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"Incentive design"
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How are pay-for-performance schemes in healthcare designed in low- and middle-income countries? Typology and systematic literature review
Background
Pay for performance (P4P) schemes provide financial incentives to health workers or facilities based on the achievement of pre-specified performance targets and have been widely implemented in health systems across low and middle-income countries (LMICs). The growing evidence base on P4P highlights that (i) there is substantial variation in the effect of P4P schemes on outcomes and (ii) there appears to be heterogeneity in incentive design. Even though scheme design is likely a key determinant of scheme effectiveness, we currently lack systematic evidence on how P4P schemes are designed in LMICs.
Methods
We develop a typology to classify the design of P4P schemes in LMICs, which highlights different design features that are a priori likely to affect the behaviour of incentivised actors. We then use results from a systematic literature review to classify and describe the design of P4P schemes that have been evaluated in LMICs. To capture academic publications, Medline, Embase, and EconLit databases were searched. To include relevant grey literature, Google Scholar, Emerald Insight, and websites of the World Bank, WHO, Cordaid, Norad, DfID, USAID and PEPFAR were searched.
Results
We identify 41 different P4P schemes implemented in 29 LMICs. We find that there is substantial heterogeneity in the design of P4P schemes in LMICs and pinpoint precisely how scheme design varies across settings. Our results also highlight that incentive design is not adequately being reported on in the literature – with many studies failing to report key design features.
Conclusions
We encourage authors to make a greater effort to report information on P4P scheme design in the future and suggest using the typology laid out in this paper as a starting point.
Journal Article
On the Role of Fairness and Social Distance in Designing Effective Social Referral Systems
Online referral systems help firms attract new customers and expand their customer base by leveraging the social relationships of existing customers. We integrate ultimatum game theory, which focuses on fairness, with motivation theories to investigate the effects of social distance and monetary incentives on the performance of three competing designs for online referral systems: rewarding only or primarily the proposer, rewarding only or primarily the responder, and dividing the reward equally or fairly between the proposer and responder. A set of controlled laboratory and randomized field experiments were conducted to test how the fairness of the split of the reward (equal/fair versus unequal/unfair split of the referral bonus) and social distance (small versus large) between the proposer and the responder jointly affect the performance of online referral systems (the proposer sending an offer and the responder accepting the offer). For a large social distance (acquaintances or weak tie relationships), equally splitting the referral bonus results in the best performance. However, for a small social distance (friends or strong tie relationships), an equal split of the referral reward does not improve referral performance, which suggests that under a small social distance, monetary incentives may not work effectively. Face validity and external validity (generalizability) are ensured using two distinct measures of social distance across several contexts. Through the analysis of the interaction effects of fairness and social distance, our research offers theoretical and practical implications for social commerce by showing that the effectiveness of fairness on the success of online social referrals largely depends on social distance.
Journal Article
The Fun and Function of Uncertainty
This research studies repetition decisions—namely, whether to repeat a behavior (e.g., a purchase) after receiving an incentive (e.g., a discount). Can uncertainty drive repetition? Four experiments, all involving real consequences for each individual participant, document a counterintuitive reinforcing-uncertainty effect: individuals repeat a behavior more if its incentive is uncertain than if it is certain, even when the certain incentive is financially better. This effect is robust; it holds in both lab and field settings and at both small and large magnitudes. Furthermore, the experiments identify two theory-driven boundary conditions for the reinforcing-uncertainty effect: the effect arises (a) only if the uncertainty is resolved immediately and not if the resolution of uncertainty is delayed, and (b) only after, not before, one has engaged in repetitions. These results support a resolution-as-reward account and cast doubt on other explanations such as reference-dependent preferences. This research reveals the hidden value of uncertain incentives and sheds light on the delicate relationship between incentive uncertainty and repetition decisions.
Journal Article
Urgency-aware optimal routing in repeated games through artificial currencies
•We study a repeated game framework for optimal routing on a parallel-arcs networks.•We propose a fair incentive mechanism based on artificial currencies.•We model the users’ decisions via a receding-horizon optimization problem.•For two arcs, we show that the aggregate routing pattern converges to the optimum.•Our framework achieves an optimal routing, reducing the users’ perceived discomfort.
