UV7649 Rev. Jan. 24, 2019 Identifying the Next High-Growth Economies In 2001, British economist Jim O’Neill, head of global economic research for Goldman Sachs in London, established the now-famous acronym “BRIC” to refer to the strong growth potential in the economies of Brazil, Russia, India, and China. He wrote, “In all four scenarios, the relative weight of the BRICs rises from 8.0% [of world GDP] at present (in current US dollars) to 14.2%...In each of these scenarios, the increasing weight is led by China, although the other three grow relative to the G7 countries also.” 1 O’Neill was correct. From 2001 to 2014, the annual average PPP-adjusted real GDP growth rates in Brazil, Russia, India, and China were 5.3%, 6.2%, 8.7%, and 8.9%, respectively. Over the same period, the United States, United Kingdom, and Germany grew at average rates of 1.7%, 1.6%, and 2.1%, respectively. 2 How do investors and managers identify the next BRIC countries? The Solow growth model forms the bedrock of economists’ thinking about growth. 3 This note summarizes the Solow approach to identifying high- growth economies, describes supplemental strategies from the investment management industry, and provides recent data on some economies to determine which countries will be the next BRICs. Solow Growth Model The Solow growth model has, at its core, two key pieces. The first is the production function, which says that a country’s output stems from its capital stock, its labor force, and the productivity with which these two factors are combined. Importantly, holding either labor or capital constant, the production function exhibits diminishing marginal returns with respect to the other factor. The second element is the capital accumulation equation, which says that capital tomorrow is equal to capital today, net of depreciation, plus investment. Combining these very general pieces and looking at the dynamics, the model evolves toward a steady state— that is, a central tendency or long-run implication—and provides quite stark predictions regarding growth. Who grows quickly? Economies catching up from below steady state 1 Jim O’Neill, “Building Better Global Economic BRICs” (working paper, Goldman Sachs Global Economics, 2001). The G7, or Group of Seven, countries are Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States. 2 PPP = purchasing power parity. Calculations by the authors based on data from Penn World Table 9.0, https://www.rug.nl/ggdc/productivity/pwt/ (accessed Nov. 16, 2017), Robert C. Feenstra, Robert Inklaar, and Marcel P. Timmer, “The Next Generation of the Penn World Table,” American Economic Review 105, no. 10 (October 2015): 3150–82. 3 For details on the Solow growth model, see Kieran James Walsh, “Growth Theory,” UVA-GEM-0168 (Charlottesville, VA: Darden Business Publishing, 2018). The original Solow growth article is Robert M. Solow, “A Contribution to the Theory of Economic Growth,” Quarterly Journal of Economics 70, no. 1 (February 1956), 65–94. This technical note was prepared by Assistant Professor Kieran James Walsh with Anthony Roberts (MBA ’18). Copyright 2018 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 in a retrieval system, used in a spreadsheet, or transmitted in any form or by any means—electronic, mechanical, photocopying, recording, or otherwise—without the permission of the Darden School Foundation. Our goal is to publish materials of the highest quality, so please submit any errata to editorial@dardenbusinesspublishing.com. Page 2 UV7649 Economies with rapidly increasing productivity Economies with high population growth (note that in the model, population growth increases GDP but actually decreases the level of GDP per capita) Who is below steady state (and thus likely to grow quickly)? Previously prosperous economies that have exogenously lost capital (e.g., from a war) Economies with recent positive shocks to savings or access to technology Economies with recent positive shocks to less direct determinants of investment and technology, such as foreign direct investment (FDI), wasteful government intervention, and debt service Solow and the BRICs Applying the Solow model lens to data available as of the end of 2000 provides insight into O’Neill’s BRICs call. Exhibit 1 shows 2000 data for emerging market economies (EMEs) with populations in excess of 30 million people. The growth potential of China is immediately evident. At the end of 2000, it had high investment, second only to South Korea among large EMEs, and above-average human capital, reflected in the education index. 