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

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Female-headed households appear to have fewer household assets than male-headed households, though the differences appear in only some types of assets. For example, there is little difference in furniture ownership between the two groups, but female-headed households are less likely to own a radio/CD player, a television/VCR, a bicycle, or a motorbike. The total value of household assets is 231 thousand FCFA for male-headed households compared to 128 thousand FCFA for femaleheaded ones (see Tables 4.1.155 and 4.1.156). Ownership of household assets is strongly correlated with per capita consumption expenditure. Of the 14 categories of assets, 10 show a positive relationship between the percentage of households owning and the expenditure category. The exceptions are "other furniture", sewing machines, bicycles, and "other assets". Sewing machines are productive assets for some households, thus explaining the fact that poor households are no less likely to own one. Bicycles are probably an inferior good at higher income levels, since motorbikes become more widespread among richer households. The overall value of household assets rises from 92 thousand FCFA in the poorest expenditure category to 471 thousand FCFA in the highest (see Tables 4.1.157 and 4.1.158). Respondents were asked how the asset was acquired by the household. For most of the asset types, at least 85 percent of the households reported that the head of household purchased the item. Inheritance, gifts, home production, and purchases by the wife are small compared to purchases by the husband. One exception is heater/ovens, which are purchased by the wife in 58 percent of the households (see Table 4.1.159). Housing characteristics Housing is one of the most important assets owned by small-scale farmers in Bénin. The size and characteristics of the house are also key elements of the standard of living of the household. For these reasons, the IFPRI-LARES Small Farmer Survey collected information on the characteristics and estimated value of household dwellings. The houses occupied by farmers in Bénin can be summarized as follows: More than three-quarters of the dwellings in rural Bénin (79 percent) have mud brick or dried mud walls. The remaining houses are divided evenly among three other types of walls: brick or stone, cement, and bamboo or straw. 97
With regard to the floors, the houses are even divided between those with dirt floors (49 percent) and those with tile or cement floors (48 percent). Houses with brick, stone, or wood floors are quite rare. Close of three-quarters of the houses (72 percent) have sheet-metal rooves. Practically all the remaining houses have roofs made of straw. Fully 86 percent of the dwellings are owned by the farm household. Most of the other houses (12 percent) and very few (2 percent) are rented. The housing characteristics do vary, however, by region. For example, houses in Mono and Ouémé are more diverse in terms of wall materials. Houses with brick or stone walls are more common in Ouémé than elsewhere, while houses with cement walls are more common in Mono. Somewhat surprisingly, these two departments also have higher proportions of houses with bamboo or straw walls. Cement or tile floors account for two-thirds of the houses in Ouémé and Atlantique, compared to about half nationwide. And metal rooves are most common in Zou, Atlantique, and Mono. These results indicate that, in general, houses in the south are more often constructed of more durable materials compared to those in the north (see Table 4.1.160). In terms of ownership patters, Ouémé and Mono are unusual in having a relatively high proportion of houses that are used without payment (26 and 17 percent, respectively). House rental is also higher in these departments than elsewhere, although even here it is rare (4-5 percent). In the other departments, 88-99 percent of the dwellings are owned by the farm households (see Table 4.1.160). There are also some differences between the dwellings of male- and female-headed households. Female-headed households tend to have lower quality walls (mud brick/mud) compared to maleheaded households, but also tend to have higher quality floors (cement/tile) and higher quality roofs (metal sheet) relative to male-headed households. These patterns seem to reflect the geographic distribution of female-headed households, rather than housing preferences of men and women. Female-headed households are also less likely to own the house and more likely to use it without payment (see Table 4.1.161). Not surprisingly, the materials used in housing are also correlated with per capita expenditure. As expenditure rises, the share of houses with mud brick or dried mud walls falls while that of houses with brick or stone rises. Even among the richest 20 percent of farm households, however, mud brick and dried mud walls are found in three-quarters of the houses. Floors are a better indicator of per capita expenditure. The incidence of dirt floors falls from 65 percent in the 98
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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
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Crop production is the main activit
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(33 percent). Livestock income is m
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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 98 and 99: non-food spending. These are follow
Page 100 and 101: Larger farms are associated with hi
Page 102 and 103: This would not be surprising in lig
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
Page 150 and 151: Table 4.1.37-Change in labor alloca
Page 152 and 153: 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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