Matt Golder’s research while affiliated with Pennsylvania State University and other places

In parliamentary democracies, the transfer of power from one government to the next is sometimes characterized by long periods of negotiations in which party leaders bar-gain over the composition and policy objectives of a new cabinet. Although these de-lays can have substantial political and economic consequences, we know surprisingly little about their determinants. Moreover, the few studies that exist reach contradic-tory conclusions. In this article, I examine how factors relating to uncertainty and bargaining complexity influence the duration of the government formation process in sixteen West European countries from 1944 to 1998. In line with my theoretical ex-pectations, I find that factors increasing uncertainty over the type of cabinet that is acceptable always lead to delays in forming governments, but that factors increasing bargaining complexity, such as the number of parties and ideological polarization in the legislature, only do so when there is sufficient uncertainty among political actors. My analysis helps to resolve the contradictory findings in the literature.

do not know why this is listed here - cannot get rid of it

Recently Mozaffar et al. [Mozaffar, S., Scarritt, J.R., Galaich, G., 2003. Electoral institutions, ethnopolitical cleavages and party systems in Africa's emerging democracies. American Political Science Review 97, 379–390] presented evidence suggesting that African party systems are somehow different from party systems elsewhere in the world. In doing so, they promoted the common notion of African exceptionalism. We believe that their conclusions are open to question because they draw inferences from a number of multiplicative interaction models in which they do not include all constitutive terms, interpret constitutive terms as unconditional marginal effects, and fail to calculate marginal effects and standard errors over a sufficiently large range of their modifying variables. By correcting these practices, we reach substantively different conclusions. Specifically, we find that African party systems respond to institutional and sociological factors such as district magnitude and ethnic fragmentation in the same way as party systems in more established democracies.

Although Duverger is traditionally seen as synonymous with the institution-list approach to party systems, this article shows that he believed social pressures were the driving force behind the multiplication of parties. Electoral institutions are important, but only because they determine the extent to which social forces are translated into political parties. Although the literature has finally come to realize that social and institutional forces interact to shape party systems, scholars still do not seem to fully understood the implications of Duverger’s theory. This article shows that existing research employs flawed statistical specifications, makes inferential errors, and does not calculate desired quantities of interest. Using a new data set that includes elections since 1946, the authors reexamine Duverger’s theory and find that modern tests largely bear out his expectations when properly specified and interpreted. This analysis also extends current research by specifically estimating the mechanical and strategic modifying effects of electoral institutions.

In this paper, we argue that claims of necessity and sufficiency involve a type of asymmetric causal claim that is useful in many social scientific contexts. Contrary to some qualitative researchers, we maintain that there is nothing about such asymmetries that should lead scholars to depart from standard social science practice. We take as given that deterministic and monocausal tests are inappropriate in the social world and demonstrate that standard multiplicative interaction models are up to the task of handling asymmetric causal claims in a multivariate, probabilistic manner. We illustrate our argument with examples from the empirical literature linking electoral institutions and party system size.

Multiplicative interaction models are common in the quantitative political science literature. This is so for good reason. Institutional arguments frequently imply that the relationship between political inputs and outcomes varies depending on the institutional context. Models of strategic interaction typically produce conditional hypotheses as well. Although conditional hypotheses are ubiquitous in political science and multiplicative interaction models have been found to capture their intuition quite well, a survey of the top three political science journals from 1998 to 2002 suggests that the execution of these models is often flawed and inferential errors are common. We believe that considerable progress in our understanding of the political world can occur if scholars follow the simple checklist of dos and don'ts for using multiplicative interaction models presented in this article. Only 10% of the articles in our survey followed the checklist.

Considerable evidence suggests that legislative fragmentation can negatively affect the survival of democratic presidential regimes. While there is a vast literature examining the determinants of legislative fragmentation, one factor that has traditionally been overlooked is the impact of presidential elections. Do presidential elections increase or decrease legislative fragmentation? Does it matter if presidents are elected by plurality rule or by runoff? Using a new dataset that covers all democratic legislative and presidential elections between 1946 and 2000, I find that presidential coattails can reduce, increase, or have no effect on legislative fragmentation depending on the number of presidential candidates. I also find strong evidence that social heterogeneity increases the number of presidential candidates when runoff systems are employed. Taken together, these results suggest that the widespread adoption of runoffs by newly democratic presidential regimes will likely increase legislative fragmentation, thereby putting their democratic survival at increased risk.

This article describes a new data set that covers the electoral institutions used in all of the democratic legislative and presidential elections in 199 countries between 1946 (or independence) and 2000. A clear and consistent classification of the electoral institutions used in these elections is followed by a concise geographical and temporal analysis. The worldwide focus of the data set reveals several striking patterns. For example, there have been almost as many elections under dictatorship as there have been under democracy. Other patterns include the fact that presidential regimes nearly always employ proportional electoral formulas, absolute majority rule has become the worldwide norm for electing presidents, and non-majoritarian systems have become more complex due to the increasing use of multiple tiers and mixed electoral formulas.

Multiplicative interaction models are common in the quantitative political science literature. This is so for good reason. Institutional arguments frequently imply that the relationship between political inputs and outcomes varies depending on the institutional context. Models of strategic interaction typically produce conditional hypotheses as well. Although con-ditional hypotheses are ubiquitous in political science and multiplicative interaction models have been found to capture their intuition quite well, a survey of the top three political science journals from 1998 to 2002 suggests that the execution of these models is often flawed and inferential errors are common. We believe that considerable progress in our understanding of the political world can occur if scholars follow the simple checklist of dos and don'ts for using multiplicative interaction models presented in this article. Only 10% of the articles in our survey followed the checklist., and two anonymous reviewers for their extremely useful comments on this paper. We also thank the research assistants at Political Analysis—Jeronimo Cortina, Tse-hsin Chen, and Seung Jin Jang—for kindly double-checking the results from our literature survey. Finally, we are grateful to those authors who have provided us with their data. To accompany this paper, we have constructed a Web page at http:// homepages.nyu.edu/;mrg217/interaction.html that is devoted to multiplicative interaction models. On this page, you will find (i) the data and computer code necessary to replicate the analyses conducted here, (ii) information relating to marginal effects and standard errors in interaction models, (iii) STATA code for producing figures illustrating marginal effects and confidence intervals for a variety of continuous and limited dependent variable models, and (iv) detailed results from our literature survey. STATA 8 was the statistical package used in this study.