Savills Chart Book

SAVILLS CHINA Province Chart Book Introduction

With a population of 1.3 billion people and a country covering 9.6 million sq km of some of the most topographically, culturally and economically diverse land in the world, China’s economy and property markets are some of the hardest to understand, especially given the fact that the property market changes from day to day. This publication looks at some of the larger trends that are influencing the country at the moment, from urbanisation and the ageing of the population, to government expenditure and the domestic savings rate. We have also taken provincial data for a number of indicators and have represented this visually, in order to highlight the differences and similarities between regions and provinces. Just as most people would not compare Texas with New York, or Nebraska with California, it does not make sense to compare with , or with . I hope you find that this gives you a different and richer understanding of the underlying trends in China at the moment.

James Macdonald Head of China Research Key

安徽 AH 北京 BJ HLJ 重庆市 CQ 福建 FJ 甘肃 GS JL 广东 GD IM 广西 GX LN 贵州 GZ XJ 海南 HaN 河北 HB SaX BJ 黑龙江 HLJ TJ 河南 HeN HeB NX 湖北 HB QH 湖南 HuN SD 内蒙古 Inner IM GS 江苏 JS TB HeN JS SaaX 江西 JX AH SH 吉林 JL SC HB 辽宁 LN CQ ZJ 宁夏 NX 青海 QH GZ HnN JX 陕西 SaaX FJ 山东 SD YN 上海 Shanghai SH GX GD 山西 SaX 四川 SC 天津 TJ 西藏 Tibet TB INDEX HaN KEY 新疆 Xinjiang XJ HIGH # UNITS 云南 YN YEAR LOW # 浙江 ZJ Contents

•Economy •Demographics •Government •Real estate •Provinces ECONOMY Economy – economic miracle

GDP growth rates, 1980–2015E HDI indicators, 1980–2010

China India Brazil France US Canada Brazil China India Japan Spain UK Germany Italy Japan Russia UK US Europe East Asia World 100% 20%

90% 15%

80% 10%

5% 70%

0% 60%

-5% 50%

-10% 40%

-15%

30%

80 83 85 86 88 89 91 92 94 95 97 98 00 01 03 04 06 07 81 82 84 87 90 93 96 99 02 05 08

11F 1980 1985 1990 1995 2000 2005 2010

10F 12F 15F 13F 14F 09E

• China has gone through a remarkable change over the last 30 years, consistently outperforming its emerging peers (Brazil, Russia, India) and major developed nations in terms of economic growth. • This has lead to many dramatic changes in society, culture, industry, built environment and demography, and in particular, to significant gains in well-being in terms of the United Nations’ Human Development Index (HDI), a composite measure of three basic dimensions of human development: health, education and income. • China's HDI rose from 0.368 in 1980 to 0.663 in 2010, which, compared to east Asia and the Pacific region as a whole, which increased from 0.391 to 0.650 during the same period, places China above the regional average.

Source: IMF; Savills China Research Source: UN; Savills China Research Economy – GDP

1165. 5 1165. 5 1023. 5 1023. 5 1023. 5 1023. 5

GDP, 2010 1165. 5 1165. 5 1165. 5 1023. 5 1023. 5 1023. 5 1023. 5

541. 881 541. 881 541. 881 541. 881 541. 881 1165. 5 1165. 5 1165. 5 1165. 5 1023. 5 1023. 5 1023. 5 1023. 5

541. 881 541. 881 541. 881 541. 881 541. 881 1165. 5 1165. 5 1165. 5 1165. 5 857. 706 857. 706 857. 706 857. 706 857. 706

GDP 5 yr CAGR growth 541. 881 541. 881 541. 881 541. 881 541. 881 541. 881 541. 881 541. 881 1165. 5 1165. 5 1165. 5 1165. 5 1165. 5 1165. 5 1165. 5 857. 706 857. 706 RMB billion 5,000 25% 541. 881 541. 881 541. 881 541. 881 541. 881 541. 881 541. 881 541. 881 411. 946 411. 946 1165. 5 1165. 5 1165. 5 1165. 5 1165. 5 1165. 5 1165. 5 1165. 5 1165. 5 1165. 5 1827. 829 1827. 829 1827. 829 857. 706

541. 881 541. 881 541. 881 541. 881 541. 881 541. 881 541. 881 541. 881 541. 881 411. 946 411. 946 1165. 5 1165. 5 1165. 5 1165. 5 1165. 5 1165. 5 1165. 5 1165. 5 1165. 5 1165. 5 1165. 5 1165. 5 1165. 5 1827. 829 1827. 829 1827. 829

4,500 541. 881 541. 881 541. 881 541. 881 541. 881 541. 881 541. 881 541. 881 541. 881 541. 881 411. 946 411. 946 411. 946 1165. 5 1165. 5 1165. 5 1165. 5 1165. 5 1165. 5 1165. 5 1165. 5 1165. 5 908. 806 908. 806 2019. 709 2019. 709 2019. 709 1827. 829 1827. 829

4,000 20% 541. 881 541. 881 541. 881 541. 881 541. 881 541. 881 541. 881 541. 881 541. 881 541. 881 541. 881 411. 946 411. 946 411. 946 411. 946 411. 946 411. 946 411. 946 1165. 5 1165. 5 1165. 5 1165. 5 1165. 5 908. 806 908. 806 2019. 709 1377. 794 2019. 709

541. 881 541. 881 541. 881 541. 881 541. 881 541. 881 541. 881 541. 881 541. 881 541. 881 541. 881 135. 043 135. 043 135. 043 135. 043 135. 043 411. 946 411. 946 1165. 5 164. 341 1165. 5 1165. 5 1165. 5 908. 806 908. 806 2019. 709 2019. 709 910. 883

3,500 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 135. 043 135. 043 135. 043 135. 043 135. 043 135. 043 135. 043 135. 043 411. 946 164. 341 164. 341 164. 341 1002. 153 1002. 153 908. 806 908. 806 2019. 709 2019. 709 2019. 709 2019. 709 3941. 62 3941. 62

50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 135. 043 135. 043 135. 043 135. 043 135. 043 135. 043 135. 043 135. 043 411. 946 411. 946 164. 341 411. 946 1002. 153 1002. 153 908. 806 908. 806 2294. 268 2294. 268 3941. 62 3941. 62 3941. 62 3,000 15% 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 135. 043 135. 043 135. 043 135. 043 135. 043 135. 043 135. 043 135. 043 411. 946 411. 946 411. 946 411. 946 1002. 153 1002. 153 908. 806 2294. 268 2294. 268 2294. 268 3941. 62 3941. 62 3941. 62

2,500 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 135. 043 135. 043 135. 043 135. 043 135. 043 135. 043 135. 043 411. 946 411. 946 411. 946 1002. 153 1002. 153 1002. 153 2294. 268 2294. 268 2294. 268 1226. 336 1226. 336 1226. 336 4090. 334 4090. 334

50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 1689. 859 1689. 859 1689. 859 1689. 859 1689. 859 411. 946 411. 946 1002. 153 1002. 153 1002. 153 2294. 268 2294. 268 2294. 268 1226. 336 1226. 336 1226. 336 4090. 334 4090. 334 2,000 10% 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 1689. 859 1689. 859 1689. 859 1689. 859 1689. 859 411. 946 411. 946 1002. 153 1002. 153 1002. 153 1580. 609 1580. 609 1580. 609 1226. 336 1226. 336 1226. 336 1226. 336 4090. 334 1687. 242

1,500 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 1689. 859 1689. 859 1689. 859 1689. 859 1689. 859 1689. 859 789. 424 789. 424 1580. 609 1580. 609 1580. 609 1580. 609 1580. 609 1226. 336 1226. 336 1226. 336 2722. 675 2722. 675

50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 50. 746 1689. 859 1689. 859 1689. 859 1689. 859 1689. 859 1689. 859 789. 424 789. 424 1580. 609 1590. 212 1590. 212 1580. 609 1580. 609 943. 501 943. 501 2722. 675 2722. 675 2722. 675

1,000 5% 722. 014 722. 014 1689. 859 1689. 859 459. 397 459. 397 459. 397 459. 397 1590. 212 1590. 212 1590. 212 1590. 212 943. 501 943. 501 943. 501 2722. 675 2722. 675

722. 014 722. 014 722. 014 722. 014 459. 397 459. 397 459. 397 459. 397 1590. 212 1590. 212 1590. 212 1590. 212 943. 501 943. 501 1435. 712 1435. 712 500 722. 014 722. 014 722. 014 722. 014 722. 014 722. 014 950. 239 950. 239 950. 239 950. 239 1590. 212 1590. 212 1590. 212 943. 501 943. 501 1435. 712 1435. 712

0 0% 722. 014 722. 014 722. 014 722. 014 722. 014 722. 014 950. 239 950. 239 950. 239 950. 239 950. 239 4547. 283 4547. 283 4547. 283 4547. 283 4547. 283

722. 014 950. 239 950. 239 950. 239 4547. 283 4547. 283 4547. 283 4547. 283 4547. 283

4547. 283 4547. 283 Jilin

Tibet 4,547

Anhui

Hebei Hubei

Fujian

Tianjin

Henan Gansu Hunan

Beijing

Shanxi

Jiangxi

Hainan

Ningxia

Yunnan Qinghai

Jiangsu 3,423

Xinjiang

Sichuan Shaanxi

Guizhou

Guangxi

Liaoning Zhejiang 205. 212 205. 212 Shanghai GDP

Shandong 2,299 Chongqing

Guangdong 205. 212 205. 212 Heilongjiang High: 4,547 RMB billion 1,175 InnerMongolia 2010 Low: 51 51 • Economic growth first started in south China with an export market that fed products through to the world, helping to establish the (PRD), and more specifically Guangdong, as one of China’s economic centres. • In the 1990s, the Shanghai clique and a revitalisation of Shanghai as a gateway to the west helped to kick-start growth that had lagged behind the growth in the south. Shanghai and the Yangtze River Delta (YRD) now accounts for 18% of the country’s economic activity, 7% to 8% of the population and 1.1% of the area. • In the 11th Five-Year Plan (2005–2010), north China and the Bohai region, were together identified as a new centre of growth, designed to counter-balance that of the PRD and YRD. Economy – tertiary industry as a percentage of GDP

0. 379521165 0. 379521165 0. 392680797 0. 392680797 0. 392680797 0. 392680797

GDP breakdown, 2009 0. 379521165 0. 379521165 0. 379521165 0. 392680797 0. 392680797 0. 392680797 0. 392680797

0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 392680797 0. 392680797 0. 392680797 0. 392680797

0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 361093134 0. 361093134 0. 361093134 0. 361093134 0. 361093134

Primary Secondary Tertiary 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 361093134 0. 361093134

100% 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 402429449 0. 402429449 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 387263763 0. 387263763 0. 387263763 0. 361093134 90% 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 402429449 0. 402429449 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 387263763 0. 387263763 0. 387263763

0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 402429449 0. 402429449 0. 402429449 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 388120782 0. 388120782 0. 35208204 0. 35208204 0. 35208204 0. 387263763 0. 387263763

80% 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 402429449 0. 402429449 0. 402429449 0. 402429449 0. 402429449 0. 402429449 0. 402429449 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 379521165 0. 388120782 0. 388120782 0. 35208204 0. 755302394 0. 35208204

0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 37121414 0. 368574864 0. 368574864 0. 368574864 0. 368574864 0. 368574864 0. 402429449 0. 402429449 0. 379521165 0. 416566911 0. 379521165 0. 379521165 0. 379521165 0. 388120782 0. 388120782 0. 35208204 0. 35208204 0. 452699451 70% 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 368574864 0. 368574864 0. 368574864 0. 368574864 0. 368574864 0. 368574864 0. 368574864 0. 368574864 0. 402429449 0. 416566911 0. 416566911 0. 416566911 0. 384800118 0. 384800118 0. 388120782 0. 388120782 0. 35208204 0. 35208204 0. 35208204 0. 35208204 0. 347177749 0. 347177749

60% 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 368574864 0. 368574864 0. 368574864 0. 368574864 0. 368574864 0. 368574864 0. 368574864 0. 368574864 0. 402429449 0. 402429449 0. 416566911 0. 402429449 0. 384800118 0. 384800118 0. 388120782 0. 388120782 0. 292647006 0. 292647006 0. 347177749 0. 347177749 0. 347177749

0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 368574864 0. 368574864 0. 368574864 0. 368574864 0. 368574864 0. 368574864 0. 368574864 0. 368574864 0. 402429449 0. 402429449 0. 402429449 0. 402429449 0. 384800118 0. 384800118 0. 388120782 0. 292647006 0. 292647006 0. 292647006 0. 347177749 0. 347177749 0. 347177749

50% 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 368574864 0. 368574864 0. 368574864 0. 368574864 0. 368574864 0. 368574864 0. 368574864 0. 402429449 0. 402429449 0. 402429449 0. 384800118 0. 384800118 0. 384800118 0. 292647006 0. 292647006 0. 292647006 0. 363929523 0. 363929523 0. 363929523 0. 395535054 0. 395535054

0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 367372609 0. 367372609 0. 367372609 0. 367372609 0. 367372609 0. 402429449 0. 402429449 0. 384800118 0. 384800118 0. 384800118 0. 292647006 0. 292647006 0. 292647006 0. 363929523 0. 363929523 0. 363929523 0. 395535054 0. 395535054 40% 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 367372609 0. 367372609 0. 367372609 0. 367372609 0. 367372609 0. 402429449 0. 402429449 0. 384800118 0. 384800118 0. 384800118 0. 387233014 0. 387233014 0. 387233014 0. 363929523 0. 363929523 0. 363929523 0. 363929523 0. 395535054 0. 593549995

30% 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 367372609 0. 367372609 0. 367372609 0. 367372609 0. 367372609 0. 367372609 0. 37893415 0. 37893415 0. 387233014 0. 387233014 0. 387233014 0. 387233014 0. 387233014 0. 363929523 0. 363929523 0. 363929523 0. 43143138 0. 43143138

0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 545650204 0. 367372609 0. 367372609 0. 367372609 0. 367372609 0. 367372609 0. 367372609 0. 37893415 0. 37893415 0. 387233014 0. 413700927 0. 413700927 0. 387233014 0. 387233014 0. 344480876 0. 344480876 0. 43143138 0. 43143138 0. 43143138 20% 0. 408379526 0. 408379526 0. 367372609 0. 367372609 0. 481966417 0. 481966417 0. 481966417 0. 481966417 0. 413700927 0. 413700927 0. 413700927 0. 413700927 0. 344480876 0. 344480876 0. 344480876 0. 43143138 0. 43143138

10% 0. 408379526 0. 408379526 0. 408379526 0. 408379526 0. 481966417 0. 481966417 0. 481966417 0. 481966417 0. 413700927 0. 413700927 0. 413700927 0. 413700927 0. 344480876 0. 344480876 0. 400807412 0. 400807412

0. 408379526 0. 408379526 0. 408379526 0. 408379526 0. 408379526 0. 408379526 0. 376215331 0. 376215331 0. 376215331 0. 376215331 0. 413700927 0. 413700927 0. 413700927 0. 344480876 0. 344480876 0. 400807412 0. 400807412

0% 0. 408379526 0. 408379526 0. 408379526 0. 408379526 0. 408379526 0. 408379526 0. 376215331 0. 376215331 0. 376215331 0. 376215331 0. 376215331 0. 457229007 0. 457229007 0. 457229007 0. 457229007 0. 457229007

0. 408379526 0. 376215331 0. 376215331 0. 376215331 0. 457229007 0. 457229007 0. 457229007 0. 457229007 0. 457229007

Jilin 0. 457229007 0. 457229007

Tibet 76%

Anhui

Hubei Hebei

Fujian

Tianjin

Gansu

Henan Hunan

Beijing

Shanxi

Jiangxi

Hainan

Ningxia

Yunnan

Qinghai

Jiangsu

Xinjiang

Sichuan Shaanxi Guizhou

Guangxi 64%

Liaoning Zhejiang

Shanghai Tertiary Industry as % of GDP 0. 452538992 0. 452538992 Shandong

Chongqing 52% Guangdong 0. 452538992 0. 452538992 Heilongjiang High: 76% % 41% InnerMongolia 2009 Low: 29% 29% • As China’s economy continues to mature, the economic structure has shifted from one dominated by the primary industry, to one dominated by the secondary industry in the 1970s, and is now slowly giving way to the tertiary industry. • By 2010, 43% of China’s gross domestic product (GDP) was supplied by the tertiary industry, still far below international norms but higher than just ten years ago (39%). • In most provinces, the tertiary industry tends to contribute about 30% to 50% to the GDP, with the exceptions being the municipalities of Shanghai and Beijing, and the , which have significantly higher contributions, and with Henan province lagging behind. Economy – GDP per capita

47174 47174 26715 26715 26715 26715

GDP per capita, 2010 47174 47174 47174 26715 26715 26715 26715

24842 24842 24842 24842 24842 47174 47174 47174 47174 26715 26715 26715 26715

24842 24842 24842 24842 24842 47174 47174 47174 47174 31232 31232 31232 31232 31232

GDP per capita 5 yr CAGR growth 24842 24842 24842 24842 24842 24842 24842 24842 47174 47174 47174 47174 47174 47174 47174 31232 31232 RMB 80,000 40% 24842 24842 24842 24842 24842 24842 24842 24842 16107 16107 47174 47174 47174 47174 47174 47174 47174 47174 47174 47174 41782 41782 41782 31232

24842 24842 24842 24842 24842 24842 24842 24842 24842 16107 16107 47174 47174 47174 47174 47174 47174 47174 47174 47174 47174 47174 47174 47174 41782 41782 41782

70,000 35% 24842 24842 24842 24842 24842 24842 24842 24842 24842 24842 16107 16107 16107 47174 47174 47174 47174 47174 47174 47174 47174 47174 25448 25448 28108 28108 28108 41782 41782

24842 24842 24842 24842 24842 24842 24842 24842 24842 24842 24842 16107 16107 16107 16107 16107 16107 16107 47174 47174 47174 47174 47174 25448 25448 28108 70251 28108

60,000 30% 24842 24842 24842 24842 24842 24842 24842 24842 24842 24842 24842 24000 24000 24000 24000 24000 16107 16107 47174 26080 47174 47174 47174 25448 25448 28108 28108 70402

16903 16903 16903 16903 16903 16903 16903 16903 16903 24000 24000 24000 24000 24000 24000 24000 24000 16107 26080 26080 26080 26848 26848 25448 25448 28108 28108 28108 28108 41147 41147

50,000 25% 16903 16903 16903 16903 16903 16903 16903 16903 16903 24000 24000 24000 24000 24000 24000 24000 24000 16107 16107 26080 16107 26848 26848 25448 25448 24401 24401 41147 41147 41147

16903 16903 16903 16903 16903 16903 16903 16903 16903 24000 24000 24000 24000 24000 24000 24000 24000 16107 16107 16107 16107 26848 26848 25448 24401 24401 24401 41147 41147 41147

40,000 20% 16903 16903 16903 16903 16903 16903 16903 16903 16903 16903 24000 24000 24000 24000 24000 24000 24000 16107 16107 16107 26848 26848 26848 24401 24401 24401 20611 20611 20611 52000 52000

16903 16903 16903 16903 16903 16903 16903 16903 16903 16903 16903 16903 21013 21013 21013 21013 21013 16107 16107 26848 26848 26848 24401 24401 24401 20611 20611 20611 52000 52000

30,000 15% 16903 16903 16903 16903 16903 16903 16903 16903 16903 16903 16903 16903 21013 21013 21013 21013 21013 16107 16107 26848 26848 26848 27615 27615 27615 20611 20611 20611 20611 52000 73297

16903 16903 16903 16903 16903 16903 16903 16903 16903 16903 16903 21013 21013 21013 21013 21013 21013 27367 27367 27615 27615 27615 27615 27615 20611 20611 20611 50024 50024

20,000 10% 16903 16903 16903 16903 16903 16903 16903 16903 16903 21013 21013 21013 21013 21013 21013 27367 27367 27615 24210 24210 27615 27615 21170 21170 50024 50024 50024

15707 15707 21013 21013 13221 13221 13221 13221 24210 24210 24210 24210 21170 21170 21170 50024 50024

10,000 5% 15707 15707 15707 15707 13221 13221 13221 13221 24210 24210 24210 24210 21170 21170 38914 38914

15707 15707 15707 15707 15707 15707 20645 20645 20645 20645 24210 24210 24210 21170 21170 38914 38914

0 0% 15707 15707 15707 15707 15707 15707 20645 20645 20645 20645 20645 43597 43597 43597 43597 43597

15707 20645 20645 20645 43597 43597 43597 43597 43597

43597 43597 Jilin

Tibet 73,297

Anhui

Hebei Hubei

Fujian

Tianjin

Henan Hunan Gansu

Beijing

Shanxi

Jiangxi

Hainan

Ningxia

Yunnan Jiangsu

Qinghai 58,278

Xinjiang

Shaanxi Sichuan

Guizhou

Guangxi

Liaoning Zhejiang 23665 23665 Shanghai GDP per capita

Shandong 43,259 Chongqing

Guangdong 23665 23665 Heilongjiang High: 73,297 RMB 28,240 InnerMongolia 2010 Low: 13,221 13,221 • GDP, however, is often misleading as it does not take into account the size of the population generating the economic activity. In this context, Shanghai’s population of 19 million has no way of competing with Shandong province’s population of 95 million. • GDP per capita tends to be highest in the east coast provinces and in the most urbanised provinces. One exception is the autonomous region, which is rich in natural resources and has a relatively low population. Economy – GDP growth

114. 9 114. 9 112. 6 112. 6 112. 6 112. 6

GDP real growth, 2010 114. 9 114. 9 114. 9 112. 6 112. 6 112. 6 112. 6

110. 6 110. 6 110. 6 110. 6 110. 6 114. 9 114. 9 114. 9 114. 9 112. 6 112. 6 112. 6 112. 6

110. 6 110. 6 110. 6 110. 6 110. 6 114. 9 114. 9 114. 9 114. 9 113. 7 113. 7 113. 7 113. 7 113. 7

Previous year = 110. 6 110. 6 110. 6 110. 6 110. 6 110. 6 110. 6 110. 6 114. 9 114. 9 114. 9 114. 9 114. 9 114. 9 114. 9 113. 7 113. 7 100 110. 6 110. 6 110. 6 110. 6 110. 6 110. 6 110. 6 110. 6 111. 7 111. 7 114. 9 114. 9 114. 9 114. 9 114. 9 114. 9 114. 9 114. 9 114. 9 114. 9 114. 1 114. 1 114. 1 113. 7

118 110. 6 110. 6 110. 6 110. 6 110. 6 110. 6 110. 6 110. 6 110. 6 111. 7 111. 7 114. 9 114. 9 114. 9 114. 9 114. 9 114. 9 114. 9 114. 9 114. 9 114. 9 114. 9 114. 9 114. 9 114. 1 114. 1 114. 1

110. 6 110. 6 110. 6 110. 6 110. 6 110. 6 110. 6 110. 6 110. 6 110. 6 111. 7 111. 7 111. 7 114. 9 114. 9 114. 9 114. 9 114. 9 114. 9 114. 9 114. 9 114. 9 113. 9 113. 9 112. 2 112. 2 112. 2 114. 1 114. 1

116 110. 6 110. 6 110. 6 110. 6 110. 6 110. 6 110. 6 110. 6 110. 6 110. 6 110. 6 111. 7 111. 7 111. 7 111. 7 111. 7 111. 7 111. 7 114. 9 114. 9 114. 9 114. 9 114. 9 113. 9 113. 9 112. 2 110. 2 112. 2

110. 6 110. 6 110. 6 110. 6 110. 6 110. 6 110. 6 110. 6 110. 6 110. 6 110. 6 115. 3 115. 3 115. 3 115. 3 115. 3 111. 7 111. 7 114. 9 113. 4 114. 9 114. 9 114. 9 113. 9 113. 9 112. 2 112. 2 117. 4

112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 115. 3 115. 3 115. 3 115. 3 115. 3 115. 3 115. 3 115. 3 111. 7 113. 4 113. 4 113. 4 114. 5 114. 5 113. 9 113. 9 112. 2 112. 2 112. 2 112. 2 112. 5 112. 5 114 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 115. 3 115. 3 115. 3 115. 3 115. 3 115. 3 115. 3 115. 3 111. 7 111. 7 113. 4 111. 7 114. 5 114. 5 113. 9 113. 9 112. 2 112. 2 112. 5 112. 5 112. 5

112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 115. 3 115. 3 115. 3 115. 3 115. 3 115. 3 115. 3 115. 3 111. 7 111. 7 111. 7 111. 7 114. 5 114. 5 113. 9 112. 2 112. 2 112. 2 112. 5 112. 5 112. 5

112 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 115. 3 115. 3 115. 3 115. 3 115. 3 115. 3 115. 3 111. 7 111. 7 111. 7 114. 5 114. 5 114. 5 112. 2 112. 2 112. 2 114. 5 114. 5 114. 5 112. 6 112. 6

112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 115. 1 115. 1 115. 1 115. 1 115. 1 111. 7 111. 7 114. 5 114. 5 114. 5 112. 2 112. 2 112. 2 114. 5 114. 5 114. 5 112. 6 112. 6

112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 115. 1 115. 1 115. 1 115. 1 115. 1 111. 7 111. 7 114. 5 114. 5 114. 5 114. 8 114. 8 114. 8 114. 5 114. 5 114. 5 114. 5 112. 6 109. 9 110 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 115. 1 115. 1 115. 1 115. 1 115. 1 115. 1 117. 1 117. 1 114. 8 114. 8 114. 8 114. 8 114. 8 114. 5 114. 5 114. 5 111. 8 111. 8

112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 115. 1 115. 1 115. 1 115. 1 115. 1 115. 1 117. 1 117. 1 114. 8 114. 5 114. 5 114. 8 114. 8 114 114 111. 8 111. 8 111. 8

108 112. 3 112. 3 115. 1 115. 1 112. 8 112. 8 112. 8 112. 8 114. 5 114. 5 114. 5 114. 5 114 114 114 111. 8 111. 8

112. 3 112. 3 112. 3 112. 3 112. 8 112. 8 112. 8 112. 8 114. 5 114. 5 114. 5 114. 5 114 114 113. 8 113. 8

112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 114. 2 114. 2 114. 2 114. 2 114. 5 114. 5 114. 5 114 114 113. 8 113. 8 106 112. 3 112. 3 112. 3 112. 3 112. 3 112. 3 114. 2 114. 2 114. 2 114. 2 114. 2 112. 2 112. 2 112. 2 112. 2 112. 2

112. 3 114. 2 114. 2 114. 2 112. 2 112. 2 112. 2 112. 2 112. 2 Jilin

112. 2 112. 2

Tibet

Anhui

Hubei Hebei

Fujian 117

Tianjin

Hunan Gansu Henan

Beijing

Shanxi

Jiangxi

Hainan

Ningxia

Yunnan

Jiangsu Qinghai

Xinjiang

Sichuan Shaanxi

Guizhou

Guangxi Liaoning

Zhejiang 116 Shanghai

115. 8 115. 8 Shandong

Chongqing GDP growth 114

Guangdong Heilongjiang High: 117 preceding 115. 8 115. 8 112 InnerMongolia 2010 Low: 110 year=100 110 • As the east coast provinces quickly mature, the pace of development has slowed to around 11% or below, with the exception of Tianjin and the northeast provinces of Liaoning and Jilin. • Development has shifted to the central and southwestern provinces, which have historically fallen behind their eastern peers, primarily as a result of poor infrastructure and the cost of transporting goods. • Yet again, the Inner Mongolia autonomous region proves to be the exception to the rule, recording growth of 16.9% in 2009 and 14.9% in 2010, primarily due to strong demand for raw materials. The 2010 figures were overshadowed, however, by strong economic growth in Tianjin and Chongqing, two of the central government’s favoured municipalities. Economy – utilized FDI

