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Why Europe looks so much like China: Big government and low
income inequalities
Vladimir Popov
Principal Research Fellow in the Central Economics and Mathematics, Institute of the Russian Academy of Sciences, Russian Federation
A R T I C L E I N F O
JEL:
H1
d63
o47
p10
p51
Keywords:
Varieties of Capitalism
Economic models
Inequalities
Size of the government
State institutional capacity
Growth rates
Catch-up development
A B S T R A C T
One view in the literature is that the East Asian economic model is superior to other models in the Global South
(i.e. in the developing world), at least in terms of catch-up development and possibly even in innovations beyond
the technological frontier. Unlike economic models in Latin America and Sub-Saharan Africa, the East Asian
model prioritizes community interests of the work collective, the neighbourhood, the nation-state, and all of
humanity over those of individuals, possibly limiting some human rights for the greater benefit of all. Crucial
features of the East Asian economic model include relatively low income and wealth inequalities, strong state
institutional capacity. The origins of the East Asian economic model can be traced to different trajectories of the
development of the Global South since the 16th century.This paper argues that European economic model and
the East Asian model have a lot in common. After controlling for the country size and the level of development, it
turns out that government consumption as a share of GDP is relatively high in both models, whereas income
inequalities are relatively low.
Introduction
Two basic economic models prevail in the Global South: one is the
replication of the Western liberal model (e.g., in Latin America, Sub-
Saharan Africa, and some former Soviet republics), and the other is
sometimes referred to as the ‘Asian values’ model. These ‘Asian values’
are understood as the prioritization of community interests, be it he
work collective, the neighbourhood, the nation state, or all the hu-
manity, over those of individuals with the possibility of limiting some
human rights for the greater benefit of all. Whereas the Western liberal
tradition considers at least some human rights inalienable, in more
traditional societies, not only in Asia but also in other parts of the Global
South, collectivist solidarity is more entrenched. The core feature of the
latter is the statistically measurable indicators of low income and wealth
inequalities that help to promote greater social cohesion and the
stronger institutional capacity of the state.
These features of the Asian model permit the existence of relatively
strong and efficient governments and promote successful catch-up
development (Popov, 2009; 2014). This collectivist economic model is
found primarily in East Asian countries, but also, to an extent, in South
Asia, the Middle East and North Africa. The European economic model,
even though it was very different several centuries ago, when it
emerged, today is very similar with relatively low inequality and large
and efficient governments, even if not highly trusted by the public.
Inequalities
Income and wealth inequalities in Asia and the Middle East and
North Africa (MENA) are lower than in Latin America (LA) and Sub-
Saharan Africa (SSA). Gini coefficients of income distribution in East
Asian countries are usually below 40%, similar to Europe, and the share
of the top 10% income group in total income1 is lower than in the US and
many developing countries such as India, Russiaand South Africa, as
Fig. 1 suggests..
In China, the Gini coefficient of income distribution is above 40%,
even close to 50% according to the new unified survey for rural and
E-mail address: vpopov@nes.ru.
1 This statistic comes from the tax data, not from household surveys that are normally used to study income distribution. Whereas the disadvantage of tax data is
that they are not based on representative samples (like household surveys), the advantage is that it takes better account of the very rich and very poor groups that are
usually not covered by household surveys.
Contents lists available at ScienceDirect
Asia and the Global Economy
Received 30 December 2021; Received in revised form 31 January 2022; Accepted 31 January 2022
Asia and the Global Economy 2 (2022) 100024
2
Fig. 1. Share of top income group in total income in selected countries,%
Source: world inequality database.
Fig. 2. Gini coefficient of income distribution in China in 2003–19,%, new official sample
V. Popov
Asia and the Global Economy 2 (2022) 100024
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Fig. 4. Average murder rates in 1960–2013 by decades, per 100,000 inhabitants, log scale (countries for which data are available for 3 and more decades)
Data are taken from different sources (mostly national data provided to WHO) and sometimes are not strictly comparable.
Source: wikipedia. list of countries by intentional homicide rate by decade (https://en.wikipedia.org/wiki/List_of_countries_by_intentional_homicide_rate_by_de
Fig. 3. Billionaire wealth from Forbes list as a% of national income in 1990–2016 in major countries
V. Popov
Asia and the Global Economy 2 (2022) 100024
4
urban regions shown in Fig. 2, but the country is so large that it should
be compared with all Europe or at least with the US.2
Accounting separately for within- and between-country/province in-
equalities produces very telling results. In China, with 29 provinces, the
general Gini coefficient of income inequality in the early 2000s was over
40% with 24 percentage points attributable to between-province dis-
parities. In the US, the Gini coefficient was similar, over 40%, but only 6
percentage points. came from disparities in income between the states. In
the EU 27, the Gini coefficient around 2005 was roughly 40% with 23
percentage points coming from between-country inequality. If China can
manage to reduce the income gap between its provinces to a level close to
the disparities between US states, then general inequality between citi-
zens will fall to a quite low level (Milanovic, 2011), one that would be
lower than in large European countries.
Moreover, the wealth inequalities (“accumulated income in-
equalities”) in China appear to be much lower than in other countries.
The “billionaire intensity” indicator, the ratio of wealth of billionaires
from the Forbes list to national income, in 2016 in China was only 6%,
whereas in the USA, Germany, France it was 10–15%, and in Russia it
was nearly 30% (Fig. 3).
Institutional capacity
Lower income and wealth inequalities make societies less polar-
ised and are usually associated with a stronger institutional capacity
of the state. The institutional capacity of the state, according to a
narrow definition, refers to the government’s ability to enforce laws
and regulations. While there are many subjective indices that are
supposed to measure state institutional capacity such as control over
corruption, rule of law, government effectiveness indices, they can be
biased and deviate from objective indicators (Popov, 2011b).
Natural objective measures of state institutional capacity are the
murder rate, i.e., non-compliance with the state’s monopoly on
ment economic regulations, such as tax payment rules. East Asia and
MENA countries are quite different from LA and SSA on both measures:
East Asian countries have one of the lowest levels of both indicators in
the developing world, comparable to that of developed countries As
shown by Figs. 4 and 5.
