Can knowledge improve population forecasts at subcounty levels?

Abstract

Recent developments in urban and regional planning require more accurate population forecasts at subcounty levels, as well as a consideration of interactions among population growth, traffic flow, land use, and environmental impacts. However, the extrapolation methods, currently the most often used demographic forecasting techniques for subcounty areas, cannot meet the demand. This study tests a knowledge-based regression approach, which has been successfully used for forecasts at the national level, for subcounty population forecasting. In particular, this study applies four regression models that incorporate demographic characteristics, socioeconomic conditions, transportation accessibility, natural amenities, and land development to examine the population change since 1970 and to prepare the 1990-based forecast of year 2000 population at the minor civil division level in Wisconsin. The findings indicate that this approach does not outperform the extrapolation projections. Although the regression methods produce more precise projections, the least biased projections are often generated by one of the extrapolation techniques. The performance of the knowledge-based regression methods is discounted at subcounty levels by temporal instability and the scale effect. The regression coefficients exhibit a statistically significant level of temporal instability across the estimation and projection periods and tend to change more rapidly at finer geographic scales.