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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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Direct impacts of input subsidies

Direct impacts of input subsidies

Chapter:
(p.124) 6 Direct impacts of input subsidies
Source:
Agricultural Input Subsidies
Author(s):

Ephraim Chirwa

Andrew Dorward

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199683529.003.0006

Abstract and Keywords

This chapter provides evidence on the direct impacts of Malawi’s Farm Input Subsidies programme, based on analysis of household level survey data collected by the authors and findings from other studies. There are, however, difficulties with maize production data, and the economy-wide effects may mask direct beneficiary impacts. Using several household welfare indicators, analysis of four panels of household data, and qualitative studies, the results suggest that subsidy receipt has positive impacts on beneficiaries’ maize production, net crop income, food consumption, household income, school enrolment, child health, and reduced supply of ganyu labour.

Keywords:   input subsidies, beneficiary impacts, welfare indicators, Malawi, maize, food security

6.1. Introduction

In this chapter we examine evidence on the direct impacts of the subsidy following the causal chain set out in Figure II.1 in the introduction to Part II. The most direct benefit of the subsidy programme is to increase maize production at household level. This is also consistent with the programme’s objective of increasing maize productivity and national and household food security in Malawi as discussed in Chapter 5. Given difficulties in obtaining maize production and productivity data (as mentioned in Chapter 1, see also Dorward and Chirwa (2010b)), we discuss various estimates of programme maize production impacts. We then draw on work reported in Chirwa et al. (2011d) to look at the contribution of the subsidy programme on welfare indicators for beneficiaries—food consumption, self-assessed poverty, income, assets, incidences of shocks and stresses, health, and education. The collection of panel data in the periodic evaluation of the subsidy programme and the incorporation of the second Integrated Household Survey (IHS2) questions in the design of 2006/7, 2008/9, and 2010/11 surveys allows comparison of the same households from the 2002/3 or 2003/4 season and thus estimation of the impact of the subsidy programme on direct beneficiary households, as compared with non-beneficiaries, over time. Impacts are also assessed using a partial equilibrium model (Dorward and Chirwa, 2012b) and with related findings in other studies.

There are, however, two main caveats to the household level analysis of direct beneficiary impacts. First, some of the indicators are subjective assessments by households: thus with difficulties of calibration and differences in the timing of interviews, caution must be exercised in interpreting the panel level results. Second, if economy-wide effects (discussed in Chapter 7) are very strong, with the subsidy benefiting many households which have not been beneficiaries, the differential impacts at household level may be weak regardless of direct benefits or the number of times a household has (p.125) had access to subsidized fertilizers in the past six agricultural seasons. In such cases the econometric analysis may not be able to pick up small differences between beneficiary and non-beneficiary households. It should also be noted that although improved seeds are part of the subsidy programme, the analysis of the impacts is focused on receipt of subsidized fertilizers as these are generally correlated with receipt of subsidized seed and involve a much larger subsidy.

The chapter is organized as follows. The next section presents the methods that are used to evaluate the direct impacts of the fertilizer subsidy on beneficiary households. In Section 6t.3, we examine direct impacts on maize production and food security. Section 6.4 evaluates the impacts on human capital (health and education). Section 6.5 assesses the impacts on incomes, self-assessed poverty, assets, and shocks, and Section 6.7 presents evidence from qualitative beneficiary interviews. Finally Section 6.8 presents concluding remarks.

6.2. Methods of evaluating direct beneficiary impacts

We report on three main methods to assess direct beneficiary impacts of the Farm Input Subsidy Programme.1 The first, reported in Chirwa et al. (2011d), is panel regression, which exploits matched panel data for different rounds of data collection: the second Integrated Household Survey (IHS2) covering the 2002/3 and 2003/4 agricultural seasons, the Agricultural Input Subsidy Survey (AISS) for the 2006/7 and 2008/9 seasons, and the Farm Input Subsidy Survey (FISS) for the 2010/11 agricultural season. This is analysed using a fixed effects panel data strategy with the following specification:

(6.1)
Y i t = α i + δ t + k = 2 k = 5 β k ( δ t * F I S P i k ) + X i + ε i t

where i is the individual household, t is the wave of the survey (2004/05, 2006/7, 2008/9, and 2010/11), k indexes the household categorization of access to subsidies over the past six years, α1 are individual fixed effects, δt is a dummy variable equal to 1 for each round of the survey (with 2004/5 as the base category), otherwise equal to 0, and (δt*FISP k) is the interaction dummy that is equal to 1 only for households that received fertilizer subsidy in access category k, Y is the impact indicator, and X is a vector of household characteristics. The coefficient β^ gives the impact of the subsidy programme. The FISS in 2010/11 tracked access to fertilizer subsidy since the programme (p.126) started, and this has enabled us to account for the number of times the household has had access to fertilizer subsidies between the 2005/06 and 2010/11 seasons. Households are categorized into five groups represented by dummy variables: never had access (base category), accessed 1–2 times, accessed 3–4 times, accessed 5 times, and accessed 6 times (continuously). The impact indicators used in the regression model include food security, education and heath, assets and welfare, and shocks.2 Alternatively, we measure access to the subsidy programme by the quantity of subsidized fertilizers in place of dummy variables. The panel analysis is based on the full panel sample (461 households) and a sub-sample of panel households that were identified as poor based on per capita expenditure in the IHS2 (227 households). The latter allows us to investigate the impact of the subsidy programme on households that had the same initial condition prior to the subsidy programme.3

Table 6.1 presents the various indicators reported from Chirwa et al. (2011d) for testing various hypotheses on the direct beneficiary impacts of the subsidy programme. In addition to the broad hypothesized relationships in Table 6.1, we also expect the subsidy to have larger impacts on households that have had access to subsidized fertilizers in all the past six seasons compared to those that have had less access. This implies that there should be a positive trend in the value of the coefficients of times of receipt of subsidy as the frequency of receipt increases from 1 to 6 times.

