kyel research paper draft final

A Cross Country Analysis
on Top Income Inequality
Kyel Governor
6/9/2016
Thispaperusescross-countrypanel datafrom1950-2014 to investigateif the rise intopincome
inequalitycanbe explainedbycapital accumulationwheninnovationandcross-countrydifferencesare
takenintoaccount. PooledOLSandfixedeffectsmodelsare usedtoinvestigatethe relationship. This
paperfindssome evidence infavourof capital accumulationdriving topincome inequalityforhighly
competitivecountries.Howeverthere isstrongerevidence suggestingthatinnovationrentsdrivetop
income inequalityratherthancapital accumulation.

1
Contents
Introduction.......................................................................................................................................2
1 Theory........................................................................................................................................3
1.1 Piketty’s General Laws of Capitalism.....................................................................................3
1.2 Schumpeterian Growth Theory and Innovation Rents ............................................................4
2 Data and Measurement...............................................................................................................5
2.1 Income Inequality................................................................................................................6
2.2 Innovation...........................................................................................................................7
2.3 Piketty’s r > g and K over Output...........................................................................................9
2.4 Control Variables.................................................................................................................9
2.5 Global Competitiveness Index (GCI) ......................................................................................9
2.6 Table of Variables and Descriptions.......................................................................................9
3 Main Empirical Analysis:The effectof innovativenessandcapital accumulationonincome
distribution ......................................................................................................................................11
3.1 Estimation Strategy............................................................................................................11
3.2 Pikettyr> g and Capital Accumulation:ResultsfromPooledOLSandFixedEffectsRegressions
11
3.3 SchumpeterianGrowthTheoryandInnovationRents:ResultsfromPooledOLSandFixed
Effects Regressions .......................................................................................................................16
3.3.1 Measure of Innovation: Patents Granted .....................................................................16
3.3.2 Measure of Innovation: Book to Market.......................................................................20
3.4 Evidence of Piketty’sr>gand Schumpeter’sInnovationRents:Interactionof Countries
Grouped by the Global Competitiveness Index ...............................................................................23
3.4.1 GCI Grouping Table.....................................................................................................24
3.4.2 Results.......................................................................................................................24
4 Discussion.................................................................................................................................27
4.1 Reverse Causality –Innovation on Income Inequality OLS Regression and Panel VAR..............27
4.2 Pre and Post Y2K Regressions – Evidence of a New Economy................................................31
5 Conclusion................................................................................................................................33
References.......................................................................................................................................34
Appendix..........................................................................................................................................36

2
Introduction
Increasingtopincome inequality indevelopedcountries hasbecome aphenomenoninthe worldtoday
and yetthere isno general agreement onthe rootcauses behind the rise in topincome inequality.This
paperdebateswhethertopincome inequalityisdrivenby capital accumulationorinnovation.Thomas
Piketty hasbroughtthe issue of growthand income redistribution backtothe forefrontof economic
debateswithhis (2014) book, Capitalin the Twenty-FirstCentury,where he argues thatcapital
accumulationbythe wealthiesttop1%is drivingtopincome inequality. Inshort, he defendsthat wealth
growsfasterthan economicoutput,centeringhisargument onthe expressionr> g (where ris the rate
of returnto capital andg isthe economicgrowthrate),andso slowergrowthleadsto a rapidincrease in
income inequality. Piketty’smore thansix-hundredpage publication inlarge partignores cross-country
differencesand the endogenousevolutionof technology anditsrelationshipwith topincome inequality
leavinghis worksusceptibletodispute (Acemogluetal,2015). Infact in the UnitedStates patentingand
top 1% income share followparallel evolutions indicatingthatinnovationmayplayarole inexplaining
the rise in top income inequality (Aghionetal,2015). Thispaperwill use cross-countrypanel datafrom
1950-2014 to investigate if the rise in topincome inequality canbe explainedby capital accumulation
wheninnovationandcross-countrydifferences are takenintoaccount.Twodifferentmeasuresare used
for innovation;total patentsgranted andthe booktomarketratio. The capital share of national income
(CSNI) ismeasuredby twovariables;the capital stocktogrossdomesticproduct ratioand r minusg.
Lastly, top income inequalityismeasuredbythe share of income heldbythe top1% and the top 10%,
and the ParetoLorenzcoefficient.
In summary,thispaperfinds some evidence validatingPiketty’sargumentthatcapital accumulation
drivestopincome inequality forhighly competitive1
countries.Howeverthere isstrongerevidence
suggestingthatinnovationrentsdrivetopincome inequalityratherthancapital accumulation.Excluding
the leastcompetitivecountries, measuresof innovation show asignificantrelationshipwith topincome
inequality where higherlevelsof innovationleadto highertop 1% and top10% income shareswhich
reflectinnovationrentsandthus isconsistentwithSchumpeteriangrowththeory. The modelpredicts
that on average a 1% increase inpatentsgrantedleadstoa 16.6% increase in the top1% share of
income anda 14.7% increase inthe top 10% share of income,aresultconsistentwithAghionetal.
(2015) U.S. cross-state analysisoninnovationandtopincome inequality. The booktomarketratio
measure of innovation alsoshowsthatonaverage innovationincreases the top1% and top 10% share of
income.Aftercontrollingforfixedeffects,regressionsshow that innovationmeasuressuchas R&D
expenditure andbooktomarketratio are significantinpredictingtopincome inequality. Furthermore,
resultsshownosignificantrelationshipbetween innovation orCSNI andthe restof the income
distribution. Wheninvestigatingwhetherincome inequality causesinnovation pooledOLSregressions
showthat on average the top10% share of income increasesinnovation andapanel vectorauto
regressionsupportsthisresult.Finally the paperfindsthatbefore the year2000 there is no evidence of
a positive relationshipbetween CSNI andtopincome inequalityandnorelationshipbetweeninnovation
1 As measured by the Global Competitive Index (2015-2016).See Section 3.4.

3
and topincome inequality.However afterthe year2000 there isa positive relationship forbothCSNI
and innovationwithtopincome inequality.
ThisanalysishasbeenmotivatedbyAcemogluandRobinson(2015) where they criticize general lawsof
capitalismproposedinPiketty’s(2014) recentwork. Contrary to Piketty,theyshow thatr> g has no
relationship withtopincome inequality. Mypaperadds to theirworkontop income inequalityby
addingan analysisof innovationdriveninequality.ThispapercloselyfollowsAghion,Akcigit, Bergeaud,
Blundell and Hémous(2015) U.S. cross-state analysisof innovationontopincome inequality exceptthis
papertakesa cross-country perspectiveandfactorsin the ParetoLorenz Coefficientasa measuresof
income inequality.Like Aghionetal.(2015),the paper dependsonthe Schumpteriangrowthmodel
presentedinAghion,AkcigitandHowitt (2013) in where innovationledgrowthis dependenton
innovation rentswhichare representedbyincreasesintopincome inequality.Therefore this
endogenous growthmodelleadsthe analysisontopincome inequalityandinnovation. Thisgrowth
model also allowsthatanincrease in topincome inequality causesanincrease ininnovation as
mentionedby Tselosis(2011) and Zweimuller(2000).So our analysisinvestigatesthislikelyevent firstby
univariate regressions andthenby apanel vectorauto regression (VAR) oninnovationandtopincome
inequality motivatedbyAtemsandJones(2015).Finally the paperallows thatthere hasbeen arecent
shifttoa newdigital economy aspresentedin Stiroh’s(2004) review andsothe return on innovation
and humancapital has dramaticallyincreased.Thishypothesisissupportedby HémousandOlsen (2014)
inwhichtheyargue that the digital economybenefitshighskilledlabourers andinnovatorsbecause of
the automationof lowskilledlabour.Thispaperlookstoinvestigate thishypothesis.
The paper isorganizedasfollows. Section1of the paperreviewsPiketty’s general lawsof capitalismand
Schumpteriangrowththeorywhere topincome inequalityisdrivenbyquality-improvinginnovations.
Section2 introducesthe datainvolvedanddescribesthe measuresof innovationandincome inequality
usedinthe paper.Section3 presentsthe mainfindings.Section4isa discussionontwoextensionsof
the analysis:first,aninvestigationintoareverse causalitywhere topincome inequalitydrives
innovation;andsecond, alookat pre Y2K andpost Y2K regressions.Section5concludes.
1 Theory
1.1 Piketty’s General Laws of Capitalism
Pikettyproposesatheoryon the longrun proclivitiesof capitalismin Capitalin the Twenty-firstCentury
(2014). He reliesonMarxianeconomics aspremise tocome to the conclusionthat the future of
capitalismisone where there ismassownershipof capital bythe wealthiestandtheirmainincome
comesfromcapital rents.
To come to his predictions, Piketty firstpresents two“fundamental laws”;
𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑠ℎ𝑎𝑟𝑒 𝑜𝑓 𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝑖𝑛𝑐𝑜𝑚𝑒 = 𝑟 𝑥 (
𝐾
𝑌
),
𝐾
𝑌
= 𝑠/𝑔,

