WWRR Vol.2.010

28-09-18 

What We Are Reading - Volume 2.010 

The enclosed 2.010 version contains - interesting articles on Indian Bull Market Prospect, Indian NBFCs, High Growth in EMs, 5-hour Rule 

for Reading, Jio’s R&D in Texas, Comparison of BRICS, Interaction of Luck and Work and Indian Household Leverage. 

• Bull Market to Broaden Morgan Stanley 

• NBFC Shutting Down as RBI Clean-Up Continues Bloomberg Quint 

• Outperformers: High Growth EM McKinsey&Company 

• 5-Hour Rule Medium 

• Jio’s Tech Team in Texas leading its R&D Factor Daily 

• India vis-à-vis BRICS Motilal Oswal 

• Luck vs Work jamesclear.com 

• Household Leverage SBI Cap

MM 

September 12, 2018 10:30 AM GMT 

India Equity Strategy 

Bull Market to Broaden 

A delay in earnings growth recovery is one of the key reasons why investors are 

not feeling bullish on Indian equities. We think this is likely to change as growth 

picks up in the coming quarters. 

Morgan Stanley does and seeks to do business with companies covered in Morgan Stanley Research. As a result, investors should be aware that the firm may have a conflict of 

interest that could affect the objectivity of Morgan Stanley Research. Investors should consider Morgan Stanley Research as only a single factor in making their investment decision. 

For analyst certification and other important disclosures, refer to the Disclosure Section, located at the end of this report. 

+= Analysts employed by non-U.S. affiliates are not registered with FINRA, may not be associated persons of the member and may not be subject to NASD/NYSE restrictions on 

communications with a subject company, public appearances and trading securities held by a research analyst account.

MM 

Contributors 

MORGAN STANLEY INDIA COMPANY PRIVATE LIMITED+ 

Sheela Rathi 

Equity Strategist 

+9122 6118-2224 

Sheela.Rathi@morganstanley.com 

MORGAN STANLEY INDIA COMPANY PRIVATE LIMITED+ 

Ridham Desai 

Equity Strategist 

+9122 6118-2222 

Ridham.Desai@morganstanley.com

MM 

India Equity Strategy 

Bull Market to Broaden 

Adelay in earnings growth recovery is one of the key reasons why 

investors are not feeling bullish on Indian equities. We think this is likely 

to change as growth picks up in the coming quarters. 

We argue that Indian equities continue to be in an uptrend mode 

backed by broad-based earnings growth acceleration. Therefore, we 

believe, rotation is inevitable and investors should not chase outperformers 

but rather invest in their favorite underperformers among 

large caps, as well as small and mid-cap (SMID). We are upping our 

index target to 42,000 for Sep. 2019, as we think the market is set 

up for a macro trade (rising correlations of returns across stocks). We 

are also adding underperforming stocks to our portfolio - namely 

SBIN, Apollo Hospitals and Prestige Estates. 

Earnings behaviour of the past eight years resembles a bear market, 

but share prices tell a different story. The Nifty is up 345% (up 208% 

in US$ compared with +108% for the MSCI EM Index) from the 

trough in 2009 without seeing a sustained drawdown in excess of 

20%; this implies that, at least technically, we have been in bull 

market since then. 

India is coming out of the deepest earnings recession that has 

extended for seven years. We think the earnings cycle is turning and 

will likely broaden in the coming months. We derive our confidence 

on the coming growth from the following three reasons: 1) our proprietary 

capex survey indicates that corporates are confident on business 

growth over the next 12 months. We are seeing signs of the 

investment rate rising and thereby profit margins should also mean 

revert; 2) our banks team thinks NPL formation for the banking 

system has peaked and slippages will drop over the next two quarters; 

and 3) the recently concluded earnings season pointed to a 

broad-based acceleration in revenue and earnings growth for broad 

as well as narrow market companies. 

As growth gains pace, we expect the broader market to see a CAGR 

of about 27% growth over the next three years, which could potentially 

take the profit share in GDP higher by 100 bps. This may also 

lead to a broadening of stock performance. 

Key risks: We believe the key risk between now and June 2019 is that 

the market turns pessimistic on the general election outcome scheduled 

in May 2019. Thus if investors start believing that the electorate 

will deliver a fragmented verdict, the index could head towards our 

bear case, especially if such a belief is combined with deteriorating 

global equity markets. Relative valuations have nudged higher 

making the market less attractive especially, if the pace of growth 

does not exceed expectations.

MM 

Contents 

5 Buy Underperformers 

6 Key Charts 

7 India – A Pain Trade 

10 The Search for Earnings 

12 We think Performance is set to Broaden Out 

14 Bear Market Behavior Doesn’t Mean It Is a Bear 

Market 

16 Portfolio implications

M 

M 

Buy Underperformers 

Why do we think India's 'pain trade' is set to end? The 

concentration of performance of the market may have 

peaked as the concentration of earnings may have peaked. 

India is coming out of the deepest earnings recession that 

has extended for seven years. We think the earnings cycle 

is turning and will likely broaden in the coming months. 

This may lead to a broadening of performance. Investors 

are likely anchored to the performance of the winners of 

the past few months and may make portfolio switches into 

these stocks, more so because the consensus opinion on 

aggregate earnings is still patchy. Our view is that this is not 

the moment to concentrate portfolios into the recent winners. 

What to do with portfolios? In an uptrending market, rotation is 

inevitable thus we think investors should not look to position themselves 

in the outperformers and chase performance, but own their 

favorite underperformers (with strong growth outlooks). We are 

upping our index target to 42,000 for Sep-19 from 36,000 in Jun-1. 

On our target the Sensex would trade at a forward PE of 16.5x (compared 

to 18x currently) and trailing PE of 20x, higher than the 25 year 

trailing average of 19x. We think SMID indices look attractive on a 

market to GDP ratio and we think investors should selectively add 

those stocks. In our Focus List, we are adding SBI, Prestige and Apollo 

Hospitals. We are removing Infy, Havells and Zee, given the recent MS 

analyst downgrades. 

Why we think growth and market performance is set to broaden 

out: We derive our confidence on the coming growth from the following 

three reasons: 1) our proprietary capex survey indicates that 

corporates are confident on business growth over the next 12M; 2) 

our banks team thinks NPL formation for the banking system has 

peaked and slippages will drop over the next two quarters; and 3) the 

recently concluded earnings season pointed to a broad-based acceleration 

in revenue and earnings growth for broad as well as narrow 

market companies. 

We believe the correlation of stock returns with the market is set to 

rise, i.e., macro has started to influence (early signs) the behavior of 

stocks. The rise in market effect means performance of individual 

stocks should be influenced more by market performance-related 

factors rather than by idiosyncratic or non-market performance-related 

issues. In such a market environment, when combined with our 

positive view on the market, it makes sense for investors to position 

themselves in their favorite underperformers. We think the market is 

prepping for a macro trade for the first time since 2016. 

Backdrop of why India has been a pain trade in 2018 - performance 

pain … Staying out of Indian equities has been painful for investors 

(as reflected in falling foreign portfolio investors overweight positioning 

and ownership) as India has outperformed the regional indices 

this year. However, investing in Indian equities was equally painful 

since a handful of stocks have accounted for India's performance this 

year and it is unlikely that most investors owned enough of them. India 

exhibited three key features of a bear market: a) large cap index outperformed 

the SMID indices; b) performance breadth collapsed; and c) 

concentration of top stocks' performance in the large cap index rose, 

i.e., the top 10 stocks in weight in the Nifty index have accounted for 

100% of the index's performance. These were also features of the bear 

markets we saw in 2013 and 2008/09. 

… combined with the earnings pain: Earnings growth has disappointed 

for most over the past few years. A fall in the investment rate 

and a rise in the current account deficit (from 2010 to 2014) made matters 

worse for earnings. Specific events, such as global trade collapse 

in 2015/16, demonetization in 2016/17 and the roll out of GST in 

2017/18, combined with the delay in the bank recap, extended the pain. 

Given the aggregate depressed earnings, large cap companies' profit 

share in aggregate has expanded sharply, like we saw the in the previous 

weak growth periods. This explains the current concentration of 

earnings. 

While GDP growth and profit growth have a close relationship in India, 

profit growth invariably has deeper cycles. We have been in one such 

deep downcycle, which means that the corporate profit share in GDP 

has fallen to near all-time lows. A likely mean reversion in this share 

accompanied by accelerating nominal GDP growth sets the stage for 

a sharp recovery in earnings in the coming quarters, in our view. 

Our economists are forecasting GDP growth of 7.6% and 7.7% in F2019 

and F2020. Fundamentally, profit margins are a function of the investment 

rate. Investments tend to be cyclical because firms overinvest in 

upcycles and then go through big adjustments in the downcycle. We 

are seeing signs of the investment rate rising and thereby profit margins 

should also mean revert. As growth gains pace, we expect the 

broader market to see a CAGR of about 27% growth over the next 

three years, which could potentially take the profit share in GDP 

higher by 100 bps. 

MORGAN STANLEY RESEARCH 5

M 

M 

Key Charts 

Exhibit 1: 

Divergence in performance of large and SMID indices indicate a bear 

market 

35% 

25% 

15% 

5% 

-5% 

-15% 

-25% 

-35% 

-45% 

-55% 

-65% 

2006 

2007 

12M returns gap - Nifty vs. Midcap 

12M returns gap - Nifty vs. Smallcap 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

2017 

2018 

Exhibit 2: 

Performance concentration being reflected even in the Nifty index 

120% 

Share of performance attribution of top 10 stocks as % of Nifty index perf. 

104% 

100% 


90% 

80% 

75% 

60% 

52% 

40% 

20% 

32% 

13% 

0% 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

2017 

2018 

Source: Company data, Morgan Stanley Research 

Source: Bloomberg, Morgan Stanley Research 

Exhibit 3: 

Breadth of revenue and net profit growth within MS coverage is 

improving 

70% 

65% 

60% 

55% 

50% 

45% 

40% 

35% 

30% 

25% 

20% 

2003 

2004 

2005 

2006 

% of Cos with >10% YoY Revenue Growth 

% of Cos with >10% YoY Net Profit Growth 

2007 

2008 

2009 

2010 

2011 

2012 

Broad Market (1441 

Companies) 

2013 

2014 

2015 

2016 

2017 

70% 

65% 

60% 

55% 

50% 

45% 

40% 

35% 

30% 

25% 

20% 

Exhibit 4: 

Investment rate pick - key kicker for profit growth 

40% 

36% 

32% 

28% 

24% 

Investment (% GDP) 

F1995 

F1996 

F1997 

F1998 

F1999 

F2000 

F2001 

F2002 

F2003 

F2004 

F2005 

F2006 

F2007 

F2008 

F2009 

F2010 

F2011 

F2012 

F2013 

F2014 

F2015 

F2016 

F2017 

F2018E 

F2019E 

F2020E 

Source: CEIC, Morgan Stanley Research, E: Morgan Stanley Research estimates 

MSe 

Source: Capitaline, Morgan Stanley Research 

Exhibit 5: 

India is prepping for a macro trade 

55% 

45% 

35% 

25% 

15% 

5% 

2003 

Stock pickers' 

time, Sep-04 

Time for macro, 

Aug-03 

2004 

2005 

2006 

Explanatory Power of Market Effect 

Stock pickers' time, Jul-06 

Time for 

macro, Jul-05 

2007 

2008 

Stock pickers' time, 

Jun-09 

Time for macro, Time for macro, 

Aug-07 Oct-10 

2009 

Source: RIMES, MSCI, Morgan Stanley Research 

2010 


time, Dec -11 

2011 

2012 

2013 

2014 


time, Mar-16 

2015 

2016 

1Y Rolling R- 

squared 

Time for macro 

2017 

2018 

Exhibit 6: 

Buy Underperformers 

MS coverage stocks 

Part of 

focus list 

12M perf 

Current ROE 

as SD from 

Avg. 

2Y EPS 

Growth 

(2018-20) 

2Y ROE 

Change 

Nifty index 20% 

ACC -15% (0.4) 29% 4.2% 

Amara Raja Batteries 1% (2.0) 23% 1.8% 

Apollo Hospitals Enterprise Yes 3% (2.6) 77% 5.8% 

Asian Paints Yes 7% (1.7) 25% 5.5% 

Grasim Industries -16% (1.7) 19% 1.3% 

ICICI Bank Yes 14% (1.3) 57% 7.5% 

Indraprastha Gas -1% (1.0) 17% 1.9% 

ITC Yes 12% (1.0) 16% 3.7% 

Larsen & Toubro Ltd 14% (0.6) 15% 1.3% 

MMFSL Yes 0% (1.6) 69% 9.8% 

Oberoi Realty Limited 8% (0.9) 45% 4.4% 

Prestige Estates Projects Yes -13% (0.5) 14% 0.8% 

Shriram Transport Finance Co. Yes 13% (0.8) 37% 5.0% 

Sobha Developers 4% (0.6) 11% 0.7% 

State Bank of India Yes 5% (2.8) NM 15.1% 

Ultratech Cement Yes 0% (0.7) 32% 4.2% 

Source: RIMES, Morgan Stanley Research Estimates. Past performance is no guarantee of future results; 

transaction costs not included; these figures are not audited. 

6

M 

M 

India – A Pain Trade 

Key debate: Indian equities have been a pain trade for 

investors in 2018 as market performance quintessentially 

represented a bear market. Owning Indian stocks has 

been as painful as not owning Indian stocks, as India has 

outperformed the EM index by a margin. Three key trends 

have emerged this year, which rhyme with a bear market: 

a) directional difference in performance of large caps 

(Nifty index) and the broader market; b) collapse in stock 

outperformance breadth; and c) high concentration in 

performance within the indices. 

Diverging performances 

The first trend that rhymes with a bear market is the approaching 

divergence in trailing performance of large and SMID (small and 

mid-cap) indices. By divergence we mean two things: first, measuring 

the gap in performance of large and SMID indices, and second, directional 

trends in the two. SMIDs outperform large caps in the long run 

and even 12M underperformance is rare. Nevertheless, thus far, 2018 

has witnessed significant underperformance by SMID with small cap 

delivering negative trailing returns while mid-cap stocks have still 

managed to show positive trailing returns. The last two instances of 

trailing outperformance of large cap indices were seen in 2013/14 and 

2008/09. These trends have historically lasted for about 12-18 

months. 

Exhibit 7: 

FPI OW positioning has been falling and is now down to decade lows 

9% 

7% 

5% 

India's Weight in GEM Funds* Relative to 

India's Weight in MSCI EM 

Exhibit 8: 

But in 2018 India has outperformed vs. EM 

5% 

0% 

-5% 

MSCI India performance relative to MSCI EM 

- local currency 

-10% 

3% 

1% 

-15% 

-20% 

MSCI India performance relative to MSCI 

EM - USD 

-1% 

Jan-00 Sep-02 May-05 Jan-08 Sep-10 May-13 Jan-16 Sep-18 

* Benchmarked to MSCI EM 

Jul-16 

Sep-16 

Nov-16 

Jan-17 

Mar-17 

May-17 


Jul-17 

Sep-17 

Nov-17 

Jan-18 

Mar-18 

May-18 

Jul-18 

Source: EPFR, Morgan Stanley Research 

Exhibit 9: 

Divergence in performance of large and SMID indices indicate a bear 

market 

35% 

25% 

15% 

5% 

-5% 

-15% 

-25% 

-35% 

-45% 

-55% 

-65% 

2006 

2007 

12M returns gap - Nifty vs. Midcap 

12M returns gap - Nifty vs. Smallcap 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

2017 

2018 

Exhibit 10: 

Broad market returns collapse in 2018 

13% 

NSE Mid 100 Nifty index 

10 Years 

10% 

7 Years 

16% 

13% 

5 Years 

25% 

16% 

3 Years 

15% 

14% 

1 Year 

9% 

18% 

0% 5% 10% 15% 20% 25% 30% 

Source: RIMES, Bloomberg, Morgan Stanley Research 



M 

M 

Collapse in outperformance breadth 

Heavy weight contribution to Nifty index rising 

The second bear market trend is the fall in the breadth of outperformance 

seen in the large cap index as well as the broad market 

index. The percentage of stocks outperforming the index has collapsed 

to levels seen in 2013. Only a third of stocks have outperformed 

the index in 2018 vs. 44% in the previous year. Historically on 

a average, the breadth of outperformance has been close to half. 

Since 2015, the breadth has been in declining mode across the narrow 

and broad markets. History suggests that such periods of narrow 

breadth are followed by a sharp comeback in the subsequent period. 

Exhibit 11: 

Breadth of performance losing its sheen since 2015, down to 2013 

levels 

85% 

Nifty YoY performance 

70% 

% of stocks outperforming the index (RS) 

65% 

60% 

45% 

50% 

25% 

40% 

5% 

30% 

-15% 

20% 

-35% 

10% 

-55% 

0% 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

2017 

2018 


Exhibit 12: 

Similar trend in the broad market 

YoY BSE 500 index performance 

Finally, the third trend comes from the fact that only a narrow list of 

stocks have been key contributors to the index performance. 

Naturally, the top 10 stocks contribute significantly to performance, 

on average just over half the performance. The top 5 stocks account 

for a third of the index performance, on average. However, in 2018, 

the contribution to returns share has risen sharply to 75% and 100% 

for top 5 and top 10 stocks, respectively. This is reminiscent of the 

trend observed in 2008 and 2013, especially in the case of top 10 

stocks by index weight. The index weights of the top 10 stocks (out 

of 50 stocks in the index) have risen to as high as 56% in 2018 - such 

a high level was last seen in 2013. Concentration in performance is 

usually a bear market signal. Why has such concentration 

occurred, and is it set to change? We answer these two questions 

in the following sections. 

Exhibit 13: 

Top index stocks key contributors to index performance 

120% 


104% 

100% 


90% 

80% 

75% 

60% 

52% 

40% 

20% 

32% 

13% 

0% 

90% 

75% 

% of BSE 500 stocks outperforming BSE 

500 Index on a 12M trailing basis 

60% 

45% 

30% 

15% 

0% 

-15% 

-30% 

-45% 

-60% 

2002 

2003 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

2017 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

2017 

2018 



8

M 

MExhibit 14: 

Investor U/W positions on large-cap stocks 

FPI 

DMF 

Total Institutions 

Reliance Industries -2.0% Reliance Industries -4.7% Reliance Industries -2.4% 

Hindustan Unilever -1.6% HDFC -3.5% HDFC -2.0% 

Infosys -0.9% TCS -2.3% HUL -1.5% 

Sun Pharma -0.6% HDFC Bank -1.3% Infosys -1.2% 

L&T -0.4% Kotak Bank -1.2% TCS -0.8% 

IOC -0.6% Indusind Bank -0.6% Axis Bank -0.6% 

Yes Bank -0.4% M&M -0.2% Bharti -0.4% 

Bharti Airtel -0.5% Yes Bank -0.4% 

Asian Paints -0.4% HCL Tech -0.3% 

TCS -0.4% Maruti -0.3% 

M&M -0.3% 

Tata Motors -0.1% 

Source: BSE, NSE, MSCI, Morgan Stanley Research 

Exhibit 15: 

Concentration of Stock Performance Rising 

Nifty Stocks YTD Name 3Y Name 5Y 

Nifty index 11% Nifty index 15% Nifty index 16% 

Bajaj Finance 63% Bajaj Finance 80% Bajaj Finance 94% 

TCS 54% Bajaj Finserv 56% Bajaj Finserv 63% 

Tech Mahindra 52% Hindalco Industries 46% Eicher Motors 54% 

Infosys 39% Tata Steel 45% Maruti Suzuki India 49% 

Reliance Industries 35% Reliance Industries 44% Yes Bank 48% 

Hindustan Unilever 30% Titan Co 39% Hindustan Petroleum Corp 47% 

Bajaj Finserv 29% Yes Bank 37% Indiabulls Housing Finance 45% 

Mahindra & Mahindra 29% Vedanta 34% UPL 40% 

Kotak Mahindra Bank 27% IndusInd Bank 32% IndusInd Bank 40% 

ITC 22% GAIL India 31% Bharat Petroleum Corp 32% 

Asian Paints 18% Maruti Suzuki India 31% Titan Co 32% 

HCL Technologies 18% Kotak Mahindra Bank 28% Kotak Mahindra Bank 31% 

IndusInd Bank 16% Hindustan Unilever 28% Axis Bank 31% 

Axis Bank 15% HDFC Bank 27% HDFC Bank 28% 

Sun Pharmaceuticals 14% Grasim Industries 24% Asian Paints 27% 

HDFC 13% Indiabulls Housing Finance 20% Adani Ports 25% 

HDFC Bank 10% Asian Paints 19% UltraTech Cement 25% 

ICICI Bank 9% HDFC 19% Indian Oil Corp 24% 

Yes Bank 9% Mahindra & Mahindra 18% Reliance Industries 24% 

Larsen & Toubro 9% Tata Consultancy Services 18% Grasim Industries 24% 

Cipla /India 9% UltraTech Cement 16% Larsen & Toubro 23% 

Coal India 9% Power Grid 16% Hindustan Unilever 23% 

Lupin 5% Eicher Motors 15% HDFC 22% 

Indiabulls Housing Finance 5% Indian Oil Corp 15% Mahindra & Mahindra 20% 

Titan Co 4% ITC 15% Tata Steel 19% 

UltraTech Cement 4% Tech Mahindra 15% ICICI Bank 19% 

Dr Reddy's Laboratories 3% NTPC 13% Hindalco Industries 18% 

Power Grid Corp of India 1% HPCL 12% Tech Mahindra 18% 

State Bank of India 0% ICICI Bank 12% GAIL India 17% 

GAIL India -1% Hero MotoCorp 11% Zee Entertainment 17% 

NTPC -3% UPL 11% TCS 16% 

Wipro -4% Axis Bank 11% Power Grid 16% 

Adani Ports & Special Economic Z -6% Zee Entertainment 10% Bharti Infratel 16% 

UPL -6% Infosys 10% State Bank of India 15% 

Maruti Suzuki India -7% Larsen & Toubro 10% HCL Technologies 15% 

Eicher Motors -8% State Bank of India 9% Infosys 13% 

Oil & Natural Gas Corp -8% BPCL 9% Hero MotoCorp 10% 

Grasim Industries -8% Bajaj Auto 7% Cipla /India 10% 

Tata Steel -9% ONGC 5% ITC 9% 

Hindalco Industries -13% Adani Ports 4% Bajaj Auto 8% 

Hero MotoCorp -14% Bharti Airtel 4% NTPC 7% 

Zee Entertainment -14% HCL Technologies 3% Bharti Airtel 5% 

Bajaj Auto -18% Wipro 2% Sun Pharmaceuticals 5% 

Indian Oil Corp -20% Cipla /India 0% Wipro 4% 

Bharti Infratel -24% Tata Motors -7% Vedanta 4% 

Bharti Airtel -28% Coal India -7% Lupin 3% 

BPCL -30% Sun Pharmaceuticals -10% Coal India 3% 

Vedanta -31% Bharti Infratel -11% Dr Reddy's Lab 2% 

Tata Motors -38% Dr Reddy's Lab -16% ONGC 2% 

HPCL -39% Lupin -21% Tata Motors -2% 

Source: Bloomberg, RIMES, Morgan Stanley Research 


M 

The Search for Earnings 

The key to concentration in performance has been 

concentration in earnings. Only a few heavyweight 

companies are driving the earnings growth, and 

thus their share in earnings in the broad market as 

well as the Nifty index has risen sharply. 

