Economic models
Big questions and big numbers
Jul 13th 2006
From The Economist print edition
http://www.economist.com/business/displaystory.cfm?story_id=7159491
We cannot live without big and ambitious economic models. But neither can we
entirely trust them.
AMONG the many gadgets, instruments and artefacts in its care, Londons
Science Museum holds a peculiar contraption that most resembles the work of a
deranged plumber. Yellow tubes connect together a number of tanks and cisterns,
around which coloured water can be pumped. Sluices and valves govern the flow
of liquid and makeshift meters record the water-levels.
The plumber responsible for this device was William Phillips. Educated as
an engineer, he later converted to economics. His machine, first built in 1949,
is meant to demonstrate the circular flow of income in an economy. It shows how
income is siphoned off by taxes, savings and imports, and how demand is
re-injected via exports, public spending and investment. At seven feet (2.1
metres) high, it is perhaps the most ingenious and best-loved of economists
big models.
Economists today use computers and software not perspex and piping, but they
share Phillipss itch to build models that faithfully mirror the real economy.
For each of the big economic questions facing the world (What do we stand to
gain from a global trade deal? By how much has expensive oil retarded growth?
What might be the economic costs of an avian flu pandemic?) there is a model
that will provide a big numerical answer ($520 billion, 1.5% of world GDP, and
$4.4 trillion, respectively). Such figures are trotted out far and wide. But
can we entirely trust them?
Economic models fall into two broad genres. Macroeconomic models, the distant
descendants of Phillipss machine, belong mostly in central banks. They capture
the economys ups and downs, providing a compass for the folks with their hands
on the monetary tiller. The second species, known as computable general
equilibrium (CGE) models, largely ignore the vagaries of the business cycle.
They concentrate instead on the underlying structure of production, shedding
light on the long-term repercussions of such things as the Doha trade round, a
big tax reform or climate change.
Both kinds of model share a debt to Leon Walras, a 19th-century French
economist. Walras was adamant that one could not explain anything in an economy
until one had explained everything. Each marketfor goods, labour and
capitalwas connected to every other, however remotely. This interdependence is
apparent whenever faster car sales in Texas result in an increase in grocery
shopping in Detroit, the home of Americas big three carmakers. Or when steep
prices for oil lead, curiously enough, to lower American interest rates,
because the money the Saudis and the Russians make from crude is spent on
American Treasury bonds. This fundamental insight moved one economist to quote
the poetry of Francis Thompson: Thou canst not stir a flower/Without troubling
of a star.
Flowers and zombies
Such thinking now comes naturally to economists. But it still escapes many
politicians, who blindly uproot flowers, ignorant of the celestial commotion
that may ensue. They slap tariffs on steel imports, for example, to save jobs
in Pittsburgh, only to find this costs more jobs in the domestic industries
that use the metal. Or they help to keep zombie companies aliverolling over
their loans, and preserving their employees on the payrollonly to discover
they have starved new firms of manpower and credit. Big models, which span all
the markets in an economy, can make policymakers think twice about the knock-on
effects of their decisions.
Wassily Leontief was one of the first to do more than just theorise about
this tangled web of interdependence. In 1941 he published his book The
Structure of American Economy, which he updated a decade later. Tucked in the
back was a 55cm x 65cm tabletoo big to be printed in the book itselfshowing
the flow of commodities and services back and forth among Americas households,
trading partners and 41 national industries. Of the $5.58 billion-worth of yarn
and cloth that passed out of Americas factory gates in 1919, for example,
$318m was exported, $41m was used up in agricultural production, $31m in making
furniture, $6m in the shoe industry, and so on. In Leontiefs blueprint, each
industry is represented by an equation. The inputs to the industry are entered
on one side of the equation, the industrys output appears on the other. Since
the output of one industry (steel, for example) serves as an input for another
(construction), one cannot solve any equation without
solving them all simultaneously.
In the palm of their hands
Short of good data, and stretched to their computational limits, the early
modellers nonetheless had high ambitions. They aimed not merely to understand
the economy, but to run it. Leontiefs book was translated into Russian; his
techniques studied by Soviet planners. Leif Johansen, a Norwegian economist
often credited with building the first CGE model, put his handiwork to use at
Norways planning ministry. During the second world war, the stewards of
Americas war effort turned to the Cowles Commission, an economics brain-trust,
to help them ration Americas resources. We imagined that we held the
well-being of the economy right in the palms of our hands, one of the Cowles
economists told a journalist, David Warsh. One measure of the modellers
prestige is the disquiet they inspired among free-market types. Leontief noted
the unconcealed alarm among businessmen, who feared that too close and too
detailed an understanding of the structure of the economic machine and
its operation might encourage undesirable attempts to regulate its course.
Such ambitions now seem quaint. In countries not cursed by socialism or war,
the market is left to decide what to produce and in what proportions. But the
state remains responsible for keeping the overall macroeconomy ticking over.
Policymakers are largely indifferent to what is in demand, so long as the tank
of demand remains full.
