Download What is new in PWT 9.0?

WhatisnewinPWT9.0?
RobertC.Feenstra,RobertInklaarandMarcelP.Timmer
June2016
ThereleaseofthePennWorldTableversion9.0representsthefirstsubstantialchange
to the ‘Next Generation of the Penn World Table’ of PWT versions 8.0 and 8.1, see
Feenstra, Inklaar and Timmer (2015). If you are a first-time user of PWT, Section I of
Feenstraetal.(2015)isstilltherecommendedstartingpoint,asthemainstructureofthe
databaseanddefinitionofitsvariablesareunchangedinPWT9.0.Thatsaid,PWT9.0
containsimportantnewandreviseddata.Thisdocumentprovidesanoverviewofthe
changes,withamoredetaileddiscussionofparticulartopicsinspecificdocuments.
Thechangesfallinthreebroadcategories,namely,i)theincorporationofnewpurchasing
powerparities(PPPs)datafromthe2011InternationalComparisonProgram(ICP)and
other sources; ii) the incorporation of revised and extended National Accounts data,
coveringtheperiodupto2014;andiii)revisedestimatesoffactorinputdataandlabor
costshares.
I.
ICP2011andothernewPPPdata
ThelatestroundofPPPsincludedinPWT8wasfortheyear2005.WithPWT9.0,weadd
thePPPsfromICP2011tothesetofPPPbenchmarksandshiftourreferenceyearfrom
2005to2011.WorldBank(2014)markedthereleaseoftheresultsofICP2011,withdata
on PPPs for consumption and investment for nearly 180 countries. Like ICP 2005,
coverage was essentially global, though with 32 additional countries notably more
extensive.FifteencountriesdidnotparticipateinanyICProundbefore2011,sothese
were not covered in PWT8, but are included in PWT9. This increases the number of
countriesfrom167to182andtheshareofworldpopulationcoveredbyPWTfrom96.9
to98.5percent.1
1Thelistofnewcountriesis:Algeria,Anguilla,Aruba,BritishVirginIslands,CaymanIslands,Curaçao,Haiti,
Montserrat,Myanmar,Nicaragua,Seychelles,SintMaarten,StateofPalestine,TurksandCaicosIslandsand
theUnitedArabEmirates.SomeofthesehadpreviouslybeencoveredinPWT7andearlierversionsbased
onalternativepriceinformationthatwasnotcomparableinqualitytoICP.
1
Inaddition,importantmethodologicalissuesthatwereidentifiedafterthereleaseofICP
2005 (see World Bank, 2013), were solved for ICP 2011. These issues were primarily
relatedtothepricecomparisonacrossmajorregionsoftheworld.InICP,pricesarefirst
compared across countries within a region, such as Africa or Asia-Pacific. In that
comparison, products that are particularly important for a region can be taken into
account.Comparingpricesacrossregionsrequiresacommonglobalproductlistand,as
turnedout,theICP2005globalproductlistincludedmanyproductsthatweretypicalin
theconsumptionbasketsofhigh-incomecountries,buthigh-pricedluxuryitemsinlowincomecountries.Duetothisbias,theICP2005pricesofregionswithpredominantly
low-incomecountrieswereoverestimatedrelativetohigh-incomeregionsandthereal
GDPlevelofcountriessuchasChinawereseverelyunderestimated–ashadalsobeen
separatelyestablishedbyFeenstra,Ma,NearyandRao(2013).
Deaton and Aten (2016) and Inklaar and Rao (2016) demonstrate that this bias was
presentandimportantinICP2005.Theyalsoconcludedthatthissourceofbiaswasnot
present in ICP 2011.Moregenerally,the ICP2011havebeenbroadlyacceptedby the
research community as the most sophisticated and reliable so far, without major
methodologicalorpracticalflaws.InklaarandRao(2016)thereforeconstructedasetof
relativepricesfortheyear2005,basedonICP2005data,butapplyingICP2011methods
and correcting for the bias in ICP 2005. In PWT 8.1, we already relied on these biasadjustedrelativeprices.ThismeansthattheincorporationofICP2011resultsdoesnot
leadtomajorshiftsinincomelevelsoflower-incomerelativetohigher-incomecountries,
thoughasshownbelow,individualcountrydifferencescanbelarge.
