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Agricultural Input SubsidiesThe Recent Malawi Experience$

Ephraim Chirwa and Andrew Dorward

Print publication date: 2013

Print ISBN-13: 9780199683529

Published to Oxford Scholarship Online: January 2014

DOI: 10.1093/acprof:oso/9780199683529.001.0001

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Targeting and access to input subsidies

Targeting and access to input subsidies

Chapter:
(p.221) 10 Targeting and access to input subsidies
Source:
Agricultural Input Subsidies
Author(s):

Ephraim Chirwa

Andrew Dorward

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199683529.003.0010

Abstract and Keywords

Targeting of input subsidies is a critical element of the Malawi agricultural subsidy programme, and a highly debated issue. This chapter analyses the experiences of targeting and presents options for improving targeting. The analysis shows that targeting at area level has considerably improved with more equitable allocations consistent with the geographic distribution of poverty. Criteria for household targeting are very broad and the analysis shows that vulnerable households such as the poor- and elderly-headed are less likely than other households to receive fertilizer coupons and they receive less of the subsidized fertilizers. However the use of open meetings in the allocation of coupons tends to favour the poor who also receive more fertilizer compared with non-open allocations of coupons. Although there are gender biases in allocation of coupons, there is no gender bias in the use of subsidized fertilizers on plots controlled by different members of the households.

Keywords:   input subsidies, targeting, gender, Malawi

10.1. Introduction

Targeting, the process of directing subsidized inputs to particular areas and households within those areas, plays a critical role in the Farm Input Subsidy Programme and is a hotly debated issue in its implementation. It is widely recognized that efficiency in targeting is one of the critical factors determining the effectiveness and impact of the subsidy programme. Targeting also has implications for private sector input market development, actual graduation, and sustainability of the programme. A well-targeted farm input subsidy programme should lead to incremental use of inputs, by minimizing displacement of commercial sales and ensuring that only those that need production support access the inputs. The choice of the types of targeting systems which are intended to deliver particular targeting outcomes depends on the targeting objectives and the objectives of the programme. Targeting objectives are determined by technical and political programme objectives and by an understanding of how subsidized inputs are used in different contexts and of how this affects input productivity and its economic and social impacts. Targeting, therefore, is not only important, it is also controversial, highly political (at national and local levels), and difficult to implement in a large-scale programme due to challenges and costs in its implementation and supervision.

This chapter sets out a conceptual framework for examining alternative targeting objectives and methods and their applicability in different situations. It then uses this conceptual framework and household survey analysis to examine the practice and outcomes of targeting in different programme years. The next section of the chapter reviews the framework for targeting farm input subsidies at national, district, and beneficiary levels. Section 10.3 examines factors that determine access to subsidized farm inputs, focusing on household characteristics and programme processes. Section 10.4 analyses how the subsidy programme affects or is affected by gender relations by (p.222) investigating intra-household allocation of farm inputs to different plots. Section 10.5 reviews the difficulties experienced by the most vulnerable groups in accessing subsidized farm inputs. Section 10.6 offers proposals for alternative targeting options and their implications. Finally, Section 10.7 provides concluding remarks.

10.2. Targeting at national, district, and beneficiary levels1

10.2.1. Targeting objectives and impacts

Targeting objectives depend on the objective of the programme and Table 10.1 illustrates how these may be related. It neither attempts to provide a comprehensive description of the range of possible programme objectives nor to explore in any depth their implications for targeting and targeting objectives. It does, however, introduce key issues that need to be considered about the impacts of targeting and the critical outcomes that targeting systems attempt to influence. In the case of the farm input subsidy, programme objectives might include increasing national and household production and food security, national food self-sufficiency, beneficiary asset building and graduation, environmental protection improving welfare of vulnerable groups, and wider, inclusive social and economic growth. There is therefore a link between programme objectives and targeting objectives.2 For instance, if the programme objective is to increase production then the targeting objective may be to maximize input use (minimizing displacement) and productivity of incremental input use. This would entail identifying geographical areas and household types with low displacement and high input use efficiency, such as poorer, able-bodied, good farmers in productive maize growing areas. Similarly, if the programme objective is to improve beneficiary household food self-sufficiency, then the targeting objective would be to target food deficit or insecure households in productive maize growing areas who are able to redeem coupons and use them effectively.

Different objectives may be related in two ways. First, some of them may be complementary (as, for example, between maximizing production, as discussed above, and promoting national food self-sufficiency). A different set of relationships between targeting systems, outcomes, and impacts is presented in Figure 10.1. This distinguishes between the targeting system (intentions, implementation, and costs), targeting outcomes (the number of beneficiaries, the inputs received per beneficiary, the characteristics of beneficiaries, and (p.223)

Table 10.1. Programme objectives and their implications for targeting

Programme objective

Targeting objectives

Implications

A1

Increased production

Maximize incremental input use (minimize displacement) & productivity of incremental input use.

Identify geographical areas & household types with low displacement (i.e. unable to buy unsubsidized inputs) & high input use efficiency—poorer, able-bodied, ‘good’ farmers in productive maize growing areas?

A2

National food self-sufficiency

As above

As above

B1

Beneficiary household food self-sufficiency

Target food deficit/insecure households in productive maize growing areas & able to redeem the coupons & use the inputs effectively—complementary safety nets to aid financing of redemption by poor targeted households

Identify such households

B2

Beneficiary household food security

As in B1 above

As in B1 above

B3

Social protection for beneficiaries

Target most vulnerable households in productive maize growing areas & able to redeem the coupons & use the inputs effectively

Identify such households. Complementary safety nets to aid financing of redemption

C1

Wider household food security

As in (A1) above

Complementary policies to promote access to maize markets with low & stable prices in rural and urban areas, higher ganyu wages, complementary social protection (e.g. cash transfers)

C2

Social protection for all households

As in (C1) above

As in (C1) above

C3

Poverty reducing broad-based growth

Some combination of (B2), (B3), and (C1) above,

Combination depends on the relative effectiveness/efficiency of direct impacts for targeted beneficiaries and indirect impacts benefiting the poor more generally

D

Programme graduation—area

As in (C3)

Together with development of (private sector) input supply systems and produce markets

E

Programme graduation—households

As in (B1)

May need mechanisms to help beneficiary household saving/other forms of affording input access to enable graduation (ability to afford unsubsidized fertilizer) after specified time as programme beneficiary

F

Environmental protection

As in (C3)

Together with focus on areas with fragile and sloping soils, particular land pressure and pressure on forested hills. Complementary promotion of integrated soil fertility management

(p.224)
Targeting and access to input subsidies

Figure 10.1. Targeting variables and impacts

Source: Dorward and Chirwa (2012c).

