1. Operational Material

2. Outline 2 Topics to be covered Introduction to Government Expenditures Compensation of Government Employees Pay and Expenditure Structure Indicators Timeline Introduction to Validation

3. Introduction to Government Expenditures (1) 3 Expenditures by government Individual consumption expenditure by government Collective consumption expenditure by government Housing Health Education Recreation and Culture Social Protection Collective Services

4. Introduction to Government Expenditures (2) 4 Health Education Collective Services Health Benefits and Reimbursements Production of Health Services Education Benefits and Reimbursements Production of Education Services Compensation of Employees Intermediate Consumption Net Taxes on Production Receipts from Sales Gross Operating Surplus Collective Services Compensation of Employees Intermediate Consumption Net Taxes on Production Receipts from Sales Gross Operating Surplus Compensation of Employees Intermediate Consumption Net Taxes on Production Receipts from Sales Gross Operating Surplus

5. Compensation of Government Employees Government Expenditure Pay & Employment Structure Indicators RemunerationsExpenditures (For Validation) PPPs for Compensation of Government Employees 1. Concept 5

6. Compensation of Government Employees 2. Data Collection Government Expenditure Pay & Employment Structure Indicators Official government pay scalesFinal government accountsRelevant official statistics 44 typical occupations (4 levels of experience each) 2 categories of government (Central + Sub-national) General indicators Data Collection Form (DCF): Basic pay Cash allowances Income in kind Employers’ social security contributions Information on hours worked Questionnaire: Wages and salaries in cash Employers contribution to social security funds Benefits in kind Information related to fixed capital formation Aggregated indicators: General Indicators such as GDP and population Government Recurrent Expenditures Indicators Wage bill Indicators Employment Indicators From Covers 3 BHs (Health, Education and Collective services) Report separately for Health, Education and Collective services Additional ratios will be computed automatically (ICP-Kit) Reference : ICP website - Operational MaterialICP website - Operational Material For Data Note 3.1.01. Information required on Government for the ICP 2011 3.1.02. Government Occupations, DCF & Indicators 3.1.03. Questionnaire on Government Expenditure 6 123

7. 3-1. Example of Government Occupations ICP Description Compensation - Health Compensation - Education Compensation - Collective 2Senior government officialXXX 7Hospital doctorX 8Specialist doctorX 9Hospital nurseX 10University teacher X 11Vocational education teacher X 12Primary school teacher X 13Secondary school teacher X 14Government accountants X 15Human resources professional X 16Database administrator X 35Driver (general duty)XXX 36Office cleanersXXX 44 181631 4 Levels of Experience: 0 year5 years10 years 20 years (starting salary) for each occupation 44 occupations X 4 levels = 176 7 Used in calculation of all three BHs

8. 8 3-2. Government Expenditure

9. 9 (1) Wage bill Indicators (2) Compression Ratios (Ratio of average total remuneration) (3) Public Sector Remuneration per GDP/capita (4) Employment Indicators Government wage bill per GDP for General/Central/Sub-national Government Managerial-professional ratio Managerial-clerical ratio for Health/Education/Collective Managerial Professional Clerical for Health/Education/Collective Employment per capita Employment per labor force (LF) The following indicators are automatically calculated in the ICP-Kit module.  they give insights when comparing countries / economies 3-3. Pay and Employment Structure Indicators

10. Actual Data Collection Forms 10 3-1. Compensation of Government Employees 3-2. Government Expenditure 3-3. Pay and Employment Structure Indicators ICP-Kit “Compensation” module Government Expenditure Questionnaire (Excel) DATAPROCESSING TOOLS

11. Validation Process NATIONAL LEVEL (Intra-Country Validation) REGIONAL LEVEL (Inter-Country Validation) Initial Data Validation Analytical Tables Finalization of Data Initial Data ValidationFinalization of Data Temporal Analysis GLOBAL LEVEL Data Compilation (Annual Data) Data Transmission NCA  RCA Data Transmission RCA  GO 11 PPPs, PLIs, etc. 1 2 3

