Round-trip investment between offshore financial centres ... · this regard, as approximately a...

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Round-trip investment between offshore financial centres and Russia: An empirical analysis

Svetlana Ledyaevaa)*, Päivi Karhunena), Riitta Kosonena), John Whalleyb)c)

a) Aalto University School of Business, Centre for Markets in Transition, P.O. Box 21210, FI-00076 Aalto, Finland. b) University of Western Ontario and NBER c) NBER *Corresponding author: [email protected]

Abstract:

In this paper we study the phenomenon of round-trip investment between offshore financial centres and Russia, which is now a significant part of foreign investment into Russia. Using firm-level data we study differences in determinants of round-trip and genuine foreign investment across Russian regions. We show that round-trip investors tend to invest in more corrupt Russian regions while genuine foreign investors - in less corrupt regions. The former finding points to the corruption component of round-trip investment. However, we find that this result is partly (but not fully) attributed to high concentration of round-trip investment in Moscow city which has rather high corruption level. Finally, we find evidence that both round-trip and genuine foreign investors choose not to invest at all into Russian regions, which have very high corruption combined with low investment potential.

Keywords: foreign investment, Russia, offshore jurisdictions, corruption

JEL classifications: G15, F21, F23, F65

Acknowledgement: We would like to thank Neil Doyle (FTI consulting, London) for providing us with useful materials on offshore jurisdictions. The second author acknowledges support from the Academy of Finland grant N 264948. The fourth author acknowledges support from the Ontario Research Fund (ORF).

1. INTRODUCTION

Offshore financial centres (OFCs) have become important players in the global financial system. This is in many

respects explained by the considerable power of advanced business services in the world economy, which they

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exercise in a large measure by operating legal, accounting and financial vehicles through the use of offshore

jurisdictions (Wójcik, 2013). It has been estimated that over half of all international bank lending and

approximately one-third of foreign direct investment (FDI) is routed through offshore jurisdictions (Christensen,

2012). OFCs have particularly strong position in both inward and outward FDI flows of emerging economies,

such as Russia or China. A significant part of these flows represents round-tripping investment, where by

definition local funds are channelled abroad by direct investors and subsequently returned back to the local

economy in the form of direct investment (IMF, 2004). It has been estimated that round-tripping would

constitute 25-50% of all FDI to China (Xiao, 2004), and most of FDI to Russia from selected OFC such as

Cyprus would be money of Russian origin (de Souza, 2008; see also Perez et al., 2012).

The round-tripping by investors from emerging economies has received increasing scholarly attention in

the 2000s, and researchers have identified various reasons explaining the popularity of OFCs as investment

destinations. These include first, financial drivers, such as the opportunity to use low- or no-tax schemes of

OFCs or the possibility to get access to financial incentives allotted to foreign investors when re-investing the

funds back to the home country (Xiao, 2004; Meyer and Boisot, 2008). Second, FDI from emerging economies

to OFCs has been explained by the push of institutional voids, whereby firms escape domestic institutional

constraints, such as lack of legal protection for property rights, poor enforcement of commercial laws, non-

transparent judicial and litigation systems, ineffective market intermediaries, political instability, unpredictable

regulatory changes, government interference, bureaucracy, and corruption in public service and government

sectors (Stal and Cuervo-Cazurra, 2011). On the one hand, the OFCs with their developed financial

infrastructure and low-or no-cost taxation schemes provide more favourable conditions for businesses, and on

the other, the secrecy rules provide protection against corrupt authorities in the home country. At the same time,

the secrecy rules can however be exploited also to launder proceeds of corruption and other illegal activity

(Christensen, 2012).

Although the motivations of emerging economy investors to transfer funds to OFCs are relatively well

understood, the other side of the round-tripping phenomenon, re-investment of funds back to the home economy,

has received only limited attention. Theoretically-driven contributions have derived almost exclusively from the

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Chinese context (Buckley et al., 2007; Morck et al., 2008; Meyer and Boisot, 2008; Ning and Sutherland, 2012),

where the tax incentives provided to foreign investors have been identified as a dominant factor for round-

tripping behaviour1. In many other emerging economies such as Brazil or Russia, however, there are hardly any

privileges for inward FDI. On the contrary, the investment climate is relatively restrictive towards foreign

investors (Stal and Cuervo-Cazurra, 2011; Ledyaeva et al., 2013b). Hence, drivers for round-tripping need to be

searched from elsewhere than from FDI incentives. In this paper we address this gap by focusing on the

reinvestment of Russian capital into Russia from OFCs. Though round-tripping between Russia and OFCs is

increasingly discussed among politicians and analysts, this study is one of the first attempts (see also Ledyaeva

et al. 2013b and Ledyaeva et al. 2013c) to formally analyse this phenomenon based on statistical data. We apply

firm-level data to study differences in determinants of FDI from OFCs and other countries across Russian

regions2. This enables us to identify dominant locational factors, which attract round-trip and genuine FDI to

Russia, and differences in the behaviour of these two groups of investors. In our empirical test we utilize a

sample of firms with foreign ownership that have been registered in Russia during the period 1997-2011. The

data is derived from the enterprise registry of Russian Federal State Statistics Service (Rosstat), which is the

most complete source for official firm-level data in Russia.

Our main results can be summarized as follows. First, we find that traditional FDI determinants such as

market and human resource potential of the region are equally important determinants of round-trip and genuine

FDI in Russia. Second, we find opposite effects of regional corruption on round-trip and genuine FDI into

Russia. In particular, we find that while regional corruption stimulates round-trip investment, it is harmful for

genuine foreign investment. The latter result is expected and goes in line with an ample of previous research on

the relationship between corruption and FDI (for a recent review see Zurawicki and Habib, 2010). The former

result indicates that round-trip investment is linked with corruption. We argue that part of the money invested to

corrupt Russian regions would be proceeds of corruption originating from that region and laundered in OFCs. In

1Recent Chinese legislation however harmonizes tax rates for foreign and indigenous Chinese businesses, so the tax incentives to move offshore have largely been removed (Ning and Sutherland, 2012) 2The Russian Federation is administratively divided into Federal Subjects, which are commonly referred to as regions. The number of regions was 89 until 2005, after which some of them were merged. The current number of regions is 83.

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addition, the more corrupt the region, the more likely would local businessmen apply offshore schemes to hide

their identity from local authorities. Finally, when compared to genuine foreign investors, round-trip investors

(being Russians by origin) would be better equipped to cope with corruption and even use it to their own benefit.

The paper is structured as follows. Section 2 discusses the phenomenon of round-trip investment

between Russia and OFCs on the basis of existing literature. Section 3 describes the data and section 4 -

empirical methodology. Section 5 presents and discusses empirical results. Finally, section 6 concludes.

2. POTENTIAL DRIVERS FOR ROUND-TRIPPING OF RUSSIAN MONEY THROUGH OFCS

As briefly discussed in the previous section, the drivers for emerging economy investors to use OFCs for round-

tripping of assets range from financial to institutional ones. In general, the attractiveness of OFCs for foreign

investors, including those from emerging economies, is based on the combination of advanced financial services,

low or no taxes and explicit secrecy rules (Gonzales & Schipke, 2011). From the economic geography

perspective, the OFCs have been conceptualized as distinctive economic spaces. Hampton (1996) introduced a

framework for analysing OFCs, which includes four dimensions of space: the secrecy space, the regulatory space,

the political space and the fiscal space (Hampton and Levi, 1999). In broad terms, the provision of advanced

financial services represents the regulatory space, low- or no-tax regimes the fiscal space, and the explicit

secrecy and confidentiality rules the secrecy space. The political space refers to the relationship between

offshore and its mainland onshore3, which is an important determinant of its usefulness as an OFC (Hampton and

Levi, 1999). We argue that the relative weight of the different attributes of OFCs as economic spaces is

contingent to the type of financial flow channelled through the OFC, i.e. whether it is licit or illicit. We discuss

3 At present, the majority of OFCs are located either in the UK Overseas Territories, or in the British Crown Dependencies, the somewhat ill-defined constitutional status of which provides room for manoeuvre (Hampton and Levi, 1999). Due to the close links of OFCs to powerful countries (onshore), the term ’offshore’ when used in the context of financial services, is strictly a political statement about the relationship between powerful states and related territories (Palan, 1999; Christensen, 2012).

