ESIC13 Aug 2026circularPrepared by Complied AI

National Metadata Structure for Payroll Data

ESIC circular · 13 Aug 2026

Official title

ESIC's National Metadata Structure (NMDS) with respect to Payroll Data

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What changed

The Employees’ State Insurance Corporation (ESIC) establishes a National Metadata Structure (NMDS) to standardize the reporting of payroll data. This framework defines the characteristics, collection methods, and dissemination policies for monthly statistical reports on registered employees. The data includes gender, age brackets, and contribution status for new and existing contributors. ESIC publishes this information on its website in PDF format. The data collection relies on administrative records from employer filings and the Insured Persons registration database. ESIC releases these statistics with a one-month lag to accommodate the statutory contribution filing deadline. This structure ensures transparency and consistency in the presentation of employment statistics under the Employees’ State Insurance Act, 1948.

Who is affected: Employees’ State Insurance Corporation (ESIC)
Insured Persons (IPs) registered under the ESIC scheme
Required action: ESIC must publish payroll data on its website on a monthly basis.
Key dates: Date of last update of the content of the metadata — 20 Jul 2025

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Source details

Source: Employees' State Insurance Corporation
Type: circular
Published by source: 13 Aug 2026
Issuing division: ICT Division, ESIC HQ
Coverage area: labour

Document text

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NMDS pertaining to ESIC:

