Paul K. Huth

Abstract: down by age, gender, and type of disease or condition. We test hypotheses about the impact of civil wars and find substantial long-term effects, even after controlling for several other factors. We estimate that the additional burden of death and disability incurred in 1999, from the indirect and lingering effects of civil wars in the years 1991-97, was approximately equal to that incurred directly and immediately from all wars in 1999. This impact works its way through specific diseases and conditions and disproportionately affects women and children.

Abstract: Why do some wars result in the intentional killing of large numbers of civilians? In this article we examine the incidence of mass killing in all wars from 1945 to 2000. In the statistical analysis of our data set of 147 wars, we find strong evidence supporting our hypothesis that mass killing is often a calculated military strategy used by regimes attempting to defeat major guerrilla insurgencies. Unlike conventional military forces, guerrilla armies often rely directly on the local civilian population for logistical support. Because guerrilla forces are difficult to defeat directly, governments facing major guerrilla insurgencies have strong incentives to target the guerrillas' civilian base of support. We find that mass killing is significantly more likely during guerrilla wars than during other kinds of wars. In addition, we find that the likelihood of mass killing among guerrilla conflicts is greatly increased when the guerrillas receive high levels of active support from the local population or when the insurgency poses a major military threat to the regime.For their helpful comments on previous versions of this article the authors thank Bear Braumoeller, Alex Downes, Jim Fearon, Hazem Goborah, Stathis Kalyvas, Gary King, Will Lowe, Matthew Krain, Lisa Martin, Manus Midlarsky, Bruce Russett, Nicholas Sambanis, Naunihal Singh, Abdulkader Sinno, Allan Stam, Jeremy Weinstein, and the anonymous reviewers of International Organization. We are also grateful to Wolfgang Moehler for his research assistance. Our coauthor Dylan Balch-Lindsay was killed in an automobile accident on 1 September 2002, cutting short a promising career. He was a gifted young scholar, without whom this article would not have been possible. He is sorely missed by his friends and colleagues. Donations in his name can be sent to the Dylan Balch-Lindsay Memorial Fund for Graduate Education/Foundation of the University of New Mexico, c/o Carol Brown, Department of Political Science, University of New Mexico, Albuquerque, NM, 87131-1121.

Abstract: Through an examination of 129 territorial disputes between 1950 and 1990, Paul Huth presents a new theoretical approach for analyzing the foreign policy behavior of states, one that integrates insights from traditional realist as well as domestic political approaches to the study of foreign policy. Huth's approach is premised on the belief that powerful explanations of security policy must be built on the recognition that foreign policy leaders are domestic politicians who are very attentive to the domestic implications of foreign policy actions. Hypotheses derived from this new modified realist mode are then empirically tested by a combination of statistical and case study analysis. ." . . a welcome contribution to our understanding of how and why some territorial disputes escalate to war."--"American Political Science Review" Paul Huth is Associate Professor of Political Science and Associate Research Scientist, Center for Political Studies, Institute for Social Research, University of Michigan.

Abstract: The use of military force to achieve foreign policy objectives is an enduring feature of international politics. Force, or the threat of force, may be used either to change the status quo or to maintain it. Threatening the use of force to maintain the status quo often takes the form of deterrence, defined by Patrick Morgan as “the threat to use force in response as a way of preventing the first use of force by someone else.”1 Deterrence sometimes succeeds and sometimes fails. Failures are attested to by numerous international wars of history. In the nuclear age, a failure could cost us our lives. The conditions of successful deterrence thus require thorough logical and empirical analysis.

Abstract: While heated arguments between practitioners of qualitative and quantitative research have begun to test the very integrity of the social sciences, Gary King, Robert Keohane, and Sidney Verba have produced a farsighted and timely book that promises to sharpen and strengthen a wide range of research performed in this field. These leading scholars, each representing diverse academic traditions, have developed a unified approach to valid descriptive and causal inference in qualitative research, where numerical measurement is either impossible or undesirable. Their book demonstrates that the same logic of inference underlies both good quantitative and good qualitative research designs, and their approach applies equally to each. Providing precepts intended to stimulate and discipline thought, the authors explore issues related to framing research questions, measuring the accuracy of data and uncertainty of empirical inferences, discovering causal effects, and generally improving qualitative research. Among the specific topics they address are interpretation and inference, comparative case studies, constructing causal theories, dependent and explanatory variables, the limits of random selection, selection bias, and errors in measurement. Mathematical notation is occasionally used to clarify concepts, but no prior knowledge of mathematics or statistics is assumed. The unified logic of inference that this book explicates will be enormously useful to qualitative researchers of all traditions and substantive fields.