(p.225) the characteristics of areas in which the beneficiaries reside and farm), and targeting impacts. These interact with other policies and stakeholder interests. The targeting system influences targeting outcomes through broad targeting design and implementation (determining the quantities of subsidized inputs in different areas, and hence the characteristics of areas receiving inputs and of potentially eligible beneficiaries) and through more detailed processes of coupon allocation, issue, and redemption (determining the quantities of subsidized inputs received by different individuals and households, and hence the characteristics of beneficiaries and the number of beneficiaries receiving different input combinations). These of course interact, and intentions are commonly modified or subverted to some extent during implementation. This needs to be explicitly allowed for in targeting system design.

The major targeting impacts are affected by four issues which determine the effectiveness of the Farm Input Subsidy Programme: displacement, input productivity, economy-wide effects, and graduation. First, displacement implies that a household’s access to subsidized inputs reduces their purchase of unsubsidized inputs such that the incremental input use from the subsidy is less than the amount of subsidized inputs. Displacement rates are affected by beneficiary characteristics, with higher displacement rates among non-poor beneficiary households and lower rates among poor beneficiary households (Ricker-Gilbert et al., 2010). It is also likely that displacement will be lower in areas where market access is poorer and inputs more expensive.3 This suggests that to reduce displacement, targeting should be aimed at areas with poorer market access and a greater proportion of poorer households, and, within those areas, at poorer households. Second, input productivity is affected by beneficiaries’ farming skills and knowledge, crop management, application of complementary inputs, timely planting and weeding, and overall rates of input application, rainfall, and soils. This implies that targeting should focus on areas with higher productivity potential in order to maximize production and on possibly less-poor households able and keen to make the most productive use of the inputs. Third, as argued in earlier chapters, economy-wide effects of the subsidy result from falling maize prices and higher wages benefiting the poor, and helping to achieve pro-poor growth objectives. Linkage or multiplier effects are also likely to be higher where poorer households are the main income beneficiaries (as argued in Chapters 7 and 9). The implications for targeting are that inputs should be focused on households yielding the greatest incremental production benefits (allowing for possible trade-offs between higher input productivity and displacement if (p.226) less poor households use inputs more productively), with concerns for wage and linkage impacts strengthening arguments for more targeting of poorer households and poorer areas Finally, graduation (described in Chapter 11 as the process by which programme benefits to poor, vulnerable households and areas enable them to improve assets and livelihood opportunities sufficiently to allow withdrawal of subsidies without reversion to their former vulnerable state) is affected by both beneficiary and area characteristics. As discussed in Chapter 11, this is critical for promoting programme impacts and controlling costs and involves the crossing of thresholds by beneficiary households and/or areas. Targeting for graduation should then try to concentrate resources on households and/or areas for whom graduation, the crossing of thresholds, is easiest. Determination of these is, however, very difficult.

It is clear that even if programme objectives have a relatively simple focus on national food self-sufficiency, targeting has to address difficult trade-offs between higher displacement and possibly higher incremental input productivity among less poor beneficiaries. There are greater and more complex trade-offs if wider pro-poor growth and graduation objectives are also important, requiring more attention to welfare gains, growth linkages, and complex graduation processes among poorer beneficiaries. Determination of ideal targeting outcomes is also made more difficult if objectives are unclear, contested, highly variable, and changeable; if there is limited information about differences in displacement, input productivity, labour market, and graduation effects of different subsidy allocations to different households and areas; and if the effectiveness of subsidies in meeting different objectives for and through different households and areas is also affected by a range of other policies and by macro-economic and other changeable and uncertain conditions.

10.2.2. Targeting criteria and processes

If targeting desirable outcomes are determined by programme and hence targeting objectives then, as set out in Figure 10.1, targeting criteria and processes should be designed and implemented to deliver these outcomes. It is helpful to consider targeting within the FISP in terms of six main stages: (1) setting of targeting criteria; (2) identification of areas and beneficiaries; (3) allocation of coupons; (4) distribution of coupons; (5) redistribution of coupons; and (6) redemption of coupons. Processes and criteria within each of these activities are formally defined by the government through the MoAFS, and Table 10.2 presents the major changes in targeting processes and criteria in the FISP from 2005/6 to 2009/10 (systems and criteria have been largely unchanged from 2009/10 to 2011/12). (p.227)

Table 10.2. Major changes in targeting processes, 2005/6–9/10

2005/6

2006/7

2007/8

2008/9

2009/10

Area targeting criteria

District allocation nominally by EPA maize & tobacco areas, but highly variable between districts. Ad hoc district allocation of supplementary coupons.

District & EPA allocation by maize & tobacco areas, but highly variable between districts. Ad hoc district allocation of supplementary coupons.

Initial district & EPA allocation by farm household & maize & tobacco areas, highly variable between districts. Ad hoc allocation of supplementary coupons. Overall criteria opaque.

Initial district & EPA allocation by farm household& maize & tobacco areas, but highly variable between districts. Ad hoc district allocation of supplementary coupons. Overall criteria opaque.

District & EPA allocation criteria not clear, variable between districts. Overall criteria opaque but more in line with farm households/district.

Beneficiary targeting criteria

Beneficiary selection criteria unclear.

Full time smallholder farmers unable to afford purchase of 1 or 2 unsubsidized fertilizer bags.

n/a

Resource poor local resident with land; guardians looking after physically challenged. Vulnerable households (child- or female-headed, PLWHA)

Resource poor local resident with land; guardians looking after physically challenged. Vulnerable households (elderly-, child-, or female-headed, PLWHA)

District/TA/Village coupon allocations

District allocation by MoAFS HQ, Village allocation by TAs,

District allocation by MoAFS HQ. Village allocation by DDC, ADCs, TAs.

District allocation by MoAFS HQ. Village allocation by DDC, ADCs, TAs.

District allocation by MoAFS HQ. EPA/village allocation by MoAFS staff, DDC, ADCs, TAs.

District allocation by MoAFS HQ. EPA/village allocation by MoAFS district staff, DDC, ADCs, TAs.

Beneficiary identification/coupon allocation

Largely by TAs, VDCs

Systems highly variable between areas—by ‘local leaders’ TAs, VDCs, MoAFS staff. Reallocation by VH common.

Systems highly variable between areas—by ‘local leaders’ TAs, VDCs, MoAFS staff. Reallocation by VH common.

Use of farm household register, open meetings for allocation led by MoAFS (participation unclear). Reallocation by VH common.

Farm household register, allocation in MoAFS led open meetings (unclear participation). Voter reg. nos & ID required. Reallocation by VH common.

Coupon distribution system

See above: allocation and distribution simultaneous

See above: allocation and distribution simultaneous

Distribution varied, more by MoAFS and VDCs. Open disbursement led by MoAFS. Redistribution by VH common

Open meetings for disbursement led by MoAFS (degree of participation unclear). Redistribution by VH common

Open meetings led by MoAFS (unclear participation). Voter reg. numbers & ID required for receipt & redemption. Redistribution by VH common

Coupon redemption systems

Only through SFFRFM & ADMARC

Fertilizers also through major retailers; flexible maize seed coupons through wide range of seed retailers

Fertilizers also through major retailers; flexible seed coupons through range of seed retailers; cotton inputs through ADDs

Fertilizers also through major retailers; flexible seed coupons through range of seed retailers; cotton inputs through ADDs

Fertilizers only through ADMARC & SFFRFM; separate maize & legume seed coupons through retailers, variable top up for maize seed max 100MK

(p.228) (p.229)

Coupon targeting and distribution processes were described in Chapter 5, Sections 5.4.1 and 5.4.3, and are not described here although they are summarized in relevant rows of Table 10.2. Here we discuss in more detail the criteria used in beneficiary selection.

