The data and programs replicate tables and figures from "Industrial Policies in Production Networks", by Ernest Liu. Please see the Readme file for additional details.

Under the global wave of intelligence, intelligent manufacturing has become a crucial means of transforming and upgrading China’s manufacturing industry. Accurate evaluation of the implementation effects of intelligent manufacturing industry policies is an urgent issue. This study uses the introduction of the “Made in China 2025” policy as a quasi-natural experiment and employs the difference-in-differences method to investigate the impact of intelligent manufacturing policies on firms’ total factor productivity (TFP) and its mechanisms. These results indicate that implementing intelligent manufacturing policies significantly enhances firms’ TFP. Mechanism analysis reveals that intelligent manufacturing policies can improve firms’ ESG performance by enhancing green technology innovation capabilities, increasing capital market attention, and reducing internal control costs, thereby enhancing firms’ TFP. Heterogeneity analysis finds that intelligent manufacturing policies have a more pronounced effect on promoting TFP in large-scale enterprises, labor-intensive enterprises, firms with higher technical employee levels, companies in highly competitive industries, and enterprises in regions with higher levels of digital infrastructure development and lower economic development as compared to their counterparts. This study provides evidence of how intelligent manufacturing policies drive the high-quality and sustainable development of enterprises and offers insights for future policy formulation and implementation.

The data and programs replicate tables and figures from "Manufacturing Revolutions: Industrial Policy and Industrialization in South Korea," by Nathan Lane. Please see the README file for additional details.

In 2023, preliminary figures showed the GDP from manufacturing activities in Indonesia was at about 3.9 quadrillion Indonesian rupiah. Over the last decade, the manufacturing sector has been the largest contributor to Indonesia's GDP and has become a significant source of investment and job creation. Manufacturing workers in Indonesia Over 19 million people were working in the manufacturing industry in Indonesia, and more than 14 percent of Indonesian workers were employed in the non-oil and gas manufacturing industry. However, the average net wage of manufacturing workers is still relatively low compared to other sectors. Riau Islands had the highest average salary for manufacturing workers in Indonesia. The number of people employed in the manufacturing sector is expected to increase, corresponding with the increasing number of manufacturing establishments in Indonesia. Improvements in manufacturing sector in Indonesia Most manufacturing companies in Indonesia are concentrated on the island of Java, where the current capital city, Jakarta, lies and where most of its population resides. Recently, the Indonesian government has started to shift its focus on developing its other islands. In December 2021, the Indonesian government launched Digital Industry Center 4.0 (PIDI 4.0) to boost the industrial sector's growth and implement better local policies toward the manufacturing industry. These actions should include improvements in connectivity and simplifying licenses and permits for investors all over the archipelago. The Indonesian government aims to develop the country into the top ten largest economies globally by 2030.

Abstract

The presence of adequate and current statistical data in various economic sectors that are considered essential for development planning, socio-economic policy formulation and economic analysis is vital in promoting the economic development of a country. Based on this general objective, the Central Statistical Agency (CSA) has been conducting surveys of various economic activities, of which, the annual Large and Medium Scale Manufacturing Industries survey is one.

Manufacturing is defined here according to International Standard Industrial Classification (ISIC Revision-3.1) as “the physical or chemical transformation of materials or components into new products, whether the work is performed by power-driven machines or by hand, whether it is done in a factory or in the worker's home, and whether the products are sold at wholesale or retail. The assembly of the component parts of manufactured products is also considered as manufacturing activities.”

CSA has been publishing results of the survey of Manufacturing and Electricity Industries on annual basis since 1968 Ethiopian Calendar to provide users with reliable, comprehensive and timely statistical data on these sectors. In this respect, this survey, which is conducted on annual basis, is the principal source of industrial statistics on large and medium scale manufacturing industries in the country.

