UVA-GEM-0207
Jan. 30, 2024
Predicting the Next High-Growth Economies
As Arturo Rodrigo rode the early-morning Metro-North train from Manhattan to Greenwich, Connecticut,
in early 2023, his thoughts turned to prospects for the global economy.1 His task this morning was to decide
which economies were poised for strong growth.
Entering 2023, there was no dearth of negative news. In some sense, the COVID-19 pandemic, followed
by the Russian invasion of Ukraine and incipient food and energy crises, made it difficult to imagine that one
day there would again be high-flying economies. Headlines from the World Bank’s semiannual flagship
publication Global Economic Prospects (GEP) had been decidedly downbeat for years, both on long-term prospects
(the June 2021 report’s title: “Global Economy: Headed for a Decade of Disappointments?”) and near-term
pain, with these less-than-encouraging words to open the January 2022 GEP:i
The global recovery is set to decelerate amid continued COVID-19 flare-ups, diminished policy
support, and lingering supply bottlenecks. The outlook is clouded by various downside risks, including
new virus variants, unanchored inflation expectations, and financial stress. If some countries eventually
require debt restructuring, the recovery will be more difficult to achieve than in the past. Climate
change may increase commodity price volatility. Social tensions may heighten as a result of the increase
in inequality caused by the pandemic.
In June 2022, the GEP led with this: “Russia’s invasion of Ukraine and its effects on commodity markets,
supply chains, inflation, and financial conditions have steepened the slowdown in global growth. One key risk
to the outlook is the possibility of high global inflation accompanied by tepid growth, reminiscent of the
stagflation of the 1970s.”
But Rodrigo knew that gloom would not last forever and additional analysis was possible. He thought back
two decades to the famous BRICs call and wondered how that analysis could inform his view of the present
situation. BRICs, the now-famous acronym established in 2001 by Jim O’Neill, then head of global economic
research for Goldman Sachs, referred to the strong growth potential in the economies of Brazil, Russia, India,
and China. O’Neill was in effect betting that the BRIC countries would grow faster than many others. He was
correct. Over the years 2001 to 2014, Brazil, Russia, India, and China posted average annual growth rates of
5%, 6%, 8.7%, and 8.9%, respectively. By comparison, over the same period the United States, the United
Kingdom, and Germany each grew at only 2% per year.
1 The terms “country” and “nation” as used in this note do not in all cases refer to a territorial entity that is a state as understood by international law
and practice, but rather cover well-defined, geographically self-contained economic areas that may not be states but for which statistical data are
maintained on a separate and independent basis.
This technical note was written by Francis E. Warnock, James C. Wheat Jr. Professor at the University of Virginia’s Darden Business School, and Kieren
J. Walsh, Senior Assistant at KOF Swiss Economic Institute at ETH Zurich. Copyright © 2024 by the University of Virginia Darden School Foundation,
Charlottesville, VA. All rights reserved. To order copies, send an email to sales@dardenbusinesspublishing.com. No part of this publication may be reproduced, stored
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related database. Our goal is to publish materials of the highest quality, so please submit any errata to editorial@dardenbusinesspublishing.com.
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Rodrigo, convinced he could learn from past episodes, decided to focus on the following questions: What
are the economics behind BRICs? How do investors and managers identify the next BRIC countries (i.e., the
next high-growth economies)? Could the economic frameworks underlying BRICs analysis provide some clarity
after the fog of the COVID-19 pandemic and war in Europe?
Identifying the Next High-Growth Economies
The Solow growth model2
The Solow growth model forms the bedrock of economists’ thinking about long-term economic growth.
The model has, at its core, two key pieces. The first is a long-run production function, which says that a
country’s long-run output or productive capacity (Yfe) stems from three components: the amount of physical
capital (𝐾) such as machinery, computers, or buildings; the size of its labor force (L); and the productivity (A)
or efficiency with which labor and capital are combined, as in Equation 1:
𝑌 = 𝐾 𝛼 (𝐴𝐿)1−𝛼 ,
(1)
where the parameter 𝛼, the “capital share,” is a number between 0 and 1 that represents the relative importance
of capital versus labor in production.3 Holding either labor or capital constant, this production function exhibits
diminishing marginal returns with respect to the other factor, meaning capital-scarce countries can get a larger
boost from adding a little more capital. The second element is a capital accumulation equation (Equation 2):
Δ𝐾 = 𝐼 − 𝛿𝐾 = 𝑠𝑌 − 𝛿𝐾.
