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on Technology and Industrial Dynamics |
| By: | Cinnirella, Francesco (University of Bergamo); Hornung, Eric (University of Cologne); Koschnick, Julius (London School of Economics) |
| Abstract: | Economic societies emerged during the late eighteenth-century. We argue that these institutions reduced the costs of accessing useful knowledge by adopting, producing, and diffusing new ideas. Combining location information for the universe of 3,300 members across active economic societies in Germany with those of patent holders and World’s Fair exhibitors, we show that regions with more members were more innovative in the late nineteenth-century. This long-lasting effect of societies arguably arose through agglomeration economies and localized knowledge spillovers. To support this claim, we provide evidence suggesting an immediate increase in manufacturing, an earlier establishment of vocational schools, and a higher density of highly skilled mechanical workers by mid-nineteenth century in regions with more members. We also show that regions with members from the same society had higher similarity in patenting, suggesting that social networks facilitated spatial knowledge diffusion and, to some extent, shaped the geography of innovation |
| Keywords: | Economic Societies ; Useful Knowledge ; Knowledge Diffusion ; Innovation ; Social Networks JEL Classification: N33 ; O33 ; O31 ; O43 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:cge:wacage:632&r= |
| By: | Costantiello, Alberto; Laureti, Lucio; Leogrande, Angelo |
| Abstract: | In this article we investigate the determinants of marketing or organizational innovators in Europe for 36 countries in the period 2010-2019. We have used data from the European Innovation Scoreboard-EIS of the European Commission. We perform different econometric models i.e. Dynamic Panel, Pooled OLS, Panel Data with Fixed Effects, Panel Data with Random Effects, WLS. Results show that the level of marketing or organizational innovators in positively associated, among others variables to “Innovation Index”, “Innovators” and “Knowledge Intensive Service Exports”, while is negatively associated with “Sales Impacts”, “Foreign Controlled Enterprises Share of Value Added” and “Government procurement of advanced technology products”. |
| Keywords: | Innovation, and Invention: Processes and Incentives; Management of Technological Innovation and R&D; Diffusion Processes; Open Innovation. |
| JEL: | O30 O31 O32 O33 O34 |
| Date: | 2022–08–09 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:114121&r= |
| By: | Quentin Plantec (TSM - Toulouse School of Management Research - UT1 - Université Toulouse 1 Capitole - Université Fédérale Toulouse Midi-Pyrénées - CNRS - Centre National de la Recherche Scientifique - TSM - Toulouse School of Management - UT1 - Université Toulouse 1 Capitole - Université Fédérale Toulouse Midi-Pyrénées); Marie-Alix Deval; Sophie Hooge; Benoît Weil |
| Abstract: | Big data, supported by AI technologies, is mainly viewed as a trigger for radical innovation. The automotive industry appears as a key example: the most critical innovative challenges (e.g., autonomous driving, connected cars) imply drawing more extensively on big data. But the degree of innovativeness of the industrial purpose of incumbents, who are already embedding such technologies in their end-products, is worth investigating. To answer this research question, we relied on a mixed-method approach and used knowledge search as a theoretical framework. First, we conducted a quantitative analysis on 46,145 patents from the top-19 automotive incumbents. By comparing AI and non-AI patents, we showed that incumbents mainly rely on knowledge exploitation for data-driven innovation leading to incremental innovations. But, surprisingly, such innovation path foster more technologically original inventions with AI, which is not the case for non-AI patents. Second, we conducted a qualitative study to better understand this phenomenon. We showed that big data and AI technologies are integrated in the industrialization phase of new vehicles development process, following creative problem-solving logics. We also retrieved technical and organizational challenges limiting data-driven innovation. Those findings are discussed regarding the knowledge search and the new product development literature in the context of automotive industry. |
| Keywords: | Big data,AI technologies,automotive industry,digital transformation |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03727359&r= |
| By: | E. Mark Curtis; Ioana Marinescu |
| Abstract: | Does the growth of renewable energy benefit US workers, and which workers stand to benefit the most? Until now, evidence on green energy jobs has been limited due to measurement issues. We use data on nearly all jobs posted online in the US, as collected by Burning Glass Technology, and we create a new measure of green jobs, defined here as solar and wind jobs. We use job titles and task requirements to define green jobs. We find that both solar and wind job postings have more than tripled since 2010, with solar jobs seeing especially strong growth that precedes the growth of new installed solar capacity. In 2019, we identify approximately 52,500 solar job openings and 13,500 wind job openings. Solar jobs are mostly (33%) in sales occupations, and in the utilities industry (16%). Wind jobs are most represented among installation and maintenance occupations (37%), and in the manufacturing industry (29%). Green jobs are created in occupations that are about 21% higher paying than average. The pay premium is even higher for jobs with a low educational requirement. Finally, green jobs tend to locate in counties with high shares of employment in fossil fuel extraction. Overall, our results suggest that the growth of renewable energy leads to the creation of relatively high paying jobs, which are more often than not located in areas that stand to lose from a decline in fossil fuel extraction jobs. |
