Six multiple regression models are developed to estimate employment and income impacts of highway expenditures on bypass, loop and radial highway improvements. The regression models are of the single linear equation type. Three of the models are solved by the ordinaryleast squares method using 1-year cross-sectional data, and three of the models are solved by the ordinary least squares and pooled methods using 4-year combined cross-sectional and time series data. Thebefore-versus-after construction approach is applied in the last three models. The economic impacts are estimated using the following dependent variables in the respective sets of equations: (a) number of manufacturing employees of an affected city, (b) number of employees of an affected county, and (c) total actual or real wages of an affected county. The research results indicate that the three economic impact variables are significantly affected by expenditures on thestudy highway improvements. The regression models developed from the combination cross-sectional and time series analysis exhibit theseeffects more clearly and are more accurate than those developed from the cross-sectional analysis. The results of this research can be used by highway planners in estimating the extent of the employment and income effects of expenditures on highway improvements like those studied. This paper appears in transportation research record no. 1305, Finance, planning, programming, economic analysis, and land development 1991.
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