YoY change in crude steel output GLOBAL The Global Macro ...pg.jrj.com.cn/acc/Res/CN_RES/MAC/2017/4/18/46ec2de... · Please refer to page 77 for important disclosures and analyst

Please refer to page 77 for important disclosures and analyst certification, or on our website

www.macquarie.com/research/disclosures.

GLOBAL

Key forecasts, changes this month

1) Tables for real GDP growth, CPI, interest

rates, currencies and commodity prices

are on pages 9-11. Online access to our

global macro forecasts is available on

request.

2) Our US real GDP growth forecasts for

2017 and 2018 (4Q on 4Q) have been

reduced to 2.0% and 1.8% respectively

(previously 2.2% and 1.9% respectively)

3) Our 2018 South Africa real GDP growth

forecast has been reduced to 1.3% (2.0%)

reflecting renewed political and policy risks

4) Our Thailand real GDP growth (2018 to

3.2% from 3.5%), CPI and policy rate

forecasts have been clipped reflecting

both the maturing global cycle and

manufacturing sector structural

weaknesses

Analyst(s) Macquarie Capital Securities (Japan) Limited Peter Eadon-Clarke +81 3 3512 7850 [email protected] Nara Song +81 3 3512 7878 [email protected] Macquarie Capital Markets Canada Ltd. David Doyle, CFA +1 416 848 3663 [email protected] Macquarie Securities (Australia) Limited James McIntyre, CFA +61 2 8232 8930 [email protected] Macquarie Capital Limited Larry Hu, PhD +852 3922 3778 [email protected] Macquarie Equities South Africa (Pty) Ltd Elna Moolman +27 11 583 2570 [email protected] Macquarie Capital (Europe) Limited Matthew Turner +44 20 3037 4340 [email protected] Colin Hamilton +44 20 3037 4061 [email protected]

18 April 2017

The Global Macro Outlook The view from the commodity pits We expect global industrial production momentum to fade over the next

few months, before rolling over during the summer. In the case of steel,

chart below, output growth slowed from 8.1% YoY globally in January to 7.8% in

February at 1.65bn tonnes annualised, Colin Hamilton and the Macquarie

commodities team are forecasting 1.6% YoY growth for 2017, and -0.3% for

2018. The key to this is Chinese construction activity, where we expect some

signs of weakening (but not collapse) to emerge during the coming months, and

an adjustment in Chinese inventories (Fig.1 and Fig.15).

Please see pages 2-5 and 11-14 for more from the commodity team.

For China, we expect 1Q17 to be the peak of PPI inflation, earning growth and

nominal GDP growth—please see pages 16-17.

Global steel output: Absolute YoY gains remain strong

Note: in February, China was +8.3% YoY, ex-China +7.3% YoY. Please note the greater breadth of the 2016 recovery versus the 2013-14 recovery. Source: worldsteel, Macquarie Research, April 2017

The tailwind from the global recovery has provided a policy window (2H

2016, 1H 2017) for countries to tackle their major issues:

1) The US Fed has been reloading its monetary policy weapon

2) The PBOC has begun to deleverage part of the financial system

3) In the Eurozone, recapitalisation of banks is proceeding very slowly

4) In Japan, labour market flexibility reform is not even on the agenda

Global real GDP growth is forecast to remain in ‘the long grinding cycle’ of

2.5-3.0% pa, Fig 17. Our 2016, 2017 and 2018 global real GDP growth forecasts

are 2.7%, 2.9% and 2.7%, respectively.

Short-term business-cycle judgements are more likely to succeed with a deep

awareness of more medium- and longer-term issues. In global macroeconomics,

there are a considerable number of the latter. We examine 24 of them from

page 28; topical issues investigated in our Macq-ro insights reports.

-150

-100

-50

0

50

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150

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

Mt a

nnua

lised

YoY change in crude steel output

World ex-China China

Macquarie Research The Global Macro Outlook

18 April 2017 2

The view from the commodity pits As detailed in the 11 April 2017 Commodities Comment: Steel slides in Asia as restock fades,

the growth in Chinese steel output over the last year has partially gone into inventories, Fig.1

and Fig.15 on page 5. This suggests the need for caution on the cycle’s longevity. Colin

Hamilton and the Macquarie commodities team are forecasting major price declines for some

leading commodities through 2017 end, e.g. hard coking coal -37% and iron ore -33%, Fig 2.

Fig 1 Chinese steel output growth has partially gone into increased inventories Fig 2 2H 2017 versus 1H 2017 forecast price

Source: NBS, China Customs, Macquarie Research, April 2017 Source: LME, MB, CRU, TSI, Macquarie Research, April 2017

Higher prices over the last twelve months are attracting back latent supply capacity in many

markets, Fig 3 and Fig 4. This is slowing inventory adjustments.

Fig 3 Proportion of supply losing money Fig 4 Supply growth, 2016 & 2017E

Source: IAI, CRU, ICSG, ILZSG, Wood Mackenzie, Macquarie Research, April 2017

As a consequence, commodity producers are in a sweet spot, generating strong free cash

flow as capital expenditure deferrals continue, Fig 5 and Fig 6. How long this is likely to

persist is best judged, we believe, by having both a historical perspective, and understanding

the role of China.

The key conclusion from the historical perspective, Fig 7, is that commodity pricing is not

homogenous, with clear differentiation apparent. Thus, whilst we speak here of ‘commodities’

and common drivers, individual commodity industry supply and demand factors are very

important. This is captured in Fig 8, which shows the different point of each commodity in its

respective cycle.

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

Jan-11 Jan-12 Jan-13 Jan-14 Jan-15 Jan-16 Jan-17

YoY change in Chinese steel apparent and "real" demand

"Real" demand (i.e after stock changes)

Apparent Consumption

18%

12%9% 9% 8% 8%

1% 0%

-1% -2%-5% -5%

-8%-10%-10%

-19%

-33%-37%-40%

-30%

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H2 2017 vs. H1 2017 forecast price

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20%

30%

40%

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Proportion of supply losing money - cash basis

As of Jan 16 Current

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Forecast supply growth, 2016 & 2017


18 April 2017 3

Fig 5 Global mining, expansion capex Fig 6 Free cash flow at diversified miners

Source: Wood MacKenzie, Macquarie Research, April 2017 Source: Company data, Macquarie Research, April 2017

Fig 7 Current price vs. 5, 15 & 40 real price averages

Source: LME, MB, CRU, TSI, Macquarie Research, April 2017

Fig 8 Positions of key mined/extracted commodities in the fundamental cycle – arrow shows 2-year progression

Source: Macquarie Research, April 2017

0

20

40

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160

1992

1993

1994

1995

1996

1997

1998

1999

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2001

2002

2003

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2005

2006

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2008

2009

2010

2011

2012

2013

2014

2015

2016

2017F

2018F

2019F

Global Mining Expansion Capex, $bn

-14

%

1%

-33%

34%

-4%

-10%

-13

%

-23%

-42%

-8%

-16%

-50%

30%

-14%

7% 1

7%

-3%

-52%

19%

-32%

-38

%

36%

-17%

62%

41%

5%

-51%

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

Co

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Alu

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Nic

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Ste

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Gold

Silv

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Ura

niu

m

Current price vs. longer term averages

vs. 5 year real average vs. 15 year real average vs. 40 year real average


18 April 2017 4

China remains the key commodities common driver. China represented two-thirds of base

metals demand growth in 2016, Fig 9, and we forecast that China’s contribution will remain

over 50% in 2017, Fig 10.

Fig 9 2016 base metal demand growth contributions Fig 10 …and 2017E

Source: IAI, CRU, ICSG, ILZSG, Wood Mackenzie, Macquarie Research, April 2017

We expect Chinese construction activity will be weakening into mid-year, slowing industrial

commodity demand.

We forecast that steel demand for China’s residential sector will resume its decline from this

summer, having peaked on a calendar year basis back in 2013, Fig 12.

Fig 11 Accumulated steel capital stock, kg per capita Fig 12 China residential real estate steel demand

Source: Customs Statistics, worldsteel, Macquarie Research, April 2017 Source: NBS, CEIC, Macquarie Research, April 2017

The importance of Chinese steel demand from 1990-2016 is shown in Fig 13.

Future steel growth is forecast to come from India and Africa, especially the latter, reflecting

urbanization trends, Fig 14.

In the near term, however, some potential cracks are emerging, rapidly rising inventories, Fig

15, and weak Chinese physical premiums, Fig 16.

Please see pages 13 and 14 for more commodity insights.

0%

10%

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Contribution to base metals demand growth in 2016

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Contribution to base metals demand growth in 2017

-

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

19

50

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Steel capital stock, kg/capita

USA

China

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China residential real estate steel demandmn tonnes


18 April 2017 5

Fig 13 World steel demand growth by decade (China in red, ROW in black)

Source: WSA, WSD, Macquarie Research, April 2017

Fig 14 Growth in urban populations, in 15-year periods

Source: UN, Macquarie research, April 2017

Fig 15 Chinese inventories, YoY Fig 16 Shanghai copper premium, US$/tonne

Source: Mysteel, SMM, SHFE, Metal Bulletin, Macquarie Research, April 2017

33%

18%

30%

22%

0%

5%

10%

15%

20%

25%

30%

35%

Iron Ore portstocks

Visiblealuminiuminventory

Steel traderinventory

Stainless steelinventory

YoY change in current Chinese inventories


18 April 2017 6

The long grinding cycle Global real GDP growth is forecast to remain in ‘the long grinding cycle’ of 2.5-3.0% pa, chart

below. Our 2016-18 global real GDP growth forecasts are 2.7%, 2.9% and 2.7%, respectively.

Fig 17 Global real GDP growth: Macquarie’s long grinding cycle forecast

Note: The 16-Total is the IMF’s 10-Advanced and 6 EM economies. Forecasts are Macquarie where available, alternatively the IMF (see pages 73 and 74). The country weights use market exchange rates, not PPP

Source: IMF, Macquarie Research, April 2017

Global real GDP growth over 1970-2011 was 3.0% pa (3.6% pa using PPP weights).

At 2.5-3.0% pa, we believe the pace of the current expansion will continue to prove to be

structurally lower than in previous decades, as a high global private investment to GDP ratio,

fiscal reconstruction, demographic factors, low TFP growth and sub-optimal global policy-making

are likely to remain as headwinds in the years ahead.

Potential real GDP growth rates are an important anchor to growth forecasting.

Cycles around the trend

Whilst the US Federal Reserve and their policies are regarded as the fulcrum of global capital

markets, the commodity complex provides a critical spyglass on global developments.

We recommend the OECD Leading indicator as the best lead indicator of global growth

momentum. It is designed to forecast advanced economies’ real GDP growth six months into

the future. Please see the 8 March 2017 A Global Macro Deep Dive: monthly advance #10 for

more analysis.

Fig 18 OECD LI, from January 2000 to latest

Source: OECD, Datastream, Macquarie Research, April 2017

2.67 2.61 2.89 2.92 2.68 2.89 2.69 2.59 2.63

0.0

1.0

2.0

3.0

4.0

5.0

2012 2013 2014 2015 2016 2017 2018 2019 2020

16-Total Average 1980-2011, 3.0% p.a The long grinding cycle, 2.5% p.a to 3.0% p.a

Average 1980-2011, 3.0% p.a

The long grinding cycle,2.5% p.a to 3.0% p.a

(Global real GDP growth)

-6

-4

-2

0

2

4

6 (%)OECD LI YoY

We believe the

OECD Leading

indicator is the best

lead indicator of

global growth

momentum

At around 2.5-3.0%

pa, we believe the

pace of the current

expansion will

continue to prove to

be structurally lower

than in previous

decades


18 April 2017 7

Global industrial production has recovered from a nadir of +0.9% YoY in December 2015 to

+3.1% YoY in December 2016. Macquarie is forecasting 2.7% YoY growth for 2017.

Fig 19 Global PMI comparison, Jan 2014 to Feb 2017 Fig 20 Global industrial production, YoY, %

Source: Markit, ISM, NBS, Macquarie Research, April 2017 Source: National data, Macquarie Research, April 2017

World export growth has picked up moderately, from -0.8% YoY (January 2016) to +3.9%

YoY (November 2016, +3.1% YoY in both December 2016 and January 2017). Please see

the 27 March 2017 A Global Macro Deep Dive: Global trade in goods for more. Fig 21 shows

how both global industrial production and global export volumes have moved up together

since the start of 2016. In Fig 22, we’ve expressed them as a ratio. Fig 22 shows how global

exports, after having grown faster than global industrial production from 1991-2008, have

subsequently grown moderately less rapidly than global industrial production.

Fig 21 World export volumes and industrial production, YoY % 3mma

Source: CPB, Macquarie Research, April 2017

Fig 22 World export volume divided by World IP, since 1991,monthly


49

50

51

52

53

54

Jan 14 Jul 14 Jan 15 Jul 15 Jan 16 Jul 16 Jan 17

Using ISM/NBS Using Markit

0.0%

0.5%

1.0%

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3.0%

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4.0%

2012 2013 2014 2015 2016 2017

3m average

0

1

2

3

4

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01/2013 07/2013 01/2014 07/2014 01/2015 07/2015 01/2016 07/2016 01/2017

(YoY%) World export volumes

World IP

55

65

75

85

95

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115

1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015

(Jan 2010=100)

World export volume / World IP

Global industrial

production and

global export

volumes have

moved up together

since the

start of 2016

Global exports, after

having grown faster

than global

industrial

production from

1991-2008, have

subsequently grown

moderately less

rapidly than global

industrial

production


18 April 2017 8

US trade policies

could lead to a

global growth

shock. We judge

this to be a risk

scenario with a high

probability, 40-50%.

A probability above

50% would need to

become our

base case

The prime risk: An activist US trade policy

A Trump administration in the US brings policy uncertainties which, from the perspective of

global growth, relate primarily to trade policy, tax reform and infrastructure spending.

We believe that President Trump, wearied by the implications of the US being the ‘consumer

of last resort’, wants to rebalance the US economy, and bring factories back to the US.

This implies that President Trump probably focusses more on the trade balance than the

current account which includes the services balance. Fig 23 contrasts the trade balance

positions of Factory Asia and the US. In line with other advanced economies, the US has a

growing surplus on its services balance.

The trade surplus of Factory Asia is now materially bigger than in the years leading to the

Global Financial Crisis (GFC). We believe that this ‘savings surplus’ was one of the main

drivers of the decline in global real interest rates over 2002-06, a contributor to the debt

build-up in advanced economies’ private sectors prior to the GFC.

Ten years later this global ‘fault line’ still exists.

Fig 23 Trade account balances: Factory Asia in surplus, the US in deficit

Note: Factory Asia is the aggregate of Japan, China, Korea, Taiwan, HK, and the ASEAN-5

Source: IMF, Datastream , Macquarie Research, April 2017

Candidate Trump advocated an activist trade policy, and President Trump is attempting to

implement this, in our opinion. Fig 24 lists our reports on this subject.

These policies could lead to a global growth shock. We judge this to be a risk scenario with

a high probability, 40-50%. A probability above 50% would need to become our base case.

Fig 24 An activist US trade policy: Macquarie reports

1 March 2017 Factory Asia at risk: the numbers

14 February 2017 The Global Macro outlook: Policy shifts in the US

8 February 2017 The US-Japan relationship

31 January 2017 An activist US trade policy, a.k.a protectionism (PowerPoint)

25 January 2017 Global slump scenarios, and the EM economies that concern us the most

19 January 2017 Fortress America: Buy America! Trade policy is great again

9 January 2017 EM economies & US policy risks


-1200

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-400

0

400

800

1200 (USbn$)

Factory Asia

United States

The trade surplus of

Factory Asia is now

materially bigger

than in the years

leading to the

Global Financial

Crisis


18 April 2017 9

Forecasts and revisions Fig 25 & Fig 26 present our principal real GDP forecasts and revisions.

For 2017, we are beneath consensus on the US, Canada, Japan, Australian and N.Z.

We are above consensus in our 2017 forecasts for the UK, the Eurozone and part of Asia.

Fig 25 Macquarie’s real GDP forecasts

Calendar Year YoY (%) 4Q on 4Q (%) 2014 2015 2016 2017 2018 2014 2015 2016 2017 2018

US 2.4 2.6 1.6 2.2 1.9 2.5 1.9 2.0 2.0 1.8 Eurozone 0.9 1.9 1.8 1.7 1.6 1.0 1.9 1.8 1.7 1.6 Japan -0.1 1.2 1.0 0.8 0.7 -0.9 1.2 1.6 0.7 0.7 UK 2.9 2.2 1.8 1.7 1.3 2.9 2.2 1.8 1.7 1.3 Canada 2.5 0.9 1.4 2.0 1.3 2.4 0.4 1.9 1.6 1.3 Australia 2.6 2.4 2.5 2.3 2.8 2.2 2.5 2.4 2.6 2.9 New Zealand 3.7 2.5 3.1 3.0 2.6 4.1 2.2 2.7 3.2 2.3

China 7.2 6.8 6.8 6.5 5.6 7.2 6.8 6.8 6.5 5.6 S. Korea 3.3 2.6 2.9 2.5 2.6 2.7 3.1 2.3 2.5 2.5 Taiwan 3.9 0.7 1.5 2.0 2.3 3.4 -1.0 2.8 2.0 2.3 Hong Kong 2.6 2.4 1.9 1.5 2.0 2.5 1.9 3.1 1.5 2.0 Indonesia 5.0 4.9 5.0 5.2 5.2 5.0 5.2 4.9 5.2 5.2 Malaysia 6.0 5.0 4.2 4.5 5.1 5.7 4.5 4.5 4.5 5.1 Singapore 3.3 1.9 2.0 2.0 2.6 2.9 1.3 2.9 2.0 2.4 Philippines 6.2 5.9 6.8 6.1 6.3 6.6 6.5 6.7 6.1 6.3 Thailand 0.8 2.9 3.2 3.3 3.2 2.1 2.7 3.0 3.3 3.1 India 6.7 7.2 7.4 6.9 7.7 6.6 6.9 7.0 7.5 7.5

South Africa 1.5 1.2 0.3 1.2 1.3 1.3 0.0 0.2 1.3 1.7


Fig 26 Macquarie’s real GDP forecasts: revisions, and versus consensus

2017 YoY (%) 2018 YoY (%) Previous

Macquarie forecast (*)

Current Macquarie

Forecast Consensus Spread (2) versus (3)

Previous Macquarie forecast (*)

Current Macquarie

Forecast Consensus Spread (6) versus (7)

(1) (2) (3) (4) (5) (6) (7) (8)

US (**) 2.2 2.0 2.3 -0.3 1.9 1.8 2.3 -0.5 Eurozone 1.7 1.7 1.6 0.1 1.6 1.6 1.5 0.1 Japan 0.8 0.8 1.2 -0.4 0.7 0.7 1.0 -0.3 UK 1.7 1.7 1.5 0.2 1.3 1.3 1.3 0.0 Canada (**) 1.5 1.6 2.0 -0.4 1.3 1.3 2.0 -0.7 Australia 2.3 2.3 2.5 -0.2 2.8 2.8 2.8 0.0 New Zealand 3.5 3.0 3.2 -0.2 2.6 2.6 2.9 -0.3

China 6.5 6.5 6.5 0.0 5.6 5.6 6.1 -0.5 S. Korea 2.6 2.5 2.5 0.0 2.6 2.6 2.5 0.1 Taiwan 2.0 2.0 1.7 0.3 2.3 2.3 1.9 0.4 Hong Kong 1.5 1.5 1.6 -0.1 2.0 2.0 1.9 0.1 Indonesia 5.2 5.2 5.2 0.0 5.2 5.2 5.3 -0.1 Malaysia 4.5 4.5 4.2 0.3 5.1 5.1 4.4 0.7 Singapore 1.6 2.0 1.6 0.4 2.6 2.6 2.0 0.6 Philippines 6.1 6.1 6.4 -0.3 6.3 6.3 6.3 0.0 Thailand 3.3 3.3 3.2 0.1 3.5 3.2 3.2 0.0 India (***) 6.8 6.8 7.0 -0.2 7.5 7.5 7.3 0.2

South Africa 1.4 1.2 1.1 0.1 2.0 1.3 1.8 -0.5

Note: (*) March 2017. Calendar year numbers for all countries bar the US and Canada. Data for countries marked with a ** are 4Q on 4Q. Data for India, marked with a ***, is for fiscal year to FY3/17 and FY3/18, and is based on the GDP-at-market-prices series. Consensus numbers for calendar year countries are from Consensus Economics, otherwise the numbers are from Bloomberg.

Source: Consensus Economics, Bloomberg, Macquarie Research, April 2017

For 2017, we are

beneath consensus

on the US, Canada,

Japan, Australian

and N.Z.

We are above

consensus in our

2017 forecasts for

the UK, the

Eurozone and part

of Asia


18 April 2017 10

We forecast the US

10-year bond yield

to have a quarter-

end cycle high of

only 2.45%

In line with the continuation of moderate real and nominal global real GDP growth, The Long

Grinding Cycle, our interest rate forecasts are commensurately subdued. Please note that we

are forecasting the US 10-year bond yield to have a quarter-end cycle-high of only 2.45%.

Fig 27 Macquarie’s interest rate forecasts

Year-end Policy or cash rate (*) (%) 10-year bond yield (%) 2014 2015 2016 2017 2018 2014 2015 2016 2017 2018

US 0.00 0.25 0.50 1.25 1.50 2.17 2.27 2.44 2.45 2.40 Eurozone 0.05 0.05 0.00 0.00 0.00 0.54 0.63 0.20 0.75 1.50 Japan 0.07 0.04 -0.06 0.00 0.00 0.33 0.27 0.04 0.00 0.20 UK 0.50 0.50 0.25 0.25 0.50 1.79 1.96 1.25 1.75 1.75 Canada 1.00 0.50 0.50 0.25 0.50 1.79 1.39 1.62 1.50 1.50 Australia 2.50 2.00 1.50 1.25 1.25 2.81 2.88 2.76 2.80 2.65 New Zealand 3.50 2.50 1.75 1.75 1.75 3.67 3.58 3.36 3.20 3.10

China 5.60 4.35 4.35 4.35 4.35 S. Korea 2.00 1.50 1.25 1.50 1.50 Taiwan 1.88 1.63 1.38 1.88 2.38 Indonesia 7.75 7.50 4.75 4.75 4.75 Malaysia 3.25 3.25 3.00 2.50 2.50 Singapore 0.50 0.88 0.88 1.80 2.00 Philippines 4.00 4.00 3.00 3.25 3.25 Thailand 2.00 1.50 1.50 1.50 1.50 India 8.00 6.75 6.25 6.25 6.00

South Africa 5.75 6.25 7.00 7.00 6.50

Note: (*) Policy or cash rate. US: Fed Funds rate. Eurozone: EMU Refi Rate. Japan: overnight call rate. UK: Repo rate. Canada: Cash rate. Australia: Cash rate. New Zealand: Official cash rate. China: 1-year working capital. South Korea: Overnight call rate. Taiwan: Official discount rate. HK: discount window base. Indonesia: 1-month SBI rate. Malaysia: Overnight policy rate. Singapore: 3-month interbank rate. Philippines: Reverse repo rate. Thailand: 14-day repo rate. India: Repo rate. South Africa: Repo rate.


We are forecasting calendar year 2017 CPI of 2.5% YoY in the US, 1.6% YoY in the Eurozone,

and 2.5% YoY in China: so moderate inflation, not deflation. In 2018, the US and Eurozone

inflation forecasts are 2.4% YoY and 1.4% YoY, respectively, China 2.5% YoY.

After a period of broad US$ strength in 2015, we believe cross-rates are being driven by more

country-specific factors. Whilst relative monetary policy stances will remain important,

resulting capital flows now have to exceed mounting current account surpluses in Japan and

the Eurozone (partially reflecting the falls in oil and other resource prices).

Fig 28 Macquarie’s CPI and currency forecasts

Year end CPI (%, YoY) Currency versus US$ (Year-end) 2014 2015 2016 2017 2018 2014 2015 2016 2017 2018

US 1.6 0.1 1.3 2.5 2.4 Eurozone 0.4 0.0 0.2 1.6 1.4 1.21 1.09 1.05 1.06 1.09 Japan 2.7 0.8 -0.1 0.3 0.5 119.8 120.4 117.1 110.0 108.0 UK 1.5 0.0 0.7 2.7 2.2 1.56 1.47 1.24 1.30 1.37 Canada 1.9 1.1 1.5 1.5 2.0 1.16 1.39 1.34 1.54 1.50 Australia 2.5 1.5 1.3 2.1 1.7 0.82 0.73 0.72 0.74 0.74 New Zealand 1.2 0.3 0.6 1.8 1.5 0.78 0.68 0.70 0.68 0.67

China 1.5 1.5 2.2 2.5 2.5 6.20 6.49 6.95 6.90 6.50 S. Korea 1.3 0.7 1.0 1.8 1.6 1099 1,173 1,160 1,150 1,150 Taiwan 1.2 -0.3 1.4 1.4 1.5 31.60 32.85 32.23 33.00 35.00 Hong Kong 4.4 3.0 2.4 2.0 2.2 7.75 7.75 7.75 7.80 7.80 Indonesia 6.4 6.4 3.5 4.2 4.3 12440 13,795 13,436 13,700 13,500 Malaysia 3.1 2.1 2.1 2.8 2.6 3.50 4.29 4.49 4.50 4.40 Singapore 1.0 -0.5 -0.5 1.0 1.4 1.31 1.42 1.45 1.46 1.44 Philippines 4.2 1.4 1.8 2.8 3.3 44.73 47.06 49.71 52.00 52.00 Thailand 1.9 -0.9 0.2 1.7 2.0 32.92 36.04 35.78 36.20 36.00 India 6.7 4.9 5.0 4.5 5.1 62.71 66.50 67.81 67.13 68.65

South Africa 6.1 4.6 6.3 5.4 5.1 11.57 15.50 13.70 14.00 14.30

Note: The currency forecasts are presented in the most common format. Normally, this is per US$, but exceptions where it is US$ per other currency include the Euro, Sterling, Australia and NZ dollars.


We are not

forecasting deflation

Whilst relative

monetary policy

stances will remain

important, resulting

capital flows now

have to exceed

mounting current

account surpluses

in Japan and the

Eurozone


18 April 2017 11

The latest Macquarie commodity team commodity price forecasts are below.

Commentary follows the table. The next page looks at oil, with comments on other

commodities on the subsequent pages.

Fig 29 Macquarie’s commodity price forecasts

2015 2016 2017 2017 2017 2017 2017 2018 2019 2020 2021 2022 LT

Unit CY CY Q1 Q2 Q3 Q4 CY CY CY CY CY CY $2017

Base Metals

Copper $/tonne 5,503 4,863 5,832 6,100 5,800 5,600 5,833 5,500 5,750 6,088 6,425 6,425 5,900

Aluminium $/tonne 1,663 1,604 1,851 1,725 1,650 1,850 1,769 1,768 1,575 1,513 1,550 1,550 1,350

Zinc $/tonne 1,932 2,092 2,780 2,850 2,950 3,100 2,920 3,100 2,600 2,325 2,188 2,188 2,300

Nickel $/tonne 11,836 9,599 10,271 11,500 11,500 10,500 10,943 11,625 12,500 13,000 14,000 14,000 15,000

Lead $/tonne 1,786 1,871 2,278 2,400 2,500 2,600 2,445 2,525 2,103 1,945 1,928 1,928 1,950

Tin $/tonne 16,077 17,991 20,028 21,500 20,500 20,200 20,557 21,375 23,000 21,000 20,500 20,500 18,000

Steel and Raw Materials

Iron ore - 62% Fe $/t CFR 56 58 86 65 50 50 63 47 50 55 60 60 60

Hard coking coal $/t FOB 102 114 285 175 150 140 188 128 130 135 140 140 115

Steel - World Export HRC

$/tonne 370 382 523 500 450 450 481 380 390 400 400 415 380

Energy

Crude Oil - Brent $/barrel 51 47 55 56 59 59 57 56 61 69 71 72 64

Crude Oil - WTI $/barrel 48 46 52 54 57 57 55 53 57 64 66 67 60

Henry Hub Gas $/MMBTU 2.6 2.5 3.0 3.2 3.5 3.5 3.3 3.2 2.7 3.1 3.1 3.2 2.8

Thermal coal - Aus Spot $/t FOB 59 66 81 75 70 73 75 67 63 60 58 58 48

Uranium $/lb 37 26 24 22 23 25 23 25 27 30 33 33 33

Lithium carbonate $/t CFR China

5,190 8,406 10,200 10,500 9,500 8,000 9,550 7,500 7,000 6,750 6,750 6,750 6,000

Precious Metals

Gold $/oz 1,160 1,248 1,219 1,175 1,250 1,325 1,242 1,381 1,375 1,400 1,388 1,425 1,250

Silver $/oz 16 17 17 17 19 21 18 21 22 22 23 23 18

Platinum $/oz 1,053 986 979 950 1,025 1,075 1,007 1,181 1,306 1,325 1,300 1,300 1,400

Palladium $/oz 692 612 765 700 650 650 691 756 825 819 763 713 800

Agriculture

Potash $/t FOB 303 245 214 230 230 220 224 230 240 250 250 316 280

Urea $/t FOB 273 199 240 230 220 220 228 220 220 230 240 268 230

Ammonia $/t FOB 385 234 308 250 250 230 260 225 230 230 245 277 230

Others

Alumina $/t FOB 301 254 340 335 330 350 339 335 288 270 275 275 240

Manganese ore $/mtu CIF 2.9 4.6 5.6 5.5 4.8 4.0 5.0 3.8 4.0 4.0 4.0 4.2 3.0

Ferrochrome (EU contract)

c/lb 107 96 165 154 145 130 149 144 140 130 133 133 110


Whilst the US Federal Reserve and their policies are regarded as the fulcrum of global capital

markets, the commodity complex provides a useful spyglass on global developments.

