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on Technology and Industrial Dynamics |
| By: | Arntz, Melanie; Genz, Sabrina; Gregory, Terry; Lehmer, Florian; Zierahn-Weilage, Ulrich |
| Abstract: | This paper examines the extent to which aggregate-level de-routinization can be attributed to firm-level technology adoption during the most recent technological expansion. We use administrative data and a novel firm survey to distinguish frontier technologies from older technologies. We find that adopters of frontier technologies contribute substantially to deroutinization. However, this is driven only by a subset of these firms: large adopters replace routine jobs and less routine-intensive adopters experience faster growth. These scale and composition effects reflect firms' readiness to adopt and implement frontier technologies. Our results suggest that an acceleration of technology adoption would be associated with faster de-routinization and an increase in between-firm heterogeneity. |
| Keywords: | technology, automation, tasks, capital-labor substitution, decomposition |
| JEL: | J21 J23 J24 O33 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewdip:283011&r=tid |
| By: | Antea Barišić; Mahdi Ghodsi (The Vienna Institute for International Economic Studies, wiiw); Robert Stehrer (The Vienna Institute for International Economic Studies, wiiw) |
| Abstract: | In recent decades, the development of novel technologies has intensified due to globalisation, prompting countries to enhance competitiveness through innovation. These technologies have significantly improved global welfare, particularly in sectors like healthcare, where they have facilitated tasks and boosted productivity, for example playing a crucial role in combating the COVID-19 pandemic. However, certain technologies, such as robots, can negatively impact employment by replacing workers and tasks. Additionally, the emergence of artificial intelligence as digital assets not only replaces specific tasks but also introduces complexities that may displace employees who are unable to adapt. While the existing literature extensively explores the heterogeneous effects of these technologies on labour markets, studies of their impact on migrant workers remain scarce. This paper presents pioneering evidence on the effects of various novel technologies on migrant employment in the European Union. The analysis covers 18 EU member states from 2005 to 2019 focusing on the impact of novel innovations, robot adoption, three types of digital assets, and total factor productivity, on migrant employment. The key findings reveal that innovations measured by the number of granted patents increase both the number and proportion of migrant workers relative to the overall workforce. While robots do replace jobs, their impact on native workers surpasses that of migrant workers, resulting in a higher share of migrant workers following robot adoption. Total factor productivity positively influences migrant workers, while the effects of digital assets are heterogeneous. Moreover, the impacts of these technologies on migrant workers vary significantly across different occupation types and educational levels. |
| Keywords: | Robot adoption, digitalisation, novel innovation, migrant workers |
| JEL: | O33 F22 D24 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:wii:wpaper:241&r=tid |
| By: | Antonia Lopez Villavicencio; Ivan Ledezma |
| Abstract: | We study how global value chain participation causally affects productivity at the firm level. We utilize an extensive dataset encompassing firms from countries of different income levels over the period 2006-2021, together with matching approaches to control for endogeneity. Our primary finding underscores that the simultaneous coordination of importing and exporting activities within a single firm leads to an increase in labor productivity. Positive effects on TFP are significant only within the subgroup of firms in the less developed countries and those operating in low-tech industries. Increased capital intensity appears as a plausible explanation of labor productivity gains. We also find higher innovation propensity, quality enhancements, and labor-cost reductions for two-way traders as channels through which GVC participation influences firms' technical change. The lower responsiveness of TFP in advanced countries can be explained by the different nature of technical change for firms operating closer to the world technology frontier. |
| Keywords: | Global value chains, productivity, firm, development, endogeneity |
| JEL: | C31 D24 F14 F15 O5 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:drm:wpaper:2024-4&r=tid |
| By: | Paolo Carioli; Dirk Czarnitzki; Gastón P Fernández Barros |
| Abstract: | Artificial Intelligence (AI) is considered to be the next general-purpose technology, with the potential of performing tasks commonly requiring human capabilities. While it is commonly feared that AI replaces labor and disrupts jobs, we instead investigate the potential of AI for overcoming increasingly alarming skills shortages in firms. We exploit unique German survey data from the Mannheim Innovation Panel on both the adoption of AI and the extent to which firms experience scarcity of skills. We measure skills shortage by the number of job vacancies that could not be filled as planned by firms, distinguishing among different types of skills. To account for the potential endogeneity of skills shortage, we also implement instrumental variable estimators. Overall, we find a positive and significant effect of skills shortage on AI adoption, the breadth of AI methods, and the breadth of areas of application of AI. In addition, we find evidence that scarcity of labor with academic education relates to firms exploring and adopting AI. |
