oecd business and finance scoreboard 2016€¦ · inward fdi positions by immediate (imd) versus...

SCOREBOARD2016

OECD Business and Finance

OECD Business and

Finance Scoreboard 2016

This work is published under the responsibility of the Secretary-General of the OECD. The opinions expressed

and arguments employed herein do not necessarily reflect the official views of OECD member countries.

This document and any map included herein are without prejudice to the status of or sovereignty over any

territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or

area.

The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli authorities. The

use of such data by the OECD is without prejudice to the status of the Golan Heights, East Jerusalem and Israeli

settlements in the West Bank under the terms of international law.

© OECD 2016

3

ABOUT THE OECD BUSINESS AND FINANCE SCOREBOARD

The OECD Business and Finance Scoreboard accompanies the OECD Business and Finance

Outlook by providing a commented overview of selected indicators and data related to corporate

performance, banking, capital markets, pensions and investments. While some of the indicators and

developments are subject to in-depth analysis in the Outlook, others appear only in the Scoreboard,

giving the reader complementary information and additional opportunities for analysis.

The Scoreboard makes use of data that is collected directly by the OECD as well as calculations

that are based on information available in different external databases. A detailed description of the

methodology for data collection and analysis is provided in the annex.

The indicators and data that feature in this edition of the Scoreboard, primarily reflect the focus

of the 2015 and 2016 OECD Business and Finance Outlooks. Going forward, the objective is to

establish a useful balance between relevant standardised indicators to be tracked over time and

quantitative information that primarily is tailored to support the policy themes that will be addressed in

subsequent editions of the Business and Finance Outlook.

5

Table of contents

About the OECD Business and Finance Scoreboard ................................................................. 3

Acronyms and abbreviations ..................................................................................................... 8

Editorial ..................................................................................................................................... 9

Global trends in productivity ................................................................................................... 11

Declining investment opportunities ......................................................................................... 12

Greater capital expenditure in emerging economies ................................................................ 14

A global surge in corporate bond issues .................................................................................. 16

The maturity divergence .......................................................................................................... 17

An increase in emerging market use of public equity markets ................................................ 18

A decline in initial public offerings by small companies ........................................................ 19

Public equity markets remain an important source of continuous financing ........................... 20

New industrial sectors using corporate bonds ......................................................................... 21

Sectoral trends in the use of public equity markets ................................................................. 22

A decline in overall corporate bond ratings ............................................................................. 23

The decline in corporate bond covenant protection ................................................................. 24

Distance-to-default has fallen .................................................................................................. 26

A measure of the value of implicit guarantees for the debt of large banks .............................. 27

Bank beta indicator .................................................................................................................. 28

Pension funds’ investments fell slightly in 2015 relatively to 2014 ........................................ 30

The low returns in 2015 mostly explain the fall in pension funds’ investments ..................... 31

Negative returns in stock markets may be behind the weak performance

of pension funds’ investments in 2015.............................................................................. 32

Global trends in FDI flows ...................................................................................................... 33

Equity capital FDI flows surged in 2015 ................................................................................. 34

Capital passing through Special Purpose Entities (SPEs) dropped significantly

in 2014 and 2015 ............................................................................................................... 36

Pace of liberalising regulatory restrictions on FDI has slowed in recent years ....................... 39

Shifting view of the origin of FDI: Investors hold investments indirectly through

Luxembourg and the Netherlands ..................................................................................... 42

Global trends in mergers and acquisitions ............................................................................... 44

Openness of banking systems .................................................................................................. 46

Saving-investment correlation ................................................................................................. 48

ANNEX: Methodology for data collection and analysis ......................................................... 49

6

Figures

Figure 1. Productivity measures for listed non-financial companies, 2002–2015 ................................. 11

Figure 2. Return on equity, cost of equity and cost of capital, 2002–2015 ............................................ 12

Figure 3. Listed non-financial company corporate finance data, 2002–2015 ........................................ 14

Figure 4. Outstanding stock of non-financial corporate bonds, 2000–2015 .......................................... 16

Figure 5. Average maturities for corporate bonds issued by non-financial companies, 2000–

2015 ........................................................................................................................................ 17

Figure 6. Initial public offerings (IPOs), 2000–2015 ............................................................................. 18

Figure 7. Small non-financial company IPOs in advanced economies, 1994–2015 .............................. 19

Figure 8. Secondary public offerings in advanced and emerging economies, 2000–2015 .................... 20

Figure 9. Corporate bond issuance by companies in energy, industrials and materials sectors,

2000–2015 .............................................................................................................................. 21

Figure 10. Distribution of public equity financing among different sectors, 2000–2015 ...................... 22

Figure 11. Rating quality and new supply of corporate bonds, 2000–2015 ........................................... 23

Figure 12. Covenant protection index for corporate bonds issued in the United States, 2000–

2014 ........................................................................................................................................ 24

Figure 13. Distance-to-default (DTD) of large listed banks, 1999–2016 ............................................... 26

Figure 14. Credit rating uplifts for the debt of large banks, 2007–2015 ................................................ 27

Figure 15. Bank beta indicator for large listed banks, 1999–2016 ......................................................... 28

Figure 16. Pension funds' real net investment rate of return in selected OECD countries,

2014–2015 .............................................................................................................................. 31

Figure 17. Pension fund asset allocation in selected asset classes in selected OECD countries,

2015 ........................................................................................................................................ 32

Figure 18. Foreign direct investment inflows by region, 2005–2015 .................................................... 33

Figure 19. Foreign direct investment by instruments, 2005–2015 ......................................................... 34

Figure 20. Total FDI outflows from selected OECD countries with resident SPEs, 2005..................... 36

Figure 21. Total FDI inflows to selected OECD countries with resident SPEs, 2005–2015 ................. 36

Figure 22. OECD FDI Regulatory Restrictiveness Index, 1997–2015 .................................................. 39

Figure 23. Inward FDI positions by major ultimate partners versus immediate partners ...................... 42

Figure 24. Domestic and Cross-Border M&A deals, 1997–2015 .......................................................... 44

Figure 25. Deviations from covered interest parity (CIP) on domestic forward (DF) and non-

deliverable forward (NDF) markets, 2015 .............................................................................. 46

Figure 26. Saving-investment correlation, 1981–2015 .......................................................................... 48

7

Tables

Table 1. Average value added and net sales data for listed companies, 2002–2015.............................. 11

Table 2. Average return on equity, cost of capital and debt data for listed companies, 2002–

2015 ........................................................................................................................................ 13

Table 3. Key financial data for non-financial listed companies, 2002–2015 ......................................... 15

Table 4. Outstanding stock of corporate bonds, 2000–2015 .................................................................. 16

Table 5. Average maturities for corporate bonds, 2000–2015 ............................................................... 17

Table 6. Initial public offerings, 2000–2015 .......................................................................................... 18

Table 7. Small non-financial company IPOs in advanced and emerging economies ............................ 19

Table 8. Secondary public offerings, 2000–2015 .................................................................................. 20

Table 9. Distribution of corporate bond issuance among different sectors, 2000–2015 ........................ 21

Table 10. Distribution of public equity financing among different sectors, 2000–2015 ....................... 22

Table 11. Distribution of corporate bond issuance among rating categories, 2000–2015 ..................... 23

Table 12. Frequency of different types of corporate bond covenants, 2000–2014 ................................ 25

Table 13. Average distance-to-default (DTD) of large listed banks, 2003–2016 .................................. 26

Table 14. Average beta calculated using MSCI regional equity indices for large listed banks,

2003–2016 .............................................................................................................................. 28

Table 15. Average beta calculated using MSCI global equity index for large listed banks,

2003–2016 .............................................................................................................................. 29

Table 16. Total investment of pension funds and all retirement vehicles, 2015 .................................... 30

Table 17. Pension funds' real net rate of investment returns in selected OECD countries,

2005–2015 .............................................................................................................................. 31

Table 18. FDI inflows by selected regions, 2005–2015......................................................................... 33

Table 19. FDI outflows by selected regions, 2005–2015....................................................................... 33

Table 20. FDI inflows and outflows by instrument for selected regions, 2005–2015 ........................... 35

Table 21. FDI outflows for countries with SPEs, 2005–2015 ............................................................... 37

Table 22. FDI inflows for countries with SPEs, 2005–2015 ................................................................. 38

Table 23. OECD FDI Regulatory Restrictiveness Index, OECD countries per sector, 2015 ................ 40

Table 24. OECD FDI Regulatory Restrictiveness Index, Non-OECD countries per sector,

2015 ........................................................................................................................................ 41

Table 25. Inward FDI positions by Immediate (IMD) versus Ultimate (ULT) partner country ............ 43

Table 26. Inward FDI positions by Immediate (IMD) versus Ultimate (ULT) partner country

for selected OECD countries .................................................................................................. 43

Table 27. Domestic and Cross-Border M&A deals, 1997–2015 ........................................................... 45

Table 28. Deviations from covered interest parity (CIP) on domestic forward (DF) markets,

2006–2015 .............................................................................................................................. 47

Table 29. Deviations from covered interest parity (CIP) on non-deliverable forward (NDF)

markets, 2006–2015 ............................................................................................................... 47

Table 30. Saving-investment correlation by region, 1986–2015 ........................................................... 48

8

Acronyms and abbreviations

BRICS Brazil, Russia, India, China, and South Africa

BRIICS Brazil, Russia, India, Indonesia, China, and South Africa

CAPEX company capital expenditure

CIP covered interest parity

COD cost of debt

COE cost of equity

COK cost of capital

DF domestic forward

DTD distance-to-default

EBITDA earnings before interest, taxes, depreciation and amortisation

EME emerging market economy

FDI foreign direct investment

GARCH Generalized Auto Regressive Conditional Heteroskedasticity

GICS Global Industry Classification Standard

IG investment grade

IPO initial public offering

M&A mergers and acquisitions

MSCI Morgan Stanley Composite Index

NDF non-deliverable forward

OECD Organisation for Economic Co-operation and Development

R&D research and development

REIT real estate investment trusts

ROE return on equity

SPEs special purpose entities

SPO secondary public offering (follow-on offering)

9

Editorial

Despite the fact that the interest rates have been kept at record lows since the financial crisis, the

world economy is still beset with major headwinds. As a consequence, there is little doubt that the

journey towards economic recovery increasingly has to rely on structural adjustments. Of particular

importance is the ability of the corporate sector to adjust to the reversal of the commodity supercycle

through a combination of orderly creative destruction and corporate strategies that promote productive

investments.

From a policy perspective, these challenges call for a better understanding of the relationships

between business behaviour and macroeconomic conditions. Notably, how monetary regimes and

regulatory reforms influence the decisions of both investors and corporations to focus on long term

value creation.

This Scoreboard provides indicators that will help policy makers to monitor changes in business

behaviour that influence firm level performance and productivity. This includes indicators of how

they use productivity enhancing strategies such as research and development, cash flow management

and corporate restructuring. It also contains indicators of how, and to what extent, corporations use

market based finance, such as equity and corporate bonds.

One important indicator of how effectively corporations use the capital that they have at their

disposal is the return-on-equity. Building on a global sample of about 11.000 large listed companies,

the Scoreboard shows that return-on-equity minus the cost-of-equity has fallen. In the last couple of

years it has even been negative in emerging markets, which indicates the existence of excess capacity

and the need for structural adjustments.

Access to equity capital is essential for long term investments, such as innovation and product

development; particularly for smaller growth companies. However, the amount of new equity capital

that corporations raised through initial public offerings (IPOs) declined in 2015. And while the

number of IPOs by growth companies in advanced economies slightly increased, the total amount of

new equity capital that they raised actually declined and remains at a low level. Considering the

consistently low level of growth company IPOs in the last decade, more can probably be done to

increase their access to public equity markets.

Bonds have become an increasingly important source of finance for corporations worldwide and

the stock of outstanding corporate bonds continued to increase in 2015. At the same time, the overall

rating quality of corporate bonds remains considerably lower than before the 2008 financial crisis.

This is mainly an effect of the high share of non-investment grade bonds and B-grade bonds being

issued. The covenant protection index for non-investment grade corporate bonds also remains at a low

level, indicating that investors in their search for yield are still willing to forego certain governance

rights.

10

Analysis of firm level data in the OECD Business and Finance Outlook suggests that some

companies with high productivity growth may use mergers and acquisitions to rationalise their

businesses in a more competitive environment. Globally, the volume of corporate mergers and

acquisitions grew by almost one third in 2015. The surge was fuelled mainly by domestic mergers and

acquisitions in advanced economies, who account for almost three quarters of the value of all mergers

and acquisitions.

To develop their business, corporations may also take advantage of entering and expanding in

overseas markets. While the global flow of foreign direct investments has remained fairly stable since

the financial crisis, 2015 saw an increase of about 25%. This increase was fuelled mainly by an

increased inflow to the European Union and the United States, while the People’s Republic of China

experienced a small decrease in inward foreign direct investments.

The OECD Business and Finance Outlook 2016 underlines the need to address the links between

the regulatory framework, the macroeconomic environment and business behaviour. By innovative

use of micro level data, this Scoreboard contributes to this effort and the shaping of coherent policies

that will support productivity, sustainable growth and better lives for all.

Adrian Blundell-Wignall

Special Advisor to the Secretary-General on Financial Markets

Director for Financial and Enterprise Affairs, OECD

11

Figure 1. Productivity measures for listed non-financial companies, 2002–2015

Table 1. Average value added and net sales data for listed companies, 2002–2015

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Value added per capita

United States 0.20 0.20 0.21 0.20 0.20 0.20 0.21 0.20 0.22 0.23 0.23 0.23 0.23 0.22

Europe 0.07 0.08 0.09 0.09 0.10 0.11 0.12 0.10 0.10 0.11 0.10 0.10 0.09 0.08

Japan 0.00 0.00 0.00 0.01 0.05 0.05 0.05 0.05 0.06 0.06 0.06 0.05 0.05 0.05

Australia & New Zealand 0.05 0.06 0.10 0.12 0.17 0.17 0.13 0.17 0.19 0.19 0.17 0.17 0.15 0.14

BRIICS 0.02 0.03 0.03 0.03 0.04 0.04 0.04 0.04 0.05 0.06 0.05 0.06 0.05 0.05

Other Asia 0.04 0.06 0.07 0.07 0.06 0.10 0.09 0.06 0.08 0.07 0.07 0.08 0.09 0.09

Other EMEs 0.03 0.03 0.04 0.04 0.04 0.04 0.06 0.05 0.06 0.06 0.06 0.06 0.06 0.06

Advanced Economies 0.11 0.11 0.12 0.12 0.13 0.13 0.13 0.12 0.13 0.14 0.14 0.14 0.13 0.12

Emerging Economies 0.03 0.03 0.04 0.04 0.04 0.06 0.06 0.05 0.06 0.07 0.06 0.07 0.06 0.06

Net sales per capita

United States 0.27 0.29 0.31 0.32 0.34 0.36 0.39 0.36 0.39 0.42 0.41 0.40 0.40 0.38

Europe 0.22 0.25 0.27 0.29 0.31 0.35 0.37 0.32 0.33 0.36 0.35 0.35 0.34 0.29

Japan 0.37 0.40 0.43 0.42 0.41 0.43 0.45 0.43 0.48 0.51 0.50 0.44 0.41 0.41


BRIICS 0.10 0.11 0.13 0.15 0.18 0.20 0.22 0.20 0.24 0.28 0.27 0.28 0.27 0.25

Other Asia 0.29 0.31 0.33 0.31 0.30 0.29 0.32 0.26 0.29 0.29 0.29 0.41 0.42 0.39

Other EMEs 0.17 0.15 0.18 0.20 0.22 0.25 0.30 0.24 0.26 0.29 0.29 0.29 0.27 0.24



Note: All data are expressed in US dollar billion per one thousand employees. Europe refers to the European Union, Norway and

Switzerland. BRIICS refers to Brazil, Russia, India, Indonesia, China and South Africa. Other Asia refers to Hong Kong (China),

Singapore, Korea and Chinese Taipei. Value added: sum of personnel expenses and EBITDA (i.e. income before interest, taxes,

depreciation and amortisation). Net sales: total operating revenues less various adjustments to gross sales.

Source: OECD calculations, Bloomberg. See Annex for details.

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

USD bln /1000

employ ees

USD bln /1000

employ ees

Advanced economies

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

USD bln /1000

employ ees

USD bln /1000

employ ees

Emerging economies

Value added Net sales (RHS)

Global trends in productivity

In recent years, productivity measured both as company value-added (wages plus EBITDA) per

employee and net sales per employee have declined. While net sales per employee in emerging

economies, as a result their increased participation in global value chains, has caught up with levels in

advanced economies, it has not allowed for a similar catch up with respect to productivity measured

as value added per employee.

12

Figure 2. Return on equity, cost of equity and cost of capital, 2002–2015

Source: Bloomberg, OECD calculations. See Annex for details.

0.00

0.50

1.00

1.50

2.00

2.50

3.00

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Ratio

Debt-to-equity ratio

Advanced economies Emerging economies

-2

0

2

4

6

8

10

12%

Advanced economies

Dividend & Buybacks ROE - COE

ROE - COK

-10

-5

0

5

10

%

Emerging economies

Dividend & Buybacks ROE - COE

ROE - COK

Declining investment opportunities

In emerging economies both the return on equity (ROE) minus the cost-of-equity and the ROE minus

cost-of-capital are today negative. This is consistent with other evidence that these economies exhibit

excess capacity in important sectors resulting from overinvestment. With a decrease in investment

opportunities, dividends and buybacks have been rising in advanced economies since the 2008

financial crisis. Debt financing is still greater in advanced economies but emerging economies are

catching up.

