Jonathan N. Katz

Throwing Out the Baby With the Bath Water: A Comment on Green, Kim and Yoon

Donald P. Green, Soo Yeon Kim, and David H. Yoon argue that many findings in quantitative international relations that use the dyad-year design are flawed. In particular, they argue that the effect of democracy on both trade and conflict has been vastly overstated, that researchers have ignored unobserved heterogeneity between the various dyads, and that heterogeneity can be best modeled by “fixed effects,” that is, a model that includes a separate dummy for each dyad.

Why Did The Incumbency Advantage In U.S. House Elections Grow

Theory: A simple rational entry argument suggests that the value of incumbency consists not just of a direct effect, reflecting the value of resources (such as staff) attached to legislative office, but also of an indirect effect, reflecting the fact that stronger challengers are less likely to contest incumbent-held seats. The indirect effect is the product of a scare-off effect-the ability of incumbents to scare off high-quality challengers-and a quality effect-reflecting how much electoral advantage a party accrues when it has an experienced rather than an inexperienced candidate. Hypothesis: The growth of the overall incumbency advantage was driven principally by increases in the quality effect. Methods: We use a simple two-equation model, estimated by ordinary least-squares regression, to analyze U.S. House election data from 1948 to 1990. Results: Most of the increase in the incumbency advantage, at least down to 1980, came through increases in the quality effect (i.e., the advantage to the incumbent party of having a low-quality challenger). This suggests that the task for those wishing to explain the growth in the vote-denominated incumbency advantage is to explain why the quality effect grew. It also suggests that resource-based explanations of the growth in the incumbency advantage cannot provide a full explanation.

A Statistical Model for Multiparty Electoral Data

We propose a comprehensive statistical model for analyzing multiparty, district-level elections. This model, which provides a tool for comparative politics research analogous to that which regression analysis provides in the American two-party context, can be used to explain or predict how geographic distributions of electoral results depend upon economic conditions, neighborhood ethnic compositions, campaign spending, and other features of the election campaign or aggregate areas. We also provide new graphical representations for data exploration, model evaluation, and substantive interpretation. We illustrate the use of this model by attempting to resolve a controversy over the size of and trend in the electoral advantage of incumbency in Britain. Contrary to previous analyses, all based on measures now known to be biased, we demonstrate that the advantage is small but meaningful, varies substantially across the parties, and is not growing. Finally, we show how to estimate the party from which each partys advantage is predominantly drawn.

Standard Voting Power Indexes Do Not Work: An Empirical Analysis

Voting power indexes such as that of Banzhaf are derived, explicitly or implicitly, from the assumption that all votes are equally likely (i.e., random voting). That assumption implies that the probability of a vote being decisive in a jurisdiction with n voters is proportional to 1/√n. In this article the authors show how this hypothesis has been empirically tested and rejected using data from various US and European elections. They find that the probability of a decisive vote is approximately proportional to 1/n. The random voting model (and, more generally, the square-root rule) overestimates the probability of close elections in larger jurisdictions. As a result, classical voting power indexes make voters in large jurisdictions appear more powerful than they really are. The most important political implication of their result is that proportionally weighted voting systems (that is, each jurisdiction gets a number of votes proportional to n) are basically fair. This contradicts the claim in the voting power literature that weights should be approximately proportional to √n.

The Reapportionment Revolution and Bias in U.S. Congressional Elections

We develop a simple formal model of the redistricting process that highlights the importance of two factors: first, partisan or bipartisan control of the redistricting process; second, the nature of the reversionary outcome, should the state legislature and governor fail to agree on a new districting plan. Using this model, we derive various predictions about the levels of partisan bias and responsiveness that should be observed under districting plans adopted under various constellations of partisan control of state government and reversionary outcomes, testing our predictions on postwar (1946{70) U.S. House electoral data. We find strong evidence that both partisan control and reversionary outcomes systematically affect the nature of a redistricting plan and the subsequent elections held under it. Further, we show that the well-known disappearance circa 1966 of what had been a long-time pro-Republican bias of about 6% in nonsouthern congressional elections can be explained completely by the changing composition of northern districting plans.