Selection of beneficiaries is supposed to be guided by targeting criteria. In 2008/9, for example, beneficiaries of FISP were supposed to meet any of the following criteria (Ministry of Agriculture and Food Security, 2008)

  • resource poor Malawian that owns a piece of land;

  • guardians looking after physically challenged persons;

  • bona fide resident of the village;

  • vulnerable, such as child-headed, female-headed, or orphan-headed and those infected or affected with HIV and AIDS.

However, there have been a number of changes in beneficiary and area targeting criteria over the life of the programme. For instance, beneficiary targeting criteria have shifted from an initial focus on ‘full time smallholder farmers unable to afford purchase of 1 or 2 unsubsidized fertilizer bags’ to put more emphasis on poor and vulnerable groups. There are, however, difficulties in applying these criteria due to ambiguities and tensions among different targeting criteria, difficulties in establishing measures for these criteria, large numbers of deserving households, and lack of understanding and other interests among those conducting beneficiary targeting. As a result even with the best will in the world there is considerable ambiguity and inconsistency in the application of these criteria, and this creates space for abuses by those able to control the selection processes. Political considerations further complicate matters. As noted earlier in Chapter 5, from 2005/6 to 2008/9 the ‘supplementary distribution’ of coupons provided major opportunities for politically motivated targeting of coupons to particular districts, to particular areas within them, and to particular individuals (Chinsinga, 2012b).

Overall, despite significant changes to improve beneficiary targeting criteria and processes, there are continuing fundamental difficulties with the lack of clarity in targeting criteria, the large numbers of households satisfying the criteria, and inconsistent application of criteria by local leaders and government staff. These difficulties continue to limit the achievement of desired beneficiary targeting outcomes.

10.2.3. Targeting outcomes

Targeting outcomes can be considered in terms of area and beneficiary targeting. Changes in area allocation criteria have led to changes in coupon distribution between regions, with increases in coupons redeemed in the (p.230)

Targeting and access to input subsidies

Figure 10.2. Maize fertilizer voucher redemption per household per region, 2005/6–10/11

Source: Dorward and Chirwa (2012c).

southern region reducing regional differences in redemptions per household. Figure 10.2 shows the changing pattern of maize fertilizer redemptions per household by region from 2005/6 to 2010/11.

It also appears that districts with higher potential (roughly categorized by altitude) were generally allocated proportionally more coupons than low potential areas in 2006/7, but differentiation fell between 2006/7 and 2010/11. This normally involved reduced allocations across the board in districts with lower allocations, not the complete exclusion of significant areas. There is no evidence of greater proportionate allocation to districts with more poor households, although this increased substantially from 2006/7 to 2010/11 due to the shift in relative coupon allocations to districts with larger numbers of poor people in the south.

This should have led to increased subsidy access by poor people and in turn reduced displacement, increased incremental production, and increased maize and labour market effects, benefiting poor non-beneficiaries as well as poor beneficiaries (School of Oriental and African Studies et al., 2008). These should, other things being equal, improve programme effectiveness and efficiency in promoting national and household food production, self-sufficiency, food security, social protection, and poverty reduction for both beneficiaries and non-beneficiaries.

These effects may, however, be undermined if incremental production per unit input is lower for new beneficiaries in the south as compared with (p.231) previous beneficiaries in the centre and north and if targeting of the poor is less effective in the south. Others, such as Mason and Ricker-Gilbert (2012), find that district allocations of the subsidized inputs appear to be politically driven with households in districts that the ruling party won in the general election receiving on average 1.7 kg more subsidized seeds and 11.4 kg more subsidized fertilizers than districts lost by the ruling party.

The targeting outcomes at beneficiary level reveal that a large proportion of households receive less than 2 fertilizer coupons, partly due to the redistribution process that takes place at village level. Survey data show that the proportion of households who lose or gain coupons as a result of redistribution (those household with only one coupon) has increased steadily from 2006/7 to 2010/11 (from 27% to 36% to 41% across 2006/7, 2008/9, and 2010/11) and that this is most common and has increased most in the south (Table 10.3). However, apart from a lower occurrence of redistribution in the north, the changes appear to be largely the result of increases in the numbers of coupons and proportions of households receiving coupons in the south—if we examine the households receiving one coupon as a percentage of households receiving any coupons (i.e. excluding households not receiving any coupons), then this remains relatively constant across the three survey seasons (around 30% in the north, between 52% and 63% in the centre, and around 57% in the south).

Rural people’s perceptions of targeting outcomes also do not suggest strong targeting to benefit poorer or more vulnerable households, nor any increases in such targeting. Table 10.4 illustrates the characteristics of rural households by the number of coupons for subsidized fertilizer in 2008/9,

Table 10.3. Fertilizer coupon receipts per household, 2006/7–10/11 (%)

Coupons/hh

Zero

1 coupon/hh

2 coupons/hh

〉2 coupons/hh

Survey year

06/7

08/9

10/11

06/7

08/9

10/11

06/7

08/9

10/11

06/7

08/9

10/11

% all households by number of coupons/hh

North

38

28

24

18

14

23

37

50

47

7

8

5

Centre

45

35

31

28

39

38

21

20

24

5

3

1

South

49

33

11

28

37

47

19

24

35

4

3

2

National

46

33

21

27

36

41

22

25

31

5

3

2

% recipient households by number of coupons/hh

North

n/a

n/a

n/a

29

19

31

60

69

63

11

11

7

Centre

n/a

n/a

n/a

52

63

60

39

32

38

9

5

2

South

n/a

n/a

n/a

55

58

56

37

38

42

8

5

2

National

n/a

n/a

n/a

50

56

55

41

39

42

9

5

3

(p.232)

Table 10.4. Mean attributes of households by number of fertilizer subsidy coupons received, 2008/9

Household characteristics

Fertilizer coupon numbers per household

Sig.

