Nonrecursive Causal Models

Ph.D., University of Minnesota, 1980. Major research and teaching interests include public policy, budgeting, state politics and research methodology. Berry's primary areas of interest are public policy, American state politics, and research methodology. Throughout his career, his work has focused on explaining why governments make the policy choices they do, and improving the methodologies available for studying policy making. Among his current research projects are analyses of the impact of electoral competition on the policy choices made by state legislators, and the effect of state welfare policy on poverty in the United States. Berry's current research on methodology focuses on the development of techniques for estimating econometric models with binary dependent variables, and methods for studying policy diffusion using geographical information systems. Berry has also been working for several years collecting data on outcomes of all state legislative elections in the U.S. since the 1960s; he has begun to use these data to study the behavior of challengers in state legislative elections, and the role of partisan and incumbent protection goals in determining the nature of redistricting in state legislatures.

Where an assumption of unidirectionality in causal effects is unrealistic, 'recursive' models cannot be used, and more complex 'nonrecursive' models are necessary. Unfortunately, many nonrecursive models (unlike recursive models) are 'unidentified', which makes meaningful parameter estimation impossible. Even when they are identified, it would be inappropriate to use OLS regression techniques (appropriate for recursive models) for the purpose of estimation. The concept of identification, and the factors that lead to it are explained; and various tests for determination are provided. Illustrations from a variety of social science disciplines are used throughout the book.