Unit Level Data/Micro Data Extraction: Hands-on Experience with MoSPI datasets
Global Research & Training
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Unit Level Data/Micro Data Extraction: Hands-on Experience
Introduction to MoSPI
Ø The Ministry of Statistics and Programme Implementation was established in 1954 as the Ministry of
Statistics.
Ø MoSPI came into
existence in 1999 after the merger of the Department of Statistics and the
Department of Programme Implementation.
Ø Its
primary goal is to collect, process, and disseminate vital statistics for the
nation.
Ø Extensive dataset: MoSPI provides a wide range of datasets
that capture the economic, social, and demographic aspects of India. The data
collected by MoSPI includes both aggregate statistics and microdata.
MoSPI collects microdata through a variety of surveys and census.
Ø The
Microdata Archive: It provides web based access to the complete metadata and unit level
data of over 172 surveys and censuses.
Ø Web-based survey cataloguing system is
powered by the National Data Archive (NADA) software developed by the International
Household Survey Network (IHSN).
Key Micro
datasets from MoSPI:
Ø ASI – Principal industrial Statistics in India.
Ø PLFS – Provides Labor Force Statistics in India.
Ø NSS – Conducts large-scale surveys on various socio-economic
issues.
Ø Economic Census – Complete economic establishment counts in India.
Ø IIP –Industrial Production Indices: a monthly measure of
industrial growth
Introduction to Microdata or unit level
data
Unit level data or Microdata
refers to the detailed information collected thru sample or census from
individual units such as households, individuals, establishments, or other
units of analysis. MoSPI relies on unit-level data from various surveys and censuses
to make informed decisions and ensure evidence-based policymaking.
Here are some of the key
datasets available on the MoSPI Microdata catalogue.
ASI, IIP, NSS, PLFS, Economic
Census, ……
Unit-Level data:
Contains detailed,
disaggregated information at the establishment or unit level.
Data at the firm or household
level, e.g., fixed capital, working capital, output, employment …etc. at a
factory level.
Aggregate data:
Summarized data across
establishments, e.g., total employment in a region or sector.
For example, aggregate data
might show the total number of workers in the manufacturing sector in a given
region, without showing data for individual factories.
Key Features of Microdata:
1. Individual-level Data: Each record represents a single
observation, such as a person, household, or business.
2. Detailed Variables: Microdata includes detailed information about
various characteristics or attributes, like age, gender, income, education,
occupation, or health status.
3. Large Scale: Microdata is often collected from large samples or
even complete populations, making it suitable for comprehensive statistical
analysis. It often involves millions of records, especially in national surveys
and censuses.
Annual Survey of Industries:
Ø Name of unit: Industrial Statistics Wing
Ø Affiliation: Ministry of Statistics & P.I, Govt. of India
Ø Principal source of Industrial Statistics in India
Ø It follows international standards like ISIC (International
Standard Industrial Classification).
Ø Sections (broad categories) like Agriculture, Mining,
Manufacturing, Education etc.
Ø Purpose: To inform government policy, economic analysis, and
industrial performance assessment.
Ø The ASI extends its coverage to the entire country up to state
level (MoSPI). Directorate of Economics & Statistics (DES) also collects
data in the states at the districts level.
Ø It covers all factories registered under Sections 2(m)(i) and
2(m)(ii) of the Factories Act, 1948, where the manufacturing process is defined
under Section 2(k) of the said Act and the Bidi and Cigar Workers (Conditions
of Employment) Act 1966.
Ø ASI Micro data has A to N blocks (total 14 blocks) but they
provide data from A to J blocks (total 10 blocks) every block has data on
different variables. Other blocks (K to N, total 4 blocks) are for official
use.
Periodic Labour
Force Survey (PLFS)
Ø It was first introduced in 2017-18
by the MoSPI.
Ø It replaced the Employment-Unemployment
Survey conducted by the NSSO and aims to provide more frequent,
comprehensive, and reliable labor market statistics.
Ø The PLFS is conducted in both urban
and rural areas, with a sample of households selected across India.