Zero

0.5 to 1

1.5 to 2

〉 2

All

% female-headed households

26

31

24

17

27

*

Owned area in hectares

1.16

1.09

1.48

2.17

1.27

**

Value durable assets (MK)

19,621

15,630

20,340

28,111

18,702

Value livestock assets (MK)

18,689

22,947

41,807

58,946

28,699

*

Subjective score of HH food consumption over past 12 months (1 = inadequate,...., 3 = more than adequate)

1.5

1.5

1.6

1.7

1.5

*

Subjective score on welfare (1 = very unsatisfied,...., 5 = very satisfied)

2.3

2.2

2.5

2.8

2.3

**

Month after harvest that maize ran out

7.2

7.1

7.9

8.6

7.4

*

Notes:

(*) = one or more differences significant at p = 0.05,

(**) = one or more differences significant at p = 0.01.

and the pattern is similar to other survey years in 2006/7 and 2010/11 as targeting continues to tend to favour the non-poor. Holden and Lunduka (2012a) find similar evidence, suggesting that the non-poor are more likely to get subsidized fertilizers than the poor. The characteristics of households receiving one coupon show a persistent pattern of poverty across the survey years. Land and other asset holdings and subjective welfare indicators suggest that across different survey years these households are consistently nearly as poor or sometimes poorer than households not receiving any coupons. The relative bias against the poor suggests that when redistribution occurs it is poorer households who share one of their coupons (less-poor households with two coupons tend to hold onto both), and poorer households who receive the redistributed coupons. This involves both exclusion errors (with exclusion of poor and vulnerable households who ought to be included according to the targeting criteria) and inclusion errors (with inclusion of less-poor households who ought to be excluded according to the targeting criteria). Holden and Lunduka (2012a) find that targeting efficiency in 2008/09 was poor and no better than under the targeted input programme in 2000/1 and 2001/2. This poor targeting is attributed to leakages of coupons and fertilizers before they reach the households (as discussed in Chapter 5) and poor targeting criteria. However, the lack of clarity in targeting criteria and the large numbers of relatively less-poor people (who can nevertheless be considered to meet the targeting criteria) make it difficult to identify exclusion and inclusion errors with any precision or confidence. (p.233)

10.3. Factors determining access to subsidies4

The lack of clarity of the targeting criteria implies that they are subject to different interpretations and application at local level. Several studies have used multivariate regression analysis to isolate factors that are important determinants in access to subsidized farm inputs (School of Oriental and African Studies et al., 2008; Chirwa et al., 2011c), whether those that received coupons were more likely to be food insecure (Holden and Lunduka, 2012a), and factors determining the quantity of subsidized fertilizers received by the household (Ricker-Gilbert, 2011). Access to inputs is measured in two ways: receipt of fertilizer coupons and amount of subsidized fertilizers acquired by the households. Chirwa et al. (2011c) use a probit regression approach for estimating the likelihood of accessing subsidized fertilizer coupons and a tobit approach for determining factors that affect access to quantities of subsidized fertilizers. Several factors are used to explain access to subsidized farm inputs and these include household characteristics (composition, headship, and assets); farming characteristics (land size, degree of commercialization, cash crop cultivation, quantity of commercial fertilizers bought in previous season); poverty and vulnerability indicators (own poverty assessment, adequacy in food consumption, participation in safety nets, receipt of subsidy in previous season); and other control variables (labour market participation, remittances, business enterprise, open forum allocation of coupons, and regional fixed effects). Table 10.5 shows results from probit and tobit regression estimates of factors affecting access to subsidized fertilizers.

Several insights emerge from the results on the determinants of access to subsidized fertilizers. First, with respect to the age of the household, the results show that age matters. As the age of household heads increases, such households are more likely to receive coupons and the probability of getting a coupon increases by 0.3%. However, households that are headed by the elderly (those above 64 years) are unlikely to receive fertilizer coupons and the probability falls by 13%. Similarly, with respect to quantity of fertilizers acquired, there is a positive relation between age and quantity acquired but the elderly are disadvantaged. This is contrary to the emphasis on special vulnerable groups that has been placed recently in the targeting criteria for the subsidy programme. It may also be the case that elderly-headed households are labour-constrained for farming activities and are least likely to use the coupons in farming.

Second, households with larger parcels of land under cultivation are more likely to receive subsidized fertilizer coupons and tend to acquire larger (p.234)

Table 10.5. Estimates for factors affecting access to subsidized fertilizer in 2008/9

(1)

Whether obtained subsidized fertilizer coupons

PROBIT

(2)

Kilograms of subsidized fertilizer acquired

TOBIT

Variables

dF/dx

z

coeff

z

Age of household head (years)

0.0032

3.11a

0.227

1.69c

Male headed household (0/1)*

0.0021

0.08

1.698

0.49

Elderly headed household (0/1)*

-0.1304

-2.75a

-7.94

-1.49

Household size (adult equivalents)

-0.0113

-2.02b

-1.172

-1.62

Value of assets in US dollars in 2008/9

0.00001

-0.67

-0.004

-1.09

Cultivated land in hectares in 2008/9

0.0561

3.03a

12.947

4.75a

Tobacco cultivation in 2008/9 (0/1)*

0.172

5.29a

27.639

7.21a

Maize marketing in 2008/9 (0/1)*

0.1126

3.22a

15.934

3.32a

Quantity of commercial fertilizers bought in 2007/8 (kg)

-0.0002

-2.50b

-0.014

-1.28

Own poverty assessment as poor in 2007/8 (0/1)*

-0.0802

-2.19b

-15.299

-2.62a

Adequate food consumption in 2008/9 (0/1)*

0.0202

0.91

6.501

2.23b

Business enterprise in 2007/8 (0/1)*

0.0051

0.23

0.432

0.15

Labour market participation in 2007/8 (0/1)*

-0.0411

-1.83c

-8.217

-2.85a

Remittance receipts in 2007/8 (0/1)*

0.0747

3.26a

5.049

1.63

Access to social safety nets in 2007/8 (0/1)*

0.0704

2.36b

4.666

1.4

Access to fertilizer coupons in 2007/8 (0/1)*

0.446

20.21a

56.109

15.82a

Open forum allocations 2008/9 and poor 2007/8 (0/1)*

0.0981

3.39a

13.167

3.36a

Central region (0/1)*

-0.0367

-1.11

-24.973

-6.35a

Southern region (0/1)*

-0.0321

-0.99

-18.023

-4.51a

Constant

-

-

3.257

0.35

Number of observations

1982

1982

Pseudo R-squared

0.2703

0.0406

Note: The dependent variable in (1) is a dummy variable for access to subsidized fertilizer coupons received in the 2008/09 agricultural season.

((*)) dF/dx (marginal effect) is for discrete change of dummy variable from 0 to 1.

The dependent variable in (2) is the quantity of subsidized fertilizers acquired in the 2008/9 season.

Robust t-statistics with superscripts a, b, and c denote significance at the 1, 5, and 10% levels, respectively.

quantities of subsidized fertilizers than those with smaller parcels. The positive relationship is expected since land is one of the main criteria for targeting smallholder farmers. Third, the household’s commercial orientation is also an important factor as reflected in the significance of tobacco cultivation and marketing of maize in both models. This implies that fertilizer coupons are likely to go to those smallholder farmers that earn cash incomes from agriculture with the potential to purchase fertilizers at prevailing market prices. This would not seem to support current targeting objectives and criteria, and (p.235) suggests the existence of inclusion errors. However, households that bought commercial fertilizers in the previous season are less likely to be allocated subsidized fertilizer coupons, and purchase of commercial fertilizers marginally leads to reduction in the probability of accessing coupons. This suggests weak adherence to targeting that should reduce inclusion errors and ineffectiveness and inefficiency from subsidizing farmers who would have bought commercial fertilizer without the subsidy.

