Statistical discrimination offers a compelling narrative on gender wage gaps among younger workers. Employers could reduce women's wages to adjust for expected costs linked to child-bearing. If this is the case, then trends toward delayed fertility should reduce the gender wage gap among young workers. We provide a novel collection of adjusted gender wage gap (AGWG) estimates among young workers from 56 countries spanning four decades and use it to test the conjecture that delayed fertility reduces gender wage inequality. We employ instrumental variables, and find that one year postponement of the first birth reduces AGWG by two percentage points (15% of the AGWG). We benchmark this estimate with the help of time-use data.

This article presents a fairness principle for evaluating decision-making based on predictions: a decision rule is unfair when the individuals directly impacted by the decisions who are equal with respect to the features that justify inequalities in outcomes do not have the same statistical prospects of being benefited or harmed by them, irrespective of their socially salient morally arbitrary traits. The principle can be used to evaluate prediction-based decision-making from the point of view of a wide range of antecedently specified substantive views about justice in outcome distributions.

Artificial intelligence (AI) is increasingly popular in the public sector to improve the cost-efficiency of service delivery. One example is AI-based profiling models in public employment services (PES), which predict a jobseeker’s probability of finding work and are used to segment jobseekers in groups. Profiling models hold the potential to improve identification of jobseekers at-risk of becoming long-term unemployed, but also induce discrimination. Using a recently developed AI-based profiling model of the Flemish PES, we assess to what extent AI-based profiling ‘discriminates’ against jobseekers of foreign origin compared to traditional rule-based profiling approaches. At a maximum level of accuracy, jobseekers of foreign origin who ultimately find a job are 2.6 times more likely to be misclassified as ‘high-risk’ jobseekers. We argue that it is critical that policymakers and caseworkers understand the inherent trade-offs of profiling models, and consider the limitations when integrating these models in daily operations. We develop a graphical tool to visualize the accuracy-equity trade-off in order to facilitate policy discussions.

This article examines one kind of conscientious refusal: the refusal of healthcare professionals to treat sexual dysfunction in individuals with a history of sexual offending. According to what I call the orthodoxy, such refusal is invariably impermissible, whereas at least one other kind of conscientious refusal—refusal to offer abortion services—is not. I seek to put pressure on the orthodoxy by (1) motivating the view that either both kinds of conscientious refusal are permissible or neither is, and (2) critiquing two attempts to buttress it.