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on Technology and Industrial Dynamics |
| By: | Albinowski, Maciej (Institute for Structural Research (IBS)); Lewandowski, Piotr (Institute for Structural Research (IBS)) |
| Abstract: | We study the age- and gender-specific labour market effects of two key modern technologies, Information and Communication Technologies (ICT) and robots, in 14 European countries between 2010 and 2018. To identify the causal effects of technology adoption, we utilise the variation in technology adoption between industries and apply the instrumental variables strategy proposed by Acemoglu and Restrepo (2020). We find that the exposure to ICT and robots increased the shares of young and prime-aged women in employment and the wage bills of particular sectors, but reduced the shares of older women and prime-aged men. The adverse effects were particularly pronounced for older women in cognitive occupations, who had relatively low ICT-related skills; and for young men in routine manual occupations, who experienced substitutions by robots. Between 2010 and 2018, the growth in ICT capital played a much larger role than robot adoption in the changes in the labour market outcomes of demographic groups. |
| Keywords: | technological change, automation, ICT, robots, employment, wages, Europe |
| JEL: | J24 O33 J23 |
| Date: | 2022–11 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp15752&r=tid |
| By: | Kuosmanen, Natalia; Maczulskij, Terhi |
| Abstract: | Abstract This paper investigates the importance of firm dynamics, including entry and exit and the allocation of carbon emissions across firms, on the green transition. Using the 2000–2019 firm-level register data on greenhouse gas emissions matched with the Financial Statement data in the Finnish manufacturing sector, we examine the sources of carbon-productivity growth and assess the relative contributions of structural change and firm dynamics. We find that continuing firms were the main drivers of carbon productivity growth whereas the contribution of entering and exiting firms was negative. In addition, the allocation of emissions across firms seems to be inefficient; its impact on carbon productivity growth was negative over the study period. Moreover, we find that there is a positive relationship between labor-intensive firms and carbon productivity but that firms with a larger market share tend to be less productive in terms of carbon use. |
| Keywords: | Carbon productivity, Decomposition, Firm dynamics, Firm-level data, Manufacturing |
| JEL: | D24 L60 Q54 |
| Date: | 2022–12–13 |
| URL: | http://d.repec.org/n?u=RePEc:rif:wpaper:99&r=tid |
| By: | Serenella Caravella; Valeria Cirillo; Francesco Crespi; Dario Guarascio; Mirko Menghini |
| Abstract: | The digital transformation is an important driver of long-run productivity growth and, as such, it has the potential to promote a more inclusive and sustainable growth. However, digital capabilities, crucial to develop and govern new digital technologies, are unevenly distributed across European regions increasing the risk of divergence and polarization. By taking advantage of a set of original indicators capturing the level of digital skills in the regional workforce, this work analyzes the factors shaping the process of digital skill accumulation in the EU over the period 2011-2018. Relying on transition probability matrices and dynamic random effects probit models, we provide evidence of a strong and persistent regional polarization in the adoption and deployment of digital skills. Further, we investigate whether European Funds (European Regional Development Fund, Cohesion Funds, and European Social Funds) are capable to shape the digitalization process and to favor regional convergence |
| Keywords: | Digital transition; Skills; Labour markets; Persistence; Regional development; EU policies |
| JEL: | O14 O30 O38 |
| Date: | 2022–10 |
| URL: | http://d.repec.org/n?u=RePEc:sap:wpaper:wp227&r=tid |
| By: | Luintel, Kul B (Cardiff Business School); Pourpourides, Panayiotis M. (Cardiff Business School) |
| Abstract: | A consensus in the growth literature is that scale effects of R&D are non-existent across mature industrialized economies. However, the scrutiny across emerging economies is lacklustre at best. The empirical studies of scale effects also leave the issues of unbalanced regression (non-standard distribution) largely unaddressed. In this paper, we conduct separate but parallel empirical scrutiny of scale effects across the panels of industrialized and emerging countries, clearly addressing these econometric issues, and employing a more realistic measure of the scale of R&D activities than has been applied hitherto. We provide parallel but novel estimates of significant scale effects across emerging countries, and their absence across developed countries. We then propose an endogenous growth model and show that scale effects exist during growth transitions but not at the vicinity of the long-run equilibrium, which reconciles our results. Thus, we shed light on a long-debated and important issue. Estimates of our model’s predictions reveal that the long-run growth rates of per capita real GDP and TFP are driven by the growth rates of technological innovation and aggregate employment, except that only the former matters for the TFP growth across emerging countries. |
| Keywords: | Endogenous Technical Change; Scale Effects; Panel Integration and Cointegration |
| JEL: | O3 O4 O14 O33 O47 |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:cdf:wpaper:2022/19&r=tid |
| By: | Dörr, Julian Oliver |
