Methodology - Industrial Skyline of India

995 views

Published on

The product has been designed to facilitate industrial analysis and research at the micro level. It provides current estimates on crucial industry related information to facilitate better decision making and business planning. This will help in understanding the vast industrial market and the wide differences across districts, states and regions overcoming any sort of constraints imposed by lack of reliable primary data.
The series includes measures that will be useful to business planners, policy makers, credit facilitating agencies, banks and financial institutions, traders as well as knowledge seekers and researchers. It enables the investors to prioritize locations at micro level as well as marketers to explore greater opportunities with the help of reliable industry related data across geographic spaces.
This product brings out information on production, workforce, number of operational units , value of input and the amount of industrial consumption of various products at all India, state and district levels. It captures both organized and unorganized sector manufacturing activities and provides the aggregate data covering the entire manufacturing activities. The development of this product involves rigorous use of several authentic and reliable data sources in India. These sources include Annual Survey of Industries (ASI), National Sample Survey Organization (NSSO), Economic Census, etc.

Published in: Business, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
995
On SlideShare
0
From Embeds
0
Number of Embeds
44
Actions
Shares
0
Downloads
19
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Methodology - Industrial Skyline of India

  1. 1. Indicus Analytics, An Economics Research Firm Industrial Skyline of India, 2008-09 Methodology The product has been designed to facilitate industrial analysis and research at the micro level. It provides current estimates on crucial industry related information to facilitate better decision making and business planning. This will help in understanding the vast industrial market and the wide differences across districts, states and regions overcoming any sort of constraints imposed by lack of reliable primary data. The series includes measures that will be useful to business planners, policy makers, credit facilitating agencies, banks and financial institutions, traders as well as knowledge seekers and researchers. It enables the investors to prioritize locations at micro level as well as marketers to explore greater opportunities with the help of reliable industry related data across geographic spaces. This product brings out information on production, workforce, number of operational units , value of input and the amount of industrial consumption of various products at all India, state and district levels. It captures both organized and unorganized sector manufacturing activities and provides the aggregate data covering the entire manufacturing activities. The development of this product involves rigorous use of several authentic and reliable data sources in India. These sources include Annual Survey of Industries (ASI), National Sample Survey Organization (NSSO), Economic Census, etc. Production: The production figures include total ex-factory value of products and by- products manufactured putting together both organized and unorganized manufacturing sector. Since our objective is to provide information directly related to industrial activities, we have chosen ‘production’ instead of ‘total output’. Workforce: We have considered ‘total persons engaged’ as the representative of workforce engaged in manufacturing activities. This includes employees and all working proprietors and others who are actively engaged in manufacturing activities. The estimations of workforce include both organized and unorganized manufacturing activities. Manufacturing units: To capture the current industrial production scenario, we have considered number of operational units instead of total units. Value of Input: This includes total delivered value of raw materials, components, chemicals, packing materials, stores and construction consumed by an industry Industrial consumption of products: This data provides information to marketers regarding consumption of their products for intermediate usage by the same as well as other industries. Our estimates have also taken care of the under-reporting over-reporting as well as any 24th April 2009
  2. 2. Indicus Analytics, An Economics Research Firm mis-reporting regarding crucial variables in the core databases used. To overcome this problem, we have used adjustment factors obtained using different relevant data sources as well as the calibration method. We have used the National Industrial Classification (NIC) 2004 three digit level definitions of the industrial products to maintain parity across several data sources. We have clubbed two NIC categories (NIC 151 and NIC 154) while reporting the data due to the change in the definition of the same overtime. Additionally, due to data constraints, we are not reporting the NIC 233 (Processing of nuclear fuel). There are many gaps in published and available data, especially when we are attempting to work at the district level. For smaller districts with lower populations such as those in the northeastern parts of India or areas such as The Dangs in Gujarat, or the more interior parts of Jammu and Kashmir, etc. the estimates maybe suggestive. Finally, in order to maintain the robustness of the estimates, the data has been rounded off and therefore, the totals will not match the reported numbers. Industrial Skyline – Estimation Methodology Annual Survey of Calibrations Industries Calibrated with data Organized Sector published by Reserve Bank 6 years time series of India & Central Statistical Census of 15k- 38k large Organization industrial units and sample survey of 26k- 76k small and medium industrial units organized sector Final Estimates Estimation State level estimates for NSSO 3 points in time Model Organized and District level estimates unorganized Sector for 2008-09 Two rounds spanning 5 years time series sample survey 150,000 & 87,00 units from the unorganized sector GDP Manufacturing District Level estimates from Indicus GDP Economic Census estimates over 7 years Latest Economic census used. Census of 8.4 million manufacturing units spanning across the organized & unorganized 24th April 2009
