588 research outputs found
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
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Growth, Governance and Corruption in the Presence of Threshold Effects: Theory and Evidence
We study the joint determination of corruption and economic growth. Our model can generate multiple equilibria when complementarity between corruption and growth is sufficiently strong. Our estimates of the impact of corruption on growth take into account that corruption is endogenous and that there may exist different growth/corruption regimes. In a cross section of countries in the 1990s,we identify two regimes, conditional on the quality of political institutions. In the regime with high quality political institutions, corruption has a negative impact on growth. In the regime with low quality institutions, corruption has, overall, little impact on growth, but, if anything, the impact is, surprisingly, positive
Some statistical procedures for the joint oil analysis program
http://archive.org/details/somestatisticalp00bar
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