Adopting qualitative methods to improve quantitative datasets and indicators

In a world dominated by data-driven decision-making, it is essential to question the adequacy of the indicators we use in capturing complex social phenomena. Thus, this contribution attempts to critically examine the use of indicators and raise some considerations and challenges. I report these concerns by presenting the example of the European indicator AROPE calculated through the EU-SILC dataset. It was adopted in the Europe 2020 strategy and 2030 targets to monitor the share of people at risk of poverty and social exclusion. Starting from its composition, I raise concerns regarding its comprehensiveness by adopting a critical theoretical framework combining post-colonial and urban studies (Santos, 2014; Wacquant, 2008). It allows and justifies a qualitative research approach. Precisely, I conducted five case studies in five European cities (Rome, Brussels, Stockholm, Bucharest, and London), where I conducted semi-structured interviews with the associations that work with the most excluded groups and the experts that study these dynamics. By comparing what emerged in the interviews with the statistical analysis of AROPE, I concluded the need to improve the current European indicator and dataset by incorporating specific dimensions and variables. This example brings some considerations and challenges to the discussion. Firstly, it shows the relevance of examining the adequacy of the indicators we use to capture social phenomena due to their multidimensionality, intersectionality and fluidity. Secondly, it spotlights the need to keep questioning the data we use and underscores the importance of critical theoretical frameworks and qualitative research approaches to enhance quantitative datasets and indicators.