In the context of population aging, stabilizing the industry is crucial for achieving high-quality economic development. However, it remains to be tested whether aging will inhibit industrialization. From a supply and demand perspective, this paper uses newly collected panel data from fifty economies spanning the years 1990 to 2018. By constructing a two-way fixed effects panel model, it aims to identify the impact of aging on industrialization and its underlying mechanisms. The research finds that there exists a U-shaped relationship between population aging and industrialization, and this conclusion remains valid after a series of robustness tests. Despite the negative supply-side mechanism of an aging labor force structure and the two negative demand-side mechanisms of domestic demand shifting towards services and international demand relocating abroad, there are also three positive supply-side mechanisms: Improvements in labor quality, technological upgrades, and capital-biased technological progress. The negative supply-demand mechanisms play a major role when the level of population aging is relatively low, while the positive supply mechanisms take the lead at higher levels of population aging. In view of this, this study enriches the empirical evidence and causal pathways of the impact of population aging on industrialization. The research conclusion provides decision-making references by advocating for sustained supply-side reforms to promote industrial development through “talent dividends” and “innovation dividends”, while also emphasizing the expansion of demand to maintain the international competitiveness of manufacturing industry.
The 20th National Congress of the Communist Party of China emphasized promoting common prosperity through high-quality development, but the the rural-urban income disparity is still relatively large. The adoption and diffusion of artificial intelligence, a major general-purpose technology represented by robots, has a profound impact on the labor market and income distribution. Based on the characteristic facts of China’s urban-rural dual economic structure, using the IFR and provincial panel data from 2005 to 2020, as well as the data of CLDS in 2014 and 2016, this paper empirically analyzes the impact of robot application on urban-rural income gap and its internal mechanism from the macro and micro levels. The results show the application of industrial robots and the income gap between urban and rural areas presents an inverted U-shaped trend, which increases first and then decreases. From a micro perspective, the growth rates of total income and wage income for rural residents are higher than those of urban residents, but this impact is mainly concentrated in the eastern regions. This papar not only provides policy reference for the government to promote the development of artificial intelligence technology, industrial structure upgrading and high-quality economic development, but also provides policy enlightenment for narrowing the urban-rural income gap, rural revitalization and common prosperity.
The new infrastructure based on information technology is not only a carrier for new technologies, elements, and business forms, but also a crucial lever to achieve high-quality development in China. This paper takes new infrastructure as the research object and analyzes the influence mechanism of the government’s new infrastructure investment in promoting China’s economic development by constructing a dynamic stochastic general equilibrium (DSGE) model including the new infrastructure sector. Furthermore, this paper makes targeted policy recommendations for China to implement the strategy of expanding domestic demand and promoting high-quality economic development from the perspective of infrastructure investment strategy. Research shows: 1) New infrastructure investment shows a differentiated growth path for the economy compared to economic and social infrastructure investment; 2) New infrastructure investment is the largest economic enabler, followed by social infrastructure investment, while economic infrastructure provides a relatively small boost; 3) Excessive increases in new infrastructure investment may weaken their investment multiplier effects, while appropriate increases in social infrastructure investment can help to strengthen the role of new infrastructure investment as a driver of the economy. The conclusion of the study shows that in the process of vigorously promoting the construction of new infrastructure, a sound new factor market system should be established, the transformation and upgrading of economic infrastructure should be intensified, and the construction of convergent infrastructure in social and livelihood areas such as medical care and education should be expedited.
