The sustained growth that large emerging
countries have experienced over the last two decades has conferred on them a
considerable growth delta over the OECD average. Combined with very large
populations, these growth differences are translating into a new world
economy. For the first time in history, the countries with the largest
economic mass are not also the richest countries. The shorthand for this
complex event is what we call “Shifting Wealth” – the recalibration of the world economy
toward the East and the South –, well documented since 2010 in the OECD
Perspectives on Global Development. The last post on this blog had
discussed potential tailwinds for Shifting Wealth that may arise from the
twin-turbo China cum India. Today, I want to reflect on four potential headwinds going forward:
·
Slower
speed of GDP convergence as the distance to the advanced countries is
shrinking;
·
The
middle-income trap in emerging countries;
·
Technological
innovations that might reduce labor demand in emerging countries;
·
Global
trade, notably normalisation and protectionism.
To be sure, there is no mechanical necessity about these risks. But policymakers, notably in middle-income emerging countries must be aware of them. Whether economists´ almost Pavlovian responses to these trends - better education, redistribution, activist industrial policy - can deal with these trands in a welfare improving way is beyond the scope of this paper, which is busy looking at trends.
Convergence Speed
China´s growth has been the engine of Shifting
Wealth since the 2000s. After almost four decades of sustained growth, the
Chinese economy has grown from an average GDP_PPP/capita of 313 US dollar in
1980 to 15,417 US dollar in 2016. Its GDP per capita is currently equivalent to
55 percent of the world's average. China, initially capital poor, has saved and
invested a large part of its annual GDP over the past 40 years. The
neoclassical Solow-Swan growth model postulates diminishing returns to capital.
This has led Krugman (1994)[1]
to make one of the most blatant errors in the history of economic prognosis:
”If growth
in East Asia runs into diminishing returns, however, the conventional wisdom about an
Asian-centered world economy needs some rethinking”…”From the perspective of
the year 2010 (sic!), current projections of Asian supremacy … may well look as silly as 1960s-vintage
forecasts of Soviet industrial supremacy did from the perspective of the
Brezhnev years”.
The hypothesis that poor economies tend to grow
faster per capita than rich ones— without conditioning on any other
characteristics of economies—is referred to as absolute convergence.
However, it has held in the past only for a fairly homogenous group of
countries, such as the OECD, or for incomes across US states (Barro and
Sala-i-Martin, 2004)[2].
The key proposition of endogenous-growth models is the absence of diminishing returns
to capital. The simplest version of a production function without diminishing
returns is the AK function Y = AK, where A is a positive constant that reflects
the level of the technology. The global absence of diminishing returns may seem
unrealistic, but the idea becomes more plausible if we think of K in a broad
sense to include human capital.
Acemoglu, Aghion and Zilibotti (2006)[3]
produced a stochastic growth model in which a country‟s “distance to the
frontier” matters for the selection of appropriate growth strategies. Countries
at early stages of development (optimally) pursue an investment-based strategy,
which relies on existing firms and managers to maximize investment under the
guidance of the government; at this stage, local entrepreneurship and home-made
innovation matter relatively less, a condition that carries implications in
shaping appropriate institutions. The three authors show that relatively backward
economies risk shifting out of the investment-based strategy too soon. Policies
that encourage the investment-based strategy, such as limits on product market
competition or investment subsidies, may be beneficial for a lengthy period in
terms of facilitating economic convergence and poverty reduction.
Table 1: BRIICS
Innovation Ranking
Country
|
China
|
Russia
|
S
Africa
|
India
|
Brazil
|
Indon´ia
|
Total
|
2017
|
22
|
45
|
57
|
60
|
69
|
87
|
127
|
2007
|
29
|
54
|
38
|
23
|
40
|
48
|
107
|
Source: World
Intellectual Property Organization, Global Innovation Index 2017 and 2007.
