CANDIDATE APPEARANCE AND

VOTE SHARE IN HONG KONG

Bauhinia Foundation Research Centre

29 June 2016

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Table of Contents

Executive Summary 3

Part One

1. Introduction

1.1 Candidate Appearance and Hong Kong Elections 4

1.2 Roadmap 5

2. Background

2.1 Summary 5

2.2 Limitations of Existing Polling Methods 6

2.3 Advantages and Limitations of Online Polling 7

2.4 The Pilot Study 10

3. Design and Implementation

3.1 Summary 11

3.2 Tinder 2.0 11

3.3 District Council Candidates 12

3.4 Data Rewards and Algorithm 13

3.5 The Second Poll 14

3.6 Internal Survey 14

3.7 Participant Recruitment Strategies 15

3.8 Lessons from the Survey 16

Part Two

4. Literature Review

4.1 Summary 17

4.2 Overseas Studies 17

5. Methodology

5.1 Summary 20

5.2 Materials and Measures 21

5.3 Participant Sample 23

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6. Results

6.1 Summary 24

6.2 Distribution of the Candidates’ Appearance Rating 24

6.3 Distribution of Appearance Rating V.S. Vote Share 25

6.4 District Council Survey (The Second Poll) 28

7. Limitations

7.1 Summary 30

7.2 Design Limitations 30

7.3 Sample Limitations 31

8. Conclusion 33

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Executive Summary

Numerous studies conducted in foreign countries such as the United States

have shown that candidate appearance has a statistically significant impact on

electoral outcomes, particularly in “low-information” elections. According to

these studies, facial traits are correlated with subconscious voter judgments

regarding the perceived competence of candidates.

In order to determine whether candidate appearance has a similar impact on

local elections, the Bauhinia Foundation Research Centre (the Centre) created

an online survey that invited users to evaluate candidates from the 2015

District Council elections based on their photographs. The Centre collected

ratings from 2,240 participants from varying age groups and political groups.

Survey results show that candidates with high appearance ratings possessed a

statistically significant advantage in terms of vote share over candidates with

low appearance ratings. When asked to select their preferred candidate based

on their candidate photos, participants selected the actual winner in 58.6% of

the elections that featured two candidates per constituency.

The major limitation stemming from the data set is that respondents do not

proportionately represent Hong Kong's general population and certainly do

not represent well the electorate who voted in the 2015 District Council

elections. Despite this, when their evaluation of candidate appearances are

applied to election results, a statistically significant relationship can be found.

Going forward, the Centre will continue to explore new means of conducting

surveys and gathering data. Online polls may be useful for collecting highly

subjective or visual-based data. But for the Centre to ever use online polls as

part of its mainstream research work, challenging issues such as sample size

and representativeness must be resolved.

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Part One

1. Introduction

1.1 Candidate Appearance and Hong Kong elections

Conventional wisdom in Hong Kong holds that voters in Legislative

Council (LegCo) elections cast ballots based on political beliefs and party

affiliation, not on candidate appearance. For example, in a poll conducted

by the Hong Kong Research Association of 1,071 eligible voters in the

2016 New Territories East LegCo by-election, 51% stated that the most

important factor in deciding a candidate was political background and

political stance.1 19% stated that “previous work performance” was the

most important consideration, while 18% responded with “political

platform.” Only 2% of the participants stated that appearance was the

primary factor in their candidate choice.

However, in many foreign countries including the United States, surveys

have shown that facial characteristics do play a significant role in

formulating voter decisions. Political candidates rated highly by voters

based on their photos tended to win more votes in the actual election as

compared to candidates whose photos were rated poorly. This trend is

especially prominent in “low-information elections” such as local council

races where voters lack information on candidate backgrounds.

With Hong Kong gearing up for LegCo elections in September 2016, the

Centre conducted an online survey in May 2016 in order to evaluate the

impact of candidate appearance in local elections. Survey participants were

invited to rate 866 candidates from the 2015 Hong Kong District Council

elections based on their official election photographs. Participants were

given four options: “super dislike,” “dislike,” “like,” and “super like”. At

the conclusion of the survey, 2,240 participants had provided candidate

ratings. Each of the 866 candidates was rated at least 20 times.

1香港研究協會 二零一六年立法會新界東選區補選民意研究計劃, Hong Kong Research

Association, February 2016, http://rahk.org/research/researchsp2016by_content.asp

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Based on the survey data, there is a significant statistical correlation

between candidate appearance ratings and vote share in Hong Kong. This

study analyses these results and explores the implications for future

elections such as the upcoming LegCo elections while overviewing

existing literature on candidate appearance and electoral outcomes.

1.2 Roadmap

This paper is divided into two parts. Part One introduces the purpose and

conclusions of the study, discusses the benefits and drawbacks of our

survey’s online platform, and describes the design and implementation

process of the study. Part Two summarises the key conclusions from

previous research conducted on the subject of candidate appearance and

vote share, overviews the methodology used in our survey, acknowledges

limitations to the study, and analyses the study results before concluding

by anticipating the implications for local politicians as well as the future

utility of online polling platforms in conducting political research.

As a preliminary matter, it bears noting that this paper does not make

concrete recommendations with respect to political parties or political

candidates in Hong Kong, due to the limitations of the survey. This is

especially the case for LegCo elections, since LegCo candidates generally

are much more well-known and receive more press coverage than

individual District Council candidates. Nonetheless, we believe that there

still are plenty of helpful findings and inferences from our survey results

that could have an impact in the political sphere and in opinion research.

2. Background

2.1 Summary

We begin with an overview of opinion polling research in Hong Kong,

focusing on the merits and disadvantages of telephone polling and online

polling. Through this exercise, we hope to explain why we decided to

break with tradition and create a pilot study that features an online polling

platform rather than a more conventional phone questionnaire.

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2.2 Limitations of Existing Polling Methods

The primary method used by research organisations such as the Public

Opinion Programme of the University of Hong Kong (HKU POP) to

assess popular opinion in Hong Kong is landline telephone polling.

Telephone polling allows surveyors to randomly select a sample of

participants with access to a landline telephone number, which helps make

the results from these polls more representative of the population. Also,

telephone polling has been conducted in Hong Kong for many years, and

it remains the preferred method for pollsters worldwide. Hong Kong

residents appear to be fairly receptive to telephone polling. The most

recent poll conducted by HKU POP, on the subject of the June 4th

incident, reportedly had effective response rates of 67.6%.2

However, many Hong Kong residents are increasingly using cell phones

as their primary form of communication. The number of mobile phones

in service in Hong Kong reached 12.3 million by 2015, while the number

of landlines dropped from 2.14 million in 2006 to 1.48 million by March

2015.3 20% of local residents do not have access to a landline telephone.

