1. Introduction
It has been stressed that in a world of global economies it is likely that the challenge for firms may not always be among the first to produce new information,
but may instead be how to recognise, obtain, employ and complement the relevant innovative information De Bondt, 1996.
Information technologies computers, mainframes, etc. henceforth, referred to as IT and telecommunications telephone, fax, modem, virtual private networks play
an important role in information processing: they increase the speed and accuracy of analysing, storing and transmitting information on input costs, production
technologies and other strategic data Toffelmire, 1992. Furthermore, they also enhance integration among firms, and foster the accessibility of large knowledge
sources such as patent publications and databank networks.
Despite the recognised importance of IT-telecommunications technologies and the productivity gains expected by using them, empirical studies aimed at measuring
their influence on economic performance have found disappointing results see for example, Morrison and Berndt, 1991; Berndt et al., 1992, and, for an extensive
review, Brinjolfsson and Yang, 1996. Robert Solow commented upon this situation by stating ‘we see computers everywhere except in the economic statistics’. This
became known as the Solow paradox or information technology productivity paradox.
We believe that perhaps this disappointing empirical evidence should stimulate further analysis of the relationship between communication technologies and firms’
productivity. Without distinguishing between information technologies and telecommunications, we refer to communications technologies that contribute to
information processing and are characterised by the existence of a community of users. The main aim of this work is in fact to determine factors that enhance or
temper firms’ pri6ate incentives to invest in communication technologies. From this point of view, even if telecommunications could be considered as public services,
our analysis also differs from recent work on returns to public infrastructure see for example, Ashauer, 1989; Berndt and Hanson, 1992; Garcia-Mila and McGuire,
1992; Nadiri and Mamuneas, 1994; Morrison and Schartz, 1996.
In particular, we consider that the usage of communications technologies allows firms to obtain knowledge and therefore to influence their rate of technological
change or total factor productivity. The role of IT investment goods in the production process differs, therefore, from the role of other capital goods: commu-
nication technologies are used to smooth the process of production and to lower transaction costs Van Mejil and Van Zon, 1933; Norton, 1992, while other capital
goods are used together with labour to produce output.
Next to this direct effect on firms’ productivity, IT investment goods also have an indirect effect via their influence on the knowledge diffusion process among the
users. We claim that IT advantages stem also from the fact that they create a network of firms, as for example, between component and material suppliers in
assembly-type firms as in the automobile industry or in service firms as in hotel and catering industries. Furthermore, the new flexibility and speed of communica-
tion systems and their links with data banks create the possibility for a wider variety of new value-added networks and new types of information services.
Communication technologies are thus effective in the distribution of knowledge among firms, creating a spillover effect: in fact, there is some empirical evidence
that excess returns in using computers and information technologies do exist Brinjolfsson and Hitt, 1993; Oliner and Sichel, 1994. We think that these
spillovers may be related to the fact that communication technologies involve a network of users, so they are likely to create a ‘network externality effect’, meaning
that the individual usage of the network is a positive function of the existent community of users
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. This effect is mostly neglected in discussion about IT. We propose an original way of modelling these externalities, stressing the importance of
the network architecture, namely the existence of direct and indirect links among users.
The main contribution of this paper thus lies in the specification of the dynamic evolution of factor productivity that we assume to be affected by network technolo-
gies usage and spillovers. We therefore focus on the impact of the extent of the network effect and the number of users on the steady state values of total factor
productivity TFP and communication investment, in a partial equilibrium model.
The main ideas of the paper are drawn from the literature on RD spillovers Dasgupta and Stiglitz, 1980; Spence, 1984; Judd, 1985; Levin and Reiss, 1988; De
Bondt, 1996; in contrast with this kind of literature, we present a rather new specification of the network effects, which differs from the usual RD spillover
effects. The endogenous growth models based on innovation mainly drive our modelling of the TFP dynamics. However, when specifying the production function
for the blueprints Grossman and Helpmann, 1991, these models concentrate on a linear relationship between total investment in research and the knowledge stock it
accumulates as a consequence. We focus on the possibility of a non-linear relation- ship between the innovative activity in our case, the telecommunication invest-
ment and general knowledge, assuming that this relationship exhibits decreasing returns. This situation is similar to a crowding of information, because later
contacts are less significant than earlier communications.
One of the most interesting results of the model, mainly obtained by numerical simulations, regards the impact of the network effect on the TFP. We find that
when the externality parameter is low, a slightly negative effect appears, but this effect is reversed when the externality is higher. This result may offer a possible
explanation for the Solow producti6ity paradox, as it can reconcile the very mixed evidence found by the econometric models. Our simulations suggest that the impact
of IT is non-neutral with respect to the characteristics of the firms analysed. Moreover, we show that an additional reason can explain the heterogeneous results
obtained by the empirical analyses: all these studies estimate reduced forms models
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The idea of network externality effects has been widely explained by the literature on telecommuni- cations residential demand Artle and Averous, 1973; Squire, 1973; Rholfs, 1974; Taylor, 1994 and on
competition among firms producing goods likely to create network effects Katz and Shapiro, 1985, 1986, 1992; Economides, 1991; Liebowitz and Margolis, 1994.
that do not distinguish between the general impact of communication technologies and the extent of the network effect.
One of the most popular explanations of the Solow productivity paradox is that output and inputs of information-using industries are not being properly measured
by conventional approaches. In our view, this measurement error also depends on having neglected some important determinants of the ITtelecommunications usage:
degree of network effects, market structure, consumer preferences and number of users. We thus suggest that some further empirical work is desirable, especially
ranking industries according to the expected extent of spillovers. Following our simulation results, we could expect that firms selling low differentiated products,
requiring a large and technologically efficient communication network and an intensive extent of communication externalities, would experience a positive impact
of ITtelecommunications on total factor productivity and then have the highest private incentive in communication technologies investment.
The remainder of the paper is organised as follows. After discussing the hypothe- ses of our model Section 2, we analyse firms’ decision making on telecommunica-
tion investment and technological level in a dynamic context Section 3. The second part of the paper focuses on the analysis of the steady state levels, together
with the welfare analysis Sections 4 and 5. The empirical implications of our model Section 6 are proposed before the conclusions Section 7.
2. Hypotheses of the model
Kategori