Turbulent growth: Business dynamism and aggregate productivity

The production of Lightweight Expanded Clay Aggregate (LECA) is a very energy-consuming process as well as depletes natural resources. Therefore, there is a need for alternative lightweight aggregates with lesser impact on the environment. This study intends to examine the possibility of using Expanded Shale (ES) instead of LECA in concrete. ES was produced via a thermal treatment process, and concrete mixtures containing ES were compared to LECA concrete in terms of mechanical performance. The methodology included compressive, tensile, and flexural strength tests, as well as petrographic analysis for alkali-silica reaction (ASR) assessment. Quantitative results showed that ES concrete achieved 28-day compressive, tensile, and flexural strengths of 19.1 MPa, 2.62 MPa, and 3 MPa, respectively, compared to 13.9 MPa, 1.64 MPa, and 2 MPa for LECA concrete. However, petrographic examination revealed ASR gel formation around volcanic aggregates in ES concrete, indicating that ASR potential requires further mitigation rather than being negligible. ES exhibits superior mechanical properties compared to LECA; however, requires life-cycle assessment and quantification of carbon footprint to qualify as a sustainable alternative. The findings suggest that ES is a promising high-performance lightweight aggregate, provided that ASR risks are addressed through mix optimization or supplementary materials.

Abstract This study decomposes aggregate labor productivity growth in Turkey from 1999 to 2023 using a chain-linked gross domestic product (GDP) series with an exactly additive decomposition method. Traditionally, this growth has been decomposed into two components: productivity growth within sectors and labor reallocation across sectors. Using the chain-linked GDP series introduces a third component: changes in relative sectoral prices. Although these relative price changes cancel out at the aggregate level, they influence sectoral contributions to overall labor productivity by altering each sector’s weight in total output. Incorporating them, therefore, provides a more comprehensive view of sectoral dynamics by capturing their contributions to aggregate productivity growth. On average, the contribution of structural change slightly exceeds that of the within component. However, both the magnitude and composition of contributions vary considerably across sub-periods. During crisis years, structural change contributed positively while the within-sector component was negative. In contrast, during non-crisis periods, aggregate labor productivity growth declined because the structural-change component weakened persistently and nearly vanished after 2018, despite a positive though limited within-sector component. At the sector level, construction, finance and real estate, community, personal, and government services, and transport and communication largely account for the slowdown, while manufacturing’s contribution stayed steady; its composition shifted away from within productivity across periods.

ABSTRACT Adam Smith's two distinct benefits from foreign trade were offered to dissuade others from adopting the mercantilist view that there is one principal gain from trade: the importation of money. Although Smith's two benefits received considerable attention in the literature, there was no consensus on what Smith meant by his two benefits. This paper offers a new interpretation of those benefits. Smith's first benefit is a micro benefit, with its impact on individuals. That benefit is the subjective value creation that results from voluntary international exchange. Both parties to the exchange experience an improvement in their living standard. Smith's second benefit is a macro benefit, impacting the economy at large. International trade extends markets and allows for production on a larger scale. A greater division of labor increases labor productivity and increases the national product. This increase in aggregate production is the second benefit from foreign trade. What makes this interpretation of Smith's two benefits attractive is the evidence that Adam Smith concurs with the interpretation.

Purpose: This paper examines the sectoral contributions to aggregate labor productivity growth in Türkiye from 2002 to 2023.Methodology: The analysis applies both conventional decomposition techniques (TRAD and CSLS) and more recent exactly additive approaches (GEAD and extended GEAD) to identify the sources of labor productivity growth at the aggregate and sectoral levels. Findings: The results indicate that within-sector growth effects are the primary drivers of aggregate labor productivity growth, while reallocation level and reallocation growth effects play a more limited role. Although the methods yield broadly consistent results at the aggregate level, they generate notable differences in sectoral contributions. Manufacturing and trade services contribute positively to aggregate productivity growth under the TRAD, GEAD, and extended GEAD frameworks, whereas the CSLS approach produces more heterogeneous sectoral outcomes. The FSGR analysis reveals a negative Baumol-type growth effect in agriculture and construction, which is partially offset by positive contributions from other sectors.Originality: By combining conventional and recent decomposition methods within a unified framework, the study provides additional evidence on the sensitivity of sectoral productivity assessments to methodological choice in the Turkish context.

