Global Purchasing Power Parities and Real Expenditures - Afristat

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Appendix F 187computing linking factors to estimate global PPPs, becauseEurostat-OECD, CIS, and South America did not makesimilar adjustments. An important consideration is thatAsia-Pacific, Africa, Western Asia, and South America usedthe same list of occupations for which average wages wereobtained. The Eurostat-OECD ring economies also providedwages for these occupations. Therefore, it was notnecessary to limit the estimation of between-region PPPs(linking factors) to data from only the ring countries.CPD regressions based on compensation data for 75economies representing all regions, including the Eurostat-OECD, were used to compute regional linking factors (seeHeston, Proposal for Linking Compensation). Occupationswere classified by skill level in the computations. The CPDincluded all economies from South America and WesternAsia and a subset of economies from the other regions. Afurther adjustment was made to compensate for differencesbetween economies included and not included ineach region. This provided regional linking factors with noproductivity adjustment. As with housing, the linking factorswere computed independently of the regional PPPs.example, Asia-Pacific and the OECD, because the latterwould not have undergone a productivity adjustment.Therefore, linking factors were not adjusted forproductivity.The comparison of within-region results with and withoutproductivity adjustments showed that the effect variedby economy. The overall effect of the productivity adjustmentwas to decrease the size of the economies that used itrelative to the Eurostat-OECD.Also of importance, however, is what the use of productivityadjustments means for comparing the 2005 resultsfor previous benchmarks. Everything else remaining thesame, the methods adopted for these sectors have the effectof producing in some regions a larger spread in real GDPper capita between rich and poor in 2005 than in previousbenchmarks. The best way to fully understand the impactof the productivity adjustment on the comparison betweenregions (as well for previous benchmarks) is to produceresults with and without productivity, using the samemethods for all regions. To the extent that reliable data canbe made available, additional studies will be conducted.Should the Between-Region PPPs forLinking Factors Also be Adjusted forProductivity?In retrospect (or in the next round), the optimal solutionwould be to estimate within-region PPPs the sameway across all regions so the question would not arise.Given the differing methods used in different regions, itseemed the better course not to make a further productivityadjustment to the between-region data. First, considercomparisons between economies across regions that dida productivity adjustment. The underlying capital-laborratios are rough approximations, so making a productivityadjustment would not greatly improve or change the comparisonbetween economies in different regions.As for the regions that did not make productivity adjustments,the direction of the effect, at least for the OECD,would have been clearly to lower the relative volumesof government consumption of the other regions, simplybecause average capital per worker is higher in the OECDeconomies. However, this would not improve the comparisonsbetween economies at a similar economic level in, forEquipment and ConstructionAsia-Pacific, Africa, Western Asia, and South Americapriced a global set of equipment items and the basket ofconstruction components; there were no separate ring lists.The four ring countries from the Eurostat-OECD regionpriced both their regional list and the global specifications.Several Africa economies and one Asia-Pacific economywere not able to price all of the basic headings for equipmentand construction. To provide real GDP volumes forall economies, PPPs were imputed for basic headings thatlacked data. All economies provided nominal expendituresfrom their national accounts for all basic headings, whichwere used as weights in combining basic headings to higherlevelaggregates.Imputation for Construction, Equipment, andGovernment Salaries in AfricaConstruction. Thirty-five economies submitted completedata for construction, government salaries, and equipment.Four countries with data for construction lacked data
188 Global Purchasing Power Parities and Real Expendituresfor either equipment or compensation. After eliminatinganother four with poor quality data, 27 countries with completedata remained. PLI data for these countries were usedto estimate the following model by ordinary least squaresregression, as in equation (F1):PLI (Y) = a1 PLI (X1) + a2 PLI (X2),where Y is construction, andX1 and X2 are equipment and compensation.(F1)No constant term was included, on the premise that theprice of construction should go to zero as the prices of theinput variables go to zero. Using the estimated regressioncoefficients (a 1and a 2), the missing values of the constructionPLIs were imputed using actual data on equipmentand compensation, where available, or the imputed values,where necessary.Equipment. Out of 48 participating economies in theAfrica region, 32 submitted equipment prices; for theremaining 16, the exchange rate to the base country (SouthAfrica) was used as a reference PPP on the grounds thatmost construction machinery and equipment are obtainedthrough international purchases. No further adjustmentswere made to account for taxes, tariffs, and other chargesbecause countries were not able to produce consistent data.In addition, some countries provide rebates for the taxes,which makes the relative prices close to exchange rates.Salaries. Forty-one countries provided data on governmentcompensation. For the seven missing countries, thePLI for government compensation was imputed from thenominal individual consumption ratio. (The nominal individualconsumption ratio is the value of a country’s individualconsumption per capita [in U.S. dollars] divided by thesame for the base country.) This procedure was adoptedon the premise that the level of compensation determines,in large part, the level of individual consumption; hence,the level of nominal compensation should reflect that ofconsumption. The imputed PLIs were used in the imputationof the construction PLI for the five countries thatlacked both construction and compensation data. The missingseven PPPs for government compensation were derivedas the product of the individual consumption ratio and theexchange rate to the base country.No data, except nominal expenditures from the nationalaccounts, are shown for the equipment and constructionaggregates for economies where results were imputed. Thegross fixed capital formation (GFCF) aggregate is footnotedwhere the individual components were imputed.Nonprofit Institutions ServingHouseholdsIndividual consumption expenditures by nonprofit institutionsserving households (NPISHs) were combined intoone basic heading in the ICP 2005 classification. However,participating economies were not able to consistently provideexpenditures for this basic heading; therefore, NPISHexpenditures in some regions could not be separated fromthe expenditures in other consumption categories, makingit difficult to ensure that basic headings were being consistentlydefined.Aggregation to GDPEstimation of PPPs for higher-level aggregates of GDP infive regions and the ring were based on the EKS method.Africa alone used the Iklé method, which provides resultswith additivity. Even though the Iklé method’s results cancontain some Gerschenkron effect, the Africa Iklé resultsin general were quite close to the EKS. The region desiredthe additivity restriction because it is easier to explain tousers how to construct aggregates and subaggregates ofGDP volumes.
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© 2008 by the International Bank f
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viContentsPart II: Technical Notes
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Acronyms and AbbreviationsADBAfDBBO
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PrefaceThis publication presents th
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AcknowledgmentsThe ICP shares a com
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Part IPurchasing Power Paritiesand
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2005 ICP:Results and Major Findings
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2005 ICP: Results and Major Finding
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Tables of Results21
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Page 154: Part IITechnical Notes of the 2005
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Page 220 and 221: Glossary 205in nonmarket production
Page 222 and 223: Glossary 207Multilateral comparison
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