Fertilizer subsidies, political influence and local food prices in sub-Saharan Africa: Evidence from Nigeria

Food Policy 54 (2015) 11–24

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Fertilizer subsidies, political influence and local food prices in sub-Saharan Africa: Evidence from Nigeria Hiroyuki Takeshima a,⇑, Lenis Saweda O. Liverpool-Tasie b a b

International Food Policy Research Institute, 2033 K Street, NW, Washington, DC 20006, USA Michigan State University, 446 W. Circle Dr, Justin S Morrill Hall of Agriculture, East Lansing, MI 48824-1039, USA

a r t i c l e

i n f o

Article history: Received 5 May 2014 Received in revised form 6 April 2015 Accepted 11 April 2015

Keywords: Fertilizer subsidies Food prices Fiscal federalism Political influence Nigeria

a b s t r a c t The last decade has seen a resurgence in the use of fertilizer subsidies in sub-Saharan Africa. However, there is limited empirical evidence on the effects of fertilizer subsidy programs on local food prices. Using an instrumental variables approach, we explore the effect of a fertilizer subsidy program on the seasonal growth rates of grain prices in Nigeria. Our results suggest that the fertilizer subsidy program had very small effects on the growth rates of grain prices between the post-planting and post-harvesting seasons. We also find that political influence played a role in the distribution of subsidized fertilizer. We discuss how the weak effects on the price growth rates may be caused by low market orientation, output market structures, greater focus on farmers’ incomes, low marginal productivity of fertilizer, and politically influenced targeting. Ó 2015 Elsevier Ltd. All rights reserved.

Introduction Many countries in Sub-Saharan Africa (SSA) have recently introduced or re-introduced fertilizer subsidy programs. Though the objectives of these programs vary from country to country, they normally include increasing fertilizer use, crop production and yields among smallholder farmers. They are also often geared to stimulate private input sector growth, raise smallholder incomes, reduce poverty and improve food security (FMARD, 2011). Since the food price spike in 2008, input subsidies in many SSA countries have been driven by, among others, an objective of mitigating the effects of global food price increases (Jayne and Rashid, 2013). This paper uses community level panel data in Nigeria to investigate the effects of a past fertilizer subsidy scheme on the growth rates of the major grain prices. We consider the three grains for which most subsidized fertilizer seems to be applied in Nigeria; rice, maize and sorghum. Though the Nigerian government currently implements a different subsidy program (using electronic vouchers), we investigate the subsidy program that was in place up until 2012; the Fertilizer Market Stabilization Program

⇑ Corresponding author. Tel.: +1 202 862 8195; fax: +1 202 467 4439. E-mail addresses: [email protected] (H. Takeshima), [email protected] (L.S.O. Liverpool-Tasie). http://dx.doi.org/10.1016/j.foodpol.2015.04.003 0306-9192/Ó 2015 Elsevier Ltd. All rights reserved.

(FMSP).1 Evaluating previous programs is still important because the scarcity of empirical evidence regarding old fertilizer subsidy programs has led the Nigerian government to frequently repeat similar programs despite their numerous challenges (Liverpool-Tasie and Takeshima, 2013). Consequently, limited empirical evidence of past subsidy schemes in Nigeria, as well as the possibility that FMSP type subsidy programs may be re-introduced again in the future, warrant the investigation of the effects of the FMSP. This study contributes to two strands of literature. First, it builds on a growing literature on the impacts of recent fertilizer subsidy programs in SSA. While most of this growing literature focusses on the effects of these input programs on fertilizer use, productivity and private market development (Dorward et al., 2008; Xu et al., 2009; Ricker-Gilbert et al., 2011; Liverpool-Tasie, 2014a,b; Mason and Jayne, 2013; Takeshima and Nkonya, 2014), very few studies investigate the effects of input subsidy programs on food prices. An exception is Ricker-Gilbert et al. (2013) who find that recent fertilizer subsidy programs in Malawi and Zambia had limited effects on equilibrium retail maize prices. A potential challenge in investigating the effect of input subsidies on food prices is that it can depend on a multitude of factors, such as the level of market orientation of farmers, trader oligopolies, or post-harvesting 1 While authors are not aware of any official written FMSP document that states containing food price increase as one of the program objectives partly because the program started in 1999 when the global food price was low, rising food price is mentioned as an increasingly serious issue in recent reports on the new fertilizer subsidy schemes (FMARD, 2012).

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H. Takeshima, L.S.O. Liverpool-Tasie / Food Policy 54 (2015) 11–24

technologies, among others. This study does not investigate such causal mechanisms, but rather focuses on estimating the reduced-form effects of FMSP on the growth rates of grain prices. This paper also contributes to the growing literature on the political economy of input subsidy allocations in developing countries. Historically, SSA governments have intervened in markets partly to consolidate power among largely agrarian populations (Holmén, 2005). Politically influential agents or communities have been known to receive more input subsidies or credit than less connected ones (Holmén, 2005; Morris et al., 2007; Olayide and Idachaba, 1987; Chinsinga, 2012; Banful, 2011; Holden and Lunduka, 2013; Mpesi and Muriaas, 2012; Mason et al., 2013). With political influence, subsidized fertilizer may be allocated sub-optimally from an efficiency perspective. In Nigeria, anecdotal evidence suggests that state governors patronize their districts of origins with preferential access to resources, including subsidized fertilizer. We find empirical evidence of this political dimension of input subsidies and incorporate it in our analytical framework. This paper proceeds as follows. Section ‘The fertilizer subsidy program and fertilizer use in Nigeria’ describes the FMSP and key fertilizer use patterns in Nigeria. Section ‘Effects of fertilizer subsidies on local grain price – conceptual prediction’ provides a conceptual framework and hypothesis of the expected food price effects of the subsidy program. Section ‘Empirical specification’ presents the empirical strategy, while Section ‘Results’ discusses the empirical results. Section ‘Summary and discussion’ concludes.

The fertilizer subsidy program and fertilizer use in Nigeria Like many other countries in SSA, Nigeria allocates a substantial portion of its agricultural budget for fertilizer subsidies (Mogues et al., 2012; Liverpool-Tasie and Takeshima, 2013). The cost of fertilizer subsidy programs between 2008 and 2010 was almost 150 million US dollars per year; the second largest among major SSA countries with similar programs (Jayne and Rashid, 2013). The FMSP was implemented in Nigeria between 1999 and 2012 (Liverpool-Tasie and Takeshima, 2013). Under the FMSP, each of the 37 state governments would submit a request to the federal government to procure a certain quantity of subsidized fertilizer. This amount would be based on their demand projections established from farm areas and recommended fertilizer application rates, as well as state agricultural budgets (Takeshima and Nkonya, 2014). Depending on the federal agricultural budget, the federal government then determined the total amount of subsidized fertilizer to be allocated to each state. The federal government then procured and sold fertilizer to the state governments at 75% of the price (i.e. providing a 25% subsidy) based on calculated national average import parity prices. States, as well as local government areas (LGAs) which are administrative units below the state, often added their subsidies to the 25% provided by the federal government.2 Though varying across states, the additional subsidies provided by states were usually of similar magnitudes as that of the federal government (25%) and ranged between 0% and 50% (Banful et al., 2010). LGAs have provided similar magnitudes of subsidies as well (authors’ assessments based on Mogues et al. (2012)). Consequently, the typical subsidy rate for subsidized fertilizer under the FMSP was in the order of 75%. Under the FMSP, each state distributed subsidized fertilizer to farmers through various outlets, largely the Agricultural Development Project (ADP) which is a state level public institution in charge of agricultural extension and other services including inputs provisions. No explicit targeting criteria or individual quota existed, and distribution was often ad hoc (Liverpool-Tasie and 2

Sometimes, states and LGAs also provided subsidized fertilizer outside the FMSP.

Takeshima, 2013). Another key distinction of the FMSP was that commercially distributed fertilizer existed in parallel to the subsidized fertilizer. Anecdotal evidence indicates that a substantial portion of subsidized fertilizer leaked into commercial markets and was sold as unsubsidized fertilizer. The quantities initially distributed as subsidized fertilizer often accounted for a significant share of total fertilizer consumption in the country (Liverpool-Tasie and Takeshima, 2013). Because of leakages, however, the amount actually received by farmers accounted for only a relatively small fraction of the intended quantity. Contrary to the ongoing fertilizer subsidy program called the Growth Enhancement Support (GES), where farmers receive subsidies through vouchers, subsidized fertilizer under FMSP was physically distributed by both federal and state governments. The aggregate quantity of subsidized fertilizer was rationed at the federal and state levels, and possibly at LGA levels under FMSP (Liverpool-Tasie and Takeshima, 2013). Subsidized fertilizer use and net sales of rice, maize and sorghum The use of subsidized fertilizer in Nigeria exhibits key patterns with important implications for its likely effects on local grain prices. We illustrate these patterns using our study dataset; the Living Standard Measurement Survey – Integrated Survey on Agriculture (LSMS data). The LSMS data were collected jointly by the National Bureau of Statistics of Nigeria and the World Bank. The data capture information from two rounds, post-planting period (August– October 2010) and post-harvesting period (February–March 2011). The data are nationally representative and contain detailed information on socio-economic characteristics and agricultural production. This includes the use of inputs such as fertilizer, crop harvests and sales and food and nonfood purchases. The LSMS data also report the sources of fertilizer obtained by each household. Following Takeshima and Nkonya (2014), subsidized fertilizer is defined as fertilizer obtained from the government or political leaders, and a very small share of fertilizer reportedly being received for free. Two key patterns of subsidized fertilizer that are of particular relevance to this paper are observed. First, most subsidized fertilizer in Nigeria is used for three crops, rice, sorghum and maize. Approximately 11%, 39% and 61% of subsidy recipients in Nigeria used subsidized fertilizer in 2010 on plots growing rice, sorghum and maize respectively, and 83% used subsidized fertilizer on at least one of these crops. The share of sorghum among plots using subsidized fertilizer is relatively high because of its ubiquity in Nigeria (Omotayo et al., 2001). Similarly, 74 (83 and 89) percent of rice (sorghum and maize) producers receiving subsidized fertilizer apply subsidized fertilizer to these crops. However, it should be noted that subsidized fertilizer accounted for a relatively small share (approximately 9–13%) of total fertilizer used on plots cultivated with rice, sorghum and maize (Table 1). The second important pattern among fertilizer subsidy recipients is that they are generally similar to non-recipients in terms of their market orientation. Table 2 summarizes the characteristics of the producers of each crop categorized by their fertilizer subsidy status. There is no statistically significant difference in the quantity being sold between subsidy recipients and non-recipients. In the case of sorghum, fertilizer subsidy recipients are actually selling less. Any differences between subsidy recipients and non-recipients are weak even if we include those giving the harvests as gifts.3 Production and sales quantities, and shares of sales 3 Motives for gift-giving may be different from market sales, such as risk-sharing, and thus effects on markets are unclear. It is however noteworthy that half of sorghum producers gave their harvests as gifts, a higher share than those selling their harvests. As in Mali, sorghum may be used as compensation for family labor (Vitale and Sanders, 2005).

