Transcript Global Strategy to Improve Agricultural and Rural Statistics

the Global Strategy
to Improve Rural and Agricultural Statistics
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Adressing short and long-term statistical needs
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importance of good data for policy analysis: a concrete example in
Tanzania (USDA/ERS)
Agriculture Policy Exchange and Learning Event
Dakar 13-16 May 2013
Christophe Duhamel
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….
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
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
Action Plan 2012-2016:
nuts and bolts
Addressing urgent needs
Country
assessments
Sectors
Plans
Technical
Assistance
Research: cost-effective methods
Training
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How it is implemented
 3 levels: Global, Regional , National
 Governance mechanisms for each level
 Trust Fund established at FAO
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Total budget (2012-2016): 84 M USD
Total committed: 41 M USD
(DFID, Gates, Italy)
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Of which: African continent: 26 M USD
already 16 M USD committed
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
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…
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
a concrete example of quick win
in terms of addressing urgent
data needs:
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
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
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
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
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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)
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.
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)
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
Conclusions
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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