Utilized FDI, 1984–2010 Utilized FDI as a percentage of GDP, 1983–2010

9% US$ billion FDI (LHS) Growth Rate (RHS) 160 160% 8% 140 140% 7% 120 120% 6% 100 100% 5% 80 80%

60 60% 4%

40 40% 3%

20 20% 2%

0 0% 1%

-20 -20% 0% 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

• China’s original economic growth spurt in the 1980s is often attributed to a surge in economic reforms, while growth in the 1990s is attributed to foreign investment. • Foreign direct investment (FDI) growth peaked in 1992 and 1993, growing to approximately 150%. Utilized FDI, in terms of the percentage of GDP, reached a peak in China in 1994 at 7.7%, but has since fallen to 1.9% in 2010. • As China’s home-grown companies, particularly state-owned enterprises (SOEs), continued to develop and evolve, FDI, relative to the overall economy, shrunk to roughly 2%. With the slowing of growth in developed economies, however, multinational corporations (MNCs) are likely to continue to move into China, helping to support FDI in the mid to long-term. Economy – utilized FDI as a percentage of GDP

0. 021204323 0. 021204323 0. 007280496 0. 007280496 0. 007280496 0. 007280496

FDI as a percentage of GDP, 2008 0. 021204323 0. 021204323 0. 021204323 0. 007280496 0. 007280496 0. 007280496 0. 007280496

0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 007280496 0. 007280496 0. 007280496 0. 007280496

0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 009954374 0. 009954374 0. 009954374 0. 009954374 0. 009954374

8% 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 009954374 0. 009954374

0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 002749451 0. 002749451 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 050978697 0. 050978697 0. 050978697 0. 009954374

7% 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 002749451 0. 002749451 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 050978697 0. 050978697 0. 050978697

0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 002749451 0. 002749451 0. 002749451 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 009945881 0. 009945881 0. 014360111 0. 014360111 0. 014360111 0. 050978697 0. 050978697

6% 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 002749451 0. 002749451 0. 002749451 0. 002749451 0. 002749451 0. 002749451 0. 002749451 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 021204323 0. 009945881 0. 009945881 0. 014360111 0. 039431329 0. 014360111

0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 003103139 0. 015558699 0. 015558699 0. 015558699 0. 015558699 0. 015558699 0. 002749451 0. 002749451 0. 021204323 0. 003667223 0. 021204323 0. 021204323 0. 021204323 0. 009945881 0. 009945881 0. 014360111 0. 014360111 0. 068364395

5% 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 015558699 0. 015558699 0. 015558699 0. 015558699 0. 015558699 0. 015558699 0. 015558699 0. 015558699 0. 002749451 0. 003667223 0. 003667223 0. 003667223 0. 012648701 0. 012648701 0. 009945881 0. 009945881 0. 014360111 0. 014360111 0. 014360111 0. 014360111 0. 017950766 0. 017950766

0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 015558699 0. 015558699 0. 015558699 0. 015558699 0. 015558699 0. 015558699 0. 015558699 0. 015558699 0. 002749451 0. 002749451 0. 003667223 0. 002749451 0. 012648701 0. 012648701 0. 009945881 0. 009945881 0. 014844303 0. 014844303 0. 017950766 0. 017950766 0. 017950766

4% 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 015558699 0. 015558699 0. 015558699 0. 015558699 0. 015558699 0. 015558699 0. 015558699 0. 015558699 0. 002749451 0. 002749451 0. 002749451 0. 002749451 0. 012648701 0. 012648701 0. 009945881 0. 014844303 0. 014844303 0. 014844303 0. 017950766 0. 017950766 0. 017950766

0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 015558699 0. 015558699 0. 015558699 0. 015558699 0. 015558699 0. 015558699 0. 015558699 0. 002749451 0. 002749451 0. 002749451 0. 012648701 0. 012648701 0. 012648701 0. 014844303 0. 014844303 0. 014844303 0. 025864543 0. 025864543 0. 025864543 0. 053800044 0. 053800044

0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 016387873 0. 016387873 0. 016387873 0. 016387873 0. 016387873 0. 002749451 0. 002749451 0. 012648701 0. 012648701 0. 012648701 0. 014844303 0. 014844303 0. 014844303 0. 025864543 0. 025864543 0. 025864543 0. 053800044 0. 053800044 3% 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 016387873 0. 016387873 0. 016387873 0. 016387873 0. 016387873 0. 002749451 0. 002749451 0. 012648701 0. 012648701 0. 012648701 0. 018812674 0. 018812674 0. 018812674 0. 025864543 0. 025864543 0. 025864543 0. 025864543 0. 053800044 0. 049480525

0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 016387873 0. 016387873 0. 016387873 0. 016387873 0. 016387873 0. 016387873 0. 02506231 0. 02506231 0. 018812674 0. 018812674 0. 018812674 0. 018812674 0. 018812674 0. 025864543 0. 025864543 0. 025864543 0. 030622944 0. 030622944 2% 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 00398404 0. 016387873 0. 016387873 0. 016387873 0. 016387873 0. 016387873 0. 016387873 0. 02506231 0. 02506231 0. 018812674 0. 023623383 0. 023623383 0. 018812674 0. 018812674 0. 037706708 0. 037706708 0. 030622944 0. 030622944 0. 030622944

0. 011142035 0. 011142035 0. 016387873 0. 016387873 0. 003040355 0. 003040355 0. 003040355 0. 003040355 0. 023623383 0. 023623383 0. 023623383 0. 023623383 0. 037706708 0. 037706708 0. 037706708 0. 030622944 0. 030622944

1% 0. 011142035 0. 011142035 0. 011142035 0. 011142035 0. 003040355 0. 003040355 0. 003040355 0. 003040355 0. 023623383 0. 023623383 0. 023623383 0. 023623383 0. 037706708 0. 037706708 0. 033736967 0. 033736967

0. 011142035 0. 011142035 0. 011142035 0. 011142035 0. 011142035 0. 011142035 0. 008916641 0. 008916641 0. 008916641 0. 008916641 0. 023623383 0. 023623383 0. 023623383 0. 037706708 0. 037706708 0. 033736967 0. 033736967

0% 0. 011142035 0. 011142035 0. 011142035 0. 011142035 0. 011142035 0. 011142035 0. 008916641 0. 008916641 0. 008916641 0. 008916641 0. 008916641 0. 036512192 0. 036512192 0. 036512192 0. 036512192 0. 036512192

0. 011142035 0. 008916641 0. 008916641 0. 008916641 0. 036512192 0. 036512192 0. 036512192 0. 036512192 0. 036512192

Jilin 0. 036512192 0. 036512192 Tibet

Anhui 6.8%

Hubei Hebei

Fujian

Tianjin

Hunan Henan Gansu

Beijing

Shanxi

Jiangxi

Hainan

Ningxia

Yunnan

Qinghai Jiangsu

Xinjiang

Sichuan Shaanxi Guizhou

Guangxi 5.2%

Liaoning Zhejiang

Shanghai 0. 056293333 0. 056293333

Shandong FDI (Utilized)

Chongqing 3.6% Guangdong 0. 056293333 0. 056293333 Heilongjiang High: 6.8% % 1.9% InnerMongolia 2008 Low: 0.3% 0.3% • FDI continues to focus on the coastal areas, such as Tianjin, Jiangsu and Liaoning provinces, as these markets remain familiar, accessible and open to foreign companies. • Investment is starting to spread to more central regions, although it is rather limited to the provinces of Anhui, Jiangxi, Hunan and Chongqing. Economy – FAI

FAI breakdown by industry, 2009 FAI, 1980–2010

FAI FAI as % of GDP Manufacturing RMb trillion 40 80% Real Estate 35 70% 11% 2% Transport, Storage & 3% 32% 30 60% 4% Postal Service 6% Environment 25 50%

9% Utilities 20 40%

11% 22% Mining 15 30%

Agriculture 10 20%

Wholesale and Retail 5 10% Trade

Other 0 0%

80 84 85 86 87 91 92 93 94 98 99 00 01 02 05 06 07 08 09 81 82 83 88 89 90 95 96 97 03 04 10

• In the past, China relied on manufacturing and the export of cheap products to overseas markets resulting in large trade surpluses, all the while the RMB was kept artificially low. • A greater emphasis has been placed on fixed-asset investment (FAI), as the government aims to construct a modern infrastructure network and, recently, ward off the pressure on its economy as its export industry feels the effects of the slowdown in the developed world. • In the 1980s and 1990s, FAI amounted to roughly 30% of GDP, a number that quickly climbed to 70% by the end of 2010. This was largely a result of the central government’s desire to build infrastructure capable of competing on a global stage. Economy – FAI as a percentage of GDP

0. 766184633 0. 766184633 0. 665614421 0. 665614421 0. 665614421 0. 665614421

FAI as a percentage of GDP, 2010 0. 766184633 0. 766184633 0. 766184633 0. 665614421 0. 665614421 0. 665614421 0. 665614421

0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 665614421 0. 665614421 0. 665614421 0. 665614421

0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 917608644 0. 917608644 0. 917608644 0. 917608644 0. 917608644

0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 917608644 0. 917608644

100% 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 766687697 0. 766687697 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 877709321 0. 877709321 0. 877709321 0. 917608644

0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 766687697 0. 766687697 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 877709321 0. 877709321 0. 877709321

90% 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 766687697 0. 766687697 0. 766687697 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 667157908 0. 667157908 0. 746768133 0. 746768133 0. 746768133 0. 877709321 0. 877709321

80% 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 766687697 0. 766687697 0. 766687697 0. 766687697 0. 766687697 0. 766687697 0. 766687697 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 766184633 0. 667157908 0. 667157908 0. 746768133 0. 392145125 0. 746768133

0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 626104458 0. 754372163 0. 754372163 0. 754372163 0. 754372163 0. 754372163 0. 766687697 0. 766687697 0. 766184633 0. 878758557 0. 766184633 0. 766184633 0. 766184633 0. 667157908 0. 667157908 0. 746768133 0. 746768133 0. 689284617

70% 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 754372163 0. 754372163 0. 754372163 0. 754372163 0. 754372163 0. 754372163 0. 754372163 0. 754372163 0. 766687697 0. 878758557 0. 878758557 0. 878758557 0. 794731523 0. 794731523 0. 667157908 0. 667157908 0. 746768133 0. 746768133 0. 746768133 0. 746768133 0. 590694298 0. 590694298

0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 754372163 0. 754372163 0. 754372163 0. 754372163 0. 754372163 0. 754372163 0. 754372163 0. 754372163 0. 766687697 0. 766687697 0. 878758557 0. 766687697 0. 794731523 0. 794731523 0. 667157908 0. 667157908 0. 72292601 0. 72292601 0. 590694298 0. 590694298 0. 590694298 60% 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 754372163 0. 754372163 0. 754372163 0. 754372163 0. 754372163 0. 754372163 0. 754372163 0. 754372163 0. 766687697 0. 766687697 0. 766687697 0. 766687697 0. 794731523 0. 794731523 0. 667157908 0. 72292601 0. 72292601 0. 72292601 0. 590694298 0. 590694298 0. 590694298

50% 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 754372163 0. 754372163 0. 754372163 0. 754372163 0. 754372163 0. 754372163 0. 754372163 0. 766687697 0. 766687697 0. 766687697 0. 794731523 0. 794731523 0. 794731523 0. 72292601 0. 72292601 0. 72292601 0. 941294066 0. 941294066 0. 941294066 0. 56686706 0. 56686706

0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 776364359 0. 776364359 0. 776364359 0. 776364359 0. 776364359 0. 766687697 0. 766687697 0. 794731523 0. 794731523 0. 794731523 0. 72292601 0. 72292601 0. 72292601 0. 941294066 0. 941294066 0. 941294066 0. 56686706 0. 56686706 40% 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 776364359 0. 776364359 0. 776364359 0. 776364359 0. 776364359 0. 766687697 0. 766687697 0. 794731523 0. 794731523 0. 794731523 0. 649287521 0. 649287521 0. 649287521 0. 941294066 0. 941294066 0. 941294066 0. 941294066 0. 56686706 0. 302795663

30% 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 776364359 0. 776364359 0. 776364359 0. 776364359 0. 776364359 0. 776364359 0. 84776024 0. 84776024 0. 649287521 0. 649287521 0. 649287521 0. 649287521 0. 649287521 0. 941294066 0. 941294066 0. 941294066 0. 45866911 0. 45866911

0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 912896583 0. 776364359 0. 776364359 0. 776364359 0. 776364359 0. 776364359 0. 776364359 0. 84776024 0. 84776024 0. 649287521 0. 607707658 0. 607707658 0. 649287521 0. 649287521 0. 930097 0. 930097 0. 45866911 0. 45866911 0. 45866911

20% 0. 765735097 0. 765735097 0. 776364359 0. 776364359 0. 675868149 0. 675868149 0. 675868149 0. 675868149 0. 607707658 0. 607707658 0. 607707658 0. 607707658 0. 930097 0. 930097 0. 930097 0. 45866911 0. 45866911

0. 765735097 0. 765735097 0. 765735097 0. 765735097 0. 675868149 0. 675868149 0. 675868149 0. 675868149 0. 607707658 0. 607707658 0. 607707658 0. 607707658 0. 930097 0. 930097 0. 571043907 0. 571043907 10% 0. 765735097 0. 765735097 0. 765735097 0. 765735097 0. 765735097 0. 765735097 0. 742713696 0. 742713696 0. 742713696 0. 742713696 0. 607707658 0. 607707658 0. 607707658 0. 930097 0. 930097 0. 571043907 0. 571043907

0% 0. 765735097 0. 765735097 0. 765735097 0. 765735097 0. 765735097 0. 765735097 0. 742713696 0. 742713696 0. 742713696 0. 742713696 0. 742713696 0. 343590362 0. 343590362 0. 343590362 0. 343590362 0. 343590362

0. 765735097 0. 742713696 0. 742713696 0. 742713696 0. 343590362 0. 343590362 0. 343590362 0. 343590362 0. 343590362

0. 343590362 0. 343590362 Jilin

Tibet 94%

Anhui

Hubei Hebei

Fujian

Tianjin

Gansu Henan Hunan

Beijing

Shanxi

Jiangxi

Hainan

Ningxia

Yunnan Jiangsu

Qinghai 78%

Xinjiang

Shaanxi Sichuan

Guizhou

Guangxi

Liaoning Zhejiang 0. 641796484 0. 641796484 Shanghai FAI as a % of GDP

Shandong 62% Chongqing

Guangdong 0. 641796484 0. 641796484 Heilongjiang High: 94% % 46% InnerMongolia 2010 Low: 30% 30% • FAI is one of the biggest contributors to the nation’s economic growth, representing 70% of its GDP in 2010. Meanwhile, gross capital formation represented roughly 48%. • As the coastal cities reoriented their economies to the manufacturing and services sectors, the proportion of their economy driven by FAI has fallen, especially when considering that a significant proportion of their infrastructure programmes are coming to an end and their real estate markets are slowing. • The central regions of Anhui and Jiangxi provinces, as well as Tibet and some of the northeastern provinces, however, still heavily rely on FAI for economic activity. Economy – real estate development as a percentage of GDP

0. 096098121 0. 096098121 0. 08237658 0. 08237658 0. 08237658 0. 08237658

Real estate development as a percentage of GDP, 2010 0. 096098121 0. 096098121 0. 096098121 0. 08237658 0. 08237658 0. 08237658 0. 08237658

0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 08237658 0. 08237658 0. 08237658 0. 08237658

0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 107380816 0. 107380816 0. 107380816 0. 107380816 0. 107380816

0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 107380816 0. 107380816

25% 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 064671219 0. 064671219 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 189610527 0. 189610527 0. 189610527 0. 107380816

0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 064671219 0. 064671219 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 189610527 0. 189610527 0. 189610527

0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 064671219 0. 064671219 0. 064671219 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 065166559 0. 065166559 0. 112136689 0. 112136689 0. 112136689 0. 189610527 0. 189610527

20% 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 064671219 0. 064671219 0. 064671219 0. 064671219 0. 064671219 0. 064671219 0. 064671219 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 096098121 0. 065166559 0. 065166559 0. 112136689 0. 210558951 0. 112136689

0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 063654326 0. 08011426 0. 08011426 0. 08011426 0. 08011426 0. 08011426 0. 064671219 0. 064671219 0. 096098121 0. 154782738 0. 096098121 0. 096098121 0. 096098121 0. 065166559 0. 065166559 0. 112136689 0. 112136689 0. 095143108

0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 08011426 0. 08011426 0. 08011426 0. 08011426 0. 08011426 0. 08011426 0. 08011426 0. 08011426 0. 064671219 0. 154782738 0. 154782738 0. 154782738 0. 11577357 0. 11577357 0. 065166559 0. 065166559 0. 112136689 0. 112136689 0. 112136689 0. 112136689 0. 082498511 0. 082498511

0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 08011426 0. 08011426 0. 08011426 0. 08011426 0. 08011426 0. 08011426 0. 08011426 0. 08011426 0. 064671219 0. 064671219 0. 154782738 0. 064671219 0. 11577357 0. 11577357 0. 065166559 0. 065166559 0. 092146345 0. 092146345 0. 082498511 0. 082498511 0. 082498511 15% 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 08011426 0. 08011426 0. 08011426 0. 08011426 0. 08011426 0. 08011426 0. 08011426 0. 08011426 0. 064671219 0. 064671219 0. 064671219 0. 064671219 0. 11577357 0. 11577357 0. 065166559 0. 092146345 0. 092146345 0. 092146345 0. 082498511 0. 082498511 0. 082498511

0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 08011426 0. 08011426 0. 08011426 0. 08011426 0. 08011426 0. 08011426 0. 08011426 0. 064671219 0. 064671219 0. 064671219 0. 11577357 0. 11577357 0. 11577357 0. 092146345 0. 092146345 0. 092146345 0. 183620517 0. 183620517 0. 183620517 0. 105171167 0. 105171167

0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 129870563 0. 129870563 0. 129870563 0. 129870563 0. 129870563 0. 064671219 0. 064671219 0. 11577357 0. 11577357 0. 11577357 0. 092146345 0. 092146345 0. 092146345 0. 183620517 0. 183620517 0. 183620517 0. 105171167 0. 105171167 10% 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 129870563 0. 129870563 0. 129870563 0. 129870563 0. 129870563 0. 064671219 0. 064671219 0. 11577357 0. 11577357 0. 11577357 0. 102380519 0. 102380519 0. 102380519 0. 183620517 0. 183620517 0. 183620517 0. 183620517 0. 105171167 0. 1173915

0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 129870563 0. 129870563 0. 129870563 0. 129870563 0. 129870563 0. 129870563 0. 205245483 0. 205245483 0. 102380519 0. 102380519 0. 102380519 0. 102380519 0. 102380519 0. 183620517 0. 183620517 0. 183620517 0. 111289148 0. 111289148

0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 017663264 0. 129870563 0. 129870563 0. 129870563 0. 129870563 0. 129870563 0. 129870563 0. 205245483 0. 205245483 0. 102380519 0. 092398334 0. 092398334 0. 102380519 0. 102380519 0. 074914833 0. 074914833 0. 111289148 0. 111289148 0. 111289148

5% 0. 124712374 0. 124712374 0. 129870563 0. 129870563 0. 121177805 0. 121177805 0. 121177805 0. 121177805 0. 092398334 0. 092398334 0. 092398334 0. 092398334 0. 074914833 0. 074914833 0. 074914833 0. 111289148 0. 111289148

0. 124712374 0. 124712374 0. 124712374 0. 124712374 0. 121177805 0. 121177805 0. 121177805 0. 121177805 0. 092398334 0. 092398334 0. 092398334 0. 092398334 0. 074914833 0. 074914833 0. 126686759 0. 126686759

0. 124712374 0. 124712374 0. 124712374 0. 124712374 0. 124712374 0. 124712374 0. 126938707 0. 126938707 0. 126938707 0. 126938707 0. 092398334 0. 092398334 0. 092398334 0. 074914833 0. 074914833 0. 126686759 0. 126686759

0% 0. 124712374 0. 124712374 0. 124712374 0. 124712374 0. 124712374 0. 124712374 0. 126938707 0. 126938707 0. 126938707 0. 126938707 0. 126938707 0. 080480907 0. 080480907 0. 080480907 0. 080480907 0. 080480907

0. 124712374 0. 126938707 0. 126938707 0. 126938707 0. 080480907 0. 080480907 0. 080480907 0. 080480907 0. 080480907

0. 080480907 0. 080480907 Jilin

Tibet 23%

Anhui

Hubei Hebei

Fujian

Tianjin

Hunan Gansu Henan

Beijing

Shanxi

Jiangxi

Hainan

Ningxia

Yunnan Qinghai

Jiangsu 18%

Xinjiang

Sichuan Shaanxi

Guizhou

Guangxi

Liaoning Zhejiang 0. 227991492 0. 227991492

Shanghai FAI (RE Development) as % of GDP

Shandong 12% Chongqing

Guangdong 0. 227991492 0. 227991492 Heilongjiang High: 23% % 7% InnerMongolia 2010 Low: 2% 2% • Aside from infrastructure projects and public schemes, the real estate market is a significant contributor to the country’s growth. However, this is still modest when compared with FAI as a whole. • Anhui and Liaoning provinces still rank quite high, but Chongqing and Hainan provinces, and Beijing are quite reliant on the real estate industry for a lot of their GDP growth. DEMOGRAPHICS Demographics – gains

Urbanisation, 1950–2050E Dependency ratio,1950–2050E

Urban (LHS) Rural (LHS) Urbanistaion Rate (RHS) million persons Child Dependency Ratio Elderly Dependency Ratio 1,600 80% 90%

1,400 70% 80%

1,200 60% 70%

60% 1,000 50%

50% 800 40% 40% 600 30% 30% 400 20% 20%

200 10% 10%

0 0% 0% 50 55 60 65 70 75 80 85 90 95 00 05 10 15 20 25 30 35 40 45 50 50 55 60 65 70 75 80 85 90 95 00 05 10 15 20 25 30 35 40 45 50

• One of the major drivers of growth in China, over and above any economic reforms, has been the massive demographic gains it has enjoyed over the last 50 years. • In 1965, only 55.4% of the population was aged between 15 and 65, while 40.2% of the population was younger than 15. As this younger population entered the workforce, the percentage of the population aged between 15 and 65 rose to 71.9% by 2010. • Going forward, however, the one-child policy will result in the dependency ratio (ratio of young and old relative to the workforce) to rise after 2010, and the workforce itself will begin shrinking after 2015. • Fortunately, a more efficient workforce and urbanisation is expected to maintain productivity for years to come.

Source: UNDP; Savills China Research Source: UNDP; Savills China Research Demographics – population density

0. 215752886 0. 215752886 0. 846407728 0. 846407728 0. 846407728 0. 846407728

Population density, 2010 0. 215752886 0. 215752886 0. 215752886 0. 846407728 0. 846407728 0. 846407728 0. 846407728

0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 215752886 0. 215752886 0. 215752886 0. 215752886 0. 846407728 0. 846407728 0. 846407728 0. 846407728

0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 215752886 0. 215752886 0. 215752886 0. 215752886 1. 436884429 1. 436884429 1. 436884429 1. 436884429 1. 436884429

Population Density 5 yr CAGR growth 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 215752886 0. 215752886 0. 215752886 0. 215752886 0. 215752886 0. 215752886 0. 215752886 1. 436884429 1. 436884429 persons per Ha 30 6% 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 632908479 0. 632908479 0. 215752886 0. 215752886 0. 215752886 0. 215752886 0. 215752886 0. 215752886 0. 215752886 0. 215752886 0. 215752886 0. 215752886 2. 954560831 2. 954560831 2. 954560831 1. 436884429

0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 632908479 0. 632908479 0. 215752886 0. 215752886 0. 215752886 0. 215752886 0. 215752886 0. 215752886 0. 215752886 0. 215752886 0. 215752886 0. 215752886 0. 215752886 0. 215752886 0. 215752886 2. 954560831 2. 954560831 2. 954560831

25 5% 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 632908479 0. 632908479 0. 632908479 0. 215752886 0. 215752886 0. 215752886 0. 215752886 0. 215752886 0. 215752886 0. 215752886 0. 215752886 0. 215752886 2. 278847967 2. 278847967 3. 813231958 3. 813231958 3. 813231958 2. 954560831 2. 954560831

0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 632908479 0. 632908479 0. 632908479 0. 632908479 0. 632908479 0. 632908479 0. 632908479 0. 215752886 0. 215752886 0. 215752886 0. 215752886 0. 215752886 2. 278847967 2. 278847967 3. 813231958 3. 813231958

20 4% 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 131019107 0. 078423341 0. 078423341 0. 078423341 0. 078423341 0. 078423341 0. 632908479 0. 632908479 0. 215752886 1. 212862244 0. 215752886 0. 215752886 0. 215752886 2. 278847967 2. 278847967 3. 813231958 3. 813231958

0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 078423341 0. 078423341 0. 078423341 0. 078423341 0. 078423341 0. 078423341 0. 078423341 0. 078423341 0. 632908479 1. 212862244 1. 212862244 1. 212862244 1. 813817186 1. 813817186 2. 278847967 2. 278847967 3. 813231958 3. 813231958 3. 813231958 3. 813231958 6. 096564475 6. 096564475

15 3% 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 078423341 0. 078423341 0. 078423341 0. 078423341 0. 078423341 0. 078423341 0. 078423341 0. 078423341 0. 632908479 0. 632908479 1. 212862244 0. 632908479 1. 813817186 1. 813817186 2. 278847967 2. 278847967 5. 679932393 5. 679932393 6. 096564475 6. 096564475 6. 096564475

0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 078423341 0. 078423341 0. 078423341 0. 078423341 0. 078423341 0. 078423341 0. 078423341 0. 078423341 0. 632908479 0. 632908479 0. 632908479 0. 632908479 1. 813817186 1. 813817186 2. 278847967 5. 679932393 5. 679932393 5. 679932393 6. 096564475 6. 096564475 6. 096564475

10 2% 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 078423341 0. 078423341 0. 078423341 0. 078423341 0. 078423341 0. 078423341 0. 078423341 0. 632908479 0. 632908479 0. 632908479 1. 813817186 1. 813817186 1. 813817186 5. 679932393 5. 679932393 5. 679932393 4. 246221142 4. 246221142 4. 246221142 7. 369183753 7. 369183753

0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 1. 661340598 1. 661340598 1. 661340598 1. 661340598 1. 661340598 0. 632908479 0. 632908479 1. 813817186 1. 813817186 1. 813817186 5. 679932393 5. 679932393 5. 679932393 4. 246221142 4. 246221142 4. 246221142 7. 369183753 7. 369183753

5 1% 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 1. 661340598 1. 661340598 1. 661340598 1. 661340598 1. 661340598 0. 632908479 0. 632908479 1. 813817186 1. 813817186 1. 813817186 3. 079144882 3. 079144882 3. 079144882 4. 246221142 4. 246221142 4. 246221142 4. 246221142 7. 369183753

0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 1. 661340598 1. 661340598 1. 661340598 1. 661340598 1. 661340598 1. 661340598 3. 506338077 3. 506338077 3. 079144882 3. 079144882 3. 079144882 3. 079144882 3. 079144882 4. 246221142 4. 246221142 4. 246221142 5. 163971893 5. 163971893

0 0% 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 0. 024974937 1. 661340598 1. 661340598 1. 661340598 1. 661340598 1. 661340598 1. 661340598 3. 506338077 3. 506338077 3. 079144882 3. 10041391 3. 10041391 3. 079144882 3. 079144882 2. 670400676 2. 670400676 5. 163971893 5. 163971893 5. 163971893

