Piercing the corporate veil system and creditors protection: Based on the investigation of debt paying ability--Empirical evidence from the 2005 Company Law amendment.

This paper uses the difference-in-differences model to research how the "piercing the corporate veil" system marked by the 2005 Company Law amendment affects the level of corporate creditor protection. The research results show that private enterprises and local state-owned enterprises are sensitive and significant to this legal amendment. In contrast, local state-owned enterprises are more sensitive and have a stronger motivation to protect the interests of creditors. The motivation of companies with weaker profitability for creditor protection lasts not only for the year of law revision but also extends to the year of implementation. With the law's implementation, the growth effect of creditor protection for local state-owned enterprises has become more significant. Further analysis shows that the main findings of this article are more significant in companies with larger debt scales, companies with a higher year-on-year growth rate of operating income, companies with controlling shareholders, and companies with higher stock market capitalization. From an empirical research view, this paper explains the economic effect and mechanism of the whole corporate personality under the complete system and adds economic evidence for how the law acts on the capital market.

Economic and financial viability of a pig farm in central semi-tropical Mexico: 2022-2026 prospective.

To estimate the economic and financial viability of a pig farm in central sub-tropical Mexico within a 5-year planning horizon, a Monte Carlo simulation model was utilized. Net returns were projected using simulated values for the distribution of input and product processes, establishing 2021 as base scenario. A stochastic modelling approach was employed to determine the economic and financial outlook. The findings reveal a panorama of economic and financial viability. Net income increased by 555%, return on assets rose from 3.36% in 2022 to 11.34% in 2026, and the probability of decapitalization dropped from 58% to 13%, respectively in the aforesaid periods. Similarly, the probability of obtaining negative net income decreased from 40% in 2022 to 18% in 2026. The technological, productive, and economic management of the production unit allowed for a favorable scenario within the planning horizon. There is a growing interest in predicting the economic sectors worth investing in and supporting, considering their economic and development performance. This research offers both methodological and scientific evidence to demonstrate the feasibility of establishing a planning schedule and validating the suitability of the pork sector for public investment and support.

Investigation on the strategy of business administration level of enterprises in the new economic environment.

In the face of the new economic environment, enterprises must continuously enhance their capabilities to achieve long-term development. In the current market scenario, business management relies on economic principles and legal accounting. Considering the current market situation, the article analyzed enterprises system reform and production planning, proposing corresponding countermeasures. Therefore, in order to achieve rapid development, it was necessary to strengthen the management of enterprises. In this paper, the current problems faced by enterprises, solutions and the significance of enterprises needed to improve their management level were explained, and the situation of enterprises was analyzed through the enterprise strategic management model. Comparing with the traditional management model in terms of the complexity of enterprise management processes, efficiency, management level score, and quarterly profit,findings reveal that the management model in the new economic environment has reduced the complexity of the enterprise process by 0.17 points. The management efficiency has increased by 0.15 points, the management score has increased by 14 points, and the quarterly profit of the company has increased by 30,000 yuan. Furthermore, it is elucidated that, in the new economy, enhancing the management level is essential for enabling enterprises to attain long-term development.

Impact of an innovative bundled payment to TB health care providers in China: an economic simulation analysis.