Table 6.2 presents the distribution of the panel sample of households in Chirwa et al. (2011d) by the number of seasons the households have had access to subsidized fertilizer. It is helpful to identify three groups regarding households’ subsidy receipt: a small proportion who never had access to subsidized fertilizer (no access), a much larger group who had access to subsidized fertilizer at least once and up to five times in six seasons (intermittent access), and those had access to subsidized fertilizer six times (continuous access). These groups accounted for 4%, 51%, and 45% of households, respectively. Most of the households are, therefore, repeat beneficiaries.4 In terms of headship of households in 2010/11, 66% and 34% of the sample households were male and female headed, respectively. The distribution of household by the poverty status in IHS2 also shows that the overall sample has equal numbers of households that were poor and non-poor. (p.127)

Table 6.1. Beneficiary household level impact indicators and hypotheses

Welfare category

Impact indicators

Impact: Alternative hypothesis

Food security

  1. 1) Adequacy in food consumption in past month

  • Positive

Schooling and health

  1. 1) Primary school enrolment at household level

  2. 2) Incidence of under-5 illness

  • Positive

  • Negative

Subjective poverty

  1. 1) Subjective assessment of poverty status

  • Positive

Shocks and stresses

  1. 1) Number of shocks experiences by household

  2. 2) Incidence of severe agriculture-related shocks

  • Negative

  • Negative

The second approach used to investigate direct beneficiary effects is qualitative analysis based on focus group discussions, key informants, and life stories of some beneficiaries, collected in the 2006/7, 2008/9, and 2010/11 periods. In each of these rounds of surveys, and each of the 8–14 districts of the surveys, detailed qualitative interviews were conducted covering systems of implementation of the subsidy programme, cropping patterns and livelihoods, and local peoples’ and beneficiary’s perceptions of the impacts of the programme on their welfare. The number of qualitative interviews varied with the number of districts covered in the surveys. For instance, in the 2010/12 survey, which had the smallest number of districts (8 districts), qualitative data was collected through 8 focus group discussions, 24 key informants’ interviews, and life histories from 64 households.

The third method used to investigate direct beneficiary effects is a partial equilibrium model, informal rural economy modelling (Dorward and Chirwa, 2012b), with analysis for two of the largest livelihood zones in Malawi from 2005/6 to 2010/11. These two livelihood zones, Kasungu–Lilongwe Plain (KAS) and Shire Highlands (SHI) between them include just over 40% of rural households in Malawi and also represent examples of less and more densely populated areas in the centre and south of the country, respectively. Cluster analysis of data from the IHS2 was used to develop a household/livelihood classification within the livelihood zones.5 We report results from simulations based on two scenarios. The basic scenario, simulated in both zones, compares model outcomes ‘with subsidy’ with actual prices against outcomes ‘without subsidy’ with equilibrium prices. The ‘basic with household savings’ scenario, only simulated in the SHI livelihood zone, is the same as the basic (p.128)

Table 6.2. Distribution of sample and number of seasons with access to subsidized fertilizer

Number of seasons

Panel households

Headship, 2010/11

Poverty status in IHS2

Proportions of poor & non-poor

Number

%

Male

Female

Poor

Non-poor

Poor

Non-poor

0

19

4

75

25

33

67

2

5

1

42

9

75

25

57

43

7

8

2

35

7

72

28

48

52

7

6

3

33

7

60

40

48

52

8

10

4

45

10

66

34

45

55

97

9

5

80

17

55

45

49

51

16

16

6

208

45

68

32

52

48

50

47

N

461

100

66

34

50

50

100

100

Note: Weighted figures.

scenario except that it assumes 20% incremental savings carried forward by each beneficiary household from increased income as a result of subsidy receipt.

6.3. Impacts on maize production

The main justification for implementing the Farm Input Subsidy Programme has been to improve maize productivity and achieve food security at household and national level. Discussion in Chapters 2 and 4 of likely subsidy impacts and linkages in Figure II.1 also suggest that increases in staple food production and productivity should be one of the major drivers of indirect subsidy impacts, through domestic price and real income effects. Determination of subsidy impacts on maize production is therefore fundamental to the assessment of programme impacts.