(2)
The capital accumulation equation says that the change in the amount of capital (ΔK) is equal to new
additions to the capital stock, which macroeconomists call investment (I), minus any depreciation of the existing
capital stock (δK). Investment is itself generated by the new savings in the economy (the savings rate, s, times
total output Y).
The key diagram from Solow is Figure 1. Fully understanding it is beyond the scope of this note, but in a
nutshell it tells us the following. First, note that everything is in per capita terms (k = K/L, y = Y/L). The
horizontal axis is the amount of capital per worker (k). From any point on the horizontal axis, going up to the
various curves tells you the amount of new investment per worker (sy), the amount of capital per worker lost
to depreciation (δk), and output per worker (y).
The model’s implications for economic growth can be divided into two time frames: the growth an
economy will eventually settle into in the long run (this is called the steady state), and transitions toward a steady
state (which can take a number of years).
Specifically, the model evolves toward a steady state, a central tendency or long-run implication (denoted
by asterisks in Figure 1) in which capital per worker and, hence, output per worker are constant. Per capita
growth in the steady state must come from sustained technological progress (which shifts up the y curve,
2 Solow won the Nobel Prize for Economics for his work on growth theory. The original Solow growth article is Robert M. Solow, “A Contribution
to the Theory of Economic Growth,” Quarterly Journal of Economics 70, no. 1 (1956): 65–94. For details on the Solow growth model, see Kieran James
Walsh, “Growth Theory,” UVA-GEM-0168 (Charlottesville, VA: Darden Business Publishing, 2018) or Felipe Saffie, “Determinants of Economic
Growth,” UVA-GEM-0202 (Charlottesville, VA: Darden Business Publishing, 2022).
3 Economists call 𝛼 the capital share because in a model with the production function of Equation 1 and competitive capital and labor markets, 𝛼 is
equal to the equilibrium amount of GDP earned by owners of capital.
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yielding higher and higher steady states), but economies can also grow strongly as they transition (over a number
of years) from their current equilibrium to the steady state.
Figure 1. The Solow diagram.
Output and investmnet per capita
3
2.5
y
2
y*
𝛿𝑘
1.5
sy
1
0.5
k*
0
0
1
2
3
4
5
6 7 8 9 10 11 12 13 14
Capital per capita (k)
Source: Created by authors.
The model provides stark predictions regarding economic growth. Specifically, it predicts that the countries
that will grow quickly are those (i) catching up from below steady state and/or (ii) with rapidly increasing
productivity.ii The second of these is easy enough to observe (as much as data allow us to observe actual
phenomena). But the first requires a bit more thinking: For example, which countries are below steady state
(and thus likely to grow quickly)? Economies below steady state might have (iii) been previously prosperous
but have exogenously lost capital (e.g., from a war); (iv) had recent positive shocks to savings or new access to
technology; or (v) experienced recent positive shocks to determinants of investment and technology, such as
foreign direct investment (FDI) or declining debt service.
Other growth factorsiii
The Solow model leaves us with the notion that sustained per capita economic growth can come only from
technological growth. Adding more capital won’t do it, because of diminishing returns. Sustained per capita
economic growth has to come from technological growth. If we are willing to step outside the narrow confines
of Solow’s original model, we notice a number of things that can fall under the umbrella of “technological
growth.”
Human capital. After Solow, many economists recognized that a narrow interpretation of capital wasn’t
realistic, in part because human capital—the knowledge, skills, and training of individuals—is so important. As
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countries prosper, they also tend to invest more resources in people through improved nutrition, schooling,
health care, and on-the-job training. This increase in human capital increases productivity.
