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.