Voters use observed economic performance to infer the competence of incumbent politicians. These economic perceptions enter the voter's utility calculations modified by a weight that is minimized when the variance in exogenous shocks to the economy is very large relative to the variance in economic outcomes associated with the competence of politicians. Cross-national variations in the political and economic context systematically increase or undermine the voter's ability to ascertain the competency of incumbents. We test one hypothesis: As policy-making responsibility is shared more equally among parties, economic evaluations will be more important in the vote decision. We employ two multilevel modeling procedures for estimating the contextual variations in micro-level economic voting effects: a conventional pooled approach and a two-stage procedure. We compare the multivariate results of a pooled method with our two-stage estimation procedure and conclude that they are similar. Our empirical efforts use data from 163 national surveys from 18 countries over a 22-year period.

In a recent paper published in this journal, Katz (2001) compares the bias in conditional and unconditional fixed effects logit estimation using Monte Carlo Simulation. This note shows that while Katz's (2001) specification has “wrong” fixed effects (in the sense that the fixed effects are the same for all individuals), his conclusions still hold if I correct his specification (so that the fixed effects do differ over individuals). This note also illustrates the danger, when using logit, of including dummies when no fixed effects are present.

In an earlier report, two of us (Bowers and Ensley, 2003, National Election Studies Technical Report, www.umich.edu/~nes) provided a general framework for understanding the particular strategy outlined by Fogarty et al. (in this issue). Fogarty et al.'s strategy is to make the face-to-face variables more like the random digit dial (RDD) telephone variables by trimming the ends in order to reduce the variance of the face-to-face (FTF) variables. Perhaps some scholars will want the FTF variables to look like the RDD variables, but that would be a fix for a specific research question. Given the significant differences in the representativeness of the samples, the processes of survey nonresponse, and the quality and character of the responses between data taken from a National Area Probability sample in person and data taken from an RDD telephone sample, research questions involving comparisons with other years in the 50-year time series will require different remedies.

I agree with the author's main point. Although I tried to fit a fixed-effects model to the simulated data, those data were generated from a model without fixed effects. In my experiment, therefore, use of the unconditional estimator was perfectly confounded with misspecification of the model. I thank the author for catching this flaw.

I analyze how the diffusion of power in parliaments affects voter choice. Using a two-step research design, I first estimate an individual-level model of voter choice in 14 parliamentary democracies, allowing voters to hold preferences both for the party most similar to them ideologically and for the party that pulls policy in their direction. While in systems in which power is concentrated the two motivations converge, in consensual systems they diverge: since votes will likely be watered down by bargaining in the parliament, outcome-oriented choice in consensual systems often leads voters to endorse parties whose positions differ from their own views. In the second step, I utilize institutional measures of power diffusion in the parliament to account for the degree to which voters in different polities pursue one motivation versus the other. I demonstrate that the more power diffusion and compromise built into the political system via institutional mechanisms, the more voters compensate for the watering down of their vote by endorsing parties whose positions differ from their own views.

Equivalent separate-subsample (two-step) and pooled-sample (one-step) strategies exist for any multilevel-modeling task, but their relative practicality and efficacy depend on dataset dimensions and properties and researchers' goals. Separate-subsample strategies have difficulties incorporating cross-subsample information, often crucial in time-series cross-section or panel contexts (subsamples small and/or cross-subsample information great) but less relevant in pools of independently random surveys (subsamples large; cross-sample information small). Separate-subsample estimation also complicates retrieval of macro-level-effect estimates, although they remain obtainable and may not be substantively central. Pooled-sample estimation, conversely, struggles with stochastic specifications that differ across levels (e.g., stochastic linear interactions in binary dependent-variable models). Moreover, pooled-sample estimation that models coefficient variation in a theoretically reduced manner rather than allowing each subsample coefficient vector to differ arbitrarily can suffer misspecification ills insofar as this reduced specification is lacking. Often, though, these ills are limited to inefficiencies and standard-error inaccuracies that familiar efficient (e.g., feasible generalized least squares) or consistent-standard-error estimation strategies can satisfactorily redress.

Two-step estimators for hierarchical models can be constructed even when neither stage is a conventional linear regression model. For example, the first stage might consist of probit models, or duration models, or event count models. The second stage might be a nonlinear regression specification. This note sketches some of the considerations that arise in ensuring that two-step estimators are consistent in such cases.

The articles in this special issue all use multilevel methods to study comparative political behavior. This is obviously a good thing, for both methodology and comparative politics. Clearly comparative politics means comparing things and not just studying nations other than the United States. This is equally true of micropolitical studies. These articles all do a very nice job of showing how one can do comparative micropolitics (and tie together micro and macro variables).

These articles demonstrate, in several different examples, the effectiveness of two-level regression: the procedure of fitting several separate regression models, and then fitting a second, higher-level, regression to the estimated coefficients (for example, fitting a separate regression model to survey data from each of several countries, then regressing the coefficient estimates on country-level predictors). For simplicity, we will refer to the first- and second-level units as “persons” and “countries,” respectively, but our points apply more generally.