Charitable conservatism, poverty radicalism and inequality aversion

In his Presidential Address to the European Economic Association, Tony Atkinson introduced the idea of a “charitable conservatism” position in public policy, which “exhibits a degree of concern for the poor, but this is the limit of the redistributional concern and there is indifference with respect to transfers above the poverty line.” This contrasts with the perspective of poverty indices, which give zero weight to those above the poverty line, which we call “poverty radicalism,” and with standard “inequality aversion” where the weights decline smoothly as we move up the income scale. The object of this paper is, first, to clarify the interrelationships between charitable conservatism, poverty radicalism and inequality aversion. We do this by showing how the patterns of welfare weights to which each of these gives rise are related to each other. Secondly, we are concerned to demonstrate the implications of these different views for optimal income taxation. In terms of levels and patterns of marginal tax rates, we show that charitable conservatism and poverty radicalism are on a continuum, and by choice of low or high inequality aversion one can approximate either outcome fairly well.     

Statistical inference for the quintile share ratio

In recent years, the Quintile Share Ratio (or QSR) has become a very popular measure of inequality. In 2001, the European Council decided that income inequality in European Union member states should be described using two indicators: the Gini Index and the QSR. The QSR is generally defined as the ratio of the total income earned by the richest 20% of the population relative to that earned by the poorest 20%. Thus, it can be expressed using quantile shares, where a quantile share is the share of total income earned by all of the units up to a given quantile. The aim of this paper is to propose an improved methodology for the estimation and variance estimation of the QSR in a complex sampling design framework. Because the QSR is a non-linear function of interest, the estimation of its sampling variance requires advanced methodology. Moreover, a non-trivial obstacle in the estimation of quantile shares in finite populations is the non-unique definition of a quantile. Thus, two different conceptions of the quantile share are presented in the paper, leading us to two different estimators of the QSR. Regarding variance estimation, [Osier, 2006] and [Osier, 2009] proposed a variance estimator based on linearization techniques. However, his method involves Gaussian kernel smoothing of cumulative distribution functions. Our approach, also based on linearization, shows that no smoothing is needed. The construction of confidence intervals is discussed and a proposition is made to account for the skewness of the sampling distribution of the QSR. Finally, simulation studies are run to assess the relevance of our theoretical results.     

Arguments for the cognitive social sciences

This article analyses the arguments for the integration between the cognitive and social sciences. We understand interdisciplinary integration as an umbrella term that includes different ways of bringing scientific disciplines together. Our focus is on four arguments based on different ideas about how the cognitive sciences should be integrated with the social sciences: explanatory grounding, theoretical unification, constraint and complementarity. These arguments not only provide different reasons why the cognitive social sciences—i.e. disciplines and research programs that aim to integrate the social sciences with the cognitive sciences—are needed but also subscribe to different visions as to how these sciences might look like. We discuss each argument in three stages: First, we provide a concrete example of the argument. Second, we reconstruct the argument by specifying its premises, inferential structure and conclusion. Third, we evaluate the argument by analyzing its presuppositions, the plausibility of its premises, the soundness of its inferences and potential conceptual ambiguities. In the final discussion, we compare these arguments and identify the most compelling reasons why the cognitive social sciences are needed.     

Societal shifts and changed patterns of poverty

Fritzell J, Ritakallio VM. Societal shifts and changed patterns of poverty Int J Soc Welfare 2010: ??: ??–??© 2010 The Author(s), Journal compilation © 2010 Blackwell Publishing Ltd and International Journal of Social Welfare. This article uses data from the Luxembourg Income Study to analyse cross‐national and cross‐temporal poverty risks in 11 Western countries. We show that poverty risks have tended to increase from the early 1980s to 2000. In line with what we would expect based on the welfare state literature, the Nordic countries tend to have the lowest poverty rates. However, the proportion of the national population with a market income below the poverty threshold has increased in all countries and the cross‐national variation in market income poverty is not apparently related to the type of welfare state regime. We perform a simulation analysis to test whether structural factors, that is, compositional differences in age, family and labour market behaviour, could account for the cross‐national variation found. Our results demonstrate the increasing importance of household labour market attachment for alleviating poverty risks, as well as for explaining the cross‐national variation in these risks.     

