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on Technology and Industrial Dynamics |
| By: | Giotopoulos, Ioannis; Kritikos, Alexander S.; Tsakanikas, Aggelos |
| Abstract: | We use the prolonged Greek crisis as a case study to understand how a lasting economic shock affects the innovation strategies of firms in economies with moderate innovation activities. Adopting the 3-stage CDM model, we explore the link between R&D, innovation, and productivity for different size groups of Greek manufacturing firms during the prolonged crisis. At the first stage, we find that the continuation of the crisis is harmful for the R&D engagement of smaller firms while it increased the willingness for R&D activities among the larger ones. At the second stage, among smaller firms the knowledge production remains unaffected by R&D investments, while among larger firms the R&D decision is positively correlated with the probability of producing innovation, albeit the relationship is weakened as the crisis continues. At the third stage, innovation output benefits only larger firms in terms of labor productivity, while the innovation-productivity nexus is insignificant for smaller firms during the lasting crisis. |
| Keywords: | Small firms,Large firms,R&D,Innovation,Productivity,Long-term Crisis |
| JEL: | L25 L60 O31 O33 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:glodps:1122&r= |
| By: | Tom Kemeny; Sergio Petralia; Michael Storper |
| Abstract: | Although technological change is widely credited as driving the last two hundred years of economic growth, its role in shaping patterns of inequality remains under-explored. Drawing parallels across two industrial revolutions in the United States, this paper provides new evidence of a relationship between highly disruptive forms of innovation and spatial inequality. Using the universe of patents granted between 1920 and 2010 by the U.S. Patent and Trademark Office, we identify disruptive innovations through their rapid growth, complementarity with other innovations, and widespread use. We then assign more- and less-disruptive innovations to subnational regions in the geography of the U.S. We document three findings that are new to the literature. First, disruptive innovations exhibit distinctive spatial clustering in phases understood to be those in which industrial revolutions reshape the economy; they are increasingly dispersed in other periods. Second, we discover that the ranks of locations that capture the most disruptive innovation are relatively unstable across industrial revolutions. Third, regression estimates suggest a role for disruptive innovation in regulating overall patterns of spatial output and income inequality |
| Keywords: | technological change; regional development; industrial revolutions; innovation; inequality |
| JEL: | O30 O33 O51 J31 |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:egu:wpaper:2211&r= |
| By: | Sander Sõna; Jaan Masso; Shakshi Sharma; Priit Vahter; Rajesh Sharma |
| Abstract: | This paper investigates which of the core types of innovation can be best predicted based on the website data of firms. In particular, we focus on four distinct key standard types of innovation – product, process, organisational, and marketing innovation in firms. Web-mining of textual data on the websites of firms from Estonia combined with the application of artificial intelligence (AI) methods turned out to be a suitable approach to predict firm-level innovation indicators. The key novel addition to the existing literature is the finding that web-mining is more applicable to predicting marketing innovation than predicting the other three core types of innovation. As AI based models are often black-box in nature, for transparency, we use an explainable AI approach (SHAP - SHapley Additive exPlanations), where we look at the most important words predicting a particular type of innovation. Our models confirm that the marketing innovation indicator from survey data was clearly related to marketing-related terms on the firms' websites. In contrast, the results on the relevant words on websites for other innovation indicators were much less clear. Our analysis concludes that the effectiveness of web-scraping and web-text-based AI approaches in predicting cost-effective, granular and timely firm-level innovation indicators varies according to the type of innovation considered. |
| Keywords: | Innovation, Marketing Innovation, Community Innovation Survey (CIS), Machine learning, Neural network, Explainable AI, SHAP |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:mtk:febawb:143&r= |
| By: | Xiang Ding; Teresa C. Fort; Stephen J. Redding; Peter K. Schott |
| Abstract: | We document the role of intangible capital in manufacturing firms' substantial contribution to non-manufacturing employment growth from 1977-2019. Exploiting data on firms' “auxiliary” establishments, we develop a novel measure of proprietary in-house knowledge and show that it is associated with increased growth and industry switching. We rationalize this reallocation in a model where irms combine physical and knowledge inputs as complements, and where producing the latter in-house confers a sector-neutral productivity advantage facilitating within-firm structural transformation. Consistent with the model, manufacturing firms with auxiliary employment pivot towards services in response to a plausibly exogenous decline in their physical input prices. |
| Keywords: | structural transformation, professional services, intangible knowledge, economic growth |
| JEL: | D24 L16 O47 |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:cen:wpaper:22-19&r= |
| By: | Marta Candeias (Universidade Nova de Lisboa); Nuno Boavida (Universidade Nova de Lisboa); António Brandão Moniz (Universidade Nova de Lisboa) |
| Abstract: | Recent developments in automation and Artificial Intelligence (AI) are leading to a wave of innovation in organizational design and changes in the workplace. Techno-optimists even named it the ‘second machine age’, arguing that it now involves the substitution of the human brain. Other authors see this as just a continuation of previous ICT developments. Potentially, automation and AI can have significant technical, economic, and social implications in firms. The paper will answer the question: what are the implications on industrial productivity and employment in the automotive sector with the recent automation trends, including AI, in Portugal? Our approach used mixed methods to conduct statistical analyses of relevant databases and interviews with experts on R&D projects related to automation and AI implementation. Results suggest that automation can have widespread adoption in the short term in the automotive sector, but AI technologies will take more time to be adopted. Findings show that adoption of automation and AI increases productivity in firms and is dephased in time with employment implications. Investments in automation are not substituting operators but rather changing work organization. Thus, negative effects about technology and unemployment were not substantiated by our results. |
