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on Efficiency and Productivity |
| By: | Jia Ming Li; Anupriya; Daniel J. Graham |
| Abstract: | Benchmarking the performance of complex systems such as rail networks, renewable generation assets and national economies is central to transport planning, regulation and macroeconomic analysis. Classical frontier methods, notably Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA), estimate an efficient frontier in the observed input-output space and define efficiency as distance to this frontier, but rely on restrictive assumptions on the production set and only indirectly address heterogeneity and scale effects. We propose Geometric Manifold Analysis (GeMA), a latent manifold frontier framework implemented via a productivity-manifold variational autoencoder (ProMan-VAE). Instead of specifying a frontier function in the observed space, GeMA represents the production set as the boundary of a low-dimensional manifold embedded in the joint input-output space. A split-head encoder learns latent variables that capture technological structure and operational inefficiency. Efficiency is evaluated with respect to the learned manifold, endogenous peer groups arise as clusters in latent technology space, a quotient construction supports scale-invariant benchmarking, and a local certification radius, derived from the decoder Jacobian and a Lipschitz bound, quantifies the geometric robustness of efficiency scores. We validate GeMA on synthetic data with non-convex frontiers, heterogeneous technologies and scale bias, and on four real-world case studies: global urban rail systems (COMET), British rail operators (ORR), national economies (Penn World Table) and a high-frequency wind-farm dataset. Across these domains GeMA behaves comparably to established methods when classical assumptions hold, and provides additional insight in settings with pronounced heterogeneity, non-convexity or size-related bias. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.16729 |
| By: | Salomé Baslandze; Zachary Edwards; John Graham; Ty McClure; Brent H. Meyer; Michael Sparks; Sonya R. Waddell; Daniel Weitz |
| Abstract: | We use novel data from a survey of nearly 750 corporate executives to study the effects of artificial intelligence (AI) on productivity and the workforce. We document substantial heterogeneity in AI adoption across firms, with more than half having already invested, though many smaller firms are only beginning to do so. Labor productivity gains are positive, vary across sectors, and are expected to strengthen in 2026, with the largest effects concentrated in high-skill services and finance. These gains are not primarily driven by firms' capital deepening but instead reflect increases in revenue-based total factor productivity, closely associated with innovation-and demand-oriented channels. We document a productivity paradox, in which perceived productivity gains are larger than measured productivity gains, likely reflecting a delay in revenue realizations. In labor markets, we find little evidence of near-term aggregate employment declines due to AI, though larger companies anticipate AI-driven workforce reductions, while smaller firms expect modest gains. We also find evidence of compositional reallocation of labor both within and across firms, with routine clerical roles declining and a relative demand for skilled technical roles increasing. We develop an index that ranks job functions most negatively affected by AI. |
| JEL: | D22 D24 G0 J01 J24 M15 O33 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34984 |
| By: | Aung, Zin Wai; Minten, Bart |
| Abstract: | We analyze paddy rice productivity and profitability for the 2024 and 2025 dry seasons, using data from the Myanmar Agriculture Performance Survey (MAPS), conducted between August 11 to October 26, 2025. The survey covered plots managed by 872 paddy producers. |
| Keywords: | productivity; extreme weather events; dry season; monsoon climate; rice; Myanmar; Asia; South-eastern Asia |
| Date: | 2025–12–01 |
| URL: | https://d.repec.org/n?u=RePEc:fpr:prnote:178419 |
| By: | Balázs Reizer (ELTE Centre for Economic and Regional Studies; Corvinus University Budapest) |
| Abstract: | I investigate the relationship between bonus payments and firm performance using Hungarian linked employer-employee data. A raw comparison shows that firms paying bonuses to 10 percentage points more of their employees are 3-5 percent more productive. Then, I construct an instrument to estimate the incentive effect of bonus payments. The IV estimates show that the incentive effect of a 10 percentage point increase in the share of employees with bonus payments increases firm productivity by 7-14 percent. Based on these results, I conclude that the raw comparison of firms with and without bonuses underestimates the incentive effects of bonus payments. Furthermore, some firms may have motivations for paying bonuses other than incentivizing employees. |
| Keywords: | Risk Management, Wage Structure, Personnel Economics |
