This article proposes a new methodology to measure precarious employment with a multidimensional approach. The adjusted multidimensional precariousness rate employed to measure job precariousness is calculated on a counting approach and exhibit several advantages, including its decomposability according to the relative contribution to total precariousness of different dimensions and sub-populations. For illustrative purposes, the methodology is applied to the Spanish case using microdata from the Encuesta de Estructura Salarial (Wage Structure Survey) and considering three precariousness dimensions of jobs (low wages, fixed-term contracts and part-time work). The evidence obtained shows that at the beginning of the economic crisis there was an increase in the incidence and intensity of precariousness for new jobs created in the Spanish economy. Moreover, obtained evidence shows that the incidence of precarious employment is particularly high in certain economic sectors and for females.
Schmid and Schmidt (2007) proposed copula-based nonparametric estimators for some multivariate extensions of Spearman's rho. In this paper, we show that two of those estimators are inappropriate since they can take values out of the parameter space and we discuss alternative proposals.
Welfare is multidimensional as it involves not only income, but also education, health or labour. The composite indicators of welfare are usually based on aggregating somehow the information across dimensions and individuals. However, this approach ignores the relationship between the dimensions being aggregated. To face this goal, in this paper we analyse the dependence between the dimensions included in the Human Development Index (HDI), namely income, health and schooling, through three copula-based measures of multivariate association: Spearman's footrule, Gini's gamma and Spearman's rho. We discuss their properties and prove new results on Spearman's footrule. The copula approach focuses on the positions of the individuals across dimensions, rather than the values that the variables attain for such individuals. Thus, it allows for more general types of dependence than the linear correlation. We base our study on data from 1980 till 2014 for the countries included in the 2015 Human Development Report. We find out that though the overall HDI has increased over this period, the dependence between its dimensions remains high and nearly unchanged so that the richest countries tend to be also the best ranked in both health and education.
In this paper, we analyze, in a novel way, the nature of economic growth in Spain after the Great Recession, in relation to its effect on poverty reduction. We use a statistical test to analyze the pro-poorness nature of economic growth using a stochastic dominance approach, not used in this context so far. We decompose changes in the difference in generalized Lorenz ordinates into a growth effect and an inequality effect and apply this to formal Spanish income data statistical tests based on dominance methods. We found that growth was pro-poor in Spain as a whole between 2013 and 2017. As regards regional growth effects, we conclude that growth was weakly pro-poor in seven of Spain’s 17 regions, it was neither pro-poor nor anti-poor in nine regions, and only weakly anti-poor in one region.
In order to contribute to providing a methodology to ensure objectivity and transparency in the measurement of multidimensional poverty, this paper proposes a new threshold for the identification of the multidimensional poor which is also applicable to each of the dimensions of poverty, suitable for identifying the severely poor in developed countries. This new methodology is applied to analyse the evolution of material deprivation in Spain during the period of economic crisis, comparing the results with those obtained using other traditional approaches.Key words: Multidimensional poverty, counting approach, poverty lines, Spain. 2 IntroductionIn recent decades, a broad academic and institutional consensus has emerged regarding the need to study poverty as a multidimensional phenomenon, integrating material deprivation and social exclusion concerns (Sen 1985) in aggregated indicators. However, despite this consensus, there are still many methodological issues when measuring multidimensional poverty that are imbued with subjectivity and a lack of transparency. Among other negative outcomes, this prevents international comparisons from being conducted. In order to construct an objective methodology for measuring severe multidimensional poverty and deprivation, in this paper, a new relative threshold for identifying multidimensional poor or deprived segments is proposed especially suited for developed countries. Using this new threshold, we have quantified and analysed the evolution of material deprivation in Spain during the economic crisis, comparing the results with those obtained using absolute and subjective thresholds proposed in the literature by other authors. order to quantify the level of poverty. In this approach, a set of highly empirical contributions summarises information of all the dimensions in a single variable using multivariate statistical methods (Townsend 1979;Desai and Shah 1988; Guio et al. 2009;Ayala, et al. 2011).Another set of contributions, which take into account the joint distribution of the poverty dimensions, has focused on the proposal of poverty indices that combine information on several dimensions (Bourguignon and Chakravarty 2003; Lemmi and Betti 2006;Foster 2007 and2011a). These contributions include those based on the counting approach, introducedby Atkinson (2003) and are suitable for both qualitative and quantitative variables. This approach, which concentrates on counting the number of dimensions in which people suffer poverty or deprivation, helps overcome the disadvantages of most of the measures that can only be calculated for quantitative variables.Within the framework of the counting approach, Foster (2007 and2011a) propose a methodology, connected with the one-dimensional analysis of the phenomenon, that relies on a method for identifying the poor or deprived and another for aggregating the data and summarising the information on multiple dimensions in one scalar. The identification method, known as dual cutoff, can be based on two kinds of thresholds. The fir...
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