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this issue next against existing literature on the role of OFCs in the financial flows to and from emerging

economies, and the empirical context of Russia.

2.1. Round-tripping of licit financial flows through OFC

The main reasons for round-tripping of Russian licit financial flows through OFCs are in our view related to the

regulatory, fiscal and secrecy dimensions of OFCs as economic spaces. The provision of a variety of financial

services, such as company residence and administration of financial transactions such as mergers and

acquisitions (M&A) efficiently and with low administrative burden is one of the factors that make OFCs

attractive for actors in the official economy, such as multinational enterprises (MNE) (Gonzales and Schipke,

2011). In addition, the residence in the OFC provides the MNE the opportunity to enjoy the low- or no-tax

regime. In ideological terms, the supporters of OFC see them as an escape valve from high tax and restrictive

regulatory regimes imposed by other governments (Christensen, 2012). Russian MNEs are not an exemption in

this regard, as approximately a third of the international subsidiaries of top 25 Russian multinationals are located

in OFCs (Skolkovo, 2007; Filippov, 2010). From the round-tripping perspective, the OFCs are frequently used

for the administration of M&A between Russian multinationals, which results in the registration of such

transactions as FDI to Russia and substantial biases in FDI statistics.

In addition to the financial considerations, we argue that emerging economy multinationals are even

more motivated to establish subsidiaries in OFCs than MNEs from developed economies due to institutional

reasons. As discussed above, in the case of China the domestic FDI policy is among the key drivers for Chinese

companies to register their businesses in offshore jurisdictions and thereby gain the status as foreign firm (e.g.

Xiao, 2004; Buckley, 2007; Luo et al., 2010). In the case of Russia, however, we argue that the main institutional

explanations for round-tripping of licit financial flows are institutional escape and institutional arbitrage. In

other words, rather than supportive home country institutions, it would be institutional imperfections that prompt

Russian firms to escape home country institutional constraints through relocating their businesses to OFCs (see

also Witt and Lewin, 2007). Such constraints include corruption, regulatory uncertainty, underdeveloped

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intellectual property rights protection, and governmental interference (Witt and Lewin, 2007; Yamakawa et al.,

2008; Luo et al., 2010). In the same vein, Loungani and Mauro (2000) observed that capital flight from Russia

was mainly driven by the “confiscatory” nature of the tax system, endemic weaknesses in its banking system,

vested interests in the energy sector, and widespread corruption.

Here, in addition to more favourable regulatory and tax regimes, the secrecy that OFCs provide for

investors is of importance. In the case of Russia, it can be argued that the relocation of businesses to OFCs is

motivated by the businesses’ desire to hide their identity and get shelter from opportunistic authorities and the

threat of hostile takeover. In contemporary Russia, it is not the outright criminal attacks on businesses4 which

undermine ownership rights but the ‘government-aided’ hostile takeovers which are implemented with the

assistance of corrupt officials (CPT, 2008). We will discuss the magnitude of corruption in Russia in the next

section to illustrate its potential significance as a determinant of round-tripping phenomenon.

The discussion above sheds light on the question why Russian investors operating in the official

economy locate their businesses in OFCs. However, there is another question: Why do these firms re-invest

capital back to Russia with its unsupportive institutional environment instead of using the OFC as a springboard

to other foreign markets? A recent theoretical concept, which suggests an explanation for such behaviour, is

institutional arbitrage coined by Meyer and Boisot (2008). It refers to the situation, where a firm is able to

exploit differences between two institutional environments. The rationale of this idea is based on the argument

that weak institutions and the associated heightened uncertainty increase transaction costs for firms operating in

an emerging economy context (Meyer, 2001). Such costs are particularly high for foreign investors, who lack

knowledge of the local business environment and local business networks. Therefore, round-trip investors (i.e.

Russian firms channelling their investment through OFCs) face lower transaction costs compared to genuine

foreign investors when investing to Russia, and, hence, have a superior competitive position. This echoes the

‘new regionalism’ (see e.g. Storper, 1997) in economic geography, which argues that the business climate is

socially and culturally reproduced and hence is embedded in specific time-space contexts that shape the

4 Such attacks posed a serious threat particularly for small firms in the first decade of economic transition in Russia (see for example Volkov, 1999)

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outcomes of FDI activities (Qiu, 2005). At the same time, the round-trip investors’ access to more favourable

institutional conditions, such as the financial expertise and lower financial costs, through the offshore investment

(Meyer and Boisot, 2008) puts them in a superior position towards purely domestic firms.

2.2. Round-tripping of illicit financial flows through OFCs

In contrast to the supporters of OFCs, who based their arguments on the neoliberalist idea of economic

harmfulness of regulation of international capital flows (Cooper, 1998; van Hulten, 2012), the critics of OFCs

point to the problems such as tax evasion and money laundering, which the lack of transparency, regulation and

secrecy at OFCs made possible (Gonzales and Schipke, 2011). In particular, it has been recognized that legalized

secrecy provides a supply side stimulus for corrupt practices (Christensen, 2012; see also Brown and Cloke,

2007) as proceeds of corruption can be safely laundered at OFCs. As concluded by Hampton and Levi (1999) it

is the secrecy space that makes OFCs most attractive for money laundering and other illicit financial activities.

In addition, the lax regulatory space assists in the laundering process by providing the opportunity to use shell

companies and other financial vehicles. The fiscal space – as Hampton and Levi (1999) somewhat ironically

note – is of minor importance here as the illicit income rarely is subject to home country taxes.

The real magnitude of illicit capital flows channelled through OFCs is extremely difficult to estimate

due to the unrecorded nature of such flows and the reluctance of OFCs to share information about the financial

transactions that they are hosting. Recent attempts to model illicit capital flows include the contribution of Perez

et al. (2012), who estimated that 6–10% of total FDI outflows and over 20% of FDI to OFCs from their sample

of East European Economies (including Russia) were made to facilitate illicit money flows. In the same vein,

Kar and Freitas (2013) estimated that over the period 1994-2011, nearly a third of capital outflows (consisting of

a mix of licit and illicit capital) from Russia was illicit capital flows.

Taken the magnitude of corruption in Russia, one can reasonable assume that a major part of the illicit

financial flows from Russia to OFCs are proceeds of corruption. As it is argued in the Financial Action Task

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Force report on money laundering and corruption, “the stolen assets of a corrupt public official are useless unless

they are placed, layered, and integrated into the global financial network in a manner that does not raise

suspicion” (FATF, 2011:6). Public sector corruption is commonly identified as one of the key problems in the

Russian society and economy, and a serious deterrent for FDI. According to the most pessimistic estimates, the

value of corruption in Russia would be a third of the country’s gross domestic product (INDEM, 2005).

Furthermore, companies participating in the BEEPS Russia 2012 survey of EBRD & World Bank identified

corruption as the fourth-biggest obstacle for conducting business in the country (EBRD, 2013). In international

corruption rankings, Russia repeatedly scores low. For example, Russia’s CPI score in 2012 was 28, which put it

on the 133rd place among 176 countries (Transparency International, 2013). Furthermore, in contemporary

Russia there is a strong nexus between business and the state, both on Federal and regional (sub-national) levels

(see, for example, Yakovlev, 2006). Hence, the strong involvement of regional authorities in the business sphere

of the region would provide them the access to profitable investment projects. Taken the high corruption in

Russia, we argue that part of the inward FDI flows from OFCs to Russia would be proceeds of corruption,

generated in the region, laundered in the OFCs and re-invested back to the same region. Theoretically, this

echoes the construct of institutional arbitrage that we introduced in the previous section, but from the illegal

point of view. In the empirical section of this paper we test the validity of this argument by investigating the

relationship between region-level corruption in Russia and distribution of FDI.

