GET Ahead Business Training Program Impact Evaluation 2013 - 2017
Kenya, 2013 - 2017
Get MicrodataCreated on
May 19, 2014
Last modified
Mar 29, 2019
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Baseline market (mkt)
Data file: r2r3publicuse2
Overview
Valid:
103
Invalid:
7011
Minimum:
2
Maximum:
20
Type:
Discrete
Decimal:
0
Start:
6703
End:
6705
Width:
3
Range:
-
Format:
Questions and instructions
Categories
| Value | Category |
|---|---|
| 1 | Bukura |
| 2 | Butali |
| 3 | Butere |
| 4 | Ekero |
| 5 | Eregi |
| 6 | Buhuru |
| 7 | Harambee |
| 8 | Kaburengu |
| 9 | Kambi ya Mwanza |
| 10 | Kambiri |
| 11 | Khayega |
| 12 | Khumusalaba |
| 13 | Khwisero |
| 14 | Koyonzo |
| 15 | Makunga |
| 16 | Malaha |
| 17 | Malava |
| 18 | Malinya |
| 19 | Manyulia |
| 20 | Matete |
| 21 | Matungu |
| 22 | Musanda |
| 23 | Musoli |
| 24 | Nambacha |
| 25 | Navakholo |
| 26 | Sabatia |
| 27 | Samitsi(includes Lutaso) |
| 28 | Shianda |
| 29 | Shiatsala |
| 30 | Shibale |
| 31 | Shinyalu |
| 32 | Birongo |
| 33 | Bobaracho |
| 34 | Daraja-mbili |
| 35 | Ekona |
| 36 | Emenwa |
| 37 | Etago |
| 38 | Gesusu |
| 39 | Ikoba |
| 40 | Itumbe |
| 41 | Kegogi |
| 42 | Kenyenya |
| 43 | Keumbu |
| 44 | Kiamokama |
| 45 | Magena |
| 46 | Marani |
| 47 | Masimba |
| 48 | Mogonga |
| 49 | Mogumo |
| 50 | Mosocho |
| 51 | Mwembe |
| 52 | Nyabigege |
| 53 | Nyacheki |
| 54 | Nyakoe |
| 55 | Nyamache |
| 56 | Nyangusu |
| 57 | Ogembo |
| 58 | Ramasha |
| 59 | Riosiri |
| 60 | Ruga |
| 61 | Sengera |
| 62 | Suneka |
| 63 | Tabaka |
| 64 | Igare |
| 65 | Etono |
| 66 | Ena |
| 67 | Gathangeri |
| 68 | Gikuuri |
| 69 | Ishiara |
| 70 | Kanyariri |
| 71 | Kanyuambora |
| 72 | Karingari(Nembure) |
| 73 | Karurumo |
| 74 | Kathangariri |
| 75 | Kathanjure |
| 76 | Kevote |
| 77 | Kiambere-Ngiri |
| 78 | Kiamuringa |
| 79 | Kianjokoma |
| 80 | Kibugu |
| 81 | Kigumo |
| 82 | Kimangaru |
| 83 | Kiriari |
| 84 | Kiritiri |
| 85 | Kithimu |
| 86 | Kivwe |
| 87 | Makengi |
| 88 | Manyatta |
| 89 | Mbuvori |
| 90 | Meka |
| 91 | Muchonoke |
| 92 | Muraru |
| 93 | Murindi |
| 94 | Mutunduri |
| 95 | Mutuobare |
| 96 | Ngandure |
| 97 | Runyenjes |
| 98 | Rwika |
| 99 | Siakago |
| 100 | Soko Mjinga |
| 101 | Ugweri |
| 102 | Endau |
| 103 | Ikanga |
| 104 | Inyuu |
| 105 | Ithiiani |
| 106 | Itoleka |
| 107 | Kabati |
| 108 | Kakeani |
| 109 | Kaliku |
| 110 | Katwala |
| 111 | Kasyala |
| 112 | Katulani |
| 113 | Kathungi |
| 114 | Kavisuni |
| 115 | Kalulini |
| 116 | Kisasi |
| 117 | Kiusyani |
| 118 | Kwa Kinyai |
| 119 | Kwa-Ngindu |
| 120 | Kwa-Vonza |
| 121 | Kyamatu |
| 122 | Kyatune |
| 123 | Matulani |
| 124 | Matinyani |
| 125 | Mbitini |
| 126 | Mbondoni |
| 127 | Miambani |
| 128 | Migwani |
| 129 | Mui |
| 130 | Mulutu |
| 131 | Musengo |
| 132 | Museve |
| 133 | Mutha |
| 134 | Mutito |
| 135 | Mutomo |
| 136 | Mutukya |
| 137 | Mwingi |
| 138 | Mwitikya |
| 139 | Nzangathi |
| 140 | Ngengeka |
| 141 | Ngenuka nenda |
| 142 | Nguutani |
| 143 | Nthogoni |
| 144 | Nguni |
| 145 | Ngomeni |
| 146 | Tulia |
| 147 | Wikililye |
| 148 | Zombe |
| 149 | Mosa |
| 150 | Ikuuni |
| 151 | Kyuso |
| 152 | Kamuongo |
| 153 | Katse |
| 154 | Waita |
| 155 | Katutu |
| 156 | Nuu |
| 157 | Mbusyani |
| Sysmiss |
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.
Others
Notes
"Baseline market"