Extracts from email to Tony Baldry MP, Chairman of UK Parliament
International Development Committee
25 June 2003
Dear Mr Baldry
I am writing to you in your capacity as Chairman of the Select Committee on International Development.
I would like, with your permission, to raise some issues for the Select Committee to consider. The thrust of the letter is to make a case for longevity as an outcome measure for hungry people....
...The first error.... is to fail to count survival as a better outcome.
...Economists have measured for example, average income in the poorest fifth, as an abstract segment of the economy. But that cannot tell us how poor people did.
...in a country where more poor people die early, the statistic on the poorest fifth will rise - simply because poor people did worse. I have been telling economists this for three years. I have told several senior members of staff in DFID; my own MP, Andrew Smith, who passed my message on to the then Secretary of State for International Development in 2001; the head of the World Development Indicators project at the World Bank; and several heads of department at UK universities. Prominent economists are now discussing the problem, led by Ravi Kanbur of Cornell University. I had two detailed conversations in 2001 with Professor Kanbur, in which I explained aspects of the theoretical problem to him...
The mortality problem extends to using the proportion of people in poverty as an outcome measure.... There is a serious problem with all Millennium Goals concerned with reducing the proportion of deprived people...
Current statistics on income cannot tell us how many people “rose out of poverty”. Nor could any statistics even on a perfect measure of annual poverty. What is more, survival is the most important outcome for poor people. Traditional “welfare economics” does not count survival as a good outcome (except for those people on higher incomes).
A second serious flaw in economic research is this. To be blunt, economists claim income gains or losses to poor people without knowing the price of food. The “income share of the poorest fifth” does not tell us, even if we know survival rates were stable, whether the poor gained or lost. In a country where poor people eat rice, you need to know the inflation rate for rice if you are going to say anything about poverty from income statistics. Economists use the overall inflation rate in a country. Why have they assumed that the price of rice changed at the same rate as non-essential goods? I have no idea.
All of the World Bank’s major research conclusions (“Assessing Aid”; “Growth is Good for the Poor”; “Globalisation, Growth and Poverty”; and measures of income inequality) suffer from these flaws.
To measure the outcome for the survivors is not to measure outcomes for people during the period. To measure income without knowing the price of staple food is not to measure poverty.
Measures of income “inequality” - income ratios or Gini indices - falsely show more inequality if poor people live longer. The extent of this problem is unknown. Fundamentally, the change in average income does not tell us the average gain. Average income falls if the poor live longer. Average income rises if people in the majority on below-mean incomes die earlier. The poor are bad for growth.
In the cases of both the mortality flaw and the inflation flaw, the poor can look as if they have done better when they have in fact done much worse, or vice versa. Martin Ravallion, who designed the methodology for the World Bank’s global poverty counts, wrote of the mortality flaw in 1996. Despite this, the World Bank has claimed that their statistics show the degree of benefit to poor people. This is not good enough in any age, but is especially alarming in the age of AIDS.
Thirdly, the Select Committee
should know that prominent academics do not
consider the main World Bank dataset to be reliable. The main dataset for research on world
poverty is the Deininger and Squire dataset of 1996. Its critics include Tony
Atkinson of Oxford, who is one of the most respected authorities on inequality
in the world, and James Galbraith of Texas. The criticisms are eminently
sensible for prima facie reasons as well as comparisons with other data. The surveys in different countries are carried out
using a wide range of different techniques, and it is unreasonable to expect
poor countries’ statistical departments to have carried out rigorous and
comparable surveys. After all, these are poor countries. The data are a jumble of gross income, net income and
expenditure, all assessed using different methods. In the document
“Growth is Good for the Poor” the authors admitted that for some data they did
not know whether the income was gross or net. Martin Ravallion is on
record as saying that the rich and the very
poor do not cooperate with surveys, so that there are gaps in the
Problems as to the reliability of the data for measuring the numerical values of income are in addition to the problems of making inferences from
a) the numerical value of income
b) purchasing power
c) economic gains, which include changes in assets and debts.
