Panel cointegration techniques applied to pooled data for 27 economies forthe period 1960-2000 indicate that: i) government spending in education andinnovation indicators are cointegrated; ii) education hierarchy is relevant whenexplaining innovation; and iii) the relation between education and innovation canbe obtained after an accommodation of a level structural break.

Joiozo, Renato

Kuhl Teles, Vladimir

English

Research Papers in Economics

 
HUMAN CAPITAL AND INNOVATION: 
EVIDENCE FROM PANEL 
COINTEGRATION TESTS 
RENATO JOIOZO 
VLADIMIR KUHL TELES
Fevereiro  
de 2010 
T Te ex xt to os s   p pa ar ra a   
D Di is sc cu us ss sã ão o   
245  
TEXTO PARA DISCUSSÃO 245 • FEVEREIRO DE 2010 •   1 
Os artigos dos Textos para Discussão da Escola de Economia de São Paulo da Fundação Getulio 
Vargas são de inteira responsabilidade dos autores e não refletem necessariamente a opinião da 
FGV-EESP. É permitida a reprodução total ou parcial dos artigos, desde que creditada a fonte. 
 
Escola de Economia de São Paulo da Fundação Getulio Vargas FGV-EESP 
www.fgvsp.br/economia 
 
 
 
 
   Human Capital and Innovation: Evidence from Panel
Cointegration Tests
Vladimir K￿ uhl Teles
Getulio Vargas Foundation*
E-mail: vkteles@fas.harvard.edu
and
Renato Joiozo
Getulio Vargas Foundation
E-mail: renato@descarpack.com.br
Panel cointegration techniques applied to pooled data for 27 economies for
the period 1960-2000 indicate that: i) government spending in education and
innovation indicators are cointegrated; ii) education hierarchy is relevant when
explaining innovation; and iii) the relation between education and innovation can
be obtained after an accommodation of a level structural break.
Key Words: Human Capital, Innovation, Panel Cointegration.
JEL Classi￿cation: O31, O50
1. INTRODUCTION
Endogenousgrowththeoriesregardendogenoustechnologicchangeasthecauseof
global growth patterns. According to the seminal paper of Nelson and Phelps (1966)
describes growth as being driven by the stock of human capital, which in turn affects
a country’s ability to innovate or catch up with more advanced countries.
However, this approach raises several empirical problems. First, as pointed out by
Krueger and Lindahl (2001), the correlation between growth and education ceases to
be signi￿cant once we restrict the analysis to OECD countries. Second, the positive
*Getulio Vargas Foundation, Sao Paulo School of Economics (EESP-FGV), Rua Itapeva, 474, 12o.
andar, 01332-000, Sao Paulo-SP, Brazil
12
correlations between education measured by stocks and growth found in the overall
country samples, may re￿ect reverse causalities from growth to education (Bils and
Klenow (2000)). Third, all correlations become insigni￿cant once one controls for
country ￿xed effects. (Aghion and Howitt, 2008)
Recently, Vandenbussche, Aghion and Meghir (2006), shortly VAM, addressed
these questions suggesting that only the tertiary educational level encourages inno-
vation, whereas the secondary fosters only the imitation of new technologies - such
scenario would be an attribute of countries that are far away from the technological
frontier. VAMmakeclearthattherelationshipbetweeninnovationandhumancapital
is neither trivial nor obvious.
The main contribution of this paper is to provide new empirical evidences about
the long-term relationship between human capital and innovation. We use new panel
cointegration tests, avoiding the reverse causality and ￿xed effects criticisms, and
we employ several alternative measures of human capital to shed light on the links
between such variables. Stock and ￿ow variables of human capital will be used in
additiontovariablesfromthedifferenteducationalhierarchiesandpublicinvestments,
testing VAM hypothesis extensively.
Independently of how they are measured, innovation and human capital tend to
be non-stationary. Due to such characteristic the use of cointegration techniques
becomes necessary for the analysis of any long-term stable relationship. However,
the restricted size of the time series limits the cointegration test’s power leading to
partial and/or ambiguous conclusions.
As consequence, this paper seeks to increase the power of the tests while compar-
ing results between two databases for the period 1963-2000: 27 countries from the
