The literature on endogenous growth cycles predicts countercyclical R&D expenditure. Aggregate R&D expenditure in G7 countries from 1973 to 1997 seems to be procyclical. Implications for future theoretical research are discussed. --Cyclical Properties of R&D Expenditure,Growth Cycles

Woitek, Ulrich

Wälde, Klaus

English

Research Papers in Economics

econstor
www.econstor.eu
Der Open-Access-Publikationsserver der ZBW – Leibniz-Informationszentrum Wirtschaft
The Open Access Publication Server of the ZBW – Leibniz Information Centre for Economics
Nutzungsbedingungen:
Die ZBW räumt Ihnen als Nutzerin/Nutzer das unentgeltliche,
räumlich unbeschränkte und zeitlich auf die Dauer des Schutzrechts
beschränkte einfache Recht ein, das ausgewählte Werk im Rahmen
der unter
→  http://www.econstor.eu/dspace/Nutzungsbedingungen
nachzulesenden vollständigen Nutzungsbedingungen zu
vervielfältigen, mit denen die Nutzerin/der Nutzer sich durch die
erste Nutzung einverstanden erklärt.
Terms of use:
The ZBW grants you, the user, the non-exclusive right to use
the selected work free of charge, territorially unrestricted and
within the time limit of the term of the property rights according
to the terms specified at
→  http://www.econstor.eu/dspace/Nutzungsbedingungen
By the first use of the selected work the user agrees and
declares to comply with these terms of use.
zbw
Leibniz-Informationszentrum Wirtschaft
Leibniz Information Centre for Economics
Wälde, Klaus; Woitek, Ulrich
Working Paper
R&D expenditure in G7 countries
and implications for endogenous
fluctuations and growth
Dresden discussion paper in economics, No. 03/03
Provided in cooperation with:
Technische Universität Dresden
Suggested citation: Wälde, Klaus; Woitek, Ulrich (2003) : R&D expenditure in G7 countries and
implications for endogenous fluctuations and growth, Dresden discussion paper in economics,
No. 03/03, http://hdl.handle.net/10419/48118Dresden University of Technology 
Faculty of Business Management and Economics 
 
 
 
 
 
 
 Dresden Discussion Paper Series  
 in Economics 
 
 
 
 
 
 
R&D expenditure in G7 countries and 
the implications for endogenous 
fluctuations and growth 
 
 
 
 
 
KLAUS WÄLDE 
ULRICH WOITEK 
 
 
 
 
 
Dresden Discussion Paper in Economics No. 3/03 
 
 
 
 
 
 
 
 
 
 
 
ISSN 0945-4829  
Address of the author(s): 
 
 
Klaus Wälde 
Technical University Dresden, 
Department of Economics 
Mommsenstr. 13 
01062 Dresden 
Germany 
 
e-mail : waelde@iwb-dresden.de 
 
 
Ulrich Woitek 
University of Munich, 
Department of Economics 
80539 Munich 
Germany 
 
e-mail : u.woitek@lrz.uni-muenchen.de 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Editors: 
Faculty of Business Management and Economics, Department of Economics 
 
Internet: 
An electronic version of the paper may be downloaded from the homepage: 
http://rcswww.urz.tu-dresden.de/wpeconomics/index.htm 
English papers are also available from the SSRN website: 
http://www.ssrn.com 
 
 
Working paper coordinators: 
Michael Berlemann 
Oliver Greßmann 
e-mail: wpeconomics@mailbox.tu-dresden.de  
 
 
Dresden Discussion Paper in Economics No. 3/03 
 
 
 
 
 
 
 
R&D expenditure in G7 countries and 
the implications for endogenous 
fluctuations and growth 
 
 
 
  Klaus Wälde  Ulrich Woitek 
  Technical University Dresden  University of Munich   
  Department of Economics  Department of Economics 
  01062 Dresden  80539 Munich 
  waelde@iwb-dresden.de  u.woitek@lrz.uni-muenchen.de 
 
 
 
 
 
Abstract: 
The literature on endogenous growth cycles predicts countercyclical R&D expenditure. Aggregate R&D expenditure in 
G7 countries from 1973 to 1997 seems to be procyclical. Implications for future theoretical research are discussed. 
 
