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on Technology and Industrial Dynamics |
| By: | Andrew Ledingham; Michael Hollins; Matthew Lyon; David Gillespie; Umar Yunis-Guerra; Jamie Siviter; David Duncan; Oliver P. Hauser |
| Abstract: | The adoption of generative artificial intelligence (AI) is predicted to lead to fundamental shifts in the labour market, resulting in displacement or augmentation of AI-exposed roles. To investigate the impact of AI across a large organisation, we assessed AI exposure at the task level within roles at the UK Civil Service (UKCS). Using a novel dataset of UKCS job adverts, covering 193, 497 vacancies over 6 years, our large language model (LLM)-driven analysis estimated AI exposure scores of 1, 542, 411 tasks. By aggregating AI exposure scores for tasks within each role, we calculated the mean and variance of job-level exposure to AI, highlighting the heterogeneous impacts of AI, even for seemingly identical jobs. We then use an LLM to redesign jobs, focusing on task automation, task optimisation, and task reallocation. We find that the redesign process leads to tasks where humans have comparative advantage over AI, including strategic leadership, complex problem resolution, and stakeholder management. Overall, automation and augmentation are expected to have nuanced effects across all levels of the organisational hierarchy. Most economic value of AI is expected to arise from productivity gains rather than role displacement. We contribute to the automation, augmentation and productivity debates as well as advance our understanding of job redesign in the age of AI. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.05659 |
| By: | Matias Ciaschi (CEDLAS-IIE-FCE-UNLP and CONICET); Guillermo Falcone (CEDLAS-IIE-FCE-UNLP and CONICET); Santiago Garganta (CEDLAS-IIE-FCE-UNLP); Leonardo Gasparini (CEDLAS-IIE-FCE-UNLP and CONICET); Octavio Bertín (CEDLAS-IIE-FCE-UNLP); Lucía Ramirez-Leira (CEDLAS-IIE-FCE-UNLP) |
| Abstract: | This paper investigates the potential distributional consequences of artificial intelligence (AI) adoption in Latin American labor markets. Using harmonized household survey data from 14 countries, we combine four recently developed AI occupational exposure indices—the AI Occupational Exposure Index (AIOE), the ComplementarityAdjusted AIOE (C-AIOE), the Generative AI Exposure Index (GBB), and the AIGenerated Occupational Exposure Index (GENOE)—to analyze patterns across countries and worker groups. We validate these measures by comparing task profiles between Latin America and high-income economies using PIAAC data, and develop a contextual adjustment that incorporates informality, wage structures, and union coverage. Finally, we simulate first-order impacts of AI-induced displacement on earnings, poverty, and inequality. The results show substantial heterogeneity, with higher levels of AI-related risk among women, younger, more educated, and formal workers. Indices that account for task complementarities show flatter gradients across the income and education distribution. Simulations suggest that displacement effects may lead to only moderate increases in inequality and poverty in the absence of mitigating policies. |
| JEL: | O33 J21 D31 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:dls:wpaper:0361 |
| By: | Giuseppe Simone |
| Abstract: | This paper investigates the structural foundations of regional productivity divergence in Italy through the lens of economic complexity. Leveraging a newly constructed Economic Complexity Index (ECI) at the NUTS-3 level, we examine how the sophistication and diversity of local productive structures shape long-run productivity trajectories of Italian provinces over the period 2000–2021. Empirical approach combines panel data models with instrumental variable (IV-GMM) techniques, spatial econometrics, and simultaneous equation systems (3SLS) to capture the direct, spatial, and bidirectional relationships between complexity and productivity. The findings reveal that economic complexity is a robust and consistent predictor of regional labour productivity. This association is particularly strong in Northern provinces, where institutional density and in- novation ecosystems amplify the returns to complexity, and where spatial spillovers from neighbouring territories enhance local outcomes. In contrast, Southern regions experience lower returns and limited externalities, reflecting persistent development traps. Crucially, I provide the first integrated empirical evidence of a cumulative, self-reinforcing loop between complexity and productivity: more complex regions become more productive, and more productive regions are better equipped to diversify into complex activities. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2536 |
| By: | Laura Alfaro; Harald Fadinger; Jan Schymik; Gede Virananda |
