Bilateral R&D Productivity and Supply Chain Networks

We study research and development productivity (RDP) transmission between 4,123 global firms across three supply chain tiers. Collecting 153,090 yearly supply chain dyad partnerships from Bloomberg, we construct a two-sided econometric model of supply chain R&D. In our empirical specification, the dependent variable measures return on R&D, and the independent variables measure supply chain partner and network effects. In our sample data, we find that a 1% R&D productivity improvement of (i) an upstream partner can increase a downstream agent’s R&D productivity by 0.14%, and (ii) a downstream partner can increase an upstream agent’s R&D productivity by 0.28%. Our findings show that having R&D-productive partners plays a significant role in transforming an agent’s R&D into revenues. Similarly, we estimate a network’s average R&D productivity elasticity on an agent as 0.23%. We further find that R&D productivity spreads more within smaller, integrated, domestic, and intra-industry networks. In our two-stage estimation, we address supply chain network endogeneity resulting from entanglement, simultaneity, and partner selection. Our findings provide operational and financial insights for R&D practitioners.