| Keywords: | Artificial Intelligence; automation; productivity; employment; automotive industry |
| JEL: | L23 L62 O30 O32 O33 |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:mde:wpaper:0165&r= |
| By: | Mundt, Philipp; Savin, Ivan |
| Abstract: | We revisit the debate on the role of technological improvement and market share reallocation in determining aggregate productivity gains. Contrary to previous work that neglects dependencies between suppliers in global value chains, we explicitly account for input linkages that impact both channels of productivity improvement. Using sector-level data from the World Input-Output Database, we show that market share reallocation has a markedly larger effect on productivity change than innovation. |
| Keywords: | input-output analysis,market share reallocation,productivity decomposition,production network,technological improvement |
| JEL: | C67 E24 L14 L16 O47 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bamber:179&r= |
| By: | Natália Barbosa (Universidade do Minho); Ana Paula faria (Universidade do Minho) |
| Abstract: | Digital technologies have the scope to engender positive effects on productivity at firm and aggregate level. However, empirical evidence and theoretical contributions are ambiguous as mixed findings and diverse explanations have been put forward. We use a rich and representative sample of Portuguese firms over the period 2014-2019 to empirically assess the relationship between digital technologies adoption and productivity. Based on estimations over the entire distribution of firm’s productivity, we find that heterogeneous digital technologies affect differently the dynamics of productivity and the convergence to the frontier. This leads to mixed findings with scope to diverse impact in the aggregate productivity. Moreover, positive and significant effects on productivity require an upgrading on the degree of sophistication and complementarity among digital technologies and benefit from the ability of firms to interact and learn with digitalised peers in the same industry. |
| Keywords: | Digital technologies, Productivity, Spillover effects |
| JEL: | L20 H81 L25 |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:mde:wpaper:0162&r= |
| By: | Bernardino Adão; Borghan N. Narajabad; Ted Temzelides |
| Abstract: | We develop a dynamic general equilibrium integrated assessment model that incorporates costs due to new technology adoption in renewable energy as well as externalities associated with carbon emissions and renewable technology spillovers. We use world economy data to calibrate our model and investigate the effects of the technology adoption channel on renewable energy adoption and on the optimal energy transition. Our calibrated model implies several interesting connections between technology adoption costs, the two externalities, policy, and welfare. We investigate the relative effectiveness of two policy instruments-Pigouvian carbon taxes and policies that internalize spillover effects-in isolation as well as in tandem. Our findings suggest that renewable technology adoption costs are of quantitative importance for the energy transition. We find that the two policy instruments are better thought of as complements rather than substitutes. |
| Keywords: | Technology adoption; Scrapping; Energy transition; Climate; Dynamic taxation |
| JEL: | H21 O14 O33 Q54 Q55 |
| Date: | 2022–07–08 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2022-45&r= |
| By: | Hempfing, Alexander; Mundt, Philipp |
| Abstract: | Using a statistical model of an evolving multiplex network, we study tie formation in global production chains within and across developed countries, their trade activities with developing economies in the intermediate goods market, and the mutual dependencies between these relationships. Our model approaches these dynamics from the perspective of individual nodes and thus identifies the driving forces behind the tie formation process. The empirical value of our approach is demonstrated by fitting the model to a panel data set from the OECD Inter-Country Input-Output Tables between 2005 and 2015. Based on these data, we find that (i) geography, two-sided heterogeneity of buyers and sellers, trade costs, as well as structural characteristics of the production network determine the formation of trade linkages between OECD country-sectors, (ii) some of these determinants have an asymmetric effect on import and export ties between OECD and non-OECD countries, and (iii) intra-OECD trade and import and export ties with non-OECD economies are mutually dependent. |
| Keywords: | trade network,network formation,stochastic actor-oriented model,multiplex dynamics,input-output analysis |
| JEL: | E23 F14 D57 R15 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bamber:181&r= |
| By: | Fouquet, Roger; Hippe, Ralph |
| Abstract: | This paper investigates the structural transformation associated with the ‘twin transition’ of decarbonisation and digitalisation in European economies by placing it in a broader historical perspective. With this in mind, this paper analyses the long run trends in energy intensity and communication intensity since 1850. The evidence indicates that these economies experienced a coevolution of energy and communication intensities during their industrialisation phase, followed by a divergence in the energy and communication intensities associated with the development of high tech and ICT. Overall, this reflects the dematerialisation of these European economies. The paper also analyses the speed of historical energy transitions and communication technology transitions in these economies, finding that communication transitions appear to be substantially faster than energy transitions. The evidence suggests that twin transitions of the decarbonisation and digitalisation of economies are likely to experience a process of imbalanced structural transformation (with ICT continuing to forge ahead). This expectation should guide policy recommendations – increasing the need for low carbon industry to develop and create synergies between the two industries in order to avoid the new industrial revolution being high-carbon. |
| Keywords: | energy transitions; ICT; twin transition; energy intensity; historical; EP/R 035288/1; ES/R009708/1 |
| JEL: | N0 |
| Date: | 2022–07–02 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:115544&r= |