The social science of the Millennium Goals
Part 1. A suggestion on the aims of international
Email to Professor Jeffrey Sachs,
chair of World Health Organisation
Commission on Macroeconomics and Health
The text of the email of 11 April 2001 begins
below at “Subj: Economics of Survival”.
“Here are some theoretical points about
mortality rates and the International Development Goals....
To me, no outcome measure is humane unless it takes into account what happened to people who started the period but didn't make it to the end. If the poorest die, the average income of those alive at the end of the period will be higher....
My suggestion is this: For any outcome
measure....account needs to be taken of those within the relevant group who did
not achieve the target, whether through death or any other path.
early 2001 my reading of material from the World Health Organisation Commission
on Macroeconomics and Health did not indicate any attention to a problem in macroeconomic
theory: statistics on the living do not
tell a researcher about aggregate outcomes.
Strictly speaking, the tradition in macroeconomics has been to confuse “the
average rise” with “the rise in the average”.
In December 2001 the Commission’s Report and at
least one background paper did make this particular distinction in relation to national
As an approach to aggregating benefits to people,
the idea of looking at trends for people is clearly right, while the idea of
looking at trends for populations in the abstract is clearly wrong. The Commission chose the former.
However, they did not, to my knowledge, say anything
further about this matter in relation to
a) future goal setting by
governments or international government agencies;
b) the adequacy of existing
international goals, targets and indicators;
c) the issue of what scientists
should say in cases where taking life length into consideration had the
potential to cause different conclusions about outcomes;
d) what social scientists
should say, or imply, about aggregate outcomes for those categorised as the
poorest or otherwise worst-off, in the absence of information on survival
e) how the general problem
of leaving out death as a cost related to disciplines other than economics;
any implications of the existence of
the error for the education of scientists, policy-makers or the public.
Commission sensibly did not set an absolute value on years of life. However, another general problem in the
Commission’s approach was that its ascriptions of benefit, while like many
claims about economics and the poorest, were strictly speaking based on a confusion between income and profit.
two areas - the cost of dying and the cost of living - form
the basis of remarks about some Millennium Goal indicators in part 2 of this
Some thoughts have recently occurred to me in
relation to the Commission’s assigning of monetary values to years of
life. These thoughts may turn out to be
The Commission estimated that if certain inputs
were made, there would be at least $186 billion of savings in income and
increased life for people.
The first thought runs along these lines:
“Other things being equal -
if the Commission’s idea is accepted that financial values can be given in
a meaningful sense to years of life, which is not necessarily uncontroversial - the
figure of $186 billion of savings seems far too low, for a reason not connected
to the value of years, but to the value of currency.
A. Two people die early, in the
B. The US citizen was
living on $1000 a year, and the Indian citizen on $333 a year.
C. Because of exchange rates, they could both
afford the same (and everything else is equal).
D. They each lose 30 years of life.
It seems to me that the logic of the Commission
would be that the
The value of wages lost by a person who dies
early might perhaps reasonably be given in real money. But when it comes to assigning a value of
the lost years, it would seem sensible to use units which relate to purchasing
power. Otherwise, the value of life would depend on
In practice, international purchasing power
estimates for people living on such amounts are not generally available.
But as things stand, other things being equal,
if the rest of the Commission’s logic is accepted, the minimum estimate should
have been over $500 billion rather than $186 billion”.
This spurs me on to another thought. Where in economic theory is the following
aspect of real life modelled?
Person A has the money equivalent of 1 bowl of
rice beyond their needs.
Persons B and C lack 1 bowl of rice each.
Person A lives in a
country where both wages and prices are double those where B and C live.
How would a theory of utility cope with such a
Subj: Economics of survival
11 April 2001
Dear Professor Sachs
I wonder if this of interest to the WHO Commission.
Here are some theoretical points about mortality rates and the International Development Goals, and then some practical points which are less simplistic.
Among the International Development Goals, progress has been faster on reducing the proportion of people in extreme poverty, and slower on child mortality.
The question no-one seems to be asking is this: Is the proportion of poor people getting smaller partly because child mortality is worse than we hoped?
Most of the goals [later correction: “several indicators” - 21 indicators for the Millennium Goals] are susceptible to the problem that if the worst-off die, we are closer to the target.
There are good grounds for thinking that the child mortality goal being on track provides a statistical safeguard among the goals - if this goal goes according to plan, it ensures that we do not get a false impression of progress towards the other goals simply through high death rates among the poorest.
Grounds for believing this include the following. Firstly, child mortality is concentrated among the poorest, so an improvement in the total may well reflect improvement among the target groups. Secondly, the child mortality rate is believed to give an indication of the rate of early deaths among adults Policies which reduce child mortality are likely to also reduce early mortality among adults.