Why earnings growth has been elusive 

Broad-based earnings growth has remained elusive for India in recent 

years. High balance-sheet leverage and negative operating leverage 

have contributed to weak profitability and low return ratios for most 

companies for a prolonged period. A fall in the investment rate and 

a rise in the current account deficit (from 2010 to 2014) made matters 

worse for earnings. Specific events such as the global trade collapse 

in 2015/16, demonetization in 2016/17, the delay in bank 

recapitalization until F2018 and the GST (goods and services tax) 

rollout in 2017/18 extended the pain. 

Earnings concentration higher than historically 

The most negative impact of such a slowdown has been on the profitability 

of corporate banks. Within banks, the public sector banks have 

borne the most pain. From a share in corporate profits of an average 

15% during 2004 to 2013, the share has collapsed below zero (due 

to losses) and profits of public sector banks now reduce aggregate 

earnings by about 25%. Over the same time private sector financials' 

share of profits multiplied fourfold. As the NPL problems became 

acute and ate into CET1 capital aggressively, they stalled growth for 

the SOE banks, driving up the pace of market share loss to ~400 bps 

every year from 100bps earlier. 

The earnings skew is not restricted to banks. Technology has witnessed 

a sharp increase in profit share in recent years, whereas 

Telecoms has reported a big decline in profits into losses. 

Exhibit 16: 

PSU banks: Key loser in the collapse in earnings 

25% 

20% 

15% 

10% 

5% 

0% 

-5% 

-10% 

-15% 

-20% 

-25% 

-30% 

Mar-04 

Oct-04 

May-05 

Dec-05 

Jul-06 

Feb-07 

Sep-07 

Apr-08 

Nov-08 

Jun-09 


Exhibit 17: 

Jan-10 

Return on equity below history 

MSCI Sectors 

Pvt financials PSU banks Share in broad market 

profits 

Aug-10 

Mar-11 

Oct-11 

May-12 

Dec-12 

Jul-13 

Feb-14 

Sep-14 

ROE as SD 

from Avg 

Apr-15 

Nov-15 

Jun-16 

Jan-17 

Aug-17 

Mar-18 

25% 

20% 

15% 

10% 

5% 

0% 

1Y Fwd 

Change in 

ROE 

Consumer Disc. (0.7) 2% 

Consumer Staples (1.6) 3% 

Energy (0.8) 1% 

Financials (2.1) 6% 

Health Care (1.8) 1% 

Industrials (0.5) 1% 

Technology (0.0) 0% 

Materials (0.6) 3% 

Telecoms (1.4) -4% 

Utilities (0.4) 1% 


-5% 

-10% 

-15% 

-20% 

-25% 

-30% 

10

M 

M 

Return ratios below history 

This absence of broad-based earnings momentum has led to a collapse 

in ROE for Indian companies. ROE across sectors has fallen 

below its long-term SD from average, reflecting not just the weakness 

in aggregate profitability for Indian companies but lower 

capacity utilization and high interest costs among companies. 

Exhibit 18: 

Large companies as well as stocks dominate in share at the aggregate 

levels 

55% 

50% 

45% 

Share of top 10 companies by net profit as % of total broad 

market net profits (trailing 4Q) 

Share of top 10 stocks by market cap to total market cap (RS) 

45% 

40% 

35% 

The weakness in earnings has also resulted in select large companies 

(no straightforward driving factor) performing better on earnings vs. 

the broader segment – the widest gap in history. Likewise, this has 

been encapsulated in their price performance, particularly in 2018. 

This trend was earlier observed in 2003, 2009 and 2013 – a bear 

market phenomenon. 

40% 

35% 

30% 

Sep-03 

Mar-04 

Sep-04 

Mar-05 

Sep-05 

Mar-06 

Sep-06 

Mar-07 

Sep-07 

Mar-08 

Sep-08 

Mar-09 

Sep-09 

Mar-10 

Sep-10 

Mar-11 

Sep-11 

Mar-12 

Sep-12 

Mar-13 

Sep-13 

Mar-14 

Sep-14 

Mar-15 

Sep-15 

Mar-16 

Sep-16 

Mar-17 

Sep-17 

Mar-18 


30% 

25% 

20% 

Exhibit 19: 

Surge in Nifty share of broad market profits 

90% 

85% 

80% 

75% 

70% 

65% 

60% 

55% 

50% 

Mar-04 

Nov-04 

Jul-05 

Mar-06 

Nifty as % of broad market 

profits (4Q trailing profits) 

Nov-06 

Jul-07 

Mar-08 

Nov-08 

Jul-09 

Mar-10 

Nov-10 

Jul-11 

Source: Company data, Capitaline, Morgan Stanley Research 

Mar-12 

Nov-12 

Jul-13 

Mar-14 

Nov-14 

Jul-15 

Mar-16 

Nov-16 

Jul-17 

Mar-18 


M 

M 

We think Performance is set to Broaden Out 

Medium-term growth outlook supports the case for 

profit to GDP to recover. Latest earnings season points to 

a broad-based recovery in corporate earnings, NPL 

formation has peaked with recovery in place in F2019, 

and finally high corporate confidence sets up the case for 

a capex recovery. Broad-based earnings growth in the 

coming months will likely expand the breadth of stock 

performance. 

Exhibit 20: 

Profit growth reflects deeper cycles vs. GDP 

1000 

Nominal GDP 

Cumulative broad market 

earnings growth 

on log scale 

Mean reversion on the cards 

100 

FY1994 

FY1996 

FY1998 

FY2000 

FY2002 

FY2004 

FY2006 

FY2008 

FY2010 

FY2012 

FY2014 

FY2016 

FY2018 

While GDP growth and earnings growth have a close relationship in 

India, profit growth invariably has deeper cycles. We have been in one 

such deep downcycle, which means that the corporate profit share 

in GDP has fallen to near all-time lows. A likely mean reversion in this 

share accompanied by accelerating nominal GDP growth sets the 

stage for a sharp recovery in earnings in the coming quarters. Our 

economists are forecasting GDP growth of 7.6% in F2019 and 7.7% 

in F2020 vs. 6.7% in F2018. 

Fundamentally, profit margins are a function of the investment rate. 

Investments tend to be cyclical because firms overinvest during up 

cycles and then go through big adjustments in the down cycle. India's 

long-term positive growth outlook driven by demographics, reforms 

and rising share of global trade means that India's investment rate 

has a secular underlying uptrending characteristic to it. This means 

that as the investment cycle turns, profit margins should also mean 

revert. We are likely sitting at the bottom of the investment cycle. 

Source: Capitaline, CEIC Morgan Stanley Research 

Exhibit 21: 

Profit to GDP: Mean reversion likely 

8% 

7% 

6% 

5% 

4% 

3% 

2% 

1% 

0% 

Corporate 

profits to GDP 

F1992 

F1993 

F1994 

F1995 

F1996 

F1997 

F1998 

F1999 

F2000 

F2001 

F2002 

F2003 

F2004 

F2005 

F2006 

F2007 

F2008 

F2009 

F2010 

F2011 

F2012 

F2013 

F2014 

F2015 

F2016 

F2017E 

Source: CMIE, CEIC Morgan Stanley Research 

Exhibit 22: 

Investment rate to rise 

40% 

Investment (% GDP) 

36% 

MSe 

32% 

28% 

24% 

F1995 

F1996 

F1997 

F1998 

F1999 

F2000 

F2001 

F2002 

F2003 

F2004 

F2005 

F2006 

F2007 

F2008 

F2009 

F2010 

F2011 

F2012 

F2013 

F2014 

F2015 

F2016 

F2017 

F2018E 

F2019E 

F2020E 

Source: RBI, CEIC Morgan Stanley Research 

12

M 

M 

What will drive earnings in the coming quarters? 

Our recent survey of 200 companies in India indicated that corporates 

are confident about improving business growth over the next 

12 months. Sentiment in the aggregate is buoyant, with attractive 

demand conditions and improving credit availability as well as credit 

ratings combined with high levels of capacity utilization. Moreover, 

for the first time since F2013, corporates are shifting focus away from 

productivity capital spending towards greenfield and brownfield 

capex. This is consistent with our view that the investment rate is 

likely rising – an important driver of corporate profitability from a 

macro perspective. The investment rate is on course to recover as 

government capex remains strong (Corporate Confidence Is Back, 

Capex to Follow). 

An additional driver of earnings comes from the healing of the 

banking system. NPL formation should see a continuous sharp 

decline in the coming quarters, in our view. The government's recapitalisation 

move is enabling state-owned (SOE) banks to recognise 

problem corporate loans as NPLs and to increase provisioning. The 

NCLT (National Company Law Tribunal) process is progressing well 

(Large Corporate Lenders – Continued Move Towards 

Normalisation). NIMs have been under pressure, driven by continued 

high NPL formation. 

Moreover, with most bad loans now classified as NPLs, reported NPL 

slippages should drop sharply from F2Q19 / F3Q19, according to our 

India Banks analysts, who believe that the peak in bad loan formation 

is past. They expect this to turn in the next two quarters as NPL formation 

slows and higher rates help NIMs expand (almost 50% of 

earning assets for these banks are funded by CASA and equity). The 

other positive will be recoveries from NPL resolution as these move 

from non-interest-earning to interest-earning. Our India Banks team 

expects the core PPoP (pre-provision operating profit) to pick up 

from F2Q19 (Advantage: Large Liquid Stocks). 

We believe a positive inflection point in growth is being reached. The 

recently concluded earnings season points to broadening of revenue 

and earnings growth trends across sectors. This should mark the end 

of India's deepest income and balance-sheet recession. Revenue 

growth in 1QF19 touched a six-year high at 17% YoY for the broad 

market. Over 61% of the broad market companies (out of 1,441 companies) 

covered reported revenue growth in excess of 10% YoY, and 

52% of the companies reported net profit growth in excess of 10% 

YoY. Accordingly, we are forecasting profit growth to accelerate 20% 

over the next couple of years. Similarly, we expect the broader 

market to see a CAGR of about 27% over the next three years, potentially 

taking the profit share in GDP up by 100 bps. Broad-based earnings 

growth in the coming months likely also changes the behavior of 

stocks. Thus, performance of stocks should broaden. 

Exhibit 23: 

Breadth of revenue and net profit growth within MS coverage is improving 

70% 

65% 

60% 

55% 

50% 

45% 

40% 

35% 

30% 

25% 

20% 

2003 

2004 

2005 


2006 

% of Cos with >10% YoY Revenue Growth 

% of Cos with >10% YoY Net Profit Growth 

2007 

2008 

2009 

2010 

2011 

2012 

Broad Market (1441 

Companies) 

2013 

2014 

2015 

2016 

2017 

70% 

65% 

60% 

55% 

50% 

45% 

40% 

35% 

30% 

25% 

20% 


M 

Bear Market Behavior Doesn’t Mean It Is a 

Bear Market 

Earnings behaviour of the past eight years resembles a 

bear market, but share prices tell a different story. The 

Nifty is up 345% (up 208% in US$ compared with +108% 

for the MSCI EM Index) from the bottom of 2009 

without seeing a sustained drawdown in excess of 20%; 

this implies that, at least technically, we have been in bull 

market since then. The dispersion in returns and several 

other indicators are not in extreme territory to suggest 

that we are in a bear market. 

We are convinced that it is a bull market 

While performance and earnings trends do not provide a clear signal, 

we don’t think this is a bear market. In fact, in 2017, we argued that 

this is a bull market (India Equity Strategy: 7 (17 Jul 2017)) and the 

longest one in India's history (almost twice the length of the previous 

one). However this bull market is yet to generate the returns that the 

previous bull markets delivered (it is currently about 60% there in 

terms of returns). Certainly, the pace of returns has been slower than 

in the previous three bull markets of the past 39 years. This may have 

to do with two factors: 1) the starting point of valuations; and 2) the 

pace of earnings growth. 

As highlighted in the previous section, we expect earnings growth to 

broaden in the coming quarters and thereby conclude that India's bull 

market is set to gain pace. The dispersion in 1-year returns between 

top- and bottom-decile-performing stocks is middling and not at the 

extremes we witness in frenzied bull markets. Return dispersion will 

likely rise in a frenzy, and its current level is actually a positive signal 

for prospective market returns. 

Exhibit 24: 

The Seven Market Cycles 

100000 

Sensex on log scale 

37922 

21206 

Market participants are not convinced about the bull market 

Investors remain sceptical about the coming broad-based recovery in 

earnings, as well as performance. Valuations are another concern. In 

our view, the underperformance in SMID this year hides the substantial 

outperformance of last year. The SMID outperformance in the 

first half of 2017 attracted higher flows in these stocks and thereby 

took valuations to expensive territory. The extreme point of returns 

and valuations earlier this year set the stage for a correction, which 

was aggravated by reflexivity between flows and performance in the 

opposite direction of that in 2017. 

There are three reasons (apart from broad-based earnings growth) 

why we think SMID stocks may have troughed: 1) The liquidity for 

these stocks has fallen to levels similar to those in July 2013 and Nov 

16 – previous troughs for SMID – and we expect the bid on these 

stocks to rise from here; 2) valuations as reflected in market to GDP 

ratios clearly suggest that broad market valuations are looking reasonable 

relative to large-cap valuations, thereby pointing to a rotation; 

and 3) despite the recent slowdown in domestic flows, we 

believe that domestic flows are in a structural uptrend which is positive 

for SMID indices in the medium term (India Equity Strategy: The 

Domestic Liquidity "DREAM" Run). 

Exhibit 25: 

Dispersion in returns not at extreme levels 

280% 

240% 

200% 

Gap between 1 yr returns of 

top and bottom decile of MSCI 

India stocks 

10000 

4546 

7697 

160% 

2904 

120% 

1000 

664 

80% 

395 

40% 

100 

124 

2002 

2003 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

2017 

1979 

1981 

1983 

1985 

1987 

1989 

1991 

1993 

1995 

1997 

1999 

2001 

2003 

2005 

2007 

2009 

2011 

2013 

2015 

2017 

Source: RIMES,MSCI, Morgan Stanley Research 


14

M 

M 

Exhibit 26: 

Mid-cap drawdown: Pain point for investors ... 

0% 

Exhibit 27: 

… Similarly, small-cap drawdown is hurting sentiment 

0% 

-10% 

-10% 

-10% 

-20% 

-30% 

-16% -16% 

-20% 

-30% 

-21% 

-15% 

-17% 

-22% 

-40% 

-40% 

-50% 

-50% 

-60% 

BSE Midcap Drawdown 

-60% 

BSE Smallcap Drawdown 

-70% 

-70% 

-80% 

2005 

2006 

2006 

2007 

2007 

2008 

2008 

2009 

2009 

2010 

2010 

2011 

2011 

2012 

2012 

2013 

2013 

2014 

2014 

2015 

2015 

2016 

2016 

2017 

2017 

2018 

2018 


-80% 

2005 

2006 

2006 

2006 

2007 

2007 

2008 

2008 

2009 

2009 

2009 

2010 

2010 

2011 

2011 

2011 

2012 

2012 

2013 

2013 

2014 

2014 

2014 

2015 

2015 

2016 

2016 

2016 

2017 

2017 

2018 

2018 


Exhibit 28: 

Liquidity has dried up for small-cap stocks 

200% 

150% 

100% 

50% 

YoY Index change minus EPS 

change for MSCI India small cap 

Exhibit 29: 

Broad market valuations have come off 

55% 

50% 

45% 

40% 

35% 

Market Cap ex Nifty to GDP 

Nifty market cap to GDP 

0% 

Jul-13 

-50% 

Dec-11 

Nov-16 

Mar-09 

-100% 

Jun-04 

Feb-05 

Oct-05 

Jun-06 

Feb-07 

Oct-07 

Jun-08 

Feb-09 

Oct-09 

Jun-10 

Feb-11 

Oct-11 

Jun-12 

Feb-13 

Oct-13 

Jun-14 

Feb-15 

Oct-15 

Jun-16 

Feb-17 

Oct-17 

Jun-18 

30% 

25% 

20% 

2009 

2009 

2010 

2010 

2010 

2011 

2011 

2011 

2012 

2012 

2012 

2013 

2013 

2013 

2014 

2014 

2014 

2015 

2015 

2015 

2016 

2016 

2016 

2017 

2017 

2017 

2018 

2018 

Source: RIMES, CEIC, Bloomberg, Morgan Stanley Research 



M 

M 

Portfolio implications 

Broader market performance means that investors should 

choose price underperformers with improving earnings 

outlooks and finally broaden their portfolios by adding SMID. 

We expect a macro trade to ensue, implying higher 

correlations across stocks. We raise our BSE Sensex target to 

42,000 by June 2019, implying upside of 11%. 

Exhibit 30: 

Correlations to rise; macro trade is on 

55% 

45% 

35% 

25% 

15% 

5% 

2003 


time, Sep-04 

Time for macro, 

Aug-03 

2004 

2005 

2006 

Explanatory Power of Market Effect 

Stock pickers' time, Jul-06 

Time for 

macro, Jul-05 

2007 

2008 

Source: RIMES, Morgan Stanley Research 

Exhibit 31: 

Stock pickers' time, 

Jun-09 

Time for macro, Time for macro, 

Aug-07 Oct-10 

2009 

2010 


time, Dec -11 

BSE Sensex Outlook: Risk-Reward for Sep-19 

48,000 

45,000 

42,000 

39,000 

36,000 

33,000 

30,000 

27,000 

24,000 

21,000 

Sep 19 Fwd probability-weighted 

outcome @ 42000 

2011 

2012 

2013 

2014 


time, Mar-16 

2015 

2016 

1Y Rolling R- 

squared 

Time for macro 

Jun-14 

Jul-14 

Sep-14 

Oct-14 

Dec-14 

Feb-15 

Mar-15 

May-15 

Jul-15 

Aug-15 

Oct-15 

Dec-15 

Jan-16 

Mar-16 

May-16 

Jun-16 

Aug-16 

Sep-16 

Nov-16 

Jan-17 

Feb-17 

Apr-17 

Jun-17 

Jul-17 

Sep-17 

Nov-17 

Dec-17 

Feb-18 

Apr-18 

May-18 

Jul-18 

Aug-18 

Oct-18 

Dec-18 

Jan-19 

Mar-19 

May-19 

Jun-19 

Aug-19 

Base Case 

(Jun 2019) 

Source: RIMES, Morgan Stanley Research forecast 

Current Price 

(Sep 10, 2018) 

37922 

Historical 

Performance 

2017 

2018 

47000(+24%) 

42000(11%) 

33000 (-13%) 

The macro trade is on 

In our view, market performance is likely broadening, and therefore 

correlations across stocks are set to rise. Our proprietary indicator, 

which is derived by averaging the correlations of stock returns to 

market returns across the broad market over time ( Exhibit 30 ), suggests 

that the macro backdrop has started to influence stock prices. 

Comparing overall stock returns with the returns of stocks in the 

Sensex highlights what has been driving stock performance – 

whether the market is being influenced by the macro environment or 

whether idiosyncratic stock-centric factors are in play. When the 

market effect starts to rise (as was the case between Aug. 2007 and 

Jun. 2009, between Oct. 2010 and Dec. 2011, and between 2012 and 

2016), it signals that the macro climate is about to wield outsize influence 

on stocks. 

However, from 2016 to the early part of 2018, the market effect 

peaked and started fading, meaning that stock picking was in vogue. 

We think now is the time to shift out of picking stocks and prepare 

for a macro trade. The rise in market effect implies that individual 

stocks will be influenced more by market performance-related factors 

than by idiosyncratic or non-market performance factors. In such 

a market environment, when combined with our view that the 

market has upside potential, it makes sense to buy one's favorite 

underperformers (both price and earnings) as performance broadens 

out. 

Index target changes 

We are revising our BSE Sensex target for Sep. 2019 to 42,000, 

implying that the index will trade at 16.5x one-year forward P/E and 

20x on trailing PE in Sep-19. Our previous target was 36,000 for June 

2019. The key drivers of a higher index are the broadening and deepening 

of earnings growth and greater conviction in a new multiyear 

earnings cycle during which we think earnings could compound at 

around 20% annually for five years. 

The key risk between now and June 2019 is that the market turns pessimistic 

on the outcome of the general elections scheduled in May 

2019. Thus, if investors starts expecting that the electorate will 

deliver a fragmented verdict with weak leadership, the index will 

likely head towards our bear case, especially if such expectations 

coincide with deteriorating global equity markets. 

16

M 

M 

Base case (50% probability) – BSE Sensex: 42,000: All outcomes are 

moderate. Growth accelerates slowly. We expect Sensex earnings 

growth of 23% YoY in F2019 and 24% YoY in F2020. 

3) Outlook. We choose stocks rated Overweight by Morgan Stanley 

and where our analysts expect a positive change in ROE along with 

EPS growth in excess of 10% over the next two years. 

Bull case (30% probability) – BSE Sensex: 47000: 

Better-than-expected outcomes, most notably on policy and global 

factors. The market starts believing in a strong election result. 

Earnings growth accelerates to 29% in F2019 and 26% in F2020. 

Bear case (20% probability) – BSE Sensex: 33,000: Global conditions 

deteriorate, and the market starts pricing in a poor election outcome. 

Sensex earnings grow 21% in F2019 and 22% in F2020. 

How to choose the underperformers 

We recommend that investors buy earnings and price underperformers. 

We use the following filters to short-list the stocks: 

1) Market underperformers. Stocks that have underperformed the 

market over the past 12M. 

Accordingly, we are adding SBIN, Apollo Hospitals and Prestige 

estates to our Focus List in place of INFY, Zee and Havells. The latter 

three have all been recently downgraded by our analysts from OW. 

Key Rationale 

SBI: The government's recapitalisation move enabled state-owned 

(SOE) banks to recognise problem corporate loans as NPLs and to 

increase provisioning. We expect improvement in overall provisioning 

and NPL slippages to drop sharply in F2H19. However, loan 

loss charges are likely to remain high as banks take coverage towards 

60% with IND-AS rolling in from 1 April 2019 (our base case). Core 

PPOP growth should start picking up from F2H19, as NPL formation 

slows down and higher rates help margins expand. Given its strong 

liability franchise and technology, SBI is better positioned relative to 

other SOE banks to sustain growth and ROE, in our view. 

2) Earnings underperformers. We look for companies whose fundamentals 

have been weak with slow earnings growth and ROE 

below the standard deviation from the average. 

Exhibit 32: 

Valuations remain middling 

7.0 

MSCI India PB relative to EM- RS 

2.6 

MSCI India PB (LS) 

2.4 

6.0 

2.2 

5.0 

2.0 

4.0 

1.8 

1.6 

3.0 

1.4 

2.0 

1.2 

Apollo Hospitals: Operational improvement in the ensuing quarters 

driven by all the three revenue segments: 1) hospitals - occupancy 

ramp up, improvement in ARPOBs and ALOS; 2) steady growth in 

Standalone Pharmacies (20%+); and 3) AHLL retail losses to be narrowed 

for target break even in 1H2020. Valuations seem inexpensive 

at 15x F20 EV/EBITDA. 

Prestige Estates: It has a balanced portfolio of developmental and 

rental projects, both of which have good scale-up potential. Presence 

in healthy south Indian cities (Bangalore, Chennai and Hyderabad), 

will be complemented by diversification to NCR and MMR markets. 