Meanwhile, the percentage of Hong Kong residents with Internet access

is relatively high. According to GO-Globe HK, the estimated number of

Internet users in Hong Kong in 2014 was 5.751 million, with an overall

Internet penetration rate of 73%.4 96% of smartphone users used their

phones to browse the Internet every day, which GO-Globe HK reports

to be the highest rate of smartphone web browsing in Asia.

Despite the popularity of Internet browsing and smartphone, the sampling

method for telephone polling typically used by organisations such as HKU

POP primarily targets landline numbers. At least 20% of Hong Kong

residents have no chance of being included in landline polls.

2 June Fourth Survey sponsored by HK01, Public Opinion Programme, The University of Hong Kong,

May 19, 2016, https://www.hkupop.hku.hk/english/report/june4_2016/index.html

3 Stuart Lau, Hong Kong pollsters prefer to stick with landline phones for surveys, South China Morning

Post, June 16, 2015, http://www.scmp.com/news/hong-kong/politics/article/1822633/hong-

kong-pollsters-prefer-stick-landline-phones-surveys

4 Internet Usage in Hong Kong- Statistics and Trends, GO-Globe HK, August 6, 2014, http://www.go-

globe.hk/blog/internet-usage-hong-kong/

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Of course, this does not necessarily mean that cold-calling mobile phone

numbers is the solution. As more Hong Kong people use social media,

WhatsApp, and email to correspond with each other, they will likely

become less responsive to unsolicited phone calls. This is especially the

case as telemarketing, fraud and phone scams become more common in

Hong Kong and phone users become more wary of unknown numbers.5

Regardless of whether they are conducted using landlines or mobile

phones, polls that rely on live interviews are expensive and time-

consuming. If pollsters choose not to use computer-recorded voice

messages, which can deter potential respondents, they must hire and train

interviewers, and supervision is required to ensure that the interviewers

are following the scripts correctly. Finally, there is an upper limit to the

number of responses that can be acquired, since only a limited number of

interviewers can be hired before the poll becomes cost-prohibitive.

2.3 Advantages and Limitations of Online Polling

According to GO-Globe HK, 97% of men aged 20-29 and 93% of women

aged 20-29 owned smartphones, while 94% of men aged 30-39 and 87%

of women aged 30-39 owned smartphones.6 77% of all smartphone users

do not leave their home without their phones, and 96% of all smartphone

users browse the Internet every day. An online polling platform that uses

volunteers rather than involuntary participants could make it more

convenient and attractive for the next generation of Hongkongers to voice

their opinions, using devices that are constantly by their side.

A smartphone polling platform would also be less susceptible to the

tendency for respondents in telephone polls to answer questions in a way

that projects a favourable image to others, otherwise known as the “Shy

5 Clifford Lo, Hong Kong businessman cheated out of record HK $58 m in phone scam, South China

Morning Post, March 6, 2016, http://www.scmp.com/news/hong-kong/law-

crime/article/1921400/hong-kong-businessman-cheated-out-record-hk58m-phone-scam

6 Smartphone Usage in Hong Kong- Statistics and Trends, GO-Globe HK, August 11, 2014,

http://www.go-globe.hk/blog/smartphone-usage-hong-kong/

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Tory effect”7 or social desirability bias. Without having to interact with a

real person, respondents can feel comfortable anonymously expressing

their viewpoints, even if they are controversial or polarising. This benefit

of anonymity could be helpful for Hong Kong political polling due to the

recent emergence of relatively controversial subjects such as Occupy

Central, Hong Kong independence and “localism” which could trigger the

“Shy Tory effect” in Hong Kong poll participants.

Online polling could also be used if the issue being investigated pertains

to a subconscious tendency or behaviour that participants may not

consciously be aware of. For example, if pollsters believe that voters tend

to instinctively weigh appearance as a factor in selecting which political

candidates to vote for, it is unlikely that voters would admit to such

behaviour when asked by an interviewer.8

Thanks to technological advances in mobile applications, as well as the

popularisation of mobile apps in general, it is possible for online polls to

be created in an engaging and interactive manner with visual components.

For instance, polls could be designed in a way that mimics popular mobile

apps, which could entice users of those apps to participate in the poll and

then encourage their friends to do so as well. If the platform is created in

a “game” format that rewards “players” with data, then the chances of

attracting a large number of participants could be even higher.

Online polls can save a great deal of time and money for pollsters while

also providing access to an exponentially larger pool of potential

respondents. As opposed to telephone polls, which can be expensive and

inefficient, a well-advertised online poll on a popular topic could attract

thousands of respondents at a fraction of the cost without requiring more

7 Jessica Elgot, How ‘shy Tories” confounded the polls and gave David Cameron victory, The Guardian, May

8, 2015, http://www.theguardian.com/politics/2015/may/08/election-2015-how-shy-tories-

confounded-polls-cameron-victory

8 In fact, a survey on this very topic was conducted prior to the February 2016 LegCo By-

election, and only 2% of the survey participants stated that appearance was their primary factor in

deciding whom to vote for. http://rahk.org/research/researchsp2016by_content.asp

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than a handful of staff.9 This could provide more flexibility to polling

centres and public opinion programmes since they will be less reliant on

funding from external organisations to conduct their research.

By making use of targeted advertising from social media networks such as

Facebook, it is possible for an online poll to recruit participants from

certain demographics of interest with much more cost-efficiency and

precision than a random telephone sample. GO-Globe HK states that

more than 3.1 million people in Hong Kong log on to Facebook every

day. 10 Approximately 64% of the total population has an active social

media account. With millions of potential poll recruiters, and with

marketing options such as Facebook Advertisements being available, we

believe that social media represents the “next frontier” of polling research

in Hong Kong, and an online platform built for smartphones may be the

best method to tap into this emerging participant base.

Online polls of course have their own drawbacks. Approximately 20.8%

of all Hong Kong residents in 2014 did not have access to the Internet on

mobile or desktop, which is higher than the percentage of residents who

do not have access to a landline. Also, not everyone owns a smartphone.

This is particularly the case for older generations; only 54% of men aged

50-54 and 36% of women aged 50-54 own smartphones.11 So it could be

argued that an online poll would be worse than landline polls in this regard

since online polls would suffer from the inability to reach a significant

number of Hong Kong residents who cannot access the polls at all.

There is currently no way of randomly selecting Internet users. Thus,

online polls rely on recruits who are not representative of the population.

