Can Subsidizing Fertilizer Boost Production and Reduce Poverty? Quantile Regression Results From Malawi.

Transcription

1 Can Subsidizing Fertilizer Boost Production and Reduce Poverty? Quantile Regression Results From Malawi. J. Ricker-Gilbert T.S. Jayne Department of Agricultural Economics, Purdue University SHAPE Seminar October 7,

2 2. In situations where there is substantial inequality, conditional mean estimates may mask the true benefits of a program. I. Overview Question: Why do experts generally refer to median income instead of mean income in the United States? 1. Most estimates of program impacts focus on the mean effect a) Key RHS variable: job training (Binary) = Coefficient Estimate is Average Treatment Effect b) More generally, estimate via OLS Y = f( Z, X, ε) Where: Y = outcome ie: Maize production Z = treatment ie: Kilograms of fertilizer X = other factors ie: farm size, assets, education ε = error term β z Coefficient estimate on Z is the conditional mean effect, or average marginal effect

3 I. Overview A. Currently widespread scaling up of fertilizer subsidies in Sub- Sharan Africa (SSA) - Stated Goals= boost staple crop production and reduce poverty - Malawi s program well publicized - Kenya, Zambia, Uganda, Senegal, Nigeria, Ghana, Tanzania as well B. Focus of this study: - Estimate how subsidizing fertilizer affects maize production and crop income. - Uses quantile regression to looks beyond the conditional mean estimates to measure impact at different points in the distribution. C. Contributions of this study: - Mainly empirical: considers the distributional effects of fert. subsidy - Deals with non-random subsidy distribution in quantile framework D. Findings: Maize production - Mean response= 2.43 kgs; 10 th %tile = 0.76 kgs; 90 th %tile=2.58kgs - May be difficult to boost production & reduce poverty at same time.

4 Outline I. Introduction & Overview II. Background & Literature III. Malawi & Malawi s Subsidy Program IV. Conceptual Framework, Methods & Data V. Results VI. Conclusions

5 Fertilizer Use (kg/ha) S.S.A. = Sub-Saharan Africa 9 II. Background Fertilizer Use Fertilizer Use by Region in S.S.A Latin America South Asia S.E. Asia Region Source: Crawford et al Asia: 50% of yield growth attributed to inorganic fertilizer uptake (Kelly 2006) S.S.A: Evidence of stagnating cereal yield growth

6 II. Relevant Literature Fertilizer Use in Africa Numerous studies investigate market participation and why fertilizer use in SSA is so low. What are the constraints to fertilizer use? Supply Side Poor infrastructure, late delivery, few input suppliers, inappropriate blending and qty recommendations (Gregory & Bumb 2006) Demand Side Credit constrained (Coady 1995, Dorward et al. 2004, Duflo et al. 2009) Not profitable a) unfavorable input / output prices (Croppendstedt et al. 2003, Duflo et al. 2008) b) poor soil quality (Marenya and Barrett 2009)

7 II. Relevant Literature Emerging literature on fertilizer subsidies Crowding out of commercial fertilizer (Xu et al. 2009; Ricker-Gilbert; Jayne & Chriwa 2011) Malawi: Mean crowding out rate = 22% poorest 1/5 = 18% ; richest 1/5 = 30% Targeting Issues (Banful 2011; Pan and Christiaenson 2011) Production Impacts (Holden and Lunduka 2011; Chibwana et al. 2011; Ricker-Gilber & Jayne 2011). Benefit/Cost calculations for fertilizer subsidies (Dorward et al. 2010) Range: 1.90 to 0.72 on average

8 III. This Study s Contribution This study s contribution to the literature: 1. Estimates distribution effects of the subsidy program 2. Application to deal with endogeneity with quantile regression in panel data context. 3. Will calculate benefit-cost estimates of the subsidy program at different points in the crop production distribution based on empirical estimates.

9 Outline I. Introduction & Overview II. Background & Literature III. Malawi & Malawi s Subsidy Program IV. Conceptual Framework, Methods & Data V. Results VI. Conclusions

10 II. Background III. Malawi Former British colony Slightly smaller than Pa. 14 million people; high pop density GDP per capita US$900 PPP; 2010 estimate Agriculture employs 90% of population; 1/3 GDP; 90% of export earnings 11% pop. Infected with AIDS in 2009 Most people engaged in subsistence maize growing. Majority of poor 10 are net consumers of food.

11 Ending Poverty Simply by Ignoring the Experts NY Times , Malawi s gov t ignored donors and initiated large scale fertilizer subsidy. Reported results: increased production & improved food security High costs direct costs: ranged from 5% to 16% of gov t spending opportunity costs so thorough evaluation of policy is warranted. 11

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13 III. Malawi Subsidy Program Subsidy Allocation Process 5. Coupons for subsidy distributed at regional level based on area under cultivation 6. Methods for local coupon allocation had the potential to vary across villages Village leaders & distribution committee, open forums. Supposed to go to people who could contribute to national level production but could not afford kg bags of fertilizer at commercial prices Female headed households officially targeted too Evaluation Standpoint: Due to non-random distribution we need to understand how people were targeted (endogeneity).

