refining the human development index: jrc suggestions · 2016. 6. 3. · human development index:...
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Michaela Saisana Second Conference on Measuring Human Progress
New York, 4-5 March 2013
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Refining the
Human Development Index:
JRC suggestions
Michaela Saisana
European Commission
Joint Research Centre
Econometrics and Applied Statistics Unit
Ispra, Italy
Michaela Saisana Second Conference on Measuring Human Progress
New York, 4-5 March 2013
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Introduction
• Achievements • The challenge • The measure • Popularity
It is exactly the “unobserved” nature of
composite indicators that is their main
limitation and their raison d'être.
180
4,460
7,730
12,600
16,200
17,80018,600
21,300
0
5,000
10,000
15,000
20,000
25,000
1985 1990 1995 2000 2005 2010 2015
Year
Sc
ho
lar
Go
og
le h
its
on
"H
um
an
de
ve
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nt
ind
ex
"
~5-fold increase
since 2000
180
4,460
7,730
12,600
18,600
21,300
77,100
115,000
0
5,000
10,000
15,000
20,000
25,000
1985 1990 1995 2000 2005 2010 2015
Year
Sc
ho
lar
Go
og
le h
its
on
"H
um
an
de
ve
lop
me
nt
ind
ex
"
0
50,000
100,000
150,000
200,000
250,000
Sc
ho
lar
Go
og
le h
its
on
"G
ros
s
do
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uc
t"
“Yet the dimensions of the HDI do not
easily meld into one. And without a
systematic method […prices…] the index
could prove difficult to explain and defend”
(J. Foster, 2013)
Michaela Saisana Second Conference on Measuring Human Progress
New York, 4-5 March 2013
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• Calibration • Goalposts • Gaterories • Cobb-Douglas HDI
Main points
Michaela Saisana Second Conference on Measuring Human Progress
New York, 4-5 March 2013
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• Calibration • Goalposts • Categories • Cobb-Douglas HDI
Main points “Frequent recalibration gives the strong suggestion that HDI values are
contingent and temporary and depend importantly on arbitrary constructs”
Foster’s suggestion:
1) ~ 10 year recalibration (as for poverty)
2) Crossover between calibration periods:
process outlined explicitly and transparently
Source: Global Innovation Index
Michaela Saisana Second Conference on Measuring Human Progress
New York, 4-5 March 2013
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Source: Global Innovation Index
Michaela Saisana Second Conference on Measuring Human Progress
New York, 4-5 March 2013
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• Calibration • Goalposts • Categories • Cobb-Douglas HDI
Main points “The HDI is typically cast and interpreted as a multidimensional measure of
size and hence is seen to be an absolute measure. […] Yet in actual
implementation, this is not necessarily the way the HDI behaves.”
Life expectancy at birth
Bounds in the HDI
After 2010: 20y – observed (83.2 y, JN)
Before : 25y – 85y
Source: Wikipedia
Michaela Saisana Second Conference on Measuring Human Progress
New York, 4-5 March 2013
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34.632.8
39.844.0
47.8
76.779.0
82.3 83.4
30
35
40
45
50
55
60
65
70
75
80
85
90
1970 1980 1990 2000 2010 2020
Lif
e e
xp
ecta
ncy a
t b
irth
(years
)
• Calibration • Goalposts • Categories • Cobb-Douglas HDI
Main points Minimum and Maximum across 194 countries
85.6
Life expectancy at birth
Suggestion: Fixed bounds
30y (Early 20th Century) – 87 years
Similarly for the other indicators
Michaela Saisana Second Conference on Measuring Human Progress
New York, 4-5 March 2013
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• Calibration • Goalposts • Categories • Cobb-Douglas HDI
Main points
Categories of Human Development
Relative (since 2010) versus Absolute (before 2010)
+ progress against other countries, rather than arbitrary numerical
cutoffs whose meaning may vary with each new calibration.
- fuzzy incentives, less practical value for the country
- many factors enter into the determination of progress (e.g. different
calibrations, performance of other countries, policies of the country,
or inclusion of new countries).
- a country can not set a meaningful numerical target to achieve over
time.
Foster’s suggestion:
1) A staggered recalibration schedule &
2) Fixed numerical cutoffs for the four HD categories
(e.g. WB grouping by income)
Michaela Saisana Second Conference on Measuring Human Progress
New York, 4-5 March 2013
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Main points
HDI Life
expectancy at
birth (years)
Mean
years of
schooling
Expected
years of
schooling
GNI per
capita
(PPP$)
…
0.6 58.2 7.9 10.8 6,487
0.8 …
Further recommendation:
To present the fixed cutoffs for the HDI
with respect to the raw data (assuming an
even performance) • Calibration • Goalposts • Categories • Cobb-Douglas HDI
Michaela Saisana Second Conference on Measuring Human Progress
New York, 4-5 March 2013
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Main points
•Calibration •Goalposts •Categories • Cobb-Douglas HDI
“[…] attempt to view the HDI more as a social evaluation function that
aggregates across dimensional variables directly”
3/13/13/1 YELW
L= life expectancy - 20 years
E =1/2 (mean years of schooling + expected years of schooling)
Y= ln (GNI per capita) – ln (100)
*/WWH W*= target social evaluation level
Michaela Saisana Second Conference on Measuring Human Progress
New York, 4-5 March 2013
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Advantages of the geometric mean versus the arithmetic mean for the HDI
1) implies only partial compensability, i.e. poor performance in one HD dimension cannot be fully
compensated by good performance in another,
2) rewards balance by penalizing uneven performance between dimensions,
3) encourages improvements in the weak dimensions, i.e. the lower the performance in a particular
HD dimension, the more urgent it becomes to improve in that dimension.
