|
on Efficiency and Productivity |
| By: | Pablo Castro (Universidad de la República (Uruguay). Facultad de Ciencias Económicas y de Administración. Instituto de Economía); Henry Willebald (Universidad de la República (Uruguay). Facultad de Ciencias Económicas y de Administración. Instituto de Economía) |
| Abstract: | This article presents a long-run estimate of total factor productivity (TFP) in Uruguayan agriculture for the period 1870–2016. Drawing on a growth accounting framework and the construction of a new historical dataset, it provides homogeneous and consistent estimates of TFP for the agricultural sector as a whole and for its main subsectors: crops and livestock. The analysis situates productivity dynamics within the broader trajectory of national development, highlighting the interaction between technological change, institutional arrangements, and international integration. The findings identify three interrelated long-term patterns: cycles of modernization associated with waves of technological adoption and external integration; prolonged episodes of adaptive stagnation, during which growth relied primarily on the extensive use of natural resources; and alternating sectoral divergence between crops and livestock, linked to differentiated technological and institutional regimes. Overall, agricultural TFP grew at an average annual rate lower than 1% between 1870 and 2016, reflecting a discontinuous path of innovation rather than a linear process of progress. Efficiency gains materialized when institutions, policies, and markets were coherently aligned, and stalled when such conditions weakened. By combining long-run TFP measurement with historical interpretation, the paper contributes to a deeper understanding of the structural determinants of agricultural productivity and of the role of the agricultural sector in Uruguay’s economic development. |
| Keywords: | TFP, agriculture, inputs, Uruguay |
| JEL: | N56 O13 O33 O47 Q16 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:ulr:wpaper:dt-04-26 |
| By: | Alexander Bick; Adam Blandin; David Deming; Nicola Fuchs-Schündeln; Jonas Jessen |
| Abstract: | AI adoption is much higher among American workers than it is among European workers. Is this widening the gap between U.S. and EU productivity growth? |
| Keywords: | generative artificial intelligence (AI); technology adoption; labor productivity |
| Date: | 2026–03–30 |
| URL: | https://d.repec.org/n?u=RePEc:fip:l00001:102954 |
| By: | Alexander Bick; Adam Blandin; David Deming; Nicola Fuchs-Schündeln; Jonas Jessen |
| Abstract: | This paper combines international evidence from worker and firm surveys conducted in 2025 and2026 to document large gaps in AI adoption, both between the US and Europe and across European countries. Cross-country differences in worker demographics and firm composition account for an important share of these gaps. AI adoption, within and across countries, is also closely linked to firm personnel management practices and whether firms actively encourage AI use by workers. Micro-level evidence suggests that AI generates meaningful time savings for many workers. At the macro level, in recent years industries with higher AI adoption rates have experienced faster productivity growth. While we do not establish causality, this relationship is statistically significant and similar in magnitude in Europe and the US. We do not find clear evidence that industry-level AI adoption is associated with employment changes. We discuss limitations of existing data and outline priorities for future data collection to better assess the productivity and labor market effects of AI. |
| Keywords: | generative artificial intelligence (AI); technology adoption; labor productivity |
| JEL: | J24 M16 O14 O33 |
| Date: | 2026–03–26 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedlwp:102950 |
| By: | Miriam Bruhn (World Bank); Asli Demirguc-Kunt (Center for Global Development); Dorothe Singer (World Bank) |
| Abstract: | This paper assesses the medium-run effects of government support to firms during the COVID-19 crisis and whether the effectiveness of this support varied with its timing. Using data from three rounds of the World Bank’s Enterprise Surveys COVID-19 Follow-up Surveys carried out between May 2020 and August 2022, it relates government support in Round 1 and 2 with firm performance in Round 3. Our results add to the existing literature on government support during the COVID-19 shock and previous crises, which has provided little evidence on how the effect of this support varies with its timing. Controlling for a host of background characteristics, firms that received support in Round 1 performed better in terms of Round 3 sales, but only if they did not have continued support. Firms that also received support in Round 2 had similar Round 3 sales to those who received no support. Firms that received government support only in Round 2 experienced no boost in Round 3 performance. The findings suggest that government support should be provided promptly, but it should also be phased out quickly. |
