What We Are Reading - Volume 2What We Are Reading - Volume 2.010 The enclosed 2.010 version contains...
102
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
Transcript of What We Are Reading - Volume 2What We Are Reading - Volume 2.010 The enclosed 2.010 version contains...
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
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.
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.
September 12, 2018 10:30 AM GMT
MM
MORGAN STANLEY INDIA COMPANY PRIVATE LIMITED+
Sheela RathiEquity Strategist
+9122 6118-2224
Contributors
MORGAN STANLEY INDIA COMPANY PRIVATE LIMITED+
Ridham DesaiEquity Strategist
+9122 6118-2222
MMIndia 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.
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 outper-formers 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 pro-
prietary capex survey indicates that corporates are confident on busi-ness 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 quar-ters; 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 poten-tially 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 sched-uled 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.
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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
MORGAN STANLEY RESEARCH 5
MWhy 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 win-ners.
What to do with portfolios? In an uptrending market, rotation is inevitable thus we think investors should not look to position them-selves 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 (com-pared 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 fol-lowing 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 accel-eration 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-re-lated issues. In such a market environment, when combined with our positive view on the market, it makes sense for investors to position
Buy Underperformers 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 posi-tioning 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 out-performed 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 disap-pointed 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 mat-ters 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 pre-vious 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 invest-ment 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 mar-gins 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.
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6
MExhibit 1:Divergence in performance of large and SMID indices indicate a bear market
-65%
-55%
-45%
-35%
-25%
-15%
-5%
5%
15%
25%
35%
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
12M returns gap - Nifty vs. Midcap
12M returns gap - Nifty vs. Smallcap
Source: Bloomberg, Morgan Stanley Research
Key ChartsExhibit 2:Performance concentration being reflected even in the Nifty index
52%
90%
104%
32%
13%
75%
0%
20%
40%
60%
80%
100%
120%
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Share of performance attribution of top 10 stocks as % of Nifty index perf.
Share of performance attribution of top 5 stocks as % of Nifty index perf.
Source: Company data, Morgan Stanley Research
Exhibit 3:Breadth of revenue and net profit growth within MS coverage is improving
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
20
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17
% of Cos with >10% YoY Revenue Growth
% of Cos with >10% YoY Net Profit Growth
Broad Market (1441 Companies)
Source: Capitaline, Morgan Stanley Research
Exhibit 5:India is prepping for a macro trade
5%
15%
25%
35%
45%
55%
20
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Explanatory Power of Market Effect 1Y Rolling R-
squared
Time for macro,
Oct-10
Time for macro,
Aug-07
Time for macro, Jul-05
Time for macro, Aug-03
Stock pickers'
time, Sep-04
Stock pickers' time, Jul-06
Stock pickers' time,
Jun-09
Stock pickers' time, Dec -11
Stock pickers' time, Mar-16
Time for macro
Source: RIMES, MSCI, Morgan Stanley Research
Exhibit 4:Investment rate pick - key kicker for profit growth
24%
28%
32%
36%
40%
F1
99
5
F1
99
6
F1
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7
F1
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F1
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9
F2
00
0
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F2
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F2
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F2
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0E
Investment (% GDP)
MSe
Source: CEIC, Morgan Stanley Research, E: Morgan Stanley Research estimates
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.
M
MORGAN STANLEY RESEARCH 7
M
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.
India – A Pain Trade
Exhibit 7:FPI OW positioning has been falling and is now down to decade lows
-1%
1%
3%
5%
7%
9%
Jan-00 Sep-02 May-05 Jan-08 Sep-10 May-13 Jan-16 Sep-18
India's Weight in GEM Funds* Relative toIndia's Weight in MSCI EM
* Benchmarked to MSCI EM
Source: EPFR, Morgan Stanley Research
Exhibit 9:Divergence in performance of large and SMID indices indicate a bear market
-65%
-55%
-45%
-35%
-25%
-15%
-5%
5%
15%
25%
35%
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
12M returns gap - Nifty vs. Midcap
12M returns gap - Nifty vs. Smallcap
Source: Bloomberg, Morgan Stanley Research
Exhibit 8:But in 2018 India has outperformed vs. EM
-20%
-15%
-10%
-5%
0%
5%
Jul-
16
Se
p-1
6
Nov-1
6
Ja
n-1
7
Ma
r-1
7
May-1
7
Jul-
17
Sep-1
7
Nov-1
7
Ja
n-1
8
Mar-
18
May-1
8
Ju
l-1
8
MSCI India performance relative to MSCI EM - USD
MSCI India performance relative to MSCI EM - local currency
Source: RIMES, MSCI, Morgan Stanley Research
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, direc-tional 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 10:Broad market returns collapse in 2018
18%
14%
16%
13%
10%
9%
15%
25%
16%
13%
0% 5% 10% 15% 20% 25% 30%
1 Year
3 Years
5 Years
7 Years
10 YearsNSE Mid 100 Nifty index
Source: RIMES, Bloomberg, Morgan Stanley Research
M
8
MCollapse in outperformance breadth
The second bear market trend is the fall in the breadth of outper-formance seen in the large cap index as well as the broad market index. The percentage of stocks outperforming the index has col-lapsed to levels seen in 2013. Only a third of stocks have outper-formed 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
0%
10%
20%
30%
40%
50%
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70%
-55%
-35%
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Nifty YoY performance
% of stocks outperforming the index (RS)
Source: RIMES, Bloomberg, Morgan Stanley Research
Exhibit 12:Similar trend in the broad market
-60%
-45%
-30%
-15%
0%
15%
30%
45%
60%
75%
90%
2002
2003
2004
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2012
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2014
2015
2016
2017
YoY BSE 500 index performance
% of BSE 500 stocks outperforming BSE500 Index on a 12M trailing basis
Source: RIMES, Bloomberg, Morgan Stanley Research
Exhibit 13:Top index stocks key contributors to index performance
52%
90%
104%
32%
13%
75%
0%
20%
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100%
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Share of performance attribution of top 10 stocks as % of Nifty index perf.
Share of performance attribution of top 5 stocks as % of Nifty index perf.
Source: RIMES, Bloomberg, Morgan Stanley Research
Heavy weight contribution to Nifty index rising
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.
