274
The final household sampling weight hw
hsq
is the product of all weighting factors: hw
sq
=
dnr
w
hs
w
hsq
Individual weights
In order to obtain the expansion factor for individuals the following calculation was made: iw
hsq
= hw
hsq
hs
hsq
the term hs
hsq
being the household size of household h in stratum s and quarter q.
IV.8.2 Kuchi population The Kuchi sample was designed on basis of the 2003-04 National Multi-sectoral Assessment of Kuchi
NMAK-2004. For this separate Kuchi stratum a community selection was implemented with PPS and a second stage selection with again a constant cluster size of ten households. The 66 clusters 660
households for this stratum were divided between the summer 30 clusters and winter 36 clusters periods in 1393 2014.
In the absence of up-to-date information about the actual number of Kuchis and the poltical sensitivity of addressing this issue, the present position taken by CSO is that the Kuchi pupolation is stable at a
number close to 1.5 million people. Apart from the sampling frame, the restriction to two seasons and the absence of the need to
accommodate population growth, the procedures for the calculation of the sampling weights for the Kuchi stratum are the same as those for the resident population
IV.8.3 Weights variables The values of the final household sample weight hw
hsq
and individual sample weight iw
hsq
are included in the ALCS 2013-14 dataset as weight variables hh_weight and ind_weight, respectively. The weight
variable hh_weight expands household-level data to the total population of households and individual- level data to the total population of individuals. The weight variable ind_weight expands household-
level data to the total population individuals.
275
ANNEX V POPULATION TABLES
Table V.1: Population, by residence, sex, and by five-year age groups in thousands
Age Urban
Rural Kuchi
National Male Female Both
sexes Male Female Both
sexes Male Female Both
sexes Male Female Both
sexes 0-4
491 467
958 1,794
1,772 3,567
143 144
286 2,428
2,383 4,811
5-9
477 452
930 1,744
1,599 3,342
161 136
297 2,382
2,187 4,569
10-14
520 460
980 1,481
1,276 2,757
123 78
201 2,124
1,814 3,938
15-19
443 455
898 1,090
1,032 2,122
77 56
133 1,610
1,543 3,153
20-24
356 341
696 823
843 1,666
51 49
100 1,230
1,233 2,463
25-29
227 237
464 670
694 1,364
42 48
91 939
980 1,919
30-34
155 158
313 487
469 956
37 36
73 679
664 1,343
35-39
143 166
309 390
446 836
32 38
70 565
650 1,215
40-44
125 134
259 365
373 738
29 24
53 519
531 1,050
45-49
119 111
230 301
343 644
20 19
39 440
474 914
50-54
94 125
218 312
308 620
18 19
38 424
451 876
55-59
72 65
136 187
168 354
13 13
26 272
245 517
60-64
77 60
136 209
166 375
16 6
22 301
232 533
65-69
44 30
74 110
68 179
7 3
9 161
102 263
70-74
39 31
70 111
59 170
7 5
12 157