Cliff Zukin, past president of the American Association for Public

Opinion Research, states that almost all online election polling is done

with samples that are not random. Zukin notes that these samples are

9 Cliff Zukin, What’s the matter with polling? The New York Times, June 20, 2015,

http://www.nytimes.com/2015/06/21/opinion/sunday/whats-the-matter-with-polling.html

10 Social Media Usage in Hong Kong, GO-Globe HK, May 16, 2015, http://www.go-

globe.hk/blog/social-media-hong-kong/

11 Smartphone Usage in Hong Kong- Statistics and Trends, GO-Globe HK, August 11, 2014,

http://www.go-globe.hk/blog/smartphone-usage-hong-kong/

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“unproven methodologically,” and the American Association for Public

Opinion Research has observed that it is impossible to calculate a margin

of error on such surveys.12 This would be a problem for a smartphone-

designed poll in Hong Kong; judging by the rates of smartphone usage, it

is likely that such a poll would be biased in favour of young and male

residents. In that case a polling centre would have to select and adjust the

sample in a way that matches the overall population and accounts for bias.

Despite these difficulties, the Centre still believes that the benefits to an

online polling platform outweigh the drawbacks. The ability to generate

and distribute polls at minimum cost is one clear advantage, along with the

potential to make these surveys fun and interactive for participants.

Regarding the potential statistical inaccuracies of the findings, we believe

it is possible in future iterations of the platform to compensate for the

biases in the sample by recruiting from underrepresented groups.

Therefore, the Centre decided to initiate a pilot study on an online polling

platform in May 2016. This would be an experiment rather than a final

product which was meant to generate initial findings while also collecting

feedback to improve the platform’s reliability and accuracy.

2.4 The Pilot Study

We decided to use the pilot study of our online platform to test for the

effect of candidate appearance on vote share in the 2015 District Council

elections. This was appropriate for the platform in several ways. While

multiple overseas studies have demonstrated that candidate appearance

has a significant impact on elections in foreign countries, no such research

on this topic has been conducted in Hong Kong.

As the LegCo elections are fast approaching, the subject of popular

elections in Hong Kong is highly pertinent. Moreover, the subject of

attractive District Council candidates has already garnered significant

media attention last year and several of these candidates could be

contesting the LegCo elections this year as a part of a party list.

12 Cliff Zukin, What’s the matter with polling? The New York Times, June 20, 2015,

http://www.nytimes.com/2015/06/21/opinion/sunday/whats-the-matter-with-polling.html

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It is impossible to measure the effect of facial appearance on vote share,

which would necessarily include a visual component, using traditional

telephone polling. Yet recruiting participants to evaluate candidates in

person could be costly and time-consuming. Online polling may be the

most viable way to recruit a large number of voters to assess candidate

appearances if efficiency is a key objective. Also, the subject of candidate

appearance is one that is probably subject to social desirability bias, since

it is unlikely that participants would admit to using candidate appearance

as a primary factor. Since online polls are anonymously filled out, there

would be less of an impact from social desirability bias.

Finally, the issue of judging candidates based on their appearance is an

ideal fit for the “gamified” survey format since a poll could be designed

that mimics the popular dating app “Tinder,” where users are also given

the opportunity to rate photos based on their appearance. This aspect of

the poll could help us determine whether an interactive poll could be

successful at collecting thousands of responses at low cost while also

inducing a societal debate on the subject of candidate appearance.

3. Design and Implementation

3.1 Summary

This section provides an overview of the thought process behind our

decision making as we designed and implemented the release of the online

polling platform, from the brainstorming stage to the marketing stage.

3.2 Tinder 2.0

When we first set out on designing the trial study, we decided to model

our user interface after the dating app “Tinder.” Tinder is most notable

for its “swipe” function- users swipe left and right on their smartphone

screens to indicate their disapproval or approval of the user photos that

they are being shown. The interface itself is very basic, with the user

photos occupying the majority of the space with almost no text or other

distracting features included. Essentially, the “swiping” on Tinder is meant

to resemble the act of flipping through a stack of cards or photographs.

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Swiping is critical for Tinder’s appeal and success because it encourages

users to make automatic, snap judgments based solely on appearance when

they are deciding whether to “like” or “dislike” another user. In fact,

studies show that we judge other people in much the same way, by making

split-second judgments based largely on appearance.13

It may seem that our research has little to do with Tinder. Yet there should

not be much intrinsic difference between snap judgments of potential

romantic partners and political candidates, because both should be based

on the same reflexive instincts. Indeed, research conducted in foreign

countries revealed that split-second appearance judgments of candidates

can predict actual election outcomes. Just as Tinder users are encouraged

not to spend more than a split second evaluating other user, we wanted to

design our platform in a way that would encourage our participants to

“like” or “dislike” the candidates as quickly as possible without any

deliberation, so that we could determine whether split-second judgments

on appearance could be used to predict elections in Hong Kong.

We also hoped that using “Tinder” as the foundation for the survey would

allow us to pique interest amongst the population while also testing out

the viability of a gamified survey in assessing public opinion. After all, a

large part of Tinder’s appeal lies in the fact that many people simply seem

to enjoy rating other people’s appearances, and we wanted to test to see if

this principle could also be applicable in the context of political candidates.

3.3 District Council Candidates

We decided to use District Council candidates instead of other political

candidates for several reasons. First, we wanted to have some initial

findings ready before the LegCo elections began in earnest. This way we

could determine if a modified version of the poll could be created for the

LegCo elections. Also, while there were 935 candidates in the District

Council elections last year, there will probably be far fewer candidates

running in the LegCo this year. The party list system of the LegCo

13 Christopher Olivola and Alexander Todorov, Elected in 100 milliseconds: Appearance-based Trait

Inferences and Voting, Journal of Nonverbal Behaviour, January 23, 2010,

http://link.springer.com/article/10.1007%2Fs10919-009-0082-1#page-1

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elections also means that there is less individual competition and less

significance placed on individual candidate appearance. This would not

bode well for our purposes since we wanted to evaluate as many

candidates as possible.

Foreign researchers have found that the significance of appearance on

vote share tends to be more pronounced in “low-information” elections

such as local council rates as compared to “high-information” races. This

is an important reason why we selected District Council candidates for our

survey, because District Council elections are “low-information elections”

in Hong Kong insofar as they are less high-profile than LegCo elections

and information about each individual candidate is not as widely dispersed

among the electorate. Thus, if the overseas research was found to apply in

Hong Kong, we would be most likely to find a relationship between

appearance and vote share in the District Council elections.

3.4 Data Rewards and the Algorithm

Unlike the real Tinder, we could not reward any of our participants with

the prospects of a romantic connection. We realised that it would be

extremely unlikely that each user would want to evaluate more than a few

of the candidate faces before getting bored or tired. We also anticipated

that users would want to receive some kind of reward or interesting factual

data before continuing on with the survey.

For this reason, we decided to design the first poll of the survey so that

participants would be asked to rate ten faces before being presented with

some data that described the candidates they had just rated and compared

their preferred candidates with the preferred candidates of other

participants. It was our belief that participants would be more likely to

continue evaluating faces if they were given this data, especially if it was

presented in an entertaining and informative manner.

