A Fair Allocation Algorithm for Predictive Police Patrolling

We address the tradeoff of developing good predictive models for police allocation vs. optimally deploying police officers over a city in a way that does not imply an unfair allocation of resources. We modify the fair allocation algorithm of [1] to tackle a real world problem: crime in the city of Bogota, Colombia. Our approach allows for more sophisticated prediction models and we ´ show that the whole methodology outperforms the current police allocating mechanism in the city. Results show that even with a simple model such as a Kernel Density Estimation of crime, one can have much better prediction than the current police model and, at the same time, mitigate fairness concerns. Although we can not provide general performance guarantees, our results apply to a real life problem and should be seriously considered by policy makers.

Predictive Algorithms in the Delivery of Public Employment Services

With the growing availability of digital administrative data and the recent advances in machine learning, the use of predictive algorithms in the delivery of labour market policy is becoming more prevalent. In public employment services (PES), predictive algorithms are used to support the classification of jobseekers based on their risk of long-term unem- ployment (profiling), the selection of beneficial active labour market programs (targeting), and the matching of jobseekers to suitable job opportunities (matching). In this chapter, we offer a conceptual introduction to the applications of predictive algorithms for the different functions PES have to fulfil and review the history of their use up to the current state of the practice. In addition, we discuss two issues that are inherent to the use of predictive algorithms: algorithmic fairness concerns and the importance of considering how caseworkers will interact with algorithmic systems and make decisions based on their predictions.

Venture investment under multi-stage incentive model based on fairness concerns

PurposeThis study explores the impact of EN's (venture entrepreneurs, simplified as EN) jealousy fairness concerns coefficient on two-stage venture capital decision-making in cases of symmetrical and asymmetrical information. It discusses the equilibrium solution of two-stage venture.Design/methodology/approachThe principal-agent model was established based on multiple periods, and differentiated contracts were established at different stages. The validity of the models and the contract was verified by numerical simulation.FindingsThe results suggest that with the increase in the EN fairness concerns coefficient, the effort level of EN decreases continuously and decreases faster in the second stage because this is the last stage. The level of VC's (venture capitalist, simplified as VC) effort declines first and then increases; that is, VC will increase the effort level when the fairness concerns coefficient increases to a certain threshold. To motivate EN to pay more effort, VC will increase the incentive to EN in the first stage. However, it will reduce the level of incentive to EN in the second stage. In the limited stage of venture investment, consider that the fairness concerns of EN do not make the profits of EN and VC achieve Pareto improvement simultaneously.Originality/valueFirst, the authors implanted fairness concerns into multi-stage venture capital and discussed the impact of fairness concerns on the efforts and returns of both parties. Second, among the influencing factors of the project output, the authors consider the bilateral efforts of EN and VC, the working capacity of EN, the initial investment scale, and the external uncertain environment.

The effects of neighbourhood and workplace income comparisons on subjective wellbeing

<p>We investigate how the incomes of a person’s neighbours and coworkers affect her happiness, using survey data on subjective wellbeing linked to unprecedentedly rich administrative data on the characteristics of survey respondents’ peer groups. Linear regressions of subjective wellbeing on peer income variables establish that people care exclusively about their ordinal rank within their peer income distribution, that workplace rank matters much more than neighbourhood rank, and that workplace comparisons are driven primarily by fairness concerns. We confirm that our results reflect a causal effect of peer income by implementing sensitivity analyses, identifying off changes in peer income over time for immobile people, exploiting plausibly exogenous moves between workplaces triggered by mass layoffs, and testing for the effects of unobservable group-level confounders.</p>

The effects of neighbourhood and workplace income comparisons on subjective wellbeing

<p>We investigate how the incomes of a person’s neighbours and coworkers affect her happiness, using survey data on subjective wellbeing linked to unprecedentedly rich administrative data on the characteristics of survey respondents’ peer groups. Linear regressions of subjective wellbeing on peer income variables establish that people care exclusively about their ordinal rank within their peer income distribution, that workplace rank matters much more than neighbourhood rank, and that workplace comparisons are driven primarily by fairness concerns. We confirm that our results reflect a causal effect of peer income by implementing sensitivity analyses, identifying off changes in peer income over time for immobile people, exploiting plausibly exogenous moves between workplaces triggered by mass layoffs, and testing for the effects of unobservable group-level confounders.</p>

A Classroom Observer Like Me: The Effects of Race-congruence and Gender-congruence Between Teachers and Raters on Observation Scores

State and local education agencies across the country are prioritizing the goal of diversifying the teacher workforce. To further understand the challenges of diversifying the teacher pipeline, I investigate race and gender dynamics between teachers and school-based administrators, who are key decision-makers in hiring, evaluating, and retaining teachers. I use longitudinal data from a large school district in the southeastern United States to examine the effects of race-congruence and gender-congruence between teachers and observers/administrators on teachers’ observation scores. Using models with two-way fixed effects, I find that teachers, on average, experience small positive increases in their scores from sharing race or gender with their observers, raising fairness concerns for teachers whose race or gender identities are not reflected by any of their raters.

Research on retail channel decisions considering consumer’s fairness concern

Online retailing provides alternative shopping channels, where the retail platform can either let manufacturers directly sell to consumers or open a self-operated channel, or even both. Regardless of sales channels, consumers often pay attention to the income gap between themselves and enterprises (named consumer's fairness concern). In this work, we explore how consumers’ fairness concerns affect the optimal decisions of both manufacturer and retail platform under different retail channel modes (single-channel mode and mixed-channel mode). The results show that consumer’s fairness concern has a negative impact on the retail price under low production cost in single-channel mode, while the retail prices in mix-channel mode are jointly determined by consumer’s fairness concern and revenue sharing ratio. Besides, if the market channel mode has not yet formed, the retail platform can choose either a self-operated channel or manufacturer consignment channel, depending on the consumer’s fairness concern level and revenue sharing ratio. By contrast, if the market channel mode has already been formed, the retail platform should make effort to reduce consumer’s fairness concern if only the self-operated channel exists, while maintain consumer’s fairness concern and revenue sharing ratio at a moderate level if there exist mixed channels.

Determinants of Consumer Perceptions of the Ethics of Online Retailers: An Investigation Using Confirmatory Factor Analysis

Though the online retailing ethics literature has been growing steadily, no research has attempted to examine the myriad aspects and concerns about the online retailers’ unethical practices and behaviours. The study aims to extend the research and re-validate the consumer perception of ethics of online retailers (CPEOR) scale in the Indian context. The methodology involves an exploratory factor analysis on a sample of 261 online shopper responses on a 7-point Likert scale. The obtained scale was then validated on a new sample of 416 users using confirmatory factor analysis. The research identifies and validates 10 factors that influence CPEOR, that is, privacy, security, non-deception, fulfilment, customer service, service recovery, fairness concerns, environmental concerns, corporate social responsibility and ethical policies. The study offers valuable insights to the managers and researchers. The study offers best practices in the different dimensions of CPEOR along with future research directions. The study provides unified and comprehensive insights on CPEOR by examining all possible dimensions of CPEOR.