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on Technology and Industrial Dynamics |
| By: | Bertschek, Irene; Erdsiek, Daniel; Niebel, Thomas; Sack, Robin; Zimmermann, Volker |
| Abstract: | Recent literature has increasingly focused on deciphering the modern productivity puzzle, with particular attention given to the link between digital technologies and firm-level productivity. So far, much of this research has primarily focused on large and publicly listed firms. Leveraging a panel dataset covering German small- and medium-sized enterprises (SMEs) over the period 2016 to 2021, we investigate whether digitalisation can help revive the sluggish productivity growth and narrow the gap between productivity frontrunners and laggards. We measure digitalisation through firms' digital capital stocks (DK) that we derive from a broad measure of digitalisation expenditures. Building on an augmented Cobb-Douglas production function, we examine the relationship between DK and labour productivity (LP ). Our findings show that higher DK is positively associated with higher LP levels, with the effect being even stronger for firms that are already more digitally advanced. Moreover, higher digitalisation expenditures appear to be related to narrowing the productivity gap between laggards and the frontier. |
| Keywords: | Heterogeneity of Digitalisation, Productivity, Firm-level Data |
| JEL: | L25 O14 O33 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewdip:340839 |
| By: | Yojiro Ito (Director and Senior Economist, Institute for Monetary and Economic Studies, Bank of Japan (E-mail: youjirou.itou@boj.or.jp)); Harumasa Shirakawa (Economist, Institute for Monetary and Economic Studies, Bank of Japan (E-mail: harumasa.shirakawa@boj.or.jp)) |
| Abstract: | The invention of new goods or services, called product innovation, and improvements in production processes, called process innovation, can have distinct impacts on firm performance. By integrating firm-level innovation survey data with financial panel data in Japan, our empirical analysis yields two key findings. First, the increasing prevalence of process innovation over product innovation has been observed over the past two decades. Second, higher market growth stimulates product innovation, which, in turn, contributes to growth in firm sales and employment over several years. In contrast, no such patterns are observed for process innovation. These results suggest that there may be a positive feedback loop between product innovation and firm growth. |
| Keywords: | Economic Growth, Productivity, Product Innovation, Process Innovation |
| JEL: | D22 L1 L25 L53 O25 O31 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:ime:imedps:25-e-18 |
| By: | Erik CANTON (European Commission) |
| Abstract: | This paper examines how employment protection legislation shapes firm-level technology adoption across OECD countries using novel survey data. We document a negative association between employment protection legislation and the adoption of artificial intelligence and other restructuring-intensive technologies, while more modular digital technologies display weaker relationships with labor market institutions. The patterns are particularly pronounced among large incumbent firms, while younger and fast-growing firms exhibit higher adoption rates. To interpret these patterns, we develop a general equilibrium model with heterogeneous firms in which workforce restructuring costs raise the productivity threshold for technology adoption. The model predicts heterogeneous firm responses: some adopters expand employment, others contract, and highly productive incumbents may optimally refrain from adoption when restructuring costs rise with scale. The framework is extended to incorporate endogenous technology arrival and diffusion through both adoption by incumbent firms and entry of new firms implementing frontier technologies. The analysis highlights how labor market institutions can affect technology diffusion and, through this channel, influence incentives to develop and commercialize new technologies. |
| Keywords: | Employment protection legislation, technology adoption, artificial intelligence, labor market institutions, firm heterogeneity, restructuring costs, general equilibrium |
| JEL: | O33 J24 J38 L25 O32 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:eug:wpaper:ki-01-26-053-en-n |
| By: | Tom Davidson; Basil Halperin; Thomas Houlden; Anton Korinek |
| Abstract: | AI labs are increasingly using AI itself to accelerate AI research, creating a feedback loop that could lead to an intelligence explosion. We develop a general semi-endogenous growth model with an innovation network, where research and automation in one sector increase the productivity of research in other sectors, and derive a clean analytical condition under which growth becomes superexponential (``explosive''). We find that automating research can offset diminishing returns to ideas by activating two reinforcing channels: a technological feedback loop across research sectors, and an economic feedback loop in which higher output finances further research. Growth becomes explosive if the combined strength of technological and economic feedback loops overcomes diminishing returns. In a simple simulation calibrated to trends in AI progress, fully automating software research and modest (5%) automation in other sectors generates a singularity within six years. Bottlenecks do not overturn the result if task automation advances sufficiently fast. |
| JEL: | O31 O33 O40 O41 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35155 |
| By: | Kathryn Bonney; Cory L. Breaux; Emin Dinlersoz; Lucia S. Foster; John C. Haltiwanger; Aditya A. Pande |
