china in 2019: keeping reasonable growth - marine money · 2019. 10. 4. ·...

China in 2019: Keeping reasonable growth

中国2019展望：保持合理增长

Wei Li 李炜渣打银行高级经济师

Senior China Economist

Standard Chartered Bank

[email protected]

How fast is China growing?

中国经济增长多快

Source: CEIC, Standard Chartered Research 2

How fast is China growing?

当前中国经济到底增长多快？Official GDP growth, % y/y; total social financing growth, real

% y/y, 3mma

0%

5%

10%

15%

20%

25%

30%

35%

40%

0%

2%

4%

6%

8%

10%

12%

14%

16%

2007 2009 2011 2013 2015 2017 2019

Official GDP (LHS)

官方GDP (左轴)

TSF (RHS)

社会融资总量 (右轴)

Growth seems blow the natural rate.

中国经济表现似乎在潜在增速之下Official GDP growth, % y/y; our estimation of economic growth

GDP增速，我们对主要产业增速的估值

0%

2%

4%

6%

8%

10%

12%

14%

16%

2005 2007 2009 2011 2013 2015 2017 2019

Official GDP

官方GDP增速

Our GDP tracker

我们的GDP估值

Infrastructure investment slowed sharply in 2018

2018年基建投资大幅放缓


Collapsing infrastructure FAI has also dragged

retail sales lower 基建投资放缓同时拖累零售餐饮行业Infrastructure FAI, retail sales, % y/y, real, 3mma

4%

6%

8%

10%

12%

14%

16%

18%

20%

-10%

0%

10%

20%

30%

40%

50%

60%

70%

2005 2007 2009 2011 2013 2015 2017 2019

Retail sales (RHS)

社会消费品零售 (右轴)

Infrastructure FAI (LHS)

基建投资 (左轴)

Slower export growth on softer external demand

外需放缓拖累中国出口增速Export, import, % y/y, real, 3mma

-10%

-5%

0%

5%

10%

15%

20%

Jan-12 Jan-13 Jan-14 Jan-15 Jan-16 Jan-17 Jan-18 Jan-19

Export growth

出口增速

Import growth

进口增速

Consumer spending appears to remain solid

居民消费似乎依然表现稳健


Consumer spending remains solid

居民消费表现稳健Household consumption and disposable income, %

y/y, 3mma

The labor market appears stable despite contracting employment PMI sub-indices劳动力市场似乎依然稳定Employment PMI sub-indices, consumer confidence index

0

30

60

90

120

150

40

45

50

55

60

65

2005 2007 2009 2011 2013 2015 2017 2019

Consumer confidence index (RHS)

消费者信心指数 (右轴)

Manufacturing employment PMI (LHS)

制造业就业指数 (左轴)

Non-manufacturing

employment PMI (LHS)

非制造业就业指数 (左轴)

0%

4%

8%

12%

16%

20%

Jan-06 Jan-08 Jan-10 Jan-12 Jan-14 Jan-16 Jan-18

Urban household disposable

income 城镇家庭可支配收入

Urban household

consumption

城镇家庭消费

Manufacturing investment re-accelerated in 2018

2018年制造业投资反弹，但盈利增速放缓


Manufacturing FAI rebounded in 2018 after slowing

for four years since 2014; 2018年制造业投资在连续四年放缓后反弹 Manufacturing FAI, % y/y, real, 3mma;

industrial profit, % y/y

-10%

-5%

0%

5%

10%

15%

20%

25%

30%


Manufacturing FAI

制造业投资

Industrial profit

工业企业利润

The financial condition for large industrial companies remains healthy大型工业企业经营指标健康Business survey of 5000 principal industrial enterprises

40

45

50

55

60

65

70

75

80


Corporate confidence

企业信心

Profitability

盈利能力

Business condition

当前经营状况

Banks’ lending attitude

银行贷款意愿

Improving credit growth is positive for 2019

信贷改善有利于2019年经济企稳


Deleveraging weighs on the economy with a 6m lag

信用增速放缓通常在6个月后拖累经济增长Our GDP tracker, % y/y, real; TSF, % y/y, real, 6m lag

16.4%

9.6%

7.1%

9.4%

0%

5%

10%

15%

20%

25%

30%

35%

40%

2%

4%

6%

8%

10%

12%

14%

16%

2009 2011 2013 2015 2017 2019

TSF, 6m lag (RHS)

社融总量, 6个月滞后(右轴)

Our GDP tracker (LHS)

我们的GDP增长估值(左轴)

The government is spending again from late-2018中国政府2018年末重新加大财政刺激力度Budget deficit, % of GDP, RHS inverted; infrastructure FAI, % y/y, real, 3mma

-4.5%

-4.2%

-3.9%

-3.6%

-3.3%

-3.0%-10%

-5%

0%

5%

10%

15%

20%

25%

Jan-17 Jul-17 Jan-18 Jul-18

Budget deficit, inverted (RHS)

财政赤字，反转 (右轴)

Infrastructure FAI (LHS)

基建投资 (左轴)

Room for further housing market turndown is limited

房地产市场表现疲软，但下行空间有限


The housing market downturn should have run

most of its course 房地产下行周期应该已经接近尾声Housing floor space sold, % y/y, 3mma; real estate

construction FAI, % y/y, real, 3mma

-40%

-20%

0%

20%

40%

60%

80%

100%

Jan-08 Jan-10 Jan-12 Jan-14 Jan-16 Jan-18

Housing floor space sold (LHS)

房屋销售面积 (左轴)