13

Table 2. Average return on equity, cost of capital and debt data for listed companies, 2002–2015

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Debt to equity ratio

United States 3.13 2.92 2.74 2.65 2.65 2.68 2.90 2.76 2.64 2.72 2.75 2.69 2.87 2.95

Europe 3.28 3.19 3.14 3.06 3.02 3.02 3.29 3.14 2.91 2.95 3.01 2.92 3.06 3.04

Japan 3.24 3.02 2.86 2.67 2.62 2.60 2.72 2.64 2.62 2.66 2.62 2.55 2.48 2.48


BRIICS 1.95 1.99 1.99 1.99 2.03 2.06 2.19 2.19 2.20 2.28 2.35 2.42 2.55 2.55

Other Asia 2.41 2.29 2.25 2.26 2.27 2.29 2.44 2.33 2.19 2.24 2.33 2.60 2.55 2.60

Other EMEs 2.17 2.09 2.03 2.00 2.08 2.06 2.23 2.24 2.22 2.27 2.25 2.25 2.37 2.41



ROE minus COE (%)

United States -1.15 7.55 8.97 10.12 10.88 10.04 1.59 4.64 7.77 7.82 4.83 6.09 4.46 0.84

Europe -3.17 3.30 9.47 10.11 10.94 11.78 5.53 1.86 6.18 5.02 2.60 4.62 4.30 1.95

Japan 1.39 3.45 5.39 5.93 5.72 4.36 -4.17 -0.65 1.76 -0.24 1.34 3.63 3.42 3.40

Australia & New Zealand 0.63 5.03 12.59 13.35 14.47 9.57 -3.00 1.06 5.07 -1.21 -3.99 -1.61 -7.22 -11.65

BRIICS 3.18 7.71 8.88 9.87 6.69 4.28 -1.19 -1.72 -0.89 -1.57 -6.01 -7.54 -11.33 -12.11

Other Asia 0.16 0.13 3.38 2.42 1.29 1.83 -5.28 -3.12 0.16 -2.98 -3.88 -3.01 -4.10 -5.05

Other EMEs 0.14 4.36 5.93 7.17 6.71 8.10 0.84 0.06 2.20 1.29 0.03 -0.84 -3.31 -5.38

Advanced Economies -1.16 4.96 8.18 8.78 9.50 8.99 1.29 1.86 5.38 4.12 2.31 3.81 2.90 0.11

Emerging Economies 1.71 5.32 7.10 8.09 5.85 4.94 -0.85 -1.40 0.05 -0.86 -4.27 -5.45 -8.57 -9.60

ROE minus COK (%)

United States -0.45 8.16 9.74 10.89 11.44 11.36 5.14 7.09 10.36 11.52 9.48 10.06 9.46 6.14

Europe -1.75 4.46 10.63 11.21 11.86 12.75 8.80 4.81 9.14 8.63 6.59 7.69 8.00 5.80

Japan 2.53 4.68 7.20 7.54 7.48 6.40 -1.28 1.70 4.07 2.60 4.36 6.32 6.38 6.36

Australia & New Zealand 5.13 8.72 16.44 17.16 17.78 13.75 3.19 4.91 8.62 3.90 0.60 2.53 -2.28 -5.72

BRIICS 5.30 9.49 10.46 11.06 9.75 7.79 3.61 1.94 3.57 3.74 0.58 -0.73 -2.54 -3.82

Other Asia 3.95 4.31 7.27 6.05 4.91 6.25 1.41 1.75 5.19 3.36 2.44 3.02 3.15 2.92

Other EMEs 2.48 6.50 8.46 9.50 9.25 11.33 6.43 4.53 6.96 7.14 5.66 3.90 2.22 0.16


Emerging Economies 4.46 7.78 9.49 9.92 8.85 8.41 4.26 2.51 4.51 4.51 1.96 0.64 -1.04 -2.24

Dividend & Buybacks (% of net sales)

United States 2.66 2.61 3.60 4.66 5.23 5.82 4.28 2.88 3.65 4.53 4.52 5.07 5.82 6.06

Europe 1.84 1.56 2.85 3.58 4.27 4.07 3.84 2.64 2.71 3.18 2.86 3.32 3.26 3.33

Japan 1.09 0.75 0.79 0.94 1.17 1.53 1.70 0.64 0.85 1.17 1.22 0.99 1.41 1.41


BRIICS 1.76 1.95 1.22 1.70 2.13 0.82 1.08 0.97 0.87 2.21 2.37 1.99 1.44 0.93

Other Asia 2.09 2.39 2.84 2.84 3.03 2.62 3.15 1.79 1.97 2.48 1.77 1.33 1.38 1.76

Other EMEs 1.99 2.16 3.37 3.22 4.22 4.95 5.17 4.00 4.26 4.37 3.47 3.91 4.08 3.89



Note: Europe refers to the European Union, Norway and Switzerland. BRIICS refers to Brazil, Russia, India, Indonesia, China and

South Africa. Other Asia refers to Hong Kong (China), Singapore, Korea and Chinese Taipei. Dividend and buybacks ratios are

expressed in percent of net sales. Debt to capital ratio: ratio of debt to the sum of debt plus equity. ROE (return on equity): ratio of net

income to common equity. COE (cost of equity): sum of dividend and buyback yield and underlying trend in EPS growth. COK (cost

of capital): weighted average (by the share of equity and debt in total assets, respectively) sum of cost of equity and cost of debt.


14

Figure 3. Listed non-financial company corporate finance data, 2002–2015


0

2

4

6

8

10

12

14

0

1

2

3

4

5

6

7

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

% of net sales% of net sales

Advanced economies

Free cashflow R&D expenditure M&A deal value Capital expenditure (RHS)

0

2

4

6

8

10

12

14

0

1

2

3

4

5

6

7

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

% of net sales% of net sales

Emerging economies

Greater capital expenditure in emerging economies

Companies in emerging economies invest much more as a percentage of net sales than companies in

advanced economies. However, advanced economies are benefitting from efficacy of three corporate

strategies that boost their productivity: higher research and development; higher free cash flow, and;

more mergers and acquisition activities to rationalise business models.

15

Table 3. Key financial data for non-financial listed companies, 2002–2015

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Capital expenditure

United States 6.13 5.29 5.48 5.68 6.43 6.63 6.70 5.79 5.87 6.39 7.06 6.98 7.39 7.20

Europe 6.86 6.09 6.34 6.52 6.82 6.97 7.13 6.96 6.37 6.38 6.44 6.43 6.36 6.54

Japan 5.68 5.29 5.53 5.84 5.56 5.65 5.92 4.97 4.58 4.86 5.30 5.06 5.00 5.00


BRIICS 11.87 11.87 11.37 11.55 12.10 13.36 13.69 13.22 11.97 11.48 11.12 10.52 9.58 9.84

Other Asia 8.19 8.69 9.58 9.23 9.16 8.55 8.08 8.27 8.23 7.87 7.48 7.35 7.16 7.93

Other EMEs 9.98 9.11 8.25 10.13 10.55 10.97 10.98 10.95 9.15 8.76 9.24 9.95 10.48 11.28



Free cash flow

United States 5.53 6.39 6.51 6.04 5.31 5.41 4.58 6.62 6.32 6.12 5.40 5.90 5.38 6.15

Europe 3.47 4.70 6.10 5.60 5.24 5.08 3.48 5.89 5.32 3.90 3.90 3.79 4.08 4.48

Japan 3.09 4.09 3.18 2.38 2.71 2.43 0.62 4.73 4.28 1.99 2.14 3.24 3.43 3.43


BRIICS 4.02 4.49 3.08 2.78 2.56 0.75 -0.87 0.92 0.27 -1.18 -0.40 0.42 1.61 2.19

Other Asia 6.24 6.27 5.60 4.23 4.06 4.25 2.38 3.26 3.42 1.76 2.47 2.45 2.54 3.83

Other EMEs 4.97 7.97 8.56 6.94 7.14 6.62 4.18 7.34 6.97 5.14 5.19 4.58 4.75 4.27



R&D expenditure

United States 2.22 2.18 2.02 1.94 2.03 1.97 1.91 2.06 2.02 2.07 2.12 2.16 2.26 2.54

Europe 2.06 2.03 2.12 1.97 2.08 2.05 1.79 1.97 1.88 1.81 1.86 1.87 2.02 2.16

Japan 2.63 2.61 2.58 2.56 2.45 2.43 2.63 2.60 2.50 2.48 2.44 2.22 2.28 2.28


BRIICS 0.28 0.27 0.26 0.27 0.30 0.31 0.35 0.43 0.48 0.56 0.63 0.72 0.87 1.00

Other Asia 1.27 1.67 1.80 1.85 1.87 1.97 1.86 1.82 1.83 2.01 2.11 1.71 1.73 1.81

Other EMEs 0.26 0.30 0.26 0.22 0.16 0.15 0.16 0.20 0.18 0.18 0.22 0.22 0.23 0.27



M&A deal value

United States 4.00 2.75 2.48 3.64 6.60 4.84 3.36 3.57 2.70 2.82 3.02 2.01 4.03 5.25

Europe 2.49 1.50 1.58 2.60 3.66 4.92 3.05 2.40 2.00 2.28 1.29 1.19 2.06 2.87

Japan 0.84 0.33 0.75 0.81 1.99 1.10 1.62 0.95 1.01 1.21 1.11 1.64 0.92 0.94


BRIICS 0.90 1.48 1.86 1.74 1.63 2.77 1.52 1.68 1.63 1.10 0.74 1.16 0.99 2.66

Other Asia 1.20 0.89 1.06 1.06 1.64 3.00 0.86 0.97 1.81 1.00 1.01 0.69 1.70 2.04

Other EMEs 0.94 0.72 0.95 0.91 1.54 3.31 1.93 1.28 3.26 1.90 1.69 1.29 1.16 0.89



Note: All data are expressed in percent of net sales. Europe refers to the European Union, Norway and Switzerland. BRIICS refers to

Brazil, Russia, India, Indonesia, China and South Africa. Other Asia refers to Hong Kong (China), Singapore, Korea and Chinese

Taipei. Capital expenditure: amount the company spent on purchases of tangible fixed assets. Free cash flow: operating cash flow

minus capital expenditures. R&D expenditure: operating expense related to the research and development of a company's products

or services. M&A deal value: declared amount effectively paid by the acquirer for the target.


16

Figure 4. Outstanding stock of non-financial corporate bonds, 2000–2015

Table 4. Outstanding stock of corporate bonds, 2000–2015

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

World 7.7 8.5 8.9 9.7 10.6 11.4 12.9 14.3 14.9 15.8 16.7 17.5 18.8 19.9 20.9 21.8

Non-financial 3.5 4.0 4.3 4.5 4.7 4.8 5.1 5.5 5.8 6.8 7.5 8.1 9.0 9.9 10.4 11.0

Financial 4.2 4.4 4.6 5.2 5.9 6.6 7.8 8.8 9.1 9.0 9.2 9.4 9.8 10.0 10.5 10.7

Investment grade 7.0 7.7 8.0 8.8 9.6 10.3 11.7 13.0 13.7 14.5 15.1 15.8 16.8 17.6 18.4 19.1

Non-investment grade 0.8 0.8 0.8 0.9 1.0 1.1 1.2 1.3 1.2 1.3 1.6 1.7 2.0 2.3 2.6 2.7

Advanced economies 7.4 8.1 8.5 9.3 10.1 10.9 12.2 13.5 13.9 14.5 15.1 15.5 16.4 17.0 17.8 18.5

Non-financial 3.4 3.9 4.1 4.3 4.5 4.5 4.8 5.1 5.3 6.1 6.6 6.9 7.7 8.3 8.8 9.3

Financial 4.0 4.3 4.5 5.0 5.7 6.4 7.4 8.4 8.6 8.4 8.5 8.6 8.7 8.7 9.0 9.2



Emerging markets 0.3 0.3 0.3 0.4 0.4 0.5 0.7 0.9 1.0 1.3 1.6 2.0 2.4 2.9 3.1 3.3

Non-financial 0.2 0.2 0.2 0.2 0.2 0.3 0.3 0.4 0.5 0.7 0.9 1.1 1.3 1.6 1.6 1.7

Financial 0.2 0.2 0.2 0.2 0.2 0.2 0.4 0.5 0.5 0.6 0.7 0.9 1.1 1.3 1.5 1.6



United States 3.3 3.7 3.9 4.1 4.4 4.5 5.0 5.5 5.5 5.5 5.7 5.7 6.0 6.4 6.8 7.4

Non-financial 2.0 2.2 2.4 2.5 2.6 2.6 2.7 2.9 2.9 3.2 3.4 3.6 3.9 4.2 4.6 5.1

Financial 1.3 1.4 1.5 1.6 1.8 1.9 2.2 2.6 2.6 2.4 2.3 2.2 2.1 2.2 2.3 2.4



Europe 3.0 3.2 3.4 3.8 4.3 4.8 5.6 6.2 6.4 6.8 7.0 7.2 7.5 7.5 7.7 7.7

Non-financial 0.7 0.8 0.9 1.0 1.1 1.1 1.3 1.4 1.5 1.9 2.1 2.2 2.4 2.6 2.7 2.8

Financial 2.3 2.4 2.5 2.8 3.2 3.7 4.3 4.8 4.9 4.9 4.9 5.0 5.0 4.9 5.0 4.9



OECD 7.4 8.1 8.5 9.3 10.1 10.9 12.2 13.4 13.9 14.5 15.0 15.5 16.3 17.0 17.8 18.4

Non-financial 3.4 3.8 4.1 4.3 4.5 4.5 4.8 5.1 5.3 6.1 6.6 7.0 7.7 8.3 8.8 9.4

Financial 4.0 4.3 4.5 5.0 5.6 6.3 7.4 8.3 8.6 8.4 8.4 8.5 8.6 8.6 8.9 9.0



Note: All data are expressed in US dollar trillion.

Source: Thomson Reuters, Bloomberg, OECD calculations. See Annex for details.

A global surge in corporate bond issues

Since 2008, corporate bonds have become an increasingly important source of financing also for non-

financial companies. Two main factors seem to have contributed to this development: first the

decrease in bank lending to non-financial companies (so-called deleveraging), and; second, the

historically low levels of bond interest rates. From being a negligible source of external finance a

decade ago, the outstanding stock of corporate bonds has reached significant levels also in emerging

economies.

17

Figure 5. Average maturities for corporate bonds issued by non-financial companies, 2000–2015

Table 5. Average maturities for corporate bonds, 2000–2015

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

World 4.9 5.6 5.8 5.8 6.0 6.5 6.8 6.8 6.2 6.5 6.8 6.7 7.2 7.3 6.9 6.9

Non-financial 6.8 7.3 7.4 8.2 7.9 9.1 9.5 9.7 7.8 7.5 7.9 7.9 8.8 8.0 8.3 8.6

Financial 4.2 4.7 5.1 4.7 5.2 5.5 5.8 5.7 5.3 5.5 6.1 5.9 5.9 6.6 5.9 5.8



Advanced economies 4.9 5.5 5.8 5.8 6.1 6.6 6.9 7.0 6.1 6.5 6.9 7.0 7.7 8.1 7.4 8.0

Non-financial 6.9 7.2 7.5 8.4 8.0 9.2 9.7 10.1 8.0 7.9 8.4 8.4 9.3 9.1 8.9 10.0

Financial 4.2 4.6 5.1 4.7 5.2 5.6 5.8 5.9 5.1 5.2 6.0 6.1 6.3 7.1 6.2 6.5



Emerging markets 4.9 6.3 5.7 5.5 5.8 5.8 6.2 5.9 6.5 6.4 6.5 5.4 5.7 5.3 5.2 4.3

Non-financial 5.4 7.6 6.3 6.7 7.3 8.6 8.1 8.2 6.7 6.1 6.1 6.0 6.9 5.4 5.8 4.8

Financial 4.6 5.6 5.3 4.8 5.0 4.6 5.5 4.9 6.3 6.7 6.8 5.1 5.1 5.1 4.8 3.9



United States 4.8 6.3 7.2 7.5 7.4 8.3 8.4 9.5 8.8 9.3 9.6 10.2 10.8 10.8 10.6 11.1

Non-financial 8.1 9.2 9.8 9.9 9.4 11.3 11.7 12.4 10.9 9.9 10.8 11.5 11.8 11.8 12.1 12.4

Financial 3.8 4.9 5.9 5.9 6.0 6.2 6.5 7.7 6.5 7.7 7.9 8.3 8.3 9.0 7.8 8.6



Europe 5.1 5.0 5.3 5.0 5.3 6.0 6.4 6.2 5.8 6.0 6.7 6.5 6.8 7.1 6.9 7.3

Non-financial 6.4 7.0 6.8 8.3 7.5 8.3 10.1 10.6 8.0 8.0 8.4 7.7 8.7 7.9 8.1 8.1

Financial 4.8 4.4 4.9 4.3 4.9 5.6 5.7 5.5 5.3 5.0 6.2 6.2 6.0 6.7 6.4 7.0



OECD 4.9 5.6 5.9 5.9 6.2 6.8 7.1 7.2 6.3 6.6 6.9 7.2 7.9 8.3 7.5 8.1

Non-financial 7.1 7.4 7.7 8.7 8.4 9.6 10.3 10.4 8.6 8.1 8.8 8.8 9.7 9.5 9.3 10.1

Financial 4.2 4.7 5.1 4.7 5.3 5.6 5.9 6.0 5.2 5.2 5.9 6.2 6.3 7.1 6.2 6.6



Note: Average maturities are expressed in number of years.

Source: Thomson Reuters, OECD calculations. See Annex for details.

The maturity divergence

The average maturities for investment grade bonds issued by non-financial companies in the United

States have always been longer than for bonds issued by companies in the rest of the world. However,

the total difference in average maturity has increased quite considerably since the financial crisis.

