Social policies and solidarity during the Greek Crisis

This article provides a succinct overview of the phenomenon of solidarization in crisis‐ridden Greece. Brief accounts of the pre‐ and post‐crisis situation in four core social policy areas (health, employment, housing, education) are combined with the presentation of four illustrative case studies, one for each of the aforementioned areas. Drawing on a broad range of primary and secondary data, including 20 in‐depth interviews with key stakeholders and end‐users of the actions examined, we argue that while the crisis provoked a further “residualization” of the institutionalized solidarity system, it also triggered the development of new solidarity ventures, both by state and non‐state actors, often even including partnerships between them. These ventures reflect the building of a bridge between institutionalized and informal solidarity in Greece and the shift towards a new type of mixed, although fragmented, mode of solidarity.     

M-quantile models with application to poverty mapping

Over the last decade there has been growing demand for estimates of population characteristics at small area level. Unfortunately, cost constraints in the design of sample surveys lead to small sample sizes within these areas and as a result direct estimation, using only the survey data, is inappropriate since it yields estimates with unacceptable levels of precision. Small area models are designed to tackle the small sample size problem. The most popular class of models for small area estimation is random effects models that include random area effects to account for between area variations. However, such models also depend on strong distributional assumptions, require a formal specification of the random part of the model and do not easily allow for outlier robust inference. An alternative approach to small area estimation that is based on the use of M-quantile models was recently proposed by Chambers and Tzavidis (Biometrika 93(2):255–268, 2006) and Tzavidis and Chambers (Robust prediction of small area means and distributions. Working paper, 2007). Unlike traditional random effects models, M-quantile models do not depend on strong distributional assumption and automatically provide outlier robust inference. In this paper we illustrate for the first time how M-quantile models can be practically employed for deriving small area estimates of poverty and inequality. The methodology we propose improves the traditional poverty mapping methods in the following ways: (a) it enables the estimation of the distribution function of the study variable within the small area of interest both under an M-quantile and a random effects model, (b) it provides analytical, instead of empirical, estimation of the mean squared error of the M-quantile small area mean estimates and (c) it employs a robust to outliers estimation method. The methodology is applied to data from the 2002 Living Standards Measurement Survey (LSMS) in Albania for estimating (a) district level estimates of the incidence of poverty in Albania, (b) district level inequality measures and (c) the distribution function of household per-capita consumption expenditure in each district. Small area estimates of poverty and inequality show that the poorest Albanian districts are in the mountainous regions (north and north east) with the wealthiest districts, which are also linked with high levels of inequality, in the coastal (south west) and southern part of country. We discuss the practical advantages of our methodology and note the consistency of our results with results from previous studies. We further demonstrate the usefulness of the M-quantile estimation framework through design-based simulations based on two realistic survey data sets containing small area information and show that the M-quantile approach may be preferable when the aim is to estimate the small area distribution function.     

ROBUST ESTIMATION OF SMALL‐AREA MEANS AND QUANTILES

Small‐area estimation techniques have typically relied on plug‐in estimation based on models containing random area effects. More recently, regression M‐quantiles have been suggested for this purpose, thus avoiding conventional Gaussian assumptions, as well as problems associated with the specification of random effects. However, the plug‐in M‐quantile estimator for the small‐area mean can be shown to be the expected value of this mean with respect to a generally biased estimator of the small‐area cumulative distribution function of the characteristic of interest. To correct this problem, we propose a general framework for robust small‐area estimation, based on representing a small‐area estimator as a functional of a predictor of this small‐area cumulative distribution function. Key advantages of this framework are that it naturally leads to integrated estimation of small‐area means and quantiles and is not restricted to M‐quantile models. We also discuss mean squared error estimation for the resulting estimators, and demonstrate the advantages of our approach through model‐based and design‐based simulations, with the latter using economic data collected in an Australian farm survey.     