Housing sales growth has flatted

多数城市房地产销售企稳Housing floor space sold, % y/y, 3mma

-45%

-30%

-15%

0%

15%

30%

45%

60%

75%


Unclassified

四线城市

Tier 3 cities

三线城市

Tier 1 cities

一线城市

Weak housing sales responsible for slowing car sales

房地产下行周期造成汽车销售下滑


Falling infrastructure investment adds to the pressure on passenger car sales 基建投资放缓加重汽车销售下滑 Housingg sales, infrastructure FAI, passenger car sales, % y/y, real, 3mma

-40%

-20%

0%

20%

40%

60%

80%

100%


Housing floor space sold (LHS)

房屋销售面积 (左轴)

Car sales

汽车销售

Slowing housing sales and collapsing

infrastructure FAI caused falling M1 growth

M1, housing sales, infrastructure FAI, % y/y, 3mma

-60%

-30%

0%

30%

60%

90%

120%

150%

-5%

4%

13%

22%

31%

40%

2006 2008 2010 2012 2014 2016 2018

M1 (LHS)

M1增速 (左轴)

Housing value sold (RHS)

住宅销售 (右轴)

We do not expect a quick recovery either

我们同样不认为房地产市场会快速反弹


Rapid growth in household debt is concerning 居民债务快速增长令人担忧Household loan-to-GDP ratio, %

19%

53%

0%

10%

20%

30%

40%

50%

60%


Household loan to GDP ratio

居民贷款与GDP比例

Household loans growth

居民贷款增速

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

4,000,000

4,500,000

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Tier 3 cities

三线城市

Tier 1 cities

一线城市

Tier 2 cities

二线城市

Housing affordability is deteriorating 购房能力下降Average housing price, CNY

110%

65%

48%

A city-by-city guidance on China’s local housing

markets 观察不同城市的房地产状况


Lining up China’s cities by average household disposable income

按家庭可支配收入分级城市Average annual disposable income per household, March 2018, CNY; the colour of the bar reflects which tier

the city belongs to

0

40,000

80,000

120,000

160,000

200,000

Tai

yuan

Sh

ijiaz

hu

ang

Lan

zho

u

Xin

ing

Yin

chu

an

Ch

on

gq

ing

Ch

ang

chu

n

Gu

iyan

g

Har

bin

Bei

hai

Uru

mq

i

Zh

eng

zho

u

Hai

kou

San

ya

Nan

chan

g

Hef

ei

Nan

nin

g

Ch

eng

du

Xia

n

Dal

ian

Ku

nm

ing

Ho

hh

ot

Sh

enya

ng

Tia

njin

Wu

han

Fu

zho

u

Jin

an

Qin

gd

ao

Wen

zho

u

Ch

ang

sha

Wu

xi

Nin

gb

o

Nan

jing

Xia

men

Han

gzh

ou

Su

zho

u

Bei

jing

Gu

ang

zho

u

Sh

ang

hai

Sh

enzh

en

Tier 1 cities: > = CNY 170,000 (USD 27,000)

Tier 2 cities: CNY 120,000 (USD19,000) – CNY 170,000 (USD 27,000)

Tier 3 cities: CNY 80,000 (USD 12,700) – CNY 120,000 (USD 19,000)

Tier 3

3线

Tier 2

2线

Tier 1

1线

Key statistics on 297 prefecture-level cities in China

297个地级市的主要指标


Tier 1 Tier 2 Tier 3 Unclassified

Average annual disposable income per capita, March 2018, CNY 182,094 142,013 102,367 NA

Average annual disposable income per capita, March 2018, USD 28,904 22,542 16,249 NA

Change of annual disposable income growth rate since 2015 33% 30% 29% NA

Urban population, 2017, mn persons 73 115 167 459

Share of urban population, 2017 9% 14% 21% 56%

Change of urban population since 2015 5% 5% 2% 12%

Ratio of “usual” residents to hukou holders, 2017 1.9x 1.3x 1.1x NA

Share of annual housing sales by floor space, May 2018 2% 12% 19% 66%

Share of annual housing sales by value, May 2018 9% 21% 20% 50%

Share of annual housing construction by floor space, May 2018 4% 13% 21% 62%

Share of annual housing fixed asset investment by value, May 2018 9% 20% 22% 49%

Average housing price2, May 2018, CNY 3,708,364 1,701,155 907,998 NA

Change in average housing price since 2015 97% 51% 38% NA

Ratio of housing price to household disposable income3, May 2018 20x 12x 9x NA

Ratio of change in housing price to income, since 2015 46% 14% 5% NA

Ratio of housing supply to sales4, May 2018, month of sales 86x 55x 52x 42x

Ratio of change in housing supply to sales, since 2015 31% -15% -28% -25%

Ratio of housing sales to local GDP, 2017 10% 17% 21% NA

Ratio of change in housing sales to local GDP, since 2015 4% 36% 43% NA

Housing prices are increasingly over-stretched in most

Tier 1&2 cities 多数一二线城市房价过高


Housing affordability looks worst in Shenzhen, Beijing, Xiamen and Sanya

深圳、北京、厦门和三亚的房价收入比最高Ratio of housing prices to household disposable income, May 2018, x (LHS); change in housing affordability

since 2015 (RHS)

