A
non-economist's view of some World Bank aims and policy research
Matt
Berkley
28 October
2004
Draft
Title changed (word “some” added); formatting changed and contact details
added: 8 September 2008
Summary
Five
mistakes by social scientists may help explain some puzzling features of
international statistics.
The
existence of the errors may be of some significance for future policy
decisions.
1.
Longevity error
I once
sought out the World Bank document "Growth is Good
for the Poor", having seen one of its conclusions in a
newspaper. The document claimed to measure average benefits
to people under different conditions and policies.
I was
surprised that the authors confused
"the average for the poorest fifth rose 1%"
with
"people in the poorest fifth had average rises of
1%".
Those are
two different statements.
I thought
to myself "they can't know the average outcome of each policy, because
they don't know how many of the poorest survived in each country".
I also
thought "if this is the way economists do their work it is the wrong
way".
Nor did I
like the idea of "poverty reduction", for the same reason: if
more poor people die, the "reduction" will happen faster.
The
confusion between "the average rose" and "on average people had
rises" is a fundamental error in the theory of economics.
So is the
error concerning "poverty reduction".
It is also
a fundamental error in the thinking of the FAO, whose head complained about the
number of hungry people going up slightly. The number will go up if
the most hungry live longer.
2. Omission
of relevant prices
A second
thing which puzzled me about the World Bank policy document was that the
authors showed no sign of having assessed the purchasing power of poor people's
money.
To omit to
estimate relevant prices under each policy would be very odd. Later I
confirmed that the World Bank did not estimate prices for poor people in
different countries or under different policies. So the policy
assessments were based on the wrong inflation rates.
No
economist has compiled prices of staple food in different countries for past
years. So all public claims as to the relative
merits of policies for the poorest people, from such international data, are
misleading.
The
British Secretary of State for International Development announced to
Parliament on 26 April 2004 that global poverty would be halved if a certain
level of GDP per capita were reached. But he has not
estimated how GDP per capita correlates with food prices. So
exactly what he means by "global poverty" is not clear. Economists, politicians and the
World Bank talk about this issue as if they had data on the cost of living, but
they have estimated neither prices nor needs. The cost of
living is a function of both.
The usual
survey method aims to measure consumption expenditure. At present,
therefore, the most prominent aims of DfID and the
World Bank amount to reducing the proportion of low spenders.
The fact that people spend 1% less does not tell a researcher
that they received 1% less. The fact that they spend more does not
tell a researcher that they "rose out of poverty".
3. Claiming
to measure economic need without estimating food need
Thirdly,
per capita figures are not suitable if the proportion of children varies. That is because adults
need more food than children. To assess need without assessing food needs
is a mistake.
4. Cost of
living depends on both prices and quantities required
Fourthly,
other aspects of need were also omitted: such as need for
rent.
5. Omission
of asset changes in claiming economic "benefits"
Fifthly,
changes in land ownership were not taken into account. But if you inherit or
lose land, in reality that is an economic gain or loss.
World Bank
policy documents have claimed to measure "average benefits" without
looking at whether people lost or gained land, or whether they increased or
decreased debt.
The same
applies to the World Bank statements about the level of global poverty.
I later
confirmed that these five errors were indeed common practice in
macroeconomics. Economists often did not know the difference between
cross-sectional and longitudinal statistics, and were steeped in a tradition of
using survey data without looking at the cost of living for the target
group.
6. Errors
may explain puzzles
I think
these errors may provide partial explanations of
why
Millennium Goal indicator 1 is ahead of the FAO indicator (the FAO adjust for
the falling proportion of children's meals needed, and the Bank do not);
and why
some countries have low GDP and high life expectancy.
They may
also help explain why the World Bank statistic is ahead of health indicators.
Given the uncertainty about prices, survival rates, items required,
and assets, it might be that the World Bank is being overpessimistic
about progress: in such a scenario, it could be that people in the World
Bank target group are living longer, and/or food is getting cheaper and/or
other expenses lower.
However,
let us bear in mind the bigger picture, including poor progress on health
goals. If people are getting richer, then would they not be
eating better? If they are eating better, would they not be getting
healthier? The Bank's continued insistence that poor people
are doing well, and that great progress has been made since 1981, without data
on food prices or survival rates, seems doubtful.
In any
case, the Bank would need to adjust for children's meals in order to comply
even with standard practice in economics. This would reduce the
claimed "poverty reduction". The FAO and the Bank
cannot both be right about the food.
In relation
to the life length-GDP puzzle, the official figure for GDP per capita depends
on the national price index. But national price indices are
mathematically dominated by items which have more money spent on them. So
if a government makes food cheap, the economist, using the inflation rate
dominated by luxury goods, thinks people are not much better off.
As a
result, GDP per capita, as a measure of basic human welfare, has a tendency
towards bias against countries where food is cheap. It also has a
tendency towards bias against countries where
life
is long,
basic services are cheap,
land
ownership is high and
rental needs are low.
The
tendencies are an inevitable consequence of the economists leaving things out
which matter to people in real life.
These
tendencies apply also to macroeconomists' policy advice from international
studies.
The
policy assessments have an undeniable tendency towards bias against policies
associated with
long
life,
cheap food,
cheap basic services,
high
land ownership,
low
rents,
low
need for rent, and
low
personal debt.
I am not
claiming that the biases affect any statistics in any significant way.
What I am saying is that the tradition in economics of equating
"income" with "profit" without estimating necessary
expenditure is unjustified. The macroeconomists have not estimated
inflation rates for poor people, or the cost of living
for anyone.
I am also
saying that economists lack basic training in the difference between
cross-sectional statistics (statistics about the economy or segments of the
economy) and longitudinal statistics (statistics about what happens to
individual people).
There is no
"average outcome" where survival rates vary.
Since it is
impossible to assess what economists' statistics are telling them about
consumption levels, it would seem wise to look elsewhere for guidance as to the
effects of policies.
An
alternative to the present situation (in which social scientists can make
fallacious claims without fear) is one where social scientists have stricter
rules.
I
propose five axioms for the social sciences. A rather poorly-written argument
for these appears at www.mattberkley.com/5axioms.htm
.
Contact
details
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