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The correlations and deviations between market prices, production prices and values are critical indexes for testing the labor theory of value. However, there is not yet a universally accepted method of solving for the production price vector, and given the complexity and volatility of economic dynamics, production prices based on the assumption of economic equilibrium cannot be accurate measuring. This paper attempts to propose a new approach to test the labor theory of value.

This paper proposes a different approach. From the perspective of disequilibrium price, the paper deduces that the range of the relative prices of commodities is determined by the relative value, the rate of surplus-value and the technical structure of production inputs, with relative price fluctuating within the value range specified by the labor theory of value under market competition influences.

With empirical research results based on China’s economic data, this paper not only affirms the scientific and practical explanatory power of the labor theory of value in a more general sense but also uncovers how surplus value is distributed across sectors, which can be used to analyze market competition and technical relations and their impacts on industrial structure and distribution.

The disequilibrium analytical framework provides a new perspective for the empirical study of labor theory of value. Moreover, it evolves the labor theory of value into a robust empirical framework, breaking through the theoretical path of the traditional labor theory of value that is mostly limited to the normative discussion of exploitation.

This study investigates the influence of capital controls on financial market structure in Sub-Saharan Africa (SSA). This is especially relevant as the former restrictions are relatively common on the sub-continent. At the same time, the sub-region’s financial markets are highly bank-based and focused on the short term, with stock markets being illiquid and stunted.

To achieve its research objectives, the study posits an original model and uses comparative statics to analyze the relation between the aforestated phenomena in a representative SSA economy. Key hypothesized conclusions derived therefrom are tested using panel econometrics.

The comparative static analysis illustrates that capital controls favor banks, making them monopolistic and inefficient. This is confirmed by the empirical investigation, as the said market restriction skews financial market structure towards a bank-dominated system.

The study limits itself to capital controls and their effects on financial market structure. It does not particularly investigate the influence of different types of these restrictions. Specifically, it dichotomizes the influence of the examined controls on bank and stock markets.

The dissimilar influence of capital controls on banks relative to stock markets is critical for decision and policymakers. This paper highlights that capital controls may have unintended adverse effects on domestic financial markets. Also, they may not be the most appropriate policy to deepen markets and enhance domestic resource retention. There is, consequently, a need to determine fitting policies that attract rather than repel financial flows. Furthermore, capital controls may engender rather than address macroeconomic misalignment.

As a social imperative, it is necessary to analyze SSA’s framework of capital restrictions to better understand how they distort market incentives and mechanisms. This would help identify adverse effects that retard social development.

This study extends existing literature by developing a novel analytical framework incorporating key characteristics of SSA economies. This helps to better understand the nature of the capital controls–financial market structure relation in imperfect market conditions.

This study aims to provide a new perspective on the factors determining a country’s tourism performance, understand the interrelationships among these factors and explore their implications for the future of tourism in high-income countries.

The study employs a fuzzy-set qualitative comparative analysis (fsQCA) using five variables from the World Economic Forum’s Travel and Tourism Development Index (TTDI). The focus is on identifying seven configurations of antecedents of Travel and Tourism Industry Gross Domestic Product (T&T Industry GDP).

The study identifies seven configurations of antecedents influencing T&T Industry GDP, revealing how these factors operate in different scenarios, specifically in countries with high and low T&T GDP. These configurations offer insights into potential future pathways for tourism development.

The study implies that tourism is a complex phenomenon influenced by multiple interacting factors. It provides a framework for understanding how different combinations of factors can lead to high or low tourism performance, offering valuable insights for anticipating and shaping the future of tourism.

This study adds value by providing a more nuanced understanding of the tourism industry, challenging the notion of singular effects of variables and highlighting the importance of analyzing multiple, interacting factors in understanding and predicting tourism performance. It contributes to the field of futures studies by offering a tool for anticipating potential future scenarios and their impact on the tourism industry.

This paper presents an original approach for learning models, partially known, of particular interest when performing source identification or structural health monitoring. The proposed procedures employ some amount of knowledge on the system under scrutiny as well as a limited amount of data efficiently assimilated.

Two different formulations are explored. The first, based on the use of informed neural networks, leverages data collected at specific locations and times to determine the unknown source term of a parabolic partial differential equation. The second procedure, more challenging, involves learning the unknown model from a single measured field history, enabling the localization of a region where material properties differ.

Both procedures assume some kind of sparsity, either in the source distribution or in the region where physical properties differ. This paper proposed two different neural approaches able to learn models in order to perform efficient inverse analyses.

Two original methodologies are explored to identify hidden property that can be recovered with the right usage of data. Both methodologies are based on neural network architecture.

We aim to elucidate the relationship between fixed-term employment and firm productivity by examining workers’ skills and considering how firm-level conversion rates influence this relationship.

We use longitudinal employer-employee data between 2011 and 2017 in the Netherlands to estimate a nonlinear regression derived from a production function proposed by Addessi (2014) and Castellani et al. (2020).

The contribution of fixed-term contracts to firm-level productivity is less than that of permanent contracts. However, this contribution is greater when firms exhibit a high conversion rate from fixed-term to permanent positions. The effect of the conversion rate is more substantial for high-skilled fixed-term workers than for low-skilled ones.

Our results suggest the extent to which firms benefit from fixed-term contracts when these are used for screening high-skilled workers for permanent employment.

The primary purpose of this study is to explore the effects of demographic transition toward aging populations on the performance of stock market indices across various economic developments. The research aims to provide valuable insights into the life-cycle hypothesis on savings patterns, investment behavior and the potential reverberations on global financial markets.

