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on Technology and Industrial Dynamics |
| By: | Lee, Kyung Min; Kim, Mee Jung; Brown, J. David; Earle, John S.; Liu, Zhen |
| Abstract: | We evaluate the contributions of immigrant entrepreneurs to innovation in the U.S. using linked survey-administrative data on 199, 000 firms with a rich set of innovation measures and other firm and owner characteristics. We find that not only are immigrants more likely than natives to own businesses, but on average their firms display more innovation activities and outcomes. Immigrant-owned firms are particularly more likely to create completely new products, improve previous products, use new processes, and engage in both basic and applied R&D, and their efforts are reflected in substantially higher levels of patents and productivity. Immigrant owners are slightly less likely than natives to imitate products of others and to hire more employees. Delving into potential explanations of the immigrant-native differences, we study other characteristics of entrepreneurs, access to finance, choice of industry, immigrant self-selection, and effects of diversity. We find that the immigrant innovation advantage is robust to controlling for detailed characteristics of firms and owners, it holds in both high-tech and non-high-tech industries and, with the exception of productivity, it tends to be even stronger in firms owned by diverse immigrant-native teams and by diverse immigrants from different countries. The evidence from nearly all measures that immigrants tend to operate more innovative and productive firms, together with the higher share of business ownership by immigrants, implies large contributions to U.S. innovation and growth. |
| Date: | 2024–04–18 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:3kycm&r=tid |
| By: | Andres Rodriguez-Pose; Zhuoying You; ; |
| Abstract: | Artificial intelligence (AI) and robotics are revolutionising production, yet their potential to stimulate innovation and change innovation patterns remains underexplored. This paper examines whether AI and robotics can spearhead technological innovation, with a particular focus on their capacity to deliver where other policies have mostly failed: less developed cities and regions. We resort to OLS and IV-2SLS methods to probe the direct and moderating influences of AI and robotics on technological innovation across 270 Chinese cities. We further employ quantile regression analysis to assess their impacts on innovation in more and less innovative cities. The findings reveal that AI and robotics significantly promote technological innovation, with a pronounced impact in cities at or below the technological frontier. Additionally, the use of AI and robotics improves the returns of investment in science and technology (S&T) on technological innovation. AI and robotics moderating effects are often more pronounced in less innovative cities, meaning that AI and robotics are not just powerful instruments for the promotion of innovation but also effective mechanisms to reduce the yawning gap in regional innovation between Chinese innovation hubs and the rest of the country. |
| Keywords: | AI, robotics, China, technological innovation, territorial inequality |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:egu:wpaper:2412&r=tid |
| By: | Emanuel Gasteiger (Institute for Mathematical Economics and Statistics, Vienna University of Technology); Michael Kuhn (International Institute for Applied Systems Analysis (IIASA)); Matthias Mistlbacher (Institute for Mathematical Economics and Statistics, Vienna University of Technology); Klaus Prettner (Department of Economics, Vienna University of Economics and Business) |
| Abstract: | While automation technologies replace workers in ever more tasks, robots, 3D printers, and AI-based applications require substantial amounts of electricity. This raises concerns regarding the feasibility of the energy transition towards mitigating climate change. How does automation interact with conventional capital in driving energy demand and how do taxes on robots and taxes on electricity affect the adoption of robots and AI? To answer these questions, we generalize a standard economic growth model with automation and electricity use. In addition, we augment the model with electricity taxes and robot taxes and show the mechanisms by which these taxes affect automation. We find that an electricity tax serves a similar purpose as a robot tax. However, a robot tax is much more difficult to implement from a practical perspective. |
| Keywords: | Automation, Robots, Growth, Electricity Use, Energy Taxes, Robot Taxes |
| JEL: | O11 O14 H21 H23 |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:wiw:wiwwuw:wuwp364&r=tid |
| By: | Sampson, Thomas |
| Abstract: | Global value chains create opportunities for North-South technology diffusion. This paper studies technology transfer in value chains when contracts are incomplete and input production technologies are imperfectly excludable. It introduces a new taxonomy of value chains based on whether the headquarters firm benefits from imitation of its supplier's technology. In inclusive value chains, where imitation is beneficial, the headquarters firm promotes technology diffusion. But in exclusive value chains headquarters seeks to limit supplier imitation. The paper analyzes how this distinction affects the returns to offshoring, the welfare effects of technical change and the social efficiency of knowledge sharing. |
| JEL: | G30 O10 |
| Date: | 2024–05–01 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:119640&r=tid |
| By: | Daan Freeman; Leon Bettendorf; Gerrit Hugo van Heuvelen; Gerdien Meijerink |
| Abstract: | This study examines the decline in firm dynamism within the Netherlands, potentially linked to the deceleration of productivity growth. We utilise a rich microdata set covering the period 2006-2016, encompassing nearly all Dutch corporations. This dataset facilitates an evaluation of start-ups’ and exiting firms’ contributions to Total Factor Productivity (TFP) growth across various industries, employing the Melitz and Polanec (2015) decomposition approach. Our findings reveal that in service sectors, the creative destruction hypothesis is substantiated, as start-ups and exiting firms positively impact overall TFP growth. In contrast, TFP growth in manufacturing is primarily driven by incumbent firms. Entry and exit dynamics in this context exert minimal or even negative influence on TFP growth. Although entrants in manufacturing initially display lower productivity than incumbents, their productivity growth outpaces that of incumbents. In services, entrants commence operations with higher initial productivity, a trait that gradually diminishes over time. Generally, entrants with relatively low productivity are predisposed to exit within five years, aligning with the ’up-or-out’ pattern. |
| Keywords: | productivity slowdown, firm dynamics, TFP, Netherlands |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_11071&r=tid |
| By: | Reinhilde Veugelers (Peterson Institute for International Economics) |