M
MORGAN STANLEY RESEARCH 9
MExhibit 14:Investor U/W positions on large-cap stocks
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%
DMFFPI Total Institutions
Source: BSE, NSE, MSCI, Morgan Stanley Research
Exhibit 15:Concentration of Stock Performance RisingNifty 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
10
MThe 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 elusiveBroad-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 mat-ters worse for earnings. Specific events such as the global trade col-lapse 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 profit-ability 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 wit-nessed 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
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-5%
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-09
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-17
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g-1
7
Ma
r-1
8
Pvt financials PSU banks Share in broad market
profits
Source: Capitaline, Morgan Stanley Research
Exhibit 17:Return on equity below history
MSCI Sectors ROE as SD
from Avg
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%
Source: RIMES, MSCI, Morgan Stanley Research
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M
MORGAN STANLEY RESEARCH 11
MReturn ratios below historyThis absence of broad-based earnings momentum has led to a col-lapse in ROE for Indian companies. ROE across sectors has fallen below its long-term SD from average, reflecting not just the weak-ness in aggregate profitability for Indian companies but lower capacity utilization and high interest costs among companies.
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.
Exhibit 19:Surge in Nifty share of broad market profits
50%
55%
60%
65%
70%
75%
80%
85%
90%
Ma
r-0
4
No
v-0
4
Jul-
05
Ma
r-0
6
No
v-0
6
Jul-
07
Ma
r-0
8
No
v-0
8
Jul-
09
Ma
r-1
0
No
v-1
0
Jul-
11
Ma
r-1
2
No
v-1
2
Jul-
13
Ma
r-1
4
No
v-1
4
Jul-
15
Ma
r-1
6
No
v-1
6
Jul-
17
Ma
r-1
8
Nifty as % of broad market
profits (4Q trailing profits)
Source: Company data, Capitaline, Morgan Stanley Research
Exhibit 18:Large companies as well as stocks dominate in share at the aggregate levels
20%
25%
30%
35%
40%
45%
30%
35%
40%
45%
50%
55%
Se
p-0
3
Ma
r-0
4
Se
p-0
4
Ma
r-0
5
Se
p-0
5
Ma
r-0
6
Se
p-0
6
Ma
r-0
7
Se
p-0
7
Ma
r-0
8
Se
p-0
8
Ma
r-0
9
Se
p-0
9
Ma
r-1
0
Se
p-1
0
Ma
r-1
1
Se
p-1
1
Ma
r-1
2
Se
p-1
2
Ma
r-1
3
Se
p-1
3
Ma
r-1
4
Se
p-1
4
Ma
r-1
5
Se
p-1
5
Ma
r-1
6
Se
p-1
6
Ma
r-1
7
Se
p-1
7
Ma
r-1
8
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)
Source: Capitaline, Morgan Stanley Research
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12
M
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.
Mean reversion on the cards
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.
We think Performance is set to Broaden OutExhibit 20:Profit growth reflects deeper cycles vs. GDP
100
1000
FY
19
94
FY
19
96
FY
19
98
FY
20
00
FY
20
02
FY
20
04
FY
20
06
FY
20
08
FY
20
10
FY
20
12
FY
20
14
FY
20
16
FY
20
18
Nominal GDP
Cumulative broad market
earnings growth
on log scale
Source: Capitaline, CEIC Morgan Stanley Research
Exhibit 21:Profit to GDP: Mean reversion likely
0%
1%
2%
3%
4%
5%
6%
7%
8%
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
Corporateprofits to GDP
Source: CMIE, CEIC Morgan Stanley Research
Exhibit 22:Investment rate to rise
24%
28%
32%
36%
40%
F1
99
5
F1
99
6
F1
99
7
F1
99
8
F1
99
9
F2
00
0
F2
00
1
F2
00
2
F2
00
3
F2
00
4
F2
00
5
F2
00
6
F2
00
7
F2
00
8
F2
00
9
F2
01
0
F2
01
1
F2
01
2
F2
01
3
F2
01
4
F2
01
5
F2
01
6
F2
01
7
F2
01
8E
F2
01
9E
F2
02
0E
Investment (% GDP)
MSe
Source: RBI, CEIC Morgan Stanley Research
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M
MORGAN STANLEY RESEARCH 13
MWhat will drive earnings in the coming quarters?
Our recent survey of 200 companies in India indicated that corpo-rates 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 recapi-talisation 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 for-mation 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 com-panies) 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, poten-tially taking the profit share in GDP up by 100 bps. Broad-based earn-ings 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
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
% of Cos with >10% YoY Revenue Growth
% of Cos with >10% YoY Net Profit Growth
Broad Market (1441 Companies)
Source: Capitaline, Morgan Stanley Research
M
14
MBear 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 marketWhile 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
124
664
395
4546
2904
21206
7697
37922
100
1000
10000
100000
19
79
19
81
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
20
01
20
03
20
05
20
07
20
09
20
11
20
13
20
15
20
17
Sensex on log scale
Source: Bloomberg, Morgan Stanley Research
Exhibit 25:Dispersion in returns not at extreme levels
40%
80%
120%
160%
200%
240%
280%
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
Gap between 1 yr returns of top and bottom decile of MSCI
India stocks
Source: RIMES,MSCI, Morgan Stanley Research
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 substan-tial 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 rea-sonable relative to large-cap valuations, thereby pointing to a rota-tion; and 3) despite the recent slowdown in domestic flows, we believe that domestic flows are in a structural uptrend which is posi-tive for SMID indices in the medium term (India Equity Strategy: The Domestic Liquidity "DREAM" Run).
M
MORGAN STANLEY RESEARCH 15
MExhibit 26:Mid-cap drawdown: Pain point for investors ...
-16% -16%
-10%
-80%
-70%
-60%
-50%
-40%
-30%
-20%
-10%
0%
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
BSE Midcap Drawdown
Source: RIMES, MSCI, Morgan Stanley Research
Exhibit 28:Liquidity has dried up for small-cap stocks
-100%
-50%
0%
50%
100%
150%
200%
Jun
-04
Fe
b-0
5
Oct-
05
Jun
-06
Fe
b-0
7
Oct-
07
Jun
-08
Fe
b-0
9
Oct-
09
Jun
-10
Fe
b-1
1
Oct-
11
Jun
-12
Fe
b-1
3
Oct-
13
Jun
-14
Fe
b-1
5
Oct-
15
Jun
-16
Fe
b-1
7
Oct-
17
Jun
-18
YoY Index change minus EPS
change for MSCI India small cap
Dec-11
Mar-09
Nov-16
Jul-13
Source: RIMES, MSCI, Morgan Stanley Research
Exhibit 27:… Similarly, small-cap drawdown is hurting sentiment
-21%
-15%
-22%
-17%
-80%
-70%
-60%
-50%
-40%
-30%
-20%
-10%
0%
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
BSE Smallcap Drawdown
Source: RIMES, MSCI, Morgan Stanley Research
Exhibit 29:Broad market valuations have come off
20%
25%
30%
35%
40%
45%
50%
55%
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
Market Cap ex Nifty to GDP
Nifty market cap to GDP
Source: RIMES, CEIC, Bloomberg, Morgan Stanley Research
M
16
MBroader 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%.
