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on Technology and Industrial Dynamics |
| By: | Stefania Albanesi; Wabitsch Alena; António Dias da Silva; Juan F. Jimeno; Ana Lamo |
| Abstract: | We examine the link between labour market developments and new technologies such as artificial intelligence (AI) and software in 16 European countries over the period 2011-2019. Using data for occupations at the 3-digit level, we find that on average employment shares have increased in occupations more exposed to AI. This is particularly the case for occupations with a relatively higher proportion of younger and skilled workers. While there exists heterogeneity across countries, only very few countries show a decline in employment shares of occupations more exposed to AI-enabled automation. Country heterogeneity for this result seems to be linked to the pace of technology diffusion and education, but also to the level of product market regulation (competition) and employment protection laws. In contrast to the findings for employment, we find little evidence for a relationship between relative wages across occupations and potential exposures to new technologies |
| Keywords: | Artificial intelligence; Employment; Occupations; Skills |
| JEL: | J23 O33 |
| Date: | 2024–11–18 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedmoi:99164 |
| By: | Panle Jia Barwick; Hyuk-Soo Kwon; Shanjun Li; Yucheng Wang; Nahim B. Zahur |
| Abstract: | This paper examines the impact of industrial policies (IPs) on innovation in the global automobile industry. We compile the first comprehensive dataset linking global IPs with patent data related to the auto industry from 2008 to 2023. We document a major shift in policy focus: by 2022, nearly half of all IPs targeted electric vehicles (EV)-related sectors, up from almost none in 2008. In the meantime, there has been a clear technological transition from internal combustion engine (GV) technologies to EV innovations. Our analysis finds a positive relationship between policy support and innovation activity. At the country level, a one-standard-deviation increase in five-year cumulative EV-targeted IPs is associated with a four-percent rise in new EV patent applications. Firm-level analyses (using OLS, IV, and PPML) indicate that a ten-percent increase in EV financial incentives received by automakers and EV battery producers leads to a similar four-percent increase in EV innovations. We confirm the importance of path dependence in the direction of technology change in the automobile industry but find no evidence that EV-targeted IPs stimulate innovation in GV technologies. |
| JEL: | H20 L5 L60 L62 O3 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33138 |
| By: | Sabrina Di Addario; Zhexin Feng; Michel Serafinelli |
| Abstract: | This paper presents direct evidence on how firms’ innovation is affected by access to knowledgeable labor through co-worker network connections. We use a unique dataset that matches patent data to administrative employer–employee records from "Third Italy"—a region with many successful industrial clusters. Establishment closures displacing inventors generate supply shocks of knowledgeable labor to firms that employ the inventors’ previous co-workers. We estimate event-study models where the treatment is the displacement of a "connected" inventor (i.e., a previous coworker of a current employee of the focal firm). We show that the displacement of a connected inventor significantly increases connected inventors’ hiring. Moreover, the improved access to knowledgeable workers raises firms innovative activity. We provide evidence supporting the main hypothesized channel of knowledge transfer through firm-to-firm labor mobility by estimating IV specifications where we use the displacement of a connected inventor as an instrument to hire a connected inventor. Overall, estimates indicate that firms exploit displacements to recruit connected inventors and the improved capacity to employ knowledgeable labor within the network increases innovation. |
| Keywords: | social connections, firm-to-firm labor mobility, patents, establishment closure |
| JEL: | J60 O30 J23 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11432 |
| By: | Thomas Licht; Klaus Wohlrabe |
| Abstract: | This paper examines the adoption of Artificial Intelligence (AI) among German firms, leveraging firm-level data from the ifo Business Survey. We analyze the diffusion of AI across sectors and firm sizes, showing a significant increase in AI usage from 2023 to 2024, particularly in manufacturing and services. The survey data allows us to explore not only sectoral patterns of adoption but also the drivers and barriers that firms face, including firm-specific characteristics and industry dynamics. Additionally, we investigate the role of managerial traits, such as risk tolerance and patience, in shaping AI adoption decisions. Finally, we assess the potential pro-ductivity impacts of AI at the firm level, with a focus on the expected long-term benefits of AI for different sectors of the German economy. Our findings contribute to the growing body of research on AI adoption by providing new evidence from a non-US context, offering valuable insights for both academia and politics. |
