2011 ICP: Validation and Experimental calculations

Transcription

1 Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized International Comparison Program [01.02] 2011 ICP: Validation and Experimental calculations Sergey Sergeev 9 th Technical Advisory Group Meeting September 25-27, 2013 Washington DC

2 S. Sergeev (Statistics Austria, ICP VEG / CoTaF Member) 2011 ICP: Validation and Experimental calculations (Annual data, state: mid Aug 13) In accordance with the ICP GO WB requests, the task of the ICP VEG / CoTaF during the validation of input annual 2011 data was the evaluation of the following points: Overall quality of price data (by region) Comparability of data at regional level Comparability of data at global level Extent of which Global Core prices represent / reflect regional price levels Density / sparsity of price data and its effect on computing linking factors Quality of resulting linking factors Quality of resulting Global PPPs for Household Consumption I. Input annual data for 2011 general overview The recent set of input data was sent by the GO ICP on 22 Aug 13. The following data was supplied see Table 1 below: 1

3 Table 1: Summary of ICP 2011 input data (Aug 13) 2

4 The supplied data was analyzed Survey by Survey. II. Analysis of Household Consumption (HHC) The ICP 2011 Global Core List (GCL) price data for HH consumption (108 BHs) were received from all 6 regions, namely Africa (50 countries), Asia (23 countries), CIS (9 countries), EU-OECD (47 countries), LAC (17 LA countries + 23 Caribbean), and Western Asia (12 countries). The GO ICP also supplied the regional BH-PPP for HHC (presented with the Regional numeraires / Currencies of the base countries). Extent of which Global Core prices represent / reflect regional price levels and quality of resulting Global PPPs for Household Consumption The African List contains 419 specific African items. The Asian List contains 404 items from GCL and 648 specific Asian items. The CIS List contains more than 1800 specific CIS items (1848 items). The EU OECD included in the Price Surveys 386 items from the Global List. The LAC List contains only 144 specific LAC items. The WA List contains 442 specific WA items. How one can evaluate the extent of which Global Core prices reflect regional price levels? It is possible on the basis of comparison of PLI obtained within the Regions on the basis of full Regional Lists (specific Regional + Global items) with the PLI obtained on the basis of Global List only. If one compare the Regional results with the World results calculated for the Global list and rescaled to the Base Regions = 100 then such comparison reflect also the potential impact of Regional fixity. The same outcome should be obtained in another way: to compare the within- Regional VIpc obtained within the Regional comparisons with the within-regional VIpc obtained within the Global comparison (something like the differences between Global CAR-Volume results and Regional Results). The reasonable consistency of the HHC PPPs (at least, for the analytical categories) obtained on the basis of full Regional list and GCL list for Asia and EU-OECD was confirmed during the former experiments (Sept 12). Other Regions should still finalize their input data. The first look was on the Regional BH-PPPs, to evaluate the quality of this part of data. The PPP for different areas can have a significant variance. However the ratios higher than 20 times indicates clear on the problems with input data. Obviously if regional BH-PPPs have very problematic points then there is no sense to use these to obtain fixity. 3

5 The quick analysis via MAX / MIN ratios of BH-PPPs showed that no. of potential problematic points in the sets of the Regional BH-PPPs was significantly decreased relatively the state of May 13 (some exotic cases like quasi-zero prices for petrol in VEN are still kept but these cases should be analyzed separately). Table 2: Ratios of MAX / MIN of the Regional BH-PPPs (Reg-Numeraire = 1) MAX-BH MIN-BH MAX / MIN Algeria DZA Angola AGO Benin BEN Botswana BWA Burkina Faso BFA Burundi BDI Cameroon CMR Cape Verde CPV Central African Republic CAF Chad TCD Comoros COM Congo COG Congo, The Democratic Republic of COD Côte d''ivoire CIV Djibouti DJI Egypt (Africa) EGY Equatorial Guinea GNQ Ethiopia ETH Gabon GAB Gambia GMB Ghana GHA Guinea GIN Guinea-Bissau GNB Kenya KEN Lesotho LSO Liberia LBR Madagascar MDG Malawi MWI Mali MLI Mauritania MRT Mauritius MUS Morocco MAR Mozambique MOZ Namibia NAM Niger NER Nigeria NGA Rwanda RWA São Tome and Principe STP Senegal SEN Seychelles SYC Sierra Leone SLE South Africa ZAF

6 Sudan (Africa) SDN Swaziland SWZ Tanzania, United Republic of TZA Togo TGO Tunisia TUN Uganda UGA Zambia ZMB Zimbabwe ZWE Bangladesh BGD Bhutan BTN Brunei Darussalam BRN Cambodia KHM China CHN Fiji (Asia) FJI Hong Kong HKG India IND Indonesia IDN Lao People''s Democratic Republic LAO Macao MAC Malaysia MYS Maldives MDV Mongolia MNG Myanmar MMR Nepal NPL Pakistan PAK Philippines PHL Singapore SGP Sri Lanka LKA Taiwan, Province of China TWN Thailand THA Viet Nam VNM Armenia ARM Azerbaijan AZE Belarus BLR Kazakhstan KAZ Kyrgyzstan KGZ Moldova, Republic of MDA Russian Federation (CIS) RUT Tajikistan TJK Ukraine UKR Albania ALB Australia (OECD) AUS Austria AUT Belgium BEL Bosnia & Herzegovina BIH Bulgaria BGR Canada CAN Chile CHL Croatia HRV

7 Cyprus CYP Czech Republic CZE Denmark DNK Estonia EST Finland FIN France FRA Germany DEU Greece GRC Hungary HUN Iceland ISL Ireland IRL Israel ISR Italy ITA Japan JPN Korea, Republic of KOR Latvia LVA Lithuania LTU Luxembourg LUX Macedonia, FYROM MKD Malta MLT Mexico MEX Montenegro MNE Netherlands NLD New Zealand (OECD) NZL Norway NOR Poland POL Portugal PRT Romania ROU Russian Federation (OECD) RUS Serbia SRB Slovakia SVK Slovenia SVN Spain ESP Sweden SWE Switzerland CHE Turkey TUR United Kingdom GBR United States USA Bolivia, Plurinational State of BOL Brazil BRA Colombia COL Costa Rica CRI Cuba CUB #DIV/0! Dominican Republic DOM Ecuador ECU El Salvador SLV Guatemala GTM Haiti HTI Honduras HND

8 Nicaragua NIC Panama PAN Paraguay PRY Peru PER Uruguay URY Venezuela, Bolivarian Republic of VEN Bahrain BHR Egypt (Western Asia) EGZ Iraq IRQ Jordan JOR Kuwait KWT Oman OMN Palestinian Territory PSE Qatar QAT Saudi Arabia SAU Sudan (Western Asia) SDO United Arab Emirates ARE Yemen YEM Anguilla AIA Antigua and Barbuda ATG Aruba ABW Bahamas BHS Barbados BRB Belize BLZ Bermuda BMU Bonaire, Saint Eustatius and Saba BES Cayman Islands CYM Curaçao CUW Dominica DMA Grenada GRD Guyana GUY #DIV/0! Jamaica JAM Montserrat MSR Saint Kitts and Nevis KNA Saint Lucia LCA Saint Vincent and The Grenadines VCT Sint Maarten SXM Suriname SUR Trinidad and Tobago TTO Turks and Caicos Islands TCA Virgin Islands, British VGB