Portfolio implications 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 ), sug-gests 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 influ-ence 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 fac-tors 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 deep-ening 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.
Exhibit 30:Correlations to rise; macro trade is on
5%
15%
25%
35%
45%
55%
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
Explanatory Power of Market Effect 1Y Rolling R-
squared
Time for macro,
Oct-10
Time for macro,
Aug-07
Time for macro, Jul-05
Time for macro, Aug-03
Stock pickers'
time, Sep-04
Stock pickers' time, Jul-06
Stock pickers' time,
Jun-09
Stock pickers' time, Dec -11
Stock pickers' time, Mar-16
Time for macro
Source: RIMES, Morgan Stanley Research
Exhibit 31:BSE Sensex Outlook: Risk-Reward for Sep-19
21,000
24,000
27,000
30,000
33,000
36,000
39,000
42,000
45,000
48,000
Ju
n-1
4Ju
l-14
Se
p-1
4O
ct-
14
Dec-1
4F
eb
-15
Mar-
15
May-1
5Ju
l-15
Au
g-1
5O
ct-
15
Dec-1
5Ja
n-1
6M
ar-
16
May-1
6Ju
n-1
6A
ug
-16
Se
p-1
6N
ov-1
6Ja
n-1
7F
eb
-17
Ap
r-1
7Ju
n-1
7Ju
l-17
Se
p-1
7N
ov-1
7D
ec-1
7F
eb
-18
Ap
r-1
8M
ay-1
8Ju
l-18
Au
g-1
8O
ct-
18
Dec-1
8Ja
n-1
9M
ar-
19
May-1
9Ju
n-1
9A
ug
-19
42000(11%)
47000(+24%) Sep 19 Fwd probability-weighted outcome @ 42000
Base Case (Jun 2019)
Current Price (Sep 10, 2018)
Historical Performance
33000 (-13%)
37922
Source: RIMES, Morgan Stanley Research forecast
The key risk between now and June 2019 is that the market turns pes-simistic 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.
M
MORGAN STANLEY RESEARCH 17
M3) 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.
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 provi-sioning 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.
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 nar-rowed 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.
Exhibit 32:Valuations remain middling
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
1.0
2.0
3.0
4.0
5.0
6.0
7.0
Se
p-9
5
Se
p-9
6
Se
p-9
7
Se
p-9
8
Se
p-9
9
Se
p-0
0
Se
p-0
1
Se
p-0
2
Se
p-0
3
Se
p-0
4
Se
p-0
5
Se
p-0
6
Se
p-0
7
Se
p-0
8
Se
p-0
9
Se
p-1
0
Se
p-1
1
Se
p-1
2
Se
p-1
3
Se
p-1
4
Se
p-1
5
Se
p-1
6
Se
p-1
7
Se
p-1
8
MSCI India PB (LS)
MSCI India PB relative to EM- RS
Source: RIMES, MSCI, Morgan Stanley Research
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.
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 underper-formers. We use the following filters to short-list the stocks:
1) Market underperformers. Stocks that have underperformed the market over the past 12M.
2) Earnings underperformers. We look for companies whose fun-damentals have been weak with slow earnings growth and ROE below the standard deviation from the average.
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18
M
Exhibit 34:Focus List: Three Changes – Adding SBI, Apollo Hospitals and Prestige Estates
YTD Perf 12m Perf
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%
2Y Fwd EPS
GrowthStocks
Rel to MSCI IndiaAvg 3M T/O
($ mn)RatingSector
Price as on
Sep 10, 2018
MCap ($
bn)
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
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.
9/26/2018 Nearly 400 NBFCs Shut Shop As RBI Clean-Up Continues - BloombergQuint
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Nearly 400 NBFCs Shut Shop As RBI Clean-Up Continues Pallavi Nahatat @PallaviNahata
Published: Sep 05 2018, 10:12 AMLast 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.
s
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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.
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 one-
fourth 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.
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*
NBFC Cancellations Rise In FY19
Chart: *: Until 31/08/2018 As Calculated By BQ • Source: RBI
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In a November 10, 2014 circular, NBFCs were asked to raise the level of net owned
funds, hold more capital and raise provisioning.
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 aneconomy. If they are unhealthy and collapse, they will send ripples throughother 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.
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.
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RBI Announces Norms For Co-Origination OfPriority Sector Loans By Banks, NBFCs
September 21 2018, 11:54 PM
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September 19 2018, 7:46 PM
View All
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
NBFC Sector Ratios(%)
Source: Financial Stability Report, RBI
SEPTEMBER 2018
OUTPERFORMERS: HIGH-GROWTH EMERGING ECONOMIES AND THE COMPANIES THAT PROPEL THEM
About MGI
Copyright © McKinsey & Company 2018Copyright © McKinsey & Company 2018
Since its founding in 1990, the McKinsey Global Institute (MGI) has sought to develop a deeper understanding of the evolving global economy. As the business and economics research arm of McKinsey & Company, MGI aims to provide leaders in the commercial, public, and social sectors with the facts and insights on which to base management and policy decisions.
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Jonathan Woetzel | Shanghai
Anu Madgavkar | Mumbai
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James Manyika | San Francisco
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Andrés Cadena | Bogota
Rajat Gupta | Mumbai
Acha Leke | Johannesburg
Hayoung Kim | Washington, DC
Shishir Gupta | Delhi
SEPTEMBER 2018
OUTPERFORMERS: HIGH-GROWTH EMERGING ECONOMIES AND THE COMPANIES THAT PROPEL THEM
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 [email protected].
Jacques Bughin Director, McKinsey Global Institute Senior partner, McKinsey & Company Brussels
James Manyika Chairman and Director, McKinsey Global Institute Senior partner, McKinsey & Company San Francisco
Jonathan Woetzel Director, McKinsey Global Institute Senior partner, McKinsey & Company Shanghai
September 2018
w
Young entrepreneur at a technology innovation center in Nairobi, Kenya. © Waldo Swiegers/Bloomberg/Getty Images
CONTENTS
HIGHLIGHTS
Rising incomes
South-south trade
The next wave?
33
63
89
In brief Page vi
Summary of findings Page 1
1. Eighteen developing economies that outperformed their peers Page 33
2. Policies that enabled exceptional growth Page 43
3. Emerging-market firms as aspiring global leaders Page 63
4. New opportunities for emerging economies in changing times Page 89
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 THEMEmerging 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.
