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on Efficiency and Productivity |
| By: | Wulong Gu; Josip Lesica |
| Abstract: | This paper examines the role of firm size in the widening labour productivity gap between Canada and the United States since 2000. Canada’s business-sector labour productivity level declined from 83% of the U.S. level in 2002 to 73% in 2019. The gap is partly explained by Canada’s higher share of small firms and their greater productivity disadvantage relative to large firms. In 2019, these two factors accounted for 60% of the 27-percentage-point productivity gap, with the remainder 40% attributable to the generally lower productivity of Canadian firms. From 2002 to 2019, both small and large Canadian firms experienced slower productivity growth than their U.S. counterparts. Large firms contributed more to the widening of the Canada–U.S. labour productivity level gap for the period from 2002 to 2019 because of significantly slower labour productivity growth among Canada’s large firms. Shift-share analysis shows that the relative weak performance of large firms accounted for 0.45 percentage points of the 0.71-point Canada–U.S. productivity growth gap, while small firms accounted for 0.16 points. The remaining 0.14 percentage points of the Canada–U.S. labour productivity growth gap were attributable to the negative effect of hours shifting toward small firms with lower labour productivity levels in Canada. The findings highlight the need to boost productivity across firm sizes. Improving small firms’ access to markets, financing, innovation and managerial capacity and enabling large firms to catch up to global productivity frontiers will be critical to narrowing the Canada–U.S. productivity gap. |
| Keywords: | role of firm size, labour productivity gap |
| JEL: | J23 M21 |
| Date: | 2025–12–22 |
| URL: | https://d.repec.org/n?u=RePEc:stc:stcp8e:202501200002e |
| By: | Jiang Li; Huju Liu |
| Abstract: | Artificial intelligence (AI) is widely recognized as a transformative technology with the potential to reshape business operations and drive productivity growth. In Canada, AI adoption among businesses has accelerated in recent years. According to Statistics Canada (Bryan et al., 2025), 12.2% of Canadian firms used AI to produce goods or deliver services in 2025—doubling the share from the previous year—and an additional 14.5% planned to adopt AI within the next 12 months. The enthusiasm surrounding AI is not unwarranted, given its projected impact on productivity. Estimates suggest that AI could lead to a rise of 0.5% to 0.7% in total factor productivity over a decade (Acemoglu, 2024) and an increase of up to 1.5 percentage points in annual labour productivity growth over a 10-year period in the United States (Goldman Sachs, 2023). For Canada, potential gains include an increase of 0.4 to 1.1 percentage points in annual labour productivity growth over the next decade (Filippucci et al., 2025). |
| Keywords: | artificial intelligence, adoption and productivity, firms |
| JEL: | J23 M21 |
| Date: | 2026–04–22 |
| URL: | https://d.repec.org/n?u=RePEc:stc:stcp8e:202600400002e |
| By: | Kim, Hyunseok |
| Abstract: | Weak total factor productivity (TFP) growth has become a central concern in explaining sluggish growth performance, particularly in emerging-market and low-income economies. At the same time, constrained fiscal and investment conditions have increased the importance of using scarce public resources effectively. These conditions make it important to investigate, from a cross-country perspective, what is associated with productivity performance and what shapes productivity losses during adverse shocks. The paper examines this issue along two margins: a structural margin focused on transportation infrastructure, a public-investment-intensive form of capital that may enhance productive efficiency, and a resilience margin through which governance and infrastructure may shape the productivity costs of adverse shocks. Using annual cross-country panel data for more than 100 countries and a dynamic panel system GMM framework, the analysis finds that transportation infrastructure is positively associated with TFP, with larger estimated payoffs in lower-income countries. Severe crises are associated with larger productivity losses in poorer economies, but stronger rule of law and political stability are linked to smaller losses in the lowest-income quartile. Road infrastructure is also associated with smaller productivity losses during moderate downturns. The findings suggest that the productivity relevance of transportation infrastructure is greater where development constraints are more binding, while governance and infrastructure shape productivity costs during adverse episodes. |
| Date: | 2026–06–08 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:11410 |
| By: | Jenny Watt; Wulong Gu; Aled ab Iorwerth |
