BLUEPAPER M Morgan Stanley does and seeks to do business with companies covered in Morgan Stanley Research. As a result, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of Morgan Stanley Research. Investors should consider Morgan Stanley Research as only a single factor in making their investment decision. For analyst certification and other important disclosures, refer to the Disclosure Section, located at the end of this report. += Analysts employed by non-U.S. affiliates are not registered with FINRA, may not be associated persons of the member and may not be subject to NASD/NYSE restrictions on communications with a subject company, public appearances and trading securities held by a research analyst account. China The Rise of China's Supercities: New Era of Urbanization W e believe Urbanization 2.0 will fuel productivity growth, allowing China to attain high-income status. By 2030 we expect the average size of the country's five supercities to reach 120mn, an 8.5x increase in commuter rail length, and a tripling of the IoT and data market to almost US$1trn. October 10, 2019 08:00 PM GMT
Transcript
BLUEPAPERM
Morgan Stanley does and seeks to do business with companies covered in Morgan Stanley Research. As a result, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of Morgan Stanley Research. Investors should consider Morgan Stanley Research as only a single factor in making their investment decision.For analyst certification and other important disclosures, refer to the Disclosure Section, located at the end of this report.+= Analysts employed by non-U.S. affiliates are not registered with FINRA, may not be associated persons of the member and may not be subject to NASD/NYSE restrictions on communications with a subject company, public appearances and trading securities held by a research analyst account.
China
The Rise of China's Supercities: New Era of Urbanization
We believe Urbanization 2.0 will fuel productivity growth, allowing China to attain high-income status. By 2030 we expect the average size of the country's five supercities to reach 120mn, an 8.5x increase in commuter rail length, and a tripling
58 Investment Theme #1: From a Consumer to an Industrial Internet
59 1a. Telecoms
63 1b. Internet
71 1c. Tech Hardware and Software
78 Investment Theme #2: Digitalization of Old-Economy Industries
79 2a. Autos
83 2b. Logistics
88 2c. Utilities and Power Equipment
91 2d. Banks
97 2e. Insurance
102 2f. Agribusiness
106 Investment Theme #3: New Lifestyles in Smart Supercities
107 3a. Transportation
111 3b. China Property
117 3c. Hong Kong Property Companies
121 3d. Materials
125 3e. Consumer IoT
132 3f. Education
139 3g. Healthcare
141 3h. Macau Gaming
144 3i. Tourism
148 Summary of Stocks Exposed to Urbanization 2.0
BLUEPAPERM
4
Other Contributors
Equity Strategy
Fran Chen Morgan Stanley Asia Limited+ Strategist [email protected] +852 2848-7135
Telecoms and Communication
Yang Liu Morgan Stanley Asia Limited+ Equity Analyst [email protected] +852 2239-1911
Camille Xu Morgan Stanley Asia Limited+ Research Associate [email protected] +852 3963-0692
Sara Wang Morgan Stanley Asia Limited+ Research Associate [email protected] +852 2239-1230
Internet
Eddy Wang, CFA Morgan Stanley Asia Limited+ Equity Analyst [email protected] +852 2239-7339
Alex Poon Morgan Stanley Asia Limited+ Equity Analyst [email protected] +852 3963-3838
Steven Tsai Morgan Stanley Asia Limited+ Research Associate [email protected] +852 2848-7217
Alex Ko Morgan Stanley Asia Limited+ Research Associate [email protected] +852 2239-1225
Technology
Yunchen Tsai Morgan Stanley Asia Limited+ Equity Analyst [email protected] +852 2848-5636
Howard Kao Morgan Stanley Taiwan Limited+ Equity Analyst [email protected] +886 2 2730-2989
Ray Wu, CFA Morgan Stanley Taiwan Limited+ Equity Analyst [email protected] +886 2 2730-2871
Daniel Yen, CFA Morgan Stanley Taiwan Limited+ Equity Analyst [email protected] +886 2 2730-2863
Lily Chou Morgan Stanley Taiwan Limited+ Research Associate [email protected] +886 2 2730-2869
Daisy Dai, CFA Morgan Stanley Asia Limited+ Research Associate [email protected] +852 2848-7310
Jeff Hsu Morgan Stanley Taiwan Limited+ Research Associate [email protected] +886 2 2730-2864
Tony Shen Morgan Stanley Asia Limited+ Research Associate [email protected] +852 2848-5657
Autos
Shelley Wang, CFA Morgan Stanley Asia Limited+ Equity Analyst [email protected] +852 3963-0047
Frank Wan Morgan Stanley Asia Limited+ Research Associate [email protected] +852 2239-1229
Logistics
JunYi Yu Morgan Stanley Asia Limited+ Equity Analyst [email protected] +852 2239-7817
Utilities and Power Equipment
Eva Hou Morgan Stanley Asia Limited+ Equity Analyst [email protected] +852 2848 6964
Banks
Anil Agarwal Morgan Stanley Asia Limited+ Equity Analyst [email protected] +852 2848-5842
Katherine Liu Morgan Stanley Asia Limited+ Research Associate [email protected] +852 2239-1924
Lu Lu Morgan Stanley Asia Limited+ Equity Analyst [email protected] +852 2239-1568
John Cai Morgan Stanley Asia Limited+ Equity Analyst [email protected] +852 2239-1885
Joey Xu Morgan Stanley Asia Limited+ Research Associate [email protected] +852 3963-0337
Insurance
Green Cai Morgan Stanley Asia Limited+ Research Associate [email protected] +852 2848-5686
Birlina Qi Morgan Stanley Asia Limited+ Research Associate [email protected] +852 3963-4087
Capital Goods and Construction
Hangjie Chen Morgan Stanley Asia Limited+ Equity Analyst [email protected] +852 2848-7168
China Property
Chloe Liu Morgan Stanley Asia Limited+ Equity Analyst [email protected] +852 2848-5497
Cara Zhu Morgan Stanley Asia Limited+ Research Associate [email protected] +852 2848-7117
Ziya Zhou Morgan Stanley Asia Limited+ Research Associate [email protected] +852 3963-0322
Consumers
Hanli Fan, CFA Morgan Stanley Asia Limited+ Equity Analyst [email protected] +852 3963-1017
Education
Elsie Sheng Morgan Stanley Asia Limited+ Equity Analyst [email protected] +852 3963-0475
Healthcare
Yolanda Hu Morgan Stanley Asia Limited+ Equity Analyst [email protected] +852 2848-5649
Laurence Tam Morgan Stanley Asia Limited+ Equity Analyst [email protected] +852 2239-1753
Ethan Ding Morgan Stanley Asia Limited+ Research Associate [email protected] +852 3963-0546
Alexis Yan Morgan Stanley Asia Limited+ Research Associate [email protected] +852 2239-7953
Materials
Sean Xiang Morgan Stanley Asia Limited+ Equity Analyst [email protected] +852 2848-8154
Hannah Yang Morgan Stanley Asia Limited+ Research Associate [email protected] +852 2239-7079
HK Real Estate and Macau Gaming
Hildy Ling Morgan Stanley Asia Limited+ Equity Analyst [email protected] +852 2239-7834
Dan Xu Morgan Stanley Asia Limited+ Research Associate [email protected] +852 2239-1227
Gareth Leung Morgan Stanley Asia Limited+ Research Associate [email protected] +852 2848-7339
BLUEPAPERM
MORGAN STANLEY RESEARCH 5
Preface Can China maintain a relatively fast pace of growth amid the chal-lenges of slowing globalization and aging demographics? Since we last wrote in-depth on China's journey to a high-income economy in November 2017, globalization has been hampered by tariffs and other barriers. As the working-age population declines, demographic headwinds will also continue to exert a drag on economic growth. We believe the answer to these challenges is a new phase of urbanization with the potential to create productivity gains by facilitating the freer movement of enterprises and workers while generating synergies between diverse industries.
The path to Urbanization 2.0
In this report we look at what the next decade could hold for China's cities and make our best attempt to identify investment opportuni-ties. We forecast that the country's urbanization rate will rise to 75% by 2030, driven by (1) the growth of city clusters , which will bring the benefits of urban agglomeration while alleviating big-city prob-lems, (2) smart cities that leverage next-generation technologies to reduce traffic, crime and pollution, and (3) agricultural modernization , which will boost labor productivity and enable more rural workers to migrate to cities.
Aiming for faster, safer, greener, more livable cities
China has experienced unprecedented urbanization and economic growth in just a few decades. In its next stage of development, how-ever, China is focusing on making cities faster, safer, greener, and more livable by embracing structural reforms (such as Hukou and land reforms) and a new era of digital technologies. By 2030, city residents should generally be able to reach their workplaces within 15 minutes. At home, current plans aim to have 5G-enabled smart appliances clean, cook, and order food when supplies run low, while virtual reality headsets will help students do homework and attend online tutoring classes with the country's top teachers.
Much of this is a reality today. It is already common to pay at grocery stores in Hangzhou or check in at the new Beijing airport using only facial recognition. By 2030, greater changes will be enabled by investments in digital infrastructure and the adoption of artificial intelligence (AI) and big data. We expect high-speed commuter trains, smart traffic control systems, shared mobility, and automated vehicle technologies to cut travel times and make streets and roads
safer than ever, while electric vehicles and green energy sources reduce pollution. This will enhance the population capacity of cities.
What could this mean for the economy?
We estimate that Urbanization 2.0 will attract an additional 220mn city dwellers by 2030 (vs. existing urban residents of 831mn). Half of them will settle in the top five superclusters, which we project will have populations of 120mn on average – close to the size of Japan's current population – enabled by an 8.5x increase in commuter rail length. The number of megacities with populations larger than New York City (8mn) should rise from nine now to 23 by 2030. Next-gen technologies, enabled by our estimated US$800bn capex in digital infrastructure, should sustain total factor productivity growth at 1.6% annually through 2030. Offsetting industrial automation, voca-tional training should help match skilled workers with high value-added manufacturing and service jobs. Labor productivity will almost double, we estimate, with 55% of the increase coming from the agglomeration effects of supercities and 40% from rural-urban migration. Most importantly, we remain confident that China will reach high-income status as annual per-capita income almost dou-bles from US$9,450 today to US$17,800 by 2030.
What could this mean for investment?
We identify three key investment opportunities from Urbanization 2.0 as disruptive technologies unleash further growth potential:
l Transition from consumer to industrial internet: 5G infra-structure, public cloud, Internet of Things (IoT) devices, and software.
l Digitalization of old-economy industries: Electric and auton-omous vehicles, smart grid and utilities, market-oriented banks and insurers, and smart farming.
l New lifestyles in smart supercities: E-commerce, smart home appliances, online tutoring and vocational education, health-care, and railway construction.
Although the views in this report are our base-case scenarios, we acknowledge that there is no way to foresee all risks that may arise from issues like automation, big data, and trade tensions, which could include job losses, a higher debt burden, or other unintended social issues. For more on risks, please see Where could we be wrong?
BLUEPAPERM
6
Key Charts at a GlanceExhibit 1:Forecasts for Urbanization 2.0
Source: Morgan Stanley Research estimates
BLUEPAPERM
MORGAN STANLEY RESEARCH 7
Exhibit 2:From Urbanization 1.0 to 2.0
Source: Morgan Stanley Research
Exhibit 3:Did you know…
Source: NBS, Haver, Morgan Stanley Research
BLUEPAPERM
8
Sector Implications Through 2030 Top Stocks for This Theme
(1) From a consumer to an industrial internet
Telecoms • 5G capex to reach US$400bn in 2019-30, 2x that of 4G • Key beneficiaries: 5G infrastructure companies
Internet • Enterprise IT spending on software and IT services to account for around 8% of global spending (vs. 3% now)• Key beneficiaries: Technology leaders that are expanding from consumer to industrial applications
Tech Hardware and Software
• IoT device market size to more than double to US$700bn • Software and IT services market size to fivefold to US$200bn • Key beneficiaries: Top players geared to IoT and 5G; software vendors focusing on digital transformation or with smart city exposure
(2) Digitalization of old-economy industries
Autos
• Shared mobility to take up 10% of total car parc (vs. 2% now) • Market share of electric vehicles to reach one-third (vs. 4% in 2018)• Share of cars with high/full automation to reach 20% (vs. 0% now)• Key beneficiaries: Early movers in EVs and autonomous vehicles
• Logistics cost/GDP to come down to 10% (vs. 15% in 2018)• Nationwide delivery time to be within 1 day (vs. 2-4 now) • Express volumes to reach 300bn deliveries per year (up 6x)• Key beneficiaries: Logistics companies with strong R&D investment
Utilities and Power Equipment
• Share of clean energy in capex to rise to 60% (vs. 40% today) • Share of clean energy in power generation approaching 40% (vs. 30% now) • Capex in smart grid to increase 2.64x, to US$80bn, in 2021-30• Key beneficiaries: Utility players with competitive edge in smart grid
Banks • Healthier credit growth, at 7% CAGR (vs. 17% in the past decade)• Key beneficiaries: More market-oriented banks
Insurance • Insurance penetration to rise to 9% (vs. 4.3% in 2018) • Key beneficiaries: Insurers with leading positions in top-tier cities and advanced technological capabilities
Agribusiness • GM corn and soybean seed application to reach 50% (vs. 0% today)• Key beneficiaries: Agribusiness entities with strong brand name and GM seed pipeline
(3) New lifestyles in smart supercities
Transportation• High-speed rail length to reach 65,000km (vs. 30,000km now)• Inter-city commuter rail to reach 17,000 km (vs. 2,000km now)• Key beneficiaries: Railway construction companies focusing on inter-city and metro rail build-up
• CRRC Corp Ltd (1766.HK)
• Haier Smart Home (600690.SS)
• Meituan Dianping (3690.HK)
• New Oriental Education & Technology Group (EDU.N)
• TAL Education Group (TAL.N)
• Jiangsu Hengrui Medicine (600276.SS)
• Aier Eye Hospital (300015.SZ)
Property• Annual incremental housing demand to sustain, at 1,450mn sqm (vs. 1,479mn sqm in 2018)• Annual housing price growth to reach 6% in five key city clusters (vs. 4% elsewhere)• Key beneficiaries: Developers with more landbank exposure to large cities and key cityclusters
Materials• Market share of top ten players in steel and cement to reach 60% (vs. 37% now) and 70% (vs. 57% now) respec-tively • Key beneficiaries: Leaders in highly concentrated industries
Consumption
• 100% penetration of smart home appliances (vs. 20% today)• IoT devices to reach 7 units per households (vs. 1 today)• E-commerce penetration to exceed 75% of total population (vs. 44% today) • Key beneficiaries: Companies with clear strategies for smart home appliances and e-commerce leaders
Education• Penetration of K-12 online tutoring to exceed 35% (vs. under 10% now)• Vocational training market to triple, to US$300bn• Key beneficiaries: Top online tutoring and vocational education players
Healthcare
• China's pharmaceutical market to reach US$0.5trn by 2030 (6.3% CAGR in 2018-30)• China's healthcare service market to reach US$2.2trn by 2030 (10.1% CAGR in 2015-30)• Key beneficiaries: Pharmas with strong, innovative pipelines, and top healthcare service providers with solid growth potential
Macau Gaming • Gaming revenue to more than double, to US$70-100bn• Key beneficiaries: Gaming operators in Macau with bigger hotel capacity
Tourism • Domestic annual tourism expenditure to reach US$1.5trn (vs. US$0.78trn in 2018). • Key beneficiaries: Top tourist destination operators
BLUEPAPERM
MORGAN STANLEY RESEARCH 9
Executive Summary
China is answering an ancient question...
Source: Shutterstock
Philosophers, policymakers and academics have debated the optimum size of cities since Aristotle walked the streets of ancient Athens. Simply put, cities that are too small lack the labor and effi-ciencies to power economic growth, and those that are too large become expensive to manage as efficiencies break down under the weight of overcrowding and decaying infrastructure.
China obviously has no problem with cities being too small. But where does it stand with respect to the latter problem now that 60% of the population lives in urban areas, up from a mere 18% in 1978? And what is the outlook for urbanization as China’s population ages and starts to decline in absolute terms?
A well-worn concern is that 'China will grow old before it gets rich'. While it is true that demographics will be a significant drag on eco-nomic growth in the years ahead, we argue that it will not stop the process of urbanization. On the contrary, we see urbanization as a solution to China's demographic pressures that will lift the economy to high-income status. In the shorter term, urbanization-related investment and productivity gains should figure as a key feature of Beijing's counter-cyclical stimulus, helping ease the effects of US-China trade tensions.
Launching China's second phase of urbanization
...with city clusters and smart cities
Source: Shutterstock
Why continued urbanization is important: China’s productivity growth has almost halved, to 2.3% annually in 2010-18 from 4.4% in 2000-09. We believe the way to address this is through continued urbanization, which can provide economic benefits such as improving the ease with which enterprises and workers can move to productive locations to facilitate matching, sharing and learning, spread ideas, form specialized supply chains, and generate synergies across dif-ferent sectors. Overcoming hurdles to urbanization will require structural reforms, including the freer flow of labor, better social welfare coverage, and larger-scale farming. Meanwhile, technology can also help enhance liveability in densely populated cities and boost productivity growth.
From Urbanization 1.0 to 2.0: Despite the substantial economic success of urbanization as China initiated reforms and opened up over the past four decades, certain bottlenecks are showing the limits of the old growth model. These include (1) big-city problems such as traffic congestion, social issues and pollution, (2) policy con-straints on labor mobility, (3) a shrinking pool of rural manpower for further urbanization, and (4) increased trade tensions with the US.
BLUEPAPERM
10
To reach the next stage of sustainable development, China is forging a different urbanization path, focusing on making cities faster, safer, greener, and more livable by embracing structural reforms and a new era of digital technologies. We see three initiatives underpinning Urbanization 2.0:
1. City clusters , knitted together by the country's advanced rail system, should continue to reap the benefits of urban agglomeration while alleviating big-city problems. We believe five key city clusters across the country – Yangtze River Delta, Jing-Jin-Ji Area, Greater Bay Area, Mid-Yangtze River Area, and Chengdu-Chongqing Area – will likely account for 75% of GDP growth and half of the urban pop-ulation increase in 2019-30.
2. Smart cities that leverage technologies like 5G, cloud, big data, the Internet of Things (IoT), and artificial intelligence (AI) should help reduce traffic, crime, and pollution, and improve the quality of city life, greatly enhancing the capacity of the cities of tomorrow. We expect the number of megacities with populations similar to or larger than New York City (8mn) will reach 23 by 2030 (vs. 9 today).
3. Agricultural modernization through land reforms and the wider adoption of smart farming should boost labor productivity, enabling more rural workers to migrate to cities. We expect China's agricultural labor productivity to more than double over the next decade, freeing up more of the rural population for further urbanization.
Exhibit 4:Three key initiatives for China's Urbanization 2.0
Source: Morgan Stanley Research
The transition toward Urbanization 2.0 is also evident in a shift in policy focus: In our view, the urbanization strategy over the past two years has been shifting to focus more on promoting mega-clus-ters in advanced regions through a combination of (1) top-down initia-tives (such as Special Districts) to strengthen coordination across local administrative boundaries within clusters, and (2) more market-oriented approaches to avoid inefficiencies from limited demand for infrastructure and services in less populated, remote inland regions. This would be distinct from past regional rebalancing initiatives (including 'Western Development' since 2000 and 'Northeast Revitalization' since 2004), which were mainly aimed at reducing regional income gaps and relieving pressure from population inflows into developed coastal regions.
As evidence of this, the August 2019 meeting of China's Central Economic and Financial Affairs Commission – the country’s highest economic policymaking body – specifically mentioned that "hub cities and city clusters are becoming the main medium for growth and development" and that policies should enhance the economic and population capacity of these regions to facilitate the agglomeration of productive factors. This marked the first time in many years that policymakers have emphasized the role of large city clusters in advanced regions. Meanwhile, we have seen strong policy support for the three initiatives in the past two years.
BLUEPAPERM
MORGAN STANLEY RESEARCH 11
Source: AlphaWise, Morgan Stanley Research
Exhibit 5:Strong government support for Urbanization 2.0
Source: Xinhua, Morgan Stanley Research
BLUEPAPERM
12
In our view, China is poised to be the global leader in smart city and city cluster development. This is underpinned by:
l The world's longest and fastest high-speed rail (HSR) system, helping realize a 'one-hour living cycle' in city clusters.
l Advanced 5G planning for an industrial Internet of Things and high e-commerce pen-etration, which have laid a solid foundation for smart city development.
l Strong human capital, with millions of gradu-ates and rapid developments in vocational training meeting demand for talent in higher value-added manufacturing and service sectors.
Exhibit 6:China's unique advantages in Urbanization 2.0
Extensive High-Speed Rail Network
Ahead-of-the-Curve 5G Planning
Stronger Human Capital
High Penetration of E-commerce and Mobile Payment
350km/h – The World's Fastest
1 Hour Living Circles in City Clusters
5G capex of US$400bn, 2x 4G
300+ prefecture cities covered by 2020
18.4% E-retail Penetration Rate
US$23bn E-hailing Market Size
86% Mobile Payment Penetration Rate
109mn Average Population in 5 City
Clusters, which is......
Annual Average No. of College Graduates
No. of International Patents
R&D Expenditure
US: 3.7mn
Japan*: 1.0mn
UK: 0.8mn
Korea: 0.6mn
China*: 11.6mn US: 56,142
China: 53,345
Japan: 49,702
Germany: 19,883
UK: 5,641
~5x that of New York ~3x that of Greater Tokyo
US: US$513bn
China*: US$297bn
Japan: US$155bn
Germany: US$103bn
Korea: US$60bn
Source: Analysys Mason, Euromonitor, PWC Global Consumer Insight Survey 2019, CEIC, Haver, Morgan Stanley Research. Note: Data as of 2018 unless otherwise noted. Annual average number of college graduates: 2013-2017 data for China and 2012-2016 data for the other countries. 2013-2016 data for Japan due to missing data in 2012, R&D Expenditure: 2018 data for China and 2016 data for the others. 2018 data for the others.
A bright outlook for urbanization through 2030
Near-term counter-cyclical easing to boost investment needed for Urbanization 2.0: The pre-vailing US-China trade tensions are bringing down-side risks to growth trajectories both globally and in China (see Global Macro Briefing: Inching Closer to a Global Recession, 25 August 2019). In response, we expect China's policymakers to step up counter-cy-clical easing, with a focus on boosting urban invest-ment. Policy guidance at the 3Q Politburo and State Council meetings on September 4 suggested that infrastructure investment will be aimed at building up city clusters (commuter rail), renovating urban facilities (car parks, cold chains for food logistics), and next-gen mobile networks (5G), which are essen-tial investments for Urbanization 2.0.
We expect China's urbanization ratio to reach 75% by 2030, up from 60% today, translating into 220mn new urban dwellers. We expect total factor productivity to be sustained at a 1.6% CAGR through 2030 (vs. 1.9% in 2014-18), as labor productivity increases by 80% from today's level. Our growth accounting analysis suggests that 55% of the labor productivity increase will come from the agglomeration effect of smart supercities, with another 40% attributable to rural-urban migration and 5% coming from agricultural modernization on the back of land reforms and smart farming. We thus remain confident that China is poised to attain high-income status by 2025, with annual per capita income almost doubling, from US$9,450 today to US$17,800 by 2030.
BLUEPAPERM
MORGAN STANLEY RESEARCH 13
and continued opening-up efforts will help attract more foreign investment. The potential for foreign inflows into China’s equity and bond markets will provide support to China’s overall balance of pay-ments and the RMB exchange rate over the longer run (see EM Strategy and Economics: The Transformation of China's Capital Flows, 11 February 2019).
Near-term catalysts and milestones of Urbanization 2.0
We expect continued Hukou and land reforms, the rapid develop-ment of high-speed inter-city rail, electric vehicles, shared mobility, investments in 5G, AI and big data technologies, and rapid growth in vocational training to be key drivers of further urbanization over the next 3-5 years. Over the longer run, more advanced smart city fea-tures, such as driverless cars, auto-delivery drones, and fully inter-connected and automated home appliances should take productivity growth to the next level. To track the progress of China's Urbanization 2.0, we will look at several markers over the next 3-5 years:
Exhibit 7:Near-term catalysts to watch
Source: NDRC, State Council, Xinhua, Morgan Stanley Research estimates
Manageable financial stability risk from Urbanization 2.0 buil-dout: Contrary to market concerns that increased capex demand for Urbanization 2.0 will lead to renewed debt problems, we believe this risk is manageable considering (1) that there is less need for massive investment given China’s strong foundation of infrastructure today (for instance, we estimate the combined capex needed for digital infrastructure, high-speed rail and the smart grid – three key compo-nents of the smart supercity buildout – will be less than US$200bn per year in 2019-30, only about 10% of China's annual infrastructure FAI in the past five years); (2) more transparent funding given local governments' shift from shadow bank borrowing to bond issuance and a potential increase in private investment with market-oriented reforms; and (3) relatively higher asset quality in digital infrastruc-ture and HSR (which will concentrate more on eastern China and inter-city commuter rail). These factors, combined with continued shadow bank tightening, affirm our view that China will stabilize its debt to GDP ratio in the next decade (see China: Blue Paper Revisit: Why we are still bullish on China, 14 November 2017).
More opportunities for foreign capital: We believe China’s improving project funding structure, higher-quality investment opportunities in industry digitalization and smart city development,
BLUEPAPERM
14
What does Urbanization 2.0 suggest for industries and companies?
Access to Chinese equity markets has been largely synchronous with the country's urbanization and globalization processes, offering unique investment opportunities at different stages to foreign inves-tors.
l Stage 1: China joins global economy and top-down, policy-driven infrastructure investment benefits Old Economy* sectors: Key defining events of this period include China re-en-tering the WTO in 2001, the opening of A-share markets to for-eign investors through the QFII program in 2002, and the IPOs of major SOEs, such as PetroChina, Sinopec and CNOOC in 2000-01, and the Big Four banks starting from 2005.
l Stage 2: Transformation to a more consumption-driven model, with New Economy** focused investments paying off: This period is defined by heightened debt levels and ROE deterioration in SOE-dominated cyclical spaces, as well as the fast growth of the internet-led New Economy (Tencent becoming the largest Hong Kong listed stock by market capital-ization, and Chinese ADRs getting included in MSCI indices in 2016).
Exhibit 8:The investment-driven Old Economy outperformed in Stage 1 and the consumption-driven New Economy is outperforming in Stage 2
0
20
40
60
80
100
120
140
160
180
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
New Economy Rel. to Old Economy
Source: FactSet, Morgan Stanley Research. Data as of end-September 2019. MSCI China sector level sub-indices are used to construct performance index for New Economy (Consumer Staples, Consumer Discretionary, Media & Entertainment, IT, Healthcare) and Old Economy (Materials, Energy, Industrials.
The transition to Urbanization 2.0 should open up significant invest-ment opportunities across sectors. The distinction between the old and new economies at the sector level may not be so clear-cut any-more. Leveraging Morgan Stanley Research's sector teams, we iden-tify three key investment themes across 16 industries to create a list of the top 18 stocks that can provide exposure to Urbanization 2.0.
1. From a consumer to an industrial internet: We expect a major transition from consumer-based to industrial-based applications, given that consumer adoption is already very mature. With strong government support for the 5G rollout (one of the major enablers), key 5G infrastructure companies will benefit. Meanwhile, the development of the industrial internet will help support leading public cloud companies. As we expect the market size of IoT devices and software to triple by 2030, we believe that Asian tech players geared to IoT and 5G are poised to gain.
2. Digitalization of old-economy industries: We expect early movers in electric and autonomous vehicles, logistics companies with strong R&D investment, and utility players with a competitive edge in the smart grid to gain the most from the development of smart cities. In the financial sector, front-runners will likely include market-oriented banks and insurers with leading positions in top-tier cities and advanced technological capabilities. Smart farming should also benefit agribusiness entities with leading posi-tion and efficient products.
3. New lifestyles in smart supercities: Digitalization is on course to change household lifestyles, benefiting e-com-merce leaders and companies with a clear strategy for smart home appliances. Meanwhile, continued population inflows into supercities should support top online tutoring and vocational education players, as well as healthcare companies with good hospital networks and advanced technologies. We also expect railway construction compa-nies focusing on inter-city rail to benefit from the govern-ment's support for city clusters.
BLUEPAPERM
MORGAN STANLEY RESEARCH 15
Exhibit 9:Top stocks exposed to the Urbanization 2.0 theme
Source: Morgan Stanley Research
BLUEPAPERM
16
Unlisted companies starting to thrive in the new era of urban development: Urbanization 2.0 offers significant opportunities in terms of market growth, penetration and consolidation as well as new technological applications, in our view. We note that a number of unlisted Chinese companies have developed leading positions in related industries.
Where could we be wrong?
Although our base-case view is for the continued development of China's smart cities and city clusters, we acknowledge that there is no way to identify all risks or unintended consequences that could occur in the years to come. Having said that, some of the key risks that could deter the progress of Urbanization 2.0 include:
l Unintended social issues: The wide adoption of automation and AI could lead to larger-than-expected job losses, particu-larly in construction and lower value-added manufacturing sec-tors. While we believe vocational training and China's developing service sector could help mitigate this problem, fric-tional unemployment could still increase if high-tech adoption takes place at a faster pace. Meanwhile, a rise in capital produc-tivity as new technologies are introduced may lead to more severe income disparities, adding to the urgency of social secu-rity and welfare reforms (we discuss factors that could mitigate these effects here ).
Exhibit 10:Chinese unicorns that could benefit from Urbanization 2.0
Company name Valuation (US$bn) Industry Investors
Toutiao (Bytedance) 75 Artificial intelligence Sequoia Capital China, SIG Asia Investments, Sina Weibo, Softbank Group
Didi Chuxing 56 Auto & transportation Matrix Partners, Tiger Global Management, Softbank Corp.,
l Government-led investment in city clusters and smart cities could result in debt problems. Investment without coordi-nated regional planning and detailed risk-reward analysis could lead to insufficient utilization of infrastructure connectivity, inefficient debt buildup and wasted resources, hampering China's productivity growth (see a more detailed discussion here ).
l Land and Hukou reforms may stall. We believe land reforms hold the key to boosting large-scale farming and freeing up more of the rural population for urbanization, while Hukou reforms will help migrant workers gain access to the social security system and have a greater sense of belonging in cities. Hence, slower-than-expected reforms on these fronts could exert a drag on further urbanization.
l Privacy concerns regarding big data: As mentioned, one advantage China has in developing smart cities is fewer hurdles in consumer data collection. However, should social concerns over data privacy increase significantly, it may slow the pace of new technology adoption, which relies heavily on big data anal-ysis.
l Tech supply chain decoupling: Currently, the global tech value chain is still highly dependent on US components, partic-ularly high-end semiconductors such as 5G- and AI-related chips. The US government included Huawei and Hikvision on its Entity List in May and October, respectively, raising concerns that chip supply could lead to delays in 5G and/or smart city development in China (see a more detailed discussion here ).
BLUEPAPERM
MORGAN STANLEY RESEARCH 17
Macro Analysis
BLUEPAPERM
18
Overview Defying structural headwinds from slowing globalization and aging demographics, we believe China's next leg of urbanization – underpinned by highly productive city clusters, smart cities, and agricultural modernization – will help sustain productivity growth, lifting the country to high-income status by 2025.
Key forecasts – Urbanization ratio to reach 75% (60% in 2018), bringing in 220mn new city-dwellers
– Total factor productivity to be sustained at a 1.6% CAGR (1.9% in 2014-18), with labor productivity almost dou-bling
– Nominal GDP per capita to almost double to US$17,800 (from US$9,450 today) as China exceeds the high-in-come threshold by 2025
China's Path to Urbanization 2.0
From Urbanization 1.0 to 2.0
Since beginning its 'reform and opening up' process, China has learned from developed nations and has achieved outsized gains: Supported by favorable demographics, a high savings rate and the globalization of supply chains, China has experienced unprece-dented progress in urbanization. The urban population has grown fivefold, to nearly one billion people, from 172mn in 1978, fueling China's rise to middle-high income status as it became a hub of global manufacturing. With 33 cities of 5mn or more, China has far outpaced the US, which has just 9 metropolitan areas at this level today. We call this phase Urbanization 1.0.
However, the market is increasingly concerned that China's urbanization is reaching its limits, in view of the following bottle-necks:
l 'Big-city diseases' are on the rise, including traffic congestion, crime, pollution, high property prices, and inadequate educa-tional and healthcare resources.
l Policy constraints on labor mobility. Hukou (city resident permit) restrictions in larger cities and a fragmented social security system have resulted in insufficient insurance coverage for migrant workers.
l Shrinking rural manpower. The rural population is aging rap-idly. Combined with slower growth in migrant workers since 2010, this has raised questions about how much more urban-ization is possible.
l Trade protectionism has increased. The escalation in US-China tariffs over the past two years has pressured manu-facturing employment, leading to worries that supply chains could migrate away from China over the long term if tensions persist. This could shrink manufacturing job opportunities for migrant workers, particularly in lower value-added sectors.
In our view, China's next phase of urbanization seeks to over-come these obstacles through three initiatives – city clusters, smart cities, and agricultural modernization. While the closely linked city clusters should continue to reap the benefits of urban agglomeration while alleviating big-city problems, advanced tech-nology and data-driven smart cities can be optimized to accommo-date larger populations. Meanwhile, agricultural modernization on the back of land reforms and the wider adoption of smart farming should boost labor productivity, enabling more rural workers to migrate to cities (see detailed discussion in the next three chapters). In our view, these initiatives will be underpinned by digital infrastruc-ture and continued Hukou and land reforms.
BLUEPAPERM
MORGAN STANLEY RESEARCH 19
China's unique advantages
In our view, China is poised to be the global leader in smart city and city cluster development. China already has the world's lon-gest and fastest high-speed rail system. As an example, it only takes 45-60 minutes to travel from Shanghai to Hangzhou by HSR (number of trains per day: 80) – much faster than the 80-120 minutes to go from London to Birmingham (number of trains per day: 65), which is a comparable distance. We expect the length of the high-speed rail network to increase to 65,000km by 2030, up from 30,000km today, within which inter-city commuter rail length would increase 8.5x, to 17,000km. This would realize a 'one-hour living cycle' in city clusters and reduce logistics costs and delivery times.
Meanwhile, China's advanced 5G planning for an industrial Internet of Things and high e-commerce penetration have laid a solid foundation for smart city development. Albeit not the first to launch globally, Chinese telcos have emphasized industrial IoT applications, including smart cities, as opposed to the US and Korea where operators have
so far focused on consumer applications like fixed wireless and smartphones. Meanwhile, China's mobile payment penetration is the highest in the world at 86%, vs. a global average of 34%, as it leads the way toward a cashless society.
As China is an early adopter of new technologies and faces fewer hur-dles to doing so than its Western counterparts when it comes to issues such as consumer data collection, its innovators have a signifi-cant advantage in big data and AI, which could eventually expand today's digital payment and e-commerce ecosystems to connect every aspect of consumers' daily lives via the Internet of Things.
On the human capital front, China's large domestic talent pool has averaged 11.6mn graduates annually over 2013-18, far exceeding levels in developed countries, and young people are graduating from college at a rapidly increasing rate ( Exhibit 14 ). Meanwhile, rapid developments in vocational training and online education should help match migrant workers with jobs in higher value-added manu-facturing and service sectors.
Exhibit 11:Significantly shorter travel times in China's key city clusters than other developed countries
1.0
0.25
1.25
0.5
1.25
0.75
2.5
2.0
0
1
2
3
SF
- P
alo
Alto
(Tra
in)
HK
- S
henzhe
n C
BD
(HS
R)
NY
C -
Sta
mfo
rd (
Ca
ror
Tra
in)
Gua
ngzho
u -
Zho
ngsha
n (
HS
R)
Lo
ndo
n -
Birm
ing
ham
(Vir
gin
Tra
ins)
Shan
gha
i -
Ha
ngzho
u(H
SR
)
Tokyo -
Osaka
(Sh
inka
nsen)
Shan
gha
i -
He
fei
(HS
R)
~45KM ~60KM ~160KM ~400km
(Hrs) Comparing Travel Time Between Major Cities
Source: Morgan Stanley Research
Exhibit 12:5G – The key building block for smart cities
Source: ITU
BLUEPAPERM
20
Exhibit 13:China's number of new graduates per year far exceeds that of key devel-oped countries
621
768
979
3,733
11,591
Korea
United Kingdom
Japan*
United States
China*
Annual Average No. of Graduates from Tertiary Education (Thousand People)
Source: World Bank, Morgan Stanley Research. Note: 2013-2017 data for China and 2012-2016 data for the other countries. 2013-2016 average data for Japan because of missing data for 2012.
Exhibit 15:Higher penetration of e-commerce...
6.7%
8.6%
8.8%
10.0%
10.8%
11.0%
13.7%
17.0%
18.4%
24.0%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0%
Switzerland
Japan
France
Australia
Taiwan
Germany
US
UK
China
South Korea
Online Shopping/Total Retail Sales, 2018
Source: Wind, Euromonitor, Morgan Stanley Research
Exhibit 14:More and more young people are graduating from college
0%
5%
10%
15%
20%
25%
30%
35%
40%
1986 1990 1994 1998 2002 2006 2010 2014 2018
Tertiary Graduates / Births 22 Years Ago
Source: Ministry of Education, NBS, Morgan Stanley Research
Exhibit 16:...and mobile payment
34%
45%
46%
64%
86%
0% 20% 40% 60% 80% 100%
Global Average
Russia
Singapore
Hong Kong SAR
Mainland China
Mobile Payment Penetration Rate*, 2019
Source: PWC Global Consumer Insight Survey 2019. *Share of mobile payment in total purchase
BLUEPAPERM
MORGAN STANLEY RESEARCH 21
Dr. Yang also has a busy day. On the way to work, he opens his tablet, which displays his patients’ real-time vital signs thanks to smart sensors connected to the 5G network. Big data analysis predicts the intra-day progress of his patients’ conditions and automatically generates his schedule based on their urgency. At the office, a double espresso is waiting on his desk, as his smart watch has detected subpar sleep quality the night before and has instructed the coffee machine to ensure he gets enough caffeine before going into surgery.
As a tech analyst, Mrs. Yang needs to make a site visit to a company that makes industrial robots. She sets off for Guangzhou at 9am to visit the factory, a trip that takes less than 15 minutes by driverless flying taxi. On the taxi she uses VR glasses to watch a video on the use cases for the company's robots.
Arriving at the factory at 9:15, she tours the facility to understand the company's competitive advantages in technology and production. At noon she takes a driverless taxi to a business lunch at headquarters with management, engineers and industrial consultants. After lunch the company takes her to visit key suppliers in Foshan. Mrs. Yang sees three suppliers and then uses VR for a real-time site visit with another in Fujian Province. Her visit ends at 5pm, and she returns to Huizhou before 6 to pick Lily up at school.
Envisioning the potential of urban life in 2030To illustrate how smart supercities could improve most aspects of urban life, we depict the daily lives of Dr. and Mrs. Yang and their daughter Lily (aged 8) in 2030. The family lives in Huizhou, a satellite city next to Shenzhen. Dr. Yang is cardiovascular surgeon and Mrs. Yang is a stock analyst at a leading investment bank. They both work in the Futian District (Shenzhen's CBD) but moved to Huizhou five years ago to enjoy extra living space and a more relaxed lifestyle.
The Yangs get up at around 7am. The smart kitchen has breakfast ready by 6:50, including congee, sandwiches, fried eggs, and warm soy milk. After eating and getting dressed, they leave home at 7:30. The couple escort Lily to school nearby and then catch the 7:55 commuter train. They don’t need to buy tickets, as smart cameras with facial recognition and GPS in their phones detect where they get on and off the train and automatically bill them by e-payment. On the platform, a mobile app indicates which seats the station's cloud computing system has assigned them. They reach Futian at 8:20 and take short walks to their offices.
VR and 5G are enabling remote surgery
Source: Shutterstock
BLUEPAPERM
22
After their busy days, the Yangs want to relax. The community’s smart butler system that's connected to all their home appliances via the internet can do most of the housework. On her way home, the house management app on Mrs. Yang’s phone automatically changes her status to 'returning home'. The smart butler receives the order and starts tracking her distance from home. To save energy, it turns on the air conditioner 10 minutes before she arrives and switches on the lights in the living room 2 minutes before. The app also asks for her dinner preferences and gives orders to the smart kitchen, which is linked to the refrigerator and can automatically prepare cooking materials. The Yangs don’t need to go grocery shopping, as the refrigerator knows which foods need restocking and orders them online. Flying drones deliver food directly to the refrigerator within 20-30 minutes by opening a window and the refrigerator door using sensors and designated QR codes.
The family comes home at 7pm and has dinner together. Afterwards, Dr. Yang clears the table and puts the remains into the trash bin, which has an automatic sorting function and sends the waste to the community's trash center. The family decides to take a walk. The neighborhood is safe even after 9pm, as smart cameras detect anyone suspicious and remind security in the community to monitor them. By 10pm, everyone is asleep.
This scenario is meant only to showcase the potential of technologies already here or in development as per what we know today.
As a prominent heart surgeon, Dr. Yang receives requests from around the country to conduct consultations, attend conferences and perform surgeries. In the afternoon, he virtually scrubs in at an operating theater in Beijing, giving verbal guidance to the surgeons. The high bandwidth and near-zero latency of 5G-connected cameras enable Dr. Yang to observe the operation in real time, allowing him to give instructions and observe any subtle changes near the incisions.
Homework hasn't gone away in the future, but at least it's more interesting
Source: Shutterstock
What about Lily? Her school believes learning by immersion is more efficient than from a textbook. In her science class, Lily puts on VR glasses to watch how tornadoes form, then takes a quiz. She hits the right answers on her e-ink tablet, earning rewards points that she can exchange for stationery. Lily likes mathematics as well. Instead of having students memorize formulas from a book, her teacher has them play interactive VR games to learn the basics. The centralized educational system tracks each student's learning curve and designs an optimal curriculum. When Lily does her homework on the tablet, for example, she has more videos to watch than her classmate Xiaohua, as she got fewer questions right in class.
