Capacity Building for Better Agricultural Statistics

Misha Belkindas and Graham Eele

Development Data Group,

World Bank

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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

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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

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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

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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

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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

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So what needs to change?

• Learn the lessons from past capacity building effortsTended to be piecemeal and uncoordinatedInitiated and led by donorsDid not address underlying institutional issuesNot sustained

• Deal with agriculture as part of a wider statistical system, rather than on a stand-alone basis

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From a vicious to a virtuous cycle

Low demand

Inadequateresources

Pooroutput

Stronger demand

Increasedresources

Betteroutput

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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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