Rather than presenting the candidate faces in a completely randomised

fashion, we decided to create an algorithm whereby seven out of every ten

faces would be a randomly selected male candidate and three out of every

ten faces would be a randomly selected female candidate. This is because

we wanted to ensure that at least a few of the faces evaluated by every

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participant were female. The percentage of actual female candidates in the

District Council elections was quite low (19.3%), and it was possible that

a random sample of ten candidates would not contain a single woman. Of

course, we recognised that this would mean that female candidates would

be rated at a higher frequency than male candidates, but we believed that

the benefits outweighed the drawbacks in this scenario.

3.5 The Second Poll

We decided to add a second poll to our survey which would present

participants with photos of candidates who competed against each other

in the same constituency in the 2015 elections. Their photos would be

shown side by side as if they were on an actual ballot. This poll was added

because we wanted to see if participants would select the actual winner of

the elections in a head-to-head competitive format based on their photos.

In this way the poll would also serve to test the effect of appearance on

electoral outcomes in the District Council elections, except in a more

direct manner, rather than generating appearance ratings for every

candidate. If the results showed that participants selected the actual

winner at percentages significantly higher than winning odds, then it could

be argued that appearance had a statistically significant effect on the

outcome of these elections, irrespective of the actual vote share won by

each candidate or the disparity in their appearance ratings.

3.6 Internal Survey

We created an experimental version of the survey and tested its viability

by presenting it internally to the 25 staff members from the Centre. The

main difference between this version and the final version of the survey

was that the experimental version only included 100 candidates rather than

all 866 candidates, and the Centre staff members were invited to evaluate

all 100 candidates. As such every candidate received the same number of

evaluations, which was not the case for the final version.

Based on this initial poll we found that candidate appearance did not have

a significant effect on vote share. This was somewhat surprising since

several overseas studies on this topic, most of which were conducted in

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the United States, found a significant correlation between candidate

appearance and vote share in foreign elections.

However, the sample used for this poll was exceedingly small and not at

all representative of the Hong Kong population, and the number of

candidates included was only a small portion of the total. For these reasons

we decided to launch our poll to the general public to see if a significant

effect could be found with a much larger and more representative sample.

3.7 Participant Recruitment Strategies

In planning our efforts to promote the poll amongst the wider public, we

decided to use Facebook ads as our primary form of recruitment.

Facebook is quite popular in Hong Kong; as previously stated,

approximately 3.1 million people log onto Facebook every day. Facebook

Ads permits businesses to target advertisements at certain demographics.

This allowed us to recruit users with an interest in politics, as well as users

between the ages of 18 to 40. We felt that users from these two

demographics would be the most likely to respond positively to our

survey, because they would be likely to have an interest in evaluating

candidates while also having some familiarity with the “Tinder” app.

While we hoped to recruit as many participants as possible, our objective

was to collect at least 20 evaluations for all 866 candidates in our study.

Assuming that each participant would evaluate around 10 faces each, this

meant that we would need to recruit at least 1,730 participants. Thus, we

decided to run our Facebook ad campaign for a week, from May 13th to

May 19th, after estimating that we would be able to acquire a few hundred

new participants each day using the Facebook ads.

Fortunately we were able to acquire 2,240 total participants by the end of

the Facebook campaign on May 19th. Since we had already collected

enough responses to satisfy our primary objective, we concluded the

survey three days after the Facebook campaign ended.

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3.8 Lessons from the Survey

There were several lessons that we learned from the survey that may affect

the design of a subsequent online poll from the Centre. For instance, we

found out that an online poll that relies on volunteers and Facebook ads

for promotion tends to draw participants from the male and under 40

years of age demographic. We do not yet know why this is the case,

although it may be related to the higher rates of smartphone ownership

and Facebook usage among these groups. We also found that in Hong

Kong, an online poll tends to attract a relatively high number of “localist”

supporters and a relatively low number of “pro-establishment” supporters

compared to the voting population. This may be related to the fact that

most participants were comparatively young with social media accounts.

We suspect that a considerable number of candidates were being evaluated

based on their political affiliations instead of their appearance. This is

because many of the candidates were quite well-known in Hong Kong,

especially among the targeted audience of young people who have an

interest in politics. We did not explicitly tell participants to consider a

particular character trait in evaluating candidates, so there may have been

some discrepancies in terms of the criteria that participants used.

To address these issues, a future online poll could be designed in a way

that anticipates that most of the participants will be male and relatively

young (under 40) and thus recruits participants from other groups such as

women and participants over 40 years of age. This could be done by

modifying the criteria used in Facebook ads to target these groups. Second,

if the poll is meant to test candidate appearance, the poll could include a

function where it asks participants to indicate if they recognised a

particular candidate from the survey. If they indicate that they recognise

the candidate, their data would not be included in the final analysis. We

could also include a control group comprised of participants from outside

Hong Kong to compare their appearance ratings with those from the

Hong Kong-based population. Finally, we could provide more guidance

in the instructions given to participants to help direct their ratings; for

instance we could tell participants to rate candidates based on

“competence” or ability to serve as a good elected representative. This

would help ensure that all participants are using the same rating criteria.

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Part Two

4. Literature Review

4.1 Summary

This section provides a brief summary of the research conducted by

foreign academics on the issue of candidate appearance in foreign

elections. No such research has been conducted in Hong Kong. Most of

the studies that were conducted in English took place in the United States

and pertained to American voters and American elections. These studies

have concluded that candidate appearance has a significant impact on vote

share in foreign elections, and the impact is more pronounced in “low-

information” elections as well as for “uninformed” voters.

4.2 Overseas Studies

Numerous overseas studies have found that voters tend to rely on physical

appearance when choosing which candidates to elect. For instance, a study

conducted by professors from the University of California at Irvine found

that candidates judged to be highly competent by participants based on

their photographs14outperformed less competent-looking candidates by

13%. 15 This trend was especially prominent in “low-information

elections” such as local races when voters lack substantive information on

candidate. 16 However, this correlation has also been found in

14 A candidate was defined as being “more competent-looking” if survey participants had ranked

his or her photograph highly on a numbered scale with regard to perceived competence, relative

to his or her opponent in the election.

15 Shawn Rosenberg, The Image and the Vote: The Effect of Candidate Presentation on Voter Preference,

American Journal of Political Science, February 1986,

http://www.jstor.org/stable/2111296?seq=1#page_scan_tab_contents

16 Christopher Olivola and Alexander Todorov, Elected in 100 milliseconds: Appearance-based Trait

Inferences and Voting, Journal of Nonverbal Behaviour, January 23, 2010,

http://link.springer.com/article/10.1007%2Fs10919-009-0082-1#page-1

18

gubernatorial and senatorial elections, which are relatively higher-profile

than local council races, as well as presidential races.17

Why do voters tend to use facial cues so prominently in making electoral

decisions? As an initial matter, human beings draw inferences about the

underlying characteristics of others based on their appearance. Moreover,

these inferences often occur spontaneously and rapidly, “leaving little

room for deliberate thought processes to inhibit or correct the resulting

judgments.” In other words, the “first impressions” that are quickly

formed about other people are difficult to reverse because “the speed,

automaticity, and implicit nature of appearance-based trait inferences

make them particularly hard to correct.”