1. 199554908 1. 199554908 1. 661340598 1. 661340598 1. 97252237 1. 97252237 1. 97252237 1. 97252237 3. 10041391 3. 10041391 3. 10041391 3. 10041391 2. 670400676 2. 670400676 2. 670400676 5. 163971893 5. 163971893

-5 -1% 1. 199554908 1. 199554908 1. 199554908 1. 199554908 1. 97252237 1. 97252237 1. 97252237 1. 97252237 3. 10041391 3. 10041391 3. 10041391 3. 10041391 2. 670400676 2. 670400676 2. 97496482 2. 97496482

1. 199554908 1. 199554908 1. 199554908 1. 199554908 1. 199554908 1. 199554908 1. 937489372 1. 937489372 1. 937489372 1. 937489372 3. 10041391 3. 10041391 3. 10041391 2. 670400676 2. 670400676 2. 97496482 2. 97496482

-10 -2% 1. 199554908 1. 199554908 1. 199554908 1. 199554908 1. 199554908 1. 199554908 1. 937489372 1. 937489372 1. 937489372 1. 937489372 1. 937489372 5. 800655737 5. 800655737 5. 800655737 5. 800655737 5. 800655737

1. 199554908 1. 937489372 1. 937489372 1. 937489372 5. 800655737 5. 800655737 5. 800655737 5. 800655737 5. 800655737

5. 800655737 5. 800655737 Jilin

Tibet 7.4

Anhui

Hebei Hubei

Fujian

Tianjin

Henan Hunan Gansu

Beijing

Shanxi

Jiangxi

Hainan

Ningxia

Yunnan Jiangsu

Qinghai 5.5

Xinjiang

Shaanxi Sichuan

Guizhou

Guangxi

Liaoning Zhejiang 2. 452790104 2. 452790104 Shanghai Population Density

Shandong 3.7 Chongqing

Guangdong 2. 452790104 2. 452790104 Heilongjiang High: 7.4 persons per 1.9

InnerMongolia 2010 Low: 0.0 Ha 0.0 • The municipalities of Shanghai, Beijing and Tianjin have been excluded from the map above to allow greater comparability, as these cities are more densely populated than other provinces. • Guangdong, Zhejiang, Jiangsu and Shandong are not only the most densely populated provinces, but also register considerable growth rates in population density. Demographics – urbanisation

0. 534 0. 534 0. 555 0. 555 0. 555 0. 555

Urbanisation, 2009 0. 534 0. 534 0. 534 0. 555 0. 555 0. 555 0. 555

0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 534 0. 534 0. 534 0. 534 0. 555 0. 555 0. 555 0. 555

0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 534 0. 534 0. 534 0. 534 0. 5332 0. 5332 0. 5332 0. 5332 0. 5332

100% 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 534 0. 534 0. 534 0. 534 0. 534 0. 534 0. 534 0. 5332 0. 5332

0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3265 0. 3265 0. 534 0. 534 0. 534 0. 534 0. 534 0. 534 0. 534 0. 534 0. 534 0. 534 0. 6035 0. 6035 0. 6035 0. 5332 90% 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3265 0. 3265 0. 534 0. 534 0. 534 0. 534 0. 534 0. 534 0. 534 0. 534 0. 534 0. 534 0. 534 0. 534 0. 534 0. 6035 0. 6035 0. 6035

80% 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3265 0. 3265 0. 3265 0. 534 0. 534 0. 534 0. 534 0. 534 0. 534 0. 534 0. 534 0. 534 0. 4599 0. 4599 0. 43 0. 43 0. 43 0. 6035 0. 6035

0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3265 0. 3265 0. 3265 0. 3265 0. 3265 0. 3265 0. 3265 0. 534 0. 534 0. 534 0. 534 0. 534 0. 4599 0. 4599 0. 43 0. 43

70% 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 3985 0. 418087206 0. 418087206 0. 418087206 0. 418087206 0. 418087206 0. 3265 0. 3265 0. 534 0. 460652591 0. 534 0. 534 0. 534 0. 4599 0. 4599 0. 43 0. 43

0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 418087206 0. 418087206 0. 418087206 0. 418087206 0. 418087206 0. 418087206 0. 418087206 0. 418087206 0. 3265 0. 460652591 0. 460652591 0. 460652591 0. 435 0. 435 0. 4599 0. 4599 0. 43 0. 43 0. 43 0. 43 0. 4832 0. 4832 60% 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 418087206 0. 418087206 0. 418087206 0. 418087206 0. 418087206 0. 418087206 0. 418087206 0. 418087206 0. 3265 0. 3265 0. 460652591 0. 3265 0. 435 0. 435 0. 4599 0. 4599 0. 377042268 0. 377042268 0. 4832 0. 4832 0. 4832

50% 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 418087206 0. 418087206 0. 418087206 0. 418087206 0. 418087206 0. 418087206 0. 418087206 0. 418087206 0. 3265 0. 3265 0. 3265 0. 3265 0. 435 0. 435 0. 4599 0. 377042268 0. 377042268 0. 377042268 0. 4832 0. 4832 0. 4832

0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 418087206 0. 418087206 0. 418087206 0. 418087206 0. 418087206 0. 418087206 0. 418087206 0. 3265 0. 3265 0. 3265 0. 435 0. 435 0. 435 0. 377042268 0. 377042268 0. 377042268 0. 421 0. 421 0. 421 0. 556 0. 556 40% 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 387 0. 387 0. 387 0. 387 0. 387 0. 3265 0. 3265 0. 435 0. 435 0. 435 0. 377042268 0. 377042268 0. 377042268 0. 421 0. 421 0. 421 0. 556 0. 556

30% 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 387 0. 387 0. 387 0. 387 0. 387 0. 3265 0. 3265 0. 435 0. 435 0. 435 0. 46 0. 46 0. 46 0. 421 0. 421 0. 421 0. 421 0. 556

0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 387 0. 387 0. 387 0. 387 0. 387 0. 387 0. 5159 0. 5159 0. 46 0. 46 0. 46 0. 46 0. 46 0. 421 0. 421 0. 421 0. 579 0. 579

20% 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 238 0. 387 0. 387 0. 387 0. 387 0. 387 0. 387 0. 5159 0. 5159 0. 46 0. 432 0. 432 0. 46 0. 46 0. 4318 0. 4318 0. 579 0. 579 0. 579

10% 0. 34 0. 34 0. 387 0. 387 0. 2989 0. 2989 0. 2989 0. 2989 0. 432 0. 432 0. 432 0. 432 0. 4318 0. 4318 0. 4318 0. 579 0. 579

0. 34 0. 34 0. 34 0. 34 0. 2989 0. 2989 0. 2989 0. 2989 0. 432 0. 432 0. 432 0. 432 0. 4318 0. 4318 0. 514 0. 514

0% 0. 34 0. 34 0. 34 0. 34 0. 34 0. 34 0. 392 0. 392 0. 392 0. 392 0. 432 0. 432 0. 432 0. 4318 0. 4318 0. 514 0. 514

0. 34 0. 34 0. 34 0. 34 0. 34 0. 34 0. 392 0. 392 0. 392 0. 392 0. 392 0. 634 0. 634 0. 634 0. 634 0. 634

0. 34 0. 392 0. 392 0. 392 0. 634 0. 634 0. 634 0. 634 0. 634

Jilin Tibet

0. 634 0. 634

Anhui

Hubei Hebei

Fujian

Tianjin

Hunan Gansu Henan

Beijing 63%

Shanxi

Jiangxi

Hainan

Ningxia

Yunnan

Qinghai

Jiangsu

Xinjiang

Shaanxi Sichuan

Guizhou Guangxi Liaoning

Zhejiang 54% Shanghai

Shandong 0. 490700985 0. 490700985

Chongqing Urbanization 44%

Guangdong Heilongjiang High: 63.4% 0. 490700985 0. 490700985 34%

InnerMongolia % 2009 Low: 23.8% 24% • The municipalities of Shanghai, Beijing and Tianjin have been excluded from the map above to allow greater comparability, as these cities are significantly more urbanised than other provinces. • The provinces near the main economic regions, ie, the YRD, the PRD and the Bohai Rim, tend to have higher degrees of urbanisation, with Liaoning outranking Zhejiang and Jiangsu. • Urbanisation rates range from 62%, all the way down to 30% in Guizhou province and 24% in Tibet. Demographics – household size

2. 91 2. 91 2. 88 2. 88 2. 88 2. 88

Household size, 2009 2. 91 2. 91 2. 91 2. 88 2. 88 2. 88 2. 88

3. 49 3. 49 3. 49 3. 49 3. 49 2. 91 2. 91 2. 91 2. 91 2. 88 2. 88 2. 88 2. 88

3. 49 3. 49 3. 49 3. 49 3. 49 2. 91 2. 91 2. 91 2. 91 3. 08 3. 08 3. 08 3. 08 3. 08 persons 5.0 3. 49 3. 49 3. 49 3. 49 3. 49 3. 49 3. 49 3. 49 2. 91 2. 91 2. 91 2. 91 2. 91 2. 91 2. 91 3. 08 3. 08

3. 49 3. 49 3. 49 3. 49 3. 49 3. 49 3. 49 3. 49 3. 58 3. 58 2. 91 2. 91 2. 91 2. 91 2. 91 2. 91 2. 91 2. 91 2. 91 2. 91 2. 86 2. 86 2. 86 3. 08

4.5 3. 49 3. 49 3. 49 3. 49 3. 49 3. 49 3. 49 3. 49 3. 49 3. 58 3. 58 2. 91 2. 91 2. 91 2. 91 2. 91 2. 91 2. 91 2. 91 2. 91 2. 91 2. 91 2. 91 2. 91 2. 86 2. 86 2. 86

3. 49 3. 49 3. 49 3. 49 3. 49 3. 49 3. 49 3. 49 3. 49 3. 49 3. 58 3. 58 3. 58 2. 91 2. 91 2. 91 2. 91 2. 91 2. 91 2. 91 2. 91 2. 91 3. 26 3. 26 3. 29 3. 29 3. 29 2. 86 2. 86 4.0 3. 49 3. 49 3. 49 3. 49 3. 49 3. 49 3. 49 3. 49 3. 49 3. 49 3. 49 3. 58 3. 58 3. 58 3. 58 3. 58 3. 58 3. 58 2. 91 2. 91 2. 91 2. 91 2. 91 3. 26 3. 26 3. 29 2. 53 3. 29

3.5 3. 49 3. 49 3. 49 3. 49 3. 49 3. 49 3. 49 3. 49 3. 49 3. 49 3. 49 3. 58 3. 58 3. 58 3. 58 3. 58 3. 58 3. 58 2. 91 3. 47 2. 91 2. 91 2. 91 3. 26 3. 26 3. 29 3. 29 2. 96

3. 58 3. 58 3. 58 3. 58 3. 58 3. 58 3. 58 3. 58 3. 58 3. 47 3. 47 3. 47 3. 16 3. 16 3. 26 3. 26 3. 29 3. 29 3. 29 3. 29 2. 98 2. 98

3.0 3. 58 3. 58 3. 58 3. 58 3. 58 3. 58 3. 58 3. 58 3. 58 3. 58 3. 47 3. 58 3. 16 3. 16 3. 26 3. 26 3. 34 3. 34 2. 98 2. 98 2. 98

3. 58 3. 58 3. 58 3. 58 3. 58 3. 58 3. 58 3. 58 3. 58 3. 58 3. 58 3. 58 3. 16 3. 16 3. 26 3. 34 3. 34 3. 34 2. 98 2. 98 2. 98 2.5 3. 58 3. 58 3. 58 3. 58 3. 58 3. 58 3. 58 3. 58 3. 58 3. 58 3. 16 3. 16 3. 16 3. 34 3. 34 3. 34 3. 04 3. 04 3. 04 2. 98 2. 98

2.0 3. 01 3. 01 3. 01 3. 01 3. 01 3. 58 3. 58 3. 16 3. 16 3. 16 3. 34 3. 34 3. 34 3. 04 3. 04 3. 04 2. 98 2. 98

3. 01 3. 01 3. 01 3. 01 3. 01 3. 58 3. 58 3. 16 3. 16 3. 16 3. 08 3. 08 3. 08 3. 04 3. 04 3. 04 3. 04 2. 98 2. 59

1.5 3. 01 3. 01 3. 01 3. 01 3. 01 3. 01 2. 75 2. 75 3. 08 3. 08 3. 08 3. 08 3. 08 3. 04 3. 04 3. 04 2. 8 2. 8

3. 01 3. 01 3. 01 3. 01 3. 01 3. 01 2. 75 2. 75 3. 08 3. 29 3. 29 3. 08 3. 08 3. 64 3. 64 2. 8 2. 8 2. 8 1.0 3. 58 3. 58 3. 01 3. 01 3. 61 3. 61 3. 61 3. 61 3. 29 3. 29 3. 29 3. 29 3. 64 3. 64 3. 64 2. 8 2. 8

0.5 3. 58 3. 58 3. 58 3. 58 3. 61 3. 61 3. 61 3. 61 3. 29 3. 29 3. 29 3. 29 3. 64 3. 64 2. 96 2. 96

3. 58 3. 58 3. 58 3. 58 3. 58 3. 58 3. 57 3. 57 3. 57 3. 57 3. 29 3. 29 3. 29 3. 64 3. 64 2. 96 2. 96

0.0 3. 58 3. 58 3. 58 3. 58 3. 58 3. 58 3. 57 3. 57 3. 57 3. 57 3. 57 3. 3 3. 3 3. 3 3. 3 3. 3

3. 58 3. 57 3. 57 3. 57 3. 3 3. 3 3. 3 3. 3 3. 3

3. 3 3. 3 Jilin

Tibet 3.8

Anhui

Hebei Hubei

Fujian

Tianjin

Gansu Henan Hunan

Beijing

Shanxi

Jiangxi

Hainan

Ningxia

Yunnan Jiangsu

Qinghai 3.4

Xinjiang

Shaanxi Sichuan

Guizhou

Guangxi

Liaoning Zhejiang 3. 75 3. 75 Shanghai Household size

Shandong 3.1 Chongqing

Guangdong 3. 75 3. 75 Heilongjiang High: 3.8 # 2.8 InnerMongolia 2009 Low: 2.5 2.5 • Tibet has been excluded from the map above to allow greater comparability as Tibet has a significantly higher household size than other provinces, at 4.7 persons per household. • The less developed, less urbanised provinces tend to have larger household sizes as the housing is less expensive and there is a lower demand on space, allowing for larger units and larger households. Government statistics indicate rural per capita living space is approximately 35 sq m, while urban per capita living space is approximately 31 sq m. Demographics – population movement

0. 006961736 0. 006961736 0. 000586871 0. 000586871 0. 000586871 0. 000586871

Registered population, 2010 0. 006961736 0. 006961736 0. 006961736 0. 000586871 0. 000586871 0. 000586871 0. 000586871

0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 000586871 0. 000586871 0. 000586871 0. 000586871

0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 002216201 0. 002216201 0. 002216201 0. 002216201 0. 002216201

Population 5 yr CAGR 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 002216201 0. 002216201 million persons 120 6% 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 - 0. 002855856 - 0. 002855856 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 007175704 0. 007175704 0. 007175704 0. 002216201

0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 - 0. 002855856 - 0. 002855856 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 007175704 0. 007175704 0. 007175704

100 5% 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 - 0. 002855856 - 0. 002855856 - 0. 002855856 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 01255626 0. 01255626 0. 009583342 0. 009583342 0. 009583342 0. 007175704 0. 007175704

0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 - 0. 002855856 - 0. 002855856 - 0. 002855856 - 0. 002855856 - 0. 002855856 - 0. 002855856 - 0. 002855856 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 006961736 0. 01255626 0. 01255626 0. 009583342 0. 049819752 0. 009583342

80 4% 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 01645949 0. 007068796 0. 007068796 0. 007068796 0. 007068796 0. 007068796 - 0. 002855856 - 0. 002855856 0. 006961736 0. 011133097 0. 006961736 0. 006961736 0. 006961736 0. 01255626 0. 01255626 0. 009583342 0. 009583342 0. 044042244

0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 007068796 0. 007068796 0. 007068796 0. 007068796 0. 007068796 0. 007068796 0. 007068796 0. 007068796 - 0. 002855856 0. 011133097 0. 011133097 0. 011133097 0. 000683892 0. 000683892 0. 01255626 0. 01255626 0. 009583342 0. 009583342 0. 009583342 0. 009583342 0. 007064413 0. 007064413

60 3% 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 007068796 0. 007068796 0. 007068796 0. 007068796 0. 007068796 0. 007068796 0. 007068796 0. 007068796 - 0. 002855856 - 0. 002855856 0. 011133097 - 0. 002855856 0. 000683892 0. 000683892 0. 01255626 0. 01255626 0. 000476235 0. 000476235 0. 007064413 0. 007064413 0. 007064413

0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 007068796 0. 007068796 0. 007068796 0. 007068796 0. 007068796 0. 007068796 0. 007068796 0. 007068796 - 0. 002855856 - 0. 002855856 - 0. 002855856 - 0. 002855856 0. 000683892 0. 000683892 0. 01255626 0. 000476235 0. 000476235 0. 000476235 0. 007064413 0. 007064413 0. 007064413

40 2% 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 007068796 0. 007068796 0. 007068796 0. 007068796 0. 007068796 0. 007068796 0. 007068796 - 0. 002855856 - 0. 002855856 - 0. 002855856 0. 000683892 0. 000683892 0. 000683892 0. 000476235 0. 000476235 0. 000476235 - 0. 005616629 - 0. 005616629 - 0. 005616629 0. 010262545 0. 010262545

0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 - 0. 004179456 - 0. 004179456 - 0. 004179456 - 0. 004179456 - 0. 004179456 - 0. 002855856 - 0. 002855856 0. 000683892 0. 000683892 0. 000683892 0. 000476235 0. 000476235 0. 000476235 - 0. 005616629 - 0. 005616629 - 0. 005616629 0. 010262545 0. 010262545

20 1% 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 - 0. 004179456 - 0. 004179456 - 0. 004179456 - 0. 004179456 - 0. 004179456 - 0. 002855856 - 0. 002855856 0. 000683892 0. 000683892 0. 000683892 0. 000481987 0. 000481987 0. 000481987 - 0. 005616629 - 0. 005616629 - 0. 005616629 - 0. 005616629 0. 010262545 0. 053007581

0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 - 0. 004179456 - 0. 004179456 - 0. 004179456 - 0. 004179456 - 0. 004179456 - 0. 004179456 0. 006116079 0. 006116079 0. 000481987 0. 000481987 0. 000481987 0. 000481987 0. 000481987 - 0. 005616629 - 0. 005616629 - 0. 005616629 0. 02131321 0. 02131321

0 0% 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 0. 016227602 - 0. 004179456 - 0. 004179456 - 0. 004179456 - 0. 004179456 - 0. 004179456 - 0. 004179456 0. 006116079 0. 006116079 0. 000481987 0. 007547926 0. 007547926 0. 000481987 0. 000481987 0. 006662688 0. 006662688 0. 02131321 0. 02131321 0. 02131321

0. 006486571 0. 006486571 - 0. 004179456 - 0. 004179456 - 0. 014082974 - 0. 014082974 - 0. 014082974 - 0. 014082974 0. 007547926 0. 007547926 0. 007547926 0. 007547926 0. 006662688 0. 006662688 0. 006662688 0. 02131321 0. 02131321

-20 -1% 0. 006486571 0. 006486571 0. 006486571 0. 006486571 - 0. 014082974 - 0. 014082974 - 0. 014082974 - 0. 014082974 0. 007547926 0. 007547926 0. 007547926 0. 007547926 0. 006662688 0. 006662688 0. 008587946 0. 008587946

0. 006486571 0. 006486571 0. 006486571 0. 006486571 0. 006486571 0. 006486571 - 0. 002473021 - 0. 002473021 - 0. 002473021 - 0. 002473021 0. 007547926 0. 007547926 0. 007547926 0. 006662688 0. 006662688 0. 008587946 0. 008587946

-40 -2% 0. 006486571 0. 006486571 0. 006486571 0. 006486571 0. 006486571 0. 006486571 - 0. 002473021 - 0. 002473021 - 0. 002473021 - 0. 002473021 - 0. 002473021 0. 025554088 0. 025554088 0. 025554088 0. 025554088 0. 025554088

0. 006486571 - 0. 002473021 - 0. 002473021 - 0. 002473021 0. 025554088 0. 025554088 0. 025554088 0. 025554088 0. 025554088

0. 025554088 0. 025554088 Jilin

Tibet 5%

Anhui

Hebei Hubei

Fujian

Tianjin

Henan Hunan Gansu

Beijing

Shanxi

Jiangxi

Hainan

Ningxia

Yunnan Qinghai

Jiangsu 4%

Xinjiang

Sichuan Shaanxi

Guizhou

Guangxi

Liaoning Zhejiang 0. 009283001 0. 009283001 Shanghai Population 5 yr CAGR

Shandong 2% Chongqing

Guangdong 0. 009283001 0. 009283001 Heilongjiang High: 5% % 0% InnerMongolia 2010 Low: -1% -1% • Unsurprisingly there still tends to be a migration to the east coast provinces of China where wages are higher and more economic activity is present. • The figures represent registered populations as opposed to a permanent population. This may result in a lag in the statistics from the actual situation as many migrants usually live in the city for a period of time before converting their residency in a long and troublesome process. • As economic growth picks up in the west, job vacancies will increase resulting in a rise in wages. We can therefore expect to the see this migration trend slow as migrants pick their home town over the crowded and expensive cities in the east. Demographics – domestic consumption

Domestic savings as a percentage of total GDP, 1960– Consumption as a percentage of GDP, 1970–2009 2009

Euro area Brazil China India Japan Russia UK US World 60%

50%

40%

30%

20%

10%

0% 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08

• China has the highest savings rate in the world. Domestic savings rose from 30% of the GDP in the 1970s to 52% by 2009, compared with the developed world which has savings rates of less than 20%, and developing countries at 30%. • Consumption as a percentage of GDP has also fallen, decreasing from 50% in the 1980s to 35% by 2009, compared to the developed world which has consumption as a percentage of GDP of around 60%. • Chinese individuals tend to save for unforeseen circumstances as the social security network is not big enough to help 1.3 billion people in their time of need. The government, however, understands the importance of changing this attitude and encouraging its people to spend if they want to shift the economy to a more sustainable footing.

Source: UN; Savills China Research Source: UN; Savills China Research Demographics – retail sales

338. 400401 338. 400401 403. 921848 403. 921848 403. 921848 403. 921848

Retail sales, 2010 338. 400401 338. 400401 338. 400401 403. 921848 403. 921848 403. 921848 403. 921848

137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 338. 400401 338. 400401 338. 400401 338. 400401 403. 921848 403. 921848 403. 921848 403. 921848

137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 338. 400401 338. 400401 338. 400401 338. 400401 350. 491606 350. 491606 350. 491606 350. 491606 350. 491606

Retail sales 5 yr CAGR growth 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 338. 400401 338. 400401 338. 400401 338. 400401 338. 400401 338. 400401 338. 400401 350. 491606 350. 491606 RMB billion 2,000 25% 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 139. 453544 139. 453544 338. 400401 338. 400401 338. 400401 338. 400401 338. 400401 338. 400401 338. 400401 338. 400401 338. 400401 338. 400401 688. 764341 688. 764341 688. 764341 350. 491606

137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 139. 453544 139. 453544 338. 400401 338. 400401 338. 400401 338. 400401 338. 400401 338. 400401 338. 400401 338. 400401 338. 400401 338. 400401 338. 400401 338. 400401 338. 400401 688. 764341 688. 764341 688. 764341

1,800 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 139. 453544 139. 453544 139. 453544 338. 400401 338. 400401 338. 400401 338. 400401 338. 400401 338. 400401 338. 400401 338. 400401 338. 400401 331. 815477 331. 815477 682. 17916 682. 17916 682. 17916 688. 764341 688. 764341

1,600 20% 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 139. 453544 139. 453544 139. 453544 139. 453544 139. 453544 139. 453544 139. 453544 338. 400401 338. 400401 338. 400401 338. 400401 338. 400401 331. 815477 331. 815477 682. 17916 622. 929887 682. 17916

137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 137. 5134613 35. 08263414 35. 08263414 35. 08263414 35. 08263414 35. 08263414 139. 453544 139. 453544 338. 400401 40. 361247 338. 400401 338. 400401 338. 400401 331. 815477 331. 815477 682. 17916 682. 17916 290. 259383

1,400 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 35. 08263414 35. 08263414 35. 08263414 35. 08263414 35. 08263414 35. 08263414 35. 08263414 35. 08263414 139. 453544 40. 361247 40. 361247 40. 361247 319. 567247 319. 567247 331. 815477 331. 815477 682. 17916 682. 17916 682. 17916 682. 17916 1462. 034362 1462. 034362

18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 35. 08263414 35. 08263414 35. 08263414 35. 08263414 35. 08263414 35. 08263414 35. 08263414 35. 08263414 139. 453544 139. 453544 40. 361247 139. 453544 319. 567247 319. 567247 331. 815477 331. 815477 800. 42238 800. 42238 1462. 034362 1462. 034362 1462. 034362 1,200 15% 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 35. 08263414 35. 08263414 35. 08263414 35. 08263414 35. 08263414 35. 08263414 35. 08263414 35. 08263414 139. 453544 139. 453544 139. 453544 139. 453544 319. 567247 319. 567247 331. 815477 800. 42238 800. 42238 800. 42238 1462. 034362 1462. 034362 1462. 034362

1,000 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 35. 08263414 35. 08263414 35. 08263414 35. 08263414 35. 08263414 35. 08263414 35. 08263414 139. 453544 139. 453544 139. 453544 319. 567247 319. 567247 319. 567247 800. 42238 800. 42238 800. 42238 419. 769582 419. 769582 419. 769582 1360. 684309 1360. 684309

18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 681. 0071535 681. 0071535 681. 0071535 681. 0071535 681. 0071535 139. 453544 139. 453544 319. 567247 319. 567247 319. 567247 800. 42238 800. 42238 800. 42238 419. 769582 419. 769582 419. 769582 1360. 684309 1360. 684309 800 10% 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 681. 0071535 681. 0071535 681. 0071535 681. 0071535 681. 0071535 139. 453544 139. 453544 319. 567247 319. 567247 319. 567247 701. 385174 701. 385174 701. 385174 419. 769582 419. 769582 419. 769582 419. 769582 1360. 684309 607. 049097

600 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 681. 0071535 681. 0071535 681. 0071535 681. 0071535 681. 0071535 681. 0071535 293. 85769 293. 85769 701. 385174 701. 385174 701. 385174 701. 385174 701. 385174 419. 769582 419. 769582 419. 769582 1024. 540758 1024. 540758

18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 18. 52501745 681. 0071535 681. 0071535 681. 0071535 681. 0071535 681. 0071535 681. 0071535 293. 85769 293. 85769 701. 385174 583. 949983 583. 949983 701. 385174 701. 385174 295. 624731 295. 624731 1024. 540758 1024. 540758 1024. 540758

400 5% 250. 008747 250. 008747 681. 0071535 681. 0071535 148. 268031 148. 268031 148. 268031 148. 268031 583. 949983 583. 949983 583. 949983 583. 949983 295. 624731 295. 624731 295. 624731 1024. 540758 1024. 540758

250. 008747 250. 008747 250. 008747 250. 008747 148. 268031 148. 268031 148. 268031 148. 268031 583. 949983 583. 949983 583. 949983 583. 949983 295. 624731 295. 624731 531. 002699 531. 002699 200 250. 008747 250. 008747 250. 008747 250. 008747 250. 008747 250. 008747 331. 19968 331. 19968 331. 19968 331. 19968 583. 949983 583. 949983 583. 949983 295. 624731 295. 624731 531. 002699 531. 002699