Barro and Lee (2001) database, and of 24 countries from the Cohen and Soto (2007)
database. To use the data in the most ef￿cient manner, we propose tests which the
null hypothesis of cointegration (McCoskey and Kao (1998) test), and extensions
with structural breaks (Westerlund (2006) test). Given that innovation is probable
caused by several factors, in addition to human capital, the structural breaks should,
if they happen, incorporate these factors as well as exogenous shocks.HUMAN CAPITAL AND INNOVATION 3
The paper is organized as follows. Section 2 introduces the econometric method-
ology. Section 3 presents the results; and Section 4 addresses ￿nal considerations.
2. EMPIRICAL METHODOLOGY
Nelson and Phelps (1966) provide a framework which implies a simple long-run
relationship between education and innovation, translated into a testable model:
Iit = ai + biHit + "it (1)
where I is the innovations of new goods, and H is the required human capital for
innovation. For a sustainable relationship between H and I, " should be stationary,
meaning that innovations are cointegrated with human capital. The basic hypothesis
of VAM is that only tertiary education has a sustainable relation with innovation. So,
if they are correct, tertiary education is cointegrated with innovation, while primary
and secondary education are not.
TherearenumerouscointegrationtestssuchasEngleandGranger(1987),Johansen
(1991)andPhilipsandOuliaris(1990)documentedinthetimeseriesliterature. How-
ever, these tests fail to take advantage of information across countries, which leads to
lossofef￿ciencyinestimation. Recently,severalauthorssuchasPedroni(1995),Kao
andChiang(1998)andKao(1999)havedevotedtheireffortstodevelopcointegration
tests with panel data. In this article, we employ the LM cointegration tests proposed
by McCoskey and Kao (1998) and Westerlund (2006) to test whether a cointegration
exists in the estimated equations. Instead of non-cointegration, the null hypothesis is
meanttoreadcointegrationinthesetests. Thisisacrucialdifferenceandanimportant
advancement in relation to the tests previously proposed given that it addresses the
critique of the non-cointegration null hypothesis being frequently rejected only if a
subset of series in the panel is cointegrated. Consequently, the LM test is the most
powerful test of cointegration in panel data, as demonstrated by McCoskey and Kao
(1999) who conducted simulations for Monte Carlo.
The residual-based test for null of cointegration in panel data proposed by Mc-
Coskey and Kao (1998) is an extension of the LM and LBI tests for an MA unit
root in the time series literature. For this test, an ef￿cient estimation technique of4
cointegrated variables becomes necessary, thus, we use the fully modi￿ed ordinary
least squares (FMOLS) given that it corrects for the possible serial correlation and
weakly exogenous regressors in a cointegrated regression.
The test considers the ￿xed effects estimation of (1) where Hit = Hit¡1 + uit;
"it = °it+²it; °it = °it¡1+µ²it; and Iit;Hit are independent across cross-sectional
units. Therefore, the null of hypothesis of cointegration is equivalent to µ = 0.
McCoskey and Kao (1998) derive the limiting distribution for the LM statistic fully
modi￿ed (FM) estimators in a cointegrated regression and next show that it is free of
nuisanceandrobusttoheteroskedasticity. ThedetaileddescriptionoftheLMstatistic
can be found in McCoskey and Kao (1998, 1999) and is not reported here.
Westerlund (2006) extended the McCoskey and Kao test allowing for multiple
structural breaks in both the level and trend of a cointegrated panel regression. The
breaks are determined endogenously by globally minimizing the sum of squared
residuals following Bai and Perron (1998, 2003).
Using sequential limit arguments, it is shown that the test has a limiting normal
distribution, i.e. free of nuisance parameters under the null hypothesis. In particular,
it is shown that the limiting distribution is invariant with respect to both the number
and locations of breaks and that the computation of different critical values for all
possible patterns of break points is not necessary.
Giventhattechnologicalinnovationislikelytobecausednotexclusivelybyhuman
capital but by innovation exogenous shocks and other factors, as fully documented in
