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

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Table 4.1.181-Regression for perceived change in well-being change Coef. Std. Err. z P>|z| 95% Conf. <strong>In</strong>terval lnexppc -.2054401 .0985885 -2.08 0.037 -.3986701 -.0122102 mktsurp .0035438 .0020357 1.74 0.082 -.000446 .0075337 hhsize -.0207655 .0124848 -1.66 0.096 -.0452351 .0037042 under15p .2679314 .2498048 1.07 0.283 -.2216771 .7575399 over65p .1332301 .4562656 0.29 0.770 -.761034 1.027494 femhead -.231811 .2441472 -0.95 0.342 -.7103307 .2467088 agehead -.0006692 .0176371 -0.04 0.970 -.0352373 .0338989 age2 -.0000754 .0001789 -0.42 0.673 -.000426 .0002752 edhead .0294055 .0267765 1.10 0.272 -.0230755 .0818864 lithead -.3547115 .1864592 -1.90 0.057 -.7201649 .0107418 spouse .0922859 .2246507 0.41 0.681 -.3480214 .5325932 edsps -.0363134 .0320437 -1.13 0.257 -.099118 .0264911 litsps -.0920824 .1654687 -0.56 0.578 -.4163951 .2322302 cotton .2613276 .1452739 1.80 0.072 -.0234041 .5460592 fsize1 .0112825 .0141542 0.80 0.425 -.0164592 .0390243 fsize2 .0517047 .0333987 1.55 0.122 -.0137557 .117165 irrig .4557959 .3337666 1.37 0.172 -.1983745 1.109966 basfond -.1837732 .1342883 -1.37 0.171 -.4469734 .079427 connect .0639005 .0943407 0.68 0.498 -.1210039 .2488049 fon -.1974852 .1333296 -1.48 0.139 -.4588064 .0638361 gv -.0965004 .1296053 -0.74 0.457 -.3505222 .1575214 tontine .0760265 .0988257 0.77 0.442 -.1176683 .2697213 extcont .4640769 .1179854 3.93 0.000 .2328298 .695324 distsp -.0052027 .0024944 -2.09 0.037 -.0100916 -.0003138 radio .0477592 .0968805 0.49 0.622 -.1421231 .2376416 tv .65714 .2322529 2.83 0.005 .2019326 1.112347 bike .0601497 .1008815 0.60 0.551 -.1375745 .2578739 mbike .1780742 .1124442 1.58 0.113 -.0423125 .3984608 dpt1 1.261566 .2358351 5.35 0.000 .7993374 1.723794 dpt3 .7922735 .22932 3.45 0.001 .3428146 1.241732 dpt4 .1048854 .2085674 0.50 0.615 -.3038991 .51367 dpt5 2859493 .1818112 1.57 0.116 -.070394 .6422927 dpt6 .21851 .1965316 1.11 0.266 -.1666847 .6037048 Ordered probit estimates Number of obs = 898 LR chi2(33) = 167.37 Prob > chi2 = 0.0000 Log likelihood = -805.19386 Pseudo R2 = 0.0941 205
Table 4.2.1-Road infrastructure Region North Center South Total Length of road in village (km) . . . . Paved road 1 0 1 1 All-season road 11 3 5 6 Dry-season road 10 8 4 6 Pistes 28 15 7 16 Period when not usable (months) . . . . Paved road 0 0 0 0 All-season road 1 0 0 0 Dry-season road 3 5 2 3 Pistes 3 6 2 3 Source: IFPRI-LARES Community Survey Table 4.2.2-Change in road length Region North Center South Total <strong>In</strong>crease 24% 10% 12% No change 69% 100% 90% 85% Decrease 7% 2% Total 100% 100% 100% 100% Source: IFPRI-LARES Community Survey Table 4.2.3-Change in road quality Region North Center South Total Improved 32% 16% 28% 27% No change 7% 68% 26% 29% Deteriorated 61% 16% 46% 45% Total 100% 100% 100% 100% Source: IFPRI-LARES Community Survey Table 4.2.4-Number of vehicles used for agricultural transport Region North Center South Total Bicycles 130 44 115 102 Push-carts 1 7 4 4 Carts 8 4 1 4 Motorbikes 26 13 26 23 Cars 1 2 1 1 Pick-up trucks 1 1 1 1 Truck 0 1 0 0 Trailer trucks 0 0 0 0 Source: IFPRI-LARES Community Survey 206
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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
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
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Table 2.4 - Gross farm revenue from
Page 31 and 32:
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
Page 37 and 38:
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
Page 46 and 47:
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
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 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 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 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
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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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Page 300 and 301:
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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Page 310 and 311:
Page 312 and 313:
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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