3,565 research outputs found

    Do Local Economic Development Programs Work? Evidence from the Federal Empowerment Zone Program

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    This paper evaluates the impact of Round I of the federal urban Empowerment Zone (EZ) program on neighborhood level labor and housing market outcomes over the period 1994-2000. Using four decades of Census data in conjunction with information on the proposed boundaries of rejected EZs, we find that neighborhoods receiving EZ designation experienced substantial improvements in labor market conditions and moderate increases in rents relative to rejected and future zones. These effects were accompanied by small changes in the demographic composition of the neighborhoods, though evidence from disaggregate Census tabulations suggests that these changes account for little of the observed improvements.Program evaluation, Local economic development, Empowerment zones

    How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility

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    Recommendation systems are ubiquitous and impact many domains; they have the potential to influence product consumption, individuals' perceptions of the world, and life-altering decisions. These systems are often evaluated or trained with data from users already exposed to algorithmic recommendations; this creates a pernicious feedback loop. Using simulations, we demonstrate how using data confounded in this way homogenizes user behavior without increasing utility

    Constrained information flows in temporal networks reveal intermittent communities

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    Many real-world networks represent dynamic systems with interactions that change over time, often in uncoordinated ways and at irregular intervals. For example, university students connect in intermittent groups that repeatedly form and dissolve based on multiple factors, including their lectures, interests, and friends. Such dynamic systems can be represented as multilayer networks where each layer represents a snapshot of the temporal network. In this representation, it is crucial that the links between layers accurately capture real dependencies between those layers. Often, however, these dependencies are unknown. Therefore, current methods connect layers based on simplistic assumptions that do not capture node-level layer dependencies. For example, connecting every node to itself in other layers with the same weight can wipe out dependencies between intermittent groups, making it difficult or even impossible to identify them. In this paper, we present a principled approach to estimating node-level layer dependencies based on the network structure within each layer. We implement our node-level coupling method in the community detection framework Infomap and demonstrate its performance compared to current methods on synthetic and real temporal networks. We show that our approach more effectively constrains information inside multilayer communities so that Infomap can better recover planted groups in multilayer benchmark networks that represent multiple modes with different groups and better identify intermittent communities in real temporal contact networks. These results suggest that node-level layer coupling can improve the modeling of information spreading in temporal networks and better capture intermittent community structure.Comment: 10 pages, 10 figures, published in PR
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