| Abstract: | Trade and industrial policies, while primarily intended to support domestic industries, may unintentionally stimulate technological progress abroad. We document this mechanism in the case of rare earth elements (REEs) – critical inputs for manufacturing at the knowledge frontier, with low elasticity of substitution, inelastic supply, and high production and processing concentration. To assess the importance of REEs across industries, we construct an input-output table that includes disaggregated REE inputs. Using REE-related patents categorized by a large language model, trade data, and physical and chemical substitution properties of REEs, we show that the introduction of REE export restrictions by China led to a global surge in innovation and exports in REE-intensive downstream sectors outside of China. To rationalize these findings and quantify the global impact of the adverse REE supply shock, we develop a quantitative general-equilibrium model of trade and directed technological change. We also propose a structural method to estimate sectoral input substitution elasticities for REEs from patent data and find REEs to be complementary inputs. Under endogenous technologies and with complementary inputs, input-supply restrictions on REEs induce a surge in REE-enhancing innovation and lead to an expansion of REE-intensive downstream sectors. |
| Keywords: | Trade Restrictions, Industrial Policy, Global Value Chains, Rare Earths, Directed Technological Change, Input-Output Linkages, Downstream Sectors, Innovation |
| JEL: | F13 F14 F42 O33 O47 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:bon:boncrc:crctr224_2025_720 |
| By: | Sang-Kyu Lee (Korea Institute for Industrial Economics and Trade) |
| Abstract: | This study is grounded on the premise that, given the transformative advances in artificial intelligence (AI) technologies occurring across the industrial landscape, AI tools should be actively implemented into the design and implementation of industrial policy. We argue that this is especially true for R&D policy, which is central to national competitiveness in science and technology, and which must consider multiple diverse variables, including the global economy, the overall industrial environment, corporate management, and technological capabilities.<p> For this study, I apply machine learning (ML)-based anomaly detection (AD) to analyze high-performing national R&D projects, and specifically assess ML-based AD that considers both input and output variables and analyzes structural patterns. Building on these analytical results, I propose firm-size-specific differentiated policy measures designed to enhance R&D performance.<p> The goal of this study is to establish a policy-decision framework that improves timeliness and precision in the operation and management of national R&D programs and, in the longer term, contributes to the realization of AI-based policy planning and operational management. |
| Keywords: | machine learning; artificial intelligence; AI; anomaly detection; DEA; SHAP; research and development; R&D; government R&D; industrial policy; South Korea |
| JEL: | I23 I28 O32 O38 |
| Date: | 2025–10–31 |
| URL: | https://d.repec.org/n?u=RePEc:ris:kieter:021804 |
| By: | Frederik Rech (School of Economics, Beijing Institute of Technology, Beijing, China); Fanchen Meng (Faculty of Economics, Shenzhen MSU-BIT University, Shenzhen, China); Hussam Musa (Faculty of Economics, Matej Bel University, Bansk\'a Bystrica, Slovakia); Martin \v{S}ebe\v{n}a (Faculty of Arts and Social Sciences, Hong Kong Baptist University, Hong Kong, China); Siele Jean Tuo (Business School, Liaoning University, Shenyang, China) |
| Abstract: | This study investigates whether firm-level artificial intelligence (AI) adoption improves the out-of-sample prediction of corporate financial distress models beyond traditional financial ratios. Using a sample of Chinese listed firms (2008-2023), we address sparse AI data with a novel pruned training window method, testing multiple machine learning models. We find that AI adoption consistently increases predictive accuracy, with the largest gains in recall rates for identifying distressed firms. Tree-based models and AI density metrics proved most effective. Crucially, models using longer histories outperformed those relying solely on recent "AI-rich" data. The analysis also identifies divergent adoption patterns, with healthy firms exhibiting earlier and higher AI uptake than distressed peers. These findings, while based on Chinese data, provide a framework for early-warning signals and demonstrate the broader potential of AI metrics as a stable, complementary risk indicator distinct from traditional accounting measures. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.02510 |
| By: | Yang, Linge |
