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Determinants of Economic Inequality - The Role of
Capital Mobility
Hannes Fauser∗
October 15, 2014
prepared for the 18th FMM Conference on
“Inequality and the future of capitalism”
Abstract
This paper investigates the relationship between capital mobility and eco-
nomic inequality in four developed countries, focusing on the post-Bretton-
Woods period. It adds to the literature in three ways: A theoretical causal
link from the increase in capital mobility to the rise of income inequality is
derived. The hypothesis claims that increased capital mobility has, via tax
competition, contributed to lower taxation of the affluent, thereby increasing
inequality. Second, this link is investigated empirically, finding some support
for the hypothesis, but little evidence for a direct link from financial openness
to income concentration. Third, proposals for further research include refine-
ments of capital control measures and panel estimations of a direct relationship
between financial openness indicators and top income shares, in the spirit of
the empirical financialisation literature.
∗E-mail: fauserha@student.hu-berlin.de. This paper is based on the author’s Master’s Thesiswith the same title. I would like to thank Nikolaus Wolf, Felix Kersting, Volker Daniel and furtherparticipants of the graduation seminar at the Institute of Economic History at Humboldt-Universitatzu Berlin for helpful comments. Financial and immaterial support from the Rosa-Luxemburg Foun-dation, and data access support by the German Research Foundation through the CollaborativeResearch Center 649 “Economic Risk” at Humboldt-Universitat zu Berlin are gratefully acknowl-edged.
Contents
1 Introduction 1
2 A short historical overview 2
3 Methodological and theoretical framework 6
3.1 Income and wealth concentration . . . . . . . . . . . . . . . . . . . . 6
3.2 Capital mobility – development and measurement . . . . . . . . . . . 7
3.3 Taxation and capital mobility . . . . . . . . . . . . . . . . . . . . . . 10
3.4 From taxation to inequality . . . . . . . . . . . . . . . . . . . . . . . 12
3.5 Deriving my hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . 13
4 Empirical analysis 15
4.1 Exploring the relationship between capital mobility and inequality
directly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.1.1 The Feldstein-Horioka indicator . . . . . . . . . . . . . . . . . 17
4.1.2 Flow-based measures . . . . . . . . . . . . . . . . . . . . . . . 18
4.1.3 Stock-based measures . . . . . . . . . . . . . . . . . . . . . . 20
4.1.4 De jure indicators . . . . . . . . . . . . . . . . . . . . . . . . 23
4.2 From capital mobility to tax competition, further to inequality . . . 26
4.3 Wrap-up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
5 Discussion 30
5.1 Relation to financialisation literature . . . . . . . . . . . . . . . . . . 31
5.2 Explaining the evolution of inequality since the 1970s . . . . . . . . 33
5.3 Suggestions for further research . . . . . . . . . . . . . . . . . . . . . 36
6 Conclusions 39
7 Bibliography 41
A Appendix 46
1 Introduction
In his widely acclaimed book “Capital in the 21st century”, Thomas Piketty (2014)
argues that the development of top income shares over the 20th century can be
explained to a large degree by changes in personal income taxation. Precisely, he
identifies the tax cuts that were implemented in the Anglo-Saxon countries since
the late 1970s, as the source of the surge of income concentration at the top of the
distribution, which has not stopped ever since. In a different strand of literature,
economic historians like Barry Eichengreen (2008) have investigated the history of
the world financial system, finding that rising capital mobility has not only played
a major role in the breakdown of the Bretton Woods System, but also for the sub-
sequent liberalisation and deregulation of financial markets. As these two develop-
ments largely coincide, I attempt to throw an exploratory glance at the relationship
between capital mobility and economic inequality in this paper.
The causes of the rise in inequality over the last three to four decades are not
yet fully understood, and they are subject to heated debate among economists and
scientists from neighbouring disciplines alike. I would like to add to this literature
by investigating the role that the increase of capital mobility has played in this de-
velopment, a point that so far received little attention in the empirical (mainstream)
literature. In short, my argument features the following narrative: Since the early
1960s, capital mobility has increased in most parts of the world, starting in liberally-
oriented developed market economies. It has contributed to the demise of Bretton
Woods, which in turn accelerated the increase of capital mobility, fueled also by
advances in technology. Alongside the spread of neoliberal thinking, this rise again
influenced decisions to cut top tax rates, as it became easier to shift funds abroad
and hence tax competition increased. As both corporate and personal income taxes
were reduced, top earners were able to boost their share of the pie at the expense of
income groups with less bargaining power over their salaries.
I try to empirically test the hypothesis that capital mobility has contributed to
lower taxation of the rich, thereby increasing inequality, along two ways: First, I
directly investigate the evolution of different measures of capital mobility and top
income shares. Second, I analyse the respective links in the functional chain of
my hypothesis, i.e. from capital mobility to tax competition, and from taxation
to inequality. Hence, I analyse four high-income countries that are contrasted as
belonging to either the Anglo-Saxon world (the United Kingdom and the United
States) or continental Europe (France and Germany).
1
Results indicate that my expectation to find a faster rise of capital mobility for
the former are not confirmed by the descriptive evidence. Instead, the increase is
large for all four countries, which points to the institutional dimension for possible
explanations of the observed inequality differentials. This notwithstanding, findings
of the second part of the empirical analysis underline the validity of the transmission
channel I propose. This paper remains at a descriptive level when working with the
data. However, I am able to decide on which indicators of capital mobility are
suitable for further research in a more rigorous way, which is proposed towards the
end.
The remainder of this paper is organised as follows: Section 2 sets out a historical
overview of the topics considered, covering the distribution of income and wealth,
and the development of the international financial system and capital mobility. From
there, chapter 3 develops the theoretical framework of the narrative: After reviewing
the theoretical and empirical literature on top income shares (3.1), capital mobility
(3.2) and tax competition (3.3), I describe the link from taxation to inequality (3.4)
and derive the hypothesis of my work (3.5). Subsequently, it is tested empirically
in section 4 by following the two paths outlined above: First, the direct relationship
between capital mobility and top income shares is analysed (4.1), then the different
steps along the transmission channel of my narrative (4.2). The section is wrapped
up by a short discussion of shortcomings and a small summary (4.3). Afterwards,
chapter 5 relates my analysis to the financialisation literature (5.1), discusses possible
further explanations for the rise in inequality (5.2) and most importantly describes
suggestions for further research (5.3), based on the theoretical and empirical analyses
of the prior chapters. Finally, section 6 concludes.
2 A short historical overview
As has been clearly demonstrated by the top income share literature (see Atkinson
et al. 2011 for an overview), since the late 1970s the “Great Compression” of the
post-war era was replaced by the “Great Divergence”: Starting in the Anglo-Saxon
countries, the distribution of income became much more unequal again. In the last 15
years, Piketty and a number of other authors have established a quite comprehensive
literature on the top income and wealth shares throughout the world. For many
developed countries, their data span over a century, starting when a personal income
tax or some sort of wealth tax was introduced. From the tax data, they have been
able to calculate decile and percentile shares of income earners and wealth owners,
2
using a coherent empirical methodology. Considering the graph of the top 1% income
share in the countries under scrutiny here, it is clear that there are substantial
differences between individual countries, as France and Germany did not experience
the same rise in inequality as the U.K. or the U.S. (see figure 1). In general, the
degree of the U-shape varies heavily, with the English-speaking Western countries as
well as India and China experiencing the strongest increases. Northern and Southern
Europe show a substantial, but somewhat smaller rise in income inequality, while
Continental Europe and Japan indicate a rather flat line with modest increases
(Atkinson et al. 2011, 5). Another interesting point to note here is that while
inequality before WWI was largely a capital income phenomenon, the rise since
the 1970s so far seems to be driven by wage inequality. Piketty (2014, 336-376)
attributes this to the fact that the build-up of fortunes takes generations, and there
has simply not passed enough time since 1945 for the wealth distribution to return
to pre-WWI-levels. Adding to this, the institutions of postwar capitalism and the
persistence of the built-up wealth of the middle classes have so far slowed the process
of wealth concentration at the top.
Figure 1: Top percentile income share, selected countries, 1890-2010
Tabelle2
Seite 1
1890
1895
1900
1905
1910
1915
1920
1925
1930
1935
1940
1945
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
5
7
9
11
13
15
17
19
21
23
25
France Germany U.K. U.S.
Sha
re o
f top
per
cent
ile in
nat
iona
l inc
ome
(%)
Source: Author’s depiction, based on data retrieved from the WTID.Note: British values for 1908-1917, 1920-1936 and 1938-1948 are interpolated using top 0.1 and0.05% shares. German figures include capital gains, as the latest data are not available without.Moreover, there are many lacunae in the German data, as e.g. in the post-WWII period tax dataare only published every three years. These caveats apply to all upcoming figures that use toppercentile income shares.
In what concerns the development of the international financial system, I focus
3
on the Bretton Woods system and the reasons for its collapse, because this is where
the work hypothesis of this paper derives. Considering the macroeconomic trilemma,
the post-WWII international financial system describes the solution where exchange
rates are stable and domestic policy objectives can be met, however at the expense
of capital mobility. The system comprised of adjustable pegged exchange rates,
the admission of capital controls and the IMF as surveillance institution and lender
to countries in financial distress. The adjustable peg and the IMF control never
functioned as in theory, according to Eichengreen (2008, 91-132). Capital controls
under Bretton Woods however worked, because they were not insulated measures,
but part of a general interventionist spirit:
“This was a period when governments intervened extensively in their
economies and financial systems. Interest rates were capped. The assets
in which banks could invest were restricted. Governments regulated
financial markets to channel credit toward strategic sectors. The need to
obtain import licenses complicated efforts to channel capital transactions
through the current account. Controls held back the flood because they
were not just one rock in a swiftly flowing stream. They were part of
the series of levees and locks with which the raging rapids were tamed.”
(Eichengreen 2008, 92)
Nevertheless, their effectiveness decreased continuously. After the restoration of
current-account convertibility in 1959, it became easier to channel funds abroad by
manipulating trade volumes. Moreover, the expansion of multinational enterprises
enabled them to transfer capital more easily. Adding to this, the financial industry
used every loophole available to improve its profitability, which had suffered from
inflationary pressures starting in the late 1960s. U.S. Commercial Banks could es-
cape from regulations when acting on the Euro-currency markets in London (Arnum
& Naples 2013, 1159 f.). As financial integration in Europe increased, and controls
on banking transactions were eased, City banks attracted funds from U.S. banks,
because they could offer a higher interest rate than their more-tightely regulated
U.S. counterparts. This was possible due to an already existing substantial stock of
dollars outside the U.S., which weakened the effectiveness of U.S. capital controls at
the border (Eichengreen 2008, 219 f.).
The Bretton Woods system featured incentives for governments not to devalue
their currencies, because this would be assessed as a failure and induce capital out-
flows. At the same time, restrictive fiscal policy was not feasible due to the postwar
social contract, so Eichengreen wonders why the system actually survived until 1971.
4
He explains it with the intensive cooperation of central banks and governments,
which however was costly. During the 1960s, the U.S. tried to prevent the collapse
by strengthening capital controls several times. Mertzanis (1999, 120) claims that
these measures worked fairly well for pursuing both the objective to reduce trade
deficits and to maintain high employment. He contends the view of a “natural”
increase in capital mobility, pointing to a shift in hegemony in economic theory and
the change of political power away from labour (a point further discussed below
and in section 5.1). Whatever the causes of current-account liberalisation are, the
Bretton Woods system eventually broke down when the pursuit of U.S. policy goals,
especially financing the Vietnam War, became too much of a burden for its European
partners (Eichengreen 2008, 126-131).