Valuations appear reasonable at 51% discount to our Mar’19 NAV. 

1.0 

Sep-95 

Sep-96 

Sep-97 

Sep-98 

Sep-99 

Sep-00 

Sep-01 

Sep-02 

Sep-03 

Sep-04 

Sep-05 

Sep-06 

Sep-07 

Sep-08 

Sep-09 

Sep-10 

Sep-11 

Sep-12 

Sep-13 

Sep-14 

Sep-15 

Sep-16 

Sep-17 

Sep-18 

1.0 



M 

M 

Exhibit 33: 

MS coverage (OW stocks): Underperformers to Own 

MS coverage stocks 

Part of 

focus list 

12M perf 

Current ROE 

as SD from 

Avg. 

2Y EPS 

Growth 

(2018-20) 

2Y ROE 

Change 

Nifty index 20% 

ACC -15% (0.4) 29% 4.2% 

Amara Raja Batteries 1% (2.0) 23% 1.8% 

Apollo Hospitals Enterprise Yes 3% (2.6) 77% 5.8% 

Asian Paints Yes 7% (1.7) 25% 5.5% 

Grasim Industries -16% (1.7) 19% 1.3% 

ICICI Bank Yes 14% (1.3) 57% 7.5% 

Indraprastha Gas -1% (1.0) 17% 1.9% 

ITC Yes 12% (1.0) 16% 3.7% 

Larsen & Toubro Ltd 14% (0.6) 15% 1.3% 

MMFSL Yes 0% (1.6) 69% 9.8% 

Oberoi Realty Limited 8% (0.9) 45% 4.4% 

Prestige Estates Projects Yes -13% (0.5) 14% 0.8% 

Shriram Transport Finance Co. Yes 13% (0.8) 37% 5.0% 

Sobha Developers 4% (0.6) 11% 0.7% 

State Bank of India Yes 5% (2.8) NM 15.1% 

Ultratech Cement Yes 0% (0.7) 32% 4.2% 

Source: RIMES, Morgan Stanley Research. Past performance is no guarantee of future results; transaction costs not included; these figures are not audited. 

Exhibit 34: 

Focus List: Three Changes – Adding SBI, Apollo Hospitals and Prestige Estates 

Stocks 

Sector 

Rating 

Price as on 

Sep 10, 2018 

MCap ($ 

bn) 

Avg 3M T/O 

($ mn) 

Rel to MSCI India 

YTD Perf 

12m Perf 

2Y Fwd EPS 

Growth 

Bajaj Auto Cons. Disc. EW 2,869 11.5 32 -19% -14% 9% 

M&M Cons. Disc. OW 937 15.3 30 17% 27% 23% 

Titan Cons. Disc. OW 858 10.5 37 -6% 18% 28% 

ITC Staples OW 307 51.8 56 9% -2% 14% 

Reliance Industries Energy OW 1,256 109.9 140 28% 34% 21% 

Bharat Financial Financials ++ 1,123 2.2 10 5% 5% 110% 

HDFC Bank Financials OW 2,041 76.5 77 2% 0% 23% 

ICICI Bank Financials OW 333 29.6 98 -0.7% 0% 46% 

Indusind Bank Financials ++ 1,829 15.2 29 4% -6% 27% 

M & M Financial Financials OW 447 3.8 11 -12% -12% 45% 

Shriram Transport Financials OW 1,192 3.7 33 -24.6% -1% 32% 

ICICI Pru Life Financials OW 374 7.4 9 -8% -25% 12% 

SBI Financials OW 285 35.1 97 -14% -8% 

Prestige Estates Financials OW 225 1.2 1 -34% -24% 8% 

Apollo Hospitals Healthcare OW 1,131 2.2 15 -12% -10% 94% 

Ashok Leyland Industrials OW 130 5.3 47 2% -2% 19% 

Adani Ports Industrials OW 372 10.6 23 -14% -16% 11% 

Eicher Motors Industrials OW 28,623 10.8 21 -12% -23% 25% 

Asian Paints Materials OW 1,293 17.1 22 5% -6% 20% 

Ultratech Cement Materials OW 4,155 15.7 19 -10% -12% 27% 

Source: RIMES, Morgan Stanley Research. Past performance is no guarantee of future results; transaction costs not included; these figures are not audited. ++ Rating and price target for this company have been removed 

from consideration in this report because, under applicable law and/or Morgan Stanley policy, Morgan Stanley may be precluded from issuing such information with respect to this company at this time 

18

9/26/2018 Nearly 400 NBFCs Shut Shop As RBI Clean-Up Continues - BloombergQuint 

s 

Nearly 400 NBFCs Shut Shop As RBI Clean-Up Continues 

Pallavi Nahata 

t @PallaviNahata 

Published: Sep 05 2018, 10:12 AM 

Last Updated: Sep 05 2018, 2:04 PM 

The RBI is weeding out small non-banking financial companies that have been 

unable to meet capital requirements, in a bid to clean-up the sector. The regulator 

has been tightening norms for NBFCs in stages since 2014 and the surge in 

cancellation of licences is a part of that. 

The central bank has cancelled 392 licenses since the beginning of this fiscal year 

till the end of August, shows data compiled by BloombergQuint based on periodic 

updates from the regulator. Most of these cancellations have taken place in the 

last two months. To be sure, the number of cancellations is a fraction of 11,402 

NBFCs that were registered with the RBI as of March 2018. 

https://www.bloombergquint.com/business/2018/09/05/nearly-400-nbfcs-shut-shop-as-rbi-clean-up-continues#gs.aWdmBag 1/15


In addition, 79 NBFCs have surrendered licenses of their own accord, with a fourth 

of them having done so in the past two months. 

The number of license cancellations is expected to remain high over the coming 

months, said Raman Aggarwal, Chairman of the Finance Industry Development 

Council, a self regulatory body for NBFCs. He explained that most of the 

cancellations are due to a failure on part of these companies to meet the minimum 

‘net owned funds’ threshold of Rs 2 crore. That requirement kicked-in starting 

March 2017. However, the RBI is enforcing it now, perhaps after giving the firms a 

grace period of a year. 

NBFC Cancellations Rise In FY19 

Year ▼ Total 

FY12 12,385 

FY13 12,225 

FY14 12,029 

FY15 11,842 

FY16 11,682 

FY17 11,522 

FY18 11,402 

FY19* 10,931* 

Chart: *: Until 31/08/2018 As Calculated By BQ • Source: RBI 

Tightening Of NBFC Regulations 

The last time the sector saw a cleanup of this scale was in 1997 when amendments 

in the RBI Act of 1934 provided for compulsory registration of all NBFCs and 

minimum net owned funds of Rs 25 lakh. Aggarwal estimates that more than onefourth 

of the estimated 40,000 NBFCs that were in existence back then, were 

forced to shut down. 

Since then the RBI has been tightening rules for NBFCs in stages. 



In a November 10, 2014 circular, NBFCs were asked to raise the level of net owned 

funds, hold more capital and raise provisioning. 

The minimum level of net owned funds was pegged at Rs 1 crore by March 

2016 and Rs 2 crore by March 2017. 

Tier-1 capital had to be raised to 8.5 percent by March 2016 and 10 percent by 

March 2018. 

Asset classification norms were to be brought in line with the 90-day NPA (non 

performing assets) recognition policy followed by banks by March 2018. 

The licence cancellations being seen now are a consequence of that circular. 

NBFCs being unable to raise even Rs 2 crore raises a legitimate question about 

their feasibility and viability, said Arun Singh, lead economist at Dun & Bradstreet, 

while explaining the rationale behind the clean up. 

“NBFCs deal with financing requirements, which is at the heart of an 

economy. If they are unhealthy and collapse, they will send ripples through 

other parts of the economy.” 

Arun Singh, Lead Economist, Dun & Bradstreet 

Health-Check of NBFC Sector 

Certain parts of the NBFC sector, particularly those in the business of consumer 

finance, have grown faster than their banking peers in the last few years. That has 

raised concerns about a potential build-up of asset quality concerns. 

Data provided by the RBI, however, shows that at an aggregate level, the NBFC 

segment remains healthy. 

According to the June edition of the RBI’s Financial Stability Report, the aggregate 

balance sheet size of the NBFC sector stood at Rs 22 lakh crore as on March 2018. 

Loans and advances rose 21 percent in 2017-18, after a 14.6 percent increase in the 

previous year. 

The gross NPA ratio fell to 5.8 percent at the end of March 2018, compared to 6.1 

percent a year ago. 



NBFC Sector Ratios 

(%) 

Year ▼ GNPA Ratio CRAR 

FY14 2.7 27.5 

FY15 2.9 26.2 

FY16 4.3 23.9 

FY17 6.1 22.0 

FY18 5.8 22.9 

Source: Financial Stability Report, RBI 

BloombergQuint 

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OUTPERFORMERS: 

HIGH-GROWTH EMERGING 

ECONOMIES AND THE COMPANIES 

THAT PROPEL THEM 

SEPTEMBER 2018

About Since its MGI founding in 1990, the McKinsey Global Institute (MGI) has sought 

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Copyright © McKinsey & Company 2018

OUTPERFORMERS: 

HIGH-GROWTH EMERGING 

ECONOMIES AND THE COMPANIES 

THAT PROPEL THEM 

SEPTEMBER 2018 

Jonathan Woetzel | Shanghai 

Anu Madgavkar | Mumbai 

Jeongmin Seong | Shanghai 

James Manyika | San Francisco 

Kevin Sneader | Hong Kong 

Oliver Tonby | Singapore 

Andrés Cadena | Bogota 

Rajat Gupta | Mumbai 

Acha Leke | Johannesburg 

Hayoung Kim | Washington, DC 

Shishir Gupta | Delhi

PREFACE 

Emerging economies are the engine of growth for the global economy, yet not all are alike. 

Some have achieved rapid growth over prolonged periods—fast enough and long enough 

to close a part or most of the gap with advanced economies—while others have not. 

To understand these divergences, and to identify lessons for all aspirants in the evolving 

global landscape, MGI has undertaken wide-ranging research into the enablers of sustained 

high economic growth among emerging economies. We focus on two intertwined aspects 

that strike us as essential but underrepresented in the literature on development economics: 

government actions that encourage higher productivity, income, and demand, and the role 

played by large, ambitious, and globally competitive companies. We aim to provide policy 

makers, corporate leaders, and global investors with a new understanding of the growth 

opportunities ahead and the characteristics of successful emerging market champions. 

This study is an analysis of the long-term economic growth patterns of emerging 

economies. It does not explore broader characteristics of economies, such as political 

processes, types of government, or the functioning of civil society. 

This research was led by Jonathan Woetzel, a McKinsey senior partner and a director 

of MGI based in Shanghai; Anu Madgavkar, an MGI partner in Mumbai; Jeongmin 

Seong, an MGI senior fellow in Shanghai; and James Manyika, the chairman of MGI, in 

San Francisco. Several McKinsey senior partners across the globe provided insight and 

guidance. They are Andrés Cadena in Bogotá; Rajat Gupta in Mumbai; Acha Leke in 

Johannesburg; Kevin Sneader, McKinsey’s managing partner and former head of the firm’s 

offices in the Asia–Pacific region; and Oliver Tonby in Singapore. We are also grateful to 

Jacques Bughin, a McKinsey senior partner and MGI director in Brussels; Tarek Elmasry, 

a McKinsey senior partner in Dubai; Sree Ramaswamy, an MGI partner in Washington; 

and Mekala Krishnan, an MGI senior fellow in Boston. Shishir Gupta, Paul Jacobson, 

Hayoung Kim, and Aditi Ramdorai led the research team in different periods. The team 

comprised Abdulla Abdulaal, Sruthi Chekuri, Nazrul Johari, Yuvika Motwani, Alberto Ramos, 

Rafael Rivera, Thea Tan, and Eleni Watts. 

Many McKinsey colleagues gave technical advice and analytical support as we developed 

this report. At MGI, we are grateful to Nicolas Grosman, Susan Lund, Jan Mischke, 

Jaana Remes, and Vivien Singer. From McKinsey offices around the world, we would 

like to thank Jorg Schubert and Yassir Zouaoui in Dubai, Elena Kuznetsova in Moscow, 

John Dowdy in London, Karel Eloot in Shanghai, Noshir Kaka and Vivek Pandit in 

Mumbai, Tim Koller in New York, and Tilman Tacke in Munich. Other McKinsey colleagues 

who gave support include Jonathan Ablett, Rishi Arora, Eduardo Doryan, Tari Ellis, 

Heather Hanselman, Nikhil Khaitan, Krzysztof Kwiatkowski, Debanwita Roy, Daniella Seiler, 

Gurneet Singh Dandona, Shivika Syal, and Soyoko Umeno. 

We are deeply indebted to our academic advisers, who challenged our thinking and 

provided valuable feedback and guidance through the research. We thank Miriam Altman, 

a member of South Africa’s National Planning Commission; Jonathan Anderson, a principal 

at Emerging Advisors Group, a China-based macroeconomic research firm; Justin Y. Lin, 

dean at the Institute of New Structural Economics at Peking University; Rakesh Mohan,

a senior fellow at the Jackson Institute for Global Affairs at Yale University; Dani Rodrik, 

Ford Foundation Professor of International Political Economy at Harvard’s John F. 

Kennedy School of Government; and Andrew Sheng, a distinguished fellow of the Asia 

Global Institute at the University of Hong Kong and chief adviser to the China Banking 

Regulatory Commission. We also thank the many business leaders, experts, investors, and 

entrepreneurs who shared their insights confidentially in our survey. 

This report was edited and produced by senior editor Mark A. Stein, editorial director 

Peter Gumbel, editorial production manager Julie Philpot, senior graphic designers 

Marisa Carder and Patrick White, and graphic design specialist Margo Shimasaki. MGI’s 

external communications team—Nienke Beuwer in Amsterdam, Cathy Gui in Shanghai, 

and Rebeca Robboy in San Francisco—managed dissemination and publicity, while digital 

editor Lauren Meling provided support for online publication and social media. We thank 

Deadra Henderson, MGI’s manager of personnel and administration, and MGI knowledge 

operations specialists Timothy Beacom, Karen P. Jones, and Nura Funda for their support. 

Photographs are by George Steinmetz. 

We are grateful for all the input we have received, but the final report is ours, and all errors 

are our own. This report contributes to MGI’s mission to help business and policy leaders 

understand the forces transforming the global economy, identify strategic locations, and 

prepare for the next wave of growth. As with all MGI research, this work is independent and 

has not been commissioned or sponsored in any way by any business, government, or 

other institution. We welcome your comments on the research at 

MGI@mckinsey.com. 

Jacques Bughin 

Director, McKinsey Global Institute 

Senior partner, McKinsey & Company 

Brussels 

James Manyika 

Chairman and Director, McKinsey Global Institute 


San Francisco 

Jonathan Woetzel 

Director, McKinsey Global Institute 


Shanghai 

September 2018

Young entrepreneur at a technology innovation center in Nairobi, Kenya. 

© Waldo Swiegers/Bloomberg/Getty Images

CONTENTS 

HIGHLIGHTS 

33 

In brief 

Page vi 

Summary of findings 

Page 1 

Rising incomes 

63 

1. Eighteen developing economies that outperformed their peers 

Page 33 

2. Policies that enabled exceptional growth 

Page 43 

South-south trade 

89 

3. Emerging-market firms as aspiring global leaders 

Page 63 

4. New opportunities for emerging economies in changing times 

Page 89 

The next wave? 

5. Looking to the next outperformers 

Page 119 

Technical appendix 

Page 137 

Bibliography 

Page 149

IN BRIEF 

OUTPERFORMERS: HIGH-GROWTH EMERGING 

ECONOMIES AND THE COMPANIES THAT PROPEL THEM 

Emerging economies are the engine of global growth, 

but the performance of individual economies varies 

considerably. In this research, we identify outperforming 

countries that have experienced strong and sustained 

growth, and focus on the economic policy choices and 

the often-overlooked contribution of large firms that have 

driven that growth. Key findings: 

• Eighteen of the 71 emerging economies we studied 

outperformed global benchmarks and their peers 

by achieving more than 3.5 percent per capita GDP 

growth over 50 years or 5 percent growth over 20 

years. They include long-term success stories such as 

China and Malaysia, recent high-growth economies 

such as India and Vietnam, and less heralded 

outperformers, including Ethiopia and Uzbekistan. 

These 18 countries have lifted about one billion people 

out of extreme poverty since 1990—730 million in 

China alone—and generated 44 percent of emerging 

market consumption growth between 1995 and 2016. 

• Outperformers develop a pro-growth agenda 

across public and private sectors aimed at boosting 

productivity, income, and demand. Steps to 

boost capital accumulation, including (sometimes) 

forced savings, are a common feature, as are deep 

connections to the global economy. Governments 

in these countries have tended to invest in building 

competence, are agile and open to regulatory 

experimentation, and are willing to adapt global 

macroeconomic practices to the local contexts. 

Critically, their competition policies create an impetus 

for productivity growth, increased investment, and the 

rise of competitive firms. 

• Large, competitive firms propel outperforming 

economies. On average, these economies have twice 

as many companies with revenue over $500 million 

as other emerging economies. Their revenue relative 

to GDP almost tripled from 22 percent between 1995 

and 1999 to 64 percent between 2011 and 2016, and 

their contribution of value added to GDP rose from 

11 percent to 27 percent in the same period, double 

the level among developing-economy peers. These 

firms bring productivity benefits by investing in assets, 

R&D, and job training, which create spillover effects 

for smaller firms. Large firms, in turn, benefit from the 

intermediary goods and services smaller companies 

provide through the supply-chain ecosystem. 

• Competition and contested leadership in the private 

sector are key features of these dynamic economies, 

with the best-performing companies subject to fierce 

competition at home. Less than half (45 percent) of 

firms that reach the top quintile of economic profit 

generation manage to stay there for a decade, 

compared with 62 percent in high-income economies, 

a consistent pattern across eight sectors. The rewards 

for those that succeed are higher: the top 10 percent 

of firms in outperforming economies capture more 

than four times the share of economic profit as their 

peers in advanced economies. 

• This competitive home environment has spawned 

innovative global players whose total return to 

shareholders is eight to ten percentage points higher 

than high-income peers. They derive 56 percent 

of their revenue from new products and services, 

eight percentage points more than advanced 

economy peers, and are 27 percentage points more 

likely to prioritize growth abroad. 

• Extending the success of outperformers to all other 

emerging economies could add $11 trillion to the 

global economy by 2030, an approximately 10 percent 

boost equivalent to the size of China. Automation 

and shifting trade patterns, along with other global 

trends, present new opportunities. There are broad 

prospects for growth in services, a traditional engine 

of employment, and in manufacturing, which can also 

stimulate demand and productivity in other sectors. 

Despite evidence of premature deindustrialization, 

we estimate that some emerging economies could 

boost the share of manufacturing employment 

as much as four percentage points by 2030 while 

also increasing the sector’s share of GDP by up to 

three percentage points. 

• Success or failure has been regionally driven, as 

emerging economies are historically more alike 

regionally than in any other way. That said, every 

region has fast-growing countries and the potential 

to achieve better outcomes. Bangladesh, Bolivia, 

the Philippines, Rwanda, and Sri Lanka, among 

others, have exceeded 3.5 percent annual per 

capita GDP growth since 2011. Laying strong policy 

foundations and fostering the growth of large firms 

could elevate these and other countries to the ranks of 

future outperformers.

Lessons from outperformers 

Of 71 emerging economies studied, 18 achieved rapid, sustained growth 

115.0% annually for 20 years 

Recent outperformers achieved 

GDP per capita growth of more than 

Long-term outperformers achieved 

GDP per capita growth of more than 

3.5% annually for 50 years 

7 

Azerbaijan 

Belarus 

Cambodia 

Ethiopia 

India 

Kazakhstan 

Laos 

Myanmar 

Turkmenistan 

Uzbekistan 

Vietnam 

5.0% 3.5% 

China 

Hong Kong 

Indonesia 

Malaysia 

Singapore 

South Korea 

Thailand 

Outperformers lifted 1 billion 

people out of extreme poverty 

in two decades, 95% of total 

People lifted out of extreme poverty, million 

1990 731 

168 158 65 2013 

China India Other Nonoutperformers 

outperformers 

A pro-growth policy agenda ... 

Measures that supported capital accumulation and 

ensured stability helped create a pro-growth agenda 

Productivity 

• Promoting competition 

• Increasing total factor 

productivity 

Income 

• 3–5pp faster annual 

wage growth 

• ~60% of growth in 

consuming classes in 

emerging economies 

Demand 

DEMAND 

• ~30% of global goods trade in 2016 

• 3pp faster annual consumption growth 

• Rank highly for global connectivity 

Two factors driving outperformance 

PRODUCTIVITY 

SUSTAINED, 

HIGH GDP 

PER CAPITA 

GROWTH 

INCOME 

... and highly competitive large companies 

Outperformers’ large firms are: 

More numerous 

2x 55% 

as many 

large firms 

for size of the economy compared 

with other emerging economies 

of firms in are displaced from their ranks 

top quintile within a decade vs only 38% of 

peers in advanced economies 

Three global trends that can help all emerging economies achieve stronger growth 

Rapidly evolving 

technology 

Automation could increase labor 

productivity in emerging 

economies by 0.8–1.2% 

More contested 

Outperforming rival firms in high-income countries 

More 

successful 

40% 

higher total 

return to 

shareholders 

Rising consumption 

from urbanization 

Consuming class in 440 cities 

could account for almost 

50% of global GDP growth 

by 2025 

Bolder 

innovators 

8pp 

more sales 

from new 

products 

Quicker 

decision 

makers 

32% 

faster 

investment 

decisions 

Aggressive 

growers 

27pp 

more cite entering 

new markets abroad 

as priority 

Growing south– 

south trade 

11x increase in trade 

between China and other 

emerging markets between 

1995 and 2016 

NOTE: The maps displayed on the MGI website and in MGI reports are for reference only. The boundaries, colors, denominations, and any other information shown on these maps do not 

imply, on the part of McKinsey, any judgment on the legal status of any territory, or any endorsement or acceptance of such boundaries.

Worker with mehndi henna design on her hands, in an electronics factory, India. 

© Dinodia Photos/Alamy Stock Photo 

viii McKinsey Global Institute Preface

SUMMARY OF FINDINGS 

Emerging economies have been a powerful engine of growth for the global economy during 

the past half century. Led by China and India, these economies accounted for almost twothirds 

of the world’s GDP growth and more than half of new consumption over the past 

15 years. Yet the catchall term “emerging economies” is misleading, for within this large 

group of countries, economic performances vary substantially. While some countries have 

truly “emerged,” achieving powerful and sustained long-term growth that has enabled these 

leaders to narrow the gap with high-income advanced economies, others have remained 

submerged, growing less strongly and steadily than the leaders, or falling behind. 

In this report, we look at the long-term economic track record of 71 developing economies 

to identify the outperformers—and determine how and why they outperformed. We focus 

on the agenda of productivity, income, and demand that has driven exceptional economic 

growth in these outperformers, and examine the underappreciated but nonetheless 

standout role that large companies have played in driving that growth. These companies 

have fought their way to the top in a propitious but often competitive macroeconomic 

environment and are emerging as formidable global competitors. If more economies can 

apply lessons from outperformers and take advantage of changing global trends, including 

rapid technological change, opportunities for growth in emerging economies will be 

abundant across all regions—and top-performing firms that have thrived through the trials of 

contested leadership will be at the forefront of that growth. 

