Post on 12-Apr-2018
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Asian Urban-Wellbeing Indicators Comparative Report : Hong Kong, Singapore, Shanghai(2016 First Report)
June 2016
Graphic Summary
Carine LaiMichael E. DeGolyer Michael E. DeGolyer
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About Civic ExchangeCivic Exchange is a Hong Kong-based nonprofit public policy think tank that was established in 2000. It is an independent organisation that has access to policy makers, officials, businesses, media and NGOs—reaching across sectors and borders. Civic Exchange has solid research experience in areas such as air quality, energy, urban planning, climate change, conservation, water, governance, political development, equal opportunities, poverty and gender.
For more information about Civic Exchange, visit www.civic-exchange.org.
About the AuthorsCarine Lai is a project manager at Civic Exchange focusing on urban liveability and wellbeing. She is also a graphic artist specialising in infographics and data visualisation. She has an MSc in urban planning and a dual BA/BFA in political science and studio art. Carine is also the co-author of From Nowhere to Nowhere: A Review of Constitutional Development Hong Kong 1997-2007 and Reflections of Leadership: Tung Chee Hwa and Donald Tsang (1997-2007).
Michael E. DeGolyer is a Civic Exchange Fellow. He is a political economist, former Director of the Masters in Public Administration Programme and Professor in the Department of Government & International Study at Hong Kong Baptist University. He was Director of the Hong Kong Transition Project, a long-term study begun in 1988 of Hong Kong people’s transition from colonial subjects to Chinese citizens with the right to amend their constitution and elect their executives and representatives. He has been President of the Hong Kong Political Science Association, a Hong Kong Country Reports and Country Forecasts Expert Contributor to the Economist Intelligence Unit (1996-2006), and regular commentator on RTHK and other media.
Data Access EnquiriesAs part of Civic Exchange’s commitment to promoting public policy research and civic engagement, the Asian Urban-Wellbeing Indicators database, on which this report is based, will be made available to the public. For data access enquiries, please contact Carine Lai at (852) 2893 0213 or clai@civic-exchange.org.
The views presented in this report are those of the authors and do not reflect the views of Civic Exchange.
Graphics credits
Car, hospital, pencil and ruler, tree, and résumé icons on p.12 by Freepik at flaticon.com, licensed under Creative Commons 3.0
Child silhouettes on p.21 by VectorOpenStock.com, licensed under Creative Commons 3.0
Seniors silhouettes on p.21 by mzacha, © RGBStock.com
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Preface and AcknowledgmentsPerception (from the Latin percipere) is the organisation, identification, and interpretation of sensory information in order to represent and understand an environment.
How people perceive their lives may be more important than how well they are actually living, as measured by commonplace objective metrics. Perceptions are linked to sentiments. Sentiments drive voting behaviour, among other things. This is an election year.
The Asian Urban-Wellbeing Indicators, as a concept, is the brainchild of Christine Loh, co-founder of Civic Exchange. The development of the tool, the execution of the survey and the analysis are the achievements of Professor Michael DeGolyer, Civic Exchange Fellow and Carine Lai, Project Manager of Civic Exchange. Initially launched in 2012, this project was supported by a number of local engagement partners in its pilot phase: Chee Anne Roño of Clean Air Asia (Manila), Stuart MacDonald of Penang Institute (Penang), Subhash Agrawal of India Focus (Delhi), Zhang Junzuo (Chengdu & Shanghai), Ni Huan Helen (Shanghai), Penny Low (Singapore), and Michele Weldon (Delhi). To these partners, we are deeply grateful. We would also like to thank our technical partners for their fieldwork contribution and advice: Raymond Sun and Alfred Chan of Consumer Search Group, and Channey KY Chan of the Centre for the Advancement of Social Sciences Research of Baptist University. We are also indebted to Pooja Pradhan for her Hindi translation work and to Evan Auyang for his advice and supplementary data analysis. Last but not least, we would like to express our appreciation to RS Group Asia and WYNG Foundation who provided funding support to make our Asian Urban-Wellbeing project possible.
We hope policy makers and society can take a good look at the survey results and ask the question:
“Why do our people feel the way they do?”
A lot more work can be done to come up with the answer(s). A responsible government should not lose time in digging deep into the areas of greatest deficiency as shown up in our survey results. Civic Exchange will make our survey data open to anyone interested. By sharing our data, we welcome everyone to join in this conversation. People deserve better answers and better results.
Maura WongCEO, Civic Exchange7 June 2016
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Table of Contents
Introduction………………………………………………………………………….5
Principles, Structure and Methodology…………………………………….6
Domain Caring and Satisfaction…………………………………………….8
Domain Priority………………………………………………………………….11
Overall Life Evaluation…………………………………………………………14
Perceptions of Improvement or Worsening…………………………16
Aspirations to Stay or Move Away………………………………………19
Perceptions of Liveability for Children and Retirees…………….21
Worry About Poverty and Supporting Your Family………………23
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We live in an age of cities. During the past decade, for the first time in human existence, more human beings dwell in urban than rural areas.1 In Asia, urbanisation is happening rapidly, with the United Nations projecting that the urbanised population will rise from 48 per cent urban in 2014 to 65 per cent urban in 2050.2 This means that the governance of cities will affect the lives and wellbeing of billions more people in the coming decades.
Policy experts have also become increasingly interested in more holistic metrics of societal progress beyond Gross Domestic Product (GDP), which was never designed to measure overall wellbeing and has well-known limitations. For example, it does not include non-market contributions to society such as parenting and volunteering, and makes no effort to distinguish between socially productive and destructive spending.3 However, most available comparative data—especially subjective data—are between countries, not cities, despite the importance and distinctiveness of cities.
The policy challenges of cities are different from those of rural areas. Cities have concentrated populations, accelerated socioeconomic activity, greater diversification and specialisation, and cities of similar sizes face similar challenges in urban planning, traffic management, congestion, environmental degradation, crime and inequality.4 Urban populations are more cosmopolitan in nature than rural residents, and their support or opposition for different policies is affected by different factors than those for their rural counterparts.
In 2012, Civic Exchange launched the project that would become the Asian Urban-Wellbeing Indicators. The Asian Urban-Wellbeing Indicators is a public opinion survey designed to measure public attitudes towards urban life. It measures how much people care about and are satisfied with 10 different policy domains—housing, medical care, education, work and business opportunities, transportation and utilities, environmental protection, community and belonging, public safety and crime control, recreation and personal time, and quality of government.
