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Plunder in the Post-Colonial Era: Quantifying Drain
from the Global South Through Unequal Exchange,
Jason Hickel, Dylan Sullivan & Huzaifa Zoomkawala
To cite this article: Jason Hickel, Dylan Sullivan & Huzaifa Zoomkawala (2021): Plunder in
the Post-Colonial Era: Quantifying Drain from the Global South Through Unequal Exchange,
1960–2018, New Political Economy, DOI: 10.1080/13563467.2021.1899153
To link to this article: https://doi.org/10.1080/13563467.2021.1899153
Published online: 30 Mar 2021.
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Plunder in the Post-Colonial Era: Quantifying Drain from the
Global South Through Unequal Exchange, 1960–2018
, Dylan Sullivan
and Huzaifa Zoomkawala
London School of Economics and Goldsmiths, University of London, London, UK;
Department of Political
Economy, University of Sydney, Sydney, Australia;
Independent Scholar, Karachi, Pakistan
This paper quantiﬁes drain from the global South through unequal
exchange since 1960. According to our primary method, which relies on
exchange-rate diﬀerentials, we ﬁnd that in the most recent year of data
the global North (‘advanced economies’) appropriated from the South
commodities worth $2.2 trillion in Northern prices —enough to end
extreme poverty 15 times over. Over the whole period, drain from the
South totalled $62 trillion (constant 2011 dollars), or $152 trillion when
accounting for lost growth. Appropriation through unequal exchange
represents up to 7% of Northern GDP and 9% of Southern GDP. We also
test several alternative methods, for comparison: we quantify unequal
exchange in terms of wage diﬀerentials instead of exchange-rate
diﬀerentials, and report drain in global average prices as well as
Northern prices. Regardless of the method, we ﬁnd that the intensity of
exploitation and the scale of unequal exchange increased signiﬁcantly
during the structural adjustment period of the 1980s and 1990s. This
study aﬃrms that drain from the South remains a signiﬁcant feature of
the world economy in the post-colonial era; rich countries continue to
rely on imperial forms of appropriation to sustain their high levels of
income and consumption.
structural adjustment; global
The dominant assumption in the ﬁeld of international development holds that the economic per-
formance of nations is due primarily to their internal, domestic conditions. High-income countries
have achieved economic success because of good governance, strong institutions and free
markets. Lower-income countries have failed to develop because they lack these things, or
because they suﬀer from corruption, red tape and ineﬃciency (Sachs 2005; Collier 2007; Rostow
1960; Moyo 2009; Calderisi 2006). Therefore, development interventions should focus primarily on
ﬁxing domestic policy in global South countries, with the assistance of aid from donor governments.
This view has long come under criticism. Methodological nationalism –analysing each country in
isolation –erases the longstanding inequitable relationships between countries that have deﬁned the
global economy for the last 500 years. When we take this history into account, it becomes evident
that the wealth of high-income nations depends on processes of appropriation from the rest of the
world. This was clear during the colonial period, but it also remains true today. In this paper we quan-
tify the value appropriated from the global South through unequal exchange since 1960, demon-
strating that the wealthy nations of the global North continue to rely on extraction to ﬁnance
economic growth and sustain high levels of consumption. This pattern is evident during the
© 2021 Informa UK Limited, trading as Taylor & Francis Group
CONTACT Jason Hickel firstname.lastname@example.org London School of Economics and Goldsmiths, University of London, London,
NEW POLITICAL ECONOMY
entire postcolonial period, from 1960 to today, but has been particularly signiﬁcant during the era of
neoliberal globalisation since the 1980s.
The historical record demonstrates that, during the colonial period, Western European nations
depended for their development on extraction from other parts of the world. Britain’s industrial revo-
lution depended in large part on cotton, which was grown on land forcibly appropriated from Indi-
genous Americans, with labour appropriated from enslaved Africans (Beckert 2015). Other crucial
inputs required by British manufacturers –hemp, timber, iron, grain –were produced using
forced labour on serf estates in Russia and Eastern Europe (Kagarlitsky 2008; Wallerstein 1974: ch.
2; Wallerstein 1989: ch. 3). Meanwhile, British extraction from India and other colonies funded
more than half the country’s domestic budget, paying for roads, public buildings, the welfare
state –all the markers of modern development –while enabling the purchase of material inputs
necessary for industrialisation (Bhambra 2017; Patnaik 2018). It is impossible to understand the
industrialisation of high-income countries without reference to the patterns of extraction that under-
The general logic of colonisation was to integrate the global South into the Europe-centered
world economy on unequal terms. The South (the ‘periphery’) was made to serve as a source of
cheap labour and raw materials for the North (the ‘core’), and as a captive market for Northern man-
ufactured goods (Davis 2002; Chang 2008). Beginning in the 1950s, economists and historians associ-
ated with dependency theory and world-systems theory argued that this relationship continues to
deﬁne the global economy in the post-colonial era (Rodney 1972; Prebisch 1950; Galeano 1973; Wal-
lerstein 1974; Frank 1967; Nkrumah 1965). Recent empirical data conﬁrms that high-income nations
continue to rely on a large net appropriation of labour and resources from the rest of the world. In
2015, this amounted to 10.1 billion tons of embodied raw material equivalents (accounting for 50%
of total consumption in high-income nations), and 182 million person-years of embodied labour
(28% of their total consumption) from low- and middle-income nations (Dorninger et al
2021). Note that these ﬁgures represent resources and labour embodied not only in primary com-
modities but also in high-technology industrial goods such as iPhones, computer chips, cars,
designer clothes, etc., which over the past few decades have come to be overwhelmingly produced
in the South.