| Keywords: | Government support, COVID-19, productivity, firms |
| JEL: | D22 D24 H81 O47 |
| Date: | 2026–03–23 |
| URL: | https://d.repec.org/n?u=RePEc:cgd:wpaper:742 |
| By: | Balazs Egert |
| Abstract: | This paper reviews the contributions of the 2025 Nobel Prize in Economics laureates, Joel Mokyr, Philippe Aghion and Peter Howitt, to our understanding of innovation-driven economic growth, situating their work within the broader evolution of modern growth theory and empirical evidence. It highlights why the Industrial Revolution marked a transition to sustained, self-reinforcing technological progress and shows how Mokyr's emphasis on knowledge, culture and institutions complements Aghion and Howitt's Schumpeterian framework, which formalises innovation as a competitive process of firm entry, exit and technological replacement. The paper then uses these frameworks to interpret the widespread productivity slowdown observed in advanced OECD economies since the mid-2000s, arguing that weakened creative destruction, slower diffusion of frontier technologies, declining business dynamism and policy headwinds are key explanatory factors. |
| Keywords: | innovation, productivity, economic growth, creative destruction, institutions |
| JEL: | O30 O40 O43 L16 N10 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12572 |
| By: | Andres Alonso-Robisco; Carlos Esparcia; Francisco Jare\~no |
| Abstract: | Generative artificial intelligence (AI) is increasingly used to write and refactor research code, expanding computational workflows. At the same time, Green AI research has largely measured the footprint of models rather than the downstream workflows in which GenAI is a tool. We shift the unit of analysis from models to workflows and treat prompts as decision policies that allocate discretion between researcher and system, governing what is executed and when iteration stops. We contribute in two ways. First, we map the recent Green AI literature into seven themes: training footprint is the largest cluster, while inference efficiency and system level optimisation are growing rapidly, alongside measurement protocols, green algorithms, governance, and security and efficiency trade-offs. Second, we benchmark a modern economic survey workflow, an LDA-based literature mapping implemented with GenAI assisted coding and executed in a fixed cloud notebook, measuring runtime and estimated CO2e with CodeCarbon. Injecting generic green language into prompts has no reliable effect, whereas operational constraints and decision rule prompts deliver large and stable footprint reductions while preserving decision equivalent topic outputs. The results identify human in the loop governance as a practical lever to align GenAI productivity with environmental efficiency. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.26712 |
| By: | Pablo Castro (Universidad de la República (Uruguay). Facultad de Ciencias Económicas y de Administración. Instituto de Economía); Henry Willebald (Universidad de la República (Uruguay). Facultad de Ciencias Económicas y de Administración. Instituto de Economía) |
| Abstract: | This paper quantifies agricultural performance in Latin America in the early 20th century, complementing previous qualitative studies with a comparative and historical perspective. The analysis covers ten countries –Argentina, Brazil, Colombia, Chile, Cuba, Mexico, Nicaragua, Peru, Uruguay, and Venezuela– during the years preceding World War I. We identify three broad agrarian paths. Argentina and Uruguay featured extensive, high-productivity, export-oriented systems that promoted broader economic development. Chile, Cuba, and Nicaragua exhibited more intensive but labour-demanding systems, with moderate productivity and uneven technological progress. Venezuela, Mexico, Colombia, Brazil, and Peru maintained low-productivity, traditional agriculture with limited potential for economic growth. These contrasting structures highlight the diversity of Latin American agrarian capitalism and help explain the uneven capacity of national economies to initiate structural transformation. Overall, differences in factor endowments played a decisive role in shaping productivity patterns, with land-abundant regions favouring labour-saving technologies. |
| Keywords: | agriculture, land productivity, labor productivity, Latin America |
| JEL: | N56 Q11 Q16 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:ulr:wpaper:dt-26-25 |
| By: | Koichiro Kamada (Faculty of Business and Commerce, Keio University) |
