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on Technology and Industrial Dynamics |
| By: | Brüll, Eduard; Mäurer, Samuel; Rostam-Afschar, Davud |
| Abstract: | We provide experimental evidence on how employers adjust expectations to automation risk in high-skill, white-collar work. Using a randomized information intervention among tax advisors in Germany, we show that firms systematically underestimate automatability. Information provision raises risk perceptions, especially for routine-intensive roles. Yet, it leaves short-run hiring plans unchanged. Instead, updated beliefs increase productivity and financial expectations with minor wage adjustments, implying within-firm inequality like limited rent-sharing. Employers also anticipate new tasks in legal tech, compliance, and AI interaction, and report higher training and adoption intentions. |
| Keywords: | Artificial Intelligence, Automation, Technological Change, Innovation, Technology Adoption, Firm Expectations, Belief Updating, Expertise, Labor Demand, White Collar Jobs, Training |
| JEL: | J23 J24 D22 D84 O33 C93 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewdip:333393 |
| By: | Colliard, Jean-Edouard (HEC Paris - Finance Department); Zhao, Junli (Bayes Business School) |
| Abstract: | This paper studies how artificial intelligence (AI) affects the finance labor market when humans and AI perform different tasks in investment projects, and workers earn agency rents that grow with project size. We identify two key effects of AI improvement: A free-riding effect raises worker rents by increasing the probability of successful investment when the worker shirks; A capital reallocation effect shifts investment toward workers with higher or lower rents, depending on which tasks AI improves. Contrary to standard predictions, AI can raise both worker rents and labor demand. We derive implications for capital allocation, labor demand, compensation, and welfare. |
| Keywords: | Artificial intelligence; labor market; automation; rents in finance |
| JEL: | O33 |
| Date: | 2025–07–08 |
| URL: | https://d.repec.org/n?u=RePEc:ebg:heccah:1576 |
| By: | Kremer, Anna |
| Abstract: | Meeting others with different backgrounds brings up new ideas. This paper shows that this not only matters for a background in heterogeneous industries or nationalities, but that regional differences matter too. Regions within a country vary in their traditions and culture. Cultural homogeneity within regions becomes mixed due to internal migration, which, like international migration, increases the diversity of a place. In a novel approach, I look at diversity in German municipalities, measured by different family names, and investigate its effect on the number of generated patents. I use a unique dataset from a 1996 phonebook and casualty lists from WWI. There is a positive association between innovation and diversity when defined by the share of new names, a deconcentration measure, or a Shannon index. Causality is established by using instrumental variables estimations with historical borders. I show that intra-country diversity affects patenting positively and conclude that regional differences matter for economic outcomes. |
| Keywords: | cultural diversity, innovation, family names, patents, local level, Germany |
| JEL: | R11 O30 Z13 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:tudcep:333401 |
| By: | Kim, Chang Hyun; Lee, Kyung Yul; Kwon, Youngsun |
| Abstract: | This study explores how knowledge search depth and breadth in green patenting influence environmental innovation in the mobile industry, a sector facing increasing scrutiny for its environmental impact. Using a panel of 42 mobile firms from 2001 to 2023, we find that search depth exhibits an inverted U-shaped relationship with green innovation, suggesting that while leveraging internal knowledge initially boosts environmental innovation, excessive reliance can hinder progress due to organizational rigidity. Conversely, search breadth demonstrates a consistently positive effect, indicating that expanding external knowledge sources enhances a firm's capacity for sustainable innovation. These findings underscore the strategic importance of balancing internal and external knowledge strategies to foster green innovation in the mobile sector. |
| Keywords: | Green Innovation, Knowledge Search, Mobile Industry, Sustainability |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:itse25:331286 |
| By: | Teo Firpo (Humboldt-Universität zu Berlin); Lukas Niemann (Tanso Technologies); Anastasia Danilov (Humboldt-Universität zu Berlin) |
| Abstract: | As firms increasingly adopt Artificial Intelligence (AI) technologies, how they adjust hiring practices for skilled workers remains unclear. This paper investigates whether AI-related skills are rewarded in talent recruitment by conducting a large-scale correspondence study in the United Kingdom. We submit 1, 185 résumés to vacancies across a range of occupations, randomly assigning the presence or absence of advanced AI-related qualifications. These AI qualifications are added to résumés as voluntary signals and not explicitly requested in the job postings. We find no statistically significant effect of listing AI qualifications in résumés on interview callback rates. However, a heterogeneity analysis reveals some positive and significant effects for positions in Engineering and Marketing. These results are robust to controlling for the total number of skills listed in job ads, the degree of match between résumés and job descriptions, and the level of expertise required. In an exploratory analysis, we find stronger employer responses to AI-related skills in industries with lower exposure to AI technologies. These findings suggest that the labor market valuation of AI-related qualifications is context-dependent and shaped by sectoral innovation dynamics. |
| Keywords: | return to skills; technological change; labor market; hiring; signaling; human capital; field experiment; ai-related skills; |
| JEL: | O33 J23 J24 I26 |
| Date: | 2025–11–17 |
| URL: | https://d.repec.org/n?u=RePEc:rco:dpaper:552 |
| By: | Damásio, Bruno; Silva, Eduardo; Mendonça, Sandro |