4
where 𝑟 isthe real interestrate, 𝐾 isthe capital stock, 𝑌 isGDP, 𝑠 is the savingrate and 𝑔 isthe growth
rate of GDP. As derivedinaSolowmodel the growthrate of capital stock 𝐾 isequal to saving 𝑠𝑌 ina
closedeconomyandso the ratio
𝐾
𝑌
overtime will approach 𝑠/𝑔due to economicgrowth. Pikettythen
combinesthe twoequationstoget;
𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑠ℎ𝑎𝑟𝑒 𝑜𝑓 𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝑖𝑛𝑐𝑜𝑚𝑒 = 𝑟 𝑥 (
𝑠
𝑔
).
Assuming 𝑟 and 𝑠 are approximatelyconstant derivesPiketty’sfirstgeneral law;
1. The capital share of national income ishigh whengrowth islow.
His secondgeneral lawstates;
2. r > g,
whichdefinesthatthe real interestrate r isalwaysgreaterthanthe growthrate g inan economy. This
lawholdswhena countryisdynamicallyefficient,meaningthatithas maximizedconsumption inevery
periodoveraninfinite timeline,otherwise itmaynotbe the case that the real interestrate exceedsthe
growthrate.
Piketty’s thirdgeneral law proposesthat;
3. Income inequalitytendsto rise wheneverr > g.
The logicbehindthispropositionisthat capital income growsapproximatelyatthe rate r where national
income growsat the rate g and since capital income is distributedmainlytothe wealthiest;their
incomesgrowfasterthanthe per capita income thusresultingincapital-drivenincome inequality.
Accordingto Piketty’stheorytopincome inequalityrisesasthe capital share of national income
increases because capital incomeismainlydistributedamongthe wealthiest. Assumingris
approximately constant,capital share of national incomewillrise asK/Yincreases(change in capital
exceedschange in GDP) and/oras g decreases makingthe distance between randg greater. Thispaper
testsbothhypotheses inordertosubstantiate Piketty’sgenerallawsof capitalism.
1.2 Schumpeterian Growth Theory and Innovation Rents
The baseline Schumpeteriangrowthmodel asdevelopedinAghionetal.(2015) offersa theoretical
frameworkforhowinnovationincreasestopincome inequality. The mainpredictionsof the model asit
relatestothispaperare;
 Innovation increasestopincome inequality.
 Higherinnovationrentleadstoincreasedlevelsof innovation.2
2 This resultis further explored in Section 4.2 where the marginal effect of innovation on income inequality is
stronger after the IT market crashed in 2000.

5
To come to these predictionsthe baseline Schumpeteriangrowthmodelisdevelopedasfollows. Given
discrete time,the economyispopulatedbytwotypesof individualseveryperiod;capital ownerswho
ownthe firmsand the productionworkers. Individualslive onlyforone period andinthe subsequent
perioda newgenerationisbornwiththose whoare bornto firmowners inheritingtheirparents’firm
and the restwork as productionworkersunlesstheysuccessfully innovate andtakeoveranincumbent.
The followingCobb-Douglastechnologyproductionfunction definesthe productionof finalgoods 𝑌𝑡:
𝑙𝑛𝑌𝑡 = ∫ 𝑙𝑛
1
0
𝑦𝑖𝑡 𝑑𝑖
𝑦𝑖𝑡 = 𝑞𝑖𝑡 𝑙𝑖𝑡
where 𝑦𝑖𝑡 isa vector of intermediateinputsusedinfinal production, 𝑙𝑖𝑡 isthe labourusedtoproduce
the intermediateinputs,and 𝑞𝑖𝑡 isthe labourproductivity; all attime t.Wheneverinnovationtakes
place inany sector 𝑖 inperiod 𝑡 the innovatorgainsa technologicalleadof 𝜂 𝐻 overthe competition and
so the innovatorproducesthe intermediateinput 𝑦𝑖𝑡 withthe productionfunction:
𝑦𝑖𝑡 = 𝜂 𝐻 𝑞𝑖,𝑡−1 𝑙𝑖𝑡
If no newinnovationsoccurinthe followingperiod,the innovator’stechnologyispartlyimitatedby
competitorsandsothe innovator’stechnological leaddecreasesto 𝜂 𝐿 where 1 < 𝜂 𝐿 < 𝜂 𝐻.
Solvingthe model yieldsthe twofollowingequationspertinenttothe paper’sanalysis;
𝑀𝐶𝑖𝑡 = 𝑤 𝑡/𝑞𝑖, 𝑡, and, 𝑝𝑖,𝑡 = 𝑤 𝑡 𝜂𝑖𝑡/𝑞𝑖, 𝑡
where 𝜂𝑖,𝑡 𝜖 {𝜂 𝐻, 𝜂 𝐿}, 𝑀𝐶𝑖𝑡 isthe marginal cost of producingthe intermediate input, 𝑤 𝑡 isthe wagespaid
to labourinvolvedinproducingthe intermediate input 𝑖,and 𝑝𝑖,𝑡 isthe price charged forthe
intermediate input;all attime t. Thusthe profitearnedbythe innovatorisgivenby;
𝜋𝑖𝑡 = ( 𝑝𝑖𝑡 − 𝑀𝐶𝑖𝑡) 𝑦𝑖𝑡 =
( 𝜂𝑖𝑡 − 1) 𝑌𝑡
𝜂𝑖𝑡
> 0
The profitsearnedbyinnovatorsinthismodel are innovationrents.Furthermore if the costof
innovationisallowedinthismodel then the costof innovationforentrantscanbe greaterthan the cost
of innovationforincumbents (Aghionetal.,2015). This leads toentrybarriersand thusreduces
innovative activity since entrantsreceive lessprofitfrominnovationthe higherthe costof innovationis
for them.
2 Data and Measurement
The paper uses cross-country unbalanced panel datacoveringthe period1950-2014. Missingvalues
betweendatapointsare approximatedbyalinearinterpolation.The 27countriescoveredare as
follows:Argentina,Australia,Canada,China,Colombia,Denmark,Finland,France,Germany,India,

6
Indonesia,Ireland,Italy,Japan,Korea,Malaysia,Netherlands,New Zealand,Norway,Portugal,
Singapore,SouthAfrica,Spain,Sweden,Switzerland,UnitedKingdom, and UnitedStates.
2.1 Income Inequality
The data on the share of income heldbythe top 1%,top 10%, and on the ParetoLorenzCoefficient was
obtained fromthe World Top Income Database (Alvaredo,Atkinson,Piketty,andSaez’sWorldTop
IncomesDatabase at http://topincomes.parisschoolofeconomics.eu/).These are ourtopincome
inequalitymeasures. Dataonthe share of income ownedbythe bottom40% was obtainedfromthe
WorldBank Databank (http://databank.worldbank.org/data).The share of income ownedbythe middle
40%-90% wasobtainedbysubtractingthe top10% and bottom40% share from100.
Figure1showsthe evolutionof top1%income inequality forFrance,Germany,SouthAfrica,Sweden,
and UnitedStates. The graphclearlyillustratessignificantcrosscountrydifferences howevera
significantupwardtrendisnoticeable forall countriesaroundafter1990. Figure 2 showsthe top 10%,
middle 40%-90%,andbottom 40% income sharesovertime forthe UnitedStates and itis evidentthat
top income sharesincrease atthe expenseof the middle class.
Figure 1
5
101520
1950 1960 1970 1980 1990 2000 2010 2020
Year
France Germany
South Africa Sweden
United States
Top 1% Income Share, 1960-2014

7
Figure 2
2.2 Innovation
We obtainpatentsgranted bythe USPTO and EPOto obtain total of patentsgranted percountryby
inventor’scountryof residence. Thisdatawasobtainedfromthe OECDdatabase
(http://stats.oecd.org/).The datacoversthe time period1999-2014. The bookto marketratio(b/m) was
createdby dividingcapital stockbymarketcapitalization. Capital stock (K) wasobtainedfromPenn
WorldTables version8.1 (www.ggdc.net/pwt) andmarketcapitalizationwasobtainedfrom the World
Bank Databank. Thisproxyfor the bookto marketratio workswell.The correlationbetweenthe actual
U.S. bookto market ratio withKovermarketcapitalizationisequal to0.9452. A low bookto market
ratiovalue impliesthatavaluationabove the worthof physical assets.A marketvaluationabove
physical assets impliesvaluableintangible assetssuchasinnovativeactivitiesandsomovementinthe
bookto marketratio isa validmeasure of innovation.
Figure 3 showsthe evolutionof patentsgrantedforFrance,Germany,SouthAfrica,Sweden,andUnited
Stateswhile Figure 4illustrates the dynamicrelationshipbetween booktomarketand patentsgranted
for the UnitedStates.Figure 4 gives evidence of arelationship between the twomeasuresof innovation
especiallywhencomparing the three yearlagof patentsgrantedwith the bookto marketratio.An
increase inpatentsgranted (lagged3years) leadstoa fall inthe book to marketratio.
1020304050
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Year
Top 10% Share of Income Mid 40%-90% Share of Income
Bottom 40% Share of Income
United States Income Distribution 1950 - 2014

8
Figure 3
Figure 4
0
50
100150
2000 2005 2010 2015
Year
France Germany
South Africa Sweden
United States
Total Patents Granted, 1999-2014
.02.025.03.035.04
BooktoMarket
80
100120140160
2000 2005 2010 2015
Year
Granted patents (in 100s)
Granted patents (in 100s), Lagged 3 yearsBook to Market
United States, Book to Market vs Patents Granted