During the 1970s, also nonbanks and hybrid lenders started to emerge in the
U.S., further increasing the growth of the financial sector. As the profit-rate in
manufacturing fell, financial activities became more and more attractive for many
firms. Moreover, the Neoclassical Synthesis Keynesianism which had dominated
economics since the 1940s, came under increasing pressure due to stagflation, con-
tributing to the rise of Monetarism in monetary policy. With low inflation as the
primary monetary objective, profitability of the financial industry was increased,
while the problems of manufacturers were exacerbated. Furthermore, the conser-
vative governments in the U.K. and the U.S., that took office in 1979 and 1980,
respectively, further reinforced the trends outlined in this paragraph. Over the
course of the 1980s and 1990s, more and more parts of the financial sector were
deregulated, while most economists adhered to what was called the “Washington
consensus” in 1990 (Arnum & Naples 2013, 1159-65).
A useful overview of the 1970s-1990s liberalisation of banking services in Europe
is found in Buch & Heinrich (2002), of which I report the three European countries
selected for my later analysis (see table 1). For the U.S. case, I report liberalisation
at the federal level, however there are substantial differences in the timing of state-
level deregulations.
To sum up, the demise of Bretton Woods, and the further development of the
international financial system, may arguably be attributed in part to the rise of
international capital mobility, which ever since has not stopped to increase. Finan-
cial markets continued to develop, fueled by new communication and information
technologies. Therefore, it became increasingly difficult for individual countries to
control capital flows, as they risked losing financial business to offshore centres and
less-regulated locations in general.
5
Table 1: Liberalisation of Banking activities in selected countries
Lifting of capitalcontrols
Interest rate de-regulation
First banking di-rective
Second bankingdirective
France 1990 1990 1980 1992Germany 1967, 1974 1981 1978 1992United Kingdom 1979 1979 1979 1993
Lifting of capitalcontrols
Interest rate de-regulation
Interstate bank-ing deregulation
Repeal of Glass-Steagall
United States 1974 1980 1994 1999
Sources: Buch & Heinrich (2002, 3), Sherman (2009)
3 Methodological and theoretical framework
This section summarises the recent literature on top income and wealth shares,
turning to studies on the measurement of capital mobility and the effects of its
increase in the light of financial deregulation, finally linking the two by analysing first
the public finance literature on capital mobility and tax competition, and second
studies on the relation of taxation and income distribution. As a consequence, I
derive my working hypothesis.
3.1 Income and wealth concentration
Since the seminal work of Piketty (2001), a substantial empirical literature has un-
folded that analyses inequality on the basis of administrative data, usually income
and wealth, e.g. estate, taxes. In contrast to survey data, which had been the
primary source for empirical research on inequality in recent decades, the tax data
cover a much longer timespan of about a century – compared to three to four decades
at maximum for survey data. They moreover allow for long-term international com-
parisons. Moreover, and can be decomposed into labour and capital income, which
is important for empirical analysis as the two usually are determined by different
economic institutions and conditions. Of course, also a number of limitations exist.
Studies of inequality in the past decades have mainly used household surveys for the
reason that tax data are not representative: First, the collection process is adjusted
to the needs of the fiscal authorities, not to those of the researcher. That is why the
concepts and definitions might change over time, and raise difficulties when compar-
ing across borders. Adding to this, of course there is tax optimisation and evasion,
as people underreport or shift income and wealth so that it receives preferable tax
treatment. Finally, tax data is not informative about the composition of income,
6
e.g. in terms of industry (Atkinson et al. 2011).
In any case, using household surveys as an alternative also has its drawbacks:
These studies usually do cover the whole distribution, but they often encounter
sampling errors, as the samples are much smaller than for tax data that covers all
tax filers. Furthermore, they perform rather poorly when measuring top incomes
and wealth, typically understating the extent of inequality. This is due to the fact
that many surveys are top-coded, because problems concerning measurement errors,
nonresponse and incomplete response especially affect the upper tail of the distribu-
tion. Atkinson et al. (2011, 29) call the response problems the “survey counterpart
of tax evasion”. For a comparison and a synthesis of household survey and tax
data for the U.S. case, see Burkhauser et al. (2012). Likewise, the use of artifi-
cial measures of inequality like the Gini or the Theil coefficients has disadvantages
compared to distribution tables. Even though it seems catchy to grasp several di-
mensions of inequality in a single number, their interpretation is less intuitive, as a
certain technical understanding of the underlying construction is necessary. More-
over, they mix different aspects of inequality, e.g. labour and capital income, that
should rather be analysed seperately for the sake of clarity in terms of underlying
economic mechanisms (Piketty 2014, 266 f.).
3.2 Capital mobility – development and measurement
“Capital is said to be mobile between two regions if some of their res-
idents may engage in interregional asset trades. Correspondingly, the
degree of capital mobility is measured by the scope for such trades, a
scope which might be limited by transaction costs, taxes, or official reg-
ulations.” (Obstfeld 1986, 55)
Standard (neoclassical) economic theory in general regards perfect capital mobility
as beneficial, as it assures the efficient international allocation of savings. Starting
with Feldstein & Horioka (1980), it was contested whether capital was actually highly
mobile, as had been assumed in conventional wisdom. The so-called “Feldstein-
Horioka puzzle” was born when the two authors found that changes in national
savings were highly correlated with changes in domestic investment. In neoclassical
theory, this should not be the case, as a country might simply borrow from abroad at
the world interest rate, if national savings do not suffice to meet investment demand.
In the early literature some more definitions for perfect capital mobility are used,
following Frankel (1992, 197):
7
• the Feldstein-Horioka condition: national saving rate has no effect on domestic
investment,
• real interest parity: interest rates equalise across borders,
• uncovered interest parity: expected returns on government bonds equalise,
regardless of exchange rate risk,
• covered interest parity: interest rates equalise when contracted in the same
currency.
The first relationship has been further investigated by a number of studies (for a
review, see Zodrow 2010, 872-881). The seminal findings have been objected on
the grounds of econometric issues with the estimation method applied, focusing
on the assumed exogenous determination of the saving rate by structural factors.
Adding to this, alternative explanations have been raised to explain the results,
e.g. that high savings-investment correlations simply reflect long-run intertemporal
budget constraints on country deficits and surpluses. Also single countries have been
analysed instead of the original cross-sectional framework. Results mostly indicate
that the relationship was closer to 1 for large, rather closed economies like the U.S.,
and also higher for countries with tight capital controls in place. Nevertheless, it
became weaker after 1972, indicating growing capital mobility (Obstfeld 1986, Taylor
1996).
The other three definitions, which all relate to the equalisation of interest rates
across borders, have also been tested empirically. There are several reasons to ex-
pect real interest parity not to hold: Currency premia that are driven by expected
exchange rate changes, exchange rate risk premia, and premia on political risks such
as taxes on capital income or even expropriation. According to Zodrow (2010, 866),
the empirical evidence also speaks against uncovered interest parity. Obstfeld et al.
(2005) study the evolution of international interest rates over 130 years, in the con-
text of the monetary policy trilemma. Using monthly data for short-term nominal
interest rates, exchange rate regimes and capital controls, they estimate different
specifications and find that “now, in the contemporary post-Bretton Woods era, we
see signs of reversion to the more globalised pattern, with increased interest-rate
transmission among fixed-rate countries” (Obstfeld et al. 2005, 424). Also Quinn &
Voth (2008) find returns of equity market correlations to increase since the 1970s,
after the expansion of capital account openness.
Measures of financial integration can be classified as either de jure or de facto in-
8
dicators. The former measure legal restrictions on capital flows, i.e. capital controls.
They are usually based on the IMF’s Annual Report on Exchange Arrangements
and Exchange Restrictions (AREAER), which tracks the exchange rate and trade
regimes of all member states of the Fund. From verbal assessments until 1966, to
binary information on current and capital account restrictions until 1996, to a more
nuanced set of 13 indicators since 1997, the informational content has been steadily
increased (Quinn et al. 2011). In the early literature, the AREAER information
was mostly used to construct binary measures that switch on whenever a country
has capital controls in place. These have been refined by coding in more detailed
information contained in the report, trying to catch the intensity of capital account
restrictions. Nevertheless, according to Kose et al. (2010, 4288 f.), they share several
shortcomings, namely that they do not capture the effectiveness of capital controls
and that they do not necessarily account for the actual degree of integration of an
economy into international financial markets.
Alternatively, de facto indicators based on actual financial flows can be used.
Here, gross (in and out-)flows prove to be less volatile and create a more compre-
hensive picture than net flows. However, Kose et al. (2010, 4289) claim that “it is
preferable to use the sum of gross stocks of foreign assets and liabilities as a ratio
to GDP”, because it alleviates the problem that annual flow data are volatile and
prone to measurement error. In the macroeconomic literature, both de jure and de
facto indicators have been used mostly for growth regressions. There, financial flows
and stocks often prove to be endogeneous, creating identification problems. How-
ever, also de jure indicators may be endogeneous, as countries in recent decades,
usually emerging market or transition economies, have adopted them mostly when
they came under pressure from financial markets. Correlations and factor analyses
indicate that de facto measures identify more variation in financial globalisation, as
they probably reflect more economic and political factors, of which de jure measures
are just one (Quinn et al. 2011, 517).
Many of the measures were built to determine what the effects of capital ac-
count openness are, in the context of the financial and economic crises in developing
countries during the 1990s, especially the Asian Crisis in 1997. Several authors,
most notably Rodrik (1998), questioned the claim that capital account openness
has positive effects on economic development, finding no correlation between finan-
cial openness and investment rates or growth. On the other side, results of Quinn
(1997) indicate the opposite, i.e. that financial liberalisation is robustly and posi-
tively associated with growth. Many more studies have been published on the issue,
9
and presumably the differing results can be explained by the choice of the capital
control measure. For an early review that concludes that at best, ambiguous effects
of capital account liberalisation are found, see Eichengreen (2001). Finding more
arguments in favor of openness, also Henry (2007) reviews the available evidence.
3.3 Taxation and capital mobility
Considering standard public finance theory, increased capital mobility should erode
the base of taxes on assets that taxpayers can then divest more easily from the
tax jurisdiction. Indeed, early models of (Nash) tax competition yield this “zero
tax result” as a consequence of a “race to the bottom”: Assuming small, open
economies competing for (perfectly) mobile capital, a source-based tax would drive
capital out until the international rate of return after taxes is reached. The tax
burden is borne by local residents, because they incur lower wages or higher prices
for non-tradables. Moreover, efficiency costs arise, in the form of transaction costs
for capital emigration or less capital-intense production (Zodrow 2010, 881).
However, these models have been updated, especially through contributions of
the New Economic Geography literature (on the latter see Baldwin & Krugman
2004; for an overview about the development of formal neoclassical models of tax
competition Krogstrup 2003). Adding imperfect competition and firm-specific eco-
nomic rents makes a difference: Especially if rents are location-specific and accrue to
multinational enterprises (MNE) which are (largely) foreign-owned, it may be ben-
eficial for local governments to tax them. In the presence of agglomeration forces,
governments in the core economies may raise higher taxes than those in the periph-
ery, up to the marginal limit that assures that industries do not leave. Another
extension to the basic models is income shifting across jurisdictions, a strategy of
tax planning mostly applied by MNEs and wealthy individuals. This includes ma-
nipulation of financial accounting, e.g. through over- or underpricing of transfers or
loans among affiliated companies. It is incentivised through differences in statutory
tax rates, as it is profitable to shift deductions to countries with high tax rates,
and revenues or profits to jurisdictions with low tax rates. Many empirical studies
show that this exploitation of tax differentials has spread in recent decades. There-
fore, competition in statutory tax rates may be even more fierce than for effective
marginal tax rates (Zodrow 2010, 883). Moreover, this expectation is reinforced by
evidence for increased competition of high-tax host jurisdictions in the form of al-
lowing tax avoidance by MNEs. For the U.S., this has been shown for the 1992-2002
period by Altshuler & Grubert (2006).