9 The ICP 2011 HHC Global Core List comprises 692 items (incl. items for Rents and Education ). The Regions and the countries priced very different no. of products from the HHC GCL: Table 3: No. of priced HHC GCL items by Regions (15 Aug 13) No. of priced items with * Sh. of * AFR-Total ASI-Total CIS-Total EU-O Total LA Total WA Total CAR Total WORLD EU-OECD priced 387 GCL HHC items, ASI 397 items but AFR 658 items and WAS 684 items. Respectively No. of prices in the Regional sets are also very different and additionally the Regional numbers depends heavily on the no. of the countries in the Regions: Table 4: No. of priced HHC GCL items by the countries (state: 15 Aug 13) No. of priced items with * Sh. of * AFR DZA AFR AGO AFR BEN AFR BWA AFR BFA AFR BDI AFR CMR AFR CPV AFR CAF AFR TCD AFR COM AFR COG AFR COD AFR CIV AFR DJI AFR EGY AFR GNQ AFR ETH AFR GAB AFR GMB AFR GHA AFR GIN AFR GNB AFR KEN

10 AFR LSO AFR LBR AFR MDG AFR MWI AFR MLI AFR MRT AFR MUS AFR MAR AFR MOZ AFR NAM AFR NER AFR NGA AFR RWA AFR STP AFR SEN AFR SYC AFR SLE AFR ZAF AFR SDN AFR SWZ AFR TZA AFR TGO AFR TUN AFR UGA AFR ZMB AFR ZWE AFRICA ASI BGD ASI BTN ASI BRN ASI KHM ASI CHN ASI FJI ASI HKG ASI IND ASI IDN ASI LAO ASI MAC ASI MYS ASI MDV ASI MNG ASI MMR ASI NPL ASI PAK ASI PHL ASI SGP ASI LKA ASI TWN ASI THA

11 ASI VNM ASIA CIS ARM CIS AZE CIS BLR CIS KAZ CIS KGZ CIS MDA CIS RUT CIS TJK CIS UKR CIS EU-OECD ALB EU-OECD AUS EU-OECD AUT EU-OECD BEL EU-OECD BIH EU-OECD BGR EU-OECD CAN EU-OECD CHL EU-OECD HRV EU-OECD CYP EU-OECD CZE EU-OECD DNK EU-OECD EST EU-OECD FIN EU-OECD FRA EU-OECD DEU EU-OECD GRC EU-OECD HUN EU-OECD ISL EU-OECD IRL EU-OECD ISR EU-OECD ITA EU-OECD JPN EU-OECD KOR EU-OECD LVA EU-OECD LTU EU-OECD LUX EU-OECD MKD EU-OECD MLT EU-OECD MEX EU-OECD MNE EU-OECD NLD EU-OECD NZL EU-OECD NOR EU-OECD POL EU-OECD PRT EU-OECD ROU

12 EU-OECD RUS EU-OECD SRB EU-OECD SVK EU-OECD SVN EU-OECD ESP EU-OECD SWE EU-OECD CHE EU-OECD TUR EU-OECD GBR EU-OECD USA EU-O LA BOL LA BRA LA COL LA CRI LA CUB 0 0 #DIV/0! LA DOM LA ECU LA SLV LA GTM LA HTI LA HND LA NIC LA PAN LA PRY LA PER LA URY LA VEN LA WA BHR WA EGZ WA IRQ WA JOR WA KWT WA OMN WA PSE WA QAT WA SAU WA SDO WA ARE WA YEM WA CARIB AIA CARIB ATG CARIB ABW CARIB BHS CARIB BRB CARIB BLZ CARIB BMU

13 CARIB BES CARIB CYM CARIB CUW CARIB DMA CARIB GRD CARIB GUY 0 0 #DIV/0! CARIB JAM CARIB MSR CARIB KNA CARIB LCA CARIB VCT CARIB SXM CARIB SUR CARIB TTO CARIB TCA CARIB VGB CAR WORLD Input data in several Regions is still under validation (regional BH-PPPs are still not final). Therefore the further analysis of the Global results was made mainly on the basis of individual country s HHC GCL price data (the CPD 3:1 procedure was run with GCL price data for 181 countries). Nevertheless between-regional linking factors were estimated as GM of PLI of the countries in the Regions (this is practically the CAR-PPP approach). Table 5: HHC Price Level Indices = Quasi-Linking factors (World= 100) Code Name of Heading AFR50 ASI23 CIS9 EU-O47 LA17 WA12 CAR23 World Rice Other cereals, flour and other products Bread Other bakery products Pasta products Beef and veal Pork Lamb, mutton and goat Poultry Other meats and meat preparations Fresh, chilled or frozen fish and seafood Preserved or processed fish and seafood Fresh milk Preserved milk and other milk products Cheese Egg and egg-based products Butter and margarine Other edible oils and fats Fresh or chilled fruit Frozen, preserved fruit and fruit - based products Fresh or chilled vegetables other than potatoes Fresh or chilled potatoes Frozen,. veget. & vegetable based products Sugar Jams, marmalades and honey

14 Confectionery, chocolate and other cocoa preparations Food products n.e.c Coffee, tea, and cocoa Mineral waters, soft drinks, fruit and vegetable juices Spirits Wine Beer Tobacco Clothing materials, other articles of clothing Garments Cleaning, Repair and Hire of Clothing Shoes and other footwear Repair and Hire of Footwear Actual and imputed rentals for housing Maintenance and repair of the dwelling Water supply Electricity Gas Other fuels Furniture and furnishings Carpets and other floor coverings Repair of furniture, furnishings and floor coverings Household Textiles Major household appliances whether electric or not Small electric household appliances Repair of Household Appliances Glassware, Tableware and Household Utensils Major Tools and Equipment Small tools and miscellaneous accessories Non-durable Household Goods Domestic services Household services Pharmaceutical products Other Medical Products Therapeutical appliances and equipment Medical Services Services of dentists Paramedical Services Motor cars Motor cycles Bicycles Fuels and lubricants for personal transport equipment Maintenance and repair of personal transport equipment Other services - personal transport equipment Passenger transport by railway Passenger transport by road Passenger transport by air Postal services Telephone and telefax equipment Telephone and telefax services Audio-visual, photo- and information proce. equipment Recording media Repair of audio-visual, photo Major Durables for outdoor and indoor recreation Other recreational items and equipment Gardens and pets Veterinary and other services for pets Recreational and sporting services Cultural services

15 Newspapers, books and stationery Education Catering services Accommodation services Hairdressing salons Appliances, articles and products for personal care Jewellery, clocks and watches Other personal effects Other financial services n.e.c Other services n.e.c Cereals and Bread (5) Meat (5) Fish (2) Milk, cheese, eggs (4) Butter and oils (2) Fruits (2) Vegetables (3) Sugar, jams, confictionery (3) Food products n.e.c. (1) Food (27) Non-alcoholic beverages (2) Alcoholic beverages (3) Tobacco (1) Clothing (3) Footwear (2) Electricity, gas, other fuel (3) Furniture, carpets, coverings (3) HH appliances (3) Major tools and equipment (2) Non-durable goods and HH services (3) Medical products (3) Medical services (3) Transport means (3) Other goods services related to transport (3) Transport services (3) Audio-visual, photo-, information proces.eq., etc. (3) Other recreational items & eq., gardens, pets, etc. (3) Recreational, sporting and cultural services (2) Personal care goods and services (2) Jewellery, clocks, watches and other pers. goods (2) Food and non-alcoholic beverages (29) Alcoholic beverages, tobacco, narcotics (4) Clothing and footwear (5) Housing, electricity, etc. (6) HH goods and services (13) Health (6) Transport (9) Post and Communication (3) Cultural goods and services (10) Catering and accomodation services (2) Other goods and services (6) HH Total (94) MAX BH-PLI MIN BH-PLI Rato MAX / MIN

