|
on Efficiency and Productivity |
| By: | Otgun, Hanifi; Fulginiti, Lilyan; Perrin, Richard |
| Abstract: | This paper provides regional level estimates of total factor productivity (TFP) growth and its components in Turkish agriculture. Using a translog stochastic frontier model with monotonicity constraints, we decompose TFP growth into technical change and efficiency change across 26 NUTS-2 regions over 2013–2020. We also examine the relationship between technical efficiency and trade openness by integrating regional agricultural trade openness into the model as an (in)efficiency driver. These contributions address gaps in the existing literature by revealing regional TFP disparities in Türkiye and investigating the previously understudied link between technical efficiency and trade openness. Additionally, we explicitly control for climatic effects, including average temperature and precipitation in the frontier, to isolate weather-driven fluctuations from inefficiency. Our results highlight pronounced regional disparities, with export-oriented regions exhibiting higher efficiency. Moreover, trade openness is significantly and positively associated with technical efficiency, suggesting that greater integration with external markets may foster TFP growth in the Turkish agricultural sector. Climate variables show nuanced impacts, with moderate temperature and rainfall positively affecting agricultural output, while extreme conditions negatively impact yields. TFP growth is predominantly driven by technical change, while efficiency change slightly detracts from overall productivity gains. |
| Keywords: | International Relations/Trade |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea25:361177 |
| By: | Sebaq, Mohamed |
| Keywords: | Production Economics, Productivity Analysis, Agribusiness |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea24:343680 |
| By: | John M. Barrios; Brian C. Fujiy; Petro Lisowsky; Michael Minnis |
| Abstract: | We examine how differences in financial reporting practices shape firm productivity. Leveraging new audit questions in the U.S. Census Bureau's 2021 Management and Organizational Practices Survey (MOPS), and complementary tax return data from the Internal Revenue Service (IRS) and detailed financial records from Sageworks, we find that (i) variation in reporting quality explains 10--20 percent of intra-industry total factor productivity dispersion, and (ii) evidence of complementarity between the effects of financial audits and management practices driving firm productivity. We then examine the underlying mechanisms. First, audits function as a managerial technology, improving the precision of internal information and raising efficiency, with stronger effects in competitive, low-margin industries and among younger firms. Second, exploiting cross-state variation in tax incentives, we show that audits constrain underreporting and mitigate the downward bias in measured productivity. Together, these results highlight the underrated importance of financial reporting quality driving firm productivity. |
| JEL: | D15 D24 G3 L2 M20 M4 M41 O33 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34536 |
| By: | Roth, Felix; Rammer, Christian |
| Abstract: | Intangible assets have increasingly been identified as a main source of productivity gains. Since the pioneering work by Corrado, Hulten, and Sichel (2005), empirical research has largely focused on macro and industry-level studies, while firm-level studies have often been confined to a limited set of intangible assets, especially Research and Development (R&D). This paper employs a unique firm-level panel database that contains information on four types of intangible assets: R&D, software & databases (S&D), firm-specific human capital (HC), and brand value (BV). For R&D, we find much lower productivity returns than for S&D and HC. R&D even loses significance once controlling for other intangibles, except for high-tech manufacturing. In contrast to R&D, we find that S&D and HC tend to be the primary drivers of productivity gains, particularly in services. Our findings have implications for research policy, suggesting a stronger focus on supporting investment in non-R&D intangibles, including S&D and HC. |
| Keywords: | Non-R&D intangibles, productivity, R&D, digitalisation, firm-specific human capital, brand value, firm-level panel data |
| JEL: | E22 O33 O38 D24 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewdip:333901 |
| By: | Carlos Esteban Posada Posada |
| Abstract: | Each production establishment is assumed to have, at any given time, a unique combination of capital and labor (a Leontief function), but the aggregate output at that same time must still be modeled with a Cobb-Douglas function (or a CES, although the latter yields less efficiency). This has two implications: 1) the total factor productivity variable of the macroeconomic function is endogenous: It depends primarily on the technical factors of the individual establishments and, secondarily, on their levels of capital and labor.; 2) the optimization processes of any establishment cannot be instantaneous, even in the absence of (monetary) adjustment costs; they are processes occurring over several time stages and depending on expectations. However, these implications do not substantially contradict what would correspond to the optimization of a hypothetical firm described by a Cobb-Douglas (or CES) function. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.15520 |