10
This notwithstanding, a number of factors also counteract against tax compe-
tition (Zodrow 2010, 884). Most notably, since the 1990s policy initiatives have
set out to limit harmful tax competition, both under the EU’s Code of Conduct
for business taxation and as part of the OECD initiative against harmful taxation
(for a critical review of these efforts see Kudrle 2008). Their efficacy is moreover
questioned most recently by Johannesen & Zucman (2014), who use unique data
retrieved from the Swiss National Bank and the BIS to study the effects of the
tax-haven crackdown implemented after the outbreak of the financial crisis. They
find that only small amounts of funds are repatriated following the new agreements,
while most are relocated to other tax havens that have signed less agreements.
Reviewing also the empirical literature, Zodrow (2010, 885) notes that statutory
corporate tax rates have declined significantly in the last three decades. At the same
time though, tax bases have been broadened, so that corporate tax revenues as a
fraction of GDP are quite stable during the past 40 years. These general findings
however require some differentiation. First of all, the data of the studies that Zodrow
reviews cover at most the early 2000s. Throwing a cursory look at the development
of statutory corporate tax rates since 2006 (KPMG 2014) reveals that since then,
corporate tax rates have declined even further, for the OECD average by about 3.5
percentage points (from 27.67 to 24.11%).
Garretsen & Peeters (2007) estimate the effect of rising capital mobility on cor-
porate tax rates of OECD countries. They use FDI relative to GDP or gross capital
formation, respectively, as a measure of financial openness and an index of capital
controls related to inward FDI as an instrumental variable. Their findings are in
line with the standard theory of tax competition, because an increase in capital
mobility decreases the corporate tax rate, even when controlling for agglomeration
effects. Still though, agglomeration matters, as core countries indeed have higher
tax rates. In line with these results, other empirical investigations emphasise the
different impact on core vs. periphery: Tax competition is shown to have much
bigger negative effects on developing countries, where revenues are not stable like in
the OECD member states, but have declined (Zodrow 2010, 886).
I have dealt only with corporate taxes so far. What about personal income
taxes? Obviously, the personal income tax base is not as mobile as the corporate
tax base. In their study of strategic behaviour of governments in terms of setting
personal income tax rates, Duncan & Gerrish (2014) only mention two other papers
that have investigated the same topic before, with opposite results. They basically
11
extend the analysis of Egger et al. (2007), who find that OECD countries tend to
follow their neighbours when setting corporate and personal income taxes. Using a
new dataset applied in a balanced panel design for 53 countries, Duncan & Gerrish
generalise these results beyond OECD countries. Interestingly, the data also allow
them to estimate both marginal and average tax rates for four income classes. Their
results indicate that mimicry of neighbouring countries holds for all income classes,
but is more important in the lower parts of the tax schedule. This is somewhat
contradictory to my intuition, but caution is necessary as the dataset does not
contain social security contributions through payroll taxes, thus possibly biasing
the results. Moreover, these findings are probably not fully relevant for top income
earners, where other comparison groups than neighbouring countries may play a
role, due to higher mobility.
3.4 From taxation to inequality
Let me now consider possible transmission channels from taxation to inequality.
The effects of taxes on the wealth distribution are presumably important, as the
reasoning of Dell (2005) for the German post-WWII case shows, who attributes
the relatively high wealth inequality to the low German inheritance tax. However,
the long-term processes of the accumulation of wealth are beyond the scope of this
paper, extensive discussions can be found in Piketty (2014, 336-429) and Piketty &
Zucman (2014). I want to focus on the effects of marginal tax rates on top income
shares, which are discussed for instance in Atkinson et al. (2011, 56-60), Piketty
(2014, 330-335) and Piketty et al. (2014).
According to the latter, there may exist several channels for the impact of lower
marginal tax rates on reported top income: In a standard supply-side story, indi-
viduals may work more and therefore earn more, stimulating the economy through
savings and entrepreneurship. Second, the tax avoidance story: As tax rates de-
crease, top earners report more of their income, which had been substituted for
other, lower- or non-taxed, forms of compensation before. This would imply that
top income shares in the past have not been much different from today, but part of
the income was simply hidden from the fiscal authorities. Third, the compensation-
bargaining story contends that remuneration at the top has little to do with marginal
productivity, but instead relies on hierarchical company structures and badly incen-
tivised compensation committees. Top earners may increase their pay by exerting
influence on corporate boards, a behaviour that is encouraged by lower taxes.
According to Piketty (2001), the progressive income tax is the most natural and
12
plausible explanation for the meagre development of big fortunes in France after
WWII. In their article, Atkinson et al. (2011, 65-67) review its influence in studies
of several countries. On one hand, top income tax rates seem to have little influence
in Canada and Indonesia, and income concentration has increased in Portugal and
the U.K. even though the tax rate has not changed for several decades. On the other
hand, the evolution of top income shares in France, Sweden, Finland, Switzerland,
Japan and the U.S. seems to be in line with the argument that progressive income
taxes matter for income concentration. Moreover, Roine et al. (2009), who are the
first to exploit the top income shares in an econometrically rigorous way using a panel
data approach for 16 countries, find that top marginal tax rates had a significant
negative impact on the rich and the upper middle class after WWII.
Adding to this, Piketty et al. (2014, 231) find that “international evidence shows
a strong correlation between top tax rate cuts and increases in top income shares in
OECD countries since 1960”. The tax-avoidance explanation does not fit the U.S.
data: Income shares based on those parts of income that allegedly had been used
to avoid the personal income tax, e.g. realised capital gains, have increased almost
exactly as much as the standard ones. Also, they find no correlation between GDP
growth and the fall in top tax rates during the period of 1960 until today, suggesting
that the first elasticity is modest in size at one hand, and confirming the bargaining
channel on the other hand, where gains for the top come at the cost of the lower
parts of the distribution. U.S. CEO pay evidence furthermore indicates that low
top tax rates have induced top managers to increase the part of their salary that is
not dependent on their performance, e.g. through stock options during the 1980s
and 1990s. Also the international micro-level evidence reassures the compensation-
bargaining story, as it “shows that CEO pay is strongly negatively correlated with
top tax rates even controlling for firm’s characteristics and performance, and that
this correlation is stronger in firms with poor governance” (Piketty et al. 2014, 232).
3.5 Deriving my hypothesis
Having summarised focal points in the literature on top income shares, capital mo-
bility measurement and development, and the links between capital mobility and
tax competition, as well as between taxation and inequality, several conclusion can
be made:
1. Inequality is most illustratory measured by top income and wealth shares, a
method that also bears superiority in terms of analytical rigour. Inequality
after WWII stayed rather flat until the 1970s in the developed world. There-
13
after, it has been on the rise, however to a substantially different extent across
different countries.
2. Whatever indicator is applied, capital mobility seems to have risen to his-
torical heights since the Bretton Woods system eroded. When exactly the
increase started, and how quickly it evolved, seems to be country-specific to
some degree, but presumably also depends on the measure applied.
3. From the former two points, one can expect a certain correlation between the
two variables. However, nothing has been said about any causality so far.
That is where possibly tax competition comes into play, as it may plausibly be
increased by rising capital mobility, and may in turn have an effect on income
and wealth distribution as well.
4. Concerning the former, my interpretation of the empirical literature is that tax
competition has indeed increased with rising financial openness. Moreover,
competition seems to take place not only for corporate taxes, but also for
personal income taxes, even though the available evidence is still thin.
5. The link from top tax rates to inequality also seems to leave little doubt, as
my reading of the available evidence suggests that the surge of top income
shares in many countries can – of course only in part – be explained through
decreased progressivity of income taxes. Presumably, the decline of corporate
taxes plays a role, too, as it increased capital incomes.
From this, I arrive at the following diagram with my assumed transmission channel:
Figure 2: The link from capital mobility to inequality
Liberalisation and Deregulation
Capital mobility
Tax competition
Top income shares
Top tax rates
Corporate tax rates
Source: Own depiction.
My hypothesis therefore is: Increased capital mobility contributed to lower tax-
ation of the rich, thereby increasing inequality.
14
4 Empirical analysis
In this section, I seek to test the hypothesis just derived against the available em-
pirical evidence. I would like to advance my analysis along the following paths:
1. Investigating the direct relationship between different measures of capital mo-
bility and inequality,
2. analysing the respective links in the functional chain of my hypothesis, i.e.
from capital mobility to tax competition, and from taxation to inequality,
3. ideally joining both into one econometrically tractable design.
It should be noted already at this point, that the third point will be left to
further research.
I confine the analysis to four large economies that feature relatively well-established
data for most of the variables needed. These are France, Germany, the United King-
dom and the United States. Apart from data availability, they have also been chosen
on the basis of taking the two leading Anglo-Saxon nations in terms of inequality, and
two continental European countries that have experienced more modest increased
(Germany) or close to stable top income shares1 (France) after 1945. This way, the
two may be contrasted and the hypothesis can be tested more specifically. If capital
mobility has indeed contributed to the rise in inequality, I expect to find a relevant
rise of its measures earlier in the Anglo-Saxon states than in continental Europe.
4.1 Exploring the relationship between capital mobility and in-
equality directly
As has been discussed in section 3.2, a variety of measures of financial openness
exist. I have been able to retrieve data for a selected number of indicators, which
are summarised in table 2.
1I stick exclusively to top income shares for two reasons: First, the dynamics that drive thewealth distribution are more long-term in nature, making it even more difficult to analyse with thedata at my disposal. Second, I could not retrieve essential post-1980 wealth shares data for theU.K., for which unfortunately only 10-year averages are provided in the online data appendix ofPiketty (2014), while detailed sources seem not to be public. Personal correspondance did not liftthe problem either, as Prof. Piketty does not dispose over more data than posted online.
15
Table 2: Overview about capital mobility indicators
Description Properties/Interpretation Available for Sources
Feldstein-Horioka Correlation of domestic savings and investment rates Annual, values closer to 0 indicatehigher capital mobility
1946-2012 WDI, Taylor (1996)
PI flow Portfolio Investment, net equity inflows, percent of GDP Annual 1970-2013 WDI
FDI flow Sum of net FDI inflows and outflows, percent of GDP Annual 1970-2012 WDI
FAL IFS Sum of foreign assets and liabilities, percent of GDP Annual 1948-2013 IFS
FAL BIS Sum of foreign assets and liabilities of reporting banks,percent of GDP
Quarterly 1978-2013 BIS
FAL LMF Sum of foreign assets and liabilities, percent of GDP Annual 1970-2004 Lane & Milesi-Ferretti(2007)
CAPITAL Capital account openness indicator, based on coding ofAREAER text
Annual, scaled 0-100, higher valueindicates less restrictions
1950-2007 Quinn (1997, 2011)
KAOPEN Capital and current-account openness indicator, basedon principal component analysis of 0/1-dummies inAREAER tables
Annual, scaled -1.8-2.54, highervalue indicates less restrictions
1970-2011 Chinn & Ito (2008)
Miniane Capital account openness indicator, based on average of0/1 dummies of 13 categories in AREAER tables
Annual, scales 0-1, higher value in-dicates more restrictions
1983-2000 Miniane (2004)
Note: Only measures are listed which are available for all countries, preferably from the same source. Whenever additional informative data for one or morestates was used, it is mentioned in the text. Online data sources for indicators taken from other researchers are listed in the bibliography, behind their respectivepublications. Data that are not publicly available, which partially applies to the IFS, WDI and BIS locational banking statistics, were accessed via Macrobond.
16
4.1.1 The Feldstein-Horioka indicator
I start with the traditional estimator, namely the correlation of domestic savings
and investment. Even though data for the period before 1950 is available, I confine
the analysis to the time thereafter, as I am primarily interested in explaining the
continuing rise in inequality since around 1980. In their seminal paper (see Feldstein
& Horioka 1980), the authors estimated an equation of the form(I
Y
)i
= α+ β
(S
Y
)i
+ εi (1)
where IY is investment relative to GDP and S
Y domestic savings relative to GDP.
The coefficient β, later dubbed “savings retention coefficient”, was found to be
close to 1, for 5-year-averages as well as for the whole sample period (1960-1974).
