A Short-term Intervention for Long-term Fairness in the Labor Market

The persistence of racial inequality in the U.S. labor market against a general backdrop of formal equality of opportunity is a troubling phenomenon that has significant ramifications on the design of hiring policies. In this paper, we show that current group disparate outcomes may be immovable even when hiring decisions are bound by an input-output notion of "individual fairness." Instead, we construct a dynamic reputational model of the labor market that illustrates the reinforcing nature of asymmetric outcomes resulting from groups' divergent accesses to resources and as a result, investment choices. To address these disparities, we adopt a dual labor market composed of a Temporary Labor Market (TLM), in which firms' hiring strategies are constrained to ensure statistical parity of workers granted entry into the pipeline, and a Permanent Labor Market (PLM), in which firms hire top performers as desired. Individual worker reputations produce externalities for their group; the corresponding feedback loop raises the collective reputation of the initially disadvantaged group via a TLM fairness intervention that need not be permanent. We show that such a restriction on hiring practices induces an equilibrium that, under particular market conditions, Pareto-dominates those arising from strategies that statistically discriminate or employ a "group-blind" criterion. The enduring nature of equilibria that are both inequitable and Pareto suboptimal suggests that fairness interventions beyond procedural checks of hiring decisions will be of critical importance in a world where machines play a greater role in the employment process.Comment: 10 page

The 3-rainbow index of a graph

Let $G$ be a nontrivial connected graph with an edge-coloring $c: E(G)\rightarrow \{1,2,...,q\},$ $q \in \mathbb{N}$, where adjacent edges may be colored the same. A tree $T$ in $G$ is a $rainbow tree$ if no two edges of $T$ receive the same color. For a vertex subset $S\subseteq V(G)$, a tree that connects $S$ in $G$ is called an $S$-tree. The minimum number of colors that are needed in an edge-coloring of $G$ such that there is a rainbow $S$-tree for each $k$-subset $S$ of $V(G)$ is called $k$-rainbow index, denoted by $rx_k(G)$. In this paper, we first determine the graphs whose 3-rainbow index equals 2, $m,$ $m-1$, $m-2$, respectively. We also obtain the exact values of $rx_3(G)$ for regular complete bipartite and multipartite graphs and wheel graphs. Finally, we give a sharp upper bound for $rx_3(G)$ of 2-connected graphs and 2-edge connected graphs, and graphs whose $rx_3(G)$ attains the upper bound are characterized.Comment: 13 page

The effect of functional role on language choice in newspapers.

Available from British Library Document Supply Centre-DSC:DXN049161 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

Exploring Corrective Feedback in Real-time Classrooms: Factors Mediating Teachers’ In-class Corrective Feedback Decisions

Abstract: Corrective feedback (CF) is the teacher’s response to the language learner’s erroneous or non-target-like output (Ellis, 2006; Li, 2010). Empirical evidence has shown that CF can effectively facilitate language acquisition. When it comes to real-time classrooms, the teacher is the sole decision maker of CF from moment to moment; however, CF is often investigated after it has been provided. This literature review outlines four contextual factors of instructors’ in-class CF decision making: student’s proficiency, curriculum design, student emotions, and teacher cognition. The paper closes with further considerations for research on CF that include classroom-to-classroom differences, teacher education programs, and student perceptions of CF.