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EDUCATIONAL INSTITUTION HIGHER EDUCATION

"National Research University

"HIGH SCHOOL OF ECONOMICS" ICEF

MASTER'S DISSERTATION

towards the preparation of "Economy"

Ownership structure and firm performance in emerging economies: the role of offshore locations

Student: Kabakov Oksana S.

Scientific adviser: academic degree

Marie-Anne Betschinger, Ph.D., assistant professor

Reviewer: academic degree

Carsten Sprenger, PhD, assistant professor

Moscow 2016



Table of Contents

Introduction

1. Literature Review on Ownership-Performance Relationship

1.1 Influence of different types of owners on financial performance: theory and evidence

1.2 Offshore ownership on financial performance: theoretical foundations and empirical evidence

2. Methodology

2.1 Data sources and method of collection

2.2 Ownership structure indicators and special characteristics of ownership in Russia

2.3 Firm's performance variables and controls

2.4 Model Specification

3. Analysis and results

3.1 Regression analysis of ownership-performance relationship for all firms

3.2 Regression analysis for firms with offshores in ownership structure

Conclusions

References

Appendices

Annotations



Introduction

Corporate governance policy includes a big number of aspects, that is the choice of capital structure, short- and long- term operational strategy, merging and acquisition activities as well as the choice of ownership structure. The letter is of particular importance for a company because the choice of ownership structure determines who will be the decision-makers of company's strategies. Thus, one of the ways to access the effectiveness of corporate governance policy is to research the link between ownership structure and financial performance. The accumulated theoretical knowledge on the impact of ownership structure on the effectiveness of the companies' activities allows us to consider it in the context of such issues as the concentration of ownership, identification of the type of owner, the nature of the relationship between ownership structure and performance of the companies, the problem of endogeneity of ownership structure and reverse causality between ownership structure and financial performance. (Demsetz and Villalonga 2001; Tsionas, Merikas, and Merika 2012; Coles, Lemmon, and Felix Meschke 2012; Huang and Boateng 2013)

Additionally, ownership structure has got special attention in the framework of transition economies like CEE, Baltics, Asian markets and CES including Russia, in particular. (Bevan, Estrin, and Schaffer 1999; Earle 1999; Lin et al. 2011) This is connected with the fact that one of the major transition processes is restructuring and privatization which leads to the formation of distinctive ownership structures in these countries. Special features of corporate ownership in transition economies include high share of institutional owners, concentrated ownership as well as underdeveloped standards of corporate information disclosure even for public companies while private companies practically do not have any standards of disclosure.(Chernykh, 2008) Thus, in current work we concentrate mainly on the influence of a particular type of ownership, namely, intermediate and ultimate parental holdings by offshore companies.

We study the link between financial performance measured by Returns on Assets and Returns on Sales and offshore ownership indicators such as intermediate and ultimate offshore holder dummy, number of intermediate offshore holders, particular offshore countries in ownership structure with special attention to their transparency characteristics, using the dataset for 151 Russian listed companies. Moreover, in current paper we address several econometric problems arising in the research agenda of this type, endogeneity of ownership and performance, in particular.

Using the dataset of 151 biggest (according to the average turnover) listed Russian companies for 2004-2013 years, we first, examine and refresh the findings of previous research devoted to the revealing of ownership structure in Russia (see Chernykh). We show that although there is a high level of private non-anonymous ultimate owners compared to anonymous ultimate ownership, there is a high number of offshore parental companies in the intermediate structure. Secondly, we found evidence of negative significant influence of both presence of offshores in ownership structure and their total number on ROA and ROS indicators of financial performance, which is explained by the arising of agency costs due to the asymmetric information among market agents towards the transparency of company's operational and financial performance. However, the number of countries in which the offshores are located influences company's performance significantly and positively, which may be reasoned by diversification as well as special features of peculiar offshore countries. We then continue our analysis of country's effects on financial performance for the sub-sample of only offshore-owned companies. We find out that the intransparency of country of offshore origin measured by Financial Secrecy Ratio negatively correlated with ROA and ROE. This means that, indeed, transparency makes difference even when applied to naturally in- or low- transparent territories. This have contribution to both academic literature devoted to the research of ownership-performance field as well as to practical questions such as de-offshorization policies and various tax-agreements.