Based on a simple up/down ratio, 2016 was another challenging year of trying to align supply

with sluggish demand growth.

Fig 30 Macquarie commodity price forecasts, YoY simple up/down ratio

2016 2017 2018 2019 2020 2021 2022

YoY, number of forecasts up 11 23 10 15 13 13 9 YoY, number of forecasts down 15 3 16 9 9 7 1 YoY, number of forecasts unchanged 5 0 0 2 4 6 16



18 April 2017 12

2017 is largely in

balance, based on

our forecasts

Oil The oil supply and demand modelling undertaken by Vikas Dwivedi indicates crude remains on a path to structural rebalance this year.

Fig 31 Global S/D balances, ’000 bpd, tight in 2017, oversupply in 2018

Source: IEA, Macquarie Research, April 2017

Oil demand continues to rise with global real GDP growth, below.

Fig 32 Oil demand remains highly correlated to global growth

Source: World Bank, BP Statistical, Macquarie Research, April 2017

Our house oil price forecasts are presented below and in Fig 16. Please see the 2 February

2017 Longer days, shorter years: Lowering oil price forecast on supply by Vikas Dwivedi for

more explanation. We expect a tighter supply-demand balance in 2017, before surpluses

return in 2018 and 2019.

Fig 33 Macquarie’s commodity price forecasts

2015 2016 1Q17 2Q17 3Q17 4Q17 2017 2018 2019 2020 2021

Long Term (2017 US$)

Crude Oil - Brent $/barrel 51 47 55 56 59 59 57 56 61 69 71 64 Crude Oil - WTI $/barrel 48 46 52 54 57 57 55 53 57 64 66 60


Continued demand

growth


18 April 2017 13

Commodities What China giveth, China can take away. Part of the support for commodities over the past

six months has been down to inventory replenishment. This restock cycle may have started in

China, but in our view has become a global phenomenon. With global industrials in a

significantly better position than six months ago (and certainly a year ago) inventory rebuild

has become the norm. Currently, global steel apparent consumption is running up ~10% YoY,

albeit off a low base. However, just as China led the cycle up, it also looks likely to lead things

lower—the cracks we were highlighting at this time last month look to be widening.

Over the past three weeks, Chinese hot-rolled coil steel prices have been on the retreat,

falling >$50/t (~12%). This is now starting to feed into Asian prices, suggesting Chinese

export volumes may be on the rise once more. The sudden drop in Chinese prices is

considered a bit of a surprise, given the strong sentiment seen preceding and coming out of

Chinese New Year. However, it has become increasingly clear that monetary policy in the

country is tightening at the margin, with the liquidity tap being tightened a notch or two. In our

view, this has changed sentiment towards future orders. Essentially, there is less confidence

that order books for steel containing products will keep improving. And this has turned a steel

restock cycle into the beginnings of a destock cycle.

It is always hard to disaggregate the stocking element of metals, particularly in terms of

downstream products. But given industrial profits in China started recovering aggressively

over Q4 2016, it is fair to assume there was both a sustaining capital and inventory

replenishment cycle in these areas over the past six months, particularly given the relatively

ready availability of credit. Using the example of steel, on our numbers, Chinese real

consumption (after adjusting for inventories) is up YoY, but not aggressively so. Helped by

lower exports, apparent consumption in China is running up over 10% YoY over Jan-Feb.

Just as we are passing the peak of the Chinese-led reflationary cycle, so we are passing (or

have passed) the peak in global steel pricing for 2017.

This is not to say underlying commodity demand is bad. Indeed, we estimate global

industrial production—the output of mining, utilities and manufacturing—was 3% higher

YoY in February, a bounce from January’s 2.4%.

Meanwhile data on car sales—another of our favourite macro-indicators of commodity

demand—is now available for March and 1Q and shows another strong month and quarter.

From our database of 64 leading markets, which account for 95% of sales of light-vehicle

(cars and light commercial vehicles), we estimate global sales rose 5.1% YoY in March. Over

1Q as a whole, sales were 4.9% higher YoY, an additional 1m vehicles, down from more than

6% in 3Q and 4Q (~1.3m vehicles more) but a considerably better performance than the ~2%

forecast growth for full-year 2017 by most analysts.

Fig 34 Chinese steel prices are now in decline, with flat products leading the way

Fig 35 While Chinese steel apparent consumption is up over 10% YoY, real consumption is up much less

Source: Mysteel, Macquarie Research, April 2017 Source: NBS, China Customs, Macquarie Research, April 2017

1,600

2,100

2,600

3,100

3,600

4,100

4,600

5,100

5,600

Ja

n-1

3

Apr-

13

Ju

l-13

Oct-

13

Ja

n-1

4

Apr-

14

Ju

l-14

Oct-

14

Ja

n-1

5

Apr-

15

Ju

l-15

Oct-

15

Ja

n-1

6

Apr-

16

Ju

l-16

Oct-

16

Ja

n-1

7

Apr-

17

HRC Rebar CRC

Rmb/t Domestic steel prices

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

Jan-11 Jan-12 Jan-13 Jan-14 Jan-15 Jan-16 Jan-17

YoY change in Chinese steel apparent and "real" demand

"Real" demand (i.e after stock changes)

Apparent Consumption

Part of the support

for commodities

over the past six

months has been

down to inventory

replenishment

Just as we are

passing the peak of

the Chinese-led

reflationary cycle,

so we are passing

(or have passed) the

peak in global steel

pricing for 2017


18 April 2017 14

Perhaps the story of the month has been metallurgical coal, where spot prices have jumped

from a low of $145/t to ~$300/t FOB Australia for premium hard coking coal following

prolonged Queensland rail outages in the aftermath of Cyclone Debbie. This once again

highlights that a commodity such as met coal will always be more exposed to potential supply

shocks given its lack of geographical supply diversity vis a vis peers, given ~50% of seaborne

tonnage comes from Queensland.

However, given actual damage to mines is limited, the overall production loss from plan may

be relatively negligible. And assuming full capacity is restored on the rails in time, there

should be a period of shipment catch-up after the outage. Add to this the fact that US met

coal is already back in the market plus supply in China looks ample at present (meaning

potential coke exports), while buyers are less ‘brand-sensitive’ than in the past, and we are

looking more at a price spike than any prolonged price shelf. Meanwhile, although there is

much less impact on the thermal coal market, given Pacific Basin pricing was already on the

rise pre-cyclone, this does make a relatively high annual benchmark price for JFY contracts

more likely.

Our recent analysis suggests that many sectors of the gold supply and demand balance in

2016 were worse, in the sense of providing more supply or taking up less demand, than in

2010, despite a similar US dollar gold price. One reason for this is that the gold price in many

local currencies was higher in 2016 than it was in 2010, a legacy of the strong dollar, and a

reminder that gold’s negative correlation with the US dollar is not just a mathematical finding.

In oil, recent weeks have seen CNOOC, Sinopec, and PetroChina all providing 2017 oil

production guidance that points to relatively moderate declines from Q4 ’16 levels. This

stands in contrast to sharp declines seen from Jun-15 to Oct-16 (over 600 KBD from a 4.4

MBD peak), but is consistent with recent performance as production has subsequently

stabilized at around 3.9 MBD. Although our global S&D balances anticipate Chinese oil

production falling by 240 KBD Q4 ’17 vs. Q4 ’16, this assumption could prove aggressive

given this guidance.

The guidance from China fits into a larger pattern we have seen in recent data for other high-

cost conventional production areas at varying levels of detail. In the US, well level results

show conventional onshore production effectively flat from Jun-16 to Sep-16 (our most timely

data); we model such production falling over 150 KBD from Dec-16 to Dec-17. Likewise,

recent data points from Canada also show stabilising conventional supply. Given exceedingly

slim margins for high-cost areas sub-$40/barrel, the sharp declines observed across much of

‘15 and ‘16 were largely expected. In contrast, the price level required to arrest these declines

was less well known; recent data appears to indicate significant improvement at $50/bbl.

While this does not alleviate long-term concerns about industry underinvestment, as a journey

of a thousand miles begins with a single step, we think this recent performance and the

guidance from China indicate greater resilience at $50/bbl than both we and the market have

been expecting.

Fig 36 Spot coking coal prices have jumped Fig 37 Global industrial production, % YoY

Source: TSI, Macquarie Research, April 2017 Source: National data, Macquarie Research, April 2017

0

50

100

150

200

250

300

350

Jan 1

6

Feb

16

Ma

r 16

Apr

16

Ma

y 1

6

Jun 1

6

Jul 16

Aug 1

6

Sep 1

6

Oct 16

No

v 1

6

De

c 1

6

Jan 1

7

Feb

17

Ma

r 17

Apr

17

Spot premium hard coking coal prices

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

2012 2013 2014 2015 2016 2017

3m average


18 April 2017 15

United States Underlying economic activity continues to grow at an above-potential pace. Survey based

data (consumer confidence, PMIs) have been notably stronger than data that enters directly

into GDP such as real consumer spending growth.

In March we updated our forecast (Fortress America: Demographics, lower potential, and the

Fed) and have pushed our estimate for 2017 real GDP growth lower to 2.1% (prev. 2.5%), for

2018 to 1.8% (prev. 2.1%), for 2019 to 1.6% (prev. 1.9%) and for 2020 and beyond to 1.4%

(prev. 1.8%). The shift in our outlook is driven by increasing conviction that labour force

supply will prove to be a constraint on growth due to an aging population.

Signs of this became more evident in March’s employment numbers. As outlined in Double

Trouble: Growth potential & the end of slack, the 12-month average in payrolls growth has

slowed to below 180K. Despite this, several measures of labour slack continue to rapidly

diminish. Even counting workers that are likely to return to the labour force (a measure of

‘effective unemployment’), the unemployment rate is now below 5%, a further sign that the

labour market is normalizing.

For the demand side of the economy in 2017, we expect outsized contributions first from

energy & mining investment, a source that we believe presents upside risks to growth, a topic

addressed in The US Economy, Oil, and Overheating.

Second, residential investment remains a high-conviction theme for the next 6-12 months. It

is low and rising as a share of GDP, making it a structural force likely to prolong the current

economic expansion further.

These developments, however, appear now to be largely discounted in financial markets.

Consistent with improving wage growth and diminishing labour slack, we anticipate that the

trend monthly pace of payrolls gains will moderate downwards to ~150K or lower by year-end

before decelerating further towards ~100K in 2018 and then towards ~50K longer-term.

As a result of an increasing need to decelerate the pace of payrolls growth and avoid

overheating, we believe the FOMC will now engage in a shorter and sharper rate hike cycle,

anticipating 3 hikes in 2017 and 1 hike in 2018, before the upper-end of the fed funds target

range reaches our new forecasted equilibrium level of 1.75% (previously 2%).

Fig 38 The number of jobs required to hold unemployment stable is poised to resume its downward trend

Source: Bureau of Labor Statistics, Macquarie Research, April 2017

0

20

40

60

80

100

120

140

160

180

Jan-86 Jan-90 Jan-94 Jan-98 Jan-02 Jan-06 Jan-10 Jan-14 Jan-18 Jan-22

US monthly "breakeven" jobs growth (five year average) (thousands)

Note: This analysis holds constant current (Jan-17) age group participation rates over time and assumes a 4.5%

unemployment rate

projection

Growth continues to

persist at an above-

trend pace

We have lowered

our growth

forecasts due to

labour supply

constraints

We anticipate 3 Fed

rate hikes in 2017

and just 1 in 2018


18 April 2017 16

China China’s economic growth in March still ran stable. That said, we expect 1Q17 to mark the

peak of the ongoing cyclical recovery.

Economy has passed the cyclical peak: The ongoing cyclical upturn is mainly driven by 3R

(real estate, reflation, and restocking). As we discussed a year ago, these factors kicked off a

new earnings cycle. While the cycle has become the key theme for the market in the past six

months, lower PPI inflation in March suggests that the current reflation cycle has already

peaked. It’s not only because of the base effect, as MoM growth of PPI has also slowed for

four months in a row, from 1.6% last Dec to 0.3%. It’s almost sure that it will continue to slow

in April. Other indicators from the top-down perspective, such as M1 growth, PMI price index

and the CRB index, all point in the same direction. Weaker price momentum has started

impacting the other leg of the recovery: restocking. Over the past three weeks, Chinese hot-

rolled coil steel prices have dropped around 12% and a similar case applies to iron ore.

Fig 39 The current recovery driven by reflation

Source: CEIC, Macquarie Research, April 2017

Monetary policy to remain tight: A natural question facing soft inflation is whether policy

makers would loosen their policy stance. The answer is NO. It’s because, at this moment, the

priority for monetary policy is NOT about the economy, but about deflating the asset bubble

and preventing financial risks. Instead of easing policy, in the near term, we are likely to see

the third mini-hike (i.e. higher repo rate) this year (the last one was on March 2016).

More cities tightened their property markets: So far this year, property sales have been

soft in tier 1 cities (GFA sales in Beijing and Shanghai down 12% and 37% YoY in Jan-Feb),

but extremely strong in lower-tier cities. Property is the best-performing sector (up 26%) in

MSCI China. However, whether the housing market becomes too cold or too hot, either way it

could pose a big headache to the government. That’s why China’s property market exhibits

very short cycles, as policy can change quickly. One year ago, the government thought that

the market was too cold, so it lowered the down-payment ratio to stimulate it. But now it

clearly views the market as too hot, so over 20 cities have tightened property policy since

March. Mortgage rate is also set to rise on window guidance and banks’ higher funding costs.

All these could weigh on the property market in the coming months.

‘Stagnation’ proved to be wrong for China: Over the past year, the market has been

concerned about the risk of ‘stagflation’ in China, in the sense that surging commodity prices

would transmit into the consumer prices. The big divergence between CPI and PPI in recent

months has proved this view is wrong. It’s wrong partly because the end-user demand is soft,

partly because Chinese companies in the downstream sectors have very limited pricing

power. Moreover, some of the key prices such as power tariff are just determined by the

government. Since downstream companies have difficulties in passing through rising input

prices to consumers, we reckon the divergence between CPI and PPI inflation is

unsustainable and most likely PPI will move toward CPI.

-8

-6

-4

-2

0

2

4

6

8

10

-10-505

10152025303540

Mar-

11

Sep-1

1

Mar-

12

Sep-1

2

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13

Sep-1

3

Mar-

14

Sep-1

4

Mar-

15

Sep-1

5

Mar-

16

Sep-1

6

Mar-

17

Industrial profits PPI (RHS)

%, y oy %, y oy

Growth close to its

peak

Policy to remain

tight

More property curbs

rolled out

No pass-through

from PPI to CPI


18 April 2017 17

FX reserves rising again in March: China’s FX reserves rose by US$4bn in March to

US$3.009tn, compared with the rise of US$69bn in Feb. That said, the weaker dollar in March

helped in terms of positive valuation effect. Stripping that out, FX reserves should drop by

US$5bn in March. In any case, from a 12-month perspective, there is no doubt that China’s

capital outflows have improved a lot. Indeed, while it was a top global macro risk 12 months

ago, we received few client requests on this issue in the past month.

Two factors are behind the improvement. First, the government has tightened capital controls

significantly since 2H16. Second, the weak US$ so far this year also helps. If the US$

strengthens later this year, China’s FX reserves will likely fall again. But it’s manageable. Our

recent channel check suggests that capital controls have been loosened for mainland

corporates, who now find it easier to transfer money offshore without government approval. In

our view, it’s a wise move on the side of policy makers. Otherwise, 12 months later, they

might need to worry about too much FX reserves again. We recently published a thematic

report on drivers of China’s capital outflows in 2016, which are very different from those in

2015 (chart below).

Fig 40 Breakdown of capital outflows in 2016

Source: CEIC, Macquarie Research, April 2017

A new city near Beijing: On April 1, the government announced to build a new city called

Xiongan, which is 100km away from Beijing, to share some of the burdens for China’s capital.

Since then, we have received quite a few inquiries asking us to quantify the macro impact of

Xiongan. Frankly speaking, it’s a bit out of our circle of competence. For the same reason, in

the past we refrained from drawing any quantitative conclusions for things like Free Trade

Zone or One-Belt-One-Road. Given so many things remain unknown at this stage, any

estimation would be more like a brain-teaser for a consulting interview than serious economic

analysis. The bottom line is that we will not change any of our forecasts because of Xiongan.

That said, Xiongan is surely an important thematic play for the stock market, because it

contains certain elements which are of great appeal to mainland retail investors, including the

official comparison to Shenzhen and Pudong, as well as the backing from top leaders. But for

the whole Chinese economy, it’s less important how much steel or cement will be poured in

Xiongan. Instead, what really matters is whether a new growth model could emerge from

there. The current one, which relies heavily on debt-fuelled investment, backed by land

financing, is not very likely to last for another decade. Like Shenzhen in 1980s and Pudong in

1990s, the Xiongan New Area could be the testing field when China is once again in strong

demand for new growth drivers.

-400

-350

-300

-250

-200

-150

-100

-50

0

50

100

Error andOmission

Net securitiesinvestment

Foreignresidentswithdraw

money fromChina banks

Chinesepaying downforeign debt

Chineseincrease

foreign assets

Directinvestment

2015 2016bn, $

Capital outflows

have improved

Markets excited

about Xiongan, a

new city near

Beijing


18 April 2017 18

Eurozone Things are pretty good in the Eurozone right now, economically speaking. Survey data is at

six-year highs, and while this almost certainly overstates the actual economy, real

improvement can be seen in a wide range of indicators. The ECB is even allowing itself to

tentatively muse about ending stimulus. The great risk, however, remains political.

The composite PMI indicator for the Eurozone ended 1Q at 56.4, a new three-year high, and

averaged over 1Q 55.7, which using Markit’s formula to translate into GDP growth implies a

rapid quarterly pace of 0.65%. We place quite a lot of faith in the PMI, and believe at this level

it will correlate to decent Eurozone QoQ growth. But not that decent.

One problem is the ‘hard’ data, while not as at odds with the ‘soft data’ as in the US,

has disappointed. In particular industrial production data available for Jan-Feb is

currently pointing to a small contraction in QoQ growth, something that in the past has

not been associated with GDP growth more than 0.4%, let alone 0.6% (Fig 41).

Another is that the PMI does not include the retail sector, and we believe this has

suffered in 1Q from the pressure on real incomes caused by higher inflation and still

subdued nominal wage growth. Data (as with IP) from Jan-Feb points to a 0.2% to

0.3% growth rate.

Fig 41 Eurozone IP growth & GDP, % QoQ Fig 42 Melenchon adds to the muddle (% 1st round)

Source: Eurostat, Macquarie Research, April 2017 Source: IFOP, Macquarie Research, April 2017

Nevertheless, there is a risk here of nit-picking. We expect growth to be 0.5% QoQ, a good

figure, and the momentum appears to be maintained at the start of 2Q, suggesting our

expectation of a slowdown to 0.3% might be misplaced. But for now we stick to that for one

simple reason—politics. The French presidential election will be held over two rounds on 23

April/7 May, and the outcome remains uncertain, which given two of the potential winners are

seen as economically damaging, is not good news.

Markets have been aware of the possibilities of a victory by the far-right candidate, Marine Le

Pen, for some while. Opinion polls show her leading the pack, so most likely to get into the

second round (Fig 42) but thereafter show her losing by roughly 60-40%, or sometimes 55-

45%, depending on her opponent. This might seem conclusive, but the nervousness stems

from scepticism opinion polls are accurate post-Brexit and Trump, and in particular whether

they can capture her supporters greater conviction compared to those of her opponents. We

can dismiss the first point—polls weren’t that bad in 2016—but the second is harder,

especially given Emmanuel Macron, her most likely opponent, has more shallow support.

More recently another concern has arisen—the rise in the polls of Jean-Luc Mélenchon, the hard-left candidate (Fig 42). He has been gaining support mostly at the expense of the moderate-left candidate Benoît Hamon to the point where he is now third in some polls. A similar surge in 2012 soon came to an end, and he remains a way off the top two. But if he was to face Le Pen in the final, there would be no market-friendly outcome.

Real improvement

can be seen in a

wide range of

indicators

French politics to

dominate first half

of 2Q


18 April 2017 19

United Kingdom Economic data in the UK remains relatively strong. It is clear that GDP growth will slow from

4Q’s rapid 0.7% pace in 1Q, and is likely to undershoot the Bank of England’s forecast of

0.6%. But there is no sign that it is has reached a hard stop, and some indicators that had

worsened through February rebounded in March, such as the services PMI.

While we still expect the UK’s exit from the EU will hit medium-term growth, the more

conciliatory line from Theresa May, the British Prime Minister, on 29 March as she filed Article

50 to start the process has raised hopes that the damage will be limited by a slow and limited

exit. May did support a vote to remain, and though she has taken a hard line since the result,

it was notable in an interview with the BBC on the day Article 50 was filed that she could not

outline any benefit she saw of leaving, something that points to her retaining close links.

Furthermore, she appears open to a temporary period of transition, something that brings to

mind Milton Friedman’s quip about nothing being so permanent as a temporary government

programme. Of course there are many potential pitfalls in the negotiations to come, not least

trying to square the compromises that will be necessary to achieve this best-case outcome

with the tabloid press, but for now there is relative calm.

This puts the focus back on the domestic economy, and the most pressing issue is how fast

and far household consumption will slow. The rise in inflation seen in February, to 2.3% YoY,

repeated in March, has not been matched by a rise in nominal wage growth. This means for

the first time in three years, real wage growth was negative in February. Employment growth

remains positive, at 1% YoY in January, though that was down from 2% in the middle of last

year. So unless either now picks up, if consumption spending is to be maintained, consumers

will need to reduce savings further.

Fig 43 Rising inflation cuts real wage growth, % YoY

Fig 44 …as Bank warns that consumption is more than just retail sales, % YoY

Source: ONS, Macquarie Research, April 2017 Source: ONS, Macquarie Research, April 2017

Retail sales are already feeling the heat, with 1Q looking like being the worst quarter since

2013, though off a high base. But the Bank of England has warned that retail sales are not a

great guide to household consumption, and sees little evidence that the wider measure is

suffering. Fig 44 shows there is a relationship between retail sales growth and household

consumption, but it supports the Bank’s contention. Sharp slowdowns or accelerations in

retail sales growth rarely correspond to similar changes in household consumption.

Why might this be the case? An obvious explanation is that only a fraction of household

consumption is through retail channels. In 2016, UK retail sales were about £400bn, just 35%

of total household consumption. That isn’t much more than the 21% of household

consumption that was housing, including ‘imputed’ rents. 11% was transport, 9% hotels and

restaurants, and 9% a wide range of services including medical, education and financial

products/services. These often see very different trends to stuff sold in the shops.

The Bank says other indicators need to be followed to give a more comprehensive view of

household consumption, such as consumer confidence indicators, especially for major

purchases, and consumer credit. We would also flag car sales—e.g. car sales rose 8.4% YoY

in March, the most important month.

A more conciliatory

line on EU exit

Retail sales slowing

but household

consumption less

clear


18 April 2017 20

Japan We are expecting the global industrial recovery and moderate export growth to support

growth over the coming year, but strong domestic growth remains elusive. The impact of

financial repression on savings is leading households to be subdued consumers. For more,

please see the 5 January 2017 Japan’s debt-deleveraging marathon. Only reform would jolt

the economy onto a higher growth trajectory, in our opinion.

Monetary policy: We believe the BOJ is now essentially subordinate to fiscal reconstruction,

working to minimize the government’s interest expense through a policy of financial

repression. The BOJ’s new arrangement to target the 10-year JGB yield “around zero” makes

this more explicit. Please see the 26 July 2016 Financial repression for decades and the

27 November 2016 Financial repression & valuation. The BOJ’s FY3/18 real GDP growth

forecast of 1.3% assumes a big boost from the government’s headline ¥28.1tr supplementary

budget. We are much more cautious.

Fig 45 Japan: key macroeconomic forecasts

CY11 CY12 CY13 CY14 CY15 CY16 CY17E CY18E CY19E

GDP (YoY, %) -0.5 1.7 1.4 0.0 0.6 0.8 0.8 0.7 0.8

CPI (YoY, %) -0.4 -0.5 0.1 2.2 0.6 -0.3 0.3 0.5 0.9

Overnight call rate (*) 0.1 0.1 0.1 0.1 0.0 -0.1 0.0 0.0 0.0

10-year JGB (*) 1.0 0.8 0.7 0.3 0.3 0.0 0.0 0.2 0.3

¥/$ (*) 76.9 86.8 105.4 119.8 120.4 117.1 110.0 108.0 106.0

Note: CPI is the headline CPI ex fresh foods. (*): per period end, Macquarie forecasts. (**) The consumption tax rate increase to 10% from 8% is now scheduled for October 2019

Source: Bloomberg, Macquarie Research, April 2017

We believe Japan is keen to sign a bilateral trade agreement with the US. Whilst the

initial US list of demands is expected to be heavy, below, the gains for Japan from improving

its relatively poor service sector labour productivity are so large that a win-win situation is

possible.

Fig 46 US-Japan trade talks: possible US agenda items

1) The cost-sharing burden of the US military bases in Japan

2) Fair trade and market access in the Japanese auto sector

3) The appropriate level of the Yen

4) Market access into Japan’s domestic service industries

5) Inward FDI (into Japan) promotion

Note: Please see the 8 February 2017 Macq-ro insights: The US-Japan relationship for more


The US-Japan bilateral auto sector trade deficit is equal to 70% of the total trade deficit,

and is unlikely to diminish over any reasonable time horizon.

Of the five major likely US agenda items, above, we believe Japan will give ground on three,

numbers 1), 4) and 5), and will neutralize 2) with an undertaking to a) further increase the

number of US-based auto assembly plants, (reducing the need for Japanese auto exports) in an

echo of the 1980-94 experience, and b) importing substantial quantities of energy from the US.

Item 3, the appropriate level of the Yen, will be a problem for Japan, in our opinion. President

Trump will be able to point to a large current account surplus in Japan, and call for domestic

demand stimulus. The latter would provide a stronger export market for US companies to sell

into. The best chance for stronger domestic demand growth in Japan, however, is by creating

new growth opportunities via structural reform. This is unlikely to occur quickly.

Our conclusion is that Japan is about to endure a protracted period of US pressure aiming to

increase the value of the Yen versus the US$.

We believe the BOJ

is now essentially

subordinate to fiscal

reconstruction,

working to minimize

the government’s

interest expense

through a policy of

financial repression

Our conclusion is that

Japan is about to

endure a protracted

period of US pressure

aiming to increase the

value of the Yen versus

the US$


18 April 2017 21

Canada In recent months Canada has found support from: i) a pickup in US activity; ii) federal fiscal

stimulus; and iii) a stabilization in energy investment. The impact from these positive forces

should moderate in coming months, but nevertheless allow expansion to continue.

The magnitude of growth, however, is likely to disappoint relative to expectations. Housing

investment, consumer leverage, demographics, and competitiveness challenges all pose

significant constraints. Housing-related macro-prudential measures announced in October

2016 are likely to slow housing activity (existing home sales volumes) substantially in 2017.