BLUEPAPERM
MORGAN STANLEY RESEARCH 23
Macro outlook toward 2030
Short term: Countercyclical easing to accelerate investment needed for Urbanization 2.0
Policymakers are stepping up countercyclical easing as persistent trade tensions put pressure on growth and the labor market. In our view, as muted private confidence will suppress the multiplier effect of any tax cut, additional easing will be in the form of direct public investment. Moreover, infrastructure investment in the current easing cycle will be geared towards city cluster buildup and next-gen mobile networks. This is evident by recent policy moves listed in Exhibit 17 .
Long term: Urbanization 2.0 to sustain productivity growth
We expect China's urbanization ratio to reach 75% by 2030, up from 60% today, largely continuing the average pace of 1.2ppt per year over the past five years (compared with an annual increase of 0.5ppt for the US during 1880-1950 and 0.9ppt for Japan during 1950-2010). This implies another 220mn rural residents will migrate to urban areas. Continued urbanization will drive China’s path to high income via a number of channels. First, migration from rural areas should help sustain return on capital (via increased labor supply) and encourage the continued investment of productive capital. Second, aggregate productivity will be supported as labor shifts to non-farm activities that tend to have higher productivity. Third, increased
Exhibit 17:Countercyclical public investments driving the next wave of urbanization
Date Government Entities Key Announcement
June, 2019The Ministry of Industry and Information
Technology • Granted 5G commercial licenses, enabling commercial launch as early as October
July, 2019 China's Politburo
• Speed up the construction of parking lots• Renovate dilapidated urban neighborhoods• Promote a new information network
August, 2019Central Financial and Economic Affairs
Commission
• Emphasized the role of large city clusters in advanced regions for the first time in many years;• Policy should enhance the economic and population capacity of these regions to facilitate the agglomeration of productive factors
September, 2019 State Council• Infrastructure investment should concentrate on transportation (particularly railways and car parks), energy (power and natural gas grid), utilities, and social welfare (healthcare, education and elderly care)
September, 2019The Ministry of Industry and Information
Technology
• Called for the build-up of 5G network and Internet of Things to promote the quality of manufacturing and service production
Source: Government websites
urban agglomeration will lead to efficiency gains and knowledge spillover, propelling China’s transition to high value-added activities. Finally, the accommodation of incremental urban residents requires more investment in infrastructure and housing, which may also ben-efit existing urban residents.
In this context, we expect China's total factor productivity growth will sustain at a robust 1.6% CAGR up to 2030 (compared with 0-1% for most developed countries since 2005, in particular a 0.4% CAGR for the US and 0.6% for Japan), contributing 36% of overall growth (compared with 30% since the 2008 global financial crisis). Meanwhile, we expect labor productivity (output per worker) to rise by 80% over the forecast horizon. Our growth accounting analysis shows that 40% of the labor productivity increase is achieved by rural residents migrating to cities, facilitated by freed-up rural labor and enhanced urban capacity; another 55% is attributed to productivity increases within cities, driven by rising agglomera-tion, capital investment (5G, HSR, etc.), and associated technological progress; finally, the remaining 5% is attributed to agricultural mod-ernization, as the fast rise in agricultural productivity is partly offset by a (desired) reduction in rural employment.
We thus reiterate our view that China will reach high-income status by 2025, i.e., its gross national income per capita will reach US$13,900, exceeding our calculated high-income threshold for 2025 (US$13,700) as defined by the World Bank, and pick up further to US$17,800 by 2030 (vs. US$9,450 today).
BLUEPAPERM
24
Exhibit 18:Sustained pace of urbanization towards 2030
2018:
59.6%
2030E:
75%
0%10%20%30%40%50%60%70%80%90%
100%
18
80
18
90
19
00
19
10
19
20
19
30
19
40
19
50
19
60
19
70
19
80
19
90
20
00
20
10
20
18
20
25E
China
Japan
US
Urbanization Ratio, % Projected
Source: UN, Morgan Stanley Research estimates
Will capex for Urbanization 2.0 lead to renewed debt problems?
A key market concern is whether increased capex demand for Urbanization 2.0 will cause a reaccelerated buildup in leverage, adding to financial stability risks. In our base case, we believe this risk is manageable, considering:
l Less need for massive investment in Urbanization 2.0: China's urbanization over the past 40 years has built a strong foundation of infrastructure, reducing the need for massive investment. For instance, we estimate the combined capex needed for digital infrastructure, high-speed rail and the smart grid – three key components of the smart supercity buildout – will be less than US$200bn per year in 2019-30, only about 10% of China's annual infrastructure FAI in the past five years.
l More transparent funding: Over the past 2-3 years, policy-makers have endeavored to improve the transparency of gov-ernment financing by gradually replacing local government financing vehicle (LGFV) loans and shadow bank financing with local government bond issuance. Meanwhile, we expect more
Exhibit 20:China to attain high-income status as early as 2025
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
1990 1995 2000 2005 2010 2015 2020E 2025E 2030E
High-Income Threshold
China: GNI per Capita (US$) Base Case
(2025) Bull Case (2023)
Bear Case
(2030)
2018
Source: NBS, E= Morgan Stanley Research estimates
Exhibit 19:China's productivity growth to remain robust in a global context
-0.5% 0.5% 1.5% 2.5% 3.5%
ItalyBelgium
United KingdomNetherlands
SwedenFrance
DenmarkCanadaFinland
AustraliaUnited States
JapanGermany
China (2019-30E)KoreaChina
Total Factor Productivity CAGR (during 2005-17 unless otherwise specified)
Source: OECD, NBS, Morgan Stanley Research estimates
Exhibit 21:China’s long-term growth model
Source: NBS, Morgan Stanley Research estimates
private investment in digital infrastructure with the liberaliza-tion of the telecommunications sector, which relies more on equity and corporate bond financing.
l Better asset quality: Railway investment will focus more on HSR in eastern China (where population density is high) and inter-city commuter rail to shorten daily commuting times, which could generate stronger investment returns. We believe the NPL risks of such high-quality projects are relatively low. Moreover, wide adoption of AI and big data analytics could help improve banks' asset allocation and risk controls, ensuring healthier credit growth.
Our China banks team estimates that the overall infrastructure interest burden is still manageable at about 1.6% of GDP in 2019 (vs. 1.59% for the US and 1.64% for Japan in 2018), and we believe policy-makers will continue to improve debt management through tight control of shadow bank financing. These factors combined echo our view that China will be able to stabilize its debt to GDP ratio in the next decade (see China: Blue Paper Revisit: Why we are still bullish on China, 14 November 2017).
BLUEPAPERM
MORGAN STANLEY RESEARCH 25
Bear case: Slower-than-expected Hukou and land reform, wasted investment and a lack of coordinated development in smart cities and city clusters, and larger-than-expected job losses due to automa-tion and a weaker-than-expected external environment could dampen urbanization momentum. In this case, we would expect the urbanization ratio to reach 70% by 2030, only 10ppt higher than today. Productivity growth could soften at a faster pace, reaching 1.2% per year during 2019-30. Consequently, China would approach, but narrowly miss, high-income status by 2030.
How could China mitigate the potential impact of automation on employment?As technology and automation become entwined in almost every aspect of city life, a key market concern is that job creation could suffer as capital inputs with higher productivity could reduce the need for labor inputs, particularly in construction and lower value-added manufacturing sectors. We concur that such a scenario is a potential challenge for further urbanization, but believe it could be somewhat mitigated by China’s developing service sector, vocational training, and further reforms to the social security system.
In the economic literature, automation typically affects labor demand in three channels:
1. Machines can completely replace labor in some sectors: History is not without episodes in which tech-enabled automation (such as mechanized looms in Britain in the nineteenth century) led to rapid and widespread labor replacement, causing, in some cases, unintended social issues for brief periods.
2. Higher labor demand in existing service sectors: A rise in capital productivity on the back of new technologies would give a boost to national income per capita. This would bring additional labor demand in existing service sectors that cannot be fully taken over by automation (such as barbers, fashion designers, health consultants, etc.).
3. Creation of new jobs alongside new technologies: The machines that replaced British textile workers also induced higher demand for engineers and maintenance workers, sales managers and delivery workers. In a more automated world, we also expect increased demand for jobs involving human interaction, creativity, and psychological counseling.
For most of the history of modernization, the latter two forces have combined to offset the first over the long run, as evident in the relatively stable share of household consumption in world aggregate output. In China, we believe the following factors could to some extent mitigate the risk of massive job losses and income disparity in the next decade:
l Strong growth potential in services: In China, the service sector only accounts for 52% of GDP today, well below 68% in Japan and 80% in the US. As international experience suggests that the service sector's share of GDP increases as per capita income rises, we expect the ratio in China to reach 60% by 2030, helping absorb job losses from the manufacturing sector. Indeed, over the past five years, the number of people employed in the service sector increased by 63mn, more than three times greater than job losses of 17.8mn in the secondary sector.
l Rapid development in vocational training business: Our education analyst, Sheng Zhong, believes that the overall vocational education and training market will grow 3x, to US$300bn in 2030. This will be supported by preferential government policies and binding subsidies. In our view, this could help displaced workers find jobs in other sectors (especially high-end, experience-driven services), reducing risk of frictional unemployment.
l Further social security and welfare reforms:Policymakers have been easing Hukou restrictions and widening access to the social security system, which could provide some basic financial support to people undergoing frictional unemployment. That said, this combined with an aging population could add to the burden on the social security system. To this end, the government has initiated SOE asset transfers over the past two years to replenish the social insurance fund. We estimate that the further transfer of SOE equity to the social insurance fund (possibly up to 30% of total SOE equity vs. 10% now) and higher SOE dividend payout ratios (up to 50% vs. 33% now) could make up for the annual average social insurance deficit of Rmb577bn in 2014-19.
Bull-bear scenariosBull case: If China can accelerate Hukou and social security reforms and complete the buildout of smart cities and city clusters at a faster pace, the country's urbanization ratio could reach 78% by 2030 (vs. our base case of 75%). Against this backdrop, we would expect total factor productivity growth to remain elevated at 2.0% during 2019-30, contributing 40% of overall growth (compared with 30% since the 2008 global financial crisis). Consequently, the country could reach high-income status by as early as 2023, and gross national income per capita could reach US$21,800 by 2030.
BLUEPAPERM
26
Exhibit 22:China's services share of GDP to rise further with increased per capita income
20%
30%
40%
50%
60%
70%
80%
0 10000 20000 30000 40000 50000 60000
China
Germany
Japan
Korea
United States
Share of services as % of GDP vs per capita income (USD)
Source: CEIC, Haver, Morgan Stanley Research. Data as of 1962-2018 for China and the US, 1965-2017 for Japan, 1972-2018 for Germany, and 1970-2018 for Korea.
Exhibit 23:State asset transfer to help address shortfall in social insurance bal-ance amid aging population
34
36
38
40
42
44
46
48-1500
-1300
-1100
-900
-700
-500
-300
-100
100
300
2006
2008
2010
2012
2014
2016
2018
20
20E
20
22E
20
24E
20
26E
20
28E
20
30E
% Rmb bn China's Demography and Social Insurance Solvency
True Balance of Social Security Fund
Age Dependency Ratio-RS
Source: CEIC, Morgan Stanley Research. E = Morgan Stanley Research estimates
BLUEPAPERM
MORGAN STANLEY RESEARCH 27
Overview The combination of extensive high-speed railways and policy support has fostered the development of five key city clusters in China: Yangtze River Delta; Jing-Jin-Ji Area; Greater Bay Area; Mid-Yangtze River Area; and Chengdu-Chongqing Area. We believe city clusters are central to boosting productivity as they amplify the effi-ciencies of urban agglomeration while alleviating big-city problems. To ensure the success of city clusters, we expect additional policy reforms to enhance regional integration, more extensive transportation networks to foster a 'one-hour living circle' within clusters, and smart logistics systems to improve factor mobility.
Key forecasts The five key city clusters will account for about 75% of GDP growth and half of the urban population increase in 2019-30. The average population size per cluster should reach 120mn by 2030 (vs. 109mn today), close to Japan's current population of 127mn.
Initiative #1: City Clusters
Why are city clusters important?
A shift in policy focus from regional rebalancing to city cluster development: As mentioned, China's top leadership put forward a new urbanization strategy in August with a focus on (1) strengthening coordination across local administrative boundaries within clusters and (2) avoiding inefficiencies from limited demand for infrastructure and services in lesser-populated, remote inland regions. This is in sharp contrast to past regional rebalancing initiatives (such as 'Western Development' since 2000 and 'Northeast Revitalization' since 2004), which aimed at reducing regional income gaps and relieving the pressure of population inflows into developed coastal regions.
In our view, city cluster development will bolster productivity growth: According to the theory of agglomeration benefits, the bigger the city, the more productive it is likely to be, as it can better match workers to jobs, facilitate the spread of ideas, shorten supply chains by gathering companies together, and generate more syner-gies across different sectors. Recent OECD studies suggest that for each doubling in population size, the productivity level of a city increases 2-5%. In our view, the benefits of agglomeration can be amplified by well-integrated city clusters, which can also alleviate problems such as traffic congestion and high property prices that are often found in large cities. A study published in 2017 showed that in China, counties enjoyed a 6% boost in productivity from being
included in the Yangtze River Delta (see Peixin Li, Chen Wang, Xueliang Zhang [2017] Did city cluster development help improve labor productivity in China? Volume 22, 2017, Journal of the Asia Pacific Economy).
What are the unique advantages of China's city clusters?
Despite the theoretical advantages of city clusters, in reality there are only a few established ones in the world today. In China, since the 13th Five-Year Plan (2016-20) identified 19 city clusters, five have stood out – (1) the Yangtze River Delta; (2) the Jing-Jin-Ji Area; (3) the Greater Bay Area; (4) the Mid-Yangtze River Area; and (5) the Chengdu-Chongqing Area ( Exhibit 24 ). We look at the advantages of these clusters in three respects:
1. Large populations: The average population size of the top five clusters reached 109mn in 2018 – five times greater than the New York Metropolitan Area, almost three times higher than Greater Tokyo, and larger than most European countries ( Exhibit 25 ). This suggests significant potential for agglomeration benefits, in our view. Meanwhile, our AlphaWise survey shows brighter job prospects and higher wage growth in the five clusters than in other areas in China (see Exhibit 26 and Exhibit 27 ), reflecting the strong attraction of these areas to new migrants.
Source: NDRC, Morgan Stanley Research. Cities in pink are cities currently with urban populations close to or above 8mn, and cities in yellow are emerging mega cities in which we expect the urban population to exceed 8mn by 2030.
2. Strong connectivity: China has the world's longest high-speed rail (HSR) network and the fastest operating speed (350km/h). This has cut travel time on major routes by more than half. Meanwhile, the average distance per trip has been decreasing since 2013 ( Exhibit 29 ), suggesting more frequent and short-distance travel within city clus-ters. We believe the rail network enables trips of less than one hour within most city clusters. Such connectivity boosts productivity growth and enhances income conver-gence among hub and satellite cities, as shown by the experiences of the Greater Bay Area and the Yangtze River Delta ( Exhibit 30 ).
3. Continued Hukou reforms: In China, possession of Hukou (city resident permit) grants access to social security sys-tems, such as pensions, healthcare and education in that city. While Hukou control in megacities like Beijing and Shanghai has remained tight, since April 2019 policymakers have completely removed Hukou restrictions for cities with populations of 1-3mn (such as Zhuhai and Zhenjiang) and loosened restrictions in cities with populations of 3-5mn (such as Hefei, Nangtong and Huizhou). We believe con-tinued reforms will create room for further population inflows to smaller cities, particularly those in key city clus-ters given brighter job prospects there.
BLUEPAPERM
MORGAN STANLEY RESEARCH 29
Exhibit 29:More frequent and short-distance travel within city clusters
Exhibit 30:Greater infrastructure connectivity has led to robust income conver-gence
y = -0.00071x + 0.124 R² = 0.50
6%
7%
8%
9%
10%
11%
12%
13%
10 30 50 70 90
Real
CA
GR
du
rin
g 2
010
-2018
GDP per capita in 2009, RMB
Source: CEIC, Morgan Stanley Research
Exhibit 27:...and higher wage growth in China's five key city clusters
56
46
39
31
39
26
51 48
37 37 36 35
Yangtze RiverDelta
ChengduChongqing
Jing-Jin-Ji Other GuangdongBay Area
Mid YangtzeRiver
Net
Sc
ore
(%
Up
Min
us
% D
ow
n)
2019 Survey: Income Growth Perception by City Cluster
Current vs. 12 months ago Next 12 months vs. Current
Source: AlphaWise, Morgan Stanley Research
Exhibit 28:China's high-speed rail is the fastest in the world
240
250
300
300
300
320
320
320
320
350
200 250 300 350
US
Russia
Korea
Taiwan
Italy
Japan
France
Spain
Germany
China
Regular Operating Speed, Km/h
Source: CIA, Morgan Stanley Research. Data as of 2018
Exhibit 25:China's top five city clusters are larger than the Greater Tokyo Area, the New York Metropolitan Area and most EU countries
20
44
46
59
65
67
82
109
0 20 40 60 80 100 120
New York Metropolitan
Greater Tokyo Area
Spain
Italy
France
U.K.
Germany
Avg of China's Five Key Clusters
Population Size (people mn)
Source: CEIC, Haver, Morgan Stanley Research. Data as of 2018
Exhibit 26:Brighter job prospects...
72 74
60
68
53 53
76 74 73 72 70 66
Mid YangtzeRiver
Yangtze RiverDelta
Jing-Jin-Ji GuangdongBay Area
ChengduChongqing
Other
(% R
ati
ng
'O
pti
mis
tic
')
Perception of Job Prospects in the City: 2019 vs. 2017
2017 Survey 2019 Survey
Source: AlphaWise, Morgan Stanley Research
BLUEPAPERM
30
Fact sheet on China's top five city clustersEconomic structure: The economic output of the top five city clusters was US$7.6trn in 2018, accounting for 54% of China’s GDP. In the Greater Bay Area, Jing-Jin-Ji and the Yangtze River Delta, the share of the service sector in GDP is higher than the national average, while the Mid-Yangtze River Area and the Chengdu-Chongqing Area are still more focused on the industrial sector.
Pillar industries in city clusters:
l Yangtze River Delta – finance, manufacturing (heavy machinery and auto), ITl Jing-Jin-Ji – manufacturing (general equipment, steel, aviation), culturall Greater Bay Area – finance, tech innovation, manufacturing (home appliances, electronics), services (professional and
Population growth: In 2010-18, population growth was strongest in the Greater Bay Area (1.4% CAGR), followed by Jing-Jin-Ji (1.1%), Yangtze (0.9%), Cheng-Yu and Mid-Yangtze (0.6% for both), and all were above the national average of 0.5%. However, population flows within hub and satellite cities show a mixed picture. In the Greater Bay Area, Cheng-Yu and the Mid-Yangtze River Area, hub cities accounted for more population inflows in 2015-18 than in 2011-14, and hub cities in the Yangtze River Delta have still maintained their attractiveness, taking up 57% of new inflows in 2011-18 as tier-2 cities, such as Hangzhou and Nanjing, have taken over the role of Shanghai (which has a strict Hukou policy) in attracting new migrants. In contrast, satellite cities in Jing-Jin-Ji are more attractive than its hub cities, as Beijing has targeted relocating 'non-capital functions' to nearby cities.
Exhibit 31:Overview of top five city clusters, 2018
Yangtze River
DeltaJing-Jin-Ji Greater Bay Area
Mid-Yangtze
River
Chengdu-
Chongqing
Five
Clusters*National*
Area (000' sqkm) 213 183 56 343 240 1,035 9,597
Population (mn) 154 89 71 127 100 541 1,403
Population Density (ppl/sqkm) 723 485 1,268 369 418 522 146
GDP (USD bn) 2,672 1,150 1,625 1,245 860 7,552 13,880
GDP per capita (USD) 17,351 12,932 22,913 9,817 8,590 13,965 9,891
Major Industries
• Finance• Internet Service• Shipping• Auto & Parts• Heavy Machinery
• Shipping• Capital Equipment
• Cultural Industry• Aerospace
• Finance• Tech & Innovation• Professional Service• Shipping• Entertainment• Home Appliance
• Heavy Industry• Capital Equipment
• Consumer Goods
• Capital Equipment
• Tourism• Food & Beverage
- -
Source: CEIC, Haver Analytics, local government websites
BLUEPAPERM
MORGAN STANLEY RESEARCH 31
Exhibit 32:GDP structure, by city cluster, 2018
0%
20%
40%
60%
80%
100%
Gre
ate
rB
ay
Are
a*
Jin
g-J
in-
Ji
Ya
ng
tze
Na
tio
nal
Ave
rag
e^
Ch
eng
-Y
u
Mid
-Y
ang
tze
Tertiary Secondary PrimaryGDP Structure, % of GDP (2018)
Source: CEIC, Morgan Stanley Research. Note: *2016 data for Hong Kong and Macau. ^National average excludes Hong Kong and Macau
Exhibit 33:Population growth of top five city clusters
0.5%
0.6%
0.6%
0.8%
0.9%
1.1%
1.4%
0.0% 0.5% 1.0% 1.5%
Total
Mid-Yangtze
Cheng-Yu
Major Clusters
Yangtze
Jing-Jin-Ji
Greater Bay Area
Annual Population growth, 2011-2018
Source: CEIC, Morgan Stanley Research
Exhibit 34:Percentage of population inflows taken by hub cities
81%
57% 61%
83%
38% 42%
57%
83%
113%
65%
0%
20%
40%
60%
80%
100%
120%
Jing-Jin-Ji Yangtze Greater BayArea
Cheng-Yu Mid-Yangtze
2011-2014 2015-2018
% of population inflow taken by hub cities
Source: CEIC, Morgan Stanley Research
Exhibit 35:Contribution to population increase, by city in the Yangtze River Delta
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2011-2014 2015-2018
Satellite Cities
Suzhou
Nanjing
Ningbo
Hangzhou
Shanghai
Hefei
Contribution to
population increase
Source: CEIC, Morgan Stanley Research
BLUEPAPERM
32
How can city clusters be promoted further?
City clusters are more than just large populations and integrated infrastructure. A common concern is that the blind development of city clusters may lead to debt problems and wasted resources, rather than productivity growth. In our view, the keys to reaping the benefits of city clusters are: (1) freer factor/labor mobility, and (2) better coor-dination among cities. In this context, we believe further reforms to ensure regional integration could be supportive, as would more advanced railway networks to enhance inter-city and intra-city com-muting, and smarter logistics systems:
1. Further policy reforms to enhance regional integration
A key challenge to city cluster development is the segmented social security system and independent government planning across cities, which has not only deterred labor mobility but also led to duplicate construction and overcapacity. We thus believe future policy reforms should focus on:
l Standardizing social security systems across cities: While policymakers have gradually reduced Hukou restrictions for smaller cities, access to city-specific pension, medical insurance, and education systems still requires lengthy administrative pro-cedures for inter-city migrants, which hinders labor mobility. This suggests a need for integrated social security systems (e.g., in the form of a universal residence passport) in city clusters, with the current degree of integration still quite limited (for example, residents of Shenzhen, Dongguan and Huizhou can claim medical insurance in 18 hospitals across the three cities).
l Establishing a public data-sharing system within clusters: To facilitate an integrated social welfare system, a transparent public data-sharing system is necessary. The shared database of social benefits, medical records, and education progress would make it easy for people to enjoy the same public services in any city within a cluster. Meanwhile, an integrated public database would enable one-stop information provision in a single govern-ment mobile app, such as traffic conditions, public events, shopping promotions, and ticket discounts at attractions in dif-ferent cities, helping people plan trips in the cluster.
l Forming a more coordinated regional development strategy: To maximize synergies across cities, local govern-ments should work together to ensure more proper industry distribution in a region, based on cities' respective strengths, so as to reduce repetitive production and competition while increasing cooperation across the supply chain. As a case in
point, the blueprint for the Shenzhen Demonstration Pilot Zone (August 2019) included several institutional frameworks for regional coordination, such as integrated planning for Shenzhen-Dongguan-Huizhou and a special coordination com-mittee for the Shenzhen-Shantou Cooperation Zone.
2. More advanced railway network
Although China already has the world's longest and fastest high-speed rail system, it's not finished yet. Our industrials analyst, Kevin Luo, expects the length of the HSR system to reach 65,000km by 2030, up from 30,000km now. Meanwhile, the HSR mix will likely improve as Kevin estimates that half of HSR lines to be completed by 2030 will be in eastern China, up from 33% in the past five years.
The team also expects the length of the inter-city commuter rail net-work to expand to 17,000km by 2030 from just 2,000km today, while the length of metro lines will reach 15,000km from 5,800km in 2018. This will further enhance inter-city connections and sub-urban commuting, bringing more population flows to suburbs and satellite cities with lower housing prices and more living space, easing congestion in city centers (see 3a. Transportation for more details).
3. Smarter logistics system
A smarter logistics system enabled by IoT, big data and a better trans-portation network would help lower logistics costs, shorten delivery times and address last-mile delivery, facilitating factor mobility in city clusters. For instance, big data analytics could help predict customer demand and automatically arrange for product to be shipped in advance to the warehouses/stores that are closest to customers, and automated sorting lines and industrial robots could make warehouse sorting and packing more efficient. Meanwhile, delivery times could be reduced, with trains gradually replacing trucks in long-haul trans-portation. With the development of technology, the adoption of unmanned drones/trucks would not only reduce efficiency losses by delivering goods around the clock, but also provide low-cost delivery options to easily reach low-density suburban areas.
In this context, transportation analyst Qianlei Fan believes average delivery times could be shortened to 24 hours nationwide by 2030 (vs. 2-4 days today) and 12 hours within each city cluster (vs. 24 hours in the Yangtze River Delta today). Meanwhile, logistics costs as a per-centage of GDP could come down to 10% by 2030 from 15% today, and express volumes could reach 300bn deliveries per year as com-pared to 50bn today (see 2b. Logistics for more details).
BLUEPAPERM
MORGAN STANLEY RESEARCH 33
Outlook: Increasing economic importance of five key clusters
We expect China's top five city clusters to account for 75% of China's real GDP growth in 2019-30 (vs. 65% in 2014-18), with annual real GDP and total factor productivity growth sustaining at around 6.0% and 2.0%, respectively – higher than the national averages of 4.6% and 1.6%. GDP per capita in these clusters, which was US$13,000 in 2018 and has already exceeded the high income threshold of US$12,300 (as of 2018), will almost double to US$25,000 by 2030, we project. Meanwhile, these clusters will likely absorb 50% of the incremental urban population, with the total urbanization ratio picking up from 67% currently to 80%, similar to the level in the US and Japan in the early 2000s. We expect the average population size of these five city clusters to reach around 120mn by 2030 (vs. 109mn today), close to Japan's total population size of 127mn today.
Exhibit 36:Productivity in the five key clusters to hold up...
2.3%
2.0% 1.9%
1.7%
1.0%
1.2%
1.4%
1.6%
1.8%
2.0%
2.2%
2.4%
2014-18 2019-30E
Five key City Clusters Nationwide
TFP Growth, CAGR %
Source: NBS, Morgan Stanley Research estimates
Exhibit 37:...helping double average income despite a high starting point
13,488
25,500
9,452
18,292
0
5,000
10,000
15,000
20,000
25,000
30,000
2018 2030E
Five key City Clusters
Nationwide
Per Capita Income, USD
Source: NBS, Morgan Stanley Research estimates
Case study: Development of the Yangtze River Delta
The Yangtze River Delta – comprising Shanghai, Zhejiang, Jiangsu, and Anhui – has led city cluster development in China. It has the highest GDP and largest population of the five key city clusters, launched high-speed rail as early as 2010, and possesses competitive advantages in diversified industries. It contains China's key financial and business center (Shanghai), the hub of internet giants (Hangzhou), the nation's key manufacturing hub for autos and heavy machinery, and a strong shipping industry.
Over the past two years, policymakers have intensified the push to enhance the region's coordinated development. In 2018, the local governments of Shanghai and three provinces in the region estab-lished the Yangtze River Delta Regional Cooperation Office, and issued a three-year action plan (2018-20) for the region's integration. This year, Beijing further promoted the region's development as a national strategy in the 2019 Government Work report, and the CPC Central Committee reviewed the central government's blueprint for the region in May, which should be published relatively soon.
In our view, policymakers will likely make more efforts on the fol-lowing aspects to enhance integration within the region:
l Unified social benefits system, which would enable residents to enjoy the same pension and healthcare services in any city within the cluster. As an initial step, the Yangtze River Delta development plan mentions the promotion of easy pension transfers within the cluster.
l Integrated public database to increase information transpar-ency. Today, the region has set up an integrated government website covering 14 cities, which provides corporate registra-tion services, pre-booking for marriage registration, and guide-lines for various cross-city administrative work, such as pension transfers and medical insurance.
l Standardized transportation network, which will facilitate travel within the cluster. The region plans to unify the metro mobile payment system in nine cities by end of this year, so that people can use the same QR code as e-tickets to take metros in different cities.
l Coordinated industrial development to reduce repetitive pro-duction and increase synergies. This could be facilitated by better industrial information sharing within the cluster.
l Simplified administrative procedures for companies doing business in different cities in the region.
BLUEPAPERM
34
Overview Data-driven smart cities can be optimized to accommodate larger populations. Specifically, we expect smart cities to enable faster commuting, provide a safer living environment by reducing crime and traffic accidents, lower pollution through the use of EVs and green energy sources, and make cities more livable via smart house-hold appliances and improved education and healthcare. We believe the pace of smart city growth in China will be faster than in many other countries, given government efforts to boost the development of fiber networks, 5G, big data, AI, and edge computing.
Key forecasts We expect the smart city buildout to boost cities' capacity to accommodate more population, lifting China's number of megacities with populations similar to or larger than New York City (8mn) to 23 by 2030 vs. 9 today.
Initiative #2: Smart Cities
What benefits can a smart city provide?
A key concern about urbanization is whether the growth of cities will reach a limit. This concern has increased in view of negative pop-ulation growth in Beijing and Shanghai over the past two years. According to a 2011 McKinsey report, the key hurdles to develop-ment are a lack of skillful planning and management to handle a growing population. This could foster various big-city problems, such as congestion, higher crime rates, severe competition for social resources, and pollution, outweighing scale benefits and leading to a deterioration in quality of life.
Exhibit 38: China's future smart cities have the potential to be faster, safer, greener, and more livable
Source: Morgan Stanley Research
That said, we believe the development of smart cities can effec-tively improve city management and boost the capacity of large cities to accommodate more people. In other words, large cities today have the potential to be even larger over the next decade. Specifically, we believe a well-developed smart city powered by advanced technologies (such as 5G and IoT, cloud computing, AI and big data analysis) could enable faster commutes, safer cities with reduced crime rates and traffic accidents, and better living environ-ments with smarter household living, easier access to public resources, and less pollution in the coming decades.
BLUEPAPERM
MORGAN STANLEY RESEARCH 35
1. Faster commutes
Traffic congestion has long been a headache for city dwellers, and the degree of congestion tends to increase with the size of the urban pop-ulation ( Exhibit 39 ). Amap’s 2018 Traffic Analysis report shows that the average weekday commute in China’s top ten cities (by urban population size) is 79 minutes, with 45% of the time spent in conges-tion, and we estimate the nationwide average commute time is about one hour. That said, this condition could significantly improve in the next decade through smarter traffic control systems, shared mobility, and auto driving. Moreover, a reduced need for people to drive suggests they will have more time for work and entertainment, increasing productivity in the economy.
l Smarter traffic control: A smart traffic control system could help optimize traffic flows by adjusting traffic lights in intersec-tions, providing route suggestions, and helping to locate parking spaces in a timely fashion. The rapid development of 5G and IoT is making this possible. Hangzhou is the first pilot city in China to adopt a smart traffic control system – its 'City Brain' project conducts real-time monitoring of traffic flows with camera systems and sensors. AI can then optimize traffic signals at over 100 intersections to reduce congestion and pri-oritize ambulances and fire engines. The City Brain system can also detect traffic accidents immediately and improve the effi-ciency of traffic police. In Shanghai, a smart parking network launched by Huawei will enable drivers to locate available parking lots nearby, reducing unnecessary driving.
l Shared mobility: Online taxi-hailing platforms should help enhance asset efficiency and reduce the number of cars on the road, alleviating traffic pressure. Our AlphaWise survey in 2Q19 showed that the percentage of surveyed households intending to buy a private car irrespective of taxi-hailing apps has declined in both large and smaller cities as compared to the 2017 survey ( Exhibit 40 ). AI and smart tariff systems could also better match customers and drivers, reducing wait times for cars.
l Autonomous vehicles: The low latency of 5G means IoT-based autonomous driving is not a distant dream. Industry body SAE sets six levels of autonomous driving, from no automation (Level 0) to full automation (Level 5) ( Exhibit 41 ). In our view, China will likely adopt autonomous vehicles at a faster pace than other countries owing to government support, rapid infra-structure buildup, and the higher possibility of commercializa-tion given the significant market size. As a case in point, in February 2019, Hubei Province started work on the country's
first 5G-based smart highway project to support self-driving cars. In this context, our automobile team forecasts that 20% of the passenger vehicles sold in 2030 will feature L4 or L5 levels of autonomous driving vs. 0% today.
Exhibit 39: Traffic congestion tends to increase with city size
Shanghai
Chongqing
Beijing
Shenzhen
Tianjin
Guangzhou
Chengdu
Wuhan Suzhou
Dongguan
25%
30%
35%
40%
45%
50%
55%
0 2 4 6 8 10 12 14 16 18 20 22 24
Tim
e S
pe
nt
on
Co
ng
es
tio
n
as
a %
of
To
tal C
om
mu
tin
g T
ime
Urban Population (Mn)
Source: Amap 2018 Traffic Analysis Report, NBS, Morgan Stanley Research
Exhibit 40: Shared mobility could dampen willingness to buy new cars
63
70
61 66
Tier 1-2 Tier 3 or lower
% Intending to buy a private car irrespective of taxi-hailing app
2017 survey 2019 survey
Source: AlphaWise, Morgan Stanley Research
Exhibit 41:Six levels of autonomous driving
Source: Shutterstock
BLUEPAPERM
36
4. More livable environment
Another challenge of big cities is quality of life, including the poten-tial for reduced personal time as a consequence of busy work sched-ules, strong competition for high-quality public resources, and rising levels of environmental pollution. We believe smart cities can help address these issues through:
l Smarter home appliances: We are already able to purchase goods and services online and remotely control household appliances via mobile apps. The wider usage of big data analysis and IoT could provide more customized services. For instance, a smart housing management system could monitor air quality and dust and decide when an air purifier or robot vacuum cleaner should be activated; a smart refrigerator could auto-matically throw away expired food, order staple foods (such as eggs and milk) online, and provide cooking materials to a smart kitchen; and a smart washing machine would sort clothes into different batches by fabric and decide on the most suitable washing mode. Moreover, the future development of autono-mous delivery associated with e-commerce could further enhance the online shopping experience.
l Smarter healthcare system: While AI and big data analytics could better match healthcare resources with patients, create cloud hospitals, and improve the accuracy of diagnosis, 5G and IoT could also enable remote surgery, breaking geographic con-straints on high-quality medical resources. Cities including Ningbo and Guangzhou have started to run online medical plat-forms for citizens, to perform pre-diagnosis and provide access to suitable healthcare services.
l Smarter education system: AI and big data analysis can better track a student's mastery of subjects and provide more person-alized assignments as well as suitable interactive online courses.
2. Safer society
Crime and traffic accidents are two of the main safety concerns in cities. Today, the development of cashless payment in China has already helped reduce cases of robbery and theft. As we have fully entered the digital age, cybersecurity is becoming increasingly impor-tant, and AI and big data analysis could help identify wire fraud by detecting unusual transactions and login locations. Meanwhile, mas-sive adoption of smart cameras with video analytics can promptly verify people’s identities and locate suspects, and AI and big data ana-lytics can help predict locations of possible crimes. We also expect there to be fewer traffic accidents in smart cities. While more intelli-gent traffic control systems should help optimize traffic flows, the wide adoption of interconnected driverless cars based on 5G and IoT could make timely adjustments based on real-time road conditions and reduce human error.
3. Greener life
We believe the wider adoption of electric vehicles and green energy sources can effectively reduce air pollution. To this end, our automo-bile team forecasts that one-third of passenger vehicles sold in China in 2030 will be electric, vs. only 4% in 2018. Meanwhile, given the Chinese government’s commitment to combating global climate change, our utility team expects the share of non-fossil fuels (hydro, nuclear, wind and solar) in total power generation to increase to 40% in 2030 from 30% in 2018. The team also believes that investment in the smart grid could increase 2.64x, to US$80bn during 2021-30, to integrate distributed renewable energy and electric vehicles and ensure the safety and reliability of the power system.
BLUEPAPERM
MORGAN STANLEY RESEARCH 37
Enablers of smart cities in China
In our view, smart cities could be developed more quickly in China than in many other countries owing to government support. Since policymakers first raised the concept of smart cities in 2012, more than 500 cities have initiated city planning toward this end. Meanwhile, strong policy support has been provided to build the key elements of smart cities, including an extensive fiber network, 5G, big data, AI, and edge computing.
Policy support
In 2012, the Ministry of Housing and Urban-Rural Development of China (MOHURD) released a notice that it was launching the Smart City Pilot Project. MOHURD defined smart cities as a new model of
urbanization, construction and management that integrates advanced technologies, information resources and business applica-tion systems. MOHURD pointed out that the construction of smart cities is an important way to realize the State Council’s guidance of innovation-driven development and urbanization in China. Qualifying cities were encouraged to take advantage of the opportu-nity and apply for the pilot project to promote industrial transforma-tion and development and improve the level of city management and services.
Starting from 2013, MOHURD has announced three tranches of trial smart cities, comprising 290 cities and districts. In 2014, eight minis-tries together published their guidance on smart city development, setting a goal to complete the construction of several characteristic smart cities by 2020, which should be the starting point for future smart city clusters.
Exhibit 42: Key trial smart cities
Source: MOHURD, Morgan Stanley Research
BLUEPAPERM
38
Exhibit 43: Key policies issued by the central government
Source: Ministry of Housing and Urban-Rural Development (MOHURD), National Development and Reform Commission (NDRC), Ministry of Land and Resources (MLR), Ministry of Transport (MOT), Ministry of Industry and Information Technology (MIIT), Ministry of Natural Resources (MNR), Ministry of Science and Technology (MST), Ministry of Public Security (MPS), Ministry of Finance (MOF); eight ministries include NDRC, MIIT, MST, MPS, MOF, MLR, MOHURD, MOT
BLUEPAPERM
MORGAN STANLEY RESEARCH 39
Infrastructure: Fiber and 5G
China is one of the most fiberized countries globally, especially con-sidering its large geographical size and relatively low population den-sity as compared with countries such as Japan and Korea. As of June 2019, according to MIIT, total optical fiber length reached 45mn kilo-meters. Fiber-to-the-home/office (FTTH/O) subs reached 400mn, representing over 90% of total broadband subs in China. Many smart home applications, such as connected home appliances, surveillance and entertainment, require extensive FTTH penetration. In China, almost all of the mobile base transceiver stations (BTS) are con-nected with fiber for transmission, which is essential to providing 4G/5G services.
5G will offer unprecedented bandwidth, low latency, fast mobility, and high capacity, which will support a set of brand new applications on top of a better experience using traditional smartphone connec-tions. Based on the network capability requirement, 5G applications can be classified into three key categories:
l Enhanced mobile broadband (eMBB): enhanced indoor and out-door broadband, enterprise collaboration, augmented and vir-tual reality
l Ultra reliable low latency communications (URLLC): autono-mous vehicles, smart grids, remote patient monitoring and tele-health, industrial automation
l Massive machine type communications (mMTC): IoT, asset tracking, smart agriculture, smart cities, energy monitoring, smart homes, remote monitoring
China is one of the leading 5G pioneers globally. More important, while other markets such as the US focus more on consumer applica-tions (e.g., fixed wireless), the Chinese government and operators have always been focused on industrial IoT applications for enter-prise and government segments.
Edge computing refers to infrastructure that enables data pro-cessing as close to the source as possible, which allows for faster pro-cessing of data, reducing latency and improving the customer experience. This type of computing will require micro-data centers close to 5G BTS. The edge computing discussion is still in the very early stages, as potential applications, like autonomous driving, are several years away. We also note that micro-data center investment could be shared by telcos (network), data center operators (physical environment) and information service providers (computing capacity), and future development will be connected with specific applications.
Cloud
China has seen a ten-fold expansion of the cloud market over the past decade. According to IDC, China was the second-largest IaaS market at the end of 2018. And the total cloud service market (IaaS+PaaS+SaaS) is expected reach US$27.5bn by 2022. The fast ramp-up of cloud could provide solid support for the rollout of smart cities as:
l It provides a platform to connect all data generated from the digitization and informatization of a city's functions, including utilities, transportation, healthcare, and education. The elimina-tion of isolated silos of information could translate into better efficiency in the daily operation of the city
l It provides a platform for better management of the massive number of end-devices connected to the network with the emergence of various new use cases
l Virtualization technology on the cloud platform indicates a better arrangement of network resources and real-time adjust-ment according to different situations and emergencies.
Cybersecurity
The security of cloud platforms, IT systems and sensitive information from the government and public infrastructure has become a signifi-cant concern for smart cities. Even small leakages or short shut-downs of key infrastructure have the potential to cause major losses for a city. On 23 December 2015, the power supply systems in three areas of the Ukraine were attacked by malicious software and shut down for six hours, affecting over 1.4mn people. Such attacks have become more frequent in the past few years and increasingly target key facilities in major cities. According to a survey done by CNCERT in 2018, malicious attacks in Beijing and Shanghai accounted for about 10% of total attacks in China. And provinces with more big cities, such as Jiangsu and Guangdong, tend be at greater risk of attack than other provinces. Therefore, we believe more advanced cyberse-curity technology and comprehensive solutions for digitalized infra-structure will help guarantee the smooth functioning of smart cities.
