Social Expenditure: Aggregated data (Edition 2016)

Purpose Revitalising the debate about how to operationalise and measure the extent of welfare states – the so-called dependent variable problem – recent research claims a close theoretical interaction between three different indicators: aggregated data on social expenditure, social rights and social benefit receipt. It is suggested that they all serve as an approximation of welfare state generosity as a dependent variable and help understand variation between as well as change of welfare states. The purpose of this paper is to investigate how these three indicators statistically relate to each other, using data on unemployment cash benefits. Design/methodology/approach To this end, a time series cross-sectional analysis is carried out, covering 16 European countries for the period 2003–2011. Findings Results confirm theoretical reflections on the link between the different indicators, whereby higher levels of social expenditure are positively associated with more generous social rights as well as higher levels of benefit receipt. Additionally, the study points to an ambivalent relation between benefit access and benefit levels within indicators as well as across them. This suggests competing policy choices in European welfare states, whereby more generous benefit access implies lower benefit levels and vice versa. Originality/value The study contributes to the existing dependent variable literature in a twofold way. First, the conceptual link between the three different indicators and the assumptions they are associated with are critically reviewed. Second, by providing a statistical analysis of the relation between the different indicators across 16 countries, the study goes beyond theoretical elaborations about their association as well as existing small-N or medium-N case time trend studies.

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Social Expenditure: Aggregated data (Edition 2015)

OECD Social Expenditure Statistics ◽

10.1787/58636bea-en ◽

2012 ◽

Cited By ~ 1

Author(s):

Keyword(s):

Social Expenditure ◽

Aggregated Data

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Idiographic Dynamics of Positive Affect in GAD

European Journal of Psychological Assessment ◽

10.1027/1015-5759/a000580 ◽

2020 ◽

Vol 36 (3) ◽

pp. 500-509

Author(s):

Hannah G. Bosley ◽

Devon B. Sandel ◽

Aaron J. Fisher

Keyword(s):

Positive Affect ◽

Structural Equation ◽

Alternative Hypothesis ◽

Equation Model ◽

Aggregated Data ◽

Regulatory Strategy ◽

Constant State ◽

Automatic Search ◽

Time Lagged ◽

Exploratory Procedure

Abstract. Generalized anxiety disorder (GAD) is associated with worry and emotion regulation difficulties. The contrast-avoidance model suggests that individuals with GAD use worry to regulate emotion: by worrying, they maintain a constant state of negative affect (NA), avoiding a feared sudden shift into NA. We tested an extension of this model to positive affect (PA). During a week-long ecological momentary assessment (EMA) period, 96 undergraduates with a GAD analog provided four daily measurements of worry, dampening (i.e., PA suppression), and PA. We hypothesized a time-lagged mediation relationship in which higher worry predicts later dampening, and dampening predicts subsequently lower PA. A lag-2 structural equation model was fit to the group-aggregated data and to each individual time-series to test this hypothesis. Although worry and PA were negatively correlated in 87 participants, our model was not supported at the nomothetic level. However, idiographically, our model was well-fit for about a third (38.5%) of participants. We then used automatic search as an idiographic exploratory procedure to detect other time-lagged relationships between these constructs. While 46 individuals exhibited some cross-lagged relationships, no clear pattern emerged across participants. An alternative hypothesis about the speed of the relationship between variables is discussed using contemporaneous correlations of worry, dampening, and PA. Findings suggest heterogeneity in the function of worry as a regulatory strategy, and the importance of temporal scale for detection of time-lagged effects.

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