Multiplex Consequences of Innovation Policy
Open Access DepositedDownloadable Content
This dissertation addresses three fundamental questions in innovation policy
How do the scientific and technological knowledge spheres interact with each other in creating new knowledge in a region? What are the conditions for R&D subsidies to be effective under varying scientific and technological landscape? How does public R&D funding, in the varying forms of basic, applied, and developmental research, influence labor productivity and total factor productivity (TFP) across different technological sectors? In answering these questions, a multiplex framework for examining knowledge complexity, R&D investment, and productivity is derived using concepts and analytic tools drawn from economic complexity theory and the Crepon-Duguet-Mairesse (CDM) productivity structural model. The dissertation takes advantage of detailed data on the academic research, technological patenting activity, public and private R&D funding, and firm business information to characterize scientific and technological knowledge dynamics embedded in a region. A multiplex structure based on the concept of relatedness is developed which combines knowledge complexity in academic research and technological patenting according to 185 technological classifications and 229 administrative geolocations, integrating multiple dimensions of complex interactions among innovation components within the Republic of Korea.Panel linear probability analysis is used to identify how much of the knowledge creation across regions and sectors in academic research and technological patenting can be attributed to complementarity between accumulated scientific knowledge and technological capabilities. Knowledge creation, measured by the emergence of new scientific and technological topics in a region, is significant when complementary scientific activities or technological capabilities are present. A policy intervention model is also estimated using Poisson count data models, in which public and private R&D investments, knowledge complexity, and innovation outcomes are jointly estimated. The findings show that government funding, particularly in basic research, is a significant driver in academic research, while private R&D investments are strongly associated with technological progress. Finally, the relationship between government R&D investments, knowledge stock, and productivity at the sectoral and regional levels is explored using a dynamic panel analysis based on the CDM productivity model. My findings reveal that government R&D expenditures have a significant positive impact on productivity, particularly in sectors reliant on basic research.This dissertation provides empirical evidence on the dynamic interplay between academic research and technological activities, challenging conventional assumptions about the direction of knowledge flow. The study contributes to the literatures on the innovation and productivity by demonstrating how knowledge accumulation, especially through technological innovation, drives productivity improvements. It also emphasizes the importance of regional and sectoral variations in the effectiveness of R&D investments. The results suggest that tailored public R&D policies, focusing on knowledge-intensive sectors and fostering knowledge spillovers, are crucial for maximizing productivity growth.
- All rights reserved
Notice to Authors
If you are the author of this work and you have any questions about the information on this page, please use the Contact form to get in touch with us.