Incentive Design Research Articles

Physician Financial Incentives to Reduce Unplanned Hospital Readmissions: A Propensity Score Weighted Cohort Study

BackgroundUnplanned hospital readmissions are associated with adverse patient outcomes and substantial healthcare costs. It remains unknown whether physician financial incentives for enhanced discharge planning can reduce readmission risk. MethodsIn 2012, policymakers in British Columbia, Canada, introduced a $75 fee-for-service physician payment to incentivize enhanced discharge planning (the “G78717” fee code). We used population-based administrative health data to compare outcomes among G78717-exposed and G78717-unexposed patients. We identified all nonelective hospitalizations potentially eligible for the incentive over a 5-year study interval. We examined the composite risk of unplanned readmission or death and total direct healthcare costs accrued within 30 days of discharge. Propensity score overlap weights and adjustment were used to account for differences between exposed and unexposed patients. ResultsA total of 5262 of 24,787 G78717-exposed and 28,096 of 136,541 unexposed patients experienced subsequent unplanned readmission or death, suggesting exposure to the G78717 incentive did not reduce the risk of adverse outcomes after discharge (crude percent, 21.1% vs 20.6%; adjusted odds ratio, 0.97; 95% CI, 0.93-1.01; P = .23). Mean direct healthcare costs within 30 days of discharge were $3082 and $2993, respectively (adjusted cost ratio, 1.00; 95% CI, 0.95-1.05; P = .93). ConclusionsA physician financial incentive that encouraged enhanced hospital discharge planning did not reduced the risk of readmission or death, and did not significantly decrease direct healthcare costs. Policymakers should consider the baseline prevalence and effectiveness of enhanced discharge planning, the magnitude and design of financial incentives, and whether auditing of incentivized activities is required when implementing similar incentives elsewhere. Trial RegistrationClinicalTrials.gov ID, NCT03256734

Understanding the municipal-level design and adaptation of pay-for-performance schemes across two states of Brazil.

The design of complex health systems interventions, such as pay for performance (P4P), can be critical to determining such programmes' success. In P4P programmes, the design of financial incentives is crucial in shaping how these programmes work. However, the design of such schemes is usually homogenous across providers within a given scheme. Consequently, there is a limited understanding of the strengths and weaknesses of P4P design elements from the implementers' perspective. This study takes advantage of the unique context of Brazil, where municipalities adapted the federal incentive design, resulting in variations in incentive design across municipalities. The study aims to understand why municipalities in Brazil chose certain P4P design features, the associated challenges and the local adaptations made to address problems in scheme design. This study was a multiple case study design relying on qualitative data from 20 municipalities from two states in Northeastern Brazil. We conducted two key informant interviews with municipal-level stakeholders and focus group discussions with primary care providers. We also reviewed municipal Primary Care Access and Quality laws in each municipality. We found substantial variation in the design choices made by municipalities regarding 'who was incentivized', the 'payment size' and 'frequency'. Design choices affected relationships within municipalities and within teams. Challenges were chiefly associated with fairness relating to 'who received the incentive', 'what is incentivized' and the 'incentive size'. Adaptations were made to improve fairness, mostly in response to pressure from the healthcare workers. The significant variation in design choices across municipalities and providers' response to them highlights the importance of considering local context in the design and implementation of P4P schemes and ensuring flexibility to accommodate local preferences and emerging needs. Attention is needed to ensure that the choice of 'who is incentivized' and the 'size of incentives' are inclusive and fair and the allocation and 'use of funds' are transparent.

Multi-Task Diffusion Incentive Design for Mobile Crowdsourcing in Social Networks

Multi-Task Diffusion Incentive Design for Mobile Crowdsourcing in Social Networks

Government subsidy and benefit distribution mechanisms for transportation PPP projects: An evolutionary game perspective