ABSTRACT This research examined the plant of an anonymous aggregate production company, hereafter called Quarry X, focusing on how to reduce failures and improve crushing efficiency. The plant has faced unplanned downtime and decreased production capacity, necessitating an assessment and proactive maintenance strategy. Key performance indicators were calculated, revealing an efficiency of 43.9%, a production availability of 61% (and therefore downtime of 39%), and mean time between failures of approximately 10 hours. This result highlighted ore, equipment, and maintenance issues as the main causes. Using Bruno simulation software, we devised solutions, such as replacing the pan feeder with a grizzly feeder, to increase capacity and enhance efficiency, ultimately boosting Quarry X’s production and industry competitiveness.

Aggregate production planning (APP) helps medium-term production, manpower, inventory, and subcontracting decisions match expected demand. Deterministic planning models are generally ineffective in manufacturing due to demand and operational variability. Fuzzy linear programming (FLP) has been frequently used to describe imprecision using membership functions and satisfaction levels. Despite its versatility, accurate approaches for solving multi-objective FLP-based APP models become computationally expensive as issue size and complexity increase. Thus, metaheuristic algorithms are widely used, although many still have premature convergence, parameter sensitivity, and restricted scalability. This study investigates the Narwhal Optimization Algorithm (NO) as a population-based metaheuristic framework. It proposes two hybrid variants to improve convergence reliability and constraint-handling capability: NO combined with the Super Modified Simplex Method (SMS) for local refinement and NO integrated with a Runge–Kutta-based optimizer (RK) for search stability. These hybrid techniques are tested for solution quality, convergence behavior, and robustness using eight response-surface benchmark functions and four constrained optimization problems. A real-parameter fuzzy APP problem with three goods and a six-month planning horizon uses the best variations. The Elevator Kinematic Optimization (EKO) algorithm, chosen for its compliance with the same mathematical framework and consistent parameter values, is used to compare the offered solutions fairly and controlled. Fuzzy programming uses a max–min satisfaction framework with linear membership functions from positive and negative ideal solutions. Computational experiments assess solution quality, stability, and efficiency for nominal and ±10% demand disturbances. The hybrid NO variants better resist premature convergence, stabilize solutions, and satisfy users more than the original NO and benchmark approaches. For small and medium-sized organizations in dynamic situations, hybrid narwhal-based optimization appears to be a reliable and scalable decision-support solution for APP problems under uncertainty.

Against the backdrop of persistent and recently widening global imbalances, the paper presents a structured framework for understanding how domestic policies can influence current account positions by altering domestic saving and investment decisions. Staff analysis finds that traditional macroeconomic policies remain the dominant drivers of imbalances, but certain types of industrial policies could also play a role. Micro industrial policies—those targeting specific sectors or firms—generally have ambiguous and limited effects on the current account depending on their impact on aggregate productivity. Macro industrial policies—those deployed economy-wide and often paired with restrictions such as capital flow management measures—can materially affect the current account but come at a cost to consumption. Trade restrictions, often deployed to counter imbalances, would only meaningfully alter current account balances when used temporarily or to support higher public savings. Using scenario analysis, the paper shows how domestic rebalancing, undertaken simultaneously, across deficit and surplus economies yields both a reduction in global imbalances and higher global output. The report concludes that the future path of global imbalances will be largely shaped by domestic macroeconomic trajectories. Durable rebalancing is a collective endeavor: it requires sound domestic policy action across major economies and works best when countries move together. To help design such policies, the Fund is pursuing a multipronged approach by strengthening data, analysis, surveillance and dialogue across the member countries.

We develop a heterogeneous-firm macroeconomic model to investigate how tax evasion affects the productivity distribution in general equilibrium. In our model, entrepreneurs choose capital and labor to produce with their firms, invest in bonds, and evade taxes to maximize their intertemporal utility, derived from dividends. Firms face leverage constraints and uninsurable productivity shocks. The results reveal that tax evasion redistributes capital toward low-productivity firms, relaxing their leverage constraints. It also increases public debt, raising the cost of capital and crowding out firms at the margin. As a result of these forces, we demonstrate that (i) the decline in high-productivity firms’ average productivity drives the negative correlation between the size of the shadow economy and aggregate productivity, and (ii) the productivity gains from reduced tax evasion are smaller in economies with higher public debt and stricter leverage constraints. • Tax evasion redistributes capital from high- to low-productivity firms. • Tax evasion raises public debt and debt crowding-out hits firms at the margin. • High-productivity firms drive shadow economy impact on Total Factor Productivity. • TFP gains from reduced evasion are smaller in financially underdeveloped economies.

