Income Diversification and Poverty Income Diversification and Poverty

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Chapter 3. Patterns and trends in diversification much growth in crop production income as higher income households in that region. The composition of this growth does, however, vary across income groups. Among the poorest quintile of farmers, the increase in yields accounts for about 60 percent of the increase in crop income (see Table 3-34). The contributions of area expansion, yield increases, higher prices, and crop diversification to crop income growth in the rural Northern Uplands shows a somewhat erratic pattern. Area expansion seems to have played a more important role in crop income growth among the households that had relatively high income. This result suggests that households with relatively high incomes in 1993 were able to use those resources to secure more land for planting crops, either through the land allocation process, through the (formal or informal) purchase of land-use certificates, or through land rental. The contribution of yield increases, though inconsistent, seems to indicate that this factor plays a more important role in the crop income growth of poor rural households. This result is plausible since yields can be increased by applying labor more intensively and through the use of improved seed and fertilizer, which are generally scale-neutral forms of agricultural technology 23 . Though the pattern is not consistent, crop diversification may play a somewhat greater role in the crop income growth of higher income rural households. This result is supported by findings from the Qualitative Social Assessment, in which higher income farmers were more likely to cite crop diversification as an explanation for increases in their income over time (see Chapter 5). Table 3-34. Sources of growth in net income from crop production by expenditure category Crop Expenditure Areas Price Higher diversicategory expansion increase yield fication Interaction Total Poorest -5 30 61 10 4 100 2 12 33 48 0 9 100 3 -18 57 68 9 -16 100 4 24 32 40 3 1 100 Richest 68 32 -3 27 -24 100 Average 16 37 43 10 -6 100 Source: Analysis of the 1993 and 1998 VLSSs (panel households only). Note: Expenditure categories are defined according to the level in 1993. These patterns are also seen across quintiles in other regions. Combining all the rural farm households together and classifying them by expenditure quintile, we see that crop diversification and area expansion contribute more to crop income growth among higher income households than among poor households. Furthermore, as in the Northern Uplands, poor households rely more on yield increases to boost the value of their crop income. 23 Because seed and fertilizer can be purchased in small quantities, this type of agricultural technology is considered more scale-neutral (benefiting small and large farmers equally) than mechanical technology. Page 78
Chapter 3. Patterns and trends in diversification 3.9 Determinants of income diversification This section examines the effect of different household characteristics on patterns of income diversification among rural households in Vietnam using data from the 1998 Vietnam Living Standards Survey. The hypothesis is that the share of income from different sources is influenced by the characteristics of the household, particularly the amount and quality of labor, the amount and quality of land, access to markets, access to electricity, and the region. We use a linear model in which the income share is a function of household characteristics 24 . The characteristics included in the analysis are 25 : • age of the head of household (years), • age squared of the head, • education of the head of household (years), • household head is a member of ethnic minority (=0 if Kinh or Hoa, =1 otherwise), • household size (members), • proportion of household members under 10 (fraction), • proportion of household members over 60 (fraction), • female-headed household (=1 if female-headed), • household has electricity in the home, • annual crop land (m2), • perennial crop land (m2), • irrigated crop land (m2), • distance from village to a paved road (km), • number of months per year road to village is impassible, and • dummy variables for region (Red River Delta is reference region). Access to credit, defined as the ability to obtain a loan, would be a useful variable to include, but the VLSS can only tell us whether or not a household has actually received credit. This variable is less useful in the regression analysis because it is endogenous. A farmer may seek a loan because he wants to diversify, so that the coefficient would be affected by simultaneity bias. The first model estimates the share of income from crop production (see Table 3-35). Crop production is represents a smaller share of net income when the head of household has more education, presumably because education opens up opportunities for wage and enterprise income. Crop income is also a smaller share of the total when the head is female, probably due to the more limited availability of family labor and the greater likelihood of remittance income (as discussed below). Not surprisingly, the crop income share is also positively associated with the amount of 24 Because income shares are “clumped” at zero and (to a lesser degree) one, the assumption of a normally distributed error term is violated in this situation. The usual method of dealing with this situation would be to use a censored regression model (similar to a Tobit) with censoring below 0 and above 1. Unfortunately, the presence of negative values for net income (particularly for livestock income and enterprise income) means that the income share may be less than zero or greater than one, so that the assumptions of the Tobit model are also violated. Both linear and Tobit models were estimated and the results are broadly consistent, so we present on the results of the linear model. 25 Some variables were not used in the analysis because they might be endogenous (influenced by the occupation decision). For example, “credit” was not included because the choice of occupation could influence the decision whether to apply for credit. The VLSS did not collect information on the availability of credit. Page 79
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Income Diversification and Poverty
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Contact information: Nicholas Minot
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USA), with assistance from Dr. Finn
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Chapter 4. Analysis of food demand
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CHAPTER ONE INTRODUCTION 1.1 Introd
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Chapter 1. Introduction into high-v
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Chapter 1. Introduction In addition
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Chapter 1. Introduction seemingly u
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Chapter 2. Background on the Northe
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Figure 2-3. Index of accessibility
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Figure 2-5. Main ethnic group in ea
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Chapter 5: Income Diversification f
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Chapter 6. Diversification from the
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Table 7.4: Structure of Marketed Su
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Chapter 8. Summary and conclusions
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References Page 200
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References Escobal, J. 2001. “The
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References Stone, R. 1981. Aspects
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Glossary Page 206
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Marshallian price elasticity: the r
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Appendix A: Supply response analysi
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Appendix B: Interview Guidelines fo
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Appendix C: Interview Guidelines fo
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Appendix C: Interview Guidelines fo
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Appendix D: Constructing a standard
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Appendix E: Details of MacroSAM Con
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Appendix E: Details of MacroSAM con
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Appendix E: Details of MacroSAM con
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Appendix E: Details of MacroSAM Con
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Table A7.3: Ha Giang: Provincial Ma
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Table A7.7: Lang Son: Provincial Ma
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Table A7.11: Phu Tho: Provincial Ma
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Table A7.15: Son La: Provincial Mac
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