determinants of economic inequality - the role of capital ... · determinants of economic...

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: [email protected]. 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.

mailto:[email protected]

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

drucken

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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


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


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


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

drucken

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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


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


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


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

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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


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


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


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

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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


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


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


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


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


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


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


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

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0102030405060708090

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France

CAPITAL top percentile income share (%)

1950

1955

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0102030405060708090

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0.00

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Germany


1950

1955

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0102030405060708090

100

0.00

2.004.00

6.00

8.00

10.0012.00

14.00

16.0018.00

United Kingdom


1950

1955

1960

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1990

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0102030405060708090

100

0.00

5.00

10.00

15.00

20.00

25.00

United States


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

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-2.5-2.0-1.5-1.0-0.50.00.51.01.52.02.5

Germany


1970

1974

1978

1982

1986

1990

1994

1998

2002

2006

2010

0.00

2.00

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6.00

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10.00

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14.00

16.00

18.00

-2.5-2.0-1.5-1.0-0.50.00.51.01.52.02.5

United Kingdom


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


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


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


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


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

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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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(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

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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

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16.00

0%10%20%30%40%50%60%70%80%90%100%

Germany


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


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


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


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


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


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


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


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

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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


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


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


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


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


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


Source: Own depiction, based on data from Lane & Milesi-Ferretti (2007) and Piketty et al. (2014).

52

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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