In this book, we have examined how to estimate productive efficiency using Stochastic Frontier Analysis. We have provided a basic understanding of the theoretical underpinnings and a practical understanding of how to estimate production, profit, and cost efficiency.

We have, in our time as academics and practitioners, seen a significant increase in the theoretical models and practical applications in this field. However, despite these developments, it is still the case that important policy, strategic, regulatory, and operational decisions are based on approaches that do not take into account all these developments and improvements.

As such, as we set out at the beginning of this book, our two main goals in writing this book were to extend the everyday application of Stochastic Frontier Analysis beyond the expert practitioner or academic and to ensure that the latest theoretical models can be implemented by as many practitioners in the area as possible. Utlimately, we hope that this will improve the decisions made in these situations, by improving the evidence base used to make such decsions. We hope to have achieved this by making it relatively easy for the reader to carry out the complex computations necessary to both estimate and interpret these models.

Through the course of the chapters, we have examined a number of different settings, including dairy farming, rice farming, electricity generation, airlines, mining, manufacturing, and economy-wide production. There are clearly countless settings in which the tools set out in this book can be applied. We hope we have inspired the reader to go away and apply these tools to their own datasets and uncover insights that had previously remained hidden within those dataset. At the very least, we hope to have informed policy makers, regulators, government advisors, companies, and the like on what tools are available and what insights can be uncovered by using them, such that they commission such analysis to be undertaken to help them in making their cirtical decions.