Individual Incentives Research Articles

On the provision of excludable public goods: General taxes or user prices?

AbstractThis paper compares some of the most common ways of financing the provision of publicly provided impure public goods/services in a unified dynamic general equilibrium framework with heterogeneous income groups. The focus is on the effects of user prices on individual incentives and how the latter shape aggregate outcomes and distribution. We show that a market‐like mechanism of user prices not only improves individual incentives and enhances aggregate efficiency, but it is also Pareto improving. However, such a mechanism is not beneficial or simply collapses when social externalities are strong, when the government adopts a mixed system that combines user prices with heavy subsidization of user costs, and when there is a minimum amount of the good that everybody needs.

Incentive-Induced Social Tie and Subsequent Altruism and Cooperation

Abstract In this paper, we studied the spillover effects of cooperative and competitive incentive schemes on subsequent decisions involving altruism and cooperation. We collected data with a laboratory experiment where subjects were asked to perform Mini Dictator games and a Public Good game after playing an incentivized real effort task. We found that cooperative incentives foster higher subsequent altruism and cooperation as compared with competitive or individual incentives. By contrast, competitive incentives induced more envy towards competitor peers and more spiteful behaviour when giving implies a larger payoff for the recipient. The effect of incentives on altruism and cooperation was moderated by relative rank in the incentivized effort task and by individual level of impulsivity.

Assessing the role of carbon pricing in global climate change mitigation strategies

Carbon pricing has emerged as a crucial policy tool in global efforts to mitigate climate change by internalizing the costs of carbon emissions and incentivizing emission reductions. This review provides an assessment of the role of carbon pricing in global climate change mitigation strategies. Carbon pricing mechanisms, including carbon taxes and emissions trading systems (ETS), have gained traction worldwide as governments seek effective ways to reduce greenhouse gas emissions. These mechanisms establish a price on carbon emissions, creating economic incentives for businesses and individuals to reduce their carbon footprint. The effectiveness of carbon pricing in mitigating climate change depends on various factors, including the stringency of pricing mechanisms, coverage of sectors and gases, and complementary policies and measures. Studies have shown that well-designed carbon pricing policies can lead to significant emission reductions while promoting innovation and investment in clean technologies. However, carbon pricing also faces challenges and limitations, including concerns about equity, competitiveness, and political acceptability. Distributional impacts on vulnerable populations and energy-intensive industries must be carefully addressed to ensure the fairness and effectiveness of carbon pricing policies. Furthermore, the success of carbon pricing depends on international cooperation and coordination, as emissions can easily shift across borders in response to pricing disparities. Global efforts to harmonize carbon pricing mechanisms and establish international carbon markets can enhance the effectiveness and efficiency of mitigation efforts. In conclusion, carbon pricing plays a crucial role in global climate change mitigation strategies by aligning economic incentives with environmental objectives. While it offers significant potential for reducing emissions and fostering the transition to a low-carbon economy, addressing challenges such as equity concerns and promoting international cooperation is essential for maximizing its effectiveness. By integrating carbon pricing into comprehensive climate policy frameworks, policymakers can accelerate progress towards a sustainable and resilient future.

A Privacy-preserving Auction Mechanism for Learning Model as an NFT in Blockchain-driven Metaverse

The Metaverse, envisioned as the next-generation Internet, will be constructed via twining a practical world in a virtual form, wherein Meterverse service providers (MSPs) are required to collect massive data from Meterverse users (MUs). In this regard, a critical demand exists for MSPs to motivate MUs to contribute computing resources and data while preserving user privacy. Federated learning (FL), as a privacy-preserving collaborative machine learning paradigm, can support distributed intensive computation in the Metaverse. In this work, we first investigate minting the machine learning models into NFT with FL assistance (referred to as FL-NFT), such that MUs as stakeholders can control the ownership and share the economic value of user-generated content (UGC). Specifically, MUs are encouraged to establish a decentralized autonomous organization (i.e., MU-DAO) to aggregate local models and mint FL-NFT. MUs and MSPs optimize the strategies by formulating an imperfect information Stackelberg game to trade off the cost and benefit. We apply the backward induction to derive the equilibrium solution. Then, we construct a privacy-preserving multi-winner sealed-bid auction mechanism (PMS-AM), in which the Hidden Markov Model assists MSPs in choosing rational bidding strategies according to historical bids, and the double auction mechanism determines the winners and price of FL-NFT. Finally, the numerical results based on theoretical analysis and simulations demonstrate that the proposed PMS-AM can increase the quality of FL-NFT and achieve the economic properties of incentive mechanisms such as individual rationality and incentive compatibility.

