Skilled emigration, business networks and foreign direct investment

In a global context foreign direct investment (FDI) and migration substitute one another in the matching process between workers and firms. However, as labor flows can lead to the formation of business networks, migration can actually facilitate FDI in the long-run. We first present a stylized model for a small open economy illustrating these offsetting effects. We then use U.S. data on bilateral labor inflows and capital outflows to measure the extent of contemporaneous substitutability and dynamic complementarity between migration and FDI. We find that brain drain and FDI inflows are negatively correlated contemporaneously but that skilled migration is associated with future increases in FDI inflows. We also find suggestive evidence of substitutability between current migration and FDI for migrants with secondary education, and of complementarity between past migration and FDI for unskilled migrants

Skilled emigration, business networks and foreign direct investment

In a global context foreign direct investment (FDI) and migration substitute one another in the matching process between workers and firms. However, as labor flows can lead to the formation of business networks, migration can actually facilitate FDI in the long-run. We first present a stylized model for a small open economy illustrating these offsetting effects. We then use U.S. data on bilateral labor inflows and capital outflows to measure the extent of contemporaneous substitutability and dynamic complementarity between migration and FDI. We find that brain drain and FDI inflows are negatively correlated contemporaneously but that skilled migration is associated with future increases in FDI inflows. We also find suggestive evidence of substitutability between current migration and FDI for migrants with secondary education, and of complementarity between past migration and FDI for unskilled migrants. Keywords; brain drain, foreign direct investment inflows, migrant ties and business networks

Skilled Emigration, Business Networks and Foreign Direct Investment

In a global context foreign direct investment (FDI) and migration substitute one another in the matching process between workers and firms. However, as labor flows can lead to the formation of business networks, migration can actually facilitate FDI in the long-run. We first present a stylized model for a small open economy illustrating these offsetting effects. We then use U.S. data on bilateral labor inflows and capital outflows to measure the extent of contemporaneous substitutability and dynamic complementarity between migration and FDI. We find that brain drain and FDI inflows are negatively correlated contemporaneously but that skilled migration is associated with future increases in FDI inflows. We also find suggestive evidence of substitutability between current migration and FDI for migrants with secondary education, and of complementarity between past migration and FDI for unskilled migrants.brain drain, foreign direct investment inflows, migrant ties and business networks

A Method to Identify and Analyze Biological Programs through Automated Reasoning.

Predictive biology is elusive because rigorous, data-constrained, mechanistic models of complex biological systems are difficult to derive and validate. Current approaches tend to construct and examine static interaction network models, which are descriptively rich but often lack explanatory and predictive power, or dynamic models that can be simulated to reproduce known behavior. However, in such approaches implicit assumptions are introduced as typically only one mechanism is considered, and exhaustively investigating all scenarios is impractical using simulation. To address these limitations, we present a methodology based on automated formal reasoning, which permits the synthesis and analysis of the complete set of logical models consistent with experimental observations. We test hypotheses against all candidate models, and remove the need for simulation by characterizing and simultaneously analyzing all mechanistic explanations of observed behavior. Our methodology transforms knowledge of complex biological processes from sets of possible interactions and experimental observations to precise, predictive biological programs governing cell function

Biocharts: a visual formalism for complex biological systems

We address one of the central issues in devising languages, methods and tools for the modelling and analysis of complex biological systems, that of linking high-level (e.g. intercellular) information with lower-level (e.g. intracellular) information. Adequate ways of dealing with this issue are crucial for understanding biological networks and pathways, which typically contain huge amounts of data that continue to grow as our knowledge and understanding of a system increases. Trying to comprehend such data using the standard methods currently in use is often virtually impossible. We propose a two-tier compound visual language, which we call Biocharts, that is geared towards building fully executable models of biological systems. One of the main goals of our approach is to enable biologists to actively participate in the computational modelling effort, in a natural way. The high-level part of our language is a version of statecharts, which have been shown to be extremely successful in software and systems engineering. The statecharts can be combined with any appropriately well-defined language (preferably a diagrammatic one) for specifying the low-level dynamics of the pathways and networks. We illustrate the language and our general modelling approach using the well-studied process of bacterial chemotaxis

A Method to Identify and Analyze Biological Programs through Automated Reasoning.

Predictive biology is elusive because rigorous, data-constrained, mechanistic models of complex biological systems are difficult to derive and validate. Current approaches tend to construct and examine static interaction network models, which are descriptively rich but often lack explanatory and predictive power, or dynamic models that can be simulated to reproduce known behavior. However, in such approaches implicit assumptions are introduced as typically only one mechanism is considered, and exhaustively investigating all scenarios is impractical using simulation. To address these limitations, we present a methodology based on automated formal reasoning, which permits the synthesis and analysis of the complete set of logical models consistent with experimental observations. We test hypotheses against all candidate models, and remove the need for simulation by characterizing and simultaneously analyzing all mechanistic explanations of observed behavior. Our methodology transforms knowledge of complex biological processes from sets of possible interactions and experimental observations to precise, predictive biological programs governing cell function.G.M. holds a career development award from the Armenise Harvard foundation, and a Telethon-DTI career award. A.G.S. is a Medical Research Council Professor