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    Home / Central Data Catalog / KEN_2004_GEHDS_V01_M / variable [F94]
central

Greater Eldoret Health and Development Survey (Round 1) 2004

Kenya, 2004
Markus Goldstein and Harsha Thirumurthy
Created on June 01, 2022 Last modified June 01, 2022 Page views 1178535 Metadata DDI/XML JSON
  • Study description
  • Data Description
  • Get Microdata
  • Data files
  • aghead_a
  • aghead_b
  • aghead_c
  • aghead_d
  • aghead_e
  • aghead_e1_2
  • agspouse_a
  • agspouse_b
  • agspouse_c
  • agspouse_c_2
  • anthrop_a
  • anthrop_b
  • anthrop_c
  • assets
  • assets_I8
  • assets_I12
  • assets_I17
  • assets_II
  • assets_II5
  • assets_II11
  • assets_III
  • assets_III7
  • assets_III13
  • assets_IV
  • assets_V
  • assets_V6
  • assets_V12
  • assets_VI
  • behavior
  • behavior_c5
  • food
  • health_c
  • health_c_I
  • health_c_II
  • health_c_III
  • health_c_IV
  • identification
  • iroster
  • othexp
  • polygamoushh
  • shocks
  • shocks_a3
  • shocks_a7
  • shocks_a11
  • shocks_a15
  • shocks_a19
  • shocks_a24
  • shocks_a27
  • shocks_b30
  • shocks_b33
  • shocks_b36
  • shocks_b40
  • shocks_c44
  • shocks_c49
  • shocks_c53
  • timealloc
  • transfers
  • transfers_a3
  • transfers_a9
  • transfers_a16
  • transfers_b3
  • youth
  • hh_roster
  • education
  • educexp
  • health
  • income
  • transfers_d
  • enterprise
CSV JSON

What did do for this work - Description (inb02a)

