Pricing Approach Research Articles

Internet price, speed, and disparity: The case of rural healthcare providers in the United States

Healthcare providers (HCPs) and patients are increasingly relying on telehealth services (healthcare provision over the internet) to provide and seek care. It turns internet access disparities into a health equity concern, i.e., poor internet access can contribute to poor health. In response, two federal programs in the United States – Healthcare Connect Fund and the Telecom Program – subsidize internet access for HCPs in rural or remote areas. I use a two-part pricing approach to examine the evolution of internet speed and price for HCPs that received internet subsidies during 2014–2020. I find that HCP internet is annually getting 30.77% faster and 5.23% cheaper. During the same period, the cost to subscribe to an internet service and the cost to raise bandwidth have annually fallen by 5.02% and 7.83%, respectively. A comparison of the trends between rural and urban HCPs suggests that the rural/urban divide in internet access has shrunk during the study years. It indicates that the subsidy programs likely have achieved their stated goal of improving rural internet.

Scalable reinforcement learning approaches for dynamic pricing in ride-hailing systems

Dynamic pricing is a widely applied strategy by ride-hailing companies, such as Uber and Lyft, to match the trip demand with the availability of drivers. Deciding proper pricing policies is challenging and existing reinforcement learning (RL)-based solutions are restricted in solving small-scale problems. In this study, we contribute to RL-based approaches that can address the dynamic pricing problem in real-world-scale ride-hailing systems. We first characterize the dynamic pricing problem with a clear distinction between historical prices and current prices. We then translate our dynamic pricing problem into Markov Decision Process (MDP) and prove the existence of a deterministic stationary optimal policy. Our solutions are based on an off-policy reinforcement learning algorithm called twin-delayed deep determinant policy gradient (TD3) that performs offline learning of the optimal pricing policy using historical data and applies the learned policy to the next time slot, e.g., one week. We enhance TD3 by creating three mechanisms to reduce our model complexity and enhance training effectiveness. Extensive numerical experiments are conducted on both small grid networks (16 zones) and the NYC network (242 zones) to demonstrate the performance of the proposed algorithm. The results show our algorithm can efficiently find the optimal pricing policy for both the small and large networks, and can significantly enhance the platform profit and service efficiency.

Customer-Centric Dynamic Pricing for Free-Floating Vehicle Sharing Systems

Free-floating vehicle sharing systems such as car or bike sharing systems offer customers the flexibility to pick up and drop off vehicles at any location within the business area and, thus, have become a popular type of urban mobility. However, this flexibility has the drawback that vehicles tend to accumulate at locations with low demand. To counter these imbalances, pricing has proven to be an effective and cost-efficient means. The fact that customers use mobile applications, combined with the fact that providers know the exact location of each vehicle in real-time, provides new opportunities for dynamic pricing. In this context of modern vehicle sharing systems, we develop a profit-maximizing dynamic pricing approach that is built on adopting the concept of customer-centricity. Customer-centric dynamic pricing here means that, whenever a customer opens the provider’s mobile application to rent a vehicle, the price optimization incorporates the customer’s location as well as disaggregated choice behavior to precisely capture the effect of price and walking distance to the available vehicles on the customer’s probability for choosing a vehicle. Two other features characterize the approach. It is origin-based, that is, prices are differentiated by location and time of rental start, which reflects the real-world situation where the rental destination is usually unknown. Further, the approach is anticipative, using a stochastic dynamic program to foresee the effect of current decisions on future vehicle locations, rentals, and profits. We propose an approximate dynamic programming-based solution approach with nonparametric value function approximation. It allows direct application in practice, because historical data can readily be used and main parameters can be precomputed such that the online pricing problem becomes tractable. Extensive numerical studies, including a case study based on Share Now data, demonstrate that our approach increases profits by up to 8% compared with existing approaches from the literature. History: This paper has been accepted for the Transportation Science Special Issue on 2021 TSL Workshop: Supply and Demand Interplay in Transport and Logistics. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2021.0524 .

Indonesian “Sixth Method” on Intercompany Sales in Coal Mining Industries

The issue of transfer pricing in extractive industries, including the coal sector, has gained global attention. To mitigate the risks of inappropriate transactions and tax evasion, resource-rich countries incorporated, among others, the arm’s length principle into their legislation. Some countries use the “sixth method”, which mandates quoted prices for affiliated transactions. Indonesia’s Government Regulation No. 15 of 2022 introduces a similar approach for coal pricing, albeit with a certain hierarchical aspect. This article explores the Indonesian “sixth method” for intercompany coal sales and its interaction with existing transfer pricing rules, including potential issues that may lead to disputes.

