National Panel Survey 2014-2015
Wave 4 - Extended Panel
Tanzania, 2013 - 2016
Get Microdata
Reference ID
TZA_2014_NPS-R4_v03_M_v03_A_EXT
Producer(s)
National Bureau of Statistics
Collection(s)
Metadata
Created on
May 20, 2019
Last modified
Dec 14, 2022
Page views
114273
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- Data files
- ag_filters
- ag_network
- ag_sec_01
- ag_sec_2a
- ag_sec_2b
- ag_sec_3a
- ag_sec_3b
- ag_sec_4a
- ag_sec_4b
- ag_sec_5a
- ag_sec_5b
- ag_sec_6a
- ag_sec_6b
- ag_sec_7a
- ag_sec_7b
- ag_sec_10
- ag_sec_11
- ag_sec_12a
- ag_sec_12b
- ag_sec_a
- com_sec_a1a2
- com_sec_cb
- com_sec_cc
- com_sec_cd
- com_sec_ce
- com_sec_cf
- com_sec_cf_id
- com_sec_cg
- hh_filters
- hh_sec_b
- hh_sec_c
- hh_sec_d
- hh_sec_e
- hh_sec_f
- hh_sec_g
- hh_sec_h
- hh_sec_i
- hh_sec_j1
- hh_sec_j3
- hh_sec_j4
- hh_sec_k
- hh_sec_l
- hh_sec_m
- hh_sec_n
- hh_sec_o1
- hh_sec_o2
- hh_sec_p
- hh_sec_q1
- hh_sec_q2
- hh_sec_r
- hh_sec_s
- hh_sec_u2
- hh_sec_v
- lf_filters
- lf_network
- lf_sec_01
- lf_sec_02
- lf_sec_03
- lf_sec_04
- lf_sec_05
- lf_sec_06
- lf_sec_07
- lf_sec_08
- lf_sec_09
- lf_sec_11a
- lf_sec_11b
- lf_sec_12
- lf_sec_13a
- lf_sec_13b
- lf_sec_a
- hh_sec_a
- consumptionnps4_extended_panel
In which ID_DISTRICT were you born? - NAME (hh_b29_1)
Data file: hh_sec_b
Overview
Valid:
1803
Invalid:
0
Type:
Discrete
Start:
127
End:
142
Width:
16
Range:
-
Format:
character
Questions and instructions
Categories
| Value | Category | Cases | |
|---|---|---|---|
| ARUMERU | 6 |
0.3%
|
|
| ARUSHA | 8 |
0.4%
|
|
| BABATI | 2 |
0.1%
|
|
| BAGAMOYO | 13 |
0.7%
|
|
| BAHI | 3 |
0.2%
|
|
| BARIADI | 46 |
2.6%
|
|
| BIHARAMULO | 4 |
0.2%
|
|
| BUHIGWE | 4 |
0.2%
|
|
| BUKOBA RURAL | 24 |
1.3%
|
|
| BUKOBA URBAN | 18 |
1%
|
|
| BUKOMBE | 11 |
0.6%
|
|
| BUNDA | 8 |
0.4%
|
|
| BURUNDI | 1 |
0.1%
|
|
| BUSEGA | 3 |
0.2%
|
|
| BUTIAMA | 5 |
0.3%
|
|
| CHAKECHAKE | 5 |
0.3%
|
|
| CHATO | 18 |
1%
|
|
| CHUNYA | 2 |
0.1%
|
|
| DODOMA URBAN | 14 |
0.8%
|
|
| GEITA | 64 |
3.5%
|
|
| GEITA VIJIJINI | 2 |
0.1%
|
|
| HAI | 6 |
0.3%
|
|
| HANANG | 1 |
0.1%
|
|
| HANDENI | 16 |
0.9%
|
|
| IGUNGA | 7 |
0.4%
|
|
| ILALA | 41 |
2.3%
|
|
| ILEJE | 2 |
0.1%
|
|
| IRAMBA | 30 |
1.7%
|
|
| IRINGA RURAL | 8 |
0.4%
|
|
| ITILIMA | 6 |
0.3%
|
|
| KABAHA | 1 |
0.1%
|
|
| KAHAMA | 43 |
2.4%
|
|
| KAKONKO | 1 |
0.1%
|
|
| KARAGWE | 14 |
0.8%
|
|
| KARATU | 12 |
0.7%
|
|
| KASKAZINI A | 6 |
0.3%
|
|
| KASKAZINI B | 5 |
0.3%
|
|
| KASULU | 10 |
0.6%
|
|
| KASULU MJINI | 2 |
0.1%
|
|
| KATABAY | 1 |
0.1%
|
|
| KATI | 15 |
0.8%
|
|
| KENYA | 2 |
0.1%
|
|
| KIBAHA | 7 |
0.4%
|
|
| KIBAHA M | 1 |
0.1%
|
|
| KIBONDO | 12 |
0.7%
|
|
| KIGOMA RURAL | 3 |
0.2%
|
|
| KIGOMA URBAN | 3 |
0.2%
|
|
| KILINDI | 1 |
0.1%
|
|
| KILOMBERO | 19 |
1.1%
|
|
| KILOSA | 18 |
1%
|
|
| KILWA | 20 |
1.1%
|
|
| KINONDONI | 72 |
4%
|
|
