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Economic indicators and electoral volatility: economic effects on electoral volatility in Western Europe, 1950–2013

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Comparative European Politics Aims and scope

Abstract

Economic voting theory assumes that on an individual level voters react to economic indicators to hold incumbents responsible for the performance of the economy. On an aggregate level, this would imply that there is an association between economic indicators and levels of volatility since voters have to switch parties if they want to punish or reward political actors. Based on a time-series cross-section analysis of the Pedersen Index for Western European countries in the period 1950–2013, we do indeed observe an association between economic indicators and levels of volatility. This effect furthermore grows stronger over time, and it is assumed that this is rendered possible by processes of partisan dealignment. The analysis suggests that European electorates are significantly more likely to shift parties in response to economic downturn now than they were a few decades ago.

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Notes

  1. Data for GDP comes from the Total Economy Database (The Conference Board, 2013), providing comparative economic data for a wide set of countries from 1950 onwards. Yearly data on unemployment and inflation comes from the OECD database. For inflation, we include the logged inflation rate in the models.

  2. Following the formula proposed by Bélanger and Gélineau (2010, p. 98), which we modify slightly to ensure a one-year time lag for the economic indicators: ρ=[ρ (t−2)*(12−σ (t))/12]+[ρ (t−1)*(σ (t)/12], where ρ is the annual economic indicator, σ is the election month and t is the election year. Data for GDP comes from the Total Economy Database, providing comparative economic data for a wide set of countries from 1950 onwards.

  3. The Bormann and Golder (2013) data run until 2011; several online election sources were consulted to complete the data on district magnitude for elections after 2011.

  4. It is important to point out that the results presented are robust to controlling for the impact of systems in the data set that are either presidential (Cyprus) or semi-presidential (France and Portugal).

  5. This exact specification is also used by Kayser and Wlezien (2011) in their aggregate-level analysis of the impact of partisanship on economic voting.

  6. Although all the indicators related to the electoral and party system are clearly closely related, zero-order correlations between these indicators (Least squares index of disproportionality, ENEP e−1, average district magnitude (ln) and electoral system change) are not too high. The Pearson correlation between district magnitude and the least squares index is −0.527, but other correlations are 0.2 or lower.

  7. Effects presented are quantities of interest from 1000 simulated observations, obtained through the Clarify-command in Stata (King et al, 2000). Simulations based on OLS Huber White estimation.

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Correspondence to Ruth Dassonneville.

Appendix

Appendix

Table A1

Table A1 Descriptive statistics of independent variables

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Dassonneville, R., Hooghe, M. Economic indicators and electoral volatility: economic effects on electoral volatility in Western Europe, 1950–2013. Comp Eur Polit 15, 919–943 (2017). https://doi.org/10.1057/cep.2015.3

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