Defining and measuring displacement: is relocation from restructured neighbourhoods always unwelcome and disruptive?

Current regeneration policy has been described as ‘state-led gentrification’, with comparisons made with the ‘social disruption’ caused by slum clearance of the 1950s and 1960s. This article takes issue with this approach in relation to the study of the restructuring of social housing areas. The terms ‘forced relocation’ and ‘displacement’ are often too crude to describe what actually happens within processes of restructuring and the effects upon residents. Displacement in particular has important dimensions other than the physical one of moving. Evidence from a recent study of people who have moved out of restructured areas shows that although there is some evidence of physical displacement, there is little evidence of social or psychosocial displacement after relocation. Prior attitudes to moving and aspects of the process of relocation—the degree of choice and distance involved—are important moderators of the outcomes. Issues of time and context are insufficiently taken into consideration in studies and accounts of restructuring, relocation and displacement

Bayesian nonparametric multivariate convex regression

In many applications, such as economics, operations research and reinforcement learning, one often needs to estimate a multivariate regression function f subject to a convexity constraint. For example, in sequential decision processes the value of a state under optimal subsequent decisions may be known to be convex or concave. We propose a new Bayesian nonparametric multivariate approach based on characterizing the unknown regression function as the max of a random collection of unknown hyperplanes. This specification induces a prior with large support in a Kullback-Leibler sense on the space of convex functions, while also leading to strong posterior consistency. Although we assume that f is defined over R^p, we show that this model has a convergence rate of log(n)^{-1} n^{-1/(d+2)} under the empirical L2 norm when f actually maps a d dimensional linear subspace to R. We design an efficient reversible jump MCMC algorithm for posterior computation and demonstrate the methods through application to value function approximation

Conclusions

Publication within the project “The V4 towards migration challenges in Europe. An analysis and recommendations” is financed by Visegrad Fund

Stepping Stone or Stumbling Block: Incrementalism and National Climate Change Legislation

This Article examines the effects of incremental domestic legislation on international negotiations to limit greenhouse gas emissions. Mitigating the effects of climate change is a global public good, which, ultimately, only an international agreement can provide. The common presumption (justified or not) is that national legislation is a step forward to an international agreement. This Article analyzes how national legislation can create a demand for international action but can also preempt or frustrate international efforts. The crucial issue, which has been largely ignored thus far, is how incremental steps at the domestic level alter international negotiations. This paper identifies four mechanisms that support the intuitive idea that national legislation will have positive effects: (1) allocating economic resources, (2) providing leadership in international negotiations, (3) creating a demand for a uniform standard, and (4) cultivating public opinion. This Article demonstrates that, on closer examination, each of these mechanisms could hinder international efforts to create a comprehensive agreement. This is by no means an argument against all efforts to curb greenhouse gas emissions at the national level. Instead, this Article calls for a more careful analysis the dynamic political impact of domestic proposals