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on Technology and Industrial Dynamics |
| By: | Ekaterina Prytkova; Fabien Petit; Deyu Li; Sugat Chaturvedi; Tommaso Ciarli |
| Abstract: | This paper measures the exposure of industries and occupations to 40 digital technologies that emerged over the past decade and estimates their impact on European employment. Using a novel approach that leverages sentence transformers, we calculate exposure scores based on the semantic similarity between patents and ISCO-08/NACE Rev.2 classifications to construct an open–access database, ‘TechXposure’. By combining our data with a shift–share approach, we instrument the regional exposure to emerging digital technologies to estimate their employment impact across European regions. We find an overall positive effect of emerging digital technologies on employment, with a one-standard-deviation increase in regional exposure leading to a 1.069 percentage point increase in the employment-to-population ratio. However, upon examining the individual effects of these technologies, we find that smart agriculture, the internet of things, industrial and mobile robots, digital advertising, mobile payment, electronic messaging, cloud storage, social network technologies, and machine learning negatively impact regional employment. |
| Keywords: | occupation exposure, industry exposure, text as data, natural language processing, sentence transformers, emerging digital technologies, automation, employment |
| JEL: | C81 O31 O33 O34 J24 O52 R23 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10955&r=tid |
| By: | Philipp Boeing; Loren Brandt; Ruochen Dai; Kevin Lim; Bettina Peters |
| Abstract: | We study the evolution of patenting in China from 1985-2019. We use a Large Language Model to measure patent importance based on patent abstracts and classify patent ownership using a comprehensive business registry. We highlight four insights. First, average patent importance declined from 2000-2010 but has increased more recently. Second, private Chinese firms account for most of patenting growth whereas overseas patentees have played a diminishing role. Third, patentees have greatly reduced their dependence on foreign knowledge. Finally, Chinese and foreign patenting have become more similar in technological composition, but differences persist within technology classes as revealed by abstract similarities. |
| Keywords: | Innovation Patents Technology China |
| JEL: | O3 |
| Date: | 2024–03–08 |
| URL: | http://d.repec.org/n?u=RePEc:tor:tecipa:tecipa-770&r=tid |
| By: | Di Girolamo, Valentina; Mitra, Alessio; Ravet, Julien; Peiffer-Smadja, Océane; Balland, Pierre-Alexandre |
| Abstract: | This paper studies the relationship between knowledge complexity and countries' technological dependency, with a focus on the EU's position vis-à-vis other major economies. Using patent data, we calculate the knowledge complexity index at technological level for a set of countries over the period 1990-2020 to assess the EU's technological capabilities. Our findings show that the EU's overall position has progressively worsened vis-à-vis the US, China, Japan, and South Korea over the last three decades, that the EU's technological base is more diversified than that of other major economies, but is disproportionally more specialised in less complex technologies than its counterparts. Finally, the EU is particularly dependent on just a few countries in most complex technologies. |
| Keywords: | Complex technologies, Technological dependencies, Strategic autonomy, Relatedness |
| JEL: | O11 O33 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:283907&r=tid |
| By: | Perez-Alaniz, Mauricio; Lenihan, Helena; Doran, Justin; Rammer, Christian |
| Abstract: | Public financial support for firm-level Research and Innovation (R&I) can generate important socio-economic returns. This is especially true if firms use this support to develop radical innovation, defined as new-to-market goods and services. However, radical innovation is risky, and prone to failure. Therefore, subsidising radical innovation can also generate sub-optimal socio-economic returns (i.e. policy failure). Understanding how public funding for R&I can be allocated in a way that encourages radical innovation, while avoiding policy failure, is crucial. Our paper investigates, for thefirst time, whether public fundingfor R&I generates more radical innovation in firms seeking to innovate by engaging in knowledge areas that are new to them, versus firms seeking to exploit their existing knowledge base. We make this distinction by using a novel approach, based on the knowledge challenges that firms face when innovating. By merging firm-level survey data with administrative data on public funding for R&I in Ireland, we find that subsidising firms seeking to engage in new knowledge areas, can result in more radical innovation and turnover from radical innovation, compared to firms seeking to exploit their existing knowledge base. These are critical insights from theoretical and policymaking perspectives, regarding the allocation of public funding for R&I. |