18

Figure 6. Initial public offerings (IPOs), 2000–2015

Table 6. Initial public offerings, 2000–2015

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

World 283 122 84 67 156 183 277 324 105 119 291 155 104 139 227 172

Non-financial 263 96 61 45 130 149 206 251 74 99 205 140 94 113 186 129

Financial 20 26 24 22 26 34 71 73 31 21 86 16 10 26 41 43

Advanced economies 244 109 63 44 122 120 149 146 46 37 130 73 59 93 142 115

Non-financial 225 83 43 31 101 105 125 127 22 31 82 64 55 80 113 89

Financial 19 26 20 12 20 15 24 19 24 6 48 9 4 12 29 26

Emerging markets 39 13 22 23 34 63 128 178 59 82 161 82 44 47 85 58

Non-financial 38 13 18 14 28 44 81 124 52 68 122 75 39 32 73 40

Financial 1 0 4 10 6 19 48 54 8 15 38 7 6 14 12 17

United States 81 48 27 13 43 37 38 35 29 17 37 28 35 47 46 24

Non-financial 74 38 15 10 33 31 32 33 7 15 33 26 33 43 31 20

Financial 7 10 12 3 11 6 6 2 22 2 4 2 2 4 16 4

Europe 119 43 20 9 40 57 74 78 10 5 25 29 10 25 58 61

Non-financial 112 29 19 6 36 51 62 69 9 2 22 23 7 19 45 47

Financial 7 14 1 3 4 6 12 10 1 2 2 6 2 6 12 14

OECD 231 106 56 40 118 122 145 141 49 33 107 70 61 94 140 113

Non-financial 213 81 40 28 96 106 121 119 25 27 79 62 55 83 111 87

Financial 18 26 16 12 23 16 23 22 24 5 29 8 5 11 29 26

Note: All data are expressed in 2015 US dollar billion.


An increase in emerging market use of public equity markets

Companies tap public equity markets for funding for the first time by making an initial public offering

(IPO). During the early 2000s, global IPO activity was dominated by companies from advanced

economies. However, in the last ten years, companies from emerging markets account for almost half

of the money raised through IPOs.

19

Figure 7. Small non-financial company IPOs in advanced economies, 1994–2015

Table 7. Small non-financial company IPOs in advanced and emerging economies

Global Advanced economies Emerging economies

2000-2007 2008-2015 2000-2007 2008-2015 2000-2007 2008-2015

Total value of IPOs with size

less than USD 100 M (2015

USD, million)

173,827 142,279 123,569 62,083 50,258 80,167

Share of all IPOs 14.5% 13.7% 10.3% 6.0% 4.2% 7.7%

Number of IPOs with size less

than USD 100 M (%) 7,765 4,926 5,881 2,699 1,884 2,227

Share of all IPOs 78.1% 70.8% 59.1% 38.8% 18.9% 32.0%


A decline in initial public offerings by small companies

The last decade has seen a fairly marked decline in the extent to which small and medium-sized

growth companies access public equity markets for external funding. This trend is particularly marked

in the United States and in Europe. Today, companies in advanced economies that actually use public

equity markets for the first time tend to be larger at the time of the IPO.

20

Figure 8. Secondary public offerings in advanced and emerging economies, 2000–2015

Table 8. Secondary public offerings, 2000–2015

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

World 457 293 265 311 391 422 460 593 648 946 733 505 517 575 606 692

Non-financial 397 239 210 227 320 307 325 440 293 476 463 317 323 437 468 517

Financial 60 54 54 84 71 115 135 154 355 469 270 189 194 137 138 175

Advanced economies 430 276 241 291 355 353 382 415 520 790 422 323 347 408 422 461

Non-financial 374 225 198 214 291 263 276 329 200 381 246 212 222 318 316 348

Financial 56 51 43 77 64 91 106 85 320 409 176 110 124 90 105 113

Emerging markets 27 18 23 20 36 69 79 178 128 155 311 183 170 167 184 232

Non-financial 23 14 12 13 29 44 49 110 93 95 218 104 100 120 151 170

Financial 4 4 11 7 7 24 29 68 35 60 94 79 70 47 33 62

United States 147 88 76 78 94 86 94 88 167 220 149 90 135 133 117 146

Non-financial 138 79 68 67 86 69 79 79 57 74 56 61 72 125 105 126

Financial 10 9 8 11 7 18 15 10 109 146 93 29 64 8 13 20

Europe 180 119 100 125 157 158 159 179 246 348 128 123 102 167 182 156

Non-financial 141 98 71 84 122 109 93 123 70 156 78 64 67 99 111 104

Financial 38 21 29 42 36 49 65 57 176 192 50 58 35 68 71 52

OECD 410 270 239 273 344 336 359 370 511 760 402 314 321 408 393 408

Non-financial 397 239 210 227 320 307 325 440 293 476 463 317 323 437 468 517

Financial 60 54 54 84 71 115 135 154 355 469 270 189 194 137 138 175



Public equity markets remain an important source of continuous financing

The role of public equity markets in providing a company with new equity capital is not limited to the

time of the initial public offering (IPO). They also provide an opportunity for companies that are

already listed to raise additional capital through a secondary public offering (SPO) or “follow-on

issue”. In fact, every year since 2000, the global amount of money raised through SPOs exceeded the

amount of money raised through IPOs. Record SPO levels were reached in the two years following

the 2008 financial crisis.

21

Figure 9. Corporate bond issuance by companies in energy, industrials and materials sectors,

2000–2015

Table 9. Distribution of corporate bond issuance among different sectors, 2000–2015

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Consumer Products and Services

Volume 20 45 21 31 53 46 61 39 40 58 58 63 88 73 80 77

Share 1% 2% 1% 1% 2% 2% 2% 1% 1% 2% 2% 2% 2% 2% 2% 2%

Consumer Staples

Volume 57 70 69 71 38 41 45 71 94 113 88 84 125 132 92 117 Share 3% 3% 3% 3% 1% 1% 1% 2% 3% 4% 3% 3% 3% 4% 3% 4%

Energy

Volume 156 220 208 232 164 153 209 292 303 528 369 365 482 464 424 368

Share 8% 9% 10% 8% 6% 5% 6% 8% 11% 17% 12% 12% 13% 13% 12% 11%

Financials

Volume 1,346 1,308 1,286 1,881 2,130 2,165 2,699 2,574 1,927 1,539 1,829 1,744 1,867 1,678 1,987 1,753

Share 65% 55% 64% 68% 75% 76% 74% 72% 68% 48% 58% 56% 52% 48% 55% 52%

Healthcare

Volume 6 44 15 37 36 31 42 79 36 124 76 89 106 88 144 188 Share 0% 2% 1% 1% 1% 1% 1% 2% 1% 4% 2% 3% 3% 3% 4% 6%

High Technology

Volume 32 46 30 22 20 28 53 46 53 65 69 80 76 111 103 155

Share 2% 2% 1% 1% 1% 1% 1% 1% 2% 2% 2% 3% 2% 3% 3% 5%

Industrials

Volume 126 170 102 154 128 112 140 135 104 247 224 221 259 310 234 225

Share 6% 7% 5% 6% 4% 4% 4% 4% 4% 8% 7% 7% 7% 9% 6% 7%

Materials

Volume 53 75 78 95 72 63 95 86 84 173 161 160 219 173 134 115 Share 3% 3% 4% 3% 3% 2% 3% 2% 3% 5% 5% 5% 6% 5% 4% 3%

Media and Entertainment

Volume 46 66 46 72 57 48 74 50 35 85 86 65 105 83 92 87 Share 2% 3% 2% 3% 2% 2% 2% 1% 1% 3% 3% 2% 3% 2% 3% 3%

Real Estate

Volume 18 30 27 32 42 40 65 53 24 41 58 55 80 109 108 91

Share 1% 1% 1% 1% 1% 1% 2% 1% 1% 1% 2% 2% 2% 3% 3% 3%

Retail

Volume 32 56 35 39 36 34 41 67 46 55 57 61 74 84 81 73

Share 2% 2% 2% 1% 1% 1% 1% 2% 2% 2% 2% 2% 2% 2% 2% 2%

Telecommunications

Volume 181 229 85 82 82 80 116 89 92 149 99 112 123 200 138 94 Share 9% 10% 4% 3% 3% 3% 3% 2% 3% 5% 3% 4% 3% 6% 4% 3%



New industrial sectors using corporate bonds

The single most important sector with respect to the use of corporate bonds is the financial services

sector. However, the relative importance of the financial services sector has declined by one fifth

during the post-crisis period and was mainly replaced by the energy, industrials and materials sectors.

This shift was also the result of a large absolute increase in the amount of money raised by these

supercycle sectors.

22

Figure 10. Distribution of public equity financing among different sectors, 2000–2015

Table 10. Distribution of public equity financing among different sectors, 2000–2015

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Consumer Products and Services

Volume 32 17 14 11 19 22 33 29 7 17 22 23 15 34 42 43

Share 4% 4% 4% 3% 3% 4% 5% 3% 1% 2% 2% 3% 2% 5% 5% 5%

Consumer Staples

Volume 15 21 13 9 23 22 28 36 47 38 41 29 27 41 34 28

Share 2% 5% 4% 2% 4% 4% 4% 4% 6% 4% 4% 4% 4% 6% 4% 3%

Energy

Volume 54 46 43 45 87 93 93 124 70 100 196 92 80 86 97 80 Share 7% 11% 12% 12% 16% 15% 13% 14% 9% 9% 19% 14% 13% 12% 12% 9%

Financials

Volume 80 80 78 106 97 149 206 227 386 490 356 205 205 164 179 218

Share 11% 19% 22% 28% 18% 25% 28% 25% 51% 46% 35% 31% 33% 23% 22% 25%

Healthcare

Volume 50 31 18 16 28 30 29 42 15 26 28 30 26 41 59 86

Share 7% 8% 5% 4% 5% 5% 4% 5% 2% 2% 3% 5% 4% 6% 7% 10%

High Technology

Volume 205 50 34 42 60 61 58 66 16 52 54 48 50 64 99 81

Share 28% 12% 10% 11% 11% 10% 8% 7% 2% 5% 5% 7% 8% 9% 12% 9%

Industrials

Volume 45 37 39 37 68 70 80 125 73 95 138 64 78 98 108 104 Share 6% 9% 11% 10% 12% 12% 11% 14% 10% 9% 13% 10% 13% 14% 13% 12%

Materials

Volume 15 20 26 30 42 45 78 124 81 132 107 104 57 61 67 72

Share 2% 5% 7% 8% 8% 7% 11% 14% 11% 12% 10% 16% 9% 9% 8% 8%

Media and Entertainment

Volume 41 20 25 14 31 27 30 28 9 27 17 17 19 37 43 38

Share 6% 5% 7% 4% 6% 4% 4% 3% 1% 3% 2% 3% 3% 5% 5% 4%

Real Estate

Volume 4 5 3 5 13 18 38 64 16 49 22 13 15 29 38 37 Share 1% 1% 1% 1% 2% 3% 5% 7% 2% 5% 2% 2% 2% 4% 5% 4%

Retail

Volume 16 18 17 16 21 12 28 21 11 22 21 27 27 34 41 39

Share 2% 4% 5% 4% 4% 2% 4% 2% 1% 2% 2% 4% 4% 5% 5% 5%

Telecommunications

Volume 182 71 40 46 58 56 36 32 22 16 21 8 22 22 27 35

Share 25% 17% 11% 12% 11% 9% 5% 3% 3% 2% 2% 1% 3% 3% 3% 4%



Sectoral trends in the use of public equity markets

At the time of the financial crisis, companies in the financial services sector significantly increased their

use of public equity markets for external financing. This strengthening of financial companies’ balance

sheets was mainly done through secondary public offerings (SPO). The use of public equity markets by

the telecommunications industry before the crisis has declined to very low levels.

23

Figure 11. Rating quality and new supply of corporate bonds, 2000–2015

Table 11. Distribution of corporate bond issuance among rating categories,

as a percentage of total, 2000–2015

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

A-grade investment 86% 78% 80% 78% 80% 83% 81% 82% 88% 71% 65% 67% 58% 53% 55% 58%

B-grade investment 9% 16% 14% 13% 11% 10% 11% 10% 10% 19% 18% 17% 25% 27% 26% 27%

B-grade non-investment 4% 6% 5% 8% 8% 6% 7% 7% 3% 10% 16% 14% 16% 18% 18% 14%

C-grade non-investment 0% 0% 0% 0% 1% 1% 1% 1% 0% 0% 1% 1% 1% 2% 1% 1%

Note: There are eleven non-investment grade categories: five from C, C to CCC+; and six from B, B- to BB+. There are ten investment

grade categories: three from B, BBB- to BBB+; and seven from A, A- to AAA. The index is weighted as one for C, two for CC and rising

to twenty one for AAA. A fall in the index indicates declining quality.


A decline in overall corporate bond ratings

Since the financial crisis, corporate bond markets have experienced a significant decrease in overall

corporate bond rating quality. This is the result of an increased share of non-investment grade bonds

and B-grade bonds being issued. As a consequence, the weighted bond rating index, which is

constructed by assigning the value 1 to the lowest credit quality rating and 21 to the highest credit

quality rating, shows an almost 20% decline in rating quality since 2008.

24

Figure 12. Covenant protection index for corporate bonds issued in the United States,

2000–2014

Note: The 34 covenant variables in the dataset are matched to the 15 covenant types. If a bond had at least one covenant that

belongs to a certain covenant type, it was considered to have that covenant type. For each covenant type, a binary variable

was generated, which is equal to 1 if the covenant type is available in the bond indenture. The binary variables are then

summed, divided by 15 to create an index that ranges from 0 to 1, with the ratio 1 (0) denoting the highest (lowest) possible

protection for bondholders. Data from the binary variables frequency in selected sub-categories are shown in Table 12

below.

Source: OECD, “Corporate Bonds, Bondholders and Corporate Governance”, OECD Corporate Governance Working

Papers, No. 16, updated with 2014 data.

The decline in corporate bond covenant protection

The increase in non-investment grade bond issues has been accompanied by a shift in the

characteristics of corporate bond indentures, which is the contract containing the main features of the

bond, including any restrictive conditions (the covenants). The covenant protection index, which

tracks the frequency of different covenants used in the United States, has declined significantly for

non-investment grade bonds since 2005. While a minor improvement can be noted in the last couple

of years, the overall decline and the accompanying decline in rating quality, suggest that bond

investors in their search for yield have been willing to trade governance rights and protection for

higher expected returns.

25

Table 12. Frequency of different types of corporate bond covenants, 2000–2014

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Number of observations

Non-IG 282 287 253 348 460 385 313 345 164 250 470 466 491 448 354

IG 664 753 597 638 456 479 490 607 530 687 582 712 823 848 864

Rest

ric

tio

ns

on

Pay

ou

ts

Dividend Payment Restrictions

Non-IG 76% 82% 86% 78% 82% 84% 65% 58% 59% 45% 50% 54% 34% 41% 42%

IG 2% 3% 1% 3% 6% 6% 1% 2% 1% 2% 2% 1% 1% 1% 0%

Share Repurchase Restrictions

Non-IG 88% 88% 91% 86% 84% 86% 67% 61% 57% 49% 53% 63% 43% 54% 46%

IG 6% 4% 4% 3% 2% 1% 0% 1% 1% 1% 2% 1% 0% 0% 1%

Rest

ric

tio

ns

on

Fin

an

cin

g A

cti

vit

ies

Funded Debt Restrictions

Non-IG 0% 0% 0% 0% 0% 0% 0% 1% 1% 0% 0% 0% 0% 0% 0%

IG 2% 1% 1% 1% 1% 1% 0% 2% 1% 0% 0% 1% 1% 1% 0%

Subordinated Debt Restrictions

Non-IG 11% 28% 36% 7% 0% 1% 2% 2% 1% 0% 0% 0% 0% 0% 0%

IG 0% 0% 0% 0% 0% 1% 1% 0% 1% 1% 0% 1% 2% 0% 1%

Senior Debt Restrictions

Non-IG 2% 1% 0% 2% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0%

IG 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

Secured Debt Restrictions

Non-IG 78% 71% 74% 76% 72% 88% 78% 66% 68% 54% 53% 57% 45% 62% 57%

IG 60% 74% 67% 66% 55% 61% 60% 60% 69% 57% 50% 51% 54% 52% 53%

Leverage Restrictions

Non-IG 91% 91% 93% 89% 89% 88% 72% 64% 61% 54% 56% 65% 48% 60% 57%

IG 14% 13% 12% 12% 15% 17% 15% 12% 8% 6% 11% 11% 11% 10% 17%

Sale & Lease Back

Non-IG 49% 41% 45% 49% 43% 44% 47% 27% 18% 29% 29% 23% 22% 26% 30%

IG 30% 36% 35% 37% 28% 31% 35% 41% 48% 44% 41% 43% 46% 43% 40%

Stock Issue Restrictions

Non-IG 62% 67% 66% 63% 70% 70% 52% 49% 48% 31% 31% 40% 23% 28% 26%

IG 4% 4% 6% 8% 12% 10% 7% 5% 3% 1% 2% 0% 0% 0% 1%

Even

t-d

riv

en

Co

ven

an

ts Rating & NW Triggers

Non-IG 1% 5% 5% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

IG 1% 2% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1%

Cross Default Provisions

Non-IG 94% 96% 95% 75% 92% 90% 73% 70% 74% 64% 67% 73% 63% 75% 74%

IG 49% 48% 40% 38% 52% 64% 59% 59% 58% 49% 74% 89% 89% 94% 89%

Poison Put

Non-IG 92% 89% 93% 86% 88% 88% 81% 92% 82% 89% 90% 91% 91% 89% 86%

IG 5% 5% 2% 2% 2% 3% 8% 39% 39% 42% 45% 38% 42% 35% 43%

Rest

ric

tio

ns

on

In

vest

men

t

Acti

vit

ies

an

d A

sset

Sa

les Asset Sale Restrictions

Non-IG 95% 99% 98% 99% 98% 98% 92% 78% 75% 67% 67% 77% 65% 75% 75%

IG 87% 92% 96% 93% 88% 90% 87% 83% 84% 59% 68% 81% 87% 89% 89%

Investment Policy Restrictions

Non-IG 9% 7% 4% 2% 2% 2% 5% 1% 4% 21% 10% 0% 1% 1% 2%

IG 2% 2% 1% 1% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0%

Merger Restrictions

Non-IG 92% 98% 97% 97% 97% 97% 92% 78% 75% 66% 67% 75% 63% 75% 75%

IG 86% 91% 95% 93% 88% 89% 88% 83% 84% 59% 66% 81% 87% 89% 88%

Source: OECD, “Corporate Bonds, Bondholders and Corporate Governance”, OECD Corporate Governance Working Papers, No. 16, updated with 2014 data.