BACKGROUND: Tuberculosis is the second most deadly infectious disease after COVID-19 and the 13th leading cause of death worldwide. Among the 30 countries with a high burden of TB, China ranks third in the estimated number of TB cases. China is in the top four of 75 countries with a deficit in funding for TB strategic plans. To reduce costs and improve the effectiveness of TB treatment in China, the NHSA developed an innovative BP method. This study aimed to simulate the effects of this payment approach on different stakeholders, reduce the economic burden on TB patients, improve the quality of medical services, facilitate policy optimization, and offer a model for health care payment reforms that can be referenced by other regions throughout the world. METHODS: We developed a simulation model based on a decision tree analysis to project the expected effects of the payment method on the potential financial impacts on different stakeholders. Our analysis mainly focused on comparing changes in health care costs before and after receiving BPs for TB patients with Medicare in the pilot areas. The data that were used for the analysis included the TB service claim records for 2019-2021 from the health insurance agency, TB prevalence data from the local Centre for Disease Control, and health care facilities' revenue and expenditure data from the Statistic Yearbook. A Monte Carlo randomized simulation model was used to estimate the results. RESULTS: After adopting the innovative BP method, for each TB patient per year, the total annual expenditure was estimated to decrease from $2,523.28 to $2,088.89, which is a reduction of $434.39 (17.22%). The TB patient out-of-pocket expenditure was expected to decrease from $1,249.02 to $1,034.00, which is a reduction of $215.02 (17.22%). The health care provider's revenue decreased from $2,523.28 to $2,308.26, but the health care provider/institution's revenue-expenditure ratio increased from -6.09% to 9.50%. CONCLUSIONS: This study highlights the potential of BPs to improve medical outcomes and control the costs associated with TB treatment. It demonstrates its feasibility and advantages in enhancing the coordination and sustainability of medical services, thus offering valuable insights for global health care payment reform.

Systematic evaluation of integration between China's digital economy and sports industry: Two-stage grey relational analysis and vector autoregressive model.

OBJECTIVES: The development of the digital economy constitutes a key component of China's endeavors to advance towards "Digital China." The sports industry functions as a new catalyst for high-quality economic growth. This study systematically evaluated the integration between these two sectors. METHODS: First, we conducted two levels of grey relational analysis to assess their integration between 2016 and 2021. Second, we conducted a VAR analysis to determine whether their integration between 2009 and 2021 represents a causal relationship. RESULTS: At the macro level, the grey relational analysis reveals that the sports industry (grade = 0.770) ranked second among China's eight key economic sectors in terms of digital economy integration. At the meso level, a wide variation (ranging from 0.606 to 0.789) existed in the grade of integration between the digital economy and the sub-sectors of the sports industry. According to the VAR model, the digital economy does not Granger cause (p = 0.344) the growth of the sports industry. CONCLUSIONS: This study yielded two added values to the existing literature: First, there exists a sectoral imbalance in the digitization process; second, the explosive growth of the sports industry was not primarily caused by the digital economy. Accordingly, the "sports + digital" complex is still in the first wave of technological integration. We propose three policy recommendations, namely, sectoral synergistic development, overtaking via esports IP, and new economy and new regulation. Collectively, these findings provide updated insights for the digital transformation towards "building a leading sports nation" and "Digital China."

How do e-commerce platforms and retailers implement discount pricing policies under consumers are strategic?

In the era of the rapid development of e-commerce, many retailers choose to launch promotional activities to become consumers' first choice for shopping. Since price discounts can greatly attract consumers, the e-commerce platforms have also begun to implement discount pricing. It is urgent for e-commerce platforms and retailers to formulate reasonable discount strategies to achieve sustainable business. In this paper, we construct a dynamic game model for implementing discount pricing on an e-commerce platform and two retailers, we study the market equilibrium between the two retailers and the e-commerce platform under various scenarios that considering consumers' strategic waiting behavior and competition between the two retailers, we further discuss the effectiveness of retailer discount pricing and the double discount pricing of the platform and retailers. We show that the optimal pricing decreases as the difference in product quality narrows under both pricing strategies. Low-quality retailers implementing a double discount pricing strategy are in relatively higher demand only when the difference in product quality is small. High-quality retailers implementing the retailer discount pricing strategy are in relatively higher demand only when the product quality difference is large. Double discount pricing is desirable for both e-commerce platforms and retailers and can be used to effectively achieve Pareto improvement in the market by increasing their expected profit. Our results emphasize the role of product quality and the value of the double discount pricing strategy. The double discount pricing strategy weakens the profit advantage that retailers and platforms gain from it as the rebate intensity and rebate redemption rates increase.

Investigating the relationship of government revenue and expenditure on economic growth using a generalized method of moments: Does state-level panel ensure sustainable growth?