| KISARAWE | 4 |
0.2%
|
|
| KITETO | 1 |
0.1%
|
|
| KONDOA | 17 |
0.9%
|
|
| KONGWA | 6 |
0.3%
|
|
| KOROGWE | 10 |
0.6%
|
|
| KUSINI | 7 |
0.4%
|
|
| KWIMBA | 15 |
0.8%
|
|
| LINDI RURAL | 22 |
1.2%
|
|
| LINDI URBAN | 19 |
1.1%
|
|
| LIWALE | 5 |
0.3%
|
|
| LUDEWA | 5 |
0.3%
|
|
| LUSHOTO | 44 |
2.4%
|
|
| MAFIA | 1 |
0.1%
|
|
| MAGHARIBI | 12 |
0.7%
|
|
| MAGU | 17 |
0.9%
|
|
| MAKETE | 27 |
1.5%
|
|
| MANYONI | 1 |
0.1%
|
|
| MARONDERA | 1 |
0.1%
|
|
| MASASI | 36 |
2%
|
|
| MASASI MJINI | 3 |
0.2%
|
|
| MASASI TC | 1 |
0.1%
|
|
| MASASI VIJIJINI | 2 |
0.1%
|
|
| MASWA | 47 |
2.6%
|
|
| MBALALI | 1 |
0.1%
|
|
| MBEYA RURAL | 14 |
0.8%
|
|
| MBEYA URBAN | 2 |
0.1%
|
|
| MBINGA | 13 |
0.7%
|
|
| MBOGWE | 1 |
0.1%
|
|
| MBOZI | 10 |
0.6%
|
|
| MEATU | 2 |
0.1%
|
|
| MICHWEWENI | 15 |
0.8%
|
|
| MISENYE | 4 |
0.2%
|
|
| MISUNGWI | 31 |
1.7%
|
|
| MJINI | 28 |
1.6%
|
|
| MKINGA | 1 |
0.1%
|
|
| MKOANI | 17 |
0.9%
|
|
| MKURANGA | 4 |
0.2%
|
|
| MLELE | 1 |
0.1%
|
|
| MOMBA | 2 |
0.1%
|
|
| MONDULI | 5 |
0.3%
|
|
| MOROGORO RURAL | 25 |
1.4%
|
|
| MOROGORO URBAN | 24 |
1.3%
|
|
| MOSHI RURAL | 29 |
1.6%
|
|
| MOSHI URBAN | 4 |
0.2%
|
|
| MOSHI VIJIJINI | 1 |
0.1%
|
|
| MPANDA | 6 |
0.3%
|
|
| MPANDA VIJIJINI | 4 |
0.2%
|
|
| MPWAPWA | 10 |
0.6%
|
|
| MSUMBIJI | 4 |
0.2%
|
|
| MTWARA MIKINDANI | 5 |
0.3%
|
|
| MTWARA RURAL | 14 |
0.8%
|
|
| MUFINDI | 13 |
0.7%
|
|
| MUHEZA | 26 |
1.4%
|
|
| MULEBA | 42 |
2.3%
|
|
| MUSOMA RURAL | 34 |
1.9%
|
|
| MUSOMA URBAN | 4 |
0.2%
|
|
| MVOMERO | 16 |
0.9%
|
|
| MWANGA | 16 |
0.9%
|
|
| NACHINGWEA | 10 |
0.6%
|
|
| NAMTUMBO | 2 |
0.1%
|
|
| NANYUMBU | 6 |
0.3%
|
|
| NEWALA | 40 |
2.2%
|
|
| NGARA | 6 |
0.3%
|
|
| NGORONGORO | 2 |
0.1%
|
|
| NJOMBE | 15 |
0.8%
|
|
| NJOMBE M | 1 |
0.1%
|
|
| NJOMBE V | 1 |
0.1%
|
|
| NJOMBE VIJIJINI | 7 |
0.4%
|
|
| NKASI | 29 |
1.6%
|
|
| NKOTAKOTA | 1 |
0.1%
|
|
| NYAMAGANA | 3 |
0.2%
|
|
| NYANG'HWALE | 2 |
0.1%
|
|
| NYASA | 2 |
0.1%
|
|
| NZEGA | 12 |
0.7%
|
|
| PANGANI | 5 |
0.3%
|
|
| ROMBO | 15 |
0.8%
|
|
| RORYA | 7 |
0.4%
|
|
| RUANGWA | 10 |
0.6%
|
|
| RUFIJI | 15 |
0.8%
|
|
| RUNGWE | 7 |
0.4%
|
|
| SAME | 10 |
0.6%
|
|
| SENGEREMA | 15 |
0.8%
|
|
| SHINYANGA RURAL | 3 |
0.2%
|
|
| SIHA | 1 |
0.1%
|
|
| SIKONGE | 8 |
0.4%
|
|
| SIMANJIRO | 2 |
0.1%
|
|
| SINGIDA RURAL | 4 |
0.2%
|
|
| SINGIDA URBAN | 1 |
0.1%
|
|
| SONGEA RURAL | 19 |
1.1%
|
|
| SONGEA URBAN | 12 |
0.7%
|
|
| SUMBAWANGA | 2 |
0.1%
|
|
| SUMBAWANGA URBAN | 4 |
0.2%
|
|
| TABORA URBAN | 12 |
0.7%
|
|
| TANDAHIMBA | 6 |
0.3%
|
|
| TANGA URBAN | 4 |
0.2%
|
|
| TARIME | 2 |
0.1%
|
|
| TEMEKE | 35 |
1.9%
|
|
| TUNDURU | 9 |
0.5%
|
|
| UGANDA | 2 |
0.1%
|
|
| UKEREWE | 7 |
0.4%
|
|
| ULANGA | 15 |
0.8%
|
|
| URAMBA | 3 |
0.2%
|
|
| UYUI | 27 |
1.5%
|
|
| WETE | 24 |
1.3%
|
|
| ZIMBABWE | 1 |
0.1%
|
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.