unido.org/statistic s Statistical Indicators of Industrial performance Shyam Upadhyaya International workshop on industrial statistics 8 – 10 July, Beijing

unido.org/statistic s Statistics for policy makers... 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. 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

unido.org/statistic s Indicators of industrial performance are compiled to: 3 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

unido.org/statistic s 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

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

unido.org/statistic s Total factor productivity from UNIDO productivity database 6

unido.org/statistic s Structural change Change in the share of sector d s - difference of the share between two periods, 0 and 1. s i – 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. 7

unido.org/statistic s 17/05/ 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

unido.org/statistic s 9 Analysis of demand supply data balance 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 When R > 1 surplus (export oriented) R= 1 – self-sufficient R < 1 – deficit (import oriented)

unido.org/statistic s Derived classification 10 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

unido.org/statistic s Share of resource-based sectors in BRICS countries in comparison with UK 11 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

unido.org/statistic s Agro-based sectors 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

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

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

unido.org/statistic s 15 Manufacturing branches by intensity of energy consumption ISICDescription of activities High energy-intensive Manufacture of textiles Paper and paper products Coke and refined petroleum products Chemical products Non-metallic mineral products Manufacture of basic metals Moderate energy- intensive Food products and beverages Wearing apparel; dressing and dyeing Manufacture of leather products Wood and wood products Printing and publishing Rubber and plastic products Fabricated metal products Low energy-intensive Tobacco products Machinery and equipment n.e.c. Office, accounting and computing machinery Electrical machinery and apparatus n.e.c. Radio, TV and communication equipment Medical, precision and optical instruments Motor vehicles, trailers and semi ‑ trailers Other transport equipment Furniture and other manufacturing n.e.c. Recycling

unido.org/statistic s A composite index 16 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

unido.org/statistic s Steps required in construction of a composite measure Dimensions and indicators Imputation and outlier cleaning Normalization Weighting and aggregation Ranking Sensitivity analysis

unido.org/statistic s CIP Components 18 DimensionsIndicators Capacity to produce and export1.Manufacturing value added per capita 2.Manufacturing export per capita Technological upgrading and deepening 3.Share of MHT activities in total MVA 4.Share of MVA in GDP 5.Share of MHT manufactures exports 6.Share of manufactures export in total exports Impact on world production and trade 7.Share of the country in world MVA 8.Share of the country in world manufactures exports

unido.org/statistic s 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

unido.org/statistic s CIP ranking of selected Asian countries 20 Global ranking

unido.org/statistic s 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