Thesis Re-print: Does Selling Fruits or Vegetables - Department of ...

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2.1.2 Supplemental Data Income elasticities data from USDA: The Economic Research Service of USDA estimated income elasticities of demand for a variety of food groups over 127 countries, using national household expenditure survey data (see “International Food Consumption Patterns: Income elasticity for food subgroups,” found at http://www.ers.usda.gov/data/internationalfooddemand). This data is used to demonstrate the expected magnitude and continued rise in demand for higher valued crops, including fresh produce. Population Information from the World Bank: The World Bank has estimates of a variety of population statistics over the past 50 years for every country in the world. This study uses their data on population growth, urban share of population growth and urban population as a percentage of total population growth. These data can be found via their databank link at the website http://data.worldbank.org/topic/urban-development. 2.1.3. Spatial Data This study uses a number of spatial variables created using the latitude and longitude coordinates of each of the 647 TIA villages. These coordinates were not always recorded very precisely in the original questionnaire. To resolve this problem, I use a list of over 10,000 village names in Mozambique and their accurate GIS latitude and longitude coordinates to reassign these verified coordinate points to the villages in the TIA, where possible. Of 647 villages, 236 are matched directly by name, 182 are matched after comparing the TIA names with the list of over 10,000 names and assigning 7
matches where appropriate 3 and 229 are not able to be matched at all. In this latter case the original TIA coordinate data is used. When projected within ESRI’s ArcMap10, 36 of the 647 villages are found to lie outside the national boundaries of Mozambique – all 36 are cases in which the original TIA coordinates were used and no match by village name to a more accurate coordinate point was made. These village-cases are eliminated from the map for the ensuing village variable creation, and their households are assigned the village-average variable values for the district in which they lie. The village level variables created with these coordinate points (later assigned to all households within each respective village) include the following: A. Average population density within a 10 km radius of the village. I generate this variable using the geoprocessing tools buffer, clip, and the “zonal statistics as table” feature within ArcMap10. The data used to create this variable are the United Nations’ 2005 estimates given in population density grids of persons/square kilometer, urban extents, and “urban points”/settlement points, obtained from the Global Rural-Urban Mapping Project (GRUMP), all of which can be obtained from the website http://sedac.ciesin.columbia.edu/gpw/global.jsp. B. Average elevation within a 5 km radius of the village. I generate this variable in a similar way as the population density variable. It uses v4.1 of the 90m digital elevation data collected by the Shuttle Radar Topography Mission (SRTM), originally produced by NASA and obtained from the Consultative Group on International Agricultural Research - Consortium for Spatial Information (CGIAR-CSI). These can be 3 The author thanks Ellen Payongayong and David Tschirley for their help in verifying the accuracy of these matches. 8
Page 1 and 2: AGRICULTURAL RESEARCH INSTITUTE OF
Page 3 and 4: ACKNOWLEDGEMENTS The Directorate of
Page 5 and 6: (2) In the absence of other insuran
Page 7 and 8: for their ongoing prayers and suppo
Page 9 and 10: 4.4.1 Improved inputs or input pack
Page 11 and 12: LIST OF FIGURES FIGURE 1: POPULATIO
Page 13 and 14: APE Average Partial Effect LIST OF
Page 15 and 16: the continent, the broad consensus
Page 17 and 18: Yet, because female-headed househol
Page 19: 2005. Because of attrition, 4,104 o
Page 23 and 24: combining the elevation, land cover
Page 25 and 26: Figure 2: Urban Share of Population
Page 27 and 28: Figure 4: Income Elasticity of Dema
Page 29 and 30: Despite some government support to
Page 31 and 32: Table 2. Household characteristics
Page 33 and 34: Table 4. Household characteristics
Page 35 and 36: Figure 6: Share of fresh produce sa
Page 37 and 38: FFV sales is less, 22% of total inc
Page 39 and 40: Figure 8: Production Behavior of Fr
Page 41 and 42: Figure 10: Maize, Fruit and Vegetab
Page 43 and 44: suggesting that different factors d
Page 45 and 46: challenges for vegetable sales pose
Page 47 and 48: 2.3.3 Persistence of Successful Fre
Page 49 and 50: Descriptive statistics for a variet
Page 51 and 52: Market Access One of the factors th
Page 53 and 54: Table 8: Comparison of Fresh Produc
Page 55 and 56: also significantly more average roa
Page 57 and 58: sense. Average elevation is higher
Page 59 and 60: Chapter 3: Modeling smallholder fre
Page 61 and 62: crop produced almost exclusively un
Page 63 and 64: Neither market access nor price inf
Page 65 and 66: Increasing productivity on fresh pr
Page 67 and 68: Second, population density at the d
Page 69 and 70: Table 9 (cont’d.) Category Asset
Page 71 and 72:
3.2.2 Strength of Asset Base Factor
Page 73 and 74:
valuation variable, also generated
Page 75 and 76:
hill or mountain and has to rise. O
Page 77 and 78:
to help control for adverse agro-ec
Page 79 and 80:
family with more adult females woul
Page 81 and 82:
decision to sell and the quantity d
Page 83 and 84:
3.4 Estimation Proceedure The Cragg
Page 85 and 86:
E 1 2 vx, x 1 1 xit + it | it i
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mean of each time-varying explanato
Page 89 and 90:
The coefficients on population dens
Page 91 and 92:
Table 11 (cont’d.) Maize Probit M
Page 93 and 94:
Table 12 (cont’d.) Fruit Probit F
Page 95 and 96:
with the typically insignificant ef
Page 97 and 98:
sale outcomes, however. These resul
Page 99 and 100:
was created for the 150-day maize g
Page 101 and 102:
to spend additional money on effort
Page 103 and 104:
Chapter 4. Key Findings Strong grow
Page 105 and 106:
generate marketable surplus differs
Page 107 and 108:
Unlike among maize sellers, the var
Page 109 and 110:
growth in use, the proliferation of
Page 111 and 112:
4.4.3. Fruit and vegetable sellers
Page 113 and 114:
APPENDICES 100
Page 115 and 116:
Appendix B: Cost-Distance Travel Ti
Page 117 and 118:
Map 2: Cost Distance Surface using
Page 119 and 120:
Page 121 and 122:
Page 123 and 124:
Map 8: Rainfall in Southern Africa
Page 125 and 126:
REFERENCES 112
Page 127 and 128:
Center for International Earth Scie
Page 129 and 130:
Report (Vol. 53E). Maputo, Mozambiq
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