Rural Income Generation and Diversification - A Case Study ... - Doria

More documents

Recommendations

Info

100 6.1.9 The results of the econometric analyses Table 18 below presents the descriptive statistics of the independent variables used in the equations. A common set of explanatory variables was used in the OLS analysis for the total income determinants and in the Tobit analyses for the determinants of income levels and shares in order to allow comparisons of the effects of the variables between the models. Table 18. Descriptive statistics for the independent variables Category Variable Min. Max. Mean Std.dev. Human capital Natural capital Age of hh head, years 19 89 49 16.63 Sex of hh head, female = 1 0 1 0.289 0.46 Education of hh head, level 0 16 4.6 3.63 Dependency ratio 0 4 0.64 0.59 Number of adults 1 9 3.96 1.88 Number of children 0 8 2.15 1.67 Cultivated land /adult, ha 0 1.75 0.49 0.34 Cultivated land, ha 0 7.60 1.74 1.15 District Dummy, Mambwe = 1 0 1 0.25 0.43 Physical Distance to capital/100, km 0.2 2.35 1.36 0.78 capital Distance to nearest town, km 6 45 22 10.82 Financial Value of household assets/100 000 0 378 14.23 35.35 capital ZMK Social Capi- Participation index, Proxy for so- 0 5 2.4 1.24 talcial capital, lowest = 0 Others Vulnerability index, lowest = 0 0 5 1.94 1.15 Source: the researcher’s own dataset 2003. Table 19 lists the results of the OLS and Tobit analyses of the determinants of the levels for total income and six individual income sources. The ordinary least squares (OLS) method was used to estimate the determinants of the total income level, since all the households included had income from one or more sources. The Tobit model was used for estimating the individual income-source determinants because it applies in situations, in which observations are censored: in this study, for example, there were households with zero income from a single source. The rationale for using the Tobit model is discussed in Chapter 5.2.4. Some heteroscedasticity was detected in the Tobit equations 51 and corrective measures were taken in 51 Some of the coefficients were found to be statistically significant when the Tobit estimation residuals were regressed on the original independent variables. This was taken into account in the final estimation by using the corrective measures available in the LIMDEP program.
the specification and estimation such as scaling the values of some of the variables and weighing those causing heteroscedasticity. An increase in the area of cultivated land per adult member, and having a male head of household, a higher participation rate and higher asset value increased the total income significantly. On the other hand, the higher the vulnerability, the lower was the total income. The corrected R-squared remained low (0.20), which suggests rather low explanatory power of the selected variables and the specified equation. Some of the variables turned out to be statistically significant in the analyses of the levels of income from each source. First, crop income increased in line with an increase in the area of cultivated land per adult household member and the participation rate. Secondly, the coefficients indicated a smaller livestock income in the female-headed households and those households in Mambwe district than in the male-headed households and those in other districts. The older the household head, the higher was the livestock income. Thirdly, the higher the dependence ratio, the more income the household obtained from forestry. Fourthly, a higher dependence ratio indicated a higher business income – thus the more children per adult, the higher as the business income. Fifthly, wage income increased the further the distance from Chipata (the provincial capital), and decreased in line with an increase in the area of cultivated land per adult. Finally, the higher the age and the educational level of the household head, the more transfer income was received, whereas the household-participation rate affected the transfer income negatively. The negative relationship between transfers and participation could be explained by the fact that food aid constituted a major share of transfer income, and households with a high participation rate received less food aid than those with a low rate. High participation was also associated with wealthier households with higher incomes. Analysis of the income-share determinants (Table 20), revealed similarities and differences in the coefficient significance compared with the analysis in income levels. First, the proportion of crop income increased the more cultivated land per adult member the household had. Secondly, the higher the age of the household head, the bigger was the proportion of livestock income, while