| Abstract: | Using novel, nationally representative data from the 2026 AI supplement to the U.S. Census Bureau’s Business Trends and Outlook Survey (BTOS), we characterize AI diffusion across three layers: firm-wide adoption, business-function deployment, and worker-task use. During Nov 2025–Jan 2026, 18% of firms used AI in at least one function (32%, employment-weighted), with adoption expected to reach 22% within six months. Use is concentrated in large firms and knowledge-intensive sectors, reaching 50%–60% (60%–70%, employment-weighted) among very large firms in Information, Professional Services, and Finance. Among adopters, scope remains limited: 57% use AI in three or fewer functions, most often Sales and Marketing (52%), Strategy (45%), and IT (41%). Worker-level use appears in 23% (41%, employment-weighted) of firms, primarily for writing, document analysis, and information search; 65% restrict use to three or fewer tasks. Evidence suggests both top-down and bottom-up diffusion: worker use can occur without firm adoption, and vice versa. Most firms (66%) use AI for task augmentation, while employment reductions are rare (2%). Regression results show a positive relationship between firm performance and AI integration breadth. However, functional deployment and operational investment are associated with employment declines, while worker-task use is not once these factors are controlled for. |
| JEL: | L23 O33 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35141 |
| By: | Sonja Dobkowitz |
| Abstract: | I study optimal implementation of climate targets in a model with distortionary fiscal policy, learning-by-doing, and directed technical change. The key mechanism is that fiscal constraints link innovation policy to labor allocation, creating a tension between directing research and directing learning-by-doing. Analytically, I show that learning-by-doing shapes the effectiveness of carbon taxation in directing research through an expertise effect: carbon taxes are more effective at steering innovation toward green technologies when green expertise is relatively high. Quantitatively, I calibrate the model to the U.S. economy to characterize the optimal policy mix consistent with climate targets. I find that carbon should be taxed heavily, persistently exceeding the social cost of carbon. While higher carbon prices raise green expertise, they induce an excessively rapid reallocation of researchers from fossil to green technologies, generating persistent innovation misallocation. A welfare analysis shows that learning-by-doing substantially amplifies the cost of distortionary taxation, in particular during the transition to net-zero emissions. |
| Keywords: | Second-best climate policy, directed technical change, learning-by-doing, Ramsey taxation, misallocation of innovation, emissions target implementation |
| JEL: | H21 H23 O38 Q54 Q55 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:diw:diwwpp:dp2161 |
| By: | Donatella Baiardi (Department of Economics and Management, University of Parma); Fabio Landini (Department of Economics and Management, University of Parma); Mario Menegatti (Department of Economics and Management, University of Parma); Ugo Rizzo (Department of Mathematics and Computer Science, University of Ferrara; Sustainability Environmental Economics and Dynamics Studies (SEEDS)); Luigi Tredicine (Department of Economics and Management, University of Parma) |
| Abstract: | This paper examines the impact of green-oriented university education on environmental quality, by developing a conceptual framework in which firm emissions depend on the joint use of green technologies and green-skilled labor. In complementarity between these inputs, an increase in the local supply of green-skilled labor induces firms to adopt more green technologies, thereby improving environmental quality. In addition, we show that this effect is stronger in more labor-intensive sectors. Guided by these theoretical insights, we perform an empirical analysis based on a novel measure of green higher education, constructed using administrative data on more than 90, 000 university course descriptions in Italy. We build an indicator of the green content of academic programs using natural language processing techniques and aggregate it at the provincial level to proxy the supply of green-skilled workers. Combining this measure with detailed data on environmental quality, proxied by different types of air emissions, including carbon dioxide (CO2), carbon monoxide (CO), and particulate matter (PM10 and PM2.5). We find that a higher supply of graduates with more intensive green skills is associated with significantly lower emissions of key pollutants, including CO2, CO, PM10, and PM2.5. This relationship is robust to a wide set of controls and fixed effects. In line with our model, the association is stronger for service-related emissions than for industrial sources. In general, these findings highlight the role of higher education as a key driver of improved environmental quality through the provision of green skills. |
| Keywords: | Higher Education; Green Skills; University; Air Pollutants; Environmental Quality |
| JEL: | I23 Q51 Q53 Q55 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:srt:wpaper:1026 |
| By: | Hassan Afrouzi; Andres Blanco; Andrés Drenik; Erik Hurst |
| Abstract: | We study how an automating technology affects career dynamics, human capital, and welfare in an economy where workers acquire skill through the tasks they perform. In a continuous-time general equilibrium model, learning-by-doing is determined jointly with the share of tasks automated, the frontier of tasks managers maintain, and the worker-to-manager career transition. Economies with high learning capacity admit pairs of stationary equilibria strictly ranked by the aggregate learning rate. Cheaper technology has opposite effects across the two: in the high-learning equilibrium, it raises welfare through the learning channel itself; in the low-learning equilibrium, it tips the economy into a human-capital trap. The planner's first-best combines a tax on automation profits with a subsidy on frontier-maintenance expenditures at a common rate. |
| JEL: | E23 E24 J24 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35157 |
| By: | Federico Atzori (Sapienza University); Luca Corazzini (University of Milan - Bicocca); Valeria Maggian (Ca’ Foscari University of Venice); Filippo Pavesi (LIUC University); Massimo Scotti (LIUC University) |