26

Figure 13. Distance-to-default (DTD) of large listed banks, 1999–2016

Table 13. Average distance-to-default (DTD) of large listed banks, 2003–2016

All Large Banks G-SIBs Other Large Banks

United

States Europe Asia

Latin

America

United

States Europe Asia

United

States Europe Asia

Latin

America

Jun-03 2.79 2.27 1.82 1.82 2.65 2.27 1.31 3.37 2.26 2.68 1.82

Dec-03 4.49 3.50 1.94 2.80 4.32 3.52 1.21 5.25 3.42 3.08 2.80

Jun-04 5.81 4.65 1.96 2.84 5.69 4.75 1.47 6.38 4.26 2.56 2.84

Dec-04 6.74 5.25 2.55 3.00 6.75 5.33 2.27 6.65 4.92 2.88 3.00

Jun-05 7.05 6.20 3.32 3.40 7.08 6.32 3.37 6.94 5.62 3.27 3.40

Dec-05 7.50 6.23 3.29 3.10 7.62 6.38 3.22 6.93 5.43 3.36 3.10

Jun-06 7.46 5.51 3.09 2.54 7.54 5.63 2.87 7.08 4.91 3.33 2.54

Dec-06 7.23 4.97 3.52 2.62 7.11 5.10 3.24 7.86 4.35 3.80 2.62

Jun-07 6.90 5.41 3.59 3.28 6.70 5.50 3.60 7.95 4.92 3.59 3.28

Dec-07 4.04 4.18 3.11 3.07 4.01 4.18 3.12 4.20 4.19 3.10 3.07

Jun-08 2.55 2.82 2.28 2.44 2.52 2.78 2.29 2.73 3.03 2.28 2.44

Dec-08 0.79 1.26 1.59 1.23 0.75 1.26 1.65 1.01 1.28 1.55 1.23

Jun-09 0.02 0.61 1.53 1.08 -0.01 0.63 1.53 0.14 0.53 1.53 1.08

Dec-09 0.56 1.07 2.24 2.20 0.56 1.13 2.15 0.57 0.85 2.31 2.20

Jun-10 2.64 2.09 3.02 2.77 2.66 2.14 3.08 2.53 1.90 2.97 2.77

Dec-10 3.08 2.32 3.51 3.13 3.10 2.32 3.80 2.98 2.31 3.32 3.13

Jun-11 3.76 3.00 3.86 3.47 3.79 3.03 3.84 3.58 2.85 3.87 3.47

Dec-11 2.36 1.85 4.03 2.79 2.29 1.87 3.88 2.71 1.73 4.13 2.79

Jun-12 2.17 1.69 4.26 2.72 2.08 1.73 4.27 2.65 1.51 4.26 2.72

Dec-12 3.54 2.34 5.02 3.31 3.35 2.35 4.54 4.50 2.27 5.32 3.31

Jun-13 4.51 2.83 4.67 3.75 4.33 2.82 3.98 5.41 2.90 5.08 3.75

Dec-13 5.26 3.62 4.27 3.60 5.09 3.67 3.72 6.13 3.39 4.57 3.60

Jun-14 5.70 4.17 4.73 3.35 5.55 4.27 4.47 6.45 3.79 4.86 3.35

Dec-14 6.15 4.57 5.10 2.79 6.04 4.60 4.58 6.70 4.44 5.35 2.79

Jun-15 5.86 4.24 4.06 2.50 5.80 4.19 4.05 6.18 4.45 4.06 2.50

Dec-15 4.80 3.74 3.22 2.39 4.79 3.67 3.26 4.82 4.01 3.21 2.39

Mar-16 4.06 3.23 3.10 2.24 4.03 3.21 2.91 4.22 3.35 3.19 2.24

Notes: Europe refers to the European Union, Norway and Switzerland. Banks included in the sample are listed in Bloomberg

regional banking indices. The horizontal 3-standard-deviation line represents a minimal level of “safety” based on

calibration from previous crises. The distance-to-default (DTD) is a measure that uses a combination of bank reported data,

and market information to calculate the number of standard deviations a bank is from the default point, where the market

values of assets equals the book value of debt. The formula is derived from the option pricing model of Black and Scholes

(1973).


0

1

2

3

4

5

6

7

8Std. dev .

United States Europe Asia Latin America

Distance-to-default has fallen

Bank default risk is measured by the distance-to-default (DTD). A bank defaults when DTD moves to

0 and below. Recently the average DTD of banks has fallen in all regions. In Asia, the DTD is back to

levels last seen in 2010. In Latin America, the DTD is at levels last seen in 2009. US banks appear to

be the strongest at this point. Nevertheless the situation bears watching closely.

27

Figure 14. Credit rating uplifts for the debt of large banks, 2007–2015

All banks

25th%

tile Median

75th%

tile Mean

2007 1 2 3 2.16

2008 1 2 3 2.33

2009 2 3 5 3.10

2010 2 3 4 3.11

2011 1 2 3 2.28

2012 1 2 3 2.42

2013 1 2 3 2.50

2014 1 2 3 2.46

2015 2 3 3 2.59

Globally systemically important banks

(G-SIBs)

25th%

tile Median

75th%

tile Mean

2007 1 2 2 1.96

2008 2 2 2 2.15

2009 2 3 4 3.00

2010 2 3 4 3.04

2011 2 3 3 2.48

2012 2 3 3 2.60

2013 2 3 3 2.63

2014 2 3 3 2.55

2015 3 3 4 2.96

Other banks

25th%

tile Median

75th%

tile Mean

2007 1 2 3 2.19

2008 1 2 3 2.36

2009 2 3 5 3.11

2010 2 3 5 3.13

2011 1 2 3 2.24

2012 1 2 3 2.37

2013 1 2 3 2.46

2014 1 2 3 2.44

2015 2 2 3 2.52

Notes: Based on the credit rating uplift due to assumed external support, obtained by subtracting (the numerical equivalent of) the

“stand-alone” credit rating from (the numerical equivalent of) the “all-in” credit rating for a sample of 204 international banks from 23

OECD countries. Mean, median and interquartile range are shown by straight line, dotted line and light-grey shaded area, respectively.

Source: OECD Secretariat calculations based on publicly available data from Moody’s website. See Annex for details.

6

0

1

2

3

4

5

6

2007 2008 2009 2010 2011 2012 2013 2014 2015

Mean Median

0

1

2

3

4

5

6

2007 2008 2009 2010 2011 2012 2013 2014 2015

0

1

2

3

4

5

6

2007 2008 2009 2010 2011 2012 2013 2014 2015

A measure of the value of implicit guarantees for the debt of large banks

One measure of the value of (the perception of) implicit guarantees for the debt of banks is the credit rating

uplift due to assumed external support. The uplift is obtained as the difference between an issuing banks’ “all-

in” and its “stand-alone” credit ratings, where the latter abstracts from external support unlike the “all-in”

rating. The average uplift for a sample of large international banks decreased from its peak levels obtained at

the end of 2009-2010, although it has been slightly increasing again more recently. It tends to be larger for

banks considered globally systemically important than for other banks.

28

Figure 15. Bank beta indicator for large listed banks, 1999–2016

Table 14. Average beta calculated using MSCI regional equity indices for large listed banks, 2003–

2016


United

States Europe Asia

Latin

America

United

States Europe Asia

United

States Europe Asia

Latin

America

Jun-03 1.24 1.29 1.10 1.47 1.31 1.34 1.49 0.95 1.06 0.43 1.47

Dec-03 1.17 1.21 1.29 1.18 1.22 1.25 1.84 0.95 1.04 0.43 1.18

Jun-04 1.08 1.12 1.09 1.07 1.11 1.13 1.69 0.91 1.07 0.36 1.07

Dec-04 1.04 1.06 0.91 1.11 1.07 1.08 1.32 0.88 0.98 0.43 1.11

Jun-05 1.02 1.07 0.73 1.03 1.03 1.08 1.04 0.96 1.01 0.38 1.03

Dec-05 0.94 1.10 0.82 1.05 0.93 1.10 1.22 1.01 1.06 0.39 1.05

Jun-06 0.97 1.10 0.79 1.20 0.97 1.11 1.19 0.96 1.08 0.37 1.20

Dec-06 1.06 1.12 0.72 1.12 1.10 1.12 1.00 0.82 1.13 0.43 1.12

Jun-07 1.17 1.11 0.73 1.02 1.22 1.12 0.92 0.86 1.06 0.55 1.02

Dec-07 1.40 1.15 0.99 1.02 1.43 1.16 1.04 1.27 1.10 0.96 1.02

Jun-08 1.61 1.27 1.04 0.97 1.64 1.29 1.24 1.46 1.16 0.90 0.97

Dec-08 1.62 1.36 0.99 1.09 1.67 1.37 1.19 1.34 1.33 0.86 1.09

Jun-09 2.03 1.56 0.96 1.08 2.08 1.57 1.19 1.76 1.53 0.80 1.08

Dec-09 2.63 1.78 0.96 1.03 2.65 1.77 1.23 2.50 1.82 0.76 1.03

Jun-10 1.52 1.50 0.81 1.01 1.52 1.51 0.86 1.53 1.47 0.77 1.01

Dec-10 1.38 1.49 0.79 1.01 1.38 1.51 0.75 1.41 1.40 0.81 1.01

Jun-11 1.37 1.32 0.81 1.10 1.36 1.33 1.03 1.42 1.28 0.67 1.10

Dec-11 1.61 1.57 0.78 1.07 1.65 1.57 0.81 1.44 1.56 0.76 1.07

Jun-12 1.71 1.66 0.79 1.02 1.76 1.67 0.75 1.47 1.66 0.82 1.02

Dec-12 1.70 1.70 0.83 1.12 1.78 1.71 0.99 1.32 1.61 0.73 1.12

Jun-13 1.43 1.68 0.87 1.21 1.49 1.72 1.18 1.16 1.47 0.68 1.21

Dec-13 1.31 1.42 0.87 1.13 1.35 1.44 1.16 1.07 1.32 0.71 1.13

Jun-14 1.21 1.28 0.80 1.16 1.25 1.27 1.01 1.04 1.28 0.69 1.16

Dec-14 1.12 1.26 0.74 1.53 1.14 1.27 1.10 1.04 1.23 0.56 1.53

Jun-15 1.14 1.21 0.78 1.50 1.17 1.24 1.03 1.04 1.09 0.66 1.50

Dec-15 1.18 1.14 0.85 1.38 1.20 1.17 1.00 1.12 0.98 0.78 1.38

Mar-16 1.28 1.25 0.82 1.37 1.31 1.28 1.00 1.17 1.10 0.74 1.37


0.00

0.50

1.00

1.50

2.00

2.50

3.00

BetaBeta calculated using Regional MSCI Indices

0.00

0.50

1.00

1.50

2.00

2.50

3.00

BetaBeta calculated using Global MSCI Index

United States Europe Asia Latin America

Bank beta indicator

Beta is defined as the covariance of a firm’s stock returns with the market divided by variance of

market returns (here calculated over a rolling window). The beta for a bank stock is a measure of the

degree to which its returns co-vary with the market and therefore cannot be eliminated through

diversification. Beta is a key input into the calculation of the bank-specific equity risk premium (the

product of bank beta and the market equity risk premium). In crisis periods, bank betas rise on

average raising their equity risk premia and tend to do so in a correlated way.

29

Table 15. Average beta calculated using MSCI global equity index for large listed banks, 2003–

2016


United

States Europe Asia

Latin

America

United

States Europe Asia

United

States Europe Asia

Latin

America

Jun-03 1.47 1.37 0.15 0.66 1.56 1.42 0.17 1.10 1.14 0.12 0.66

Dec-03 1.34 1.28 0.46 0.70 1.40 1.32 0.64 1.08 1.14 0.18 0.70

Jun-04 1.13 1.14 1.01 1.24 1.16 1.16 1.50 0.97 1.05 0.43 1.24

Dec-04 1.07 1.11 0.71 1.57 1.10 1.14 0.88 0.91 1.00 0.51 1.57

Jun-05 1.11 0.98 0.58 1.27 1.14 0.99 0.82 1.00 0.95 0.33 1.27

Dec-05 0.93 1.09 0.57 1.48 0.92 1.09 0.80 0.97 1.06 0.33 1.48

Jun-06 0.85 1.37 0.60 2.15 0.87 1.38 0.85 0.77 1.29 0.33 2.15

Dec-06 0.84 1.50 0.72 2.27 0.89 1.50 0.94 0.56 1.52 0.50 2.27

Jun-07 1.00 1.42 0.66 2.01 1.04 1.43 0.65 0.74 1.40 0.67 2.01

Dec-07 1.35 1.42 0.60 2.00 1.39 1.43 0.33 1.16 1.34 0.83 2.00

Jun-08 1.45 1.62 0.63 1.69 1.49 1.65 0.61 1.24 1.44 0.65 1.69

Dec-08 1.75 1.49 0.59 1.75 1.83 1.49 0.52 1.35 1.45 0.64 1.75

Jun-09 2.25 1.70 0.54 1.61 2.33 1.70 0.46 1.86 1.67 0.59 1.61

Dec-09 2.68 2.19 0.50 1.36 2.72 2.18 0.48 2.51 2.25 0.51 1.36

Jun-10 1.38 2.11 0.41 1.35 1.38 2.12 0.26 1.36 2.08 0.51 1.35

Dec-10 1.28 2.03 0.43 1.18 1.27 2.06 0.26 1.30 1.90 0.55 1.18

Jun-11 1.18 1.76 0.53 1.02 1.17 1.78 0.48 1.23 1.67 0.56 1.02

Dec-11 1.58 1.97 0.43 1.12 1.61 1.97 0.30 1.38 1.95 0.51 1.12

Jun-12 1.70 2.16 0.38 1.15 1.76 2.16 0.23 1.42 2.15 0.48 1.15

Dec-12 1.54 2.45 0.44 1.37 1.62 2.48 0.40 1.17 2.31 0.46 1.37

Jun-13 1.41 2.27 0.48 1.29 1.47 2.34 0.48 1.11 1.98 0.48 1.29

Dec-13 1.29 1.70 0.63 1.20 1.35 1.73 0.58 1.00 1.59 0.66 1.20

Jun-14 1.19 1.53 0.57 1.11 1.24 1.53 0.60 0.95 1.54 0.55 1.11

Dec-14 1.27 1.51 0.22 0.86 1.29 1.51 0.23 1.16 1.47 0.21 0.86

Jun-15 1.26 1.44 0.27 1.61 1.28 1.48 0.24 1.15 1.26 0.28 1.61

Dec-15 1.27 1.24 0.52 1.65 1.28 1.29 0.54 1.21 1.04 0.51 1.65

Mar-16 1.36 1.34 0.54 1.50 1.39 1.38 0.50 1.24 1.18 0.56 1.50

Notes: Europe refers to the European Union, Norway and Switzerland. Banks included in the sample are listed in Bloomberg

regional banking indices. Beta is defined as the covariance of a bank's stock returns with MSCI benchmark (either related

regional or global benchmarks) divided by variance of market returns. It is an indicator of the systemic importance a bank

with respect to the economy.


30

Table 16. Total investment of pension funds and all retirement vehicles, 2015

Pension funds All retirement

vehicles

Millions of

national

currency

Millions of

USD % of GDP % change

% of all

retirement

vehicles

% of GDP

Australia 1,894,431 1,454,923 117.7 10.1 97.1

121.2

Austria 19,646 21,389 5.8 3.3

Belgium 24,117 27,018 5.9 8.5

Canada 1,583,494 1,182,241 79.8 5.2 50.8

157.0

Chile 109,433,421 154,711 69.6 8.9 100.0

69.6

Czech Republic 373,069 15,029 8.3 10.0 100.0

8.3

Denmark 888,707 130,118 44.8 -4.7 22.0

203.3

Estonia 2,613 2,844 12.8 18.5 88.2

14.5

Finland 105,258 114,594 50.8

France 12,200 13,282 0.6 17.6 5.6

10.0

Germany 199,197 216,865 6.6 2.4

Greece 1,135 1,236 0.6 2.8

Hungary 1,381,292 4,819 4.1 5.7 72.3

5.7

Iceland 3,266,214 25,204 149.2 12.0 94.8

157.3

Ireland 105,400 114,749 49.1

91.2

53.9

Israel 627,569 160,833 54.5 5.1

Italy 114,600 124,765 7.0 6.5 79.5

8.8

Japan 159,757,300 1,325,787 32.0 1.6 100.0

32.0

Korea 136,427,700 116,356 8.8 25.6 30.2

29.0

Luxembourg 1,444 1,572 2.8 -2.7

Mexico 2,789,870 162,140 15.4 4.2 93.0

16.6

Netherlands 1,210,321 1,317,676 178.4 1.7

New Zealand 53,235 36,317 22.2 21.8 100.0

22.2

Norway 283,126 32,137 9.0 8.7

Poland 142,810 36,608 8.0 -5.6 94.0

8.5

Portugal 18,164 19,775 10.1 3.8

Slovak Republic 8,037 8,750 10.3 1.2 100.0

10.3

Slovenia 1,641 1,786 4.3 4.2 61.0

7.0

Spain 103,862 113,074 9.6 3.7 66.2

14.5

Sweden 380,000 45,019 9.1 4.4 13.6

67.4

Switzerland 797,648 804,000 124.7 2.6

Turkey 42,959 14,762 2.2 24.0

United Kingdom 1,818,507 2,694,846 97.5 1.9

United States 14,299,033 14,299,033 79.7 -0.9 59.9

133.1

OECD

24,794,259 84.5

Source: OECD Global Pension Statistics; the French Asset Management Association; Bank of Korea; Bank of Japan; Bank

of Mexico; Reserve Bank of New Zealand; Willis Towers Watson, Global Pension Assets Study 2016 (Switzerland). See

Annex for details.