Investment-based development strategies may
also have significant long-run costs, if the country holds on too long, failing
to reach the world technology frontier and finding itself devoid of domestic
entrepreneurs and innovation capacity. Reassuringly, China has managed to reach
the world technology frontier and is blessed with millions of highly innovative
entrepreneurs. The Global Innovation Index 2017 (published annually since 2007 at
the World Intellectual Property Organization) provides
detailed metrics about the innovation performance of 127 countries and
economies around the world, with 81 indicators. China ranked at 22, the only
middle-income country among the top 25 innovative countries. Note that, China
and Russia aside, no other BRIICS country was able to move up the WIPO
innovation ranking over the past decade (Table 1).
The Middle Income Trap
Closely related is the concern that
middle-income countries can be ´trapped´ in growth slowdowns. Gill and Kharas (2015)[4]
had coined the term ´middle-income trap´ in 2005, because they had found growth
theory wanting for the group of middle-income countries (which the World Bank in 2018 classifies as the large group of countries
with GNI per capita between $1,005 and $12,235): endogenous growth theories
addressed the problem in high-income economies, and the Solow growth model was
still the work-horse for understanding the growth problem in low-income countries,
but neither were satisfactory for understanding what to do in countries where 5
billion (out of 7billion) people in the world live.
The middle-income trap, whereby GDP per capita
growth slows down once a country approaches an intermediate level of
development, is particularly persistent in Latin America[5].
Although average per capita income in the region was relatively high in the
mid-20th century, most Latin American countries have been unable to reduce
significantly the income gap with advanced economies and reach high-income
status. The few regional exceptions are Chile, Uruguay and some Caribbean
countries. These trends contrast with European and Asian countries, much more
effective in joining the high-income group during the last half of the 20th
century. Even though ´Emerging Asia´ has made remarkable progress over the past
four decades in raising income levels, it will take from several years
(Malaysia, China) to decades (Indonesia, India) to join in the ranks of the
advanced high-income countries, according to OECD (2014)[6]
estimates (Fig. 1). The jury is still out as to whether middle-income ASEAN
countries like Indonesia, Malaysia, the Philippines, Thailand or Vietnam can
expect to replicate the growth experience of the Asian Tigers, or whether they
will follow the trajectories of Latin America.
Figure 1: Estimated years
required to become high-income country, 2014
Source: SEAO
2014, http://dx.doi.org/10.1787/888932937111
To be sure, there is nothing mechanical about
the development transition from middle-income to high-income country, not even
in Asia. Since 2000 (tiny island economies aside), six European and two Latin
American countries have joined the ranks of high-income economies as classified
by the World Bank, but no Asian country.
Technological Innovations and Labor Demand
Especially the 1990s opening phase of Shifting
Wealth had been rooted in the abundance of labor with basic skills in poor
countries that got connected to demand in advanced countries via trade and
investment, resulting initially in manufacturing job creation in China above
all. The world entered a new phase of globalisation:
Information and communication technology, trade liberalisation and lower
transport costs have enabled firms and countries to fragment the production
process into global value chains (GVCs). However, the current proliferation of
artificial intelligence, such as industrial robots, and other forms of
worker-replacing technological progress have the potential to disrupt global
labor markets (Korinek and Stiglitz, 2017)[7].
Technology innovation, such as 3D printing, may facilitate reshoring and would
deepen the domestic (rather than global) division of labor for major economies,
including China. Policymakers in middle-income countries have started to worry
about the “end of global value chains” (Arbache, 2016)[8].
Robots could alter the global pattern of
comparative advantage, for example in shoe production, which would shift from
skill-scarce to skill-abundant countries if skilled workers with robots could
make shoes more efficiently than unskilled workers[9].