Biofilms are resistant to conventional sanitizers used in food-associated environments. Essential oils are plant-derived liquids that have many biological activities. Essential oils have emerged as promising agents for controlling biofilms, aligning with the trend toward natural product-based solutions. They exhibit antibiofilm properties against various foodborne bacteria. The active compounds include carvacrol, thymol, linalool, limonene, 1,8-cineole, terpinen-4-ol, α-pinene, eugenol, and cinnamaldehyde. The antibiofilm effects of essential oils can be improved through formulation as nanoemulsions and bioactive packaging. There are several targets for essential oils to inhibit biofilm formation. These mechanisms include bacterial surface attachment, swimming and swarming motilities, aggregation, extracellular polysaccharide and protein production, and quorum-sensing. Overall, essential oils represent a promising, environmentally friendly alternative to conventional chemical disinfectants, aligning with the growing demand for natural and sustainable solutions.

Parkinson's disease (PD) is characterised by progressive neurodegeneration and is marked by the formation of Lewy bodies, which are intracellular aggregates primarily composed of α-synuclein. Mitochondrial dysfunction and impaired protein degradation pathways are thought to play critical roles in PD progression, contributing to the loss of dopaminergic neurons in the substantia nigra. Phosphorylation of α-synuclein has been shown to promote its aggregation, underscoring its potential role in disease progression. Parkin, an E3 ubiquitin ligase, is widely regarded as a pleiotropic neuroprotective protein that modulates the mitochondrial quality control, as well as metabolic turnover and the accumulation of α-synuclein. Death-associated protein kinase 1 (DAPK1), which is involved in the regulation of apoptosis and autophagy, has recently emerged as an important factor in neurodegeneration. While DAPK1 has been implicated in Alzheimer's disease through its role in tau aggregation and amyloid-β production, our findings suggest that DAPK1 may also influence PD-related pathways by phosphorylating parkin at Ser136 and Ser198. This phosphorylation promotes the mitochondrial transport of parkin, enhancing interaction with mitochondria-localised E3 ubiquitin ligase MITOL and consequently leading to the degradation of parkin. Given the neuroprotective role of parkin, its reduction increases the vulnerability of neurons to 6-hydroxydopamine-induced toxicity, potentially contributing to decreased neuronal survival. Together, these findings suggest that DAPK1 functions as a previously unrecognised modulator of parkin and could potentially influence PD-related neurodegenerative processes. This pathway may provide a mechanistic link between mitochondrial dysfunction, α-synuclein pathology and neuronal cell death.

Global imbalances denote the distribution of countries’ current account balances, identically equal to the difference between two forward-looking aggregate variables: national savings and domestic investment. Industrial and trade policies have traditionally not been considered important drivers of aggregate savings or investment, and therefore of current account balances. The former because most industrial policies are small in scope; the latter because permanent tariffs have no intertemporal effect in the textbook model, with an offsetting appreciation of the real exchange rate. The rapidly growing use of both industrial and trade policies in recent years calls for a reassessment. This paper presents a framework to think about the role of both policies. For industrial policy, we make the important distinction between the traditional sector-specific policies via subsidies or other targeted instruments (‘micro IP’) and broader policies (‘macro IP’) that aim to promote industrial developments and competitiveness through the deployment of more aggregate instruments such as financial repression, foreign reserve accumulation, or capital controls. A key finding is that ‘micro IP’ tends to increase external balances if it fails to raise aggregate productivity. By contrast, ‘macro IP’ can, under some conditions, boost the current account, forcing other countries to adjust. Yet, these policies often come at the cost of suppressed domestic consumption and possibly domestic welfare. Our analysis confirms that tariffs are a weak tool to improve current account balances. Finally, traditional macroeconomic drivers—such as fiscal policy, demographics or credit cycles—remain critical drivers of global imbalances, especially for the US and China.

Current debate around artificial intelligence frequently centers on workforce displacement. However, mounting empirical evidence indicates AI primarily functions as augmentation technology—amplifying human capabilities rather than replacing workers. This article synthesizes recent theoretical and empirical findings to examine how AI-driven productivity gains and distributional outcomes fundamentally depend on human capital investments. Drawing on task-based economic models where workers remain essential across all tasks, we demonstrate that aggregate productivity improvements from AI advancement depend critically on two forms of human capital: specialized AI expertise and complementary non-AI skills. The supply of AI-literate workers amplifies productivity gains while attenuating wage inequality effects. Meanwhile, the distribution of complementary skills across the workforce shapes whether AI improvements generate productivity bottlenecks or concentration-driven inequality. For organizational leaders and policymakers, these mechanisms highlight that technological advancement alone proves insufficient—maximizing AI's economic potential requires strategic investments in workforce capability development, ranging from widespread AI fluency programs to targeted cultivation of higher-order judgment skills that remain distinctively human.