This net appropriation occurs because prices are systematically lower in the South than in the
North. For instance, wages paid to workers in the South are on average one-ﬁfth the level of Northern
wages (Cope 2019, p. 80). This means that for every unit of embodied labour and resources the South
imports from the North, they have to export many more units to pay for it. This pattern was ﬁrst
described by Adam Smith (1981, p. 141–145), Karl Marx (1991,p. 344–346) and Dadab-
hai Naoroji (1902). It was theorised more fully by Arghiri Emmanuel (1972), Samir Amin (1976) and
Stephen Bunker (1985) as a process of ‘unequal exchange’, which constitutes a ‘hidden transfer of
value’from South to North.
Theorists of unequal exchange argue that global price inequalities are artefacts of historical and
contemporary forces that depress the cost of labour and resources in the South. During the colonial
period, dispossession and the destruction of subsistence economies created a surplus of unem-
ployed labour (Davis 2002; Patnaik and Patnaik 2017; Dunaway 2010). Following independence,
when Southern governments attempted to improve wages and resource prices, Western powers
often intervened to remove them from power, as in the Republic of Congo (1960), Indonesia
(1965), and Chile (1973) (Hickel 2017: ch. 4). In the contemporary era, subsidised grain exports
from the North, and land grabs by multinational companies, continue to undermine subsistence
economies, placing downward pressure on wages (McMichael 2014; Pearce 2012). Structural adjust-
ment programmes (SAPs) imposed on the South by the IMF and World Bank have cut public sector
wages and employment, while rolling back labour rights and curtailing unions (Khor 1995; Petras and
2J. HICKEL ET AL.
Veltmeyer 2002). Finally, the South’s dependence on external ﬁnance means that Southern govern-
ments must compete with one another to oﬀer cheaper wages and resources to attract foreign
investment (Hickel 2017: ch. 6). Low wages are ultimately maintained through militarised borders,
which preclude easy migration from South to North, and thus prevent international wage
Just as Southern prices are kept artiﬁcially low, Northern prices are kept artiﬁcially high. Northern
ﬁrms control 97% of patents (Chang 2008,p.141)–a form of monopoly power that, bolstered by
the TRIPS agreement under the WTO, enables them to extract returns well in excess of free market
rates. Moreover, high-income nations exercise monopoly power within the core institutions of econ-
omic governance. In the World Bank and the IMF, the G8 hold a majority share of votes, allowing
them to determine the rules of international ﬁnance. In the World Trade Organization, bargaining
power is determined by market size, enabling high-income nations to set trade rules in their interests.
Neoliberal policies imposed by these institutions have forced global South governments to remove
tariﬀs, subsidies and other infant industry protections, preventing them from developing the industrial
capacity to compete with the North (Chang 2008). As a result, a relatively small number of ﬁrms from
high-income countries have grown so large that they now control an overwhelming share of the world
economy, with revenues that exceed the GDP of most sovereign countries (Vitali et al. 2011;Anderson
and Cavanagh 2000). These ﬁrms can set ﬁnalpricesthatareeﬀectively insulated from competition,
while depressing input costs across their supply chains (Suwandi 2019; Cope 2019: ch. 3).
The deployment of geopolitical and monopoly power by Northern states and corporations main-
tains price diﬀerentials that enable them to appropriate labour and resources from the South
through international trade. This pattern sustains high levels of income in the global North, and pre-
serves levels of material consumption well above equitable and ecologically sustainable levels
(O’Neill et al 2018). At the same time, it depresses the South’s potential trade revenues, denying
them access to resources that could be used for investment in public services, economic develop-
ment and poverty reduction. In light of this, one might ask how much the South has been losing
(and the North gaining) as a consequence of this relationship.
Several attempts have been made to estimate the scale of the South’s losses through unequal
exchange. Samir Amin (1976, p. 144) calculated that ‘if the rewards of labor were equivalent to
what they are at the center, with the same productivity,’the South’s revenues from exports to the
North would have been $152 billion higher in 1966 (updated to constant 2011 dollars). This
method has been improved upon by Zak Cope (2019, p. 81), who devised a way to distinguish
between losses the South suﬀers due to the ‘undervaluation’of their exports (comparing the
South’s wages to global average wages), and losses they suﬀer due to the ‘overvaluation’of
imports (comparing the North’s wages to global average wages). He ﬁnds that in 2010, the South
lost $2.8 trillion in hidden value appropriated by the North.
In the late-1990s, a second method for quantifying unequal exchange was developed by Gernot
Köhler (Köhler 1998; Köhler and Tausch 2002,p.43–100). Instead of looking at wage diﬀerentials,
Köhler uses the distortion factor between market exchange rates (MER) and purchasing power
parity (PPP) as a proxy for calculating how much higher Southern export prices would be if
valued in Northern prices. Using this approach, Köhler estimates that the South lost $134 billion
in unequal exchange in 1965, a ﬁgure which rose to $2.586 trillion in 1995 (updated to constant
2011 dollars). Köhler (2003) also employed a simpliﬁed version of his formula (using arithmetic
rather than weighted averages to estimate exchange rate distortion) to construct annual estimates
of unequal exchange from 1960 to 1998. His data showed that the South lost $27.7 trillion (updated
to constant 2011 dollars) over that period.
This paper builds on these previous attempts in order to provide annual estimates of unequal
exchange over the past ﬁve to six decades, using both methods for comparison. The data allows
us to measure the South’s total losses over multiple decades, and enables us to analyse the
impacts of policy changes in the world economy over time. We conclude with a discussion that
addresses critiques of unequal exchange, while assessing the limitations of this analysis.