7311990 2013168 158 65
PRODUCTIVITY
INCO
ME
DEM
AND
SUSTAINED, HIGH GDP
PER CAPITA GROWTH
Productivity Promoting competition
Increasing total factor productivity
Income 3–5pp faster annual
wage growth
~60% of growth in consuming classes in emerging economies
Demand ~30% of global goods trade in 2016
3pp faster annual consumption growth
Rank highly for global connectivity
Lessons from outperformersOf 71 emerging economies studied, 18 achieved rapid, sustained growth
711Recent outperformers achievedGDP per capita growth of more than 5.0% annually for 20 years
Long-term outperformers achievedGDP per capita growth of more than
3.5% annually for 50 years
Outperformers lifted 1 billion people out of extreme poverty in two decades, 95% of totalPeople lifted out of extreme poverty, million
Outperformers’ large firms are:
Two factors driving outperformance
are displaced from their ranks within a decade vs only 38% of peers in advanced economies
55%55% of firms in top quintile
for size of the economy compared with other emerging economies2x2x as many
large firms
Three global trends that can help all emerging economies achieve stronger growthRapidly evolving technology
Automation could increase labor productivity in emerging economies by 0.8–1.2%
Rising consumption from urbanization
Consuming class in 440 cities could account for almost 50% of global GDP growth by 2025
Growing south–south trade
11x increase in trade between China and other emerging markets between 1995 and 2016
More numerous
More contested
Outperforming rival firms in high-income countries
5.0% 3.5%
China India Non-outperformers
Other outperformers
32%
Quicker decision makers
faster investment decisions
40%
higher total return to shareholders
More successful
27pp
Aggressive growers
more cite entering new markets abroad as priority
8pp
Bolder innovators
more sales from new products
... and highly competitive large companiesA pro-growth policy agenda ...Measures that supported capital accumulation and ensured stability helped create a pro-growth agenda
Azerbaijan
Belarus
Cambodia
Ethiopia
India
Kazakhstan
Laos
Myanmar
Turkmenistan
Uzbekistan
Vietnam
China
Hong Kong
Indonesia
Malaysia
Singapore
South Korea
Thailand
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.
viii McKinsey Global Institute Preface
Worker with mehndi henna design on her hands, in an electronics factory, India. © Dinodia Photos/Alamy Stock Photo
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 two-thirds 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 BENCHMARKSWe 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.
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2 McKinsey Global Institute Summary of findings
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.
Box E1. Our categorization of developing economies
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.
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.
3McKinsey Global Institute Outperformers: High-growth emerging economies and the companies that propel them
These 18 countries not only showed exceptional average economic performance but also demonstrated consistency by exceeding the benchmark growth rate in at least three-fourths 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 of the 71 emerging 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 “middle-income 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
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.
Exhibit E1
GDP per capita growth among outperforming economies has far exceeded that of other emerging economies.
Archetype
Compound annual growthrate, 1965–2016%
GDP,20162
% share
3.9 4
2.0 59
1.7 16
54.7
7.3 13
SOURCE: World Bank; McKinsey Global Institute analysis
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.
GDP per capita1
Index: 100 = 1965
1,500
1,000
2,500
3,000
500
2,000
0
3,500
4,000
20009585 20161090 051965 807570
China
High income
Non-outperformers
Recentoutperformers
Long-termoutperformers(excluding China)
ES and report
Popu-lation,20162
% share
22
13
31
6
19
OutperformersESmc 0829
4 McKinsey Global Institute Summary of findings
Exhibit E2
High income2
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
Eighteen emerging economies sustained long-term GDP per capita growth, outperforming their peers.
N = 91 countries1
SOURCE: World Bank; McKinsey Global Institute analysis
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 lower-middle-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 <0.95% compound annual growth rate of GDP per capita over the period 1965–2016 is equivalent to <50% of the rate achieved by the United States over the same period.
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.
Long-term outperformers3
Outpaced US growth consistently from 1965–2016 China Hong Kong Indonesia Malaysia Singapore South Korea Thailand
Recent outperformers4
Outpaced US growth consistently from 1996–2016 Azerbaijan Belarus Cambodia Ethiopia India Kazakhstan Laos Myanmar Turkmenistan Uzbekistan Vietnam
Middlers5
No relative change: No or inconsistent improvement relative to US from 1965–2016Very 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
Underperformers6
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
ES and report
5McKinsey Global Institute Outperformers: High-growth emerging economies and the companies that propel them
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
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.
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.
Exhibit E3
Outperformers lifted approximately 1.1 billion people out of extreme poverty and increased household consumption by about $3.2 trillion.
SOURCE: PovcalNet, World Bank; UNDP; McKinsey Global Institute analysis
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.NOTE: Figures may not sum to 100% because of rounding.
Population lifted out of extreme poverty, 1990–20131
Million
1,057
65
158
168
731
1,12365
Non-outperformers
Total
Otheroutperformers
India
China
ES and report
Household consumption expenditure change, 1995–2016$ billion
Number of countries
1
1
102
46
583,158
3,975
1,202
1,026
931
3,975 7,133
% of total 94 6 44 56
6 McKinsey Global Institute Summary of findings
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 pro-growth 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 agendaThe 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.
7McKinsey Global Institute Outperformers: High-growth emerging economies and the companies that propel them
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.
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.
Exhibit E4
GDP growth decompositionContribution to real GDP growth, 1990–2016 (%)1
N = 83 countries
Capital accumulation and total factor productivity have been major drivers of economic growth for outperforming economies.
3.7
4.3
5.1
3.8
2.3
1.7
1.7
Recent outperfomers(except India)
Long-term outperformers(except China)
China
Underperformers
India
Middlers
High income
SOURCE: Economics Analytics Platform; World Bank; The Conference Board Total Economy Database; McKinsey Global Institute analysis
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.
0.1
4.3
0.6
1.5
0.1
-1.0
-0.2 0.2
0.2
0.3
0.6
0.4
0.3
0.4 0.7
0.7
0.8
0.8
0.8
1.0
0.4
Differentiating factors
Capital accumulation
Total factor productivity2
Labor quality contribution3
Labor quantity contribution
Labor productivityLabor force participation
ES and report
8 McKinsey Global Institute Summary of findings
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 long-term 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 demandOutperforming 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.
9McKinsey Global Institute Outperformers: High-growth emerging economies and the companies that propel them
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 growthMany 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.
