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on Efficiency and Productivity |
| By: | Akira Kohsaka (Osaka School of International Public Policy, The University of Osaka) |
| Abstract: | This paper examines China’s industrial structural transformation over the past several decades, comparing it with Japan, Korea and the US. Using expanded sets of international comparable databases, we decompose aggregate productivity growth into sectoral productivity growth and inter- sector resource reallocation. Our findings reveal notable trend changes in labor shares across sectors with significant time differences, earlier de-industrialization in the US and latest industrialization in China. Notably, in the US, manufacturing has lagged behind trade, finance and business services in labor share, and ICT in productivity for years. In contrast, in China, while these service sectors remain minimal in labor share, their relative productivities surpass those of manufacturing. Despite her remarkable productivity growth, significant gaps persist in all sectoral productivity levels between China and the others. We explore how fast these gaps could be narrowed by current sectoral productivity growth trends. |
| Keywords: | structural transformation, productivity growth, reallocation, growth decomposition, China |
| JEL: | O47 O57 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:osp:wpaper:26e002 |
| By: | Chun Pang Chow; Hiroyuki Kasahara; Yoichi Sugita |
| Abstract: | We establish nonparametric identification of production functions, total factor productivity (TFP), price markups, and firms' output prices and quantities, as well as consumer demand, using firm-level revenue data, without observing output quantity, in a monopolistically competitive environment with a fully nonparametric demand system. This result overturns the widely held view -- formalized by Bond, Hashemi, Kaplan, and Zoch (2021) -- that output elasticities and markups are not nonparametrically identifiable from revenue data without quantity information. Under the additional restriction that demand satisfies the homothetic single-aggregator (HSA) structure of Matsuyama and Ushchev (2017), we further nonparametrically identify the representative consumer's utility function from firm-level revenue data. This new identification result enables counterfactual welfare analysis without parametric assumptions on preferences. We propose a semiparametric estimator that is feasible for standard firm-level datasets under a Cobb--Douglas production specification. Monte Carlo simulations show that the estimator performs well, while treating revenue as output induces substantial bias. Applying the estimator to Chilean manufacturing data, we reject the CES specification in favor of HSA, and find that market power reduces welfare by approximately 3%--6% of industry revenue in the three largest manufacturing industries in 1996. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.01492 |
| By: | Yan Bai; Dan Lu; Xu Tian; Yajie Wang |
| Abstract: | This paper reassesses the role of financial frictions in capital misallocation through a model disciplined by both firm-level borrowing costs and the average revenue product of capital (ARPK). Using Chinese manufacturing data, we document substantial dispersion in ARPK, alongside a strong positive relationship between ARPK and the borrowing costs firms face---patterns absent in U.S. data. We develop a heterogeneous-firm model with endogenous firm-specific borrowing costs and additional capital distortions modeled as exogenous wedges. In this model, eliminating financial frictions raises total factor productivity (TFP) by 25 percentage points. In contrast, without other capital distortions, removing financial frictions increases TFP by less than 2 percentage points. The stark difference arises from the interaction between financial frictions and permanent firm-level distortions, which generate endogenous financial heterogeneity and selection, making productive firms the most constrained. Our findings suggest that financial frictions can be highly distortionary when other sources of misallocation are present. |
| JEL: | E2 F3 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34930 |
| By: | Filip Premik (Monash University); Dan Yu (University of Alberta) |
| Abstract: | We study how heterogeneity in capital inputs affects firm performance. Drawing on detailed data on municipal bus fleets in Poland, we exploit plausibly exogenous variation generated by public procurement and nationally coordinated sales behavior of bus manufacturers to identify the causal effects of variety in fleet composition across brands and other technical dimensions. More heterogeneous fleets exhibit lower vehicle utilization and, for a fixed level of output, require more units of capital and generate higher costs. Our results emphasize that the pro- ductive capacity of capital depends on its internal structure, not only on its aggregate quantity or value. |