0 0% 250. 008747 250. 008747 250. 008747 250. 008747 250. 008747 250. 008747 331. 19968 331. 19968 331. 19968 331. 19968 331. 19968 1745. 8444 1745. 8444 1745. 8444 1745. 8444 1745. 8444

250. 008747 331. 19968 331. 19968 331. 19968 1745. 8444 1745. 8444 1745. 8444 1745. 8444 1745. 8444

1745. 8444 1745. 8444 Jilin

Tibet 1,746

Anhui

Hubei Hebei

Fujian

Tianjin

Henan Hunan Gansu

Beijing

Shanxi

Jiangxi

Hainan

Ningxia

Yunnan Jiangsu

Qinghai 1,314

Xinjiang

Shaanxi Sichuan

Guizhou

Guangxi

Liaoning Zhejiang 63. 928631 63. 928631 Shanghai Retail sales

Shandong 882 Chongqing

Guangdong 63. 928631 63. 928631 Heilongjiang High: 1,746 RMB billion 450 InnerMongolia 2010 Low: 19 19 • While consumption as a percentage of GDP is falling, it is not because of a lack of growth in retail sales, rather other sectors of the economy are growing at faster rates. Demographics –disposable income (urban)

17698. 15 17698. 15 13856. 51 13856. 51 13856. 51 13856. 51

Disposable income (urban), 2010 17698. 15 17698. 15 17698. 15 13856. 51 13856. 51 13856. 51 13856. 51

13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 17698. 15 17698. 15 17698. 15 17698. 15 13856. 51 13856. 51 13856. 51 13856. 51

13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 17698. 15 17698. 15 17698. 15 17698. 15 15411. 47 15411. 47 15411. 47 15411. 47 15411. 47

Disposable income (Urban) 5 yr CAGR growth 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 17698. 15 17698. 15 17698. 15 17698. 15 17698. 15 17698. 15 17698. 15 15411. 47 15411. 47 RMB 40,000 16% 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13188. 55 13188. 55 17698. 15 17698. 15 17698. 15 17698. 15 17698. 15 17698. 15 17698. 15 17698. 15 17698. 15 17698. 15 17712. 58 17712. 58 17712. 58 15411. 47

13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13188. 55 13188. 55 17698. 15 17698. 15 17698. 15 17698. 15 17698. 15 17698. 15 17698. 15 17698. 15 17698. 15 17698. 15 17698. 15 17698. 15 17698. 15 17712. 58 17712. 58 17712. 58

35,000 14% 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13188. 55 13188. 55 13188. 55 17698. 15 17698. 15 17698. 15 17698. 15 17698. 15 17698. 15 17698. 15 17698. 15 17698. 15 15647. 66 15647. 66 16263. 43 16263. 43 16263. 43 17712. 58 17712. 58

13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13188. 55 13188. 55 13188. 55 13188. 55 13188. 55 13188. 55 13188. 55 17698. 15 17698. 15 17698. 15 17698. 15 17698. 15 15647. 66 15647. 66 16263. 43 29072. 93 16263. 43

30,000 12% 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13643. 77 13854. 99 13854. 99 13854. 99 13854. 99 13854. 99 13188. 55 13188. 55 17698. 15 15344. 49 17698. 15 17698. 15 17698. 15 15647. 66 15647. 66 16263. 43 16263. 43 24292. 6

14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 13854. 99 13854. 99 13854. 99 13854. 99 13854. 99 13854. 99 13854. 99 13854. 99 13188. 55 15344. 49 15344. 49 15344. 49 15695. 21 15695. 21 15647. 66 15647. 66 16263. 43 16263. 43 16263. 43 16263. 43 19945. 83 19945. 83

25,000 10% 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 13854. 99 13854. 99 13854. 99 13854. 99 13854. 99 13854. 99 13854. 99 13854. 99 13188. 55 13188. 55 15344. 49 13188. 55 15695. 21 15695. 21 15647. 66 15647. 66 15930. 26 15930. 26 19945. 83 19945. 83 19945. 83

14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 13854. 99 13854. 99 13854. 99 13854. 99 13854. 99 13854. 99 13854. 99 13854. 99 13188. 55 13188. 55 13188. 55 13188. 55 15695. 21 15695. 21 15647. 66 15930. 26 15930. 26 15930. 26 19945. 83 19945. 83 19945. 83

20,000 8% 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 13854. 99 13854. 99 13854. 99 13854. 99 13854. 99 13854. 99 13854. 99 13188. 55 13188. 55 13188. 55 15695. 21 15695. 21 15695. 21 15930. 26 15930. 26 15930. 26 15788. 17 15788. 17 15788. 17 22944. 26 22944. 26

14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 15461. 16 15461. 16 15461. 16 15461. 16 15461. 16 13188. 55 13188. 55 15695. 21 15695. 21 15695. 21 15930. 26 15930. 26 15930. 26 15788. 17 15788. 17 15788. 17 22944. 26 22944. 26

15,000 6% 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 15461. 16 15461. 16 15461. 16 15461. 16 15461. 16 13188. 55 13188. 55 15695. 21 15695. 21 15695. 21 16058. 37 16058. 37 16058. 37 15788. 17 15788. 17 15788. 17 15788. 17 22944. 26 31838. 08

14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 15461. 16 15461. 16 15461. 16 15461. 16 15461. 16 15461. 16 17532. 43 17532. 43 16058. 37 16058. 37 16058. 37 16058. 37 16058. 37 15788. 17 15788. 17 15788. 17 27359. 02 27359. 02

10,000 4% 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 14980. 47 15461. 16 15461. 16 15461. 16 15461. 16 15461. 16 15461. 16 17532. 43 17532. 43 16058. 37 16565. 7 16565. 7 16058. 37 16058. 37 15481. 12 15481. 12 27359. 02 27359. 02 27359. 02

16064. 54 16064. 54 15461. 16 15461. 16 14142. 74 14142. 74 14142. 74 14142. 74 16565. 7 16565. 7 16565. 7 16565. 7 15481. 12 15481. 12 15481. 12 27359. 02 27359. 02

5,000 2% 16064. 54 16064. 54 16064. 54 16064. 54 14142. 74 14142. 74 14142. 74 14142. 74 16565. 7 16565. 7 16565. 7 16565. 7 15481. 12 15481. 12 21781. 31 21781. 31

16064. 54 16064. 54 16064. 54 16064. 54 16064. 54 16064. 54 17063. 89 17063. 89 17063. 89 17063. 89 16565. 7 16565. 7 16565. 7 15481. 12 15481. 12 21781. 31 21781. 31

0 0% 16064. 54 16064. 54 16064. 54 16064. 54 16064. 54 16064. 54 17063. 89 17063. 89 17063. 89 17063. 89 17063. 89 23897. 8 23897. 8 23897. 8 23897. 8 23897. 8

16064. 54 17063. 89 17063. 89 17063. 89 23897. 8 23897. 8 23897. 8 23897. 8 23897. 8

23897. 8 23897. 8 Jilin

Tibet 31,838

Anhui

Hebei Hubei

Fujian

Tianjin

Hunan Henan Gansu

Beijing

Shanxi

Jiangxi

Hainan

Ningxia

Yunnan Qinghai

Jiangsu 27,176

Xinjiang

Sichuan Shaanxi

Guizhou

Guangxi

Liaoning Zhejiang 15581. 05 15581. 05 Shanghai Disposable income (Urban)

Shandong 22,513 Chongqing

Guangdong 15581. 05 15581. 05 Heilongjiang High: 31,838 0 17,851 InnerMongolia 2010 Low: 13,189 13,189

• Inequality tends to be an issue present in most emerging economies and it is no different in China. China has a Gini coefficient of 47%, which compares favourably with Brazil (57%) but is far behind India (36.8%), Russia (39.9%), and more developed countries like the US (40.8%) and the UK (36%). Inequality would be even higher if grey income (unreported) is taken into account as it tends to vastly favour the higher income bracket. • Urban disposable income averages RMB19,109 per annum while rural disposable income averages RMB5,919 per annum. • There are also significant differences between urban disposable incomes in the west and the east, with provinces such as Zhejiang in the east at RMB25,000 per annum and Xinjiang in the west at RMB12,000 per annum. Demographics – consumption expenditure (urban)

13994. 62 13994. 62 10683. 92 10683. 92 10683. 92 10683. 92

Consumption expenditure (urban), 2010 13994. 62 13994. 62 13994. 62 10683. 92 10683. 92 10683. 92 10683. 92

10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 13994. 62 13994. 62 13994. 62 13994. 62 10683. 92 10683. 92 10683. 92 10683. 92

10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 13994. 62 13994. 62 13994. 62 13994. 62 11679. 04 11679. 04 11679. 04 11679. 04 11679. 04

Consumption Expenditure (Urban) 5 yr CAGR growth 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 13994. 62 13994. 62 13994. 62 13994. 62 13994. 62 13994. 62 13994. 62 11679. 04 11679. 04 RMB 25,000 15% 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 9895. 35 9895. 35 13994. 62 13994. 62 13994. 62 13994. 62 13994. 62 13994. 62 13994. 62 13994. 62 13994. 62 13994. 62 13280. 04 13280. 04 13280. 04 11679. 04

10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 9895. 35 9895. 35 13994. 62 13994. 62 13994. 62 13994. 62 13994. 62 13994. 62 13994. 62 13994. 62 13994. 62 13994. 62 13994. 62 13994. 62 13994. 62 13280. 04 13280. 04 13280. 04

10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 9895. 35 9895. 35 9895. 35 13994. 62 13994. 62 13994. 62 13994. 62 13994. 62 13994. 62 13994. 62 13994. 62 13994. 62 9792. 65 9792. 65 10318. 32 10318. 32 10318. 32 13280. 04 13280. 04

20,000 12% 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 9895. 35 9895. 35 9895. 35 9895. 35 9895. 35 9895. 35 9895. 35 13994. 62 13994. 62 13994. 62 13994. 62 13994. 62 9792. 65 9792. 65 10318. 32 19934. 48 10318. 32

10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 10197. 09 9613. 79 9613. 79 9613. 79 9613. 79 9613. 79 9895. 35 9895. 35 13994. 62 11334. 43 13994. 62 13994. 62 13994. 62 9792. 65 9792. 65 10318. 32 10318. 32 16561. 77

9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9613. 79 9613. 79 9613. 79 9613. 79 9613. 79 9613. 79 9613. 79 9613. 79 9895. 35 11334. 43 11334. 43 11334. 43 11821. 88 11821. 88 9792. 65 9792. 65 10318. 32 10318. 32 10318. 32 10318. 32 13118. 24 13118. 24

9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9613. 79 9613. 79 9613. 79 9613. 79 9613. 79 9613. 79 9613. 79 9613. 79 9895. 35 9895. 35 11334. 43 9895. 35 11821. 88 11821. 88 9792. 65 9792. 65 10838. 49 10838. 49 13118. 24 13118. 24 13118. 24 15,000 9% 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9613. 79 9613. 79 9613. 79 9613. 79 9613. 79 9613. 79 9613. 79 9613. 79 9895. 35 9895. 35 9895. 35 9895. 35 11821. 88 11821. 88 9792. 65 10838. 49 10838. 49 10838. 49 13118. 24 13118. 24 13118. 24

9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9613. 79 9613. 79 9613. 79 9613. 79 9613. 79 9613. 79 9613. 79 9895. 35 9895. 35 9895. 35 11821. 88 11821. 88 11821. 88 10838. 49 10838. 49 10838. 49 11512. 55 11512. 55 11512. 55 14357. 49 14357. 49

9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 12105. 09 12105. 09 12105. 09 12105. 09 12105. 09 9895. 35 9895. 35 11821. 88 11821. 88 11821. 88 10838. 49 10838. 49 10838. 49 11512. 55 11512. 55 11512. 55 14357. 49 14357. 49 10,000 6% 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 12105. 09 12105. 09 12105. 09 12105. 09 12105. 09 9895. 35 9895. 35 11821. 88 11821. 88 11821. 88 11450. 97 11450. 97 11450. 97 11512. 55 11512. 55 11512. 55 11512. 55 14357. 49 23200. 4

9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 12105. 09 12105. 09 12105. 09 12105. 09 12105. 09 12105. 09 13335. 02 13335. 02 11450. 97 11450. 97 11450. 97 11450. 97 11450. 97 11512. 55 11512. 55 11512. 55 17858. 2 17858. 2

9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 9685. 54 12105. 09 12105. 09 12105. 09 12105. 09 12105. 09 12105. 09 13335. 02 13335. 02 11450. 97 11825. 33 11825. 33 11450. 97 11450. 97 10618. 69 10618. 69 17858. 2 17858. 2 17858. 2

5,000 3% 11074. 08 11074. 08 12105. 09 12105. 09 10058. 29 10058. 29 10058. 29 10058. 29 11825. 33 11825. 33 11825. 33 11825. 33 10618. 69 10618. 69 10618. 69 17858. 2 17858. 2

11074. 08 11074. 08 11074. 08 11074. 08 10058. 29 10058. 29 10058. 29 10058. 29 11825. 33 11825. 33 11825. 33 11825. 33 10618. 69 10618. 69 14750. 01 14750. 01

11074. 08 11074. 08 11074. 08 11074. 08 11074. 08 11074. 08 11490. 08 11490. 08 11490. 08 11490. 08 11825. 33 11825. 33 11825. 33 10618. 69 10618. 69 14750. 01 14750. 01

0 0% 11074. 08 11074. 08 11074. 08 11074. 08 11074. 08 11074. 08 11490. 08 11490. 08 11490. 08 11490. 08 11490. 08 18489. 53 18489. 53 18489. 53 18489. 53 18489. 53

11074. 08 11490. 08 11490. 08 11490. 08 18489. 53 18489. 53 18489. 53 18489. 53 18489. 53

18489. 53 18489. 53 Jilin

Tibet 23,200

Anhui

Hubei Hebei

Fujian

Tianjin

Hunan Henan Gansu

Beijing

Shanxi

Jiangxi

Hainan

Ningxia

Yunnan Qinghai

Jiangsu 19,804

Xinjiang

Sichuan Shaanxi

Guizhou

Guangxi

Liaoning Zhejiang 10926. 71 10926. 71

Shanghai Consumption Expenditure (Urban)

Shandong 16,407 Chongqing

Guangdong 10926. 71 10926. 71 Heilongjiang High: 23,200 RMB 13,010 InnerMongolia 2010 Low: 9,614 9,614 • Incomes have a very significant impact on the ability of individuals to consume. The distribution of consumption in China closely mirrors that of disposable incomes, with differences only showing clearly when we look at savings rates of the ratio of consumption expenditure versus disposable income. Demographics – savings rates (urban)

0. 209260855 0. 209260855 0. 22896025 0. 22896025 0. 22896025 0. 22896025

Savings rates (urban), 2010 0. 209260855 0. 209260855 0. 209260855 0. 22896025 0. 22896025 0. 22896025 0. 22896025

0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 22896025 0. 22896025 0. 22896025 0. 22896025

0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 242185204 0. 242185204 0. 242185204 0. 242185204 0. 242185204

0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 242185204 0. 242185204

40% 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 249701446 0. 249701446 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 250248129 0. 250248129 0. 250248129 0. 242185204

0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 249701446 0. 249701446 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 250248129 0. 250248129 0. 250248129

35% 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 249701446 0. 249701446 0. 249701446 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 374177992 0. 374177992 0. 365550809 0. 365550809 0. 365550809 0. 250248129 0. 250248129

0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 249701446 0. 249701446 0. 249701446 0. 249701446 0. 249701446 0. 249701446 0. 249701446 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 209260855 0. 374177992 0. 374177992 0. 365550809 0. 314328484 0. 365550809

30% 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 252619327 0. 306113537 0. 306113537 0. 306113537 0. 306113537 0. 306113537 0. 249701446 0. 249701446 0. 209260855 0. 261335502 0. 209260855 0. 209260855 0. 209260855 0. 374177992 0. 374177992 0. 365550809 0. 365550809 0. 318238064

0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 306113537 0. 306113537 0. 306113537 0. 306113537 0. 306113537 0. 306113537 0. 306113537 0. 306113537 0. 249701446 0. 261335502 0. 261335502 0. 261335502 0. 24678421 0. 24678421 0. 374177992 0. 374177992 0. 365550809 0. 365550809 0. 365550809 0. 365550809 0. 342306638 0. 342306638

25% 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 306113537 0. 306113537 0. 306113537 0. 306113537 0. 306113537 0. 306113537 0. 306113537 0. 306113537 0. 249701446 0. 249701446 0. 261335502 0. 249701446 0. 24678421 0. 24678421 0. 374177992 0. 374177992 0. 319628807 0. 319628807 0. 342306638 0. 342306638 0. 342306638

0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 306113537 0. 306113537 0. 306113537 0. 306113537 0. 306113537 0. 306113537 0. 306113537 0. 306113537 0. 249701446 0. 249701446 0. 249701446 0. 249701446 0. 24678421 0. 24678421 0. 374177992 0. 319628807 0. 319628807 0. 319628807 0. 342306638 0. 342306638 0. 342306638

20% 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 306113537 0. 306113537 0. 306113537 0. 306113537 0. 306113537 0. 306113537 0. 306113537 0. 249701446 0. 249701446 0. 249701446 0. 24678421 0. 24678421 0. 24678421 0. 319628807 0. 319628807 0. 319628807 0. 270811627 0. 270811627 0. 270811627 0. 3742448 0. 3742448

0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 217064567 0. 217064567 0. 217064567 0. 217064567 0. 217064567 0. 249701446 0. 249701446 0. 24678421 0. 24678421 0. 24678421 0. 319628807 0. 319628807 0. 319628807 0. 270811627 0. 270811627 0. 270811627 0. 3742448 0. 3742448

15% 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 217064567 0. 217064567 0. 217064567 0. 217064567 0. 217064567 0. 249701446 0. 249701446 0. 24678421 0. 24678421 0. 24678421 0. 286915795 0. 286915795 0. 286915795 0. 270811627 0. 270811627 0. 270811627 0. 270811627 0. 3742448 0. 271300279

0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 217064567 0. 217064567 0. 217064567 0. 217064567 0. 217064567 0. 217064567 0. 239408342 0. 239408342 0. 286915795 0. 286915795 0. 286915795 0. 286915795 0. 286915795 0. 270811627 0. 270811627 0. 270811627 0. 347264632 0. 347264632

10% 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 353455532 0. 217064567 0. 217064567 0. 217064567 0. 217064567 0. 217064567 0. 217064567 0. 239408342 0. 239408342 0. 286915795 0. 286155731 0. 286155731 0. 286915795 0. 286915795 0. 31408774 0. 31408774 0. 347264632 0. 347264632 0. 347264632

0. 310650663 0. 310650663 0. 217064567 0. 217064567 0. 288801887 0. 288801887 0. 288801887 0. 288801887 0. 286155731 0. 286155731 0. 286155731 0. 286155731 0. 31408774 0. 31408774 0. 31408774 0. 347264632 0. 347264632

5% 0. 310650663 0. 310650663 0. 310650663 0. 310650663 0. 288801887 0. 288801887 0. 288801887 0. 288801887 0. 286155731 0. 286155731 0. 286155731 0. 286155731 0. 31408774 0. 31408774 0. 322813458 0. 322813458

0. 310650663 0. 310650663 0. 310650663 0. 310650663 0. 310650663 0. 310650663 0. 326643573 0. 326643573 0. 326643573 0. 326643573 0. 286155731 0. 286155731 0. 286155731 0. 31408774 0. 31408774 0. 322813458 0. 322813458

0% 0. 310650663 0. 310650663 0. 310650663 0. 310650663 0. 310650663 0. 310650663 0. 326643573 0. 326643573 0. 326643573 0. 326643573 0. 326643573 0. 226308279 0. 226308279 0. 226308279 0. 226308279 0. 226308279

0. 310650663 0. 326643573 0. 326643573 0. 326643573 0. 226308279 0. 226308279 0. 226308279 0. 226308279 0. 226308279

0. 226308279 0. 226308279 Jilin

Tibet 37%

Anhui

Hebei Hubei

Fujian

Tianjin

Henan Hunan Gansu

Beijing

Shanxi

Jiangxi

Hainan

Ningxia

Yunnan Jiangsu

Qinghai 33%

Xinjiang

Sichuan Shaanxi

Guizhou

Guangxi

Liaoning Zhejiang 0. 298717994 0. 298717994 Shanghai Savings Rate (Urban)

Shandong 29% Chongqing

Guangdong 0. 298717994 0. 298717994 Heilongjiang High: 37% % 25% InnerMongolia 2010 Low: 21% 21% • The distribution of savings rates indicates that individuals residing in the centre and the east of China save a significant percentage of their money, while those in the northeast, and Guangdong and Sichuan provinces tend to spend a larger amount of their overall income. • This could possibly be due to high property costs, meaning that many have to save their money to afford a property, or it could be an effect of remittance by migrant workers to their home provinces. Demographics – minimum wages

900 900 880 880 880 880

Minimum wages, 2010 900 900 900 880 880 880 880

960 960 960 960 960 900 900 900 900 880 880 880 880

960 960 960 960 960 900 900 900 900 820 820 820 820 820

2010 5 yr CAGR 960 960 960 960 960 960 960 960 900 900 900 900 900 900 900 820 820

RMB per month 960 960 960 960 960 960 960 960 760 760 900 900 900 900 900 900 900 900 900 900 900 900 900 820

1,400 35% 960 960 960 960 960 960 960 960 960 760 760 900 900 900 900 900 900 900 900 900 900 900 900 900 900 900 900

960 960 960 960 960 960 960 960 960 960 760 760 760 900 900 900 900 900 900 900 900 900 850 850 900 900 900 900 900

1,200 30% 960 960 960 960 960 960 960 960 960 960 960 760 760 760 760 760 760 760 900 900 900 900 900 850 850 900 960 900

960 960 960 960 960 960 960 960 960 960 960 750 750 750 750 750 760 760 900 710 900 900 900 850 850 900 900 920

1,000 25% 950 950 950 950 950 950 950 950 950 750 750 750 750 750 750 750 750 760 710 710 710 760 760 850 850 900 900 900 900 920 920

950 950 950 950 950 950 950 950 950 750 750 750 750 750 750 750 750 760 760 710 760 760 760 850 850 800 800 920 920 920

800 20% 950 950 950 950 950 950 950 950 950 750 750 750 750 750 750 750 750 760 760 760 760 760 760 850 800 800 800 920 920 920

950 950 950 950 950 950 950 950 950 950 750 750 750 750 750 750 750 760 760 760 760 760 760 800 800 800 720 720 720 960 960

600 15% 950 950 950 950 950 950 950 950 950 950 950 950 850 850 850 850 850 760 760 760 760 760 800 800 800 720 720 720 960 960

950 950 950 950 950 950 950 950 950 950 950 950 850 850 850 850 850 760 760 760 760 760 900 900 900 720 720 720 720 960 1120

950 950 950 950 950 950 950 950 950 950 950 850 850 850 850 850 850 680 680 900 900 900 900 900 720 720 720 1100 1100 400 10% 950 950 950 950 950 950 950 950 950 850 850 850 850 850 850 680 680 900 850 850 900 900 720 720 1100 1100 1100

830 830 850 850 830 830 830 830 850 850 850 850 720 720 720 1100 1100

200 5% 830 830 830 830 830 830 830 830 850 850 850 850 720 720 900 900

830 830 830 830 830 830 820 820 820 820 850 850 850 720 720 900 900

0 0% 830 830 830 830 830 830 820 820 820 820 820 1030 1030 1030 1030 1030

830 820 820 820 1030 1030 1030 1030 1030

Jilin 1030 1030

Tibet 1,120

Anhui

Hubei Hebei

Fujian

Tianjin

Gansu

Henan Hunan

Beijing

Shanxi

Jiangxi

Hainan

Ningxia

Yunnan

Qinghai

Jiangsu

Xinjiang Sichuan

Shaanxi 1,010

Guizhou Guangxi

Liaoning Zhejiang

830 830 Shanghai

Shandong Minimum Wage 900

Chongqing Guangdong 830 830 Heilongjiang High: 1,120 RMB per 790 InnerMongolia 2010 Low: 680 month 680 • Wages have increased as the cost of living increases. Many factory owners, however, try to keep wages low, leaving it to the government to introduce, adjust and enforce minimum wages. • The eastern provinces have some of the highest minimum wages, and thus costs, while the central provinces still keep minimum wages comparatively low. Demographics – average wage growth

115. 5 115. 5 111. 8 111. 8 111. 8 111. 8

Average wage growth, 2010 115. 5 115. 5 115. 5 111. 8 111. 8 111. 8 111. 8

115. 9 115. 9 115. 9 115. 9 115. 9 115. 5 115. 5 115. 5 115. 5 111. 8 111. 8 111. 8 111. 8

115. 9 115. 9 115. 9 115. 9 115. 9 115. 5 115. 5 115. 5 115. 5 111. 8 111. 8 111. 8 111. 8 111. 8

Previous year = 115. 9 115. 9 115. 9 115. 9 115. 9 115. 9 115. 9 115. 9 115. 5 115. 5 115. 5 115. 5 115. 5 115. 5 115. 5 111. 8 111. 8 130 100 115. 9 115. 9 115. 9 115. 9 115. 9 115. 9 115. 9 115. 9 108. 8 108. 8 115. 5 115. 5 115. 5 115. 5 115. 5 115. 5 115. 5 115. 5 115. 5 115. 5 112. 8 112. 8 112. 8 111. 8

115. 9 115. 9 115. 9 115. 9 115. 9 115. 9 115. 9 115. 9 115. 9 108. 8 108. 8 115. 5 115. 5 115. 5 115. 5 115. 5 115. 5 115. 5 115. 5 115. 5 115. 5 115. 5 115. 5 115. 5 112. 8 112. 8 112. 8

115. 9 115. 9 115. 9 115. 9 115. 9 115. 9 115. 9 115. 9 115. 9 115. 9 108. 8 108. 8 108. 8 115. 5 115. 5 115. 5 115. 5 115. 5 115. 5 115. 5 115. 5 115. 5 117. 8 117. 8 113. 2 113. 2 113. 2 112. 8 112. 8

125 115. 9 115. 9 115. 9 115. 9 115. 9 115. 9 115. 9 115. 9 115. 9 115. 9 115. 9 108. 8 108. 8 108. 8 108. 8 108. 8 108. 8 108. 8 115. 5 115. 5 115. 5 115. 5 115. 5 117. 8 117. 8 113. 2 112. 8 113. 2

115. 9 115. 9 115. 9 115. 9 115. 9 115. 9 115. 9 115. 9 115. 9 115. 9 115. 9 111. 2 111. 2 111. 2 111. 2 111. 2 108. 8 108. 8 115. 5 112. 9 115. 5 115. 5 115. 5 117. 8 117. 8 113. 2 113. 2 117. 2

120 110 110 110 110 110 110 110 110 110 111. 2 111. 2 111. 2 111. 2 111. 2 111. 2 111. 2 111. 2 108. 8 112. 9 112. 9 112. 9 112. 9 112. 9 117. 8 117. 8 113. 2 113. 2 113. 2 113. 2 113. 3 113. 3

110 110 110 110 110 110 110 110 110 111. 2 111. 2 111. 2 111. 2 111. 2 111. 2 111. 2 111. 2 108. 8 108. 8 112. 9 108. 8 112. 9 112. 9 117. 8 117. 8 110. 8 110. 8 113. 3 113. 3 113. 3

110 110 110 110 110 110 110 110 110 111. 2 111. 2 111. 2 111. 2 111. 2 111. 2 111. 2 111. 2 108. 8 108. 8 108. 8 108. 8 112. 9 112. 9 117. 8 110. 8 110. 8 110. 8 113. 3 113. 3 113. 3