Public-private partnerships (PPP), as an important model for collaboration between the public and private sectors, is an urgent and critical topic due to the serious financial losses of governments involved in transportation PPP projects in recent years. Current research focuses on the government subsidy model, in which the effective implementation of government subsidies relies on the design of incentives for stakeholder behavior. Although the positive externalities are strong, they are prone to the problem of “free riding,” which leads to low project performance and challenges in compensating for the government's financial losses. Therefore, this study proposes a novel dynamic subsidy mechanism that can be adjusted based on actual changes in transportation demand and that is linked to project performance. We use evolutionary game theory to construct a two-party evolutionary game model of the government and social capital, focusing on the stability and influencing factors of these interactions. Our research unveils that reaching specific thresholds in both the incentive coefficient and benefit distribution ratio induces an “positive management–negative management” shift in the behavior of involved parties, leading to enhanced project outcomes. Notably, fluctuations in operational quality substantially enhance the efficiency of the active management of private sector, with no discernible impact on the subsidy efficiency of the government. Therefore, our study provides a theoretical framework for improving the revenue allocation and government subsidy mechanism, which has theoretical and practical implications for enhancing the effect of government incentives and improving the quality of operational social capital.

Does pay-for-performance design matter? Evidence from Brazil.

Pay-for-performance (P4P) schemes have been shown to have mixed effects on health care outcomes. A challenge in interpreting this evidence is that P4P is often considered a homogenous intervention, when in practice schemes vary widely in their design. Our study contributes to this literature by providing a detailed depiction of incentive design across municipalities within a national P4P scheme in Brazil [Primary Care Access and Quality (PMAQ)] and exploring the association of alternative design typologies with the performance of primary health care providers. We carried out a nation-wide survey of municipal health managers to characterize the scheme design, based on the size of the bonus, the providers incentivized and the frequency of payment. Using OLS regressions and controlling for municipality characteristics, we examined whether each design feature was associated with better family health team (FHT) performance. To capture potential interactions between design features, we used cluster analysis to group municipalities into five design typologies and then examined associations with quality of care. A majority of the municipalities included in our study used some of the PMAQ funds to provide bonuses to FHT workers, while the remaining municipalities spent the funds in the traditional way using input-based budgets. Frequent bonus payments (monthly) and higher size bonus allocations (share of 20-80%) were strongly associated with better team performance, while who within a team was eligible to receive bonuses did not in isolation appear to influence performance. The cluster analysis showed what combinations of design features were associated with better performance. The PMAQ score in the 'large bonus/many workers/high-frequency' cluster was 8.44 points higher than the 'no bonus' cluster, equivalent to a difference of 21.7% in the mean PMAQ score. Evidence from our study shows how design features can potentially influence health provider performance, informing the design of more effective P4P schemes.

Trust and reliance in the cognitive institutions of cryptocurrency

AbstractThe stated aim of cryptocurrencies is to free the monetary system from the need to trust financial intermediaries, by relying on incentive design and technology. Many descriptive studies, however, have questioned cryptocurrencies’ delivery on the promise of trustlessness. This paper promotes a normative analysis of trust in cryptocurrencies by discussing (i) whether trust is in principle eliminable, and (ii) whether trustlessness is in itself a desirable goal. These issues are closely related, we argue, to the further issue of what kind of institutions cryptocurrencies represent. We discuss the cognitive functions played by cryptocurrencies through the lens of the “extended mind” hypothesis in the philosophy of mind and hence conceive of cryptocurrencies as mind-extending institutions. As the models of institutional mind extension differ in the fiduciary bond they assume exists between individuals and institutional resources, we compare the reliance-based model of “scaffolding institutions” with the trust-based model of “cognitive institutions,” showing that the ineliminability and desirability of trust lead to seeing cryptocurrencies as instances of the latter. In the end, our discussion suggests that trust is a necessary component of cryptocurrencies’ cognitive functions and its promotion helps to perform such cognitive functions more effectively and sustainably

Trends in low-value cancer care during the COVID-19 pandemic.