| Abstract: | Canada has experienced relatively low productivity growth in the construction sector over the last several decades. This study examines the evolution of labour productivity in residential construction by using firm-level data from the National Accounts Longitudinal Microdata File. The residential construction industry is dominated by small firms, with those employing fewer than 20 workers accounting for 66.1% of total employment in the industry in 2023. Labour productivity growth, measured as real gross output per worker, declined by a cumulative 37.3% from 2001 to 2023—an average decrease of 2.1% per year—in the residential construction industry. The decline occurred across all firm-size categories, with smaller firms experiencing the largest decrease. A decomposition shows that smaller firms with fewer than 20 employees accounted for the dominant share of the decline. From 2001 to 2023, the share of firms with fewer than 20 employees decreased, while the share of firms with 20 employees or more increased. This reallocation toward relatively larger firms made a small positive contribution (less than 5%) to aggregate labour productivity growth over the period, since the productivity advantage of larger firms in the residential construction industry is small. There is great variation between geographic areas—labour productivity in residential construction is falling in most parts of Canada, but some provinces and cities have managed to achieve positive growth. Finally, the study finds that the residential construction industry is characterized by high rates of firm entry and exit, which are typically associated with productivity improvements, though high exit rates may also point to a difficult business environment for residential construction firms |
| Keywords: | labour productivity, construction, subprovincial analysis |
| JEL: | J23 M21 |
| Date: | 2026–02–25 |
| URL: | https://d.repec.org/n?u=RePEc:stc:stcp8e:202600200003e |
| By: | Sofi, Irfan (Head, Department of Econ, Islamic University of Science & Technology, Kashmir, India); Mehrotra, Santosh (Higher School of Economics) |
| Abstract: | This study examines the relationship between employment protection legislation (EPL), measured through pro-worker judicial outcomes (PWCJ), and firm performance in India's formal manufacturing sector. We construct a novel state-level indicator of EP based on 1, 471 high court judgments covering 18 states from 1999 to 2022. Using plant-level panel data from the Annual Survey of Industries, we analyse the impact of EP on labour productivity growth, employment growth, and wage growth. The results indicate a non-linear (inverted Ushaped) relationship. A 1% increase in PWCJ is associated with a 3% rise in labour productivity, 2.59% increase in employment, and 2.95% increase in wages. However, beyond a threshold, further increases in PWCJ negatively affect all three outcomes. The results are robust to alternative specifications and controls. Our findings highlight the importance of balancing job security with flexibility to foster industrial performance. The study contributes new empirical insights to the debate on labour laws and firm outcomes in India. |
| Keywords: | employment protection, labour disputes, court judgements, productivity, employment, wages, India |
| JEL: | K31 L51 L60 D24 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18687 |
| By: | Kuosmanen, Natalia; Kuosmanen, Timo; Maczulskij, Terhi |
| Abstract: | Abstract A substantial share of firm entry and exit observed in register-based data reflects mergers, acquisitions, spin-offs, and other forms of corporate restructuring, instead of genuinely new firms or firm closures. This distinction is important for productivity decompositions, which typically interpret market entry and exit as manifestations of the Schumpeterian creative destruction. Using linked register-based data on Finnish manufacturing firms and employees for the period 2010–2022, we identify restructuring events through clustered worker flows, and incorporate this classification into a structural productivity decomposition framework. The results show that firms involved in restructuring events exhibit significantly higher productivity levels than genuinely entering or exiting firms. Nevertheless, the contribution of restructuring-related entry and exit to aggregate productivity growth remains modest, whereas genuine creative destruction by newly established firms and closing down make a larger positive contribution to productivity growth. Firms undertaking acquisitions exhibit a temporary decline in labor productivity around the time of acquisition, followed by a gradual recovery. These findings highlight the need to distinguish restructuring events from genuine market entry and exit when analyzing productivity dynamics. |
| Keywords: | Labor productivity, Mergers and acquisitions, Corporate restructuring, Worker flows, Productivity decomposition |
| JEL: | D24 L25 L60 O47 |
| Date: | 2026–06–18 |
| URL: | https://d.repec.org/n?u=RePEc:rif:wpaper:142 |
| By: | Korinek, Anton; McKelvey, Patrick |
| Abstract: | We construct a macroeconomic estimate of total AI production for the United States, combining inference and R&D/training activities and applying quality adjustments based on the evolution of API prices at fixed performance levels and the pace of algorithmic progress. We 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. We then employ our 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. Our measures complement traditional national accounts by providing visibility into a fast-moving sector whose activity is difficult to isolate in existing statistics, and they may serve as building blocks for satellite accounts that track AI’s growing role in the economy. |