 
JEL-Classification: C2, E32, O41 
 
Keywords: Cyclical Properties of R&D Expenditure; Growth Cycles 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
We are grateful to Gunter Coenen, Antonio Fatas, Gerhard Glomm, Alfred Mausner, Bernd Süssmuth and Mark Weder 
for many constructive comments and discussions. 1 Introduction
Growth rates of virtually all economic variables are time varying. The rea-
sons for these ﬂuctuations are manyfold. Some of the factors implying non-
constant growth rates can reasonably be considered to be exogenous. Oil
price shocks, shifts in preferences or sometimes also shifts in expectations
can be put in this category. However, ﬂuctuations can also be the result of
events that are endogenous to an economy. As Bental and Peled (1996), Mat-
suyama (1999, 2001) and Wälde (1999, 2002) have stressed, intentional R&D
by proﬁt maximizing ﬁrms can not only be the source for positive long-run
growth rates but also for short-run ﬂuctuations.
Models presented by these authors combine capital accumulation with
R&D. Generally speaking, these model economies feature a growth path on
which periods of high growth are followed by periods of low growth and vice
versa. Similarily, periods of high innovative activity are followed by periods of
low innovative activity. Whereas in Matsuyama’s work, a temporary patent
protection for innovators leads to a synchronization of innovative activity, it
is relative returns for investors in Bental and Peled and Wälde’s approach
that coordinates investment decisions.
A common prediction of these models is a countercyclical allocation of
resources to R&D.2 In periods of high returns to capital (or no patent protec-
tion) and high growth of GDP, few resources are allocated to R&D. With low
returns to capital (or temporary patent protection) and low growth, resource
allocation to R&D is high. Empirically speaking, resource allocation to R&D
should be negatively correlated with growth rates of GDP. The present paper
t e s t st h i sp r e d i c t i o n .
A widely used technique to do this is to detrend the data using the
Hodrick-Prescott (HP) ﬁlter and calculate descriptive statistics based on the
ﬁltered series. However, it is well known that the HP ﬁlter can create spuri-
ous cyclical structure and spurious correlations (Harvey and Jaeger, 1993) if
the series is I(0). Given the limited reliability of unit-root tests (e.g. Banerjee
et al. 1993) to discriminate between I(0) and I(1) series, and the shortness
of our time series, we adopt the approach proposed by Canova (1998). We
compare the outcome for ﬁltering techniques with diﬀerent implications for
the nature of the non-stationarity, and judge the robustness of our results by
comparing the outcome.
Evidence on cyclical behaviour of R&D is provided by Saint-Paul (1993)
and Geroski and Walters (1995). Saint-Paul uses a VAR approach and ﬁnds
2Francois and Lloyd-Ellis (forthcoming) obtain this result in a multi-sector economy
without capital accumulation.
2”very little evidence of any pro- or countercyclical behavior” of R&D activity
(measured by R&D expenditure). Geroski and Walters, using UK innova-
tions and patents data, ﬁnd some procyclical behavior of these quantities
(correlation coeﬃcients of .62 and .23, respectively). Fatas (2000) provides a