The survey was developed over 3 years in collaboration with local partners from five diverse Asian cities—Chengdu, Delhi, Hong Kong, Manila and Penang—in order to ensure that the resulting instrument could be used in a broad range of Asian cities. The first survey wave was conducted in August 2015 to January 2016 in three selected Asian cities, Hong Kong, Shanghai and Singapore, all major commercial ports and financial centres with Chinese heritage and extensive international connections. It is hoped that the findings will provide insights into city dwellers’ attitudes and priorities in order to identify areas for further research and to provoke discussions on how urban policymakers can better meet people’s needs.
This graphic summary presents a brief overview of the key findings from the full comparative report, “Asian Urban-Wellbeing Indicators—Hong Kong, Shanghai and Singapore (2016 First Report)” which is available at Civic Exchange’s website at http://civic-exchange.org/en/publications/8290304. However, even the full report only manages to scratch the surface of a rich and complex dataset. Researchers interested in conducting their own analyses are welcome to approach Civic Exchange for access to the database (see Data Access Enquiries, p.2).
1. United Nations, Department of Economic and Social Affairs, Population Division (2014), World Urbanization Prospects: The 2014 Revision, Highlights (ST/ESA/SER.A/352)., http://esa.un.org/unpd/wup/Publications/Files/WUP2014-High-lights.pdf (accessed 6 June 2016).
2. Ibid.
3. OECD (2011), How’s Life?: Measuring Well-being, OECD Publishing, http://dx.doi.org/10/1787/9789264121164-en
4. Bettencourt, L. & West, G. (2010), “A unified theory of urban living”, Nature, vol. 467, 21 October 2010, pp. 912-913, http://depts.washington.edu/urbdpphd/symposium/Nature_Cities%20copy.pdf (accessed 6 June 2016.
1 Introduction
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2.1 Principles
• City focused The survey focuses on cities, not countries.
• People focused Unlike other well-known urban liveability indices, the Asian Urban-Wellbeing Indicators focuses on people’s subjective attitudes and experiences rather than on policy inputs (e.g. educational spending) or policy outputs (e.g. graduation rates).
• Inclusive In cities where migrant workers (defined as workers without full legal residency status) make up a large proportion of the population, efforts were made to include them in the survey despite difficulties in contacting them.
• Comprehensive, but in-depth The survey instrument was designed to cover a broad spectrum of 10 policy domains enabling comparison of residents’ priorities. However, it also included in-depth questions about specific domains selected by the respondent.
• Methodologically flexible In order to achieve the best chance of obtaining a representative sample in each city, the survey mode was determined by each city’s level of telecommunications penetration and economic development.
2.2 Structure
The survey is structured in three parts. At the start of the survey, all respondents are asked a set of core questions about their perceptions of their city as a place to live, their overall life satisfaction, their overall satisfaction with each domain, and how much they care about each domain. Each respondent is then asked to select the domain they think the government should make its top priority.
National Level City Level
ObjectiveData
UN Human Development Index
EIU’s Global LiveabilityRankings
Mercer’s Quality of Living Rankings
AT Kearney’s Global Cities Index
Subjective Data
Gallup World Poll
Asian Barometer
Asian Urban Wellbeing Indicators
Legatum Prosperity Index
OECD Better Life Initiative
Table 1: Positioning of the Asian Urban-Wellbeing Indicators Compared with Other Major Indices
2 Principles, Structure and Methodology
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Based on their response to this question, respondents are directed to the second part of the survey, which contains more in-depth questions about their selected domain. (This summary does not cover the domain-specific questions. Please consult the individual city reports, to be released in late 2016, for details.) Finally, all respondents are asked the same set of demographic questions.
2.3 Methodology
• Approximately 1,500 randomly contacted respondents aged 18-65 were interviewed in each city.
• Interlocking quotas were set based on age (under 40 and 40 and over) and gender according to the most recent available official census or household survey data. Within the interlocking quotas, non-interlocking quotas were set for age bands 18-29, 30-39, 40-49, 50-59 and 60-65.
• The majority of interviews were carried out through Computer Assisted Telephone Interviewing (CATI). In Hong Kong and Singapore where household landline penetration rates in 2014 were 100.38% 5 and 99%6 respectively, landline dialling was used. In Shanghai, although the official household landline penetration rate was 90.9%,7 as many households did not answer or plug in their telephones, dialling was expanded to mobile phones.
• In Shanghai and Singapore, quotas were set for contacting migrant workers according to the most recent available official data. In Shanghai, this was 43 per cent of the overall sample and the figure for Singapore was 20 per cent. The Singapore quota only included those on temporary work permits, which are given exclusively to low-skilled workers, as it was assumed that this group had the least telephone access. Migrant workers were interviewed through random street intercepts using Computer Assisted Personal Interviewing (CAPI).
• In Hong Kong, interviews were carried out in Cantonese, Putonghua and English. In Shanghai, Putonghua was used. In Singapore, languages included Mandarin, English, Tagalog and Hindi.
• For a detailed discussion of the methodology and limitations, please see Appendix 1 of the full comparative report, available on Civic Exchange’s website at http://civic-exchange.org/en/publications/8290304.
5. Office of the Communications Authority, HKSAR Government (2015), “Key Communications Statistics”, http://www.ofca.gov.hk/en/media_focus/data_statistics/key_stat/, (accessed 13 August 2015).
6. InfoComm Development Authority of Singapore, Government of Singapore (2014), “Statistics on Telecomm Services for 2014 (Jan-Jun)” http://www.ida.gov.sg/Tech-Scene-News/Facts-and-Figures/Telecommunications/Statistics-on-Telecom-Services/Statistics-on-Telecom-Services-for-2014-Jan-Jun (accessed 12 August 2015).
7. Shanghai Municipal Statistics Bureau (2014), “Table 15.17, Postal and Telecoms Level in Main Years”, Shanghai Statisti-cal Yearbook 2014, http://www.stats-sh.gov.cn/tjnj/nje14.htm?d1=2014tjnje/E1517.htm. Shanghai data for household landlines only includes registered households.