As an important way to gather talents, employee stock ownership plan(ESOP) has received close attention from the academic circle. This paper examines the industrial and regional peer effects of ESOP, the motives of the peer effect in ESOP. We find that there are significant industrial and regional peer effects in ESOP, and this conclusion is still valid after various robustness tests. Information motivation, competition motivation and risk aversion motivation are the potential reasons why the target company imitates the ESOP of peers. Industry followers tend to learn from leaders, and when information transparency is low, competition is high and executives are more risk-averse, the industry and regional peer effect of ESOP is more obvious. Further research shows that the peer effect is stronger in industries and regions where ordinary employees hold shares than in executives. Moreover, under the influence of collectivism culture and in non-state-owned enterprises, the peer effect of ESOP is more obvious, and non-state-owned enterprises will learn from state-owned enterprises’ employee stock ownership plans. This peer effect is also helpful to retain employees and increase market value. This paper provides a new explanation for the driving factors of the ESOP, the reasons for mutual imitation, and then verifies.
Traditional linear predictive regression models perform poorly in out-of-sample stock return predictions. One competing hypothesis for this result is that structural changes in the financial markets introduce model instability. This paper constructs a two-state multi-asset time-varying regime switching (TVTP-RS) model to investigate industry stock return predictability. In this model, the time-varying industry expected returns are driven by economic variables, but the relation between them may change due to shifts in market states, where market states are unobservable and follow a Markov chain with time-varying transition probabilities. In-sample estimation results reveal that jointly utilizing information from industry returns and economic variables can effectively identify latent market states, and the relation between economic variables and expected returns indeed depends on market states — The coefficients of some economic variables reverse in different market states. Out-of-sample industry return predictions and industry allocation strategies based on this model consistently outperform benchmark models and linear predictive regression models. This study provides new evidence of the time-varying relation between economic variables and industry expected returns and demonstrates that considering the impact of market state transitions can effectively reduce the model instabilities. The proposed TVTP-RS model also offers a reliable solution for industry rotation strategies in practice.
With the increase in life expectancy and uncertainty of morbidity rates, how to effectively diversify longevity and health risks has become a major challenge in risk management for insurance companies. In line with the “Notice on Conducting the Pilot Business of Converting Liabilities between Life Insurance and Long-term Care Insurance”, this paper utilizes CHARLS data to estimate and predict the health state transition probabilities of residents. Subsequently, by constructing a multivariate duration model, we identify the optimal product weights for natural hedging of longevity and health risks. Moreover, the hedging effectiveness is evaluated using the volatility and variance reduction ratio of the expected present value of liabilities across the different insurance product portfolios. The following results are concluded. Firstly, mortality rates for both men and women are expected to decline, with significant differences in their disability rates. Secondly, the multivariate duration model facilitates the hedging of risks in insurance product combinations that include both longevity and health risks. Thirdly, the effectiveness of risk hedging varies with the product features, changes in mortality and morbidity rates, and the insured’s gender and age.
The latest literature on variance risk premium (VRP) finds that the different components of the VRP have significant differences in the performance of predictive return regressions. Based on two cutting edge approaches to decomposing the VRP, we construct an analytical framework for the decomposition of VRP. Under this new analytical framework, we use the data come from the Shanghai Stock Exchange 50 ETF (SSE 50ETF)market and SSE 50ETF options market, and apply a variety of model-free estimation methods to estimate the variance risk premium, upside and downside variance risk premiums, signed jump risk premium and its continuous and jump components, tail jump risk premium, and then investigate the predictive performance of each risk premium to the future returns of the SSE 50ETF. The results show that, the variance risk premium and the upside and downside variance risk premiums of the SSE 50ETF during the sample period were significantly negative. The variance risk premium has a negative predictive effect on future returns, but the result is not robust. The upside(downside) variance risk premium has a significant positive (negative) predictive effect on future returns and the prediction performance is robust. The signed jump risk premium and its continuous and jump components are all significantly negative, and all three have negative predictive effect on future returns, the prediction performance of the first two is robust, while the prediction performance of the jump component of the signed jump risk premium is not robust. The left risk-neutral jump tail variation is larger than the right risk-neutral jump tail variation, and the former has a positive effect on future returns, the latter has a negative effect on future returns, but the prediction performance of both is not robust. The return prediction model which considers the upside (or downside) variance risk premium, the continuous part of signed jump risk premium and the decomposition of its jump part has the best prediction performance.
