Search query: General purpose technologies, engines of growth

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abstract: Whole eras of technical progress and economic growth appear to be driven by a few key technologies, which we call General Purpose Technologies (GPT's) Thus the steam engine and the electric motor may have played such a role in the past, whereas semiconductors and computers may be doing as much in our era. GPT's are characterized by pervasiveness (they are used as inputs by many downstream sectors) inherent potential for technical improvements, and innovational complementarities' meaning that the productivity of RD on the other hand this phenomenon makes it difficult for a decentralized economy to fully exploit the growth opportunities offered by evolving GPT's. In particular; if the relationship between the GPT and its users is limited to arms length market transactions, there will be "too little, too late" innovation in both sectors. Likewise, difficulties in forecasting the technological developments of the other side may lower the rate of technical advance of all sectors. Lastly, we show that the analysis of GPT's has testable implications in the context of R&D and productivity equations, that can in principle be estimated.
author_names: Timothy F Bresnahan

Manuel Trajtenberg
title: General Purpose Technologies "Engines of Growth?"
year: 1992

abstract: Since Bresnahan and Trajitenberg's original 1995 article on 'General Purpose Technology (GPT) Engines of Growth' the concept of GPT has slowly but steadily influenced the literature on business ICT (information and communication technology) adoption. In considering business ICT, within the framework of GPT allows to focus on the externalities in ICT adoption which poses challenges for policy and society as a whole. In the literature, these externality benefits of business ICT have rarely been examined. In this context, the article provides a survey of recently published empirical studies (from 2004 to 2013) citing the original article of Bresnahan and Trajenberg. In using the science citation index and citations derived from Google Scholar, we found 1090 articles fulfilling these criteria; from these articles just 57 studies provided some empirical estimation of the productivity impact of ICT. Our survey indicates if GPT refers to whole range of ICT, this might generate misleading results. In general, our results indicate a shift in the discussion on GPT focusing on business ICT. The literature demonstrates that with the availability of better data increasingly a better distinction between infrastructure technologies and applications should be used. While infrastructure technologies are uniformly adopted among small and large enterprises, the adoption of applications is a more complex phenomenon. Although some authors argue that there is a productivity impact of ICT, this impact is mediated by employment, wage and size variables. In particular, the adoption of business ICT within small and medium sized enterprises (SMEs) has rarely been examined.
author_names: S Sadaf Bashir

Bm Bert Sadowski
title: General Purpose Technologies: A Survey, a Critique and Future Research Directions
year: 2014

abstract: This dissertation tackles the different aspects of the creation and transmission of (new) knowledge in the context of the characteristics of a general purpose technology (GPT) Particular emphasis is put on the role of the composition of knowledge as well as the corresponding (presumed) knowledge spillovers on the one hand and on the concrete impact of collaboration and knowledge sharing in innovator networks on the other hand. The thesis offers a coherent literature review in its first part, analysing the theoretical role of knowledge for innovation and growth as well as the role of knowledge diffusion and sharing. Although the development of GPTs is particularly knowledgeand innovation intensive and GPTs are found to be 'engines of growth' the role of knowledge for innovation in GPTs has not been distinctive subject to investigation yet. Therefore, the two mentioned sets of research questions were tackled empirically in this thesis using the showcase example of nanotechnology. Nanotechnology is argued to be the key technology of the future, and empirical analyses in this thesis using patent and publication data provided evidence that there is sensible reason to consider nanotechnology as GPT. The first array of research questions is concerned with the role of local knowledge composition and spillovers for the development of nanotechnology. Two different approaches capture these issues. The first one investigates how the characteristics of the regional technological nano knowledge base as approximated (mainly) by patents influence the creation of new nanoknowledge. Panel negative binomial regression analyses are employed to disentangle the effects. The second approach captures the performance of nano firms depending on the local endowment with knowledge as investigated by means of OLS and fixed effects panel analyses. The central finding is that the regional endowment with knowledge impacts the development of nanotechnology. Concerning the composition of the knowledge bases, evidence suggests that specialisation and diversity are positively impacting innovation in nanotechnology. More particularly both are necessary to support nanotechnology's characteristics both as high technology and as GPT. Focusing on the role of collaboration and knowledge sharing in networks, the second array of research questions is tackled by another two analyses. One analysis focuses on the development of the role of collaboration and networking. The means of social network analysis of German nanotechnology patents' co contributorship networks shed light on the relationship between collaboration, the efficiency of the networks and the technological overlap (and hence the potential for cooperation) and the development of nanotechnology. The second analysis more particularly puts an emphasis on the factors that impact the generality of a patent. Therefore variables such as intensity of collaboration, access to knowledge, experience and overlap of technological background are included into fractional logit analyses. Findings include that the performance of a GPT can be enhanced through collaboration by offering efficient means for the organisation and coordination of knowledge sharing and knowledge spillovers and by fostering an increase in the technology's generality level due to knowledge sharing in teams and networks.
author_names: Nina Teichert
title: Innovation in General Purpose Technologies How Knowledge Gains when It Is Shared
year: 2013