Theshiftinreferenceyearfrom2005to2011meansthatallvariablesthatweredenoted
in2005USdollarsinPWT8.0and8.1arenowdenotedin2011USdollars.Fortheperiod
1950to2005,thisshiftinreferenceyearhasnoeffectonrealGDP,otherthantoincrease
allvaluesby12percent–theincreaseintheUSGDPdeflatorbetween2005and2011.2
For the years from 2006 onwards, however, PPPs are revised as consumption and
investment PPPs are now based on interpolation between the 2005 and 2011 ICP
benchmarkresults,whiletheywereextrapolatedfromICP2005inPWT8.
2RevisionstoNationalAccountswillalsoleadtochanges,seeSectionII.
2
There are three sets of countries for which PPPs and real GDP change in a different
fashion:
1. NineteencountriesfromCentralAmericaandtheCaribbeandidnotparticipateinICP
2005,butdidparticipateinICP2011andatleastoneearlierICPround.InPWT8,the
PPPsandrealGDPnumbersforthesecountrieswereextrapolatedfromthatearlier
ICPround(1996inmanycases)to2011,thelatestyearinPWT8.InPWT9thePPPs
forthatperiodareinterpolatedusingdatafromtheirearlierICPbenchmarkandthe
new2011round.3
2. ForcountriesintheEuropeanUnion(EU)and/orOECD,PWTreliesnotonlyonthe
ICP benchmark data, but also includes their more frequent PPP benchmark
comparisons;i.e.annualdataforEUcountriesandtriennialdatafornon-EUOECD
countries.InPWT8,EUPPPdatauntil2010wasused;fornon-EUOECDcountriesthe
mostrecentPPPdatawerefor2008.InPWT9.0,forEUcountriesweincorporatePPP
dataupto2014.,Fornon-EUOECDcountriesthe2011ICPPPPsarethemostrecent
available.
3. TurkmenistanandUzbekistanhaveonlyparticipatedinICP1996,sotheirPPPdata
continuestobeextrapolated.
InadditiontothenewconsumptionandinvestmentPPPsfromICP2011,wealsoextend
thePPPinformationforexportsandimports.AsinPWT8,thesetradePPPsarebasedon
theframeworkintroducedbyFeenstraandRomalis(2014),butwhiletradePPPswere
previouslyavailablefortheperiodfrom1984to2007,thetradePPPdatainPWT9.0
extendthrough2014.
Toillustratetheimpactofthesechanges,Figure1plotstheratiooftheGDPopricelevel
inPWT9.0relativetothepricelevelinPWT8.1fortheyear2011forthe142countries
that participated in ICP 2005 and in ICP 2011. In PWT 8.1, the price levels for these
countrieswerebasedonextrapolationsfrom2005basedonrelativeinflation,whilein
PWT9.0,weusetheICP2011PPPsincombinationwithnewlyestimatedtradePPPs.As
thefigureshows,thedifferencescanbesubstantial,withpricelevels(relativetotheUS)
3ThelistofcountriesnotinICP2005butinICP2011andinanearlierICProundis:AntiguaandBarbuda,
Bahamas, Barbados, Belize, Bermuda, Costa Rica, Dominica, Dominican Republic, El Salvador, Grenada,
Guatemala, Honduras, Jamaica, Panama, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the
Grenadines, Suriname and Trinidad and Tobago. Zimbabwe did participate in ICP 2005, but the results
werenotincorporatedinPWT8duetothedistortingimpactofhyperinflationonpricesandexchangerates.
3
lowerby,onaverage,6percentand16countriesforwhichthedifferenceislargerthan
25percent(upordown).
Asdiscussedabove,thereareno(remaining)systematicdifferencesintheunderlying
measurementmethodologyofICP2005andICP2011andthepricelevelsarealsonot
systematicallydifferent–i.e.thedifferencesarenotrelatedtoincomelevel.Yetindividual
countrydifferencesarelarge.ThishaslongbeenafeatureofconsecutiveICPbenchmark
rounds and, as the current results demonstrate, even better-funded and more closely
harmonizedICProundssufferfromthis.
DifferenceinPL_GDP&,PWT9.0/PWT8.1−1
-0.4
-0.2
0.0
0.2
0.4
Figure1,Differenceinthe2011GDPopricelevel,PWT9.0vs.8.1,forcountriesin
ICP2005andICP2011
MWI
TCD
DJI
LAO
AZE
YEM IDN IRQ EGY
VEN
OMN
GNQ
MAC
JOR
6
8
10
LogofCGDP&percapita(PWT9.0)
12
Note:CGDP&percapitafromPWT9.0isin2011USdollars.PL_GDP&fromPWT8.1wasinUlatedbyafactor
1.123toaccountforUSinUlation.Includedarethe142countriesthatwereinICP2005andICP2011.