-30%

0%

30%

60%

90%

0

9

18

27

36

Tai

yuan

Sh

ijiaz

hu

ang

Lan

zho

u

Yin

chu

an

Xin

ing

Ch

on

gq

ing

Ch

ang

chu

n

Har

bin

Gu

iyan

g

Bei

hai

Uru

mq

i

Zh

eng

zho

u

Hai

kou

San

ya

Nan

chan

g

Hef

ei

Nan

nin

g

Ch

eng

du

Xia

n

Dal

ian

Ho

hh

ot

Ku

nm

ing

Sh

enya

ng

Tia

njin

Wu

han

Fu

zho

u

Jin

an

Qin

gd

ao

Wen

zho

u

Ch

ang

sha

Wu

xi

Nin

gb

o

Nan

jing

Xia

men

Han

gzh

ou

Su

zho

u

Bei

jing

Gu

ang

zho

u

Sh

ang

hai

Sh

enzh

en

The colour of the bar reflects which tier the city belongs to (LHS)

The height of the bar shows the ratio of housing prices to average household disposable incomes in May-2018 (LHS)

The red dotted lines are thresholds above which we think housing prices are becoming over-stretched (LHS)

The purple dot shows how much housing affordability has deteriorated since 2015 (RHS)

Sanya, Haikou, Zhengzhou, Nanning, Wuhan and Hangzhou are more vulnerable to

housing downturns 三亚、海口、郑州、武汉和杭州更易受到房地产回调的冲击


Sanya, Haikou, Zhengzhou, Nanning, Wuhan and Hangzhou are more vulnerable to housing downturns

三亚、海口、郑州、武汉和杭州更易受到房地产回调的冲击Ratio of housing sales to local GDP, 2017 (LHS); change in the ratio since 2015 (RHS)

-80%

-40%

0%

40%

80%

120%

160%

200%

-10%

0%

10%

20%

30%

40%

50%

60%

70%

Tai

yuan

Sh

ijiaz

hu

ang

Lan

zho

u

Yin

chu

an

Xin

ing

Ch

on

gq

ing

Ch

ang

chu

n

Har

bin

Gu

iyan

g

Bei

hai

Uru

mq

i

Zh

eng

zho

u

Hai

kou

San

ya

Nan

chan

g

Hef

ei

Nan

nin

g

Ch

eng

du

Xia

n

Dal

ian

Ho

hh

ot

Ku

nm

ing

Sh

enya

ng

Tia

njin

Wu

han

Fu

zho

u

Jin

an

Qin

gd

ao

Wen

zho

u

Ch

ang

sha

Wu

xi

Nin

gb

o

Nan

jing

Xia

men

Han

gzh

ou

Su

zho

u

Bei

jing

Gu

ang

zho

u

Sh

ang

hai

Sh

enzh

en

The colour of the bar reflects which tier the city belongs to (LHS)

The height of the bar shows the ratio of housing sales to local GDP (LHS)

The purple dot shows how much the ratio of housing sales to local GDP

has changed since 2015 (RHS) Above the red dotted lines means the

housing sales to GDP ratio has risen since 2015 (RHS)

86%

M1 growth and housing sales are closely correlated

M1增速和房屋销售高度相关


Falling M1 growth indicates pressure on PPI inflation M1下行暗示工业品出厂价格指数可能继续下跌M1, 9m lag, % y/y; PPI % y/y (6.3% in 2017, 3.3% in 2018)

-10%

-5%

0%

5%

10%

15%

0%

9%

18%

27%

36%

45%

2005 2007 2009 2011 2013 2015 2017 2019

M1, 9m lag (LHS)

M1增速，滞后9个月 (左轴)

PPI (RHS)

生产者价格指数 (右轴)

Why is M1 and M2 growth diverging?

广义和狭义货币增长背离的原因M1, M2 and TSF, % y/y, nominal

-5%

0%

5%

10%

15%

20%

25%

30%

35%

40%

2005 2007 2009 2011 2013 2015 2017 2019

TSF

社会融资总量

M2

广义货币

M1

狭义货币

Tepid inflation enhances policy flexibility

低通胀增加货币政策灵活性


We expect CPI to average 2.0% in 2019, versus

2.1% in 2018, and core CPI to edge lower to 1.7%

from 1.9% from 2018-19

CPI, core CPI & forecast, % y/y

We expect food inflation to rise to 2.9% in 2019 from 1.8% in 2018, on a positive base effect and a rebound in pork prices after pig supplies fell sharply following the African swine fever outbreak

-2%

0%

2%

4%

6%

8%

10%

2005 2007 2009 2011 2013 2015 2017 2019

CPI

消费者价格指数

Core CPI

核心消费者价格指数

40%

45%

50%

55%

60%

65%

70%

-30%

-15%

0%

15%

30%

45%

60%

75%

Jan-10 Jan-12 Jan-14 Jan-16 Jan-18

Pork slaughtered-to-supply (RHS)

生猪屠宰-存栏比例 (右轴)

Pork CPI inflation (LHS)

猪肉价格 (左轴)

Tackling the ‘dual track’ interest rate problem

推进利率市场“两轨并一轨”


We expect the PBoC to phase out the benchmark

rate and cut the MLF rate by 40bp in 2019

我们预计银行将退出对基准利率的使用

What is likely to happen next：

1. Phasing out the benchmark lending and deposit rates,

and adopting a policy rate system which includes the

7-day reverse repo rate and medium-term lending

facility (MLF) rates within an interest rate corridor

2. Introducing a new methodology for calculating the loan

prime rate (LPR) to ensure it is closely linked to policy

rates, and encouraging banks to price new loans

based on the LPR.

