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on Technology and Industrial Dynamics |
| By: | Davide Antonioli (University of Ferrara); Alberto Marzucchi (Gran Sasso Science Institute); Francesco Rentocchini (European Commission, Joint Research Centre (JRC), Seville, Spain; Department of Economics Management and Quantitative Methods (DEMM), University of Milan); Simone Vannuccini (Science Policy Research Unit, University of Sussex) |
| Abstract: | We exploit firm-level data on robot adoption and use an event-study approach to study the unexplored relationship between robotisation and innovation. Instead of an enabling effect, we find a negative association between robot adoption and the probability to introduce product innovations, as well as their number; the results emerge using different proxy of product innovation. However, large-scale investments in mechanisation cancel-out the negative effect and show a positive association with R&D expenditure. We rationalise and interpret the findings suggesting that a piecewise substitutive relationship exists between process and product innovation. Large investments relax the product-process trade-off, as substantial R&D investments to accrue absorptive capacity are mobilised; as a result, they make less binding the allocation dilemma between implementing robot technology and designing and trialling new products. Finally, we discuss whether industrial robots studied here and in the literature feature enabling capabilities at all. The study has important implications for our understanding of the role of robots for firms operations and strategies, as well as for policy design. |
| Keywords: | robots, automation, product innovation, absorptive capacity, Spain |
| JEL: | O31 O33 |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:aiw:wpaper:21&r= |
| By: | Aleksandra Parteka (Gdansk University of Technology, Gdansk, Poland); Aleksandra Kordalska (Gdansk University of Technology, Gdansk, Poland) |
| Abstract: | In this paper we analyse the effects of technological innovation in the artificial intelligence (AI) domain on productivity. We embed the recently released data on patents and publications related to AI into an augmented panel model of productivity growth, estimated for OECD countries, and compared to a non-OECD sample. Our instrumental variables' estimates, accounting for AI endogeneity, provide evidence in favour of the modern (AI) productivity paradox. We show that the development of AI technologies remains a niche innovation phenomenon with a negligible role in the officially recorded productivity growth process. This general result, i.e. the lack of a strong relationship between AI and productivity growth, is robust to changes in the country sample, in the way we quantify labour productivity or the creation of AI technology, in the specification of the empirical model (control variables) or in estimation methods. |
| Keywords: | technological innovation, productivity paradox, productivity growth, artificial intelligence, patents |
| JEL: | O33 O47 |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:gdk:wpaper:67&r= |
| By: | Daron Acemoglu; Gary Anderson; David Beede; Catherine Buffington; Eric Childress; Emin Dinlersoz; Lucia Foster; Nathan Goldschlag; John Haltiwanger; Zachary Kroff; Pascual Restrepo; Nikolas Zolas |
| Abstract: | This paper provides a comprehensive description of the adoption of automation technologies by US firms across all economic sectors by leveraging a new module introduced in the Census Bureau’s 2019 Annual Business Survey. The module collects data from over 300,000 firms on the use of five advanced technologies: AI, robotics, dedicated equipment, specialized software, and cloud computing. We document that the adoption of these technologies remains low (especially for AI and robotics), varies substantially across industries, and concentrates on large and younger firms. However, because larger firms are much more likely to adopt them, 12-64% of US workers and 22-72% of manufacturing workers are exposed to these technologies. Firms report a variety of motivations for adoption, including automating tasks previously performed by labor. Consistent with the use of these technologies for automation, adopters have higher labor productivity and wages and lower labor shares. In particular, the use of these technologies is associated with a 15% increase in labor productivity, which accounts for 20–30% of the higher labor productivity achieved by the largest firms in an industry. Adopters report that these technologies raised skill requirements and led to greater demand for skilled labor, but brought limited or ambiguous effects to their employment levels. |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:cen:wpaper:22-12&r= |
| By: | Janos Ferencz; Javier López González; Irene Oliván García |
| Abstract: | Artificial intelligence (AI) has strong potential to spur innovation, help firms create new value from data, and reduce trade costs. Growing interest in the economic and societal impacts of AI has also prompted interest in the trade implications of this new technology. While AI technologies have the potential to fundamentally change trade and international business models, trade itself can also be an important mechanism through which countries and firms access the inputs needed to build AI systems, whether goods, services, people or data, and through which they can deploy AI solutions globally. This paper explores the interlinkages between AI technologies and international trade and outlines key trade policy considerations for policy makers seeking to harness the full potential of AI technologies. |
| Keywords: | Data flows, Digital trade, Innovations, Regional Trade Agreements, Trade policy |
| JEL: | F13 F14 O33 |
| Date: | 2022–04–22 |
| URL: | http://d.repec.org/n?u=RePEc:oec:traaab:260-en&r= |
| By: | Pang, Ziyun |