Fourth, households that view themselves as poor are less likely to receive coupons. In the first two years of the subsidy, evidence of households having cash for coupon redemption was a precondition in some communities for households to receive fertilizer coupons (Imperial College et al., 2007; School of Oriental and African Studies et al., 2008). With respect to the quantity of fertilizers acquired, the poor acquire 15.4 kg less subsidized fertilizers than the non-poor. School of Oriental and African Studies et al. (2008) find similar results on the effect of own poverty evaluation on the likelihood of receiving fertilizers, with wealthier households receiving disproportionately more coupons than poor households.

Fifth, participation in the labour market either through salaried or ganyu employment in the 2007/8 season reduced the household’s chances of receiving coupons in the 2008/9 season. Similarly, households that participated in the labour market tended to acquire 8.2 kg less subsidized fertilizers than non-participants in the labour market. This implies that those in salaried employment are excluded as they are capable of purchasing fertilizers at commercial prices and those in ganyu employment may be those households that do not have adequate land and use their labour resource in ganyu labour. Nonetheless, ganyu labour is also the second most important source of cash for redeeming the coupons (School of Oriental and African Studies et al., 2008; Dorward et al., 2010b).

Sixth, receipt of remittances in the previous season increases the probability of receiving coupons, but this does not significantly determine the quantity of subsidized fertilizers acquired by the household. Remittances are, however, an important source of cash for redemption of coupons and for purchase of farm inputs in the rural areas.

Seventh, access to other social safety nets in the previous season is positively associated with receipt of fertilizer coupons in the 2008/09 season, although this does not significantly affect the quantity of subsidized fertilizers acquired by the household. This implies that participants in other social safety nets are not excluded from the fertilizer vouchers, and if these safety nets are well targeted then they can provide additional information about the vulnerable households in the communities. Some of the social safety nets, such as cash-for-work or public works programmes, if well coordinated can ease the cash constraint of vulnerable households and enable them to redeem the fertilizer coupons. (p.236)

Eighth, households that benefited from the subsidy in the previous season were more likely to receive the coupons in the next season. The probability of receiving fertilizer coupons increases by 45% for households targeted in the previous season who tend to acquire 56.1 kg more subsidized fertilizers than those that did not receive coupons in the previous season. The targeting impacts of this of course depend upon the criteria used in targeting the previous year and on criteria used in excluding previous beneficiaries and including new ones.

Finally, transparency and accountability in allocation of coupons at the local level tends to be beneficial for the poor. Open forums for allocating coupons increase the chance of targeting those that ranked themselves in the poor category. Similarly, the poor tend to acquire 13.2 kg more of subsidized fertilizer when open forums are used than when coupon allocations are discrete. This suggests that community-based targeting may be superior to allocations that involve traditional leaders and committees, as was previously the case in the 2005/6 up to the 2007/8 season.

Overall, the results suggest that although the poor are not excluded from access to subsidized farm inputs, where they receive subsidized inputs they tend to receive fewer coupons and acquire less subsidized fertilizers than the non-poor. Holden and Lunduka (2012a), Ricker-Gilbert (2011), and to a lesser extent Chibwana et al. (2010) reach similar conclusions, with households receiving coupons being better off in terms of their livestock endowments and assets than those that did not receive coupons. The re-distribution of coupons at the village level tends to increase such a bias in which the poor tend to share the coupons and the non-poor tend to retain the two expected fertilizer coupons. The results also suggest that the fortunes of the poor in accessing subsidized farm inputs, and hence improvements in targeting efficiency, can increase with increased use of coupon allocation processes such as open forums. Hence, transparency and accountability of systems are critical in achieving development results and outcomes. School of Oriental and African Studies et al. (2008) and Ricker-Gilbert and Jayne (2011) also found that in 2006/7 and 2008/9 the receipt of subsidized fertilizer was also associated with the presence of a resident MP in the community.

10.4. Gender and use of subsidized inputs5

Gender issues in FISP are considered in the targeting criteria, where female-headed households are categorized as part of vulnerable groups requiring particular attention in the targeting of subsidized farm inputs. However, (p.237) it is also important to consider how gender relations are affected or affect the use of subsidized inputs at household level.

The analysis of gender issues in the FISP has mostly concentrated on differential access between male-headed and female-headed households. Figure 10.3 shows the proportion of male-headed and female-headed households receiving fertilizer coupons from survey data in the 2006/7, 2008/9, and 2010/11 agricultural seasons. A relatively higher proportion of male-headed households had access to subsidized fertilizer coupons as compared with female-headed households in 2006/7 and 2010/11, while in the 2008/9 season a slightly higher proportion of female-headed households got subsidized fertilizer coupons than male-headed households.

However, School of Oriental and African Studies et al. (2008) also find that male-headed recipient households tended to receive more maize fertilizer coupons than female-headed recipient households, with male-headed households receiving on average 1.55 coupons compared to 1.45 coupons received by female-headed households in 2008/9 (with 1.7 compared to 1.3 coupons received per households in 2006/7). Holden and Lunduka (2012a), in a study of six districts in central and southern Malawi, find that 11% of female-headed households received the full package of 2 bags compared to 29% of male-headed households. With respect to communities’ perceptions on who is likely to receive coupons, there were no significant differences between

Targeting and access to input subsidies

Figure 10.3 . Proportion of male- and female-headed households receiving fertilizer coupons, 2006/7–10/11 (%)

Source: Computed from School of Oriental and African Studies et al. (2008), Dorward et al. (2010b), and Dorward and Chirwa (2011a).

(p.238) male-headed and female-headed households across regions (Dorward et al., 2010b).

Chirwa et al. (2011e) exploit detailed plot level information on decision-making on farming activities by specific members of the household to understand intra-household decision making in allocation of subsidized fertilizers on male- and female-controlled plots. Using probit regression models, the gender of the household member who controls input and farming decisions on the plot is the main variable of interest. The control variables in the model include farmer characteristics and other household characteristics such as plot size, age of household head, headship of household, cultivation of tobacco, sale of maize, access to safely nets, previous access to subsidized fertilizers, and district dummies. Female membership was interacted with household receipt of fertilizer coupons, male-headed membership, and with household with commercial fertilizers. Table 10.6 presents results of probit regressions showing: (1) intra-household use in households with any fertilizers (regardless of the source of the fertilizers); (2) intra-household use in households with subsidized fertilizer (with or without additional unsubsidized fertilizer); and (3) intra-household use in households that only used subsidized fertilizers (with no purchases of unsubsidized fertilizer).

