How do fertilizer subsidy programs affect total fertilizer use in sub-Saharan.pdf

8/10/2019 How do fertilizer subsidy programs affect total fertilizer use in sub-Saharan.pdf

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AGRICULTURALECONOMICS

Agricultural Economics 44 (2013) 687703

How do fertilizer subsidy programs affect total fertilizer use in sub-SaharanAfrica? Crowding out, diversion, and benet/cost assessments

T.S. Jayne a,, David Mather a, Nicole Mason a, Jacob Ricker-Gilbert ba Department of Agricultural, Food and Resource Economics, Michigan State University

b Department of Agricultural Economics, Purdue University

Received 1 April 2013; received in revised form 15 August 2013; accepted 15 August 2013

Abstract

A major determinant of input subsidy programs effects on theachievement of national policy goals is theextent to which they raise total fertilizeruse. This study synthesizes recent literature on how the new generation of targeted input subsidy programs has affected national fertilizer useafter accounting for crowding effects, and derives benetcost (BC) estimates of the fertilizer subsidy programs for Kenya, Malawi, and Zambiaafter accounting for crowding out and diversion. We highlight two major ndings. First, accounting for the illicit diversion of program fertilizercan profoundly inuence estimates of how fertilizer subsidy programs affect total fertilizer use and program impacts. Given recent evidence that33% or more of total program fertilizer may be diverted before being received by intended beneciary farmers, the failure to account for programdiversion is shown to overestimate the contribution of the subsidy programs to national fertilizer use by 67.3% in the case of Malawi, by 61.6% forZambia, and by 138.0% for Kenya. The second major nding is that the incremental value of maize output produced from these subsidy programsis considerably less than their costs in most years, except under unusually high maize price assumptions. Conventional BC analyses that do notaccount for crowding out and diversion may seriously overestimate the benets of input subsidy programs. Greater attention to program designand implementation details to reduce problems of crowding out and diversion can substantially raise the returns to such programs.

JEL classications : Q12, Q13, Q18

Keywords : Input subsidies; Fertilizer; Crowding out; sub-Saharan Africa

1. Introduction

Fertilizer subsidy programs typically have multiple objec-tives, including the raising of crop productivity, food supplies,rural incomes, and food security. The degree to which an in-put subsidy program achieves these objectives depends on theextent to which it raises total fertilizer use.

This takes us quickly to the issue of crowding in/out.Concerns about crowding outthe displacement of commer-

cial activity in the presence of government programshavebeen examined in many economic contexts (e.g., Cutler andGruber, 1996; Easterly, 2006; Kronick and Gilmer, 2002;Spencer and Yohe, 1970) but only quite recently in the con-text of input subsidy programs. Because sub-Saharan African

Corresponding author. Tel.: + 1-517-432-9802. E-mail addr ess:[email protected] (T.S. Jayne).

Data Appendix Available Online

A data appendix to replicate main results is available in the online version of this article.

governments are spending at least US$1.0 billion each year onfertilizer subsidy programs (Jayne and Rashid, 2013), even rel-atively modest reductions in crowding out could massively pro-mote the achievement of important national policy objectives.

There are offsetting a priori reasons why a ton of fertiizer distributed through a subsidy program may result in eithermore or less than one ton being applied to farmers elds. Inareas where only a small proportion of farmers use fertilizer,where market and agronomic conditions would justify appli-

cation rates higher than those observed, and/or where ruralincomes are so low as to depress effective demand for com-mercial inputs, then a subsidy program might raise fertilizeruse by at least the quantity distributed through the program. Inthis situation a subsidy program could potentially generate newinvestment in input retailing or raise farmer incomes in waysthat crowd in additional purchases of commercial fertilizer,such that total fertilizer use increases beyond the quantitiesdistributed through the subsidy program.

By contrast, if a ton of subsidized fertilizer is distributedto farmers who already purchase commercial fertilizer at near

C 2013 International Association of Agricultural Economists DOI: 10.1111/agec.12082


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688 T.S. Jayne et al./ Agricultural Economics 44 (2013) 687703

prot-maximizing application rates, then the program mayinduce these farmers to reduce their purchases of commer-cial fertilizer, thereby adding less than one ton to total fer-tilizer use. Because the underlying market conditions in whichfertilizer subsidy programs are implemented in sub-SaharanAfrica (SSA) vary considerably, and because implementationmodalities also tend to vary, the degree of crowding in/out isultimately an empirical question that inuences the overall ben-ets and costs of such programs. Understanding the degree towhich fertilizer subsidy programs in SSA have affected totalfertilizer use is crucial for achieving greater consensus on theimpacts of such programs. This study synthesizes the evidenceto date on the topic and derives program benetcost (BC)estimates after accounting for crowding out.

Despite the longstanding post-independence history of in-put subsidy programs in Africa, until recently there has been adearth of quantitative estimates of how they affect total fertil-izer use. Using a cross-section of Malawian farm households,Nyirongo (2005) compared commercial fertilizer purchasesbetween recipients of the governments Targeted Inputs Pro-gramme (TIPS) and nonrecipients. Even though nonrecipientswere found to be 20% more likely to purchase commerciallydistributed inputs than recipients, Nyirongo concludes that theTIPS program had little effect on commercial fertilizer demandbehavior. 1 The rst estimates of crowding out using large-scalehousehold panel survey data capable of controlling for unob-served heterogeneity was by Xu et al. (2009), who found thatfertilizer subsidies distributed to regions of Zambia where pri-vate input distribution systems were weak or nonexistent hadno impact on commercial fertilizer purchases, whereas in ar-eas where private input distribution systems were active, the

distribution of subsidized fertilizer almost totally crowded outcommercial purchases, resulting in no increase in total fertilizerused by farmers. More recent studies using large-scale panelsurvey data in Malawi, Zambia, and Kenya were conductedby Ricker-Gilbert et al. (2011), Mason and Jayne (2013), andMatherand Jayne (2013), which both controlled forunobservedheterogeneity and treated the allocation of subsidized fertilizeras endogenous to commercial fertilizer demand. These studieswere conducted in a coordinated way using relatively similarmethods and form the basis for the review in this article. 2 Otherrecent studies drawn upon for this study include two from Nige-ria (Liverpool-Tasie, 2012; Takashima et al., 2012).

To our knowledge, this is the rst study to synthesize the re-

search evidence on how the new generation of so-called smart

1 The TIPS program in the early 2000s was much smaller than Malawiscurrent subsidy program, the Farm Inputs Support Program (FISP). The TIPSgave recipients 10 kg of fertilizer, compared to 50100 kg for most recipientsof FISP, so the smaller size of the TIPS may have minimized the magnitude of crowding out.

2 Another important set of studies to consider are those conducted by AndrewDorwardand associateson theMalawi fertilizersubsidyprogram(e.g.,Dorwardet al., 2008; Dorward and Chirwa, 2011). Their estimates of crowding out drawlargely from theanalysisof Ricker-Gilbert et al.(2011),who were alsoinvolvedin the initial reports produced by Dorward and associates, and hence we do notexplicitly review their reports in this analysis.

subsidy programs implemented in SSA since the early 2000shas affected total fertilizer use. The study is also novel in thatit demonstrates how accounting for the diversion of programfertilizer (following Mason and Jayne 2013) affects the BC esti-mates of the programs, which are presented for Malawi, Kenya,and Zambia. A major conclusion of the study is that failureto account for diversion of program fertilizer into estimates of crowding out may result in a substantial overestimation of theincrease in total fertilizer resulting from a subsidy program andconsequently overestimate the programs benets relative to itscosts. By providing a synthesis of the new literature on thistopic, this review aims to reconcile conicting understandingsof how and why targeted input subsidy programs may lead todifferent crowding out/in outcomes and contributes to a generalunderstanding of the importance of addressing crowding outproblems in input subsidy program design and implementation.

2. Description of input subsidy programs in Malawi,

Zambia, and Kenya

The Abuja Declaration in 2006 was a watershed moment inthe agricultural policy environment in SSA. Many African gov-ernments resolved at that time to revive input subsidy programsas the vehicle for greatly raising fertilizer use and agriculturalproductivity in the region. Subsequent surges in world food andfertilizer prices in 2007 and 2008 created a heightened sense of urgency in meeting these important goals.

In contrast to the government-led input subsidy programsof the pre-structural adjustment era in SSA and Asia, whichtypically took the form of monopolistic state control of inputdistribution and a pan-territorial subsidized input price for allbuyers, the recentwaveof input subsidy programs was designedto work through, and support the development of, private sec-tor input distribution systems. By utilizing the private sector inprogram implementation, so-called smart subsidy programswere conceived to overcome well-known inefciencies of theearlier state-led approaches, including the problem of crowd-ing out of commercial input delivery systems. 3 Although notadopted by all programs, the feature of targeting vouchers tosmallholder farmers according to specic characteristics and al-lowing them to redeemthevouchers for fertilizer at thestoresof private retailers was one of the most important smart featuresof the new programs.