Subsidy impacts on maize production have been estimated in a variety of ways. The simplest and most commonly quoted indicators of impact have been changes in estimated national maize production before and after the subsidy (see, for example, Dugger (2007) and Denning et al. (2009)). These show dramatic increases in estimates of national maize production, with estimated maize production of 1.2 million MT in 2004/5 (before the subsidy) followed by estimates of 2.6, 3.2, 2.8, 3.8, 3.4, 3.8, and 3.6 million MT in the seven subsidy years 2005/6 to 2011/12 (these estimates are presented in Figure 6.1 in terms of incremental production above pre-subsidy estimates). There are, however, a number of difficulties with the use of increases in (p.129) national production estimates as estimates of subsidy impacts, or even as indicators of subsidy success. First, there is no way of separating the impacts of the subsidy on maize production from the impacts of other variables. The most obvious of these is the weather, but other variables may also be important—thus, poor production in 2004/5 was caused by a combination of poor rains and late and limited commercial fertilizer deliveries and sales.

Second, it is not clear why estimated production should have increased so dramatically during the life of FISP (as opposed to increasing mainly following its inception). This appears to be the result of increasing estimated hybrid maize yields (which rise by 20% from around 2,500 to around 3,000 kg/ha from 2005/6 to 2011/12), an increasing proportion of maize area under hybrid varieties (with a 70% increase in hybrid maize area over the same period), by a small increase in overall maize area (7%), and by a very large differential between hybrid and local maize yields (with hybrid maize yields increasing from 160 to 240% of local maize yields). However, although hybrid maize areas may have increased with increased volumes of subsidized seeds, it is not clear what can have driven the large increases in hybrid maize yields when volumes of subsidized fertilizer were falling back from 2007/8, although Holden and Lunduka (2010b) estimate rising maize yields for both local and hybrid maize from 2006 to 2009. A possible explanation for this is that fertilizer use in one year has dynamic effects on yield in the following year. Thus, Ricker-Gilbert and Jayne (2011) estimate that 100 kg of subsidized fertilizer receipt increases immediate maize yield by 165 kg and receipt of 100kg fertilizer per year over three years yields an extra 316 kg of maize in the fourth year. Holden and Lunduka (2010b) also estimate that rising maize yields are associated with small falls in overall maize area and maize area share (with a net increase in maize production). Chibwana et al. (2012), however, estimate increases in maize and tobacco area with the subsidy.

Third, the hybrid yield estimates are not consistent with other estimates of farmers’ hybrid maize yields (Government of Malawi and World Bank, 2006; Ricker-Gilbert et al., 2009; Chibwana et al., 2010; Holden and Lunduka, 2010c; National Statistical Office, 2010a). While these are admittedly very variable, they are almost all below 2,000 kg/ha (Dorward and Chirwa, 2010a).

The very high national production figures are not consistent either, with very high domestic prices from early in 2008 through much of 2009. These discrepancies are illustrated in Figure 7.4 in Chapter 7, which plots estimated maize availability against price by market season, with remarkably high prices in many of the marketing seasons when production estimates would have suggested that greater maize production and availability would have led to low prices. We discuss possible reasons for this in Chapters 8 and 9, but note here that one likely partial explanation is that production was over-estimated in these years, most notably following the 2006/7 harvest with (p.130) export of a little over 300,000MT of maize to Zimbabwe in the 2007/8 season. Dorward and Chirwa (2010a) also show that there are difficulties in reconciling national production figures with estimates of consumption and export—national production figures suggest that there should be greater availability of maize than required by the population, and this in turn is not consistent with high levels of food insecurity and child malnutrition (as shown for the latter, for example, in Table 7.5)

Finally, there are doubts regarding the methodology used in deriving these figures. This involves field extension workers taking samples of fields and estimating yields for these and crop areas, and then the aggregation of yield and area estimates up to district, Agricultural Development Division (ADD), and national estimates. Sampling and estimation methodologies actually employed by field extension workers are not very clear, and aggregate estimates are subject to technical adjustments. Given past examples of over-estimation of cassava areas and yields, tendencies for crop production figures to be used in judging extension performance, the political importance of national figures, and instances of apparent attempts in President Mutharika’s government to influence sensitive statistics,6 there appear to be considerable risks of upward bias of these estimates.

With estimates of national maize production therefore not being able to provide estimates of the impacts of the subsidy programme, a better alternative should be to estimate incremental production from the use of subsidised inputs, fertilizer, and improved seeds. Here we would ideally use estimates of maize yield responses to incremental fertilizer and seed inputs resulting from the subsidy programme. These could then be multiplied and added across seed types for beneficiaries and for the programme as a whole. Estimates of incremental input use as a result of the programme are examined later in Chapter 8. We briefly consider here estimates of incremental production per unit incremental input use made in a number of studies.

A number of studies have attempted to estimate yield responses to nitrogen on farmers’ fields and on-farm yield differences between varieties, from on-farm trials and from farm surveys. School of Oriental and African Studies et al. (2008) reviewed a number of these and concluded that 12, 15, and 18 kg grain per kg of N were reasonable estimates of responses of local (or traditional), Open Pollinated (OPV) and hybrid varieties respectively, under reasonable farmer management, with further gains of 100 and 200 kg per ha for OPVs and hybrid varieties over local varieties, in the absence of fertilizer. (p.131) A small number of studies since then have specifically examined fertilizer and seed yield responses. School of Oriental and African Studies et al. (2008) and Dorward and Chirwa (2010b) report that attempts to gather sufficiently reliable information from farm household surveys in 2006/7 and 2008/9 had not been successful, and the latter reported very wide differences in yield estimates from different surveys with little attempt to reconcile differences.