Introduction: Knowledge Construction and Creation in Organizations*

While adopting a knowledge‐based perspective on organizations has been valuable, since, among other things, it enables us to see links between organizational learning and a firm's competitive advantage through the development of idiosyncratic capabilities, it has nonetheless tended to treat organizational knowledge as ‘given’, exploring how it is related to other ‘given’ variables. The focus of this special issue is to unpack the notion of organizational knowledge by exploring the processes and practices through which knowledge is constructed and created in organizations. A constructivist perspective assumes that ‘knowledge’ presupposes work and seeks to explore how what comes to be considered as organizational knowledge is established and validated (or fails to do so). By seeing organizational knowledge as work we can further probe into how knowledge is shaped by organizational strategies and incentives and, more radically, how power and politics influence the struggle between competing bodies of knowledge in organizations.     

Spatial Risk Analysis of Power Systems Resilience During Extreme Events

The increased frequency of extreme events in recent years highlights the emerging need for the development of methods that could contribute to the mitigation of the impact of such events on critical infrastructures, as well as boost their resilience against them. This article proposes an online spatial risk analysis capable of providing an indication of the evolving risk of power systems regions subject to extreme events. A Severity Risk Index (SRI) with the support of real‐time monitoring assesses the impact of the extreme events on the power system resilience, with application to the effect of windstorms on transmission networks. The index considers the spatial and temporal evolution of the extreme event, system operating conditions, and the degraded system performance during the event. SRI is based on probabilistic risk by condensing the probability and impact of possible failure scenarios while the event is spatially moving across a power system. Due to the large number of possible failures during an extreme event, a scenario generation and reduction algorithm is applied in order to reduce the computation time. SRI provides the operator with a probabilistic assessment that could lead to effective resilience‐based decisions for risk mitigation. The IEEE 24‐bus Reliability Test System has been used to demonstrate the effectiveness of the proposed online risk analysis, which was embedded in a sequential Monte Carlo simulation for capturing the spatiotemporal effects of extreme events and evaluating the effectiveness of the proposed method.     

Employability management practices in the Polish ICT sector

This article examines current career thinking and employability management practices within the Polish Information and Communication Technology (ICT) sector. The aim of this contribution is to identify career management problems and to determine obstacles for implementing employability management practices at a company level. Semi-structured interviews aimed at establishing company needs were conducted with 18 managers of small and medium-sized ICT enterprises in Poland. These firms appear to apply various developmental approaches to stimulate competitive advantage. Faced with a more demanding environment, firms aim for versatility rather than adopting simplified solutions. Managing the careers and employability of ICT professionals is acknowledged as vitally important for the survival and development of ICT companies.     

Team resilience: A scoping review of conceptual and empirical work

ABSTRACT

The purpose of this scoping review was to examine the literature on team resilience to gain insight into current thinking regarding its definition and conceptualisation, and to identify how researchers have operationalised and measured this concept. We conducted a systematic scoping review using the 5-phase approach proposed by Arksey and O’Malley. A total of seven databases were searched, followed by a citation search of eligible papers via Google Scholar. Of the 275 articles identified via the search process, 27 papers were deemed eligible for review. Several key findings regarding the literature on team resilience were observed: (i) definitions varied in terms of content (e.g. input or process), breadth (e.g. unidimensional versus multidimensional), and quality (e.g. essential and necessary attributes of key components); (ii) there was a predominance of single-level conceptualisations of team resilience; and (iii) there has been a reliance on cross-sectional research designs in empirical studies, which is incongruent with the dynamic nature of this concept. Key recommendations from this scoping review focus on definitional, theoretical, and methodological issues.     

Outlier Robust Small‐Area Estimation Under Spatial Correlation

Modern systems of official statistics require the estimation and publication of business statistics for disaggregated domains, for example, industry domains and geographical regions. Outlier robust methods have proven to be useful for small‐area estimation. Recently proposed outlier robust model‐based small‐area methods assume, however, uncorrelated random effects. Spatial dependencies, resulting from similar industry domains or geographic regions, often occur. In this paper, we propose an outlier robust small‐area methodology that allows for the presence of spatial correlation in the data. In particular, we present a robust predictive methodology that incorporates the potential spatial impact from other areas (domains) on the small area (domain) of interest. We further propose two parametric bootstrap methods for estimating the mean‐squared error. Simulations indicate that the proposed methodology may lead to efficiency gains. The paper concludes with an illustrative application by using business data for estimating average labour costs in Italian provinces.