Expenditure and Consumption Survey, PECS 2004
Palestine, 2004 - 2005
Get Microdata
Reference ID
PAL_PECS_2004_HD_V2.0
Producer(s)
Economic Research Forum, Palestinian Central Bureau of Statistics
Created on
Feb 19, 2014
Last modified
Aug 14, 2022
Page views
212197
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- Data files
- Palestine04-IND-V2
- Palestine04-HH-V2
Industry classification for the main job [Standardized version] (PIND)
Data file: Palestine04-IND-V2
Overview
Valid:
5173
Valid (weighted):
5136.123
Invalid:
15412
Invalid (weighted):
15439.511
Type:
Discrete
Decimal:
0
Start:
138
End:
140
Width:
3
Range:
10 - 999
Format:
Numeric
Weighted variable:
V2403
Questions and instructions
Categories
| Value | Category | Cases | Weighted | |
|---|---|---|---|---|
| 10 | Agriculture and Fishing | 978 | 851 |
16.6%
|
| 20 | Mining | 0 | 0 |
0%
|
| 30 | Manufacturing | 0 | 0 |
0%
|
| 35 | Mining and Manufacturing | 748 | 811 |
15.8%
|
| 40 | Electricity and Utilities | 0 | 0 |
0%
|
| 50 | Construction | 801 | 891 |
17.4%
|
| 60 | Commerce | 782 | 826 |
16.1%
|
| 70 | Transportation, Storage and Communication | 253 | 269 |
5.2%
|
| 80 | Financial, Insurance and Real Estate | 0 | 0 |
0%
|
| 90 | Public Administration | 0 | 0 |
0%
|
| 100 | Other services | 1611 | 1486 |
28.9%
|
| 998 | Unspecified | 0 | 0 |
0%
|
| 999 | Not stated | 0 | 0 |
0%
|
| Sysmiss | 15412 | 15440 |
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
Ever-employed Active individuals aged 10+ years
Source of information
Data Dictionary & Questionnaire/Codebook
Imputation and derivation
Coder instructions
Original survey variable recoded/renamed (Ref: Data dictionary.pdf)
Others
Notes
GENERAL NOTES
----------------------
In many datasets national classifications are used.
The variable should generally cover the active sample (employed and unemployed if previously employed).
While for Unemployed never previously employed, the variable was coded missing.
Active sample reported as "not stated (code 999)" should normally include employed for whom the status was not identified and unemployed previously employed if status in last job is not reported.
Those unemployed and do not search for a job are considered inactive, hence their previous employment information, if they were ever active, is recoded missing.
COUNTRY SPECIFIC NOTES
----------------------------------
Economic activities provided are already aggregated and they are not following the international classifications 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 an exceptional code following all the codes of actual categories (Ex: mining and manufacturing will be coded 35 following the codes 2 and 3)
----------------------
In many datasets national classifications are used.
The variable should generally cover the active sample (employed and unemployed if previously employed).
While for Unemployed never previously employed, the variable was coded missing.
Active sample reported as "not stated (code 999)" should normally include employed for whom the status was not identified and unemployed previously employed if status in last job is not reported.
Those unemployed and do not search for a job are considered inactive, hence their previous employment information, if they were ever active, is recoded missing.
COUNTRY SPECIFIC NOTES
----------------------------------
Economic activities provided are already aggregated and they are not following the international classifications 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 an exceptional code following all the codes of actual categories (Ex: mining and manufacturing will be coded 35 following the codes 2 and 3)