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H. Takeshima, L.S.O. Liverpool-Tasie / Food Policy 54 (2015) 11–24 Table 1 Inorganic fertilizer use by plots in Nigeria (2010 rainy season, 1000 ton)a. Source: Authors’ calculations. Type of plots

Subsidized fertilizer

Rice plots Sorghum plots Maize plots

20 66 43

Non-subsidized fertilizer

95% CI

a

[5, 35] [46, 85] [27, 60]

All fertilizer

95% CI 192 452 394

Share (%) of subsidized fertilizer 95% CI

[136, 247] [395, 509] [337, 450]

211 518 437

[154, 269] [458, 577] [378, 495]

9 13 10

Adjusted for sample weights. Multiple crops can be grown on these plots, and thus figures may be double-counted.

Table 2 Sales/uses of harvests by fertilizer users, subsidy recipients.a Source: Authors’ calculations. Rice

Sorghum

Subsidy recipient

a b

Others

Subsidy recipient

Maize Others

Subsidy recipient

Others

Number of observations

23

310

105

980

64

1264

Sold harvest (%) Sold or gave harvest as gift (%) Median production (unprocessed, kg)b Average share of sales to total production (%)b Average share of gifts to total production (%)b % Some household member was engaged in non-agricultural work in the past 7 days Non-farm business sales from previous month (USD) – median % With literate household head Household asset (USD) – median Livestock asset (USD) – median % Owning at least some plots Non-food item expenditure per capita/year (USD) – median Total farm size cultivated (ha)

63 93 500 31 5 83⁄

60 80 1000 30 3 61

13⁄ 56 600 4 5 78⁄

19 59 600 7 4 69

27 50 450 12 5 84⁄⁄

31 48 500 22 4 61

57  57 404 767⁄⁄ 22 87 0.8

0 53 347 202 21 53 1.1

0 57 255 1023⁄⁄ 26 39 0.6⁄

0 50 285 352 22 39 0.8

0 58 320 267⁄⁄ 22 48 0.8⁄

0 55 270 69 21 63 0.4

Asterisks (⁄⁄1%; ⁄5%;  10%) indicate statistically significant differences from non-recipients, based on non-weighted sample test. Approximately 10% of farmers have not reported harvest. We dropped those observations to calculate these statistics.

Table 3 Share (%) of farm households buying rice, sorghum and maize by subsidy recipient status.a Source: Authors’ calculations. Type of farmers

All Number of Observations

a

Among producers of this crop Share (%)

Number of observations

p

h

Share (%) p

h

Rice All Subsidy recipients Other

2972 158 2814

41 47 40

39 47 39

333 23 310

42 49 42

31 41 30

Sorghum All Subsidy recipients Other

2972 158 2814

17 20 17

11 22 11

1095 105 990

29 20 30

18 26 17

Maize All Subsidy recipients Other

2972 158 2814

21 34 20

14 24 13

1328 64 1264

25 43 23

14 31 12

ap = post-planting data; h = post-harvesting data. Percentage adjusted for sample weights.

or gifts to total production are also not statistically significantly different between subsidy recipients and non-recipients. Importantly, subsidy recipients tend to earn higher off-farm incomes and may be better endowed with livestock assets. Similarly, subsidy recipients are not necessarily less likely to purchase these crops in the market. Table 3 presents the share of households purchasing each crop, differentiated by household subsidy recipient status. These are based on consumption during the seven days prior to the interviews in both post-planting and post-harvest seasons. The shares of households who purchased these crops were relatively low; 41%, 17% and 21% for rice, sorghum and maize, respectively, for the post-planting season. For the post-harvesting season, they were 39%, 11% and 14%, respectively. These shares are qualitatively similar among those who also

produce these crops. Subsidy recipients generally exhibit similar shares; for example, their shares in the post-planting survey were 47%, 20%, and 34%, respectively. Contrary to the expectation that subsidy recipients may produce more and purchase less food from the market, they are at least as likely as non-recipients to purchase crops from the market, potentially affecting their market prices. These patterns do not allow us to test directly whether receiving subsidies affect the crop purchase behaviors. They, however, indicate that the effect of subsidies on grain prices through farm households’ purchases may be complex. While this study does not formally investigate the causal mechanisms of the impacts of FMSP on the growth rates of grain prices, the relatively significant subsistence level of many recipients (as observed above) can potentially limit such impacts. Combined

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H. Takeshima, L.S.O. Liverpool-Tasie / Food Policy 54 (2015) 11–24

Panel A Grain Price

P0

Panel B

Panel C

D

S = MC

A0

P0

A0 = A1

P0

A0

margin

MC 0

75% reduction P1

P1

A1

A1

margin

MC1 Q'

Q

Q'

Q

Q'

Q

Fig. 1. Fertilizer subsidies and food grain prices in district j. Source: Authors.

with other potentially limiting factors (including trader oligopoly and high post-harvest loss that are often common in SSA), the observed characteristics of fertilizer subsidies recipients indicate that conditions are unfavorable for the FMSP to affect grain prices. We provide formal reduced-form evidence of such limited impacts in the remaining sections. Effects of fertilizer subsidies on local grain price – conceptual prediction Prior to the empirical analyses, we briefly illustrate conceptually, how fertilizer subsidies may lower grain prices in the local market. The objective here is to assess the order of magnitude of grain price changes likely to occur due to fertilizer subsidies provisions, and obtain some references for evaluating our empirical results. The exposition is simplified in a partial equilibrium framework. Fig. 1 illustrates some simple mechanisms through which rationed fertilizer subsidies affect grain prices in district j. These mechanisms differ depending on the type of the market, as illustrated in different panels in Fig. 1. We first describe the common factors across all types of markets, and later distinguish these markets. In the short run, grain prices in the market are determined by the local supply and demand curves (S and D). Under reasonable assumptions, S is the marginal cost (MC) of producing each unit of output. Fertilizer subsidies affect the grain prices through their effects on the MC curve. A salient feature of such mechanisms is illustrated by the drop in MC curve. Upon the receipt of subsidized fertilizer, the MC curve drops from MC0 to MC1 for the range of output up to Q = Q0 . The quantity Q0 is the output that can be produced with subsidized fertilizer alone, without using non-subsidized fertilizer. Once the farmer uses up subsidized fertilizer and produce Q0 , the farmer must begin to use regular fertilizer and thus the MC curve jumps up at that point. Equilibria A0 and A1 refer to ‘‘before’’ and ‘‘after’’ the provision of subsidized fertilizer, respectively. Typical subsidy rates in 2010 were possibly up to 75%, as mentioned above. In certain circumstances, the rationed provision of a 75% subsidy for fertilizer lowers the MC curve by 75% for the range Q = {0, Q0 }. One way this can be seen is through a solution to a simple input use optimization problem. With production function Q = f (QH) and input price vectors WH (where QH is a vector of quantities of inputs H including fertilizer, F), interior solutions of optimal

number of H. Suppose WF drops by 75% from WF0 to WF1 through subsidies, where 0 and 1 denote ‘‘before’’ and ‘‘after’’ a fertilizer subsidy provision, respectively. This first raises PF/W. Under a reasonable assumption of strictly concave production function, the farmer increases fertilizer use while reducing the use of other inputs until the uses across all inputs satisfy PH1⁄/WH1⁄ = PF1⁄/WF1⁄. In a special case where PF1⁄ is constant for a fairly large range of fertilizer quantity, D is perfectly inelastic, and only subsidized fertilizer is used in A1 (so that non-subsidized fertilizer price is irrelevant), PH1⁄/WH1⁄ = PF1⁄/WF1⁄ = PH0⁄/WH0⁄/(1–0.75), and MC1⁄ = NH  WH⁄1/PH⁄1 = 0.25  NH  WH⁄0/PH⁄0 = 0.25MC0⁄, and therefore P1  0.25P0. However, such price effects can be smaller depending on various factors. If the marginal productivity of fertilizer is rapidly decreasing, the reduction in MC1⁄ will be less since the increase in fertilizer use induced by the subsidy quickly raises WF1/PF1 (or lowers PF1/WF1).4 Second, while Panel A illustrates the case where only subsidized fertilizer is used in A1, markets could be more similar to Panel B where non-subsidized fertilizer is used in addition to subsidized fertilizer. In Panel B, rationed fertilizer subsidies have no price effect because the MC depends on the non-subsidized fertilizer price, and not the subsidized price. Third, if the margins between farm gate and retail prices are large as in Panel C, a 75% drop in farm gate price will not translate into the same percentage drop at the retail level because the margin is unaffected by the fertilizer subsidies. Our empirical analysis focuses on the average of these price effects across markets. The average effects depend on the aforementioned mechanisms within individual markets, as well as on the allocation of subsidized fertilizer across markets. In summary, the average effects depend on whether (1) the amount of subsidized fertilizer used for the grain produced for market sales is large (which leads to a large Q0 ), and (2) the receipt of subsidized fertilizer which shifts the demand curve (D) backwards (through increased subsistence grain production which can reduce market demand). The effects are greater if the (3) marginal productivity of fertilizer does not diminish rapidly; (4) demand is inelastic; (5) subsidized fertilizer leads to new equilibria that are still interior solutions; (6) the margin between farm gate and retail prices is small; and if (7) more subsidized fertilizer is provided to markets of type depicted in Panel A.

inputs used satisfy the conditions PF⁄/WF⁄ = PH⁄/WH⁄, where PH is the marginal product of input H, H is a set of inputs other than F, and ‘‘⁄’’ denotes equilibrium. Meanwhile, we have MC⁄ = o(Total Cost)⁄/oQ = RH [oQH⁄/oQ]  WH⁄ = NH  WH⁄/PH⁄, where NH is the

4 Potential factors that can also lead to a rapidly decreasing marginal productivity of fertilizer include various value chain deficiencies, trader oligopoly, and post-harvest losses.