4 Yet China’s GDP per capita was behind that of economies comparable or worse on these measures (such as Thailand, Iran, South Africa, Colombia, and Egypt), suggesting 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 (less than half of China’s), despite above-average investment. India’s human capital lagged behind China’s, but it was above the levels in Iran and Pakistan, economies with higher output per person. Overall, with extremely low GDP per capita, relatively high investment, and decent human capital, India also appeared to be below steady state. While Brazil was much wealthier than China and India in terms of GDP per capita in 2000, the country 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. Identifying the Next BRICs Exhibits 2 and 3 show, for many large EMEs, recent data on direct Solow factors such as GDP per capita, population growth, the investment/GDP ratio, and more indirect measures, such as ease of doing business rankings and infrastructure quality indexes (both of which indirectly affect the savings and technology Solow 4 The human capital index 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. For further details, see “Human Capital in PWT 9.0,” University of Groningen, https://www.rug.nl/ggdc/docs/human_capital_in_pwt_90.pdf (accessed Nov. 16, 2018). Page 3 UV7649 factors). One could use such publicly available data and attempt to identify the next BRICs. There are also a number of other supplemental approaches. Professional forecasters One way to forecast is simply to look at the forecasts of experts. For example, the biannual International Monetary Fund’s World Economic Outlook (WEO) provides five-year real GDP forecasts for all countries; these projections, for selected EMEs, are shown in Exhibit 2 . As of spring 2018, Ethiopia and India were at the top of the list, with average expected real GDP growth of around 8% per year through 2023, while Nigeria, South Africa, and Russia were at the other end, with expected growth of 2% or less per year through 2023. Ruchir Sharma For a truly rounded view of emerging markets, my approach is to monitor everything from per capita income levels to the top-ten billionaire lists, the speeches of radical politicians, the prices of black- market money changers, the travel habits of local businessmen (for example, whether they are moving money home or offshore), the profit margins of big monopolies, and the size of second cities (oversized capital cities often indicate excessive power in the hands of the political elite). 5 Ruchir Sharma, head of emerging markets at Morgan Stanley Investment Management, emphasizes some direct Solow factors like income per capita and population growth, but describes his approach as relying on experiential on-the-ground and “canary in the coalmine” leading indicators of factors only indirectly related to the Solow growth model. That said, almost everything Sharma emphasizes can be translated into Solow. 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/services he purchases when he travels. “A rule of the road: if the local prices in an emerging- market country feel expensive even to a visitor from a rich nation, that country is probably not a breakout nation.” 6 Publicly available cross-country data ( Exhibit 3 ) proxy for much of what Sharma calls experiential competitiveness. 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.” 7 Sharma argues that producing billionaires is an indication that an economy has the capacity to innovate, create wealth, and 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 5 Ruchir Sharma, Breakout Nations: In Pursuit of the Next Economic Miracles (New York: W. W. Norton & Company, 2012). 6 Sharma, Breakout Nations 7 Ruchir Sharma, The Rise and Fall of Nations: Forces of Change in the Post-Crisis World (New York: W. W. Norton & Company, 2016). Page 4 UV7649 growth prospects. Exhibits 4 and 5 , taken from his books, show Sharma’s billionaire indicators for many countries. 