Figure2showsthatthedifferencesarenotablylargerforthesetofcountriesthatwere
partofICP2011andanearlierICProundbutdidnotparticipateinICP2005.Forinstance,
thepricelevelforBarbados(BRB)is80percenthigherinPWT9.0thaninPWT8.1,while
thepricelevelforElSalvador(SLV)is80percentlower.ThePPPforZimbabweis920
percenthigherandnotshowninthefigure.Thesenewerfiguresshouldbeconsideredan
improvementoverthepreviousestimates.Thisis,inpart,becausewewouldexpectmore
recentpricecomparisonstomoreaccuratelyreflecttherelativepricelevelofacountry
comparedwithextrapolationsfrom(much)earliercomparisons,butalsobecausetheICP
4
2011 round relied on more extensive data collection and improved methodologies,
especiallycomparedtotheICProundsbeforeICP2005.Thisisalsoillustratedbythefact
that in PWT8, the observations for El Salvador in recent decades had been flagged as
outliersduetoimplausiblyhighrelativepricelevels.
DifferenceinPL_GDP&,PWT9.0/PWT8.1−1
-1.0
-0.5
0.0
0.5
1.0
Figure2,Differenceinthe2011GDPopricelevel,PWT9.0vs.8.1,forcountriesin
ICP2011butnotinICP2005
BRB
DMA
LCA
BLZ
HND
DOM
CRI
VCT
GTM
BHS
PAN
GRD
JAM
SUR
KNA
TTO
ATG
BMU
SLV
8.5
9
9.5
10
LogofCGDP&percapita(PWT9.0)
10.5
11
Note:CGDP&percapitafromPWT9.0isin2011USdollars.PL_GDP&fromPWT8.1wasinTlatedbyafactor
1.123toaccountforUSinTlation.Includedarethe19countriesthatwerenotinICP2005butwere
inICP2011andinPWT8.1;Zimbabwe,withadifferenceof9.2,isomitted.
ThesefiguresillustratethataPPPestimateforaspecificgivencountryinaspecificyear
issubjecttoasizeablelevelofuncertainty,especiallyiftheestimateisnotbasedona
recentICPbenchmarkbutextrapolatedoverlongerperiods.Aswealsoremarkedinour
‘UserGuidetoPWT8’(Feenstra,InklaarandTimmer,2013),thisimpliesthatcautionis
inorderwhenrelyingonthepointestimateofrelativeincomeforaparticularcountryin
aparticularyearandthat‘true’incomelevelsmaybe10–20percenthigherorlower.4
Thebroadercross-countrypatternofpricesismuchlessaffectedbythisuncertainty.This
is most easily demonstrated using the Balassa-Samuelson/Penn-effect relationship
betweenthelogpricelevelandlogincomelevel–measuredasexchange-rate-converted
GDPpercapita.Estimatingthisrelationshipforthe142countriesfromFigure1forthe
4SeealsoRaoandHajarghast(2016),whoestimatestandarderrorsofrelativeprices,whichimplyasimilar
confidenceinterval.
5
logpricelevelfromPWT9.0yieldsanearlyidenticalcoefficientaswhenusingthelog
pricelevelfromPWT8.1,namely0.22.
II.
GDPdatafromtheNationalAccounts
Revisions in the PWT are due to incorporation of new PPP data from the ICP, which
mainlyaffectspricelevels,aswellasnewNationalAccounts(NA)dataofcountrieswhich
mainlyaffectsnominalGDPlevelsandrealgrowthrates.Whiletypicallyreceivingless
attentioninthediscussions,thissourceofrevisionscanbeasleastasimportantthanPPP
revisionsformakingrealGDPcomparisonsacrosscountries.NationalAccountsdatain
PWT8coveredtheperiodupto2011andwerefromtheversionoftheUnitedNations
National Accounts Main Aggregates Database compiled in 2012. Since then, nearly all
countries have revised their National Accounts data, in part because more complete
sourcematerialhasbecomeavailableforthemostrecentyears,butmanycountrieshave
alsomadecomprehensiverevisions.Agrowingnumberofcountrieshasshiftedfromthe
accountingrulesofthe1993editionoftheSystemofNationalAccounts(SNA)tothe2008
edition,whichrequirescapitalizationofresearchanddevelopment(R&D)expenditure
(amongstmanyotherchanges).
