Assessing the influence of the Responsibility to Protect on the UN Security Council during the Arab Spring

This article challenges those perspectives which assert first, that the Security Council’s engagement with the Responsibility to Protect (R2P) during the Arab Spring evidences a generally positive trend, and second, that the response to the Arab Spring, particularly Syria, highlights the need for veto restraint. With respect to the first point, the evidence presented in this article suggests that the manner in which R2P has been employed by the Security Council during this period evidences three key trends: first, a willingness to invoke R2P only in the context of Pillar I; second, a pronounced lack of consensus surrounding Pillar III; and third, the persistent prioritisation of national interests over humanitarian concerns. With respect to veto restraint, this article argues that there is no evidence that this idea will have any significant impact on decision-making at the Security Council; the Council’s response to the Arab Spring suggests that national interests continue to trump humanitarian need.

Macroeconomic information, structural change, and the prediction of fiscal aggregates

Previous research on the prediction of fiscal aggregates has shown evidence that simple autoregressive models often provide better forecasts of fiscal variables than multivariate specifications. We argue that the multivariate models considered by previous studies are small-scale, probably burdened by overparameterization, and not robust to structural changes. Bayesian Vector Autoregressions (BVARs), on the other hand, allow the information contained in a large data set to be summarized efficiently, and can also allow for time variation in both the coefficients and the volatilities. In this paper we explore the performance of BVARs with constant and drifting coefficients for forecasting key fiscal variables such as government revenues, expenditures, and interest payments on the outstanding debt. We focus on both point and density forecasting, as assessments of a country’s fiscal stability and overall credit risk should typically be based on the specification of a whole probability distribution for the future state of the economy. Using data from the US and the largest European countries, we show that both the adoption of a large system and the introduction of time variation help in forecasting, with the former playing a relatively more important role in point forecasting, and the latter being more important for density forecasting.