The authors originally used a cross-section design; and the 5-year averages to deal
with business cycle fluctuations. The methodology has been under debate in the
subsequent two decades (see e.g. Taylor 1996, 8-18; or Zodrow 2010, 872-881), but
I restrict the analysis to time-series estimations of single countries. I use data taken
from the WDI database of the World Bank as primary source, and the appendix of
Taylor (1996) as additional source. Variables used are gross capital formation and
gross domestic savings, both as a percentage of GDP. I estimate an error-correction
model along the lines of Taylor Taylor (1996, 18-25) for all four countries, basically
adding some 20 years to the sample used by Taylor. As a simple bivariate relationship
is estimated, the Engle-Granger two-step method can be used, and the first-order
ECM specification therefore is:
∆
(I
Y
)t
= ω0 + ω1∆
(S
Y
)t
+ ω2zt−1 + εt (2)
with zt being the error-correction term, which is the residual of a first-step re-
gression in levels (I
Y
)t
= θ0 + θ1
(S
Y
)t
+ zt (3)
The coefficients of regression 4, also called cointegrating regression, can be inter-
preted as the long-term relationship to which the short-term dynamics of regression
3, the actual ECM, adjust. Alternatively, again following Taylor (1996, 20 f.), one
17
can redefine the parameters and use the following equation
∆
(I
Y
)t
= αECM+βECM∆
(S
Y
)t
+γECM
((S
Y
)t−1
−(I
Y
)t−1
)+δECM
(S
Y
)t−1
+εt
(4)
This has the advantage of convenient interpretation, as βECM can be interpreted
as the short-run savings retention coefficient, and the significance of γECM as a test
for cointegration. I have estimated the time-series for different periods, in order to
detect possible shifts in the coefficient βECM . First, I used 1971 as separating year to
analyse whether Bretton Woods and the time thereafter differ. Next, I tried 1980 as
it adds some more observations to the first period, and coincides with conservatives
taking over the government in the Anglo-Saxon countries. The results can be found
in table 3 in the appendix.
In general, the they are in line with the estimation results of Taylor (1996, 41 f.).
It is noteworthy that they seem not to fit to my expectations, as only Germany shows
a decrease in the ”savings retention coefficient” when comparing the periods before
and after the separating years. This would indicate a decrease of capital mobility
in France, the U.K. and the U.S., if interpreted in the traditional Feldstein-Horioka
way. Moreover, in half of the cases the γ-cofficient is not significant, indicating that
no cointegration is present between the two time series. In these cases, the ECM-
specification does therefore not deliver meaningful results. This implies that there
is no meaningful connection to the rise of inequality either, except if one were to
cherry-pick the German case which luckily fits the story of a modest increase in both
indicators.
4.1.2 Flow-based measures
Next, I consider indicators that are based on de facto financial flows. As is argued
in section 3.2, one may suppose that they capture more variation in financial glob-
alisation than de jure indicators. All of the measures reviewed here will be analysed
as a ratio to GDP.
To begin with, I assess flow measures of portfolio and foreign direct investment,
all derived from the World Bank’s WDI database. The first indicator I have at my
disposal are net portfolio equity inflows, which are available from 1970-2013 for most
countries (the french series only starts in 1983). Comparing to the evolution of top
1% income shares, the ratio is depicted in figure 3. Note that the low frequency –
around 40 annual observations – of the quite volatile data does not allow for mean-
18
ingful time-series analysis, also concerning the application of smoothing techniques
or fitting a linear trend. In the figure, a simple, centralised 5-year moving average is
shown, but also Holt-Winters filtering has been tried. Therefore, I cannot directly
detect a sensible relationship between the rise of inequality and the development of
net portfolio equity inflows relative to GDP. In what concerns my expectation to find
a faster rise of capital mobility in the two Anglo-Saxon countries, this claim cannot
be confirmed based on these data. Moreover, when considering the changes between
10- or 5-year averages summarised in table 4 in the appendix, only for the U.K. in
the 1980s a systematically faster rise can be detected. This may reflect its role as a
financial center whose services are important for the circumvention of regulations.
Moreover, the U.K. shows a substantially higher level of flows relative to GDP than
the others, the U.S. a significantly smaller one, a pattern that will hold throughout
the upcoming analysis.
Figure 3: Portfolio investment and top 1% income share, 1970-2012
Seite 1
1970
1974
1978
1982
1986
1990
1994
1998
2002
2006
0.0
0.5
1.0
1.5
2.0
2.5
0.0
2.0
4.0
6.0
8.0
10.0
France
Portfolio equity inflows, relative to GDP top percentile income share (%)
1971
1974
1977
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
0.00.2
0.4
0.6
0.81.0
1.2
1.41.6
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
Germany
Portfolio equity inflows, relative to GDP top percentile income share (%)
1970
1974
1978
1982
1986
1990
1994
1998
2002
2006
2010
0.0
1.0
2.0
3.0
4.0
5.0
6.0
0.02.04.06.08.010.012.014.016.018.0
United Kingdom
Portfolio equity inflows, relative to GDP top percentile income share (%)
1970
1974
1978
1982
1986
1990
1994
1998
2002
2006
2010
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
0.0
5.0
10.0
15.0
20.0
25.0
United States
Portfolio equity inflows, relative to GDP top percentile income share (%)
Source: Own depiction, based on World Bank WDI and WTID data.Left axis: Net portfolio equity inflows as a ratio to GDP. French series only start in 1983. The bluelines show simple centralised 5-year moving averages.
The next flow measure to be analysed is the sum of net inflows and net outflows
of foreign direct investment, again relative to GDP. The series are somewhat less
19
volatile than portfolio investment, and seem to coincide with a direct link from
capital mobility to inequality for the Anglo-Saxon countries, as can be interpreted
when considering figure 4. However, the relative rise of the measure is not too
different across countries, all show an increase of considerable magnitude over time.
This becomes clear when considering changes over 5-year averages (table 5 in the
appendix), which once more seem to be contradictory to my expectation of a faster
rise in the U.K. and the U.S. versus France and Germany.
Figure 4: FDI flows and top 1% income share, 1970-2012
Seite 1
1970
1974
1978
1982
1986
1990
1994
1998
2002
2006
0.0
2.0
4.0
6.0
8.0
10.0
12.0
0.0
2.0
4.0
6.0
8.0
10.0
France
FDI, relative to GDP top percentile income share (%)
1970
1974
1978
1982
1986
1990
1994
1998
2002
2006
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
Germany
FDI, relative to GDP top percentile income share (%)
1970
1974
1978
1982
1986
1990
1994
1998
2002
2006
2010
0.02.04.06.08.0
10.012.014.016.018.0
0.02.04.06.08.010.012.014.016.018.0
United Kingdom
FDI, relative to GDP top percentile income share (%)
1970
1974
1978
1982
1986
1990
1994
1998
2002
2006
2010
0.0
1.0
2.0
3.0
4.0
5.0
0.0
5.0
10.0
15.0
20.0
25.0
United States
FDI, relative to GDP top percentile income share (%)
Source: Own depiction, based on World Bank WDI and WTID data.Left axis: Sum of FDI net inflows and net ouflows, as a ratio to GDP. French series on net outflowsonly start in 1975. The blue lines show simple centralised 5-year moving averages.
4.1.3 Stock-based measures
The next set of indicators reviewed here are financial stocks, as these are less volatile
than flow data. The first source for the sum of foreign assets and liabilities is the
IFS database. The results are depicted in figure 5, compared to top 1% income
shares during the same period (1948-2008). It has to be noted that there were a
number of problems when collecting the data: For France and Germany, there are
possibly structural breaks in 1999, as one can detect a substantial decrease in foreign
20
liabilities of more than a third in the French case2, similarly for Germany. This does
not seem plausible in the light of the relative macroeconomic stability at the time.
Moreover, suitable data for the UK only starts in 1962, for France in 1969.
Figure 5: Foreign assets & liabilities and top 1% income share, 1948-2012drucken
Seite 1
1948
1954
1960
1966
1972
1978
1984
1990
1996
2002
2008
0.0
0.5
1.0
1.5
2.0
0.0
2.0
4.0
6.0
8.0
10.0
12.0
France
For. ass. & liab., relative to GDP top percentile income share (%)
1948
1953
1958
1963
1968
1973
1978
1983
1988
1993
1998
2003
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
Germany
For. ass. & liab., relative to GDP top percentile income share (%)
1948
1953
1958
1963
1968
1973
1978
1983
1988
1993
1998
2003
2008
0.0
1.0
2.0
3.0
4.0
5.0
6.0
0.02.04.06.08.010.012.014.016.018.0
United Kingdom
For. ass. & liab., relative to GDP top percentile income share (%)
1948
1953
1958
1963
1968
1973
1978
1983
1988
1993
1998
2003
2008
0.00.10.20.30.40.50.60.70.80.9
0.0
5.0
10.0
15.0
20.0
25.0
United States
For. ass. & liab., relative to GDP top percentile income share (%)
Source: Own depiction, based on IMF IFS and WTID data. Left axis: Sum of foreign assets andliabilities, as a ratio to GDP.
Eyeballing on the data reveals that foreign assets and liabilities as a ratio to
GDP clearly increased in all four countries. The levels and the pace are different,
though, because the U.K. shows the highest level as well as the sharpest overall rise.
10-year averages as well as changes between the decades can be found in table 6 in
the appendix, contrasted with the development of top income shares at the same
time. In line with my expectations, the rise in France seems to be rather modest
in the last three decades. However, the U.S. increase seems roughly similar to the
German case at the same time. One may speculate about possible explanations
for the cross-country differences in levels, for instance by pointing at the rather big
home bias of U.S. investment. In my interpretation, the overall picture does neither
2I assume this is due to shifts in definitions, between notations used before and after the intro-duction of the Euro: “Liabilities excl. from the National Definition of Broad Money” vs. “Non-Monetary Liabilities”. Moreover, also the choice of GDP series imposes a certain arbitrariness. Asthe source and the covered timespan are the same for all four countries, I finally sticked to the IMFIFS data even though there are small differences to data from national statistical offices.
21
strengthen nor weaken my hypothesis.
More evidence is obviously necessary, so I proceed with alternative data for the
same indicator. This time, it originates from the BIS locational banking statis-
tics, thus covering assets and liabilities of reporting and resident banks vis-a-vis all
sectors3 between 1978-2013. Results can be found in figure 6 and table 7 (in the
appendix). I would like to highlight the cross-country differences, as it is obvious
that all nations experienced a substantial rise in banks’ foreign assets and liabilities
relative to GDP. Again, eyeballing seems to reveal some correlation with the rise of
inequality in the U.K. and U.S. cases4. On the other hand, the rise of assets and lia-
bilities in France and Germany is not that different from the Anglo-Saxon countries,
when comparing the changes between 5- or 10-year averages. The biggest relative
rise can even be detected for Germany, with the U.K. showing a more modest rela-
tive increase, however starting from a higher level. At the time when according to
my expectations a faster rise should be detectable for the U.K. and the U.S., i.e. in
the 1980s and 1990s, they do not seem to outpace France and Germany. Thus, the
overall picture does not change much compared to the IFS data, but again it is at
most partly consistent with my expectations.
The last source to consider for foreign assets and liabilities is Lane & Milesi-
Ferretti (2007), who have collected the large “External Wealth of Nations mark
II”-database, covering 145 countries over the years 1970-2004. Fortunately, also
subaggregates are available, namely portfolio equity, FDI and debt assets and li-
abilities (portfolio debt + other investment). From the total foreign assets and
liabilities, I exclude central bank reserves and financial derivatives. I assume the
former not to reflect capital mobility, and for the latter only partial data for France
is available. Results similar to the above are reported in figure 7 and table 8 in
the appendix. The picture is the most complete of the three data sources, and it
confirms the image drawn above.
3In the BIS locational banking statistics, banks are defined as all deposit-taking financial in-stitutions that grant credit or invest in securities on their own account, central banks included.Non-banks are therefore all entities not defined as banks, e.g. the government sector or publiccorporations. The residence of a bank “is determined by the location where it has its centre of pre-dominant economic interest”, not its nationality (BIS 2014). Thus, the category “resident banks”also includes foreign banks operating in a jurisdiction, while “reporting banks” does not. Here, Ionly report results for resident banks, which are somewhat higher than for reporting banks, exceptfor the U.S. (see figure 14 in the appendix). Transactions of reporting banks are also disaggregatedfor counterparty, i.e. the bank and the non-bank sector, in the statistics available to me.