By itself, these new accounting rules have a fairly modest effect, increasing GDP by
around2–3percentinadvancedeconomies(Eurostat,2014).Achangeintheaccounting
systemcanalsobeanoccasionforotherchanges,suchasshiftstonewsourcesorarebenchmarking.5Forexample,thetransitiontoSNA2008intheEUledtoGDPrevisions
dueto‘statisticalimprovements’of1.4percentfortheEU-28asawhole,butlargerfor
individualcountries:e.g.amountingto2.6percentofGDPintheUKand5.9percentin
theNetherlands(Eurostat2014).
Butwhilethesechangesarecertainlynoteworthy,theyaresmallcomparedtorevisions
in several African countries. In recent years, statistical systems for measuring GDP in
countries like Nigeria and Ghana have been overhauled and revamped with major
consequencesforlevelsandgrowthratesofGDP.InGhana,thelevelofGDPwasrevised
upwards by 60 percent in 2010 (Jerven, 2013), while in Nigeria the GDP level was
5InNationalAccountspractice,itiscommonformanyaggregatestobeextrapolatedfromabenchmark
yearusing,forinstance,moretimelybutlesscomprehensivesourcematerial.
6
increased by 89 percent.6 These revisions are welcome, since they provide a more
comprehensiveviewoftheseeconomies,butalsoalarming,astheysuggestsubstantial
uncertaintyaboutthetruesizeofAfricaneconomies.Suchconcernsaboutthereliability
ofNationalAccountsestimatesarenotnew.Previousresearchhasaimedtoprovidean
alternative perspective based on detailed Demographic and Health Surveys (Young,
2012) and on nighttime light intensity (Henderson, Storeygard and Weil, 2012). On a
morepositivenote,areportbytheAfricanDevelopmentBank(2013)showsthatthesize
of the revisions in Ghana and Nigeria have been exceptions rather than the rule for
Africancountriesupdatingtheiraccountingmethodologies.
Figure3,RevisionstothelevelGDPinlocalcurrencyunitsfortheyear2011
0.6
NGA
COD
GDPrevisions,PWT9.0/PWT8.1−1
-0.2
0.0
0.2
0.4
SDN
TZA
LBRMWI
IRQ
ZWE
KEN ZMB
GNB
BGD MRT
SWZ MDV
MNG
ECU
ARG
SYR
GMB
-0.4
GAB
4
6
8
10
LogofCGDP%percapita(PWT9.0)
12
Moregenerally,theyarguethatAfricancountriesaremakinggreatereffortstoproduce
timelyandreliableNationalAccountsstatistics.Yetotherevidenceinthereportsuggests
there could be future surprises comparable to Ghana or Nigeria: of the 44 countries
surveyed,27reliedonaNationalAccountsbenchmarkthatwas10oreven20yearsout
of date. Given that the once-a-decade re-benchmarking in some European countries
6
http://www.economist.com/news/finance-and-economics/21600734-revised-figures-show-nigeriaafricas-largest-economy-step-change.
7
already lead to sizeable revisions, it would not be surprising if there were very large
revisionsinsomeAfricancountriesinthefuture.
ToillustraterecentrevisionstoPWTsourcedata,Figure3showsthechangeinthelevel
ofGDPinlocalcurrencyunitsfortheyear2011betweentheNationalAccountsdatathat
wereusedforPWT8andthedatausedforPWT9.Nigeria’s(NGA)revisionisthelargest
buttherearelargeupward(andalsosomedownward)adjustmentsinothercountries,
suchasGabon(GAB),Liberia(LBR)andBangladesh(BGD).
Figure4,RevisionstogrowthofGDP,annualandfive-year
.1
Five-year(2006–2011)
.1
Annual(2010–2011)
BDI
BDI
.05
GDPgrowthrevisions,PWT9.0/PWT8.1−1
-.05
0
.05
ZWE
-.05
0
MDV
MWI
TCD
SDN
OMN
6
8
10
LogofCGDP%percapita(PWT9.0)
-.1
-.1
GMB
12
6
8
10
LogofCGDP%percapita(PWT9.0)
12
Dataoneconomicgrowthisalsosubjecttochangingmethodsandrevisions.Aprominent
exampleistherevisionofthemethodsforestimatingIndia’seconomicgrowthinJanuary
2015,whichimpliedmuchfastergrowththanhadpreviouslybeenreported:the20132014 GDP growth rate was revised upwards from 4.7 to 6.9 percent.7 These revisions
continuetoexercisepolicymakersandanalysts,raisingquestionsaboutthe‘true’Indian
7Seehttp://in.reuters.com/article/india-gdp-idINKBN0L319Z20150130.