Because the implementation modalities of the targeted fer-tilizer subsidy programs are reviewed in detail in companioncountry-specic articles in this issue, we do not review themhere.4 For the cases of Malawi, Zambia, and Kenya, which arethe focus of this study, the subsidy programs included the distri-bution of improved maize seed and were primarily focused onincreasing maize production. In Malawi, and in Zambia since

3 The criteria for smart subsidies were rst laid out in Morris et al. (2007)and later in the World Bank (2007). These criteria are summarized in Jayne andRashid (2013).

4 See also Dorward and Chirwa (2011) for the case of Malawi and Matherand Jayne (forthcoming) for Kenya.


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T.S. Jayne et al./ Agricultural Economics 44 (2013) 687703 6

2010/2011, the programs also included inputs for other crops.However, in all three case study countries, fertilizer constitutedthe vast majority of the input costs of the programs. The re-mainder of this section highlights some common features aboutthese targeted subsidy programs in relation to the problem of crowding out of commercial fertilizer.

First, major differences have surfaced in the three countriesbetween smart subsidy conceptsand actual subsidy programim-plementation. Most important for the present study on crowdingin/out is that the private retailers, who were supposed to havefound their operations expanded through their involvement inthe distribution of fertilizer, were in most years excluded fromdistributing fertilizer in Malawi, and were never involved inZambias program. 5 In these two countries, farmers could ob-tain fertilizer only from government-arranged depots. Only inKenya have private retailers consistently been involved in itsfertilizer distribution program.

Second, it is important to distinguish between volumes soldby private traders and volumes sold through commercial chan-nels. Some private traders have experienced a marked declinein the quantity of fertilizer sold to farmers on commercial termseven whiletheir overall sales have increasedafter being selectedto distribute subsidized fertilizer on behalf of government. Thiscan lead to an atrophy of commercial distribution operations asprivate traders gear up their activities to meet the needs of thesubsidy program. The larger set of traders not selected to par-ticipate in the governments subsidy program generally suffera loss in sales as they are competing against heavily subsidizedfertilizer being distributed by rms participating in the govern-ment program (Dorward et al., 2008; Takeshimaet al., 2012; Xuet al., 2009), leading to rms exit from fertilizer distribution

and increased concentration of the sector. Anecdotal reports in-dicatethat some rms awarded governmentsupply tenders havetaken extreme measures to ensure that they are able to continuein this role.

A third general issue concerns targeting. The generalizedsmart subsidy guidelines specied in Morris et al. (2007) andMinde et al. (2008) were to target households who would not beable to afford commercial fertilizer. This guideline has provendifcult to implement in practice. In each of the three countries,targeting guidelines were vague and sometimes contradictory(Dorward et al., 2008; Mather and Jayne, 2013; World Bank 2010). Zambias program in particular subordinated the target-ing of households with little effective demand for fertilizer to

the goal of raising food supplies, based on the assumption thatlarger farmers were more efcient users of fertilizer (Masonet al., 2013). As will be shown, this exacerbated crowding outandadverselyaffected theextent to which thesubsidy programswere effective in raising total fertilizer use, a conclusion alsoreached by Banful et al. (2010) and Takeshima et al. (2012)

5 In Malawi, certain input wholesalers were allowed to sell fertilizer directlyto farmers during the2006/2007 and2007/2008seasons, whileretail shops wereprohibited from doing so in all years of the program. Since 2007/2008, farmershave been able to redeem their subsidy vouchers in exchange for fertilizer onlyfrom government and/or national cooperative depots (Dorward and Chirwa2011).

based on their study of Nigerias fertilizer subsidy programsprior to 2010. By contrast, Liverpool-Tasie (2012) found ev-idence of crowdingin of commercial fertilizer demand in apilot subsidy scheme in one district of Nigeria, the success of which appears to be related to the fact that fertilizer voucherswere mainly targeted to areas where private commercial mar-kets were relatively weak and to households that were relativelypoor. These studies linking contrasting ndings to variations inprogram design and implementation can provide useful guid-ance about how to raise the benets of fertilizer voucher pro-grams through modicationsto programdesign and/or targetingcriteria.

A fourth issueand one of the main contributions of thisstudyconcerns the measurement of crowding out under con-ditions where some program fertilizer is diverted by programauthorities before being distributed to intended farmer recipi-ents. Mason and Jayne (2013) rst explored this issue for thecase of Zambia. Illegal diversion of program fertilizer is com-monly identied as a ubiquitous feature of subsidy programsin developing countries. By diversion, we are referring to fer-tilizer procured by the government for its subsidy program,illegally diverted at the wholesale level, and thus not forwardedthrough the normal government subsidy (GS) program distri-bution channels. That is, by diversion we are not referring tothe leakage of vouchers at the village levelthat is, wherea targeted voucher recipient decides to sell his/her voucher orsubsidized fertilizer to another farmer (or a trader) rather thanapply it on his/her elds. However, the magnitude of diversionis difcult to measure, hence it has typically not been con-sidered in analyses of subsidy program impacts. While mostof the diverted program fertilizer may ultimately be resold to

and used by farmers (except for quantities that are smuggledacross borders or spoiled), farmers may believe that they arepurchasing commercial fertilizer and refer to it as such whenresponding to surveys when in fact they purchased governmentfertilizer intended for distribution under the subsidy program.This means that the quantity of fertilizer procured by fertilizerwholesalers and retailers for commercial distribution is actuallyless than the total amount of fertilizer purchased by farmersthrough commercial channels according to farm survey data.Mason and Jayne (2013) compute the magnitude of diversionfrom Zambias Fertilizer Support Programme by comparing thequantities of program fertilizer specied in ofcial governmentdocuments against the receipts of subsidized fertilizer by farm-

ers in nationally representative Crop Forecast Surveys.6

For thannual surveys conducted between 2002/2003 and 2011/2012,the weighted quantity of Farmer Support Programme fertilizerreceived by farmers averaged only 62% of the quantities dis-tributed under the program according to the Ministry of Agri-culture and Livestock, implying that 38% was diverted. Theshare of program fertilizer that was diverted ranged from a lowof 13% in 2007/2008 to a high of 63% in 2004/2005. Following

6 The Government of Zambias Crop Forecast Survey is considered statisti-cally representative at the district level in Zambia and is used to produce thecountrys ofcial annual crop production estimates.


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the same approach in Malawi, Lunduka et al. (2013) used thenationally representative Integrated Household Survey III to es-timate the total quantity of subsidized fertilizer that householdsreceived through the subsidy program in 2009/2010, and con-clude that between 25% and 42% of the fertilizer imported fordistribution through the subsidy program was illicitly divertedto middlemen who subsequently resold the fertilizer to farmers.Liverpool and Takeshima (2013) conclude that more than 50%of the quantity of fertilizer distributed through Nigerias sub-sidy program in the late 2000s was likely to have been divertedby authorities. As will be shown later, the consideration of pro-gram diversion can make an enormous difference in estimatingthe contribution of fertilizer subsidy programs to total fertilizeruse and program benets relative to their costs.

3. Conceptual modeling of crowding out after accountingfor program diversion 7

The main studies on this topic starting with Xu et al. (2009)dene crowding in/out as the change in commercial fertilizerpurchases ( comm) given a one-unit increase in government-subsidized fertilizer received by a household ( govt ). Becausetotal fertilizer use ( total ) is the sum of fertilizer from the twosources, then the change in total fertilizer use is one plus thecrowding in/out estimate, that is:

total = govt + comm, (1)

totalgovt

=govt govt

+commgovt

= 1 +commgovt

. (2)

If there is diversion of fertilizer intended for the GS programand it is resold through commercial retailers at prices at ornear market levels (making it indistinguishable for farmers andresearchers from other fertilizer sold by commercial retailers),then some of the commercial fertilizer is actually divertedgovernment fertilizer ( leaked ). In equation form, this is:

govt = nonleaked + leaked, (3)

and

comm = allcomm leaked, (4)

where nonleaked is government fertilizer that stays in the gov-ernment channel and allcomm is all fertilizer acquired by endusers through commercial channels. Plugging Eqs. (3) and (4)into (1) and taking the derivative with respect to govt gives:

totalgovt

= (govt + allcomm leaked )

govt

= 1 +allcomm

govt

leaked govt

. (5)

7 This section draws on Mason and Jayne (2013).

In Eq. (5), allcomm and govt are observable in survey dataand hence allcommgovt (the household-level change in commercialpurchases given a change in subsidized fertilizer receipts, i.e.,household-level crowding in/out) can be econometrically es-timated via a factor demand equation for allcomm . However,to go from this estimate of crowding in/out to the change innational total fertilizer use given a change in subsidized fer-tilizer ( totalgovt ), the diversion effect (

leaked govt ) also needs to be

accounted for, reecting the fact that some of farmers com-mercial fertilizer purchases were not supplied through com-mercial input distribution systems but were rather fertilizer di-verted from the GS program and then resold to traders andultimately to farmers. 8 Failure to account for diversion of pro-gram fertilizer results in upwardly biased estimates of the con-tribution of subsidized fertilizer to total national fertilizer use.Farmers purchases of diverted program fertilizer are gener-ally not specied as such in survey data, but as discussed inSection 2, the magnitude of diversion, leaked govt , can be estimatedas 1 y/z, where y is the national quantity of fertilizer receivedby farmers through thesubsidy program according to nationallyrepresentative survey data with appropriate weighting factors,and z is the national quantity of fertilizer distributed throughthe subsidy program according to ofcial government gures.Based on the magnitude of this term from the several stud-ies reviewed in Section 2, we use a conservative estimate of 33%. We report the results of sensitivity analysis to provide animpression of how sensitive program benet/cost ratios are toalternative assumptions about the magnitude of diversion.