9
2.3 Piketty’s r > g and K over Output
Data on real interestrates (r) were retrievedfromthe WorldBankDatabankand data on GDP was
obtainedfromPennWorldTablesversion8.1and Madison’sdataset
(http://www.ggdc.net/maddison/maddison-project/data.htm).The GDPgrowthrate (g) iscomputed
and the variable r> g is computedbysimplysubtractinggfromr. Our capital to outputratio (K/Y) isalso
computedusingKand GDP data.
2.4 Control Variables
The paper controlsforthe size of the financial sector andthe size of the governmentsectorbecause
theyare likelyto have directeffectsoninnovationandtopincome share (Aghionetal,2015).
Furthermore the analysiscontrolsfor macroeconomicvariablessuchaspopulationgrowth,the
employmentrate, andhumancapital;andResearchand Development(R&D) variables suchasR&D
expenditure percapitaandnumberof R&D researchers. Controllingforthe employmentrate ismainly
to reduce time varyingcrosscountrydifferencesinordertohave more efficientresults. Humancapital is
controlled forsince thislikelyhasaneffectonincome inequalitymeasuresandinnovation. FinallyR&D
iscontrolledforsince itiscorrelatedwithpatentsgrantedandincome inequalitymeasures;alsoR&D
may be consideredameasure of innovation.
2.5 Global Competitiveness Index (GCI)
The paper groupscountriesbasedontheirGCI obtainedfromthe WorldEconomicForum Global
CompetitivenessReport (http://reports.weforum.org/global-competitiveness-report-2015-2016/) in
orderto see the relationshipof CSNIandinnovation ontopincome inequality acrossdifferenteconomic
stages.The GCI is a composite of sub-indexes suchasinstitutions,infrastructure, macroeconomic
environment,healthandprimaryeducation,highereducationand training, goodsmarketefficiency,
labourmarket,financial marketdevelopment,technological readiness,marketsize,business
sophistication,andinnovation. The table inSection3.4.1showshow countriesare grouped.
2.6 Table of Variables and Descriptions
Variable Number of
Observations
(annual)
Mean Standard
Deviation
Time
Span
Retrieval Description
Income Inequality Variables
Top 1% Share
of Income (%)
1177 12.057 3.425 1950-
2014
World Top
Incomes
Database
Share of national
income held by the
top 1% earners
Top 10%
Share of
Income (%)
962 36.6 6.809 1950-
2014
World Top
Incomes
Database
Share of national
income held by the
top 10% earners
Pareto Lorenz
Coefficient
1068 1.844 .224 1950-
2014
World Top
Incomes
Database
Measure of the
income distribution
Piketty and Innovation Variables
Patents
Granted
416 8.872 22.643 1999-
2014
OECD
Database
Number of patents
granted by the EPO

10
(in 1000s) and USPTO
Market
Capitalization
(% of GDP)
799 93.502 45.774 1975-
2014
World
Bank
Databank
Share price x number
of sharesoutstanding
for listed domestic
companies
r (%) 946 2.736 3.554 1961-
2014
World
Bank
Databank
Real interest rate
g (%) 1620 1.224 2.103 1951-
2010
Madison’s
dataset
GDP growth rate
K (in mil.
2005US$)
1644 8724524 1.27e+07 1950-
2011
Penn
World
Tables
Capital Stock
GDP (in mil.
2005US$)
1644 2856685 4178096 1950-
2011
Penn
World
Tables
Real GDP
Control Variables
R&D per
capita
expenditure (%
of GDP)
346 42289.59 20903.95 1996-
2010
World
Bank
Databank
Expenditures for
research and
development both
public and private
Number of
R&D
researchers
391 3345.405 1427.127 1996-
2013
World
Bank
Databank
Professionals
engaged in the
creation of new
knowledge,products,
processes, methods,
or systems
Index of
Human Capital
per person
1644 3.133 .337 1950-
2011
Penn
World
Tables
Based on years of
schoolingandreturns
to education
Share of
Government
Consumption
(%)
1644 .139 .034 1950-
2011
Penn
World
Tables
Share of government
consumption at
current PPPs
Services per
capita (% of
GDP)
1014 68.641 8.288 1960-
2014
World
Bank
Databank
Services correspond
to ISICdivisions50-99
(includes financial)
Employment
to population
ratio, 15+, total
(%) (national
estimate)
817 58.957 8.937 1980-
2014
World
Bank
Databank
Proportion of a
country's population
that is employed
Population
growth (annual
%)
1484 .989 .467 1950-
2011
Penn
World
Tables
Population growth
rate
Global N/A N/A N/A 2015-
2016
World
Economic
Integrates
macroeconomic and

11
Competitivene
ss Index
(1-7 (best))
Forum micro/business
aspects of
competitiveness into
a single index
3 Main Empirical Analysis:The effect of innovativeness and capital
accumulationon income distribution
3.1 Estimation Strategy
The main empirical analysis involveslookingatwhetherPiketty’svariables,r-g andcapital accumulation
measuredbyK/Y,or innovationmeasuredby b/mandtotal patentsgranted are responsible for
increasesintopincome shares. The mainestimatedequationis:
log( 𝑦𝑖𝑡) = 𝐴 + 𝐵𝑖 + 𝐵𝑡 + 𝐵1𝑗( 𝑃𝑖𝑘𝑒𝑡𝑡𝑦𝑗) + 𝐵2 𝑙𝑜𝑔(𝑖𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛) + 𝐵3𝑖 𝐺𝐶𝐼 𝑘−1 ∗ 𝑙𝑜𝑔(𝑖𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛)
+ 𝐵4𝑗𝑖 𝐺𝐶𝐼 𝑘−1 ∗ ( 𝑃𝑖𝑘𝑒𝑡𝑡𝑦𝑗) + 𝐵5 𝑋𝑖𝑡 + 𝜀𝑖𝑡
where 𝑦𝑖𝑡 isthe measure of inequality, 𝐵𝑖 isacountry fixedeffect, 𝐵𝑡 isa yearfixedeffect, 𝑃𝑖𝑘𝑒𝑡𝑡𝑦𝑗 isa
vectorof Pikettyvariableswhich include r-g(j=1) andK/Y(j=2), 𝑖𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛isinnovativeness measured
by b/mor total patentsgranted, 𝐺𝐶𝐼 𝑘−1 are GCI dummies and 𝑋 isa vector of control variables.
In Section 3.2 𝐵2, 𝐵3 and 𝐵4 are setto zero and a seriesof Pikettyregressionsare estimatedby pooled
OLS and fixedeffects.The restrictionon 𝐵2 isremoved inSection3.3and the model isestimatedagain
by pooledOLS andfixedeffects. InSection3.4all restrictionsare removedandthe model isestimated
by fixedeffects. All resultsare clusterbootstrappedand include time dummies.3
3.2 Piketty r > g and Capital Accumulation: Results from Pooled OLS and
Fixed Effects Regressions
The resultsfroma seriesof Pikettyregressionsare presentedbelowinTable 1.In (3) and(4) of Table 1 it
isassumedthat all capital marketsare openand cross countryvariationinr iszero andso r is
normalizedto0, therefore we have r-g=- g. The regressionsshow nosignificantrelationshipbetween
the top 1% share of income andr > g for pooledOLSand fixedeffectsestimates asseeninAcemogluand
Robinson(2015). ResultsfromTable 1 illustrate thatcrosscountrydifferences have astronginfluence
on Piketty’s r> g since fixedeffectscoefficientsare significantlylessthanpooledOLScoefficients.The
lastcolumnin Table 1 estimatesthatthe coefficientonPiketty’sK/Yissignificantbutnegativeinafixed
effectsregression.Thisimpliesthat anincrease inCSNIdecreasestopincome inequalitywhich
contradictsPiketty’s theory.
To continue withthe resultsfoundin(6) of Table 1 the nexttwotable shows the resultsfrom
regressions againstdifferentcontrolsandfordifferenttimespans. The resultsshow that K/Yremains
negative andsignificantafteraddingthe controls howeveroverthe period1999-2010 itbecomes
3 Additional regressionsarekept in the appendix.

12
insignificantasshownin(4) of Table 3. Overthe same periodin(3) of Table 3 r > g ispositive and
significant.
Table 1
Piketty's r > g and Capital Accumulation
1961-2010 1951-2010 1961-2010
(1) (2) (3) (4) (5) (6)
Top 1%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
r - g 0.00921
(1.34)
-0.00246
(-1.02)
0.0103
(1.43)
0.00122
(0.48)
normalizing
r=0 (r-g=-g)
0.00818
(0.96)
-0.00155
(-0.79)
K/Y (Log) -0.125
(-0.64)
-0.536**
(-2.36)
Country Fixed
Effects
No Yes No Yes No Yes
Observations 829 829 1339 1339 829 829
R2 0.284 0.202 0.293
R2 overall 0.233 0.194 0.173
R2 between 0.152 0.173 0.0217
R2 within 0.561 0.462 0.617
# of countries
in regression
27 27 27 27 27 27
t statistics in parentheses
Cluster Bootstrapped Standard Errors
* p < 0.1, ** p < 0.05, *** p < 0.01

13
Table 2
Piketty's r > g and Capital Accumulation with different controls
1961-2010
(1) (2) (3) (4)
Top 1% Share
of Income
(Log)
Top 1% Share
of Income
(Log)
Top 1% Share
of Income
(Log)
Top 1% Share
of Income
(Log)
r - g 0.00184
(0.77)
0.00221
(0.92)
0.00120
(0.47)
0.00259
(1.15)
K/Y (Log) -0.498**
(-2.46)
-0.487**
(-2.56)
-0.559**
(-2.45)
-0.506***
(-2.85)
Index of human
capital per person
(Log)
-0.752*
(-1.82)
-0.605
(-1.55)
Share of
Government
Consumption
-1.930***
(-3.22)
-1.762***
(-3.21)
Population growth
(annual %)
-0.0182
(-0.89)
-0.0369*
(-1.94)
Country Fixed
Effects
Yes Yes Yes Yes
Observations 829 829 829 829
R2 overall 0.193 0.358 0.155 0.331
R2 between 0.0544 0.214 0.0120 0.187
R2 within 0.643 0.651 0.618 0.672
# of countries in
regression
27 27 27 27
* p < 0.1, ** p < 0.05, *** p < 0.01