Nevertheless, triangulation across national yield, production, and consumption estimates does provide some guidance on which estimates are likely to be more or less reliable.

  • Chibwana et al. (2010) estimate from farm survey mean yield differences of around 210 kg/ha between hybrid and local varieties, with fertilizer responses of 11.5 and 9 kg grain per kg N for local and hybrid maize respectively.7 While the basic differential between local and hybrid is broadly consistent with other findings, the higher response to local than to improved maize is not, and may be the result of the parsimonious production function employed with no fertilizer/variety interaction terms.

  • Holden and Lunduka (2010c) report an average difference of 323 kg/ha between ‘matched’ local and hybrid maize yields in their farm surveys, and a fertilizer response of 9 kg grain per kg N.

  • Makumba et al. (2012) derive fertilizer response estimates of 16.8 kg grain per kg N for hybrid maize farmer-managed trials across the country. These were achieved with high rates of fertilizer use (over 250 kg/ha), but comparison of high and low fertilizer rates on researcher-managed on-farm trials suggests that response rates might be considerably higher than this with lower fertilizer rates. Questions about how marginal response rates depend upon overall fertilizer rates, upon incremental application of subsidized inputs, and upon other aspects of crop management (such as the time of planting, fertilizer application, and weeding) further complicate the issue.

The approach taken by School of Oriental and African Studies et al. (2008), with an approximate +/- 20% range to allow for the effects of over- or under-estimation, therefore still seems as reasonable as any. This discussion, however, highlights the importance of improving maize yield and production estimates in Malawi, not only for consideration of the direct impacts of the subsidy programme, but for wider economic, agricultural sector and food security planning and management. (p.132)

Figure 6.1 therefore compares estimates of incremental maize production for each year of the programme using first national production estimates and then the approach taken by School of Oriental and African Studies et al. (2008), with a +/- 20% range allowing for possible over- or under-estimation and downward adjustments in some years (2005/6, 2007/8, and 2011/12) to allow for the effects of poor rainfall in some parts of Malawi. It should be noted that all these estimates ignore the impacts of storage losses (Denning et al., 2009). Also for the purpose of obtaining a relatively simple estimate of production impacts, the approach taken by School of Oriental and African Studies et al. (2008) assumes that the nutrients in subsidized tobacco fertilizers from 2005/6 to 2008/9 provided benefits equivalent to those that would have been obtained if the nutrients had been applied to maize, and benefits from these nutrients are therefore measured in maize production. This should lead to a limited over-estimate of incremental maize production but not, when these figures are used in the benefit–cost analysis in Chapter 9, an over-estimate of the value of programme benefits. Over-estimates of maize production should be limited because tobacco fertilizers accounted for between 10% and 17% of total subsidized fertilizers in these three years, and suffered from much higher estimated displacement rates (School of Oriental and African Studies et al., 2008). Figure 6.1 shows that if incremental national maize production estimates are all attributed to the effects of FISP then this requires a fertilizer response approximately double the responses used in the School of Oriental and African Studies et al. (2008) estimates.

Direct impacts of input subsidies

Figure 6.1. Increases in maize production estimates above 2002/3 and 2003/4 base

Source: MoAFS Annual Crop Estimates, author calculations.

(p.133)

Estimates of increased maize production among beneficiaries are also consistent with qualitative evidence from focus group discussions and life stories of beneficiaries in Chirwa et al. (2011d) and discussed later in Section 6.7. These suggest that the subsidy has increased food (maize) production at household level. However, most of the more positive stories came from households that were already buying commercial fertilizers before the programme and those that received two bags of subsidized fertilizers (rather than those that received less as a result of sharing of fertilizers).

6.4. Impact on food consumption

Improvements in maize production should lead to improved food availability and food security for beneficiary households. In all the panel surveys, households were asked whether they considered their food consumption in the month before the survey inadequate or adequate. In order to assess the impact of food security, Chirwa et al. (2011d) created a dummy variable representing adequacy in food consumption equal to one if the household revealed that food consumption was adequate or more than adequate, and to zero if it was inadequate and to investigate, using fixed effects, the impact of the subsidy programme on food security. Estimation of the impact of the number of times that subsidized fertilizer is received (as outlined earlier) indicates that among households that received subsidized fertilizers continuously (six times) about 22% more reported adequate food production as compared with non-recipients, with the coefficient being statistically significant at the 5% level. Among those who had received subsidized fertilizers less than six times, increasing frequency of fertilizer use also led to increasing frequency of reported adequate food production. Separate estimation of the impact of the quantity of subsidized fertilizer received also provided evidence of a positive and significant impact on food consumption adequacy. These findings are consistent with the evidence of increased maize production reported above, with qualitative reports from focus group discussions (School of Oriental and African Studies et al., 2008; Dorward and Chirwa, 2010a; Chirwa et al., 2011d) and with the findings of Holden and Lunduka (2010a). They report that receipt of subsidized inputs increases the probability of households being net sellers rather than net buyers of maize, and also that 66% and 69% of surveyed households reported improvements in household and community food security as a result of the subsidy programme (although 60% of the households in their sample were still net buyers of maize despite the subsidy programme). However, only 30% of households reported that the subsidy programme led to increased maize consumption. (p.134)

