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on Technology and Industrial Dynamics |
| By: | Gianluca Biggi; Martina Iori; Julia Mazzei; Andrea Mina |
| Abstract: | This paper investigates the contribution of Artificial Intelligence (AI) to environmental innovation. Leveraging a novel dataset of USPTO patent applications from 1980 to 2019, it explores the domain of Green Intelligence (GI), defined as the application of AI algorithms to green technologies. Our analyses reveal an expanding landscape where AI is indeed used as a general purpose technology to address the challenge of sustainability and acts as a catalyst for green innovation. We highlight transportation, energy, and control methods as key applications of GI innovation. We then examine the impact of inventions by using measures and econometric tests suitable to establish 1) how AI and green inventions differ from other technologies and 2) what specifically distinguishes GI technologies in terms of quality and value. Results show that AI and green technologies have a greater impact on follow-on inventions and display greater originality and generality. GI inventions stand out even further in these dimensions. However, when we examine the market response to these inventions, we find positive results only for AI, indicating a mismatch between the technological vis-Ã -vis market potential of green and GI technologies, arguably due to greater uncertainty in their risk-return profiles. |
| Keywords: | Artificial Intelligence, Environmental innovation, Green Intelligence (GI), Twin transition, Digitalization, Green technologies |
| Date: | 2024–09–19 |
| URL: | https://d.repec.org/n?u=RePEc:ssa:lemwps:2024/23 |
| By: | Lauren Cohen; Umit Gurun; Katie Moon; Paula Suh |
| Abstract: | Analyzing millions of patents granted by the USPTO between 1976 and 2020, we find a pattern where specific patents only rise to prominence after considerable time has passed. Amongst these late-blooming influential patents, we show that there are key players (patent hunters) who consistently identify and develop them. Although initially overlooked, these late-blooming patents have significantly more influence on average than early-recognized patents and are associated with significantly more new product launches. Patent hunters, as early detectors and adopters of these late-blooming patents, are also associated with significant positive rents. Their adoption of these overlooked patents is associated with a 6.4% rise in sales growth (t = 3.02), a 2.2% increase in Tobin’s Q (t = 3.91), and a 2.2% increase in new product offerings (t = 2.97). We instrument for patent hunting, and find strong evidence that these benefits are causally due to patent hunting. The rents associated with patent hunting on average exceed those of the original patent creators themselves. Patents hunted are closer to the core technology of patent hunters, more peripheral to writers, and in less competitive spaces. Lastly, patent hunting appears to be a persistent firm characteristic and to have an inventor-level component. |
| JEL: | L1 O31 O33 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32965 |
| By: | Patricia Peñalosa; Lukas Kleine-Rueschkamp |
| Abstract: | This paper explores the geography of “green innovation hubs” and the relationship between green patents and local labour markets. The analysis considers the spatial distribution and evolution of patenting activity for green inventions and identifies green innovation hubs, i.e., regions demonstrating notable strength in green patenting. It also explores the relationship between the regional level of green patenting, economic activity, education, and local labour dynamics across OECD regions. Greater Copenhagen (a cross-border area including parts of Denmark and Southern Sweden) is used as an example to illustrate one region's green innovation ecosystem, assessing its progress, unique opportunities, and challenges. |
| Date: | 2024–09–24 |
| URL: | https://d.repec.org/n?u=RePEc:oec:cfeaaa:2024/09-en |
| By: | Ina Ganguli; Jeffrey Lin; Vitaly Meursault; Nicholas F. Reynolds |
| Abstract: | As distorted maps may mislead, Natural Language Processing (NLP) models may misrepresent. How do we know which NLP model to trust? We provide comprehensive guidance for selecting and applying NLP representations of patent text. We develop novel validation tasks to evaluate several leading NLP models. These tasks assess how well candidate models align with both expert and non-expert judgments of patent similarity. State-of-the-art language models significantly outperform traditional approaches such as TF-IDF. Using our validated representations, we measure a secular decline in contemporaneous patent similarity: inventors are “spreading out” over an expanding knowledge frontier. This finding is corroborated by declining rates of multiple invention from newly-digitized historical patent interference records. In contrast, selecting another single representation without validating alternatives yields an ambiguous or even opposing trend. Thus, our framework addresses a fundamental challenge of selecting among different black-box NLP models that produce varying economic measurements. To facilitate future research, we plan to provide our validation task data and embeddings for all US patents from 1836–2023. |
| JEL: | C81 L19 O31 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32934 |
| By: | Xiao, Jing (CIRCLE, Lund University); Lindholm Dahlstrand, Åsa (CIRCLE, Lund University) |
| Abstract: | Recently, acqui-hiring, which refers to the acquisitions driven by gaining access to target human capital, has emerged as a proliferating phenomenon in acquisitions of small technology firms. However, we still know little about this phenomenon, particularly outside the community of Silicon Valley. This study sheds new light on the nature of acqui-hiring by focusing on what drives acqui-hiring. Using a sample of 213 technological acquisitions of Swedish technology firms, our results show that firms tend to be acqui-hired when they are younger and when they are based on the development of deep tech, a group of emerging disruptive technologies, of which the technological base involves high levels of technological newness and complexity. The results show a support to our initial idea that acqui-hiring could be driven by the acquiring firm’s need to acquire complex knowledge and/or new capabilities that are embodied in target key employees or engineering teams. In addition, we develop a typology and identify four types of acqui-hiring. We use case illustrations of deep-tech acqui-hiring to demonstrate four differentiated acquisition strategies, including technology strengthening, product expansion, product experimentation and technology experimentation. |
| Keywords: | Acqui-hiring; deep tech; technological acquisitions; technological newness and complexity; combined methods; Sweden |
| JEL: | G34 L26 O32 O33 |
| Date: | 2024–09–04 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:lucirc:2024_011 |
| By: | Valeria Cirillo; Andrea Mina; Andrea Ricci |