Pension funds’ investments fell slightly in 2015 relatively to 2014

Pension funds’ investments fell slightly in the OECD area in 2015 relatively to their 2014 level (USD

25.2 trillion according to the last report Pension Markets in Focus 2015). They were still close to USD

25 trillion at the end of 2015. Pension funds’ investments, expressed in national currency, grew in most

countries between 2014 and 2015, except Denmark, Luxembourg, Poland and the United States. In

Denmark however, private pension investments are mainly channelled through life insurance

companies instead of pension funds. The overall size of investments in the pension system in Denmark

exceeded two times its GDP. The decline in OECD total pension fund investments was partly driven by

the United States, which represented 58% of all pension funds’ investment in the OECD area.

31

.

Figure 16. Pension funds' real net investment rate of return in selected OECD countries, 2014–2015

Table 17. Pension funds' real net rate of investment returns in selected OECD countries, 2005–2015

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Australia 10.1 8.9 12.9 -11.4 -10.2 5.6 5.3 0.6 10.3 8.3 6.4

Austria 9.0 3.8 -1.8 -14.4 7.3 3.7 -6.0 5.5 2.9 6.2 1.2

Belgium 10.3 10.3 7.7 -22.3 13.4 4.4 -4.6 9.2 5.8 10.7

Canada 10.7 10.8 1.0 -16.9 10.3 7.6 1.8 7.9 9.8 7.8 1.4

Chile 5.0 14.4 4.4 -24.1 22.0 8.3 -6.0 5.1 3.5 8.1 1.5

Czech Republic 2.7 1.3 -2.0 -1.5 -0.6 0.7 0.5 0.2 0.2 1.2 0.9

Denmark 14.7 1.4 -3.3 5.1 1.2 7.1 12.2 5.3 -4.5 16.6 0.8

Estonia 7.2 2.2 -5.4 -32.4 14.8 2.1 -8.0 5.2 0.9 4.7 2.1

Finland

5.2 6.0 6.2 5.3

Germany 3.4 3.2 1.0 0.5 3.9 3.6 1.0 2.7 2.8 4.4

Greece

2.3 0.3 -7.8 -5.6 5.0 7.7 6.5 4.6

Hungary 7.6 1.2 -3.9 -21.7 12.8 4.2

7.8 7.0 9.6 3.7

Iceland 11.8 8.8 0.4 -23.1 0.9 1.3 2.3 7.1 4.9 7.2 6.8

Ireland

-7.4 -35.7

Israel 7.2 5.7 3.5 -16.3 20.2 6.9 -4.3 7.9 8.3 5.8 3.6

Italy 6.1 2.1 0.3 -5.3 5.3 1.2 -2.8 4.0 3.9 5.7 2.6

Korea 0.6 6.0 0.6 -2.7 -2.2 2.1 0.0 3.3 2.6 2.3 Luxembourg

4.9 -2.5 -11.4 6.5 0.7 -2.3 6.0 1.7 8.3 0.6

Mexico 4.8 5.6 -0.1 -7.8 7.5 6.6 1.2 9.7 -1.5 4.7 Netherlands 10.9 6.8 0.6 -17.3 11.5 8.9 4.3 9.5 1.6 15.1 7.1

New Zealand 4.3 8.8 5.0 -5.5 -9.5 10.5 3.1 1.6 9.5 7.2

Norway 9.2 7.4 3.1 -10.6 9.8 5.5 -0.1 6.1 7.9 5.1 1.1

Poland 12.9 13.4 1.5 -17.3 8.9 7.2 -9.1 1.6 2.7

-4.0

Portugal 7.1 7.1 5.5 -13.2 11.6 -3.0 -7.3 5.8 4.9 6.9 2.1

Slovak Republic

-0.1 -8.9 1.0 0.0 -3.8 0.4 1.1 3.9 1.0

Slovenia

-1.0 -5.4 4.2 1.8 -1.8 4.5 2.5 6.7 2.4

Spain

-9.9 6.9 -2.2 -2.3 3.7 7.9 8.0 2.0

Sweden

-1.0 7.9 6.7

Switzerland 9.2 5.3 0.2 -13.8 9.9 2.8 0.6 7.5 5.9 7.2

Turkey 22.1 1.4 13.2 0.9 17.6 1.9

9.6 -7.6 5.6 -5.9

United Kingdom 19.8 10.3 0.9 -15.9 13.4 11.2 8.3 9.0 5.4 5.2 3.0

United States 1.4 5.4 -1.5 -24.4 8.6 5.3 -4.3 4.6 11.2 3.3 -1.7

Note: All data are expressed in percent.

Source: OECD Global Pension Statistics and other national sources. See Annex for details.

7.16.8

6.45.3

4.63.73.6

3.33.0

2.62.4

2.12.12.12.0

1.51.41.21.1

1.00.90.8

0.60.4

-1.7-4.0

-5.9

-8 -6 -4 -2 0 2 4 6 8

NetherlandsIceland

AustraliaFinlandGreece

HungaryIsrael

JapanUnited Kingdom

ItalySloveniaEstonia

PortugalSimple average

SpainChile

CanadaAustriaNorway

Slovak RepublicCzech Republic

DenmarkLuxembourg

Weighted averageUnited States

PolandTurkey %

The low returns in 2015 mostly explain the fall in pension funds’ investments

Real net investment returns were low in 2015, with a weighted OECD average at 0.4%. These low real

net returns help explain the relative fall in OECD pension funds’ investments. Real investment rates of

return ranged from positive values, peaking at 7.1% in the Netherlands, to negative values, down to -

5.9% for Turkey’s personal plans. Twenty-two countries witnessed positive investment rates of return,

but lower than 2% for eight of them. Three OECD countries had negative returns in 2015: Poland,

Turkey and the United States which dragged the OECD weighted average close to 0.

32

Figure 17. Pension fund asset allocation in selected asset classes in selected OECD countries,

2015

Note: All data are expressed in percent of total investment.

Source: OECD Global Pension Statistics; Australian Bureau of Statistics; Bank of Japan. See Annex for details.

0 10 20 30 40 50 60 70 80 90 100

Poland

Australia

United States

Iceland

Finland

Chile

Belgium

Netherlands

Ireland

Norway

Austria

Estonia

Canada

Luxembourg

United Kingdom

Italy

Portugal

Turkey

Denmark

Spain

Greece

Japan

Hungary

Israel

Germany

Slovak Republic

Slovenia

Czech Republic

Shares Bills and bonds Other

Negative returns in stock markets may be behind the weak performance of pension funds’

investments in 2015

Pension funds’ low performance may be due to the negative evolution of stock markets in 2015. The

S&P 500 and FTSE 100 indices exhibited lower levels at the end of 2015 compared to 2014. The MSCI

Pacific Index slightly increased by 0.4% at the end of 2015 compared to its level at the end of 2014.

These developments may have weakened pension funds’ investment performance, especially in the case

of the United States, where pension funds invested 47% of their portfolios in shares in 2015.

33

Figure 18. Foreign direct investment inflows by region, 2005–2015

Table 18. FDI inflows by selected regions, 2005–2015

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

Total World 982 1,447 2,000 1,577 1,181 1,505 1,700 1,470 1,578 1,400 1,833

European Union (EU) 458 527 828 317 377 380 415 294 319 282 434

United States 113 243 221 310 150 206 236 194 217 112 385

China 104 124 156 172 131 244 280 241 291 268 250

Other G20 118 237 413 429 251 314 371 339 400 348 275

Rest-of-the-world 189 316 382 349 272 360 398 402 351 391 490

OECD 619 955 1,316 845 672 715 874 699 777 572 1,063

G20 countries 619 871 1,152 1,038 697 911 1,063 875 1,033 808 1,020

G20-OECD countries 445 641 826 629 417 461 558 442 544 339 614

G20 -non OECD countries 174 230 326 408 280 450 505 433 489 468 406

Table 19. FDI outflows by selected regions, 2005–2015

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

Total World 852 1,375 2,182 1,730 1,089 1,408 1,529 1,215 1,283 1,368 1,626

European Union (EU) 556 664 1,217 752 348 478 480 256 279 290 508

United States 36 245 414 329 310 301 419 340 329 337 320

China 14 24 17 57 44 58 48 65 73 123 188

Other G20 81 220 270 371 212 247 296 298 344 335 286

Rest-of-the-world 165 223 265 221 175 324 286 256 259 284 325

OECD 728 1,148 1,897 1,414 871 1,029 1,213 921 926 875 1,183

G20 countries 404 805 1,408 1,196 786 860 1,042 826 816 881 871

G20 - OECD countries 363 698 1,303 1,029 689 711 904 705 636 635 617

G20 - non OECD countries 41 108 104 167 96 149 138 121 180 245 254

Notes: All data are expressed in US dollar billion. p: preliminary data; Data are updated as of April 2016. By definition, inward and

outward FDI worldwide should be equal. However, in practice, there are statistical discrepancies between inward and outward FDI.

Reference to ‘global FDI flows’ refer to the average of these two figures.

Source: OECD Foreign direct investment statistics database and IMF. See Annex for details.

0

300

600

900

1 200

1 500

1 800

2 100

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

USD bln

EU United States China Other G20 Economies Rest-of-the-world OECD

Global trends in FDI flows

Global foreign direct investment (FDI) flows stagnated following the global financial crisis, and,

despite a 25% increase in 2015, still remain about one tenth below their pre-crisis peak. The

prolonged recovery in FDI is largely due to persistent weakness in the EU, and, to a much lesser

extent, the United States following the financial crisis. In contrast, FDI inflows to China are higher

than before the crisis. FDI inflows to the non-OECD G20 economies fell in 2015 largely due to

drops for Brazil, Indonesia, Russia, and South Africa as these economies struggled with lower

commodity prices.

34

Figure 19. Foreign direct investment by instruments, 2005–2015

Source: OECD Foreign direct investment statistics database and IMF.

0

200

400

600

800

1000

1200

1400

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

USD bln

OECD

0

200

400

600

800

1000

1200

1400

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

USD bln

Non-OECD

Equity Reinv estment of earnings Debt

Equity capital FDI flows surged in 2015

In OECD economies, the immediate fall after the financial crisis in foreign direct investments (FDI)

was mainly the result of a fall in reinvested earnings and debt. While both reinvested earnings and

debt financing picked up in 2009, equity investments continued to decline quite substantially until

2014. In 2015, equity capital more than doubled from the previous year; debt FDI also increased. For

non-OECD economies, reinvested earnings and to some extent debt have come to play a more

important role as a source of funding of FDI after the financial crisis.

35

Table 20. FDI inflows and outflows by instrument for selected regions, 2005–2015

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

FDI inflows - Equity capital

World 597 806 1202 1145 705 827 776 692 735 567 927

OECD 392 532 824 727 452 469 419 345 363 255 609

G20 396 492 761 746 426 497 525 454 546 336 583

OECD G20 283 346 546 492 275 289 287 243 289 141 383

Non OECD G20 113 146 215 254 151 208 238 212 257 195 200

Rest of the World 91 128 164 164 102 151 119 134 115 117 118

FDI inflows - Reinvestment of earnings

World 258 395 500 291 309 549 587 551 547 630 568

OECD 156 258 322 123 128 243 244 255 257 311 267

G20 155 216 238 164 152 324 341 306 320 352 290

OECD G20 113 163 182 87 59 141 142 154 175 195 154

Non OECD G20 42 52 56 77 93 184 199 151 145 157 136

Rest of the World 61 85 122 90 88 122 143 145 146 163 165

FDI inflows - Debt

World 127 245 298 141 167 129 337 227 295 202 338

OECD 71 164 170 -6 91 4 211 99 157 6 187

G20 68 164 153 128 118 90 196 115 167 119 147

OECD G20 49 132 98 51 83 32 129 45 79 3 77

Non OECD G20 19 32 55 77 36 58 68 70 87 116 70

Rest of the World 37 49 72 70 39 67 59 58 51 80 81

FDI outflows - Equity capital

World 495 782 1185 992 570 685 686 576 571 461 724

OECD 437 671 1039 846 439 486 541 425 339 199 531

G20 225 419 690 625 344 421 446 385 383 272 348

OECD G20 204 354 637 544 277 314 351 281 201 105 226

Non OECD G20 21 65 54 82 67 106 95 104 183 167 121

Rest of the World 37 46 92 64 64 93 49 47 50 95 73

FDI outflows - Reinvestment of earnings

World 266 562 679 444 461 679 717 645 638 734 628

OECD 226 507 607 354 381 541 555 518 499 573 480

G20 168 438 524 407 347 490 541 475 469 525 444

OECD G20 156 418 500 366 328 449 494 441 431 464 372

Non OECD G20 11 20 24 41 19 41 47 35 38 61 72

Rest of the World 29 36 49 50 60 97 115 92 101 100 77

FDI outflows - Debt

World 91 31 318 294 58 44 127 -6 74 173 274

OECD 65 -29 251 213 50 2 117 -23 88 104 172

G20 11 -51 194 163 95 -51 55 -35 -37 84 80

OECD G20 3 -74 167 119 85 -52 58 -17 5 67 19

Non OECD G20 9 22 27 44 10 2 -3 -17 -41 17 61

Rest of the World 17 38 40 36 -3 40 13 34 27 53 40

Notes: All date are expressed in US dollar billion. For OECD countries who did not report FDI aggregates by instrument to

the OECD, instruments were estimated using data on instruments available from the IMF BOP database; or by using

instrument shares observed in non-revised for historical years. Missing instruments for 2015 were estimated by distributing

total FDI equally among the instruments. Instruments for non OECD G20 countries were gathered from national source

websites and from the IMF. Missing instruments for those countries were estimated by distributing total FDI equally among

instruments or by applying the average instrument shares observed in previous periods. Instruments for the rest of the world

were estimated by applying the instruments shares observed on data available in the IMF BOP database for the rest of the

world. 2015 data were estimated by applying the same instrument shares as observed in 2014. World is equal to OECD plus

non-OECD G20 plus Rest of the World. Data are updated as of April 2016.

Source: OECD and IMF.

36

Figure 20. Total FDI outflows from selected OECD countries with resident SPEs, 2005–2015

Figure 21. Total FDI inflows to selected OECD countries with resident SPEs, 2005–2015

Source: OECD calculations, OECD and IMF.

0

100

200

300

400

500

600

700

800

900

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

USD bln

Non-SPE SPE

0

200

400

600

800

1000

1200

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

USD bln

Thousands

Non-SPE SPE

Capital passing through Special Purpose Entities (SPEs) dropped significantly in 2014 and 2015

Special Purpose Entities (SPEs) are entities whose role is to facilitate the internal financing of

multinational enterprises but that have little or no physical presence in an economy. By excluding

such entities from their FDI statistics, countries have a much better measure of the FDI into their

country that is having a real impact on their economy. FDI flows to and from SPEs are significant for

certain countries and are much more volatile than FDI flows into non-SPE or operating affiliates.

While flows to and from SPEs dropped significantly in 2014 and 2015, it is too soon to say whether

this reflects a long term decline in the use of SPEs.

37

Table 21. FDI outflows for countries with SPEs, 2005–2015

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

Data including SPEs:

Austria 78,074 11,226 66,751 28,534 12,355 -14,065 32,533 20,492 6,703 5,051 14,780

Chile2

6,212 10,534 13,617 17,040 8,388 11,803 15,513

Denmark 17,506 7,260 16,985 11,741 2,397 -1,423 9,627 -13,008 6,960 7,600 11,910

Hungary 12,656 19,084 67,478 70,686 4,498 -41,146 21,436 12,358 -2,819 4,813 -5,224

Iceland2

460 -295 -594

Luxembourg 124,542 114,537 265,999 135,226 227,057 205,556 374,294 369,305 518,540 235,198 304,222

Netherlands 248,511 461,992 205,473 364,080 385,931 210,620 388,351 257,720 417,751 98,586 78,251

Poland3 2,864 7,660 3,490 3,437 3,657 6,148 3,677 -2,660 -1,346 1,595 2,832

Portugal 1,643 6,214 5,262 1,163 -367 -9,783 13,447 -8,210 -2,043 4,108 8,167

Total 485,795 627,973 631,438 614,868 641,740 366,442 856,983 653,037 952,593 368,460 429,858

Data excluding SPEs.

Austria 11,139 11,981 35,847 28,652 10,999 9,585 21,933 13,114 15,565 5,066 12,403

Chile2 2,183 2,171 2,573 8,041 6,487 10,226 12,470 17,252 8,780 11,857 15,550

Denmark 13,145 14,462 13,115 15,271 3,689 1,382 11,278 7,359 7,174 8,401 13,219

Hungary 2,171 4,346 4,300 2,638 1,852 1,173 4,713 11,717 1,870 3,521 1,534

Iceland2 7,084 5,495 10,105 -4,250 2,248 -2,368 18 -3,205 460 -257 -599

Luxembourg 9,034 7,183 73,363 11,737 6,709 20,842 9,052 2,771 25,278 23,438 39,378

Netherlands 105,999 72,534 55,691 68,345 26,267 68,363 34,818 6,174 69,960 55,971 113,449

Poland3 1,347 3,857 1,682 1,858 1,807 6,149 1,028 2,905 -451 1,975 4,060

Portugal 2,634 4,435 5,457 722 814 -9,956 13,917 -8,096 -1,035 4,539 7,979

Total 154,736 126,464 202,131 133,013 60,872 105,396 109,228 49,991 127,601 114,509 206,971

SPEs:

Austria 66,936 -755 30,905 -118 1,356 -23,651 10,600 7,378 -8,862 -15 2,377

Chile

-274 308 1,147 -212 -393 -54 -37

Denmark 4,361 -7,202 3,870 -3,530 -1,293 -2,804 -1,651 -20,367 -214 -801 -1,308

Hungary 10,484 14,739 63,178 68,049 2,646 -42,319 16,723 641 -4,689 1,292 -6,758

Iceland

0 -37 5

Luxembourg 115,508 107,354 192,636 123,490 220,348 184,714 365,242 366,534 493,262 211,761 264,844

Netherlands 142,512 389,458 149,782 295,735 359,664 142,257 353,533 251,546 347,791 42,615 -35,198

Poland 1,516 3,803 1,808 1,579 1,850 -1 2,649 -5,565 -895 -379 -1,227

Portugal -991 1,779 -194 441 -1,181 174 -470 -113 -1,008 -431 189

Total 340,326 509,175 441,985 485,646 583,116 258,678 747,773 599,842 824,992 253,951 222,887

Notes: All data are expressed in US dollar million. |: breaks in series; p: Preliminary data; Data are updated as of April 2016.