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3 Domain Caring and Satisfaction
Respondents were asked about their satisfaction with each of the 10 domains, and then were asked how much they cared about each domain. This enables a comparison to be made between the two dimensions, i.e. the caring-satisfaction gap. This indicator sheds light on the degree to which a city’s performance meets residents’ expectations. A domain is underperforming where satisfaction dips far below caring. The spider diagrams below illustrate the caring-satisfaction gaps for each domain, while the scatter graphs plot the two variables relative to each other. Caring-Satisfaction Gap
How much do you care about the following issues? A lot, some not much, or not at all? How satisfied are you with the following issues? Very satisfied, satisfied, dissatisfied or very dissatisfied?
Mean Caring Score
Mean Satisfaction Score4 = very satisfied3 = satisfied2 = dissatisfied1 = very dissatisfied
4 = care a lot3 = care some2 = care not much1 = care not at all
3.23.3
3.0
3.0
3.1
3.2
2.8
2.9
3.2
3.2
2.82.0
2.8
2.3
2.82.22.5
2.7
3.02.0
1.0
2.0
4.0Medical Care
Quality & Costof Housing
Transport &Utilities
EnvironmentalProtection
Work &BusinessOpportunities
Education
Community &Belonging
Recreation &Personal Time
Public Safety &Crime Control
Quality ofGovernment
3.0
Hong Kong
Shanghai
3.5
3.5
3.2
3.3
3.3
3.43.0
3.0
3.4
3.1
2.72.5
2.8
2.8
3.0
4.0
2.9
2.9
3.1
2.9
1.0
2.0
3.0
4.0Medical Care
Quality & Costof Housing
Transport &Utilities
EnvironmentalProtection
Work &BusinessOpportunities
Education
Community &Belonging
Recreation &Personal Time
Public Safety &Crime Control
Quality ofGovernment
3.4
3.3
3.2
3.2
3.2
3.4
3.0
3.1
3.4
3.33.0
2.7
3.0
3.1
2.9
3.23.1
2.9
3.4
3.1
1.0
2.0
3.0
4.0Medical Care
Quality & Costof Housing
Transport &Utilities
EnvironmentalProtection
Work &BusinessOpportunities
Education
Community &Belonging
Recreation &Personal Time
Public Safety &Crime Control
Quality ofGovernment
Singapore
Figure 1: Caring and Satisfaction, Hong Kong
Medical Care
Housing
Transport & UtilitiesEnvironmental
Protection
Work & Business Opportunities
Education
Community & Belonging
Recreation & Personal Time
Public Safety &Crime ControlQuality of Government
2.50
3.00
3.50
1.50 2.00 2.50 3.00 3.50
Very dissatisfied Very satisfied
Care a lot
Mea
n Ca
ring
Scor
e: 1
= n
ot a
t all;
4 =
a lo
t
Mean Satisfaction Score: 1 = very dissatisfied; 4 = very satisfied
Caring vs. Satisfaction, Hong Kong
How satisfied are you with the following issues? Very satisfied, satisfied, dissatisfied or very dissatisfied? How much do you care about the following issues? A lot, some, not much or not at all?
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Caring-Satisfaction GapHow much do you care about the following issues? A lot, some not much, or not at all? How satisfied are you with the following issues? Very satisfied, satisfied, dissatisfied or very dissatisfied?
Mean Caring Score
Mean Satisfaction Score4 = very satisfied3 = satisfied2 = dissatisfied1 = very dissatisfied
4 = care a lot3 = care some2 = care not much1 = care not at all
3.23.3
3.0
3.0
3.1
3.2
2.8
2.9
3.2
3.2
2.82.0
2.8
2.3
2.82.22.5
2.7
3.02.0
1.0
2.0
4.0Medical Care
Quality & Costof Housing
Transport &Utilities
EnvironmentalProtection
Work &BusinessOpportunities
Education
Community &Belonging
Recreation &Personal Time
Public Safety &Crime Control
Quality ofGovernment
3.0
Hong Kong
Shanghai
3.5
3.5
3.2
3.3
3.3
3.43.0
3.0
3.4
3.1
2.72.5
2.8
2.8
3.0
4.0
2.9
2.9
3.1
2.9
1.0
2.0
3.0
4.0Medical Care
Quality & Costof Housing
Transport &Utilities
EnvironmentalProtection
Work &BusinessOpportunities
Education
Community &Belonging
Recreation &Personal Time
Public Safety &Crime Control
Quality ofGovernment
3.4
3.3
3.2
3.2
3.2
3.4
3.0
3.1
3.4
3.33.0
2.7
3.0
3.1
2.9
3.23.1
2.9
3.4
3.1
1.0
2.0
3.0
4.0Medical Care
Quality & Costof Housing
Transport &Utilities
EnvironmentalProtection
Work &BusinessOpportunities
Education
Community &Belonging
Recreation &Personal Time
Public Safety &Crime Control
Quality ofGovernment
Singapore
Caring-Satisfaction GapHow much do you care about the following issues? A lot, some not much, or not at all? How satisfied are you with the following issues? Very satisfied, satisfied, dissatisfied or very dissatisfied?
Mean Caring Score
Mean Satisfaction Score4 = very satisfied3 = satisfied2 = dissatisfied1 = very dissatisfied
4 = care a lot3 = care some2 = care not much1 = care not at all
3.23.3
3.0
3.0
3.1
3.2
2.8
2.9
3.2
3.2
2.82.0
2.8
2.3
2.82.22.5
2.7
3.02.0
1.0
2.0
4.0Medical Care
Quality & Costof Housing
Transport &Utilities
EnvironmentalProtection
Work &BusinessOpportunities
Education
Community &Belonging
Recreation &Personal Time
Public Safety &Crime Control
Quality ofGovernment
3.0
Hong Kong
Shanghai
3.5
3.5
3.2
3.3
3.3
3.43.0
3.0
3.4
3.1
2.72.5
2.8
2.8
3.0
4.0
2.9
2.9
3.1
2.9
1.0
2.0
3.0
4.0Medical Care
Quality & Costof Housing
Transport &Utilities
EnvironmentalProtection
Work &BusinessOpportunities
Education
Community &Belonging
Recreation &Personal Time
Public Safety &Crime Control
Quality ofGovernment
3.4
3.3
3.2
3.2
3.2
3.4
3.0
3.1
3.4
3.33.0
2.7
3.0
3.1
2.9
3.23.1
2.9
3.4
3.1
1.0
2.0
3.0
4.0Medical Care
Quality & Costof Housing
Transport &Utilities
EnvironmentalProtection
Work &BusinessOpportunities
Education
Community &Belonging
Recreation &Personal Time
Public Safety &Crime Control
Quality ofGovernment
Singapore
Figure 2: Caring and Satisfaction, Shanghai
Medical CareHousing
Transport & Utilities
Environmental Protection
Work & Business Opportunities
Education
Community & Belonging
Recreation & Personal Time
Public Safety
Quality of Government
2.50
3.00
3.50
1.50 2.00 2.50 3.00 3.50
Very dissatisfied Very satisfied
Care a lot
Caring vs. Satisfaction, Shanghai
Mea
n Ca
ring
Scor
e: 1
= n
ot a
t all;
4 =
a lo
t
Mean Satisfaction Score: 1 = very dissatisfied; 4 = very satisfied
Of the three cities, Hong Kong exhibited the largest gaps between caring and satisfaction, especially in the domains of housing (-1.3 points), quality of government (-1.2 points), education (-1 point) and environmental protection (-0.7 points). The maximum possible gap is -3 points.