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10 McKinsey Global Institute Summary of findings
THE ROLE OF PRODUCTIVE FIRMS IS A KEY CHARACTERISTIC OF GROWTH OF OUTPERFORMING ECONOMIESGrowth 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
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.
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
11McKinsey Global Institute Outperformers: High-growth emerging economies and the companies that propel them
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.
SOURCE: World Bank Worldwide Governance Indicators 2017; McKinsey Global Institute analysis
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.
Absolute change in Worldwide Governance Indicators score, 1996–2016Simple average across archetypesScore ranges from approximately -2.5 (weak) to 2.5 (strong)
0.1
0.1
0.2
0.4
0.7
0.5
-0.2
0.2
0.2
0.2
0.2
0
0
-0.2
0.1
0.4
0.3
0.3
0
-0.4
-0.2
ES and report
Government effectiveness1 Regulatory quality2 Rule of law3
Change42016 total Change4
2016 total Change4
2016 total
Long-term outperformers(except China) 1.1 1.0 0.8
China 0.4 -0.3 -0.2
Recent outperformers(except India) -0.5 -0.9 -0.8
India 0.1 -0.3 -0.1
Middlers -0.1 -0.1 -0.2
Underperformers -0.6 -0.6 -0.8
High income 1.5 1.4 1.5
12 McKinsey Global Institute Summary of findings
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 medium-size enterprises in their supply chains—and in turn depend on these SMEs to provide 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 E6
N = 25 economies; 6,474 companies1,2
Large companies have been important to the growth of outperforming developing economies.
SOURCE: World Bank; McKinsey Corporate Performance Analytics; McKinsey Global Institute analysis
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 $500 million in revenue in 2016.3 Simple average across countries; 5-year averages taken due to year-on-year volatility.4 Gross value added has been calculated as the difference between revenue and cost of goods sold; GVA contribution of large financial services firms has
been estimated.NOTE: Figures may not sum to 100% because of rounding.
Ratio of large-company revenue to GDP, 1995–20163
%
11 1826 27
6460
+42
2011–16
2005–10
2000–04
41
1995–99
22
Outperformers
13 13106
29
2000–04
+16
2005–10
28
2011–16
1995–99
1321
Non-outperformers
18 26 30 33
59
1995–99
69
2000–04
41
2011–16
2005–10
77 +36
ES and report
Revenue
Value added4
High income
13McKinsey Global Institute Outperformers: High-growth emerging economies and the companies that propel them
Exhibit E7
Firms from outperforming countries operate in a wide variety of sectors.
SOURCE: IMF; McKinsey Corporate Performance Analytics; McKinsey Global Institute analysis
NOTE: Hong Kong omitted as large firm revenue >300% of GDP; Singapore agriculture, forestry, and fishing omitted as outlier.
Large firm revenue Large firm revenue as % of GDP
<3 >103–10
Bubble size represents sector revenue as % of total large firm revenue in each country
South Korea Singapore Thailand China Malaysia India Indonesia
Accommodation, food services, and entertainment/recreation activities
Agriculture, forestry, and fishing
Automotive and assembly
Construction and real estate
Energy and basic materials
Financial and insurance services
Healthcare
Manufacturing: Consumer packaged goods
Manufacturing: High tech
Manufacturing: Other (chemicals, steel, textiles, etc)
Manufacturing: Pharmaceuticals and medical products
Telecommunications, media, and technology services
Travel, transport, and logistics
Wholesale and retail trade
Other
Total large firm revenue$ billion 1,684 220 237 5,123 140 866 158
Share of GDP% 129 75 58 54 41 35 15
14 McKinsey Global Institute Summary of findings
COMPETITIVE EMERGING-MARKET FIRMS AS ASPIRING GLOBAL LEADERSRising 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 economyOne 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.
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15McKinsey Global Institute Outperformers: High-growth emerging economies and the companies that propel them
The most competitive companies from emerging economies are becoming global players that outperform their counterparts in advanced economiesThe 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.
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.
Exhibit E8
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.
15
23
62
Drop tothe bottomquintile
High income
Drop tothe middle 3quintiles
Remain inthe topquintile
23
32
45
Outperformers
Emerging economies exhibit greater contested leadership among top firms.
SOURCE: McKinsey Strategy Practice (Beating the Odds model v20.0); McKinsey Corporate Performance Analytics; McKinsey Global Institute analysis
Distribution of trajectory for top quintile economic profit generators over 10 years1
% (N = 48 countries and 2,284 total companies2,3)
76
68
63
63
62
58
50
36
France
UnitedStates
Canada
UnitedKingdom
Japan
Australia
Germany
Switzerland
60
43
34
20Malaysia
China andHong Kong
South Korea
India
Country
Firms remaining in top quintile%
Firms remaining in top quintile%Country
ES and report
16 McKinsey Global Institute Summary of findings
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.
17McKinsey Global Institute Outperformers: High-growth emerging economies and the companies that propel them
company earned $273 million in profit in 2015–16 while the African airline industry overall lost $900 million.33
NEW OPPORTUNITIES FOR EMERGING ECONOMIES IN CHANGING TIMESGlobal 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 for emerging economies 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.
Exhibit E9
+3
+8
0.8
1.3
-6
+33 +1.1
+0.5 +0.3
+1.1
-7.9
-6.0
+13
+27
Top firms in outperformer economies are bolder, quicker, and more forceful than their peers.
SOURCE: McKinsey 2017 Firm Survey; McKinsey Corporate Performance Analytics; McKinsey Global Institute analysis
Comparison of self-reported performance and practices for top-performing firms across archetypes1,2
Absolute difference compared to top-performing firms from high-income economiesN = 7 countries, 2,172 companies3
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.
Innovation and digital disruption InvestmentGeographic expansion
Innovation assessmentSales from new products (percentage points)
Innovation practices4
Score (1 to 10)
Disruption proactive-ness5
Percentage points
Digital disruption response6
Score (1 to 8)
Investment levels7
Capex to depreciation ratio
Investment speedNumber of weeks
Expansion priorityMarkets outside home (percentage points)
Outperformersvs high income
Non-outperformers vs high income
High income 48 6.5 25 3.2 1.5 18.7 47
ES and report
18 McKinsey Global Institute Summary of findings
Global trends in demographics, trade and other flows, and technology imply emerging markets will be the main battleground for global growthWe 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, cross-border 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.
19McKinsey Global Institute Outperformers: High-growth emerging economies and the companies that propel them
a significant training and retraining challenge to governments, educational institutions, and companies.38
Manufacturing has continued strong growth opportunitiesManufacturing 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 complicates 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 labor-intensive 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.
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.
