Research

This paper studies how shocks to firm productivity or scale of operations propagate over a production network through the availability of cheaper intermediate inputs, demand creation, and changes to the competitive structure of the economy. To this end, an endogenous measure of market power is derived capturing firms’ ability to endogenise their position in the production network to extract monopoly rents. The paper first explores the relation between network position and endogenous markups under general non-parametric production functions and then quantifies the three channels of shock propagation under a simple parametric model. Using granular administrative data from Rwanda and an instrumental variable strategy based on exogenous border closures, the paper tests the empirical validity of the derived measures. It then uses the universe of firm-to-firm interactions in the country to study the network-position of Foreign Direct Investment in Rwanda and assess the General Equilibrium effects of FDI on the domestic economy via efficiency gains, demand creation, and changes to the competition.

This paper provides evidence of spillover effects from foreign direct investment (FDI) through forward linkages, a relatively neglected channel to enhance national competitiveness that is likely to become more important as countries seek to bolster domestic competitiveness and resilience to geo-economic shocks. Using granular information on the universe of firm-to-firm transactions and inward FDI in Rwanda, we find substantial and persistent effects on value-added, employment, and productivity of domestic firms after beginning to source from foreign-owned enterprises. These effects are more pervasive than those associated with selling to foreign-owned firms - the backward linkages emphasised in the literature. Suggestive evidence reveals that foreign-owned firms provide higher-quality intermediate inputs than domestic suppliers, particularly in specialized business and professional services that are difficult to import, and that these inputs complement rather than crowd out domestically sourced inputs.

This paper investigates the identification of treatment effects in panel data using Synthetic Control methods when the treatment consists of an interaction over an observed network. It first defines treatment in such a setting and then extends existing approaches to optimal weight-estimation for regularised Synthetic Control estimators to include dyadic information from the network of interactions. Using simulation studies, the performance of the introduced estimator is then compared to commonly used benchmarks.