Recent economic turbulence in several emerging economies has tested some investors’ 

confidence. In this report, we take a long view of developing economies, looking back at 

their real performance over decades and looking forward to where they could be in 2030. 

QUANTIFYING SUCCESS AMONG DEVELOPING ECONOMIES: 18 OF 71 

COUNTRIES OUTPERFORMED THEIR PEERS AND GLOBAL BENCHMARKS 

We analyzed the per capita GDP growth of 71 economies over 50 years, starting in 1965 

(see Box E1, “Our categorization of developing economies”). Of these, we identified 18 as 

outperformers, about one in four. 

Seven economies achieved or exceeded real annual per capita GDP growth of 3.5 percent 

for the entire 50-year period. This threshold is the average growth rate required by low- and 

lower middle-income economies to achieve upper middle-income status over a 50-year 

period, as defined by the World Bank. 1 That growth rate is 1.6 percentage points above the 

per capita GDP growth of the United States in the same period. The seven are China, Hong 

Kong, Indonesia, Malaysia, Singapore, South Korea, and Thailand. 

1 

The World Bank assigns the world’s economies into four income groups: high, upper middle, lower middle, 

and low. We set the threshold growth rate for long term outperformers at 3.5 percent, which is the annual 

average growth rate required over a 50-year period for low-income and lower middle-income economies 

to achieve upper middle-income status. For low-income economies alone, the threshold growth rate 

is 4.3 percent, and for lower middle-income economies it is 2.8 percent. The Data Blog, “New country 

classifications by income level: 2016-2017,” blog entry by World Bank Data Team, July 1, 2016, blogs. 

worldbank.org.

Box E1. Our categorization of developing economies 

For our analysis, we started with a list of 218 countries tracked by the World Bank, then 

excluded 99 countries with fewer than five million people in 2016, a further 28 countries 

because of a lack of data, and 20 high-income countries. 1 Of the remaining sample of 71, 

we identified the 18 outperformers: the long-term outperformers over 50 years, which 

represented 24 percent of the world’s population and 18 percent of global GDP as of 2016, 

and the recent outperformers, which represented 22 percent of global population but only 

4 percent of worldwide GDP in 2016. 

In most of the developing economies we studied, per capita GDP increased relative to 

the United States but by a lower margin than for the outperformers, or less consistently. 

While these middling economies shared some broad traits, they represent a range of 

performances. Some, such as Bangladesh and Ghana, have seen recent growth spurts; 

others, such as Bulgaria, Pakistan, and Tanzania, have grown more consistently, while the 

economies of a third grouping, including Argentina and Kenya, have been highly volatile. 

Some emerging economies have underperformed, with their per capita GDP declining 

relative to the United States from 1965 to 2016. These countries include Lebanon, Russia, 

South Africa, Ukraine, Venezuela, Zambia, and Zimbabwe. 

For several economic indicators, such as capital accumulation and total factor productivity, 

reliable data are not available for the 50 years we review. Where this occurs, we use the 

longest available time series of reliable data and state the time frame in the text and exhibits. 

We took the simple average of indicators across countries to avoid overriding the growth 

experience of smaller economies. 

Our analysis is based on data up to 2016, and for the sake of consistent analysis it does not 

take into account more recent developments. 

1 

We include Greece, Portugal, and South Korea in our analysis of emerging economies because the World 

Bank only classified them as high-income countries in the 1990s. We also include Hong Kong and Singapore, 

which were classified as high-income countries in 1987. See technical appendix for details. 

While the economic transformation stories of these Asian countries, especially China, have 

been widely studied (including by us), they remain remarkable in their scale and speed. Our 

analysis found a second group of 11 more recent, less heralded and more geographically 

diverse outperformers, across regions and income levels. These countries achieved real 

average annual per capita GDP growth over the 20 years between 1996 and 2016 of at 

least 5 percent. This was enough to lift themselves by one income bracket as defined by 

the World Bank—and 3.5 percentage points above the per capita GDP growth of the United 

States. 2 The 11 are Azerbaijan, Belarus, Cambodia, Ethiopia, India, Kazakhstan, Laos, 

Myanmar, Turkmenistan, Uzbekistan, and Vietnam (Exhibit E1). 

2 

For recent outperformers, we set the threshold growth rate at 5.0 percent. Under the World Bank’s income 

classification, low- and lower middle-income countries must attain average annual growth of 5.4 percent to 

move up one income level over a 20-year period. Growth of 3.7 percent is needed for the move from low to 

lower-middle income, while 7.1 percent growth is needed to rise from lower-middle to upper-middle income. 

Ibid. 

2 McKinsey Global Institute Summary of findings

Exhibit E1 

GDP per capita growth among outperforming economies has far exceeded that of other emerging economies. 

GDP per capita 1 

Index: 100 = 1965 

4,000 

3,500 

Archetype 

Compound 

annual growth 

rate, 1965–2016 

% 

GDP, 

2016 2 

% share 

Population, 

2016 2 

% share 

3,000 

China 

7.3 13 

19 

2,500 

2,000 

Long-term 

outperformers 

(excluding China) 

4.7 

5 

6 

1,500 

Recent 

outperformers 

3.9 4 

22 

1,000 

High income 

2.0 59 

13 

500 

Non-outperformers 

1.7 16 

31 

0 

1965 70 75 80 

85 90 95 2000 05 10 2016 

1 Calculated using GDP per capita (constant 2010 $) and based on simple averages. 

2 Excluded economies account for 3% of global GDP and 9% of population. 

NOTE: Figures may not sum to 100% because of rounding. 

SOURCE: World Bank; McKinsey Global Institute analysis 

These 18 countries not only showed exceptional average economic performance but also 

demonstrated consistency by exceeding the benchmark growth rate in at least threefourths 

of the 50 and 20 years, respectively. Some other countries such as Brazil, Ghana, 

and Poland that have also experienced strong periods of growth did not make the cut, 

as they have gone through sharp downturns following the booms. Exhibit E2 shows our 

classification ES of the and 71 emerging report economies and, for outperformers and select others, 

highlights their progress across a range of economic performance dimensions that we 

consider in our analysis. 3 Overall we find little evidence to support notions of a “middleincome 

trap”—that is, that countries which relied for growth on low wages and technology 

adoption from higher-income nations could lose their competitive advantage as they 

become more prosperous and move up to middle-income status. 4 

Outperformers 

ES 

mc 0829 

3 

Prior MGI research has shown that advancing the participation and role of women in the economy can give a 

significant boost to GDP, and this is also true of emerging economies. For this research, we did not explicitly 

include gender equality-related metrics in our economic performance indicators, as female participation in the 

labor force is heavily influenced by non‐economic factors such as cultural barriers and household preferences 

about how to manage unpaid care work. In many emerging economies, therefore, we see a nuanced 

relationship between economic factors, like household income and urbanization, and progress on gender 

equality. See The power of parity: Advancing women’s equality in Asia Pacific, McKinsey Global Institute, June 

2018; The power of parity: How advancing women’s equality can add $12 trillion to global growth, McKinsey 

Global Institute, September 2015. 

4 

See, for example, Shekhar Aiyar et al., Growth slowdowns and the middle-income trap, IMF working paper 

WP/13/71, March 2013, imf.org; Pierre-Richard Agénor and Otaviano Canuto, Middle-income growth traps, 

World Bank policy research working paper number 6210, September 2012; and David Bulman, Maya Eden, 

and Ha Nguyen, “Transitioning from low-income growth to high-income growth: Is there a middle-income 

trap?” Journal of the Asia Pacific Economy, January 2017, Volume 22, Number 1, pp. 5–28. 

McKinsey Global Institute 

Outperformers: High-growth emerging economies and the companies that propel them 

3

Exhibit E2 

Eighteen emerging economies sustained long-term GDP per capita growth, outperforming their peers. 

N = 91 countries 1 

High income 2 

• Australia 

• Austria 

• Belgium 

• Canada 

• Denmark 

• Finland 

• France 

• Germany 

• Israel 

• Italy 

• Japan 

• Netherlands 

• Norway 

• Saudi Arabia 

• Spain 

• Sweden 

• Switzerland 

• United Arab 

Emirates 

• United 

Kingdom 

• United States 

Long-term outperformers 3 

Outpaced US growth 

consistently from 1965–2016 

• China 

• Hong Kong 

• Indonesia 

• Malaysia 

• Singapore 

• South Korea 

• Thailand 

Recent outperformers 4 

Outpaced US growth 

consistently from 1996–2016 

• Azerbaijan 

• Belarus 

• Cambodia 

• Ethiopia 

• India 

• Kazakhstan 

• Laos 

• Myanmar 

• Turkmenistan 

• Uzbekistan 

• Vietnam 

Middlers 5 

No relative change: No or inconsistent improvement relative to 

US from 1965–2016 

Very recent 

accelerators 

• Bangladesh 

• Dominican 

Republic 

• Ghana 

• Mozambique 

• Peru 

• Philippines 

• Poland 

• Rwanda 

• Sri Lanka 

Consistent 

growers 

• Bulgaria 

• Chile 

• Colombia 

• Czech 

Republic 

• Ecuador 

• Egypt 

• Hungary 

• Morocco 

• Nepal 

• Pakistan 

• Portugal 

• Romania 

• Serbia 

• Slovak 

Republic 

• Tanzania 

• Turkey 

• Uganda 

Volatile 

growers 

• Algeria 

• Angola 

• Argentina 

• Brazil 

• Greece 

• Guatemala 

• Honduras 

• Iran 

• Jordan 

• Kenya 

• Mexico 

• Nigeria 

• Paraguay 

Underperformers 6 

Fallen behind: Slower 

relative growth than 

US from 1965–2016 

• Bolivia 

• Cameroon 

• Côte d’Ivoire 

• El Salvador 

• Kyrgyz Republic 

• Lebanon 

• Nicaragua 

• Russia 

• Senegal 

• South Africa 

• Ukraine 

• Venezuela 

• Zambia 

• Zimbabwe 

1 We excluded economies with populations of less than 5 million in 2016 and those with limited data availability. 

2 For the purposes of this report, we have defined high income economies as those that had gross national income per capita of $6,000 or more in 1987, when 

the World Bank first started classifying countries by income bands. The two exceptions are Hong Kong and Singapore, which are classified as outperformers 

in our report due to the high rate of growth during the period analyzed. 

3 The long-term outperformer threshold of 3.5% compound annual growth rate of GDP per capita is the average growth rate required by low (4.3%) and lowermiddle-income 

(2.8%) economies to achieve upper middle-income status over a 50-year period. 

4 The recent outperformer threshold of 5% compound annual growth rate is derived from the average growth rate of 5.4% required by low (3.7%) and lower 

middle (7.1%) income to move up one income level over a 20-year period (from low to lower middle or lower middle to upper middle). 

5 The middler threshold was between 0.95% and 3.5% compound annual growth rate over the period 1965–2016, or where economies did not meet the criteria 

for other cohorts. Very recent accelerators’ GDP per capita growth outpaced long-term outperformers’ (>3.6% compound annual growth rate) from 2006–16. 

Consistent growers‘ GDP per capita grew consistently (albeit slowly) from 1965–2016 with a low coefficient of variation. Volatile growers’ GDP per capita 

regressed and/or exhibited a high coefficient of variation over at least one 10-year period from 1965–2016. Coefficient of variation defined as standard 

deviation of year-on-year growth divided by simple average year-on-year growth 1965–2016. 

6 The underperformer threshold of

Collectively, the outperformers have been the engine for lifting about one billion people 

out of extreme poverty, helping to meet a key United Nations Sustainable Development 

Goal. 5 Indeed, rising prosperity in these countries has not just reduced poverty, but also 

enabled the emergence of a new wave of middle and affluent classes. Between 1990 and 

2013, the latest year for which comprehensive data are available, the number of people 

living in extreme poverty in the 71 emerging economies fell from 1.84 billion to 766 million. 

Outperformers accounted for almost 95 percent of that change. Less than 11 percent 

of the world’s population now lives in extreme poverty, down from 35 percent in 1990. 6 

While China and India led the way, lifting some 900 million people out of extreme poverty 

(approximately 730 million and 170 million, respectively), Indonesia also elevated over 

80 million people out of extreme poverty (Exhibit E3). 7 

Exhibit E3 

Outperformers lifted approximately 1.1 billion people out of extreme poverty and increased household consumption 

by about $3.2 trillion. 

Population lifted out of 

extreme poverty, 1990–2013 1 

Million 

Household consumption 

expenditure change, 1995–2016 

$ billion 

Number of 

countries 

China 

731 

931 

1 

India 

168 

1,026 

1 

Other 

outperformers 

158 

1,202 

10 2 

Nonoutperformers 

65 

3,975 

46 

Total 

1,057 

65 1,123 

3,158 

3,975 7,133 

58 

% of total 94 6 44 56 

1 Defined as individuals earning less than $1.90 per day (PPP $ 2005), N = 63 economies. 

2 Data unavailable for outperformers: Azerbaijan, Ethiopia, Laos, Myanmar, Turkmenistan, and Uzbekistan; non-outperformers: Angola, Côte d’Ivoire, Ghana, 

Nepal, Venezuela, Zambia, and Zimbabwe. 


SOURCE: PovcalNet, World Bank; UNDP; McKinsey Global Institute analysis 

At the same time, growing numbers of residents of these countries joined what we call 

the “consuming class”—that is, people with incomes high enough to become significant 

consumers of goods and services. 8 Globally, these highly urbanized consumers have 

become a powerful motor for global economic growth. We estimate that 440 cities globally 

could account for close to half of world GDP growth by 2025, largely because of additional 

spending by the consuming class. 9 The outperformers accounted for almost half of the 

growth in household spending of all emerging economies in the past 20 years. 

ES and report 

5 

The World Bank defines extreme poverty as living on less than $1.90 a day. 

6 

Poverty and shared prosperity 2016: Taking on inequality, World Bank, 2016. 

7 

Atlas of Sustainable Development Goals: No poverty, World Bank, 2018, datatopics.worldbank.org/sdgatlas. 

8 

We define consuming class or consumers as those individuals with an annual income of more than $3,600, or 

$10 per day at purchasing power parity (PPP), using constant 2005 PPP dollars. See Urban world: Cities and 

the rise of the consuming class, McKinsey Global Institute, June 2012, on McKinsey.com. 

9 

Ibid. 



5

In the turbulent period for the global economy following the 2008 financial crisis, including 

the volatile commodity price cycle, some of the outperformers nonetheless recorded 

3.5 percent annual GDP per capita growth between 2011 and 2016, even as a few of the 

exceptional historical performers, including Singapore, experienced a deceleration of 

growth. At the same time, a number of other countries have undergone growth spurts. 

They include Bangladesh, Bolivia, the Dominican Republic, Ghana, Poland, the Philippines, 

Rwanda, and Sri Lanka. Some but not all of these countries are also putting in place progrowth 

policies that are strengthening their economic fundamentals, as we discuss later. 

GOVERNMENT POLICIES ENABLED A PRO-GROWTH CYCLE BASED ON 

PRODUCTIVITY, INCOME, AND DEMAND 

While the 18 outperformers vary considerably, spanning different income levels, sizes, 

regions (with the exception of Latin America, the Middle East, and North Africa), and factor 

endowments, our analysis suggests they share foundations of similar pro-growth cycles 

of rising productivity, income, and demand. Part and parcel of these foundations are 

competition policies that created an impetus for productivity growth and helped forge the 

big companies that have driven a significant part of GDP growth. 

Policies aimed at supporting capital accumulation and ensuring stability helped 

create a pro-growth agenda 

The pro-growth cycle starts with growing productivity, made possible by accumulating 

capital and technology. The fruits of improved productivity are then distributed throughout 

the economy in the form of more jobs and higher wages for workers, lifting more people into 

the middle class, and in turn supporting higher levels of consumption and savings. 

Companies see increased profits, and governments collect additional tax revenue they 

can use to improve essential infrastructure. Wage growth translates into more disposable 

income, which boosts personal savings—some of it through mandatory payroll deductions 

for retirement savings—as well as investment and household consumption. This, along with 

better access to global markets, increases overall demand for goods. The outperformers we 

identify have historically stood out as better performers on most of these metrics, although 

opportunities remain. 

For all the outperformer countries, increased productivity rather than a larger labor supply 

drove high rates of GDP growth. 10 Rising productivity, or total factor productivity (TFP) 

growth, which represents the efficient use of resources through technology, innovation, and 

better management, has in turn been enabled by capital accumulation and income growth 

(Exhibit E4). 11 

10 

In the 50-year period between 1964 and 2014, the total labor force in G-19 countries and Nigeria doubled, 

contributing about 48 percent of GDP growth in these economies, while rising productivity generated 

52 percent. With slowing growth or declines in the working-age population in many countries, the onus on 

future GDP growth will fall more heavily on productivity improvements. For details, see Global growth: Can 

productivity save the day in an aging world? McKinsey Global Institute, January 2015, on McKinsey.com. 

11 

Robert E. Hall and Charles I. Jones, “Why do some countries produce so much more output per worker than 

others?” The Quarterly Journal of Economics, February 1999, Volume 114, Number 1, pp. 83–116. 


Exhibit E4 

Capital accumulation and total factor productivity have been major drivers of economic growth for 

outperforming economies. 

GDP growth decomposition 

Contribution to real GDP growth, 1990–2016 (%) 1 

N = 83 countries 

Labor productivity 

Differentiating factors 

Labor force 

participation 

Capital 

accumulation 

Total factor 

productivity 2 

Labor quality 

contribution 3 

Labor quantity 

contribution 

Long-term outperformers 

(except China) 

3.7 

0.1 

0.4 0.7 

China 

4.3 

4.3 

0.3 

0.7 

Recent outperfomers 

(except India) 

5.1 

0.6 

0.4 

0.8 

India 

3.8 

1.5 

0.6 

0.8 

Middlers 

2.3 

0.1 

0.3 

0.8 

Underperformers 

1.7 

-1.0 

0.2 

1.0 

High income 

1.7 

-0.2 0.2 

0.4 

1 Simple average across economies within cohorts and across years within countries. 1995–2016 for recent outperformers. 

2 Long-term outperformers’ low rate of total factor productivity growth was caused, in part, by the 1997 Asian financial crisis. Further, capital accumulation and 

total factor productivity were likely lower for long-term outperformers over this period as the growth accelerations in these economies commenced prior to 

1990. For example, from 1965 to 1990, South Korea’s average growth of output attributable to total factor productivity is estimated to be 2.39%, while 

capital’s contribution was 4.27% compared to total output growth averaging 8.78% per year (Nirvikar, Singh, and Hung Trieu, 1996). 

3 Labor quality contribution data are constructed using data on employment and compensation by educational attainment. These data are collected from 

various sources, including Eurostat, World Input-Output Database and various country-specific KLEMS (capital, labor, energy, material and services) 

databases. 

SOURCE: Economics Analytics Platform; World Bank; The Conference Board Total Economy Database; McKinsey Global Institute analysis 

Indeed, more than two-thirds of the GDP growth in outperforming countries over the past 

30 years is attributable to a rapid rise in productivity correlated with industrialization: an 

annual average productivity gain of 4.1 percent versus 0.8 percent for the other developing 

economies. 12 That rapid development initially drives the pro-growth cycle by creating wealth 

and boosting demand, which translates into more jobs. 

ES and report 

Capital accumulation—enabled by high rates of investment and domestic savings— 

contributed an average of approximately four percentage points to economic growth 

each year between 1990 and 2016 for the seven 50-year outperformers in our sample, 

and five percentage points for the 11 shorter-term outperformers, between 1995 and 

2016. Investment as a share of GDP averaged 30 percent for long-term outperformers 

and 20 percent for recent outperformers, or three to 13 percentage points higher than 

investment in other developing economies. The difference in domestic savings as a share of 

GDP was ten to 30 percentage points higher. 

12 

We used McKinsey & Company’s proprietary Global Growth Model to simulate the effects of the productivity 

increase. For details of the model, see Luis Enriquez, Sven Smit, and Jonathan Ablett, Shifting tides: Global 

economic scenarios for 2015–25, McKinsey & Company, September 2015, on McKinsey.com. 



7

The outperformers could tap into higher levels of domestic savings, some of which was 

required by government-run pension savings schemes, such as Singapore’s Central 

Provident Fund, and some of which was encouraged by governments developing strong 

financial institutions and convenient digital banking services. 13 Higher domestic savings 

enabled more investment in infrastructure, among other areas. Outperformers also attracted 

the largest share of foreign investment, almost 70 percent, of the approximately $900 billion 

invested in emerging markets between 2000 and 2016. 14 

For its part, total factor productivity accounted for one percentage point of annual GDP 

growth on average from 1995 to 2016 for the 20-year outperformers, compared with having 

limited or even negative effects in other developing economies and advanced economies. 

The 1997 Asian financial crisis took a toll on TFP among long-term outperformers, but in 

China, which was less affected by that crisis, TFP accounted for 4.4 percentage points of 

annual GDP growth from 1990 to 2016. 15 

Strong productivity growth in the 18 outperformers translated into exceptional income 

growth. Real wages and benefits rose by an average 4.6 percent annually in the seven longterm 

outperforming countries between 1980 and 2014. China led the way, with incomes 

there rising by 8.6 percent annually. Among the more recent outperforming countries, real 

wages and benefits grew by 6.0 percent per year between 1995 and 2014. This was about 

triple the level in other developing and advanced economies. Household consumption 

spending generated by rising incomes grew about three percentage points faster in the 18 

outperforming countries than in other developing or advanced economies. 

Another essential feature of these countries has been their ability to achieve macroeconomic 

stability, even at a time of global volatility, by adapting economic policies to fit their local 

context and changing conditions. For example, governments took quick action to ensure 

rapid recovery from volatile episodes such as the Asian financial crisis of 1997 and the global 

financial crisis of 2008 and 2009. When, in 2013, the prospect of central banks’ unwinding 

of quantitative easing led to the so-called taper tantrum in financial markets in emerging 

economies, several countries, including India and Indonesia, implemented monetary, fiscal, 

and exchange-rate stabilization measures that served as a buffer to market pressure. 

Outperforming economies are more connected to foreign markets, enabling 

them to tap into global demand 

Outperforming economies have benefited from their ability to tap into global demand 

growth through export markets, giving them greater economies of scale. 16 This higher 

export orientation is reflected in MGI’s Connectedness Index, which assesses the extent 

of countries’ engagement with the global economy through inflows and outflows of goods, 

services, finance, people, and data. 17 

13 

What is the Central Provident Fund (CPF), Singapore Ministry of Manpower, mom.gov.sg. 

14 

PitchBook Deal Analytics. 

15 

Nirvikar Singh and Hung Trieu, Total factor productivity growth in Japan, South Korea, and Taiwan, University 

of California, Santa Cruz, working paper, July 1996. 

16 

Jonathan Anderson, How to think about emerging markets (2018 edition), Emerging Advisors Group, April 24, 

2018. 

17 

MGI’s Connectedness Index offers a comprehensive look at how countries participate in inflows and outflows 

of goods, services, finance, people, and data. The index takes into account the size of each flow for a country 

relative to its own GDP or population (flow intensity) as well as its share of each total global flow. Digital 

globalization: The new era of global flows, McKinsey Global Institute, March 2016, on McKinsey.com. 