Underpinning our perspective is the tremendous divergence in the leveraging cycles of the

two economies, a thesis outlined in The Depth and Breadth of Domestic Divergence. While

the US is emerging from a period of deleveraging, Canada has just completed a period of

‘hyper-leveraging’.

The result should be an unprecedented divergence in monetary policy in the two economies

and a weaker Canadian dollar over time. Our currency forecast remains well below the

consensus—as markets move towards discounting a further monetary divergence, this should

widen the two-year sovereign yield differential between Canada and the US and lead

CADUSD to weaken to $0.65 by end-17 (USDCAD = $1.54).

Hopes for a reorientation in the economy towards non-energy exports have not transpired.

Volumes of non-energy exports have weakened further and are declining at their fastest pace

since 2009. Canada’s manufacturing sector continues to be challenged from a prolonged

period of weak investment and the rise of Mexico. These factors will limit the potential benefit

to Canada’s outlook despite improvements in the US.

In Canada’s recent 4Q GDP release, where business investment slowed further and as a

share of nominal GDP is now near 20 year lows. Unfortunately, hopes for a rebound in 2017

remain too optimistic, in our view. A recent survey of capex intentions indicated while

stabilization may occur in the year ahead, growth was likely to remain weak.

Fig 47 Non-energy goods export volumes are sending a cautious signal

Source: Statistics Canada, Macquarie Research, April 2017

-25%

-20%

-15%

-10%

-5%

0%

5%

10%

15%

Mar-98 Mar-00 Mar-02 Mar-04 Mar-06 Mar-08 Mar-10 Mar-12 Mar-14 Mar-16

Canada - non-energy exports goods volumes (% change YoY in 3 MMA)

The Canadian and

US economies have

never been more

desynchronized


18 April 2017 22

Australia Two very different storms erupted in Australia over the past month—cyclone Debbie, and

concerns around house prices and financial stability. The cyclone’s impact on Australia’s

economy is likely to be relatively benign from an activity, and inflation, perspective. Although

coal exports will be partially disrupted by infrastructure damage, the increase in prices is likely

to deliver a net boost to Australia’s income and terms of trade—albeit a temporary one.

Cyclones have previously impacted inflation through disruptions to fruit & vegetable

production—especially bananas, a product where imports are severely restricted on

biosecurity grounds. In this instance, damage affects crops with multiple growing districts and

less severe restrictions. In any case, the RBA looks through any temporary impacts on

headline inflation measures, with the focus on underlying inflation metrics.

Continued strength in house prices, especially Sydney and Melbourne, have prompted a

flurry of regulatory comments and responses over the past month. The prudential regulator,

APRA, announced a further tightening of macro prudential policy measures, with the major

measure a quantitative limit (30%) on the share of all new lending (to owner occupiers and

investors) that could be interest-only.

Additional follow-up measures—including lowering the 10% pa growth speed limit for the

stock of investor lending, and revisiting the underlying minimum income assumptions lenders

use in serviceability assessments—were flagged, and are likely to progressively be

introduced over the next 6-18 months. The delayed introduction of these measures in part

reflects an awareness from regulators of the large supply of dwellings—particularly

apartments—currently under construction, with a light-touch regulatory response favoured

until demand-supply balances are stabilised.

Fig 48 Rising financial stability concerns have promoted a further incremental tightening of macro prudential policy measures—including a quantitative limit on the share of new interest-only lending.

Source: APRA, Macquarie Research, April 2017

Whilst housing market conditions and lending standards are a concern, there remains a

tension in the broader policy mix. Domestic demand weakness remains with retail sales

declining and consumer sentiment relatively tepid. Labour market outcomes are flattering at

the headline level, but underlying metrics point to a continued accumulation of slack (Aussie

Macro Moment – Labour force: March madness! )

Our broad thesis on the Australia remains unchanged. Whilst the A$ remains well supported

by commodity prices, conditions in the domestic economy are likely to remain weak under the

weight of ongoing fiscal drag and the downswing in the mining investment boom.

Pass-through of commodity prices into the economy is likely to be limited, or delayed. Weak

domestic conditions and labour demand are likely weigh on wages and domestically inflation,

and could still drag the RBA into further easing.

25

30

35

40

45

50

25

30

35

40

45

50

Mar-08 Mar-10 Mar-12 Mar-14 Mar-16

Australia: New housing loans (share of new loan approvals) %%

Interest onlyloan share

APRAtarget *

Financial stability

concerns have seen

regulators act to

tighten macro

prudential measures.

Additional measures

are likely over the next

6-18 months


18 April 2017 23

New Zealand New Zealand’s economic growth surprised on the downside at the end of 2016, with the

economy growing at a 2.7%YoY pace (3.2% expected).

A range of temporary factors were considered to be the driver of the weak growth outcome,

with the RBNZ maintaining that the growth outlook remains positive.

That outlook remains underpinned by strong population growth, a massive pipeline of

construction activity, and surging tourist inflows. Labour market conditions remain firm, with

strong net migration—particularly the low departure rate of NZ residents—supporting supply.

Consistent with the recent assessment from RBNZ Governor Graeme Wheeler that the

balance of risks to the NZ economy, and monetary policy outlook, have become more ‘equally

weighted’ our outlook for OCR remains one of a persistent period of stable rates.

Housing market conditions have moderated, on the back of tighter regulatory restrictions and

lending conditions. But strong migration continues to place pressure on demand, crimping

scope for the RBNZ to ease further in support of dampening the currency and accelerating

the expected return to the mid-point of the target band.

The NZ$ has depreciated somewhat over recent months, and is currently ~4% below the

RBNZ’s forecast profile.

Despite this, the RBNZ remains of the view that further depreciation is needed to achieve

more balanced growth in the economy.

Our outlook incorporates a further ~4% depreciation of the NZ$ on a trade-weighted basis

over the course of 2017 and 2018 as the NZ$ progresses towards US$0.67.

Fig 49 The NZ$ has declined since the RBNZ’s February MPS, but the RBNZ maintains that ‘further depreciation is needed to achieve more balanced growth’.

Source: RBNZ, Macquarie Research, April 2017

64

69

74

79

84

64

69

74

79

84

Jun-10 Jun-12 Jun-14 Jun-16 Jun-18

Index Index

New Zealand: Trade Weighted Index(Index, RBNZ forecasts)

RBNZforecasts

Nov-16

Aug-16

Feb-17

Jun-16

Weaker than

expected GDP

growth in 4Q16 is a

reflection of

temporary factors.

The broader growth

outlook remains

well-supported by

migration,

construction and

tourism strength.

Our outlook

incorporates a

further ~4%

depreciation of the

NZ$ on a trade-

weighted basis over

the course of 2017

and 2018 as the NZ$

progresses towards

US$0.67


18 April 2017 24

South Africa We trimmed our forecasts in response to renewed political and policy risks, but our general

views remain unchanged. It should be kept in mind that these adjustments do not represent

the total impact of the credit rating downgrades and political turmoil but just further

adjustments. For example, the consensus economic growth forecast for 2017 is now just

1.1%, down from 2.7% when polled two years ago (for 2017). The original forecast implied

growing real per capita income, and adequate growth for near-term fiscal stability; the latest

forecasts do not. We still expect a very modest growth rebound, from 0.3% in 2016 to 1.2% in

2017. In 2016, the agricultural sector subtracted 0.2ppt off economic growth, which should be

reversed. The mining sector subtracted 0.4ppt, which we do not expect to recur. When seen

from the expenditure side, we have lowered our gross fixed-capital formation (GFCF)

forecasts further (to a 0.9ppt contraction), and remain concerned about downside risks. We

still expect positive real consumption expenditure growth in 2017 as inflation moderates amid

steady employment and wage settlements, while inventory restocking and net exports should

also support the growth rebound. We have lowered our 2018 forecasts slightly more, and now

only expect 1.3% economic growth.

From a fiscal perspective, our view remains that in line with the latest ANC policy discussion

documents, the big threat is not a general deliberate change in the fiscal path of consolidation

and an expenditure ceiling. The fiscal risks rather stem from sluggish economic growth and

weak SOE balance sheets, for which the promised governance and business model reform

must generally still be delivered upon, and it remains to be seen if the new cabinet will ensure

an affordable wage settlement next year. Furthermore, there is significant focus on nuclear

power procurement and the resulting risk to the government’s contingent liabilities, but even

in the absence of nuclear procurement, additional electricity supply (alongside other

infrastructure requirements, even with more private sector participation) will increasingly add

to the fiscal pressure given the weak SOE balance sheets and credit ratings. Our near-term

view is that SA will retain some of its investment-grade ratings, but SA is on a path towards

an unsustainable fiscal situation unless there is decisive intervention from government to

restore the SOEs’ balance sheets and improve the efficiency of government spending, and

higher economic growth.

The rand has recovered slightly from its recent worst levels and may gain slightly further in

the coming weeks in the absence of real political shocks. But our view remains that the

upside risks to the rand and growth are limited, whereas the medium-term negative risks are

growing. Our base case view is that there will be threats of a fundamental change in policy

direction (such as a constitutional change to support expropriation without compensation) but

not in the near-term radical actual shifts. In the context of a relatively small current account

deficit and attractive yields, foreign bond inflows may remain resilient. We are more

concerned about the growth than the rand risks from recent events at this stage.

Fig 50 Foreigners’ net bond and equity purchases (cumulative) Fig 51 GDP growth composition

Source: Inet-Bridge, Macquarie Research, April 2017 Source: SARB, Macquarie Research, April 2017

-100000

-50000

0

50000

100000

150000

-50000

0

50000

100000

150000

200000

250000

300000

350000

400000

Jan-00 Jan-02 Jan-04 Jan-06 Jan-08 Jan-10 Jan-12 Jan-14 Jan-16

ZARmnZARmn

Equities Bonds (RHS)

-4

-2

0

2

4

6

2009 2010 2011 2012 2013 2014 2015 2016 2017E

%

Households Government GFCF Inventories

Residual Net exports GDP

We still expect a

very modest growth

rebound, from 0.3%

in 2016 to 1.2% in

2017

We have lowered

our 2018 forecasts

and now only expect

1.3% economic

growth


18 April 2017 25

India Reported real GDP growth understated the impact of the slowdown in the economy on

a cash squeeze, in our view: Considering GDP in India is measured using formal sector

economic indicators, we were mindful of the fact that the reported growth numbers will not

reflect the extent of the slowdown in the economy on account of demonetisation, especially in

the informal sector. Accordingly, growth measured on a real GVA (gross value added) basis

stood at 6.6% YoY in the Dec-16 quarter compared with 6.7% YoY in the Sept-16 quarter, not

showing any significant slowdown in economic activity post the cash squeeze seen since 8th

November on account of demonetisation. This does not seem to reconcile with other high-

frequency data that we track, including auto sales, cement despatches, power demand, etc,

amongst others that pointed towards slowing economic activity post demonetisation. There

are indications on the ground that consumer discretionary spending was impacted adversely

on a cash squeeze in the short term, even though it is now recovering on remonetisation.

Small & medium enterprises and the rural economy, in which cash transactions are quite

prevalent, have been at a disadvantage. The supply chain disruptions and adverse impact on

productivity also resulted in delaying the recovery in the investment cycle. We believe there

are certain asset classes that are likely to see a long-lasting impact, including real estate/land

sales, jewellery, etc, as the unorganised segment shrinks.

Growth to bounce back in FY18: As the cash situation eases, we expect growth on a GDP-

at-market-prices basis to bounce back from an estimated 6.8% YoY in FY17 to 7.5% in FY18.

On a GVA basis, we expect growth to recover by 80bp, to 7.4% in FY18 from the 6.6%

estimated in FY17. Indeed, we expect growth in FY18 to be largely supported by higher

consumption demand on remonetisation, a lower cost of capital, higher wages & salaries on

state pay commission awards and increased government spending, especially in rural areas.

However, the investment cycle recovery led by private corporate capex is likely to be pushed

forward to FY19. The low capacity utilisation, slow corporate deleveraging and banks’ asset

quality concerns continue to be laggards. Export growth is likely to stabilise in FY18 on

improved global growth prospects.

Challenges for FY18 include:

a) Upside risks to inflation emanating from pent-up consumption demand, rising rural

wages, higher minimum support prices for agricultural produce and gradual narrowing of

the output gap. We believe India’s rate-cut cycle is mostly over as of now, and any scope

for further monetary easing will be largely data-dependent.

b) Complexity and lag in GST implementation in the initial phase. We expect the

government to implement GST by Jul-17.

c) Global factors including movement in global commodity prices (especially oil) and

uncertainties regarding the direction of US macroeconomic policies.

Fig 52 Real GDP growth Fig 53 CPI inflation forecasts

Source: Inet-Bridge, Macquarie Research, April 2017 Source: SARB, Macquarie Research, April 2017

5.5%

6.5%

7.2%

7.9%

6.8%

7.5%

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

FY13 FY14 FY15 FY16 FY17E FY18E

YoY%

0%

2%

4%

6%

8%

10%

12%

Mar-12 Jan-13 Nov-13 Sep-14 Jul-15 May-16 Mar-17 Jan-18

YoY%, 3MMAProjected path of CPI inflation

RBI's inflation target (4% +/-2%)

We believe the

upside risk to

inflation clearly

persists in FY18

We expect growth

on a GDP-at-market-

prices basis to

bounce back from

an estimated 6.8%

YoY in FY17 to 7.5%

in FY18


18 April 2017 26

ASEAN Real GDP growth has been fading across ASEAN since 2012, Fig 54, as the region looks for

a new growth engine, and unwinds the credit excesses of the past.

Fig 54 Real GDP growth for ASEAN-5 (weighted by GDP in current US$), 2000 to 2020. 2016-20 are Macquarie forecasts.

Note: Please see Fig 182 and Fig 183 for the underlying data. The horizontal dashed line above is 5% pa real GDP growth. The ADB estimates 4.9% pa over 2011-20 and 4.2% pa over 2021-30, whilst the Economic Complexity Project forecasts 4.3% pa. 5% pa is therefore a potential future ‘lid’.

Source: IMF, World Bank, Macquarie Research, April 2017

A subdued external environment and restrained credit expansion is suggestive of real GDP

growth modestly beneath potential, which we estimate at around 4.5% to 5.0% pa for the

ASEAN-5, Fig 54, and the room for monetary policy to have an easing bias, despite the US

Fed’s being in a slow and cautious rate rising cycle.

For EM economies generally, ongoing credit cycle and real property price adjustments temper

the industrial cyclical relief, as does expected weak inward FDI. Please see the 7 November

2016 EM growth struggles & the US Fed and the 5 December 2016 Eastern Europe, ASEAN

& India for more.

We believe Thailand is representative of the challenges facing ASEAN.

Thai manufacturing and total payroll job growth hit an all-time-low in 2016 with weak private

investment growth, contraction in manufacturing loans and rising new NPL formation. The

manufacturing sector is very important for the Thai exports, the job market (41.6% of total

payroll jobs) and the banking system (21% of system loans).

Fig 55 Thailand total exports: flat to down since 2012

Source: BOT, Macquarie Research, April 2017

0

2

4

6

8

10

2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020

(%)

ASEAN

-20%-16%-12%-8%-4%0%4%8%12%16%20%24%28%

0

50

100

150

200

250

US$ bn

Export (US$ bn) Growth (% YoY)

Drift

Real GDP growth

has been fading

across ASEAN

since 2012, as the

region looks for a

new growth engine,

and unwinds the

credit excesses of

the past

We believe Thailand

is representative of

the challenges

facing ASEAN


18 April 2017 27

The structural weakness of manufacturing has resulted in payroll job growth that has

remained weak, with all-time-low payroll job growth, continued low new hiring in the

manufacturing sector which employs the most payroll jobs, and rising unemployment benefit

claims. One consequence has been moderate Thai private consumption growth, Fig 56.

Fig 56 Thailand private consumption

Source: BOT, Macquarie Research, April 2017

A notable weakness is manufacturing investment. 4Q 2016 private investment (nominal term)

grew only 0.4% YoY being one of the lowest contributions to GDP, at 17.8%. FDI in 2016 hit a

near record low, with only the 2011 major flood year being lower, Fig 57. Thai overseas FDI

was strong, Fig 58, which implies that the better investment opportunities for both Thai and

foreign companies are elsewhere.

Weak private investment and FDI implies that the competitive position of Thailand

manufacturing is still deteriorating.

Fig 57 Foreign direct investment Fig 58 Thai overseas FDI

Source: BOT, Macquarie Research, April 2017 Source: BOT, Macquarie Research, April 2017

Another area of weakness lies in the banking system, which continues to face a sub-par loan

growth. The weak loan growth in 2016 could be attributed to the loan contraction in

manufacturing (-1.4% YoY), the sector that accounts for 21% of total system loan.

Both NPL ratio and new NPL formation from the manufacturing sector remain on a rising

trend which is the key reason behind the loan contraction, in our opinion.

We believe that to support GDP growth, government spending will remain the key factor.

-4%

-2%

0%

2%

4%

6%

8%

10%

12%

0

1

2

3

4

5

6

7

8

Bt trn

PCFE % YoY

8,634 8,562

6,411

14,747

2,474

12,899

15,936

4,975

9,004

3,286

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

US$m

FDI

1,5472,340

5,996

8,1317,176

14,261

12,121

5,7424,991

13,307

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

US$m

TDI

Moderate Thai

private consumption

growth

Weak private

investment and FDI

implies that the

competitive position

of Thailand

manufacturing is

still deteriorating


18 April 2017 28

Macq-ro insights Short-term business-cycle judgements are more likely to succeed with a deep awareness of

more medium- and longer-term issues. In global macroeconomics, there are a considerable

number of the latter. The following pages provide ‘executive summaries’ on:

Fig 59 Medium- and longer-term global macro issues

1) Global policy coordination 9) Financial repression for decades 17) FDI, following the smart money

2) Global services 10) Housing, globally, a good news story 18) Local factors determine core inflation

3) Innovation clusters/’brain belts’ 11) Global banking’s broken pipes 19) Income shares: from labour to capital

4) The private sector’s appetite for credit 12) Global liquidity & EM credit cycles 20) The corporate savings “glut”

5) Global trade in goods 13) Chinese corporate debt 21) Global income distribution

6) Global trade in services 14) Oil & sovereign defaults 22) Demographic Tectonics

7) Fading fiscal policy tailwinds 15) What’s next for EM economies? 23) Demographics & productivity growth

8) Big government’s impact on growth 16) Global private investment indicators 24) The long grinding cycle continues

Note. Macquarie Research, April 2017

Our Macq-ro insights series have been tackling these with an emphasis on quality data,

presented in numerous tables and charts.

Fig 60 Macq-ro insights 2017 publications

Date Title Topic Pages Data banks

5-Jan Japan’s debt-deleveraging marathon Debt structures are limiting growth - globally 32 BOJ

9-Jan EM economies & US policy risks Protectionism, infrastructure spending and capital flows 13 UNCTAD, World Bank, IMF

11-Jan Following the new smart money MNC political networks and FDUI flows 25 UNCTAD

25-Jan Global slump scenarios and the EM economies that concern us the most

Risks in the US, Eurozone and China, and the vulnerability of EM commodity-exporters

64 Latin America and Net Oil Exporters data build

8-Feb The US-Japan relationship Prospects for a bi-lateral trade agreement 60

1-Mar Factory Asia: the numbers East Asia’s integrated export supply chain & Trump 43 Tariff import volume sensitivities

15-Mar Fault lines and broken pipes Global imbalances and global financial system problems 25 Target2 balances


Fig 61 Macq-ro insights 2H 2016 publications

Date Title Topic Pages Data banks

18 July Fiscal winds of change Global fiscal policy tailwinds fade significantly from 2017 30 IMF, World Bank fiscal sustainability databases

22 July Residential investment, globally A good news story: around 4% of global GDP and will grow between 3.5% and 4.0% pa through 2020

58 Residential investment/GDP ratios, BIS price indices

26 July Financial Repression for decades A conscious policy choice to minimise public debt funding costs that began in Japan and is spreading to Europe

21 Global real interest rates, inflation-linked bonds

11 Aug. The Great Divergence: Inflation and Policy

Core inflation is determined by local factors, such that policy divergence is expected to persist

28 Model-based monetary policy rules

26-Aug Timing the exit from unconventional monetary policies: The ECB & BOJ

Using the US experience as a roadmap, but the analysis is complicated by relative fiscal reconstruction priorities

24

7-Nov EM growth struggles & the US Fed A negative reaction, but short-lived and muted to the next Fed Funds hike, but a new growth engine remain elusive

50 UNCTAD FDI, Conference Board productivity statistics

9-Nov Trump and the long durable expansion Post US election analysis for the US and overseas 16

5-Dec Eastern Europe, ASEAN & India Seeking opportunities through contrasts 32 Eastern Europe data build

6-Dec The US economy, oil, and overheating Oil price scenarios, including a US$80 risk case 31

14-Dec The OECED LI: monthly advance #8 The global recovery is maturing, but not yet late cycle 25 OECD LI


Medium- and longer-

term issues


18 April 2017 29

Global policy coordination Over 2016-18, national perspectives are expected to dominate:

1) The US, having achieved domestic balance, will slowly raise policy interest rates

2) The Eurozone will nurture the revival of its private sector

3) China will focus on the quality of its growth

Consumption is the key to global growth forecasts. At the global level, we are expecting

investment to be a mild headwind (pages 54-59), whilst fiscal spending is shifting to a mild

headwind. Trade growth is expected to remain modest and, therefore, we do not expect it to

be a material catalyst of efficiency gains. This leaves consumption, including residential

investment which we believe will be a pocket of strength.

We expect the G7/G20 forums to shift focus from fiscal stimulus to boosting consumption.

Seeking a high-frequency indicator for global consumption, Fig 62 presents the OECD US

and Eurozone consumer confidence monthly series along with the Oxford Economics global

real private consumption series (quarterly data, 4Q ma).

Consumer confidence in both the US and the Eurozone remain at high levels. The benefit to

household real incomes from plunging oil prices is still positive, though at a diminishing rate.

Retail sales data in advanced economies is reassuring, Fig 63.

Fig 62 Global real private consumption is growing around 2.5% pa, consumer confidence indices for the US and the Eurozone, 2006-4Q16

Source: OECD, Oxford Economics, Macquarie Research, April 2017

Fig 63 Retail sales volumes (3m % YoY)

Source: Datastream, Macquarie Research, April 2017

Within consumption, services are becoming ever more important. For example, 67% of US

personal consumption expenditures are now services, with goods just 33%.

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

94

95

96

97

98

99

100

101

102

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

(%) US - Consumer Confindence [LHS] Eurozone - Consumer Confidence [LHS]World - Consumption [RHS]

(%)

-10

-8

-6

-4

-2

0

2

4

6

8 (%)

UK US Japan Eurozone

We expect the

G7/G20 forums to

shift focus from

fiscal stimulus to

boosting

consumption

Consumption

growth has softened

over the last six

months


18 April 2017 30

We expect services

will form a greater

share of consumer

spending and total

output in the

years ahead

Global services The service sector has been understudied due to its relative stability. This is despite the

increasing role it is playing in the global economy as its share of output has grown steadily.

Over the past quarter century, ~1 billion new jobs have been created globally; ~75% of these

have been in services. This has caused services employment to rise from ~34% of the global

total in 1991 to ~46% in 2016, a gain of half a percentage point per year. Output data tells a

similar story, with the services share of GDP having risen steadily in both developed and

emerging markets over the past four decades, Fig 64.

Fig 64 Services as a share of GDP, 1970-2014: a secular shift to services

Source: United Nations, Macquarie Research, April 2017

These trends are likely to persist. Growth in per-capita incomes (particularly in EMs),

demographics (aging populations), and firmer relative trends in services inflation all suggest

services will form a greater share of consumer spending and total output in the years ahead.

Fig 65 Services become more important with a higher GDP per capita

Source: IMF, UN, Macquarie Research, April 2017

Country 1970 1975 1980 1985 1990 1995 2000 2005 2010 2014

change

1970 to

2014

change

1995 to

2014

Australia 53.8 57.1 56.9 60.0 66.3 67.8 70.2 69.1 69.0 70.1 16.4 2.3

Canada 59.4 58.7 58.3 61.4 65.8 66.4 64.5 65.8 70.8 69.9 10.5 3.5

France 59.9 62.9 65.2 67.9 69.6 72.7 74.3 76.6 78.6 78.9 19.0 6.1

Germany 48.9 55.2 56.8 59.0 61.1 66.0 68.0 69.8 69.1 69.0 20.1 2.9

Italy 53.4 55.4 57.4 62.4 65.6 67.6 70.0 71.9 73.7 74.3 21.0 6.7

Japan 49.8 54.6 58.1 59.4 59.6 65.2 67.3 70.6 71.3 72.0 22.2 6.8

Republic of Korea 41.2 40.9 46.3 49.5 51.6 54.6 57.5 59.4 59.3 59.4 18.2 4.8

Spain 47.8 49.4 55.0 59.4 60.5 65.1 65.1 66.5 71.4 75.1 27.3 9.9

United Kingdom 58.5 61.6 60.1 62.6 67.0 68.5 72.0 76.0 78.5 78.4 19.9 9.9

United States 65.5 66.5 67.2 70.0 72.6 74.5 76.3 77.5 78.8 78.4 12.9 3.9

DM average (level) 53.8 56.2 58.1 61.2 64.0 66.8 68.5 70.3 72.0 72.5 18.7 5.7

Argentina 48.1 47.0 55.1 56.1 59.1 68.3 69.2 56.9 60.9 63.0 14.8 -5.3

Brazil 52.6 48.9 49.2 46.7 52.9 68.5 67.7 65.9 67.8 71.0 18.4 2.5

China 24.7 22.2 21.8 29.0 32.0 33.3 39.5 40.9 43.7 47.7 23.0 14.3

India 32.5 34.4 33.9 35.9 36.6 39.1 44.2 46.4 48.2 53.0 20.5 13.8

Indonesia 40.1 40.9 37.7 47.1 47.9 47.6 42.9 44.8 41.8 43.3 3.3 -4.2

Mexico 54.1 53.5 49.5 46.8 53.0 57.9 57.1 58.1 58.3 59.0 4.9 1.1

Russian Federation 32.5 53.5 54.0 57.0 61.4 59.1 26.6 5.6

Saudi Arabia 36.8 27.3 27.8 55.2 45.7 45.5 41.5 35.0 39.2 40.6 3.7 -5.0

South Africa 54.4 51.0 45.7 51.5 55.4 61.3 64.8 67.1 67.2 68.0 13.6 6.8

Turkey 41.0 43.4 49.5 51.0 47.8 50.0 59.4 61.4 64.2 64.9 23.9 14.9

EM average 42.7 41.0 41.1 46.6 46.3 52.5 54.0 53.3 55.3 57.0 15.3 4.5

Emerging markets

Services share of GDP

Developed markets

$-

$10

$20

$30

$40

$50

$60

$70

40 45 50 55 60 65 70 75 80 85

GD

P p

er cap

ita (th

ou

san

ds) U

S$

Services share of output (%)

AUS

CAN

DEU

USA

GBR

FRA

ITAJPN

KOR

BRA

ZAF

TUR

ARG

MEX

RUS

IND

CHN

IDN

SAU

line of best fit

GDP per capita vs. services share of output (2014)

ESP

Over the past

quarter century,

~1 billion new jobs

have been created

globally; ~75% of

these have been in

services


18 April 2017 31

The rising importance of services has played a key role in our view that the current expansion

should continue to be characterized by structurally lower growth rates, but also by incredible

persistency and resiliency. We see five major implications:

1) Reduced volatility in economic growth

Services output is much less volatile than industrial output leading to more stable

employment trends. This also means that the business cycle will be less cyclical

supporting longer expansions and limiting the severity of downturns.

2) Central bank policy divergence and less inflation variability

The rise of services means that inflation prospects are going to be increasingly driven by

domestic forces, such as wage growth, and less by external factors such as commodity

prices and exchange rates. As a result, divergent domestic outlooks are likely to enable

desynchronized central bank policy.

Fig 66 US services inflation has shown far more stability relative to goods

Fig 67 ... and has been stronger on average with lower variation

Source: BEA, Macquarie Research, April 2017 Source: Bloomberg, Macquarie Research, April 2017

Fig 68 Euro area services inflation has shown greater stability than goods...