Non-linear factors are often selectively ignored in the modification process of financial asset pricing models. This study employs feedforward neural network methods from machine learning, integrating prior classification information to construct three optimized multi-layer perceptron (MLP) models: The hybrid neuron MLP, single-category Hybrid MLP, and multi-category hybrid MLP. These models predict returns in the Chinese stock market and compare their effects with linear regression models based on ordinary least squares and partial least squares estimation methods (OLS and PLS), support vector machines (SVM), extreme gradient boosting(XGBoost), recurrent neural networks (RNN), long-short-term memory networks (LSTM), and the classical MLP. The research found that the optimized MLP models with prior information can effectively reveal the complex relationships between market anomalies and stock excess returns; they significantly outperform traditional models that lack prior information in terms of prediction performance and investment strategies, demonstrating superior out-of-sample prediction results and robustness, and enhancing investment returns. The multi-category hybrid MLP model exhibits the best predictive performance and investment results. The prior information-based optimized MLP models developed in this study succeed in capturing the non-linear pricing structure between market anomalies and stock excess returns, substantially contributing to and enriching the theory of financial asset pricing.
Considering the current problem of insufficient supply and demand and inefficient incentive in China’s carbon financial market, this paper constructs a three-party evolutionary game model of governments, enterprises and financial institutions from the perspective of reputation incentive. This paper analyzes the influences of government certification, reputation rent and time preference on the strategy evolution process of supply and demand players in the carbon financial market. Moreover, the effectiveness of reputation incentive mechanism to stimulate the supply and demand of carbon financial market is further verified through a case study. The research conclusions are as follows: First, whether the governments establish a green label certification system is crucial to the development of carbon finance market. Compared with financial institutions, the governments tend to provide green label certification services to enterprises, which is more conducive to promoting the prosperity of carbon finance market. Secondly, the higher the initial value of reputation rent, the higher the level of product quality, the greater the influence coefficient of product quality on the accumulated value of reputation rent, and the faster the evolution speed of carbon financial market to reach the equilibrium state of supply and demand. Thirdly, time preference significantly affects the strategic behaviors of players in carbon financial market. Compared with financial institutions, weakening the time preference of enterprises can more effectively and comprehensively mobilize the initiative of enterprises, financial institutions and governments to participate in the carbon financial market.
This study aims to analyze the impact of digital platform recommendation algorithms on specialized production and consumer welfare. Based on consumer search theory, this paper constructs a theoretical model to analyze enterprise production decisions, profits and consumer welfare under different recommendation mechanisms. The results show that the preference recommendation mechanism can promote specialized production of enterprises on the platform and improve the overall profits of enterprises and total consumer welfare. This effect increases with the increase in the number of enterprises on the platform and the basic utility of products. Although the use of bidding sorting by digital platforms can increase platform profits, it will cause enterprises with higher rankings to choose mass production, thereby reducing the overall profits and total consumer welfare of enterprises. The degree of welfare improvement of different consumers under the preference recommendation mechanism is not the same. When there is a risk of privacy leakage of consumer data, the preference recommendation mechanism will lead to a decrease in the welfare level of consumers who purchase products from lower-ranked enterprises. This paper expands the analysis of the impact of digital platforms on consumer welfare and provides policy recommendations for the further development of digital platforms.
Ecological label certification can give enterprises an advantage in the fierce competition. However, some enterprises have taken advantage of their certification qualifications to engage in illegal production activities. Aiming at the problem of eco-label fraud under the background of government supervision, this paper establishes a game model of the eco-label product company with label fraud and the ordinary product company with or without peer monitoring. Through theoretical analysis and comparison of companies’ optimal strategies and numerical experiments, we found that: Peer monitoring by the ordinary product company can reduce the fraudulent eco-label product company’s demand and profit, and improve ordinary products’ demand. The ordinary product company will withdraw from peer monitoring when the market supervision is lax, and adopts peer monitoring when the market supervision is strict. Increasing the unit sales penalty or reducing the peer monitoring cost coefficient can improve the ordinary product company’s peer monitoring level. When the peer monitoring cost coefficient is low, the government maintains a low regulation level that can motivate the ordinary product company to invest in higher peer monitoring level, thereby reducing the non-compliant eco-label product company’s profit, in this case, the government can achieve the curbing label fraud goal with lower regulatory input.
