Estimating productivity and identifying the frontier - Mohamad, Noorihsan; Wan Husain, Wan Rohaida (2020)
Analysis of productivity at industry and sectoral-level is often too broad to be a practical basis for policy and strategy formulations. To better translate the sectoral productivity into feasible and effective policies and incentives, it is therefore essential to understand how enterprise-level productivity patterns evolve by taking into account the heterogeneity between firms. On the basis on this backdrop and in an effort to facilitate and support productivity enhancement at the enterprise level, a preliminary exploration had been conducted to measure the productivity performance across enterprises for benchmarking purposes quantitatively. The main aim is to identify the most productive firms (frontier) within the same subsector to be presented as an exemplary and lead model as a channel to spread good practices for other business entities (non-frontier). The proposed benchmarking mechanism and framework for evaluating and tracking enterprise-level productivity is drawn upon a productivity measurement model called DEA. DEA or Data Envelopment Analysis measures the relative efficiency performance of business entities in terms of resource utilization that can assist in the identification of benchmark peers. Such identification enables possible efficiency and productivity improvements that may help the non-frontier firms or laggards to reach their relative potential. As this is among the first attempts to assess the micro-level productivity, the scope of this study is only limited to enterprises and firms that are publicly listed in the primary board of Bursa Malaysia, covering 149 companies across eight specific priority subsectors. This is mainly driven by the aim of collecting comparable data across firms while achieving the broadest possible coverage from the available published sources. Data were gathered and compiled from the respective firms’ annual reports for the most recent published years covering 2017 to 2019.