115 110 110 110 110 110 110 110 110 110 110 111. 2 111. 2 111. 2 111. 2 111. 2 111. 2 111. 2 108. 8 108. 8 108. 8 112. 9 112. 9 112. 9 110. 8 110. 8 110. 8 116. 1 116. 1 116. 1 112. 9 112. 9

110 110 110 110 110 110 110 110 110 110 110 110 115. 7 115. 7 115. 7 115. 7 115. 7 108. 8 108. 8 112. 9 112. 9 112. 9 110. 8 110. 8 110. 8 116. 1 116. 1 116. 1 112. 9 112. 9

110 110 110 110 110 110 110 110 110 110 110 110 115. 7 115. 7 115. 7 115. 7 115. 7 108. 8 108. 8 112. 9 112. 9 112. 9 119. 8 119. 8 119. 8 116. 1 116. 1 116. 1 116. 1 112. 9 113. 3 110 110 110 110 110 110 110 110 110 110 110 110 115. 7 115. 7 115. 7 115. 7 115. 7 115. 7 113. 9 113. 9 119. 8 119. 8 119. 8 119. 8 119. 8 116. 1 116. 1 116. 1 111. 2 111. 2

110 110 110 110 110 110 110 110 110 115. 7 115. 7 115. 7 115. 7 115. 7 115. 7 113. 9 113. 9 119. 8 111. 8 111. 8 119. 8 119. 8 117. 4 117. 4 111. 2 111. 2 111. 2

105 111. 6 111. 6 115. 7 115. 7 110. 9 110. 9 110. 9 110. 9 111. 8 111. 8 111. 8 111. 8 117. 4 117. 4 117. 4 111. 2 111. 2

111. 6 111. 6 111. 6 111. 6 110. 9 110. 9 110. 9 110. 9 111. 8 111. 8 111. 8 111. 8 117. 4 117. 4 114 114

111. 6 111. 6 111. 6 111. 6 111. 6 111. 6 112. 3 112. 3 112. 3 112. 3 111. 8 111. 8 111. 8 117. 4 117. 4 114 114

100 111. 6 111. 6 111. 6 111. 6 111. 6 111. 6 112. 3 112. 3 112. 3 112. 3 112. 3 110. 9 110. 9 110. 9 110. 9 110. 9

111. 6 112. 3 112. 3 112. 3 110. 9 110. 9 110. 9 110. 9 110. 9 Jilin

110. 9 110. 9

Tibet

Anhui Hebei

Hubei 124

Fujian

Tianjin

Gansu

Hunan Henan

Beijing

Shanxi

Jiangxi

Hainan

Ningxia

Yunnan

Jiangsu Qinghai

Xinjiang

Sichuan Shaanxi

Guizhou

Guangxi Liaoning

Zhejiang 120 Shanghai

124. 1 124. 1 Shandong

Chongqing Average Wage Growth 116

Guangdong Heilongjiang High: 124 Previous 124. 1 124. 1 113 InnerMongolia 2010 Low: 109 Year = 100 109 • Real wage growth follows real GDP growth quite closely, with central and some of the northeast provinces, and Inner Mongolia recording more than 15% growth per annum, while the eastern provinces, where economic growth is slowing, recording closer to 12% growth per annum. Demographics – training the workforce

University undergraduate numbers, 1977–2010

University undergraduates % of total million 25 2.0%

20 1.6%

15 1.2%

10 0.8%

5 0.4%

0 0.0%

77 79 81 82 84 86 88 89 91 93 95 96 98 00 02 03 04 05 07 09 10 78 80 83 85 87 90 92 94 97 99 01 06 08

• One of the basic criteria for moving the economy onto a higher value-added footing is to educate the workforce, that has, until recently, been largely uneducated and low cost. In order to do this, it is essential to have the institutions in place to facilitate this education. • By the end of 2010, there were 22.3 million people enrolled at university or approximately 1.7% of the population – up from 0.4% of the population in 2000. Demographics – higher institution students as a percentage of the population

University undergraduates as a percentage of the 0. 014530051 0. 014530051 0. 018529404 0. 018529404 0. 018529404 0. 018529404

0. 014530051 0. 014530051 0. 014530051 0. 018529404 0. 018529404 0. 018529404 0. 018529404

population, 2009 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 018529404 0. 018529404 0. 018529404 0. 018529404

0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 019381833 0. 019381833 0. 019381833 0. 019381833 0. 019381833

4.0% 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 019381833 0. 019381833

0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 013716391 0. 013716391 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 019737601 0. 019737601 0. 019737601 0. 019381833

3.5% 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 013716391 0. 013716391 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 019737601 0. 019737601 0. 019737601

0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 013716391 0. 013716391 0. 013716391 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 015971214 0. 015971214 0. 015075202 0. 015075202 0. 015075202 0. 019737601 0. 019737601

3.0% 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 013716391 0. 013716391 0. 013716391 0. 013716391 0. 013716391 0. 013716391 0. 013716391 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 014530051 0. 015971214 0. 015971214 0. 015075202 0. 033429345 0. 015075202

0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 011193998 0. 007856092 0. 007856092 0. 007856092 0. 007856092 0. 007856092 0. 013716391 0. 013716391 0. 014530051 0. 012086372 0. 014530051 0. 014530051 0. 014530051 0. 015971214 0. 015971214 0. 015075202 0. 015075202 0. 033054977

2.5% 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 007856092 0. 007856092 0. 007856092 0. 007856092 0. 007856092 0. 007856092 0. 007856092 0. 007856092 0. 013716391 0. 012086372 0. 012086372 0. 012086372 0. 023694274 0. 023694274 0. 015971214 0. 015971214 0. 015075202 0. 015075202 0. 015075202 0. 015075202 0. 016820734 0. 016820734

0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 007856092 0. 007856092 0. 007856092 0. 007856092 0. 007856092 0. 007856092 0. 007856092 0. 007856092 0. 013716391 0. 013716391 0. 012086372 0. 013716391 0. 023694274 0. 023694274 0. 015971214 0. 015971214 0. 014428302 0. 014428302 0. 016820734 0. 016820734 0. 016820734

2.0% 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 007856092 0. 007856092 0. 007856092 0. 007856092 0. 007856092 0. 007856092 0. 007856092 0. 007856092 0. 013716391 0. 013716391 0. 013716391 0. 013716391 0. 023694274 0. 023694274 0. 015971214 0. 014428302 0. 014428302 0. 014428302 0. 016820734 0. 016820734 0. 016820734

0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 007856092 0. 007856092 0. 007856092 0. 007856092 0. 007856092 0. 007856092 0. 007856092 0. 013716391 0. 013716391 0. 013716391 0. 023694274 0. 023694274 0. 023694274 0. 014428302 0. 014428302 0. 014428302 0. 01431711 0. 01431711 0. 01431711 0. 021403586 0. 021403586

0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 012656494 0. 012656494 0. 012656494 0. 012656494 0. 012656494 0. 013716391 0. 013716391 0. 023694274 0. 023694274 0. 023694274 0. 014428302 0. 014428302 0. 014428302 0. 01431711 0. 01431711 0. 01431711 0. 021403586 0. 021403586 1.5% 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 012656494 0. 012656494 0. 012656494 0. 012656494 0. 012656494 0. 013716391 0. 013716391 0. 023694274 0. 023694274 0. 023694274 0. 021836731 0. 021836731 0. 021836731 0. 01431711 0. 01431711 0. 01431711 0. 01431711 0. 021403586 0. 026694898

0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 012656494 0. 012656494 0. 012656494 0. 012656494 0. 012656494 0. 012656494 0. 016935957 0. 016935957 0. 021836731 0. 021836731 0. 021836731 0. 021836731 0. 021836731 0. 01431711 0. 01431711 0. 01431711 0. 016727722 0. 016727722 1.0% 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 010434783 0. 012656494 0. 012656494 0. 012656494 0. 012656494 0. 012656494 0. 012656494 0. 016935957 0. 016935957 0. 021836731 0. 015873135 0. 015873135 0. 021836731 0. 021836731 0. 017902971 0. 017902971 0. 016727722 0. 016727722 0. 016727722

0. 008610829 0. 008610829 0. 012656494 0. 012656494 0. 00787446 0. 00787446 0. 00787446 0. 00787446 0. 015873135 0. 015873135 0. 015873135 0. 015873135 0. 017902971 0. 017902971 0. 017902971 0. 016727722 0. 016727722

0.5% 0. 008610829 0. 008610829 0. 008610829 0. 008610829 0. 00787446 0. 00787446 0. 00787446 0. 00787446 0. 015873135 0. 015873135 0. 015873135 0. 015873135 0. 017902971 0. 017902971 0. 016715853 0. 016715853

0. 008610829 0. 008610829 0. 008610829 0. 008610829 0. 008610829 0. 008610829 0. 010880189 0. 010880189 0. 010880189 0. 010880189 0. 015873135 0. 015873135 0. 015873135 0. 017902971 0. 017902971 0. 016715853 0. 016715853

0.0% 0. 008610829 0. 008610829 0. 008610829 0. 008610829 0. 008610829 0. 008610829 0. 010880189 0. 010880189 0. 010880189 0. 010880189 0. 010880189 0. 013841969 0. 013841969 0. 013841969 0. 013841969 0. 013841969

0. 008610829 0. 010880189 0. 010880189 0. 010880189 0. 013841969 0. 013841969 0. 013841969 0. 013841969 0. 013841969 Jilin 0. 013841969 0. 013841969

Tibet 3.3%

Anhui

Hebei Hubei

Fujian

Tianjin

Hunan Henan Gansu

Beijing

Shanxi

Jiangxi

Hainan

Ningxia

Yunnan

Qinghai

Jiangsu

Xinjiang Sichuan

Shaanxi 2.7%

Guangxi Guizhou Liaoning Zhejiang Higher Instution Students As % of Population

Shanghai 0. 016443344 0. 016443344 Shandong

Chongqing 2.1% Guangdong Heilongjiang High: 3.3% 0. 016443344 0. 016443344 % 1.4% InnerMongolia 2009 Low: 0.8% 0.8% • The leading universities in China all wish to have a presence in the main business and political centres and vice versa, and as such Beijing, Tianjin and Shanghai come out on top with between 2.5% and 3.5% of their populations being enrolled students. • Apart from these municipalities, provinces such as Shanxi and Hubei, and more particularly their capitals of Xi’an and respectively, have large institutions, closely followed by Jiangsu province and the northeastern provinces. • The largest universities in China in 2011 were Zhejiang University, Beijing University, Qinghua University, Shanghai Jiaotong University and Fudan University. GOVERNMENT Government – government state organs

National People’s Congress (NPC)

Standing Committee of the NPC President

Central Military Supreme People’s Supreme People’s State Council Commission Court Procuratorate Government – administrative divisions

Central (1) People's Republic of China (1)

Provincial (33) Special administrative regions Provinces (22) Autonomous regions (5) Municipalities (4) (2)

Prefectural (333) Prefectures (17) Prefecture-level cities (283) Autonomous prefectures (30) Leagues (3)

County (2,858) Counties County-level Autonomous Autonomous Special Forestry areas Districts (855) Banners (49) (1,464) cities (367) counties (117) banners (3) districts (2) (1)

Township (40,858) Townships Sub-districts Ethnic townships Towns (19,141) Sumu (181) District public (2) Ethnic sumu (1) (14,848) (6,686) (1,099)

Village (not fixed) Government – ministries under the State Council

National Ministry of Ministry of Ministry of Ministry of Development and Ministry of Industry and Science and Foreign Affairs National Defense Reform Education Information Technology Commission Technology

State Ethnic Ministry of Public Ministry of State Ministry of Ministry of Civil Affairs Ministry of Justice Security Security Supervision Affairs Commission

Ministry of Ministry of Ministry of Ministry of Human Ministry of Land Housing and Ministry of Environmental Finance Resources and and Resources Urban-Rural Transport Protection Social Security Development

Ministry of Ministry of Water Ministry of Ministry of Ministry of Ministry of Health Railways Resources Agriculture Commerce Culture

National Population and People's Bank of National Audit Family Planning China Office Commission Government – active role

National government expenditure, 2009 Government surplus, 1978–2010

Education RMB billion National Government Central Government Local Government 3,000 General Public Service

Social Security & 2,000 16% 14% Employment 5% Agriculture & Water 1,000 12% Conservancy 6% Industry, Commerce & Banking 0 6% 10% Community Affairs 7% 9% National Defense -1,000 7% 8% Public Security -2,000 Transportation -3,000 Medical & Health Care

Other -4,000 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

• The government plays a very active role in determining what happens in China. • China has a one-party system and is therefore capable of rolling out new regulations/initiatives very quickly with little or no opposition, yet at the same time, with very little debate or oversight. • This has resulted in the central government having a very tight rein over the financial system, able to dictate exchange rates, bank lending practices and many other aspects. • As the economy continues to grow and becomes more integrated with the global economy, and as the general public becomes more outspoken on certain topics, the government’s power will gradually deteriorate. Government – central government subsidies

102319. 59 102319. 59 123841. 74 123841. 74 123841. 74 123841. 74

Central government subsidies, 2009 102319. 59 102319. 59 102319. 59 123841. 74 123841. 74 123841. 74 123841. 74

92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 102319. 59 102319. 59 102319. 59 102319. 59 123841. 74 123841. 74 123841. 74 123841. 74

92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 102319. 59 102319. 59 102319. 59 102319. 59 95082. 56 95082. 56 95082. 56 95082. 56 95082. 56

RMB million Central Government Subsidies 5 yr CAGR growth 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 102319. 59 102319. 59 102319. 59 102319. 59 102319. 59 102319. 59 102319. 59 95082. 56 95082. 56 400,000 40% 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 88450. 11 88450. 11 102319. 59 102319. 59 102319. 59 102319. 59 102319. 59 102319. 59 102319. 59 102319. 59 102319. 59 102319. 59 109801. 15 109801. 15 109801. 15 95082. 56

92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 88450. 11 88450. 11 102319. 59 102319. 59 102319. 59 102319. 59 102319. 59 102319. 59 102319. 59 102319. 59 102319. 59 102319. 59 102319. 59 102319. 59 102319. 59 109801. 15 109801. 15 109801. 15

350,000 35% 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 88450. 11 88450. 11 88450. 11 102319. 59 102319. 59 102319. 59 102319. 59 102319. 59 102319. 59 102319. 59 102319. 59 102319. 59 81532. 5 81532. 5 126302. 34 126302. 34 126302. 34 109801. 15 109801. 15

92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 88450. 11 88450. 11 88450. 11 88450. 11 88450. 11 88450. 11 88450. 11 102319. 59 102319. 59 102319. 59 102319. 59 102319. 59 81532. 5 81532. 5 126302. 34 36772. 02 126302. 34

300,000 30% 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 92773. 83 40122. 74 40122. 74 40122. 74 40122. 74 40122. 74 88450. 11 88450. 11 102319. 59 32070. 91 102319. 59 102319. 59 102319. 59 81532. 5 81532. 5 126302. 34 126302. 34 28163. 44

47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 40122. 74 40122. 74 40122. 74 40122. 74 40122. 74 40122. 74 40122. 74 40122. 74 88450. 11 32070. 91 32070. 91 32070. 91 102823. 87 102823. 87 81532. 5 81532. 5 126302. 34 126302. 34 126302. 34 126302. 34 114159. 96 114159. 96

250,000 25% 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 40122. 74 40122. 74 40122. 74 40122. 74 40122. 74 40122. 74 40122. 74 40122. 74 88450. 11 88450. 11 32070. 91 88450. 11 102823. 87 102823. 87 81532. 5 81532. 5 174648. 17 174648. 17 114159. 96 114159. 96 114159. 96

47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 40122. 74 40122. 74 40122. 74 40122. 74 40122. 74 40122. 74 40122. 74 40122. 74 88450. 11 88450. 11 88450. 11 88450. 11 102823. 87 102823. 87 81532. 5 174648. 17 174648. 17 174648. 17 114159. 96 114159. 96 114159. 96

200,000 20% 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 40122. 74 40122. 74 40122. 74 40122. 74 40122. 74 40122. 74 40122. 74 88450. 11 88450. 11 88450. 11 102823. 87 102823. 87 102823. 87 174648. 17 174648. 17 174648. 17 118971. 31 118971. 31 118971. 31 87971. 19 87971. 19

47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 247020. 08 247020. 08 247020. 08 247020. 08 247020. 08 88450. 11 88450. 11 102823. 87 102823. 87 102823. 87 174648. 17 174648. 17 174648. 17 118971. 31 118971. 31 118971. 31 87971. 19 87971. 19

150,000 15% 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 247020. 08 247020. 08 247020. 08 247020. 08 247020. 08 88450. 11 88450. 11 102823. 87 102823. 87 102823. 87 130420. 03 130420. 03 130420. 03 118971. 31 118971. 31 118971. 31 118971. 31 87971. 19 49370. 6

47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 247020. 08 247020. 08 247020. 08 247020. 08 247020. 08 247020. 08 66944. 37 66944. 37 130420. 03 130420. 03 130420. 03 130420. 03 130420. 03 118971. 31 118971. 31 118971. 31 62824. 22 62824. 22

100,000 10% 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 47094. 65 247020. 08 247020. 08 247020. 08 247020. 08 247020. 08 247020. 08 66944. 37 66944. 37 130420. 03 137398. 59 137398. 59 130420. 03 130420. 03 97166. 57 97166. 57 62824. 22 62824. 22 62824. 22

114761. 28 114761. 28 247020. 08 247020. 08 92081. 66 92081. 66 92081. 66 92081. 66 137398. 59 137398. 59 137398. 59 137398. 59 97166. 57 97166. 57 97166. 57 62824. 22 62824. 22

50,000 5% 114761. 28 114761. 28 114761. 28 114761. 28 92081. 66 92081. 66 92081. 66 92081. 66 137398. 59 137398. 59 137398. 59 137398. 59 97166. 57 97166. 57 51145. 54 51145. 54

114761. 28 114761. 28 114761. 28 114761. 28 114761. 28 114761. 28 99525. 11 99525. 11 99525. 11 99525. 11 137398. 59 137398. 59 137398. 59 97166. 57 97166. 57 51145. 54 51145. 54

0 0% 114761. 28 114761. 28 114761. 28 114761. 28 114761. 28 114761. 28 99525. 11 99525. 11 99525. 11 99525. 11 99525. 11 90796. 78 90796. 78 90796. 78 90796. 78 90796. 78

114761. 28 99525. 11 99525. 11 99525. 11 90796. 78 90796. 78 90796. 78 90796. 78 90796. 78

90796. 78 90796. 78 Jilin

Tibet 247,020

Anhui

Hubei Hebei

Fujian

Tianjin

Henan Hunan Gansu

Beijing

Shanxi

Jiangxi

Hainan

Ningxia

Yunnan Qinghai

Jiangsu 192,060

Xinjiang

Shaanxi Sichuan

Guizhou

Guangxi

Liaoning Zhejiang 27180. 12 27180. 12 Shanghai Central Government Subsidies

Shandong 137,100 Chongqing

Guangdong 27180. 12 27180. 12 Heilongjiang High: 247,020 RMB 82,140

InnerMongolia 2009 Low: 27,180 million 27,180 • The central government receives a significant proportion of taxes and runs a large surplus before taking into account local government subsidies. • More recently, the government spent a significant percentage of its subsidies in supporting Sichuan province in the wake of the 2008 earthquake and in the development of the central provinces. Government – central government subsidies as a percentage of GDP

Central government subsidies as a percentage of GDP, 0. 105048217 0. 105048217 0. 14422003 0. 14422003 0. 14422003 0. 14422003

0. 105048217 0. 105048217 0. 105048217 0. 14422003 0. 14422003 0. 14422003 0. 14422003

2009 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 14422003 0. 14422003 0. 14422003 0. 14422003

0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 130630342 0. 130630342 0. 130630342 0. 130630342 0. 130630342

120% 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 130630342 0. 130630342

0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 261102711 0. 261102711 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 072178289 0. 072178289 0. 072178289 0. 130630342

0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 261102711 0. 261102711 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 072178289 0. 072178289 0. 072178289

100% 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 261102711 0. 261102711 0. 261102711 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 110803296 0. 110803296 0. 073280431 0. 073280431 0. 073280431 0. 072178289 0. 072178289

0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 261102711 0. 261102711 0. 261102711 0. 261102711 0. 261102711 0. 261102711 0. 261102711 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 105048217 0. 110803296 0. 110803296 0. 073280431 0. 030257491 0. 073280431

0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 216910791 0. 3710705 0. 3710705 0. 3710705 0. 3710705 0. 3710705 0. 261102711 0. 261102711 0. 105048217 0. 236981253 0. 105048217 0. 105048217 0. 105048217 0. 110803296 0. 110803296 0. 073280431 0. 073280431 0. 037442172 80% 0. 3710705 0. 3710705 0. 3710705 0. 3710705 0. 3710705 0. 3710705 0. 3710705 0. 3710705 0. 261102711 0. 236981253 0. 236981253 0. 236981253 0. 125858491 0. 125858491 0. 110803296 0. 110803296 0. 073280431 0. 073280431 0. 073280431 0. 073280431 0. 033678833 0. 033678833

0. 3710705 0. 3710705 0. 3710705 0. 3710705 0. 3710705 0. 3710705 0. 3710705 0. 3710705 0. 261102711 0. 261102711 0. 236981253 0. 261102711 0. 125858491 0. 125858491 0. 110803296 0. 110803296 0. 089653001 0. 089653001 0. 033678833 0. 033678833 0. 033678833

60% 0. 3710705 0. 3710705 0. 3710705 0. 3710705 0. 3710705 0. 3710705 0. 3710705 0. 3710705 0. 261102711 0. 261102711 0. 261102711 0. 261102711 0. 125858491 0. 125858491 0. 110803296 0. 089653001 0. 089653001 0. 089653001 0. 033678833 0. 033678833 0. 033678833

0. 3710705 0. 3710705 0. 3710705 0. 3710705 0. 3710705 0. 3710705 0. 3710705 0. 261102711 0. 261102711 0. 261102711 0. 125858491 0. 125858491 0. 125858491 0. 089653001 0. 089653001 0. 089653001 0. 118228598 0. 118228598 0. 118228598 0. 025530494 0. 025530494

0. 174556704 0. 174556704 0. 174556704 0. 174556704 0. 174556704 0. 261102711 0. 261102711 0. 125858491 0. 125858491 0. 125858491 0. 089653001 0. 089653001 0. 089653001 0. 118228598 0. 118228598 0. 118228598 0. 025530494 0. 025530494

40% 0. 174556704 0. 174556704 0. 174556704 0. 174556704 0. 174556704 0. 261102711 0. 261102711 0. 125858491 0. 125858491 0. 125858491 0. 100624199 0. 100624199 0. 100624199 0. 118228598 0. 118228598 0. 118228598 0. 118228598 0. 025530494 0. 032812125

0. 174556704 0. 174556704 0. 174556704 0. 174556704 0. 174556704 0. 174556704 0. 102518021 0. 102518021 0. 100624199 0. 100624199 0. 100624199 0. 100624199 0. 100624199 0. 118228598 0. 118228598 0. 118228598 0. 027326343 0. 027326343

0. 174556704 0. 174556704 0. 174556704 0. 174556704 0. 174556704 0. 174556704 0. 102518021 0. 102518021 0. 100624199 0. 105208156 0. 105208156 0. 100624199 0. 100624199 0. 126929177 0. 126929177 0. 027326343 0. 027326343 0. 027326343

20% 0. 18600637 0. 18600637 0. 174556704 0. 174556704 0. 235341658 0. 235341658 0. 235341658 0. 235341658 0. 105208156 0. 105208156 0. 105208156 0. 105208156 0. 126929177 0. 126929177 0. 126929177 0. 027326343 0. 027326343

0. 18600637 0. 18600637 0. 18600637 0. 18600637 0. 235341658 0. 235341658 0. 235341658 0. 235341658 0. 105208156 0. 105208156 0. 105208156 0. 105208156 0. 126929177 0. 126929177 0. 041797421 0. 041797421

0. 18600637 0. 18600637 0. 18600637 0. 18600637 0. 18600637 0. 18600637 0. 128267892 0. 128267892 0. 128267892 0. 128267892 0. 105208156 0. 105208156 0. 105208156 0. 126929177 0. 126929177 0. 041797421 0. 041797421

0% 0. 18600637 0. 18600637 0. 18600637 0. 18600637 0. 18600637 0. 18600637 0. 128267892 0. 128267892 0. 128267892 0. 128267892 0. 128267892 0. 02299668 0. 02299668 0. 02299668 0. 02299668 0. 02299668

0. 18600637 0. 128267892 0. 128267892 0. 128267892 0. 02299668 0. 02299668 0. 02299668 0. 02299668 0. 02299668 Jilin 0. 02299668 0. 02299668

Tibet 37%

Anhui

Hubei Hebei

Fujian

Tianjin

Hunan Gansu Henan

Beijing

Shanxi

Jiangxi

Hainan

Ningxia

Yunnan

Qinghai

Jiangsu

Xinjiang

Sichuan Shaanxi Guangxi

Guizhou 28%

Liaoning Zhejiang

Shanghai Central government subsidies as % of GDP 0. 164308764 0. 164308764 Shandong

Chongqing 20% Guangdong Heilongjiang High: 0.4 0. 164308764 0. 164308764 11%

InnerMongolia % 2009 Low: 0.0 2% • Tibet has been excluded from the map above to allow greater comparability, as Tibet’s central government subsidy is significantly higher than any other province. This can be clearly seen when looking at subsidies as a percentage of GDP. • In the provinces along the south and east coasts, subsidies typically account for less than 5% of GDP, in central provinces subsidies account for close to 15%, while in the western provinces they account for more than 20%. REAL ESTATE Real estate – investment

Real estate investment year-on-year by property type, Real estate investment by property type, 1996–2010 1997–2010

RMB trillion Ordinary Resi Deluxe Resi Economic Resi Office Retail Other Ordinary Resi Deluxe Resi Economic Resi Office Retail Other 80% 6

5 60%

4 40%

3 20%

2 0%

1 -20%

0 -40% 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

• Real estate is a pillar of the Chinese economy. The figures for 2009 are as follows: • 70,817 construction companies and 36 million people employed in the construction industry • Total output of RMB7.6 trillion, value added of RMB1.56 trillion and total profit of RMB271 billion • 5.9 billion sq m of floor space under construction and 2.5 billion sq m of floor space completed • These figures are for the construction industry and do not take into account upstream industries, such as cement and steel manufacturing, and downstream industries, such as developers and brokerage houses. Real estate – residential market

China’s premier commercialised residential market, China’s commercialised residential market, 1997–2010 1997–2009

million sqm Supply (LHS) Demand (LHS) Price (RHS) RMB per sqm million sqm Supply (LHS) Demand (LHS) Price (RHS) RMB per sqm 1,000 5,000 50 10,000

900 4,500 45 9,000

800 4,000 40 8,000

700 3,500 35 7,000

600 3,000 30 6,000

500 2,500 25 5,000

400 2,000 20 4,000

300 1,500 15 3,000

200 1,000 10 2,000

100 500 5 1,000

0 0 0 0 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

• The overall residential market has witnessed spectacular growth over the last 15 years. In the initial stages, supply outstripped demand, and over the last six years demand has exceeded supply and residential prices have recorded stellar growth. • In 2010, 600 million sq m of new supply came onto the market and there was more than 900 million sq m of take-up. Prices exceeded RMB4,500 per sq m for the first time in history. • The premier market, as defined by the National Bureaus of Statistics, accounts for about 5% of the total residential market, with average prices now close to RMB10,000 per sq m. Real estate – economical housing

