1. Agriculture Policy Exchange and Learning Event Dakar 13-16 May 2013 Christophe Duhamel the Global Strategy to Improve Rural and Agricultural Statistics Adressing short and long-term statistical needs importance of good data for policy analysis: a concrete example in Tanzania (USDA/ERS)

2. Why a Global Strategy (GS)?  Lacking capacity to produce statistics for monitoring national trends or inform international development debate;  Decline of quality and availability of agricultural statistics, particularly in Africa over the last 20 years  Growing demand and urgency: food crisis, price volatility, food security, impact of climate change, urgent data needs for national policies, community of donors and aid impact….

3. Purpose of the GS  a framework to enhance and expand the availability and the use of basic agricultural data for evidence-based decision making: more reliable, timely data  a blueprint for a coordinated, long-term initiative: sustainability ownership mutual accountability

4. Scope: 3 pillars  Minimum set of core data urgent data needs  Integration into National Statistical Systems long-term, demand driven  better governance and statistical capacity building sustainability

5. Action Plan 2012-2016: nuts and bolts Country assessments Sectors Plans Technical Assistance Training Research: cost-effective methods Addressing urgent needs

6. How it is implemented  3 levels: Global, Regional, National  Governance mechanisms for each level  Trust Fund established at FAO Total budget (2012-2016): 84 M USD Total committed: 41 M USD (DFID, Gates, Italy) Of which: African continent: 26 M USD already 16 M USD committed

7. Implementation in Africa  AfDB and UNECA: implementing partners  40 countries targeted by 2016  12 priority countries in 2013 : country assessments and sector plans  Short-term needs support to in all countries

8. Short vs Long-term: friends or foes? FRIENDS! but needs to be balanced  Long-term vision needed (core of the GS) Building the overall statistical capacity and institutional architecture needs time: – improving coordination and dialogue mechanisms; – legislative framework; – linkage with national policies (example of NSDS into PRSP), statistical capacity building as a component of CIP; – 5- 10 year vision taking into account capacities of absorption; – Use of new methods: integrated survey framework; – staff trained; – infrastructures in place…

9. Short vs Long-term: friends or foes?  But urgent data needs also to be addressed Quick wins are possible: example of Tanzania to follow, GS will develop light approaches such as Farm Structure Surveys covering economic, social and environmental dimensions; Matching CAADP M&E indicators with minimum set of core data proposed by GS: on-going; But should not jeopardize long term processes: ownership, use of country systems, should encourage south-south cooperation processes.  Processes may be conducted in parallel but must be coordinated at country level and responding to national demand

10. a concrete example of quick win in terms of addressing urgent data needs:

11. The importance of good data for policy analysis: an example from Tanzania Cheryl Christensen Economic Research Service (ERS) United States Department of Agriculture (USDA) May 10, 2013

12. The Global Strategy from the perspective of data users Input from data users is critical to the assessment process Improved statistical systems are not an end in themselves— they must be used in order to have impact The needs and priorities of users should be built into the process of creating improved statistical systems Analysis and strong analytic capacity are key to creating value from better data The costs of poor data Bad data lead to ineffective or even harmful policies Lack of accurate and timely data reduce the efficiency of markets and raise transactions costs Recognizing the cost of poor data can strengthen the demand for better data

13. An example from Tanzania USDA engagement with Tanzania ERS and the National Agricultural Statistics Service (NASS) conducted an initial USDA assessment July 2011 to evaluate the agricultural statistical system ERS extended the assessment to the food security information system ( ) Developed strong tie to policy analysis—ERS participated in a USAID SERA Project analysis of Tanzania’s export ban Commitment from the Government of Tanzania This is a country driven model Good links to data users both among high level officials and working level offices Prime Minister’s Office Department of Food Security at the Ministry of Agriculture, Food Security and Cooperatives (MAFC) Recognition by the Prime Minister’s Office that there was a need to change the way that food security is measured Coordination among stakeholders and donors USAID – USDA – USAID consultants – FAO – World Bank

14. Linking better data to policy analysis: Tanzania Food Crops Export Ban Link to data Production data collected from extension agents not statistically reliable ERS evaluation found that the method used to compute food security requirements overstated the need for maize The food security requirement calculation was used to determine if national supplies were adequate When national maize supplies were inadequate export bans were imposed Export bans Since the 1990s Tanzania has periodically used export bans to address food security concerns Bans have strong regional impacts in Tanzania as well as eastern Africa They are incompatible with export-led growth model of Southern Agricultural Growth Corridor (SAGCOT)

15. Key steps in changing the export ban Concept Note sent to the Government of Tanzania in October, 2011 Government approved work plan in November Research prepared by ERS, international and local consultants during March-June, 2012 President committed to removing export ban in May as part of G8 Implementation Framework Workshops for Government and all stakeholders in June Policy Brief prepared in August PM Announced an end to export bans on Sept. 6, 2012 and an openness to developing more accurate measures of food security.

17. Next steps: Short term strategies Develop a measure of food security that more accurately reflects Tanzania’s diverse consumption—existing information could be used to estimate a food basket Measures changes in cost of acquiring representative food basket Measures access rather than availability Data needs Calorie shares of foods important people’s diets Retail prices Per capita income ERS has conducted pilot food basket estimates in two districts (Mara and Mbeya) working with USAID and the Department of Food Security at the Ministry of Agriculture, Food Security and Cooperatives (MAFC )

18. Next steps: Short term strategies Construct supply and use balances for products important to agricultural markets and food security At the beginning use existing information supplemented by information gleaned from NGO surveys and interviews with the private sector Update and improve balances as new and better data are available from statistics offices Improve the collection, dissemination and use of key market prices Wholesale prices are disseminated for selected crops, but not key food security crops such as cassava Both wholesale and retail prices are collected for a much wider range of crops at the district level, but are not systematically organized and disseminated in a timely way

19. Conclusions The quality (good or bad) and use (appropriate or inappropriate) of data can have significant implications for policy Better data can create opportunities for re-evaluating existing policies, as well as laying a foundation for better future policies Data linked to analysis and research can support policy change Government engagement and support is critical Short term improvements in data and analytic methods can lead to improved outcomes even as work to establish a better overall agricultural statistical system is ongoing