| Keywords: | artificial intelligence, AI, ifo business survey, productivity |
| JEL: | M15 O30 C83 L20 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11459 |
| By: | MOTOHASHI Kazuyuki; ZHU Chen |
| Abstract: | While innovation performance at country level has been analyzed using a variety of STI indicators, the relationship between them such as the patent-new product relationship is under-investigated. Historically, the relationship between technology and industrial output has been analyzed using technology-industry concordance matrices, but the granularity of output information is bounded by the industrial classification system. In this study, we use the text information in both patent and product-related keywords extracted from company’s web site contents to come up with detailed concordance information between technology and products, and compare them across three countries, China, Japan and the United States. First, we apply a dual attention model to extract product/service information from web page information. Then, using the textual information of both patent abstracts and product/service keywords, we develop a machine learning model to predict products/services from a particular type of technology. Then, we use this transformation model (from technology to product) to understand the difference in innovation processes of the three countries. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:eti:dpaper:24075 |
| By: | Francesco Filippucci; Peter Gal; Matthias Schief |
| Abstract: | The paper studies the expected macroeconomic productivity gains from Artificial Intelligence (AI) over a 10-year horizon. It builds a novel micro-to-macro framework by combining existing estimates of micro-level performance gains with evidence on the exposure of activities to AI and likely future adoption rates, relying on a multi-sector general equilibrium model with input-output linkages to aggregate the effects. Its main estimates for annual aggregate total-factor productivity growth due to AI range between 0.25-0.6 percentage points (0.4-0.9 pp. for labour productivity). The paper discusses the role of various channels in shaping these macro-level gains and highlights several policy levers to support AI's growth-enhancing effects. |
| Keywords: | Artificial Intelligence, Productivity, Technology adoption |
| JEL: | E1 O3 O4 O5 |
| Date: | 2024–11–22 |
| URL: | https://d.repec.org/n?u=RePEc:oec:comaaa:29-en |
| By: | Kim, Jinhwan (Stanford U); Valentine, Kristen (U of Georgia) |
| Abstract: | Innovative public firms sell 9.6% (615) more patents in the last month, relative to the first half of the fiscal year. Consistent with reporting incentives driving these sales, they are more pronounced among firms with strong incentives to meet earnings expectations. Patents sold in the last month are litigated more frequently because they are disproportionately sold to “patent trolls†, who opportunistically acquire patents to engage in litigation. We find anti-troll laws reduce opportunistic acquisition among trolls. We highlight a novel consequence of corporate reporting incentives: its contribution to strategic patent sales, which in turn impact the market for innovation. |
| JEL: | D23 M40 M41 O30 O31 O32 O34 |
| Date: | 2024–02 |
| URL: | https://d.repec.org/n?u=RePEc:ecl:stabus:4162 |
| By: | Dietmar Harhoff; David Heller; Paul P. Momtaz |
| Abstract: | We show that firm and industry, rather than inventor and invention factors, explain more than half of the variation in inventor returns in administrative employer-inventor-patent-linked data from Germany. Between-firm variation in inventive rents is strongly associated with inventor mobility. Inventors are more likely to make a move just before a patent is filed than shortly thereafter and benefit from their move through a mobility-related marginal inventor return. Employers that pay inventor returns in excess of the expected return gain a favorable position in the market for inventive labor with subsequent increases in patent quality and quantity. Consistent with theoretical arguments, effect sizes also depend on employer-inventor technological complementarity, degree of competition, and invention quality. |
| Keywords: | inventor returns, labor mobility, patents, inventive productivity |
| JEL: | O31 J24 J62 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11449 |
| By: | Sergio Petralia; ; ; ; |
| Abstract: | This article outlines a method to link organizations’ patenting activities at the United States Patent and Trademark Office (USPTO) with their Open Source Software (OSS) contributions in GitHub, the most popular code-hosting service platform. It also provides two ready-to-use databases that are easy to connect to related data sources. The first includes information about all contributions (6, 091, 653) made to 54 of the most popular OSS projects until June 2024, amounting to over 49 million file changes and more than 3.3 billion line modifications. The second includes information on patents granted until June 2024 (1, 719, 510) to 1, 328 organizations with activity in GitHub. This novel data can be used to explore the dynamics and mechanisms driving innovation within modern technological ecosystems, where the lines between proprietary and open-source development are becoming blurry. It offers an opportunity to investigate several unresolved puzzles in the economics of OSS literature, such as disentangling the intrinsic and extrinsic motivations behind individual contributions to OSS, understanding the strategic reasons organizations engage in OSS, and exploring collaboration and geographical concentration mechanisms in the production of digital technologies. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2437 |
