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    Home / Central Data Catalog / WBG_2010_PECS_V01_M / variable [F2]
central

Expenditure and Consumption Survey, 2010

West Bank and Gaza, 2010 - 2011
Reference ID
WBG_2010_PECS_v01_M
Producer(s)
Palestinian Central Bureau of Statistics
Metadata
DDI/XML JSON
Study website
Created on
Jul 10, 2013
Last modified
Mar 29, 2019
Page views
22513
Downloads
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  • Pal_2010_HH
  • Pal_2010_Person

International Standard Industrial Classification of main job (PIND)

Data file: Pal_2010_Person

Overview

Valid: 0
Valid: 0
Invalid: 0
Invalid: 0
Type: Discrete
Decimal: 0
Start: 95
End: 96
Width: 2
Range: -
Format:

Questions and instructions

Categories
Value Category
1 Agriculture and Fishing
4 Electricity and Utilities
5 Construction
6 Commerce
7 Transportation, Storage and Communication
8 Financial, Insurance and Real Estate
9 Public Administration
10 Other Services
11 Unspecified
23 Mining, quarrying and Manufacturing
99 Not stated
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.

Description

Definition
Industry classification of the main (current or last held) job, with as much detail as possible.
Universe
Active individuals aged 7+

Others

Notes
GENERAL NOTES
----------------------
In many datasets national classifications are used.
The variable should generally cover the active sample (employed and unemployed).
Active sample reported as "not stated (code 99)" should normally include employed for whom the industry was not identified and unemployed if industry of last job is not reported.


COUNTRY SPECIFIC NOTES
----------------------------------
Economic activities provided are already aggregated and they are not following the international classification used for harmonization, hence some activities are grouped and cannot be split (Ex: manufacturing is already grouped with mining in the original data received while the international classification tend always to differentiate between different activities).

In order not to drop the variable, the provided grouping was used and labels were adjusted for this data set; giving the aggregated categories a composite code of all the codes of actual categories (Ex: mining and manufacturing will be coded 23 aggregating the codes 2 and 3)
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