Fig. 5. Murder rate in China and in the world per 100,0000 inhabitants
Table 1
Share of shadow economy in GDP in 2005 (%) and murder rate per 100,000
inhabitants in 2002 in selected countries.
REGION, COUNTRY
Murder rate in 2002 per
100,000 inhabitants
Shadow economy in
2005,% of GDP
SUB-SAHARA AFRICA
Angola
40
45
Algeria
12
34
Congo, Dem. Rep.
21
51
Ethiopia
21
42
Nigeria
23
59
South Africa
43
28
LATIN AMERICA
Argentina
9
27
Brazil
33
42
Colombia
72
43
Mexico
10
32
FORMER USSR
Belarus
13
51
Kazakhstan
20
45
Russian Federation
33
47
Ukraine
15
55
Uzbekistan
4
35
MIDDLE EAST AND
NORTH AFRICA
Egypt, Arab Rep.
2
35
Iran, Islamic Rep.
4
20
Saudi Arabia
3
18
Turkey
3
33
SOUTH ASIA
Bangladesh
7
38
India
5
25
Pakistan
4
39
EAST ASIA
China
3
17
Indonesia
9
24
Japan
1
9
Korea, Rep.
2
28
Malaysia
9
31
Philippines
21
44
Thailand
9
54
Vietnam
4
16
EUROPE, USA, CANADA
Canada
1
15
France
1
14
Germany
1
16
Italy
1
25
Spain
1
21
United Kingdom
1
12
United States
5
8
Source: WHO; Schneider, 2007. Measures of the shadow economy are derived
from divergence between output dynamics and electricity consumption, demand
for real cash balances, etc.).
2 Three Chinese provinces, Guangdong, Shandong, and Henan, have pop-
ulations exceeding 95 millionand several provinces have populations of more
than 50 million . Therefore, China should be compared with multicountry re-
gions such as the EU or ASEAN rather than with individual countries.
3 Crimes are registered differently in different countries; higher crime rates in
developed countries seem to be the result of more accurate crime records. But
grave crimes, such as murder, appear to be recorded quite accurately even in
developing countries, so international comparisons of murder rates are
appropriate.
V. Popov
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In China, for instance, in recent decades there were only 1–2 murders
per 100,000 inhabitants compared to 1–2 in Europe and Japan and 5 in
the US. Only a few developing countries, mostly in the MENA region,
had such low murder rates; these rates are typically higher by an order of
magnitude in LA, SSA, and many former Soviet Union states.
It is notable that murder rates in most countries are quite stable over
time (Fig. 4. 4), but in China the murder rate has fallen since the 1990s
by nearly 80%, from 2.3 in 1995 to 0.5 in 2018 per 100,000 inhabitants
The same pattern applies to the shadow economy: it constitutes less
than 17% of Chinese GDP, lower than in Belgium, Portugal, and Spain.
In developing countries, the proportion is typically around 40%,
sometimes even greater than 60% (Table 1). Only a few developing
countries have such a low shadow-economy share, in particular Viet-
nam and several MENA countries such as Iran, Jordan, Saudi Arabia,
and Syria (Popov, 2011a).
Needless to say, growth rates of productivity and per capita GDP
depend on the institutional capacity of the state (Popov, 2015) ,4 so
countries with the strongest institutional capacity, ceteris paribus, are
morel likely to become growth miracles. So far only 5 coun-
tries/territories from the Global South, Hong Kong, Japan, Singapore,
South Korea, Taiwan, managed to join the club of countries due to their
high economic growth rates.5 In recent decades Southeast Asia and
China have been catching up with the developed countries as well.6
Inequality, state capacity, trust in the government and patriotism
Low income inequality is generally tied to strong institutional ca-
pacity as indicated by a low murder rate and a small shadow economy,
but to be more nuanced, it may make sense to distinguish between three
groups of countries (Popov, 2014; 2020):
• low inequality and strong institutions (e.g., developed countries;
some East Asian and MENA states);
• relatively low inequality and poor institutions (e.g., former socialist
countries and some MENA and East Asian states);
• and high inequality and poor institutions (e.g., LA and SSA).
There are virtually no countries with high inequality and a low
murder rate and a small shadow economy by which we mean an income
Gini above 45%, murder rate below 5 per 100,000 inhabitants and
shadow economy below 40% of GDP.
Similar, but not identical, results can be observed by plotting several
subjective measures of social solidarity from the World Value Survey
such as trust in government and willingness to fight for one’s own
country7 (Fig. 6) against the murder rate, an objective indicator of
institutional strength. We can distinguish between four groups of
Fig. 6. Trust-in-government index and patriotism index, log scale
Source: World Value Survey.
4 The cross-country regression equations of growth rates of GDP per capita in
1960-2013 on the objective indicators of the state institutional capacity
(shadow economy and murder rate) are reported below from Popov (2015): y =
0.0003*** Ycap75 - 0.03*MURDERS –0.14***SHADOW + 5.32*** (- 4.95) (1.67) ( 4.82)
(8.55) N = 80, R2
= 0.38, robust standard errors, T-statistics in brackets below.
Here and elsewhere in this paper asterisks – ***, **, * – indicates that co-
efficients are significant at 1, 5 and 10% level, respectively; y =
0.003***POPDENS – 0.0002*** Ycap75 - 0.023 MURDERS –0.067***SHADOW
+ 5.04*** (4.08) ( 4.33) ( 1.62) ( 4.40) (7.67) N = 80, R
2
= 0.40, robust
standard errors, T-statistics in brackets below, where y –annual average growth
rates of per capita GDP in 1960-2013, %, POPDENS – number of residents per 1
square km in 2000, Ycap75 – per capita PPP GDP in 1975 in dollars, MURDERS
– number of murders per 100,000 inhabitants in 2002, SHADOW – share of
shadow economy in GDP in 2005, %. <ADDED ABOVE> Data on growth,
population density and PPP GDP per capita are from WDI, data on murders are
from WHO, data on shadow economy are from Schneider, 2007 (measures of
the shadow economy are derived from divergence between output dynamics
and electricity consumption, demand for real cash balances, etc.).
