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on Efficiency and Productivity |
| By: | Klaus Friesenbichler; Agnes Kügler; Andreas Reinstaller (Austrian Productivity Board) |
| Abstract: | Recent evidence suggests that Chinese imports negatively impact firm-level productivity growth in the EU. We argue that this effect is moderated by the technological frontier, as proposed by Acemoglu et al. (2006). Using three distinct measures of the frontier, we examined firm-level data from twenty-three EU countries between 2003 and 2022 and found mixed results. We find that an increase in Chinese import intensity positively affects catching up to a productivity frontier. However, the productivity growth of firms operating in less technologically advanced sectors is adversely affected by an increase in Chinese import competition. The results for the country-level measure of national innovation system performance are inconclusive. We partly attribute the productivity growth slowdown to Chinese import competition which lowers growth of non-frontier firms. The findings also have implications for EU trade policy in the context of centralized negotiations. |
| Keywords: | Import competition, Productivity, Manufacturing, EU, China, Frontier, Productivity slowdown |
| Date: | 2026–04–01 |
| URL: | https://d.repec.org/n?u=RePEc:wfo:wpaper:y:2026:i:727 |
| By: | Nikolaj Broberg; Luca Marcolin; Elettra Sartori |
| Abstract: | Business investment has weakened across OECD economies in recent decades. Using firm-level data for 17 OECD countries over 2003–2022, this paper documents a marked decline in the responsiveness of tangible investment to firm productivity following the Global Financial Crisis, with only a partial recovery thereafter, pointing to a weakening of capital reallocation toward more productive firms. The decline is broad-based across countries and sectors, holds after accounting for intangible investment, and is confirmed under an instrumental-variables strategy. Both frontier and non-frontier firms experienced a reduction in responsiveness, though the decline is larger and more persistent among non-frontier firms, while frontier firms proved more resilient. Partial-equilibrium counterfactuals suggest that maintaining pre-crisis responsiveness would translate into substantially higher aggregate investment and measurable productivity gains. Policy and market conditions shape how strongly investment responds to productivity. Responsiveness is weaker in sectors where firms depend more on external finance and in countries with less efficient insolvency regimes, the latter most apparent at the frontier. More concentrated markets are associated with lower responsiveness, particularly among non-frontier firms, whereas greater trade openness is associated with stronger responsiveness across the productivity distribution. |
| Keywords: | Business dynamism, Cross-country firm-level data, Frontier firm divergence, Investment responsiveness, Multifactor productivity, Tangible investment |
| JEL: | C23 C55 D22 D24 E22 O47 |
| Date: | 2026–06–30 |
| URL: | https://d.repec.org/n?u=RePEc:oec:ecoaac:40-en |
| By: | Jaumandreu, Jordi |
| Abstract: | Many exercises need to estimate firm-level production functions. The doubts start with what production function, i.e. which functional form and what heterogeneity. This paper develops a semiparametric specification for variable factors that only assumes common scale and firm’s cost minimization. It is easy to apply and use for testing restrictions. When size of the firm is not correlated with the heterogeneity of factor proportions, is equivalent to a loglinear specification with firm and time-varying elasticities. It nests the commonly used CES and homogeneous Translog production functions. It admits Hicks-neutral and biased productivity and non-competitive outcomes in the input markets. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:21492 |
| By: | World Bank |
| Abstract: | This paper provides the first firm-level assessment of agglomeration economies in Mongolia, focusing on Ulaanbaatar, the country’s dominant urban center. Using data from the 2021 Enterprise Census, the paper estimates total factor productivity for a large sample of enter-prises and examines its relationship with localization, urban diversity, and peer productivity. The results indicate robust positive agglomeration effects, alongside suggestive evidence of diminishing returns to localization consistent with an inverted U-shaped pattern. These non-linearities are most apparent in Ulaanbaatar and in manufacturing, although they prove sensitive to alternative agglomeration and productivity measures. Although the cross-sectional nature of the data limits causal inference, the analysis offers new micro-level evidence on how spatial concentration interacts with congestion, infrastructure strain, and potential spatial misallocation. The findings underscore the importance of urban planning, infrastructure investment, and the development of regional hubs to sustain productivity growth in Mongolia’s highly concentrated urban system, with implications for diversification beyond mining in resource-dependent economies. |
| Date: | 2026–06–29 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:11415 |
| By: | Baslandze, Salomé; Edwards, Zachary; Graham, John; McClure, Ty; Sparks, Michael; Meyer, Brent; Waddell, Sonya; Weitz, Daniel |
| 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. |
| Keywords: | Artificial intelligence; Productivity; Technological change; Labor markets; Occupations |
| JEL: | O33 D22 J24 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:21313 |
| By: | Fort, Teresa; Goldschlag, Nathan; Liang, Jack; Schott, Peter; Zolas, Nikolas |
