Elections with candidate filtering and two mechanisms of demobilization effect: the prologue to Hong Kong’s authoritarian turn

ABSTRACT

Previous studies of electoral authoritarianism identified that manipulations demobilize opposition supporters. Yet, less is known about whether radicals are more prone to abstention than moderates in manipulated elections. To answer this question, we disentangle two mechanisms of demobilization effect—the efficacy mechanism and the electoral supply mechanism—that have different expectations on the turnout rate of radicals and moderates. Our research leverages the disqualification controversy in Hong Kong in 2016, after which radical candidates who advocate self-determination or independence were filtered out from the electoral market. Using both aggregate-level and individual-level data, our analysis shows that a substantive demobilization effect exists. Crucially, we find that radicals and moderates are demobilized to a similar extent, and the decreases in perceived electoral fairness and importance of voting are similar between the two factions. These findings suggest that the efficacy mechanism is a more plausible explanation of the demobilization effect. Overall, this study extends our understanding of voting behavior and political attitude of opposition supporters in face of autocratization.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Acknowledgment

An earlier version of this article was presented at the graduate colloquium of the Division of Social Science of HKUST, and Hong Kong Political Science Association Annual Conference 2020.

Notes

1 Throughout this article, we understand moderates and radicals based on whether a candidate advocates self-determination or independence, as political conflicts in the period concerned (2012-2018) were mainly about issues related to mainland-Hong Kong integration. Yet, one should note that the notions of moderates and radicals can relate to other dimensions, such as economic and democratization strategies, and their meaning is shaped by the political context.

2 Before the electoral reform in 2021, half of the seats in the legislature were filled by the FCs that mostly represent major business and professional groups. The other half was chosen by universal suffrage based on the five GCs (Ma 2007).

3 The most likely turnout-enhancing manipulation in Hong Kong would be phantom voters, i.e. pro-government organizations use false addresses to register establishment camp supporters (Fong 2017a). We use a local news search engine to check the severity of this issue. Although there were sporadic reports on vote rigging from 2000 to 2018, these cases usually entailed legal consequences and were mini-scale. Only one confirmed election fraud case (the 2011 King’s Park case) involved more than a handful of votes, and a re-election was ordered in that case.

4 It is plausible that some opposition supporters give a void vote to express discontent. However, our analysis cannot explore this possibility because the Registration and Electoral Office does not provide such data.

5 Since counting-station level data is used, we can only identify the number of votes obtained by radical and moderate candidates. Although one cannot be completely certain that voters who voted radical (moderate) candidates identify with the radical (moderate) faction, we consider the counting-station level data is a good proxy. It is because, from the HKES September 2016 General Election dataset, 58% of voters feeling closest to a moderate party voted for a moderate candidate, while 77% of those feeling closest to a radical party voted for a radical candidate.

6 We also repeated our analysis with counting stations being classified into two categories; the substantive interpretation remains unchanged.

7 The placebo test cannot control for all confounds that took place during 2012-2016. Some important confounds that could boost voter turnout include the Umbrella Movement in 2014 and the Mong Kok civil unrest in 2016. However, these confounds should affect both the February 2016 by-election and the subsequent elections.

8 The scale of is from 1–5 in the Sep 2016 election, while it is 1–10 in the March 2018 election.

9 We use the plotplain scheme created by Bischof (2017) to make all graphs.

10 Readers should note that the small number of radicals in the by-election dataset leads to relatively large standard errors in estimations.