Intangibles Trade and MNEs: Supply-Chain Trade in R&D Services and Innovative Subsidiaries

This paper contributes to the literature on vertical specialization in R&D (or ‘R&D fragmentation’) and trade in intermediate services by examining the role of multinational enterprises (MNE) activities associated with bilateral foreign direct investment (FDI). Prior work in production networks (or global value chains [GVCs]) and R&D fragmentation suggests a complementarity relationship between FDI in R&D and technological knowledge flows. The paper examines this proposition empirically for R&D services trade by extending the gravity framework of supply-chain trade for intermediate services with bilateral MNE operations as economic mass variables. The results are partially consistent with the hypothesized complementarity. The econometric strategy accounts for zero trade observations. The latter addresses possible selection and consistency issues of traditional gravity trade specifications, and allows exploring extensive vs. intensive margin of trade. Understanding the role of MNEs in these transactions may be useful for policies aimed at increasing participation and upgrading in MNE-driven GVCs.     

Predictable forward performance processes: Infrequent evaluation and applications to human-machine interactions

We study discrete-time predictable forward processes when trading times do not coincide with performance evaluation times in a binomial tree model for the financial market. The key step in the construction of these processes is to solve a linear functional equation of higher order associated with the inverse problem driving the evolution of the predictable forward process. We provide sufficient conditions for the existence and uniqueness and an explicit construction of the predictable forward process under these conditions. Furthermore, we find that these processes are inherently myopic in the sense that optimal strategies do not make use of future model parameters even if these are known. Finally, we argue that predictable forward preferences are a viable framework to model human-machine interactions occurring in automated trading or robo-advising. For both applications, we determine an optimal interaction schedule of a human agent interacting infrequently with a machine that is in charge of trading.