The three-level joint reserve model of emergency supplies can effectively improve the response speed of emergency material support and the security level of emergency supply chain. This paper takes the emergency supply chain composed of the government, manufacturers and suppliers as the research object, considers the corporate social responsibility (CSR) investment behavior of manufacturers and suppliers and the information asymmetry of manufacturers’ risk aversion, and establishes a three-stage game model of emergency supply chain. Through model analysis, the optimal government option contract under CSR investment is designed, and the impact of CSR investment on the equilibrium of the emergency supply chain is investigated. On this basis, the impact of CSR undertaked by manufacturers and suppliers on the emergency material reserve strategy and the design of government contracts is further considered. The results show that the CSR investment of manufacturers and suppliers always brings positive externalities to the government. Although there are differences in the impact of CSR investment on government costs and the profits of manufacturers and suppliers, the decline in government costs and the increase in supplier profits always prevail, thus compensating for some of the lost profits of manufacturers. Therefore, when manufacturers and suppliers make CSR investments, they can always effectively improve the overall efficiency of the emergency supply chain.
This paper verifies the optimization effect of customer digital transformation on supplier resource allocation and its transmission mechanism from the perspective of supply chain spillover. The research results show that customer digital transformation has a backward spillover effect, which significantly improves the resource allocation efficiency of supplier. When the geographical distance of the supply chain is long, and the supplier’s customer concentration is low and the scale is large, the spillover effect is more significant. The mechanism test shows that the spillover effect of customer digital transformation mainly improves supplier’s resource allocation efficiency by optimizing the matching of supply and demand, reducing capital costs, and facilitating digital innovation collaboration. Further research shows that the spillover effect is mainly reflected in the reduction of supplier labor mismatch, and the negative demand shock caused by the COVID-19 pandemic weakens the spillover effect. Based on the perspective of supply chain spillover, this paper expands the application scope of the theory of vertical relationship of industrial organization in the field of digital supply chain management and resource allocation, and enriches the research on the interaction mechanism between upstream and downstream firms in the supply chain. It provides practical inspiration on how to use digitalization to empower supply chain management, promote the construction of vertical coordination mechanism of supply chain and improve supply chain resilience.
With the change of global economic pattern and the intensification of international competition, improving the security and efficiency of supply chain has become a key factor to enhance the competitiveness of national economy. Based on the CVC (corporate venture capital) investment data of A-share listed companies and the information of their top five suppliers and customers from 2011 to 2021, this paper constructs a sample set of supplier-customer-annual observations, and discusses the influence of customer CVC on the bullwhip effect and its mechanism from the perspective of supply chain information spillover. The study finds that customer CVC can effectively mitigate the supply-demand bullwhip effect by improving information transparency, reliability and synergy. Heterogeneity analysis shows that the information spillover effect of customer CVC is more significant when the supplier’s data element utilization level is low and the environmental uncertainty is high. Further research finds that the information spillover effect of focus firm CVC also exists in the multilevel supply chain network, which can mitigate the overall bullwhip effect of the supply chain network; and from the perspective of economic consequences, the mitigation of bullwhip effect by CVC can further enhance the total factor productivity of the supply chain. This paper provides a new perspective to understand how CVC improves the operational efficiency of supply chain and promotes the synergistic development of industrial chain through information spillover effect.