NEW POLITICAL ECONOMY 3
Methodology: Exchange Rate Diﬀerentials
Köhler measures value transfer through unequal exchange by starting with the exchange rate
disparities between Northern countries and Southern countries. For instance, Köhler notes that
India’s GDP per capita in 1995 was US$1,400 in PPP terms (i.e. measured at the US price level),
but only US$340 in MER. Dividing PPP by MER yields what Köhler calls the ‘Exchange Rate Deviation
Index’, or ERDI. For India in 1995, ERDI was 4.12. Put diﬀerently, prices in the US were 4.12 times
higher than in India. For Northern countries, by contrast, ERDI is generally very close to 1. Köhler pro-
poses that we can use ERDI to measure value transfer. His formula is as follows:
T = value transferred through unequal exchange
X = exports from periphery to core
d = the ratio of the peripheral country’s ERDI to the core country’s ERDI
There are two ways to conceptualise Köhler’s approach to value transfer. Some scholars have
interpreted it as the amount of additional income that the South would have earned on its
exports under conditions of fair-trade (Köhler 1998; Somel 2003). In other words, value transfer is
calculated under the assumption that Southern exporters could receive Northern prices in a fairer
world. One might criticise this approach on the grounds that it is impossible for all countries to
achieve Northern prices, given that Northern prices are high because of imperial power, which
cannot be universalised. But there is another, more robust way to conceptualise Köhler’s approach,
namely, as measuring the value of commodities that the South transfers uncompensated to the
North in terms of the Northern price level.
This represents commodities that the South could have sold on world markets, as well as labour
and resource inputs that could have been used to meet domestic needs, but which were instead
transferred gratis to the North. It also represents a signiﬁcant windfall for the North, in terms of
the money saved by acquiring goods from the South, on unequal terms, rather than producing
them domestically at Northern prices. These savings are available for reinvestment in Northern econ-
omic development and to enhance the North’s economic and geopolitical power, which further
enables unequal exchange.
To calculate ERD indices, we rely on Penn World Table version 9.1 (Groningen Growth and Devel-
opment Centre, 2019). We use GDPe instead of GDPo, because the latter makes assumptions about
export prices that are invalid for our purposes (in short, GDPo calculations assume that price diﬀer-
ences of internationally traded goods are caused principally by quality diﬀerences, rather than diﬀer-
ences in bargaining power; see Feenstra and Romalis 2014; Feenstra et al. 2015).
We use the IMF’s
(2019) Direction of Trade Statistics (DOTS) for data on exports. All exports are measured Free On
Board (FOB), and do not include Cost Insurance and Freight (CIF). Since DOTS does not include
data on imports, FOB, we calculate each country’s imports as the sum of other countries’exports
to that country.
One limitation of Köhler’s method is that the export ﬁgures include the import content of exports
(ICE). To overcome this, we subtract the ICE, as recorded by the OECD (2020), from each country’s
export data. Import content data is not available for all countries, however, and is not available
for years prior to 2005 or after 2016. For countries without available data, we have assigned them
the global average ICE percentage in the relevant year. For instance, we do not have data for
Albania in 2008, so we have taken the average ICE in 2008 (27%) and assigned this to Albania.
For years prior to 2005 and after 2016, we have extrapolated from each country’s 2005–2016
mean. For instance, Argentina’s mean ICE was 10% from 2005 to 2016; we apply this ﬁgure to all
4J. HICKEL ET AL.
Using Köhler’s formula, we calculate the annual loss or gain from unequal exchange for all
countries with available data, for all years between 1960 and 2017. We have categorised each
country as either ‘core’(global North) or ‘periphery’(global South), based on the IMF’s
(2020a)‘Advanced economies’and ‘Emerging and Developing economies’categories. For each per-
ipheral country, we calculate the quantity of value lost through the undervaluation of their exports
to core countries as a group. For each core country, we calculate the amount they gain through the
undervaluation of their imports from peripheral countries as a group. Like Köhler, we weight the
core’s average ERDI by imports from the periphery, and the periphery’s average ERDI by exports
to the core. Since the IMF’s DOTS are listed in current dollars, we have used IMF (2020b) data on
the US Consumer Price Index to convert all results to 2011 constant dollars.
Results and Analysis
Figure 1 shows the scale of annual value transfer due to unequal exchange (calculated as the sum of
all losses suﬀered by the ‘peripheral’group) from 1960 to 2017. In the 1960s, the South lost on
average $38 billion a year (constant 2011 dollars), a signiﬁcant sum at the time. Yet the scale of
value transfer increased dramatically over the following decades, with particularly rapid growth
between 1983 and 2005, during the height of the structural adjustment period and the establish-
ment of the WTO trade system. Value transfer reached a maximum of almost $3 trillion per year
before declining somewhat after the global ﬁnancial crisis.
In 2017, the most recent year of data, drain through unequal exchange amounted to $2.2 trillion;
in other words, it was equivalent to the quantity of Northern commodities that one could buy in that
year with $2.2 trillion. This represents a signiﬁcant loss for the South. For perspective, $2.2 trillion is
enough to end extreme poverty ﬁfteen times over (i.e., with reference to the poverty gap at $1.90 per
day in 2011 PPP, or the rough equivalent of Northern prices).
For the North, this represents $2.2 tril-
lion in savings, which can be invested in technological development, military power, etc., while
maintaining high consumption levels. Aggregate value transfer over the whole period sums to a
total of $62 trillion.
There are two factors that drive ﬂuctuation in the scale of value transfer over time: a) the volume
of international trade, and b) the ratio of the South’s export-weighted average ERDI to the North’s
import-weighted average ERDI (in other words, the price distortion factor, or d), which represents
the intensity of exploitation. These trends are represented in Figures 2 and 3. We can distinguish
between two diﬀerent types of growth in unequal exchange. The ﬁrst is extensive growth, where
Figure 1. Drain from the global South, constant 2011 dollars, billions (1960-2017).
NEW POLITICAL ECONOMY 5
transfers increase because of an increase in the volume of exports, even if the price distortion factor
remains unchanged. Though exploitative, extensive growth allows the South to increase its export
earnings. Intensive growth in unequal exchange, on the other hand, happens when the price distor-
tion factor increases, even if the scale of trade remains unchanged.
