Assessment of National Agricultural Statistical Systems in Africa

1. UGANDA BUREAU OF STATISTICS ASSESSMENT OF NATIONAL AGRICULTURAL STATISTICAL SYSTEMS IN AFRICA by Prof. Ben Kiregyera PARIS21 CONSULTANT

2. 1 COVERAGE • I. Introduction • Review of Current National • Agricultural Statistical Systems (NASSs) • III. Way Forward - Paradigm shift • IV. Recommendations

3. 2 I. INTRODUCTION • Millennium Development Goals (MDGs) • 8 goals • Eradication of extreme poverty and hunger • Poverty Reduction Strategy Papers (PRSPs) • national planning frameworks and development strategies • instruments for relations with donors • basis for concessional lending/debt relief (HIPC) • PRSP and Agriculture Linkage • agriculture plays a central role in economy (see next slide) • agric. sector central to improved economic performance, increased incomes, raising standards of living and poverty reduction.

4. Contribution of agriculture to national economies Country Contribution of agriculture to: GDP Exports Employment Ethiopia 50 90 80 Kenya 30 50 75 Tanzania 49 85 80 Malawi 37 85 90 Rwanda 44 - 90 Uganda 43 90 80 3

5. 4 II. REVIEW OF NATIONAL AGRICULTURAL STATISTICAL SYSTEMS (NASSs) • A: Forty years on, no satisfactory NASSs • project and piecemeal ad hoc approach • Success of projects = success of NASSs • Quotation FAO (1997) • B: Audit/scan of NASS • Triple dilemma • agendas made elsewhere • weak capacity to deliver • seemingly intractable methodological problems • What has gone wrong?

6. 4B • C: Summary of what has gone wrong • NSSs are unstructured with no strategic direction • NSSs largely donor funded and driven with limited • government commitment • uncoordinated and prioritized • wide use of “quick fixor ad hoc” approach with • long-term planning taking a back seat • inadequate data – inaccurate, conflicting, • insufficiently processed and analyzed, • insufficiently disaggregated and not easily • accessible • no lasting benefits – capacity building and raising • the profile of statistics • methodological problems

7. 5 D: Paradox of data gaps • Data Demand outstrips Supply Demand for good data Supply of good data • Demand versus Resources demand resources Time

8. 6 • Paradox of data gaps Yawning gaps on some indicators and a plethora of data on other indicators which are not used. Quotation - Cisse (1990) • Critical data gaps • profile of rural populations • household food security • nutrition • on-farm stocks • disaggregated poverty levels • post-harvest losses • yields for staples such as cassava and bananas • horticultural production • environment and forestry • gender (especially role of women), etc.

9. 7 E: Lack of coordination – a serious problem • horizontal coordination to avoid working at • cross-purpose • generally poor • destructive rivalry between MOA and CSO • technical coordination to ensure mutual consistency • of data from different sources • generally poor • leads to conflicting data • Quotation – Blackwood (1997) • F: Main sources of data • Agricultural Reporting Services • reports by extension staff • administrative registers • Generally data suspect

10. 8 • Agricultural censuses • Countries participating in World Census Programme 1930 1950 1960 1970 1980 1990 1 3 16 22 17 14 • Few countries been able to repeat the census • Long period between censuses • lack of census data constrained long-term planning and investment decisions • unable to build expertise; dependence syndrome • Based on small samples; unable to provide small • area statistics

11. 8B

12. Agricultural sample surveys • timeliness • less cost • increased data quality • unable to provide small area statistics • lack of expertise and dependence on TA • Data collection methodologies • guess estimates • self-enumeration • farmer interviews • physical (objective) measurement • household budget surveys • special problems of data collection • cropping systems (mixed cropping, continuous planting and harvesting, etc) • production of root crops • In production environment that occurs in family smallholder sector in Africa, neither objective nor subjective methods have proved reliable 9

13. 10 • Data management • data processing • computer hardware & software no longer a problem • problem is with computer personnel (liveware) • data analysis • (see next slide)

14. 11 G: Data cycle Planning Stage 1 Stage 2 Implementation Dissemination Feedback Stage 3 Reporting Processing Analysis/Interpretation

15. 12 H: Data versus Information End Users Intermediate User (researchers) Data Producers Raw Data (low level information) Data Analysis Information Add value to data

16. 13 Policy-related information information End users Policy/ decision- maker Policy-related Analysis Basic Analysis Tables Raw Data