It should come as no surprise, then, that appearance can be significant in

the political realm. A study conducted by Princeton University

psychologist Alexander Todorov showed that facial competence

judgments accurately predicted the vote share of real senatorial and

gubernatorial elections. While the study assessed many character traits,

such as extraversion and agreeableness, perceived competence rankings

were the most reliable predictor of electoral results. Candidates judged by

participants to be more competent-looking won 69% of subsequent

gubernatorial races and 72% of Senate races, even controlling for the

typical advantages that incumbents have over other candidates.

Initial impressions of competence based on facial traits are often made in

a split second. For example, Todorov conducted another study which

showed that competence judgments made after 100 milliseconds of

exposure to real political candidate’s faces with no additional information

were almost as accurate in predicting actual election outcomes as

judgments made after unlimited exposure to the candidate faces.18 This

proves that initial impressions of candidate competence can be formed

rapidly from a single photo, without any deliberation whatsoever.

17 J. Scott Armstrong, Predicting Elections from Politicians’ Faces, University of Pennsylvania Scholarly

Commons, June 11, 2008.

18 Charles Ballew and Alexander Todorov, Predicting political elections from rapid and unreflective face

judgments, Proceedings of the National Academy of Sciences of the USA, November 13, 2007,

http://www.pnas.org/content/104/46/17948.full.pdf

19

Additionally, “once formed, these impressions can influence voting

decisions, and this influence may not even be recognised by voters.”

Most of these studies involved American participants and candidates. But

the appearance effect is also apparent when foreign, unfamiliar faces are

introduced. A study conducted by John Antonakis and Olaf Dalgas from

the University of Lausanne found that facial competence judgments of

French parliamentary candidates made by Swiss participants could predict

the results of French elections.19 Additionally, judgments made by Swiss

children (aged five to thirteen) were just as accurate in predicting French

electoral outcomes as those made by Swiss adults. Based on these results,

Antonakis and Dalgas argued that “appearance-based trait inferences

develop quite early and are surprisingly stable throughout a person’s life.”

This would explain why age was not an important factor in determining

whether participants could predict the French elections.

A study by Daniel Benjamin and Jesse Shapiro from the University of

Chicago found that purely visual cues can outweigh verbal cues in

determining electoral outcomes.20 In their study, participants were able to

predict electoral outcomes more accurately when they were shown 10-

second silent debate clips as compared to when they were shown the clips

with audio included. Since the audio of the clips enabled participants to

infer the candidate’s party affiliation, this is a surprising result.

According to Todorov, however, the finding from the Benjamin and

Shapiro study is not unusual after all. He argues that voters tend to make

decisions from rapid, unreflective and appearance-based impressions, not

from more deliberative consideration. Therefore, the introduction of

additional information such as political platform and party affiliation can

disrupt one’s ability to predict an election, since the average voter at the

local level does not vote based on such information. Indeed, Todorov’s

experiments have affirmed that voters are more likely to weigh appearance

as a factor when they are less familiar with the candidates. Politically

19 John Antonakis and Olaf Dalgas, Predicting elections: Child’s play, Science Magazine, February 27,

2009, http://science.sciencemag.org/content/323/5918/1183

20 Daniel Benjamin and Jesse Shapiro, Thin-slice forecasts of gubernatorial elections, Review of

Economics and Statistics, November 2006, http://www.nber.org/papers/w12660

20

knowledgeable voters are less likely to use appearance as a factor and are

more likely to decide who to vote for after thoughtful deliberation.

If candidate appearance is merely correlated with other variables, such as

incumbency or candidate spending, then appearance may be an effect

rather than a direct cause of electoral success. To address this issue,

professors at the University of California at Berkeley conducted a survey

wherein one group of voters was shown a ballot with real candidate photos

shortly before Election Day while a control group was shown the same

ballot without any photos.21 Candidates were drawn from a variety of

California state elections, including primary and general races.

Despite the fact that both groups were shown the same relevant and

substantive information such as the candidate’s names, political affiliation

and occupation, the group of voters that was exposed to the candidate

photos in their ballots reported that they intended to vote for appearance-

advantaged candidates at higher rates and appearance-disadvantaged

candidates at lower rates than the voters in the control group. Since this

discrepancy could not be explained by other variables, such as superior

candidate spending or incumbency, the study concluded that candidate

appearance does in fact have a direct, causal effect on American voters.

5. Methodology

5.1 Summary

Section Three of Part One discussed the design and implementation of

the survey prior to its official public release. This section will go in further

detail and explain the methodology of the survey including the materials

and measures of the online platform as well as the participant sample.

21 Douglas J. Ahler, Jack Citrin, and Michael C. Dougal, Can Your Face Win you Votes? Experimental

Tests of Candidate Appearance’s Influence on Electoral Choice, University of California at Berkeley,

January 2015, https://www.ocf.berkeley.edu/~glenz/cfwv/cfwv.pdf

21

5.2 Materials and Measures

We used 866 photos of candidate’s faces from the 2015 Hong Kong

District Council elections. Photos were official headshots from the

elections.gov.hk website. All photos were of the same size and nearly every

photo featured the same white background. While 935 candidates were

nominated for the 2015 elections, 68 ran uncontested and won

automatically. One candidate did not submit a photo. This left the total

number of candidates with contested elections and photos at 866. Since

our survey was intended to compare candidate appearance with vote share,

we did not include the photos of uncontested candidates in our survey

since they did not have any votes to compare with their appearance ratings.

We did not employ any selection criteria for filling out the survey, and we

did not intend to draw upon a specific group of participants. Nonetheless,

since our survey could only be completed online, we decided to recruit

participants using Facebook ads targeted at Chinese-speaking Hong Kong

residents between the ages of 18 and 40 who had an interest in politics.

Thus, a side-effect of our recruitment strategies was that our participants

were biased towards the younger demographic. We did not make efforts

to recruit participants from outside these demographics.

Participants were informed that all of the photos in the survey belonged

to candidates from the 2015 District Council elections, and they would be

asked to evaluate the candidates based on their photos. Links to the survey

were shared on social media by Centre members and in Hong Kong

newspaper articles. The Facebook ad campaign began on Friday, May 13th,

2016 and ended on Friday, May 20th, 2016. The survey concluded soon

afterwards, on Monday, May 23rd, 2016.