3. Narrowing the width of the interest rate corridor to

c.100-200bps through improved liquidity management

4. Enhancing the concept of funds transfer pricing (FTP)

to ensure banks maintain healthy and sustainable net

interest margins (NIMs) after benchmark lending and

deposit interest rates are removed. In particular, given

China banks’ RoE fell to c.14% from 22% in 2012-17

(US banks’ RoE recovered to 13% from 9% in 2013-

18), and their NIM stands at c.2.5% (at par with US

banks’ NIM).1%

2%

3%

4%

5%

6%

7%

Jan-15 Jan-16 Jan-17 Jan-18 Jan-19

1Y IRS 7d repo

General loans

PBoC 7d reverse repo

1Y corporate

bond

Mortgage

1Y lending

benchmark

1Y MLF

We expect PBoC to cut the MLF rate by 40bp in 2019

我们预计2019年央行降低MLF40个基点


We expect the PBoC to cut the one-year MLF rate

by about 40bps this year to 2.9% from 3.3%

currently 我们预计央行降低MLF40个基点Weighted lending rate, %; our GDP tracker, % y/y

We expect a total of 300bps of cuts in the RRR我们预计2019年银行共降准300点RRR, TSF, M2, % y/y

5.4%

5.9%

0%

2%

4%

6%

8%

10%

12%

2011 2013 2015 2017 2019

Our GDP tracker

我们的GDP估值

Weighted lending rate

贷款加权利率

8.4%

10.4%

13.5%

0%

5%

10%

15%

20%

25%

2011 2013 2015 2017 2019

M2

广义货币

Big bank RRR

大银行存款准备金率

TSF

社会融资总量

Consumption has become China’s major growth driver

消费超过投资成为拉动中国经济增长的主要引擎


Q3

Consumption has surpassed investment as the

key driver of China’s economy

消费超过投资成为拉动中国经济增长的主要引擎Contribution to domestic demand growth, %

39%

46%

70%

61%

54%

30%

0%

15%

30%

45%

60%

75%

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Consumption contribution

消费贡献率Investment contribution

投资贡献率

China no longer needs to grow as fast中国不再需要保持10%的经济增速China GDP and growth needed to be equally fast as 2018

为创造与2018相同的经济增量，需要的中国GDP总量及增长

20

90

14.6%

9.3%

6.6%

3.7%

0%

5%

10%

15%

20%

25%

30%

35%

0

15

30

45

60

75

90

105

1970 1980 1990 2000 2010 2020 2030

GDP, CNY trn

经济总量，万亿元

How fast China needs to grow to generate the

same amount of incremental GDP as in 2017

创造与2017年相同的经济增量，中国需要多快增长

As the poor catch up with the rich

内陆省市追赶沿海省市


The catch-up of poorer inland regions in China with its rich parts is another key growth driver for Chin

更加平衡的发展为中国经济提供新引擎GDP per capita by province, CNY; the gap between lower tier provinces and tier 1 provinces in per capita

capital stock and urbanisation rate, %

20,000

40,000

60,000

80,000

100,000

120,000

140,000

Gan

suY

un

nan

Gu

izho

uT

ibet

Sh

anxi

Gu

ang

xiH

eilon

gjian

gA

nh

ui

Qin

gh

aiS

ichu

anX

injian

gJian

gxi

Hen

anH

ebei

Hain

anH

un

anN

ing

xiaL

iaon

ing

JilinS

haan

xiH

ub

eiC

ho

ng

qin

gIn

ner M

on

go

liaS

han

do

ng

Gu

ang

do

ng

Fu

jianZ

hejian

gJian

gsu

Tian

jinS

han

gh

aiB

eijing

Tier 4 (CNY 43,224)

Tier 3 (CNY 59,593)

Tier 2 (CNY 82,243)

Tier 1 (CNY 119,981)

-80%

-70%

-60%

-50%

-40%

-30%

-20%

-10%

0%

Gan

su

Yu

nn

an

Gu

izho

u

Tib

et

Sh

anxi

Gu

ang

xi

Heilo

ng

jiang

An

hu

i

Qin

gh

ai

Sich

uan

Xin

jiang

Jiang

xi

Hen

an

Heb

ei

Hain

an

Hu

nan

Nin

gxia

Liao

nin

g

Jilin

Sh

aanxi

Hu

bei

Ch

on

gq

ing

Inn

er Mo

ng

olia

Sh

and

on

g

Gu

ang

do

ng

Fu

jian

Zh

ejiang

Capital stock per capita compared with Tier 1 provinces, %

人均固定资产存量与一线省份差距，%

Urbanisation

rate 城镇化率

Source: WIOD, CEIC, Standard Chartered Research 20

New power: consumption & industrial upgrade

消费和产业升级成为新动能

Which industries will benefit from industrial upgrading in China? 哪些行业将从产业升级中获益Difference in value added ratios between developed economies and China, ppt

-6%

-3%

0%

3%

6%

Fo

od

&to

bacco

食品、饮料、烟草

Textiles& ap

parel 服

装

Ed

ucatio

n 一般教育

Ho

me ap

plian

ce 传统家电

Telecom

mu

nicatio

fees 电信缴费

Fu

el-energ

y cars 传统动力汽车

Wareh

ou

sing

储藏物流

Ho

tel&fo

od

service 住宿

&餐饮

Ch

emical p

rod

ucts 化

学品制造

En

viron

men

tal pro

tection

环保

Air tran

spo

rt 航空运输

Mo

tion

pic&

bo

oks 影

视&书籍发行

New

gen

eration

IT 新一代信息技术

Insu

rance 保

险、再保险

Health

care 人体健康、护理

Wh

olesale&

retail 批发

&零售

Real estate 房

地产买卖租赁估价

More demand

需求加速上升

Less demand

需求增速下降

Which industries will benefit from consumption upgrading in China? 哪些行业将从消费升级中获益Difference in the share of consumption between developed economies and China, ppt