Ø PLFS tracks labor force participation,
employment and unemployment rates, sectoral employment, wages and salaries, and
migration patterns.
Ø The objective of PLFS is primarily
twofold:
Ø Employment and Unemployment indicators (viz.
Worker Population Ratio, Labour Force Participation Rate, Unemployment
Rate) in the short time interval of three months for the urban areas only in
the Current Weekly Status (CWS)
Ø To estimate employment and unemployment
indicators in both usual status (ps+ss) and CWS in both rural and urban areas
annually.
National Sample Survey (NSS)
Ø NSSO is an organization under the Ministry of Statistics and
Programme Implementation (MOSPI) in India.
Ø The NSSO conducts
large-scale surveys on various socio-economic issues to collect data that helps
in shaping government policies and decisions.
Ø Household consumption expenditure Survey
Ø Unorganized enterprises
Ø Education and health status of household
Ø Debt and investment
Ø Agriculture household
Ø Land and Livestock Holding Surveys
Ø Employment and Unemployment survey
Ø Annual Survey of Unincorporated Sector Enterprises (ASUSE)
Ø Time Use Survey (TUS)
Ø AYUSH of NSS
Ø INDIA - Multiple Indicator Survey (MIS)
Ø etc.
The Index of Industrial Production (IIP)
Ø The Index of Industrial Production (IIP)
was first compiled in India in 1950-51. It was introduced by the Central
Statistical Organisation (CSO), which is now part of the Ministry of Statistics
and Programme Implementation (MoSPI).
Ø It is compiled and
published monthly by the Central Statistics Office (CSO) with a time lag of six
weeks from the reference month.
Ø The
IIP is a key economic indicator that measures the growth rate of the industrial
sector by tracking the production of a fixed basket of industrial goods. The
IIP is calculated based on a fixed base year, with the current base year being
2011-12.
Economic Census
Ø The Economic Census is a large-scale survey
that provides detailed data on the economic activities of businesses,
establishments, and industries in India.
Ø It collects
information on the structure, distribution, and types of economic
establishments across the country.
Ø The survey includes various sectors such as
agriculture, manufacturing, trade, services, construction, and others.
Ø The Economic
Census in India was first conducted in 1977 and the most recent
census is the 7th Economic Census conducted in 2019-20.
Ø 7th Economic Census 2019-20
Ø 6th Economic Census 2013-14
Ø 5th Economic Census 2005
Ø 4th Economic Census 1998
Ø 3rd Economic Census 1990
Ø 2nd Economic Census 1980
Ø 1st Economic Census 1977
Hands-on Exercise
How
to Access MoSPI’s Microdata: Microdata Extraction 1:
Steps:
Visit
the MoSPI website (https://microdata.gov.in).
v Home page = DOWNLOAD
TABLES DATA = Microdata = select
required data = select survey year = Microdata = login = download data
v Home page =
Microdata = select required data = select survey year = Microdata = login =
download data
v Search Microdata catalog = select required data and year = Microdata =
login = download data
Or ICSSR Data Service
CSSR
Data Service https://icssrdataservice.in
Home
page = Microdata catalog = select required data = Microdata = login = download
data
o Search Filters: it can be used for dataset and time
period
o Downloading Data: Accessing datasets in various
formats (CSV, STATA, SPSS, …etc)
Required documents
File Merging and
Reshaping:
File Merging: Why It's Necessary
o ASI
data is stored in multiple files (10 blks), each containing different
types of information (e.g., employment in one, output in another).
o Merging
these files provides a comprehensive view of each unit.
o Identify
key variables for merging: Unit ID.
o Use
statistical software to merge the files by matching these key variables.
File Reshaping: why It’s Necessary
o Reshaping
is needed to change the data format, either long-format (one row per
observation) or wide-format (multiple variables per row).
o Helps
in data analysis and visualization.
Steps for Reshaping Data
o Long
to Wide: Converting data where each row represents one unit
(establishment) and different columns represent attributes.
Identify key variables
and Sr. No for reshaping data.
Thank You and Best Wishes
Raghavendra Yadav
Global Research & Training
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