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

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heads. This suggests that some of the female-headed households are widows. On the other hand, the dependency ratios are almost the same for male- and female-headed households (see Table 4.1.3). How does the household composition vary between rich and poor households 3 ? First, poor households tend to have more members than richer households. Households have around 10 members in the poorest two categories, compared to 7-8 members in the richest two categories. Second, , the dependency ratio is higher for poor households (55 percent in the first category) than for richer households (44 percent in the fifth category). The difference in dependency ratios is largely due to the fact that poor households have more children (see Table 4.1.4). The proportion of female-headed households does not appear to vary with expenditure category. This finding is somewhat surprising given the common belief that female-headed households are poorer and disadvantaged. The relatively small sample of female-headed households, however, makes this finding somewhat tentative. The results also show that polygamy is found in equal numbers across all income groups in rural areas, contradicting the view that only richer men can support a polygamous household (see Table 4.1.4). Education Education and literacy levels are relatively low among farm households of Bénin. The average adult (15 years and older) has 1.4 years of education and less than one in five is literate, defined as being able to read and/or write. These figures show significant regional variation. The departments of Ouémé and Mono have the highest literacy rates (33 and 25 percent, respectively) and the average number of years of schooling is two or more. In contrast, the adult literacy rate is around 10 percent in Borgou and Atacora (see Table 4.1.5). 3 We use the value of per capita consumption expenditure to measure the standard of living of households. Consumption expenditure is defined as the sum of four components: cash expenditures on food, cash expenditure on non-food goods and services, the estimated value of food produced by the household for its own consumption, and the rental equivalent of owner-occupied housing. We define five expenditure categories based on the quintiles of per capita consumption expenditure. Thus, the first category contains the poorest 20 percent of the farm households, the second contains the next-poorest 20 percent, and so on. Expenditure is considered a better measure of the standard of living than income because it is less variable over time and because it measures more directly the material well-being of the household. 37
If we focus our attention on the heads of household, the situation is only slightly more favorable. Twenty-seven percent are literate and the average amount of schooling is 2.1 years. Once again, those in Ouémé and Mono have the highest levels of literacy and schooling. For example, 48 percent of the heads of household in Ouémé are literate, as are 38 percent of those in Mono (see Table 4.1.6). Male-headed households are both more educated and more likely to be literate than female-headed households. For example, the literacy rate is 28 percent among male heads but just 6 percent among female heads. The schooling and literacy of the head of household are strongly correlated with per capita expenditure. For example, literacy is just 20 percent among the heads of households in the poorest expenditure category, but it rises to almost twice that figure (38 percent) among heads of households in the richest category (see Table 4.1.7). This is partly because literacy and education increase income-earning potential, but it is also due to the fact that both variables (education and income) are positively influenced by the income and education of one’s parents. Are education and literacy levels improving over time? The survey results suggest that the new generations are significantly more likely to be educated than the older ones. For example, 43 percent of children between 6 and 10 years old are attending school and 46 percent of those between 11 and 15 are. This implies that rural children are often more educated than their parents and that the average levels of literacy and education will rise as these children become adults. Furthermore, the rates of attendance do not seem to vary between male- and female-headed households (see Table 4.1.8). On the other hand, educational disparities persist among school-aged children: School attendance is significantly higher in the south (Ouémé, Atlantique, and Mono) than in the north (Atacora and Borgou) (see Table 4.1.9) School attendance is markedly higher among higher-income households than among the poor (see Table 4.1.10). 38
Page 1 and 2: IMPACT OF AGRICULTURAL MARKET REFOR
Page 4 and 5: TABLE OF CONTENTS CHAPTER 1 - INTRO
Page 6 and 7: 5.1.10 Expenditure patterns .......
Page 8 and 9: CHAPTER 1 - INTRODUCTION 1.1 Backgr
Page 10 and 11: villageois, (GV)) was needed as the
Page 12 and 13: CHAPTER 2: POLICY BACKGROUND FOR BE
Page 14 and 15: 2.3 Evolution of Economic Policy 2.
