China: Where’s the growth? Rachel Morarjee, Director, ECN Beijing For Finnish Embassy October 2019 corporatenetwork.com
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The dispute is about technology as much as tariffs The US is concerned about losing its position at the technology frontier corporatenetwork.com 2
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 corporatenetwork.com
Trade war exposure in China corporatenetwork.com 4
A split in the global trading system is our top risk Currency wars, USD debt and a China slowdown are also on our mind High USD debt No-deal levels: 12 Brexit: 8 Global China trade credit system Italian bubble splits: 20 banking Korean bursts: 10 crisis: 4 War: 5 Oil price spike: 8 SCS conflict: Currency 8 war: 15 Major cyber- attack: 6 corporatenetwork.com
The Economy: Structural weakness corporatenetwork.com
Growth is falling, but from a much higher base 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. corporatenetwork.com 7 7
Trade war less of a threat to growth than SOEs For private and foreign firms, China’s economy is not a level playing field. corporatenetwork.com 8
Crisis watch: international comparisons 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 corporatenetwork.com 9
Demographics: opportunity and risk corporatenetwork.com
Labour force will stabilise, then fall fast post 2025 Labour force (15-64 years) Labour force (m); LHS Change in labour force (%); RHS 1,020 2.0% 1,000 1.5% 980 1.0% 960 0.5% 940 920 0.0% 900 -0.5% 880 -1.0% 860 840 -1.5% Source: EIU corporatenetwork.com 11
China ’ s demographic dividend is over Rapid ageing means that by 2030 China ’ s population structure is likely to be similar to that of today ’ s Japan Population of 1990 (m) Population of 2020 (m) Population of 2000 (m) 90-94 90-94 90-94 75-79 75-79 75-79 60-64 60-64 60-64 45-49 45-49 45-49 30-34 30-34 30-34 15-19 15-19 15-19 0-4 0-4 0-4 0.0 50.0 100.0 150.0 0.0 50.0 100.0 150.0 0.0 50.0 100.0 150.0 Population of 2030 (m) India Population Japan Population in 2030 (m) in 2018 (m) 90-94 90-94 90-94 75-79 75-79 75-79 60-64 60-64 60-64 45-49 45-49 45-49 30-34 30-34 30-34 15-19 15-19 15-19 0-4 0-4 0-4 0.0 50.0 100.0 150.0 0.0 50.0 100.0 150.0 -3.0 2.0 7.0 12.0 corporatenetwork.com 12
Ageing from a regional perspective Ageing by province Increase in ageing population 2018-2030;LHS Number of population age 65+ in 2018; LHS 65+ % of population in 2018; RHS 25 18.0% 16.0% Sichuan Jiangsu 20 14.0% Jilin 12.0% 15 10.0% 8.0% 10 6.0% 4.0% 5 2.0% 0 0.0% Source: EIU corporatenetwork.com 13
Regional Rebalancing corporatenetwork.com 14
“Continuously narrowing the gap” corporatenetwork.com
Redrawing the economic map corporatenetwork.com
A tale of five regions corporatenetwork.com
Growing apart corporatenetwork.com
Underlying cracks 1 corporatenetwork.com
Underlying cracks 2 corporatenetwork.com
Rebalancing, ready or not corporatenetwork.com
Seizing the future corporatenetwork.com
Demographic deficit corporatenetwork.com
End of migration? Central and western cities rise 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 Shanghai Tianjin Jiangsu Xinjiang Hebei Ningxia Qinghai Tibet Yunnan Gansu Hunan Anhui Sichuan Guizhou Hubei Guangdong Beijing Zhejiang Shandong Shanxi Liaoning Fujian Hainan Heilongjiang Inner Mongolia Shaanxi Jilin Jiangxi Chongqing Guangxi Henan 2000--18 2019--30 • Aless mobile population • Traditional labour exporters see reverse migration:Anhui, Sichuan, Chongqing, Hubei • Repopulation in manufacturing centres: Guangdong, Zhejiang, Jiangsu Source: The Economist Intelligence Unit. corporatenetwork.com 24