4. Estimation approach

The basic approach taken in the Malawi (Ricker-Gilbertet al., 2011), Kenya (Mather and Jayne, 2013), and Zam-bia (Mason and Jayne, 2013; Xu et al., 2009) studies wasto estimate a household-level factor demand model to ob-tain estimates of allcommgovt . See the individual studies fordetails. The dependent variable in these factor demand mod-els is allcomm (the kilograms of fertilizer purchased by thefarmer from commercial retailers in a given year). The mainexplanatory variable of interest is govt (the kilograms of subsi-dized fertilizer acquired by the household). The models controlfor other exogenous factors including the expected prices of maize andother crops (typically na ve expectations); the market

prices of fertilizer and other inputs, includingagricultural labor;agro-ecological conditions; and household characteristics andquasi-xed factors of production such as landholding size,farm equipment, household size, age, gender and educationof the household head, distances to roads, towns, and /ormarkets, etc.

8 Anecdotal reports from Zambia and Malawi indicate that those doing thediverting may include government and nongovernment local authorities, andthat diverted fertilizer tends to be recycled through local retail markets, privateagro-dealer stores, and even directly by government extension agents.


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Our a priori hypothesis is that the degree of crowding in/outmay vary in magnitude and potentially in sign between areaswhere commercial fertilizer demand has already been estab-lished and where it has not. To allow for these differentialeffects, separate demand models are estimated for areas of rel-atively high versus low commercial demand. Doing so wassupported by Chow Test results in all three countries. The es-timates from the high and low commercial demand areas arethen weighted (by the number of households in each area) andcombined to obtain a national estimate of crowding in/out.

Corner-solution dependent variable and choice of the doublehurdle (DH) model

The demand models are estimated using panel data fromeach country. Because a large proportion of households buyno commercial fertilizer (i.e., allcomm is zero for many house-holds), the equations are estimated via a truncated normal hur-dle model, also known as a DH model (Cragg 1971). Poolingthe data across all survey years in each country, only 16% of Zambian smallholders purchased commercial fertilizer versus35% for Malawi, and 79% for Kenya. A DH model is moreappropriate for corner-solution variables like allcomm than is aselection model because inorganic fertilizer has been availablefordecades in Kenya, Malawi, andZambia, so it is reasonable toassume that thevast majorityof farmers areaware of it.Howeverdue to market and agronomic conditions many farmers choosenot to use fertilizer. Therefore the zeros in the data reect thefarmers decision not to buy commercial fertilizer rather thanrepresenting a missing value. All three studies conducted a like-

lihood ratio test to test the DH model against a Tobit alternative,which is also appropriate for modeling corner-solution depen-dent variables but is more restrictive than the DH. Test resultssuggest that the DH model is favored in all cases.

Controlling for unobserved heterogeneity

In models estimated with panel data, a key concern is time-invarianthousehold-level unobserved heterogeneitythatmay becorrelated with the observed covariates. Failure to control forit leads to biased and inconsistent estimates of crowding in/outandotherfactorsaffecting commercial fertilizer demand. Fixed-

effects approaches to dealing with unobserved heterogeneitylead to the so-called incidental parameters problem in nonlin-ear models like the DH so should not be used (Wooldridge2002). Fortunately, another approach, known as the correlatedrandom effects (CRE) approach or the MundlakChamberlaindevice following Mundlak (1978) and Chamberlain (1984)works well with nonlinear models. The CRE approach is imple-mented by including as additional covariates in the DH modelthe household-level time averages of the observed explanatoryvariables (for more on the CRE framework, see Wooldridge2002).

Controlling for the potential endogeneity of subsidized fertilizer to commercial fertilizer demand

Even after controlling for time-invariant unobserved hetero-geneity, we still might be concerned that subsidized fertilizer(govt ) is correlated with time-varying shocks affecting commer-cial fertilizer demand ( allcomm ). Such endogeneity would alsolead to biased and inconsistent estimates of crowding in/outand the other parameters in our DH model. Endogeneity is ofparticular concern in our studies because the quantity of subsi-dized fertilizer allocated to a given household is not random butrather is affectedby targeting criteria as well as government andlocal leaders interpretation and implementation thereof. OurMalawi, Zambia, and Kenya studies all use the control function(CF) method to test and control for the potential endogeneity ofsubsidized fertilizer. 9 The CF approach as implemented in theKenya, Malawi, and Zambia case studies entails rst estimatinga reduced form CRE Tobit model of govt on all the exogenouexplanatory variables from the demand model. The results fromthese reduced form CRE Tobits also shed light on the factorsaffecting targeting of subsidized fertilizer. The residuals fromthe reduced form Tobits are then included as additional regres-sors in the main DH demand model. A t -test of those residualtests the null hypothesis that subsidized fertilizer is exogenousagainst the alternative that it is endogenous.

These reduced form CRE Tobits also need to include aninstrumental variable (IV) that is correlated with household re-ceipt of subsidized fertilizer but that is uncorrelated with thetime-varying, household-level shocks affecting allcomm . Thstudies used several sets of instruments. Following previousstudies (Banful 2011), we hypothesize that the distribution of

government-funded input subsidies may be driven in part bypolitical economy factors. We therefore use constituency-leveldata on electoral results (from the most recent presidential elec-tion) to construct various IVs.

For the Kenya models, the constituency-level electoral IVsare electoral threat and the proportion of votes earned bythe runner-up in the most recent presidential election. Elec-toral threat is dened as the ratio of the proportion of votesfor the runner-up over the proportion of votes for the presi-dential winner (Chang 2005). The Kenya model also uses asIVs the percentage of the population in each district belong-ing to particular ethnic groups. The Zambia studies consideredthree candidate IVs for subsidized fertilizer: (i) a dummy vari-

able equal to one if the households constituency was won bythe ruling party in the last presidential election; (ii) the ab-solute value of the percentage point spread between the rul-ing party and the lead opposition party in the constituency inthe last presidential election (to measure the closeness of therace); and (iii) the interaction of (i) and (ii). The IV used in theMalawianalysis was thenumberof years that thehouseholdhaslived in the village, a social capital indicator hypothesized to be

9 For further details on the CF approach, see Rivers and Vuong (1988), Smithand Blundell (1986), Vella (1993), and Lewbel (2004).


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correlated with receipt of subsidized fertilizer but not correlatedin any obvious way with commercial fertilizer demand.

5. Data

The household panel surveys used in each country recordedinformation on household demographics, farm/household land-holding and assets, and recall information on a range of eco-nomic activities during that agricultural year, including landuse, input use and access to farm services, agricultural and live-stock production, and nonfarm income activities. Regardingfertilizer use in particular, the survey instruments asked house-holds about the quantity and source of commercial purchases,fertilizer acquired from barter, gifts, and other sources, and theprices paid. The surveys also asked farmers about the quanti-ties of fertilizer and other inputs acquired through governmentprograms, where they acquired these inputs, and prices paid.

The following briey describes other salient features of the

three country data sets. The studies use either balanced (Kenya)or unbalanced (Malawi and Zambia) panels. Given attritionbetween survey rounds, attrition bias is a potential problem.However, regression-based tests for attrition bias as describedin Wooldridge (2002) nd little evidence of attrition bias orshow that attrition has little impact on coefcient estimates inall cases.

Kenya

The data used by Mather and Jayne (2013) are primarilydrawn from a nationwide rural household survey rst imple-mented in 1997 by The Tegemeo Institute of Egerton Univer-sity. This survey covered the main and short harvest periods of the agricultural years of 1995/1996 and 1996/1997. Subsequentpanel waves were conducted in 2000, 2004, 2007, and 2010.The sampling frame for the 1997 survey was prepared in con-sultation with the Central Bureau of Statistics. Households anddivisions were selected randomly within purposively chosendistricts within thecountrys eightagriculturally orientedzones;further sampling details are provided in Argwings-Kodhek et al. (1998). A total of 1,514 sedentaryfarming households cul-tivating less than 20hectareswere surveyedin 1997, drawn from106 villages in 24 districts. The 2009/2010 sample contains1,257 households of the 1,514 sedentary households sampled, a

re-interview rate of 83%. For this study, we also drop 111house-holds from two regions with marginal potential for maize pro-duction and inorganic fertilizer use in which thegovernment-ledinput subsidy programs were not active. We also drop house-holds which were not observed in each of the ve panel years,leaving a sample of n = 1,065 households each year.