the related literature, structural breaks are expected in the human capital and innova-
tion relationship. Therefore, the Westerlund test allows for the investigation of this
long-term relationship in a more precise way.
3. EMPIRICAL INVESTIGATION
3.1. Data Description
This section presents the main features of our data-set. We use a panel data of 27
countries for the period 1963-2000. The 27 countries1 were chosen through a unit
1The countries are:, Australia, Austria, Belgium, Brazil, Canada, China, Denmark, England, Fin-
land, France, Holland, Hon Kong, India, Ireland, Israel, Italy, Japan, Malaysia, Mexico, Norway, New
Zeeland, Russia, South Africa, Spain, Switzerland, and Sweden.HUMAN CAPITAL AND INNOVATION 5
root test for each innovation serie of each country. Only countries which the series
were not stationary were kept, given that an existing stationarity could disqualify
the cointegration tests and countries with a nearly inactive innovatory section would
eventually be included.
As a measure of the innovative output within a region we use the count of granted
patentapplicationsthatinventorsresidingineachregion￿ledwiththeU.S.Patentand
Trademark Of￿ce between 1963 and 2000. As it is generally done in this literature
(see Jaffe et al., 1993), each patent is attributed to the ￿rst inventor listed in the patent
application. Patents have long been considered, not without controversy, as the best
measure of output of innovative activity. Although not all inventions are patented,
the ones that are patented have to meet minimal standards of novelty, originality and
potential use. Therefore patents can be considered a good proxy of "economically
pro￿table ideas" for testing theories on innovation (Bottazi and Peri, 2003).
Additionally, three different types of measures of human capital are used. The
￿rst measure is given by per capita public spending in education (GGEDUC). Such
variable is given as follows:
GGEDUC = GEPERC ¤ RGDPL ¤ G (2)
where, GEPERC corresponds to the percentage of the government’s expenditure
in education in relation to the total government’s expenditure (source:UNESCO),
RGDPL corresponds to the real GDP per capita (constant prices: Laspeyres) ad-
justed by PPP(base year 2000, source: Penn World Tables 6.1) and G is the total
government expenditure as a percentage of the GDP (source: Penn World Tables
6.1).
The second measure of human capital corresponds to the population’s average
number of study years (AV GSCH). The third human capital measurement, as
suggested by VAM, seek to evaluate which type of human capital, in relation to the
hierarchy,mattersforinnovation. Suchmeasurementsaretheaverageyearsoftertiary
(yearsT)andprimary-secondary(yearsPS)education. Thelatterwerederivedfrom
the Barro and Lee (2001) and Cohen and Soto (2007) databases. The Barro and Lee
database is the most used in studies that perform comparisons among countries. The6
Cohen and Soto database has recently come out as an alternative to the Barro and Lee
database since it improves data quality using surveys based on uniform classi￿cation
systems of education over time, and intensi￿es the use of information by age groups.
There are seven categories of schooling in these databases, so we can de￿ne yearsT
and yearsPS as:
yearsT = (p6 + p7)n6 + p7n7 (3)
and
yearsPS =
5 X
i=1
(
7 X
j=i
pi)ni (4)
wherepi isthefractionofthepopulationwhichhavereachedtheleveliandni isthe
numberofextrayearsofeducationwhichtheindividualinthecategoryihasaccumu-
latedmorethananindividualinacategory(i¡1). Wehave(n1;n2;n3;n4;n5;n6;n7)
= (0, 3, 3, 3, 3, 2, 2). The variable yearsT represents the average number of years
of education of the tertiary level of the average adult population. Based on these
premises, a college graduation contributes with 12 years in the variable yearsPS
and 4 years in the variable yearsT.
3.2. Empirical Results
The cointegration tests include trend and constants for all the cases. The lags were
selected using the Akaike Information Criteria (AIC). The existence of a level break
was allowed for the Westerlund test. The results of the tests are presented in Table 1.
The results make clear that it is not possible to reject a long-term relationship
between human capital and innovation given that in all variables of human capital the
cointegration relationship was obtained. Such results strongly support the hypothesis
