Household Budget Survey 2017-2018

Tanzania, 2017 - 2018

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Reference ID

TZA_2017_HBS_v01_M

Producer(s)

National Bureau of Statistics

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Mar 04, 2022

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May 15, 2022

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4.2.2. In which district were %rostertitle% born? (S4_2_2)

Data file: HBS_2017-18 Consumption Aggregate and Poverty Analysis Variables

Overview

Valid: 9389

Invalid: 74

Minimum: 11

Maximum: 1210

Type: Discrete

Decimal: 0

Start: 5012

End: 5015

Width: 4

Range: 11 - 1210

Format: Numeric

Questions and instructions

Literal question

Section 4: Citizenship and Migration
In which region and district was [NAME] born? District Name

Categories

Value	Category	Cases
11	KONDOA	102	1.1%
12	MPWAPWA	71	0.8%
13	KONGWA	56	0.6%
14	CHAMWINO	67	0.7%
15	DODOMA URBAN	49	0.5%
16	BAHI	39	0.4%
17	CHEMBA	38	0.4%
21	MONDULI	49	0.5%
22	MERU	71	0.8%
23	ARUSHA URBAN	42	0.4%
24	KARATU	64	0.7%
25	NGORONGORO	37	0.4%
26	ARUSHA RURAL	40	0.4%
27	LONGIDO	33	0.4%
31	ROMBO	97	1%
32	MWANGA	47	0.5%
33	SAME	82	0.9%
34	MOSHI RURAL	187	2%
35	HAI	64	0.7%
36	MOSHI URBAN	34	0.4%
37	SIHA	29	0.3%
41	LUSHOTO	143	1.5%
42	KOROGWE RURAL	44	0.5%
43	MUHEZA	59	0.6%
44	TANGA URBAN	65	0.7%
45	PANGANI	20	0.2%
46	HANDENI	76	0.8%
47	KILINDI	30	0.3%
48	MKINGA	30	0.3%
49	KOROGWE URBAN	21	0.2%
51	KILOSA	97	1%
52	MOROGORO RURAL	65	0.7%
53	KILOMBERO	48	0.5%
54	ULANGA	66	0.7%
55	MOROGORO URBAN	23	0.2%
56	MVOMERO	56	0.6%
57	GAIRO	18	0.2%
61	BAGAMOYO	59	0.6%
62	KIBAHA RURAL	19	0.2%
63	KISARAWE	50	0.5%
64	MKURANGA	41	0.4%
65	RUFIJI	84	0.9%
66	MAFIA	10	0.1%
67	KIBAHA URBAN	16	0.2%
71	KINONDONI	85	0.9%
72	ILALA	87	0.9%
73	TEMEKE	68	0.7%
81	KILWA	88	0.9%
82	LINDI RURAL	86	0.9%
83	NACHINGWEA	51	0.5%
84	LIWALE	30	0.3%
85	RUANGWA	42	0.4%
86	LINDI URBAN	24	0.3%
91	MTWARA RURAL	54	0.6%
92	NEWALA	87	0.9%
93	MASASI RURAL	80	0.9%
94	TANDAHIMBA	55	0.6%
95	MTWARA MIKINDANI	14	0.1%
96	NANYUMBU	31	0.3%
97	MASASI URBAN	40	0.4%
101	TUNDURU	66	0.7%
102	SONGEA RURAL	50	0.5%
103	MBINGA	97	1%
104	SONGEA URBAN	36	0.4%
105	NAMTUMBO	38	0.4%
106	NYASA	17	0.2%
111	IRINGA RURAL	103	1.1%
112	MUFINDI	131	1.4%
113	IRINGA URBAN	41	0.4%
114	KILOLO	68	0.7%
115	MAFINGA	16	0.2%
121	CHUNYA	35	0.4%
122	MBEYA RURAL	93	1%
123	KYELA	58	0.6%
124	RUNGWE	134	1.4%
125	ILEJE	23	0.2%
126	MBOZI	62	0.7%
127	MBALALI	60	0.6%
128	MBEYA URBAN	47	0.5%
129	MOMBA	2	0%
131	IRAMBA	80	0.9%
132	SINGIDA RURAL	90	1%
133	MANYONI	55	0.6%
134	SINGIDA URBAN	43	0.5%
135	IKUNGI	49	0.5%
136	MKALAMA	32	0.3%
141	NZEGA	99	1.1%
142	IGUNGA	50	0.5%
143	UYUI	48	0.5%
144	URAMBA	39	0.4%
145	SIKONGE	21	0.2%
146	TABORA URBAN	39	0.4%
147	KALIUA	10	0.1%
151	KALAMBO	54	0.6%
152	SUMBAWANGA RURAL	131	1.4%
153	NKASI	83	0.9%
154	SUMBAWANGA URBAN	39	0.4%
162	KIBONDO	66	0.7%
163	KASULU RURAL	92	1%
164	KIGOMA RURAL	51	0.5%
165	KIGOMA URBAN	70	0.7%
166	UVINZA	31	0.3%
167	BUHIGWE	38	0.4%
168	KAKONKO	34	0.4%
169	KASULU URBAN	43	0.5%
171	SHINYANGA RURAL	168	1.8%
172	KISHAPU	94	1%
173	SHINYANGA URBAN	54	0.6%
174	KAHAMA RURAL	142	1.5%
175	KAHAMA URBAN	40	0.4%
181	KARAGWE	86	0.9%
182	BUKOBA RURAL	80	0.9%
183	MULEBA	98	1%
184	BIHARAMULO	31	0.3%
185	NGARA	59	0.6%
186	BUKOBA URBAN	26	0.3%
187	MISSENYI	43	0.5%
188	KYERWA	25	0.3%
191	UKEREWE	71	0.8%
192	MAGU	99	1.1%
193	NYAMAGANA	40	0.4%
194	KWIMBA	140	1.5%
195	SENGEREMA	118	1.3%
196	ILEMELA	33	0.4%
197	MISUNGWI	61	0.6%
201	TARIME	128	1.4%
202	SERENGETI	58	0.6%
203	MUSOMA RURAL	83	0.9%
204	BUNDA	65	0.7%
205	MUSOMA URBAN	20	0.2%
206	RORYA	58	0.6%
207	BUTIAMA	35	0.4%
211	BABATI RURAL	59	0.6%
212	HANANG	38	0.4%
213	MBULU	70	0.7%
214	SIMANJIRO	17	0.2%
215	KITETO	18	0.2%
216	BABATI URBAN	27	0.3%
221	NJOMBE URBAN	38	0.4%
222	WANGING'OMBE	75	0.8%
223	MAKETE	71	0.8%
224	NJOMBE RURAL	57	0.6%
225	LUDEWA	64	0.7%
226	MAKAMBAKO	32	0.3%
231	MPANDA URBAN	43	0.5%
232	MPANDA RURAL	42	0.4%
233	MLELE	34	0.4%
241	BARIADI	134	1.4%
242	ITILIMA	76	0.8%
243	MEATU	57	0.6%
244	MASWA	118	1.3%
245	BUSEGA	41	0.4%
251	GEITA	101	1.1%
252	NYANG'HWALE	40	0.4%
253	MBOGWE	11	0.1%
254	BUKOMBE	20	0.2%
255	CHATO	28	0.3%
261	SONGWE URBAN	8	0.1%
262	ILEJE	43	0.5%
263	MBOZI	112	1.2%
264	MOMBA	26	0.3%
265	TUNDUMA MJI	1	0%
410	HANDENI URBAN	7	0.1%
1210	TUNDUMA	2	0%
Sysmiss		74

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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