During the 1960s and 1970s, the volume of trade grew while the price distortion factor remained
relatively steady. The intensity of exploitation increased somewhat from 1960 to 1972, but declined
during the 1970s as the South managed to raise the prices of its labour and resources relative to
Northern prices. This coincides with the period of progressive developmentalist policies in the
global South, and the rise of the anti-colonial movement, which altered the balance of global econ-
omic power at the time. Any growth in value transfer that happened during this period was for the
most part extensive in nature.
This pattern changed in the 1980s and 1990s, under structural adjustment and neoliberal globa-
lisation. During this period, rising value transfer was driven primarily by an increase in the intensity of
exploitation, with the price distortion factor doubling from 1.4 to 2.8. By cheapening labour and
resources in the South, structural adjustment caused a dramatic decline in Southern prices relative
to Northern prices. In fact, during the 1980s, the market value of the South’s exports actually fell,
even though the quantity of commodities exported (measured at Northern prices) was rising. In
Figure 2. South-North exports, constant 2011 dollars, billions (1960-2017).
Figure 3. Price Distortion Factor (1960–2017).
6J. HICKEL ET AL.
1993, Southern exports had a lower market price than they did in 1980, even though the value the
North was receiving increased signiﬁcantly. In other words, the South’s exports continued to increase
even while their earnings on those exports stagnated. This represents purely intensive growth in
In the early 2000s, the price distortion factor began to decline quite rapidly. This likely reﬂects the
commodities boom, which increased the bargaining power of Southern primary commodity produ-
cers. Nevertheless, despite this decline in price distortion, the early-2000s saw an extraordinary
increase in South–North trade, due to the rise of the WTO system, and the outsourcing of most man-
ufacturing activity to the periphery. As a result, value extraction continued to increase despite the
decline in the intensity of exploitation. This represents extensive growth in unequal exchange. It
is only after 2011 that value transfer itself began to decline, due to a decline in the volume of
North–South trade in the aftermath of the global ﬁnancial crisis.
We should note that most of the decline in value transfer since 2005 is due to a change in the
position of China. If we take China out, we see that value transfer from the rest of the South has
remained largely unchanged, or even increased (Figure 4). Over the past decade China has played
a diminishing role in unequal exchange. Value transfer from China declined from 42% of total
value transfer in 2005, to only 16% in 2017. It is worth noting that, unlike the rest of the global
South, China’s economy was never forcibly structurally adjusted. Figure 5 shows the price distortion
factor outside China.
While there is a decline from 1999 to 2013 (during the commodities boom,
which was itself driven by growing Chinese demand for resources), it has increased again since
2013. In other words, China is responsible for most of the improvement in the price distortion
factor, due perhaps to its greater degree of control over economic policy and greater bargaining
power in international trade.
Figure 6 represents the sum of all Northern gains as a percentage of the North’s GDP, and the sum
of all Southern losses as a percentage of the South’s GDP. Since our estimates of value transfer are
measured at the Norths’price level, for comparison to the North’s GDP we use MER, and for compari-
son with the South’s GDP we use PPP.
When calculating total GDP we excluded countries with no
unequal exchange data in any given year. For the period 1960–2005, value transfer represented a
rapidly growing share of GDP for both the North and South. The two lines converge in 2008, at a
point when value transfer represented nearly 7% of GDP for both regions. In the years since,
value transfer has declined relative to GDP. The North however still appropriates transfers equivalent
to 5% of its GDP.
Table 1 shows the ten peripheral countries that suﬀered the highest absolute losses due to
unequal exchange with the core in 2017.
The results show that China suﬀered the greatest absolute
Figure 4. Drain from the global South, excluding China, constant 2011 dollars, billions (1960-2017).
NEW POLITICAL ECONOMY 7
losses, equivalent to $357 billion, and a total of $19 trillion over the whole period. This aggregate loss
is equivalent to nearly $14,000 for each person presently living in China. And yet China’s annual
losses amount to only 2% of the country’s annual output. Vietnam, by contrast, has suﬀered
losses that amount to 17% of its output. Note that these results are not a measure of each country’s
total losses through unequal exchange, but rather only of losses in relation to the core. Extra-periph-
eral states like India may suﬀer losses not only to the core but to semi-peripheral states. Meanwhile
semi-peripheral states like China and Russia may be able to oﬀset part of their loss to the core by
exploiting other peripheral states.
It is worth noting that, if the value of outward transfers had been retained by the South, it could in
theory have been reinvested for national economic development. If we assume a rate of growth con-
sistent with that of Southern GDP in each year, cumulative losses over the period sum to $152 trillion,
signiﬁcantly higher than the $62 trillion represented here.
Table 2 shows the ten core countries that appropriated the highest absolute gains through
unequal exchange in 2017. The United States has enjoyed the greatest absolute gains, equivalent
to $856 billion, and a total of $18 trillion over the period –equivalent to $56,000 for each American
citizen living in the country today. There is a slight discrepancy between total peripheral losses and
Figure 5. Price Distortion Factor excluding China (1960–2017).
Figure 6. Value transfer as a percentage of GDP (1960-2017).
8J. HICKEL ET AL.
total core gains. This is due to two reasons. First, it is not possible to calculate a loss or gain for
countries for which ERDI data is unavailable. As a result, export ﬁgures from peripheral countries
without ERD indices are not included in the sum of peripheral losses, while export ﬁgures to core
countries without ERD indices are not included in the sum of core gains. In all years except for
the period running from 1993 to 1996, we capture more export data when measuring core gains
than peripheral losses.
The second reason has to do with a ﬂaw in Köhler’s method. To calculate the periphery’s losses,
Köhler multiplies each country’s export ﬁgure by the ratio of that country’s ERDI to the core’s import-
weighted ERDI. As a result, aggregate exports are multiplied by the ratio of the export-weighted per-
ipheral ERDI to the import-weighted core ERDI (i.e. the price distortion factor, d, shown in Figure 3).