| Abstract: | This paper examines the substitutability between labor and machinery in U.S. agriculture using a translog cost function and county-level data from the 2002 and 2022 Censuses of Agriculture. We estimate own-price and cross-price elasticities, along with Allen-Uzawa and Morishima elasticities of substitution, to evaluate the evolving relationship between these inputs. Our results indicate that labor and machinery have been strong substitutes in the past two decades, reflecting the sector’s capacity to mechanize tasks traditionally performed by human labor. However, elasticity estimates reveal a notable decline in substitutability in the past twenty years, which might be explained by the onset of technological saturation. As basic agricultural tasks become increasingly automated, the remaining labor-intensive activities, such as fruit harvesting and livestock care, pose greater challenges to mechanization. We also observe a declining own-price elasticity of labor, indicating reduced responsiveness of labor demand to wage changes. A shift toward skilled labor and broader structural changes in the agricultural economy may drive this trend. Regional analysis highlights heterogeneity in substitution patterns, with some areas maintaining strong substitutability while others exhibit mixed or complementary relationships. These results carry important policy implications. High substitutability supports continued investment in mechanization and informs the design of subsidies and R&D funding. This paper also contributes to a deeper understanding of input dynamics in agricultural production and offers evidence-based guidance for innovation and labor policy in the sector. |
| Keywords: | Labor, mechanization, elasticity of substitution |
| JEL: | Q12 Q16 Q18 |
| Date: | 2025–10–21 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:126570 |
| By: | Burga, Carlos; Cespedes, Jacelly; Parra, Carlos R; Ricca, Bernardo |
| Abstract: | A long-standing debate concerns whether technological change widens wage gaps by benefiting skilled labor. We show that financial technologiesspecifically, instant payment systemscan instead reduce wage inequality. Using an administrative dataset covering all registered employees in Brazil, we study the nationwide rollout of Pix, an instant payment platform introduced in late 2020. Our empirical strategy is a triple difference-in-differences design that exploits variation in preexisting mobile penetration across municipalities, the differential benefits of Pix for cash-intensive versus non-cash-intensive sectors, and the timing of Pixs rollout. A one standard deviation increase in mobile penetration leads to a 1.2 percent wage increase in cash-intensive sectors relative to non-cash-intensive sectors following Pixs introduction. These wage gains are concentrated among workers with less education, reducing the college wage premium by 1 percentage point. Further evidence suggests that increased small-business labor demand, amplified by local labor market frictions, drives these effects. Overall, instant payment systems disproportionately benefit small, cash-intensive businesses, enhancing labor demand in sectors reliant on low-skill workers and highlighting how financial technologies can shape distributional outcomes differently from skill-biased technologies. |
| JEL: | J31 O33 G23 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:idb:brikps:14416 |
| By: | Ashwin Bhattathiripad; Vipin P Veetil |
| Abstract: | This paper develops an algorithm to reconstruct large weighted firm-to-firm networks using information about the size of the firms and sectoral input-output flows. Our algorithm is based on a four-step procedure. We first generate a matrix of probabilities of connections between all firms in the economy using an augmented gravity model embedded in a logistic function that takes firm size as mass. The model is parameterized to allow for the probability of a link between two firms to depend not only on their sizes but also on flows across the sectors to which they belong. We then use a Bernoulli draw to construct a directed but unweighted random graph from the probability distribution generated by the logistic-gravity function. We make the graph aperiodic by adding self-loops and irreducible by adding links between Strongly Connected Components while limiting distortions to sectoral flows. We convert the unweighted network to a weighted network by solving a convex quadratic programming problem that minimizes the Euclidean norm of the weights. The solution preserves the observed firm sizes and sectoral flows within reasonable bounds, while limiting the strength of the self-loops. Computationally, the algorithm is O(N2) in the worst case, but it can be evaluated in O(N) via sector-wise binning of firm sizes, albeit with an approximation error. We implement the algorithm to reconstruct the full US production network with more than 5 million firms and 100 million buyer-seller connections. The reconstructed network exhibits topological properties consistent with small samples of the real US buyer-seller networks, including fat-tails in degree distribution, mild clustering, and near-zero reciprocity. We provide open-source code of the algorithm to enable researchers to reconstruct large-scale granular production networks from publicly available data. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.02362 |
| By: | Gary Gereffi (Duke University) |