3. DATA DESCRIPTION

Our empirical analysis makes use of Rosstat (Russian State Statistical Agency) dataset, which provides

information on the location choice of 20,165 firms with foreign capital registered in Russia in the period 1997-

2011. This dataset includes information on firms of two ownership types: full ownership of foreign entities and

joint ventures of foreign owners (foreign entities and foreign citizens) with Russian private owners (Russian

entities and citizens). For each firm, we use data that Rosstat records on:

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• Industry information, including the six-digit OKVED code (Russian equivalent to SIC six-digit codes) of

the primary industry in which a firm operates;

• Ownership structure, including information about firms` owners (country of origin, company`s name,

share in charter capital) and ownership status;

• Location information, including a region;

• Year of registration;

• Charter capital size at the moment of registration;

• Annual gross revenues in the period of 1998-2011.

From this dataset we extract two types of firms. First group (round-trip investors) consists of firms which foreign

ownership is represented by investors from main offshore financial centers. Following Haberly and Wojcik

(2013), we utilize an expert agreement definition of tax havens as jurisdictions appearing on a sufficient

percentage (in this study – 50%) of 11 tax haven lists produced by different researchers (compiled by Palan et al.

2010; see Appendix 1). This group comprises 9909 firms. The second group consists of firms for which foreign

ownership is represented by genuine foreign owners. We define genuine foreign investors as investors from all

countries excluding offshore financial jurisdictions with more than 25% agreement as defined in Palan et al.

study (see Appendix 1) and the Netherlands5. This group comprises 7743 firms.

In table 1 we present the structure of our data by investor country (separately for round-trip and genuine

foreign investors` groups).

5 The Netherlands is also a popular location among Russian natural resource companies to set up their financial subsidiaries and, at the same time, is one of the most important source countries of foreign investment into Russia.

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Table 1 Countries – main investors into Russia

Round-trip investors Genuine foreign investors

Country Number of firms

Cumulative revenues, %

Country Number of firms

Cumulative revenues, %

Cyprus 6015 (60.7%) 71.7 Germany 887 (11.5%) 13.7

British Virgin Islands (BVI) 1688 (17%) 5.7 Belorussia 740 (9.6%) 3.3

Seychelles 420 (4.2%) 0.5 Ukraine 627 (8.1%) 2.0

Switzerland 370 (3.7%) 10.1 USA 488 (6.3%) 5.3

Belize 268 (2.7%) 0.3 China 388 (5%) 1.0

Joint Cyprus and BVI 152 (1.5%) 3.2 Finland 285 (3.7%) 6.6

Luxembourg 116 (1.2%) 0.5 Austria 274 (3.5%) 3.5

Others 880 (8.9%)* 7.9 Italy 232 (3%) 1.4

Latvia 217 (2.8%) 0.4

Kazakhstan 194 (2.5%) 0.3

Belgium 66 (0.9%) 8.5

Japan 71 (0.9%) 5.2

South Korea 84 (1.1%) 3.5

France 168 (2.2%) 3.4

Others 3022 (39%)* 41.9

Total 9909 (100%) 100 Total 7743 (100%) 100

Note: 1) *This number also includes joint ownership of investors from different countries in the corresponding group; 2) By shadow we mark countries which are significant investors into Russia by cumulative revenues but not by number of established firms.

Source: Rosstat and authors’ calculations.

As we can see, the country structure of offshore (round-trip) investment is not diversified. Around 61% of

established firms in this group and around 72% of their cumulative revenues belong to investors from Cyprus.

Other two significant offshore investors are BVI (by number of established firms) and Switzerland (by

cumulative revenues). The most important genuine foreign investors by number of established firms are

Germany, Belorussia, Ukraine, USA and China; and by cumulative revenues – Germany, Belgium, Finland,

USA and Japan. In table 2 we present industrial distribution of firms in the two groups of investors.

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Table 2 Industrial distribution of firms established in the period of 1997-2011

Sector Number of firms and (%)

Cumulative gross annual revenues (% to total)

Round-trip investors

Genuine foreign

investors

Round-trip investors

Genuine foreign

investors Agriculture, hunting, forestry, fishing (01 to 05)

175 (2%) 288 (4%) 0.75% 0.9%

Resource extraction (10 to 14) 282 (3%) 104 (1%) 3.5% 1.6%

Manufacturing industries (15 to 37) 971 (10%) 1590 (21%) 12.8% 25.6%

Production and distribution of electricity, gas and water (40-41)

63 (1%) 32 (0.4%) 0.5% 4.5%

Construction (45) 682 (7%) 552 (7%) 3.86% 2.2%

Trade and repair (50 to 52) 2235 (23%) 3146 (41%) 49.5% 57%

Hotels and restaurants (55) 155 (2%) 112 (1%) 0.25% 0.4%

Transport and communications (60 to 64)

593 (6%) 461 (6%) 2.9% 2%

Financial activities (65 to 67) 1083 (11%) 182 (2%) 13.8% 2%

Real estate and related services (70 to 74)

3428 (35%) 1138 (15%) 11.8% 3%

Others 242 (2%) 138 (2%) 4.46% 3.5%

Total 9909 (100%) 7743 (100%) 100% 100%

Note: Two-digit OKVED (Russian classification of economic activities) codes in parentheses.

Source: Rosstat and authors’ calculations.

As we can see from the table, round-trip investors have significantly larger shares than genuine foreign ones in

financial activities and real estate and related services. For other industries the structure does not differ much

between the two groups of investors. On maps 1 and 2 we depict regional distribution of firms across Russia for

round-trip and genuine foreign investors, respectively.

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Cumulative number of established firms in the period of 1997-2011 across Russian regions

Map 1 Round-trip investors Map 2 Genuine foreign investors

Note: White colour denotes so-called ‘autonomous okrugs’ of regions for which data is not available separately but included in the corresponding regions.

Source: Rosstat and authors` calculations.

In general regional distributions of firms look very similar between the two groups of investors. However, we

can notice that round-trip investors tend to establish more firms in Siberia (i.e. in more resource abundant

regions) and slightly fewer firms in the Western part of Russia compared to real foreign investors.

We should also note that both round-trip and genuine foreign firms are highly concentrated in three

Russian regions, namely, Moscow city, Saint-Petersburg city and Moscow region. 55% of firms established by

offshore investors are registered in Moscow city, 9% - in Moscow region and 5.5% - in Saint-Petersburg. The

corresponding shares for real foreign investors are 38, 8.3 and 8%. The large shares of firms in Moscow city is

partly explained by the fact that companies have their head offices in Moscow (which is the financial centre of

Russia) but their real production activities are located in other regions. Unfortunately, from our data we cannot

separate those firms that conduct real business in other regions but are registered in Moscow.

On maps 3 and 4 we depict regional distribution of round-trip and genuine foreign investment measured

by cumulative gross annual revenues in the period of 1998-2011.

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Figure 2 Cumulative gross annual revenues in the period of 1998-2011 (in million Euros) across Russian

regions

Map 3 Round-trip investors Map 4 Genuine foreign investors

Note: White colour denotes ‘autonomous okrugs’ of regions for which data is not available separately but included in the corresponding regions and zero observations. White colour also corresponds to the regions where cumulative revenues are less than 4/7 million Euros for round-trip/genuine foreign investors, respectively.