| Abstract: | This paper analyzes the relationship between labor automation and economic growth. I build a task-based framework and utility to evaluate how labor automation range can maximize economy in different conditions. I also analyze relationships among labor automation, capital, consumption, and investment. I considered the fact that automation will be expanded due to technological advancements while labor tends to obtain new skills to be more competitive. The best labor automation depends on labor productivity and machine productivity. When labor productivity exceeds machine productivity, labor automation will be less than half of the total tasks. In the opposite case, labor automation will be more than half of the total tasks. I also demonstrated that the investment decreases rapidly when workers become more competitive. When disruptive technologies are introduced, consumption will increase sharply together with labor automation, which is consistent with the first conclusion. |
| Keywords: | Labor automation, economic growth, consumption, investment, technology. |
| JEL: | O4 |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:112457&r= |
| By: | Fabienne Kiener; Christian Eggenberger; Uschi Backes-Gellner |
| Abstract: | We study how information technology (IT) progress affects returns to specialization and social skills by developing a theoretical model and empirically analyzing its implications. Our model shows how IT progress can lead to increasing returns to specialization and social skills. Using rich skill data from Swiss occupational training curricula, we empirically investigate the wage returns to specialization and social skills depending on IT progress in different industries. Our individual fixed-effects analyses show that IT progress leads to increasing wage returns for specialized workers. Furthermore, our results suggest that workers with high shares of social skills benefit from IT progress only if they are also specialized. |
| Keywords: | digitalization, IT progress, skills, education, human capital |
| JEL: | I26 J24 O33 |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:iso:educat:0192&r= |
| By: | Claude Diebolt (BETA - Bureau d'Économie Théorique et Appliquée - UNISTRA - Université de Strasbourg - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Karine Pellier (BETA - Bureau d'Économie Théorique et Appliquée - UNISTRA - Université de Strasbourg - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement) |
| Abstract: | This paper examines the structural and spatial dynamics of patents in France, Germany, Japan, the United Kingdom and the United States. The time series are extracted from international, comparative and historical databases on the long-term evolution of patents in 40 countries from the 17th century to 1945 and in more than 150 countries from 1945 to present (Diebolt and Pellier 2010). We have found strong evidence of infrequent large shocks resulting essentially from the major economic and political events formed by the two World Wars in the 20th century. Our results question the autonomous process, i.e. the internal dynamic of the patent systems. Wars seem to drive innovation and, finally, the very process of economic growth. We further investigated the role of innovation in economic growth through a causality analysis between patents and GDP per capita. Our major findings support the assumption that the accumulation of innovations was a driving force only for France, the United Kingdom and the United States during the post-World War II period. |
| Keywords: | Comparisons in time and space,Outliers,Causality,Patents,Shock analysis,Cliometrics,Database |
| Date: | 2022–03–16 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-02929514&r= |
| By: | Kleiner-Schaefer, Timo; Schaefer, Kerstin J. |
| Abstract: | University-industry collaborations (UICs) are one of the main sources of external knowledge and technologies for industrial firms, particularly in the context of emerging markets (EMs) and firm development. It is thus highly relevant to identify potential barriers internal to the firm as well as in the regional innovation system that might prevent firms from using UICs for innovation, in particular in an EM context. In order to address this issue, we conduct a firm-level study of the R&D-related segment of the manufacturing industry in Istanbul. Logistic regression analysis is used to test the effect of potential barriers on using UICs for innovative activities. With this approach, we are able to identify barriers that prevent innovation-related UICs and thus form a bottleneck to collaborations in the first place. Our findings show that lack of information about UIC opportunities as well as lack of financial support for UICs are the most relevant barriers that inhibit firms’ usage of UICs for innovation. This firm-level evidence points out the importance of university technology transfer offices in regional innovation systems and for fruitful UICs. We further find that administrative barriers have no significant effect, while barriers related to trust and skill matching with scientific partners even have a reverse effect to what we would have expected from the literature. This finding might point towards an effect of perceived versus deterring barriers that has been observed in innovation studies before and might be relevant for studying UICs as well. |
| Keywords: | barrier; emerging market; innovation; research and development; Turkey; university–industry collaboration |
| JEL: | O30 O32 O38 |
| Date: | 2022–02–04 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:113840&r= |
| By: | Alejandra Bellatin; Gabriela Galassi |