Population. The effect of population growth, not shown in the baseline model (Figure 1), is ambiguous. On
the one hand, population growth will decrease the level of GDP per capita, because if we hold capital constant,
then with more workers each has less capital to work with and hence produces less, albeit while increasing
overall GDP (there are more people, each producing a little less, but overall output increases). Offsetting that
effect is that innovations and hence productivity improvements often come from clusters, suggesting that more
population can lead to increased productivity. Overall, population growth has a mechanical negative effect on
per capita GDP but can spur productivity growth.
Open-economy forces. The baseline Solow model is of a closed economy. Allowing for trading of goods and
assets across borders can have two effects. One, the country now has access to foreign savings, which could
increase s. Two, some foreign capital, such as FDI, might come with knowledge spillovers that can increase
productivity.
Government. The baseline Solow model assumes no government. Including government has two effects:
One, governments can increase savings by running budget surpluses or decrease savings by running fiscal
deficits. Two, governments can enable productivity growth and, hence, long-run living standards, by improving
infrastructure (e.g., highways, bridges, utilities, dams, and airports); helping people build human capital through
educational policies, work training, worker relocation programs, and health programs; incentivizing
entrepreneurial activity (e.g., by reducing red tape); and encouraging research and development (e.g., by
supporting scientific research, which has positive externalities). Of course, corruption and government
inefficiencies can have the opposite effect.
Institutions. Institutions can incentivize innovation and productivity growth, whereas hostile environments
for innovation can harm productivity.
The BRICs call
Applying the lens of the Solow model and other growth factors to data available as of the end of 2000
provides insight into O’Neill’s BRICs call. Exhibit 1 shows year 2000 data for emerging-market economies
(EMEs) with populations in excess of 30 million people.4
The growth potential of China is immediately evident. In 2000, it had high investment, second only to
South Korea among large EMEs, and above-average human capital, as reflected in the education index.5 Yet
China’s GDP per capita was behind economies that were comparable or worse on these measures (such as
Thailand, Iran, South Africa, Colombia, and Egypt), suggesting that in 2000, China was well below steady state
and primed to enter a high-growth “catching-up” period.
India’s GDP per capita was even further behind (it was less than half of China’s), despite above-average
investment. India’s human capital also lagged China’s, but it was above the levels in Iran and Pakistan,
4 There is no single way to identify which countries are EMEs, but it basically comes down to income levels (i.e., per capita GDP), size (nominal
GDP, population), and trade and financial linkages with the rest of the world. See Rupa Duttagupta and Ceyla Pazarbasioglu, “Miles to Go,” International
Monetary Fund (IMF), Finance and Development, Summer 2021, https://www.imf.org/external/pubs/ft/fandd/2021/06/the-future-of-emergingmarkets-duttagupta-and-pazarbasioglu.htm (accessed Sept. 10, 2022) for the IMF’s methodology for identifying EMEs.
5
The human capital index (“Human Capital in PWT 9.0,” Groningen Growth and Development Centre,
https://www.rug.nl/ggdc/docs/human_capital_in_pwt_90.pdf [accessed Sept. 10, 2022]) used in this note represents both years of schooling and quality
of education. A high index value, like 3.7 for the United Kingdom in 2014, means people in that country generally receive high-quality education. A low
index value, like 1.2 in Ethiopia in 2000, means the opposite.
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economies with higher output per person. Overall, with extremely low GDP per person, relatively high
investment, and decent human capital, India also appeared to be below steady state.
Brazil, while much wealthier than China and India in terms of GDP per capita in 2000, was substantially
poorer than Mexico and Argentina. With above-average investment and average human capital, Brazil was,
according to Solow factors, also poised to grow.
In 2000, Russia had very high human capital, second only to South Korea among large EMEs, and slightly
below-average investment. However, Russia was poorer in GDP per capita than Poland and Argentina,
economies with lower human capital and only slightly higher investment. Therefore, Solow factors also
indicated growth potential in Russia.
High Growth Is in the Eye of the Beholder
While growth in GDP per capita correlates with key measures of standards of living (and so is clearly
important), why should a manager or investor care about GDP growth? The answer to this reasonable question
depends on one’s perspective.
