Impact Of Agricultural Market Reforms On Smallholder Farmers In ...

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non-food spending. These are followed by clothing (16 percent), health (11 percent), and transportation (11 percent) (see Table 4.1.145). - Non-food spending patterns vary somewhat across departments, but the differences are not large. Households in Atacora spend a larger proportion of their non-food budget on social events and “leisure and other” than do other departments. Those in Atlantique allocate a larger share to housing, transportation, and health. Households in Borgou devote more of the non-food budget to clothing and household goods. The departments of Mono, Ouémé, and Zou have non-food budgets that closely resemble the national average with one exception: households in Mono spend one quarter of their non-food budget on social events, more than any other department. Nonetheless, it should be kept in mind that large and infrequent budget items such as social events and housing repair, are subject to larger measurement errors than small frequent purchases (see Table 4.1.146). The structure of non-food spending does not differ much between male- and female-headed households. Female-headed households appear to spend a somewhat larger share of the non-food budget to clothing, housing, and household goods. On the other hand, male-headed households allocate a larger share to “leisure and other” (see Table 4.1.147). The composition of non-food spending does vary significantly across expenditure categories. The share of the non-food budget devoted to clothing, household goods, and energy fall with per capita expenditure. For example, clothing accounts for 25 percent of non-food spending among households in the poorest category, but the percentage falls to just 11 percent in the highest expenditure category. Other types of non-food spending become more important as expenditure rises: housing, transportation, and “leisure and other.” For example, spending on housing (repairs and maintenance) rise from just 1 percent of non-food spending among the poorest households to 13 percent among those in the highest category (see Table 4.1.148). Regression analysis of expenditure This section uses regression analysis to examine the relationship between various household characteristics and its standard of living, as measured by per capita consumption expenditure. The result is an equation that “predicts” per capita expenditure on the basis of these household characteristics 17 . This type of analysis sheds light on some of the causes of poverty and 17 Regression analysis finds the equation that best fits the data, given one dependant and various independent (or explanatory) variables. The analysis generates coefficients for each independent variable that 91
identifies the variables that are most closely associated with poverty. This may help policymakers focus efforts on addressing the root causes of poverty. In addition, this analysis can provide a method of targeting assistance on the poor, based on easily observable characteristics of the households. In some countries, government assistance is provided to households identified as poor based on variables known to be related to poverty, such as education, ownership of certain assets, and housing characteristics. Table 4.1.149 shows the results of the regression analysis. The value of R 2 is 0.39, indicating that the regression equation “explains” 39 percent of the variation in per capita expenditure. This value is not unusually low for this type of analysis, but it does remind us that there is a lot of variation in per capita expenditure that cannot be capture by the variables included here. The regression analysis includes 39 independent variables, of which 15 are statistically significant at the 5 percent level 18 . The results can be summarized as follows: Larger households are poorer households, other things being equal. Each additional household member is associated with a 4 percent reduction in per capita consumption expenditure 19 . This is a common pattern in household expenditure data and is thought to reflect the difficulties in supporting a large household. A household with a large proportion of children under the age of 15 is more likely to be poor. This is expected, since children do not contribute as much to household income as an adult does. The education of the head of household is positively associated with the per capita expenditure of the household. Each additional year is associated with a 3 percent increase in per capita expenditure. This implies that even among farm households, education contributes to the productivity and/or the employment opportunities. The effect of the education of the spouse is positive but statistically insignificant. describe the relationship between that variable and the dependent variable, holding all other variables constant. 18 Statistical significance at the 5 percent level implies that there is only a 5 percent probability that the relationship found in the data could occur by chance. 19 If the coefficient is B, then a one unit increase in the independent variable increases per capita expenditure by a factor of exp(B). 92
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IMPACT OF AGRICULTURAL MARKET REFOR
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TABLE OF CONTENTS CHAPTER 1 - INTRO
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5.1.10 Expenditure patterns .......
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CHAPTER 1 - INTRODUCTION 1.1 Backgr
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villageois, (GV)) was needed as the
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CHAPTER 2: POLICY BACKGROUND FOR BE
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2.3 Evolution of Economic Policy 2.