Fig 69 ...and a similar story in China, with services more stable and stronger than goods

Source: Bloomberg, Macquarie Research, April 2017 Source: Bloomberg, Macquarie Research, April 2017

-5%

-3%

-1%

1%

3%

5%

1991 1994 1997 2000 2003 2006 2009 2012 2015

US PCE price index YoY % change

services

goods

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

Average STDEV

Services

PCE price inflation descriptive statistics: 1991 to 2015

GoodsServices

Goods

-3

-2

-1

0

1

2

3

4

5

6

7

1991 1994 1997 2000 2003 2006 2009 2012 2015

services

goods

Euro area CPI price index YoY % change

-4

-2

0

2

4

6

8

10

12

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

goods

services

China CPI price index YoY % change

The current

expansion should

continue to be

characterized by

structurally lower

growth rates, but

also by incredible

persistency and

resiliency


18 April 2017 32

3) Increased trade in services

Trade in services has been rising rapidly worldwide, but a steep run-up in commodity

prices between 1998 and 2014 led to disproportionate gains in the trade of goods,

particularly from EM exports. Going forward, our expectation for a flatter commodity price

environment means that services exports are likely to make up a greater share of overall

export growth and rise in importance worldwide. Please see page 38.

4) Relative investment in IT and intellectual property

Service economies, which tend to be centred on knowledge-based industries, are increasingly

investing in IT and intellectual property relative to other investment areas. This shift is likely

to influence growth globally, as these forms of investment tend to be less trade-dependent

and contribute the majority of the economic benefit to the domestic economy.

Fig 70 Intellectual property products have risen to one-third of non-commodity US business investment

Fig 71 ...and the biggest increases have come from the R&D and software components

Source: BEA, Macquarie Research, April 2017 Source: BEA, Macquarie Research, April 2017

5) Lower productivity growth and real rates

The services sector has historically had lower productivity levels and growth rates than the

goods/manufacturing sector. A greater share of services employment and output will act as

a drag on productivity growth as a higher share of output will come from areas with lower

productivity growth.

In the years ahead, EMs are likely to continue to experience the strongest output gains as

productivity growth rates tend to be highest when countries undergo urbanization. This allows

workers with very low marginal productivity in the agricultural sector to be redeployed into

higher productivity employment in either the industrial/goods or service sector.

0%

10%

20%

30%

40%

50%

60%

70%

80%

1959 1964 1969 1974 1979 1984 1989 1994 1999 2004 2009 2014

all other non-resi

investment

intellectual property

products

information processing

equipment

Share of US nonresidential private investment (ex mining and oil & gas)

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

1959 1964 1969 1974 1979 1984 1989 1994 1999 2004 2009 2014

Share of US nonresidential private investment excl mining and oil & gas

Software

R&D

Enetertainment, literary, and

artistic orginals


18 April 2017 33

Innovation clusters, ‘Brain belts’ After a week visiting the Southern California brain belt, bringing to life the book by Antoine

van Agtmael and Fred Bakker: The Smartest Places on Earth (2016), we are optimistic about

a new global growth engine.

The catalyst for the above book was the insight that “Making things cheap to gain an edge

over high-cost Western companies just wasn’t cutting it anymore. The days of the low-cost

advantage were essentially over”. This led to an understanding that cheap is giving way to

smart, where high value-added products are being created in collaborative environments,

in ‘brain belts’. The latter, to quote Antoine van Agtmael and Fred Bakker are:

Research facilities with deep, specialist knowledge; educational institutions; government

support for basic research; appealing work and living environments; capital; and, most

important, the atmosphere of trust and the freedom of thinking that stimulates unorthodox

ideas and accepts failure as a necessary part of innovation—different from the hierarchical,

regimented thinking so prevalent in many Asian and MIST economies.

MIST stands for Mexico, Indonesia, South Korea and Turkey.

In addition to the brain belts listed in Fig 41, Antoine van Agtmael and Fred Bakker identified

many others in their book, but virtually all were in North America and Europe.

Fig 72 ‘Brain belts’: The Smartest Places on Earth

Country Region Name/place Focus Universities, research institutes, hospitals

United States: Well-known

California West Silicon Valley IT, bioscience, electric car, next gen bendable and wearable electronic devices

Stanford, University of California, Caltech

Massachusetts East Cambridge (and Route 128) Bioscience, robotics MIT, Harvard

Texas South Austin (Silicon Hills) Computers, new materials, bioscience University of Texas

Focus of the book

North Carolina South-East Durham-Raleigh-Chapel Hill (Research Triangle Park)

Bioscience, new materials, energy (LED) Duke, UNC, NC State

New York East Albany (Hudson Tech Valley) Semiconductors SUNY, RPI

Ohio Midwest Akron New materials, polymers University of Akron, Kent State

Minnesota Midwest Minneapolis-St. Paul Medical devices/bioscience University of Minnesota

Oregon West Portland (Silicon Forest) Bioscience OHSU

Northern Europe: in book

Netherlands Eindhoven (High Tech Campus)

Semiconductors. New materials Technical University

Sweden Lund-Malmo (Ideon) Life science, new materials Lund University

Finland Oulu (Technopolis) Medical instruments, wireless Oulu University

Germany Dresden (Silicon Saxony) Semiconductors Max Planck

Switzerland Zurich Life science Technical University

Note: from the book by Antoine van Agtmael and Fred Bakker: The Smartest Places on Earth (2016)

Source: Antoine van Agtmael and Fred Bakker above, Macquarie Research, April 2017

Beyond expensive, multidisciplinary, complex new product development, these innovation

clusters are also undertaking manufacturing, highly automated, custom runs, using 3-D

printers, produced in close proximity to the customer.

From an employment perspective, Antoine van Agtmael and Fred Bakker are optimistic, citing

Enrico Moretti’s book The New Geography of Jobs (2013).

For each new urban high-tech job there are five additional jobs created, three for

professionals and two for lower-wage non-professionals.

Cheap is giving way

to smart, where high

value-added

products are being

created in

collaborative

environments, in

‘brain belts’

For each new urban

high-tech job there

are five additional

jobs created


18 April 2017 34

The private sector’s appetite for credit Debt is not inherently a problem. Intermediated by the financial system, it is the main way that

savers earn returns. Savers, to be sustainably paid, however, require debt to be deployed into

debt-serviceable activities. We regard private sector non-financial credit growth broadly in line

with nominal GDP growth to be healthy.

The revival of private sector credit demand is a prerequisite for the successful withdrawal of

extraordinary central bank support for the economy.

In the case of Japan, the excess of credit growth over nominal GDP growth over the 1980s

‘bubble’ was followed by a protracted period of balance sheet adjustment (credit growth

beneath nominal GDP growth, Fig 73. We believe Japan endured a vicious cycle of policy

and regulatory mistakes, which US policy avoided after the Global Financial Crisis, Fig 74.

Fig 73 Japan: private non-financial credit growth vs nominal GDP growth

Fig 74 US private sector nonfinancial credit is growing faster than GDP for the first time since 2008

Source: FRB of St Louis, Datastream, Macquarie Research, April 2017 Source: FRB of St. Louis, Macquarie Securities, April 2017

Nevertheless, even in the US it took four years between the year private sector credit growth

turned positive (2011) until it exceeded nominal GDP growth (2015).

The Eurozone is behind the US because of the 2011-13 fiscal squeeze, Fig 75, such that

private sector credit growth double-dipped and has only gone positive again in 2015, four

years behind the US (2011), Fig 76.

Fig 75 The fiscal impulse: the Eurozone’s 2011-13 fiscal squeeze

Fig 76 Euro Area: private nonfinancial credit growth vs Nominal GDP growth

Note: Year-over-year change in the cyclically adjusted net lending (+) or net borrowing (-) of general government, adjusted based on potential GDP, excessive deficit procedure. Forecasts for 2015-17 from the European Commission. Source: EC Annual Macroeconomic Database, Macquarie Research, April 2017 Source: FRB of St Louis, Datastream, Macquarie Research, April 2017

-12

-9

-6

-3

0

3

6

1992 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015

(YoY % change) Private nonfinancial creditNominal GDP

latest

-4

-2

0

2

4

6

8

10

12

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016

(YoY% ) Private nonfinancial credit

Nominal GDP

latest

-2

-1

0

1

2

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

(% of potential GDP)

YoY change in cyclically adjusted budget balance

-6

-4

-2

0

2

4

6

8

10

12

14(YoY % ) Private nonfinancial credit

Nominal GDP

The case of Japan

The US

The Eurozone


18 April 2017 35

The BIS Financial cycle

Claudio Borio in his December 2012 BIS working paper: The financial cycle and

macroeconomics: What have we learnt? (http://www.bis.org/publ/work395.pdf) detailed the

existence of financial cycles: “Arguably, the most parsimonious description of the financial

cycle is in terms of credit and property prices (Drehmann et al (2012)). These variables tend

to co-vary rather closely with each other, especially at low frequencies, confirming the

importance of credit in the financing of construction and the purchase of property.”

Like business cycles, financial cycles are not mechanistic, but do appear as patterns in the

data. One problem is that whilst business cycles average 3-4 years, financial cycles average

16. Averages are just that, so let's say 16 years plus or minus 5. A lot depends on how policy

responds in the business cycle downturns prior to the financial cycle's peak/bust. On average

there will be three prior business cycle downturns, and if policy aggressively counteracts them

then moral hazard/credit leverage builds. We believe the private sectors of the major

economies/blocs are at very different stages in this ‘financial cycle’, below. The credit cycle

in EM economies is examined on page 45.

Fig 77 BIS-defined 16-year private sector ‘financial cycle’


Fig 78 presents the last 35 years of financial history in one chart. The rise and fall in the ratio

of private non-financial credit/GDP has coincided with major events. China is included to put

the relentless rise in its private non-financial credit/GDP ratio into context. China’s corporate

debt issue is examined on page 49.

Fig 78 Private non-financial credit as a % of GDP: significant peaks*

Note: (*) 1) 1990 saw the start of Japan’s lost decade 2) 1995 was Mexico’s peso crisis 3) 1997 saw Thailand kick-off the Asian crisis 4) 2008 was the USA’s subprime crisis 5) 2011 was the start of the Euro crisis that included Spain

Source: The Economist, BIS, Macquarie Research, April 2017

0

50

100

150

200

250(% of GDP)

Japan US Spain Mexico Thailand China

Like business

cycles, financial

cycles are not

mechanistic, but do

appear as patterns

in the data

We believe the

private sectors of

the major

economies/blocs

are at very different

stages in this

“financial cycle”


18 April 2017 36

Global trade in goods The 1980-2008 6% pa global world trade volume growth average has stepped down, in our

opinion, to a new trend growth rate of 2-3% pa, Fig 79.

Fig 79 World Merchandise Trade, from 1992 to latest, rolling 3 months, YoY


This reflects:

1) The maturing of a 50-year containerization trend.

2) The completion of the integration of China into global supply chains post its

December 2001 WTO entry.

3) The move by MNCs to regionalize production to dampen exchange rate risk.

4) The transition of China’s investment and credit-driven growth model into an import

substitution stage for intermediate and capital goods, especially by those companies

that perceive their cost of capital to be very low.

The late 2014/early 2015 air pocket in emerging market economies’ import growth, along with

the trend decline in its growth rate, is shown in Fig 80, red line, which uses rolling three-month

average YoY data.

Fig 80 EM & Advanced economies import volumes


One consequence of the above is the muted response relative to history of the trade account

to exchange rate movements. This probably results in greater currency volatility.

In the case of EM currency depreciations, the consequence is likely to be more import volume

compression than export volume expansion.

For the identification of global trade trends, we recommend the comprehensive database of

the CPB Netherlands Bureau for Economic Policy Analysis. Data for both volume and price/

unit values is available geographically on a monthly basis. Importantly, the data is seasonally

adjusted. Their website is here: http://www.cpb.nl/en and their key summary table is Fig 81.

-25

-20

-15

-10

-5

0

5

10

15

20

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

World Merchandise Trade, YoY

(%)

-5

0

5

10

15

20

25

2010 2011 2012 2013 2014 2015 2016 2017

Advanced EM

(per 12 month % change)

LAST 3 MONTHS ON YEAR AGO

Please note the late

2014/early 2015 air

pocket in emerging

market economies’

import growth,

along with the trend

decline in its

growth rate


18 April 2017 37

For those interested in going deeper into last year’s developments in China, the May 2016

IMF working paper by Joong Shik Kang and Wei Liao: Chinese imports, What’s behind the

slowdown? provides a comprehensive analysis of recent trends in Chinese trade.

Fig 81 World merchandise trade (% changes) – latest month January 2017

YoY QoQ momentum (note #1) MoM

Volumes (s.a) 2014 2015 2016 2016q2 2016q3 2016q4 2016m12 2017m01 2016m11 2016m12 2017m01

World trade 2.7 2.0 1.3 -0.3 0.6 1.4 1.4 2.4 2.6 0.9 0.0

World imports 2.6 1.9 1.2 -0.3 0.7 1.0 1.0 1.9 2.1 0.7 0.7

Advanced economies 3.2 3.6 1.9 -0.2 0.5 0.0 0.0 0.5 0.9 0.1 0.6 US 5.1 6.2 0.7 -0.7 0.5 2.0 2.0 3.4 1.5 1.6 1.9 Japan 2.2 0.2 0.3 -1.4 0.6 -0.2 -0.2 1.7 0.7 0.4 3.7 Eurozone 2.7 3.6 2.3 -0.8 0.3 1.0 1.0 0.7 1.1 -1.4 0.1 Other advanced economies 2.6 2.2 2.9 1.9 0.6 -4.1 -4.1 -3.4 -0.1 1.4 -1.4

Emerging economies 1.7 -0.5 0.4 -0.4 1.1 2.4 2.4 4.0 3.9 1.6 0.8 Asia 3.2 -0.1 2.1 0.3 1.8 3.4 3.4 5.0 4.3 1.0 0.9 Central & Eastern Europe -10.9 -23.2 9.4 -0.2 0.4 -0.6 -0.6 2.4 2.0 -0.5 8.9 Latin America 3.1 -1.4 -2.9 -2.1 1.9 -0.5 -0.5 2.5 5.7 6.8 -2.0 Africa & Middle East 0.0 10.4 -8.4 -2.2 -3.0 1.0 1.0 0.7 1.1 0.4 0.1

World exports 2.7 2.2 1.4 -0.3 0.4 1.9 1.9 2.8 3.0 1.0 -0.7

Advanced economies 2.1 1.8 0.9 -0.1 0.6 1.5 1.5 1.7 1.5 1.4 -1.1 US 3.2 -1.1 0.0 0.4 3.8 -1.1 -1.1 0.0 -0.9 3.6 0.1 Japan 1.7 2.7 2.3 1.1 0.7 2.8 2.8 2.2 3.4 -1.6 -1.1 Eurozone 1.9 2.2 1.7 0.4 -0.1 1.2 1.2 1.0 1.7 -0.1 -1.1 Other advanced economies 2.0 2.8 -0.8 -2.1 -0.3 3.8 3.8 4.8 2.1 4.3 -1.9

Emerging economies 3.5 2.7 1.9 -0.6 0.2 2.5 2.5 4.2 4.7 0.6 -0.2 Asia 4.2 1.4 1.0 -0.6 -0.3 2.8 2.8 4.0 4.1 1.0 -1.4 Central & Eastern Europe -0.5 0.1 3.1 1.0 0.5 3.3 3.3 5.9 5.5 0.8 1.5 Latin America 4.7 9.2 3.9 -4.1 0.3 1.5 1.5 8.1 11.4 -0.8 7.8 Africa & Middle East 1.6 4.5 3.4 2.3 1.9 1.2 1.2 -0.1 0.8 -0.1 -3.5

Prices/unit values US$ (s.a.)

World trade -1.6 -13.4 -5.2 2.5 0.0 0.0 0.0 0.0 -0.7 0.4 1.0

World imports -1.4 -13.2 -5.2 2.5 -0.3 0.2 0.2 0.3 -0.3 0.2 1.5

Advanced economies -1.1 -14.0 -4.5 2.6 -0.4 -0.7 -0.7 -0.4 -0.3 0.0 1.6 US -1.1 -10.2 -3.3 1.8 0.8 0.6 0.6 0.7 -0.1 0.4 0.6 Japan -4.7 -20.3 -6.6 3.6 5.1 -1.4 -1.4 -3.6 -0.7 -4.2 0.1 Eurozone -1.5 -17.4 -2.7 3.0 -0.1 -0.8 -0.8 -0.8 -1.3 -0.1 2.6 Other advanced economies 1.0 -9.1 -8.5 2.1 -3.8 -1.6 -1.6 0.2 1.6 0.6 1.2

Emerging economies -1.8 -12.0 -6.0 2.4 -0.1 1.4 1.4 1.2 -0.5 0.4 1.3 Asia -2.4 -12.9 -6.5 3.1 0.1 1.3 1.3 0.6 -0.8 0.7 0.5 Central & Eastern Europe -2.9 -14.7 -10.6 -5.3 -1.9 0.4 0.4 6.9 -1.3 1.5 21.4 Latin America -3.0 -9.1 -5.1 1.5 -0.6 2.8 2.8 2.3 -0.9 -1.4 0.6 Africa & Middle East 2.5 -11.0 -1.9 3.1 0.9 1.1 1.1 1.6 1.9 0.2 -0.2

World exports -1.7 -13.7 -5.3 2.5 0.2 -0.2 -0.2 -0.4 -1.1 0.7 0.5

Advanced economies -0.9 -12.9 -3.1 2.5 0.0 -1.2 -1.2 -1.3 -0.6 -0.5 0.8 US -0.5 -6.3 -3.2 1.3 0.6 0.3 0.3 0.6 0.1 0.4 0.2 Japan -5.2 -11.9 0.5 3.2 4.5 -3.4 -3.4 -5.3 -3.1 -3.9 1.8 Eurozone 0.0 -15.2 -1.3 2.6 -0.3 -1.7 -1.7 -2.3 -1.1 -1.2 0.9 Other advanced economies -1.6 -12.7 -8.1 2.9 -1.6 0.0 0.0 1.4 1.2 1.6 0.8

Emerging economies -2.7 -14.7 -7.8 2.4 0.4 1.0 1.0 0.7 -1.6 2.1 0.0 Asia -0.8 -6.9 -5.4 1.5 -0.1 -0.1 -0.1 -0.8 -1.4 0.2 0.1 Central & Eastern Europe -5.4 -32.0 -19.4 2.0 3.4 4.6 4.6 8.7 2.3 4.4 8.5 Latin America -5.9 -19.2 -6.0 5.2 1.5 2.6 2.6 0.6 -2.0 3.4 -6.2 Africa & Middle East -6.8 -37.9 -18.3 8.2 2.0 5.1 5.1 7.5 -5.2 13.8 3.5

World prices/unit values in US$

Fuels (HWWI) -6.8 -44.1 -14.6 28.9 3.6 12.0 12.0 10.4 -6.2 14.4 0.9 Primary commodities ex fuels -9.2 -22.8 -0.8 10.2 1.3 6.4 6.4 10.1 6.5 2.2 2.9

Notes: (#1): average of the three months up to the report month over average of the preceding three months



18 April 2017 38

Global trade in services The share of global trade in services has been flat for close to 20 years, Fig 82, as much of

the delta in global trade growth has come from EM goods.

The result of this development is that services have become far less trade-intensive relative

to goods. To provide some context, the average dollar value of world exports per employee

in the goods sector is ~$11,000, nearly 3x the same figure for the services sector, Fig 83.

This represents a significant increase from just ~2x in 1991.

Fig 82 Flat global services export share, 1982-2015 Fig 83 A higher trade intensity in goods

Source: WTO, Macquarie Research, April 2017 Source: WTO, Macquarie Research, April 2017

Partially fuelled by a boom in commodity prices from 1998 to 2014, global trade growth has

been increasingly driven by EMs, where exports are more concentrated in goods. We believe

this contributed to outsized gains in the exports of goods.

Prior to 1998, growth in services exports for both EMs and DMs outpaced the growth in goods

(Fig 84) whereas after 1998 EM goods exports outperformed (Fig 85).

Fig 84 Services trade outperformed from 86 to 98 … Fig 85 Underperformed during the commodity boom

Source: WTO, Macquarie Research, April 2017 Source: WTO, Macquarie Research, April 2017

Going forward, it may be many years before there is another commodity boom. As a result,

global trade growth is likely to come increasingly from services.

Policymakers are already showing signs of awareness on this front. Recent trade

agreements, such as the Trans-Pacific Partnership, have chapters dedicated specifically to

liberalizing the trade in services and removing barriers.

5%

10%

15%

20%

25%

30%

1982 1987 1992 1997 2002 2007 2012

Share of exports that are services (USD basis)

G20-DM

G20-EM

world

$-

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

1991 1994 1997 2000 2003 2006 2009 2012

goods

services

World exports (USD basis) per employee

0

1

2

3

4

5

1982 1984 1986 1988 1990 1992 1994 1996 1998

Export growth - G20 countries (1982= 1.00)

EM services

DM services

DM goods

EM goods

1982 = 1.00

During flat commodity price period EM service

exports outperformed

0

1

2

3

4

5

6

7

8

1998 2000 2002 2004 2006 2008 2010 2012 2014

DM goods

Export growth - G20 countries (1998 = 1.00)

EM goods

EM services

DM services

1998 = 1.00

During commodity boom EM goods

exports outperformed

The share of global

trade in services

has been flat for

close to 20 years

Going forwards,

global trade growth

is likely to come

increasingly from

services


18 April 2017 39

Global fiscal policy

has now reached an

inflexion point, as it

is forecast to

become a

significantly weaker

cyclical tailwind,

with aggregate

budget balances

forecast to improve

modestly (by around

0.5% of GDP each in

2017 and 2018)

Those countries with

larger budget

deficits (horizontal

scale) are forecast,

on average, to see

the biggest

improvements

(reduction in the

budget deficit) 2017

versus 2016

(vertical scale)

Fading fiscal policy tailwinds In the immediate aftermath of the global financial crisis (GFC), global fiscal policy was very

expansionary, with global budget deficits widening by nearly 2% of GDP in 2008 and a further

5% of GDP in 2009, Fig 86. Aggregate revenue weakness was accommodated and spending

was increased. However, the fiscal expansion was tempered from 2010, when global budget

balances improved by 1% of GDP (each) in 2010 and 2011, as economies began reloading

their fiscal arsenal.

This continued, albeit less aggressively, in 2012-13, with an average improvement of 0.7%

of GDP per year. From 2014-16, however, aggregate fiscal budget balances deteriorated

modestly (on average by 0.2% of GDP per year), as the global economic recovery became

less sure-footed and put pressure on developing economy tax revenues in particular. Global

fiscal policy has now reached an inflexion point again, as it is forecast to become a

significantly weaker cyclical tailwind, with aggregate budget balances forecast to improve

modestly (by around 0.5% of GDP each in 2017 and 2018).

Fig 86 Advanced versus developing countries: primary budget balances


Fig 87 Budget balance 2016 and change to 2017


-6.00

-5.00

-4.00

-3.00

-2.00

-1.00

0.00

1.00

2.00

3.00

% of GDP, YoY change

Advanced weighted Developing economies (weighted)

Philippines

Hungary

Mauritius

Thailand

Indonesia SingaporeIsrael Chile

Brazil SAUSNZ

IndiaChinaNigeria

Croatia

MalaysiaMexico

TurkeyCanada

Ireland

Japan Australia

ArgentinaUK

EgyptRussia

Swaziland

Namibia

-0.5

0.0

0.5

1.0

1.5

2.0

-12 -10 -8 -6 -4 -2 0 2

2017 - 2016 (% of GDP)

(2016, % of GDP)

Fiscal headwind that fades

Fiscal tailwind that intensifies

Fiscal tailwind that fades


18 April 2017 40

Big government’s impact on growth The role of the government, and its impact on economic development and real GDP growth is

a vast area of academic study, with many ongoing controversies. Government has a vital role

to play, providing the conditions for markets to operate (national and domestic security,

regulatory oversight of institutions providing the rule of law etc.) and the provision of ‘public

goods’ that the market is incapable of providing. Alternatively, the presence of the

government creates opportunities to extract ‘economic rents’ by creating market distortions

through the use of political access.

Named for four economists (Barro, Armey, Rahn and Scully), the inverted-U BARS curve

perceives the government first aiding growth, and then, as its presence grows, detracting

from growth on a net basis. The literature concludes that the optimal size of government

spending is between 17% and 40% of GDP, with most estimates being between 20% and

30%. Nonetheless, economists have struggled to conclusively identify a negative relationship

between increased government size and lower rates of economic growth.

We believe that the relationship is between the persistence of large government and lower

rates of economic growth. Rather than using annual pairs, as is most commonly done, we

believe it is necessary to use decade averages. Andreas Bergh & Magnus Henrekson do this

in Fig 88.

Fig 88 Cross-country correlations between growth and government size using averages over decades rather than annual values of growth and government size, OECD, 1970-2005

Note: From the research paper “Government size and implications for economic growth” by Andreas Bergh & Magnus Henrekson, 2010. Government size is measured as tax revenue over GDP. The plots are average growth for the 1970s, 1980s, 1990s and 2000-2005 against average government size in the respective periods

Source: American Enterprise Institute for Public Policy Research above, Macquarie Research, April 2017

For a final perspective, we quote from the European Central Bank (ECB) working paper

number 1399, November 2011 ‘Economic performance and government size’ by Antonio

Afonso & Joan Tovar Jalles, and from their conclusions:

Our results allow us to draw several conclusions regarding the effects on economic growth of

the size of the government: 1) there is a significant negative effect of the size of government

on growth, 2) institutional quality has a significant positive impact on the level of real GDP per

capita, 3) government consumption is consistently detrimental to output growth irrespective of

the country sample considered (OECD, emerging and developing economies); 4) moreover,

the negative effect of government size on GDP per capita is stronger at lower levels of

institutional quality, and the positive effect of institutional quality on GDP per capita is stronger

at smaller levels of government size.

Government has a

role to play

Alternatively, the

presence of the

government creates

opportunities to

extract “economic

rents” by creating

market distortions

The negative effect

of government size

on GDP per capita is

stronger at lower

levels of

institutional quality


18 April 2017 41

Financial repression for decades It would be healthier if real interest rates were broadly in line with real GDP growth.

Please see the 26 July 2016 Macq-ro insights: Financial repression for decades for more.

Real interest rates have also been moving lower progressively, from the 4% level of 1985-97.

Having moved down modestly to 3.5% over 1998 through 2001, there was a substantial slide

over 2002-06 (attributable to the ’saving surplus’ coming out of Asia), and another substantial

slide over 2010-12 (attributable to the imbalances and associated austerity inside the

Eurozone).

Our expectation is for real interest rates to stay low, as explained on the next page.

Fig 89 10-year real bond yields, G7 ex Italy, 1985-2013, quarterly, %

Note: National Bureau of Economic research (NBER) working paper “Measuring the world interest rate” by Mervyn King & David Low (2014) The US enters the sample in 1999 with the first TIPS issue.

Source: NBER above, Macquarie Research, April 2017

The chart above comes from the Mervyn King & David Low National Bureau of Economic

research (NBER) working paper ‘Measuring the world interest rate’. The authors make the

following observation about their methodology:

The ‘real rate’ requires careful definition. For most purposes the relevant concept is an ex

ante rate, which subtracts from the actual nominal rate the expected rate of inflation. Reliable

quantitative measures of inflation expectations are notoriously hard to come by and refer only

to expectations over time horizons too short to be useful for analysing saving and investment.

So in this note we use measures of real rates on government bonds that are issued with

inflation protection.

Such ex-ante measures of real rates are much less volatile than ex post rates when there are

significant and unexpected changes in inflation, as in the 1970s and 1980s, or in equilibrium

real rates, as seen more recently.

The following table provides period averages.

Fig 90 10-year real bond yields, period averages, %

1985-1989 4.27 1990-1994 4.15 1995-1999 3.88 2000-2004 2.86 2005-2009 1.85 2010-2013 0.48

Source: NBER, Macquarie Research, April 2017

Usefully, Mervyn King & David Low also provide a proxy for their G7 ex Italy real 10-year

bond yield, Fig.76. The 2026, 10-year US TIPS yield is currently around 45bp.