The paper is structured in 4 parts. To make the statement about the importance and relevance of ownership-performance relationship we first study the literature in order to find out theoretical and empirical evidence. Next part covers methodology, where the data, propositions and model specification are discussed. It is followed by the analysis and results section in which we show the estimated models for ownership-performance relationship with the emphasis on offshore ownership as well as country specifics for offshore-owned companies. In last part we discuss the conclusions, limitations and future directions of the research.



1. Literature Review on Ownership-Performance Relationship

Since the corporate ownership structure questions were first brought up to the academic literature by Berle and co-authors (1933) who discussed the effects of separation between ownership and control, the research agenda towards the ownership structure has significantly grown.

The first bunch of literature studies the relationship between ownership dispersion or, which attracted much attention of scholars recently, concentration on firm financial results. It was shown that ownership dispersion has negative influence on financial efficiency (Monsen, Chiu, and Cooley 1968), while later studies on the ownership concentration showed that there is positive effect explained by the low likelihood of conflict between different shareholders and other benefits of control if ownership is concentrated (Claessens and Djankov 1999; Tsionas, Merikas, and Merika 2012), not significant effects of concentrated ownership on performance (La Porta, Lopez-De-Silanes, and Shleifer 1999) or even negative effects because of the excessive monitoring. (Buck et al. 1999)

1.1 Influence of different types of owners on financial performance: theory and evidence

Another direction of analysis of ownership-performance relationship is concentrated on the influence of particular types of blockholders, such as institutional, state, insider (CEO, Directors) or family (founders), foreign and anonymous owners. These effects are theoretically explained by the difference in objective functions, treatment of shareholder value growth strategy, costs of control and monitoring, risk aversion, initial wealth for various types of blockholders. (Xu and Wang, 1997)

Cornett and co-authors (2007) find that there is a significant positive relation of the percentage of institutional stock ownership and the number of institutional stockholders on the operating cash flow returns. Fung and Tsai (2012) also find positive significant effect of institutional owners using the sample of Canadian firms and explain this result by institutional firm's advisory and monitoring capabilities.

Chhibber and Majumdar (1998) show that there is negative significant influence of state ownership on ROA and ROS performance measures of Indian listed firms. At the same time, analyzing the performance of Chinese real-estate firms, Huang and Boateng (2013) show the evidence that state ownership has significant negative effect on performance in pre-boom stage and positive influence in boom period. The effect may be explained by the bureaucracy and political interventions. Authors also include other indicators of ownership, such as insiders (significant positive effect), legal person (significant positive effect) ownership and concentration of ownership (significant positive influence).

Towards family control, there are several main findings connected with the role of the family in the firm's management practices. Villalonga and Amit (2006) shows on the USA that there is significant positive effect of family control only when the CEO or Chairman of the firm is its founder, and the results are supported by European firms data as well. (Barontini and Caprio 2006) Also, as it is revealed from the literature analysis, it can be noted that family ownership is a peculiar characteristic of Asian ownership structures. (Claessens, Djankov, and Lang 1999; Claessens, Djankov, and Lang 2000; Yeh, Lee, and Woidtke 2001) This is mainly explained by historical and cultural features of these countries.

Insider management ownership, in turn, has non-monotonic influence on corporate performance as shown by previous research. (Morck, Shleifer, and Vishny 1988; Chen, Hexter, and Hu 1993) There are two main theoretical models which explain the relationship between insiders ownership and performance. The first one is introduced by Jensen and Meckling (1976) who suggest that insider ownership by managers leads to the private benefits extraction which harms the firm's value. This is connected with managers' preferences of their own benefits over the maximization of corporate value goals, which brings so-called agency costs. Secondly, Brealey, Leland, and Pyle (1977) follow the idea of signaling effects of ownership structure arguing that insider's ownership can be an informative sign of firm's quality.

As for the foreign ownership, it is argued that this type of owners has a dramatically important role in the financial performance of firms from emerging and transition economies. There is an empirical evidence that foreign ownership influences firm performance positively. For instance, Chhibber and Majumdar (1999) show evidence that for Indian firms that conditioning on several controls as property rights, there is a positive significant influence on ROA and ROS values. Moreover, Djankov and Murrell (2002) analyzing the relationship between foreign ownership, productivity and restructuring efficiency claim that there is indeed positive influence, especially for transitioning markets. The main reason for this relationship is that foreign owners are superior to domestic ones in managerial effectiveness and monitoring practices.

