1. AGEC 640 – Ag Development & Policy Measuring Impacts October 30 th, 2014 Today: Focus on Malawi’s subsidy program Readings: Chibwana, C. et al. (2014) “Measuring the Impacts of Malawi’s Farm Input Subsidy Program.” Forthcoming in African Journal of Agricultural and Resource Economics. Fisher, M. and G. Shively (2005) “Can Income Programs Reduce Tropical ForestPressure? Income Shocks and Forest Use in Malawi.” World Development 33(7): 1115–1128.

2. Price Quantity S=MC S=MC - σ other maize labor fert maize fert D

3. What do we know about input subsidies? Great for farmers (therefore popular with politicians) Expensive (therefore unpopular with donors) Problematic (therefore popular with researchers)

4. Issues surrounding fertilizer subsidies Is fertilizer a private good or a public good? Does public provision undermine or “crowd-out” the private sector? Do subsidies reach the intended beneficiaries? What are their short-run and long-run impacts? –Fertilizer use –Crop choice/land allocation –Unintended impacts (e.g. forests)

5. A dynamic perspective on what might be going on and why we should care A t+1 = A t + f(A t, X t ) – c t

6. Malawi’s 2009 FISP Program goal: maize self-sufficiency Targeted 1.7 million farm households Inputs: –Maize fertilizer (NPK): 150,000 mt –Tobacco fertilizer: 20,000 mt –Maize seed (OPV + Hybrid): 4,750 mt –Cotton seed, legume seed, cotton chemicals and grain storage pesticides Program cost: US$221million –13.5% of national budget –5.5% of GDP

7. FISP details Program delivered via voucher/coupon system “Typical” voucher –100 kg fertilizer for maize (50 + 50) –2 kg of improved seed (Hybrid or OPV) –5.5% of GDP Distribution:  Ministry of Agriculture  District Officials  Local chiefs  Village heads and village development committees Stated Targets: FHOH, residents, vulnerable

8. Research questions: 1. Who benefited from the subsidy program? 2. Did the program boost smallholder’s use of fertilizer and maize output? 3. Did the participation influence crop choice? 4. [ additionally: what effect (if any) did the subsidy have on area expansion and forests? ]

9. Data Household panel covering 2002, 2006, 2009 Kasungu and Machinga Districts Approx. 400 respondents Results presented here are primarily based on our 2009 survey, with some additional insights drawn from the 2002 and 2006 surveys. IV estimation strategy

10. Sample statistics VariableMean Age47 Household size6.3 Land owned per HH (ha)1.6 Female-headed (1=yes)0.15 Net buyer of maize (1=yes)0.73 Education None15% Some primary72% Some secondary13% Land sharesMean Traditional maize0.38 Improved maize0.25 Tobacco0.06 Other crops0.31 21% of households in rural Malawi are female-headed. 13% of all coupon recipients in sample were female-headed Yields (kg/acre)Mean Traditional maize1196 Improved maize1391 (National smallholder average: 1483)

11. Program participation CategoryNone Seed only Fertilizer only Seed & fertilizer Male0.120.060.180.64 Female0.210.040.250.50 Poor0.180.050.200.58 Non-poor0.090.060.180.67 Male, non-poor0.090.070.170.67 Male, poor0.160.060.180.60 Female, non-poor0.130.040.210.63 Female, poor0.280.030.280.41 Participation cannot be considered exogenous…

12. Proportion of subsidy in total fertilizer For the poor, the subsidy represented more than two-thirds of total fertilizer used. For women, the subsidy represented about 3/4 of total fertilizer used.

13. Question 1: Who received coupons? Estimated using probit, multinomial logit and Tobit Dependent variables: –Probit: receipt of any coupon (0/1) –MNL: type of coupon received – 4 or 7 categories e.g. no coupon, seed only, fertilizer only, both seed and fertilizer, etc. –Tobit: monetary (market) value of subsidized inputs Identification variables for 1 st stage: –FHOH, years of residency, village size, asset poverty –Relevant and satisfy standard overidentification tests

14. Findings for question 1 Female-heads less likely to receive coupons Asset-poor HHs less likely to receive coupons Residency matters Village size does not matter (cf. Jayne – member of parliament matters!)