When people choose routes minimizing their individual delay, the aggregate congestion can be much higher compared to that experienced by a centrally-imposed routing. Yet centralized routing is incompatible with the presence of self-interested users. How can we reconcile the two? In this paper we address this question within a repeated game framework and propose a fair incentive mechanism based on artificial currencies that routes selfish users in a system-optimal fashion, while accounting for their temporal preferences. We instantiate the framework in a parallel-network whereby users commute repeatedly (e.g., daily) from a common start node to the end node. Thereafter, we focus on the specific two-arcs case whereby, based on an artificial currency, the users are charged when traveling on the first, fast arc, whilst they are rewarded when traveling on the second, slower arc. We assume the users to be rational and model their choices through a game where each user aims at minimizing a combination of today’s discomfort, weighted by their urgency, and the average discomfort encountered for the rest of the period (e.g., a week). We show that, if prices of artificial currencies are judiciously chosen, the routing pattern converges to a system-optimal solution, while accommodating the users’ urgency. We complement our study through numerical simulations. Our results show that it is possible to achieve a system-optimal solution whilst significantly reducing the users’ perceived discomfort when compared to a centralized optimal but urgency-unaware policy.
Journal Article
Strategically Targeting Plug-In Electric Vehicle Rebates and Outreach Using “EV Convert” Characteristics
To expand markets for plug-in electric vehicles (EVs) beyond enthusiastic early adopters, investments must be strategic. This research characterizes a segment of EV adoption that points the way toward the mainstream: EV consumers with low or no initial interest in EVs, or “EV Converts.” Logistic regression is utilized to profile EV Convert demographic, household, and regional characteristics; vehicle-transaction details; and purchase motivations—based on 2016–2017 survey data characterizing 5447 rebated California EV consumers. Explanatory factors are rank-ordered—separately for battery EVs (BEVs) and plug-in hybrid EVs (PHEVs), to inform targeted outreach and incentive design. EV Converts tend to have relatively “lower” values on factors that might have otherwise “pre-converted” them to EV interest: hours researching EVs online; motivation from environmental impacts and carpool-lane access; and solar ownership. PHEV Converts more closely resemble new-car buyers than other EV adopters, and BEV Converts actually tend to be younger and less-frequently white/Caucasian than new-car buyers. BEV Converts also tend to: lack workplace charging, be moderately motivated by energy independence, and reside in Southern California or the Central Valley. Predictors that not only help target consumers, but also help convert them, include rebates for BEV consumers and, modestly, fuel-cost savings for PHEV consumers.
Journal Article
Designing two-period decentralized service chain incentives with the consideration of customer acquisition and retention
PurposeThe authors address a two-dimensional (both customer acquisition and retention) incentive in a decentralized service chain consisting of a risk-neutral brand and agent (or averse). Design/methodology/approachThe authors focus on the relationship between acquisition and retention, that is, retained customers (repeated purchases) are based on and come from the acquired (new) customers in the former period. The authors also design a two-period separate incentive on both dimensions.FindingsThe authors found that a targeted incentive strategy should be applied for achieving more revenue when the incentive intensities are relatively small. Otherwise, the brand needs to adjust the targeted incentive strategy into incentivizing the opposite dimension, particularly on acquisition. Under the optimal contract, the brand needs to be very careful with deciding the fixed part of the incentive salary and the incentive intensities on both dimensions. For example, the fixed salary initially decreases and then increases in the incentive intensities. For the optimal incentive policies, the brand should incentivize acquisition but outsource retention if the agent is risk-neutral. When the agent is becoming risk-averse, the brand should lower its incentive intensity as the risk degree and variances become larger. Interestingly, the brand may benefit from introducing risks.Originality/valueThe study contributes to the literature by considering the following points. First, the authors extend the principal-agent incentive model by considering two-period decisions of customer acquisition and retention. Second, based on the two-period principal-agent problem, the authors design separate incentive intensities on acquisition and retention, respectively. While, most of the literature focused on acquisition incentives. Third, different from other works focusing on either risk-neutral or risk-averse environments, the authors consider both and compare the cases of risk-neutral and risk-averse to analyze the impact of risk on the optimal decisions and the brand's expected profit.