1.2 Offshore ownership on financial performance: theoretical foundations and empirical evidence

offshore ownership economy

However, existing research on the influence of foreign anonymous and, in particular, offshore ownership (offshore may be defined as the company registered in country with lower tax levels and softer regime of state monitoring of companies' activities) on firm financial performance is not as extant, as for the `true' foreign ownership and other types of beneficiaries. The main findings are made by Mueller, Dietl, and Peev (2003) (statistically insignificant negative influence of offshore ownership on profitability for Bulgarian firms) and Gugler, Ivanova, and Zechner (2014) who found that there is a significant negative influence of anonymous (offshore) ownership on Q Tobin value for firms from 11 CEE countries.

From theoretical point of view, such effect may be explained similarly to the insiders' and other ownership types, namely, through agency costs and asymmetry of information and signaling. These effects arise from the nature of anonymous ownership in offshores which are often characterized by low transparency, which has both negative (tax avoidance, money laundering) and positive effects (property rights protection) which may give an ambiguous effect on performance of offshore-owned companies. In more details, the negative effects of offshore may include tax saving purposes, entrenchment by controlling owners over non-controlling owners, corruption, fraud and money laundering with avoiding of administrative and law prosecution. Offshore ownership may also hide the disparity of voting and cash flow rights, insider ownership, affiliation to business groups and particular personalities. Still, there are some advantageous features of offshore locations like protection of property rights, mitigation of political risks and resolving lack of trust between market agents, mitigation of financial risks, asset protection and conflicts of interest (affiliation with some companies, hide sources of capital).

The effects of foreign and offshore ownership, similar to the family ownership in Asian countries, thus, may be considered to be a special feature of emerging and transition economies and, particularly, for Russia as it was shown by Chernykh, (2008) who collected ownership data for 145 Russian firms during 2000-2002 years that there is a high share of offshore ownership.. Still, there is little empirical and theoretical research of relationship between offshore ownership structure and firm's performance for Russian companies, while most research concentrate on other types of blockholders: Earle (1999) studied the influence of private ownership, Buck and co-authors (1999) analyzed insider ownership while, for instance, Guriev and Rachinsky (2005) studied influence of oligarch ownership on firm's productivity. So, there is a gap in both theoretical and empirical literature on the topic, despite the high importance of offshore locations in transition and emerging economies as suggested by the ownership structure patterns in these countries.

Still, there is some empirical evidence on the topic. In 2013, company Gradient Alpha Consulting made a survey as part of the meeting of financial market experts and top executives of biggest Russian companies (i.e. Lukoil, Sistema, TMK, etc.) devoted to the `The Offshore Havens: The new rules of the game in Russia and across the Globe' topic. The main question of this survey was the reasons of using offshore jurisdictions by Russian companies and the answers revealed the following pattern: 86% of experts consider the property rights protection as the main reason, second most popular choice is confidentiality of business owner, tax optimization gets the third place (58%), while the criminal reasons such as money laundering and fraud are only forth (45%), the least popular answers are short-term speculations (28%) and inheritance issues (8%) (based on 140 firm's managers and owners responses). (Gagarin 2013) The latter supports the idea that the influence of offshore ownership on financial performance can be indeed ambiguous for Russian companies as it is unclear whether the confessions of market players in their intentions to use offshore structures purely for their property rights is true and it overweigh the asymmetry of agency costs connected with negative instances of using offshore locations in ownership structures.



2. Methodology

2.1 Data sources and method of collection

In order to reveal the influence of ownership structure we manually collected the ownership data for 200 Russian biggest companies according to average turnover for 2004-2013 years which was later reduced to 151 due to absence of data and outliers.

The distribution of companies in sample by industry is presented in the Table 1, where the Manufacturing industry cluster is prevailing with the per cent rate of 54%.

In general, the industry distribution corresponds to the whole economy with some bias towards manufacturing and follows the industrial pattern of top-400 biggest companies rating “Raexpert-400” (2013).