| By: | Blagica Petreski; Marjan Petreski |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:ftm:policy:2025-12/58 |
| By: | Owusu Ansah, Michael; Skevas, Theodoros; Grashuis, Jasper |
| Abstract: | This study examines the temporal effects of mergers and acquisitions (M&A) on the productivity of U.S. grain marketing cooperatives from 2004 to 2020. While prior research has explored the drivers and outcomes of M&A, limited attention has been paid to how the passage of time since an M&A event affects productivity, particularly in terms of total factor productivity (TFP). Using a system GMM model, we explore how M&A age, defined as the number of years since a cooperative’s most recent merger of acquisition, influences TFP. M&A age serves as a temporal measure of post-M&A experience, reflecting the integration period during which cooperatives absorb operational, managerial, and cultural changes. The results show that M&A age does not significantly impact productivity, suggesting that the integration and adjustment process may offset productivity gains in the short term. Secondary findings reveal that exporting cooperatives tend to have higher productivity, while those exiting the sector experience declines. These results highlight the need for cooperatives to develop long-term strategies to manage post M&A integration. This paper contributes to a deeper understanding of the temporal dynamics of M&A in agricultural cooperatives and offers insights for strategic decision-making within the sector. |
| Keywords: | Productivity Analysis, Research and Development/Tech Change/Emerging Technologies |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea25:361178 |
| By: | Ufuk Akcigit; Harun Alp; Jeremy Pearce; Marta Prato |
| Abstract: | Why do some entrepreneurs drive economic growth while others do not? This piece discusses new work that studies entrepreneurs using a comprehensive dataset from Denmark. We study who becomes an entrepreneur, along with their hiring and business decisions, and find that a distinct minority are “transformative.” These individuals, who generate disproportionate productivity gains, tend to have high IQ scores, be well-educated, and hire technical (R&D) workers. The data support the idea of productivity growth being driven by the symbiotic relationship between transformative entrepreneurs and R&D workers. For policymakers, the lesson is that when an economy has more R&D workers and transformative entrepreneurs, they sustain higher long-run productivity growth. |
| Keywords: | entrepreneurship; R&D; innovation; productivity growth |
| JEL: | O31 O38 |
| Date: | 2026–01–05 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednls:102298 |
| By: | Diego Restuccia |
| Abstract: | I examine the empirical properties of firm-level productivity and distortions across countries using the newly released World Bank Enterprise Surveys (WBES). Using a standard framework of production heterogeneity, I show that the gap in productivity and distortions between high and low productivity firms is larger in developing countries, generating large aggregate productivity losses from misallocation. A key empirical property of distortions in developing countries is that they systematically weaken the relationship between firm size and firm productivity. I exploit a unique feature of the WBES data to document which specific aspects of the economic environment faced by firms, such as financial constraints, regulation, corruption, and weak infrastructure, are consistent with the empirical pattern of distortions across countries. |
| JEL: | O11 O14 O4 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34573 |
| By: | Ali Zeytoon-Nejad; Barry Goodwin |
| Abstract: | The conventional functional form of the Constant-Elasticity-of-Substitution (CES) production function is a general production function nesting a number of other forms of production functions. Examples of such functions include Leontief, Cobb-Douglas, and linear production functions. Nevertheless, the conventional form of the CES production specification is still restrictive in multiple aspects. One example is the fact that the marginal effect of increasing input use always has to be to increase the variability of output quantity by the conventional construction of this function. This paper proposes a generalized variant of the CES production function that allows for various input effects on the probability distribution of output. Failure to allow for this possible input-output risk structure is indeed one of the limitations of the conventional form of the CES production function. This