Shanghai also showed significant gaps, but on a smaller scale than Hong Kong, in areas such as housing (-1 point), medical care (-0.8 points), education (-0.6 points) and environmental protection (-0.5 points).
However, in Singapore, satisfaction levels came very close to meeting, and in a couple of cases, exceed caring levels in all domains except for housing (-0.6 points) and to a lesser extent medical care (-0.4 points). To a large degree, dissatisfaction with housing in Singapore came from migrant workers, who lived in employer-provided dormitories and rented accommodations, sometimes in very crowded conditions.
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Caring-Satisfaction GapHow much do you care about the following issues? A lot, some not much, or not at all? How satisfied are you with the following issues? Very satisfied, satisfied, dissatisfied or very dissatisfied?
Mean Caring Score
Mean Satisfaction Score4 = very satisfied3 = satisfied2 = dissatisfied1 = very dissatisfied
4 = care a lot3 = care some2 = care not much1 = care not at all
3.23.3
3.0
3.0
3.1
3.2
2.8
2.9
3.2
3.2
2.82.0
2.8
2.3
2.82.22.5
2.7
3.02.0
1.0
2.0
4.0Medical Care
Quality & Costof Housing
Transport &Utilities
EnvironmentalProtection
Work &BusinessOpportunities
Education
Community &Belonging
Recreation &Personal Time
Public Safety &Crime Control
Quality ofGovernment
3.0
Hong Kong
Shanghai
3.5
3.5
3.2
3.3
3.3
3.43.0
3.0
3.4
3.1
2.72.5
2.8
2.8
3.0
4.0
2.9
2.9
3.1
2.9
1.0
2.0
3.0
4.0Medical Care
Quality & Costof Housing
Transport &Utilities
EnvironmentalProtection
Work &BusinessOpportunities
Education
Community &Belonging
Recreation &Personal Time
Public Safety &Crime Control
Quality ofGovernment
3.4
3.3
3.2
3.2
3.2
3.4
3.0
3.1
3.4
3.33.0
2.7
3.0
3.1
2.9
3.23.1
2.9
3.4
3.1
1.0
2.0
3.0
4.0Medical Care
Quality & Costof Housing
Transport &Utilities
EnvironmentalProtection
Work &BusinessOpportunities
Education
Community &Belonging
Recreation &Personal Time
Public Safety &Crime Control
Quality ofGovernment
Singapore
Figure 3: Caring and Satisfaction, SingaporeM
ean
Carin
g Sc
ore:
1 =
not
at a
ll; 4
= a
lot
Mean Satisfaction Score: 1 = very dissatisfied; 4 = very satisfied
Medical Care
Housing
Transport & Utilities
Environmental Protection
Work & Business Opportunities
Education
Community &BelongingRecreation &
Personal Time
Public Safety & Crime Control
Quality of Government
2.50
3.00
3.50
1.50 2.00 2.50 3.00 3.50
Very dissatisfied
Very satisfied
Care a lot
Caring vs. Satisfaction, Singapore
Caring-Satisfaction GapHow much do you care about the following issues? A lot, some not much, or not at all? How satisfied are you with the following issues? Very satisfied, satisfied, dissatisfied or very dissatisfied?
Mean Caring Score
Mean Satisfaction Score4 = very satisfied3 = satisfied2 = dissatisfied1 = very dissatisfied
4 = care a lot3 = care some2 = care not much1 = care not at all
3.23.3
3.0
3.0
3.1
3.2
2.8
2.9
3.2
3.2
2.82.0
2.8
2.3
2.82.22.5
2.7
3.02.0
1.0
2.0
4.0Medical Care
Quality & Costof Housing
Transport &Utilities
EnvironmentalProtection
Work &BusinessOpportunities
Education
Community &Belonging
Recreation &Personal Time
Public Safety &Crime Control
Quality ofGovernment
3.0
Hong Kong
Shanghai
3.5
3.5
3.2
3.3
3.3
3.43.0
3.0
3.4
3.1
2.72.5
2.8
2.8
3.0
4.0
2.9
2.9
3.1
2.9
1.0
2.0
3.0
4.0Medical Care
Quality & Costof Housing
Transport &Utilities
EnvironmentalProtection
Work &BusinessOpportunities
Education
Community &Belonging
Recreation &Personal Time
Public Safety &Crime Control
Quality ofGovernment
3.4
3.3
3.2
3.2
3.2
3.4
3.0
3.1
3.4
3.33.0
2.7
3.0
3.1
2.9
3.23.1
2.9
3.4
3.1
1.0
2.0
3.0
4.0Medical Care
Quality & Costof Housing
Transport &Utilities
EnvironmentalProtection
Work &BusinessOpportunities
Education
Community &Belonging
Recreation &Personal Time
Public Safety &Crime Control
Quality ofGovernment
Singapore
However, as the scatter graphs make clear, in Singapore and Shanghai, even where there were caring-satisfaction gaps, the average satisfaction score was greater than 2.5, which meant that a majority of the respondents were either satisfied or very satisfied. The exception was with housing in Shanghai, which fell just below the neutral point. On the other hand, Hong Kong had a cluster of domains—housing, quality of government, education and environmental protection—where a majority of respondents were dissatisfied or very dissatisfied.