Table 1 Sample distribution by industry (NACE coding)

Industry	Freq. in sample	%	Population*
Agriculture, Forestry, Fishing and Hunting	2	1%	14.42%
Mining, Quarrying, and Oil and Gas Extraction	24	16%	12%
Construction	19	13%	11.55%
Manufacturing	82	54%	24.24%
Wholesale Trade	9	6%	26.77%
Retail Trade	3	2%	2%
Information	4	3%	3%
Real Estate and Rental and Leasing	2	1%	8.72%
Professional, Scientific, and Technical Services	6	4%	2.32%
Total	151	100%	-

*Source: Russian Statistic Agency “Rosstat” URL:rosstat.ru

Following the approaches suggested in previous research (La Porta, Lopez-De-Silanes, and Shleifer 1999; Chernykh 2008; Almeida et al. 2011) ownership data was collected manually using company's quarterly and annual reports (or lists of affiliated personalities when reports were not available) which are in most cases disclosed at Center of corporate financial information web-site (http://www.e-disclosure.ru) and, in some cases, only on corporate web-sites.

The reporting of ownership information is regulated by Russian legislation of public companies under which these entities are obliged to provide information on direct beneficiaries who own at least 5% of ordinary shares or capital.

The data is provided through quarterly reports in sections 6.2 (current ownership structure at 20% threshold) and 6.5 (historical changes of ownership structure and share amount at 5% threshold).

However, there is still not considered to be transparent disclosure as this reports provide only first or, maximum second level (for public direct owners) of ownership.

So, in order to reveal the whole ownership structure and ultimate owners.

We thus turned to either the quarterly reports again for public companies, SPARK Interfax data for private companies (if possible) and public information for other companies, especially offshores.

There is a big amount of private investigations of ownership structures of biggest Russian companies made by journalists from Kommersant, RBK, Forbes and other business journals as well as private web-sites like compromat.ru or the informational leaks like Panama papers case (for example, offshoreleaks.icij.org/ portal).

As an example of how this worked see Figure 1, where the ownership structure for the Electrotsink company ownership structure in 2013 year is presented.

For financial indicators included in empirical models such as sales, total and current assets, equity, debt as well as companies' characteristics like age and industrial affiliation we used Bureau van Dijk Ruslana database.



Figure 1 Ownership structure data collection example

2.2 Ownership structure indicators and special characteristics of ownership in Russia

Based on previous research (La Porta, Lopez-De-Silanes, and Shleifer, 1999; Chernykh, 2008) we chose a set of ownership variables of interest, both ultimate and intermediate.

In terms of ultimate ownership, we calculated the ultimate owner's control and cashflow rights in order to get the ownership concentration rate (at 25 % threshold), share of unreported ultimate ownership and identity of ultimate owner(s) (see Table 2) which can be interesting for us as controls of ownership.



Table 2 Ultimate ownership indicators

Indicator (at 25% threshold)	Categories
Ultimate owner identity	State
	Private known ultimate owner
	Private offshore/nominee ultimate owner
	Dispersed firm
Ownership concentration of ultimate owner	Total share of voting rights
Multiple ultimate owners	Number of ultimate owners
Unreported ownership	Share of unreported ultimate ownership

In the research sample, 18% of companies are ultimately owned by Government (either State, Regional or Municipal), 38% - by known private owners, 16% ultimate owners are anonymous that is either offshore or nominees, while the rest of the firms (27%) are widely held at 25% control rights threshold. Average ownership concentration based on voting rights is 40%. The unreported ultimate ownership average rate is 28%.

In order to catch the effects of offshore ownership, we reveal the intermediate ownership structure of companies in the sample as well.

As it is stated by Russian financial market experts, one of the peculiarities of offshore ownership in Russia is that companies register in tax havens parent holdings owning assets in home country in comparison with general international tendency to move firm's subsidiaries to offshore locations.

According to the most recent analytics data, approximately 40% of Russian largest companies were owned by entities registered in offshore zones. (Gagarin, 2013)

The distribution of offshore ownership in the collected data corresponds to this estimation as among 151 companies in the sample 41% of companies has offshore owners in the intermediate ownership structure.