-1

0

1

2

3

4

5

6

1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013

(%) Weighted real rate Unweighted real rate

We disagree that

record low bond

yields are signalling

a global slump

Rather, we believe

that it is a

conscious policy

choice to minimise

public debt funding

costs that began in

Japan and is

spreading to Europe

Our expectation is

for real interest

rates to stay low


18 April 2017 42

Fig 91 Unweighted 10-year real bond yields, ‘World’, versus the yield on US TIPS, 1985-2013, quarterly, %

Note: There are risks with simply using the US TIPS: 1) it includes expectations of changes in the real exchange rate of the US$, 2) in the case of the US, there is the potential of a safe haven negative risk premium

Source: NBER, Macquarie Research, April 2017

Fig 92 US TIPs yields, %

Source: US Department of the Treasury, FED, Macquarie Research, April 2017

Real interest rates to stay low

In addition to the two major global disequilibria noted above, we believe a third major factor,

financial repression, needs to be incorporated.

We believe fiscal reconstruction (reloading the fiscal policy tool weapon) using the seductive

tool of financial repression, is a conscious policy choice to minimise public debt funding costs

that began in Japan and is spreading to Europe. Budget realities are shown below.

Fig 93 G7 public debt/GDP ratios, %

2007 2016E 2020E

Gross Net Gross Net Gross Net

US 64.0 44.5 108.2 82.2 107.9 83.4 Japan 183.0 80.5 250.4 127.9 254.5 132.2 Germany 63.5 47.9 68.2 45.4 58.9 38.5 UK 42.2 37.1 89.0 80.5 84.3 75.8 France 64.4 57.7 97.2 89.2 95.9 88.0 Italy 99.8 85.9 133.2 113.8 127.5 108.9 Canada 66.8 22.1 92.1 26.9 84.6 19.6

G7 80.9 52.0 121.7 84.3 120.2 83.7 Euro area 64.9 44.9 91.7 67.4 86.2 63.7

Note. 2016 and 2020 estimates IMF. Source: IMF, Macquarie Research, April 2017

-2

-1

0

1

2

3

4

5

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

(yield, %)Treasury inflation protected securities - 5 year

Treasury inflation protected securities - 7 year



Treasury inflation protected securities - long term

In addition to the

two major global

disequilibria noted

above, we believe a

third major factor,

financial repression,

needs to be

incorporated


18 April 2017 43

We disagree that record low bond yields are signalling a global slump. Rather, we believe that

it is a conscious policy choice to minimise public debt funding costs that began in Japan and

is spreading to Europe. The principal driver of record low bond yields since 2009 has evolved

from a private sector balance sheet recession (completed in the US and Japan; ongoing, but

progressing in the Eurozone) to fiscal reconstruction (reloading the fiscal policy tool weapon)

using the seductive tool of financial repression. Japan illustrates well the pressures being

faced by policymakers.

Japan’s public debt to GDP ratio built over the 24 years 1990-2014 from around 65% to

240%. Only in 2006 and 2007 did the ratio decline. Japan’s public debt to GDP has far

exceeded the previous high in 1945, Fig 94.

Please note, however, that Japan’s public debt ratio has stabilised over the last few years.

Fig 94 Japan’s gross government debt, as a percentage of GDP

Source: IMF, Datastream, Macquarie Research, April 2017

Japan’s considerable progress over the last three years towards stabilizing its public debt to

GDP ratio can be illustrated by using the formula for the primary budget balance necessary

for stabilization: (Bond yields minus nominal GDP growth) x (Public debt/GDP)

In 2012, Japan needed a primary budget surplus of 2.4% (1% minus 0% times 2.4). Today,

thanks to the consequences of financial recession, Japan only needs a primary budget deficit

of 2.4% (zero minus 1% times 2.4) to stabilize the public debt to GDP ratio.

That’s a 4.8% of GDP swing. There is no ‘free lunch’, however.

An implicit tax on savers resulting in weak consumption

Savings behaviour is not mechanical, but the hypothesis that households smooth their

consumption by saving for retirement seems to work well in practice. Reducing the returns

on savings leads to higher required savings. The implicit tax on savings leads to weak

consumption. If consumption stagnates, companies do not invest and do not raise wages.

Achieving fiscal reconstruction with financial repression, in the context of very high (around

250% of GDP) household financial assets, has led Japan into a low growth trap.

Fig 95 Household saving rate, 1990 to latest

Source: Datastream, Macquarie Research, April 2017

0

50

100

150

200

250

1875 1885 1895 1905 1915 1925 1935 1945 1955 1965 1975 1985 1995 2005 2015

Gross government debt(as % of GDP)

Gross government debt (as % of GDP)

Bubble bursts

1936 Takahashi assassinated for trying to rein in military expenditures

(%)

1927 Takahashi first appointed Finance Minister

1936 Takahashi assassinated for trying to rein in military expenditures

-5

0

5

10

15

20

1990 1992 1995 1997 2000 2002 2005 2007 2010 2012 2015

Housholds saving rate(%, seasonally adjusted, 6m average)

Japan’s public debt

ratio has stabilised

If consumption

stagnates,

companies do not

invest and do not

raise wages

The principal driver

of record low bond

yields since 2009

has evolved to fiscal

reconstruction

(reloading the fiscal

policy tool weapon)

using the seductive

tool of financial

repression


18 April 2017 44

Housing, globally, a good news story Private sector credit growth needs to revive for central banks to end their extraordinary

support. A recovery in home loan demand could be the key driver. Home loans are on

average ~40% of total loans.

A good news story: We believe residential investment is around 4% of global GDP and will

grow between 3.5% and 4.0% pa through 2020. The latter is supported by recovery in many

advanced economies, whilst financial deepening and the upgrading of the housing stock

underpin growth in EM economies. There are exceptions.

Housing is a lead indicator and an important driver of the business cycle, as well as being

intertwined with credit growth and financial stability. Nonetheless, there is a paucity of globally

consistent data.

Fig 96 10-Advanced economies residential investment to GDP ratio

Note: 10-Advanced economies are US, Japan, Germany, France, Italy, Spain, UK, Australia, Canada, and South Korea. Source: IMF, National sources, Oxford Economics, Macquarie Research, April 2017

The 22 July 2016 Macq-ro insights: Residential investment, globally looked at residential

investment data and residential price trends across 23 countries. The key observations are

summarized below.

Fig 97 Key residential investment observations

Country

USA 10% pa real growth through 2019 would only return residential investment/GDP to the post-1970 average level

Germany & France

Germany has been recovering since 2010, whilst France is still at cyclical lows. We believe a mild recovery is beginning

Japan The Affluent Elderly is supporting renovation and reform, with the upgrading of the housing stock a major government priority

China Due to lower mortgage rates and aggressive policy relaxation, 2016 will be remembered as a year of a property up-cycle like 2013. From a long-term perspective, we believe China’s housing demand has peaked


Fig 98 Residential investment to GDP ratio for Germany and France

Source: Oxford Economics, Macquarie Research, April 2017

3.5

4.0

4.5

5.0

5.5

6.0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

10-Total

4.0

5.0

6.0

7.0

8.0

9.0 (%) Germany residential investment - % share of GDPFrance residential investment - % share of GDP

Residential

investment is

around 4% of global

GDP and will grow

3.5-4.0% pa through

2020

Recovery in many

advanced

economies...

Financial deepening

and the upgrading

of the housing stock

underpin growth in

EM economies

We looked at

residential

investment data and

residential price

trends across 23

countries


18 April 2017 45

Global banking’s broken pipes Imbalances, disequilibria lead to financial flows, with global intermediation by banks having

been associated with four major boom-bust cycles since 1980 (Latin America in the early

1980’s, Japan in the late 1980’s, Asia ex Japan in the late 1990’s and US & Europe in the late

2000’s). The 15 March 2017 Fault lines and broken pipes has more. Thus, we believe the

three major global disequilibria are interconnected:

1) The current surplus of Factory Asia; the current account deficit of the US

2) The imbalances inside the Euro (the first chart below is a proxy for this)

3) The cross-country funding ratio, Fig 96 and Fig 97: ongoing financial sector

deleveraging is driving dysfunction in the interbank lending market and the FX swap

market – these are the broken pipes.

Fig 99 Euro Target2 balances

Note: aggregate 1 consists of Germany, Luxembourg, Netherlands and Finland and aggregate 2 consists of Greece, Ireland, Italy, Portugal and Spain.

Source: Euro crisis monitor, Based on data of the ECB's Statistical Data Warehouse, Macquarie Research, April 2017

The main actors extending US$-denominated foreign claims (loans) are not US banks, the

blue shading in Fig 23, left-hand chart. With global trade and financial transactions (outside

intra-Eurozone Euro activity) being largely US$-denominated, this implies most cross-border

activity globally is being conducted with the assistance of non-US banks.

Fig 100 US$-denominated foreign positions of banks: the ‘Euro-dollar’ market

Notes: (1) Latest data at end-June 2016. (2) “Non-US banks’ US$-denominated foreign claims” and Non-US banks’ US$-denominated foreign liabilities” are calculated as US$-denominated foreign claims and liabilities of all reporting countries after excluding those of US banks, respectively. (3) “Non-US banks’ cross-currency funding ratio” is calculated as “Non-US banks’ US$-denominated foreign claims” less “Non-US banks’ US$-denominated foreign liabilities,” divided by “Non-US banks’ US$-denominated foreign claims.”

Source: BOJ, BIS, Macquarie Research, April 2017

-1200

-700

-200

300

800

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Aggregate 1

Aggregate 2

(bn €)

A funding gap,

usually filled

through FX-swaps


18 April 2017 46

When non-US banks extend credit, they need to find dollar funding. Since 2000, there has

always been a funding gap between their US$ assets (claims, loans) and liabilities (Fig 23

left- and right-hand charts) which is usually filled through FX-swaps. Please note:

1) The non-US banks cross-currency funding ratio has been trending up since 2000

2) The funding ratio declines sharply during financial crises – this occurs through the

sudden reduction of claims/loans, acting as an accelerant during the crisis

3) It is important that the FX-swap market functions smoothly (please see the next

page)

Beneath the surface, there have been notable shifts by bank nationality, Fig 97. Most

European banks began to shrink their balance sheets from 2008 (UK banks from 2012),

whilst US banks kept expanding until 2014. Japanese banks continue to expand.

Fig 101 Foreign claims by bank nationality, US$ trillions

Notes: (1) Latest data as at end-June 2016. (2) Euro area claims for German and French banks are excluded

Source: BOJ, BIS, Macquarie Research, April 2017

Unfortunately, the FX swap market is not working smoothly. Fragmentation of the Eurozone’s

financial system along national borders, in combination with regulatory requirements for

banks to reduce their balance sheet leverage ratios, has led to contraction of liquidity in the

money markets. Fig 98 is the US$ 3-month average cross-country basis swap for five leading

currencies in basis points.

Fig 102 US$ 3-month cross-currency basis swap – EUR / JPY/ GBP / CHF / CAD


-60

-50

-40

-30

-20

-10

0

3 mns cross-currency basis swap average bp

US banks kept

expanding until

2014

Japanese banks

continue to expand

Unfortunately, the

FX swap market is

not working

smoothly


18 April 2017 47

Global liquidity & EM credit cycles Global liquidity, as defined by the BIS, is the ease of financing by companies in international

markets.

Global liquidity moves in irregular and powerful waves, appearing to be stronger when the

US$ is depreciating, and weaker when the US$ is appreciating, chart below.

Fig 103 Cumulative flows, gross, in US$-denominated cross-border loans outside the US (US$tr): The dark shaded areas indicate depreciations in the US dollar effective exchange rate

Sources: S Avdjiev, C Koch and H S Shin “Exchange rates and foreign borrowing in international currencies” (2015, in progress); BIS effective exchange rate statistics; BIS locational banking statistics by residence. In addition to cross-border bank loans shown above, the BIS include bond issues in their total debt data

Source: BIS as noted above, Macquarie Research, April 2017

Swings in global liquidity are associated with credit booms and subsequent financial

vulnerability in emerging market economies (EMEs). Into the credit-growth contraction phase

across EMEs, we expect 2016 to see some major debt restructurings, but no cascading

cross-border banking crisis.

This will provide opportunities for well-funded MNCs. Please see the 21 January 2016

Macq-ro insights: The corporate savings “glut” & cross-border M&A.

We believe the EM private non-financial credit cycle peaked with the May-June 2013 ‘taper

tantrum’. However, the following three charts on the next page suggest that this could be

optimistic as private sector credit ratios are still increasing in most EMEs.

The distribution of debt by currency and sectors

We believe that within foreign currency denominated debt, private sector debt is more

vulnerable to destabilization than sovereign debt (the government sector), Fig 74. For more

comprehensive tables, please see the 25 January 2016 Macq-ro insights: Global liquidity.

Fig 104 Key debt ratios, % of GDP in 2015

Singapore Malaysia Thailand Indonesia China Brazil S. Korea

External debt1/GDP NA 40 NA 32 10 31 13

US$-denominated corporate debt2/GDP

NA 6 NA 12 8 9 8

Corporate debt2/GDP 82 64 51 23 157 49 105

Notes: (1): all sectors, all currencies, (2): non-financial corporate debt

Source: BIS, Macquarie Research April 2017

Chinese corporate debt is examined in the next section.

Global liquidity, as

defined by the BIS,

is the ease of

financing by

companies in

international

markets

We believe the EM

private non-financial

credit cycle peaked

with the May-June

2013 ‘taper tantrum’


18 April 2017 48

Fig 105 Private non-financial sector credit as a % of GDP: selected EMEs #1

Note: The BIS does not have data for Nigeria

Source: BIS, Macquarie Research, April 2017

Fig 106 Private non-financial sector credit as a % of GDP: selected EMEs #2

Note: The BIS does not have data for Taiwan. Japan and Korea are included as benchmarks


Fig 107 Private non-financial sector credit as a % of GDP: ASEAN

Note: The BIS does not have data for the Philippines and Vietnam


10

25

40

55

70

85

100 (% of GDP)

Turkey Poland Mexico BrazilArgentina Russia Saudi Arabia

0

50

100

150

200

250(% of GDP) Japan China India Korea

0

40

80

120

160

200 (% of GDP) Indonesia Thailand Singapore Malaysia


18 April 2017 49

Chinese corporate debt Please see Fig 78 on page 35 and Fig 74 on page 45 for context. For more details, please

see Larry Hu’s 8 June 2016 China’s debt: Myths and Realities.

It’s popular to argue that China will run into a debt crisis in either local government debt or

corporate debt. By contrast, our long-held view is that China’s debt problem is very different

from many other countries. The risk of a debt crisis is small because policy-makers could

reshuffle debt among different entities: central government, local government, SOEs and

banks. In 2015, China’s total government debt was RMB37tn, of which RMB27tn was owned

by local governments, Fig 108 and Fig 73). Since last year, China has conducted local

government debt swaps, which essentially converts local government debt to sovereign debt.

Banks also share the costs by swapping loans to bonds, which are safe but offer lower yields.

Fig 108 Corporate leverage is high in China

Fig 109 China’s debt breakdown

Source: CEIC, Macquarie Research, April 2017 Source: CEIC, Macquarie Research, April 2017

In 2015, China’s total corporate debt was RMB106tn, or 157% of GDP. While it’s large, over

70% of China’s corporate debt is owned by SOEs. The discussion on debt/equity swap

suggests that it’s possible to transfer the SOE debt burden to governments and banks.

By doing so, the government also lowers the odds for a corporate debt crisis.

Fig 110 Annual new loans vs. interest payments Fig 111 Leverage ratio: SOE vs. private enterprises


In our view, the real risk behind China’s debt is capital misallocation, as the majority of credit

is poured to the less-efficient SOEs and local governments rather than the private sector. The

debt swap for local governments and SOEs could worsen the problem if it is not designed

well, as it could delay the exit of inefficient firms and worsen the soft budget constraints for

SOEs. Such capital misallocation could undermine China’s potential growth in the long run,

causing the country to become stuck in the so-called ‘middle-income trap’.

0

20

40

60

80

100

120

Non-financial corporate Government Households

RMB tn China's debt breakdown

SOE

Private enterprise

Local govt

Central govt

11 13 14 15 17 21 24 32 40 46 5462 70 76

2 3 3 4 5 6 79

1214

18

2125

30

1 2 2 2 23

4

6

89

10

13

15

19

2 2 2 3 45

5

6

7

7

8

9

10

11

1 2 2 3 45

6

9

11

13

16

20

24

27

0

20

40

60

80

100

120

140

160

180 SOE debt

Private corporate debt

Household debt

Central governmt debt

Local government debt

RMB tn

5

10

87

89

10

12

2 23

44

56 6

0

2

4

6

8

10

12

14

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

Annual new loans

Loan interest payment

RMB tn

50

52

54

56

58

60

62

64

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

SOE

Private enterprise

% Industrial enterprise: Debt-to-assets ratio

Government debt

Corporate debt

Capital misallocation


18 April 2017 50

Fig 112 Gross fixed capital formation, % of GDP, 1970-2015

Source: CAO, World Bank, Macquarie Research, April 2017

During the Asian Financial Crisis, the average step-down in gross capital formation to GDP,

1996-98, for Malaysia, Thailand, Indonesia and Korea was approximately 14%. The catalyst

was a hard-stop by capital providers, cross-border bank lending. The equivalent today in

China would be a hard-stop of capital provision by the household sector, i.e. bank deposits

leaving the Chinese banking system en masse.

Fig 113 Core debt of the non-financial sectors, as a % of GDP

Level in 2014 Change since end-2007 (1)

Households Corporate Government Total Households Corporate Government Total

Advanced economies (2) 73 81 110 265 -7 1 41 36

Australia 119 77 35 230 11 -4 26 33

Canada 93 104 70 267 15 15 19 49

France 56 124 109 288 10 20 43 72

Germany 54 54 82 191 -7 -1 18 10

Italy 43 78 151 272 5 3 46 53

Japan 66 105 222 393 0 5 72 77

Spain 71 111 110 293 -10 -14 74 50

Sweden 83 165 47 295 18 27 7 52

Switzerland 120 91 34 245 13 15 -4 26

United Kingdom 87 77 107 271 -9 -11 61 41

United States 78 69 92 239 -18 -1 39 21

Eurozone 61 103 106 270 1 4 39 45

Emerging market economies (2) 30 94 44 167 10 35 4 50

Argentina (3) 6 9 43 58 2 -1 -1 -1

Brazil (4) 25 49 64 139 12 18 1 32

China 36 157 41 235 17 58 6 82

Hong Kong SAR 66 217 5 287 14 85 3 103

India 9 50 66 125 -1 8 -8 -1

Indonesia 17 23 25 64 6 8 -8 5

Korea 84 105 38 228 12 14 14 40

Malaysia (3) 69 64 53 186 15 4 13 32

Mexico 15 22 33 70 1 7 13 21

Russia (3) 20 58 18 95 8 16 9 33

Singapore 61 82 99 242 22 24 13 59

South Africa 37 33 55 125 -4 -2 23 17

Thailand 69 51 30 151 25 5 7 37

Turkey 21 33 34 108 9 28 -8 30

Notes: (1): in percentage points of GDP. (2): weighted average of the economies listed based on rolling GDP and PPP exchange rates. (3): breakdown of household and corporate debt is estimated based on bank credit data. Please note that Government debt data is the BIS core debt (credit to the government) at market values except for countries where only nominal values are available.


10

15

20

25

30

35

40

45

50 (% of GDP) Thailand Malaysia Indonesia Korea

The Asian

Financial Crisis


18 April 2017 51

Oil & sovereign defaults The last great collapse in oil and commodity prices from the end of the 1970s led to a

decade-long wave of sovereign defaults, below. The defaults data is available both by creditor

and defaulting country, below.

Fig 114 Sovereign debt default as a share of world public debt and world GDP

Source: CRAG, Macquarie Research, April 2017

Fig 115 Total sovereign debt in default, by creditor

Source: CRAG, Macquarie Research, April 2017

In the 1980s, there was a reluctance to address the fiscal deficits that resulted from less tax

revenues from lower oil and commodity prices. Fifteen countries saw an increase of public

debt to GDP in excess of 40%. With global capital markets up until 1980 consisting largely of

fixed exchange rates and national controls on capital flows, falling exports led to current

account deficits and persistent forex reserves erosion, Fig 116.

Fig 116 Foreign exchange reserves, in US$, the 1980s and now

1980 forex reserves

as a % of GDP Decline in forex reserves,

start 1980 to 1980s low End-2015 reserves

as a % of GDP

Forex reserves to GDP difference:

2015 vs 1980

Nigeria 4.9 -93% 5.9 1.0 South Africa 8.2 -79% 14.4 6.2 Argentina 3.9 -77% 4.4 0.5 Saudi Arabia 10.1 -66% 98.6 88.5 Brazil 5.8 -64% 19.8 14.0 Colombia 9.1 -64% 17.0 7.9 Indonesia 4.9 -38% 12.1 7.3 Malaysia 14.3 -23% 27.7 13.4 Venezuela 10.4 -15% 12.4 2.0

Note: The above does not include an analysis of forward book positions, which is important for sum including Brazil. Source: IMF/Oxford Economics/Haver, Macquarie Research, April 2017

0

1

2

3

4

5

6

71980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

(%) % of World Public Debt % of Gross World Product

0

100

200

300

400

500 US$bnIMF World Bank Paris Club Other official creditorsPrivate creditors FC Bank Loans FC Bonds LC Debt

Falling exports led

to current account

deficits and

persistent forex

reserves erosion

A decade-long wave

of sovereign

defaults


18 April 2017 52

Whilst this time, greater exchange rate flexibility is absorbing some of the risk related to

capital flight, Fig 117, cross-border capital account flows are enormously more important.

The decade from 2011

For countries with a high export dependency on oil and commodities, the decade from 2011

could evolve as follows:

1) A major fall in export revenues leading to a current account deficit, and the policy

decision to allow the currency to depreciate.

2) The latter could be of the order of 30-50%, chart below, causing a significant

increase in inflation, and the need for nominal local interest rates to increase.

Fig 117 Emerging Market currencies (nominal)

Note: Latest data point: 13 April 2017


3) Initially, there is a reluctance to increase policy interest rates rapidly given concerns

for economic growth, but capital outflows and an increase in sovereign risk spreads

lead to a tightening of monetary policy.

4) The fiscal position deteriorates with a fall in oil- and commodity-related taxes, and the

weakening economy. The initial reluctance to cut public expenditures is reinforced by

the tightening of monetary policy’s negative impact on economic growth, and the

escalating debt service costs.

At this point, fiscal deficits become persistent.

The ratio of public debt to GDP begins to trend higher.

The following formula gives the primary budget balance (excludes interest expense)

necessary for public debt/GDP stabilization:

(Bond yields minus nominal GDP growth) x (Public debt/GDP)

For example, 10% bond yields, nominal GDP growth of 5% and a public debt/GDP ratio of 50%

would require a primary budget balance of 2.5% of GDP (a surplus).

As the public debt/GDP ratio rises over time, then the required primary budget balance

becomes bigger, e.g. at 100%, not 50%, the required primary balance would be a surplus of 5%

of GDP, not 2.5% of GDP.

The last great collapse in oil and commodity prices from the end of the 1970s led to a

decade-long wave of sovereign defaults.

We believe another wave is coming, involving multiple debt restructurings over

many years.

60

80

100

120

1999 2001 2003 2005 2007 2009 2011 2013 2015

Emerging Market Currencies (nominal)

Fiscal deficits

become persistent


18 April 2017 53

What’s next for EM economies? An extended period of subdued growth: The major six emerging market economies’

aggregate real GDP growth is expected to trend around 4.6% pa 2017-20, some 2.8% pa

less than the 7.4% pa achieved over 2003-11.

Versus the ten advanced economies, the real GDP growth premium was +5.2% pa over

2003-11, falling to an expected +2.9% over 2013-18. Contributions from all three growth

accounting factors (labour supply, capital accumulation, and total factor productivity)

are fading.

Fig 118 Seven headwinds for emerging market economies since 2011

1) The headwind from falling commodity prices since 2011

2) The turn in the EME credit cycle post the 22 May 2013 to 28 June 2013 ‘Taper tantrum’

3) The deceleration in global industrial production growth since early 2014 has led to a fading of the export growth engine

4) The US$, which had been appreciating gradually since mid-2011, rose rapidly from mid-2014 through end-2015

5) Oil prices breaking decisively through US$100 from July 2014

6) Declining import volumes into China since early 2015 partially reflecting import substitution. Whilst not a net negative for EM economies initially, second round effects such as the tightening of policy by countries suffering deteriorating trade balances is a negative.

7) The start of a US Fed Funds policy interest rate up-cycle since December 2015, which puts upward pressure on nominal local rates in fixed/semi-fixed exchange rate regimes

Note: Remembering the good times: The 2009-11 commodity price boom, a weak US$ facilitating access to global capital markets and strong credit growth had enabled rapid growth to persist across the EM economies. However, over the past five years, conditions have become progressively tougher


Emerging market economies (EME) have been battered by seven negatives since 2011, table

above. Whilst some of these are abating, two new headwinds worry us: falling property values

with the credit down-cycle, and declining FDI inflows as the elevated global investment-to-GDP

ratio adjusts.

These are headwinds for consumption and investment, respectively.

Fig 119 Real residential property prices, YoY growth

Note: Housing price movements are deflated by the CPI. Aggregates are weighted by GDP in current US$. Advanced consists of 10 advanced countries (US, Japan, Germany, France, Italy, Spain, UK, Aust, Canada and South Korea) and emerging consists of 6 emerging countries (Brazil, Mexico, Russia, Turkey, India and China). 16-Total is the sum of advanced and emerging countries. ASEAN consists of 5 ASEAN countries (Indonesia, Malaysia, Singapore, Philippines and Thailand).

Source: BIS, IMF, Macquarie Research, April 2017

-12

-9

-6

-3

0

3

6

9

12

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

(%) Advanced Emerging 16-Total ASEAN

Two new headwinds

worry us:

1) Falling

property

values with

the credit

down-cycle

2) Declining FDI

inflows as the

elevated global

investment-to-GDP

ratio adjusts

These are

headwinds for

consumption and

investment,

respectively


18 April 2017 54

Global private investment indicators This is a six-page section, as we believe that there is widespread misunderstanding about the

existing elevated level of global private investment. Given the relative volatility of private

investment, this implies that it is the major downside risk to global growth.

Lead indicators of capital expenditure

We are unaware of any composite global machinery order indicators. We present indicators

for the US, China, Germany and Japan. The overall impression is one of softness.

We believe Germany is the key series to follow, Fig 122, below (bottom), for the reasons

shown in Fig 120 and Fig 121.

On the basis of exports as a percentage of that country’s GDP, Germany is the most exposed

to emerging markets, at roughly 11.0%, Fig 120. Japan has the highest exports to GDP ratio

to China but emerging markets in total are just 7%. The US is approximately 5%.

Fig 120 Emerging Market export exposures Fig 121 Germany: Goods exports to China

Source: Oxford Economics/Haver Analytics, Macquarie Research, April 2017

Source: Oxford Economics/Haver Analytics, Macquarie Research, April 2017

Fig 122 shows trends in Germany, which are currently a little beneath zero.

Fig 122 Germany VDMA plant & machinery orders, 1999 to latest

Source: VDMA, Bloomberg, Macquarie Research, April 2017

Fig 123 is the well-known US non-defence capital goods orders (excluding aircraft). The case

for excluding aircraft orders relates to both the lumpiness of orders historically and the long

lag between order receipt and the start of work on such orders.

Energy and other commodity-related capital expenditures are expected to stay weak, but the

fall is almost complete, in our opinion.

-40%

-30%

-20%

-10%

0%

10%

20%

30%Germany Plant&Machinery Orders(YoY%, 3mma)

The overall

impression is one of

softness


18 April 2017 55

Fig 123 US non-defence capital goods orders (excluding aircraft), 2000 to latest

Source: US Census Bureau, Macquarie Research, April 2017

Japanese machinery orders are broadly stable to soft; Fig 124 and Fig 125.

For Larry Hu’s recent insights on the China business cycle, please see his Macro Monday

reports from which Fig 126 and Fig 127 come.

