Does Group Deliberation Mobilize? The Effect of Public Deliberation on Willingness to Participate in Politics


Proponents of public deliberation suggest that engaging in deliberation increases deliberators’ subsequent participation in other forms of politics. We evaluate this “deliberative participation hypothesis” using data drawn from a deliberative field experiment in which members of medically underserved communities in Michigan deliberated in small groups about the design of that state’s Medicaid program. Participants were randomly assigned to deliberate about the program in a group or to think about the decision individually, and then completed a post-survey that included measures of willingness to engage in a variety of political acts. We measured willingness to engage in common forms of political participation, as well as willingness to participate in particularistic resistance to adverse decisions by insurance bureaucracies. Contrary to the claims of much of the existing literature, we find no impact of deliberation on willingness to engage in political participation. These results suggest that the ability of public deliberation to increase broader political engagement may be limited or may only occur in particularly intensive, directly empowered forms of public deliberation.

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  1. 1.

    For similar statements of the hypothesis, see Fishkin (2009, p. 141), Jacobs et al. (2009, pp. 87, 88 and 158).

  2. 2.

    In describing his version of the “deliberative participation hypothesis,” Fishkin draws an explicit contrast between deliberation in mini-publics and Mutz’s finding that everyday political discussion with those who hold different political views depresses political participation. “Without efforts to create a safe public space or civil discourse, exposure to strong partisan differences may well depress participation” (p. 3, fn 7). We are primarily concerned here with the potential educative effects of deliberative mini-publics, but also note that other scholars have found mixed support for Mutz’s basic finding of conflict between participation and deliberation (Pattie and Johnston 2009; Klofstad et al. 2013; Minozzi et al. 2017).

  3. 3.

    Our study compares group deliberation to an individual-reflection control condition in which participants were given educational materials about Medicaid and asked to perform the same prioritization task, though as individuals instead of in a group. These control-group participants may have engaged in what Goodin (2000; Goodin and Niemeyer 2003) refer to as “deliberation within.” Thus, our test evaluates the added value of group deliberation above and beyond this kind of individual reflection.

  4. 4.

    Similarly, Fishkin (2009, p. 103) hypothesizes that “once citizens are actively engaged in the discussion of politics, particularly if it is in a context where they feel their voice matters, they may want to continue engagement.”

  5. 5.

    Frustration with the difficulty in testing the educative hypothesis is evident in Mansbridge’s 1999 summary of the history of the idea. Notably, she bemoans the difficulty in conducting internally and externally valid experiments that have sufficient power to conclusively rule in favor or against the hypothesis (pp. 315–320).

  6. 6.

    There are some differences in methodology between these studies. In addition to the pre-post comparison among attendees, Jacobs et al. (2009, Ch. 6)’s study of ADSS compare deliberators to a set of people who were invited to attend the forum and declined, as well as a random sample of the population from who the invitation list was drawn. Neither of these are particularly compelling quasi-control groups: given the small number of people who self-select into attending mini-publics (Jacquet 2017), both those who decline the invitation and a general population sample are almost certainly different in important ways than those who choose to accept an invitation. Grönlund et al. (2010) do randomly assign subjects to two different deliberative conditions, but do not have a non-deliberation control condition. Thus, most of their conclusions about the effect of deliberation on political participation are drawn based on comparison of pre- and post-survey responses within experimental conditions.

  7. 7.

    For example, Luskin and Fishkin (2002) compare self-reported participation before the National Issues Convention to self-reported participation in the 10-month period following the event; however, the 10-month period after the National Issues Convention included most of the 2000 Presidential Election Cycle, a time when we might expect participants to have many more opportunities to participate in politics.

  8. 8.

    Myers et al. (2018) describe the development of the Medicaid CHAT tool and the priorities chosen by participants in greater detail.

  9. 9.

    For example, in the “Hospitals” area, participants could choose no funding (0 markers), level 1 funding (15 markers), or level 2 funding (21 markers). The no funding level was described as “Medicaid does not pay for inpatient care,” level 1 as “People who have an emergency do not have to pay for inpatient care. Other admissions cost $50.” and level 2 as “Admission to hospitals is covered at no cost.” The costs and benefits of each level were chosen to reflect actual policy options, and to reflect the actual costs of providing coverage (see Myers et al. 2018 for details).

  10. 10.

    This method of simple random assignment, as opposed to complete random assignment, produced slightly different sample sizes in the two arms of the study. 15 subjects in the treatment condition and 8 in the control condition dropped out prior to completing the political participation battery on the post-survey, producing final sample sizes of 194 in the treatment condition and 173 in the control condition. We report the demographic characteristics of the treatment and control groups in Appendix Table A1, and pre-survey measures of whether they had participated in politics in the last 6 months in Table A2. We observe only one statistically significant difference in demographic characteristics between the treatment and control groups: 57% of the treatment group was at or below the federal poverty level, versus 45% of the control group. However, these two groups did not differ significantly on any pre-survey measure of participation in politics, suggesting that the two groups were balanced on underlying propensity to participate in politics.

