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PARIS21

This presentation discusses a comprehensive framework for assessing data quality in statistical capacity. It covers background information, the framework's description, current and potential uses, and concluding remarks. The framework includes dimensions of data quality, elements, indicators, and focal issues tailored to specific datasets.

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PARIS21

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Presentation Transcript

  1. PARIS21 Task Force On Statistical Capacity IMF Statistics Department May 21, 2001

  2. Premise of the presentation Measuring statistical capacity Equals Assessing data quality

  3. Plan of the presentation • Background • Description of the framework • Current and potential uses of the framework • Concluding remarks

  4. Background A framework: its uses dictate that it be: • Comprehensive • Balanced between experts’ rigor and generalists’ bird’s eye view • Widely applicable: • Across various stages of statistical development • Across the major datasets • Designed to give transparent results • Arrived at by drawing on national statisticians’ best practices

  5. Framework suite of tools Generic (3-digit) “Lite” Summary of Results etc. Dataset (6-digit) etc. DatasetSpecific (5-digit) etc. GFS BOP NA

  6. Dimensions of data quality

  7. A cascading structure Five dimensions (plus prerequisites) of quality And for each dimension; Elements that can be used in assessing quality And for each element; Indicators that are more concrete and detailed; And for each indicator; Focal issues that are tailored to the dataset Structure of the framework

  8. Structure of the framework (cont’d) The cascading structure—an example • For serviceability, one of the five dimensions: • Four elements are identified as being useful in assessing that dimension: relevance, timeliness and periodicity, consistency, and revision policy and practice • For consistency, three indicators are identified to provide detail and concreteness for that element: intertemporal, internal, and intersectoral consistency • For internal consistency, the focal issues in [dataset] are…

  9. Framework preview • Purpose: serve as a diagnostic preview or for a nonstatistician’s assessment • 13 specific (three-digit) indicators were identified (in the handout): • Relatively nontechnical • Relatively easy to get information • Formatted (in the handout) as a worksheet

  10. Framework summary • Summary presentation of results • Purpose: for nonstatisticians—such as policy advisors and potential investors—after a full assessment based on the DQAF • For clarity and comparability (in handout): • For each dataset, a one-page table • At the two-digit level (21 elements) • On a 4-point scale, from “practice observed” to “practice not observed” • With an “n.A.” Column • With a “comments” column

  11. Uses of the framework • To guide IMF staff • In assessing data for IMF’s use in surveillance and operations; • In preparing ROSCs; and • In designing technical assistance • To guide country efforts (including self-assessments) in strengthening statistical capacity • To guide data users—to complement the SDDS and GDDS

  12. Concluding remarks • General reaction • Welcome initiative • Fills important gap • Is careful and thoughtful • Provides basis for coherent and practical way forward in a complex field

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