The familiar benefits of industrialization as a
development strategy might be eroded if robot-based automation makes
industrialization more difficult or causes it to yield substantially less
manufacturing employment than in the past (Mayer, 2017[10]). To
be sure, technical feasibility of workplace automation does not imply economic
profitability, especially in developing countries with low wage levels. And Central European countries, it has been argued recently by Professor Dalia Marin (LMU Munich) at a globalisation conference run by the Hans-Boeckler Foundation (https://www.boeckler.de/veranstaltung_107484.htm), have been able to advance to high-income status despite sharply rising wage cost thanks to relatively massive robotisation. Robotisation as a way to beat the middle-income trap?
Figure
2: Vulnerability to robot-based automation in manufacturing
Source: UNCTAD (2017), "Robots,
industrialization and inclusive growth", chapter 3 in Trade and Development Report
2017
Figure 2
suggests “that on current technological and economic
indicators, developed countries and developing countries other than
least-developed countries (LDCs) are exposed to robot-based automation in
manufacturing to a larger extent than LDCs. It should be noted that this
evidence only refers to exposure to robot-based automation and does not take
account of the risks to employment from other forms of automation. But it
suggests that robot-based automation per se does not invalidate the traditional
role of industrialisation as a development strategy for lower income countries.
Yet, the dominance of robot use in sectors higher up on the skill ladder
implies greater difficulty for latecomers in attaining sectoral upgrading and
may limit their scope for industrialisation to low-wage and less dynamic (in
terms of productivity growth) manufacturing sectors. This could seriously
stifle these countries’ economic catch-up and leave them with stagnant
productivity and per capita income growth” (Wood, 2017).
For the
past decade, 3D printing has become popular due to availability of low-cost 3D
printers such Fab@Home and better software. The brand name Fab@Home is
suggestive of the technological possibilities to move production closer to the
consumer, which may impact on trade, global value chains and shifting Wealth. Ishengoma
and Mtaho (2014)[11] discuss 3D printing from
a developing-country perspective, both challenges and opportunities. In
developing countries, 3D printers are increasingly used in fabrication
laboratories and engineering faculties, for example to produce 3D-printed prosthetic limbs. Major (economic)
challenges are the lack of 3D experts and losses in manufacturing employment
substituted for home platforms. Opportunities are lie in moving developing
countries close to world supply chains For example, developing countries can
use 3DP technology to manufacture local equipment such as farming tools and
goods such as clothing.
Slowdown of Global Trade
Policymakers
in low- and middle-income countries worry about the slowdown of the most
important vehicle of Shifting Wealth, global trade. World trade growth was
rapid in the two decades prior to the global financial crisis but has halved
subsequently. There are both structural and cyclical reasons for the slowdown.
A deceleration in the rate of trade liberalisation post 2000 was initially
obscured by the ongoing expansion of global value chains and associated rapid
emergence of China in the world economy. Post the financial crisis global value
chains started to unwind and Chinese weakened markedly (Haugh et al., 2016)[12].
Figure 3: Drop
in China’s export openness since 2007
Source: Banque de France (2017)“The role of China in the trade
slowdown”, Rue de la Banque #30, September.
In recent
years, global trade and global production have grown at similar rates, whereas
before 2008, global trade grew at twice the rate. Apart from increasing
protectionism (see Evenett and Fritz, 2016[13]),
this slowdown in global trade was initially (2008-2010) largely due to China’s
rebalancing towards its domestic demand and its services sector (Banque de
France, 2016[14]). In the 2010s, however, the
near-parallel growth for global trade and production seems to have been caused
by changes in the supply chain (Los and Timmer, 2016[15]).
Degain,
Meng, and Wang (2017) find that there has been a reduction in cross-country
production sharing in complex global value chains (GVC) during the current
economic recovery, contrary to the rapid production globalization driven by the
growth of complex GVC activities in previous periods[16].