plot of growth rates of R&D expenditure and GDP for the USA from 1961 to
1996 and argues that they are procyclical. We complement these studies by
being more comprehensive both with respect to countries and years covered,
by using diﬀerent data and methods and by stressing the link to theories of
endogenous ﬂuctuations and growth.
2R e s u l t s
2.1 The data and descriptive statistics
We study G7 countries and use annual data for the period from 1973 - 1997.
This time period and frequency was largely dictated by the availability of data
on R&D. Data on industrial R&D expenditure in current national prices are
from OECD (1997, 2000a). GDP data in current national prices and price
deﬂators used to express time series in 1995 prices are from OECD (2000b).
R&D and GDP data were divided by total employment (OECD, 2000b) and
the resulting per-capita (i.e. per number of workers) series were used for all
computations.
Time series for Germany cover West Germany only. As data for West
Germany was not available up to 1997, data for West Germany for 1991
to 1997 were constructed by multiplying data for Germany as a whole by
the 1991 - 1994 average ratio of West German to German data. As results
obtained for Germany do not qualitatively diﬀer from results for other G7
countries, these added observation errors do not seem to be substantial.
The annual (unweighted) average growth rate of real per-capita GDP
of G7 countries amounts to 1.6%, ranging from .9% for Canada and 2.3%
for Japan. The annual (unweighted) average growth rate of real per-capita
R&D expenditure of G7 countries lies at 3.1% with extreme values at 1.3%
for the UK and 4.7% for Japan and Canada. In each country, except the
UK, the growth rate of R&D expenditure lies above the growth rate of GDP.
The ratio of R&D expenditure to GDP in 1997 lies between .5% in Italy and
2.1% in Japan. Volatility (measured by the standard deviation of the cyclical
component of a time series detrended by the HP ﬁlter with a smoothing
parameter λ =1 0 0 ) of R&D exceeds volatility of GDP by a factor between
approx. 2.1 for the UK and 5.6 for Canada. Due to this much higher volatility
(unweighted average 3.6), R&D expenditure can be considered to behave very
3similarily to ”normal” investment.
2.2 Cross correlations
The trend and cyclical components of a time series were obtained by applying
the various ﬁlters on logarithms of per-capita GDP and per-capita R&D data.
We use the HP ﬁlter, a modiﬁed Baxter-King ﬁlter (BKM), the Christiano-
Fitzgerald ﬁlter (CF) and a diﬀerence ﬁlter (DF), i.e. we computed growth
rates by computing the diﬀerences of logs of time series. The smoothing
parameter for the HP ﬁlter was λ =1 0 0and λ =6 .25 (suggested recently by
Ravn and Uhlig, 2002), the modiﬁed Baxter-King (1999) ﬁlter has a cut-oﬀ
frequency of 15 years, the ﬁlter length is 3. The undesireable sidelobes in the
gain function are taken care of by the so called Lanczos’s σ-factors (as used
in A’Hearn and Woitek, 2001). For the Christiano-Fitzgerald (1999) ﬁlter,
the cut-oﬀ frequency is again 15 years.
Correlation coeﬃcients and corresponding t− and p−values were obtained
by regressing centered cyclical components of per-capita R&D data on cen-
tered cyclical components of per-capita GDP, correcting standard errors for