If all this is true, then the closer we are to the required rate of progress on child mortality, the more poor people there are. Slow progress on child mortality (as now) makes for fewer poor people, so the poverty goal looks closer - simply because fewer poor people are alive, not because more of the survivors have raised their living standards.
My hunch is that slower progress on total child mortality means much slower progress on child mortality among the poorest. If this is so, then the effect is stronger.
To me, no outcome measure is humane unless it takes into account what happened to people who started the period but didn't make it to the end. If the poorest die, the average income of those alive at the end of the period will be higher than the average when the group included the poorest, even if none of the survivors' income has gone up. It even looks higher, if enough of the poorest die, when the average among the survivors goes down somewhat - simply because the poorest are no longer there to pull the average down.
If we measure the income of those alive in 1995 and then the income of those alive in 2000, we will not notice the decline in income of someone who died in 1998. The average income of those alive will be exactly the same as if he had survived and raised his income to the average of the group. In fact, since most people in poor countries work on the land, vulnerability is seasonal, and therefore the people who die may have a declining income for a few weeks or months before they die. This is too fast for measurements taken every five years.
My suggestion is this: For any outcome measure - reducing poverty, achieving 100% schooling - account needs to be taken of those within the relevant group who did not achieve the target, whether through death or any other path.
[Later note: “Reducing poverty” is strictly speaking not an outcome measure but an expression of an opinion.].
In practice, the relationship between child mortality and statistical progress on the goals would appear to need careful research (see below).
In real life, there may not be such a clear division between the poorest and the less-poor. However, DHS data seem to point to assets as important determinants of child mortality - the lowest 10% can be far more vulnerable than the next 10% (Bonilla-Chacin and Hammer, "Life and Death among the Poorest", 1999, revised version 2001 forthcoming, World Bank). There may be a clear division, for example, in some geographical areas, between the landed and the landless.
In real life, policies which reduce the proportion of people living under $1 a day may also save the most vulnerable from death. This cannot be assumed, and may depend on the relative vulnerability of the poorest (see previous point).
In real life, the poorest may produce more children to replace those who have died. The total number of poor children could conceivably be the same in 2015 whatever the child mortality rate. But if adults as well as children die in hard times this is unlikely.
The statistical relationship between mortality and outcome measures can only be determined by careful research, together with an intimate knowledge of household behaviour.
Statistical progress on the goals needs to be translated into human terms. If there is any suspicion that apparent progress on any of the goals is helped by lack of progress on any of the others, then this is an argument for tackling the goals that are furthest behind, not the ones that are furthest ahead.
A better title for the email would perhaps have been “statistics and
survival”. My suggestion above to Professor Sachs related to any outcome
Perhaps a better hypothesis than
“the proportion of people may be falling due to child
might have been this:
puzzle that fast reported progress on Goal 1 has co-existed with slow progress
on child mortality may be partially explicable by the fact that its indicators
fail to take into account the costs of dying”.
Notes on Millennium Goal indicators, description and
Millennium Goal indicators listed below fail to take survival rates into
account. They are not in themselves guides to the aggregate progress of
individuals. They risk being mistaken
points below relate to the interpretation of country-level trends as well as
Common sense is needed to consider which indicators may be problematic for
which countries and time periods. For example, indicator 22
on malaria may not be problematic at all in its context, which includes
indicator dealing with malaria deaths.
The application of common sense does not necessarily mean the application of traditional
inferences in a particular social science. The guiding
principle might reasonably be one of caution in inferring benefits to people
without evidence on survival rates.
Indicator 18 on HIV would seem to be more problematic than that on malaria,
given the absence of any indicator relating to HIV deaths.
The text of the letter to Professor Sachs, and notably the suggestion, deal
with a fundamental principle of social science. A
reasonable line of argument might be this: it is important for social
scientists to understand the principles, so that methods can deal with any
reasonably foreseeable circumstances.
The letter to Professor Sachs and this note do not claim any indicators have
shown the wrong trend as a result of mortality differences. Nor do
they claim that any will do so in the future. They
raise possibilities for discussion.
However, the context is of falls in life expectancy in some countries during
the 1990s. The effects of AIDS on demographic projections in some
African countries have been large. Reasonably foreseeable
circumstances which would affect the statistics may already have come to
It is perhaps worth noting here a point I made to Professor Frances Stewart of
The mortality flaw is a flaw in the logic of arguments which have been used by
i) Two aspects to the mortality flaw are mentioned in
the letter to Professor Sachs.
is that aggregate outcomes are not measurable without considering survival
rates. Again, common sense should
override this rule where necessary.