China has underspent on cybersecurity compared with other coun-tries in the past. However, it is expected to be the sector with the fastest growth in terms of IT spending in the next decade. According to IDC, spending on cybersecurity in China because of government encouragement will be the main growth driver. The Chinese govern-ment has launched several laws and regulations, including the first China Cybersecurity Law, Critical Information Infrastructure Security Protection Regulation, Graded Protection 2.0 Policy, etc. We believe
BLUEPAPERM
40
the strong push from those policies and detailed standards should help boost growth in the smart security supply chain and accelerate smart city development.
Outlook: More megacities to emerge in the next decade
In the past, Hukou restrictions, in part a response to rising urban problems in larger cities, have restricted the growth of China's larger cities. Contrary to common perceptions, China’s large cities are too small to be consistent with Zipf’s law, which posits that the growth rate of cities should be independent of their size because the effi-ciency gains from agglomeration will offset related costs, and as a result, for a group of cities, if one plots the log of population and log of corresponding ranking (in terms of population size) in a scatter chart, the graph will mimic a straight line, often with a slope of -1 ( Exhibit 44 ). For China in 2018, as in Exhibit 45 , more large cities deviated away from the fitted line, whose slope is also much steeper than -1, suggesting that the growth rate of China's cities has been neg-atively correlated with their size.
We believe smart city development could effectively enhance the capacity of the cities of tomorrow. We expect the combination of smarter traffic control systems, shared mobility, and auto driving could significantly reduce congestion, shortening the average com-mute time from home to office to merely 15 minutes by 2030. The deployment of cybersecurity, AI, and automated vehicle technolo-gies should help make streets and roads safer than ever. Moreover, digitalization and advanced technology can enhance connectivity in every aspect of urban life. Lillian Lou, our China consumer analyst, believes that average consumer IoT devices per household could reach 7 units by 2030 (vs. 1 today), which could free people from cleaning, cooking, doing laundry, and grocery shopping in favor of work and entertainment.
In this context, we expect the 50 largest cities in China to expand 3% per year (measured by urban population) towards 2030, with the remaining cities growing by 2.5%, reversing the trend in 2010-18, when the top 50 cities grew by 2.4%, compared with 3.1% in other cities. Consequently, the number of Chinese cities with populations exceeding 8mn will likely jump to 23 in 2030 from 9 today, while those with more than 5mn will also pick up significantly, to 50 from 33.
Exhibit 44:Zipf's law for the US
y = -1.00x + 10.91 R² = 0.98
0
1
2
3
4
5
6
5 6 7 8 9 10
Natu
ral
Lo
g o
f R
an
k
Natural Log of Population
Size/Rank Distribution of Top 250 US Metropolitan Areas
Source: Haver, Morgan Stanley Research
Exhibit 45:Zipf's law for China
y = -1.52x + 5.91 R² = 0.95
0
1
2
3
4
5
6
0 1 1 2 2 3 3 4 4
Natu
ral
Lo
g o
f R
an
k
Natural Log of Urban Population
Size/Rank Distrubution of Top 250 Chinese Cities (2018)
Source: Government website, Morgan Stanley Research
Exhibit 46:Continued urbanization...
2018:
59.6%
2030E:
75%
0%10%20%30%40%50%60%70%80%90%
100%
18
80
18
90
19
00
19
10
19
20
19
30
19
40
19
50
19
60
19
70
19
80
19
90
20
00
20
10
20
18
20
25E
China
Japan
US
Urbanization Ratio, % Projected
Source: UN, NBS, Morgan Stanley Research estimates
Exhibit 47:…will create larger cities
0
10
20
30
40
50
>8m >5m
Number of Cities Above Certain Size
2018 2030E
Source: Local government websites, Morgan Stanley Research estimates
BLUEPAPERM
MORGAN STANLEY RESEARCH 41
Key smart city applications
Smart security
Increased security concerns have become a major obstacle to the expansion of cities. According to the National Bureau of Statistics, total crime cases filed by police departments almost doubled from 2001 to 2017, along with the accelerating rampup of urbanization in China, from 38% to 58%. This situation is even more obvious at the city level, as highly populated cities tend to have a lower sense of safety. Based on a study by Numbeo, major cities in China can be
divided into three groups – populations of below 15mn, between 15mn and 20mn, and above 20mn – and the crime index of these cities steps up with the population. Apart from public security con-cerns, risks from traffic, fire and other infrastructure incidents increase significantly. Therefore, improving a city's security manage-ment through technology is one of the major steps for further expan-sion of cities.
Policy support from government could be the main growth driver. For public security, mainly surveillance and traffic monitoring systems, the government has been rolling out several nationwide projects since 2000.
Exhibit 50: Major surveillance projects launched by government since 2000
Source: Morgan Stanley Research
Exhibit 51: Cybersecurity policies issued in recent yearsDate Policy Authority
Feb-14 The Central Leading Group for Cyberspace Affairs State Council
Aug-14Government Guidance on Enhancing Telecom and
Internet SecurityMinistry of Industry and Information Technology
14-OctGovernment Opinion on Enhancing Military IT
SecurityCentral Military Commission
Nov-16 Cybersecurity Law National People's Congress
Dec-16 13th Five-Year Plan for National IT Industry National Development and Reform Commission
Jan-17Opinions on Promoting the Sound and Orderly
Development of the Internet State Council
Mar-18Guiding Opinions on Promoting Capital Markets in
Serving the Cyberspace Power Building The Central Leading Group for Cyberspace Affairs
Source: Morgan Stanley Research
Exhibit 48: Total number of crime cases and urbanization rate in China (2001-17)
Source: National Bureau of Statistics, Morgan Stanley Research
Exhibit 49: Comparison of the crime index and populations of different cities in China
Source: Numbeo, Morgan Stanley Research. Data as of 2018.
BLUEPAPERM
42
How will smart security be built?
New smart security use cases keep emerging as the requirements of security continue to evolve. With the expansion of city sizes, higher populations, increasing traffic volumes, and greater residential inten-sity, demand for security management will increase.
In the current stage, the use cases for smart security are mainly in the following areas:
l Crime investigationl Traffic monitoringl Residential area surveillance l Industrial and public infrastructure security
The adoption of new technologies, especially AI, and the maturing domestic supply chain also aid in the accelerating development of smart security in China.
l Edge computing: facial recognition, object detection and video structuralization
l Cloud platform: big data analysis, database searching, and smart analysis
How large could the surveillance market be?
With AI embedded, surveillance cameras are becoming an important tool to improve efficiency and productivity in smart cities. We esti-mate the professional surveillance camera market will grow at an 8% CAGR during 2018-30, fuelled by new installations until 2025 and replacement demand thereafter.
We expect demand from the government to grow fastest, with the installation base rising at a 20% CAGR during 2018-30. Additional cameras will be installed to deter crime. Traffic monitoring systems combine surveillance cameras from train stations, airports, and main roads, and traffic data is analyzed in real time at the back-end server with AI enabled. Such systems can better manage traffic and avoid traffic jams. Other than human and vehicle identification, these AI cameras are capable of floating object detection, sewage disposal detection, and wild animal detection.
We expect the professional surveillance camera installation base to reach 897mn by 2030, at a 12% CAGR during 2018-30. The number of surveillance cameras will increase to 62 per 100 people by 2030 from 16 per 100 people in 2018, we estimate. This would be signifi-cantly higher than levels in the rest of the world, which we expect to reach 17 cameras per 100 people by 2030. We expect penetration of AI-embedded cameras to reach 14% by 2030 from less than 1% in 2019. Growth will be supported by demand from the government (such as the Xueliang Project) and commercial applications, such as unmanned supermarkets.
We also expect the consumer surveillance camera market to grow at a 30% CAGR during 2018-30. According to IDC, 9.7mn security cam-eras were shipped to consumers in China in 2018, mainly for home security. We believe more households have demand for these cam-eras, which are able to recognize strange faces, detect abnormal behaviors such as falling down, and to monitor and record the impor-tant moments of babies or pets. IHS estimates that the number of households with cameras will increase to 67mn by 2030 from just 8.3mn in 2018, and camera installations per household will increase to 5.9 by 2030 from 2.3 in 2018.
Exhibit 52:China: Surveillance camera installation base
0
200
400
600
800
1,000mn
Source: IHS, Morgan Stanley Research estimates
Exhibit 53:China: Surveillance camera shipments
0
40
80
120
160
200mn
Net Add Replacement
Source: IHS, Morgan Stanley Research estimates
BLUEPAPERM
MORGAN STANLEY RESEARCH 43
How can smart security enable the development of smart cities?
There have already been quite a few successful cases in Shanghai and Hangzhou:
l Shanghai: AI surveillance cameras, working as electronic police, have captured hundreds of motorists running red lights. The whole system is an integrated application, including video anal-ysis, motion tracking, and facial recognition. Surveillance cam-eras are also applied to monitoring garbage sorting.
l Hangzhou: It would normally take roughly 30 days and 1,500 police officers to search 250 hours of video from over 10,000 cameras, but AI systems need just several minutes to do this. AI cameras can also trigger alarms upon recognizing anyone behaving suspiciously at big events, such as the 2016 G20 Summit in Hangzhou.
We are expecting smart security to be one of the top three growth drivers of smart city spending, of which the majority will be in the public sector.
V2X adoption
Continuous penetration of autonomous vehicles requires signif-icant connection to the network. V2X (vehicle-to-everything) is the new generation of communications technology that connects vehi-cles to other vehicles, infrastructure, pedestrians, and networks. The
Exhibit 54:China: Surveillance camera ASP and surveillance equipment market size
20
30
40
50
60
0
5,000
10,000
15,000
20,000
25,000USD USD mn
Revenue ASP
Source: IHS, Morgan Stanley Research estimates
primary objective of adopting V2X is to reduce accidents, improve efficiency and save resources. V2X contains three key aspects: vehi-cle-to-infrastructure (V2I), vehicle-to-vehicle (V2V) and vehicle-to-network (V2N). While both V2I and V2V will likely play the key role in providing connectivity to cars, we believe that in a world where there is full car autonomy, mobile networks will need to play a crucial role in the ecosystem in the following areas:
l Human safety. In our view, vehicle connectivity to mobile net-works can address crashes that cannot otherwise be prevented by current technology (using camera and sensors) or vehicle-to-vehicle platforms. In short, network-connected vehicles are not restricted by line-of-sight limitations. Essentially, data on acci-dents, congestion/traffic jams and road blockages could be transmitted via 5G.
l Unleashing the power of data. The key finding from our work and interviews is that autonomous vehicles will generate data on an unprecedented scale. Essentially, even with the contin-uous evolution of technology, local processing power within vehicles is probably insufficient, meaning the use of cloud com-puting will become essential.
l Back-up/contingency. We believe 5G will prove complemen-tary to both V2V and V2I technologies. Indeed, we believe 5G can be used as support in the event of slow networks (capacity blockages) and outages (power, cyber attacks). This would be similar to the way that wireless networks and WiFi interact.
Exhibit 55:Number of surveillance cameras per 100 people
0
20
40
60
80
2018 2030E
China
Global
Source: IHS, Morgan Stanley Research estimates
BLUEPAPERM
44
Exhibit 56: Summary of the different connectivity platforms for AVs
Source: Morgan Stanley Research
Exhibit 57: Overview of connected car ecosystem
Source: Qualcomm, Morgan Stanley Research
BLUEPAPERM
MORGAN STANLEY RESEARCH 45
Rollout of V2X in China is enabled by policy push and technology readiness. China has a good foundation in the development of V2X thanks to the scale of its automobile market, solid highway and telecom infrastructure, and comprehensive value chain. Several industry associations have started working on setting industry standards, including application defi-nitions, technology requirements and secu-rity. The Chinese government has established multiple trial programs to explore the com-mercialization of V2X and business models together with all value chain players.
The automobile supply chain's focus has also been shifting to smart road/vehicle-infra-structure cooperative systems (VICS) in recent years. Along the V2X value chain, there are communication chipset/sensor module/end-equipment makers, auto OEMs, opera-tors, and service providers. Major suppliers that have already made related announce-ments include Hikvision, Navinfo, TUS, China Transinfo, and Desay SV.
Exhibit 58: Architecture of Vehicle-Infrastructure Cooperative Systems (VICS)
Source: Morgan Stanley Research
We believe the commercialization of V2X will follow these stages:
1. Establishment of connections. China has already started the trial of this stage by upgrading road infra-structure in areas of high automobile density. Users are starting to recog-nize and become interested in V2X with basic applications pre-installed in cars.
2. Capability enhancement. Coverage expands along with penetration of commercial users. To support the enlarged user base and more new applications, network upgrades will become necessary, including multiple layers of computing capacity.
3. Application upgrades. ADAS will evolve to autonomous driving with the help of 5G-V2X technology. Cooperative traffic will also become available.
Exhibit 59: V2X trials in ChinaTrial place Related parties Key applications Scale
Shanghai Shanghai International Auto City Group Smart cars, V2X connections 18 roads and 3000 vehicles in 2018-19
Wuxi Ministry of Public Security, China Mobile, Huawei LTE-V2X information convergence 211 crossings and 5 highways
Chongqing CAERI Smart cars already has 9.6km trial road
Beijing Beijing Innovation Center for Mobility Intelligent (BICMI) PCW (Pedestrian Collision Warning) already has 12km trial road
Changchun FAW Security Warning, smart cars, smart transportation NA
Hangzhou Local government Smart cars, smart transportation 34 LTE-V2X stations
Wuhan Local government Autonoumous driving, smart transportation To build demonstration district in 5 years
Source: Morgan Stanley Research
Exhibit 60: Local governments are competing to build 5G-enabled pilot zones
Source: Morgan Stanley Research. *PV=passenger vehicles, CV=commercial vehicles
BLUEPAPERM
46
Shared mobility Exhibit 61: Major types of shared mobility
Source: CIVITAS
Planning a smart city that delivers effective and equitable urban mobility solutions is one of the most pressing problems for cities throughout the world. Shared mobility is a business model innova-tion that improves the utilization rate of social resources and the effi-ciency of public transportation, such as online taxi hailing and shared cars/bikes. It also makes a meaningful contribution to reducing traffic congestion. Despite the different operating model, all shared-mo-bility solutions/apps have some common factors, such as: 1) reliance on mobile application to enable users to enter into rental/lease or usage contracts; 2) a strong social component for users to evaluate and share their experiences; and 3) customization of transportation services, to some extent. We believe shared mobility can enhance smart cities in terms of both environmental and social impact.
l Environmental. We expect that car sharing will be a real opportunity for reducing car ownership levels, especially in large urban areas. Bike sharing could also significantly reduce car use. We are also expecting car-sharing schemes to result in the replacement of old vehicles with more environmentally friendly ones.
l Social. By reducing the cost of transportation through sharing schemes, the prevalence of shared mobility services will encourage more citizens to come out and travel, in our view. Moreover, the fast ramp-up of the geographical coverage of shared-mobility services due to economies of scale could quickly expand the boundaries of cities.
China is a global leader in this kind of B2C business model inno-vation. Though the capital-driven model, price competition and a lack of regulation may distort the market in the short run, we believe shared mobility will bring long-term value creation as it lifts the utili-zation rate of private cars, reduces matching times between taxis and passengers, and gives more choice to customers.
Internet of Medical Things and remote healthcare
Smart wearable devices and IoMT: People can use wearable devices to access their health status or fitness regime without any profes-sional help. Such devices can help check blood pressure, body tem-perature, heartbeat, cardiovascular problems, vision quality, and chronic ailments. People can monitor their health condition in real time, and smart devices can flag abnormal patterns.
Remote healthcare: Leveraging IoMT technology, doctors can mon-itor patients' health remotely and analyze all of the data collected to prescribe highly personalized treatments. Patients may even be able to 3D print pills at home. Meanwhile, through data connectivity, doc-tors can also conduct remote surgeries. On 27 August 2019, doctors at a Beijing hospital successfully conducted a remote robotic surgery, on a patient more than 136km away, using 5G wireless technology.
Increasing hospital capacity: Through smart patient flow planning, hospitals can improve the quality of services and the efficiency utili-zation of resources. Hospitals can connect beds or medical machinery to improve efficiency. For example, GE partnered with a hospital in New York to connect and track hospital beds using sen-sors. These sensors enabled hospital operators to tell when a bed was free and helped reduce emergency room wait times by as much as four hours, according to Business Insider. IoMT can also monitor the status of medical machines to reduce the probability of outages.
Smart ambulances: Smart ambulances can send a patient's data col-lected by sensors to the hospital's database while on the road, and traffic signals could be adjusted to ensure the ambulance arrives at the hospital as fast as possible. At the same time, staff at the hospital can work on preparing treatments before a patient arrives.
BLUEPAPERM
MORGAN STANLEY RESEARCH 47
Exhibit 62: Apple's health platforms and tools provide the foundation for a healthcare ecosystem
Source: Morgan Stanley Research
Online education and adaptive learning
With online education, quality educational resources will not be restricted by location. Experienced teachers and high-quality content can be made available to all students nationally, especially together with the government’s efforts to unify the gaokao test paper and upgrade the syllabus. Students from remote suburban locations will have similar access to experienced teachers and learning resources as students in urban areas.
With adaptive learning, learning efficiency can be greatly enhanced. Teaching and learning progress can be digitalized through online edu-cation, and big data analysis can be used to form personalized learning plans for students and teaching suggestions for teachers. Longer term, with more adaptive learning of teaching behaviors, AI teachers could also become available.
Many commercial education products are already yielding statisti-cally positive results by improving study performance through adap-tive learning tools. For instance, DreamBox Learning's online individualized instruction program and Carnegie Learning's interac-tive math tutoring software achieve statistically positive effects in improving study performance.
China's government is currently promoting Educational Digitalization 2.0, which emphasizes developing smart campuses, improving students' and teachers' use of online resources, and inte-grating nationwide educational databases. Under Educational Digitalization 2.0, we believe the sharing of excellent online educa-tional resources and the promotion of online learning will be critical to narrowing the gap in teacher quality between schools.
BLUEPAPERM
48
Exhibit 63: Score growth in US Southwest Allen County schools after usage of Dreambox
Source: Dreambox
Exhibit 64: Dreambox: Percentage of students who tested as proficient and distin-guished in statewide exams
Source: Dreambox
Smart homes
Smart home apps are making life more secure and convenient
Source: Shutterstock
Current smart home applications are mostly single purpose, e.g., for controlling lights, air conditioners, entertainment systems, etc. With IoT technology becoming more mature, smart home applications could serve several purposes at the same time, with multiple systems connected through a home automation hub, allowing users to create combined actions for particular situations. For example, a saved com-mand 'returning home from work' might include turning on the lights and air conditioner, preparing dinner, and suggesting entertainment or exercise options based on data collected through sensors at home (sleeping hours, dietary records, etc.).
A smart grocery system could automatically order food if it detects a shortage in the refrigerator through smart cameras. Orders could be delivered by drones, and the refrigerator would be refilled auto-matically.
Smart home security and surveillance systems can enable people to lock doors remotely through mobile apps and signal any abnormal situation. People can monitor their home security while on vacation. Security systems can also dial emergency calls in case of fire or inva-sion. Cameras installed on the door can record the presence of any suspicious persons.
BLUEPAPERM
MORGAN STANLEY RESEARCH 49
Case study: Smart city development in ShenzhenAccording to the China Smart City Assessment Report released by the Center for Informatization Study (CIS), Shenzhen ranked #1 among Chinese smart cities in 2018. Historically, Shenzhen has focused on informatization development and has been a pilot city for several national informatization projects, including the Smart Cities Project in 2012.
Front-runner in smart city development:
Shenzhen targeted smart city development even before the official launch of the Smart Cities Project. In 2011, the 'Smart Shenzhen Development Plan 2011-20' was released to focus on informatization development. The project aims to use new technologies (converged networks, smart sensors and computing power) to improve urban management and quality of life and promote industry upgrades.
In 2012, Shenzhen was selected as a pilot city in the first batch of the Smart Cities Project.
In 2016, Shenzhen set up a special work team for smart city construction, emphasizing support from local enterprises, especially technology giants such as ZTE, Tencent and Huawei.
In 2016, the 'Shenzhen Smart City Construction Plan 2016-20' was published with the following guidance:
l Public services: Building an integrated internet service network covering healthcare, education and community services.
l Society governance: Security information sharing across various departments to improve emergency management and urban security.
l Digitalization: Accelerating integration of internet, big data and IoT to promote new business models and industrial digitalization.
l Environment: Improving energy-saving in public buildings, accelerating the establishment of smart transportation and realizing dynamic and real-time monitoring of land utilization and waste disposal.
Further development: In 2018, the 'Shenzhen Smart City Construction Plan 2018-20' put forward new guidance, with the target of being a world-leading smart city by 2020.
l One integrated support network system: A comprehensive communication network combining monitoring and computing capacity, providing fundamental IT support and data collection.
l Two centers: (1) The data center is in charge of data management and sharing resources and (2) the smart city operation center focuses on trans-department cooperation and decision support services.
l Four applications: (1) Smart public services include government, education, healthcare, and community services; (2) smart security includes public security, emergence management and safety production; (3) smart city governance focuses on improving transportation, environmental protection and water utilities; and (4) smart industry targets smart industrial parks and factories.
BLUEPAPERM
50
Exhibit 67:
Framework of Shenzhen's smart city in 2018-20 plan
Source: Shenzhen government data, Morgan Stanley Research
Exhibit 68:
Smart city comparison
City Application/Programs
Smart transportation
Smart government
Smart services industry
Smart healthcare
Smart community
Smart transportation
Smart government
Smart safety
Smart healthcare
Smart living
Smart community
Smart business district
Smart industry park
Smart village
Target
ShanghaiBuilding a new smart
city landmark
Hangzhou
ShenzhenBeing a world-leading
smart city
Building "Smart
Brains" for cities
Source: Morgan Stanley Research
Achievements:
Strong information infrastructure: Shenzhen has built a robust broadband network with 100Mbps for individual users and 1000Mbps for enterprise users. Free WiFi is available in public areas, and IoT applications have been developed for utility meters.
Smart government: Different departments are required to co-build an integrated service platform to provide services, with a goal of saving investment and improving efficiency through synergies. Citizens have access to the integrated platform through websites and mobile apps. In 2016, the resource-sharing platform integrated 385 categories of information and resources of 29 units, including over 3.8bn terms of data. 49 directly affiliated municipal departments and all district governments shared information via this platform. More than 90% of government data was shared on the platform, and the utilization rate of shared resources was around 80% in 2017.
Smart healthcare: The healthcare department has developed a platform that enables citizens to have access to various healthcare services, such as doctor appointments, hospital information, research, and medical consultation, through mobile apps. The installment of WeChat payment for medical insurance in Shenzhen’s hospitals helps to save an average of 46 minutes of waiting time.
Smart transportation: Shenzhen's transportation department teamed with Huawei to build a transportation system with a 'Smart Brain' to improve traffic safety and congestion. The system can recognize traffic violations through algorithms and AI-based facial recognition. Shenzhen has built a camera-and-sensor network to monitor license plates, accidents and traffic flows, collecting more than 700mn records per month. Coupled with big data analysis, the transportation department can reduce traffic jams by optimizing traffic flows. Also, the transportation system is linked to the integrated platform, which allows citizens to deal with mobile parking payment and online processing of traffic violations and fines.
Exhibit 65:
Policy support in Shenzhen's smart city construction
2011 2012 2013 2014 2015 2016
2
2017
2
2018
Smart Shenzhen development plan (2011-2020)
Shenzhen was selected in smart cities pilot project
Shenzhen set up a special work team for smart city construction
Shenzhen smart city construction plan 2016-2020
Shenzhen smart city construction plan 2018-2020
Source: Shenzhen government data, Morgan Stanley Research
Exhibit 66:
Shenzhen technology companies
Artificial Intelligence Big data and Cloud IoT
Source: Morgan Stanley Research
BLUEPAPERM
MORGAN STANLEY RESEARCH 51
Exhibit 69: Smart services via mobile apps
Frequently used government services
Government services to
individuals and enterprises
Real-time updates on bus transportation
Source: Morgan Stanley Research
Exhibit 70: Integrated services in mobile apps
Search for services
Navigation of various
services like healthcare,
security and transportation
Healthcare services
Doctor's appointments, hospital locations,
Automated External Def ibrillator (AED) etc.
Source: Morgan Stanley Research
Exhibit 71: Smart transportation services
Transportation services
Online tickets; Real-time updates on bus
transportation
Online processing of traffic violations and
fines; Parking availability information
Traffic information
Source: Morgan Stanley Research
Exhibit 72: Access to medical insurance via WeChat Mini-program
Payment for medical insurance and details of personal insurance
information
Source: Morgan Stanley Research
BLUEPAPERM
52
Overview Moving rural workers into cities while preserving China's food security is critical to urbanization. Today, the key hurdles to China's agricultural modernization are uncertainty over land use and a fragmented and small-scale farming model. That said, we believe this can be resolved by the government's ongoing land reform efforts that started in 2014, as well as wider adoption of smart farming equipment such as drones, automated irrigation sys-tems, and precision seeding equipment.
Key forecasts We expect China's agricultural labor productivity to more than double over the next decade, releasing more of the rural population who can then contribute to further urbanization.
Initiative #3: Agricultural Modernization
Why is it important to enhance agricultural productivity?
World Agricultural Labor Productivity in 2017 (2010 US$)
Source: World Bank, Morgan Stanley Research
In our view, the key to unleashing more rural man-power for urbanization while preserving China's food security is enhancing productivity. While China's agriculture productivity has increased at an 8.1% CAGR over the past decade, from just 1.6% in 1969-78, on the back of reforms (see following box), its level remains lower than that in most key econo-mies. Over the past few years, the country's trade def-icit in agricultural products has been widening as a result of rising domestic food demand and a shrinking area of arable land. With the UN estimating that China's population will continue to increase until 2030, this means a steady increase in food demand and continued downward pressure on the agricul-tural trade balance, suggesting the urgency of boosting agricultural productivity.
Exhibit 74:...and its agricultural trade deficit is widening
-100
-80
-60
-40
-20
0
20
40
60
1994 1998 2002 2006 2010 2014 2018
Agriculture Imports
Agriculture Exports
Agriculture Trade Balance
USD Bn
Source: CEIC, Morgan Stanley Research
BLUEPAPERM
MORGAN STANLEY RESEARCH 53
China's agricultural policy reforms since 1978China moved away from the collective agricultural system to the household registration system (HRS) in 1978-85. This provided more incentives for farmers to improve production, as HRS allowed farm households to either consume or sell any production beyond a fixed quota to be delivered to the country. To improve the demand-supply dynamics of agricultural products, policymakers also conducted market-oriented price reforms in 1985-93, rescinded SOEs’ monopolies on the trading of strategic products (e.g., cotton, soybeans) at the end of the 1990s, and further lowered tariffs on strategic agricultural products after China joined the World Trade Organization in 2001.
Exhibit 75:Timeline of China's agricultural policy reform
Source: Morgan Stanley Research
How can China's agricultural sector modernize?
l Uncertainty over land use: In China, land is characterized by collective ownership, and farm households can only use land on a leased basis. Despite the adoption of the household regis-tration system, uncertainty over land usage discourages farmers from preserving and protecting land resources by replacing poor quality farm chemicals with more expensive
l Small-scale and scattered farms: China’s agricultural struc-ture is significantly smaller-scaled than global counterparts. This means it is difficult for farms to benefit from economies of scale because of a lack of communication and cooperation. Meanwhile, it imposes challenges on new technology adoption, which is capital-intensive and largely unaffordable for small farms, and information gathering for ICT data analysis (there are 40,000 agriculture-related websites nationwide, which have fragmented and incompatible data).
modern technologies, and this can also result in pollution and food safety issues. Exhibit 76 , for instance, shows that China’s pesticide application rate is among the highest in the world. Moreover, the inherent business risk from land rights may dampen private investment in the agricultural sector.
In our view, uncertainty over land rights and the existing fragmented and small-scale farming model have been the key challenges in recent decades, discouraging private investment, reducing the bene-fits of economies of scale, and slowing the adoption of new tech-nology:
BLUEPAPERM
54
In this context, we believe continued land reforms and the wider adoption of advanced agricultural technologies hold the key to boosting large-scale farming and modernizing the sector:
1. Continued land reforms
China has taken up a series of measures to facilitate large-scale farming. Since December 2014, China has started piloting reforms to separate farmland ownership, contract rights, and operating rights, which makes it possible for farms to collect scattered lands for large-scale planting. In August 2019, China's top legislature also adopted a revision to the land administration law, which gives farmers more property rights, enhances the protection of basic arable land, extends the duration of existing farmland use contracts by another 30 years upon expiry, and improves the transparency of rural land requisition. Beijing has also been promoting different forms of agri-cultural business entities in recent years, such as family farms and farm cooperatives, to improve the fragmented and small-scale farming model.
2. Wider adoption of smart farming
Exhibit 76:Overuse of pesticides in China
0.31
0.89
1.1
1.56
1.87
2.57
2.63
3.14
4.85
11.41
12.04
13.06
0 2 4 6 8 10 12 14
Africa
South East Asia
Australia
Canada
Mexico
World
US
EU
South America
Japan
South Korea
China
Global Pesticide Usage in Agriculture in 2016, kg/ha
Source: FAO, Morgan Stanley Research
Most of China's farms don't yet have the scale to justify costly automated farm technologies
Source: Shutterstock
Exhibit 77:Relatively small farm sizes in China
0.7
1.5
2.5
79.8
177.7
331.8
0 50 100 150 200 250 300 350
China
South Korea*
Japan*
UK
US
Canada
Average Farm Size in 2016 (Hectare/Farm Households)
Source: USDA, Statistics Canada, China NBS, Statistics Korea, Japan Agriculture Ministry, UK Agriculture Department. Note: Data in 2015 for Japan and South Korea
Since 2014 policymakers have focused on promoting smart farming, which is based on advanced technologies, such as IoT, cloud com-puting, big data analytics, and automation, to increase the quality and quantity of agricultural products. After precisely measuring varia-tions in fields, smart farming techniques help form the best strategies and use automation to conduct actual farming work. Specifically, smart farming is concentrated on three areas:
l Smart monitoring: Use of satellites, drones, cameras, and in-field sensors to enable real-time monitoring and geographic data collection. For example, satellites provide weather predic-
tions, flying drones with cameras provide a bird’s eye view of plant health and pest conditions, and in-field sensors monitor soil conditions, sunlight levels, temperature, moisture, and air quality. Using these tools saves time compared with manual field checks.
l Smart analysis: Based on real-time farming data, big data ana-lytics can provide predictive insights and help make timely deci-sions. For instance, smart farming apps can suggest optimal times for planting, whether more irrigation, fertilization, or weeding is needed, which fields need pest control, and whether crops are ready to harvest.
l Smart control: Automated equipment can conduct work via remote control. For example, drones can be used for large-scale pesticide spraying and seeding from the air, precision seeding equipment can plant seeds at the correct depth and with the appropriate spacing to allow for optimal growth, and robots can water, fertilize, cultivate, harvest, and sort.
BLUEPAPERM
MORGAN STANLEY RESEARCH 55
In our view, the development of 5G and IoT, AI, and cloud computing will further integrate these three areas of smart farming, making full automation pos-sible over the medium term. For instance, on a smart farm with an IoT-enabled monitoring system and auto-mated equipment, when in-field sensors detect insuffi-cient moisture levels in the soil, automated irrigation systems can immediately apply more water. Similarly, pest control and harvesting can be carried out without the need for human intervention. This would improve productivity and change the labor-intensive nature of China’s agricultural sector.
Exhibit 78:How does smart farming work?
Source: Morgan Stanley Research
Higher agricultural productivity to support further urbanization
Still some room for the rural population to migrate to urban areas... The aging rural population and slower growth in migrant workers over the past decade has led to market concerns about the future potential for urbanization ( Exhibit 79 ). However, an NBS survey shows that there are still about 400mn rural people aged 5-50 (70% of the rural population) who will become or remain working age over the next decade ( Exhibit 80 ). This points to room for further urbaniza-tion, albeit at a slower pace than in the past.
...which needs to be enabled by higher agricultural productivity: International experience suggests that the share of agriculture in GDP tends to decline with higher GDP per capita as a consequence of industrializa-tion and developments in service sectors, and higher agricultural productivity is needed to meet rising domestic food demand ( Exhibit 81 ). In the US, Japan and Korea, agricultural labor productivity increased exponentially when the share of agriculture in GDP fell to 3-4% ( Exhibit 82 ). Our econometrics model sug-gests that when China’s per capita income increases to US$17,800, a level we forecast it will reach by 2030 (vs. US$9,450 in 2018), the share of agriculture in GDP would fall to 2% from 7% today. Assuming that China follows a path similar to that of developed countries, through successful implementation of rural reforms, inviting more private investment and FDI, and the adop-tion of smart farming, we believe China’s agricultural productivity could more than double over the next decade, releasing more rural workers for further urbanization.
BLUEPAPERM
56
Exhibit 79:Despite an aging rural population and slower growth of migrant workers...
Exhibit 80:...there is still a rural population of 400mn aged 5-50
0
50
100
150
200
250
300
350
400
450
500
65%
66%
67%
68%
69%
70%
71%
72%
73%
2010 2011 2012 2013 2014 2015 2016 2017 2018
Person mn-RS
% of Rural Population-LS
Rural Population at the age of 5-50
Source: CEIC, NBS, Morgan Stanley Research
Exhibit 81:Share of agriculture in GDP tends to decline with higher GDP per capita
y = -4.9ln(x) + 50.4 R² = 0.7
0
2
4
6
8
10
12
14
16
18
20
0 10000 20000 30000 40000
China
GDP Per Capita, USD
Ag
ricu
ltu
re S
hare
in
GD
P
Data as of 2017
Source: World Bank, IMF, Morgan Stanley Research
Exhibit 82:China's agricultural labor productivity would more than double by 2030 assuming it follows the path of the US, Japan and Korea
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
50,000
0% 5% 10% 15%
Korea US
Japan China
Agriculture Share in GDP
Ag
ricu
ltu
re L
ab
or
Pro
du
ctiv
ity
, P
PP
(In
tern
ati
on
al
Do
lla
r P
er
Pe
rso
n,
20
10
Pri
ce)
China (2030E)
US (1950)
US (2000)
Japan (1980)
Japan (2014)
Korea (1992)
Korea (2017)
China (2018)
Source: World Bank, IMF, Morgan Stanley Research estimates
BLUEPAPERM
MORGAN STANLEY RESEARCH 57
Micro Implications
BLUEPAPERM
58
Exhibit 83:Investment Theme #1: Summary
Th
em
e 1
: F
rom
a c
on
su
mer
to a
n
ind
ustr
ial
inte
rnet
Telecoms 5G infrastructure companies5G capex: US$400bn in 2019-30
(2x 4G capex)
Top Stocks
• China Tower (0788.HK)
• Alibaba (BABA.N)
• GigaDevice Semiconductor Beijing (603986.SS)
• HIKVision Digital Technology (002415.SZ)
• Yonyou Network Technology (600588.SS)
• VenusTech (002439.SZ)Total value of connected devices: US$684bn
(vs. US$301bn in 2018)
Top players geared to IoT and 5G; software vendors focusing on digital
transformation or with smart city exposure
Tech Hardware and
Software
Key Beneficiary Key 2030 Forecasts
InternetTechnology leaders that are expanding from consumer to
industrial applications
Software and IT services spending: US$200bn (5x 2018)
Source: Morgan Stanley Research
Investment Theme #1: From a Consumer to an Industrial Internet
BLUEPAPERM
MORGAN STANLEY RESEARCH 59
1a. TelecomsOverview: China's extensive fiber network and pioneering 5G rollout are the major enablers of smart cities. Globally, tele-
coms operators are struggling to identify business cases to monetize 5G investments, but we believe smart cities will emerge as an early B2G application.
Key forecasts: We project 5G capex of US$400bn in 2019-30, which is double the level of 4G capex. We forecast IoT connec-tions (connected with carrier networks) will increase 10x, from 800mn in 2018 to 8bn in 2030.
Investment implications:
China Tower is our preferred 5G play, while China Communication Services should be a key beneficiary of smart city development.
China's telecoms infrastructure ranks among the best in the world, and it includes extensive fiber coverage and a pioneering 5G rollout, paving the way for smart city development. We project total capex of US$400 bn in 5G (including transmission) in 2019-30. Although 5G use cases remain immature today, Chinese telcos tend to focus on to-business (2B) and to-government (2G) industrial applications, and we believe smart cities could emerge as one of the early 5G monetiza-tion opportunities for operators, given policy support. Among the three Chinese telcos, we believe China Telecom (CT) and China Unicom (CU) are better positioned than China Mobile (CM), given higher exposure to the government/enterprise segment and net-work sharing. For telecoms infrastructure, we believe China Tower (CTC), China Communications Services (CCS) and data centers are the key beneficiaries.
Fiber: China is one of the most fiberized countries globally. As of June 2019, according to MIIT, total optical fiber length reached 45mn km. Fiber-to-the-home/office (FTTH/O) subscribers reached 400mn, representing more than 90% of broadband subscribers in China. Many smart home applications, such as connected home appliances, surveillance and entertainment, require extensive FTTH penetration. In China, almost all of the mobile base transceiver stations (BTS) are connected with fiber for transmission, which is essential to providing 4G/5G services.
l 'Broadband China' policy: In 2013, the State Council announced its 'Broadband China' plan, setting 2020 targets for the number of broadband subscribers, network speed, capacity, and penetration. Most of the targets were aggressive compared to the broadband network at the time. However, as of June 2019, most of the targets had been exceeded, making China one of the most fiberized countries globally.
l 'Speed Upgrade Tariff Reduction' initiatives: Since 2015, the State Council has been setting 'Speed Upgrade Tariff Reduction' targets, which lower broadband tariffs substantially for both household and enterprise subscribers, facilitating the penetra-tion of the fiber network.
BLUEPAPERM
60
Exhibit 84:'Broadband China' targets
Index Unit 2013 2015 2020 Jun-19
Broadband subs Mn 210 270 400 435
FTTH/O subs Mn 30 70 NA 398Urban subs Mn 160 200 NA 306Rural susb Mn 50 70 NA 129
3G/4G Subs Mn 330 450 1200 1586
Broadband penetration % 40 50 70 94
Urban subs % 55 65 NA NA
Rural susb % 20 30 NA NA
3G/4G subs penetration % 25 32.5 85 114
Urban Area Mbps 20 80% subs 20 50 100
Developed cities Mbps NA100 some
cities
1000 some
subs1000 (some cities)
Rural Areas Mbps 4 85% subs 4 12 NA
Speed for enterprise Mbps >100 >1000 NA
FTTH subs Mn 130 200 300 344
3G/LTE BTS '000 950 1200 NA 4450
Broadband penetration in
administrative villages% 90 95 >98 >98
Internet users Mn 700 850 1100 854
Rural users Mn 180 200 NA 318
E-commerce transaction
volumeBn 1000 1800 NA 3660 (by 2018)
Broadband China Targets
1. Broadband users
3. Broadband capability
4. Broadband information application
2. Broadband penetration
Source: State Council, MIIT, Morgan Stanley Research
5G: China has become the world's front-runner for 5G network buil-dout, a key building block of smart city development.
l Timetable: 5G spectrum was allocated in December 2018, with 3.5GHz for CU and CT (100MHz each) and 2.6/4.9GHz for CM (160MHz and 100MHz, respectively). The sufficient sub-6GHz spectrum is well positioned for 5G development. 5G licenses were then released in June 2019. Telcos could launch commer-cial 5G services as early as late-2019.
l Capex: We expect Chinese telcos to spend total 5G capex (including on wireless networks, core networks, transmission, and towers) of Rmb2.8trn (US$400bn) in 2019-30.
l SA vs. NSA: Unlike most other countries, which started 5G net-work deployment under a non-standalone (NSA) model, China insists on developing its 5G networks using a standalone (SA) model, preparing telcos for 5G applications in Ultra Reliable
Low Latency Communications (URLLC) and Massive Machine Type Communications (mMTC), which are mainly B2B use cases, facilitating smart city development.
l Use cases: Encouraged by the government, telcos have been conducting trials for various use cases, especially for smart cities, such as smart transportation, manufacturing automation, remote education and healthcare, autonomous driving, and smart homes.
l Network slicing and edge computing: Smart cities may need network slicing owing to cybersecurity requirements, which enables telcos to exert higher pricing power. This technology enables telcos to sell 'slices' of the network, at different param-eters, customized for specific end-applications. Meanwhile, emerging technologies, such as edge computing, enable smart city applications to run smoothly on a city-wide basis.
BLUEPAPERM
MORGAN STANLEY RESEARCH 61
Infrastructure sharing: The Chinese government has been sup-portive of infrastructure sharing, which we believe will improve the efficiency of smart city development.
l Tower sharing: Encouraged by the government, in 2014-15 China Tower (CTC) was established to take over new tower construction from the three telcos and start a tower-sharing scheme: CTC meets telcos' new tower demands by using other telcos' existing towers. CTC then acquired all existing tower assets from the three telcos and improved network efficiency through co-locating tower demands from telcos by offering co-location discounts.
l DAS sharing: The government encourages CTC to take leader-ship in the coordination of indoor distributed antenna system (DAS) deployment. CTC negotiates with local governments to acquire social resources (e.g., light poles) and consolidates the resources to meet telcos' demands, improving network rollout efficiency.
l 5G network sharing: In September 2019, CU and CT together announced they will build a full-scale nationwide 5G network, which we believe is positive for smart city development given the efficiency of co-building networks.
districts or sole builder Region Province/CityRatio of construction
districts or sole builder
15 Cities 25 Provinces
Beijing Hebei
Tianjin Henan
Zhengzhou Heilongjiang
Qingdao Jilin
Shijiazhuang Liaoning
Shanghai Inner Mongolia
Chongqing Shandong
Guangzhou Shanxi
Shenzhen Anhui
Hangzhou Fujian
Nanjing Gansu
Suzhou Guangxi
Changsha Guizhou
Wuhan Hainan
Chengdu Hubei
Guangdong and Zhejiang Hunan
Guangdong Province /CU: 9 prefecture cities
CT: 10 prefecture cities Jiangsu
Zhejiang Province /CU: 5 prefecture cities
CT: 5 prefecture cities Jiangxi
Ningxia
Qinghai
Shaanxi
Sichuan
Xizang
Xinjiang
Yunnan
8 Northern Provinces CU
17 Southern Provinces CT
CU:CT = 6:45 Northen Cities
10 Southern Cities CU:CT = 4:6
Source: Company data, Morgan Stanley Research
CT and CU are better positioned than CM: Telcos have been strug-gling to identify use cases to monetize 5G capex investment. Consumer applications are more mature, but monetization is diffi-cult given a lack of pricing power; enterprise applications potentially offer significant upside, but applications are very immature. We believe initial use cases will be government-driven applications focused on smart cities and smart agriculture. Currently, 60-80% of Chinese telcos' revenues come from the consumer market, and we believe the telcos are well positioned to benefit from incremental revenue from enterprise and government, given supportive policies, which, in our view, is not reflected in the share prices.
