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on Technology and Industrial Dynamics |
| By: | Iñaki Aldasoro; Leonardo Gambacorta; Rozalia Pal; Debora Revoltella; Christoph Weiss; Marcin Wolski |
| Abstract: | This paper provides new evidence on how the adoption of artificial intelligence (AI) affects productivity and employment in Europe. Using matched EIBIS-ORBIS data on more than 12, 000 non-financial firms in the European Union (EU) and United States (US), we instrument the adoption of AI by EU firms by assigning the adoption rates of US peers to isolate exogenous technological exposure. Our results show that AI adoption increases the level of labor productivity by 4%. Productivity gains are due to capital deepening, as we find no adverse effects on firm-level employment. This suggests that AI increases worker output rather than replacing labor in the short run, though longer-term effects remain uncertain. However, productivity benefits of AI adoption are unevenly distributed and concentrate in medium and large firms. Moreover, AI-adopting firms are more innovative and their workers earn higher wages. Our analysis also highlights the critical role of complementary investments in software and data or workforce training to fully unlock the productivity gains of AI adoption. |
| Keywords: | artificial intelligence, firm productivity, Europe, digital transformation |
| JEL: | D22 J24 L25 O33 O47 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1325 |
| By: | Yukun Zhang; Tianyang Zhang |
| Abstract: | This paper develops a micro-founded economic theory of the AI industry by modeling large language models as a distinct asset class-Digital Intelligence Capital-characterized by data-compute complementarities, increasing returns to scale, and relative (rather than absolute) valuation. We show that these features fundamentally reshape industry dynamics along three dimensions. First, because downstream demand depends on relative capability, innovation by one firm endogenously depreciates the economic value of rivals' existing capital, generating a persistent innovation pressure we term the Red Queen Effect. Second, falling inference prices induce downstream firms to adopt more compute-intensive agent architectures, rendering aggregate demand for compute super-elastic and producing a structural Jevons paradox. Third, learning from user feedback creates a data flywheel that can destabilize symmetric competition: when data accumulation outpaces data decay, the market bifurcates endogenously toward a winner-takes-all equilibrium. We further characterize conditions under which expanding upstream capabilities erode downstream application value (the Wrapper Trap). A calibrated agent-based model confirms these mechanisms and their quantitative implications. Together, the results provide a unified framework linking intelligence production upstream with agentic demand downstream, offering new insights into competition, scalability, and regulation in the AI economy. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.12339 |
| By: | Bijnens, Gert; Konings, Jozef; Putseys, Aaron |
| Abstract: | This paper identifies a firm-level Productivity J-curve induced by intangible investments. Using novel microdata on business-to-business transactions for Belgian firms, we construct a comprehensive measure of intangible investment covering software, R&D, design, training, and organizational capital. Our analysis shows that returns on intangibles substantially exceed those of traditional production factors, highlighting their central role in value creation. However, because intangible expenditures are rarely capitalized and are often recorded as intermediate inputs, they are not properly accounted for in conventional measures of total factor productivity (TFP). This misclassification creates systematic mismeasurement, whereby inputs are overstated relative to output in the short run, leading to an underestimation of TFP. Exploiting the lumpy nature of intangible expenditures within a difference-in-differences event-study framework, we document that such mismeasurement results in a persistent underestimation of TFP, by about 3% over a seven-year horizon. Given average measured TFP growth of 1% annually, this represents a substantial distortion. The bias is strongest among small, young, and low- capital intensive firms, reflecting slower absorption of intangible assets. By clarifying how intangible capital and emerging technologies such as AI systematically distort measured productivity, our findings provide new empirical insights into the productivity slowdown and the role of mismeasurement in modern economies. |
| Keywords: | Productivity |
| JEL: | E01 E22 E23 O32 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:335205 |
| By: | Yueran Ma; Mengdi Zhang; Kaspar Zimmermann |