Malawi

Data used in this analysis update the data used in Ricker-Gilbert et al. (2011). Data used in the earlier study come from

two nationally representative, stratied random samples col-lected by the Government of Malawis National StatisticalOfce. The rst wave of panel data comes from the nation-ally representative Integrated Household Survey-II (IHHS2),covering two cropping seasons; 2002/2003 and 2003/2004. Astratied random sample of 11,280 households was collectedfrom IHHS2. The second panel wave comes from the 2007Agricultural Inputs Support Survey (AISS), conducted after the2006/2007 growing season. The budget for AISS1 was muchlower than for IHHS2, so only certain districts of the coun-try were selected for resampling, however the sample is stillconsidered nationally representative. In total 3,287 householdswere surveyed in AISS1. The third wave of data that is newfor this analysis, called the AISS2 survey, was collected in the2008/2009 growing season, and built on earlier survey roundsin Malawi. In total we use an unbalanced panel of 7,311 house-holds where 1,593 were surveyed in the rst two rounds and1,375 households were surveyed in all three rounds.

Zambia

The Zambia study (Mason and Jayne, 2013) used datafrom a three-wave, nationally representative panel survey of smallholder households (the Supplemental Survey to the Post-Harvest Survey). These surveys were conducted by the Min-istry of Agriculture and Cooperatives in mid 2001, 2004, and2008 to capture information on the 1999/2000, 2002/2003, and2006/2007 agricultural years and subsequent crop marketingyears. A total of 6,922 households were interviewed in 2001,5,358 (77.4%) of which were re-interviewed for in 2004. Of the households interviewed in 2004, 4,286 (80.0%) were re-interviewed for the 2008 Supplemental Surveys. The analysisused theunbalanced panel of households from these three years.

Household characteristics based on the Kenya, Malawi, and Zambia survey data

The averages of the variables used in the analysis are pre-sented in Table 1, by survey wave. Income denitions are con-sistently dened for each country across years, but differ acrosscountries. During the rst survey round in Malawi, there wasa relatively small fertilizer subsidy program in operation, andcommercial purchases accounted for about 94% of farmers to-

tal fertilizer use. In the rst survey wave of 2002/2003/2004,35% of small-scale farmers purchased fertilizer from privateretailers. This proportion fell to 12% in 2006/2007, the rstsurvey period after the initiation of the Agricultural Inputs Sup-port Programme. The proportion of farmers receiving subsi-dized fertilizer rose from 35% in the rst wave to 59% in thesecond wave, to 71% in 2008/2009. The proportion of house-holds purchasing commercial fertilizer recovered to 34% in2008/2009, but the median quantity purchased commerciallyfell to 50 kg per household in that year, down from 100 kgin the two previous survey rounds. The proportion of total


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Table 1Household characteristics from the farm surveys

Malawi Zambia Kenya

Smallholder household characteristic 2002/2003 ***** 2006/ 2008/ 1999/ 2002/ 2006/ 2006/ 20092007 2009 2000 2003 2007 2007 2010

GeneralTotal gross HH income per adult equivalent ($US real), mean * 104 58 121 244 177 199 1016 984Total gross HH income per Adult Equivalent ($US real), median 56 27 46 131 92 107 635 612HH total landholding (ha) 1.08 1.04 1.07 2.82 2.30 2.71 2.15 1.89% of total crop production value marketed (%), mean 13.7 8.0 7.5 13.4 26.2 24.8 46.1 41.9% of total crop production value marketed (%), median 0 0 0 5.9 14.3 16.5 47.1 42.8

Market access **

Distance to nearest paved road (km) 16.9 16.8 17.1 26.3 7.4 6.6Distance to nearest feeder/motorable road (km) 3.3 0.5 0.4Distance from village to nearest fertilizer retailer (km) 19.6 9.9 11.4 2.9 3.5

Input use% HHs that purchased/acquired hybrid *** maize seed (%) 52 71 11 28 24 72 82% HHs acquiring no fertilizer (%) **** 38 34 18 79 71 71 21 13% HHs purchasing commercial fertilizer (%) **** 35 12 34 16 17 20 79 79% HHs receiving subsidized fertilizer (%) **** 35 59 71 7 14 14 0 9Quantity of commercial fertilizer purchased, among users (kg), median 100 100 50 150 150 200 154 139Quantity of subsidized received, among recipients (kg), median 10 100 50 200 100 200 0 100% households that received credit for farm inputs (%) 5 7 11 13 13 12 52 58

Notes: * Income values are in $US in the year of the most recent survey from each country.**Distance to nearest road measures in Zambia are from 2000.*** Includes both hybrids and improved OPVs for Malawi.**** % acquiring commercial fertilizer, subsidized fertilizer, and no fertilizer do not add up to 100% because commercial category includes some who receivedsubsidized fertilizer, and vice versa.***** The Integrated Household Survey II for Malawi covered both the 2002/2003 and 2003/2004 crop seasons.

fertilizer use accounted for by commercial purchases fell from94% in 2002/2003/2004 to 26% by 2006/2007. Total fertilizeruse rose by 11% for households in the balanced panel betweenthe rst and second survey years.

A small proportion of Zambias smallholder farmers pur-chased commercial fertilizer in the early 2000s, and this hasrisen slowly over the course of the 2000s. The scale of Zam-bias input subsidy program hasbeen smaller than Malawis andthe proportion of farmers receiving subsidized fertilizer throughthe program rose from 7% in 1999/2000 to 14% in 2002/2003and 2006/2007.

A much higher proportion of smallholder farmers purchasecommercial fertilizer in Kenya than in Malawi or Zambia. Thisproportion stood at 79% in 2006/2007 before the National Ac-celerated Agricultural Input Access Programme (NAAIAP) be-gan and remained at 79% in 2009/2010 even after several yearsof NAAIAP implementation and the scaling up of a concurrentgovernment fertilizer subsidy program implemented throughthe governments grain parastatal, the National Cereals andProduce Board (NCPB). However, the scale of the combinedNAAIAP and NCPB programs has been relatively small, withonly 9% of sampled farmers having received subsidized fertil-izer through either of these programs in 2009/2010.

One observation that does not come out in the full sampledata reported in Table 1 is the considerable spatial variationin commercial demand for fertilizer. In each country, there areareas where a relatively high proportion of households had

purchased commercial fertilizer prior to the subsidy program,whereas many other areas exhibit very low commercial fertil-izer purchases throughout the panel periods, owing mainly todifferences in soil, rainfall, and market conditions.

6. Main ndings

As a prelude to the econometric results, we rst present thebivariate relationship between changes over time in commer-cial fertilizer purchases and receipt of subsidized fertilizer byrecipient households for one of the case studies, Malawi. Eachpoint in Fig. 1 represents a farm household surveyed in two suc-cessive survey years. The slope of the line measures the changein a households commercial purchases of fertilizer per addi-tional unit of fertilizer acquired from the government. 10 Doand the solid regression line represent the relationship in areas

of low initial commercial fertilizer demand before the subsidyprograms were scaled up, while Xs and the dotted regres-sion line represent the relationship in areas of relatively highinitial demand. While recipient farmers were supposed to havereceived only 100 kg of subsidized fertilizer, the data in Fig. 1indicate that many households received more than 100 kg.

The data in Fig. 1 reveal that, at least in Malawis casethe relationship between changes in acquisition of subsidized

10 The slope is a locally weighted bivariate regression line (Lowess).


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- 2 0 0 0

- 1 5 0 0

- 1 0 0 0

- 5 0 0

0

5 0 0

1 0 0 0

c h a n g e

i n q u a n

t i t y d e m a n

d e

d o f c o m m e r c

i a l f e r t

i l i z e r

( k g

)

-500 -300 -100 100 300 500 700 900change in quantity of subsidized fertilizer received (kg)

Notes: Lines computed by Statas lowess nonparametric command withbandwidth 0.8. Dots and solid lowess regression line represent the changein household commercial fertilizer use in communities of relatively lowprivate sector fertilizer retailing activity; Xs and dotted lowess regressionline represent the change in household commercial fertilizer use in areas of relatively high private sector activity.

Fig. 1. Change in household commercial fertilizer quantity demanded giventhe change in household subsidized fertilizer quantity received, 2002/2003,2003/2004, and 2006/2007, by low and high private sector activity (PSA)zones, Malawi.

fertilizer and changes in commercial fertilizer purchases in ar-eas of high initial commercial demand is clearly negative, whilein areas of low initial demand, the relationship is basically at.This conforms to our a priori expectation that some crowd-ing out of commercial fertilizer purchases would be expectedin areas where private retailers are active and where a rela-tively high proportion of farmers are purchasing fertilizer oncommercial terms. The slope of the line for the areas of highinitial demand is around 0.85, indicating that an increase in 1kg of subsidized fertilizer received by a household results in a0.85 kg reduction in commercial fertilizer purchases, and a 0.15increase in total fertilizer use. However, these bivariate resultsdo not account for other factors or the many estimation chal-lenges mentioned in Section 4.

Government targeting behavior

As indicated in Section 4, Tobit models were estimated toidentify factors associated with the quantity of subsidized fertil-izerreceived by households. Thissection reports themainstatis-tically signicant ndings of these models for Kenya, Malawi,and Zambia as summarized in Table 2.

