Measuring Development - Wikiprogress

Angus Deaton, Princeton University
8 th AFD/EUDC Conference: Measure for measure
Paris, December 1 st , 2010
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My themes
• Recent explosion in data availability
Allows us to map the world
Content and country coverage
• Additional dimensions of development
Health (itself with many dimensions)
Happiness (also with many dimensions)
In addition to income/consumption/GDP
• Which has also improved in its own right
Sen agenda: measuring development in broader spaces
• Stiglitz report (Stiglitz/Sen/Fitoussi)
• Brought new insights about development
But also new puzzles: different from what we thought!
And also some contradictions: some of what we thought must be wrong!
• Different data, different conclusions
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My examples
• 1. The International Comparison Project
New insights from the 2005 round
• Global inequality and global poverty
Puzzles
• 2. Measuring hunger
New insights from Gallup polls
Puzzles on calories and heights
• 3. Health and income
New insights from Demographic Health Surveys
Links & differences between health and income
Health and income together paint a different picture
of development than does income alone
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Why do we need PPPs?
• Who is poor and who is rich?
How many poor are there in the world?
• How big are the differences?
What is the ratio of US to Chinese income?
• The global distribution of income?
Over countries
Over the citizens of the world
• For these, and other questions, we use national
incomes
not in local currencies
not in US $ at market exchange rates: non‐traded goods
International PPP currency, usually dollars
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Where do PPPs come from?
• The Penn World Table & the World Bank’s World
Development Indicators, among others
• Ultimately from the International Comparison Program
• ICP collects prices on comparable goods in many countries
To construct multilateral price indexes for each country relative
to a base, such as the US
For consumption, investment, GDP, etc
Used to deflate nominal local currency amounts to give “real”
common unit international PPP measures
• PPPs are international price indexes, with all usual price
index issues
Multilateral (compare A with B, C, D, …..Z all at once) rather than
bilateral (compare A with B) price indexes
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ICP 1993
• Until now using 1993 round of ICP (PWT6)
• Important missing countries, including India
and China, both imputed not measured
• A regional system with each region collecting
prices on its own, and calculating its own
PPPs in regional currency
• Weak center with ad hoc links between
regions
• UN (1997) report concluded that the ICP 1993
had lost credibility
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ICP 2005
• Much better: global office housed by World
Bank
• 146 countries (186 in 2011, next ICP)
Including India and China
Many African countries never previously included
• Carefully designed procedures
Goods and services more precisely defined
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Headline result
• Per capita GDP of both India and China both much reduced
using the new data
• China in 2005 from $6,757 to $4,088
• India in 2005 from $3,452 to $2,222
• The US is numeraire/base country
So we could just as well say that the US got richer
India and China moved further away from the US and other rich
countries
Ratio of US to China went from 6.2 times to 10.2 times
Their PPPs relative to the US increased, so “real” amounts fell
• Not only India and China
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Congo, DR
Sao Tome & Principe
Ratio of new to old PPP for 2005
.5 1 1.5 2 2.5
Burundi
Guinea Bissau
Ethiopia
Cape Verde
Guinea Lesotho
Ghana
Togo
Cambodia
Bangladesh
Philippines
China Namibia
India Fiji
Tonga
Vietnam
Tanzania
Bolivia
Angola
Nigeria
Gabon
Yemen Congo, R Lebanon
Kuwait
6 7 8 9 10 11
Logarithm of per capita GDP in 2005 international $
11

.5 .52 .54 .56 .58 .6
Gini coefficient for per capita GDP,
weighted by population
Post 2005 ICP
Pre 2005 ICP
1970 1980 1990 2000 2010
year
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.45 .5 .55 .6
PWT 6.2, 1993 prices
Gini coefficient for per capita GDP,
weighted by population
PWT 5.6, 1985 prices
WDI 2008, 2005 prices
WDI 2007, 1993 prices
1960 1970 1980 1990 2000 2010
year