4Simply computing the correlation between the variables however delivers no more insightfulresults, neither for contemporary nor for lagged values. The same is true for 5-year averages, andthe changes from period to period.
22
Figure 6: Foreign assets & liabilities from banks and top 1% income share, 1978-2012drucken
Page 1
1978
1981
1984
1987
1990
1993
1996
1999
2002
2005
2008
2011
0.0
0.5
1.0
1.5
2.0
2.5
0.01.02.03.04.05.06.07.08.09.010.0
France
FAL of resident banks, relative to GDP top percentile income share (%)
1978
1981
1984
1987
1990
1993
1996
1999
2002
2005
2008
2011
0.00.20.40.60.81.01.21.41.61.8
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0Germany
FAL of resident banks, relative to GDP top percentile income share (%)
1978
1981
1984
1987
1990
1993
1996
1999
2002
2005
2008
2011
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.0
5.0
10.0
15.0
20.0
25.0United States
FAL of resident banks, relative to GDP top percentile income share (%)
1978
1981
1984
1987
1990
1993
1996
1999
2002
2005
2008
2011
0.00.51.01.52.02.53.03.54.04.55.0
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
United Kingdom
FAL of resident banks, relative to GDP top percentile income share (%)
Source: Own depiction, based on BIS locational banking statistics, WDI and WTID data.Left axis: Sum of foreign assets and liabilities of resident banks, as a ratio to GDP.
Therefore, one may sum up that there is no obvious difference between the two
Anglo-Saxon and the two continental European countries examined here, in what
concerns the extent of the rise of capital mobility as measured by the sum of foreign
assets and liabilities relative to GDP. The data can be interpreted as reflecting the
role of the U.K. as a financial center, as well as the home bias of U.S. investment.
However, the available evidence on foreign financial stocks and flows does not confirm
my expectation. Nevertheless, it should be noted that these findings do not rule out
a possible effect of the (more or less similar) rise in capital mobility on inequality
per se. A possible causality could run through different institutional settings in the
countries affected, where the continental European states might not be affected (as
much) by a rise in capital mobility as the Anglo-Saxon states.
4.1.4 De jure indicators
I would like to finish the descriptive analysis of a possible direct relationship between
capital mobility and inequality with selected measures of capital controls. The first
one is the “CAPITAL”-indicator that has originally been constructed by Quinn
(1997), subsequently enhanced to cover 122 countries over the period 1950-2007. It
23
Figure 7: Foreign assets & liabilities and top 1% income share, 1970-2004drucken
Seite 1
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
0,01,02,03,04,05,06,07,08,09,010,0
France
For. ass. & liab., relative to GDP top percentile income share (%)
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
0,0
2,0
4,0
6,0
8,0
10,0
12,0
14,0Germany
For. ass. & liab., relative to GDP top percentile income share (%)
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
0,0
2,0
4,0
6,0
8,0
10,0
12,0
14,0United Kingdom
For. ass. & liab., relative to GDP top percentile income share (%)
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
0.00.20.40.60.81.01.21.41.61.82.0
0,0
5,0
10,0
15,0
20,0
25,0
United States
For. ass. & liab., relative to GDP top percentile income share (%)
Source: Own depiction, based on data from Lane & Milesi-Ferretti (2007) and the WTID.Left axis: Sum of foreign assets and liabilities, as a ratio to GDP.
is based on coding of verbal assessments in the IMF’s AREAER, which are considered
for capital flows by residents and nonresidents, also taking into account the severity
of restrictions (Quinn et al. 2011, 492). Results are depicted in figure 8, as usual
compared to top 1% income shares. Only for the case of the U.K., the evolution of
capital account restrictiveness seems to coincide with the rise in inequality. Note
however that the liberalisation of the British capital account was just one of a
number of steps taken by the Tory government in the early 1980s which possibly
affected the rise in inequality thereafter. Furthermore, the U.S. show a completely
free capital account except for a limited period in the late 1960s according to the
indicator, similarly to Germany which had restored full convertibility by the late
1950s. France shows a rather gradual liberalisation of the capital account until the
late 1990s, but as is well-known a rather stable evolution of top income shares.
The next capital control indicator considered here is KAOPEN, constructed by
Chinn & Ito (2008) and available for the period 1970-2011. It is based on the binary
dummies reported in the AREAER tables for both capital account and financial
current account restrictions in four categories: presence of multiple exchange rates,
24
Figure 8: Capital account openness and top 1% income share, 1950-2007drucken
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1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
0102030405060708090
100
0.00
2.00
4.00
6.00
8.00
10.00
12.00
France
CAPITAL top percentile income share (%)
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
0102030405060708090
100
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
Germany
CAPITAL top percentile income share (%)
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
0102030405060708090
100
0.00
2.004.00
6.00
8.00
10.0012.00
14.00
16.0018.00
United Kingdom
CAPITAL top percentile income share (%)
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
0102030405060708090
100
0.00
5.00
10.00
15.00
20.00
25.00
United States
CAPITAL top percentile income share (%)
Source: Own depiction, based on data from Quinn (1997, updated online) and the WTID.Left axis: CAPITAL indicator, higher value = more openness. For details of construction, see text.
restrictions on current accounts, restrictions on capital transitions and requirements
of the surrender of export proceeds. The authors attempt to capture the extent and
intensity of controls, relying on a data reduction exercise: They reverse the value of
the binary variables of the AREAER categories they apply, and additionally take
a moving average of year t plus the preceding four years for controls on capital
transactions. The final index has a mean of zero, with higher values indicating
more financial openness. Unfortunately, much like the previous, this indicator does
not capture much of the information about the evolution of capital mobility I am
interested in: There is no variation at all for Germany and the U.S., only for the
U.K. the story seems to fit while for France the gradual increase does not coincide
with more inequality (see figure 9).
The last de jure indicator I apply covers the shortest time period, namely 1983-
2000. On the other hand, it contains a somewhat higher degree of variation for
some countries. It is based on 13 subcategories for capital account transaction
restrictions derived from the extended post-1996 AREAER tables. These are ex-
trapolated backwards using text information until 1983, in the end taking the mean
of the 13 categories. Thus, the final measure takes on values 0-1, where a higher
25
Figure 9: Financial openness and top 1% income share, 1970-2011drucken
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1970
1974
1978
1982
1986
1990
1994
1998
2002
2006
0.001.002.003.004.005.006.007.008.009.00
10.00
-2.5-2.0-1.5-1.0-0.50.00.51.01.52.02.5
France
KAOPEN top percentile income share (%)
1970
1974
1978
1982
1986
1990
1994
1998
2002
2006
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
-2.5-2.0-1.5-1.0-0.50.00.51.01.52.02.5
Germany
KAOPEN top percentile income share (%)
1970
1974
1978
1982
1986
1990
1994
1998
2002
2006
2010
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
-2.5-2.0-1.5-1.0-0.50.00.51.01.52.02.5
United Kingdom
KAOPEN top percentile income share (%)
1970
1974
1978
1982
1986
1990
1994
1998
2002
2006
2010
0.00
5.00
10.00
15.00
20.00
25.00
-2.5-2.0-1.5-1.0-0.50.00.51.01.52.02.5
United States
KAOPEN top percentile income share (%)
Source: Own depiction, based on data from Chinn & Ito (2008, updated online) and the WTID.Right axis: KAOPEN indicator, more positive value = more openness. For details of construction,see text.
figure indicates more restrictions. Results are depicted in figure 10. They prove to
be of equally low value for the analysis conducted here as those of the two indicators
before. Somehow contradictory to the former reports, the U.S. show a small decline
of capital account restrictions over this time period, also Germany to an even smaller
degree. The French and British values seem to be in line with the former measures
for this period.
Concluding, it may be noted that the capital control measures applied above, and
presumably most existing AREAER-based indicators, are less suited to analyse a
possible relationship between capital mobility and inequality than de facto measures.
Indeed, they have been mostly used in cross-country-studies that predominantly deal
with problems of emerging market economies in the light of financial crises.
4.2 From capital mobility to tax competition, further to inequality
To decriptively investigate the transmission channel outlined in section 3.5, I start
with examining the link from capital mobility to tax competition, both in terms
26
Figure 10: Financial openness and top 1% income share, 1983-2000drucken
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1983
1985
1987
1989
1991
1993
1995
1997
1999
4.00
5.00
6.00
7.00
8.00
9.00
10.00
11.00
12.00 0.00.10.20.30.40.50.60.70.80.91.0
France
Miniane-indicator top percentile income share (%)
1983
1985
1987
1989
1991
1993
1995
1997
1999
4.05.06.07.08.09.0
10.011.012.013.014.0 0.0
0.10.20.30.40.50.60.70.80.91.0
Germany
Miniane-indicator top percentile income share (%)
1983
1985
1987
1989
1991
1993
1995
1997
1999
4.0
6.0
8.0
10.0
12.0
14.0 0.0
0.2
0.4
0.6
0.8
1.0
United Kingdom
Miniane-indicator top percentile income share (%)
1983
1985
1987
1989
1991
1993
1995
1997
1999
5.07.09.0
11.013.015.017.019.021.023.0 0.0
0.10.20.30.40.50.60.70.80.91.0
United States
Miniane-indicator top percentile income share (%)
Source: Own depiction, based on data from Miniane (2004) and the WTID.Right axis: Miniane indicator, lower value = more openness. For details of construction, see text.
of corporate and personal income taxes. Following the theoretical tax competition
literature, the countries of interest can be expected to be able to set comparatively
high (corporate) taxes. This is due to them being all rather large economies with
substantial agglomerations that can be taxed at higher rates even in the presence of
(perfect) capital mobility.
A glance at the evolution of statutory corporate tax rates reveals that they
have decreased substantially in all four countries since the early 1980s, from 46-
56% to 15-35% (see figure 11). The largest cut took place in Germany, while the
U.S. rates have not changed since Reagon’s 1986 Tax Reform Act. In terms of
statutory tax rates, the claim of the new economic geography literature seems valid
only for Germany until around 2000, and the U.S. and France since the same year.
During these periods, their rates are above the OECD average. The rate reductions
notwithstanding, corporate tax revenues relative to GDP are quite stable across the
sample, fluctuating around 2-3% (figure 15 in the appendix). Relating the corporate
tax rates to my preferred capital mobility indicator shows no systematic relationship
accessible to eyeballing (see figure 16 in the appendix). Only in the German case,
increases of capital mobility precede cuts in tax rates, as one would expect in the
27
Figure 11: Statutory corporate tax rates, 1981-2013
drucken1
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1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
0%
10%
20%
30%
40%
50%
60%
France OECD average Germany United Kingdom United States
Source: Own depiction, based on OECD statistics.
case of a large economy that reacts to tax competition from smaller jurisdictions.
Unfortunately, no comprehensive data on the actual tax burden of corporations is
at hand, which most surely underestimates the extent of tax competition and tax
cuts, as has been argued in section 3.3. In the U.S. today, the effective corporate tax
rate seems to be on the magnitude of 20 percentage points lower than the statutory
rate (GAO 2013).
Concerning personal income taxes, I have time-series at my disposal that show
the development since 1960, but for the sake of beauty I show a graph which covers
the whole 20th century (figure 12) taken from Piketty (2014). German rates since
the 1950s are relatively stable compared to the others, of which the U.K. and the
U.S. have cut rates most aggressively.
As has been discussed in section 3.3, after consulting the relevant literature
it is arguably less obvious that a rise in capital mobility translates into cuts in
personal income tax rates. Therefore, I expect only a rather small direct explanatory
contribution from this link. If one takes the most comprehensive de facto indicator
from above, and depicts it together with the statutory top tax rate, again there is
some coincidence (see figure 17 in the appendix).
