Performance assessment of mining operations using nonparametric production analysis: A bootstrapping approach in DEA

This paper presents a Data Envelopment Analysis (DEA) model combined with bootstrapping to assess performance in mining operations. Since DEA-type indicators based on nonparametric production analysis are simply point estimates without any standard error, we provide a methodology to assess the performance of strip mining operations by means of a DEA bootstrapping approach. This methodology is applied to a sample of fifteen Illinois strip coal mines using publicly available data (Thompson et al., 1995). The applied approach uses a mixed mine environmental performance indicator (MMEPI) that is derived by means of a VRS DEA environmental technology treating overburden as an undesirable output under the weak disposability assumption, and we compare this measure with a traditional output-oriented mine performance indicator (MPI) omitting overburden. Although omitting undesirable output results in biased performance estimates, these findings are based on sample specific results and indicate this bias is not statistically significant. The confidence intervals derived by the bootstrapping of the proposed MMEPI point estimates indicate that significant inefficiency has taken place in the analyzed sample of Illinois strip mines.