heteroskedasticity and autocorrelation (Newey and West, 1987). Results on
contemporanous correlations are presented in table 1.3
One, two and three asterisks indicate signiﬁcance at the ten, ﬁve and
one percent level, respectively. The overall impression is one of a positive
correlation. 25 out of 35 estimates are positive, of which 15 are signiﬁcant.
Negative correlation coeﬃcients were estimated for Canada (4 out of 5), for
Italy (5 out of 5) and the U.S. (1 out of 5) of which only one is signiﬁcant.
Correlations for lagged R&D expenditure were computed as well and are
available upon request. No easily identiﬁable pattern emerged. We also
looked at the correlation between investment (gross ﬁxed capital formation
from OECD, 2001) and R&D expenditure.4 Here, only 4 out of 35 estimated
correlation coeﬃcients were negative, and none was signiﬁcant. By contrast,
9 positive correlation coeﬃcients were signiﬁcant. The hypothesis of a neg-
ative correlation between R&D expenditure and GDP (or investment) made
by theory is clearly rejected.
3A visual impression of the cyclicality of R&D expenditure can be gained from the plot
on the last page of this paper, obtained by using the HP ﬁlter with λ = 100.
4It could be argued that the most direct prediction of existing models of endogenous
ﬂuctuations is the switching allocation of savings to either capital accumulation or R&D
and hence their negative correlation. The negative correlation between R&D expenditure
and GDP is an immediate consequence.
4HP100 HP6.25 DF BKM CF
CAN ρ -0.028 -0.164 -0.020 -0.195 0.006
t-value -0.124 -0.893 -0.099 -0.888 0.021
p-value 0.902 0.381 0.922 0.386 0.983
FRA ρ 0.132 0.000 0.353∗∗∗ 0.081 0.031
t-value 0.639 0.000 3.225 0.444 0.119
p-value 0.529 1.000 0.004 0.662 0.906
GER ρ 0.341∗∗ 0.312∗ 0.275∗∗ 0.330∗ 0.337∗
t-value 2.330 1.797 2.132 1.807 1.953
p-value 0.029 0.085 0.044 0.087 0.065
ITA ρ -0.227 -0.115 -0.113 -0.145 -0.355∗
t-value -1.322 -0.666 -0.743 -0.840 -2.076
p-value 0.199 0.512 0.465 0.412 0.051
JPN ρ 0.478∗ 0.571∗∗∗ 0.522∗∗∗ 0.640∗∗∗ 0.461
t-value 1.783 4.869 3.375 3.535 1.547
p-value 0.087 0.000 0.003 0.002 0.138
GBR ρ 0.488∗∗∗ 0.499∗∗∗ 0.423∗∗∗ 0.368∗∗ 0.456∗∗
t-value 2.956 3.932 5.119 2.317 2.115
p-value 0.007 0.001 0.000 0.032 0.047
USA ρ 0.170 0.116 0.141 -0.053 0.245
t-value 0.873 0.675 0.568 -0.258 1.015
p-value 0.392 0.506 0.576 0.799 0.322
Table 1: Correlation coeﬃcients between GDP and R&D expenditure for G7 countries
3C o n c l u s i o n
What do these ﬁndings imply for further research? Bental and Peled (1996)
brieﬂy and Francois and Lloyd-Ellis (forthcoming) extensively discuss empir-
ical evidence on countercyclical R&D expenditure.5 These authors provide
evidence in favour of their - and therefore also - Matsuyama’s and Wälde’s
approach. Generally speaking, some R&D activities (in a broad sense in-
cluding reorganization of production or managment processes and training)
indeed seem to increase during downturns. On the other hand, it seems fair
to argue that there is stronger evidence for procyclical rather than counter-
cyclical behavior of R&D expenditure from the analysis undertaken here.
While data might remain inconclusive for some time, future research
should focus on making models more ﬂexible. Other speciﬁcations might
5Cf. also Saint Paul (1993, 1997) on the related ”opportunity cost” literature.