CU's and CT's plan to build a nationwide 5G network is a significant positive for both companies, given the substantial capex savings. CU and CT indicated that the combined 5G capex will be similar to that of CM, even with network sharing, which means CM's network advan-tage is likely to diminish during the 5G era.
BLUEPAPERM
62
Exhibit 87:China telcos: Enterprise revenue contribution is still relatively small
Personal Mobile Market 73%
Household Market 7%
Corporate Market 11%
Emerging Business 9%
CM 2018 Service Revenue Structure
Internet Dedicated Line Access
Revenue 27%
Others 73%
CU 2017 Fixed-line Broadband Access Revenue
Source: Company data, Morgan Stanley Research
Exhibit 88:China telcos: CU and CT together spent less capex than CM during the 4G rollout (2014-18)
419
240
-
50
100
150
200
250
300
350
400
450
Mobile Transmission
Thousands
Rmb bn CM
160
96
187
48
348
144
Mobile Transmission
CT CU
Source: Company data, Morgan Stanley Research
Exhibit 89:China Tower: EBITDA and net profit
41.8 44.7
49.1
55.1 62.1
69.6
2.7
5.5
8.6
12.6
17.4
22.6
0
5
10
15
20
25
0
10
20
30
40
50
60
70
80
2018 2019E 2020E 2021E 2022E 2023E
Net Profit (Rmb bn)
EBITDA (Rmb bn) China Tower: EBITDA vs. Net Profit
EBITDA
Net Profit
Net Profit 2018-20 CAGR: 80%
2020-23 CAGR: 38%
Source: Company data, Morgan Stanley Research estimates
China Tower: CTC is a key beneficiary of 5G investment and smart city applications. Its tower business should benefit from the com-mencement of the 5G capex cycle in 2H19, which requires denser macro cells supplemented by small cells. Meanwhile, its DAS busi-ness will benefit from the indoor coverage needs of 5G applications. Furthermore, we believe CTC's trans-sector site application and information (TSSAI) business is well positioned to benefit from smart city applications, e.g., surveillance, weather monitoring, traffic data collection, etc., leveraging its tower assets.
Data centers: In our view, data center operators are well positioned to benefit from secular demand growth in computing and storage capability by providing infrastructure to public cloud vendors or gov-ernment and enterprises for their private IT deployment. The con-struction of smart cities would further increase overall internet traffic and data volumes. In addition, the government's increasing focus on environmental protection, particularly in key city clusters, raises the hurdle for new entrants. We believe leading incumbents like GDS, Sinnet and 21Vianet will enjoy rising value from their existing capacity and a competitive advantage in future expansion.
China Communication Services: CCS is a major beneficiary of smart city construction. It has a deep understanding and rich experience in smart city construction. It has market-leading capabilities in top-level planning, turnkey ICT solutions, systems integration, operation and maintenance. In addition, close relations with municipal govern-ments and a neutral platform for all partners make it even more com-petitive in smart city-related tendering. The geographical expansion of smart cities and deepening of smart applications are key drivers of CCS's revenue and profit growth, supported by the government's high investment priority and fiscal budget.
Exhibit 91:CCS: Non-telecom revenue growth
-
20
40
60
80
100
120
140
2016 2017 2018 2019E 2020E 2021E 2022E 2023E
Rm
b b
n
CCS Non-telecom revenue (primarily smart city and related)
11% 5-year CAGR
Source: Company data, Morgan Stanley Research estimates
Exhibit 90:CCS: Smart city development
Source: Company data
BLUEPAPERM
MORGAN STANLEY RESEARCH 63
Overview: China's internet giants will likely be key enablers of Urbanization 2.0, as they are technology leaders and are expanding from consumer to industrial applications (including cloud computing and smart solutions), which enable the development of smart cities and digitize enterprises in each vertical market.
Key forecasts: We project software and IT services spending will grow at a 13% CAGR, to US$200bn in 2018-30, with global spending share reaching 8% (vs. 3% in 2018).
We project online retail GMV to reach about Rmb30trn in 2030, rising at a 11% CAGR from 2018, with over 1bn online shoppers (vs. 610mn in 2018), thanks to urbanization driving up internet penetration and improved logis-tics helping e-commerce penetration in categories such as FMCG and fresh groceries, on top of an expanding internet population.
We project food delivery GTV will reach Rmb2.2trn in 2030, rising at a 13.5% CAGR from 2018, with average daily orders ramping up to 126mn (vs. 29mn in 2018), as we note China has more than 150 cities with a popula-tion of over 1mn (vs. only around 10 cities of such size in the US), where urban appetites should continue to fuel industry growth.
Investment implications:
Alibaba and Tencent should be key beneficiaries in view of cloud computing demand. Baidu is facing a bigger chal-lenge in its core business due to the rise of Bytedance, so the potential benefits from the industrial internet could be partially offset by weakness in the core business. Urbanization should benefit physical goods and ser-vice e-commerce leaders in lower-tier cities, favoring Alibaba and Meituan given their market-leading positions and strong ecosystems.
1b. Internet
In an era of slowing internet use growth by consumers, China's major internet companies are ramping up efforts to develop an industrial internet with respect to infrastructure investment (cloud), capabilities (AI, big data, industry applications), and organizational restructuring. Their digita-lization initiatives aim to cover a wide range of sectors such as retailing, manufacturing, transportation, and government/smart cities by working with various business partners to form ecosystems. We believe internet companies are enabling various stakeholders (enterprises and govern-ments) to enhance their capabilities to support further urbanization and create better household living in China.
BLUEPAPERM
64
Exhibit 92:Alibaba’s industrial internet footprint
Source: Company data, Morgan Stanley Research
Exhibit 93:Tencent’s industrial internet footprint
Source: IDC, McKinsey Global Institute, Morgan Stanley Research
BLUEPAPERM
MORGAN STANLEY RESEARCH 65
However, as it did with the consumer internet, we believe China is likely to leapfrog other countries in the industrial internet. Digital transformation in the US evolved in three major steps: IT > Cloud > AI & big data, whereas in China these three development steps for enterprises could happen at the same time. We expect Chinese internet companies to play a meaningful role in certain fields of enterprise IT spending, including IT services (cloud infrastructure ser-vices, consulting, implementation, managed services and business process outsourcing) and software (data analytics and business intel-ligence, supply chain, ERP, CRM and vertical industry-specific applica-tions).
China's software and IT services market has significant growth poten-tial given that the country is underspending on software relative to the US ( Exhibit 95 ). According to Gartner, China's enterprise IT spending by segment (ranked from highest to lowest) is: telecom ser-vices, devices, datacenter systems, IT services, internal services, and software. In contrast, the ranking for US enterprises is: IT services, software, internal services, telecom services, data center systems, and devices. Therefore, we forecast China's software and IT services spending to grow to US$200bn in 2030.
Industrial internet carries ample potential...: China's government has announced several initiatives to upgrade the country's manufac-turing sector to monetize the sizeable amount of manufacturing data available. Both the public and private sectors are focusing on technol-ogies such as AI and automation to transform China into an innova-tive, high-tech powerhouse. We think this is further enhanced by the following points: 1) the rising cost of human capital – wages have grown by more than 10% annually over the past eight years; 2) maturing cloud service development – we expect the public cloud adoption rate to move from 4% in 2016 to 16% in 2020; 3) the rapid adoption of mobile internet (60% of the population); and 4) the gov-ernment's strategic focus should fuel the development of the 2B ser-vice market in China.
Despite significant efforts by the government and enterprises in the past couple of years, we believe there are still ample opportunities for further digitalization in both the 2B and 2G segments, in that Chinese enterprises have relatively narrower adoption of digitaliza-tion as compared with US and global enterprises.
Exhibit 94:China’s enterprise IT spending was only 18% that of US enterprises in 2018
169
120
178 177 197
250
210
322
0
50
100
150
200
250
300
350
IT Internet IT Internet IT Internet IT Internet
2016 2017 2018E 2019E
China Enterprise IT Spending vs. Internet Aggregated Revenue
Others Data Center Systems Software + IT Services Internet
(US$bn)
Source: Gartner, Morgan Stanley Research estimates
Exhibit 95:75% of China’s enterprise IT spending relates to hardware, with only 25% related to software and IT services
50% 51% 52% 54% 55% 56% 58%
58% 59% 60% 61% 63% 64% 65%
21% 22% 23% 25% 27% 29%
31%
0%
10%
20%
30%
40%
50%
60%
70%
2016 2017 2018E 2019E 2020E 2021E 2022E
Software + IT Services as % of Enterprise IT Spending
World Wide US China
Source: Gartner (E) estimates, Morgan Stanley Research
Exhibit 96:China's software and IT services spending to grow at a 13% CAGR through 2030...
45 50 57
66 76
87 99
113 127
143 161
180
200
0
50
100
150
200
250
20
18
20
19
E
20
20
E
20
21
E
20
22
E
20
23
E
20
24
E
20
25
E
20
26
E
20
27
E
20
28
E
20
29
E
20
30
E
(US$bn) Software IT Services
Source: Gartner, Morgan Stanley Research estimates
Exhibit 97:…accounting for almost 8% of global spending (up from 3% in 2018)
3.0% 3.3%
3.6% 3.9%
4.2% 4.4% 4.7%
5.1% 5.6%
6.0% 6.5%
7.1%
7.6%
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
20
18
20
19
E
20
20
E
20
21
E
20
22
E
20
23
E
20
24
E
20
25
E
20
26
E
20
27
E
20
28
E
20
29
E
20
30
E
Source: Gartner, Morgan Stanley Research estimates
BLUEPAPERM
66
& Smart Industries Group to offer enterprise digitalization solutions across retail, healthcare, education, transportation, and municipal services. It aims to expand from IaaS to PaaS and SaaS over time, leveraging AI, location-based services, and payment among other core technologies. Baidu is cultivating its new AI businesses (i.e., DuerOS, Apollo and AI Cloud) to tap into the enterprise solutions market and smart city projects. Additionally, Baidu has said it wants to position its AI cloud beyond delivering infrastructure, and it tar-gets to deliver data insights and solutions.
...with monetization to come from the public cloud and other infrastructure: We believe the benefits of digitalization will take time to be realized and that efficiency improvements may initially be difficult to quantify. At the same time, smart cities rely on data while the cloud will be a crucial platform and supplies resources/capabili-ties for data accumulation and processing. We therefore think infra-structure such as cloud computing/storage/platforms is likely to be developed first.
We believe that one of the early successes of China's industrial internet development will be the public cloud market as enterprises shift their storage/computing/application solutions. The public cloud market in China was US$7bn in 2018 sales, according to IDC, making up merely 6% of the global market. This is well below the 10-15% global value share for China's other tech products – PCs, smartphones and servers – implying low public cloud adoption (11%, on our estimates). Nonetheless, we believe China is set for wider public cloud adoption given government support, improving product quality, lower costs, and rising demand for new technology (AI, big data, IoT). We project that China's public cloud market will reach US$18.8bn by 2020, with public cloud penetration rising to 22% from 11% in 2018.
Additionally, information security concerns and the government's efforts to have key products and components 'made in China', should be an important tailwind for domestic players like AliCloud. We fur-ther analyze the competitive dynamics for BAT's cloud businesses: Alibaba: Among China's public cloud companies, we think Alibaba is a clear leader given: 1) its sheer scale, from leveraging third-party datacenters to building in-house, 2) its demonstrated ability to expand, as shown by the Double 11 event, 3) data security concerns should benefit domestic competitors, and 4) it is expanding its product offerings from IaaS to SaaS. Tencent has established a Cloud
Exhibit 98:China’s public cloud market made up only 4% of the global public cloud market in 2018
Global public cloud ~US$183bn
US public cloud ~US$110bn
China’s pu li loud ~US$7 n
Source: IDC, Morgan Stanley Research
Exhibit 99:We project that China's public cloud market will reach US$18.8bn in 2020, with penetration rising to 22%
2.5 4.1
7.3
12.0
18.8
5% 8%
11% 16%
22%
57%
64%
78%
66%
56%
0%
20%
40%
60%
80%
100%
0
5
10
15
20
2016 2017 2018 2019E 2020E
Public cloud revenue in China (LHS)
Public cloud adoption rate (RHS)
YoY (RHS)
(US$ bn)
Source: IDC, Morgan Stanley Research estimates
Internet companies are facilitating the smart city concept: Smart city initiatives are complex projects, involving many interrelated domains such as transportation, public services, healthcare, security, infrastructure (5G and cloud) and other capabilities (AI and block-chain). Chinese internet companies are serving as key enablers and pathfinders for such initiatives and have demonstrated initial prog-ress.
Take, for example, Alibaba's City Brain project in Hangzhou, Zhejiang province. In late 2016, Alibaba announced it was cooperating with the Hangzhou government to develop ET City Brain, an AI-driven project helping the government better manage urban areas. In its 2019 Apsara (cloud computing) Conference, it illustrated City Brain's smart transportation system, which enables traffic light adjustment and allows drivers to adjust travel routes based on real-time traffic predictions, alleviates road congestion in Hangzhou, which was once one of the top five most congested cities in China and now has improved to the national average level. It also helps with the integra-tion of data platforms and systems across multiple government agencies, which speeds up public services. In the past, citizens in Hangzhou had to experience a time-consuming process with a number of visits to various government agencies in different loca-tions get the required documents and procedures done for just one
BLUEPAPERM
MORGAN STANLEY RESEARCH 67
application. Now, citizens can get about 90% of public services ful-filled with at most one physical visit to a local government agency.
E-commerce penetration to be driven by urbanization: We esti-mate that the e-commerce penetration rate (e-shoppers/total popu-lation) was 34% in low-tier cities and rural areas in 2018 vs. 71% in tier 1 and 2 cities, given low internet penetration and less-developed logistics infrastructure. Low penetration in less-developed regions resulted in a mere 44% overall e-commerce user penetration in China, vs. 60% in the US. We believe continuous urbanization along with improved infrastructure will boost the rate to 77% by 2030, or an online shopping population of over one billion. E-commerce's wallet share is also expected to increase, driven by product category
Exhibit 100:Online shopping population to reach 1bn by 2030, driven by urbaniza-tion and improved infrastructure
610
1,095
0
200
400
600
800
1,000
1,200
2018 2030E
(mn)
Source: CNNIC, Morgan Stanley Research estimates
Exhibit 101:E-commerce GMV to Rmb31trn
8,865
31,151
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
2018 2030E
(Rmb bn)
Source: iResearch, Morgan Stanley Research estimates
Exhibit 102:Food delivery average daily transactions to reach 126mn by 2030, driven by urbanization and higher purchase frequency
29.2
125.5
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
2018 2030E
Average daily transactions (mn)
Source: Company data, Morgan Stanley Research estimates
Exhibit 103:Food delivery GTV to reach Rmb2.2trn by 2030
479.0
2,187.6
0.0
500.0
1,000.0
1,500.0
2,000.0
2,500.0
2018 2030E
China Food Delivery GTV (Rmb bn)
Source: iResearch, Morgan Stanley Research estimates
expansion into segments such as fresh groceries as major players improve their e-commerce infrastructure. We forecast e-commerce GMV to reach Rmb31trn in 2030, implying a 11% CAGR from 2018.
Urban appetites to fuel food delivery industry growth: We believe e-commerce platforms for services (i.e., food delivery, in-store services) should continue to benefit from the urbanization theme. We estimate food delivery GTV to reach Rmb2.2trn in 2030, rising at a 13.5% CAGR from 2018, with average daily orders ramping up to 126mn (vs. 29mn in 2018), followed by higher purchase fre-quency on a per-user basis.
BLUEPAPERM
68
Tencent
l Reorganization to expand from consumer to industrial internet: In September 2018, Tencent announced an important reorganization to enable it to embrace the consumer and indus-trial internet. It set up a dedicated Cloud and Smart Industries Group, as it now aims to pursue the growth potential of the industrial internet. It also plans to set up a technology com-mittee, pulling in internal resources and enhancing fundamental research.
l China's second-largest public cloud vendor: Tencent has been strategically focused on its public cloud business, with strong growth momentum from a low base. It has grown to become China's second-largest public cloud vendor, with around 12% IaaS revenue market share in 2H18, per data from IDC.
l Tapping into 2B business by leveraging success in 2C: Tencent has emphasized that it believes the industrial internet will provide the next wave of internet opportunities. It will focus on offering integrated solutions to digitalize enterprises across retail, healthcare, education, transportation, manufac-turing, and municipal services. To do this, Tencent plans to leverage its core AI, big data, cloud, and mini program technolo-gies to bring offline enterprises onto the internet.
Baidu
l Top-line growth slowdown amid structural challenge in online ad and macro risks: Baidu may not be immune to macro risks, and the top-line growth boosted by the Baidu App newsfeed and iQIYI business lines are likely to gradually slow in 2019. In addition, the rise of competitor Bytedance has created structural challenges for Baidu and other apps. We thus expect Baidu to experience slower revenue growth than its China internet peers until new mobile initiatives (i.e., short videos – Haokan video app) start to make meaningful progress, and the commercialization of new AI businesses (i.e., DuerOS, apollo, AI Cloud) takes place.
l Well positioned, but patience needed: We believe Baidu is well positioned for the enterprise solutions market and smart city projects, backed by its 2B business, data edge, technology roots and dedicated investments. Baidu has also seen progress in certain fields (i.e., autonomous driving, IoT ecosystem) with commercialization on the way, which we view as a long-term positive. We believe Baidu will be able to capture 2B/2G oppor-tunities post the ecosystem buildup.
Stock implications
We believe the industrial internet presents ample growth potential for early movers in the 2B and 2G segments, including Alibaba, Tencent and Baidu, and we expect them to be the major beneficiaries when industrial internet investments begin to bear fruit. Each has successful cash cow consumer internet businesses to support early investment in the industrial internet. However, these investments will affect their profitability, especially as it will likely take a long time for profits to materialize.
In terms of their positioning in the data era, we believe Baidu has a better technological foundation, given its core business in search engines. It has specified two strategic focuses in AI: 1) autonomous cars and 2) DuerOS, which is an operating system for IoT products. Nonetheless, Baidu is likely to face structural headwinds in the online ad business because of the rise of Bytedance, so its core business growth could be much slower than either that of Alibaba or Tencent. On the other hand, in terms of user scenarios, we believe Alibaba and Tencent are better positioned, given their richer user and merchant databases. In view of this, we expect BAT to excel in different areas in the data era.
Alibaba
l Data technology as the key edge: We believe Alibaba's closed-loop ecosystem provides good quality data for analytics. We also like Alibaba's new retail strategy as a 2B initiative, as it aims to improve the efficiency of online and offline retailers.
l China's largest public cloud vendor: Alibaba's public cloud services commanded 43% market share by IaaS revenue in China in 2018, according to IDC, which we think lays a solid foundation to grow services for industrial applications. In addi-tion, it is expanding services from IaaS to PaaS/SaaS by pro-viding digitization solutions for enterprises, which could provide future growth.
l Increased investments in new technology: Alibaba set up DAMO Academy to invest in future technologies, and it also set up a semi company to develop proprietary AI chipsets in 2018, demonstrating its ambition to lead in this new area, which has developed and released CPU IP (Xuantie 910), an SoC chip plat-form (Wujian), and an NPU (Hanguang 800) in 2019.
BLUEPAPERM
MORGAN STANLEY RESEARCH 69
Online recruitment
Although we forecast the total employed population in China to remain stable, at around 770-780mn through 2030, urbanization will lift the total number of urban employees from 426mn in 2017 to 585mn in 2030, accounting for 54% of the employed population, per our analysis.
Meituan
l Urban appetites to fuel food delivery industry growth: Meituan is China’s leading e-commerce platform for services capitalizing on the booming food delivery market. We believe Meituan should continue to ride the urbanization wave in China. Take its core business food delivery, for example. The China market has higher growth potential than overseas mar-kets thanks to China's higher population density and consump-tion upgrade trend. In addition, higher order volumes should drive better efficiencies, leading to higher profitability over the long term.
l Traffic redirection to drive future monetization: Meituan has effectively leveraged its success in high-frequency food ser-vices to expand into low-frequency services (i.e., hotels, travel) on its one-stop platform. Moreover, thanks to its industry lead-ership, Meituan’s logistics infrastructure and merchant solu-tions are superior to peers, in our view. We believe Meituan will continue to empower merchants in a fragmented service supply chain.
Exhibit 104:Number of urban employees
40%
45%
50%
55%
60%
65%
70%
75%
80%
0
100
200
300
400
500
600
700
2013
2014
2015
2016
2017
2018
2019E
2020E
2021E
2022E
2023E
2024E
2025E
2026E
2027E
2028E
2029E
2030E
Number of urban employees % of total employment(mn)
Source: NBS, Morgan Stanley Research estimates
According to the China Internet Network Information Center, China's mobile internet users reached 817mn in 2018 thanks to the improving penetration of low-cost smartphones. We expect online recruitment to keep increasing on the back of mobile penetration. According to iResearch, the number of online job seekers will increase 8%, to 193mn in 2019, despite the weak macro economy, and we expect online job seekers to further increase to 316mn in 2030, representing 41% of the total employed population.
Exhibit 105:Number of online job seekers
10%
15%
20%
25%
30%
35%
40%
45%
0
50
100
150
200
250
300
350
2013
2014
2015
2016
2017
2018
2019E
2020E
2021E
2022E
2023E
2024E
2025E
2026E
2027E
2028E
2029E
2030E
Number of online job seekers % of total employment(mn)
Source: iResearch, Morgan Stanley Research estimates
BLUEPAPERM
70
Exhibit 106:Online food delivery market is expected to grow
Further, according to the China Federation of Logistics & Purchasing, the YoY growth of the E-commerce Logistics Index rose steadily to 8% in July 2019 following a bumpy period starting in November 2018. The recovery indicates a positive trend in the development of e-com-merce amid headwinds in the economy; as a result, demand for blue-collar workers from the e-commerce logistics industry should remain resilient, in our view.
In addition, we expect strong job demand in the service industry to persist. Despite near-term macro headwinds, demand for workers from O2O and logistics remains resilient. According to iResearch, online food delivery accounted for 16.3% of the total O2O market in China in 2018, showcasing the rapid growth of the past five years. Analysys projects that the total market size of online food delivery will expand to Rmb934bn in 2021, with a three-year sales CAGR of 28%, underpinning continuing demand for workers.
Recently, Alibaba's local service arm launched an open platform to digitalize brick-and-mortar retailers, and it already covers more than 10,000 large-sized supermarkets and nearly 200,000 chain stores in 676 cities. The expansion of delivery service from catering to other segments is also driving up demand for blue-collar workers.
Exhibit 107:Recovery in E-commerce Logistics Index in 1H19
Source: China Federation of Logistics & Purchasing, Morgan Stanley Research
BLUEPAPERM
MORGAN STANLEY RESEARCH 71
1c. Tech Hardware and SoftwareOverview: Asia's tech companies are key enablers of Urbanization 2.0, as smart cities require security solutions, IoT net-
works comprising sensors, MCUs, communication ICs and power ICs, and extensive software for data manage-ment and analysis.
Key forecasts: The total value of connected devices should more than double from US$301bn in 2018 to US$684bn in 2030, we estimate, with the major driver being public sector infrastructure investment. The household segment will eventually outgrow the public sector, given low penetration currently. Meanwhile, the total market size of soft-ware and IT services could grow fivefold, to US$200bn by 2030, per our projections.
Investment implications:
Tech companies geared to IoT and 5G should benefit from Urbanization 2.0, including TSMC (leading-edge semi foundry), MediaTek (5G and IoT chip supplier), Win Semi (5G PA foundry), Realtek ( connectivity chip), Will Semi (sensors), ASM Pacific (volume play for semis), Macronix (NOR), GigaDevice (NOR+MCU); HIKVision (surveil-lance), ZTE and Accelink (telecom equipment); Foxconn Industrial Internet (hardware + software solutions).
We believe software vendors will become major enablers of smart cities, and we like names exposed to the dig-ital transformation of industries, such as domestic software solutions leader Yonyou and construction software leader Glodon; we also like vendors with direct exposure to smart cities, like cybersecurity leader VenusTech.
Information technology is changing the evolution of cities. Smart cities run on IoT technologies, with data used to improve energy man-agement, reduce environmental footprints, increase the safety of cit-izens, and improve the maintenance of public infrastructure and buildings. A smart city needs an IoT network comprising MCUs, sen-sors, communication ICs, and power ICs, with extensive software. The total solution also comprises an integrated wireless sensor network platform that can monitor, control, and deliver smart city informa-tion and services. The platform can also be interfaced to motion, envi-ronmental sensing, and proximity sensor boards.
New actors will be added to the IoT. Besides smartphones, all kinds of computers, vehicles, streets, buildings, and household appliances will become part of a comprehensive communications infrastruc-ture. The focus is no longer on direct communication between indi-vidual people or devices but on linking countless users, devices and systems with each other. Roads, lights, parking lots, water supplies, and a whole range of smart city applications need sensors, pro-
cessing, and access to information. Smart cities depend on sensors, gateways, control centers and the cloud communicating efficiently through a mesh network with hierarchical elements. Each data gath-ering, aggregation or decision point will have its own requirements that can only be met through flexible, scalable semiconductor solu-tions.
For people to enjoy the full benefits of smart cities, some kind of com-munications system must connect all manner of smart things. Smartphones, tablets, and other portable devices today allow citi-zens to easily connect to many devices to get real-time data. Many IoT applications will not need to stress these new reliability require-ments. Smartphones and many other consumer-facing connected devices are good enough, but other applications such as medical devices, self-driving cars and many industrial/infrastructure IoT applications are mission-critical devices that require cutting-edge technology to guarantee reliability.
BLUEPAPERM
72
Exhibit 109:Smart built-in devices to reach 2.2bn units by 2023
Source: Canalys
Exhibit 108:Smart home devices expected to reach 3.6bn units by 2023
Source: Canalys
Technology companies must understand the entire IoT stack’s requirements and, in some cases, create an end-to-end solution that facilitates market adoption. High-performance, high-bandwidth and multichannel operation is critical, and scalable technology devices, software and solutions are increasingly important. A typical IoT stack with a representative electronic subsystem consists of:
l Sense. Typically the device that contains the sensors. It could be a mobile phone or a medical device that captures a person’s heartbeat.
l Analyze. The IoT platform also has the capability of inter-preting data and sending analytics up through an application that provides insights and alerts to people or providers.
l Communicate. Both the device and the IoT platform it commu-nicates with have the capability to analyze and organize the data received.
l Security. This is a critical component at every step of the journey, from the time the data is collected to the time it is served up in the application.
l Cloud. This is a service outside the IoT stack, which includes the cloud platform, analytics/AI, and ease of cloud integration.
Exhibit 110:IoT stack diagram
Security
Encryption
TPM
Data Centers/ Cloud
(analytics, management and archive)
IoT devices
(Sensors and Actuators)
Communication
Communication
Communication
IoT gateways
(data aggregation, A/D measurement, control)
Edge IT
(analytics, pre-processing)
Source: Morgan Stanley Research
BLUEPAPERM
MORGAN STANLEY RESEARCH 73
Exhibit 111:Connected devices in China: Shipments in 2018 vs. 2030
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
2018 2030E 2018 2030E
Annual shipment Installed base
Public Sector Household Personal
mn units
CAGR = 9%
CAGR = 15%
Source: IDC, Morgan Stanley Research
Exhibit 112:Connected devices in China: Value in 2018 vs. 2030
0
500
1,000
1,500
2,000
2,500
3,000
2018 2030E 2018 2030E
Annual shipment value Installed base value
Public Sector Household Personal
US$bn
CAGR = 7%
CAGR = 12%
Source: IDC, Morgan Stanley Research
Connected device demand explores when everything needs to be connected
Connected device market value to more than double in 2030: Increased connectivity among various devices allows for seamless data transmission and enables AI innovation to enhance quality of life in smart cities. In 2018, total connected device volume amounted to 1.3bn units, among which infrastructure connections and smart-phones were two major applications. We estimate total connected device shipments will increase at a 9% CAGR to reach 3.9bn units by 2030, with a split of 65% public sector, 23% household and 12% per-sonal usage.
In view of already saturated smartphone penetration currently and high value adds in public connected devices, we estimate total con-nected device value will grow to US$684bn in 2030, more than double the level of US$301bn in 2018, implying a 7% CAGR. The con-tribution from public sector applications will also increase to 75% of total value in 2030 vs. 49% in 2018.
Total installed connected device value to amount to US$2.7trn in 2030: We assume infrastructure and household connected devices have replacement cycles of 4-5 years while personal devices are replaced every 2 years. This implies that total installed connected devices will amount to 16.6bn units in 2030, up from 3.1bn units in 2018, or a 15% CAGR.
We therefore estimate the total value of installed connected devices at US$2.7trn in 2030, more than triple the level of US$684bn in 2018. The substantial increase will be fueled by the rapid adoption of connected devices in the public sphere (connected vehicles, infra-structure connections, etc.) and households (lighting, home moni-toring/security, smart speakers, etc.).
Key technology requirements of smart cities
Smart cities will depend on the Internet of Things (IoT) applied on a city-wide basis. The technology required for individual sensing nodes in smart cities will be miniature and affordable, while con-suming very little power. The metropolitan networks for power, communications, transportation, resource management, and other services will be integrated, and all of these areas will be served by countless points of information collection feeding into the cloud.
The ability of an IoT sensor or device to sense its surroundings, com-municate its state, and process collected data to determine the best response to its environment require low-power consumption, high-speed precision, high-performance, integration and affordability.
– Sensors and micro-actuators sense and act. Sensors are going to play a critical role in collecting and processing data across a variety of industries. Sensors must perform for years without interruption or failure. This requires ultra low-power ICs with prolonged battery life through the use of energy harvested. They also vary in complexity, combining multiple elements for temperature, sunlight, radar, lidar or chemical detection, together with analog-to-digital conversion (ADC) and signal amplification, local processing, a communications interface, wireless transmission, a battery, and power management.
Video cameras produce a large data stream, making bandwidth- and power-reduction techniques especially important. Cameras can wake up at intervals, or only when motion is detected. Frames can be scanned within the unit for objects of interest, allowing the selection
BLUEPAPERM
74
We expect demand from the government to grow fastest, with the installation base rising at a 20% sales CAGR during 2018-30. Additional cameras will be installed to deter crime. AI cameras are able to recognize faces and compare them against databases, which can save hundreds of hours as compared with using manpower alone. Traffic monitoring systems combine surveillance cameras from train stations, airports, and main roads, and traffic data is analyzed in real time at the back-end server with AI enabled. Such systems can better manage traffic and avoid traffic jams. For example, in Guangzhou, there are over 80,000 surveillance cameras as part of the traffic monitoring system, covering major roads and high-traffic areas. We expect this will first be applied in urban areas and then penetrate into rural areas in China. Other than human and vehicle identification, these AI cameras are capable of floating object detection, sewage disposal detection, and wild animal detection.
In commercial use, AI cameras can be applied in various sectors, such as retail, manufacturing, education, healthcare, and energy. Surveillance companies such as Hikvision are cooperating with shop-ping malls on pedestrian counting and behavior analysis, with preci-sion manufacturing plants on flaw detection, and with schools on distance education. In some scenic areas, cameras are helping people to view scenery via VR.
We expect the professional surveillance camera installation base to reach 882mn by 2030, at a 17% CAGR during 2018-30. The number of surveillance cameras will increase to 62 per 100 people by 2030 from 16 per 100 people in 2018, we estimate. This would be signifi-cantly higher than the rest of the world, which we expect to have 17 cameras per 100 people by 2030. We expect penetration of AI-embedded cameras to reach 14% by 2030, up from less than 1% in 2019. Growth will be supported by demand from the government (such as the Xueliang Project) and commercial applications such as unmanned supermarkets.
We also expect the consumer surveillance camera market to grow at a 30% sales CAGR during 2018-30. According to IDC, 9.7mn security cameras were shipped to consumers in China in 2018, mainly for home security. We believe more households have demand for these cameras, which can recognize strange faces, detect abnormal behav-iors such as falling down, and monitor and record the important moments of babies or pets. IHS estimates that the number of house-holds with cameras will increase to 67mn by 2030 from just 8.3mn in 2018, and camera installations per household will increase to 5.9 by 2030 from 2.3 in 2018.
of only the most essential information for transmission. Advanced compression technology will keep to a minimum the bandwidth needed for communication. Object recognition and compression are enabled by video processors with acceleration for high-speed signal processing.
– High-performance processors, ultra-low power microcon-trollers and security process information. The aggregation of transmitted data from multiple sensor and camera nodes is process-ing-intensive and requires high-performance computing to evaluate incoming data and decide what action should be taken locally. It is more efficient, for example, for traffic control on part of an expressway to be processed at the edge rather than the main gateway.
– Ultra-low power communication modules. Wireless communi-cation plays a pivotal role in enabling a wide network of sensors, driving the need for stable technologies with low power require-ments. As such, advanced CMOS technologies are needed to enable low-power consumption and low cost.
l Analog and mixed signal components translate the information l Connectivity protocols, cloud providers, analytics and system
integratorsl Power and energy-management modules keep the system run-
ning in the most energy-efficient way. Efficiently managing sleep cycling is critical to IoT implementation.
– Storage efficiency. Given the amount of data that the IoT is going to generate, storage requirements and costs should not increase exponentially, but rather data optimization and filtering should be used to limit storage. Software-defined storage can be used to bal-ance and optimize the usage of available storage.
– AI-embedded software platform for data management and cyber security. AI-embedded software will allow efficient data man-agement in the integrated platform and, most importantly, provide effective feedback and/or predictions to assure sound operation in cities. Cyber security is also increasingly important to prevent attacks and reduce unnecessary disruption.
Surveillance cameras in smart cities
With AI embedded, surveillance cameras are becoming an important tool to improve efficiency and productivity in smart cities. We esti-mate the professional surveillance camera market will grow at an 8% sales CAGR during 2018-30, fuelled by new installations until 2025 and replacement demand thereafter.
BLUEPAPERM
MORGAN STANLEY RESEARCH 75
Trend toward localization creates new opportunities for Greater China semiconductors
The potential global economic slowdown is likely to hit demand for tech products given their discretionary nature. We are therefore unsure whether logic semi inventory digestion will continue, thus making the cycle recovery slower than expected. Although we are starting to see some inventory depletion, the demand outlook is still uncertain.
Although the timing of a recovery in the logic semi cycle is unclear, we think the trend towards localization will continue, and offset potential order cuts in smartphone units in 4Q19. In May, we thought US semi components would run out in 2-3 months. However, demand from Chinese system houses has sustained, given better allocation of critical component usage. Also, US semi companies have resumed some shipments of legacy components such as 4G FPGA for 4G base stations.
With trade tensions leading to uncertainty for the supply chain, we believe the push for China to localize its semiconductor industry will continue. We believe the design/R&D would be a way for China's semi industry to pursue an industry upgrade. And this is unfolding rapidly, with Huawei’s captive design house Hisilicon launching several proj-ects in the Taiwan supply chain.
Increasing focus on efficiency and innovation drive long-term demand for software and IT services
We believe rising demand for software and outsourced IT services is being driven by Chinese enterprises' increasing focus on internal effi-ciency to combat slowing top-line growth and higher labor costs. Further tailwinds are coming from technological innovation (in areas like artificial intelligence), previous underinvestment in software (historically low software spending as a % of GDP) and government policy support (top down digital transformation requirement in public sector and the general promotion of domestic technology in private sector). We expect China's overall software and IT services market to grow at a 15% CAGR through 2022, then at a 13% CAGR through 2030. We forecast software spending as a % of nominal GDP will more than double from 0.34% in 2018 to 0.76% in 2030. Cloud services, industrial-related software and cybersecurity are potential submarkets that could outgrow the industry average.
Exhibit 113:HIKVision: AI cameras can identify persons and their vehicles once facial features are analyzed at the back end
Source: AI exhibition at HIKVision's office building, Morgan Stanley Research
Within Greater China semiconductors, we think TSMC and MediaTek are best positioned. TSMC accounts for 50% of global logic chip pro-duction, and it is likely to be a key enabler of future 5G and AIoT appli-cations. Its leadership should be sustained for at least another five years, in our view, when Moore’s Law approaches to 3nm.
Will Semi
Will Semi is the #3 vendor of CMOS image sensors worldwide, with 12% revenue market share in 2018, behind Sony and Samsung. As we move toward a more connected world, more and more electronic devices will have to be empowered with machine vision functions to communicate with each other. This increase in camera usage will inevitably translate into more sensor demand, allowing Will Semi to address a larger market.
GigaDevice
GigaDevice is the largest NOR Flash and MCU design house in China. The company is the key beneficiary of IoT growth in the Chinese market, as both NOR and MCU are critical components in IoT devices. The company has also invested in D-RAM manufacturing, which is a key strategic move for Chinese semi localization.
HIKVision
In our view, HIKVision is well positioned in the video surveillance market thanks to a combination of scale and the ability to offer com-prehensive customized solutions in smart city development. We expect its revenue growth to remain at 20% YoY in 2019 and 2020 with sustained gross margin at 40%, thanks to China domestic demand recovery. In the mid to long term, we anticipate additional sustainable revenue growth from: (1) AI enlarging the surveillance industry market size; (2) expanding business into new machine vision related fields, such as mobile robots/drones/auto cameras/industrial cameras; and (3) vertical integration of storage and AI chipset.
Accelink Technologies
Accelink is one of the few domestic suppliers with in-house chip man-ufacturing capabilities. Ongoing demand for transmission compo-nents in the 5G era presents a bigger opportunity than does wireless business for Accelink; the datacom segment could present further
Key challenges for technology companies
IoT is a nascent and fragmented market with different end-cus-tomer needs. What the IoT industry seems to lack is consistent stan-dards that enable interoperability and security of data. It is impossible for a single company to possess all the technologies or provide a blanket solution to various sub-segments of the IoT. As such, building an ecosystem of companies is critical, but striving to be the control point that manages the whole solution on behalf of eco-system partners would be extremely challenging. Another challenge is on the trusted platform where numerous security gaps are created during the integration and implementation of IoT systems.
Making sense of the data. Data production rates from devices and machines are exponential, and processing and analyzing this data is challenging. New approaches are required, such as search algo-rithms, computation, visualization, and cloud-based processing. In addition, data privacy, data security and data quality research are essential.
The coming limitations of Moore's Law will be a challenge for the development of cheaper and more powerful semiconductors that will enable the data era. Fundamentally, companies are not in the business of continuously shrinking transistors but making attractive returns on investment and building useful products. But computing progress is becoming less predictable as we approach the limits of Moore's Law, impacting performance, power consumption, and the costs of computing. In addition, the required capital investments and operational costs of next-generation factories are likely to rapidly exceed US$20bn within five years. Finally, the industry's ability to create chips that run faster while also using less power ended about a decade ago. To continue advancing computing capabilities at reduced cost with economy-wide benefits will require entirely new semiconductor processes and device technologies.
Dependency on US technology and trade tensions. Dependence on US technology in semiconductors is one of the key hurdles to China's smart city development. For example, GPUs and FPGAs that are used in AI servers are still supplied by US semi companies. US-China trade tensions are forcing supply chain disruption, which is detrimental to smart city development.
High implementation costs. Implementation costs could be higher for AI servers and 5G infrastructure given the need to use leading-edge foundry processes.
BLUEPAPERM
MORGAN STANLEY RESEARCH 77
upside. Both 5G infrastructure and data centers are developing along with urbanization in China. Our price target implies 46x 2019 and 34x 2020 earnings, based on our estimates. Examining historical pat-terns, we note that Accelink has traded at above 40x during upcycles, i.e., 2014-16 (4G cycle) and 2009-10 (3G cycle). With the approaching 5G upcycle, we believe that a valuation around 40x is justified. Further, because of Accelink's in-house laser chip capability, we believe A-share semi stocks can also serve as a comparable valuation reference.
Foxconn Industrial Internet
8K+5G has been the key strategic development for Foxconn Industrial Internet (FII). Its full-range offerings, from 8K cameras, con-tent creation (real-time raw data recording) and processing (cloud storage, network transmission) to data-driven analysis should allow it to win a number of smart city projects, such as those in Shanghai and Guangzhou, over the next 12-18 months. It is also leveraging part-nerships to enlarge its ecosystem, including strategic partners for joint solution development (Advantech, Cognex, SAP), partners for integrated solutions (Yonyou, MegVII, CyberInsight) and service part-ners for local system integration and service support (Henry Waltz, ViTex, IMRobotic).
Yonyou
Yonyou has been the largest enterprise software vendor in China and develops cloud services for further upgrades. It aims to leverage its cloud offering capability to create an integrated big data platform for smart city operation – open for multiple vendor solutions to enhance data management and analysis. Its AI-empowered software analytics can also help the city operate efficiently and safely. Policy support as an overseas vendor substitution fuels Yonyou's business potential in China.
Glodon
Glodon is a leading construction software vendor and a pioneer in the industry's digital transformation. With building information mod-eling (BIM) technology, Glodon's construction management product helps constructors to save material and labor costs, optimizes pro-cesses to save time, and increases security in the construction stage. It can also help building owners to do predictive maintenance, secu-rity checks and energy management. We expect the government's top-down encouragement of BIM adoption in smart cities as well as bottom-up constructors' increasing focus on efficiency to drive the long-term growth of Glodon's product.
