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    Home / Central Data Catalog / TZA_2018_RFS_V01_M / variable [F1]
central

Resilience Firm Survey 2018, Dar es Salaam, Tanga and Dodoma

Tanzania, 2018
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Reference ID
TZA_2018_RFS_v01_M
Producer(s)
Alvina Erman, Jun Rentschler, Julie Rozenberg, Stephane Hallegatte
Metadata
DDI/XML JSON
Study website
Created on
Sep 07, 2022
Last modified
Sep 07, 2022
Page views
6713
Downloads
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  • Study Description
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  • rfs_tanzania_2018

4.1. Supplier # (q4_1_3)

Data file: rfs_tanzania_2018

Overview

Valid: 453
Invalid: 0
Type: Discrete
Start: 1215
End: 1257
Width: 43
Range: -
Format: character

Questions and instructions

Categories
Value Category Cases
3 3
0.7%
A 1
0.2%
A3 1
0.2%
Aggregate pebbles supplier 1
0.2%
Aglex 1
0.2%
Ako catering 1
0.2%
Alexandria 1
0.2%
Anti virus suppliers 1
0.2%
Assagee 1
0.2%
Azam 1
0.2%
B 9
2%
B.M Book Shop 1
0.2%
B3 2
0.4%
BINGWA PRINTERS 1
0.2%
BMTL 1
0.2%
BMW 1
0.2%
Bahari Pharmacy 1
0.2%
Bahari pharmacy 1
0.2%
Bakhresa 1
0.2%
Be Forward Jp 1
0.2%
Blue pharmacy 1
0.2%
Bnbm 1
0.2%
C 52
11.5%
C.D.H traders 1
0.2%
C3 1
0.2%
CUMPUTER SALES AND SERVICES 1
0.2%
Carpentry furniture 1
0.2%
Cfao motors 1
0.2%
Chonge hardware 1
0.2%
Chuwa 1
0.2%
Cleaning materials suplers 1
0.2%
Coca cola 1
0.2%
Cocacola 1
0.2%
Cocacola suppliers 1
0.2%
Cooking oil suppliers 1
0.2%
Cotex 1
0.2%
D 5
1.1%
D3 2
0.4%
DESKS 1
0.2%
DIY 1
0.2%
DROP 1
0.2%
Dar Electronic 1
0.2%
Dents 1
0.2%
Different building items like paints nails, 1
0.2%
E 2
0.4%
E3 1
0.2%
East African cables ltd 1
0.2%
Emmnuel 1
0.2%
F 3
0.7%
Fabrics supplier 1
0.2%
Farmer 3 1
0.2%
Floula 1
0.2%
Food bevarages 1
0.2%
G 5
1.1%
G3 1
0.2%
GEREZANI ELECTRONICS 1
0.2%
GREEN SUPLY 1
0.2%
General Supplier 1
0.2%
Goodone 1
0.2%
Government 1
0.2%
Grants Care 1
0.2%
H 9
2%
H3 2
0.4%
HDH 1
0.2%
Halaf 1
0.2%
Hard drinks suppliers 1
0.2%
ICT WORLD 1
0.2%
INFOR TEC 1
0.2%
INTERNAL PRINTING 1
0.2%
J 6
1.3%
JACOB 1
0.2%
JASMIN 1
0.2%
JOMBA 1
0.2%
JOYCE 1
0.2%
JUMA 1
0.2%
James Shop 1
0.2%
Jumbo Suppliers 1
0.2%
K 5
1.1%
K1 1
0.2%
KIFUU STATIONARY 1
0.2%
KIJIJI GENERAL SUPLLY 1
0.2%
KIM 1
0.2%
KISIMA 2
0.4%
Kariakoo 1
0.2%
Kato auto parts 1
0.2%
Kilimanjaro drinking water 1
0.2%
Kilombero sugar suppliers 1
0.2%
L 2
0.4%
Lazaro 1
0.2%
Lorenzo auto parts 1
0.2%
M 10
2.2%
MAMA WAHENGA 1
0.2%
MASOUD AUTO 1
0.2%
MEKU REPAIR 1
0.2%
MICHAEL 1
0.2%
MOROGORO AUTO SPARE 1
0.2%
MUSHI 1
0.2%
MWIBA 1
0.2%
Majengo Tailoring 1
0.2%
Mandalia 1
0.2%
Mangi 1
0.2%
Meat 1
0.2%
Meru 1
0.2%
Morogoro supply 1
0.2%
Muna 1
0.2%
N 2
0.4%
NCI 1
0.2%
NYASA TRADERS 1
0.2%
Nails supplier 1
0.2%
Nails suppliere 1
0.2%
Nayan interpraise 1
0.2%
O 3
0.7%
O3 1
0.2%
OPEN SUPPLY 1
0.2%
Omary packaging 1
0.2%
Orange gas 1
0.2%
Ornaments 1
0.2%
P 3
0.7%
PATEL 1
0.2%
Paroko shop 1
0.2%
Paul 1
0.2%
Pepsi 1
0.2%
Pharmacy 1
0.2%
Planet Pharmacy 1
0.2%
Printer ink 1
0.2%
Public 1
0.2%
Q3 2
0.4%
R 6
1.3%
R3 1
0.2%
Rashid ramadhan 1
0.2%
Ready made clothes 1
0.2%
Rk Pharmacy 1
0.2%
S 13
2.9%
S1 1
0.2%
S2 1
0.2%
S3 19
4.2%
SHAKUL 1
0.2%
SHUBI 1
0.2%
SIMPLE MOVERS 1
0.2%
STATIONARY 1
0.2%
SUMBURG 1
0.2%
SYNPYONY 1
0.2%
Saba general 1
0.2%
Sawe shop 1
0.2%
Sbs Company limited 1
0.2%
Single source supplier 1
0.2%
Sink 1
0.2%
Slipers 1
0.2%
Soft drinks 1
0.2%
Southern Sun Hotel 1
0.2%
Spokes supplier 1
0.2%
Stationaries 1
0.2%
Stationaries material 1
0.2%
Stationaries supplier 1
0.2%
Stationary 1
0.2%
Stationery 1
0.2%
Stationery requirements 1
0.2%
Stephano 1
0.2%
Super dealer 1
0.2%
Swissport 2
0.4%
T 8
1.8%
T2 1
0.2%
T3 4
0.9%
TONNER 1
0.2%
TOTAL 1
0.2%
TOTAL OIL 1
0.2%
Tanzania cable 1
0.2%
Temesa 2
0.4%
Threads suppliers 3
0.7%
Toothpaste suppliers 1
0.2%
Toyota 2
0.4%
Twiga cement 1
0.2%
U 3
0.7%
UNLCE MA 1
0.2%
Ustadhi 1
0.2%
VICTOR 1
0.2%
VN 1
0.2%
VYOMBO VYA CHAKULA NA MAJI 1
0.2%
Vf 1
0.2%
W 5
1.1%
W1 2
0.4%
W3 19
4.2%
We 1
0.2%
Weaving suppliers 1
0.2%
Wires and pipes suppliers 1
0.2%
X 5
1.1%
X2 2
0.4%
X3 16
3.5%
X4 1
0.2%
XXX 1
0.2%
Xy 1
0.2%
Xy2 1
0.2%
Xy3 15
3.3%
Y 11
2.4%
Y2 2
0.4%
Y3 4
0.9%
Yt 1
0.2%
Yutelex 1
0.2%
Z 16
3.5%
Z mbao 1
0.2%
Z3 1
0.2%
Zp 1
0.2%
Zuber 1
0.2%
Zuku 1
0.2%
c 1
0.2%
z 1
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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