| Abstract: | Relatively flat US productivity growth versus rising R&D expenditures is often interpreted as evidence that ideas are getting harder to find. We build a new 45-year panel tracking the universe of US firms' patenting to investigate the micro underpinnings of this conclusion, separately examining the relationships between research inputs and ideas (patents) versus ideas and growth. We find that average patents per R&D input are increasing, the elasticity of patents to R\&D inputs is flat or rising, and there is not systematic evidence of a secular decline in patenting after controlling for research inputs. We then document a positive, significant, and fairly steady relationship between firms' patent and labor productivity growth rates. Average firm growth after controlling for patent growth, however, declines. Together, these results suggest that firms' innovative efforts play a key role in sustaining growth that has not diminished over the last four decades. |
| Keywords: | Innovation; Productivity; Patents |
| JEL: | O31 O32 O33 O47 D24 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:21555 |
| By: | González, Xulia; Lach, Saul; Miles, Daniel; Pazó Martínez, María Consuelo |
| Abstract: | This paper examines how managerial type shapes firms’ investment decisions, focusing on the distinction between owner (family)-managed firms and professionally-managed firms. We estimate a flexible investment policy function which depends on productivity, capital, labor and on other firm-level state variables, and is allowed to vary systematically with managerial type. Since firm-level productivity is not directly observed, we estimate it in a first step, addressing both the endogeneity of input choices and the lack of information on physical quantities. Our analysis draws on a rich panel of Spanish manufacturing firms from 1993 to 2016 that identifies whether firm owners, or their relatives, hold managerial positions. We find that, after controlling for state variables, family-managed firms invest more on average than professionally-managed firms. Managerial type also matters for how investment responds to changes in its determinants. In particular, family-managed firms exhibit stronger investment responses to changes in productivity and capital and display more procyclical investment behavior. |
| Keywords: | Family firms; Productivity |
| JEL: | L11 L60 D22 D24 D25 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:21383 |
| By: | Bilgin, Nuriye Melisa; Ottaviano, Gianmarco |
| 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 |
| JEL: | O33 L25 D22 O14 J3 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:21506 |
| By: | Masayuki Morikawa |
| Abstract: | This study documents the adoption of AI in the workplace and its impact on productivity among workers in the Japanese machinery industry. At the end of 2025, 34% of workers use AI in their jobs, with R&D accounting for the largest proportion of AI-utilized jobs. Among AI users, the mean share of tasks using AI, efficiency gains, and resulting productivity effects are 12%, 20%, and 4%, respectively. Most workers use AI for only a small fraction of their overall job tasks. The productivity effect is larger for continuous AI users than for new AI users, suggesting selection and learning effects of AI adoption. The use of AI at work is projected to increase labor productivity in the industry by 0.3-0.4 percentage points annually over the next several years. If the use of AI in R&D activities improves the efficiency of R&D investment, it is likely to generate productivity gains that extend beyond simple labor-saving effects. Finally, more than 80% of workers hold positive views toward expanding the use of AI in the workplace, with stronger support among those already using AI and those facing severe labor shortages. |
| Keywords: | artificial intelligence, machinery industry, productivity |
| JEL: | J24 L60 O33 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:een:camaaa:2026-49 |
| By: | Prados de la Escosura, Leandro |
| Abstract: | Recent research confirms that per capita income in early modern Spain improved only marginally overall, while also revealing sustained growth through much of the sixteenth and eighteenth centuries, alongside a continued decline from the late sixteenth to the mid-seventeenth 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 the subsequent 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: | Spain; Britain |
| JEL: | E24 J24 N13 O47 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:21503 |
| By: | Bilgin, Nuriye Melisa; Ottaviano, Gianmarco |
| Abstract: | We study how digital infrastructure relaxes constraints on the diffusion and economic impact of artificial intelligence (AI). Using administrative data and a nationally representative enterprise survey from Turkey (2021–2024), we document significant disparities in AI adoption. Adoption is concentrated among large firms and in regions with high-speed broadband and proximity to data centers, particularly for software-intensive and cloud-based applications. To identify causal effects, we exploit the staggered expansion of Turkey’s national natural gas pipeline network, which serves as a conduit for fiber-optic deployment. Because pipeline routing is determined by energy distribution priorities rather than digital demand, it provides plausibly exogenous variation in connectivity. Difference-in-differences estimates show that improved connectivity significantly increases AI adoption, particularly for software-intensive technologies and among small and medium-sized enterprises. Instrumental-variable estimates indicate that infrastructure-driven AI adoption raises labor productivity and export intensity while shifting labor composition toward ICT-related roles. These findings highlight digital infrastructure as a primary determinant of both the pace of AI diffusion and its resulting economic returns. |
| Keywords: | Artificial intelligence; Digital infrastructure; Broadband; Technology diffusion; Firm productivity; Cloud computing |
| JEL: | O33 L86 D24 J24 O14 R12 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:21385 |