8
rateofgrowth.8TheissueofNationalAccountsrevisionsandtheirimpactonresearch
was also raised more generally by Johnson, Larson, Papageorgiou and Subramanian
(2013), who show that cross-country growth regression studies relying on annual
growth rates of GDP can be severely affected by moving from one vintage of National
Accountsdatatothenext.Figure4illustratesthispoint,showingrevisionstotheannual
growthofthevolumeofGDP(inlocalcurrencyunits)between2010and2011andthe
revisiontotheaverageannualgrowthratebetween2006and2011.Theaveragegrowth
overthefive-yearperiodshowsnotablysmallerrevisionsthantheannualgrowthrate,
confirmingtheJohnsonetal.(2013)finding.
III.
Capital,laborandTFP
InadditiontorevisionsinPPPsandGDP,PWT9alsoincludesimprovementsinthesource
materialanddatacompilationforthelaborandcapitalinputdata.Adiscussionofthese
changesisgivenbelowforeachtopic.Thebasicmethodusedtomeasurecapitalandlabor
hasnotbeenchanged,forafullexposition,seeFeenstra,InklaarandTimmer(2015),
specifically (online) Appendix C. In conjunction with these new estimates, we also
providenewdetailedsourcematerial,tobemoreusefulandmoretransparent.
Specifically,wenowprovideadetailedlaborfile,thatdetailsthesourcesandmethods
forthedataonemployment,yearsofschoolingandthelaborshare,aswellasthevarious
alternativelaborsharemeasuresthatcanbeusedtoassessthesensitivityofourchoices.
Wealsoprovideadetailedcapitalfile,thatprovidesabreakdownofinvestment,capital
stocksanddepreciationbyfourassets–structures,machinery,transportequipmentand
otherassets,whichincludesoftwareandotherintellectualpropertyproducts.
•
Investmentdata.OneofthemaininnovationsinPWT8wasthereintroductionof
capitalstocksseriesbasedonestimatesofinvestmentbyasset.Thoseestimateswere
based on National Accounts data, detailed expenditure data from ICP benchmarks,
andestimatesbasedonoutputintheconstructionindustryandsupply(production+
imports – exports) of machinery and equipment, the so-called Commodity Flow
Method(CFM).
8Seee.g.http://www.economist.com/news/finance-and-economics/21696546-few-economists-
wholeheartedly-believe-indias-stellar-growth-rate-elephant
9
In PWT 9.0, this basic approach is unchanged, but its implementation has greatly
improved.First,weincludesubstantiallymoreinvestmentdata,directlytakenfrom
national accounts sources, reducing our reliance on the indirect CFM estimates.
Second,weincorporatedatacompiledunderthenewSystemofNationalAccounts,
which includes investment in R&D. This improved dataset is the result of a
collaborationwithTheConferenceBoardandwillunderlienotjustPWT9.0,butalso
upcomingversionsoftheTheConferenceBoard’sTotalEconomyDatabase.9Ajoint
paperprovidingamoredetaileddiscussionoftheconstructionandfeaturesofthese
dataisplannedforreleaseinJune2016.
•
Laborshare.InPWT8weintroducedthevariableLABSHthatgivesestimatesofthe
shareoflaborincomeinnominalGDP.Itisrelativelystraightforwardtodetermine
theshareoflaborincomeofemployeesinGDP,asthisinformationisaregularpartof
the National Accounts of countries. Estimating the labor income of self-employed
workersismorechallenging.Ifacountryreportsthetotalincomeofself-employed,
knownasmixedincome,thereisaclearupperboundtooveralllaborincome,leading
toareasonableestimate.Whensuchinformationisnotavailable,PWT8assumedselfemployed earn the same average wage as employees or alternatively that selfemployed labor income equaled value added in agriculture, depending on which
methodleadstoalowerlaborshare.10Thisapproachwasmotivatedtominimizethe
risk of overestimation, which is high when using the former. However, this
conservativeprocedurecouldleadtounderestimationoflaborshares.
InPWT9.0,weattempttoidentifysuchcases,byconsideringtwocriteria:(1)does
thechosenmethod(sameaveragewageorvalueaddedinagriculture)leadtoalabor
shareoflessthan40percent,onaverage?And(2)istheshareofGDPgoingtofixed
assets larger than 50 percent, on average? The first criterion is motivated by the
observationthat,whenevermixedincomedataisavailable,thelaborshareisonly
rarely smaller than 40 percent (i.e. in less than 10 percent of cases). The second
criterionreliesonanestimateoftheincomeflowingtoownersoffixedassets.Tothat
9 See https://www.conference-board.org/data/economydatabase/. While the Total Economy Database
alsoestimatestotalfactorproductivitygrowth,onlyPWTprovidesestimatesofcomparativecapitalinput
levels.
