Caltech Social Science Working Paper #1266. There are many examples of allocation problems where the final allocation affects more than one agent, but the models developed to study them typically allow for side payments between agent. However, there are political economy applications where it is hard to imagine monetary transfers between the agents, at least not legal ones. In this paper we propose a general political economic framework for the study of allocation problems with externalities without side payments. We consider a setup with complete information and we formulate the problem as one where the status quo describes an initial allocation that can altered in a sequence of proposals. The number of these proposals is restricted. In the context of our main application, bidding for slots on a legislative agenda, such restriction can be interpreted as scarcity of plenary time for considering the possible bills to move the policy. The intuition for our model comes out of framing the problem as a special type of a multi-good auction. We show that equilibria generically exist within the general model.
Caltech Social Science Working Paper #1267R. Since the passage of he “Help America Vote Act” in 2002, nearly half of the states have adopted a variety of new identification requirements for voter registration and participation by the 2006 general election. There has been little analysis of whether these requirements reduce voter participation, espe- cially among certain classes of voters. In this paper we document the effect of voter identification requirements on registered voters as they were imposed in states in the 2000 and 2004 presidential elections, and in the 2002 and 2006 midterm elections. Looking first at trends in the aggregate data, we find no evidence that voter identification requirements reduce participation. Using individual-level data from the Current Population Survey across these elections, however, we find that the strictest forms of voter identification re- quirements — combination requirements of presenting an identification card and positively matching one’s signature with a signature either on file or on the identification card, as well as requirements to show picture identification — have a negative impact on the participation of registered voters relative to the weakest requirement, stating one’s name. We also find find evidence that the stricter voter identification requirements depress turnout to a greater ex- tent for less educated and lower income populations, but no racial differences.
Caltech Social Science Working Paper #1293. Ordinal variables — categorical variables with a defined order to the cat- egories, but without equal spacing between them — are frequently used in social science applications. Although a good deal of research exists on the proper modeling of ordinal response variables, there is not a clear directive as to how to model ordinal treatment variables. The usual approaches found in the literature for using ordinal treatment variables are either to use fully unconstrained, though additive, ordinal group indicators or to use a numeric predictor constrained to be continuous. Generalized additive models are a useful exception to these assumptions (Beck and Jackman 1998). In con- trast to the generalized additive modeling approach, we propose the use of a Bayesian shrinkage estimator to model ordinal treatment variables. The es- timator we discuss in this paper allows the model to contain both individual group level indicators and a continuous predictor. In contrast to traditionally used shrinkage models that pull the data toward a common mean, we use a linear model as the basis. Thus, each individual effect can be arbitrary, but the model “shrinks” the estimates toward a linear ordinal framework ac- cording to the data. We demonstrate the estimator on two political science examples: the impact of voter identification requirements on turnout (Al- varez, Bailey, and Katz 2007), and the impact of the frequency of religious service attendance on the liberality of abortion attitudes (e.g., Singh and Leahy 1978, Tedrow and Mahoney 1979, Combs and Welch 1982).
Caltech Social Science Working Paper #1294R. Misreporting is a problem that plagues researchers that use survey data. In this paper, we develop a parametric model that corrects for misclassified binary responses using information on the misreporting patterns obtained from auxiliary data sources. The model is implemented within the Bayesian framework via Markov Chain Monte Carlo (MCMC) methods, and can be easily extended to address other problems exhibited by survey data, such as missing response and/or covariate values. While the model is fully general, we illustrate its application in the context of estimating models of turnout using data from the American National Elections Studies.
Caltech Social Science Working Paper #1304. This paper deals with a variety of dynamic issues in the analysis of time-series--cross-section (TSCS) data. While the issues raised are more general, we focus on applications to political economy. We begin with a discussion of specification and lay out the theoretical differences implied by the various types of time series models that can be estimated. It is shown that there is nothing pernicious in using a lagged dependent variable and that all dynamic models either implicitly or explicitly have such a variable; the differences between the models relate to assumptions about the speeds of adjustment of measured and unmeasured variables. When adjustment is quick it is hard to differentiate between the various models; with slower speeds of adjustment the various models make sufficiently different predictions that they can be tested against each other. As the speed of adjustment gets slower and slower, specification (and estimation) gets more and more tricky. We then turn to a discussion of estimation. It is noted that models with both a lagged dependent variable and serially correlated errors can easily be estimated; it is only OLS that is inconsistent in this situation. We then show, via Monte Carlo analysis shows that for typical TSCS data that fixed effects with a lagged dependent variable performs about as well as the much more complicated Kiviet estimator, and better than the Anderson-Hsiao estimator (both designed for panels).