The next part of the transmission channel obviously links taxation and inequality.
There is little left to explore anew in terms of descriptive evidence, as the facts are
outlined superbly by Piketty (2014, 493-514) and more technically by Piketty et al.
28
Figure 12: Top income tax rates, 1900-2013
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Source: Piketty (2014), online appendix.
(2014). The most instructive chart has been copied to the appendix (see figure 18),
relating the U.S. development of inequality to the top marginal tax rate over the
20th century. Similar charts are produced for the four countries of interest, with
somewhat shorter time series covering the last 50 years (figure 13). It seems obvious
that the large tax cuts in the U.K. and the U.S. went along with the surge of income
concentration, while more moderate tax cuts in France and Germany coincide with
an appropriately moderate rise in their top 1% income shares.
4.3 Wrap-up
As described in the outline, ideally one were to bring the pieces together in one
model, or at least test the different parts of the transmission channel described
above in a structural way using econometric estimation techniques. I abstain from
doing this here, due to several reasons. First of all, writing space and time are
limited, so the tasks necessary to develop models to be estimated are beyond the
scope of this paper. Second, I also lack the necessary data at this point, adding to
time constraints. Nevertheless, in the upcoming section 5.3, I describe suggestions
for further research, including the required data and possible estimation strategies.
Summing up the empirical analysis, it can be stated that while the Feldstein-
Horioka indicator and capital control measures are ill-suited for the analysis set out
in this section, de facto measures contain a lot more variation that can be further
analysed more rigorously. My expectation to find a faster rise of capital mobility in
29
Figure 13: Top 1% income shares and top marginal tax rates, 1960-2010drucken
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1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
0%10%20%30%40%50%60%70%80%90%100%
France
top income tax rate top percentile income share (%)
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
0%10%20%30%40%50%60%70%80%90%100%
Germany
top income tax rate top percentile income share (%)
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
0%10%20%30%40%50%60%70%80%90%100%
United Kingdom
top income tax rate top percentile income share (%)
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
0.0
5.0
10.0
15.0
20.0
25.0
0%10%20%30%40%50%60%70%80%90%100%
United States
top income tax rate top percentile income share (%)
Sources: Author’s depiction, based on data retrieved from Piketty et al. (2014) and the WTID.Left axis: Top income shares. Right axis: Top marginal tax rate.
the countries that also experienced a faster rise in inequality, was not met by the
descriptive evidence. However, the evidence seems not to contradict the transmission
channel. The most important missing descriptive data would cover the effective rates
for both corporate and personal income taxes, as well as historical estimations of
funds in tax havens.
5 Discussion
This section further discusses the rise in inequality in recent decades and the cross-
country differences that have been highlighted, arguing for the role of the transmis-
sion channel outlined in this paper. Therefore, I firstly comment on the relation of my
hypothesis to the financialisation literature. Next, I discuss alternative approaches
that are prominent in the literature, most notably skill-biased technological change
and deunionisation. Lastly, I present suggestions for further research, drawing on
insights from the prior theoretical and empirical analyses.
30
5.1 Relation to financialisation literature
It is noteworthy, that my hypothesis has been derived considering almost exclusively
works in the mainstream of economics, mostly published in working paper series of
established institutions like the NBER or in prestigious economics journals. Never-
theless, it fits quite well into a rather heterodox strand of work, namely studies on
financialisation. Epstein (2002, 3) defines the latter as the “increasing importance of
financial markets, financial motives, financial institutions, and financial elites in the
operation of the economy and its governing institutions, both at the national and
international levels”. The alleged role of “conventional economics” in the process of
financialisation, most notably for the promotion of liberalisation at the macro-level
and the importance of shareholder value at the micro-level, has been outlined by
Palley (2008, 3-5).
Several authors have examined the effects of financialisation on inequality, ar-
guing along the following lines: As financial services became less regulated, and
financial institutions seeked new business ventures, indebtedness in the economy ac-
celerated. Interestingly, 1979 is identified as a breaking point, after which financial
sector debt increased much faster than non-financial sector debt Palley (2008, 5).
This is associated with speculative trade, layered financial activity, and compara-
bly weak growth of the real sector – in short, a domination of the financial sector
over the rest of the economy. As firms relied increasingly on financial rather than
production activities, owners’ and elite workers’ bargaining power relative to other
workers was strengthened (Lin & Tomaskovic-Devey 2013, 2). While top managers
and wealthy individuals, epecially in financial corporations, were able to increase
their share of the pie, income of the middle classes stagnated, contributing to the
rise in inequality. This argumentation is often focussed on the course of events in the
U.S., however one can arguably derive implications for other developed economies,
particularly large ones.
Only very recently, these considerations have been tested empirically in a number
of studies. They however rely heavily on other measures of inequality than favored
in this paper, namely Gini coefficients and the functional income distribution, i.e.
wage and profit shares in national accounts. The first study to apply the latter is
Stockhammer (2009, 32-52), who investigates the impact of financialisation on wage
shares in 22 developed countries from 1979-2007. For a nonoverlapping five-year
average specification, he finds his financial openness variable (the sum of foreign as-
sets and liabilities relative to GDP) to have a significant negative effect on the wage
share, thereby explaining a fall of about 4.2%. Lin & Tomaskovic-Devey (2013) use
31
panel data at the U.S. industry level for the years 1970-2008, to study the effect
on three measures of inequality in a single equation error correction model frame-
work. They find that while the U.S. labour share declined by 5 percentage points,
top executive pay has risen constantly, and earnings dispersion of core workers has
increased by about 40% in the past thirty years. Assa (2012) applies standard fixed-
effects panel regressions to OECD-country data for the years 1970-2008, finding that
financialisation (measured by value added in finance relative to total value added, or
employment in finance relative to total employment) is significantly associated with
higher inequality (measured by the Gini coefficient). Adding to this, Kus (2012)
also analyses 20 OECD countries for the period 1995-2007 in a panel design using a
GMM specification. Controlling, inter alia, for unemployment, globalisation, union
density, female participation in the labour market and wage bargaining centralisa-
tion, his results indicate that there is a strong correlation between financialisation
indicators (value of stock traded relative to GDP, bank profitability, securities under
bank assets and a composite index of these) and Gini-coefficient income inequality.
Finally, Arnum & Naples (2013) also seek to explain the U.S. Gini-coefficient, in
terms of value added by FIRE (finance, insurance and real estate) industries rela-
tive to GDP as one of their dependent variables. They, too, control for a variety of
other factors, but find that their financialisation measure is of modest importance
for explaining the rise in income inequality in the U.S., instead stressing worsening
economic conditions and reductions in minimum wages.
One more aspect is interesting to mention: Even though they write without ever
mentioning the financialisation literature, Roine et al. (2009, 29) confirm its basic
reasoning unintentionally, at least in my interpretation. In their panel estimation
spanning more than a hundred years, they find that financial development, mea-
sured by bank deposits, stock market capitalisation and total market capitalisation
(the sum of the former two), is highly positively associated with top income shares.
Sticking to mainstream economic theory, they interpret this in a different way than
authors in the financialisation literature presumably would, though: Theory pos-
tulates that financial development should first benefit the rich, but later also the
whole population as it creates growth via access to credit for formerly constrained
households. They take the result that top income earners benefit particularly at low
levels of development as consistent with this claim.
Concluding, the financialisation literature arguments are not too far away from
works like Eichengreen (2001) in what concerns the development of capital mobility,
32
but strip the latter off its deterministic part5, i.e. they do not share the tendency
to regard financial liberalisation as a natural development driven mostly by market
forces. There is of course some technological component in the rise of capital mobil-
ity, and unfettered markets work in a way that promotes capital mobility, but the
financialisation argument equally stresses the political nature of the process. More-
over, the empirical analyses in this spirit have generated important insights that
ought to be considered in the upcoming suggestions for further research.
5.2 Explaining the evolution of inequality since the 1970s
The most prominent mainstream story for the explanation of rising inequality con-
cerns what is called skill-biased technological change (SBTC), which assumes that
technological change is not factor-neutral, but biased in favor of more skilled labour
(for a formal representation, see Violante 2009). The usual argument (for reviews
of the central points, see Acemoglu 2002 or Autor 2014) then points at the rise of
education wage premia since the 1970s – U.S. median wages stagnated from 1975
onwards, while wages of the first decile declined and those of the ninth decile soared
- and posits that the spread of new technologies like computers and the Internet
favoured higher-skilled workers over lower-skilled ones. According to Autor (2014,
847), the accumulation of skills slowed down during the 1980s and thereafter, lead-
ing to higher premia for a post-secondary education. Of course, proponents of this
explanation do not neglect other factors that may have played a role, but claim that
up to two third of the rise in the U.S. earnings dispersion between 1980 and 2005
may be explained by SBTC (Autor 2014, 843).
In essence, the SBTC story is based on market forces, i.e. the development of de-
mand and supply for labour of different skill-levels, and it may well account for some
part of inequality shifts in the lower parts of the income distribution. Nevertheless,
some criticisms have been raised starting more than a decade ago: As Card & Di-
Nardo (2002) note, SBTC cannot explain why wage inequality stabilised during the
1990s, after having risen during the 1970s and especially the 1980s. Whatever indi-
cator they apply, they do not find a concomitant slowdown of technological change,
which seems plausible in the light of advances in computer technology and the spread
of the Internet during the 1990s. Moreover, they find it difficult to reconcile other
aspects of wage inequality, e.g. the development of the gender or the race pay gap,
5This deterministic spirit is well-summarised in the following quote: “International financialliberalization, to paraphrase Marx, may be just another instance of the more developed countriesshowing their less-developed counterparts an image of their future” (Eichengreen 2001, 360). Seealso the quote of the same author in section 2, which uses the metaphore of overwhelming waterflows to describe capital movements.
33
with SBTC. This is why Card & DiNardo (2002) rather point at the development
of the minimum wage as an explanation for the rise of wage inequality. This expla-
nation is also favoured by Piketty (2014, 304-315), who contrasts the development
of the minimum wage in the U.S. and France (see figure 19 in the appendix).
Adding to this, the comparison to France points to another weakness of the SBTC
story: It cannot explain why inequality in countries that encountered the same
technological development, like France or Germany, did not rise in the same way
as in the U.S. Consequently, one should rather focus attention on (labour market)
institions and other factors that influence the bargaining power of workers in different
brackets of the income distribution. For instance, a vast literature has investigated
the link between unionisation and wage inequality. I do not attempt to review it
here, but rather point the reader to Card et al. (2004) and Lemieux (2008, 33-39).
For the purpose at hand, it suffices to state that the majority of studies finds a
significant negative impact of deunionisation on the wage and income distributions.
This is also largely confirmed by the first Granger-causality panel study of this
relationship, which finds causality running from unionisation to inequality for 10
out of 24 countries, and reversed causality for six countries (Tongur & Elveren
2014). If one then considers the development of union membership across countries,
it is clear that the level is much higher in continental European countries compared
to the Anglo-Saxon world, and also did not decline as much between 1960 and
2007 (Schmitt & Mitukiewicz 2012). According to the same study, union coverage
remains even higher, at 90% for France (up by 5 percentage points) and 62.8% for
Germany (down by 24.2 percentage points), compared to 34.6% in the U.K. (down
by 36.4 percentage points) and 13.3% in the U.S. (down by 12.4 percentage points).
Therefore, I conclude that institutions matter more than the purely market-based
SBTC explanation, which however has its merits for catching the development of
some long-run factors, especially education.