The main objectives of the annual survey of Large and Medium Scale Manufacturing and Electricity Industries are to: 1.Obtain basic statistical data that are essential for policy makers, planners and researchers by major industrial group. 2.Collect basic quantitative information on employment, volume of quantitative information on employment, volume of production and raw materials, structure and performance of the country's Large and Medium Scale Manufacturing and Electricity Industries. 3.Compile statistical data which will be an input to the System of National Accounts (SNA), on Large and Medium Scale Manufacturing and Electricity establishments as a whole and by major industrial group. 4.Obtain the number of proprietors engaged in these sectors and find out the major problems that create stumbling blocks for their activities.

Geographic coverage

National

Analysis unit

Establishment/ Enterprise

Universe

The universe of the large and medium scale manufacturing survey is confined to those establishments which engaged 10 persons and above and use power-driven machines and covers both public and private industries in all Regions of the country.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Not applicable - the survey enumerated all manufacturing industries/ enterprises that qualified as large and medium manufacturing industry category.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questinnaire contains the following sections/ items:

Item 1.1. Adress of the establishments: This section has varibles that identify the questionnaire uniquely. The variables are; Killil, Zone, Wereda, Town, Higher, Kebele, House no, Year, ISIC, Establishmnet no, Eelephone no and P.O.Box codes or numbers.

Item 1.2. Address of Head Office if Separated From Factory: In this section information about factory head office is collected (if the factory is separated from the head office). The varibles used to collect the information are; Killil, Zone, Wereda, Town, Higher, Kebele, House no, Telephone no and P.O.Box.

Item 2. Basic Information About The Establishment: This section has questions related to basic information about the establishment.

Item 3.1. Number of Persons Engaged: This section has variables (questions) that used to collect establishment's employees number by employees occupation.

Item 3.2. Number of Persons Engaged by Educational Status: This section has varabils (questions) that used to collect establishment's employees number by their educational status.

Item 3.3. Number of Persons Engaged by Age Group: Contains variables that used to collect information about employees number by employees age group.

Item 3.4. Wages and Salaries and Other Employee Benefits Paid: This section has variables related to wages and other employees benefits by employee occupation.

Item 3.5. Number of Permanent Employees by Basic Salary Group: This section has variables related to salary groups by sex of employees

Item 4.1. Products and By-products: This section has questions related to product produced, produced quantity and sales.

Item 4.2. Service and Other Receipts: Contains questions related to income from different source other than selling the products.

Item 5. Value of Stocks: Contains questions that related to information about materials in the stock.

Item 6.1. Cost and Quantity of Raw Materials, Parts and Containers Used: This section has questions related to principal raw materials, raw material type, quantity, value and source (local or imported).

Item 6.2. Other Industrial Costs: This sections has questions related to other industrial costs including cost of energy and other expenses.

Item 6.3. Other Non-industrial Expenses: Contains questions related to non-industrial expenses like license fee, advertising, stationary, etc.

Item 6.4. Taxes Paid: This section has questions related to taxes like indirect tax and income tax.

Item 7. Fixed Assets and Investment: This section has questions related to fixed assets and investment on fixed assests and working capital.

Item 8.1. Annual Production at Full Capacity: This section has questions about quantity and value of products if the establishment uses its full capacity.

Item 8.2. Estimated Value and Quantity of Raw Materials Needed, at Full Capacity: This section has questions about the estimate of quantity and value of raw materials that needed to function at full capacity.

Item 8.3. The three major problems that prevented the establishment from operating at full capacity.

Item 8.4. The three major problems that are facing the establishment at present.

Cleaning operations

Editing, Coding and Verification: A number of quality control steps were taken to ensure the quality of data. The first step taken in this direction was, to revise the questionnaire, to make it easier for internal consistency checking or editing, both at field and office level. Furthermore, based on this revised questionnaire, revised instruction manual with field editing procedures were prepared in Amharic for both enumerators and supervisors (field editors). Using this manual, some editing and coding were carried out by field editors during the data collection stage.

After the majority of the completed questionnaires were brought back to head office, final editing, coding and verification were performed by editors, statistical technicians and statisticians. Finally, the edited and coded questionnaires were checked and verified by other senior professionals.

