The fundamental importance of economic institutions for economic growth through their impact on technological change has long been argued by Joseph Schumpter and others. Recent empirical studies have reconfirmed such arguments. Robert Barro (1997) finds that economic and political institutions are the most important factors in explaining differences in growth across economies. New growth theory has made major breakthroughs in endogenizing technological changes. However, although some insightful and inspiring discussions of institutional impacts of innovation are provided, there is little attempt in these models to explain what, aside from capital, labor inputs, and knowledge accumulation, determines innovation. An attempt is made to fill the gap in literature by examining how financial institutions affect technological innovation and thus affect growth.

When a nation can finance its investments via foreign-currency denominated debt or domestic-currency claims, what is the optimal capital structure of the nation? Building on the functions of fiat money as both medium of exchange, and store of value like corporate equity, our model connects monetary economics, fiscal theory, and international finance under a unified corporate finance perspective. With frictionless capital markets both a Modigliani–Miller theorem for nations and the classical quantity theory of money hold. With capital market frictions, a nation’s optimal capital structure trades off inflation dilution costs and expected default costs on foreign-currency debt. Our framing focuses on the process by which new money claims enter the economy and the potential wealth redistribution costs of inflation.

This paper develops a model of the lender of last resort (LOLR) from a Central Bank (CB) viewpoint. The model in a static setting suggests that the CB would only rescue banks which are above a threshold size, consistent with the insight of “too big to fail”. In a dynamic setting, CB’s optimal policy in liquidity support depends on the trade off between contagion and moral hazard effects. Our results show that contagion is the key factor affecting CB’s incentives in providing LOLR and they also provide a rationalization for “constructive ambiguity”.