5 Some developing countries became rich not due to rapid economic growth,
but because of improved terms of their external trade – increased relative prices
for their export goods. The best-known example is oil exporting states of the
Persian Gulf: with the exception of Oman, these countries did not enjoy high
growth rates of physical output, but their per capita income approached the
level of developed countries due to the increase in oil prices since 1973.
6 From the 1920s– to the1970s, the USSR and East European countries were
catching up with the developed countries, but later they slowed down consid-
erably and, in the 1990s, experienced transformation recessions. In 1950–2020,
high average growth rates (over 3% per capita GDP annual growth) in addition
to East Asia, were observed in Botswana, Israel, and Oman.
7 The patriotism index and trust-in-government index are computed as the
ratio of positive answers to negative answers in Round 6 (2010–14) of the
World Value Survey. Question about patriotism (V66): Of course, we all hope
that there will not be another war, but if it were to come to that, would you be
willing to fight for your country? Question about trust in government (V115):
How much confidence you have in the government (in your nation’s capital): is
it a great deal of confidence, quite a lot of confidence, not very much confidence
or none at all?
V. Popov
Asia and the Global Economy 2 (2022) 100024
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countries in Fig. 7:
From these figures we can see that
• East Asian and MENA countries have generally low murder rates and
higher patriotism and trust in the government;
• developed countries have low murder rates and low trust and
patriotism;
• many LA and SSA countries have lower indicators of trust and
patriotism and high murder rates;
• many states of the former Soviet Union (e.g., Belarus, Kazakhstan,
Kyrgyzstan, and Russia) have high murder rates along with high trust
in the government and patriotism.
It can be hypothesised that higher trust in government institutions
and stronger patriotism (i.e., willingness to fight for one’s own country)
can build social cohesion and solidarity in difficult times, even if
objective measures of institutional strength (e.g., murder rate and the
shadow economy) are not that impressive. Conversely, strong in-
stitutions may not be enough to respond effectively to crises if social
solidarity is weak. This trend may explain why, in advanced countries
struck by the coronavirus, quarantine and isolation measures were less
strict and enacted after a delay when compared to East Asian and MENA
countries, leading to much higher infection and death rates in the former
A related measure of social cohesion is the Inglehart–Welzel cultural
map of the world (Fig. 8) that is based on questions of the World Value
Survey.
Patriotism and trust in the government are regarded in this classifi-
cation as the traditional values that “emphasize the importance of reli-
gion, parent-child ties, deference to authority, absolute standards and
traditional family values. People who embrace these values also reject
divorce, abortion, euthanasia and suicide. Societies that embrace these
values have high levels of national pride and a nationalistic outlook”.
Self-expression values, as opposed to survival values, “give high priority
to subjective well-being, self-expression and quality of life” (Inglehart
Self-expression naturally becomes more important with economic
progress, so that developed countries are more oriented towards self-
expression and less to survival than developing countries. It is inter-
esting though that on a secular- traditional scale (the vertical axis of
Fig. 8) there are clearly two groups of developing countries, East Asia
and the former communist countries that are quite “modern” and
secular, whereas Latin America and Sub-Saharan Africa are more
traditional while the Middle East and North Africa and South Asia are in
between. It could be hypothesized that East Asia and former communist
countries maintain collectivist values and low inequalities in a “modern”
rather than in a “traditional” way. This modern way implies the greater
reliance on individual responsibility enforced by the power of the gov-
ernment (state capacity), and less on family and the community.
Inequality and government size
The raw data on income inequalities and the share of government
consumption8 in GDP (Fig. 9) show a weak negative relationship; the
higher government consumption, the lower is the Gini coefficient of
income. This negative relation, however, is driven by the differences
between rich and poor countries. With the growth of per capita income,
the share of government consumption increases, whereas income in-
equalities decline. As per capita income increases, the government
provides more and more public goods such as health care, education,
infrastructure, and expands social programs that mitigate income in-
equalities. To eliminate this factor, one has to control for the level of
development or per capita GDP.
Besides, larger countries generally have smaller governments as they
can enjoy economies of scale and they are less vulnerable to shocks from
the world market. Rodrik (1998) shows that more open economies have
larger governments, and this holds for developed and developing
countries. Small countries are naturally more open due to their higher
share of external trade and capital flows in GDP, so, other things being
equal, smaller states have bigger governments. As was already
mentioned, inequalities in larger countries are likely to be higher
because of the uneven development of various regions of the large
country.
Regressions of Gini coefficients and government consumption on PPP
GDP and PPP GDP per capita are reported below:
Averages for the period 2016–19 (88 countries)
• GINIcoef = -0.00015*** GDPcap +3.22×10e
13 * GDP + 39.7***,
• -3.9) (1.7) (32.0)
• Number of obs. = 88, R-squared = 0.1710, robust standard errors, T-
statistics in brackets below;
• GOVcons = 16.0 - 0.77**lnGDP + 2.1*** lnGDPcap, (1.5) (-2.2)
(3.2)
• Number of obs. = 88, R-squared = 0.1311, robust standard errors, T-
statistics in brackets below;
Fig. 7. Patriotism index, trust in government, and murder rate
8 The comparable data on all countries are available only for the government
consumption (final government purchases of goods and services). Total gov-
ernment expenditure is equal to the sum of government consumption and
government transfers (pensions, allowances, etc.).
V. Popov
Asia and the Global Economy 2 (2022) 100024
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where
• GINIcoef – average Gini coefficient of income distribution in
2016–19,%,
• GOVcons – average final general government consumption as a% of
GDP, in 2016–19,%,
• GDP – average PPP GDP in 2016–19 in 2017 constant international
dollars,
• GDPcap – average PPP GDP per capita in 2016–19 in 2017 constant
international dollars. *** means what?