SOURCE: UNCTAD; McKinsey Global Institute analysis
1 Global imports of goods; north and south defined as developed and emerging markets respectively.NOTE: Figures may not sum to 100% because of rounding.
Goods trade by development status1
%; $ trillion (current $)
55 5143
35 33
2930
31
32 30
8 912
14 16
69 10
5 6 8 10 10
China-north
South-south3
1995 10
4
2000 201605
China-south
South-north
North-north
100% = 5 7 11 15 16
Change in value of trade, 1996–2016Multiple
6x
11x
6x
3x
2x
ES and report
20 McKinsey Global Institute Summary of findings
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.
21McKinsey Global Institute Outperformers: High-growth emerging economies and the companies that propel them
Exhibit E11
Manufacturing can remain an important source of employment and growth for low-income economies.
SOURCE: Groningen Growth and Development Centre; McKinsey Global Growth Model; McKinsey Global Institute analysis
Change in employment and value added in manufacturing in productivity boost scenario, 2015–30
<5,000 5,000–10,000 10,000–20,000 >20,000
Growing manufacturing productivity
Lower contribution from manufacturing
Growing manufacturing sector employment and productivity
There is still opportunity for growth through manufacturing,
especially for countries with lower
GDP per capita
GDP per capita, 2015 (constant 2010 $)
SIMULATION
ES and report
1.0
2.5
-2.5 -0.5-2.0 0.5-1.5 -1.0 0 3.5
-0.5
1.5
0
2.0
2.0
2.5
0.5
3.0
3.0
1.0
4.0
1.5
-1.0
DominicanRepublic
Indonesia
Potential change in share of employmentPercentage points
Rwanda
Tanzania
Mozambique
Colombia
Nigeria
SouthAfrica
Peru
Singapore
Potential change in share of value addedPercentage points
Kenya
Philippines
Bangladesh
Nepal
Pakistan
Senegal
Uganda
Zambia
Zimbabwe
China
MexicoRomaniaMalaysia
RussiaTurkey
IndiaHonduras
Chile
Argentina
Cameroon
Australia
Japan
Ghana
SpainSouth Korea
Nicaragua
Vietnam
France
Ethiopia
UnitedKingdom
Thailand
Guatemala
Italy
Morocco
Germany
Saudi Arabia
UnitedStates
Brazil
22 McKinsey Global Institute Summary of findings
Services can create jobs and open productivity-growth opportunities as the relative contribution from manufacturing declinesServices 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.
23McKinsey Global Institute Outperformers: High-growth emerging economies and the companies that propel them
Exhibit E12
Average contribution to GD
P
Emerging economy firms have opportunities to increase productivity in manufacturing and services.
SOURCE: World Input-Output Database, 2016; McKinsey Global Institute analysis
NOTE: Not to scale.
3020100 50 15014013012070 8040 60 11010090
IndonesiaUnitedStatesIndia
Turkey
JapanSouthKoreaMexicoBrazil
70 13011030 600 50 100 1701504020-10 120 160140908010
IndonesiaIndia United
StatesMexicoSouthKoreaBrazil
RussiaRomania
16080-20 2202001801401201006040200
Turkey
GermanySouthKorea
Mexico UnitedStatesIndonesiaBrazil
RomaniaBulgariaIndia
12080 10060 7040 50 17020 1401300 180160150110903010
GermanyPortugalUnitedStatesIndia
IndonesiaBrazil Japan
SouthKoreaMexico
0-10 20 4010 30 1009070 806050
Mexico UnitedStates
SouthKoreaTurkeyPoland
Indonesia Brazil
India
UnitedKingdom
0 130110 15012010080 9070605030 1402010 40
Germany
UnitedKingdom
MexicoHungary
BrazilIndonesiaIndia
SouthKorea
UnitedStates
20 8030 60100 70 90 10040 50
RussiaIndia United
StatesBrazil
MexicoCzechRepublicIndonesia
SouthKorea
PolandUnitedKingdom
0 20 1206040 110100908070503010
Brazil RomaniaIndonesiaIndia Poland
UnitedStates MexicoTurkeyRussia
SouthKorea
170 20090 1401201107050100 604020 160 180 19015010080 13030
SpainBrazilSouth
KoreaIndia
UnitedStatesGermanyMexicoRussiaIndonesia
UnitedKingdom
110908070605040302010-10 1000
UnitedStatesGermanyPoland
MexicoIndonesia
IndiaBulgaria
JapanSouthKorea
Brazil
Typical contributions in emerging economies
Value added (% of GDP)
Employment (% of total jobs)
1–7 1–5
Manufacturing
Food and beverages
Manufacture of metals
Electrical equipment and electronics
Auto and transport
Textiles and apparel
Machinery and equipment
Retail and trade
Legal, account-ing, and administration
Financial services
Construction
2–5 1–5
1–6 1–4
1–5 1–4
1–5 1–9
<4 1–3
7–19 5–20
1–15 1–8
4–12 1–4
3–12 5–18
Productivity per sector (annual value added per employee, average 2010–14, $ thousand, constant 2010 $)
Outperformers Non-outperformers High income
Services
24 McKinsey Global Institute Summary of findings
An $11 trillion boost awaits the global economy if all emerging economies match the historical productivity growth of outperformersProductivity 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 CYCLESWe 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.
user
Highlight
25McKinsey Global Institute Outperformers: High-growth emerging economies and the companies that propel them
Exhibit E13
► Regions2Central
Asia
East and Southeast
AsiaSouth Asia
Central and
Eastern Europe
Sub-Saharan
AfricaLatin
America
Middle East and
North AfricaD
escrip-tion % of emerging market
population 1 36 30 7 12 10 5
% of emerging market GDP 1 47 10 16 5 19 2
Economic
perform-
ance
Average GDP per capitaReal $ 2016 5,283 12,604 1,703 12,644 1,751 6,885 4,461
Average GDP per capita growthCAGR, 1996–2016, %
5.5 4.4 3.7 3.1 2.5 1.9 1.6
Productivity drivers
Domestic savingsCAGR, 1996–2016, %
Government effectivenessChange, 1996–2016, %
Market capitalization of listed domestic companiesCAGR, 1996–2016, %
Global Innovation IndexRank change, 2013–16
Foreign direct investmentCAGR, 1996–2016, %
InflationAverage, 2000–16
Government health expenditureCAGR, 2000–15, %Government education expenditureCAGR, 1996–2016, %Incom
e and dem
and driversHousehold incomeCAGR, 1996–2014, %
Corporate incomeCAGR, 1996–2014, %
ExportsCAGR, 1996–2016, %
MGI Connectedness IndexScore, 2016
Infrastructure investmentCAGR, 2000–15, %
Our heat map analysis on 13 growth metrics highlights strong regional patterns.