H. Takeshima, L.S.O. Liverpool-Tasie / Food Policy 54 (2015) 11–24

From Table 1, subsidized fertilizer accounted for about 10% of all fertilizer used. The discussion in the previous section (Table 2) suggests that these shares are not particularly greater for the market-oriented portion of production. Similarly, discussions on Table 3 suggest that the receipt of subsidized fertilizer may not necessarily shift D backward. Thus, conditions (1) and (2) are unlikely to hold. If subsidized fertilizer were all provided in markets as depicted in Panel A, and these markets also account for 10% of the market (so that the same amount of fertilizers are used for each market), and if conditions (3)–(7) hold, the average change in grain prices may be in the order of 7.5% for all crops (=75% ⁄ 10% + 0% ⁄ 90%). While the illustration here does not explicitly incorporate intra-market trades, if demand is inelastic in each market, the price reduction effects will remain because the movement of food grains from low price markets to high price markets may raise prices in the former but lower prices in the latter due to market saturation. The figure of 7.5% should not be treated as definitive, but rather as one benchmark which allow us to interpret our empirical findings more contextually, as the actual effect can depend on a number of factors. The effects can be smaller if fewer of conditions (3)–(7) hold.5 It is possible that a reduction in the price of fertilizer may not translate into a large reduction in MC, for example, if fertilizer accounts for a very small share of total costs. In addition, other factors like corruption and leakages associated with subsidy distributions may reduce the effective subsidies to significantly less than 75%. However, the effect can also be greater than 7.5% under certain circumstances. For example, if subsidized fertilizer could mitigate liquidity constraints and crowd in investments into production technology enhancement (for example, small inexpensive irrigation equipment or small farm machineries) that can further lower the MC curve. In some cases, fertilizer subsidies can also further lower MC curve by improving fertilizer supply network and reducing the price of unsubsidized fertilizer (Liverpool-Tasie, 2014a,b). In both cases, an additional decrease in the MC curve is realized, aside from the direct effects of lower fertilizer price. The discussion in this section should be interpreted with these potential factors in mind. Empirical specification We build on the conceptual framework used by Ricker-Gilbert et al. (2013) which models equilibrium maize prices in Malawi and Zambia as being determined by the interactions of supply and demand functions, as well as market margins. They specify the supply of maize as a function of the farm gate maize price and supply shifters, as well as quantities of subsidized fertilizer. They also model the demand for maize as a function of the market price and other demand shifters. Through the market clearing condition in the presence of market margins, they arrive at a reduced form empirical specification. We generalize their framework by including the prices of substitute crops, and incorporating not only the margin between farm gate and local market, but also between markets. The equilibrium price of crop k in district j is determined in the following way. Notation k is suppressed when expressions are general across all k.

Q Sj ¼ Q S ðP; F j ; W j ; Hj ; sj ðT j Þ; TÞ

ð1Þ

5 In particular, condition (3) may not hold in West Africa. For example, more fertilizer responsive hybrid maize varieties are scarce in West Africa unlike in Eastern and Southern Africa where European settlers introduced them (Smale et al., 2011). Similarly, due to insufficient domestic R&D, despite the diverse local production environment, virtually no domestically-bred varieties have been newly introduced for lowland rice in Nigeria since mid-80s (Takeshima, 2014), potentially suppressing fertilizer response for rice in the country.

15

Crop supply in j(QSj ) is a function of the vector P of retail prices of commodities including substitutes in all districts that are integrated with district j, fertilizer subsidy received in j(Fj); input prices vector (Wj) and other supply shifter Hj. The retail price of commodity k in particular district j, Pkj , is an element in P and is related to the farm gate price through the market margin sj that is affected by factors Tj. Factors T (such as the level of infrastructure) affect margins that relate prices in vector P across districts. Crop demand in j

Q Dj ¼ Q D ðP; PO ; dj ; TÞ

ð2Þ

is a function of P, a vector of exogenous retail prices of some consumer substitutes PO (such as imported rice), and demand shifter dj. We observe a vector of equilibrium retail prices P⁄ which solve the above set of equations. The retail price of crop k in particular district j (Pk⁄ j ) is empirically estimated by, O Pk j ¼ f ðF j ; P ; W j ; Hj ; dj ; T j ; TÞ:

ð3Þ

Empirical specification The primary units of our empirical analysis are Enumeration Areas (EA). Multiple EAs exist within each LGA in Nigeria. Consequently, our empirical model explains EA level crop prices by the amount of subsidized fertilizer received at the LGA level and other factors. We assign subscript j to EA, and J to LGA that contains EA j. We modify (3) into the first difference of prices as follows:

D ln Pjt ¼ ln Pjt  ln Pjt1 ¼ b þ bF F j þ bO D ln POit þ bW W j þ bH Hj þ bd dj þ bs T j þ bs Statedummy þ bs Dyjt þ v jt : ð4Þ Here t and t  1 are the post-harvesting and post-planting period, respectively. Pjt is the price of local rice, maize and sorghum in j at t, so that Dln Pjt is the growth rate. Fj is an indicator of the fertilizer subsidy received by farmers in J. If there are multiple j within J, Fj’s are the same f or these EAs. vjt is an idiosyncratic error. Other variables are as defined above. We focus on the first difference specification as in (4) instead of the level specification, as it eliminates the unobserved EA specific effects. This is important in countries like Nigeria where price levels can vary across markets due to heterogeneous production environments and high intra-market margins. Ignoring such unobserved EA specific effects can lead to biased estimates of the effects of fertilizer subsidies. While first-differencing eliminates time-invariant effects in (3), many time-invariant factors are still included in (4). This is because, as is described below, t and t  1 are only about six months apart in our data so that Dln Pjt is a relatively short-term price fluctuation. In developing countries like Nigeria with incomplete and relatively slow market integration, short-term price fluctuations in local markets may be substantial and be affected by local production environments as well as fertilizer subsidies. Specification (4) therefore exploits the variations in such short-term price changes and explains them with both time-varying and time-invariant factors. We also use growth rates instead of absolute differences in prices. This is more appropriate because the price levels of staple crops often reflect the nominal prices of other commodities as well as incomes. Since the growth rates standardize the price differences by the price levels, they have a more direct welfare implication. Fertilizer subsidies (Fj) are captured using two measures; (a) the average quantity of subsidized fertilizer received by the farmers in J; (b) the share of farmers receiving subsidized fertilizer in J. We use (a) as the primary measure and (b) for a robustness check. We distinguish between (a) and (b) because even when the total subsidy in the district is the same, if small subsidy amounts were allocated to many farmers, instead of fewer farmers receiving large

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H. Takeshima, L.S.O. Liverpool-Tasie / Food Policy 54 (2015) 11–24

quantities, the implications on the aggregate MC curve of the district may be different. This is particularly important when subsidized fertilizer is fixed in each district; as was often the case under the FMSP. The share of farmers receiving subsidized fertilizer in a J is also likely to be less susceptible to measurement errors. Unlike the case of Malawi and Zambia studied by Ricker-Gilbert et al. (2013), no LGA or state level figures of subsidized fertilizer are available from government offices in Nigeria. In addition, leakages are potentially rampant. We therefore calculate Fj from the data. Since the denominator for both (a) and (b) are farm households, Fj can be high if there are fewer farm households in J even though J as a whole received less subsidy.6 PO jt is proxied by the prices of imported rice. Consumption of imported rice has grown significantly over time in Nigeria and is an important substitute for the staple crops we study (Johnson et al., 2013). The price of imported rice which is determined at the international market and subject to import taxes is likely to be exogenous to Pj. As was done for the dependent variables, the price of rice is log-transformed and first differenced. If the degree of substitutability between local and imported rice is high, a larger growth rate in the price of imported rice should have positive effects on Dln Pjt. Wj includes median daily wage of land clearing/preparation for adult males and the share of farmers who use government tractors in J. Government tractor hiring service, when accessible to farmers, is provided at lower price than private hiring service. Wj also includes the Euclidean distance from j to the capital of its state (Dj), which can affect the access to other agricultural services such as seeds and extension services often provided by the ADP or the State Ministry of Agriculture; located mainly at the state capitals. Lower inputs prices can lower the production costs, making j more likely to be a food surplus area. Since Dln Pjt is measured in the short term before the inter-market trades fully occur, as was mentioned above, lower inputs prices may lower Dln Pjt. Hj includes various agro-ecological characteristics of j such as whether the alluvial soil is dominant, and the Euclidean distance to the nearest river (FAO, 2000) that can affect irrigation costs. Compared to other types of soils, alluvial soils are generally more suitable for intensive use of inputs such as chemical fertilizer in humid and semi-arid tropics (Subbian et al., 2000) such as Nigeria.7 Applying the same arguments as for the effect of lower inputs prices discussed above, favorable soils and proximity to rivers may lower Dln Pjt. Other agro-ecological characteristics including average rainfall, temperature and solar radiation which can all affect the production are highly correlated with the state dummy variables described below, and therefore excluded. dj includes the average values of non-productive assets and livestock assets in J at the post-planting season. These assets are good indicators of households’ wealth that affect demand for grains, including their consumption smoothing behaviors that can affect the change in food demand between the post-planting and post-harvest seasons. Assets are also generally more exogenous to food consumption relative to incomes. Furthermore, asset-wealthier households may have means to access grain markets in more diverse locations, and can switch from one market to the other depending on the prices, compared to asset-poor households. Thus, in districts with higher asset values, absolute values of Dln Pjt may be smaller. Since Dln Pjt in our sample is

