اندازه گیری تغییر در بهره وری تحقیق و توسعه صنعت داروسازی ژاپنی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|11878||2008||8 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Research Policy, Volume 37, Issue 10, December 2008, Pages 1829–1836
This paper presents a data envelopment analysis (DEA)/Malmquist index methodology for measuring the change in R&D efficiency at both firm and industry levels. Letting each of ten firms in each year be a separate decision-making unit, and employing one input and three outputs in a DEA case of R&D activity input–output lag, we measure “total factor R&D efficiency” change of Japanese pharmaceutical firms for decade 1983–1992 as defined by the period of R&D input. Decomposing Malmquist index into catch-up and frontier shift components and using “cumulative indices” proposed in this study, we evaluate R&D efficiency change for each firm and empirically show that R&D efficiency of Japanese pharmaceutical industry has almost monotonically gotten worse throughout the study decade.
This paper measures R&D efficiency of Japanese pharmaceutical firms and examines how R&D efficiency at industry level has changed over time. R&D in firms, which can be considered as a stage prior to production, would be as important as production. But we have not quantitatively analyzed R&D efficiency so much as productivity. The lack of how to measure R&D efficiency would be a main reason. In considering R&D activity input and output, we cannot immediately specify what to be as the output, compared with R&D investment as the input. Geisler (1995) and Brown and Svenson (1998) list published articles, patents, new products, etc. as the output. That is, we cannot help considering multiple outputs of R&D. This multiplicity of output prevents from analyzing R&D efficiency by means of ordinary production function, i.e., parametric, approach. Thus it is not easy to measure R&D efficiency, so that we have seldom observed its chronological transition at industry level. Has it gradually gotten better as incorporating some innovations into process as productivity could be expected? For the recent Japanese industry, it might not, or might have even worsened (Sakakibara and Tsujimoto, 2003). For also pharmaceutical industry in the world, it is said that R&D efficiency is recently in decline (Tollman et al., 2004). After all, the recent change in R&D efficiency has yet been elusive. Taking up Japanese pharmaceutical industry, we verify whether R&D efficiency has gotten better or worse for the study period. In order to analyze R&D efficiency, we employ data envelopment analysis (DEA) (e.g., Cooper et al., 2000). DEA is a non-parametric method that can measure the relative efficiency, i.e., DEA efficiency, of objects called decision-making units (DMUs) with multiple inputs and multiple outputs. Although DEA could be applied to various fields other than the standard efficiency analysis (e.g., Hashimoto and Ishikawa, 1993 and Hashimoto, 1996), its characteristic that is able to deal with multiple outputs has enabled measuring efficiencies of a novelty of DMU sets even in the standard analysis. For example, Nasierowski and Arcelus (2003) recently measure the efficiency of 45 national innovation systems with two inputs and three outputs. However, we can find no DEA analyses of firms’ R&D efficiency except for Honjo and Haneda (1998). They try to analyze R&D efficiency of fourteen Japanese pharmaceutical firms with one input and two outputs for period 1977–1991. Refining their analyses, we also preparatorily do DEA analyses using panel data from ten pharmaceutical firms for the study period. But we should note that ordinary DEA cannot analyze as taking DEA efficiency frontier shifting over time into consideration. Then, we introduce DEA/Malmquist index analysis (e.g., Färe et al., 1994 and Thanassoulis, 2001) to examine time series change in R&D efficiency at industry level. The Malmquist index can measure the ratio of DEA efficiencies in two different time periods with shifting DEA efficiency frontiers. Although we have some DEA/Malmquist index applications ( Färe et al., 1994, Coelli et al., 1998 and González and Gasćon, 2004; etc.), they are all to productivity change. The Malmquist index can be decomposed into two components: “catch-up” and “frontier shift.” While the former measures how much closer to the frontier a DMU, i.e., a firm, moves, the latter does movement of the frontier. Since the frontier is composed of “DEA efficient” DMUs among all firms in a time period, the frontier shift means change at industry level. Using this frontier shift, we devise to quite obviously display R&D efficiency change of Japanese pharmaceutical industry throughout the study period.
نتیجه گیری انگلیسی
This paper presented a DEA/Malmquist index methodology for measuring the change in total factor R&D efficiency of Japanese pharmaceutical firms. Using the Malmquist index decomposition into catch-up and frontier shift, we found that both diffusion and innovation of R&D technology had not taken place so much for decade 1983–1992. This study decade was defined by the period of R&D activity input in the 8 years input–output lag case. For the frontier shift especially, by means of the cumulative index proposed in this study, we could quantitatively show the time series change in R&D efficiency at industry level, which had empirically seemed elusive. That is, we found a great R&D efficiency loss by the Japanese pharmaceutical industry for the decade and that the industry’s R&D efficiency had dropped in year 1992 to 50% of the start year 1983, though a few innovator firms existed. The firms have continued to increase R&D expenditure every year despite that R&D efficiency has not improved. Possibly, firms might have found another meaning of R&D expenditure than R&D itself. (Haneda and Odagiri (1998) indicate that R&D investment affects the corporate value.) However, it is certain that there has been the lack of firms’ R&D efficiency evaluation. The methodology presented in this study, which is able to measure the R&D efficiency change at both firm and industry levels, would provide useful information on firm’s R&D activity management.