Data Entry, Cleaning and Tabulation: The data were entered and verified on personal computers using CSpro (Census and Survey Processing System) Software. Fifteen CSA data entry staff and one data cleaner participated in this activity for fifteen days with close supervision of the activities by two professionals. Then, the data entered were cleaned hundred percent using personal computers in combination with manual cleaning for some serious errors. Finally, the tabulation of the results was processed using the same software by one programmer with technical assistance from Industry, Trade and Services Statistics Department staff.

The data and programs replicate tables and figures from "AI-tocracy", by Beraja, Kao, Yang, and Yuchtman. Please see the README file for additional details.

These codes use the original sources of data to add in replicating the article and its online appendix. The articles uses UNIDO and iMaPP (IMF) data to test the effect of macroprudential policies on the growth of the manufacturing industries. The article's tables are the same as for the BIS working paper, but their numbering is slightly different because Table 1 in the paper draft was moved to be Table A.1 in the appendix.

The basic data (i.e. mat file) included in this dataset is the Multi Regional Input Output Tables (MRIO) compiled by the Asian Development Bank (ADB) from 2009 to 2022 at constant prices. This input-output table includes 62 economies including China, the United States, and rest of the world (RoW), each of which includes 14 manufacturing sectors and 19 non manufacturing sectors. According to the manufacturing industry classification standards released by the Organization for Economic Co operation and Development (OECD) in 2011, the 14 manufacturing sectors mentioned above can be further divided into low, medium, and high-tech manufacturing industries. The code file (i.e. m file) included in this dataset is a MATLAB program file, which calculates the international market share (RIMS) and explicit comparative advantage index (RRCA) of the manufacturing industry in China and the United States, as well as manufacturing industries with different levels of technology, under the value-added caliber. The dynamic changes are decomposed using a structural decomposition analysis model.

The determinants of total factor productivity (TFP) of the manufacturing industries, in particular innovation and industrial policy, constituted the seminal contribution of Crépon, Duguet and Mairesse in the late 1990. This study aims to explain the TFP of the 5 branches composing the manufacturing industries in Morocco on the 1985-2013 period through its panel regression on innovation, trade openness and industrial policy variables, as well as their interactions, while testing the model assumptions in the light of the Moroccan reality. This study will also provide conclusions related to the relevance of the original model as well as extensions and implications for further research.

Abstract

Introduction

The Annual Survey of Industries (ASI) is one of the large-scale sample survey conducted by Field Operation Division of National Sample Survey Office for more than three decades with the objective of collecting comprehensive information related to registered factories on annual basis. ASI is the primary source of data for facilitating systematic study of the structure of industries, analysis of various factors influencing industries in the country and creating a database for formulation of industrial policy.

The main objectives of the Annual Survey of Industries are briefly as follows:

(a) Estimation of the contribution of manufacturing industries as a whole and of each unit to national income.

(b) Systematic study of the structure of industry as a whole and of each type of industry and each unit.

(c) Casual analysis of the various factors influencing industry in the country: and

(d) Provision of comprehensive, factual and systematic basis for the formulation of policy.

The Annual Survey of Industries (ASI) is the principal source of industrial statistics in India. It provides statistical information to assess changes in the growth, composition and structure of organised manufacturing sector comprising activities related to manufacturing processes, repair services, gas and water supply and cold storage. The Survey is conducted annually under the statutory provisions of the Collection of Statistics Act 1953, and the Rules framed there-under in 1959, except in the State of Jammu & Kashmir where it is conducted under the State Collection of Statistics Act, 1961 and the rules framed there-under in 1964.

Geographic coverage

The ASI is the principal source of industrial statistics in India and extends to the entire country except Arunachal Pradesh, Mizoram & Sikkim and the Union Territory of Lakshadweep. It covers all factories registered under Sections 2m(i) and 2m(ii) of the Factories Act, 1948.