6 We also tried calculating Fj including non-farm households in J. We find that results and implications remain robust. 7 This is partly because alluvial soils have various unique properties such as high cation exchange capacities (Bakri, 2001; Binswanger-Mkhize and Savastano, 2014). Using FAO/IIASA/ISRIC/ISSCAS/JRC (2009), we classify fluvisol, gleysols and vertisols as alluvial soils.

positive on average, higher asset values may lower Dln Pjt. The effects of wealth may also vary between maize and other crops mainly due to the differences in seed characteristics. In developing countries like Nigeria, grains are sometimes purchased as seeds for the next planting season. Due to the cross-pollinating nature of maize, more seed attributes are lost in maize grains compared to rice or sorghum grains. Wealthier households may be more likely to wait until the planting time to buy more expensive but higher quality maize seeds, while poorer farmers may choose to buy low quality but cheaper seeds due to their cash constraints. Extra demands for maize grains as seeds in the post-harvest season may raise maize prices. Here as well, wealth would be negatively correlated with the growth rate of maize prices. This differentiation may be less for rice or sorghum as their food grains and seeds are more similar. Transactions cost (Tj) is proxied by the distance from j to the nearest town with a population of 20,000 or more, which is measured as travel time in hours (HarvestChoice, 2012). Tj can affect Dln Pjt if certain shocks can damage road conditions in either post-planting or post-harvest and such shocks are likely to increase Tj. The changes in key time-varying factors Dyit are included as well to jointly proxy for the remaining changes in Hj, dj and Tj. They include a share of farmers in J who had experienced harvest losses due to flood or poor rain in the 2010 production season, and the existence of any new positive development interventions implemented in j between t  1 and t. Flood or poor rain during the production season can reduce the harvests and raise crop prices in the post-harvesting season, raising Dln Pjt. However, Dln Pjt may be actually lowered if flood or poor rain induce temporary out-migration and reduces local food demand. Since crops such as sorghum tolerate drought more and are often grown on upland plots that are less prone to flood damage, its supply may be less affected by these shocks. Consequently, if the effects of shocks dominate, Dln Pjt of sorghum may be lower than those of rice or maize. Positive development interventions include road or other types of transportation improvements, constructions or rehabilitations of health care facilities, schools, or public boreholes, all of which took place between January through March 2011. Since relatively few EAs experienced these projects between these periods, we aggregated all these types of projects to construct the variable. These positive improvements may increase both supply and demand for food in j, and expectations of the signs of their effects on Dln Pjt are not unilateral. Their omission however, is likely to lead to biased estimates. Lastly, a State dummy variable (Statedummy) controls for a number of factors. First, it controls for state-level variations in the levels of market integration (T). Statedummy also partly controls for variations in agro-ecological characteristics across states such as climate, which may not be fully captured in Hj. It also indirectly controls for the state level release of grain reserves, which have been observed in a handful of states (NAERLS, 2009) and can capture State specific variations in Dln Pjt. Endogeneity of subsidized fertilizer (Fj) Empirically Fj is determined by,

F j ¼ c þ cG Gj þ cA GjA þ cW Wj þ cH Hj þ ct T j þ cs Statedummy þ ujt :

ð5Þ

Fj can be endogenous when estimating DPjt in (4) if ujt and vjt are correlated. We therefore estimate (4) and (5) using three-stage least squares (3SLS) estimation method where Fj is endogenous. We exploit the exogenous variations in Fj based on anecdotal evidence that state governors may favor their district-of-origins and allocate more subsidized fertilizer there. We therefore instrument Fj with two variables Gj and GAj . Gj is the Euclidean distance from j

H. Takeshima, L.S.O. Liverpool-Tasie / Food Policy 54 (2015) 11–24

to the centroid of the LGA that is the district of origin of the state governor in 2010. GAj is a similar distance to the nearest district-of-origin of any state governor. GAj < Gj if j is closer to the district-of-origin of the governor of the neighboring state than it is to its own state. Note that Gj and GAj are different from Dj which measures the distance to state capital. Proximity to the state governor’s district-of-origin can affect access to subsidized fertilizer because fertilizer is often traded informally by connected individuals and state governors can influence the quantity of fertilizer allocated to particular LGAs (Liverpool-Tasie and Takeshima, 2013). We argue that while being from the district-of-origin of the state governor might increase the quantity of subsidized fertilizer allocated to J, there is no reason why being from the district-of-origin of the state governor should affect local grain prices except through its effect on production costs when one has controlled for other factors such as access to markets (Dj). Each state in Nigeria, as was mentioned above, has its own policy on its fertilizer subsidy rate and procurement quantity. Gj may matter more than GAj if subsidies are much more generous in j’s own state than in neighboring states. We use the centroid when measuring the distances from EAs due to the lack of information on the exact locations of governors’ origins within district-of-origins. In Section ‘Results’, we check the robustness of these approaches by also using an alternative measure based on the district border instead of the centroid. Gj and GAj are good IVs for Fj because once subsidized fertilizer is controlled for, Gj and GAj should have no effect on grain prices. This is because fertilizer subsidy often accounts for a substantial share of federal and state agricultural budgets in Nigeria (Mogues et al., 2012) and is likely to be a major policy instrument whose geographical allocation may be influenced by state governors. One may argue that Gj and GAj can overlap with Dj if many governors are originally from the state capitals. However, that is not the case in our sample. The districts-of-origins and capital districts are the same in only 18% of the states (or 7 out of 37 states). These shares are similarly low in the sample EAs used. Moreover, these shares are even lower when GAj is concerned because some EAs are closer to the district-of-origin of the neighboring state’s governor rather than that for their own state governor. In all equations estimated, Gj and GAj are different from Dj in more than 80% and 90% of sample EAs, respectively. Therefore, Gj and GAj add important external variations not captured by Dj. Though the fertilizer subsidy variable (Fj) is censored at 0, and also at 1 if it is the share of farmers receiving subsidized fertilizer, (4) and (5) are still consistently estimated through 3SLS (Kelejian, 1971). If Fj is the average quantity of subsidized fertilizer, the Tobit model can be used in place of the ordinary least squares (OLS) in the first stage. However, the Tobit model is less appropriate in our case because of the large number of state dummy variables that must be included in (5) to account for the strong state governments’ influence on fertilizer subsidy provision in Nigeria (Moser and Barrett, 2006).8 In addition, the measurement error associated with Fj may cause heteroskedasticity which makes Tobit model inconsistent. Other exogenous variables are the same as in (4), except time-variant factors DPO jt and Dyit. They are not included in (5) because Fj is determined before the realizations of these variables. The first stage (5) is not a structural equation, and Fj may be generally affected by government’s decisions on how to allocate subsidized fertilizer. It is therefore generally difficult to determine the expected signs of coefficients in (5) based on an economic theory. We focus on the effects of fertilizer subsidy in J only, without controlling for subsidies in neighboring LGAs. Our data indicate

8 The Tobit model often breaks down if many dummy variables are included and the sample size is small.

17

that farmers are unlikely to travel across LGAs to obtain fertilizer. 83% of farmers buying fertilizer obtained it within or near the village or in a town, usually within their own LGAs (Table 4). The quantity purchased also does not significantly vary across locations, contrary to the perceptions that farmers traveling long distance would buy a larger quantity to exploit economies of scale. Therefore, subsidized fertilizer received in J is not likely to substantially affect crop productions in other LGAs. Studies examining similar effects of fertilizer subsidy on grain prices (Ricker-Gilbert et al., 2013) also include other potential factors that could affect the marketing margins, such as commercial lending interest rates and petrol prices. We, however, have only two-rounds of survey data with a short interval of about 6 months. We therefore exclude these variables assuming that they are relatively constant across the two survey periods. The conceptual framework and specification in (4) imply that fertilizer subsidies in J would affect prices within j mainly through local crop production; in other words, these crops must be grown in most of our sample LGAs. This is appropriate for sorghum and maize which are grown widely across Nigeria, and also for rice. In Nigeria, thanks to sufficient rainfall, rice is widely grown on dry land and rained lowland as opposed to irrigation-dominated Sahelian countries (Ezedinma, 2005; Johnson et al., 2013). If output markets are well integrated, the effects of Fj on Pkj are attenuated as they can be diffused across districts. The information on how these effects vary depending on the level of market integration is another important issue for policy makers. However, the literature on this is still relatively thin. While food markets have become increasingly integrated in parts of West Africa (Araujo et al., 2012), large price differentials across markets are observed in Nigeria due to high transport costs (Atkin and Donaldson, 2012) or other factors such as ethnicity differences across markets (Aker et al., 2014). Generally, poor infrastructure, market failures in credit and insurance markets can raise transactions costs in SSA, often preventing cross regional trades (Barrett, 2008). However, formal investigation of these mechanisms is beyond the scope of this paper. Our empirical analysis rather informs whether fertilizer subsidies can be effective in lowering local grain prices, with the existing level of market integration in Nigeria. Data Our primary data, the LSMS dataset consist of 500 EAs that were randomly selected from about 600,000 EAs across the country. Our empirical analysis uses the data collected at the EA level, supplemented by those aggregated from household level data. The LSMS data capture the EA level prices of food crops including rice, maize and sorghum reported in both post-planting and post-harvesting periods. These price data allow the construction of EA level panel data. Grain prices Pjt are from the community surveys conducted as part of LSMS data collection in the same EAs as the household survey. Grain prices are therefore from the market where the EA residents principally transact. All commodities except maize are identically captured in both surveys. For maize, while maize as a crop is aggregated in the post-planting survey, it is separated into white and yellow maize in the post-harvesting survey. Thus, for maize, we use the average of white and yellow maize price in the post-harvesting survey to construct Pjt. LSMS also has household level data which are cross-sectional. From them, we extract LGA level variables that represent various factors in the aforementioned conceptual framework. EAs serve as sub-components within each LGA We use LGA level values of variables instead of EA level because certain policies including fertilizer subsidies are set at LGA level. It also allows us to minimize measurement errors of district level variables. As is described

18

H. Takeshima, L.S.O. Liverpool-Tasie / Food Policy 54 (2015) 11–24

Table 4 Purchase locations of inorganic fertilizer (among all farmers who acquired inorganic fertilizer in 2010).a Source: Authors’ calculations.

