Performance-based aid has been proposed as an alternative to the failed traditional approach whereby donors make aid conditional on the reform promises of recipient countries. However, hardly any empirical evidence exists on whether ex post rewards are effective in inducing reforms. We attempt...

Foreign aid from China is often characterized as ‘rogue aid’ that is not guided by recipient need but by China’s national interests alone. However, no econometric study so far confronts this claim with data. We make use of various datasets, covering the 1956-2006 period, to empirically...

NGO aid is still widely believed to be superior to official aid (ODA). However, the incentives of NGOs to excel and target aid to the poor and deserving are increasingly disputed. We contribute to the emerging literature on the allocation of NGO aid by performing panel Tobit estimations for...

Using panel data for a maximum of 109 countries over the years 1976-2000, we empirically analyze the impact of decentralization on the occurrence of transnational terror. Our results show that expenditure decentralization reduces the number of transnational terror events in a country, while...

We develop a two-country, two-sector model of trade where the only difference between the two countries is their distribution of human capital endowments. We show that even if the two countries have identical aggregate human capital endowments the pattern of trade depends on the properties of...

We test whether, in addition to economic conditions, IMF credit is influenced by political factors. On the basis of a panel model for 128 countries over the period 1972-1998, we find that debt service scaled to exports, international reserve holdings scaled to imports and economic growth, as...

We develop a two-sector, two-country model where trade is driven by technological differences. Each country is populated by large number of heterogeneous workers distinguished by their level of skills. Given that one country has a technological advantage in the skilled intensive good when we...