By contrast, when calculating the core’s losses, Köhler multiplies each core country’s import ﬁgure by
the ratio of the export-weighted peripheral ERDI to the relevant core country’s ERDI. This is equival-
ent to multiplying total imports by the import-weighted average of the ratio of the periphery’s
export-weighted mean ERDI to each core country’s ERDI (henceforth, D). Köhler’s method produces
a discrepancy between total gains and total losses because ddiﬀers from D. Our calculations suggest
that Dis systematically larger than d; between 1960 and 2017, the former was on average 0.08 points
higher than the latter. It is not clear why this happens,or what the discrepancy means in terms of
political economic theory. This could be an avenue for future research.
To keep our estimates of value transfer conservative, we use total peripheral losses in Figure 1.
However, there is no reason to assume dis a more reliable indicator of global price diﬀerences
than D. It may be that the hidden transfer of value from South to North since 1960 was not $62 tril-
lion but $68 trillion, equivalent to $65,517 for everyone living in the core of the world-system.
We can also calculate the scale of value transfer by region. Figure 7 illustrates drain from each
region of the global South, accumulated by the core economies since 1960.
Table 1. Peripheral losses due to unequal exchange, constant 2011 dollars.
2017 losses Aggregate losses (1960–2017)
Country Total (billions) Per capita % of GDP, PPP Total (billions) Per capita % of GDP, PPP
China $357 $257 2% $18,760 $13,531 102%
Mexico $202 $1,619 9% $2,838 $22,742 120%
India $200 $150 2% $3,378 $2,524 40%
Russia $155 $1,070 5% $4,057 $28,077 120%
Poland $118 $3,119 11% $1,428 $37,597 132%
Vietnam $106 $1,120 17% $1,158 $12,237 188%
Indonesia $98 $371 3% $3,502 $13,232 122%
Malaysia $93 $2,983 11% $2,359 $75,837 287%
Thailand $90 $1,298 8% $2,222 $32,101 193%
Turkey $81 $998 4% $984 $12,138 46%
Peripheral Total $2,228 $357 3% $62,086 $9,951 97%
Table 2. Core gains due to unequal exchange, constant 2011 dollars.
2017 gains Aggregate gains (1960–2017)
Country Total (billions) Per capita % of GDP, MER Total (billions) Per capita % of GDP, MER
United States $856 $2,634 5% $18,291 $56,255 103%
Japan $212 $1,671 3% $11,995 $94,609 180%
Germany $170 $2,061 4% $5,752 $69,588 140%
Hong Kong $162 $21,873 56% $2,822 $381,841 971%
Netherlands $140 $8,150 14% $4,416 $257,788 452%
Korea $103 $1,996 7% $1,681 $32,660 118%
Great Britain $97 $1,469 3% $3,597 $54,446 121%
Australia $77 $3,116 4% $1,349 $54,849 75%
France $73 $1,099 2% $3,354 $50,160 110%
Italy $73 $1,210 3% $3,496 $57,758 155%
Core Total $2,356 $2,266 5% $68,113 $65,517 134%
NEW POLITICAL ECONOMY 9
Methodological Modiﬁcation 1: Value Transfer Compared to a Fair-Trade World
There is another method for measuring value transfer that is worth considering. Instead of measur-
ing the quantity of commodities transferred for free from South to North, at Northern price levels, we
can measure the South’s losses in terms of the market dollars the South would receive if the North’s
geopolitical and commercial monopolies were dismantled, and prices converged at a global average
level. To do this, we can compare the South’s ERDI not to the North’s ERDI, but rather to global
average ERDI. This allows us to distinguish between two diﬀerent kinds of losses: losses that the
South suﬀers due to the ‘undervaluation’of its exports to the North, and losses that the South
suﬀers due to the ‘overvaluation’of its imports from the North. The formula for this is:
T1 = value transfer due to the undervaluation of Southern exports
X1 = exports from South to North
d1 = the ratio of the South’s export-weighted average ERDI to the global average ERDI, weighted
by GDP, PPP.
T2 = value transfer due to the overvaluation of Northern exports
X2 = exports from North to South
d2 = the ratio of the North’s export-weighted average ERDI to the global average ERDI, weighted
by GDP, PPP.
The South’s total losses, T = T1 + T2
This method is derived from Cope (2019), but instead of looking at deviations in wages, as Cope
does, we use ERDI. Figure 8 shows our results. This method yields a similar curve, but the ﬁgures are
smaller for the most part, with maximum annual transfers reaching just over $1.6 trillion prior to the
global ﬁnancial crisis. The diﬀerence in scale can be explained by the fact that this approach rep-
resents value transfer in terms of global average prices, rather than Northern prices. That said,
these results are higher for the 1960s and early 1970s. The reason may be that this method accounts
for the South’s imports, rather than just exports, and during this period the former exceeded the
Figure 7. Drain from the global South (1960–2017).
10 J. HICKEL ET AL.
This indicates that Köhler’s original method underestimates the scale of value transfer when
the South runs a current account deﬁcit. Future research may be able to modify Köhler’s approach to
account for this, while still rendering value transfer at Northern price levels.
The utility of this approach is that it allows us to meaningfully compare value transfer to the South’s
GDP in MER, and to any other ﬁnancial stocks or ﬂows measured in terms of market dollars. For
instance, in recent years, the South has suﬀered annual losses of $1.4 trillion through unequal
exchange, $486 billion through proﬁt repatriation (Griﬃths 2014), and $1.1 trillion through illicit
ﬁnancial ﬂows (Kar and Spanjers 2015), which together sum to $3 trillion (note however that
Griﬃths (2014) uses somewhat diﬀerent country groupings, so is not directly comparable).