Treatment Allocation with Strategic Agents

There is increasing interest in allocating treatments based on observed individual characteristics: examples include targeted marketing, individualized credit offers, and heterogeneous pricing. Treatment personalization introduces incentives for individuals to modify their behavior to obtain a better treatment. Strategic behavior shifts the joint distribution of covariates and potential outcomes. The optimal rule without strategic behavior allocates treatments only to those with a positive conditional average treatment effect. With strategic behavior, we show that the optimal rule can involve randomization, allocating treatments with less than 100% probability even to those who respond positively on average to the treatment. We propose a sequential experiment based on Bayesian optimization that converges to the optimal treatment rule without parametric assumptions on individual strategic behavior. This paper was accepted by Vivek Farias, data science. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.01629 .

The Influence of Teacher Incentives on Student Academic Achievement in Public Secondary Schools

The education sector, in general, faces challenges that suffer quite a lot in terms of quality of education services, curriculum, teacher motivation, and government role that influences school performance and student achievement. One important factor that influences school performance is how teachers get incentives, which makes them motivated and satisfied with their jobs and organization. Performance-based incentives for teachers have considerably attracted attention as a policy tool to improve student outcomes. Education departments intend to allocate large amounts of money as incentives for the teachers based on their performance. This paper evaluates the influence of teacher incentives on student achievement in public secondary schools. Specifically, the study examines the influence of monetary incentives for teachers on student academic achievement in public secondary schools. In addition, it establishes the effect of tangible non-monetary incentives for teachers on student academic achievement in public secondary schools. Moreover, it investigates the influence of intangible non-monetary incentives for teachers on student academic achievement in public secondary schools. The study will revise past literature related to this research topic to obtain all information necessary for the research. The researcher will gather findings and results obtained by the former authors, which are related to the main purpose and objectives of the study. Based on the literature reviewed by the researcher, the study expects that individual incentives will have more effect on the student’s achievements. Literature also shows that merit pay systems have very little influence on student achievement. Lastly, the teacher’s monetary incentives are a function of students’ achievements in their final year of high school.

Costly Information Acquisition in Decentralized Markets: Experimental Evidence

This study tests the rationality of the decisions to purchase information, the informational efficiency of prices, and the optimality of the resulting allocations in decentralized markets with a series of laboratory experiments. The theory predicts that markets with dispersed information and natural buyers and sellers converge to a fully revealing equilibrium. It is profitable to pay for information, and as such, the Grossman-Stiglitz paradox does not emerge. Statistically significant improvements in both price efficiency and allocative efficiency are documented across trading periods. In contrast to the theory, which predicts that the higher the number of informed agents, the stronger the individual incentives to invest in information, the and the more data present the opposite result. The price of information is higher when there are fewer informed agents. The result can be explained by boundedly rational decision making. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: This work was supported by the National Science Foundation [Price Quality in Dark Markets Grant 1426428]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.4976 .

Improving Working Conditions in Retail During the COVID-19 Pandemic

The COVID-19 pandemic has significantly changed the patterns of employees’ behaviour, especially in the retail sector, which requires continuous contact with consumers. The anti-pandemic measures based on physical distance, wearing medical and protective equipment, application of barriers, frequent disinfection of work surfaces and space, use of strict protocols, and safety standards significantly complicate working conditions. This paper aims to analyse and define incentives for upgrading work and working conditions in retail during the COVID-19 pandemic. The scientific importance of the paper is reflected in the fact that analytically and systematically, it defines incentives that will support the behaviour of employees during crises affecting the retail sector. Unlike existing research in trade and retail, which indicated the importance of individual incentives for improving working conditions in periods of crisis, the contribution of this paper is in the comprehensive analysis, systematization, and definition of the importance of all incentives for improving the working conditions of employees in the retail sector. In this context, empirical research was conducted on a sample of 255 employees and it tested the importance of incentives to improve working conditions such as: maintaining a safe and healthy working environment, how to treat infected workers and workers in contact, the existence of detailed instructions for employees and consumers, organizing transportation for workers, financial incentives for employees, reduction of the scope of business activities, shortening of working hours of facilities, security protocols and digitalization of work processes. The survey was conducted among employees in SMEs and large retail chains. The set hypotheses were tested using the Path Analysis or Structural Modeling (SEM) method. The correlation between the retailer size and the differences in the incentives’ impact was assessed by employing Multiple Regression Analysis. The research results showed significant deviations in the mentioned incentives depending on the size of the retailer. Based on the obtained results, a set of measures was proposed that social policymakers and retail facility management should implement to improve working conditions in retail and establish a social safety net that will support employees in times of crisis. Guidelines for future research are provided in the paper. Keywords: working conditions, work incentives, position of workers, retailing, COVID-19 pandemic.