1. Information on resident SPEs for Estonia and Sweden (FDI flows only) is confidential. This information is not yet available

separately for Canada, Ireland and Mexico. The information is available separately for Austria, Chile, Denmark, Hungary, Iceland,

Korea, Luxembourg, the Netherlands, Norway, Poland, Portugal, Spain, Sweden, Switzerland and the United Kingdom. However,

the information is not displayed in the table for all countries, due to limited availability of historical data or to differences in data

vintages. Resident SPEs are not present or not significant in Australia, the Czech Republic, Finland, France, Germany, Greece,

Israel, Italy, Japan, New Zealand, the Slovak Republic, Slovenia, Turkey, and the United States.

2. Data for Chilean SPEs are not available from 2005 to 2008. Data for Iceland's SPEs are available from 2013 onward.

3. Data for 2015 for Poland correspond to asset/liability figures, while data for earlier years correspond to directional figures.


38

Table 22. FDI inflows for countries with SPEs, 2005–2015

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

Data including SPEs:

Austria 77,903 5,557 56,752 7,144 11,140 -21,694 17,184 7,372 -3,765 9,955 5,803

Chile2

12,371 16,583 16,674 24,977 17,878 21,231 20,176

Denmark 13,437 1,646 10,909 -4,007 -430 -12,547 9,598 -18,592 605 2,171 2,156

Hungary 20,300 19,597 70,003 72,408 5,170 -37,264 23,628 15,050 -2,745 8,704 -4,001

Iceland2

412 439 -128

Luxembourg 116,107 128,557 191,162 105,765 204,341 222,023 412,774 410,089 709,000 193,338 250,784

Netherlands 189,851 313,143 334,444 282,344 339,086 135,774 349,932 259,371 370,492 129,847 90,759

Poland3 9,723 18,379 21,663 13,857 11,892 12,799 18,290 7,130 2,734 11,934 6,211

Portugal 3,462 10,600 2,876 3,542 1,611 2,424 7,435 8,873 2,672 7,616 6,031

Total 430,783 497,479 687,809 481,052 585,181 318,098 855,515 714,269 1,097,285 385,235 377,790

Data excluding SPEs.

Austria 10,778 4,757 25,489 7,212 9,269 2,576 10,625 3,990 5,719 9,326 3,838

Chile2 6,984 7,298 12,534 15,150 12,051 15,220 16,815 24,960 18,170 21,161 20,028

Denmark 8,552 9,306 7,269 -811 392 -9,167 11,488 418 1,050 3,471 3,643

Hungary 7,711 6,817 3,952 6,314 1,998 2,195 6,315 14,427 3,406 7,489 1,271

Iceland2 3,076 3,858 6,822 919 79 245 1,107 1,025 397 447 -136

Luxembourg 5,976 31,802 -28,266 11,194 20,667 35,661 13,302 4,423 15,368 12,074 24,600

Netherlands 39,077 13,901 119,733 5,751 38,748 -7,185 24,391 20,121 51,363 52,200 72,663

Poland3 8,207 14,576 19,855 12,279 10,043 12,800 15,953 12,441 3,626 12,532 7,438

Portugal 4,360 7,227 3,086 2,099 1,282 1,507 5,997 8,963 2,405 7,630 7,381

Total 94,720 99,544 170,474 60,109 94,528 53,852 105,994 90,768 101,504 126,330 140,726

SPEs:

Austria 67,126 800 31,263 -68 1,871 -24,269 6,558 3,381 -9,483 629 1,965

Chile

320 1,363 -141 17 -291 70 148

Denmark 4,885 -7,660 3,640 -3,196 -822 -3,380 -1,890 -19,010 -445 -1,299 -1,487

Hungary 12,589 12,780 66,052 66,093 3,172 -39,458 17,313 623 -6,151 1,214 -5,272

Iceland

15 -8 8

Luxembourg 110,132 96,754 219,428 94,570 183,675 186,362 399,473 405,666 693,632 181,264 226,184

Netherlands 150,774 299,242 214,710 276,592 300,338 142,959 325,541 239,251 319,129 77,648 18,095

Poland 1,516 3,803 1,808 1,579 1,850 -1 2,337 -5,311 -892 -598 -1,227

Portugal -898 3,372 -210 1,443 329 917 1,438 -90 267 -15 -1,350

Total 346,123 409,091 536,691 437,012 490,732 264,492 750,629 624,527 995,780 258,905 237,064

Notes: All data are expressed in US dollar million. |: breaks in series; p: Preliminary data; Data are updated as of April 2016.

1. Information on resident SPEs for Estonia and Sweden (FDI flows only) is confidential. This information is not yet available

separately for Canada, Ireland and Mexico. The information is available separately for Austria, Chile, Denmark, Hungary, Iceland,

Korea, Luxembourg, the Netherlands, Norway, Poland, Portugal, Spain, Sweden, Switzerland and the United Kingdom. However, the

information is not displayed in the table for all countries, due to limited availability of historical data or to differences in data vintages.

Resident SPEs are not present or not significant in Australia, the Czech Republic, Finland, France, Germany, Greece, Israel, Italy,

Japan, New Zealand, the Slovak Republic, Slovenia, Turkey, and the United States.

2. Data for Chilean SPEs are not available from 2005 to 2008. Data for Iceland's SPEs are available from 2013 onward.

3. Data for 2015 for Poland correspond to asset/liability figures, while data for earlier years correspond to directional figures.


39

Figure 22. OECD FDI Regulatory Restrictiveness Index, 1997–2015

Notes: The OECD FDI Regulatory Restrictiveness Index gauges the restrictiveness of a country’s FDI rules across 22

economic sectors and across four types of restrictions: foreign equity restrictions; screening or approval mechanisms;

restrictions on key foreign employment; and operational restrictions to FDI. Restrictions are scored on a range from 0 (open) to

1 (closed). The Index covers only statutory measures discriminating against foreign investors. Other important aspects of an

investment climate (e.g. the implementation of regulations and state monopolies among other) are not considered. Country

coverage expands over time. In 1997, there were 44 economies covered. In 2015, all 34 OECD countries and 25 non-OECD

countries are covered, including all G20 members. Data reflects regulatory restrictions as of December each year. The increase

in average scores observed for the entire sample of countries in 2012 and 2013 are mainly due to the addition of new countries

(4 countries in 2012 and 1 country in 2013) for which historical scores have not been compiled.

Source: OECD FDI Regulatory Restrictiveness Index database.

0.00

0.05

0.10

0.15

0.20

0.25

1997 2003 2006 2010 2011 2012 2013 2014 2015

Average OECD Average G20 All countries

OECD FDI Regulatory Restrictiveness Index (open=0; closed=1)

Pace of liberalising regulatory restrictions on FDI has slowed in recent years

Foreign direct investment (FDI) is a key element in international economic integration. From a broad

perspective, countries have significantly liberalised restrictions on international investment over time,

albeit at a slower pace more recently. Yet, there still remains significant variation across countries in

terms of statutory restrictions on FDI and, worldwide, many service sectors remain partly off limits to

foreign investors, holding back potential economy-wide productivity gains. In more recent periods, a

few countries have also become more sensitive to investment in primary sectors, and have tightened

the regime for foreign investors in these sectors (notably agriculture and mining and quarrying).

40

Table 23. OECD FDI Regulatory Restrictiveness Index, OECD countries per sector, 2015

Countries Total

FDI

Index

Primary Manufacturing Electricity Distribution Transport Media Communications Financial

services

Business

services

Real estate

investment

Australia 0.14 0.14 0.08 0.08 0.08 0.26 0.20 0.40 0.13 0.08 0.40

Austria 0.11 0.15 1.00 0.18 0.00 0.32 0.20

Belgium 0.04 0.04 0.02 0.02 0.02 0.11 0.02 0.02 0.02 0.25 0.02

Canada 0.17 0.19 0.10 0.10 0.10 0.27 0.71 0.57 0.07 0.10 0.01

Chile 0.06 0.15 0.41 0.19 0.02 0.01

Czech

Republic 0.01 0.03 0.08 0.01 0.02

Denmark 0.03 0.06 0.08 0.00 0.36 0.05

Estonia 0.02 0.02 0.15 0.00 0.15

Finland 0.02 0.02 0.01 0.08 0.01 0.09 0.01 0.01 0.01 0.05

France 0.05 0.16 0.15 0.05 0.05 0.00

Germany 0.02 0.07 0.20 0.03 0.01

Greece 0.03 0.08 0.15 0.11 0.02 0.06

Hungary 0.03 0.17 0.01 0.45

Iceland 0.17 0.24 0.11 0.56 0.11 0.20 0.11 0.11 0.12 0.11 0.24

Ireland 0.04 0.14 0.13 0.01 0.25

Israel 0.12 0.06 0.02 0.77 0.02 0.40 0.26 0.40 0.04 0.02 0.22

Italy 0.05 0.13 0.20 0.36 0.02

Japan 0.05 0.07 0.00 0.03 0.00 0.28 0.20 0.27 0.10

Korea 0.14 0.25 0.42 0.51 0.56 0.33 0.05

Luxembourg 0.00 0.08 0.00

Mexico 0.19 0.32 0.10 0.10 0.18 0.53 0.53 0.10 0.13 0.10 0.17

Netherlands 0.02 0.06 0.08 0.00

New Zealand 0.24 0.33 0.20 0.20 0.20 0.28 0.20 0.40 0.23 0.20 0.20

Norway 0.09 0.16 0.35 0.13 0.07 0.31 0.25

Poland 0.07 0.05 0.09 0.30 0.08 0.00 0.90

Portugal 0.01 0.01 0.08 0.02

Slovak

Republic 0.05 0.08 0.00 1.00

Slovenia 0.01 0.15 0.00 0.01

Spain 0.02 0.01 0.08 0.23 0.00 0.11

Sweden 0.06 0.14 0.29 0.20 0.20 0.00 0.05

Switzerland 0.08 0.50 0.25 0.47 0.07 0.40

Turkey 0.06 0.01 0.38 0.20 0.13 0.55

United

Kingdom 0.06 0.16 0.02 0.02 0.02 0.11 0.25 0.02 0.02 0.02

United States 0.09 0.18 0.20 0.55 0.25 0.11 0.04

OECD 0.07 0.10 0.02 0.12 0.02 0.22 0.16 0.09 0.03 0.07 0.16


41

Table 24. OECD FDI Regulatory Restrictiveness Index, Non-OECD countries per sector, 2015

Countries

Total

FDI

Index

Primary Manufacturing Electricity Distribution Transport Media Communications Financial

services

Business

services

Real estate

investment

Argentina 0.04 0.08 0.04 0.50

Brazil 0.10 0.19 0.03 0.03 0.03 0.28 0.55 0.03 0.11 0.03 0.03

China 0.39 0.44 0.20 0.48 0.21 0.55 1.00 0.75 0.51 0.33 0.18

Colombia 0.03 0.04 0.05 0.12 0.21 0.02

Costa Rica 0.05 0.10 0.12 0.33 0.05 0.05 0.03

Egypt 0.06 0.33 0.02 0.10 0.33

India 0.24 0.32 0.05 0.06 0.23 0.16 0.31 0.18 0.30 0.56 1.00

Indonesia 0.34 0.43 0.07 0.10 0.56 0.41 0.89 0.29 0.24 0.58 1.00

Jordan 0.30 0.16 0.10 0.10 0.62 0.75 0.46 0.10 0.23 0.41 0.85

Kazakhstan 0.14 0.23 0.05 0.05 0.05 0.33 0.55 0.40 0.10 0.05 0.05

Kyrgyzstan 0.08 0.06 0.04 0.04 0.04 0.22 0.04 0.04 0.04 0.04 0.24

Latvia 0.03 0.03 0.01 0.01 0.01 0.08 0.01 0.01 0.01 0.01 0.23

Lithuania 0.03 0.07 0.01 0.01 0.01 0.28 0.01 0.01 0.01 0.01 0.11

Malaysia 0.21 0.25 0.50 0.44 0.10 0.65 0.25 0.20 0.09 0.30

Mongolia 0.10 0.13 0.09 0.09 0.09 0.20 0.09 0.09 0.09 0.09 0.09

Morocco 0.07 0.17 0.27 0.03 0.03 0.40

Myanmar 0.37 0.40 0.31 0.14 0.21 0.38 0.64 0.11 0.68 0.21 0.46

Peru 0.08 0.05 0.05 0.05 0.05 0.43 0.30 0.05 0.05 0.05

Philippines 0.41 0.66 0.07 0.50 0.28 0.67 0.96 0.67 0.11 1.00 0.53

Romania 0.01 0.17 0.00

Russia 0.18 0.16 0.10 0.03 0.05 0.35 0.35 0.10 0.43 0.18 0.33

Saudi

Arabia 0.37 0.61 0.21 0.21 0.25 0.45 0.61 0.30 0.27 0.30 1.00

South

Africa 0.06 0.01 0.01 0.01 0.01 0.19 0.30 0.01 0.05 0.26 0.06

Tunisia 0.21 0.21 0.04 0.03 0.63 0.27 0.09 0.20 0.24 0.22 0.20

Ukraine 0.12 0.13 0.08 0.06 0.08 0.26 0.27 0.08 0.08 0.08 0.41

Notes: The OECD FDI Regulatory Restrictiveness Index covers only statutory measures discriminating against foreign investors (e.g. foreign

equity limits, screening & approval procedures, restriction on key foreign personnel, and other operational measures). Other important aspects of

an investment climate (e.g. the implementation of regulations and state monopolies among other) are not considered. All 34 OECD countries and

25 non-OECD countries are covered, including all G20 members. Data of December 2015.


42

Figure 23. Inward FDI positions by major ultimate partners versus immediate partners

As a share of total inward FDI position*, at end 2014 or latest available year

Notes:* For 7 OECD countries which report FDI by ultimate investing country and FDI by immediate partner country to the

OECD: Czech Republic, France, Germany, Iceland, Italy, Poland and the United States. Austria report inward FDI by UIC to

the OECD but the data are excluded from the analysis because inward FDI by immediate counterparty is not publishable.

Source: OECD Foreign Direct Investment statistics database.

0%

4%

8%

12%

16%

Inv esting country

By Ultimate counterpart By Immediate counterpart

Shifting view of the origin of FDI: Investors hold investments indirectly through Luxembourg

and the Netherlands

Standard FDI statistics are presented according to the immediate investing country but presenting the

statistics according to the country of the ultimate investor better captures where the investment in a

country is coming from. These statistics show the country of the direct investor who ultimately

controls the investment and, thus, bears the risks and reaps the rewards of the investment. These

statistics reveal substantial differences in the distribution of inward positions by partner country. The

United States, Germany, and the United Kingdom are generally found to be more important investors

than the standard FDI statistics show while Luxembourg and the Netherlands are generally found to

be much less important. This indicates that investors from the United States, Germany, and the United

Kingdom—as well as other countries—hold their investments in other countries indirectly through

Luxembourg and the Netherlands.

43

Table 25. Inward FDI positions by Immediate (IMD) versus Ultimate (ULT) partner country*,

at end 2014 or latest available year

Immediate counterpart Ultimate counterpart

Total inward FDI position 5,332.7 100.0% 5,332.7 100.0%

Of which from: 0.0

0.0

United Kingdom 662.4 12.4% 718.2 13.5%

Germany 380.5 7.1% 535.9 10.1%

United States 186.4 3.5% 518.8 9.7%

Japan 418.8 7.9% 444.2 8.3%

France 390.3 7.3% 432.4 8.1%

The Netherlands 786.0 14.7% 375.6 7.0%

Switzerland 405.1 7.6% 333.1 6.2%

Canada 267.2 5.0% 317.2 5.9%

Luxembourg 672.0 12.6% 228.9 4.3%

Ireland 31.9 0.6% 173.9 3.3%

Italy 103.3 1.9% 140.4 2.6%

Belgium 208.6 3.9% 115.4 2.2%

Spain 125.6 2.4% 113.2 2.1%

Sweden 73.0 1.4% 79.2 1.5%

Australia 51.9 1.0% 61.0 1.1%

Austria 75.2 1.4% 53.0 1.0%

Korea 44.5 0.8% 43.9 0.8%

Norway 24.2 0.5% 39.8 0.7%

Denmark 44.8 0.8% 35.4 0.7%

United Arab Emirates 9.9 0.2% 33.3 0.6%

Mexico 18.0 0.3% 32.7 0.6%

Israel 11.3 0.2% 32.6 0.6%

Bermuda -0.3

26.0 0.5%

Finland 18.4 0.3% 25.9 0.5%

Brazil 0.6 0.0% 23.6 0.4%

Hong Kong (China) 10.4 0.2% 23.1 0.4%

Singapore 24.4 0.5% 22.7 0.4%

Russian Federation 11.8 0.2% 19.7 0.4%

Czech Republic 3.2 0.1% 19.4 0.4%

China 13.6 0.3% 16.6 0.3%

India 8.5 0.2% 15.5 0.3%

Saudi Arabia 0.8 0.0% 15.0 0.3%

Chinese Taipei 5.9 0.1% 11.4 0.2%

Poland 1.1 0.0% 10.6 0.2%

Table 26. Inward FDI positions by Immediate (IMD) versus Ultimate (ULT) partner country

for selected OECD countries*, at end 2014 or latest available year

Inward FDI positions from:

To

tal

Wo

rld

Un

ited

Kin

gd

om

Ger

ma

ny

Un

ited

Sta

tes

Ja

pa

n

Fra

nce

Th

e

Neth

erla

nd

s

Sw

itzerla

nd

Ca

na

da

Lu

xem

bo

urg

Irela

nd

Czech Republic IMD 122 3 16 5 1 7 31 5 0 15 0

ULT 122 5 32 9 2 7 6 3 0 4 0

France IMD 797 85 80 84 15 0 122 75 4 141 2

ULT 797 98 97 158 16 31 52 82 3 82 8

Germany IMD 957 80 0 86 26 74 226 78 1 173 11

ULT 957 132 33 208 41 70 95 71 c 79 3

Iceland** IMD 8 0 0 -3 0 0 1 0 0 7 0

ULT 8 1 0 5 0 0 0 0 0 0 0

Italy IMD 340 39 27 7 3 62 65 17 0 69 0

ULT 340 5 25 38 7 61 21 21 1 30 1

Poland IMD 209 7 34 8 1 24 36 5 0 25 2

ULT 209 12 36 21 4 23 17 4 2 11 2

United States IMD 2901 449 224 0 373 223 305 224 261 243 16

ULT 2901 466 313 80 375 240 185 152 311 24 160

Notes: All data are expressed in US dollar billion. c: confidential. *For 7 OECD countries which report FDI by Ultimate investing country and FDI by immediate partner country to the OECD: Czech Republic, France, Germany, Iceland, Italy, Poland and the United

States. Austria report inward FDI by UIC to the OECD but the data are excluded from the analysis because inward FDI by immediate

counterparty is not publishable. ** Data exclude resident Special Purpose Entities (SPEs). Data for France and Germany in Table 26 correspond to FDI positons at-end 2013. Data are updated as of April 2016.