Poststratification Without Population Level Information on the Poststratifying Variable With Application to Political Polling

We investigate the construction of more precise estimates of a collection of population means using information about a related variable in the context of repeated sample surveys. The method is illustrated using poll results concerning presidential approval rating (our related variable is political party identification). We use poststratification to construct these improved estimates, but because we do not have population level information on the poststratifying variable, we construct a model for the manner in which the poststratifier develops over time. In this manner, we obtain more precise estimates without making possibly untenable assumptions about the dynamics of our variable of interest, the presidential approval rating.

What to Do (and Not Do) with Time-Series Criss-Section Data

We examine some issues in the estimation of time-series cross-section models, calling into question the conclusions of many published studies, particularly in the field of comparative political economy. We show that the generalized least squares approach of Parks produces standard errors that lead to extreme overconfidence, often underestimating variability by 50% or more. We also provide an alternative estimator of the standard errors that is correct when the error structures show complications found in this type of model. Monte Carlo analysis shows that these “panel-corrected standard errors” perform well. The utility of our approach is demonstrated via a reanalysis of one “social democratic corporatist” model.

Estimating Partisan Bias of the Electoral College Under Proposed Changes in Elector Apportionment

Abstract In the election for President of the United States, the Electoral College is the body whose members vote to elect the President directly. Each state sends a number of delegates equal to its total number of representatives and senators in Congress; all but two states (Nebraska and Maine) assign electors pledged to the candidate that wins the state’s plurality vote. We investigate the effect on presidential elections if states were to assign their electoral votes according to results in each congressional district, and conclude that the direct popular vote and the current electoral college are both substantially fairer compared to those alternatives where states would have divided their electoral votes by congressional district.

8. Nathaniel Beck and Jonathan N. Katz. 1995. “What to Do (and Not to Do) with Time-Series Cross-Section Data.” American Political Science Review 89 (September): 634–47 Cited 398 times.

Much as we would like to believe that the high citation count for this article is due to the brilliance and clarity of our argument, it is more likely that the count is due to our being in the right place (that is, the right part of the discipline) at the right time. In the 1960s and 1970s, serious quantitative analysis was used primarily in the study of American politics. But since the 1980s it has spread to the study of both comparative politics and international relations. In comparative politics we see in the 20 most cited Review articles Hibbss (1977) and Camerons (1978) quantitative analyses of the political economy of advanced industrial societies; in international relations we see Maoz and Russetts (1993) analysis of the democratic peace; and these studies have been followed by myriad others. Our article contributed to the methodology for analyzing what has become the principal type of data used in the study of comparative politics; a related article (Beck, Katz, and Tucker 1998), which has also had a good citation history, dealt with analyzing this type of data with a binary dependent variable, data heavily used in conflict studies similar to that of Maoz and Russetts. Thus the citations to our methodological discussions reflect the huge amount of work now being done in the quantitative analysis of both comparative politics and international relations.

An Audit of Political Behavior Research

What are the most important concepts in the political behavior literature? Have experiments supplanted surveys as the dominant method in political behavior research? What role does the American National Election Studies (ANES) play in this literature? We utilize a content analysis of over 1,100 quantitative articles on American mass political behavior published between 1980 and 2009 to address these questions. We then supplement this with a second sample of articles published between 2010 and 2018. Four key takeaways are apparent. First, the agenda of this literature is heavily skewed toward understanding voting to a relative lack of attention to specific policy attitudes and other topics. Second, experiments are ascendant, but are far from displacing surveys, and particularly the ANES. Third, while important changes to this agenda have occurred over time, it remains much the same in 2018 as it was in 1980. Fourth, the centrality of the ANES seems to stem from its time-series component. In the end, we conclude that the ANES is a critical investment for the scientific community and a main driver of political behavior research.