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4 Domain Priority
1
1
3
4
5
6
15
16
16
33
0 10 20 30 40
Recreation
Community
Transport & Utilities
Environment
Work & Business Opportunities
Public Safety
Medical Care
Education
Housing
Hong Kong
Quality of Government
%
Transport & Utilities
Work & Business Opportunities
1
2
3
6
9
10
12
18
19
20
0 10 20 30
Community
Recreation
Public Safety
Environment
Education
Medical Care
Housing
Shanghai
Quality of Government
%
Transport & Utilities
Work & Business Opportunities
3
4
4
6
10
10
13
15
16
20
0 10 20 30
Recreation
Community
Environment
Public Safety
Quality of Government
Education
Housing
Medical Care
Singapore
%
Priority of <10% of respondents Priority of 10-14% of respondentsPriority of ≥ 15% of respondents
Out of your [n*] choices, what is the number 1 issue that the government should address?
* n = The number of issues the respondent said that he or she cared a lot about. If the respondent did not care a lot about any issues, he/she was asked about the issues he/she cared about “some”. If the respondent did not care “some” about any issues, he/she was asked about the issues he/she cared “not much” about.
Later I’d like to ask you some more detailed questions about the issue you think the government should focus on the most. Out of your [n]8 choices, what is the number 1 issue that the government should address?
8. n = The number of issues the respondent said that he or she cared a lot about. If the respondent did not care a lot about any issues, he/she was asked about the issues he/she cared about “some”. If the respondent did not care “some” about any issues, he/she was asked about the issues he/she cared “not much” about.
9. For example, in November 2015 (which coincided with the Asian Urban-Wellbeing Indicators survey period), the Uni-versity of Hong Kong’s Public Opinion Programme reported that 45.4% of respondents gave the HKSAR Government
Figure 4: Domain Priorities in Hong Kong, Shanghai and Singapore
In all three cities, housing was in the top three priorities for government action; however in Hong Kong it was not only the top issue, it exceeded the next most popular priority, education, by 18 percentage points. In general, the three cities also assigned a high level of priority to education and medical care.
Medical care was the top issue in Singapore, and virtually tied with housing for the top issue in Shanghai. In Singapore, public attention on medical care may have been elevated due to the government’s November 2015 roll-out of the MediShield Life programme, Singapore first universal health care policy, which coincided with the survey’s timing.
The cities did have some unique concerns. In Hong Kong, quality of government tied for second place with education, selected by 16 per cent of respondents. Given the political events of the last two years and reported widespread dissatisfaction with government performance,9 this was to be expected.
12
Craft
, Tra
des &
Fa
ctor
y
Man
ager
s &
Adm
inist
rato
rs
Prof
essio
nals
Asso
c. P
rofe
ssio
nals
& Te
chni
cian
s
Cler
ks
Serv
ice
and
Sale
s
Elem
enta
ry
Occ
upati
ons
Hom
emak
ers
Retir
ed
Une
mpl
oyed
Stud
ents
1st
2nd
3rd
4th
29 33 38 41 31 35 28 33 32 37
23 20 19 18 25 15 23 22 32 22
18 17 14 15 13 14 16 14 11 16
8 12 8 12 10 14 13 9 11 7
Hong Kong
Craft
, Tra
des
&
Fact
ory
Man
ager
s &
A
dmin
istr
ator
s
Prof
essi
onal
s
Ass
oc. P
rofe
ssio
nals
&
Tec
hnic
ians
Cler
ks
Serv
ice
and
Sale
s
Elem
enta
ry
Occ
upati
ons
Hom
emak
ers
Retir
ed
Une
mpl
oyed
Stud
ents
Arti
sts
&
Perf
orm
ers
Shanghai
1st
2nd
3rd
4th
30 22 21 23 30 24 43 21 20
20 20 21 18 17 22 14 17 19
20 14 17 18 13 13 14 12 16
8 13 12
25
20
15
12 12
24
21
18
14 12
22
19
19
12 11 11 10 15
Craft
, Tra
des &
Fa
ctor
y
Man
ager
s &
Adm
inist
rato
rs
Prof
essio
nals
Asso
c. P
rofe
ssio
nals
&
Tech
nici
ans
Cler
ks
Serv
ice
and
Sale
s
Elem
enta
ry
Occ
upati
ons
Hom
emak
ers
Retir
ed
Une
mpl
oyed
Stud
ents
Singapore
1st
2nd
3rd
4th
27 19 34 28 23 24 41 24 20
23 17 18 19 17 24 18 15 20
13 16 11 15 15 11 18 14 14
10 14 10 12 14
36
13
12
10 11 8 14 10
經理及行政人員
專業人士
技術人員
文員
服務業手工艺
非技術
家庭主婦退休人士失業學生全部
家庭主婦
Housing
Medical Care
Education
Insu�cient Data
Public Safety and Crime Control
Environmental Protection
Transport and Utilities
Figures show percentage of respondents who chose the domain as their number 1 issue for the government to address.
In cases where there are more than one domain tied for 4th place, the more popular domain overall is shown.
Work and Business Opportunities
Quality of Government
Figure 5: Top Four Domain Priorities by Occupation10
Chi-square = 337.2 with 90 df p ≤ 0.0001
13
In Shanghai, environmental protection was chosen by 12 per cent compared to just 4 per cent in the other two cities. Given the severity of air pollution in Shanghai it was understandable that environmental protection had captured the priority of a small but significant segment of the public.
In Singapore, work and business opportunities stood out as the second ranked priority, chosen by 16 per cent of respondents. In Hong Kong and Shanghai, it did not even achieve double digits. However, this prioritisation of work opportunities was disproportionately found among low-wage migrant workers, who made up 20 per cent of the sample but 45 per cent of those who wanted the government to address it first.
Breaking down respondents’ priorities by occupation (see Figure 5), a few data points stood out. A large plurality of retirees in all three cities wanted the government to prioritise medical care. This was to be expected as medical need tends to increase with age. Perhaps the more surprising finding was that in Hong Kong, although retirees were the only occupational group that did not put housing first, medical care only exceeded the second choice (housing) by 11 percentage points, compared to 23 percentage points in Singapore and 29 points in Shanghai.