Policy Responses To Fake News On Social Media Platforms: A Law And Economics Analysis

Abstract Fake News is one of the major techno-policy challenges faced by modern societies. As popular discourse shifts to social media platforms, Fake News on the internet has increased drastically, generating significant costs for society. Consequently, there is a regulatory movement across the globe to mitigate fake news on these platforms. A law and economics analysis offers valuable insights towards devising an appropriate regulatory approach to tackling this issue. In economic terms, Fake News can be conceptualized as a negative externality, while Fact-Checking and Content Moderation Services may be defined as public goods. There exists a misplaced individual incentive to create fake news on social media platforms. A clear case of market failure and thus a need for state intervention can be made out. While direct state regulation of platforms is the preferred approach by regulators for mitigating fake news, this paper cautions against over-reliance on such a ‘negative state’ regime, due to censorship risks and enforcement costs. This paper recommends adoption of a multi-pronged strategy, including a statutory Pigouvian tax to internalize social costs of fake news on social media platforms within the market, by channelling resources towards promoting positive state measures to mitigate the broader effects of fake news on these platforms. This paper’s analysis is largely located in the Indian context, with references to other jurisdictions.

Motivation attitudes of managers and ordinary employees of anti-tuberculosis organizations

The purpose of the study was to identify differences in the level of general professional satisfaction and motivational preferences of the managers and ordinary personnel of tuberculosis institutions.
 Materials and methods. An anonymous sociological survey of 75 employees of medical institutions of phthisiatric profile using the developed questionnaire was conducted. The level of satisfaction with occupational activity and motivational attitudes of medical workers was studied, and recommendations for improving the complex system of motivation of medical personnel were developed.
 Results. Managers are more likely to be dissatisfied with the key incentives compared to doctors. The priorities for doctors are loyalty to occupational duty, financial well-being and stability, for managers — self-realization, professional growth and social status. For doctors, the main motives are material, managers believe labour efficiency to increase due to developing a system of individual incentive payments and maintaining interpersonal relationships in the team. Conflict situations with a manager occur due to the imposition of additional responsibilities according to ordinary employees, to the managers’ opinion — their failure. The degree of employee satisfaction with work can be characterized as high, which is confirmed by the desire of a large number of employees to continue working in their previous position.
 Limitations: truthfulness, hasty and rash answers, distortion of results due to socio-psychological influence, small number of respondents, belonging to a narrow sphere of activity.
 Conclusion. When developing a motivation system for employees of anti-tuberculosis institutions, it is necessary to agree on satisfaction with working conditions of all occupational categories, improve incentive methods while maintaining existing favourable factors, create an individual incentive system based on investments in human resources, manage the motivational environment taking into account group affiliation, individual characteristics, preferences and personal qualities of employees, as well as assessment of employee satisfaction with innovations through regular questionnaires, sociological surveys

Justice, inclusion, and incentives

How does justice affect individual incentives and efficiency in a political economy? We show that elementary principles of distributive justice guarantee the existence of a self-enforcing contract whereby agents non-cooperatively choose their inputs and derive utility from their pay. Chief among these principles is that your pay should not depend on your name, and a more productive individual should not earn less. We generalize our analysis to incorporate inclusivity, ensuring basic pay to unproductive agents, implemented through progressive taxation and redistribution. Our findings show that without redistribution, any self-enforcing agreement may be inefficient, but a minimal level of redistribution guarantees the existence of an efficient agreement. Our model has several applications and interpretations. In addition to highlighting the structure of economies and organizations in which fairness and efficiency are compatible, we develop an application to the formation of rent-seeking political alliances under the threat of fake news.

The Difference of Influencing Factors of Entrepreneurs' Entrepreneurial Choice

Entrepreneurship is a crucial catalyst for enhancing economic vitality and sustaining a country's healthy economic growth. Therefore, what leads entrepreneurs to relinquish their comfort zone and abandon traditional employment to pursue entrepreneurship remains a pertinent inquiry. Entrepreneurs represent the core of entrepreneurial activity. Based on empirical evidence, entrepreneurs' reasons for starting a business vary considerably. For instance, some are motivated by the desire to innovate, while others aim to maximize their earnings. This study specifically examines the singular influence of individual factors, summarizes the primary factors that impact entrepreneurial decision-making, provides explanations and analyses, and elucidates the specific role of each factor and its impact on entrepreneurs' choices. Furthermore, this study introduces the impact of economic cycles and unemployment on individual entrepreneurial behavior, which has received insufficient attention until now. People commonly believe that starting a business is more feasible during periods of economic growth rather than during recessions, but certain entrepreneurial behaviors suggest that this belief may be misguided. Through analysis and research, recommendations are provided for both individual entrepreneurial decisions and government entrepreneurial incentive policies.