Pari-Mutuel Betting Markets: Racetracks and Lotteries Revisited

This survey discusses the state of the art in research in racetrack and lottery investment markets. Market efficiency and the pricing of various wagers are studied along with new developments since the Thaler & Ziemba (1988) review. The weak form inefficient market pricing approach using stochastic programming optimization models changed racetrack betting from handicapping to a financial market allowing professional syndicates to operate as hedge funds. Topics discussed include arbitrage and risk arbitrage, syndicates, betting exchange rebates, behavioral biases, and fundamental and mispricing information in racetrack and lottery markets. Similar models can be used to successfully trade stock market anomalies. Supplemental Materials are included online.

Valuasi Ekonomi Wilayah Adat Taman Sunsong Kabupaten Sekadau

This article aims to investigate the valuation of the indigenous territory of Taman Sunsong in Sekadau Regency, with a focus on uncovering the cultural, economic, and environmental values inherent in the Taman Sunsong Indigenous Community. The research employs a mixed-method approach, combining qualitative and quantitative methods. Field surveys were conducted to gather data on the social, cultural, and economic profiles of the indigenous community. In-depth interviews were carried out with traditional leaders and community members to gain a comprehensive understanding. Economic data was collected through surveys, market price approaches, and benefit transfer methods. The findings reveal that the Taman Sunsong Indigenous Community possesses unique cultural traits and a way of life governed by customary values, rituals, and religious practices. Their social life is rooted in strong communal interactions, while a close relationship with the surrounding environment also plays a significant role. The significance of preserving nature and culture is evident in the high value placed on the indigenous forest. The forest holds cultural significance and profound knowledge about ecosystem balance and sustainable resource utilization. The economy of the Taman Sunsong indigenous region relies on fields, gardens, traditional craftsmanship, and the potential for ecotourism. To assess the economic value of the indigenous territory, market price approaches and benefit transfer methods are employed. Market prices are used to gauge the value of agricultural commodities and forest products. The benefit transfer method is utilized to estimate the value of environmental services such as oxygen production by the forest.

A semi‐Lagrangian ε$$ \varepsilon $$‐monotone Fourier method for continuous withdrawal GMWBs under jump‐diffusion with stochastic interest rate

AbstractWe develop an efficient pricing approach for guaranteed minimum withdrawal benefits (GMWBs) with continuous withdrawals under a realistic modeling setting with jump‐diffusions and stochastic interest rate. Utilizing an impulse stochastic control framework, we formulate the no‐arbitrage GMWB pricing problem as a time‐dependent Hamilton‐Jacobi‐Bellman (HJB) Quasi‐Variational Inequality (QVI) having three spatial dimensions with cross derivative terms. Through a novel numerical approach built upon a combination of a semi‐Lagrangian method and the Green's function of an associated linear partial integro‐differential equation, we develop an ‐monotone Fourier pricing method, where is a monotonicity tolerance. Together with a provable strong comparison result for the HJB‐QVI, we mathematically demonstrate convergence of the proposed scheme to the viscosity solution of the HJB‐QVI as . We present a comprehensive study of the impact of simultaneously considering jumps in the subaccount process and stochastic interest rate on the no‐arbitrage prices and fair insurance fees of GMWBs, as well as on the holder's optimal withdrawal behaviors.

Online machine learning approach for system marginal price forecasting using multiple economic indicators: A novel model for real-time decision making

In comparison to other countries, South Korea has a high reliance on energy, with the majority of its electricity being generated by a government-run company to ensure a stable and affordable supply. Unlike the "pay-as-bid" pricing approach, South Korea utilizes the system marginal price (SMP), also known as "pay-as-clear." Accurate SMP forecasting is crucial for guaranteeing steady economic growth because manufacturing is South Korea's flagship industry and accounts for 50 % of the nation's power consumption. In this study, a combination of machine learning-based batch learning and online learning techniques were employed to forecast the SMP in South Korea, utilizing a dataset consisting of five energy sectors, two financial sectors, and one transportation sector. The analysis of the F-value revealed that the coal sector, which is one of the energy sectors, had the most significant influence on SMP indicating the greatest score of 2,328. In this study, three machine learning models, namely support vector regression, simple deep neural network, and deep neural network, were suggested and compared for batch learning to determine the best-trained model. The evaluation metrics were used to assess the performance of these models. Based on the results obtained, the simple deep neural network was found to outperform the other models in terms of accuracy. Furthermore, two methods such as weight modification and time interval updating between inputs and output were employed for online learning based on the trained batch model. Upon the implementation of model updates, an ongoing assessment of its performance transpired utilizing the metrics of coefficient of determination, root mean square error, mean absolute error, and mean absolute percentage error. The average values for these metrics were observed to be 0.924, 7.991, 5.035, and 0.052, respectively. This study is expected to provide direct assistance in the formulation of energy plans for decision-makers in the industrial sector.

