Efficiency Evaluation of Korean Biopharmaceutical Companies: An Application Data Envelopment Analysis with Negative and Missing Data

Efficiency Evaluation of Korean Biopharmaceutical Companies: An Application Data Envelopment Analysis with Negative and Missing Data

Since the beginning of the evolutionary development of molecular biology in 1970, the biopharmaceutical industry has been rapidly growing worldwide. The biopharmaceutical industry improves the health management system (HMS) and plays an essential role in South Korea’s economy. Therefore, efficiency in the biopharmaceutical industry is a critical aspect of research for national policy regulators. Data envelopment analysis (DEA) is a powerful nonparametric technique for measuring the efficiency and performance of decision making units (DMUs) that convert multiple homogeneous inputs into multiple outputs. Conventional DEA models measure efficiency by assuming that inputs and outputs are known, fixed, and strictly positive. However, these assumptions are not applicable in every real-life efficiency problem. In many situations, inputs and outputs are either missing or negative and even sometimes both. This paper proposes an algorithm to estimate missing values and translate the negative inputs and outputs into positive values. It also extends our algorithm to measure the efficiency of the South Korean biopharmaceutical industry by using the base-point DEA approach with BCC, SBM, and super efficiency.