Portfolio equity investors
The returns to an arms-length portfolio equity investor depend on dividend yields and capital gains (and,
for international investors, currency returns). While there are many drivers of each, from dividend payout
policies to market fickleness or exuberance, both are ultimately tied to GDP growth. Dividends depend on
earnings, which depend on sales and thus national income and aggregate demand. Capital gains and equity
prices are more fickle, though ultimately tied to expectations of income and demand. Therefore, the armslength equity investor should indeed care about GDP growth.
That said, how GDP growth translates into equity returns varies from country to country. One factor
determining the “pass through” of GDP growth to equity returns is how much of a country’s growth comes
from exchange-listed firms. Another factor is investor protection regulations, which determine how much of a
listed firm’s growth arms-length investors actually get. Overall, as evidenced in Exhibit 2, stock market returns
have been positively (but not perfectly) correlated with growth in many countries.6
Exporters
GDP determines national income and hence demand, so it is natural for an exporter to assess possible
recipient countries’ GDP growth prospects. Specific income levels and demographics of target clients, as well
as the nature of trade agreements, are also important.
Vertical foreign direct investors
Vertical FDI is when a multinational corporation (MNC) owns a stage of production—often a supplier—
in a foreign country. For example, a Japanese automotive company might own a supplier in South Korea that
produces parts for the company’s Japan-based production facilities. While an MNC must consider many factors
when deciding where to set up a vertical FDI operation (see, for example, Intel’s decision to produce in Costa
6 In the optimal corporate ownership theory of the home bias, investors are more likely to enter countries with poor governance with some protection,
that is, as foreign direct investors rather than arms-length portfolio investors. See Bong-Chan Kho, René M. Stulz, and Francis E. Warnock, “Financial
Globalization, Governance, and the Evolution of the Home Bias,” Journal of Accounting Research 47, no. 2 (2009): 597–635.
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Rica), vertical FDI primarily involves locating production where it is cheapest (broadly defined) and most
efficient. GDP growth prospects in the vertical FDI location may not matter because the goods produced there
are to be exported elsewhere (e.g., back to the company’s home country).
Horizontal foreign direct investors
Horizontal FDI, when the MNC owns a similar stage of production abroad, is a type of FDI in which the
identification of high-growth economies is important (in addition to costs and regulations). Horizontal FDI
occurs when it is cost effective to produce in a foreign country to serve that market (or export from there)
rather than to produce at home.
A number of factors determine whether to set up a horizontal FDI operation. On one side are revenue
considerations that will depend on the location’s (and the neighboring countries’) growth prospects. Assume a
country’s growth prospects are indeed promising. Then the MNC must decide whether to produce at home
and export to that country or, taking into account costs and quality, to produce in that country (horizontal
FDI). Producing in the country might reduce costs, and then the products can be sold in that country and
perhaps other nearby countries. Cost considerations influencing this decision include trade restrictions
(countries might have stricter restrictions on imports than on horizontal FDI), shipping costs (these would
presumably be lower if closer to the ultimate buyer), and general costs of production.
We note that over time the high-growth economy that is attractive for horizontal FDI might experience
rising costs (e.g., wage increases that outstrip efficiency gains). See, for example, the increase in wages in China
that pushed production to Vietnam and other countries with lower wages.
Updated Analysis of Growth Prospects: The Next BRICs?
Which countries are likely to be the next highfliers? Rodrigo appreciated the old analysis by Goldman
Sachs, but wanted to canvas the literature to find additional, complementary ways of thinking about growth
prospects.
About a decade after O’Neill’s BRICs call, Ruchir Sharma, then head of emerging markets at Morgan
Stanley Investment Management, thought a broader approach was warranted. Sharma emphasized some direct
Solow factors like income per capita and population growth, but also relied on experiential on-the-ground and
“canary in the coal mine” leading indicators of factors only indirectly related to the Solow growth model.iv
•
Experiential competitiveness. Flexible labor markets, efficient business practices, and effective intermediate
goods sourcing reduce costs, make firms more competitive, and allow economies to produce more
output at given levels of capital and labor. Sharma gets a sense of an economy’s potential international
competitiveness through comparing across countries the relative expensiveness of various products
and services that he purchases when he travels. “A rule of the road: if the local prices in an emergingmarket country feel expensive even to a visitor from a rich nation, that country is probably not a
breakout nation.”v
•
Inequality: Good billionaires and bad billionaires. In The Rise and Fall of Nations, Sharma writes, “Measuring
changes in the scale, rate of turnover, and sources of billionaire wealth can help to provide some insight
into whether an economy is creating the kind of productive wealth that will help it grow in the future.
