1. Rural Investment and Policy Analysis (RIAPA) Modeling Toolkit
Rui Benfica (IFAD-SKD) and James Thurlow (IFPRI)
IFAD Learning Day Workshop, 25 February 2016

2. Outline Part 1: Introduction to RIAPA
What is RIAPA? What does it add to IFAD analytics?
RIAPA entry Points in IFAD’s operational Cycle
Depth vs. Breadth of the Analysis
Spatial Scale of the Analysis
Potential RIAPA Countries
RIAPA Products and Timeframe
Part 2: RIAPA Demonstration (“proof of concept”)
Level 1 Analysis: Identifying agricultural investment opportunities with strong links to poverty reduction
Level 2 Analysis: Evaluating economic impacts of value-chain development

3. Part 1
Introducing the RIAPA Model

4. What is RIAPA? What does it add to IFAD analytics?
“Rural Investment and Policy Analysis” (RIAPA)
New tool being developed by IFAD and IFPRI for analyzing the contribution of IFAD’s investments to broader rural transformation goals (e.g., poverty, food security, nutrition and employment)
Ex ante economy-wide analysis of projects and programs:
Identifies agricultural sectors/sub-sectors with greatest impact on poverty
Helps prioritize between project design options (e.g., which crops to target?)
Estimates project impacts on job markets, food security, poverty, and nutrient availability
Assesses potential benefits and constraints when scaling-up interventions
Unlike other approaches, RIAPA explicitly captures two important considerations for IFAD projects:
Economy-wide linkages (e.g., project spillovers)
Macro-micro interactions (i.e., linking production to poverty)

5. I. Project Spillovers (1)
Direct impacts on treated + Indirect impacts on control group
Example: Targeted Input Subsidy Program
Observed effect with no spillovers
Observed effect with negative spillovers (overstates impact)
Observed effect with positive spillovers (understates impact)
Treated
Control

6. I. Project Spillovers (2)
Larger-scale programs (if successful) are more likely to generate spillovers at local and national levels
Via labor markets…
Labor wages may change, affecting more than targeted households/sectors
Workers move across-sectors in response
Workers may migrate between cities, towns and rural areas
Via product markets…
Real commodity prices may rise or fall
Increased competition for inputs (e.g., fertilizer)
Changes in imports and exports may affect foreign exchange rates
Not accounting for spillovers can greatly reduce the measured benefits of IFAD’s projects (and possible constraints to scaling-up)
e.g., Spillover effects from Malawi’s Farm Input Subsidy Program (FISP) are estimated to be about a third of total program benefits

7. II. Production-Poverty Linkages (1)
Linking farming to poverty depends on many factors…
Types of new jobs created and the wages they offer
Displacement of existing jobs
Households’ land and labor endowments and access to capital
Changes in product prices, and poor households’ consumption patterns
Plus macro considerations:
Import competition and export opportunities
Project financing mechanisms (i.e., “Robin Hood” effects)

8. II. Production-Poverty Linkages (2)
Complex projects generally have more complex linkages to poverty
For example…
Larger-scale projects generate more spillovers (i.e., non-targeted households may benefit or lose from an IFAD project)
Value chains, by design, affect both farm and nonfarm workers and households
Investing in food production affect relative food prices for many rural (and urban) consumers
Promoting export-oriented crops may displace food crops (i.e., food availability may fall even through incomes are rising)
Not accounting for certain “transmission channels” can understate (or overstate) a project’s contribution to poverty reduction

9. Entry Points in IFAD’s Operational Cycle
What are the knowledge gaps in IFAD’s operational cycle that RIAPA’s operationally-relevant research could address?
Level 1: Informing COSOPs
Understanding “business-as-usual” trends
Prioritize investment areas for IFAD
Measuring IFAD’s contribution to rural
development and poverty reduction
Level 2: Informing Concept Notes
Focus on value-chain development
Supplement EFAs with estimates of potential growth, employment and poverty impacts

10. Depth vs. Breadth of Analysis
COSOPs require a broad assessment of trends and opportunities
PDRs require in-depth analysis of specific projects
IFAD already has tools for detailed project design
RIAPA can supplement EFAs
Esp. for concept notes that need to show potential poverty impacts
RIAPA can also help with learning from ex-post impact m evaluation studies

11. Spatial Scale of Analysis
RIAPA compromises between having a detailed spatial focus, and the time and cost of implementing and conducting the analysis
E.g., local models are usually for once-off analysis and require primary data collection, which means more time and resources (e.g., surveys)