Optimal economic-emission load dispatch in microgrid incorporating renewable energy sources by golden jackal optimization (GJO) and Mexican Axolotl optimization (MAO)

This article proposes the use of hybrid technique to achieve balanced and compromised solution among the generation cost and the emission of pollutants. Microgrids (MGs) are the restricted power energy system that transmitted, generated, and distributed. The renewable energy sources (RESs) are used in their fullest extent. Advantages of MG include reducing cost and transmission losses. Operated in different modes like wind turbine (WT), microturbine (MT) and fuel cell (FC). The proposed technique used to execute the golden jackal optimization (GJO) and Mexican Axolotl optimization (MAO) named as GJO–MAO technique. The objective of the technique is to solve dissimilar optimization issues in MG reduces the computational cost and maximize performance. The objectives of economic dispatch are based on fractional scheduling and restricted environment. Three different scenarios, low-voltage MG system are investigated. GJO–MAO techniques used to optimize various issues on MG by using renewable energy. The proposed technique performance is done in the MATLAB. When the time-of-use (TOU) energy market price strategy with the fixed pricing approach, the economic dispatch is calculated by time-of-use electricity market pricing method, generating cost decreases by 18.5%, 13.5% if the FP-related combined economic emission dispatch (CEED) is examined, and 15% after evaluating the environmental-constrained-economic-dispatch (ECED). The MG producing cost targets for ECED and ECD are according to renewable energy sources. The best and most system is used for finding a fair compromise between the cost of generating and emission. The smallest values of implementation time and standard deviation of superiority and robustness are achieved.

Share pledging of controlling shareholders and corporate social responsibility disclosure

This paper employs a large sample of A-share listed firms in China from 2011 to 2019 to examine whether controlling shareholders strategically improve corporate social responsibility disclosure under the unique situation of share pledges. The result indicates that controlling shareholders’ share pledging is positively associated with corporate social responsibility disclosure. The mechanism analysis demonstrates that the corporate social responsibility disclosure level increases when the firm’s stock price approaches the liquidation line. This finding indicates that controlling shareholders use strategic social responsibility disclosure for risk management after pledging their shares; however, controlling shareholders focus on improving the comprehensiveness of disclosure content rather than its credibility and standardization.

Dynamic pricing for shared mobility systems based on idle time data

In most major cities today, various shared mobility systems such as car or bike sharing exist. Maintaining these systems is challenging, and, thus, public and private providers strive to improve operational performance. An important metric which is regularly recorded and monitored in practice for this purpose is idle time, i.e., the time a vehicle stands unused between two rentals. Usually, it is available for different temporal and spatial granularities. At the same time, dynamic pricing has been shown to be an efficient means for increasing operational performance in shared mobility systems, but data necessary for traditional dynamic pricing approaches, like unconstrained demand, is much less available in practice. Thus, dynamic pricing based on idle time data appears promising and first ideas have been proposed. However, the existing approaches are based either on simple business rules or on myopic optimization. In this work, we develop a novel dynamic pricing approach that determines prices by online optimization and thereby anticipates future profits through the integration of idle time data. The core idea is quantifying the remaining profitable time by using idle times. With regard to application in practice, the developed approach is generic in the sense that different types of readily available historical idle time data can be seamlessly integrated, meaning data of different spatio-temporal granularities. In an extensive numerical study, we demonstrate that the operational performance increases with higher granularity and that the approach with the highest one outperforms current pricing practice by up to 11% in terms of profit.