Dollar a day poverty?
• Poor world is now poorer relative to the rich
world
• Many more people than before live beneath a
dollar a day using ICP 2005 than using ICP 1993
Because PPPs convert $1 into higher amounts in local
currencies in poor countries, and more people below
• Holding $‐a‐day fixed in $
World poverty is 1.76 billion in 2005
Compared with 931 million using old PPPs
• Holding $‐a‐day fixed in Rupees
World poverty is 940 million in 2005
Compared with 931 million using old PPPs
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World Bank poverty
• World Bank uses only poor country poverty lines
So more like rupee line on previous slide
• But they revised 2005 global poverty to 1.37 billion, up from
931 million
Because they changed the countries in the average
• Dropping India (low line) in favor of countries with higher lines
• There is no “correct” way of updating when PPPs change
Though it certainly is confusing!
• Whether we should maintain a rich world standard or a
poor world standard is a matter of debate
Rich world standard seems more like what people perceive when
they think of what a $ a day means
Rich world citizens are the audience for such numbers
Limited interest in poor countries
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Does the level matter?
• Given that downward trend is similar?
• There are nearly 200 million Indians living between $1.00
and $1.25 a day
Many fewer Africans
• Raising the line makes global poverty relatively more
Indian, and relatively less African
Numbers shape consciousness of global poverty
• India will now no longer meet Millennium Development
Goal for poverty reduction
Rate of reduction is the same, but base is higher
Though not clear anyone much cares about this!
• PPPs are only one of the problems!
Survey and NAS inconsistencies also very important
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What happens between
rounds?
• Nothing!
• World Bank discards previous ICP results
Takes current benchmarks
Growth rates from domestic national accounts
• Makes sense if revisions are mostly methodological
So new ICP is just better than previous one
The leading position
• In principle, this violates theory underlying PPP
adjustment in the first place
At least over long enough time periods
An alternative would be to only use benchmark years

.45 .5 .55 .6
PWT 6.2, 1993 prices
Gini coefficient for per capita GDP,
weighted by population
PWT 5.6, 1985 prices
WDI 2008, 2005 prices
WDI 2007, 1993 prices
1960 1970 1980 1990 2000 2010
year

Puzzles
• Why did the PPPs rise so much for the poor
countries?
Many stories, none convincing
• How should we update PPPs between benchmarks?
Don’t know and will become central issue for 2011 round
• Making comparisons between very different
countries is subject to much uncertainty
Disguised by the ICP
Ratio of Laspeyres to Paasche indexes is one measure of
uncertainty
• China and India ratios are 1.66 and 1.61
• The international income data are much more fragile
than most people think!
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Political economy
• Who cares about global poverty and global
inequality?
More particularly about measures in international
dollars
• They do not matter to individual countries
Who have their own poverty and inequality measures
• Does it matter if we are out by 50 percent in
measuring relative income of US and China, or
France and Mali?
It matters within Europe, because of the EEC
It matters to NGOs and IFIs and Aid Agencies
But mostly for raising money in rich countries
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What is hunger?
• In principle, easier to deal with
No PPPs, no poverty lines
Individuals are hungry, not households
Hunger is what people think of in global poverty
Also part of MDG1
• But what exactly is hunger?
Not having enough to eat
• Undernutrition
Physical/medical evidence of poor nutrition
• Malnutrition
• Requires measuring people: weight and height
• Skinny kids and short adults
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Intake measures
• FAO makes annual estimates of intake
• Estimates total food and calorie availability in
each country
“Food balance sheets”
• Divides by number of people to get average
• Uses survey data to estimate dispersion over
people: some get more and some get less
• This gives a distribution and an estimate of
people getting less than 2,100 calories a day
(with some flexibility)
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Difficulties. . .
• Poor correlation with malnutrition
But they are different things
Malnutrition depends on disease, work load, and things other
than calories
So I don’t think this is a problem, but much confusion
• Latest years, which get the headlines, are based on
forecasts by USDA
• Distributional data are very weak, especially in Africa
• Balance sheets are not very accurate
Animal feed hard to estimate
Cereals pretty good, other foods much harder
Stocks hard to measure
FAO Indian data disagree with both surveys and Indian
Department of Agricultural estimates, even in trend
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Why don’t we just ask?
• Gallup World Poll did just that
“Have there been times in the last 12 months when you did
not have enough money to buy food that you and your
family needed?”
• Gallup has been sampling “all of the population of
the world”
Annual basis since 2006
2010 is almost complete
Samples of 1,000 or so in each country
155 countries, not all in every year (most)
Identical questionnaires in all countries
• Also collects income, so can check global poverty
numbers
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Millions hungry
East Asia
Europe & Cent. Asia
Latin America
Middle E/ N Africa
Sub‐Saharan Africa
South Asia
2006 2007 2008 2009
383
50
108
78
409
511
404
54
110
78
364
400
365
50
113
102
410
367
400
55
113
67
411
439
Total
1,539
1,410
1,407
1,485
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Evaluation
• World Poll shows large increase 78 million into
2009, following food price spike and financial
crisis
Like FAO, but a bit smaller
And from a larger base
• Might defend the calorie based question
But the data are weak
Calories are a bad measure even if well measured
Calories are falling in India in spite of rapid growth &
poverty reduction
Not having enough money to buy food is important in
itself
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Health
• Income is important, but it isn’t everything
Income is useless if you are not alive to enjoy it
Or for people who are disabled in some way
• who have been prevented by circumstances from attaining
their full potential of physical and mental health
• How does the world look if we look at income and
health simultaneously?
Various ways of doing this
Perhaps most famous is Preston curve, showing the
positive relationship between life expectancy and GDP per
capita
Life expectancy (health) is low when income is low
• Correlated burden of deprivation
• True within countries too