12. Potential RIAPA Countries
RIAPA relies on existing country data
i.e., Social Accounting Matrices (SAMs) and national household surveys
Data exists for most IFAD countries
Except in West and Central Africa (WCA)
National SAMs need to be disaggregated across subnational regions
IFAD is partnering with global SAM data networks to reduce burden on in-house resources

13. RIAPA Products and Timeframe
RIAPA will have a fairly standardized approach and sets of products
RIAPA is expected to take about 8 weeks to implement, including drafting (and revising) research briefs/reports

14. Part 2.1
Informing COSOPs
Identifying agricultural sectors (value-chains) with the strongest linkages to rural poverty

15. Using RIAPA to Identify Opportunities
Which agricultural activities or subsectors are most effective at reducing rural and national poverty?
Answer depends on the structural macro-micro relationships:
Factor and skill intensity of production (land, labor and capital)
Poor households ownership of factor resources
Changes in product prices (for own produced goods and for goods in domestic and foreign markets)
Poor households consumption patterns

16. Malawi RIAPA Model 58 economic sectors: 16 factors of production:
27 in agriculture (incl. crops, livestock, forestry and fishing)
20 in industry (incl. food processing)
11 in services (incl. trade, transport and finance)
16 factors of production:
Rural and urban workers by three education levels
Crop land and livestock by small/medium/large smallholders, and estates
Agricultural and nonagricultural capital
30 representative households:
Rural and urban households by per capita expenditure quintiles
Rural further separated by small/medium/large smallholders, and nonfarm
Reference: Diao, Thurlow, Benin, Fan Strategies and Priorities for African Agriculture: Economywide Perspectives from Country Studies. Washington DC: IFPRI.

17. Malawi Case Study Run model over 2010-2020
Baseline is a continuation of recent trends
Then accelerate growth in each agricultural subsector by increasing total factor productivity (TFP)
Measure change in final year poverty rate (using microsimulation model)
Agricultural Growth
Rural Poverty
Crop GDP
2010
Baseline
(business-as-usual)
Counterfactual (accelerated growth)
2015
2020
Poverty rate
2010
2015
2020
Impact

18. Results: Baseline Scenario

19. Closing Yield Gaps Target the same increase in total agricultural GDP
For smaller crops, this requires larger yield increases

20. Identifying Priority Sectors
Poverty effect: Some sub-sectors are better at reducing poverty
We use poverty growth elasticities (PGEs) to measure how effective growth in a sector is in reducing poverty
𝑃𝐺𝐸= ∆ GDP growth rate ∆ poverty headcount rate
Growth linkage effect: Some sectors are able to generate growth outside of agriculture (either directly along own value chain, and indirectly via other value chains)
We use economy-wide linkage ratios (ELRs) to measure how effective growth in an agricultural sector in generating growth in nonagricultural sectors
𝐸𝐿𝑅= ∆ nonagricultural GDP ∆ agricultural GDP

21. Results: Ranking Activities
National PGE
Rural
PGE
ELR
Vegetables
-4.42
-4.30
0.26
Cotton
-3.33
-3.47
-0.26
Forestry
-2.84
-2.83
0.15
Pulses
-2.23
-2.33
0.07
Tobacco
-1.75
-1.69
2.53
Fisheries
-1.55
-1.41
0.66
Maize
-1.34
-1.40
0.25
Fruits
-1.21
-1.19
0.46
Groundnuts
-0.97
-1.22
-0.54
Cassava
-0.96
-0.89
Other export crops
-0.87
-0.91
0.79
Other cereals
-0.60
-0.62
0.21
Livestock
-0.58
-0.45
0.18
Poultry
-0.51
-0.44
0.51
Potatoes
-0.42
0.34
Agricultural activities with strong poverty reducing linkages and positive nonagricultural growth effects
Poverty-reducing agricultural activities with little or no downstream processing

22. Summary RIAPA Level 1 analysis is useful for..
Projecting “business-as-usual” agricultural and poverty trends
Identifying agricultural activities whose growth is most effective at reducing poverty, and/or generating stronger downstream growth (e.g., value chains)
Can also look at productivity gains in the trade and processing sectors
Can also prioritize based on nutrition and environmental outcomes, etc.
Can be run at national level and for subnational regions
Limitations of Level 1 analysis:
Focuses on individual sectors, rather than whole value-chains
Does not identify specific investments or characteristics of new value chains

23. Informing Project Concept Notes
Part 2.2
Informing Project Concept Notes
Measuring the agricultural and poverty impacts of projects with major value-chain components

24. Malawi Case Study
Highly dependent on tobacco for both export earnings and smallholder incomes
Government is exploring options to diversify exports
e.g., soybeans, groundnuts, biofuels
Existing crops are not export-oriented and so new value-chains will need to be developed
Reference: Schuenemann, Thurlow and Zeller Leveling the field for biofuels: Comparing the economic and environmental impacts of biofuel and other export crops in Malawi. Discussion Paper 1500, IFPRI.

