Production and Sustainability in Türkiye's Fisheries Sector: The Role of Economic Variables

PurposeDairy farms, along with livestock and specialty crop farms, face a tight labor supply and increasing labor costs. To overcome the challenging labor market, farm managers can increase labor-use efficiency through both human resource and capital investments. However, little is known about the relationship between such investments and farm profitability. The purpose of this paper is to examine the relationship between dairy farm financial performance and labor-use efficiency, as measured by labor productivity (milk sold per worker equivalent); labor costs (hired labor cost per unit of milk sold and hired labor cost per worker); and investment in labor-saving equipment.Design/methodology/approachCluster analysis is applied to partition dairy farms into three performance categories (high/middle/low), based on farms’ rate of return on equity, asset turnover ratios and net dairy income per hundredweight of milk. Next, the annual financial rank is fitted into both random- and farm-level fixed-effects ordered logit and linear models to estimate the relationship between dairy farms’ financial performance and labor-use efficiency. This study also investigates the implications of using a single financial indicator as a measure of financial performance, which is the dominant approach in literature.FindingsThe study finds that greater labor productivity and cost efficiency (as measured by hired labor cost per unit of milk sold) are associated with better farm financial performance. No statistically significant relationship is found between farm financial performance and both hired labor cost per worker and advance milking systems (a proxy of capital investment in labor-saving technology). Future studies would benefit from better measurements of labor-saving technology. This study also demonstrates inconsistency in regression results when individual financial variables are used as a measure of financial performance. The greater labor-use efficiency on high-performing farms may be a combination of hiring more-skilled workers and managerial strategies of reducing unnecessary labor activities. The results emphasize the importance of managerial strategies that improve overall labor-use efficiency, instead of simply minimizing total labor expenses or labor cost per worker.Originality/valueThis study examines the importance of labor productivity and labor cost efficiency for dairy farm management. It also develops a novel approach which brings a more comprehensive financial performance evaluation into regression models. Furthermore, this study explicitly demonstrates the potential for inconsistent results when using individual financial variable as a measure of financial performance, which is the dominant measurement of financial performance in farm management studies.

This study analyzes the relationship between production, labor force and capital investments in the fisheries sector in the Marmara, Aegean, Mediterranean, Western Black Sea and Eastern Black Sea regions of Türkiye and examines their effects on sectoral growth and employment. In the study, total fish production, number of employees in the sector and capital investments (number of vessels) variables are used in the panel data analysis covering the period 2006-2023. According to the results of the analysis, the labor force has a positive and significant effect on production, but the effect of capital on production is negative. This shows that capital investments in the Turkish fisheries sector have not been able to provide the expected productivity growth. Moreover, capital investments are found to support employment by increasing labor demand. The long-run cointegration results reveal a strong equilibrium relationship between the variables. This study contributes to the existing research in the literature and provides strategic recommendations for the development of sustainable growth and productivity policies in Türkiye's fisheries sector. In particular, supporting aquaculture activities, modernizing capital investments and taking regional differences into account are critical for the long-term sustainability of the sector.

This paper studies the mechanism of how China?s state-owned enterprise (SOEs) reform can influences economic growth, and distinguishes the capital efficiency between state-owned and private enterprises. The results show that: 1) the capital allocation efficiency among state-owned enterprises is lower than private enterprise due to an insufficiently released productivity of state-owned enterprises; 2) although with a higher capital allocation efficiency, the improvement of technology progress of private enterprises at a much slower pace compared to its rapidly increasing share in China?s economy. In case of poor allocation with private sector, blindly reforming ownership of state-owned enterprises cannot effectively alleviate the problem of efficiency losses. State-owned enterprise reform can boost economic growth by increasing capital marginal output, improving capital dynamic allocation efficiency, promoting TFP growth and exerting external spillovers on other firms. At present, China is exploring the endogenous power of economic growth, improving the market institutions and promoting the state-owned enterprises reform with positive and steady pace. By properly re-allocation SOEs into the private sector, which has significant influence on improving economic efficiency and promoting sustained economic growth.

