The legislative agenda in most parliamentary systems is controlled tightly by the government and bills offered by individual members of parliament have low rates of success. Yet, members of parliament (MPs) do seek to present (private) members’ bills even where the rate of adoption is very low. We argue that members’ bills serve as an electoral connection but also as an opportunity for MPs to signal competence to their co-partisans. To demonstrate the presence of an electoral connection we take advantage of the random selection of private members’ bills in the New Zealand House of Representatives and show that survey respondents approve more of electorate MPs whose bills were drawn on the ballot. In addition, we show that MPs respond to the incentives created by the voters and parties’ willingness to reward legislative effort and, consequently, that electorally vulnerable legislators are more likely to place members’ bills on the ballot.

There is abundant empirical evidence that the plurality rule constrains party competition and favors two-party systems. This reduction of party system fragmentation may be due to parties deciding not to enter elections for which they are not viable and/or voters voting strategically. Yet, no prior research has attempted to estimate the respective role of parties and voters in this process. To fill this gap, we conducted a unique laboratory experiment where some subjects played the role of parties and others played the role of voters, and where the two were able to respond to each other just as in real-life elections. We find that the reduction due to party strategic exit is higher than that due to strategic voting. We conclude that parties play a key role in the effect of the plurality rule on party system fragmentation.

The expansion of digital interconnectivity has simultaneously increased individuals’ access to media and presented governments with new opportunities to regulate information flows. As a result, even highly democratic countries now issue frequent censorship and user data requests to digital content providers. We argue that government internet censorship occurs, in part, for political reasons, and seek to identify the conditions under which states censor. We leverage new, cross-nationally comparable, censorship request data, provided by Google, to examine how country characteristics co-vary with governments’ digital censorship activity. Within democracies, we show that governments engage in more digital censorship when internal dissent is present and when their economies produce substantial intellectual property. But these demand mechanisms are modulated by the relative influence that democratic institutions provide to narrow and diffuse interests; in particular, states with proportional electoral institutions censor less.

Research on presidential distributive politics focuses almost exclusively on federal domestic spending. Yet, presidential influence on public policy extends well-beyond grant allocation. Since the early 20th Century, for example, the president has had substantial discretion to adjust tariff schedules and non-tariff barriers “with the stroke of a pen.” These trade adjustments via presidential directive allow us to test the logic of presidential particularism in an area of policy understudied among presidency scholars. We examine unilateral adjustments to US trade policies between 1917 and 2006, with a detailed analysis of those made between 1986 and 2006, and find that presidents—in accordance with electoral incentives—strategically allocate trade protections to industries in politically valuable states. In general, states in which the president lacks a comfortable electoral majority are systematically more likely to receive protectionist unilateral orders. Overall, our results show that the president’s distributive imperative extends into the realm of foreign affairs, an arena in which the president has substantial authority to influence public policy.

In contrast to non-partisan Get Out the Vote (GOTV) campaigns, political parties do not aim to increase turnout across the board. Instead, their principal goal is to affect the outcome of an election in their favor. To find out how they realize this aim, we carried out a randomized field experiment to evaluate the effect of campaign visits and leafleting by Conservative Party canvassers on turnout in a marginal English Parliamentary constituency during the 2014 European and Local Elections. Commonly-used campaign interventions, leaflets and door-knocks, changed the composition of the electorate in favor of the Conservative Party, but did not increase turnout overall. Supporters of rival parties, particularly Labour self-identifiers, were significantly less likely to mobilize in response to Conservative campaign contact than Conservative supporters. In contrast to the non-partisan GOTV literature, we show that impersonal campaign leaflets were as effective in shaping the local electorate in the Conservative’s favor as personal visits. The common practice of contacting all constituents irrespective of their party preferences was effective as a campaign tactic, but had no civic benefits in the aggregate.

Members and parties have electoral incentives to address issues on the congressional agenda to satisfy public demand. When determining which issues to address, majorities seek to minimize their uncertainty about the costs and electoral benefits of legislating by revisiting policy areas previously addressed. This theory is tested using error-correction models that demonstrate that policy activity within each chamber is in a long-term equilibrium and that the passage of legislation, even important bills, promotes future policymaking in the same policy area. This relationship is stronger when the majority has less information about the costs of lawmaking—specifically, when it faces a chamber controlled by the opposite party and when it is a new majority.