location in the valley (Mambwe district) seemed to decrease it. Thirdly, the proportion of forest income was bigger in households with higher vulnerability and dependence ratio, and smaller in those that had older heads, were situated far from the capital, and cultivated larger areas. Fourthly, higher dependence ratio and location in the Mambwe district suggested a higher proportion of business income, and fifthly, the wage-income share was higher the longer the distance to the provincial capital and the smaller the size of cultivated land per adult. Finally, the higher the age and educational level of the household head, the higher was the proportion of transfer income, while a higher participation rate had the opposite effect. 101
Page 1 and 2:
Kaisa Karttunen Rural Income Genera
Page 3 and 4:
University of Helsinki Department o
Page 5 and 6:
CONTENTS 1. Introduction ..........
Page 7 and 8:
Table 6. Percentages of income from
Page 9:
WFP World Food Programme WHO World
Page 12 and 13:
12 man 2002). These include income-
Page 14 and 15:
14 The following questions were for
Page 16 and 17:
16 of household comsumption is also
Page 18 and 19:
18 Zambian farmers fall into three
Page 20 and 21:
20 impact on rural poverty. Startin
Page 22 and 23:
22 1992 consumer and transportatio
Page 24 and 25:
24 The Government of Zambia (GRZ) i
Page 26 and 27:
26 inadequate monitoring of the act
Page 28 and 29:
28 Distributor Scheme was fully in
Page 30 and 31:
30 as tobacco and cotton the out-gr
Page 32 and 33:
32 households were directly afflict
Page 34 and 35:
34 If it had no control over the m
Page 36:
36 According to the AHM, a househol
Page 39 and 40:
icultural economy was opened up to
Page 41 and 42:
Similar concerns were shared by Tay
Page 43 and 44:
order to reduce income variablity.
Page 45 and 46:
of infrastructure development, the
Page 47 and 48:
which implies that diversification
Page 49 and 50: Haggblade et al. (2002) argue that
Page 51 and 52: In many cases the processing of the
Page 53 and 54: 4.9 Rural income diversification in
Page 55 and 56: Table 3. Rural income-diversificati
Page 57 and 58: 1990s - made the farmers reluctant
Page 59 and 60: In addition to asset endowments and
Page 61 and 62: 5.1.2 The 2003 dataset The second s
Page 63 and 64: clarifying some concepts and assess
Page 65 and 66: 5.2 Methods The theoretical grounds
Page 67 and 68: able random variables not explicitl
Page 69 and 70: Incshare 25 or Inclevel 26 = β0 +
Page 71: 5.2.4 Choosing the estimation metho
Page 74 and 75: 74 Both cash and in-kind income wer
Page 76 and 77: 76 Share of income 100 90 80 70 60
Page 78 and 79: 78 The households considered market
Page 80 and 81: 80 The proportion of income generat
Page 82 and 83: 82 The seasonality effect was clear
Page 84 and 85: 84 Since a lack of money was given
Page 86 and 87: 86 % of income 100 90 80 70 60 50 4
Page 88 and 89: 88 100 90 80 70 60 50 40 30 20 10 0
Page 90 and 91: 90 6.1.7 Grouping households accord
Page 92 and 93: 92 number of adult members, was cri
Page 94 and 95: 94 more likely to use fertilisers a
Page 96 and 97: 96 holds. According to the responde
Page 98 and 99: 98 The following process was follow
Page 102 and 103: 102 The categorisation of household
Page 104 and 105: 104 Table 20. Determinants of the s
Page 106 and 107: 106 In some cases the former househ
Page 108 and 109: 108 indicated thus an opposite tren
Page 110 and 111: 110 The Simpson diversification ind
Page 112 and 113: 112 tares - the difference being st
Page 114 and 115: 114 which brought more purchasing p
Page 116 and 117: 116 diversification index. Block an
Page 118 and 119: 118 evidence that households had no
Page 120 and 121: 120 such as piecework and forest la
Page 122 and 123: 122 7.2 Changes in the households b
Page 124 and 125: 124 lower soil fertility, and the l
Page 126 and 127: 126 8. Conclusions and recommendati
Page 128 and 129: 128 Rural non-farm activities are l
Page 130 and 131: 130 Politiikkamuutosten seurauksena
Page 132 and 133: 132 References Abdulai, A. & CroleR
Page 134 and 135: 134 CSO. 1997. Living Conditions Mo
Page 136 and 137: 136 Ellis, F., Kutengele, M. & Nyas
Page 138 and 139: 138 Heltberg, R. (mimeo) 2001. Comm
Page 140 and 141: 140 Lanjouw, J.O. & Lanjouw, P. 200
Page 142 and 143: 142 Ruotsi, J., Lindberg, O., Seger
Page 144 and 145: 144 Zulu, B., Nijhoff, J.J., Jayne,
Page 146 and 147: 146 Annex 2. The theoretical framew
Page 148 and 149: 148 1,050 millimetres, while in the
Page 150 and 151:
150 Annex 4. Schedule of the field
Page 152 and 153:
152 Annex 5. Three household cases
Page 154 and 155:
154 In the 1970s and 1980s, and eve
show all

Rural Income Generation and Diversification - A Case Study ... - Doria

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?