First, the results show that significant gender differentials exist in the allocation of fertilizers to plots within the households, with female-controlled plots less likely to have fertilizer applications compared to male-controlled plots. This is only in the case where we pool the sample of subsidized and unsubsidized households. The probability of applying fertilizer falls by 0.28 points for female-controlled plots, and the marginal effect is statistically significant at the 1% level. These results are similar to the findings in other studies in African agriculture such as Doss and Morris (2001) and Chirwa (2005), although in both those studies the coefficients of female control were statistically insignificant. However, model (1) results also show that female-controlled plots in coupon-recipient households were more likely to be fertilized as compared with male-controlled plots and female-controlled plots in female-headed households. Access to subsidized fertilizers improves the odds for female-controlled plots, with the probability of fertilizer application increasing by 35% compared to female-controlled plots in male-headed and non-coupon recipient households. This implies that for a female household member in a coupon recipient household the mean increase in the probability of applying fertilizer on the plot is 0.07 points compared to a decrease of 0.28 points for a female member in a household without subsidized fertilizers.

Female-controlled plots in male-headed households were less likely to be fertilized than either male-controlled plots or female-controlled plots in female-headed households. This is consistent with observations in focus (p.239)

Table 10.6. Marginal effects from probit estimates of intra-household fertilizer use

Dependent variable: Plot controlled by member in household was fertilized (0/1)

(1)

(2)

(3)

All households

Use any fertilizer

Only use subsidy fertilizer

dF/dx

t-ratio

dF/dx

t-ratio

dF/dx

t-ratio

Female household member*

-0.2844

-3.50a

0.078

1.3

0.0401

0.42

Female member in coupon recipient household*

0.3502

13.09a

-

-

-

-

Female member in male-headed household*

-0.2848

-3.32a

-0.1581

-2.03b

-0.073

-0.65

Female in household with commercial fertilizer*

0.2154

7.30a

0.0729

2.66a

-

-

Plot size in hectares

0.4308

12.59a

0.4664

11.99a

0.4502

8.42a

Male-headed households*

0.1223

1.65c

0.0535

0.84

0.012

0.12

Age of household head

-0.0008

-1.45

-0.0003

-0.64

0

-0.07

Number of adult equivalents

-0.0043

-1.12

-0.0085

-2.37b

-0.0086

-1.66c

Log of household land size in hectares

-0.2389

-15.05a

-0.1672

-13.34a

-0.2527

-11.52a

Household that grew tobacco*

0.1368

6.88a

0.1067

6.19a

0.0755

2.51b

Household that sold maize*

0.1255

4.90a

0.0817

3.59a

0.0937

2.82a

Household had commercial fertilizers in 2007*

0.151

8.59a

0.0776

4.58a

0.0101

0.31

Household own assessment as poor in 2007*

-0.063

-2.99a

-0.0447

-2.29b

0.0069

0.22

Household had access to safety nets 2007*

0.0109

0.49

0.0017

0.08

0.0276

0.96

Household had subsidized fertilizers 2007*

0.1698

9.44a

0.057

3.05a

0.0389

1.43

District fixed effects?

Yes

Yes

Yes

Number of observations

4727

3551

1944

Pseudo R-squared

0.2281

0.1826

0.2003

Notes:

((*)) dF/dx is for discrete change of dummy variable from 0 to 1.

Superscripts a, b, and c denote statistically significant at 1, 5, and 10% level, respectively.

group discussions in Chirwa et al. (2011c) that typically, in male-headed households, resources are likely to be controlled by husbands. However, this is only the case when commercial fertilizers are also available to the household (models (1) and (2)) but it is not the case when households have access to subsidized fertilizers only (model 3). The results show that being a female member controlling a plot in a male-headed household reduces the probability of applying fertilizers by 28% in the model of subsidized and unsubsidized households (model (1)), but this bias reduces to 15% in subsidized households (model (2)). Hence, the bias against female-controlled plots in male-headed households is reduced as compared with the case when commercial (p.240) fertilizer is also available at the household level. In model (1), the results imply that the mean decrease in the probability of a female-controlled plot being fertilized in a coupon-recipient and male-headed household is 0.21 points. In model (2), the decrease in the mean probability of applying fertilizer on female-controlled plots in male-headed households is only 0.08 compared to a decrease of 0.57 points for the same situation in model (1).

Second, the results also show that access to commercial fertilizers in the 2008/09 season also favoured women-controlled plots in the application of fertilizers and raised the probability of application of fertilizers on the plot by 21% compared to male-controlled or female-controlled plots in households without commercial fertilizers. This is lower than the increase in the probability of 32% with household receipt of subsidized fertilizer. Third, larger plots are more likely to be fertilized than smaller plots. However, plots that belong to households with larger land holdings tend to be less fertilized. This may be due to the fact that most rural households are cash constrained to afford fertilizers and tend to be very selective on the plots that they apply fertilizers to.

Fourth, commercialization of agricultural activities, using indicators such as cultivation of tobacco and sale of maize, and acquisition of commercial fertilizer in the previous season by households is positively related to the probability of the plots being fertilized. This commercialization enables households to invest in fertilizers across all plots. Fifth, self-reported poverty in the 2007/8 season may be one of the constraints to the 2008/9 application of fertilizers by households, with plots that belong to poor households less likely to be fertilized regardless of availability of commercial or subsidized fertilizers. Finally, households’ access to subsidized fertilizers in the previous season increases the probability of the plot being fertilized, demonstrating the positive cumulative effects of fertilizer adoption or continued access to subsidized fertilizers. However, this relationship is only statistically significant at the 1% level in models where commercial fertilizers are also available among households but not significant among purely subsidized households.

Overall, although female-headed households are less likely to receive coupons, potentially joint decision making prevails when it comes to use of subsidized fertilizers within the household, hence reducing the bias against female-controlled plots. This may be due to the fact that most of the subsidized fertilizer is meant for the cultivation of maize for subsistence needs, in which case women may have a stronger countervailing power as providers of basic food needs at the household level. It is therefore important that analysis of gender issues in the subsidy programme goes beyond examination of differential access of subsidized fertilizers among male-headed and female-headed households, and also includes examination of intra-household use of subsidized fertilizers. The study implies that social transfers that focus on (p.241) provision of basic services, such as input subsidy for household food security, are likely to be efficiently used even if they are targeted at the household level instead of at individual household members.

10.5. Challenges of access for the most vulnerable groups6

As noted in Section 10.2 above the targeting criteria in the FISP have recently emphasized the need to reach out to the most vulnerable groups, such as resource poor female-headed households, resource poor elderly-headed households, resource poor orphan-headed households, HIV-positive resource poor household heads, resource poor physically-challenged households, and resource poor households looking after elderly and/or physically challenged persons (Farmers Union of Malawi, 2011). These vulnerable groups may experience more challenges in accessing coupons and acquiring subsidized fertilizers due to the processes and problems experienced in the implementation of the programme. Mvula et al. (2011) provide a detailed analysis of some of the challenges that the most vulnerable households experience in accessing subsidized farm inputs, and we highlight some of the major issues in this section. The problems of access to farm inputs relate to access to coupons and access to subsidized fertilizers.