It’s a bad sign if the billionaire class owns a bloated share of the economy, becomes an entrenched and
inbred elite, and produces its wealth mainly from politically connected industries.”vi Sharma argues that
producing billionaires is an indication that an economy has the capacity to innovate, create wealth, and
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produce highly profitable companies. Lacking billionaires can reflect growth impediments such as
excessively redistributive governments or institutional barriers to amassing wealth, which reduce
incentives for work and innovation. However, Sharma continues, billionaires are not indicative of
growth potential if their wealth predominantly stems from government connections, natural resource
extraction, inheritance, or real estate. Furthermore, he claims that low turnover of billionaires, wealth
concentration among billionaires, a high fraction of GDP going to billionaires, and billionaire wealth
in government-controlled industries are signs of crony capitalism and corruption and thus suggest poor
growth prospects.
•
Demographics, urbanization, and second cities. The density of cities allows for rapid diffusion of ideas and
reduces the cost of moving labor and capital to their most productive uses. The ability of an economy
to sustain and generate large cities is thus an indicator of growth potential. For Sharma, the key metric
is the size of the “second city,” for example Rio de Janeiro in Brazil, Busan in Korea, St. Petersburg in
Russia, and Kaohsiung in Taiwan. He argues that growth may be limited if, unlike in these instances,
the population of the second city is less than one-third of that of the main city. On the other hand, low
urbanization can signal future expansion. For example, part of the recent Chinese growth miracle was
the result of policies allowing underutilized rural populations to migrate to cities and realize their
potential productivity.7
•
Infrastructure. The efficient movement of capital, intermediate goods, and labor requires infrastructure
(e.g., quality roads, trains, and airports). Hence, while the level of investment is important, so is its
composition.
•
The natural resource curse. Sharma emphasized two problems with growth driven by natural resource
extraction. The first is the well-known concept of “Dutch disease”: commodities like oil are usually
traded in foreign currencies (e.g., US dollars) from the perspective of emerging markets. Therefore,
natural resource revenue leads to a glut of foreign currency, which causes appreciation of the domestic
currency as domestic beneficiaries try to convert foreign currency into domestic currency in large
quantities. This appreciation makes other industries less globally competitive. Second, abundant natural
resources attract foreign and domestic capital and labor at the expense of other sectors. Both Dutch
disease and the capital/labor shift can prevent long-term development and innovation in more
sustainable industries robust to volatile global commodity prices and the depletion of resource reserves.
Exhibits 3, 4, and 5 show recent data for a range of countries on direct Solow factors such as GDP per
capita, population growth, savings, and the investment/GDP ratio; some measures emphasized by Sharma,
such as ease of doing business rankings and infrastructure quality indexes (both of which indirectly affect the
savings and technology Solow factors); and Sharma’s good/bad billionaires indicator.8 One could use such
publicly available data and attempt to identify the next BRICs.9
7 This is the “Lewis turning point.” St. Lucian Nobel Prize–winning economist Arthur Lewis explained that an economy could achieve rapid growth
through a surplus of low-wage farming labor fueling low-cost industrialization. The turning point arrives when the rural labor surplus is exhausted, and
there is upward pressure on industrial wages (and thus costs).
8 The underlying data are available at “The World’s Billionaires,” Forbes, https://www.forbes.com/billionaires/list/ (accessed Nov. 16, 2018). For
updated good/bad billionaire estimates, see Sharma’s May 14, 2021, Financial Times article, “The Billionaire Boom: How the Super-Rich Soaked up Covid
Cash,” which shows that Mexico and Russia still have very high bad billionaires scores, and Taiwan, China, and South Korea have high good billionaires
scores.
