Kenya Integrated Household Budget Survey 2005-2006
Kenya, 2005 - 2006
Kenya National Bureau of Statistics
Created on
September 14, 2022
Last modified
September 14, 2022
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- Data files
-
Section U
Credit -
Section R
Transfers -
Section S Other
Income -
Section T
Recent Shocks -
consumption
aggregated data -
Section A
Identification -
Section H2
Energy -
Section G
Housing -
Section H1
Water
Sanitation -
Section C
education -
Section D
Health -
Section B
Household
member
Information -
section D
Deaths -
Section D
Fertility -
Section E
Labour -
Section Q
Household
Enterprises -
Section F Child
health -
Section N
Agriculture
Holding -
Section O
Agriculture
Output -
Section P1
Livestock -
Section P2
Livestock
District code (district)
Data file: Section A Identification
Overview
Valid:
13212
Valid (weighted):
35533585.6
Invalid:
0
Invalid (weighted):
0
Minimum:
101
Maximum:
808
Questions and instructions
Literal question
DISTRICT
Categories
| Value | Category | Cases | Weighted | |
|---|---|---|---|---|
| 101 | Nairobi | 672 | 2811412 |
7.9%
|
| 201 | Kiambu | 268 | 983765 |
2.8%
|
| 202 | Kirinyaga | 190 | 541866 |
1.5%
|
| 203 | Muranga | 167 | 367386 |
1%
|
| 204 | Nyandarua | 190 | 541615 |
1.5%
|
| 205 | Nyeri | 249 | 757361 |
2.1%
|
| 206 | Thika | 247 | 723303 |
2%
|
| 207 | Maragua | 170 | 468141 |
1.3%
|
| 301 | Kilifi | 169 | 603872 |
1.7%
|
| 302 | Kwale | 169 | 735141 |
2.1%
|
| 303 | Lamu | 170 | 80472 |
0.2%
|
| 304 | Mombasa | 240 | 890596 |
2.5%
|
| 305 | Taita Taveta | 167 | 306935 |
0.9%
|
| 306 | Tana River | 170 | 273838 |
0.8%
|
| 307 | Malindi | 170 | 387814 |
1.1%
|
| 401 | Embu | 169 | 367098 |
1%
|
| 402 | Isiolo | 169 | 113698 |
0.3%
|
| 403 | Kitui | 179 | 644013 |
1.8%
|
| 404 | Makueni | 219 | 990023 |
2.8%
|
| 405 | Machakos | 260 | 1201430 |
3.4%
|
| 406 | Marsabit | 159 | 145581 |
0.4%
|
| 407 | Mbeere | 170 | 205423 |
0.6%
|
| 408 | Meru Central | 195 | 602696 |
1.7%
|
| 409 | Moyale | 170 | 75782 |
0.2%
|
| 410 | Mwingi | 170 | 384254 |
1.1%
|
| 411 | Nyambene | 192 | 731215 |
2.1%
|
| 412 | Tharaka | 170 | 119873 |
0.3%
|
| 413 | Meru South | 169 | 251043 |
0.7%
|
| 501 | Garissa | 169 | 398676 |
1.1%
|
| 502 | Mandera | 170 | 318791 |
0.9%
|
| 503 | Wajir | 170 | 388843 |
1.1%
|
| 601 | Gucha | 169 | 514570 |
1.4%
|
| 602 | Homa Bay | 165 | 307296 |
0.9%
|
| 603 | Kisii | 178 | 534568 |
1.5%
|
| 604 | Kisumu | 195 | 650846 |
1.8%
|
| 605 | Kuria | 169 | 194053 |
0.5%
|
| 606 | Migori | 186 | 568593 |
1.6%
|
| 607 | Nyamira | 180 | 571379 |
1.6%
|
| 608 | Rachuonyo | 170 | 400420 |
1.1%
|
| 609 | Siaya | 199 | 552509 |
1.6%
|
| 610 | Suba | 161 | 162577 |
0.5%
|
| 611 | Bondo | 169 | 252380 |
0.7%
|
| 612 | Nyando | 170 | 343501 |
1%
|
| 701 | Baringo | 170 | 377416 |
1.1%
|
| 702 | Bomet | 163 | 462240 |
1.3%
|
| 703 | Keiyo | 170 | 193744 |
0.5%
|
| 704 | Kajiado | 179 | 486700 |
1.4%
|
| 705 | Kericho | 180 | 477983 |
1.3%
|
| 706 | Koibatek | 170 | 186691 |
0.5%
|
| 707 | Laikipia | 170 | 423588 |
1.2%
|
| 708 | Marakwet | 153 | 178366 |
0.5%
|
| 709 | Nakuru | 349 | 1331206 |
3.7%
|
| 710 | Nandi | 190 | 770953 |
2.2%
|
| 711 | Narok | 165 | 472382 |
1.3%
|
| 712 | Samburu | 150 | 199219 |
0.6%
|
| 713 | Trans Mara | 158 | 198722 |
0.6%
|
| 714 | Trans Nzoia | 200 | 879601 |
2.5%
|
| 715 | Turkana | 170 | 538949 |
1.5%
|
| 716 | Uasin Gishu | 210 | 761022 |
2.1%
|
| 717 | West Pokot | 168 | 376668 |
1.1%
|
| 718 | Buret | 170 | 412072 |
1.2%
|
| 801 | Bungoma | 249 | 1188441 |
3.3%
|
| 802 | Busia | 168 | 533678 |
1.5%
|
| 803 | Mt. Elgon | 170 | 225199 |
0.6%
|
| 804 | Kakamega | 208 | 667143 |
1.9%
|
| 805 | Lugari | 170 | 269514 |
0.8%
|
| 806 | Teso | 166 | 252687 |
0.7%
|
| 807 | Vihiga | 189 | 618793 |
1.7%
|
| 808 | Butere/Mumias | 188 | 585967 |
1.6%
|
Warning: these figures indicate the number of cases found in the data file. They cannot be interpreted as summary statistics of the population of interest.
var_qstn_ivuinstr
Please fill in A02 with the required information on the province
Question pretext
WRITE CODE and NAME for District
Description
Definition
The district where the cluster for enumeration is located. Administratively, a provinces is divided into districts and within each district are the divisions etc