8 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 in 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. This is the idea behind 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). Exhibit 3 provides data on urbanization in many emerging markets. Infrastructure. The efficient movement of capital, intermediate goods, and labor requires infrastructure (e.g., quality roads, trains, and airports). Hence, the composition of investment matters beyond its level. The natural resource curse. Sharma emphasizes 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 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. Mark Mobius Mark Mobius is a renowned emerging markets fund manager at Franklin Templeton Investments. For 25 years, he served as lead manager of the GBP1.8 billion Templeton Emerging Markets Investment Trust, the United Kingdom’s largest global emerging markets investment trust. 9 Mobius is fundamentally a value investor, trying to find underpriced assets, and he believes company-specific independent research is the key to gaining a market advantage. He does this by traveling extensively to gather information on the ground about investment opportunities. According to Mobius, the four best sources of information for emerging markets investors are: 10 1. The staff of a company in which you are considering an investment, 2. The staff of the company’s competitors, 3. The audited financial statements, and 4. The company’s customers. 8 The underlying data are available at “The World’s Billionaires,” Forbes , https://www.forbes.com/billionaires/list/ (accessed Nov. 16, 2018). 9 GBP = British pounds. 10 Mark Mobius, The Little Book of Emerging Markets: How to Make Money in the World’s Fastest Growing Markets (Singapore: John Wiley & Sons Singapore, 2012). Page 5 UV7649 “It is important to incorporate both perspectives by having local and country-specific information collated, digested, and then contrasted to global data. This analytic process yields much more powerful results than research that leans heavily on one or the other source of information.” 11 High Growth Is in the Eye of the Beholder While growth in GDP per capita correlates with key measures of standards of living, 12 why should an investor care about GDP growth? The answer depends on the perspective of the investor. Portfolio equity investors The returns to an arms-length portfolio equity investor depend on dividend yields and capital gains. Dividends are tied to earnings, which depend on sales and thus national income and aggregate demand. Equity prices, and thus capital gains, are more fickle, though ultimately tied to expectations of income and demand. Therefore, the arms-length 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 outside investors actually get. However, as evidenced in Exhibit 6 , stock market returns have recently been correlated with growth in many places. The exhibit shows the growth of Morgan Stanley Capital International (MSCI) country stock market indexes (in US dollars), which can be traded in the United States for exchange-traded funds (ETFs), for most of the countries mentioned in this note over 2001 to 2014. The exhibit clearly illustrates that stock market returns were positively correlated with GDP growth over this period. However, Ireland (which had decent GDP growth but the poorest stock market performance of these countries) and Colombia (which yielded the highest return) show that portfolio equity investors cannot always expect perfect correlation between growth and returns. Exporters GDP determines national income and hence demand. Therefore, an exporter to emerging markets should assess GDP growth prospects, although the specific income levels and demographics of target clients, as well as the nature of trade agreements, are also important. Vertical foreign direct investors Through FDI, a multinational corporation (MNC) has operations—or control of operations—in a foreign country. Vertical FDI is when an MNC owns a stage of production in a foreign country. For example, a Japanese automotive company might produce parts in one country (e.g., South Korea) to incorporate into their cars sold in Japan. Vertical FDI involves locating production where it is cheapest and most efficient. 