Another shortcoming of the SBTC theory is its low explanatory power for the
rise of top incomes, which is furthermore underestimated in the inequality mea-
sures used in the SBTC literature. Here, the assumption that wages mostly reflect
marginal productivity of labour stands in stark contrast to any realistic reasoning:
As a general problem, it is hard to measure individual productivity in any mod-
ern occupation, that is why Piketty (2014, 305) calls it “in some respects limited
and naive”. Indeed, the implication that top managers over the past 40 years have
become as more productive as their salaries have risen, is by any means utterly
unrealistic, to be frank. Propenents of the determination of pay by pure market
34
forces have nevertheless come up with explanations like the “superstar” theory, in
which labour market reward depends largely on relative performance. It is argued
that these markets, which are marked by a winner-takes-all payoff structure, have
first been common in entertainment and sports, where technological change has in-
creased the size of audience. With the onset of new communication and trading
technologies, this setting has spread especially to the banking sector, but also for
top-rank lawyers the rise of salaries is allegedly driven by market demand (Gordon
& Dew-Becker 2008, 19-28).
I do not find this explanation fully plausible, as it neglects power relations. Nei-
ther does Piketty (2014, 315-321), who notes that once more, it cannot account for
cross-country differences, because the rise of “supermanagers” is mainly an Anglo-
Saxon phenomenon. Why should a universal change in technology affect those coun-
tries differently than others? Moreover, Bakija et al. (2012, 35) report interesting
data from U.S. tax statistics: The share of the arts, media and sports professions –
who are usually used to illustrate the “superstar” theory – in total top 1% income
earners has not moved from its low value of 1.6% between 1979 and 2005. On the
contrary to the SBTC story, the numbers seem to support the financialisation expla-
nation: The share of financial professions individuals among the top 1% has soared
at the same time, from 7.7 to 13.9%. The largest decline is found for non-finance
executives, managers and supervisors, who constituted 31% of top 1% earners in
2005 compared to 36% in 1979. For CEOs, the bargaining-power theory that has
been described in section 3.4, seems to account for the biggest share of the increase
in top income shares, as has been argued by Piketty et al. (2014).
Caution however is necessary, because it is difficult to empirically disentangle all
the factors mentioned so far. It is for instance likely that union power also influences
minimum wages, or unions react to technological change. While studies based on
microeconomic data, especially panels, can usually account for these problems by
using somewhat sophisticated estimation strategies like Difference in Differences,
this is much harder for macroeconomic problems. I would argue that the rise of
top income and wealth inequality is largely a macroeconomic policy phenomenon.
Over the course of my analysis, it became clear that the single-country approach
I have chosen would have major difficulties when trying to rigorously estimate any
interesting relationship: The number of observations for the yearly data that I have
at my disposal is simply too low, which results in bad explanatory power. Therefore,
this paper remains mostly at the descriptive level and leaves the actual econometric
estimation to further ventures.
35
5.3 Suggestions for further research
Concerning the direct link from capital mobility to top income shares, it was evident
that especially financial stock and flow measures could be utilised in further analysis.
A natural next step would be to estimate another panel approach, as has been done
by several authors in the financialisation literature. Indeed, for instance Kus (2012,
490 f.) notes that the effect of financialisation is probably most pronounced at the
top of the income distribution, but abstains from using top income shares due to
problems with lacunae in the data. Nevertheless, one could take OECD countries
as a starting point, as presumably the data availability is best for them. Hence, the
lack of variation for single countries could be mitigated, however presumably at the
cost of typical macroeconomic panel estimation problems like heterogeneity across
countries.
To investigate into the institutional differences of several groups of countries,
it suggests itself to use the definitions of Schmitt & Mitukiewicz (2012, 270), or
a comparable “varieties of capitalism” approach from the political economy liter-
ature. Assumed that the samples for the respective categories are still sufficiently
large to yield robust estimates, there could be some insights generated from panel
estimations for these subgroups, or from introducing dummies into a general panel.
Without claiming to be complete, the data required would comprise of the following:
Top income shares as dependent variable, furthermore capital mobility measures of
course, plus a possible range of controls like unionisation indicators, financialisa-
tion measures (probably highly correlated with capital mobility), an indicator of
political affiliation of government, GDP per capita, maybe also some indicator of
technological change in terms of computer and Internet use.
A number of challenges for this type of analysis are laid out by Stockhammer
(2009, 35f.) in a very similar context (investigating the impact of financialisation
on wage shares for a sample of OECD countries): For a valid panel estimation, it
has to be assumed that the cofficient of a variable has the same effect across coun-
tries, which is unlikely to be correct when checked for with unit root tests. Thus
the pooling restriction is very likely violated and the estimated coefficients should
be interpreted with caution, showing the mean effects over a range of heteroge-
neous countries. As a standard fixed effects estimator is probably attributed with
autocorrelation problems, an estimator in first differences, which also transforms
non-stationary time series to stationarity, can be applied to cope with this issue.
All these approaches however come at the cost of abandoning some information, as
well as the third approach that Stockhammer uses: With non-overlapping 5-year
36
averages, the restriction of uniform coefficients is still unlikely to be met, but more
plausible. It also mitigates unit root issues, as the residuals have less autocorrelation
problems. Adding to this, it is attractive for variables that show little variation over
time, e.g. labour market institutions or in my case capital controls.
In order to extract more information from the latter, a number of improvements
over the simple descriptive analysis in section 4.1.4 can be thought of: For instance
in a single-country-estimation, for CAPITAL and similar indicators one alternative
would be to consider the global mean which shows more variation but probably is
only economically meaningful for countries that did not have strong capital outflow
controls at the same time. Otherwise, a reduction of the general world-wide restric-
tiveness, in terms of capital inflows, would not really matter. Another improvement
of de jure indicators could be to construct a weighted average, taking into account
the country itself plus the most important foreign investment target countries. This
in turn requires data on bilateral financial flows, which in principle must exist at
the BIS6. The presumably most tedious approach would be to construct a new
AREAER-based indicator that measures only capital outflow restrictions, starting
with simple binary variables. Arguably, this would be most appropriate in terms of
the transmission channel I describe from capital mobility via tax competition to top
income distribution. In fact, this is because such a new indicator may capture some
of the pressure on legislators to reduce taxes, which in turn is generated by easier
ways of tax evasion that come about with the reduction of capital controls (and ad-
vances in communication technology which should also be accounted for). However,
this would require access to the AREAER online database, where digitised data is
available for the years 1999-2012. Data for earlier periods (1950-1998) would have
to be collected from AREAER paper reports or pdf-files, and moreover the more
limited diversity of indicators before 1997 would have to be taken into account.
Another possible avenue for further research concerns the steps along the causal
chain of my hypothesis. Here, the claim that capital mobility influences corporate
tax competition seems to be well-established in the literature (see section 3.3). Even
though personal income taxes, especially their degree of progressivity, are plausibly
less-related to the mobility of capital, there could still be some influence: As capital
could be shifted abroad much easier, it became harder to tax movable forms of
income at high rates. This is especially relevant for dividends, rents, and other
forms of capital income. Still though, I think that the major tax reductions, first
6Johannesen & Zucman (2014) have got access to such data for their analysis of offshore wealth,but these are not publicly available.
37
and foremost in the Anglo-Saxon countries, were motivated mainly by neoliberal
ideology. Nevertheless, one could exploit the “World tax indicators” (WTI) dataset
constructed by Peter et al. (2010), or alternatively the publicised data used by
Piketty et al (2014), to empirically analyse the relationship between different capital
mobility indicators and top income tax rates. The WTI offers the advantage that also
marginal and average tax rates for four different income classes have been computed.
This is of course not yet ideal, but offers more variation than simple statutory tax
rates.
A further line of investigation could be opened if there is bilateral data on banking
flows and stocks to offshore financial centres available, that reaches back until the
1980s or even 1970s. Then, for tax competition analyses it could be fruitful to
construct an indicator that measures the pressure from tax havens for the countries
under scrutiny. For instance, the share of funds from residents of the respective
country in particular tax havens could be used to weight for the importance of
regulation changes, e.g. through taxation agreements, with respect to this tax haven.
Also, the sum of funds in offshore centres as a ratio to total foreign assets would be
an interesting indicator.
As a next step, one could analyse the link from personal income tax progressivity
to inequality, something that to my knowledge has not been done with data from
the WTI and the WTID. A simple starting point would be the model presented in
Atkinson et al. (2011, 60), which estimates a central concept in the literature, namely
the elasticity of reported earnings with respect to the net-of-tax rate (one minus the
the marginal tax rate). Again, the choice between choosing a single-country or panel
approach would have to be made. However, the sophisticated analysis of Piketty
et al. (2014) sets the standard that would have to be met to contribute additional
insights to the literature. Nevertheless, even here refinements could be achieved by
updating their estimations with the more nuanced data from the WTI. Indeed, also
Atkinson et al. (2011, 60) mention that the lack of series on marginal tax rates by
income groups so far has been a limiting factor. Unfortunately, the WTI do not
cope with the problem that effective top marginal tax rates may differ substantially
from statutory rates, due to various exemptions and the like. Moreover, in the past
only a very small number of individuals was subject to the very high tax rates, e.g.
in the U.S. or the U.K., so the marginal tax rate of the 1% as a group was much
lower than the top statutory rate.
38
6 Conclusions
The goal of this paper was to empirically analyse the relationship between capital
mobility and economic inequality. The empirical section has been embedded into
a historical narrative that posits a possible causal link of growing capital mobility
to lower taxation of corporations and high incomes, further to increasing inequality.
Findings of the descriptive statistical analysis are in line with this hypothesis, but
my expectation to find a faster rise of capital mobility in Anglo-Saxon countries
has not been met. Instead, capital mobility seems to have increased strongly in all
countries under scrutiny. In general, this study has its limitation concerning the
data and the methods applied, which mostly remain at the descriptive level.
The empirical analysis has confirmed a theoretical supposition from the litera-
ture, which claims that the sum of foreign assets and liabilities relative to GDP is
the best measure of capital mobility in the present context. The Feldstein-Horioka-
indicator, which looks at the correlation of domestic savings and investment, seems
to be unsuitable for capturing the aspects of capital mobility I am interested in.
The capital control measures that are available in the literature showed only limited
variation for the four countries (DEU, FR, UK, US) considered here, so they would
need additional refinements if they were used in more rigorous econometric appli-
cations. I propose to construct a new AREAER-based indicator on capital outflow
restrictions, or using a weighted average of existing indicators that adjusts for the
importance of financial flow target countries. De facto measures of financial flows
and stocks may be used in further research, too. I suggest to continue with a panel
approach for OECD countries, in the spirit of the recent empirical financialisation
literature. Concerning the single steps along my narrative, possible further research
may be directed at exploiting more nuanced data on marginal and average personal
income tax rates to clarify the link between taxation and income concentration.
Summing up the discussion about determinants of economic inequality, I would
like to highlight my impression that skill-biased technological change does only yield
low explanatory power for the rise of income inequality, at the bottom as well as at
the top. Especially, it performs rather poorly in explaining cross-country differences
among developed nations. Nevertheless, as it concentrates on technology, it has some
validity in outlining the long-term developments in high-income market economies.
Explanations that are based solely on marginal productivity are difficult to reconcile
with common sense. That is why Piketty (2014, 331) notes:
“(O)nce we introduce the hypothesis of imperfect information [...] the
39
very notion of ‘individual marginal productivity’ becomes hard to define.
In fact, it becomes something close to a pure ideological construct on the
basis of which a justification for higher status can be elaborated.”
In my opinion, political institutions such as labour market regulations that determine
reservation wages, or the strength of unions play a more important role to explain
the bulk of shifts in the income distribution. At the top, my interpretation of the
data and the literature is that changes of top tax rates were crucial for the rise in
concentration, as the pay of the majority of top earners is determined by bargaining
power which is used more heavily if the untaxed rewards are higher.
At the normative side, again I share the conclusion of Piketty (2014, 493-539)
that at least in developed market economies, we need a more progressive personal
income tax schedule, the closing of tax loopholes, and a global tax on capital. These
measures would help to reduce the negative impacts of inequality, contributing to
recreate a more human society after more than thirty years of neoliberal enrichment
of a few. Indeed, such taxes are a rather mild form of redistribution, and the wealthy
and powerful should be more aware of the consequences of societies drifting apart.
Modern means of communication and data processing have created the necessary
technical conditions to levy such taxes. What is missing is the political will to
introduce them, but such considerations of power relations are left to works in the
sphere of political economy.