China’s economical residential market, 1997–2009

million sqm Supply (LHS) Demand (LHS) Price (RHS) RMB per sqm 70 3,500

60 3,000

50 2,500

40 2,000

30 1,500

20 1,000

10 500

0 0 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

• After a lot of new supply at the start of the 21st century the economical housing market saw supply fall until 2005, when it stabilized at about 30 million sq m of new supply per year once again, accounting for approximately 5% of the total residential market. • The government has embarked upon an aggressive plan to increase the volume of economical housing being developed in China in order to address concerns that housing prices are becoming unaffordable for low-income households. The target for 2011 is to build 10 million units. Real estate – commercial real estate

China’s retail market, 1997–2010 China’s office market, 1997–2010

million sqm Supply (LHS) Demand (LHS) Price (RHS) RMB per sqm million sqm Supply (LHS) Demand (LHS) Price (RHS) RMB per sqm 90 9,000 24 12,000 22 11,000 80 8,000 20 10,000 70 7,000 18 9,000 60 6,000 16 8,000 14 7,000 50 5,000 12 6,000 40 4,000 10 5,000 30 3,000 8 4,000 6 3,000 20 2,000 4 2,000 10 1,000 2 1,000 0 0 0 0 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

• The commercial real estate market also recorded strong growth, with government figures indicating that in 2010 close to 80 million sq m of retail space and 18 million sq m of office space was completed. • Supply has continually outstripped demand in the retail market, supposedly with developers withholding projects from the market after construction. In 2010, demand outstripped supply in the office market, however, with a very active strata- sales market supporting activity. • Office developments are primarily located in key markets (typically first-, second- and third-tier cities), while retail markets can be spread throughout the whole nation, accounting for the significantly larger figure. Real estate – land deficit

Land sales and local government revenue, 2000–2010 Land area purchased, 1996–2010

Local government revenue (LHS) Revenue from land sales (LHS) million sqm 600 % of local government revenue (RHS) RMB billion 4,500 90% 500 4,000 80%

3,500 70% 400 3,000 60%

2,500 50% 300

2,000 40% 200 1,500 30%

1,000 20% 100 500 10%

0 0% 0 00 01 02 03 04 05 06 07 08 09 10 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

• Land sales are an essential component of many local government tax bases. In 2010, land sale revenue is believed to have accounted for more than 70% of local government revenue. • This revenue helps to generate capital used in constructing new infrastructure and developing local public facilities, which are a great burden for local governments. • This also has a large negative impact as it discourages local governments from cooling the market as this would have a direct and very real impact on their ability to generate revenue. Real estate – residential supply

Residential supply, 1995–2010 Residential supply, 1995–2010

million sqm Urban Residential Rural Residential Commodity Residential Urban (non commodity) Residential Rural Residential 2,000 100% 1,800 90% 1,600 80% 1,400 70% 1,200 60% 1,000 50% 800 40% 600 30% 400 20% 200 10% 0 0% 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

• Residential supply, as calculated by the National Bureaus of Statistics, is broken down into three categories: total, urban and commodity. • In the 1990s, only a third of the population lived in urban areas and the vast majority of residential supply was in rural areas. This has shifted with the population, and in recent years, supply has gradually moved to urban centres and now accounts for approximately 45% of all residential supply. Commodity housing, or freely traded properties – which are a form of urban supply – account for about 35% of the total. Real estate – residential prices vs incomes

Residential prices, 1999–2010 Disposable incomes (urban), 1999–2010

RMB per sqm C E N NE NW S SW RMB C E N NE NW S SW 8,000 25,000

7,000 20,000 6,000

5,000 15,000

4,000

10,000 3,000

2,000 5,000 1,000

0 0 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

• China always records staggering statistics whether they be absolute values or growth rates and the residential market is no different. • Average residential prices started off at just less than RMB2,000 per sq m in all regions in China at the start of the 21st century, and as low as RMB1,000 per sq m in central China. • By 2010, prices had risen to RMB7,500 per sq m in the east of China and RMB3,000 per sq m in central China. • Over the same period of time, incomes have risen from RMB5,000 to RMB8,000 per annum to RMB15,000 to RMB25,000 per annum. Real estate – residential prices vs incomes (cont)

Residential price YoY growth, 2000–2010 Disposable income (urban) YoY growth, 2000–2010

C E N NE NW S SW C E N NE NW S SW 35% 35%

30% 30%

25% 25%

20% 20%

15% 15%

10% 10%

5% 5%

0% 0%

-5% -5% 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

• At the start of the 21st century, the residential market was not in the fervent state it currently finds itself in. Prices were growing at more moderate levels of less than 10% per year. • China’s east was the first to get caught-up in the cycle of price rises, recording growth in excess of 10% in both 2002 and 2003. The rest of the country was quick to jump on the bandwagon and price growth remained between 10% and 30% until the government clamped down on the market in 2008. Once the government took their foot off the brakes, growth quickly returned in 2009 and remained strong in 2010 despite government efforts to cool the market. • In comparison to the volatile residential prices ,disposable incomes are believed to have recorded growth consistently above 5%, and as high as 15% in 2007 when labour shortages and a strong global economy forced prices up. Real estate – residential prices vs incomes (cont)

Residential price growth less disposable income Residential prices as a multiple of income, 2000–2010 (urban) growth, 2000–2010

C E N NE NW S SW C E N NE NW S SW 30% multiples of income Decreasing affordability 12.0 25%

20% 10.0

15% 8.0 10%

5% 6.0 0%

-5% 4.0

-10% 2.0 -15% Increasing affordability -20% 0.0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

• The difference in growth rates, especially over the last two years, has resulted in decreasing affordability for many people, but especially in the coastal regions (N, E, S, NE) where price growth has been strongest. • Prices as a multiple of income is a rather crude measure of affordability, especially in China where only mean figures rather than median figures are available. However, given provincial data for disposable income per capita, average household size and prices, while assuming a 90 sq m unit, we can see that house prices in the north and the east are the least affordable at some ten times the annual household income, up from approximately eight times just two years ago. However, the west of China has managed to maintain prices at less than six times their income. Real estate – residential prices

2981. 184729 2981. 184729 3487. 999356 3487. 999356 3487. 999356 3487. 999356

Residential prices, 2010 2981. 184729 2981. 184729 2981. 184729 3487. 999356 3487. 999356 3487. 999356 3487. 999356

2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2981. 184729 2981. 184729 2981. 184729 2981. 184729 3487. 999356 3487. 999356 3487. 999356 3487. 999356

2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2981. 184729 2981. 184729 2981. 184729 2981. 184729 3451. 133996 3451. 133996 3451. 133996 3451. 133996 3451. 133996

Price (Residential) 5 yr CAGR 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2981. 184729 2981. 184729 2981. 184729 2981. 184729 2981. 184729 2981. 184729 2981. 184729 3451. 133996 3451. 133996 RMB per sqm 18,000 45% 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2911. 130907 2911. 130907 2981. 184729 2981. 184729 2981. 184729 2981. 184729 2981. 184729 2981. 184729 2981. 184729 2981. 184729 2981. 184729 2981. 184729 4300. 306868 4300. 306868 4300. 306868 3451. 133996

2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2911. 130907 2911. 130907 2981. 184729 2981. 184729 2981. 184729 2981. 184729 2981. 184729 2981. 184729 2981. 184729 2981. 184729 2981. 184729 2981. 184729 2981. 184729 2981. 184729 2981. 184729 4300. 306868 4300. 306868 4300. 306868

16,000 40% 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2911. 130907 2911. 130907 2911. 130907 2981. 184729 2981. 184729 2981. 184729 2981. 184729 2981. 184729 2981. 184729 2981. 184729 2981. 184729 2981. 184729 3325. 752939 3325. 752939 3450. 261009 3450. 261009 3450. 261009 4300. 306868 4300. 306868

2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2911. 130907 2911. 130907 2911. 130907 2911. 130907 2911. 130907 2911. 130907 2911. 130907 2981. 184729 2981. 184729 2981. 184729 2981. 184729 2981. 184729 3325. 752939 3325. 752939 3450. 261009 17151. 10283 3450. 261009 14,000 35% 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2877. 484183 2892. 281037 2892. 281037 2892. 281037 2892. 281037 2892. 281037 2911. 130907 2911. 130907 2981. 184729 3106. 863846 2981. 184729 2981. 184729 2981. 184729 3325. 752939 3325. 752939 3450. 261009 3450. 261009 7912. 636709

12,000 30% 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2892. 281037 2892. 281037 2892. 281037 2892. 281037 2892. 281037 2892. 281037 2892. 281037 2892. 281037 2911. 130907 3106. 863846 3106. 863846 3106. 863846 3665. 113345 3665. 113345 3325. 752939 3325. 752939 3450. 261009 3450. 261009 3450. 261009 3450. 261009 3817. 681045 3817. 681045

2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2892. 281037 2892. 281037 2892. 281037 2892. 281037 2892. 281037 2892. 281037 2892. 281037 2892. 281037 2911. 130907 2911. 130907 3106. 863846 2911. 130907 3665. 113345 3665. 113345 3325. 752939 3325. 752939 2856. 302937 2856. 302937 3817. 681045 3817. 681045 3817. 681045

10,000 25% 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2892. 281037 2892. 281037 2892. 281037 2892. 281037 2892. 281037 2892. 281037 2892. 281037 2892. 281037 2911. 130907 2911. 130907 2911. 130907 2911. 130907 3665. 113345 3665. 113345 3325. 752939 2856. 302937 2856. 302937 2856. 302937 3817. 681045 3817. 681045 3817. 681045

2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2892. 281037 2892. 281037 2892. 281037 2892. 281037 2892. 281037 2892. 281037 2892. 281037 2911. 130907 2911. 130907 2911. 130907 3665. 113345 3665. 113345 3665. 113345 2856. 302937 2856. 302937 2856. 302937 3906. 964978 3906. 964978 3906. 964978 5548. 669503 5548. 669503

8,000 20% 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 3984. 799488 3984. 799488 3984. 799488 3984. 799488 3984. 799488 2911. 130907 2911. 130907 3665. 113345 3665. 113345 3665. 113345 2856. 302937 2856. 302937 2856. 302937 3906. 964978 3906. 964978 3906. 964978 5548. 669503 5548. 669503

2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 3984. 799488 3984. 799488 3984. 799488 3984. 799488 3984. 799488 2911. 130907 2911. 130907 3665. 113345 3665. 113345 3665. 113345 3499. 93311 3499. 93311 3499. 93311 3906. 964978 3906. 964978 3906. 964978 3906. 964978 5548. 669503 14212. 78154

6,000 15% 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 3984. 799488 3984. 799488 3984. 799488 3984. 799488 3984. 799488 3984. 799488 4040. 437478 4040. 437478 3499. 93311 3499. 93311 3499. 93311 3499. 93311 3499. 93311 3906. 964978 3906. 964978 3906. 964978 9322. 017272 9322. 017272

2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 2772. 044725 3984. 799488 3984. 799488 3984. 799488 3984. 799488 3984. 799488 3984. 799488 4040. 437478 4040. 437478 3499. 93311 3011. 945699 3011. 945699 3499. 93311 3499. 93311 2958. 649496 2958. 649496 9322. 017272 9322. 017272 9322. 017272 4,000 10% 2893. 276809 2893. 276809 3984. 799488 3984. 799488 3142. 437477 3142. 437477 3142. 437477 3142. 437477 3011. 945699 3011. 945699 3011. 945699 3011. 945699 2958. 649496 2958. 649496 2958. 649496 9322. 017272 9322. 017272

2,000 5% 2893. 276809 2893. 276809 2893. 276809 2893. 276809 3142. 437477 3142. 437477 3142. 437477 3142. 437477 3011. 945699 3011. 945699 3011. 945699 3011. 945699 2958. 649496 2958. 649496 6076. 064934 6076. 064934

2893. 276809 2893. 276809 2893. 276809 2893. 276809 2893. 276809 2893. 276809 3381. 865435 3381. 865435 3381. 865435 3381. 865435 3011. 945699 3011. 945699 3011. 945699 2958. 649496 2958. 649496 6076. 064934 6076. 064934

0 0% 2893. 276809 2893. 276809 2893. 276809 2893. 276809 2893. 276809 2893. 276809 3381. 865435 3381. 865435 3381. 865435 3381. 865435 3381. 865435 7006. 287835 7006. 287835 7006. 287835 7006. 287835 7006. 287835

2893. 276809 3381. 865435 3381. 865435 3381. 865435 7006. 287835 7006. 287835 7006. 287835 7006. 287835 7006. 287835

7006. 287835 7006. 287835 Jilin

Tibet 17,151

Anhui

Hubei Hebei

Fujian

Tianjin

Hunan Gansu Henan

Beijing

Shanxi

Jiangxi

Hainan

Ningxia

Yunnan Qinghai

Jiangsu 13,556

Xinjiang

Sichuan Shaanxi

Guizhou

Guangxi

Liaoning Zhejiang 8800. 142222 8800. 142222 Shanghai Price (Residential)

Shandong 9,962 Chongqing

Guangdong 8800. 142222 8800. 142222 Heilongjiang High: 17,151 RMB per 6,367

InnerMongolia 2010 Low: 2,772 sqm 2,772 • Residential sales prices vary dramatically between the east coast and the interior, all the way from RMB17,000 per sq m in Beijing to less than RMB3,000 per sq m in Tibet. • While price growth is slightly higher on the east coast, the remainder of China is not that far behind, with every province apart from Yunan recording in excess of 10% five-year CAGR. Real estate – retail prices

7148. 704832 7148. 704832 5791. 903473 5791. 903473 5791. 903473 5791. 903473

Retail prices, 2010 7148. 704832 7148. 704832 7148. 704832 5791. 903473 5791. 903473 5791. 903473 5791. 903473

5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 7148. 704832 7148. 704832 7148. 704832 7148. 704832 5791. 903473 5791. 903473 5791. 903473 5791. 903473

5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 7148. 704832 7148. 704832 7148. 704832 7148. 704832 5096. 737033 5096. 737033 5096. 737033 5096. 737033 5096. 737033

Price (Retail) 5 yr CAGR 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 7148. 704832 7148. 704832 7148. 704832 7148. 704832 7148. 704832 7148. 704832 7148. 704832 5096. 737033 5096. 737033 RMB per sqm 25,000 20% 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 4302. 995262 4302. 995262 7148. 704832 7148. 704832 7148. 704832 7148. 704832 7148. 704832 7148. 704832 7148. 704832 7148. 704832 7148. 704832 7148. 704832 6517. 608793 6517. 608793 6517. 608793 5096. 737033

5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 4302. 995262 4302. 995262 7148. 704832 7148. 704832 7148. 704832 7148. 704832 7148. 704832 7148. 704832 7148. 704832 7148. 704832 7148. 704832 7148. 704832 7148. 704832 7148. 704832 7148. 704832 6517. 608793 6517. 608793 6517. 608793

5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 4302. 995262 4302. 995262 4302. 995262 7148. 704832 7148. 704832 7148. 704832 7148. 704832 7148. 704832 7148. 704832 7148. 704832 7148. 704832 7148. 704832 5473. 940656 5473. 940656 5642. 941206 5642. 941206 5642. 941206 6517. 608793 6517. 608793

20,000 15% 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 4302. 995262 4302. 995262 4302. 995262 4302. 995262 4302. 995262 4302. 995262 4302. 995262 7148. 704832 7148. 704832 7148. 704832 7148. 704832 7148. 704832 5473. 940656 5473. 940656 5642. 941206 22452. 3991 5642. 941206

5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5625. 677734 5295. 729446 5295. 729446 5295. 729446 5295. 729446 5295. 729446 4302. 995262 4302. 995262 7148. 704832 4888. 080983 7148. 704832 7148. 704832 7148. 704832 5473. 940656 5473. 940656 5642. 941206 5642. 941206 10546. 27009

7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 5295. 729446 5295. 729446 5295. 729446 5295. 729446 5295. 729446 5295. 729446 5295. 729446 5295. 729446 4302. 995262 4888. 080983 4888. 080983 4888. 080983 6330. 293039 6330. 293039 5473. 940656 5473. 940656 5642. 941206 5642. 941206 5642. 941206 5642. 941206 5609. 689429 5609. 689429

7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 5295. 729446 5295. 729446 5295. 729446 5295. 729446 5295. 729446 5295. 729446 5295. 729446 5295. 729446 4302. 995262 4302. 995262 4888. 080983 4302. 995262 6330. 293039 6330. 293039 5473. 940656 5473. 940656 5562. 714886 5562. 714886 5609. 689429 5609. 689429 5609. 689429 15,000 10% 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 5295. 729446 5295. 729446 5295. 729446 5295. 729446 5295. 729446 5295. 729446 5295. 729446 5295. 729446 4302. 995262 4302. 995262 4302. 995262 4302. 995262 6330. 293039 6330. 293039 5473. 940656 5562. 714886 5562. 714886 5562. 714886 5609. 689429 5609. 689429 5609. 689429

7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 5295. 729446 5295. 729446 5295. 729446 5295. 729446 5295. 729446 5295. 729446 5295. 729446 4302. 995262 4302. 995262 4302. 995262 6330. 293039 6330. 293039 6330. 293039 5562. 714886 5562. 714886 5562. 714886 6683. 585623 6683. 585623 6683. 585623 7871. 158526 7871. 158526

7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 6220. 755211 6220. 755211 6220. 755211 6220. 755211 6220. 755211 4302. 995262 4302. 995262 6330. 293039 6330. 293039 6330. 293039 5562. 714886 5562. 714886 5562. 714886 6683. 585623 6683. 585623 6683. 585623 7871. 158526 7871. 158526 10,000 5% 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 6220. 755211 6220. 755211 6220. 755211 6220. 755211 6220. 755211 4302. 995262 4302. 995262 6330. 293039 6330. 293039 6330. 293039 7727. 712498 7727. 712498 7727. 712498 6683. 585623 6683. 585623 6683. 585623 6683. 585623 7871. 158526 15735. 17231

7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 6220. 755211 6220. 755211 6220. 755211 6220. 755211 6220. 755211 6220. 755211 8003. 202084 8003. 202084 7727. 712498 7727. 712498 7727. 712498 7727. 712498 7727. 712498 6683. 585623 6683. 585623 6683. 585623 10558. 51767 10558. 51767

7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 7740. 080506 6220. 755211 6220. 755211 6220. 755211 6220. 755211 6220. 755211 6220. 755211 8003. 202084 8003. 202084 7727. 712498 5273. 343653 5273. 343653 7727. 712498 7727. 712498 5348. 549675 5348. 549675 10558. 51767 10558. 51767 10558. 51767

5,000 0% 6489. 516588 6489. 516588 6220. 755211 6220. 755211 7014. 598143 7014. 598143 7014. 598143 7014. 598143 5273. 343653 5273. 343653 5273. 343653 5273. 343653 5348. 549675 5348. 549675 5348. 549675 10558. 51767 10558. 51767

6489. 516588 6489. 516588 6489. 516588 6489. 516588 7014. 598143 7014. 598143 7014. 598143 7014. 598143 5273. 343653 5273. 343653 5273. 343653 5273. 343653 5348. 549675 5348. 549675 10235. 84357 10235. 84357

6489. 516588 6489. 516588 6489. 516588 6489. 516588 6489. 516588 6489. 516588 6980. 757022 6980. 757022 6980. 757022 6980. 757022 5273. 343653 5273. 343653 5273. 343653 5348. 549675 5348. 549675 10235. 84357 10235. 84357

0 -5% 6489. 516588 6489. 516588 6489. 516588 6489. 516588 6489. 516588 6489. 516588 6980. 757022 6980. 757022 6980. 757022 6980. 757022 6980. 757022 12878. 29723 12878. 29723 12878. 29723 12878. 29723 12878. 29723

6489. 516588 6980. 757022 6980. 757022 6980. 757022 12878. 29723 12878. 29723 12878. 29723 12878. 29723 12878. 29723

12878. 29723 12878. 29723 Jilin

Tibet 22,452

Anhui

Hubei Hebei

Fujian

Tianjin

Henan Gansu Hunan

Beijing

Shanxi

Jiangxi

Hainan

Ningxia

Yunnan Qinghai

Jiangsu 17,915

Xinjiang

Shaanxi Sichuan

Guizhou

Guangxi

Liaoning Zhejiang 6124. 453835 6124. 453835 Shanghai Price (Retail)

Shandong 13,378 Chongqing

Guangdong 6124. 453835 6124. 453835 Heilongjiang High: 22,452 RMB per 8,840

InnerMongolia 2010 Low: 4,303 sqm 4,303 • Growth rates have been relatively consistent throughout the country, growing, on average, around 10% per annum. • Most provinces have an average retail sales price of RMB5,000 to RMB8,000 per sq m, but prices in the coastal provinces of Guangdong, Zhejiang and Fujian and the municipalities of Beijing and Shanghai are much higher (RMB10,000 to RMB20,000 per sq m). Real estate – office prices

6252. 59036 6252. 59036 4315. 788192 4315. 788192 4315. 788192 4315. 788192

Office prices, 2010 6252. 59036 6252. 59036 6252. 59036 4315. 788192 4315. 788192 4315. 788192 4315. 788192

8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 6252. 59036 6252. 59036 6252. 59036 6252. 59036 4315. 788192 4315. 788192 4315. 788192 4315. 788192

8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 6252. 59036 6252. 59036 6252. 59036 6252. 59036 3415. 251692 3415. 251692 3415. 251692 3415. 251692 3415. 251692

Price (Office) 5 yr CAGR 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 6252. 59036 6252. 59036 6252. 59036 6252. 59036 6252. 59036 6252. 59036 6252. 59036 3415. 251692 3415. 251692 RMB per sqm 35,000 35% 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 3794. 970372 3794. 970372 6252. 59036 6252. 59036 6252. 59036 6252. 59036 6252. 59036 6252. 59036 6252. 59036 6252. 59036 6252. 59036 6252. 59036 8106. 154153 8106. 154153 8106. 154153 3415. 251692

8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 3794. 970372 3794. 970372 6252. 59036 6252. 59036 6252. 59036 6252. 59036 6252. 59036 6252. 59036 6252. 59036 6252. 59036 6252. 59036 6252. 59036 6252. 59036 6252. 59036 6252. 59036 8106. 154153 8106. 154153 8106. 154153

30,000 30% 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 3794. 970372 3794. 970372 3794. 970372 6252. 59036 6252. 59036 6252. 59036 6252. 59036 6252. 59036 6252. 59036 6252. 59036 6252. 59036 6252. 59036 5628. 472674 5628. 472674 4707. 728845 4707. 728845 4707. 728845 8106. 154153 8106. 154153

8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 3794. 970372 3794. 970372 3794. 970372 3794. 970372 3794. 970372 3794. 970372 3794. 970372 6252. 59036 6252. 59036 6252. 59036 6252. 59036 6252. 59036 5628. 472674 5628. 472674 4707. 728845 23412. 70409 4707. 728845 25,000 25% 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 8988. 616675 2193. 088206 2193. 088206 2193. 088206 2193. 088206 2193. 088206 3794. 970372 3794. 970372 6252. 59036 5277. 779582 6252. 59036 6252. 59036 6252. 59036 5628. 472674 5628. 472674 4707. 728845 4707. 728845 13854. 54741

20,000 20% 2193. 088206 2193. 088206 2193. 088206 2193. 088206 2193. 088206 2193. 088206 2193. 088206 2193. 088206 3794. 970372 5277. 779582 5277. 779582 5277. 779582 5516. 490487 5516. 490487 5628. 472674 5628. 472674 4707. 728845 4707. 728845 4707. 728845 4707. 728845 6664. 743472 6664. 743472

2193. 088206 2193. 088206 2193. 088206 2193. 088206 2193. 088206 2193. 088206 2193. 088206 2193. 088206 3794. 970372 3794. 970372 5277. 779582 3794. 970372 5516. 490487 5516. 490487 5628. 472674 5628. 472674 8276. 171771 8276. 171771 6664. 743472 6664. 743472 6664. 743472

15,000 15% 2193. 088206 2193. 088206 2193. 088206 2193. 088206 2193. 088206 2193. 088206 2193. 088206 2193. 088206 3794. 970372 3794. 970372 3794. 970372 3794. 970372 5516. 490487 5516. 490487 5628. 472674 8276. 171771 8276. 171771 8276. 171771 6664. 743472 6664. 743472 6664. 743472

2193. 088206 2193. 088206 2193. 088206 2193. 088206 2193. 088206 2193. 088206 2193. 088206 3794. 970372 3794. 970372 3794. 970372 5516. 490487 5516. 490487 5516. 490487 8276. 171771 8276. 171771 8276. 171771 6208. 222009 6208. 222009 6208. 222009 7894. 466576 7894. 466576

10,000 10% 8845. 057488 8845. 057488 8845. 057488 8845. 057488 8845. 057488 3794. 970372 3794. 970372 5516. 490487 5516. 490487 5516. 490487 8276. 171771 8276. 171771 8276. 171771 6208. 222009 6208. 222009 6208. 222009 7894. 466576 7894. 466576

8845. 057488 8845. 057488 8845. 057488 8845. 057488 8845. 057488 3794. 970372 3794. 970372 5516. 490487 5516. 490487 5516. 490487 5530. 291129 5530. 291129 5530. 291129 6208. 222009 6208. 222009 6208. 222009 6208. 222009 7894. 466576 18888. 3391

5,000 5% 8845. 057488 8845. 057488 8845. 057488 8845. 057488 8845. 057488 8845. 057488 9536. 724373 9536. 724373 5530. 291129 5530. 291129 5530. 291129 5530. 291129 5530. 291129 6208. 222009 6208. 222009 6208. 222009 11036. 72884 11036. 72884

8845. 057488 8845. 057488 8845. 057488 8845. 057488 8845. 057488 8845. 057488 9536. 724373 9536. 724373 5530. 291129 3742. 287061 3742. 287061 5530. 291129 5530. 291129 8256. 084222 8256. 084222 11036. 72884 11036. 72884 11036. 72884 0 0% 6731. 771984 6731. 771984 8845. 057488 8845. 057488 5227. 820393 5227. 820393 5227. 820393 5227. 820393 3742. 287061 3742. 287061 3742. 287061 3742. 287061 8256. 084222 8256. 084222 8256. 084222 11036. 72884 11036. 72884

-5,000 -5% 6731. 771984 6731. 771984 6731. 771984 6731. 771984 5227. 820393 5227. 820393 5227. 820393 5227. 820393 3742. 287061 3742. 287061 3742. 287061 3742. 287061 8256. 084222 8256. 084222 8731. 541461 8731. 541461

6731. 771984 6731. 771984 6731. 771984 6731. 771984 6731. 771984 6731. 771984 7827. 650434 7827. 650434 7827. 650434 7827. 650434 3742. 287061 3742. 287061 3742. 287061 8256. 084222 8256. 084222 8731. 541461 8731. 541461

-10,000 -10% 6731. 771984 6731. 771984 6731. 771984 6731. 771984 6731. 771984 6731. 771984 7827. 650434 7827. 650434 7827. 650434 7827. 650434 7827. 650434 15213. 63946 15213. 63946 15213. 63946 15213. 63946 15213. 63946

6731. 771984 7827. 650434 7827. 650434 7827. 650434 15213. 63946 15213. 63946 15213. 63946 15213. 63946 15213. 63946

15213. 63946 15213. 63946 Jilin

Tibet 23,413

Anhui

Hubei Hebei

Fujian

Tianjin

Gansu Hunan Henan

Beijing

Shanxi

Jiangxi

Hainan

Ningxia

Yunnan Jiangsu

Qinghai 18,108

Xinjiang

Sichuan Shaanxi

Guizhou

Guangxi

Liaoning Zhejiang 6446. 255321 6446. 255321 Shanghai Price (Office)

Shandong 12,803 Chongqing

Guangdong 6446. 255321 6446. 255321 Heilongjiang High: 23,413 RMB per 7,498

InnerMongolia 2010 Low: 2,193 sqm 2,193 • The growth in office prices has been a lot more uneven, with provinces with higher office prices recording stronger growth rates over the last five years. • Higher office prices tend to cluster around the economic centres of YRD, PRD and the Bohai Rim, as well as the coastal regions. Real estate – land purchased