In Singapore, most occupations aside from professionals, clerks, elementary occupations and students put medical care in first place. Aside from migrant workers (all of whom were categorised as elementary workers) putting work opportunities first, students also wanted the government to prioritise work opportunities alongside housing, with 20 per cent choosing the two domains. Clerks also put housing ahead of medical care by quite a large margin, 28 per cent to 19 per cent.
In Shanghai, the pattern of responses was more dispersed among housing, education and medical care. However, students stood out in choosing environmental protection as their top government priority, just ahead of job opportunities. Young educated people in Shanghai may be leading a change in environmental awareness and it will be interesting to see how this develops in the future.
a “negative” or “very negative” rating, compared to 25.9% who gave a “positive” or “very positive” rating. Hong Kong University Public Opinion Programme (2016), “People’s Satisfaction with the HKSAR Government - per poll (31/5/2016) - Table”, https://www.hkupop.hku.hk/english/popexpress/sargperf/sarg/, accessed 2 June 2016.
10. Occupational categories were defined according to the International Labour Organization’s International Standard Clas-sification of Occupations 2008 (ISCO-08) classification system. See International Labour Organization (2008), “ISCO-08 Structure and preliminary correspondence with ISCO-88”, http://www.ilo.org/public/english/bureau/stat/isco/isco08/index.htm, accessed 2 June 2016.
14
5 Overall Life EvaluationOn a scale of 0 to 10, with 0 representing the worst possible life for you and 10 representing the best possible life for you, what score would you give to your life as a whole?Using the same scale, what score would you give to your life in 5 years?
Hong Kong respondents gave their lives lower scores than respondents in Singapore and Shanghai (see Figure 6). However, a majority of them still scored their lives at 6 or above. This contrasted with their much more negative views about life in Hong Kong, as will be shown in later sections. This is indicative of a difference between personal dissatisfaction and sociopolitical dissatisfaction.
Shanghai is one of China’s richest cities and its mean score of 7.4 was considerably higher than the national mean score (5.245) reported by the Gallup World Poll in 2013-15, which used the same question.11 Non-residents in Shanghai (those without household registration) gave significantly lower scores than residents, at 6.9 vs. 7.8. Since eligibility for social services in China is tied to household registration, rural migrants to cities cannot access public benefits from subsidised housing to education. They also face social and employment discrimination. Many (but not all) work in low-wage positions in the service industry, manufacturing and elementary occupations. Their lower satisfaction scores may reflect their lower social status.
However, in Singapore, the pattern appeared to be reversed, with non-citizens scoring their lives more highly than citizens, at 7.8 vs. 6.9 (close to the 6.739 recorded by Gallup)12. It would be interesting to carry out further research into why Singapore’s migrant workers, who come from abroad, work in low-wage positions, and are vulnerable to labour abuses, are so much more satisfied with their lives relative to citizens than Shanghai’s migrant workers are to residents.
Respondents were also asked what score they would give to their lives in 5 years’ time as a measure of optimism or pessimism. Shanghai’s respondents were the most optimistic, with the mean score rising from 7.4 to 8.2. Hong
11. Heilliwell, J., Huang, H. and Wang, S. (2016), “The Distribution of World Happiness” in World Happiness Report 2016, Update (Vol. 1), eds. Heillwell, J, Layard, R. and Sachs, J., New York: Sustainable Development Solutions Network
12. Ibid.
1 1 1
6
9
22 21
24
12
1 20 0 0 1 1
7
20 21 2220
8
0 0 1 1 2
1315
27
24
6
11
0
5
10
15
20
25
30
0 1 2 3 4 5 6 7 8 9 10
Hong Kong Shanghai Singapore
%
HK M
ean
5.8
Sing
Mea
n 7.
1
SH M
ean
7.4
Figure 6: Present Life Evaluation Score, 0-10 Scale, Hong Kong, Shanghai and Singapore
15
Kong respondents were marginally pessimistic, with the mean score dropping from 5.8 to 5.7. Singapore’s mean score was unchanged.
Analysing life evaluation by age, in all three cities, older respondents were more likely to give the “best” scores (9-10). The pattern was especially pronounced in Shanghai. However, in Singapore and Hong Kong, all age groups had fairly similar distributions of life evaluation scores and differences were barely statistically significant.
5.8
7.4 7.1
5.7
8.2
7.1
0
1
2
3
4
5
6
7
8
9
10
Hong Kong Shanghai Singapore
Mean present life evaluation score
Mean life evaluation score in 5 years' timeLi
fe e
valu
ation
scor
e
Figure 7: Present and Future Life Evaluation Scores, Hong Kong, Shanghai and Singapore
4
19
2255
18-292
19
2552
330-39
4
17
1761
240-49
3
9
2459
450-59
3
12
2553
8
60-65
Hong Kong
0-2
3-4
5
6-8
9-10
Life Evaluation ScoresPercentage of respondents
12
11
66
20
18-291 3
9
69
19
30-392 4
70
24
40-491 3
54
42
50-592
3959
60-65
Shanghai
3 10
70
17
18-293
12
67
18
30-395
13
68
14
40-4923
16
63
17
50-593
17
56
24
60-65
Singapore
Chi square ≤ 0.0001
Note: Totals do not always
add up to 100% due to
rounding.
Figure 8: Life Evaluation Scores by Age, Hong Kong, Shanghai and Singapore
16
6 Perceptions of Improvement or WorseningSince you started living in [city], overall, has it become a better or worse place to live?
2
5
22
47
23
1
Hong Kong
20
3138
6 5
Shanghai All
26
41
23
8
1 1
Singapore All
Much betterBetterSameWorseMuch worse Don't know
30
2035
7 7
Shanghai Residents
6
4641
5
2
Shanghai Non-residents
28
46
233
Singapore Citizens
25
39
24
10
2 2
SingaporeNon-citizens
Out of the three cities, Hong Kong was the most negative, with 70 per cent of respondents saying their city has become worse or much worse, whereas only 11 per cent and 9 per cent in Shanghai and Singapore said the same, respectively.
In Shanghai, about half of both residents and non-residents said Shanghai has got better, although residents were more likely to pick the extreme response categories of “much better” or “much worse”. This is perhaps expected since 65 per cent of migrant respondents had lived there for 5 years or less, and have therefore had less time to experience changes.