Source: Rosstat and authors` calculations.

The distribution patterns are very similar to those found for cumulative number of firms (maps 1 and 2). In

particular, in general, round-trip investors earn higher revenues in Siberia while genuine foreign investors – in

the Western part of Russia.

Cumulative revenues earned by round-trip investors in Moscow city count for 50%, in Moscow region –

4.6% and in St. Petersburg – 5.7%. The corresponding percentages for genuine foreign investors are 33%

(Moscow city), 30% (Moscow region) and 10% (St. Petersburg).

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4. METHODOLOGY AND VARIABLES

We estimate the determinants of round-trip and genuine foreign investment across Russian regions using the

following specification:

iittitiiitititi

tititiiiit

tit

uRIRaTAXaDEMaCityaMarkPotaMarketaRFPa

RIPaRESaRoadsaPortaEDUaCORRDummiesYeary

++++++++

++++++++=

−−−−−

−−−∑ε

αδα

1,141,1312111,101,91,8

1,71,61,54321 _

(1)

Our dependent variable, ity , is measured by the natural logarithm of cumulative gross annual revenues

(transformed from roubles into USD) in a year t (2002-2011) in a region i (1,…,76) earned by firms established

either by round-trip or genuine foreign investors. As an alternative dependent variable we utilize per capita

version of this indicator. The explanatory and control variables are described below. We also include year

dummies to control for unobserved systematic period effects. na and tδ are the parameters to be estimated. itε is

an idiosyncratic error term. iu is unobserved regional heterogeneity (a region-specific effect).

The explanatory variables were selected according to the existing literature on the determinants of

foreign investment, data availability, and the particularities of the Russian economy. The time-varying

explanatory and control variables are lagged by one year. The use of lagged explanatory variables helps to solve

possible endogeneity problems. The lagged explanatory variables further relate to a simple hypothesis for the

foreign investor`s decision-making process: foreign investors are assumed to make an investment decision for a

given year by referring to the observable variables of the previous year (see, for example, Ledyaeva, 2009;

Ledyaeva et al. (2013a)).

Corruption in a region i, iCORR , is measured using the corruption dimension provided by the Moscow

Carnegie Center`s Index of Democracy for the period 2000-2004. It is measured on a 5-point scale, where 1

indicates the highest level of corruption and 5 indicates the lowest. This indicator refers mainly to state

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corruption in a broader sense, that is, the interconnections between political and business elites and their

interventions in the political decision-making process. To our knowledge, this is the only indicator of corruption

that is available for all of the Russian regions.6

The educational background of the population in a region, i , iEDU , is measured using a natural

logarithm of the share of the population with at least a medium level of professional education compared to the

share of the population with no professional education in a particular Russian region i in the year 2002 (the data

come from the Rosstat Population Census for 2002).

The third and fourth control variables measure the existing transport infrastructure in a particular

Russian region, which should have an impact on the transportation costs incurred by a foreign investor. The

variable iPort reflects the presence of a seaport in a particular Russian region i (a dummy variable that is equal

to 1 if there is at least one sea port in a region and to 0 otherwise). The variable 1, −tiRoads reflects the regional

development of railways and highways and is measured by the average density of railways and highways in a

particular region, i , in a given year, t-1 (where data is not available – for the nearest year).

The natural resources` potential, 1, −tiRES 7 , regional institutional potential, 1, −tiRIP 8, and regional

financial potential, 1, −tiRFP 9 are measured using an online Expert RA journal corresponding rankings10 for a

particular region, i, in a given year, t-1 (from 1 to 89: 1 corresponds to the highest potential and 89 corresponds

to the lowest potential). The market size variable, 1, −tiMarket , is the first principal component of three variables

(gross regional product, total population, and population density) for a particular region i, in a given year, t-1.

This indicator for the market size in Russian regions was introduced in a study by Iwasaki and Suganuma (2005).

The proportion of variance of the first component reach 67%, and furthermore, its eigenvector and component

loading show that this variable is suitable as a general index of market size.

6 The only alternative is the index of corruption of Transparency International and Fund INDEM (2002). However, the index was only computed for 40 Russian regions, which would pose serious limitation on our study. 7 This indicator reflects the average weighted availability of balanced stocks of principal natural resources in the Russian regions. 8 This indicator reflects the level of development of principal market institutions in the Russian regions. 9 This indicator reflects the volume of tax base, profitability of the enterprises and income of population in the Russian regions. 10 http://www.raexpert.ru/ - official webpage of Expert Rating Agency (RA), the most respected rating agency in the CIS and Eastern Europe.

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We also include a surrounding-market potential variable, 1, −tiMarkPot (see Blonigen et al., 2007). For

a region, i , it is defined as the sum of the market sizes (measured using the Market variable) of the surrounding

regions within a distance of 500 km (between the capital of a particular Russian region and the capital of a

neighboring (but not necessarily bordering) region). This distance threshold between neighboring regions has

been chosen based on the “trial-and-error” method. We further control for 13 Russian cities with population

over 1 million inhabitants. In particular, we include dummy, iCity , which equals to one for Russian regions in

which there is a million city and zero otherwise.

We measure democracy in a Russian region i (i=1,…,76), iDEM , using a simple average of all the

dimensions of the Moscow Carnegie Center`s Index of Democracy for the period 2000-2004 except corruption

dimension. Each dimension is measured on a 5-point scale, where 1 indicates the lowest level of democracy and

5 indicates the highest. The corruption dimension is excluded because our objective is to assess its separate

influence on foreign firms` locational choice and also because it does not correlate highly with the other

dimensions: While all the dimensions except corruption correlate highly with one another (for all pairs, the

correlation coefficients are more than 0.5), all of the correlation coefficients between the corruption dimension

and the other dimensions are less than 0.5. This allows us to suggest that the corruption dimension reflects

patterns that are somewhat different from the other dimensions of the index and therefore the corruption

dimension should be considered as a separate explanatory variable.

The level of regional taxes, 1, −tiTAX , is measured by the ratio of regional tax revenues to gross regional

product for a particular region, i , in a given year, t-1. In general this measure reflects the average tax rate at

regional level. The data come from Rosstat. Finally, regional investment risk, iRIR , is an online Expert RA

journal ranking11 ranging from 1 to 89 for a particular Russian region, i, in a given year, t-1(1 is assigned to a

region with the smallest risk in Russia, and 89 is assigned to a region with the largest risk).

11 This is a qualitative indicator that simultaneously reflects political, economic, social, criminal, financial, ecological, and legislative risks for investment activities in the Russian regions.

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5. RESULTS

5.1. Baseline results

In tables 3-6 we present our baseline estimation results of the equation (1). We report panel data model results

with random effects. We do not report results with fixed effects (they are available upon request) because five

out of 13 explanatory variables are time-invariant and thus subsumed by regional fixed effects. We also report

Hausman test statistics. In many models it is statistically significant meaning that fixed effects model is preferred.

However, comparing results between models with fixed and random effects, we did not find substantial

differences between the corresponding coefficients of time-variant explanatory variables (in particular, signs of

the coefficients are the same in most cases and statistical significance differs considerably in only few cases).

The descriptive statistics and correlation matrix of the dependent and explanatory variables are presented

in Appendix 2. From correlation matrix we can conclude that a multicollinearity problem might be rather serious

in our estimations. To test for possible multicollinearity problems in our data, we recorded standard errors and

Variance Inflation Factors (VIFs). The standard errors and VIFs indicate that there is no serious multicollinearity

problem in our data: the critical value of VIF is 10 (Belsley et al., 1980) although some authors use a more

conservative rule that VIF does not exceed 5. For two variables in our model, regional institutional potential, RIP,

and regional financial potential, RFP, the VIF exceeds 5 and, hence, to control for possible multicollinearity we

estimate our model including these two variables and City and Market variables (which correlates most highly

with RIP and RFP) separately (models 2-5 in each table).