Cotton is one of the most important cash crops in China’s agricultural sector. However, increasing cotton yield and improving farmers’ income remain major challenges for participants in cotton cultivation. In order to incentivize cotton farmers and promote active production, the government has introduced a subsidy policy based on target prices for cotton. Additionally, the futures market provides a new avenue for mitigating price risks in agricultural commodities. Therefore, this study, based on practices in the Xinjiang region and considering the output uncertainty of cotton, constructs decision models for four operating modes: No subsidy and no futures, no subsidy with futures, subsidy without futures, and subsidy with futures. By comparing the expected profits of heterogeneous cotton farmers under different modes, the study explores the impact of the futures market and subsidy policies on farmers’ decision-making and profits. We find that: i) Under all four operating modes, the optimal production quantity and profit of cotton farmers with higher production efficiency decrease with a reduction (increase)in output heterogeneity (heterogeneity coefficient) between farmers. However, the optimal production quantity of the other cotton farmer initially increases and then decreases with the heterogeneity coefficient, while their profit increases with an increase in the heterogeneity coefficient. ii) Implementing target price subsidies alone may potentially increase the profits of all cotton farmers, depending on the magnitude of the target price subsidies. iii) Solely adopting futures trading can lead to profit losses for cotton farmers with lower production efficiency, while the profits of the other cotton farmer increase when the heterogeneity coefficient is small. iv) Subsidies and futures trading also need to be implemented with significant subsidy intensity to increase the profits of all cotton farmers. However, the magnitude of profit growth may not necessarily surpass that of using subsidies or futures alone. Furthermore, when considering the intensity of market competition among cotton farmers, the presence of acquiring companies, and the quality differences in cotton, the impact of subsidies, futures trading, and their combination on the optimal decisions and profits of cotton farmers aligns with the basic model.
The aim of this paper is to establish a noncooperative-cooperative biform game model for optimizing sales pricing, warranty service and derivative service outsourcing strategies and profit distribution in the dual channel supply chain of new energy vehicles. In the noncooperative game part, any competitive situation is formed by using the sales price of derivative service between the 4S store and third-party outsourcing service agency as a strategy profile. In the cooperative game part, for any competitive situation, the new energy vehicle manufacturer, 4S store, and third-party outsourcing service agency form a coalition to optimize the direct sales price and retail price of new energy vehicles, the new energy vehicle warranty service levels and derivative service levels of the 4S store, the new energy vehicle warranty service levels and derivative service levels of the third-party outsourcing service agency, and create the value of coalitions. The profits of the three players are allocated by using the Myerson value of the cooperative game with a restricted communication structure. Hereby the profits of the 4S store and third-party outsourcing service agency are used as their payoff functions in the noncooperative game. The optimal sales price of derivative service of the 4S store and third-party outsourcing service agency are solved, i.e., Nash equilibrium strategies. The research results show that: 1) The increase in competition intensity of new energy vehicles will lead to an increase in the direct sales price and retail price of new energy vehicles, as well as the warranty service levels provided by the 4S store and third-party outsourcing service agency. However, the improvement of warranty service levels is limited, and a vicious increase in competition intensity for new energy vehicles will damage the profits of all members of the new energy vehicle dual channel supply chain. 2) As the competition intensity of derivative service increases, the 4S store and third-party outsourcing service agency sales price of derivative service will increase. It is possible to appropriately increase the competition intensity of the derivative service to increase their profits of the new energy vehicle manufacturer, 4S store, and third-party outsourcing service agency. However, it is necessary to avoid excessive competition in the derivative service leading to vicious price wars, which will affect the overall profitability and sustainable development of the new energy vehicle supply chain. 3) The increase in the sensitivity coefficient of warranty service levels will promote the increase in direct sales price and retail price of new energy vehicles. The new energy vehicle manufacturer, 4S store, and third-party outsourcing service agency can increase the promotion, innovation, and efficiency of warranty service, enhance consumer sensitivity to the warranty service levels, balance service improvement and cost control, and ensure market stability and healthy development.
