Preferences for Government Size and their Effect on Labor-Leisure Decisions

While many economists have theorized and/or empirically demonstrated that labor-leisure decisions are influenced by the rate of taxation, this note introduces a new mechanism in which the collecting of taxes on income may affect such decisions. Although standard models assume that agents have no preference for the size and scope of government activity, recent and past political rhetoric suggests that preferences do exist. We examine how labor-leisure decisions can be affected when taxes are derived from income and agents' utility functions include a preference for government size.

Does the distribution of New Deal spending reflect an optimal provision of public goods?

Since 1969 more than a dozen studies have explored the grossly unequal state-level distribution of New Deal spending. Why did small population rural states such as Nevada, Montana, and Wyoming receive up to six times as many federal dollars per capita as densely populated states such as Connecticut, Rhode Island, and New York? Empirical studies employing economic and political variables have had mixed results in explaining this distribution. What past studies neglect is that a large proportion of New Deal dollars went towards the creation of public goods, which had spillover effects particularly upon those who lived in close proximity to these projects. This note suggests that the state-level distribution of per capita expenditures during the 1930s is consistent with what would be expected to follow from an economically efficient allocation of public goods.

Exact Post Model Selection Inference for Marginal Screening

We develop a framework for post model selection inference, via marginal screening, in linear regression. At the core of this framework is a result that characterizes the exact distribution of linear functions of the response $y$, conditional on the model being selected (``condition on selection" framework). This allows us to construct valid confidence intervals and hypothesis tests for regression coefficients that account for the selection procedure. In contrast to recent work in high-dimensional statistics, our results are exact (non-asymptotic) and require no eigenvalue-like assumptions on the design matrix $X$. Furthermore, the computational cost of marginal regression, constructing confidence intervals and hypothesis testing is negligible compared to the cost of linear regression, thus making our methods particularly suitable for extremely large datasets. Although we focus on marginal screening to illustrate the applicability of the condition on selection framework, this framework is much more broadly applicable. We show how to apply the proposed framework to several other selection procedures including orthogonal matching pursuit, non-negative least squares, and marginal screening+Lasso

Nonlinear bound states on weakly homogeneous spaces

We prove the existence of ground state solutions for a class of nonlinear elliptic equations, arising in the production of standing wave solutions to an associated family of nonlinear Schr\"odinger equations. We examine two constrained minimization problems, which give rise to such solutions. One yields what we call $F_\lambda$-minimizers, the other energy minimizers. We produce such ground state solutions on a class of Riemannian manifolds called weakly homogeneous spaces, and establish smoothness, positivity, and decay properties. We also identify classes of Riemannian manifolds with no such minimizers, and classes for which essential uniqueness of positive solutions to the associated elliptic PDE fails.Comment: 49 page

A Framework for Dialogue within Service-Learning: Lessons from Philosophy for Children

This article argues that the field of community service-learning should adopt a “community of inquiry” model for classroom dialogue. Specifically, the author claims that communities of inquiry, as expounded by the Philosophy for Children movement, can help to overcome certain educational barriers that have been identified in both the theoretical and empirical service-learning literature by providing a general model for productive classroom dialogue. Moreover, this dialogical model, given its relative parsimony, promises to be amenable to many of the distinct (and potentially incompatible) theoretical models that inform service-learning approaches

Non-parametric Cosmology with Cosmic Shear

We present a method to measure the growth of structure and the background geometry of the Universe -- with no a priori assumption about the underlying cosmological model. Using Canada-France-Hawaii Lensing Survey (CFHTLenS) shear data we simultaneously reconstruct the lensing amplitude, the linear intrinsic alignment amplitude, the redshift evolving matter power spectrum, P(k,z), and the co-moving distance, r(z). We find that lensing predominately constrains a single global power spectrum amplitude and several co-moving distance bins. Our approach can localise precise scales and redshifts where Lambda-Cold Dark Matter (LCDM) fails -- if any. We find that below z = 0.4, the measured co-moving distance r (z) is higher than that expected from the Planck LCDM cosmology by ~1.5 sigma, while at higher redshifts, our reconstruction is fully consistent. To validate our reconstruction, we compare LCDM parameter constraints from the standard cosmic shear likelihood analysis to those found by fitting to the non-parametric information and we find good agreement.Comment: 13 pages. Matches PRD accepted versio