THE LOCATION OF NONMETROPOLITAN HIGH-TECH INDUSTRIES: A Comparison of Regional Determinants

A study of the location of three classifications of high-tech industries in rural areas was undertaken using ordinary least squares, Tobit, and a two-step estimation procedure suggested by Heckman (1976). The results indicate that the location decision for high tech firms is influenced primarily by the county population, adjacency to an SMSA, and the expenditures on public infrastructure (exclusive of education expenditures). However, location near interstate highways was generally not a significant factor. Results were similar between branch and unit (single ownership) plants. Some differences in signs of orders of magnitude occurred among the estimation procedures. Making a choice between Tobit and Heckman approaches depends upon the goal of the research.     

Diversification Categories in Investment Real Estate

This paper continues previous work evaluating the benefits of diversification and analyzes the various dimensions within the commercial real estate opportunity set. The database is large and extends through the 1982 downturn in property values. Due to the low levels of systematic risk, current distinctions by region and property type make little sense in a world of costly diversification. More exacting categories combining property type, SMSA growth rate and lease maturity offer promise for more efficient diversification within the real estate portfolio.     

The Atkinson Inequality Measure and its Sampling Properties: Bayesian and Classical Approaches

This paper examines several Bayesian methods of obtaining posterior probability density functions of the Atkinson inequality measure and its associated social welfare function, in the context of grouped income distribution data. The methods are compared with asymptotic standard errors. The role of the number of income classes is investigated using a simulated distribution. If only a small number of groups is available in published data, there is a clear gain from generating the posterior probability density function when using an explicit income distribution assumption. Even with a small number of groups, the Bayesian approach gives results that are close to the sample values obtained using the corresponding individual observations.