25. Using RIAPA for Value Chain Analysis
1. Characterize the new value chain
Identify key stages in the supply chain
Estimate labor and other inputs used at each stage (i.e., production costs)
Determine market for finished product (i.e., export or domestic)
2. Insert new value chain into RIAPA and run simulations
Outcome
(poverty)
Time (project horizon)
Baseline
(without value chain)
Counterfactual
(with value chain)
Project Impact

26. Stages in Malawi’s Biofuel Value-Chain
Typical two step production process
Farming of biofuel feedstock crop (e.g., sugarcane)
Processing of feedstock into liquid biofuel
Transaction costs generate incomes for traders and transporters
Moving goods from farm to processing plant and then to export market
Stage 1
Feedstock farming
Stage 2
Biofuel processing
Transport to processing plant
Transport to border for export

27. Sugarcane-Ethanol “Technologies”
Farming System Options
Irrigated outgrowers
Rainfed outgrowers
Liquid yield (liter/mt)
70.0
Feedstock required (1000 mt)
14,286
Land yield (mt/ha)
99.0
42.0
Land required (ha)
144,000
340,000
Workers employed (people)
53,669
100,634
Feedstock
53,298
100,263
Processing
371
Capital needs (units)
12,142
9,984

28. Comparing Biofuel and Existing Crops

29. Simulating Biofuels Expansion
Jointly model feedstock and downstream biofuels processing
Biofuels sectors start very small and are then expanded by injecting capital
Capital for biofuels production is from foreign direct investment
All profits are repatriated
Biofuels sectors compete for land and labor resources, and demand intermediate inputs
All biofuels are exported (equivalent to reducing fuel imports)
Product markets
Labor
Exports
Capital
Raw feedstock
Agro-processing
Trade & transport
Land
Services
Industry
Agriculture
House-holds
Supply
Domestic sales
Repatriated profits

30. Results: Macroeconomic Impacts
Large increase in exports
Exchange rate appreciates
Existing export crops decline
Effect on food crops depends on land displacement
We assume almost no new lands are cleared for biofuel crop cultivation (+14,000 hectares)
National GDP rises
Biofuel crops generate more GDP per hectare and worker than the activities it displaces

31. Results: Land Use Change
Rainfed farmers have lower yields and so need more feedstock land
Leads to a displacement of food crops and falling food production

32. Results: Household Incomes and Poverty
Welfare and poverty improves in the irrigation scenario
Esp. for poorer farm households
Incomes increase from higher land and labor productivity
Food prices fall
Poverty worsens in rainfed scenario
Income gains are modest because of negligable productivity increase
Food prices rise

33. Results: Biofuels vs. Other Export Crops
Compare biofuels to other smallholder export crops
Tobacco
Soybeans
Biofuel outperforms…
No food crop displacement
Larger national GDP and household welfare gain
Larger reduction in poverty

34. Summary
In Malawi, only irrigated smallholder value chains generate unambiguous benefits
Increase national GDP and household welfare
Reduce poverty and food insecurity
Outperforms other (selected) smallholder export crops
RIAPA Level 2 analysis is useful for..
Measuring benefits and trade-offs between different value chains
Linking value chain development to poverty and food security
Can also link to nutrition, GHG emissions, water use, etc.
Can be run at national level and for subnational regions

35. Some Questions for Discussion
For RIAPA to be successful and sustainable, it needs to be:
Useful for addressing IFADs needs (i.e., operationally-relevant)
Accessible to in-house analysts (i.e., standardized inputs and outputs)
Maintainable (e.g., minimize time spent building and updating datasets)
Is RIAPA’s focus on COSOPs and concept notes correct?
Is the information that RIAPA generates operationally relevant?
Is RIAPA’s timeframe too long? Would results be timely enough?
Is it possible to supply the value chain data RIAPA needs?