Published online by Cambridge University Press: 12 July 2019
In this paper, the location patterns of Multinational Enterprises are modeled by an evolutionary two-country model in which producing in a developed economy offers strong cost-reducing externalities of within-country spillovers and opting for a developing economy entails cheap labor but also extra operational costs due to the undersupply of public goods. The offshoring process, that is, manufacturing activity outsourced in the developing economy, increases the bargaining power of its workers and, with it, its labor cost. The investigation underlines that an increasing labor-productivity remuneration in the developing economy may spark a reshoring process that depends on the agglomeration and endowment drivers characterizing an industry. The reshoring process can be narrowed by a flexible labor remuneration scheme, with wages indexed to the domestic concentration of manufacturing activity. The presence of sub-optimal location patterns points out the existence of a trade-off between stability and efficiency, which underlines that policy measures designed to make a country a more efficient location are neither sufficient nor necessary for preventing offshoring or ensuring reshoring.
The authors wish to thank Pasquale Commendatore, Michael Kopel, Mario Pezzino, Jan Tuinstra, and two anonymous referees for their helpful suggestions and discussions and gratefully acknowledge financial support from EU COST Action IS1104 “The EU in the New Economic Complex Geography: Models, Tools and Policy Evaluation.” The authors also thank participants at the XLI AMASES Annual Meeting in Cagliari (2017), at the International Conference ESCoS (The Economy as a Spatial Complex System) in Naples (2018), and at the 59a SIE meeting in Bologna (2018). The research of the paper was supported by the research project “Dynamic Models for Behavioral Economics,” DESP–University of Urbino, Italy. Fabio Lamantia and Davide Radi gratefully acknowledge support by VŠB-TU Ostrava under the SGS Project SP2019/5.