Studying the Welfare State by Analysing Time-Series-Cross-Section Data

Federico Podestà

No 2023-03, FBK-IRVAPP Working Papers from Research Institute for the Evaluation of Public Policies (IRVAPP), Bruno Kessler Foundation

Abstract: For a few decades now, quantitative researchers interested in studying welfare states have been analysing time-series-cross-section (TSCS) data relatively regularly. Given that welfare state researchers operate within an observational data framework, they seek to exploit the characteristics of TSCS data to make causal inferences. However, this objective remains quite difficult. Accordingly, the chapter aims to critically illustrate some of the most relevant TSCS techniques used in recent years. Much of the chapter regards TSCS regression, as it is the most widely used econometric tool for estimating causal effects regarding several welfare state features in a TSCS setting. The concluding part of the chapter regards the synthetic control method. This method requires a dedicated section because, although it has been widely used in numerous strands of research, it has arguably not yet been sufficiently exploited for the study of social policy.

Keywords: time-series-cross-section analysis; welfare state; causal inference; regression; synthetic control method. Acknowledgments: (search for similar items in EconPapers)
Date: 2023-05
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