Singapore’s non-citizens, however, viewed changes in the city even more positively than citizens did, with 74 per cent compared to 64 per cent saying that Singapore had got better. This was despite the fact that 60 per cent of migrant respondents had been present in Singapore for 5 years or less. Further investigation is needed to explain this finding.
Analysing responses by age, in Hong Kong, younger respondents were more likely to say that Hong Kong has worsened: 79 per cent of 18-29-year-olds say Hong Kong has worsened, compared to 61 per cent of 60-65-year-olds.
In Shanghai, differences by age among residents and non-residents were insignificant or weakly significant, respectively. In Singapore, older respondents said that Singapore has got better. Among non-residents, there was no statistically signficant relationship by age as virtually everyone said Singapore has improved or stayed the same.
Figure 9: Perceptions of Improvement or Worsening, Hong Kong, Shanghai and Singapore
17
28 22 26 33 38
2520 19
2020
3544 39 31
34
12 14 16 179
0
20
40
60
80
100
18-29 30-39 40-40 50-59 60-65
Much better Better Same Worse and Much worse*
6 7 5 8
3955
51 45
5132
34 37
5 6 10 10
0
20
40
60
80
100
18-29 29-39 40-49 50-65**
Residents Non-residents
* The categories of “Worse” and “Much worse” were combined due to very few responses in the latter category.** Age groups 50-59 and 60-65 were combined as there were very few non-residents aged 60-65.
Chi-square = 19.58 with 12 df p ≤ 0.0754 Chi-square = 30.74 with 12 df p ≤ 0.0022
Shanghai
% %
3 6 6 13 1218 18 23
26 27
57 49 4440 47
22 28 27 21 14
0
20
40
60
80
100
18-29 30-39 40-49 50-59 60-65
Better and Much better*
Same
Worse
Much worse
* “Better” and “Much better” were combined due to very few responses in the latter category.
Hong Kong
%
Chi-square = 59.20 with 12 dfp ≤ 0.0001
Singapore
32 2516
3657
51
3217
33
1 1 0
0
20
40
60
80
100
18-29 30-39 40-59**
Much better Better Same Worse***
1423 23
3550
4437 41
42
31
33 29 18
17 139 12 18
6 6
0
20
40
60
80
100
18-29 30-39 40-49 50-59 60-65
Citizens and Residents
Much better Better Same Worse and Much worse*
% %
Non-residents
* The categories of “worse” and “much worse” were combined due to very few responses in the latter category.** Age groups 40-49 and 50-59 were combined as there were very few non-residents aged 50-59. There was no-one aged 60 and above.*** No non-residents selected the “Much worse” category.
Chi-square = 63.46 with 8 df p ≤ 0.0001 Chi-square = 9.226 with 6 df p ≤ 0.1613
Figure 10: Perceptions of improvement or Worsening by Age, Hong Kong, Shanghai and Singapore
18
Singapore also had no significant relationship between perceptions of improvement and worsening and education or monthly household income.
However, in Shanghai, higher education was connected to perceptions of improvement (although post-graduates were more polarised), but in Hong Kong the opposite was true. A similar pattern existed for household income, whereby higher-income respondents in Shanghai were more likely to think their city has got better, but in Hong Kong they were more apt to say their city has got worse.
Much better
Better
SameWorse and Much worse*
Better and Much better**
Same
Worse
Much worse
Shanghai
Hong Kong
<6,000 RMB 6,000-7,999 RMB
8,000-11,999 RMB
12,000-19,999 RMB
20,000+RMB
<10,000 HKD 10,000-19,999 HKD
20,000-29,999 HKD
30,000-49,999 HKD
50,000+ HKD
%
%
11 6 10 6 7
31 32 2315 20
35 42 5054 46
23 21 17 24 28
14 10 15 21 2828 35
3736 25
50 44 38 33 36
8 11 11 10 11
*The categories “Worse” and “Much worse” were combined due to very few responses in the latter category.**The categories “Better” and “Much better” were combined due to very few responses in the latter category.“Don’t know” and ”Refuse” responses not shown.
17 16 1224 31
31 30 4130 16
38 4339 35
33
14 10 8 12 20
Shanghai
18 8 6 6 10
2727 22 17 17
4243
43 52 49
13 22 29 25 24
Hong Kong
%
%
Primary or Less Secondary Vocational, Technical or Associate
University Post-graduate
Much better
BetterSameWorse and Much Worse*
Better and Much Better**
Same
WorseMuch Worse
*The categories “Worse” and “Much worse” were combined due to very few responses in the latter category.**The categories “Better” and “Much better” were combined due to very few responses in the latter category.“Don’t know” responses excluded.
Figure 11: Perceptions of Improvement or Worsening by Educational Attainment, Hong Kong and Shanghai
Figure 12: Perceptions of Improvement or Worsening by Monthly Household Income, Hong Kong and Shanghai
Chi-square = 46.61 with 12 df p ≤ 0.0001
Chi-square = 40.57 with 12 df p ≤ 0.0001
Chi-square = 45.27 with 12 df p ≤ 0.0001
Chi-square = 58.70 with 15 df p ≤ 0.0001
19
7 Aspirations to Stay or Move AwayIf you could freely choose to live anywhere in the world, would you stay or move away?
Respondents were asked whether given a free choice, they would choose to stay in their city or move away. This question was worded aspirationally so that respondents would answer based on their desire rather than practical constraints. However, when comparing across cities, extra care should be taken because the populations being compared are different in wealth, education and international exposure. The cities being compared are also different in important ways. Singapore is a city-state with a strong national identity, while Shanghai is a wealthy first-tier city within a much broader national setting. Hong Kong has a very different history as a former British colony and a node for mass immigration and emigration.
The survey found that only 55 per cent of Hong Kong respondents would ideally want to stay, compared to 81 per cent of Shanghai respondents and 74 per cent of Singapore respondents.
As a proxy measure of international exposure, respondents were asked the question below.
5542
3
81
172
74
20
6
Move away Stay Don't know
Hong Kong Shanghai Singapore
% % %
Figure 13: Aspirations to Stay or Move Away, Hong Kong, Shanghai and Singapore
42% (All)
24% (Citizens Only)
7%24%
Figure 14: Do you currently have any parents, children, brothers, sisters, or spouses living abroad?