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 Rica), but the identification of high-growth economies is not necessary. Proximity to high- growth economies may be important, but a vertical FDI operation need not be placed in a high-growth economy. Vertical FDI is driven by cost considerations. An MNC may want to locate part of its production chain in the country with the lowest costs of production. For example, primary and secondary stages of production are often located in developing countries with low wages for the basic labor tasks that these stages of production 11 Mobius. 12 Walsh, “Growth Theory.” Page 6 UV7649 require. Because the location of production may not be the primary source of sales and revenues, cost considerations dominate. 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. 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 shop (horizontal FDI). These include trade restrictions, shipping costs, and general costs of production. For example, a company with a US-based cost structure might find it difficult to export profitably to poorer countries but could lower its costs by producing in the low-income (and presumably low-cost) country. Horizontal FDI can also be a platform to export to other foreign countries by reducing production and shipping costs and perhaps jumping trade barriers. Horizontal FDI is driven by revenue considerations. A firm may acquire a foreign entity that permits it to sell products in that country and other proximate countries at a lower cost than would be possible by simply exporting to the foreign market. The benefits from the acquisition depend on the revenues/sales to which the acquirer gains access. Costs of production are less relevant if the firm already has an established/fixed location of production and the acquisition is only to facilitate sales in the foreign market. This is not to suggest that costs are irrelevant for horizontal FDI. The high-growth economy that is attractive for horizontal FDI might experience rising costs (e.g., wage increases that outstrip efficiency gains). Low-margin operations are particularly susceptible to rising costs—see, for example, the increase in wages in China that has pushed production of many low-margin goods (e.g., footwear and clothing) to Vietnam and other countries with lower wages. But the production of high-margin goods (e.g., consumer electronics and pharma) is less vulnerable to local cost increases. Page 7 UV7649 Exhibit 1 Identifying the Next High-Growth Economies Solow and the BRICs in 2000 Investment / GDP (%) Education index Real GDP per capita (in 2011 US dollars) GDP (in billions of US dollars) Stock market capitalization (in billions of US dolla 171 rs) Real GDP growth, 2001–06 (%) South Korea 33 3.2 22,541 562 4.9 China 26 2.2 4,118 1211 10.2 Thailand 24 2.2 7,336 126 29 6.7 Iran 24 1.7 7,470 110 27 13.9 Mexico 21 2.4 12,076 684 125 4.2 Bangladesh 21 1.6 1,362 53 2 4.4 India 21 1.8 2,009 462 8.4 Brazil 20 2.0 8,628 655 226 3.0 Poland 20 3.0 13,610 172 31 2.8 Vietnam 19 2.0 2,100 31 8.1 Argentina 19 2.7 14,189 284 46 2.6 Tanzania 18 1.5 1,125 10 0 8.2 Indonesia 17 2.2 3,888 176 27 4.6 Philippines 17 2.4 4,315 81 26 1.9 Russia 16 3.2 10,516 260 7.0 South Africa 15 2.1 8,806 136 204 5.2 Colombia 14 2.2 7,026 100 4.3 Egypt 14 2.0 4,823 100 5.3 Pakistan 13 1.6 2,740 74 7 5.7 Ukraine 13 3.1 4,590 31 8.7 DR Congo 10 1.6 502 19 0.0 Myanmar 9 1.5 1,174 9 11.8 Ethiopia 9 1.2 529 8 6.6 Sudan 6 1.4 1,874 12 9.7 Nigeria 6 1.5 761 46 33.8 Average 17 2.1 5,924 217 71 7.3 Note: The table shows Penn World Table 9.0 Solow factors from 2000 for EMEs with population in excess of 30 million. Real GDP (in 2011 US dollars) is adjusted for purchasing power parity (PPP) to account for differences in the cost of living across countries. The education index is a human capital index that reflects years and quality of schooling. DR Congo refers to the Democratic Republic of Congo. Source: Nominal GDP and stock market capitalization are from the World Development Indicators, https://data.worldbank.org/ (accessed Nov. 16, 2018). Page 8 UV7649 Exhibit 2 Identifying the Next High-Growth Economies Country Data IMF real growth forecast (%) Investment / GDP (%) GDP per capita (in US dollars) Population growth (%) Real capital per capita (in 2011 US dollars) Gross domestic saving / GDP (%) Year 2019–23 2017 (*2016) 2017 2017 2014 2015 (*2016) Ethiopia 8.1 39 768 2.5 3,954 24 