16 Generally the Regional PLIs do not look too bad. Maybe, CARIB countries have too high PLIs but it is difficult to evaluate this specific Region and these specific small countries without specific knowledge about their economies. The first global calculations for the HHC GCL showed that there are still several problematic points in the input data at the country s level maximal country s BH- CV of CUP-In are still very high in many cases. Table 6: ICP 2011 HHC - Maximal country s BH-CV of CUP-In However it seems that these inconsistencies have no a systematical character - the general BH CV of Cup-In are not so high in many cases. 15

17 Table 7: ICP 2011 HHC general BH-CV of CUP-In BH code BH name BH-Var Rice Other cereals, flour and other products Bread Other bakery products Pasta products Beef and veal Pork Lamb, mutton and goat Poultry Other meats and meat preparations Fresh, chilled or frozen fish and seafood Preserved or processed fish and seafood Fresh milk Preserved milk and other milk products Cheese Egg and egg-based products Butter and margarine Other edible oils and fats Fresh or chilled fruit Frozen, preserved or processed fruit and fruit - based products Fresh or chilled vegetables other than potatoes Fresh or chilled potatoes Frozen, preserved, processed vegetables & veget. based products Sugar Jams, marmalades and honey Confectionery, chocolate and other cocoa preparations Food products n.e.c Coffee, tea, and cocoa Mineral waters, soft drinks, fruit and vegetable juices Spirits Wine Beer Tobacco Clothing materials, other articles of clothing and clothing accessories Garments Cleaning, Repair and Hire of Clothing Shoes and other footwear Repair and Hire of Footwear Actual and imputed rentals for housing Maintenance and repair of the dwelling Water supply Electricity Gas Other fuels Furniture and furnishings Carpets and other floor coverings Repair of furniture, furnishings and floor coverings Household Textiles Major household appliances whether electric or not Small electric household appliances Repair of Household Appliances Glassware, Tableware and Household Utensils Major Tools and Equipment Small tools and miscellaneous accessories Non-durable Household Goods Domestic services Household services Pharmaceutical products Other Medical Products

18 Therapeutical appliances and equipment Medical Services Services of dentists Paramedical Services Motor cars Motor cycles Bicycles Fuels and lubricants for personal transport equipment Maintenance and repair of personal transport equipment Other services in respect of personal transport equipment Passenger transport by railway Passenger transport by road Passenger transport by air Postal services Telephone and telefax equipment Telephone and telefax services Audio-visual, photographic and information processing equipment Recording media Repair of audio-visual, photographic & information processing eq Major Durables for outdoor and indoor recreation Other recreational items and equipment Gardens and pets Veterinary and other services for pets Recreational and sporting services Cultural services Newspapers, books and stationery Education Catering services Accommodation services Hairdressing salons and personal grooming establishments Appliances, articles and products for personal care Jewellery, clocks and watches Other personal effects Other financial services n.e.c Other services n.e.c..0 The author used the obtained PPPs to estimate the VIpc (World181=100) for HHC (EKS aggregation), to evaluate general reliability of the whole set of input data (price data in the combination with NA data). 1 Table 8: ICP 2011 HHC with BH-PPPs from GCL - VIpc (World = 100) HHC (w/o Rents) Popul. PPP PLI RV RVpc VIpc 1 DZA Algeria 2 AGO Angola 3 BEN Benin 4 BWA Botswana 5 BFA Burkina Faso 6 BDI Burundi 7 CMR Cameroon 8 CPV Cape Verde 9 CAF Central African Republic 10 TCD Chad 11 COM Comoros (mio. NC) (mio.) (World = 1) (World = 100) (mio. I$) (Int. $) (W = 100) Of course, these VIpc for HHC do not show Actual Consumption because of different country s shares of NPISH and GG-Individual. 17

19 12 COG Congo COD Congo, The DR of CIV Côte d''ivoire DJI Djibouti EGY Egypt (Africa) GNQ Equatorial Guinea ETH Ethiopia GAB Gabon GMB Gambia GHA Ghana GIN Guinea GNB Guinea-Bissau KEN Kenya LSO Lesotho LBR Liberia MDG Madagascar MWI Malawi MLI Mali MRT Mauritania MUS Mauritius MAR Morocco MOZ Mozambique NAM Namibia NER Niger NGA Nigeria RWA Rwanda STP São Tome & Principe SEN Senegal SYC Seychelles SLE Sierra Leone ZAF South Africa SDN Sudan (Africa) SWZ Swaziland TZA Tanzania, United Republic of TGO Togo TUN Tunisia UGA Uganda ZMB Zambia ZWE Zimbabwe Africa BGD Bangladesh 52 BTN Bhutan 53 BRN Brunei Darussalam 54 KHM Cambodia 55 CHN China 56 FJI Fiji (Asia) 57 HKG Hong Kong 58 IND India 59 IDN Indonesia 60 LAO Lao People''s DR

20 61 MAC Macao MYS Malaysia MDV Maldives MNG Mongolia MMR Myanmar NPL Nepal PAK Pakistan PHL Philippines SGP Singapore LKA Sri Lanka TWN Taiwan, Province of China THA Thailand VNM Viet Nam Asia ARM Armenia AZE Azerbaijan BLR Belarus KAZ Kazakhstan KGZ Kyrgyzstan MDA Moldova, Republic of RUT Russian Federation (CIS) TJK Tajikistan UKR Ukraine CIS ALB Albania 84 AUS Australia (OECD) 85 AUT Austria 86 BEL Belgium 87 BIH Bosnia & Herzegovina 88 BGR Bulgaria 89 CAN Canada 90 CHL Chile 91 HRV Croatia 92 CYP Cyprus 93 CZE Czech Republic 94 DNK Denmark 95 EST Estonia 96 FIN Finland 97 FRA France 98 DEU Germany 99 GRC Greece 100 HUN Hungary 101 ISL Iceland 102 IRL Ireland 103 ISR Israel 104 ITA Italy 105 JPN Japan 106 KOR Korea, Republic of 107 LVA Latvia 108 LTU Lithuania

21 109 LUX Luxembourg MKD Macedonia, FYROM MLT Malta MEX Mexico MNE Montenegro NLD Netherlands NZL New Zealand (OECD) NOR Norway POL Poland PRT Portugal ROU Romania RUS Russian Federation (OECD) SRB Serbia SVK Slovakia SVN Slovenia ESP Spain SWE Sweden CHE Switzerland TUR Turkey GBR United Kingdom USA United States EU-OECD BOL Bolivia, Plurinational State of BRA Brazil COL Colombia CRI Costa Rica CUB Cuba DOM Dominican Republic ECU Ecuador SLV El Salvador GTM Guatemala HTI Haiti HND Honduras NIC Nicaragua PAN Panama PRY Paraguay PER Peru URY Uruguay VEN Venezuela, Bolivarian R. of LAC BHR Bahrain 148 EGZ Egypt (Western Asia) 149 IRQ Iraq 150 JOR Jordan 151 KWT Kuwait 152 OMN Oman 153 PSE Palestinian Territory 154 QAT Qatar 155 SAU Saudi Arabia 156 SDO Sudan (Western Asia)