To assess the association between the onset of the COVID-19 pandemic and change in low-value cancerservices. In this retrospective cohort study, we used administrative claims from the HealthCore Integrated Research Environment, a repository of medical and pharmacy data from US health plans representing more than 80 million members, between January 1, 2016, and March31, 2021. We used linear probability models to investigate the relation between the onset of the COVID-19 pandemic and 4 guideline-based metrics of low-value cancer care: (1) conventional fractionation radiotherapy instead of hypofractionated radiotherapy for early-stage breast cancer; (2) non-guideline-based antiemetic use for minimal-, low-, or moderate- to high-risk chemotherapies; (3) off-pathway systemic therapy; and (4) aggressive end-of-life care. We identified patients with new diagnoses of breast, colorectal, and/or lung cancer. We excluded members who did not have at least 6 months of continuous insurance coverage and members with prevalent cancers. Among 117,116 members (median [IQR] age, 60[53-69] years; 72.4% women), 59,729 (51.0%) had breast cancer, 25,751 (22.0%) had colorectal cancer, and 31,862(27.2%) had lung cancer. The payer mix was 18.7% Medicare Advantage or Medicare supplemental and 81.2% commercial non-Medicare. Rates of low-value cancer services exhibited minimal changes during the pandemic, as adjusted percentage-point differences were 3.93 (95%CI, 1.50-6.36) for conventional radiotherapy, 0.82 (95% CI, -0.62 to 2.25) for off-pathway systemic therapy, -3.62 (95% CI, -4.97 to -2.27) for non-guideline-based antiemetics, and 2.71(95% CI, -0.59 to 6.02) for aggressive end-of-life care. Low-value cancer care remained prevalent throughout the pandemic. Policy makers should consider changes to payment and incentive design to turn the tide against low-value cancer care.

Removing Bias and Incentivizing Precision in Peer-grading

Most peer-evaluation practices rely on the evaluator’s goodwill and model them as potentially noisy evaluators. But what if graders are competitive, i.e., enjoy higher utility when their peers get lower scores? We model the setting as a multi-agent incentive design problem and propose a new mechanism, PEQA, that incentivizes these agents (peer-graders) through a score-assignment rule and a grading performance score. PEQA is designed in such a way that it makes grader-bias irrelevant and ensures grader-utility to be monotonically increasing with the grading-precision, despite competitiveness. When grading is costly and costs are private information of the individual graders, a modified version of PEQA implements the socially optimal grading-choices in equilibrium. Data from our classroom experiments is consistent with our theoretical assumptions and show that PEQA outperforms the popular median mechanism, which is used in several massive open online courses (MOOCs).

Collaborative Vehicle-to-Grid Operations in Frequency Regulation Markets

Problem definition: We study the operations of electric vehicles (EVs) providing frequency regulation services to the electric grid in vehicle-to-grid (V2G) systems. In particular, individually owned EVs collaboratively bid in the regulation market, coordinated by a platform that operates the network of charging equipment. We study how the platform determines optimal pricing incentives for drivers to plug in their EVs, accounting for heterogeneous driving schedules. Methodology/results: We model the platform’s pricing optimization problem as a bilevel program: At the upper level, the platform determines hourly rebates for EV owners to plug in their EVs and capacity bids in the regulation market; at the lower level, individual travelers optimize their travel and charging schedules in response to pricing incentives. To account for uncertainties and heterogeneity in regulation market prices and travel patterns, we adopt distributionally robust optimization techniques to formulate the problem as a mixed-integer second-order cone program. We conduct a computational study based on the California Household Travel Survey data set and actual frequency regulation prices. Our results show that the ability to offer time-varying rebates and install workplace chargers can significantly improve the V2G platform’s expected profits. Managerial implications: As EV adoption progresses past the nascent stage, V2G business models become more viable. Successful implementation of V2G provides economic incentive for switching to EVs, potentially helps sustain adoption growth, and complements the growth of renewable power by helping stabilize the grid. Our findings shed light on the design of driver incentives for V2G systems. Funding: R. Tang acknowledges support from the National Natural Science Foundation of China [Grant 72201206]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0133 .

Dollars and megabits: A comparative analysis of Telecom and Healthcare Connect Fund

Two federal programs in the United States subsidize internet access for rural healthcare providers, namely, Healthcare Connect Fund (HCF) and the Telecom Program. HCF uses a subsidy mechanism that strongly incentivizes healthcare providers to shop for faster or cheaper internet. Telecom does not incentivize shopping. Theoretically, this predicts that HCF must achieve faster or cheaper internet than Telecom. I empirically test this question and find that Telecom subsidy recipients pay 132-179% more than HCF subsidy recipients on similar internet plans. Evidence point to Telecom's poor incentive design as the root cause. Eliminating this price gap would save American taxpayers $143 million annually. The findings highlight the power of program design, showcase the unintended consequences of misaligned incentives, and encourage policymakers to thoroughly examine program features, the impact on consumer behavior, and the effect on competition and market outcomes.

