Optimal Degree of Foreign Ownership under Uncertainty

This paper studies the integration strategies of multinational firms in a multiperiod model under incomplete contracts and uncertainty. I incorporate continuous levels of integration to the study of organizational choice in an existing model of foreign direct investment (Antras and Helpman, 2004) and extend the model to a multi-period framework of learning. The joint productivity of the two partners in an integrated firm is unknown initially to both sides and is revealed only after continued joint production. The model gives rise to a nondegenerate distribution of foreign ownership at the firm level and shows that the optimal level of integration rises with the age of the firm. These patterns are supported by detailed plant-level data on share of foreign ownership. The model predicts that the degree of foreign ownership is an increasing function of joint productivity and intra-firm trade should rise over time as a result of increased control by multinationals. I test the implications of my theory with plant-level data from Turkey and find support for the predictions of the model.partial ownership, vertical integration, multinationals, uncertainty

Lending Cycles and Real Outcomes: Costs of Political Misalignment. LEQS Paper No. 139/2018 December 2018

We document a strong political cycle in bank credit and industry outcomes in Turkey. In line with theories of tactical redistribution, state-owned banks systematically adjust their lending around local elections compared with private banks in the same province based on electoral competition and political alignment of incumbent mayors. This effect only exists in corporate lending as opposed to consumer loans. It creates credit constraints for firms in opposition areas, which suffer drops in employment and sales but not firm entry. There is substantial misallocation of financial resources as provinces and industries with high initial efficiency suffer the greatest constraints

An information theory based behavioral model for agent-based crowd simulations

Crowds must be simulated believable in terms of their appearance and behavior to improve a virtual environment’s realism. Due to the complex nature of human behavior, realistic behavior of agents in crowd simulations is still a challenging problem. In this paper, we propose a novel behavioral model which builds analytical maps to control agents’ behavior adaptively with agent-crowd interaction formulations. We introduce information theoretical concepts to construct analytical maps automatically. Our model can be integrated into crowd simulators and enhance their behavioral complexity. We made comparative analyses of the presented behavior model with measured crowd data and two agent-based crowd simulators