China: Wheres the growth? Rachel Morarjee, Director, ECN Beijing - - PowerPoint PPT Presentation

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China: Where’s the growth?

Rachel Morarjee, Director, ECN Beijing For Finnish Embassy

October 2019

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The US is concerned about losing its position at the technology frontier

The dispute is about technology as much as tariffs

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Trump started a trend by using trade as a weapon

The underlying battle over technology will increasingly move to the forefront

Latest announcements would cover 98% of Chinese exports to US by year-end, as well as virtually all US exports to China (not aircraft, pharma, semiconductors) China-US escalation from here is in tech, investment, financial and security areas: where pain will last USMCA still not ratified, and autos from EU and Japan at risk: auto tariffs would signal a global trade war, but likely just a negotiation tactic for free-trade agreements WTO dispute settlement mechanism to cease to function in December Bright spots: CPTPP, Latin America

Source: Chad Bown, PIIE

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Trade war exposure in China

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A split in the global trading system is our top risk

Currency wars, USD debt and a China slowdown are also on our mind

Global trade system splits: 20 Italian banking crisis: 4 Korean War: 5 Major cyber- attack: 6 No-deal Brexit: 8 SCS conflict: 8 Oil price spike: 8 China credit bubble bursts: 10 High USD debt levels: 12 Currency war: 15

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The Economy: Structural weakness

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Growth is slower in percentage term but is coming from a much higher growth, so there are still growing markets for foreign companies to tap.

Growth is falling, but from a much higher base

7

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For private and foreign firms, China’s economy is not a level playing field.

Trade war less of a threat to growth than SOEs

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The steep acceleration in Chinese debt shows reflects how hard it is to manage risk when debt grows that fast. Credit chains are complex and difficult to pin down

Crisis watch: international comparisons

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Demographics: opportunity and risk

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Labour force will stabilise, then fall fast post 2025

• 1.5%
• 1.0%

900

• 0.5%

880 0.0% 0.5% 980 1.0% 960 1.5% 2.0% 840 860 920 940 1,000 1,020

Labour force (15-64 years)

Labour force (m); LHS Change in labour force (%); RHS

11

Source: EIU

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Rapid ageing means that by 2030 China’s population structure is likely to be similar to that of today’s Japan

China’s demographic dividend is over

0.0 50.0 100.0 150.0 90-94 75-79 60-64 45-49 30-34 15-19 0-4

Population of 1990 (m) Population of 2020 (m)

0.0 50.0 100.0 150.0 90-94 75-79 60-64 45-49 30-34 15-19 0-4

Population of 2000 (m)

0.0 50.0 100.0 150.0 90-94 75-79 60-64 45-49 30-34 15-19 0-4 0.0 50.0 100.0 150.0 90-94 75-79 60-64 45-49 30-34 15-19 0-4

India Population in 2030 (m)

• 3.0

2.0 7.0 12.0 0-4 15-19 30-34 45-49 60-64 75-79 90-94

Japan Population in 2018 (m)

0.0 50.0

12

100.0 150.0 90-94 75-79 60-64 45-49 30-34 15-19 0-4

Population of 2030 (m)

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Ageing from a regional perspective

Source: EIU Jiangsu Sichuan Jilin 12.0% 15 10.0% 8.0% 10 6.0% 0.0% 2.0% 4.0% 14.0% 16.0% 18.0% 5 20 25

Ageing by province

Increase in ageing population 2018-2030;LHS Number of population age 65+ in 2018; LHS 65+ % of population in 2018; RHS

13

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Regional Rebalancing

14

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“Continuously narrowing the gap”

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Redrawing the economic map

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A tale of five regions

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Growing apart

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Underlying cracks 1

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Underlying cracks 2

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Rebalancing, ready or not

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Seizing the future

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Demographic deficit

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End of migration? Central and western cities rise

• Aless mobile population
• Traditional labour exporters see reverse migration:Anhui, Sichuan, Chongqing, Hubei
• Repopulation in manufacturing centres: Guangdong, Zhejiang, Jiangsu

Reverse migration to inland along with conglomeration in the east

Net migrant inflow (m)

15.0 10.0 5.0 0.0

• 5.0
• 10.0

Guangdong Beijing Shanghai Zhejiang Tianjin Jiangsu Xinjiang Shandong Hebei Shanxi Liaoning Ningxia Qinghai Fujian Hainan Tibet Heilongjiang Inner Mongolia Shaanxi Jilin Yunnan Jiangxi Chongqing Gansu Hunan Anhui Guangxi Sichuan Guizhou Hubei Henan 2000--18 2019--30

Source: The Economist Intelligence Unit. 24

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Cities of the future

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But not all regions are equal