Hong Kong Shanghai Singapore
At 42 per cent Singapore clearly had the highest proportion of respondents with close relatives overseas, due to its large population of foreign origin. When only citizens were included, the figure dropped to 24 per cent, comparable to Hong Kong. For Shanghai, the figure was only 7 per cent.
As Table 2 overleaf shows, having relatives abroad had no correlation with whether or not respondents want to stay or move away in any of the three cities.
20
Table 2: Prefer to Stay or Move Way by Relatives Abroad
Relatives Abroad
Hong Kong Shanghai Singapore
Yes No Yes No Yes No
Prefer to Stay 55% 57% 79% 83% 80% 78%
Prefer to Move Away
45% 43% 21% 17% 20% 22%
Total 100% 100% 100% 100% 100% 100%
However, the desire to stay or move away was strongly associated with perceptions of whether the city has got better or worse over time. As Figure 15 shows, the association was strong in Singapore and Hong Kong and weaker in Shanghai. In all three cities, 90 per cent or more respondents who said their city has become much better wished to stay. Among respondents who thought their city has become much worse, only a quarter to a fifth wanted to stay in Singapore and Hong Kong, respectively, but two-thirds still wanted to stay in Shanghai. This implies that in Shanghai, factors besides perceptions of liveability influence the desire to leave or stay more than in Singapore or Hong Kong.
9482
57
20
90 85
80
53
6678
62
44
24
0
20
40
60
80
100
Much better Better Same Worse Much worse
Hong Kong Shanghai Singapore
%
Figure 15: Percentage of Respondents Who Wish to Stay by Perceptions of their City Becoming Better or Worse Over Time
“Don’t know” and
“refuse” responses
excluded.
21
8 Perceptions of Liveability for Children and RetireesIn your view, is [city] a good place for children to grow up or not? In your view, is [city] a good place for retirees to live or not?
In Shanghai and Singapore, huge majorities—83 per cent and 87 per cent respectively—said their city was a good or very good place for children to grow up. In contrast, only 32 per cent of Hong Kong respondents said the same.
On the question of retirement, while Hong Kong respondents were still the most negative, with 61 per cent saying that Hong Kong was not a good place for retirees, significant minorities in Shanghai (37 per cent) and Singapore (39 per cent) also said their cities were not good for retirees.
Interestingly though, in Hong Kong, retirees themselves were significantly more positive than average, with 59 per cent of them saying Hong Kong was a good place to retire. Further investigation is warranted into how
Hong Kong Shanghai Singapore
Very good Good Not so good Not good at all Don't know
0
%
100
1
2
3118 28
65 59
15 111
22 1
52
14
Hong Kong Shanghai Singapore
Very good Good Not so good Not good at all Don't know
0
%
1001 1 4
9
30
44
13
5
2
15
4632
49
13
36
Figure 16: Perceptions of Hong Kong Shanghai and Singapore as a Good Place for Children to Grow Up
Figure 17: Perceptions of Hong Kong Shanghai and Singapore as a Good Place for Retirees
22
7263
25
6
87 8985
54 55
94 95
83
51 53
0
20
40
60
80
100
Much better Better Same Worse Much worse*
Hong Kong Shanghai Singapore
%
79
5259
37
15
6967
62
4051
75
65
50
15 60
20
40
60
80
100
Much better Better Same Worse Much worse*
Hong Kong Shanghai Singapore
%
Figure 18: Percentage of Respondents Who Say Their City Is a Good Place for Children by Perceptions of Improvement or Worsening
Figure 19: Percentage of Respondents Who Say Their City Is a Good Place for Retirees by Perceptions of Improvement or Worsening
much this positive perception was due specifically to their experience of retirement, and how much of it was related to older respondents’ more positive outlook in general. As noted in previous sections, 60-65-year-olds gave themselves somewhat higher life evaluation scores and were less likely to think that Hong Kong had worsened as a place to live.
It was also found that perceptions of whether cities were good for children were strongly correlated with whether people thought their cities had got better or worse, especially in Hong Kong. Perceptions about whether cities were good for retirees were also linked to perceptions of improvement or worsening, especially in Hong Kong and Singapore. In general, the association was not as strong with retirees as with children, except for in Singapore.
* For Figures 18 and 19, “Much worse” responses are so few in number in Shanghai and Singapore that cross-tabs of extremes of the scale are not reliable.“Don’t know” responses excluded. Chi Square ≤ 0.0001 or 0.0000.
23
Hong Kong
Very worried Worried Not so worried Not worried at all Don't know
17
51
27
4
12
3644
8
Shanghai
10
18
46
25
1
7
28
44
21
14
33
37
15
2
Singapore
20
3338
91
Inner circle: Worry about providing for own family’s daily needsOuter circle: Worry about poverty in [city]
9 Worry About Poverty and Supporting Your FamilyHow worried are you about poverty in [city]? How worried are you about being able to provide for you and your family’s daily needs?
Figure 20: Worry About Poverty and Supporting Your Family, Hong Kong, Shanghai and Singapore
The two questions above focused on perceptions of economic struggle from different angles. The first, about poverty, focuses on sociopolitical perceptions of the city, while the second focuses on personal worry. Again, Hong Kong showed a different response pattern to the other two cities.
In Hong Kong, generalised worry about poverty (“worried” and “very worried”) far exceeded respondents’ worries about supporting their own families. While 68 per cent were worried or very worried about poverty, only 48 per cent were worried or very worried about supporting their own families.
In Shanghai and Singapore, however, personal worry exceeded sociopolitical worry: 35 per cent in Shanghai and 53 per cent in Singapore expressed worry about supporting their families, but only 28 per cent and 47 per cent respectively were worried about poverty.
Data Access EnquiriesAs part of Civic Exchange’s commitment to promoting public policy research and civic engagement, the Asian Urban-Wellbeing Indicators database, on which this report is based, will be made available to the public. For data access enquiries, please contact Carine Lai at (852) 2893 0213 or clai@civic-exchange.org.
23/F, Chun Wo Commercial Centre, 23-29 Wing Wo Street, Central, Hong KongT (852) 2893 0213 F (852) 3105 9713 www.civic-exchange.org
©Civic Exchange, June 2016The views expressed in this report are those of the authors and do not necessarily represent the views of Civic Exchange.