Proportion of farmers (%) by locations [95% CI] Average quantity bought (kg/household) [95% CI] a

Within the village

Near the village

Within the town

Near the town

Urban center

Others

26 [23, 29] 144 [119, 168]

33 [29, 36] 181 [155, 206]

17 [15, 20] 149 [114, 184]

7 [5, 9] 156 [112, 200]

15 [12, 17] 190 [154, 226]

6 [4, 8] 159 [93, 225]

Shares may not add up to 100 because some farmers obtained fertilizer from multiple locations. Figures are adjusted for sample weights.

Table 5 Explanatory variables and descriptive statistics. Source: Authors’ calculations.

a

Variables

Number of observations

Mean

Standard deviation

Growth rate (%) in local rice prices (in local currency) Growth rate (%) in sorghum prices (in local currency) Growth rate (%) in maize prices (in local currency) % of farmers receiving subsidized fertilizer Average quantity of subsidized fertilizer (kg) Distance to the nearest towns of 20 thousand inhabitants or more (hours) Whether tractor was used in j Whether the dominant soil is alluvial in j Distance to the nearest river (geographical minute) Average value of non-productive assets in J (USD) Average value of livestock assets in J (USD) Average nominal wage of land clearing/preparation for adult male in J (USD/day) Whether any farmer experienced harvest loss due to flood or poor rain in 2010 rainy season Distance to the state capital (Dj) (geographical minute) Distance from LGA j to districts of origin (Gj) (geographical minute) – within the state – global

137 106 127 187 187 187 187 187 187 187 187 187 187 187 187 187

10 26 24 6 8 2.3 .064 .064 .017 770 1410 5.6 .35 .72 .77 .64

83 54 76 13 23 2.1 .246 .246 .012 928 3789 4.4 .48 .50 .55 .41

Changes in other factors (DYj) Growth rate (%) in imported rice price in ja Whether new positive development happened in j

187 187

35 .053

76 .226

Exclude Gombe state where import rice prices are highly questionable.

below, we only include in the analysis LGAs with at least 10 farmers interviewed, so that LGA level variables are reliable. In the LSMS, 10 households (including non-farm households) are interviewed in each EA, so that the number of farm households in each EA is not more than 10. Only one EA was sampled in most LGAs. Therefore, raising the threshold to more than 10 would substantially reduce the sample size. We therefore choose 10 as a cut-off point. EAs not reporting prices of key commodities are excluded. EAs with questionable geographic coordinates were also excluded because their locations are important for our analyses. Overall, our primary sample for each crop consists of 187 EAs. Table 5 presents the descriptive statistics for the 187 EAs used in the analysis. Although grain prices were generally expected to drop after the harvesting season, nominal prices increased for rice, sorghum and maize in the sample by 10–26% on average between post-planting and post-harvesting surveys. An increase in prices is unlikely to be due to exchange rates as they remained stable during this period. Sharp increases may rather be partly due to worldwide price increases for major cereals, which had affected the domestic grain prices through the price of imported rice. Based on FAO, cereal price indices increased almost 40% from 182.1 in August 2010 to 254.6 in February 2011, which correspond to the timing of two surveys (FAO, 2013). In Table 5, the standard deviation of growth rates of grain prices are relatively large, indicating that growth rates varied considerably across locations within Nigeria. Overall, however, general grain price increases during the survey period make it more relevant to investigate how fertilizer subsidies might have mitigated domestic grain prices increase.

Results Table 6 represents the results from estimating (5) for each crop for both measures of fertilizer subsidies, while Table 7 shows the results of (5) run on the entire sample (including all crops) as a

robustness check. Regressions are adjusted for sample weights, which are the size of the population represented by each observation, EA. As the model holding for the sample data may be very different from the model holding for the population before sampling, failure to account for the sample selection process might bias the inference (Pfeffermann, 1993).9 We adjust the analyses for sample weights because price changes in EAs with greater population sizes have greater welfare implications. We ran (5) for each crop separately because some EAs do not report the prices of all three crops and we can only run (4) for EAs with price information. While an alternative may be to estimate (5) using the same samples regardless of crops, our approach allows the use of standard 3SLS where samples do not change in the first and the second stages. Also, subsidized fertilizer may be distributed differently depending on what staple crops are in the EA, which is indicated by whether their prices are reported. In any case, all models are consistent and not over-identified.10 The statistical significance of state dummies indicated in the tables is based on the state with the lowest p-value. Next, Table 8 shows the results of the structural Eq. (4).

First stage results While the first stage (5) is not a structural equation, its results provide useful insights on the effect of various factors on access to subsidized fertilizer. Tables 6 and 7 reveal that a higher share of farmers received subsidized fertilizer in LGAs closer to the district-of-origin of the state governor. Similarly, proximity to the 9 Specifically, we use aweights (analytic weights) option in reg3 command in STATA. While probability weights (pweights) instead of aweights can be used to obtain heteroskedasticity-robust covariance matrix (Roodman, 2009), they tend to be inefficient when sample size is small (MacKinnon and White, 1985) like ours. 10 Overidentification is tested using the STATA command ‘‘overid’’ (Baum et al., 2006). Since the properties of overidentification in weighted regressions are not well known, we tested them using non-weighted results.

19

H. Takeshima, L.S.O. Liverpool-Tasie / Food Policy 54 (2015) 11–24 Table 6 First stage.a Source: Authors’ calculations. Dependent variable

Average subsidy amount (kg) in LGA j

Share of direct subsidy recipients in LGA j

Crops

Rice

Sorghum

Maize

Rice

Sorghum

Maize

Distance to governor origin LGA – within state

State dummy Constant

8.737 (7.585) 21.512* (9.645) 8.505 (7.576) 2.175 (1.387) 427.907* (200.916) 13.488  (7.814) .523 (2.077) .932 (.662) 2.190 (10.372) 2.342* (.985) Yes** Yes

4.398 (9.108) 30.278* (12.305) 4.831 (8.629) .089 (1.655) 531.304  (242.040) 21.990  (11.505) 3.004 (3.568) 1.020 (1.117) 9.126 (12.146) 5.822** (1.076) Yes** Yes

7.565 (9.405) 29.531** (10.718) 6.946 (8.117) .071 (.961) 358.417  (213.925) 3.541 (10.475) 3.782 (3.097) .204 (.873) 5.020 (10.716) 5.534** (.913) Yes** Yes

.084  (.047) .077 (.052) .043 (.042) .010 (.008) 1.932  (1.090) .077  (.042) .004 (.011) .000 (.004) .010 (.057) .001 (.005) Yes** Yes

.068 (.048) .094 (.058) .028 (.047) .001 (.009) 3.164* (1.291) .135* (.062) .015 (.019) .001 (.006) .061 (.065) .011  (.006) Yes** Yes

.102* (.049) .063 (.055) .044 (.042) .000 (.005) 1.956  (1.110) .003 (.054) .020 (.016) .002 (.005) .038 (.056) .010* (.005) Yes* Yes

Number of observations p-value (H0: overall insignificance) p-value (H0: Distances to district-of-origins are jointly insignificant)

137 .000 .006

106 .000 .007

127 .000 .001

137 .000 .004

106 .000 .024

127 .000 .004

Distance to governor origin LGA – global Distance to state capital LGA Distance to nearest 20 k town (hours) Distance to nearest river (geographical minutes) Alluvial soil (1/0) Average household asset values (USD1000) Average livestock asset values (USD1000) Government tractor (Share of users in the LGA) Farm labor wage (USD/day)

**

1% is significant at 1% level. 5% is significant at 5% level. 10% is significant at 10% level. a Regressions are adjusted for sample weights. Numbers in parentheses are standard errors. *

 

district-of-origin of the state governor is positively associated with the average subsidy amount received in J. In all specifications, the two variables measuring distance to the governor’s origin LGA are jointly statistically significant at the 10% level.11 These results generally hold if we run the same regressions for all the LGAs, using OLS as well as Beta regression (Ferrari and Cribari-Neto, 2004) and Tobit regression that account for the proportional and censored nature of the dependent variable respectively (Table 7).12 Our results are in line with other studies that have demonstrated the political influence of the allocation of input subsidies in SSA (Sadanandan, 2012; Mason et al., 2013; Mason and Ricker-Gilbert, 2013; Pan and Christiaensen, 2012). We also find that the receipt of subsidized fertilizer varies significantly across states supporting the fact that state governments are influential in subsidy provision. In addition, more subsidized fertilizer appears to reach favorable production areas (as captured by their proximity to the nearest river), and areas with higher wages.