22 157 ARE United Arab Emirates YEM Yemen WAS AIA Anguilla ABW Aruba ATG Antigua and Barbuda BHS Bahamas BRB Barbados BLZ Belize BMU Bermuda BES Bonaire, S. Eustatius & Saba CYM Cayman Islands CUW Curaçao DMA Dominica GRD Grenada GUY Guyana JAM Jamaica MSR Montserrat KNA Saint Kitts and Nevis LCA Saint Lucia VCT Saint Vinc.& The Grenadines SXM Sint Maarten SUR Suriname TTO Trinidad and Tobago TCA Turks and Caicos Islands VGB Virgin Islands, British CARIBIC World Generally the results look as not bad taking into account different ratios between HHC and AIC in different countries. Housing Rents Many Regions did not finalize still the treatment of data for this area. In some Regions (like Asia) even the approach which should be used is still not agreed. Therefore all present versions of Global PPPs for the Rentals are very rough and should be further analysed. A. Heston calculated the between regional linked factors relatively EU-OECD on the basis of available price data: Table 9: Direct Rentals between-regional PLI (OECD=1) Region Linking Factor Africa Asia Caribbean Islands Latin America OECD Western Asia

23 P. Konjin (Eurostat) calculated direct Volume indices on the basis of No. of dwellings and 3 Quality indicators (Electricity, Water supply, Inside toilet) and respective indirect PPP/PLIs. These indirect PPP/PLIs are presented below parallel with Alan s direct PLI: Table 10: Rentals - Indirect and Direct between-regional PPP/PLIs Africa Asia CIS EU-O LA WA CARIB VIpc (EU-O = 100) Indirect PPPs (EU-O = 1) Ind. PLIs (EU-O = 100) Dir. PLIs, Alan (EU-O = 100) At the first look, PLI by two methods are very different. However it is not so clear at the 2 nd look. It seems that sometimes we operate with the term Between-regional Linking factors too flexible, too inexact. For example, Alan s Linking factors for the OECD, ADB, AfDB, LA, CAR, WAS are in reality the linking factors between 32 AfDB countries, 20 ADB countries, 13 LA countries, 11 WAS countries, 16 CAR countries and 24 OECD countries 2. Paulus s indirect PLIs were calculated for a broader set of regional countries. Therefore both sets are named as Between- Regional Linking factors but these are hardly comparable due to different bases of the presentation. Alan indicated clearly himself that Linking factors on the basis of price data should be used with big reservation. What is with the Linking factors obtained by the Quantity approach? At the first look, they are not too bad. However this is not really so. Let us look in the Rent results for the EU-OECD countries obtained by the official EU-OECD approach and the results for the same countries obtained by the method proposed for the Global linking (all results with the base AUT = 100): Table 11: EU-OECD Rentals VIpc (AUT = 100) - the Linking and Official approaches VIpc (AUT = 100) Paulus-Link OECD-EU Albania ALB 30.4 Australia (OECD) AUS 71.7 Austria AUT Belgium BEL 78.7 Bosnia & Herzegovina BIH Bulgaria BGR Canada CAN Chile CHL Croatia HRV Cyprus CYP Czech Republic CZE Denmark DNK Respectively, to use Alan s scaling factors in the World comparison, one should know these countries and do numerous rescalings of the Regional PPPs. Pualus s results are also not fully straightforward even formally because the countries for which no expenditure or no BH PPP were found were not excluded from the Volume comparison. 22

24 Estonia EST Finland FIN France FRA Germany DEU 93.5 Greece GRC Hungary HUN Iceland ISL Ireland IRL Israel ISR 82.8 Italy ITA Japan JPN 92.4 Korea, Republic of KOR 75.4 Latvia LVA Lithuania LTU Luxembourg LUX Macedonia, FYROM MKD Malta MLT Mexico MEX 23.0 Montenegro MNE Netherlands NLD 76.6 New Zealand (OECD) NZL 77.9 Norway NOR Poland POL Portugal PRT Romania ROU Russian Federation (OECD) RUS Serbia SRB Slovakia SVK Slovenia SVN Spain ESP 63.2 Sweden SWE Switzerland CHE 97.7 Turkey TUR United Kingdom GBR 99.1 United States USA The differences in many cases are very significant (especially for less developed countries). There is an opinion that the method should be evaluated from the aim for which it is used and bad results for individual countries does not mean that the method is bad for the Between-Regional comparisons. It seems that this is an incorrect concept. This is something like one said: For the comparisons within the Regions we should use very tight product definitions but for the Between-Regional comparisons the use of Unit Values would be sufficient. ;-) We should evaluate the reliability of the method on the basis of its features. One can agree that the comparison on the basis of no. of dwellings and only 3 Quality indicators can bring plausible results in the case we compare the countries with similar structure of dwelling stock. The assumption behind is that parameters which 23

25 were not included are ~ similar. [For example, the results by the price approach and the quantity approach within the official EU_OECD are similar for similar countries.] This is not the case for the whole set of the countries within the EU-OECD and for the Regions for Rents and the very high differences are visible. The regional average results appear not to be very dubious but these should be applied for the intercountry comparison. Therefore one should look on the reliability of the method per se. Regional averaging can have (and has) the compensation effect but this does not help to obtain correct inter-country ratios. What would be if one uses Between-regional links for the Global comparison calculated by Paulus? The author multiplied the within-regional BH-PPPs for Rents with the Linking Coefficients (OECD = 1) to should obtain the linked PPPs for the World comparison with the base DE=1. Further the author calculated with these linked PPPs the country's VIpc (DE = 100). 24

