asian urban-wellbeing indicators comparative report...

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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 [email protected].

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





Education





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





Education





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



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





Education





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





Education





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





Education





Singapore


Mean Caring Score



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





Education





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





Education





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





Education





Singapore

Figure 2: Caring and Satisfaction, Shanghai

Medical CareHousing

Transport & Utilities

Environmental Protection


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


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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Mean Caring Score



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





Education





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





Education





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





Education





Singapore

Figure 3: Caring and Satisfaction, SingaporeM

ean

Carin

g Sc

ore:

1 =

not

at a

ll; 4

= a

lot


Medical Care

Housing




Education

Community &BelongingRecreation &

Personal Time

Public Safety & Crime Control


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


Mean Caring Score



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





Education





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





Education





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





Education





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


Environment


Public Safety

Medical Care

Education

Housing

Hong Kong


%



1

2

3

6

9

10

12

18

19

20

0 10 20 30

Community

Recreation

Public Safety

Environment

Education

Medical Care

Housing

Shanghai


%



3

4

4

6

10

10

13

15

16

20

0 10 20 30

Recreation

Community

Environment

Public Safety


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.

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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

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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

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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

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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


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


Figure 5: Top Four Domain Priorities by Occupation10

Chi-square = 337.2 with 90 df p ≤ 0.0001

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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


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





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


% % %

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?


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


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


%

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


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


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*


%

79

5259

37

15

6967

62

4051

75

65

50

15 60

20

40

60

80

100

Much better Better Same Worse Much worse*


%

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 [email protected].

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.

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