9 A shortcut is to simply look at the forecasts of experts. For example, the biannual IMF World Economic Outlook (WEO) provides five-year real
GDP forecasts for all countries; these projections, for selected EMEs, are also shown in Exhibit 3.
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Identifying the Next BRICs and the Next Fragile Five
In early 2023, after almost three years of the COVID-19 pandemic and a year of war in Europe, there were
severe concerns about incomes across much of the world. To Rodrigo, the entire world seemed headed for a
period of sluggish economic growth and high inflation. But he knew “seemed” was not sufficient, so, having
internalized the economics behind BRICs, as well as more updated analysis, he cleared his head and turned to
analyzing data. Which countries are likely to be the next high-growth economies?
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Exhibit 1
Predicting the Next High-Growth Economies
Solow and the BRICs in Year 2000
GDP (in
billions of
US dollars)
Argentina
Bangladesh
Brazil
China
Colombia
DR Congo
Egypt
Ethiopia
India
Indonesia
Iran
Mexico
Myanmar
Nigeria
Pakistan
Philippines
Poland
Russia
South Africa
South Korea
Sudan
Tanzania
Thailand
Ukraine
Vietnam
Average
284
53
655
1,211
100
19
100
8
462
176
110
684
9
46
74
81
172
260
136
562
12
10
126
31
31
217
Real GDP
Per Capita
(in 2011 US
dollars)
14,189
1,362
8,628
4,118
7,026
502
4,823
529
2,009
3,888
7,470
12,076
1,174
761
2,740
4,315
13,610
10,516
8,806
22,541
1,874
1,125
7,336
4,590
2,100
5,924
Gross Domestic
Saving / GDP (in
percentage)
Investment /
GDP (in
percentage)
Depreciation
Rate (in
percentage)
Population
Growth (in
percentage)
Human
Capital
Index
16
19
17
37
14
10
13
19
21
20
26
14
10
14
9
21
17
24
21
9
6
13
17
20
16
15
33
6
18
24
13
19
17
3.4
4.7
4.3
4.7
9.9
3.9
5.8
3.8
4.1
3.7
3.9
3.6
5.7
3.7
5.6
4.1
4.3
2.6
4.3
4.7
5.7
4.6
5.5
2.8
2.8
4.5
1.1
2.0
1.5
0.8
1.5
2.5
1.8
2.9
1.8
1.4
1.6
1.4
1.2
2.5
2.3
2.1
−1.0
−0.4
1.5
0.8
2.4
2.6
1.0
−1.0
1.3
1.4
2.7
1.6
2.0
2.2
2.2
1.6
2.0
1.2
1.8
2.2
1.7
2.4
1.5
1.5
1.6
2.4
3.0
3.2
2.1
3.2
1.4
1.5
2.2
3.1
2.0
2.1
26
31
37
22
39
16
16
18
39
19
35
27
10
31
25
26
24
Stock Market
Capitalization (in
billions of US
dollars)
46
2
226
50
2
5
21
27
27
125
0
7
26
31
16
204
171
0
29
53
Real GDP growth,
2001–2006 (in
percentage)
2.6
4.4
3.0
10.2
4.3
0.0
5.3
6.6
8.4
4.6
13.9
4.2
11.8
8.8
5.7
1.9
2.8
7.0
5.2
4.9
9.7
8.2
6.7
8.7
8.1
6.3
Note: The table shows Penn World Table 9.0 Solow factors from 2000 for emerging market economies (EMEs) with population in excess of 30 million. Real GDP and real capital per capita (in 2011 US dollars)
are adjusted for purchasing power parity (PPP) to account for differences in the cost of living across countries. The human capital index, which reflects years and quality of schooling, ranges from 1 (low) to 4
(best). Nominal GDP, gross domestic savings, and population growth are from the World Development Indicators (WDI), https://data.worldbank.org/ (accessed December 2017). Stock market capitalization
is also from the WDI if end-2000 data is available; for countries for which end-2000 data are not available in the WDI (China, Colombia, Egypt, India, Nigeria, and Russia), it is the market capitalization deemed
“investable” by the International Finance Corporation. Gross domestic savings are calculated as GDP less final consumption expenditure (total consumption).