Fig 124 Japanese machinery orders: total (¥tr) Fig 125 ...and by geography (¥tr)

Source: CAO, Macquarie Research, April 2017 Source: CAO, Macquarie Research, April 2017

Fig 126 Chinese fiscal spending Fig 127 Chinese fixed asset investment


-30

-25

-20

-15

-10

-5

0

5

10

15

20 (YoY%, 3mma) Non-defence capital goods orders, ex aircraft

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6

2.8

3.0

04/0

5

10/0

5

04/0

6

10/0

6

04/0

7

10/0

7

04/0

8

10/0

8

04/0

9

10/0

9

04/1

0

10/1

0

04/1

1

10/1

1

04/1

2

10/1

2

04/1

3

10/1

3

04/1

4

10/1

4

04/1

5

10/1

5

04/1

6

10/1

6

(¥tr)Machinery orders (total, SA)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

04/0

5

11/0

5

06/0

6

01/0

7

08/0

7

03/0

8

10/0

8

05/0

9

12/0

9

07/1

0

02/1

1

09/1

1

04/1

2

11/1

2

06/1

3

01/1

4

08/1

4

03/1

5

10/1

5

05/1

6

12/1

6

Domestic Overseas

(¥tr)


18 April 2017 56

The following charts from the IMF include residential investment; 2015-16 are IMF forecasts

Please note that at the global level, private investment is already at elevated levels.

Fig 128 World investment (US$tr) Fig 129 World investment (% of current GDP)

Source: IMF WEO database, Macquarie Research, April 2017 Source: IMF WEO database, Macquarie Research, April 2017

Fig 130 Advanced economies’ investment (US$ tr)

Fig 131 Advanced economies’ investment (% of current GDP)


Fig 132 US investment (US$tr) Fig 133 US investment (% of current GDP)


0

5

10

15

20

25

1980

1982

1984

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1990

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2008

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(US$tr) World investment

20

21

22

23

24

25

26

27

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

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98

20

00

20

02

20

04

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20

08

20

10

20

12

20

14

20

16

(%)World investment, % of GDP

0

2

4

6

8

10

12

1980

1982

1984

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1988

1990

1992

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2004

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(US$tr) Advanced economies, investment

15

17

19

21

23

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27

1980

1982

1984

1986

1988

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1994

1996

1998

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2002

2004

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(%) Advanced economies, investment, % of GDP

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

1980

1982

1984

1986

1988

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1996

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2004

2006

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(US$tr) US's investment

10

12

14

16

18

20

22

24

26

1980

1982

1984

1986

1988

1990

1992

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1996

1998

2000

2002

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2006

2008

2010

2012

2014

2016

(%GDP) US's investment, % of GDP


18 April 2017 57

Anxiety normally revolves around China, which is the difference between a currently strong

level of world investment activity, Fig 129, and the depressing picture presented in Fig 131.

As Fig 132 and Fig 133 show, the IMF has positive 2015-16 growth forecasts for the US.

Fig 134 World investment by economic groups

Fig 135 World investment by economic groups (% of current GDP)


Fig 136 Emerging & developing economies’ investment (US$tr)

Fig 137 Emerging & developing economies’ investment (% of current GDP)


Fig 138 China’s investment (US$tr) Fig 139 China’s investment, % of GDP


0

5

10

15

20

25

1980

1982

1984

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(US$tr) Emerging and developing economies investment

Advanced economies, investment

15

17

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27

29

31

33

35

1980

1982

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(%) Advanced economies, investment, % of GDP

Emerging and developing economies investment, % of GDP

0

2

4

6

8

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12

1980

1982

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(US$tr) Emerging and developing economies investment

20

22

24

26

28

30

32

34

1980

1982

1984

1986

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1990

1992

1994

1996

1998

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2002

2004

2006

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2010

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2014

2016

(%)

Emerging and developing economies investment, % of GDP

0.0

1.0

2.0

3.0

4.0

5.0

6.0

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

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2006

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2010

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(US$tr) China's investment

25

30

35

40

45

50

1980

1982

1984

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1994

1996

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2004

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2008

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2012

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2016

(%GDP) China's investment, % of GDP


18 April 2017 58

Whilst China is still growing in absolute terms (Fig 138, previous page), a slow rebalancing of

the economy is underway (Fig 139). EM ex China has been stagnant, 2011-15, Fig 140.

Fig 140 Emerging and developing economies ex China investment

Fig 141 Emerging and developing economies ex China investment, % of GDP


Fig 142 India’s investment Fig 143 India’s investment, % of GDP


Fig 144 Brazil’s investment Fig 145 Brazil’s investment, % of GDP


0.0

1.0

2.0

3.0

4.0

5.0

6.0

1980

1982

1984

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1994

1996

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2008

2010

2012

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2016

(US$tr) Emerging and developing economies ex China

15

17

19

21

23

25

27

29

1980

1982

1984

1986

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1996

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2000

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2004

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2016

(%) Emerging and developing economies ex China, % of GDP

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

(US$tr) India's investment

15

20

25

30

35

40

45

1980

1982

1984

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2002

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2016

(%GDP) India's investment, % of GDP

0.0

0.1

0.2

0.3

0.4

0.5

0.6

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

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2004

2006

2008

2010

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2016

(US$tr) Brazil's investment

10

12

14

16

18

20

22

24

26

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

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2006

2008

2010

2012

2014

2016

(%GDP) Brazil's investment, % of GDP


18 April 2017 59

Fig 146 Russia’s investment Fig 147 Russia’s investment, % of GDP


Fig 148 Indonesia’s investment Fig 149 Indonesia’s investment, % of GDP


Fig 150 Investment by World ex China

Fig 151 Investment by World ex China, % of GDP


0.0

0.1

0.2

0.3

0.4

0.5

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

(US$tr) Russia's investment

10

15

20

25

30

35

40

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

2018

2020

(%GDP) Russia's investment, % of GDP

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

(US$tr) Indonesia's investment

0

5

10

15

20

25

30

35

40

45

501980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

(%GDP) Indonesia's investment, % of GDP

0

4

8

12

16

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

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20

00

20

02

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12

20

14

20

16

(US$tr)

Investmet by World ex China

18

19

20

21

22

23

24

25

26

27

19

80

19

82

19

84

19

86

19

88

19

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19

92

19

94

19

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19

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20

00

20

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06

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08

20

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12

20

14

20

16

(% GDP)

Investment by World ex China, % of GDP


18 April 2017 60

FDI, following the smart money Gross capital flows to EM economies are likely to remain under pressure during the US Fed’s

tightening cycle. Macquarie forecasts the latter to last into 2019. Contracting since 1Q 2015,

the blue portion in Fig 152, is a proxy for the global liquidity cycle. The red portion is FDI, and

we expect a 40% shrinkage in total global FDI flows from the 2015 high through 2018.

Fig 152 Emerging Market economies: Gross capital flows

Note: Other investment includes cross-border bank lending and international debt issues

Source: Oxford Economics, Haver Analytics, Macquarie Research, April 2017

The 11 January 2017 Macq-ro insights: following the new smart money identified four key FDI

trends that are expected to persist over the next three years:

1) A 40% shrinkage of inward FDI from the 2015 high. UNCTAD estimates that 2016

saw a 10-15% decline

2) Relative strength in flows to advanced economies versus EM economies

3) Pronounced strength in flows to India versus China, reflecting absolute growth and

material YoY declines respectively

4) Pronounced weakness in FDI flows to commodity-exporting economies

Fig 153 FDI flows, total and greenfield only for the US and China, US$bn

Note: Inward FDI data is Oxford Economics and the greenfield data series is from UNCTAD.

Source: UNCTAD, Oxford Economics, Macquarie Research, April 2017

-2%

0%

2%

4%

6%

8%

10%

1995 1998 2001 2004 2007 2010 2013 2016

Portfolio investmentOther investmentDirect investment

% of combined GDP

0

50

100

150

200

250

300

350

400 US Inward FDI China Inward FDI

US Greenfield China Greenfield(in bnUS$)

The new smart

money is

disengaging from

China


18 April 2017 61

Local factors determine core inflation One popular perspective holds a global view of inflation – that global slack determines local

inflation. However, there is significant evidence that local conditions drive underlying inflation.

For example, Fig 154 shows that divergence in CPI-services for the US and the Eurozone.

Of personal consumption expenditures in advanced economies, three-quarters is on services.

Fig 154 A dramatic divergence between the US and Euro area services inflation


We believe Fig 155 explains the confusion. Currently, the average pair-wise correlation on

core inflation measures is near zero, and near 40-year lows. At the same time, the average

pair-wise correlation on headline inflation measures is as elevated today as during the first oil

shock of the early 1970s. From late 2014 until February 2016 oil prices plunged 80%.

Fig 155 International synchronisation of inflation (headline and core)

Notes: From Bank of England Governor, Mark Carney’s speech at the 2015 Jackson Hole symposium: Inflation in a globalised world. Average pair-wise rolling (15 years) correlation of seasonally-adjusted quarterly headline and core inflation. Source: OECD, Global Financial Data, DataStream, National sources, and Bank Staff calculation

Source: BoE, Macquarie Research, April 2017

We believe that core inflation is driven locally, through the influence of local output gaps and

the slack in the local labour market.

The US: The CPI services for the US, shown in Fig 154, reflects the acceleration in US wage

growth, following six years of employment growth above labour force growth, and the

unemployment rate approaching full employment.

The Eurozone: Conversely, the tightening of fiscal policy in the Eurozone over 2011-13 and

a second credit crunch there during 2011-12 leaves the Eurozone approximately four years

behind the US in the business cycle. Nonetheless, we are forecasting accelerating Eurozone

inflation from 0.0% in 2015, to 0.5% this year, and to 1.5% in 2017.

0%

1%

2%

3%

4%

5%

1998 2000 2002 2004 2006 2008 2010 2012 2014 2016

1998-2011 correl = +0.47 2012-16 correl = -0.55

Services CPI

US

Euro area

We believe that core

inflation is driven

locally, through the

influence of local

output gaps and the

slack in the local

labour market

Of personal

consumption

expenditures in

advanced

economies, three-

quarters is on

services


18 April 2017 62

Income shares: from labour to capital To the benefit of capital, there has been a 5% decline in the labour share globally since 1980,

Fig 156.

We believe the main driver has been technology, facilitated by globalisation with MNCs as the

main agents of change. As a consequence, MNCs have been the major beneficiaries.

Fig 156 Declining global labour share: 0.65 means 65%

Note: Loukas Karabarbounis & Brent Neiman, The global decline of the labour share, June 2013. “The figure shows year fixed effects from a regression of corporate and overall labour shares that also include country fixed effects to account for entry and exit during the sample. The regressions are weighted by corporate gross value added and GDP measured in U.S. dollars at market exchange rates. We normalize the fixed effects to equal the level of the global labour share in our dataset in 1975”

Source The global decline of the labour share above, Macquarie Research, April 2017

MNCs are a global macro dimension needing attention. The last 30 years have seen the

emergence of global oligopolistic competition. Following waves of M&A activity, the top MNCs

now dominate their global markets, entrenching their position by undertaking the vast proportion

of R&D in their industries. The result is the control of leading technologies and brands.

We believe one consequence of global oligopolistic competition is to limit the diffusion of new

techniques and labour productivity growth across the economy.

Fig 157 Labour productivity growth: the issue of diffusion

Note: Each line shows the average labour productivity (value added per worker). The “Top 5%” and “Top 100” are the globally most productive firms in each two-digit industry. “Non-frontier firms” is the average of all firms, excluding the Top 5%. Included industries are manufacturing and business services, excluding the financial sector. The coverage of firms in the dataset varies across the 24 countries in the sample and is restricted to firms with at least 20 employees. Source: OECD preliminary results based on Andrews, D., C. Criscuolo and P. Gal (2016), “Mind the Gap: Productivity Divergence between the Global Frontier and Laggard Firms”, OECD Productivity Working Papers, forthcoming; Orbis data of Bureau van Dijk Source: OECD, Macquarie Research, April 2017

We believe one

consequence of

global oligopolistic

competition is to

limit diffusion of

new techniques,

labour productivity

growth across the

economy

The last 30 years

have seen the

emergence of global

oligopolistic

competition

We believe the main

driver has been

technology,

facilitated by

globalisation with

MNCs as the main

agents of change

There has been a

5% decline in the

labour share

globally since 1980


18 April 2017 63

The corporate savings ‘glut’ Savings-Investment balances, net lending/borrowing by sector, the net acquisition of financial

assets (NAFA) are terms used interchangeably in the literature.

The corporate sector in most advanced economies is running a savings-investment surplus.

We believe this is an unintended by-product of the monetary policy response to credit bubble/

busts that transfers income from the household sector to the corporate sector: the lowering of

policy interest rates to near zero and the flattening of the yield curve.

The corporate sector in Japan is a representative example. Japan’s data go back to 1994,

just far enough to capture the swing from net investing (borrowing) to net saving (lending),

below. Looking at the numbers:

The ¥35.4tr swing over 1994-2013 from annual net borrowing (-¥2.0bn) to net lending

(¥33.4bn, pale red highlights) compares to reduced interest payments of ¥28.6tr (from

¥32.2bn to ¥3.6bn, blue highlights).

We’ve also highlighted (in orange) how the surplus of gross fixed capital formation over

consumption of fixed capital has shrunk from ¥11.9tr in 1994 to ¥2.0tr in 2013.

In turn, this narrowing of ¥9.9tr reflects a gradual upward drift in the consumption of fixed

capital charge (¥2.7tr) and a marked fall in gross fixed capital formation (¥7.2tr).

Dividend payments increased from ¥4.6tr in 1994 to ¥15.1tr in 2013, for an increase over the

period of ¥10.5tr.

So rather than paying interest, non-financial corporations have paid down net debt.

Rather than growing their fixed capital base, non-financial corporations have paid out

dividends.

Fig 158 Non-financial corporations’ flow of funds (¥tr), 1994 to latest available (2013)

CY 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Gross fixed capital formation 72.9 74.5 75.6 81.1 74.7 70.6 74.2 71.0 64.6 65.3 66.3

Consumption of fixed capital 61.0 59.5 60.2 63.3 64.9 64.0 63.7 63.6 63.3 61.8 61.8 changes in inventories -1.4 1.6 2.2 2.5 1.4 -3.5 -0.5 -0.1 -1.9 -0.4 1.7 purchases of land, net -7.7 3.9 -3.1 0.5 -0.2 -0.3 9.3 5.7 -4.9 3.7 -1.8 Net lending/net borrowing -2.0 -13.7 -2.5 -8.3 22.3 9.3 3.4 3.8 28.4 21.5 29.8 Net saving -3.7 1.7 6.7 8.3 5.2 11.7 17.3 13.2 19.7 24.0 31.4 Net capital transfers 4.5 5.0 5.2 4.3 28.1 0.4 5.6 3.7 3.3 4.3 2.7 Interest payable 32.2 28.8 23.8 21.9 17.3 13.9 12.1 9.4 7.0 6.3 5.3 Dividends

4.6 5.1 5.2 5.2 5.0 5.6 4.8 5.3 7.1 6.8 9.3

CY

Gross fixed capital formation 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Consumption of fixed capital 66.3 69.2 73.4 76.9 74.8 62.2 61.2 62.6 64.8 65.3 66.3 changes in inventories 61.8 62.2 64.4 66.5 68.6 67.7 65.0 63.4 62.8 61.8 61.8 purchases of land, net 1.7 0.5 -0.5 1.7 2.9 -5.2 1.1 -1.7 -0.9 -0.4 1.7 Net lending/net borrowing -1.8 -8.7 -0.1 5.1 3.8 5.3 -0.6 5.8 0.2 3.7 -1.8 29.8 32.0 17.7 13.1 12.5 29.9 39.3 27.4 33.5 21.5 29.8 Net saving Net capital transfers 31.4 28.0 24.0 28.1 23.3 22.1 33.0 27.2 31.6 24.0 31.4 2.7 2.8 2.1 2.1 2.1 2.5 3.0 3.5 3.3 4.3 2.7 Interest payable Dividends 5.3 4.6 4.9 5.8 5.1 4.8 4.7 4.2 3.7 6.3 5.3

Source: CAO, ESRI, CEIC, Datastream, Macquarie Research April 2017

The corporate savings ‘glut’: variations by geography

Fig 159 presents the US data. In the case of the US, below, the weakness of investment post

the TMT Bubble (1998-2001) led to a small corporate surplus over the years 2002-05. The

post Global Financial Cycle corporate Saving-Investment surpluses, however, have been

much more substantial, 2-3% versus 1%. The corporate surplus calculation used in Fig 159 is

net of both dividend payments and share buy-back activity.

The corporate

sector in most

advanced

economies is

running a savings-

investment surplus

… an unintended

by-product of the

monetary policy

response to credit

bubble/busts that

transfers income

from the household

sector to the

corporate sector

The US


18 April 2017 64

Fig 159 Net lending of US non-financial corporations

Note: Savings (the blue line) is calculated as the undistributed profits of non-financial corporations, that is after-tax profits less dividends to shareholders. Investment (the red line) represents spending by non-financial companies on capital formation. Any excess of savings over investment represents net lending to the rest of the economy, the black bars at the bottom of the charts. More precisely:

Source: Gruber, Joseph W., and Steven B. Kamin (2015). The Corporate Saving Glut in the Aftermath of the Global Financial Crisis International Finance Discussion Papers 1150, Macquarie Research, April 2017

Japan is as has been indicated earlier: A rapid growth in gross saving since the 1987-1991

bubble-bust, and a trend decline in gross investment. The trend decline in investment in

Japan began a decade earlier in Japan than in the USA (1991 versus 2001). The post-GFC

corporate Saving-Investment surplus has averaged 5%.

Germany has seen similar trends, a trend increase in gross savings, a trend decrease in

gross investment, but the latter could be described two ways: either as a very mild downtrend

since 1991, or broadly sideways until the Global Financial Crisis, and then a step-decline

thereafter. However, the German corporate sector surplus is less than 1%.

The trends are far from uniform. Canada has seen a trend improvement in gross savings,

but gross investment has been broadly stable. The UK does not appear to have experienced

a trend improvement in gross savings, but a trend decline in gross investment has occurred.

Both Canada and UK do have corporate sector surpluses, of 1-2%.

However, since 1990, the corporate sectors in both France and Italy have had persistent

deficits of 2-3%. As a consequence, a weighted average of the corporate Saving-Investment

balances for the three euro-zone majors of Germany, France and Italy is mildly negative.

We believe the above is facilitating global cross-border M&A, which in 2015 was 40% above

its previous high of 2007. We expect 2016 to be another year of intense cross-border M&A,

with the trend towards global oligopolistic competition amongst MNCs continuing.

Fig 160 Total & cross-border M&A, 2007 to latest

Total M&A (US$bn) Cross-border M&A (US$bn) % of cross-border M&A to

total M&A Global Japan Global Japan Global Japan

2007 2391.6 111.7 1050.3 52.4 43.9 46.9 2008 1398.9 138.2 569.4 87.1 40.7 63.1 2009 1033.3 85.2 286.5 38.4 27.7 45.1 2010 1369.9 82.0 551.6 51.7 40.3 63.0 2011 1465.8 139.1 556.2 94.5 37.9 67.9 2012 1405.4 123.5 598.8 89.6 42.6 72.5 2013 1489.5 78.1 468.0 62.1 31.4 79.6 2014 2400.9 71.9 1075.4 58.3 44.8 81.2 2015 3429.4 127.3 1298.0 102.8 37.9 80.7

2016 3838.3 143.5 1971.1 115.3 51.4 80.4

Note: Data for Japan from RECOF. Cross-border for Japan is both In-Out and Out-In.

Source: FactSet, RECOF, Macquarie Research, April 2017

Japan

Germany

Canada, U.K.

France and Italy

We expect 2016 to

be another year of

intense cross-

border M&A


18 April 2017 65

Global income distribution Income gains have varied markedly over the 20 years since 1988, Fig 161. The relative winners

have been ‘the top 1%’ and the one-fifth of the world’s population in the 40th to 60th percentile

range. In nine out of ten cases, the latter live in the emerging Asia economies of China, India,

Thailand, Vietnam and Indonesia, and are around the median income earners in their countries.

Whilst they are often referred to in the media as the “emerging global middle class” they are still

economically below the lower middle class of the rich nations, as commonly understood.

Fig 161 Relative gain in real per capita income by global income level, 1988-2008

Note: World Bank Economic Review, Lakner, Christoph & Branko Milanovic, 2015, Global income distribution: From the fall of the Berlin Wall to the great Recession. The following notes come from the book Global inequality by Branko Milanovic:

This graph shows relative (percentage) gain in household per capita income (measured in 2005 international dollars) between 1988 and 2008 at different points of the global income distribution (ranging from the poorest five percentiles, 0 to 5, to the richest global percentile, at 100). Real income gains were greatest around the 50th percentile of the global income distribution (the median; at point A) and among the richest (the top 1%; at point C). They were lowest among people who were around the 80th percentile globally (point B), most of whom are in the lower middle class of the rich world

Source: World Bank above, Macquarie Securities, April 2017

The relative losers are the lower middle class of the rich nations, who cluster around the 80th

percentile, point B above. As a consequence estimates of global income distribution have

shown declined inequality, but increased inequality in advanced economies such as the US.

Please note that US inequality is currently much lower than global inequality.

Fig 162 Global and US inequality, 1820-2011

Note: This graph shows global and US income inequalities (calculated across world and US citizens, respectively). In the recent period, global inequality is decreasing while US inequality is going up.

Sources: Milanovic Branko: Global inequality, page 124, with the 2003, 2008 and 2013 estimates and the 2035 forecast for global inequality from the paper The future of worldwide income distribution, April 2015, by Tomas Hellebrandt and Paolo Mauro

Source: Various as noted above, Macquarie Research, April 2017

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

Cum

ulat

ive

gain

in re

al i

ncom

e (in

%)

Ventile/percentile of global income distribution

A

B

C

30

35

40

45

50

55

60

65

70

75

1800 1850 1900 1950 2000 2050

US inequality Global (B-M series) Global (L-M series) Global (H-M series)

The relative winners

have been ‘the top

1%’ and the one-fifth

of the world’s

population in the

40th to 60th

percentile range

The relative losers

are the lower middle

class of the rich

nations, who cluster

around the 80th

percentile

As a consequence

estimates of global

income distribution

have shown

declined inequality,

but increased

inequality in

advanced

economies such as

the US

Please note that US

inequality is

currently much

lower than global

inequality


18 April 2017 66

Demographic Tectonics

Fig 163 Key Demographic Tectonic reports so far have included:

a) China – A V-shaped age profile

b) ASEAN – The rise of ASEAN affluence

c) Japan – Aging Japan it’s wonderful (the Affluent Elderly)

d) India – The young and restless

e) Global consumption – Global implications for the apparel & footwear industries


To take the example of the Affluent Elderly, for those that have prepared well for retirement,

this demographic cluster has both the cash-flow and the time to be a prime consumer. One

example is Japan. The Affluent Elderly are approximately 20% of Japan’s population, and

30% of annual consumption. Their total income in 2015, US$1tr, makes them equal to about

half the size of Italian GDP, and unlike Italy, Japan’s Affluent Elderly is still a growth story.

Fig 164 Japan’s Affluent Elderly: a massive market that will continue to grow

Persons, by household head age and income bracket (millions) 2005 2015 2025 2025 vs 2015

65+ with income $50,000 to $120,000 13.3 18.4 20.0 +1.6

65+ with income $120,001 plus 3.0 5.0 5.7 +0.7

Affluent Elderly households’ total 16.3 23.4 25.7 +2.3

Affluent Elderly households’ total

household income (US$ billions) 629 1,022 1,223 +201

Note: The total income of the above affluent elderly in 2015 at US$1tr makes them equal to about half the size of Italian GDP. Source: Global Demographics Ltd, Macquarie Research, April 2017

This group are consumers of premium services and goods: a) luxury brands, b) increased

quality of living through the progressive upgrading in the unit size of dwellings and the overall

building and furnishing standards; and c) increased wellness & health expenditures –

increased expenditures on wellbeing, covering everything from skin care and nutritional

supplements through to medical expenses.

The global labour supply

This is a demographic headwind.

Fig 165 Global changes in working age populations (2025 less 2015)

Note: To calculate working age population we have used the definition of 15-64 years for economies including Africa, Central & South America, Developing Asia, India, North Africa & Middle East and South Asia. For the rest, we have used the definition of 15-74 years to calculate working age population, as the life expectancy in these countries is higher.

Source: Global Demographics, Macquarie Research, April 2017

100

5344 39 35

2814

1 0

-1 -5

-29

-60

-20

20

60

100

140

India

Nort

h A

fric

a&

Mid

dle

Ea

st

Afr

ica

Develo

pin

g A

sia

South

Asia

Centr

al

& S

outh

Am

erica

No

rth

Am

eri

ca

We

ste

rn E

uro

pe

Rus

sia

Easte

rn E

uro

pe

Aff

lue

nt

As

ia

Chin

a

Persons mn

Affluent Elderly

demographic

opportunities


18 April 2017 67

The world’s working age population is now growing quite modestly, Fig 165. An extra 260m

over a decade on 4.6bn is approximately 0.5% pa. Please note the divergence in working age

populations, 2025 versus 2015, for India and China.

Fig 166 shows the impact of labour retirement ages.

Fig 167 shows the ongoing fade in growth of the working population in EM economies.

Fig 166 Global population & labour supply Fig 167 Annual growth in the working population

Note: from the December 2015, Bank of England staff working paper No. 571, Secular drivers of the global real interest rate by Lukasz Rachel and Thomas D Smith. Black lines shows growth in the global working aged 20 to retirement. Retirement ages are calculated using OECD data on average effective retirement ages. Over the future, global retirement ages are assumed to grow by one and a half years every decade to keep pace with expected rises in longevity

Source: UN population projections, OECD, Macquarie Research, April 2017

Source: Oxford Economics, Macquarie Research, April 2017

The US labour supply

One of David Doyle’s key themes has been lower potential growth for the United States, of

around 1.4 pa, with US labour force growth only able to provide 0.4% pa of that over the long-

term. Combining shifting demographic trends with current participation rates allows David to

compute a measure “breakeven jobs growth”. This is the number of jobs an economy needs

to create in order to keep unemployment from rising. For 2020, for the US, this is only 45,000

per month: 8 March 2017 Fortress America: Demographics, lower potential, and the Fed.

Fig 168 The downward trend in breakeven jobs growth is poised to resume

Source: Bureau of Labour Statistics, Macquarie Research, April 2017

0

20

40

60

80

100

120

140

160

180

Jan-86 Jan-90 Jan-94 Jan-98 Jan-02 Jan-06 Jan-10 Jan-14 Jan-18 Jan-22

US monthly "breakeven" jobs growth (five year average) (thousands)

Note: This analysis holds constant current (Jan-17) age group participation rates over time and assumes a 4.5%

unemployment rate

projection

US potential growth

of around 1.4 pa,

with US labour force

growth only able to

provide 0.4% pa of

that over the long-

term


18 April 2017 68

The charts below extend the analysis to Europe, Japan and China.

We believe the trends have largely gone unnoticed in recent years because of spare capacity

that was created in the economic downturn of 2007-9. But as that slack has nearly dissipated,

particularly in the US, it means that economies are likely to confront the issue of very weak (if

not negative) labour force growth in coming years.

While US labour force growth will be low (+0.4% per annum), the dynamics are even more

severe in other major economies.

Without a jump in age group participation rates, labour forces in will shrink through 2030:

1) Japan (-0.6% p.a.)

2) Europe (-0.5% p.a.)

3) China (-0.1% p.a.)

Fig 169 US breakeven jobs growth has been nearly cut in half since 2010 and is set to fall further ...

Fig 170 … the decline in breakeven jobs in China is poised to become more severe

Source: United Nations, Macquarie Research, April 2017 Source: United Nations, Macquarie Research, April 2017

Fig 171 In Europe, breakeven jobs growth is set to move increasingly into negative territory

Fig 172 … while in Japan it has stabilized (albeit in negative territory)

Source: United Nations, Macquarie Research, April 2017 Source: United Nations, Macquarie Research, April 2017

113

149

121

110

75

60

41

31

0

20

40

60

80

100

120

140

160

180

1990-95 1995-00 2000-05 2005-10 2010-15 2015-20 2020-25 2025-30

US - monthly breakeven jobs growth (estimate in thousands)

Note: Assumes constant age group participation rates over time (Jan-17 US

rates)

954895

988

647

396

38

-138

-281

-500

-300

-100

100

300

500

700

900

1100

1990-95 1995-00 2000-05 2005-10 2010-15 2015-20 2020-25 2025-30

China - monthly breakeven jobs growth (estimate in thousands)

Note: Assumes constant age US age group participation rates have been used, but have

been upwardly adjusted by 12.5% (the amount that China's 15 & over participation rate exceeds the US 15 & over participation rate)

98

73 73

50

-49

-76

-101-113

-150

-100

-50

0

50

100

150

1990-95 1995-00 2000-05 2005-10 2010-15 2015-20 2020-25 2025-30

Europe- monthly breakeven jobs growth (estimate in thousands)

Note: Assumes constant age group participation rates over time (Europe 2016

rates )

31

16

-1

-19

-33-31

-27

-35-40

-30

-20

-10

0

10

20

30

40

1990-95 1995-00 2000-05 2005-10 2010-15 2015-20 2020-25 2025-30

Japan- monthly breakeven jobs growth (estimate in thousands)

Note: Assumes constant age group participationrates over time (2016 Japan rates)

While US labour

force growth will be

low (+0.4% per

annum), the

dynamics are even

more severe in

other major

economies


18 April 2017 69

Demographics and productivity growth For the past 2.5 years we have held the view that the major significant driver of low

productivity growth was demographic forces. This is a topic we addressed in Demographics,

productivity, and wage growth and our conclusion is supported by a broad range of academic

evidence.

