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

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CHAPTER 5 – RESULTS FROM THE MALAWI SMALLHOLDER FARMER AND COMMUNITY SURVEYS 5.1: Results from the Smallholder Farmer Survey 5.1.1 Survey methods Sampling Malawi is administratively divided into three regions: The North, the Center and the South. The Center occupies the largest surface area (38 percent of the total country area) and is considered the bread-basket of Malawi. The North has the smallest surface area (28 percent) and is sparsely populated while the South is the most densely populated region. The extent of poverty is higher in the South than elsewhere in the country (National Economic Council, 2000) 28 . The three regions are sub-divided into 155 Extension Planning Units (EPAs): 31 in the North, 68 in the Center, and 56 in the South. Each EPA is sub-divided into sections, which are managed by Field Assistants working for the MOAI. In total, there are over 2,000 sections. In turn, sections are divided into blocks, which are the equivalent of villages. The methodology used for selecting the farmer sample was based on a geographically stratified random sample technique. First, 40 of the 155 EPAs were randomly selected. Second, two villages from each EPA were randomly selected with the purpose of choosing 10 households in each. Finally, since female-headed households constitute about 30 percent of all households, in case where information was available about the gender of the household head, seven male-headed households and three female-headed households were randomly selected to make up the 10 households in each village. Otherwise, 10 households were selected randomly in each village with no regard to gender distribution. Selection of villages was done with replacement to take into consideration the possibility that a village was: a) inaccessible due to bad roads or security problems, b) had some extraordinary circumstances such as a funeral, or c) had less than 15 households. Replacements for selected households were also used in case a household refused to 28 Based on the 1997-98 Integrated Household Survey analyzed by the Poverty Monitoring System of the National Economic Council of Malawi (2000), the extent of rural poverty was 69 percent in the South compared to 65 percent in the Center and 62 percent in the North. 231
participate in the survey or if there was no household member present during the survey to answer knowledgeably about farm production. The total sample included 800 farm families covering the three regions of the country. The list of EPAs and villages selected in the final sample are presented in Table 5.1. Because each farmer in Malawi did not have an equal probability of being selected for the sample, weights are applied in analyzing the data. The weights (also called expansion factors) are the inverse of the probability of selection. Thus, if farmers in a region were under-represented in the sample, the weights compensate for this by giving greater weight to these farms in the calculation of national averages and percentages. Because the sample was selected in a threestage process, the calculation of the weighting factor involves three terms: W hg EPAr epa r V v e e H h vg vg where W hg = the weight for household h with head of gender g EPA r = the total number of EPAs in region r epa r = the number of EPAs selected for the sample in region r V e = the total number of villages in EPA e v e = the number of villages selected for the sample in EPA e H vg = the total number of households of gender g in village v h vg = the number of households selected for the sample of gender g in village v The first term extrapolates from the EPAs selected for the sample to the entire region (North, Center, and South). The second term extrapolates from the selected villages to the entire EPA. Note that v e is equal to two for all EPAs in the sample. The third term extrapolates from the households in the sample to all the households in the village. In order to ensure that femaleheaded households represented 30 percent of the sample (reflecting secondary data about farm households in Malawi), the weights are different for male- and female-headed households. For female-headed households, the weight uses the number of female-headed households in the village (H vg ) divided by the number of female-headed households in the sample (h vg ). The same procedure is used for male-headed households. 232
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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
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It is no more common among richer h
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production, and women actually spen
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Seed use by crop Although only 18 p
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Looking at the results by departmen
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Ouémé presents an unusual case: j
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Market prices have significant effe
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policies of SONAPRA to discourage
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crops are responsible for this incr
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Respondents were asked whether the
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Female-headed households, not surpr
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households are not considered credi
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In almost every category of agricul
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This discussion implies that farms
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eturns per hectare and labor-intens
