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Work session convened by the Friends of the Chair Group on Integrated Economic Statistics

Bern, 6-8 June 2007

Session 3(c)

DISSEMINATION STANDARDS (DATA AND METADATA), DATA EXCHANGE AND

REVISION POLICY

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DISSEMINATION STANDARDS

Main aim of presentation

To present a simple framework of dissemination standards for data and metadata

Draws extensively from OECD Data and Metadata Reporting and Presentation Handbook – published in late 2006

Available at: http://www.oecd.org/dataoecd/46/17/37671574.pdf

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DISSEMINATION STANDARDS

Presentation also covers some elements of this Framework:

• Data and metadata exchange standards

• Importance of terminology

• Guidelines for the reporting and dissemination of metadata

• Data revision

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DISSEMINATION STANDARDS

“Dissemination standards” in context of this presentation cover issues related to:

• The presentation of statistics and related metadata in various dissemination media used by national agencies and international organisations

• Mechanisms for the efficient exchange of statistics and metadata between agencies (Data exchange) – including to international organisations

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DISSEMINATION STANDARDS

• Existing international guidelines touch on dissemination issues but references are partial or incomplete

• The elements of statistics and metadata dissemination are interrelated

• Need for an integrated approach on dissemination fits into the theme of this Work Session – Integrated Economic Statistics

• OECD has been an advocate of the development of a more modular approach in the development of statistical guidelines at the international level

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DISSEMINATION STANDARDS

Modular approach on dissemination standards could be applied to other statistical standards currently being developed, such as:

• Distributive trade statistics (IRDTS)

• Industrial production

• National accounts (SNA update)

This means using either the same text on dissemination or links to dissemination standards

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DISSEMINATION STANDARDS

There are three broad problems when comparing data over time and between agencies.

• Conceptual: Differences in variable definitions, units and classifications.

• Operational: Differences in data collection and processing practices.

• Different data dissemination practices – data may look different even if they aren’t – complicates comparisons

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DISSEMINATION STANDARDS

The need for articulation of a comprehensive set of standards in one source is therefore driven by two broad imperatives. To:

• Improve data quality

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DISSEMINATION STANDARDS

IMPROVE DATA QUALITY

• Interpretability facilitated through the provision of appropriate metadata.

• Statistical transparency embodied in UN Fundamental Principles of Official Statistics

• Responsibility of all statistical agencies to accompany their statistics with appropriate metadata; and

• Provide efficient facilities for the dissemination of their metadata

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DISSEMINATION STANDARDS

The need for articulation of a comprehensive set of standards in one source is therefore driven by two broad imperatives. To:

• Improve data quality

• Minimise reporting burden in provision of data and metadata to international organisations

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DISSEMINATION STANDARDS

MINIMISE REPORTING BURDEN

A lot of activity to reduce reporting burden of member countries international organisations, through:

• Coordinated collection of data.• Use of common questionnaires.• Data sharing between international organisations

More can be done to improve the efficiency of data exchange between organisations

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ELEMENTS OF DRAFT DISSEMINATION STANDARDS FRAMEWORK

• Simple framework presented in paper

• Intended only to outline main elements

• Could be further elaborated in future

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ELEMENTS OF DRAFT DISSEMINATION STANDARDS FRAMEWORK

Different types (e.g. absolute figures; indices; growth rates) and forms (e.g. original; seasonally adjusted; trend-cycle) of data:.

Different types (e.g. absolute figures; indices; growth rates) and forms (e.g. original; seasonally adjusted; trend-cycle) of data:.

Presentation and dissemination of metadataPresentation and dissemination of metadata

Key data presentation practicesKey data presentation practices

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ELEMENTS OF DRAFT DISSEMINATION STANDARDS FRAMEWORK

Key data presentation practices

• for revised data • for breaks in time series• For information about sampling and non-sampling errors• of base years in the presentation of indices• For data and metadata citation • for the reporting of administrative data

• for revised data • for breaks in time series• For information about sampling and non-sampling errors• of base years in the presentation of indices• For data and metadata citation • for the reporting of administrative data

Initial list in OECD Presentation Handbook – Obviously, there are other issues

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DATA EXCHANGE

SDMX

• Consortium of BIS, ECB, Eurostat, IMF, OECD, UNSD, World Bank

• To develop standards for the more efficient exchange of statistical and metadata – from countries to I/Os – between I/Os.

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DATA EXCHANGE

SDMX

Developing two distinct but complementary sets of standards

• The technical standards - provide a data model for exchanging a dataset using a “Data Structure Definition” - Version 2.0 released in 2006

• Content standards

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DATA EXCHANGE

SDMX Content standards – requires

Standardization of concepts - Common Metadata Items

Standardization of terminology - Metadata Common Vocabulary

Standardization of Data/Metadata Structure Definitions

Responsibility of the Domain Groups; identified using the Statistical Domain Lists

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GUIDELINES FOR REPORTING AND DISSEMINATION OF METADATA

• Provision of adequate metadata still a problem despite efforts at national and international levels.

• Absence makes international comparisons difficult

• Numerous initiatives and forums have developed metadata standards over the years – ISO, DDI, Dublin Core + work of METIS, etc

• Still a need for further standards on where and how metadata should be presented – OECD Presentation Handbook

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GUIDELINES FOR REPORTING AND DISSEMINATION OF METADATA

OECD Presentation Handbook

• Further emphasizes need for metadata

• Access to metadata (free of charge, linkage to data, structuring, searchable, etc)

• Adoption of a set of common metadata items – SDMX content standards

• Adoption of a common set of terminology for metadata preparation – SDMX content standards

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DATA REVISION

Defined as any change in value of a statistic released to the public by an official national statistical agency

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DATA REVISION

• Data revision is a fact of statistical life

• Many users still have difficulty with the fact that data needs to be revised for any one or combination of reasons

• Therefore, all organisations should develop a sound revisions policy and practices that are transparent to users

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DATA REVISION

• OECD Handbook touches on data revision in detail

• Work derived from previous IMF material

• Both IMF and OECD believe that implementation of corporate revisions strategy is still an exception at both national and international levels

• Such a strategy touches on eight elements - to be outlined shortly

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DATA REVISION- Causes -

• Incorporation of more complete source data

• Incorporation of source data that more closely match concepts

• Replacement of first release estimates from judgmental or statistical techniques when data become available or as a result of benchmarking

• Incorporation of updated seasonal factors

• Updating of base period

• Changes in statistical methods

• Changes in concepts, definitions or classifications

• Correction of errors

Source data

Routine recalcu-lations

Improve-ments in metho-dology

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DATA REVISION

Eight elements of corporate revisions strategy embedded in recommendations outlined in OECD Presentation Handbook

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DATA REVISIONRecommended practices

• Obtain user views before developing a revisions policy

• Provide a short summary statement on when to expect revisions and why – Make this readily available to users

• Attempt to maintain a stable revisions cycle from year to year

• Balance need to introduce new concepts / methods against user need for “stable” data

• Backcast data back several years to give a consistent time series

• Make documentation on revisions readily available to users

• Undertake analyses to give users an indication of the size of revisions based on past history

• Correct mistakes in a transparent and timely manner

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DATA REVISIONDocumentation of revisions

Should cover:

• Whether data are preliminary or provisional

• Provision of advance notice of major changes in concepts, classifications or methods, etc.,

• Explanation of the sources of revisions when the revised series are released

• Breaks in series when backcasting cannot be undertaken