Moving backward or forward? : robots and value capture in the automotive global value chains

How the use of industrial robots affects value capture in fragmented global production networks, represented by global value chains (GVCs), remains an open question. I focus on the automotive industry - the largest consumer of industrial robots and one of the most traded manufacturing industries - to examine how robot adoption affects value capture through backward and forward GVC participation. In addition, I assess how automation shapes an industry's position within the value chain, using upstreamness as a proxy. Using data from OECD TiVa, OECD ICIO, and IFR for 56 countries and 14 manufacturing industries from 1997 to 2017, I apply OLS, OLS-IV, and Poisson pseudo maximum likelihood fixed effects models to analyze the impact of automation on GVC integration. My findings reveal that robot adoption significantly increases forward GVC participation, particularly among factory economies, while also driving higher upstreamness. These results indicate that robots enable industries to capture more value in GVCs while maintaining their upstream positions in GVCs, which are symbolized by forward linkages and trade in intermediate goods.