An NBER working paper published in 2016 The Effect of population aging on economic

growth, the labour force, and productivity (Maestas et. al) provides further support for our

conclusion. The paper uses variation in the rate of population ageing across US states and

finds that “a 10% increase in the fraction of the population aged 60+ decreases the growth

rate of GDP per capita by 5.5%. Two-thirds of this reduction is due to slower growth in the

labour productivity of workers across the age distribution, while one-third arises from slower

labour force growth”.

The drag on productivity from demographics comes from two channels. First is the aging

channel. Individuals that are 55+ are less likely to improve their productivity, this acts as a

drag on productivity growth when this group is growing as a share of the workforce. Second is

the retirement channel. This means workers with several years of experience are replaced

with those with little to no experience, creating a further productivity drag.

Fig 173 Productivity growth has been very low for the past six years

Source: BLS, Macquarie Research, April 2017

Fig 174 Worker wages tend to rise steadily until the mid-40s, before stabilizing and then declining

Fig 175 Annual wage gains by age group are an approximation for productivity growth

Source: BLS, Macquarie Research, April 2017 Source: BLS, Macquarie Research, April 2017

0

1

2

3

4

1995 2000 2005 2010 2015

US - nonfarm business sector labor productivit y (annual % change)

avg:1995-2010 = 2.5%

avg: 2011-2016 = 0.5%

0

200

400

600

800

1000

16-19 20-24 25-34 35-44 45-54 55-64 65+

Median usual weekly earnings of full-timewage and salary workers (US$)

-2%

-1%

0%

1%

2%

3%

4%

5%

6%

20-24 25-34 35-44 45-54 55-64 65+

Estimated annual wage gains by age group (%)

The drag on

productivity from

demographics

comes from two

channels:

First is the aging

channel: individuals

that are 55+ are less

likely to improve

their productivity,

this acts as a drag

on productivity

growth when this

group is growing as

a share of the

workforce

Second is the

retirement channel:

This means workers

with several years

of experience are

replaced with those

with little to no

experience, creating

a further

productivity drag


18 April 2017 70

The Long Grinding Cycle continues We believe the pace of the current expansion should continue to prove to be structurally

lower than in previous cycles, as fiscal reconstruction, demographic factors, low productivity

growth and sub-optimal global policy making are likely to remain as headwinds.

Fig 176 shows data since 1999, breaking real GDP growth down into contributing parts:

labour, “capital deepening” (labour quality, non-IT capital, IT capital) and total factor

productivity (TFP) growth. Fig 177 provides more detail on TFP.

Fig 176 Contributions of sources of growth to aggregate GDP growth, 1999-2015

GDP Growth Labour Labour Quality Non-IT Capital IT Capital TFP Growth

World

99-06 4.2 0.6 0.2 1.8 0.6 0.9 07-13 3.3 0.2 0.1 2.4 0.5 0.1 2013 3.0 0.3 0.1 2.2 0.4 0.0 2014 3.2 0.6 0.1 2.1 0.3 0.0 2015 2.7 0.6 0.1 2.0 0.3 -0.3

Mature

99-06 3.0 0.3 0.3 1.2 0.7 0.5 07-13 1.1 -0.1 0.1 0.9 0.5 -0.3 2013 1.2 0.1 0.1 0.7 0.4 -0.1 2014 2.0 0.8 0.1 0.8 0.4 -0.1 2015 2.3 0.8 0.1 0.8 0.4 0.1

Emerging

99-06 5.6 0.9 0.1 2.6 0.4 1.6 07-13 5.4 0.6 0.1 3.8 0.4 0.4 2013 4.4 0.5 0.1 3.4 0.3 0.1 2014 4.1 0.4 0.1 3.3 0.3 0.0 2015 3.1 0.4 0.1 3.0 0.3 -0.7

United States

99-06 3.3 0.4 0.2 1.4 0.9 0.5 07-13 1.1 -0.1 0.1 0.8 0.5 -0.2 2013 1.6 0.7 0.1 0.8 0.4 -0.5 2014 2.6 1.1 0.1 0.9 0.4 0.1 2015 2.7 1.2 0.1 0.9 0.4 0.1

Japan

99-06 1.6 -0.4 0.4 0.7 0.7 0.1 07-13 0.6 -0.4 0.1 0.3 0.5 0.1 2013 1.5 0.0 0.1 0.3 0.3 0.7 2014 0.2 0.2 0.1 0.4 0.3 -0.8 2015 1.0 0.2 0.2 0.3 0.3 -0.1

Euro Area

99-06 2.3 0.5 0.2 1.1 0.4 0.1 07-13 0.2 -0.3 0.1 0.6 0.3 -0.7 2013 -0.3 -0.8 0.1 0.3 0.2 -0.2 2014 0.9 0.4 0.1 0.3 0.2 -0.2 2015 1.7 0.7 0.1 0.4 0.3 0.2

China

99-06 8.7 0.5 0.2 5.0 0.8 2.3 07-13 8.3 0.2 0.1 6.3 0.4 1.3 2013 6.2 0.2 0.1 5.5 0.2 0.2 2014 6.0 0.2 0.1 5.3 0.3 0.1 2015 4.1 0.2 0.1 4.8 0.2 -1.3

India

99-06 6.6 1.4 0.1 4.1 0.8 0.1 07-13 7.2 0.1 0.1 4.9 1.5 0.6 2013 6.1 0.5 0.1 3.5 1.2 0.9 2014 6.8 0.7 0.1 3.4 1.0 1.6 2015 7.0 0.8 0.1 3.3 0.9 1.9

Brazil

99-06 2.9 1.1 0.1 1.3 0.2 0.1 07-13 3.7 0.6 0.2 1.9 0.1 0.9 2013 2.7 0.2 0.2 2.0 0.1 0.2 2014 0.1 0.5 0.2 1.6 0.1 -2.2 2015 -3.9 0.1 0.2 0.7 0.0 -5.0

Source: The Conference Board Summary Table, Macquarie Research, April 2017

Fiscal

reconstruction,

demographic factors,

low productivity

growth and sub-

optimal global policy

making are likely to

remain as headwinds

Ma

cq

ua

rie R

es

ea

rch

T

he

Glo

ba

l Macro

Outlo

ok

18

Ap

ril 201

7

71

Fig 177 Growth of total factor productivity (TFP) per annum, %, aggregates weighted by GDP in current US$ shares

Country Name 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Advanced US 0.3 -0.1 0.3 0.4 0.5 0.2 -0.2 -0.2 -0.4 -0.1 0.7 -0.1 0.0 -0.2 0.0 0.0 Japan 0.1 -0.2 0.0 0.0 0.2 0.0 0.0 0.1 -0.1 -0.5 0.6 -0.1 0.1 0.1 -0.1 0.0 Germany 0.1 0.1 0.0 -0.1 0.0 0.1 0.1 0.1 -0.1 -0.4 0.2 0.2 0.0 0.0 0.0 0.0 France 0.1 0.0 0.1 0.0 0.0 0.0 0.1 0.0 -0.1 -0.2 0.1 0.0 -0.1 0.0 -0.1 0.0 Italy 0.1 0.0 -0.1 -0.1 0.0 0.0 0.0 0.0 -0.1 -0.3 0.1 0.0 -0.1 0.0 0.0 0.0 Spain 0.0 0.0 0.0 0.0 -0.1 -0.1 0.0 0.0 -0.1 -0.1 0.0 0.0 0.0 0.0 0.0 0.0 UK 0.1 0.0 0.0 0.1 0.0 0.0 0.1 0.0 -0.2 -0.2 0.0 0.1 -0.1 0.0 0.0 0.0 Australia 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.1 0.0 0.0 0.0 0.0 Canada 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.1 -0.1 -0.1 0.0 0.0 0.0 0.0 0.0 0.0 S. Korea 0.0 0.0 0.1 0.0 0.1 0.1 0.1 0.1 0.0 0.0 0.1 0.1 -0.1 0.1 0.0 0.0

10-advanced total 0.8 -0.2 0.4 0.3 0.6 0.3 0.1 -0.1 -1.2 -1.8 1.7 0.0 -0.3 -0.1 -0.1 0.0

Emerging Brazil 0.3 -0.1 0.1 -0.2 0.3 0.1 0.2 0.5 0.2 -0.1 0.5 0.0 -0.1 0.0 -0.3 -0.5 Mexico 0.6 -0.5 -0.6 -0.1 0.1 -0.2 0.2 0.0 -0.3 -0.5 0.0 0.1 0.0 -0.1 0.0 0.0 Russia 0.6 0.4 0.3 0.7 0.7 0.7 0.9 0.9 0.6 -0.7 0.4 0.4 0.3 0.2 0.1 -0.3 Turkey 0.3 -0.4 0.3 0.2 0.3 0.1 0.0 0.0 -0.2 -0.4 0.2 0.1 -0.1 0.0 0.0 0.0 India 0.0 -0.1 0.1 0.0 -0.2 0.2 0.2 0.1 0.0 -0.1 0.3 0.1 0.0 0.1 0.2 0.2 China (Official) 0.8 0.8 0.6 1.3 1.2 1.6 1.6 2.4 1.3 0.4 0.9 1.0 0.3 0.4 0.3 0.8

6-EM Total 2.7 0.2 0.8 2.0 2.4 2.5 3.1 3.7 1.5 -1.4 2.3 1.6 0.4 0.6 0.2 0.1

16-Total 1.0 -0.1 0.4 0.6 0.9 0.6 0.6 0.7 -0.6 -1.7 1.9 0.5 -0.1 0.1 0.0 0.1

Other Indonesia 0.9 0.2 0.9 1.0 0.4 1.0 0.7 -0.2 0.5 0.1 0.7 0.7 0.6 0.5 0.3 0.5 Malaysia 1.1 -0.2 0.6 0.6 0.7 0.7 0.5 0.7 0.2 -0.8 0.2 0.2 0.0 -0.2 0.1 0.0 Singapore 0.8 -1.1 0.5 0.8 1.5 1.1 0.8 0.7 -0.6 -0.5 1.0 0.2 -0.3 -0.1 -0.1 0.2 Philippines 0.8 0.0 0.2 0.1 0.5 0.1 0.4 0.5 0.0 -0.2 0.3 -0.2 0.4 0.3 0.3 0.3 Thailand 0.5 0.4 0.9 1.0 0.6 0.3 0.3 0.4 -0.2 -0.8 0.9 -0.5 0.8 0.3 0.0 0.2

ASEAN 5-Total 4.2 -0.7 3.1 3.4 3.6 3.2 2.7 2.2 -0.1 -2.2 3.1 0.4 1.4 0.7 0.6 1.3

China (alternative) 1.9 1.5 2.8 1.1 1.7 4.8 4.5 4.7 -2.3 0.9 5.2 1.2 -1.1 0.2 0.1 -1.3

Note: estimated as a Tornqvist index

Source: The Conference Board, IMF, Macquarie Research, April 2017

Fig 178 China’s total factor productivity (TFP) per annum, %, 1995-2015, adjusted version

Country 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

China (Alternative) 2.8 1.0 1.7 -5.5 0.4 1.9 1.5 2.8 1.1 1.7 4.8 4.5 4.7 -2.3 0.9 5.2 1.2 -1.1 0.2 0.1 -1.3 China (Official) 5.1 3.8 3.4 1.6 1.4 2.4 2.0 1.7 3.2 3.2 4.3 4.4 6.2 3.0 0.8 2.0 2.1 0.6 0.8 0.6 1.2


The Conference board provides an explanation of the methodological differences between the old and new series here: https://www.conference-board.org/retrievefile.cfm?filename=FAQ-for-China-GDP-vs4_10nov15.pdf&type=subsite. “The Maddison-Wu approach, which the Conference Board has adopted, reconstructs aggregate real GDP growth from the bottom-up, on a sector-by-sector basis. The biggest adjustments are for the industrial sector and the so-called “non-material services” sector, which includes banking & financing, real estate, professional services, education, healthcare, culture & entertainment services, and government services. We argue that the upward bias in the published GDP numbers in China is attributable to two sources: (1) a ‘misreporting effect” which accounts for about two-thirds of the upward bias; and (2) a “price effect” which accounts for the remaining one third. The price effect includes the adjustment for the “non-material services” sector, where the Conference Board has introduced judgemental labour productivity growth estimate of 1% pa 1982-91 and 2% from 1992 onwards – in line with common experience at China’s level of development. In contrast, the official data has 6% pa growth on average.”

Source: The Conference Board, Macquarie Research, April 2017


18 April 2017 72

TFP is the increase in the overall efficiency in production, reflecting innovation and the

adoption of new technologies.

However, TFP growth has been very weak recently. TFP in the US has fallen from 0.9% pa

2000-07 to 0.5% pa 2010-14 to zero pa 2013-14, Fig 179.

Fig 179 Growth of total factor productivity (TFP), per annum, %

2000-07 2010-14 2013-14 2013-14 minus 2000-07

Brazil 0.3 -0.9 -1.6 -1.9 Mexico -0.3 -0.7 -1.7 -1.4 Russia 5.3 2.2 0.4 -4.9 Turkey 0.3 -0.3 -1.9 -2.2 India 1.4 1.3 0.6 -0.8 China – old methodology 4.8 1.1 0.2 -4.6 China – new methodology 2.1 -1.3 -2.4 -4.5

US 0.9 0.5 0.0 -0.9


Source: The Conference Board, IMF, Macquarie Research, April 2017

This decline of 0.9% pa 2013-14 versus 2000-07 has been exceeded by all the major EM

economies with the exception of Turkey (-0.8% pa). Russia and China have seen TFP per

annum declines 2013-14 versus 2000-07 of over 4%. For a more extensive discussion,

please see the 19 April 2016: The Global Macro Outlook – The long grinding cycle,

pages 41-58. Fig 180 provides a decomposition of our global growth expectations.

Fig 180 Global growth and components, per annum, %, 2016-20

Labour supply

growth Capital

deepening TFP Total

Advanced economies 0.2 0.8 0.5 1.5

EM economies 0.8 2.3 1.0 4.0

Global 0.4 1.4 0.7 2.5

Note: Macquarie estimates. Weights used: advanced economies 0.6, EM economies 0.4. Pale red shading: most vulnerable, highest downside risk. Blue shading: most upside risk, in our opinion


Our real GDP growth forecasts by country are shown in Fig 181, which shows the aggregates

out to 2020. The long grinding cycle continues.

Fig 181 Real GDP growth for ten advanced economies and six emerging market economies, in current US$: The long grinding cycle continues

Note: Global real GDP growth (10-Advanced & 6-Emerging) is forecast to be 2.2% in 2016, 2.6% in 2017, and 2.7% in 2018. Macquarie forecasts where available. Please see Fig 182 and Fig 183 for the constituents & weights used. The above uses market exchange rates. Using PPP weights would boost the numbers by about 0.5-0.6% pa

Source: IMF, World Bank, OECD, Macquarie Research, April 2017

-6

-4

-2

0

2

4

6

8

10

12

2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020

Advanced Emerging 16-Total(%)

Russia and China

have seen TFP per

annum declines

2013-14 versus

2000-07 of over 4%

The long grinding

cycle continues

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Fig 182 GDP in current US$ for 10-Advanced countries and 6-Emerging market economies. Also for the 5-ASEAN majors

Country Name

2000 % of total

2001 % of total

2002 % of total

2003 % of total

2004 % of total

2005 % of total

2006 % of total

2007 % of total

2008 % of total

2009 % of total

2010 % of total

2011 % of total

2012 % of total

2013 % of total

2014 % of total

2015 % of total

10-advanced countries (GDP in trillion)

US 10.3 38% 10.6 40% 11.0 40% 11.5 37% 12.3 36% 13.1 35% 13.9 35% 14.5 33% 14.7 31% 14.4 32% 15.0 30% 15.5 29% 16.2 29% 16.8 30% 17.3 30% 17.9 32%

Japan 4.7 18% 4.2 16% 4.0 14% 4.3 14% 4.7 13% 4.6 12% 4.4 11% 4.4 10% 4.8 10% 5.0 11% 5.5 11% 5.9 11% 6.0 11% 4.9 9% 4.6 8% 4.1 7%

Germany 1.9 7% 1.9 7% 2.1 7% 2.5 8% 2.8 8% 2.9 8% 3.0 8% 3.4 8% 3.7 8% 3.4 8% 3.4 7% 3.8 7% 3.5 6% 3.7 7% 3.9 7% 3.4 6%

France 1.4 5% 1.4 5% 1.5 5% 1.8 6% 2.1 6% 2.2 6% 2.3 6% 2.7 6% 2.9 6% 2.7 6% 2.6 5% 2.9 5% 2.7 5% 2.8 5% 2.8 5% 2.4 4%

Italy 1.1 4% 1.2 4% 1.3 5% 1.6 5% 1.8 5% 1.9 5% 1.9 5% 2.2 5% 2.4 5% 2.2 5% 2.1 4% 2.3 4% 2.1 4% 2.1 4% 2.1 4% 1.8 3%

Spain 0.6 2% 0.6 2% 0.7 3% 0.9 3% 1.1 3% 1.2 3% 1.3 3% 1.5 3% 1.6 3% 1.5 3% 1.4 3% 1.5 3% 1.4 2% 1.4 2% 1.4 2% 1.2 2%

UK 1.5 6% 1.5 6% 1.7 6% 1.9 6% 2.3 7% 2.4 7% 2.6 7% 3.0 7% 2.8 6% 2.3 5% 2.4 5% 2.6 5% 2.6 5% 2.7 5% 3.0 5% 2.8 5%

Australia 0.4 2% 0.4 1% 0.4 1% 0.5 2% 0.6 2% 0.7 2% 0.7 2% 0.9 2% 1.1 2% 0.9 2% 1.1 2% 1.4 3% 1.5 3% 1.6 3% 1.5 2% 1.3 2%

Canada 0.7 3% 0.7 3% 0.8 3% 0.9 3% 1.0 3% 1.2 3% 1.3 3% 1.5 3% 1.5 3% 1.4 3% 1.6 3% 1.8 3% 1.8 3% 1.8 3% 1.8 3% 1.6 3%

S. Korea 0.6 2% 0.5 2% 0.6 2% 0.7 2% 0.8 2% 0.9 2% 1.0 3% 1.1 3% 1.0 2% 0.9 2% 1.1 2% 1.2 2% 1.2 2% 1.3 2% 1.4 2% 1.4 2%

(Advanced) 23.3 87% 23.1 87% 23.9 86% 26.6 86% 29.4 85% 30.9 84% 32.4 82% 35.0 79% 36.7 77% 34.8 76% 36.3 74% 38.8 71% 39.0 71% 39.1 69% 39.8 68% 38.0 68%

6-emerging market economies (GDP in trillion)

Brazil 0.6 2% 0.6 2% 0.5 2% 0.6 2% 0.7 2% 0.9 2% 1.1 3% 1.4 3% 1.7 3% 1.6 4% 2.1 4% 2.5 5% 2.2 4% 2.2 4% 2.4 4% 1.8 3%

Mexico 0.7 3% 0.7 3% 0.7 3% 0.7 2% 0.8 2% 0.9 2% 1.0 2% 1.0 2% 1.1 2% 0.9 2% 1.1 2% 1.2 2% 1.2 2% 1.3 2% 1.3 2% 1.1 2%

Russia 0.3 1% 0.3 1% 0.3 1% 0.4 1% 0.6 2% 0.8 2% 1.0 2% 1.3 3% 1.7 3% 1.2 3% 1.5 3% 1.9 4% 2.0 4% 2.1 4% 2.0 3% 1.3 2%

Turkey 0.3 1% 0.2 1% 0.2 1% 0.3 1% 0.4 1% 0.5 1% 0.5 1% 0.6 1% 0.7 2% 0.6 1% 0.7 1% 0.8 1% 0.8 1% 0.8 1% 0.8 1% 0.7 1%

India 0.5 2% 0.5 2% 0.5 2% 0.6 2% 0.7 2% 0.8 2% 0.9 2% 1.2 3% 1.2 3% 1.4 3% 1.7 3% 1.8 3% 1.8 3% 1.9 3% 2.0 3% 2.1 4%

China 1.2 4% 1.3 5% 1.5 5% 1.6 5% 1.9 6% 2.3 6% 2.7 7% 3.5 8% 4.5 10% 5.0 11% 5.9 12% 7.3 13% 8.2 15% 9.2 16% 10.4 18% 10.9 19%

(Emerging) 3.5 13% 3.6 13% 3.8 14% 4.3 14% 5.1 15% 6.1 16% 7.2 18% 9.1 21% 10.9 23% 10.7 24% 13.1 26% 15.5 29% 16.3 29% 17.5 31% 18.9 31% 17.9 31%

Total 26.9 26.7 27.7 30.9 34.5 37.0 39.6 44.1 47.5 45.5 49.4 54.3 55.3 56.7 58.7 55.9

5-Asean Countries (GDP in trillion)

Indonesia 0.2 30% 0.2 30% 0.2 33% 0.2 35% 0.3 34% 0.3 34% 0.4 36% 0.4 36% 0.5 37% 0.5 39% 0.8 42% 0.9 43% 0.9 43% 0.9 41% 0.9 40% 0.9 40%

Malaysia 0.1 17% 0.1 17% 0.1 17% 0.1 16% 0.1 16% 0.1 17% 0.2 16% 0.2 16% 0.2 17% 0.2 15% 0.3 14% 0.3 15% 0.3 14% 0.3 14% 0.3 15% 0.3 14%

Singapore 0.1 17% 0.1 17% 0.1 15% 0.1 14% 0.1 15% 0.1 15% 0.1 15% 0.2 15% 0.2 14% 0.2 14% 0.2 13% 0.3 13% 0.3 13% 0.3 13% 0.3 14% 0.3 14%

Philippines 0.1 15% 0.1 14% 0.1 14% 0.1 12% 0.1 12% 0.1 12% 0.1 12% 0.1 12% 0.2 12% 0.2 12% 0.2 11% 0.2 11% 0.3 12% 0.3 12% 0.3 13% 0.3 14%

Thailand 0.1 22% 0.1 22% 0.1 21% 0.2 22% 0.2 22% 0.2 22% 0.2 22% 0.3 21% 0.3 21% 0.3 20% 0.3 19% 0.4 18% 0.4 18% 0.4 19% 0.4 18% 0.4 18%

5-ASEAN 0.6 100 0.5 100 0.6 100 0.7 100 0.8 100 0.8 100 1.0 100 1.2 100 1.4 100 1.4 100 1.8 100 2.1 100 2.2 100 2.2 100 2.2 100 2.1 100

Note: The GDP trillions numbers are to one decimal point, so rounding plays an important part in the ASEAN part of the table


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Fig 183 Real GDP growth (annual %), totals weighted by GDP in current US$ shares, Macquarie where available (shaded in grey)

Country Name 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Advanced US 1.9 -0.1 3.6 2.7 4.0 2.7 3.8 4.5 4.5 4.7 4.1 1.0 1.8 2.8 3.8 3.3 2.7 1.8 -0.3 -2.8 2.5 1.6 2.2 1.7 2.4 2.6 1.6 2.2 1.9 1.6 1.5

Japan 5.6 3.3 0.8 0.2 0.9 1.9 2.6 1.6 -2.0 -0.2 2.3 0.4 0.3 1.7 2.4 1.3 1.7 2.2 -1.0 -5.5 4.7 -0.5 1.7 1.4 0.0 1.2 1.0 0.8 0.7 0.8 0.8 Germany 5.7 5.0 1.5 -1.0 2.5 1.8 0.9 1.9 1.8 1.8 3.2 1.8 0.0 -0.7 0.7 0.9 3.9 3.4 0.8 -5.6 4.0 3.7 0.7 0.6 1.6 1.5 1.7 1.4 1.4 1.3 1.3 France 2.9 1.0 1.6 -0.6 2.3 2.1 1.4 2.3 3.6 3.4 3.9 2.0 1.1 0.8 2.8 1.6 2.4 2.4 0.2 -2.9 2.0 2.1 0.2 0.6 0.6 1.3 1.3 1.3 1.6 1.7 1.8 Italy 2.1 1.5 0.8 -0.9 2.2 2.3 1.3 1.8 1.6 1.6 3.7 1.8 0.2 0.2 1.6 1.0 2.0 1.5 -1.1 -5.5 1.7 0.6 -2.8 -1.7 -0.3 0.8 0.8 0.9 1.1 0.9 0.9 Spain 3.8 2.5 0.9 -1.3 2.3 4.1 2.4 3.9 4.5 4.7 5.1 4.0 2.9 3.2 3.2 3.7 4.2 3.8 1.1 -3.6 0.0 -1.0 -2.6 -1.7 1.4 3.2 3.1 2.2 1.9 1.9 1.8 UK 0.7 -1.1 0.4 2.5 3.9 2.5 2.5 3.1 3.2 3.3 3.7 2.7 2.4 3.5 2.5 3.0 2.5 2.6 -0.6 -4.3 1.9 1.5 1.3 1.9 3.1 2.2 1.8 1.7 1.3 1.6 1.7

Australia 1.5 -1.1 2.7 4.0 4.9 3.0 4.3 4.3 4.6 4.3 3.2 2.5 4.0 3.0 4.1 3.2 2.7 4.5 2.6 1.8 2.3 2.7 3.6 2.0 2.7 2.4 2.5 2.3 2.8 3.0 3.0 Canada 0.2 -2.1 0.9 2.7 4.5 2.7 1.6 4.3 3.9 5.2 5.2 1.8 3.0 1.8 3.1 3.2 2.6 2.1 1.0 -3.0 3.1 3.1 1.7 2.2 2.5 0.9 1.4 2.0 1.3 1.2 1.1 S. Korea 9.8 10.4 6.2 6.8 9.2 9.6 7.6 5.9 -5.5 11.3 8.9 4.5 7.4 2.9 4.9 3.9 5.2 5.5 2.8 0.7 6.5 3.7 2.3 2.9 3.3 2.6 2.9 2.5 2.6 2.5 2.4

10-Total 3.7 1.4 1.6 2.0 2.9 2.5 2.7 2.4 0.0 -3.6 2.9 1.5 1.4 1.2 1.8 2.1 1.6 1.8 1.7 1.5 1.5 Emerging Brazil -4.2 1.0 -0.5 4.7 5.3 4.4 2.2 3.4 0.3 0.5 4.4 1.4 3.1 1.1 5.8 3.2 4.0 6.1 5.1 -0.1 7.5 3.9 1.9 3.0 0.1 -3.8 -3.3 0.5 1.5 2.0 2.0 Mexico 5.2 4.2 3.5 2.3 4.7 -5.8 5.9 7.0 4.7 2.7 5.3 -0.6 0.1 1.4 4.3 3.0 5.0 3.1 1.4 -4.7 5.1 4.0 4.0 1.4 2.2 2.5 2.1 2.3 2.6 2.9 3.0 Russia n/a n/a n/a -8.7 -12.7 -4.1 -3.6 1.4 -5.3 6.4 10.0 5.1 4.7 7.3 7.2 6.4 8.2 8.5 5.2 -7.8 4.5 4.0 3.5 1.3 0.7 -3.7 -0.8 1.1 1.2 1.5 1.5 Turkey 9.3 0.9 6.0 8.0 -5.5 7.2 7.0 7.5 3.1 -3.4 6.8 -5.7 6.2 5.3 9.4 8.4 6.9 4.7 0.7 -4.8 9.2 8.8 2.1 4.2 3.0 4.0 3.3 3.0 3.2 3.3 3.5 India 5.5 1.1 5.5 4.8 6.7 7.6 7.6 4.1 6.2 8.5 4.0 4.9 3.9 7.9 7.8 9.3 9.3 9.8 3.9 8.5 10.3 6.6 5.6 6.6 7.2 7.2 7.4 6.9 7.7 7.5 7.5 China 3.9 9.2 14.3 13.9 13.1 11.0 9.9 9.2 7.8 7.6 8.4 8.3 9.1 10.0 10.1 11.3 12.7 14.2 9.6 9.2 10.6 9.5 7.9 7.8 7.3 6.8 6.8 6.5 5.6 5.4 5.8

6-Total 6.5 3.9 5.3 6.5 7.9 7.8 8.9 9.6 6.2 3.8 8.8 7.1 5.7 5.7 5.2 4.6 4.9 5.1 4.8 4.8 5.0

16-Total 4.1 1.7 2.1 2.7 3.7 3.4 3.8 3.9 1.4 -1.8 4.5 3.1 2.7 2.6 2.9 2.9 2.7 2.9 2.7 2.6 2.6

ASEAN Indonesia 9.0 8.9 6.5 8.0 7.5 8.2 7.8 4.7 -13.1 0.8 5.0 3.6 4.5 4.8 5.0 5.7 5.5 6.3 7.4 4.7 6.4 6.2 6.0 5.6 5.0 4.9 5.0 5.2 5.2 5.0 5.0 Malaysia 9.0 9.5 8.9 9.9 9.2 9.8 10.0 7.3 -7.4 6.1 8.7 0.5 5.4 5.8 6.8 5.0 5.6 6.3 4.8 -1.5 7.5 5.3 5.5 4.7 6.0 5.0 4.2 4.5 5.1 5.1 5.1 Singapore 10.0 6.7 7.1 11.5 10.9 7.0 7.5 8.3 -2.2 6.1 8.9 -1.0 4.2 4.4 9.5 7.5 8.9 9.1 1.8 -0.6 15.2 6.2 3.7 4.7 3.3 1.9 2.0 2.0 2.6 2.6 2.6

Philippines 3.0 -0.6 0.3 2.1 4.4 4.7 5.8 5.2 -0.6 3.1 4.4 2.9 3.6 5.0 6.7 4.8 5.2 6.6 4.2 1.1 7.6 3.7 6.7 7.1 6.2 5.9 6.8 6.1 6.3 6.0 6.0 Thailand 11.6 8.4 9.2 8.7 8.0 8.1 5.7 -2.8 -7.6 4.6 4.5 3.4 6.1 7.2 6.3 4.2 5.0 5.4 1.7 -0.7 7.5 0.8 7.2 2.7 0.8 2.9 3.2 3.3 3.2 3.2 3.2

5-ASEAN 6.1 2.2 4.8 5.5 6.5 5.4 5.9 6.6 4.6 1.5 8.1 4.8 5.9 5.0 4.3 4.3 4.4 4.4 4.6 4.5 4.5

Note: Totals are weighted by the respective year ‘GDP in current US$’ weights from Fig 182. 2016-20 use 2015 weights.


