اندازه گیری بهره وری نسبی پروژه های "تحقیق و توسعه" تحت حمایت دولت: یک رویکرد سه مرحله ای

Without considering differences in operating environment, traditional methods of efficiency evaluation are suffering from external environmental influences. This study presents an alternative approach for assessing the relative efficiency of government-sponsored research and development projects (GSP). A three-stage approach employing data envelopment analysis to evaluate efficiency and Tobit regression to control external variables was applied to 110 projects over 9 years. This study finds that firm size, industry, and ratio of public subsidy on research and development (R&D) budget of recipient firm significantly influences the technical efficiency of GSP in Taiwan. After controlling these external variables, the mean value of technical efficiency in the third stage increases and becomes significantly different to that in the first stage. Most GSPs increase their returns when their projects are scaled up. Furthermore, government policy makers must establish the upper-limit ratio of subsidies on R&D budgets of recipient firms to avoid inefficient use of public funds.

As increased attention is paid to governmental support for private sector research and development (R&D), policy makers must offer an economic rationale for their support of public–private technology partnerships, and to require methods of performance evaluation to determine efficiency of R&D projects (Revilla, Sarkis, & Modrego, 2003). Through promulgating the Government Performance and Results Act (GPRA) of 1993 in the United States, which stressed the accountability of performance, agencies have been required to pay serious attention to evaluate impact of government subsidy (Corley, 2007). Public subsidies intended to stimulate the R&D progress of firms are wasted if resources are not used efficiently.

Government policy makers face intense regulatory pressure to achieve best-practice performance in circumstances involving measurable goals. If public subsidies are not used efficiently, government resources devoted to stimulating private R&D activity will be wasted. This study proposed a three-stage DEA approach for assessing the relative efficiency of a GSP by controlling external variables. The commonly used non-parametric DEA technique is applied in both stages I and III. Stage II applies Tobit regression that adopts input slacks generated from the first stage as the dependent variable and eliminates external environmental effects out of the efficiency evaluation. This study demonstrated a methodology for investigating R&D efficiency that can reliably assess the aggregate performance of a GSP. After controlling the external variables using Tobit regressions, the mean value of technical efficiency in the third stage increases and differs significantly to that in the first stage. The result of Tobit regressions indicated that the external environmental variables – including firm size, industry and ratio of public subsidy on R&D budget of recipient firms – influence the measurement of technical efficiency.