| By: | Kim, Jinhwan (Stanford U); Shi, Terrence Tianshuo (Harvard U); Verdi, Rodrigo S. (MIT) |
| Abstract: | We examine how the efficiency of the judicial system impacts corporate innovation. To do so, we exploit a pilot program introduced by the U.S. Congress in 2011, which allowed judges with expertise(as opposed to randomly selected judges) to preside over more patent cases to facilitate efficient ruling. We find firms headquartered in counties subject to the Patent Pilot Program increase patent-based innovation by 5.2% to 6.2%, relative to firms in counties not under the program. Our results are concentrated among firms with high legal costs and uncertainty: firms that engage in innovation with “fuzzy boundaries†, that have high litigation risk, and that are more resource-constrained. However, we also find non-random assignment has an adverse impact on firms more likely to be assigned to judges that are favorably-biased towards non-practicing entities (NPEs) or “patent trolls†, who engage in frequent, frivolous litigation. Taken together, our findings underscore the important role of judicial efficiency in helping firms better allocate their resources towards innovation investment, but also indicate that judicial efficiency programs can exacerbate the negative effects of judicial biases in certain contexts. |
| JEL: | J24 K11 K42 M41 O31 O38 O39 |
| Date: | 2024–01 |
| URL: | https://d.repec.org/n?u=RePEc:ecl:stabus:4161 |
| By: | Saul Estrin; Andrea Herrmann; Moren Levesque; Tomasz Mickiewicz; Mark Sanders |
| Abstract: | We present a Schumpeterian growth model with new venture creation, under uncertainty, which explains the tradeoff between speed-to-breakeven, revenue-at-breakeven and relates this to the level of innovation. We then explore the tradeoffs between these outcomes empirically in a unique sample of 331 information and communication technology (ICT) ventures using a multi-input, multi-output stochastic frontier model. We estimate the contribution of financial capital and labor input to the outcomes and the tradeoffs between them, as well as address heterogeneity across ventures. We find that more innovative (and therefore more uncertain) ventures have lower speed-to-breakeven and/or lower revenue-at-breakeven. Moreover, for all innovativeness levels, new ventures face a tradeoff between speed-to-breakeven and revenue-at-breakeven. Our results suggest that it is the availability of proprietary resources (founder equity and labor) that helps ventures overcome bottlenecks in the innovation process, and we propose a line of research to explain the (large) unexplained variation in venture creation efficiency. Plain English Summary. This study examines how new businesses deal with uncertainty, focusing on the tradeoff between how quickly they become profitable (speed-to-breakeven) and how much revenue they generate when they do. We analyze data from 331 ICT ventures to understand these tradeoffs better, considering factors like financial resources and labor inputs. We find that more innovative ventures, which tend to be more uncertain, often take longer to reach profitability and may earn less when they do. Moreover, regardless of their level of innovation, all new ventures face a tradeoff between speed-to-breakeven and revenue. The study highlights that unique resources, such as founder equity and founder labor, help businesses overcome challenges in the innovation process. It also suggests further research to understand why some ventures are more efficient than others in the early stage of creating new businesses. |
| Keywords: | entrepreneurship, innovation, new venture creation, proprietary resources, stochastic frontier analysis, Schumpeterian growth model |
| Date: | 2024–11–15 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2054 |
| By: | Korneliusz Pylak; Jason Deegan; Tom Broekel; ; |
| Abstract: | This paper explores the phenomenon of mimicry in the selection of economic domains for Smart Specialisation Strategies (S3) and discusses the regional policy implications of strategic mimicry. By analysing S3 documents from European regions, we identify and distinguish between two general types of mimicry: ‘Follow the Peers’ and ‘Follow the Role Models, ’ against the more desirable ‘Follow the Indicators’ priority selection strategy. Our findings reveal that although regions rely on their strengths by following the crucial indicators, thus exhibiting non-mimetic behaviour, there is a stronger tendency for regions to mimic popular domain portfolios, particularly those chosen by neighbouring regions and national strategies. Understanding these patterns in the selection of priority domains helps decision-makers balance mimicry and diversification, promoting specialization, new economic activities, and regional uniqueness. |
