Composite science and technology (S&T) indices are essential to overall understanding and evaluation of national S&T status, and to formulation of S&T policy. However, only a few studies on making these indices have been conducted so far since a number of complications and uncertainties are involved in the work. Therefore, this study proposes a new approach to employ fuzzy set theory and to make composite S&T indices, and applies it. The approach appears to successfully integrate various S&T indicators into three indices: R&D input, R&D output, and economic output. We also compare Korea's S&T indices with those of five developed countries (France, Germany, Japan, the United Kingdom, and the United States) to obtain some implications of the results for Korea's S&T.

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Modeling firms' R&D expenditures often become complicated due to the zero values reported by a significant number of firms. The maximum likelihood (ML) estimation of the Tobit model, which is usually adopted in this case, however, is not robust to heteroscedastic and/or non-normal error structure. Thus, this paper attempts to apply symmetrically trimmed least squares estimation as a semi-parametric estimation of the Tobit model in order to model firms' R&D expenditures with zero values. The result of specification test indicates the semi-parametric estimation outperforms the parametric ML estimation significantly.

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