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Ethiopian Rural Household Survey 1989-2009

Ethiopia, 1989 - 2009
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Reference ID
eth-csae-erhs-1989-2009-v1
Producer(s)
International Food Policy Research Institute (IFPRI), Centre for the study of African Economies (CSAE), Department of Economics, Addis Ababa University (AAU)
Metadata
DDI/XML JSON
Created on
Oct 15, 2012
Last modified
Oct 26, 2015
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Name of village/neighbourhood/got/kushet within the pa (village)

Data file: ERHS 2009_R7p1_s1b

Overview

Valid: 1910
Invalid: 0
Type: Discrete
Decimal: 0
Width: 22
Range: 1 - 232
Format: Numeric

Questions and instructions

Categories
Value Category Cases
1 Mokor Chebote 7
0.4%
2 Meklo Sefer 18
0.9%
3 Gonde 8
0.4%
4 Burka 3
0.2%
5 Maekel 14
0.7%
6 Haresew 35
1.8%
7 Limeat 43
2.3%
8 Gichi 15
0.8%
9 Sirba 55
2.9%
10 Awaeho 11
0.6%
11 Yetmen 1
0.1%
12 Gelegel Yetmen 9
0.5%
13 Densit 11
0.6%
14 Arogea Ambo 6
0.3%
15 Minchit 0
0%
16 Yetmen Lay 9
0.5%
17 Lay Ambo 5
0.3%
18 Yetmen Abbo 4
0.2%
19 Felege Selam 1
0.1%
20 Karafio 23
1.2%
21 Dibe 13
0.7%
22 Ballo 1
0.1%
23 Woyenue Eshete 6
0.3%
24 Tekeleye Meshesha 0
0%
25 Mender Mesereto 9
0.5%
26 Aleka Merawi 2
0.1%
27 Golbo 4
0.2%
28 Dahadeba 0
0%
29 Fino 16
0.8%
30 Golele 4
0.2%
31 Ealka 0
0%
32 Beressa 9
0.5%
33 Mehal Faji 7
0.4%
34 Milki 11
0.6%
35 Tach Faji 12
0.6%
36 Asefa Meshesha 1
0.1%
37 Boka Fiya 3
0.2%
38 Boka Bodo 14
0.7%
39 Atabahiye 7
0.4%
40 Borkema 12
0.6%
41 Koyo 5
0.3%
42 Yerer 10
0.5%
43 Yesemie 1
0.1%
44 Yada 1
0.1%
45 Addis Ketema 12
0.6%
46 Gedereha 1
0.1%
47 Yerezeb 2
0.1%
48 Yetabhe 0
0%
49 Godety 6
0.3%
50 Turife 101
5.3%
51 Wotera 32
1.7%
52 Ketchema 4
0.2%
53 Tena Kelo 0
0%
54 Oda Mecro 3
0.2%
55 Morom Sefer 3
0.2%
56 Begejo 42
2.2%
57 Gonda Begajo 2
0.1%
58 Konisa 1
0.1%
59 Kobe G/Zaw 18
0.9%
60 Alini 0
0%
61 Worgbn 8
0.4%
62 Fino & Golele 9
0.5%
63 Makro 6
0.3%
64 Arekesa Kelo 8
0.4%
65 Gende Bele 19
1%
66 Adadie 3
0.2%
67 Yaboo 7
0.4%
68 Laftole Mesreta 0
0%
69 Boto 23
1.2%
70 Ebicho 48
2.5%
71 Alemgena 27
1.4%
72 Chelo 24
1.3%
73 Gera 9
0.5%
74 Woyde Tokisa 11
0.6%
75 Hago 61
3.2%
76 Wattro 13
0.7%
77 2
0.1%
78 Godo 1
0.1%
79 Weyde 1
0.1%
80 Koysha Totisa 3
0.2%
81 Cheba Tokisa 5
0.3%
82 Bako 0
0%
83 Muleta 2
0.1%
84 Laye Wite 6
0.3%
85 Tache Wite 9
0.5%
86 Azemish Kida 1
0.1%
87 Dokea 4
0.2%
88 Shafa 3
0.2%
89 2nd Bulla 5
0.3%
90 Mehkelawi 7
0.4%
91 Laye Deboo 7
0.4%
92 Merob Godana 2
0.1%
93 Laye Wonko 1
0.1%
94 Tach Wonko 5
0.3%
95 Wite 1
0.1%
96 Bulla 2
0.1%
97 Fuchere 13
0.7%
98 Meriager 6
0.3%
99 Alela Ager 19
1%
100 Telem Amba 10
0.5%
101 Gema 7
0.4%
102 Merere 10
0.5%
103 Tilko 2
0.1%
104 Dinki 41
2.1%
105 Genda Gerbi 12
0.6%
106 Kuslto 26
1.4%
107 G/Haro 6
0.3%
108 Gole Gendo Koclto 23
1.2%
109 Gole 5
0.3%
110 Tabia 4
0.2%
111 Galon 12
0.6%
112 Gole Genda Barentu 2
0.1%
113 H/Goro 2
0.1%
114 Doma 44
2.3%
115 Kokob 16
0.8%
116 Negot 3
0.2%
117 Kae Sefer 6
0.3%
118 Wacho 1
0.1%
119 Keysha 3
0.2%
120 Megazen Sefer 0
0%
121 Yemenged Sefer 0
0%
122 Omala 11
0.6%
123 Church Sefer 2
0.1%
124 Enquiry Beret 12
0.6%
125 Shume Sha 40
2.1%
126 Deorwo 28
1.5%
127 Aba Aregay 8
0.4%
128 Tewala 15
0.8%
129 Shikt 1
0.1%
130 Addis Amba 2
0.1%
131 Dendawhl 2
0.1%
132 Takora 8
0.4%
133 Denekel/ Bonito 4
0.2%
134 Semidoro 30
1.6%
135 Kosien 23
1.2%
136 Weielawer 20
1%
137 Errata 14
0.7%
138 Buko 16
0.8%
139 Degaga 37
1.9%
140 Emo 11
0.6%
141 Shalota 17
0.9%
142 Amola 8
0.4%
143 Arda 17
0.9%
144 Arda chikota 3
0.2%
145 Alelu 1
0.1%
146 kura 29
1.5%
147 Mona Barumso 8
0.4%
148 Tineshu Kamo 4
0.2%
149 Kokola 17
0.9%
150 Gerdi 10
0.5%
151 Habt 6
0.3%
152 Abeyi 1
0.1%
153 Abejede 11
0.6%
154 Yebbo 4
0.2%
155 Abukako 6
0.3%
156 Famo 0
0%
157 Yero 3
0.2%
158 Buraya 4
0.2%
159 Wayyu 0
0%
160 Somdo 2
0.1%
161 Lalo Mabe 0
0%
162 Kajaro 18
0.9%
163 Kulto 11
0.6%
164 Gole Ganda Sole 8
0.4%
165 Genda Muuda 6
0.3%
166 Kura 1
0.1%
167 Elemo 2
0.1%
168 Boba Sodere 0
0%
169 Dodota 2
0.1%
170 Dolo 8
0.4%
171 Bursa 12
0.6%
172 Adado 40
2.1%
173 Hago 17
0.9%
174 Waissa 15
0.8%
175 Kisha 30
1.6%
176 Bukicho 16
0.8%
177 Invester Chample /Roba 6
0.3%
178 Hassene/ Waissa 2
0.1%
179 Dude 3
0.2%
185 6
0.3%
232 3
0.2%
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.
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