| Abstract: | Theory suggests that new market entrants play a special role for the creation of new technological pathways required for the development and diffusion of more sustainable forms of production, consumption, mobility and housing. Unconstrained by past technological investments, entrants can introduce more radical environmental innovations than incumbent firms whose past R&D decisions make them locked into path-dependent trajectories of outdated technologies. Yet, little research exists which provides empirical evidence on new ventures' role in the technological transition towards decarbonization and dematerialization. This is mainly because patenting is rare among start-ups and also no historical track record about their R&D investments exists, both data sources commonly used to determine a company's technological footprint. To enable the identification of clean technology-oriented market entrants and to better understand their role as adopters and innovators for sustainable market solutions, this paper presents framework that systematically maps new ventures' business models to a set of well-defined clean technologies. For this purpose, the framework leverages textual descriptions of new entrants' business summaries that are typically available upon business registration and allow for a good indication of their technological orientation. Furthermore, the framework uses textual information from patenting activities of established innovators to model semantic representations of technologies. Mapping company and technology descriptions into a common vector space enables the derivation of a fine-granular measureof entrants' technological orientation. Applying the framework to a survey of German start-up firms suggests that clean technology-oriented market entrants act as accelerators of technical change: both by virtue of their existing products and services and through a high propensity to introduce additional environmental innovations. |
| Keywords: | Clean technologies,technological orientation,environmental innovation,sustainable entrepreneurship,text modeling,natural language processing |
| JEL: | C38 O13 Q55 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewdip:22057&r=tid |
| By: | Barth, Erling (Institute for Social Research, Oslo); Bryson, Alex (University College London); Dale-Olsen, Harald (Institute for Social Research, Oslo) |
| Abstract: | We utilize a new survey on Norwegian firms' digitalization and technology investments, linked to population-wide register data, to show that the pandemic massively disrupted the technology investment plans of firms, not only postponing investments, but also introducing new technologies. More productive firms innovated, while less productive firms postponed investments. Most innovations were permanent, not due to acceleration of existing plans, thus the pandemic yields longterm influence in directions unanticipated before the pandemic. The new technologies are associated with increased labour demand for skilled workers, and reduced demand for unskilled workers, particularly for the more productive firms. |
| Keywords: | technology investments, digitalization, labour demand, pandemic, COVID-19 |
| JEL: | D22 D24 F14 L11 L60 |
| Date: | 2022–11 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp15762&r=tid |
| By: | Fabio Montobbio (Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy – BRICK, Collegio Carlo Alberto, Torino, Italy – ICRIOS, Bocconi University, Milano, Italy); Jacopo Staccioli (Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy – Institute of Economics, Scuola Superiore Sant’Anna, Pisa, Italy); Maria Enrica Virgillito (Institute of Economics, Scuola Superiore Sant’Anna, Pisa, Italy – Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy); Marco Vivarelli (Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy – UNU-MERIT, Maastricht, The Netherlands – IZA, Bonn, Germany) |
| Abstract: | What have we learned, from the most recent years of debate and analysis, of the future of work being threatened by technology? This paper presents a critical review of the empirical literature and outlines both lessons learned and challenges ahead. Far from being fully exhaustive, the review intends to highlight common findings and main differences across economic studies. According to our reading of the literature, a few challenges—and also the common factors affecting heterogeneous outcomes across studies—still stand, including (i) the variable used as a proxy for technology, (ii) the level of aggregation of the analyses, (iii) the deep heterogeneity of different types of technologies and their adopted mix, (iv) the structural differences across adopters, and (v) the actual combination of the organisational practices in place at the establishment level in affecting net job creation/destruction and work reorganisation. |
| Keywords: | Technology, Employment, Skills, Occupations, Tasks, Future of Work |
| JEL: | O33 |
| Date: | 2022–11 |
| URL: | http://d.repec.org/n?u=RePEc:ctc:serie5:dipe0028&r=tid |
| By: | Lee G. Branstetter; Guangwei Li; Mengjia Ren |
| Abstract: | Are Chinese industrial policies making the targeted Chinese firms more productive? Alternatively, are efforts to promote productivity undercut by efforts to maintain or expand employment in less productive enterprises? In this paper, we attempt to shed light on these questions through the analysis of previously underutilized microdata on direct government subsidies provided to China’s publicly traded firms. We categorize subsidies into different types. We then estimate total-factor productivity (TFP) for Chinese listed firms and investigate the relationship between these estimates of TFP and the allocation of government subsidies. We find little evidence that the Chinese government consistently “picks winners”. Firms’ ex-ante productivity is negatively correlated with subsidies received by firms, and subsidies appear to have a negative impact on firms’ ex-post productivity growth throughout our data window, 2007 to 2018. Neither subsidies given out under the name of R&D and innovation promotion nor industrial and equipment upgrading positively affect firms’ productivity growth. On the other hand, we find a positive impact of subsidy on current year employment, both for the aggregated and employment-related subsidies. These findings suggest that China’s increasingly prescriptive industrial policies may have generated limited effects in promoting productivity. |