Analysis unit

The primary unit of enumeration in the survey is a factory in the case of manufacturing industries, a workshop in the case of repair services, an undertaking or a licensee in the case of electricity, gas & water supply undertakings and an establishment in the case of bidi & cigar industries. The owner of two or more establishments located in the same State and pertaining to the same industry group and belonging to census scheme is, however, permitted to furnish a single consolidated return. Such consolidated returns are common feature in the case of bidi and cigar establishments, electricity and certain public sector undertakings.

Universe

The survey cover factories registered under the Factory Act 1948.

Establishments under the control of the Defence Ministry,oil storage and distribution units, restaurants and cafes and technical training institutions not producing anything for sale or exchange were kept outside the coverage of the ASI.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Procedure

The sampling design followed in ASI 1998-99 is a Circular Systematic one. All the factories in the updated frame (universe) are divided into two sectors, viz., Census and Sample.

Census Sector: Census Sector is defined as follows:

a) All the complete enumeration States namely, Manipur, Meghalaya, Nagaland, Tripura and Andaman & Nicobar Islands. b) For the rest of the States/ UT's., (i) units having 200 or more workers, and (ii) all factories covered under Joint Returns.

Rest of the factories found in the frame constituted Sample sector on which sampling was done. Factories under Biri & Cigar sector were not considered uniformly under census sector. Factories under this sector were treated for inclusion in census sector as per definition above (i.e., more than 200 workers and/or joint returns). After identifying Census sector factories, rest of the factories were arranged in ascending order of States, NIC-98 (4 digit), number of workers and district and properly numbered. The Sampling was taken within each stratum (State X Sector X 4-digit NIC) with a minimum of 8 samples in each stratum in the form of 2 sub-samples. For the first time, all electricity undertakings other than captive units, Government Departmental undertakings such as Railway Workshops, P & T workshops etc. were kept out of coverage of ASI.

Sampling deviation

There was no deviation from sample design in ASI 1998-99.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Pre-data entry scrutiny was carried out on the schedules for inter and intra block consistency checks. Such editing was mostly manual, although some editing was automatic. But, for major inconsistencies, the schedules were referred back to NSSO (FOD) for clarifications/modifications.

The final unit level data of ASI 98-99 is available now in electronic media. This document describes additional information regarding ASI 98-99 data from the point of data processing. Users of ASI 98-99 data are requested to read this document carefully before they attempt to process the unit level data for their own purpose. They are also requested to refer to the schedule and the instruction manual for filling up the schedule before interpreting contents of various data fields. A. Contents The CD (or any other media) should contain the following files: ASI99.TXT This file contains unit level detail data of ASI 98-99 as per structure given in ANNEXURE- Total no. of records: 104740 XASI98.TXT (Metadata created from this .TXT file) This file contains unit level detail data of ASI 97-98 for those factories which were found not responding during the survey of ASI 98-99. The record layout is already available with the Computer Centre, New Delhi. Record Length: 135 Total no. of records: 6974 README.DOC This file.

B. Tabulation procedure The tabulation procedure by CSO(ISW) includes both the ASI 98-99 data and the extracted data from ASI 97-98 for all tabulation purpose. To make results comparable, users are requested to follow the same procedure. For calculation of various parameters, users are requested to refer instruction manual/report for the respective years. Please note that a separate inflation factor (Multiplier) is available for each factory against records belonging to Block-A ,pos:38-46 (Please refer ANNEXURE-I) for ASI 98-99 data. Since the data extracted from ASI 97-98 belong to Census Sector no such inflation (Multiplier) factor is required. Industry code as per Return(5-digit level of NIC-98) Industry code as reported by the factories in Block-A, Item 1 has been further codified because of the following two policies practiced at CSO(ISW). Tabulation policy: As per the latest tabulation policy, it has been decided to publish detail information regarding factories belonging to 01 to 37 of industry codes( 2-digit, NIC-98). Factories belonging to other industry groups would be clubbed together and to be published under 'Others'. Accordingly all industry codes other than 01 to 37 were replaced with a 5-digited code 'YYYYY'. Merging and suppression of identity: To suppress the identity of factories, less frequent industry codes were modified accordingly. Example: if a reported industry code is found as 2930Z, this is to be treated as 'other merged industry code under industry group 2930 (4-digit NIC'98)'. Similarly if the reported industry code is found as 293ZZ, the same as to be treated as 'other merged industry code under industry group 293 (3-digit NIC'98)' and so on.