Abstract: We study rare events data, binary dependent variables with dozens to thousands of times fewer ones (events, such as wars, vetoes, cases of political activism, or epidemiological infections) than zeros ("nonevents"). In many literatures, these variables have proven difficult to explain and predict, a problem that seems to have at least two sources. First, popular statistical procedures, such as logistic regression, can sharply underestimate the probability of rare events. We recommend corrections that outperform existing methods and change the estimates of absolute and relative risks by as much as some estimated effects reported in the literature. Second, commonly used data collection strategies are grossly inefficient for rare events data. The fear of collecting data with too few events has led to data collections with huge numbers of observations but relatively few, and poorly measured, explanatory variables, such as in international conflict data with more than a quarter-million dyads, only a few of which are at war. As it turns out, more efficient sampling designs exist for making valid inferences, such as sampling all variable events (e.g., wars) and a tiny fraction of nonevents (peace). This enables scholars to save as much as 99% of their (nonfixed) data collection costs or to collect much more meaningful explanatory variables. We provide methods that link these two results, enabling both types of corrections to work simultaneously, and software that implements the methods developed.

Abstract: We study rare events data, binary dependent variables with dozens to thousands of times fewer ones (events, such as wars, vetoes, cases of political activism, or epidemiological infections) than zeros (“nonevents”). In many literatures, these variables have proven difficult to explain and predict, a problem that seems to have at least two sources. First, popular statistical procedures, such as logistic regression, can sharply underestimate the probability of rare events. We recommend corrections that outperform existing methods and change the estimates of absolute and relative risks by as much as some estimated effects reported in the literature. Second, commonly used data collection strategies are grossly inefficient for rare events data. The fear of collecting data with too few events has led to data collections with huge numbers of observations but relatively few, and poorly measured, explanatory variables, such as in international conflict data with more than a quarter-million dyads, only a few of which are at war. As it turns out, more efficient sampling designs exist for making valid inferences, such as sampling all available events (e.g., wars) and a tiny fraction of nonevents (peace). This enables scholars to save as much as 99% of their (nonfixed) data collection costs or to collect much more meaningful explanatory variables. We provide methods that link these two results, enabling both types of corrections to work simultaneously, and software that implements the methods developed.

Abstract: Researchers typically analyze time-series-cross-section data with a binary dependent variable (BTSCS) using ordinary logit or probit. However, BTSCS observations are likely to violate the independence assumption of the ordinary logit or probit statistical model. It is well known that if the observations are temporally related that the results of an ordinary logit or probit analysis may be misleading. In this paper, we provide a simple diagnostic for temporal dependence and a simple remedy. Our remedy is based on the idea that BTSCS data are identical to grouped duration data. This remedy does not require the BTSCS analyst to acquire any further methodological skills, and it can be easily implemented in any standard statistical software package. While our approach is suitable for any type of BTSCS data, we provide examples and applications from the field of International Relations, where BTSCS data are frequently used. We use our methodology to reassess Oneal and Russett's (1997) findings regarding the relationship between economic interdependence, democracy, and peace. Our analyses show that (1) their finding that economic interdependence is associated with peace is an artifact of their failure to account for temporal dependence yet (2) their finding that democracy inhibits conflict is upheld even taking duration dependence into account.

Abstract: A collection of seven essays that serves as an introductory text on complexity theory and computer modelling in the social sciences, and as an overview of the current state of the art in this field. The articles move beyond the basic paradigm of the "Prisoner's Dilemma" to study a rich set of issues, including how to cope with errors in perception or implementation, how norms emerge, and how political actors and regions of shared culture can develop. They use the shared methodology of agent-based modelling, a technique that specifies the rules of interaction between individuals and uses computer simulation to discover emergent properties of the social system.