The current research project investigates the correlation between economic growth, government spending, and public revenue in seventeen Indian states spanning the years 1990 to 2020. An analysis of the relationship between key fiscal policy variables and economic growth was conducted utilising a panel data approach, the Generalised Method of Moments (GMM), and fully modified Ordinary Least Squares (FMOLS & DOLS) estimation. In our investigation, we assessed the impacts of non-tax revenue, development plan expenditure, tax revenue, and development non-plan expenditure on (i) the net state domestic product (NSDP) and (ii) the NSDP per capita. The findings indicate that the selected fiscal variables are significantly related. The results indicate that expeditious expansion of the fiscal sector is obligatory to stimulate economic growth in India and advance the actual development of the economies of these states.

Exploring the nexus between FDI inflows and economic growth: A sectoral level analysis.

This study investigates the relationship between Foreign Direct Investment (FDI) inflows and economic growth at sectoral levels in Bangladesh, employing a panel study framework. Utilizing sectoral-level panel data spanning six sectors from 2007-08 to 2018-19, the analysis is conducted using Panel Vector Error Correction Model (Panel VECM). Results from panel unit root tests confirm that all variables are integrated of order one I (1), indicating stationarity. The Pedroni panel co-integration test further supports the presence of co-integration among the variables. Notably, the Panel VECM reveals evidence of a unidirectional causal relationship from Real Gross Domestic Product (RGDP) to Real Foreign Direct Investment (RFDI) across all six sectors of Bangladesh. The findings underscore the significance of formulating pragmatic policies and implementing them effectively to attract FDI across sectors, thereby contributing to the overall economic growth of Bangladesh.

Measurement of efficiency and its drivers in the Chilean banking industry.

This paper estimates efficiency measures for the banking system in Chile for the period 2000-2019. In contrast to previous studies, we use input-distance functions, introduce the nonparametric slack-based model, and choose the intermediate inputs approach in determining inputs and outputs. Our results suggest that the Chilean system has achieved relatively high levels of efficiency, although with no significant variation over the sample period. Ownership (government, foreign and public) and size had a positive impact on efficiency. On average, mergers and acquisitions seem to have targeted highly efficient banks in order to improve the overall efficiency of the controlling institution in the short run. Other sources of efficiency gains could be an increase in bond funding or a reduction in expenses and capital holdings. The latter could be induced by deepening the local derivatives market.

Wealthy individual investors and stock markets' tail risk.

This paper employs a unique data set to analyze the trading behavior of wealthy individual investors across Mainland China and their impact on Chinese stock markets' tail risk. Results show that the wealthy individual investors' trading behavior can explain Chinese stock markets' tail risk, and the daily investment portfolios based on the network density of wealthy individual investors have significant excess returns. This paper also investigates the determinants of wealthy individual investors' trading behavior with the social network method and the spatial econometric model, and reveals that wealthy individuals benefit from the spillover effect of their trading behavior through the investor networks. The results of this paper not only reveal micro evidence for the formation mechanism of asset prices, but also provide insight into the behavior of wealthy individual investors.

The coupling coordination relationship between sports industry agglomeration and economic resilience in the Yangtze River Delta region.

With a rapidly growing sports industry worldwide, one may argue that sports industry agglomeration can play a crucial role in the economy of the sports industry. In particularly, the coupling linkage between sports industry agglomeration and economic resilience can be leveraged to promote both economic quality and efficiency. Based on data on three provinces and one city in the Yangtze River Delta region during the 2011-2020 period, this study uses the entropy-weighted TOPSIS method, coupling coordination degree model, and relative development models to explore the coupling coordination relationship between sports industry agglomeration and economic resilience in this region. The results show that: (1) Sports industry agglomeration shows an overall increasing albeit fluctuating trend with inter-provincial differences. (2) Economic resilience has steadily increased, while the economic resilience kernel density curve generally shows a "dual peaks" trend. (3) The coupling coordination between sports industry agglomeration and economic resilience remains in a fluctuating, albeit coordinated state. These findings are relevant for the integration and high-quality development of the sports industry in the Yangtze River Delta region.

Construction and application of economic resilience evaluation model for megacities.