VenusTech
VenusTech, as the biggest cybersecurity vendor in China, is well posi-tioned to benefit, with the most comprehensive product portfolio, especially for industrial internet and public infrastructure systems. VenusTech has established close relationships with high-profile cus-tomers, such as governments and big SOEs, and built a strong brand image in cybersecurity. Security operation service (SOS) is underpen-etrated in China. The construction of smart cities could be an inflec-tion point for managed security services, due to the centralization of sensitive data and related security demand. VenusTech recently issued convertible bonds and plans to use the proceeds to build four security operation centers. We expect managed security will drive VenusTech's security service revenue at a 37% CAGR, 2018-21, with managed security's revenue contribution rising from 17% in 2018 to 24% in 2021.
BLUEPAPERM
78
Exhibit 115:Investment Theme #2: Summary
Th
em
e 2
: D
igit
ali
zati
on
of
old
-eco
no
my i
nd
ustr
ies
Insurance
Insurers with leading positions in top-
tier cities and advanced technological
capabilities
Insurance penetration: 9%
(vs. 4.3% in 2018)
AgribusinessAgribusiness entities with strong brand
name and GM seed pipeline
GM corn and soybean seed application:
50%
(vs. 0% today)
Utility players with competitive edge in
smart grid
Share of clean energy in capex: 60%
(vs. 40% today)
Utilities and Power
Equipment
Banks More market-oriented banksCredit growth: 7% 2018-30 CAGR
(vs. 17% in the past decade)
Early movers in EVs and autonomous
vehicles
EV Sales:
8.4mn units (8x vs. 2018)Autos
Top Stocks
• S.F. Holding (002352.SZ)
• NARI Technology (600406.SS)
• Ping An Bank (000001.SZ)
• Ping An Insurance Company (2318.HK)
• Yuan Longping High-tech
Agricultural
(000998.SZ)
Logistics companies with strong R&D
investment
Express Volume:
300bn deliveries (6x vs. 2018)Logistics
Key Beneficiary Key 2030 Forecasts
Source: Morgan Stanley Research
Investment Theme #2: Digitalization of Old-Economy Industries
BLUEPAPERM
MORGAN STANLEY RESEARCH 79
shared, electric and autonomous vehicles. Along with the growing impact of millennials, the auto value chain will be reshaped by a rising focus on environmental issues and energy efficiency, as well as rapid transition into the new economy.
Wealth created by China Auto 2.0 may surpass prior cycles: In the new mobility cycle, we expect non-traditional automotive players, such as autonomous driving solution providers, entertainment/telematics content providers and shared mobility providers, will con-tribute up to 80% of China's market capitalization in the auto value chain by 2040. We believe the market is overly conservative in its view of new auto technologies, and while we acknowledge these changes are some way in the future, we believe the impact they will have is beginning to be felt now and is being reflected in investor per-ceptions, strategic actions (M&A, tie-ups), and stock valuations. In the future, we think car sales volume is likely to be a less relevant metric when evaluating the auto business; instead, we think innova-tion, miles traveled and ARPU will become more important metrics in boosting market capitalization.
2a. AutosOverview: China's smart cities will drive three important trends in the auto segment: shared mobility, electric vehicles, and
autonomous driving.
Key forecasts: By 2030 we expect 27.6mn passenger vehicles to be used in shared mobility in China, accounting for 10% of the total car parc vs. only 2% in 2018. We forecast that 33% of passenger vehicles sold in China in 2030 will be elec-tric, vs. only 4% in 2018. We expect 20% of the PVs sold in 2030 to feature L4 or L5 levels of autonomous driving, compared with none in 2018.
Investment implications:
OEMs with sufficient R&D resources should benefit, such as SAIC, Dongfeng and GAC. In auto parts, early movers into EVs/vehicle autonomy realms, like Huayu, Nexteer, Navinfo, should benefit from the growing electri-fication of fleets and the development of smart traffic that should hasten the adoption of autonomous driving.
New opportunities from China Auto 2.0
China continues to drive new mobility adoption: China is the world’s largest car market in terms of the number of new cars sold per year, and we think its use of electric and autonomous vehicles will also lead the world in the next decade. This will profoundly influence the pace of technological adoption globally. However, rather than seeing traditional OEMs compete with tech giants, as in developed markets (Detroit vs. Silicon Valley), in China we see closer collabora-tion between internet bellwethers like Baidu, Alibaba and Tencent (BAT), and traditional OEMs in developing electric and/or autono-mous vehicles in terms of hardware and software. We believe such omni-channel collaboration will redefine the future of mobility in China at a faster pace than in the rest of the world.
Major changes in auto technologies/operation will create new dynamics in China: We believe new auto market entrants will accel-erate their efforts to make inroads into this Auto 2.0 market of
BLUEPAPERM
80
Exhibit 117:Potential industry value generated from Auto 2.0
Source: Morgan Stanley Research estimates
Exhibit 116:Reshaping the China auto value chain's capital allocation
Source: Morgan Stanley Research estimates
of EV portfolios will likely take place in association with the suspension of investment in ICE. Migration could take more time but it's unlikely that OEMs and tier 1 players would sud-denly turn back to ICE, given the lengthy lead time required for new model development. Toyota officially targets 50% of its global sales to come from EVs by 2025, five years ahead of its previous schedule. BMW announced it was speeding up the plan to electrify its model range by introducing 25 EVs by 2023, two years ahead of its previous target.
l Likely irreversible pursuit of electrified user experience: 30%+ of new car purchases are by millennials – a tech-savvy group of early EV adopters. The rise in the car-buying mix of the younger generation bodes well for the shift to EVs.
The major pushbacks from investors regarding our EV forecasts include:
l Will there be real demand for EVs to back our bullish forecasts, as current demand is policy-driven? Although it is true that cur-rent demand is mainly policy-driven, the cost of EVs has been falling at a rapid pace thanks to scale benefits and tech upgrades. We look for EV demand to meaningfully take off beyond 2022 when they reach cost parity with ICE vehicles. Meanwhile, quality upgrades will give EVs more traction with Chinese consumers, especially younger ones.
l Can the EV supply chain in China support such growth? We believe China’s EV supply chain is competitive on all fronts, from materials to components, given the country's early-mover advantage. China is also opening the market more aggressively to global players, which should enhance the development of the industry through foreign capital, talent and technology.
Outlook for China's electric vehicles
We remain optimistic on the long-term growth trajectory for EVs: We stay constructive on EV demand over a three- to five-year timeframe. We look for 1.2mn units of passenger EV sales for 2019 (vs. consensus for 1.4-1.6mn units), but forecast a 19% CAGR over 2018-30, to 8.4mn units. That implies 33% market penetration by end-2030.
Our positive stance mainly rests on the following reasons:
l Multiple regulatory levers for the Chinese government to pull: We believe EVs remain a key strategic focus area for the gov-ernment, with an eye toward global automotive dominance.
l Strong model pipeline: Most if not all global OEMs will have more serious model launches from 2019 – for example, Mercedes-Benz EQC, BMW iX3 and Audi e-tron. Meanwhile, EV startups and local brands are also vying to launch EVs, with over 50 new models scheduled for 2019-20. We believe the profusion of new model launches should further increase EVs' mindshare.
l Huge sunk costs from investments in China, from both the gov-ernment and private sector: According to the Ministry of Industry and Information Technology (MIIT), the accumulated investment in developing China's EV market topped Rmb2trn by January 2019. We believe this will hinder any potential attempt to change course by either the auto companies or the government.
l Major global OEMs and tier-one suppliers have brought for-ward their EV technology/product roadmaps: The development
BLUEPAPERM
MORGAN STANLEY RESEARCH 81
China AD 2.0 (2022-25) – L4 autonomy – Tech driven: Parts/system makers we talked to expect L4 models to come to market as early as 2021 and achieve mid-sin-gle-digit penetration of new car sales by 2025. The key contents added will be execu-tion functions (steering, braking, parking, acceleration) and algorithms (chips, MCUs), and applied in certain areas (e.g., logistics, shuttles).
China AD 2.0 (2026-30) – L5 autonomy – Morality driven: Most of the industry con-tacts we talked to expect L5 autonomous driving vehicles to hit the road in China after 2025, as the moral and ethical issues have yet to be clearly defined. We think L5 vehi-cles will first be adopted by fleet runners for logistics and shared PVs, rather than by pri-vate users. Fleet operators would have a stronger incentive, i.e., large savings in labor costs, scale benefits from higher vehicle uti-lization, and additional revenue streams from a sizable road data pool. By 2030, we expect 20% of new vehicles sold to be equipped with L4 or L5 autonomous driving, vs. only 5% in 2025 and zero in 2018.
Exhibit 118:We remain constructive on the long-term outlook for EVs in China – we forecast a 19% sales CAGR over 2018-30
Source: CAAM, Morgan Stanley Research estimates
Outlook for China's autonomous driving
We expect China's autonomous driving development to go through three stages:
China AD 1.0 (2018-21) – L1-3 autonomy – Competition driven: We expect L1-2 models to gradually penetrate the market and make up 25% of new car sales by 2020, as we notice OEMs are leveraging new fea-tures and smart car branding to boost sales. We expect to see incremental demand for radar/cameras, HD navigation maps, and car connectivity functions. OEMs and the supply chain, per our checks, now expect volume-manufactured L3 vehicles inte-grating multiple ADAS functions to come after 2020.
Exhibit 119:Investment focus for different stages of China's autonomous driving (AD) development
Source: CAAM, Morgan Stanley Research estimates
Exhibit 120:Autonomous driving levels explained
Source: SAE International, Morgan Stanley Research
BLUEPAPERM
82
Shared mobility and smart traffic systems to solve congestion
Expect shared mobility to improve the utili-zation rate of vehicles: We forecast 10% of China's total car parc will be used in shared mobility in 2030, vs. only 2% in 2018. We expect a vehicle used in shared mobility could travel 5x more per year than a privately-owned vehicle, and the ride-sharing service could reduce the number of vehicles needed on the road, improving the efficiency of the traffic system.
Smart maps to improve the efficiency of road traffic: It has become a common practice for people living in cities to check road condi-tions before going out to avoid traffic conges-tion. NavInfo (002405.SZ) in China launched dynamic traffic information digital map prod-ucts. The dynamic traffic information is devel-oped and operated by Cennavi, a subsidiary of NavInfo. The company supplies products and services in 340+ cities across China as well as some countries and regions in Southeast Asia. It has over 4mn vehicle users and over 500mn Internet users, and more than 100 government and enterprise users.
Vertical parking lots to expand capacity for total vehicles: The Volkswagen Group has built two car towers at Autostadt in Wolfsburg, Germany. According to Volkswagen, each of the fully automated, high-rise stacks has space for up to 400 vehicles. The new cars are rolled over from the neigh-boring Volkswagen plant using a robotic-pallet system mounted on rails. The cars are loaded into and fetched from the towers using two 'car shuttles' or lifts per tower, each servicing 180° of the silo. We believe similar designs could serve as potential solution for the lack of parking space in major cities in China.
Exhibit 121:NavInfo provides lane-level road condition information and prediction
Source: NavInfo
Exhibit 122:Autostadt car towers
Source: Autostadt
Exhibit 123:China's cities have insufficient parking slots
Source: Xinhua, People.com.cn, Morgan Stanley Research.*Beijing's data is residential only: residential parking slots divided by residential vehicles
BLUEPAPERM
MORGAN STANLEY RESEARCH 83
Overview: Thanks to the development of city clusters and new technologies, we expect China's logistics costs to fall dra-matically. We expect 2030 express volumes to be five times higher than in 2018, with fulfilment times cut in half.
Key forecasts: – Logistics costs as a percentage of GDP: 14.8% in 2018 and 10% in 2030.– Express volumes: 300bn deliveries in 2030, up from 50bn in 2018.– Delivery times: 12 hours within city clusters and 24 hours nationwide.
Investment implications:
We like logistics companies that invest more in R&D, such as S.F. Holding and Deppon Logistics.
2b. Logistics
Exhibit 124:A snapshot of technology applications in the logistics industry
Source: Deloitte
BLUEPAPERM
84
Lower costs
We expect social logistics costs to drop from 14.8% of GDP in 2018 to 10% in 2030, vs. 8% for the US in 2018.
Exhibit 125:China: Social logistics costs as a % of GDP
10%
12%
14%
16%
18%
20%
22%
24%
-
2,000
4,000
6,000
8,000
10,000
12,000
14,000
19
92
19
94
19
96
19
98
20
00
20
02
20
04
20
06
20
08
20
10
20
12
20
14
20
16
20
18
China: Logistics cost (Rmb bn) as % of GDP
Source: CEIC
How to achieve lower costs
l Supply chain: Less administrative work; lower inventory levels; lower risks (i.e., insurance fees); lower damage rates
l First mile: Optimization of cargo loading; higher utilization for trucks; less administrative work; cost-saving from the use of unmanned drones
l Logistics hubs: Automation and robots replacing human laborl Line-haul: Unmanned trucks; route and fuel-consumption opti-
mization; remote control; 'road to rail'l Last mile: Data analytics and dynamic planning; automation
(i.e., smart lockers, robots and unmanned drones); electric vehi-cles; less administrative work
According to PwC, logistics costs could drop by 47% between 2018 and 2030 worldwide.
Exhibit 126:China: Breakdown of social logistics costs (2018)
Transportation
cost
52%
Storage cost
35%
Overhead
expenses
13%
Source: NDRC
Faster fulfilment
Faster shipments: Shipment speeds can be increased two ways: faster line-haul shipments and faster last-mile delivery. For line-haul, railways could gradually replace trucks in long-haul transportation. The maximum speed of trucks is required to be no more than 120km/h in China, while the speed of railways can reach 160km/h, 250km/h and even higher. For last mile-delivery, we believe unmanned drones, which can easily reach low-density suburban areas, will provide low-cost instant delivery options.
Lower efficiency loss: Efficiency loss happens at certain stages, such as (1) pick-up, (2) sorting, (3) line-haul, and (4) delivery. In each area, we expect efficiency to be improved by adopting new technolo-gies or hardware.
l Pick-up/delivery: smart lockers, unmanned dronesl Sorting: automation, robotsl Line-haul: unmanned drones/trucks, high-speed rail to allow
shipment at night
Moreover, with the help of big data, fulfilment times can be cut as optimized transport routes are adopted.
More efficient supply chain: In manufacturing & assembly, IoT and big data will enable predictive demand forecasts and real-time reac-tions to changes in demand and supply. This will reduce lead times and increase the utilization rate of warehouse space and other resources.
BLUEPAPERM
MORGAN STANLEY RESEARCH 85
Exhibit 127:Case study: How a parcel gets from Shanghai to Nanjing
Source: Morgan Stanley Research. Note: Parcel delivered in August 2019
For express parcels, by 2030, we expect fulfilment times to fall to 12 hours within city clusters (vs. 24 hours in the Yangtze River Delta today) and 24 hours nationwide (vs. 2-4 days today).
In September 2019, Cainiao, together with ZTO, Yunda, YTO, and other express companies, announced 24-hour fulfilment for parcel shipments that start and end in 26 cities in the Yangtze River Delta.
Larger volumes
We expect China's express deliveries to grow at a 16% CAGR in 2019-30, to 300bn per year, driven by increasing disposable income per capita and the e-commerce boom. We expect parcels per capita to reach 365 annually in major city clusters and 110 annually outside these areas. In 2017, Jack Ma mentioned at a smart logistics summit that he thinks annual express volumes in China will reach 365bn by 2025 (Securities Daily, May 24, 2017). This is more aggressive than our forecasts.
Exhibit 128:Express parcel volume per capita (2018 vs. 2013)
Exhibit 129:2030 population in major city clusters and other areas
Major Clusters 41%
Other Cities 52%
Remaining Admin Areas
7%
Source: Morgan Stanley Research estimates
BLUEPAPERM
86
Stock implications
We prefer companies with higher R&D and IT spending. We expect the application of new technologies and hardware to significantly help players to lower costs, improve efficiency/service quality, leading to competitive advantages and higher pricing power compared with their peers. We also expect innovations in logistics to help reshape traditional business flows and cash flows in the economy, leading to new business areas for logistics players.
Exhibit 130:China's express firms: R&D/IT spending as a percentage of total sales
0.0
0.5
1.0
1.5
2.0
2.5
3.0
SF
De
pp
on
Yu
nd
a
ST
O
YT
O
FY18 1H19
Source: Company data. Note: S. F.: R&D expensed and capitalized; Deppon: IT spending
Exhibit 131:Absolute spending on R&D/IT in 2018 (Rmb mn)
-
500
1,000
1,500
2,000
2,500
SF
De
pp
on
Yu
nd
a
ST
O
YT
O
FY18 1H19
Source: Company data. Note: S. F.: R&D expensed and capitalized; Deppon: IT spending
Case study: S.F. Holding
Exhibit 132:S.F. Holding: Application of technologies in logistics
Source: Company data
BLUEPAPERM
MORGAN STANLEY RESEARCH 87
Case Study: Deppon Logistics
Exhibit 133:Deppon: Application of technologies in logistics
Smart Sorting Hub Digital Twin Finding CargoDi Lu
System
Full Process
Visualization
Intelligent GIS
Services
Volume
Forecasting and
Load Balancing
Intelligent Package
Collection and
Delivery
Electronic
WaybillSmart Voice
Multi-Functional
Handset
Mixed Parcel
Sorting
Second
Generation PDA
Deppon
D Plus
Self-driving
Cars
Digitalization Improves Management
Intelligent Services Improve Customer Experience
New Technology Hardware improvement
Source: Company data, Morgan Stanley Research
BLUEPAPERM
88
Overview: Given the Chinese government’s commitment to fighting climate change, we expect China to continue to increase clean energy (mainly nuclear, hydropower, wind, and solar) capacity. By 2030, the grid system will involve a com-plex distribution network to integrate distributed renewable energy and electric vehicles. Also, the grid system will need to embrace more renewable energy (mainly wind and solar). A smart grid is needed to ensure the safety and reliability of the power system.
Key forecasts: – The power generation capacity of non-fossil fuels will increase to 60% by 2030 from 40% in 2018, driven by solar, wind and nuclear, per our forecasts. As a result, power generation from non-fossil fuel sources will reach 37.5% in 2030 from 29.8% in 2018.
– We forecast that China’s investment in the smart grid will increase 2.6x, to US$80bn during 2021-30 vs. US$30bn over 2011-20.
Investment implications:
We expect companies with exposure to nuclear power plant operations, such as CGN Power, and those exposed to smart grid equipment manufacturing, such as NARI Tech, will benefit.
2c. Utilities and Power Equipment
High targets for non-fossil fuel generation by 2030
The Chinese government has committed to reducing the proportion of fossil fuel consumption, and aims to further increase non-fossil fuel generation to 50% in 2030, according to 'The revolution strategy of energy production and consumption (2016-2030)' announced in December 2016. China is well on track to accomplish its goal of non-fossil fuel generation of 30% in 2020, compared to 26.9% in 2015 and 19.2% in 2010.
China's total power capacity reached 1,900GW, including thermal power of 1,147GW (60.4%), hydropower of 350GW (18.4%), wind power of 184GW (9.7%), solar power of 175GW (9.2%), and nuclear power of 45GW (2.3%) as of the end of 2018. Total electricity genera-
tion in 2018 was 6,990bn kWh, of which non-fossil fuel generation accounted for 30.9%, contributed mainly by hydropower, wind, nuclear, and solar at 17.9%, 5.2%, 4.2%, and 2.5%, respectively.
We expect China to continue to contain new thermal power capacity during 2021-30, and add more wind and solar capacity, which are more economical and flexible and also less controversial than devel-oping hydropower and nuclear power. We expect acceleration of nuclear approvals in 2020 upon commissioning of China's first Hualong One reactor.
The power generation capacity of non-fossil fuels will increase to 1,856GW or 60% of total capacity by 2030 from 907GW or 40% of total in 2018, driven by solar (26.9%), wind (14.8%) and nuclear (3.7%), based on our forecasts. As a result, power generation from non-fossil fuel sources will reach 37.5% in 2030 from 29.8% in 2018.
BLUEPAPERM
MORGAN STANLEY RESEARCH 89
Exhibit 134:Cumulative capacity breakdown
-
500
1,000
1,500
2,000
2,500
3,000
3,500
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
E
20
20
E
20
21
E
20
22
E
20
23
E
20
24
E
20
25
E
20
26
E
20
27
E
20
28
E
20
29
E
20
30
E
Solar Nuclear
Wind Hydropower
Thermal
(GW)
Source: China Electricity Council, Morgan Stanley Research estimates
Exhibit 135:Power generation breakdown
-
2,000
4,000
6,000
8,000
10,000
12,000
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
E
20
20
E
20
21
E
20
22
E
20
23
E
20
24
E
20
25
E
20
26
E
20
27
E
20
28
E
20
29
E
20
30
E
Solar Nuclear
Wind Hydropower
Thermal
(TWh)
Source: China Electricity Council, Morgan Stanley Research estimates
Future grid system: Embracing renewable energy plus complex distribution networks
China will continue to connect to renewable energy, and we forecast its capacity contribution will rise to 23.3% in 2020 and further to 33.5% by end-2025. Besides the scalable renewable projects trans-ported via transmission networks across vast distances, the govern-ment is also encouraging renewable energy to be generated locally and connected directly to distribution networks.
Besides greater fluctuations on the supply side, a new form of energy demand – electric vehicles – is been emerging. EV charging infra-structure has experienced a high double-digit CAGR in the past five years, and is likely to maintain high growth in the future, driven by rising demand from EVs in light of advances in technology, changing economics, and climate concerns. Increasing EV penetration will raise the need to reconfigure distribution networks to alleviate con-gestion caused by EV charging demand. Energy demand will become more dynamic and complex.
Exhibit 136:The future of the power grid system: Embracing more renewable energy for transmission involves a complex distribution network integrating distrib-uted renewable energy and electric vehicles
Smart
dispatch system
High voltage to
medium voltage
primary substation
Medium voltage to low
voltage secondary
substation
High voltage
step up
substation
Power flow
Information flow
Storage
Source: Morgan Stanley Research; Note: Red icon stands for new elements in the grid system
BLUEPAPERM
90
Grid spending to remain high over 2021-25, requiring more smart grid/secondary equipment
Smart grid/secondary equipment refers to software-oriented prod-ucts that control, regulate, protect, and monitor the grid system. With the evolution of both transmission and distribution networks, we expect the overall secondary equipment investment penetration in transmission and distribution networks will increase to 20% during 2021-25 and 25% during 2026-30, up from 11% in 2016-20, from both incremental and replacement demand. The replacement cycle of secondary equipment is normally 5-10 years. As a result, we expect demand for smart grid/secondary equipment will see high growth of 2.6x (to US$80bn) over 2021-30 compared US$30bn in 2011-20.
Exhibit 138:Spending on smart grid/secondary equipment to increase by 2.64x during 2021-30 vs. 2001-20
215
568
-
100
200
300
400
500
600
2011-2020e 2021-2030e
(Rmb bn)
2.64x
Source: China Electricity Council, Morgan Stanley Research estimates
Exhibit 137:Growing overall smart grid/secondary equipment demand/penetra-tion driven by grid system evolving
Source: China Electricity Council, Morgan Stanley Research estimates
Stock implications
NARI Technology
Stable competitive landscape in high-end smart grid/secondary equipment market: Given the nature of secondary equipment for ensuring the operational safety of the grid network and the reliability of electricity supply, grid companies (State Grid and China Southern Grid) only have a short list of qualified domestic suppliers, especially for high-end equipment involving control and monitoring functions, despite conducting a open tender for equipment procurement. Secondary equipment includes software-oriented or software and hardware integrated products, thus pricing and gross margins (>20%) are relatively stable. When adopting new functions, the ASPs of upgraded products are likely to be increased. The competitive landscape in the high-end secondary equipment market remains stable. There are a few players, including NARI, XJ Electric, Sifang Automation, Sieyuan Electric, Changyuan Group, and Guodian Nanjing Automation.
Leading market position backed by strong R&D: NARI (ultimate parentco, State Grid Electric Power Research Institute) is actually the R&D center of State Grid for high-end secondary equipment, and has attracted, cultivated, and retained a group of talented experts in the grid system. NARI has replaced international players in the past decade, to become the only supplier of a grid dispatch platform at state and provincial levels. NARI also supplies the state level dispatch system for the Philippines. The company has a higher market share in some other high-end secondary equipment, such as high voltage level substations (>35%), high voltage level relay protection equip-ment (>40%), as well as master stations (50%).
CGN Power
As the Chinese government phases out coal power, the only remaining power sources available will be gas, nuclear, hydropower, biomass, wind, and solar. Comparing various power sources (with the exception of hydropower), we believe nuclear is competitive vs. coal as a power source for base-load, as well as vs. other clean and renew-able power sources including gas, wind and solar. Moreover, among all these sources, nuclear is the most cost-competitive at Rmb430-435/MWh, and it provides a high level of energy security.
China's nuclear power development was halted during 2016-18, with three years of no new approvals, until 2019 when the government approved three projects – Shandong Rongcheng CAP1400, Fujian Zhangzhou and Guangdong Talpingling, both Hualong One units. We expect an acceleration of nuclear approvals in 2020 upon the com-missioning of China's first Hualong One reactor.
BLUEPAPERM
MORGAN STANLEY RESEARCH 91
Overview: Urbanization 2.0 should support consistent credit demand from infrastructure projects, industrial upgrading, and consumers. Smart cities and advanced technologies are likely to enhance banks' efficiency and risk management capabilities, expand the retail client base, improve capital allocation, and help lower financing costs to the real economy.
Key forecasts: Household financial leverage will rise by around 10ppts through 2030, to 56% of GDP, similar to the pace in Japan in the 1960s and early 1970s when the urbanization rate was approaching 70%. Overall system leverage will rise to around 300% of GDP by 2030 from 275% in 2018, on healthy total credit growth of 7-8% annually (vs. 17% in the past decade), per our projections.
Investment implications:
We expect market-oriented banks to be the key beneficiaries of Urbanization 2.0, with Industrial, PAB and CMB remaining our top picks.
2d. Banks
Enablers of Urbanization 2.0
China's banks have in-depth experience and sufficient capital to finance and facilitate infrastructure projects, in our view:
l Infrastructure accounts for around 30% of total credit out-standing in China and requires mostly debt financing, where banks have expertise and long experience. The relationships built between banks and local governments and SOEs in terms of infrastructure cooperation also help, providing a stable funding source for projects and better risk control for banks.
l In addition, as an asset-heavy sector, infrastructure financing requires much capital. Currently around 80% of total system credit comes from the banking system in China, indicating banks have sufficient capacity to continue to play a major role in infrastructure financing.
BLUEPAPERM
92
Exhibit 139:Infrastructure accounts for around 30% of total credit outstanding in China
Amount (Rmb bn) 2016 2017 2018
Manufacturing 20,187 20,080 20,603
Wholesale retail trade 14,083 14,780 14,306
Real estate 17,639 20,529 23,326
Construction (excluding infrastructure related) 11,165 11,923 12,243
Local government bonds 10,625 14,745 18,070Bank restructuring debt 810 810 810
Government credit 23,423 28,990 33,761
Total credit to real economy 204,285 228,348 247,505
GDP 74,359 82,712 89,577
Total real economy leverage 275% 276% 276%
Infra credit % total 31% 31% 30%
Infra credit (incl local government bonds) % total 36% 37% 37%
Source: PBOC, CEIC, Wind, China Trust Association, 01Caijing, P2P Eye, WDZJ, Morgan Stanley Research
BLUEPAPERM
MORGAN STANLEY RESEARCH 93
A beneficiary of more support for credit demand, enhanced efficiency, risk management, and capital allocation, enabled by advanced technologies
More support for credit demand from infrastructure development and industry upgrading
We see still significant growth potential for infrastructure, and the interest burden for local governments is still manageable as a whole (for details see How market misperceptions undervalue China's banks). The infrastructure interest burden is running at around 1.61% of GDP in China in 2019, compared with 1.59% for the US and 1.64% for Japan in 2018, and China's government controls more assets and
resources that could support interest payments in addition to tax revenue. On the other hand, FAI growth has historically been highly correlated with FAI funding growth. As a result, we believe infrastruc-ture credit demand going forward will support credit growth, which will be largely in line with nominal GDP growth.
However, we expect future infrastructure financing to be carried out via loans, bonds (including local government bonds), and ABS rather than, as previously, through non-standard credit assets, as China's financial cleanup puts stricter requirements on asset/liability dura-tion matching, the gradual removal of implicit guarantees, and look-through risk control for underlying assets.
In addition, industry upgrades in smart manufacturing and smart agri-culture will be capital-intensive. This will be another source of credit demand supporting banks' asset growth and profitability, in our view.
Exhibit 140:Infrastructure interest burden accounts for around 1.61% for China in 2019, vs. 1.59% for the US and 1.64% for Japan in 2018
1.70%
1.61%
1.59%
1.64%
1.52%
1.54%
1.56%
1.58%
1.60%
1.62%
1.64%
1.66%
1.68%
1.70%
1.72%
China 2018 China 2019E US 2018 Japan 2018
Interest burden as % of GDP
Source: Wind, CEIC, Morgan Stanley Research
Exhibit 141:FAI growth has historically been highly correlated with FAI funding growth
-
10,000
20,000
30,000
40,000
50,000
60,000
70,000
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
45.0%
FAI (RHS) FAI funding (RHS)FAI yoy FAI funding yoy bn Rmb
Source: CEIC, Morgan Stanley Research
BLUEPAPERM
94
More support for credit demand from Chinese households
We see potential for steadily rising household credit demand alongside improving household financial assets and higher disposable income, facilitated by Urbanization 2.0. We forecast that household financial leverage will rise to around 56% in 2030 from 46% in 2018, similar to the pace of household leverage increase in Japan in the 1960s and early 1970s as the urbanization rate was approaching 70%. We forecast household consumption credit growth of 8.1% annually through 2030. We expect the overall system leverage ratio (total credit as a percentage of GDP) to rise to 300% in 2030 vs. 275% in 2018, implying credit growth at a 7.1% CAGR (vs. 17% in the past decade).
Exhibit 142:With improving household financial assets and higher disposable incomes, facilitated by Urbanization 2.0, we see potential for steadily rising household credit demand
Source: Federal Reserve, Bank of Japan, Bank of Korea, Wind, CEIC, Morgan Stanley Research. Data as of 2018.
Exhibit 143:Japan experienced a similar increase in household leverage as its urbanization ratio rose to 70-75% in the 1960s and early 1970s
2%
12%
22%
32%
42%
52%
62%
72%
82%
60
65
70
75
80
85
90
95
Japan urban population % total Japan HH loan % total (RHS)%
Source: CEIC, Wind, Morgan Stanley Research
Exhibit 144:We forecast household financial leverage to rise to around 56% in 2030, with the overall system leverage ratio (total credit as a percentage of GDP) rising to 300% in 2030
2016 2017 2018 2030E
Total credit to real economy 204,285 228,348 247,505 562,247
GDP 74,006 82,075 90,031 187,416
Total real economy leverage 276% 278% 275% 300%
Retail credit % total 13% 15% 17% 26%
Household consumption credit % GDP 37% 42% 46% 56%
Source: PBOC, CEIC, Wind, China Trust Association, 01Caijing, P2P Eye, WDZJ, Morgan Stanley Research
BLUEPAPERM
MORGAN STANLEY RESEARCH 95
Advanced technologies and smart cities to help improve client base, efficiency, risk management and capital allocation
The Urbanization 2.0 process will also help expand banks' target client bases with improved efficiency. On one hand, banks still largely target more prime customers because of their risk-averse nature and lower loan yield offered as compared with alternative financing channels. Financially healthier households amid the urban-ization process should help enlarge banks' borrower candidate pool. On the other hand, advanced technologies should help enhance banks' efficiency with existing manpower in terms of screening and risk control, thus enlarging their client bases.
We also expect improved risk management capabilities at banks with reduced NPL risks. Smart cities connect households' and cor-porates' data points to arrive at a more comprehensive borrower pro-file via advanced technologies such as 5G, AI, and big data analysis. This should help improve banks' proficiency in risk assessment and credit pricing. In addition, smart city and manufacturing technologies
will make trade related information, product and cash flow more transparent and traceable, which should help reduce trade finance credit risks, a key source of NPL, with a cumulative 30% NPL on trade finance credits digested in the past four years.
More efficient and effective capital allocation enabled by advanced technologies. Before 2017 and amid Urbanization 1.0, China's fast growth was, to a large extent, fueled by excessive credit growth. This has resulted in a waste and misallocation of credit resources and oversupply issues. With the help of more data on busi-nesses, the economy and financial markets, we expect improved cap-ital allocation amid Urbanization 2.0, with more effective use of credit to support the real economy.
Lower operating expenses and credit costs, as well as more efficient and effective capital allocation could help create a wider profit buffer for banks when guided to lower financing cost to better serve the real economy. Support on credit demand will also help with banks' asset expansion and revenue generation.
Exhibit 145:Before 2017 and amid Urbanization 1.0, China's fast growth was, to a large extent, fueled by excessive credit growth...
18.0%
12.4%
10.9%
10.0% 9.3%
8.4%
9.5%
0%
4%
8%
12%
16%
20%
12/2
014
3/2
01
5
6/2
01
5
9/2
01
5
12/2
015
3/2
01
6
6/2
01
6
9/2
01
6
12/2
016
3/2
01
7
6/2
01
7
9/2
01
7
12/2
017
3/2
01
8
6/2
01
8
9/2
01
8
12/2
018
3/2
01
9
6/2
01
9
20
19E
Nominal GDP yoy Reported credit yoy Total credit yoy
Source: PBOC, CEIC, Wind, China Trust Association, 01Caijing, P2P Eye, WDZJ, Morgan Stanley Research. E = Morgan Stanley Research estimates
Exhibit 146:...this has resulted in waste and misallocation of credit resources and oversupply issues
0
10
20
30
40
50
60
70
Jun-0
3
Mar-
04
Dec-0
4
Sep-0
5
Jun-0
6
Mar-
07
Dec-0
7
Sep-0
8
Jun-0
9
Mar-
10
Dec-1
0
Sep-1
1
Jun-1
2
Mar-
13
Dec-1
3
Sep-1
4
Jun-1
5
Mar-
16
Dec-1
6
Sep-1
7
Jun-1
8
Mar-
19
Year-over-year change, %
Nominal GDP
FAI of industrial industryLagging industrial supply vs. demand
Source: CEIC, Morgan Stanley Research
BLUEPAPERM
96
Stock implications
We expect market-oriented banks to be the key beneficiary of Urbanization 2.0. Notably, Industrial, PAB and CMB remain our top picks for the Urbanization 2.0 theme.
l Industrial Bank has traditionally been strong in asset sourcing, especially in the areas of property and infrastructure. The bank also has good relationships with developers, local governments and SOEs. It is also strong in bond underwriting. Urbanization 2.0 should create more credit demand, while the development of capital markets should increase bond underwriting demand and more standardized asset management products. Industrial Bank, in our view, should be a key beneficiary in terms of gaining infra credit and bond underwriting market share. Industrial Bank is also developing its retail arm, with some improvement in retail funding and loan extension, which should also help take advantage of more credit demand.
l PAB has a strong retail business, while it is also developing its corporate business and should benefit from Urbanization 2.0. On one hand, PAB targets mostly well-off clients, a segment which Urbanization 2.0 can help expand. On the other hand, PAB guided that a major focus for its corporate business and WMP underlying assets is infrastructure, an arena in which Urbanization 2.0 should help generate sufficient high-quality assets.
l CMB is well known for its strong retail business. The potential for more household credit demand should help support CMB's loan growth, expand its client base, and contribute to the bottom line.
BLUEPAPERM
MORGAN STANLEY RESEARCH 97
City clusters can improve operational and distribution efficiency: Chinese insurers are already amongst the largest globally in life, non-life and reinsurance. However, their expense ratios are not yet the lowest. Hong Kong insurers tend to have higher operational efficiency than their Chinese peers given that they operate in a compact city. AIA HK's G&A ratio is half that of Chinese peers despite gross premiums being 80% smaller. Its distribution efficiency is also higher, partly because of a high concentration of wealthy people and better city connectivity. As city clusters grow in China, the efficiency gap with Hong Kong insurers should start to narrow. We note that domestic insurers are already catching up by refocusing on metropolitan areas, recruiting better educated agents and utilizing digital tools to improve efficiency.
Overview: Continued urbanization should lift distribution efficiency and reduce claims, while new opportunities are created in agricultural insurance.
Key forecasts: China's total insurance penetration ratio will rise to 9% in 2030 from 4.3% in 2018, with total insurance pre-miums reaching US$2.3trn, implying a CAGR of 13%.
Investment implications:
Insurers with leading positions in top-tier cities and advanced technological capabilities, such as Ping An, should benefit the most. Insurers with dominant positions and strong expertise in agricultural insurance, such as PICC P&C, should also benefit.
2e. Insurance
Exhibit 147:Hong Kong vs. Mainland insurers: G&A ratio*
8.8
7.0
4.1 3.5
.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
Pin
g A
n L
ife
Ch
ina
life
HS
BC
HK
*
AIA
HK
%
*HSBC data as of FY15, others as of FY18
Source: Company data, Morgan Stanley Research
Exhibit 148:Hong Kong vs. Mainland insurers: Agent productivity*
98,278
59,982
7,771 4,392 20,000
40,000
60,000
80,000
100,000
120,000
AIA
HK
PC
A
Pin
g A
n L
ife
Ch
ina
life
Rmb
Case productivity
1.70 1.13 1.22 1.00
*First year premium per agent per month, as of FY18
Source: HKIA, company data, Pi Fsi, Morgan Stanley Research
Exhibit 149:China: Life insurance market projections
Total life premium 1,269 1,586 2,169 2,604 2,626 3,050 3,623 4,364 13,004
Growth rate % 25 37 20 1 16 19 20 13
Penetration % 2.0 2.3 2.9 3.2 3.0 3.2 3.5 3.9 6.9
Source: CBIRC, Bureau of Statistics, Morgan Stanley Research estimates
BLUEPAPERM
98
Smart cities can improve profitability by reducing fraud and claims risks: China's auto insurance claims frequency has been run-ning at over 20% – much higher than the 5-6% in developed econo-mies. This is partially a consequence of lower road safety awareness and inefficient traffic management systems. With smart city initia-tives, most cities have now installed AI detectors in major streets and adopted more advanced traffic management systems to monitor real-time violations and ease traffic congestion. We believe these ini-tiatives will also help to reduce China's auto claims ratio. Ongoing digitalization and wider adoption of big data analytics are also helping insurers reduce fraudulent claims, which should help P&C insurers improve their profitability. We are expecting a potential 20ppt reduction in CoR due to technology advances in the future ( Exhibit 151 ).
Aside from being key beneficiaries, insurers with advanced techno-logical capabilities, such as Ping An, can also serve as enablers of China's smart city initiatives. Ping An has already built up its Smart City ecosystem by exporting core technologies to help local govern-ments provide better citizen services and manage local economies. Some of Ping An's key platforms under its Smart City ecosystem are listed in Exhibit 153 . The company has already secured smart city projects in more than 100 cities, and it is still looking to expand the scale of this business.
Exhibit 150:China vs. US: Car damage claims frequency
100
89
61
51 53 51 46
41
30 28 23
5.4 5.5 5.7 5.8 5.6 5.7 5.9 6.0 6.1 6.1 6.1
0
20
40
60
80
100
120
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
China US%
Source: Insurance Association of China, CBIRC, III, Morgan Stanley Research
Exhibit 151:Technologies can further reduce the combined ratio by 17-21ppts
Source: Annual reports from European insurers, interviews, BCG Insurance Benchmark Database, BCG case experience, BCG Analysis, Morgan Stanley Research
BLUEPAPERM
MORGAN STANLEY RESEARCH 99
Exhibit 152:Ping An's Smart City Initiatives
Government services Economic development People's livelihood
Crop insurance Big dataDigital risk identification system
for catastrophe prevention
Livestock insuranceAI/Facial recognition for
livestockAnti-fraud
Source: Company data, Morgan Stanley Research
Exhibit 154:China vs. US: Agricultural production and premiums
1,695
445
8.5
9.9
0
2
4
6
8
10
12
0
200
400
600
800
1,000
1,200
1,400
1,600
1,800
1 2
USD bn USD bn
Agricultural gross output value
Agricultural premium
China US China US
Source: CEIC, A.M. Best, China Insurance Yearbook, Morgan Stanley Research
New opportunities in agricultural insurance: Despite an 11-fold increase in agricultural insurance premiums thanks to strong policy and fiscal support over the past decade, China's agricultural insur-ance protection level remains low as compared with the US level ( Exhibit 154 ), partly as a result of a lack of data and effective claims assessment tools stemming from the country's fragmented and small-scale farming model. Wider adoption of smart farming and continued land reforms to facilitate large-scale farming could help insurers better underwrite and control risks in agricultural insurance as well as expand coverage to a wider variety of agricultural prod-ucts.
Large insurers, such as PICC P&C, have already deployed advanced technologies in this line to make risks more quantifiable. For example, the company is using drones to do image-based underwriting and claims assessment for farms in remote areas and is adopting facial recognition of pigs to prevent fraudulent claims. We forecast that agricultural premiums will grow at a 17% CAGR to 2030.
Overall, we expect China's total insurance penetration ratio to rise to 9% in 2030 (vs 4.3% in 2018), within which life penetration should reach 7% and non-life should reach 2%.
Exhibit 156:China's agricultural and total P&C insurance market projections
*Protection level = sum assured/agricultural gross output value
Source: Bureau of Statistics, CBIRC, Morgan Stanley Research
BLUEPAPERM
MORGAN STANLEY RESEARCH 101
PICC P&C
PICC P&C is the dominant player in China's agricultural insurance market with a 50% market share. This is enabled by its large amount of data, deep relationships with local governments, and strong underwriting expertise. We believe the modernization of agriculture could present new opportunities for the company, as the wider adop-tion of smart farming and the transition to large-scale farming make insurance coverage more viable and available to farmers at an afford-able price. The company should also benefit the most from smart city development and see secular improvements in profitability.
