1. AGEC 640 – Ag Development & Policy Measuring Impacts and Unintended consequences October 25, 2018
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 Forest Pressure? Income Shocks and Forest Use in Malawi.” World Development 33(7): 1115–1128.

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

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
At+1 = At + f(At, Xt) – ct

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
Objective of FISP was to promote food self-sufficiency through increased use of modern maize varieties and fertilizer.
Program was implemented through a voucher/coupon system. Distribution: Ministry of Ag – local chiefs – village heads and their development committees – farmers. 2 fertilizer vouchers per household for maize (100kg), redeemed at MK800 (~$6 each; 1 seed voucher (free); 2 fertilizer vouchers for tobacco-producing households.
The program was to target farmers who were:
Residents of their villages who owned land
Vulnerable (female-headed or households keeping orphans)

7. FISP details Program delivered via voucher/coupon system
“Typical” voucher
100 kg fertilizer for maize ( )
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
Objective of FISP was to promote food self-sufficiency through increased use of modern maize varieties and fertilizer.
Program was implemented through a voucher/coupon system. Distribution: Ministry of Ag – local chiefs – village heads and their development committees – farmers. 2 fertilizer vouchers per household for maize (100kg), redeemed at MK800 (~$6 each; 1 seed voucher (free); 2 fertilizer vouchers for tobacco-producing households.
The program was to target farmers who were:
Residents of their villages who owned land
Vulnerable (female-headed or households keeping orphans)

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 Variable Mean Age 47 Household size 6.3
Land owned per HH (ha)
1.6
Female-headed (1=yes)
0.15
Net buyer of maize (1=yes)
0.73
Education
None
15%
Some primary
72%
Some secondary
13%
Land shares
Mean
Traditional maize
0.38
Improved maize
0.25
Tobacco
0.06
Other crops
0.31
Yields (kg/acre)
Mean
Traditional maize
1196
Improved maize
1391
(National smallholder average: 1483)
21% of households in rural Malawi are female-headed.
IHS2: 21% of households in rural Malawi are female-headed).
Asset-poverty variable constructed from PCA using ownership of durable assets and housing quality
13% of all coupon recipients in sample were female-headed

11. Program participation
Category
None
Seed only
Fertilizer only
Seed & fertilizer
Male
0.12
0.06
0.18
0.64
Female
0.21
0.04
0.25
0.50
Poor
0.05
0.20
0.58
Non-poor
0.09
0.67
Male, non-poor
0.07
0.17
Male, poor
0.16
0.60
Female, non-poor
0.13
0.63
Female, poor
0.28
0.03
0.41
13% of all coupon recipients were female-headed
These 4 categories of coupon receipt make up my dependent variable for participation
Participation cannot be considered exogenous…

12. Proportion of subsidy in total fertilizer
For women, the subsidy represented about 3/4 of total fertilizer used.
For the poor, the subsidy represented more than two-thirds 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 1st 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!)
As the World Bank notes, “once established, subsidies can be difficult to re-target or eliminate because they create politically significant constituencies which demand continuing payouts.”

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 fertilizer
135.5
189.6*
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%
Poor households seem to use less fertilizer when the price of fertilizer is high relative to that of maize. This might indicate that non-use of fertilizer reflects cash constraints. For households that are net buyers of maize, there is perhaps competition for cash between immediate consumption and purchases of fertilizer. For households producing improved maize, result suggests complementarities between improved maize varieties and fertilizer.

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]
596 kg/acre: improved maize with coupons
424kg/acre: traditional maize without coupons

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
The F-statistic for the test of the hypothesis that all variables in the equations are jointly zero is At the 95% confidence level, we can reject the hypothesis of joint insignificance of the explanatory variables. The likelihood ratio (LR) statistic for a test of symmetry of cross-price effects is 12.83, which is not significant at the 95% level. We therefore fail to reject the null hypothesis of symmetry.

22. Overall impact on maize, revisited
What is the total maize output gain associated with coupon receipt?
change 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
Tobacco has a dual effect on forests: the demand for land and the derived demand for trees for either curing tobacco or constructing tobacco drying sheds.

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)
48% of the respondents classified the forests cleared as customary, 44% as private and the remaining 4% as public
72% of the households in the sample are net buyers of food, and therefore subsistence-driven. In the long run, higher agricultural output prices could still drive forest expansion, despite the lack of evidence in these data.

26. Findings from Fisher and Shively
48% of the respondents classified the forests cleared as customary, 44% as private and the remaining 4% as public
72% of the households in the sample are net buyers of food, and therefore subsistence-driven. In the long run, higher agricultural output prices could still drive forest expansion, despite the lack of evidence in these data.

27. Findings from Fisher and Shively
48% of the respondents classified the forests cleared as customary, 44% as private and the remaining 4% as public
72% of the households in the sample are net buyers of food, and therefore subsistence-driven. In the long run, higher agricultural output prices could still drive forest expansion, despite the lack of evidence in these data.
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 ) 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