Capacity Building for Better Agricultural Statistics

1. Capacity Building for Better Agricultural Statistics Misha Belkindas and Graham Eele Development Data Group, World Bank

2. Why agriculture? “75 percent of all poor people — those living under $1 a day — live in rural areas. Of those rural people, 86 percent are involved in agriculture in some way… Despite its importance to poor people, agriculture as a sector has been neglected over the last two decades” World Development Report 2007: Agriculture for Development

3. Why statistics? Statistics are needed to provide the evidence for sound policy making, to: • Achieve issue recognition • Inform program design and policy choice • Forecast the future • Monitor policy implementation • Evaluate policy effects and impact Chris Scott “Measuring up to the Measurement Problem: The Role of Statistics in Evidence-based Policy Making” PARIS21

4. The problem • The neglect of agriculture is in part because of a failure of agricultural statistics • Statistical systems, especially those in the poorest countries, have failed to provide the evidence needed to identify problems, to develop effective policies and to monitor change • To a large extent this is a problem of capacity

5. But not because of neglect • Since the Millennium Declaration interest in statistics has increased significantly • Major effort by the international community to improve statistics, stimulated by the MDGs • Increased focus in countries on managing for results • Major focus on monitoring

6. New initiatives • The Partnership in Statistics for Development in the 21st Century (PARIS21) – coordination, advocacy and support • The Marrakech Action Plan for Statistics – targeted actions to strengthen national capacity and improve international support and coordination • Major efforts by donor agencies to scale-up support for statistics – up to an additional US $2 billion may be needed over the next seven years

7. So what needs to change? • Learn the lessons from past capacity building efforts • Tended to be piecemeal and uncoordinated • Initiated and led by donors • Did not address underlying institutional issues • Not sustained • Deal with agriculture as part of a wider statistical system, rather than on a stand-alone basis

8. From a vicious to a virtuous cycle Low demand Stronger demand Inadequate resources Increased resources Poor output Better output • Stronger demand • MDGs, focus on agriculture, results-based management • Better output • Improved data quality and dissemination • More resources • Budgets, skilled staff, financial and technical assistance

9. Developing a new approach • Must be country led • Based on a comprehensive and realistic assessment of strengths and weaknesses of the whole statistical system • Be feasible, setting priorities in the light of constraints • Integrated with national planning processes • Take a long-term view • Focus on results

10. Country leadership • Effective capacity building can never be imposed from the outside • There must be strong political leadership • Need for wide ownership based on comprehensive consultation • Need for agreement on vision, goals and targets • Mechanism for review to respond to a changing environment

11. Assessment • Needs to be comprehensive • Look at agricultural statistics as part of a larger system • Examine both the external environment and internal structures and processes • Be driven by the needs of users, both current and anticipated • Compare with international recommendations and good practice

12. Setting priorities • Identify and document constraints • Goals and targets must be financially, technically and organizationally feasible • Sequencing is important • Process is usually iterative and must involve stakeholders • Results need to be sustained

13. Integration with national processes • The demand for statistics should be driven by national plans and strategies • Financial constraints will be determined by budget processes and plans such as Medium-Term Expenditure Frameworks • Manpower plans must be realistic and in line with government targets • Need to address regional and international obligations

14. Looking at the long-term • Many statistical activities are based on a 10-year cycle • Planning and implementing major new statistical programs can take several years • Developing skills and building competencies takes time • Need to look forward and anticipate demand • Need for long-term financing plan

15. Focusing on results • Need to emphasize capacity to generate better results • Results will generally be improved dissemination of statistics, outcomes will be achieved when these statistics are used • Important to measure what is achieved and to report on progress • Need for mechanisms to monitor user satisfaction

16. Implications for agricultural statistics • Management and staff need to think strategically and develop their own plans • Need for greater coordination with the statistical system • Important to interact with users to understand and anticipate data needs • Make better use of technology • Research to identify solutions to technical problems

17. Implications for statistical systems • Agricultural development is a mainstream concern in most developing countries • Need to give priority to statistics on agriculture and rural development • Strengthen coordination • Support statistical operations in other agencies • Establish frameworks for improving data quality and dissemination

18. Implications for donors • Emphasize importance of statistics • Support the strategic planning process • Support strategies and follow agreed priorities • Provide long-term support, both financial and technical • Support research to address technical problems, especially in data collection • Encourage cooperation between statistical agencies in developed and developing countries