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Under pressure from the World Bank
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anking system has been restored to
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Fertilizer can be imported outside
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particularly urban wage-earners. On
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Soulé (1996) describes the evoluti
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led to a more sustainable agricultu
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Table 2.4 - Gross farm revenue from
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Malawi’s economy is heavily depen
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the reduction of its operations in
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crops is also difficult because tob
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Table 3.1 CROP 1982/83 1983/84 1984
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CHAPTER 4: RESULTS OF THE BÉNIN SM
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where W v is the weight for a house
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heads. This suggests that some of t
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And school attendance is much highe
Page 48 and 49: Crop production is the main activit
Page 50 and 51: (33 percent). Livestock income is m
Page 52 and 53: Somewhat surprisingly, the relation
Page 54 and 55: It is no more common among richer h
Page 56 and 57: production, and women actually spen
Page 58 and 59: Seed use by crop Although only 18 p
Page 60 and 61: Looking at the results by departmen
Page 62 and 63: Ouémé presents an unusual case: j
Page 64 and 65: Market prices have significant effe
Page 66 and 67: policies of SONAPRA to discourage
Page 68 and 69: crops are responsible for this incr
Page 70 and 71: Respondents were asked whether the
Page 72 and 73: Female-headed households, not surpr
Page 74 and 75: households are not considered credi
Page 76 and 77: In almost every category of agricul
Page 78 and 79: This discussion implies that farms
Page 80 and 81: eturns per hectare and labor-intens
Page 82 and 83: farm household and tends to be smal
Page 84 and 85: Surprisingly, the value of sales as
Page 86 and 87: Finally, proximity to an all-season
Page 88 and 89: Changes in crop marketing In this s
Page 90 and 91: Poor households are more likely to
Page 92 and 93: it is clear that virtually all grow
Page 94 and 95: production to total expenditure is
Page 96 and 97: a significant number of household (
Page 100 and 101: Larger farms are associated with hi
Page 102 and 103: This would not be surprising in lig
Page 104 and 105: Female-headed households appear to
Page 106 and 107: poorest expenditure category to jus
Page 108 and 109: headed household were more likely t
Page 110 and 111: Households in every expenditure cat
Page 112 and 113: An important result of this analysi
Page 114 and 115: oad network, although those citing
Page 116 and 117: questions posed by the IFPRI-LARES
Page 118 and 119: With regard to cotton marketing, al
Page 120 and 121: Village leaders were asked to ident
Page 122 and 123: Marketing patterns Village leaders
Page 124 and 125: 4.3: Results of the Bénin Survey o
Page 126 and 127: 4.3.2 Survey methods At the time of
Page 128 and 129: officers have CM2 certificates. Thi
Page 130 and 131: Suppliers The GV representatives we
Page 132 and 133: Cotton area per GV ranges from 145
Page 134 and 135: The largest cost items are schools
Page 136 and 137: members and 10 percent of the membe
Page 138 and 139: Table 4.1.1-Description of sample o
Page 140 and 141: Table 4.1.7-Age, education, and lit
Page 142 and 143: Table 4.1.13-Ethnic background of h
Page 144 and 145: Table 4.1.19-Importance of each sou
Page 146 and 147: Table 4.1.25-Number of plots and ar
Page 148 and 149:
Table 4.1.31-Land tenure by expendi
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Table 4.1.37-Change in labor alloca
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Table 4.1.42-Seed use by sex of hea
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Table 4.1.47-Perceived change in av
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Table 4.1.54-Share of producers of
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Table 4.1.59-Fertilizer use by farm
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Table 4.1.64-Characteristics of fer
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Table 4.1.69-Change in fertilizer a
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Table 4.1.75-Allocation of labor by
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Table 4.1.82-Allocation of labor by
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Table 4.1.89-Change in credit avail
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Table 4.1.94-Percent of households
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Table 4.1.102-Area allocation by cr
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Table 4.1.106-Area allocation by cr
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Table 4.1.110-Marketed share of out
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Table 4.1.114-Marketed share of out
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Table 4.1.117-Regression for market
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Table 4.1.120-Change in the type of
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Table 4.1.128-Perceived change in r
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Table 4.1.135-Main reason for stori
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Table 4.1.140-Summary of food expen
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Table 4.1.144-Food consumption by e
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Table 4.1.149-Results of regression
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Table 4.1.152-Value of household as
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Table 4.1.156-Value of household as
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Table 4.1.160-Housing characteristi
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Table 4.1.162-Housing characteristi
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Table 4.1.165-Main source of water
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Table 4.1.170-Source of information
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Table 4.1.176-Source of information
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Table 4.1.181-Regression for percei
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Table 4.2.5-Change in number of veh
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Table 4.2.12-Availability and sourc
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Table 4.2.21-Number of GFs per vill
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Table 4.2.30-Percentage of villages
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Table 4.2.34-Distribution of villag
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Table 4.3.1-Distribution of GVs by
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Table 4.3.9-Number of each type of
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Table 4.3.12-Percentage of secretar
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Table 4.3.19-Distribution of GVs by
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Table 4.3.24-Percentage of inputs s
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Table 4.3.32-Average value of curre
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Table 4.3.40-Percentage of GVs in w
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CHAPTER 5 - RESULTS FROM THE MALAWI
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The weights are used to calculate a
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On average, around 20 percent of th
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agricultural work. Other less commo
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5.1.4 Time allocation In Malawi, as
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attributed it to the fact that fert
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insignificant. The coefficient on t
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As in the results from the previous
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most important sources of input on
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applied for credit compared to 20 p
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less than 1 mt per ha which are fai
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5.1.9 Agricultural marketing Market
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Households that belong to a club al
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most important contractors are the
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indicates that the extent of povert
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wheat, and fruits and nuts, a highe
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Surprisingly, the education of the
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Housing characteristics The overwhe
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It was also important to find out w
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Surprisingly, tobacco growers are m
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Over three-quarter of the EPAs repo
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The changes in the number and types
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5.2.11 Storage Facilities On averag
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Table 5.2 - Household characteristi
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Table 5.7 - Percentage of household
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Table 5.12 - Farm labor use and all
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Table 5.15 - Determinants of the de
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Table 5.17 - Sources of fertilizer
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Table 5.22 - Access and use of cred
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Table 5.25 - Percent of households
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Table 5.34 - Crop disposal by type
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Table 5.38 - Determinants of Market
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Table 5.42 - Summary of expenditure
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Table 5.45 - Food consumption by so
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Table 5.51 - Food consumption by ex
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Table 5.56 - Determinants of per ca
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Table 5.59 Percent of households ow
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Table 5.62 - Characteristics of the
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Table 5.68 - Determinants of percei
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Table 5.71 - Average yields per cro
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Table 5.77 - Changes in the availab
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Figure 5.1 Distribution of househol
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Figure 5.7 - Percentage of Househol
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CHAPTER 6 - SUMMARY, CONCLUSIONS AN
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Fertilizer Half of the far
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Regression analysis indicates that
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Among those seeing improvement, the
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Agricultural markets Three quart
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The economic reforms have boosted p
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department. The farms with very lit
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Strengthen the extension service by
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The most time-consuming agricultura
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Most common means of transport to t
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Small farmers in Malawi are mainly
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households have less assets and les
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Place more serious efforts to impro
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REFERENCES Ahoyo Adjovi, N.R. and F
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