17. 14 I: Other issues • involvement of of subject-matter specialists and • experts (starting in some countries) • production of new analytical products e.g. poverty • and vulnerability maps using GIS functionality • (starting in a few countries) • Databases and data warehouses • (recognized but not enough done)

18. 15 J: Major problems and constraint • limited political commitment • organizational problems • insufficient coordination/collaboration/networking and • information sharing • limited coordination • user/producer, producer-produce, producer/research/ training institutions • Human resources • shortage of critical skills and expertise • Methodological • given above • Data quality problems • inconsistency, incompleteness (data gaps), inaccuracy, • lack of timeliness; insufficient small area statistics • Data management problems

19. 16 • knowledge management A way of promoting integrated approach to identifying, capturing, retrieving, sharing and evaluating organization’s information assets. Information assets: • databases • documents • policies and procedures • library services • tacit expertise & experience stored in peoples’ heads Experience in countries • poor or no documentation of methods/procedures • no institutional memory • experience in people’s heads • datasets and no databases

20. 17 • PARADIGM SHIFT: WINDOW I WINDOW II WINDOW I APPROACH Ad hoc Largely donor driven, limited government commitment INPUTS • data which are inadequate • no database • yawning gaps OUTPUTS

21. 18 WINDOW II • Identify Partners • Master Plan Coordinated System • user driven • ownership • long-term • partnerships • prioritized • Capacity building Main Feature • government • donor Inputs • adequate data • data base • sustainable system Outputs

22. 19 A: Develop an Integrated Framework • Process Analysis Planning Implementation monitoring evaluation • establish long- • tem objectives • generating • actionable • strategies • development of • statistical • programme • identify • activities • outputs • indicators • plan • budgets • external environment • users and • producers • coordination arrangements • current and future data needs • crate awareness • positioning the NSS • sticking to priorities and • implementation plan • track inputs, activities, • outputs • monitoring schedule • evaluation

23. 20 B: Address statistical governance Issues • All National Statistical Systems grappling with governance-related questions: • What is our mission? • How do we perform and can we do better? • How do we convince government that statistics • useful and adequate resources are needed? • Some governance issues: • Improving relevance • Improving coordination, networking, • partnerships • Benefiting from technical assistance • Knowledge management • Improving data quality • Improving data analysis, dissemination, access • Better data management (Databases)

24. 21 C: Improving relevance • Advocacy for statistics • raise awareness about and create demand • raise profile of statistics • resource mobilization • create partnerships for statistics • stakeholders to take ownership • increase relevance and funding for NSS • make national statistics demand-driven • User-producer Committees D: Improving coordination, partnerships & collaboration

25. 22 Improving Coordination, partnerships and collaboration National Statistical System DATA PRODUCERS Main data producers DATA USERS Other data producers • government (s) • public/private sector • NGOs • research/training orgs. • donors/international orgs. • press • wider public NSO Research/Training Organs Partnerships

26. 23 E: Improve benefits from technical assistance Follow UN guidelines • exchange expertise • development of skills & expertise • demand driven • not distort national priorities • not undermine national institutions and authority • F: Improve knowledge management Especially • documentation of methodologies and procedures • develop writing and reporting skills

27. 25 G: Improving data quality • Consistency- improved coordination • - system-wide adoption/standardization of • concepts, definitions, classifications • Completeness- Strategic Plan for the Statistical Institute - comprehensive programme (Master Plan) • Accuracy- use of “best methods” • - human resources/capacity development • - proper handling of data after collection • - need for adaptation/research • Timeliness- release calendar and sticking to it • Small area statistics - increase sample size - combine data from surveys and censuses

28. 26 H: Improve data management Enable • networking • sharing of information • data archiving • creation of user-friendly and accessible databases • creation of data warehouses/data mining

29. 27 I: Others • Role of NSO • set standards, promote “best practices” • need realignment of Statistics Act • Role of Technical Assistance • need f to follow UN Guidelines • many countries not following guidelines • Capacity is not built as it should • Opportunities for developing NASSs • great demand for statistics to track progress • increased international partnership • Quotation – Clare Short • PARIS21 • advances in information technology (IT)

30. IV: RECOMMENDATIONS International Community • Multi-country methodological research project • World Training and research Centre for Food and Agricultural Statistics • Statistical advocacy • Technical cooperation Countries • Role of National Statistical Office • Development and implementation of Integrated Framework • Staying ahead of demand • Role of technical assistance • Improve knowledge management • improve statistical products and services 28

31. 29 Thank You END