Source: Created by authors.
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Exhibit 2
Predicting the Next High-Growth Economies
Stock Market Returns and GDP Growth (2001–14)
Annualized Percentage Change in Dollar Stock
Index (2001–14)
25
COL
20
IDN
15
THA
KOR
MEX
CZE
ZAF
10
PHL
AUS
5
JPN
0
ITA
-5
0
CAN
SWE
USA
DEU
GBR
FRA
ARG
CHE
HUN POL
PAK
MYS
BRA RUS
TUR
EGY
IND
CHN
IRL
3
6
9
Annualized Real GDP Growth 2001–14 (percentage)
12
Notes: Real GDP is adjusted for purchasing power parity (PPP) to account for differences in the cost of living across countries. For each country, the dollar
stock market return is the percentage change in the dollar MSCI price index. For country codes, see “Country Codes,” World Bank,
https://wits.worldbank.org/WITS/wits/WITSHELP/Content/Codes/Country_Codes.htm (accessed Jan. 10, 2024).
Data source: Penn World Table 9.0, https://www.msci.com/ (accessed Nov. 16, 2018).
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Exhibit 3
Predicting the Next High-Growth Economies
Recent Data on Growth Prospects (I)
IMF LongTerm Real
Growth
Forecast (in
percentage)
Year
Argentina
Bangladesh
Brazil
China
Colombia
Egypt
Ethiopia
India
Indonesia
Mexico
Nigeria
Pakistan
Poland
Russia
Serbia
South Africa
Thailand
Ukraine
Vietnam
Average
2027
2.0
6.9
2.0
4.6
3.3
5.9
7.0
6.2
5.1
2.1
2.9
5.0
3.1
0.7
4.0
1.4
3.0
6.8
4.0
Investment /
GDP (in
percentage)
GDP Per
Capita (in
US dollars)
Population
Growth (in
percentage)
2019–21
average
15
32
17
42
19
15
31
28
32
20
28
13
18
21
22
14
23
14
31
23
2021
10,636
2,458
7,057
12,556
6,104
3,699
925
2,257
4,333
10,046
2,066
1,505
18,000
12,195
9,230
7,055
7,066
4,836
3,756
6,620
2021
0.9
1.1
0.5
0.1
1.1
1.7
2.6
0.8
0.7
0.6
2.4
1.8
−0.4
−0.4
−0.9
1.0
0.2
−0.8
0.8
0.7
Gross
Domestic
Saving / GDP
(in
percentage)
2019–21
average
20
26
17
45
13
6
21
28
33
23
27
6
24
30
16
17
31
9
34
22.4
Depreciation Rate
(in percentage)
2019
3.5
4.0
4.8
5.2
3.8
5.4
4.7
5.7
4.2
3.9
3.4
7.5
5.1
3.4
4.1
5.2
6.3
2.6
5.4
4.6
Note: Gross domestic savings are calculated as GDP less consumption expenditure.
Sources: Created by authors. Data are from the World Development Indicators, https://datatopics.worldbank.org/world-development-indicators/, except
depreciation, which is from Penn World Tables 10.0, https://www.rug.nl/ggdc/productivity/pwt/, and the International Monetary Fund (IMF) real GDP
growth forecast, which is from the fall 2022 World Economic Outlook database, https://www.imf.org/en/Publications/WEO/weodatabase/2022/October (all accessed Jan. 2, 2023).
