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on Efficiency and Productivity |
| By: | Prados de la Escosura, Leandro |
| Abstract: | Recent research confirms that per capita income in early modern Spain improved onlymarginally overall, while also revealing sustained growth through much of the sixteenth and eighteenth centuries, alongside a continued decline from the late sixteenth to the midseventeenth centuries. These phases shaped Spain's relative position within Western Europe and contributed to the Reversal of Fortune. This paper finds that labour productivity, proxied by output per working-age population, improved during the first three-quarters of the sixteenth century, then declined until the mid-seventeenth century, and that thesubsequent recovery never reached the levels of the 1570s. What caused these episodes of growth and decline: changes in resource endowments or in the efficiency of their use? Phases of labour productivity growth were often driven by factor intensity, but efficiency losses underpinned periods of stagnation or decline, which contradicts the stylised view that factor intensity is the main driver of labour productivity in a pre-industrial economy. Compared with Great Britain, Spain showed an inverse, divergent pattern, moving from similar levels to less than half of Britain's by 1800. Efficiency was the main driver of the widening gap. Ingenuity appears, therefore, to be the driving force behind the Reversal of Fortune. |
| Keywords: | Output per working-age population; Dual TFP; Efficiency; Factor intensity; Reversal of Fortune; Spain; Britain |
| JEL: | J24 E24 N13 O47 |
| Date: | 2026–05–21 |
| URL: | https://d.repec.org/n?u=RePEc:cte:whrepe:50135 |
| By: | DiMaria, charles-henri |
| Abstract: | This document examines how three structural megatrends—the expansion of services, the diffusion of ICT, and deepening globalization - have reshaped the production structure and, by extension, the measurement of productivity in advanced economies. It argues that classical growth‑accounting approaches, rooted in Solow (1957), under‑state performance in knowledge‑based systems because they insufficiently capture the contribution of intangible assets. Using experimental macroeconomic estimates of intangibles from EUKLEMS–INTANProd applied to Luxembourg and a set of comparator economies (US and EU peers), the analysis shows that including intangibles raises the level of value added per hour and modestly alters growth rankings, with Luxembourg’s post‑2007 decline turning into a slight positive trend—yet still falling short of its pre‑2007 trajectory. While measurement constraints and data comparability issues remain, the results align more closely with a knowledge‑based economy narrative and provide a practical foundation for improving productivity metrics going forward. |
| Keywords: | intangible assets; productivity measurement; EUKLEMS–INTANProd; knowledge‑based economy. |
| JEL: | O4 O40 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:129021 |
| By: | Bloom, Nicholas; Kawakubo, Taka; Meng, Charlotte; Mizen, Paul; Riley, Rebecca; Senga, Tatsuro; Van Reenen, John |
| Abstract: | We link new forecast and management data on over 20, 000 firms to data on productivity in manufacturing and services. The panel survey was administered in the UK in July 2017 and November 2020, coinciding with two periods of considerable uncertainty from Brexit and Covid. We find that better-managed firms make more accurate forecasts for firm level turnover and macro-level GDP. Uniquely, we show better-managed firms are also aware that they make more accurate forecasts and have greater confidence in their predictions. This highlights how superior forecasting ability enables well-managed firms to make improved operational and strategic choices. |
| Keywords: | management; productivity; expectations; uncertainty; forecasting |
| JEL: | O32 O33 |
| Date: | 2025–12–18 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:138480 |
| By: | Nuriye Melisa Bilgin; Gianmarco Ottaviano |
| Abstract: | Do the determinants of technology adoption depend on technological architecture? Using administrative data on Turkish firms from 2021 to 2024, we compare the adoption of traditional and generative artificial intelligence (GenAI).We show that GenAI adoption is driven by workforce skill intensity and is not positively associated with firm size, whereas traditional AI depends on both scale and skills. Firms that adopt both technologies are distinct and represent the most persistent adoption mode. Conditional on adoption, the skill-to-size ratio governs technology choice, and transition dynamics indicate a sequential process in which firms adopt GenAI before expanding to hybrid use. Exploiting the release of ChatGPT as a quasi-experimental reduction in access costs, we find that high-skill firms differentially increased GenAI adoption, while firm size played a limited role. These results suggest that the canonical size-based diffusion pattern is not universal but depends on the cost structure of technologies, with implications for innovation policy and productivity dispersion. |
| Keywords: | artificial intelligence, generative AI, technology adoption, firm heterogeneity |
| Date: | 2026–05–21 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2184 |
| By: | Anton Korinek (Peterson Institute for International Economics); Patrick McKelvey (Bank of Canada) |