Averages for the period 2011–19 (144–217 countries)
• GINIcoef = -2.5*** lnGDPcap +2.4 × 10e
13 * GDP + 60.8***,
• (-5.4) (1.8) (14.2)
• Number of obs. = 144, R-squared = 0.1206, robust standard errors,
T-statistics in brackets below;
• GOVcons = -0.75*** lnGDP + 1.2*** lnGDPcap + 24.7***,
• (-2.9) (3.1) (2.6)
• Number of obs. = 217, R-squared = 0.0929, robust standard errors,
T-statistics in brackets below;
where
• GINIcoef – average Gini coefficient of income distribution in
2011–19,%,
• GOVcons – average final general government consumption as a% of
GDP, in 2011–19,%,
• GDP – average PPP GDP in 2011–19 in 2017 constant international
dollars,
• GDPcap – average PPP GDP per capita in 2011–19 in 2017 constant
international dollars.
The residuals from these regressions show the deviation of the
relative share of government consumption in GDP and Gini coefficients
of income inequalities from the predicted levels (given the size of the
country and the level of development). The simplified general picture is
described by the scheme below (see Appendix).
Scheme. Classification of economic models – deviation of income
inequalities and the size of the government from predicted levels
INEQUALITY
(horizontal)———————–
SIZE OF THEGOVERNMENT
(vertical)
LOW
HIGH
BIG
= Big government=
High state capacity=
Low inequalityEAST
ASIA, EUROPE
= Big government=
Low state capacity=
High
inequalityLATIN
AMERICA, SUB-
SAHARA AFRICA
(Argentina,
Botswana, Brazil,
Djibouti, Eswatini,
Mozambique,
Namibia, Seychelles,
South Africa, Togo,
Zimbabwe), RUSSIA
SMALL
= Small government=
Low state capacity=
Low
inequalityFORMER
USSR and socialist
countries (Albania,
Kosovo, Lao PDR, North
Macedonia, Mongolia,
Vietnam), SOUTH ASIA
and MENA
(Bangladesh, India,
Mauritania, Morocco,
Pakistan, Sudan, UAE)
= Small
government= Low
state capacity= High
inequalityLATIN
AMERICA, SUB-
SAHARAN AFRICA,
USA
There are four group of countries, those with:
Fig. 8. Inglehart–Welzel cultural map of the world
V. Popov
Asia and the Global Economy 2 (2022) 100024
8
• relatively low inequality and big governments – East Asia and
Europe;
• relatively high inequality and small governments – Latin America
and Sub-Sahara Africa, USA;
• relatively low inequality and small governments (former USSR,
South Asia and MENA);
• w relatively high inequality and big government (LA, SSA, Russia).
More detailed data are in the charts below and in the Appendix.
For the period of 2016–19 relatively low income inequalities and
high government consumption group includes European countries
(Austria, Belgium, Croatia, Czech Republic, Denmark, Estonia, Finland,
France, Germany, Greece, Hungary, the Netherlands, Norway, Poland,
Portugal, Slovak Republic, Slovenia, Sweden, Ukraine); East Asia
(China, Myanmar, Thailand). Besides, there are Bhutan, Liberia, Kyrgyz
Republic, West Bank and Gaza that do not exactly fit into the model (See
Relatively high income inequalities and low share of the government
consumption in GDP in 2016–19 are observed in Sub-Sahara Africa
countries (Angola, Gabon, Ghana, Malawi, Mauritius, Tanzania,
Uganda); Latin American countries (Chile, Colombia, Dominican Re-
public, Ecuador, Honduras, Mexico, Panama, Paraguay, Peru) and the
United States; and several “outsiders’ (Iran. Ireland, Lithuania,
Luxembourg, Sri Lanka, Switzerland, Turkey).
For the longer period (2011–19) there are more countries in the first
group (low inequalities and high government consumption): Bosnia and
Herzegovina, Iceland, Italy and the UK in Europe (+ European offshoots
– Australia and Canada); Japan in East Asia; and Algeria, Burkina Faso,
Fiji, Guinea, Iraq, Timor-Leste, Tunisia elsewhere (Fig. 12; Table 2A of
the Appendix). Whereas the second group (high inequalities and low
government consumption) is being supplemented by SSA countries
(Benin, Cabo Verde, Cameroon, Chad, Comoros, Congo, Cote D’Ivoire,
Kenya, Malawi, Ruanda, Zambia), LA countries (Bolivia, Costa Rica, El
Salvador, Guatemala, Nicaragua, Uruguay) and ‘outliers” (Bhutan,
Bulgaria, Georgia, Indonesia, Malaysia, Philippines).
Thus, East Asia and Europe in most cases appear to be in the same
group of countries with relatively low inequality and big governments
with strong institutional capacity, whereas LA and SSA and USA are in
the opposite group of countries with relatively high inequality and small
governments with weak institutional capacity. Trust of the public in
government institutions is usually high in developing countries of East
Asia, but low to moderate in Europe and the US.
If such commonalities between East Asia and Europe really exist, this
could be a contribution to the Variety of Capitalism literature (Lee,
2019). The collectivist economic model based on low inequality and big
and efficient government appears to be most competitive in terms of
catch-up development and possibly in terms of innovative growth at the
technological frontier and beyond. This low inequality-strong state
model is found in developed world (Europe and Japan) and in devel-
oping countries (China, ASEAN). It is inherently consistent and has the
potential to become the dominant one in other regions.
Fig. 9. Gini coefficient of income inequalities and the share of government
consumption in GDP, average for 2016–19 and 2011–19,%
Source: WDI.
Fig. 10. Excess government consumption as a% of GDP and deviation of Gini
coefficient of income distribution, p.p. (2016–19 data)
Source: WDI (see Appendix).
V. Popov
Asia and the Global Economy 2 (2022) 100024
9
Concluding remarks
The low inequality–big government model comes from two different
historical trajectories. The European model can be traced back to the
16th century, but it emerged in its current form only in the 20th century.