In this paper the assignment of cross-trained and temporary workers to tasks on an assembly line is investigated. Cross-trained workers are skilled to perform more than one task on the assembly line in the production process. Temporary workers are viewed as either trained or untrained and may be hired or laid off as required. The solution procedure may be divided into three parts. During the first part a model is formulated to determine an optimal assignment of the workers to the production tasks. During the second part the model is extended to determine the effect of the assignment of both trained and untrained temporary workers to the tasks on the assembly line. During the final part of the model an optimal sequence of tasks in the assembly line is determined that minimises the resulting execution times of these tasks. During the first part the objective is to maximise the total production utility. This is achieved by implementing a two-phase model. The first phase maximises the utility of pro-duction by minimising labour shortage in the assembly line. During the second phase the improvement of the workers’ levels of skill is maximised while the effect of the learning and forgetting of skills is taken into consideration. A learn-forget-curve model (LFCM) is implemented to model the effect of this human characteristic on the master model. This approach ensures that the advantageous cross-trained nature of the workers is maintained and optimized, without a large deviation from the solution determined by the first phase. The objective of the second part is to minimise the labour cost of production by determin-ing the best type of workers for a certain task as well as the manner in which they should be hired or laid off. A worker is classified as either permanently or temporarily employed. Tem-porarily employed workers are further classified as either untrained or cross-trained workers. The assignment of workers to tasks on the assembly line is achieved by means of a Master Production Scheduling (MPS) model. The MPS has as its objective the minimisation of the total labour cost of performing all the tasks. The labour cost is defined as the sum of the temporary workers’ daily wages, the overtime cost of permanent workers, the overtime cost of temporary workers and the cost of employing and laying off temporary workers. Finally, during the third part an optimal sequence of tasks is determined in the production process in order to minimise the total production time. This is achieved by means of a two-phase dynamic assembly line balancing model, which is adjusted to incorporate the critical path method. During the first phase, an optimal task sequence is determined, while during the second phase, an optimal assignment of tasks to workstations and the timing thereof, is determined. The practical applicability of the model is demonstrated by means of a real life case study. The production of various styles of shoes in a leatherworks factory is considered. The production of each style requires a different set of tasks and each task requires a different level of skill. The factory under consideration employs both cross-trained and temporary workers and data sets were obtained empirically by observation, interviews and questionnaires. Upon execution of the first phase of the assignment model, an optimal utility is found and the second phase is able to maximise the increase of the workers’ skill level without deviation from this optimum. Upon execution of the employment model, it is found that labour costs are minimized by increasing the use of temporary workers and by assigning the maximum allowable number of overtime hours to them. Upon application of the scheduling model, an improved time is obtained compared to the standard execution time of each style. The results obtained from the case study indicate that the application of the model presented in this paper shows a substantial improvement in production, while reducing the cost of labour as well as improving the overall level of workers’ skills. A multi-objective model is thus developed which successfully maximises production utility, maximises skill development of workers, minimises labour costs and the occurrence of idle workers as well as minimises total execution time.

With the development of the digital economy and the guidance of environmental regulation, more and more companies are embarking on a green transition. Capital allocation is an important guarantee for the long-term development of enterprises, and efficient capital allocation can better help enterprises carry out green transformation. Therefore, this paper makes recommendations for the green transformation of companies by examining the relationship between environmental regulations, digital economy and capital allocation efficiencies. The listed enterprises of intelligent manufacturing in China are selected from 2015 to 2021 as a sample. The results show that environmental regulations inhibit capital allocation efficiencies, and the digital economy promotes them. For high-tech-capital enterprises, capital allocation efficiencies are more subject to the synergistic effect of environmental regulations and the digital economy. In contrast, environmental regulations exert a more inhibitory effect on capital allocation efficiencies of high-knowledge-capital enterprises. The influence of environmental regulations on capital allocation efficiencies is affected by the dual-threshold effect of the digital economy. In contrast, the single-threshold effect of environmental regulations influences that of the digital economy on capital allocation efficiencies. With the digital economy’s development, environmental regulations’ influence on enterprises’ capital allocation efficiencies shifts from an inhibitory effect to a promoting effect. The digital economy plays a critical role in improving capital allocation efficiencies and the inhibitory effects of environmental regulations. The government should consider the threshold effects of the digital economy and environmental regulations when developing strategies for greening business transformation. Furthermore, specific policies made for intelligent manufacturing enterprises with high-tech-capital factors are necessary to effectively counteract the inhibitory effects of environmental regulations on their allocation efficiencies.