5imply R&D expenditure whose cyclical behavior depends on parameter val-
ues. This might then help to ﬁnd new empirical strategies that would allow
to understand why there is both pro- and countercyclical evidence of R&D
in a broad sense.
References
[1] A’Hearn, B. and Woitek, U., 2001, More International Evidence on the
Historical Properties of Business Cycles. Journal of Monetary Economics
47: 321 - 346.
[2] Banerjee, A., Dolado, J., Galbraith, J. and Hendry, D., 1993, Cointegra-
tion, Error-Correction and the Econometric Analysis of Non-Stationary
Data. Oxford University Press, Oxford.
[3] Baxter, M. and King, R. G., 1999, Measuring Business Cycles. Approxi-
mate Band-Pass Filters for Economic Time Series. Review of Economics
and Statistics 81: 575-593.
[4] Bental, B., Peled, D., 1996, The accumulation of wealth and the cyclical
generation of new technologies: A Search Theoretic Approach. Interna-
tional Economic Review 37, 687 - 718.
[5] Canova, F., 1998, Detrending and Business Cycle Stylized Facts. Journal
of Monetary Economics 41: 475-512.
[6] Christiano, L. and Fitzgerald, T., 1999, The Band Pass Filter. NBER
Working Paper No 7257.
[7] Fatás, A., 2000, Do Business Cycles Cast Long Shadows? Short-Run
Persistence and Economic Growth. Journal of Economic Growth 5: 147
-1 6 2 .
[8] Francois, P. and Lloyd-Ellis, H., forthcoming, Animal Spirits Through
Creative Destruction. American Economic Review.
[9] Geroski, P. A. and Walters, C. F., 1995, Innovative Activity over the
Business Cycle. Economic Journal 105: 916 - 928.
[10] Harvey, A. C. and A. Jaeger, 1993, Detrending, Stylized Facts and the
Business Cycle. Journal of Applied Econometrics 8: 231-247.
[11] Matsuyama, K., 2001, Growing through Cycles in an Inﬁnitely Lived
Agent Economy. Journal of Economic Theory 100: 220 - 234.
6[12] Matsuyama, K., 1999. Growing through cycles. Econometrica 67, 335 -
347.
[13] Newey, W. K. and West, K. O., 1987, A Simple, Positive Semi-deﬁnite,
Heteroskedasticity and Autocorrelation Consistent Covariance Matrix.
Econometrica 55: 703 - 708.
[14] OECD, 2001, International sectoral database. OECD [Organization for
Economic Co-Operation and Development], Paris.
[15] OECD , 2000a, Research and Development Expenditures in Industry
1977-1998. OECD, Paris.
[16] OECD, 2000b, Statistical Compendium Edition 1.2000 (on CD). OECD,
Paris.
[17] OECD , 1997, ANBERD: Business Enterprise R-D Expenditures in In-
dustry 1973-1995 (on diskettes). OECD, Paris.
[18] Ravn, M. O.; Uhlig, Harald, 2002, On Adjusting the Hodrick-Prescott
Filter for the Frequency of Observations. Review of Economics and
Statistics 84: 371 - 376.
[19] Saint-Paul, G., 1993, Productivity Growth and the Structure of the
Business Cycle. European Economic Review 37: 861 - 883.
[20] Saint-Paul, G., 1997, Business Cycles and Long-Run Growth. Oxford
Review of Economic Policy 13: 145 - 153.
[21] Wälde, K., 2002, The Economic Determinants of Technology Shocks in a
Real Business Cycle Model. Journal of Economic Dynamics and Control
27: 1 - 28.
[22] Wälde, K., 1999, Optimal Saving under Poisson Uncertainty. Journal of
Economic Theory 87: 194 - 217.
7Figure 1: Cyclical components of per-capita GDP and per-capita R&D ex-
penditure (using the HP ﬁlter with λ =1 0 0 )
8Dresden Discussion Paper Series in Economics 
 
