APHRC Online Microdata Library
    Home / Central Data Catalog / NUHDSS / APHRC-NUHDSS-OUTMIGRATION-V1.0 / variable [V22]
NUHDSS

NUHDSS - Outmigration Registration 2002-2015

KENYA, 2002 - 2015
Nairobi Urban Health & Demographic Surveillance System
African Population and Health Research Center
Last modified June 02, 2017 Page views 40297 Documentation in PDF Metadata DDI/XML JSON
CSV JSON

district in Kenya to which outmigrated (omg_outmigt_district)

Data file: Outmigration_2002-2015

Overview

vald 174786
invd 0
Interval discrete
Decimal 0
Range 100 - 9999995

Questions and instructions

Literal question
What is the name of the area where (NAME) went to?
Categories
Value Category Cases
100 central province-district unknown 0
0%
101 kiambu 2671
1.5%
102 kirinyaga 472
0.3%
103 maragua 842
0.5%
104 murang'a 6362
3.6%
105 nyandarua 864
0.5%
106 nyeri 1353
0.8%
107 thika 1912
1.1%
108 nyahururu 25
0%
109 limuru 7
0%
110 nanyuki 4
0%
111 kerugoya 1
0%
200 coast province-district unknown 0
0%
201 kilifi 46
0%
202 kwale 28
0%
203 lamu 30
0%
204 malindi 36
0%
205 mombasa 1381
0.8%
206 taita taveta 178
0.1%
207 tana river 20
0%
208 voi 1
0%
209 shinyanga 0
0%
210 changamwe 0
0%
300 eastern province-district unknown 1
0%
301 embu 513
0.3%
302 isiolo 898
0.5%
303 kitui 3203
1.8%
304 machakos 11512
6.6%
305 makueni 4588
2.6%
306 marsabit 313
0.2%
307 mbeere 253
0.1%
310 meru 949
0.5%
311 moyale 633
0.4%
312 mwingi 421
0.2%
313 tharaka 25
0%
314 kangundo 32
0%
315 athi river 3
0%
316 matuu 6
0%
317 mwanza 7
0%
318 mbooni 5
0%
319 loitoktok 3
0%
320 kibwezi 4
0%
321 tala 1
0%
322 ndhiwa 0
0%
323 yatta 4
0%
324 garba tula 0
0%
400 nairobi province-district unknown 21359
12.2%
401 central 1639
0.9%
402 dagoretti 565
0.3%
403 embakasi 43437
24.9%
404 kasarani 18949
10.8%
405 kibera 734
0.4%
406 makadara 5932
3.4%
407 pumwani 1712
1%
408 westlands 383
0.2%
500 north eastern province-district unknown 0
0%
501 garissa 292
0.2%
502 ijara 28
0%
503 mandera 937
0.5%
504 wajir 185
0.1%
505 elwak 14
0%
600 nyanza province-district unknown 6
0%
601 bondo 944
0.5%
603 homa bay 880
0.5%
604 kisii central 2240
1.3%
605 kisumu 3041
1.7%
606 kuria 458
0.3%
607 migori 671
0.4%
608 kisii 2206
1.3%
609 nyando 526
0.3%
610 karachuonyo 460
0.3%
611 siaya 6340
3.6%
612 suba 115
0.1%
613 oyugis 2
0%
614 ugenya 10
0%
615 nyakach 8
0%
616 alego 2
0%
617 gem 6
0%
618 kendu bay 1
0%
619 ahero 0
0%
620 asembo 0
0%
621 gwasi 0
0%
700 rift valley province-district unknown 0
0%
701 baringo 20
0%
702 bomet 117
0.1%
703 bureti 23
0%
704 kajiado 914
0.5%
705 keiyo 21
0%
706 kericho 136
0.1%
707 koibatek 40
0%
708 laikipia 150
0.1%
709 nakuru 1378
0.8%
711 nandi 75
0%
712 narok 160
0.1%
713 samburu 23
0%
714 transmara 12
0%
715 trans nzoia 302
0.2%
716 turkana 30
0%
717 uasin gishu 293
0.2%
718 west pokot 5
0%
719 kitale 22
0%
720 molo 3
0%
721 bureti 5
0%
722 kinangop 4
0%
723 nyambene 0
0%
724 maseno 0
0%
725 kapsabet 0
0%
800 western province-district unknown 0
0%
801 bungoma 924
0.5%
802 busia 2541
1.5%
803 butere/mumias/bunyore 3087
1.8%
804 kakamega 3584
2.1%
805 lugari 243
0.1%
806 elgon 299
0.2%
807 teso 164
0.1%
808 vihiga 1907
1.1%
809 webuye 12
0%
810 amagoro 0
0%
900 outside Kenya 1156
0.7%
9996 other 0
0%
9997 refused 1
0%
9998 don't know 691
0.4%
9999 NIU (not in universe) 0
0%
9999995 missing:impute 2755
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.
Question pretext
IF THE OUT MIGRANT MOVED OUTSIDE KENYA, CODE 9 IN PROVINCE AND 999 UNDER DISTRICT
Question post text
na
Interviewer instructions
This question follows your respondent answer to 3.1 above. You should be very careful when selecting the codes provided by probing as much as you can to ensure you get the precise answer.

For Example: Assume that somebody is moving to WOODLEY (in KIBERA). In 3.1, you will write “NAIROBI/NAIROBI/KIBERA/WOODLEY and then you will code “4” for “Nairobi non-slum”. You will note here that, although this migrant moved to KIBERA, the area of destination is not necessarily a slum. This is valid for all the slums. It is important, therefore, to assess whether the village or estate the migrant is moving to is a slum area. There are several ways of probing to ascertain whether the village of destination is a slum or not. One way is to ask about the materials of the walls of houses in the neighbourhood, or ask whether the environment resembles the slum where the interview is taking place.



[Note: code 8 should be used only in extreme cases, and only when all the probing has failed, this option is not acceptable if the respondent is the migrant him\herself]

Description

Text
The district in Kenya to which the outmigrant moved to.
Universe
All households that had an out-migrant.