20061370 20061370 11378145 11378145 11378145 11378145

Land purchased, 2010 20061370 20061370 20061370 11378145 11378145 11378145 11378145

5235376 5235376 5235376 5235376 5235376 20061370 20061370 20061370 20061370 11378145 11378145 11378145 11378145

5235376 5235376 5235376 5235376 5235376 20061370 20061370 20061370 20061370 9359056 9359056 9359056 9359056 9359056

Land Space Purchased 5 yr CAGR 5235376 5235376 5235376 5235376 5235376 5235376 5235376 5235376 20061370 20061370 20061370 20061370 20061370 20061370 20061370 9359056 9359056 million sqm 40 40% 5235376 5235376 5235376 5235376 5235376 5235376 5235376 5235376 2794842 2794842 20061370 20061370 20061370 20061370 20061370 20061370 20061370 20061370 20061370 20061370 36520054 36520054 36520054 9359056

5235376 5235376 5235376 5235376 5235376 5235376 5235376 5235376 5235376 2794842 2794842 20061370 20061370 20061370 20061370 20061370 20061370 20061370 20061370 20061370 20061370 20061370 20061370 20061370 36520054 36520054 36520054

5235376 5235376 5235376 5235376 5235376 5235376 5235376 5235376 5235376 5235376 2794842 2794842 2794842 20061370 20061370 20061370 20061370 20061370 20061370 20061370 20061370 20061370 8385559 8385559 28442618 28442618 28442618 36520054 36520054

30 30% 5235376 5235376 5235376 5235376 5235376 5235376 5235376 5235376 5235376 5235376 5235376 2794842 2794842 2794842 2794842 2794842 2794842 2794842 20061370 20061370 20061370 20061370 20061370 8385559 8385559 28442618 8587483 28442618

5235376 5235376 5235376 5235376 5235376 5235376 5235376 5235376 5235376 5235376 5235376 1124326 1124326 1124326 1124326 1124326 2794842 2794842 20061370 5488116 20061370 20061370 20061370 8385559 8385559 28442618 28442618 6524601

20 20% 45456 45456 45456 45456 45456 45456 45456 45456 45456 1124326 1124326 1124326 1124326 1124326 1124326 1124326 1124326 2794842 5488116 5488116 5488116 5794877 5794877 8385559 8385559 28442618 28442618 28442618 28442618 32905295 32905295

45456 45456 45456 45456 45456 45456 45456 45456 45456 1124326 1124326 1124326 1124326 1124326 1124326 1124326 1124326 2794842 2794842 5488116 2794842 5794877 5794877 8385559 8385559 28643227 28643227 32905295 32905295 32905295

45456 45456 45456 45456 45456 45456 45456 45456 45456 1124326 1124326 1124326 1124326 1124326 1124326 1124326 1124326 2794842 2794842 2794842 2794842 5794877 5794877 8385559 28643227 28643227 28643227 32905295 32905295 32905295

10 10% 45456 45456 45456 45456 45456 45456 45456 45456 45456 45456 1124326 1124326 1124326 1124326 1124326 1124326 1124326 2794842 2794842 2794842 5794877 5794877 5794877 28643227 28643227 28643227 26110691 26110691 26110691 22242387 22242387

45456 45456 45456 45456 45456 45456 45456 45456 45456 45456 45456 45456 9633095 9633095 9633095 9633095 9633095 2794842 2794842 5794877 5794877 5794877 28643227 28643227 28643227 26110691 26110691 26110691 22242387 22242387

45456 45456 45456 45456 45456 45456 45456 45456 45456 45456 45456 45456 9633095 9633095 9633095 9633095 9633095 2794842 2794842 5794877 5794877 5794877 15296845 15296845 15296845 26110691 26110691 26110691 26110691 22242387 4324365

0 0% 45456 45456 45456 45456 45456 45456 45456 45456 45456 45456 45456 9633095 9633095 9633095 9633095 9633095 9633095 13692156 13692156 15296845 15296845 15296845 15296845 15296845 26110691 26110691 26110691 18336899 18336899

45456 45456 45456 45456 45456 45456 45456 45456 45456 9633095 9633095 9633095 9633095 9633095 9633095 13692156 13692156 15296845 10960866 10960866 15296845 15296845 7771503 7771503 18336899 18336899 18336899

-10 -10% 10313903 10313903 9633095 9633095 9801003 9801003 9801003 9801003 10960866 10960866 10960866 10960866 7771503 7771503 7771503 18336899 18336899

10313903 10313903 10313903 10313903 9801003 9801003 9801003 9801003 10960866 10960866 10960866 10960866 7771503 7771503 15324653 15324653

10313903 10313903 10313903 10313903 10313903 10313903 11838840 11838840 11838840 11838840 10960866 10960866 10960866 7771503 7771503 15324653 15324653

-20 -20% 10313903 10313903 10313903 10313903 10313903 10313903 11838840 11838840 11838840 11838840 11838840 17556124 17556124 17556124 17556124 17556124

10313903 11838840 11838840 11838840 17556124 17556124 17556124 17556124 17556124

17556124 17556124 Jilin

Tibet 36,520,054

Anhui

Hebei Hubei

Fujian

Tianjin

Henan Hunan Gansu

Beijing

Shanxi

Jiangxi

Hainan

Ningxia

Yunnan Qinghai

Jiangsu 27,401,405

Xinjiang

Sichuan Shaanxi

Guizhou

Guangxi

Liaoning Zhejiang 5201607 5201607 Shanghai Land Space Purchased

Shandong 18,282,755 Chongqing

Guangdong 5201607 5201607 Heilongjiang High: 36,520,054 sqm 9,164,106 InnerMongolia 2010 Low: 45,456 45,456 • In 2010, 410 million sq m of land was purchased, up from 320 million sq m in 2009. • The vast majority of land purchases have focused on the central and northeast provinces, with provinces such as Liaoning recording the sale of 37 million sq m in 2010. Real estate – land purchased per capita

0. 811993417 0. 811993417 0. 296984717 0. 296984717 0. 296984717 0. 296984717

Land purchased per capita, 2010 0. 811993417 0. 811993417 0. 811993417 0. 296984717 0. 296984717 0. 296984717 0. 296984717

0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 296984717 0. 296984717 0. 296984717 0. 296984717

0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 340796547 0. 340796547 0. 340796547 0. 340796547 0. 340796547

0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 340796547 0. 340796547

sqm Land purchased per capita 5 yr CAGR growth 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 109279149 0. 109279149 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 834814254 0. 834814254 0. 834814254 0. 340796547 1.0 50% 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 109279149 0. 109279149 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 834814254 0. 834814254 0. 834814254

0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 109279149 0. 109279149 0. 109279149 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 23480995 0. 23480995 0. 395837922 0. 395837922 0. 395837922 0. 834814254 0. 834814254

0.8 40% 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 109279149 0. 109279149 0. 109279149 0. 109279149 0. 109279149 0. 109279149 0. 109279149 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 811993417 0. 23480995 0. 23480995 0. 395837922 0. 437860589 0. 395837922

0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 240008061 0. 199819007 0. 199819007 0. 199819007 0. 199819007 0. 199819007 0. 109279149 0. 109279149 0. 811993417 0. 870942893 0. 811993417 0. 811993417 0. 811993417 0. 23480995 0. 23480995 0. 395837922 0. 395837922 0. 504288765

0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 199819007 0. 199819007 0. 199819007 0. 199819007 0. 199819007 0. 199819007 0. 199819007 0. 199819007 0. 109279149 0. 870942893 0. 870942893 0. 870942893 0. 155244684 0. 155244684 0. 23480995 0. 23480995 0. 395837922 0. 395837922 0. 395837922 0. 395837922 0. 343503937 0. 343503937

0.6 30% 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 199819007 0. 199819007 0. 199819007 0. 199819007 0. 199819007 0. 199819007 0. 199819007 0. 199819007 0. 109279149 0. 109279149 0. 870942893 0. 109279149 0. 155244684 0. 155244684 0. 23480995 0. 23480995 0. 304638804 0. 304638804 0. 343503937 0. 343503937 0. 343503937

0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 199819007 0. 199819007 0. 199819007 0. 199819007 0. 199819007 0. 199819007 0. 199819007 0. 199819007 0. 109279149 0. 109279149 0. 109279149 0. 109279149 0. 155244684 0. 155244684 0. 23480995 0. 304638804 0. 304638804 0. 304638804 0. 343503937 0. 343503937 0. 343503937

0.4 20% 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 199819007 0. 199819007 0. 199819007 0. 199819007 0. 199819007 0. 199819007 0. 199819007 0. 109279149 0. 109279149 0. 109279149 0. 155244684 0. 155244684 0. 155244684 0. 304638804 0. 304638804 0. 304638804 0. 438831381 0. 438831381 0. 438831381 0. 28276652 0. 28276652

0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 119787498 0. 119787498 0. 119787498 0. 119787498 0. 119787498 0. 109279149 0. 109279149 0. 155244684 0. 155244684 0. 155244684 0. 304638804 0. 304638804 0. 304638804 0. 438831381 0. 438831381 0. 438831381 0. 28276652 0. 28276652

0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 119787498 0. 119787498 0. 119787498 0. 119787498 0. 119787498 0. 109279149 0. 109279149 0. 155244684 0. 155244684 0. 155244684 0. 267251031 0. 267251031 0. 267251031 0. 438831381 0. 438831381 0. 438831381 0. 438831381 0. 28276652 0. 187859472 0.2 10% 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 119787498 0. 119787498 0. 119787498 0. 119787498 0. 119787498 0. 119787498 0. 474661142 0. 474661142 0. 267251031 0. 267251031 0. 267251031 0. 267251031 0. 267251031 0. 438831381 0. 438831381 0. 438831381 0. 336908809 0. 336908809

0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 015141068 0. 119787498 0. 119787498 0. 119787498 0. 119787498 0. 119787498 0. 119787498 0. 474661142 0. 474661142 0. 267251031 0. 1668734 0. 1668734 0. 267251031 0. 267251031 0. 174376112 0. 174376112 0. 336908809 0. 336908809 0. 336908809

0.0 0% 0. 224379963 0. 224379963 0. 119787498 0. 119787498 0. 282071922 0. 282071922 0. 282071922 0. 282071922 0. 1668734 0. 1668734 0. 1668734 0. 1668734 0. 174376112 0. 174376112 0. 174376112 0. 336908809 0. 336908809

0. 224379963 0. 224379963 0. 224379963 0. 224379963 0. 282071922 0. 282071922 0. 282071922 0. 282071922 0. 1668734 0. 1668734 0. 1668734 0. 1668734 0. 174376112 0. 174376112 0. 415367357 0. 415367357

-0.2 -10% 0. 224379963 0. 224379963 0. 224379963 0. 224379963 0. 224379963 0. 224379963 0. 257217186 0. 257217186 0. 257217186 0. 257217186 0. 1668734 0. 1668734 0. 1668734 0. 174376112 0. 174376112 0. 415367357 0. 415367357

0. 224379963 0. 224379963 0. 224379963 0. 224379963 0. 224379963 0. 224379963 0. 257217186 0. 257217186 0. 257217186 0. 257217186 0. 257217186 0. 168318282 0. 168318282 0. 168318282 0. 168318282 0. 168318282

0. 224379963 0. 257217186 0. 257217186 0. 257217186 0. 168318282 0. 168318282 0. 168318282 0. 168318282 0. 168318282

-0.4 -20% 0. 168318282 0. 168318282 0.9

0.7 Jilin 0. 599849646 0. 599849646

Tibet Land purchased per capita 0.4

Anhui

Hebei Hubei

Fujian

Tianjin

Hunan Gansu Henan Beijing

Shanxi 0. 599849646 0. 599849646

Jiangxi

Hainan

Ningxia

Yunnan Jiangsu

Qinghai High: 0.9

Xinjiang Sichuan

Shaanxi 0.2

Guizhou

Guangxi Liaoning

Zhejiang sqm Shanghai

Shandong 2010 Low: 0.0 0.0

Chongqing

Guangdong

Heilongjiang InnerMongolia • When viewed on a land space per capita basis, the data strongly indicates that Liaoning and Ningxia provinces, and Inner Mongolia have all seen a large amount sold in relation to their populations. • The high figure from Inner Mongolia may be due to the area’s new-found wealth as a result of recently discovered resources, and in Liaoning province it may be due to support from the central government and its two key economic powerhouses, and cities. • Ningxia province, however, seems like an anomaly, as while it is in close proximity to Shaanxi province and Inner Mongolia, it is one of the smallest provinces and has very few resources. • Hainan province’s real estate industry seems to be recovering with a significant increase in land sold per capita of 20% in 2010. Real estate – affordability

7. 610940598 7. 610940598 10. 31562145 10. 31562145 10. 31562145 10. 31562145

Affordability, 2009 7. 610940598 7. 610940598 7. 610940598 10. 31562145 10. 31562145 10. 31562145 10. 31562145

8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 7. 610940598 7. 610940598 7. 610940598 7. 610940598 10. 31562145 10. 31562145 10. 31562145 10. 31562145

8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 7. 610940598 7. 610940598 7. 610940598 7. 610940598 8. 207265454 8. 207265454 8. 207265454 8. 207265454 8. 207265454

multiples of income 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 7. 610940598 7. 610940598 7. 610940598 7. 610940598 7. 610940598 7. 610940598 7. 610940598 8. 207265454 8. 207265454

25 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 10. 20550604 10. 20550604 7. 610940598 7. 610940598 7. 610940598 7. 610940598 7. 610940598 7. 610940598 7. 610940598 7. 610940598 7. 610940598 7. 610940598 10. 2612992 10. 2612992 10. 2612992 8. 207265454

8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 10. 20550604 10. 20550604 7. 610940598 7. 610940598 7. 610940598 7. 610940598 7. 610940598 7. 610940598 7. 610940598 7. 610940598 7. 610940598 7. 610940598 7. 610940598 7. 610940598 7. 610940598 10. 2612992 10. 2612992 10. 2612992

8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 10. 20550604 10. 20550604 10. 20550604 7. 610940598 7. 610940598 7. 610940598 7. 610940598 7. 610940598 7. 610940598 7. 610940598 7. 610940598 7. 610940598 8. 071022547 8. 071022547 9. 478631181 9. 478631181 9. 478631181 10. 2612992 10. 2612992

20 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 10. 20550604 10. 20550604 10. 20550604 10. 20550604 10. 20550604 10. 20550604 10. 20550604 7. 610940598 7. 610940598 7. 610940598 7. 610940598 7. 610940598 8. 071022547 8. 071022547 9. 478631181 19. 21803414 9. 478631181

8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 807538067 8. 463684518 8. 463684518 8. 463684518 8. 463684518 8. 463684518 10. 20550604 10. 20550604 7. 610940598 8. 473531365 7. 610940598 7. 610940598 7. 610940598 8. 071022547 8. 071022547 9. 478631181 9. 478631181 10. 79364705

7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 8. 463684518 8. 463684518 8. 463684518 8. 463684518 8. 463684518 8. 463684518 8. 463684518 8. 463684518 10. 20550604 8. 473531365 8. 473531365 8. 473531365 10. 96178427 10. 96178427 8. 071022547 8. 071022547 9. 478631181 9. 478631181 9. 478631181 9. 478631181 8. 699729703 8. 699729703

15 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 8. 463684518 8. 463684518 8. 463684518 8. 463684518 8. 463684518 8. 463684518 8. 463684518 8. 463684518 10. 20550604 10. 20550604 8. 473531365 10. 20550604 10. 96178427 10. 96178427 8. 071022547 8. 071022547 8. 010283535 8. 010283535 8. 699729703 8. 699729703 8. 699729703

7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 8. 463684518 8. 463684518 8. 463684518 8. 463684518 8. 463684518 8. 463684518 8. 463684518 8. 463684518 10. 20550604 10. 20550604 10. 20550604 10. 20550604 10. 96178427 10. 96178427 8. 071022547 8. 010283535 8. 010283535 8. 010283535 8. 699729703 8. 699729703 8. 699729703

7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 8. 463684518 8. 463684518 8. 463684518 8. 463684518 8. 463684518 8. 463684518 8. 463684518 10. 20550604 10. 20550604 10. 20550604 10. 96178427 10. 96178427 10. 96178427 8. 010283535 8. 010283535 8. 010283535 11. 21713856 11. 21713856 11. 21713856 9. 687201034 9. 687201034

7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 12. 69023225 12. 69023225 12. 69023225 12. 69023225 12. 69023225 10. 20550604 10. 20550604 10. 96178427 10. 96178427 10. 96178427 8. 010283535 8. 010283535 8. 010283535 11. 21713856 11. 21713856 11. 21713856 9. 687201034 9. 687201034 10 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 12. 69023225 12. 69023225 12. 69023225 12. 69023225 12. 69023225 10. 20550604 10. 20550604 10. 96178427 10. 96178427 10. 96178427 10. 69142917 10. 69142917 10. 69142917 11. 21713856 11. 21713856 11. 21713856 11. 21713856 9. 687201034 15. 92821849

7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 12. 69023225 12. 69023225 12. 69023225 12. 69023225 12. 69023225 12. 69023225 10. 38475704 10. 38475704 10. 69142917 10. 69142917 10. 69142917 10. 69142917 10. 69142917 11. 21713856 11. 21713856 11. 21713856 13. 73616218 13. 73616218

7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 7. 727643932 12. 69023225 12. 69023225 12. 69023225 12. 69023225 12. 69023225 12. 69023225 10. 38475704 10. 38475704 10. 69142917 7. 44398165 7. 44398165 10. 69142917 10. 69142917 6. 962706359 6. 962706359 13. 73616218 13. 73616218 13. 73616218

5 9. 603852678 9. 603852678 12. 69023225 12. 69023225 11. 06692222 11. 06692222 11. 06692222 11. 06692222 7. 44398165 7. 44398165 7. 44398165 7. 44398165 6. 962706359 6. 962706359 6. 962706359 13. 73616218 13. 73616218

9. 603852678 9. 603852678 9. 603852678 9. 603852678 11. 06692222 11. 06692222 11. 06692222 11. 06692222 7. 44398165 7. 44398165 7. 44398165 7. 44398165 6. 962706359 6. 962706359 12. 25897462 12. 25897462

9. 603852678 9. 603852678 9. 603852678 9. 603852678 9. 603852678 9. 603852678 9. 317255673 9. 317255673 9. 317255673 9. 317255673 7. 44398165 7. 44398165 7. 44398165 6. 962706359 6. 962706359 12. 25897462 12. 25897462

0 9. 603852678 9. 603852678 9. 603852678 9. 603852678 9. 603852678 9. 603852678 9. 317255673 9. 317255673 9. 317255673 9. 317255673 9. 317255673 10. 70290261 10. 70290261 10. 70290261 10. 70290261 10. 70290261

9. 603852678 9. 317255673 9. 317255673 9. 317255673 10. 70290261 10. 70290261 10. 70290261 10. 70290261 10. 70290261

10. 70290261 10. 70290261 Jilin

Tibet 19

Anhui

Hubei Hebei

Fujian

Tianjin

Gansu Henan Hunan

Beijing

Shanxi

Jiangxi

Hainan

Ningxia

Yunnan

Qinghai Jiangsu

Xinjiang 16

Shaanxi Sichuan

Guizhou

Guangxi

Liaoning Zhejiang 16. 47603814 16. 47603814 Shanghai Affordability

Shandong 13 Chongqing

Guangdong 16. 47603814 16. 47603814 Heilongjiang High: 19 multiples of 10

InnerMongolia 2009 Low: 7 income 7 • One of the most controversial indicators in China, due to the lack of clarity and meaningful data, is affordability, as incomes are typically under-reported and only mean (no median) data is available. • The analysis takes a 90 sq m unit at the mean housing price and assumes a household of mean size and mean disposable income for the province, eg, in 2009, Beijing had an average residential price of RMB13,224 per sq m, therefore a unit value of RMB1.2 million, a household size of 2.5 and an average disposable income of RMB24,500 (combination of rural and urban) and therefore a household income of RMB61,250. PROVINCES A tale of two halves

• The - Line is a geo-demographic demarcation, splitting China roughly in two. To the west of the line is 57% of the country’s land mass but 9% of the population (2000 figures). East of the line is 43% of the land mass and 91% of the population. As such it is very similar to Australia. • The vast majority of the major cities are located on the east coast, and much of the economic activity takes place there. Manufacturing Coal Husbandry Agriculture Resources Tourism Hi-tech Finance Heavy industry Political Oil Heavy industrial and agricultural base; government support for development

Resource rich; fast pace of economic growth; NE government support

Under-developed economy; resource rich; large government spending N

NW Extremely wealthy; hi-tech and higher up the value chain

E Under-developed economy; policy support C from central government SW FAI focused; agricultural base; low labour costs; potential for reverse brain drain S Manufacturing and export focused; strong ties with Hong Kong, Taiwan and Southeast Asia Northeast China – Heilongjiang

Type: Province Capital city: Harbin US state equiv: Minnesota Industries: Equipment manufacturing; petrochemical; food processing; energy Advantages: Oil reserves; fertile land; proximity to Russia Disadvantages: Over reliance on oil 1 Harbin 2 3 4 Heihe % of Unit Figure Rank 5 national 6 Area sq km 45,265 4.8 6 7 Population million 38 2.9 15 8 Population density '000 per sq km 0.8 -- 26 9 GDP RMB billion 1,024 2.4 16 10 Tertiary industry % of GDP 37.4 -- 13 11 GDP per capita RMB 26,715 -- 16 12 Yichun Disp income (urban) RMB 13,857 -- 28 13 Daxing'anling Consumption (urban) RMB 10,684 -- 23 Residential price RMB per sq m 3,488 -- 16 HDI % 80.8 -- 11 Engel % 35.3 -- 11 Northeast China – Jilin

Type: Province Capital city: US state equiv: Michigan Industries: Automobiles; petrochemicals Advantages: Birth-place of heavy industry; infrastructure; traditional Chinese medicine production Disadvantages: Changing to green and hi-tech industries 1 Changchun 2 3 4 Jilin % of Unit Figure Rank 5 national 6 Siping Area sq km 19,112 2.0 12 7 Population Million 27 2.1 21 8 Population density '000 per sq km 1.4 -- 23 9 Yanbian GDP RMB billion 858 2.0 22 Tertiary industry % of GDP 36.3 -- 19 GDP per capita RMB 31,232 -- 11 Disp income (urban) RMB 15,411 -- 24 Consumption (urban) RMB 11,679 -- 15 Residential price RMB per sq m 3,451 -- 17 HDI % 81.5 -- 9 Engel % 33.3 -- 5 Northeast China – Liaoning

Type: Province Capital city: Shenyang US state equiv: Indiana Industries: Machinery; metallurgy; food processing; petrochemical Advantages: Northeast China’s economic centre; rich mineral resources; heavy industry focus but leading development of hi-tech industry 1 Shenyang Disadvantages: Dependence on heavy industry 2 Dalian 3 4 % of Unit Figure Rank 5 Chaoyang national 6 Area sq km 14,806 1.6 21 7 Population million 44 3.3 14 8 Population density '000 per sq km 3.0 -- 15 9 GDP RMB billion 1,828 4.2 7 10 Tertiary industry % of GDP 37.1 -- 16 11 GDP per capita RMB 41,782 -- 8 12 Disp income (urban) RMB 17,713 -- 9 13 Consumption (urban) RMB 13,280 -- 10 14 Residential price RMB per sq m 4,300 -- 9 HDI % 83.5 -- 7 Engel % 38.0 -- 19 North China – Beijing

Type: Municipality Capital city: Beijing US equiv: Washington Industries: Financial services; retail trade; hi- tech and IT services Advantages: Political, cultural and educational centre; consumption base; Chinese financial hub Disadvantages: Over-crowding; traffic 1 Dongcheng District 2 Xicheng District 14 3 Chaoyang District 4 Haidian District % of Unit Figure Rank 5 Fengtai District national 16 15 6 Shijingshan District Area sq km 1,641 0.2 29 7 Mentougou District Population million 20 1.5 26 8 Fangshan District Population density '000 per sq km 12.0 -- 2 11 13 10 9 Tongzhou District GDP RMB billion 1,378 3.2 13 10 Shunyi District Tertiary industry % of GDP 75.0 -- 1 7 4 11 Changping District GDP per capita RMB 70,251 -- 3 6 21 3 5 12 Daxing District Disp income (urban) RMB 29,073 -- 2 9 13 Pinggu District 8 Consumption (urban) RMB 19,934 -- 2 12 14 Huairou District Residential price RMB per sq m 17,151 -- 1 15 Miyun County HDI % 89.1 -- 2 16 Yanqing County Engel % 33.2 -- 4 North China – Tianjin

Type: Municipality Capital city: Tianjin US equiv: Chicago Industries: Mechanics; textiles; future finance/commodities hub Advantages: Strategic location; large port; Binhai New Area development with central government support Disadvantages: Reliance of FAI for GDP; competition from Beijing 1 Heping District 6 3 2 Hexi District 5 3 Hebei District 4 1 14 4 Nankai District % of 2 Unit Figure Rank 5 Hedong District national 6 Hongqiao District Area sq km 1,192 0.1 30 12 7 Binhai New Area Population million 13 1.0 27 8 Jinnan District Population density '000 per sq km 10.9 -- 3 13 16 9 Dongli District GDP RMB billion 911 2.1 20 10 Xiqing District 11 Tertiary industry % of GDP 45.3 -- 6 11 Beichen District 9 GDP per capita RMB 70,402 -- 2 12 Baodi District Disp income (urban) RMB 24,293 -- 4 10 8 7 13 Wuqing District Consumption (urban) RMB 16,562 -- 5 15 14 Ji County Residential price RMB per sq m 7,913 -- 5 15 Jinghai County HDI % 87.5 -- 3 16 Ninghe County Engel % 36.5 -- 14 North China – Shanxi

Type: Province Capital city: US state equiv: Pennsylvania Industries: Coal; coke; metallurgy; electrical power Advantages: Second largest producer of coal Disadvantages: Pollution; inefficient energy usage

1 Taiyuan 2 3 4 % of Unit Figure Rank 5 national 6 Area sq km 15,671 1.6 20 7 Lüliang Population million 36 2.7 18 8 Population density '000 per sq km 2.3 -- 18 9 GDP RMB billion 909 2.1 21 10 Tertiary industry % of GDP 37.0 -- 17 11 GDP per capita RMB 25,448 -- 18 Disp income (urban) RMB 15,648 -- 20 Consumption (urban) RMB 9,793 -- 29 Residential price RMB per sq m 3,326 -- 20 HDI % 80.0 -- 14 Engel % 32.8 -- 2 North China – Hebei

Type: Province Capital city: US state equiv: New Jersey Industries: Metallurgy (iron and steel); pharmaceuticals; electronics Advantages: Proximity and importance to Beijing and Tianjin; vibrant iron and steel industry Disadvantages: Pollution; future iron and steel 1 Shijiazhuang consolidation 2 3 4 % of Unit Figure Rank 5 national 6 Area sq km 18,843 2.0 13 7 Population million 72 5.4 6 8 Population density '000 per sq km 3.8 -- 10 9 GDP RMB billion 2,020 4.7 6 10 Tertiary industry % of GDP 34.3 -- 26 11 GDP per capita RMB 28,108 -- 12 Disp income (urban) RMB 16,263 -- 14 Consumption (urban) RMB 10,318 -- 25 Residential price RMB per sq m 3,450 -- 18 HDI % 81.0 -- 10 Engel % 33.6 -- 7 North China – Inner Mongolia