40
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A Appendix
Table 3: Results of an error correction model of the investment-savings relationship
Country Period N Adj. R2 α β γ δ
France 1949-1971 22 0.28 0.002 0.37*** 0.35** -0.02(0.06) (0.17) (0.14) (0.23)
France 1972-2012 41 0.61 -0.007 0.96*** 0.11 0.04(0.01) (0.14) (0.10) (0.06)
France 1949-1980 31 0.41 0.04 0.45*** 0.50*** -0.18(0.03) (0.13) (0.13) (0.14)
France 1981-2012 32 0.60 -0.02 0.96*** 0.06 0.11(0.03) (0.17) (0.11) (0.14)
Germany 1950-1971 21 0.55 -0.08* 1.08*** 0.50** 0.26(0.04) (0.26) (0.23) (0.15)
Germany 1972-2012 41 0.49 0.003 0.65*** 0.03 -0.02(0.02) (0.12) (0.05) (0.08)
Germany 1950-1980 30 0.44 0.01 0.80*** 0.14 -0.04(0.03) (0.21) (0.16) (0.11)
Germany 1981-2012 32 0.52 -0.02 0.70*** 0.03 0.06(0.03) (0.13) (0.07) (0.13)
United Kingdom 1946-1971 26 0.54 0.02** 0.24 0.10 -0.12**(0.009) (0.15) (0.20 ) (0.05)
United Kingdom 1972-2012 41 0.37 0.02 0.75*** 0.31** -0.10(0.02) (0.18) (0.12) (0.08)
United Kingdom 1946-1980 35 0.44 0.02* 0.41*** 0.32** -0.11*(0.01) (0.14) (0.15) (0.06)
United Kingdom 1981-2012 32 0.45 0.01 0.88*** 0.23* -0.06(0.02) (0.19) (0.12) (0.09)
United States 1946-1971 26 0.92 -0.003 0.87*** 0.53*** 0.005(0.01) (0.07) (0.17) (0.07)
United States 1972-2012 41 0.71 0.009 1.12*** 0.10 -0.03(0.01) (0.12) (0.09) (0.06)
United States 1946-1980 35 0.92 -0.01 0.92*** 0.49*** 0.05(0.008) (0.06) (0.14) (0.04)
United States 1981-2012 32 0.67 -0.001 1.14*** 0.07 0.02(0.02) (0.15) (0.10) (0.07)
Note: Standard errors in parentheses. Significance codes: *** 0.01; ** 0.05; * 0.1Author’s own calculations. See text for the specification. Data sources: World Bank WDI for theyears 1970-2012, Taylor (1996) for the years 1946-1969.
46
Table 4: Net portfolio equity inflows, as a ratio to GDP
France Germany U.K. U.S.
Mean for the years1970-74 0.09 0.25 0.111975-79 0.15 0.14 0.081980-84 0.05 0.11 0.17 0.111985-89 0.43 0.41 1.47 0.181990-94 0.58 0.08 1.15 0.031995-99 1.32 1.00 2.62 0.552000-04 1.44 0.81 3.28 0.862005-09 1.74 0.62 1.44 1.20
1970-79 0.12 0.19 0.091980-89 0.32 0.26 0.82 0.141990-99 0.95 0.54 1.88 0.292000-09 1.59 0.71 2.36 1.03
Change to previous period, 5 year averages1975-79 0.71 -0.45 -0.281980-84 -0.28 0.24 0.381985-89 7.06 2.81 7.77 0.701990-94 0.35 -0.80 -0.22 -0.851995-99 1.27 11.49 1.29 19.762000-04 0.09 -0.19 0.25 0.572005-09 0.21 -0.24 -0.56 0.39
Change to previous period, 10 year averages1980-89 1.12 3.28 0.561990-99 1.94 1.12 1.30 1.022000-09 0.67 0.31 0.25 2.58
Source: Author’s calculations, based on World Bank WDI data. Note: French series only start in1983.
47
Table 5: Sum of net FDI inflows and outflows, as a ratio to GDP
France Germany U.K. U.S.
Mean for the years1970-74 0.98 3.16 0.691975-79 0.77 0.69 3.40 0.951980-84 0.87 0.70 2.92 0.831985-89 1.77 1.30 5.94 1.521990-94 3.40 1.29 4.08 1.471995-99 5.37 6.43 10.27 3.172000-04 8.65 2.62 9.85 3.142005-09 8.43 4.15 14.91 3.94
1970-79 0.60 0.84 3.28 0.821980-89 1.32 1.00 4.43 1.181990-99 4.38 3.86 7.18 2.322000-09 8.54 3.39 12.38 3.54
Change to previous period, 5 year averages1975-79 -0.29 0.08 0.381980-84 0.12 0.01 -0.14 -0.121985-89 1.04 0.85 1.03 0.831990-94 0.92 -0.01 -0.31 -0.031995-99 0.58 3.98 1.51 1.162000-04 0.61 -0.59 -0.04 -0.012005-09 -0.03 0.58 0.51 0.26
Change to previous period, 10 year averages1980-89 0.70 0.20 0.35 0.441990-99 2.32 2.85 0.62 0.972000-09 0.95 -0.12 0.73 0.53
Source: Author’s calculations, based on World Bank WDI data.Note: French series on net outflows only start in 1975. Therefore, the 10-year average for the 1970sis computed based on 1975-79 data. Otherwise, only taking into account net inflows for 1970-74would presumably introduce a downward bias.
48
Table 6: Sum of foreign assets and liabilities (IFS), as a ratio to GDP
France Germany U.K. U.S.
Mean for the years1960-69 0.14 0.43 0.091970-79 0.33 0.22 1.17 0.121980-89 0.71 0.32 2.20 0.181990-99 0.89 0.51 2.26 0.252000-09 1.33 1.13 3.81 0.60
Change between decades1960s to 1970s 0.56 1.69 0.381970s to 1980s 1.16 0.42 0.89 0.491980s to 1990s 0.26 0.62 0.03 0.391990s to 2000s 0.48 1.20 0.69 1.40
Source: Author’s calculations, based on IMF IFS data. Note: French series only start in 1969, thusno values for the 1960s are reported. British series only start in 1962.
Table 7: Sum of foreign assets and liabilities of resident banks vis-a-vis all sectors,as a ratio to GDP
France Germany U.K. U.S.
Mean for the years1978-1982 0.42 0.17 1.51 0.121983-1987 0.52 0.22 2.36 0.191988-1992 0.62 0.31 2.01 0.211993-1997 0.72 0.43 2.13 0.191998-2002 0.88 0.93 2.57 0.212003-2007 1.53 1.32 3.65 0.362008-2012 1.79 1.19 4.24 0.46
1980-1989 0.51 0.22 2.09 0.181990-1999 0.71 0.47 2.09 0.202000-2009 1.42 1.24 3.50 0.34
Change to previous period, 5 year averages1983-1987 0.25 0.29 0.56 0.561988-1992 0.18 0.41 -0.15 0.071993-1997 0.16 0.40 0.06 -0.081998-2002 0.23 1.15 0.21 0.112003-2007 0.74 0.41 0.42 0.682008-2012 0.17 -0.10 0.16 0.27
Change to previous period, 10 year averages1990-1999 0.39 1.14 0.00 0.072000-2009 0.99 1.66 0.67 0.71
Source: Author’s calculations, based on BIS locational banking statistics (foreign assets and liabil-ities) and World Bank WDI (GDP).
49
Table 8: Sum of foreign assets and liabilities, as a ratio to GDP
France Germany U.K. U.S.
Mean for the years1970-1974 0.41 0.39 1.48 0.301975-1979 0.53 0.51 1.90 0.341980-1984 1.08 0.98 3.42 0.701990-1994 1.49 1.11 3.60 0.861995-1999 2.20 1.65 4.87 1.382000-2004 3.57 2.95 6.66 1.67
1970-1979 0.47 0.45 1.69 0.321980-1989 0.94 0.80 2.98 0.581990-1999 1.85 1.38 4.24 1.12
Change to previous period, 5 year averages1975-1979 0.29 0.32 0.28 0.161980-1984 0.50 0.21 0.34 0.351985-1989 0.35 0.57 0.34 0.511990-1994 0.38 0.13 0.05 0.231995-1999 0.48 0.49 0.35 0.602000-2004 0.62 0.79 0.37 0.21
Change to previous period, 10 year averages1980-1989 0.99 0.78 0.77 0.831990-1999 0.97 0.72 0.42 0.92
Source: Author’s calculations, based on Lane & Milesi-Ferretti (2007). Note: My representation of(total) foreign assets and liabilities includes portfolio equity assets and liabilities, debt assets andliabilities (portfolio debt + other investment), and FDI assets and liabilities.
50
Figure 14: Foreign assets and liabilities of reporting banks vs. resident banks, as aratio to GDP, 1978-2013
Seite 1
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
Reporting banks, France Resident banks, France Reporting banks, Germany Resident banks, Germany
Reporting banks, U.K. Resident banks, U.K. Reporting banks, U.S. Resident banks, U.S.
Source: Own depiction based on BIS locational banking statistics.
Figure 15: Corporate taxes, percentage of GDP, 1965-2012drucken2
Seite 1
1965
1968
1971
1974
1977
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
0
1
2
3
4
5
6
France Germany United Kingdom United States
Cor
pora
te ta
x re
venu
es, %
of
GD
P
Source: Own depiction, based on OECD statistics.
51
Figure 16: Corporate tax rates and foreign assets & liabilities (as a ratio to GDP),1981-2004 drucken
Seite 1
1981
1984
1987
1990
1993
1996
1999
2002
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
0%
10%
20%
30%
40%
50%
60%France
Corporate tax rate For. ass. & liab., relative to GDP
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
0%
10%
20%
30%
40%
50%
60%Germany
Corporate tax rate For. ass. & liab., relative to GDP
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
0%
10%
20%
30%
40%
50%
60%United Kingdom
Corporate tax rate For. ass. & liab., relative to GDP
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
0.00.20.40.60.81.01.21.41.61.82.0
0%
10%
20%
30%
40%
50%
60%United States
Corporate tax rate For. ass. & liab., relative to GDP
Source: Own depiction, based on data from Lane & Milesi-Ferretti (2007) and the OECD.
Figure 17: Top income tax rates and foreign assets & liabilities (as a ratio to GDP),1970-2004 drucken
Seite 1
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
20%
30%
40%
50%
60%
70%
80%
90%
100%
France
Top income tax rate For. ass. & liab., relative to GDP
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
20%
30%
40%
50%
60%
70%
80%
90%
100%
Germany
Top income tax rate For. ass. & liab., relative to GDP
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
20%
30%
40%
50%
60%
70%
80%
90%
100%
United Kingdom
Top income tax rate For. ass. & liab., relative to GDP
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
20%
30%
40%
50%
60%
70%
80%
90%
100%United States
Top income tax rate For. ass. & liab., relative to GDP
Source: Own depiction, based on data from Lane & Milesi-Ferretti (2007) and Piketty et al. (2014).
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Figure 18: Top 1% income shares and top marginal tax rate, U.S.
Source: Piketty et al. (2014, 246).
Figure 19: Minimum wage in France and the U.S., 1950-2013
$0.0
$1.2
$2.4
$3.6
$4.8
$6.0
$7.2
$8.4
$9.6
$10.8
$12.0
0 €
1 €
2 €
3 €
4 €
5 €
6 €
7 €
8 €
9 €
10 €
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Hou
rly m
inim
um w
age
Expressed in 2013 purchasing power, the hourly minimum wage rose from $3.8 to $7,3 between 1950 and 2013 in the U.S., and from €2.1€ to €9,4 in France. Sources and series: see piketty.pse.ens.fr/capital21c.
Figure 9.1. Minimum wage in France and the U.S., 1950-2013
France (2013 euros, left hand scale)
United States (2013 dollars, right hand scale)
,
Source: Piketty (2014), online appendix.
53
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