Generating and using data for Poverty Reduction Strategies

1. Generating and using data for Poverty Reduction Strategies Neil Fantom, Development Data Group Ghislaine Delaine, Africa Results and Learning

2. Statistical systems and Poverty Reduction Strategies • Data are needed for monitoring and managing • Example: Madagascar MAP includes indicators on: • GDP, Poverty, family size, malaria prevention, agriculture, crime and security, public finance, investment, education, health, transport, water, sanitation… • More than indicators • Data needed for management, allocation of resources, etc (health and use of census data is a good example) • More than censuses and surveys • Making use of administrative data is very important too

3. Data Sources: Censuses • Fundamental statistical baseline, especially in countries with limited vital registration systems • Burundi: Last census 1990, next 2008 • Madagascar: Last census 1993, next 2009 • Niger: Last census 2001, next 2011 • Censuses are expensive, funding is often problematic, also political considerations • Lengthy delays between data collection and the release of results are discouraging for data users • Inter-censal estimates are important as well

4. Data Sources: Household Surveys • Only source of many social indicators, including poverty incidence • Some common problems: • Over-reliance on donor funding • Programs incoherent over time and between surveys • Data not accessible or not well-used

5. Surveys in Last Five Years • Burundi: • None recorded • Madagascar: • Demographic and Health Survey, 2003 • Agricultural census, 2004 • Niger: • Living conditions survey, 2005 • Demographic and Health Survey, 2006 Source: International Household Survey Network, www.surveynetwork.org

6. Administrative systems • Source of many key indicators often relevant to PRSPs • E.g. Disease prevalence; educational enrolment and completion; trade; crime and justice; migration; transport • Where systems exist, often easy to harvest data • But even then data may not be precisely what is needed • And data weaknesses are inherited from weaknesses in administrative systems • Often collected directly by line ministries, but role of central statistical agency is very important

7. Assessing Data Quality • Are they relevant? • Are the accurate? • Are they timely? • Are the accessible? • Can they be interpreted? • Are the coherent? • Are they “fit for the purpose”

8. Need to break the cycle of under performance and under investment Low investment in statistics Weak statistical systems Donors and others create parallel systems Poor quality data and poor user interface Weak demand by in-country users

9. Building institutional capacity to produce good statistics • Need to invest in: • “Physical” infrastructure • “Statistical” infrastructure • Human capacity • Statistical methods • Information technology • Improving data access • Need coherent and comprehensive improvement strategy: most importantly, based on user needs • Beware: donor investments in data are not always “system-building”

10. Investment in household surveys matters • Poverty, consumption, demography, health, education, labor, ….. • Still too few countries with established national household survey programs: many surveys “donor-driven” • Not enough access to data • International Household Survey Network promotes international coordination • Accelerated Data Program helps improve access to data and survey design

11. Investment also needed in administrative systems

12. And in using data from administrative systems and records

13. And making data accessible

14. Some practical steps to improve investment in statistical capacity The ICP and PPPs • Statisticians: • Develop a realistic and prioritized plan for improving statistical capacity • Encourage governments and donors to invest in their implementation in a coordinated way • Improve access and dissemination • Donors and finance ministries: • Support realistic and prioritized plans for improvement • Incorporate statistical capacity improvement plans in strategic planning processes, including PRSs • And support data collection activities which help build country systems

15. 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

17. 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)

19. Some messages from the case studies • NSDS process has been catalytic in reforming statistical systems because it came when: • Political environments were favorable • Demands were articulated in PRSPs • Statistical leadership was responsive • NSDS has promoted: • Better donor collaboration • Increased funding • Countries have made use of improved ICT • For production and dissemination/accessibility

20. Discussion points • PRSs often demand data at regional and local levels: what are country experiences in terms of data needs and weaknesses? • What kind of surveys or survey programs have successfully supported PRS data needs? • What are country experiences of developing and implementing a national strategy for improving statistics? Have there been results in terms of improving data for PRSs?