Cost estimation, optimization and competitiveness of different composite floor systems—Part 1: Self-manufacturing cost estimation of composite and steel structures

Rural revitalization is intricately tied to national prosperity, with listed companies in agriculture, forestry, animal husbandry, and fisheries playing pivotal roles in driving this transformative agenda. However, these sectors face critical challenges, notably resource constraints and environmental pressures, which underscore the necessity of evaluating capital utilization efficiency. This study employs a combined three-stage DEA model and Malmquist index to analyze capital utilization efficiency across 85 listed companies within China’s agriculture, forestry, animal husbandry, and fishery industries over the period 2018–2022. The primary objectives are to quantify capital utilization efficiency, identify pathways for industry optimization, advance ecological agricultural sustainability, and offer valuable insights to investors and policymakers. Key findings include: (1) When grouped by sub-sector and adjusted to account for external environmental factors and random disturbances, the initial comprehensive capital utilization efficiency in agricultural companies was found to be significantly overestimated. Subsequent third-stage adjustments revealed decreases in average comprehensive efficiency, technical efficiency, and scale efficiency by 29.79%, 3.03%, and 27.37%, respectively, largely driven by a marked decline in scale efficiency, ultimately diminishing overall efficiency. (2) The capital utilization efficiency in agricultural trading firms remains suboptimal, with scale efficiency posing a critical limitation. High dependency on government subsidies and excess specialized personnel further constrain efficiency improvements. (3) Dynamic analysis using the DEA-Malmquist model indicates low total factor productivity in the agricultural sub-sector, primarily due to inefficiencies in capital management and suboptimal scale allocation. These findings underscore the need for targeted strategies to enhance resource allocation and management, bolster talent development and financial management frameworks, and drive technological research and development, innovation, and efficient capital allocation across the agriculture, forestry, animal husbandry, and fishery sectors.

ECONOMETRIC MODELING IN FORMATION OF OPTIMAL PRICE FOR IMPLEMENTATION OF AGRICULTURAL PRODUCTS

PurposeThe present study aims to investigate the impact of internal control effectiveness on supply chain management efficiency (SCME) and capital allocation efficiency for companies listed in the Tehran Stock Exchange (TSE). In addition, it investigates the mediating role of supply chain management efficiency in the relationship between internal controls and capital allocation efficiency.Design/methodology/approachThe data about 191 companies in 2014–2022 were examined. The sales per inventory ratio was used to calculate SCME. The present study also applied the Generalized Method of Moments (GMM) for endogeneity concerns.FindingsThe results showed that internal control effectiveness has a significant positive effect on SCME. Moreover, internal control effectiveness and SCME significantly positively affect capital allocation efficiency. SCME has a mediating role in the relationship between internal control effectiveness and capital allocation efficiency. These findings remained robust even after several robustness tests. In addition, this study tested the results' robustness by dividing data into the pre-COVID-19 and post-COVID-19 years. The previous results were also confirmed according to the robustness test of COVID-19.Originality/valueChallenges in the supply chain often hinder capital allocation efficiency. In addition, enterprises should try to establish strong internal controls to ensure SCME. Therefore, the relationship between internal control effectiveness, SCME and capital allocation efficiency is complex and underscores the importance of robust internal controls in optimizing resource allocation within organizations. Interestingly, this topic has not been extensively researched in accounting and business research, and there is a lack of empirical evidence on these effects. Consequently, this study aims to fill the gap and identify potential opportunities for new research directions.

The pharmacoeconomics of neuromuscular blocking drugs.