FIXED ASSETS (Block-C) Columnwise relationship (please refer schedule) may not hold true for data in this block. This is because of the lack of information available from the factory owners. E. EMPLOYMENT AND LABOUR COST (Block-E) It has been found that a larger number of factory owners were unable to provide detailed break-up of information regarding provident fund (Block-E, Col.7). Instead they provide total provident fund as a whole for all employees (Block-E, Srl. No. 7, Col.7). Users are requested to use Srl.9, Col.7 for information on provident fund. The total of srl.6 to 8 for Col.7 may not tally with srl.9, col.7. F. ASICC codes in Block H, I & J Because of the proximity of various item's description, it is possible that same ASICC code may appear against multiple records in these blocks. They should not be treated as duplicates. They are clubbed together at the time of tabulation to provide information at ASICC level. G. Record Identification Key Record identification key for each factory is Despatch Serial No. (DSL, pos: 4-8) X Block code (Blk, pos: 3). Please refer ANNEXURE-I for item level identification key for each factory.

Sampling error estimates

Relative Standard Error (RSE) is calculated in terms of worker, wages to worker and GVA using the formula (Pl ease refer to Estimation Procedure document in external resources). Programs developed in Visual Faxpro are used to compute the RSE of estimates.

Data appraisal

To check for consistency and reliability of data the same are compared with the NIC-2digit level growth rate at all India Index of Production (IIP) and the growth rates obtained from the National Accounts Statistics at current and constant prices for the registered manufacturing sector.

Moderating effect of institutional environment.

United States ROB Manufacturing: Buying Policy: Production Materials: 5 Days data was reported at 14.000 Day in Apr 2020. This records an increase from the previous number of 12.000 Day for Mar 2020. United States ROB Manufacturing: Buying Policy: Production Materials: 5 Days data is updated monthly, averaging 14.000 Day from Jan 1958 (Median) to Apr 2020, with 748 observations. The data reached an all-time high of 35.000 Day in Mar 2009 and a record low of 1.000 Day in Jul 1974. United States ROB Manufacturing: Buying Policy: Production Materials: 5 Days data remains active status in CEIC and is reported by Institute for Supply Management. The data is categorized under Global Database’s United States – Table US.S002: Institute for Supply Management: Purchasing Manager Index. [COVID-19-IMPACT]

DIPP: IPI: 1993-94=100: Basic Metal and Alloy Industries: Drums and Barrels data was reported at 170.510 1993-1994=100 in Mar 2011. This records an increase from the previous number of 155.220 1993-1994=100 for Feb 2011. DIPP: IPI: 1993-94=100: Basic Metal and Alloy Industries: Drums and Barrels data is updated monthly, averaging 128.925 1993-1994=100 from Apr 2004 (Median) to Mar 2011, with 84 observations. The data reached an all-time high of 209.580 1993-1994=100 in Jul 2009 and a record low of 77.200 1993-1994=100 in Dec 2008. DIPP: IPI: 1993-94=100: Basic Metal and Alloy Industries: Drums and Barrels data remains active status in CEIC and is reported by Ministry of Commerce and Industry. The data is categorized under India Premium Database’s Mining and Manufacturing Sector – Table IN.BAA016: Industrial Production Index: 1993-94=100: Department of Industrial Policy and Promotion. Rebased from 1993-1994 base to 2004-2005 base. Replacement series ID: 285480902

Based on the State Ownership Policy document approved in late December 2022 by Egyptian President Abdel-Fattah El-Sisi, the Egyptian government planned to withdraw over three years, maintain or decrease, or maintain or increase its presence in several economic sectors and activities. In the manufacturing sector, the Egyptian government planned to withdraw from 90 percent of the textile industry in the next three years. Moreover, government involvement in the mining industry was expected to be maintained or decreased by 60 percent. In contrast, the government planned to hold or increase presence by 17 percent in pharmaceutical manufacturing.