Before the 16th century all countries had roughly the same per capita
income, low savings rate and virtually zero growth rates. Only the
destruction of the collectivist institutions (the community) in the 16th
century and beyond, leading to the growth in inequality, allowed the
West to raise the savings and investment rates and the capital/labor
ratio. The result was the acceleration of growth rates of productivity and
per capita income by orders of magnitude, but it came at a price: high
inequality led to the weakening of state institutions. In particular, the
murder rate was in double digits per 100,000 of inhabitants in the 14th
to 17th centuries, and life expectancy in the 16–17th century fell
This weakening of state institutions, however, was relatively short-
lived: the murder rate in Western Europe fell to the current levels (sin-
gle digits per 100,000 inhabitants) by the 18–19th century (Popov,
2009; 2014). And in the 20th century, especially after the Second world
war, income inequalities in Western countries declined dramatically due
to the proliferation of social and welfare programs in response to the
competition from socialism with free health care and education, strong
social guarantees and low income inequalities (Popov and Jomo, 2015).
In the Global South the collectivist model emerged in a different way.
Since the 16th century the Western model of growth was adopted in
many developing countries through colonialism, as in Latin America and
Sub-Sahara Africa, or via voluntary Westernization in an attempt to
catch up with the West, as in the Russian Empire. Such a proliferation of
the Western model has resulted in the destruction of traditional in-
stitutions, increases in income inequality, and worsening of starting
positions for catch-up development. This group of countries replicated
the Western exit from the Malthusian trap because they experienced an
immediate increase in income differentiation, hiogher savings and in-
vestment rates and the growth of productivity, but at the price of rising
social inequality and deterioration of institutional capacities.
Other developing countries, inEast Asia, and to an extent in South
Asia, and Middle East and North Africa, were less affected by colonialism
and managed to retain their traditional institutions. This delayed the
transition to modern economic growth until mid-20th century but
allowed them to preserve a good starting position for economic growth
due to their low inequality and strong institutions. Eventually slow
technical progress allowed them to find another and less painful exit
from the Malthusian trap because increased income permitted an
oncrease in the share of investment in GDP without a major increase in
income inequality without worsening of institutional capacity and
decreasing in life expectancy.
This less Westernized region of the developing world became
another reincarnation of the low inequalities-big government collec-
tivist economic model. It turned into the fastest growing region of the
developing world and started to catch up with Western levels of per
capita income due to the fast growth of productivity, not due to the
favorable terms of trade shifts (like some oil exporting nations). Japan, a
developing country in 1950 (less than 18% of US per capita income),
slowed down since the 1990s, and Hong Kong slowed down since 2004,
but other East Asian dragons such as South Korea, Taiwan, Singapore
continued to grow at record rates bridging the gap with the US income
level. Singapore even surpassed the US levels of GDP per capita (Fig. 13).
Fig. 11. Countries with relatively high government consumption and low Gini coefficient of income distribution in 2016–19
Source: WDI (see Appendix).
V. Popov
Asia and the Global Economy 2 (2022) 100024
10
Table 1A
General government final consumption (% of GDP), Gini coefficient of income distribution (%), PPP GDP and PPP GDP per capita (constant 2017 international dollars),
average for 2016–19.
Countryname
Countrycode
General government final
consumption,% of GDP
Gini coefficient of
income distribution,%
PPP GDP per
capita
PPP GDP
Excess government
consumption, p.p.
Deviation of Gini
coefficient from predicted
values, p.p.
Albania
ALB
11.40
33.45
13,287.90
3.81E+10
3.97
4.34
Angola
AGO
11.91
51.30
7120.93
2.16E+11
2.98
12.55
Argentina
ARG
16.88
41.53
22,902.49
1.01E+12
0.77
4.83
Armenia
ARM
12.43
33.50
12,446.71
3.67E+10
2.86
4.41
Austria
AUT
19.48
30.25
54,976.11
4.85E+11
0.89
1.60
Bangladesh
BGD
6.13
32.40
4319.14
6.94E+11
8.54
6.91
Belarus
BLR
15.72
25.30
18,534.42
1.76E+11
0.05
11.77
Belgium
BEL
23.08
27.50
50,963.68
5.81E+11
4.79
4.97
Bhutan
BTN
17.37
37.40
11,274.55
8.46E+09
2.17
0.68
Bolivia
BOL
17.54
43.60
8501.56
9.59E+10
2.55
5.09
Brazil
BRA
20.23
53.50
14,553.53
3.04E+12
4.77
14.93
Bulgaria
BGR
16.12
40.50
21,808.27
1.54E+11
0.10
3.91
Canada
CAN
20.98
33.30
48,573.78
1.79E+12
2.88
0.09
Chile
CHL
14.25
44.40
23,967.57
4.46E+11
1.94
8.04
China
CHN
16.48
38.50
14,797.97
2.06E+13
1.00
5.68
Colombia
COL
15.06
50.30
14,456.32
7.12E+11
0.39
12.46
Costa Rica
CRI
17.17
48.33
19,193.40
9.55E+10
1.35
11.39
Croatia
HRV
19.58
30.65
27,105.96
1.12E+11
3.15
5.15
Cyprus
CYP
15.35
32.15
38,165.22
3.33E+10
1.94
2.01
Czech Republic
CZE
19.21
25.15
39,267.91
4.17E+11
1.84
8.97
Denmark
DNK
24.35
28.45
56,026.73
3.24E+11
5.67
3.20
Djibouti
DJI
21.58
41.60
5085.90
4.84E+09
6.86
2.62
Dominican
Republic
DOM
10.79
43.87
17,256.65
1.83E+11
4.88
6.61
Ecuador
ECU
14.60
45.03
11,526.65
1.95E+11
0.63
6.93
Egypt, Arab
Rep.
