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AbstractGrowing reports indicate the presence of frauds in microfinance institutions (MFIs), as it can occur in any organization in countries where there are weak institutions, weak rule of law, and fraudulent behavior of MFI officers for personal gain. While there are increasing calls to launch financial governance of these NGO MFIs, there are concerns as to whether frauds of this nature can damage MFIs' contributions to the credit market, particularly in the bank‐linkage program where the NGO MFIs act as third party intermediary. The purpose of this study was to analyze the collusion decisions faced by MFIs and their impact on the bank‐linkage program, which has been offered as a solution to help overcome adverse selection and moral hazard problems in the credit market by harnessing local information via MFIs. Our results show that even when there is a chance of collusion between MFI and the borrower, the linkage between MFI and bank can still increase the probability that the borrower puts in full effort, and therefore decreases the probabilities of both credit rationing and strategic default. Such linkage in financing viable projects can make micro‐financing more effective in achieving inclusive financial development and thereby poverty reduction in rural areas.

In this paper we develop a model to examine the effect of information technology (IT) in the banking industry. IT can reduce operational cost and create network externality. Empirical studies, however, have shown inconsistency, the so‐called Solow paradox, which we explain by stressing the heterogeneity in banking services. In a differentiated model, we characterize the conditions to identify these two effects and explain how the two seemingly positive effects turn negative. Using a panel data set of 68 US banks over 1986–2005, our results show that the profitability effect of IT spending is negative, reflecting a negative network competition effect in the banking industry.

This paper assesses the macroeconomic impact of fiscal policy shocks for four key emerging market economies - Brazil, Russia, India and China (BRICs) – using a Bayesian Structural Vector Auto-Regressive (BSVAR) approach, a Sign-Restrictions Vector Auto-Regressive framework and a Panel Vector Auto-Regressive (PVAR) model. To get a deeper understanding of the government's behaviour, we also estimate fiscal policy rules using a Fully Simultaneous System of Equations and analyze the mportance of nonlinearity using a smooth transition (STAR) model. Drawing on quarterly frequency data, we find that government spending shocks have strong Keynesian effects for this group of countries while, in the case of government revenue shocks, a tax hike is harmful for output. This suggests that there is no evidence in favour of 'expansionary fiscal contraction' in the context of emerging economies where spending policies are largely pro-cyclical. Our findings also show that considerations about growth (in the case of China), exchange rate and inflation (for Brazil and Russia) and commodity prices (in India) drive the nonlinear response of fiscal policy to the dynamics of the economy. All in all, our results are consistent with the idea that fiscal policy can be a powerful stabilization tool and can provide an important short-term economic boost for emerging markets, in particular, in the context of severe downturns as in most recent financial turmoil. ; Fundação para a Ciência e a Tecnologia ...