1. 6TH Western Asian ICP Workshop
GOVERNMENT COMPENSATION
January , 2013
Istanbul, Turkey

2. 2011 ICP Government Compensation
Session Outline
Introduction
Overview of Validation Steps
Intra-
Country
Review of Country Submissions
Intra-Country Analysis
Inter-
Country
Analysis of 1st QTs

3. Overview of Validation Steps

4. Validation Process NATIONAL LEVEL (Intra-Country Validation)
Data Compilation (Annual Data)
NATIONAL LEVEL (Intra-Country Validation)
Initial Data Validation
Finalization of Data 1
Data Transmission NCA  RCA
REGIONAL LEVEL (Inter-Country Validation)
Initial Data Validation 2
Analytical Tables 3
Temporal Analysis
Finalization of Data
PPPs, PLIs, etc.
Data Transmission RCA  GO
GLOBAL LEVEL

5. ? ? ?  Validation Steps(1) 1. NATIONAL Initial Data Validation Step1:
Add remunerations and metadata to data collection tool
Step2:
Check added codes and metadata for errors and discrepancies
Step3:
Check that remunerations are plausible within the same occupation
a. Within the same level of experience
b. With different levels of experience
Usually one observation per occupation
Sometimes different salary scales in different local governments
Usually, remunerations increase as experience increases
e.g.
Database administrator 35,256
(Municipal A, 5-year experience)
Database administrator 74,662
(Municipal B, 5-year experience)
e.g.
Hospital doctor 50,653
(0 year of experience)
Hospital doctor 45,367
(10 years of experience) ? ?
Step4:
Check that remunerations are plausible between related occupations
<Within the same level of experience>
e.g. 1
Hospital Doctor (5 years) 62,556
Hospital Nurse (5 years) 75,698
e.g. 2
Hospital Doctor (0 year) 62,556
Hospital Nurse (20 years) 65,698 ? 

6. Validation Steps(2) 1. NATIONAL Initial Data Validation – cont’d
Check that remunerations are temporally plausible by comparing them to previous ICP round
ICP 2011
ICP 2005
Code
Occupation 32
Firefighter
215
Fire Fighter 33
Policeman/woman
213 34
Prison guard
214
Prison Guard 35
Driver (general duty)
221
Chauffeur 36
Office cleaners
212
Cleaner 38
Messengers
209
Messenger
Step6:
Compare remuneration data and expenditure data by using structure indicators. (Remuneration x Employment  Expenditure)
Step7:
Analyze price data and metadata for flagged cases

7. Validation Steps(3) 2. REGIONAL Initial Data Validation
Add remunerations and metadata to data validation tool
Step2:
Re-base data from all the countries to reference hours worked
e.g.
Country
Remuneration
Regular Hours worked per week
Actual Hours Worked per week
Never land
60,000 32 25
Timberland
70,000 40 60
Re-base
Country
Remuneration
Rebased on Regular Hours (40 hours)
Rebased on Actual Hours (40 hours)
Never land
60,000 (a)
{(a)/32}*40 = 75,000
{(a)/25}*40 = 96,000
Timberland
70,000 (b)
{(b)/40}*40 = 70,000
{(b)/60}*40 = 46,667
*Recommended to use “actual hours” if information is available.
Step3:
Convert remunerations in local currency into base currency using annual average exchange rate

8. Validation Steps(4) 2. REGIONAL Initial Data Validation – cont’d
Check that remunerations are plausible within a country
e.g. Primary school teacher
> Secondary school teacher
e.g. University teacher < Firefighter ? ?
County A
County B
County C
County D
County E 10
University teacher
9,086
16,182
17,109
28,697
35,387 12
Primary school teacher
1,214
4,652
5,108
9,920
25,562 13
Secondary school teacher
1,959
5,259
4,221
14,313
26,960 16
Database administrator
1,400
17,396
3,799
4,744
23,362 30
Building caretaker
965
5,947
1,931
2,542
8,605 32
Firefighter
560
7,569
18,199
4,660
13,719 33
Policeman/woman
1,380
8,235
3,257
5,422
12,501 34
Prison guard
836
2,457
3,820
13,918 35
Driver (general duty)
999
4,308
2,531
3,558
8,024 36
Office cleaners
3,499
1,858
8,595
4,221
560
18,199
Step5:
Check that remunerations are plausible for a remuneration across the countries ?
e.g. 560 in country A could be too small. 18,199 in country C would be too large.
Step6:
Check that remunerations are temporally plausible by comparing them to previous ICP round
Step7:
Analyze price data and metadata for flagged cases

9. PPPs, PLI, C.V., CUP ratio, etc.
Validation Steps(5)
3. REGIONAL Analytical Tables  Temporal Analysis
As same as HHC, analysis using Tables make it possible to conduct detailed comparisons.
iteration
Quaranta Tables
Dikhanov Tables
Temporal Analysis – with data from previous round
PPPs, PLI, C.V., CUP ratio, etc.
Analytical Tables

10. Review of Country Submissions

11. Intra-Country Analysis

12. Inter-Country Analysis
Analysis of 1st QTs

13. Thank you