On the fuzziness, the average number of total offshore owners is 1.61 with maximum of 16 entities. In year-firm dimension, for the companies which have offshore owners, 64% have an offshore parental companies from single country, 29% - from 2 countries and other from 3 to 5 countries.

Table 3 Intermediate ownership structure indicators

Indicator	Category
Offshore ownership There is an ongoing update on the classification of offshore countries, which is mainly concentrated on tax issues and, thus, addressed as tax havens issued by Organisation for Economic Co-operation and Development on the annual basis. Moreover, there are some other sources of classification sources, which are analyzed by Gravelle (2009). The author combines different views on offshore locations definitions and classifications and provides the exhaustive list of tax havens, which will be used for current research goals. (see Appendix 1)
Extent of offshore ownership	Dummy of offshore usage
Fuzzyness of offshore ownership	Number of offshore owners
	Number of offshore countries in the structure
Offshore country	Country of origin of the offshore with highest ownership share
Foreign and Nominee ownership
Domestic nominee	Dummy
Foreign nominee	Dummy
Foreign nominee country	Country of origin of foreign nominee
Insider Ownership	Dummy if owned by the insider (Management/Board of directors)
Difference in owner's countries of origin	Financial Secrecy Index Financial Secrecy Index provides ranking for a group of jurisdictions based on their transparency and offshore activities. The index is a composite value of set of 15 indicators including banking secrecy, corporate ownership and accounts secrecy, tax evasion, etc. The countries which are less transparent get the high FSI index value, while less secretive jurisdictions have a low rank. See Appendix 2 for the rankings and URL: http://www.financialsecrecyindex.com for more details

There are 13 offshore countries from which the company's in the sample have intermediate owners. The most frequent choice is Cyprus (more than 80%), the second popular are British Virgin Islands, followed by Bermuda. The other countries are Gibraltar, Cayman Islands, Hong Kong, Ireland, Luxemburg, Isle of Man, Panama, Saint Kitts and Nevis, Liechtenstein and Belize.

Moreover, 15% of companies in the sample are owned by foreign nominees, while 24% - by insiders.

Following the empirical evidence presented in the literature review and theoretical background section, we propose that there is negative influence of intermediate offshore ownership on a firm's p

erformance. This is mainly connected with the fact that presence of in- or low-transparent, in particular, offshore beneficiaries causes the problem of asymmetric information among market agents driven by such negative offshore ownership instances as corruption, insider trading, tax avoidance, hiding capital and profits of the company, etc. Previously, it was shown by Gugler, Ivanova and Zechner (2014) that there is indeed negative significant influence of anonymous (offshore and nominees) ultimate ownership on companies financial performance measured by Q-Tobin indicator.

However, the effect of offshore ownership may be different when we look at intermediate, not ultimate, structure. In this case, the offshore ownership is not completely anonymous anymore as the ultimate owner is known, it can have been revealed through official disclosure or through private investigations or information leaks. This means that the asymmetry of information problem might be less severe and positive effects of owning company through entities registered in offshore countries could result from such as a better property rights protection or better financial infrastructure. Thus, the overall effect of (intermediate or ultimate) offshore ownership on the financial performance of a company may potentially be positive if positive factors outweigh negative ones listed above.

Some offshore ownership structures can be more likely used for “cheating”. With an increased fuzziness of the intermediate ownership structure, such as when companies use a higher number of offshore companies or have them located in a number of different offshore countries, it is easier for firms to hide activities and take out corporate belongings, assets or profits for the ultimate owners' own benefit. This could be expected to translate into a lower performance of the focal firm. Moreover, the country where the offshore company is registered could be crucial. What can also matter is the country of origin of offshore country as in case of foreign ownership it was shown that for US firms there is a sufficient significant positive influence of foreign investments from related countries on productivity. (Ford, Rork, and Elmslie 2008)

For the offshores, positive and negative effects may be also be driven by the country of origin. If firms have (intermediate) offshore beneficiaries registered in offshore locations which are considered relatively transparent in financial matters (measured by Financial Secrecy Ratio), they should find it relatively difficult to use these locations for hiding their activities.

Other advantages, such as better property rights protection or a better financial infrastructure in these locations than at home could have been of larger importance for the offshore ownership choice. This could potentially enable positive effects of offshore ownership on the financial results of the companies.