EGY
9.38
31.50
11,234.97
1.10E+12
5.82
6.93
El Salvador
SLV
16.08
38.87
8538.81
5.47E+10
1.09
0.38
Estonia
EST
19.88
30.80
34,591.12
4.57E+10
2.86
3.89
Eswatini
SWZ
23.35
54.60
8468.15
9.58E+09
8.36
16.11
Finland
FIN
23.09
27.25
47,608.43
2.62E+11
5.06
5.61
France
FRA
23.44
31.75
45,121.38
3.02E+12
5.61
2.36
Gabon
GAB
12.36
38.00
15,015.27
3.14E+10
3.14
0.46
Georgia
GEO
13.84
36.97
13,950.69
5.20E+10
1.57
0.73
Germany
DEU
20.05
31.90
53,260.11
4.41E+12
1.58
1.47
Ghana
GHA
9.16
43.50
5081.96
1.50E+11
5.56
4.48
Greece
GRC
19.63
34.70
28,928.52
3.11E+11
3.05
0.90
Honduras
HND
13.67
51.23
5589.46
5.32E+10
1.09
12.31
Hungary
HUN
19.86
30.45
30,695.78
3.00E+11
3.15
4.89
Indonesia
IDN
9.10
38.17
11,162.69
2.97E+12
6.10
0.88
Iran, Islamic
Rep.
IRN
12.65
40.40
13,650.54
1.11E+12
2.74
2.32
Ireland
IRL
12.04
32.80
80,353.68
3.90E+11
8.52
4.68
Israel
ISR
22.57
39.00
39,256.93
3.46E+11
5.20
4.90
Italy
ITA
18.87
35.55
41,893.30
2.53E+12
1.29
1.13
Kazakhstan
KAZ
9.91
27.35
25,242.39
4.59E+11
6.38
8.83
Kosovo
XKX
13.92
27.85
10,743.37
1.92E+10
1.24
10.31
Kyrgyz
Republic
KGZ
17.14
27.27
5086.33
3.19E+10
2.41
11.72
Latvia
LVA
14.96
29.50
20,741.35
7.61E+11
0.98
7.43
Lesotho
LSO
39.52
44.90
2769.69
5.82E+09
24.97
5.58
Liberia
LBR
17.11
35.30
1489.21
7.09E+09
2.66
4.20
Lithuania
LTU
16.61
37.85
34,635.10
9.77E+10
0.41
3.15
Luxembourg
LUX
16.52
33.95
113,741.90
6.84E+10
6.62
10.80
Malawi
MWI
14.22
44.70
1041.10
1.87E+10
0.20
5.13
Maldives
MDV
15.68
31.30
18,492.32
9.35E+09
0.08
5.72
Malta
MLT
16.13
29.15
42,798.56
2.05E+10
1.52
4.33
Mauritius
MUS
15.31
36.80
21,785.12
2.76E+10
0.71
0.25
Mexico
MEX
11.64
45.85
19,817.30
2.49E+12
4.23
8.22
Moldova
MDA
14.81
25.97
12,004.10
3.27E+10
0.45
12.01
Mongolia
MNG
12.79
32.50
11,621.14
3.65E+10
2.45
5.53
(continued on next page)
V. Popov
Asia and the Global Economy 2 (2022) 100024
11
ASEAN countries and China are following suit.
The European collectivist economic model may be experiencing
difficulties since the 1980s. Europe in 1923–33 and after the Second
world war and until the 1980s was the fastest growing part of the
developed world, growing faster than the US. Since the 1980s, though,
growth rates of Western Europe have slowed down, and its relative per
capita income stagnated at a level of about 70% of the US level (Fig. 14).
One possible reason for these difficulties is the increase in inequality
taking place since the early 1980s (Fig. 15). Even before the collapse of
the Berlin Wall, the USSR and East European countries lost social
dynamism and were no longer perceived as a threat by the West (Popov
and Jomo, 2015), so the need to contain the increase of inequality to
remain competitive vis-`a-vis world communism disappeared. But the
growing inequalities created domestic problems by undermining the
growth potential.
As Fig. 15 suggests, there was an increase in inequality in recent
decades in most countries, developed and developing. But developing
countries as a group did not experience the reduction of inequality in the
1930s-80 s that was observed in developed countries. And Europe and
East Asia still remain regions with the lowest income and wealth in-
equalities. The ratio of income of top 10% of the population to the in-
come of the bottom 50% is 2 to 3 in Europe and East Asia, but 5 to 6 in
Latin America, Sub Sahara Africa and MENA countries (World Inequality
For wealth distribution, similar ratios are 14 –15 in Europe and East
Asia and up to 77 in Latin America (World Inequality Report, 2022). The
top 1% of population owns 25 –30% of total wealth in Europe and East
Asia and 34 – 46% in all other regions. The average wealth of the top
10% of the population exceeds the average wealth of the bottom 50% by
a fraction of 66 to 70 in Europe, East and South Asia, but by a fraction of
351 to 630 in Sub Saharan Africa and Latin America.
There is always a chance, however, that Europe and East Asia may
change in the future, so the growth potential of the low inequality–big
government–high state capacity collectivist economic model may be
Table 1A (continued)
Countryname
Countrycode
General government final
consumption,% of GDP
Gini coefficient of
income distribution,%
PPP GDP per
capita
PPP GDP
Excess government
consumption, p.p.
Deviation of Gini
coefficient from predicted
values, p.p.
