As it was previously indicated, firm assessment based on financial and economic ratios has been widely accepted by practitioner circles, since this analysis brings signals linked to firm performance. Nevertheless, it is important to remark the limitations derived from firm analysis based on accounting ratios. Thus, accounting analysis allows the evaluation of firm performance, but considering only one activity dimension, either linked to firm inputs or outputs in a specific period.
The CEBR has a broader view of firm assessment, and consequently, we offer to our corporate customers a more robust economic and statistic analysis based on productivity analysis.
To attain this, we make use of sophisticated non-parametric linear programming mathematical techniques that allow us for evaluating both firm and industry efficiency. More importantly, the CEBR has the most appropriate tools for undertaking an analysis that break traditional standards, since to carry out our analysis we take into consideration a highly relevant economic fact: firms need several inputs to obtain their outputs.
Through this technique it is possible to create an production function (envelop surface) that allows us to compute efficiency scores for each firm under assessment. In addition, from our analysis we can identify the main factors that positively of negatively affect firm efficiency.
The main advantage of this technique lies on the fact that we do not impose any functional form to the production function, which represents a methodological advance. Furthermore, we can incorporate a longitudinal perspective to our efficiency analysis, leading to evaluate efficiency trends shown by firms and industry sectors, which is a valuable tool for industrial analysis.
We in the CEBR are aware of the limitation of this analytical technique, which mainly lies on the difficulties for statistical treatment. In order to offer a high quality product we propose to overcome this through the application of recently developed statistical techniques that will permit us to carry out robust statistical analyses of the efficiency scores obtained from the linear program which increases the quality of our analysis.