Time-series cross-section (TSCS) data are prevalent in political science, yet many distinct challenges presented by TSCS data remain underaddressed. We focus on how dependence in both space and time complicates estimating either spatial or temporal dependence, dynamics, and effects. Little is known about how modeling one of temporal or cross-sectional dependence well while neglecting the other affects results in TSCS analysis. We demonstrate analytically and through simulations how misspecification of either temporal or spatial dependence inflates estimates of the other dimension’s dependence and thereby induces biased estimates and tests of other covariate effects. Therefore, we recommend the spatiotemporal autoregressive distributed lag (STADL) model with distributed lags in both space and time as an effective general starting point for TSCS model specification. We illustrate with two example reanalyses and provide R code to facilitate researchers’ implementation—from automation of common spatial-weights matrices (W) through estimated spatiotemporal effects/response calculations—for their own TSCS analyses.

A prominent line of research on electoral systems and income redistribution argues that proportional representation (PR) leads to tax-and-transfer policies that benefit the poor at the expense of the rich. This is because PR produces encompassing center-left coalitions that protect the poor and middle classes. Yet countries with PR electoral systems tend to rely heavily on consumption taxes and tax profits lightly, both of which are inconsistent with this expectation. Both policies are regressive and seem to benefit the rich at the expense of the poor. This article argues that PR electoral institutions, when combined with trichotomous multipartism, are not as hostile to the rich as commonly believed, and that it is important to understand how electoral and party systems interact with labor market institutions in order to explain the puzzling pattern of taxation that is observed. The author develops a theoretical model and evaluates its empirical implications for a world in which production has become multinational.

Most agree that models of binary time-series-cross-sectional data in political science often possess unobserved unit-level heterogeneity. Despite this, there is no clear consensus on how best to account for these potential unit effects, with many of the issues confronted seemingly misunderstood. For example, one oft-discussed concern with rare events data is the elimination of no-event units from the sample when estimating fixed effects models. Many argue that this is a reason to eschew fixed effects in favor of pooled or random effects models. We revisit this issue and clarify that the main concern with fixed effects models of rare events data is not inaccurate or inefficient coefficient estimation, but instead biased marginal effects. In short, only evaluating event-experiencing units gives an inaccurate estimate of the baseline risk, yielding inaccurate (often inflated) estimates of predictor effects. As a solution, we propose a penalized maximum likelihood fixed effects (PML-FE) estimator, which retains the complete sample by providing finite estimates of the fixed effects for each unit. We explore the small sample performance of PML-FE versus common alternatives via Monte Carlo simulations, evaluating the accuracy of both parameter and effects estimates. Finally, we illustrate our method with a model of civil war onset.

Spatial interdependence—the dependence of outcomes in some units on those in others—is substantively and theoretically ubiquitous and central across the social sciences. Spatial association is also omnipresent empirically. However, spatial association may arise from three importantly distinct processes: common exposure of actors to exogenous external and internal stimuli, interdependence of outcomes/behaviors across actors (contagion), and/or the putative outcomes may affect the dimensions along which the clustering occurs (selection). Accurate inference about any of these processes generally requires an empirical strategy that addresses all three well. From a spatial-econometric perspective, this suggests spatiotemporal empirical models with exogenous covariates (common exposure) and spatial lags (contagion), with the spatial weights being endogenous (selection). From a longitudinal network-analytic perspective, the same three processes are identified as potential sources of network effects and network formation. From that perspective, actors' self-selection into networks (by, e.g., behavioral homophily) and actors' behavior that is contagious through those network connections likewise demands theoretical and empirical models in which networks and behavior coevolve over time. This paper begins building such models by, on theoretical side, extending a Markov type-interaction model to allow endogenous tie-formation, and, on empirical side, merging a simple spatial-lag logit model of contagious behavior with a simple p*-logit model of network formation. One interesting consequence of network-behavior coevolution—identically, endogenous patterns of spatial interdependence—emphasized here is how it can produce history-dependent political dynamics, including equilibrium phat and path dependence (Page 2006). The paper concludes with an illustrative application to alliance formation and conflict behavior among the great powers in the first half of the twentieth century.

Duration data are often subject to various forms of censoring that require adaptations of the likelihood function to properly capture the data generating process, but existing spatial duration models do not yet account for these potential issues. Here, we develop a method to estimate spatial-lag duration models when the outcome suffers from right censoring, the most common form of censoring. We adapt Wei and Tanner's (1991) imputation algorithm for censored (non-spatial) regression data to models of spatially interdependent durations. The algorithm treats the unobserved duration outcomes as censored data and iterates between multiple imputation of the incomplete, that is, right censored, values and estimation of the spatial duration model using these imputed values. We explore the performance of an estimator for log-normal durations in the face of varying degrees of right censoring via Monte Carlo and provide empirical examples of its estimation by analyzing spatial dependence in states' entry dates into World War I.