For three decades after the war they carried out this duty with remarkable
success, aided and abetted by macroeconomic models in the spirit of Phillipss
machine. Empirical economists put a lot of effort into teasing out the
historical relationships between macroeconomic variables, such as inflation and
unemployment. These measurements were fed into their models, which in turn
guided their policy advice.
In 1958, for example, Phillips showed that for long stretches of British
history, high unemployment coincided with low wage inflation, and vice versa.
Many macroeconomic models therefore featured a trade-off between the two: doves
could choose low unemployment at the expense of high inflation; hawks the
opposite.
But in the 1970s these trusted relationships broke down. And in 1976 Robert
Lucas, of the University of Chicago, explained why. Such trade-offs, he argued,
existed only if no one expected policymakers to exploit them. Unanticipated
inflation would erode the real value of wages, making workers cheaper to hire.
But if central bankers tried to engineer such a result, by systematically
loosening monetary policy, then forward-looking workers would pre-empt them,
raising their wage claims in anticipation of higher inflation to come. Cheap
money would result in higher prices, leaving unemployment unchanged.
In short, one could not judge how the macroeconomy would respond to a new
policy based on its behaviour under the old regime. The Lucas critique, as it
was called, brought its author fame and a Nobel prize. But it dealt a big blow
to the confidence of model-makers. As Christopher Sims of Princeton University
has put it, Use of quantitative models as a guide to real-time policy advice
was cast into such deep disrepute that academic research on the topic nearly
completely ceased.
It did not start again until academic economists found new foundations for
their models, foundations that would not shift under their feet when policies
changed. They located this bedrock in the microfoundations of macroeconomic
behaviour. Mr Lucas and his disciples, echoing Margaret Thatcher, believe there
is no such thing as society. Everything that happens at the level of the
economy as a whole is simply the sum of the actions of individual households or
firms. If you know how the representative firm or household makes its
choices, the argument goes, you can forecast how the economy might respond to a
policy, even if that policy has never been tried before.
In the past decade, a number of central banksand even the International
Monetary Fund (IMF)have reared a new generation of practical macroeconomic
models, all of them sporting microfoundations. First-born was Canadas
Quarterly Projection Model in the mid-1990s; its close siblings include the
Bank of England Quarterly Model (BEQM) introduced in 2004; the SIGMA model
groomed by the Federal Reserves International Finance Department; and the
IMFs new Global Economic Model (GEM). Old hands doubt whether the new
microfoundations are quite as secure as they seemthe macroeconomy is surely
rather more than the sum of its partsbut no self-respecting theorist can now
be seen in public without them.
Stabilising the macroeconomy is only one of the responsibilities of
governments in a market economy. They must also raise taxes and most feel the
need to impose tariffs, both of which put rocks in the stream of economic life.
When they contemplate big changes to these policies, most governments cannot
resist turning to CGE models to forewarn them of the consequences.
These models were, for example, a weapon of choice in the battles over the
1994 North American Free-Trade Agreement (NAFTA). The pacts opponents had the
best lines in the debateRoss Perot, a presidential candidate in 1992, told
Americans to listen out for the giant sucking sound as their jobs disappeared
over the border. But the deals supporters had the best numbers. More often
than not, those with numbers prevail over those without. As Jean-Philippe
Cotis, chief economist of the OECD, has put it, orders of magnitude are useful
tools of persuasion.
Pick a number, any number
But how plausible were the numbers? Twelve years on, economists have shown
little inclination to go back and check. One exception is Timothy Kehoe, an
economist at the University of Minnesota. In a paper published last year, he
argued that the models drastically underestimated NAFTAs impact on trade
flows (if not on jobs). The modellers assumed the trade pact would allow people
to buy more of the goods for which they had already shown some appetite. In
fact, the agreement set off an explosion in the exports of many products Mexico
had scarcely traded before. Cars, for example, amounted to less than 1% of
Mexicos exports to Canada before the agreement. By 1999, however, they
accounted for more than 15%. The only comfort economists can draw from their
efforts, Mr Kehoe writes, is that their predictions fared better than Mr
Perots. A low bar indeed.
Dubious computations also helped to usher the Uruguay round of global trade
talks to a belated conclusion in 1994. Peter Sutherland, head of the General
Agreement on Tariffs and Trade, the ancestor of the World Trade Organisation
(WTO), urged negotiators to close the deal lest they miss out on gains as great
as $500 billion a year for the world economy. This figure came, of course, from
a big model.
Even staunch free-traders, such as Arvind Panagariya, an economist now at
Columbia University, thought these claims extravagant and overblown. They
escaped scrutiny, he argued in 1999, because they emanated from gigantic
models, which were opaque even to other economists. Why then did these models
thrive? Supply and demand. Given the appetite of the press and politicians for
numerical estimates and the publicity they readily offer researchers, these
models are here to stay, Mr Panagariya concluded.
That appetite was undiminished at the onset of the next round of trade
negotiations, launched in Doha, the capital city of Qatar, in 2001. Two years
into the round, as trade ministers gathered for a summit in Mexico, the World
Bank was pushing another extravagant simulation. It argued that an ambitious
Doha agreement could raise global incomes by $290 billion-520 billion and lift
144m people out of poverty by 2015. Those figures found a ready place in almost
every news report about the Doha round that autumn.