Economic resilience provides a new perspective for megacities to achieve sustainable development when facing multiple shocks, and its accurate evaluation is an essential prerequisite for optimizing urban governance. There are currently no generally accepted methods for empirical evaluation or measuring economic resilience, and the present study aims to contribute to in both the research field and methodology. The present study sets dimensions and indicators based on economic resilience's theoretical and empirical research and used Decision Making Trial and Evaluation Laboratory (DEMATEL) and Interactive Structural Modeling (ISM) methods to exclude the effect indicators and divide the indicator hierarchy, respectively. Subsequently, the present study conducts model validation using Chinese megacities as a case study. The game theory weighting method, which combines the Analytic Hierarchy Process (AHP) and Entropy methods, is used to calculate indicator weights, and the VIKOR (VIseKriterijumska Optimizacija i KOmpromisno Resenje) method is used to evaluate and compare economic resilience of megacities. The research findings indicate that the evaluation model constructed in the present study included 15 indicators (after excluding three effect indicators) divided into four levels. After merging the levels, they correspond to three dimensions: resistance, recoverability, and adaptability. In addition, using Chinese megacities as a case study, the evaluation results found that Beijing, Shanghai, and Shenzhen have high economic resilience, Tianjin and Guangzhou have moderate economic resilience, Chengdu has low economic resilience, and Chongqing has the lowest economic resilience. This result is consistent with previous studies and verifies the model's effectiveness. The present study also found that megacities with lower levels of economic resilience exhibit a more significant upward trend, as well as the highest and higher proportion of economic resilience in Chinese megacities depending on time passes, indicating that megacities' economic resilience is weakening. The evaluation result obtained in the present study is more specific, precise, and focused on depicting the distribution differences and development trends of economic resilience at the urban level.

Heteroscedasticity effects as component to future stock market predictions using RNN-based models.

Heteroscedasticity effects are useful for forecasting future stock return volatility. Stock volatility forecasting provides business insight into the stock market, making it valuable information for investors and traders. Predicting stock volatility is a crucial task and challenging. This study proposes a hybrid model that predicts future stock volatility values by considering the heteroscedasticity element of the stock price. The proposed model is a combination of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and a well-known Recurrent Neural Network (RNN) algorithm Long Short-Term Memory (LSTM). This proposed model is referred to as GARCH-LSTM model. The proposed model is expected to improve prediction accuracy by considering heteroscedasticity elements. First, the GARCH model is employed to estimate the model parameters. After that, the ARCH effect test is used to test the residuals obtained from the model. Any untrained heteroscedasticity element must be found using this step. The hypothesis of the ARCH test yielded a p-value less than 0.05 indicating there is valuable information remaining in the residual, known as heteroscedasticity element. Next, the dataset with heteroscedasticity is then modelled using an LSTM-based RNN algorithm. Experimental results revealed that hybrid GARCH-LSTM had the lowest MAE (7.961), RMSE (10.466), MAPE (0.516) and HMAE (0.005) values compared with a single LSTM. The accuracy of forecasting was also significantly improved by 15% and 13% with hybrid GARCH-LSTM in comparison to single LSTMs. Furthermore, the results reveal that hybrid GARCH-LSTM fully exploits the heteroscedasticity element, which is not captured by the GARCH model estimation, outperforming GARCH models on their own. This finding from this study confirmed that hybrid GARCH-LSTM models are effective forecasting tools for predicting stock price movements. In addition, the proposed model can assist investors in making informed decisions regarding stock prices since it is capable of closely predicting and imitating the observed pattern and trend of KLSE stock prices.

Reducing delivery insurance costs through risk score model for food delivery company.

In this paper, we propose a novel pricing model for delivery insurance in a food delivery company in Latin America, with the aim of reducing the high costs associated with the premium paid to the insurer. To achieve this goal, a thorough analysis was conducted to estimate the probability of losses based on delivery routes, transportation modes, and delivery drivers' profiles. A large amount of data was collected and used as a database, and various statistical models and machine learning techniques were employed to construct a comprehensive risk profile and perform risk classification. Based on the risk classification and the estimated probability associated with it, a new pricing model for delivery insurance was developed using advanced mathematical algorithms and machine learning techniques. This new pricing model took into account the pattern of loss occurrence and high and low-risk behaviors, resulting in a significant reduction of insurance costs for both the contracting company and the insurer. The proposed pricing model also allowed for greater flexibility in insurance contracting, making it more accessible and appealing to delivery drivers. The use of estimated loss probabilities and a risk score for the pricing of delivery insurance proved to be a highly effective and efficient alternative for reducing the high costs associated with insurance, while also improving the profitability and competitiveness of the food delivery company in Latin America.

