Search

Using data on the Spanish firm-level production network we show that firms learn about international trade opportunities and related business know-how from their production network peers. Our identification strategy leverages the panel structure of the data, import origin variation, and network structure. We find evidence of both upstream and downstream network effects, even after accounting for sectoral and geographical spillovers. Larger firms are better at absorbing valuable information but worse at disseminating it. Connections with geographically distant firms provide more useful information to start importing., Comment: 34 pages, 8 tables

Poor economies not only produce less; they typically produce things that involve fewer inputs and fewer intermediate steps. Yet the supply chains of poor countries face more frequent disruptions---delivery failures, faulty parts, delays, power outages, theft, government failures---that systematically thwart the production process. To understand how these disruptions affect economic development, we model an evolving input--output network in which disruptions spread contagiously among optimizing agents. The key finding is that a poverty trap can emerge: agents adapt to frequent disruptions by producing simpler, less valuable goods, yet disruptions persist. Growing out of poverty requires that agents invest in buffers to disruptions. These buffers rise and then fall as the economy produces more complex goods, a prediction consistent with global patterns of input inventories. Large jumps in economic complexity can backfire. This result suggests why "big push" policies can fail, and it underscores the importance of reliability and of gradual increases in technological complexity., Comment: Main text: 10 pages, 6 figures. Supplementary Information: 15 pages, 1 figure

We study a general set of models of social network evolution and dynamics. The models consist of both a dynamics on the network and evolution of the network. Links are formed preferentially between 'similar' nodes, where the similarity is defined by the particular process taking place on the network. The interplay between the two processes produces phase transitions and hysteresis, as seen using numerical simulations for three specific processes. We obtain analytic results using mean field approximations, and for a particular case we derive an exact solution for the network. In common with real-world social networks, we find coexistence of high and low connectivity phases and history dependence., Comment: 11 pages, 8 figures

We review recent results on the dynamics of social networks which suggest that the interplay between the network formation process and volatility may lead to the occurrence of discontinuous phase transitions and phase coexistence in a large class of models. We then investigate the effects of negative links -- links inhibiting local growth of the network -- and of a geographical distribution of the agents in such models. We show, by extensive numerical simulations, that both effects enhance this phenomenology, i.e. it increases the size of the coexistence region., Comment: 6 pages, 4 figures, Proceedings of Granada Workshop 2005

We introduce and study a general model of social network formation and evolution based on the concept of preferential link formation between similar nodes and increased similarity between connected nodes. The model is studied numerically and analytically for three definitions of similarity. In common with real-world social networks, we find coexistence of high and low connectivity phases and history dependence. We suggest that the positive feedback between linking and similarity which is responsible for the model's behaviour is also an important mechanism in real social networks., Comment: 6 pages, 3 figures

We propose a simple model of the evolution of a social network which involves local search and volatility (random decay of links). The model captures the crucial role the network plays for information diffusion. This is responsible for a feedback loop which results in a first-order phase transition between a very sparse network regime and a highly-connected phase. Phase coexistence and hysteresis take place for intermediate value of parameters. We derive a mean-field theory which correctly reproduces this behavior, including the distribution of degree connectivity and the non-trivial clustering properties., Comment: 5 pages 2 figures

A model of communication that is able to cope simultaneously with the problems of search and congestion is presented. We investigate the communication dynamics in model networks and introduce a general framework that enables a search of optimal structures., Comment: Proceedings of the Conference "Statistical Mechanics of Complex Networks", Sitges, Spain, June 2002