6.5. Impacts on education and health

6.5.1. Primary school enrolment

Chirwa et al. (2011d) also investigate the impact of beneficiaries’ access to subsidized inputs on schooling based on enrolment of the primary school age group between 5–13 year olds, while controlling for household characteristics. This analysis uses a two-period panel, IHS2 (in 2003/4 and 2004/5) and FISS (in 2010/11), in which members of households that were more than 5 years old were asked whether they were in school. This enabled the generation of an indicator of primary school enrolment at household level, computed as the number of primary school age children in school divided by the total number of primary school-going age children in the household. The results indicate that the subsidy has a positive impact on schooling. Across all households, there was a general increase in school enrolment between the two periods, a change that was universally confirmed in focus group discussions and key informant interviews. The coefficients of the dummies for receipt of subsidized fertilizer 1–2 times, 5 times, and 6 times are statistically significant at the 5%, 1%, and 10% level, respectively, although there is no clear trend in the value of the coefficients of the number of times of receipt and primary school enrolment. Similar but weaker relationships are observed for the model estimated only for households categorized as poor in the IHS2.

The estimated positive impact of subsidy receipt on educational enrolment is consistent with anecdotal reports on programme impacts, with focus group discussion reports (School of Oriental and African Studies et al., 2008; Dorward and Chirwa, 2010a), and with Holden and Lunduka (2010a) who report that 65% of respondent households perceived that there was a positive impact of subsidy receipt on school attendance.

6.5.2. Health and nutrition

Improvements in food availability at household level due to access to subsidized fertilizers may improve beneficiaries’ health in a number of ways—through improved food security and nutrition from increased own production and income, and from increased ability to finance health care. This can be investigated in a number of ways. Chirwa et al. (2011d) examine the impact of subsidy receipt on incidence of illness using data for households that had under-5 members in 2004/5 and 2010/11. On average, about 59% of households in 2004/5 had under-5 members who were ill in the two weeks before the survey, but this fell to 49% in 2010/11. The econometric evidence of the impact of the subsidy programme on the health of children in beneficiary households shows a negative relationship between access to subsidy and (p.135) incidence of under-5 illness: households that had access to subsidized fertilizer five or six times were significantly more likely (at the 5% level) to have under-5 children that had not fallen ill in the two weeks before the survey.

This impact was not commonly articulated in focus group discussions and key informant interviews. However Holden and Lunduka (2010a) also explored people’s perceptions of subsidy receipt on health, and report that 40% of respondents perceived that subsidy receipt improved health. Further evidence on the impacts of subsidy access on health, but not of access to FISP itself, is provided by Ward and Santos (2010), who examined the impact on stunting from access to Targeted Input Programme inputs prior to 2004/5. They found a significant reduction in stunting for each year of receipt of TIP inputs, and based on strong international evidence on the relationship between adult height and wages, discuss possible long-term beneficial effects of increased adult height on earnings.

6.6. Welfare impacts

We now consider evidence for direct impacts of subsidy receipt on a number of different variables related to welfare: subjective assessment of well-being, real incomes, assets, and shocks and stresses.

6.6.1. Subjective wellbeing assessment

The panel surveys collected consistent information on self-assessment of well-being, using households’ subjective assessment of their poverty status based on a ladder ranging from 1 representing the poorest to 6 representing the richest. As reported in Chirwa et al. (2011d) we use these subjective measures as outcome indicators of participation in the Farm Input Subsidy Programme. The mean self-assessment of well-being for panel households increased from 1.66 in 2004/5 to 2.34 in 2010/11(Chirwa et al., 2011d). After controlling for household and year effects, households’ self-assessments were higher by 54%, 69%, and 68% in the 2006/7, 2008/9, and 2010/11 surveys, respectively as compared with the pre-subsidy survey.8 However, the estimated impacts of subsidy receipt by beneficiaries’ households are small and not statistically significant (with some estimates marginally negative). (p.136) We therefore cannot reject the null hypothesis that receipt of the subsidy does not statistically affect changes in self-assessment of poverty among beneficiaries, and this suggests that the subsidy programme may have only weak direct income effects on beneficiary households.

These results are consistent with sentiments expressed in qualitative interviews in which most households report that they are not able to produce surplus maize which could be sold to earn extra cash income. As discussed below, life histories with selected households revealed that although some have had access to subsidized fertilizers continuously they may still struggle to produce maize that takes them to the next harvest and have to rely on ganyu to earn income to purchase food. Small but insignificant positive effects are consistent with small direct improvements from subsidy receipt which may be overshadowed by wider positive changes affecting all households through indirect market effects of the subsidy and other positive changes from 2002/3 and 2003/4 to 2006/7 and subsequent years. However, the differences between the estimated dummy coefficients for 2008/9 and 2010/11 are very small, suggesting that after a substantial improvement in perceived well-being from the pre-subsidy to 2006/7 surveys, and a smaller improvement from 2006/7 to 2008/9, there may have been little or no further improvement in perceived well-being from 2008/9 to 2010/11. We discuss these issues in Chapter 7.

In contrast with these results, however, Ricker-Gilbert and Jayne (2010b) do find a significant increase in satisfaction with life with increased receipt of subsidized fertilizer between the pre-subsidy and 2008/9 surveys.