Item No	Concept Name	Definition	Guidelines	Response
1	Contact	Individual or organisational contact points for the data or metadata, including information on how to reach the contact points.
1.1	Contact Organization	The name of the organisation of the contact points for the data or Metadata.	Provide the full name (not just acronym/code name) of the organisation responsible for the processes and outputs (data and metadata) that are the subject of the report.	Employees’ State Insurance Corporation (ESIC), Ministry of Labour and Employment
1.2	Compiling agency	Organisation collecting and/or elaborating the data being reported.	Provide the full name of the Department/ Division under the organisation responsible for the processes and outputs (data and metadata) that are the subject of the report.	ICT Division, ESIC HQ
1.3	Contact Details	The details of the contact points for the data or metadata.	(a) Name of Organisation owning the processes and outputs (b) Author (if different from (a)) (c) Disseminating Agency (if different from (a) and (b)) (d) Name (first and last names) (e) Designation (f) Postal address (g) E-mail address (preferably designation based) (h) Contact number (i) Fax number	(a) Employees’ State Insurance Corporation (b) Same as above (c) Same as above (d) Sh. Gaurang Bhatnagar (e) Deputy Director (ICT) (f) Panchdeep Bhawan, CIG Marg, New Delhi – 110002 (g) ic-ict@esic.nic.in (h) 011-23604765
2	Statistical Presentation and Description	Description of the disseminated data which can be displayed to users as tables, graphs or maps		Monthly
2.1	Data description	Main characteristics of the data set, referring to the data and indicators disseminated.	Describe brieﬂy the main characteristics of the data in an easily and quickly understandable manner, referring to the main variables disseminated.	payroll data showing the number of registered employees under ESIC by gender and age brackets. Categories include new registrations, new contributors, and existing contributors.
2.2	Classification system	Arrangement or division of objects into groups based on characteristics which the objects have in common	List all classiﬁcations and breakdowns that are used in the data (with their detailed names) and provide links (if publicly available). Type of dis-aggregation available in the data sets - for example rural-urban, male-female, etc. and whether data is available at the sub-national level or not, should be clearly specified.	Classification based on gender (Male, Female, Others), age group brackets (e.g., <18, 18-21, 22-25...), and contribution status (New, Existing).
2.3	Sector coverage	Main economic or other sectors	List the main economic or other sectors covered by the data and the size classes used, for example, Health/ Education/ Manufacturing etc for sectors and classes based on number of employees for size classes.	All ESIC-covered employment sectors.
2.4	Statistical concepts and definitions	Statistical characteristics of statistical observations, variables	Deﬁne and describe brieﬂy the main statistical variables that have been observed or derived. Indicate their types.	Variables: New Registrants, New Contributors, Existing Contributors Unit: Individual employee. Types: Categorical.
2.5	Statistical unit	Entity for which information is sought and for which statistics are ultimately compiled.	Deﬁne the type of statistical unit about which data are collected, e.g. enterprise, household, etc.	Individual Insured Persons (employees registered under ESIC scheme).
2.6	Statistical population	The total population of a deﬁned class of people, objects or events	Deﬁne the target population of statistical units for which information is sought. For example, agricultural household, general household, industrial unit, etc.	IPs registered under ESIC scheme
2.7	Reference Period	The length of time for which data are available	State the time period(s) for which data is collected.	Continuous with monthly reporting cut-off.
2.8	Data Confidentiality	Rules applied for treating the datasets to ensure statistical confidentiality and prevent unauthorised disclosure	Describe the procedures that are used in protecting conﬁdentiality, viz., anonymisation, legal provision, if any.	Non-sensitive data is published on ESIC website. No PII data is published.
3	Institutional Mandate	Law, set of rules or other formal set of instructions assigning responsibility as well as the authority to an organisation for the collection, processing, and dissemination of statistics
3.1	Legal acts and other agreements	Legal acts or other formal or informal agreements that assign responsibility as well as the authority to an agency for the collection, processing, and dissemination of statistics.	State the national legal acts and/or other reporting agreements	Employees’ State Insurance Act, 1948.
3.2	Data sharing	Arrangements or procedures for data sharing and coordination between data producing agencies.	Describe the arrangements, procedures or agreements to facilitate data sharing and exchange between data producing agencies within the national statistical system.	Published on ESIC website on monthly basis.
3.3	Release policy	Rules for disseminating statistical data to all interested parties.	State if the release of the products is governed by some policy etc.	Published on ESIC website on monthly basis.
3.4	Release calendar	The schedule of statistical release dates.	State whether there is a release calendar for the statistical outputs from the process being reported, and if so, whether this calendar is publicly accessible and if yes, give a link or reference.	Monthly.
3.5	Frequency of dissemination	The time interval at which the statistics are disseminated over a given time period.	State the frequency with which the data are disseminated, e.g. monthly, quarterly, yearly.	Monthly.
3.6	Data access	The conditions and modalities by which users can access, use and interpret data	State the conditions and link on website from where the user can access the data: For easy access of users, following details should also be mentioned about the dataset: Title: Name by which the data is known Dataset Edition: Edition of data (ex: first, second, final etc) Dataset Reference data type: Type of data entered in the ﬁeld (ex: .txt, .dbf, .xls) Presentation Format: Presentation format of the data (ex: document, map, table, etc.) Dataset Language: language of any text in the data Status/Version: How updated is the data?	Data is published on ESIC website in pdf format. Language: English.
4	Quality Management	Systems and frameworks in place within an organisation to manage the quality of statistical products and processes.
4.1	Documentation on methodology	Descriptive text and references to methodological documents available.	List reference metadata ﬁles, methodological papers, summary documents and handbooks relevant to the statistical process.	NA
4.2	Quality documentation	Documentation on procedures applied for quality management and quality assessment.	List relevant quality related documents, for example, other quality reports, studies.	NA
