Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
In this article, we compare standard approaches used to handle losses in logarithmic profit models with a simple novel approach. We estimate translog stochastic profit frontiers, and discuss discriminatory power, rank stability and the precision of Profit Efficiency (PE) scores. Contrary to existing methods, our approach does not result in a loss of observations. Our new method enhances rank stability and discriminatory power, and improves the precision of PE scores.