Such extravagance did not last. The World Bank has since cut these figures
drastically, in part because the ambitions of the Doha negotiators have fallen
short of the banks expectations. One estimate made last year had cut the
increase in global incomes to $95 billion and projected 6.2m people might
instead move out of poverty. But even as they curb their enthusiasm for Doha,
proponents of freer trade argue that CGE models do not show their cause to its
best advantage.
Trades virtuous effects are of two distinct kinds. First, trade helps
countries make the most of what they already have. It frees countries to
allocate their resourceswhether they be cheap labour, fertile land or educated
mindsas efficiently as possible. But, secondly, trade can also allow countries
to accumulate resources more quickly. Indeed, the biggest prizes lie in faster
growth, not heightened efficiency; in accumulation and innovation, not
allocation.
By their nature, CGE models are better suited to capturing the first effect
than the second. They provide before and after snapshots of the economy at
two points in time. They are therefore good at capturing the one-off gains that
might arrive from a redeployment of the economys resources. They are much less
good at capturing the continuing gains that result from a faster accumulation
of capital, or a quickened pace of productivity growth. Most trade models,
indeed, hold productivity fixed.
In a recent article, Dominique van der Mensbrugghe, of the World Bank,
illustrates the much bigger numbers the modellers could produce given a free
hand. He assumes that the very act of exporting raises the productivity of
firms, because selling on world markets forces companies to raise their game
while exposing them to new ideas and techniques. This alternative assumption
raises the gains from free trade in goods by $174 billion (or thereabouts).
These rival assumptions are not right or wrong, but they illustrate how far
the results of CGE models flow from the presuppositions of their authors. Most
empirical exercises confront theory with numbersthey test theories against the
data; sometimes they even reject them. CGE models, by contrast, put numbers to
theory. If the modeller believes that trade raises productivity and growth, for
example, then the models results will mechanically confirm this. They cannot
do otherwise. In another context, Robert Solow, a Nobel prize-winner, has noted
the tendency of economists to congratulate themselves for retrieving juicy
plums that they themselves planted in the pudding.
In a recent article, Roberta Piermartini and Robert Teh, two economists at
the WTO, urge modellers to demystify their creations, making it clear to
their audience what makes their models tick. A failure to do this, they argue,
risks bringing a useful analytical tool into disrepute and may even induce
unwarranted cynicism about the economic case for open trade.
To be fair, most modellers are quite open about the theoretical principles
that underlie their simulations. But to compute an economic model, this theory
has to be given concrete form, spelt out in definite algebraic terms. Alfred
Marshall, one of the fathers of neo-classical economics, distrusted mathematics
for this very reason. To be expressed in mathematical form, he complained, many
important economic considerations had to be clipped and pruned till they
resembled the conventional birds and animals of decorative art. Economic
theory gives only the roughest guide to this pruning. It points out, for
example, that supply rises when prices increase. But does it rise in a straight
line or curve upwards? Perhaps, as prices rise, supply traces out an inverted
U-shape or an S-shape?
Such choices of form matter more than most modellers recognise, argues Ross
McKitrick, of the University of Guelph in Canada. In a 1998 paper, he ran two
simulations of the Canadian economys response to a tax rise. The two
projections shared the same Walrasian philosophy, used identical data and
examined the same 10% tax on the purchase of services; they differed only in
the way they clipped and pruned households and companies, giving different
mathematical expression to the laws of demand and supply. But these subtleties
of expression had profound effects. In the first of his simulations, the tax
rise allowed government spending to increase by more than 60%; in the second,
spending could rise by just 14%. The first simulation makes the tax sorely
tempting to any big-spending Canadian politician; the second much less so. But
the policymakers who swallow these simulations have little way of knowing what
is driving the results: is it deep theory, solid data, or arbitrary
pruning? Sometimes the model-maker himself does not know.
The lost art of plumbing
Phillipss pump-action model was, he wrote, meant for exposition rather than
accurate calculation. But all models should ultimately be seen as pedagogical
devices, their calculations a means to the end of helping policymakers think
through their decisions. Unfortunately, Phillipss model was rather better at
this than many of its more sophisticated successors. It was transparent: you
could see through its casing, trace the flow of expenditures through its pipes
and watch wealth accumulating in its tanks. Get things wrong and prosperity
drained away in front of your eyes. The model was also easy to tinker with:
valves could be loosened, sluices opened and taps tightened. It was clear what
was governing its results.
Shantayanan Devarajan, of the World Bank, and Sherman Robinson, of the
International Food Policy Research Institute, point out that policymakers need
not grasp exactly how a model works, any more than a pilot needs to understand
the insides of a flight simulator. This may be true. But too many policymakers
never even fly their models. They just want to know where they will land. If
they were instead prepared to work through the simulations they might find
inconsistencies in their thought, unforeseen implications of their policies, or
new reasons for their actions. The big number that sums up a models story$520
billion, 1.5% of world GDP, $4.4 trillionis often the least interesting thing
about it.
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