In Malawi, villages with a farmer credit organization re-ceive signicantly less subsidized fertilizer than other villages.Households further from a road receive signicantly more sub-sidized fertilizer. These results indicate that the government isdistributing subsidized fertilizer to farmers in areas with weak access to credit and infrastructure. Household assets and land-

holding size are both positively correlated with the quantity of subsidized fertilizer received. These coefcients are signicantat the 4% and 1% levels, respectively.

In all three countries, households with larger landholdingswere more likely to acquire subsidized fertilizer. In Malawi, thetargetingof households with larger farmsand asset holdings haslessened between the last two surveys, indicating movement tosomewhat more progressive distributional effects. In Malawi,female-headed households were likely to receive 12 kg lesssubsidized fertilizer than male-headed households in the rst orsecond wave, however there is some evidence that targeting of female-headed households may have improved in recent years(Lunduka et al., 2013). No such nding of targeting in favor of male-headed households was found in Kenya or Zambia.

It is interesting to understand how input subsidies are tar-geted in relation to market access conditions, since one of themotivations for subsidy programs is the perceived underdevel-opment of private sector input retail networks in remote areas.In Kenya and Malawi, households further from the main districtmarkets and motorable roads did tend to acquire more subsi-dized fertilizer than households with better access to markets.By contrast, in Zambia, farmers further from the main townsand roads tended to get less than households closer to marketsand roads.

Political factors loomed large in subsidy targeting in all threecountries. In Malawi and Zambia, local administrative unitswhere the ruling party won the prior presidential election re-ceived more fertilizer. In Zambias case, the greater the rulingpartys margin of victory, the more was distributed to recipienthouseholds. In Kenya, prior election results also inuenced thequantity of subsidized fertilizer distributed to the constituency,

but in this case, more was given in 2009/2010 to households inconstituencies with a larger proportional turnout for the chal-lenger in the 2007 presidential election. The statistical signi-cance and importance of these political variables, and their lack of correlation with commercial fertilizer demand, demonstratethe appropriateness of the variables as IVs to control for theendogeneity of subsidized fertilizer in the commercial fertilizerdemand models.

Evidence on crowding in/out without accounting for diversion

The main econometric results on crowding out are summa-rized in Table 3. Unconditional average partial effects (APEs)show the estimated kilogram change in a households demandforcommercial fertilizer based on a 1 kg increase in thequantityof subsidized fertilizer received by that household. 11 These re-sults assume that there is no diversion of program fertilizer into

11 Unconditionalrefers to theeffect onthe average householdin thesample,regardless of whether or not they received subsidized fertilizer. This is in con-trast to estimates that are conditional on receipt of subsidized fertilizer fromthe probit stageof the truncatednormal hurdle model. The conditional estimatesof crowding out are much higher (more crowding out) than the unconditionalestimates.


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Table 2Household (HH) and village characteristics of recipients of subsidized fertilizer

HH or village characteristic Malawi Zambia Kenya

HH total landholding HHs with larger landholding andasset wealth get more; less of anissue in recent years

HHs with larger landholding getmore

HHs with larger landholding getmore

Gender of HH head Female-headed HHs less likely toreceive subsidized fertilizer

Female-headed HHs equally likely toreceive subsidized fertilizer

Female-headed HHs equally likely treceive subsidized fertilizer

Market access HHs farther from main districtmarket get more

HHs farther from main districtmarket and/or feeder road get less

HHs farther from motorable road gemore

Political economy factors(electoral results)

Districts where ruling party won lastpresidential election get more

Constituencies where ruling partywon last presidential election getmore (and more so the larger theruling partys margin of victory)

Constituencies with more electoralsupport for challenger in the lastpresidential election get more

Table 3Unconditional average partial effects (APEs) of a 1 kg increase in the quantity of subsidized fertilizer received by a household on the kilograms of commercialfertilizer purchased, not accounting for program diversion

APE P-value signicance 95% CI

National (full sample)Malawi 0.180 0.000 *** [ 0.26 to 0.1Zambia 0.134 0.000 *** [ 0.29 to 0.1Kenya 0.431 0.005 *** [ 0.74 to 0.1

HHs in low PSA zonesMalawi 0.103 0.000 *** [ 0.15 to 0.0Zambia 0.070 0.000 *** [ 0.10 to 0.0Kenya 0.125 0.079 * [ 0.26 to 0.02]

HHs in high PSA zonesMalawi 0.265 0.000 *** [ 0.37 to 0.1Zambia 0.228 0.000 *** [ 0.16 to 0.1Kenya 0.534 0.029 ** [ 1.02 to 0.0

HHs in bottom 50% of total HH landholding (Kenya, Malawi); HHs with < 2 ha cultivated (Zambia)Malawi 0.127 0.000 *** [ 0.18 to 0.0Zambia 0.110 0.000 *** [ 0.13 to 0.0Kenya 0.235 0.046 * [ 0.47 to 0.01]

HHs in top 50% of total HH landholding (Kenya, Malawi); HHs with 2 ha cultivated (Zambia)Malawi 0.251 0.000 *** [ 0.35 to 0.1Zambia 0.210 0.000 *** [ 0.25 to 0.1Kenya 0.647 0.004 *** [ 1.08 to 0.1

Notes: PSA = private sector fertilizer retailing activities. In Zambia, high PSA refers to HHs in districts in the top tercile of mean HH commercial fertilizer quantityused in 1999/2000. In Kenya, high PSA refers to the medium and high potential zones. In Malawi, high PSA refers to the top half of mean community commercialfertilizer quantity used in 2002/2003 and 2003/2004.*Indicates statistical signicance at 1% level.** Indicates statistical signicance at 5% level.*** Indicates statistical signicance at 10% level.Sources : Ricker-Gilbert & Jayne (forthcoming), Mason & Jayne (2013), Mather and Jayne (2013), and authors calculations

commercial distribution channels and hence that householdsreporting of commercial purchases in survey data is fully

representing fertilizer distributed through private rms nor-mal commercial operations. The APEs are negative and highlysignicant in all three countries, indicative of crowding out.Across the full samples, an additional ton of subsidized fertil-izer distributed in Malawi, Zambia, and Kenya would crowd out180 kg, 134 kg, and 431 kg, respectively, of commercial fertil-izer purchased by farmers. This means that an additional ton of subsidized fertilizer would add 820 kg, 866 kg, and 569 kg tototal fertilizer use if diversion is not accounted for.

Table 3 results also show that crowding out is higher forsubsidized fertilizer distributed to relatively large farms. In all

three countries, an additional ton of subsidy fertilizer allocatedto farmers in the top half of the farm size distribution leads to

a doubling or more in the magnitude of crowding out (e.g., forMalawi, 251 kg of commercial sales are displaced as opposedto 127 kg if the subsidized fertilizer were allocated to farms inthe bottom half of the landholding size distribution).

Estimates of crowding out are also, as expected, highly sen-sitive to where the fertilizer was distributed. In areas of lowdemand for commercial fertilizer, there is virtually no crowd-ing out. In such areas, there is very little commercial fertilizerdemand that could be crowded out, thus fertilizer subsidies dis-tributed to such areas clearly make the greatest contribution tototal fertilizer use. For example, one ton(1,000kg)of additional


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subsidized fertilizer crowds out only 103 kg of commercial fer-tilizer in areas of Malawi where commercial demand was rela-tively low. By contrast, an additional ton of subsidized fertilizerdistributed in areas of Malawi where commercial demand wasrelatively high would crowd out 265 kg of commercial pur-chases. The most extreme case of crowding out has been inthe high-potential areas of western Kenya, where commercialfertilizer channels are relatively well developed and where over90% of sampled households purchased fertilizer in the surveyprior to the subsidy program. In these areas, one additional tonof subsidized fertilizer was found to crowd out 534 kg of com-mercial fertilizer and contribute 466 kg to total fertilizer use.This is quite substantial, especially considering that some of thecommercial purchases made by farmers are actually recycledsubsidy program fertilizer after it was diverted to commercialchannels.

However, this does not necessarily mean than targeting sub-sidized fertilizer to areas of low commercial fertilizer demandconstitutes best practice. In areas where fertilizer use isprotable on one or more crops, there is likely to be effec-tive demand that attracts input suppliers to meet this demand.Conversely, areas of low private sector activity may reectpoor underlying economics of using fertilizer and low cropresponse rates (average/marginal products) of fertilizer appli-cation. Hence, whether or not subsidized fertilizer contributesmore or less crop output if distributed in high- or low-demandareas depends on two potentially offsetting effects:how the dis-tribution of subsidized fertilizer affects total fertilizer use, andthe average/marginal products of fertilizer application in cropoutput.