Effects of fertilizer subsidies on the growth rate of local food price (second stage) The higher the average subsidy amount in J, (for rice and sorghum) the lower the growth rates of their local prices between the post-planting and post-harvesting seasons (Table 8). These effects are, however, very small. At the national average, the 2010 fertilizer subsidy reduced the growth rates of rice and 11

If only one of these two variables is used, it is statistically significant. We, however, maintain two variables because an IV estimator with only one external IV (exact identification) does not possess a mean, where hypothesis testing can be challenging (Deaton, 1995). 12 Due to the aforementioned fact that Tobit model often breaks down with too many dummy variables, we used dummy variables for six geo-political zones instead of states for the Tobit regression.

sorghum price by approximately 4 and 3 percentage points, respectively. These effects are calculated as estimated coefficients (0.11) times the national average of the EA average subsidized fertilizer quantity (4 kg) and likewise for sorghum over the study period.13 These effects are smaller than the 7.5% that is feasible under ideal conditions as discussed in Section ‘Effects of fertilizer subsidies on local grain price – conceptual prediction’. In developing countries like Nigeria where irrigation is still rare (Takeshima and Edeh, 2013), production is highly seasonal and crop prices are likely to bounce back to their original levels by the next planting season. The estimated price reduction effects, if averaged over the entire year, may be even smaller. As was described earlier, rice, sorghum and maize capture a substantial share of subsidized fertilizer in Nigeria, and therefore effects on the growth rates of prices of other crops are expected to be negligible. The signs of other coefficients are intuitive and consistent with the discussions in Section ‘Empirical specification’. Given the substitutability between different types of rice, imported rice price growth significantly affects the growth rate of the local price of rice, but not other crops. Better access to government tractor hiring service is associated with slower price growths possibly because of reduced land preparation cost and increased rice supply. This is observed only for rice, due to the high correlation of tractor use and rice cultivation in Nigeria (Takeshima et al., 2013). Higher asset values are associated with lower price growth for maize, but not for rice or sorghum, as anticipated in the methodology 13 The ‘‘4 kg’’ is obtained by taking the sample weighted adjusted average, across all EAs, of the average subsidized fertilizer quantity. This included EAs with few or no farmers interviewed and thus excluded from the regression analysis. For the EAs with no farmers interviewed, we assume ‘‘zero’’ for the EA average subsidized fertilizer quantity. This is because in these EAs the local grain supply is likely to be small relative to the demand (as number of farm households is small relative to non-farm households), and likely to be in the Panel B of Fig. 1 where fertilizer subsidies, even if they were received, were unlikely to change the grain price in the market.

20

H. Takeshima, L.S.O. Liverpool-Tasie / Food Policy 54 (2015) 11–24

Table 7 First stage (all crop samples combined).a Source: Authors’ calculations. Dependent variable

Average subsidy amount (kg) in LGA j

Share of direct subsidy recipients in LGA j

Models

OLS

OLS

Distance to governor origin LGA – within state Distance to governor origin LGA – global Distance to state capital LGA Distance to nearest 20 k town Distance to nearest river Alluvial soil (1/0) Average household asset values (USD 1000) Average livestock asset values (USD 1000) Government tractor (Share of users in the LGA) Farm labor wage (USD/day) State dummy Geo-political zone dummy Constant Number of observations p-value (H0: overall insignificance)

8.610 (9.054) 22.011* (10.343) 5.697 (7.799) .360 (.972) 369.157* (181.356) 6.868 (7.837) 1.299 (2.088) 1.162* (.583) .246 (10.682) 4.210** (.790) Yes**

Tobit 30.593 (19.501) 44.525  (26.118) 31.301* (15.296) 5.813 (3.659) 804.398  (434.436) 16.064 (17.500) 6.033 (6.857) .811 (1.972) 35.856 (24.049) 4.047** (1.388)

Betab *

.099 (.046) .022 (.052) .040 (.039) .002 (.005) 1.894* (.918) .055 (.040) .006 (.011) .001 (.003) .012 (.054) .007  (.004) Yes**

.658 (.425) .123 (.485) .143 (.357) .045 (.047) 14.558  (7.847) .674* (.330) .070 (.082) .001 (.026) .045 (.447) .039 (.033) Yes**

Yes

Yes** Yes

Yes

Yes**

187 .000

187 .000

187 .000

187 .000

**

1% is significant at 1% level. 5% is significant at 5% level. 10% is significant at 10% level. a Regressions are adjusted for sample weights. Numbers in parentheses are standard errors. b For Beta regression, we transformed the 0 and 1 observations into non-zero and one values using the method suggested by Smithson and Verkuilen (2006). *

 

section. Sorghum price growth is contained in areas closer to the river, with alluvial soils, possibly because such environments allow greater sorghum productions exploiting residual moistures (Takeshima and Edeh, 2013). Sorghum price growth is faster in areas with higher farm wages potentially because, as was mentioned above, it is sometimes used to compensate for farm labor. Lastly, sorghum price growth was lower in areas affected by flood and drought, while this was not the case for rice or maize. These results are generally robust. When we use the share of subsidy recipients in J instead of average quantities of subsidized fertilizer, the set of statistically significant variables are almost identical. The only exception is the statistically insignificant effect of the share of the subsidy on sorghum price growth. However, it is still broadly consistent with the interpretations above that the effects of fertilizer subsidy on sorghum price growth rates are small. Some variables including Fj are calculated from observations within each LGA. If sampling errors are uncorrelated with the true values of these variables, then calculated values such as our variables are automatically correlated with the sampling errors, which can lead to the classical errors-in-variables (EIV) issue (Wooldridge, 2002). Many studies simply use these generated regressors if they are not the variables of main interests but rather included to reduce omitted variable biases. By using 3SLS where Fj is treated as endogenous, the potential bias for Fj is mitigated as well. In addition, as was mentioned above, limiting our analysis to LGAs with at least 10 farmers interviewed excludes the LGAs with extremely large errors, because sampling errors of calculated averages reduce as the sample size increases. Sample statistics based on as few as 10 observations, however, can still carry substantial errors. We therefore also tried the paired bootstrap method (Efron, 1979) to assess finite-sample performances. We

use a two-step sampling procedure where EAs are randomly selected in the first stage, and then random samples are selected from within these EAs, in order to best replicate the sampling method used in the LSMS survey. We find that bootstrapping generally doubles the standard error of the coefficient for Fj, making it statistically insignificant. Those results are not shown here since the exact bootstrapping methods are not well-known in this circumstance. However, they suggest that we need to carefully interpret the findings of significant 3SLS coefficients for Fj in rice and sorghum equations in Table 8. In any case, having them statistically insignificant does not affect our overall message; that fertilizer subsidies had generally weak effects on the growth rates of grain prices between the pre and post-planting period. Limiting our focus to LGA’s with at least 10 observations could also cause sample selection bias. For example, some unexpected disaster could have prevented households in certain LGAs (where more farmers are located) from being interviewed. When such disasters could also affect the likelihood or amount of subsidies received by these LGAs this introduces a bias. In order to test the robustness of our results to small sample bias, we also estimate the model using different cut-off points of eight and nine, instead of 10. In addition, growth rates of prices were somewhat variable across locations and in some cases questionable. We therefore also estimated the model excluding EAs reporting more than 100% increase in prices. We find that the results are qualitatively similar and the key messages are maintained, i.e., first, the closer to the governor’s district-of-origin, the better the access to subsidized fertilizer and second, the effects of subsidized fertilizer on the price growth rates are often insignificant and relatively small where significant (Table 9). We also checked the robustness of our results to using the distance to the district border rather than the distances to the

21

H. Takeshima, L.S.O. Liverpool-Tasie / Food Policy 54 (2015) 11–24 Table 8 Effects of fertilizer subsidy on local grain prices.a Source: Authors’ calculations. Crops Dependent variable = Growth rate of prices (1100%) Average subsidized fertilizer (kg)

Rice

Sorghum

Maize

.011* (.005)

.008  (.004)

.001 (.004)

Rice

Sorghum

Maize

1.654 (1.083) .023 (.117) .029 (.027) 8.735  4.889 .683* (.269) .038 (.063) .009 (.018) .308 (.231) .056* (.024) .021 (.187) .217** (.076) .072 (.193) Yes** Yes

.159 (.914) .088 (.105) .007 (.015) .402 (3.879) .151 (.183) .095  (.056) .007 (.015) .122 (.189) .013 (.020) .138 (.141) .098 (.081) .212 (.280) Yes** Yes

106 .000 .133

127 .000 .463

State dummy Constant

.029 (.103) .045 (.027) 5.679 (3.954) .012 (.168) .045 (.037) .005 (.012) .487** (.171) .009 (.022) .591** (.115) .110 (.076) .022 (.160) Yes* Yes

.046 (.099) .028 (.025) 8.400* (4.267) .613** (.214) .037 (.059) .000 (.018) .259 (.187) .086** (.032) .049 (.185) .224** (.087) .059 (.185) Yes** Yes

.091 (.099) .007 (.015) .326 (3.771) .144 (.177) .098  (.055) .006 (.014) .115 (.172) .010 (.028) .130 (.142) .126 (.087) .204 (.279) Yes** Yes

1.701  (.894) .013 (.097) .036 (.024) 3.917 (3.554) .003 (.159) .047 (.033) .016 (.011) .526** (.170) .019 (.016) .599** (.108) .099 (.066) .011 (.148) Yes* Yes

Number of observations p-value (H0: overall insignificance) p-value (H0: not over-identified)

137 .000 .872

106 .000 .376

127 .000 .362

137 .000 .453

Share of subsidy recipients Distance to state capital LGA Distance to nearest 20 k town Distance to nearest river Alluvial soil (1/0) Average household asset values (USD 1000) Average livestock asset values (USD 1000) Government tractor (Share of users in the LGA) Farm labor wage (USD/day) Growth rate of imported rice price Flood/poor rain (Share of affected farmers within LGA) Positive event in EA in 2011 (1/0)

**

1% is significant at 1% level. 5% is significant at 5% level. 10% is significant at 10% level. a Regressions are adjusted for sample weights. Numbers in parentheses are standard errors. *

 