 
 
5/00  Kalus, Falk: Analyse der Straßenverkehrsnachfrage und des Verkehrsunfallrisikos - Eine 
vergleichende Betrachtung westdeutscher und gesamtdeutscher Modelle 
 
6/00  Friedrich, Cornelia: Pfadabhängigkeit, Kritische Masse, Lock-In und Netzwerke. Logische 
Interdepenzen der evolutorischen Methoden zur Analyse des Wettbewerbs 
7/00  Berlemann, Michael: Monetary Policy under Uncertain Planning Horizon 
8/00  Arnold, Lutz G.: Growth in Stages 
9/00  Wälde, Klaus / Wood, Christina: The empirics of trade and growth: where are the policy 
recommendations? 
10/00  Wälde, Klaus: Does trade cause growth? A policy perspective 
1/01  Graff, Michael / Karmann, Alexander: Does Financial Activity Cause Economic Growth? 
2/01  Blum, Ulrich / Dudley, Leonard: Transport and Economic Development 
3/01  Blum, Ulrich: Volkswirtschaftliche Grundlagen: Die Neue Ökonomie des Internets 
4/01   Choi, Jay Pil / Thum, Marcel: The Dynamics of Corruption with the Ratchet Effect 
5/01  Berlemann, Michael / Schmidt, Carsten: Predictive Accuracy of Political Stock Markets. Empirical 
Evidence from an European Perspective 
6/01  Berlemann, Michael: Forecasting Inflation via Electronic Markets: Results from a Prototype Market 
7/01  Weiß, Pia / Wälde, Klaus: Globalisation is good for you: Distributional effects of mergers caused by 
globalisation 
8/01  Blum, Ulrich: Borders Matter! Regional Integration in Europe and North America 
9/01  Wälde, Klaus: Capital accumulation in a model of growth and creative destruction 
10/01  Hott, Christian: National vs. International Welfare Effects of Horizontal Mergers 
11/01  Lehmann-Waffenschmidt, Marco: Konstruktivismus und Evolutorische Ökonomik 
12/01  Lehmann-Waffenschmidt, Marco: Kontingenz und Kausalität bei evolutorischen Prozessen 
01/02  Rosenberg, Stanislav: Dresden's Transition Into The Market Economy And The Impact On Its 
Business Community 
02/02  Karmann, Alexander / Greßmann, Oliver / Hott, Christian: Contagion of Currency Crises - Some 
Theoretical and Empirical Analysis 
03/02  Buschle, Nicole-Barbara: Der Einfluß von Konsumenten auf die Determinanten wirtschaftlicher 
Entwicklung. Ein evolutorisches Simulationsmodell  
04/02  Albert, Max / Meckl, Jürgen: Immigration and Two-Component Unemployment 
05/02  Blum, Ulrich / Veltins, Michael: Wettbewerbsleitbilder für die Cyber-Ökonomie 
06/02  Hansen, Sabine / Wälde, Klaus: Warum ist Deutschland Exportweltmeister? Der Einfluß laufender 
internationaler Transfers 
07/02  Weimann, Marco: OCA theory and EMU Eastern enlargement. An empirical application 
08/02  Albrecht, Karl-Friedrich / Mende, Werner / Orlamünder, Dirk: Elektroenergieverbrauch als 
Wachstumsindikator – Eine empirische Untersuchung 
01/03  Berlemann, Michael / Markwardt, Gunther: Partisan Cycles and Pre-Electoral Uncertainty 
02/03  Choi, Jay Pil / Thum, Marcel: Corruption and the Shadow Economy 
03/03  Wälde, Klaus / Woitek, Ulrich: R&D expenditure in G7 countries and the implications for 
endogenous fluctuations and growth 

A Simple, Positive Semi-deﬁnite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix.

Business Cycles and Long-Run Growth.

Cointegration, Error-Correction and the Econometric Analysis of Non-Stationary Data.

Detrending and Business Cycle Stylized Facts.

Detrending, Stylized Facts and the Business Cycle.

Do Business Cycles Cast Long Shadows? Short-Run Persistence and Economic Growth.

forthcoming, Animal Spirits Through Creative Destruction. American Economic Review.

Growing through Cycles in an Inﬁnitely Lived Agent Economy.

Innovative Activity over the Business Cycle.

International sectoral database. OECD [Organization for Economic Co-Operation and Development],

Measuring Business Cycles. Approximate Band-Pass Filters for Economic Time Series.

More International Evidence on the Historical Properties of Business Cycles.

On Adjusting the Hodrick-Prescott Filter for the Frequency of Observations.

Optimal Saving under Poisson Uncertainty.

Productivity Growth and the Structure of the Business Cycle.

The accumulation of wealth and the cyclical generation of new technologies: A Search Theoretic Approach.

The Band Pass Filter.

The Economic Determinants of Technology Shocks in a Real Business Cycle Model.

R&D expenditure in G7 countries and implications for endogenous fluctuations and growth

http://econstor.eu/bitstream/10419/48118/1/363817255.pdf

R&D expenditure in G7 countries and implications for endogenous fluctuations and growth

Abstract

Similar works

Full text

Available Versions

Research Papers in Economics