Our survey included two polls, both using the same set of 866 photos. The

first poll displayed ten photos consecutively, one at a time. No other

candidate information besides the photo was given. Participants were

asked to evaluate each candidate from his or her photo by selecting one of

four options: “super dislike,” “dislike,” “like” and “super like”.

Participants were not given any guidance as to how they should vote or

what quality (such as facial competence) they should base their vote on.

22

Each set of ten photos was selected using an algorithm that picked seven

male candidates and three female candidates from the pool of 866

candidates at random. The algorithm also prevented the same photos from

being shown more than once per user. Since only 19.3% of the candidates

who contested the 2015 elections were female, this meant that female

candidates were rated more frequently than male candidates. However, all

866 candidates were rated at least 20 times by the conclusion of the survey.

The second poll, which was displayed immediately after the first, presented

participants with a set of photos from candidates who had competed

against each other to win one seat in a given constituency. In most cases

only two photos were shown since most races had two candidates. Unlike

the first poll, these photos were shown side by side.

Participants were asked to choose one of the photos as their preferred

candidate. As with the first poll, no additional candidate information was

provided and no guidance was given as to what factors should be

considered when selecting a candidate. The photos shown all belonged to

candidates in the same district, but the districts were selected randomly.

A total of 3,743 “votes” were cast for this second poll across all of the

district races. Each “vote” was counted individually, even if multiple votes

came from the same user. After completing the second poll, participants

were given the option to continue evaluating faces or to stop. Most of the

2,240 users opted to stop rating faces after the second poll. However, an

undetermined number of participants opted to continue evaluating faces

and casting votes for the second poll, which is why the number of votes

cast for the second poll is greater than the number of users.

It should be noted that neither of the two polls included a design to avoid

participants rating candidates that they recognised. Therefore it is very

likely that our participants recognised prominent politicians who were

included in the survey. It would have been possible to remove candidates

who we anticipated would be recognised by our participants, such as

LegCo members, from the survey. We could also have created some

mechanism whereby participants who recognised a candidate would have

their responses thrown out of our analysis. This would resemble similar

mechanisms in overseas studies. However, we decided that it would be too

23

arbitrary to determine which candidates would be likely to be recognised

by participants, and we did not feel that a mechanism removing participant

responses in case they recognised a candidate would be efficient.

5.3 Participant Sample

Survey participants were asked to provide their gender, age range, and

political affiliation before they were allowed to evaluate faces. No contact

was made with the participants, and no efforts were made to verify the

accuracy of the participants’ information given.

Participants were 2,240 people with Internet access. 72.3% identified

themselves as male. In terms of age, 71% of users reported that they were

18-29 years old, 23.5% were 30-44 years old, 3% were 45-64 years old, and

2.5% were over 65 years old. In terms of political affiliation, 33.5% of all

participants identified themselves as pan-democrats, 31.4% identified

themselves as independents or having no political affiliation, 23.5%

identified as localists, and 11.6% identified as pro-establishment. A

summary of the participant demographics is shown below.

Table 1: Participants by Gender

Gender Participants Percentage of Total

Male 1619 72.3%

Female 621 27.7%

Table 2: Participants by Age

Age Participants Percentage of Total

18-29 Years 1591 71.0%

30-44 Years 525 23.5%

45-64 Years 68 3.0%

65+ Years 56 2.5%

Table 3: Participants by Political Affiliation

Political Affiliation Participants Percentage of Total

Pan-Democrat 751 33.5%

Independent/No Affiliation 703 31.4%

Localists 526 23.5%

Pro-Establishment 260 11.6%

24

No control group of participants was used in our study. This could have

been done for instance by recruiting participants from other regions of the

world, who should not have been able to recognise any of the District

Council candidates, to evaluate the same faces. If this control group gave

candidates similar average ratings, then we could show that our own

survey was not significantly impacted by the occurrence of participants’

recognising candidate faces and changing their evaluations accordingly.

However, we chose not to use a control group, primarily for expediency.

6. Results

6.1 Summary

In this section, we will analyse the data collected from both of the polls in

our survey using charts and graphs. In addition, we will overview key

conclusions from regression analyses which compared user-provided

information such as appearance rating along with publicly available

candidate information such as their age, gender, incumbent status, and

party affiliation with the vote share won by each candidate. It should be

noted that publicly available candidate information was limited and this

may have resulted in omitted-variable bias in the regression analyses.

6.2 Distribution of the Candidates’ Appearance Rating

Chart 1 shows the distribution of all 866 candidates’ appearance rating.

Participants were given four options to rate candidates and each of these

options corresponded to a score from 1 to 4 in our analysis. “Super

Dislike” corresponded to a score of 1, “Dislike” corresponded to a score

of 2, “Like” corresponded to a score of 3, and “Super Like” corresponded

to a score of 4. Most candidates were given similar average ratings and

concentrated around the mean of 1.86, with the ± 1 standard deviation

covering around 75% of the candidates (under normal distribution, ± 1

standard deviation covers 68% of the sample). If candidates ranked in the

top 100 are excluded (blue dots), the variation of ratings is even smaller.

25

Chart 1

6.3 Distribution of Appearance Rating V.S. Candidates’ Vote Share

Charts 2 to 4 plot the candidates’ rating against his/her vote share under

three groups (i) all candidates, (ii) top 20% and bottom 20%, and (iii)

middle 80%.

The reason for dividing the candidates into these three groups based on

their rating was to test our hypothesis that the effect of appearance on

vote share would be more pronounced when the candidates were given

appearance ratings on the extreme ends of the spectrum, rather than

ratings closer to the mean. In other words, we believed that very attractive

candidates would receive more of a boost in vote share than a moderately

attractive candidate, while very unattractive candidates would be

“penalised” in vote share more so than a moderately unattractive

candidate. Isolating the top 20% and the bottom 20% as well as the middle

80% based on appearance rating from the entire candidate pool would

enable us to test the validity of this hypothesis.

The study found that the candidates’ rating has a positive and significant

effect on the vote share for all the three groups. It is also noted that the

vote shares of the top 20% and bottom 20% candidates differ significantly

(45% and 33%, while the average vote share of all candidates is 42%).

1

2

3

4

1 101 201 301 401 501 601 701 801

Candidate

Appearance ratingScore

+1SD:2.09

Mean:1.86

-1SD:1.63

26

Chart 2: All candidates

Chart 3: Top 20% and bottom 20% candidates

Chart 4: Middle 80% candidates

y = 16.606x + 11.076R² = 0.0375

0

10

20

30

40

50

60

70

80

90

100

1 2 3 4

Appearance rating vs. vote share for all candidates

Score

Vote Share

y = 18.507x + 3.6979R² = 0.0972

0

10

20

30

40

50

60

70

80

90

100

1 2 3 4

Appearance rating vs. vote share for top 20% and bottom 20% candidates

Score

Vote share

y = 19.591x + 7.0413R² = 0.0178

0

10

20

30

40

50

60

70

80

90

100

1 2 3 4

Appearance rating vs. vote share for middle 80% candidatesVote Share

Score

27

Regressions were run for the aforementioned three groups in order to provide

further evidence (Table 4). Key observations are listed as below:

a) Rating score had a positive and significant effect on the vote share for all the

three groups. The effect was stronger for the top 20% and bottom 20%.

b) Incumbent status had a positive and significant effect, while the number of

competitors had a negative and significant effect.

c) The effects of age and sex were insignificant.

d) Pro-establishment and pro-democrat candidates had an advantage, while

candidates from the “Localism camp” had no advantage in getting higher vote

share than independents.