Table 9 Sensitivity of key coefficients.a Source: Authors’ calculations. Alternative specifications

Effects of average subsidized fertilizer quantity

Effects of Share of subsidy recipients

Rice

Sorghum

Maize

Rice

Sorghum

Maize

Using LGA with at least 8 farmers

No. obs Coefficients Std.err

173 .008 (.005)

129 .006 (.004)

157 .003 (.004)

173 1.702 (1.140)

129 .792 (.713)

157 .995 (.804)

Using LGA with at least 9 farmers

No. obs Coefficients Std.err

166 .008  (.005)

126 .005 (.004)

149 .003 (.004)

166 1.656  (.964)

126 .755 (.679)

149 .661 (.739)

Using EA with 100 % price changes or less

No. obs Coefficients Std.err

102 .008* (.004)

94 .004 (.003)

101 .004 (.003)

102 1.382  (.745)

94 1.355  (.765)

101 1.263 (.798)

.002 .002 .014

.004 .004 .011

.000 .000 .001

.006 .003 .017

.003 .001 .013

.000 .000 .005

p-value (H0 Using LGA with at least 8 farmers Using LGA with at least 9 farmers Using EA with than 100 % price changes *  

5% is significant at 5% level. 10% is significant at 10% level. a Regressions are adjusted for sample weights. Numbers in parentheses are standard errors.

centroid. Table 10 compares the estimated coefficients of fertilizer subsidy variables in the second stage regression with these two different measures of proximity to the district-of-origin of the state governor. Table 10 also presents the joint statistical significance of the distance to the district-of-origin variables in the first stage regression. Estimated coefficients are generally similar or of smaller magnitudes while the distances to district-of-origin of the state

governor consistently affect the fertilizer subsidy variables. This also confirms the robustness of our results. Overall, the relatively weak effects of fertilizer subsidies on the growth rates of local grain prices are consistent with the presence of various factors discussed in earlier sections. Most markets may be more like Panels B or C than Panel A in Fig. 1, possibly due to low market orientation of producers, high market margins and

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H. Takeshima, L.S.O. Liverpool-Tasie / Food Policy 54 (2015) 11–24

Table 10 Robustness check based on alternative measures of distance to district-of-origins.a Source: Authors’ calculations Coefficients of fertilizer subsidy variables in the second stage regression Effects of average subsidized fertilizer quantity

Effects of Share of subsidy recipients

Rice

Sorghum

Maize

Rice

Sorghum

Maize

Based on district centroids

Coefficients Std.err

.011* (.005)

.008  (.004)

.001 (.004)

1.701  (.894)

1.654 (1.083)

.159 (.914)

Based on district borders

Coefficients Std.err

.010  (.005)

.005 (.004)

.003 (.004)

1.620  (.866)

.759 (.654)

.611 (.744)

.007

.001

.001

.000

.000

p-value (H0: Distances to governor origin LGA are jointly insignificant) Based on district borders .002 *  

5% is significant at 5% level. 10% is significant at 10% level. a Regressions are adjusted for sample weights. Numbers in parentheses are standard errors.

Table 11 Relative contributions of fertilizer subsidies to the growth rate of crop prices. Source: Authors’ calculations. Methods

Contributions to

Fertilizer subsidy variables Average subsidy amount in LGA j

Share of direct subsidy recipients in LGA j

Rice (%)

Sorghum (%)

Rice (%)

Standardized coefficients

Predicted value Actual value

21 16

16 6

17 12

Pseudo-average stepwisea

Predicted value Actual value

1.2 1.1

1.1 0.7

1.0 0.7

a Estimated using ‘‘tuples’’ command in STATA. Contributions are low because we use predicted values of fertilizer subsidies variables from the first stage, which are likely to be highly correlated with other variables.

post-harvesting losses that are common in rural SSA, as well as generally low marginal productivity of fertilizer. Effects may also be suppressed due to political influences evidenced in the first stage results in Table 6. Resource allocations based on political incentives are often inefficient (Buchanan, 1950; Samuelson, 1954; Musgrave, 1959; Oates, 1972, 1999). If subsidized fertilizer is not allocated in ways that maximize productivity, price effects in the output market can be limited. Further considerations: The relative contribution of fertilizer subsidies on grain price growth rates While the results from the 3SLS suggest that the effects of fertilizer subsidies are weak, it is useful to see whether they still account for a significant part of the growth rates of grain prices. One way to capture the amount of grain price growth rates accounted for by subsidized fertilizer is to decompose the variance of the predicted values into the contributions by each variable, including fertilizer subsidy variables. The literature provides various methods, such as standardized coefficients methods and step-wise methods; mostly applicable to the case of ordinary least squares (Grömping, 2007). In standardized coefficients methods, the coefficients are obtained after all variables are standardized with mean zero and standard deviation of one. It treats the squared coefficients as indicators of the relative importance of each variable. Step-wise methods run regressions with all combinations of a subset of explanatory variables together with the variable of interest, obtain increases in R2 due to the variable of interest, and average over all regressions. Complications arise when we apply these methods to 3SLS. The ‘‘Standardized coefficients’’ method is questionable if explanatory variables are not orthogonal to each other (Bring, 1996).This is likely to occur in 3SLS because instrumented F is correlated with

the linear combination of other explanatory variables, with additional variations coming only from excluded IVs. Stepwise methods which average r-squares overcome such collinearity problems, but they only work if stepwise regressions are all the same type of least squares. In our case, the problem arises when the endogenous variable F is dropped, as the specification changes from 3SLS to OLS (because there is no endogenous variable left). Consequently, R2 cannot be compared directly, and the logic behind stepwise methods breaks down. One way around this problem is to assume that the instrumented F correctly measures the true exogenous portions of fertilizer subsidies, so that the model can be treated as OLS using the instrumented F. The literature is still thin on the case of 3SLS regarding other possible methods. Because of these limitations, we try both methods and treat results only as indirect evidence of the robustness of our 3SLS results. Table 11 summarizes these results. Results are presented only for the case where the fertilizer subsidy variable was statistically significant in 3SLS. Fertilizer subsidies generally make only small contributions to the growth rate of prices of rice and sorghum. Based on the standardized coefficient methods, fertilizer subsidy contributed only 6–16% of the actual variations, and 14–21% of the predicted variations. The stepwise methods suggest that fertilizer subsidy contributions are even smaller; in the order of 1%, indicating that standardized coefficients methods may suffer from the collinearity problems mentioned above. The results are robust to the exclusions of all state dummy variables as well as all statistically insignificant variables. The estimated 8% and 6% contributions to the growth rates of rice and sorghum prices, mentioned above were based on the average subsidized fertilizer quantities used and fall within the range of relative contributions shown in Table 11. Our inferences are therefore robust from the perspectives of statistical decompositions of R2.

H. Takeshima, L.S.O. Liverpool-Tasie / Food Policy 54 (2015) 11–24

Summary and discussion Recently, many SSA countries have re-introduced fertilizer subsidy programs. Global food price increase since 2008 has been increasingly recognized as one of the drivers of fertilizer (and other inputs) subsidies in SSA (Jayne and Rashid, 2013). However, there is limited empirical evidence of such food price effects of input subsidies in countries like Nigeria. This paper investigated the effects of fertilizer subsidies under the FMSP in 2010 on the growth rates of grain prices in Nigeria using an instrumental variables approach with community and district level panel data. We find that the FMSP generally had weak effects on the growth rates of local prices of rice, maize and sorghum between post-planting and post-harvesting seasons. Such weak effects are consistent with the possibility that production and marketing structure of these crops in Nigeria are closer to Panels B and C of Fig. 1, rather than Panel A, due to the lower market orientation of producers and high market margins that are common in SSA countries. In addition, subsidy allocation was politically influenced, potentially further lowering the efficiencies of subsidies from the production perspective. Lastly, the findings of weak effects on the growth rates of grain prices should not be used as the sole criterion for evaluating the validity of fertilizer subsidies. If the primary goal of subsidies was to benefit the producers rather than consumers, assessing the farm income effects of subsidies will be more appropriate. Assessing the effects of fertilizer subsidies from diverse aspects are required to more holistically evaluate the fertilizer subsidy programs in Nigeria as well as other SSA countries. Acknowledgments We are grateful to the World Bank LSMS team for providing open access to the data which enabled this study. We also thank the participants at the Annual Meeting of The Agricultural & Applied Economic Association, Washington DC in 2013 for their constructive comments and suggestions. We appreciate the feedback from two anonymous reviewers and the editor. All remaining errors are our own. References Aker, J.C., Klein, M.W., O’Connell, S.A., Yang, M., 2014. Borders, ethnicity and trade. J. Dev. Econ. 107, 1–16. Araujo, C., Araujo-Bonjean, C., Brunelin, S., 2012. Alert at Maradi: preventing food crises by using price signals. World Dev. 40 (9), 1882–1894. Atkin, D., Donaldson, D., 2012. Who’s Getting Globalized? The Size and Nature of Intranational Trade Costs. Yale University (Manuscript). Bakri, D., 2001. Towards developing a geoscientific approach to sustainable agricultural and rural development. Environ. Geol. 40 (4–5), 543–556. Banful, A.A., 2011. Old problems in the new solutions? Politically motivated allocation of program benefits and the ‘‘New’’ fertilizer subsidies. World Dev. 39 (7), 1166–1176. Banful, A., Nkonya, E., Oboh, V., 2010. Constraints to fertilizer use in Nigeria: Insights from agricultural extension service. IFPRI Discussion Paper 01010. Barrett, C.B., 2008. Smallholder market participation: concepts and evidence from eastern and southern Africa. Food Policy 33, 299–317. Baum, C.F., Schaffer, M.E., Stillman, S., Wiggins, V., 2006. Overid: Stata module to calculate tests of overidentifying restrictions after ivreg, ivreg2, ivprobit, ivtobit, reg3. . Binswanger-Mkhize, H., Savastano, S., 2014. Agricultural intensification: the status in six African countries. World Bank Policy Research Working Paper 7116. World Bank, Washington, DC. Bring, J., 1996. A geometric approach to compare variables in a regression model. Am. Stat. 50 (1), 57–62. Buchanan, J., 1950. Federalism and fiscal equity. Am. Econ. Rev. 40 (4), 583–599. Chinsinga, B., 2012. The political economy of agricultural policy processes in Malawi: a case study of the fertilizer subsidy programme. Future Agricultures Working Paper No. 039. (accessed May 2013).