One difference between my writings and those of economists is that I refer to all the problems, rather than considering one or two at a time. (Economists are forever telling each other to adjust for children’s needs, or to get better purchasing-power parity information. But those don’t solve all the serious problems).
Another difference is that I take the commonsense view that without the required data, a social scientist does not have a firm conclusion. That is not to say that social scientists should not speculate, or assume, or infer. The point is that speculation, assumption and inference should be labelled as such. The social scientist might infer a conclusion from general knowledge, but that is not the same as saying that they have calculated the answer. Oddly, it is the tradition in economics to state, for example, that there “is” a relationship between a policy and economic gains to poor people, without the required data on prices.
Professor Kanbur, and the economic theorist Kenneth Arrow, have both suggested to me that the problem of food prices is not economists’ fault, because national statistical agencies do not provide those data. They are right in the sense that absence of data is not the fault of economists. However, that is neither the point which I make nor relevant to it. My observation is simply a matter of fact: the vast majority of development economists claim, without the required evidence on food prices, to know the level of economic gains to the poor.
[MB note 14 April 2018: I am not sure I should have been that definite in making the claim about 'the vast majority'.]
Smith noted an observable difference between the inflation rate for the poor
and for other people in 1776. It is somewhat surprising that two
hundred years later, for all the equations and long words, economists are using more primitive methods than
that. To ask around about the cost of rice and get a rough idea
would be simple. To get an accurate idea would be complex. A sensible solution is to ask a few basic questions
before saying what the answer is.
The numbers on the faces of the coins do not tell us whether hungry people were able to eat more or less. Development experts have made similar excuses to me about data availability when I discussed the mortality flaw. In both cases, with all due respect to the experts, their thinking was the wrong way round. To think that the standard assumption is acceptable just because it is standard is not to take a scientific approach. The standard assumptions are merely conventional, with no theoretical or empirical support.
I now turn to practicalities. The complexity involved in household surveys is enormous even without price surveys: income surveys can involve up to two hundred questions to a single family. In the case of prices, this second necessary task would also be complex. Those in charge of the International Comparison Programme, hosted by the World Bank to set purchasing-power parity rates, are well aware of the problems. Poor people often pay higher prices because they have to buy in small quantities and cannot buy more when prices are cheap. Further, there are no standard prices for rice: your results will depend on which markets you go to and how many people live in each place. Obtaining a price for a whole country would be complex and expensive. Suppose we had the required data on life length - would that make economic statistics meaningful measures of poverty? Maybe, if we knew about prices, age structure and extra items of necessary expenditure.
But let us consider the
a) Income statistics are not in themselves sufficient to measure poverty.
b) Life length statistics are necessary.
c) To some extent, life length statistics are sufficient. Amartya Sen has argued for life length as a measure of economic success.
Are income statistics (adjusted for prices, age and extra items) necessary?
Perhaps not. When we consider the technical difficulties in using income to measure the progress of hungry people, we may decide that measuring life length is a far cheaper, more practical, more transparent and less corruptible option.
developing a set of minimum criteria for the UN Statistics Division.
The criteria are not demanding. They include such things as these:
1) outcome statistics should take survival into account;
2) statements about progress should be based on a credible assessment of the reliability of the data, and
3) income statistics should only be used where prices of staple foods (and any changes in necessary expenditure due for example to the proportion of people in cities, or the changing proportion of adults) are estimated.
It would help the effort to feed hungry people - in both the short and long terms, by whatever means are chosen - if the UK Government were to adopt some such guidelines. Otherwise, we will not know what the statistics mean.
Economic statistics on “poverty” in all
large-scale studies also ignore the facts that the proportion of adults is
rising and adults need more food than children. The World Bank’s
method for counting the global poor suffers from this flaw. Other things
being equal, the Bank must have overestimated the reduction in the proportion
of people living on the original level of per-day consumption
considering their age and size. A dollar a day, even if we knew its value in
food terms, would not be an appropriate measure where the proportion of
children is changing, as now.]
Current economic statistics do not meet those minimum criteria. There is a more cost-effective option.
The economics of hunger is measured in years.
Thank you for taking the time to read this letter.