We study the impact of policy and institutional constraints, and reforms undertaken to remedy them, on relative price efficiency and cost of the private manufacturing sector of Egypt. We undertake this study using a generalized cost function, which subsumes the standard neoclassical cost function as a special case. This approach allows us to assess the impact of such constraints, which include labor market, energy and financial sector ones, on relative prices and the structure of production, including factor demands, shares and cost. Our findings indicate the presence of substantial distortions in relative prices, and hence on cost, due to the policy environment. We also find improvements in relative price efficiency and cost performance as a result of policy reforms initiated to remove the constraints.

This study aims to explore the driving factors of green innovation, and uses the micro- and macro-data from China’s sports goods manufacturing industries. In particularly, sports goods manufacturing enterprises are identified by the textual analysis of information disclosure, and the competitive environment faced by each enterprise is built through their unique closest rivals. Empirically, this study finds that competition and policy can promote green innovation in sports goods manufacturing industries, and industrial policy can moderate the role of product market competition in promoting green innovation. Considering the characteristics of the Chinese market, more industrial policies may intensify the competition among manufacturing enterprises, forcing such enterprises to obtain competitive advantages through innovation outcomes. It is worth noting that the association between product market competition and green innovation changes as financial constraints increase, and this may be caused by the impact of industrial policy on the interactions among enterprises. After implementing the strict environmental policy, product market competition and industrial policy can both promote green innovation. In high-polluting industries, sports goods manufacturing enterprises get more social attention and suffer from higher penalties for environmental violations, so that such enterprises will get more motivations from industrial policies to support green innovation. In addition, we also find that there is a significant inverted-U shape relationship between industrial policy and green innovation in sports goods manufacturing industries. As financial constraints increase, the non-linear relationship between product market competition and green innovation converts from a U shape relationship to an inverted-U shape relationship. Our findings can provide a better understanding of the investment of sports goods manufacturing enterprises in green innovation.

India DIPP: IPI: 1993-94=100: Other Manufacturing Industries: Process Control Instruments data was reported at 733.550 1993-1994=100 in Mar 2011. This records an increase from the previous number of 300.480 1993-1994=100 for Feb 2011. India DIPP: IPI: 1993-94=100: Other Manufacturing Industries: Process Control Instruments data is updated monthly, averaging 251.645 1993-1994=100 from Apr 2004 (Median) to Mar 2011, with 84 observations. The data reached an all-time high of 870.270 1993-1994=100 in Mar 2008 and a record low of 79.500 1993-1994=100 in Oct 2005. India DIPP: IPI: 1993-94=100: Other Manufacturing Industries: Process Control Instruments data remains active status in CEIC and is reported by Ministry of Commerce and Industry. The data is categorized under India Premium Database’s Mining and Manufacturing Sector – Table IN.BAA016: Industrial Production Index: 1993-94=100: Department of Industrial Policy and Promotion.

United States ROB Manufacturing: Buying Policy: Production Materials: 30 Days data was reported at 33.000 Day in Apr 2020. This records an increase from the previous number of 28.000 Day for Mar 2020. United States ROB Manufacturing: Buying Policy: Production Materials: 30 Days data is updated monthly, averaging 38.000 Day from Jan 1958 (Median) to Apr 2020, with 748 observations. The data reached an all-time high of 48.000 Day in Apr 2010 and a record low of 7.000 Day in Feb 1974. United States ROB Manufacturing: Buying Policy: Production Materials: 30 Days data remains active status in CEIC and is reported by Institute for Supply Management. The data is categorized under Global Database’s United States – Table US.S002: Institute for Supply Management: Purchasing Manager Index. [COVID-19-IMPACT]