| Keywords: | Artificial Intelligence, CIS data, skills shortage |
| Date: | 2024–02–08 |
| URL: | http://d.repec.org/n?u=RePEc:ete:msiper:735893&r=tid |
| By: | Yuan Gao (School of Economics, University of East Anglia); Emiliya Lazarova (School of Economics, University of East Anglia) |
| Abstract: | The research on measuring technological innovation quality has evolved with our understanding of the origin of novelty. Patents have been widely used in such studies because they are a form of copyright-protected outcome of inventions deemed to be valuable. The quality of technological innovation can be measured in multiple dimensions. In this paper, we make a methodological contribution to the literature on ex-ante technological novelty and propose two new indices based on a network approach: the Inverse Recombination Intensity Index (IRII) to capture the extent to which an invention is the outcome of a novel combination of pre-existing technological components; and the New Technology Ratio (NTR) to measure the share of new knowledge elements in the invention. Through an in-depth empirical study of patents filed in the Pharmaceuticals and Computer Technology sectors, we show that our proposed indices are correlated with some of the conventional patent quality indicators and go beyond that to reveal previously unnoticed features of the inventions process, of which some are sector-specific. Moreover, through our regression analysis, we demonstrate that IRII and NTR are important predictors of a patents’ potential impact on future inventions, which confirms the ex-ante nature of our indices. In the regression analysis we also include sector-country-specific R&D input variables as controls to test the robustness of our results. Our analysis suggests that the distinct characteristics of each sector affect how the quality of innovation is related to the ex-ante measures of technological novelty. We argue, therefore, that future analysis of the link between ex-ante novelty and ex-post quality of innovation needs to take into consideration the recombinant content of the invention and account for sectoral characteristics. |
| Keywords: | Innovation quality, Disruptive novelty, Ex-ante novelty, Patent, Network |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:uea:ueaeco:2024-02&r=tid |
| By: | Jan Malek; Melissa Newham; Jo Seldeslachts; Reinhilde Veugelers |
| Abstract: | This paper analyzes M&A patterns of R&D projects in the antidiabetics industry. For this purpose, we construct a database with all corporate individual antidiabetics R&D projects over the period 1997 - 2017, and add detailed information on firms’ technology dimension using patent information, next to their position in product markets. This allows us to identify the identity of targets and acquirers (who), the timing of acquisitions along the R&D process (when), and which type of R&D projects changes hands in terms of technology novelty (what). The main results can be summarized as follows. First, most of the action in M&As is in early R&D stages, still far from product markets. Second, most of the early-stage projects that change hands are high-risk/high-gain novel projects. Third, the industry leaders in the product markets are rather inactive in acquiring those novel early-stage projects. The likely acquirers of such projects are small or pipeline firms. Our results put in perspective the narrative that large incumbents acquire small targets with low-risk projects close to product launch. |
| Keywords: | M&As, innovation, R&D, pharmaceutics, technology, novelty, patents |
| Date: | 2024–02–06 |
| URL: | http://d.repec.org/n?u=RePEc:ete:msiper:735739&r=tid |
| By: | Daniel P. Gross; Bhaven N. Sampat |
| Abstract: | We introduce new historical administrative data identifying U.S. government-funded patents since the early twentieth century. In addition to the funding agency, the data report whether the government has title to the patent (“title” patents) or funded a patent assigned to a private organization (“license” patents). The data include a large number of “license” patents that cannot be linked to government funding from patent text or other sources. Combining the historical data with modern administrative sources, we present a public, consolidated data series measuring U.S. government-funded patents—including funding agencies—through 2020, and we provide code to extend this series in the future. We use the data to document long-run patterns in U.S. government-funded patents and federal patent policy, propose ways in which these data can be used in future research, and discuss limitations of the data. |
| JEL: | N42 N72 O31 O34 O38 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32136&r=tid |
| By: | De Monte, Enrico |
| Abstract: | This paper investigates the evolution of aggregate productivity and markups among French manufacturing firms between 1994 and 2016, by focusing on the role of reallocation with respect to both aggregate measures. Firm-level productivity and markups are estimated based on a gross output translog production function using popular estimation methods. I find an aggregate productivity growth of about 34% over the whole period while aggregate markups are found to remain relatively stable. As a key finding the study shows that over time reallocation of sales shares affects differently aggregate productivity and markups: Before 2000 both aggregate productivity and markups are importantly driven by reallocation effects; Post-2000, instead, the contribution of reallocation to aggregate productivity becomes negligible, inducing a slowdown in aggregate productivity growth, while I measure persistent reallocation of sales shares from lower to higher markup firms. Policy relevant implications of these dynamics are discussed. |