14
Table 3
Piketty's r > g and Capital Accumulation over different time spans
1980-2010 1999-2010
(1) (2) (3) (4)
Top 1% Share
of Income
(Log)
Top 1% Share
of Income
(Log)
Top 1% Share
of Income
(Log)
Top 1% Share
of Income
(Log)
r - g 0.0106
(1.34)
0.000222
(0.09)
0.0151*
(1.83)
-0.00308
(-1.52)
K/Y (Log) -0.0937
(-0.48)
-0.394**
(-2.21)
-0.0992
(-0.45)
-0.0203
(-0.09)
Country Fixed
Effects
No Yes No Yes
Observations 681 681 235 235
R2 0.284 0.101
R2 overall 0.200 0.0174
R2 between 0.0449 0.0110
R2 within 0.624 0.165
# of countries in
regression
27 27 27 27
* p < 0.1, ** p < 0.05, *** p < 0.01
In Table 4 all the control variables are includedandthe Pikettyvariablesare regressedonseveral
measuresof topincome inequality.The regressions show thatincreasingr-gincreasesthe top1% share
of income asseenin (1) of Table 4 butwhencountryfixedeffectsare includedthe coefficientbecomes
insignificant.In(6) of Table 4 K/Y has a significantandpositiverelationshipwiththe ParetoLorenz
coefficientandsoan increase inK/Ydecreasesincome inequality whichiswhatwasfoundinTable 1 to
3. This leadstoconflictingresultsintermsof analyzingPiketty’sargument.Favourable resultsforPiketty
are foundinthe pooledOLS regressionsbut notinfixedeffectsregressions.

15
Table 4
Piketty's r > g and Capital Accumulation – Top Income Inequality
1980-2010
(1) (2) (3) (4) (5) (6)
Top 1%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Pareto
Lorenz
Coefficient
(log)
Pareto
Lorenz
Coefficient
(log)
r - g 0.00904**
(2.06)
0.000327
(0.15)
0.00433
(1.03)
-0.000450
(-0.32)
-0.00850**
(-1.99)
-0.000450
(-0.32)
K/Y (Log) -0.116
(-0.67)
-0.380
(-1.38)
-0.110
(-0.65)
0.110
(0.46)
0.179
(1.13)
0.379**
(1.99)
Index of
human capital
per person
(Log)
-0.680*
(-1.75)
-0.673
(-1.06)
-0.293
(-0.89)
-0.132
(-0.24)
0.234
(0.69)
0.197
(0.42)
Share of
Government
Consumption
-4.549***
(-3.89)
-1.896***
(-2.68)
-2.050***
(-2.73)
-0.232
(-0.45)
2.592***
(3.43)
1.172**
(2.01)
Population
growth (annual
%)
0.0549
(1.05)
-0.0361
(-1.60)
-0.00665
(-0.13)
-0.0144
(-0.72)
-0.0144
(-0.33)
0.0125
(0.58)
Services per
capita (% of
GDP)
-0.00134
(-0.13)
-0.00357
(-0.08)
-0.00235
(-0.28)
-0.00361
(-0.07)
0.00128
(0.19)
-0.00387
(-0.20)
Employment to
population
ratio, 15+, total
(%) (national
estimate)
-0.00987*
(-1.75)
-0.00485
(-0.79)
-0.00943**
(-2.21)
-0.00218
(-0.76)
-0.00173
(-0.28)
0.00206
(0.40)
Country Fixed
Effects
Observations 502 502 444 444 450 450
R2 0.713 0.597 0.539
R2 overall 0.509 0.283 0.366
R2 between 0.468 0.233 0.227
R2 within 0.642 0.538 0.659
# of countries
in regression
26 26 23 23 24 24

16
* p < 0.1, ** p < 0.05, *** p < 0.01
3.3 Schumpeterian Growth Theory and Innovation Rents: Results from
Pooled OLS and Fixed Effects Regressions
So far our analysisprovides conflictingevidence forPiketty’sargumentonCSNI drivingtopincome
inequality. Basedonfixedeffectsthe analysisis inagreementwithAcemogluandRobinson(2015).In
thissectioninnovationisallowedinthe model andisfirstmeasuredby total patentsgranted andthen
by bookto market.The resultsmetfrombothmeasuressupport Schumpeteriangrowththeorybutdoes
not debunkPiketty’sargumentforcapital accumulationincreasingtopincome inequality.
3.3.1 MeasureofInnovation:PatentsGranted
Table 5 displays the results whentotal patentsgrantedis includedinthe regression. The pooledOLS
regression ontop10% share of income showssignificantresults findinga1% increase inpatentsgranted
increasesthe top10% share of income by6.6%. Alsointhe table,(1) shows r-gis positive andsignificant
and K/Y becomesinsignificantforall regressions givingevidence thatinnovationisanomittedvariable in
Section3.2 regressions.
The resultsinTable 5 give some evidence insupportof Schumpeteriangrowththeoryandinnovation
rentsand evidence againstPiketty’s supposition.Howeverthe regressionsinTable 5covera short time
span, 1999-2010, and so general inferencescannotbe drawn.
To ignore the insignificantcoefficientsonpatentsgrantedin (1) and(2) of Table 5 wouldbe unjust since
the top 1% share of income is one of the income inequalitymeasuresof interest. There isanapparent
omittedvariable whichis R&Dactivitysince thisisverylikelycorrelatedwithpatentsgrantedand can
likelybe correlatedwithourdependentvariables. R&Dactivityapproximatesthe total costof
innovations.AccordingtoSchumpeteriangrowththeoryasoutlinedinSection1.2,innovatorswill
produce an innovation if the expectedtotal profit4
fromaninnovation isequal toorgreaterthan the
expected costof innovation. If aninnovation,measuredbypatentsgranted,occurs thenthe innovator
has incurredthe cost of innovation whichisapproximatedbyR&Dactivity. Therefore R&Dactivity
affectspatentsgrantedwhere higherR&Dactivityleadstoan increase inpatentsgranted.R&Dactivity
can alsoaffectinnovationrents since the costof innovationhasanimpacton innovationrents. R&D
activitycan thushave a positive effectoninnovationrentsif the returnonR&D activityispositive anda
negative effectif the returnonR&D activityisnegative.
Table 6 controlsforR&D activityand providesasignificantand twice aslarge coefficientforpatents
grantedon the top 10% share of income pooledOLSregression thanwhatwaspresentedinTable 5,
4 Profitas defined by the equation 𝜋𝑖𝑡 = ( 𝑝𝑖𝑡 − 𝑀𝐶𝑖𝑡
) 𝑦𝑖𝑡 in Section 1.2. Expected total profitis the expected
present valueof the stream of all profits earned up to time T. See Aghion, Akcigitand Howitt (2013).

17
howeverregressionsonthe top1% share of income remaininsignificant.5
The keyresultinTable 6is in
(1) where r-gremains positive andsignificantinexplainingthe top 1% share of income.
Table 5 – Check appendix Table A1 for complete list of excluded countries.
Innovation Measure: Patents Granted – Top Income Inequality
1999-2010
(1) (2) (3) (4) (5) (6)
Top 1%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Pareto
Lorenz
Coefficient
(log)
Pareto
Lorenz
Coefficient
(log)
Patents
Granted (Log)
0.0243
(0.45)
0.0368
(0.55)
0.0664**
(2.19)
0.0410
(0.79)
-0.00404
(-0.15)
-0.0186
(-0.63)
r - g 0.0107*
(1.85)
-0.00403
(-1.29)
0.00422
(0.83)
-0.000130
(-0.05)
-0.00636
(-1.38)
0.00313**
(2.36)
K/Y (Log) -0.156
(-0.62)
-0.0816
(-0.19)
-0.234
(-1.07)
-0.0103
(-0.05)
0.119
(0.70)
-0.0280
(-0.11)
Index of
human capital
per person
(Log)
-0.811
(-0.99)
1.784
(1.05)
-0.594
(-0.98)
0.831
(1.01)
0.106
(0.23)
-0.665
(-1.02)
Share of
Government
Consumption
-3.994**
(-2.24)
-0.151
(-0.13)
-1.206
(-0.99)
-0.508
(-1.41)
2.163**
(2.25)
-0.370
(-0.79)
Population
growth
(annual %)
0.0954
(1.28)
-0.0113
(-0.28)
0.0551
(1.07)
-0.000113
(-0.01)
-0.0214
(-0.55)
-0.000475
(-0.02)
Services per
capita (% of
GDP)
-0.00186
(-0.09)
0.0961
(1.49)
0.00370
(0.31)
0.0391
(1.20)
0.00350
(0.39)
-0.0680***
(-3.05)
Employment
to population
ratio, 15+,
total (%)
(national
estimate)
-0.00779
(-0.95)
0.00377
(0.38)
-0.0100
(-1.56)
-0.00575
(-1.36)
-0.00516
(-0.72)
-0.00766
(-1.43)
5 By addingR&D activity to the regressions a few countries were dropped however Table 5 results were consistent
when the same countries were excluded and therefore the results arenot tainted by any selection bias.

