Giving money to strangers: European welfare states and social trust

Jensen C, Svendsen GT. Giving money to strangers: European welfare states and social trust Int J Soc Welfare 2011: 20: 3–9 © 2009 The Author, Journal compilation © 2009 Blackwell Publishing Ltd and the International Journal of Social Welfare. Why would you give money to strangers? That is the fundamental question posed by a new body of research into the relationship between social trust and willingness to accept high taxes and extensive welfare states. The literature argues that generalised trust causes and upholds universal welfare state institutions, an entirely plausible explanation of the Scandinavian social democratic welfare states. However, it cannot explain the presence of very large welfare states in Continental Europe, where the level of generalised trust is much lower than in Scandinavia. The article adds to the existing literature by arguing that the ‘bumblebee’ of conservative welfare states is characterised by particularistic trust and familiaristic welfare institutions, which are functional equivalents to the mechanisms found in Scandinavia. Future research into the trust–welfare state relationship should therefore focus on the trust profile of a country to understand how the welfare state provides its citizens with benefits.     

Valid Resampling of Higher-Order Statistics Using the Linear Process Bootstrap and Autoregressive Sieve Bootstrap

We show that the linear process bootstrap (LPB) and the autoregressive sieve bootstrap (AR sieve) are, in general, not valid for statistics whose large-sample distribution depends on moments of order higher than two, irrespective of whether the data come from a linear time series or not. Inspired by the block-of-blocks bootstrap, we circumvent this non-validity by applying the LPB and AR sieve to suitably blocked data and not to the original data itself. In a simulation study, we compare the LPB, AR sieve, and moving block bootstrap applied directly and to blocked data.     

Asymptotics of improved generalized moment estimators for spatial autoregressive error models

Abstract

This article considers linear models with a spatial autoregressive error structure. Extending Arnold and Wied (2010) Arnold, M., Wied, D. (2010). Improved GMM estimation of the spatial autoregressive error model. Econ. Lett. 108:65–68.[Crossref], [Web of Science ®] , [Google Scholar], who develop an improved generalized method of moment (GMM) estimator for the parameters of the disturbance process to reduce the bias of existing estimation approaches, we establish the asymptotic normality of a new weighted version of this improved estimator and derive the efficient weighting matrix. We also show that this efficiently weighted GMM estimator is feasible as long as the regression matrix of the underlying linear model is non stochastic and illustrate the performance of the new estimator by a Monte Carlo simulation and an application to real data.