The study adopts a comprehensive global perspective, scrutinizing the effects of aging populations on stock market indices across developed, developing and transitional economies through the panel data analysis. Using annual data spanning the period from 1991 to 2020, encompassing a sample of 10 countries from each economic development level, the study employs the panel autoregressive distributed lag (ARDL) model with fixed effect estimation.

The findings unveil a statistically significant positive impact of the elderly population proportion on global stock market indices. However, the magnitude and contours of this impact exhibit considerable heterogeneity across different country groups. Specifically, the study finds that while the aging population significantly influences stock market performance in developed nations, its effect is overshadowed by other economic factors, such as consumer price indices and interest rates, in developing countries and economies in transition.

The originality and value of this study lie in its comprehensive global perspective, which encompasses a diverse array of economies at varying developmental stages. The research contributes to an understanding of the effects of demographic transitions on stock market performance on a global scale. The insights derived from this study hold significant implications for policymakers, financial institutions and investors seeking to navigate the challenges and opportunities posed by aging societies in an increasingly interconnected global economy. Additionally, the findings highlight the need for specific strategies and policies that account for the unique economic characteristics and developmental stages of different nations.

The deployment of a field hospital can play an important role in the response to an emergency. This paper is concerned with the management of emergency staff to a field hospital from a roster of volunteers with different characteristics. This paper aims to propose a mathematical optimisation model that selects the necessary profiles of the roster according to several criteria and provides travel planning taking into account the total cost of the operation.

This study uses a multi-criteria optimisation model to take into account the preferences of the three main stakeholders involved in the deployment of the field hospital: the cooperation organisation, the staff and the end users. The model considers the possibility of using commercial or chartered flights, allows staff to indicate their preferred availability, considers the grading of volunteers according to their skills and training and provides a final flight schedule for all the medical personnel needed to operate the field hospital. Compromise programming is used to provide a Pareto optimal solution, which is compared with solutions provided by Goal programming.

The model has been validated using data from the operation in a case study of the deployment of the Spanish START hospital in Turkey 2023, demonstrating the practical utility of the model in similar operations.

The study innovates by considering a multi-criteria model that takes into account the main actors involved in the response – cooperation organisation, staff and end users – in an integrated way and proposes new measures of efficiency.

The rapid increase in importance of the remanufacturing operation in the present scenario is just because of its ability to retrieve the functional value of the End-of-Use or End-of-Life products which is as good as the original product. However, customers are still concerned about the reliability of the remanufactured product which is considered as one of the major problems in the area of remanufacturing. The purpose of this paper is to study and analyse the behavioural pattern of the mixture failure rate of a remanufactured product.

In order to analyse the behavioural pattern of the mixture failure rate, different proportions of new and remanufactured products are mixed. In this paper, a two-parameter Weibull distribution is used to observe the mixture failure rate characteristics. Also, the mixture failure rate of the remanufactured product is evaluated under two conditions, that is when the shape parameter of new and remanufactured components is the same and when the shape parameter values are different.

From the analysis, it is observed that the mixture failure rate is always decreasing in nature when the shape parameter values are same. In that case, the value of the mixture failure rate depends only on the proportion of the new components. When the shape parameter values are different, the mixture failure rate characteristics depend upon the shape parameter value of the remanufactured product.

The results of the research can be applied to any remanufactured automotive product. This study also shows the behavioural characteristics of the mixture failure rate of a remanufactured product at different mixture proportions.

This study develops a novel method for mitigating credit risk through the use of structured derivatives, focusing in particular on the use of European put options as a strategic hedging tool. Inspired by the work of Merton (1974), our approach introduces the concept of default triggered by the stock price S_T breaching a predefined barrier B. By establishing a distributional equivalence between an existing default model and P(ST<B) for a given time T, we demonstrate the potential for reducing the necessary capital allocation for a projected loss X(T) by partially hedging with a European put option. We formulate and solve an optimization problem w.r.t. a specific risk measure to determine the optimal strike price for the option, and our numerical analysis confirms a reduction in the Solvency Capital Requirement (SCR) in markets with and without jumps. Our findings provide (insurance) companies with a pragmatic approach to mitigating losses while maintaining their current risk management framework.

ESG issues are gaining increasing attention from investors, but the environmental, social and governance (ESG) rating disagreement caused by different standards of rating agencies misleads investors' investment decisions. This can lead to an increased risk of stock price crashes, causing turbulence in the financial markets and reducing investors' confidence. The paper investigates whether ESG rating disagreement of the current period increases stock price crash risk and the mechanism to mitigate this impact.

With the sample of the listed companies of Shanghai and Shenzhen Stock Exchanges from 2010 to 2022 this paper examines the impact of ESG rating disagreement itself on stock price crash risk. Moreover, this paper examines the mechanisms by analyzing the moderating effect of distraction of investors; digital economy and corporate intelligence maturity.

This paper finds that ESG rating disagreement itself would amplify the stock price crash risk. When exploring the moderating effect of institutional investors' distraction, digital economic development level and corporate intelligence, the paper found that they would mitigate the impact of ESG rating disagreement on stock price crash risk. The relationship between ESG rating disagreement and stock price crash risk is more pronounced in the context of heavily-polluted, state-owned enterprises (SOEs) and enterprises with star analysts.

Currently, few articles discuss ESG rating disagreement, especially the impact of current ESG rating disagreement on stock price crash risk. This paper focuses on this topic and provides strategies to mitigate the impact of current ESG rating divergence on stock price crash risk.