United States ROB Manufacturing: Buying Policy: Production Materials: 90 Days data was reported at 20.000 Day in Apr 2020. This stayed constant from the previous number of 20.000 Day for Mar 2020. United States ROB Manufacturing: Buying Policy: Production Materials: 90 Days data is updated monthly, averaging 13.000 Day from Jan 1958 (Median) to Apr 2020, with 748 observations. The data reached an all-time high of 33.000 Day in Oct 1973 and a record low of 3.000 Day in Mar 2009. United States ROB Manufacturing: Buying Policy: Production Materials: 90 Days data remains active status in CEIC and is reported by Institute for Supply Management. The data is categorized under Global Database’s United States – Table US.S002: Institute for Supply Management: Purchasing Manager Index. [COVID-19-IMPACT]

Abstract

Introduction

The Annual Survey of Industries (ASI) is one of the large-scale sample survey conducted by Field Operation Division of National Sample Survey Office for more than three decades with the objective of collecting comprehensive information related to registered factories on annual basis. ASI is the primary source of data for facilitating systematic study of the structure of industries, analysis of various factors influencing industries in the country and creating a database for formulation of industrial policy.

The main objectives of the Annual Survey of Industries are briefly as follows: (a) Estimation of the contribution of manufacturing industries as a whole and of each unit to national income. (b) Systematic study of the structure of industry as a whole and of each type of industry and each unit. (c) Casual analysis of the various factors influencing industry in the country: and (d) Provision of comprehensive, factual and systematic basis for the formulation of policy.

The Annual Survey of Industries (ASI) is the principal source of industrial statistics in India. It provides statistical information to assess changes in the growth, composition and structure of organised manufacturing sector comprising activities related to manufacturing processes, repair services, gas and water supply and cold storage. The Survey is conducted annually under the statutory provisions of the Collection of Statistics Act 1953, and the Rules framed there-under in 1959, except in the State of Jammu & Kashmir where it is conducted under the State Collection of Statistics Act, 1961 and the rules framed there-under in 1964.

Geographic coverage

The ASI is the principal source of industrial statistics in India and extends to the entire country except Arunachal Pradesh, Mizoram & Sikkim and the Union Territory of Lakshadweep. It covers all factories registered under Sections 2m(i) and 2m(ii) of the Factories Act, 1948.

Analysis unit

The primary unit of enumeration in the survey is a factory in the case of manufacturing industries, a workshop in the case of repair services, an undertaking or a licensee in the case of electricity, gas & water supply undertakings and an establishment in the case of bidi & cigar industries. The owner of two or more establishments located in the same State and pertaining to the same industry group and belonging to same scheme (census or sample) is, however, permitted to furnish a single consolidated return. Such consolidated returns are common feature in the case of bidi and cigar establishments, electricity and certain public sector undertakings.

Universe

The survey cover factories registered under the Factory Act 1948. Establishments under the control of the Defence Ministry,oil storage and distribution units, restaurants and cafes and technical training institutions not producing anything for sale or exchange were kept outside the coverage of the ASI.

Kind of data

Census and Sample survey data [cen/ssd]

Sampling procedure

Sampling Procedure

The sampling design followed in ASI 2000-01 is a Circular Systematic one. All the factories in the updated frame (universe) are divided into two sectors, viz., Census and Sample.

Census Sector: Census Sector is defined as follows:

a) All the complete enumeration States namely, Manipur, Meghalaya, Nagaland, Tripura and Andaman & Nicobar Islands. b) For the rest of the States/ UT's., (i) units having 100 or more workers, and (ii) all factories covered under Joint Returns.