6.6.2. Household real incomes

Reliable estimates of real incomes are difficult to obtain in survey data, without extensive and thorough analysis of expenditure data. Ricker-Gilbert (2011) reports an analysis of the impact of subsidy receipt on reported incomes using the same panel data set as Chirwa et al. (2011d), but excluding the 2010/11 panel. He finds no significant impacts of subsidy receipt on non-farm income or on total household income, although net value of rainy season crop production (a measure of farm income) is positively affected by subsidy receipt in the year of receipt (but not subsequent years), with each extra kg of fertilizer received increasing net crop income by 174MK (p = 0.01). While net crop income is estimated from production estimates, non-farm income is derived from respondent estimates, and total income is the sum of farm and non-farm income. National Statistical Office (2005a) estimate mean (median) household expenditure and consumption for 2002/3 to 2003/4 years as 99,532MK (72,000MK) and reported income as 50,000MK (36,123MK): reported incomes are approximately 50% of reported expenditure and consumption suggesting considerable under-estimates in reported incomes. Mean (median) non-farm (p.137) incomes for 2002/3 to 2003/4, 2006/7, and 2008/9 reported by Ricker-Gilbert are 31,000MK (9700MK), 10,000MK (2600MK), and 39,000MK (13,000MK), respectively. The extraordinary drop in 2006/7 (which is also reflected in total household income estimates) is not explained and does not match patterns of self-assessed well-being reported above or asset holdings reported below. This raises serious questions about the reliability of non-farm and total household income data and hence of the findings reported by Ricker-Gilbert (2011) on the impacts of subsidy receipt on non-farm and total household incomes.

These concerns about the reliability of data on reported income suggest that other analytical approaches may be preferred for estimating subsidy impacts on real incomes. Changes in real incomes of targeted poor households were therefore examined using the informal rural economy model introduced in Section 6.2 and described more fully in Dorward and Chirwa (2012b). This allows comparison of real income estimates for ‘target households’ (that is poor male- and female-headed types) with and without the subsidy (with an average receipt of 75 kg and 2 kg of subsidized fertilizer and hybrid maize seed respectively per household) but with constant prices (that is without any wider market equilibrium effects). Gains averaging around 7% (just under 1000MK) across poorer beneficiary households are estimated in the Shire Highlands with lower gains (around 4%, just under 450MK) in the Kasungu–Lilongwe Plains where poverty is less severe and poor households are less capital constrained and have lower returns to capital. School of Oriental and African Studies et al. (2008) also state that increases in beneficiary incomes were reported in a number of focus group discussions in 2007.

6.6.3. Assets

Increased productivity and incomes resulting from subsidy receipt may allow beneficiary households to increase their investment in assets. Increases in human capital or assets (in education and health) were considered earlier in Section 6.5. Here we examine evidence on investment in physical and livestock assets.

Holden and Lunduka (2010a) examined the impacts of subsidies on the value of assets and on livestock ownership measured in tropical livestock units. They find a general build-up in the real value of assets from 2006 to 2009 (particularly from 2007 to 2009), suggesting ‘that welfare has improved on a broad scale’ (pp. 20), but they find no evidence of direct impacts of subsidy receipt on asset accumulation. They conclude that their results ‘strengthen the impression that the direct targeting effect of the subsidy program is less important than the economy-wide effect of the program when it comes to growth effects in the economy’ (pp. 21–2). There is no evidence of a general increase in livestock endowments, nor of direct subsidy impacts on this. (p.138)

Ricker-Gilbert (2011) also explores possible direct impacts of subsidy receipt on asset holdings. He reports no significant impact of subsidy receipt on household livestock and durable assets for subsidy received in the survey year or in each of the previous three years. However, in an earlier presentation Ricker-Gilbert and Jayne (2010b) report positive but weak impact of subsidy receipt aggregated over the previous three years on household total assets, consumption, and productive assets (p = 0.12, p = 0.36, and p = 0.16, respectively). Ricker-Gilbert (2011) also finds a very large increase in the value of assets in the sample as a whole, with an increase of 73% in mean value of assets per household and 27% in median value of assets per household, although mean values show a large increase from 2002/3 and 2003/4 to 2007, while median values show a large increase from 2007 to 2009. The large increase from 2007 to 2009 was also observed by Holden and Lunduka (2010a).

6.6.4. Shocks and stresses

Changes in the vulnerability of households to shocks and stresses are another possible impact of subsidy receipt on household welfare. Households experience a number of shocks and stresses and many of these are agricultural related.

Simple comparison of the frequency of reported shocks from the IHS2 (pre-subsidy) survey to the FISS (2010/11) survey shows a decline from 24% to 13% in households that experienced lower crop yields due to weather or rainfall as the most severe shocks between IHS2 and FISS, respectively. Other agriculture-related shocks whose incidence declined were large falls in the sale price of crops and a large rise in the price of food. The relative importance of chronic and acute illnesses appears to have risen as a result of the decline in importance of severe agricultural shocks.