The impact of remote work on green space values in regional housing markets

We examine the extent to which the increased prevalence of work from home (WFH) due to the COVID-19 pandemic has made green amenities more desirable.22Data provided by Zillow through the Zillow Transaction and Assessment Dataset (ZTRAX). More information on accessing the data can be found at http://www.zillow.com/ztrax. The results and opinions are those of the authors and do not reflect the position of the Zillow Group. Specifically, we focus on ten metropolitan statistical areas (MSAs) in the United States (Baltimore, Chicago, Cleveland, Los Angeles, Minneapolis/St. Paul, New York City, Philadelphia, Pittsburgh, St. Louis, and Tampa) and use a hedonic pricing approach to identify changes in the implicit prices of yard space and park proximity. We use a combination of data sources, including the Zillow Transaction and Assessment Database (ZTRAX), Open Street Maps, Longitudinal Employer Household Dynamics Origin–Destination Employment Statistics (LODES), National Land Cover Database (NLCD), and the Environmental Protection Agency’s EnviroAtlas to study the interactions between the exposure of a given neighborhood to WFH shock and private yard space, as well as proximity to the nearest park. Our findings suggest that home buyers in all the MSAs, except Minneapolis and Pittsburgh, assigned a greater value to private green amenities during the post-COVID period. However, for the MSAs of Los Angeles, New York, Philadelphia, and Pittsburgh, the marginal willingness to pay (MWTP) for private green spaces decreased in the post-COVID period in the areas with large WFH shocks. No significant change in the MWTP for proximity to public green spaces is noted in the post-COVID period for most MSAs. An event study of yard space reveals that most MSAs experienced an increase in the hedonic price of yard space in the post-COVID period. In the pre-COVID period, for many MSAs, the hedonic price of yard space was decreasing over the years, and this trend reversed in the post-COVID period. These results suggest that the preferred amenity bundles of people living in major cities in the U.S. have shifted as a result of changes in their commutes and work habits.

Analisis Perhitungan Pokok Produksi dengan Menggunakan Metode Full Costing : Studi Kasus pada Apem Singkong Keju Pak Gede

The title of this research is "Micro, Small, and Medium Enterprises (MSMEs) Study: Apem Cassava Cheese by Mr. Gede. The research aims to 1) Determine the calculation of production costs using the Full Costing method in the MSME Apem Cassava Cheese by Mr. Gede, and 2) Assess the income of the MSME Apem Cassava Cheese by Mr. Gede. The objectives of this research are 1) To understand the calculation of production costs using the Full Costing method in the MSME Apem Cassava Cheese by Mr. Gede, and 2) To analyze the income generated by the MSME Apem Cassava Cheese by Mr. Gede. This research employs a qualitative method through interviews, observations, and documentation. All data are qualitatively analyzed in descriptive qualitative analysis. The informants in this study are the owner of Apem Cassava Cheese by Mr. Gede. The results of the research show that 1) The calculated production cost according to the company is Rp 1,060, with a production cost of Rp 42,382,000, which includes raw material costs, labor, packaging, and electricity. The selling price of the MSME's product has been traditionally set at Rp 1,500, resulting in a profit margin of 41.51% according to the MSME's estimation. 2) The highest sales in 2022 occurred in December with a total of 40,000 pieces and a net profit of Rp 17,618,000. The lowest sales were recorded in June with a total of 30,750 pieces and a net profit of Rp 11,654,639. The total sales in 2022 amounted to Rp 182,926,890, with an average monthly profit of Rp 15,488,983. The cost-plus pricing approach is used to determine the product's selling price, with a desired profit margin of 41.51%.

Day Ahead Solar Generation Forecasting in Smart Grids to Minimize Electricity Bill

Abstract: In order to meet the increasing growth of energy demand, integration of renewable resources into residential applications appears to be a viable solution. In the proposed model,a residential house is considered where the consumer is able to generate his own energy from a microgrid consisting of solar panels and wind turbines. In this study, an optimization method is employed to minimize the overall electricity bill of a residential home over a periodof 24 hours. In smart grids, energy management is imperative in reducing the electricity cost of consumers under a real time pricing approach. Nowadays, learning-based modeling methods are utilized to build a precise forecast model for renewable power sources. In this paper, we propose a long short-term memory (LSTM) recurrent neural network-based framework, which is one of the most popular techniques of deep learning. This technique predicts day ahead solar irradiation and wind speed data. We also consider an energy storage system (ESS) for efficient energy utilization. Optimization is done and from the obtained results a substantial reduction in electricity bill is observed

Not one, but many: developing a multi-indication pricing model for medicines in Belgium.

Back ground: Current pricing and reimbursement models that focus on one indication at a time are not suited to address the market access of multi-indication medicines. Therefore, the aim of this study is to co-create with Belgian stakeholders a multi-indication pricing model and procedural pathway, to identify conditions for implementation, and to illustrate the multi-indication pricing model with a case study. Methods: Different multi-indication pricing models were identified from the literature, case studies and pilots in other countries. Semi-structured interviews were conducted with 21 representatives from the National Institute for Health and Disability Insurance, insurance funds, clinicians, patients, the policy cell of the Minister of Health, pharmaceutical industry and academia. These provided insight in the opinions of stakeholders about possible multi-indication pricing models and their feasibility in the Belgian context. Agreement on the preferred multi-indication pricing model and procedural pathway was reached in a multi-stakeholder round table. Results: The international review generated four main multi-indication pricing models that vary in terms of whether a uniform price or differential prices are applied, whether prices are adjusted for the volume and/or value of the medicine in each indication, and whether a proactive or retroactive dynamic pricing approach is used. However, Belgian stakeholders preferred a fifth model, which sets a single price as the volume- and value-weighted average price across all indications at launch. Over time, the price is adapted based on volume and value of the medicine in real-life practice for each indication. To implement this model, a legal framework, horizon scanning and early dialogue, data infrastructure, an evidence plan for the medicine, technical expertise and governance model need to be developed. Conclusion: Although the multi-indication pricing model preferred by Belgian stakeholders raises the administrative burden, it allows for the price of a medicine to vary during the lifecycle based on its initial and real-life performance in multiple indications.