| JEL: | G32 M5 J31 J23 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:has:discpr:2516 |
| By: | Dubey, Rohan (National Institute of Public Finance and Policy); Chakraborty, Lekha (National Institute of Public Finance and Policy) |
| Abstract: | The rapid diffusion of artificial intelligence (AI) has generated widespread expectations of substantial productivity gains, yet empirical evidence on its macroeconomic effects remains limited. This paper provides across-country empirical assessment of the relationship between AI adoption and labour productivity using a newly constructed panel dataset covering G20 over the period 2012–2023. We develop two composite indices of AI adoption that capture both relative cross-country positioning and within-country evolution overtime, drawing on indicators of investment, innovation, computational capacity, and scientific output. Employing panel regressions with country and time fixed effects and a rich set of macroeconomic controls, we find evidence of a statistically significant short-run effect of AI diffusion on aggregate labour productivity. These results are robust across alternative index constructions and model specifications. We then extend our analysis to human development indicators and find that AI diffusion is positively associated with UNDP the Human Development Index (HDI). At the sametime, the magnitude and dynamics of the estimated effects suggest that productivity gains from AI are likely to materialize gradually and depend on complementary investments and structural conditions. Beyond the regression results, the indices developed in this paper provide a transparent framework for tracking AI diffusion and identifying areas of AI preparedness and technological lag, offering useful insights for future research and policy design. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:npf:wpaper:26/445 |
| By: | Coulibaly, Yacouba; Coulibaly, Aissata |
| Abstract: | This paper examines the effect of women’s political empowerment on public spending efficiency in developing countries. Using a large panel of 126 developing countries over 1995–2021, the paper constructs public spending efficiency scores based on Stochastic Frontier Analysis, capturing governments’ ability to transform public expenditures into socioeconomic outcomes. The analysis employs a fractional regression model with a bootstrap and instrumental variable approach, complemented by alternative identification strategies. The results consistently show that higher levels of women’s political empowerment significantly improve public spending efficiency. These findings remain robust in alternative estimators, additional controls, subsamples, and alternative measures of women’s empowerment. In addition, a transmission channel analysis further reveals that this positive effect operates primarily through improved governance quality, particularly stronger control of corruption, while fiscal capacity and education spending play complementary but less dominant roles. These findings suggest that policies promoting women’s effective participation in political decision-making—beyond symbolic representation—should be integrated into fiscal governance and anti-corruption strategies to improve public sector performance in developing countries. |
| Date: | 2026–03–18 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:11336 |
| By: | Tamás Krisztin (International Institute for Applied Systems Analysis); Philipp Piribauer (WIFO) |
| Abstract: | This document introduces the R library estimateW to estimate spatial weight matrices for Bayesian spatial econometric panel models. The approach focuses on spatial weights that are binary prior to row-standardization. However, unlike recent literature our approach requires no strong a priori assumptions on (socio-)economic distances between the spatial units. The estimation approach relies on efficient Bayesian Gibbs sampling techniques and the library supports a variety of the most common spatial econometric panel specifications. estimateW moreover supports to elicit flexible shrinkage priors, which allow to estimate spatial spillovers even in settings where the number of time period is small relative to number of cross-sectional units. An empirical illustration for European NUTS-1 regions demonstrates that the method recovers plausible spatial dependence patterns, interpretable spillover effects, and meaningful clustering in the estimated network structure. |
| Keywords: | Bayesian spatial econometrics, spatial weight matrix estimation, regional economic growth, R |
| Date: | 2026–03–17 |
| URL: | https://d.repec.org/n?u=RePEc:wfo:wpaper:y:2026:i:722 |
| By: | Jose Maria Barrero; Nicholas Bloom; Philip Bunn; Steven J. Davis; Kevin Foster; Aaron Jalca; Brent Meyer; Paul Mizen; Michael Navarrete; Pawel Smietanka; Gregory Thwaites; Ben Wang; Ivan Yotzov |