Table 4: Regression on vote share (First poll)

Variables All Candidates Vote Share

Top 20% /Bottom 20% Vote Share

Middle 80% Vote Share

Rating score 0.123*** (0.0246)

0.171*** (0.0308)

0.0783* (0.0454)

Incumbent (0/1) 0.192*** (0.0113)

0.185*** (0.0186)

0.188*** (0.0124)

No. of competitors -0.0851*** (0.00631)

-0.0835*** (0.00976)

-0.0896*** (0.00712)

Age -0.000511 (0.000451)

0.000764 (0.000762)

-0.000791 (0.000496)

Sex (0/1) 0.00577 (0.0127)

0.00117 (0.0201)

0.00248 (0.0145)

Pro-establishment (0/1)

0.0669*** (0.0123)

0.0670*** (0.0198)

0.0638*** (0.0135)

Pan-democrat (0/1)

0.0843*** (0.0124)

0.0874*** (0.0197)

0.0816*** (0.0136)

Localism camp (0/1)

-0.0105 (0.0319)

-0.0349 (0.0420)

0.0223 (0.0455)

Constant 0.235*** (0.0605)

0.0767 (0.0883)

0.348*** (0.0958)

Observations 802 322 642

R-squared 0.533 0.566 0.519

Notes: Standard errors in parentheses.

*** p<0.01, ** p<0.05, * p<0.1

28

Candidates were defined as being pan-democratic, pro-establishment,

independent, or localist based on their party affiliation on their candidate

applications. Some parties were difficult to classify, either because they were

newly formed or because their party’s stance towards democracy issues was

unknown. It was particularly difficult to assign parties to the “localism” camp

because the term “localism” is still somewhat ambiguous. For this reason, our

classifications of candidates were somewhat arbitrary, and it is possible that

our findings could have been affected as a result.22

6.4 District Council Survey (The Second Poll) This section discusses the data from the second poll. The analysis for this

section will be different because the second poll did not assign any

appearance ratings to candidates. Instead, participants were asked to select a

candidate among the actual list of candidates who ran in a given constituency.

Table 5 shows that users’ preference helps predict the actual winning

candidate within each constituency. For example, more than 50% of the

votes went to the actual winner for two-candidate constituency elections, and

40.4% of three-candidate constituency elections are matched by users’

preference (better than winning odds (33.3%) of each candidate). In column

3, the term “Matched” refers to the number of constituencies where a

majority of the participants selected the actual winner of the election. In

column 4, the percentage of correct prediction refers to the percentage of

“matched” constituencies compared with the number of all constituencies

with the same number of candidates.

22 Candidates from the Democratic Party, Civic Party, Labour Party, Association for Democracy and

People’s Livelihood, Professional Commons, People Power, League of Social Democrats,

Neighbourhood and Worker’s Service Centre, and Neo-Democrats were classified as pan-democrats.

Candidates from the Democratic Alliance for the Betterment and Progress of Hong Kong, Business

and Professionals Alliance, Hong Kong Federation of Trade Unions, Liberal Party, New People’s

Party, and Kowloon West Dynamic were classified as pro-establishment. Candidates from

Youngspiration and Civic Passion were classified as localist. All others were classified as independent.

29

Table 5: Predictions from the Second Poll

No. of Candidates

No. of Constituencies (A)

No. of Matched (B)

Percentage of correct prediction

2 261 153 58.6%

3 89 36 40.4%

4 20 8 40%

5 1 0 0%

6 2 2 100%

Two regressions were run to confirm the effect of users’ preference on the

vote share (Table 6). “Preference” refers to the candidates who were selected

by the majority of participants per constituency. In other words, “preferred”

candidates were candidates who received more “votes” by survey participants

in the second poll than their opponents from the same constituency.

Key observations are listed as below:

a) Users’ preference had a positive and significant effect on vote share.

b) Incumbent status remained a key factor and younger candidates had a slight

edge in two-candidate constituency elections.

Table 6: Regression on vote share (Second poll)

Variables All Elections Vote Share

Two-candidate constituency Vote Share

Preference 0.247*** (0.0202)

0.0799*** (0.0214)

Incumbent status (0/1)

0.192*** (0.0113)

0.190*** (0.0119)

Age -0.0851*** (0.00631)

-0.00117** (0.000487)

Sex -0.00108 (0.0134)

-0.00315 (0.0136)

Constant 0.265*** (0.0260)

0.433*** (0.0277)

Observations 802 469

R-squared 0.451 0.373

Notes: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

30

7. Limitations 7.1 Summary This section discusses the limitations of the survey which affected the ability

of the survey to generate conclusions that would apply to the Hong Kong

electoral context. Since the survey was relatively informal, it is possible that

the accuracy and reliability of the survey’s findings were negatively impacted.

7.2 Design Limitations Survey participants were online volunteers who never corresponded with the

Centre outside of the survey. We did not make any efforts to confirm that

their self-provided personal information was accurate. It is possible that

participants provided false information, either intentionally or accidentally.

We did not give participants any suggestions or instructions to guide their

evaluation of candidates beyond the “Super dislike,” “Dislike,” “Like” and

“Super Like” buttons. For instance, we did not instruct participants to rate

any particular trait such as honesty or trustworthiness for each candidate. We

also did not inform participants that each of the options would be recorded

on a scale of 1 to 4. This could have created confusion and induced some

users to rate candidates based on factors other than their facial appearance,

despite the fact that no information besides their photographs was given.

Overseas studies typically asked participants if they recognised any of the

candidate faces. If any participant recognised a face, his or her ratings were

removed from the study. By contrast, we did not ask any of our participants

if they recognised any of the candidate faces in the survey. It is very likely that

candidates who were well-known LegCo members or who otherwise received

media attention during the 2015 District Council elections were recognised

by participants. This is particular the case because the Facebook ads were

targeted at users with an interest in politics. For this reason it is probable that

some participants based their candidate ratings on factors other than

appearance, such as the political beliefs of each candidate.

31

Participants were differentiated based on their IP addresses. If the same

participant accessed the survey several times using different IP addresses,

then the survey would have recorded this participant’s data as being derived

from different participants. It is not known how often this occurred, nor is it

known whether this had any effect on our survey findings.