This paper quantifies the magnitude and evolution of the efficiency of resource allocation in the Polish enterprise sector over 1993–2023 using a near-census of non-financial firms with 10+ employees. The baseline approach follows Hsieh and Klenow (2009) and shows that hypothetical elimination of within-industry inefficiencies generate sizeable and rising gains in productivity and value added, increasing from close to 40% of value added in the 1990s to roughly 70–75% in the early 2020s. Deteriorating allocation efficiency of resources is robust to changes in the identification scheme and to changes in the underlying model (accounting for wedges related to the use of materials or allowing for non-unit economies of scale and markups that vary across sectors). The efficiency of allocation worsens primarily in services and is due to entry and exit, rather than within-cohort dynamics. Firm-level regressions show that high-productive firms and middle-sizedfirms are disproportionately too small, that non-exporters and more profitable firms exhibit larger wedges, and that subsidies are conductive to better allocation of resources, while the same characteristics often have opposing effects on actual growth of firms’ value added. It implies that market forces and policies do not systematically steer firms toward efficient scales. Deteriorating allocation efficiency can also be a factor explaining the slowdown of productivity growth in the Polish economy since the 2000s.

Driven by persistent scepticism about FDI, many countries adopt protectionist measures – regulatory restrictions being a key tool. This study argues that such restrictions hinder FDI, raising the key question: do FDI regulatory restrictions impede the flow of outward FDI (OFDI)? Building on Melitz-type heterogeneous firm models [Melitz, M. J. (2003). The impact of trade on intra-industry reallocations and aggregate industry productivity. Econometrica, 71(6), 1695–1725; Helpman, E., Melitz, M. J., & Yeaple, S. R. (2004). Export versus FDI with heterogeneous firms. American economic review, 94(1), 300–316], the study examines how regulatory restriction influences Indian OFDI. Using firm-level bilateral data on Indian OFDI and the OECD's FDI regulatory restrictiveness index, the analysis reveals that OFDI does not respond uniformly to restrictions – equity limits, screening requirements, and personnel rules have varying effects. Additionally, a battery of heterogeneity checks is conducted to examine how foreign ownership decisions, sector-specific and cross-sectoral restrictions, and the North–South divide interact with regulatory barriers to influence OFDI. Results show that service-sector restrictions are particularly deterrent. The findings confirm strong sectoral complementarity and a pronounced North–South divide in regulatory sensitivity.

India’s horticulture sector plays a major role in the economy, contributing about one-third of agricultural GVA, with vegetables dominating production and exports due to favorable climatic diversity. Among vegetables, cabbage (Brassica oleracea var. capitata) is an important, nutrient-rich cash crop with significant health benefits and global importance in food security. The present study examined the production and export performance of cabbage in India using secondary data obtained from various governmental departmental repositories and official websites. Statistical techniques like mean, standard deviation, CV (Coefficient of variation), regression and CAGR (Compound Annual Growth Rate) were applied for analysis. Cabbage has contributed a 4.92 % share of the mean aggregate production of Indian vegetables over the last ten years. Although India contributed 8.29% share of global cabbage production with a CAGR of 1.65 % over the last 10 years (2014-15 to 2024-25), ranked 2nd after China, but the share of global cabbage exports was estimated at only 0.10 % over the same period. The major destinations for cabbage export from India were emerging markets like Vietnam and Poland showing positive trends indicating opportunities for growth of export. Hence, India has to explore more export markets without sacrificing domestic demand. The lower growth rate (CAGR) of productivity (0.14 %) of cabbage compared to the area (2.60 %) over two decades (2000-01 to 2024-25) harnesses technological breakthroughs. Two major cabbage producing states, viz. West Bengal and Odisha accounted 34.45 % of national production. In terms of productivity, West Bengal (28.35 t/ha) and Uttar Pradesh (27.98 t/ha) ranked highest in the country. Therefore, priority should be given to enhancing productivity through technological advancement, alongside the development of adequate market infrastructure and improvements in post-harvest management, particularly processing, to strengthen the overall production and export potential of cabbage.

