Assessing the Capacity of Statistical Systems

1. Assessing the Capacity of Statistical Systems Development Data Group

2. Summary • Overview of the assessment process • Some tools and frameworks • Assessing organization and management • Indicators of statistical capacity building Assessing Statistical Capacity and Improving Data Quality for Development

3. Part 1: Overview of the assessment process

4. Assessing statistical capacity • The statistical system • Inputs • Financial and human resources • Legislative and regulatory framework • Statistical and physical infrastructure • Intermediate processes • Statistical operations and procedures • Organization and management • Outputs • Statistical products and services Assessing Statistical Capacity and Improving Data Quality for Development

5. Looking at outputs • Assessing data quality • The Data Quality Assessment Framework (DQAF) • Data coverage and dissemination • Comparison with international frameworks and good practice • General Data Dissemination System (GDDS) • Meeting users needs • Balance between supply and demand • Anticipation of new needs and demands Assessing Statistical Capacity and Improving Data Quality for Development

6. Intermediate processes • Reviewing statistical operations and procedures (DQAF and GDDS) • Appropriateness and correspondence with good practice • Communications with providers and actions to reduce data burden and protect privacy • Quality awareness and control • Assessing management and coordination • Financial management and control • Human resource management • Effectiveness of logistics Assessing Statistical Capacity and Improving Data Quality for Development

7. Inputs • Financial and human resources • Levels and trends in recurrent and development budgets • Numbers and levels of skills/training • Legislative and regulatory framework • Compliance with fundamental principles • Statistical infrastructure • Adequacy of registers, sampling frames etc, • Physical infrastructure • Adequacy of buildings, computers and communications equipment Assessing Statistical Capacity and Improving Data Quality for Development

8. Part 2: Some tools and frameworks

9. Data Quality Assessment Framework • Monitors the quality of economic and social data: • Quality of the statistical product • Quality of the statistical agency • Used by IMF for data part of Reports on Standards and Codes (ROSCs) • Monitors extent to which observed procedures follow good practice Assessing Statistical Capacity and Improving Data Quality for Development

10. Coverage • General DQAF as well as separate frameworks for: • Main economic statistics frameworks: • National accounts; Balance of payments; Government finance; Money and banking; Consumer price index • Socio-demographic statistics (being prepared by World Bank) • Income poverty (completed); Education; Health; Population (in preparation) Assessing Statistical Capacity and Improving Data Quality for Development

11. Structure • Six dimensions of quality 0.Prerequisites of quality • Integrity • Methodological soundness • Accuracy and reliability • Serviceability • Accessibility • Hierarchical structure • Dimensions • Elements • Indicators • Focal issues and key points Assessing Statistical Capacity and Improving Data Quality for Development

12. GDDS • Sets out objectives for data production and dissemination in four “dimensions”: • Data: coverage, periodicity, and timeliness • Quality • Integrity • Access by the public • Provides a framework for development • National authorities set their own priorities and timing to achieve their objectives Assessing Statistical Capacity and Improving Data Quality for Development

13. Participation • Voluntary and involves three actions: 1.Commitment to use the GDDS as a framework for statistical development 2.Designation of a country coordinator 3. Publication of metadata, descriptions of– • current statistical production and dissemination practices • plans for short- and longer-term improvements • need for support including technical assistance Assessing Statistical Capacity and Improving Data Quality for Development

14. Coverage • Economic and financial data – responsible agencies and main data series • Real sector • Fiscal sector • Financial sector • External sector • Socio-demographic data – responsible agencies and main data series • Population • Health • Education • Poverty Assessing Statistical Capacity and Improving Data Quality for Development

15. Part 3: Assessing the organization and management of statistical agencies

16. One approach • Effectiveness of a statistical system is determined by • The products it produces and the services it provides • Its functional and organizational structure • Carry out a SWOT analysis of • The internal organization • The external environment in which the system operates Assessing Statistical Capacity and Improving Data Quality for Development

17. Internal organization • Structure • Coordination • Human resources • Infrastructure • Management systems Assessing Statistical Capacity and Improving Data Quality for Development

18. External environment • Statistical legislation and regulations • Budgets • Accountability and reporting • Relationships with users • Public image Assessing Statistical Capacity and Improving Data Quality for Development

19. Part 4: Indicators of statistical capacity building

20. Assessing capacity • 16 quantitative indicators • Resources • Inputs • Statistical products • 18 qualitative indicators • Environment • Core statistical processes • Quality of statistical products Assessing Statistical Capacity and Improving Data Quality for Development

21. The quantitative indicators • Resources • Annual budget - recurrent and development, locally and externally funded • Inputs • Data sources – censuses, surveys and administrative data • Statistical products • Media and topics covered Assessing Statistical Capacity and Improving Data Quality for Development

22. Using quantitative indicators • Provide rough measure of extent of statistical activities • Usefulness limited by: • Lack of benchmarks • Do not measure efficiency or effectiveness • Need to be interpreted using contextual information provided by qualitative indicators Assessing Statistical Capacity and Improving Data Quality for Development

23. Qualitative indicators • Cover a broader view of factors determining capacity • Based on DQAF Framework • Six indicators on institutional prerequisites • Two indicators on data integrity • One indicator on methodological soundness • Four indicators on accuracy and reliability • Three indicators on serviceability • Two indicators on accessibility Assessing Statistical Capacity and Improving Data Quality for Development

24. Coverage • Legal and institutional environment • Professional and cultural setting • Methodological expertise • Adequacy of data sources • Analytical and processing capacity and quality control • Relevance of products to users needs • Effectiveness of dissemination Assessing Statistical Capacity and Improving Data Quality for Development

25. Measurement and recording • Quantitative indicators use four point assessment scale • Level 1 – largely underdeveloped • Level 2 – developing but with observed deficiencies • Level 3 – moderately well developed • Level 4 – highly developed, in line with good practice Assessing Statistical Capacity and Improving Data Quality for Development