| Keywords: | radical innovation, public financial support, knowledge base, policy failure, additionality |
| JEL: | D83 O31 O32 O33 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewdip:283615&r=tid |
| By: | Alexander Cuntz; Carsten Fink; Hansueli Stamm |
| Abstract: | The emergence of Artificial Intelligence (AI) has profound implications for intellectual property (IP) frameworks. While much of the discussion so far has focused on the legal implications, we focus on the economic dimension. We dissect AI's role as both a facilitator and disruptor of innovation and creativity. Recalling economic principles and reviewing relevant literature, we explore the evolving landscape of AI innovation incentives and the challenges it poses to existing IP frameworks. From patentability dilemmas to copyright conundrums, we find that there is a delicate balance between fostering innovation and safeguarding societal interests amidst rapid technological progress. We also point to areas where future economic research could offer valuable insights to policymakers. |
| Keywords: | Artificial Intelligence, Intellectual Property, Patents, Copyright |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:wip:wpaper:77&r=tid |
| By: | Daron Acemoglu |
| Abstract: | Does capital accumulation increase labor demand and wages? Neoclassical production functions, where capital and labor are q-complements, ensure that the answer is yes, so long as labor markets are competitive. This result critically depends on the assumption that capital accumulation does not change the technologies being developed and used. I adapt the theory of endogenous technological change to investigate this question when technology also responds to capital accumulation. I show that there are strong parallels between the relationship between capital and wages and existing results on the conditions under which equilibrium factor demands are upward-sloping (e.g., Acemoglu, 2007). Extending this framework, I provide intuitive conditions and simple examples where a greater capital stock leads to lower wages, because it triggers more automation. I then offer an endogenous growth model with a menu of technologies where equilibrium involves choices over both the extent of automation and the rate of growth of labor-augmenting productivity. In this framework, capital accumulation and technological change in the long run are associated with wage growth, but an increase in the saving rate increases the extent of automation, and at first reduces the wage rate and subsequently depresses its long-run growth rate. |
| JEL: | C65 O31 O33 |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32190&r=tid |
| By: | Miguel Antón; Florian Ederer; Mireia Giné; Martin C. Schmalz |
| Abstract: | Firms have inefficiently low incentives to innovate when other firms benefit from their inventions and the innovating firm therefore does not capture the full surplus of its innovations. We show that common ownership of firms mitigates this impediment to corporate innovation. By contrast, without technological spillovers, innovation has the effect of stealing market share from rivals; in that case, more common ownership reduces innovation. Empirically, the association between common ownership and innovation inputs and outputs decreases with product market proximity and increases with technology proximity. The sign and magnitude of the overall relationship between common ownership and corporate innovation thus varies considerably across the universe of firms depending on their relative proximity in technology and product market space. These results persist if we use only variation from BlackRock's acquisition of BGI. Our results inform the debate about the welfare effects of increasing common ownership among U.S. corporations. |
| JEL: | G30 L20 L40 O31 |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32192&r=tid |
| By: | David Kreitmeir; Paul A. Raschky |
| Abstract: | We analyse the individual productivity effects of Italy's ban on ChatGPT, a generative pretrained transformer chatbot. We compile data on the daily coding output quantity and quality of over 36, 000 GitHub users in Italy and other European countries and combine these data with the sudden announcement of the ban in a difference-in-differences framework. Among the affected users in Italy, we find a short-term increase in output quantity and quality for less experienced users and a decrease in productivity on more routine tasks for experienced users. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.01964&r=tid |
| By: | Nana, Ibrahim; Tabe-Ojong, Martin Paul |