The applicability of the survey results to the LegCo elections is unclear.

Compared to District Council elections, LegCo elections are “high-

information.” Since the LegCo is elected using party lists and proportional

representation with less individual head-to-head competition, and since there

is comparatively more media attention and importance on each individual

candidate’s political beliefs and party affiliation, it is probable that candidate

appearance will not have as much of an effect on vote share in the LegCo.

7.3 Sample Limitations Previous studies made an effort to recruit a balanced sample of participants,

with participants from different ages, genders and political affiliations being

represented in rough equilibrium. We made no efforts to ensure that the

sample was balanced and unbiased, for instance by ensuring that older

participants and female participants were recruited. Our Facebook ads

specifically targeted participants aged 18 to 40 and with an interest in politics.

The demographics of the survey participants were not representative of the

demographics of the actual voters of the 2015 District Council elections.

Firstly, male participants were overrepresented. Although men constituted

over 70% of the sample, the proportion of male voters in the 2015 elections

was only 49.7%, according to the Electoral Affairs Commission’s data on

voter turnout in 2015.23 Participants aged 18-29 were also overrepresented.

While users from this age group made up 71% of the total participants, voters

aged 18-29 comprised a mere 12.7% of all voters in the actual elections.

23 Electoral Affairs Commission, 2015 DCE Age Group and Gender of Voter Turnout by District,’

March 16, 2016. Retrieved at:

http://www.elections.gov.hk/dc2015/pdf/2015_DCE_Age_Group_and_Gender_of_Voter_Turno

ut_by_District.xlsx

32

Pro-establishment voters were underrepresented in the survey. The

Democratic Alliance for the Betterment and Progress of Hong Kong (DAB),

the flagship pro-establishment party, won 21% of all the votes cast in the

2015 District Council elections.24 Yet only 11.6% of the survey participants

identified themselves as pro-establishment. By contrast, localists were

overrepresented. While it can be arbitrary to label a candidate as being

“localist,” media sources reported that “localist” and “umbrella soldier”

candidates only succeeded in winning 2% of the seats in the 2015 District

Council elections. 25 This number is much lower than the proportion of

localist supporters in our study (23.5%).

Discrepancies between the survey sample and the actual voters could have

distorted the results of the study. For instance, if younger participants tended

to rate certain candidates more highly than older participants, then the

appearance ratings would be biased in favour of younger voters. Also, if male

participants tended to rate certain candidates more highly than female

participants, then the appearance ratings would be biased in favour of male

voters. It is not clear at this point whether or not this is in fact the case. What

can be said is that the sample for our study was not representative of the

Hong Kong population at large or the voters in the 2015 District Council

elections, and standard practice in experimental studies is to have a

representative sample to ensure accuracy in the results of such studies.

24 Tony Cheung, Occupy and Hong Kong youth take toll on pro-Beijing parties, South China Morning Post,

November 24, 2015, http://www.scmp.com/news/hong-kong/politics/article/1882463/occupy-

and-hong-kong-youth-take-toll-pro-beijing-parties

25 Donny Kwok and Clare Baldwin, Hong Kong’s “Umbrella Soldiers” win seats in local elections, Reuters,

November 23, 2015, http://www.reuters.com/article/us-hongkong-politics-election-

idUSKCN0TA0WL20151123

33

8. Conclusion

Considering the fact that online opinion polls are still relatively rare in Hong

Kong, at least with regard to the political sphere, we believe that the “Tinder”

online poll for District Council candidates is an encouraging first step for the

Centre’s foray into mobile polling. We were able to recruit over 2,200 participants

in ten days at minimal cost by using a creative and interactive user interface,

Facebook advertisements, and organic “sharing” on social media. However,

there are clearly many limitations and issues for future consideration related to

the survey’s methodology, which we have attempted to address in this paper.

To that end, we are currently contemplating modifications to the online

platform in addition to the ones already mentioned. While the pilot study was

web-based, we may create a mobile “application” for a subsequent version.

This way we could include more interactive features while eliminating some of

the issues related to distinguishing participants based on IP addresses. The next

modification we may make is to use Facebook ads to overcome demographic

issues in the participant sample. As previously stated, women and participants

over 40 years of age were underrepresented in the pilot survey, so it would

make sense to recruit from these groups using Facebook’s targeted ads system.

Even with targeted Facebook ads, however, our survey will still face sampling

challenges because our sampling method may result in a non-probability

sampling. We would try to ensure that the sample size is adequately large so

that our conclusions would not be invalidated. In any case we will report our

results cautiously to ensure that we have accounted for the survey limitations.

We may also consider using longitudinal studies that track respondents over an

extended period of time. This could enable us to conduct more of an in-depth

and less time-sensitive analysis into the factors behind candidate ratings that

would distinguish short-term phenomena from long-term phenomena.

No matter what modifications we make, we will have to balance the need to

preserve the anonymity and privacy of our participants with the need to collect

detailed and granular data. If the data that we collect from our participants is too

general or broad, this may prevent us from making specific conclusions regarding

34

voter preferences. But since our platform would rely on volunteers, we will have

to gauge the willingness of the participants to provide their personal information.

The results from our survey also had implications for the Centre’s analysis of the

political situation in Hong Kong. It is not surprising, for example, that Hong

Kong is similar to overseas countries such as the United States in that physical

appearance is one of the many determining factors used by voters. Since

researchers have concluded that voters were subconsciously (rather than

intentionally) using physical appearance as a heuristic, it would make sense for

Hong Kong voters to employ similar heuristics at a subconscious level.

There is no evidence that physical appearance is correlated with actual

performance as an elected official. Attractive candidates, in other words, may be

elected over less attractive candidates without necessarily being any more

competent or capable at being elected representatives. For this reason we believe

that Hong Kong voters should be discouraged from using appearance as a

determining heuristic, especially in “low-information” races like the District

Council elections. Party affiliation, work experience and political beliefs should

ideally be prioritised by voters. For this to happen, it is necessary to increase the

level of political awareness among all voters in the District Council elections.

District Council candidates will need to find ways to increase their profile in the

community if they hope to be judged on their platform rather than on their

appearance. Meanwhile, government and election authorities in District Council

elections could consider providing more resources and opportunities for

candidates to promote themselves, which could mitigate the need to rely on

physical appearance as a determining factor for voters. Independent candidates

who do not enjoy the support of a political party and who could not otherwise

afford to launch a robust campaign could benefit from such an arrangement.

Of course, it is within every voter’s right to select candidates based on whatever

criteria they see fit. However, we believe that this survey has managed to draw

some attention to subconscious or unintentional voter tendencies regarding

candidate appearance in Hong Kong. At the least, we hope that voters and

candidates will be more aware of this trend by the time the next elections arrive.