| JEL: | J23 Q52 |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:30332&r= |
| By: | Sunil Kanwar (Department of Economics, Delhi School of Economics) |
| Abstract: | This paper explores the nexus between innovation and bureaucratic performance where we focus on the effect of actual bureaucratic performance, rather than bureaucratic capacity. A conditional difference in differences estimation using an unbalanced panel of nations spanning the period 2004-2018, provides strong evidence that better bureaucratic performance underlies better innovation outcomes, ceteris paribus. At the median stock of knowledge capital, a one-unit improvement in bureaucratic performance raises the patent applications of a sample country by about 651, and ‘international’ patents by about 340, which constitute a 1.8% and 5.5% increase over the sample mean patent applications and sample mean ‘international’ patents, respectively. Second, this effect is heterogenous, with these responses becoming more pronounced at higher percentiles of the knowledge capital stock owned by a country. Thus, at the 95th percentile, a one-unit increase in bureaucratic performance raises patent applications by about 3.3%, and ‘international’ patents by about 6.7%, of their respective sample means. Third, the strong significance of bureaucratic performance for innovation is found to be fairly broad-based across technology groups such as Electrical/Electronics Technology, Professional and Scientific Equipment, Pharmaceuticals, Chemicals, and Machinery (Non-electrical), and is not driven by just one or two of these groups. The results are robust to several robustness checks. Key Words: Innovation, Bureaucratic performance, Nonlinear influence JEL Codes: O34, O38, O43 |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:cde:cdewps:328&r= |
| By: | Hoffreumon, Charles; Labhard, Vincent |
| Abstract: | In this article, we present a new perspective on forecasting technology adoption, focused on the extensive margin of adoption of multiple digital technologies in multiple countries. We do this by applying a Bayesian hierarchical structure to the seminal model of technology diffusion. After motivating the new perspective and the choices of priors, we apply the resulting framework to a cross-continental data set for EU and OECD countries and different digital technologies adopted by either households/individuals or by businesses. The results illustrate that the Bayesian hierarchical structure may be used to assess and predict both the adoption process and the uncertainty surrounding the data, and is robust to the use of alternative priors. They point to heterogeneity across countries and across technologies, mostly in the timing of adoption and, although to a lesser extent, the steady-state adoption rate once technologies are fully diffused. This suggests that characteristics of countries and technologies matter for technology diffusion. JEL Classification: C11, C52, C53, O33, O57 |
| Keywords: | adoption, diffusion, maximum, speed, timing |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20222700&r= |
| By: | Stamegna, Marco |
| Abstract: | The empirical literature on induced technical change has explored the long-run relationship between real wages and labour productivity but still lacks an explicit treatment of the implications of the wage-productivity nexus for the business cycle. The present paper aims to fill this gap. By employing a four-dimensional structural vector autoregressive (SVAR) model for the US economy (1948-2019), we test an extended version of the Goodwin model that includes aggregate demand and decomposes the labour share into real wages and labour productivity. This paper adds to the existing literature in some respects. First, it contributes to the induced innovation literature, by showing that wage shocks have positive and persistent effects on labour productivity at business cycle frequencies. Second, it adds to the debate and empirical evidence on the distributive cycle. Impulse response functions show that, even when decomposing the labour share, empirical evidence supports the Goodwin pattern, although the profit-led regime turns out to be driven more by technology than distributive shocks. Finally, we address two relevant cyclical stylized facts of the US economy: since the mid-1980s, the procyclical pattern of labour productivity has vanished, and real wages have no longer been correlated with employment over the business cycle. We explore the hypothesis that the two changes are linked. In light of the theory of induced innovation, we argue that the decline in the cyclical correlation between output and labour productivity can be explained by a lessened incentive to invest in labour-saving innovations due to missing wage growth in the upturn of the business cycle. Impulse response functions qualitatively support this intuition. |
| Keywords: | Labour productivity; endogenous technical change; income distribution; SVAR |
| JEL: | E12 E24 E25 E32 |
| Date: | 2022–07–21 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:113855&r= |
