Cultural Diversity in the United States and Its Impact on Human Development

Previous studies have yielded mixed results on the impact of cultural diversity on economic performance. We find a positive relationship in the United States between cultural diversity and a comprehensive measure of human development that incorporates health, education, and income. We also disaggregate cultural diversity into three components including ethnicity, language, and religion. We find a positive relationship between human development and both religious and language diversity, and a negative relationship with ethnic diversity. These relationships are robust, using several alternative mathematical measures of diversity. Our results are consistent with diversity generating benefits from exposure to a variety of experiences, ideas, and skills while introducing costs due to difficulty in communication, difference in preferences, and conflict between polarized groups. We conclude that strong institutions are essential to maximize the benefits of diversity while mitigating the associated costs

Diversity in the Heartland of America: The Impact on Human Development in Indiana

This article is the third in a series of studies measuring the impact of cultural diversity on human development. We disaggregate cultural diversity into three components: ethnicity, language, and religion. The first study examined the impact of diversity internationally. We found that countries are worse off with greater diversity, especially religious diversity; however, we found that more-prosperous countries with strong institutions benefited from increased diversity. We concluded that strong institutions are essential to maximize the benefits of diversity while mitigating the associated costs. The second study examined the impact of diversity within the United States, where institutional strength was assumed to be relatively great and similar between states. We found an overall negative impact from diversity. Ethnic diversity was negatively associated with human development, while religious and language diversity had a positive impact. We concluded that in the United States, there is more tolerance for religious and language differences compared to ethnic differences. In this third study, we examine the impact of diversity within the state of Indiana. As with our national results, we find a generally negative relationship between human development and diversity. Ethnic diversity has a negative impact, while religious and language diversity are generally positive influences. Strong political and legal institutions may not be sufficient to extract net benefits from diversity if social attitudes that guide behavior are not supportive. The results suggest that net benefits from diversity in Indiana may depend on improvement of social attitudes and in commitment to social services that support historically disadvantaged minority groups

Data-Mining a Large Digital Sky Survey: From the Challenges to the Scientific Results

The analysis and an efficient scientific exploration of the Digital Palomar Observatory Sky Survey (DPOSS) represents a major technical challenge. The input data set consists of 3 Terabytes of pixel information, and contains a few billion sources. We describe some of the specific scientific problems posed by the data, including searches for distant quasars and clusters of galaxies, and the data-mining techniques we are exploring in addressing them. Machine-assisted discovery methods may become essential for the analysis of such multi-Terabyte data sets. New and future approaches involve unsupervised classification and clustering analysis in the Giga-object data space, including various Bayesian techniques. In addition to the searches for known types of objects in this data base, these techniques may also offer the possibility of discovering previously unknown, rare types of astronomical objects.Comment: Invited paper, to appear in Applications of Digital Image Processing XX, ed. A. Tescher, Proc. S.P.I.E. vol. 3164, in press; 10 pages, a self-contained TeX file, and 3 separate postscript figure

When It\u27s Moonlight On The Prairie : There\u27s A Parson Only Twenty Miles Away

https://digitalcommons.library.umaine.edu/mmb-vp/5012/thumbnail.jp

The source and value of voting rights and related dividend promises

This paper examines the relative share pricing of 98 firms with two classes of common stock trading in the United States from 1984 to 1999. The firms feature common stock classes with differential voting rights and, in some cases, differential rights to dividends. The observed voting premiums are higher than those reported in previous studies of U.S. firms and are dependent on the form of dividend promise to the low-vote shareholder. The voting premium is higher in the presence of a control threat, when insiders do not hold controlling voting power, and during periods of poor firm performance

Issues in knowledge representation to support maintainability: A case study in scientific data preparation