Finally, in tables 3-6 we exclude zero observations of the dependent variable. Zero revenues in most

cases mean that there are no or very few foreign firms in these regions and, hence, might reflect more the

decision “invest or not invest” but not the decision “how much to invest” which we test in this paper. However,

for robustness checking purposes we summarize results when zero observations are included in section 5.2.

below.

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Table 3 Baseline results for genuine foreign investors: Panel data model with random effects Dependent variable is natural logarithm of annual cumulative revenues in USD in a Russian region i (1,…,76) in a year t (2002-2011). Zero observations are excluded. Explanatory variable

M1 M2 M3 M4 M5 VIF

Constant 16.3 (1.1)*** 13.7 (1.1)*** 13 (1.04)*** 14.7 (1.07)*** 15.7 (1.07)***

iPort 0.09 (0.5) -0.003 (0.5) -0.13 (0.5) -0.04 (0.5) 0.04 (0.5) 1.51 (11)

1, −tiTAX 1.4 (1.6) 1.4 (1.7) 1.5 (1.7) 1.7 (1.65) 1.3 (1.6) 1.34 (13)

iEDU 2.3 (0.86)*** 2.87 (0.87)*** 2.9 (0.9)*** 2.7 (0.8)*** 2.4 (0.8) 1.74 (9)

iCORR 0.5 (0.3)* 0.1 (0.3) -0.02 (0.3) 0.26 (0.29) 0.4 (0.3) 1.73 (10)

1, −tiMarkPot 0.06 (0.03)* 0.06 (0.03)** 0.06 (0.03)* 0.04 (0.03) 0.07 (0.03)** 1.49 (12)

1, −tiRIR -0.003 (0.002) -0.004 (0.002)* -0.005 (0.002)** -0.004 (0.002) -0.003 (0.002) 1.79 (8)

1, −tiRES 0.02 (0.01)*** 0.01 (0.01) 0.01 (0.01) 0.01 (0.006)* 0.02 (0.01)*** 2.33 (5)

iDEM 0.02 (0.4) 0.42 (0.37) 0.9 (0.33)*** 0.47 (0.34) 0.25 (0.33) 2.16 (6)

1, −tiRoads 0.002 (0.002) 0.004 (0.001)***

0.004 (0.002)***

0.004 (0.001)***

0.003 (0.001)* 2.67 (4)

iCity 0.4 (0.52) 1.7 (0.5)*** 2.10 (7)

1, −tiMarket -0.02 (0.15) 0.2 (0.15) 3.17 (3)

1, −tiRIP -0.01 (0.01)* -0.03 (0.01)*** 6.63 (2)

1, −tiRFP -0.04 (0.01)***

-0.05 (0.01)*** 7.36 (1)

N. obs. 703 704 703 704 704

R-sq overall 0.62 0.56 0.54 0.6 0.62

Hausman test 17.8(0.33) 59.6 (0.000)*** 970 (0.000)*** 62.8 (0.000)*** 19. (0.15)

Note: 1) * if p < 0.10, ** if p < 0.05; *** if p < 0.01; 2) standard errors in parentheses; 3) for VIFs the place of variable (from the highest to the lowest value) in parentheses.

19

Table 4 Baseline results for round-trip investors: Panel data model with random effects Dependent variable is natural logarithm of annual cumulative revenues in USD in a Russian region i (1,…, 76) in a year t (2002-2011). Zero observations are excluded. Explanatory variable

M1 M2 M3 M4 M5 VIF

Constant 19.1 (1.03)*** 17.3 (1.1)*** 17 (0.99)*** 17.6 (1.03)*** 18.6 (1.03)***

iPort -0.5 (0.4) -0.65 (0.5) -0.68 (0.45) -0.75 (0.4)* -0.6 (0.4) 1.47 (12)

1, −tiTAX -3.7 (2)* -3.9 (1.9)** -3.6 (1.9)* -3.6 (1.9)* -3.9 (1.9)** 1.36 (13)

iEDU 2.3 (0.8)*** 3.05 (0.86)*** 2.7 (0.8)*** 3.06 (0.8)*** 2.7 (0.8)*** 1.71 (10)

iCORR -0.5 (0.3)* -0.8 (0.3)*** -0.8 (0.3)*** -0.8 (0.3)*** -0.6 (0.3)** 1.78 (9)

1, −tiMarkPot 0.03 (0.03) 0.02 (0.03) 0.02 (0.03) 0.02 (0.033) 0.03 (0.03) 1.49 (11)

1, −tiRIR -0.001 (0.003) -0.002 (0.003) -0.003 (0.003) -0.002 (0.003) -0.001 (0.003) 1.79 (8)

1, −tiRES 0.005 (0.007) -0.002 (0.007) -0.003 (0.01) -0.004 (0.007) 0.005 (0.007) 2.31 (5)

iDEM 0.5 (0.3) 0.9 (0.4)** 1.2 (0.32)*** 1.1 (0.3)*** 0.8 (0.3)*** 2.21 (6)

1, −tiRoads 0.0004 (0.002) 0.003 (0.002)* 0.002 (0.002) 0.003 (0.001)**

0.002 (0.001) 2.61 (4)

iCity 0.7 (0.5) 1.65 (0.5)*** 2.11 (7)

1, −tiMarket 0.3 (0.2) 0.44 (0.2)*** 3.12 (3)

1, −tiRIP -0.004 (0.006) -0.02 (0.01)*** 6.13 (2)

1, −tiRFP -0.03 (0.01)*** -0.04 (0.01)*** 6.98 (1)

N. obs. 712 713 712 713 713

R-sq overall 0.61 0.53 0.53 0.54 0.59

Hausman test 383.3 (0.000)***

82.9 (0.000)***

3675.6 (0.000)***

--- 181 (0.000)***

Note: 1) * if p < 0.10, ** if p < 0.05; *** if p < 0.01; 2) standard errors in parentheses; 3) for VIFs the place of variable (from the highest to the lowest value) in parentheses; 4) --- - model fitted on these data fails to meet the asymptotic assumptions of the Hausman test.

20

Table 5 Baseline results for genuine foreign investors: Panel data model with random effects Dependent variable is natural logarithm of annual cumulative revenues in USD per capita in a Russian region i (1,…,76) in a year t (2002-2011). Zero observations are excluded.

Explanatory variable M1 M2 M3 M4 M5

Constant 8.2 (1.2)*** 6.5 (1.04)*** 6.07 (1.02)*** 7.2 (1.08)*** 8 (1.1)***

iPort 0.3 (0.5) 0.24 (0.5) 0.1 (0.5) 0.23 (0.5) 0.3 (0.47)

1, −tiTAX 1.6 (1.6) 1.7 (1.6) 1.7 (1.6) 1.9 (1.6) 1.6 (1.6)

iEDU 2.6 (0.9)*** 2.8 (0.9)*** 2.9 (0.9)*** 2.7 (0.85)*** 2.5 (0.85)***

iCORR 0.6 (0.3)** 0.4 (0.3) 0.3 (0.03) 0.54 (0.3)* 0.6 (0.3)**

1, −tiMarkPot 0.06 (0.03)** 0.06 (0.03)** 0.06 (0.03)** 0.05 (0.03) 0.07 (0.03)**

1, −tiRIR -0.002 (0.002) -0.004 (0.002) -0.004 (0.002)* -0.003 (0.002) -0.002 (0.002)

1, −tiRES 0.02 (0.007)*** 0.01 (0.006)** 0.01 (0.007)** 0.02 (0.006)** 0.02 (0.007)***

iDEM -0.07 (0.4) 0.18 (0.36) 0.45 (0.3) 0.14 (0.34) -0.03 (0.34)

1, −tiRoads 0.002 (0.002) 0.002 (0.001) 0.003 (0.002)* 0.002 (0.001) 0.001 (0.001)

iCity -0.04 (0.5) 0.76 (0.5)

1, −tiMarket -0.12 (0.15) -0.02 (0.15)

1, −tiRIP -0.007 (0.007) -0.02 (0.006)***

1, −tiRFP -0.03 (0.01)*** -0.03 (0.007)***

N. obs. 703 704 703 704 704

R-sq overall 0.49 0.51 0.5 0.51 0.49

Hausman test 28.1 (0.03)** 14.6 (0.33) 20.9 (0.11) 13.5 (0.5) 26 (0.03)**

Note: 1) * if p < 0.10, ** if p < 0.05; *** if p < 0.01; 2) standard errors in parentheses.

