172 research outputs found

    The Distributional Impacts of Economic Development Incentives: Three Essays

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    Economic Development Incentives (EDIs) are among the most common and costly tools used by state and local governments in the United States to promote economic development. While literature has predominantly focused on the efficiency of EDIs, comparatively less focus has been paid to the distributional impacts of business attraction or the impact on individual welfare. In three papers, this dissertation seeks to better understand and evaluate how business attraction supported by EDIs impacts current residents. The first paper critiques EDI evaluations that focus on bottom-line growth instead of metrics that can show changes to the quality of life and welfare of current resident. A new framework, Distributive Welfare Evaluations, is proposed. A second paper examines how business attraction impacts wages and employment rates in local economies, using large warehouses as a natural experiment. Analysis shows that jobs were filled by shifting commuting patterns and had minimal benefits for incumbent workers. Finally, a third paper, co-authored with Jeremy Moulton and Scott Wentland, measures the impacts of EDI announcements on housing markets. We find highly variable results across 114 cases but demonstrate significant increases in prices when many jobs are promised. Together, evidence contributes to a growing body of work arguing that EDIs have limited or even negative impacts on the welfare of current residents and can contribute to growing inequalityDoctor of Philosoph

    Kentucky Annual Economic Report 2023

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    This report is one of the important ways that the Center for Business and Economic Research fulfills its mission to examine various aspects of Kentucky’s economy as directed by the Kentucky Revised Statutes (KRS 164.738). The analysis and data presented here cover a variety of topics that range from a discussion of Kentucky’s current economic climate to a broad presentation of factors affecting the economy

    The Democratic Imperative to Make Margins Matter

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    Tempering the Adversary: An Exploration into the Applications of Game Theoretic Feature Selection and Regression

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    Most modern machine learning algorithms tend to focus on an average-case approach, where every data point contributes the same amount of influence towards calculating the fit of a model. This per-data point error (or loss) is averaged together into an overall loss and typically minimized with an objective function. However, this can be insensitive to valuable outliers. Inspired by game theory, the goal of this work is to explore the utility of incorporating an optimally-playing adversary into feature selection and regression frameworks. The adversary assigns weights to the data elements so as to degrade the modeler\u27s performance in an optimal manner, thereby forcing the modeler to construct a more robust solution. A tuning parameter enables tempering of the power wielded by the adversary, allowing us to explore the spectrum between average case and worst case. By formulating our method as a linear program, it can be solved efficiently, and can accommodate sub-population constraints, a feature that other related methods cannot easily implement. We feel that the need to generate models while understanding the influence of sub-population constraints should be particularly prominent in biomedical literature, and though our method was developed in response to the ubiquity of sub-population data and outliers that exist in this realm, our method is generic and can be applied to data sets that are not exclusively biomedical in nature. We additionally explore the implementation of our method as an adversarial regression problem. Here, instead of providing the user with a fitting of parameters for the model, we provide the user with an ensemble of parameters which can be tuned based on sensitivity to outliers and various sub-population constraints. Finally, to help foster a better understanding of various data sets, we will discuss potential automated applications of our method which will enable data scientists to explore underlying relationships and sensitivities that may be a consequence of sub-populations and meaningful outliers

    Texture and Colour in Image Analysis

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    Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

    Valores reales para juegos cooperativos con función característica difusa

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    La cooperación es un comportamiento social relevante, y en algunos contextos como la economía o la ciencia políıtica, tiene un papel fundamental. Una de las perspectivas desde las que se analizan las situaciones de cooperación es desde la teor´ıa de juegos cooperativos. En ella se estudia el problema fundamental de cómo repartir los beneficios o costes que la cooperaci´on en un proyecto com´un genera. El modelo que emplea la teor´ıa cl´asica asume que se conoce con precisi´on el pago que cada posible coalici´on puede obtener. Sin embargo, hay situaciones en las que los jugadores solo tienen unas expectativas imprecisas sobre el beneficio o coste que puede lograr cada coalici´on. En la literatura se han propuesto distintos modelos para abordar tales situaciones. Uno de esos modelos son los juegos cooperativos con funci´on caracter´ıstica difusa, en los que el pago de cada coalici´on viene dado por un n´umero difuso. Al igual que en los juegos cooperativos cl´asicos, el principal problema que abordan estos modelos es c´omo repartir entre los jugadores el beneficio o coste derivado de la cooperaci´on. Para ello, en este trabajo se proponen reglas de asignaci´on, basadas en los valores de Shapley y Banzhaf, para juegos cooperativos con funci´on caracter´ıstica difusa. Para cada valor propuesto se proporciona una caracterizaci´on con propiedades razonables. Adem´as, se presenta una aplicaci´on de estos modelos a los llamados problemas de aeropuerto. Estos problemas estudian c´omo repartir el coste de mantenimiento de una pista de aterrizaje en funci´on del tama˜no de las aeronaves que la utilizan. Para el modelo propuesto se presenta una regla de asignaci´on y se proporciona adem´as una axiomatizaci´on. Tambi´en se ha desarrollado un algoritmo en Python para el c´alculo de esta regla de reparto

    Bibliographie Moderner Fremdsprachenunterricht 2018 (4)

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    Insights from Systematically Analyzing Microbial Phenotypic Profiles

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    Following classical genetic approaches to understanding gene function, high-throughput phenotyping methods have emerged as a new way of studying gene functions, especially in microorganisms, which are highly amenable to high-throughput experimental design. As more high-throughput microbial phenotype data as well as the low-throughput data become available, systematically managing, displaying, and analyzing these data become a pivotal part in discovering unknown functions for genes. In this work, I have curated some datasets for high-throughput microbial phenotype data that contain genomic-scale phenotypes from E. coli tested under hundreds of conditions. Next, I conducted systematic and unbiased statistical analysis of these phenotype datasets and showed that the phenotypic profiles within these datasets are highly correlated with various functional annotations. The phenotype-function correlation has also been seen when a curated cell-cycle related phenotypic profile of S. cerevisiae is used with Gene Ontology annotations. Furthermore, I have displayed the preliminary results of using machine learning techniques to predict gene functions using high-throughput phenotype data of complete annotations, given more functional annotations as labels. Lastly, I describe a software package written in R that is potentially useful in analyzing high-throughput microbial phenotype data
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