from 1% to 2% is doable, but increasing it from 25% to 26% is effectively impossible).


A quote from Keynes

“There is nothing a government hates more than to be well-informed; for it makes the process of arriving at decisions much more complicated and difficult.”

John Maynard Keynes, The Times (March 11, 1937); Collected Writings, vol. 21, p. 409

(via Bruce Bartlett (via Brad DeLong))

Ahhh … the sweet, soothing succour of cynicism. šŸ™‚


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 Iā€™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

Managing the news cycle

Peter Martin draws attention to the Australian Treasury press release listing the adjusted figures for the government surplus in 2006-07:

Preliminary estimates indicate that the Australian Government general government sector recorded an underlying cash surplus of $17.3 billion for 2006-07, which is $3.7 billion higher than expected at the time of the 2007-08 Budget.

A commenter on Peter’s site asks the obvious question:

Is it a good thing that treasury gets it’s numbers so consistently wrong? Who is responsible for the mistake – in this case an error of 27%. If it had gone 27% the other way who would have copped it?

27% is indeed a very large adjustment, and it’s rather difficult to imagine this sort of revision being made in the other direction. It is possible that the adjustment is just as much a surprise to Mr Costello as it (nominally) is to the media, but I suspect that it was always known – or at least believed – in the Treasury that the figures included in the 2007-08 Budget were too low. They will have, at best, decided to err on the side of caution (in case their internal numbers were wrong or there was a sudden crisis that worked against it) and, at worst, knowingly understated the true figures in order to guarantee the political capital boost they’d get later in the election cycle (i.e. now) when the upwardly revised figures were released.

I’ll leave it up to the audience to come to their own conclusions on which is the more likely explanation.