14 III. Subsidy Program Characteristics 2005/ / / /09 Metric tons of subsidized fertilizer distributed Metric tons of subsidized seed distributed % of households reached by the program (officially) % Subsidy rate for fertilizer 131, , , ,278 NA 4,524 5,541 5,365 NA Cost as % of national budget Source: Dorward & Chirwa Subsidy program scaled up starting in the 2005/06 growing season Fertilizer prices drop by about 50% in 2009/10 14

15 Outline I. Introduction & Overview II. Background & Literature III. Malawi & Malawi s Subsidy Program IV. Conceptual Framework, Methods & Data V. Results VI. Conclusions

16 IV. Conceptual Framework Standard Output Supply Model: Y=f(P input, P output ) Singh, Squire, Strauss (1986) farm household model Other factors affect Input Choice and output Production of staple crop Y Y = f(p input, P output, S, T, C, M, A, ε) S = kgs of subsidized fertilizer T = transfer costs: distance to road C = credit availability M = management ability A = land/soil quality ε = error term

17 Differences in management (M) and soil quality (A) may partly explain heterogeneous returns to inorganic fertilizer. Fertilizer Response is likely an increasing function of M Fertilizer Response and Soil quality? Does the MP of inorganic fertilizer increase or decrease as soil quality increases? MP Inorganic fertilizer IV. Conceptual Framework MP Inorganic fertilizer A B Soil organic matter Soil organic matter

18 If poor HH farm plots with low soil organic matter, then offering them subsidized fertilizer may not be optimal. IV. Conceptual Framework Marenya and Barrett find complimentary (von Leibig) relationship between fertilizer and Soil fertility in Western Kenya

19 0 Percent IV. Distribution of Dependent Variable: Household Maize Production in Kgs Kilograms of Maize Produced Note: N=7, HH produced >5,000 kgs. of maize

20 VII. Methodology: Estimator Choice Quantile Regression Model: y = β 0 + Xβ 1 + μ OLS: Minimize Sum of Squared Residuals N Min β0, β (y 1 i=1 i - β 0 - X i β 1 ) 2 Quantile Regression: Least Absolute Deviations (LAD) estimator. N Min β0, β y 1 i=1 i - β 0 - X i β 1 Advantage: LAD allows us to estimate marginal effects at different points in distribution of Y. 25%, 50%, 75% etc. Drawback: LAD is non-linear operator so more difficult to deal with endogeneity (Wooldridge 2011)

21 VII. Methodology: Maize production for HH(i) at Time (t) Maize Production it = β 0 + β 1 Sub. fert it + β 2 Credit it + β 3 Road_dist it + β 4 Assets it + β 5 Land it + β 6 HH_characteristics it + β 7 Prices it + β 8 Rainfall it + β 9 Soil Quality + β 10 Year it + β 11 Region i + C i + V it β 1 is the marginal product of subsidized fertilizer C i = time-constant unobservable factors (management ability, soil) V it = time-varying unobservable shocks (intra HH issues) for this application consider V it to be i.i.d. normal Red Indicates potentially endogenous variable Blue indicates dummy variable

22 IV. Methodology: Correlation between Sub. fert it & C i Can t use fixed effects in non-linear models like Quantile Regression; incidental parameters problem. Use Correlated Random Effects Framework (CRE) following Mundlak (1978) and Chamberlain (1984). Model distribution of C i in following way: c i = ψ + δ Xi + a i, a i x i ~ N(0, σ 2 a ) Include household time averages Xi of all covariates that vary over time. Applications using Quantile Regression Smoking on birthweight (Abraveya & Dahl 2008) Piped water on IMR (Gamper-Rabindran et al. 2010)

23 Red = Potentially endogenous variable; Blue = Dummy Variable Dependent Variable: Kgs. of maize produced IV. Methodology Covariates in maize production model RHS Variables Kg s of subsidized fertilizer acquired Distance to paved road Distance to main town Farm credit organization in village Log of value of livestock and durable assets Size of landholding Log of adult equivalence in HH Log age of household head HH head attended school HH headed by female Death of family member over previous 2 yrs Maize price per kg. Tobacco price per kg. Commercial price of fertilizer per kg. Ag labor wage rates. Current year rainfall Past rainfall (expectation) Std deviation of past rainfall Soil quality dummies Slope of plot Texture of soil