With respect to access to subsidized fertilizer coupons, several problems were documented, which include shortage of coupons earmarked for the villages, missing of beneficiary names that were identified and verified, sharing of coupons, alleged sales of coupons by government agents and traditional leaders, and the process of beneficiary identification and coupon distribution. These findings are consistent with assessment by Farmers Union of Malawi (2011) where they find that among the 30% of respondents reporting problems of coupon distribution, the main problems were: not enough coupons (34% of respondents reporting problems, 10% of all respondents); not receiving coupons though eligible (23% of respondents reporting problems, 7% of all respondents) and being forced to share a coupon with those who did not register (17% of respondents reporting problems, 5% of all respondents). Although these problems tend to be common to all beneficiaries, they tend to be worse for vulnerable groups. For instance, limited numbers of coupons available for villages against the number of resource poor households and vulnerable households tends to result in the most vulnerable households being left out. Similarly, the widespread reported practice of sharing of coupons on average favours less-poor beneficiaries (in that poor beneficiaries share their coupons but less-poor beneficiaries do not) and makes vulnerable groups (p.242) benefit less than the official entitlement. The poor tend to share among the poor or share with the less-poor not in the beneficiary list. Less-poor beneficiaries tend to be less affected by the village level politics of sharing and usually retain their normal share of the coupons. In addition, the requirements for identification documents excluded some of the most vulnerable groups from access to the subsidy.7

Even with a coupon there are severe challenges in the process of acquiring subsidized fertilizers, with major implications for the most vulnerable groups. These challenges, discussed in Chapter 5, include long queues at the coupon redemption points, payment of ‘tips’, stock-outs, presence of thieves at the markets, distances to markets, lack of money to redeem coupons, and rudeness of some input selling clerks. First, long queues at input suppliers (requiring some households to spend days and nights buying inputs) and long distances to selling points are a major challenge for the most vulnerable groups such as female-headed, physically challenged, and elderly-headed households. Second, frequent stock-outs at markets lead to scrambles for farm inputs whenever they are in stock and this disadvantages female-headed households and the elderly, particularly where there is no provision for special queues for vulnerable groups. Third, where ‘tips’ or bribes are demanded by sellers of subsidized inputs these are not affordable for the most vulnerable groups. Finally, incidents of theft, difficulties in transportation of inputs, and lack of money to buy inputs are problems that particularly affect women and the elderly.

Overall, the problems in accessing coupons for most vulnerable households were not widespread, while the difficulties in redeeming coupons were most severe for most households, particularly the most vulnerable groups. Access to subsidized inputs was more problematic for vulnerable groups due to long queues, frequent stock-outs, long distances to markets, and payment of tips and bribes. These raise the transaction costs and opportunity costs which most vulnerable groups could not afford. The most vulnerable households had particular challenges in finding money to redeem the coupons let alone payment of tips to purchase subsidized inputs.

10.6. Options for targeting8

Given the difficulties noted in previous sections with targeting processes, criteria, and outcomes, we now consider three possible alternative targeting (p.243) approaches. We consider first a universal but smaller per household subsidy providing 50 kg of fertilizer to all households (termed the ‘universal programme’), second ‘tighter pro-poor targeting’ where the same total volume of subsidized fertilizer is targeted with a 100 kg ration to the poorest households, and third ‘pro-poor mixed targeting’ where the same proportion of households get 100 kg and 50 kg of fertilizer as in 2010/11, but these are better targeted with the poorest households getting 100 kg, less poor households getting 50 kg, and the least poor getting none.9

The first approach, universal provision of 50 kg fertilizer, is effectively legitimizing and extending the widespread practice of redistribution. It has a number of advantages:

  • Elimination of targeting costs and difficulties.

  • Increased transparency and accountability, as all households know their entitlement.

  • High correspondence between planned targeting outcomes and those achieved.

  • Increased effectiveness in targeting the poor as compared with 2010/11, as all the poor would receive some subsidized inputs.

  • Despite some increase in the number of less-poor households receiving fertilizers, the total quantity of fertilizer going to less-poor households would be similar to 2010/11 as households would receive only 50 kg per household. This may be seen as offering compensation for lower prices for less poor farmers’ surplus maize.

  • Reduced demands on coupon allocation and distribution processes may allow earlier coupon distribution and input purchase and use, greater farmer confidence in subsidy receipt, and also release staff time for more extension support to farmers.

There are, however, also difficulties with this approach. First, it may appear to be a reversion to the former ‘starter pack’ approach, although there are substantial differences with the larger scale of the subsidized ‘pack’ and in its objectives, and this may make it politically unacceptable. Second, there are concerns that incremental production from a smaller ration of subsidized inputs for each household may not provide poor households with enough productivity gains to ‘lift’ them over productivity and asset thresholds needed for graduation. Finally, graduation could only be achieved if the whole programme were withdrawn from all beneficiaries in an area at the same time. (p.244) Progressive beneficiary graduation and targeting would undermine the core benefits of universal targeting. However graduation might be pursued by progressive lowering of the subsidy with increasing beneficiary redemption payments, with cash transfers to households not able to graduate.

The second approach, tight pro-poor targeting of 100 kg fertilizer, is broadly the approach that is supposed to be used currently. If implemented effectively this would provide the lowest displacement and the highest pro-poor growth potential. There are, however, serious difficulties in applying this method, as discussed in this chapter, and targeting outcomes do not match aspirations. Improving the implementation of this approach must address current difficulties in both setting and applying measurable targeting criteria.

The third approach, mixed pro-poor targeting of 50 and 100 kg fertilizer, is closest to the approach that is actually currently used, where there is redistribution of subsidy coupons. However, whereas in the current system most redistribution seems to involve sharing by poor recipients with poor non-recipients, a more pro-poor approach would prioritize poorer recipients keeping their 100 kg fertilizer allocation, while less-poor recipients would get 50 kg each, and the least poor would get nothing. While this lacks the strong transparency and accountability of the universal approach, it may provide better targeting and have wider community and political support than the tight pro-poor approach. In some ways this might allow easier implementation—but it will still run up against the interests of powerful people who may be excluded from subsidy benefits, and will still face challenges in setting and applying criteria to identify target households. These are likely to make it more difficult to implement. It might also allow a natural beneficiary graduation system with households being shifted from a 100 to 50 kg to zero fertilizer allocation.

Nonetheless, all systems face major practical challenges in determining the number of eligible farm families in each area. Attention is also needed to processes of coupon redemption, as these can be highly exclusionary to poorer and more vulnerable people. Options include distribution centre committees, more private sector involvement in subsidized input sales to promote competition (as discussed in Chapter 8), more effective market monitoring and auditing, and better integration with cash transfers for the productive poor who cannot afford redemption payments. In addition, the development of methods for better identifying beneficiaries is a key requirement for improving targeting, unless it is accepted that difficulties with this (together with power, politics, and problems of lack of accountability and transparency) make the universal approach the best practical approach.

