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What determines whether children grow up to be rich or poor? Arguing that parental actions are some of the most important sources of wealth inequality, Casey B. Mulligan investigates the transmission of economic status from one generation to the next by constructing an economic model of parental preferences.

In Mulligan's model, parents determine the degree of their altruistic concern for their children and spend time with and resources on them accordingly—just as they might make choices about how they spend money. Mulligan tests his model against both old and new evidence on the intergenerational transmission of consumption, earnings, and wealth, including models that emphasize "financial constraints." One major prediction of Mulligan's model confirmed by the evidence is that children of wealthy parents typically spend more than they earn.

Mulligan's innovative approach can also help explain other important behavior, such as charitable giving and "corporate loyalty," and will appeal to a wide range of quantitatively oriented social scientists and sociobiologists.

This collection illustrates how nonlinear methods can provide new insight into existing political questions. Politics is often characterized by unexpected consequences, sensitivity to small changes, non-equilibrium dynamics, the emergence of patterns, and sudden changes in outcomes. These are all attributes of nonlinear processes. Bringing together a variety of recent nonlinear modeling approaches, Political Complexity explores what happens when political actors operate in a dynamic and complex social environment.

The contributions to this collection are organized in terms of three branches within non-linear theory: spatial nonlinearity, temporal nonlinearity, and functional nonlinearity. The chapters advance beyond analogy towards developing rigorous nonlinear models capable of empirical verification.

Contributions to this volume cover the areas of landscape theory, computational modeling, time series analysis, cross-sectional analysis, dynamic game theory, duration models, neural networks, and hidden Markov models. They address such questions as: Is international cooperation necessary for effective economic sanctions? Is it possible to predict alliance configurations in the international system? Is a bureaucratic agency harder to remove as time goes on? Is it possible to predict which international crises will result in war and which will avoid conflict? Is decentralization in a federal system always beneficial?

The contributors are David Bearce, Scott Bennett, Chris Brooks, Daniel Carpenter, Melvin Hinich, Ken Kollman, Susanne Lohmann, Walter Mebane, John Miller, Robert E. Molyneaux, Scott Page, Philip Schrodt, and Langche Zeng.

This book will be of interest to a broad group of political scientists, ranging from those who employ nonlinear methods to those curious to see what it is about. Scholars in other social science disciplines will find the new methodologies insightful for their own substantive work.

Diana Richards is Associate Professor of Political Science, University of Minnesota.