Data Quality Reference Site

Work on Economic Data Quality by the IMF Staff

• Mrkaić, Mićo, “Data dissemination standards and the statistical quality of the IMF's world economic outlook forecasts” (September 2010). This paper analyzes the effects of IMF member countries participation in the IMF’s Data Standards Initiatives (DSI) on the statistical quality of WEO forecasts. Results show that WEO forecasts for SDDS subscribers are in general better than for GDDS participants and those member countries than do not participate in the DSIs.
   
• Gonzalez-Garcia, Jose and Gonzalo Pastor, “Benford’s law and Macroeconomic data quality” (January 2010). This paper examines the usefulness of testing the conformity of macroeconomic data with Benford’s law as indicator of data quality. Most of the macroeconomic data series tested conform with Benford’s law. However, questions emerge on the reliability of such tests as indicators of data quality once conformity with Benford’s law is contrasted with the data quality ratings included in the data module of the Reports on the Observance of Standard and Codes (data ROSCs).
   
• Grünewald, Werner, Lucie Laliberté, and Lauren Probst, "Data Quality: A Comparison of IMF’s Data Quality Assessment Framework (DQAF) and Eurostat"s Quality Definition" (January 2004). In this paper, the IMF and Eurostat compare their approaches to data quality and find that they complement each other. The IMF Data Quality Assessment Framework is process-oriented, leading to qualitative measures, while Eurostat takes an output-oriented approach seeking quantitative results.  
   
• Carson, Carol S., Sarmad Khawaja, and Thomas K. Morrison, "Revisions Policy For Official Statistics: A Matter of Governance" (August 2003). This paper, presented to the International Statistical Institute, argues that the time has come to bring revisions more fully out in the open and to draw on statistical experience from around the world to work towards identifying a set of good practices. 
   
• "Fifth Review of the Fund"s data Standards Initiatives: Data Quality Assessment Framework and Data Quality Program" (July 2003). This paper, presented to the IMF"s Executive Board on July 9, 2003, presents an update on the DQAF and introduces the IMF data quality program. A summary of the DQAF, a brief description of a set of refinements to the DQAF, the role of the DQAF in the data quality program, and priorities for the work ahead, are also discussed.  
   
• Carson, Carol S. and Lucie Laliberté, "Assessing Accuracy and Reliability: A Note Based on Approaches Used in National Accounts and Balance of Payments Statistics" (February 2002). This paper explores four approaches that shed light on the accuracy and reliability of both the national accounts and the balance of payments: examination of statistical discrepancies, comparison with other data, analysis of revisions, and judgmental evaluation.
   
• Allum, Peter and Mehmet Agca, “Economic Data Dissemination: What Influences Country Performance on Frequency and Timeliness” (November 2001).This paper explores and identifies the various economic, social, and demographic factors underlying the large differences in macroeconomic data dissemination among countries.  
   
• "Fourth Review of the Fund"s Data Standards" Initiatives: Supplement on the Data Quality Assessment Framework" (July 2001). This paper, presented to the IMF"s Executive Board, outlines the methodology for assessing data quality. Applications of the assessment methodology, including in the Reports on the Observance of Standards and Codes (ROSC) data modules, are also discussed.  
   
• Carson, Carol S. and Claire Liuksila, "Further steps Toward a Framework for Assessing Data Quality" (May 2001). This paper was presented at the International Conference on Quality in Official Statistics that took place May 14-15, 2001 in Stockholm (Sweden).
   
• Carson, Carol S., "Toward a Framework for Assessing Data Quality" (October 2000). This paper builds on the paper noted below and discusses the stimuli that prompted the IMF’s ongoing work on data quality, presents the data quality assessment framework that is emerging from the iterative and consultative process undertaken by the IMF, and discusses the work ahead to refine the framework while seeking further comments on the approach taken. The framework synthesizes elements covering the quality of the institution the superstructure for producing and disseminating statistics and the quality of the individual statistical product. The paper discusses possible applications of the frameworks, including a "lite" assessment tool approach, and gives examples of practical applications of the framework.
   
• Carson, Carol S., "What is Data Quality? A Distillation of Experience" (August 2000). This paper examines a sample of reviews of statistical systems and statistical products to distill some answers to the question "What is data quality?" and to derive some of the elements, or key features, that represent data quality. The paper describes the IMF staff"s work toward a framework for assessing data quality and, in light of the criteria and key features of the sample reviewed, invites comments on the usefulness of the framework under development.