Download omposite measure of industrial performance for cross

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The 59th World Statistics Congress
Hong Kong, 25-30 August 2013
Composite measure of industrial performance
for cross-country analysis
Shyam Upadhyaya
UNIDO
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Outline of the presentation
Composite measures in international practice
What is different in UNIDO’s CIP index
Scope, dimensions and construction procedure
Sensitivity analysis
Some results and conclusion
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Composite measures in international practice
Jeder nach seinen
Fähigkeiten, jedem nach
seinen Bedürfnissen!
From each according to his
ability, to each according to
his need!
- Karl Marx (1875)
178 composite indices are
compiled worldwide in different
frequencies
Happiness index
Welfare Index
Global climate risk index
... ...
Political instability index
From each international agency at least one
composite index according to their need ...
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Why international agencies are so
fond of composite index?
• Single measure of indicating a country’s
development performance
• Easy for policymakers to understand
• Benchmarking and country comparison
• Ranking
Shift in the rank generates public debate
• Media attraction (visibility)
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What is risk?
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•
Composite measure combines too many
things into one, but precisely, it may not
measure anything
•
To construct the index, one needs source
data for all indicators; which may limit the
country coverage
Even when all underlying statistics
are available … there is no way of
capturing the entire wealth of
knowledge embedded in a set of
numbers in one real number.
Or under temptation of getting larger
coverage, the compiler may compromise the
quality of estimates when underlying data
are not readily available (HDI discussions)
- Amartya Sen, 1994
•
Policymakers may actually not see the value
of large amount of data that are produced
behind the scene
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UNIDO’s Competitive Industrial
Performance (CIP) Index
• UNIDO’s mandate on industrial
development
• Sectoral perspectives
Other similar indices:
• Based on output measures to capture
the production performance
Global Competitive Index (GCI)
by the World Economic Forum
• Solely quantitative measures, no
perception indicators
World Competitiveness
Scoreboard (WCS) by the Institute
for Management Development
• Reflects country’s capacity to produce
and compete in the world market
Doing Business Index (DBI) by the
World Bank
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Structure of CIP index
Dimensions
Capacity to produce
and export
Technological
upgrading and
deepening
Indicators
1. Manufacturing value added (MVA) per
capita
2. Manufacturing export per capita
Weight
1/6
1/6
3. Share of MHT activities in total MVA
4. Share of MVA in GDP
1/12
1/12
5. Share of MHT in manufactures exports
6. Share of manufacturing in total exports
1/12
1/12
7. Share of the country in world MVA
Impact on world
8. Share of the country in world manufactures
production and trade
exports
1/6
1/6
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Compilation procedure
X i , j  min( X i , j )
Normalization: conversion of real value of
S ik, j 
S ik, j  0,1
k
k
varying scale to obtain a common score between
max( X i , j )  min( X i , j )
0 to 1
Sik, j score obtained from k-th variable of
i-th indicator and j-th country
k
Aggregation of individual scores to CIP value
Equal weights for three dimensions and
aggregation through geometric mean
k
q
CIPj   Sij
i 1
wi
 wi  1
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CIP’s fitness for its purpose as a
performance index
A powerful tool for policy advice
Country comparator
Component indicators can be used for
industrial diagnostics
Comparison with other composite
measures
Rank correlation coefficient with HDI = 0.79
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Country Ranks in latest CIP publication
Top 10 countries
Bottom 10 countries
1
Japan
126
Sudan
2
Germany
127
Haiti
3
United States
128
Niger
4
Republic of Korea
129
Rwanda
5
China, Taiwan
130
Ethiopia
6
Singapore
131
Central African Republic
7
China
132
Burundi
8
Switzerland
133
Eritrea
9
Belgium
134
Gambia
135
Iraq
10 France
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Sensitivity analysis
• Composite measures are compiled through several
dilemmas
• Often, there is no clear path to selection of one way against
another
• The main purpose of the sensitivity analysis is to examine
the impact of methodological choices in the final results
• Methodological choices in CIP construction:
Number of indicators and weights
Normalization method
Aggregation method
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Results of sensitivity analysis
Methodological choices
Absolute
difference*
Spearman
correlation**
Four vs. eight indicators
13.71
0.901
Arithmetic vs. geometric mean
13.21
0.914
z-score vs. Min-Max normalization
12.81
0.923
Linear interpolation vs. last price interpolation
9.932
0.972
Product-based technology classification vs. activity5.732
0.975
based
*Year-average of average absolute difference in ranks between the modified and default method
** Year-average of correlation between ranks of new method and default method
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Conclusion
• Composite index is a powerful tool to communicate with policy
makers
• Behind the single measure there is a vast amount of data and
statistical work
• CIP index depicts a country’s overall measure of industrial
performance
• Users should pay attention equally to its component indicators,
which provide more specific measures of key aspects of
industrial performance
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Thank you!
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or
[email protected]
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