Stock implications
Ping An
Ping An ranks #1 in terms of life market share in Beijing, Shanghai, Guangzhou, and Shenzhen, owing to its focus on higher-tier cities, better-educated agents, and higher productivity empowered by advanced digital tools. As large city clusters grow, we believe Ping An will be a key beneficiary as it continues to leverage its technologies to improve distribution and operational efficiency and solidify its leading position in large cities. Its smart city initiatives, which help local governments build smart city platforms, could also create new revenue streams for the group.
BLUEPAPERM
102
Overview: As farmland is consolidated, we believe demand for genetically modified seed will be boosted as it can reduce labor inputs, achieve higher yields and improve the economics of farming.
Key forecasts: We expect GM corn and soybean seed application to grow from zero now to 50%.
Investment implications:
Leading companies should be able to consolidate the fragmented agribusiness market and gain market share with efficient products. We prefer Longping High-tech for its strong brand name and GM seed pipeline.
2f. Agribusiness
China's low per-person agricultural output. China's gross output per worker is just 1% of the US level, according to World Bank data. We think this is because China's farmland area per person is tiny. Also, farm efficiency is damaged by smallholding operations that have intensive labor requirements and are only semi-mechanized.
Exhibit 158:China's arable land per person is tiny
Source: World Bank, Morgan Stanley Research
Exhibit 157:Agricultural gross output per worker in selective regions, 2017; China is far behind
Source: World Bank, Morgan Stanley Research
BLUEPAPERM
MORGAN STANLEY RESEARCH 103
Farmland consolidation a crucial reform. We believe China needs to consolidate this system of smallholdings into industrial-scale farms given risks to food security, inferior farm economics, the aging rural population, and ongoing urbanization.
1. Urbanization will depopulate rural areas. We believe urbanization is unstoppable as the economy grows in any country. The urban population as a proportion of the total population is still far below that of developed countries at more than 80%.
2. Chinese farmers are migrant workers. Chinese farmers do not focus solely on farming. Half of the rural population work in urban areas for most of the year and only return to their homes during festivals or busy farming seasons.
3. Farmers' major source of income is not crops. Jobs in urban areas are now the major contributor to rural resi-dents' incomes, whereas in the 1980s over 80% of rural incomes came from farming. As salaries have continued to rise, many farmers have abandoned farming altogether.
4. Increasing opportunity cost of unpaid labor. For the past decade, grain production needed an incentive policy to maintain good margins for farmers. The government's key incentive policy has been to raise grain prices to offset the increasing opportunity cost of unpaid labor (and fertilizer costs) and to keep the profitability of grain production attractive. However, grain price hikes raise the burden on the lower-income population, so we think this policy will not be sustained. Also, rising labor costs could be a long-term structural trend with the potential to impair China's food security.
Exhibit 159:Cash cost structure for rice production, 2017
Source: NDRC, Morgan Stanley Research
Exhibit 160:Opportunity cost of unpaid labor is the biggest of all rice production costs
Source: NDRC, Morgan Stanley Research
BLUEPAPERM
104
Exhibit 161:China has higher unit costs but lower yields in corn production than the US
China Corn Production Cost 2014: yield=3,035kg/acre
Rmb/mu US$/acre Cent/kg
As a % of
total cost
Fertilizer 130 129 4.2 12%
Seed 55 54 1.8 5%
Chemicals 15 15 0.5 1%
Machinery leasing 105 104 3.4 10%
Hired labor 28 28 0.9 3%
Farmland rent 24 24 0.8 2%
Others 59 58 1.9 6%
Total cash cost 417 411 13.5 39%
Opportunity cost of unpaid labor 446 440 14.5 42%
Opportunity cost of land 200 198 6.5 19%
All-in cost 1,064 1,049 34.6 100%
The US Corn Production Cost 2014: yield=4,320kg/acre
Rmb/mu US$/acre Cent/kg
As a % of
total cost
Fertilizer 151 149 3.5 23%
Seed 102 101 2.3 15%
Chemicals 30 29 0.7 4%
Fuel, lube & power 33 33 0.8 5%
Hired labor 3 3 0.1 0%
Custom operation 19 18 0.4 3%
Others 27 26 0.6 4%
Total cash cost 365 360 8.3 55%
Capital recovery of machinery 101 99 2.3 15%
Opportunity cost of unpaid labor 25 25 0.6 4%
Opportunity cost of land 178 176 4.1 27%
All-in cost 669 660 15.3 100%
Difference % - China over the US
Cash cost 14% 14% 63%
All-in cost 59% 59% 126%
Source: NDRC, USDA, Morgan Stanley Research
Farmland consolidation to create efficiency. Because of its small-er-scale farms, China's grain production has higher unit costs than US levels. China also has lower yields per hectare for corn and soybeans. If China opens up grain imports, domestic grain production would sharply decrease. Soybeans are a good example of a crop where domestic production and planted area have been decreasing since 2000.
Genetically modified crops' penetration in the Americas. After the commercialization of the herbicide glyphosate in the 1970s, Monsanto began developing glyphosate-tolerant crops through genetic engineering with the intent of increasing glyphosate usage. A
Exhibit 162:China has higher unit costs but lower yields in soybean production than the US
China Soybean Production Cost 2014: yield=872kg/acre
Rmb/mu US$/acre Cent/kg
As a % of
total cost
Fertilizer 47 46 5.3 7%
Seed 39 38 4.4 6%
Chemicals 16 16 1.8 2%
Machinery leasing 77 76 8.7 11%
Hired labor 20 19 2.2 3%
Farmland rent 65 64 7.3 10%
Others 25 25 2.8 4%
Total cash cost 288 284 32.5 43%
Opportunity cost of unpaid labor 197 194 22.3 30%
Opportunity cost of land 183 180 20.7 27%
All-in cost 667 658 75.4 100%
The US Soybean Production Cost 2014: yield=1,307kg/acre
Rmb/mu US$/acre Cent/kg
As a % of
total cost
Fertilizer 38 37 2.9 8%
Seed 61 60 4.6 13%
Chemicals 28 27 2.1 6%
Fuel, lube & power 22 22 1.7 5%
Hired labor 3 3 0.2 1%
Custom operation 10 10 0.8 2%
Others 24 23 1.8 5%
Total cash cost 186 183 14.0 41%
Capital recovery of machinery 89 87 6.7 20%
Opportunity cost of unpaid labor 18 18 1.4 4%
Opportunity cost of land 161 158 12.1 35%
All-in cost 454 447 34.2 100%
Difference % - China over the US
Cash cost 55% 55% 132%
All-in cost 47% 47% 121%
Source: NDRC, USDA, Morgan Stanley Research
glyphosate-tolerant soybean was introduced to the market in 1996, followed by GM corn in 1998. Afterwards, insect resistance was developed as well as stacked traits – a combination of more than two GM traits.
As GM crops are able to reduce labor inputs, the new varieties have quickly surpassed conventional varieties over the past two decades, especially in the Americas. Currently, GM crops account for 81%, 64%, 29%, and 23% of the global planted area of soybean, cotton, corn, and canola. In the US – where GM crops originated – coverage of GM crops is much higher, at 94%, 92% and 94% of planted areas of soybean, corn and cotton.
BLUEPAPERM
MORGAN STANLEY RESEARCH 105
Forecasting 50% GM seed penetration for corn and soybeans: China imports over 80% of its soybean demand and those imports are all genetically modified. Chinese consumers have been eating GM soybeans and corn for a long time. If urbanization and farmland con-solidation continue, large-scale farming is needed to reduce labor inputs by more than half, and GM seeds are a must if China hopes to lower its farming costs and become competitive in the global market. It is unrealistic to use labor to remove weeds on large farms, and in addition labor will be less available in future. We believe that China’s aggressive GM tech M&A in recent years shows it ultimately plans to adopt GM seed. Given that most of China's labor input on farms is mainly for weed removal and crop protection, we forecast a 50% GM penetration rate (GM seed sales as % of total seed annual sales) for corn and soybean by 2030 as China introduces large-scale farms.
Stock implications
Longping High-tech
We believe Longping High-tech is the most exposed to the agricul-tural modernization theme for the following reasons:
l Leading rice seed breeder. Longping is a diversified seed breeding company with a particular strength in rice. The com-pany's rice strain is the best in class in China and has the largest planted area among all hybrid strains, which has enabled the company to maintain a gross margin of around 45% over the past decade.
l Benefits from farmland consolidation. Hybrid rice occupies half of the total rice planted area in China. Farmers who do not really focus on yield and resistant traits tend to prefer conven-tional strains, which can be collected and saved for the next planting. Nevertheless, we believe hybrid seeds will see accel-erating penetration owing to industrial-scale farming.
l GM pipeline is a significant opportunity. The Brazilian asset acquired in late 2017 greatly improved the company's GM tech-nology and positioned it to benefit from China's coming GM revolution.
Exhibit 163:GM crop penetration, by country
Source: USDA (United States Department of Agriculture), Morgan Stanley Research
Exhibit 164:GM corn, soybean and cotton planted areas (as a percentage of total planted area)
Source: USDA (United States Department of Agriculture), Morgan Stanley Research
GM crop application is just a matter of time in China. GM corn and soybeans have proved their safety as food. We think the current low adoption of GM crops is mainly because food supply independence is a core part of China's food security policy.
We believe China's opposition to GM crops will end as the country seeks to increase efficiency through industrial-scale farming. China's conventional corn and soybeans will be largely replaced by GM vari-eties, in our opinion, as indicated by the acquisition of GM technolo-gies by Chinese companies in recent years.
BLUEPAPERM
106
Exhibit 165:Investment Theme #3: Summary
Th
em
e 3
: N
ew
lif
esty
les
in
sm
art
su
perc
itie
s
Tourism
Pharmas with strong, innovative pipelines, and top healthcare service providers with
solid growth potential
Gaming operators in Macau with bigger hotel capacity
Top tourist destination operators
Leaders in highly concentrated industriesMaterials
Domestic annual tourism expenditure: US$1.5trn (vs. US$0.78trn in 2018)
EducationTop online tutoring and vocational education
playersVocational training market: US$300bn
(3x 2018)
Healthcare
Macau Gaming
Top Stocks
• CRRC Corp Ltd (1766.HK)
• Haier Smart Home (600690.SS)
• Meituan Dianping (3690.HK)
• New Oriental Education & Technology Group (EDU.N)
• TAL Education Group (TAL.N)
• Jiangsu Hengrui Medicine (600276.SS)
• Aier Eye Hospital (300015.SZ)
Market share of top ten players in steel and cement: 60% and 70% (vs. 37% and 57% now)
Consumer IoTCompanies with clear strategies for smart
home appliances and e-commerce leadersSmart home appliance sales:
US$220bn (>5x vs. 2018)
Key Beneficiary Key 2030 Forecasts
TransportationRailway construction companies focusing on
inter-city and metro rail build-upInter-city commuter rail: 17,000 km
(vs. 2,000km now)
PropertyDevelopers with more landbank exposure to
large cities and key cityclusters
Annual housing price growth: 6% in five key city clusters
(vs. 4% elsewhere)
China's healthcare service market size: US$2.2trn (10.1% CAGR in 2015-30)
Gaming revenue: US$70-100bn(>2x 2018)
Source: Morgan Stanley Research
Investment Theme #3: New Lifestyles in Smart Supercities
BLUEPAPERM
MORGAN STANLEY RESEARCH 107
Exhibit 166:Breakdown of new operational HSR mileage in 2019-23, 2024-30 compared to 2014-18
Overview: China already has the world's longest and fastest high-speed rail system, and it's not finished yet. We expect a further buildup of high-speed rail, inter-city rail, and metro lines over the next decade, making travel even more convenient for people in key city clusters.
Key forecasts: The high-speed rail system will grow from about 30,000km now to 65,000km by 2030, with most of the new construction focused on eastern and central China. Inter-city rail networks will increase from 2,000km to 17,000km, and the length of metro lines will reach 15,000km, up from about 5,800km in 2018.
Investment implications:
Within our coverage, CRRC and CRCC should benefit the most from Urbanization 2.0. CRRC is our top pick for its exposure to rapidly growing inter-city and metro rail networks. CRCC is also exposed to this segment and has higher margins and returns than other China infrastructure plays.
3a. Transportation
China's high-speed rail mix is improving. Contrary to market per-ceptions, we believe HSR construction will focus on eastern China rather than the mid-west over the next decade.
It is a widely held view that given the advanced HSR networks in the central and eastern parts of China, future HSR lines will be concen-trated in western China, where the economy is less developed. The concern is that demand for HSR travel will be weaker in western China, resulting in lower train density allocated to those lines. However, we regard this as a misunderstanding of the long-term out-look for the development of China's HSR network.
Based on the HSR lines currently under construction and their com-pletion schedules, we project that 17,600km and 17,000km will be completed in 2019-23 and 2024-30 in eastern, central, and western China, with respective distances of roughly 7,200km, 6,400km, and 4,000km in 2019-23 and 7,700km, 6,300km, and 3,000km in 2024-30, accounting for 41%, 36%, and 23% of the total length in 2019-23 and 45%, 37% and 18% of the total length in 2024-30, vs. 33%, 34% and 33% in 2014-18. This suggests that there will be an even larger proportion of HSR lines starting up eastern and central China.
In addition to the '8-Vertical and 8-Horizontal' arterial HSR network, China will build inter-city railway networks in several key provinces and economic zones. Some target areas mentioned in the plan include Beijing-Tianjin-Hebei, the Yangtze River Delta, and Pearl River Delta.
Elsewhere in this report, our economics team highlights the five city clusters that have the most growth potential: the Guangdong-Hong Kong-Macao Greater Bay area, the Yangtze River Delta, Jing-Jin-Ji, the Mid-Yangtze River area, and Chengdu-Chongqing. The team believes it will be highly beneficial for economic development if there are advanced inter-city railways to connect cities within the zones.
We also highlight that some of the city clusters are very light on inter-city rail. For example in Guangdong province, which contributed 11% of China's total GDP in 2018, the total distance of HSR (including inter-city rail) was only 1,542km, or 5% of China's total, by the end of 2018. We believe these regions have significant potential to build inter-city networks in the future.
Some provinces within clusters have released aggressive inter-city construction plans:
l Jiangsu expects to construct 980km of inter-city rail in the province. Including lines now under construction, the new com-pletion length will reach over 2,200km in 2019-2030.
Exhibit 168:New operational HSR mileage, by region
l Guangdong plans to construct 1,000km as part of its Pearl River Delta inter-city railway network.
l In Jing-Jin-Ji, nine inter-city rail projects with a total length of 1,100km will start construction by the end of 2020 in an attempt to achieve a 0.5-1.0 hour commuting circle.
l In Chengdu-Chongqing, nine inter-city railway projects with a total length of 1,000km are planned, of which 671km will be in Sichuan and 337km in Chongqing.
l Hubei aims to construct a rapid inter-city passenger network with Wuhan at the center. According to the plan, 700km of inter-city rail will go into operation in 2019-2030.
l Shandong has set inter-city rail targets of 700km by 2025 and 1,500km by 2034, up from 113km at the end of 2017.
Our confidence is also supported by the inter-city railway construc-tion plans announced by different provinces. Summing up all long-term plans, we estimate that more than 15,000km of inter-city railway lines will be built in 2019-30. Based on the projections of new starts in the past and estimated completion schedules, we expect 5,000km to be completed in 2019-23 and 10,000km will be com-pleted beyond 2023.
In addition, the plan also mentioned some HSR extension lines that will connect some smaller cities with inter-city or arterial HSR net-works. We estimate that 6,570km of extension lines will be com-pleted in 2019-30.
BLUEPAPERM
MORGAN STANLEY RESEARCH 109
Exhibit 169:A glance at China's inter-city railways to be built in 2019-30, according to local governments' published plans
2,266
1,375 1,369 1,275
1,128 1,120
995 959 875
746 713
569
400 395 375 310
213 118 114 72
0
500
1,000
1,500
2,000
2,500
2019-2030 intercity railway new completion mileage (km)
Source: NDRC, Morgan Stanley Research
Some of the inter-city railway construction plans belong to 'long-term plans' and construction may not be completed before 2030. Nonetheless, we are optimistic on the outlook for inter-city rail, and we assume that in the period of 2024-30, 50% of current scheduled inter-city railways and other non-arterial HSRs will be completed. This suggests that over 2019-30, the annual average distance com-pleted will be 1,253km. Given that '8-Vertical and 8-Horizontal' will be fully completed by 2030, we estimate total new HSR completions in 2019-30 will average 2,900km annually.
Buildup of metro rail lines to enhance inter-city and city-suburb connectivity
Metro rail has enjoyed rapid development over the past decade owing to local government encouragement and the constant expan-sion of cities. In 2018, China's metro rail mileage (including subways, light rail, monorail, rapid transit, trams, maglev transport, and APM) totalled 5,767km, covering total 35 cities, growing at a CAGR of 16.5% since 2014. According to the China Urban Rail Transit Association, by the end of 2018 plans for urban rail networks in 63 cities had been approved, with construction already under way in 61 cities, with a total planned length of 7,611km.
We expect China's metro rail mileage in operation to grow by 18% in 2019, 18% in 2020 and 16% in 2021, after a 16% dip in 2018. The
decline in 2018 stemmed from NDRC's suspension of approvals for new construction in August 2017 after a subway project in Baotou was halted, with the approval process restarting in August 2018.
We believe increased urbanization must be accompanied by the rapid expansion of urban rail networks. In July 2018, the NDRC released a document raising the economic hurdles cities need to clear before undertaking subway and light rail construction. For subways, a city's public budget income and gross regional product (GRP) must exceed Rmb30bn and Rmb300bn, respectively, up from Rmb10bn and Rmb100bn previously. For light rail, a city's public budget income and GRP must exceed Rmb15bn and Rmb150bn, up from Rmb6bn and Rmb60bn previously. We believe this will help China's urban rail net-works achieve healthy and sustainable development.
Stock implications
CRRC and CRCC
Within our coverage, CRRC and CRCC should benefit most from Urbanization 2.0. CRRC is our top pick for its exposure to rapidly growing inter-city and metro rail networks. CRCC is also exposed to this segment and has higher margins and returns than other China infrastructure plays.
BLUEPAPERM
110
Exhibit 170:China's total metro rail mileage in operation to grow strongly in 2019-21...
Source: China Urban Rail Transit Association, Morgan Stanley Research
BLUEPAPERM
MORGAN STANLEY RESEARCH 111
Overview: We believe property demand will be sustainable through 2030, as China's urbanization ratio rises from 60% to almost 75% by 2030, per our projection, on the back of smart cities and city clusters.
Key forecasts: We forecast incremental housing demand at 1,450mn sqm annually through 2030 (vs. 1,479mn sqm in 2018). Housing prices could increase by a 6% CAGR in city clusters and a 4% CAGR in non-city clusters.
Investment implications:
Companies with high landbank exposure to top-tier cities, such as Sunac, CR Land, and Longfor, should benefit most.
3b. China Property
Demographics suggest sustainable property demand through 2030
Property demand in China may have passed its 'golden age', but our analysis of the country's demographics indicates demand should be relatively stable until 2030.
1. Shortage of urban housing stock
We estimate there were 244mn units of housing stock in 2018 for 300mn urban households, indicating a shortage of 56mn units. (Units of housing stock refers to houses with separate kitchens and bathrooms.)
2. Continuous urbanization
China's urbanization rate was slightly under 60% in 2018, making it similar to Japan's level in 1958. As Japan's urbanization rate was nearly 95% in 2018, we believe China still has significant potential for urban-ization. Our economist forecasts that China's urbanization rate will increase to 74.5% in 2030, indicating an urban population increase of 19mn each year, providing solid support for urban property demand.
3. Improvement in poor living conditions in lower-tier cities
According to China's 2010 census, only 50% of households in tier 3-5 cities have tap water and showers. The lack of basic necessities in lower-tier cities indicates the strong potential for upgrade demand in China.
4. Key urbanization trends through 2030:
We forecast incremental housing demand at 1,450mn sqm annually through 2030, driven by continuing urbanization, a shortage of urban housing, and improved living conditions in lower-tier cities.
l Our economist expects the urban population to reach 875mn/970mn/1,056mn in 2020/25/30, representing urbaniza-tion ratios of 62.3%/68.5%/74.5%, compared with 59.6% in 2018
l We estimate the average size of urban households will drop from 2.77 people in 2018 to 2.6 in 2030, compared with 2.33 in Japan and 2.54 in the US currently
l We estimate the number of urban housing units per household will increase from 0.81 in 2018 to 1.00 in 2030, which is still lower than that in Japan (1.16) and the US (1.15) currently
l Thus, we forecast residential demand at 1,450mn sqm per year through 2030, vs. 1,479mn sqm in 2018.
BLUEPAPERM
112
Exhibit 173:Housing stock vs. number of urban households
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
-
50
100
150
200
250
300
350
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
(unit/household) (mn) China urban residential housing stock China number of urban households
China number of urban unit per household (RSH)
Source: CEIC, NBS, Morgan Stanley Research
China's urbanization process and the formation of metropolitan areas
China's urbanization ratio increased to 59.6% in 2018 but is still lower than that of developed countries. According to our economist's fore-cast, China's urbanization rate will increase to 74.5% in 2030. We believe the following factors are driving urbanization and population migration in China:
1. Economic growth, especially in secondary and tertiary indus-tries
China's GDP growth has slowed from 9.5% in 2011 to 6.6% in 2018 but is still at a relatively high level. The contribution of tertiary industries to total GDP increased from 40% in 2000 to 52% in 2018 but was
still lower than Japan's 72% in 2015. We believe economic growth and the development of tertiary industries will be key drivers of further urbanization.
2. Income gap
We use the ratio of tier 1 cities' average wages to tier 5 cities' average wages as an indicator of the income gap in China ( Exhibit 177 ). Although the ratio decreased from 2.27x in 2005 to 1.82x in 2017, it is still relatively high, which could attract population inflows to large cities.
BLUEPAPERM
MORGAN STANLEY RESEARCH 113
Exhibit 176:China's GDP breakdown by industry structure vs. urbanization rate
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1953
1957
1961
1965
1969
1973
1977
1981
1985
1989
1993
1997
2001
2005
2009
2013
2017
Tertiary GDP Secondary GDPPrimary GDP Urbanisation rate (RHS)
Source: CEIC, Morgan Stanley Research
Exhibit 177:China's income gap
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
-20
-10
0
10
20
30
40
1955
1958
1961
1964
1967
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
2006
2009
2012
2015
(mn) Net increase of urban population
T1 / T5 cities' average wages (RHS)
Source: CEIC, Morgan Stanley Research
Exhibit 174:China's urbanization rate
0%
20%
40%
60%
80%
100%
-
250
500
750
1,000
1,250
1,500
1949
1952
1955
1958
1961
1964
1967
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
2006
2009
2012
2015
2018
(mn) China rural population
China urban population
China urbanisation rate (RHS)
Source: CEIC, Morgan Stanley Research
Exhibit 175:China's GDP growth vs. urban population growth
-10%
-5%
0%
5%
10%
15%
20%
-20
-10
0
10
20
30
40
1955
1958
1961
1964
1967
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
2006
2009
2012
2015
2018
(mn) Net increase of urban population
Real GDP growth (RHS)
Source: CEIC, Morgan Stanley Research
3. 1/5 cities have population inflows; large cities becoming larger
Based on our analysis of China's 287 prefecture cities, we find the following key points:
l Tier 1 & 2 cities have much larger usual residents than do low-er-tier cities. In 2017, tier 1 cities had an average population of 18.2mn, vs. 8.9mn in tier 2 cities, 4.9mn in tier 3 cities, 4.2mn in tier 4 cities, and 2.6mn in tier 5 cities.
l Large cities are becoming larger. From 2010 to 2017, tier 1 cities saw annual usual residents increases of 258k, vs. 103k in tier 2 cities, 26k in tier 3 cities, and 12-14k in tier 4-5 cities.
l The usual resident increase in higher-tier cities was mainly driven by migration instead of local urbanization. Usual resi-dents over the registered population in 2017 was 76% in tier 1 cities and 19% in tier 2 cities in 2017, showing population inflows. The ratio was 0%/-12%/-8% in tier 3/4/5 cities, indi-cating outflows.
BLUEPAPERM
114
Exhibit 178:Usual residents in China's cities
18.2
8.9
4.9 4.2
2.6
0
2
4
6
8
10
12
14
16
18
20
Tier 1 Tier 2 Tier 3 Tier 4 Tier 5
(mn people) 2000 2010 2017
Source: CEIC, China 2000 and 2010 Census, Morgan Stanley Research
Exhibit 180:China's usual residents over registered population
76%
19%
0%
-12% -8%
-20%
-10%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Tier 1 Tier 2 Tier 3 Tier 4 Tier 5
2000 2010 2017
Source: CEIC, China 2000 and 2010 Census, Morgan Stanley Research
Exhibit 182:China's urbanization sources
44%
34%
18%
8% 8%
3%
14%
29%
36% 35%
0%
10%
20%
30%
40%
50%
60%
Tier 1 Tier 2 Tier 3 Tier 4 Tier 5
Urban population growth from local urbanisation, etc.
Urban population growth from migration
Source: CEIC, China 2000 and 2010 Census, Morgan Stanley Research
Source: CEIC, China 2000 and 2010 Census, Morgan Stanley Research
Exhibit 181:China's urbanization rate
84%
55%
38%
28% 27%
37%
89%
68%
53%
40% 38%
50%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Tier 1 Tier 2 Tier 3 Tier 4 Tier 5 National
2000 urbanisation rate 2010 urbanisation rate
Source: CEIC, China 2000 and 2010 Census, Morgan Stanley Research
Exhibit 183:Percentage of China's new urban population that rent homes
71%
40%
20%
-1% 0% -10%
0%
10%
20%
30%
40%
50%
60%
70%
80%
Tier 1 Tier 2 Tier 3 Tier 4 Tier 5
Rent population net increase/ urban population net increase
Source: CEIC, China 2000 and 2010 Census, Morgan Stanley Research
BLUEPAPERM
MORGAN STANLEY RESEARCH 115
Smart cities and city clusters lift population capacity
Smart cities
The development of smart cities will help improve efficiency and the capacity to accommodate more people by alleviating issues caused by growing populations, such as congestion and pollution.
From a property perspective, smart cities can be built up by devel-oping ToD (Transit Adjacent Development) projects and under-ground cities, and developing more detailed and comprehensive city planning to enhance commuting efficiencies and increase population densities. The key for the development of smart cities in central areas is to increase the plot ratio, as the current plot ratio in the central areas of Beijing and Shanghai are relatively low, at around 2.5. Increasing plot ratios involves redeveloping old buildings in central areas. However, urban redevelopment policies vary; in cities where the local government is reliant on land sales revenue, the govern-ment will take back the redeveloped land and resell it in a public auc-tion at a much higher price. Under such circumstances, developers have less incentive to get involved in urban redevelopment projects, and, if governments have to do it on their own, it will take a longer time to redevelop the land. Currently, only some cities in the Greater
Exhibit 184:China's smart cities
1. Smart cities in central areas: Development of ToD projects and underground cities could improve city efficiency
and capacity to accommodate more people
2. City clusters: Development of high-speed rail and metro rail to connect satellite cities with hubs and build up city
clusters
1. Increased plot ratios in central areas: The development of smart cities in central areas involves urban
redevelopment, and in cities that are highly reliant on land sales revenue the government is often responsible for
urban redevelopment and progress can be slow
2. Dilution of central cities: The expansion of city clusters could dilute the attractiveness of central cities and may
affect property prices there. This could discourage governments from promoting city cluster development.
Beneficiaries Companies with high landbank exposure to tier 1 & 2 cities and strong execution: Sunac, CR Land, Longfor
Hurdles
Enablers
Source: Morgan Stanley Research
Bay Area, such as Shenzhen, Guangzhou, Zhuhai, and Huizhou, have supportive urban redevelopment policies and allow developers to get land after redevelopment, which speeds up the progress of urban redevelopment.
City clusters
City clusters are also an efficient way to improve population capacity, and the key is to develop high-speed rail and metro links to connect satellite cities with the core city. This requires infrastructure con-struction and may dilute the attractiveness of central cities.
Regarding property price expectations, we expect prices in the five key city clusters to be more resilient given population inflows, strong industry support and abundant resources (including healthcare and education). We expect 6% annual housing price CAGR in the five key city clusters, and 4% CAGR for other cities toward 2030.
Stock implications
We prefer developers with high landbank exposure to higher-tier cities and city clusters due to: (1) solid population inflows and industry support; (2) lower inventory risk; and (3) benefits from spill-over demand from nearby center cities and metro development.
Source: Company data, Morgan Stanley Research estimates
Exhibit 186:Developers' landbanks, by city cluster
6% 7% 7%
20%
3% 10%
4% 9%
4% 12%
0%
11% 9% 0%
8%
43%
9% 0%
13% 17% 13%
48%
19%
15%
67%
18%
46% 32% 40%
8%
0%
28% 30%
30% 20%
18%
25%
10%
25%
43%
23%
5% 55%
20%
15%
27%
4% 25%
7%
16%
78% 5%
20% 39%
23%
14%
6%
83% 19%
6%
2%
9%
3%
6%
7%
7% 6%
5%
13%
5%
0%
8%
10%
10%
12%
10%
6%
6%
4%
7%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Ag
ile
CIF
I
CM
SK
CO
LI
Countr
y G
ard
en
CR
Land
Futu
re L
an
d A
&H
Gem
dale
Gre
enla
nd
GZ
R&
F
Logan
Longfo
r
Po
ly A
SH
Shim
ao
Sh
imao
Sin
o-O
cean
Su
nac
Sh
enzhen Inv
Va
nke
Yu
zhou
Central West Mid-Yangtze Greater Bay Area Yangtze River Delta Pan Bohai Rim
Source: Company data, Morgan Stanley Research estimates
BLUEPAPERM
MORGAN STANLEY RESEARCH 117
development of new central business districts (CBDs) away from congested old city centers, speeding up integration. Moreover, well regarded hotels, better managed shopping areas and high-end resi-dences serve as necessary complements. Property companies in HK, by leveraging their experience, can serve as developers and landlords (property management).
Policy support has been and will be in place: In the development of Qianhai, the government designated land sites for investment property purposes to HK companies with experience in developing and managing investment properties in Hong Kong of at least 1.5mn sqft GFA. This helped reduce competition in land auctions in favor of HK property companies. We expect additional supportive policy reforms, such as for foreign company holdings, capital repatriation and expat tax exemptions.
HK property companies have witnessed and participated in the devel-opment of major CBDs in mainland China over the past 30 years, including some of the most iconic buildings in city CBDs. Some exam-ples are:
Overview: Hong Kong developers have built mixed-used projects in key areas like West Kowloon and Island East, as well in tier 1 & 2 cities, such as Shanghai and Chengdu. They should continue to benefit as they build projects that enable higher population densities.
Investment implications:
SHKP and Hang Lung are the most exposed to quality mixed-used projects in tier 1 & 2 cities within China's key city clusters.
3c. Hong Kong Property Companies
What role do Hong Kong property developers play in building city clusters?
History lessons from Hong Kong: Since the 1960s, HK has under-gone several transformations, changing from an industrial hub to a global financial center. The city saw the formation of various new 'clusters' such as Tsim Sha Tsui in the 1960s, Central in the 1980s, the reclaimed areas of HK Station and Kowloon Station in the 2000s, and more recently HK Island East and New Kowloon East. We believe the formation of city clusters in mainland China is in some ways similar to the development of new economic clusters in HK, but on a much bigger scale. Commercial real estate is part of the infrastructure buildup.
Commercial real estate is the first building block: Apart from building homes, sustainable job prospects and economic develop-ment in these new areas are crucial to new migrants. Grade A offices bring in multinational corporations and high-profile domestic com-panies, which in turn attracts new migrants and brings back home-grown companies that had previously left. This helps facilitate the
BLUEPAPERM
118
Exhibit 187:Highlighted developments of HK companies
Major CBDs/Iconic Developments Time Related Company
Exhibit 188:HK property companies' landbanks in mainland China
96
76
66
49
40
32
18
31 29 24
14 12 9 6
0
10
20
30
40
50
60
70
80
90
100
CK
A
NW
D
HK
La
nd
SH
KP
Wh
arf
He
nd
ers
on
HLP
NW
D
Wh
arf
HLP
SH
KP
Ke
rry
Sw
ire
Pro
p
He
nd
ers
on
GFA in sq.ft
Properties under development in China
GFA in sq.ft
Properties under development in China Existing investment properties in China
Source: Company data, Morgan Stanley Research
*Data as of December 2018, Link REIT as of Sep-2018, no new breakdown disclosed
Exhibit 189:CKA's development landbank (96mn sqft as of December 2018)
Source: Company data, Morgan Stanley Research
*Data as of December 2018, no new breakdown disclosed
Beneficiaries: Hong Kong companies with exposure to mainland China
China exposure: Among HK property companies, CK Asset has the largest landbank in terms of GFA in mainland China. The company had 96mn sqft of development property landbank as of December 2018, of which 40% is located in 'Chengdu Chongqing', one of the five key city clusters that we expect to continue attracting population inflows. CKA has 71% of its development landbank in tier 2 cities, and these areas should benefit as city clusters continue to expand. SHKP, Henderson and Sino also have over 50mn sqft of development land-bank in China.
Upside from China exposure: We estimate that there is potential NAV upside of over 60% for Hong Kong real estate companies with China exposure, given that we expect property asset prices in tier 1 & 2 cities (provincial capitals) to double by 2030. We identify Wharf, HLP, and Kerry as the most 'mainland China exposed' companies with at least 50% of their assets in these cities. Wharf has invested in the 'Chengdu Chongqing' cluster by building the high-profile IFS complex with a luxury hotel, Grade A offices and discretionary malls. Meanwhile, Hang Lung saw 1H19 retail sales in Wuxi (Yangzte River Delta cluster) and Dalian grow 25-29% YoY, while malls in Tianjin (Jing-Jin-Ji cluster) also saw a turnaround with positive sales growth of 3% YoY. In Wuhan (Mid-Yangzte River Delta cluster) Spring City 66 was opened in August 2019 and Wuhan and Hangzhou will be added to the group's portfolio.
BLUEPAPERM
MORGAN STANLEY RESEARCH 119
Exhibit 193:Hang Lung to add more retail malls in key city clusters
Source: Company data, Morgan Stanley Research estimates
Exhibit 191:HK property companies' NAV exposure to China
53% 45% 43%
32%
21% 21% 19% 14%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Wharf HLP Kerry NWD Swire
Prop
HK Land SHKP CKA
China DP China IP HK property OthersAs % of NAV
Source: Company data, Morgan Stanley Research estimates
Exhibit 192:Wharf China's rental portfolio to continue to grow
1.3
2.0 2.3 2.4
2.6
3.4
4.1 4.4 4.6
0.8 1.0
1.2 1.3 1.5
1.9
2.3 2.5 2.6
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
China rental revenue China rental EBITHK$bn
Source: Company data, Morgan Stanley Research estimates
Exhibit 194:Hang Lung China's rental portfolio to grow
3.5 3.9
4.2 4.0 4.0 4.2
4.5
5.2
5.8
2.7 2.8 2.7 2.5 2.5 2.7 2.8
3.2 3.5
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
China rental revenue China rental EBITHK$bn
Source: Company data, Morgan Stanley Research estimates
BLUEPAPERM
120
What could hinder growth? Hong Kong property developers tend to have a lower appetite for high net-debt-to-equity ratios, and none of them has a gearing of over 50%. Their cautious mindsets could hinder their ability to aggressively bid for newly devel-oped areas or any new satellite cities.
Exhibit 195:HK property companies have lower net gearings than their mainland peers
Source: Company data, Morgan Stanley Research. As of December 2018.
BLUEPAPERM
MORGAN STANLEY RESEARCH 121
Overview: We believe continued urbanization means demand from property and infrastructure will decline at a mild pace in the medium to long term, contrary to market expectations of a sharp decrease. In addition, industry consolida-tion should improve significantly by 2030.
Key forecasts: The steel industry should be much more consolidated by 2030, with the market share of the top 10 players reaching 60% from 37% now. We estimate the same ratio for the cement industry will hit 70% by 2030 from 57% now. Construction demand will be down around 6% in 2030 vs. 2019's level.
Investment implications:
Leaders in highly concentrated industries can offset the negative effects of a mild demand slowdown, including Anhui Conch, CNBM and Baosteel.
3d. Materials
The supply side should also see improvements. Since supply-side reform began in 2016, production capacity for many materials has come under better control. However, many industries remain very fragmented. The government aims to push for industry consolidation as the next step in supply-side reform. Utilization in the steel industry has increased to 95% in 2019 from 60% in 2015, but pricing power is still poor because of low concentration, at 37%. While the govern-ment aims to increase the top 10 players' market share to 60% by 2025, we think the actual progress might take longer to 2030 as over 200mnt of capacity under different steel enterprises will need to be consolidated to achieve the goal. For the cement segment, the market share of the top 10 players is currently 57%. Because leading players such as Anhui Conch are still planning to expand capacity and market share, and as China has banned building new capacity (enter-prises can only do capacity swaps), we think their target can only be achieved by engaging in M&A, which would help raise the concentra-tion level to 70% by 2030.
Beneficiary of urbanization and improving infrastructure
Building materials: Our economics team expects China's urbaniza-tion ratio to rise from under 60% in 2018 to almost 75% by 2030. This should sustain demand for urban housing. Our property team fore-casts incremental housing demand at 1,450mn sqm annually through 2030, which is similar to the current level (1,479mn sqm for 2018). Our infrastructure team expect a further buildup of high-speed rail, inter-city rail, and metro lines over the next decade, making travel even more convenient for people in key city clusters.
The market has been cautious on the long-term demand outlook for materials because of concerns about the property market and infra-structure construction. In contrast, we expect demand will continue to be well supported over the medium to long term, with only a mild slowdown and not a sharp decline.
BLUEPAPERM
122
Exhibit 198:Examples from developed countries indicate steel demand could decrease by 6-15% from 2019 to 2030China's income per capita
Note: GDP per capita statistics are on purchasing power parity (PPP) and constant 2011 USD basis
Source: World Steel Association, NBS, Haver Analytics, Morgan Stanley Research estimates
Aluminum and copper to benefit from EVs and grid system upgrade: Our auto team expects EV penetration of 32.6% by 2030. This suggests more demand for aluminum (lightweight cars) and copper (more wiring in EVs, charging piles, and local grid and trans-former upgrades). More power grid investment should also translate into higher demand for copper, as power equipment accounts for around 40% of copper demand.
Contrary to market expectations of a sharp decline (30-50% slowdown) in demand, our analysis suggests the construction demand slowdown should be mild (a 6% decline in 2030 vs. 2019). First, we expect real GDP growth to slow regardless of the pace of transition in China’s growth model, which implies an accom-panying deceleration in the old economy given its primary role in sup-porting growth in recent decades. Second, the change in growth mix – specifically as the investment share in the economy declines – also implies slower demand growth for sectors in the near term. Experiences from countries that have successfully made the eco-nomic transition also point to a more subdued demand outlook for the old economy in the longer term. For instance, trends in the steel industry across a number of developed markets suggest that per capita steel consumption tends to peak 10-20 years after the share of secondary industry in GDP has peaked. In China’s case, the ratio of secondary industry to GDP peaked around 2005, implying that China’s per capita steel consumption would have started to flatten by now. From a per capita income perspective, although other coun-tries’ historical consumption of various commodities shows that China’s per capita consumption has yet to peak, China’s age depen-dency ratio has already inflected, contrary to the demographic dynamics in these countries at around the same per capita income levels. The subsequent structural shift in the relative importance of investment and consumption will therefore imply more subdued commodity consumption.
Exhibit 196:CR10 of steel industry in 2018
Source: Company data, Morgan Stanley Research.
Exhibit 197:CR10 of cement industry in 2018
CNBM, 22%
Anhui Conch, 11%
Jidong Cement, 6%
CR Cement, 4%
Huaxin Cement, 3% Shanshui Cement,
3% Hongshi Cement, 3% TCC, 2%
Tianrui Cement, 2%
ACC, 1%
Others, 43%
Source: Company data, Digital Cement, Morgan Stanley Research
BLUEPAPERM
MORGAN STANLEY RESEARCH 123
Exhibit 199:In China, the share of secondary industry in GDP peaked in 2005
Source: World Steel Association, World Bank, Morgan Stanley Research
Exhibit 201:China's cement consumption per capita is above international peers' sustainable levels
China
US
US
Japan
UK
Germany France
Australia
Canada
South Korea
-
200
400
600
800
1,000
1,200
1,400
1,600
1,800
2,000
- 10,000 20,000 30,000 40,000 50,000
Cement consumption per
cap (kg)
GDP per cap (US$, constant price at 2010 dollar)
Cement consumption per cap vs GDP per cap
China
US
Japan
UK
Germany
France
Australia
Canada
South Korea
Source: Cembureau, World Bank, Morgan Stanley Research
Exhibit 203:China's aluminum consumption per capita is still on an upward trajec-tory
South Korea
China
US
Japan
UK
Germany
France
Australia Canada
-
5
10
15
20
25
30
35
- 10,000 20,000 30,000 40,000 50,000
Aluminum consumption per cap
(kg)
GDP per cap (US$, constant price at 2011 dollar)
Aluminum consumption per cap vs GDP per cap
Source: Woodmac, World Bank, Morgan Stanley Research
Exhibit 200:Sustainable steel consumption per capita: most developed economies range from 200-600kg/capita – China is within this range
China
US
Japan
UK
Germany
France Australia
Canada
South Korea
-
200
400
600
800
1,000
1,200
1,400
- 10,000 20,000 30,000 40,000 50,000
Steel consumption per cap (kg)
GDP per cap (US$, constant price at 2011 dollar)
Steel consumption per cap vs GDP per cap
China US Japan
UK Germany France
Australia Canada South Korea
Source: WSA, Japan Iron & Steel Federation, Korea Customs Service, World Bank, Morgan Stanley Research. Note: Japan, South Korea and China steel consumption has been adjusted for steel content exported with auto, machinery and ships
Exhibit 202:China's coal consumption per capita leveling off at the higher end of global peers' sustainable levels
China
US
Japan
UK
Germany
France
Australia
Canada
South Korea
-
500
1,000
1,500
2,000
2,500
3,000
- 10,000 20,000 30,000 40,000 50,000
Coal consumption per cap (kg of oil
equivalent)
GDP per cap (US$, constant price at 2011 dollar)
Coal consumption per cap vs GDP per cap
China US Japan
UK Germany France
Australia Canada South Korea
Source: BP Statistics, World Bank, Morgan Stanley Research
Exhibit 204:China's copper consumption per capita has more room to grow com-pared with global peers' sustainable levels
China
US
Japan
UK
UK
Germany
France
Australia
Canada
South Korea
-
2
4
6
8
10
12
14
16
18
20
- 10,000 20,000 30,000 40,000 50,000 60,000
Refined copper consumption per cap
(kg)
GDP per cap (US$, constant price at 2011 dollar)
Refined copper consumption per cap vs GDP per cap
China USJapan UKGermany FranceAustralia CanadaSouth Korea
Source: Woodmac, World Bank, Morgan Stanley Research
BLUEPAPERM
124
Exhibit 205:Morgan Stanley estimates of downstream demand drivers for different commodities: property and infrastructure are important drivers of demand
Steel Cement Copper Aluminum Coal
Property 42% 33% 9% 35%
Infrastructure 25% 33%
Auto 6% 10% 16%
Power 47% 16% 53%
White Goods 2% 15% 8%
Electronics 8% 5%
Machinery 15%
Shipbuilding 3%
Rural 33%
Manufacturing
Packaging 11%
Metallurgy 18%
Building Materials 8%
Others 7% 11% 9% 21%
Source: Morgan Stanley Research estimates
Stock implications
Baosteel
Benefiting from smart city construction. Baosteel is one of the largest steelmakers in China, with a focus on high-end flat product. The company produces hot-rolled sheet, cold-rolled sheet, down-stream value-added coated products, seamless tubes and wire rods. In particular, Baosteel has the largest market share of auto sheet pro-duction in China, which we think will benefit from the trend of shared mobility and electric vehicles, as mentioned by our autos team. Besides the construction of infrastructure, commercial real estate projects should also help create stable demand for steel in the medium term.