Two main approaches may be considered for improving targeting: proxy wealth/income measures, and community targeting. Both these methods (p.245)

  • require formal identification of targeting criteria and systems that, when implemented, provide improvements that justify their costs;

  • pay insufficient attention to difficulties associated with the large number of households clustered around the poverty cut-off point, and hence local concerns about ‘fairness’; and

  • need to overcome interests of less-poor groups, with enforcement of more transparent and accountable allocation and distribution processes–with open and inclusive processes and/or published recipients lists and allocation criteria.

There is potential merit in the use of proxy poverty indicators, for example, but also major costs and challenges in gathering and using reliable data. Houssou and Zeller (2011) propose an indicator-based system for setting targeting criteria for FISP and argue that this approach would be more target- and cost-effective than the 2006/7 system in improving welfare transfers to the poor.10 This approach presupposes (a) that the integrated household survey data and its estimation of income poverty (with its various challenges)11 provide more valid poverty measures than more subjective local definitions which may take account of wider definitions of poverty,12 (b) that poverty targeting is the most effective way of meeting the range of programme objectives discussed earlier in Section 10.2, (c) does not recognize the complex interactions between area and beneficiary targeting that are important in the practicalities of targeting, and (d) does not pay sufficient attention to difficulties noted earlier with large numbers of households clustered around the poverty cut-off point, and hence local concerns about ‘fairness’.13

Nevertheless, given the cost implications, it may be useful to consider and develop alternative ways of implementing this (for example, criteria might be developed by a process of participatory consultations with rural people, and a small number of low cost indicators combined into a points system for household prioritization in subsidy allocation). Community targeting with open meetings is the approach supposed to be used for identifying FISP (p.246) beneficiaries. There is widespread concern that traditional leaders, government officials, and others are appropriating coupons and/or directing them to themselves and/or friends and relatives. This perception is promoted by lack of transparency in allocation, misunderstanding of coupon allocations and targeting processes, and widespread belief that there should be more coupons. It may be difficult for targeting to be perceived to be fair if less than around 80% of households are targeted, and community targeting needs fairly costly training and facilitation with checks and balances to stop elite capture.

10.7. Summary

Targeting is one of the critical elements of the Farm Input Subsidy Programme with implications for displacement, productivity, economy-wide effects, and graduation. It is also important that targeting criteria and processes are consistent with the objectives of the programme in order to maximize the impact. Different programme objectives may entail different targeting objectives with implications for targeting criteria and processes. Hence, there should be a strong link between programme objectives, targeting systems, targeting outcomes, and programme impacts. These links have not been clearly articulated in the Farm Input Subsidy Programme, although targeting occurs at both area and beneficiary levels. While changes have occurred over the life time of the programme, the alignment between programme objectives and targeting objectives and outcomes, and their interaction with political objectives and processes, remains an important issue in the implementation of the programme.

Changes in area targeting have resulted in more equitable distribution of input vouchers per household with per household regional differences narrowing over time. There has been considerable scope for and some evidence of political considerations and processes affecting area distributions, particularly in the earlier years of the programme. Major issues remain on how allocations to areas, villages, and perhaps most importantly to beneficiaries are determined. No major changes have taken place in the targeting criteria and processes of targeting at beneficiary level, apart from increasing emphasis on vulnerable households and the promotion of open forums at community level in the identification of beneficiaries and allocation and distribution of coupons.

The broadness of the beneficiary targeting criteria, covering a large proportion of poor households, has allowed wide variations in the application of the criteria at community level. This has resulted in biases in receipt of subsidized farm input coupons against the poor, with the non-poor more (p.247) likely to get coupons and then likely to get more coupons than the poor. The wide and increasing practice of redistribution and ‘sharing’ of coupons reduces the bias where by the poor are less likely to receive coupons. On the other hand, however, it increases the likelihood of poorer recipients receiving fewer coupons than less-poor recipients. However, open forum meetings for allocation of coupons appear to increase the likelihood of the poor receiving fertilizer coupons and acquiring more than the poor in areas where the coupon allocation was not made in an open manner. There are also gender biases in receipt of coupons and access to subsidized fertilizers, with female-headed households receiving fewer coupons than male-headed households. However, this gender bias is not evident in the allocation of subsidized fertilizers on plots controlled by different members of the households. The analysis of intra-household use of inputs shows that female-controlled plots are less likely to have fertilizer applied when commercial fertilizer is available in the household, but this bias vanishes among households that acquire subsidized fertilizer inputs. Overall, however, the extent of elite capture does not appear to be as great as that reported by Pan and Christiaensen (2012) in Tanzania.

Options for targeting have been considered for improving patterns of coupon distribution among poorer and less-poor households, with discussion of alternative targeting criteria and processes to achieve these patterns. Regressive patterns appear to be undesirable due to associated high displacement (leading to low incremental production even if there is higher input productivity) and low linkage effects. Three alternative approaches are considered—‘tight pro-poor targeting’, ‘mixed pro-poor targeting’, and universal (but more tightly rationed) access. Although ‘tight pro-poor targeting’ is the current desired outcome, difficulties in setting criteria and with distribution and redistribution processes lead to outcomes that are very different from those that are desired. In any case, except for the universal approach, targeting requires efficient and cost-effective ways of improving the criteria for identifying beneficiary households.

Notes:

(1) This section draws heavily on Dorward and Chirwa (2012c).

(2) Dorward and Chirwa (2012c) provide a detailed analysis of the links between programme objectives and targeting objectives and their implications.

(3) Ricker-Gilbert et al. (2010) report that participation in unsubsidized fertilizer purchase is depressed with increasing distance to a paved road, whereas subsidized purchases increase with distance to a paved road. Chirwa et al. (2011b) do not find any significant effect of distance to paved road on participation in unsubsidized fertilizer purchases.

(4) This section draws heavily on Chirwa et al. (2011c).

(5) This section draws heavily on Chirwa et al. (2011e).

(6) This section relies heavily on Mvula et al. (2011).

(7) As noted in chapter, voter ID cards have been required for beneficiary registration from the 2009/10 season. This proved particularly difficult for child-headed and elderly-headed households that were either under voting age or too old to participate in the general elections.

(8) This section draws on Dorward and Chirwa (2012c).

(9) For simple exposition, and also reflecting the high economic and social value of fertilizer, we frame these options in terms of fertilizer allocations. In practice matching allocations of maize and legume seed should be considered with fertilizer allocations.

(10) Ten indicators are proposed (household size, radio ownership, cement floor of house, bicycle ownership, use of electricity for lighting, panga ownership, educational qualification in household, use of bed net, rubbish disposal facility, and household head literacy) and also area based factors based on Agricultural Development Divisions.

(11) See, for example, Chirwa et al. (2012) on poverty estimation difficulties as a result of seasonality.

(12) See for example, World Bank (2000) for discussion of issues such as vulnerability, power, voice, assets, wealth, and well-being as poverty concepts alongside income or expenditure measures.

(13) Houssou and Zeller (2011) do consider different patterns of distribution, including a ‘fair targeting’ approach that does not lift anyone above the poverty line—but this involves reducing subsidy receipts for households just below the poverty line to ensure that it does not lift them over it—a very challenging process, both politically and administratively.