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Table 6 Baseline results for round-trip investors: Panel data model with random effects Dependent variable is natural logarithm of annual cumulative revenues in USD per capita in a Russian region i (1,…,76) in a year t (2002-2011). Zero observations are excluded.

Explanatory variable M1 M2 M3 M4 M5

Constant 10.8 (1.04)*** 10.2 (0.95)*** 10 (0.9)*** 10.1 (+.9)*** 10.7 (1)***

iPort -0.3 (0.4) -0.4 (0.4) -0.4 (0.4) -0.44 (0.4) -0.37 (0.4)

1, −tiTAX -3.4 (1.9)* -3.4 (1.9)* -3.3 (1.9)* -3.3 (1.9)* -3.4 (1.9)*

iEDU 2.8 (0.8)*** 3.04 (0.77)*** 2.9 (0.8)*** 3.1 (0.75)*** 2.9 (0.76)***

iCORR -0.35 (0.27) -0.47 (0.26)* -0.5 (0.26)* -0.5 (0.26)* -0.4 (0.26)

1, −tiMarkPot 0.03 (0.03) 0.03 (0.03) 0.03 (0.03) 0.03 (0.03) 0.03 (0.03)

1, −tiRIR -0.0002 (0.003) -0.001 (0.003) -0.001 (0.003) -0.001 (0.003) -0.0002 (0.003)

1, −tiRES 0.005 (0.007) 0.002 (0.006) 0.001 (0.006) 0.001 (0.006) 0.005 (0.007)

iDEM 0.5 (0.34) 0.6 (0.3)* 0.75 (0.3)** 0.75 (0.3)** 0.6 (0.3)*

1, −tiRoads -0.00004 (0.002) 0.001 80.002) 0.0004 (0.002) 0.001 (0.001) 0.001 (0.001)

iCity 0.4 (0.5) 0.77 (0.4)*

1, −tiMarket 0.11 (0.16) 0.21 (0.15)

1, −tiRIP 0.001 (0.006) -0.005 (0.006)

1, −tiRFP -0.01 (0.008) -0.02 (0.007)**

N. obs. 712 713 713 713 713

R-sq overall 0.5 0.49 0.49 0.48 0.49

Hausman test 65.4 (0.000)*** 44.8 (0.000)*** 71.6 (0.000)*** 122.7 (0.000)*** 49.7 (0.000)***

Note: 1) * if p < 0.10, ** if p < 0.05; *** if p < 0.01; 2) standard errors in parentheses.

From tables 3-6 we can see that the most robust and highly statistically significant result for both types of

investors is the positive relationship between educational background of population and foreign investment in

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Russian regions. There is also rather strong evidence that both genuine and round-trip foreign investment is

positively related to institutional and financial potentials, market size and surrounding market potential

(including the presence of a “million” city in a Russian region) and transport infrastructure. All these results are

expected and go in line with previous literature.

Our empirical analysis also reveals some remarkable differences between the two types of foreign

investors. In particular, we find rather strong evidence that, while genuine foreign investors invest in less corrupt

Russian regions, their round-trip counterparts invest in more corrupt regions. These results are robust and rather

highly statistically significant (especially for the positive relationship between the level of corruption and round-

trip foreign investment). While the negative relationship between corruption and genuine foreign investment is

expected and has been previously found in numerous academic studies, the positive relationship between

corruption and round-trip investment needs to be explained. First, we suggest that round-trip investors will invest

into more corrupt Russian regions if their purpose is corruption money laundering (i.e. they launder corrupt

money in offshore jurisdictions and then return them back into the same region). Second, Russian businessmen

might utilize round-trip investment as a mean for securing the secrecy of an investor`s identity from corrupt

regional authorities. Finally, round-trip investors being Russians by origin have better knowledge about corrupt

practices in Russia and, hence, how to overcome them and even how to benefit from them.

There are other differences between the determinants of round-trip and genuine foreign investment. First,

we find that regional tax level is negatively associated with round-trip investment as expected. However, the

corresponding coefficients for genuine foreign investment are even positive though not statistically significant.

These results indicate that costs associated with local taxes are more important for round-trip investors than for

genuine foreign ones. Second, we find that round-trip investment is positively associated with regional

democracy while for genuine foreign investment this relationship, being also positive, is not statistically

significant in most cases. In general positive relationship between the level of democracy and foreign investment

is expected and has been documented in numerous previous studies (see Asiedu and Lien, 2011; Biswas, 2002;

Busse, 2004; Harms and Ursprung, 2002; Jakobsen and Soysa, 2006; Jensen, 2003; Madhu, 2009; Schulz, 2009).

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5.2. Robustness checking

In baseline results we excluded zero observations of the dependent variable. However, for robustness checking

purposes in table 7 we report our main estimation results (i.e. model 1 in tables 3-6) when zero observations are

included (i.e. we take natural logarithm of the corresponding revenues plus 0.001).

Table 7 Baseline results: Panel data model with random effects Dependent variable is natural logarithm of annual cumulative revenues in USD per capita in a Russian region i (1,…,76) in a year t (2002-2011). Zero observations are included.

Type of investor Genuine foreign Round-trip investors

Variable Abs. DepVar Per capita DepVar Abs. DepVar Per capita DepVar

Constant 13.3 (3.1)*** 6 (2.3)*** 18.4 (3.3)*** 10.3 (2.5)***

iPort 1.23 (1.2) 1.1 (0.9) .6 (1.4) .52 (1.02)

1, −tiTAX 13.3 (6.4)** 9.7 (4.5)** -6.2 (5.3) -4.9 (3.9)

iEDU 3.4 (2.3) 3.1 (1.7)* .6 (2.5) 1.4 (1.9)

iCORR 2.2 (0.8)*** 1.7 (0.6)*** 1.4 (0.8) /pv=0.11/ .98 (0.63) /pv=0.12/

1, −tiMarkPot .24 (0.11)** .2 (0.08)** -.2 (0.1)** -.12 (0.07)*

1, −tiRIR -.03 (0.01)*** -.02 (0.01)*** -.008 (0.008) -.006 (0.006)

1, −tiRES .03 (0.02) .03 (0.02)* .02 (0.02) .01 (0.02)

iDEM .46 (0.97) .3 (0.7) .5 (1.1) .41 (0.8)

1, −tiRoads -.005 (0.005) -.004 (0.004) -.003 (0.005) -.003 80.004)

iCity -1.26 (1.4) -.97 (1.1) -.05 (1.6) -.14 (1.2)

1, −tiMarket -.48 (0.5) -.43 (0.4) .23 (0.47) .2 (0.3)

1, −tiRIP -.003 (0.02) -.001 (0.02) -.07 (0.02)*** -.05 (0.01)***

1, −tiRFP -.15 (0.03)*** -.09 (0.02)*** -.04 (0.03) -.02 (0.02)

N. obs. 758 758 758 758

R-sq overall 0.46 0.45 0.33 0.31

24

Hausman test 31.06 (0.011)** 38.8 (0.001)*** --- 1823.7 (0.000)***

Note: 1) * if p < 0.10, ** if p < 0.05; *** if p < 0.01; 2) standard errors in parentheses; 3) --- - model fitted on these data fails to meet the asymptotic assumptions of the Hausman test. As we can see from the results, when zero observations are included, the positive relationship between

corruption and round-trip investment disappears. Furthermore, it becomes negative though statistically

insignificant (albeit very close to be marginally significant). The negative relationship between corruption and

genuine foreign investment becomes even stronger (considerably). In general, these results enable us to suggest

that both round-trip and genuine foreign investors choose not to invest at all into regions with very high

corruption. Such regions as a rule score low in investment potential, and are economically and socially unstable.