limitation may result in false inferences about input-driven output risk. In light of this, the present paper proposes a solution to this problem. First, it is shown that the familiar CES formulation suffers from very restrictive structural assumptions regarding risk considerations, and that such restrictions may lead to biased and inefficient estimates of production quantity and production risk. Following the general theme of Just and Pope's approach, a CES-based production-function specification that overcomes this shortcoming of the original CES production function is introduced, and a three-stage Nonlinear Least-Squares (NLS) estimation procedure for the estimation of the proposed functional form is presented. To illustrate the proposed approaches in this paper, two empirical applications in irrigation and fertilizer response using the famous Hexem-Heady experimental dataset are provided. Finally, implications for modeling input-driven production risks are discussed. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.20910 |
| By: | Masaya Nishihata; Suguru Otani |
| Abstract: | We examine whether team affinity differs across skill dimensions in team production. Using a novel nonparametric framework that accommodates task-level structure, role asymmetry, and latent affinity, we decompose team performance into skill-specific productivity and unobserved match affinity. As an illustrative application, we analyze elite women's bobsleigh data, where performance can be separated into start and riding phases with distinct individual skill inputs. The estimates reveal heterogeneous, task-specific affinities: coordination and complementarity are stronger in the start phase but weaker and more dispersed during riding, underscoring skill-specific heterogeneity in unobserved team affinity. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.21460 |
| By: | Joanne Lindley (King’s Business School, King’s College London, Bush House, Aldwych, London, WC2B 4B, UK); Steven McIntosh (School of Economics, University of Sheffield, Sheffield S10 2TU, UK) |
| Abstract: | This study investigates the relationship between worker-reported job quality characteristics and both work-related wellbeing and labour productivity, utilizing data from the European Working Conditions Surveys (EWCS) of 2005 and 2015, and the European Working Conditions Telephone Survey (EWCTS) of 2021. We construct composite Job Quality Scores (JQS) for wellbeing and productivity based on 24 key job quality characteristics, weighted by their correlation with each respective outcome. Our analysis reveals a divergence in trends between 2015 and 2021, with average JQS for work-related wellbeing significantly declining while the JQS for labour productivity increased. By decomposing the changes, we identify specific job quality characteristics, such as increased repetitive hand/arm movements, working at high speed, carrying heavy loads, and working to tight deadlines, as key drivers of this opposing trend. Conversely, increased computer use, reduced physically demanding postures, appropriate reward for effort, and reduced exposure to dangerous chemicals are identified as factors that could simultaneously enhance both productivity and wellbeing. Furthermore, we explore the role of occupational shifts in explaining these changes, finding that the observed increases in key job characteristics listed above are largely occurring within occupations rather than solely due to changes in occupational composition of the workforce. These findings offer valuable insights for managers seeking to balance economic performance with worker wellbeing, highlighting specific areas for intervention to foster a more harmonious and productive work environment. |
| Keywords: | Job quality, wellbeing, productivity, occupations |
| JEL: | J20 J21 J24 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:shf:wpaper:2025013 |
| By: | Wagner, Joachim |
| Abstract: | The use of advanced technologies like artificial intelligence, robotics, or smart devices will go hand in hand with higher productivity, higher product quality, and lower trade costs. Therefore, it can be expected to be positively related to export activities. This paper uses firm level data for manufacturing enterprises from the 27 member countries of the European Union collected in 2025 to shed further light on this issue by investigating the link between the use of advanced technologies and extensive margins of exports. Applying a new machine-learning estimator, Kernel-Regularized Least Squares (KRLS), which does not impose any restrictive assumptions for the functional form of the relation between margins of exports, use of advanced technologies, and any control variables, we find that firms which use more advanced technologies do more often export and do export to more different destinations. |
| Keywords: | Advanced technologies, exports, firm level data, Flash Eurobarometer 559, kernel-regularized least squares (KRLS) |
| JEL: | D22 F14 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:kcgwps:334537 |