24 IV. Data From Three HH Surveys in Malawi First Survey collected during 2002/03 & 2003/04 season Subsidy scaled up during 2005/06 season Second Survey collected during 2006/07 season Third Survey collected during the 2008/09 season 1,593 hh surveyed in waves 1 and 2 1,375 hh surveyed in all three waves Total N= 7,311

25 Outline I. Introduction & Overview II. Background & Literature III. Malawi & Malawi s Subsidy Program IV. Conceptual Framework, Methods & Data V. Results VI. Conclusions

26 V. Results Descriptive Statistics across the sample Variable Mean Median Mean Median Mean Median Maize produced per hh (in kg) Real crop income (Kwacha) 14,676 9,416 17,170 6,724 24,457 14,682 Landholding (in Ha) Livestock & durable asset value (Kwacha) 32,871 10,543 10,232 46,645 13,800 56,413 Maize price (Kwacha) Commercial fertilizer price (Kwacha) 2003/ / / Note: N=7,311 US $1.00 = 150 Malawian Kwacha

27 V. Results Pooled Quantile Results for Maize Production Covariates 1 Cond. mean est. 10%tile 25%tile 50%tile 75%tile 90%tile Kg sub. fertilizer 3.96*** 0.96*** 1.65*** 2.97*** 4.06*** 4.48*** Km to main town -1.37*** -0.43*** -0.59*** -0.52*** -0.73*** Log real hh assets 145*** 19*** 40*** 68*** 108*** 146*** Ha of landholding 269*** 32*** 56*** 110*** 203*** 446*** District dummies Included Soil Quality dummies Included Subsidized fertilizer assumed to be exogenous POLS Pooled Quantile Regression Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Note: *, **, *** indicates that corresponding coefficients are significant at the 10%, 5%, and 1% level respectively 1 Table displays key covariates that are statistically significant

28 Covariates 1 Cond. mean est. 10%tile 25%tile 50%tile 75%tile 90%tile Kg sub. fertilizer 2.43*** 0.75*** 1.11*** 2.04*** 2.76*** 2.61** Km to main town NA -0.32** -0.63*** -0.52** -0.95*** -1.18* Log real hh assets 77*** 11*** 29*** 52*** 77*** 94*** Ha of landholding 240*** 34*** 50*** 94*** 179*** 347*** District dummies Included V. Results CRE Quantile Results for Maize Production Controls Correlation between Sub. Fert. and unobserved heterogeneity First Difference CRE Quantile Regression Yes Yes Yes Yes Yes Yes Soil Quality dummies Included Yes Yes Yes Yes Yes Yes Note: *, **, *** indicates that corresponding coefficients are significant at the 10%, 5%, and 1% level respectively. 1 Table displays key covariates that are statistically significant POLS Pooled Quantile Regression Kg sub. fertilizer 3.96*** 0.96*** 1.65*** 2.97*** 4.06*** 4.48***

29 V. Results Crop Income (Kwacaha) Covariates POLS Cond. mean est. Pooled Quantile Regression 10%tile 25%tile 50%tile 75%tile 90%tile Kg sub. fertilizer 104*** -4 21*** 51*** 83*** 184** Covariates First Difference Cond. mean est. CRE Quantile Regression 10%tile 25%tile 50%tile 75%tile 90%tile Kg sub. fertilizer *** 42*** 44*** 121** Note: *, **, *** indicates that corresponding coefficients are significant at the 10%, 5%, and 1% level respectively. US $1.00 = 150 Malawian Kwacha

30 VI. Conclusions 1. Returns to subsidizing fertilizer differ across maize production and crop income distribution. a. HH at bottom of distribution obtain lower returns from subsidized fertilizer. b. HH towards the top of distribution obtain higher returns from subsidized fertilizer. i. assuming these people are wealthier, offering them subsidy will cause displacement of some of their commercial purchases. (Xu et al. 2009; Ricker-Gilbert, Jayne & Chirwa 2011). 2. When unobserved heterogeneity controlled, returns to subsidizing fertilizer much lower.

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32 Thank you for your time!

33 Answer: Outliers!

34 Income Distributions Skewed Mean and Median may reveal different information

35 IX. Results Significant factors affecting subsidized fertilizer acquisition (reduced form) Variables dep var: Subsidized fert Tobit estimator N = 4,812, R 2 =.03 Yrs lived in village (IV) 0.09** Farm credit in village *** Distance to nearest paved road 0.08** Assets (1,000 Mk) 0.02*** Land holding in ha 3.29*** Female headed household ** # of males over 65 yrs 12.84** Death in family over previous two years 5.31* Last season s harvest maize price (May Oct) -0.93*** Fertilizer price during planting (Oct May) -0.45*** Long run rainfall (in cm) 0.08*** Note: Coefficients are Average Partial Effects (APE); 35 ***, **, * denotes sign at 10%, 5%, and 1% respectively