Abstract

There is a broad consensus on the need to observe information on households’ income and consumption and other socio-economic variables within the same micro-dataset. However, in most European Union countries, these data are collected through two independent surveys: the Statistics on Income and Living Conditions (SILC) and Household Budget Survey (HBS). Previous literature points to statistical matching techniques as the most appropriate way to combine data from these sources. This paper presents a novel matching strategy between SILC and HBS applicable to all European Union countries (with complete information) for the 2010 and 2015 reference years. It results in a robust merging of the household disposable income (from SILC) and total consumption expenditure (from HBS) variables and all their respective components in a synthetic dataset. The strategy relies on a matching procedure implemented through a nearest-neighbor distance algorithm that considers as matching variables reliable proxies for the variables of interest that indirectly capture the information of common variables of the surveys once transformed into a single dimension. Apart from measuring welfare, inequality and poverty more comprehensively, our results can enhance the representation of households in economic models and Distributional National Accounts and serve to study relationships among the integrated variables.

 

SPEAKER:

Manuel Tomás is a researcher at the Basque Centre for Climate Change and is developing his Ph.D. Thesis in Economics at the University of the Basque Country. Before that, he worked as an Associate Lecturer at the University of Castilla-La Mancha. His research focuses on modelling consumer behaviour using different economic models to assess the macroeconomic, distributional, and environmental effects of energy and climate policies.