한국생산성학회

Research and development is generally considered the driving force of innovation. Innovation is a crucial factor for firms in terms of industrial competitiveness and economic growth as it directly impacts firm productivity and further R&D activities. However, due to the uncertainty of R&D results, R&D strategy needs to choose the timing of investment. Many studies have shown that there is a close relationship between R&D expenditure and patents, but these have not been studied at a technical level. This study aims to empirically estimate distributed lag effects between R&D investment and patent by industry and firm size. To analyze the lag structure between them, we used a dataset of the “Business Activity Survey” compiled by Statistics Korea from 2006 to 2015 and employed a polynomial distributed lag model. This is because it is highly likely that multicollinearity will emerge, if a distributed lag model is applied to multiple regression analysis. R&D expenditure and the number of patents held were used to measure R&D input and output, respectively. The main results are as follows. First, the empirical results indicate the presence of bilateral causality between R&D expenditure and patents (R&D→Patents; R&D←Patents). Our data indicate that the traditional causality is stronger although the reverse causality is also acceptable. Second, the time-lag between R&D investment and patent application was three years. Third, we found that there are differences among industries with a similar level of technology with regard to the time-lag effect. In particular, high technology industries have a longer time-lag than other industries. These results are similar to previous studies that suggested industries with higher technology levels determine long-term investments. Finally, medium-sized firms have a shorter time-lag between R&D investment and patents than that of large or small firms. This study would be a new approach using technology level, which would be able to provide a benchmarking idea to firm owners and policy makers. Firm owners have to control their input effectively, sometimes reducing or transferring resources such as R&D investment and employees according to many factors such as technology level, size, and so on. Attempting to predict the size and timing of R&D results is an effective way of responding to the uncertainty of the R&D and to reduce the management risk before making decisions regarding R&D investment.