-20%

-10%

0%

10%

20%

30%

40%

Traditio

nal fin

ance 传

统金融

Telecom

mu

nicatio

fees 电信缴费

Real estate 房

地产买卖租赁估价

Wh

olesale&

retail 批发

&零售

Mo

tion

pic&

bo

oks 影

视&书籍发行

Insu

rance 保

险、再保险

Wareh

ou

sing

储藏物流

Ch

emical p

rod

ucts 化

学品制造

Air tran

spo

rt 航空运输

Scien

tific research 科研

Hig

h-en

d eq

uip

men

t 高端装备

Ph

armaceu

tical 基本医药

Health

care 人体健康、护理

En

viron

men

tal pro

tection

环保

New

gen

eration

IT 新一代信息技术

More profitable

利润率上升Less profitable

利润率下降

The era of cheap labour in China has ended

中国廉价劳动力时代一去不返

Source: CEIC, Bloomberg, Standard Chartered Research21

China has passed the Lewis turning point

中国已经越过了刘易斯拐点 Working-age and old-age

population changes, mn; old-age dependency ratio

0%

10%

20%

30%

40%

50%

-20

-10

0

10

20

30

40

1950 1970 1990 2010 2030 2050

Working age population (LHS)

劳动适龄人口（左轴）

Old-age dependency ratio (RHS)

老龄人口抚养比（右轴）

Old age population (LHS

老龄人口（左轴）

Economic growth “naturally” slows as a country

gets richer 随着经济体量变大，增速“自然”放缓GDP per employed person, 2011 PPP, y/y %

-6%

-3%

0%

3%

6%

9%

12%

15%

0 20,000 40,000 60,000 80,000 100,000 120,000

GDP per employed person (X axis)

劳动生产率（X轴）

Labor productivity growth (Y axis)

劳动生产率增速（Y轴）

22Source: IMF, Standard Chartered Research

1. Nominal credit to the nonfinancial sector more than doubled in

the last five years. China’s leverage ratio increased from 145%

of GDP in 2008 to 253% as of December-2018.

2. Out of 43 cases of credit booms in which the credit-to-GDP

ratio increased by more than 30ppts over a 5-year period., only

5 cases ended without a major growth slowdown or a financial

crisis immediately afterwards.

3. Several China-specific factors—high savings, current account

surplus, small external debt, and various policy buffers—can

help mitigate near-term risks of a disruptive adjustment and buy

time to address risks. But, if left unaddressed, these factors will

likely not eliminate the eventual adjustment.

4. Decisive policy action is needed to deflate the credit boom

smoothly: (i) deemphasize the high and hard GDP targets to

contain excessive credit expansion, and reduce credit demand

by the least efficient users to increase credit efficiency; (ii)

institute reforms to boost consumption to support growth while

credit expansion slows.

Policymakers likely slow the pace of deleveraging

短期去杠杆力度可能减弱Outstanding debt by sector, % of GDP

0%

52%

104%

156%

208%

260%

2008 2010 2012 2014 2016 2018

Government debt (40%)

政府债务（40%）

Corporate debt (160%)

企业债务（160%）

Household debt (53%)

居民债务（53%）

Moderate the intensity of deleveraging

把握好结构性去杠杆的力度和节奏

Stretched leverage cannot be ignored

过度杠杆风险不容忽视

23

Leverage heatmap – China has the biggest concentration of credit risk (in the corporate sector); Korean, Thai and Australian households are also stretched杠杆热力图 – 中国信贷风险最为集中（企业部门）；韩国，泰国和澳大利亚的家庭杠杆程度高

* Difference between 5Y CAGR of credit growth and 5Y CAGR of nominal GDP growth; a difference of more than 5ppt (500bps) is our threshold for a red flag

# China and India data are as of December 2016; all other numbers as of June 2016

+ Interest payments/government revenue figures are 2017 forecasts from Moody’s

Source: Bloomberg, Moody’s, BIS, IMF, Standard Chartered Research

Colours indicate leverage and potential stress Low Moderate/sustainable High

Arrows indicate change from June 2015: ↑ Moderate increase ↑↑ Fast increase ↓ Decrease

CN IN ID KR MY PH TW TH HK SG AU JP US

Economy

Overall leverage indicator ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↑ ↑ ↑ ↓

Total credit/GDP 246.4↑ 134.7↓ 68.5↑ 220.4↑ 236.3↓ 104.8↑ 142.8↑ 191.2↓ 302.8↑ 302.4↑ 247.5 393.1↑ 244.6

*Credit-GDP growth spread (5-yr avg, bps) 543.0↓ -41.8↓ 391.7↓ 184.6↓ 38.3↓ 332.6↑ -27 120.2↓ 621.3↑ 213.2↓ 315.1↓ 14.2↓ 14.5

> Private non-financialTotal borrowings/GDP 188.8↑ 67.3↓ 39.4↓ 182.2↑ 185.5↓ 70.8↑ 107.2↑ 150.0↓ 302.8↑ 190.2↑ 205.5↓ 160.8↑ 149.4↑

DSR 17.9↑ 8.5↓ 4.2↓ 19.8 13.1↓ 9.8↓ 26.0↑ 19.2↑ 20.9↓ 14.5↑ 14.9↑

– Corporates

Business borrowings/GDP 139.8↑ 54.1↓ 22.3↓ 98.3↓ 101.3 61.4↑ 61.5↑ 72.4↓ 232.2↑ 117.9↑ 75.3↓ 103.4↑ 72.2↑

