A PEDAGOGICAL TOOL FOR ILLUSTRATING THE REAL IMPACT OF THE FINANCIAL SECTOR

We devise a simple way of incorporating the financial sector into a growth model that is useful pedagogically. Financial innovation raises the efficiency of financial intermediation, which facilitates capital accumulation. The model may be extended to include real R&D as a symbiotic source of endogenous growth.Financial innovations, economic growth

Modelling the Impact of Network Social Capital on Business and Technological Innovations

In this paper, we construct a macroeconomic growth model where social capital embedded in collaborative networks of firms (such as corporate partnerships and research consortia) increase the rate of technological and business innovations in high-tech industries. Social capital is created via network-building activities and through “learning-by-doing”. We derive the optimal quantity of resources that should be channelled away from pure production into activities that build network social capital, and study both the comparative statics and transitional dynamics of the model. We also examine the implications of the model for policymakers interested in formulating innovation policies.Technological progress, business innovations, social capital, economic growth

MODELLING SOCIAL CAPITAL AND GROWTH

This paper proposes three theoretical growth models incorporating social capital, based on varied expositions on the concept of social capital and the empirical evidence gathered to date. In these models, social capital impacts growth by assisting in the accumulation of human capital, by affecting financial development through its effects on collective trust and social norms, and by facilitating networking between firms that result in the creation and diffusion of business and technological innovations. We solve for the optimum allocation of human capital or labor towards social capital formation in each model, and examine their comparative statics and transitional dynamics.Economic Growth; Social Capital; Financial Development; Technological Change; Human Capital

Technological Revolutions and Financial Innovations

In this paper, we study the symbiotic relationship between financial innovation and technological innovation. In particular, we construct a theoretical macroeconomic growth model that correspond to the thesis presented in Perez (2002) that all the technological revolutions and their associated development surges since the Industrial Revolution have been both beneficiaries and stimulants of financial development. We explain the microeconomic foundations of the model and present its steady state solution, emphasizing how the growth rate of the economy depends on parameters characterising the R&D and financial sectors. We then analyze the impact of specific types of financial innovations that predominate in each phase of the technological cycle on the optimum allocation of resources in the economy.Financial Innovation, Technological Revolutions, Economic Growth

Explaining Africa's Growth Tragedy: A Theoretical Model of Dictatorship and Kleptocracy

In this paper, we construct a dynamic model of a kleptocratic dictatorship to explain sub-Saharan Africa’s dismal economic performance between the early 1970s and the mid-1990s. The dictator’s objective is to maximize a discounted stream of revenue generated through theft of the economy’s output by choosing the optimal expropriation rate and the size of the security force employed to enforce his rule. The model is used to evaluate alternative intervention options open to developed countries such as unconditional, conditional and selective foreign aid, financial and military assistance to rebel groups, as well as medical relief to combat the HIV/AIDs pandemic.Economic performance, dictatorship, foreign aid, Africa

Prediction of protein submitochondria locations by hybridizing pseudo-amino acid composition with various physicochemical features of segmented sequence

BACKGROUND: Knowing the submitochondria localization of a mitochondria protein is an important step to understand its function. We develop a method which is based on an extended version of pseudo-amino acid composition to predict the protein localization within mitochondria. This work goes one step further than predicting protein subcellular location. We also try to predict the membrane protein type for mitochondrial inner membrane proteins. RESULTS: By using leave-one-out cross validation, the prediction accuracy is 85.5% for inner membrane, 94.5% for matrix and 51.2% for outer membrane. The overall prediction accuracy for submitochondria location prediction is 85.2%. For proteins predicted to localize at inner membrane, the accuracy is 94.6% for membrane protein type prediction. CONCLUSION: Our method is an effective method for predicting protein submitochondria location. But even with our method or the methods at subcellular level, the prediction of protein submitochondria location is still a challenging problem. The online service SubMito is now available at