Information Asymmetry, Rents, and Cost Risk Allocation: Theory with Evidence from BMW

Abstract

Several features of automotive procurement distinguish it from the prototypical supply chain in the academic literature: pass-through pricing that reimburses auto part suppliers for raw material costs; informational asymmetries that imbue suppliers with pricing power; and contractual commitments that span multiple production periods. We formalize a model of automotive procurement by considering an automaker that procures components from an upstream supplier to assemble cars over several production periods, and where period demands and raw material costs are both stochastic. Our model identifies four key market factors that drive protocol preference: asymmetric information in raw material costs, the price elasticity of consumer demand, supplier R&D and tooling costs, and raw material price variability. Then, using proprietary contract and supplier data from BMW, we empirically validate this model and show that it reflects BMW's reality: the factors that should theoretically go into automotive procurement decisions in fact do so. Our analysis also reveals that existing contracting protocols in this context are not optimal for procurement under asymmetric information. We therefore propose an alternative procurement protocol that achieves a second-best outcome, which is the best the automaker can do under asymmetric information. We calibrate our model and estimate an automaker's performance improvement from this optimal contract over the status quo.

Keywords: asymmetric information, risk management, commodity, automotive, empirical