2.3 Firm's performance variables and controls

In this part we discuss what are the appropriate measures of financial performance in the framework of firm performance - offshore ownership relationship analysis.

As the review of literature showed, in most papers Q Tobin is chosen as financial performance indicator while analyzing this empirical relationship. (Himmelberg, Hubbard, and Palia, 1999; Demsetz and Villalonga, 2001; Gugler, Ivanova, and Zechner, 2014)

This indicator may be calculated as sum of market capitalization of the firm and book value of total debt divided by book value of assets. Its advantage over pure accounting measures is that it incorporates the market agents' judgement about the efficiency of all strategic corporate governance decisions of the company and, in case of effective markets assumptions, should reflect the fair market value of the company. However, there are several problems connected with using this indicator, especially, in case of companies from developing markets.

Firstly, Q Tobin is a forward-looking indicator meaning that it reflects the future growth opportunities of the company while as it discussed by Demsetz and Villalonga (2001) it is much more relevant to concentrate on previous performance of past or present corporate governance rather then estimate outcomes of future strategy when accessing the effects of ownership on firm's financial performance. Secondly, as Q Tobin calculation relies on market data, it is highly exposed to investor mood towards company and market as a whole which may not always be rational. Thirdly, and most importantly, emerging markets suffer from low accounting standards, characterized by underdeveloped stock markets and low informational efficiency making it difficult to use this indicator in transition economies context. (Bevan, Estrin, and Schaffer 1999)

Another market-based indicators of performance used in performance-ownership empirical analysis are share price (Kirchmaier and Grant, 2005) and firm's market value (Lemmon and Lins, 2003) which, however, obviously suffer from the same problems as Tobin's Q indicator.

Alternatively, authors employ accounting-based indicators of firm financial performance while accessing the influence of ownership structure in transition markets. Fir example, Earle (1999) used labor productivity as indicator of performance, productivity proxied by added value (Gorodnichenko and Grygorenko, 2008), total factor productivity (Peter, Svejnar, and Terrell 2012), different growth measures, or either try several indicators of financial performance as dependent variables in their analysis (Kouznetsov and Muravyev 2001).

Moreover, as far as the literature analysis shows, the most common and most suitable indicator of firm's performance for the analysis in the context of emerging economies is profitability (Kocenda and Svejnar, 2002).

Performance, in turn, may be measured through a set of financial ratios which are Returns on Equity (ROE), which indicates the efficiency of the firm's equity to generate earnings, calculated as net income divided by total equity Returns on Assets (ROA), capturing the efficiency of the firm's assets to generate profits, calculated as net income divided by total value of assets operating income margin (Returns on Sales, ROS), working as indicator of profitability as well as growth opportunities and operational efficiency calculated as ratio of net income before taxes and interest (EBIT or Operating Income) and total value of sales.

We chose ROS and ROA as the main financial performance indicators to include in the model as dependent variable as, firstly, it was noted by market experts that special feature of offshore usage in Russia is transfer of of revenues and assets rather than common worldwide practice to transfer profits. (Gagarin, 2013) So, we will use the ratios which capture these indicators and may potentially be influenced by offshore ownership. Moreover, ROS and ROA were successfully employed in the analysis of relationship between concentration and type of ownership and performance, which showed the significant positive influence of foreign industrial parental companies, and other research. (Hanousek, Kocenda, and Svejnar 2004; P. Chhibber and Majumdar 1998; P. K. Chhibber and Majumdar 1999)

Additionally, it is also important to discuss which additional variables other than ownership structure characteristics may influence performance and, thus, should be included in regression analysis as controls. Firm-specific characteristics other than ownership structure which may potentially influence financial performance are firm size, liquidity ratio, leverage and intangibles indicators, as well as company age and industrial affiliation.

Firm size is measured as logarithm of total assets in constant prices. Due to the potential of larger firm size to reduce the risk of bankruptcy, economies of scale and access to investment projects which demand more funds and at the same time have more potential to generate profits, this indicator may have positive influence on profitability. (Brailsford and Pua, 2002)

At the same time, increase in size lead to the increasing of agent and monitoring costs which lead to the decrease in operational efficiency of a company. (Dhawan 2001) Thus, we suggest that firm size has negative effect on the performance, which is may be neglected by the increase of financial stability and access to better due to growth in size.