In this paper, we demonstrate the econometric consequences of different specification and estimation choices in the analysis of spatially interdependent data and show how to calculate and present spatial effect estimates substantively. We consider four common estimators—nonspatial OLS, spatial OLS, spatial 2SLS, and spatial ML. We examine analytically the respective omitted-variable and simultaneity biases of nonspatial OLS and spatial OLS in the simplest case and then evaluate the performance of all four estimators in bias, efficiency, and SE accuracy terms under more realistic conditions using Monte Carlo experiments. We provide empirical illustration, showing how to calculate and present spatial effect estimates effectively, using data on European governments' active labor market expenditures. Our main conclusions are that spatial OLS, despite its simultaneity, performs acceptably under low-to-moderate interdependence strength and reasonable sample dimensions. Spatial 2SLS or spatial ML may be advised for other conditions, but, unless interdependence is truly absent or minuscule, any of the spatial estimators unambiguously, and often dramatically, dominates on all three criteria the nonspatial OLS commonly used currently in empirical work in political science.

Spatial/spatiotemporal interdependence—that is, that outcomes, actions or choices of some unit-times depend on those of other unit-times—is substantively important and empirically ubiquitous in binary outcomes of interest across the social sciences. Estimating and interpreting binary-outcome models that incorporate such spatial/spatiotemporal dynamics directly is difficult and rarely attempted, however. This article explains the inferential challenges posed by spatiotemporal interdependence in binary-outcome models and recent advances in their estimation. Monte Carlo simulations compare the performance of one of these consistent and asymptotically efficient methods (maximum simulated likelihood, using recursive importance sampling) to estimation strategies naïve about (inter-) dependence. Finally, it shows how to calculate, in terms of probabilities of outcomes, the estimated spatial/spatiotemporal effects of (and response paths to) hypotheticals of substantive interest. It illustrates with an application to civil war in Sub-Saharan Africa.

The view that multiparty elections in changing authoritarian regimes should be held sooner rather than later has been increasingly under attack. Critics argue that, under conditions of low institutional development, multiparty elections may lead to violence and civil war, rather than to the peaceful allocation of authority that everyone desires. Starting from the premise that elections are strategically timed and endogenous in transitioning authoritarian regimes, that is, more likely to be held when violence is imminent, we show that for Africa, the continent with the lowest levels of political institutionalization, elections do not increase the probability of a civil war initiation. In fact, for the post-Cold War period, the holding of multiparty elections is actually associated with a substantial reduction in the probability of civil war onset. To account for this pattern, we develop an informational theory of elections held under conditions that prevail in the post-Cold War, when foreign powers are reluctant to provide direct support for dictators (or their opponents) and elections are more reflective of the true level of a leader’s strength. We argue that, under these conditions, elections may prevent the eruption of a civil war that is already imminent, through two mechanisms: they may deter a weak opposition from initiating a war they are likely to lose or they may induce a weak dictator to offer ways to share power with the opposition.

According to the embedded liberalism thesis, governments committed to free trade provide insurance and other transfers to compensate those who lose economically from expanded trade. The goal of this spending is to maintain public support for trade liberalization. We provide a micro-level test of the critical assumption behind the embedded liberalism thesis that government programs designed to protect individuals harmed by imports reduce opposition to free trade. Our micro results have important implications for the macro relationship between trade and government spending, which we also test. We find empirical support for the embedded liberalism thesis in both our micro- and macro-level analyses.Earlier versions of this article were presented at the Midwest Political Science Association's 2002 Meeting and at the University of Illinois during summer 2003. We thank the respective panel and seminar participants for their feedback. In addition, we want to acknowledge valuable comments from William Bernhard, Rebecca Blank, Kerwin Charles, Alan Deardorff, John DiNardo, John Freeman, Brian Gaines, Jim Granato, Nathan Jensen, William Keech, Layna Mosley, Robert Pahre, Ken Scheve, Marina Whitman, two anonymous reviewers, and Lisa Martin. They, of course, are not responsible for any errors.

This article contributes to the growing literature on the role that domestic political institutions play in mediating globalization pressures by arguing that the capital tax constraints arising from international economic integration are the most severe for countries with majoritarian political institutions. In doing so, the author solves a tax puzzle that challenges conventional thinking about how institutions condition the relationship between economic globalization and domestic politics. He presents a formal, game-theoretic model to sharpen the basic logic of his argument and then tests some of the model's predictions empirically using both quantitative and qualitative evidence.