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Policy and Blame Attribution: Citizens’ Preferences, Policy Reputations, and Policy Surprises

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Abstract

Negativity bias suggests that the attribution of blame to governments, for alleged or actual policy failures, is disproportionately pertinent for their popularity. However, when citizens attribute blame for adverse consequences of a policy, does it make a difference which policy was it, and who was the political agent that adopted the policy? We posit that the level of blame citizens attribute to political agents for policy failures depends on three policy-oriented considerations: (1) the distance between a citizen’s ideal policy and the agent’s established policy position; (2) the distance between a citizen’s ideal policy and the agent’s concrete policy choice; and (3) the distance between the agent’s established policy position and her concrete policy choice. The inherent relationship between these three policy-oriented considerations renders their integration in one model a theoretical and methodological imperative. The model provides novel observable predictions regarding the conditions under which each of the three policy-oriented factors will produce either pronounced or subtle observable effects on blame attribution. We test the model’s predictions in two survey experiments, in Israel and in Germany. The results of both experiments are highly consistent with the model’s predictions. These finding offer an important contribution by specifying the ways in which individual-level preferences interact with politicians’ policy reputations and policy choices to shape blame attribution. Our model entails unintuitive revisions to several strands of the literature, and in the “Discussion” section we provide tentative support for the applicability of this model to other political judgments beyond blame attribution.

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Notes

  1. The formal structure of this policy preferences relies on its implementation in spatial models of voting (Adams et al. 2005, p. 26; Downs 1957a); but such distances were also found to account for other political evaluations (e.g., Yair and Sulitzeanu-Kenan 2015).

  2. This expectation is consistent with the logic of spatial models in general and with several behavioral studies that have found such policy effects on positive attitudes such as support (Boudreau and MacKenzie 2014; Bullock 2011; Carsey and Layman 2006). Note, however, that all of these behavioral studies estimated the behavioral responses to concrete policy information and partisan identification, disregarding the established policy position of the political agent. Given the contribution of established policy positions of political agents (agent distance) to understanding political attitudes and behavior beyond the effect of partisan identification, we suspect that the partisan cue treatment effects estimated in these studies comprise both partisan identification and established policy position proximity.

  3. The probability of partisan identification under proximate agent distance is higher compared to this probability under distance agent condition: \(p(pid|AD = 0) - p(pid|AD = 1) = \Delta p^{pid} ;1 > \Delta p^{pid} > 0\).

  4. The theoretical effects of shifting from congruent to surprising policy of proximate and distant agents are as follows: for proximate agents (a move from cell (a) to (c)): \((\Delta p^{pid} \beta + \gamma + \delta ) - (\Delta p^{pid} \beta ) = \gamma + \delta\); for distant agents (a move from cell (d) to (b)): \((\alpha + \delta ) - (\alpha + \gamma ) = \delta - \gamma\). Since we do not assume to know the sizes of γ and δ, and given that γ > 0 and δ < 0, we cannot predict whether the net effect of γ + δ is positive or negative. We do know, however, that the net effect of δ − γ is negative and its absolute value is larger than the absolute value of the γ + δ effect. Therefore, we expect a larger reduction in blame for agent non-supporters by an additional 2γ.

  5. Age, gender, country of birth, ideological self-placement, religiosity, income, vote choice in the 2009 national elections, vote intention. Religiosity plays a particularly important role in defining collective identity and political attitudes in Israeli society (Shamir and Arian 1999).

  6. The ideological self-placement scale ranges from 1 (extreme hawk) to 5 (extreme dove). It was subsequently reversed and set to range from 0 (extreme dove) to 1 (extreme hawk).

  7. We assessed the robustness of the results in less extreme agent and policy ideological placements. Agent and concrete policy distance effects retain a relatively stable mean effect across agent/policy extremity levels, but the variances of these effects become large at less extreme levels. These results are provided in Online Appendix 6.

  8. Successful manipulation of policy in/congruence was verified (for details see Online Appendix 5).

  9. Age, gender, education, ideological self-placement, intention to vote, vote choice, political interest, political knowledge.

  10. The ideological self-placement scale was based on a slider which respondents were able to locate on a line between “left” and “right”. The position of the slider was recorded as a figure between 1 (left) and 33 (right). For the purpose of the analyses, it was set to range from 0 (left) to 1 (right).

  11. As in the Israeli experiment, we assessed the robustness of the results in less extreme agent and policy ideological placements. Agent and concrete policy distance effects retain a relatively stable mean effect across agent/policy extremity levels, except from the very moderate level (0.4/0.6). These results are provided in Online Appendix 6.

  12. Given that both experiments were conducted in multi-party systems, partisanship and agent-distance were not expected to be overly correlated. Indeed multicollinearity is not an issue in both of the experiments (Israel: agent-distance VIF = 1.42, partisanship VIF = 1.15, mean VIF = 1.33; Germany: agent-distance VIF = 1.26, partisanship VIF = 1.11, mean VIF = 1.10).

  13. In the Israeli experiments respondents were coded as evaluating an ideologically distant agent (AD = 1) if they identified as “extreme hawk” or “hawk” and the political agent was Livni, or if they identified as “center”, “dove” or “extreme dove” and the policy agent was Netanyahu or Liberman. They were coded as evaluating an ideologically proximate agent (AD = 0) otherwise. Respondents were coded as evaluating a large policy-distance (CPD = 1) if they identified as “extreme hawk” or “hawk” and the policy was dovish, or if they identified as “center”, “dove” or “extreme dove” and the policy was hawkish. They were coded as evaluating an ideologically proximate policy (CPD = 0) otherwise (the results also hold when coding those identified as “center” as “hawks”). In the German experiment respondents were coded as evaluating an ideologically distant agent (AD = 1) if their ideological measure was less than 15 (median) and the political agent was a CDU/CSU and FDP coalition, or if their ideological measure was 15 or higher and the political agent was a SPD and Greens coalition. They were coded as evaluating an ideologically proximate agent (AD = 0) otherwise. Respondents were coded as evaluating an ideologically distant policy (CPD = 1) if their ideological measure was less than 15 and the policy was labor-market liberalization; or if their ideological measure was 15 or higher and the policy was minimum wage. They were coded as evaluating a proximate policy (CPD = 0) otherwise.

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Acknowledgements

The authors are grateful to Michael Bechtel, Brian Burgoon, Gail Gilboa-Freedman, Guy Grossman, Bernhard Kittel, Dan Miodownik, Mattan Sharkansky, Rune Slothuus, Florian Stoeckel, Pieter Vanhuysse, Barbara Vis, Omer Yair, seminar participants at the University of Amsterdam, Ben-Gurion University, The European Centre for Social Welfare Policy and Research, The Hebrew University, the Technion – Israel Institute of Technology, and at the 2014 MPSA and ECPR conferences, for their thoughtful comments and suggestions. Funding of part of this project by the University of Heidelberg’s Frontier Research Fund is gratefully acknowledged. . Replication files are available at Political Behavior’s Dataverse page: http://dx.doi.org/10.7910/DVN/SHADUV.

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Correspondence to Raanan Sulitzeanu-Kenan.

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Sulitzeanu-Kenan, R., Zohlnhöfer, R. Policy and Blame Attribution: Citizens’ Preferences, Policy Reputations, and Policy Surprises. Polit Behav 41, 53–77 (2019). https://doi.org/10.1007/s11109-017-9441-5

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