Estimates of crowding out after accounting for diversion

We now estimate the issue of main interest in this article:how a ton of subsidized fertilizer affects total fertilizer use afteraccounting for diversion of program fertilizer. Table 4 presentsthese estimates based on three alternative estimates of programdiversion (16.5%, 33%, and 40%). Recall from Section 2 thatanalysis to date indicatesthat between 25%and 40%of programfertilizer (or more in the case of Nigeria) appears to be divertedby program authorities prior to reaching farmer beneciaries(Holden and Lunduka, 2013; Liverpool and Takeshima, 2013;Lunduka et al., 2013; Mason and Jayne, 2013). Using Eq. (5),

and based on the national (full sample) estimates of crowdingout presented earlier in Table 3 and an estimated diversion rateof 33%, we nd that an additional ton of fertilizer distributedthrough the subsidy program is found to displace 490 kg, 464kg, and 761 kg of commercial fertilizer purchases in Malawi,Zambia, and Kenya, and contribute 510 kg, 536 kg, and 239kg to total fertilizer use (Table 4). Note that if the computationof crowding out does not account for program diversion, theestimated contribution of the subsidy programsto total fertilizeruse is overestimated by 67.3% in the case of Malawi, by 61.6%in the case of Zambia, and by 138.0% in the case of Kenya.

If we reduce our estimate of subsidy program diversion byhalf (from 0.33 to 0.165), then the estimated contribution tototal fertilizer use from a one ton increase in the quantity of subsidized fertilizer is 655 kg in Malawi, 701 kg in Zambia, and404 kg in Kenya. A failure to account for even this more modestlevel of program diversion would overestimate the contributionof the subsidy programs to total fertilizer use by 25.2% in thecase of Malawi, by 23.5% in the case of Zambia, and by 40.8%in the case of Kenya.

Table 4 shows how total additional fertilizer use from a oneunit increase in the quantity of subsidized fertilizer use is af-fected by alternative assumptions about the magnitude of pro-gram fertilizer diversion. Analyses of input subsidy programsthat do not account for crowding out of commercial demandand particularly the diversion componentare likely to seri-ously overestimate the national food production response tosuch programs.

Benet/cost analysis

BC analysis provides a means of assessing the incrementalbenets of GS programs (the value of the incremental maizeproduction and associated welfare effects of price changes) rel-ative to their costs (the incremental change in government andfarmer expenditures on fertilizer used on maize). The term in-cremental refers to the difference between total benets andcosts in a with program scenario as compared with those ina without program scenario. 12 Tables 57 show BC cal-culations for ve years each for Malawi, Zambia, and Kenya,respectively. We have put as much description as possible ineach row of the tables to make them relatively self-explanatory.

The sources of all information used in the tables are providedin the table notes. More detailed information on our methodsand data sources are contained in the Data Appendix.

Our analysis includes the associated welfare effects of changes in maize prices resulting from the subsidy programs,but not potential effects of these programs on wage rates. Thatsaid, analysis to date has indicated that the effects of these pro-grams on food prices and wages are quite small and/or statisti-cally insignicant from zero, even in countries with relativelylarge subsidy programs (Ricker-Gilbert et al., 2013, Ricker-Gilbert, 2013; Takeshima and Liverpool-Tasie, 2013).

We report both nancial and economic (or social) BC ra-tios. Differences between the nancial and economic BC ratiosare due to two main differences in the methods used in eachtype of analysis. The rst and most signicant difference is that

12 For example, the incremental benet of the GS program in a given yearis the difference between the value of aggregate national maize produced thatyear with the GS program (i.e., the with program scenario) and value of aggregate national maize production that we assume would have obtained inthe absence of the GS program (the without program scenario). Similarly,the incremental cost of the GS program is the difference between the totalgovernment and farmer expenditure on fertilizer applied to maize in the withand without program scenarios.


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Table 4Estimated kilogram change in total smallholder fertilizer acquisition given a 1 kg increase in the quantity of subsidy program fertilizer

Contribution of 1 kg additionalsubsidized fertilizer distribution to

total fertilizer use (kg)

Adjusted by alternative % overestimation of total fertilizer us

estimates of the % of program if program diversion is not accountedAPE of 1 kg subsidized fertilizer Not accounting fertilizer that is diverted ** for, based on diversion rate of on household commercial fertilizer for program

Country use unadjusted for diversion (kg) * diversion 16.5% 33% 40% 16.5% 33% 40%

Malawi 0.180 0.820 0.655 0.490 0.420 25.2% 67.3% 95.2%Zambia 0.134 0.866 0.701 0.536 0.466 23.5% 61.6% 85.9%Kenya 0.431 0.569 0.404 0.239 0.169 40.8% 138.0% 236.4%

Notes: * APEs from Table 3.**Mason & Jayne (2013) found a diversion range between 13% and 71% in Zambia between 1999 and 2012, depending on the year, but a multi-year average of 33% for their SS household survey wave years. Holden & Lunduka (2010) estimate a diversion rate of 33% in Malawi and Lunduka et al. (2013) estimate 42% for2009/2010.

total incremental costs in the nancial analysis includes the to-

tal government expenditure on the GS program plus farmersexpenditure on their incremental fertilizer used on maize. Bycontrast, the total incremental costs in the economic analysis donot include the governments expenditure on subsidized fertil-izer that displaces commercial fertilizer (that the farmer wouldhave purchased at the full market rate in the without programscenario). This is because economic analysis considers this ex-penditure to be a transfer from the government to farmers andnotan incremental cost. Thus, the incremental cost of govern-ment expenditure is lower in the economic analyses. Second,in the nancial analysis, incremental maize output produced onaccount of the GS program is valued at its observed retail mar-ket price, while in the case of the economic analysis, additional

maize output is valued at the import parity retail price of maize,which is often higherthan observed market pricesin these coun-tries, depending on the year. As these countries import all of their fertilizer,we value fertilizer prices at observedretail prices.

Row J in Tables 57 shows the contributions to total fertilizeruse resulting from a one ton increase in the quantity distributedthrough thesubsidy programs,basedon theunconditional APEsfor the national (full sample) in Tables 3 and 4, and based on theassumption that 33% of GS fertilizer is diverted to private sec-tor channels. Row L shows the tonnage of additional fertilizerutilized on farmers elds; because of crowding out, this is con-siderably less than the total quantity of government-subsidizedfertilizer intended for distribution in Row A. Row K reports the

average product of fertilizer on maize output, the main crop onwhich the programs in each country were focused. These av-erage product estimates come from farm survey crop responsefunctions reported in Ricker-Gilbert and Jayne (forthcoming),Burke (2012), and Sheahan et al. (2013), which are derivedfrom the same national panel household survey data we useto estimate crowding in/out effects of subsidized fertilizer onhousehold total fertilizer use.

One of the most widely varying parameters affecting the es-timated benets of subsidy programs is the price of the output.

Maize prices vary widely across years, and hence we report

program benets for a wide range of maize output prices, in-cluding the annual average retail price in the capital city (RowN.i), the import parity price in the capital city during the leanseason period (Row N.ii), and nally, the maize price at whichthe benets of the subsidy program equal program costs (ac-cording to both thenancial andeconomic analyses [Rows N.iiiand N.iv]). The corresponding gross revenue of the additionalmaize output valued at these prices is shown in Rows O.i andii. Based on these output values and program costs as shownin Row I, we compute the nancial and economic BC ratios inrows P and Q.

The salient conclusion from these computations in Table 5 isthat under noscenario inany of thethree countries(exceptin one

year in Malawi, 2008/2009) do the nancial program benetsoutweigh the program costs. The mean BC ratios across allyears for Malawi, Zambia, and Kenya are 0.55, 0.52, and 0.52.A BC ratio of 1.0 would mean that the program benets equaltheir costs. The maize price levels required for the programto break even in nancial analysis (Row N.iii) are in all casesupward of US$300 per mt in Malawi, US$350 in Zambia, andUS$381 in Kenya, and often far higher than that. These break-even price levels are generally higher than import costs to thecapital city in each country experienced over the past decade.

The economic BC analysis produces more varied results. BCratios are below one in four of the ve years of subsidy programimplementation in Malawi, with a mean BC ratio of 0.77. In

Zambia, BC ratios are below one inthree of the ve years, withamean BC ratio of 0.79. In Kenya, BC ratios are greater than onein three of the ve years, although the mean BC ratio across theve years of program implementation is 0.89. 13 The relativelhigh maize response rates to fertilizer application in Kenya

13 The ve-year BC ratio reported in Tables 57 are not the average of theratiosacrossveyears, butrather the ratioof the aggregateincremental programbenets overve yearsto the aggregate incremental costs over ve years. Thus,the ve-year BC ratio is weighted by relative differences in program size, costsand maize prices over time.