26 Table 12: ICP 2011 VIpc for Rents (DE=100) by indirect between-regional PPPs AFR Asia CIS EU-O LA WA CAR DZA 4.8 BGD 20.7 ARM 74.6 ALB 32.5 BOL 21.7 BHR 31.1 AIA 52.2 AGO 36.2 BTN 39.2 AZE 36.3 AUS 76.8 BRA 42.8 EGZ 27.9 ATG #DIV/0! BEN 7.8 BRN BLR 57.1 AUT COL 70.7 IRQ 28.0 ABW BWA 21.9 KHM 21.1 KAZ 36.7 BEL 84.1 CRI 25.2 JOR 38.6 BHS BFA 5.0 CHN 45.0 KGZ 21.1 BIH 35.7 CUB #DIV/0! KWT 66.2 BRB BDI 11.6 FJI 59.2 MDA 39.5 BGR 62.5 DOM 70.9 OMN 30.1 BLZ 93.7 CMR 16.5 HKG RUT 60.1 CAN ECU 46.6 PSE 0.8 BMU CPV 48.6 IND 38.1 TJK 11.2 CHL 40.4 SLV #DIV/0! QAT 43.1 BES #DIV/0! CAF 2.1 IDN 52.7 UKR 49.8 HRV 59.9 GTM #DIV/0! SAU 68.2 CYM TCD 4.7 LAO 26.7 CYP HTI #DIV/0! SDO 3.8 CUW COM 18.6 MAC CZE 65.9 HND 22.4 ARE DMA #DIV/0! COG 10.3 MYS DNK 99.7 NIC 43.9 YEM 8.1 GRD 48.5 COD 5.8 MDV 29.9 EST 45.9 PAN GUY #DIV/0! CIV 12.1 MNG 41.0 FIN 85.4 PRY 16.0 JAM 35.6 EGY 99.0 MMR 22.6 FRA 96.9 PER 38.6 MSR 75.0 GNQ 29.8 NPL 19.1 DEU URY KNA 87.2 ETH 3.9 PAK 43.6 GRC 86.1 VEN 35.9 LCA 49.6 GMB 2.6 PHL 54.8 HUN 62.8 VCT 52.1 GHA 3.4 SGP ISL SXM 65.9 GIN 15.8 LKA 38.1 IRL 97.0 SUR 7.2 GNB 2.6 TWN ISR 88.6 TTO 31.3 KEN 9.1 THA 81.7 ITA 83.1 TCA LSO 23.2 VNM 32.0 JPN 99.0 VGB MDG 0.7 KOR 80.8 MWI 10.2 LVA 54.4 MLI 3.9 LTU 52.6 MRT 14.1 LUX MUS 65.3 MKD 42.5 MAR 64.5 MLT 81.8 MOZ 3.5 MEX 24.6 NAM 32.9 MNE 38.9 NER 2.5 NLD 82.0 NGA 1.9 NZL 83.4 RWA 17.4 NOR STP 16.1 POL 49.7 SEN 21.2 PRT 53.5 SYC ROU 33.1 SLE 6.5 RUS 66.8 ZAF 73.8 SRB 44.3 SDN 14.3 SVK 59.3 SWZ 53.9 SVN 58.9 TZA 0.1 ESP 67.8 TGO 4.3 SWE TUN 78.1 CHE UGA 9.2 TUR 81.9 ZMB 11.5 GBR ZWE 3.6 USA

27 As one could expect EU-OECD figures look logically but many figures in other Regions are (very) questionable: for example, HKG has VIpc more than 3 times higher (338%) than DE but PSE only ~1%!? The figure for HKG shows clearly the limitation of the evaluation of the plausibility of Regional averages for inter-country comparison. Asian average Volume index (Linking Volume factor) = ~50% of the EU- OECD level look as more or less plausible. However Asian average is an average mainly from the CHI and IND (very large countries) and is very close to VI for CHI and IND. These countries have VIpc = 12-14% from HKG in the Asian Regional comparison. Respectively HKG VIpc relatively EU-OECD will be very high by this approach. [Nobody is perfect: present state of Asian comparison for Rents is very questionable but it is clear that the Linking method proposed by Eurostat is very rough and hardly applicable for the sets of heterogeneous countries]. Obviously, one part of strange results goes from the weak expenditure data but other very important part goes for the weakness (very rough) of the linking method used by Paulus. I demonstrated that this method produced very different results (from the official results) even within the EU-OECD comparison. This method can be used for more or less homogeneous set of countries (from point of view of similarity of dwelling stock) but not for very heterogeneous sets. Education Most of the Regions use the Input Cost approach (some of them with PA for Salaries of Teachers, etc.) but the EU-OECD used in the ICP 2011 the Output approach (direct Volume index on the basis of no. of students-years with QA by PISA scores). These approaches have significant methodological and drastic numerical differences. The experiments showed that the results by Input Cost approach with PA go in fully opposite direction with the results by the Output approach (Volumes by the Output approach gravitate to the demographic indicators and PA on the basis of the whole economy estimations are very high for Education in many cases) see respective calculations of the Eurostat results for (exactly this time ESTAT moved to the Output approach) Table 13: Education VIpc (EU27=100) I_T paper GEO/TIME PA 2004 Price appr. Output appr. (USA=1) Price appr. with PA EU Belgien Bulgarien Czech R Dänemark Germany Estland Irland Greece Spain Frankreich Italien Cyprus

28 Lettland Litauen Luxemburg Hungary Malta Niederlande Austria Polen Portugal Rumänien Slowenien Slowakei Finnland Schweden UK Island Norwegen Schweiz Kroatien FYROM Türkei Serbien : Albanien : BA : Montenegro : Therefore, first of all, it is difficult to link straightforwardly the Regions with very different methodologies and, secondly, even this is mechanically possible then between-regional incomparability at the country s level will be very high. The OECD proposed to link the EU-OECD results for Education to the results of other Regions via 5 LA countries. Of course, it is possible to link these indirect PPPs after some procedure with other regional results for Education. However, as it was indicated above, the link of the whole EU-OECD on the basis of only 5 LA countries is not straightforward. For example, the results between OECD countries and LA countries in 2005 are very different from the present OECD computations. Table 14 ICP 2011 vs. ICP 2005 VIpc Education: some LA countries 2011_Output Approach (with QA) 2005_Input Costs Approach 2005_Input Costs Approach OECD 47 = 100 OECD47 = 100 World = 100 Brazil Peru Colombia Uruguay OECD-EU Additionally, Francette s results refer to the Education-Total (=Private + Public = expenditure of HH, NPISH and GG). In effect, the EU-OECD countries have equal BH-PPPs for all Education BHs. What classification should be used in the Global 27

29 calculations? Many technical points (beside fundamental) are still open. Therefore there will be as special TAG ICP session of this point. III. Salaries GG First of all, what should be exact sets of occupations for concrete areas. Some nonprofessional occupations (secretary, cleaner ) can be used in several areas (Health, Education, GG). However there is only one BH Compensation-Total for each area. Therefore a realistic structure (professional occupation vs. nonprofessional occupations) should be kept. In this aspect the following nonprofessional occupations in the Health and Education were used: Health: Cooks (Average of four levels of experience) Cooks (Entry level) Cooks (5 year experience) Cooks ( 10 year experience) Cooks ( 20 year experience) Office cleaners (Average of four levels of experience) Office cleaners (Entry level) Office cleaners (5 year experience) Office cleaners ( 10 year experience) Office cleaners ( 20 year experience) Education Secretaries (Not medical) (Average of 4 levels of experience) Secretaries (Not medical) (Entry level) Secretaries (Not medical) (5 year experience) Secretaries (Not medical) ( 10 year experience) Secretaries (Not medical) ( 20 year experience) Building caretaker (Average of four levels of experience) Building caretaker (Entry level) Building caretaker (5 year experience) Building caretaker ( 10 year experience) Building caretaker ( 20 year experience) Office cleaners (Average of four levels of experience) Office cleaners (Entry level) Office cleaners (5 year experience) Office cleaners ( 10 year experience) Office cleaners ( 20 year experience) 28

30 Between-regional PLIs are presented below Table 15: ICP 2011 Salaries-GG Between-Regional PLI (World = 100) GM- AFR50 GM- ASI23 GM- CIS9 GM_EU- OECD47 GM- LA17 GM- WA12 CAR- Jam GM- World Health-Salaries (GG) Education-Salaries (GG) Coll. Cons.-Salaries (GG) GG Salaries Total (3) *) EU-OECD has no Salary data for Education (Output approach) The differences between the Regions are high but the differences in the country s PLIs are extremely high. It means that all Regions need to use PA. Table 16: ICP 2011 Salaries Comparative Price Level Indices (World = 100) Health Education GG Max-PLI Min-PLI Ratio Max-PLI Min-PLI Ratio Max-PLI Min-PLI Ratio Africa Asia CIS EU-OECD LA WA CARIB (only JAM) World It is clear that to be comparable these original PLIs need a Productivity Adjustment (PA). M. Timmer and R. Inklaar (Groningen university) provided PA Coefficients with the base USA=1 (lower Coefficient shows higher Productivity than USA). The general tendency is correct but concrete country s figures are questionable in many cases. Table 17: Some extracts from Groningen PA file Region countrycode ky_nac pl_kg rkg adj_klg WAS QAT % 0.56 WAS KWT % 0.69 AS MAC % 0.71 AS BRN % 0.84 OES LUX % 0.85 OES ITA % 0.85 WAS OMN % 0.88 OES NOR % 0.88 OES IRL % 0.88 LAC VEN % 0.89 AS SGP % 0.89 OES ESP % 0.92 AS HKG %

