Impact Of Agricultural Market Reforms On Smallholder Farmers In ...
Impact Of Agricultural Market Reforms On Smallholder Farmers In ... Impact Of Agricultural Market Reforms On Smallholder Farmers In ...
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
- Page 48 and 49: Crop production is the main activit
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- 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
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- 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
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- Page 142 and 143: Table 4.1.13-Ethnic background of h
- Page 144 and 145: Table 4.1.19-Importance of each sou
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non-food spending. These are followed by clothing (16 percent), health (11 percent), and<br />
transportation (11 percent) (see Table 4.1.145).<br />
-<br />
Non-food spending patterns vary somewhat across departments, but the differences are not large.<br />
Households in Atacora spend a larger proportion of their non-food budget on social events and<br />
“leisure and other” than do other departments. Those in Atlantique allocate a larger share to<br />
housing, transportation, and health. Households in Borgou devote more of the non-food budget to<br />
clothing and household goods. The departments of Mono, Ouémé, and Zou have non-food budgets<br />
that closely resemble the national average with one exception: households in Mono spend one<br />
quarter of their non-food budget on social events, more than any other department. Nonetheless, it<br />
should be kept in mind that large and infrequent budget items such as social events and housing<br />
repair, are subject to larger measurement errors than small frequent purchases (see Table 4.1.146).<br />
The structure of non-food spending does not differ much between male- and female-headed<br />
households. Female-headed households appear to spend a somewhat larger share of the non-food<br />
budget to clothing, housing, and household goods. <strong>On</strong> the other hand, male-headed households<br />
allocate a larger share to “leisure and other” (see Table 4.1.147).<br />
The composition of non-food spending does vary significantly across expenditure categories. The<br />
share of the non-food budget devoted to clothing, household goods, and energy fall with per capita<br />
expenditure. For example, clothing accounts for 25 percent of non-food spending among<br />
households in the poorest category, but the percentage falls to just 11 percent in the highest<br />
expenditure category. Other types of non-food spending become more important as expenditure<br />
rises: housing, transportation, and “leisure and other.” For example, spending on housing (repairs<br />
and maintenance) rise from just 1 percent of non-food spending among the poorest households to<br />
13 percent among those in the highest category (see Table 4.1.148).<br />
Regression analysis of expenditure<br />
This section uses regression analysis to examine the relationship between various<br />
household characteristics and its standard of living, as measured by per capita consumption<br />
expenditure. The result is an equation that “predicts” per capita expenditure on the basis of these<br />
household characteristics 17 . This type of analysis sheds light on some of the causes of poverty and<br />
17<br />
Regression analysis finds the equation that best fits the data, given one dependant and various<br />
independent (or explanatory) variables. The analysis generates coefficients for each independent variable that<br />
91
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