10Inaddition,somecountriesreportanemployeecompensationshareofmorethan70percent.Sincesuch
highsharesoccurveryrarelyifmixedincomedataisavailable,thissuggeststheemployeecompensation
sharesalreadyincludesanestimateofself-employedlabourincome.
10
end,wesubtracttheWorldBank’sestimateofnaturalresourcerentsfromGDP.Take
SaudiArabia:itsestimatedlaborshare(basedonmixedincomedata)is25percentof
GDP, natural resource rents account for 40 percent of GDP, leaving 35 percent for
owners of fixed assets, such as buildings and machinery. Whenever data on mixed
incomeisavailable,theshareofincomegoingtoownersoffixedcapitalisonlyrarely
larger than 50 percent. For both criteria, we consider the average across the full
periodtoidentifycountrieswherethelaborshareisclearlyunderestimated.Inthose
countries,weusethelarger,ratherthanthesmaller,ofthetwoalternativemethods.11
While we view this is a worthwhile refinement, the labor share estimates remain
uncertain,especiallyincountrieswherenoinformationisavailableonmixedincome.
For this reason, we provide additional detailed data underlying our compilations,
namelythevariouslaborsharealternativesandinformationonwhichmethodisused
forwhichspecificobservations.
•
AveragehoursworkedandTFP.PWT8andPWT9reporttheaveragehoursworked
by persons engaged (variable AVH), sourced from The Conference Board’s Total
EconomyDatabase.Thesedatacover65countriesandsincethisislessthanthe111
countriesforwhichTFPcouldbecomputedbasedonlaborandcapitalestimates,the
data on average hours worked were not taken into account when computing TFP
levels(CTFP)orgrowth(RTFPNA)inPWT8.
InPWT9,wechangedthecomputationmethodtotakeintoaccounttheavailabledata
onaveragehoursworkedforcountriesandyearsforwhichtheseareavailable,by
imputing missing values. For countries without any information, average hour
workedissetequaltoaveragehoursworkedintheUS.ThisensuresthatCTFPisnot
affectedbythischoice,thoughaconsequenceisthatTFPgrowthishigher,onaverage,
sinceUSaveragehoursworkedhavedeclinedatanaverageannualrateof0.2percent
since 1950. This approach is clearly no substitute for careful measurement (or
econometricmodeling)ofaveragehoursworked,as,forexample,increasesinincome
levels tend to lead to decreases in average hours worked. The current approach
11Therearetwoexceptions,SudanandSierraLeone.InSudan,thisapproachleadstoalaboursharethat
isexceptionallyhighin1996-1998,sowesetthelabourshareat0.9inthoseyears.InSierraLeone,new
vintageNationalAccountsdataleadtoveryhighestimatesofthelabourshareafter2001,soweusethe
employeecompensationshareforthoseyears.
11
shouldthusbeseenmoreasa‘weakprior’ratherthanatruecorrectionforhours
worked.Forcountrieswithdataforpartoftheirtimeseries,weassumednochange
in average hours worked before the first observation. This ensure CTFP reflects
higherorloweraveragehoursworked,whileavoidingstringentassumptionsabout
theevolutionovertime.
NotethatPWT9continuestoshowdataonaveragehoursworkedonlyforcountries
andyearsforwhichtheTotalEconomyDatabasealsoreportsdata,astheimputed
figures are removed after computing CTFP and RTFPNA. See the PWT program
package(specificallygen_pwt.do)forthepreciseimplementationoftheimputation.
•
Human capital. In PWT8, a human capital index was estimated using data on the
average years of schooling from Barro and Lee (2013) and rates of return on
education from Psacharopoulos (1994). As we describe in more detail in our
document‘HumancapitalinPWT9.0’,theBarroandLee(2013)dataforarangeof
countriesishardtosquarewithseveralalternativesources.InPWT9,wetherefore
drawinpartuponthedatafromBarroandLee(2013)andinpartondatabyCohen
and Leker (2014), which updated the work of Cohen and Soto (2007). The precise
implementationofthiscombinationofsourcesisalsodiscussedinthe‘Humancapital
in PWT9.0’ document on the PWT website. This change in source material has a
relatively small effect on cross-country comparisons, but more notable effects on
growthratesforseveralcountries.
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