31 WAS SAU % 0.93 OES JPN % 0.93 OES CYP % 0.94 OES BEL % 0.95 OES FRA % 0.95 OES AUT % 0.95 OES FIN % 0.95 AF GNQ % 0.96 WAS ARE % 0.98 OES NLD % 1.00 OES USA % 1.00 QTA, VEN and the like are exotic cases. However if one looks in the OECD figures then there are many questions: GRC has higher Productivity than CHE; MLT than DE, PRT & HRV than GBR, HUN than SWE, ALB than ISR, etc. Table 18: Groningen PA for the OECD countries Region countrycode ky_nac pl_kg rkg adj_klg OES LUX % 0.85 OES ITA % 0.85 OES NOR % 0.88 OES IRL % 0.88 OES ESP % 0.92 OES JPN % 0.93 OES CYP % 0.94 OES BEL % 0.95 OES FRA % 0.95 OES AUT % 0.95 OES FIN % 0.95 OES NLD % 1.00 OES USA % 1.00 OES DNK % 1.03 OES MLT % 1.05 OES DEU % 1.06 OES GRC % 1.07 OES CHE % 1.09 OES AUS % 1.09 OES CAN % 1.12 OES HRV % 1.15 OES PRT % 1.15 OES KOR % 1.16 OES CZE % 1.18 OES GBR % 1.18 OES HUN % 1.20 OES SWE % 1.21 OES SVN % 1.22 OES ISL % 1.23 OES SVK % 1.28 OES ROU % 1.29 OES LVA %

32 OES MEX % 1.31 OES EST % 1.35 OES NZL % 1.36 OES ALB % 1.37 OES ISR % 1.39 OES TUR % 1.41 OES LTU % 1.44 OES SRB % 1.44 OES MKD % 1.47 OES MNE % 1.51 OES BIH % 1.53 OES POL % 1.58 OES CHL % 1.62 OES RUS % 1.75 OES BGR % 1.76 No. of very questionable figures is very high. It will be impossible to explain to users Why EU, ECB and IMF request from GRC, ESP, CYP, the reform of GG due to its inefficiency but we believe that GG-Productivity in these countries are higher or similar with USA, DEU, SWE, CHE? Exactly, this situation led in the past to the OECD decision not to use PA. In the opinion of the author, the use of reference PPP from Individual Market Consumption (IMC) for GG-Salaries is more straightforward 3, more transparent and more justifiable way. Salaries of GG-employees show What worth do these occupations give society? If we want to measure their real values then it is important to know - How much goods and services can buy GG-employees for their nominal Salaries. The use of IMC-PPPs as ref. PPPs for GG-Salaries seems to be the most practicable way based on more realistic and much less no. of assumptions. For example, CPI figures (national inter-time PPPs for IMC) are used for the Adjustment of Salaries on the Inflation. The experiments showed that in the practice the use of ref. IMC-PPPa and PA lead bring similar results. 4 IV. Machinery and Equipment The 1 st calculations showed that the allocation of price data for the EU-OECD countries was not fully correct. Therefore the 2 nd delivery was needed. The global results for Machinery & Equipment look not so bad at the aggregated level and in many cases even at the BH level. As one could expect, in many cases PLI are not very far from 100 (PPPs gravitate to the XRs because of they are mostly imported products of international brands). 3 To the point, PA approach used by Asia and Africa in the ICP 2005 was non-invariant relatively the Regional base country. The differences are not drastic but in some cases very remarkable. 4 See more on this point in the paper Comparison of Non-Market Services at Cross Roads: experience, considerations, proposals" (S.Sergeev) 31

33 Table 19: ICP2011 Machinery & Eq.: Regional PLI for BH and AH (World GM = 100) Version of calculation: CPD (w/o weights) Code of BH Name of basic heading AFR ASI CIS EU-OECD LA WA Fabricated metal products General purpose machinery Special purpose machinery Electrical and optical equipment Other manufactured goods Motor vehicles and trailers Other road transport Software Machinery, etc. (5) Transport equipment (2) Software (1) Machinery and Eq. Total (7) Survey-Total (8) Nevertheless BH-CV of CUP-In are systematically very high. Table 20: ICP 2011 M&Eq. GCL: BH-CV of PPP-indices (23/08/2013) BH code BH name BH-Var Fabricated metal products General purpose machinery Special purpose machinery Electrical and optical equipment Other manufactured goods Motor vehicles and trailers Other road transport Software 95.1 It means that validation at the item level should be continued. V. Construction Not all Regions used the ICP 2011 approach systematically: in the CIS only RUS priced the GCL and in the EU-OECD only several countries. The Regions supplied the same price data for all 3 kinds of Construction. ICP GO deleted prices irrelevant for a concrete construction type (mainly, for Materials). Countries specific weights for Materials, Equipment, and Labor were established by the ICP GO. With one look at PLIs for some countries and CV of CUP-In it is visible that price data (especially for Materials) still has many rough mistakes. However the main problem seems to be the Labor: 32

34 Table 21: ICP 2011 Construction: Between-Regional PLI Code of BH Name of basic heading GM-AFR GM-Asia GM-CIS GM_EU-O GM-LA GM-WA Materials - Res.Constr Equipment- Res. Constr Labour - Res. Constr Materials - Non-res. Constr Equipment - Non-res. Constr Labour - Non-res. Constr Materials - Civil eng. Works Equipment - Civil eng. Works Labour - Civil eng. Works Residential buildings (3) Non-residential buildings (3) Civil engineering works (3) Construction-Total (9) The country s differences are very high in all regions and present Labor-PLI for OECD countries are extremely high. Table 22: ICP 2011 Construction PLI-Labor (World = 100) DNK AUS NLD CAN USA FIN GBR HKG HUN PRT BRN TWN IRQ SGP PSE ZAF RUT RUS URY KWT PER MAC ARE PAN JOR CHN MNG MYS SAU GAB QAT VEN CRI GTM NIC DOM EGY

35 EGZ NGA ECU YEM BHR SYC MUS BRA OMN BEN MOZ FJI CPV MDV BOL 93.1 COD 92.8 COG 91.8 HND 90.1 ZWE 87.8 DZA 87.2 HTI 84.7 SDO 84.7 LSO 82.5 TUN 80.9 COL 77.0 VNM 76.5 COM 75.2 NAM 73.1 SDN 70.1 GHA 68.2 DJI 63.0 THA 62.9 SLE 62.8 LKA 57.1 LAO 55.3 IDN 54.1 GNQ 54.0 PHL 53.6 RWA 52.9 BFA 51.3 GIN 50.8 CMR 49.6 BTN 48.8 MLI 47.6 GMB 45.6 NER 45.2 NPL 40.4 TCD 40.0 CAF 39.7 KHM 38.3 CIV 38.1 ZMB 37.8 PAK 37.1 SEN 34.1 KEN 33.5 IND 32.3 BGD 32.1 MDG 32.0 MMR 27.8 BDI 26.6 MAR 25.2 STP