| JEL: | E01 O33 O47 E22 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:21571 |
| By: | Achintya Ranjan; Uma Ranjan |
| Abstract: | The GDP of a country is modelled as the relative interaction between two agents - working hours, reflecting the social choice of a population, and Total Factor Productivity, reflecting the collective investment in productivity enhancers. It is shown that a Random Forest model can accu- rately predict the GDP from these two factors. The differences in the choices made by Germany and USA are analysed though Gini importance, SHAP plots and partial dependency. It is shown that the differences in the social structure of the countries are reflected in the relative contribution of working hours and productivity to the GDP. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.01234 |
| By: | OECD |
| Abstract: | Veneto is one of Europe’s manufacturing powerhouses, generating 9.5% of Italy’s GDP. While labour productivity growth has been slower than in peer regions since 2005, the region has maintained international competitiveness, supported by contained labour costs and high employment rates. The analysis highlights opportunities to raise productivity by further developing high value added activities, including by strengthening links between manufacturing and services and by expanding private business investment. There is scope for improving skills matching, notably in STEM fields, leveraging the strong enrolment of local students in those subjects. Veneto’s labour market is also gradually adapting to the green transition, with around 30% of recent job postings involving green tasks |
| Keywords: | drivers of productivity, international comparison, labour market policies, manufacturing servitisation, skills, SMEs, subnational productivity |
| JEL: | D24 J21 J24 L11 O3 O47 R11 |
| Date: | 2026–06–25 |
| URL: | https://d.repec.org/n?u=RePEc:oec:cfeaaa:2026/10-en |
| By: | Princewill Okwoche (Namibia University of Science and Technology, Windhoek, Namibia; School of Economics, University of Cape Town; Environment Centre, Charles University, Prague, Czech Republic); Milan Scasny (Environment Centre, Charles University, Prague, Czech Republic) |
| Abstract: | Improving energy efficiency is a cornerstone of the EU Fit-for-55, competitiveness, and energy-security strategy, yet performance across the new member states remains uneven despite price convergence and common regulatory frameworks. This study examines how energy prices, energy price uncertainty, and regulatory quality jointly shape sectoral energy efficiency in transition economies. We estimate a Shephard energy distance frontier model for seven new EU member states across ten industrial sectors over 1995–2015, modelling energy inefficiency directly as a function of these determinants and discrete reform episodes. Methodologically, we employ a consistent true fixed effects stochastic frontier estimated via the pairwise-difference estimator of Belotti and Ilardi (2018), which resolves the incidental parameters problem and disentangles inefficiency from unobserved heterogeneity. To our knowledge, this is the first joint one-step frontier estimation of price, price uncertainty, and governance as direct drivers of inefficiency, closing a gap between energy-pricing theory and applied frontier econometrics. Average efficiency is relatively high, with scope for roughly 21% energy savings from eliminating existing inefficiencies. Higher real energy prices significantly reduce inefficiency, confirming the price-discipline hypothesis. Energy price uncertainty robustly raises inefficiency, with a markedly stronger effect during the pre-accession adjustment phase and a weaker effect in high energy-intensive sectors. Regulatory quality is, counterintuitively, associated with higher transient inefficiency, plausibly reflecting adjustment costs. Results are robust to a balanced sub-sample and to Brent-based prices extending coverage to 2022 and eight countries. The findings imply that stabilising and credibly anchoring price signals matter more than raising average prices alone. |
| Keywords: | Energy prices, energy price uncertainty, stochastic energy distance frontier, regulatory governance, energy efficiency |
| JEL: | C23 O52 Q41 Q43 Q48 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:fau:wpaper:wp2026_15 |
| By: | Gondauri, Davit; Batiashvili, Mikheil |