| Keywords: | Heterogeneous capital, capital utilization, productivity, fleet composition, orga- nization of production. |
| JEL: | D24 L23 L62 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:mos:moswps:2026-04 |
| By: | Ales Marsal (National Bank of Slovakia); Patryk Perkowski (Yeshiva University) |
| Abstract: | We examine how generative AI impacts productivity across the task-based framework using a field experiment at the National Bank of Slovakia. In our experiment, we randomly assign generative AI access to central bank employees completing workplace tasks that mirror the theoretical task-based framework. Our results indicate that generative AI access leads to large improvements in both quality and efficiency for the majority of participants. We find a strong complementarity between generative AI and non-routine work, both on average and for most participants. We also find some support for generative AI as both cognitive-biased and specialist-biased, though smaller in magnitude than our tests of routine-biased. While workers in routine jobs experience larger individual performance gains, generative AI is less effective for the routine task content of their work. The mismatch between generative AI’s task- versus worker-level impacts is economically large, and results from a simulation exercise suggest the organization can increase output by 7.3% by changing how workers are assigned to tasks in the presence of generative AI. Additionally, we find differences in how the benefits of generative AI relate to worker skills: low-skill workers benefitmost in terms of quality while high-skill workers benefit in terms of efficiency. Our findings provide empirical support on generative AI and task-level complementarities, with important implications for how generative AI will impact workers, organizations, and labor markets more broadly. |
| JEL: | J24 M15 E58 C93 O33 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:svk:wpaper:1128 |
| By: | Ali, Amjad; Jabeen, Riffat; Ahmad, Khalil |
| Abstract: | This study aims to analyze the impact of trade secrets, i.e., intangible assets, patents, on an entity’s financial performance in respect of varying elements, e.g., revenue, leverage, capex, especially the profitability measured in terms of return on assets. We used a dataset of US firms and statistical and econometric methods, including regression analysis, multicollinearity test, endogeneity & robustness tests, were employed during this research to study the impact of ownership of trade secrets on a firm’s performance, and the dataset used consisted of an unbalanced panel of US firms. The outcome of this study establishes that the entities that own trade secrets tend to be more profitable as compared to those that do not. More specifically, a directly proportional relationship also exists between the amounts of trade secrets owned by a firm versus its return on assets. Further, regardless of the selected model, the use of robustness checks also establishes the validity of these findings, which solidifies the importance of trade secrets being a source of competitive advantage and profitability for the firm. |
| Keywords: | Trade Secrets, Financial Performance, Return on Assets, Intangible Assets, Competitive Edge |
| JEL: | M1 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:127530 |
| By: | José Alves; João Jalles; Lucas Menescal |
| Abstract: | This paper examines how expansions of the public-sector wage bill and employment affect government performance when political incentives and institutional constraints shape bureaucratic behaviour. Using data for 41 emerging and developing economies over 1997-2019, we construct annual measures of public-sector efficiency based on frontier methods and analyses how different sources of payroll growth translate into subsequent efficiency. To distinguish politically discretionary payroll expansions from externally induced adjustments, we decompose wage-bill changes into a component driven by natural disasters and a residual component reflecting policy-driven variation. This distinction contrasts emergency-driven administrative responses with payroll growth more likely to reflect patronage, weak accountability, or soft budget constraints. We find that discretionary increases in the wage bill are systematically followed by declines in public-sector efficiency, whereas disaster-driven payroll changes have small and transitory effects. These effects are conditioned by fiscal decentralization and institutional quality: stronger governance and subnational accountability mitigate efficiency losses. The results contribute to the public choice literature on bureaucratic incentives and the political economy of government size. |
| Keywords: | Government Efficiency; Public Sector Employment; Fiscal Decentralization; Government Wages; Panel Data Analysis; Local Projections; Nonlinear Effects. |
| JEL: | C23 H11 H72 H77 E62 J45 O43 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:ise:remwps:wp04082026 |