36 BWA 22.5 LBR 22.3 SWZ 21.2 MWI 20.2 ETH 18.9 UGA 10.1 TZA 8.3 It seems that PA (or TFP) is strongly requested in Construction. It is interesting to look on the consistency of the results by the Construction Projects and the Input Costs approaches for the countries which have both sets of price data. Table 23: Construction 2011 PLI by two approaches (OECD10 = 100) ICP 2011 Construction OECD 2011 Construction DNK DNK AUS AUS NLD NLD CAN CAN USA USA FIN FIN GBR GBR 94.2 HUN 52.7 HUN 54.2 PRT 52.6 PRT 54.1 RUS 36.5 RUS GM GM It is visible that the differences in some cases (especially for RUS) are very high. These cases should be further investigated. VI. Provisional aggregation The input data are still not finalized in several regions. There are also many open methodological questions. Therefore it was impracticable to attempt to carry out an exact aggregation in accordance with all agreed technical details. To obtain rough HHC, AIC and GDP figures, the author carried out the free EKS aggregation for 181 countries. The following PPPs (incl. ref. PPPs) were used: - HHC => PPPs obtained on the basis of GCL (gaps were filled in by unweighted CPD at the level of 12 main product Groups) - Private Education => XRs - Rents => ref. PPP from Individual Market Consumption (this is not very appropriate for the countries with weak exp. NA Rental data but not mere worse than present other available versions) - Salaries (also in respective Construction parts) => ref. PPP from Individual Market Consumption (experiments showed that these ref. PPPs bring similar results as with PA for Salaries) - Machinery and Equipment (also in respective Construction parts) => XRs. The following results for the GDP were obtained: 35

37 Table 24: 20 largest economies - ICP 2011 GDP ( ) GDP Population PPP PLI RV RVpc VIpc (mio. NC) (mio.) (World = 1) (World = 100) (mio. Int. $) (Int. $) (World = 100) 129 USA United States CHN China IND India JPN Japan DEU Germany RUT RUS (CIS) RUS RUS (OECD) BRA Brazil FRA France GBR United Kingdom ITA Italy IDN Indonesia MEX Mexico ESP Spain CAN Canada SAU Saudi Arabia KOR Korea, Republic of AUS Australia (OECD) TUR Turkey TWN Taiwan, Province of China

38 Table 25: ICP GDP per capita order ( ) GDP Population PPP PLI RV RVpc VIpc (mio. NC) (mio.) (World = 1) (World = 100) (mio. Int. $) (Int. $) (World = 100) 154 QAT Qatar LUX Luxembourg MAC Macao KWT Kuwait BRN Brunei Darussalam ARE United Arab Emirates CHE Switzerland NOR Norway SGP Singapore BMU Bermuda USA United States SAU Saudi Arabia AUS Australia (OECD) HKG Hong Kong NLD Netherlands AUT Austria SWE Sweden CYM Cayman Islands DNK Denmark BEL Belgium IRL Ireland TWN Taiwan, Province of China DEU Germany CAN Canada OMN Oman BHR Bahrain FIN Finland ISL Iceland FRA France JPN Japan GBR United Kingdom ITA Italy GNQ Equatorial Guinea ESP Spain

39 161 ATG Antigua and Barbuda CYP Cyprus NZL New Zealand (OECD) ISR Israel KOR Korea, Republic of TCA Turks and Caicos Islands SVN Slovenia CZE Czech Republic GRC Greece MYS Malaysia MLT Malta PRT Portugal CUW Curaçao SVK Slovakia RUT Russian Federation (CIS) RUS Russian Federation (OECD) KAZ Kazakhstan EST Estonia BHS Bahamas POL Poland LTU Lithuania HUN Hungary CHL Chile TTO Trinidad and Tobago LVA Latvia HRV Croatia AIA Anguilla SYC Seychelles MEX Mexico KNA Saint Kitts and Nevis URY Uruguay PAN Panama TUR Turkey GAB Gabon AZE Azerbaijan MUS Mauritius BLR Belarus

40 131 BRA Brazil BRB Barbados ROU Romania VEN Venezuela, Bolivarian Republic of THA Thailand ABW Aruba SUR Suriname CRI Costa Rica DZA Algeria BGR Bulgaria MNE Montenegro ZAF South Africa COL Colombia MDV Maldives BWA Botswana PER Peru ECU Ecuador DOM Dominican Republic CHN China IRQ Iraq JOR Jordan SRB Serbia MKD Macedonia, FYROM EGZ Egypt (Western Asia) GRD Grenada IDN Indonesia TUN Tunisia EGY Egypt (Africa) FJI Fiji (Asia) LCA Saint Lucia BIH Bosnia & Herzegovina MSR Montserrat DMA Dominica UKR Ukraine BTN Bhutan VGB Virgin Islands, British ALB Albania

41 70 LKA Sri Lanka NAM Namibia MNG Mongolia VCT Saint Vincent and The Grenadines SXM Sint Maarten PRY Paraguay AGO Angola BLZ Belize GTM Guatemala ARM Armenia SLV El Salvador CPV Cape Verde JAM Jamaica PHL Philippines SWZ Swaziland MAR Morocco BOL Bolivia, Plurinational State of COG Congo IND India VNM Viet Nam PAK Pakistan MDA Moldova, Republic of HND Honduras NIC Nicaragua KGZ Kyrgyzstan MMR Myanmar YEM Yemen NGA Nigeria LAO Lao People s Democratic Republic GHA Ghana SDN Sudan (Africa) MRT Mauritania ZMB Zambia SDO Sudan (Western Asia) CMR Cameroon BGD Bangladesh CIV Côte d'ivoire

42 54 KHM Cambodia STP São Tome & Principe NPL Nepal DJI Djibouti TJK Tajikistan SEN Senegal LSO Lesotho KEN Kenya TCD Chad BEN Benin HTI Haiti MLI Mali RWA Rwanda TZA Tanzania, United Republic of UGA Uganda ZWE Zimbabwe SLE Sierra Leone GNB Guinea-Bissau GMB Gambia TGO Togo MDG Madagascar BFA Burkina Faso PSE Palestinian Territory GIN Guinea MOZ Mozambique ETH Ethiopia MWI Malawi NER Niger CAF Central African Republic BDI Burundi COD Congo, The Democratic Republic of COM Comoros LBR Liberia BES Bonaire, Saint Eustatius and Saba CUB Cuba GUY Guyana World

43 There was not enough time to look comprehensively on the consistency of the World results with the Regional results. This was done only for the EU-OECD countries: Table 26: ICP 2011 GDP VIpc (USA = 100) World ICP EU-OECD AUS AUT BEL CAN CHL CZE DNK EST FIN FRA DEU GRC HUN ISL IRL ISR ITA JPN KOR LUX MEX NLD NZL NOR POL PRT RUS SVK SVN ESP SWE CHE TUR GBR USA It is visible that the consistency is not bad taking into account that the EU-OECD Regional comparison does not use any Productivity Adjustments (therefore less developed countries have higher GDPpc and more developed countries lower GDPpc). 42