| Abstract: | This article develops Agentic Capital as a measurable and econometrically testable productive category of the agentic economy. The core argument is that models, data, robots, compute, protocols, and human-sovereignty systems are no longer auxiliary digital inputs; they increasingly constitute a composite stock of productive assets that enables economic action to be selected, executed, verified, and governed. To operationalise this concept, the study constructs a six-pillar Agentic Capital framework composed of model capital, data capital, robotic physical capital, compute capital, protocol capital, and human sovereignty capital. The methodology adapts the diagnostics-first logic of the Gondauri Index by using robust normalisation, additive and geometric aggregation, balance and gap diagnostics, non-compensatory adjustment and scenario pathways. The key transformation is the Gondauri-adjusted Agentic Capital Index, which integrates macro-financial resilience into the agentic-capital measurement architecture so that technological readiness is not interpreted separately from systemic resilience. Empirically, the article applies a country-year panel design for nine economies over 2005-2024 and estimates the relationship between agentic capital and labour productivity growth through a layered econometric strategy. The analysis combines pooled OLS, random effects, fixed effects, Hausman model selection, panel unit-root diagnostics, Pesaran cross-sectional dependence testing, Driscoll-Kraay robust standard errors, dynamic panel GMM, IV-2SLS, panel quantile regression, Hansen-style threshold estimation, mediation analysis, component-level fixed effects, and robustness checks. The results indicate that geometric agentic capital is positively associated with labour productivity performance, that structural imbalance weakens the maturity of the system, and that human sovereignty and governance conditions shape the productivity value of agentic systems. The article contributes to capital theory by shifting the analytical focus from accumulated productive assets toward accumulated capacity for governed economic action. It also provides a reproducible empirical architecture for assessing how countries, sectors, and firms can transform AI readiness into legitimate, auditable, and productivity-relevant agentic capacity. |
| Keywords: | agentic capital, agentic economy, Gondauri Index, artificial intelligence preparedness, composite index, panel econometrics, dynamic GMM, IV-2SLS, human sovereignty capital, macro-financial resilience |
| JEL: | C23 C43 C53 D24 O33 O47 L86 M15 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:341306 |
| By: | Lindenlaub, Ilse; Oh, Ryungha; Rodriguez, Maria Alejandra; Veldkamp, Laura |
| Abstract: | We document and explain the gap between measures of AI exposure and measures of AI adoption in the workplace. This leads us to propose a new AI adoption index based on comparative advantage. Using the representative German DiWaBe employee survey linked to worker and establishment information, we compare worker-reported AI use to prominent exposure measures and find that the relationship is weak. Motivated by this gap, we develop a framework in which adoption depends not only on technical feasibility—AI’s absolute advantage measured by exposure—but also on profitability—AI’s comparative (dis)advantage relative to a specific worker—balancing AI productivity against AI user costs and worker productivity against wages. We operationalize this framework at the task level by (i) estimating worker productivity relative to pay, (ii) mapping exposure indices into AI productivity, and (iii) inferring task-specific AI user costs from revealed-preference adoption. The resulting occupation-level index accounts for 60% of the cross-occupation variation in observed AI adoption, compared with 14% for an exposure-only model. The two approaches diverge substantially for approximately 30% of workers, highlighting that comparative advantage—not exposure alone—is crucial for assessing AI’s labor-market impact. |
| Keywords: | Artificial intelligence; Comparative advantage; Technology diffusion; Worker productivity |
| JEL: | E24 D24 J24 O33 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:21589 |
| By: | Alexandra Carine Marlyne Sedogo (INERA - Institut de l'Environnement et Recherches Agricoles [Ouagadougou] - CNRST - Centre national de la recherche scientifique et technologique [Ouagadougou]); Patrice Rélouendé Zidouemba (UNB - Université Nazi Boni [Bobo-Dioulasso, Burkina Faso]); Souleymane Ouedraogo (INERA - Institut de l'Environnement et Recherches Agricoles [Ouagadougou] - CNRST - Centre national de la recherche scientifique et technologique [Ouagadougou]) |
| Abstract: | ABSTRACT The tomato sector plays a significant role in socio-economic development. However, the heterogeneity of farming practices leads to profitability disparities between farms. This study analyzes the economic performance of tomato producers in the Kadiogo and Oubri regions of Burkina Faso. A total of 292 producers were surveyed using the snowball sampling method. Data processed using Stata 16 and R allowed for the creation of a producer typology based on analysis of mixed data (FAMD) and hierarchical cluster analysis (HCA), and the development of a farm account. The results reveal three production systems. The traditional system is characterized by an estimated yield of 15.3 t/ha and a relative net margin of 7, 542, 452 FCFA. The semi-intensive system is distinguished by a high yield of 18.5 t/ha and an estimated relative net margin of 10, 910, 595 FCFA. The intensive system yielded 17.9 t/ha and a relatively lower net margin of 10, 275, 243 FCFA. These results indicate that the semi-intensive system is the most economically efficient, due to the rational use of production factors. Overall, tomato production is profitable across all identified systems. Therefore, improving the sector's competitiveness requires differentiated policies based on farm type, as well as targeted capacity building for farms. Keywords : tomato, production system, profitability, performance, Burkina Faso |
