6TH Western Asian ICP Workshop GOVERNMENT COMPENSATION January 21 - 27, 2013 Istanbul, Turkey

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

Overview of Validation Steps

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

? ? ?  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 ? 

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

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

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

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

Review of Country Submissions

Intra-Country Analysis

Inter-Country Analysis Analysis of 1st QTs

Thank you