Life Edu GNI stdev
HDI
(arithmetic)
HDI 2011
(geometric)
Liberia’s
improvement
Mali .496 .270 .346 .115 .371 (176) .359 (175)
Liberia .580 .439 .140 .225 .386 (175) .329 (182)
Option A .680 .439 .140 .419 .347 5.5%
Option B .580 .439 .240 .419 .394 19.8%
More on the geometric mean in the case of the HDI…
Michaela Saisana Second Conference on Measuring Human Progress
New York, 4-5 March 2013
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More on the “quality” of the HDI… (Implicit Weights)
We suggest to use as a measure of importance
of a variable in an index what is known as:
‐ Pearson’s correlation ratio
‐ First order effect
‐ Top marginal variance
- Main effect
…
Source: Paruolo, Saisana, Saltelli, 2013, J.Royal Stat. Society A
Using these points we can compute a statistics that tells us:
How much (on average) would the variance of the HDI scores be reduced if one could
fix “Life expectancy”?
HDI
Life Expectancy
Michaela Saisana Second Conference on Measuring Human Progress
New York, 4-5 March 2013
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More on the “quality” of the HDI… (Implicit Weights)
HDI
Life Expectancy
HDI 2011 Nominal
Weights (wi)
Implicit
Weights (Si)
Life expectancy .333 .83 [.81 .85]
Education .333 .88 [.83 .87]
GNI .333 .90 [.88 .91]
We could reduce the variation of the
HDI scores by 83% by fixing ‘Life
expectancy”.
Quality check:
The HDI is balanced in its three underlying dimensions (Si values are very similar)
Michaela Saisana Second Conference on Measuring Human Progress
New York, 4-5 March 2013
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More on the “quality” of the HDI… (Marginal weights)
Recommendation: To plot Change in HDI
versus life expectancy instead to evidence
that countries with low life expectancy are
more encouraged to improve
Marginal Weights=
Michaela Saisana Second Conference on Measuring Human Progress
New York, 4-5 March 2013
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Some recent criticism…
Source: M. Ravallion (2012) Troubling
tradeoffs in the HDI, J. Dev. Economics,
99:201-209
Tradeoffs = marginal rate of
substitution, i.e. how much of one
dimension must be given up for an
extra unit of another, keeping the
index constant.
Previous HDI
The new HDI has devalued
longevity, especially in poor
countries.
Michaela Saisana Second Conference on Measuring Human Progress
New York, 4-5 March 2013
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Final considerations
Simply take the log of GNI just once (now logged twice) Take the arithmetic average the two education indicators (now geometric) Use the generalized mean of the three dimensions (a compromise solution between arithmetic-geometric averaging)
/1
)(3
1
YELHDI
mean arithmetic ,1
mean geometric ,0
5.0
Michaela Saisana Second Conference on Measuring Human Progress
New York, 4-5 March 2013
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Assess any new calibration formula in terms of:
Implicit weights (reduction in the HDI variance by fixing one dimension at a time)
Marginal weights (impact on HDI of 1% increase in one of the dimensions)
Marginal rate of substitution (how much of one component must be given up for an extra unit of another, keeping the index constant)
More reading at:
http://composite-indicators.jrc.ec.europa.eu
(first Google hit on “composite indicators” over the last 10 years!)
Michaela Saisana Second Conference on Measuring Human Progress
New York, 4-5 March 2013
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1. Paruolo P., Saisana M., Saltelli A., 2013, Ratings and Rankings: voodoo or science?. J
Royal Statistical Society A 176(2).
2. Saisana M., Saltelli A., 2012, JRC audit on the 2012 WJP Rule of Law Index, In Agrast, M.,
Botero, J., Martinez, J., Ponce, A., & Pratt, C. WJP Rule of Law Index® 2012.
Washington, D.C.: The World Justice Project.
3. Saisana M., Philippas D., 2012, Sustainable Society Index (SSI): Taking societies’ pulse along
social, environmental and economic issues, EUR 25578, Joint Research Centre, Publications
Office of the European Union, Italy.
4. Saisana M., D’Hombres B., Saltelli A., 2011, Rickety Numbers: Volatility of university
rankings and policy implications. Research Policy 40, 165–177.
5. Saisana M., Saltelli A., Tarantola S., 2005, Uncertainty and sensitivity analysis
techniques as tools for the analysis and validation of composite indicators. J Royal
Statistical Society A 168(2), 307-323.
6. OECD/JRC, 2008, Handbook on Constructing Composite Indicators. Methodology and user
Guide, OECD Publishing, ISBN 978-92-64-04345-9.
References and Related Reading