Page 12
UVA-GEM-0207
Exhibit 4
Predicting the Next High-Growth Economies
Recent Data on Growth Prospects (II)
Year
Argentina
Bangladesh
Brazil
China
Colombia
Egypt
Ethiopia
India
Indonesia
Mexico
Nigeria
Pakistan
Poland
Russia
Serbia
South Africa
Thailand
Ukraine
Vietnam
Average
Ease of
Doing
Business
Rank
External
Debt (as
percentage
of GNI)
2019
126
168
124
31
67
114
159
63
73
60
131
108
40
28
44
84
21
64
70
83
2021
51
21
39
15
56
37
27
20
36
48
18
38
28
68
41
43
70
39
39
CPI
Inflation
(as
percentage)
2020–22
Average
54.3
5.8
7.0
1.8
5.3
6.2
26.9
6.2
2.7
5.7
16.4
10.6
7.4
8.0
5.7
4.9
2.2
10.9
2.9
10.0
Urbanization
(as
percentage)
2020
92
38
87
61
81
43
22
35
57
81
52
37
60
75
56
67
51
70
37
58
Infrastructure
Human
Capital
Index
Natural
Resources
Rents (as
percentage of
GDP)
2018
2.8
2.4
2.9
3.8
2.7
2.8
2.1
2.9
2.9
2.9
2.6
2.2
3.2
2.8
2.6
3.2
3.1
2.2
3.0
2.8
2019
3.1
2.1
3.1
2.7
2.6
2.7
1.5
2.2
2.3
2.8
2.0
1.8
3.5
3.4
3.5
2.9
2.8
3.3
2.9
2.7
2020
1.8
0.3
4.0
1.1
3.8
2.7
5.1
1.9
2.8
2.1
6.2
0.9
0.6
10.2
1.0
3.9
1.3
1.5
2.6
2.8
Notes: The infrastructure number is an index that measures “quality of trade and transport-related infrastructure (1 = low to 5 = high).” “Ease of
Doing Business” ranks countries in the world from 1 (best) to 190 (worst). GNI is gross national income (GDP plus net factor payments).
Sources: Created by authors. Data, current as of January 2, 2023, are from the World Development Indicators, except the human capital index,
which reflects years and quality of schooling, ranges from 1 (low) to 4 (best), and is from Penn World Tables 10.0, and inflation, which is from the
fall 2022 IMF WEO.
Page 13
UVA-GEM-0207
Exhibit 5
Predicting the Next High-Growth Economies
Good Billionaires, Bad Billionaires (The Rise and Fall of Nations)
Total Billionaire
Wealth / GDP
Bad Billionaire Wealth / Total
Billionaire Wealth
Inherited Billionaires’
Wealth / Total
Billionaire Wealth
Brazil
China
India
Indonesia
Mexico
Poland
Russia
South Korea
Taiwan
Turkey
EME average
8%
5%
14%
7%
11%
2%
16%
5%
16%
6%
9%
5%
27%
31%
12%
71%
44%
67%
4%
23%
22%
31%
43%
1%
61%
62%
38%
0%
0%
83%
44%
57%
50%
Australia
Canada
France
Germany
Italy
Japan
Sweden
Switzerland
United Kingdom
United States
AE Average
5%
8%
9%
11%
7%
2%
21%
15%
6%
15%
10%
45%
11%
5%
1%
3%
9%
5%
29%
25%
10%
14%
41%
47%
67%
73%
51%
14%
77%
62%
32%
34%
50%
Country
Note: AE = advanced economies; EME = emerging-market economies. Ruchir Sharma defines “bad” billionaires as those whose wealth stems
from natural resource extraction, real estate, and government connections.
Source: “The World’s Billionaires,” Forbes, March 2015, https://www.forbes.com/billionaires/list/ (accessed Nov. 16, 2018).
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UVA-GEM-0207
Endnotes
i The World Bank’s current “Global Economic Prospects” report is at https://www.worldbank.org/en/publication/global-economic-prospects
(accessed Nov. 30, 2022). To access any past report from that page, click on Downloads (currently at upper right of page) and then choose Report
Archive.
ii See Charles I. Jones, “The End of Economic Growth? Unintended Consequences of a Declining Population,” American Economic Review 112, no. 11
(2022): 3,489–527.
iii For more on these growth factors, see the “Solow Outside the Box” appendix in Felipe Saffie, “Determinants of Economic Growth,” UVA-GEM0202 (Charlottesville, VA: Darden Business Publishing, 2022).
iv See Ruchir Sharma, Breakout Nations: In Pursuit of the Next Economic Miracles (New York: W. W. Norton & Company, 2012).
v Sharma, Breakout Nations.
vi Ruchir Sharma, The Rise and Fall of Nations: Forces of Change in the Post-Crisis World (New York: W. W. Norton & Company, 2016).