1. Generating and Using Data for Poverty Reduction Strategies
Neil Fantom, Development Data Group

2. What data are needed to:
Analyze and understand poverty?
Monitor economic growth?
Determine the location of new schools and monitor their performance?
Design health care policy?

3. In the countries you work with:
What statistical data are needed for managing and monitoring the PRS?
Are statistical data available to meet those needs?
What data quality attribute is most important?
Accuracy Timeliness
Frequency Relevance
Comparability Accessibility
What is most expensive?

4. Some points Data needs can be broad
Madagascar PRSP: GDP, poverty, family size, malaria, agriculture, crime and security, investment, education, health, transport, water, public finance, sanitation…
Need indicators, but more than indicators
Need data to understand policy choices and manage service delivery
More than surveys
Use of administrative data is very important e.g. in education, health, crime & justice, etc.

5. Data Sources: Surveys and Censuses

6. Censuses
Fundamental statistical baseline, especially in countries with limited vital registration systems
Common problems:
Censuses are expensive and infrequent, so funding is often problematic
Censuses are often highly political
Long delays between data collection and release of results
Give “point in time” estimates only
Inter-censal estimates, including at sub-national levels, are important as well but often inaccurate (e.g. because of poor birth, mortality and migration estimates)
Commonly under-count (the issue is by how much)

8. Household Surveys
Key data source, both for indicators (like poverty incidence), and for research and analysis
A lot of HH survey data in many developing countries come from just a few internationally-sponsored surveys
DHS, MICS, LSMS, CWIQ
Common problems:
Relatively infrequent
Relatively expensive
Over-reliance on donor funding
Large sample sizes needed for geographical disaggregation
Incoherence over time and between surveys
Inaccessible or under-utilized data

9. Household survey coverage: IDA countries in Africa 2000-2004
“Poverty” surveys
Health surveys
62 % of population
97 % of population
 Available  Not conducted /unavailable  Non-IDA country

10. Measuring access to improved water sources in Ghana
Lack of comparability
Measuring access to improved water sources in Ghana
CWIQ 2003
CENSUS 2000
GLSS 1998
DHS 2003

11. Measuring access to improved water sources in Ghana
Lack of comparability
Measuring access to improved water sources in Ghana
Difficult to create a series on access to a protected well from these surveys
CWIQ 2003
CENSUS 2000
GLSS 1998
DHS 2003

12. Data sources: Administrative Systems

14. Data sources: Administrative Systems
Source of many key indicators that are often relevant to PRSPs
Where systems exist, often easy and cheap to harvest frequent data that can be disaggregated
Examples: disease prevalence; educational enrolment and completion; trade; crime and justice; migration; transport
The common problems:
Data may not be precisely what is needed
Data weaknesses are inherited from weaknesses in administrative systems (inaccurate, not timely..)
Usually part of a line ministry system; not easy for statistics office to influence

15. National statistical agencies

19. National statistical agencies
Have mandate and basic institutional capacity to collect and disseminate statistics across public sector
Provide coordination and standards-setting function
The common problems:
Basic capacity may be poor, often under funded by government or heavily reliant on donors
Often remote from policy discussion
Linkage to, and influence over, statistics produced by line ministries may be very weak

20. National statistical systems
Different models in different countries
Oversight/governance
Independent commissions or Boards
Political oversight by government or parliament
Status of central statistical authority
Statistical legislation
May be centralized or decentralized (most countries are a mixture)
Geographically e.g. statistical offices of national statistical office at sub-national levels
Sectorally e.g. statistical functions within line ministries directly controlled by national statistical agency

21. Centralized or decentralized?

22. Build institutional capacity to produce good statistics
Basis should be a coherent and comprehensive improvement strategy: most importantly, based on user needs
Institutional change and reform is often crucial
Need to invest in:
“Physical” infrastructure and “statistical” infrastructure
Human capacity
Statistical methods
Information technology
Improving data access
Beware: donor investments in data and statistics are not always “system-building”

23. A Frequently Asked Question: What is a good statistics strategy?
Integrated into national development policy
Developed in an inclusive way, with stakeholder consultation
Basis for sustained improvement in statistics that are “fit for purpose”
Contains assessment of current situation
Has plan for how statistics should be developed
(From PARIS21,

24. Checklist from PARIS21

25. Case study 1. Nigeria Baseline at end of 1990s: Key turning points:
Statistics “atrocious”; Federal Bureau of Statistics “in decay”
Weak coordination between many under-funded data collection agencies
Lack of interest by government, low productivity of staff
Data often stale, integrity of data in question
Key turning points:
Statistical Master Plan for Federal Office of Statistics (FOS), 2003
National Economic Empowerment and Development Strategy – NEEDS – emphasizes statistics
New Statistics Bill: National Bureau of Statistics formed from FOS and National Databank. Results in major overhaul and financial support from government and donors

27. Case study 2. Kenya Baseline at end of 1990s: Key turning points:
Statistics “poor and in decline”
System supported by donor-sponsored surveys, slow publication of results
Major weaknesses in key datasets e.g. external trade, education, national accounts
High turnover of senior management
Key turning points:
Strategic Plan for reform of Central Bureau of Statistics in 2003; new management and IMF General Data Dissemination System played key role
Key reforms: Statistics Act, NBS now autonomous with a Board of Directors, increase in recurrent funding
Attracted donor support, significant improvements made by 2005 (dissemination, survey program)

29. Some messages from the case studies
Capacity building processes are more successful when:
Political environments are favorable
Demands are articulated in PRSPs
Statistical leadership is responsive
Statistical planning process promotes:
Better donor collaboration
Increased funding
Countries have made use of improved ICT
For production and dissemination/accessibility

30. Support available (1) TFSCB STATCAP ADP
Trust Fund for Statistical Capacity Building, small grants up to $400,000 (over 80 projects supported)
STATCAP
Lending program for statistical investment projects
Approved: Burkina Faso ($10m), Ukraine (32m), Russia ($10m), Kenya ($20m), Tajikistan ($1m)
Pipeline: Mongolia, Bolivia, Tanzania, India (DPL), Indonesia
ADP
Accelerated Data Program, for documentation and improvement of household surveys

31. Support available (2) Statistical Capacity Assessment Training
On line database maintained by DEC
Other tools, including IMF Data ROSC frameworks
Training
New on-line course being developed
Documents
PARIS21 e.g. “Developing a Policy-Based NSDS” at
… and of course DECDG staff, and experienced statistics TTLs

35. A New Initiative: Statistics for Results Facility
Response to need to do more, in more countries
To help raise funds for improving statistics at national level
Based on notion of “sector-wide” statistics plan targeted at PRS
i.e. implementation plan for NSDS discussed and agreed by country-level partnership group (using PRS M&E processes if practical)
Finance through central fund
New $140m multi-donor Trust Fund to provide resources to meet funding shortfalls
Pilot phase
Possible countries are Ghana, Mozambique, Niger, Afghanistan, Ethiopia, Cambodia…

36. The ICP and PPPs What you can do
Raise statistical capacity weaknesses in policy dialogue
Incorporate statistical capacity improvement plans in strategic planning processes, particularly PRSs
Support realistic and prioritized plans for improving statistics
Support data collection activities which help build country systems

37. Intranet keyword: DATA
then “Statistical Development and Partnership”
Web:
then “Statistical Capacity Building”

38. Discussion points In the field, what surveys are most useful?
What are data needs at regional and local levels?
What is experience of NSDS, and getting results in terms of better data for PRSPs?