Page 16 and 17: Under pressure from the World Bank
Page 18 and 19: anking system has been restored to
Page 20 and 21: Fertilizer can be imported outside
Page 22 and 23: particularly urban wage-earners. On
Page 24 and 25: Soulé (1996) describes the evoluti
Page 26 and 27: led to a more sustainable agricultu
Page 28: Table 2.4 - Gross farm revenue from
Page 31 and 32: Malawi’s economy is heavily depen
Page 33 and 34: the reduction of its operations in
Page 35 and 36: crops is also difficult because tob
Page 37 and 38: Table 3.1 CROP 1982/83 1983/84 1984
Page 40 and 41: CHAPTER 4: RESULTS OF THE BÉNIN SM
Page 42 and 43: where W v is the weight for a house
Page 46 and 47: 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 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 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
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
Page 154 and 155:
Table 4.1.47-Perceived change in av
Page 156 and 157:
Table 4.1.54-Share of producers of
Page 158 and 159:
Table 4.1.59-Fertilizer use by farm
Page 160 and 161:
Table 4.1.64-Characteristics of fer
Page 162 and 163:
Table 4.1.69-Change in fertilizer a
Page 164 and 165:
Table 4.1.75-Allocation of labor by
Page 166 and 167:
Table 4.1.82-Allocation of labor by
Page 168 and 169:
Table 4.1.89-Change in credit avail
Page 170 and 171:
Table 4.1.94-Percent of households
Page 172 and 173:
Page 174 and 175:
Table 4.1.102-Area allocation by cr
Page 176 and 177:
Table 4.1.106-Area allocation by cr
Page 178 and 179:
Table 4.1.110-Marketed share of out
Page 180 and 181:
Table 4.1.114-Marketed share of out
Page 182 and 183:
Table 4.1.117-Regression for market
Page 184 and 185:
Table 4.1.120-Change in the type of
Page 186 and 187:
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:
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 238 and 239:
CHAPTER 5 - RESULTS FROM THE MALAWI
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
Page 288 and 289:
Table 5.12 - Farm labor use and all
Page 290 and 291:
Table 5.15 - Determinants of the de
Page 292 and 293:
Table 5.17 - Sources of fertilizer
Page 294 and 295:
Table 5.22 - Access and use of cred
Page 296 and 297:
Table 5.25 - Percent of households
Page 298 and 299:
Page 300 and 301:
Table 5.34 - Crop disposal by type
Page 302 and 303:
Table 5.38 - Determinants of Market
Page 304 and 305:
Table 5.42 - Summary of expenditure
Page 306 and 307:
Table 5.45 - Food consumption by so
Page 308 and 309:
Page 310 and 311:
Page 312 and 313:
Table 5.51 - Food consumption by ex
Page 314 and 315:
Table 5.56 - Determinants of per ca
Page 316 and 317:
Table 5.59 Percent of households ow
Page 318 and 319:
Table 5.62 - Characteristics of the
Page 320 and 321:
Table 5.68 - Determinants of percei
Page 322 and 323:
Table 5.71 - Average yields per cro
Page 324 and 325:
Table 5.77 - Changes in the availab
Page 326 and 327:
Figure 5.1 Distribution of househol
Page 328 and 329:
Figure 5.7 - Percentage of Househol
Page 330 and 331:
CHAPTER 6 - SUMMARY, CONCLUSIONS AN
Page 332 and 333:
Fertilizer Half of the far
Page 334 and 335:
Regression analysis indicates that
Page 336 and 337:
Among those seeing improvement, the
Page 338 and 339:
Agricultural markets Three quart
Page 340 and 341:
The economic reforms have boosted p
Page 342 and 343:
department. The farms with very lit
Page 344 and 345:
Strengthen the extension service by
Page 346 and 347:
The most time-consuming agricultura
Page 348 and 349:
Most common means of transport to t
Page 350 and 351:
Small farmers in Malawi are mainly
Page 352 and 353:
households have less assets and les
Page 354 and 355:
Place more serious efforts to impro
Page 356 and 357:
REFERENCES Ahoyo Adjovi, N.R. and F
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