18
Country Fixed
Effects
Observations 220 220 202 202 202 202
R2 0.516 0.574 0.388
R2 overall 0.0623 0.00183 0.000000567
R2 between 0.0437 0.000400 0.00327
R2 within 0.255 0.270 0.370
# of countries
in regression
25 25 23 23 24 24
* p < 0.1, ** p < 0.05, *** p < 0.01

19
Table 6 - Check appendix Table A1 for complete list of excluded countries.
Innovation Measure: Patents Granted controlling for R&D – Top Income Inequality
1999-2010
(1) (2) (3) (4) (5) (6)
Top 1%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Pareto
Lorenz
Coefficient
(log)
Pareto
Lorenz
Coefficient
(log)
Patents
Granted (Log)
0.105
(1.39)
0.0367
(0.54)
0.122***
(2.90)
0.0447
(0.68)
0.0459
(0.94)
-0.00978
(-0.33)
R&D per
capita
expenditure
(Log)
-0.362
(-1.51)
0.136
(1.28)
-0.355***
(-2.58)
0.0360
(0.37)
-0.211
(-1.34)
-0.103
(-1.54)
R&D
researchers
(Log)
0.246
(1.00)
-0.108
(-1.18)
0.283**
(2.50)
-0.0496
(-0.75)
0.130
(0.86)
0.0746
(1.16)
r - g 0.0129**
(2.01)
-0.00294
(-0.89)
0.00455
(0.97)
-0.000283
(-0.10)
-0.00665
(-1.44)
0.00304*
(1.96)
K/Y (Log) -0.141
(-0.54)
-0.0495
(-0.12)
-0.196
(-1.03)
0.0442
(0.20)
0.131
(0.71)
-0.0704
(-0.28)
Index of
human capital
per person
(Log)
-0.950
(-0.91)
2.372
(1.60)
-0.582
(-1.03)
1.078
(1.05)
-0.0223
(-0.04)
-0.949
(-1.12)
Share of
Government
Consumption
-4.648***
(-2.74)
-0.289
(-0.55)
-1.801*
(-1.90)
-0.328
(-0.92)
1.910**
(2.00)
-0.456
(-0.75)
Population
growth (annual
%)
0.107
(1.34)
-0.0106
(-0.27)
0.0654
(1.40)
-0.000541
(-0.03)
-0.0164
(-0.46)
-0.00186
(-0.09)
Services per
capita (% of
GDP)
0.00965
(0.44)
0.0937*
(1.78)
0.00941
(0.77)
0.0383
(1.09)
0.0102
(0.84)
-0.0650**
(-2.47)
Employment
to population
ratio, 15+,
total (%)
-0.00521
(-0.51)
-0.00369
(-0.60)
-0.00973
(-1.50)
-0.00499
(-1.22)
-0.00343
(-0.44)
-0.00421
(-0.89)

20
(national
estimate)
Country Fixed
Effects
Observations 209 209 197 197 192 192
R2 0.625 0.707 0.484
R2 overall 0.0597 0.00287 0.0153
R2 between 0.0231 0.000901 0.0176
R2 within 0.359 0.274 0.411
# of countries
in regression
24 24 23 23 23 23
* p < 0.1, ** p < 0.05, *** p < 0.01
3.3.2 MeasureofInnovation:Bookto Market
As statedinthe previoussubsection,there isashorttime spanof available dataforpatentsgrantedand
so the advantage gainedfromusingthe booktomarket ratioas a measure of innovationisalongertime
span. The disadvantage,however,isthat b/misan indirectmeasure of innovative activity since itdoes
not directly capture innovative activitylike the more commonlyusedmeasure patentsgranted, but
insteaditapproximates the aggregate value of innovation. AccordingtoSchumpeteriangrowth theory
as presentedinSection1.2,the value of an innovationdecreasesovertime whichcorrespondstoan
increasingb/m.If a newinnovation occurs ina givenperiod thenthe value of thatinnovation isgreater
than the one that precededit andso there will be acorrespondingdecreasein the b/m.
The results fromregressingincome inequalitymeasureson b/mare displayedin Table 7.The regressions
inTable 7 coverthe period1980-2010 whichisthe same periodcoveredinthe Pikettyregressions in
Section3.2. The table predicts thatboth r-g andb/m contribute to the top 1% share of income however
r-g affectsthe top1% share of income ina pooledOLSregressionwhereasb/maffectsthe top1% share
of income ina fixedeffectsregression.Betweenthe resultsinSection3.3.1and Table 7 there is some
evidence thatinnovation increasestopincome inequalityandalsothatina pooledOLSthere isa
positive andsignificantrelationship betweenr-gandthe top 1% share of income.
Kitchensinkregressions6
ontopincome inequalityare performedandthe resultsare capturedinTable
8. The mostsignificantresultsare foundfor pooledOLS regressionon the top1% and top10% share of
income.Bothinnovationmeasuresare significantinthe correctdirectionindicatingthatinnovation
increasestopincome inequalityandmore sofor the top 1% share of income thanthe top 10% share of
income. Furthermore the Pikettyvariable r-gbecomesinsignificant. Table 8providessome more
evidence thatinnovationrentsexplain topincome inequalityratherthanthe Pikettyvariables.However
the resultsare not robustto countryfixedeffects.
6 Kitchen sink regression includes all variables of interestand controls.

21
Innovation Measure: Book to Market – Top Income Inequality
1980-2010
(1) (2) (3) (4) (5) (6)
Top 1%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Pareto
Lorenz
Coefficient
(log)
Pareto
Lorenz
Coefficient
(log)
Book to
Market (Log)
-0.0447
(-0.89)
-0.0578*
(-1.78)
-0.0542
(-1.43)
-0.0149
(-0.80)
0.000460
(0.01)
0.00975
(0.51)
r - g 0.00763*
(1.65)
-0.000391
(-0.11)
0.00436
(1.08)
0.00150
(0.67)
-0.00202
(-0.45)
0.00163
(0.54)
Index of
human capital
per person
(Log)
-1.051**
(-2.36)
-1.220**
(-2.04)
-0.299
(-1.00)
-0.340
(-0.71)
0.215
(1.00)
0.517
(1.12)
Share of
Government
Consumption
-3.669***
(-3.18)
-1.340
(-1.50)
-1.765**
(-2.08)
-0.0889
(-0.18)
1.852***
(3.06)
1.320
(1.40)
Population
growth
(annual %)
0.0436
(0.91)
-0.0137
(-0.55)
-0.0215
(-0.41)
-0.0109
(-0.56)
-0.0186
(-0.50)
0.000551
(0.02)
Services per
capita (% of
GDP)
-0.00677
(-0.93)
-0.0146
(-0.29)
-0.00271
(-0.39)
-0.00653
(-0.12)
0.00373
(0.75)
0.00459
(0.20)
Employment
to population
ratio, 15+,
total (%)
(national
estimate)
-0.00382
(-0.77)
-0.00816
(-1.26)
-0.00966**
(-1.98)
-0.00413*
(-1.78)
-0.00723**
(-1.99)
0.00440
(0.76)
Country Fixed
Effects
Observations 433 433 408 408 388 388
R2 0.723 0.593 0.600
R2 overall 0.582 0.430 0.370
R2 between 0.444 0.362 0.155
R2 within 0.670 0.550 0.648
# of countries
in regression
25 25 23 23 23 23

22
* p < 0.1, ** p < 0.05, *** p < 0.01
Kitchen Sink – Top Income Inequality
1999-2010
(1) (2) (3) (4) (5) (6)
Top 1%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Pareto
Lorenz
Coefficient
(log)
Pareto
Lorenz
Coefficient
(log)
Book to
Market (Log)
-0.138*
(-1.72)
-0.0608
(-0.89)
-0.116*
(-1.93)
-0.0338
(-1.07)
0.0521
(0.90)
-0.0184
(-0.43)
Patents
Granted (Log)
0.166**
(2.33)
0.0408
(0.50)
0.147***
(3.93)
0.0562
(0.85)
0.0199
(0.40)
0.00391
(0.11)
R&D per
capita
expenditure
(Log)
-0.659***
(-2.66)
0.0508
(0.24)
-0.517***
(-4.04)
-0.0274
(-0.24)
-0.0921
(-0.55)
-0.189*
(-1.75)
R&D
researchers
(Log)
0.413*
(1.90)
-0.0785
(-0.64)
0.368***
(3.69)
-0.0358
(-0.56)
0.0778
(0.56)
0.0833
(0.95)
r - g 0.0111
(1.48)
-0.00428
(-0.78)
0.00280
(0.67)
-0.0000555
(-0.02)
-0.00373
(-0.69)
0.00365
(1.61)
Index of
human capital
per person
(Log)
-0.873
(-1.06)
2.269
(1.26)
-0.256
(-0.51)
0.952
(1.02)
-0.103
(-0.23)
-0.828
(-0.70)
Share of
Government
Consumption
-3.661**
(-2.15)
-0.144
(-0.23)
-1.400
(-1.37)
-0.265
(-0.72)
1.362*
(1.67)
-0.0669
(-0.11)
Population
growth (annual
%)
0.0658
(0.92)
-0.00620
(-0.18)
0.0324
(0.70)
0.00180
(0.11)
0.00152
(0.05)
0.00275
(0.13)

23
Services per
capita (% of
GDP)
0.0162
(0.98)
0.113**
(2.15)
0.0152
(1.43)
0.0475
(1.61)
0.00586
(0.62)
-0.0610**
(-2.01)
Employment
to population
ratio, 15+,
total (%)
(national
estimate)
0.00337
(0.45)
-0.00219
(-0.27)
-0.00837
(-1.45)
-0.00390
(-0.82)
-0.00734
(-1.18)
-0.00424
(-0.81)
Country Fixed
Effects
Observations 195 195 189 189 178 178
R2 0.666 0.744 0.463
R2 overall 0.0505 0.0166 0.00676
R2 between 0.0324 0.00713 0.0112
R2 within 0.368 0.286 0.442
# of countries
in regression
24 24 23 23 23 23
* p < 0.1, ** p < 0.05, *** p < 0.01
3.4 Evidence of Piketty’s r>g and Schumpeter’s Innovation Rents:
Interaction of Countries Grouped by the Global Competitiveness Index
The countriesinthe panel have verydifferenteconomies andsosuch differencesshouldbe considered.
The fixedeffectsestimatorisappropriate forcapturingthese differences.Howeveritisimportantto
recognize thatdifferenteconomieswillreactdifferentlytochangesininnovationandPikettyvariables.
Ratherthan interactingeachcountrywitheachmeasure of innovationandeachPikettyvariable the
countriesare firstgroupedintermsof theircompetitivenessasmeasuredbythe GCI and then the
interactionbetweenthe variablesof interestandthe GCIgroupsare estimated.Fourgroupsare created
as seeninthe table belowwhere GCI0isthe group withthe mostcompetitive countriesandGCI3isthe
groupwiththe leastcompetitive countries.