In 1980, outperformers accounted for 7 percent or less of global inflows and outflows across 

goods, services, and finance. By 2016, they had increased their share to 19 percent or 

more. The greatest increase came from goods trades. Outperformer economies captured 

almost 30 percent of global share by 2016—of which China accounted for 13 percentage 

points—compared with 1 percent in 1980. Indeed, seven of the outperformers rank in the 

top 30 countries globally for connectedness, including Singapore in second place, China in 

ninth, South Korea 15th, Malaysia 20th, Thailand 21st, Vietnam 26th, and India 30th. 

Competition policies created impetus for productivity growth 

Many outperformer countries recognized the importance of competitive private-sector firms 

and nurtured environments in which they could invest and compete, even as they created 

incentives for productivity improvements. Rather than picking winning sectors or winning 

companies within sectors, they focused on boosting productivity and enabling competition 

within sectors. As a result, sectors with a larger share of big firms grew faster, increased 

productivity by more, paid workers better, and realized greater levels of investment. In 

some but not all countries, governments helped incubate competitive domestic companies 

through sector-wide support for infant industries, including low-cost loans, preferential 

exchange rates, low tax rates, and R&D subsidies. However, protection was gradually lifted 

as these industries became more competitive, limiting market distortions. In some cases, 

support was tied to conditions that encouraged firms to increase productivity. For example, 

South Korea’s import policy in the 1960s strictly limited all but strategic imports and 

imposed high tariffs, but the country gradually transitioned to a more (but still not entirely) 

open scheme in the 1980s. 18 

Attracting foreign investors, in the form of foreign invested enterprises (FIEs) and foreign 

direct investment, has also been a way for governments to contribute to productivity growth. 

China used joint venture structures and favorable FDI policies for FIEs, including preferential 

treatment, for example. Local firms can benefit from the technology spillover from these 

foreign firms, and FIEs help emerging economies participate in the global value chain. 19 

In China, for example, they account for about half of exports, according to the Ministry of 

Commerce. 20 Improving government effectiveness helps attract foreign investment (see 

Box E2, “Outperforming economies benefit from improved government effectiveness”). 

Governments also collaborated with the private sector to co-create solutions in multiple 

areas, including infrastructure, technology, and financial services. Vietnam, for example, 

moved rapidly from being a socialist-market economy without a private sector to becoming 

a deregulated capitalist economy that has seen an influx of private enterprise and foreign 

investment. China allowed intercity and interprovincial competition, plus competition among 

state-owned and private-sector companies, including for foreign direct investment. 

18 

Kwan S. Kim, The Korean miracle (1962–1980) revisited: Myths and realities in strategy and development, 

Kellogg Institute working paper number 166, November 1991. 

19 

John Van Reenan and Linda Yueh, Why has China grown so fast? The role of international technology 

transfers, Oxford University Department of Economics, working paper, January 2012. 

20 

Foreign direct investment—The China story, World Bank, July 16, 2010. 



9

THE ROLE OF PRODUCTIVE FIRMS IS A KEY CHARACTERISTIC OF GROWTH 

OF OUTPERFORMING ECONOMIES 

Growth and development economists over the decades have extensively documented 

policies that have driven growth in emerging economies. 21 Less studied is the contribution 

to that growth of globally competitive, nimbly managed, and highly productive companies 

founded in and based in developing economies. In the 18 outperforming countries, we find 

that these firms, backed by macroeconomic and other enabling policies, not only helped 

boost GDP but also are catalysts for change at home. 

21 

See, for example, Alice H. Amsden, Rise of “The Rest”: Challenges to the West from Late-Industrializing 

Economies, Oxford, UK: Oxford University Press, 2001; Edward K.Y. Chen, Hypergrowth in Asian Economies: 

A Comparative Study of Hong Kong, Japan, Korea, Singapore, and Taiwan, London: Macmillan, 1979; and 

Richard R. Nelson and Howard Pack, “The Asian miracle and modern growth theory,” The Economic Journal, 

July 1999, Volume 109, Number 457. 

Box E2. Outperforming economies benefit from improved 

government effectiveness 

Government effectiveness is a characteristic of the outperformers, as reflected 

in their above-average improvement in the World Bank’s Government 

Effectiveness Score (Exhibit E5). 1 

Firms in many of the outperforming economies face fewer regulatory and 

tax barriers compared with companies in other countries, and this in turn 

encourages business creation and improved efficiency. According to data 

from the World Bank Enterprise Survey, firms in the outperformer economies 

are less likely than those in other developing economies to consider tax 

management a major obstacle (9 percent of respondents versus 23 percent). 

Similarly, fewer firms in outperformer economies reported customs delays 

and trade barriers (9 percent versus 16 percent), facilitating exporting and 

importing activities. Senior managers in other developing economies report 

spending 11 percent of their time on government regulatory issues, while their 

peers in outperformer economies say they spend only 5 percent. 2 

Outperformer governments have used pilot programs and experiments to test 

new ideas in a variety of contexts, modifying and updating them as necessary, 

and then scaling up policies that work. China famously used special economic 

zones to test policies before introducing them broadly. Regulatory sandboxes, 

such as those used by the Monetary Authority of Singapore, also facilitated 

policy experiments while containing consequences of failure. Governments 

have also worked to improve the capabilities of the public sector, including 

hiring better government clerks, inspectors, and regulators. For example, 

South Korea invested in sending some of its civil servants to train in more 

advanced economies, while China systematically rotates its bureaucrats by 

function and geography. 3 

1 

World Bank Worldwide Governance Indicators, 2017. 

2 

World Bank Enterprise Survey. 

3 

The focus of our analysis is on the role of government and policies as they relate to economic 

performance and does not explore political processes, types of government, or the 

functioning of civil society. 


We define large firms here as public companies with annual revenues of at least 

$500 million. 22 From 1995 to 2016, their revenue relative to GDP has almost tripled in 

outperformer developing economies, growing from the equivalent of 22 percent of GDP 

to 64 percent, close to levels in high-income economies and dwarfing levels in other 

developing economies. At the same time, we estimate that the contribution of value added 

by these outperformer firms to national GDP also grew rapidly, from 11 percent in 1995 to 

27 percent in 2016—or double the share among non‐outperforming emerging economies 

(Exhibit E6). 

22 

For certain of our analyses including that of total shareholder returns, we use slightly different definitions, 

which we note where relevant. For our company analyses, we looked at more than 13,000 listed companies in 

27 countries using McKinsey & Company’s Corporate Performance Analytics tool. See technical appendix. 

Box E2. Outperforming economies improved 

government effectiveness (continued) 

Exhibit E5 

Outperforming developing economies improved policy and institutional effectiveness. 

Absolute change in Worldwide Governance Indicators score, 1996–2016 

Simple average across archetypes 

Score ranges from approximately -2.5 (weak) to 2.5 (strong) 

Government effectiveness 1 Regulatory quality 2 Rule of law 3 

Change 4 total Change 4 total Change 4 total 

2016 

2016 

2016 

Long-term outperformers 

(except China) 

0.5 

1.1 0.2 1.0 0.3 0.8 

China 0.7 0.4 0 -0.3 0.3 -0.2 

Recent outperformers 

(except India) 

0.4 

-0.5 0.2 -0.9 0.4 -0.8 

India 0.2 

0.1 0.2 -0.3 -0.4 

-0.1 

Middlers 0.1 

-0.1 0 -0.1 0.1 -0.2 

Underperformers -0.2 

-0.6 -0.2 

-0.6 -0.2 

-0.8 

High income 0.1 

1.5 0.2 1.4 0 

1.5 

1 Reflects perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of 

policy formulation and implementation, and the credibility of the government's commitment to such policies. 

2 Reflects perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector 

development. 

3 Reflects perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, 

property rights, the police, and the courts, as well as the likelihood of crime and violence. 

4 Changes show only the difference between 1996 and 2016 and do not reflect declines early in that period or steady scores more recently. 

SOURCE: World Bank Worldwide Governance Indicators 2017; McKinsey Global Institute analysis 



11

Exhibit E6 

Large companies have been important to the growth of outperforming developing economies. 

N = 25 economies; 6,474 companies 1,2 

Ratio of large-company revenue to GDP, 1995–2016 3 

% 

High income 

59 

69 

77 

+36 

Outperformers 

60 

64 

+42 


Revenue 

41 

41 

Value 

added 4 

18 

26 

30 33 

22 

11 

18 

26 27 

13 

6 

21 

10 

28 

29 

13 13 

+16 

1995– 

99 

2000– 

04 

2005– 

10 

2011– 

16 

1995– 

99 

2000– 

04 

2005– 

10 

2011– 

16 

1995– 

99 

2000– 

04 

2005– 

10 

2011– 

16 

1 Outperformers include China, India, Indonesia, Malaysia, Singapore, South Korea, and Thailand; high-income economies include Canada, France, Germany, 

Italy, Japan, the United Kingdom, and United States; non-outperformers include Argentina, Brazil, Egypt, Mexico, Nigeria, Pakistan, Poland, Russia, 

Philippines, South Africa, and Turkey; Hong Kong is excluded as an outlier (large-company revenue is equivalent to more than 340% of GDP). 

2 Publicly listed companies with more than $500 million in revenue in 2016. 

3 Simple average across countries; 5-year averages taken due to year-on-year volatility. 

4 Gross value added has been calculated as the difference between revenue and cost of goods sold; GVA contribution of large financial services firms has 

been estimated. 


SOURCE: World Bank; McKinsey Corporate Performance Analytics; McKinsey Global Institute analysis 

Large firms tend to focus on sectors that tap into global demand and which have helped 

drive a greater share of exports for the outperforming economies. They bring productivity 

benefits by investing in assets, R&D, and job training at a higher rate than small and 

medium-size enterprises—and they tend to pay higher wages, upward of 75 percent more 

in countries such as Indonesia and South Korea. 23 Along with these direct effects, large 

firms indirectly stimulate the creation, growth, and productivity of small and mediumsize 

enterprises in their supply chains—and in turn depend on these SMEs to provide 

ES and report 

intermediate inputs for their ecosystem (Exhibit E7). 

23 

This wage gap also has some less positive effects, including the potential to exacerbate income inequality. 

Lucia Cusmano, Small, Medium, Strong: Trends in SME Performance and Business Conditions, Paris, France: 

OECD Publishing, 2017; Kim Kyung-ho, “Wage gap widening between SMEs, large firms,” Korea Herald, 

August 31, 2016. 


Exhibit E7 

Firms from outperforming countries operate in a wide variety of sectors. 

Large firm revenue 

Bubble size represents sector 

revenue as % of total large firm 

revenue in each country 

Large firm revenue as % of GDP 

10 

South 

Korea Singapore Thailand China Malaysia India Indonesia 

Accommodation, food services, and 

entertainment/recreation activities 

Agriculture, forestry, and fishing 

Automotive and assembly 

Construction and real estate 

Energy and basic materials 

Financial and insurance services 

Healthcare 

Manufacturing: Consumer packaged 

goods 

Manufacturing: High tech 

Manufacturing: Other (chemicals, 

steel, textiles, etc) 

Manufacturing: Pharmaceuticals 

and medical products 

Telecommunications, media, and 

technology services 

Travel, transport, and logistics 

Wholesale and retail trade 

Other 

Total large firm revenue 

$ billion 

Share of GDP 

% 

1,684 220 237 5,123 140 866 158 

129 75 58 54 41 35 15 

NOTE: Hong Kong omitted as large firm revenue >300% of GDP; Singapore agriculture, forestry, and fishing omitted as outlier. 

SOURCE: IMF; McKinsey Corporate Performance Analytics; McKinsey Global Institute analysis 



13

COMPETITIVE EMERGING-MARKET FIRMS AS ASPIRING GLOBAL LEADERS 

Rising to the top in the outperforming emerging economies—and then staying there—is by 

no means a foregone conclusion for large firms, many of which are far from the common 

stereotype of outsize government-protected oligopolies. Our analysis finds that the 

competitive dynamics in many (but not all) of the outperforming countries can be brutal, 

with only the strongest surviving. Domestic competition, in turn, has enabled the winners to 

earn a disproportionate share of revenue and income and to outperform their counterparts 

in advanced economies across key dimensions, including total returns to shareholders. 

For companies in high-income countries, the developing world has thus become both an 

opportunity for growth and the source of tough new global competition. 

It’s hard to be a winning company in an outperforming economy 

One indication of the competitive corporate environment is that outperforming countries 

have about twice as many big companies per trillion dollars of GDP as other emerging 

economies, just over 160 firms per $1 trillion in 2016 versus 80 firms in non‐outperforming 

peers (and 95 in high-income countries). 24 As a result, revenue growth is shared more 

widely. In high-income countries, for example, 8 percent of firms account for 80 percent of 

all big-company revenue growth. In the outperformers, that figure is 22 percent of firms. 

Contested leadership is a vital sign of the competitive environment. Less than half 

(45 percent) of firms that reached the top quintile in terms of economic profit generation 

between 2001 and 2005 managed to stay in place for a decade, according to our analysis. 

That was far less than incumbents in high-income economies, 62 percent of which stayed 

in the top quintile for the same decade. 25 This churn holds true for virtually all the sectors we 

studied and for all the outperformer countries for which data were available (Exhibit E8). 26 

The rewards for the successful companies that stay on top are substantial: the top 

10 percent of large firms in terms of value creation in the outperforming countries captured 

454 percent of the net economic profits generated by all companies. That is more than 

four times the proportion in high-income countries, where the top 10 percent captures only 

106 percent of all net economic profit. But the penalties for failure are larger, too: the bottom 

10 percent of firms in outperformer emerging economies accrues losses equivalent to 

289 percent of the total, compared with 31 percent of the respective profit pool for top large 

firms in advanced economies. 

24 

In 1995, the outperformers had almost three times as many companies per trillion dollars of GDP, but the ratio 

has come down as GDP has grown. In non‐outperforming developing economies, the number has stayed flat. 

25 

See technical appendix for details of our methodology in calculating contested leadership. 

26 

In our discussion of successful large firms in this report, we highlight the aggregate trends we found in 

our research but do not systematically list the companies themselves, especially given the high churn rate 

among top-quintile firms. We are also conscious that some emerging-economy firms may have high debt 

levels or may be creating economic profit largely because of market forces outside their control, for example 

commodity prices. 


Exhibit E8 

Emerging economies exhibit greater contested leadership among top firms. 

Distribution of trajectory for top quintile economic profit generators over 10 years 1 

% (N = 48 countries and 2,284 total companies 2,3 ) 

Remain in 

the top 

quintile 

62 

Country 

Canada 

Australia 

Firms 

remaining in 

top quintile 

% 

36 

50 

45 

Country 

Malaysia 

China and 

Hong Kong 

Firms 

remaining in 

top quintile 

% 

20 

34 

Japan 

58 

South Korea 

43 

Germany 

62 

India 

60 

Drop to 

the middle 3 

quintiles 

Drop to 

the bottom 

quintile 

23 

15 

France 

Switzerland 

United 

States 

United 

Kingdom 

63 

63 

68 

76 

32 

23 

High income 

Outperformers 

1 Quintiles based on rankings within archetype by economic profit generation between 2001–05 and 2011–15. Economic profit defined as net operating profit 

less adjusted taxes (NOPLAT) – [invested capital x weighted average cost of capital]. 

2 Outperformers include China, India, Indonesia, Malaysia, Thailand, Hong Kong, Singapore, and South Korea; high-income countries include Australia, 

Austria, Belgium, Canada, Denmark, Finland, France, Germany, Israel, Italy, Japan, Netherlands, Norway, Saudi Arabia, Spain, Switzerland, United Arab 

Emirates, the United Kingdom, and the United States; non-outperformer emerging economies include Argentina, Brazil, Chile, Colombia, Czech Republic, 

Egypt, Greece, Hungary, Mexico, Morocco, Nigeria, Pakistan, Peru, Philippines, Poland, Portugal, Russia, Slovak Republic, South Africa, and Turkey. 

3 Publicly listed companies with more than $500 million in revenue in 2016, of which 457 were top quintile. 

SOURCE: McKinsey Strategy Practice (Beating the Odds model v20.0); McKinsey Corporate Performance Analytics; McKinsey Global Institute analysis 

The most competitive companies from emerging economies are becoming 

global players that outperform their counterparts in advanced economies 

The emerging-market firms that survive this rite of passage emerge hardened and 

formidable competitors on the global stage. They cover a wide range of sectors, with 

significant differences depending on the structure of national economies. 

ES and report 

Between 1995 and 2016, large, publicly listed companies in the outperforming countries 

grew their net income each year four to five percentage points faster than firms in other 

emerging economies. On a global level, they contributed about 40 percent of the revenue 

and net income growth of all large public companies from 2005 to 2016, even though they 

accounted for only about 25 percent of total revenue and net income in 2016. More than 120 

of these companies have joined the Fortune Global 500 list since 2000. 

The best-performing companies also outdid firms in advanced economies on a key 

performance indicator: total return to shareholders. Between 2014 and 2016, total return to 

shareholders from the top quartile of outperformer companies was 23 percent on average, 

compared with 15 percent for top-quartile firms in high-income countries and 13 percent 

in non‐outperformer emerging economies. However, return on invested capital was higher 

among companies in high-income countries, which tend to focus more on maximizing profit 

margins over revenue growth. 



15

To understand the contribution of these big companies more fully, we surveyed executives 

from more than 2,000 companies across seven countries and ten industries. Three 

characteristics stand out: 

Top firms in emerging economies devote more attention to innovation, deriving 56 percent 

of their revenue from new products and services, eight percentage points more than 

their peers in advanced economies. Many top companies take the lead in addressing 

technological and digital disruption in their industries (Exhibit E9). This, in turn, is helping 

some cities, especially in China, India, and South Korea, emerge as clusters of innovation 

as a result. The number of patents granted annually in Bangalore, Beijing, and Shanghai 

grew more than twice as fast as in Silicon Valley, the largest innovation cluster in the world. 

Individual examples of creative innovation abound. The Chinese phone manufacturer 

Transsion is one: it has become the leading brand of smart and feature phones in Africa by 

making handsets that not only are affordable but can accommodate up to four SIM cards to 

let customers in many African countries avoid the high cost of calling someone who uses a 

different mobile provider. It is now growing rapidly in India, making inroads against market 

leader Samsung in some markets just a year after launching its four brands. 27 

Second, these companies are more aggressive in their investment strategies and nimbler 

in allocating resources. 28 They invest almost twice as much as comparable businesses in 

advanced economies, measured as a ratio of capital spending to depreciation. This gap 

holds across most industries we analyzed. In India, for example, Reliance Jio, a mobile 

network operator that launched in September 2016, has already invested $30 billion in its 

fourth generation (4G) VoLTE mobile network, leapfrogging incumbents that were gradually 

transitioning out of older technologies. In less than two years of operations, the company 

has become the third-largest telecom operator in India by market share. 29 These leading 

companies are also faster in assigning resources. On average, they make important 

investment decisions six to eight weeks faster than similar companies in advanced 

economies. 30 That amounts to about 30 to 40 percent less time. 

Third, the most successful large companies in emerging economies are 27 percentage 

points more likely than their peers in high-income countries to prioritize growth outside 

their home markets—and in doing so, have become powerful global competitors. 31 The 

Thai conglomerate CP Group is one example. Focused on agribusiness, real estate, 

retail, and telecommunications, CP Group was the first foreign investor in China´s first 

special economic zone in Shenzhen in 1981; today, its Chinese businesses account for a 

significant portion of its $40 billion to $50 billion annual sales. 32 In Africa, Ethiopian Airlines 

has expanded rapidly through acquisitions, including large stakes in Malawian Airlines 

(49 percent) and Zambia Airways (45 percent), and partnerships, such as the one with 

the Guinean government to start Guinea Airlines and with ASKY Airlines in Togo. The 

27 

Writankar Mukherjee, “Chinese phone maker Transsion Holdings eyes top three slots in Indian market,” 

Economic Times, August 23, 2017, economictimes.indiatimes.com; and Li Tao, “How an unknown Chinese 

phone maker became No 3 in India by solving the oily fingers problem,” South China Morning Post, January 

12, 2018, scmp.com. 

28 

One explanation for this difference is that the ownership structure of these companies and strong family or 

state control may allow for long-term investment and scale. See Playing to win: The new global competition 

for corporate profits, McKinsey Global Institute, September 2015. 

29 

Promit Mukherjee, “Reliance lifts Jio investment above $30 billion after record year,” Reuters, April 25, 2017, 

in.reuters.com. 

30 

McKinsey 2017 Firm Survey. 

31 

Ibid. 

32 

Usanee Mongkolporn, “New Charoen Pokphand CEO unveils ‘CP 4.0’ plan,” The Nation, February 24, 2017. 


company earned $273 million in profit in 2015–16 while the African airline industry overall lost 

$900 million. 33 

Exhibit E9 

Top firms in outperformer economies are bolder, quicker, and more forceful than their peers. 

Comparison of self-reported performance and practices for top-performing firms across archetypes 1,2 

Absolute difference compared to top-performing firms from high-income economies 

N = 7 countries, 2,172 companies 3 

Innovation and digital disruption 

Investment 

Geographic 

expansion 

Innovation 

assessment 

Sales from 

new 

products 

(percentage 

points) 

Innovation 

practices 4 

Score 

(1 to 10) 

Disruption 

proactiveness 

5 

Percentage 

points 

Digital 

disruption 

response 6 

Score 

(1 to 8) 

Investment 

levels 7 

Capex to 

depreciation 

ratio 

Investment 

speed 

Number of 

weeks 

Expansion 

priority 

Markets 

outside 

home 

(percentage 

points) 

Outperformers 

vs high income 

+8 

1.3 

+33 +1.1 

+1.1 

-6.0 

+27 


vs high income 

+3 

0.8 

-6 

+0.5 +0.3 

-7.9 

+13 

High income 48 6.5 25 3.2 1.5 18.7 47 

1 Top-performing defined as top quartile of self-reported revenue growth (over past 3 years) adjusted for country and industry. 

2 All reported statistics are calculated as weighted averages across countries within archetype. 

3 Outperformers include China, India, and Indonesia; non-outperformer emerging economies include Brazil and South Africa; high income includes Germany 

and the United States. 

4 Score marks number of dimensions for which respondent answered either “Strongly agree” or “Agree” among 10 dimensions that describe the company’s 

current innovation capabilities and practices. 

5 Proactiveness measured as answering either "We have changed our longer-term corporate strategy to address the disruption” or "We initiated the 

disruption(s)” to question “Which of the following statements best describes your company’s approach to addressing the technological and digital disruptions 

that have affected your industry in the past three years?" 

6 Score marks number of “changes [made] to the strategy of individual business units…in response to technological and digital disruptions that have affected 

your industry in the past three years.” 

7 Based on financial data for large publicly listed companies with more than $500 million in annual revenue; top performing defined as top quartile in terms of 

total return to shareholders adjusted by industry. 

NOTE: Not to scale. 

SOURCE: McKinsey 2017 Firm Survey; McKinsey Corporate Performance Analytics; McKinsey Global Institute analysis 

NEW OPPORTUNITIES FOR EMERGING ECONOMIES IN CHANGING TIMES 

Global conditions are changing. Manufacturing seems to be peaking earlier than it used to 

in developing countries, for example, and cross-border trade flows have lost some of their 

dynamism since the 2008 financial crisis. With these changes come not only challenges but 

also new opportunities 

ES and 

for emerging 

reporteconomies 

in both manufacturing and services. 