| Abstract: | We collect new data to document the long-run evolution of the firm size distribution in ten market-based economies in Asia, Europe, North America, and Oceania, where we can obtain comprehensive coverage of the population of firms. Around the world, we observe prevalent increases in the concentration of sales, net income, and equity capital over the past century. These trends hold in the aggregate and at the industry level. Meanwhile, employment concentration has been stable over the long run in most cases. The evidence shows that the rising dominance of large firms is a pervasive phenomenon, not limited to the recent decades or the United States, and that large firms often achieve greater scale without proportionally more workers. |
| JEL: | E01 L1 N1 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34711 |
| By: | FUKAO, Kyoji; KIM, YoungGak; KWON, Hyeog Ug |
| Abstract: | This study examines the dynamics of total factor productivity (TFP) by firm size to clarify the recent drivers of productivity growth in the Japanese economy, utilizing firm-level financial data from Teikoku Databank (TDB) spanning the years 1999 to 2020. In particular, we examine Japan’s distinctive “negative exit effect” by differentiating among various types of firm exit, including bankruptcy, closure, dissolution, and mergers. Our analysis shows that while within-firm productivity improvements at large firms played a dominant role in driving productivity growth through the 2000s, reallocation effects have become increasingly important since the 2010s. Notably, a substantial share of high-productivity firms exited the market through mergers, accounting for nearly half of the overall negative exit effect. Furthermore, while TFP among acquiring firms tends to stagnate in the short term after mergers, their labor productivity shows a significant and sustained increase, likely driven by capital deepening. These findings provide new insights into the shifting drivers of productivity growth in Japan—from within-firm productivity growth to market-driven resource reallocation—as well as into firm-size heterogeneity and the role of mergers in shaping productivity dynamics. |
| Keywords: | productivity dynamics, firm size heterogeneity, negative exit effect, mergers and acquisitions, resource reallocation, total factor productivity, SMEs, Japan |
| JEL: | O47 D24 L25 O53 G34 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:hit:tdbcdp:e-2025-03 |
| By: | Isaak Mengesha; Debraj Roy |
| Abstract: | Global inequality has shifted inward, with rising dispersion increasingly occurring within countries rather than between them. Using 8, 790 newly harmonised Functional Urban Areas (FUAs), micro-founded labour-market regions encompassing 3.9 billion people and representing approximately 80% of global GDP, we show that national aggregates systematically, and increasingly, misrepresent the dynamics of growth, convergence, and structural change. Drawing on high-resolution global GDP data and country-level capability measures, we find that the middle-income trampoline that previously drove global convergence is flattening. This divergence in the lower-income regime does not reflect poverty traps: low-income FUAs exhibit positive expected growth, and the transition curve displays no stable low-income equilibrium. Instead, productive capabilities, proxied by the Economic Complexity Index, define distinct growth regimes. FUAs converge within capability strata but diverge across them, and capability upgrading follows a predictable J-curve marked by short-run disruption and medium-run acceleration. These findings suggest that national convergence policies may be systematically misaligned with the geographic scale at which capability accumulation operates. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.12158 |
| By: | David Autor; B. N. Kausik |
| Abstract: | A central socioeconomic concern about Artificial Intelligence is that it will lower wages by depressing the labor share - the fraction of economic output paid to labor. We show that declining labor share is more likely to raise wages. In a competitive economy with constant returns to scale, we prove that the wage-maximizing labor share depends only on the capital-to-labor ratio, implying a non-monotonic relationship between labor share and wages. When labor share exceeds this wage-maximizing level, further automation increases wages even while reducing labor's output share. Using data from the United States and eleven other industrialized countries, we estimate that labor share is too high in all twelve, implying that further automation should raise wages. Moreover, we find that falling labor share accounted for 16\% of U.S. real wage growth between 1954 and 2019. These wage gains notwithstanding, automation-driven shifts in labor share are likely to pose significant social and political challenges. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.06343 |
| By: | Joshua S. Gans |