| By: | Cruces, Guillermo; Fernandez Meijide, Diego; Galiani, Sebastian; Galvez, Ramiro; Lombardi, María |
| Abstract: | Does generative artificial intelligence (AI) reinforce or reduce productivity differences across workers? Existing evidence largely studies AI within firms and occupations, where organizational selection compresses educational heterogeneity, leaving unclear whether AI narrows productivity gaps across individuals with substantially different levels of formal education. We address this question using a randomized online experiment conducted outside firms, in which 1, 174 adults ages 25–45 with heterogeneous educational backgrounds complete an incentivized, workplace-style business problem-solving task. The task is a general (not domain specific) exercise, and participants perform it either with or without access to a generative-AI assistant. Unlike prior work that studies heterogeneity within relatively homogeneous worker samples, our design targets the between–education-group productivity gap as the primary estimand. We find that AI increases productivity for all participants, with substantially larger gains for lower-education individuals. In the absence of AI access, higher-education participants outperform lower-education participants by 0.548 standard deviations; with AI access, this gap falls to 0.139 standard deviations, implying that generative AI closes about three quarters of the initial productivity gap. We interpret this pattern as evidence that generative AI narrows effective productivity differences in task execution by relaxing cognitive constraints that are more binding for lower-education individuals, even though underlying skill differences remain, as reflected in persistent education gaps in task performance and in a follow-up exercise without AI assistance. |
| Keywords: | Productivity; Inequality |
| JEL: | J24 O33 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:21299 |
| By: | Arnarson, Björn Thor; Buus, Magnus Tolum; Moxnes, Andreas; Munch, Jakob Roland; Xiang, Chong |
| Abstract: | We study how firm growth reorganizes the division of labor across firms in global value chains. Using a novel dataset linking cross-border firm-to-firm transactions to matched employer–employee data, we show that demand shocks increase trade between firms while reducing occupational similarity, implying greater specialization. We develop and estimate a model of task outsourcing in which firms expand by reallocating tasks to suppliers. The model matches the data and implies endogenous scale economies. Eliminating outsourcing reduces average labor productivity by 25 percent and increases input costs by 10 percent, highlighting the central role of specialization in shaping firm performance. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:21543 |
| By: | Görg, Holger; Mao, Haiou; Driffield, Nigel |
| Abstract: | Divestments by foreign multinationals are an important phenomenon that is largely neglected in the literature. We use firm‐level panel data from China to estimate the impact of such divestments on the performance of domestic firms in the local economy. To the best of our knowledge, there is no empirical study that has looked at these effects. Our results suggest that, overall, domestic firms may be able to benefit from divestments by foreign firms through spillovers. We find evidence suggesting that the positive overall effect for private firms is driven by the movement of workers from the divested firm to the local firm, as well as by a reduction in competition reducing crowding out. By contrast, local firms are negatively affected by the loss of technology transfer and customer–supplier relationships with foreign firms. While most effects are short‐lived, the negative impact on technology transfer persists over time. |
| Keywords: | foreign divestment, multinational enterprises, spillovers |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:ifwkie:335592 |
| By: | Research and Statistics Department (Bank of Japan) (Bank of Japan) |
| Abstract: | The Research and Statistics Department of the Bank of Japan has revised the methodology for calculating the output gap and potential growth rate, which are regularly estimated and released, taking into account the benchmark year revision of 2020 in GDP statistics and recent changes in economic structure. The main changes include: (1) regarding the capital utilization rate, the source data has been changed from a quantity basis to a "value-added basis, " which accounts for quality improvements, resulting in an adjustment of the downward bias that has occurred in capital utilization rate for the manufacturing sector; (2) for the structural unemployment rate, the estimation method has been revised to more accurately capture mismatches in the labor market, given the recent shift from the use of the Public Employment Security Office to the use of private employment agencies; and (3) the potential growth rate has been re-estimated using the 2020 base-year GDP and capital stock statistics, through the calculation of the total factor productivity growth rate. In order to assess economic and price developments, using estimates of the output gap to identify the aggregate supply and demand balance remains critical. However, in recent years, and amid intensifying labor supply constraints, developments in labor input and labor market tightness appear to exert an increasingly significant influence on economic activity in labor-intensive sectors and price trends. Against this backdrop, this paper also conducts a brief empirical performance exercise of labor market indicators that are considered to be suitable for complementary monitoring of the output gap, given their relevance to forecasting wages and prices. |
| Keywords: | output gap; potential growth rate; GDP; phillips curve; labor market |
| JEL: | E23 E24 E31 E32 J20 O47 |
| Date: | 2026–06–30 |
| URL: | https://d.repec.org/n?u=RePEc:boj:bojron:ron260630a |