Source: OECD Foreign Direct Investment statistics database.

44

Figure 24. Domestic and Cross-Border M&A deals, 1997–2015

Source: Dealogic M&A Analytics database.

0

1000

2000

3000

4000

5000

6000

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

USD bln

Domestic and cross border M&As

Domestic M&A Cross border M&A

0

20

40

60

80

100

120

140

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

USD mln

Average deal size

0%

5%

10%

15%

20%

25%

30%

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Share of EMEs in total global M&As

Global trends in mergers and acquisitions

In 2015 the global value of mergers and acquisitions (M&A) reached USD 4.2 trillion. This was an

increase by 32% compared to 2014 and one of the highest levels in the last 15 years. In general, the

annual average value of M&A during the period 2008-2015 exceeded the annual average value of

M&A during the period 2000-2007. In 2015, the average deal size exceeded USD 100m for the first

time since 2007. Despite the fact that emerging market economies in recent years have experienced

declines in capital inflows, their share of global M&A activity reached 27% in 2015, which is the

highest share on record.

45

Table 27. Domestic and Cross-Border M&A deals, 1997–2015

Domestic

M&A -

USD

millions

Cross-

border

M&A -

USD

millions

M&A

volumes in

OECD -

USD

millions

M&A

volumes in

emerging

markets -

USD

millions

Total global

M&A

volumes -

USD

millions

Total

number of

deals

Average deal

size - USD

millions

1997 1,228,039 317,691 1,399,737 145,993 1,545,730 18,032 86

1998 1,678,094 545,693 2,054,871 168,916 2,223,787 24,057 92

1999 1,951,220 851,014 2,601,494 200,741 2,802,234 28,668 98

2000 2,794,213 1,220,241 3,743,627 270,827 4,014,454 32,019 125

2001 1,625,082 648,996 2,110,513 163,566 2,274,079 27,914 81

2002 1,147,128 413,340 1,413,725 146,743 1,560,468 26,187 60

2003 1,128,658 346,287 1,311,568 163,378 1,474,946 23,800 62

2004 1,738,793 449,728 1,975,769 212,752 2,188,521 26,938 81

2005 2,153,935 725,639 2,564,745 314,828 2,879,573 32,381 89

2006 3,047,761 1,027,946 3,578,813 496,895 4,075,708 38,430 106

2007 3,615,902 1,707,255 4,626,368 696,789 5,323,157 44,340 120

2008 2,922,992 1,326,387 3,526,041 723,338 4,249,379 44,168 96

2009 2,107,207 659,504 2,270,212 496,498 2,766,711 38,479 72

2010 1,932,719 785,217 2,014,722 703,214 2,717,936 42,460 64

2011 2,029,208 1,022,038 2,330,809 720,437 3,051,246 45,750 67

2012 2,028,863 745,478 2,179,541 594,800 2,774,341 44,216 63

2013 2,133,504 827,832 2,263,033 698,303 2,961,336 38,852 76

2014 2,291,449 897,618 2,512,619 676,447 3,189,066 41,215 77

2015 3,136,991 1,089,175 3,095,146 1,131,020 4,226,165 39,603 107

Source: Dealogic M&A Analytics database.

46

Figure 25. Deviations from covered interest parity (CIP) on domestic forward (DF) and non-

deliverable forward (NDF) markets, 2015

Note: Deviations from CIP is measured as the 1-year average conditional volatility of CIP which is the conditional standard

deviation calculated using a GARCH (1,1) model. Along with interest parities, the conditional variance of CIPs might be a

measure of dynamic capital mobility. Indeed with greater capital mobility, not only covered differential rates but also the

variance would decline over time. CIP is calculated using deliverable and non-deliverable forward rates. The greater the

volatility, the more CIP is deviating from the 0 equilibrium. This phenomenon is observed in countries with strong capital

control measures.

Source: Thomson Reuters Datastream, OECD calculations. See Annex for details.

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

GARCH conditional std devDomestic forward market

1

0.00

0.50

1.00

1.50

2.00

2.50

3.00

GARCH conditional std devNon-Deliverable forward market

9

Openness of banking systems

The measure of the openness of a country’s banking system is based on the persistence of deviations

from covered interest parity (CIP). A larger score implies a less open banking system. Some countries

are more open than others, and some have over time moved down the index of restrictiveness.

Countries with non-deliverable forward markets (NDF) are those where controls are so strong that an

offshore parallel foreign exchange market emerges to serve investor needs.

47

Table 28. Deviations from covered interest parity (CIP) on domestic forward (DF) markets,

2006–2015

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Australia 0.01 0.03 0.18 0.07 0.06 0.06 0.05 0.04 0.04 0.05

Canada 0.03 0.05 0.15 0.06 0.03 0.02 0.02 0.01 0.01 0.02

China 0.30 0.64 1.07 0.57 0.44 0.51 0.62 0.57 0.56 0.26

Czech Republic 0.04 0.05 0.21 0.15 0.06 0.04 0.03 0.02 0.02 0.11

Denmark 0.03 0.04 0.15 0.05 0.03 0.05 0.03 0.01 0.01 0.05

Euro Area 0.03 0.03 0.13 0.03 0.04 0.05 0.02 0.01 0.01 0.02

Hong Kong (China) 0.04 0.05 0.09 0.02 0.02 0.02 0.01 0.01 0.01 0.02

Hungary 0.24 0.30 0.41 0.72 0.34 0.30 0.26 0.18 0.20 0.36

India 0.20 0.46 0.54 0.18 0.17 0.31 0.18 0.25 0.16 0.11

Indonesia 0.22 0.44 0.43 0.13 0.17 0.13 0.14 0.11 0.34

Israel 0.05 0.06 0.16 0.08 0.06 0.11 0.08 0.04 0.06 0.04

Japan 0.06 0.06 0.14 0.03 0.03 0.04 0.02 0.01 0.01 0.03

Korea 0.03 0.17 0.45 0.14 0.09 0.08 0.05 0.06 0.04 0.03

Malaysia 0.05 0.04 0.15 0.06 0.05 0.06 0.05 0.04 0.06 0.06

Mexico 0.08 0.06 0.32 0.19 0.11 0.09 0.08 0.07 0.07 0.05

Morocco 0.21 0.23 0.20 0.23 0.19 0.18 0.14 0.19 0.28 0.25

New Zealand 0.08 0.08 0.14 0.15 0.11 0.10 0.07 0.07 0.06 0.11

Norway 0.03 0.04 0.22 0.06 0.04 0.05 0.03 0.02 0.02 0.02

Pakistan 0.16 0.16 0.65 0.29 0.18 0.32 0.17 0.21 0.29 0.15

Philippines 0.18 0.22 0.32 0.20 0.59 0.28 0.20 0.18 0.08 0.06

Poland 0.03 0.04 0.25 0.11 0.08 0.09 0.12 0.06 0.08 0.09

Russia 0.22 0.23 1.77 1.09 0.19 0.29 0.12 0.09 0.50 0.60

Saudi Arabia 0.11 0.08 0.06 0.11 0.19 0.20

Singapore 0.04 0.07 0.14 0.06 0.02 0.10 0.02 0.01 0.02 0.09

South Africa 0.06 0.10 0.18 0.16 0.09 0.07 0.09 0.08 0.09 0.10

Sweden 0.08 0.06 0.13 0.17 0.11 0.11 0.08 0.07 0.06 0.15

Switzerland 0.05 0.04 0.15 0.03 0.06 0.08 0.04 0.02 0.02 0.07

Chinese Taipei 0.24 0.46 0.39 0.42 0.80 0.32 0.20 0.25 0.18 0.26

Thailand 0.93 6.08 2.09 0.57 0.53 0.83 0.72 0.90 0.61 1.10

Turkey 0.31 0.21 0.39 0.15 0.13 0.17 0.17 0.19 0.18 0.16

United Kingdom 0.01 0.03 0.14 0.03 0.01 0.01 0.01 0.01 0.01 0.01

United States 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Table 29. Deviations from covered interest parity (CIP) on

non-deliverable forward (NDF) markets, 2006–2015

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Argentina 0.82 1.32 2.91 2.18 0.89 1.91 2.43 3.22 3.82 8.85

Brazil 0.50 0.18 0.47 0.30 0.20 0.47 0.18 0.17 0.12 0.17

Chile 0.37 0.28 0.39 0.65 0.28 0.31 0.28 0.22 0.27 0.25

China 0.33 0.64 1.10 0.57 0.71 0.73 0.74 0.63 1.18 0.95

Colombia 0.23 0.42 0.39 0.31 0.45 0.44 0.25 0.15 0.22 0.24

Egypt 0.07 0.14 0.81 2.58 0.31 1.34 1.31 2.37 1.25 2.25

India 0.60 1.02 2.10 1.35 1.18 1.20 1.30 1.54 0.94 0.78

Indonesia 1.11 1.41 3.05 2.92 1.38 2.02 1.54 2.67 1.71 1.68

Korea 0.66 0.84 3.24 2.45 1.97 1.87 0.98 1.06 1.11 1.55

Malaysia 0.38 0.64 1.10 1.22 1.05 1.19 0.84 1.08 0.87 1.95

Peru 0.20 0.19 0.74 0.33 0.25 0.27 0.18 0.28 0.36 0.67

Philippines 0.64 1.26 2.06 1.68 1.20 1.14 0.74 0.89 0.83 0.86

Russia 0.46 0.32 1.65 4.62 0.94 1.56 1.72 0.44 1.64 1.20

Chinese Taipei 0.79 0.62 1.30 1.24 0.97 1.24 0.66 0.77 0.68 1.31


48

Figure 26. Saving-investment correlation, 5 years quarterly rolling window, 1981–2015

Table 30. Saving-investment correlation by region, 5 years quarterly rolling window, 1986–2015

Date range Advanced economies Emerging economies OECD countries BRICS

1986 Q3 0.42 0.22 0.37 0.74

1987 Q3 0.45 0.26 0.43 0.83

1988 Q3 0.46 0.32 0.45 0.85

1989 Q3 0.47 0.34 0.47 0.86

1990 Q3 0.47 0.41 0.50 0.81

1991 Q3 0.46 0.45 0.52 0.77

1992 Q3 0.46 0.53 0.56 0.68

1993 Q3 0.48 0.59 0.59 0.79

1994 Q3 0.48 0.63 0.61 0.85

1995 Q3 0.46 0.66 0.61 0.85

1996 Q3 0.45 0.74 0.60 0.81

1997 Q3 0.44 0.79 0.60 0.79

1998 Q3 0.39 0.77 0.55 0.76

1999 Q3 0.31 0.76 0.47 0.72

2000 Q3 0.18 0.67 0.33 0.62

2001 Q3 0.10 0.63 0.23 0.59

2002 Q3 0.04 0.61 0.18 0.60

2003 Q3 0.02 0.62 0.16 0.63

2004 Q3 -0.01 0.60 0.14 0.68

2005 Q3 -0.02 0.56 0.13 0.78

2006 Q3 -0.05 0.49 0.09 0.84

2007 Q3 -0.06 0.44 0.06 0.85

2008 Q3 -0.06 0.38 0.03 0.83

2009 Q3 0.01 0.39 0.07 0.82

2010 Q3 0.07 0.44 0.12 0.82

2011 Q3 0.13 0.47 0.18 0.83

2012 Q3 0.16 0.49 0.21 0.84

2013 Q3 0.21 0.53 0.25 0.84

2014 Q3 0.24 0.53 0.28 0.81

2015 Q3 0.34 0.53 0.34 0.80

2015 Q4 0.38 0.53 0.38 0.80

Note: Saving-investment correlation is measured by the β coefficient associated to the national gross capital formation

indicator when regressed on the national gross saving indicator over 5-years rolling sample time period.


-0.20

0.00

0.20

0.40

0.60

0.80

1.00

1981

Q

4

1983

Q

4

1985

Q

4

1987

Q

4

1989

Q

4

1991

Q

4

1993

Q

4

1995

Q

4

1997

Q

4

1999

Q

4

2001

Q

4

2003

Q

4

2005

Q

4

2007

Q

4

2009

Q

4

2011

Q

4

2013

Q

4

2015

Q

4

Advanced economies Emerging economies OECD countries BRICS

Saving-investment correlation

The saving-investment correlations are indicators of the financial openness of an economy. The

BRICS are the least open and have made no progress to reduce the correlation in recent years.

Advanced and OECD economies’ correlations declined in the 1990’s and early 2000’s, but have

begun to rise since the crisis. Other EMEs made progress compared to the BRICS, but correlations

there have also increased since the crisis.

49

ANNEX: Methodology for data collection and analysis

Non-financial company data and sample description

Company data are based on the Bloomberg World Equity Index (BWEI). The sample includes all

companies which have been listed in the BWEI over the period 2002-2015. 10,098 listed companies

in 76 countries were selected (i.e. 6,460 in advanced economies and 4,638 in emerging economies)

operating in 20 GICS industry sectors. Annual consolidated financial statements were extracted from

Bloomberg. The unbalanced panel dataset is denominated in current US dollars. Potential outliers

were removed from the sample.

To examine the financial characteristics of firms, the following financial variables are considered

and are defined as follows:

Value added: Sum of personnel expenses and EBITDA (income before interest, taxes,

depreciation and amortisation).

Number of employees: Number of people employed by the company, based on the number

of full time equivalents. If unavailable, then the number of full time employees is used,

excluding part time employees.

Net sales: Total operating revenues less various adjustments (i.e. returns, discounts,

allowances, excise taxes, insurance charges, sales taxes, and value added taxes).

Capital expenditure: Amount the company spent on purchases or upgrade of tangible fixed

assets. It may include intangible assets when not disclosed separately.

Free cash flow: Operating cash flow minus capital expenditures. It represents the cash that

a company is able to generate after laying out the money required to maintain or expand its

asset base.

Dividends and buybacks: Sum of dividend paid and buybacks of common shares.

R&D expenditure: Operating expense related to the research and development of a

company's products or services.

Debt-to-capital ratio: Total long-term borrowings divided by the sum of long-term

borrowing and equity capital. Long-term borrowing includes all interest-bearing financial

obligations that are not due within a year. It may also include shares issued by subsidiaries if

the group has an obligation to transfer economic benefits in connection with these shares.

Equity capital is share capital, plus retained earnings and minus treasury stock.

Return on equity (ROE): Ratio of net income to common equity. Net income is the profit

after all expenses have been deducted. Common equity is the amount that all common

shareholders have invested in a company.

Value of completed M&A deals: Declared amount effectively paid by the acquirer

company for the target company.

Cost of equity (COE): Sum of dividend and buyback yield and underlying trend in earnings

per share growth.

50

Cost of capital (COK): Weighted average (by the share of equity and debt in total assets,

respectively) cost of equity and cost of debt.

Corporate bonds

Primary corporate bond market data are based on original OECD calculations using data

obtained from Thomson Reuters ThomsonOne New Issues Database, an international deal-level

database on new issues of corporate bonds. The database provides a detailed set of information for

each corporate bond issue, including the identity, nationality and sector of the issuer; the type, interest

rate structure, maturity date and rating category of the bond, the amount of and use of proceeds

obtained from the issue.

Prior to any exclusion, the database covers 244,137 observations in the period from January 2000

to December 2015. From this initial set, the deals that were registered but were not consummated

(31,089), sukuk bonds (2,957), convertible bonds (16,412), preferred shares (2,787) and bonds with

an original maturity less than 1 year (16,436) or an issue size less than USD 1 million (394) have been

excluded. After eliminating observations with improper or missing fields (1,749), the dataset covers

172,313 bond issues from 108 countries. When tranches under the same bond package are counted as

a single issue, this figure reduces to 146,267.

Outstanding amounts are calculated based on annual net issuance amounts. Actual call date data

obtained from Bloomberg were used on net issuance calculations. Outstanding amount are at current

prices.

Given that a significant portion of bonds are issued internationally, it is not possible to assign

such issues to a certain country of issue. For this reason, the country breakdown was carried out based

on the domicile country of the issuer. Issuance amounts are in 2015 USD adjusted by US GDP

deflator.