Estimated Savings From Using Added Therapeutic Benefit and Therapeutic Reference Pricing in United States Medicare Drug Price Negotiations

ObjectivesUS Medicare will begin negotiating prices for top-selling drugs in 2023. This study describes and estimates potential savings from a therapeutic reference pricing approach, linking comparative effectiveness with the costs of existing therapeutic alternatives, that Medicare could use to adjust the starting point for price negotiations. MethodsFirst, we identified target drugs likely to be selected for Medicare negotiation. Second, we identified comparative effectiveness ratings for target drugs based on French Haute Autorité de Santé reports. For target drugs with minor or no added benefit, we identified therapeutic alternatives based on the French reports and US clinical guidelines. For each target drug with minor or no added benefit, we computed the difference between spending based on the drug’s estimated statutory ceiling price and spending based on the weighted average cost of therapeutic alternatives or the lowest cost therapeutic alternative. Finally, we calculated potential annual savings from using a starting point in negotiations based on costs of therapeutic alternatives. ResultsPotential drug-level savings to Medicare from using a starting point in negotiations based on average spending across therapeutic alternatives, compared with using the statutory ceiling price alone, ranged from $186 541 340 to $2 173 441 197. Potential savings from using a starting point based on the lowest cost alternative ranged from $199 872 163 to $3 605 904 765. ConclusionsAlthough we do not expect Medicare to rely on French comparative effectiveness assessments, this study demonstrates the potential for additional savings when using comparative effectiveness and costs of therapeutic alternatives to inform the starting price for negotiations.

Franchisors’ Strategic Pricing Approaches for Franchise Fee Decisions and the Moderating Role of the Competitive Condition: Evidence from the Korean Franchising Market

As franchising provides several benefits to both corporations and small business owners, a growing number of contracts have been written through which corporations offer the right to use their brand name and business model, and small business owners pay fees for accepting the offers. In this franchisor–franchisee market, the franchise fee plays a pricing role in the exchange between two parties. In this context, we investigate the influence of franchisors’ strategic pricing approaches (i.e., cost- and value-based approaches) on franchise fee decisions. Furthermore, by examining the moderating effect of the competitive condition on the relationships between pricing approaches and franchise fees, we uncover franchisors’ pricing practices in greater detail. The results show that both pricing approaches have significant influences on franchise fee decisions, and the competitive condition moderates the relationship between the value-based approach and franchise fees but does not moderate the relationship between the cost-based approach and franchise fees. The findings contribute to the franchising and pricing literature and to industry practitioners.

An efficient unified approach for spread option pricing in a copula market model

An efficient unified approach for spread option pricing in a copula market model

Service-quality based pricing approach for charging electric vehicles in smart energy communities

Guiding electric vehicles (EVs) in smart energy communities to participate in demand response can significantly benefit users and the load aggregator. However, when the user's travel plans are uncertain, the EV may end up charging before the planned time, leaving the user with an insufficient power supply. This can occur because the control program chooses to charge the EV during periods of low electricity prices to reduce user costs. To address this issue, a new strategy for charging pricing based on service-quality is proposed to help users deal with the uncertainty of travel plans and promote user participation in demand response. In this paper, the proposed charging pricing of EVs consists of three parts. Firstly, the model of charging packages with different service qualities is designed for users. The differences in charging packages are reflected in the percentage of power supply and price within each time slot. Charging packages provide users with at least a certain amount of power in each time slot, allowing them to better cope with the uncertainty of their travel plans. Secondly, a subscription model for charging packages is built to match the user's travel plans. Finally, the pricing model of the charging package is established, representing the master-slave game between the load aggregator and the user. The model is linearized by the KKT condition. The results show that the proposed strategy helps users cope with the uncertainty of their travel plans, reducing charging cost for users by 16.39% while increasing revenue for the load aggregator by 14.8%.

A Dual Approach for Dynamic Pricing in Multidemand Markets

A Dual Approach for Dynamic Pricing in Multidemand Markets