Inference on the Sources of Innovative Inefficiency Using Data Envelope Analysis and Semi-Parametric Bootstrapping

Abstract

Using the US states plus the District of Columbia, output-oriented innovative efficiency scores are computed using data envelope analysis (DEA) where total patent counts serve as the measure of innovative output and industry and academic research and development expenditures, and numbers of research scientists, graduate students, and postdoctoral positions are inputs. In the second stage the extent to which state and local government fiscal policy explain the estimated inefficiency is considered. Using a truncated regression model where parameter inference is based on semi-parametric bootstrapping, our results indicate that reducing government administration expenditure share, increasing total education expenditure share, and reducing the share of revenues collected from institutions of higher learning all correlate with greater efficiency.