Anhui Conch
Well positioned for city cluster formation. Anhui Conch has strong exposure to three of the four highly mentioned urban agglom-erations in China: the Yangtze River Delta, the Guangdong-Hong Kong-Macau Greater Bay Area, and the Sichuan-Chongqing Urban Agglomeration. The construction of infrastructure facilities in these regions, as well as property demand because of continual population inflows in these regions, will secure relatively stable cement demand. Because of limited new capacity announced in these regions, we believe sustainable pricing will also ensure strong earn-ings for Conch.
Chalco
Continuous urbanization and demand for higher living stan-dards drives up aluminum consumption. Chalco is the largest alu-minum and alumina producer in China. Solid property and auto demand through 2030 should help support aluminum consumption, as a respective 35% and 16% of demand is driven by these two seg-ments. In addition, increasing white goods demand, along with increasing property demand, could further support aluminum demand. Furthermore, China has capped the country's total alu-minum capacity at 44-45mnt, which limits supply.
Jiangxi Copper
Key beneficiary of copper price recovery driven by infrastruc-ture stimulus. Jiangxi Copper is one of the largest copper smelters in China. China has stepped up its quantitative easing efforts heading into 2H19. The Ministry of Finance has required all special govern-ment bonds to be issued by end-September and all funds to be paid to related projects by end-October. Part of the 2020 special bond quota will be front loaded to end-2019 to support infrastructure cap-ital expenditure. The infrastructure stimulus in China should signifi-cantly increase demand for copper, especially from the grid investment side, which should benefit Jiangxi Copper.
BLUEPAPERM
MORGAN STANLEY RESEARCH 125
Exhibit 206:Market size of China's home appliance industry
616 660 810 818 819
906 996
1,602
-
200
400
600
800
1,000
1,200
1,400
1,600
1,800
2015 2016 2017 2018 2019E 2020E 2021E 2030E
Rmb'bn
White goods Small appliances Others
Source: iResearch, All View Cloud (AVC), Morgan Stanley Research estimates
3e. Consumer IoTOverview: In our view, urbanization will drive overall demand for home appliances. This will lead to sustainable growth in
home appliances through 2030, with the penetration of smart appliances reaching 100% by then.
Key forecasts: We forecast a 2018-30 sales CAGR of 15% for IoT smart appliances given our outlook for 100% penetration in 2030 vs. 20% in 2018. This compares with a 2018-30 CAGR of 6% for the overall home appliances segment. In turn, the number of IoT appliances per household could rise to 7 units by 2030 (vs. just 1 today).
Investment implications:
Companies with clear strategies for IoT smart appliances should benefit from the trend, such as Haier Smart Home, Haier Electronics, Midea, and Viomi.
Urbanization will provide a substantial growth opportunity for China's consumer IoT segment, supported by advances in infrastruc-ture, technology, and growing personal wealth. Currently, home appliances are the main use case for consumer IoT, with smartphones and speakers serving as the interface to control them.
We expect China's home appliance market to reach annual sales of US$220bn by 2030, implying a 2018-30 CAGR of 6%. By 2030, all home appliances will be 'smart', we believe, which implies a 15% CAGR. We expect smart home appliances to be adopted in four
stages ( Exhibit 208 ). We are currently at Stage II, and the number of IoT appliances per household is about 1 unit. By 2030, we expect to be somewhere in Stage IV, with the number of IoT appliances per household reaching 7 units.
Companies with clear IoT strategies should benefit from this trend. We believe key beneficiaries will include Haier Smart Home (600690.SS), Haier Electronics (1169.HK), Midea (000333.SZ), and Viomi (VIOT.N).
Exhibit 207:Market size of China's IoT-enabled smart home appliances
71 117 201
315 421
529 615
1,602
-
200
400
600
800
1,000
1,200
1,400
1,600
1,800
2015 2016 2017 2018 2019E 2020E 2021E 2030E
Rmb'bn
White goods Small appliances Others
Source: iResearch, Morgan Stanley Research estimates
BLUEPAPERM
126
Stage III: Cross-scenario application – the genuine smart home. The next stage is to get all scenarios connected to achieve better solutions for consumers. This requires better human-machine inter-action based on AI and IoT technologies. Daily life could be more easily supported by smart home appliances and high-speed data transmission. We would achieve real smart homes under Stage III.
Stage IV: Free connection of all products. Stage IV allows for the free connection of all equipment that a consumer uses in daily life, including household electronics, home appliances and autos. The free connection is not confined to the home but can expand to the living community, schools, offices, hospitals, etc. This is a stage of real connection across all scenarios within a person's daily life regardless of location. Due to infrastructure restrictions, we are not living in this stage. We expect a certain level of Stage IV to be achieved by 2030, with the number of smart appliances per household reaching 7 units.
Participants in the smart home appliances segment
Smart home appliances are the main use case for consumer IoT at home. We place participants in the smart home appliances segment into four categories: tech hardware companies; internet companies (e-commerce and search engine); traditional home appliance compa-nies; and traditional retailers.
Development of smart home appliances
Exhibit 208:Four stages of AI + IoT enabled smart appliances
Source: Morgan Stanley Research
Stage I: Isolated smart home appliances. Most of the appliances currently called 'smart' have achieved this stage. Historically, tradi-tional appliances performed a singular function, and were not intelli-gent or connected. In recent years, we have seen rapid growth in the use of connected home appliances that consumers control remotely via mobile apps. Although these home devices are connected to the internet, they are generally isolated from one another, requiring con-sumers to download different mobile apps to operate them.
Stage II: Connected smart home appliances under a scenario. We are currently at this stage of development, and we have seen many real applications of stages. A 'scenario' is a key concept in the smart home appliances industry. Locations of daily living scenarios at home include bedroom, living room, kitchen, bathroom, and dining room. From a solutions point of view, whole home solutions include air, water, laundry, security, health, and dining. Each scenario requires many interconnected appliances. For example, in a living room sce-nario, door locks, lights, curtains, and air conditioners should be inter-connected to achieve smart control and interact when people leave or arrive home. Based on our estimates, the penetration of smart appliances is about 1 unit per household.
BLUEPAPERM
MORGAN STANLEY RESEARCH 127
Strengths: Internet companies can provide the best support for con-nectivity and cloud computing, and they have the best customer exposure, particularly for e-commerce companies, such as Amazon, Alibaba, and JD, which control the sales channel. Tech companies gen-erally have strong product quality control and understand the needs of customers.
Weaknesses: Currently, internet companies are still burning cash to attract users and expand market share, but have been unable to find better ways to monetize the user traffic from IoT home appliances at this stage.
Traditional home appliance companies
Traditional manufacturers have noted the importance of the growing smart home appliance market. Companies with strong brand recog-nition and R&D capabilities have tapped into the market, targeting different customer and product mixes. In China, Haier is leading the market as the first mover and can provide integrated services by con-necting all devices it makes under Haier U+. Midea is also in the market using the M-Smart platform, but it focuses on small devices such as safety items, cameras, lighting, and power switches. Gree has made less progress. In the global market, Samsung is the leader, with its self-developed SmartThing platform.
Traditional manufacturers usually provide closed ecosystems by only connecting own-brand products at the early stage of IoT devel-opment, particularly for large brands, such as Haier.
Strengths: Appliance companies have better control over offline distribution channels, which span the country. Distributors can help with expansion, particularly in lower-tier cities.
Weaknesses: Online sales channels are controlled by internet com-panies (e-commerce) and distributors’ online platforms (Suning.com, GOME.com, etc). Home appliance hardware companies are at a disad-vantage when competing with them, particularly internet compa-nies. Hardware companies which have special focuses or strengths, such as Gree in air conditioning, may not be able to provide home integrated solutions.
Participants in the smart home appliances segment:
l Tech hardware companies¡ China: Xiaomi, Huawei¡ Global: Apple
l Internet companies – e-commerce¡ China: Alibaba, JD.com¡ Global: Amazon
l Internet companies – search engine¡ China: Baidu¡ Global: Google
l Traditional home appliance companies¡ China: Haier, Midea¡ Global: Whirlpool, Electrolux, Samsung
l Traditional retailers¡ China: Suning, GOME
Tech hardware and internet companies
As internet traffic growth from computers and smartphones/pads is leveling off, internet companies are looking for new portals, such as smart home products, to increase traffic from users. In the US, Amazon and Google are the leading players, with 90% of the smart speaker market share, according to IDC. In addition, Apple is another large player in the market. In China, Alibaba, Xiaomi, Baidu, and JD are key leading players, while we have seen limited progress from Tencent.
As tech and internet companies do not manufacture appliances, they usually use self-branded smart speakers as the interface between consumers and devices, and they integrate home appliances from third-party devices. Tech and internet companies use their strong platforms in AI, big data, cloud computing, and e-commerce to estab-lish open ecosystems to attract home appliance brands and accel-erate consumer adoption. Entry barriers are high, and the space is dominated by internet giants.
BLUEPAPERM
128
Exhibit 209:Summary of smart home appliance participants
Company Market position/strategy Platform Central con-troller
Hardware focus Manufacturing
Hardware companies
Xiaomi Starting from Xiaomi smartphone, connecting prod-ucts from itself and ecosystem partners. Mi Home Xiaomi AI
speakerAll home appli-ances Own production + OEMs
HuaweiHuawei established HiLink Alliance by integrating top home appliance manufacturers. More than 50 brands have joined HiLink Alliance.
HiLink Huawei router/app
All home appli-ances No manufacturing
Apple
HomeKit lets people control connected accessories using Siri or the Home app on iPhone, iPad, and Apple Watch. Many leading worldwide brands offer accessories that are compatible with HomeKit and Apple devices. Apple's HomeKit is currently available in China, and many appliance brands have joined the platform.
HomeKitiPhone/iPad/AppleTV/Apple Watch
All home appli-ances, with focus on small devices
No manufacturing
Internet companies (AI/e-commerce)
AlibabaAs the leading e-commerce platform and cloud computing provider, Alibaba’s open source platform provides integrated solutions for home appliances.
Alibaba cloud
TmallGenie speaker
All home appli-ances
No manufacturing (Alibaba is an e-commerce and cloud computing provider)
Baidu
Baidu is an AI-oriented IoT platform provider, pow-ered by the DuerOS system. Baidu has also signed many strategic alliances with home appliance man-ufacturers such as Midea and Haier.
DuerOS Baidu speaker All home appli-ances
No manufacturing (Baidu is an AI solution provider)
JDAll home appliances purchased from JDSmart (smarthome.jd.com) can be connected via JD Weilian
JD Weilian NA All home appli-ances
No manufacturing (JD Weilian is an e-commerce and platform provider)
Google
Google uses the Google Home speaker as the voice control center to connect third-party devices to Google’s platform. Google Home and related ser-vices are not available in China.
Google Home
Google Home speaker
Focus on small devices like safety products, switches, lights, sensors, thermo-stats.
No manufacturing (pro-viding portal for third-party products)
AmazonAmazon uses Alexa, its cloud-based voice service, to connect devices via its Echo speaker. Alexa and related services are not available in China.
Alexa Echo speaker
Focus on small devices like safety products, switches, lights, sensors, thermo-stats.
No manufacturing (pro-viding portal for third-party products, and also enables e-commerce via own online channel)
Traditional home appliance companies
HaierUses the U+ platform to connect Haier's own brands (including Haier, Casarte, GEA) and other brands.
U+ Inter-control All home appli-ances Own production
Midea Uses the M-Smart platform to connect Midea's products. M-Smart M-Smart app Small devices Own production
Samsung Connects Samsung and third-party products to the platform. SmartThing NA Small devices Own production + other
brandsRetailers
Suning As sales channel providers, they integrate online/offline sales platforms and provide an offline-experi-ence/online-sales business model to consumers, with a focus on one-stop shopping.
NA NA Anything sold at the store No manufacturing
GOME NA NAAnything sold at the store
No manufacturing
Source: Company data, Morgan Stanley Research
BLUEPAPERM
MORGAN STANLEY RESEARCH 129
Collaboration between different participants
All participants are looking for new users/consumers to expand their businesses. However, the focuses of smart home appliance partici-pants are different.
The purpose of internet companies is to build an open system to attract as many smart products and brands to the platform as pos-sible. Traditional appliance companies have their own systems (such as Haier's U+ and Midea's M-Smart) while also participating in other systems. For example, Haier, as the traditional home appliance manu-facturer, has its own smart platform Haier U+, which is a closed system for Haier's own products. However, Haier also collaborates with other internet companies. The collaboration between Baidu's DuerOS and Haier U+ expanded the network for both companies. In
addition, consumers can also purchase Haier-branded smart prod-ucts at Tmall (Alibaba) and JD.com, which are specifically designed for the platform.
Xiaomi, which started as a smartphone brand, is building a closed IoT system that is only compatible products made by Xiaomi and Xiaomi ecosystem companies. For example, Viomi, which is 33% owned by Xiaomi and Xiaomi's related party, started as one of Xiaomi's eco-system partners, and launched its first product, a Xiaomi-branded water purifier in 3Q15. Currently, Viomi provides Xiaomi-branded water purifiers, range hoods, and gas stoves to Xiaomi's IoT system. Xiaomi curates a wide range of additional products by investing in and managing an ecosystem of over 200 companies, among which more than 100 companies are focused on the development of smart hardware.
What does the participant have? What is the participant looking for?
Tech hardware companies
l Strong technological capabilities (AI/IoT/5G)
l Leading smartphone sales
l Large user base from sales of smartphones
l Home appliances manufacturing capabilities
l Brand position as a consumer IoT company
l Ecosystem, which could be a closed ecosystem
Internet companies (e-commerce and search engines)
l Online platform and large user base
l Ability to collaborate with different brands
l Strong software capabilities
l Looking for new user traffic
l Attracting more brands/products to the platform
Home appliance companies
l Strong brand position as a home appliance
company
l Control over the whole production value chain
l Product upgrading or premiumization
l New growth driver as the traditional home appliances
sector is plateauing
BLUEPAPERM
130
Exhibit 210:Smart kitchen scenario
Source: Morgan Stanley Research
Exhibit 211:Interconnection of different scenarios
Source: Morgan Stanley Research
Deep dive into 'scenarios'
'Scenarios' are a key concept of consumer IoT. Within each scenario, IoT companies are promoting bundled sales to consumers, meaning that consumers need to purchase all related products within the same platform/system to maximize the benefits. Scenarios at home can be defined under different categories:
l By location: living room, kitchen, bathroom, bedroom, dining room.
l By function: air treatment, water treatment, laundry, cleaning, security, interaction, health.
Exhibit 210 gives an example of a kitchen scenario at 6pm. The smart refrigerator can suggest a recipe based on eating habits and food currently available in the refrigerator. The recipe can be trans-ferred to the screen above the stove to make cooking easier. The smart range hood can identify fumes and change power levels auto-matically. Meanwhile, the smart refrigerator can detect when food is running low and order online. When the food is ready, an alert can be issued through the screen above the stove or a smartphone.
The scenario in Exhibit 211 is achievable currently. In coming years, due to the variabilities of interaction and the wide application of smart appliances, smart functionality will not be confined to a single terminal or a single scenario. We expect interconnection among many scenarios. This should significantly improve the user experi-ence when interacting with consumer IoT products.
The ultimate purpose is to achieve the free connection of all con-sumer IoT equipment both inside and outside the home. In 2030, based on a unified connection platform and data protocol, smart homes, community services, and automobiles will be further con-nected and merged into a larger uni-scenario. The ultimate purpose is for consumers to have easier and better lives.
Entertainment Life management Life services Healthcare
HomeSmart TVSmart speaker
Home security monitorSmart lockerSmart doorHome power management
Smart washing machineSmart refrigeratorSmart air conditioner
Smart scaleSmart blood pressure meter
AutomobileCar audio and entertainment system
Car security systemParking system
Car navigation systemCar air purifier systemCar alert system
Community Community activity management
Car managementGate locker systemMonitor systemSmart lighting system
Car services systemConvenience store retail systemHouse cleaning services system
Healthcare services system
BLUEPAPERM
MORGAN STANLEY RESEARCH 131
Bottlenecks in consumer IoT development
Technological advances in AI, IoT and 5G have assisted the develop-ment of consumer IoT, facilitating the implementation of different scenarios and finally targeting a free-connection environment. However, we still see bottlenecks during the evolution of the con-sumer IoT industry.
Compatibility among operating platforms: Connectivity is the key to IoT. Currently, different parties are running their own platforms for smart appliances ( Exhibit 209 ), while appliances under dif-ferent systems cannot connect to each other. Compatibility could be a bottleneck given that consumers may have their own brand prefer-ences when purchasing appliances. For example, Haier is the leader for washing machines and refrigerators, Gree is the top brand for air conditioners, and Robam is the premium brand for kitchen appli-ances. A unified operating platform that can connect appliances from different platforms would accelerate the popularity of smart appli-ances.
Key components/modules costs: Traditional home appliances have reduced the cost of consolidation in the production value chain. However, to implement smart functionality, the appliances should be installed with high-tech modules such as 5G modules, AI modules, voice/image recognition modules. Currently, the cost of these mod-ules is still high, making the price of smart appliances higher than tra-ditional ones. Price reductions of key components/modules would raise the penetration of consumer IoT products.
Consumer switch costs: Large consumer IoT products such as home appliances and automobiles have long lifecycles. It could take longer for consumers to change from non-smart products to IoT products. In addition, home appliances and automobiles have a larger ticket size than other consumer electronics. In the ultimate free-connec-tion scenario, it is important that smart functionalities are applicable in all scenarios, which requires solid infrastructure.
Stock implications
Haier Smart Home (600690.SS) and Haier Electronics (1169.HK)
Haier is a first mover and one of the leading smart appliance pro-viders in China. In March 2014, Haier launched its U+ Smart Life Platform, in which smart home appliances interact with one another and with third-party services to provide 'smart life' solutions. The launch of Haier's smart solutions initiative was ahead of similar such efforts from JD, Alibaba, Huawei, and Xiaomi, which were all launched after February 2015. 'Going smart' encourages bundled sales of con-nected smart home appliances to achieve better connectivity. In addi-tion, Haier's 'smart' strategy is applied across all brands, which allows overseas consumers to purchase small home appliances under the brands GEA, FPA, AQUA, and Candy.
Midea (000333.SZ)
Midea has been promoting its 'Smart Home + Smart Manufacturing' strategy. With continuing research and investments in AI, chips, sen-sors, big data, cloud computing, and other new technologies, Midea has built the biggest AI team in the household appliances industry, which is committed to enabling products, machines, production pro-cesses, and systems that can sense, perceive, understand, and make decisions in order to keep human-machine interactions to a minimum.
Viomi (VIOT.N)
Viomi's main business consists of innovative IoT-enabled products (including water purifiers, refrigerators, range hoods, gas stoves) together with a suite of complementary consumable products (e.g., water purifier filters) and value-added businesses (including both hardware and services). Viomi, 33% owned by Xiaomi and Xiaomi's related party, is part of Xiaomi's ecosystem, initially focusing on water purifiers. Viomi currently provides Xiaomi-branded water puri-fiers, range hoods, and gas stoves to Xiaomi. As of June 30, 2019, Viomi had over 2.3mn household users.
Exhibit 212:Smart home appliance statistics for Haier Smart Home, Midea, Xiaomi, and Viomi
Devices Users Financials
Haier Smart HomeSold 28.3mn smart appliances in China from
Jan'15 to Jun'1860mn U+ Smart family users as of 2018
Revenue from smart home appliances:
2015: Rmb11.2bn
2016: Rmb19.6bn, +75% yoy, accounting for 15% of total revenue
2017: Rmb33.2bn, +69% yoy, accounting for 22% of total revenue
2018: Rmb59.7bn, +80% yoy, accounting for 33% of total revenue
Xiaomi151mn connected devices (excl. smartphones
and laptops) as of Dec'18
(1) 20.3mn Mi Home App MAU as of Dec'18
(2) 2.3mn users with more than 5 Xiaomi IoT devices
(excl. smartphones and laptops) as of Dec'18
Revenue from ecosystem products of IoT segment:
2016: Rmb9.0bn
2017: Rmb15.0bn, +67% yoy, accounting for 13% of total revenue
2018: Rmb27.4bn, +83% yoy, accounting for 16% of total revenue
ViomiSold 2.5mn smart IoT products in China from
Jan'16 to Jun'18
(1) 1.7mn household users as of Dec'18
(2) 243,000 household users with more than 2
connected products oas of Dec'18
Viomi total revenue:
2016: Rmb313mn
2017: Rmb873mn, +179% yoy
2018: Rmb2,561mn, +193 yoy
Source: Company data, Morgan Stanley Research
BLUEPAPERM
132
3f. EducationOverview: Education demand is positively correlated with urbanization. We forecast a 7% revenue CAGR for the overall
education industry in 2018-30, reaching US$1.9trn, and believe online and vocational education will be in strong demand owing to better technology and support for industry upgrades.
Key forecasts: We expect online K-12 tutoring revenues to grow fastest, rising 22x to US$160bn by 2030, thanks to more advanced technology application and an increase in penetration from less than 10% in 2018 to over 35% in 2030. (For details please refer to our online education report.) We estimate vocational education and training revenues will grow 3x, to US$300bn in 2030, driven by an increasing penetration rate.
Investment implications:
After-school tutoring will likely become more consolidated amid the city cluster trend; leaders EDU and TAL should benefit most. TAL, as the largest online tutoring player, should also benefit from the increasing online tutoring trend. China Education Group is, in our view, best placed within our coverage to benefit from the voca-tional education trend.
Analysis of educational resources in the five key city clusters
Insufficient educational resources in the five leading city clus-ters, especially for primary schools and vocational education: Junior education is highly insufficient ( Exhibit 213 ) in some cities in the Greater Bay Area (such as Shenzhen, Dongguan and Foshan) and the Yangtze Delta (such as Wuxi and Suzhou), even with the current population. Overall, the average number of primary students per school in the five city clusters (786) is already 28% higher than the national average level (612), and demand will only rise as populations in these clusters grow.
We also expect stronger demand for vocational education, as urban-ization and industry upgrades should increase the need for more and better skilled workers in these regions. According to the Ministry of Human Resources and Social Security, demand for highly skilled workers (level 8 technicians, the highest level among the current total of 165mn technical workers) in 2018 was double the supply of just 47.9mn. In 1H19, the percentage of high-tech manufacturing-add-ed-value among total manufacturing-added-value was much higher in Guangdong (over 50%) and Beijing (over 40%) than the national average of 13.8%, highlighting a stronger need for skilled workers in these regions.
We expect online education and vocational education will support and benefit from the growth of city clusters.
l Junior education: Access to quality educational resources is a key consideration in where families choose to work and live. With more online education and government policy support on promoting equalized junior education, we believe quality edu-cation will be available to most city residents in 2030.
l Higher education: As industry upgrades and urbanization create demand for more and better skilled workers, vocational educa-tion can directly connect skills training with job requirements to improve both knowledge and practical job training. The gov-ernment has emphasized supportive policies for vocational edu-cation in 2019 by enlarging enrollment quotas and providing more fiscal support.
BLUEPAPERM
MORGAN STANLEY RESEARCH 133
Exhibit 213:Insufficient primary and middle resources
0
200
400
600
800
1,000
1,200
1,400
1,600
Jing-Jin-Ji Yangtze Greater BayArea
Mid-Yangtze Cheng-Yu
Average no. of K12 student per school (2017)
Primary School Middle School
National average - Primary National average - Middle
Source: CEIC, MoE, Morgan Stanley Research
Exhibit 215:Shortage of teachers in compulsory schools, and especially primary schools
0.4
5.4
10.4
15.4
20.4
25.4
Jing-Jin-Ji Yangtze Greater Bay Area Mid-Yangtze Cheng-Yu
Student to teacher ratio (2017)
Primary School Middle School
National average - Primary National average - Middle
Source: CEIC, NBS, Morgan Stanley Research
Exhibit 214:Greater Bay Area also has strong demand for higher education
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
Jing-Jin-Ji Yangtze Greater BayArea
Mid-Yangtze Cheng-Yu
Average no. of student per school (2017)
Higher education Higher vocational educationNational average - Higher education National average - Higher vocational
Source: CEIC, MoE, Morgan Stanley Research
Exhibit 216:Developed areas like Beijing and Guangdong have higher % of high-tech manufacturing-added-value, implying higher demand for skilled workers
14%
53%
40%
0%
10%
20%
30%
40%
50%
60%
National Guangdong Beijing
% of High-tech mnufaturing-added-value among total manufaturing-added-value in 1H19
Source: NBS, Provincial government.
Urbanization will boost after-school tutoring demand: As house-hold incomes and education levels are higher in major cities, parents are more willing to pay for their children's education, driving up demand for K12 after-school tutoring (AST). In Beijing and Shanghai, 45% and 32% of residents have at least a college degree – far higher than the national average of 13% – and the cities' disposable incomes per capita were Rmb62k and Rmb64k in 2018, compared with the national average of Rmb28k. Beijing and Shanghai have been big mar-kets for K12 AST over the past decade, despite having a lower average number of students per school. We believe this shows that higher income and education levels are supportive of strong K12 AST demand.
Given that we expect the five major city clusters to have a higher GDP per capita than the national average in 2030 – at Rmb181k and Rmb94k, respectively – as well as higher education levels as more educated workers move in, we anticipate stronger demand for K12 AST in these regions.
Exhibit 217:Jing-Jin-Ji residents are the most likely to have a tertiary degree, and there is strong demand for AST in this region. We expect this to happen in other city clusters, as well
19%
15% 13%
12% 11%
0%2%4%6%8%10%12%14%16%18%20%
-
5
10
15
20
25
30
Jing-Jin-Ji Yangtze Greater BayArea
Mid-Yangtze Cheng-Yu
% of population with college or above degree
Population with college or above degree % of Bachelor among total
Source: CEIC, NBS, Morgan Stanley Research
BLUEPAPERM
134
Population density improvements imply better learning center coverage: For offline K12 AST, people usually go to learning centers close to them (within 3-5km), and thus higher population densities in the city clusters imply that each learning center may be able to accommodate more students.
Online learning will enable quality education resource sharing
Adaptive learning online could disrupt education... Learning should be personalized, but this is hard to achieve in offline settings because of limited resources, mainly teachers and physical locations. However, learning in offline groups is becoming more personalized and adaptive through online education. This is backed by the applica-tion of Artificial Intelligence in Education (AIED) in each component of the learning process. And the pace of change is accelerating, as seen in how business models are getting clearer after the past sev-eral years of trial and exploration.
…empowered by new technology: The evolution of communica-tion infrastructure is accelerating the development of edtech and online education. Education companies are able to do in-depth data collection and analytics as more vendors offer technical support and more cloud computing service providers offer on-demand IaaS, PaaS, and SaaS for educational products. Meanwhile, with higher penetra-tion and the faster iteration of smart devices, we expect more online educational products with higher levels of interaction to be intro-duced to the market. The launch of 5G will also enable better learning experiences through live streaming, virtual reality, and other tech-nologies.
Exhibit 219:spARk, an edtech start-up, uses AR technologies and tangible tool kits to demonstrate wind power generation
Source: Company data, Morgan Stanley Research
Exhibit 218:IT infrastructure development accelerates application of edtech and online education products
Source: Morgan Stanley Research
Exhibit 220:Squirrel AI Learning is devoted to improving study outcomes through its adaptive learning system and has achieved very positive results
Levels of difficulty for all
knowledge points
Number of tags for each
knowledge point
Minimum questions in test bank
required for data analysis
Score improvement*
Competitors
3-4 9
*The test result is based on a teaching competition held in October 2017, when Squirrel AI compares teaching
outcomes of a four-day middle school math bootcamp. The bootcamp is held for two groups of students who are
taught by Squirrel AI's adaptive learning system and senior teachers who have more than 17 years teaching
experiences.
4-6 30+
+26.18 points on
average
+36.13 points on
average
20mn 20,000
Source: Company data, Morgan Stanley Research
BLUEPAPERM
MORGAN STANLEY RESEARCH 135
Barriers to entry in online education will be significantly height-ened… The online education market is developing ways to provide high-level adaptive learning environments. Databases of, and algo-rithms for, students' learning behaviors will become core assets for edtech companies, helping them widen their competitive moats over time. However, with the fast growth in online education user num-bers – and therefore the fast accumulation of user data – we believe barriers to entry will rise higher and higher as the leaders advance in adaptive learning.
...and the OMO (Online Merges Offline) model will become more popular: As mentioned, the key to sustainable success in AI educa-tion is to develop proprietary databases of, and algorithms for, learning behaviors. We think that cannot be achieved with pure online education platforms. The main reason is that algorithms for learning behaviors have higher requirements for data collection in terms of standardization, consistency, and granularity, and must be rooted in observation of traditional offline education. That's why
many edtech companies in the US, such as Knewton and DreamBox, have chosen the B2B2C model. The data collection from students can filter out more 'noise' stemming from non-study-related data, and it is easier to track the same students' data over time.
China has a large offline AST market. Thus, we expect the OMO model to be successful in both the B2B2C and B2C scenarios. In the B2C scenario, we expect the leading market players, which have broader offline learning center networks and high offline retention rates, can make the most of the OMO model. The large presence of students in centralized classrooms can provide sufficient datasets, and the companies bear lower customer acquisition costs in launching new online education products. Meanwhile, smaller edu-cational companies and newcomers can seize opportunities with B2B2C models to get access to target students. For instance, a start-up featuring an AI tutor, Xizi-AI, cooperates with New Oriental to gain massive amounts of analyzable data for faster product itera-tion while saving on customer acquisition costs.
Exhibit 221:OMO model should enjoy high adoption rates in B2C and B2B2C scenarios
Offline LC only Small local AST institutionB2B2C
(OMO available)
Boxfish, 17zuoye, Knowbox,
TAL's Magic School, Xizi-AI,
Knewton, Dreambox
Online to Online Hujiang, GSX, Udemy
Online platform
onlyLAIX, Xuebajun, Chegg, Byju B2B2C
Tencent Classroom, Hujiang,
GSX, Coursera, EdxOnline to Offline Qingqing
Offline LC + Online
platform
(OMO available)
EDU, TAL, Onesmart
B2C B2B2C C2B2C
Source: Morgan Stanley Research
BLUEPAPERM
136
Vocational education will be critical to supporting industry upgrades and urbanization
Government emphasizing vocational education through subsi-dies and preferential policies: Vocational education used to be seen as the place that the poorest performing students would end up, and was long neglected by society and government. However, vocational education will become more critical as urbanization accel-erates tertiary industry development and as the modernization of agriculture releases more of the rural workforce.
In 2014, the government established preferential policies and binding targets to enhance the quality of vocational education. For example, the government set binding subsidy targets for both formal and informal education, as shown in Exhibit 222 . It also emphasizes integration between vocational education and work experience for
Exhibit 222:Government's binding targets for formal and informal vocational education
Secondary vocational
education
On-site apprenticeship
programs
EnrollmentAdditional 1mn new
enrolled studentsAll enrolled student All enrolled student
Enroll 500k students
annually50mn
Timeline By 2019 Since 2015 Since 2015 Since 2021 2019-2021
Subsidy NAMin. Rmb12,000 per
student annually
Reasonably higher than
funding standard of
regular high schools
≥R b4,000 per stude t annually
Total Rmb100 bn
Formal vocational educationInformal vocational
educationTertiary vocational education
Source: MoE, Morgan Stanley Research
China is determined to promote skilled workers' social recogni-tion and compensation: Learning from the experiences of Germany, Japan, South Korea and Australia, the government is determined to promote skilled workers' educational attainment and social recogni-tion by (1) establishing unimpeded pathways for vocational students to attain tertiary education and transfer to regular academic educa-tion ( Exhibit 223 ), and (2) establishing a national qualification system to include all vocational skill certificates and diplomas from degree-granting institutions into a standardized and comparable framework ( Exhibit 224 ).
frontline workers by promoting the 'School-Company Cooperation' initiative, which requires over 80% of large and medium companies to participate in vocational education through apprenticeship and internship programs by 2020.
BLUEPAPERM
MORGAN STANLEY RESEARCH 137
Exhibit 223:China intends to establish unimpeded pathways for vocational students to attain upper tertiary educa-tion and transfer to regular academic education
Source: MoE, Morgan Stanley Research
Exhibit 224:Guangdong has set up the first provincial qualifications framework to bridge credentials between formal and informal education
Academic education Vocational education
Level 7Academic Doctors
degree diploma
Vocational Doctors
degree diploma
Level 6Academic Masters
degree diploma
Vocational Masters
degree diplomas1st Level
Level 5Academic bachelor
degree diploma
Vocational bachelor
degree diploma2nd Level
Level 4Academic associate
degree diploma
Vocational associate
degree diploma3rd Level
Level 3
Academic upper
secondary school
diploma
Vocational upper
secondary school
diploma
4th Level
Level 2 5th Level
Level 1
Formal educationQualifications
Level
Credentials in formal and informal education
Primary school diploma
Lower secondary school diploma
Informal education
National Vocational
Qualification Certificates,
Vocational trainings certificates
and others
-
-
Source: Administration of Quality and Technology Supervision of Guangdong Province, Morgan Stanley Research
BLUEPAPERM
138
Location is key for vocational education: As the employment pros-pects of graduates from vocational schools largely depend on local economic conditions, students will tend to choose good quality voca-tional schools in developed regions. Vocational schools in China have been viewed as taking a backseat to bachelor's degree programs, and enrollment quotas at vocational schools have not been filled in recent years in most provinces. Although the government has been encouraging the development of vocational education by targeting an increase of 1mn in higher vocational student enrollment this year, we think the key beneficiaries will still be quality vocational schools with high employment rates.
City clusters should be more attractive to students considering their higher GDP per capita and greater demand for skilled workers. The State Council aims to establish 50 high-standard vocational schools by 2020 and encourages cooperation between vocational schools and enterprises, which is more likely to happen in developed regions. Among the top five provinces that we have identified as the most attractive for higher education, three are within the five city clusters (Guangdong, Jiangxi and Sichuan).
Stock implications
TAL
We expect strong tutoring demand will be sustainable, supported by rising incomes and higher tutoring participation rates. TAL is a leading K12 AST institution, with both offline and online tutoring. With its well-established reputation and mature operations, we believe TAL will benefit, especially from its heavy investment in online tutoring, which is a more scalable business and will likely be a more consoli-dated market.
EDU
EDU is the largest tutoring institution in China and has the widest learning center network across different tier cities. It is especially competitive in secondary school student tutoring. We believe EDU is best positioned to capture fast-growing demand from non-tier 1 cities, as well as demand from secondary students due to fiercer com-petition in the university entrance examination.
China Education Group
As a pure higher education operator with six of its nine schools located in the five key city clusters, we believe China Education Group will benefit from strong demand for higher education talent and skilled workers. It has the largest enrollment size among listed peers and the highest adjusted net margin, with a good track record of M&A execution.
BLUEPAPERM
MORGAN STANLEY RESEARCH 139
3g. Healthcare
The rise of smart cities and clusters is aligned with the govern-ment's plan to reform the healthcare system: One key pillar of healthcare reform is the tiered care system – redirecting patient flow from major urban centers back to local lower-tier hospitals. China's 1,000 or so class 3 hospitals (the highest level) are overcrowded but also have the best doctors and the latest equipment and technology. Rural patients tend to travel to urban healthcare centers to seek treatment. China currently lacks a referral system staffed by primary care physicians (general practitioners) at the grassroots level. This contrasts with more developed economies, where many common conditions can be readily treated and managed at the community level. As China provides more incentives for doctors to work at grass-roots institutes, the patient burden on large urban hospitals can be alleviated as long as the enabling infrastructure is in place.
1. Electronic patient records and filings: One key hurdle to over-come is transferring patient files (X-ray images, drug histories, prior diagnoses and surgeries) across hospitals. This avoids duplicate testing efforts and frustration. The buildup of a national filing system linking all public hospitals is as yet incomplete, not to mention linkage to the county level, where the majority of patients live. A number of large, university-affiliated hospitals have hospital clusters around them, i.e., smaller hospitals that can refer patients to them, whose systems are linked up with the parent hospital. These hubs are increasingly being developed with the help of private enterprise management techniques. However, it remains a daunting task to keep accurate medical histories of China's vast population in a digital
format. The effort will require technologies in database manage-ment, software programming, and cloud computing.
2. Electronic prescription: The Chinese government has discussed allowing online drug prescription and filling but progress has been slow. If implemented, prescriptions are likely to be limited to common drugs that treat common chronic conditions. Critical condi-tions requiring more complex diagnostic algorithms will require face-to-face consultation with a doctor. The emergence of platforms like Ali Health and Ping An Good Doctor may enable online prescriptions and drug dispensing. This will not only save trips to hospitals and clinics for patients, but enable hospital clusters to develop, espe-cially for remote hospitals that may not necessarily have the resources to invest in hospital pharmacy space and dispensing sys-tems.
3. Telemedicine: This is closely related to patient record filing. Some online platforms have been established, with patient forums, for patients to seek medical advice directly from doctors remotely. Some platforms even allow video-conferencing with doctors for chronic, non-critical conditions. Telemedicine requires high band-width connectivity, which may be lacking in remote areas due to affordability. Remote imaging, storage, and computing, for example, require supercomputers and real-time data transfer. The idea is to link up local imaging systems with imaging experts in large urban hos-pitals, so patients do not have to travel long distances to take tests. This also requires a local certified technician who can operate the
Overview: Urbanization will facilitate the formation of a tiered healthcare system in China by directing more patients to low-tier or private hospitals. The growing disposable income and an improving medical insurance system will favor the development of innovative drugs. The emergence of electronic prescriptions should also enable easier drug dispensing, especially in remote areas.
Key forecasts: We forecast the sizes of China's pharmaceutical and healthcare service markets to reach US$0.5trn and US$2.2trn by 2030, respectively, representing revenue CAGRs of 6.3% (2018-30) and 10% (2015-30).
Investment implications:
1) Leaders in the pharmaceutical industry with a strong innovative pipeline, e.g., Jiangsu Hengrui Medicine (600276.SS);
2) Healthcare service providers with leading market shares and growth potential in their specialties, e.g., Aier Eye Hospital (300015.SZ).
BLUEPAPERM
140
equipment. Oftentimes, these are sales reps or distributor reps sent by the manufacturer of the equipment locally. Other tests and vital signs can be taken with monitors or wearable devices.
4. Wearables: Mobile patient monitors like glucose meters, oxygen meters and 24-hour Holters have been around for quite a long time. Apps have also been developed for patients to download their time series data onto USBs and transfer remotely to their physicians. Connectivity not only leads to convenience but more accurate tracking of vital signs. However, applications remain limited at the moment. Diabetes (glucose level monitoring), cardiovascular (blood pressure monitoring), cardiology (episodes of syncope and brady-cardia) are well developed. But other critical conditions still require on-site testing and physical examinations.
The key goals of healthcare reform in China are to improve patient access to the best drugs available internationally, control reimburse-ment budgets, and provide basic healthcare to all. The large-scale urbanization in China over the past 50 years has focused medical resources on the large urban centers, where the patients are much better insured through urban resident insurance and employers' insurance. The current reform aims to decentralize medical resources and distribute them more evenly. The development of smart cities, with higher connectivity to smaller hospitals in hospital clusters, can expedite this aspect of healthcare reform.
Key market size projections: We forecast China's pharmaceutical market to grow at a 6.3% CAGR in 2018-30, from US$0.2trn in 2018 to US$0.5trn in 2030, after 8.2% in 2014-18, in view of: 1) continued launches of innovative drugs vs. further cuts in generic drug prices by the government; 2) increasing affordability of innovative drugs thanks to growing disposable income and better insurance coverage; and 3) the emergence of electronic prescriptions, which should enable easier drug dispensing. We expect China's healthcare service market to grow at a 10.1% CAGR in 2015-30, to US$2.2trn by 2030, considering: 1) an 11.7% CAGR in China's aggregate hospital revenue in 2014-18, according to the NHFPC; 2) an emerging tiered healthcare system in China that could lead to the burgeoning of low-tier or pri-vate specialty hospitals; and 3) more diverse medical needs, such as medical aesthetics and assisted reproductive services.