Loss-Optimising-Management-Algorithm and the usage in an energy management for low-voltage grids

Since the amount of decentralised power generation is increasing it is important to develop an energy management system for low-voltage grids. This paper presents a method to operate such a management. The system is designed for managing a network of smart homes which can consume or supply electrical energy. The aims are to reduce the transmission losses and to stay within the limits of both, the voltage drop and the utilisation of lines and transformers. The reduction of the losses is implemented in the Loss-Optimising-ManagementAlgorithm (LOMA). This algorithm tries to find the power flow situation where minimal losses occur. The results of LOMA, the actual power situation (in low- and medium-voltage system) and the maximum power situation (based on the grid parameters) are summarised in an individual incentive signal for every smart home. The simulations show the feasibility of such an energy management and that significant loss reduction is achieved.

Optimal Spatial–Temporal Scheduling for EVs With Stochastic Behaviors and Possible Inauthentic Information

This paper studies the spatial-temporal scheduling for electric vehicles with stochastic charging behaviors. A new spatial-temporal scheduling model is proposed to achieve both the spatial coordination and temporal coordination for EV users and is modeled as a non-convex optimization problem which can be solved by the coordinate descent method. Further, a new billing method of charging cost is proposed based on the Vickrey-Clarke-Groves to avoid possible inauthentic information from users. Specially, the individual rationality and incentive compatibility of the method are theoretically proved. Moreover, a stochastic optimization problem is proposed to describe the stochastic charging behavior of users, which can fit the real application better than the deterministic one. Consequently, an efficient charging strategy is obtained via chance constraint method. Simulation results and comparison studies show the effectiveness of both the model and the billing method.

Incentive Mechanism Design for Emergency Frequency Control in Multi-Infeed Hybrid AC-DC System

In multi-infeed hybrid AC-DC (MIDC) systems, the emergency frequency control (EFC) with LCC-HVDC systems participating is of vital importance for system frequency security. Nevertheless, when regional power systems are operated by different decision-makers, the LCC-HVDC systems and their connected AC systems might be unwilling to participate in the EFC due to the costs and losses. In this paper, to incentivize the LCC-HVDC systems and their connected adjacent AC systems to participate in the droop-based EFC, a novel control-parameter-based incentive mechanism is proposed, which can flexibly deal with various possible power imbalances. Then, a non-cooperative-based incentive game model is formulated to quantitatively analyze the incentive mechanism in the MIDC system. An algorithm for seeking the Nash equilibrium is designed, and the uniqueness of Nash equilibrium is proven. Moreover, the individual rationality, incentive compatibility and social optimality of the proposed mechanism are analyzed and proven. The effectiveness of the proposed incentive mechanism is verified through a case study.

ОСОБЕННОСТИ ИСПОЛЬЗОВАНИЯ СИСТЕМ МАТЕРИАЛЬНОГО И НЕМАТЕРИАЛЬНОГО СТИМУЛИРОВАНИЯ ПРИ УПРАВЛЕНИИ ПЕРСОНАЛОМ ПРЕДПРИЯТИЙ В СОВРЕМЕННЫХ УСЛОВИЯХ

The article analyzes various aspects of motivational mechanisms in modern organizations, with an emphasis on the importance of fulfilling critical conditions for the successful achievement of results. Particular attention is paid to the development of effective non-financial motivation programs that correspond to the strategic directions of the organization and are adapted to the needs of various groups of employees. The authors analyze the experience of foreign and Russian companies, emphasizing the importance of an integrated approach to motivating personnel and justify the need for regular updating of motivational programs in response to changes in the market environment and the structure of labor resources. The purpose of the article is to study the features of the use of financial and non-financial incentive systems in modern conditions at enterprises. For this work, a comprehensive methodological approach was used, including the use of general scientific and private scientific methods, analysis and a systematic approach; the current practice in international companies was analyzed and the main practices of material and non-material incentive systems were identified in a comparative analysis based on the results of existing research at various enterprises. The main features of the use of material and non-material incentive systems at enterprises in modern conditions are identified, both financial and non-financial approaches to motivating employees are considered, including individual and group, short-term and long-term strategies, the features of various types of incentives for employees are analyzed and conclusions are drawn about the need for implementation a systematic approach that takes into account the interaction of various elements of the incentive system in a complex to achieve the best results of the enterprise. This work has revealed that the effective use of financial and non-financial incentive systems in enterprises requires flexibility, result orientation, and openness. As a result, it can be concluded that the development of individual employee incentive programs, taking into account the specifics of the enterprise’s industry and the needs of employees are key elements of a successful staff motivation system.