44 CIS ICP 2011 treatment (different experiments) Annex 1 Two scenarios for linking CIS to the Global comparison were requested by the GO: 1- by using Russia as bridge to OECD/Eurostat results then the global results 2- by linking CIS using the global core approach directly to the global results. First of all, one should consider a general fundamental problem - the methodological differences between the Regional and the World comparisons. If all Regions and the World comparison use the same methodology then the results should be ~ the same by any technical procedure. For example, the Russian results in the World comparison for 181 countries are very close for both cases (RUT as CIS country and RUS as OECD country). No. NAME OF ANALYTICAL CATEGORY RUT-CIS RUS-OECD 1 ACTUAL CONSUMPTION of HH (nat.) FOOD and Non-ALCOHOLIC BEVERAGES Food Bread and cereals Meat and fish Meat Fish Milk, cheese and eggs Oils and fats Fruits, vegetables, potatoes Other food Non-alcoholic beverages ALCOHOLIC BEV., TOBACCO, NARCOTICS Alcoholic beverages Tobacco and narcotics CLOTHING and FOOTWEAR Clothing Footwear HOUSING, WATER, ELECTR., FUEL Rentals for housing Maintenance, repair, water, misc.services Electricity, gas and other fuel FURNISHING, HOUSEHOLD EQ. & OPERATION Furniture, carpets, textiles Household appliances Other household goods & services HEALTH Medical products, appl. & equipment Pharmaceutical products (HH+GG) Health services Health services (price approach) Health services (input costs approach) TRANSPORT Transport equipments

45 35 Operation of equipments Purchased transport services COMMUNICATION RECREATION and CULTURE Equip. for recreation & culture (+veter.serv.) Recreational, cultural services (+pack.holid.) Books, newspapers, magazines, stationery EDUCATION RESTAURANTS and HOTELS Miscel. GOOS & SERVICES (+NPI&GG-Soc.sec.) NET PURCHASES ABROAD FINAL CONSUMPTION EXP. of GG INDIVIDUAL SERVICES by GG GENERAL PUBLIC SERVICES GROSS FIXED CAPITAL FORMATION MACHINERY and EQUIPMENT Metal products and equipment Electrical and optical equipment Transport equipment CONSTRUCTION Residential buildings Non-residential buildings Other construction, etc OTHER PRODUCTS CHANGES in INVENTOR. & VALUABLES BALANCE of IMPORTS & EXPORTS GROSS DOMESTIC PRODUCT HH domestic exp. (w/o Rents) Salaries-Total Rents and related services HH + NPISH Final consumption expenditure (HH+NPI+GG) GCF = GFCF + Ch. in Inv. & Valuables GROSS DOMESTIC PRODUCT GOODS-TOTAL Consumer goods Consumer goods: Non-Durables Consumer goods: Semi-Durables Consumer goods: Durables Investment goods (GFCF) SERVICES-TOTAL (HH; NPI; GG) Individual Services-TOTAL (HH-NPI-GG) Consumer services Non-Market Services - Individual Non-Market Services - Collective (GG-Coll) Non-Market services-total (NPISH+GG)

46 However if the methodological differences are significant then the differences can be also high. For example, the most of the countries are interested, first of all, in the comparison with the USA. The EU-OECD comparison does not use Productivity Adjustment (PA) => therefore Volume RUS relatively USA is overestimated (it is clearly that USA has much higher Productivity) and the results for other CIS countries too. The Word comparison should use PA. So, if CIS is participated in the World comparison as the Region then the CIS countries results relatively USA should be lower than they are via RUS from the OECD comparison. The CIS region does not use also the PA => therefore Volumes CIS countries relatively RUT are also overestimated (it is very likely that RUS has much higher Productivity than many other CIS countries). The differences between the VIpc from the World comparison (where a kind of PA was used) and original CIS results (both with the base => RUS = 100) are not drastic but remarkable: Table A.1: CIS ICP 2011 VI p.c. (RUS = 100): Ratios CIS Regional results vs. World Results No. NAME OF ANALYTICAL CATEGORY ARM AZE BLR KAZ KGZ MDA RUT TJK UKR 1 ACTUAL CONSUMPTION of HH (nat.) FOOD and Non-ALCOHOLIC BEVERAGES Food Bread and cereals Meat and fish Meat Fish Milk, cheese and eggs Oils and fats Fruits, vegetables, potatoes Other food Non-alcoholic beverages ALCOHOLIC BEVERAGES, TOBACCO, NARCOTICS Alcoholic beverages Tobacco and narcotics CLOTHING and FOOTWEAR Clothing Footwear HOUSING, WATER, ELECTR., FUEL Rentals for housing Maintenace, repair, water, misc.services Electricity, gas and other fuel FURNISHING, HOUSEHOLD EQ. & OPERATION Furniture, carpets, textiles Household appliances Other household goods & services HEALTH Medical products, appl. & equipment Pharmaceutical products (HH+GG) Health services Health services (price approach) Health services (input costs approach) TRANSPORT Transport equipments Operation of equipments Purchased transport services COMMUNICATION RECREATION and CULTURE Equip. for recreation & culture (+veter.serv.) Recreational, cultural services (+pack.holid.) Books, newspapers, magazines, stationery

47 42 EDUCATION RESTAURANTS and HOTELS MISCELL. GOOS and SERVICES (+NPI&GG-Soc.sec.) NET PURCHASES ABROAD FINAL CONSUMPTION EXP. of GG INDIVIDUAL SERVICES by GG GENERAL PUBLIC SERVICES GROSS FIXED CAPITAL FORMATION MACHINERY and EQUIPMENT Metal products and equipment Electrical and optical equipment Transport equipment CONSTRUCTION Residential buildings Non-residential buildings Other construction, etc OTHER PRODUCTS CHANGES in INVENTOR. & VALUABLES BALANCE of IMPORTS & EXPORTS GROSS DOMESTIC PRODUCT HH domestic exp. (w/o Rents) Salaries-Total Rents and related services HH + NPISH Final consumption expenditure (HH+NPI+GG) GCF = GFCF + Ch. in Inv. & Valuables GROSS DOMESTIC PRODUCT GOODS-TOTAL Consumer goods Consumer goods: Non-Durables Consumer goods: Semi-Durables Consumer goods: Durables Investment goods (GFCF) SERVICES-TOTAL (HH; NPI; GG) Individual Services-TOTAL (HH-NPI-GG) Consumer services Non-Market Services - Individual Non-Market Services - Collective (GG-Coll) Non-Market services-total (NPISH+GG) An additional experiment for the CIS countries was carried out. OECD published the 2008 benchmark results together with the (some) CIS countries. These results were obtained via the link with RUS from the official OECD47 comparison. The author carried out for the ICP 2011 two calculations for the 8 CIS countries (VIpc, OECD47 = 100): 1) Link via RUS 2) CIS countries are included in the OECD55 comparison with the fixity for OECD47 (BH-PPPs were linked via RUS in the official OECD set) As one see from Table below, the differences can be substantial. The CIS is a very heterogeneous Region (e.g., UKR vs. TJK or ARM vs. KRZ). Therefore the link via one country only is not desirable practically 5. 5 For example, the CIS countries (ECP 96 Group III) were linked to the common ECP 96 results via 4 countries (RUS, BLR, MDA and UKR) which participated in the Group II (together with AUT) and in Group III. 46

48 Table A.2: ICP 2011 Ratios EU-OECD-CIS VIpc to CIS results via RUS (EU-O47 =110) 47