SOURCE: World Bank; OECD; IMF; WIPO; INSEAD; WFE; WHO; UNESCO; McKinsey Global Growth model; Global Insight; McKinsey Global Institute analysis
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.
Performance within emerging markets (quartile)1 ThirdFirst Fourth Second
26 McKinsey Global Institute Summary of findings
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 pro-growth 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
27McKinsey Global Institute Outperformers: High-growth emerging economies and the companies that propel them
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 oil-related. 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.
28 McKinsey Global Institute Summary of findings
Looking to the next outperformersAcross 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 pro-growth 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.
SOURCE: McKinsey Global Institute analysis
3.5%
1
Countries with top quartile heat map scores and top quartile growth rates
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
2 3
ES and report
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.
Quartile 4
1
Quartile 2
9
Quartile 1
2
8
2
5
4
Quartile 3
6
3
0
7
1
4
3
Peru
Ecuador
PakistanKenya
Sri Lanka
Morocco
Tanzania
Nigeria
Paraguay
GDP per capitaCAGR 2011–16, %
Philippines
Heat map score, 2011–16
Rwanda DominicanRepublic
Mozambique
Cameroon
Algeria
Angola
Bangladesh
Colombia
Côte d’Ivoire
Bolivia
Senegal
29McKinsey Global Institute Outperformers: High-growth emerging economies and the companies that propel them
•••
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.
9/26/2018 5-Hour Rule: If you’re not spending 5 hours per week learning, you’re being irresponsible
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Michael Simmons in The MissionLearning How To Learn Speaker & Teacher / Serial Entrepreneur / Bestselling Author /Forbes, Fortune, Time, HBR Contributor / Site: http://t.co/T32xDLUBLJOct 12, 2017 · 10 min
5-Hour Rule: If you’re not spending 5 hours perweek 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?
HOME THE MISSION DAILY IT VISIONARIES PODCASTS TOP STORIES NEWSLETTER STUDIOS SUB
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Why has the best investor in history, Warren Bu�ett, invested 80% of his time in
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, self-
made 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.
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This chart from Diamandis’ book Abundance shows how we’ve demonetized
$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.
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While goods and services are becoming demonetized, knowledge is becoming
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 self-
driving car team, gives the example of Uber paying $700 million for Otto, a six-
month-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?
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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
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move so quickly and so much information is transmitted,” he said, reading gave
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 knowledgeeconomy
“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�er
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
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getting a job that pays well, getting a raise, building a successful business, selling
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
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Before his daughter was born, successful entrepreneur Ben Clarke focused on
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 stepsThe 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
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I spent dozens of hours and created a free masterclass to help you master your
learning ritual too!
Sign up for the free Learning How To Learn webinar here >>
. . .
If you enjoyed this story, please click the 👏 button and share to helpothers �nd it! Feel free to leave a comment below.
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Companies (/Companies)
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Jio’s under-the-radar tech team in Texasleading its R&D
Sunny Sen (https://factordaily.com/author/sunny-sen/)Anand Murali (https://factordaily.com/author/anand/) June 1, 2018 6 min
Y ou are the world’s fastest growing telco. Your customer base ismore than the Russian population in less than two years of
launch. You have the backing of a conglomerate whose core refiningbusiness turns in profits of over Rs 32,000 crore a year. You havetaken outlandish bets both at backward integration (branded phones)
9/21/2018 Jio’s under-the-radar tech team in Texas leading its R&D | FactorDaily
https://factordaily.com/reliance-jio-research-development-team-texas-for-blockchain-ai-ml/ 2/13
and forward integration (content from music to movies to TV todigital). The man at your helm deeply believes in the ‘Data is new oil’adage and sees voice and data services as just a gateway toextracting value in tomorrow’s digital economy, not a revenue spinner.
Would you want to be held hostage to fast and tectonic changes inthe digital and tech world? No, not if you are Mukesh Ambani, therichest Indian who is betting India’s largest company’s future on a wildbet called Reliance Jio into telecom and digital services spending overRs 200,000 crore to set up an all-4G phone network. (4G is short forfourth generation, data rich telecom network.)
We need to go back five years to understand Ambani’s technology betin telecom when he authorised the setting up of a tech outpost inFrisco – near Dallas in Texas, not to be confused with San Francisco,California. “Telecom is massive distribution channel, and manybusinesses can fold into it,” said a Reliance executive, who is notauthorised to speak to the media. “To make that happen, Jio needs tobecome more of a technology company than a vanilla telecomservices company.”
The Texan outpost has grown into a significant even if small-sizednerve centre for Jio, which is making investments in technologistsand projects in artificial intelligence, machine learning, and blockchaintechnologies. “There are multiple use cases that we can build withthese new age technologies,” said the source quoted above.
9/21/2018 Jio’s under-the-radar tech team in Texas leading its R&D | FactorDaily
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The office that started to develop applications for Jio and forgepartnerships with large technology and devices companies such asApple, Samsung, Qualcomm, and others is slowly becoming a spoutof new technology – and patents – that the telco plans to bring toIndia in the years to come.
Reliance Jio Infocomm USA, the 100% subsidiary of Reliance Jio setup in 2013, has a second office in Palo Alto besides in Frisco, which isabout 28 miles north of Dallas, widely considered the telecomsoftware hub in the US. The Jio unit is one of four such overseassubsidiaries(http://www.ril.com/DownloadFiles/List%20of%20Subsidiaries%20and%20As
9/21/2018 Jio’s under-the-radar tech team in Texas leading its R&D | FactorDaily
https://factordaily.com/reliance-jio-research-development-team-texas-for-blockchain-ai-ml/ 4/13
– one more in the US. and the other two being in Singapore and theUK – incorporated to facilitate Jio’s international long distancecommunication business.
Deep tech, but WIP stillA team of 50-odd people, largely software developers, in Texas isreadying new technologies that will shape Jio’s and indeed RelianceGroup’s future. “Jio will become one of the (technology) vendors toReliance Industries,” says a second source, who is also not authorisedto talk to the media.
To start with,
that Ambani is making a bet on. Thesecond source said that Jio intends moving the distribution andmanagement of SIM cards on to blockchain – with each card with aunique number put on the ledger. “It would make distribution muchmore simpler and will make it easier to manage the retail network ofthousands of small merchants,” the source added.