Scientific data preparation is the process of extracting usable scientific data from raw instrument data. This task involves noise detection (and subsequent noise classification and flagging or removal), extracting data from compressed forms, and construction of derivative or aggregate data (e.g. spectral densities or running averages). A software system called PIPE provides intelligent assistance to users developing scientific data preparation plans using a programming language called Master Plumber. PIPE provides this assistance capability by using a process description to create a dependency model of the scientific data preparation plan. This dependency model can then be used to verify syntactic and semantic constraints on processing steps to perform limited plan validation. PIPE also provides capabilities for using this model to assist in debugging faulty data preparation plans. In this case, the process model is used to focus the developer's attention upon those processing steps and data elements that were used in computing the faulty output values. Finally, the dependency model of a plan can be used to perform plan optimization and runtime estimation. These capabilities allow scientists to spend less time developing data preparation procedures and more time on scientific analysis tasks. Because the scientific data processing modules (called fittings) evolve to match scientists' needs, issues regarding maintainability are of prime importance in PIPE. This paper describes the PIPE system and describes how issues in maintainability affected the knowledge representation used in PIPE to capture knowledge about the behavior of fittings

Using machine learning techniques to automate sky survey catalog generation

We describe the application of machine classification techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Palomar Observatory Sky Survey provides comprehensive photographic coverage of the northern celestial hemisphere. The photographic plates are being digitized into images containing on the order of 10(exp 7) galaxies and 10(exp 8) stars. Since the size of this data set precludes manual analysis and classification of objects, our approach is to develop a software system which integrates independently developed techniques for image processing and data classification. Image processing routines are applied to identify and measure features of sky objects. Selected features are used to determine the classification of each object. GID3* and O-BTree, two inductive learning techniques, are used to automatically learn classification decision trees from examples. We describe the techniques used, the details of our specific application, and the initial encouraging results which indicate that our approach is well-suited to the problem. The benefits of the approach are increased data reduction throughput, consistency of classification, and the automated derivation of classification rules that will form an objective, examinable basis for classifying sky objects. Furthermore, astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems given automatically cataloged data

Intelligent assistance in scientific data preparation

Scientific data preparation is the process of extracting usable scientific data from raw instrument data. This task involves noise detection (and subsequent noise classification and flagging or removal), extracting data from compressed forms, and construction of derivative or aggregate data (e.g. spectral densities or running averages). A software system called PIPE provides intelligent assistance to users developing scientific data preparation plans using a programming language called Master Plumber. PIPE provides this assistance capability by using a process description to create a dependency model of the scientific data preparation plan. This dependency model can then be used to verify syntactic and semantic constraints on processing steps to perform limited plan validation. PIPE also provides capabilities for using this model to assist in debugging faulty data preparation plans. In this case, the process model is used to focus the developer's attention upon those processing steps and data elements that were used in computing the faulty output values. Finally, the dependency model of a plan can be used to perform plan optimization and run time estimation. These capabilities allow scientists to spend less time developing data preparation procedures and more time on scientific analysis tasks

A Case For Teaching Business Ethics In A Cost-Benefits Framework: Are Business Students More Discriminating In Their Decision Making?

Based on a survey questionnaire of 299 university students, we find that business majors act more ethically than other majors in some cases and less ethically in others. Business students appear more likely to adopt the consequentialist framework to evaluate ethical dilemmas. Our results are consistent with business students being more discriminating based on the perceived costs and benefits of each case. We find differences in behavior based on active versus passive unethical behavior and based on the identity of the potentially harmed party. This evidence suggests that business school curricula that focus on acting ethically because it is the right thing to do may be ineffective. Our results indicate it may be important to openly discuss unethical behavior in a framework that considers the long-term consequences to all affected stakeholders. As a result, business students and future professionals may conclude that ethical behavior is supported by careful cost-benefit analysis

The Role of Insider Influence in Mutual-to-Stock Conversions

Using a sample of 347 demutualizing thrifts from 1991 to 2004, we show that the level of inside participation is not a traditional signal of firm performance. We conclude that unanticipated inside participation reflects the incentives of insiders to reduce the size of the offer to influence the level of expected IPO returns. We find unanticipated inside participation is related to lower offer size and higher initial returns, but we do not find a relationship between inside participation and post-IPO performance