It's the biology, stupid! Proxy failures in economic decision making.

Utilitarian characterizations of economic decision making fail to capture the complex, conditional, and heterogeneous motivations underlying human behavior as shaped by the predictive, multicriterial drivers of biological regulation. Unless economic models start to acknowledge that humans have bodies and a biology with its own adaptive logic and tradeoffs, economic policies will be systematically exposed to, and systematic generators of, proxy failures.

Modeling the economic value of cardiometabolic virtual-first care programs.

OBJECTIVES: This study simulated the potential multiyear health and economic benefits of participation in 4 cardiometabolic virtual-first care (V1C) programs: prevention, hypertension, diabetes, and diabetes plus hypertension. STUDY DESIGN: Using nationally available data and existing clinical and demographic information from members participating in cardiometabolic V1C programs, a microsimulation approach was used to estimate potential reduction in onset of disease sequelae and associated gross savings (ie, excluding the cost of V1C programs) in health care costs. METHODS: Members of each program were propensity matched to similar records in the combined 2012-2020 National Health and Nutrition Examination Survey files based on age, sex, race/ethnicity, body mass index, and diagnosis status of diabetes and/or hypertension. V1C program-attributed changes in clinical outcomes combined with baseline biometric levels and other risk factors were used as inputs to model disease onset and related gross health care costs. RESULTS: Across the V1C programs, sustained improvements in weight loss, hemoglobin A1c, and blood pressure levels were estimated to reduce incidence of modeled disease sequelae by 2% to 10% over the 5 years following enrollment. As a result of sustained improvement in biometrics and reduced disease onset, the estimated gross savings in medical expenditures across the programs would be $892 to $1342 after 1 year, and cumulative estimated gross medical savings would be $2963 to $4346 after 3 years and $5221 to $7756 after 5 years. In addition, high program engagement was associated with greater health and economic benefits. CONCLUSIONS: V1C programs for prevention and management of cardiometabolic chronic conditions have potential long-term health and financial implications.

Epidemiological and health economic implications of symptom propagation in respiratory pathogens: A mathematical modelling investigation.

BACKGROUND: Respiratory pathogens inflict a substantial burden on public health and the economy. Although the severity of symptoms caused by these pathogens can vary from asymptomatic to fatal, the factors that determine symptom severity are not fully understood. Correlations in symptoms between infector-infectee pairs, for which evidence is accumulating, can generate large-scale clusters of severe infections that could be devastating to those most at risk, whilst also conceivably leading to chains of mild or asymptomatic infections that generate widespread immunity with minimal cost to public health. Although this effect could be harnessed to amplify the impact of interventions that reduce symptom severity, the mechanistic representation of symptom propagation within mathematical and health economic modelling of respiratory diseases is understudied. METHODS AND FINDINGS: We propose a novel framework for incorporating different levels of symptom propagation into models of infectious disease transmission via a single parameter, α. Varying α tunes the model from having no symptom propagation (α = 0, as typically assumed) to one where symptoms always propagate (α = 1). For parameters corresponding to three respiratory pathogens-seasonal influenza, pandemic influenza and SARS-CoV-2-we explored how symptom propagation impacted the relative epidemiological and health-economic performance of three interventions, conceptualised as vaccines with different actions: symptom-attenuating (labelled SA), infection-blocking (IB) and infection-blocking admitting only mild breakthrough infections (IB_MB). In the absence of interventions, with fixed underlying epidemiological parameters, stronger symptom propagation increased the proportion of cases that were severe. For SA and IB_MB, interventions were more effective at reducing prevalence (all infections and severe cases) for higher strengths of symptom propagation. For IB, symptom propagation had no impact on effectiveness, and for seasonal influenza this intervention type was more effective than SA at reducing severe infections for all strengths of symptom propagation. For pandemic influenza and SARS-CoV-2, at low intervention uptake, SA was more effective than IB for all levels of symptom propagation; for high uptake, SA only became more effective under strong symptom propagation. Health economic assessments found that, for SA-type interventions, the amount one could spend on control whilst maintaining a cost-effective intervention (termed threshold unit intervention cost) was very sensitive to the strength of symptom propagation. CONCLUSIONS: Overall, the preferred intervention type depended on the combination of the strength of symptom propagation and uptake. Given the importance of determining robust public health responses, we highlight the need to gather further data on symptom propagation, with our modelling framework acting as a template for future analysis.