  3. 3. Indicus Analytics, An Economics Research Firm Methodology The manufacturing sector in India comprises of the large scale manufacturing units, the registered small scale manufacturing units and the un-registered manufacturing units. These units are generally classified by the National Industrial Classification System (NIC). We have followed the NIC 2004 Classification which is based on the international classification ISEC Rev 3.1 Most of the public data available provide information on only subsets of the manufacturing sector. While the data published by the ASI only provide information on the registered units, the NSSO provides information for the un-registered units. These extremely useful and robust data sources cover the manufacturing sector. However, these are for different points in time and present their findings in a manner that it is not easy for users to get a comprehensive picture of the manufacturing sector. Industrial Skyline is an attempt to not only combine information from these data sources but to update it and also to provide estimates at the district level. Data sources: Annual Survey of Industries (ASI) is as the name suggests a yearly survey cum census of manufacturing units in India which are registered under Section 2m(i) and 2m (ii) of the Factories Act 1948 and the Bidi and Cigar Workers (Conditions of Employment) Act, 1966. It captures a host of characteristics of manufacturing units from inputs, workers to gross value added. For this exercise we have used data from 2000-01 to 2005-06, with census of 15,000-38,000 large scale industrial units and a sample survey of 26,000-76,000 organized units. Survey of unorganized manufacturing (National Sample Survey Organization (NSSO)) is a survey of the unorganized manufacturing units in India. This survey is conducted once in five years. For this exercise we have used the data for NSS 56th (1999-2000) and 62nd (2005-06) rounds with sample size 1.5 lakh and 83,000 enterprises, respectively. Economic Census 2005: A census of all establishments undertaking economic activities is conducted by the Ministry of Statistics and Programme Implementation in collaboration with the various state governments. For this exercise we have used the economic census conducted in 2005. Reserve Bank of India: Data of schedule commercial bank credit and deposits to the manufacturing sector from the RBI has been used. For this exercise we have used the data from 1999-00 to 2007-08. Central Statistical Organization: The Central Statistical Organization (CSO) provides annual estimates of the GDP - Manufacturing Sector for All India as well as State level. For this exercise we have used the GDP for the period 1999-00 to 2007-08. Additionally, we have also used the latest I-O (Input-Output) matrix for 2003-04 published by the CSO. 24th April 2009
  4. 4. Indicus Analytics, An Economics Research Firm Methodology The basic methodology was to combine the estimates from the organized and unorganized sectors. The following function denotes the theoretical concept. (P)NIC = (Po) NIC + (Pu) NIC (P)NIC = Total Production for the given industry as per NIC (Po) NIC = Organized sector Production for the given industry as per NIC (Pu) NIC = Unorganized sector Production for the given industry as per NIC However, the data was not available for all the years for both the sectors. Also the latest data was available for on 2005-06. Thus two steps were required. First to estimate the data for all the years at the state level for various NIC and second to estimate it for the recent year. A function of the following form was estimated for the organized and unorganized sector separately. (Pij)NIC = fn (GDP, (Lij) NIC , (Xij) NIC, KNIC , (Aij) NIC ) (P)NIC = Ʃij(Yij)NIC (Pij)NIC = Production of the industry as per NIC GDP = Manufacturing Sector GDP (Lij) NIC = Labour employed by the industry as per NIC (Xij) NIC = Income earned by the labour employed in the industry as per NIC K = Capital employed ANIC = A factor for technical efficiency of the industry as per NIC i = category based on workforce employed j = category based on production The relationship established between GDP and production has been used to estimate the production for various years at the state level and updating of the total production. Once the production was estimated, the other parameters viz the employment, the number of units and the input consumed and industrial consumption were determined using a the following function (Charij)NIC = fn ((Pij)NIC, (Lij)NIC , (Aij)NIC) (Char)NIC = Ʃij(Charij)NIC 24th April 2009
  5. 5. Indicus Analytics, An Economics Research Firm Char = Characteristics of the industry (units, input, etc.) (Pij)NIC = Production of the industry as per NIC (Lij) NIC = Labour employed by the industry as per NIC ANIC = A factor for technical efficiency of the industry as per NIC i = category based on workforce employed j = category based on production A similar function was used to determine the production at the district level. For estimating inter-industrial consumption for various sectors, the latest Input-Output (IO) matrix published by CSO was used. The actual estimation involved the IO coefficients for each industry at NIC3 level under the technology assumption. The inter-industrial consumption estimates in the data involve a basic assumption regarding the state of technology of the economy. Research studies show that the impact of technology on the output is very small especially in the manufacturing sector (Sumon Kumar Bhaumik, S.K, and Kumbhakar S.C, Impact of Reforms on Plant-level Productivity and Technical Efficiency: Evidence from the Indian Manufacturing Sector). In order to maintain the robustness of the estimates at such micro level, we have rounded off the figures to the hundredth place. Therefore, the numbers will not add up to the reported totals. Also, on the basis of employment, certain industries have been identified to have a trivial contribution in the total industrial output of the state or district. In such cases, we have considered the values of production, inputs and units to be relatively insignificant because these industries will not contribute extensively to the total industrial output in a state or district. It is an apparent fact that all the industries will not be present in all states & districts. Consequently, for industries that do not have a presence in certain areas, we have quoted “NA”. 24th April 2009

×