Debt/equity 78.6↓ 45.3↓ 61.1↓ 49.8↓ 56.3 89.2↓ 51.5↑ 67.0↓ 54.9↓ 62.7↓ 52.4↓ 61.2↓ 95.7

EBITDA/interest expense 8.0↑ 4.6↑ 9.3↑ 14.2↑ 8.2↑ 8.2 18.5↓ 10.6↓ 13.9↑ 7.4↑ 13.3↑ 33.0↑ 8.8↑

– Household

Household borrowing/GDP 49.0↑ 13.2 17.0↑ 83.8↑ 84.2↓ 9.4↑ 45.6↑ 77.5↓ 70.6↑ 72.3↓ 130.3↑ 57.4↑ 77.2↓

Borrowing/household income 112.2↑ 13.3↑ 29.9↓ 159.8↑ 196.6↓ 16.6↑ 71.5↑ 171.1↑ 176.3↑ 139.2 188.6↑ 129.5↑ 104.4↑

Debt service ratio 12.3↑ 1.8↑ 4.8↓ 15.7↑ 20.7↓ 1.9↑ 6.4↑ 19.9↑ 16.0↑ 13.4 20.7↑ 10.8 10.3↑

> GovernmentGovernment debt/GDP 57.6↓ 67.3↓ 29.2↑ 38.2↑ 50.7↓ 34.0↓ 35.6↓ 41.2↓ 0.1↓ 112.2↑ 42.0↑ 232.3 95.2

Int. payments/govt revenue 7.2↑ 22.2↓ 11.7↑ 4.4↓ 12.5↑ 13.9↓ 4.2↓ 3.9↓ 4.3↑ 5.6↓ 7.7

> Private financialTier 1 capital adequacy ratio 11.1 11.0↑ 22.7↑ 13.8↑ 14.3↑ 12.7↓ 11.8↑ 15.1↑ 16.6↑ 15.4↑ 12.4↑ 14.2↑ 13.5↑

Non-performing loans ratio 1.7↑ 10.0↑ 2.6↓ 0.9↓ 1.5↓ 1.6↓ 0.3 3.1↑ 0.7↓ 1.4↑ 0.9↓ 1.2↓ 1.1↓

External debt

External debt/GDP 5.1↓ 14.4↓ 32.2 13.0↓ 42.0↓ 16.3↓ 31.1↓ 20.2↓ 92.5↑ 81.3 45.2↓ 45.2↑

Total Ext. debt (incl fin. sector)/GDP 13.5 20.1↓ 35.2↓ 25.9↓ 65.0↓ 23.1↓ 31.4↓ 459.2↑ 432.0↓ 108.2↓ 73.9↑

FCY share of total external debt 61.0↑ 60.6↑ 79.0↑ 71.0↑ 66.0↑ 91.3↑ 68.9↑ 93.0↑ 26.0↑ 35.0↑

External debt/FX reserves 0.2↑ 0.9↓ 2.5↓ 0.5 1.4 0.6↓ 0.4 0.5↓ 0.7↑ 1 10.8↓ 1.8↑

Rising tensions between US and China

中美贸易摩擦升级

Source: Standard Chartered Research 24

Rising US goods trade deficit with China

美国与中国的货物贸易赤字上升US goods trade deficit, rolling annual sum, USD bn

-900

-740

-580

-420

-260

-100

1990 1994 1998 2002 2006 2010 2014 2018

US trade deficit with China

(USD 413bn in Nov 2018)

美国与中国贸易赤字

US trade deficit with others

美国与其它国家贸易赤字

US economic dependency on China and foreign

demand is rising

Percentage of US and China economies driven by

foreign demand, WIOD

6.3%

3.0%

0.2%

1%

8.9%

0%

2%

4%

6%

8%

10%

2000 2002 2004 2006 2008 2010 2012 2014 2016 2018

US contribution to China’s GDP

中国经济对美国的依存度

Foreign demand contribution to US GDP

美国经济对外需的依存度

China’s contribution to US GDP

美国经济对中国的依存度


How big is the impact of a trade war?

贸易战对中美经济的损伤多大？1. We estimate that a 25ppt increase in US tariffs on a total of

USD 250bn of China-made goods could cut China’s annual

GDP growth by up to 0.6ppt over the next 12 months.

2. If the US bans all imports of Chinese computer, electronic and

optical products, electrical equipment, machinery, vehicles

and other transport equipment (about 50% of China’s total

direct exports to the US, with a weighted average value-added

ratio of 76%), the total economic losses for China, from all

economic activity associated with the production and exports

of hi-tech products to the US, should be about 1.3% of China

GDP.

3. If the US limiting all direct imports from China (note that 20%

of China exports for US consumers arrive in the US indirectly

through our other countries), our model suggests the total

economic losses for China could rise up to 2.6% of China’s

GDP.

4. If the US is capable of banning all imports from China for its

own consumption, including those being imported indirectly

from other countries with China components, our model

suggests the total economic losses for China could rise

further to 3.2% of China’s GDP.

5. The economies that are likely to suffer most from a slowdown

in China’s exports to the US include Taiwan, Korea, Australia

and Japan, with the impact amounting to 0.8%, 0.4%, 0.2% and

0.1% of their respective GDP.

1. If China bans imports of US agricultural products and

transport equipment. Such products account for about 30%

of US total direct exports to China. In this scenario, we

estimate that total economic losses for the US could be

c.0.2% of US GDP.

