Transportation and access to markets

Afghanistan NRVA 2005 57 The table below shows that rural households have the most diversified sources of income; 48 have only one source of income undefined group out of the 8 above while 40 have two sources of income, 12 have three sources of income, and 1 have four sources of income and less than half percent have five sources of income. 25 In contrast, urban dwellers have much less diversified sources of income; 84 have only one source of income trade and services, non-farm or manufacture would be the most likely while15 have two sources of income, 2 have three sources of income and only 0 have four or more sources of income. The exposure to environmental variability in the rural or Kuchi households is dramatically higher than that of the rural dwellers; therefore, income diversification is higher than in urban areas. In contrast, Kuchi households are less diversified than rural households. Table 51: Income diversification Increasing number of groups of income Total Categories 1 2 3 4 5 No. weighted observations Kuchi 62 32 6 1 185,148 100 Rural 48 40 12 1 2,980,859 100 Urban 84 15 2 607,062 100 National 55 35 10 1 3,773,069 100 The groups are any of those in section 3.7 livestock, agriculture, opium, trade and services, manufacture, remittance, other and non-farm. The national percentage of households with only one income group was 55; this percentage was broken down as follows using un-weighted observations: Table 52: Households within the one income group Income No. un-weighted observations Livestock 1,487 9 Agriculture 4,496 27 Opium 110 1 Trade and services 4,696 29 Manufacture 321 2 Remittances 374 2 Other 648 4 Non-farm 4,357 26 Total 16,489 100 The table below presents the combinations of groups for households with two groups of income. Nationally, there were 10,489 households with two groups of income. The most frequent combination of income sources was agriculture with a non-farm activities 22, b livestock 22, or c trade and services 11. 26 A similar pattern was also evident in the rural communities. In comparison, but not surprisingly, the Kuchi were more likely to combine livestock with a non-farm activities 39, b trade and services 10 and c agriculture 17. Households in the urban areas were more likely to combine trade and services with a non-farm activities 26, b manufacturing 15 or c agriculture 13. 25 Because of rounding, anything less than 0.5 is recorded as zero in the tables throughout this report. Real zeros are indicated with empty cells or cells with one dot. 26 The entries in the table depict the interactions of income groups that could eventually be traced to monetary values. The interaction is based on frequencies of households combining two groups of income or employment, rather than on a correlation based on market values derived from these activities. Afghanistan NRVA 2005 58 Table 53: Household income from two sources Group Agriculture Opium Trade an d service s Manufac ture R e m it ta n ce s Othe r Non far m Total Livestock 22 4 1 1 1 6 35 Agriculture 3 11 3 4 2 22 46 Opium 1 1 Trade and services 2 2 2 7 13 Manufacture 2 2 Remittances 2 3 Other 1 1 Total Natio nal 22 4 16 6 7 5 41 100 Natio nal Livestock 17 1 10 2 3 8 39 81 Agriculture 2 2 1 1 6 12 Opium 2 2 Trade and services 1 3 4 Manufacture Remittances 1 1 Other 1 1 Total Kuchi 17 3 12 4 4 8 51 100 Kuchi Livestock 24 4 1 1 1 5 35 Agriculture 4 12 3 4 2 25 50 Opium 1 1 Trade and services 1 2 1 5 9 Manufacture 1 2 Remittances 2 3 Other 1 1 Total Rural 24 4 16 5 7 4 40 100 Rural Livestock 5 3 1 10 Agriculture 13 2 1 7 24 Opium 1 Trade and services 15 4 9 26 54 Manufacture 1 6 7 Remittances 2 2 Other 2 2 Total Urban 5 1 17 17 5 11 44 100 Urban Note: Number of un-weighted observations—national = 10,489, Kuchi = 540, rural = 8994, and urban = 955. The table above was carried out to the second level of income diversification group for the national, Kuchi, rural and urban household categories. A third level, for example, households involved in agriculture, livestock and opium, would have a set of linkages with other sources of income. Even though these figures do not represent flows of income in Afghans they represent frequencies of households or livelihoods attached to different sectors of the economy. These relationships and the frequency of their occurrence can be related with perceptions of well being or risk as well as quantifiable socio-economic variables in NRVA 2005.