Heights & malnutrition
• We want to know how well people function when
they are alive, not just whether or not they are alive
Measuring this is difficult
Because there are so many dimensions to health
• One useful measure is adult height
Genetics are important for individual height
• Less so (or not so) for populations
Early childhood disease and nutrition plays a large role, and
heights do not change (much) in adulthood
So adult heights provide a “tree‐ring” record of past
environment
Long term measure of hunger and disease
Height is important in its own right

• Taller people are (on
average)
More intelligent
Lead better lives
(Except on airplanes)
• Though not once we adjust
for childhood experience
Genetic height does nothing
Actual height marks
childhood deprivation
• What do heights look like
around the world?

Average height
145 150 155 160 165 170
Africa
China
South Asia
Central Asia
Europe
Latin America
& Caribbean
6 7 8 9 10
Log of real GDP per head in year of birth
US
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Men’s heights
161 162 163 164 165 166
HEIGHTS IN INDIA
Men (right scale)
10 20 30 40 50
age
Slope is –0.050 cm per year
Women (left scale)
Slope is –0.016 cm per year
Women’s heights
151 152 153 154 155 156

Learning from heights
• Africa is much poorer than South Asia or Latin America
But is better nourished
Also has higher infant and child mortality
• Income and health measures differ
• Different health measures are different
• Morbidity and mortality move in different directions
• Indians are getting taller
Less quickly than Chinese (except in S. India)
Men more than women (except in S. India)
Why?
• Heights are weakly negatively correlated with income
Except in Europe in 20C
• No guarantee that income growth will make people better
off in this (or other) dimension(s)

Health and income summary
• Health (deprivation) and income (deprivation) go
hand in hand
• World is even more unequal than by income alone
• Heights improve with living standards too
And cognitive ability perhaps
Capabilities to lead a fuller life
But there is much we don’t understand here!
• BUT, we should not think that, if economies grow,
health will look after itself
POLICY is important and often causes health and income to
move differently
India and China important examples

Endnotes
• More detailed results (and qualifications!) in the paper
• Better data on more outcomes and more countries is
changing our view of development
See also 2010 Human Development Report
• Health and income are correlated but are different things
For well‐being measurement, usually complement and
strengthen each other
Rich are richer and poor and poorer
For policy, we must not assume that growth automatically brings
health
• Much more work needs to be done to reconcile the
findings, and gaining an integrated understanding of
development in a broad perspective
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