2. If China bans all direct imports from the US (note that

around 32% of US exports due to China demand arrive in

China indirectly via other countries). In this case, our model

suggests an economic loss for the US equivalent to 0.6% of

its GDP.

3. If China bans all direct and indirect imports from the US, the

economic losses for the US could amount to 0.9% of its GDP,

according to our model.

4. The top five US industries that are likely to suffer the most

from China’s ban of imports from the US are air transport,

non-vehicle transport equipment, computer, electronic and

optical products, agricultural products and machinery

equipment industries. Their production could slow between

3% and 6% according to our model.

5. The economies that are likely to be affected most by a

slowdown in US exports to China are Canada, Mexico,

Ireland, Taiwan and Korea (note that Taiwan and Korea

economies appear to be most vulnerable to collateral

damage, since they are closely linked to both China and US

exports).

China is the second largest economy in the world

中国是世界第二大经济体

Source: IMF, Standard Chartered Research 26

China is the second-largest economy and growing, nearly overtaking the euro area when measured

by nominal GDP at market exchange rates

Size represents 2018F percentage share of level of world GDP at market exchange rates; shade represents

whether share is increasing/decreasing vs past-5-year average

China is ¼ of world GDP growth at market exchange

rates 中国贡献全球经济增长的四分之一

27

We expect global growth to accelerate or remain steady in 2018 versus past 5Y levels; the UK and

MENAP are the only exceptions 我们预计2018年全球增长较过去5年平均水平将加速或持平；但英国和中东/

北非/巴基斯坦也许是个例外Size represents 2018F ppt contribution to world GDP growth at market exchange rates; shade represents rate

of contribution vs past-5-year average; percentages represent estimated share of world GDP growth in 2018

Source: IMF, Standard Chartered Research

China 中国26%

Euro area欧元区9%

United States美国17%

AXCJ除中日外亚洲15%

Japan日本2%

Latin America 拉丁美洲4%

MENA

3%

Africa非洲1%Rest of World 全球其他地区

20%

United Kingdom英国1%

Canada加拿大1%

Other Europe

欧洲其他地区2%

Fast Neutral Slow

P中东/北非/巴基斯坦

US import duties focus on “China Manufacturing 2025”

美国关税主要针对“中国制造2025”

Source: Office of the US Trade Representative, Standard Chartered Research

https://ustr.gov/sites/default/files/2018-0018%20notice%206-15-2018_.pdf28

Key sector Selected tariff line

New information

technology

新一代信息技术产业

• Communications satellites

• Instruments and apparatus

for telecommunications

High-end numerically

controlled machine tools

and robots

高档数控机床和机器人

• Numerically controlled

machines

• Industrial robots

Aerospace equipment

航空航天装备

• Air combat flying simulators

• Airplane and parts

Ocean engineering

equipment and high-end

vessels

海洋工程装备及高技术船舶

• Oceanographic instruments

and appliances

• Tanker, ferry and fishing

boats

High-end rail

transportation equipment

先进轨道交通装备

• Railway or tramway

coaches

• Train parts

Key sector Selected tariff line

Energy-saving cars and

new energy cars

节能与新能源汽车

• Parts of electric vehicles

• Motor vehicles

Electrical equipment

电力装备

• Various electrical

instruments and apparatus

Farming machines

农机装备

• Agricultural, horticultural and

forestry machinery and parts

New materials

新材料• Various chemical products

Bio-medicine and high-end

medical equipment

生物医药及高性能医疗器械

• Vaccines

• X-ray machine parts

Which industries are likely to suffer most in a trade

war? 贸易战对哪些行业冲击最大？

Source: CEIC, WIOD, Standard Chartered Research 29

Chinese industries most affected by a trade war

贸易战影响最大的中国行业% of industrial production affected

5% 6% 6%6% 7%

7%

9%10%

14%

16%

0%

3%

6%

9%

12%

15%

18%

Oth

er mach

inery

其他机械

Fab

ricated m

etal

金属制品

Pap

er pro

du

cts

纸制品

Ch

emical p

rod

ucts

化学产品

Electrical eq

uip

men

t

电气设备

Ru

bb

er & p

lastic

橡胶、塑料

Textiles & ap

parel

纺织

Air tran

spo

rt

航空运输

Co

mp

,elec,op

ti

电脑电子光学…

Fu

rnitu

re

家具

What the US exports to China in 2018

2018年美国向中国主要出口货物（总量1300亿美元）% of US total exports to China of USD 130bn

Civilian aircraft

& engines (13%)

Passenger cars (8%)

Semiconductors (5%)

Crude oil (3%)

Plastic materials (3%)

Others (47%)

Medicinal equipment (3%)

Soybeans (10%)

Industrial machines (4%)

Pulpwood & wood pulp (3%)

Logs & lumber (2%)

CNY recovered 3.5% against USD since G20 on 1 December

人民币对美元自2018年12月1日G20会议以来升值3.5%

Source: CEIC, Bloomberg, Standard Chartered Research 30

CNY recovered 3.5% against USD since the G20 meeting on 1 December 2018, while CFETS up 3% and DXY down 0.7% (CNY up 2.3% against USD, CFETS up 2.1% and DXY up 0.2% ytd 2019, versus CNY -5.7%, CFETS -1.7% and DXY +5.4% in 2018)人民币对美元自2018年12月1日G20会议以来升值3.5%，人民币指数升值3.0%，美元指数贬值0.7%

