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As a contribution to the theorization of the planning process, this article gives a brief account of the probit model and its applications to Hong Kong; a review of the literature on the use of development control data in planning research; advances the argument for the use of statistical methods that do not operate on aggregate sampling data; and demonstrates the applicability of the probit model in development control studies. Possible applications discussed are the identification of factors for predicting success in planning applications, measurement of zone separation, assessment of the effectiveness of the planning system in tackling externalities, verification of economic theories of the behaviour of planning authorities and evaluation of public participation.

Migration is the principal component of demographic change in most states and local areas. Consequently, the methods used to estimate and project migration deserve special attention. Conventional estimation and projection procedures focus on individual movement and the number of moves that have already oc curred. Recent research on migrant behavior strongly suggests, however, that the appropriate unit of analysis in the migration decision is the household, not the individual, and that greater attention should be devoted to the study of intentions to migrate, rather than to the fact of migration. This paper presents a multivariate probit model of migrant behavior focusing on household migration plans. Probit analysis permits a more rigorous specification of the relation of demographic, economic, and social factors to the individual migration decision. Empirical findings are presented for an application of the model to a 1976 survey of 3,610 metropolitan Delaware households. Problems and procedures are also discussed surrounding the construction of probit models incorporating aggregate informa tion, an approach to model building considered potentially useful for planning and policy research.

Missing data are common in observational studies due to self-selection of subjects. Missing data can bias estimates of linear regression and related models. The nature of selection bias and econometric methods for correcting it are described. The econometric approach relies upon a specification of the selection mechanism. We extend this approach to binary logit and probit models and provide a simple test for selection bias in these models. An analysis of candidate preference in the 1984 U.S. presidential election illustrates the technique.