| Abstract: | This featured article is authored by Gary Gereffi, one of the architects of the Global Value Chains (GVC) framework for understanding global trade and a major contributor to the body of scholarship on international trade.<p> The article is part of a series of contributions by global scholars to be published in IER leading up to the 50th anniversary of KIET's founding.<p> In contrast to the relatively fluid process of trade and FDI growth at the height of the globalization era (from the 1980s to mid-2000s), the period from the financial crisis of 2008 to the present is characterized more by disruption and geopolitical fragmentation. The bipolar international order of the Cold War (1950s-1980s) followed a short-lived period of US hegemony in the 1990s and early 2000s; the present environment is a bitterly-contested multipolar global regime that presents MNEs with major uncertainties. This paper describes how this new environment is shaping GVCs today, and the implications carried for firms and governments alike. |
| Keywords: | global value chains; GVCs; GVC governance; industrial policy; value-added trade; global trade; weaponized interdependence; economic security |
| JEL: | F13 F15 F23 F51 F52 F60 |
| Date: | 2025–10–31 |
| URL: | https://d.repec.org/n?u=RePEc:ris:kieter:021803 |
| By: | Bas Gorrens |
| Abstract: | Carbon pricing is a central policy instrument for reducing emissions, but governments face a trade-off: faster decarbonization can raise output losses and carbon leakage, while gradual implementa-tion slows emission reductions. This paper studies how EU carbon policies have shaped firms’ adoption of abatement technologies and identifies the optimal trajectory to reach the EU’s 2050 net zero target, particularly in a unilateral context. I develop a dynamic heterogeneous-firm model in which forward-looking manufacturing firms choose when to adopt discrete abatement technologies under a gradually tightening carbon price. I estimate it using panel data on EU ETS firms from 2005-2019. The model rationalizes the low carbon prices of the 2010s as a consequence of gradual policy and firm anticipation. Emission reduc-tions arise mainly from large, productive, and initially polluting firms. Anticipation of future tightening mitigates half of the short-run output losses in 2025 and two-thirds by 2050, keeping overall output losses below 2%. A moderately faster tightening could cut cumulative emissions by 15% at an additional cost of only 0.11% of output. Finally, because firms anticipate future policy changes, unilateral and global carbon pricing yield nearly identical effects on domestic output and carbon leakage. |
| Keywords: | trade and environment, technology adoption, firm decisions, climate policy, carbon leakage |
| Date: | 2025–11–26 |
| URL: | https://d.repec.org/n?u=RePEc:ete:vivwps:777266 |
| By: | Paolo Pedotti |
| Abstract: | The paper is related to the identification of firm's features which serve as determinants for firm's total factor productivity through unsupervised learning techniques (principal component analysis, self organizing maps, clustering). This bottom-up approach can effectively manage the problem of the heterogeneity of the firms and provides new ways to look at firms' standard classifications. Using the large sample provided by the ORBIS database, the analyses covers the years before the outbreak of Covid-19 (2015-2019) and the immediate post-Covid period (year 2020). It has been shown that in both periods, the main determinants of productivity growth are related to profitability, credit/debts measures, cost and capital efficiency, and effort and outcome of the R&D activity conducted by the firms. Finally, a linear relationship between determinants and productivity growth has been found. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.19627 |
| By: | Shuhui Xiang (Graduate Institute of International and Development Studies (IHEID)); Xinran Yin (Graduate Institute of International and Development Studies (IHEID)); Yuan Zi (Graduate Institute of International and Development Studies (IHEID) and CEPR) |
| Abstract: | This paper constructs a new database based on China's WTO subsidy notifications (2001–2022) and provides the first systematic overview of China's industrial subsidies over the past two decades. Five findings emerge. First, subsidies expanded rapidly, but direct fiscal support stabilized around 0.8 percent of GDP after 2008. Second, China has employed more subsidies than its income level would suggest, with striking policy persistence. Third, subsidies and tax incentives for FDI have declined, while those targeting specific industries and promoting innovation have grown. Fourth, wealthier and more trade-oriented provinces provide more local subsidies. Finally, subsidies are concentrated in a few sectors, and measures based on counts versus values reveal different patterns. These patterns reveal how China's subsidy strategy has evolved, offering insights to state-led development in the 21st century. |
| Keywords: | Industrial Policy; Industrial Subsidies; Chinese Economy |
| JEL: | F13 O25 H2 |
| Date: | 2025–12–11 |
| URL: | https://d.repec.org/n?u=RePEc:gii:giihei:heidwp18-2025 |