24
3.4.1 GCIGroupingTable
Global Competitive Index (2015-2016) Categories
Label Value Countries Total
GCI0 GCI ≥ 5.5 Switzerland, Singapore, United States, Germany,
Netherlands, Japan, Finland
7
GCI1 5.4 ≥ GCI ≥ 5 Sweden, United Kingdom, Norway, Denmark,
Canada, New Zealand, Malaysia, Australia, France,
Ireland, Korea Rep.
11
GCI2 4.9 ≥ GCI ≥ 4.5 China, Spain, Indonesia, Portugal, Italy 5
GCI3 4.4 ≥ GCI South Africa, India, Columbia, Argentina 4
3.4.2 Results
Table 9 displays resultsfromfixedeffectsregressions7
showingthatanincrease in r>g increases the top
1% share of income forGCI0 countriesand thatthe effectof anincrease inr>g is nearlyzero or negative
for mostnon-GCI0countries.Alsothe table showsthat the innovationmeasure patentsgranted hasno
effectontopincome inequalitybutR&Dexpenditure does.ExcludingpatentsgrantedandtreatingR&D
as a measure of innovation,(5) and(6) show that the coefficienton R&D expenditure percapitais
significantandpositive forthe top1% and top10% share of income forGCI0 countriesbutfor the top
10% share of income is nearlyzeroornegative forall othercountries.
These resultsshowthat aftercontrollingforfixedeffects andinnovation the Pikettyvariable r>g isnot
significantinexplainingtopincome inequalityforthe most competitive economies.Thisisevidence
againstPiketty’stheoryoncapital driventopincome inequality. Furthermore the resultsgive some
justificationforSchumpeteriangrowththeory andinnovationrents.
7 Pooled OLS regressions areincluded in Appendix Table A6.

25
Global Competitive Index Interaction Results – GCI0 is base
Piketty: r>g, K/Y
1980-2010
Patents Granted
1999-2010
R&D: Expenditure
1996-2010
(1) (2) (3) (4) (5) (6)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
r - g 0.0110*
(1.88)
0.00121
(0.46)
0.00141
(0.23)
0.00425
(0.93)
0.000675
(0.20)
0.000542
(0.19)
GCI1 x r - g -0.0131*
(-1.67)
-0.00312
(-0.82)
-0.00830
(-0.97)
-0.00704
(-1.35)
-0.00926*
(-1.65)
-0.00542
(-1.47)
GCI2 x r - g -0.0146**
(-2.38)
-0.00193
(-0.51)
-0.00354
(-0.19)
-0.00251
(-0.25)
-0.00247
(-0.49)
-0.00217
(-0.64)
GCI3 x r - g -0.0104
(-1.49)
0.00176
(0.57)
-0.00243
(-0.36)
-0.00352
(-0.63)
-0.00221
(-0.70)
-0.00384
(-1.29)
Patents
Granted (Log)
0.0951
(0.67)
0.111
(1.28)
GCI1 x Patents
Granted (Log)
-0.0752
(-0.48)
-0.104
(-1.17)
GCI2 x Patents
Granted (Log)
-0.121
(-0.70)
-0.114
(-1.18)
GCI3 x Patents
Granted (Log)
-0.101
(-0.69)
-0.0783
(-0.90)
R&D per
capita
expenditure
(Log)
0.168*
(1.67)
0.0993
(1.01)
0.571***
(2.72)
0.357***
(2.69)
GCI1 x R&D
per capita
expenditure
(Log)
-0.363
(-1.59)
-0.326**
(-2.42)
GCI2 x R&D
per capita
expenditure
-0.519*
(-1.94)
-0.377**
(-2.21)

26
(Log)
GCI3 x R&D
per capita
expenditure
(Log)
-0.334
(-1.41)
-0.407***
(-3.82)
K/Y (Log) -0.717
(-1.39)
0.216
(0.77)
0.0334
(0.07)
0.106
(0.47)
0.344
(0.96)
0.353
(1.64)
GCI1 x K/Y
(Log)
0.373
(0.60)
-0.107
(-0.20)
GCI2 x K/Y
(Log)
0.467
(0.57)
-0.656
(-1.03)
GCI3 x K/Y
(Log)
-0.232
(-0.23)
0.0525
(0.16)
R&D
researchers
(Log)
-0.0977
(-1.12)
-0.0333
(-0.53)
-0.121
(-0.99)
-0.0380
(-0.55)
Index of
human capital
per person
(Log)
-0.756
(-1.14)
-0.0911
(-0.17)
2.286
(1.47)
0.942
(0.95)
0.835
(0.82)
1.048*
(1.74)
Share of
Government
Consumption
-1.722**
(-1.97)
-0.287
(-0.61)
-0.444
(-0.69)
-0.561
(-1.50)
-1.108
(-1.64)
-0.327
(-0.79)
Population
growth (annual
%)
-0.0411*
(-1.66)
-0.0112
(-0.64)
-0.00980
(-0.26)
-0.00161
(-0.11)
-0.0399
(-1.34)
-0.0222*
(-1.69)
Services per
capita (% of
GDP)
-0.00545
(-0.13)
-0.00182
(-0.03)
0.0972*
(1.89)
0.0401
(1.04)
0.0673
(1.09)
0.0431
(1.01)
Employment to
population
ratio, 15+, total
(%) (national
estimate)
-0.00515
(-0.79)
-0.00183
(-0.72)
-0.00368
(-0.56)
-0.00576
(-1.40)
0.00617
(1.09)
0.000102
(0.02)
Country Fixed Yes Yes Yes Yes Yes Yes

27
Effects
Observations 502 444 209 197 278 255
R2 overall 0.0403 0.167 0.0433 0.000163 0.00318 0.00781
R2 between 0.000164 0.0887 0.0153 0.00104 0.0127 0.00819
R2 within 0.662 0.558 0.382 0.351 0.540 0.543
# of countries
in regression
26 23 24 23 25 23
* p < 0.1, ** p < 0.05, *** p < 0.01
4 Discussion
4.1 Reverse Causality –Innovation on Income Inequality OLS Regression and
Panel VAR
It isworthwhile toinvestigatethe regressionof innovationonincome inequality inordertotruly
investigateSchumpeteriangrowththeory.Accordingtotheory,highertopincome inequalityshouldlead
to greaterinnovative activitysince inventors receive largerinnovationrentsandthus have a greater
incentive toinnovate. If this isthe case then the coefficientonpatentsgrantedin ourregressionsontop
income inequalitymeasures inSection3.3.1can likelysufferfromendogeneitycaused byreverse
causalitysince patentsgrantedisameasure of innovative activity. The regressionsonbooktomarketin
Section3.3.2 avoid some of this problembecause booktomarketis a measure of the value of innovative
activitiesratherthaninnovative activity.
Table 10 displaysthe resultswhenpatentsgrantedisregressedonincome inequalitymeasures. An
increase inthe top10% share of income significantlyincreasespatentsgrantedasshownin (3) of Table
10. Thisresultsuggeststhathighertop 10% share of income increasesinnovativeactivity.However,the
mostsignificantresultfromthe table isthatR&D expenditure percapitahasa significantlystrong
positive effectonpatentsgranted whichremainsrobustafteraccountingfor countryfixedeffects.