Once implemented, the blockchain use case can be replicated amongReliance Retail’s suppliers. “Managing supply chain is a critical aspectof the retail business… there are so many parts to it. Once blockchainis implemented, it improves transparency and reduces wastage andleakages,” a third source with the company told FactorDaily, addingthis deployment could take time.
blockchain is one new technology(https://telecom.economictimes.indiatimes.com/news/airtel-vodafone-reliance-jio-seek-blockchain-deployments-to-create-revenue-streams/64262488)
9/21/2018 Jio’s under-the-radar tech team in Texas leading its R&D | FactorDaily
https://factordaily.com/reliance-jio-research-development-team-texas-for-blockchain-ai-ml/ 5/13
Insiders have said that Reliance’s big plan is to integrate its retailnetwork with Jio users by offering discounts and bring in tie-ups withconsumer brand. It is already in talks with kirana stores to use the Jiophone network and point-of-sale machine. “We will see how to put thecouponing system onto blockchain,” said the second source.
The other area where work is being done is on cash flow and creditmanagement. When orders are placed with suppliers, for instance,some money is paid as advance. Once supplies start coming in, theremaining payments are to be made. “Managing ledger for thesesuppliers is a lot of work. There will be more transparency withimplementation of blockchain,” the second source said.
9/21/2018 Jio’s under-the-radar tech team in Texas leading its R&D | FactorDaily
https://factordaily.com/reliance-jio-research-development-team-texas-for-blockchain-ai-ml/ 6/13
And, why is this being spearheaded from the US? “Talent inblockchain, artificial intelligence and machine learning is scarce inIndia. Talent is (relatively) easily available in the US… We need to bepresent where the action is,” said a fourth source.
Jio is also planning to set up a unit in Estonia, news daily Mint has
. Estonia isconsidered the most friendly global jurisdiction for cryptocurrencyand blockchain companies and after the recent clampdown on suchbusinesses in India, several companies are
via the country’s e-residency program.
Networks, people, patentsBlockchain is not the only area where Jio is making investments – alarge number of patents are being filed in the area of networktechnology. Jio is the only 100% 4G telco in India. This means thatjust 15% to 20% of the bandwidth is used by voice calls, leaving theremaining for internet browsing, online shopping, watching movies,listening to music, and other uses.
Besides telecom and blockchain, the Frisco team has members withbackgrounds in AI, machine learning, internet of things, among othertechnologies. The people heading R&D in such frontier technologiesseem to operate out of the US subsidiary.
A key executive is Gautham Reddy, the at Jio since
July 2013. Reddy had earlier worked at Motorola, AT&T Wireless, andMetro PCS, where he worked as the technical head of mobile devicesand applications. At Jio, he was involved with the Jio Chat app, thecompany’s messaging application, and did work that ended in patentsrelating to communication processing and applications that work over
reported(https://www.livemint.com/Companies/39syF2sZymyjCLerdGMKyM/ReliancJio-plans-overseas-expansion-eyes-Estonia-unit.html)
moving to Estonia(https://factordaily.com/india-blockchain-ico-companies-migrate-estonia/)
director of product design(https://www.linkedin.com/in/gautam-reddy-47a9793/)
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Voice over Wifi (VoWiFi) and voice over long-term evolution (VoLTE)service, show patents filed with the United States Patent andTrademark Office. There are
Reddy and histeam have filed at USPTO.
other patents(https://patents.google.com/?inventor=Gautam+G.+Reddy&oq=Gautam+G.+Reddy)
One of the US patents held by Reliance JIo’s Texas team
9/21/2018 Jio’s under-the-radar tech team in Texas leading its R&D | FactorDaily
https://factordaily.com/reliance-jio-research-development-team-texas-for-blockchain-ai-ml/ 8/13
Jio has been filling for patents in India, too. According to a PTI
, the RIL’s annual report for 2016-17 statedthat Reliance Jio had applied for 54 global patents. This helps giventhat patents are protection given by jurisdiction. “Patents are territorialin nature and companies usually file patents when they want toprotect their product in a particular territory,” says Sai Krishna,managing partner of Sai Krishna and Associates, an intellectualproperty legal firm.
The US unit is also working on AI projects. Research firm Markets andMarkets predicts the global
at $21.46 billion this year and to top $190billion by 2025 – a compound annual growth rate of 36.62%.
newsreport (http://www.ptinews.com/news/8839138_Reliance-Jio-files-54-global-patents-in-FY17)
AI market(https://www.marketsandmarkets.com/PressReleases/artificial-intelligence.asp%20.asp)
9/21/2018 Jio’s under-the-radar tech team in Texas leading its R&D | FactorDaily
https://factordaily.com/reliance-jio-research-development-team-texas-for-blockchain-ai-ml/ 9/13
“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 sourcesaid. This, it is expected, will work especially in serving new usersegments such as new rural cohorts of customers. “As the machine isfed with more data, and more customer interact with the platform wewill be able to offer better services… it is learning that happens withtime,” the first executive said.
To get more stories like this on email, and subscribeto our daily brief.
Visuals: Rajesh Subramanian
click here
(1) (4) (3)
1 1
(7)(1)
5 12
127
187
149
68
126
80
233
153
261
(% of GDP)
(bp)
Data is average of the last four quarters
I
Absolute Success is Luck. Relative Success is Hard Work.
n 1997, Warren Buffett, the famous investor and multi-
billionaire, 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
jamesclear.com
10 mins read
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.
user
Highlight
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.
SBI ECOWRAP
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 lev-erage 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!
SEPTEMBER 21, 2018 ISSUE NO: 48, FY19
ECOWRAP ‘Be the Bank of Choice for a Transforming India’
SBI ECOWRAP
2
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 3: India’s Household Debt (% of GDP) Graph 4: Cross-Country Household Debt (% of GDP)
Source: SBI Research
Graph 1: Household Savings % to Gross Domes�c Savings
Graph 2: Composi�on of Household Savings
Source: SBI Research
SBI ECOWRAP
3
�� 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 struc-
ture, 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% fi-nanced 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 non-institutional sources in coming years.
Graph 5: Growth in Net Financial Assets
Source: RBI; SBI Research
MEANWHILE HOUSEHOLDS NET FINANCIAL ASSET SURGED �� Household financial saving which is the most important
source of funds for investment in the economy is declin-ing 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.
SBI ECOWRAP
4
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 out-standing per credit card (average) increased from Rs 9,580 in 2016 to Rs 11,309 in 2017, a growth of whop-ping 18.1%. During the same period real outstanding had also increased by 14.3%. This might, in turn, con-tribute 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)
Source: RBI; SBI Research Source: RBI; SBI Research
*****