| Abstract: | We investigate how generative AI shapes creative performance and human-AI interaction in an open-ended writing task that employs a laboratory experiment in which participants are randomly assigned to either receive access to a large language model (ChatGPT-4.2) or not. Creative performance is measured by the average score assigned by independent evaluators recruited through the Prolific platform, and detailed logs of human-AI interaction are analyzed to measure AI use, prompting intensity, ideation requests, and the textual overlap between AI outputs and participants' final writings. Three main results emerge. First, AI access increases performance, but the gain is entirely driven by active use: participants with access who do not submit queries perform no better than those without AI. Second, the relationship between interaction intensity and performance is concave, peaking at roughly eight queries, consistent with iterative exploration rather than mechanical copying. Third, structural mediation analyses show that ideation requests affect performance primarily indirectly, by increasing downstream incorporation of AI-generated language; the direct effect of requesting an idea from the AI is negligible once execution-stage reliance is accounted for. We further document heterogeneity in AI reliance: cultural capital (proxied by books owned) predicts lower AI use, while prior AI exposure predicts higher use. By contrast, incentive schemes have limited effects on both outcomes and AI-related behaviors. |
| Keywords: | Human-AI Interaction; Creativity; Generative AI; Laboratory Experiment |
| JEL: | C91 D83 J24 O33 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ven:wpaper:2026:16 |
| By: | Erhan Bayraktar |
| Abstract: | Automation raises productivity and reduces paid human labor, but it also reallocates income and ownership claims. This paper studies that tradeoff in a static benchmark and in a stationary heterogeneous-agent general equilibrium. Firms choose automation from a profit function. Households differ by skill and wealth, save in a capital/equity claim, and face incomplete insurance. Wages and returns are determined by market clearing from a Cobb--Douglas final-good firm, while the wealth distribution is pinned down by a Hamilton--Jacobi--Bellman (HJB) equation and a Kolmogorov forward equation (KFE). The paper is deliberately two-sided. With strong productivity growth, high-skill complementarity, low obsolescence, and broad ownership, automation raises output, capital, and consumption. With strong exposure of low-wealth, high-marginal-propensity-to-consume (high-MPC) households and concentrated ownership, privately chosen automation can be excessive even though it raises high-skilled labor income. The central object is the derivative of household consumption demand and collective wage bill with respect to automation. Fiscal policy is modeled as a government problem rather than as an abstract planner: a tax changes the firm's automation first-order condition, raises revenue only on the remaining automation base, and must specify rebates and administrative losses. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.05127 |
| By: | Ernst, Christoph,; Michelena, Gabriel,; Bertin, Pablo, |
| Abstract: | Foreign direct investment and the activities of multinational enterprises play an essential role in shaping employment outcomes, particularly in developing and emerging economies. With globalization and the expansion of global value chains, understanding how FDI influences labour markets has become increasingly relevant. This paper quantifies the global employment supported by foreign affiliates, examining both direct and indirect effects across regions and sectors. Our estimates suggest that foreign MNEs supported around 125 million jobs worldwide by 2021, a significant increase from earlier baselines. Employment is geographically concentrated in the European Union, China, North America, and East Asia, with services accounting for over 40 per cent of total MNE-supported jobs, though manufacturing remains key in China. Poorer countries are further marginalized. The contribution of this paper lies in two main areas. First, it provides a consistent global estimate of employment associated with MNEs by combining firm-level datasets with ILO labour statistics. Second, it provides evidence about the heterogeneity of employment effects across regions and sectors: while the European Union, China, North America, and East Asia account for the largest employment shares, a shift towards services in productive activities and employment has been observed, but manufacturing still remains central in countries such as China. |
| Keywords: | foreign investment, value chains., labour market, multinational enterprise |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ilo:ilowps:995691071902676 |
| By: | Ryan R. Hill; Carolyn Stein |
| Abstract: | We study how a frontier AI model affects scientific discovery by examining the release of the AlphaFold2 algorithm and its impact on structural biology and related fields of science. Structural biology is the field of science concerned with understanding the structure and function of proteins. Researchers in this field historically devoted substantial time and resources to experimentally solving three-dimensional protein structures. AlphaFold can predict these structures without running experiments. In July 2021, researchers gained access to hundreds of thousands of these AI-predicted structures virtually overnight. Yet, to date, we find that the rate of experimental structure determination has remained almost unchanged. Instead, researchers appear to use predicted structures to facilitate and complement experimental structure determination. Looking at downstream science that builds on protein structures, we find that basic research on proteins that had no structure information prior to AlphaFold increases by 15 to 40% relative to proteins that already had a structure, shifting the direction of research toward less-studied proteins. However, we find no evidence so far that more applied, early-stage drug development is targeting these proteins, though such activity may emerge in the future. |
| JEL: | I23 J24 L65 O31 O33 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35143 |