33 

Ethiopian becomes strategic partner in new Malawi airlines, Ethiopian Airlines press release, July 13, 2013, 

ethiopianairlines.com; Tom Collins, “Ethiopian Airlines on the up,” African Business Magazine, August 8, 2017, 

africanbusinessmagazine.com; Abdi Latif Dahir, “How Africa’s largest airline will dominate the continent’s 

skies,” Quartz Africa, January 20, 2018, qz.com. 



17

Global trends in demographics, trade and other flows, and technology imply 

emerging markets will be the main battleground for global growth 

We highlight three fundamental changes in the global landscape that all emerging 

economies will have to navigate: changing demographics, rising global prosperity, and 

urbanization, which will influence consumption; shifting patterns of trade and other 

cross-border flows; and the increased adoption of digital and automation technologies, 

which could challenge some traditional development paths even as they potentially boost 

productivity and GDP growth. The combined effect of these trends is to heighten the 

importance of emerging markets in the global economy both as sources of demand and 

as competition. 

Demographic change is already affecting the global economy, with a decline in the 

working-age population in some countries such as Germany and Japan acting as a 

drag on growth. At the same time, we see a powerful countertrend in the form of rising 

urbanization in emerging economies, which is boosting consumption as people move to 

cities and join the burgeoning consuming class. We expect emerging economies overall to 

represent 62 percent of total consumption growth between 2015 and 2030, the equivalent 

of $15.5 trillion, with 22 percent of that coming from China alone—a country that is also 

undergoing the aging phenomenon. 34 

Growth in global trade in goods and services slowed following the 2008 financial crisis, and 

trade and migration face a political backlash in some countries. At the same time, crossborder 

digital flows have grown apace, by 147 times from 2005 to 2017, and have assumed 

a major role in global commerce. 35 Recent MGI research has shown that, for the first time 

in history, developing economies participate in more than half of global trade of goods, and 

“south-south” trade—shorthand for trade among emerging economies, even if they are 

not in the Southern Hemisphere—is growing faster than north-south or north-north trade. 

China is a significant driver of this south-south trade. As it develops, it is focusing more on 

R&D and capital-intensive manufacturing; this is creating opportunities in labor-intensive 

manufacturing for Vietnam, India, and other low-income emerging economies in recent 

times. 36 Overall, the share of goods trade among emerging markets, both south-south and 

China-south, has risen from 8 percent in 1995 to 20 percent in 2016 (Exhibit E10). 

A digital revolution is also unfolding. Recent rapid advances in automation and artificial 

intelligence could give a much-needed boost to productivity and per capita GDP growth 

globally, helping counter the demographic changes noted above. We estimate that 

automation has the potential to increase productivity in developing economies by 0.8 to 

1.2 percentage points a year between 2015 and 2030. 37 Digital technologies have already 

enabled new business models and opened new markets. In Kenya, for example, M-Pesa 

allows mobile money transfers, while in Indonesia, Go-Jek, a motorcycle-hailing application, 

has opened new frontiers in transportation using technology. 

While many jobs will be displaced by adoption of the new technologies in the workplace, 

our research suggests that enough new work will likely be created, especially in emerging 

economies, to offset those jobs lost. Jobs of the future including in emerging economies will 

nonetheless require new skills and higher educational attainment than today’s jobs, posing 

34 

Urban world: The global consumers to watch, McKinsey Global Institute, March 2016, on McKinsey.com. 

35 

McKinsey Global Flows database 2.0. 

36 

China’s share of emerging economies’ labor-intensive manufactured exports rose from 33 percent in 2000 

to 56 percent in 2014, but declined to 53 percent in 2016, while its share of emerging economies’ R&D and 

capital-intensive manufacturing increased. 

37 

This estimate is based on a scenario for the pace of automation adoption in the midpoint of our range, 

between the fastest and the slowest adoption outlined in our January 2017 automation report and 

subsequently updated. A future that works: Automation, employment, and productivity, McKinsey Global 

Institute, January 2017, on McKinsey.com. 


a significant training and retraining challenge to governments, educational institutions, 

and companies. 38 

Exhibit E10 

The share of goods trade among emerging markets (south-south and China-south) increased from 8 percent in 1995 

to 20 percent in 2016. 

Goods trade by development status 1 

%; $ trillion (current $) 

100% = 5 7 11 15 16 

5 6 8 10 10 

3 

4 

8 

6 

9 

9 10 

12 

29 

30 

31 

14 

16 

South-south 

China-south 

China-north 

Change in value of 

trade, 1996–2016 

Multiple 

6x 

11x 

6x 

32 

30 

South-north 

3x 

55 

51 

43 

35 33 

North-north 

2x 

1995 2000 05 

10 

2016 

1 Global imports of goods; north and south defined as developed and emerging markets respectively. 


SOURCE: UNCTAD; McKinsey Global Institute analysis 

Manufacturing has continued strong growth opportunities 

Manufacturing has been a powerful engine of economic growth and employment in 

outperforming economies over the past three decades, and has tended to follow a similar 

pattern: its share of employment eventually peaks and starts to decline, at which point 

the service sector takes over as leading job creator. Researchers recently found that this 

peak is occurring earlier and earlier in the development process, a phenomenon that Dani 

Rodrik, an economist at Harvard University, has dubbed “premature deindustrialization.” 39 

This phenomenon ES and complicates report but may not frustrate developing economies’ ambitions; 

we find that manufacturing may still have room to grow, especially in low-income countries, 

and it can remain a source of job creation, especially where low wages and a strategic 

location make a country an attractive destination for garment makers and other laborintensive 

manufacturers. 

38 

See Jobs lost, jobs gained: Workforce transitions in a time of automation, McKinsey Global Institute, 

December 2017, on McKinsey.com; Skill shift: Automation and the future of the workforce, McKinsey Global 

Institute, May 2018, on McKinsey.com. 

39 

Dani Rodrik, “Premature deindustrialization,” Journal of Economic Growth, March 2016, Volume 21, 

Number 1, pp.1–33. 



19

Our analysis shows that more than 20 countries can still increase the share of employment 

and value-added of manufacturing sectors in the economy (Exhibit E11). Some developing 

economies, for example, are benefiting from China’s shift away from the manufacture and 

export of labor-intensive goods. In Bangladesh, manufacturing’s contribution to GDP rose 

to 22 percent from 16 percent between 2005 and 2016, and its share of the labor force 

increased to 14 percent from 11 percent. Vietnam posted similar gains, with manufacturing’s 

share of GDP climbing to 21 percent from 16 percent from 2009 to 2016. 40 Countries, 

especially those with relatively lower levels of manufacturing share to begin with, can 

generate manufacturing-led growth, provided they focus on creating mechanisms to help 

businesses to compete. 

Much of that opportunity is likely to come from growing consumer demand in developing 

economies as incomes increase. Indeed, China and India’s growth in imports of 

manufactured goods to 2030 could surpass the import growth registered by the 

United States and Western Europe in the 1980s and 1990s, according to our estimate. 

Manufacturing does not just create jobs and growth in manufacturing-related sectors, 

but has a broader impact on productivity and employment in the economy. An illustrative 

analysis of manufacturing and services in five emerging economies—Bangladesh, Ethiopia, 

India, Mexico, and Vietnam—suggests that, including these induced effects, manufacturing 

has a significant multiplier effect on employment of more than five times, compared with 

three times for services. The multiplier effect for output is about 2.3 times, compared with 

1.9 times for services. 

A closer look at three industry sectors by way of example highlights some of the 

growth opportunities. 

• Textiles and apparel could grow annually at 4 percent until 2030, double the rate since 

1995. 41 Just five economies—Bangladesh, China, Indonesia, Turkey, and Vietnam—are 

responsible for 51 percent of global growth in exports of textiles and apparel in the past 

five years. 

• Electronics and electrical equipment has grown at 5 percent per year since 1995 

and could maintain that pace at least until 2030, with developing economies’ share of 

global value added rising to 65 percent in 2030 from 52 percent in 2016. 42 This sector 

is particularly effective at boosting technology adoption and higher productivity. In 

Vietnam, for example, global players including Foxconn, Intel, Samsung, and Wintek 

have invested more than $15 billion since 2010 to set up production facilities and build 

partnerships with local parts manufacturers. 43 

• The automotive industry presents another opportunity, as the focus of global 

production moves to emerging economies. Some 46 percent of all global growth in 

exports since 2011 came from five emerging economies: China, the Czech Republic, 

Hungary, Mexico, and the Slovak Republic. 

40 

World Input-Output Database Socioeconomic Accounts 2016. 

41 

Estimates of consumption by IHS Markit. Consumption measured in total merchandise value. 

42 

Estimates from IHS Markit. 

43 

Based on data from Vietnam Electronic Industries Association and Aranca. 


Exhibit E11 

Manufacturing can remain an important source of employment and growth for 

low-income economies. 

SIMULATION 

Change in employment and value added in manufacturing in productivity boost scenario, 2015–30 

GDP per capita, 2015 (constant 2010 $) 

20,000 

Potential change in share of value added 

Percentage points 

3.0 

Growing 

manufacturing 

productivity 

Growing manufacturing 

sector employment and 

productivity 

Nepal 

2.5 

2.0 

Pakistan 

Kenya 

Senegal 

Bangladesh 

Rwanda 

Uganda 

Ethiopia 

Zimbabwe 

Tanzania 

1.5 

1.0 

Zambia Cameroon 

There is still 

Ghana 

Mozambique opportunity for 

Philippines Honduras Nicaragua 

growth through 

India 

manufacturing, 

especially for 

Morocco 

countries with lower 

Nigeria Vietnam 

GDP per capita 

Guatemala 

Peru 

South 

Dominican 

Africa 

Republic 

Indonesia 

0.5 

Colombia China 

Argentina 

Thailand 

Romania 

Mexico 

Malaysia 

Brazil 

Turkey Russia 

Saudi Arabia 

0 Chile Australia Singapore 

Italy 

South Korea 

France 

Spain 

Japan United 

Germany 

United Kingdom 

-0.5 

States 

Lower contribution 

from manufacturing 

-1.0 

-2.5 -2.0 -1.5 -1.0 -0.5 0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 

4.0 

Potential change in share of employment 

Percentage points 

SOURCE: Groningen Growth and Development Centre; McKinsey Global Growth Model; McKinsey Global Institute analysis 

ES and report 



21

Services can create jobs and open productivity-growth opportunities as the 

relative contribution from manufacturing declines 

Services account for more than 60 percent of GDP and more than half the jobs in emerging 

economies, but in most countries the service sector has not historically been a significant 

contributor to productivity growth. That is now changing, partly thanks to technology, which 

enables providers ranging from call-center workers to radiologists to more easily compete 

around the world. The share of services as a proportion of total global exports has risen 

from 19 percent in 1995 to 24 percent today. The share of employment in services is also 

becoming more relevant at an earlier stage of development. 

It is particularly important for emerging economies to simultaneously increase productivity 

and employment in service sectors such as construction and trade because they typically 

absorb the greatest number of workers leaving agriculture work. In studying 19 emerging 

economies over the past decade, we found most countries were able to lift productivity and 

employment in those sectors—though the growth was not always even or automatic. Our 

analysis of several sectors finds new opportunities for productivity growth in services. For 

example, trade in business and IT services doubled to more than $2 trillion between 2005 

and 2016, and global demand is expected to grow by 3 percent annually to 2025, with digital 

spending becoming the main driver of growth. In India, a major provider, IT and business 

process revenue has expanded at 9 percent annually since 2012, while employment has 

grown by more than 6 percent. 44 Productivity has risen 4 percent annually since 2000. 45 

In retail, we see potential productivity growth across emerging economies of more than 

5 percent, with almost 60 percent of that potential achieved by shifting more sales to 

hyperstores, supermarkets, big-box stores, and other modern retail formats that are 

typically at least three times as productive as small-scale traditional stores. Online retailing 

is even more productive, and in countries with substantial e‐commerce penetration, such 

as Brazil, India, and Indonesia, productivity in the retail sector has grown by more than 

5 percent per year since 2000. 46 Exhibit E12 highlights the productivity opportunity for 

emerging economies in some sectors, both in manufacturing and in services. 

44 

Jobs and skills: The imperative to reinvent and disrupt, NASSCOM, May 2017; Indian IT-BPM industry—FY 

2013 performance review, FY 2014 outlook, NASSCOM, February 2013. 

45 

World Input-Output Database Socioeconomic Accounts 2016. 

46 

Ibid. 


Exhibit E12 

Emerging economy firms have opportunities to increase productivity in manufacturing and services. 

Typical contributions in emerging economies 

Value added (% of GDP) 

Employment (% of total jobs) 

Productivity per sector (annual value added per employee, 

average 2010–14, $ thousand, constant 2010 $) 

Outperformers Non-outperformers High income 

Average contribution to GDP 

Manufacturing 

Food and 

beverages 

1–7 1–5 

Manufacture 

of metals 

2–5 1–5 

Electrical 

equipment and 

electronics 

1–6 1–4 

India 

Turkey 

Brazil 

Indonesia Mexico 

South 

Korea 

Japan 

United 

States 

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 

Indonesia 

India 

Romania 

Russia 

Brazil 

Mexico 

United 

States 

South 

Korea 

-10 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 

Turkey 

Bulgaria Romania 

Mexico 

India 

Brazil 

Indonesia 

Germany 

South 

Korea 

United 

States 

-20 0 20 40 60 80 100 120 140 160 180 200 220 

Auto and 

transport 

India 

Portugal 

Brazil 

Indonesia 

Mexico 

South 

Korea 

Japan 

Germany 

United 

States 

Services 

1–5 1–4 

Textiles and 

apparel 

1–5 1–9 

Machinery and 

equipment 

An $11 trillion boost awaits the global economy if all emerging economies 

match the historical productivity growth of outperformers 

Productivity growth will determine the pace at which incomes—and consumption—continue 

to rise in developing economies. Consensus forecasts that serve as our baseline anticipate 

that the 53 developing economies that are either middling or underperforming may increase 

their productivity growth to 1.3 percent per year on average between 2015 and 2030. 47 

What would happen if these economies could match the historical productivity gains of 

the 18 outperformers? It would require them to lift their annual average productivity growth 

from the 1.4 percent rate between 2000 and 2015 to 4.1 percent, the average annual rate 

achieved by the outperformers. To estimate the impact, both for the emerging economies 

and for the global economy, we simulated this increase using a macroeconomic model. 48 

The effects are striking: for developing economies, the overall per capita GDP growth 

rate could rise to 4.6 percent. This could push their average per capita GDP more than 

50 percent above the consensus forecasts for 2030 and lift 200 million people to the 

consuming class and 140 million more people out of poverty—an increase of almost two full 

percentage points of the global population. 

How credible is such a scenario? Tripling productivity growth rates is certainly an ambitious 

goal, but the precedent has already been set: this is what the 11 recent outperformers 

achieved between 1995 and 2015 compared with the baseline period of 1980 to 1995. 

The global economy would experience a bounce, growing at an average rate of 3.5 percent 

a year, compared with consensus forecasts of 2.8 percent. That growth could directly add 

$11 trillion to global GDP by 2030. About $8 trillion of that would come directly from the 

53 hitherto middling and underperforming emerging economies. The remaining $3 trillion 

would come indirectly, as increased economic activity and income in the 53 nations affect 

global demand in advanced and outperforming emerging economies. The $11 trillion boost 

to global output amounts to roughly 10 percent of the world’s economy and would be 

equivalent to adding another China. 

GEOGRAPHIC REGIONS HAVE STRENGTHS AND WEAKNESSES IN COMMON, 

AND ALL HAVE POTENTIAL TO STRENGTHEN THEIR PRO-GROWTH CYCLES 

We analyzed the strengths and challenges of all 71 emerging economies in our sample 

by using 13 indicators of economic performance and potential that highly correlate to 

per capita GDP growth as demonstrated by the outperformers. These indicators track 

performance across a range of dimensions, including elements of productivity, income, and 

demand that contribute to the pro-growth agenda mentioned earlier. 49 A heat map of our 

findings provides a snapshot of both the strengths and the challenges of the seven regions 

(Exhibit E13). 

47 

Consensus forecasts from the Economist Intelligence Unit, IHS Economics, and Oxford Economics. 

48 

We used McKinsey & Company’s Global Growth Model to simulate the effects of the productivity increase. 

49 

The 13 indicators are: domestic savings, foreign direct investment, market capitalization of listed domestic 

companies, Global Innovation Index, government effectiveness, inflation, government health expenditure, 

government education expenditure, household income, corporate income, infrastructure investment, exports, 

and connectedness to the global economy through cross-border flows of trade in goods, services, finance, 

people, and digital. 


Exhibit E13 

Our heat map analysis on 13 growth metrics highlights strong regional patterns. 

Performance within emerging markets (quartile) 1 

First 

Second 

Third 

Fourth 

Description 

Economic 

performance 

Productivity 

drivers 

► Regions 2 

% of emerging market 

population 

% of emerging market 

GDP 

Average GDP per capita 

Real $ 2016 

Average GDP per capita 

growth 

CAGR, 1996–2016, % 

Domestic savings 

CAGR, 1996–2016, % 

Government effectiveness 

Change, 1996–2016, % 

Market capitalization of 

listed domestic companies 

CAGR, 1996–2016, % 

Global Innovation Index 

Rank change, 2013–16 

Central 

Asia 

East and 

Southeast 

Asia 

South 

Asia 

Central 

and 

Eastern 

Europe 

Sub- 

Saharan 

Africa 

Latin 

America 

Middle 

East and 

North 

Africa 

1 36 30 7 12 10 5 

1 47 10 16 5 19 2 

5,283 12,604 1,703 12,644 1,751 6,885 4,461 

5.5 4.4 3.7 3.1 2.5 1.9 1.6 

Income and 

demand drivers 

Foreign direct investment 

CAGR, 1996–2016, % 

Inflation 

Average, 2000–16 

Government health 

expenditure 

CAGR, 2000–15, % 

Government education 

expenditure 

CAGR, 1996–2016, % 

Household income 

CAGR, 1996–2014, % 

Corporate income 

CAGR, 1996–2014, % 

Exports 

CAGR, 1996–2016, % 

MGI Connectedness Index 

Score, 2016 

Infrastructure investment 

CAGR, 2000–15, % 

1 Represents which quartile of the 71 economies the average of the archetype would fall in. For example, a green-colored square means the average of this 

archetype has a similar level in an indicator as top-quartile countries. 

2 Central Asia: Azerbaijan, Kazakhstan, Kyrgyz Republic, Turkmenistan, and Uzbekistan. East and Southeast Asia: Cambodia, China, Hong Kong, Indonesia, 

South Korea, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam. South Asia: Bangladesh, India, Nepal, Pakistan, and Sri Lanka. 

Central and Eastern Europe: Belarus, Bulgaria, Czech Republic, Greece, Hungary, Poland, Romania, Russian Federation, Serbia, Slovak Republic, Turkey, 

and Ukraine. Sub-Saharan Africa: Angola, Cameroon, Côte d’Ivoire, Ethiopia, Ghana, Kenya, Mozambique, Nigeria, Rwanda, Senegal, South Africa, 

Tanzania, Uganda, Zambia, and Zimbabwe. Latin America: Argentina, Bolivia, Brazil, Chile, Colombia, Dominican Republic, Ecuador, El Salvador, 

Guatemala, Honduras, Mexico, Nicaragua, Paraguay, Peru, and Venezuela. Middle East and North Africa: Algeria, Egypt, Iran, Jordan, Lebanon, and 

Morocco. 

SOURCE: World Bank; OECD; IMF; WIPO; INSEAD; WFE; WHO; UNESCO; McKinsey Global Growth model; Global Insight; McKinsey Global Institute 

analysis 



25

One insight of this analysis is that countries within geographic regions have more in 

common with each other than clusters defined by income level, growth archetype, or 

recent growth experience. Most outperformers are from Asia, for example, whereas none 

is from Latin America, the Middle East, or North Africa. Our analysis suggests that most 

countries still need to fix many elements of their economies in order to strengthen a progrowth 

cycle. Even the best-performing region, East and Southeast Asia, faces challenges 

to sustain its growth. Some of the recent outperformers, including Azerbaijan, Belarus, and 

Kazakhstan, face slowing growth, partly because of the decline in resource prices in that 

period. Conversely, even in regions that have produced few outperformers, there are still 

standout countries. 

• Central Asian economies are highly dependent on resources but have avoided the 

“resource curse” so far, thanks to high growth rates of savings and income, as well 

as improved government effectiveness. Domestic investment rates in Azerbaijan, 

Kazakhstan, and Turkmenistan, for example, average 32 percent of GDP in 2010–15, 

compared with 16 percent in Nigeria, another resource-dependent economy. While the 

region accounts for just 1 percent of the GDP of all 71 emerging economies in 2016, 

four of the five countries rank among the recent outperformers. Although growth has 

been slowing in Azerbaijan and Kazakhstan, momentum continues to be strong in 

Turkmenistan and Uzbekistan. 

• East and Southeast Asia has been the best-performing region, lifted by the soaring 

economies of all seven long-term outperformers as well as four recent outperformers 

(Cambodia, Laos, Myanmar, and Vietnam). This is also the biggest economic region, 

accounting for 47 percent of the GDP of the emerging economies we examined. 

Sustaining growth remains challenging, nonetheless: some long-term outperformers in 

this region including Singapore and South Korea have experienced decelerating GDP 

growth in the past few years, given lagging rates of productivity improvement. More 

recent outperformers such as Cambodia and Vietnam are still “works in progress” and 

have varied shortcomings across productivity, income, and demand. Most countries 

in the region will need to ensure broad-based income growth and address rising 

income inequality. 

• With mainly low- and lower middle-income countries, South Asia needs greater 

global connectedness and export diversity. For now, only India ranks among the 

outperformers. Exports contribute on average 18 percent of GDP in 2010–15, less 

than one-third the average for outperformers, and many countries in the region export 

mainly textiles and apparel. South Asia has significant inequality in part because a high 

percentage of its labor force still works in agriculture, though countries in the region 

are transitioning people into more productive sectors at a high rate. The region has an 

opportunity to improve the quality of its institutions and bureaucracy and could use 

its experience in information technology consulting services to boost the local digital 

economy and technology adoption in companies. 

• Central and Eastern Europe accounts for 16 percent of the GDP of the 71 emerging 

economies, and GDP per capita, at more than $12,600, is the highest of all regions, 

yet only one of the 12 countries—Belarus—ranks as a recent outperformer. Capital 

investment in the region is low, and growth in wages and household consumption is 

sluggish. Countries in the region could reduce dependence on foreign direct investment 

by boosting domestic savings and tapping their supply of highly educated yet 

affordable workers to build knowledge-intensive services that may benefit from coming 

technological disruption. Some countries, such as Poland, have attracted companies 

from Western Europe and the United States, including Hewlett-Packard, which set up 

back-office and support operations. The region now employs nearly 300,000 people 


in outsourcing and offshoring work. 50 However, total employment in Belarus, Bulgaria, 

Greece, Romania, and Ukraine has declined 1 percent annually or more since 2010, 

while remaining almost flat in Russia and the Czech Republic. 51 

• Sub-Saharan Africa is the region with the second-lowest average per capita GDP, 

at about $1,750, but several countries have made great strides in recent years. Labor 

productivity growth at 2.5 percent annually between 2010 and 2015—the highest rate 

outside Asia—and government effectiveness registered significant improvement in 

countries such as Rwanda and Côte d’Ivoire. For now, only one of the 15 countries— 

Ethiopia—ranks among the recent outperformers. In general, connectedness to other 

regions is poor and exports from countries in sub-Saharan Africa lack diversity. For 

example, more than 90 percent of goods exported from Nigeria and Angola are oilrelated. 

