1. MIFIRA Framework Lecture 12 Local supply responsiveness Chris Barrett and Erin Lentz March 2012

2. Lecture Overview 2 What do we do when we can’t compute the degree of market integration? Estimate prospective equilibrium effects Overview: –Theoretical approach to drawing supply curves –Example: estimate changes in demand due to transfer / procurement –Example: estimate responsiveness of supply to change in demand, using local wholesale trader information

3. Supply Responsiveness 3 Approaches to estimate equilibrium: –Link marginal costs with changes in demand –Recover marginal costs to draw supply curve Marketing margins and ability to expand –Utilize demand estimates: Combine elasticities and MPCF and expected size of the intervention

4. Price Effects of Different Supply Patterns 4 Source: Barrett (2009) MIFIRA

5. Supply Responsiveness: Marketing Margins Revisited Marginal costs are often elicited as the costs associated with buying one more unit of product Costs vary with the number of units purchased How much more volume can be moved under the same marginal cost structure? At what volume will marketing margins increase? –By how much will the margins increase? –How much more can be moved at that margin? 5

6. Supply Responsiveness: Discussions with traders Objective for discussions with traders is to learn: –Are traders at capacity? Room for expansion? How much? –Do traders face barriers to expansion? –The greater traders’ capacity to increase delivery volumes at the pre-existing price or a level near it, the greater the scope for cash-based response. 6

7. Supply Responsiveness: Discussions with traders Questions to ask traders: –If demand increases, how much more can a trader import / sell at current prices? If this is not concrete enough, specify demand increase in tons or percentage –If demand increases and prices increase by 10%, how much more can a trader sell? –If entire stock was purchased today, how much time would a trader need to restock? –What constrains the volume traded? 7

8. Supply Responsiveness: Discussions with traders Eliciting supply responsiveness data from traders can be difficult –Larger market actors generally have fewer competitors Larger actors may not be willing to participate –Traders may have incentives to overstate their ability to meet demand –Quite difficult to generate a statistically significant sample of major market actors More effective to approach traders as key informants 8

9. Marketing Margins: Estimating Induced Price Effects Additional Marginal Volume in metric tons Additional Procureme nt Cost Additional Transporta tion Cost Storage Costs Taxes or Fees Processing Costs Interest or Short- term credit costs Other Costs Total Marginal Cost Costs by trader Trader 1 1.0019002001220210019002604 2.0019004501220210019002854 5.002500750025010030003900 Trader 2 20.0019002001220210019002604 50.002500500025010020003550 Trader 3 4.0019002001220210019002604 12.0020005001520210025003067 9

10. Marketing Margins: Estimating Induced Price Effects 10 Sorted volumes by Marginal costs Total Marginal Cost Aggregate Added Supply Trader 11.0026041.00 Trader 220.00260421.00 Trader 34.00260425.00 Trader 12.00285427.00 Trader 312.00306739.00 Trader 250.00355089.00 Trader 15.00390094.00

11. Estimating Marginal Costs 11 Source: Barrett (2009) Food Insecurity

12. Demand Side 12 Demand response –Size of the transfer, current prices –Elasticities –Marginal propensity to consume –Upper and lower bounds for sensitivity

13. Adding in Demand: Estimating increased volume demanded for food due to cash distribution 13 Market Expected Cash Dispersal Cost of staple per metric ton Amount of food that could be purchased with cash Income elasticity of Demand: More elastic (closer to 1) Income elasticity of Demand: Less elastic (further from 1) Additional Volume demanded: High estimate Additional Volume Demanded: Low estimate Market A100,000190052.630.60.331.5815.79 Market B200,0001900105.260.60.363.1631.58

14. Marketing Margins: Estimating Induced Price Effects (Initial Price: 2600) 14 Income elasticity of demand scenario Additional Supply Needed (MT) Marginal Cost (find from ordered AS schedule) Induced price change (%) Scenario 1: low (0.3) market A15.7926040.2% Scenario 2: high (0.6) market A31.58306718.0% Scenario 3: low (0.3) market B31.58306718.0% Scenario 4: high (0.6) market B63.16355036.5%

15. Example: Estimating Rice Demand in Sirajganj Estimate increase in demand if cash replaced food aid in a community receiving food aid SHOUHARDO-MCHN program distributed 12 kilograms of wheat to each of 6500 district recipients over a single month The total distribution of 78 MT of wheat Assume 1:1 substitution of rice for wheat IFPRI MPCs: 0.3-0.45 15

16. Example: Estimate rice demand in Sirajganj Number of recipient house-holds (hh) Grain given to each hh per month (kg) Total food aid delivered per month (MT) Marginal propensity to consume (MPC) food Demand adjusted by MPC, per month (MT) Sirajganj MCHN recipients 650012780.4535.1 Total MCHN recipients 85,000121,0200.45459 16 …What about linking supply response to demand increases? Source: Barrett (2009) MIFIRA

17. Example: Simple estimate of Sirajganj’s rice volume and market behavior 17 What is the level of competition at the wholesaler level? How diverse and numerous are upstream suppliers? What is current wholesaler volume?