Real-time high-resolution modelling of grid carbon emissions intensity

Reducing greenhouse gas emissions in the energy industry is broadly acknowledged as important with numerous countries implementing targets to reduce emissions. Understanding the carbon emissions intensity of electricity grids is an important step in this process, and higher spatial and temporal granularity estimates are critical for delivering actionable insights. Existing emissions intensity models are limited in terms of their real-time availability and, in particular, their spatial granularity. Here we outline a method for estimating emissions intensity of large-scale interconnected electricity networks in real-time at substation-level resolution. This is realized by integrating nation-wide high-resolution electricity generation and demand data, regional population data, a detailed model of the electricity grid, and power flow simulation and flow tracing models. Results for Australia's National Electricity Market reveal that most capital city locations have higher emissions intensity than neighbouring regional areas, though the variation within a city can also be substantial. In general, emissions intensity is higher in areas closer to, or directly fed by emissions intensive coal and gas generators. These results have important implications for the design of energy efficiency incentives and demand response strategies, in the rollout of distributed renewable generation, and in carbon accounting.

An incentive mechanism design for federated learning with multiple task publishers by contract theory approach

In the process of model training of the federated learning system, how to design an incentive mechanism to attract more high-quality worker nodes to join is a key issue. The existing researches on federated learning incentive mechanism only consider the scenario of single task publisher with multiple worker nodes. In the scenario of multi task publishers and multiple worker nodes, the competition between different task publishers makes the entire research process more complicated, and the contract design in the single task publisher scenario cannot be directly applied. To solve this problem, this paper proposes an incentive mechanism based on contract theory in the multi task publisher scenario and studies its application in federated learning. Simulation experiments show that this mechanism is effective for federated learning and can achieve the purpose of encouraging worker nodes to join and improve the efficiency of federated learning.

Incentive Design for Heterogeneous Client Selection: A Robust Federated Learning Approach

Incentive Design for Heterogeneous Client Selection: A Robust Federated Learning Approach

Disturbing the Quiet Life? Competition and CEO Incentives

ABSTRACT Although it is well understood that product market competition acts as a disciplining mechanism that reduces inefficiencies, our understanding of the implications for firms’ incentive design choices is still limited. We use a comprehensive new measure of competition and examine its effect on four major choices: CEO equity portfolio incentives, annual bonus plan incentives, choice of performance measures, and difficulty of financial performance targets. We find that competition reduces firm profits and total CEO compensation, including equity grants, which then also weakens portfolio incentives. Firms respond by adjusting annual bonus plans to restore incentives. Specifically, we find that competition goes together with stronger bonus plan incentives, more challenging annual performance targets, and a greater emphasis on long-term performance measures. Finally, we show that competition increases performance relative to annual bonus targets, which we interpret as evidence that CEOs work harder but get paid less in highly competitive environments. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: M41; M52.

Agency in Hierarchies: Middle Managers and Performance Evaluations

Abstract This paper studies the optimal joint design of incentives and performance rating scales in a principal-manager-worker hierarchy. The principal wants to motivate the worker to exert unobservable effort at the minimum feasible cost. Given the worker’s effort, two signals are realized: public and verifiable output and a private non-verifiable signal known only to the manager. The principal may try to elicit the manager’s private information by requiring her to evaluate the worker’s performance. Payments may depend on output and the manager’s evaluation. I show that the principal can achieve no more than what is feasible with a binary rating scale. I also identify scenarios where subjective evaluations are valuable (non-valuable), reduced transparency is advantageous, and forced ranking outperforms individual evaluations.