  11. 11.

    Subjects in the treatment condition spent an average of 18 minutes in round one setting priorities individually and then reviewing educational materials on their own. They spent an average of 16 minutes in round two and 48 minutes in round three deliberating in small groups and then as a complete group. They then spent 6 minutes in round 4 again deciding priorities individually. Subjects in the information-only control spend 21 minutes in round 1 selecting individual priorities and then reviewing educational materials. They then spent an average of seven minutes in round 4 again setting priorities individually.

  12. 12.

    Specifically, the highest rating was 4.6 (out of 5) on “Discussion during the game was open and honest,” 4.5 on “During the exercise, I was treated with respect,” and 4.4 (out of 5) on “All positions were considered with equal respect,” and “I had lots of chances to share my views”.

  13. 13.

    Specifically, the most negative statements were two negatively coded statements: “A few people dominated the discussion” (3 out of 5) and “People in the group argued by referring to what would be best for themselves” (2.8 out of 5).

  14. 14.

    Thanks to Jamila Michener for helping to write these survey questions.

  15. 15.

    As a simpler test, we also conducted a series of t-tests that compare the mean willingness to participate in the deliberation and control arms. The results, reported in Online Appendix B, are substantively the same.

  16. 16.

    We also conducted a multivariate analysis of variance (MANOVA) using all 13 items as outcomes and did not find significant difference between the two groups (p = 0.33).

  17. 17.

    In particular, we cannot rule out a substantively meaningful effect for any of the 13 dependent variables among the “no response” group, as there were only 40 people in this group. For 10 of the 13 variables we cannot rule out a meaningful negative effect of deliberation, while for 7 of 13 we cannot rule out a meaningfully positive effect of deliberation.

  18. 18.

    Gastil et al. (2008) do not report the average length of the jury deliberations in their study, though they do report that the average hung jury deliberated for 9.03 hours and the average jury that reached a verdict deliberated for 3.57 hours.

  19. 19.

    Niemeyer 2014. Mini-publics can, of course, have systemic effects through mechanisms other than direct effects on participants (e.g. MacKenzie and Warren 2012; Warren and Gastil 2015).

  20. 20.

    Appendix D reports an attempt to show the frequency with which deliberative exercises comparable to Medicaid CHAT occur in the published literature. We re-examined the 105 studies described in the 2014 AHRQ literature review of deliberative methods to determine how many lacked decision-making authority and were 1 day or shorter or, if they reported the length of discussion, featured two or fewer hours of discussion. We excluded 14 studies because we were unable to determine the structure of deliberation used or were unable to find the source cited in the AHRQ review, and 9 studies because the source described something other than deliberation among the lay public. Of the remaining exercises, 49 were either longer than 1 day or included more than 2 hours of face-to-face conversation, while 1 of those remaining appeared to have a more direct connection to policy-making than our CHAT exercise. The remaining 32 studies (out of 82 not excluded) appear comparable on these dimensions to CHAT.

  21. 21.

    We thank an anonymous reviewer for suggesting this possibility.

  22. 22.

    See, for example Morrell (2005)’s study of deliberation and political efficacy; he finds that deliberation increases efficacy to engage in discursive participation but has no effect on general political efficacy. For more general reviews of the effects of mini-public participation see Myers and Mendelberg (2013) and Gastil (2018).


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This research was funded by the Agency for Healthcare Research and Quality (1-R21-HS-023566-01). Dr. Goold is supported, in part, by a CTSA grant from the Michigan Institute for Clinical and Health Research (Contract #UL1TR000433). The authors thank Cengiz Salmon, Lisa Szymecko, Edith Kieffer, A. Mark Fendrick, Karen Calhoun, Lynnette LaHahnn, Caro Ledon, Marion Danis, and Eric Campbell for assistance with the Medicaid-CHAT project. We also thank the participants in the Medicaid-CHAT sessions for their time and insights, and the steering committee for the Medicaid-CHAT project. Michael Neblo, William Minozzi, and three anonymous reviewers provided helpful feedback on drafts of this paper. All errors are, of, course, our own. Dr. Goold and her institutions could benefit from future paid licenses (royalties) for the CHAT tool used in this study. Replication data and code can be found at

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Correspondence to C. Daniel Myers.

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Myers, C.D., Gordon, H.G., Kim, H.M. et al. Does Group Deliberation Mobilize? The Effect of Public Deliberation on Willingness to Participate in Politics. Polit Behav 42, 557–580 (2020).

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  • Political participation
  • Turnout
  • Deliberation
  • Medicaid
  • Health policy
  • Particularistic resistance