| JEL: | O25 O32 |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:30699&r=tid |
| By: | Edward Lazear; Kathryn L. Shaw; Grant E. Hayes; James M. Jedras |
| Abstract: | Wages have been spreading out across workers over time – or in other words, the 90th/50th wage ratio has risen over time. A key question is, has the productivity distribution also spread out across worker skill levels over time? Using our calculations of productivity by skill level for the U.S., we show that the distributions of both wages and productivity have spread out over time, as the right tail lengthens for both. We add OECD countries, showing that the wage-productivity correlation exists, such that gains in aggregate productivity, or GDP per person, have resulted in higher wages for workers at the top and bottom of the wage distribution. However, across countries, those workers in the upper income ranks have seen their wages rise the most over time. The most likely international factor explaining these wage increases is the skill-biased technological change of the digital revolution. The new AI revolution that has just begun seems to be having a similar skill-biased effects on wages. But this current AI, called “supervised learning,” is relatively similar to past technological change. The AI of the distant future will be “unsupervised learning,” and it could eventually have an effect on the jobs of the most highly skilled. |
| JEL: | J00 J30 M50 |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:30734&r=tid |
| By: | Axenbeck, Janna; Berner, Anne; Kneib, Thomas |
| Abstract: | The ongoing digital transformation has raised hopes for ICT-based climate protection within manufacturing industries, such as dematerialized products and energy efficiency gains. However, ICT also consume energy as well as resources, and detrimental effects on the environment are increasingly gaining attention. Accordingly, it is unclear whether trade-offs or synergies between the use of digital technologies and energy savings exist. Our analysis sheds light on the most important drivers of the relationship between ICT and energy use in manufacturing. We apply flexible tree-based machine learning to a German administrative panel data set including more than 25,000 firms. The results indicate firm-level heterogeneity, but suggest that digital technologies relate more frequently to an increase in energy use. Multiple characteristics, such as energy prices and firms' energy mix, explain differences in the effect. |
| Keywords: | digital technologies,energy use,manufacturing,machine learning |
| JEL: | C14 D22 L60 O33 Q40 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewdip:22059&r=tid |
| By: | Simon Bunel; Benjamin Hadjibeyli |
| Abstract: | The Innovation tax credit (crédit d’impôt innovation, CII) is an extension of the Research tax credit (crédit d’impôt recherche, CIR) intended to boost the incentive effect of the latter on SMEs to encourage them to engage in the creation of new products via the development of prototypes or pilot plants. Introduced in 2013, it amounted to €120 million of tax credit in 2014 for some 5,300 recipients. This article seeks to measure the impact of the introduction of this scheme on its beneficiaries over the period from 2013 to 2016. Using a difference-in-differences method following propensity score matching, we find a greater increase in employment in the short term for firms benefiting from the scheme, along with a more pronounced increase in their sales turnover in the medium term. A greater increase in the number of new products produced by the beneficiaries is also observed. Finally, the introduction of the CII went along with a reduction in the research expenditure reported under the CIR. |
| Keywords: | Innovation, Tax credit, Evaluation, Products |
| JEL: | C21 D22 H32 L25 O31 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:bfr:banfra:887&r=tid |
| By: | CIRILLO Valeria; RINALDINI Matteo; VIRGILLITO Maria Enrica; DIVELLA Marialuisa; MANICARDI Caterina; MASSIMO Francesco Sabato; CETRULO Armanda; COSTANTINI Eleonora; MORO Angelo; STACCIOLI Jacopo |
| Abstract: | A full understanding of the technological complexity underlying robotics and automation is still lacking, most of all when focusing on the impacts on work in services. By means of a qualitative analysis based on over 50 interviews to HR managers, IT technicians, workers and trade union delegates, this work provides evidence on the main changes occurring at shopfloor level in selected Italian companies having adopted technological artefacts potentially affecting labour tasks by automating processes. The analysis of interviews complemented with visits to the companies and desk research on business documents highlights that so far labour displacement due to the adoption of automation technologies is not yet in place, while tasks and organizational reconfiguration appear more widespread. Major heterogeneity applies across plants due to the final product/service produced, the techno-organizational capabilities of the firm and the type of strategic orientation versus technological adoption. These elements also affect drivers and barriers to technological adoption. Overall, the analysis confirms the complexity in automating presumably low-value-added phases: human labour remains crucial in conducting activities that require flexibility, adaptability and reconfiguration of physical tasks. Further, human agency and worker representation, in particular the role of trade unions, are almost disregarded and not considered by the firms when deciding to introduce a new technology. |