This approach also allows us to make meaningful comparisons with ODA. The original method
yields results that can be compared with ODA from the perspective of the North (i.e. seeing ODA
as a cost to the North), in the sense that the North saves through unequal exchange a sum that’s
many times more than it gives out in aid. But from the perspective of the South, this modiﬁed
method works better, because ODA receipts are subject to the global price level. So, over the
past few years the South has lost around $1.4 trillion per year in unequal exchange, while receiving
around $100 billion per year in aid. In other words, for every $1 the South receives in aid it loses $14
through unequal exchange (or $30, if we include losses due to proﬁt repatriation and illicit ﬁnancial
Methodological Modiﬁcation 2: Wage Rate Diﬀerentials
The primary limitation of using exchange rate deviation to estimate value transfer is that PPP
exchange rates are calculated using price surveys of consumption and investment products,
which include imported goods but not exports. As Feenstra et al. (2015) explain, this means that
countries with favorable terms of trade have lower estimated GDPe price levels than the real
price level of their output. In other words, the estimated price level of US GDPe may be unrealistically
low because it incorporates the price of cheap goods imported from the periphery, but fails to
account for expensive US exports. In light of this, the unequal exchange ﬁgures presented above
are probably underestimates.
An alternative method for calculating unequal exchange is presented by Cope (2019), who relies
on wage diﬀerentials. Instead of measuring Southern exports in Northern prices using ERDI, we can
calculate the price distortion factor as the ratio of the North’s import-weighted average wage to the
Figure 8. Peripheral losses compared to fair-trade, constant 2011 dollars, billions (1960 -2017).
NEW POLITICAL ECONOMY 11
South’s export-weighted average wage. To calculate these average wages, we use the ILO’s(2020)
data on ‘mean nominal monthly earnings of employees.’
One problem with this dataset is that
some countries have wage ﬁgures which are clearly inaccurate. For instance, Benin’s monthly
wage is listed as $0.18 in 2011, while Belarusian workers are recorded as earning $99.8 million a
month in 2000. To overcome this problem, we removed 61 cells which contained data that appeared
Another problem with the ILO’s data is that wage ﬁgures are not consistently available for any
country. Any time series based on ILO ﬁgures would ﬂuctuate wildly due to changes in the avail-
ability of data. To overcome this issue, we have linearly interpolated the intermediate years. We
then extrapolated backwards and forwards using average regional growth rates. For instance, in
1990 the average rate of nominal wage growth in the periphery (including the growth of interp-
olated ﬁgures) was 1.06%. As such, for countries with data in 1989 but not 1990, we extrapolated
forward by multiplying their 1989 wage by 1.0106. For those countries that already had data in
1990, but not 1989, we extrapolated backwards by dividing their 1990 wage by 1.0106.
Given these assumptions, our results should be interpreted with caution. For any country that has
not grown close to the average rate, our wage ﬁgure will be inaccurate. Nevertheless, since we have
extrapolated from historically accurate wage ﬁgures with historically accurate growth rates, these
ﬁgures give us a meaningful picture of the scale and trend of unequal exchange. Figure 9 shows
that, according to this method, value transfers reached almost $13 trillion prior to the global
ﬁnancial crisis, before declining to about $10 trillion in 2018, measured at the Northern price
level. These ﬁgures are higher than our previous estimates because the wage price distortion
factor is higher than the ERDI price distortion factor. Figure 10 shows that, according to this
method, prices are up to eight times higher in the North than the South.
We can also use wage disparities to estimate the South’s losses from unequal exchange in global
prices. We can do this using the same formula as in Figure 8, but calculating the distortion factors
with wages, such that d1 is the ratio of the population-weighted global average wage to the
South’s export-weighted average wage, and d2 is the ratio of the population-weighted global
average wage to the North’s export-weighted average wage. Figure 11 shows our results. According
to this method, in an equal-exchange world, the South would have received $3.5 trillion more in its
trade with the North in 2011.
Estimating value transfer with wage disparities allows us to avoid the issues associated with PPP
calculations, but is limited in that it proceeds on the assumption that international trade can be
measured in labour alone, as if labour were the only factor of production. This is a signiﬁcant limit-
ation, because exports are comprised of a range of inputs: labour, but also land, energy, and raw
Figure 9. Drain from the South, measured with wage deviation, constant 2011 dollars, billions (1969–2018).
12 J. HICKEL ET AL.
materials. Of these, global price inequalities are bigger for labour than for other inputs. If we could
account for the full range of inputs to exports, the scale of unequal exchange would likely be smaller
than the estimates presented here. While Köhler’s method may underestimate unequal exchange,
Cope’s method likely overstates it.
Conclusions and Discussion
Our results aﬃrm that drain from the global South remains a signiﬁcant feature of the world-
economy in the post-colonial era. ‘Advanced economies’rely on unequal exchange to facilitate
their economic growth and to sustain high levels of income and material consumption. In recent
years, the drain has amounted to around $2.2 trillion per year (constant 2011 dollars) in Northern
prices, or $1.3 trillion per year in global average prices, when calculated according to exchange-
rate diﬀerentials. The intensity of exploitation and the scale of unequal exchange increased signiﬁ-
cantly during the structural adjustment period of the 1980s and 1990s. These patterns of appropria-
tion through North–South trade are a major driver of global inequality and uneven development.
Figure 10. Price Distortion Factor, measured with wages.
Figure 11. Peripheral losses compared to fair-trade, measured with wage deviation, constant 2011 dollars, billions (1969-2018).
NEW POLITICAL ECONOMY 13
It is worth considering possible critiques of these results. Subasat (2013) has argued that PPP
exchange rates cannot meaningfully be used to estimate value transfer, because the South’s rela-
tively low price levels (i.e. high ERD indices) may be driven not only by power imbalances in the
world economy, but also other factors unrelated to global exploitation (2013, p. 374–375). While
we agree that other factors may be involved, these are unlikely to have a major impact on our
results. For instance, Subasat (2013, p. 375) postulates that exchange rate asymmetries may be
driven by ‘quality improvements’in high-income countries’non-traded sector. Yet the International
Comparisons Programme follows ‘well-established procedures for identifying matching qualities of
goods in each country’(Kravis et al. 1978, p. 32). These procedures should prevent systematic over-
estimates of price inequalities. Another possible cause of price inequality, according to Subasat
(2013, p. 375), is declining transport costs, which may allow rich countries to export goods more
eﬃciently, leading to exchange rate appreciation. But the eﬀect of this on Northern price levels
would be mixed, since declining transport costs should allow domestically traded goods to be pro-
vided at lower prices. In short, while these factors may create some noise in the calculation of value
transfer, they are unlikely to account for the large price asymmetries demonstrated in this paper.
