1. unido.org/statistics Composite measure of industrial performance for cross-country analysis Shyam Upadhyaya UNIDO The 59th World Statistics Congress Hong Kong, 25-30 August 2013

2. unido.org/statistics 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 2

3. unido.org/statistics 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... 3

4. unido.org/statistics 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) 4

5. unido.org/statistics What 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 5

6. unido.org/statistics UNIDO’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 6

7. unido.org/statistics Structure of CIP index 7 DimensionsIndicators Weight Capacity to produce and export 1.Manufacturing value added (MVA) per capita 2.Manufacturing export per capita 1/6 Technological upgrading and deepening 3.Share of MHT activities in total MVA 4.Share of MVA in GDP 1/12 5.Share of MHT in manufactures exports 6.Share of manufacturing in total exports 1/12 Impact on world production and trade 7.Share of the country in world MVA 8.Share of the country in world manufactures exports 1/6

8. unido.org/statistics Compilation procedure 8 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 score obtained from k-th variable of i-th indicator and j-th country

9. unido.org/statistics 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 9

10. unido.org/statistics Country Ranks in latest CIP publication Top 10 countriesBottom 10 countries 1Japan126Sudan 2Germany127Haiti 3United States128Niger 4Republic of Korea129Rwanda 5China, Taiwan130Ethiopia 6Singapore131Central African Republic 7China132Burundi 8Switzerland133Eritrea 9Belgium134Gambia 10France135Iraq 10

11. unido.org/statistics 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 11

12. unido.org/statistics Results of sensitivity analysis 12 Methodological choices Absolute difference* Spearman correlation** Four vs. eight indicators13.710.901 Arithmetic vs. geometric mean13.210.914 z-score vs. Min-Max normalization12.810.923 Linear interpolation vs. last price interpolation9.9320.972 Product-based technology classification vs. activity- based 5.7320.975 * 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

13. unido.org/statistics Conclusion 13 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

14. Thank you! S.Upadhyaya@unido.org or stat@unido.org S.Upadhyaya@unido.org stat@unido.org 14