| Abstract: | New technologies can shape the production process by affecting the way in which inputs are embedded in the organization, their quality, and their use. Using an original employer-employee dataset that merges firm-level data on digital technology adoption and other characteristics of production with employee-level data on worker entry and exit rates from the administrative archive of the Italian Ministry of Labor, this paper explores the effects of new digital technologies on labor flows in the Italian economy. Using a Difference-in-Difference approach, we show that digital technologies lead to an increase in the firm-level hiring rate – particularly for young workers - and reduce the firm-level separation rate. We also find that digital technologies are positively associated with workplace training, proxied by the share of trained employees and the amount of training costs per employee. Furthermore, we explore the heterogeneity of effects related to different technologies (robots, cybersecurity and IoT). Our results are confirmed through several robustness checks. |
| Keywords: | Industry 4.0; Digital technologies; Hiring rate; Separation rate; Skills; Training; Employer-Employee data |
| Date: | 2024–09–19 |
| URL: | https://d.repec.org/n?u=RePEc:ssa:lemwps:2024/22 |
| By: | Nicholas Bloom; Marcela Carvalho; Tarek A. Hassan; Aakash Kalyani; Joshua Lerner; Ahmed Tahoun |
| Abstract: | We identify phrases associated with novel technologies using textual analysis of patents, job postings, and earnings calls, enabling us to identify four stylized facts on the diffusion of jobs relating to new technologies. First, the development of economically impactful new technologies is geographically highly concentrated, more so even than overall patenting: 56% of the most economically impactful technologies come from just two U.S. locations, Silicon Valley and the Northeast Corridor. Second, as the technologies mature and the number of related jobs grows, hiring spreads geographically. But this process is very slow, taking around 50 years to disperse fully. Third, while initial hiring in new technologies is highly skill biased, over time the mean skill level in new positions declines, drawing in an increasing number of lower-skilled workers. Finally, the geographic spread of hiring is slowest for higher-skilled positions, with the locations where new technologies were pioneered remaining the focus for the technology’s high-skill jobs for decades. |
| Keywords: | employment; geography; innovation; research and development |
| JEL: | O31 O32 |
| Date: | 2024–08–26 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedlwp:98770 |
| By: | Gagliardi, Nicola (Free University of Brussels); Grinza, Elena (University of Turin); Rycx, François (Free University of Brussels) |
| Abstract: | We investigate the impact of rising temperatures on firm productivity using longitudinal firm-level balance-sheet data from private sector firms in 14 European countries, combined with detailed weather data. Our findings, based on control-function techniques and fixed-effects regressions, reveal that global warming significantly and negatively impacts firms' TFP. Labor productivity declines markedly as temperatures rise, while capital productivity remains unaffected – indicating that TFP is primarily affected through the labor input channel. Sensitivity tests show that firms involved in outdoor activities, such as agriculture and construction, are more adversely impacted. Manufacturing, capital-intensive, and blue-collar-intensive firms also experience significant productivity declines. Geographically, the negative impact is most pronounced in temperate and mediterranean climate areas. |
| Keywords: | climate change, global warming, firm productivity, Total Factor Productivity (TFP), semiparametric methods to estimate production functions, longitudinal firm-level data |
| JEL: | D24 J24 Q54 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17241 |
| By: | Elisabetta Cappa; Francesco Lamperti; Gianluca Pallante |
| Abstract: | A rapid transition towards renewable energy sources is crucial to address climate change and improve local energy independence. However, the acceptability of this transition often faces resistance due to concerns about potential job-losses in the fossil-intensive sectors, while the employment potential of renewable energy technologies remains unclear. In this study, we address this concern by employing a novel and detailed geolocalized dataset of energy power units across four technologies and three decades, to examine theemployment impacts of renewable energy investments in four large European countries. To mitigate for the possible non-random allocation of renewable energy technologies, we leverage the physical potential of each region in relation to renewable energy sources, to isolate its exposure to technology-specific investments. We find that the deployment of renewable energy plants has a positive and long-lasting impact on employment. Our central estimates suggest that 1 MW of new renewable energy installed capacity creates around 40 jobs in 7 years locally, indicating that 1 Million USD invested in renewable energy technologies generates approximately 15 jobs over the same time frame. These estimates are mostly driven by the effects generated by the solar and wind installations on the construction sector. We find evidence of substantial heterogeneities across regional features, where rural and low-income areas are the ones experiencing the largest employment effect from renewable energy deployment. Overall, our findings suggest that green energy investments can constitute as a strategic asset to spur local jobs and encourage rural development. |
| Keywords: | renewable energy, employment multiplier, green stimulus, shift-share |
| Date: | 2024–09–11 |
| URL: | https://d.repec.org/n?u=RePEc:ssa:lemwps:2024/21 |
| By: | Bal\'azs Mark\'o |
| Abstract: | This paper argues that military buildups lead to a significant rise in greenhouse gas emissions and can disrupt the green transition. Identifying military spending shocks, I use local projections to show that a percentage point rise in the military spending share leads to a 1-1.5% rise in total emissions, as well as a 1% rise in emission intensity. Using a dynamic production network model calibrated for the US, I find that a permanent shock of the same size would increase total emissions by between 0.36% and 1.81%, and emission intensity by between 0.22% and 1.5%. The model indicates that fossil fuel and energy-intensive firms experience a considerable expansion in response to such a shock, which could create political obstacles for the green transition. Similarly, investment in renewables and green R&D could be crowded out by defence spending, further hindering the energy transition. Policymakers can use carbon prices or green subsidies to counteract these effects, the latter likely being more efficient due to political and social constraints. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.16419 |