In this paper we identify and measure the effects of credit shocks in a small open economy. To incorporate information from a large number of economic and financial indicators we use the structural factor-augmented VARMA model. In the theoretical framework of the financial accelerator, we approximate the external finance premium with credit spreads. We find that an adverse global credit shock generates a significant and persistent economic slowdown in Canada; the Canadian external finance premium rises immediately while interest rates and credit measures decline. Variance decomposition reveals that the credit shock has an important effect on real activity measures, including price and leading indicators, and credit spreads. On the other hand, an unexpected increase in the Canadian external finance premium shows no significant effect in Canada, suggesting that the effects of credit shocks in Canada are essentially caused by the unexpected changes in foreign credit market conditions. Given the identification procedure our structural factors have an economic interpretation.

With extensive country- and firm-level data sets we first document that the financial sectors of most sub-Saharan African countries remain significantly underdeveloped by the standards of other developing countries. We also find that population density appears to be considerably more important for banking sector development in Africa than elsewhere. To better understand how countries can overcome the high costs of developing viable banking sectors outside large metropolitan areas, we focus on Kenya, which has made significant strides in financial inclusion and development in recent years. We find a positive and significant impact of Equity Bank, a leading private commercial bank on financial access, especially for under-privileged households. Equity Bank’s business model—providing financial services to population segments typically ignored by traditional commercial banks and generating sustainable profits in the process—can be a potential solution to the financial access problem that has hindered the development of inclusive financial sectors in many other African countries.

The paper reconsiders the conflicting results in the debate connected to the effects of technology shocks on hours worked in the bivariate system. Given major dissatisfaction with the just-identifying long-run restrictions, I analyze whether the restrictions used in the literature are consistent with the data. Modeling volatility of shocks using Markov switching structure allows to obtain additional identifying information and perform tests of the restrictions that were just-identifying in classical structural vector autoregression analysis. Using four datasets where hours worked are modeled differently, I find that the standard restriction, identifying the technology shocks as the only sources of variation in labor productivity, has major support by the data. Taking into account important low frequency movements in the hours worked series yields a result consistent with the recent findings: hours decline in response to a positive technology shock. I also show that the use of a standard Hodrick-Prescott filter may be problematic in the context.