Generating and Using Data for Poverty Reduction Strategies

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

11. 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, www.paris21.org)

24. Checklist from PARIS21

25. Case study 1. Nigeria • Baseline at end of 1990s: • 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: • 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 • 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 • 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 www.paris21.org • … 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. What you can do The ICP and PPPs • 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: www.worldbank.org/data 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?