Assessing Statistical Capacity and Improving Data Quality for Development Session 3 Slide 2
Summary
Overview of the assessment processSome tools and frameworksAssessing organization and managementIndicators of statistical capacity building
Assessing Statistical Capacity and Improving Data Quality for Development Session 3 Slide 4
Assessing statistical capacity
The statistical systemInputs• 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 Session 3 Slide 5
Looking at outputs
Assessing data qualityThe Data Quality Assessment Framework (DQAF)
Data coverage and disseminationComparison with international frameworks and good practiceGeneral Data Dissemination System (GDDS)
Meeting users needsBalance between supply and demandAnticipation of new needs and demands
Assessing Statistical Capacity and Improving Data Quality for Development Session 3 Slide 6
Intermediate processes
Reviewing statistical operations and procedures (DQAF and GDDS)
Appropriateness and correspondence with good practiceCommunications with providers and actions to reduce data burden and protect privacyQuality awareness and control
Assessing management and coordinationFinancial management and controlHuman resource managementEffectiveness of logistics
Assessing Statistical Capacity and Improving Data Quality for Development Session 3 Slide 7
Inputs
Financial and human resourcesLevels and trends in recurrent and development budgetsNumbers and levels of skills/training
Legislative and regulatory frameworkCompliance with fundamental principles
Statistical infrastructureAdequacy of registers, sampling frames etc,
Physical infrastructureAdequacy of buildings, computers and communications equipment
Assessing Statistical Capacity and Improving Data Quality for Development Session 3 Slide 9
Data Quality Assessment Framework
Monitors the quality of economic and social data:
Quality of the statistical productQuality 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 Session 3 Slide 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 Session 3 Slide 11
Structure
Six dimensions of quality0. Prerequisites of quality1. Integrity2. Methodological soundness3. Accuracy and reliability4. Serviceability5. Accessibility
Hierarchical structureDimensions• Elements
– Indicators– Focal issues and key points
Assessing Statistical Capacity and Improving Data Quality for Development Session 3 Slide 12
GDDS
Sets out objectives for data production and dissemination in four “dimensions”:
Data: coverage, periodicity, and timelinessQualityIntegrityAccess by the public
Provides a framework for developmentNational authorities set their own priorities and timing to achieve their objectives
Assessing Statistical Capacity and Improving Data Quality for Development Session 3 Slide 13
Participation
Voluntary and involves three actions:1. Commitment to use the GDDS as a
framework for statistical development2. Designation of a country coordinator3. 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 Session 3 Slide 14
Coverage
Economic and financial data – responsible agencies and main data series
Real sectorFiscal sectorFinancial sectorExternal sector
Socio-demographic data – responsible agencies and main data series
PopulationHealthEducationPoverty
Assessing Statistical Capacity and Improving Data Quality for Development Session 3 Slide 16
One approach
Effectiveness of a statistical system is determined by
The products it produces and the services it providesIts functional and organizational structure
Carry out a SWOT analysis ofThe internal organizationThe external environment in which the system operates
Assessing Statistical Capacity and Improving Data Quality for Development Session 3 Slide 17
Internal organization
StructureCoordinationHuman resourcesInfrastructureManagement systems
Assessing Statistical Capacity and Improving Data Quality for Development Session 3 Slide 18
External environment
Statistical legislation and regulationsBudgetsAccountability and reportingRelationships with usersPublic image
Assessing Statistical Capacity and Improving Data Quality for Development Session 3 Slide 20
Assessing capacity
16 quantitative indicatorsResourcesInputsStatistical products
18 qualitative indicatorsEnvironmentCore statistical processesQuality of statistical products
Assessing Statistical Capacity and Improving Data Quality for Development Session 3 Slide 21
The quantitative indicators
ResourcesAnnual budget - recurrent and development, locally and externally funded
InputsData sources – censuses, surveys and administrative data
Statistical productsMedia and topics covered
Assessing Statistical Capacity and Improving Data Quality for Development Session 3 Slide 22
Using quantitative indicators
Provide rough measure of extent of statistical activitiesUsefulness limited by:
Lack of benchmarksDo 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 Session 3 Slide 23
Qualitative indicators
Cover a broader view of factors determining capacityBased on DQAF Framework
Six indicators on institutional prerequisitesTwo indicators on data integrityOne indicator on methodological soundnessFour indicators on accuracy and reliability Three indicators on serviceabilityTwo indicators on accessibility
Assessing Statistical Capacity and Improving Data Quality for Development Session 3 Slide 24
Coverage
Legal and institutional environmentProfessional and cultural settingMethodological expertiseAdequacy of data sourcesAnalytical and processing capacity and quality controlRelevance of products to users needsEffectiveness of dissemination
Assessing Statistical Capacity and Improving Data Quality for Development Session 3 Slide 25
Measurement and recording
Quantitative indicators use four point assessment scale
Level 1 – largely underdevelopedLevel 2 – developing but with observed deficienciesLevel 3 – moderately well developedLevel 4 – highly developed, in line with good practice