In multi-category product management, substitution and complementation effects will both affect consumers’ purchasing decisions, and it is urgent to integrate their advantages to carry out multi-category assortment planning. Therefore, this study targets to build a multi-assortment joint planning model that considers the substitution and complementation conditions comprehensively to extend the classical exogenous demand model, to propose the related methods for estimating model parameters and choosing assortments, and to employ more than 570,000 real sales data from 64 stores of a domestic large retail supermarket to assess our model’s prediction accuracy and robustness and evaluate the impact of the planning model on expected revenue. The results support our model’s accuracy and robustness to solve the substitution and complementation probability, and the assortment planning model brings 5.1%, 11.1% and 3.0% increase in the potential income of the three pairs of related categories respectively, which also proves the planning effect of the model. The study integrates two stages of parameter estimation and assortment planning, enriches the assortment planning method, and provides decision support for assortment planning practice.
Due to the characteristics of controlling activities dominated and work continuity constraints, the cascading effect of repetitive projects subject to disturbance is very significant, leading to project delays and economic losses. In this paper, we study the reactive scheduling problem of repetitive projects considering soft logic based on the characteristics of controlling activities. First, the disturbance absorbing capability and transfer rule of controlling activities in line of balance method are explored, based on which a specific soft logic reactive strategy is designed. Second, the research problem is defined, its integer linear programming model is constructed, and constraint programming is designed for the problem characteristics to quickly obtain a high-quality feasible solution. Last, a highway project is used to illustrate the research, and a sensitivity analysis of key parameters is performed. The results show that the strategy in this paper can effectively reduce reactive scheduling costs in a way that does not consume additional resources, which is mainly implemented for backward and start controlling activities; the sensitivity of reactive costs to the number of backward and start controlling activities increases as the number of disturbances increases. This study can provide quantitative decision support for repetitive project managers to deal with uncertainty perturbations in resource-constrained situations.
System degradation occurs during operation due to factors including inherent attributes and external environment. Maintenance is an effective means to enhance system reliability. Currently, most maintenance decision-making research considers costs over an infinite time horizon to facilitate modeling and computation. However, in practice, many systems have finite and uncertain lifecycles. Concurrently, numerous scenarios in production processes involve finite-time costs, such as equipment leasing, product warranties, and batch processing of scheduling tasks. Therefore, this study aims to optimize system maintenance costs over a finite time period. First, for a class of multi-component systems, a condition-based replace/ment strategy incorporating corrective, preventive, and opportunistic replacements is formulated, considering economic dependence among components. Second, the system lifecycle is divided into four stages. Through periodic inspections assessing component degradation levels, the maintenance status of multiple components at inspection times and the corresponding probabilities of maintenance combinations are determined. Based on this, the number of inspections, the opportunistic replacement threshold, and the preventive replacement threshold are used as decision variables to minimize the total cost over the finite time horizon. Finally, a genetic algorithm is employed to optimize the model. Using an FU-type multi-axis drilling machine as an example, the effectiveness of the decision model is validated through comparisons with traditional maintenance strategies and sensitivity analysis of relevant parameters.
Condition-based maintenance (CBM) involves monitoring the performance, status, and real-time health of machines to identify optimal maintenance opportunities and prevent failures in a cost-effective manner. In manufacturing systems, degradation in machine condition significantly affects reliability, product quality, and overall throughput performance. Therefore, this paper proposes a CBM decision-making method for serial lines with machine condition and product quality degradation. The objective is to facilitate the process of making maintenance decisions and improve production performance. Specifically, for two-machine lines, a CBM decision model is formulated as a Markov decision process, with a dynamic programming algorithm utilized to determine the optimal maintenance policy. For longer serial lines, an aggregated method is proposed, based on the two-machine case, to approximate the optimal maintenance policy. Numerical experiments validate the effectiveness and efficiency of the proposed CBM decision-making method.