Data file: income

Overview

Valid: 460
Invalid: 0
Type: Discrete
Width: 10
Range: -
Format: character

Questions and instructions

Literal question
What did [NAME] do in this work? What kind of trade, industry or business is it connected with? Description.
Categories
Value Category Cases
A ATTENDAN 1
0.2%
A FARMER 2
0.4%
A P 1
0.2%
A/CARE 1
0.2%
A/POLICE 2
0.4%
ACC CLERK 1
0.2%
ACC/CLERK 1
0.2%
ACCOUNTANT 1
0.2%
ADM POLICE 1
0.2%
ADMINISTRA 1
0.2%
AGRI ADVIS 1
0.2%
AH OFFICER 1
0.2%
ATTENDANT 3
0.7%
BAKERY 1
0.2%
BAR MAID 1
0.2%
BRICKS 1
0.2%
BURSAR 1
0.2%
C FARMING 2
0.4%
C/FARMERS 1
0.2%
C/FARMING 4
0.9%
CARGO 1
0.2%
CASUAL 16
3.5%
CASUAL JOB 3
0.7%
CASUAL LAB 4
0.9%
CASUALWORK 24
5.2%
CATERESS 1
0.2%
CHAIN SAW 1
0.2%
CHARCOAL 3
0.7%
CLEANER 3
0.7%
CLERGYMAN 1
0.2%
CLERICAL 2
0.4%
CLERK 2
0.4%
CON/WORKER 1
0.2%
CONSTRUCTI 2
0.4%
CONSTRUCTO 3
0.7%
COOK 6
1.3%
CUT TREES 1
0.2%
D/BOREHOLE 1
0.2%
DIG LAND 2
0.4%
DIGLATRINE 1
0.2%
DIP ATT 1
0.2%
DISHWASHER 1
0.2%
DOME WORK 4
0.9%
DOMESTIC 2
0.4%
DRIVER 22
4.8%
DRIVING 1
0.2%
EIS 1
0.2%
ELECTRICAL 1
0.2%
EMPLOYEE 2
0.4%
ENGINEER 1
0.2%
F/LABOUR 2
0.4%
FARM WORK 4
0.9%
FARMING 2
0.4%
FARMLABOUR 3
0.7%
FARMWORK 1
0.2%
FEED PIGS 1
0.2%
FOOTBALLER 1
0.2%
FOREMAN 1
0.2%
FRONT DESK 1
0.2%
GARDENER 1
0.2%
GRAZING 1
0.2%
GSU 1
0.2%
H20 SUPPLY 1
0.2%
HAIR DRESS 1
0.2%
HAWKER 1
0.2%
HERDING 3
0.7%
HERDSBOY 7
1.5%
HERDSMAN 5
1.1%
HOUSE MAID 1
0.2%
HOUSEHELP 3
0.7%
HOUSEMAID 2
0.4%
HOUSEWORK 6
1.3%
IT 1
0.2%
KENYA ARMY 1
0.2%
LAB TECH 1
0.2%
LABOURER 1
0.2%
LS OFFICER 1
0.2%
MAID 1
0.2%
MANAGER 4
0.9%
MASON 2
0.4%
MASONARY 5
1.1%
MATATU CON 1
0.2%
MATATU DRI 1
0.2%
MATRON 1
0.2%
MECHANIC 1
0.2%
MESSENGER 1
0.2%
MILITARY 6
1.3%
MILKING 1
0.2%
MILLER 1
0.2%
MILLITARY 1
0.2%
MOPPING 1
0.2%
NURSE 3
0.7%
NURSE AID 1
0.2%
OFFICER 5
1.1%
OPERATOR 3
0.7%
P TIMBER 1
0.2%
PASTOR 1
0.2%
PETROL ATT 1
0.2%
PHONEDEALE 1
0.2%
PICK TEA 9
2%
PICKINGTEA 3
0.7%
PLAITING 1
0.2%
PLANT TEA 1
0.2%
PLANTGRASS 1
0.2%
PLUMBERING 1
0.2%
POLICE 5
1.1%
POLICEMAN 6
1.3%
POSHO MILL 3
0.7%
PRE WELLS 1
0.2%
PREACHER 1
0.2%
PRINTING 1
0.2%
PUBLICWORK 1
0.2%
QUARRY 1
0.2%
RESTAURANT 1
0.2%
S LOAVES 1
0.2%
SALE COWS 1
0.2%
SALESMAN 2
0.4%
SALOONIST 3
0.7%
SCH COOK 1
0.2%
SECRETARY 3
0.7%
SECURICOR 2
0.4%
SECURITY 6
1.3%
SEL CLOTHS 1
0.2%
SELL MEAT 1
0.2%
SHAMBA 3
0.7%
SHAMBA BOY 3
0.7%
SHAMBABOY 1
0.2%
SHOPKEEPER 2
0.4%
SLAUGHTER 1
0.2%
STOREKEEPE 1
0.2%
SUPERVISOR 1
0.2%
SURB STAFF 1
0.2%
T ASSIST 1
0.2%
T DRIVER 9
2%
T OPERATOR 1
0.2%
T/DRIVER 2
0.4%
TAILOR 1
0.2%
TEA HARVES 1
0.2%
TEACHER 40
8.7%
TEACHING 27
5.9%
TEAPICKING 2
0.4%
TECHNICIAN 2
0.4%
TRADER 2
0.4%
TRUCK DRIV 1
0.2%
TURN BOY 1
0.2%
TUTOR 1
0.2%
TYPIST 1
0.2%
VETOFFICER 2
0.4%
VILL ELDER 1
0.2%
W/DOMESTIC 1
0.2%
WAITER 8
1.7%
WARDEN 1
0.2%
WASHDISHES 1
0.2%
WATCHMAN 10
2.2%
WEED MAIZE 1
0.2%
WEED TEA 3
0.7%
WEEDING 25
5.4%
WELDING 1
0.2%
WILDLIFE 1
0.2%
WORKER 1
0.2%
WORKING 3
0.7%
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.
var_qstn_ivuinstr
Write down the exact description of the job of the individual in block letters. Then find the code for the type of job that most closely fits the description. Write the code number in the column 2b, which is labeled “Occupation Code.” For example, suppose that the individual is employed as a truck driver for a company. This means the individual occupation is “Transport worker - code 5.” You should write the following:
Descprition: TRUCK DRIVER Occupation Code: 5

** If the individual worked as a wage or salaried employee in more than one job in the past 7 days, then you should begin by asking about the job in which he/she spent the most amount of time in the past 7 days.
Question post text
If more than one job of this type, choose the one that they spent the most time on in the past 12 months.

Description

Universe
All household members 8 years and older who worked as an EMPLOYEE in the past 7 days.
Source of information
Primary male respondent
Kenya National Data Archive (KeNADA)

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