Decomposing four types of value-added
creation activities (pure domestic production, traditional trade production,
simple GVC, and complex GVC) unveils the structure of value-added creation
during the economic recovery since 2011. That structure is found quite
different from that during the three previous economic growth periods in the
last 20 years. Unlike the rapid production globalization driven by the growth
of complex GVC activities in previous periods, during the current economic
recovery the pattern was reversed, with less cross-border production-sharing
activities in complex GVCs. This may also mean that the China centric growth of
middle- and low-income countries observed during the 2000s has been lower since
2011.
Protectionism
is clearly another concern for the future of Shifting Wealth. The Economist
(2016)[17]
asked YouGov, a polling outfit, to survey 19 countries to gauge people's attitudes towards immigration, trade
and globalisation. The data
revealed a split between emerging markets and the West. Unsurprisingly, the countries that are the biggest
enthusiasts of globalisation are the ones that have benefitted most from it –
the poorer nations of East and South East Asia. In Asia, belief that
globalisation is a force for good reaches at least 70% in all countries
surveyed, and as high as 91% in Vietnam. In France, the US, the UK, Australia
and Norway, less than 50 per cent thought that globalization had been a “force
for good”. From disillusionment with globalisation to populist
protectionism is a short distance.
Figure 4: Global Trade Liberalisation Index
Source: Haugh et
al (2016), OECD Economic Policy Papers No.18
Research has
confirmed that the slowdown of trade liberalisation and rising protectionism is
taking its toll on trade flows (Evenett and Fritz, 2015, op.cit.). Less
international fragmentation of production processes than in recent decades
cannot be excluded with labour saving innovations, increasing domestic
capabilities to substitute for imported intermediate goods, and subsequent
reshoring. And while support for globalization has dropped in some advanced
countries below the 50 percent threshold, resort to protectionism and retreat
from multilateralism, notably in advanced countries, are a potential threat for
Shifting Wealth present and future.
[2] Robert
Barro and Xavier Sala-i-Martin (2004), Economic
Growth, 2nd edition, MIT Press: Cambridge, Mass.
[3] Daron Acemoglu,
Philippe Aghion and Fabrizio Zilibotti (2006), “Distance to Frontier, Selection
and Economic
Growth.”
Journal of the European Economic Association 4.3, pp. 37–74.
[4] Indermit Gill and Homi Kharas (2015), “The Middle-Income Trap Turns Ten”, World Bank Policy Research
Working Paper No. 7403, August.
[5] Angel Melguizo, Sebastián Nieto-Parra, José Perea and Jaime Perez
(2017), “No
sympathy for the devil!
Policy
priorities to overcome the middle-income trap in Latin America”, OECD Development Centre Working Paper No. 340, September.
[7] Anton Korinek and Joseph Stiglitz (2017), “Artificial Intelligence and Its
Implications for Income Distribution and Unemployment”, NBER Working Paper No. 24174,
December.
[8] Jorge Arbache (2016), “The
end of the global value chains”, Valor
Econômico, June.
[9] Adrian Wood
(2017), “Variation in
structural change around the world, 1985–2015: Patterns, causes, and
implications”, UNU-WIDER: WIDER Working Paper 2017/34, February.
[11] Fredrick
Ishengoma and Adam Mtaho (2014), “3D Printing: Developing Countries
Perspectives”,
International Journal of Computer Applications, 104 .11,
pp. 30-34.
[12] David Haugh, Alexandre Kopoin, Elena Rusticelli, David Turner,
Richard Dutu (2016), “Cardiac
Arrest or Dizzy Spell: Why is World Trade So Weak and What can Policy Do About
It?”, OECD Economic Policy Papers
No.18, September.
[14] Guillaume Gaulier, Walter Steingress & Soledad Zignago (2016),
“The
role of China in the trade slowdown”, Rue de la Banque #30, Banque de
France, September.
[15] Bart Los and Marcel Timmer (2016), “Peak
trade? An Anatomy of the Recent Global Trade Slowdown”, University of
Groningen, May.
[16] Christophe Degain, Bo Meng, and Zhi Wang (2017), Global Value Chain Development Report 2017, Chapter 2.
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