This study proposes a hybrid optimization and control framework for wind farm operation that integrates Quantum-Inspired Multi-Agent Reinforcement Learning (QI-MARL) with Non-dominated Sorting Genetic Algorithm III (NSGA-III) within a federated constraint negotiation (FCN) architecture. The framework is designed to address the coupled challenges of wake-induced aerodynamic interactions, stochastic atmospheric turbulence, and noise-regulation constraints through decentralized, cooperative turbine-level decision-making. The proposed methodology simultaneously optimizes inherently competing objectives (maximization of aggregate power production, reduction of structural fatigue loading, and mitigation of acoustic emissions) while strictly enforcing operational and regulatory constraints. Wake interactions are modeled using the Bastankhah Gaussian wake formulation, and the approach is evaluated on a five-turbine array of utility-scale 5-MW horizontal-axis wind turbines. Performance is assessed over 30 independent stochastic wind realizations, incorporating variability in wind speed and turbulence intensity. Relative to established control and optimization baselines (including greedy axial-induction control, conventional yaw-based wake steering, plain multi-agent reinforcement learning without quantum-inspired encoding, and NSGA-II-based multi-objective optimization) the proposed framework achieves an average 7.8% increase in total wind-farm power output, a 18.6% reduction in maximum blade-root damage-equivalent loads, and a 3.4 dBA decrease in far-field acoustic levels at the designated receptor location. In addition, Pareto-front quality is significantly improved, with a 22% increase in hypervolume and a 27% reduction in inverted generational distance relative to NSGA-III alone. These results demonstrate that the proposed quantum-inspired, federated multi-agent framework provides a robust, scalable, and wake-aware control paradigm capable of delivering fatigue-conscious, noise-compliant, and resilient wind-farm operation under complex and uncertain atmospheric conditions, thereby supporting the deployment of intelligent control strategies in next-generation sustainable wind energy systems.

Mineral processing may decisively influence recycled aggregate (RA) production, yet it is systematically underreported. This critical review screened 338 Scopus-indexed publications (2004–2024) and retained 204 studies after eligibility assessment. Reporting on comminution was limited: ~52% (105 studies) of studies did not explicitly mention crushing, while ~26% (53 studies) identified the crusher type, and only about 1% (two articles) reported operating conditions, which undermines reproducibility and cross-study comparability. RA quality is application-/market-dependent. The literature was classified into cement-based materials (46.1%), pavement applications (44.6%), and fundamental studies without application (9.3%). For cement-based materials, water absorption and compressive strength were the most frequently reported primary and secondary properties, respectively. For pavement applications, particle-size distribution and optimum moisture content predominated. Overall, mineral processing directly governs the primary attributes of recycled aggregates (RAs) and indirectly influences their secondary performance outcomes. The main gap identified in the literature is the lack of clear recommendations for processing procedures, which limits the reproducibility and comparability of reported results. To address this limitation, this article proposes a mineral-processing framework intended to standardize both RA processing and reporting practices, thereby improving crosslink study comparability, experimental reproducibility, and evidence-based specification according to end-use requirements.

Evaluation of the stability of an IgG2 in citrate buffer, upon exposure to near-UV and visible light and in the presence of relevant trace amounts of iron. We monitored the oxidation of amino acid residues, the formation of protein aggregates, fragments and charge variants by SDS-PAGE and two-dimensional gel electrophoresis (2-DIGE). Degradation products in individual gel spots were isolated and characterized by HPLC-MS/MS. We detected an increase in the formation of DOPA, protein aggregates, and fragments with increasing concentrations of added Fe3+. Light exposure resulted in the generation of more acidic products, evident from a shift of the protein pI detected by 2-DIGE. Oxidation products of Tyr, His, Cys, and Trp were detected. The addition of EDTA or DTPA showed a significant protection against degradation. IgG2 was significantly modified by the photo-Fenton reaction in citrate buffer. We demonstrated the oxidation of Tyr, His, Cys, and Trp residues, the formation of aggregation and degradation products, as well as the formation of different charge variants.

Aggregate production planning (APP) requires balancing workforce and operational decisions over a medium-term horizon. This study formulates and applies a two-stage stochastic mixed integer linear program (MILP) for APP with the primary objective of evaluating strategic workforce planning decisions under demand uncertainty. Workforce decisions are modeled as here-and-now commitments, while operational decisions are optimized as recourse actions in response to realized demand. The framework is demonstrated in an illustrative furniture manufacturing setting over a 12-month horizon with seasonally varying cost parameters. Demand scenarios are generated by combining Holt–Winters point forecasts with forecast-error scenarios obtained through a rolling-origin procedure and a moment-matching approach, yielding demand trajectories that reflect the statistical properties and temporal dependence of forecast uncertainty. Using these scenarios, the model quantifies cost–service trade-offs under alternative backorder penalty severities. To assess the robustness of the resulting workforce plans, this study conducts an out-of-sample evaluation based on observed demand from a holdout year and a wait-and-see benchmark, a validation perspective that has received limited attention in the APP literature. The out-of-sample results indicate that the stochastic model produces feasible and cost-effective workforce decisions that remain near-optimal under observed demand. Overall, the proposed framework serves as an effective decision-support tool for APP under demand uncertainty, supporting the evaluation of workforce and operational decisions within a unified stochastic framework.