| Abstract: | We present the first representative international data on firm-level AI use. We survey almost 6, 000 CFOs, CEOs, and executives from stratified firm samples across the US, UK, Germany, and Australia. We find four key facts. First, around 70 percent of firms actively use AI, particularly younger, more productive firms. Second, while over two-thirds of top executives regularly use AI, their average use is only 1.5 hours a week, with one quarter reporting no AI use. Third, firms report little impact of AI over the last three years, with more than 80 percent of firms reporting no impact on either employment or productivity. Fourth, firms predict sizable impacts over the next three years, forecasting AI will boost productivity by 1.4 percent, increase output by 0.8 percent, and cut employment by 0.7 percent. We also survey individual employees who predict a 0.5 percent increase in employment in the next three years as a result of AI. This contrast implies a sizable gap in expectations, with senior executives predicting reductions in employment from AI and employees predicting net job creation. |
| Keywords: | artificial intelligence; productivity; employment |
| JEL: | E0 |
| Date: | 2026–03–24 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedawp:102928 |
| By: | Òscar Jordà; Fernanda Nechio; Toan Phan; Felipe Schwartzman |
| Abstract: | We show, both theoretically and empirically, that tight financial conditions shift investment toward cheaper but less energy-efficient capital. In a small open-economy model with vintage capital, higher financing costs reduce the present value of future energy savings, tilting firms’ choices along a cost efficiency frontier. Using 150 years of macroeconomic and energy data from 17 advanced economies, we find that tighter financial conditions reduce output, capital, and total energy consumption, but raise the amount of energy per unit of capital (energy intensity), a composition effect that persists for 6 to 8 years. Tight financial conditions lower energy use in the short run by depressing activity, but increase energy use in the medium run through worse energy efficiency. |
| Keywords: | energy efficiency; capital vintages; monetary policy; interest rates; local projections; small open economy |
| Date: | 2026–02–26 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedfwp:102910 |
| By: | Jarosław Bełdowski; Łukasz Dąbroś; Wiktor Wojciechowski |
| Abstract: | In this study, we use data on Polish civil and commercial courts of first instance to examine the determinants of the court output measured by the number of cases they adjudicate. Besides taking into account a caseload, number of serving judges and auxiliary court staff members, the novelty of the research is that it pays particular attention to the problem of the heterogeneity of cases on the docket which both types of courts are dealing with. Using a set of fixed effects panel data models and addressing potential endogeneity, we test whether this variation promotes court performance or, on the contrary, reduces it. The results confirm that judges play a significant role in resolving cases albeit it considerably varies between distinguished type of adjudications. The auxiliary court staff members also turned out to affect court output in a different way, depending mainly on the type of cases under examination. The results indicate that there can be both synergy and competition in resolving certain types of cases. This synergy can be explained by either judicial backlash or an increase in experience in judges and support staff that makes the judicial process more time-efficient. The competition between certain types of cases may be indicative of opportunistic behaviour in some courts. |
| Keywords: | judicial efficiency, court performance, panel models, case heterogeneity |
| JEL: | C23 K41 K15 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:sgh:kaewps:2024103 |
| By: | Haotian Deng (Department of Economics, Ghent University, Belgium); Sam Desiere (Department of Economics, Ghent University, Belgium; IZA Institute of Labor Economics, Germany); Bart Cockx (Department of Economics, Ghent University, Belgium; IZA Institute of Labor Economics, Germany; IRES/LIDAM, UCLouvain, Belgium; CESIfo, Germany; ROA, Maastricht University, the Netherlands); Gert Bijnens (National Bank of Belgium) |
| Abstract: | This paper studies how employment subsidies for start-ups shape their performance. We exploit an unexpected policy reform in Belgium that permanently exempted start-ups hiring their first employee from payroll taxes for that employee. Using firm-level administrative data and a regression-discontinuity-in-time design, we find that subsidized post-reform startups employed fewer workers and generated lower output, value added, and profits compared to pre-reform start-ups. However, post-reform start-ups were more likely to survive as employers. These effects emerged within the first year after hiring and remained stable over a medium horizon of three years. Our findings indicate a compositional shift: the subsidy primarily induced low-productivity firms to enter the market. As most firms nowadays are nonemployers, our results meaningfully generalize the theoretical implications of standard neoclassical entrepreneurship models (employee–employer margin) and fill the important gap of the nonemployer–employer margin. |