Subasat suggests that another possible cause of price inequality may have to do with the Balassa-
Samuelson eﬀect (BSE) hypothesis:
a country that experiences productivity increase in its exportables experiences exchange rate appreciation that
increases the price of non-tradables as well as average prices in international currency (US Dollars) …This does
not imply, however, that the country with lower average prices (with low exportable productivity) is exploited
more than the one with higher average prices. (Subasat, 2013, p. 375)
Subasat acknowledges there are problems with the BSE (namely, that it fails to explain price diﬀer-
ences between countries exporting diﬀerent types of commodities), but he argues that productivity
diﬀerences may play a role (Subasat, 2013, p. 377). There is little evidence, however, to suggest that
the North does in fact have a productivity advantage over the South when it comes to production for
international trade (Fischer 2011). Most Southern export industries use advanced technologies pro-
vided by foreign capital. According to Amin (1976, p. 143), at least 75% of the South’s exports in 1966
were produced in ‘the ultra-modern capitalist sector (oil, mining and primary processing of minerals,
modern plantations –like those of United Fruit in Central America, or of Unilever in Africa and Malay-
sia).’Given the extent of oﬀshoring since the 1980s, it is likely that the proportion has only increased.
Furthermore, while Balassa (1964) and Samuelson (1964) consider the impact of technology and
capital on productivity, we should also consider the impact of diﬀerent modes of labour control.
Workers in the South are subject to rigid Taylorist rules that would fall foul of labour law in the
North. Chinese workers who produce smart phones describe being ‘trapped in a concentration
camp of labor discipline’where foreign corporations ‘sacriﬁce our dignity as people for production
eﬃciency’(Foxconn workers, quoted in Ngai and Chan, 2012, p. 398). Indeed, the US business press
admits Apple outsources manufacturing to China because repressive labour control grants the
‘eﬃciency needed to race products out the door’(Goldman 2012). In terms of the model proposed
by Balassa and Samuelson (where prices are determined by relative productivity) the highly eﬃcient
labour discipline in Chinese factories should be associated with high wages and prices. Yet Clelland
(2014, p. 103) estimates the price of an iPad is US$1,077 below its true labour and ecological value,
due to Apple’s monopsonistic control over its supply chain. Contra Balassa and Samuelson, prices are
not determined by productivity, but by the monopoly power of Northern governments and multi-
Claims about productivity diﬀerences could also be used to critique Cope’s approach to unequal
exchange. One might argue that the higher wages of workers in the North reﬂect their greater pro-
ductivity. Yet this assumption is belied by a 1971 study of export processing zones in Mexico, which
found that Mexican metal workers, electronics workers and seamstresses produced 10%-40% more
output in an hour than their US counterparts (Baerresen 1971, p. 33). Presumably, this productivity
advantage has increased as the US has oﬀshored plants and equipment to Mexico. If so, we can
14 J. HICKEL ET AL.
conclude that Southern wages are lower than Northern wages despite the fact that Southern
workers are more productive and eﬃcient.
Smith (2010, p. 198) has criticised the use of exchange rates to estimate unequal exchange on the
grounds that ‘southern exporters are actually paid in ‘overvalued’dollars, they therefore enjoy the
full domestic beneﬁts of the dollar’s greater domestic purchasing power.’This may be a fair critique
of theorists who argue that unequal exchange is driven by imbalances in currency markets (e.g.
Köhler 1998; Reich 2007). However, it does not undermine the validity of ERDI as a measure of
value transfer. ERDI simply tells us how much higher a country’sprices need to be to reach parity
with the US. Regardless of whether Southern exporters are paid in dollars, their prices remain low.
Ecuador’s national currency is the US dollar. Nevertheless, Ecuador had an ERDI of 1.73 in 2017,
because prices are lower in Ecuador than the US.
In fact, Smith’s analysis conﬁrms that ERDI is a meaningful proxy measure for the undervaluation
of Southern exports. In his critique of the BSE, Smith (2010, p. 205–206) argues it is the ‘suppression
of international labour mobility’combined with ‘the destitution of a large part of the [South’s]
working population’which explains ‘why a haircut or a bus journey in Dhaka is so much cheaper
than in Amsterdam,’not ‘the allegedly so much lower productivity of workers in the tradeable
goods sector.’If Smith is correct that diﬀerences in price levels reﬂect structural inequalities in
labour markets, then ERDI is suitable for measuring unequal exchange.
It is worth noting that estimates of unequal exchange may not be correlated to GDP growth
rates. In other words, some countries (like China) that suﬀer substantial losses due to unequal
exchange may grow faster than countries with lower losses. Subasat (2013, pp. 376–377) has
suggested that this implies Köhler’s method lacks ‘empirical validity.’However, there is no
reason we should expect value transfer to correlate with GDP growth. As Köhler (1998, p. 167)
points out, countries that do not trade with the core may be worse-oﬀbecause they cannot
access the resources controlled by Northern monopoly capital. This dilemma is comparable to
that of an employee who must either submit to exploitative working conditions or face unemploy-
ment and severe deprivation.
Unequal exchange represents a loss for the South. But it is not a loss relative to exclusion from the
world-economy; rather, it is a loss relative to an alternative world of fair-trade. The closest we have
come to such a world was during the 1960s and 1970s, when the Non-Aligned Movement was on the
rise and international prices (as measured by ERDI) were relatively equal. During those decades, the
economic growth achieved by the South was, on average, higher than what has been achieved since
(Chang 2008,p.27–28). If the North’s monopoly power were dismantled, the South’s capacity to
ﬁnance development would likely be even greater. Our estimates give some indication as to how
large these gains might have been.
