A Median Voter Theorem for Postelection Politics

We analyze a model of 'postelection politics', in which (unlike in the more common Downsian models of 'preelection politics') politicians cannot make binding commitments prior to elections. The game begins with an incumbent politician in office, and voters adopt reelection strategies that are contingent on the policies implemented by the incumbent. We generalize previous models of this type by introducing heterogeneity in voters' ideological preferences, and analyze how voters' reelection strategies constrain the policies chosen by a rent-maximizing incumbent. We first show that virtually any policy (and any feasible level of rent for the incumbent) can be sustained in a Nash equilibrium. Then, we derive a 'median voter theorem': the ideal point of the median voter, and the minimum feasible level of rent, are the unique outcomes in any strong Nash equilibrium. We then introduce alternative refinements that are less restrictive. In particular, Ideologically Loyal Coalition-proof equilibrium also leads uniquely to the median outcome.

Is the Median Voter Decisive? Evidence of 'Ends Against the Middle' From Referenda Voting Patterns

This paper examines whether the voter with the median income is decisive in local spending decisions. Previous tests have relied on cross-sectional data while we make use of a pair of California referenda to estimate a first difference specification. The referenda proposed to lower the required vote share for passing local educational bonding initiatives from 67 to 50 percent and 67 to 55 percent, respectively. We find that voters rationally consider future public service decisions when deciding how to vote on voting rules, but the empirical evidence strongly suggests that an income percentile below the median is decisive for majority voting rules. This finding is consistent with high income voters with weak demand for public educational services voting with the poor against increases in public spending on education.

Creating Useful Products From Connecticut's 2000 LIDAR Data Set JHR 08-314 Project 07-2

The State of Connecticut owns a LIght Detection and Ranging (LIDAR) data set that was collected in 2000 as part of the State’s periodic aerial reconnaissance missions. Although collected eight years ago, these data are just now becoming ready to be made available to the public. These data constitute a massive “point cloud”, being a long list of east-north-up triplets in the State Plane Coordinate System Zone 0600 (SPCS83 0600), orthometric heights (NAVD 88) in US Survey feet. Unfortunately, point clouds have no structure or organization, and consequently they are not as useful as Triangulated Irregular Networks (TINs), digital elevation models (DEMs), contour maps, slope and aspect layers, curvature layers, among others. The goal of this project was to provide the computational infrastructure to create a first cut of these products and to serve them to the public via the World Wide Web. The products are available at http://clear.uconn.edu/data/ct_lidar/index.htm.

Building a Globally Distributed Historical Sheet Map Set

This paper describes the procedures used to create a distributed collection of topographic maps of the Austro-Hungarian Empire, the Spezialkarte der Öesterriechisch-ungarnischen Monarchie, Masse. 1:75,000 der natur. This set of maps was published in Vienna over a period of years from 1877 to 1914. The part of the set used in this project includes 776 sheets; all sheets from all editions number over 3,665. The paper contains detailed information on how the maps were converted to digital images, how metadata were prepared, and how Web-browser access was created using ArcIMS Metadata Server. The project, funded by a 2004 National Leadership Grant from the Institute for Museums and Library Science (IMLS), was a joint project of the Homer Babbidge Library Map and Geographic Information Center at the University of Connecticut, the New York Public Library, and the American Geographical Society’s Map Library at the University of Wisconsin Milwaukee.