Weights variables The values of the final household sample weight hw

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