Type: Autonomous region Capital city: US state equiv: Texas Industries: Energy; chemicals; metallurgy; equipment manufacturing; farm produce; hi-tech Advantages: Livestock industry in strong demand; coal/other resources and energy; support from central government Disadvantages: Poor energy efficiency; dependence on coal 1 Alxa 2 Bayan Nur 3 4 Ordos % of Unit Figure Rank 5 national 6 Hohhot Area sq km 114,512 12.0 3 7 Population million 25 1.9 23 8 Xilin Gol Population density '000 per sq km 0.2 -- 28 9 GDP RMB billion 1,166 2.7 15 10 Tertiary industry % of GDP 35.9 -- 22 11 Hinggan GDP per capita RMB 47,174 -- 6 12 Disp income (urban) RMB 17,698 -- 10 Consumption (urban) RMB 13,995 -- 8 Residential price RMB per sq m 2,981 -- 24 HDI % 80.3 -- 13 Engel % 30.5 -- 1 East China – Shandong

Type: Province Capital city: US state equiv: Illinois Industries: Manufacturing; food processing; energy Advantages: Largest agricultural base; improvements in infrastructure; major ports; tourist destination Disadvantages: Largest coal consumer in China; 1 Jinan impacted by push for energy 2 efficiency 3 4 % of Unit Figure Rank 5 national 6 Area sq km 15,713 1.7 19 7 Population million 96 7.2 2 8 Population density '000 per sq km 6.1 -- 5 9 GDP RMB billion 3,942 9.1 3 10 Tertiary industry % of GDP 36.6 -- 18 11 GDP per capita RMB 41,147 -- 9 12 Tai'an Disp income (urban) RMB 19,946 -- 8 13 Consumption (urban) RMB 13,118 -- 11 14 Residential price RMB per sq m 3,818 -- 13 15 HDI % 82.8 -- 8 16 Engel % 32.9 -- 3 17 East China – Jiangsu

Type: Province Capital city: US state equiv: Rhode Island Industries: Electronics; telecommunications; chemicals; machinery; equipment; textiles; metallurgy Advantages: Diversified economy; large FDI; largest solar panel producer; private business thriving; securitisation; top spender on R&D; location 1 Nanjing 2 Disadvantages: Income inequality 3 Huai'an 4 % of Unit Figure Rank 5 national 6 Area sq km 10,674 1.1 24 7 Population million 79 5.9 5 8 Taizhou Population density '000 per sq km 7.4 -- 4 9 GDP RMB billion 4,090 9.5 2 10 Tertiary industry % of GDP 23.5 -- 31 11 GDP per capita RMB 52,000 -- 4 12 Disp income (urban) RMB 22,944 -- 6 13 Consumption (urban) RMB 14,357 -- 7 Residential price RMB per sq m 5,549 -- 8 HDI % 83.7 -- 6 Engel % 36.3 -- 12 East China – Anhui

Type: Province Capital city: US state equiv: Virginia Industries: Electrics; machinery equipment; energy production Advantages: Fertile land; copper rich; cheap labour; improving FDI Disadvantages: Reliance on FAI

1Hefei 2Anqing 3Bengbu 4Bozhou % of Unit Figure Rank 5Chaohu national 6Chizhou Area sq km 14,013 1.5 22 7Chuzhou Population million 60 4.5 8 8Fuyang Population density '000 per sq km 4.2 -- 9 9Huaibei GDP RMB billion 1,226 2.8 14 10Huainan Tertiary industry % of GDP 33.8 -- 27 11Huangshan GDP per capita RMB 20,611 -- 27 12Lu'an Disp income (urban) RMB 15,788 -- 18 13Ma'anshan Consumption (urban) RMB 11,513 -- 16 14Suzhou Residential price RMB per sq m 3,907 -- 12 15Tongling HDI % 75.0 -- 26 16Wuhu Engel % 39.6 -- 21 17Xuancheng East China – Zhejiang

Type: Province Capital city: US state equiv: Connecticut Industries: Textiles; electrical equipment; machinery Advantages: Entrepreneurs; small and medium enterprises; private enterprises; largest port city Disadvantages: High property prices; 1 Hangzhou manufacturing losing out to 2 cheaper locations 3 4 % of Unit Figure Rank 5 national 6 Area sq km 10,540 1.1 25 7 Population million 54 4.1 10 8 Population density '000 per sq km 5.2 -- 8 9 Taizhou GDP RMB billion 2,723 6.3 4 10 Tertiary industry % of GDP 43.1 -- 8 11 GDP per capita RMB 50,024 -- 5 Disp income (urban) RMB 27,359 -- 3 Consumption (urban) RMB 17,858 -- 4 Residential price RMB per sq m 9,322 -- 3 HDI % 84.1 -- 5 Engel % 33.6 -- 8 East China – Shanghai

Type: Municipality Capital city: Shanghai US equiv: New York City Industries: Financial services; retail and wholesale; real estate Advantages: Leader in efficiency; strong service sector; national financial centre; transport hub; hi-tech potential; international city; livability 1 Huangpu District Disadvantages: Rising costs; ageing society 2 Xuhui District 3 Changning District 4 Jing'an District % of Unit Figure Rank 17 5 Putuo District national 6 Zhabei District Area sq km 824 0.1 31 12 11 7 Hongkou District Population million 23 1.7 24 8 Yangpu District 8 Population density '000 per sq km 27.9 -- 1 6 7 5 9 Pudong New Area 4 1 GDP RMB billion 1,687 3.9 9 15 3 10 Minhang District 2 9 Tertiary industry % of GDP 57.0 -- 2 11 Baoshan District GDP per capita RMB 73,297 -- 1 14 10 12 Jiading District Disp income (urban) RMB 31,838 -- 1 13 Jinshan District Consumption (urban) RMB 23,200 -- 1 16 14 Songjiang District Residential price RMB per sq m 14,213 -- 2 13 15 Qingpu District HDI % 90.8 -- 1 16 Fengxian District Engel % 35.0 -- 10 17 Chongming County South China – Fujian

Type: Province Capital city: US state equiv: South Carolina Industries: Leather products; mineral products; telecommunications; electronics; developing finance and insurance Advantages: Ties with Taiwan – Westbank Economic Zone; tourism Disadvantages: No longer unique entry point for 1 Fuzhou Taiwanese investment 2 3 4 % of Unit Figure Rank 5 national 6 Area sq km 12,402 1.3 23 7 Population million 37 2.8 17 8 Population density '000 per sq km 3.0 -- 14 9 GDP RMB billion 1,436 3.3 12 Tertiary industry % of GDP 39.2 -- 12 GDP per capita RMB 38,914 -- 10 Disp income (urban) RMB 21,781 -- 7 Consumption (urban) RMB 14,750 -- 6 Residential price RMB per sq m 6,076 -- 7 HDI % 80.7 -- 12 Engel % 39.7 -- 22 South China – Guangdong

Type: Province Capital city: Guangzhou US state equiv: California Industries: Electrical appliances; consumer goods Advantages: Foreign trade leader; leader in value-added sectors and tech industries; financial hub Disadvantages: Economic restructuring; rising 1Qingyuan 12Shanwei labour costs and disputes 2Shaoguan 13Jieyang 3Heyuan 14Shantou 4Meizhou 15Zhanjiang % of Unit Figure Rank 5Chaozhou 16Maoming national 6Zhaoqing 17Yangjiang Area sq km 17,981 1.9 15 7Yunfu 18Jiangmen Population million 104 7.8 1 8Foshan 19Zhongshan Population density '000 per sq km 5.8 -- 6 9Guangzhou 20Zhuhai GDP RMB billion 4,547 10.5 1 10Dongguan 21Shenzhen Tertiary industry % of GDP 44.6 -- 7 11Huizhou GDP per capita RMB 43,597 -- 7 Disp income (urban) RMB 23,898 -- 5 Consumption (urban) RMB 18,490 -- 3 Residential price RMB per sq m 7,006 -- 6 HDI % 84.4 -- 4 Engel % 36.9 -- 15 South China – Guangxi

Type: Autonomous region Capital city: US state equiv: Tennessee Industries: Automobiles; heavy mining machinery; electrical and electronics; instruments and apparatus Advantages: Agricultural base; tourism – natural beauty; Southeast Asia port; improving infrastructure Disadvantages: Brain drain; low value-added 1 2 industry 3 4 % of Unit Figure Rank 5 national 6 Area sq km 23,756 2.5 9 7 Nanning Population million 46 3.5 11 8 Population density '000 per sq km 1.9 -- 20 9 GDP RMB billion 950 2.2 18 10 Tertiary industry % of GDP 35.0 -- 23 11 GDP per capita RMB 20,645 -- 26 12 Disp income (urban) RMB 17,064 -- 12 13 Consumption (urban) RMB 11,490 -- 17 14 Yulin Residential price RMB per sq m 3,382 -- 19 HDI % 77.6 -- 20 Engel % 39.9 -- 24 South China – Hainan

Type: Province Capital city: US state equiv: Florida Industries: Fisheries; vegetables and fruits; rubber production; tourism Advantages: Tourism; five-star hotels; climate; agricultural base; oil and gas reserves; Disadvantages: Property market is volatile 1Haikou 11Ding'an 2Sanya 12Tunchang 3Wenchang 13Changjiang 4Qionghai 14Baisha % of Unit Figure Rank 5Wanning 15Qiongzhong national 6Wuzhishan 16Lingshui Area sq km 3,535 0.4 28 7Dongfang 17Baoting Population million 9 0.7 28 8Danzhou 18Ledong Population density '000 per sq km 2.5 -- 17 9Lingao Paracels, 10Chengmai Spratlys, & GDP RMB billion 205 0.5 28 *19 Tertiary industry % of GDP 46.1 -- 5 Zhongsha GDP per capita RMB 23,665 -- 23 Iss. Disp income (urban) RMB 15,581 -- 21 Consumption (urban) RMB 10,927 -- 21 Residential price RMB per sq m 8,800 -- 4 HDI % 78.4 -- 16 Engel % 44.7 -- 30 Central China – Jiangxi

Type: Province Capital city: US state equiv: Georgia Industries: Smelting and pressing of ferrous and non-ferrous metal; military industry Advantages: Natural resources; cheap labour and land Disadvantages: Low-tech industries; brain drain 1 Nanchang 2 Fuzhou 3 4 Ji'an % of Unit Figure Rank 5 national 6 Area sq km 16,689 1.8 17 7 Population million 45 3.3 13 8 Population density '000 per sq km 2.7 -- 16 9 GDP RMB billion 944 2.2 19 10 Yichun Tertiary industry % of GDP 32.2 -- 29 11 GDP per capita RMB 21,170 -- 24 Disp income (urban) RMB 15,481 -- 22 Consumption (urban) RMB 10,619 -- 24 Residential price RMB per sq m 2,959 -- 25 HDI % 76.0 -- 25 Engel % 39.9 -- 23 Central China – Hunan

Type: Province Capital city: US state equiv: Idaho Industries: Smelting and pressing of ferrous and non-ferrous metals Advantages: Entertainment industry; strong low- value industry, developing heavy industry; low wages Disadvantages: Low energy efficiency; FAI 1 Changsha dependence 2 3 4 % of Unit Figure Rank 5 national 6 Area sq km 21,185 2.2 10 7 Population million 66 4.9 7 8 Population density '000 per sq km 3.1 -- 12 9 GDP RMB billion 1,590 3.7 10 10 Tertiary industry % of GDP 39.3 -- 11 11 GDP per capita RMB 24,210 -- 21 12 Disp income (urban) RMB 16,566 -- 13 13 Consumption (urban) RMB 11,825 -- 13 14 Xiangxi Residential price RMB per sq m 3,012 -- 23 HDI % 78.1 -- 19 Engel % 38.6 -- 20 Central China – Hubei

Type: Province Capital city: Wuhan US state equiv: Ohio Industries: Metallurgy; automobiles; chemicals; construction materials; foodstuff; machine building; textiles; electronics; ship building Advantages: Wuhan’s development attraction to companies; agricultural base 1 Wuhan Disadvantages: FAI dependent 2 3 4 % of Unit Figure Rank 5 national 6 Jingzhou Area sq km 18,589 2.0 14 7 Population million 57 4.3 9 8 Population density '000 per sq km 3.1 -- 13 9 GDP RMB billion 1,581 3.7 11 10 Tertiary industry % of GDP 37.3 -- 14 11 GDP per capita RMB 27,615 -- 13 12 Disp income (urban) RMB 16,058 -- 16 13 Enshi Consumption (urban) RMB 11,451 -- 18 14 Residential price RMB per sq m 3,500 -- 15 15 Qianjiang HDI % 78.4 -- 16 16 Engel % 40.4 -- 26 17 Central China – Henan

Type: Province Capital city: US state equiv: New York Industries: Foodstuffs; chemicals; automobiles and parts; machinery production; textiles Advantages: Key transportation hub; cheap labour; agricultural producer Disadvantages: Dependence on FAI 1 Zhengzhou 11 2 12 3 13 4 14 % of Unit Figure Rank 5 15 national 6 16 Area sq km 16,554 1.7 18 7 17 Population million 94 7.1 3 8 Nanyang 18 Population density '000 per sq km 5.7 -- 7 9 11 Sanmenxia GDP RMB billion 2,294 5.3 5 10 12 Shangqiu Tertiary industry % of GDP 28.1 -- 30 GDP per capita RMB 24,401 -- 20 Disp income (urban) RMB 15,930 -- 17 Consumption (urban) RMB 10,838 -- 22 Residential price RMB per sq m 2,856 -- 30 HDI % 78.7 -- 15 Engel % 34.2 -- 9 Southwest China – Guizhou

Type: Province Capital city: US state equiv: West Virginia Industries: Electricity production; tobacco; beverages; tourism Advantages: Agricultural base; tourism – natural environment Disadvantages: Poor infrastructure; weak investments 1 Prefecture 2 3 4 % of Unit Figure Rank 5 national 6 Guiyang Area sq km 17,615 1.9 16 7 Qianxinan Population million 35 2.6 19 8 Qiannan Population density '000 per sq km 2.0 -- 19 9 Qiandongnan GDP RMB billion 459 1.1 26 Tertiary industry % of GDP 47.1 -- 4 GDP per capita RMB 13,221 -- 31 Disp income (urban) RMB 14,143 -- 27 Consumption (urban) RMB 10,058 -- 27 Residential price RMB per sq m 3,142 -- 21 HDI % 69.0 -- 30 Engel % 41.5 -- 28 Southwest China – Chongqing

Type: Municipality Capital city: Chongqing US equiv: St Louis Industries: Automobiles; military; iron and steel; aluminum Advantages: Government support/focus; automobile manufacturing hub Disadvantages: Unrealistic goal of becoming financial centre; environmental 1 Yuzhong D. 21 Tongnan C. issues 2 Dadukou D. 22 Fuling D. 28 3 Jiangbei D. 23 Nanchuan D.

33 4 Shapingba D. 24 Dianjiang C. % of 4 3 5 Jiulongpo D. 25 Fengdu C. 1 6 30 Unit Figure Rank 32 6 Nan'an D. 26 Wulong C. national 5 34 2 29 7 Banan D. 27 Wanzhou D. Area sq km 8,227 0.9 26 31 27 8 Beibei D. 28 Chengkou C. Population million 29 2.2 20 9 Yubei D. 29 Fengjie C. 24 35 Population density '000 per sq km 3.5 -- 11 21 10 Bishan C. 30 Kai C. 12 38 11 11 Changshou D. 31 Liangping C. GDP RMB billion 789 1.8 23 20 8 9 25 17 22 12 Hechuan D. 32 Wushan C. Tertiary industry % of GDP 36.1 -- 21 10 19 14 36 13 Jiangjin D. 33 Wuxi C. GDP per capita RMB 27,367 -- 14 16 7 26 37 14 Shuangqiao D. 34 Yunyang C. 23 Disp income (urban) RMB 17,532 -- 11 13 15 Wansheng D. 35 Zhong C. Consumption (urban) RMB 13,335 -- 9 18 15 40 16 Yongchuan D. 36 Qianjiang D. 17 Dazu C. 37 Pengshui A.C. Residential price RMB per sq m 4,040 -- 10 39 18 Qijiang C. 38 Shizhu A.C. HDI % 78.3 -- 18 19 Rongchang C. 39 Xiushan A.C. Engel % 37.7 -- 17 20 Tongliang C. 40 Youyang A.C. Southwest China – Sichuan

Type: Province Capital city: US state equiv: Colorado Industries: Agriculture; coal; energy; iron and steel; building materials; wood; processing; foodstuffs; silk processing Advantages: Agricultural base; natural gas production; tourism centre of the west; highly skilled workforce; investment after earthquake 1Garzê 11Suining 2Ngawa 12Guang'an Disadvantages: Unequal growth 3Mianyang 13Meishan 4Guangyuan 14Ziyang % of Unit Figure Rank 5Nanchong 15Leshan national 6Bazhong 16Neijiang Area sq km 48,406 5.1 5 7Dazhou 17Zigong Population million 80 6.0 4 8Ya'an 18Yibin Population density '000 per sq km 1.7 -- 22 9Chengdu 19Luzhou GDP RMB billion 1,690 3.9 8 10Deyang 20Liangshan Tertiary industry % of GDP 34.6 -- 25 GDP per capita RMB 21,013 -- 25 Disp income (urban) RMB 15,461 -- 23 Consumption (urban) RMB 12,105 -- 12 Residential price RMB per sq m 3,985 -- 11 HDI % 76.3 -- 24 Engel % 40.4 -- 27 Southwest China – Tibet

Type: Autonomous region Capital city: Lhasa US state equiv: Alaska Industries: Tourism; Tibetan medicines; unique plateau biology; handicrafts; mining Advantages: Government support; booming tourism Disadvantages: Undeveloped; weak infrastructure; small population; geographical/topographical 1 Ngari 2 Nagqu limitations 3 Qamdo 4 Xigazê % of Unit Figure Rank 5 Lhasa national 6 Shannan Area sq km 120,207 12.6 2 7 Population million 3 0.2 31 Population density '000 per sq km 0.0 -- 31 GDP RMB billion 51 0.1 31 Tertiary industry % of GDP 54.3 -- 3 GDP per capita RMB 16,903 -- 28 Disp income (urban) RMB 14,980 -- 26 Consumption (urban) RMB 9,686 -- 30 Residential price RMB per sq m 2,772 -- 31 HDI % 63.0 -- 31 Engel % 50.7 -- 31 Southwest China – Yunnan

Type: Province Capital city: US state equiv: Louisiana Industries: Sugar; cigarettes; cloth; steel and steel products; hydropower; raw coal; cement; machine-made paper and paperboards Advantages: Infrastructure; tourism; tobacco industry; natural reserves 1Kunming Disadvantages: Poor; reliance on tobacco 2Qujing 3Yuxi 4Baoshan % of Unit Figure Rank 5Zhaotong national 6Lijiang Area sq km 38,319 4.0 8 7Pu'er Population million 46 3.4 12 8Lincang Population density '000 per sq km 1.2 -- 25 9Dehong GDP RMB billion 722 1.7 24 10 Nujiang Tertiary industry % of GDP 40.0 -- 9 11 Diqing GDP per capita RMB 15,707 -- 30 12 Dali Disp income (urban) RMB 16,065 -- 15 13 Chuxiong Consumption (urban) RMB 11,074 -- 20 14 Honghe Residential price RMB per sq m 2,893 -- 27 15 Wenshan 16 Xishuangbanna HDI % 71.0 -- 28 Engel % 43.7 -- 29 Northwest China – Shaanxi

Type: Province Capital city: Xi’an US state equiv: Pennsylvania Industries: Agriculture; equipment manufacturing; aviation; tourism Advantages: Energy giants; hi-tech development; vastly improved infrastructure Disadvantages: Commercial advantages yet to be 1 Xi'an realised 2 3 4 % of Unit Figure Rank 5 national 6 Area sq km 20,579 2.2 11 7 Population million 37 2.8 16 8 Population density '000 per sq km 1.8 -- 21 9 Yan'an GDP RMB billion 1,002 2.3 17 10 Yulin Tertiary industry % of GDP 36.2 -- 20 GDP per capita RMB 26,848 -- 15 Disp income (urban) RMB 15,695 -- 19 Consumption (urban) RMB 11,822 -- 14 Residential price RMB per sq m 3,665 -- 14 HDI % 77.3 -- 22 Engel % 37.3 -- 16 Northwest China – Ningxia

Type: Autonomous region Capital city: US state equiv: Utah Industries: Agriculture; coal; metallurgy; chemicals; wool textiles Advantages: Larger, more resource rich neighbours promoting development of entire region Disadvantages: Size (smaller than Chongqing); no 1 Yinchuan natural advantages 2 3 Wuzhong 4 % of Unit Figure Rank 5 national Area sq km 5,195 0.5 27 Population million 6 0.5 29 Population density '000 per sq km 1.2 -- 24 GDP RMB billion 164 0.4 29 Tertiary industry % of GDP 39.6 -- 10 GDP per capita RMB 26,080 -- 17 Disp income (urban) RMB 15,344 -- 25 Consumption (urban) RMB 11,334 -- 19 Residential price RMB per sq m 3,107 -- 22 HDI % 76.6 -- 23 Engel % 33.4 -- 6 Northwest China – Qinghai

Type: Province Capital city: US state equiv: Wyoming Industries: Petroleum and natural gas; power generation; nonferrous metals; saline chemicals Advantages: Resources; major rivers; connection to Tibet and its natural resources; development of renewable energy 1 Haixi 2 Haibei Disadvantages: Underdeveloped; lacks investment 3 Xining 4 % of Unit Figure Rank 5 Hainan national 6 Huangnan Area sq km 71,748 7.5 4 7 Yushu Population million 6 0.4 30 8 Golog Population density '000 per sq km 0.1 -- 30 GDP RMB billion 135 0.3 30 Tertiary industry % of GDP 34.9 -- 24 GDP per capita RMB 24,000 -- 22 Disp income (urban) RMB 13,855 -- 29 Consumption (urban) RMB 9,614 -- 31 Residential price RMB per sq m 2,892 -- 28 HDI % 72.0 -- 27 Engel % 40.4 -- 25 Northwest China – Xinjiang

Type: Autonomous region Capital city: Ürümqi US state equiv: Alaska Industries: Oil and petrochemicals; foodstuffs and beverages; textiles; metallurgy; building materials; electric power Advantages: Resources; central government support; agriculture Disadvantages: Unrest; geographical/topographical 1Altay 11 Ili limitations 2Bortala 12 Kizilsu 3Tacheng 13 4Karamay 14 Tumxuk % of Unit Figure Rank 5Shihezi 15 Aksu national 6Changji 16 Aral Area sq km 166,490 17.5 1 7Wujiaqu 17 Population million 22 1.6 25 8Ürümqi 18 Bayingolin Population density '000 per sq km 0.1 -- 29 9Turpan 11 Ili GDP RMB billion 542 1.3 25 10 12 Kizilsu Tertiary industry % of GDP 33.3 -- 28 GDP per capita RMB 24,842 -- 19 Disp income (urban) RMB 13,644 -- 30 Consumption (urban) RMB 10,197 -- 26 Residential price RMB per sq m 2,877 -- 29 HDI % 77.4 -- 21 Engel % 36.3 -- 13 Northwest China – Gansu

Type: Province Capital city: US state equiv: New Mexico Industries: Mining; mineral extraction; electricity generation; petrochemicals; oil exploration machinery; building materials Advantages: Renewable energies; nickel resources 1 Disadvantages: Largely desert 2 Jiayuguan 3 4 % of Unit Figure Rank 5 Wuwei national 6 Area sq km 40,409 4.3 7 7 Lanzhou Population million 26 1.9 22 8 Linxia (Hui) Population density '000 per sq km 0.6 -- 27 9 Gannan (Tibetan) GDP RMB billion 412 1.0 27 10 Tertiary industry % of GDP 37.3 -- 15 11 GDP per capita RMB 16,107 -- 29 12 Disp income (urban) RMB 13,189 -- 31 13 Consumption (urban) RMB 9,895 -- 28 14 Residential price RMB per sq m 2,911 -- 26 HDI % 70.5 -- 29 Engel % 37.8 -- 18 China vs the US

US China • While there are many differences between the two countries, there are also many similarities, for Land area 9.2 million sq km 9.3 million sq km example, the division of power between Population 304 million 1,330 million province/state and central governments and total land GDP US$14.6 trillion US$5.9 trillion mass. However, as the US has prospered, the similarities between states began to outweigh the GDP per capita US$48,000 US$6,100 differences. In China this has not yet occurred, with many provinces showing marked differences. Provinces vs states

Population Disposable US GDP Province Capital city Description (million Income Comparison (RMB billion) people) (RMB) Manufacturing / agricultural Liaoning Shenyang Indiana 1,521 43 15,761 economies NE Jilin Changchun Michigan Automotive & blue collar 763 27 14,006

Heilongjiang Harbin Minnesota Cold, beer production & forests 859 38 12,566

Beijing* Beijing Washington Capital city 1,215 18 26,738 Commodities exchange & diverse Tianjin* Tianjin Chicago 752 12 21,402 economy Spill over affect from Beijing (NYC) N Hebei Shijiazhuang New Jersey 1,724 70 14,718 on economy and culture Shanxi Taiyuan Pennsylvania Coal 744 34 13,997 Inner Hohhot Texas Oil-based economy & cowboys 974 24 15,849 Mongolia Shanghai* Shanghai New York City Economic and cultural centre 1,505 19 28,838

Jiangsu Nanjing Rhode Island Affluent 3,446 77 20,552

E Zhejiang Hangzhou Connecticut Affluent & strong financial industry 2,299 52 24,611

Anhui Hefei Virginia Good universities & traditional 1,006 61 14,086 Agriculture-based economy & Shandong Jinan Illinois 3,390 95 17,811 large population * Municipalities, therefore, city comparison given. Provinces vs states

Population Disposable US GDP Province Capital city Description (million Income Comparison (RMB billion) people) (RMB) Fujian Fuzhou South Carolina Reliant on ports 1,260 36 19,577 Risk takers, strong IT industry & Guangdong Guangzhou California 3,948 96 21,575 mixed with southern cultures S Textile industry, mountainous & Guangxi Nanning Tennessee 776 49 15,451 tourism Hainan Haikou Florida Hot climate & tourism 165 9 13,751 Jiangxi Nanchang Georgia Agriculture 766 44 14,022 Henan Zhengzhou New York Large population 1,948 95 14,372 C Hubei Wuhan Ohio Middle in ranking 1,324 57 14,367 Hunan Changsha Idaho 1,306 64 15,084 A major river link & a centre for a Chongqing* Chongqing St Louis 653 29 15,749 Go West policy Sichuan Chengdu Colorado Laid back, R&D & mountains 1,415 82 13,839 Coal, mountainous & behind the SW Guizhou Guiyang West Virginia 391 38 12,863 times Yunnan Kunming Louisiana Culturally diverse & poor 617 46 14,424 Tibet Lhasa Alaska Mountainous, sparsely populated 44 3 13,544 Shaanxi Xi’an Pennsylvania 817 38 14,129 Gansu Lanzhou New Mexico Dessert 339 26 11,930 NW Qinghai Xining Wyoming Barren 108 6 12,692 Ningxia Yinchuan Utah Dessert 135 6 14,025 Xinjiang Urumqi Alaska Oil & rugged people 428 22 12,258

* Municipalities, therefore, city comparison given. • NOTES: • All data, unless otherwise stated, is sourced from CEIC, which receives data directly from government institutions.

• This document is prepared by Savills for information only. Whilst reasonable care has been exercised in preparing this document, it is subject to change and these particulars do not constitute, nor constitute part of, an offer or contract; interested parties should not rely on the statements or representations of fact but must satisfy themselves by inspection or otherwise as to the accuracy. No person in the employment of the agent or the agent's principal has any authority to make any representations or warranties whatsoever in relation to these particulars and Savills cannot be held responsible for any liability whatsoever or for any loss howsoever arising from or in reliance upon the whole or any part of the contents of this document.