28
Reverse Causality – Patents Granted on Top Income Inequality
1999-2010
(1) (2) (3) (4) (5) (6)
Patents
Granted
(Log)
Patents
Granted
(Log)
Patents
Granted
(Log)
Patents
Granted
(Log)
Patents
Granted
(Log)
Patents
Granted
(Log)
Top 1% Share
of Income
(Log)
0.950
(1.55)
0.157
(0.69)
Top 10% Share
of Income
(Log)
2.817**
(2.43)
0.492
(0.96)
Pareto Lorenz
Coefficient
(log)
1.450
(0.94)
-0.137
(-0.55)
R&D per capita
expenditure
(Log)
2.138***
(3.00)
0.447
(1.33)
2.279***
(3.61)
0.779***
(2.77)
2.184***
(2.76)
0.562*
(1.68)
R&D
researchers
(Log)
-0.567
(-0.76)
0.443*
(1.73)
-1.058*
(-1.74)
0.334
(1.62)
-0.549
(-0.60)
0.413
(1.51)
r - g 0.00561
(0.32)
-0.00712*
(-1.71)
0.00865
(0.40)
-0.00606
(-0.94)
0.0266
(1.30)
-0.00836*
(-1.75)
K/Y (Log) 0.00361
(0.00)
0.986
(1.04)
0.471
(0.56)
0.522
(0.74)
-0.289
(-0.26)
1.325
(1.32)
Index of human
capital per
person (Log)
1.281
(0.39)
1.307
(0.43)
1.718
(0.62)
2.140
(0.56)
0.393
(0.11)
2.737
(0.80)
Share of
Government
Consumption
1.413
(0.30)
2.711
(1.06)
2.328
(0.54)
1.768
(0.69)
-5.480
(-0.97)
2.885
(0.86)
Population
growth (annual
%)
-0.270
(-0.96)
0.0272
(0.36)
-0.338*
(-1.83)
-0.00215
(-0.03)
-0.169
(-0.58)
0.0380
(0.46)
Services per -0.186*** -0.0567 -0.154*** -0.0490 -0.196*** -0.0676

29
capita (% of
GDP)
(-3.89) (-0.51) (-3.24) (-0.57) (-3.35) (-0.59)
Employment to
population
ratio, 15+, total
(%) (national
estimate)
-0.00384
(-0.13)
-0.0236
(-1.04)
0.0242
(0.70)
-0.0132
(-0.60)
-0.00461
(-0.12)
-0.0325
(-1.37)
Country Fixed
Effects
Observations 209 209 197 197 192 192
R2 0.925 0.930 0.923
R2 overall 0.793 0.707 0.806
R2 between 0.744 0.685 0.765
R2 within 0.642 0.764 0.651
# of countries
in regression
24 24 23 23 23 23
* p < 0.1, ** p < 0.05, *** p < 0.01
To furtherinvestigatethe causal relationshipbetween topincome inequality andinnovation the paper
conductstwo panel VARestimation;the firstbetweenthe top1% share of income and b/m, andthe
secondbetweenthe top10%share of income and b/m.The numberof lagschosenisfour,basedon
whatwas done inAtemsand Jones(2015). The resultsfromgrangercausalitytests showninFigure A1in
the appendix support whatwas foundinthe precedingregressions;increasesinthe top10% share of
income causesincreasesininnovation. Figure 5and Figure 6 show selected impulseresponse functions
fromboth multivariate regressions.

30
Figure 5 – Impulse Response Function: Impulse (Book to Market), Response (Top 1% Share of Income)
Figure 6 - Impulse Response Function: Impulse (Top 10% Share of Income), Response (Book to Market)
-2
-1.5
-1
-.5
0
0 5 10
bm : top1inc
IRF
step
impulse : response
-.005
0
0 5 10
top10inc : bm
IRF
step
impulse : response

31
4.2 Pre and Post Y2K Regressions – Evidence of a New Economy
So far the analysishasconsistentlygivendifferentresultsbetweenregressionswithtime spansstarting
fromand after1997 to 2010 and regressionswherethe time periodcoveredextendfromthe 1980s to
2010. Therefore thissectionfurtherexploresthe difference inresults.Table 11displaysresultsfor
regressionoverthe full period, pre-2000 andpost-2000.
The year 2000 can be consideredaturningpointineconomichistory.The endof the dot-combubble
had begunandit putout many incumbentsinthe relativelyyounginformationtechnology(IT) industry.
Between1990 and 2000 there washeavyinvestmentintothe ITindustrywhichledtoan IT stock bubble
whichiscommonlyreferred toasthe dot-combubble.8
The endof the dot-combubble resultedinless
incumbentfirmsinthe ITindustryandexcessITinfrastructure,whichgave rise tolowerentrybarriers
and thusincreasingcompetitionandinnovativeactivitiesinthe ITindustry. The post2000 IT industry
differsfromotherindustriesbecauseitrequireslittle capital,littlelabour,andhighabilityworkers(high
levelsof humancapital) forfirmstobe successful.The dot-combubbleenableddigitalizationwhichhas
transformed the advancedeconomiesintoadigital economy(Stiroh,2004). HémousandOlsen(2014)
suggestthatthe digital economycreateslargerhumancapital andinnovationrents resultinginhigher
income inequality.
Before 2000 innovationplaysnorole inexplainingincreases intopincome inequality andhumancapital
issignificantandnegativelycorrelatedwiththe top1% share of income asshowninTable 11. However
after2000 Table 11 showsthat innovationincreasestop1% and top10% share of income forGCI0
countries while humancapital becomesinsignificantwithapositivecoefficient.Table 11also showsthat
the coefficienton r-ghas become significantandpositiveforGCI0countriespost2000 whichgivessome
indicationforPiketty’shypothesis thatthe future of capitalismtendsto capital accumulationdriven
income inequality.The increasingsignificanceof innovationandhumancapital inTable 11 provides
evidence thatincreasesininnovationandhumancapital rents explainsrecenttrendsintopincome
inequality.
8 See “The Boom and Bust in Information Technology Investment” by Mark Doms. http://www.frbsf.org/economic-
research/files/er19-34bk.pdf.

32
Y2K Regressions: Book to Market pre and post 2000 – GCI0 is base
1980-2010 1980-2000 2000-2010
(1) (2) (3) (4) (5) (6)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Book to
Market (Log)
-0.0812
(-1.10)
-0.00325
(-0.14)
-0.0534
(-0.66)
0.00927
(0.26)
-0.116**
(-2.03)
-0.0465*
(-1.70)
GCI1 x Book
to Market
(Log)
0.0228
(0.29)
-0.0242
(-1.12)
0.0208
(0.22)
-0.0142
(-0.44)
0.0380
(0.51)
-0.00547
(-0.20)
GCI2 x Book
to Market
(Log)
0.100
(0.67)
-0.000403
(-0.00)
0.0941
(0.35)
0.0140
(0.14)
0.00673
(0.02)
-0.0175
(-0.15)
GCI3 x Book
to Market
(Log)
-0.0920
(-1.10)
0.0488
(1.34)
0.131
(0.94)
-0.0409
(-0.72)
0.0340
(0.31)
0.0986***
(2.58)
r - g 0.0102**
(2.38)
0.00288
(1.21)
0.00590
(0.97)
-0.00126
(-0.57)
0.00517
(1.02)
0.00670*
(1.74)
GCI1 x r - g -0.0144**
(-2.07)
-0.00281
(-0.79)
-0.0119
(-1.56)
-0.000886
(-0.28)
-0.0129
(-1.43)
-0.00878*
(-1.94)
GCI2 x r - g -0.0162
(-0.76)
-0.00102
(-0.06)
-0.0206
(-0.55)
-0.000654
(-0.04)
-0.00319
(-0.06)
-0.000171
(-0.01)
GCI3 x r - g -0.0110**
(-2.31)
-0.0000623
(-0.02)
-0.00789
(-0.99)
-0.000938
(-0.30)
-0.00707
(-1.46)
-0.00882***
(-2.86)
Index of
human capital
per person
(Log)
-1.210**
(-1.99)
-0.349
(-0.70)
-1.723**
(-2.42)
-0.572
(-1.51)
1.391
(0.91)
0.544
(0.73)
Share of
Government
Consumption
-1.344
(-1.59)
0.0252
(0.05)
-0.568
(-0.45)
0.338
(0.47)
-0.351
(-0.64)
-0.495
(-1.47)
Population
growth (annual
-0.0136
(-0.59)
-0.0115
(-0.59)
0.0154
(0.13)
-0.00795
(-0.22)
-0.00565
(-0.22)
-0.00421
(-0.53)

33
%)
Services per
capita (% of
GDP)
-0.0132
(-0.30)
-0.00716
(-0.12)
-0.00184
(-0.03)
0.00168
(0.04)
0.121**
(2.21)
0.0414*
(1.83)
Employment
to population
ratio, 15+,
total (%)
(national
estimate)
-0.00741
(-1.16)
-0.00374*
(-1.70)
-0.0106
(-1.27)
-0.00385
(-1.37)
0.000607
(0.12)
-0.00283
(-0.89)
Country Fixed
Effects
Yes Yes Yes Yes Yes Yes
Observations 433 408 271 256 183 172
R2 overall 0.606 0.0668 0.502 0.264 0.0470 0.171
R2 between 0.572 0.0191 0.263 0.212 0.0418 0.103
R2 within 0.692 0.567 0.589 0.479 0.435 0.391
# of countries
in regression
25 23 22 21 24 22
* p < 0.1, ** p < 0.05, *** p < 0.01
5 Conclusion
Thispaperhas analyzedthe effectof CSNI andinnovationon topincome inequality.The resultsshow
that neitherCSNI norinnovationaffects the restof the income distributionasmeasuredbythe Pareto
Lorenzcoefficient.Itisfoundthatinnovation issignificantinexplainingtopincome inequality where an
increase ininnovationincreasesthe top1% andtop 10% share of income aspredictedinSchumpeter’s
growthmodel;howeverthese resultsdonotholdforthe leastcompetitive economies. Furthermore
regressions predictthatincreasesinthe top10% share of income leadtoincreases ininnovativeactivity
whichfurthervalidatesSchumpeteriangrowththeory. Insupportof Pikettythe paperfinds evidence
that as an economybecomesmore competitive r-gbecomesmore significantinexplainingtopincome
inequality trends.Lastly,resultsindicate that humancapital rents mightplay asignificantrole in
explainingtopincome inequalitytrends inthe future.
The limitations realizedinthispaperare a few.The paperwas limited toarelativelysmall numberof
observationsforthe analysisbutthisissue wasmitigatedbyusingpairs-clusterbootstrapprocedure
whichhas betterfinite sample propertiesthanclusteredstandarderrors.Alsointermsof observations,
the paperwas limitedto27 countrieswithmostof the observationscomingfrom OECDcountriesin
Europe and NorthAmericaandthus presentingsignificantselectionbias,yetthe analysisislargelybased
on topincome inequalityof advancedeconomies andtherefore beingrestrictedtothese countriesis