Using Predicted Academic Performance to Identify At-Risk Students in Public Schools

Measures of student disadvantage—or risk—are critical components of equity-focused education policies. However, the risk measures used in contemporary policies have significant limitations, and despite continued advances in data infrastructure and analytic capacity, there has been little innovation in these measures for decades. We develop a new measure of student risk for use in education policies, which we call Predicted Academic Performance (PAP). PAP is a flexible, data-rich indicator that identifies students at risk of poor academic outcomes. It blends concepts from emerging early warning systems with principles of incentive design to balance the competing priorities of accurate risk measurement and suitability for policy use. In proof-of-concept policy simulations using data from Missouri, we show PAP is more effective than common alternatives at identifying students who are at risk of poor academic outcomes and can be used to target resources toward these students—and students who belong to several other associated risk categories—more efficiently.

Game Theory-Based Incentive Design for Mitigating Malicious Behavior in Blockchain Networks

This paper presents an innovative incentive model that utilizes graph and game theories to address the issue of node incentives in decentralized blockchain networks such as EVM blockchains. The lack of incentives for nodes within EVM networks gives rise to potential weaknesses that might be used for various purposes, such as broadcasting fake transactions or withholding blocks. This affects the overall trust and integrity of the network. To address this issue, the current study offers a network model that incorporates the concepts of graph theory and utilizes a matrix representation for reward and trust optimization. Furthermore, this study presents a game-theoretic framework that encourages cooperative conduct and discourages malicious actions, ultimately producing a state of equilibrium according to the Nash equilibrium. The simulations validated the model’s efficacy in addressing fraudulent transactions and emphasized its scalability, security, and fairness benefits. This study makes a valuable contribution to the field of blockchain technology by presenting an incentive model that effectively encourages the development of secure and trusted decentralized systems.

A mathematical programming based decision support system for waste to resource market trading

In a linear economy, consumers typically dispose end-of-life products which are eventually incinerated or landfilled. Digital platforms could unlock rich waste reutilization opportunities and economic benefits by connecting the waste producers with potential waste buyers in online marketplaces. While previous studies mostly focus on input-output matching, this study proposes a decision support system that can be incorporated to an online marketplace to optimize the waste trading based on economic considerations. Moreover, the decision support system could facilitate higher level decisions such as capacity planning and incentive design. Based on a case study on Singapore’s organic waste streams, the waste trading platform presents economic benefits for agents that sell their waste. Additionally, enforcing waste reutilization targets would require relevant policy support in terms of incentives or levies, where the levies can be implemented as co-payment rate for the agents. For instance, results showed that when the food waste reutilization target is set as 6 t/d, increasing the co-payment rate from 0 to 20% allows the waste trading network to be self-sustaining without external funding. Finally, sensitivity analyses show that collecting more waste records in the database helps in optimizing the investment decisions, preferably when the number of records exceeds 64.

Exploring the prospective acceptability of a healthy food incentive program from the perspective of people with type 2 diabetes and experiences of household food insecurity in Alberta, Canada.

FoodRx is a 12-month healthy food prescription incentive program for people with type 2 diabetes (T2DM) and experiences of household food insecurity. In this study, we aimed to explore potential users' prospective acceptability (acceptability prior to program use) of the design and delivery of the FoodRx incentive and identify factors influencing prospective acceptability. We used a qualitative descriptive approach and purposive sampling to recruit individuals who were interested or uninterested in using the FoodRx incentive. Semi-structured interviews were guided by the theoretical framework of acceptability, and corresponding interview transcripts were analysed using differential qualitative analysis guided by the socioecological model. Individuals living in Alberta, Canada. In total, fifteen adults with T2DM and experiences of household food insecurity. People who were interested in using the FoodRx incentive (n 10) perceived it to be more acceptable than those who were uninterested (n 5). We identified four themes that captured factors that influenced users' prospective acceptability: (i) participants' confidence, views and beliefs of FoodRx design and delivery and its future use (intrapersonal), (ii) the shopping routines and roles of individuals in participants' social networks (interpersonal), (iii) access to and experience with food retail outlets (community), and (iv) income and food access support to cope with the cost of living (policy). Future healthy food prescription programs should consider how factors at all levels of the socioecological model influence program acceptability and use these data to inform program design and delivery.

Optimal Design of V2G Incentives and V2G-Capable Electric Vehicles Parking Lots Considering Cost-Benefit Financial Analysis and User Participation

Optimal Design of V2G Incentives and V2G-Capable Electric Vehicles Parking Lots Considering Cost-Benefit Financial Analysis and User Participation