| Keywords: | entrepreneurship; start-up; employment subsidy; tax reduction; labor demand; small firms |
| JEL: | H25 J23 J24 J38 L25 L26 M51 |
| Date: | 2026–02–04 |
| URL: | https://d.repec.org/n?u=RePEc:ctl:louvir:2026004 |
| By: | Abouzaid Badr (ENCGT - École Nationale de Commerce et de Gestion de Tanger); Koross Mohsine (ENCGT - École Nationale de Commerce et de Gestion de Tanger) |
| Abstract: | Objective: The objective of this study is to examine the role of ISO 50001 as a structured energy management system within the field of green supply chain management (GSCM). More specifically, it aims to map the intellectual structure, thematic evolution, and research trends related to ISO 50001 and its contribution to sustainable supply chain practices. Theoretical Framework: This research is grounded in the theoretical foundations of green supply chain management, energy management systems, and sustainability-oriented organizational strategies. Core concepts related to energy efficiency, sustainable development, renewable energy integration, and organizational performance provide the conceptual basis for analyzing the literature on ISO 50001 and GSCM. Method: The study employs a bibliometric research design, utilizing a dataset of 422 peer-reviewed journal articles published between 2013 and 2024, indexed in the Scopus and Web of Science databases. Bibliometric performance indicators and science mapping techniques were applied to identify influential journals, authors, institutions, and countries, as well as to explore keyword co-occurrence patterns and thematic clusters shaping this research domain. Results and Discussion: The findings reveal a sustained growth in academic interest at the intersection of ISO 50001 and GSCM, reflecting the increasing strategic relevance of energy management within supply chain sustainability research. Dominant research themes primarily relate to energy efficiency, sustainable development, renewable energy adoption, and organizational performance. However, the analysis also highlights underexplored areas, particularly those associated with digital technologies, data-driven energy management, and the strategic integration of ISO 50001 into supply chain decision-making processes. Research Implications: The results provide both theoretical and managerial implications by clarifying how ISO 50001-based energy management systems support sustainability-oriented supply chain strategies and by offering insights for organizations seeking to improve environmental and competitive performance. Originality/Value: This study offers one of the first comprehensive bibliometric syntheses focusing specifically on the contribution of ISO 50001 to green supply chain management, positioning the standard as a strategic lever for embedding energy management into sustainable supply chain governance. |
| Keywords: | Renewable Energy., Environmental Performance, Sustainability, Energy Efficiency, ISO 50001, Green Supply Chain |
| Date: | 2026–01–29 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05488073 |
| By: | Danilo Cascaldi-Garcia; Matteo Iacoviello |
| Abstract: | We construct a news-based index of deregulation for the United States from 1960 through 2025 using AI to semantically classify newspaper articles. We distinguish articles discussing deregulation from those discussing increased regulation, assigning intensity scores that reflect both the centrality of deregulatory content and whether articles discuss advocacy, proposals, or enacted measures. Human validation confirms strong agreement between AI and human classifications. The deregulation index captures major reform episodes including transportation and telecommunications liberalization in the 1970s--1980s, financial deregulation in the 1980s-1990s, and recent deregulatory activity. We decompose the index by sector, type of deregulation, and policy stage. We validate the news-based index against a parallel index constructed using Federal Register documents: the news-based index leads the Federal Register index by nearly one year, consistent with media coverage reflecting policy intentions before formal implementation. Unlike measures based on detailed statutory coding or Federal Register counts that weigh all rules equally, our approach covers the entire economy and weighs naturally by newsworthiness, capturing regulatory shifts before they materialize in law. Positive shocks to deregulation boost investment, productivity, stock prices, profits, and GDP. Industry-specific deregulation shocks boost industry-level stock returns, consistent with our finding that deregulation involves measures that may impact incumbent profitability and operational efficiency more than competitive entry. |
| Keywords: | Economic uncertainty; Productivity; Economic regulation |
| JEL: | D80 E66 L51 |
| Date: | 2026–03–06 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgif:102902 |