| Abstract: | RESUME La filière tomate joue un rôle important dans le développement socio-économique. Cependant, l'hétérogénéité des pratiques engendre des écarts de rentabilité entre exploitations. Cette étude analyse les performances économiques des producteurs de tomates dans la région du Kadiogo et de l'Oubri au Burkina Faso. Au total, 292 producteurs ont été enquêtés à partir de la méthode boule de neige. Les données traitées à partir de Stata 16 et R ont permis de réaliser une typologie des producteurs à partir d'une analyse factorielle de données mixtes (AFDM) et une classification ascendante hiérarchique (CAH) et d'établir un compte d'exploitation. Les résultats révèlent trois systèmes de production. Le système traditionnel est caractérisé par un rendement estimé à 15, 3 t/ha et une marge nette relative de 7.542.452FCFA. Le système semi intensif se distingue par un rendement élevé de 18, 5 t/ha et une marge nette relative estimée à 10.910.595 FCFA. Le système intensif affiche un rendement de 17, 9 t/ha et une marge nette relative moins importante de 10.275.243 FCFA. Ces résultats indiquent que le système semi intensif est économiquement le plus performant, en raison de l'utilisation rationnelle des facteurs de production. Globalement, la production de tomate est rentable dans l'ensemble des systèmes identifiés. Par conséquent, l'amélioration de la compétitivité de la filière nécessite des politiques différenciées selon le type d'exploitations ainsi qu'un renforcement ciblé des capacités techniques des exploitations. Mots clés : tomate, système de production, rentabilité, performance, Burkina Faso |
| Keywords: | tomato, production system, profitability, performance, Burkina Faso, tomato production system profitability performance Burkina Faso |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05562238 |
| By: | Rocio Carrillo-Labella (Universidad de Málaga [Málaga] = University of Málaga [Málaga]); Fatiha Fort (UMR MoISA - Montpellier Interdisciplinary center on Sustainable Agri-food systems (Social and nutritional sciences) - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - IRD - Institut de Recherche pour le Développement - CIHEAM-IAMM - Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier - CIHEAM - Centre International de Hautes Études Agronomiques Méditerranéennes - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement); Louis-Antoine Saïsset (UMR MoISA - Montpellier Interdisciplinary center on Sustainable Agri-food systems (Social and nutritional sciences) - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - IRD - Institut de Recherche pour le Développement - CIHEAM-IAMM - Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier - CIHEAM - Centre International de Hautes Études Agronomiques Méditerranéennes - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement) |
| Abstract: | The adoption of Environmental Management Systems (EMS), such as the ISO 14001 standard, is a key strategy for advancing towards sustainable production. This study addresses the causal ambiguity between ISO 14001 certification and financial performance (FP), a critical and unresolved gap in the literature, particularly in the agri-food SME sector. The central dilemma lies in discerning whether the improvement in FP is due to the effect of certification (treatment effect) or to prior financial strength (selection effect). A longitudinal analysis of 255 Spanish olive oil-producing SMEs (2021–2023) was employed, focusing on key objective accounting indicators such as ROE, ROA, ATR (Asset Turnover Ratio), EBIT, and EBITDA, using methodologies designed to isolate and contrast the causal effects. Our findings confirm the existence of a positive and significant association with FP. Specifically, a marked contrast in causality was identified: financial health (ROE) significantly influences the propensity for adoption (selection effect), acting as an entry barrier for companies with lower FP. On the other hand, certification leads to a significant improvement in profitability, manifesting in higher ROE, EBIT, and EBITDA (treatment effect), albeit without a significant impact on overall operational efficiency (ROA). These results demonstrate the existence of a circular causality operating under a "virtuous circle, " where the selection and treatment effects mutually reinforce each other. The findings validate ISO 14001 as a market differentiator that translates into higher benefits, positioning sustainable practices as a key driver of financial success in the agri-food industry. |
| Keywords: | Olive oil, Circular causality, Treatment effect, Selection effect, SMEs, Financial performance, ISO 14001 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05628184 |
| By: | Ruzic, Dimitrije |