6.2

6.3

6.4

6.5

6.6

6.7

6.8

6.9

7.087

89

91

93

95

97

99

101

103

105

Jan-16 Apr-16 Jul-16 Oct-16 Jan-17 Apr-17 Jul-17 Oct-17 Jan-18 Apr-18 Jul-18 Oct-18 Jan-19 Apr-19

USD/CNY, inverted (RHS)

美元-人民币（右轴反转）

CNY index (LHS)

人民币指数（左轴）

DXY (LHS)

美元指数（左轴）

We expect China’s C/A to turn deficit in 2019

我们预计2019年中国经常账户逆差将达到GDP的0.5%


We expect China’s C/A deficit to be 0.5% of GDP in 2019 我们预计2019年中国经常账户逆差达到GDP的0.5%Current account balance, USD bn, yearly C/A, % of GDP

-34

0.4%

2.8%

-4%

-2%

0%

2%

4%

6%

8%

10%

12%

-50

-25

0

25

50

75

100

125

150


C/A balance (LHS)

经常账户规模（左轴）

Yearly C/A, % of GDP (RHS)

经常账户顺差占GDP比例（右轴）

Yearly trade surplus, % of GDP (RHS)

经常货物贸易顺差占GDP比例（右轴）

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

-100

-75

-50

-25

0

25

50

75

100


FX reserves (RHS)

外汇储备（右轴）

PBoC FX purchases (LHS)

央行外汇占款（左轴）

FX reserves stabilised at around USD 3.1tn

中国外汇储备稳定在 3.1 万亿美元附近FX reserves & change in PBoC FX purchases, USD bn

FX reserves stabilised but the current account has

中国外汇储备稳定在3.1万亿美元附近，但是经常账


Foreigners have increased holdings in CNY asset

外国机构增持人民币资产CNY assets held by foreigners in China, CNY bn

We see foreign inflows rising to CNY 0.8-1tn in 2019. Total passive inflows upon full inclusion by the three indices are likely to be c.USD 280bn during 2019-21

1,152

1,712

0

500

1,000

1,500

2,000

2,500

3,000


Bond

债券

Equity

股票

Forecasts for China in 2019

2019年中国主要经济指标预测

2016 2017 20182019

预测

GDP growth

GDP 增长, %6.7 6.8 6.6 6.4

CPI, % (年均) 2.0 1.6 2.1 2.0

1-yr base saving rate

1年存款基准利率, %

1.50 1.50 1.50 1.50

USD-CNY (年底) 6.95 6.55 6.88 6.65

Current account/GDP

经常项目/GDP (%)1.8 1.4 0.4 -0.5

Fiscal balance ,

% of GDP

财政余额, GDP占比 %*

-3.8 -3.6 -4.2 -4.2

*Based on our definition following widely accepted fiscal accounting, covering general public and government funds budget.

Source: Standard Chartered Research

33

1. We expect China to expand 6.4% in 2019, compared with 6.6% in

2018. The government has the will and tools to achieve above

6.2% growth in 2019-20 in order to build a moderately prosperous

society by 2020. 我们预测2019年中国增长在6.4，较2018年估计的6.6%略为放缓。政府有意愿也有手段实现2019-20年间6.2%以上的增长，以便在2020年全面建成“小康社会”。

2. We are optimistic about China’s medium-term economic prospects.

Consumption has replaced investment as China’s key growth

driver, contributing 71% of overall domestic economic growth.

Urbanisation (at 59%) has a long way to rise. 我们对中国中长期经济前景较为乐观。消费已经取代投资，成为中国经济增长的主引擎，对国内经济增长贡献达70%。城镇化率（目前在59%）仍具备广阔上升空间。

3. The opening-up saw continued strong inflows into China’s capital

markets (net inflows up more than 80% y/y). We estimate index

inclusion by three global equity/bond index providers to result in

USD 50bn of capital inflows into China’s stock markets in 2019,

and USD 286bn of foreign capital inflows into China’s bond

markets by 2021. 改革开放40年见证了庞大的资金流入中国资本市场（净流入同比增长超80%）。我们估算中国股市/债市被纳入全球三大股指/债指体系将推动2019年流入中国证券市场的资金达500亿美元，到2021年中国债券市场外国资本流入或达到2860亿美元。

4. We see USD-CNY appreciating to 6.65 by end-2019 on a likely

US-China ceasefire and portfolio inflows. 我们预测到2019年底USD-CNY在6.65，主要考虑到中美贸易摩擦可能缓和和资产组合的流入。


Global economic forecasts

全球主要经济指标预测


Global FX forecasts

全球外汇预测

Global Disclaimer

Analyst Certification Disclosure: The research analyst or analysts responsible for the content of this research report certify that: (1) the views expressed and attributed to the research analyst or analysts in the research report accurately reflect their

personal opinion(s) about the subject securities and issuers and/or other subject matter as appropriate; and, (2) no part of his or her compensation was, is or will be directly or indirectly related to the specific recommendations or views contained in this

research report. On a general basis, the efficacy of recommendations is a factor in the performance appraisals of analysts.

36Continued on next page

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reasonable care has been taken in preparing this document and data obtained from sources believed to be reliable, no responsibility or liability is accepted for errors of fact or for any opinion expressed herein. This document does not purport to contain

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investments discussed may not be suitable for all investors. Users of this document should seek professional advice regarding the appropriateness of investing in any securities, financial instruments or investment strategies referred to in this document

and should understand that statements regarding future prospects may not be realised. Opinions, forecasts, assumptions, estimates, derived valuations, projections and price target(s), if any, contained in this document are as of the date indicated and

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