| Abstract: | Generative AI systems often display highly uneven performance across tasks that appear “nearby”: they can be excellent on one prompt and confidently wrong on another with only small changes in wording or context. We call this phenomenon Artificial Jagged Intelligence (AJI). This paper develops a tractable economic model of AJI that treats adoption as an information problem: users care about local reliability, but typically observe only coarse, global quality signals. In a baseline one-dimensional landscape, truth is a rough Brownian process, and the model “knows” scattered points drawn from a Poisson process. The model interpolates optimally, and the local error is measured by posterior variance. We derive an adoption threshold for a blind user, show that experienced errors are amplified by the inspection paradox, and interpret scaling laws as denser coverage that improves average quality without eliminating jaggedness. We then study mastery and calibration: a calibrated user who can condition on local uncertainty enjoys positive expected value even in domains that fail the blind adoption test. Modelling mastery as learning a reliability map via Gaussian process regression yields a learning-rate bound driven by information gain, clarifying when discovering “where the model works” is slow. Finally, we study how scaling interacts with discoverability: when calibrated signals and user mastery accelerate the harvesting of scale improvements, and when opacity can make gains from scaling effectively invisible. |
| JEL: | D83 O33 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34712 |
| By: | Joel M Thomas; Abhijit Chakraborty |
| Abstract: | This study investigates the economic complexity of Indian states by constructing a state-industry bipartite network using firm-level data on registered companies and their paid-up capital. We compute the Economic Complexity Index and apply the fitness-complexity algorithm to quantify the diversity and sophistication of productive capabilities across the Indian states and two union territories. The results reveal substantial heterogeneity in regional capability structures, with states such as Maharashtra, Karnataka, and Delhi exhibiting consistently high complexity, while others remain concentrated in ubiquitous, low-value industries. The analysis also shows a strong positive relationship between complexity metrics and per-capita Gross State Domestic Product, underscoring the role of capability accumulation in shaping economic performance. Additionally, the number of active firms in India demonstrates a persistent exponential growth at an annual rate of 11.2%, reflecting ongoing formalization and industrial expansion. The ordered binary matrix displays the characteristic triangular structure observed in complexity studies, validating the applicability of complexity frameworks at the sub-national level. This work highlights the usefulness of firm-based data for assessing regional productive structures and emphasizes the importance of capability-oriented strategies for fostering balanced and sustainable development across Indian states. By demonstrating the usefulness of firm registry data in data constrained environments, this study advances the empirical application of economic complexity methods and provides a quantitative foundation for capability-oriented industrial and regional policy in India. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.12356 |
| By: | Tatsuru Kikuchi |
| Abstract: | This paper develops a unified framework for analyzing technology adoption in financial networks that incorporates spatial spillovers, network externalities, and their interaction. The framework characterizes adoption dynamics through a master equation whose solution admits a Feynman-Kac representation as expected cumulative adoption pressure along stochastic paths through spatial-network space. From this representation, I derive the Adoption Amplification Factor -- a structural measure of technology leadership that captures the ratio of total system-wide adoption to initial adoption following a localized shock. A Levy jump-diffusion extension with state-dependent jump intensity captures critical mass dynamics: below threshold, adoption evolves through gradual diffusion; above threshold, cascade dynamics accelerate adoption through discrete jumps. Applying the framework to SWIFT gpi adoption among 17 Global Systemically Important Banks, I find strong support for the two-regime characterization. Network-central banks adopt significantly earlier ($\rho = -0.69$, $p = 0.002$), and pre-threshold adopters have significantly higher amplification factors than post-threshold adopters (11.81 versus 7.83, $p = 0.010$). Founding members, representing 29 percent of banks, account for 39 percent of total system amplification -- sufficient to trigger cascade dynamics. Controlling for firm size and network position, CEO age delays adoption by 11-15 days per year. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.04246 |