Socio-economic Planning Sciences最新文献

This paper aims to explore the correlation between individual income and tax evasion. We develop a novel theoretical model based on the argument that the level of public goods provision and the perceived fairness of public services significantly influence an individual's attitude towards taxation. The model reveals a concave pattern, indicating the presence of an income threshold. Initially, as an individual's income increases, tax evasion decreases due to the perceived fairness of their contributions relative to the benefits received from public services. However, beyond this threshold, the tax burden is perceived as unfair, leading to an increase in tax evasion as income levels rise. We also proof that this income threshold is influenced by the tax rate and enforcement system. We also conduct a panel data analysis on a sample of 35 OECD countries from 2007 to 2020 to support the theoretical findings. The empirical investigation confirms a U-shaped relationship between income and the underground economy. Our findings suggest that policymakers should introduce measures to improve the quality of public goods provision, considering the potential adverse effect of higher income on economic growth. This study is the first to theoretically investigate the non-linear relationship between income and tax evasion while also providing empirical evidence.

The integration of the Internet with traditional medical services is poised to transform health insurance policies. This study aims to explore the coordinating role of differentiated health insurance policies within the context of Internet healthcare. A four-stage sequential game decision-making model is developed within a queuing framework to address scenarios involving online patient referrals and misdiagnoses. The model begins by analyzing the equilibrium arrival strategy of patients, followed by the determination of optimal service capacity strategies for a nonprofit community health center (CHC) and optimal pricing strategies for a for-profit general hospital (GH). Additionally, the model describes an optimal differentiated subsidy strategy for the government aimed at minimizing total social costs. Analysis reveals that under certain conditions, an increase in the service price at GH relative to CHC can lead to a higher influx of online patients visiting GH in person. Furthermore, when the number of online patients exceeds a specific threshold, it not only prompts the government to increase the disparity in health insurance subsidies between the two hospital tiers but also encourages GH to reduce its service prices and offer free services to online patients. Numerical experiments explore the effects of government budgets, sharing ratios, and other variables on the system's equilibrium state, providing several managerial insights. Notably, when patients' misdiagnosis costs are partially covered, increasing GH's misdiagnosis cost-sharing ratio not only enhances the patient arrival rate but also enhances GH's profitability.

Environmental consciousness is a multi-dimensional construct that encompasses several dimensions related to pro-environmental attitudes, beliefs and behaviours. The academic literature has attempted to conceptualise and operationalise environmental consciousness over the last 20 years, resulting in a wide variety of measures.

However, the available measures are country-specific and with a predominant U.S. focus, based on convenience samples, and rather limited in terms of interpretability and external validity. To overcome these limitations, the present study develops an index of environmental consciousness at both the micro (consumer) and macro (country) levels, taking into account the four main dimensions of environmental consciousness: the affective, cognitive, active and dispositional dimensions. By analysing more than 27 000 “Eurobarometer 92.4” responses from consumers in the 28 EU Member States in 2019, this paper develops a comprehensive measure of consumer environmental consciousness that captures the heterogeneity across European countries. To assess the robustness of the index, the link between environmental consciousness and life satisfaction is also examined. The index is also compared with a big data-based index using Google Trends data on environmental search categories. The results show differences in environmental consciousness between European countries. The link between environmental consciousness and life satisfaction is also supported, in line with previous research in this area. Finally, the index appears to be strongly correlated with actual consumer search patterns on Google. The findings have implications for businesses willing to enter in new markets and policy makers on how to measure and assess environmental consciousness.

The high-speed railway (HSR) plays a crucial role in bolstering regional connectivity and economic and social progress. Nevertheless, there is an ongoing debate around the impact of HSR on the economic and social development of the developing regions. On one hand, developing regions necessitate the implementation of HSR systems to facilitate their progress. On the other hand, they must also mitigate the potential drawbacks and adverse consequences associated. By employing accessibility analysis, urban potential analysis, and geographically-temporal weighted regression modeling, this study aims to comprehensively elucidate the spatially variable impact of HSR on developing regions such as Southwest China. The findings indicate that the implementation of HSR has yielded significant benefits in terms of accessibility, economic growth, and social development in developing regions. However, there is a growing disparity between cities that have HSR connections and those that do not. Additionally, smaller and medium-sized cities situated along the middle section of the HSR route experience relatively less pronounced impacts. This study posits that there is a necessity to enhance the expansion of the HSR network in developing regions, concurrently with the reinforcement of inter-city cooperation and the establishment of urban agglomerations, thereby promoting high-quality and balanced development of the developing regions.

This research presents a novel approach for enhancing power grid resilience with a focus on social equity in light of increasing natural disasters. Utilizing a two-stage stochastic optimization model for flood mitigation investments, we optimize substation hardening and power flow decisions to minimize the weighted combination of expected load shed and expected equity metric. Our method uniquely approximates community power outages stemming from transmission grid disruptions, sidestepping the complexities of the distribution grid, and it incorporates two equity metrics (affected population and duration of loss) to address the uneven postdisaster well-being loss across communities. Our approach’s novelty lies in integrating this equity-informed resilience model with realistic flood scenario generation and utilizing a large-scale synthetic but a realistic power grid of Texas. The findings highlight the importance of the composite objective function in altering power flow decisions to prioritize electricity provision and save communities in disadvantaged areas even without investing in substation hardening. The results also quantify the equity and load shed benefits of substation hardening as a function of the investment budget with a parameterized analysis. With an attention to equity, power outages increase in nonvulnerable communities — a trade-off made to mitigate well-being loss in the most vulnerable areas. We further explore a justice model inspired by the government’s Justice40 initiative but find it less effective than our equity-informed models at preventing well-being loss. Our research, enriched by a comprehensive sensitivity analysis, offers valuable insights for policymakers, grid operators, and utilities aiming for a more resilient and equitable power grid.