| Abstract: | Global value chains offer countries unique opportunities to participate in and benefit from international trade by specializing in specific production stages and tasks. We investigate the evolution of the integration of African countries into Global Value Chains (GVCs) and establish their relationship with economic growth and income inequality. Using recent export decomposition methods and world Input-Output tables from EORA-Multi-Region Input-Output Tables (MRIOs), we track the evolution of African countries along GVCs, identify specialization patterns and generate sector/task level "GVCs participation measures". We then use the GVCs participation measures to investigate the relationship between GVCs participation and position with economic growth and income inequality. We also explore which sectors explain these relationships. Our analysis is based on a panel data set constructed from a combination of different data sources of 48 African countries over the period 1990-2016. Using several empirical strategies, including a panel fixed effect estimator, instrumental variables, and local projection techniques, we find GVCs to be positively associated with both GDP per capita and income inequality for African countries. These results are consistent for GVCs position and different proxies of income inequality like income share and the standardized Gini coefficients. We also find suggestive evidence from the sectoral assessment of GVCs that this relationship may be driven by trade in knowledge-intensive goods and services. Based on these findings, we discuss some policy insights and implications, key of which is the importance in promoting GVCs and investments in knowledge intensive goods. To this end, various initiatives like skill upgrading, skill-based technological change and various education and labour market programs should take central stage. |
| Keywords: | Global Value Chains, Trade, Growth, Inequality, Africa |
| JEL: | F14 F15 F43 O55 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:sgscdp:284371&r=tid |
| By: | Füner, Lena; Berger, Marius; Bersch, Johannes; Hottenrott, Hanna |
| Abstract: | New business formation is a key driver of regional transformation and development. While we know that a region's attractiveness for new businesses depends on its resources, infrastructure, and human capital, we know little about the role of local business networks in promoting or impeding the birth of new firms. We construct local business networks connecting more than 350 million nodes consisting of managers, owners and firms using administrative data on all German businesses from 2002 to 2020. Differentiating between serial and de-novo entrepreneurs, we show a positive but decreasing relation between a region's connectedness and firm entry of serial entrepreneurs. Networks are, moreover, positively linked to firm survival. Relating our findings to a measure of ownership concentration, we show that networks provide additional explanations for regional variation in new business formations. These patterns are robust to synthetic instrumental variable estimations |
| Keywords: | New Firm Formation, Business Networks, Serial Entrepreneurship, RegionalDynamics, Ownership Concentration |
| JEL: | L14 L26 M13 O31 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewdip:283589&r=tid |
| By: | Berlingieri, Giuseppe; Blanchenay, Patrick; Criscuolo, Chiara |
| Abstract: | This paper provides new evidence on the increasing dispersion in wages and productivity using a unique micro-aggregated firm-level data source, representative for the full population of firms in 12 countries. First, we document an increase in wage and productivity dispersions, for both manufacturing and market services, and show that the increase is mainly driven by the bottom of the wage and productivity distributions. Second, we show that between-firm wage dispersion increased more in sectors that experienced an increase in productivity dispersion; the estimated elasticity is larger at the bottom than at the top of the wage/productivity distributions, consistent with a framework in which more productive firms charge higher mark-ups and/or larger wage mark-downs. Third, we find that both globalisation and digitalisation strengthen the link between productivity and wage dispersion. Our results suggest that policies designed to mitigate wage inequality must take into consideration gaps between firms of the same sectors, and how both globalisation and digitalisation affect these gaps. |
| Keywords: | digitalisation; dispersion; globalisation; productivity; wages |
| JEL: | J50 |
| Date: | 2024–04–01 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:122046&r=tid |
| By: | Carvajal, Daniel (Dept. of Economics, Norwegian School of Economics and Business Administration); Franco, Catalina (Center for Applied Research (SNF)); Isaksson, Siri (Dept. of Economics, Norwegian School of Economics and Business Administration) |