Myanmar
MMR
18.58
30.70
4848.54
2.60E+11
3.87
8.39
Netherlands
NLD
24.47
28.35
55,677.27
9.57E+11
5.82
3.55
Nigeria
NGA
5.32
35.10
5191.46
1.00E+12
9.42
4.18
Norway
NOR
24.05
27.75
62,979.21
3.33E+11
4.84
2.89
Panama
PAN
11.50
49.83
30,575.13
1.27E+11
5.20
14.54
Paraguay
PRY
11.24
47.63
12,571.93
8.69E+10
4.07
9.72
Peru
PER
13.20
43.23
12,635.12
4.01E+11
2.11
5.23
Poland
POL
17.83
30.45
30,982.46
1.18E+12
1.10
5.13
Portugal
PRT
17.16
34.50
33,487.80
3.45E+11
0.23
0.44
Romania
ROU
16.24
35.20
27,724.37
5.41E+11
0.24
0.65
Russian
Federation
RUS
16.11
35.85
26,207.44
6.16E+11
0.26
0.25
Rwanda
RWA
15.15
43.70
2060.35
2.51E+10
0.66
4.28
Serbia
SRB
16.33
37.50
17,078.44
1.20E+11
0.67
0.24
Sierra Leone
SLE
10.12
35.70
1658.68
1.26E+10
4.35
3.78
Slovak Republic
SVK
19.05
25.20
31,429.72
1.71E+11
2.28
9.99
Slovenia
SVN
18.53
24.50
37,228.00
7.72E+10
1.31
9.81
Spain
ESP
18.80
35.25
39,840.08
1.86E+12
1.38
0.74
Sri Lanka
LKA
8.89
39.80
12,703.57
2.74E+11
6.43
1.85
Sweden
SWE
26.08
29.20
52,810.36
5.34E+11
7.65
2.99
Switzerland
CHE
11.94
32.85
67,704.23
5.74E+11
7.64
2.82
Tanzania
TZA
8.78
40.50
2555.60
1.38E+11
5.75
1.11
Thailand
THA
16.36
36.60
17,691.21
1.23E+12
0.65
0.93
Turkey
TUR
14.83
41.73
27,926.82
2.28E+12
1.67
5.35
Uganda
UGA
8.21
42.80
2114.92
8.88E+10
6.28
3.36
Ukraine
UKR
19.99
25.70
12,138.51
5.14E+11
4.71
12.41
United
Kingdom
GBR
18.78
34.80
46,044.63
3.05E+12
0.87
0.82
United States
USA
14.07
41.10
60,783.55
1.98E+13
4.97
3.87
Uruguay
URY
14.67
39.63
21,334.73
7.35E+10
1.31
3.00
Vietnam
VNM
6.49
35.50
7387.80
7.03E+11
8.42
3.37
West Bank and
Gaza
PSE
21.61
33.70
6344.71
2.87E+10
6.79
5.10
Note: Excess government consumption is computed as a residual from regression of general final government consumption as a% of GDP on PPP GDP and PPP GDP per
capita. Deviation of Gini coefficient from predicted values is computed as a residual from regression of actual Gini coefficient on PPP GDP and PPP GDP per capita.
Figures in green are for countries with positive excess government consumption and negative deviation of Gini coefficients from predicted values. Figures in red are for
countries with negative excess government consumption and positive deviation of Gini coefficients from predicted values.
Source: WDI database.
V. Popov
Asia and the Global Economy 2 (2022) 100024
12
jeopardized. Moreover, it remains to be seen whether the collectivist
model will be competitive at the technological frontier, if and when the
productivity levels in these countries will become the highest in the
world.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Fig. 12. Countries with high/low government consumption and low/high Gini coefficient of income distribution (2011–19 data)
Source: WDI (see appendix).
V. Popov
Asia and the Global Economy 2 (2022) 100024
13
Table 2A
General government final consumption (% of GDP), Gini coefficient of income distribution (%), PPP GDP and PPP GDP per capita (constant 2017 international dollars),
average for 2011–19 .
Countries with high government consumption and low Gini coefficient
Country
CountryCode
General government final
consumption,% of GDP
Gini coefficient of
income distribution,%
PPP GDP per
capita
PPP GDP,
billion $
Excess government
consumption, p.p.
Deviation of Gini
coefficient from predicted
values, p.p.
Algeria
DZA
19.5
27.6
11,506.3
458.1
3.6
10.3
Australia
AUS
18.3
34.4
48,039.4
1147
1.3
0.1
Austria
AUT
19.7
30.5
54,044.5
467
1.9
3.6
Belgium
BEL
23.6
27.7
49,721.2
560.4
6.1
6.6
Bosnia and
Herzegovina
BIH
21.6
33.0
12,817.4
44.12
3.8
4.5
Burkina Faso
BFA
16.1
35.3
1961.3
35.76
0.5
6.9
Canada
CAN
20.9
33.6
47,695.2
1710
4.3
1.1
China
CHN
16.1
40.1
12,781.7
17,570
2.9
1.6
Croatia
HRV
20.2
31.6
25,343.6
106
2.2
4.2
Czech Republic
CZE
19.3
25.9
36,626.3
387
1.9
9.1
Denmark
DNK
25.3
28.2
53,754.0
306
7.2
5.9
Estonia
EST
19.4
32.8
31,933.7
42.15
0.5
2.5
Fiji
FJI
19.0
36.7
12,393.8
10.82
0.2
0.9
Finland
FIN
23.7
27.2
46,454.4
254.1
5.6
7.2
France
FRA
23.7
32.5
44,039.2
2924
7.6
2.7
Germany
DEU
19.7
31.3
51,743.5
4228
3.7
3.8
Greece
GRC
20.3
35.5
28,673.0
311.6
3.0
0.1
Guinea
GIN
16.5
33.7
2205.4
25.54
0.5
8.2
Hungary
HUN
20.0
30.5
28,147.0
277.1
2.6
5.1
Iceland
ISL
23.9
26.7
52,065.3
17.48
3.7
7.3
Iraq
IRQ
20.7
29.5
10,586.6
374.7
4.8
8.6
Italy
ITA
19.3
35.2
41,487.1
2501
3.1
0.0
Japan
JPN
20.0
32.9
39,635.1
5037
4.4
3.0
Kyrgyz Republic
KGZ
17.8
27.7
4788.7
28.66
1.0
12.3
Liberia
LBR
16.9
34.3
1540.0
6.882
0.3
8.5
Myanmar
MMR
17.8
34.4
4245.2
223.9
2.7
5.9
Netherlands
NLD
25.1
28.1
53,910.5
915.5
7.9
6.0
Norway
NOR
22.7
26.7
61,882.4
320.2
4.4
7.0
Poland
POL
18.0
32.1
28,348.1
1077
1.7
3.7
Slovak Republic
SVK
18.6
26.4
29,425.9
159.7
0.8
9.1
Slovenia
SVN
19.2
25.3
34,998.8
72.31
0.5
9.8
Sweden
SWE
25.9
28.6
51,209.4
504
8.2
5.6
Timor-Leste
TLS
63.8
28.7
3228.6
3.876
12.2
Tunisia
TUN
19.4
32.8
10,435.0
116.9
2.7
5.3
Ukraine
UKR
19.1
25.1
12,194.1
531.5
3.3
12.7
United Kingdom
GBR
19.6
33.5
44,633.9
2907
3.4
1.7
West Bank and
Gaza
PSE
23.7
34.1
6142.4
26.29
6.4
5.3
Countries with low government consumption and high Gini coefficient
Country
CountryCode
General government final
consumption,% of GDP
Gini coefficient of
income distribution,%
PPP GDP per
capita
PPP GDP,
billion $
Excess government
consumption, p.p.