Another critique that Subasat (2013, p. 376) levels against Köhler’s method is that it implies poor
countries were not exploited through trade prior to 1980, ‘the years when unequal exchange theory
[was] particularly popular.’Subasat presents two scatter charts showing that there was no positive
correlation between price levels and GDP per capita in 1960 or 1980. However, our calculations indi-
cate that Northern prices were in fact higher than Southern prices in those decades. As Figure 3
shows, from 1960 to 1980, the average annual price distortion factor was 1.54. In other words, the
South’s losses from unequal exchange amounted to 54% of their export earnings. Why Northern
prices were relatively high, despite the lack of a correlation between price and income levels, is a
question for future research.
While an investigation of global price diﬀerences before 1960 is beyond the scope of this paper, a
brief overview of the economic history literature contradicts Subasat’s notion that exchange rate
asymmetries only developed post-1980. Robert Allen (2001,p. 424) ﬁnds that from the ﬁfteenth
through to the late-eighteenth century, there was a ‘great divergence’in consumer prices
between Western and Eastern Europe. Similarly, Allen et al. (2011, p. 23) ﬁnd that a major price
gap arose between Europe and China in the mid-nineteenth century. While these examples are
NEW POLITICAL ECONOMY 15
not conclusive, they indicate core–periphery price inequalities have their roots in monopolistic
trading relationships established in the colonial period.
Ultimately, these critiques remind us that it is not possible to determine deviation of export prices
from ‘real’values, because ‘real’values are an abstraction, and the proxy measures we use (wage devi-
ation and ERDI) are not perfect. Measuring drain from the periphery is inherently diﬃcult because it is,
by deﬁnition, hidden in the price structure of the world-economy. Nevertheless, the South’s wages and
real exchange rates indicate that Southern prices have been kept artiﬁcially low, which enables pat-
terns of imperial appropriation that remain a dominant feature of the world economy.
1. We calculated this ﬁgure using data from Köhler 2003, p. 384.
2. The ratio of GDP, PPP to GDP, MER (i.e. ERDI) is equivalent to the inverse of the price level (Köhler and Tausch
2002, p. 96 - 97). As such, we calculate each country’s ERDI by ﬁnding the inverse of the price level of their GDPe
(denoted as PL_DA in PWT9.1). Since PWT’s GDP, PPP data is rendered in 2011 prices, ERD indices calculated as
the inverse of PL_DA indicate how much higher a country’s prices would be if they were at parity with the US in
2011. This does not mean that we estimate the Northern value of Southern exports by inﬂating the export
ﬁgures to 2011 prices. Since we multiply the market price of exports by the ratio of the peripheral ERDI to
the core ERDI, the Northern value of exports is always measured at the Northern price level in the relevant year.
3. We calculated the poverty gap in 2017 with data from the World Bank (2021).
4. To exclude China from the price distortion factor we sum the Northern value of all exports from peripheral
countries other than China, and then divide that ﬁgure by the market price of the same exports.
5. PWT9.1 does not contain data on GDP, MER (constant 2011 dollars). We therefore calculated GDP, MER by divid-
ing each country’s GDP in constant 2011 local currency units (the q_gdp variable), by their 2011 exchange rate
(XR_2). The q_gdp and XR_2 variables are in the ‘National Accounts data’ﬁle on the PWT website. For GDP, PPP
we used RGDPe.
6. One limitation of these time series is that the availability of data changes over time. For instance, with the excep-
tion of 1961, we do not have export data for China until 1978. Since these and other export ﬂows are left out of
our calculations, we may underestimate unequal exchange in the early years. Nevertheless, the trends we have
discussed here can be observed for countries that have data for the entire series.
7. Tables 1 and 2include per capita ﬁgures calculated with population data from the World Bank (2020). When
calculating total population, we excluded countries without unequal exchange data.
8. For instance, we assume that if the South’s 1961 losses ($29.7 billion) were invested in Southern development in
1962, they would have grown at the South’s 1962 growth rate (6%), yielding a total of $31 billion at year’s end. In
1963, the South would then be able to re-invest this $31 billion plus the scale of value transfer in 1962 ($25
billion), with this entire sum growing by the 1963 growth rate (7%). Our calculations here include years of nega-
tive returns. For instance, in 1961 the South’s growth rate was −0.5%. As such, we assume that, if the South had
invested their 1960 losses ($23 billion) in 1961, they would have lost $115 million. These growth rates are cal-
culated with the RGDPe variable from PWT9.1.
9. Another reason Figure 8 records higher estimates in the 1960s and early-1970s is that we calculated Figure 1
as the sum of all country-level peripheral losses. By contrast, we calculated Figure 8 by multiplying aggregate
exports by d1. This means Figure 1 only includes exports from countries with corresponding ERDI data,
whereas Figure 8 includes all available export data. This is not, however, the primary cause of the
10. We ﬁltered the ILO data by economic activity: ‘Aggregate Total,’by sex: ‘Total,’and by currency: ‘US dollars.’This
ensures that, as much as possible, we are comparing average wages across all economic activities and both
sexes, in US dollars.
11. Ecuador’s ERDI is calculated as the ratio of the US price level (PL_DA) to Ecuador’s price level (PL_DA).
No potential conﬂict of interest was reported by the author(s).
Notes on contributors
Jason Hickel is an economic anthropologist and a Fellow of the Royal Society of Arts. He writes on global inequality,
international development and ecological economics.
16 J. HICKEL ET AL.
Dylan Sullivan is a graduate student in the Department of Political Economy at the University of Sydney. His work
focuses on global inequality, colonial history, environmental justice, and the economics of socialist planning.
Huzaifa Zoomkawala is an independent scholar, data analyst and poet based in Karachi. His work focuses on feminist
and anti-colonial approaches to contemporary social issues.
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