Land Value Taxation: A Spatially Explicit Economic Experiment with Endogenous Institutions

An economic experiment with endogenous institutions informs the political economy of land value taxation relative to uniform property taxation in terms of efficiency and sprawl reduction. Heterogeneous type distributions were used so that land value taxation was earnings-rational, relative to uniform property taxation, for 40, 60, and 80 percent of the participants. The model’s induced values predict land value taxation leads to less sprawl, more earnings, and more tax revenue than uniform property taxation. Experimental data do not consistently match this prediction, where both tax institutions led to more sprawl and lower earnings than predicted. Results show participants voted for the tax institution that does not maximize their individual earnings in 16.7 percent of rounds. These earnings-irrational choices occurred when the type distributions were 40 and 60 percent in favor of land value taxation. The experiment results nonetheless show the absolute advantage of land value taxation for producing less sprawl, more tax revenue, and more earnings. Moreover, the behavioral evidence suggests that relative advantage of land value taxation in reducing sprawl is greater than predicted by the model. This suggests further inquiry about whether land value taxation promotion activities may best be targeted towards cities using uniform property taxation where economies are vibrant, land uses are already relatively intensive, and greater-than-average population density already exists.

A High-Resolution and Low-Complexity DOA Estimation Method with Unfolded Coprime Linear Arrays

The direction-of-arrivals (DOA) estimation with an unfolded coprime linear array (UCLA) has been investigated because of its large aperture and full degrees of freedom (DOFs). The existing method suffers from low resolution and high computational complexity due to the loss of the uniform property and the step of exhaustive peak searching. In this paper, an improved DOA estimation method for a UCLA is proposed. To exploit the uniform property of the subarrays, the diagonal elements of the two self-covariance matrices are averaged to enhance the accuracy of the estimated covariance matrices and therefore the estimation performance. Besides, instead of the exhaustive peak searching, the polynomial roots finding method is used to reduce the complexity. Compared with the existing method, the proposed method can achieve higher resolution and better estimation performance with lower computational complexity.