There are other remarkable differences with baseline estimations. First, educational background variable,

EDU, loses considerably its statistical significance. Second, we find positive and statistically significant impact

of regional tax level on genuine foreign investment. This result is unexpected. The explanation might be

connected with our measurement of the tax variable. In particular, high ratio of tax revenues to gross regional

product might also reflect intensive business activities and, hence, favourable business environment. In addition,

it may signal more favourable business environment in terms of lower share of shadow economy. Third, now we

find strong evidence of the negative relationship between investment risk and genuine foreign investment which

is expected. Finally, now we find negative relationship between surrounding market potential and round-trip

investment. This indicates that the decision of round-trip investors not to invest at all in a particular Russian

region might be also attributed to the choice of the region with the highest potential in the clusters of

neighbouring regions.

As we have mentioned in “data description” section, both round-trip and genuine foreign investment are

highly concentrated in the Moscow city. This might create bias in our estimates. Hence, to check for this, in table

8 we report our main estimation results when Moscow city is excluded.

25

Table 8 Baseline results: Panel data model with random effects

Dependent variable is natural logarithm of annual cumulative revenues in USD per capita in a Russian region i (1,…,75) in a year t (2002-2011). Moscow city is excluded.

Zero obs. are excluded Zero obs. are included

Type of investor Genuine foreign Round-trip Genuine foreign Round-trip

Variable Abs. DepVar

Per capita DepVar

Abs. DepVar Per capita DepVar

Abs. DepVar

Per capita DepVar

Abs. DepVar

Per capita DepVar

Constant 16.3 (1.1)***

8.2 (1.2)*** 19 (1.04)*** 10.7 (1.05)***

13.5 (3.1.)***

6.2 (2.3)***

18.6 (3.33)***

10.5 (2.5)***

iPort .2 (0.5) .34 (0.49) -.37 (0.42) -.23 (0.43) 1.4 (1.2) 1.2 (0.93) .82 (1.4) .67 (1)

1, −tiTAX 1.8 (1.6) 1.9 (1.6) -3.2 (1.9) -3.01 (1.9) 13.25 (2.4)**

9.6 (4.6)** -6.2 (5.4) -4.8 (4)

iEDU 1.8 (0.9)** 2.3 (0.96)** 1.9 (0.8)** 2.4 (0.83)***

2.6 (2.4) 2.6 (1.8) -.28 (2.6) .78 (2)

iCORR .6 (0.3)** .73 (0.32)** -.34 (0.27) /pv=0.22/

-.27 (0.28) /pv=0.34/

2.26 (0.77)***

1.8 (0.58)***

1.5 (0.85)* 1.1 (0.64)*

1, −tiMarkPot .07 (0.03)** .07 (0.03)** .04 (0.03) .04 (0.03) .25 (0.11)**

.2 (0.08)** -.2 (0.1)** -.11 (0.07)

1, −tiRIR -.003 (0.002)

-.002 (0.002) -.0005 (0.003)

-.0001 (0.003)

-.03 (0.01)***

-.02 (0.007)***

-.01 (0.01) -.006 (0.006)

1, −tiRES .02 (0.01)***

.02 (0.007)***

.003 (0.007) .004 (0.007)

.03 (0.02) .02 (0.02) .01 (0.02) .01 (0.02)

iDEM -.03 (0.37) -.1 (0.4) .5 (0.33) .44 (0.34) .5 (0.97) .33 (0.73) .5 (1.1) .4 (0.8)

1, −tiRoads .002 (0.002) .001 (0.002) -.0004 (0.002)

-.001 (0.002)

-.005 (0.005)

-.004 (0.004)

-.004 (0.005)

-.004 (0.004)

iCity .09 (0.54) -.23 (0.56) .4 (0.49) .22 (0.5) -1.3 (1.46) -.99 (1.1) -.27 (1.6) -.33 (1.2)

1, −tiMarket .44 (0.25)* .17 (0.26) .78 (0.25)*** .5 (0.25)* -.72 (0.78) -.6 (0.57) .32 (0.78) .28 (0.58)

1, −tiRIP -.01 (0.007)*

-.007 (0.007) -.003 (0.007) .002 (0.006)

-.004 (0.02) -.002 (0.02) -.07 (0.02)***

-.05 (0.01)***

1, −tiRFP -.04 (0.008)***

-.03 (0.008)***

-.02 (0.008)**

-.01 (0.01) -.15 (0.03)***

-.1 (0.02)***

-.04 (0.03) -.02 (0.02)

N. obs. 693 693 702 702 748 748 748 748

R-sq overall 0.60 0.48 0.58 0.47 0.45 0.45 0.31 0.31

Hausman test 8.5 (0.9) 28 (0.03)** 216.3 (0.000)***

73.3 (0.000)***

34.5 (0.005)***

40.4 (0.001)***

--- ---

Note: 1) * if p < 0.10, ** if p < 0.05; *** if p < 0.01; 2) standard errors in parentheses; 3) --- - model fitted on these data fails to meet the asymptotic assumptions of the Hausman test.

26

In general, the results do not differ much from our baseline results and the corresponding results in table 7 in this

section. However, when zero observations of the dependent variable and Moscow city are excluded, the positive

relationship between corruption and round-trip investment loses statistical significance. However, when we

estimated our model separately including City, Market, RIP or RFP variables (as in tables 3-6, columns M2-M5),

in most cases this positive relationship remains statistically significant.

6. CONCLUSIONS

This paper sheds light on a virtually unexplored phenomenon: roundtrip investment from Russia to offshore

financial centres and back to Russia. Our overview of statistics on Russia’s outward and inward foreign

investment shows that offshore financial centres, such as Cyprus and British Virgin Islands, are both key

destinations of Russian outward FDI, and main sources of inward FDI to Russia. This provides support to the

existence of round-tripping phenomenon of Russian capital via offshore financial centres back to Russia in the

form of FDI.

Conceptually, we anchor our analysis in the existing international business literature on the role of OFCs

in FDI from and to emerging economies, in addition to which we apply the concept of OFCs as economic spaces,

rooted in economic geography. As regards to outward direction of round-tripping, we argue that in the case of

Russia, the OFCs representing distinctive regulatory, fiscal and secrecy spaces provide strong motivations for

transferring capital to them. This argument is supported by the notion of institutional escape as a key driver for

outward FDI from emerging economies. In particular, Russian capital would seek an ‘escape valve’ from

arbitrary regulation and heavy taxation, and use the secrecy space as a shelter from opportunistic and corrupt

Russian authorities. On the other hand, we maintain that taken the severity of corruption problem in Russia, the

secrecy space in combination with the financial instruments provided by OFCs is used also to launder the

proceeds of corruption generated in Russia.