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farm household and tends to be smal
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Surprisingly, the value of sales as
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Finally, proximity to an all-season
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Changes in crop marketing In this s
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Poor households are more likely to
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it is clear that virtually all grow
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production to total expenditure is
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a significant number of household (
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non-food spending. These are follow
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Larger farms are associated with hi
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This would not be surprising in lig
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Female-headed households appear to
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poorest expenditure category to jus
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headed household were more likely t
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Households in every expenditure cat
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An important result of this analysi
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oad network, although those citing
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questions posed by the IFPRI-LARES
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With regard to cotton marketing, al
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Village leaders were asked to ident
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Marketing patterns Village leaders
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4.3: Results of the Bénin Survey o
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4.3.2 Survey methods At the time of
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officers have CM2 certificates. Thi
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Suppliers The GV representatives we
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Cotton area per GV ranges from 145
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The largest cost items are schools
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members and 10 percent of the membe
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Table 4.1.1-Description of sample o
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Table 4.1.7-Age, education, and lit
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Table 4.1.13-Ethnic background of h
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Table 4.1.19-Importance of each sou
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Table 4.1.25-Number of plots and ar
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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 4.1.98-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
Page 188 and 189: Table 4.1.135-Main reason for stori
Page 190 and 191: Table 4.1.140-Summary of food expen
Page 192 and 193: Table 4.1.142-Percent of households
Page 194 and 195: Table 4.1.144-Food consumption by e
Page 196 and 197: Table 4.1.149-Results of regression
Page 198 and 199: Table 4.1.152-Value of household as
Page 200 and 201: Table 4.1.156-Value of household as
Page 202 and 203: Table 4.1.160-Housing characteristi
Page 204 and 205: Table 4.1.162-Housing characteristi
Page 206 and 207: Table 4.1.165-Main source of water
Page 208 and 209: Table 4.1.170-Source of information
Page 210 and 211: Table 4.1.176-Source of information
Page 212 and 213: Table 4.1.181-Regression for percei
Page 214 and 215: Table 4.2.5-Change in number of veh
Page 216 and 217: Table 4.2.12-Availability and sourc
Page 218 and 219: Table 4.2.21-Number of GFs per vill
Page 220 and 221: Table 4.2.30-Percentage of villages
Page 222 and 223: Table 4.2.34-Distribution of villag
Page 224 and 225: Table 4.3.1-Distribution of GVs by
Page 226 and 227: Table 4.3.9-Number of each type of
Page 228 and 229: Table 4.3.12-Percentage of secretar
Page 230 and 231: Table 4.3.19-Distribution of GVs by
Page 232 and 233: Table 4.3.24-Percentage of inputs s
Page 234 and 235: Table 4.3.32-Average value of curre
Page 236 and 237: Table 4.3.40-Percentage of GVs in w
Page 240 and 241: The weights are used to calculate a
Page 242 and 243: On average, around 20 percent of th
Page 244 and 245: agricultural work. Other less commo
Page 246 and 247: 5.1.4 Time allocation In Malawi, as
Page 248 and 249: attributed it to the fact that fert
Page 250 and 251: insignificant. The coefficient on t
Page 252 and 253: As in the results from the previous
Page 254 and 255: most important sources of input on
Page 256 and 257: applied for credit compared to 20 p
Page 258 and 259: less than 1 mt per ha which are fai
Page 260 and 261: 5.1.9 Agricultural marketing Market
Page 262 and 263: Households that belong to a club al
Page 264 and 265: most important contractors are the
Page 266 and 267: indicates that the extent of povert
Page 268 and 269: wheat, and fruits and nuts, a highe
Page 270 and 271: Surprisingly, the education of the
Page 272 and 273: Housing characteristics The overwhe
Page 274 and 275: It was also important to find out w
Page 276 and 277: Surprisingly, tobacco growers are m
Page 278 and 279: Over three-quarter of the EPAs repo
Page 280 and 281: The changes in the number and types
Page 282 and 283: 5.2.11 Storage Facilities On averag
Page 284 and 285: Table 5.2 - Household characteristi
Page 286 and 287: 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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