Using the panel surveys we investigate whether there is a relationship between the extent of subsidization and shocks experienced by households. Chirwa et al. (2011d) estimate two fixed-effect regression models using IHS2 and FISS survey data, one using all panel households in the sample, the other only using households that were classified as poor in the IHS2 survey before subsidy implementation. Both models show that the number of shocks declined between 2004/5 and 2010/11. However, with respect to the relationships between shocks and frequency of subsidized fertilizer receipt, the estimated coefficients for dummy variables show that recipients of fertilizer subsidies tend to experience more shocks than non-recipients (with all coefficients statistically significant at the 10% or 1% levels and larger coefficients for recipients that access subsidized fertilizer less than five times). A possible explanation for this is that there is some season specific targeting of the subsidy to households who have experienced shocks. (p.139)

These issues were explored further with examination of the relationships between frequency of access to subsidized fertilizers and the incidence of severe agricultural related shocks (identified where these were reported as the most severe shock). This analysis gave mixed results. For results obtained with all panel households, subsidy receipt is not significantly related with the incidence of agricultural shocks (estimated coefficients are positive but not significant), but the overall incidence of severe agriculture-related shocks has declined over time (with the decline significant at 5%). However, for the sub-sample of panel households identified as poor in IHS2, there is no evidence that severe agricultural related shocks have declined (the coefficient is marginally positive but not significant). In contrast to the results above, however, it is striking that among poor households those with access to subsidized fertilizers are less likely to have agriculture-related shocks as their most severe shock (estimated coefficients are negative and significant at 1%, 5%, or 10%), but there is no clear trend suggesting that higher frequency of access to subsidized fertilizer is associated with more or fewer agricultural related shocks.

In summary, the evidence on changes in shocks and stresses is rather mixed. Overall, the number of shocks experienced by beneficiary households has fallen significantly over time, although those with access to subsidized fertilizers continue to experience shocks and stresses like other households. However, among poor beneficiary households, agriculture-related shocks are less likely to be the most severe shocks; hence the subsidy appears to have helped poor households to become cushioned or resilient against agriculture-related shocks.

6.7. Impacts in life stories of beneficiary households

Analysis of beneficiary life stories gathered in 2010/11 reveals a mix of perceived impacts of subsidy receipt on their well-being. While there are positive stories about the increase in food production at household level among most households that receive subsidies, the life histories illustrate the challenges of the programme in delivering sustained direct benefits to beneficiary households. In most life histories of beneficiaries, particularly among the most vulnerable groups (female-headed, elderly-headed, and child-headed households), the stories were that the subsidy programme has enabled them to produce ‘a bit more food’ than when they had no access to the subsidy. The qualitative analysis points to the following issues:

  • In most cases, households that report success with the subsidy programme are those that are already relatively well-to-do and purchased commercial fertilizers before the subsidy programme. For instance, one of the beneficiaries who has had access to the subsidy over (p.140) five seasons, was also buying coupons that enabled him to profit from tobacco cultivation, and claimed that together this transformed his life from the ‘poor’ to the ‘well-to-do’ category.

  • Households that reported receipt and use of two fertilizer coupons, are likely to talk positively about the extent to which the subsidy improved their food production for such years compared to households that received less than two bags of subsidized fertilizers.

  • Sharing of coupons is widespread, and most households that have participated in the subsidy programme attribute the perceived failure of the programme to significantly change their lives to inadequate amounts of fertilizers. This is particularly the case for households that have never used fertilizers prior to the subsidy programme. There are many life stories that described how the full package of the subsidy was beginning to change their lives, only to experience drifting back to poverty due to the dilution of the subsidy as a result of the redistribution that takes place at village level.

  • There is also a tendency for beneficiaries to thinly spread the subsidized inputs over a larger parcel of land. Even among households that received two bags of subsidized fertilizers, the sentiments were that the subsidized fertilizer was not adequate for the amount of land the household had for maize cultivation. This is exacerbated by the lack of technical advice on the appropriate use of fertilizers, with most households reporting lack of access to agricultural extension services.

  • There is widespread recognition that the subsidy has helped beneficiary households to produce a ‘bit more maize’ and more importantly has reduced the purchase price of maize even in the lean months of January and February. Most of the beneficiaries interviewed, particularly those that were still not able to produce enough own maize to last them to the next season, consider the low price of maize as one major benefit of the programme.

  • Households that are not able to produce maize to last to the next harvest tend to purchase from the market. Most poor households engage in ganyu to earn incomes to buy maize and most reported that ganyu wage rates have been increasing while maize prices have been falling and maize is locally available. This has enabled the poor to afford purchase of maize based on ganyu incomes which have also improved over time. Due to higher wages, households reported that they have reduced the amount of time they spend on ganyu and there has also been an increase in opportunities to operate off-farm income generating activities.

  • Poor and vulnerable households such as female- and/or elderly-headed households that received subsidy fertilizers rarely supplement the supply of fertilizers with commercial purchases, leading to application (p.141) of subsidized fertilizers on larger parcels of land. Generally, where subsidized fertilizers are supplemented by commercial fertilizers, such households were buying commercial fertilizers prior to the subsidy or they are better off households that are also receiving subsidies. The quantitative analysis also shows that among beneficiaries there is much lower use of supplementary commercial fertilizers by poor households as compared with non-poor households.

As Chirwa et al. (2011d) note, the case studies of beneficiaries highlighted two challenges that have implications on direct beneficiary impacts of the subsidy programme: targeting and sharing of coupons at village level. We discuss these issues in Chapters 10 and 11.