| Abstract: | We use data from online job postings listed on a job board to study how the de- mand for jobs linked to new technologies during the COVID-19 crisis responded to pandemic mitigation policies. We classify job postings into a standard occupation classification, using text analytics, and we group occupations according to their involvement in the production and use of digital technologies. We leverage the variation in the stringency of containment policies over time and across provinces. We find that when policies become more stringent, job postings in occupations that are related to digital infrastructure that allow for remote work fare relatively better than postings in more traditional occupations. Job postings for positions in occupations with low risk of automation recover faster during reopenings than postings for more traditional occupations. Occupations typically populated by disadvantaged groups (e.g., women and low-wage workers) post relatively few job postings if they are not linked to new technologies. We also find that cities with scarce pre-pandemic job postings related to digital technologies post fewer job ads overall when policies become more stringent. |
| Keywords: | Coronavirus disease (COVID-19); Econometric and statistical methods; Labour markets |
| JEL: | J23 J24 O14 |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocawp:22-17&r= |
| By: | Coad, Alexander; Amaral-Garcia, Sofia; Bauer, Peter; Domnick, Clemens; Harasztosi, Péter; Pál, Rozália; Teruel, Mercedes |
| Abstract: | The effect of the COVID shock on European economies has been severe and also unequal, with some firms being affected much more strongly than others. To improve the effectiveness of policy interventions, policymakers need to understand which types of vulnerable firms have been suddenly pushed into dire circumstances. We seek to fill this important gap in our knowledge by providing evidence from the EIBIS (European Investment Bank Investment Survey, 2016-2020) on how the COVID shock has affected the investment activity and investment-related framework conditions of vulnerable firms. While data on actual investment activity post-COVID is not yet available to us, we focus on investment expectations. We exploit the fact that the same questions relating to investment expectations have been asked in several previous survey waves, which enables a difference-indifferences approach to investigate how investment expectations might have suddenly changed, for vulnerable groups of firms, immediately after the onset of the COVID crisis. We focus on 4 groups of vulnerable firms: High-Growth Enterprises (HGEs), young and small firms, R&D investors and nonsubsidiary firms. R&D investors are more likely to be pessimistic about investment plans as a consequence of the COVID shock, and (similarly) HGEs are less likely to be optimistic about investment plans. R&D investors are less likely to be optimistic about the availability of internal finance, while HGEs and R&D investors are more likely to be pessimistic about the availability of external finance. Subsidiary firms, interestingly, are more likely to report a decrease in expected investment, although this could be part of a conservative group-level strategy and coordinated group-level reduction in investment, however that is not caused by any detectable lack of access to (internal or external) finance. Event study graphs generally confirm our regression results. |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:eibwps:202204&r= |
| By: | Nomaler, Önder (UNU-MERIT, Maastricht University); Verspagen, Bart (UNU-MERIT, Maastricht University) |
| Abstract: | We aim to contribute to the literature on product space and diversification by proposing a number of extensions of the current literature: (1) we propose that the alternative but related idea of a country space also has empirical and theoretical appeal; (2) we argue that the loss of comparative advantage should be an integral part of (testing the empirical relevance of) the product space idea; (3) we propose several new indicators for measuring relatedness in the product space; and (4) we propose a non-parametric statistical test based on bootstrapping to test the empirical relevance of the product space idea. |
| Keywords: | Diversification, specialization, product space, country space, path dependence of specialization, relatedness |
| JEL: | F14 F63 O11 O25 |
| Date: | 2022–04–04 |
| URL: | http://d.repec.org/n?u=RePEc:unm:unumer:2022011&r= |
| By: | Sinha, Avik (Asian Development Bank Institute); Shah, Muhammad Ibrahim (Asian Development Bank Institute); Mehta, Atul (Asian Development Bank Institute); Sharma, Rajesh (Asian Development Bank Institute) |
| Abstract: | In order to reduce greenhouse gas (GHG) emissions and to achieve the Sustainable Development Goals (SDGs), Asian countries are trying to realize the potential of energy innovation. However, several structural issues might deter the expected impact of energy innovation on GHG emissions. Given the ecologically unsustainable economic growth trajectory of Asian countries, achieving the full potential of energy innovation is necessary, and therefore an efficient development and diffusion of these solutions requires a policy reorientation. Given the present situation of Asian countries in attaining SDG objectives, there is a void in the academic literature in terms of a policy framework, and there lies the contribution of our study. We shed light on how regional integration and social inequality can moderate the desired environmental impact of energy innovation. Based on the outcomes of the study conducted on 24 Asian countries over the period 1990–2019, we recommend a multipronged SDG-oriented policy framework. This policy framework is developed by considering the internal and external structural issues with Asian countries, and, using a phase-wise policy implementation approach, a way to address the objectives of SDGs 7, 9, and 13 is discussed. |
| Keywords: | energy innovation; GHG emissions; Asia; regional integration; social inequality |
| JEL: | Q48 Q53 Q55 Q56 |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:ris:adbiwp:1304&r= |