of the endogenous growth models which state that a public policy toward investment
inhumancapitalincreasesinnovationsandthereforetheeconomicgrowthinthelong
run.HUMAN CAPITAL AND INNOVATION 7
TABLE 1.
LM Cointegration Tests
McCoskey and Kao Westerlund
GGPEDUC 8.06* 3.56
Barro and Lee (2001) Cohen and Soto (2007)
McCoskey and Kao Westerlund McCoskey and Kao Westerlund
AVGSCH 11.35* 2.39 18.80* 1.42
YEARST 7.05* 1.94 2.66 1.69
YEARSPS 8.56* 2.34 17.92* 8.76*
(*) null hypothesis of cointegration rejected at 1%
Additionally, all the variables tested were relevant in explaining innovation. As a
result we might suppose that public spending in education is productive in the long
run, spurs innovation, technical progress, and growth.
There is no indication, from the Barro-Lee database, which sustains the relevance
of the educational hierarchy, in opposition to the conclusion obtained by VAM. VAM
anticipated that the tertiary education is the driving force of innovations. However,
when the Cohen-Soto database is used the results are different from the scenario
describedabove. Theresultsobtainedwiththisdatabase(consideredabetterdatabase
in relation to Barro-Lee) leans toward the positive effect of the innovation hierarchy,
meaning that this is indeed a relevant factor when determining innovations. Such
resultsareobtainedthroughtheLMtestwithbreaks,ofWesterlund,wherethetertiary
education is very signi￿cant when determining innovation while the primary and
secondary educational levels are irrelevant.
The traditional McCoskey and Kao test does not provide evidence that there is
a cointegration relationship between human capital and innovation for the majority
of the variables. That is so because, at 1% level of signi￿cance, the cointegration
hypothesis is signi￿cantly rejected for all the variables, except for the tertiary study,
from the Cohen-Soho database.
However, the Westerlund test, which includes a structural break at the intercept,
brings up an opposing conclusion. In this case, the cointegration can not be rejected
inalmostall thecasesif onlythe caseofprimary andsecondary studiesofthe Cohen-8
Soto database is excluded. This result strongly suggests structural breaks in the
innovation series which might be caused by different reasons such as global trade
openness, exogenous technological and patent legislation shocks. As anticipated by
Westerlund, the use of series longer than previous studies should have included break
periods. The expanded period, aiming to increase the power of the tests, increases
the possibility of having more errors as consequence of breaks, such trade-off is
satisfactory resolved by this test.
4. CONCLUSION
Thegoalofthisstudyistoevaluatethecontributionofthehumancapitaltotechno-
logicalinnovation. Thisrelationshipiskeywithinseveralendogenousgrowthmodels
where the human capital, being the driving force behind innovation, is essential to
the economic growth and should be prioritized by sustainable growth policies.
Tothebestofourknowledge,thisisthe￿rststudythatusescointegrationtechniques
withpaneldataaimingtoinvestigatethisrelationandtheimportanceoftheeducational
hierarchy for innovation. Furthermore, the study uses a cointegration panel test
recently developed allowing for structural breaks (Westerlund test) and an up-to-date
re￿ned human capital database (Cohen and Soto database). These two applications
substantially alter the results and are relevant for the question under analysis.
The results signi￿cantly show that there is a clear long-run relationship between
humancapitalstockandthequantityofinnovation-thatisso,onlyifthepossibilityof
abreaklevelisconsidered. Thisrelationshipisre￿ectedonthecointegrationbetween
thenumberofpatentsandpublicspendingineducationandinthepopulation’saverage
yearsofeducation. Moreover,becauseofthepeculiaritiesoftheCohen-Sotodatabase,
wecansaythatonlythetertiaryeducationcointegrateswithinnovation,thesamedoes
not hold true for the primary and secondary educational levels as predicted by VAM.
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Human capital and innovation: evidence from panel cointegration tests

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Human capital and innovation: evidence from panel cointegration tests

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