| Abstract: | The promise of generative AI to increase human productivity relies on developing skills to become proficient at it. There is reason to suspect that women and men use AI tools differently, which could result in productivity and payoff gaps in a labor market increasingly demanding knowledge in AI. Thus, it is important to understand if there are gender differences in AI-usage among current students. We conduct a survey at the Norwegian School of Economics collecting use and attitudes towards ChatGPT, a measure of AI proficiency, and responses to policies allowing or forbidding ChatGPT use. Three key findings emerge: first, female students report a significantly lower use of ChatGPT compared to their male counterparts. Second, male students are more skilled at writing successful prompts, even after accounting for higher ChatGPT usage. Third, imposing university bans on ChatGPT use widens the gender gap in intended use substantially. We provide insights into potential factors influencing the AI adoption gender gap and highlight the role of appropriate encouragement and policies in allowing female students to benefit from AI usage, thereby mitigating potential impacts on later labor market outcomes. |
| Keywords: | Artificial intelligence; ChatGTP; gender; education; technology adoption |
| JEL: | I24 J16 J24 O33 |
| Date: | 2024–03–14 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:nhheco:2024_003&r=tid |
| By: | S. Alex Yang; Angela Huyue Zhang |
| Abstract: | The rapid advancement of generative AI is poised to disrupt the creative industry. Amidst the immense excitement for this new technology, its future development and applications in the creative industry hinge crucially upon two copyright issues: 1) the compensation to creators whose content has been used to train generative AI models (the fair use standard); and 2) the eligibility of AI-generated content for copyright protection (AI-copyrightability). While both issues have ignited heated debates among academics and practitioners, most analysis has focused on their challenges posed to existing copyright doctrines. In this paper, we aim to better understand the economic implications of these two regulatory issues and their interactions. By constructing a dynamic model with endogenous content creation and AI model development, we unravel the impacts of the fair use standard and AI-copyrightability on AI development, AI company profit, creators income, and consumer welfare, and how these impacts are influenced by various economic and operational factors. For example, while generous fair use (use data for AI training without compensating the creator) benefits all parties when abundant training data exists, it can hurt creators and consumers when such data is scarce. Similarly, stronger AI-copyrightability (AI content enjoys more copyright protection) could hinder AI development and reduce social welfare. Our analysis also highlights the complex interplay between these two copyright issues. For instance, when existing training data is scarce, generous fair use may be preferred only when AI-copyrightability is weak. Our findings underscore the need for policymakers to embrace a dynamic, context-specific approach in making regulatory decisions and provide insights for business leaders navigating the complexities of the global regulatory environment. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.17801&r=tid |
| By: | Voraprapa Nakavachara; Tanapong Potipiti; Thanee Chaiwat |
| Abstract: | Generative AI technologies such as ChatGPT, Gemini, and MidJourney have made remarkable progress in recent years. Recent literature has documented ChatGPT's positive impact on productivity in areas where it has strong expertise, attributable to extensive training datasets, such as the English language and Python/SQL programming. However, there is still limited literature regarding ChatGPT's performance in areas where its capabilities could still be further enhanced. This paper aims to fill this gap. We conducted an experiment in which economics students were asked to perform writing analysis tasks in a non-English language (specifically, Thai) and math & data analysis tasks using a less frequently used programming package (specifically, Stata). The findings suggest that, on average, participants performed better using ChatGPT in terms of scores and time taken to complete the tasks. However, a detailed examination reveals that 34% of participants saw no improvement in writing analysis tasks, and 42% did not improve in math & data analysis tasks when employing ChatGPT. Further investigation indicated that higher-ability students, as proxied by their econometrics grades, were the ones who performed worse in writing analysis tasks when using ChatGPT. We also found evidence that students with better digital skills performed better with ChatGPT. This research provides insights on the impact of generative AI. Thus, stakeholders can make informed decisions to implement appropriate policy frameworks or redesign educational systems. It also highlights the critical role of human skills in addressing and complementing the limitations of technology. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.01770&r=tid |