Liquidity measures the efficiency of asset management. We use current liquidity ratio calculated as current assets divided by current liabilities.

It was empirically shown that there is a significant negative relationship between profitability and liquidity level explained by the avoidance of risk to be not able to cover short-term liabilities. (Abuzar, 2004)

Leverage indicates the relationship between total debt and total assets. Together with firm's size it may have both positive and negative effects. The positive effect is driven by the access to additional investment opportunities, while negative is explained by the risk of bankruptcy. We additionally include company's age as a proxy of life-cycle stage, industry in Russia there might be some biases in performance towards particular industry types as Oil and Manufacturing.

We also include year dummies as controls in order to capture year fixed effects which are relevant because of the changes in economic situation during the sample period (2004-2007 - growth, 2008-2010 - crisis, 2011-2013 - recovery) as well as the increase in quality of available data including ownership.

The descriptive statistics on the financial variables for our sample of 151 Russian companies for 2004-2013 years are the following. The average age of firms in the sample is 42 years, with maximum of 274-year history. Average number of employees in sample companies is 6065. ROA is ranged in -0.62 to 14.45 value with the average of 0.1563, while ROE has minimum of -1.32 and maximum of 0.9 with average 0.12.

The average leverage in the sample is 2.65 meaning that on average companies in the sample have a debt which is twice more high than assets. Average level of liquidity is 2.24 and average logarithm of assets in constant 2004 year prices (i.e. measure of Size) is 10.6.

2.4 Model Specification

The model specification for analyzing the relationship between ownership structure is the following:



, (1)

where is a set of firm control characteristics, is a set of ownership variables including offshore ownership (offshore ownership dummy, number of offshores, number of offshore countries and controls like state ownership dummy and share of undisclosed ultimate ownership), is a set of year dummies, all discussed above, is an unobservable term, , and are vectors of unknown estimated parameters.

The baseline techniques for model estimation of panel data are fixed-effects and random effects model. Both methods are usually superlative to pooled OLS regression estimates which are usually suffer from bias and inconsistency because of the unobserved heterogeneity in data.

When estimating using fixed-effects in case of financial performance regression, there is an inference that there is a random variable for each company in the sample which may be correlated with observed independent variables in the model. Moreover, there are potentially some fixed time effects in financial performance regression models.

Nevertheless, these models may not be the best in case of performance ownership relationship analysis as there is a potential problem of reversed causality between performance and ownership structure connected with the fact that possession of share in firm's voting and cash-flow rights by particular intermediate and ultimate beneficiaries may not be exogenous and conditioned on particular characteristics of a firm such as financial indicators, for instance. Thus, fixed-effects technique may not be the most appropriate one in this case as it does not allow us to solve such problem.

Although very first studies on the relationship between ownership and performance does not consider potential problems of reverse causality and endogeneity in more recent studies these problems are considered crucial for the reliability of the model and its results.

Demsetz and Villalonga (2001) and (Earle and Estrin 1997) discuss in details the endogeneity problem while studying the privatization impact on firm's performance, the endogeneity issue in insider ownership framework is discussed in Himmelberg et. al (1999) and Palia (2001).

There are a few approaches of dealing with this issue which are suggested in the existing research devoted to the ownership-performance relationship analysis. Tsionas, Merikas, and Merika (2012) used 2-stage least-square method. Another way is binary logistic regression (Mueller et. al., 2003). Claessens and Djankov (1999) and Hanousek, Kocenda, and Svejnar (2004) utilize IV-GMM approach. In their papers, authors study the effects of ownership in transition economies with concentration on privatization, they claim that pre-privatization firm characteristics such as asset value, number of shares, industry, sales, employment and other indicators are appropriate instruments.

As for the instrumental variables to include in the model estimation, previous research showed that ownership structure may be instrumented by pre-privatization indicators such as sales growth, number of employees and date of privatization. (Hanousek, Kocenda, and Svejnar 2004)

Following the last methodology, another group of authors suggest that one more appropriate way of dealing with endogeneity issues in ownership-performance research is dynamic panel GMM technique proposed by Arellano and Bond (1991). (Gedajlovic and Shapiro 2002; de Miguel, Pindado, and de la Torre 2004) Firstly, it allows to include the lagged performance variable into the model, which may significantly influence current level , secondly, it incorporates IV-GMM features with the possibility to use lags of variables in the model as instruments. As we lack this data due non-availability in open sources and databases, we turn to the Arellano-Bond model estimation due to the feature discussed above.

