Responsible artificial intelligence for measuring efficiency: a neural production specification

In this paper, we develop a flexible neural production functional form to measure efficiency and productivity in the US food sector. By utilizing machine learning techniques, our production specification offers exceptional flexibility and accuracy in estimating these measures. We employ the Stochastic Frontier Approach to calculate efficiency scores for the industry and present an appropriate estimation method. It is worth emphasizing the importance of responsible Artificial Intelligence (AI) in economic research, as our methodology ensures transparency and accountability. Based on our findings, the employed specification performs exceptionally well, and the US food industry demonstrates nearly constant returns to scale. Furthermore, the total factor productivity of the US food sector is estimated to be 0.65%, indicating slow growth in its technical level. Additionally, the food industry achieves satisfactory levels of technical efficiency, reaching 82%. This suggests that the US food sector has the potential to reduce its production costs by 18% without altering its input mix. Our study highlights the advantages of utilizing machine learning techniques, particularly neural networks, in analyzing complex economic data and provides valuable insights for further research in the field. We underscore the significance of responsible and transparent AI in ensuring the ethical and equitable use of these techniques. Neural networks hold immense potential for various applications, including deep learning techniques that can enhance business performance.