15. Question 2: Did coupons increase fertilizer use? Estimation approach: IV /Tobit Dependent variable: fertilizer/ha Explanatory variables: –demographics, farm size, location –fertilizer-maize price ratio –idiosyncratic shocks –“instrumented” variable for receipt of coupon (either from MNL or direct 2SLS in the case of value)

16. Question 2: Did coupons increase fertilizer use? Model 1 Observed coupon Model 2 Instrumented coupon Model 3 Instrumented w/ lagged fertilizer Seed coupon only-4.214-60.81-50.54 100kg fertilizer135.5189.6*161.82

17. Question 2: Did coupons increase fertilizer use? Model 1 Observed coupon Model 2 Instrumented coupon Model 3 Instrumented w/ lagged fertilizer Coupon value (100 Mk) 1.21*0.97*0.50*

18. Findings for question 2: positively correlated with coupon receipt Intensity falls with farm size positive correlation with use of improved maize net buyers of maize used less fertilizer adding 2002 and 2006 fertilizer intensity reduces point estimate for coupon by approximately 50%

19. Maize yield response to fertilizer Marked points on the graph correspond to the following fertilizer-yield combinations: A [114 kg/ha, 1302 kg/ha] B [165 kg/ha, 1245 kg/ha] C [136 kg/ha, 1373 kg/ha] D [175 kg/ha, 1477 kg/ha]

20. Question 3: Did the FISP influence land allocation? Estimation approach: 2SLS, SUR Dependent variable: land shares –traditional maize –hybrid maize –tobacco –other crops

21. Findings for question 3 Farmer response to price signals is weak Maize and “other crops” act as substitutes Maize and tobacco are complements Results robust to inclusion of 2006 fertilizer use Coupon for maize seed and fertilizer led to: –16-22% more land to maize –3-8% more land to tobacco –20-26% less land to “other crops

22. Overall impact on maize, revisited What is the total maize output gain associated with coupon receipt? c hange in yield x ( 1 + change in maize area ) ≈ 456 kg on average  about half of the gain is from seed, half from fertilizer  But... maize area comes at the expense of other crops displaced, and the value of the output of these other crops constitute about 50% of the average gain.

23. Overall impact on maize, revisited

24. Question 4: Any unintended consequences? Approach: IV/Tobit Dependent variable: area of forest cleared –mean: 0.16 acres/household (including 0s) –14% of sample reported forest clearing activity Explanatory variables: farm size, agricultural prices, forest access and control, shocks, coupon receipt variables

25. Findings for Question 4 Farm size has a negative correlation with forest clearing Rates of clearing higher on private and communal forests Rates lower in presence of a Forest User Group (more likely to protect what is increasingly scarce?) Forest clearing not driven by higher agricultural output prices, at least in the short run Less forest clearing among recipients of maize coupons (no forest left to clear?) Derived demand for forest resources for tobacco (land less directly affected than timber for drying sheds)

26. Findings from Fisher and Shively

27. Average value of SPS ≈ 450 Mk, so ≈ 1062 fewer hours, or drop of more than 50%.

28. Conclusions The FISP appears to have had a positive impact on fertilizer use and maize output in the survey year (450 kg gross (≈ half this net) = +25% boost) Accessing seed and fertilizer is much better than accessing fertilizer only Program seems to have increased land allocation to maize & tobacco at expense of other crops Did program help reduce forest pressure? –Probably (as with the SPS in 1999-2000) but…there were some negative effects through tobacco

29. Implications Who should be subsidized? Targeting at the poor could be improved Subsidies should be part of a comprehensive strategy to improve agricultural productivity Too much emphasis on fertilizer? Package = 2kg maize seed and 100kg fertilizer Half of observed gains came from improved seed Improved seed delivery systems needed Reinforce research and extension