Open Access

Welfare is the largest expenditure category in all advanced democracies. Consequently, much literature has studied partisan effects on total and policy-specific welfare expenditure. Yet, these results cannot be trusted: The methodological standard is to apply time-series cross-section-regressions to annual observation data. But governments hardly change annually. Thus, the number of observations is artificially inflated, leading to incorrect estimates. While this problem has recently been acknowledged, it has not been convincingly resolved. We propose Mixed-Effects Models as a solution, which allow decomposing variance into different levels and permit complex cross-classification data structures. We argue that Mixed-Effects models combine the strengths of existing methodological approaches while alleviating their weaknesses. Empirically, we study partisan effects on total and on disaggregated expenditure in 23 OECD-countries, 1960-2012, using several measures of party preferences.