LAUSR

The identification of environmental factors that explain differences in efficiency is essential for improving the results of public universities. A two-stage, semi-parametric approach with the single and double bootstrap procedure (Algorithm #1 and Algorithm #2) proposed by Simar and Wilson (J Econom 136(1):31–64, 2007) was used in this article for making valid inferences about the impact of environmental factors on university efficiency. A data envelopment analysis (DEA) efficiency estimator was used in the first stage to estimate technical efficiency scores for Spanish public universities. It is common to explore the determinants of (in)efficiency in a second stage. To provide valid inference, Simar and Wilson (2007) suggested a parametric bootstrap of the truncated regression (Algorithm #1). Alternatively, they recommended a bootstrap procedure to obtain bias-corrected technical efficiency scores used in the second-stage truncated regression; valid inference can be obtained by using a second bootstrap procedure applied to the truncated regression (Algorithm #2). Under both algorithms, three environmental factors were statistically significant predictors of efficiency. Our results confirmed that universities with a higher percentage of academics with tenure, outgoing Erasmus students, and state grantees tend to be less inefficient.