Statistical Indicators of Industrial performance Shyam Upadhyaya

1. International workshop on industrial statistics 8 – 10 July, Beijing Statistical Indicators of Industrial performanceShyam Upadhyaya

2. Statistics for policy makers ... • We produce a lot of data, they need a few synthesized indicators • We provide figures, they would like to read a story around the figures • We should convert statistics into information and information into knowledge Sir Roland Fisher (1890 – 1962) In the original sense of the word, ‘Statistics’ was the science of Statecraft: to the political arithmetrician of the eighteenth century, its function was to be the eyes and ears of the central government.

3. Indicators of industrial performance are compiled to: • Reflect the major policy relevant issues of industrial development • Synthesize the large volume of data to some meaningful statistics • Help to carry out the cross-country comparison • Indicate the relative position of the country in industrial development

4. Construction of performance indicators by key policy issues Performance indicators constructed in UNIDO mainly addresses the following three dimensions relevant to development strategy and monitoring of industrial performance • Productivity • Structural change • Competitiveness

5. Productivity Labour productivity Value added per employee Value added per worked hour Capital productivity Value added per unit of capital Value added capital increment ratio Generalized measure of industrial productivity of the whole population is measured by MVA per capita Multifactor productivity index • share of compensation of employees (as labour input) in value added • - share of other components (capital input) in value • ILand IK as defined in relation (4). • - Index of the number of employees and fixed assets (adjusted with price changes)

6. Total factor productivity from UNIDO productivity database http://www.unido.org/data1/wpd/Index.cfm

7. Change in the share of sector ds- difference of the share between two periods, 0 and 1. si – share of i-th sector in total value Integral coefficient of structural change n – number of observations (sectors) The coefficient lies between 0 and 1. Its value more than 0.5 would mean significant structural change, while less than 0.1 indicate the identical structure in both time points. Rank correlation of Spearman Lack of correlation of ranks in two periods would mean the presence of structural change Coefficient of diversification It equals to 0 when the value is concentrated in one branch of industry, and to 1, when all the branches has equal value indicating a perfect diversification. Structural change

8. Competitiveness • Ability to sell the products in the market. • International competitiveness is measured by the share of export in domestic output • Share of resource-based and high-tech products in total manufactured export

9. Overall equilibrium C – Apparent consumption Y – Domestic output M – Import X - Export Relative variables Ratio of domestic output to consumption Share of import in total consumption Share of export in total output Analysis of demand supply data balance When R > 1 surplus (export oriented) R= 1 – self-sufficient R < 1 – deficit (import oriented)

10. Derived classification • Create a smaller group of industrial sectors based on some policy relevant criteria • Resource based sectors • Agro-based sectors • Classification based on technological intensity • Classification based on energy intensity • ICT goods producing sectors

11. Share of resource-based sectors in BRICS countries in comparison with UK Resource-based sectors account for a considerable part of manufacturing in emerging economies So far, only China has succeeded in reducing its dependence on resource-based industries

12. Sub-set of resource-based industry, excluding the processing of mineral resources Lower technological innovation, labour intensive Share of agro-based sectors falls as industry diversifies and moves towards high-technology sectors Agro-based sectors

13. Classification by technological intensityBased on R&D expenditure per unit of value added Medium-high and high technology industries by ISIC rev-3 24 Manufacture of chemicals and chemical products 29 Manufacture of machinery and equipment n. e. c. 30 Manufacture of office, accounting and computing machinery 31 Manufacture of electrical machinery and apparatus n. e. c. 32 Manufacture of radio, television and communication equipment 33 Manufacture of medical, precision and optical instruments 34 Manufacture of motor vehicles 35 Manufacture of other transport equipment

14. Classification based on energy intensity Classification is based on: Ranking of industries for sample countries zij - rank score of j-th industry in i-th sample Z max – maximum value of z (m x n)

15. Manufacturing branches by intensity of energy consumption

16. A composite index • UNIDO constructs and disseminates data on a composite index - the Competitive industrial performance index (CIP Index) • This is a consolidated measure of industrial performance based on a number of indicators capturing different dimensions • CIP index is useful to benchmark and compare a country’s performance • The index is used to rank countries and reveal their relative position. It serves as a tool for policy makers and attracts attention of public and media

17. Dimensions and indicators Imputation and outlier cleaning Normalization Weighting and aggregation Ranking Sensitivity analysis Steps required in construction of a composite measure

18. CIP Components

19. Construction of CIP index • A normalized component index for i-th country, j-th year and k-th indicator is given by: where x represents the actual value of the indicator • CIP index is computed as the geometric mean of individual indices

20. CIP ranking of selected Asian countries Global ranking

21. Indicators help to write a story... Performance indicators provide more synthesized and analytical information for policy makers The process described here is more about the construction of indicators Indicators help to write a story for communicating statistics to the policy makers Writing story does not end in compilation of indicators, it only starts