Prehospital Care Standards in the Federal Wildland Fire Service

Abstract Objective Defining the role of federal wildland firefighters (WFF) in the context of emergency medical services (EMS) is a gap in the literature. Our work highlights the importance of standardizing and enhancing the EMS capabilities of federal WFF for their own occupational safety and well-being, while emphasizing their potential for responding to all disasters and providing medical safety net in rural communities. Methods A comprehensive academic literature review on the topic returned one non-peer reviewed manuscript. Study members analyzed National Wildfire Coordinating Group (NWCG) EMS policy to assess current EMS standards within the WFF service. Results The study reveals a lack of standardized, comprehensive support for EMS capabilities among federal WFF, despite need for their expertise in wide-ranging emergency situations. There are few incentives for individuals to obtain and maintain EMS certification. Various innovations, such as the Alaska Fire Medic Program and the BLM Operational Medical Support Program, are potential models for improving EMS across the entire NWCG. Conclusions To enhance the safety, professionalization, and efficiency of WFF, the NWCG should consider standardizing EMS guidelines, especially in providing consistent support for medical certifications. EMS certified WFF could contribute to nationwide disaster response capabilities if supported by NWCG.

Boosting Weak-to-Strong Agents in Multiagent Reinforcement Learning via Balanced PPO.

Multiagent policy gradients (MAPGs), an essential branch of reinforcement learning (RL), have made great progress in both industry and academia. However, existing models do not pay attention to the inadequate training of individual policies, thus limiting the overall performance. We verify the existence of imbalanced training in multiagent tasks and formally define it as an imbalance between policies (IBPs). To address the IBP issue, we propose a dynamic policy balance (DPB) model to balance the learning of each policy by dynamically reweighting the training samples. In addition, current methods for better performance strengthen the exploration of all policies, which leads to disregarding the training differences in the team and reducing learning efficiency. To overcome this drawback, we derive a technique named weighted entropy regularization (WER), a team-level exploration with additional incentives for individuals who exceed the team. DPB and WER are evaluated in homogeneous and heterogeneous tasks, effectively alleviating the imbalanced training problem and improving exploration efficiency. Furthermore, the experimental results show that our models can outperform the state-of-the-art MAPG methods and boast over 12.1 % performance gain on average.

Enhancing Data Freshness in Air-Ground Collaborative Heterogeneous Networks through Contract Theory and Generative Diffusion-Based Mobile Edge Computing.

Mobile edge computing is critical for improving the user experience of latency-sensitive and freshness-based applications. This paper provides insights into the potential of non-orthogonal multiple access (NOMA) convergence with heterogeneous air-ground collaborative networks to improve system throughput and spectral efficiency. Coordinated resource allocation between UAVs and MEC servers, especially in the NOMA framework, is addressed as a key challenge. Under the unrealistic assumption that edge nodes contribute resources indiscriminately, we introduce a two-stage incentive mechanism. The model is based on contract theory and aims at optimizing the utility of the service provider (SP) under the constraints of individual rationality (IR) and incentive compatibility (IC) of the mobile user. The block coordinate descent method is used to refine the contract design and complemented by a generative diffusion model to improve the efficiency of searching for contracts. During the deployment process, the study emphasizes the positioning of UAVs to maximize SP effectiveness. An improved differential evolutionary algorithm is introduced to optimize the positioning of UAVs. Extensive evaluation shows our approach has excellent effectiveness and robustness in deterministic and unpredictable scenarios.

‘Party position, electoral incentives, and attention to European Union issues in the 2019 European Parliament election’

Under what conditions do Members of the European Parliament (MEPs) give attention to EU issues in European Parliament (EP) election campaigns? This study examines how party positioning on European integration and individual electoral incentives affected MEPs’ attention to EU issues in the 2019 EP election campaign. Based on data collected from Twitter, the findings show that pro-EU parties and individual vote-seeking incentives increased attention to EU issues. More specifically, they show that MEPs from pro-EU mainstream parties had incentives to debate about EU issues, especially when they competed for votes. The findings help to unfold the interplay between party-level and legislator-level factors and its effects on attention to EU issues and aim to contribute to research on EU issue salience and EP election campaigns.