Cities of the future corporatenetwork.com
City clusters are an emerging priority But not all regions are equal corporatenetwork.com 26 26
Big cities draw in population Transport connections primarily benefit the larger hubs corporatenetwork.com 27 27
Emerging city rankings Central and eastern cities have the highest growth potential • Index includes 9 growth indicators: Emerging city rankings 2018 • GDP , F AI, FDI, Metropolitan built 100% area 90% • Metropolitan population, disposable 80% income, urban consumption 70% • Fiscal revenue 60% • No. of national level development 50% zones 40% • 30% Full ranking includes 98 prefecture cities (out of 292) 20% • Metropolitan population >1m by 10% 2022 0% Top 30 ranking 31--60 ranking 61--98 East Central West Northeast Source: The Economist Intelligence Unit. corporatenetwork.com 28
Overall growth rankings: top 10 T op 10 are populous cities with lower cost and central policy supports Rank Province City Tier • National policies • Central Rise, W estern 1 Anhui Suzhou 4 Development (BRI), 2 Hunan Yueyang 3 Revitalisation of the Northeast, city 3 Henan Luoyang 3 clusters 4 Hubei Xiangyang 3 • City expansion 5 Hunan Xiangtan 3 • F AI, metropolitan 6 Xinjiang Urumqi 3 population 7 Jilin Changchun 2 • Close connections with 8 Guizhou Guiyang 3 provincial capital 9 Shandong T ai'an 3 10 Henan Xinxiang 3 Source: The Economist Intelligence Unit. corporatenetwork.com 29
Cost advantage of emerging cities Emerging cities mostly have low labour cost Annual wage in 2018 (,000 Rmb) • Urumqi, Guiyang, 120 Changchun have 100 high labour cost but strategic 80 importance 60 40 • Most top 10 20 emerging cities (in yellow) have 0 Beijing Nanjing Shenzhen Hangzhou Urumqi Wuhan Hefei Xi'an Changchun Chongqing Luoyang Xiangyang Jinzhou Yueyang Nanyang Guiyang aiyuan National average Xiangtan ai'an Suzhou (Anhui) Xinxiang lower labour cost • T Industrial T transfer 30
Metropolitan migration inflow will concentrate in tier 4 Urbanisation will continue Metropolitan net migration inflow (m) 14.0 12.0 • Further urbanisation • Move rural residents to cities 10.0 • Official target: 70% by 2030 (59% in 2018) 8.0 • Hukou reform in 2019 (NDRC) 6.0 • Metro pop 1m-3m no restriction 4.0 • 3m-5m relax requirements (high-skill workers) 2.0 • >5m increase hukou quota, prioritise long-term residents 0.0 2001--18 2019--30 with long history of social security payment Tier 1 Tier 2 Tier 3 Tier 4 31
Sector ranking: economy (liveability) Sector rankings are snapshots of the city performance: 7 sectors, 42 indicators 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 corporatenetwork.com 32
Shenzhen tops the gain in metropolitan population But is exposed to the trade war corporatenetwork.com 33
Smart cities China ’ s 5G rollout accelerated amid trade tension • T o 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 corporatenetwork.com 34
China ’ s regional potential: consumer market Lower tier cities as a whole have potential Retail sales and population, High-income consumers (m) 286 prefectures, 2018 (%) 7.0 100% 6.0 90% 22% 34% 80% 5.0 70% 26% 4.0 60% 3.0 50% 37% 40% 2.0 37% 30% 1.0 20% 24% 0.0 10% 16% 6% 0% Retail sales Population First tier Second tier 2018 2019--30 increase Third tier Fourth tier Note. High-income consumers are defined as those with annual disposable income of more than Rmb150,000. Source: The Economist Intelligence Unit. corporatenetwork.com 35
Attract talents Universities and industries 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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