abstract: Since Bresnahan and Trajitenberg's original 1995 article on 'General Purpose Technology (GPT) Engines of Growth' the concept of GPT has slowly but steadily influenced the literature on business ICT (information and communication technology) adoption. In considering business ICT, within the framework of GPT allows to focus on the externalities in ICT adoption which poses challenges for policy and society as a whole. In the literature, these externality benefits of business ICT have rarely been examined. In this context, the article provides a survey of recently published empirical studies (from 2004 to 2013) citing the original article of Bresnahan and Trajenberg. In using the science citation index and citations derived from Google Scholar, we found 1090 articles fulfilling these criteria; from these articles just 57 studies provided some empirical estimation of the productivity impact of ICT. Our survey indicates if GPT refers to whole range of ICT, this might generate misleading results. In general, our results indicate a shift in the discussion on GPT focusing on business ICT. The literature demonstrates that with the availability of better data increasingly a better distinction between infrastructure technologies and applications should be used. While infrastructure technologies are uniformly adopted among small and large enterprises, the adoption of applications is a more complex phenomenon. Although some authors argue that there is a productivity impact of ICT, this impact is mediated by employment, wage and size variables. In particular, the adoption of business ICT within small and medium sized enterprises (SMEs) has rarely been examined. 1 Corresponding author: S.Bashir@tue.nl
author_names: S Sadaf Bashir

Bert M Sadowski
title: 1 General Purpose Technologies A survey a critique and future research directions
year: 2014

abstract: This paper presents a critique of the General Purpose Technology (GPT) framework. It argues that the GPT framework is fundamentally flawed as an approach to understanding growth as it focuses on what are input production technologies (hereafter input technologies) and not on the associated primary inputs and resulting outputs. Computers don't produce output; rather, they produce information which is necessary for the production of output. Dynamos don't produce output, rather they transform prime movers (steam, hydraulics, fossil fuels) into electricity which is transmitted to machines that ultimately produce output. Steam engines don't produce output; rather, they too transmit prime movers (fossil fuels) into output. The problem with the GPT framewoek lies with the implicit assumption that there exists a one to one relationship between the intermediate input and the primary input. We will show that this assumption was violated in all three classic GPT cases, which explains what Paul Davd referred to as the "electricity paradox" and what Robert Solow referred to as the "information paradox." We then proceed to present an alternative framework based on the concept of enabling technologies.
author_names: Bernard C Beaudreau
title: A Critique of the General Purpose Technology Framework
year: 2016

abstract: We present a dynamic, non scale general equilibrium model with two human capital types where Schumpeterian R&D and human capital accumulation are the engines of growth and wage inequality. In particular, wage inequality is encouraged by relative changes in supply and demand of both human capital types. Relative supply restricts employed human capital levels. Relative demand is instantly affected by a new general purpose technology and, as in the skill biased technological change literature, by technological knowledge bias. By considering substitutability between technologies and complementarity between inputs, the bias is driven by the price channel (not by the market size channel) and is affected by human capital accumulation.
author_names: Oscar Afonso
title: Growth and Wage Inequality in a Scale Independent Model with R&D and Human Capital Accumulation
year: 2010