34
acceptable.Lastlythe time spanpresented significantchallengeswhichthe paperattemptedto
overcome byintroducingbooktomarketas a measure of innovation.
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36
Appendix
Table A1
Tables Excluded Countries
Table 1 None
Table 2 None
Table 3 None
Table 4 (1)-(2) India
(3)-(4) Argentina, Columbia, India, Indonesia
(5)-(6) Argentina, Finland, India
Table 5 (1)-(2) India, Argentina
(5)-(6) Argentina, Finland, India
Table 6 (1)-(2) India, Argentina, Indonesia
(5)-(6) Argentina, Finland, India, Indonesia
Table 7 (1)-(2) India, Indonesia
Table 9 (1) India
(2) Argentina, Columbia, India, Indonesia
(3) India, Argentina, Indonesia
(5) India, Indonesia
Table 11 (1) India, Indonesia
(3) India, Indonesia, Canada, Columbia, China
(4) India, Argentina, Indonesia, Canada, Columbia,
(5) India, Indonesia, Portugal
(6) Argentina, Columbia, India, Indonesia, Portugal

37
Table A2
Innovation Measure: Patents Granted
1999-2014
(1) (2) (3) (4) (5) (6)
Top 1%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Pareto
Lorenz
Coefficient
(log)
Pareto
Lorenz
Coefficient
(log)
Patents
Granted (Log)
-0.00792
(-0.25)
0.0428
(0.71)
0.0317
(1.58)
0.0354
(0.88)
-0.00540
(-0.40)
-0.0320
(-1.02)
Country Fixed
Effects
Observations 303 303 284 284 276 276
R2 0.0320 0.113 0.0600
R2 overall 0.000542 0.110 0.0281
R2 between 0.000126 0.0913 0.000743
R2 within 0.150 0.130 0.233
# of countries
in regression
26 26 23 23 25 25
* p < 0.1, ** p < 0.05, *** p < 0.01
Table A3
Innovation Measure: Patents Granted
1999-2010
(1) (2) (3) (4) (5) (6)
Top 1%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Pareto
Lorenz
Coefficient
(log)
Pareto
Lorenz
Coefficient
(log)
Patents
Granted (Log)
-0.0120
(-0.29)
0.0495
(0.84)
0.0456*
(1.95)
0.0507
(1.30)
-0.00219
(-0.12)
-0.0266
(-0.75)
r - g 0.0199*
(1.89)
-0.00269
(-0.98)
0.00872
(1.13)
0.000323
(0.14)
-0.00735
(-1.10)
0.00171*
(1.68)
K/Y (Log) -0.140
(-0.59)
-0.161
(-0.45)
-0.243
(-1.11)
-0.0480
(-0.26)
0.123
(1.00)
0.0507
(0.22)
Country Fixed
Effects
Observations 229 229 210 210 211 211
R2 0.131 0.248 0.103

38
R2 overall 0.000312 0.156 0.00727
R2 between 0.0000748 0.103 0.00183
R2 within 0.164 0.152 0.223
# of countries
in regression
26 26 23 23 25 25
* p < 0.1, ** p < 0.05, *** p < 0.01
Table A4
Innovation Measure: Book to Market
1980-2014
(1) (2) (3) (4) (5) (6)
Top 1%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Pareto
Lorenz
Coefficient
(log)
Pareto
Lorenz
Coefficient
(log)
Book to
Market (Log)
-0.0395
(-0.46)
-0.0139
(-0.51)
-0.101*
(-1.90)
-0.00175
(-0.14)
0.0150
(0.42)
0.00322
(0.16)
Country Fixed
Effects
Observations 685 685 640 640 621 621
R2 0.225 0.256 0.302
R2 overall 0.216 0.0911 0.295
R2 between 0.128 0.00617 0.0537
R2 within 0.225 0.525 0.426 0.535
# of countries
in regression
25 25 23 23 23 23
* p < 0.1, ** p < 0.05, *** p < 0.01

39
Table A5
Innovation Measure: Book to Market
1980-2010
(1) (2) (3) (4) (5) (6)
Top 1%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Pareto
Lorenz
Coefficient
(log)
Pareto
Lorenz
Coefficient
(log)
Book to
Market (Log)
-0.121**
(-2.18)
-0.0251
(-0.81)
-0.107*
(-1.91)
-0.00251
(-0.18)
0.0143
(0.43)
0.00401
(0.24)
r - g 0.00489
(0.77)
-0.00121
(-0.32)
0.00334
(0.68)
0.000753
(0.38)
0.00150
(0.34)
0.00239
(0.97)
Country Fixed
Effects
Observations 595 595 570 570 550 550
R2 0.351 0.268 0.324
R2 overall 0.294 0.0835 0.319
R2 between 0.164 0.000296 0.0331
R2 within 0.591 0.511 0.574
# of countries
in regression
25 25 23 23 23 23
* p < 0.1, ** p < 0.05, *** p < 0.01
Table A6
Global Competitive Index Interaction Results – GCI0 is base
Piketty: r>g, K/Y
1980-2010
Patents Granted
1999-2010
R&D: Expenditure
1996-2010
(1) (2) (3) (4) (5) (6)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
r - g 0.00770
(0.95)
-0.00149
(-0.30)
0.0179*
(1.85)
0.00484
(0.73)
0.0184
(1.39)
0.00694
(0.91)
GCI1 x r - g -0.00442
(-0.42)
0.00360
(0.57)
-0.0187
(-1.46)
-0.00863
(-0.96)
-0.0230
(-1.63)
-0.00849
(-1.08)
GCI2 x r - g -0.000829
(-0.08)
0.000689
(0.09)
-0.00864
(-0.26)
-0.00378
(-0.30)
-0.0155
(-0.95)
-0.00931
(-0.86)

40
GCI3 x r - g -0.00816
(-0.94)
0.00431
(0.64)
-0.0174
(-1.26)
-0.0155*
(-1.82)
-0.0140
(-0.95)
-0.00329
(-0.33)
Patents
Granted (Log)
0.101
(0.86)
0.115**
(2.35)
GCI1 x Patents
Granted (Log)
0.0126
(0.12)
0.0545
(1.02)
GCI2 x Patents
Granted (Log)
-0.00923
(-0.05)
-0.0609
(-0.81)
GCI3 x Patents
Granted (Log)
-0.142
(-0.89)
-0.229**
(-2.52)
R&D per
capita
expenditure
(Log)
-0.234
(-0.85)
-0.271**
(-1.98)
-0.0521
(-0.30)
-0.103
(-0.78)
GCI1 x R&D
per capita
expenditure
(Log)
0.00147
(0.08)
-0.00572
(-0.60)
GCI2 x R&D
per capita
expenditure
(Log)
-0.0257
(-0.68)
-0.00836
(-0.39)
GCI3 x R&D
per capita
expenditure
(Log)
0.0259
(0.50)
0.0412
(1.19)
K/Y (Log) -0.0854
(-0.46)
-0.00443
(-0.03)
-0.121
(-0.38)
0.0256
(0.13)
-0.0316
(-0.13)
-0.117
(-0.58)
GCI1 x K/Y
(Log)
-0.0255
(-0.19)
-0.0882
(-1.07)
GCI2 x K/Y
(Log)
-0.275*
(-1.73)
-0.181**
(-2.17)
GCI3 x K/Y 0.186 0.328

41
(Log) (0.92) (1.14)
R&D
researchers
(Log)
0.188
(0.65)
0.155
(1.31)
0.0935
(0.48)
0.227*
(1.79)
Index of
human capital
per person
(Log)
-0.913**
(-2.16)
-0.304
(-0.79)
-0.601
(-0.47)
0.00836
(0.02)
-0.857
(-0.77)
-0.278
(-0.44)
Share of
Government
Consumption
-4.068***
(-3.06)
-1.684**
(-2.07)
-3.354*
(-1.93)
-1.339
(-1.63)
-4.414**
(-2.32)
-1.792
(-1.62)
Population
growth (annual
%)
0.0276
(0.47)
-0.0221
(-0.46)
0.137*
(1.87)
0.0605
(1.62)
0.0564
(0.91)
0.0311
(0.65)
Services per
capita (% of
GDP)
-0.00345
(-0.33)
-0.00414
(-0.61)
0.0110
(0.32)
0.0195
(1.22)
-0.00774
(-0.53)
-0.0131
(-1.37)
Employment to
population
ratio, 15+, total
(%) (national
estimate)
-0.00971*
(-1.74)
-0.00850**
(-2.26)
-0.00523
(-0.57)
-0.00649
(-1.32)
-0.00746
(-0.81)
-0.00715
(-1.24)
Country Fixed
Effects
No No No No No No
Observations 502 444 209 197 278 255
R2 0.783 0.717 0.695 0.839 0.686 0.726
# of countries
in regression
26 23 24 23 25 23
* p < 0.1, ** p < 0.05, *** p < 0.01

42
Figure A1 – Granger Causality Tables
ALL 14.438 4 0.006
bm 14.438 4 0.006
top1inc
ALL 6.120 4 0.190
top1inc 6.120 4 0.190
bm
Equation Excluded chi2 df Prob > chi2
ALL 6.517 4 0.164
bm 6.517 4 0.164
top10inc
ALL 11.512 4 0.021
top10inc 11.512 4 0.021
bm
Equation Excluded chi2 df Prob > chi2

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