4.3	Quality assurance	All systematic activities implemented that can be demonstrated to provide conﬁdence that the processes will fulﬁl the requirements for the statistical output.	Describe the procedures (such as use of a general quality management system based on ISO 9000 series) to promote general quality management principles in the organisation. Describe the quality assurance framework used to implement statistical quality principles. Describe the quality assurance procedures speciﬁcally applied to the statistical process for which the report is being prepared, for example training courses, process monitoring, benchmarking, assessments, and use of best practices. Describe any ongoing or planned improvements in quality assurance procedures.	Data is taken from ESIC database.
4.4	Quality assessment	Overall assessment of data quality, based on standard quality criteria.	Summarise the results of the most recent quality assessments and cross reference to the chapters in the report where the results are presented in more detail.	NA
5	Accuracy and Reliability	Accuracy of data is the closeness of computations or estimates to the exact or true values that the statistics were intended to measure. Reliability of the data, deﬁned as the closeness of the initial estimated value to the subsequent estimated value.
5.1	Sampling error	That part of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a subset of the population is enumerated.	If probability sampling is used: • for user reports-provide the range of variation of the A13 indicator among key variables at user report level of detail; • for producer reports-provide the range of variation of the A1 indicator among key variables at producer report level of detail; • indicate the impact of sampling error on the overall accuracy of the results; • state how the calculation of sampling error is aﬀected by adjustments for nonresponse, misclassiﬁcations and other sources of uncertainty, such as outlier treatment. If non-probability sampling is used: provide an assessment of representativeness, a motivation for the invoked model for estimation and risk of sampling bias	NA
6	Timeliness	The timeliness of the data collection release to be compiled.
6.1	Timeliness	Length of time between data availability, the event or phenomenon the data describe, and ﬁnal release to its users.	Outline the reasons for the time lag, if any. Outline eﬀorts to reduce time lag in future.	Data is published with a lag of 1 month as monthly contribution is allowed to be filed by the 15th of next month.
7	Coherence and Comparability	Adequacy of statistics to be reliably combined in diﬀerent ways and for various uses and the extent to which diﬀerences between statistics can be attributed to diﬀerences between the true values of the statistical characteristics
7.1	Comparability – over time	The extent to which statistics are comparable or reconcilable over time.	Provide information on possible limitations in the use of data for comparisons over time. Distinguish three broad possibilities: 1. There have been no changes, in which case this should be reported. 2. There have been some changes but not enough to warrant the designation of a break in series. 3. There have been suﬃcient changes to warrant the designation of a break in series.	NA
7.2	Coherence	The extent to which statistics are reconcilable with System of National Accounts.	For producer reports only. Where relevant, the results of comparisons with the System of National Account framework / Other Statistical Standards and feedback from System of National Accounts / Other Statistical Standards with respect to coherence and accuracy problems should be reported and should be a trigger for further investigation.	NA
8	Statistical Processing	Any statistical processing undertaken to finalise the data
8.1	Source data type	Characteristics and components of the raw statistical data used for compiling statistical aggregates.	Indicate if the data are based on a survey, administrative data, multiple data sources, or macro-aggregates. In the event of multisource or macro-aggregate processes describe each source. For each dataset from an administrative source, summarise the source, its primary purpose, and the most important data items acquired	Administrative data via employer filings and IP registration database.
8.2	Frequency of data collection	Time interval at which the source data are collected.	Indicate the frequency of data collection (e.g. monthly, quarterly, annually, or continuous).	Continuous with monthly reporting cut-off.
8.3	Data collection method	Method applied for gathering data for oﬃcial statistics.	For each source of survey data: • describe the method(s) used to gather data from respondents; • annex or hyperlink the questionnaires(s). For each source of administrative data: • describe the acquisition process and how it was tested. For all sources: • describe the types of checks applied at the time of data entry.	Data extracted from ESIC database. Further, some validation checks applied at application level.
8.4	Data validation	Process of monitoring the results of data compilation and ensuring the quality of statistical results.	Describe the procedures for checking and validating the source data and how the results are monitored and used. Describe the procedures for validating the aggregate output data (statistics) after compilation, including checking coverage and response rates, and comparing with data for previous cycles and with expectations. List other output datasets to which the data relate and outline the procedures for identifying inconsistencies between the output data and these other datasets	NA
8.5	Data compilation	Operations performed on data to derive new information according to a given set of rules.	Describe the procedures for imputation, the most common reasons for imputation and imputation rates within each of the main strata. Describe the likely impact of imputation. Describe the procedures to derive new variables and to calculate aggregates and complex statistics. Describe the procedures for adjustment for non-response and the corrections to the design weights to account for differences in response rates. Describe the calculation of design weights, including calibration (if used). Describe the procedures for combining input data from different sources. Provide the ratio of the number of replaced values to the total number of values for a given variable. Speciﬁc reference to formula shall be made. The formula or mathematical equation used while computing diﬀerent variables in the report may be described here in a structured format showing the Numerator; Denominator and Multiplier used for computing the same.	NA
9	Metadata Update	The date on which the metadata element was inserted or modified in the database.
9.1	Metadata last posted	Date of the latest dissemination of the metadata.	The date when the complete set of metadata was last disseminated as a block should be provided (manually, or automatically by the metadata system).	Metadata is not published.
9.2	Metadata last update	Date of last update of the content of the metadata.	The date when any metadata were last updated should be provided (manually, or automatically by the metadata system).	21-Jul-25

2,170 words

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