We propose a remedy for the discrepancy between the way political scientists analyze data with missing values and the recommendations of the statistics community. Methodologists and statisticians agree that "multiple imputation" is a superior approach to the problem of missing data scattered through one's explanatory and dependent variables than the methods currently used in applied data analysis. The reason for this discrepancy lies with the fact that the computational algorithms used to apply the best multiple imputation models have been slow, difficult to implement, impossible to run with existing commercial statistical packages, and demanding of considerable expertise. In this paper, we adapt an existing algorithm, and use it to implement a generalpurpose, multiple imputation model for missing data. This algorithm is considerably faster and easier to use than the leading method recommended in the statistics literature. We also quantify the risks of current missing data practices, ...

Estimating the impact of economic conditions on the likelihood of civil conflict is difficult because of endogeneity and omitted variable bias. We use rainfall variation as an instrumental variable for economic growth in 41 African countries during 1981–99. Growth is strongly negatively related to civil conflict: a negative growth shock of five percentage points increases the likelihood of conflict by one-half the following year. We attempt to rule out other channels through which rainfall may affect conflict. Surprisingly, the impact of growth shocks on conflict is not significantly different in richer, more democratic, or more ethnically diverse countries. I.

In this paper we prove theoretically and demonstrate empirically that all existing measures of incumbency advantage in the congressional elections literature are biased or inconsistent. We then provide an unbiased estimator based on a very simple linear regression model. We apply this new method to congressional elections since 1900, providing the first evidence of a positive incumbency advantage in the first half of the century.

This paper builds a stochastic model of the processes that give rise to observed patterns of representation and bias in congressional and state legislative elections. The analysis demonstrates that partisan swing and incumbency voting, concepts from the congressional elections literature, have determinate effects on representation and bias, concepts from the redistrictihg literature. The model shows precisely how incumbency and increased variability of partisan swing reduce the responsiveness of the electoral system and how partisan swing affects whether the system is biased toward one party or the other. Incumbency, and other causes of unresponsive representation, also reduce the effect of partisan swing on current levels of partisan bias. By relaxing the restrictive portions of the widely applied "uniform partisan swing " assumption, the theoretical analysis leads directly to an empirical model enabling one more reliably to estimate responsiveness and bias from a single year of electoral data. Applying this to data from seven elections in each of six states, the paper demonstrates that redistricting has effects in predicted directions in the short run: partisan gerrymandering biases the system in favor of the party in control and, by freeing up seats held by opposition party incumbents, increases the system's responsiveness. Bipartisan-controlled redistricting appears to reduce bias somewhat and dramatically to reduce responsiveness. Nonpartisan redistricting processes substantially increase responsiveness but do not have as clear an effect on bias. However, after only two elections, prima facie evidence for redistricting effects evaporate in most states. Finally, across every state and type of redistricting process, responsiveness declined significantly over the course of the decade. This is clear evidence that the phenomenon of "vanishing marginals, " recognized first in the U.S. Congress literature, also applies to these different types of state legislative assemblies. It also strongly suggests that redistricting could not account for this pattern. 1.

We propose a remedy to the substantial discrepancy between the way political scientists analyze data with missing values and the recommendations of the statistics community. With a few notable exceptions, statisticians and methodologists have agreed on a widely applicable approach to many missing data problems based on the concept of \multiple imputation, " but most researchers in our eld and other social sciences still use far inferior methods. Indeed, we demonstrate that the threats to validity from current missing data practices rival the biases from the much better known omitted variable problem. As it turns out, this discrepancy is not entirely our fault, as the computational algorithms used to apply the best multiple imputation models have been slow, di cult to implement, impossible to run with existing commercial statistical packages, and demanding of considerable expertise on the part of the user (even experts disagree on how to use them). In this paper, we adapt an existing algorithm, and use it to implement a generalpurpose, multiple imputation model for missing data. This algorithm is between 65 and

Although integrating multiple levels of data into an analysis can often yield better inferences about the phenomenon under study, traditional methodologies used to combine multiple levels of data are problematic. In this paper, we discuss several methodologies under the rubric of multilevel analysis. Multilevel methods, we argue, provide researchers, particularly researchers using comparative data, substantial leverage in overcoming the typical problems associated with either ignoring multiple levels of data, or problems associated with combining lower-level and higher-level data (including overcoming implicit assumptions of fixed and constant effects). The paper discusses several variants of the multilevel model and provides an application of individual-level support for European integration using comparative political data from Western Europe.

Social scientific theories frequently posit that multiple causal mechanisms may produce the same outcome. Unfortunately, it is not always possible to observe which mechanism was responsible. For example, IMF scholars conjecture that nations enter IMF agreements both out of economic need and for discretionary domestic political reasons. Typically, though, all we observe is the fact of agreement, not its cause. Partial observability probit models (Poirier 1980, Journal of Econometrics 12:209–217; Braumoeller 2003, Political Analysis 11:209– 233) provide one method for the statistical analysis of such phenomena. Unfortunately, they are often plagued by identification and labeling difficulties. Sometimes, however, qualitative studies of particular cases enlighten us about causes when quantitative studies cannot. We propose exploiting this information to lend additional structure to the partial observability approach. Monte Carlo simulation reveals that by anchoring ‘‘discernible’ ’ causes for a handful of cases about which we possess qualitative information, we obtain greater efficiency. More important, our method proves reliable at recovering unbiased parameter estimates when the partial observability model fails. The paper concludes with an analysis of the determinants of IMF agreements. A member shall be entitled to purchase the currencies of other members from the Fund...[provided] the member represents that it has a need to make the purchase because of its balance of payments or its reserve position or developments in its reserves. —International Monetary Fund Articles of Agreement [IMF] negotiations sometimes enable government leaders to do what they privately wish to do, but are powerless to do domestically. —Robert Putnam (1988, p. 457)

Regression models are often used to infer causation from association. For instance, Yule [79] showed – or tried to show – that welfare was a cause of poverty. Path models and structural equation models are later

Abstract: In this paper I combine two existing policy theories, institutional rational choice and the Advocacy Coalition Framework, to explain actor perceptions of the effectiveness of public policies targeting common-pool resource dilemmas in coastal watersheds. Survey data from estuaries with and without the USEPA’s National Estuary Program provides evidence for two main hypotheses. First, perceived policy effectiveness is a function of “collective-action beliefs”: beliefs about situational variables that determine the benefits and transaction costs of collective action within the estuary action arena. Second, the effects of policy-core beliefs and institutional structure on perceived policy effectiveness are interdependent. In particular, governance institutions have a favorable effect on perceived policy effectiveness among political actors whose policy-core beliefs are congruent with the structure of the institution. Why do political actors believe public policies are effective? Perceived effectiveness refers to a belief on the part of involved actors that public policies are achieving their set goals. This paper attempts to answer this question in the context of common-pool resource (CPR) dilemmas by synthesizing two existing theories of the public policy process, institutional rational choice and Sabatier and Jenkins-Smith’s (1993) Advocacy Coalition Framework (Schlager and Blomquist 1995). Research in the

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