| Keywords: | smart specialisation, regional strategy, regional policy, innovation policy, mimicry |
| JEL: | O25 O38 R11 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2439 |
| By: | Tacsir, Ezequiel; Pereira, Mariano; Favata, Federico; Leone, Julian |
| Abstract: | Using a multilevel analysis and the new Harmonized Latin American Innovation Surveys Database (or LAIS database) augmented with indicators from the U.S. Census Bureau's Survey of Business Owners (SBO) and the World Banks World Integrated Trade Solution (WITS), this paper presents estimates of the effects of import competition and distance to the technological frontier on firm innovation in Latin American countries. Although innovation is recognized as a multilevel phenomenon, with investment decisions not solely affected by the firm characteristics but also by the context in which each firm is embedded, the empirical literature adopting a multilevel design is still nascent and scarce. Using a two-level random slope model allows us to overcome some of the pitfalls of traditional regression models when dealing with the hierarchical structure of data while allowing us to capture the influence of contextual factors. The results suggest that the fostering effect of foreign competition depends on the firms distance to the technological frontier. The estimates suggest that the lower the foreign competition and the greater the productivity gap, the lower the probability of firms engaging in innovation. In contrast, when a firm operates in a sector that is relatively closer to the technological frontier, firms invest in innovative activities to remain at the top. These results offer a clear and useful guide for designing policies in Latin America regarding innovation among firms. While it is important to promote and stimulate innovation efforts by firms, these factors should not be overlooked as considerations: sectoral characteristics associated with the economies, sectoral openness to foreign competition, and firms distance to the technological frontier. |
| Keywords: | innovation;Latin America;multilevel modeling;LAIS database;Productivity;Competition |
| JEL: | O31 O32 C21 C25 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:idb:brikps:13833 |
| By: | Anton Korinek; Jai Vipra |
| Abstract: | This paper examines the evolving structure and competition dynamics of the rapidly growing market for foundation models, with a focus on large language models (LLMs). We describe the technological characteristics that shape the AI industry and have given rise to fierce competition among the leading players. The paper analyzes the cost structure of foundation models, emphasizing the importance of key inputs such as computational resources, data, and talent, and identifies significant economies of scale and scope that may create a tendency towards greater market concentration in the future. We explore two concerns for competition, the risk of market tipping and the implications of vertical integration, and we evaluate policy remedies that aim to maintain a competitive landscape. Looking ahead to increasingly transformative AI systems, we discuss how market concentration could translate into unprecedented accumulation of power, highlighting the broader societal stakes of competition policy. |
| JEL: | D43 K21 L4 L86 O33 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33139 |
| By: | Prashant Garg; Thiemo Fetzer |
| Abstract: | We analyze over 44, 000 economics working papers from 1980–2023 using a custom language model to construct knowledge graphs mapping economic concepts and their relationships, distinguishing between general claims and those supported by causal inference methods. The share of causal claims within papers rose from about 4% in 1990 to 28% in 2020, reflecting the “credibility revolution.” Our findings reveal a trade-off between factors enhancing publication in top journals and those driving citation impact. While employing causal inference methods, introducing novel causal relationships, and engaging with less central, specialized concepts increase the likelihood of publication in top 5 journals, these features do not necessarily lead to higher citation counts. Instead, papers focusing on central concepts tend to receive more citations once published. However, papers with intricate, interconnected causal narratives—measured by the complexity and depth of causal channels—are more likely to be both published in top journals and receive more citations. Finally, we observe a decline in reporting null results and increased use of private data, which may hinder transparency and replicability of economics research, highlighting the need for research practices that enhance both credibility and accessibility. |
| Keywords: | knowledge graph, credibility revolution, causal inference, narrative complexity, null results, private data, large language models |
| JEL: | A10 B41 C18 C80 D83 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11462 |