| Keywords: | automation, digitalization, robots, digital society |
| Date: | 2022–11 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc129691&r=tid |
| By: | Dieppe, Alistair; Matsuoka, Hideaki |
| Abstract: | This paper investigates how the sector-specific source or the changing sectoral composition of labor productivity has contributed to Ø-convergence, using a newly constructed eightsector database. The main findings are twofold. First, both within and sectoral reallocation have become important drivers of Ø-convergence in labor productivity. Second, agricultural productivity growth has been a significant contributor to Ø-convergence, whereas catch-up in other sectors has only contributed a small amount to convergence. The strong growth of the agriculture sector has been the most important driver of aggregate productivity convergence even though agricultural productivity itself in low-income countries is not converging to that in advanced economies. |
| Keywords: | Labor productivity,Shift-share decomposition,Ø-decomposition,New sectoral database |
| JEL: | O1 O11 O4 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bofitp:bdp2022_004&r=tid |
| By: | Maria Aristizabal-Ramirez (Federal Reserve Board of Governors); John V. Leahy (University of Michigan & NBER); Linda L. Tesar (University of Michigan & NBER) |
| Abstract: | This paper is motivated by a set of cross-country observations on growth, structural transformation, and investment rates in a large sample of countries. We observe a hump-shaped relationship between a country's investment rate and its level of development, both within countries over time and across countries. Advanced economies reach their investment peak at a higher level of income and at an earlier point in time relative to emerging markets. We also observe the familiar patterns of structural change (a decline in the agricultural share and an increase in the services share, both relative to manufacturing). The pace of change observed in the 1930 to 1980 period in advanced economies is remarkably similar to that in emerging markets since 1960. We develop a two-region model of the world economy in which regions are isolated from each other up to the point of capital market liberalization in the early 1990s. At that point, capital flows from advanced economies to emerging markets and accelerates the process of structural change in emerging markets. The majority of gains from financial liberalization accrue to emerging economies. We consider the impact of a Òsecond waveÓ of liberalization when China fully opens its economy to capital inflows. |
| Keywords: | growth, structural change, financial liberalization |
| JEL: | E2 F62 O10 |
| Date: | 2022–10 |
| URL: | http://d.repec.org/n?u=RePEc:mie:wpaper:685&r=tid |
| By: | Romagnoli, Matteo |
| Abstract: | The paper investigates the effect of electricity liberalisation on the variety of clean energy patent’ search space to asses whether a more competitive electricity market can foster the development of radical clean-energy technologies. This idea is tested using a cross-section of patents filed in the period 1990-2017, a set of patent-level indicators and an instrumental variable approach. Results show that electricity liberalisation pushes clean-energy patents to cite knowledge from technological fields other than their own. However, the reform does not significantly affect the overall breath of the knowledge base of these patents. Additional insights are drawn by looking at the correlation between electricity liberalisation and an indicator of novelty in patents’ search space. The results are consistent with the claim that electricity liberalisation has a positive effect on the development of radical clean-energy technologies. At the same time, by describing how the reform changes clean-energy patents’ search space, they define this effect more precisely. |
| Keywords: | Research and Development/Tech Change/Emerging Technologies, Resource /Energy Economics and Policy |
| Date: | 2022–12–19 |
| URL: | http://d.repec.org/n?u=RePEc:ags:feemwp:329738&r=tid |
| By: | Tamay Besiroglu; Nicholas Emery-Xu; Neil Thompson |
| Abstract: | Since its emergence around 2010, deep learning has rapidly become the most important technique in Artificial Intelligence (AI), producing an array of scientific firsts in areas as diverse as protein folding, drug discovery, integrated chip design, and weather prediction. As more scientists and engineers adopt deep learning, it is important to consider what effect widespread deployment would have on scientific progress and, ultimately, economic growth. We assess this impact by estimating the idea production function for AI in two computer vision tasks that are considered key test-beds for deep learning and show that AI idea production is notably more capital-intensive than traditional R&D. Because increasing the capital-intensity of R&D accelerates the investments that make scientists and engineers more productive, our work suggests that AI-augmented R&D has the potential to speed up technological change and economic growth. |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2212.08198&r=tid |