Journal Article
Sales Force Compensation Design for Two-Sided Market Platforms
The authors study the use of sales agents for network mobilization in a two-sided market platform that connects buyers and sellers, and they examine how the presence of direct and indirect network effects influences the design of the sales compensation plan. They employ a principal–agent model in which the firm tasks sales agents to mobilize the side of the platform that it monetizes (i.e., sellers). Specifically, the presence of network effects alters the agency relationship between the firm and the sales agent, requiring the platform firm to alter the compensation design, and the nature of the alteration depends on whether the network effects are direct or indirect and positive or negative. The authors first show how the agent's compensation plan should account for different types of network effects. They then establish that when the platform firm compensates the agent solely on the basis of network mobilization on the side cultivated by the agent (sellers), as intuition would suggest, it will not fully capitalize on the advantage of positive network effects; that is, profit can be lower under stronger network effects. To overcome this limitation, the platform should link the agent's pay to a second metric, specifically, network mobilization on the buyer side, even though the agent is not assigned to that side. This design induces a positive relation between the strength of network effects and profit. This research underlines the complexity and richness of network effects and provides managers with new insights regarding the design of sales agents' compensation plans for platforms.
Journal Article
Possible Ways of Extending the Biogas Plants Lifespan after the Feed-In Tariff Expiration
Energy production from biogas can play a pivotal role in many European countries, and specifically in Italy, for three main reasons: (i) fossil fuels are scarce, (ii) imports cover large shares of internal demand, and (iii) electricity and heat production from biogas is already a consolidated business. Nonetheless, in Italy, current legislation and incentive policies on electricity generation from biogas are causing a stagnation of the entire sector, which may lead to the shutting down of many in-operation plants in the years 2027–2028 and the consequent loss of 573 MWel over a total of 1400 MWel. This work aims to investigate the potential of revamping biogas power plants in prolonging operation until the end of the plants’ useful life, regardless of the implementation of a new government’s incentive schemes. Based on the time-series analysis of electricity prices in Italy and a case study representative of the vast set of in-operation power plants, our findings show that 700 plants will likely shut down between 2027 and 2028 unless the government adequately rewards electricity produced and fed into the grid via incentive schemes. In detail, our results show that the investment to revamp the plant exhibits a highly negative Net Present Value.
Journal Article
Dynamic Incentive Design in Public Transit Subsidization Under Double Moral Hazard: A Continuous-Time Principal-Agent Approach
Public transit subsidization often suffers from a double (or bilateral) moral hazard problem, where both regulators and operators may reduce their efforts due to information asymmetry, thereby compromising service quality despite significant public investment. This paper develops a continuous-time principal-agent model to investigate optimal subsidy contract design under such conditions, where both parties exert costly, unobservable efforts that jointly determine stochastic service outcomes. Using stochastic dynamic programming and exponential utility functions, we derive closed-form solutions for the optimal contracts. Our analysis yields three key findings. First, under standard technical assumptions, the optimal subsidy contract takes a simple linear form based on final service quality, facilitating practical implementation. Second, the contract’s incentive intensity decreases with environmental uncertainty, highlighting a fundamental trade-off between risk-sharing and effort inducement. Third, a unique and mutually agreeable contract emerges as the parties’ risk preferences and productivity levels converge. This study extends the classic principal-agent framework by incorporating bilateral moral hazard in a dynamic setting, offering new theoretical insights into public-sector contract design. For policymakers, the results suggest that performance-based subsidies should be calibrated to account for operational uncertainty, and that regulators are active co-producers of service quality whose own unobservable efforts—distinct from the subsidy itself—are critical to outcomes.The proposed framework provides actionable guidance for designing effective, incentive-compatible subsidies to enhance public transit service delivery.
Journal Article