## Dissertation

Uses two new estimators (see below) that are implemented in my R package, FAiR, to test traditional political economy theories of preferences for redistribution and other welfare-state programs against alternative theories that stem from sociology, psychology, and lab experiments. In short, the alternative theories perform better, and the new methods produce stronger evidence for this conclusion than do standard methodological techniques.

## Published Papers

A Comment on "Rewarding Impatience" (pdf) (data) (Stata code) (BibTeX) Published in International Organization 60(2):499-513.

## Public Software

The "stable" version (0.4-x) of my R package to estimate structural equation models with latent variables is available here. The "development" version (0.6-0) is available here. It is mostly functional but some things may not work (correctly) at any particular point in time, and they may or may not correspond to the (lack of) documentation. Some of the working papers depend on functionality in FA$i$R 0.6-x.

## Working Papers

This paper derives an algorithm to indirectly successfully solve an optimization problem proposed by Louis Leon Thurstone in the 1930s and worked on by Louis Guttman in the 1950s and various engineers even today. At the optimum, it is possible to infer the number of inputs to the data-generating process of the observed variables and under certain verifiable conditions, to estimate the proportion of each variable that consists of random noise.
This paper applies the methods developed in the previous two (and following) papers to cross-country survey-data in order to test theories of preferences for redistribution. It is an article-length statement of the main conclusion of my dissertation, namely that traditional political economy theories that assume a voter's utility depends only on private variable do a demonstrably poor job of explaining the variation in preferences for redistribution within countries.
This APSA paper describes a new algorithm for estimating a factor analysis model where a specified number of exclusion restrictions are imposed on the model but not which coefficients are subject to exclusion restrictions. The algorithm, which is implemented in the stable version of FA$i$R, finds the best-fitting combination of the locations of the null coefficients and the values of the non-zero parameters. A generalization of this idea will be implemented for LISREL models sometime in the reasonably near future.