City clusters are an emerging priority

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Transport connections primarily benefit the larger hubs

Big cities draw in population

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Central and eastern cities have the highest growth potential

Emerging city rankings

• Index includes 9 growth indicators:
• GDP

, F AI, FDI, Metropolitan built area

• Metropolitan population, disposable

income, urban consumption

• Fiscal revenue
• No. of national level development

zones

• Full ranking includes 98 prefecture cities

(out of 292)

• Metropolitan population >1m by

2022

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Top 30 ranking 31--60 ranking 61--98

Emerging city rankings 2018

East Central West Northeast

Source: The Economist Intelligence Unit. 28

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T

• p 10 are populous cities with lower cost and central policy supports
• National policies
• Central Rise, W

estern Development (BRI), Revitalisation of the Northeast, city clusters

• City expansion
• F

AI, metropolitan population

• Close connections with

provincial capital

Source: The Economist Intelligence Unit. 29

Overall growth rankings: top 10

Rank Province City Tier 1

Anhui Suzhou 4

2

Hunan Yueyang 3

3

Henan Luoyang 3

4

Hubei Xiangyang 3

5

Hunan Xiangtan 3

6

Xinjiang Urumqi 3

7

Jilin Changchun 2

8

Guizhou Guiyang 3

9

Shandong T ai'an 3

10

Henan Xinxiang 3

Cost advantage of emerging cities

• Urumqi, Guiyang,

Changchun have high labour cost but strategic importance

• Most top 10

emerging cities (in yellow) have lower labour cost

• Industrial

transfer Emerging cities mostly have low labour cost

Annual wage in 2018 (,000 Rmb)

120 100 80 60 40 20

30

Beijing Nanjing Shenzhen Hangzhou Urumqi Wuhan Hefei Guiyang Xi'an Changchun Chongqing T aiyuan National average Xiangtan T ai'an Luoyang Xiangyang Suzhou (Anhui) Jinzhou Yueyang Nanyang Xinxiang

Metropolitan migration inflow will concentrate in tier 4

Urbanisation will continue

Metropolitan net migration inflow (m)

14.0 12.0 10.0 8.0 6.0 4.0 2.0 0.0

• Further urbanisation
• Move rural residents to cities
• Official target: 70% by 2030

(59% in 2018)

• Hukou reform in 2019 (NDRC)
• Metro pop 1m-3m no

restriction

• 3m-5m relax requirements

(high-skill workers)

• >5m increase hukou quota,

prioritise long-term residents with long history of social security payment

2001--18 Tier 1 Tier 2 2019--30 Tier 4 Tier 3

31

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Sector rankings are snapshots of the city performance: 7 sectors, 42 indicators

Sector ranking: economy (liveability)

9 measures on livability:

• Innovation (university

, aging)

• Openness (FDI)
• Transportation (rail,

population density)

• Job market (services,

wage) Tier 1 cities are different

• Shenzhen: youngest

(aging 3.2%>65)

• Beijing oldest: (13.8%)
• Shanghai: open, dense
• Guangzhou: transport

32

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But is exposed to the trade war

Shenzhen tops the gain in metropolitan population

33

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China’s 5G rollout accelerated amid trade tension

• T
• commercilise 5G

in 2020

• Issued licenses

ahead of schedule

• China Mobile and

China Unicom pilot 5G in 40 cities

• Xiongan
• Zhangjiakou
• China T

elecom has a different set of cities

• Qionghai

(county)

• Y

ingtan

Smart cities

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Lower tier cities as a whole have potential

China’s regional potential: consumer market

16% 6% 37% 24% 26% 37% 22% 34% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Retail sales Population

Retail sales and population, 286 prefectures, 2018 (%)

First tier Third tier Second tier Fourth tier

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0

High-income consumers (m)

2018 2019--30 increase

• Note. High-income consumers are defined as those with annual disposable

income of more than Rmb150,000.

Source: The Economist Intelligence Unit. 35

Universities and industries

Attract talents

Fierce competition to attract talents

• Hukou
• Housing subsidy
• Startup funds
• T

ax breaks for investment Some inland and northeastern cities have good labour pools

• Lanzhou, Hefei
• Changchun,

Shenyang

36

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Accounts for 10% of China’s GDP. Five clusters by 2022: in electronics, smart home appliances, automotive, green petrochemicals and robotics

Guangdong has a reliance on low cost labour and low margin assembly

Greater Bay Area: Ecosystem advantage?

3 7

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Hong Kong

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Competing visions of reality. Jarring for business and China’s future

Clash of the titans or David and Goliath

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HK’s contribution to GDP misses its vital role for China

HK: Look beyond the numbers

China can remain so closed because Hong Kong’s economy is so vital and so open.

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Any questions?