| Keywords: | productivity decomposition, production function estimation, business dynamism, market power, entry and exit |
| JEL: | C13 D21 D24 L16 L60 O47 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewdip:283014&r=tid |
| By: | Antea Barišić; Mahdi Ghodsi (The Vienna Institute for International Economic Studies, wiiw); Michael Landesmann (The Vienna Institute for International Economic Studies, wiiw) |
| Abstract: | This paper explores the complex interplay between technology adoption, specifically robotisation and digitalisation, and international migration within the EU and other advanced economies, including Australia, the UK, Japan, Norway and the US, over the period 2001-2019. Utilising a gravity model approach grounded in neoclassical migration theory, the study analyses how technological advancements influence migration flows. It examines two key technological variables the extent of digitalisation, represented by ICT capital per person employed, and the adoption of industrial robots, measured by the stock of robots per thousand workers. The research uniquely integrates these technological factors into migration analysis, considering both push and pull effects. Additionally, it accounts for various other migration determinants such as macroeconomic conditions, demography and policy factors. The findings reveal insightful dynamics about the relationships between technological progress, labour market conditions and migration patterns, contributing significantly to the current literature and informing future migration policies and the impact of technology adoption. |
| Keywords: | Robot adoption, digitalisation, novel innovation, migrant workers |
| JEL: | O33 F22 D24 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:wii:wpaper:242&r=tid |
| By: | Laatsit, Mart (CIRCLE, Lund University); Boschma, Ron (Utrecht University) |
| Abstract: | Policy networks are an important source of information for policy making. Yet, we have only a limited understanding of how policy networks are structured among innovation policy makers and which factors shape their structure. This paper studies how proximities can explain what drives the connections in policy networks. More specifically, we look at innovation policy networks between EU member states. We use social network analysis based on our own data to map the networks of the 28 EU innovation policy directors, consisting of 756 potential connections, and study the proximities shaping these networks. Geographical and cultural proximity turn out to be strong predictors for symmetric and asymmetric ties, but we do not find a relationship between policy proximity (in terms of similarities in business environment regulations and innovation policy) and policy network formation between countries. |
| Keywords: | Innovation policy; policy networks; proximities; policy proximity; social network analysis |
| JEL: | O33 O38 |
| Date: | 2024–02–14 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:lucirc:2024_003&r=tid |
| By: | Daron Acemoglu (Massachusetts Institute of Technology); Philippe Aghion (Collège de France; INSEAD; London School of Economics and Political Science); Lint Barrage (ETH Zurich); David Hémous (University of Zurich) |
| Abstract: | To combat climate change without sacrificing long-term economic growth, innovation must be redirected toward green technologies. The authors review recent literature that has developed a directed technical change framework where innovation can be endogenously targeted either toward fossil-fuel enhancing technologies or clean energy sources (such as renewables). They provide empirical evidence of path dependence in firms' choice between green and dirty innovation. They then draw implications of this path dependence for the design of environmental policy and for economic growth. In particular, they show that their framework has distinctive implications regarding unilateral environmental policies, international cooperation, the use of intermediate energy sources such as natural gas, and the role of civil society. |
| Keywords: | green growth, endogenous growth, directed technical change, climate change, innovation, environmental policy |
| JEL: | F18 O30 O41 O44 Q43 Q54 Q55 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:iie:wpaper:wp23-14&r=tid |
| By: | Mr. Serhan Cevik |
| Abstract: | The rise of fintech is revolutionizing the financial landscape, with products and companies advancing innovative technologies to improve and automate financial services. In this paper, I use a novel dataset and implement a dynamic modelling to investigate the relationship between fintech and economic growth in a panel of 198 countries over the period 2012–2020. This cross-country approach—utilizing direct measures of fintech and dealing with potential endogeneity—provides interesting empirical insights. First, the impact magnitude and statistical significance of fintech on real GDP per capita growth depend on the type of instrument (digital lending vs. digital capital raising). While digital lending has a statistically significant positive effect on economic growth, digital capital raising has a large but insignificant effect. Second, the overall impact of fintech including all instruments is positive and statistically significant because of the overwhelming share of digital lending in total. Finally, while the positive relationship between fintech and growth is stronger in magnitude in advanced economies, the statistical significance of this effect is higher in developing countries. Taken as a whole, these results confirm Schumpeter’s prediction that financial innovation can promote growth, but not every type of fintech becomes an accelerator. |
| Date: | 2024–02–02 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2024/020&r=tid |