A top-down movement with grass-roots effects? Alexei Navalny’s electoral campaign

Today’s Russia is a hostile environment for genuine political activity, and especially for movements that aim at changing the current power structure. This is due to the factually limited manoeuvre space of oppositional actors who face obstacles in the form of repression, surveillance and restricted access to the public sphere. Moreover, society is largely apolitical, with political activity often considered futile, immoral, or dangerous. In this profile, we portray the electoral campaign of the opposition politician and anti-corruption activist Alexei Navalny, who built a popular movement around his bid to participate in the 2018 presidential elections. Although the campaign failed to build up sufficient pressure for Navalny to be granted access to the elections, and despite the strong hierarchy inside his campaign, we argue that it contributed to the politicization of parts of the younger generation in the country’s provinces – which may have greater long-term effects than any concrete projects envisioned or controlled by the campaign’s strategists.     

The ABCE Model of Volunteer Motivation

To meet the growing social service needs of our societies, the social services and other Volunteer organizations need to understand the needs and motives of their volunteers to keep them retained. Although volunteer motivation scales are available and tested, different organizations have to amend and add volunteering motives to best fit their organization and environment. Furthermore, not much guidance is available to volunteer organizations to understand or measure motivation of their volunteers raising a need for a unified model that can be a guideline for managers. This study discusses different approaches to volunteering motivation and links them into four areas of affiliation using an ABCE model: affiliation (A), beliefs, (B) career development, (C) and (E) egoistic. Participants were 496 volunteers from a variety of NGOs including 239 (48.2%) from an international Faith Based Organization (FBO). Findings show that although differences exist in volunteering motivation the actual best fit was an ABCE model. Future research is needed on testing the scale with different cultures and different organizations. A deeper knowledge of volunteer motivations will enable organizations to prosper and utilize the continuous experience of the volunteers and their engagement, thereby ensuring enhanced quality social service delivery.     

Instrumental and Non-instrumental Factors of Social Innovation Adoption

We investigate social innovation adoption and extend instrumental factors of adoption intention by including non-instrumental factors. We argue that, from a market-based perspective, conventional innovation adoption models based on instrumental factors do not fully consider specific characteristics of social innovations. Our objective is to improve the explanatory power of conventional adoption models by including symbolic, emotional, and motivational factors as non-instrumental factors. We conducted two empirical studies in which we investigated younger adults’ adoption intentions regarding time-bank-based senior cooperatives. The results extend the findings of conventional adoption research and show that explanatory power can be substantially improved by including non-instrumental factors. The results contribute to the understanding of social innovation adoption and indicate limited generalizability of conventional innovation adoption research to social innovations. Implications and directions for future research are discussed.     

Robust adaptive Metropolis algorithm with coerced acceptance rate

The adaptive Metropolis (AM) algorithm of Haario, Saksman and Tamminen (Bernoulli 7(2):223?C242, 2001) uses the estimated covariance of the target distribution in the proposal distribution. This paper introduces a new robust adaptive Metropolis algorithm estimating the shape of the target distribution and simultaneously coercing the acceptance rate. The adaptation rule is computationally simple adding no extra cost compared with the AM algorithm. The adaptation strategy can be seen as a multidimensional extension of the previously proposed method adapting the scale of the proposal distribution in order to attain a given acceptance rate. The empirical results show promising behaviour of the new algorithm in an example with Student target distribution having no finite second moment, where the AM covariance estimate is unstable. In the examples with finite second moments, the performance of the new approach seems to be competitive with the AM algorithm combined with scale adaptation.