unido.org/statistics composite measure of industrial performance for cross-country analysis shyam...
TRANSCRIPT
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Composite measure of industrial performance for cross-country analysis
Shyam UpadhyayaUNIDO
The 59th World Statistics CongressHong Kong, 25-30 August 2013
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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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unido.org/statisticsComposite 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 indexWelfare 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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unido.org/statisticsWhy 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
• RankingShift in the rank generates public debate
• Media attraction (visibility)
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unido.org/statisticsWhat is risk?
• 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
Or under temptation of getting larger coverage, the compiler may compromise the quality of estimates when underlying data are not readily available (HDI discussions)
• Policymakers may actually not see the value of large amount of data that are produced behind the scene
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.
- Amartya Sen, 1994
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unido.org/statisticsUNIDO’s Competitive Industrial
Performance (CIP) Index
Other similar indices:
Global Competitive Index (GCI)by the World Economic Forum
World Competitiveness Scoreboard (WCS) by the Institute for Management Development
Doing Business Index (DBI) by the World Bank
• UNIDO’s mandate on industrial development
• Sectoral perspectives
• Based on output measures to capture the production performance
• Solely quantitative measures, no perception indicators
• Reflects country’s capacity to produce and compete in the world market
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Structure of CIP index
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Dimensions Indicators Weight
Capacity to produce and export
1. Manufacturing value added (MVA) per capita
2. Manufacturing export per capita
1/6
1/6
Technological upgrading and deepening
3. Share of MHT activities in total MVA 4. Share of MVA in GDP
1/121/12
5. Share of MHT in manufactures exports 6. Share of manufacturing in total
exports
1/121/12
Impact on world production and trade
7. Share of the country in world MVA8. Share of the country in world
manufactures exports
1/6
1/6
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Compilation procedure
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Normalization: conversion of real value of varying scale to obtain a common score between 0 to 1
Aggregation of individual scores to CIP value Equal weights for three dimensions and aggregation through geometric mean
10,S)Xmin()Xmax(
)Xmin(XS k
j,ikj,i
kj,i
kj,i
kj,ik
j,i
score obtained from k-th variable of i-th indicator and j-th country
kj,iS
11
i
q
i
wijj wSCIP i
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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
10 France 135 Iraq
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unido.org/statisticsSensitivity 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 methodAggregation method
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Results of sensitivity analysis
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Methodological choicesAbsolute 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. activity-based
5.732 0.975
*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
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• 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