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Table 5Benetcost estimates for government fertilizer subsidy programs in Malawi, 2005/2006 to 2009/2010 crop years

2005/2006 2006/2007 2007/2008 2008/2009 2009/2010 Five-year total

Estimated program costs to government and farmers *

(A) Total quantity of GS fertilizer intended for distribution tofarmers (MT) **

131,388 179,000 216,553 202,278 160,000 889,219

(B) Total ofcial government costs of GS fertilizer program ($US) ** $55,710,000 $88,690,000 $114,620,000 $274,920,000 $114,600,000 $648,540,000(C) Pan-territorial farmer price for GS fertilizer ($US/MT) ** $147 $132 $126 $110 $67(D) Total farmer spending on incremental fertilizer use as result of GS

program, accounting for crowding out and diversion (A C J)$9,463,878 $11,577,720 $13,369,982 $10,902,784 $5,252,800 $50,567,164

Incremental government program costs for economic analysis(E) Total government costs of GS fertilizer less administration costs $52,924,500 $84,255,500 $108,889,000 $261,174,000 $108,870,000(F) GS program administration costs of GS fertilizer program (B 5%) $2,785,500 $4,434,500 $5,731,000 $13,746,000 $5,730,000(G) Total government spending on the portion of GS fertilizer received

by farmers that results in incremental aggregate fertilizer use,accounting for crowding out and diversion (E J)

$25,933,005 $41,285,195 $53,355,610 $127,975,260 $53,346,300

(H) Total government spending on GS fertilizer that is diverted atwholesale level to commercial channels (E 33% diversion 90%wholesale share of retail price) ***

$15,718,577 $25,023,884 $32,340,033 $77,568,678 $32,334,390

(I) Total incremental government costs of GS fertilizer program,accounting for crowding out and diversion (F + G + H)

$44,437,082 $70,743,579 $91,426,643 $219,289,938 $91,410,690 $517,307,931

Estimated incremental benets(J) Estimated average partial effect of a 1 kg increase in GS fertilizer

on total household fertilizer use, accounting for crowding out anddiversion (kg) +

0.490 0.490 0.490 0.490 0.490

(K) Estimated average product of maize (MT) per additional MTof fertilizer

3.32 3.32 3.32 3.32 3.32

(L) Incremental national fertilizer use as a result of GS fertilizerprogram, accounting for crowding out and diversion (A J) (MT)

64,380 87,710 106,111 99,116 78,400

(M) Incremental maize output produced as a result of GS fertilizerprogram, accounting for crowding out and diversion (K L) (MT)

213,742 291,197 352,288 329,066 260,288

(N) Maize grain prices ($US/MT) &

(i) Annual average retail price in Lilongwe (nancial analysis) # $158 $212 $399 $292 $198(ii) Annual average retail IPP in Lilongwe (economic

analysis) $156 $259 $463 $294 $236

(iii) Price at which GS program breaks even (nancial analysis) $305 $344 $363 $869 $460(v) Price at which GS program breaks even (economic analysis) $252 $283 $297 $700 $371(O) Value of incremental maize output (M) at prices in N ($US)

(i) Annual average retail price in Lilongwe (nancial analysis) $34,125,834 $62,382,011 $142,038,992 $97,096,144 $52,078,163 $387,721,144(ii) Annual average retail IPP in Lilongwe SA/Moz (economic

analysis)$33,730,579 $76,209,043 $164,699,683 $97,648,716 $61,983,181 $434,271,202

Benetcost ratios of GS fertilizer program(P) Financial BC ratio of incremental benets (value of incremental

maize output in (O.i)) to total government program costs (B) andincremental farmer costs (D)

0.524 0.622 1.110 0.340 0.435 0.555

(Q) Economic BC ratio of incremental benets (value of incrementalmaize output in (O.ii)) to incremental costs to government (I) andincremental farmer costs (D)

0.626 0.926 1.572 0.424 0.641 0.765

Notes: All gures are in nominal $US. GS = government-subsidized.*The term incremental refers to the difference between benets and costs in a with program and a without program scenario.** Dorward and Chirwa (2011) and Logistics Unit.** * 33% of GS fertilizer assumed to be diverted.+ APE adjusted for crowding out and diversion as per Table 4.Ricker-Gilbert and Jayne (forthcoming) reports an average product of fertilizer of 3.32.&Average retail prices for May/April marketing year following harvest.#Ministry of Agriculture and Fisheries.IPP computed as average of Angonia and Lichinga (Mozambique) market price (SIMA) during marketing year + $30 transport to Lilongwe + 10% retail markup. Value of additional maize output in terms of net social surplus computed as [M (observed retail price + without-subsidy price)/2] where without-subsidy price is1.012 observed price as per Ricker-Gilbert et al. (2013).


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Table 6Benetcost estimates for government fertilizer subsidy programs in Zambia, 2006/2007 to 2010/2011 crop years

2006/2007 2007/2008 2008/2009 2009/2010 2010/2011 Five-year total


(A) Total quantity of GS fertilizer intended for distribution to farmers(MT)**

84,000 50,000 80,000 100,000 178,000 492,000

(B) Total ofcial government costs of GS fertilizer program ($US) *** $45,673,367 $45,965,458 $118,862,163 $100,631,894 $108,616,555 $419,749,437(C) Pan-territorial farmer price for GS fertilizer ($US/MT) + $229 $286 $289 $204 $204(D) Total farmer spending on incremental fertilizer use as result of GS

program, accounting for crowding out and diversion (A C J)$10,387,440 $7,722,000 $12,484,800 $11,016,000 $19,608,480 $61,218,720

Incremental government program costs for economic analysis(E) Total government costs of GS fertilizer less administration costs $43,389,699 $43,667,185 $112,919,055 $95,600,299 $103,185,727(F) GS program administration costs of GS fertilizer program (B 5%) $2,283,668 $2,298,273 $5,943,108 $5,031,595 $5,430,828(G) Total government spending on the portion of GS fertilizer received


$23,430,437 $23,580,280 $60,976,290 $51,624,161 $55,720,293

(H) Total government spending on GS fertilizer that is diverted atwholesale level to commercial channels (E 33% diversion 90%wholesale share of retail price)

$12,886,741 $12,969,154 $33,536,959 $28,393,289 $30,646,161

(I) Total incremental government costs of GS fertilizer program ,accounting for crowding out and diversion (F + G + H)

$38,600,846 $38,847,707 $100,456,357 $85,049,045 $91,797,281 $354,751,237


on total household fertilizer use, accounting for crowding out anddiversion (kg) &

0.540 0.540 0.540 0.540 0.540

(K) Estimated average product of maize (MT) per additional MT of fertilizer #

3.56 3.56 3.56 3.56 3.56


45,360 27,000 43,200 54,000 96,120


161,482 96,120 153,792 192,240 342,187

(N) Maize grain prices ($US/MT)

(i) Annual average retail price in Lusaka (nancial analysis) $260 $327 $312 $242 $236(ii) Annual average retail IPP in Lusaka (economic

analysis) $$408 $356 $331 $335 $454

(iii) Price at which GS program breaks even (nancial analysis) $347 $559 $854 $581 $375

(v) Price at which GS program breaks even (economic analysis) $303 $484 $734 $500 $326(O) Value of incremental maize output (M)at prices in N ($US)

(i) Annual average retail price in Lusaka (nancial analysis) $42,489,039 $31,808,415 $48,558,901 $47,080,345 $81,725,253 $251,661,953(ii) Annual average retail IPP in Lusaka (economic analysis) $66,615,949 $34,672,145 $51,543,562 $65,236,238 $157,225,882 $375,293,776



0.758 0.592 0.370 0.422 0.637 0.523

(Q) Economic BC ratio of incremental benets (value of incrementalmaize output in (O.ii)) to incremental costs to government [I] andincremental farmer costs (D)

1.360 0.745 0.456 0.679 1.411 0.902

Notes: All gures are in nominal $US. GS = government subsidized.*The term incremental refers to the difference between benets and costs in a with program and a without program scenario.** MAL (2012).** * MFNP (20052012). FSP/FISP government spending 90% to obtain estimated government cost of fertilizer portion of the program (program includes bothfertilizer and hybrid maize seed).+ MACO (20052012) and MAL (20052012). Farmer contribution varied by district in 2006/2007 and 2007/2008. Value in (C) for these years is weighted averagefarmer contribution, where weights are share of total GS fertilizer allocated to a given district.33% of GS fertilizer assumed to be diverted as per Table 4.&APE adjusted for crowding out and diversion as per Table 4.#Burke et al. (2012).average retail prices for May/April marketing year following harvest. CSO (2012).$IPP computed as SAFEX price + $100 transport + $20 non-GMO price premium + 10% retail markup.Value of additional maize output in terms of net social surplus computed as [M (observed retail price + without-subsidy price)/2] where without-subsidy price is1.024 observed price per Ricker-Gilbert et al. (2013).


