Development of a business climate rating system and its application to investment location decisions

Industry recruitment strategies and business location decisions are complex. Business climate ratings purportedly simplified place evaluations, but most systems were flawed. The purpose of this research is to improve local government recruitment policies and private-sector analyses of investment locations. Factors underlying business ratings were compared to those identified in geographic and other literature. Determinants were measured for roles in actual investment decisions. Representative variables were assembled from Census data for metropolitan statistical areas (MSAs), 1980 to 1994. Factor analytic procedures distilled variables into principal components, used as independent variables in stepwise regression models to predict new establishments. Components received scores for predictive capabilities. Scores comprised weights to rate MSAs for business climate in ten industries. Cluster and discriminant analyses then identified patterns within the American urban system. Tests of hypothetical business location decisions and a development recruitment program for Knoxville, TN were conducted. Past climate studies had shortcomings: inadequately defined scales, questionable sources, and subjective analyses. Weighting systems were removed from sound location principles, and inconsistent treatment of location factors prevailed. The system built here included one-digit Standard Industrial Classification (SIC) models showing factors behind actual business investment decisions. Industry interdependencies were revealed in 11 principal components, making them better predictors of establishment growth. Predictive power ranged from R₂=0.312 for the agriculture/forestry/fishing model, to R₂=0.653 for the services model. Four-digit SIC models were generally unsuccessful. Five categories of business ratings from the models allowed assessment of MSAs. Rating criteria were adaptable to unique business decisions. The cluster and discriminant analyses differentiated economic conditions of cities. Clusters showed suitable cities for investment and enabled measurement of community strengths and needs. They aided industry targeting and evaluation of public incentives. The Knoxville assessment revealed strong ratings in most sectors, but the city was grouped with economically stagnant MSAs. Knoxville's economic assets indicated potential for exceptional growth, however, certain forces limited its performance. This project's system is a useful tool to assess investment choices and economic policies. Individual treatment of different investments (branch establishments, relocations, start-ups) and inclusion of multivariate dependents should improve subsequent models. Functional-area modeling can supplement SIC-specific predictors.