23

Deaton, A., 1995. Data and econometric tools for development analysis, . 3rd ed.. In: Berhman, J., Srinivasan, T.N. (Eds.), Handbook of Development Economics 3rd ed., vol. III North Holland, Amsterdam, pp. 1785–1882. Dorward, A., Chirwa, E., Kelly, V., Jayne, T.S., Slater, R., Boughton, D., 2008. Evaluation of the 2006/07 Agricultural Input Subsidy Programme, Malawi. Final Report. Lilongwe, Malawi. Efron, B., 1979. Bootstrap methods: another look at the jackknife. Ann. Stat. 7, 101– 118. Ezedinma, C.I., 2005. Impact of Trade on Domestic Rice Production and the Challenge of Self-sufficiency in Nigeria. Ibadan, Nigeria. FAO (Food and Agriculture Organization), 2000. Rivers of Africa. FAO, Rome. FAO/IIASA/ISRIC/ISSCAS/JRC, 2009. Harmonized World Soil Database (Version 1.1). FAO and IIASA, Rome, Italy and Laxenburg, Austria. FAO, 2013. FAO Food Price Index. Rome, Italy. Federal Ministry of Agricultural & Rural Development (FMARD), 2011. Agricultural Transformation Agenda: We Will Grow Nigeria’s Agricultural Sector. Draft, Abuja, Nigeria. Computer Disk, Washington DC. Federal Ministry of Agricultural & Rural Development (FMARD), 2012. Technology Enabled Fertilizer Market Stabilization Scheme. A Consultant Report Prepared by Cellulant Corporation and International Fertilizer Development Center. Draft, Abuja, Nigeria. Computer Disk, Washington DC. Ferrari, S.L.P., Cribari-Neto, F., 2004. Beta regression for modeling rates and proportions. J. Appl. Stat. 31 (7), 799–815. Grömping, U., 2007. Estimators of relative importance in linear regression based on variance decomposition. Am. Stat. 61 (2), 139–147. HarvestChoice, 2012.. Average travel time to nearest town over 20K (hours) (2000). (accessed October 10, 2012). Holden, S.T., Lunduka, R., 2013. Who benefit from Malawi’s targeted farm input subsidy program? Forum Dev. Stud. 40 (1), 1–25. Holmén, H., 2005. The state and agricultural intensification in sub-Saharan Africa. In: Djurfeldt, G., Holmen, H., Jirstrom, M. (Eds.), The African Food Crisis: Lessons from the Asian Green Revolution. CABI Publishing Series, London, UK. Jayne, T., Rashid, S., 2013. Input subsidy programs in sub-Saharan Africa: a synthesis of recent evidence. Agric. Econ. 44 (6), 547–562. Johnson, M., Takeshima, H., Gyimah-Brempong, K., 2013. Assessing the potential and policy alternatives for achieving rice competitiveness and growth in Nigeria. IFPRI Discussion Paper 01301. Kelejian, H.H., 1971. Two-stage least squares and econometric systems linear in parameters but nonlinear in the endogenous variables. J. Am. Stat. Assoc. 66 (334), 373–374. Liverpool-Tasie, L.S., 2014a. Do vouchers improve government fertilizer distribution? Evidence from Nigeria. Agric. Econ. 45 (4), 393–407. Liverpool-Tasie, L.S., Takeshima, H., 2013. Input promotion within a complex subsector: fertilizer in Nigeria. Agric. Econ. 44 (6), 581–594. Liverpool-Tasie, L.S., 2014b. Fertilizer subsidies and private market participation: the case of Kano State, Nigeria. Agric. Econ. 45 (6), 663–678. MacKinnon, J.G., White, H., 1985. Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties. J. Econ. 29 (3), 305– 325. Mason, N.M., Ricker-Gilbert, J., 2013. Disrupting demand for commercial seed: input subsidies in Malawi and Zambia. World Dev. 45, 75–91. Mason, N.M., Jayne, T.S., 2013. Fertilizer subsidies and smallholder commercial fertilizer purchases: crowding out, leakage, and policy implications for Zambia. J. Agr. Econ. 64 (3), 558–582. Mason, N.M., Jayne, T.S., Van de Walle, N., 2013. Fertilizer Subsidies and Voting Behavior: Political Economy Dimensions of Input Subsidy Programs. Mimeo. Mogues, T., Morris, M., Freinkman, L., Adubi, A., Ehui, S., 2012. Agricultural public spending in Nigeria. In: Mogues, T., Benin, S. (Eds.), Public Expenditures for Agricultural and Rural Development in Africa. Routledge, Taylor and Francis Group, London, New York. Morris, M., Kelly, V., Kopicki, R., Byerlee, D., 2007. Fertilizer use in African agriculture: Lessons Learned and Good Practice Guidelines. World Bank, Washington, DC. Moser, C.M., Barrett, C.B., 2006. The complex dynamics of smallholder technology adoption: the case of SRI in Madagascar. Agric. Econ. 35 (3), 373–388. Mpesi, A.M., Muriaas, R.L., 2012. Food security as a political issue: the 2009 elections in Malawi. J. Contemp. Afr. Stud. 30, 377–383. Musgrave, R., 1959. The Theory of Public Finance; A Study in Public Economy. McGraw-Hill, New York. NAERLS (National Agricultural Extension and Research Liaison Services), 2009. Crops Data from Annual Agricultural Survey Report of the 2008 Wet Season. Federal Ministry of Agriculture and Rural Development, Abuja, Nigeria. Oates, W., 1972. Fiscal Federalism. Harcourt Brace Janovich, New York. Oates, W., 1999. An essay on fiscal federalism. J. Econ. Lit. 37 (3), 1120–1149. Olayide, S.O., Idachaba, F.S., 1987. Input and output marketing systems: a Nigerian case. In: Mellor, J., Delgado, C.L., Blackie, M.J. (Eds.), Accelerating Food Production in Sub Saharan Africa. John Hopkins Press, Baltimore. Omotayo, A., Chikwendu, O.D., Adebayo, K., 2001. Two decades of World Bank assisted extension services in Nigeria: lessons and challenges for the future. J. Agric. Educ. Ext. 7 (3), 143–152. Pan, L., Christiaensen, L., 2012. Who is vouching for the input voucher? Decentralized targeting and elite capture in Tanzania. World Dev. 40, 1619– 1633. Pfeffermann, D., 1993. The role of sampling weights when modeling survey data. Int. Stat. Rev. 61 (2), 317–337.

24

H. Takeshima, L.S.O. Liverpool-Tasie / Food Policy 54 (2015) 11–24

Ricker-Gilbert, J., Jayne, T.S., Chirwa, E., 2011. Subsidies and crowding out: a doublehurdle model of fertilizer demand in Malawi. Am. J. Agr. Econ. 93 (1), 26–42. Ricker-Gilbert, J., Mason, N., Jayne, T., Darko, F., Tembo, S., 2013. What are the effects of input subsidy programs on equilibrium maize prices? Evidence from Malawi and Zambia. In: Paper Presented at the Agricultural & Applied Economics Association (AAEA) Annual Meeting. Washington, DC, August 4–6, 2013. Roodman, D., 2009. How to do xtabond2: an introduction to difference and system GMM in Stata. Stata J. 9 (1), 86–138. Sadanandan, 2012. Patronage and decentralisation: the politics of poverty in India. Comp. Polit. 44 (2), 211–228. Samuelson, P., 1954. The pure theory of public expenditures. Rev. Econ. Stat. 36 (4), 387–389. Smale, M., Byerlee, D., Jayne, T., 2011. Maize revolutions in sub-Saharan Africa. Policy Research Working Paper 5659. World Bank, Washington, DC. Smithson, M., Verkuilen, J., 2006. A better lemon squeezer? Maximum likelihood regression with beta-distributed dependent variables. Psychol. Methods 11 (1), 54–71. Subbian, P., Lal, R., Subramanian, S., 2000. Cropping systems effects on soil quality in semi-arid tropics. J. Sust. Agric. 16 (3), 7–38.

Takeshima, H., Edeh, H., 2013. Typology of Farm Household and Irrigation Systems: Some Evidence from Nigeria. IFPRI Discussion Paper 01267. Takeshima, H., Nin Pratt, A., Diao, X., 2013. Agricultural Mechanization Patterns in Nigeria: Insights from Farm Household Typology and Agricultural Household Model Simulation. IFPRI Discussion Paper 01291. Takeshima, H., Nkonya, E., 2014. Government fertilizer subsidy and commercial sector fertilizer demand: evidence from the Federal Market Stabilization Program (FMSP) in Nigeria. Food Policy 47, 1–12. Takeshima, H., 2014. Importance of Rice Research and Development in Rice Seed Policies: Insights from Nigeria. IFPRI Discussion Paper 01343. Vitale, J.D., Sanders, J.H., 2005. New markets and technological change for the traditional cereals in semiarid sub-Saharan Africa: the Malian case. Agric. Econ. 32 (2), 111–129. Wooldridge, J., 2002. Econometric analysis of cross section and panel data. MIT Press, Cambridge, Massachusetts, London, England. Xu, Z., Burke, W.J., Jayne, T.S., Govereh, J., 2009. Do input subsidy programs ‘‘Crowd In’’ or ‘‘Crowd Out’’ commercial market development? Modeling fertilizer use decisions in a two-channel marketing system. Agric. Econ. 40 (1), 79–94.