| Abstract: | The authors construct a macroeconomic estimate of total AI production for the United States, combining inference and research and development/training activities and applying quality adjustments based on the evolution of API prices at fixed performance levels and the pace of algorithmic progress. They estimate that nominal AI compute spending grew over 140 percent per year each in 2024 and 2025, raw compute capacity grew over 200 percent per year, and quality-adjusted AI output grew over 2, 000 percent per year. These growth rates reflect three compounding forces: expanding data-center capacity, continued improvements in chip efficiency, and rapid algorithmic progress. The authors then employ their estimates to develop a nascent framework for "AI GDP" that tracks the AI economy as a coherent whole rather than dispersed across standard industry classifications. Quality-adjusted AI GDP grew by more than 2, 500 percent each in 2024 and 2025. The measures in the paper complement traditional national accounts by providing visibility into a fast-moving sector whose activity is difficult to isolate in existing statistics. The measures may serve as building blocks for satellite accounts that track AI's growing role in the economy. The conceptual framework and methodology used in this paper are described in detail in the technical appendix. |
| Keywords: | artificial intelligence, national accounts, GDP mismeasurement, AI satellite accounts, quality-adjusted prices, algorithmic progress, AI GDP |
| JEL: | E01 O33 O47 E22 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:iie:wpaper:wp26-9 |
| By: | Jesus Cañas; Emily Kerr |
| Abstract: | Learning how businesses use artificial intelligence (AI) helps policymakers understand changing economic conditions, particularly involving employment and productivity. |
| Keywords: | artificial intelligence (AI); labor; manufacturing; economic surveys |
| Date: | 2024–06–25 |
| URL: | https://d.repec.org/n?u=RePEc:fip:d00001:98445 |
| By: | Zulfiqar, Ammara; Aziz, Mahwish; Wahid, Abdul |
| Abstract: | Structural Equation Modeling (SEM) is a commonly used and prevalent method to describe the relationships between latent and observed variables. If these variables contain outliers and leverage-points, the estimation by existing SEM is problematic and leads to biased and inefficient estimators. In this article, we propose the Least Mahalanobis Distance-based Trimmed (LMDT) model which uses Mahalanobis distance for the identification of outliers in SEM and trimming approach for dealing with such types of influential observations. By using this suggested technique, instead of maximum likelihood and least squares criteria, the LMDT is resistant to outliers in both measurement error and latent factors. A FAST-iterative algorithm is constructed and implemented for computing the LMDT. Both a simulation study and a real data analysis indicate that the proposed robust method has good performance in terms of bias and efficiency on contaminated and non-normal skewed data and it outperforms the two non-robust and one robust existing estimation methods. |
| Keywords: | Structural equation modeling; outliers; non-normality; Mahalanobis distance; trimming |
| JEL: | C15 C51 J28 |
| Date: | 2026–05–11 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:129065 |
| By: | Carrillo, Paul (George Washington University); Donaldson, Dave (Massachusetts Institute of Technology and NBER); Pomeranz, Dina (University of Zurich); Singhal, Monica (University of California, Davis, and NBER) |
| Abstract: | This paper develops new tools to study misallocation that do not require assumptions about the heterogeneity of firms’ technologies. We show how features of the distribution of marginal products can be identified from exogenous variation in firms’ input use and used both to test for misallocation and to quantify its resulting welfare losses. We apply this method to a setting with exogenous demand shocks from public procurement contracts for construction services in Ecuador. Our results reject the null of efficiency but our estimates of the resulting welfare losses from misallocation are small. |
| Keywords: | procurement, misallocation, firms |
| JEL: | D24 C14 O12 H57 D61 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18612 |
| By: | Anton Korinek (Peterson Institute for International Economics); Patrick McKelvey (Bank of Canada) |
| Abstract: | The AI economy in the United States has been growing at an unprecedented rate, but this extraordinary growth is largely invisible in conventional statistics. The authors propose developing an "AI GDP" framework to better measure AI's growing role in the economy. -Key Takeaways -Quality-adjusted AI production in the United States grew at over 2, 000 percent per year in 2024 and 2025, driven by three compounding forces: expanding data-center capacity, hardware efficiency gains, and—the largest of the three—algorithmic progress. -Treating the AI sector as a coherent economic entity yields preliminary estimates of nominal AI GDP at approximately $250 billion in 2025, growing at roughly 2, 600 percent per year in quality-adjusted real terms. -National economic statistics accounts were not designed to track this kind of activity. Statistics agencies should begin developing AI-focused satellite accounts now, before the measurement gap becomes a policy gap. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:iie:pbrief:pb26-7 |
| By: | Ali Aouad; Thodoris Lykouris; Huiying Zhong |
| Abstract: | Generative Artificial Intelligence (AI) tools are rapidly adopted in the workplace and in education, yet the empirical evidence on AI's impact remains mixed. We propose a model of human-AI interaction to better understand and analyze several mechanisms by which AI affects productivity. In our setup, human agents with varying skill levels exert utility-maximizing effort to produce certain task outcomes with AI assistance. We find that incorporating either endogeneity in skill development or in AI unreliability can induce a productivity paradox: increased levels of AI assistance may degrade productivity, leading to potentially significant shortfalls. Moreover, we examine the long-term distributional effect of AI on skill, and demonstrate that skill polarization can emerge in steady state when accounting for heterogeneity in AI literacy -- the agent's capability to identify and adapt to inaccurate AI outputs. Our results elucidate several mechanisms that may explain the emergence of human-AI productivity paradoxes and skill polarization, and identify simple measures that characterize when they arise. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.11350 |