Improving infrastructure and continuing to build out government effectiveness to 

attract foreign investment remain important opportunities for the region. 

• Latin America accounts for almost 20 percent of the GDP of the 71 emerging 

economies, but it trails in all dimensions of the pro-growth agenda. All countries are in 

the bottom half of annual productivity growth rankings, without a single country of the 

15 we analyzed breaking through into the outperformers’ ranks. Stringent regulation, 

low savings and income growth, and fragmented rule of law are major obstacles. While 

the region has produced globally competitive companies—including Mexico’s Grupo 

Alfa, Brazil’s Embraer, and Argentina’s Tenaris—companies can be fettered by restrictive 

labor laws and regulations. 52 Most countries in the region also have low savings and 

investment rates, and room to improve income inequality. On average, as of 2015, 

Latin America had the highest inequality of any region, as measured by the average 

Gini coefficient. 53 

• Middle East and North Africa countries also have no outperformers. 54 Indeed, the 

region on average has negative total factor productivity, limited income and demand 

growth, and the lowest improvement in education spending. A lack of economic diversity 

hobbles some countries in the region—about 95 percent of Algeria’s exports of goods 

and more than 60 percent of Iran’s are oil-based products, for example. 55 It is also a 

region with few large, publicly listed companies. This region was the only one where 

emerging economies’ per capita GDP declined in recent years, falling 0.6 percent per 

year from 2010 to 2015, while labor productivity grew only 0.9 percent annually in the 

same period. Recent MGI research found that 73 percent of GDP growth in the region 

from 2000 to 2015 was explained by an expanding workforce, while only 27 percent 

was attributable to labor productivity growth. 56 The region’s policy makers could 

improve business productivity by encouraging the adoption of technology in production, 

stimulating consumption, and making bureaucracies more professional. 

50 

A new dawn: Reigniting growth in Central and Eastern Europe, McKinsey Global Institute, December 2013, on 

McKinsey.com. 

51 

From the Conference Board Total Economy Database, conference-board.org. 

52 

Where will Latin America’s growth come from? McKinsey Global Institute, April 2017, on McKinsey.com. 

53 

The Gini coefficient measures income distribution in a country. The higher the score, the higher the levels of 

inequality. Data collected between 2010 and 2015. 

54 

Saudi Arabia and United Arab Emirates are classified by the World Bank as high-income economies and thus 

are not included in our analysis here. 

55 

The Atlas of Economic Complexity, Harvard University, Center for International Development, 2018, 

atlas.cid.harvard.edu. 

56 

Where will Latin America’s growth come from? McKinsey Global Institute, April 2017, on McKinsey.com. 



27

Looking to the next outperformers 

Across this varied global landscape, we identify individual countries that are aspiring 

newcomers to the list of outperformers. These are countries that are putting in place and 

strengthening their economic fundamentals, in accordance with the elements of our progrowth 

agenda, as mapped in the heat map analysis. Some of them are already achieving 

GDP per capita growth that exceeded 3.5 percent in 2011 to 2016. Exhibit E14 calls out 

a number of these potential future outperformers, which fall into three groupings. Five 

countries—Bangladesh, Bolivia, the Philippines, Rwanda, and Sri Lanka—exceeded the 

3.5 percent annual per capita growth rate in 2011 to 2016 and also rank in the top 25 percent 

of our performance index. A second cluster of countries consists of Kenya, Mozambique, 

Paraguay, Senegal, and Tanzania. These countries have moved into the top quartile of our 

pro-growth performance scores, reflecting improvement in key productivity, income, and 

demand drivers, but have not yet achieved consistent 3.5 percent GDP per capita growth. 

Two other countries achieve the 3.5 percent GDP growth benchmark, but their pro-growth 

performance is less exceptional, and puts them in the second quartile. They are Côte 

d’Ivoire and Dominican Republic. 

Exhibit E14 

Countries that achieved high GDP per capita growth and strong momentum on fundamental indicators since 2011 

have the potential to join the next wave of outperformers. 

GDP per capita 

CAGR 2011–16, % 

9 

8 

7 

6 

Countries with top quartile 

heat map scores and top 

quartile growth rates 

1 

Côte d’Ivoire 

5 

4 

3 

2 

1 

0 

1 

2 

3 

Bangladesh 

Philippines 

Sri Lanka 

Rwanda 

Dominican 

Republic 

Bolivia 

Tanzania Mozambique 

Kenya 

Peru 

Paraguay 

Colombia 

Pakistan 

Cameroon 

Senegal 

Morocco 

Algeria 

Ecuador 

Nigeria 

2 Angola 

3 

Countries with top quartile 

heat map scores but lower 

growth, indicating that they 

can leverage their strong 

fundamentals to achieve 

higher growth rates 

Countries with second 

quartile heat map scores, 

indicating that they need 

to further strengthen 

fundamental drivers to 

sustain and/or improve 

growth rates 

3.5% 

4 

Quartile 1 

Quartile 2 

Quartile 3 

Quartile 4 

Heat map score, 2011–16 

NOTE: Heat map score is based on each country's performance across the 13 drivers of growth. Results are normalized for each indicator and summed with a 

weight based on the indicator's simple correlation to GDP per capita growth. Quartiles represent where the total country score falls. Overall correlation 

between heat map scores and GDP per capita growth is 0.8. 

SOURCE: McKinsey Global Institute analysis 


••• 

Developing economies can continue to be engines of global economic growth well into the 

future, lifting many more millions of people out of poverty, expanding the middle class, and 

boosting global GDP growth. To realize these potential benefits, our research suggests, 

will require policy makers to hew to a pro-growth agenda based on boosting productivity, 

income, and demand, as well as on the expansion of a vibrant private sector, characterized 

by highly competitive firms that cut their teeth in domestic competition before becoming 

global players. That combination, which has proved so successful for the outperformers 

examined in this report, will likely remain key elements for future development, in times of 

change. The rise of automation and shifting trade patterns, among other trends, present 

new opportunities, with potentially big rewards for those sufficiently flexible to harness them. 

The 18 outperformers have blazed the trail. Now it is the turn of other developing countries— 

and advanced economies—to learn from that experience and keep the momentum going 

(Exhibit 15). The global economy, and millions of people who still live in poverty, will be more 

prosperous as a result. 



29

9/26/2018 5-Hour Rule: If you’re not spending 5 hours per week learning, you’re being irresponsible 

Get started 

HOME THE MISSION DAILY IT VISIONARIES PODCASTS TOP STORIES NEWSLETTER STUDIOS SUB 

Michael Simmons in The Mission 

Learning How To Learn Speaker & Teacher / Serial Entrepreneur / Bestselling Author / 

Forbes, Fortune, Time, HBR Contributor / Site: http://t.co/T32xDLUBLJ 

Oct 12, 2017 · 10 min 

5-Hour Rule: If you’re not spending 5 hours per 

week learning, you’re being irresponsible 

Photo credit from left to right: Pete Souza, gatesnotes.com, Wikipedia Commons 

“In my whole life, I have known no wise people (over a broad subject matter 

area) who didn’t read all the time — none. Zero.” — Charlie Munger, Self-made 

billionaire & Warren Bu ett’s longtime business partner 

Why did the busiest person in the world, former president Barack Obama, read an 

hour a day while in o ce? 

https://medium.com/amp/p/791c3f18f5e6 1/9


Why has the best investor in history, Warren Bu ett, invested 80% of his time in 

Get started 

reading and thinking throughout his career? 

Why has the world’s richest person, Bill Gates, read a book a week during his 

career? And why has he taken a yearly two-week reading vacation throughout his 

entire career? 

Why do the world’s smartest and busiest people nd one hour a day for deliberate 

learning (the 5-hour rule), while others make excuses about how busy they are? 

What do they see that others don’t? 

The answer is simple: Learning is the single best investment of our time that we can 

make. Or as Benjamin Franklin said, “An investment in knowledge pays the best 

interest.” 

This insight is fundamental to succeeding in our knowledge economy, yet few 

people realize it. Luckily, once you do understand the value of knowledge, it’s 

simple to get more of it. Just dedicate yourself to constant learning. 

Knowledge is the new money 

“Intellectual capital will always trump nancial capital.” — Paul Tudor Jones, selfmade 

billionaire entrepreneur, investor, and philanthropist 

We spend our lives collecting, spending, lusting after, and worrying about money 

— in fact, when we say we “don’t have time” to learn something new, it’s usually 

because we are feverishly devoting our time to earning money, but something is 

happening right now that’s changing the relationship between money and 

knowledge. 

We are at the beginning of a period of what renowned futurist Peter Diamandis 

calls rapid demonetization, in which technology is rendering previously expensive 

products or services much cheaper — or even free. 



This chart from Diamandis’ book Abundance shows how we’ve demonetized 

Get started 

$900,000 worth of products and services you might have purchased between 1969 

and 1989. 

This demonetization will accelerate in the future. Automated vehicle eets will 

eliminate one of our biggest purchases: a car. Virtual reality will make expensive 

experiences, such as going to a concert or playing golf, instantly available at much 

lower cost. While the di erence between reality and virtual reality is almost 

incomparable at the moment, the rate of improvement of VR is exponential. 

While education and health care costs have risen, innovation in these elds will 

likely lead to eventual demonetization as well. Many higher educational 

institutions, for example, have legacy costs to support multiple layers of hierarchy 

and to upkeep their campuses. Newer institutions are nding ways to dramatically 

lower costs by o ering their services exclusively online, focusing only on training 

for in-demand, high-paying skills, or having employers who recruit students 

subsidize the cost of tuition. 

Finally, new devices and technologies, such as CRISPR, the XPrize Tricorder, better 

diagnostics via arti cial intelligence, and reduced cost of genomic sequencing will 

revolutionize the healthcare system. These technologies and other ones like them 

will dramatically lower the average cost of healthcare by focusing on prevention 

rather than cure and management. 



While goods and services are becoming demonetized, knowledge is becoming 

Get started 

increasingly valuable. 

“The central event of the twentieth century is the overthrow of matter. In technology, 

economics, and the politics of nations, wealth in the form of physical resources is 

steadily declining in value and signi cance. The powers of mind are everywhere 

ascendant over the brute force of things.” —George Gilder (technology thinker) 

Perhaps the best example of the rising value of certain forms of knowledge is the 

self-driving car industry. Sebastian Thrun, founder of Google X and Google’s selfdriving 

car team, gives the example of Uber paying $700 million for Otto, a sixmonth-old 

company with 70 employees, and of GM spending $1 billion on their 

acquisition of Cruise. He concludes that in this industry, “The going rate for talent 

these days is $10 million.” 

That’s $10 million per skilled worker, and while that’s the most stunning example, 

it’s not just true for incredibly rare and lucrative technical skills. People who 

identify skills needed for future jobs — e.g., data analyst, product designer, 

physical therapist — and quickly learn them are poised to win. 

Those who work really hard throughout their career but don’t take time out of their 

schedule to constantly learn will be the new “at-risk” group. They risk remaining 

stuck on the bottom rung of global competition, and they risk losing their jobs to 

automation, just as blue-collar workers did between 2000 and 2010 when robots 

replaced 85 percent of manufacturing jobs. 

Why? 



Get started 

People at the bottom of the economic ladder are being squeezed more and 

compensated less, while those at the top have more opportunities and are paid 

more than ever before. The irony is that the problem isn’t a lack of jobs. Rather, it’s a 

lack of people with the right skills and knowledge to ll the jobs. 

An Atlantic article captures the paradox: “Employers across industries and regions 

have complained for years about a lack of skilled workers, and their complaints are 

borne out in U.S. employment data. In July [2015], the number of job postings 

reached its highest level ever, at 5.8 million, and the unemployment rate was 

comfortably below the post-World War II average. But, at the same time, over 17 

million Americans are either unemployed, not working but interested in nding 

work, or doing part-time work but aspiring to full-time work.” 

In short, we can see how at a fundamental level knowledge is gradually becoming 

its own important and unique form of currency. In other words, knowledge is the 

new money. Similar to money, knowledge often serves as a medium of exchange 

and store of value. 

But, unlike money, when you use knowledge or give it away, you don’t lose it. 

Transferring knowledge anywhere in the world is free and instant. Its value 

compounds over time faster than money. It can be converted into many things, 

including things that money can’t buy, such as authentic relationships and high 

levels of subjective well-being. It helps you accomplish your goals faster and better. 

It’s fun to acquire. It makes your brain work better. It expands your vocabulary, 

making you a better communicator. It helps you think bigger and beyond your 

circumstances. It puts your life in perspective by essentially helping you live many 

lives in one life through other people’s experiences and wisdom. 

Former President Obama perfectly explains why he was so committed to reading 

during his Presidency in a recent New York Times interview: “At a time when events 



move so quickly and so much information is transmitted,” he said, reading gave 

Get started 

him the ability to occasionally “slow down and get perspective” and “the ability to 

get in somebody else’s shoes.” These two things, he added, “have been invaluable to 

me. Whether they’ve made me a better president I can’t say. But what I can say is 

that they have allowed me to sort of maintain my balance during the course of eight 

years, because this is a place that comes at you hard and fast and doesn’t let up.” 

6 essentials skills to master the new knowledge 

economy 

“The illiterate of the 21st century will not be those who cannot read and write, but 

those who cannot learn, unlearn, and relearn.” — Alvin To 

So, how do we learn the right knowledge and have it pay o for us? The six points 

below serve as a framework to help you begin to answer this question. I also 

created an in-depth webinar on Learning How To Learn that you can watch for 

free. 

1. Identify valuable knowledge at the right time. The value of knowledge isn’t 

static. It changes as a function of how valuable other people consider it and how 

rare it is. As new technologies mature and reshape industries, there is often a 

de cit of people with the needed skills, which creates the potential for high 

compensation. Because of the high compensation, more people are quickly 

trained, and the average compensation decreases. 

2. Learn and master that knowledge quickly. Opportunity windows are 

temporary in nature. Individuals must take advantage of them when they see 

them. This means being able to learn new skills quickly. After reading thousands 

of books, I’ve found that understanding and using mental models is one of the 

most universal skills that EVERYONE should learn. It provides a strong 

foundation of knowledge that applies across every eld. So when you jump into 

a new eld, you have preexisting knowledge you can use to learn faster. 

3. Communicate the value of your skills to others. People with the same skills 

can command wildly di erent salaries and fees based on how well they’re able to 

communicate and persuade others. This ability convinces others that the skills 

you have are valuable is a “multiplier skill.” Many people spend years mastering 

an underlying technical skill and virtually no time mastering this multiplier skill. 

4. Convert knowledge into money and results. There are many ways to 

transform knowledge into value in your life. A few examples include nding and 

https://medium.com/amp/p/791c3f18f5e6 6/9 

er


getting a job that pays well, getting a raise, building a successful business, selling 

Get started 

your knowledge as a consultant, and building your reputation by becoming a 

thought leader. 

5. Learn how to nancially invest in learning to get the highest return. Each of 

us needs to nd the right “portfolio” of books, online courses, and 

certi cate/degree programs to help us achieve our goals within our budget. To 

get the right portfolio, we need to apply nancial terms — such as return on 

investment, risk management, hurdle rate, hedging, and diversi cation — to our 

thinking on knowledge investment. 

6. Master the skill of learning how to learn. Doing so exponentially increases the 

value of every hour we devote to learning (our learning rate). Our learning rate 

determines how quickly our knowledge compounds over time. Consider 

someone who reads and retains one book a week versus someone who takes 10 

days to read a book. Over the course of a year, a 30% di erence compounds to 

one person reading 85 more books. 

To shift our focus from being overly obsessed with money to a more savvy and 

realistic quest for knowledge, we need to stop thinking that we only acquire 

knowledge from 5 to 22 years old, and that then we can get a job and mentally 

coast through the rest of our lives if we work hard. To survive and thrive in this new 

era, we must constantly learn. 

Working hard is the industrial era approach to getting ahead. Learning hard is the 

knowledge economy equivalent. 

Just as we have minimum recommended dosages of vitamins, steps per day, and 

minutes of aerobic exercise for maintaining physical health, we need to be rigorous 

about the minimum dose of deliberate learning that will maintain our economic 

health. The long-term e ects of intellectual complacency are just as insidious as the 

long-term e ects of not exercising, eating well, or sleeping enough. Not learning at 

least 5 hours per week (the 5-hour rule) is the smoking of the 21st century and this 

article is the warning label. 

Don’t be lazy. Don’t make excuses. Just get it done. 

“Live as if you were to die tomorrow. Learn as if you were to live forever.” — Mahatma 

Gandhi 



Before his daughter was born, successful entrepreneur Ben Clarke focused on 

Get started 

deliberate learning every day from 6:45 a.m. to 8:30 a.m. for ve years (2,000+ 

hours), but when his daughter was born, he decided to replace his learning time 

with daddy-daughter time. This is the point at which most people would give up on 

their learning ritual. 

Instead of doing that, Ben decided to change his daily work schedule. He shortened 

the number of hours he worked on his to do list in order to make room for his 

learning ritual. Keep in mind that Ben oversees 200+ employees at his company, 

The Shipyard, and is always busy. In his words, “By working less and learning more, 

I might seem to get less done in a day, but I get dramatically more done in my year 

and in my career.” This wasn’t an easy decision by any means, but it re ects the 

type of di cult decisions that we all need to start making. Even if you’re just an 

entry-level employee, there’s no excuse. You can nd mini learning periods during 

your downtimes (commutes, lunch breaks, slow times). Even 15 minutes per day 

will add up to nearly 100 hours over a year. Time and energy should not be excuses. 

Rather, they are di cult, but overcomable challenges. By being one of the few 

people who rises to this challenge, you reap that much more in reward. 

We often believe we can’t a ord the time it takes, but the opposite is true: None of 

us can a ord not to learn. 

Learning is no longer a luxury; it’s a necessity. 

Start your learning ritual today with these three steps 

The busiest, most successful people in the world nd at least an hour to learn 

EVERY DAY. So can you! 

Just three steps are needed to create your own learning ritual: 

1. Find the time for reading and learning even if you are really busy and 

overwhelmed. 

2. Stay consistent on using that “found” time without procrastinating or falling prey 

to distraction. 

3. Increase the results you receive from each hour of learning by using proven 

hacks that help you remember and apply what you learn. 

Over the last three years, I’ve researched how top performers nd the time, stay 

consistent, and get more results. There was too much information for one article, so 



I spent dozens of hours and created a free masterclass to help you master your 

Get started 

learning ritual too! 

Sign up for the free Learning How To Learn webinar here >> 

. . . 

If you enjoyed this story, please click the

9/21/2018 Jio’s under-the-radar tech team in Texas leading its R&D | FactorDaily 

Companies (/Companies) 

Jio’s under-the-radar tech team in Texas 

leading its R&D 

Sunny Sen (https://factordaily.com/author/sunny-sen/) 

Anand Murali (https://factordaily.com/author/anand/) June 1, 2018 6 min 

( p ( p p 

jio-research-deve for y 

u=https%3A%2F%2Ffactorda 

radar%20tech%20team 

developme 

ml/&title=Jio’s%2 http 

You are the world’s fastest growing telco. Your customer base is 

more than the Russian population in less than two years of 

launch. You have the backing of a conglomerate whose core refining 

business turns in profits of over Rs 32,000 crore a year. You have 

taken outlandish bets both at backward integration (branded phones) 

https://factordaily.com/reliance-jio-research-development-team-texas-for-blockchain-ai-ml/ 1/13


and forward integration (content from music to movies to TV to 

digital). The man at your helm deeply believes in the ‘Data is new oil’ 

adage and sees voice and data services as just a gateway to 

extracting value in tomorrow’s digital economy, not a revenue spinner. 

Would you want to be held hostage to fast and tectonic changes in 

the digital and tech world? No, not if you are Mukesh Ambani, the 

richest Indian who is betting India’s largest company’s future on a wild 

bet called Reliance Jio into telecom and digital services spending over 

Rs 200,000 crore to set up an all-4G phone network. (4G is short for 

fourth generation, data rich telecom network.) 

We need to go back five years to understand Ambani’s technology bet 

in telecom when he authorised the setting up of a tech outpost in 

Frisco – near Dallas in Texas, not to be confused with San Francisco, 

California. “Telecom is massive distribution channel, and many 

businesses can fold into it,” said a Reliance executive, who is not 

authorised to speak to the media. “To make that happen, Jio needs to 

become more of a technology company than a vanilla telecom 

services company.” 

The Texan outpost has grown into a significant even if small-sized 

nerve centre for Jio, which is making investments in technologists 

and projects in artificial intelligence, machine learning, and blockchain 

technologies. “There are multiple use cases that we can build with 

these new age technologies,” said the source quoted above. 



The office that started to develop applications for Jio and forge 

partnerships with large technology and devices companies such as 

Apple, Samsung, Qualcomm, and others is slowly becoming a spout 

of new technology – and patents – that the telco plans to bring to 

India in the years to come. 

Reliance Jio Infocomm USA, the 100% subsidiary of Reliance Jio set 

up in 2013, has a second office in Palo Alto besides in Frisco, which is 

about 28 miles north of Dallas, widely considered the telecom 

software hub in the US. The Jio unit is one of four such overseas 

subsidiaries 

(http://www.ril.com/DownloadFiles/List%20of%20Subsidiaries%20and%20As 



– one more in the US. and the other two being in Singapore and the 

UK – incorporated to facilitate Jio’s international long distance 

communication business. 

Deep tech, but WIP still 

A team of 50-odd people, largely software developers, in Texas is 

readying new technologies that will shape Jio’s and indeed Reliance 

Group’s future. “Jio will become one of the (technology) vendors to 

Reliance Industries,” says a second source, who is also not authorised 

to talk to the media. 

To start with, blockchain is one new technology 

(https://telecom.economictimes.indiatimes.com/news/airtelvodafone-reliance-jio-seek-blockchain-deployments-to-createrevenue-streams/64262488) 

that Ambani is making a bet on. The 

second source said that Jio intends moving the distribution and 

management of SIM cards on to blockchain – with each card with a 

unique number put on the ledger. “It would make distribution much 

more simpler and will make it easier to manage the retail network of 

thousands of small merchants,” the source added. 

Once implemented, the blockchain use case can be replicated among 

Reliance Retail’s suppliers. “Managing supply chain is a critical aspect 

of the retail business… there are so many parts to it. Once blockchain 

is implemented, it improves transparency and reduces wastage and 

leakages,” a third source with the company told FactorDaily, adding 

this deployment could take time. 



Insiders have said that Reliance’s big plan is to integrate its retail 

network with Jio users by offering discounts and bring in tie-ups with 

consumer brand. It is already in talks with kirana stores to use the Jio 

phone network and point-of-sale machine. “We will see how to put the 

couponing system onto blockchain,” said the second source. 