abstract: The model we propose in this paper is an extension of the one described in Freeman et al. [Freeman, S. Hong, D. and Peled, D. (1999) Endogenous Cycles and Growth with Indivisible Technological Developments. Review of Economics Dynamics, 2, 403 432] In our model, we incorporate the process of diffusion of major innovations and analyze macroeconomic effects on consumption, capital and aggregate output. Following Bresnahan and Trajtenberg [Bresnahan, T. and Trajtenberg, M. (1995) General Purpose Technologies: Engines of Growth? Journal of Econometrics, 65, 83 108. Helpman [Helpman, E. (ed. (1998) General Purpose Technologies and Economic Growth. MIT Press] and Lipsey et al. [Lipsey, R.G. Carlaw, K. and Bekar, C. (2005) Economic Transformations: General Purpose Technologies and Long Term Economic Growth. Oxford University Press. we assimilate major innovations with the emergence of certain GPTs, and we suggest that the diffusion process for these technologies, at a large scale, might follow an S shaped pattern. The proposed model presents optimum stationary solutions which are cyclical and have a wave dynamic within each cycle. The cycles are characterized by certain co movements in consumption, R&D investment, capital accumulation and output. Consideration of the innovation diffusion process highlights new aspects of endogenous cycles and long run growth.
author_names: Julio Sanchez-Choliz

Francisco Fatas-Villafranca

Gloria Jarne

Isabel Perez-Grasa
title: ENDOGENOUS CYCLICAL GROWTH WITH A SIGMOIDAL DIFFUSION OF INNOVATIONS
year: 2008

abstract: Google is an American multinational technology company which specializes in internet related services and products. The products and services offered by Google include online advertising technologies, search, cloud computing, and software. Google's ownership structure consists of 95% equity and 5% debt. Google's board of directors have authorized three classes of stock Class A, Class B common stock, and Class C capital stock. The continuing shift from an offline to online world has contributed to the growth of Google's business. Google generate revenue primarily by delivering both performance advertising and brand advertising. Google have many competitors in different industries, including general purpose search engines and information services, vertical search engines and e commerce websites, social networks, providers of online products and services, other forms of advertising. Acquisition is an important strategy of growth for Google. Google has emerged as the one of the biggest and most successful acquirers in the technology industry. Google went public on August 19, 2004.
author_names: Rajesh Kumar
title: Wealth creation analysis of Google
year: 2017

abstract: This paper presents the latest update for historical scaling dynamics research including new technoloies such as general purpose technologies (e.g. steam engines) and small end use technologies (e.g.,cellphones, e bikes) Scaling refers to technology growth that is rapid and extensive, occurring at different levels, both unit and industry. It also studies the importance of the formative phase in the historical diffusion of energy technologies. So, what are the characteristics of the formative phase in the case of fast and intense adoptions? What is the infuence of the formative phase in the overall diffusion? Empirical analysis uses logistic models to exlore the growth of energy technologies observed historically. The formative phase is defined here as the early stage of diffusion before technology up scales at unit level; the operational critera adoped is that formative phase ends when diffusion reaches 10% of cumulative total unit numbers. The hisorical evidence confirms that larger transitions require more time for experimentation and maturation in the formative periods, especially in the case of complex innovations with high infrastrcture needs. In addition, small size technologies with high turnover rates present the fastest diffusion. Moreresearch is needed to refine the definition of the moment when the technology completes the formativ phase and acquires enough maturity to pass on to mass commercialization. KEYWRODS: technological change; innovation; economies of scale; logistic growth; formative phases
author_names: Nuno Bento
title: New Evidence in Technology Scaling Dynamics and the Role of the Formative Phase
year: 2013

abstract: BackgroundWith the growth of biological data in volume and heterogeneity, web search engines become key tools for researchers. However, general purpose search engines are not specialized for the search of biological data.DescriptionHere, we present an approach at developing a biological web search engine based on the Semantic Web technologies and demonstrate its implementation for retrieving gene and protein centered knowledge. The engine is available at http:/www.integromedb.org.ConclusionsThe IntegromeDB search engine allows scanning data on gene regulation, gene expression, protein protein interactions, pathways, metagenomics, mutations, diseases, and other gene and protein related data that are automatically retrieved from publicly available databases and web pages using biological ontologies. To perfect the resource design and usability, we welcome and encourage community feedback.
author_names: Michael Baitaluk

Sergey Kozhenkov

Yulia Dubinina

Julia V Ponomarenko
title: IntegromeDB: an integrated system and biological search engine
venue: BMC Genomics
year: 2011