The Retrenchment Hypothesis and the Extension of the Franchise in England and Wales

Does an extension of the voting franchise increase public spending or can it be a source of retrenchment? We study this question in the context of public spending on health-related urban amenities in a panel of 75 municipal boroughs in England and Wales in 1868, 1871 and 1886. We \u85nd evidence of a U-shaped relationship between spending on urban amenities and the extension of the local voting franchise. We argue that this retrenchment e¤ect arose because middle class taxpayers were unwilling to pay the cost of poor sanitation and the urban elites, elected on a narrow franchise, were instrumental for sanitary improvements. Our model of taxpayer democracy suggests that the retrenchment e¤ect is related to enfranchisement of the middle class through nation-wide reforms

Sampling intervals

THE STATUS OF SAMPLING INTERVALS BETWEEN OIL ANALYSIS HAS BEEN REVIEWED AND A FRAMEWORK FOR AN ALTERNATIVE APPROACH TO THE DETERMINATION OF SAMPLING INTERVALS IS DISCUSSED.supported with funds provided by Kelly Air Force Base, (SA-ALC/MME1), Texas, 79241http://archive.org/details/samplinginterval00jay

Semi-supervised Eigenvectors for Large-scale Locally-biased Learning

In many applications, one has side information, e.g., labels that are provided in a semi-supervised manner, about a specific target region of a large data set, and one wants to perform machine learning and data analysis tasks "nearby" that prespecified target region. For example, one might be interested in the clustering structure of a data graph near a prespecified "seed set" of nodes, or one might be interested in finding partitions in an image that are near a prespecified "ground truth" set of pixels. Locally-biased problems of this sort are particularly challenging for popular eigenvector-based machine learning and data analysis tools. At root, the reason is that eigenvectors are inherently global quantities, thus limiting the applicability of eigenvector-based methods in situations where one is interested in very local properties of the data. In this paper, we address this issue by providing a methodology to construct semi-supervised eigenvectors of a graph Laplacian, and we illustrate how these locally-biased eigenvectors can be used to perform locally-biased machine learning. These semi-supervised eigenvectors capture successively-orthogonalized directions of maximum variance, conditioned on being well-correlated with an input seed set of nodes that is assumed to be provided in a semi-supervised manner. We show that these semi-supervised eigenvectors can be computed quickly as the solution to a system of linear equations; and we also describe several variants of our basic method that have improved scaling properties. We provide several empirical examples demonstrating how these semi-supervised eigenvectors can be used to perform locally-biased learning; and we discuss the relationship between our results and recent machine learning algorithms that use global eigenvectors of the graph Laplacian

Some statistical procedures for the joint oil analysis program

http://archive.org/details/somestatisticalp00bar