Deviation of Gini
coefficient from predicted
values, p.p.
Angola
AGO
51.3
15.5
212.0
7623.6
0.4
12.4
Benin
BEN
45.6
10.7
31.6
2968.9
5.5
4.5
Bhutan
BTN
38.1
18.1
7.4
10,194.5
0.7
0.0
Bolivia
BOL
45.7
15.9
86.1
7899.1
0.7
7.0
Bulgaria
BGR
37.7
16.3
143.1
19,991.8
1.2
1.2
Cabo Verde
CPV
42.4
17.9
3.4
6528.2
1.0
3.2
Cameroon
CMR
46.6
11.8
79.9
3412.9
3.9
5.8
Chad
TCD
43.3
5.4
24.1
1711.1
10.4
0.8
Chile
CHL
45.2
13.2
421.6
23,358.4
3.6
9.0
Colombia
COL
51.7
14.5
665.1
13,907.5
1.3
14.2
Comoros
COM
45.3
10.3
2.3
2997.7
7.9
4.2
Congo, Rep.
COG
48.9
16.2
23.3
4821.2
0.8
9.0
Costa Rica
CRI
48.6
17.3
87.9
18,105.7
0.4
11.9
Cote d’Ivoire
CIV
41.5
10.8
102.6
4367.2
5.0
1.3
Dominican
Republic
DOM
45.3
10.4
159.1
15,422.3
6.6
8.2
Ecuador
ECU
45.6
14.1
188.5
11,590.6
2.4
7.8
El Salvador
SLV
40.8
16.1
51.7
8167.9
1.0
2.1
Gabon
GAB
38.0
13.4
29.4
15,146.3
4.9
0.9
Georgia
GEO
37.8
13.9
47.3
12,693.3
3.8
0.2
Ghana
GHA
43.0
10.4
133.5
4768.1
5.3
3.0
Guatemala
GTM
48.3
10.8
125.6
8055.7
5.6
9.6
Honduras
HND
52.3
14.8
48.7
5328.2
1.8
12.6
Indonesia
IDN
39.1
9.3
2642.0
10,202.1
5.1
0.4
Iran, Islamic
Rep.
IRN
39.3
11.3
1046.0
13,308.5
4.1
1.6
(continued on next page)
V. Popov
Asia and the Global Economy 2 (2022) 100024
14
Table 2A (continued)
Countries with low government consumption and high Gini coefficient
Country
CountryCode
General government final
consumption,% of GDP
Gini coefficient of
income distribution,%
PPP GDP per
capita
PPP GDP,
billion $
Excess government
consumption, p.p.
Deviation of Gini
coefficient from predicted
values, p.p.
Kenya
KEN
40.8
13.6
185.0
3846.6
1.6
0.3
Lithuania
LTU
36.2
17.0
90.4
31,256.3
1.4
0.9
Luxembourg
LUX
33.0
16.7
63.1
110,645.4
3.5
0.8
Malawi
MWI
44.7
14.2
17.1
1020.3
1.2
0.9
Malaysia
MYS
42.1
12.8
753.3
24,788.5
3.6
6.0
Mauritius
MUS
37.7
14.7
25.3
20,036.4
4.1
1.2
Mexico
MEX
47.3
11.9
2346.0
19,250.7
3.4
10.2
Nicaragua
NIC
46.2
14.3
34.1
5464.0
2.6
6.6
Panama
PAN
50.7
11.1
112.2
28,126.1
7.0
15.1
Paraguay
PRY
48.6
11.1
79.1
11,792.7
6.1
10.9
Peru
PER
43.7
12.3
368.8
12,005.0
3.8
5.9
Philippines
PHL
45.5
11.1
758.3
7398.3
3.8
6.4
Rwanda
RWA
44.4
14.1
21.6
1877.6
1.9
2.1
Serbia
SRB
39.2
17.3
113.4
16,009.5
0.1
2.2
Sri Lanka
LKA
39.5
8.5
247.4
11,740.3
7.8
1.7
Turkey
TUR
41.2
14.3
2032.0
25,795.3
1.4
4.9
United States
USA
40.9
14.7
18,720.0
58,402.9
0.4
2.7
Uruguay
URY
40.2
14.0
69.9
20,473.2
4.0
3.8
Zambia
ZMB
57.1
13.8
54.5
3419.9
2.2
16.3
Most of the countries are from Europe (not highlighted). Asian countries are highlighted in red, MENA countries – in green, other – in blue.
Note: Excess government consumption is computed as a residual from regression of general final government consumption as a% of GDP on PPP GDP and PPP GDP per
capita. Deviation of Gini coefficient from predicted values is computed as a residual from regression of actual Gini coefficient on PPP GDP and PPP GDP per capita.
European countries highlighted in blue, Asia countries – in red; all other countries are either in SSA or Americas (LA and the USA).
Source: WDI database.
Fig. 13. PPP GDP per capita in some East Asian countries as a% of the US level
V. Popov
Asia and the Global Economy 2 (2022) 100024
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Fig. 14. PPP GDP per capita (in 1990 international geary-khamis dollars) as a% of the US level
V. Popov
Asia and the Global Economy 2 (2022) 100024
16
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