Exceptions to this rule are where variations in survival rates have previously been excluded on reasonable grounds
as insignificant or irrelevant. The point here is that
some people’s outcomes are left out.
aspect is that differences between statistics across time, place or
circumstance may be caused by differences in survival rates between people at
different levels of the variable being studied.
third aspect of the mortality flaw is that the effects of deaths may not be
limited to those who die. Parents generally prefer their children
to stay alive. An economist might observe that where children die,
the work put into being pregnant and raising them is wasted.
Similar considerations might apply to other In England, if a person dies early,
any suffering of relatives is generally considered important.
Note that indicators may also be affected by birth rates -
not just by the effects of birth rates on age structure, but also by the
direct effects of birth rates on population numbers. It is important to understand the difference
between “there are fewer people in a category now” and “people came out of the
category”. More detail on this is given at the end of
list comprises 21 of the 48 indicators. The descriptions are those
provided by the United Nations Statistics Division.
1. Proportion of population below $1 (1993 PPP) per day (World Bank)
2. Poverty [* see note by MB below] gap ratio [incidence x depth of poverty]
3. Share of poorest quintile in national consumption [**] (World Bank)
4. Prevalence of underweight children under five years of age (UNICEF-WHO)
5. Proportion of population below minimum level of dietary energy consumption
6. Net enrolment ratio in primary education (UNESCO)
8. Literacy rate of 15-24 year-olds (UNESCO)
15. Proportion of 1 year-old children immunized against measles (UNICEF-WHO)
17. Proportion of births attended by skilled health personnel (UNICEF-WHO)
18. HIV prevalence among pregnant women aged 15-24 years (UNAIDS-WHO-UNICEF)
19. Condom use rate of the contraceptive prevalence rate (UN Population
19a. Condom use at last high-risk sex (UNICEF-WHO)
19b. Percentage of population aged 15-24 years with comprehensive correct
knowledge of HIV/AIDS (UNICEF-WHO)
19c. Contraceptive prevalence rate (UN Population Division)
20. Ratio of school attendance of orphans to school attendance of non-orphans
aged 10-14 years (UNICEF-UNAIDS-WHO)
22. Proportion of population in malaria-risk areas
using effective malaria prevention and treatment measures (UNICEF-WHO)
29. Proportion of population using solid fuels (WHO)
30. Proportion of population with sustainable access to an improved water
source, urban and rural (UNICEF-WHO)
31. Proportion of population with access to improved sanitation, urban and
32. Proportion of households with access to secure tenure (UN-HABITAT)
45. Unemployment rate of young people aged 15-24 years, each sex and total
46. Proportion of population with access to affordable essential drugs on a
sustainable basis (WHO)
47. Telephone lines and cellular subscribers per 100 population
48. Personal computers in use per 100 population and Internet users per 100 population (ITU)
Note on indicator 2, “Poverty gap ratio [incidence x depth of poverty]”:
It is not
clear why the United Nations Statistics Division used the word “poverty”
here. The data are mostly on what people said they spent, rather than
on economic shortfall relative to need.
think poverty is excess of needs over resources.
researcher has information about resources.
How might they come to a judgement about poverty without looking at
needs? Needs include needs for food,
rent, water and so on. It is
not clear how an argument might be advanced in favour of the idea that lowness
or highness of resources might measure prosperity or poverty without reference
to needs. Surely, if you earn X-plus-1
units but need to spend 2 more units on health or rent or food or water, you
are worse off than your friend who earns X units.
One type of
argument in favour of looking at resources without looking at needs might stem
from hypotheses about general trends in real life. A person might say “in the real world, income
differentials, across time, place and level, are so big that they knock out the
effects of things like people moving to cities and needing to pay rent”. Intuitively, I do not find this line of reasoning
very appealing. If landlessness is a
big problem in some countries, then its most
immediately obvious effects on command of material resources may be compounded
by the fact that people in cities may have to pay rent, and/or live in worse
conditions than others who stay in villages.
Quantifying the value to people of living in a slum versus a village
seems to me an enterprise which would have a significant element of
subjectivity. But even ignoring this
problem, the idea that the costs of rent can safely be left out in an era of
fast urbanisation on the grounds that incomes will be rising fast enough to
render this kind of thing insignificant seems to me to be implausible, or at
least in need of empirical examples.