A large class of theoretical models posits that voters choose candidates on the basis of issue congruence, but convincing empirical tests of this key claim remain elusive. The most persistent difficulty is obtaining comparable spatial estimates for winning and losing candidates, as well as voters. We address these issues using candidate surveys to characterize the electoral platforms for winners and losers, and large issue batteries in 2008 and 2010 to estimate voter preferences. Questions that were answered by both candidates and citizens allow us to jointly scale these estimates. We find robust evidence that vote choice in congressional elections is both strongly associated with spatial proximity and that individual-level and contextual variables commonly associated with congressional voting behavior condition the magnitude of its importance. Our results have important implications for theories of voter decision-making and electoral institutions.

The effect of conditioning on an additional covariate on confounding bias depends, in part, on covariates that are unobserved. We characterize the conditions under which the interaction between a covariate that is available for conditioning and one that is not can affect bias. When the confounding effects of two covariates, one of which is observed, are countervailing (in opposite directions), conditioning on the observed covariate can increase bias. We demonstrate this possibility analytically, and then show that these conditions are not rare in actual data. We also consider whether balance tests or sensitivity analysis can be used to justify the inclusion of an additional covariate. Our results indicate that neither provide protection against overadjustment.

Comparable household income measures are crucial for most social science analyses of cross-national public opinion survey data. However, income questions in many cross-national surveys suffer from comparability and interpretability limitations that have not been adequately addressed by the existing literature. In this article, we examine the income measure in one major survey, the World Values Survey (WVS), arguing that a variety of problems arise when drawing inferences—descriptive or causal, individual or aggregate—using the standard ten-category measure. We then propose and implement a number of corrections to these potential biases and present a series of diagnostics that confirm the importance of our proposed corrections. We conclude by documenting some of the same challenges in the income measures used in other cross-national surveys. The accompanying data set can be merged with the WVS to make better use of the income measure.

We argue that political sciexntists can provide additional evidence for the predictive validity of observational and quasi-experimental research designs by minimizing the expected prediction error or generalization error of their empirical models. For observational and quasi-experimental data not generated by a stochastic mechanism under the researcher’s control, the reproduction of statistical analyses is possible but replication of the data-generating procedures is not. Estimating the generalization error of a model for this type of data and then adjusting the model to minimize this estimate—regularization—provides evidence for the predictive validity of the study by decreasing the risk of overfitting. Estimating generalization error also allows for model comparisons that highlight underfitting: when a model generalizes poorly due to missing systematic features of the data-generating process. Thus, minimizing generalization error provides a principled method for modeling relationships between variables that are measured but whose relationships with the outcome(s) are left unspecified by a deductively valid theory. Overall, the minimization of generalization error is important because it quantifies the expected reliability of predictions in a way that is similar to external validity, consequently increasing the validity of the study’s conclusions.

Methodologists and econometricians advocate the partial observability model as a tool that enables researchers to estimate the distinct effects of a single explanatory variable on two partially observable outcome variables. However, we show that when the explanatory variable of interest influences both partially observable outcomes, the partial observability model estimates are extremely sensitive to misspecification. We use Monte Carlo simulations to show that, under partial observability, minor, unavoidable misspecification of the functional form can lead to substantial large-sample bias, even though the same misspecification leads to little or no bias under full observability.

Lagged dependent variables (LDVs) have been used in regression analysis to provide robust estimates of the effects of independent variables, but some research argues that using LDVs in regressions produces negatively biased coefficient estimates, even if the LDV is part of the data-generating process. I demonstrate that these concerns are easily resolved by specifying a regression model that accounts for autocorrelation in the error term. This actually implies that more LDV and lagged independent variables should be included in the specification, not fewer. Including the additional lags yields more accurate parameter estimates, which I demonstrate using the same data-generating process scholars had previously used to argue against including LDVs. I use Monte Carlo simulations to show that this specification returns much more accurate coefficient estimates for independent variables (across a wide range of parameter values) than alternatives considered in earlier research. The simulation results also indicate that improper exclusion of LDVs can lead to severe bias in coefficient estimates. While no panacea, scholars should continue to confidently include LDVs as part of a robust estimation strategy.