18. Example: Sirajganj trader volume and ability to respond to demand Number of recipient house- holds (hh) Grain given to each hh per month (kg) Total food aid delivered per month (MT) Marginal propensity to consume (MPC) food Demand adjusted by MPC, per month (MT) Monthly volume of largest seller in Sirajganj (MT) Share trader would have to increase his trade volume Sirajganj MCHN recipients 650012780.4535.1148.80.236 Total MCHN recipients 85,000121,0200.45459N/A 18 Source: Barrett (2009) MIFIRA

19. Example from Northern Kenya 19 Estimate demand, using elicited MPCF Estimate supply responsiveness Barriers to trade expansion

20. Example from Northern Kenya 20 Estimating demand by eliciting MPCs in the field: –Ask likely recipient households how they would spend a one-time gift of specified value. –Using proportional piling, households indicate what proportion would be spent on food. –The denominator is the size of the one-time gift –The numerator is the value of the gift that would be spent on a certain commodity. MPCF = Amount spent on food/ Value of gift

21. Estimated Value of Food Demand Generated by Cash Transfer HH popula tion Average value of food aid basket Lower Bound MPC Lower value of food demanded based on transfer to 40% of pop Lower value of food demanded based transfer to entire pop Upper Bound MPC Upper value of food demanded based on transfer to 40% of pop Upper value of food demanded based on transfer to entire pop ABC D=AxBxCx0.4E=AxBxCF G=A*B*F*0. 4H=A*B*F Dirib Gombo11701,7970.53445,7281,113,8630.75630,7471,576,868 Kargi18311,3490.49484,1241,210,2040.75741,0061,852,514 Logologo11312,2630.47481,1771,203,1980.75767,8361,919,590 Loiyangalani19581,1420.42375,654938,9240.75670,8111,677,027 North Horr22941,2950.53629,7951,574,5970.75891,2192,228,048 21 Source: Mude et al. (forthcoming)

22. Example from Northern Kenya 22 Estimate supply responsiveness –For key commodities, what is the trader’s maximum supply capacity at any one time given their current access to storage, transport, credit, etc., without increasing prices. ? –All else equal, how many days does a trader need in order to fully restock?

23. Value of Maximum Possible Wholesale Supply Capacity of Top 3 Commodities Value of max one-off capacity per wholesaler Max monthly restock frequenc y/2 Value of max monthly capacity per wholesaler No of wholesalers Total value of wholesaler monthly capacity Total value of current monthly wholesaler sales Value of Excess capacity AB*C=AxBDE=CxDF**G=E-F Marsabit Town***4,056,2505.020,281,25010202,812,50021,975,000180,837,500 Kargi637,0004.02,548,000410,192,000588,5009,603,500 Logologo372,1254.01,488,50022,977,000529,3002,447,700 Loiyangalani1,024,8134.54,611,659418,446,63410,284,0008,162,634 North Horr1,193,6011.01,193,60189,548,8083,001,5006,547,308 23 Source: Mude et al. (forthcoming)

24. Induced Demand for Top 3 Commodities as a Fraction of Excess Capacity 24 Value of Excess capacity Value of food demand generated by food basket value income transfer to entire pop Cash-transfer generated demand as a fraction of excess capacity. ABA/B*100 Marsabit Town 180,837,5001,113,8630.6% Kargi 9,603,5001,210,20412.6% Logologo 2,447,7001,203,19849.2% Loiyangalani 8,162,634938,92411.5% North Horr 6,547,3081,574,59724.0% Source: Mude et al. (forthcoming)

25. Example from Northern Kenya 25 Barriers to trade expansion –What would need to change for the trader to be willing to increase his or her capacity beyond the current maximum at current sales prices?

26. Factors Necessary for Traders to Increase their Maximum Stocking Capacity at Current Sales Prices 26 Source: Mude et al. (forthcoming)

27. Factors Affecting the Speed at which Extra Supply is Sources 27 Source: Mude et al. (forthcoming)

28. Supply Responsiveness 28 Approach: –Consider current capacity and barriers to expansion –Elicit information on volume expansion and cost effects, as well as barriers –Be skeptical examine competition, historical pricing patterns to triangulate Limitations of the analytic –Hypothetical situations –Marginal costs are difficult and time consuming to elicit