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Fig 184 CPI, YoY, forecasts Macquarie where available (shaded in grey), totals weighted by GDP in current US$ shares

Country Name 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Advanced US 5.4 4.2 3.0 3.0 2.6 2.8 2.9 2.3 1.6 2.2 3.4 2.8 1.6 2.3 2.7 3.4 3.2 2.9 3.8 -0.4 1.6 3.2 2.1 1.5 1.6 0.1 1.3 2.5 2.4 2.4 2.4 Japan 3.0 3.3 1.7 1.3 0.7 -0.1 0.1 1.8 0.7 -0.3 -0.7 -0.8 -1.3 0.2 0.0 -0.3 0.2 0.1 1.4 -1.3 -0.7 -0.3 0.0 0.4 2.8 0.8 -0.1 0.3 0.5 0.9 1.3 Germany 5.1 4.4 2.7 1.7 1.4 1.9 0.9 0.6 1.5 2.0 1.4 1.0 1.7 1.5 1.6 2.3 2.6 0.3 1.1 2.1 2.0 1.5 0.8 0.2 1.2 1.5 1.6 1.8 1.9 France 3.4 3.2 2.4 2.1 1.7 1.8 2.0 1.2 0.6 0.5 1.7 1.6 1.9 2.1 2.1 1.7 1.7 1.5 2.8 0.1 1.5 2.1 2.0 0.9 0.6 0.1 1.0 1.1 1.3 1.5 1.7 Italy 6.5 6.3 5.1 4.5 4.0 5.2 4.0 2.0 2.0 1.7 2.5 2.8 2.5 2.7 2.2 2.0 2.1 1.8 3.4 0.8 1.5 2.7 3.0 1.2 0.2 0.2 0.7 1.0 1.1 1.2 1.3 Spain 6.7 5.9 5.9 4.6 4.7 4.7 3.6 2.0 1.8 2.3 3.4 3.6 3.1 3.0 3.0 3.4 3.5 2.8 4.1 -0.3 1.8 3.2 2.4 1.4 -0.2 -0.3 0.9 1.0 1.2 1.4 1.5 UK 7.0 7.5 4.3 2.5 2.0 2.7 2.5 1.8 1.6 1.3 0.8 1.2 1.3 1.4 1.3 2.0 2.3 2.3 3.6 2.2 3.3 4.5 2.8 2.6 1.5 0.0 0.7 2.7 2.2 1.7 1.8 Australia 7.3 3.2 1.0 1.8 1.9 4.6 2.6 0.3 0.9 1.5 4.5 4.4 3.0 2.8 2.3 2.7 3.5 2.3 4.4 1.8 2.8 3.4 1.8 2.4 2.5 1.5 1.3 2.1 1.7 2.5 2.0 Canada 4.8 5.6 1.5 1.8 0.2 2.2 1.6 1.6 1.0 1.7 2.7 2.5 2.3 2.8 1.9 2.2 2.0 2.1 2.4 0.3 1.8 2.9 1.5 0.9 1.9 1.1 1.5 1.5 2.0 2.0 2.0 S. Korea 8.6 9.3 6.3 4.7 6.3 4.5 4.9 4.4 7.5 0.8 2.3 4.1 2.8 3.5 3.6 2.8 2.2 2.5 4.7 2.8 3.0 4.0 2.2 1.3 1.3 0.7 1.0 1.8 1.6 1.8 2.0

10-Total 2.1 2.0 1.3 1.8 2.0 2.3 2.4 2.2 3.2 0.0 1.4 2.5 1.8 1.4 1.5 0.3 1.0 1.9 1.9 2.0 2.0 Emerging Brazil 2948 432.8 951.6 1928 2076 66.0 15.8 6.9 3.2 4.9 7.0 6.8 8.5 14.7 6.6 6.9 4.2 3.6 5.7 4.9 5.0 6.6 5.4 6.2 8.9 8.9 6.3 5.2 5.0 4.8 4.6 Mexico 26.7 22.7 15.5 9.8 7.0 35.0 34.4 20.6 15.9 16.6 9.5 6.4 5.0 4.5 4.7 4.0 3.6 4.0 5.1 5.3 4.2 3.4 4.1 3.8 4.0 2.8 3.0 3.0 3.0 3.0 3.0 Russia 874.6 307.6 197.5 47.7 14.8 27.7 85.7 20.8 21.5 15.8 13.7 10.9 12.7 9.7 9.0 14.1 11.7 6.9 8.4 5.1 6.8 7.8 15.8 8.6 7.3 5.0 4.0 4.0 Turkey 60.3 66.0 70.1 66.1 106.3 88.1 80.3 85.7 84.6 64.9 54.9 54.4 45.0 25.3 10.6 10.1 9.6 8.8 10.4 6.3 8.6 6.5 8.9 7.5 8.9 7.4 7.0 6.5 6.5 6.5 6.5 India 9.0 13.9 11.8 6.4 10.2 10.2 9.0 7.2 13.2 4.7 4.0 3.7 4.4 3.8 3.8 4.2 6.1 6.4 8.4 10.9 12.0 8.9 9.3 10.9 6.7 4.9 5.0 4.5 5.1 5.0 5.0 China 3.1 3.5 6.3 14.6 24.2 16.9 8.3 2.8 -0.8 -1.4 0.3 0.7 -0.8 1.2 3.9 1.8 1.5 4.8 5.9 -0.7 3.3 5.4 2.7 2.6 1.5 1.5 2.2 2.5 2.5 2.5 2.5

6 -Total 9.4 7.9 6.6 6.9 5.7 5.2 4.5 5.6 7.6 3.9 5.5 6.3 4.5 4.8 4.3 8.6 7.9 7.6 7.4 7.2 7.1

16-Total 3.0 2.8 2.0 2.5 2.5 2.8 2.7 2.9 4.2 0.9 2.5 3.6 2.6 2.4 2.4 2.9 3.2 3.7 3.6 3.6 3.7 ASEAN Indonesia 7.8 9.4 7.5 9.7 8.5 9.4 8.0 6.2 58.4 20.5 3.7 11.5 11.9 6.6 6.2 10.5 13.1 6.4 9.8 4.8 5.1 5.4 4.3 6.4 6.5 6.4 3.5 4.2 4.3 4.3 4.3 Malaysia 2.6 4.4 4.8 3.5 3.7 3.5 3.5 2.7 5.3 2.7 1.5 1.4 1.8 1.0 1.5 3.0 3.6 2.0 5.4 0.6 1.7 3.2 1.7 2.1 2.1 2.1 2.1 2.8 2.6 2.5 2.5 Singapore 3.5 3.4 2.3 2.3 3.1 1.7 1.4 2.0 -0.3 0.0 1.4 1.0 -0.4 0.5 1.7 0.4 1.0 2.1 6.5 0.6 2.8 5.3 4.5 2.4 -0.4 -0.5 -0.5 1.0 1.4 1.5 1.5 Philippines 12.7 18.5 8.6 6.9 8.4 6.7 7.5 5.6 9.3 5.9 4.0 5.3 2.7 2.3 4.8 6.5 5.5 2.9 8.3 4.2 3.8 4.6 3.2 3.0 1.4 1.4 1.8 2.8 3.3 3.0 3.0 Thailand 5.9 5.7 4.1 3.3 5.0 5.8 5.8 5.6 8.0 0.3 1.6 1.6 0.7 1.8 2.8 4.5 4.6 2.2 5.5 -0.8 3.3 3.8 3.0 2.2 -0.8 -0.9 0.2 1.7 2.0 2.0 2.0

5-ASEAN 2.5 5.0 4.7 3.2 3.8 5.9 7.1 3.7 7.5 2.4 3.8 4.7 3.6 4.0 2.9 2.8 1.9 2.9 3.1 3.1 3.1

Note: On the above data, for advanced countries the CPI increases at 1.8% pa1999-2020, emerging economies 5.3% and the 16-Total 2.6%. Brazil’s inflation 1990-1994 has been rounded to the nearest percent.

For advanced countries the GDP deflator increases at 1.5% pa1999-2014, emerging economies 6.8% and the 16-Total 2.5%

Source: World Bank, IMF, Macquarie Research, April 2017

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Fig 185 G20 real GDP growth softened over 3Q and 4Q 2015, YoY based on quarterly data

2011 2012 2013 2014 2015 2016

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

G20 4.8 4.4 3.9 3.5 3.5 3.2 3.0 2.7 2.8 3.1 3.4 3.7 3.7 3.4 3.3 3.4 3.4 3.5 3.3 3.1 3.0 3.0 2.9 ..

Argentina 9.1 5.2 5.6 4.3 0.6 -3.6 -1.3 0.2 1.4 5.4 2.8 0.1 -1.2 -2.5 -4.0 -2.4 0.4 3.2 4.1 2.9 0.3 -3.3 -3.5 ..

Australia 1.9 2.5 3.1 3.1 4.5 3.9 3.3 2.9 2.0 2.1 2.0 2.3 3.1 3.0 2.8 2.4 2.5 2.0 2.4 2.6 2.5 3.1 1.9 2.4

Brazil 5.1 4.8 3.6 2.6 1.6 1.0 2.5 2.5 2.5 4.1 2.8 2.6 3.3 -0.3 -0.6 -0.3 -1.9 -2.9 -4.5 -5.8 -5.4 -3.6 -2.9 ..

Canada 3.1 2.8 3.5 3.1 2.4 2.6 1.4 0.7 1.7 2.0 2.6 3.6 2.5 2.9 2.6 2.2 1.9 0.7 0.8 0.4 1.3 1.1 1.4 1.9

China 10.2 9.9 9.4 8.7 8.1 7.6 7.5 8.1 7.9 7.6 7.9 7.7 7.4 7.5 7.1 7.2 7.0 7.0 6.9 6.8 6.7 6.7 6.7 6.8

France 2.8 2.1 1.9 1.6 0.4 0.3 0.2 0.0 0.0 0.9 0.7 0.9 0.9 0.4 0.7 0.6 1.3 1.2 1.1 1.2 1.2 1.1 0.9 1.2

Germany 5.6 3.6 3.3 2.4 1.0 0.9 0.7 0.2 -0.4 0.5 0.7 1.6 2.3 1.3 1.1 1.6 1.1 1.8 1.7 1.3 1.8 1.7 1.7 1.8

India 9.8 8.5 5.3 4.7 4.2 4.3 6.2 5.8 6.1 6.5 6.3 6.9 6.8 7.1 7.4 7.1 7.3 7.5 7.3 7.5 7.9 7.2 7.2 7.2

Indonesia 6.3 6.2 6.2 6.0 6.1 6.1 6.0 5.9 5.7 5.6 5.5 5.4 5.2 5.0 4.9 4.8 4.8 4.8 4.9 5.0 5.0 5.1 5.0 4.9

Italy 2.0 1.5 0.5 -1.1 -2.2 -3.2 -3.2 -2.8 -2.8 -2.1 -1.3 -0.7 0.3 0.3 0.0 0.1 0.4 0.6 0.6 0.9 1.0 0.8 1.0 1.0

Japan 0.7 -0.8 -0.4 0.1 2.7 2.9 0.1 0.3 0.5 1.9 3.0 2.7 2.7 -0.3 -1.0 -0.3 -0.0 1.7 2.1 1.2 0.3 0.9 1.0 1.6

Korea 4.8 3.7 3.4 2.9 2.6 2.4 2.1 2.1 2.2 2.7 3.2 3.5 3.9 3.5 3.3 2.7 2.4 2.2 2.8 3.1 2.8 3.2 2.6 2.3

Mexico 4.2 3.7 4.2 4.2 3.9 4.5 3.3 3.4 3.1 0.7 1.6 1.1 1.1 3.0 2.3 2.7 -2.6 -4.3 -3.5 -3.6 2.3 1.5 2.0 2.4

Russia 3.3 4.2 4.0 4.6 5.3 4.3 3.1 1.8 0.6 1.1 1.2 2.1 0.6 1.1 0.9 0.2 -2.8 -4.5 -3.7 -3.8 -1.2 -0.6 -0.4

Saudi Arabia 10.0 11.2 13.5 5.6 8.5 6.7 4.9 1.7 0.2 1.9 3.7 4.9 6.1 3.6 2.4 2.6 3.2 4.8 4.0 2.0 1.5 1.4 .. ..

South Africa 3.9 3.8 2.9 2.6 2.1 2.4 2.4 2.0 2.0 2.1 2.2 3.0 2.2 1.4 1.6 1.4 2.3 1.6 1.1 0.2 -0.6 0.7 0.7 ..

Turkey 11.5 11.5 11.6 9.9 6.3 4.9 3.7 4.3 7.9 9.8 9.3 7.0 8.2 2.8 4.0 5.7 3.5 7.0 6.5 7.3 4.5 4.2 -1.5 ..

United Kingdom 2.3 1.3 1.2 1.3 1.2 1.0 1.8 1.3 1.5 2.1 1.7 2.4 2.6 3.1 3.1 3.5 2.8 2.4 1.8 1.7 1.6 1.7 2.0 2.0

United States 1.9 1.7 1.2 1.7 2.8 2.5 2.4 1.3 1.3 1.0 1.7 2.7 1.6 2.4 2.9 2.5 3.3 3.0 2.2 1.9 1.6 1.3 1.7 1.9

European Union (28 countries) 2.8 1.8 1.5 0.7 -0.1 -0.4 -0.5 -0.7 -0.6 0.1 0.5 1.2 1.6 1.5 1.6 1.8 2.1 2.2 2.1 2.1 1.8 1.8 1.9 1.9

Euro area (19 countries) 2.8 1.8 1.4 0.5 -0.5 -0.8 -1.0 -1.1 -1.2 -0.4 0.0 0.7 1.3 1.0 1.1 1.3 1.8 2.0 1.9 2.0 1.7 1.6 1.8 1.7

Note: all data from the OECD with the exception of the pale green highlighted numbers which come from National Statistics sources

Aggregates above use PPP weights. Using market exchange rates would reduce the G20 real GDP growth numbers by 0.5-0.6%

“The G7 was established in 1976 as an informal forum of seven major industrial economies: Canada, France, Germany, Italy, Japan, the United Kingdom and the United States of America. It was re-named the G8 after the entry of Russia in 1998. In March 2014, the G7 voted to suspend Russia in response to escalating tensions with Ukraine that led to Russia's annexation of Crimea. However, the suspension is designed to be temporary. Where the G7 seeks agreement on current economic issues based on the interests of those countries, the G20 reflects the wider interests of both industrial and emerging-market economies. The G20 is made up of the finance ministers and central bank governors of 19 countries: Argentina, Australia, Brazil, Canada, China, France, Germany, India, Indonesia, Italy, Japan, Mexico, Russia, Saudi Arabia, South Africa, South Korea, Turkey, the United Kingdom, the United States of America” - plus the EU (Source: the Telegraph newspaper)

Source: OECD, Macquarie Research, April 2017


18 April 2017 77

Important disclosures:

Recommendation definitions

Macquarie - Australia/New Zealand Outperform – return >3% in excess of benchmark return Neutral – return within 3% of benchmark return Underperform – return >3% below benchmark return Benchmark return is determined by long term nominal GDP growth plus 12 month forward market dividend yield

Macquarie – Asia/Europe Outperform – expected return >+10% Neutral – expected return from -10% to +10% Underperform – expected return <-10%

Macquarie – South Africa Outperform – expected return >+10% Neutral – expected return from -10% to +10% Underperform – expected return <-10%

Macquarie - Canada Outperform – return >5% in excess of benchmark return Neutral – return within 5% of benchmark return Underperform – return >5% below benchmark return

Macquarie - USA Outperform (Buy) – return >5% in excess of Russell 3000 index return

Neutral (Hold) – return within 5% of Russell 3000 index return Underperform (Sell)– return >5% below Russell 3000 index return

Volatility index definition*

This is calculated from the volatility of historical price movements. Very high–highest risk – Stock should be expected to move up or down 60–100% in a year – investors should be aware this stock is highly speculative. High – stock should be expected to move up or down at least 40–60% in a year – investors should be aware this stock could be speculative. Medium – stock should be expected to move up or down at least 30–40% in a year. Low–medium – stock should be expected to move up or down at least 25–30% in a year. Low – stock should be expected to move up or down at least 15–25% in a year. * Applicable to Asia/Australian/NZ/Canada stocks only

Recommendations – 12 months Note: Quant recommendations may differ from

Fundamental Analyst recommendations

Financial definitions

All "Adjusted" data items have had the following adjustments made: Added back: goodwill amortisation, provision for catastrophe reserves, IFRS derivatives & hedging, IFRS impairments & IFRS interest expense Excluded: non recurring items, asset revals, property revals, appraisal value uplift, preference dividends & minority interests EPS = adjusted net profit / efpowa* ROA = adjusted ebit / average total assets ROA Banks/Insurance = adjusted net profit /average total assets ROE = adjusted net profit / average shareholders funds Gross cashflow = adjusted net profit + depreciation *equivalent fully paid ordinary weighted average number of shares All Reported numbers for Australian/NZ listed stocks are modelled under IFRS (International Financial Reporting Standards).

Recommendation proportions – For quarter ending 31 March 2017

AU/NZ Asia RSA USA CA EUR Outperform 47.26% 55.50% 38.46% 45.47% 59.09% 48.21% (for global coverage by Macquarie, 8.20% of stocks followed are investment banking clients)

Neutral 38.01% 29.31% 42.86% 48.77% 37.88% 36.79% (for global coverage by Macquarie, 8.25% of stocks followed are investment banking clients)

Underperform 14.73% 15.19% 18.68% 5.76% 3.03% 15.00% (for global coverage by Macquarie, 8.00% of stocks followed are investment banking clients)

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Chan Hwang (Korea) (822) 3705 8643

Suresh Ganapathy (India) (9122) 6720 4078

Sameer Bhise (India) (9122) 6720 4099

Gilbert Lopez (Philippines) (632) 857 0892

Ken Ang (Singapore) (65) 6601 0836

Passakorn Linmaneechote (Thailand) (662) 694 7728

Conglomerates

David Ng (China, Hong Kong) (852) 3922 1291

Conrad Werner (Singapore) (65) 6601 0182


Consumer and Gaming

Linda Huang (Asia, China, Hong Kong) (852) 3922 4068

Zibo Chen (China, Hong Kong) (852) 3922 1130

Terence Chang (China, Hong Kong) (852) 3922 3581

Sunny Chow (China, Hong Kong) (852) 3922 3768

Satsuki Kawasaki (Japan) (813) 3512 7870

Kwang Cho (Korea) (822) 3705 4953

KJ Lee (Korea) (822) 3705 9935

Stella Li (Taiwan) (8862) 2734 7514

Amit Sinha (India) (9122) 6720 4085

Fransisca Widjaja (65) 6601 0847 (Indonesia, Singapore)

Karisa Magpayo (Philippines) (632) 857 0899

Chalinee Congmuang (Thailand) (662) 694 7993

Emerging Leaders

Jake Lynch (Asia) (852) 3922 3583

Aditya Suresh (Asia) (852) 3922 1265

Timothy Lam (China, Hong Kong) (852) 3922 1086

Kwang Cho (Korea) (822) 3705 4953

Corinne Jian (Taiwan) (8862) 2734 7522

Marcus Yang (Taiwan) (8862) 2734 7532

Conrad Werner (ASEAN) (65) 6601 0182

Industrials

Janet Lewis (Asia) (813) 3512 7856

Patrick Dai (China) (8621) 2412 9082

Kunio Sakaida (Japan) (813) 3512 7873

William Montgomery (Japan) (813) 3512 7864

James Hong (Korea) (822) 3705 8661

Benson Pan (Taiwan) (8862) 2734 7527

Inderjeetsingh Bhatia (India) (9122) 6720 4087

Justin Chiam (Singapore) (65) 6601 0560

Internet, Media and Software

Wendy Huang (Asia, China) (852) 3922 3378

David Gibson (Asia, Japan) (813) 3512 7880

Hillman Chan (China, Hong Kong) (852) 3922 3716

Soyun Shin (Korea) (822) 3705 8659

Abhishek Bhandari (India) (9122) 6720 4088

Oil, Gas and Petrochemicals

Polina Diyachkina (Asia, Japan) (813) 3512 7886

Aditya Suresh (Asia, China, India) (852) 3922 1265

Anna Park (Korea) (822) 3705 8669

Isaac Chow (Malaysia) (603) 2059 8982

Pharmaceuticals and Healthcare

Abhishek Singhal (India) (9122) 6720 4086

Wei Li (China, Hong Kong) (852) 3922 5494

Property

Tuck Yin Soong (Asia, Singapore) (65) 6601 0838


Raymond Liu (China, Hong Kong) (852) 3922 3629

Wilson Ho (China) (852) 3922 3248

William Montgomery (Japan) (813) 3512 7864


Abhishek Bhandari (India) (9122) 6720 4088

Aiman Mohamad (Malaysia) (603) 2059 8986

Kervin Sisayan (Philippines) (632) 857 0893

Patti Tomaitrichitr (Thailand) (662) 694 7727

Resources / Metals and Mining

Polina Diyachkina (Asia, Japan) (813) 3512 7886

Coria Chow (China) (852) 3922 1181

Anna Park (Korea) (822) 3705 8669

Sumangal Nevatia (India) (9122) 6720 4093

Technology

Damian Thong (Asia, Japan) (813) 3512 7877

George Chang (Japan) (813) 3512 7854

Daniel Kim (Korea) (822) 3705 8641

Allen Chang (Greater China) (852) 3922 1136

Jeffrey Ohlweiler (Greater China) (8862) 2734 7512

Patrick Liao (Greater China) (8862) 2734 7515

Louis Cheng (Greater China) (8862) 2734 7526

Kaylin Tsai (Greater China) (8862) 2734 7523

Telecoms

Soyun Shin (Korea) (822) 3705 8659

Prem Jearajasingam (ASEAN) (603) 2059 8989

Kervin Sisayan (Philippines) (632) 857 0893

Transport & Infrastructure

Janet Lewis (Asia) (852) 3922 5417


Azita Nazrene (ASEAN) (603) 2059 8980

Utilities & Renewables

Patrick Dai (China) (8621) 2412 9082

Candice Chen (China) (8621) 2412 9087

Alan Hon (Hong Kong) (852) 3922 3589


Prem Jearajasingam (Malaysia) (603) 2059 8989

Karisa Magpayo (Philippines) (632) 857 0899

Commodities

Colin Hamilton (Global) (44 20) 3037 4061

Ian Roper (65) 6601 0698

Jim Lennon (44 20) 3037 4271

Lynn Zhao (8621) 2412 9035

Matthew Turner (44 20) 3037 4340

Economics

Peter Eadon-Clarke (Global) (813) 3512 7850

Larry Hu (China, Hong Kong) (852) 3922 3778

Quantitative / CPG

Gurvinder Brar (Global) (44 20) 3037 4036

Woei Chan (Asia) (852) 3922 1421

Danny Deng (Asia) (852) 3922 4646

Per Gullberg (Asia) (852) 3922 1478

Strategy/Country

Viktor Shvets (Asia, Global) (852) 3922 3883

Chetan Seth (Asia) (852) 3922 4769


Peter Eadon-Clarke (Japan) (813) 3512 7850

Chan Hwang (Korea) (822) 3705 8643

Jeffrey Ohlweiler (Taiwan) (8862) 2734 7512


Jayden Vantarakis (Indonesia) (6221) 2598 8310

Anand Pathmakanthan (Malaysia) (603) 2059 8833


Conrad Werner (Singapore) (65) 6601 0182

Passakorn Linmaneechote (Thailand) (662) 694 7728

Find our research at Macquarie: www.macquarieresearch.com/ideas/ Thomson: www.thomson.com/financial Reuters: www.knowledge.reuters.com Bloomberg: MAC GO Factset: http://www.factset.com/home.aspx CapitalIQ www.capitaliq.com Email [email protected] for access

Asia Sales Regional Heads of Sales

Miki Edelman (Global) (1 212) 231 6121

Jeff Evans (Boston) (1 617) 598 2508

Jeffrey Shiu (China, Hong Kong) (852) 3922 2061

Sandeep Bhatia (India) (9122) 6720 4101

Thomas Renz (Geneva) (41 22) 818 7712

Riaz Hyder (Indonesia) (6221) 2598 8486

Nick Cant (Japan) (65) 6601 0210

John Jay Lee (Korea) (822) 3705 9988

Nik Hadi (Malaysia) (603) 2059 8888

Gino C Rojas (Philippines) (632) 857 0861

Regional Heads of Sales cont’d

Paul Colaco (San Francisco) (1 415) 762 5003

Amelia Mehta (Singapore) (65) 6601 0211

Angus Kent (Thailand) (662) 694 7601

Ben Musgrave (UK/Europe) (44 20) 3037 4882

Christina Lee (UK/Europe) (44 20) 3037 4873

Sales Trading

Adam Zaki (Asia) (852) 3922 2002

Stanley Dunda (Indonesia) (6221) 515 1555

Sales Trading cont’d

Suhaida Samsudin (Malaysia) (603) 2059 8888

Michael Santos (Philippines) (632) 857 0813

Chris Reale (New York) (1 212) 231 2555

Marc Rosa (New York) (1 212) 231 2555

Justin Morrison (Singapore) (65) 6601 0288

Daniel Clarke (Taiwan) (8862) 2734 7580

Brendan Rake (Thailand) (662) 694 7707

Mike Keen (UK/Europe) (44 20) 3037 4905

This publication was disseminated on 17 April 2017 at 18:00 UTC.

Documents

YoY change in crude steel output GLOBAL The Global Macro ...pg.jrj.com.cn/acc/Res/CN_RES/MAC/2017/4/18/46ec2de... · Please refer to page 77 for important disclosures and analyst