| Abstract: | This paper focuses on the innovation angle in green industrial policy design. The innovation system, delivering new and improved technology solutions for the clean energy transition, can be the cornerstone of a successful transition that reconciles decarbonization, competitive value creation and jobs, and strategic autonomy on a global scale. This, however, requires the innovation system to be properly directed. This paper first lays out the principles of a policy design that properly steers the innovation system. It then documents the current performance on clean energy innovations and clean energy policymaking globally, with focus on the Inflation Reduction Act (IRA) and the Net-Zero Industry Act (NZIA) trends in clean tech policymaking in the United States and European Union, respectively. The evidence shows that the innovation system is not at full potential, and there is still ample room to improve the current clean energy policymaking and international policy coordination. |
| Keywords: | climate change, clean tech, innovation, green innovation policy, strategic autonomy |
| JEL: | O31 O38 Q55 |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:iie:wpaper:wp24-5&r=tid |
| By: | Orsetta Causa; Emilia Soldani; Maxime Nguyen; Tomomi Tanaka |
| Abstract: | Climate change mitigation policies affect the allocation of workers on the labor market: jobs in high-polluting industries will contract, while jobs in the “green” sector will grow. A just transition in the labour market requires policies to improve the allocation of workers and their deployability, for instance towards performing green tasks; as well as to manage and minimise scarring effects associated with job losses in polluting industries. Using an econometric analysis, this paper investigates the role of structural and policy factors in shaping a number of relevant labour market transitions, uncovering heterogeneity across different groups of workers. Education is the most important individual-level driver of transitions from non-employment to green jobs, with a particularly strong effect from graduating in scientific fields for young people entering the labour market. Women are significantly less likely than men to move into green jobs out of non-employment. Workers employed in high-polluting occupations face higher displacement risks than other workers, but this does not translate into higher long-term unemployment risks. In terms of policies, the paper finds that the labour market implications of the greening economy can be addressed by general structural policies favouring labour market efficiency in terms of workers’ reallocation, labour market inclusiveness in terms of promoting equality of opportunities and minimising long-term scars. Results also suggest that place-based policies are needed to mitigate scarring effects for displaced workers. |
| Keywords: | green transition, labour markets, policy analysis |
| JEL: | J08 J21 Q52 Q48 |
| Date: | 2024–05–07 |
| URL: | http://d.repec.org/n?u=RePEc:oec:ecoaaa:1803-en&r=tid |
| By: | Jiachen T. Wang; Zhun Deng; Hiroaki Chiba-Okabe; Boaz Barak; Weijie J. Su |
| Abstract: | Generative artificial intelligence (AI) systems are trained on large data corpora to generate new pieces of text, images, videos, and other media. There is growing concern that such systems may infringe on the copyright interests of training data contributors. To address the copyright challenges of generative AI, we propose a framework that compensates copyright owners proportionally to their contributions to the creation of AI-generated content. The metric for contributions is quantitatively determined by leveraging the probabilistic nature of modern generative AI models and using techniques from cooperative game theory in economics. This framework enables a platform where AI developers benefit from access to high-quality training data, thus improving model performance. Meanwhile, copyright owners receive fair compensation, driving the continued provision of relevant data for generative model training. Experiments demonstrate that our framework successfully identifies the most relevant data sources used in artwork generation, ensuring a fair and interpretable distribution of revenues among copyright owners. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.13964&r=tid |
| By: | Joaquin Vespignani (Tasmanian School of Business and Economics, University of Tasmania, Australia); Russell Smyth (Department of Economics, Monash University, Clayton, Australia) |
| Abstract: | This paper employs insights from earth science on the financial risk of project developments to present an economic theory of critical minerals. Our theory posits that back-ended critical mineral projects that have unaddressed technical and nontechnical barriers, such as those involving lithium and cobalt, exhibit an additional risk for investors which we term the “back-ended risk premium”. We show that the back-ended risk premium increases the cost of capital and, therefore, has the potential to reduce investment in the sector. We posit that the back-ended risk premium may also reduce the gains in productivity expected from artificial intelligence (AI) technologies in the mining sector. Progress in AI may, however, lessen the back-ended risk premium itself through shortening the duration of mining projects and the required rate of investment through reducing the associated risk. We conclude that the best way to reduce the costs associated with energy transition is for governments to invest heavily in AI mining technologies and research. |
| Keywords: | Critical Minerals, Artificial Intelligence, Risk Premium |
| JEL: | Q02 Q40 Q50 |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:mos:moswps:2024-08&r=tid |
| By: | Rodríguez-Pose, Andrés; Dijkstra, Lewis; Poelman, Hugo |
| Abstract: | While in recent times many regions have flourished, many others are stuck—or are at risk of becoming stuck—in a development trap. Such regions experience decline in economic growth, employment, and productivity relative to their neighbors and to their own past trajectories. Prolonged periods in development traps are leading to political dissatisfaction and unrest. Such discontent is often translated into support for antisystem parties at the ballot box. In this article we study the link between the risk, intensity, and duration of regional development traps and the rise of discontent in the European Union (EU)—proxied by the support for Eurosceptic parties in national elections between 2013 and 2022—using an econometric analysis at a regional level. The results highlight the strong connection between being stuck in a development trap, often in middle- or high-income regions, and support for Eurosceptic parties. They also suggest that the longer the period of stagnation, the stronger the support for parties opposed to European integration. This relationship remains robust whether considering only the most extreme Eurosceptic parties or including parties with more moderate levels of Euroscepticism. |
| Keywords: | discontent; euroscepticism; development trap; economic growth; employment; productivity; regions; EU; Taylor & Francis deal |
| JEL: | D72 R58 R11 |
| Date: | 2024–04–17 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:122411&r=tid |