Stock implications
Jiangsu Hengrui Medicine
Jiangsu Hengrui Medicine is a pharmaceutical company focusing on the development, manufacturing, and commercialization of oncology drugs, surgery drugs, and other pharmaceutical products. It holds a leading position in China in oncology and anesthesia drugs. With a strong innovative oncology portfolio highlighted by anti-PD-1 mAb camrelizumab and small molecule TKIs apatinib and pyrotinib, we think it will benefit significantly from the growth of disposable income in China, the improving medical insurance system that favors the development of innovative drugs, and the emergence of elec-tronic prescriptions that should facilitate drug dispensing.
Aier Eye Hospital
Aier Eye Hospital is the largest private ophthalmology medical insti-tution in China. The company principally provides ophthalmology medical services through the commercial mode of three-level linkage of ophthalmocace diagnosis, ophthalmocace treatment, pharmaceuticals distribution, and medical optometry. The company operated 300+ hospitals in China by the end of 2018. We think Aier is well positioned to exploit the growing demand from lower-tier markets, thanks to its continued efforts to establish a tiered network with ophthalmology hospitals, optometric clinics and eye care cen-ters.
BLUEPAPERM
MORGAN STANLEY RESEARCH 141
Overview: Macau's penetration rate in China is less than 2%, compared to 14% for Las Vegas in the US. With nominal GDP tripling by 2030, per our economics team's outlook, we expect Macau to benefit from improving infrastructure and rising affluence.
Key forecasts: We forecast that Macau's gaming revenue will double or more by 2030, from US$38bn to US$70-100bn. Furthermore, we believe Macau gaming's market cap could go as high as roughly US$300bn (3x the current market cap).
Investment implications:
Sands China, with the highest market share of hotel rooms, and Galaxy, with the biggest room supply increase outlook, should benefit the most from China's visitation growth.
3h. Macau Gaming
Growing in line with nominal GDP
Macau is the only place in greater China where gambling is allowed. There are six concessionaires that generated roughly US$38bn of gross gaming revenue (GGR) in 2018. GGR is strongly correlated with China's nominal GDP (US$13.6tn in 2018) with an average ratio of 0.34% since 2007. Based on our GDP projection range of US$21.1-31.5tn by 2030, we estimate Macau's GGR will range from US$70bn to US$100bn. This will be driven by growing GDP per capita and improved connectivity to Macau (high-speed rail, the HZMB bridge and the Hengqin development).
Mass as a percentage of revenue has gone up from 25% of the total in 2010 to 55% in 2018. Assuming mass contributes 75% of GGR and has a 3x higher margin, we estimate Macau's EBITDA at US$18-27bn in 2030, compared with US$10bn in 2018. Assuming a long-term average of 12x EV/EBITDA, this would imply a market cap range for the six concessionaires of US$206-314bn compared with around US$100bn currently. Market cap upside would be even higher if we assume higher EV/EBITDA multiples or lower FCFE yield as they start paying higher dividend yields and the risk of concession renewal goes away.
Exhibit 225:China's nominal GDP vs. Macau GGR
-100%
-50%
0%
50%
100%
150%
5%
7%
9%
11%
13%
15%
17%
19%
21%
1Q
11
3Q
11
1Q
12
3Q
12
1Q
13
3Q
13
1Q
14
3Q
14
1Q
15
3Q
15
1Q
16
3Q
16
1Q
17
3Q
17
1Q
18
3Q
18
1Q
19
China nominal GDP (LHS)
Macau market cap (RHS)Growth YoY Growth YoY
Source: CEIC, DICJ, Morgan Stanley Research estimates
Exhibit 226:China GDP per capita vs. Macau mass revenue per Chinese visitor
-20%
-10%
0%
10%
20%
30%
40%
0%
5%
10%
15%
20%
25%
30%
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
China GDP per capita (LHS)
Mass revenue per Chinese visitor (RHS)
Growth YoY Growth YoY
Source:CEIC, DICJ, DSEC, Morgan Stanley Research estimates
BLUEPAPERM
142
Beneficiary of urbanization and improving infrastructure
In our 2019 primer on Macau gaming, we highlighted that visitation to Macau from lower-tier cities is rising. Between 2013 and 2018, visi-tation from lower-tier cities has grown at a 7.5% CAGR, as compared to 5% from tier 1 cities. However, since the opening of HZMB in October 2018, visitation from tier 1 cities, especially Guangdong, has
Exhibit 228:Visitation from higher-tier cities to start rising
9.1 11.6
6.4 7.9
9.5
13.7
7.4 9.0
-
5.0
10.0
15.0
20.0
25.0
30.0
35.0
20
13
20
14
20
15
20
16
20
17
20
18
20
18
YT
D
20
19
YT
D
Visitors from lower tier cities (mn)
Visitors from tier 1 cities (mn)
Total visitors in Macau (mn)
Source: DSEC, Morgan Stanley Research. Note: tier 1 cities include Beijing, Shanghai and Guangdong province
Exhibit 229:Visitation from tier 1 cities will start growing again
49% 46% 47%
51% 54% 53%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2013 2018 2019
YTD
Visitors from low-tier cities
as a % of total Chinese
visitors
Visitors from tier 1 cities as
a % of total Chinese visitors
Visitor by tier of cities
Source: DSEC, Morgan Stanley Research. Note: tier 1 cities include Beijing, Shanghai and Guangdong province
Exhibit 230:Tier 1 cities vs. lower-tier cities' visitation to Macau (Jan-Aug 2019)
% as of Chinese visitors 51% 53% 52% 52% 54% 54% 54% 53%
YoY growth 17% 19% -7% 1% 13% 14% 16% 17%
Source: DSEC, Morgan Stanley Research. Note: tier 1 cities include Beijing, Shanghai and Guangdong province
started rising. Year to date, visitation from tier 1 cities (defined here as Shanghai, Beijing and Guangdong province) has grown by 22%, higher than lower-tier city growth of 21%.
Morgan Stanley Research now expects the high-speed rail network to expand more in eastern China (contrary to consensus expecta-tions), and thus we see more upside from tier 1 and new urban regions (23 more cities to have more than 8mn population by 2030), driving visitation and revenue upside.
Exhibit 227:Macau's GGR, mass revenue and EBITDA are strongly correlated with China's nominal GDP
Macau Mass & slot as % to China Nominal GDP % 0.03% 0.04% 0.04% 0.06% 0.07% 0.08% 0.09% 0.09% 0.05% 0.05% 0.06% 0.07% 0.06%
Source: CEIC, DICJ, Morgan Stanley Research. Note: Average ratio of Macau EBITDA and China nominal GDP is from 2008, after the sector-wise EBITDA normalized.
BLUEPAPERM
MORGAN STANLEY RESEARCH 143
Stock implications
We believe the key beneficiary of Urbanization 2.0 will be Sands China (the largest mass operator with the biggest room inventory), followed by Galaxy (biggest planned increase in room supply).
Mass revenue is largely a function of visitation and spending per capita. With the improved connectivity and the emergence of city clusters, fueling GDP per capita growth, we believe the mass seg-
ment will see the greatest upside. Sands China had 30% mass market share as of 2Q19 and is best positioned to capture the benefits of mass segment upside.
Exhibit 231:Sands China is the largest mass operator with 30% mass market share
Mass revenue (US$ mn) 2014 2015 2016 2017 2018
Mass revenue
CAGR 2014-18 2Q19
Mass
market share
Sands China 4,910 3,816 3,867 4,646 5,405 2% 1,390 29%
Hotel rooms allow guests to stay longer and potentially gamble more. The number of hotel rooms can help operators gain mass market share. Sands China has been the market leader in terms of hotel room supply (12.3k rooms in 2018), and we expect Galaxy to become the second-largest room supplier behind Sands by 2020, with 5.2k rooms and 18% room market share.
Exhibit 232:Sands and Galaxy have the biggest room inventories among the six operators
No of hotel room (average) 2014 2015 2016 2017 2018
Hotel rooms
CAGR 2014-18 2020e
Hotel supply
market share
Sands China 9,296 9,296 10,598 12,696 12,360 7% 13,020 44%
Source: Company data, Morgan Stanley Research estimates
Exhibit 233:Hotel room growth driving mass revenue growth
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
60%
2Q
12
4Q
12
2Q
13
4Q
13
2Q
14
4Q
14
2Q
15
4Q
15
2Q
16
4Q
16
2Q
17
4Q
17
2Q
18
4Q
18
2Q
19
Casino hotel room (ex satellite)
Mass and slot revenue
Growth YoY
Source: Company data, Morgan Stanley Research
BLUEPAPERM
144
Domestic tourism expenditure to almost double to US$1.5trn by 2030: Our expectation was based on:
l Real GDP per capita: Domestic travel frequency correlates strongly with real GDP per capita. We expect travel frequency to improve from 4x in 2018 to 7x by 2030, a level similar to that of US during 2015-17.
l Travel expenditure as % of GDP: We expect this to gradually decrease from 1.4% in 2018 to 0.8% by 2030, similar to that of US during 2015-17. Higher travel frequency could means shorter stay per travel or lesser spending each time. Chinas domestic travel expenditure as a % of GDP as been declining gradually from 4.6% in 2002 to 2.0% in 2001 to 1.4% in 2018.
3i. TourismOverview: We believe urbanization will lift demand for sightseeing and holidays. Online travel agencies (OTAs), which are
positioned as one-stop shops, are consumers' key booking channels for domestic travel.
Key forecasts: We estimate domestic tourism expenditure will grow at an 8% CAGR, from US$0.78trn in 2018 to US$0.9trn in 2020, and then at a 5% CAGR, to US$1.5trn to 2030. We expect domestic travellers to grow at a 10% CAGR, from 5.5trn trips in 2018 to 6.7trn trips in 2020, and then at a 4% CAGR, to 10trn trips to 2030. Online penetra-tion of travel booking (hotels, transportation and others travel related products) grew from 11% in 2013 to 37% in 2018, and we expect that to reach 46% by 2022.
Investment implications:
Tourist destination operators like CYTS and OTAs such as Ctrip and Tongcheng-Elong should benefit.
Exhibit 234:China's domestic travel spending to reach US$1.5trn by 2030
2018, 777
2020(E), 905
2030(E), 1,540
-
200
400
600
800
1,000
1,200
1,400
1,600
1,800
Domestic Travel Expenditures (US$bn)
MS Est.
*
Source: China Ministry of Culture and Tourism, Morgan Stanley Research estimates
BLUEPAPERM
MORGAN STANLEY RESEARCH 145
Urbanization promotes sightseeing
Urban residents tend to travel more for sightseeing and holiday purposes than people in rural areas, according to a survey by the China Tourism Academy in 2017 ( Exhibit 237 ).
Exhibit 237:Domestic travel by purpose of visit, 2017
14%
48%
8%
27%
1% 1% 6%
36%
13%
32%
5% 8%
0%
10%
20%
30%
40%
50%
60%
Sig
hts
ee
ing
Ho
lid
ay
Bu
sin
ess
Fa
mily
vis
it
Me
dic
al
Oth
ers
Domestic Travel % By Purpose
Urban Citizens Rural Citizens
Source: China Tourism Academy, Morgan Stanley Research
Exhibit 236:Domestic travel frequency per person – China vs. the US
2018, 4.0
2018, 7.0
-
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
Domestic Travel Trips Per Person Per Annum
China
US
MS Est.
Times/Year
Source: China Ministry of Culture and Tourism, US Travel Association, Morgan Stanley Research
Growth in short leisure trips
The development of city clusters and improving transportation networks should help promote short trips to tourist destinations in city clusters. We believe growing household incomes and the rise of the middle class will increase the frequency of domestic travel ( Exhibit 235 ). In addition, China's domestic travel frequency of 4x per year in 2018 was lower than the US level of 7x. We expect China's domestic travel frequency to catch up to the US level by 2030 ( Exhibit 236 ).
Exhibit 235:Domestic travel frequency is highly correlated with GDP
y = 8E-06x2 + 0.0005x + 0.3608
R² = 0.9986
-
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
- 100 200 300 400 500 600 700
Per Person Domestic Travel Trips vs Real GDP in China (1995-
2018)
Real GDP Index (1995=100)
Trips / Persons
Source: China Ministry of Culture and Tourism, US Travel Association, China National Bureau of Statistics, US Bureau of Economic Analysis, Morgan Stanley Research
BLUEPAPERM
146
Stock implications
CYTS
To benefit from Jing-Jin-Ji and Yangtze River Delta cluster devel-opment: Over 95% of CYTS's profits from 1H16 to 1H19 came from two tourist destinations – Wuzhen in the Yangtze River Delta (near Shanghai and Hangzhou) and Gubei in Jing-Jin-Ji (near Beijing). CYTS is developing a third destination, Puyuan, near Wuzhen. The com-pany should benefit from short-term leisure travel growth as city clusters develop with better infrastructure. In particular, we believe that Gubei has strong growth potential. Gubei has the same area as Wuzhen, but in 1H19 its visitation level was only 23% of Wuzhen's, and its net profit was only 14% of Wuzhen's.
Ctrip and Tongcheng-Elong
Ctrip is the leading OTA in China and offers the most comprehensive travel products and round-the-clock customer service including more than 60 travel-related value added services (VAS) in and out-side of China. Tongcheng-Elong, which has about one-third the domestic room night market share of Ctrip and offers over 30 travel-related VAS, is geared toward domestic travel and lower-tier cities. We believe rising urbanization in China and new transportation infra-structure will drive inorganic growth for the OTAs as 1) online pene-tration of travel booking has risen from 11% in 2013 to 37% in 2018 and will hit 46% in 2022, we estimate, 2) OTAs remain the key channel for travel booking, according to our AlphaWise survey in May, and 3) OTAs are positioned as one-stop shops, providing travel products from 'pre-departure' to 'on the road' to 'arrival'.
Exhibit 238:Online penetration of overall travel booking market will continue to rise, benefiting the OTAs
Source: Modelware, Morgan Stanley Research. Data as of September 30, 2019 for A-shares, October 4, 2019 for HK listed names and October 7 for US listed names. *This table only summarizes potential beneficiaries that are covered by Morgan Stanley Research.
Summary of Stocks Exposed to Urbanization 2.0
BLUEPAPERM
MORGAN STANLEY RESEARCH 149
Disclosure SectionThe information and opinions in Morgan Stanley Research were prepared or are disseminated by Morgan Stanley Asia Limited (which accepts the responsibility for its contents) and/or Morgan Stanley Asia (Singapore) Pte. (Registration number 199206298Z) and/or Morgan Stanley Asia (Singapore) Securities Pte Ltd (Registration number 200008434H), regulated by the Monetary Authority of Singapore (which accepts legal responsibility for its contents and should be contacted with respect to any matters arising from, or in connection with, Morgan Stanley Research), and/or Morgan Stanley Taiwan Limited and/or Morgan Stanley & Co International plc, Seoul Branch, and/or Morgan Stanley Australia Limited (A.B.N. 67 003 734 576, holder of Australian financial services license No. 233742, which accepts responsibility for its contents), and/or Morgan Stanley Wealth Management Australia Pty Ltd (A.B.N. 19 009 145 555, holder of Australian financial services license No. 240813, which accepts responsibility for its contents), and/or Morgan Stanley India Company Private Limited, regulated by the Securities and Exchange Board of India (“SEBI”) and holder of licenses as a Research Analyst (SEBI Registration No. INH000001105); Stock Broker (BSE Registration No. INB011054237 and NSE Registration No. INB/INF231054231), Merchant Banker (SEBI Registration No. INM000011203), and depository participant with National Securities Depository Limited (SEBI Registration No. IN-DP-NSDL-372-2014) which accepts the responsi-bility for its contents and should be contacted with respect to any matters arising from, or in connection with, Morgan Stanley Research, and/or PT. Morgan Stanley Sekuritas Indonesia and their affiliates (collectively, "Morgan Stanley").
For important disclosures, stock price charts and equity rating histories regarding companies that are the subject of this report, please see the Morgan Stanley Research Disclosure Website at www.morganstanley.com/researchdisclosures, or contact your investment representative or Morgan Stanley Research at 1585 Broadway, (Attention: Research Management), New York, NY, 10036 USA.
For valuation methodology and risks associated with any recommendation, rating or price target referenced in this research report, please contact the Client Support Team as follows: US/Canada +1 800 303-2495; Hong Kong +852 2848-5999; Latin America +1 718 754-5444 (U.S.); London +44 (0)20-7425-8169; Singapore +65 6834-6860; Sydney +61 (0)2-9770-1505; Tokyo +81 (0)3-6836-9000. Alternatively you may contact your investment representative or Morgan Stanley Research at 1585 Broadway, (Attention: Research Management), New York, NY 10036 USA.
Analyst Certification
The following analysts hereby certify that their views about the companies and their securities discussed in this report are accurately expressed and that they have not received and will not receive direct or indirect compensation in exchange for expressing specific recommendations or views in this report: Charlie Chan; Elly Chen; Grace Chen; Praveen K Choudhary; Qianlei Fan, CFA; Tim Hsiao; Jenny Jiang, CFA; Shawn Kim; Simon H.Y. Lee, CFA; Lillian Lou; Jack Lu; Kevin Luo, CFA; Sharon Shih; Laura Wang; Sean Wu; Richard Xu, CFA; Jack Yeung; Gary Yu; Rachel L Zhang; Sheng Zhong.
Unless otherwise stated, the individuals listed on the cover page of this report are research analysts.
Global Research Conflict Management Policy
Morgan Stanley Research has been published in accordance with our conflict management policy, which is available at www.morganstanley.com/institutional/research/conflictpolicies. A Portuguese version of the policy can be found at www.morganstanley.com.br
Important US Regulatory Disclosures on Subject Companies
The analyst or strategist (or a household member) identified below owns the following securities (or related derivatives): Praveen K Choudhary - Alibaba Group Holding(GDR); Laura Wang - New Oriental Education &Technology Group(GDR, common or preferred stock).
As of September 30, 2019, Morgan Stanley beneficially owned 1% or more of a class of common equity securities of the following companies covered in Morgan Stanley Research: 21Vianet, Alibaba Group Holding, Anhui Conch Cement Co. Ltd, Baidu Inc, China Communication Service Co Ltd, China CYTS Tours Holding Co Ltd, China Railway Construction, China Telecom, CRRC Corp Ltd, Ctrip.Com International Ltd, Dongfeng Motor Group, Goldwind, Jiangsu Hengrui, Jiangxi Copper, MediaTek, Meituan Dianping, New Oriental Education &Technology Group, TAL Education Group, Viomi Technology Co Ltd, WIN Semiconductors Corp, ZTE Corporation.
Within the last 12 months, Morgan Stanley managed or co-managed a public offering (or 144A offering) of securities of China National Building Material Company, GDS Holdings Ltd, Industrial Bank Co. Ltd., Longfor Group Holdings Ltd., New Oriental Education &Technology Group, Ping An Insurance Company, Sunac China Holdings Limited, Tencent Holdings Ltd., Tongcheng-Elong Holdings Ltd.
Within the last 12 months, Morgan Stanley has received compensation for investment banking services from Alibaba Group Holding, China National Building Material Company, China Tower Corp Ltd, Industrial Bank Co. Ltd., Longfor Group Holdings Ltd., Ping An Insurance Company, Sunac China Holdings Limited, Tencent Holdings Ltd..
In the next 3 months, Morgan Stanley expects to receive or intends to seek compensation for investment banking services from 21Vianet, Alibaba Group Holding, Aluminum Corp. of China Ltd., Anhui Conch Cement Co. Ltd, Baidu Inc, CGN Power Co., Ltd, China Communication Service Co Ltd, China Merchants Bank, China National Building Material Company, China Railway Construction, China Resources Land Ltd., China Telecom, China Tower Corp Ltd, China Unicom, CK Asset Holdings Ltd, CRRC Corp Ltd, Ctrip.Com International Ltd, Dongfeng Motor Group, Foxconn Industrial Internet Co. Ltd., Galaxy Entertainment, Goldwind, Guangzhou Automobile Group, Hang Lung Properties Ltd., Henderson Land, HIKVision Digital Technology, Huayu Automotive, Industrial Bank Co. Ltd., Jiangxi Copper, Kerry Properties, Longfor Group Holdings Ltd., MediaTek, Meituan Dianping, Midea Group Co Ltd., New Oriental Education &Technology Group, Nexteer Automotive Group, Ping An Bank, Ping An Insurance Company, S.F. Holding Co Ltd, SAIC Motor Corp. Ltd., Sino Land, Sun Hung Kai Properties, Sunac China Holdings Limited, TAL Education Group, Tencent Holdings Ltd., Tongcheng-Elong Holdings Ltd, Viomi Technology Co Ltd.
Within the last 12 months, Morgan Stanley has received compensation for products and services other than investment banking services from 21Vianet, Alibaba Group Holding, Baidu Inc, China Merchants Bank, China National Building Material Company, China Railway Construction, China Unicom, CRRC Corp Ltd, Ctrip.Com International Ltd, Henderson Land, Industrial Bank Co. Ltd., Longfor Group Holdings Ltd., MediaTek, Meituan Dianping, Midea Group Co Ltd., New Oriental Education &Technology Group, Ping An Bank, Ping An Insurance Company, Sun Hung Kai Properties, Sunac China Holdings Limited, Tencent Holdings Ltd., Tongcheng-Elong Holdings Ltd, Wharf Real Estate Investment Company Ltd, ZTO Express.
Within the last 12 months, Morgan Stanley has provided or is providing investment banking services to, or has an investment banking client relationship with, the following company: 21Vianet,
BLUEPAPERM
150
Alibaba Group Holding, Aluminum Corp. of China Ltd., Anhui Conch Cement Co. Ltd, Baidu Inc, CGN Power Co., Ltd, China Communication Service Co Ltd, China Merchants Bank, China National Building Material Company, China Resources Land Ltd., China Telecom, China Tower Corp Ltd, China Unicom, CK Asset Holdings Ltd, CRRC Corp Ltd, Ctrip.Com International Ltd, Dongfeng Motor Group, Foxconn Industrial Internet Co. Ltd., Galaxy Entertainment, GDS Holdings Ltd, Goldwind, Guangzhou Automobile Group, Hang Lung Properties Ltd., Henderson Land, HIKVision Digital Technology, Huayu Automotive, Industrial Bank Co. Ltd., Jiangxi Copper, Kerry Properties, Longfor Group Holdings Ltd., MediaTek, Meituan Dianping, Midea Group Co Ltd., New Oriental Education &Technology Group, Nexteer Automotive Group, Ping An Bank, Ping An Insurance Company, S.F. Holding Co Ltd, SAIC Motor Corp. Ltd., Sino Land, Sun Hung Kai Properties, Sunac China Holdings Limited, TAL Education Group, Tencent Holdings Ltd., Tongcheng-Elong Holdings Ltd, Viomi Technology Co Ltd.
Within the last 12 months, Morgan Stanley has either provided or is providing non-investment banking, securities-related services to and/or in the past has entered into an agreement to provide services or has a client relationship with the following company: 21Vianet, Alibaba Group Holding, Baidu Inc, China Merchants Bank, China National Building Material Company, China Railway Construction, China Unicom, CRRC Corp Ltd, Ctrip.Com International Ltd, Henderson Land, Industrial Bank Co. Ltd., Longfor Group Holdings Ltd., MediaTek, Meituan Dianping, Midea Group Co Ltd., New Oriental Education &Technology Group, PICC P&C Company Ltd, Ping An Bank, Ping An Insurance Company, Sun Hung Kai Properties, Sunac China Holdings Limited, Taiwan Semiconductor Manufacturing Co Lt, TAL Education Group, Tencent Holdings Ltd., Tongcheng-Elong Holdings Ltd, TSMC, Wharf Real Estate Investment Company Ltd, ZTO Express.
An employee, director or consultant of Morgan Stanley is a director of Industrial Bank Co. Ltd.. This person is not a research analyst or a member of a research analyst's household.
Morgan Stanley & Co. LLC makes a market in the securities of 21Vianet, Alibaba Group Holding, Baidu Inc, China Mobile Limited, China Unicom, Ctrip.Com International Ltd, New Oriental Education &Technology Group, Taiwan Semiconductor Manufacturing Co Lt, TAL Education Group, TSMC, Viomi Technology Co Ltd.
The equity research analysts or strategists principally responsible for the preparation of Morgan Stanley Research have received compensation based upon various factors, including quality of research, investor client feedback, stock picking, competitive factors, firm revenues and overall investment banking revenues. Equity Research analysts' or strategists' compensation is not linked to investment banking or capital markets transactions performed by Morgan Stanley or the profitability or revenues of particular trading desks.
Morgan Stanley and its affiliates do business that relates to companies/instruments covered in Morgan Stanley Research, including market making, providing liquidity, fund management, commercial banking, extension of credit, investment services and investment banking. Morgan Stanley sells to and buys from customers the securities/instruments of companies covered in Morgan Stanley Research on a principal basis. Morgan Stanley may have a position in the debt of the Company or instruments discussed in this report. Morgan Stanley trades or may trade as principal in the debt securities (or in related derivatives) that are the subject of the debt research report.
Certain disclosures listed above are also for compliance with applicable regulations in non-US jurisdictions.
STOCK RATINGS
Morgan Stanley uses a relative rating system using terms such as Overweight, Equal-weight, Not-Rated or Underweight (see definitions below). Morgan Stanley does not assign ratings of Buy, Hold or Sell to the stocks we cover. Overweight, Equal-weight, Not-Rated and Underweight are not the equivalent of buy, hold and sell. Investors should carefully read the definitions of all ratings used in Morgan Stanley Research. In addition, since Morgan Stanley Research contains more complete information concerning the analyst's views, investors should carefully read Morgan Stanley Research, in its entirety, and not infer the contents from the rating alone. In any case, ratings (or research) should not be used or relied upon as investment advice. An investor's decision to buy or sell a stock should depend on individual circumstances (such as the investor's existing holdings) and other considerations.
Global Stock Ratings Distribution
(as of September 30, 2019)
The Stock Ratings described below apply to Morgan Stanley's Fundamental Equity Research and do not apply to Debt Research produced by the Firm.
For disclosure purposes only (in accordance with NASD and NYSE requirements), we include the category headings of Buy, Hold, and Sell alongside our ratings of Overweight, Equal-weight, Not-Rated and Underweight. Morgan Stanley does not assign ratings of Buy, Hold or Sell to the stocks we cover. Overweight, Equal-weight, Not-Rated and Underweight are not the equivalent of buy, hold, and sell but represent recommended relative weightings (see definitions below). To satisfy regulatory requirements, we correspond Overweight, our most positive stock rating, with a buy recommendation; we correspond Equal-weight and Not-Rated to hold and Underweight to sell recommendations, respectively.
Coverage Universe Investment Banking Clients (IBC)Other Material Investment Services Clients
(MISC)Stock Rating
CategoryCount % of Total Count % of Total IBC % of Rating Category Count % of Total Other MISC
Data include common stock and ADRs currently assigned ratings. Investment Banking Clients are companies from whom Morgan Stanley received investment banking compensation in the last 12 months. Due to rounding off of decimals, the percentages provided in the "% of total" column may not add up to exactly 100 percent.
BLUEPAPERM
MORGAN STANLEY RESEARCH 151
Analyst Stock Ratings
Overweight (O). The stock's total return is expected to exceed the average total return of the analyst's industry (or industry team's) coverage universe, on a risk-adjusted basis, over the next 12-18 months.
Equal-weight (E). The stock's total return is expected to be in line with the average total return of the analyst's industry (or industry team's) coverage universe, on a risk-adjusted basis, over the next 12-18 months.
Not-Rated (NR). Currently the analyst does not have adequate conviction about the stock's total return relative to the average total return of the analyst's industry (or industry team's) coverage universe, on a risk-adjusted basis, over the next 12-18 months.
Underweight (U). The stock's total return is expected to be below the average total return of the analyst's industry (or industry team's) coverage universe, on a risk-adjusted basis, over the next 12-18 months.
Unless otherwise specified, the time frame for price targets included in Morgan Stanley Research is 12 to 18 months.
Analyst Industry Views
Attractive (A): The analyst expects the performance of his or her industry coverage universe over the next 12-18 months to be attractive vs. the relevant broad market benchmark, as indicated below.
In-Line (I): The analyst expects the performance of his or her industry coverage universe over the next 12-18 months to be in line with the relevant broad market benchmark, as indicated below.
Cautious (C): The analyst views the performance of his or her industry coverage universe over the next 12-18 months with caution vs. the relevant broad market benchmark, as indicated below.
Benchmarks for each region are as follows: North America - S&P 500; Latin America - relevant MSCI country index or MSCI Latin America Index; Europe - MSCI Europe; Japan - TOPIX; Asia - relevant MSCI country index or MSCI sub-regional index or MSCI AC Asia Pacific ex Japan Index.
Important Disclosures for Morgan Stanley Smith Barney LLC Customers
Important disclosures regarding the relationship between the companies that are the subject of Morgan Stanley Research and Morgan Stanley Smith Barney LLC or Morgan Stanley or any of their affiliates, are available on the Morgan Stanley Wealth Management disclosure website at www.morganstanley.com/online/researchdisclosures. For Morgan Stanley specific disclosures, you may refer to www.morganstanley.com/researchdisclosures.
Each Morgan Stanley Equity Research report is reviewed and approved on behalf of Morgan Stanley Smith Barney LLC. This review and approval is conducted by the same person who reviews the Equity Research report on behalf of Morgan Stanley. This could create a conflict of interest.
Other Important Disclosures
Morgan Stanley & Co. International PLC and its affiliates have a significant financial interest in the debt securities of Alibaba Group Holding, Baidu Inc, China Railway Construction, China Resources Land Ltd., CRRC Corp Ltd, Ctrip.Com International Ltd, Industrial Bank Co. Ltd., Longfor Group Holdings Ltd., Sands China Ltd., Sunac China Holdings Limited, Tencent Holdings Ltd..
Morgan Stanley Research policy is to update research reports as and when the Research Analyst and Research Management deem appropriate, based on developments with the issuer, the sector, or the market that may have a material impact on the research views or opinions stated therein. In addition, certain Research publications are intended to be updated on a regular periodic basis (weekly/monthly/quarterly/annual) and will ordinarily be updated with that frequency, unless the Research Analyst and Research Management determine that a different publication schedule is appropriate based on current conditions.
Morgan Stanley is not acting as a municipal advisor and the opinions or views contained herein are not intended to be, and do not constitute, advice within the meaning of Section 975 of the Dodd-Frank Wall Street Reform and Consumer Protection Act.
Morgan Stanley produces an equity research product called a "Tactical Idea." Views contained in a "Tactical Idea" on a particular stock may be contrary to the recommendations or views expressed in research on the same stock. This may be the result of differing time horizons, methodologies, market events, or other factors. For all research available on a particular stock, please contact your sales representative or go to Matrix at http://www.morganstanley.com/matrix.
Morgan Stanley Research is provided to our clients through our proprietary research portal on Matrix and also distributed electronically by Morgan Stanley to clients. Certain, but not all, Morgan Stanley Research products are also made available to clients through third-party vendors or redistributed to clients through alternate electronic means as a convenience. For access to all available Morgan Stanley Research, please contact your sales representative or go to Matrix at http://www.morganstanley.com/matrix.
Any access and/or use of Morgan Stanley Research is subject to Morgan Stanley's Terms of Use (http://www.morganstanley.com/terms.html). By accessing and/or using Morgan Stanley Research, you are indicating that you have read and agree to be bound by our Terms of Use (http://www.morganstanley.com/terms.html). In addition you consent to Morgan Stanley processing your personal data and using cookies in accordance with our Privacy Policy and our Global Cookies Policy (http://www.morganstanley.com/privacy_pledge.html), including for the purposes of setting your preferences and to collect readership data so that we can deliver better and more personalized service and products to you. To find out more information about how Morgan Stanley processes personal data, how we use cookies and how to reject cookies see our Privacy Policy and our Global Cookies Policy (http://www.morganstanley.com/privacy_pledge.html).
If you do not agree to our Terms of Use and/or if you do not wish to provide your consent to Morgan Stanley processing your personal data or using cookies please do not access our research.
Morgan Stanley Research does not provide individually tailored investment advice. Morgan Stanley Research has been prepared without regard to the circumstances and objectives of those who receive it. Morgan Stanley recommends that investors independently evaluate particular investments and strategies, and encourages investors to seek the advice of a financial adviser. The appropriateness of an investment or strategy will depend on an investor's circumstances and objectives. The securities, instruments, or strategies discussed in Morgan Stanley Research
BLUEPAPERM
152
may not be suitable for all investors, and certain investors may not be eligible to purchase or participate in some or all of them. Morgan Stanley Research is not an offer to buy or sell or the solicitation of an offer to buy or sell any security/instrument or to participate in any particular trading strategy. The value of and income from your investments may vary because of changes in interest rates, foreign exchange rates, default rates, prepayment rates, securities/instruments prices, market indexes, operational or financial conditions of companies or other factors. There may be time limitations on the exercise of options or other rights in securities/instruments transactions. Past performance is not necessarily a guide to future performance. Estimates of future performance are based on assumptions that may not be realized. If provided, and unless otherwise stated, the closing price on the cover page is that of the primary exchange for the subject company's securities/instruments.
The fixed income research analysts, strategists or economists principally responsible for the preparation of Morgan Stanley Research have received compensation based upon various factors, including quality, accuracy and value of research, firm profitability or revenues (which include fixed income trading and capital markets profitability or revenues), client feedback and competitive factors. Fixed Income Research analysts', strategists' or economists' compensation is not linked to investment banking or capital markets transactions performed by Morgan Stanley or the profitability or revenues of particular trading desks.
The "Important US Regulatory Disclosures on Subject Companies" section in Morgan Stanley Research lists all companies mentioned where Morgan Stanley owns 1% or more of a class of common equity securities of the companies. For all other companies mentioned in Morgan Stanley Research, Morgan Stanley may have an investment of less than 1% in securities/instruments or derivatives of securities/instruments of companies and may trade them in ways different from those discussed in Morgan Stanley Research. Employees of Morgan Stanley not involved in the preparation of Morgan Stanley Research may have investments in securities/instruments or derivatives of securities/instruments of companies mentioned and may trade them in ways different from those discussed in Morgan Stanley Research. Derivatives may be issued by Morgan Stanley or associated persons.
With the exception of information regarding Morgan Stanley, Morgan Stanley Research is based on public information. Morgan Stanley makes every effort to use reliable, comprehensive information, but we make no representation that it is accurate or complete. We have no obligation to tell you when opinions or information in Morgan Stanley Research change apart from when we intend to discontinue equity research coverage of a subject company. Facts and views presented in Morgan Stanley Research have not been reviewed by, and may not reflect information known to, professionals in other Morgan Stanley business areas, including investment banking personnel.
Morgan Stanley Research personnel may participate in company events such as site visits and are generally prohibited from accepting payment by the company of associated expenses unless pre-approved by authorized members of Research management.
Morgan Stanley may make investment decisions that are inconsistent with the recommendations or views in this report.
To our readers based in Taiwan or trading in Taiwan securities/instruments: Information on securities/instruments that trade in Taiwan is distributed by Morgan Stanley Taiwan Limited ("MSTL"). Such information is for your reference only. The reader should independently evaluate the investment risks and is solely responsible for their investment decisions. Morgan Stanley Research may not be distributed to the public media or quoted or used by the public media without the express written consent of Morgan Stanley. Any non-customer reader within the scope of Article 7-1 of the Taiwan Stock Exchange Recommendation Regulations accessing and/or receiving Morgan Stanley Research is not permitted to provide Morgan Stanley Research to any third party (including but not limited to related parties, affiliated companies and any other third parties) or engage in any activities regarding Morgan Stanley Research which may create or give the appearance of creating a conflict of interest. Information on securities/instruments that do not trade in Taiwan is for informational purposes only and is not to be construed as a recommendation or a solicitation to trade in such securities/instruments. MSTL may not execute transactions for clients in these securities/instruments.
Certain information in Morgan Stanley Research was sourced by employees of the Shanghai Representative Office of Morgan Stanley Asia Limited for the use of Morgan Stanley Asia Limited.
Morgan Stanley is not incorporated under PRC law and the research in relation to this report is conducted outside the PRC. Morgan Stanley Research does not constitute an offer to sell or the solicitation of an offer to buy any securities in the PRC. PRC investors shall have the relevant qualifications to invest in such securities and shall be responsible for obtaining all relevant approvals, licenses, verifications and/or registrations from the relevant governmental authorities themselves. Neither this report nor any part of it is intended as, or shall constitute, provision of any consultancy or advisory service of securities investment as defined under PRC law. Such information is provided for your reference only.
Morgan Stanley Research is disseminated in Brazil by Morgan Stanley C.T.V.M. S.A. located at Av. Brigadeiro Faria Lima, 3600, 6th floor, São Paulo - SP, Brazil; and is regulated by the Comissão de Valores Mobiliários; in Mexico by Morgan Stanley México, Casa de Bolsa, S.A. de C.V which is regulated by Comision Nacional Bancaria y de Valores. Paseo de los Tamarindos 90, Torre 1, Col. Bosques de las Lomas Floor 29, 05120 Mexico City; in Japan by Morgan Stanley MUFG Securities Co., Ltd. and, for Commodities related research reports only, Morgan Stanley Capital Group Japan Co., Ltd; in Hong Kong by Morgan Stanley Asia Limited (which accepts responsibility for its contents) and by Morgan Stanley Asia International Limited, Hong Kong Branch; in Singapore by Morgan Stanley Asia (Singapore) Pte. (Registration number 199206298Z) and/or Morgan Stanley Asia (Singapore) Securities Pte Ltd (Registration number 200008434H), regulated by the Monetary Authority of Singapore (which accepts legal responsibility for its contents and should be contacted with respect to any matters arising from, or in connection with, Morgan Stanley Research) and by Morgan Stanley Asia International Limited, Singapore Branch (Registration number T11FC0207F); in Australia to "wholesale clients" within the meaning of the Australian Corporations Act by Morgan Stanley Australia Limited A.B.N. 67 003 734 576, holder of Australian financial services license No. 233742, which accepts responsibility for its contents; in Australia to "wholesale clients" and "retail clients" within the meaning of the Australian Corporations Act by Morgan Stanley Wealth Management Australia Pty Ltd (A.B.N. 19 009 145 555, holder of Australian financial services license No. 240813, which accepts responsibility for its contents; in Korea by Morgan Stanley & Co International plc, Seoul Branch; in India by Morgan Stanley India Company Private Limited; in Indonesia by PT. Morgan Stanley Sekuritas Indonesia; in Canada by Morgan Stanley Canada Limited, which has approved of and takes responsibility for its contents in Canada; in Germany and the European Economic Area where required by Morgan Stanley Europe S.E., regulated by Bundesanstalt fuer Finanzdienstleistungsaufsicht (BaFin); in Spain by Morgan Stanley, S.V., S.A., a Morgan Stanley group company, which is supervised by the Spanish Securities Markets Commission (CNMV) and states that Morgan Stanley Research has been written and distributed in accordance with the rules of conduct applicable to financial research as established under Spanish regulations; in the US by Morgan Stanley & Co. LLC, which accepts responsibility for its contents. Morgan Stanley & Co. International plc, authorized by the Prudential Regulatory Authority and regulated by the Financial Conduct Authority and the Prudential Regulatory Authority, disseminates in the UK research that it has prepared, and approves solely for the purposes of section 21 of the Financial Services and Markets Act 2000, research which has been prepared by any of its affiliates. RMB Morgan Stanley Proprietary Limited is a member of the JSE Limited and A2X (Pty) Ltd. RMB Morgan Stanley Proprietary Limited is a joint venture owned equally by Morgan Stanley International Holdings Inc. and RMB Investment Advisory (Proprietary) Limited, which is wholly owned by FirstRand Limited. The information in Morgan Stanley Research is being disseminated by Morgan Stanley Saudi Arabia, regulated by the Capital Market Authority in the Kingdom of Saudi Arabia , and is directed at Sophisticated investors only.
Morgan Stanley Hong Kong Securities Limited is the liquidity provider/market maker for securities of Anhui Conch Cement Co. Ltd, China Merchants Bank, China Mobile Limited, China Resources
BLUEPAPERM
MORGAN STANLEY RESEARCH 153
Land Ltd., China Telecom, China Tower Corp Ltd, China Unicom, CK Asset Holdings Ltd, Galaxy Entertainment, Henderson Land, Jiangxi Copper, Meituan Dianping, PICC P&C Company Ltd, Ping An Insurance Company, Sands China Ltd., Sun Hung Kai Properties, Sunac China Holdings Limited, Tencent Holdings Ltd. listed on the Stock Exchange of Hong Kong Limited. An updated list can be found on HKEx website: http://www.hkex.com.hk.
The information in Morgan Stanley Research is being communicated by Morgan Stanley & Co. International plc (DIFC Branch), regulated by the Dubai Financial Services Authority (the DFSA), and is directed at Professional Clients only, as defined by the DFSA. The financial products or financial services to which this research relates will only be made available to a customer who we are satisfied meets the regulatory criteria to be a Professional Client.
The information in Morgan Stanley Research is being communicated by Morgan Stanley & Co. International plc (QFC Branch), regulated by the Qatar Financial Centre Regulatory Authority (the QFCRA), and is directed at business customers and market counterparties only and is not intended for Retail Customers as defined by the QFCRA.
As required by the Capital Markets Board of Turkey, investment information, comments and recommendations stated here, are not within the scope of investment advisory activity. Investment advisory service is provided exclusively to persons based on their risk and income preferences by the authorized firms. Comments and recommendations stated here are general in nature. These opinions may not fit to your financial status, risk and return preferences. For this reason, to make an investment decision by relying solely to this information stated here may not bring about outcomes that fit your expectations.
The trademarks and service marks contained in Morgan Stanley Research are the property of their respective owners. Third-party data providers make no warranties or representations relating to the accuracy, completeness, or timeliness of the data they provide and shall not have liability for any damages relating to such data. The Global Industry Classification Standard (GICS) was developed by and is the exclusive property of MSCI and S&P.
Morgan Stanley Research, or any portion thereof may not be reprinted, sold or redistributed without the written consent of Morgan Stanley.
Indicators and trackers referenced in Morgan Stanley Research may not be used as, or treated as, a benchmark under Regulation EU 2016/1011, or any other similar framework.