| Abstract: | This paper proposes two measures on economic slackness, the survey-based output gap and the survey-based input gap. Our method extends Carlson and Parkin’s (1975) method of transforming qualitative data into quantitative data and uses Hirose and Kamada’s (2003) method of estimating a time-varying model. We show theoretically how a survey asking firms whether business conditions are favorable or unfavorable generates an estimate of the output gap in a country. Our method is also applicable to the estimation of the capital input gap and the labor input gap, which are combined to produce the survey-based input gap in a country. The Japanese survey-based output gap and input gap are estimated, based on the TANKAN survey conducted by the Bank of Japan. The estimated gap measures are consistent with the official reference dates of business cycle and have tight confidence intervals. The two measures surprisingly coincide with each other, but sometimes deviate from each other, particularly in severe recessions. The deviation is likely to be caused by changes in total factor productivity. |
| Keywords: | output gap, input gap, survey data, qualitative data, Carlson and Parkin, time-varying model |
| JEL: | C13 C22 E32 |
| Date: | 2025–03–17 |
| URL: | https://d.repec.org/n?u=RePEc:keo:dpaper:dp2026-004 |
| By: | Vaishnavi; Lokesha; H.; Vedamurthy; K. B.; Manojkumar Patil |
| Abstract: | Agricultural pricing policies are crucial for farm profitability and food security in India. This study analysed how input and output prices significantly influence the profitability of cereals in Karnataka, with the strategic support prices playing a crucial role in maintaining the price parity. The average annual TFP growth was 1.041 per cent. Rising input costs, particularly for human labour, led to reduced profitability for Jowar (6.12 per cent) and Ragi (4.89 per cent). The net effect was adverse for Jowar (-1.50 per cent) and Ragi (-0.86 per cent) due to rising input costs outpacing output prices. The study recommended increasing the MSP for Jowar (60 per cent) and Ragi (46.24 per cent) above the existing levels. A strategic price adjusted for changing input costs can stabilise farm incomes and promote sustainable production, enabling efficient pricing policies. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.25696 |
| By: | Moh Hosseinioun; Brian Uzzi; Henrik Barslund Fosse |
| Abstract: | Investment in artificial intelligence (AI) has grown rapidly, yet its returns to scientific research remain poorly understood. We study how AI reshapes the production of science using a comprehensive dataset of research proposals submitted to a large international funding agency, including both funded and unfunded projects. Combining keyword extraction with large language model classification, we identify the presence, type, and functional role of AI within each proposal and link these measures to detailed budget allocations, team structure, and subsequent publication outcomes. We find that, in the short run, AI adoption is associated with modest improvements in scientific outcomes concentrated in the upper tail. Instead, its primary effects arise in the organization of research: AI-enabled projects reallocate resources toward human capital, involve larger teams, and undertake a broader set of tasks. These patterns are consistent with a reorganization of the scientific production process rather than immediate efficiency gains, in line with theories of general-purpose technologies. Task-level analyses further show that activities expanded in AI-enabled projects, particularly ideation and experimentation, are increasingly compatible with large language model capabilities, suggesting potential for future productivity gains as these technologies mature. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.27956 |
| By: | Andrew Johnston; Christos A. Makridis |
| Abstract: | Does artificial intelligence (AI) increase productivity - and does it displace workers? We examine aggregate effects using administrative data covering essentially all U.S. employers in a difference-in-differences design exploiting occupational AI exposure across industries and states. A one standard deviation increase in exposure raises output by 7%, with effects emerging in 2021 when enterprise AI tools entered the market. Employment effects follow the same timing but diverge by exposure type: where AI likely requires human collaboration, employment rises 4%; where AI can perform tasks independently, we find no significant employment effect. Results are robust to state-by-year and industry-by-year fixed effects and suggest AI has caused a decrease in the labor share of income. |
| Keywords: | artificial intelligence, generative AI, aggregate productivity, labor market, technological change |
| JEL: | O33 J24 J23 E24 O47 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12579 |