6.8. Summary

This chapter has reviewed the direct beneficiary impacts of the subsidy programme using quantitative and qualitative data collected over time in a variety of different studies. Use of a partial equilibrium model also helped to triangulate the results from the analysis of the quantitative and qualitative data. A broadly consistent picture of direct subsidy impacts emerges from this, which we summarize in Table 6.3 and discuss below.

As Table 6.3 shows, the evidence examined in this chapter suggests that subsidy receipt has immediate or current season beneficial impacts on beneficiaries’ maize production, net crop income, food consumption, and household income (though impacts may be limited, particularly with food consumption). There appear to be no immediate impacts on ownership of physical assets, estimates of immediate impacts on subjective well-being are mixed (one study finding positive impacts, the other finding no impacts) and the observed positive relationship with shocks is counter-intuitive and perhaps best explained by reverse causality. There is then evidence of lagged impacts on beneficiaries’ maize production and food consumption (again with limited impacts). There appear to be no lagged impacts on ownership of physical assets, on net crop income, or on subjective well-being, there is no evidence from sufficiently robust data to draw any conclusions on lagged impacts on household income, and the observed positive relationship with shocks is again counter-intuitive and possibly due to reverse causality. The models estimated for examining direct subsidy impacts on school enrolment and child health do not allow examination of immediate impacts separately from lagged impacts.

The finding of lagged impacts on maize production is consistent with residual effects of fertilizer application on soil nutrients (as suggested by Ricker-Gilbert (2011)) and/or with reduced cash flow constraints as postulated earlier in Chapter 4. Lagged impacts on food consumption are then consistent with (p.142)

Table 6.3. Summary of findings on direct subsidy impacts

Current season impacts

Lagged season impacts

Wider seasonal changes

Maize production

+ve

+ve

+ve

Net crop income

+ve

X

+ve

Food consumption

+ve but limited

+ve but limited

+ve for 2006/7 & 8/9

School enrolment

?

+ve

+ve

Child health

?

+ve

+ve

Subjective well-being

Mixed (+ve, X)

X

+ve

Household income

?, +ve

?

?

Physical assets

X

Mixed (weak +ve, X)

+ve

Shocks

+ve*

+ve*

-ve

Notes:

(*) Possible reverse causality.

+ve: evidence for positive change; X: evidence does not suggest change.

-ve: evidence of negative change; ?: lack of evidence.

this. The lack of lagged impacts on net crop income, despite these impacts on maize production, are consistent with the observed lack of lagged impacts of subsidy receipt on tobacco production despite the existence of immediate positive impacts (Ricker-Gilbert, 2011). If the limited immediate and lagged gains from increased maize production are invested in food consumption, school enrolment, and health, this may explain the lack of direct lagged impact on subjective well-being and on investment in physical assets. The ability of econometric models to identify direct impacts may also be limited by the widespread practice of sharing of subsidized fertilizer (as described in Chapter 5) and by general increases in maize yields and production, net crop income, food consumption, school enrolment, child health, subjective well-being, and asset ownership (as summarized in the last column of Table 6.3).

As regards the impact of sharing of subsidized fertilizer, it was certainly the opinion of a number of respondents in focus group discussions and life histories that this severely reduced the scale and persistence of subsidy impacts, raising questions about possible thresholds for persistent impacts. However, the absence of apparent impacts on accumulation of physical assets should not obscure the important long-term social and economic benefits of investment in ‘consumption’ that lead to increased school enrolment and improved child health.

It is also important that the possible roles of the subsidy in driving general improvements should not lead to under-estimates of overall subsidy impact, through a narrow focus on direct beneficiary impacts. This is therefore the focus of Chapter 7.

Notes:

(1) Estimation of maize production impacts also uses other methods, as explained in Section 6.3.

(2) Panel data on education and health is only available from IHS2 and the 2010/11 FISS and the panel analysis is based on two periods.

(3) Ricker-Gilbert (2011) shows that OLS cross-sectional data analysis finds apparently significant direct subsidy impacts when regressing some measures of beneficiary impact against subsidy receipt, but that these relationships are not significant when investigated using fixed effects or first difference models. We therefore rely on our own and others’ results only when they take account of the effects of possible endogeneity of subsidy receipt.

(4) However, these figures reflect receipt of subsidized fertilizers and do not account for the quantity received and the last time it was received for those that received it less than six times.

(5) Dorward and Chirwa (2012b) provide details of the informal rural economy modelling and specification of household types and construction of individual household livelihood models.

(6) See, for example, the delayed and very limited release of yield estimates in National Statistical Office (2010a), the 2012 Malawi Revenue Authority (MRA) scandal (where the MRA borrowed money to inflate its reported cash holdings), and apparent downward estimates of the consumer price index noted in National Statistical Office (2012) and discussed in Chapter 7.

(7) These figures are calculated from figure 2 in Chibwana et al. (2010) and assume that fertilizer is applied as 50% 23:21: 0 and 50% urea.

(8) Estimates for model (1) for all households and with dummies for the number of times subsidized fertilizer was received. Similar positive coefficients of the year dummies were also found with models (a) estimated only for households categorized as poor in the pre-subsidy survey and (b) assessing subsidy receipt in terms of the total quantity of subsidized fertilezer received from 2005/6 to 2010/11. Coefficients were statistically significant at 1% or (for 2010/11 with the poor households sample) at 5%.