Public equity

Initial public offering (IPO) and secondary public offering (SPO) data are based on original

OECD calculations using data obtained from Thomson Reuters ThomsonOne New Issues Database.

Data exclude Real Estate Investment Trusts (REITs), closed-investment funds, over-the-counter

(OTC) markets and unit/trust offerings.

The IPOs of companies that were listed in an organised market after the IPO but currently traded

in OTC markets are included. SPO covers all share issues of listed companies after an IPO. The

country breakdown was carried out based on the domicile country of the issuer. Issuance amounts are

in 2015 USD adjusted by US GDP deflator.

Industry classification for corporate bond and public equity data

The data on corporate bonds and public equity follow Thomson Reuters’ industry classification.

The main categories and their subcategories are the following:

Consumer products and services: Educational services, employment services, home

furnishings, legal services, travel services, professional services, and others.

51

Consumer Staples: Agriculture and livestock, food and beverage, household & personal

products, textiles & apparel, tobacco, and others.

Energy: Oil & gas, petrochemicals, pipelines, power, water and waste management,

alternative energy sources, and others.

Healthcare: Pharmaceuticals, biotechnology, healthcare equipment & supplies, healthcare

providers & services, hospitals, and others.

High technology: Computers & peripherals, e-commerce / B2B, electronics, IT consulting &

services, internet software & services, semiconductors, software, and others.

Industrials: Aerospace & defence, automobiles & components, building/construction &

engineering, industrial conglomerates, machinery, transportation & infrastructure and others.

Materials: Chemicals, construction materials, containers & packaging, metals & mining,

paper & forest products, and others.

Media and entertainment: Broadcasting, cable, publishing, recreation & leisure, advertising

and marketing, hotels and lodging, motion pictures & audio visual, casinos & gaming and

others.

Real estate: Non-residential, residential, REITs, real estate management & development, and

others.

Retail: Food & beverage retailing, discount and department store retailing, apparel retailing,

computers & electronics retailing, internet and catalog retailing, automotive retailing, home

improvement retailing and others.

Telecommunications: Space & satellite, telecommunications equipment,

telecommunications services, wireless, and others.

Covenant data

The analysis on covenants is based on OECD report Corporate Bonds, Bondholders and

Corporate Governance (2015, OECD Corporate Governance Working Papers, No. 16) using data

from Mergent Fixed Investment Securities Database (FISD), a database providing issue-level

covenant data for publicly-offered bonds in the United States.

The dataset covers issues from January 2000 to October 30, 2014. After excluding convertible

bonds, private placements (excluding Rule 144A transactions), issues with missing data, and bonds

with original maturity less than 1 year leads to a final dataset of 15,046 bonds.

The 34 covenant variables in the dataset are matched to the 15 covenant types defined in Billett,

King and Mauer (2007, “Growth Opportunities and the Choice of Leverage, Debt Maturity, and

Covenants”, The Journal of Finance, Vol. 62, No. 2, pp. 697-730.) If a bond had at least one covenant

that belongs to a certain covenant type, it was considered to have that covenant type. For each

covenant type, a binary variable was generated, which is equal to 1 if the covenant type is available in

the bond indenture. The binary variables are then summed, divided by 15 to create an index that

ranges from 0 to 1, with the ratio 1(0) denoting the highest (lowest) possible protection for

bondholders.

It should be noted that this index provides only a rough measure of covenant quality, since the

measure changes based only on the existence or non-existence of a given covenant. Therefore the

52

index cannot account for the changes in the level of protection a given covenant may provide

depending on how it is worded.

Rating data

For each bond that has rating information in the corporate bond dataset, developed based on data

from Thomson Reuters and Bloomberg, a value of 1 to the lowest credit quality rating (C) and 21 to

the highest credit quality rating (AAA for S&P and Fitch and Aaa for Moody’s) is assigned. There are

eleven non-investment grade categories: five from C, C to CCC+; and six from B, B- to BB+. There

are ten investment grade categories: three from B, BBB- to BBB+; and seven from A, A- to AAA.

This index is weighted as one for C, two for CC and rising to twenty one for AAA. A fall in the index

indicates declining quality.

Distance-to-default

The distance-to-default indicator DTDt is the number of standard deviations away from the

default point. To derive the measure, it is assumed that a bank defaults (or is bankrupt) when the

market value of assets equals (or is lower) than the book value of debt (Vt = Dt). The formula to

calculate the distance-to-default is derived from the option pricing model of Black and Scholes (1973)

and is as follows:

𝐷𝑇𝐷𝑡 =log (

𝑉𝑡𝐷𝑡

) + (𝑟𝑓 −𝜎𝑡

2

2 ) . 𝑇

𝜎𝑡√𝑇

where:

𝑉𝑡: Market value of bank’s asset at time t,

𝑟𝑓: Risk-free interest rate,

𝐷𝑡: Book value of the debt at time t,

𝜎𝑡: Volatility of bank’s asset at time t,

𝑇: Maturity of the debt.

However, the market value of assets (Vt) and its volatility (𝜎𝑡) have to be estimated. Equity-

holders have the residual claim on a firm’s assets and have limited liability. As first realised by

Merton (1977), equity can be modelled as a call option on the underlying assets of the bank, with a

strike price equal to the total book value of the bank’s debt. Thus, option-pricing theory can be used to

derive the market value and volatility of bank’s underlying assets from equity’s market value (VE)

and volatility (𝜎𝐸), by solving:

𝑉𝑡 =𝑉𝐸𝑡 + 𝐷𝑡𝑒−𝑟𝑓𝑇𝑁(𝑑2)

𝑁(𝑑1)

𝜎𝑡 =𝑉𝐸𝑡

𝑉𝑡

𝜎𝐸,𝑡

𝑁(𝑑1)

where:

𝑑1 =log (

𝑉𝑡𝐷𝑡

) + (𝑟𝑓 +𝜎𝑡

2

2 ) . 𝑇

𝜎𝑡√𝑇

𝑑2 = 𝑑1 − 𝜎𝑡√𝑇

𝑉𝐸: Value of bank’s equity,

N: The cumulative normal distribution,

𝜎𝐸 : Equity’s volatility.

53

A bank defaults (or is bankrupt) when DTDt equals to 0 (or is negative). All data are extracted

from Bloomberg. The total annual debt liabilities (i.e., the difference of the annual total assets and

annual total equity) is interpolated using a cubic spline to yield daily observations (Dt). The volatility

of equity (𝜎𝐸) is the standard deviation of daily return multiplied by √252 (i.e., 252 trading days by

year). The expiration date of the option (T) equals the maturity of the debt and it is assumed to be 1 in

accordance with the common practice. The risk free interest rate (𝑟𝑓) is the 12 months interbank rate.

Implicit guarantees for the debt of large banks

Estimates based on the credit rating uplift due to assumed external support, obtained by

subtracting (the numerical equivalent of) the “stand-alone” credit rating from (the numerical

equivalent of) the “all-in” credit rating for a sample of 204 international banks from 23 OECD

countries. Sample as in Blix-Grimaldi et.al. (“Estimating the size and incidence of bank failure

resolution costs”, forthcoming in OECD Journal: Financial Market Trends in 2016), without Mexico

and Turkey. Numerical equivalents of credit ratings are obtained through a simple linear mapping

(e.g. Aaa to 20, Aa1 to 19, etc.) and the units shown are "notches" of credit ratings (with one notch

being the difference between two subsequent credit rating values). Values as of end-of-the-year.

Details on the estimation method are available in Schich and Lindh (2012, “Implicit Guarantees

for Bank Debt: Where Do We Stand?”, OECD Journal: Financial Market Trends) and on the

interpretation of the economic drivers of the estimated values in Estrella and Schich (2015, “Valuing

guaranteed bank debt: Role of strength and size of the bank and the guarantor”, Journal of Economic

& Financial Studies). Note that other methods have been developed to measure the value of implicit

bank debt guarantees and that there is no agreed best measure.

Conditional volatility of covered interest parity

The 1-year average conditional volatility of Covered Interest Parity (CIP) is measured as the

conditional standard deviation calculated using a GARCH (p,q) model. If CIP is denoted generically

y, a time-series model that captures the autoregressive (AR) structure in both the mean and the

variance can be written as:

𝑦𝑡 = 𝛼0 + 𝛼1𝑦𝑡−1 + ⋯ + 𝛼ℎ𝑦𝑡−ℎ + 𝜉𝑡 𝑤𝑖𝑡ℎ 𝜉𝑡 ~𝑁(0, 𝜎𝑡2)

𝜎𝑡2 = 𝛽0 + ∑ 𝛽1

𝑞

𝑡=1

𝜉𝑡−12 + ∑ 𝛽2

𝑝

𝑡=1

𝜎𝑡−𝑗2

Where ∑ 𝛽1𝜉𝑡−12 is the ARCH term (the squared error term in the previous time period) of q

order, generally being news about volatility from the previous period; ∑ 𝛽2𝜎𝑡−𝑗2 is the GARCH term

(the conditional variance in the previous time period) of p order. Thus, yt follows an AR (h) process

with a conditional variance equation described by a GARCH (p, q) process. The GARCH model is

implemented via maximum likelihood estimation of the log-likelihood function. The estimated

conditional variance 𝜎𝑡2

will give us an indication of the evolution of capital mobility. In this

Scoreboard, GARCH (1,1) model is adopted, which is sufficient to capture the dynamics of the

conditional variance of y. The properties of the dataset are examined before the analysis of the

empirical results. Augmented Dickey–Fuller and Phillips−Perron (PP) tests are used to check the

54

stationarity of the times series. CIP time series are driven by an AR (1) process. It is also identified

that the estimated coefficients are significant and all the diagnostic statistics are reasonable.

Along with interest parities, the conditional variance of CIPs might be a measure of dynamic

capital mobility. With greater capital mobility, not only covered differential rates but also the variance

would decline over time. CIP is calculated using deliverable and non-deliverable forward rates. The

greater the volatility, the more CIP is deviating from the 0 equilibrium. This phenomenon is observed

in countries with strong capital control measures.

The Savings Investment Correlation

The analysis on savings-investment correlation is based on “Integration versus Interdependence and

Complexity in Global Trade and Finance in the Post-War Period” (Blundell-Wignall, A., Atkinson, P., Roulet,

C., 2013, in 50 Years of Money and Finance: Lessons and Challenges, Edited by M. Balling, Larcier Edition,

Chapter 6: 195-228).

Countries which open up to foreign private participation in domestic investment opportunities for

technology transfer, synergies in the global supply chain, or resources development reasons will see

S-I correlations decline over time as this opening up occurs. Countries that are not open, or which are

excessively selective in their openness, should see higher more stable S-I correlations.

To explore this proposition, revised and internationally consistent quarterly data for a constant

sample of 43 countries’8 savings and investment from 1977 are compiled. Panel regressions are run

for the OECD as a group, advanced economies, emerging economies and BRICS countries. To

explore the changing degree of openness, the following empirical model is specified in equation (1),

where the subscripts i and t denote the country and the period, respectively:

GFCFi,t = αi,t + β SAVi,t + εi,t (6.1)

where GFCFi,t is national gross capital formation and SAVi,t is national gross saving. Both

variables are expressed in per cent of national gross domestic product. β corresponds to the coefficient

of openness. This equation is estimated using ordinary least squares (OLS). From 1977, the panel

regressions are run in 5-years rolling sample time period format.

Pension funds' total investments

Data for 2015 are preliminary. Data about pension funds in Estonia only refer to the mandatory

funded system; data for Finland only cover the main mandatory earnings-related pension schemes

(TyEL and MEL); German figures only include data of "Pensionskassen" and "Pensionsfonds"

supervised by BaFin; Norway's data do not fully cover all Norwegian pension funds; data about

pension funds in Slovenia only cover mutual pension funds; data for Turkey only refer to personal

pension plans. Data for Switzerland are OECD estimates based on Willis Towers Watson's report

Global Pension Assets Study 2016, and are not official data from the Swiss Statistical Office.

Data for 2015 refer to the end of 2015, except for: Australia where data refer to end of June

2015; New Zealand where data refer to end of March 2015; Belgium, Canada, and IRAs in the United

States where data refer to the end of Q3 2015.

The change in value of pension funds' investment, expressed in national currency, was calculated

between June 2014 and June 2015 for Australia, and between March 2014 and March 2015 for New

Zealand.

55

The share of pension funds’ investments within the whole funded pension systems refers to 2014

for Canada, France, Korea, Mexico and Spain. The share of pension funds' investment within the

whole funded pension systems is underestimated for Estonia and Slovenia.

OECD pension funds’ investment as a percentage of GDP (84.5%) is a weighted average;

weights are pension funds’ investments in 2015.

Calculation of pension funds' real net investment rate of return

Methods for calculating the average investment returns (IRR) of pension funds vary greatly from

country to country, hindering international comparability of these statistics. With a view to increasing

data comparability across countries, the OECD and its Working Party on Private Pensions therefore

decided that it would be worth applying the same calculation method for IRR across countries, which

would be calculated by the OECD, using variables already collected as part of the Global Pension

Statistics’ framework. In order to reach a consensus on the most appropriate formula for the IRR

calculation, an electronic discussion group was created, composed of selected country experts.

Drawing on preliminary consultations, the OECD Secretariat proposed five formulas to the

electronic discussion group for comments. A consensus was reached within the group and

subsequently endorsed by the OECD Task Force on Pension Statistics on the following formula for

the average IRR, in each year N:

𝐶𝑎𝑙𝑐𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐼𝑅𝑅𝑁 =𝑁𝑒𝑡 𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 𝐼𝑛𝑐𝑜𝑚𝑒𝑁

(𝑇𝑜𝑡𝑎𝑙 𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡𝑁−1 + 𝑇𝑜𝑡𝑎𝑙 𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡𝑁)/2. 100

Net investment income comprises income from investments, value re-adjustments on

investments and income from realised and unrealised capital gains and losses. It includes rents

receivable, interest income, dividends and realised and unrealised capital gains, before tax and after

investment expenses.

Real IRRs in this Scoreboard have been calculated using this common formula for the average

nominal net investment return (ratio between the net investment income at the end of the year and the

average level of assets during the year) for all the countries, except for Austria (2011-2012, 2015),

Finland (2015), Ireland (2007-2008), Israel, Italy (2015), Poland (2015), Slovak Republic (2015),

Sweden (2011-2013), Turkey (2013-2014) and the United States for which values have been provided

by the countries or come from national official publications.

Pension funds' real net investment rates of return of the year N are calculated over the period

December N-1 - December N for all the other countries, except for Australia (June N-1 – June N) and

Canada in 2015 (December 2014 - September 2015). Data for 2015 are preliminary.

There is a break in series in 2011 for Finland which is due to the exclusion of public buffer funds

which were included before. The break in series in 2011 in Hungary corresponds to the pension

reform leading to a decrease in the assets of mandatory pension funds in 2011. Data for Israel refer to

new pension funds only. Data for Mexico and Turkey refer to personal pension plans only. For

Poland, the financial result (i.e. the sum of result on investment and the realized and unrealized

profits/losses on investment/valuation of investment and the income from the coverage of the deficit)

is used as a proxy for net investment income. Since 2007, the financial result of Poland's occupational

pension plans has been included (1% of pension funds total assets).

56

Pension fund asset allocation

The OECD Global Pension Statistics database provides information about investments in

Collective Investment Schemes and the look-through of Collective Investment Schemes' investments

in cash and deposits, bills and bonds, shares and other. When the look-through was not provided by

the countries, estimates were made assuming that collective investment schemes’ allocation in bills

and bonds, shares and other was the same as pension funds' direct investments in these categories.

Therefore, pension fund asset allocation data in this Scoreboard include both direct investment in

shares, bills and bonds and indirect investment through Collective Investment Schemes. The "Other"

category includes cash and deposits, loans, land and buildings, unallocated insurance contracts, hedge

funds, private equity funds, structured products, other mutual funds (i.e. not invested in bills and

bonds, or shares) and other investments.

Data are preliminary. Data for Ireland only refer to DB plans. The high value for the “Other”

category in Japan is mainly driven by outward investments in securities and accounts payable and

receivable.

Foreign direct investment trends

OECD, EU, World, G20 aggregates: FDI outward and inward flows for these aggregates were

compiled using directional figures when available. Missing quarterly directional figures were

approximated using the ratio between annual asset liability and directional figures; or by distributing

annual directional figures equally among the four quarters; or using unrevised historical data. When

directional figures were not available and could not be approximated, asset liability figures were used.

Resident SPEs from Austria, Belgium, Chile, Denmark, Hungary, Iceland, Luxembourg, Mexico,

the Netherlands, Norway, Poland, Portugal, Spain and Sweden are excluded.

The European Union aggregate corresponds to member country composition of the reporting

period: EU15 for data up to and including 2003, EU25 for data between 2004 and 2006, EU27 for

data between 2007 and 2012 and EU28 starting from 2013.

The government of Argentina declared a state of emergency in the national statistical system on

07 January 2016. As a consequence, Argentina's Instituto Nacional de Estadística y Censos (INDEC)

has temporarily suspended publication of certain official statistics under its responsibility, pending re-

organization. As a consequence, Argentina has been excluded from the calculation of the G20

aggregate from Q2 2015 onwards.

World aggregate: World totals for FDI flows are based on available data at the time of update as

reported to the OECD and IMF. Missing data for countries for Q3 and Q4 2015 were estimated using

the overall growth rate observed between, respectively, Q2 2015 and Q3 2015 and Q3 2015 and Q4

2015. Growth rates were calculated from data for OECD countries, for non-OECD G20 countries, and

for 50 non-OECD and non-G20 countries in Q3 and 15 non-OECD and non-G20 countries in Q4.

World totals for FDI positions are based on available FDI data at the time of update as reported to

OECD and IMF for the year ended or the latest available year.

By definition, inward and outward FDI worldwide should be equal. However, in practice, there

are statistical discrepancies between inward and outward FDI.

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