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Table 7Benetcost estimates for government fertilizer subsidy programs in Kenya, 2006/2007 to 2010/2011 crop years

2006/2007 2007/2008 2008/2009 2009/2010 2010/2011 Five-year total


(A.1) Total quantity of NAAIAP fertilizer intended for distribution (MT) ** 0 3,550 9,200 17,725 31,066 61,541(A.2) Total quantity of NCPB fertilizer intended for distribution (MT) ** 16,137 9,506 52,608 8,388 45,264 131,903(B) Total estimated government costs of GS fertilizer programs ($US) *** $8,512,290 $10,175,234 $80,864,354 $21,638,203 $61,571,039 $182,761,120(C) Farmer price for NCPB fertilizer ($US/MT) + $352 $520 $872 $552 $538(D) Total farmer spending on incremental fertilizer use as result of GS

program, accounting for crowding out & diversion (A.2 C J)$1,356,291 $1,180,426 $10,966,571 $1,107,464 $5,817,559 $20,428,311

Incremental government program costs for economic analysis(E) Total government costs of GS fertilizer less administration costs $8,086,675 $9,666,473 $76,821,136 $20,556,293 $58,492,488(F) GS program administration costs of GS fertilizer program (B 5%) $425,614 $508,762 $4,043,218 $1,081,910 $3,078,552(G) Total government spending on the portion of GS fertilizer received


$1,932,715 $2,310,287 $18,360,252 $4,912,954 $13,979,705

(H) Total government spending on GS fertilizer that is diverted atwholesale level to commercial channels (E 33% diversion 90%wholesale share of retail price)

$2,401,742 $2,870,942 $22,815,877 $6,105,219 $17,372,269

(I) Total incremental government costs of GS fertilizer program,accounting for crowding out and diversion (F + G + H)

$4,760,072 $5,689,991 $45,219,347 $12,100,083 $34,430,525 $102,200,018


on total household fertilizer use, accounting for crowding out anddiversion (kg) &

0.239 0.239 0.239 0.239 0.239

(K) Estimated average product of maize (MT) per additional MTof fertilizer #

6.72 6.72 6.72 6.72 6.72


3,857 3,120 14,772 6,241 18,243


25,917 20,969 99,269 41,940 122,592

(N) Maize grain prices ($US/MT)

(i) Annual average retail price in Nairobi (nancial analysis) $305 $429 $350 $342 $331(ii) Annual average retail IPP in Nairobi (economic

analysis) $$361 $315 $275 $345 $415

(iii) Price at which GS program breaks even (nancial analysis) $381 $542 $925 $542 $550(v) Price at which GS program breaks even (economic analysis) $236 $328 $566 $315 $328

(O) Value of incremental maize output (M) at prices in N ($US)(i) Annual average retail price in Nairobi (nancial analysis) $7,897,079 $8,995,658 $34,746,216 $14,352,652 $40,576,591 $106,568,197(ii) Annual average retail IPP in Nairobi (economic analysis) $9,365,071 $6,605,372 $27,290,664 $14,487,464 $50,881,001 $108,629,572



0.800 0.792 0.378 0.631 0.602 0.524

(Q) Economic BC ratio of incremental benets (value of incrementalmaize output in (O.ii)) to incremental costs to government (I) andincremental farmer costs (D)

1.531 0.961 0.486 1.097 1.264 0.886

Notes: All gures are in nominal $US. GS = government subsidized.*The term incremental refers to the difference between benets (costs) in a with program and a without program scenario.** NAAIAP (2010), LOG Associates (2011), and NCPB (2013).** * Program cost for fertilizer derived from IPP of DAP (U.S. gulf port) and Urea (Black sea) + shipping to Mombassa-Nakuru + 10% retail markup, plus assumed5% program administration costs.+ Tegemeo survey households that received NCPB-subsidized fertilizer paid an average of 70% of the commercial fertilizer price in 2009/2010 for NCPB-subsidizedfertilizer, thus we multiply 0.7 by the average of our rural retail IPP of DAP and Urea fertilizer.33% of GS fertilizer assumed to be diverted as per Table 4.&APE adjusted for crowding out and diversion as per Table 4.#Sheahan et al. (2013).Average retail prices for October/September marketing year following harvest. Ministry of Agriculture (20052012).$Maize retail IPP computed as SAFEX price + shipping to Mombassa-Nairobi + 10% retail markup.


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(6.7 kg of maize per kg of fertilizer product applied, Row K) isthe main reason why Kenya BC ratios are relatively favorable.National maize response rates to fertilizer (the average prod-uct of fertilizer) are considerably lower in Malawi and Zam-bia according to national household survey data (Burke 2012;Ricker-Gilbert and Jayne, forthcoming).

Therefore, the main conclusion from the computations in Ta-bles 57 is that, after accounting for diversion of program fertil-izer in the derivation of how program fertilizer crowds out com-mercial fertilizer, the standardeconomic BC ratios are generallyunfavorable in the three countries considered here. The Kenyasubsidy program does produce economic BC ratios exceedingone in three of the ve years even though the ve-year averageof this ratio is also below one. While various actions could betaken to raise program benetsfor example, reduce diversion,improve targeting to farmswith little effective demand for com-mercial inputs, extension programs to raise the efciency withwhich farmers use fertilizerthe social and political circum-stances of the countries involved require a sober assessment asto the scope and time frame for achieving such benets.

7. Conclusions and implications for policy

This study is motivated by the need to better understand thebenets and costs of fertilizer subsidy programs in SSA. Cru-cial for addressing this question is accurate information on thedegree to which fertilizer subsidy programs in SSA raise totalfertilizer use. Questions of crowding out and illicit diversion of program fertilizer, while long understood to be potential prob-lems, have until recently neither been rigorously measured, nor

have their impacts on total fertilizer use and program bene-ts and costs been quantied. This study contributes to ourunderstanding of input subsidy program impacts in two ways.First, we review and update the recent evidence on crowdingout based on large-scale farm panel survey data in eastern andsouthern Africa after accounting for the likely range of diver-sion of program fertilizer onto commercial markets. The secondcontribution of the study is to examine how alternative targetingcriteria and estimates of diversion affect the extent of crowdingout, andhencethe benets of theprograms relative to their costs.

The rst major nding of this study is that the magnitude of diversion can profoundly inuence estimates of how fertilizersubsidy programs affect total fertilizer use and program im-

pacts. Basedon estimates of programdiversion,every additionalton of fertilizer distributed through the subsidy programs raisetotal fertilizer use by 490 kg in Malawi, by 536 kg in Zambia,and by 239 kg in Kenya. Not accounting for potential diversionhas led prior studies to seriously overestimate the extent towhich subsidy programs contribute to total fertilizer use. Givenrecent evidence that 33% or more of total program fertilizermay be diverted before being received by intended beneciaryfarmers, the failure to account for program diversion is shownto overestimate the contribution of the subsidy programs tonational fertilizer useby 67.3% in thecase of Malawi, by 61.6%in the case of Zambia, and by 138.0% in the case of Kenya.

This has important implications for the benets of the subsidyprograms relative to their costs. The second major nding ofthis study is that after taking account of both crowding out anddiversion problems, under no scenario in any of the three coun-tries (except for Malawi in 2008/2009) do the program benetsoutweigh program costs according to nancial BC analysis.The maize price levels required for the programs to break evenare generally well over US$350 per MT. These price levels aregenerally higher than import costs to the capital city experi-enced over thepast decade. If world food pricescontinue to rise,as they have in the recent past, the BC ratios may become morefavorable, but these considerations would also need to accountfor the offsetting probable increases in world fertilizer prices aswell. Economic BC ratios are more favorable, particularly forKenya, but in allthreecases, mean ve-yearBC ratiosarebelowone. The main conclusion that emerges from this analysis isthat, after account for crowding out and diversion, the standardeconomic BC ratios are generally unfavorable in the threecountries considered here, especially for Malawi and Zambia.

These estimates of the contribution of subsidy programs tototal fertilizer use all pertain to the recent set of smart sub-sidy programs, which were intended to overcome the majortargeting and diversion problems of universal input subsidyprograms commonly implemented in the 1970s and 1980s inAfrica. Empirical evidence from Asia and high-income coun-tries show that thecosts of universal subsidiesoften outweighedthe benets as input suppliers usually captured a large part ofthe subsidy, because the price discount was not fully passedon to farmers (Brooks et al., 2008). The three targeted inputsubsidy programs examined in this article produce BC ratiosthat are similarly negative on average. Many factors contribute

to low BC ratios, including low crop response rates to fertilizerapplication, the diversion of signicant quantities of programfertilizer at least in some countries, and de facto targeting criteria. Crowding out tends to increase when the main driver of thesubsidy programs is increasing food supplies and where by de-sign or in practice the subsidies areallocateddisproportionatelyto households with relatively larger farms, higher incomes, andthe ability to purchase fertilizer. By contrast, when input sub-sidies are targeted to poorer households, crowding out is oftenconsiderably lower.

If the political process determines that fertilizer subsidy pro-grams are to continue regardless of their questionable economicmerits to date, then the fundamental challenge is to raise their

benets by seriously addressing the program targeting and im-plementation modalities that result in high levels of crowd-ing out and diversion of program fertilizer. Raising the re-turns to input subsidy programs would also require assistingfarmers to raise the efciency with which they use fertilizer,through more effective farmer extension programs guided byevidence from agronomic, agricultural engineering, and cropscience research. All of these measures would raise the outputresponse from a given quantity of fertilizer distributed throughthesubsidyprograms.In principal, overcomingthese challengesseems within reach. In practice, however, there has been limitedprogress in effectively addressing these challenges despite their


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longstanding recognition and despite explicit attempts of thenew generation of smart subsidy programs to do so. More at-tention to these design and implementation detailsand theirunderlying governance dimensionsis clearly necessary in or-der to translate the theoretical benetsof smart subsidy conceptsinto reality.

Acknowledgments

This study has beneted from the useful comments of HansBinswanger, Eric Crawford, Mulat Demeke, Gershon Feder,Milu Muyanga, and Elaine Ronchi. The authors gratefully ac-knowledge funding for this study from the Bill and MelindaGates Foundation, USAID/Bureau for Food Security, andUSAID missions in Zambia, Kenya, and Malawi.

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