12. e.g. Database administrator35,256 (Municipal A, 5-year experience) Database administrator74,662 (Municipal B, 5-year experience) Validation Steps(1) 12 1. NATIONAL Initial Data Validation Add remunerations and metadata to data collection tool Check added codes and metadata for errors and discrepancies 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. Hospital doctor 50,653 (0 year of experience) Hospital doctor45,367 (10 years of experience) Step1:Step2: Step3: Check that remunerations are plausible between related occupationsStep4: 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 ? ? ? ? ? ?

13. Validation Steps(2) 13 1. NATIONAL Initial Data Validation – cont’d Check that remunerations are temporally plausible by comparing them to previous ICP round Step5: ICP 2011 ICP 2005 CodeOccupationCodeOccupation 32Firefighter 215Fire Fighter 33Policeman/woman 213Policeman/woman 34Prison guard 214Prison Guard 35Driver (general duty) 221Chauffeur 36Office cleaners 212Cleaner 38Messengers 209Messenger Analyze price data and metadata for flagged cases Step7: Compare remuneration data and expenditure data by using structure indicators. (Remuneration x Employment  Expenditure) Step6:

14. Validation Steps(3) 14 Re-base data from all the countries to reference hours worked Step2: e.g. 2. REGIONAL Initial Data Validation Convert remunerations in local currency into base currency using annual average exchange rate Step3: CountryRemunerationRegular Hours worked per week Actual Hours Worked per week Never land60,0003225 Timberland70,0004060  Re-base *Recommended to use “actual hours” if information is available. CountryRemunerationRebased on Regular Hours (40 hours) Rebased on Actual Hours (40 hours) Never land60,000 (a){(a)/32}*40 = 75,000{(a)/25}*40 = 96,000 Timberland70,000 (b){(b)/40}*40 = 70,000{(b)/60}*40 = 46,667 Add remunerations and metadata to data validation tool Step1:

15. Validation Steps(4) 15 2. REGIONAL Initial Data Validation – cont’d Step7: Analyze price data and metadata for flagged cases County A County B County CCounty DCounty E 10University teacher 9,086 16,182 17,109 28,697 35,387 12Primary school teacher 1,214 4,652 5,108 9,920 25,562 13Secondary school teacher 1,959 5,259 4,221 14,313 26,960 16Database administrator 1,400 17,396 3,799 4,744 23,362 30Building caretaker 965 5,947 1,931 2,542 8,605 32Firefighter 560 7,569 18,199 4,660 13,719 33Policeman/woman 1,380 8,235 3,257 5,422 12,501 34Prison guard 836 2,457 3,820 5,422 13,918 35Driver (general duty) 999 4,308 2,531 3,558 8,024 36Office cleaners 965 3,499 1,858 3,558 8,595 4,221 18,199 560 e.g. Primary school teacher > Secondary school teacher e.g. University teacher < Firefighter e.g. 560 in country A could be too small. 18,199 in country C would be too large. Check that remunerations are plausible within a country Step4: Check that remunerations are plausible for a remuneration across the countries Step5: Check that remunerations are temporally plausible by comparing them to previous ICP round Step6: ? ? ? ? ? ?

16. Validation Steps(5) 16 3. REGIONAL Analytical Tables  Temporal Analysis Analytical Tables PPPs, PLI, C.V., CUP ratio, etc. 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

17. Data Transmission (all data) NCA  RCA Data Transmission (all data) RCA  GO Timeline 17 4 th Quarter, 2012 1 st Quarter, 2013 4 th Quarter, 2012 Data Transmission (all data) NCA  RCA Data Transmission (all data) RCA  GO 1 st Quarter, 2012 2 nd Quarter, 2012 1 st Quarter, 2012 Latest Year2011 data

18. 18 THANK YOU