Rest of the factories found in the frame constituted Sample sector on which sampling was done. Factories under Biri & Cigar sector were not considered uniformly under census sector. Factories under this sector were treated for inclusion in census sector as per definition above (i.e., more than 100 workers and/or joint returns). After identifying Census sector factories, rest of the factories were arranged in ascending order of States, NIC-98 (4 digit), number of workers and district and properly numbered. The Sampling fraction was taken as 12% within each stratum (State X Sector X 4-digit NIC) with a minimum of 8 samples except for the State of Gujarat where 9.5% sampling fraction was used. For the States of Jammu & Kashmir, Himachal Pradesh, Daman & Diu, Dadra & Nagar Haveli, Goa and Pondicherry, a minimum of 4 samples per stratum was selected. For the States of Bihar and Jharkhand, a minimum of 6 samples per stratum was selected. The entire sample was selected in the form of two independent sub-sample using Circular Systematic Sampling method.

Sampling deviation

There was no deviation from sample design in ASI 2000-01

Mode of data collection

Statutory return submitted by factories as well as Face to face

Research instrument

Annual Survey of Industries Questionnaire (in External Resources) is divided into different blocks:

BLOCK A.IDENTIFICATION PARTICULARS BLOCK B. PARTICULARS OF THE FACTORY (TO BE FILLED BY OWNER OF THE FACTORY) BLOCK C: FIXED ASSETS BLOCK D: WORKING CAPITAL & LOANS BLOCK E : EMPLOYMENT AND LABOUR COST BLOCK F : OTHER EXPENSES BLOCK G : OTHER INCOMES BLOCK H: INPUT ITEMS (indigenous items consumed) BLOCK H1: FUELS, ELECTRICITY AND WATER CONSUMPTION BLOCK I: INPUT ITEMS – directly imported items only (consumed) BLOCK J: PRODUCTS AND BY-PRODUCTS (manufactured by the unit)

Cleaning operations

Pre-data entry scrutiny was carried out on the schedules for inter and intra block consistency checks. Such editing was mostly manual, although some editing was automatic. But, for major inconsistencies, the schedules were referred back to NSSO (FOD) for clarifications/modifications.

Validation checks are carried out on data files. Code list, State code list, Tabulation program and ASICC code are may be refered in the External Resources which are used for editing and data processing as well..

B. Tabulation procedure

The tabulation procedure by CSO(ISW) includes both the ASI 2000-01 data and the extracted data from ASI 99-00 for all tabulation purpose. For extracted returns, status of unit (Block A, Item 12) would be in the range 17 to 20. To make results comparable, users are requested to follow the same procedure. For calculation of various parameters, users are requested to refer instruction manual/report. Please note that a separate inflation factor (Multiplier) is available for each unit against records belonging to Block-A for ASI 2000-01 data. The multiplier is calculated for each stratum (i.e. State X NIC'98(4 Digit)) after adjusting for non-response cases.

.

C. Merging of unit level data

As per existing policy to merge unit level data at ultimate digit level of NIC'98 (i.e., 5 digit) for the purpose of dissemination, the data have been merged for industries having less than three units within State, District and NIC'98(5 Digit) with the adjoining industries within district and then to adjoining districts within a state. There may be some NIC'98(5 Digit) ending with '9' which do not figure in the book of NIC '98. These may be treated as 'Others' under the corresponding 4-digit group. To suppress the identity of factories data fields corresponding to PSL number, Industry code as per Frame (4-digit level of NIC-98) and RO/SRO code have been filled with '9' in each record.

It may please be noted that, tables generated from the merged data may not tally with the published results for few industries, since the merging for published data has been done at aggregate-level to minimise loss of information.

Sampling error estimates

Relative Standard Error (RSE) is calculated in terms of worker, wages to worker and GVA using the formula (Pl ease refer to Estimation Procedure document in external resources). Programs developed in Visual Faxpro are used to compute the RSE of estimates.

Data appraisal

To check for consistency and reliability of data the same are compared with the NIC-2digit level growth rate at all India Index of Production (IIP) and the growth rates obtained from the National Accounts Statistics at current and constant prices for the registered manufacturing sector.