Change in hierarchy of the financial networks: A study on firms of an emerging market in Bangladesh.

We investigate the hierarchical structure of Dhaka stocks' financial networks, known as an emerging market, from 2008 to 2020. To do so, we determine correlations from the returns of the firms over a one-year time window. Then, we construct a minimum spanning tree (MST) from correlations and calculate the hierarchy of the tree using the hierarchical path. We find that during the unprecedented crisis in 2010-11, the hierarchy of this emerging market did not sharply increase like in developed markets, implying the absence of a compact cluster in the center of the tree. Noticeably, the hierarchy fell before the big crashes in the Bangladeshi local market, and the lowest value was found in 2010, just before the 2011 Bangladesh market scam. We also observe a lower hierarchical MST during COVID-19, which implies that the network is fragile and vulnerable to financial crises not seen in developed markets. Moreover, the volatility in the topological indicators of the MST indicates that the network is adequately responding to crises and that the firms that play an important role in the market during our analysis periods are financial, particularly the insurance companies. We notice that the largest degrees are minimal compared to the total number of nodes in the tree, implying that the network nodes are somewhat locally compact rather than globally centrally coupled. For this random structure of the emerging market, the network properties do not properly reflect the hierarchy, especially during crises. Identifying hierarchies, topological indicators, and significant firms will be useful for understanding the movement of an emerging market like Dhaka Stock exchange (DSE), which will be useful for policymakers to develop the market.

Assessing the contribution of nonmarket factors to the market value generated by cow-calf operations in rangelands of the western USA: A true cost accounting approach.

Food system transformation requires a better understanding of the negative and positive externalities involved in food production and consumption. Although negative externalities have received substantial attention, positive externalities have been largely overlooked. True Cost Accounting (TCA) is an economic assessment aimed at accounting for externalities in food systems. The beef industry is an important part of the US food system. In the western USA, beef cattle production is a major land use and economic activity that involves direct links among the cattle, range ecosystems, range management, climate, and ranchers' decisions and welfare. We present a case study based on a TCA assessment to quantify and monetize the contribution of human, social, natural, and produced capitals, as well as farm structure, to the market value generated by cow-calf operations, a key component of the USA beef industry. We estimated an Ordinary Least Square regression model based on indicators of these capitals and of farm structure derived from publicly available data sources at the county level. From model coefficients, we estimated the marginal revenue product of these factors. Results show that nonmarket factors linked with human and social capitals support market performance by contributing to the market value of cow-calf production. These factors operate at scales above the ranch, usually remain hidden, and seldomly are considered in policy decision-making which can lead to policies that inadvertently hamper or eliminate these positive externalities.

Impacts of financial development and green trade on the shadow economy: An insight of eagle countries using Bayesian approaches.

The existence of a shadow economy is recognized as an impediment to sustainable development. By applying the Bayesian approaches, the current article investigates the linkage between financial development, green trade, and the scope of the shadow economy, aiming to contribute to a comprehensive understanding of how these factors address the challenge posed by the shadow economy in Emerging and Growth-Leading Economies (EAGLE) from 2003 to 2016. The results demonstrate that (i) The progress of the financial sector is expected to diminish the scale of the shadow economy. Specifically, the expansion of financial institutions and markets has a strong and negative influence on the shadow economy. (ii) Increased involvement in green trade is likely to result in a decreased shadow economy. Empirical findings provide evidence for effective policymaking in simultaneously promoting sustainable trade practices, strengthening financial systems, and curtailing informal economic activities for inclusive economic development.