Data Dictionary

Mer information

Fält	Värde
Data senast uppdaterad	16 oktober 2023
Metadata senast uppdaterad	19 december 2025
Skapad	16 oktober 2023
Format	CSV
Licens	Licens ej angiven
Data extraction page	B-01: Main workers, marginal workers, non-workers and those marginal workers, non-workers seeking/available for work classified by age and sex
Data insights	Data Insights that can be drawn:Regional Differences: By examining the data across different states and districts, one can discern patterns related to employment and demographics, possibly highlighting more prosperous regions versus those requiring more developmental attention.Gender Dynamics: The dataset classifies information by gender, enabling insights into gender disparities in employment, literacy, and other socio-economic factors.Literacy & Employment: The relationship between literacy levels and types of occupation or employment status can be explored.Social Group Insights: The categorization by social group might shed light on societal structures, possibly indicating which groups might be more marginalized in terms of employment and educational opportunities.Age Dynamics: The classification by age can be used to understand the age structure of workers in different occupations, providing insights into youth employment, elderly participation, and more.
Data last updated	2 011
Data retreival date	6/23/2022
Datastore active	True
District no	729
Frequency	Decadal
Geo columns	['state_code', 'district_code']
Granularity	District
Has views	True
Id	efefb405-bd30-4041-bd36-5e6b0d9432ff
Idp ready	True
Lgd mapping	yes
Methodology	The data is collected as part of the national census conducted by the Government of India. Enumerators are trained and deployed across the country to gather detailed demographic information at the household level, which is then aggregated and reported at various administrative divisions, including at the district level. This particular dataset focuses on the working dynamics of the population, capturing the intricacies of employment, unemployment, and the nature of jobs people are engaged in.
No indicators	1
Package id	2a41af7b-7922-4d8f-83c9-d5ebcb8a5f54
Position	1
Size	23,4 MiB
Sku	moha-census_pca_demography-dt-dc-abc
State	active
States uts no	36
Url type	upload
Years covered	2 011
Methodology	The data is collected as part of the national census conducted by the Government of India. Enumerators are trained and deployed across the country to gather detailed demographic information at the household level, which is then aggregated and reported at various administrative divisions, including at the district level. This particular dataset focuses on the working dynamics of the population, capturing the intricacies of employment, unemployment, and the nature of jobs people are engaged in.
Indicators
Similar Resources
Granularity Level	District
Data Extraction Page	B-01: Main workers, marginal workers, non-workers and those marginal workers, non-workers seeking/available for work classified by age and sex
Data Retreival Date	6/23/2022
Data Last Updated	2011
Sku	moha-census_pca_demography-dt-dc-abc
Dataset Frequency	Decadal
Years Covered	2011.0
No of States/UT(s)	36
No of Districts	729
No of Tehsils/blocks
No of Gram Panchayats
Mer information
Number of Indicators	1
Insights from the dataset	Data Insights that can be drawn:Regional Differences: By examining the data across different states and districts, one can discern patterns related to employment and demographics, possibly highlighting more prosperous regions versus those requiring more developmental attention.Gender Dynamics: The dataset classifies information by gender, enabling insights into gender disparities in employment, literacy, and other socio-economic factors.Literacy & Employment: The relationship between literacy levels and types of occupation or employment status can be explored.Social Group Insights: The categorization by social group might shed light on societal structures, possibly indicating which groups might be more marginalized in terms of employment and educational opportunities.Age Dynamics: The classification by age can be used to understand the age structure of workers in different occupations, providing insights into youth employment, elderly participation, and more.
IDP Ready	Yes
LGD Mapping	Yes
Mapping Status %
Geo Columns	['state_code', 'district_code']