The other area where work is being done is on cash flow and credit 

management. When orders are placed with suppliers, for instance, 

some money is paid as advance. Once supplies start coming in, the 

remaining payments are to be made. “Managing ledger for these 

suppliers is a lot of work. There will be more transparency with 

implementation of blockchain,” the second source said. 



And, why is this being spearheaded from the US? “Talent in 

blockchain, artificial intelligence and machine learning is scarce in 

India. Talent is (relatively) easily available in the US… We need to be 

present where the action is,” said a fourth source. 

Jio is also planning to set up a unit in Estonia, news daily Mint has 

reported 

(https://www.livemint.com/Companies/39syF2sZymyjCLerdGMKyM/Relianc 

Jio-plans-overseas-expansion-eyes-Estonia-unit.html) . Estonia is 

considered the most friendly global jurisdiction for cryptocurrency 

and blockchain companies and after the recent clampdown on such 

businesses in India, several companies are moving to Estonia 

(https://factordaily.com/india-blockchain-ico-companies-migrateestonia/) 

via the country’s e-residency program. 

Networks, people, patents 

Blockchain is not the only area where Jio is making investments – a 

large number of patents are being filed in the area of network 

technology. Jio is the only 100% 4G telco in India. This means that 

just 15% to 20% of the bandwidth is used by voice calls, leaving the 

remaining for internet browsing, online shopping, watching movies, 

listening to music, and other uses. 

Besides telecom and blockchain, the Frisco team has members with 

backgrounds in AI, machine learning, internet of things, among other 

technologies. The people heading R&D in such frontier technologies 

seem to operate out of the US subsidiary. 

A key executive is Gautham Reddy, the director of product design 

(https://www.linkedin.com/in/gautam-reddy-47a9793/) at Jio since 

July 2013. Reddy had earlier worked at Motorola, AT&T Wireless, and 

Metro PCS, where he worked as the technical head of mobile devices 

and applications. At Jio, he was involved with the Jio Chat app, the 

company’s messaging application, and did work that ended in patents 

relating to communication processing and applications that work over 



Voice over Wifi (VoWiFi) and voice over long-term evolution (VoLTE) 

service, show patents filed with the United States Patent and 

Trademark Office. There are other patents 

(https://patents.google.com/? 

inventor=Gautam+G.+Reddy&oq=Gautam+G.+Reddy) Reddy and his 

team have filed at USPTO. 

One of the US patents held by Reliance JIo’s Texas team 



Jio has been filling for patents in India, too. According to a PTI news 

report (http://www.ptinews.com/news/8839138_Reliance-Jio-files-54- 

global-patents-in-FY17) , the RIL’s annual report for 2016-17 stated 

that Reliance Jio had applied for 54 global patents. This helps given 

that patents are protection given by jurisdiction. “Patents are territorial 

in nature and companies usually file patents when they want to 

protect their product in a particular territory,” says Sai Krishna, 

managing partner of Sai Krishna and Associates, an intellectual 

property legal firm. 

The US unit is also working on AI projects. Research firm Markets and 

Markets predicts the global AI market 

(https://www.marketsandmarkets.com/PressReleases/artificialintelligence.asp%20.asp) 

at $21.46 billion this year and to top $190 

billion by 2025 – a compound annual growth rate of 36.62%. 



“That’s the future. It will help us understand what the customer wants, 

what’s their behaviour and how to serve them better,” the third source 

said. This, it is expected, will work especially in serving new user 

segments such as new rural cohorts of customers. “As the machine is 

fed with more data, and more customer interact with the platform we 

will be able to offer better services… it is learning that happens with 

time,” the first executive said. 

To get more stories like this on email, click here 

to our daily brief. 

and subscribe 

Visuals: Rajesh Subramanian 


233 

261 

127 

187 

149 

126 

1 1 

80 

153 

5 12 

(1) (4) (3) 

68 

(7) 

(1)

(% of GDP) 

(bp)

Data is average of the last four quarters

Absolute Success is Luck. 

Relative Success is Hard 

Work. 

jamesclear.com 

10 mins read 

In 1997, Warren Buffett, the famous investor and multibillionaire, 

proposed a thought experiment. 

“Imagine that it is 24 hours before you are going to be born,” he said, 

“and a genie comes to you.” 

“The genie says you can determine the rules of the society you are 

about to enter and you can design anything you want. You get to 

design the social rules, the economic rules, the governmental rules. 

And those rules are going to prevail for your lifetime and your 

children's lifetime and your grandchildren's lifetime.” 

“But there is a catch,” he said. 

“You don't know whether you're going to be born rich or poor, male 

or female, infirm or able-bodied, in the United States or Afghanistan. 

All you know is that you get to take one ball out of a barrel with 5.8 

billion balls in it. And that's you.” 

“In other words,” Buffett continues, “you're going to participate in 

what I call the Ovarian Lottery. And that is the most important thing 

that's ever going to happen to you in your life. It's going to determine 

way more than what school you go to, how hard you work, all kinds 

of things.” 

Buffett has long been a proponent for the role of luck in success. In 

his 2014 Annual Letter, he wrote, “Through dumb luck, [my business 

partner] Charlie and I were born in the United States, and we are

forever grateful for the staggering advantages this accident of birth 

has given us.” 

When explained in this way, it seems hard to deny the importance of 

luck, randomness, and good fortune in life. And indeed, these factors 

play a critical role. But let's consider a second story. 

The Story of Project 523 

In 1969, during the fourteenth year of the Vietnam War, a Chinese 

scientist named Tu Youyou was appointed the head of a secret 

research group in Beijing. The unit was known only by its code name: 

Project 523. 

China was an ally with Vietnam, and Project 523 had been created to 

develop antimalarial medications that could be administered to the 

soldiers. The disease had become a huge problem. Just as many 

Vietnamese soldiers were dying from malaria in the jungle as were 

dying in battle. 

Tu began her work by looking for clues anywhere she could find 

them. She read manuals about old folk remedies. She searched 

through ancient texts that were hundreds or thousands of years old. 

She traveled to remote regions in search of plants that might contain 

a cure. 

After months of work, her team had collected over 600 plants and 

created a list of almost 2,000 possible remedies. Slowly and 

methodically, Tu narrowed the list of potential medications down to 

380 and tested them one-by-one on lab mice. 

“This was the most challenging stage of the project,” she said. “It was 

a very laborious and tedious job, in particular when you faced one 

failure after another.” 

Hundreds of tests were run. Most of them yielded nothing. But one 

test—an extract from the sweet wormwood plant known as 

qinghao—seemed promising. Tu was excited by the possibility, but 

despite her best efforts, the plant would only occasionally produce a 

powerful antimalarial medication. It wouldn’t always work.

Her team had already been at work for two years, but she decided 

they needed to start again from the beginning. Tu reviewed every test 

and re-read each book, searching for a clue about something she 

missed. Then, magically, she stumbled on a single sentence in The 

Handbook of Prescriptions for Emergencies, an ancient Chinese text 

written over 1,500 years ago. 

The issue was heat. If the temperature was too high during the 

extraction process, the active ingredient in the sweet wormwood 

plant would be destroyed. Tu redesigned the experiment using 

solvents with a lower boiling point and, finally, she had an 

antimalarial medication that worked 100 percent of the time. 

It was a huge breakthrough, but the real work was just beginning. 

The Power of Hard Work 

With a proven medication in hand, it was now time for human trials. 

Unfortunately, there were no centers in China performing trials for 

new drugs at the time. And due to the secrecy of the project, going to 

a facility outside of the country was out of the question. 

They had reached a dead end. 

That’s when Tu volunteered to be the first human subject to try the 

medication. In one of the boldest moves in the history of medical 

science, she and two other members of Project 523 infected 

themselves with malaria and received the first doses of their new 

drug. 

It worked. 

However, despite her discovery of a breakthrough medication and her 

willingness to put her own life on the line, Tu was prevented from 

sharing her findings with the outside world. The Chinese government 

had strict rules that blocked the publishing of any scientific 

information. 

She was undeterred. Tu continued her research, eventually learning 

the chemical structure of the drug—a compound officially known as

artemisinin—and going on to develop a second antimalarial 

medication as well. 

It was not until 1978, almost a decade after she began and three years 

after the Vietnam War had ended, that Tu’s work was finally released 

to the outside world. She would have to wait until the year 2000 

before the World Health Organization would recommend the 

treatment as a defense against malaria. 

Today, the artemisinin treatment has been administered over 1 billion 

times to malaria patients. It is believed to have saved millions of 

lives. Tu Youyou is the first female Chinese citizen to receive a Nobel 

Prize, and the first Chinese person to receive the Lasker Award for 

major contributions to medical science. 

Luck or Hard Work? 

Tu Youyou was not fabulously lucky. My favorite fact about her is that 

she has no postgraduate degree, no research experience abroad, and 

no membership in any of the Chinese national academies—a feat that 

has earned her the nickname “The Professor of the Three No's”. 

But damn was she a hard worker. Persistent. Diligent. Driven. For 

decades she didn't give up and she helped save millions of lives as a 

result. Her story is a brilliant example of how important hard work 

can be in achieving success. 

Just a minute ago, it seemed reasonable that the Ovarian Lottery 

determined most of your success in life, but the idea that hard work 

matters feels just as reasonable. When you work hard you typically 

get better results than you would with less effort. While we can't 

deny the importance of luck, everyone seems to have this sense that 

hard work really does make a difference. 

So what it is? What determines success? Hard work or good fortune? 

Effort or randomness? I think we all understand both factors play a 

role, but I'd like to give you a better answer than “It depends.” 

Here are two ways I look at the issue.

Absolute Success vs. Relative Success 

One way to answer this question is to say: Luck matters more in an 

absolute sense and hard work matters more in a relative sense. 

The absolute view considers your level of success compared to 

everyone else. What makes someone the best in the world in a 

particular domain? When viewed at this level, success is nearly 

always attributable to luck. Even if you make a good initial 

choice—like Bill Gates choosing to start a computer company—you 

can’t understand all of the factors that cause world-class outcomes. 

As a general rule, the wilder the success, the more extreme and 

unlikely the circumstances that caused it. It's often a combination of 

the right genes, the right connections, the right timing, and a 

thousand other influences that nobody is wise enough to predict. 

As a general rule, the wilder the success, the more 

extreme and unlikely the circumstances that caused it. 

Then there is the relative view, which considers your level of success 

compared to those similar to you. What about the millions of people 

who received similar levels of education, grew up in similar 

neighborhoods, or were born with similar levels of genetic talent? 

These people aren't achieving the same results. The more local the 

comparison becomes, the more success is determined by hard work. 

When you compare yourself to those who have experienced similar 

levels of luck, the difference is in your habits and choices. 

Absolute success is luck. Relative success is choices and habits. 

There is an important insight that follows naturally from this 

definition: As outcomes become more extreme, the role of luck 

increases. That is, as you become more successful in an absolute 

sense, we can attribute a greater proportion of your success to luck.

As Nassim Taleb wrote in Fooled by Randomness, “Mild success can 

be explainable by skills and labor. Wild success is attributable to 

variance.” 

Both Stories are True 

Sometimes people have trouble simultaneously holding both of these 

insights. There is a tendency to discuss outcomes in either a global 

sense or a local sense. 

The absolute view is more global. What explains the difference 

between a wealthy person born in America and someone born into 

extreme poverty and living on less than $1 per day? When discussing 

success from this angle, people say things like, “How can you not see 

your privilege? Don’t you realize how much has been handed to you?” 

The relative view is more local. What explains the difference in 

results between you and everyone who went to the same school or 

grew up in the same neighborhood or worked for the same company? 

When considering success from a local viewpoint, people say things 

like, “Are you kidding me? Do you know hard I worked? Do you 

understand the choices and sacrifices I made that others didn’t? 

Dismissing my success as luck devalues the hard work I put in. If my 

success is due to luck or my environment, then how come my 

neighbors or classmates or coworkers didn’t achieve the same thing?” 

Both stories are true. It just depends on what lens you are viewing 

life through. 

The Slope of Success 

There is another way to examine the balance between luck and hard 

work, which is to consider how success is influenced across time. 

Imagine you can map success on a graph. Success is measured on the 

Y-axis. Time is measured on the X-axis. And when you are born, the 

ball you pluck out of Buffett's Ovarian Lottery determines the

y-intercept. Those who are born lucky start higher on the graph. 

Those who are born into tougher circumstances start lower. 

Here's the key: You can only control the slope of your success, not 

your initial position. 

In Atomic Habits, I wrote, “It doesn’t matter how successful or 

unsuccessful you are right now. What matters is whether your habits 

are putting you on the path toward success. You should be far more 

concerned with your current trajectory than with your current 

results.” 

You can only control the slope of your success, not 

your initial position. 

With a positive slope and enough time and effort, you may even be 

able to regain the ground that was lost due to bad luck. I thought this 

quote summarized it well: “The more time passes from the start of a 

race, the less the head-start others got matters.” 

This is not always true, of course. A severe illness can wipe out your 

health. A collapsing pension fund can ruin your retirement savings. 

Similarly, sometimes luck delivers a sustained advantage (or 

disadvantage). In fact, one study found that, if success is measured by 

wealth, then the most successful people are almost certainly those 

with moderate talent and remarkable luck. 

In any case, it is impossible to divorce the two. They both matter and 

hard work often plays a more important role as time goes on. 

This is true not only for overcoming bad luck, but also for capitalizing 

on good luck. Bill Gates might have been incredibly fortunate to start 

Microsoft at the right time in history, but without decades of hard 

work, the opportunity would have been wasted. Time erodes every 

advantage. At some point, good luck requires hard work if success is 

to be sustained.

How to Get Luck on Your Side 

By definition, luck is out of your control. Even so, it is useful to 

understand the role it plays and how it works so you can prepare for 

when fortune (or misfortune) comes your way. 

In his fantastic talk, You and Your Research, the mathematician and 

computer engineer Richard Hamming summarized what it takes to do 

great work by saying, “There is indeed an element of luck, and no, 

there isn't. The prepared mind sooner or later finds something 

important and does it. So yes, it is luck. The particular thing you do is 

luck, but that you do something is not.” 

You can increase your surface area for good luck by taking action. The 

forager who explores widely will find lots of useless terrain, but is 

also more likely to stumble across a bountiful berry patch than the 

person who stays home. Similarly, the person who works hard, 

pursues opportunity, and tries more things is more likely to stumble 

across a lucky break than the person who waits. Gary Player, the 

famous golfer and winner of nine major championships, has said, 

“The harder I practice, the luckier I get.” 

In the end, we cannot control our luck—good or bad—but we can 

control our effort and preparation. Luck smiles on us all from time to 

time. And when it does, the way to honor your good fortune is to 

work hard and make the most of it. 

Footnotes 

1. Buffett has told this story on multiple occasions. The quotes 

in this section are a combination of his versions from the 

1997 Berkshire Hathaway annual shareholders meeting and 

a speech he gave to students at the University of Florida in 

1998. The quotes have been lightly edited for clarity. Also, 

I'd like to thank J.D. Roth as I originally discovered this 

story through his site, Get Rich Slowly. 

2. 5.8 billion was the number of people in the world in 1997. 

Today, that bucket would contain over 7.6 billion balls.

3. I believe Buffett is paraphrasing a moral theory known as 

the “Veil of Ignorance” and originally proposed by the 

philosopher John Rawls. Buffett (and Rawls) use this 

thought experiment as a way to discuss what the types of 

social systems we should build in society. Buffett finishes by 

saying, “Now, what kind of world do you want to design? 

You're going to want a system that does not leave behind 

the person who accidentally got the wrong ball and is not 

well-wired for this particular system.” 

4. 2014 Letter to Berkshire Shareholders by Warren Buffett. 

5. “From branch to bedside: Youyou Tu is awarded the 2011 

Lasker~DeBakey Clinical Medical Research Award for 

discovering artemisinin as a treatment for malaria” by 

Ushma S. Neill. September 12, 2011. 

6. “Chinese Scientist Wins Nobel Prize in Medicine; China 

Hails the Laureate with Reflection” by Luxiao Zou. October 

6, 2015. 

7. Tweet from @mmay3r. May 26, 2017. 

8. “Talent vs Luck: the role of randomness in success and 

failure” by Pluchino. A. E. Biondo, A. Rapisarda. 

9. This is an adaptation of a quote from Matt Ridley, “One of 

the peculiar features of history is that time always erodes 

advantage.” 

10. The same can be said for bad luck. The particular hardship 

you go through is bad luck and random, but that you 

experience some hardship is not. Life comes from everyone 

at some point. This is one reason why it is important to 

practice inversion and prepare for hardship even though 

you do not know which form it will take. 

11. I believe this idea of “increasing your surface area for luck” 

originally came from The Startup of You by Ben Casnocha 

and Reid Hoffman, but I heard about it through Greg Nance.

ECOWRAP 

‘Be the Bank of Choice for a Transforming India’ 

SEPTEMBER 21, 2018 

ISSUE NO: 48, FY19 

HOUSEHOLD LEVERAGE STILL AT LOW 

LEVELS IN INDIA 

There has been recently a lot of brouhaha over increasing 

household leverage in India. However, such fears are largely 

unsubstantiated by hard facts. 

While, it is true that India’s gross domestic savings rate has 

climbed to a historic high level of 36.8% in FY08 but 

thereafter declined gradually to 30.0% in FY17. However this 

decline was primarily on account of decline in physical assets 

share in HS from 56.4% in FY17 to 67.3% in FY12, while share 

of financial savings jumped. Hence to concur that household leverage 

is going up because of a decline in household savings 

misses the sectoral savings divergence. 

Meanwhile, household debt, which was the originator of global 

financial crisis in 2007 has been increasing significantly in 

developed countries but declining over the years in all most all 

developing countries, except China. India’s household debt is low 

and stagnant in last few years hovering around in the range of 

9-10% of GDP. 

The problem in India’s household debt composition is of debt 

structure. For example, non-institutional sources like landlord, 

professional money lender and friends and relatives still play an 

important role in financing household debt. As per the latest 

available data, in 2016 out of total debt to GDP ratio of 9.89%, 

non-institutional sources contributed 6.17% and remaining 

3.72% were financed by Banks, co-operative banks and other 

financial institution. 

Interestingly, in FY18, the household financial liabilities increased 

to 5.63% from 3.33%, suggesting in part opening of Jan-Dhan 

account in large scale thus changing the financial behavior of 

household in India. This will clearly mean a decline in household 

debt from non-institutional sources in coming years. 

The other good news is that during 2018 the increase in real 

household debt is muted and almost stagnant that was earlier 

rising. In 2018 (till Jun), the average real debt was Rs 8,479 

almost same as the level of 2017 (Rs 8,471) of 3.3%. This would 

mean better financial stability after all! 

SBI ECOWRAP

SBI ECOWRAP 

PHYSICAL HOUSEHOLD SAVINGS DIPS 

India’s gross domestic savings (GDS) rate has climbed to 

a historic high level of 36.8% in FY08 but 

thereafter declined gradually to 30.0% in FY17 (latest 

available). The definitions and composition of savings has 

changed with the change in base year to 2011-12 and 

comprises of 3 sectors: households savings, 

private corporations savings (financial & non-financial) 

and the Government. The decline in GDS rate is 

mainly due to the decline in household savings (HS). 

With the change in the base year to 2011-12, the 

definition of HS has changed and now defined as the sum 

of net financial savings (gross financial savings minus 

financial liabilities), savings in physical assets and 

savings in the form of valuables. 

The share of financial savings (FS) in HS has increased to 

41.5% in FY17 from 31.1% in FY12, and share of savings 

in ornaments (gold & silver) in HS has increased to 2.1% 

in FY17 from 1.6% in FY12. While, the savings in 

physical assets share in HS has been declined to 56.4% 

in FY17 from 67.3% in FY12. 

HOUSEHOLD DEBT INCREASING 

GLOBALLY 

With increasing global debt which has touched to a 

record high of $164 trillion in 2016, twice the figure of 

world GDP, is indicating that it will be harder for 

countries to respond to any kind of future crisis as their 

debt pay off ability will be limited under tightening 

finance condition. The top three borrowers in the world 

are United States, China, and Japan who accounts more 

than half of the global debt. 

Graph 1: Household Savings % to Gross Domesc Savings 

Graph 2: Composion of Household Savings 

Source: SBI Research 

Graph 3: India’s Household Debt (% of GDP) 

Graph 4: Cross-Country Household Debt (% of GDP) 

Source: SBI Research 

2

SBI ECOWRAP 

Household Debt, which was the originator of global financial crisis in 2007 has been declining over the years in 

all most all developing countries, except China. India’s household debt is low and stagnant in last few years 

hovering around in the range of 9-10%. 

In US and Japan’s household debt to GDP ratio is close to 80%, but interesting in most of the Asian economies, 

the debt to GDP ratio is less even than 20% which further suggesting that these countries are doing better 

when it comes to burdening their families with huge loans. 

COMPOSITION OF INDIA’S HOUSEHOLD DEBT 

Though India’s Household Debt remain stagnant post crisis, but if we look at the composition of debt structure, 

it is clearly visible that the non-institutional sources like landlord, professional money lender and friends 

and relatives still plays an important role in financing household debt. As per the latest available data, in 2016 

out of total debt to GDP ratio of 9.89%, non-institutional sources contributed 6.17% and remaining 3.72% financed 

by Banks, co-operative banks and other financial institution. 

Interestingly, in 2017-18, the household financial liabilities increased to 5.63% from 3.33%, suggesting better 

financial literacy and availability of institutional credit and opening of Jan-Dhan account in large scale has 

changed the financial behavior of household in India. This will mean a decline in household debt from noninstitutional 

sources in coming years. 

MEANWHILE HOUSEHOLDS NET 

FINANCIAL ASSET SURGED 

Household financial saving which is the most important 

source of funds for investment in the economy is declining 

and reduced to 6.7% in FY17 down from 8.1% in 

FY16. The share of household assets to household saving 

is also declining over the years which was 4.9% in FY10 

is now reduced to 2.8% in FY18. 

But if we compare the growth of net financial assets over 

the years, it is quite uneven ranging between 36% to – 

13% since global financial crisis. 

Post demonetization, the net financial assets of the 

household sector has increased to 17.2% in FY18 

from –8.8% in FY16 largely on account of an increase 

in households’ assets in the form of currency. 

Graph 5: Growth in Net Financial Assets 

Source: RBI; SBI Research 

3

SBI ECOWRAP 

THE GOOD THING IS THAT HOUSEHOLDS REAL DEBT IS NOW STAGNANT 

Household debt as measured by credit outstanding per credit card in India rose rapidly both in nominal and real 

terms (after being adjusted for consumer price index, or CPI inflation) during 2017. In nominal terms, the outstanding 

per credit card (average) increased from Rs 9,580 in 2016 to Rs 11,309 in 2017, a growth of whopping 

18.1%. During the same period real outstanding had also increased by 14.3%. This might, in turn, contribute 

to the building up of financial risks and make it difficult for households to manage their balance sheets. 

However, during 2018 the increase in real household debt is muted and almost stagnant. In 2018 (till Jun), the 

average real debt was Rs 8,479 almost same as the level of 2017 (Rs 8,471) though the average CPI in 2018 

(till Jun) was 4.7% much higher than the 2017 average of 3.3%. 

Graph 6: Nominal & Real Household Debt (in Rs) 

Graph 7: Real Household Debt (% YoY) 



***** 

4

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