96 252
75-79
18 11
29 39
20 59
2 1
3 59
32 90
80-84
16 7
23 33
14 47
2 1
3 51
22 73
85+
10 5
15 19
8 26
1 1
29 13
42
Total
3,425 3,317
6,742 10,163
9,657 19,821
781 676
1,458 14,370
13,651 28,020
Table V.2: Population, by residence, sex, and by five-year age groups in percentages
Age Urban
Rural Kuchi
National Male Female Both
sexes Male Female Both
sexes Male Female Both
sexes Male Female Both
sexes 0-4
14.3 14.1
14.2 17.7
18.4 18.0
18.3 21.2
19.6 16.9
17.5 17.2
5-9
13.9 13.6
13.8 17.2
16.6 16.9
20.6 20.1
20.3 16.6
16.0 16.3
10-14
15.2 13.9
14.5 14.6
13.2 13.9
15.7 11.6
13.8 14.8
13.3 14.1
15-19
12.9 13.7
13.3 10.7
10.7 10.7
9.9 8.3
9.1 11.2
11.3 11.3
20-24
10.4 10.3
10.3 8.1
8.7 8.4
6.6 7.2
6.9 8.6
9.0 8.8
25-29
6.6 7.2
6.9 6.6
7.2 6.9
5.4 7.1
6.2 6.5
7.2 6.8
30-34
4.5 4.8
4.6 4.8
4.9 4.8
4.7 5.4
5.0 4.7
4.9 4.8
35-39
4.2 5.0
4.6 3.8
4.6 4.2
4.1 5.6
4.8 3.9
4.8 4.3
40-44
3.6 4.0
3.8 3.6
3.9 3.7
3.7 3.5
3.6 3.6
3.9 3.7
45-49
3.5 3.4
3.4 3.0
3.5 3.2
2.6 2.9
2.7 3.1
3.5 3.3
50-54
2.7 3.8
3.2 3.1
3.2 3.1
2.4 2.8
2.6 3.0
3.3 3.1
55-59
2.1 2.0
2.0 1.8
1.7 1.8
1.7 1.9
1.8 1.9
1.8 1.8
60-64
2.2 1.8
2.0 2.1
1.7 1.9
2.0 1.0
1.5 2.1
1.7 1.9
65-69
1.3 0.9
1.1 1.1
0.7 0.9
0.9 0.4
0.6 1.1
0.7 0.9
70-74
1.2 0.9
1.0 1.1
0.6 0.9
0.9 0.8
0.8 1.1
0.7 0.9
75-79
0.5 0.3
0.4 0.4
0.2 0.3
0.3 0.1
0.2 0.4
0.2 0.3
80-84
0.5 0.2
0.3 0.3
0.1 0.2
0.3 0.1
0.2 0.4
0.2 0.3
85+
0.3 0.2
0.2 0.2
0.1 0.1
0.1 0.0
0.1 0.2
0.1 0.2
Total
100.0 100.0
100.0 100.0
100.0 100.0
100.0 100.0
100.0 100.0
100.0 100.0
276
Table V.2: Population, by sex, and by province, age groups in thousands
Province, Sex
age group Male
Female Both sexes
Total
14,370 13,651
28,020
0-14
6,934 6,384
13,318
15-24
2,840 2,776
5,616
25-39
2,184 2,294
4,477
40-59
1,956 1,933
3,889
60+
456 264
720
Badakhshan
517 484
1,001
0-14
242 228
470
15-24
105 98
203
25-39
75 80
155
40-59
75 69
144
60+
18 9
27
Badghis
301 254
555
0-14
150 119
269
15-24
49 42
90
25-39
56 56
112
40-59
41 35
76
60+
6 2
8
Baghlan
446 451
897
0-14
212 214
425
15-24
94 96
190
25-39
59 68
127
40-59
66 64
130
60+
15 9
24
Balkh
655 663
1,318
0-14
288 277
565
15-24
147 149
296
25-39
96 116
212
40-59
94 97
191
60+
31 24
54
Bamyan
245 230
476
0-14
107 106
214
15-24
54 49
103
25-39
40 38
77
40-59
34 32
66
60+
10 5
16
277
Daykundi
210 208
418
0-14
106 99
205
15-24
36 39
75
25-39
30 37
68
40-59
29 29
58
60+
9 3
13
Farah
278 261
539
0-14
140 128
268
15-24
55 58
113
25-39
43 43
86
40-59
30 27
57
60+
10 5
15
Faryab
516 520
1,036
0-14
234 229
463
15-24
102 112
214
25-39
79 83
162
40-59
75 79
154
60+
26 17
43
Ghazni
686 595
1,280
0-14
313 256
569
15-24
142 122
264
25-39
103 102
206
40-59
106 106
212
60+
21 8
29
Ghor
372 353
725
0-14
188 179
367
15-24
59 59
119
25-39
69 69
138
40-59
50 42
92
60+
5 4
9
Helmand
494 415
909
0-14
257 220
477
15-24
108 75
