The origins of redistributive policy preferences: political socialisation with and without a welfare state

Research on the impact of the macroeconomy on individual-level preferences for redistribution has produced varying results. This paper presents a new theory on the presence of an expansive welfare state during one’s formative years as a source of heterogeneity in the effect that macroeconomic conditions have on individuals’ preferences for redistributive policy. This theory is tested using cohort analysis via the British Social Attitudes surveys (1983–2010), with generations coming of age between the end of World War I and today. Findings confirm that cohorts that were socialised before and after the introduction of the welfare state react differently to economic crises: the former become less supportive of redistribution, while the latter become more supportive. The research sheds light on the long-term shifts of support for the welfare state due to generational replacement

Party identification and party closeness in comparative perspective

The present analysis uses data from 1974 and 1981 U. S. cross sections, which incorporate a panel, to compare the standard NES measure of party identification (ID) with a measure of partisanship derived from a party closeness question widely employed in cross-national research. Important features of the two scales are examined by transforming the closeness measure into a scale of very close, fairly close, not very close, and no preference corresponding to the seven-point ID scale. The scales are highly correlated and are similar in their reliability. More than 75% of the “independents” in the ID scale choose a party in the closeness version, and over half of these select the “fairly close” category. Respondents do not volunteer that they are independents when that alternative is not stated in the question.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45482/1/11109_2004_Article_BF00990552.pd

It's About Time: Modelling Political and Social Dynamics

Extending the basic regression model to the analysis of processes, which entail modelling time. The example demonstrates using Box?Jenkins ARIMA intervention and transfer function models, and error correction models, and introduces and interprets statistical tests such as the Q?test and the Dickey?Fuller t?ratio