This study aims to determine the profitability of domestic chicken business in Jember Regency, to find out the factors that influence the profitability of chicken farming in Jember Regency, determine the development strategy domestic chicken in Jember Regency. This researche used survey method. The research was conducted in 3 (three) Subdistricts namely Bangsalsari, Umbulsari, and Gumukmas Subdistricts, in Jember Regency. The data used in this researche include primary data and secondary data. The data is done qualitatively and quantitatively. The analysis used is profit analysis, multiple regression analysis and SWOT analysis. This researche concludes that (1) Wild Chicken Jowo Super (Joper) Business in Jember Regency is profitable with an average revenue gain of Rp. 9,749,038 per production process, and the total production costs are Rp. 9,082,346 per production process. The average business profit rate of Buras Joper Chicken in Kabupaten Jember is IDR 666,693 / production process / 404 heads. (2) Business Benefits of Burst Joper Chicken are influenced by 5 independent variables namely price (X1), production (X2), cost of production facilities (X3), labor costs (X4), and other costs (X5) with a positive relationship and significant at the real level of five percent. This is indicated by the results of the calculated F test greater than F table at the real level of 99%. Simultaneously the factors have a significant effect on profits including price, production, production facilities, labor costs and other costs. But partially significant factors include the price of output and production, while the ones that are not significant include the costs of means of production, labor costs, and other costs. (3) Based on the results of the SWOT Analysis, the Buras Chicken Business in Jember Regency is in position / quadrant I (Growth / Aggressive / progressive). This position signifies a strong and potential business, meaning that the business is carried out in prime condition. So that it is truly possible to continue to expand, increase growth and achieve maximum progress.

How to improve the efficiency of global energy interconnection capital allocation? Analysis from the perspective of spatial heterogeneity and driving factors

In this study, we explore the relationship between financial structure and the efficiency of capital allocation to determine a scientific and effective approach to transform and upgrade China’s financial sector. Using China’s provincial data from 2005 to 2018, we detected the threshold effect of financial structure on capital allocation efficiency. We found that financial structure, when using its three agency indicators (credit scale, insurance scale, and stock market size) as threshold variables, and capital allocation efficiency show an inverted ‘U’ relationship. Moreover, the relationship between financial efficiency, insurance scale, and capital allocation efficiency changes from negative to positive, and the relationship between foreign trade scale and capital allocation efficiency shows a significant negative correlation. This research improves the understanding of the relationship between China’s financial structure and the efficiency of capital allocation. Our results show that by optimizing the financial structure through expanding the stock market and the scale of bank credit and appropriately expanding the scale of insurance, more loanable funds will flow into the production sector, and providing certain guarantees can greatly promote improved capital allocation efficiency.

Improving the efficiency of corporate capital allocation is the microfoundation for promoting high-quality economic development. Starting from the Cobb-Douglas production function, this paper constructs a theoretical model of the impact of finance technology on capital allocation efficiency. Based on this, an empirical test of the impact and mechanism of financial technology on capital allocation efficiency is conducted using data from Chinese A-share listed companies from 2008 to 2023. The research indicates that: (I) The improvement of financial technology levels could significantly reduce the deviation of capital allocation from the ideal state and improve capital allocation efficiency; (II) Financial technology could enhance corporate governance levels, thereby improving the efficiency of corporate capital allocation; (III) The enhancement of financial technology levels could restrain excessive investment and underinvestment behaviors in enterprises, promoting the improvement of capital allocation efficiency; (IV) The higher the level of financial technology is, the more significant effect on improving capital allocation efficiency would be.

This paper calculates the yearly capital allocation efficiency of twenty eight manufacturing industries in China from 1999 to 2005 and analyzes influential factors of capital allocation efficiency with panel data model and sectional data model. Econometrical results show that the influential factors include FDI, foreign capital's monopoly degree on high-tech and capital-intensive industries, labor population's reflecting coefficient on industrial growth and capital return rate. The higher ratio of foreign fixed equity is, the higher allocation efficiency is; the higher foreign capital's monopoly degree in high tech and capital-intensive industries is, the lower allocation efficiency is; the higher labor population's reflecting coefficient on industrial growth is, the higher capital allocation efficiency is; the higher capital return rate is, the lower allocation efficiency is. Accordingly, when making the most use of foreign capital's allocation effect its monopoly on high-tech and capital-intensive industries should be avoided. Meanwhile the government should distribute investing scale, total credit quota and information of supply and demand in the market to make the enterprises and financial structure know economic condition of now and future to avoid negative influence that over affect manufacturing industry's capital allocation efficiency when industrial structure upgrades.