| By: | Massimo FLORIO (Department of Economics, Management, and Quantitative Methods, University of Milan (Italy)); Paolo CASTELNOVO (Department of Economics, Management, and Quantitative Methods, University of Milan (Italy)); Veronica LUPI (Department of Economics, Management, and Quantitative Methods, University of Milan (Italy)); Valentina MORRETTA (Department of Economics, Management, and Quantitative Methods, University of Milan (Italy)); Davide VURCHIO (Department of Economics and Finance, University of Bari “Aldo Moro†(Italy)); Lorenzo ZIRULIA (Department of Economics, Management, and Quantitative Methods, University of Milan (Italy)); Simonetta DI CIACCIO (Italian Space Agency (Italy)); Mauro PIERMARIA (Italian Space Agency (Italy)) |
| Abstract: | The purpose of this work is assessing the impact of the Italian Space Agency (ASI) on the innovation and performance of the Italian space system. Based on descriptive evidence from three surveys and econometric analysis using balance-sheet, patent and scientometric data, we find that, when considering upstream companies and downstream intermediate users in the field of Earth observation (EO) as a whole, the socio-economic benefit – taxpayer cost ratio is higher than 1, and is particularly high in the downstream EO sector. As regards the upstream sector, the econometric analysis shows a significant effect of procurement on economic performance and innovation. Also for the downstream sector (companies and research centres), descriptive evidence from the surveys shows a positive effect of EO data on economic performance and innovation. Finally, we observe a significant impact of ASI also on scientific productivity. |
| Keywords: | space industry, Earth observation, socio-economic impact |
| JEL: | D61 H50 O32 O38 |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:crc:wpaper:2201&r= |
| By: | David Popp; Francesco Vona; Myriam Grégoire-Zawilski; Giovanni Marin |
| Abstract: | The costs of low-carbon energy fell dramatically over the past decade, leading to rapid growth in its deployment. However, many challenges remain to deploy low-carbon energy at a scale necessary to meet net zero carbon emission targets. We argue that developing complementary technologies and skills must feature prominently in the next wave of low-carbon energy innovation. These include both improvements in physical capital, such as smart grids to aid integration of intermittent renewables, and human capital, to develop the skills workers need for a low-carbon economy. We document recent trends in energy innovation and discuss the lessons learnt for policy. We then discuss the need for complementary innovation in both physical capital—using smart grids as an example of how policy can help—and human capital, where we show how a task approach to labor informs policy and research on the worker skills needed for the energy transition. |
| Keywords: | low-carbon energy, innovation, patents, human capital, skills |
| JEL: | J24 O31 O38 Q42 Q55 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_9878&r= |
| By: | César Ducruet (EconomiX - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique); Hidekazu Itoh (Kwansei Gakuin University) |
| Abstract: | Based on untapped shipping and urban data, this article compares the diffusion of steam and container shipping at the port city level and at the global scale between 1880 and 2008. A temporal and multi-layered network is constructed, including the pre-existing technologies of sailing and breakbulk. The goal is to check the differences a) between innovations and their predecessors and b) between innovations, from an urban network perspective. Main results show that despite certain differences, such as historical context, voyage length, speed of diffusion, and geographical spread, the two innovations share a large quantity of similarities. They both fostered port concentration, were boosted by city size and port connectivity, bypassed upstream port sites, and diverged gradually from older technologies. This research thus contributes to the literature on cities, networks, innovation, and maritime transport. |
| Keywords: | Containerization,Maritime transport,Port cities,Regional disparity,Spatial networks,Steam shipping,Technological change |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:halshs-03719062&r= |
| By: | Shreya Roy; Sugata Marjit; Bibek Ray Chaudhuri |
| Abstract: | Artificial Intelligence (AI) has the potential to significantly impact the income of individuals. Cross-country data shows that introduction of AI is inequality enhancing in developing and less developed countries. In this paper, we attempt to understand the reason for increase in wage inequality across labourers due to introduction of AI, in a finite change General Equilibrium (GE) set up which allows for emergence of a new activity. AI-induced technological shock is introduced in the non-traded sector of an open economy with heterogeneous skills. We show how the advent of AI (which was initially non-existent) in the non-traded sector separates the skills of the once homogenous workers, thus, creating an intra-sectoral wage gap. What proportion of the low-skilled workers can move to the higher wage paying sector depends on an adaptability factor that acts as an eligibility criterion in fragmenting the erstwhile homogenous labourers and also works towards rising intra-group wage gap. |
| Keywords: | artificial intelligence, finite change, sectoral wage gap |
| JEL: | O33 J31 D50 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_9862&r= |