But in this
case, mostly the data are not on resources, but on spending. I am not sure what argument might be
advanced in support of the idea that “if you spent more, you got richer.” One argument could be of the form “on
average people spend more when they have more”. This type of argument would show nothing
about any particular period of history, or any particular place. In this particular period of history AIDS
might reasonably be thought to raise need for expenditure.
arises as to how social scientists might describe the existing statistics more
accurately. “Poverty” seems wrong, since the data give no specific
information about either prices or needs for the relevant people. Income, spending and the money value of
consumption could not measure economic
poverty as a whole, even in a narrow sense related to legally-defined personal
command over resources, since they leave out assets and debts. They could not measure income poverty or
consumption poverty, since they do not measure consumption need or relevant
prices. They do not measure necessary
A more accurate description of this
indicator would be “spending gap ratio [prevalence x depth of spending
gap]”. However, this would still not be entirely
accurate even ignoring the demographic problems. This is so for at least
data related to spending are not on what people spent, but on what they said
who are destitute may not be reachable. In order to understand
the potential importance of this factor, it is necessary to understand how the
mathematics works. The whole point of
these types of measures is to place more mathematical emphasis on people who
are further from the official poverty line (“poorer”). So, for
instance, measures like this take each person’s number, see how far the number
is from the official line, and multiply the figure by itself. This makes sense in theory, at least from
the philosophical standpoint implied by the traditional language of development
macroeconomics, since the stated aim is to find out not only “how many poor
people there are” but also “how poor they are”. I say the stated
aim, because from a different philosophical standpoint, any claim to have
measured prosperity or poverty is logically false. Part of the argument here would be that
needs have no meaning except by reference to particular purposes; beyond bare survival, the relative value of
these purposes in the end reduce to value judgements about what activities are
important. Another part of the
argument would be that in practice assessing consumption needs of different
people is too complex to be feasible at any reasonable cost. For instance, to come to a judgement about
even one person’s food consumption needs you would need to know at least their
size, age, gender, workload, the nutritional value of each component of
their diet and how the components worked more or less successfully together to
make a balanced diet for their body and their activities. For practical reasons, in one sense it is
not surprising that official statistics tend to look at the cost of calories
only. But calories do not tell me about
how well nourished you are. They are a
measure of energy consumption. In
another sense, it is surprising that anyone might think calories measured
mathematical procedures such as those used for what are traditionally called
“measures of the depth of poverty”, the absence of destitute people may be
important. Without knowing about homeless people, even in the
absence of other philosophical, theoretical or data problems, it is not clear
how social scientists can justifiably claim to know what has happened to the
poorest or to have measured the “poverty gap”.
data are not all on what people said they spent. Some data are on what people said they
earned. Some are on money values which researchers assigned to people’s
answers about what they grew for themselves to eat.
“incidence” is strictly speaking, according to the most common usage,
tradition in economics is to use the word “incidence” for what people usually
is the number of incidents over a period. It is “how often
something happens to real people over time”.
is “the prevailing rate in the population at one
If incidence rises and so does mortality, prevalence can fall.
is about real people.
is about populations, not what happens to real people.
discrepancy between the use of the terminology in economics and other social
sciences can be seen in the list above.
Note on indicator 3, “share of poorest quintile in national consumption”:
is not clear why the UN Statistics Division referred to “consumption”
rather than “consumption expenditure”..
compiled by the World Bank from national statistical agencies were on financial
amounts, not consumption amounts. The data are mostly on what
people said they spent. It is important to distinguish
“consumption” from “consumption expenditure”.
is in addition to the problem about mortality. The indicator could not
show how much people in the quintile (fifth of the population) ate even if the
statistics had been on food. This is strictly speaking a confusion between a fifth of the population and a fifth of
aspect of demographic change is birth rates.
relevant aspects of falling birth rates are 1) while reducing total food need
they increase average food need, and 2) they lower the count of people
One line of
argument might be this: none of the financial indicators for the
Millennium Goals measures poverty, because they do not consider food and
shelter needs. Globally, the proportion of children is
falling. Among countries, one example is this: other
things being equal economists have underestimated current poverty in
counterargument might be that life lengths have risen, so counteracting some of
the effects of falling birth rates on age structure and efficiency. In the case of
addition, neither the World Bank nor FAO methods consider the difference
between “fewer babies being born” and “people eating better”.
still be the case even if they did actually measure consumption adequacy (which
is not really possible even in principle, as it is partially a subjective
concept) in different years.
severity of hunger is less if fewer babies are born to hungry
families. But that is different from saying that people ate better,
which would be the usual inference drawn, if not by social scientists or
politicians, then by the public.
a change, even in the absence of other methodological, theoretical and
philosophical problems, would not show how much people’s consumption
increased. Nor would it show how much their consumption
dated 14 January 2006