182
25-39
65 64
129
40-59
53 51
104
60+
11 5
16
Herat
966 955
1,922
0-14
464 434
898
15-24
207 214
420
25-39
124 151
275
40-59
136 139
276
60+
36 17
53
Jawzjan
280 252
532
0-14
114 102
215
15-24
59 52
112
25-39
48 50
98
40-59
49 44
93
60+
9 5
14
278
Kabul
2,263 2,170
4,433
0-14
965 887
1,852
15-24
541 543
1,083
25-39
355 353
708
40-59
318 328
646
60+
85 59
143
Kandahar
615 601
1,216
0-14
325 294
619
15-24
94 110
205
25-39
90 97
187
40-59
87 87
174
60+
18 12
31
Kapisa
218 217
435
0-14
99 99
198
15-24
47 46
93
25-39
34 35
68
40-59
29 33
61
60+
9 5
14
Khost
322 287
609
0-14
171 147
318
15-24
59 48
107
25-39
46 51
97
40-59
39 38
77
60+
7 4
11
Kunarha
240 240
480
0-14
128 129
257
15-24
48 48
96
25-39
30 32
62
40-59
28 28
55
60+
6 4
10
Kunduz
537 505
1,042
0-14
259 233
492
15-24
109 108
217
25-39
86 85
171
40-59
63 70
133
60+
19 9
28
Laghman
249 241
490
0-14
136 130
266
15-24
42 45
87
25-39
36 37
73
40-59
30 27
57
60+
5 2
7
Logar
224 238
461
0-14
117 122
239
15-24
24 30
54
25-39
55 53
108
40-59
24 30
53
60+
3 3
7
279
Nangarhar
943 789
1,732
0-14
527 431
959
15-24
162 121
283
25-39
124 130
254
40-59
114 98
211
60+
16 10
26
Nimroz
85 76
161
0-14
44 36
80
15-24
18 17
35
25-39
12 12
23
40-59
10 10
20
60+
2 1
3
Nooristan
78 64
143
0-14
42 33
75
15-24
12 11
23
25-39
13 12
26
40-59
8 7
15
60+
3 1
4
Paktika
231 198
430
0-14
113 79
192
15-24
39 44
84
25-39
43 36
80
40-59
35 39
74
60+
Paktya
299 278
577
0-14
153 131
284
15-24
53 53
106
25-39
43 49
92
40-59
39 41
80
60+
10 4
15
Panjsher
76 74
150
0-14
33 32
65
15-24
19 18
37
25-39
9 11
20
40-59
11 11
23
60+
3 2
6
Parwan
358 345
703
0-14
167 150
317
15-24
77 81
158
25-39
52 50
101
40-59
51 57
108
60+
12 6
18
280
Samangan
199 185
383
0-14
97 85
182
15-24
39 36
74
25-39
28 31
59
40-59
28 28
57
60+
7 4
11
Sar-e-Pul
300 294
594
0-14
142 137
279
15-24
59 57
115
25-39
44 53
97
40-59
46 41
86
60+
9 7
16
Takhar
492 475
967
0-14
239 225
463
15-24
94 94
188
25-39
73 86
159
40-59
68 63
131
60+
18 7
25
Urozgan
180 204
384
0-14
103 121
224
15-24
27 33
60
25-39
26 28
54
40-59
20 19
39
60+
4 3
7
Wardak
328 344
672
0-14
165 179
344
15-24
46 50
96
25-39
61 64
125
40-59
47 45
93
60+
8 5
14
Zabul
169 184
353
0-14
95 112
207
15-24
15 17
32
25-39
34 34
69
40-59
22 18
41
60+
2 2
5
281
ANNEX VI TECHNICAL NOTE ON SURVEY TO SURVEY IMPUTATION: POVERTY PROJECTION FOR
AFGHANISTAN
The ‘survey-to-survey imputation’ technique uses a model built from existing data from one survey to estimate missing data in another survey. Using the NRVA 2011-12 survey, which has consumption data,
we used the survey-to-survey imputation method to estimate household consumption expenditure and poverty rates for the ALCS 2013-14, which does not have consumption data. This annex describes in
detail the survey-to-survey imputation methodology.
68
VI.1 Data