27

As for the outward investment from Russia to OFCs, we propose that the inward component of round-

tripping investment would be institutionally-driven as well. We base our argumentation to the construct of

institutional arbitrage, which refers to the opportunity for exploiting the differences between two institutional

environments in FDI. In the case of licit financial flows between Russia and OFCs, we maintain that the

channelling of investment through OFCs would put Russian firms, established in OFCs, into superior

competitive position on the Russian market. On the one hand, the location at an OFC with its advanced financial

infrastructure and no or low taxes establishes a competitive advantage against purely domestic firms. On the

other, the Russian firm’s local knowledge and networks put them into a stronger position vis-à-vis genuine

foreign investors, which lack such assets. Similarly, we argue that the institutional arbitrage concept can be

applied also to such inward investment from OFCs to Russia, which has illicit origin. In particular, we refer to

the proceeds of corruption, which taken the severity of corruption problem in Russia can be expected to form a

substantial component of illicit funds channelled through the OFCs. First, corrupt Russian officials would

benefit from the secrecy and sophisticated financial instruments hosted by OFCs, which provide the opportunity

to launder the proceeds of corruption. Second, taken the nexus of business and the state in Russia, public sector

officials have the access to profitable investment projects in Russia, which motivates the re-investment of the

capital back to Russia.

In our empirical analysis we apply firm-level data to study differences in determinants of FDI from

OFCs and other countries across Russian regions. This enables us to identify dominant locational factors, which

attract round-trip and genuine FDI to Russia, and differences in the behaviour of these two groups of investors.

In particular, we explore the argument whether there is a linkage between corruption and round-trip investment.

First, we find that traditional FDI determinants such as market and human resource potential of the region are

equally important determinants of round-trip and genuine FDI in Russia.

Second, we find opposite effects of regional corruption on round-trip and genuine FDI into Russia, i.e.

round-trip investors tend to invest into more corrupt Russian regions, whereas genuine foreign ones prefer less

corrupt regions. This result gives support for the proposition of laundering the proceeds of corruption via round-

trip investment (in particular it’s high significance for the combined financial and real estate sector). It further

28

indicates that round-trip investors may indeed be better equipped to cope with institutional deficiencies, e.g.,

corruption (in particular, the result`s significance in manufacturing sector). Finally, Russian businessmen might

utilize round-trip investment as a mean for securing the secrecy of an investor`s identity from corrupt regional

authorities.

Third, we show that the positive relationship between corruption and round-trip investment can be partly

(but not fully) attributed to high concentration of round-trip investment in Moscow city which has rather high

corruption level. Our results also enable us to preliminary conclude that both round-trip and genuine foreign

investors choose not to invest at all into Russian regions with very high corruption. These are regions, which are

socially and politically unstable, and have limited investment potential.

Finally, to find potential support for our institutional arbitrage argument that round-trip investors would

be in better competitive position vis-á-vis other foreign investors, we also looked at the profitability of the

investment. Preliminary, we found that in average profitability is higher for offshore investors than for genuine

foreign ones (see Appendix 3).

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Appendix 1: Tax Havens, OFC Score (from Palan et al. 2010)

>75% Agreement

11 Malta (EU)*; Bahamas*; Bermuda**; Cayman Islands**; Guernsey**; Jersey**; Panama.

10 Barbados*; Isle of Man**; Cyprus (EU)*; Liechtenstein; Netherlands Antilles (NL); Vanuatu*; Virgin Islands, British**;

9 Singapore*; Switzerland (OECD); Hong Kong (CN)*; Gibraltar (EU)**; St Vincent and the Grenadines*; Turks and Caicos**.

>50% Agreement

8 Antigua and Barbuda*; Cook Islands (NZ); Grenada*; Ireland (OECD, EU); Luxembourg (OECD, EU); Monaco; St. Kitts and Nevis*; Belize*; Nauru.

7 Andorra; Anguilla**; Marshall Islands Republic of (US); Mauritius*; Bahrain, Kingdom of*; Costa Rica.

6 Aruba (NL); Samoa; Seychelles*; St. Lucia*; Dominica*; Liberia.

>25% Agreement

5 Lebanon; Niue (NZ).

4 Macao (CN) ; Montserrat**; Malaysia*; Maldives*.

3 United Kingdom (OECD, EU); Brunei Darussalam*.

** Current UK Overseas Territories and Crown Dependencies * Former UK colonies (post WWII). Superscript abbreviations indicate current territories/dependencies of other states.

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Appendix 2: Descriptive statistics and correlation matrix

Table A2_1: Descriptive statistics Variable Obs Mean Std. Dev. Min Max DV_real 705 17,32 2,47 7,48 23,97 DV_ofc 714 18,33 2,50 4,68 25,83 DV_real_pc 705 10,06 2,21 1,19 15,14 DV_ofc_pc 714 11,06 2,19 -0,63 16,47 Port 760 0,21 0,41 0,00 1,00 Tax 759 0,11 0,03 0,02 0,46 Edu 760 0,57 0,22 -0,21 1,31 Corr 760 2,76 0,71 1,00 5,00 MarkPot 760 1,26 3,26 -5,27 12,22 RIR 760 40,08 23,51 1,00 85,00 RES 760 42,98 23,76 1,00 89,00 Dem 760 2,96 0,63 1,67 4,67 Roads 760 146,21 107,46 1,51 623,50 City 760 0,21 0,41 0,00 1,00 Market 759 0,00 1,00 -0,72 7,29 RIP 760 39,60 22,75 1,00 81,00 RFP 760 40,55 22,48 1,00 83,00

Table A2_2: Correlation matrix Var DV_real DV_ofc DV_real_pc DV_ofc_pc Port Tax Edu Corr MarkPot RIR RES Dem Roads City Market RIP RFP

DV_real 1,00

DV_ofc 0,57 1,00

DV_real_pc 0,95 0,43 1,00

DV_ofc_pc 0,48 0,95 0,44 1,00

Port 0,00 0,04 0,03 0,07 1,00

Tax 0,01 -0,06 0,01 -0,07 0,04 1,00

Edu 0,30 0,32 0,29 0,32 0,37 0,21 1,00

Corr 0,08 -0,11 0,15 -0,06 -0,22 0,04 0,02 1,00

MarkPot 0,30 0,21 0,32 0,23 -0,30 -0,07 -0,05 0,10 1,00

RIR -0,38 -0,20 -0,28 -0,08 0,19 -0,13 -0,03 -0,05 -0,21 1,00

RES 0,17 -0,04 0,22 -0,01 -0,32 0,03 -0,11 0,13 0,43 -0,34 1,00

Dem 0,29 0,30 0,22 0,24 0,14 0,05 0,38 0,39 -0,12 0,01 -0,09 1,00

Roads 0,47 0,24 0,38 0,14 -0,20 0,07 0,03 -0,01 0,37 -0,49 0,54 -0,02 1,00

City 0,41 0,45 0,23 0,28 0,04 0,09 0,25 0,00 -0,02 -0,25 -0,07 0,45 0,22 1,00

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Market 0,55 0,57 0,35 0,39 -0,01 0,09 0,40 -0,19 0,09 -0,30 0,10 0,25 0,53 0,55 1,00

RIP -0,51 -0,56 -0,25 -0,32 -0,06 -0,03 -0,31 0,17 -0,01 0,38 0,13 -0,42 -0,31 -0,65 -0,61 1,00

RFP -0,46 -0,54 -0,19 -0,29 -0,11 -0,06 -0,30 0,17 0,06 0,32 0,30 -0,38 -0,16 -0,65 -0,62 0,89 1,00

Note: correlation coefficients higher than 0.4 are denoted by bold and underlined.

Appendix 3: Firms profitability

Table A3_1: Profitability of firms

Genuine foreign

investors

Offshore investors

Total

Average revenues, mil USD 46.8 113.8

Average profits, mil USD 3 12.7

Ratio of average profits to average revenues, % 6.4% 11.2%

Manufacturing


Average profits, mil USD 4.6 14.3

Ratio of average profits to average revenues, % 8% 9.6%

Trade




Financial and real estate sector




Note: average revenues and profits are calculated as simple averages of cumulative revenues and profits during

the period of 2002-2011 across firms in the sample (in corresponding groups).

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