| Abstract: | This paper reevaluates the longstanding debate on capital-labor substitution by examining the role of external inputs: raw materials, intermediate goods and services, imports, offshoring. Both a meta-analysis (analyzing existing estimates of substitution) and direct estimation (using U.S. data for 1963-2016) indicate that external inputs disproportionately displace labor. These findings imply (1) that the capital-labor ratio responds 40-80% more strongly to the price of labor than to the price of capital, (2) that value added cannot be modeled separably from gross output, and (3) that historical disagreements regarding substitution can be recast as an omitted variable bias involving external inputs. |
| Keywords: | Capital-labor substitution; Production; Trade; Non-separability; Intermediate inputs |
| JEL: | E23 F16 O47 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:21591 |
| By: | Khalifa, Zayed; Rahal, Imene |
| Abstract: | Artificial Intelligence (AI) is rapidly reshaping the global economic landscape as a transformative general-purpose technology with far-reaching implications. This paper provides a comprehensive review of the economics of AI by examining its effects on productivity, labor markets, capital accumulation, firm dynamics, innovation systems, and income distribution. It synthesizes theoretical frameworks and empirical evidence to analyze how AI alters traditional economic models, particularly through modifications to production functions and factor substitution relationships. The study further highlights the growing importance of data as a key economic input and strategic asset, as well as the emergence of “superstar firms” that leverage AI-driven advantages to achieve market dominance. In addition, the paper explores macroeconomic consequences, including productivity growth, structural transformation, and shifts in economic organization, while addressing critical challenges such as inequality, labor market polarization, market concentration, and regulatory limitations. The review concludes by outlining policy considerations aimed at ensuring that AI contributes to inclusive, equitable, and sustainable economic development. |
| Keywords: | Artificial Intelligence; Digital Economy; Productivity Growth; Labor Market Transformation; Economic Inequality; Innovation Systems; Data Economics; Structural Change |
| JEL: | O3 |
| Date: | 2026–01–10 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128546 |
| By: | Alexander Quispe |
| Abstract: | We study whether adoption of an AI coding assistant causally expands the technological frontier of individual software developers. We exploit the staggered rollout of Claude Code across GitHub between May 2025 and January 2026 in a panel of 5, 838 developers observed monthly over 28 months, with treatment defined by the developer's first Claude-co-authored commit and not-yet-treated developers as controls. Using the doubly robust Callaway and Sant'Anna (2021) estimator, we find positive and significant effects on monthly commits (+41), repositories contributed to (+1.5), distinct programming languages used (+0.83), Shannon language entropy (+0.14), newly-used languages (+0.31), and cumulative lifetime languages (+0.51). The cumulative-languages effect grows with time since adoption, matching a Bayesian-learning model in which AI provides free signals about unfamiliar technologies and lowers the switching barrier. Results are robust to two stricter activity filters. The estimates document a sharp, persistent shift in developer behavior coincident with AI adoption; identification limits prevent a strict causal claim and we outline an agenda for cleaner tests. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.25438 |
| By: | Carter McCormack; Ryan Macdonald |
| Abstract: | Canada shares a border with the largest economy in the world. The ability of U.S. businesses to allocate resources to new technologies and new production processes has raised the living standards of U.S. citizens over time. Given the longstanding and deeply integrated economic relationship between Canada and the United States, knowledge of how Canada’s economic performance compares with that of its southern neighbour can provide valuable insights into potential changes in relative living standards between the two countries. |
| Keywords: | measuring economic, performance relative |
| JEL: | J23 M21 |
| Date: | 2026–03–25 |
| URL: | https://d.repec.org/n?u=RePEc:stc:stcp8e:202600300004e |
| By: | Alam, Afroza; Diegmann, André |
| Abstract: | This paper provides new causal evidence on how patent allowances affect firms and their employees based on quasi-random assignment of patent applications to examiners. Exploiting employer-employee records with newly linked German firm data and web-scraped patent documents, it shows that patent-induced shocks reduce firm exit, improve productivity, and increase wages, with rent-sharing elasticities between 0.10 and 0.21. Wage gains are broadly observed across occupational tasks, with substantial heterogeneity: managers benefit dispro portionately in publicly traded firms, whereas broader wage increases accrue to workers in non-traded firms. The findings highlight the role of institutional features and firm organiza tion in shaping how rents are shared. |
| Keywords: | Innovation, Firm Performance, Worker Compensation, Rent Sharing |
| JEL: | O31 O34 J31 D22 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewdip:341411 |