Russian monetary policy has been challenged by large and continuous private capital outflows and a sharp drop in oil prices during 2014. Both contributed to significant depreciation pressures on the ruble and led the central bank to give up its exchange rate management strategy. Against this background, this work estimates a small open economy model for Russia, featuring an oil price sector and extended by a specification of the foreign exchange market to correctly account for systematic central bank interventions. We find that shocks to the oil price and private capital flows substantially affect domestic variables such as inflation and output. Simulations for the estimated actual strategy and alternative regimes suggest that the vulnerability of the Russian economy to external shocks can substantially be lowered by adopting some form of inflation targeting. Strategies to target the nominal exchange rate or the ruble price of oil prove to be inferior.

Using daily data for 34 emerging markets in the period 1994–2016, we find robust evidence that higher export commodity prices are associated with lower sovereign default risk, as measured by lower EMBI spreads. The economic effect is especially pronounced for heavy commodity exporters. Examining the drivers, we find that, first, commodity dependence is higher for countries that export large volumes of commodities, whereas other portfolio characteristics like volatility or concentration are less important. Second, commodity-sovereign risk dependence increases in times of recessions and expansionary U.S. monetary policy. Third, the importance of raw material prices for sovereign financing can likely be mitigated if a country improves institutions and tax systems, attracts FDI inflows, invests in manufacturing, machinery and infrastructure, builds up reserve assets and opens capital and trade accounts. Fourth, the country’s government indebtedness or amount of received development assistance appear to be only of secondary importance for commodity dependence.

Using a global sample, this paper investigates the determinants of wealth inequality capturing various economic, financial, political, institutional, and geographical indicators. Using instrumental variable Bayesian model averaging, it reveals that only a handful of indicators robustly matters and finance plays a key role. It reports that while financial depth increases wealth inequality, efficiency and access to finance reduce inequality. In addition, redistribution and education are associated with lower inequality whereas wars and openness to international trade contribute to greater wealth inequality.