India 8.0 31 1,940 1.1 17,364 30 Bangladesh 7.0 31 1,517 1.0 9,606 25 China 6.0 44 8,827 0.6 50,663 47 Egypt 5.8 15 2,413 1.9 14,209 3 Indonesia 5.6 33 3,847 1.1 52,885 34 Pakistan 4.9 16 1,548 2.0 8,219 7 Serbia 3.9 21 5,900 − 0.5 55,745 12 Ukraine 3.7 21 2,640 − 0.4 35,253 14 Thailand 3.6 22* 6,594 0.3 54,708 33* Colombia 3.5 23 6,302 0.8 39,263 18 Argentina 3.2 19 14,402 1.0 49,983 16 Poland 3.0 20 13,812 0.0 53,702 24 Mexico 2.9 23 8,903 1.3 53,256 23 Brazil 2.3 16 9,821 0.8 66,262 17 Nigeria 2.0 15* 1,969 2.6 11,037 13* South Africa 1.8 19 6,161 1.2 41,225 20 Russia 1.5 24 10,743 0.1 53,731 30 Note: Real capital per capita (in 2011 US dollars) is adjusted for purchasing power parity (PPP) to account for differences in the cost of living across countries. Gross domestic savings are calculated as GDP less final consumption expenditure (total consumption). Source: All data are from the World Development Indicators, https://data.worldbank.org/ (accessed Nov. 16, 2018), except the IMF real GDP growth forecast, which is from the spring 2018 World Economic Outlook, https://www.imf.org/en/Publications/WEO/Issues/2017/09/19/world- economic-outlook-october-2017 (accessed Nov. 16, 2018), and real capital per capita, which is from the Penn World Table 9.0. Page 9 UV7649 Exhibit 3 Identifying the Next High-Growth Economies Country Data Ease of doing business rank External debt (% of GNI) CPI inflation (%) Urbanization (%) Infrastructure Natural resources rents (% of GDP) Year 2017 2016 2016 2017 2016 2016 India 100 20 4.9 34 3.3 1.9 Ethiopia 161 32 7.3 20 2.1 12.0 Bangladesh 177 18 5.5 36 2.5 0.8 China 78 13 2.0 58 3.8 1.1 Pakistan 147 24 3.8 36 2.7 1.2 Egypt 128 20 13.8 43 3.1 4.0 Indonesia 72 35 3.5 55 2.6 2.5 Serbia 43 83 1.1 56 2.5 1.5 Ukraine 76 128 13.9 69 2.5 3.8 Colombia 59 43 7.5 80 2.4 3.5 Thailand 26 31 0.2 49 3.1 1.2 Argentina 117 36 92 2.9 1.3 Poland 27 − 0.6 60 3.2 0.8 Mexico 49 41 2.8 80 2.9 2.6 Brazil 125 31 8.7 86 3.1 3.1 South Africa 82 51 6.3 66 3.8 4.7 Nigeria 145 8 15.7 50 2.4 5.4 Russia 35 42 7.0 74 2.4 11.5 Note: 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). Source: All data are from the World Development Indicators, https://data.worldbank.org/ (accessed Nov. 16, 2018). Page 10 UV7649 Exhibit 4 Identifying the Next High-Growth Economies Good Billionaires, Bad Billionaires ( The Rise and Fall of Nations ) Inherited billionaires’ Total billionaire Bad billionaire wealth / total Country wealth / total wealth / GDP billionaire wealth billionaire wealth Brazil 8% 5% 43% China 5% 27% 1% India 14% 31% 61% Indonesia 7% 12% 62% Mexico 11% 71% 38% Poland 2% 44% 0% Russia 16% 67% 0% South Korea 5% 4% 83% Taiwan 16% 23% 44% Turkey 6% 22% 57% EME average 9% 31% 50% Australia 5% 45% 41% Canada 8% 11% 47% France 9% 5% 67% Germany 11% 1% 73% Italy 7% 3% 51% Japan 2% 9% 14% Sweden 21% 5% 77% Switzerland 15% 29% 62% United Kingdom 6% 25% 32% United States 15% 10% 34% AE Average 10% 14% 50% Note: AE and EME stand for advanced economies and emerging market economies. Ruchir Sharma defines “bad” billionaires as the ones with wealth stemming 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). Page 11 UV7649 Exhibit 5 Identifying the Next High-Growth Economies The Billionaires Index ( Breakout Nations ) Country Number of billionaires Total net worth (in billions of US dollars) Total net worth / GDP (%) Average net worth of top 10 (in billions of US dollars) Russia 100 432 29 17 Malaysia 9 44 20 5 India 55 247 17 15 Taiwan 25 62 15 4 Mexico 11 125 13 12 Saudi Arabia 7 55 12 8 Turkey 38 64 9 3 Brazil 30 131 7 9 Philippines 4 11 6 3 Indonesia 14 32 5 3 Korea 16 40 4 3 China 115 230 4 6 Sources: “World Economic Outlook, September 2011: Slowing Growth, Rising Risks” (Washington, DC: International Monetary Fund, 2011); Forbes Billionaires List, April 2011. Page 12 UV7649 Exhibit 6 Identifying the Next High-Growth Economies AUS EGY PHL USA Annualized % Change in Dollar Stock Index (2001–14) Stock Market Returns and GDP Growth (2001–14) 25 COL 20 IDN 15 THA KOR ZAF SWE PAK CHN ARG CAN CHE DEU MEX HUN POL GBR CZE IND 10 MYS RUS BRA TUR 5 JPN FRA 0 ITA IRL -5 0 3 6 9 12 Annualized Real GDP Growth 2001–14 (%) Note: 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. Sources: Penn World Table 9.0; https://www.msci.com/ (accessed Nov. 16, 2018).