The escape interdiction problem within the context of attacker activities on a transportation network is addressed in this study. In the absence of traffic within the network, the attacker attempts to flee the city by choosing one of the shortest paths from the crime scene to a randomly selected exit point. However, in the presence of traffic, the attacker strategically selects the optimal route that minimizes his time to reach a randomly selected exit point. On the other side, defenders try to interdict the attacker on his escape route. Defenders face the daunting challenge of interdicting the attacker’s escape route while operating under limited resources. Dealing with a real city road network further adds complexity to the scenario. A simulation-based model is proposed for the optimal allocation of resources to tackle this issue. The focus then shifts to the development of an advanced search strategy that involves routing with optimal resource allocation. This paper presents the first comparative study for escape interdiction problems within a simulation environment, explicitly focusing on solution methodologies. An optimal resource allocation approach is proposed in the presence of traffic, constituting a novel contribution that has not been previously implemented in escape interdiction problems. In addition, the paper introduces a Genetic Algorithm (GA)-based meta-heuristic approach within a simulation environment. This approach generates optimal paths for defenders, wherein each node is associated with a fixed time window, representing the defender’s waiting time. In this proposed methodology, defenders undertake a tour of the network rather than remaining stationary at a single location. This approach expands the network search capabilities, thereby requiring optimization to ascertain the optimal routes and schedules for the defender vehicles. A case study is conducted using the map of IIT Kharagpur, India, to evaluate the effectiveness of this approach. By employing this approach and conducting in-depth analyses, the aim is to provide valuable insights into the efficiency and practicality of the developed methods on real-world transportation networks.

In recent years, concern has increased about affordable and high-quality early childhood education and care (ECEC) services. Such services can help balance work and family life, increase workforce participation, promote gender equality, and increase fertility rates. ECEC services are then essential, especially for countries facing ultralow fertility rates like Italy. To face these challenges, the Italian government has included an unprecedented investment in the recovery and resilience plan financed with Next Generations EU funds to increase the supply of ECEC services and make them more equitably distributed across the country. However, socio-economic policies need a comprehensive understanding of demand dynamics and the intricate interplay between supply and demand of such services. Therefore, in this article, we propose a novel analysis of the complex combination of supply and demand of Italian ECEC services. We explore patterns of this combination in terms of groups of spatial areas sharing similar structures of service coverage and public expenditure rates in ECEC, as well as other socio-demographic and economic factors such as female employment, education, and grandparent rates. Our empirical findings confirm how Italy is characterized by several distinct territorial models and how some are shared across multiple regions. Current empirical findings suggest a lack of uniform governance across the Italian territory. Policymakers can use these results to plan, discuss, and implement area-specific welfare strategies regarding ECEC services.

Staged trees are a relatively recent class of probabilistic graphical models that extend Bayesian networks to formally and graphically account for non-symmetric patterns of dependence. Machine learning algorithms to learn them from data have been implemented in various pieces of software. However, to date, methods to assess the robustness and validity of the learned, non-symmetric relationships are not available. Here, we introduce validation techniques tailored to staged tree models based on non-parametric bootstrap resampling methods and investigate their use in practical applications. In particular, we focus on the evaluation of transport services using large-scale survey data. In these types of applications, data from heterogeneous sources must be collated together. Staged trees provide a natural framework for this integration of data and its analysis. For the thorough evaluation of transport services, we further implement novel what-if sensitivity analyses for staged trees and their visualization using software.

Unemployment rate is one of the most important macroeconomic indicators used by governments for setting economic policy, as it provides worthwhile information on a country's labour market condition, on the health of its economic system and on its future growth. In Italy, since the turn of the century, this indicator steadily decreased until the onset of the 2007–2008 global financial crisis, after which it saw rapid growth that continued in the subsequent years and intensified with the 2011 European sovereign debt crisis. A reversal of this trend occurred only since 2015, after almost eight years of growth, when unemployment rate began a slow decline. Using quarterly data derived from official statistics produced by the Labour Force Survey, this study proposes a counterfactual approach based on interrupted time series analysis to measure the severity of the immediate impact and persistence of the 2008 global financial crisis and the 2011 European sovereign debt crisis on the Italian unemployment rate as well as the intensity of the economic recovery in the years after 2015. Differences across population age sub-groups were considered to highlight the effects on youth unemployment, gender, macro-regions, citizenship and level of education and thus obtain a more in-depth analysis.

Carbon quota allocation, a precise implementation approach within the cap-and-trade policy, exerts an enduring impact on the production and economic activities of supply chain members. Recognizing the dynamic fluctuations in product goodwill, this study formulates differential game models involving both the manufacturer and retailer within the supply chain. Subsequently, differences in carbon emission reduction efforts, advertising expenditures, product goodwill, and profitability among supply chain members within each contractual framework are comprehensively compared and analyzed. Numerical illustrations are then utilized to validate the theoretical findings and strengthen the subsequent key insights. First, the implementation of carbon quotas allocation, especially those grounded in the benchmarking-based contract, enhances carbon reduction and advertising efforts by supply chain members. Second, both grandfathering-based and benchmarking-based regulations improve the product goodwill, with the latter yielding more substantial improvements. Third, the escalation in unit emission permit price positively impacts the dedication and profitability of supply chain members, although variations in profit enhancements between the manufacturer and retailer hinge on the specific carbon quota allocation methods employed. Lastly, the grandfathering-based total carbon quotas exclusively benefit the manufacturer's profitability, whereas the per-unit carbon quotas under benchmarking-based contract fosters enhanced individual efforts and profitability for all supply chain members.