| Abstract: | Recent years have recorded a growth in the number of patent applications filed by digital platforms. This paper argues that by profiling these patent portfolios, we can obtain insightful patterns on platforms' business and innovation strategies. For this purpose, we build a dataset of over 380, 000 patent applications filed at least by one of ten large US and Chinese digital platforms between 1986 and 2024. A significant rise in patent activity has taken shape since 2012, largely due to an impressive number of applications filed by Chinese platforms. Platforms tend to patent alone and concentrate their patenting activity on computer technology and electric communication, with machine learning being an overarching theme. However, some platforms like Apple pursue the development of a diversified patent portfolio, while others build one more specialized and aligned with their core business. Additionally, platform applications receive a significant number of citations, despite a skewed distribution which is only slightly challenged by Apple. Finally, applications by Chinese platforms have a more limited international protection when compared to their American counterparts, as attested by their patent family sizes. |
| Keywords: | patents, digital platforms, portfolio, China |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:itse25:331264 |
| By: | Aakash Bhalothia; Gavin Engelstad; Gaurav Khanna; Harrison Mitchell |
| Abstract: | We estimate the economic value of cities worldwide, using detailed job histories for 513 million workers in 220, 000 cities across 191 countries. These estimates allow us to identify why some cities are more productive than others and to quantify the earnings gains from migration throughout the development process. Our data contain job spells—with start and end dates, establishment names, locations, job titles, and effective salaries—enabling an event-study movers design with individual and time fixed effects. Moving to higher-value cities leads to immediate increases in job seniority, shifts into better-paid industries and occupations, and large overall earnings gains. The global scope of the data lets us compare internal and international moves and assess how the productivity advantages of cities differ by country income level. Across borders, 93% of wage changes reflect city effects, while within countries this share ranges from 45–73%. High-income countries exhibit stronger ability-based sorting, reducing the proportion attributable to place. City effects rise with industrial diversity and population, consistent with agglomeration economies, and more productive cities allocate workers to higher-productivity firms. The wide dispersion of city effects within countries highlights substantial potential gains from migration, particularly in low-income, less-urbanized economies. Reallocating workers to match the US distribution yields sizable wage gains in developing countries. |
| JEL: | J38 J6 O15 R12 R23 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34503 |
| By: | Diego R. Känzig; Charles T. Williamson |
| Abstract: | We explore the increasing divergence between economic growth and energy consumption through energy-saving technical progress. Proposing a new measure of energy-saving technology, we study the underlying drivers in a semi-structural model of the U.S. economy. Our analysis shows that energy price shocks reduce consumption and stimulate energy-saving innovation, but also cause economic downturns and crowd out other innovations. Only energy-saving technology shocks can explain the negative co-movement between output and energy use. These sudden efficiency gains emerge as the primary driver of energy-saving technical change. Our findings highlight the importance of fostering energy-saving innovations in transitioning to a low-carbon economy. |
| JEL: | E0 O30 Q32 Q43 Q55 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34511 |
| By: | Terada, Shinichiro |
| Abstract: | This paper investigates whether the activities of OpenAI—particularly those related to its generative AI product, ChatGPT—have affected major U.S. technology firms, Big Tech, collectively referred to as GAFAM (Alphabet/Google, Apple, Meta/Facebook, Amazon, and Microsoft). Using a short-term event study methodology, we analyze the abnormal stock returns of these firms in response to key OpenAI-related events, including product launches, corporate investments, and technological integrations. Our empirical analysis reveals three main findings. First, the release of ChatGPT had no statistically significant effect on the stock returns of each of GAFAM firms. Second, Microsoft's direct investments in OpenAI, including a $1 billion and a multi-billion-dollar deal, resulted in neutral market responses, suggesting a balance between cost and expected strategic benefit. Third, collaborations that integrated OpenAI's technology into Microsoft's Bing and Edge, and Apple's iOS ecosystem yielded statistically significant positive abnormal returns for the respective firms. These results imply that OpenAI's competitive impact on GAFAM is not direct rivalry but rather complementary enhancement. OpenAI's AI capabilities function as a core competence that strengthens the existing revenue-generating platforms of Big Tech. This suggests a business ecosystem in which AI technologies are not isolated strategic assets, but rather enablers of broader corporate competitiveness. |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:itse25:331311 |
| By: | Claeys, Peter; Gómez-Bengoechea, Gonzalo; Jung, Juan; Van Der Wielen, Wouter; Weiss, Christoph |
| Abstract: | This article analyzes if the Internet of Things (IoT) can contribute to increasing energy productivity across firms that adopt this technology. The empirical analysis is based on a sample of more than 8, 000 firms from various sectors across 26 European countries, surveyed by the European Investment Bank across the years 2022 and 2023. Methodologically, we combine two-way fixed-effects models (TWFE), differences-in-differences and matching methods. Our results indicate significant effects of IoT on energy productivity, although these effects seem to be concentrated among manufacturing/construction sectors and medium/big firms only. Our findings suggest that digital technologies such as IoT can potentially play a key role in energy transitions toward more sustainable economies. |
| Keywords: | Internet of Things, Digitization, Energy productivity, Energy efficiency |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:itse25:331263 |