Another important issue is the exogeneity of firm-specific financial indicators included in model as regressors. Following the methodology suggested by Barbosa and Louri (2005), we will consider these control variables to be predetermined, however, we will take into account that they are not strictly endogenous while specifying the Arellano Bond estimation procedure.

In this case we will estimate the following model:

(2)

where is lagged performance, is a set of firm control characteristics, is a set of ownership variables including offshore ownership (offshore ownership dummy, number of offshores, number of offshore countries and controls like state ownership dummy and share of undisclosed ultimate ownership), is a set of year dummies, all discussed above, is an unobservable term, , and are vectors of unknown estimated parameters. Also, there are instruments which are year dummies as excluded IV instruments and lagged values of performance, firm-specific characteristics and ownership parameters as GMM instruments.



3. Analysis and results

3.1 Regression analysis of ownership-performance relationship for all firms

In order to analyze the relationship between offshore ownership indicators and firm's financial performance we start with the estimation of Model (1) presented above. The first two specifications for both ROS and ROA includes separately offshore dummy plus number of offshore countries variables and total number of offshores in ownership structure, respectively. (See Appendices 4 and 5). The third specification which we will discuss includes all these three variables together.

We first use pooled Ordinary Least Squares, Fixed Effects (FE), Random Effects (RE) specifications for both dependent variables, ROS and ROA. Based on Hausman test the Fixed Effects model is chosen out of this set of models. However, as we previously discussed there is an endogeneity of ownership structure and, additionally, current performance may be influenced by lagged value, so we turn to the set of Dynamic Panel Models (DPM) and Arellano Bond procedure as specified in Model (2).

We ran System one-step and Two-Stage estimation procedures, with robust standard errors and small (to account for the small sample size and to obtain t-stats rather than f-stats). We treat year dummies as strictly endogenous regressors and include them as Instrumental Variables (IV)-style excluded instruments, while firm-specific variables are considered predetermined but not strictly exogenous and ownership variables, both offshore and other included controls of ownership characteristics, are treated as endogenous and included in General Method of Moments (GMM)-style instruments which are instrumented by their lags. (Roodman, 2006)

Firstly, all estimated models passed through Hansen test for over-identifying restrictions as well as Arellano Bond tests for AR(1) and AR(2) in first-differences. Then we again ran Hausman test (See Appendix 3 for extended Stata outputs), it showed that for the first two specifications the efficient case is two-step Arellano Bond, while for the third one one-step system Arellano Bond is consistent for both performance measures. As for the results, we will discuss the third specification for both performance measures as they include all variables of interest.

The lagged performance has significant and positive influence on current ROS and ROA.

Liquidity has significant positive effect in ROS variable, suggesting that companies who maintain the sufficient levels of current liabilities coverage by current assets may have significant operational efficiencies on Russian market. Liquidity also influences positively, but insignificantly ROA measure.

Leverage has positive significant influence on ROS, meaning that potential access to investment opportunities are higher than agency costs of bankruptcy. So, higher leverage on average leads to higher operational efficiency and growth opportunities. There is no significant influence on ROA measure, however.

Table 4 Arellano Bond model estimation results for ROS and ROA performance measures

	ROS	ROA
Dependent variable	AB one-step	AB one-step
Liquidity	0.00933**	0.00624
	(0.00361)	(0.00546)
Leverage	0.000610***	-0.0000463
	(0.000216)	(0.000424)
Size	0.0354*	0.0138
	(0.0196)	(0.0181)
Age	0.000698	-0.000498
	(0.000523)	(0.00101)
Year 2005	0.0500***	0.0670***
	(0.0176)	(0.0254)
Year 2006	0.0534***	0.0707***
	(0.0161)	(0.0203)
Year 2007	0.0276**	0.0284
	(0.0131)	(0.0241)
Year 2008	0.00420	0.0159
	(0.0145)	(0.0222)
Year 2009	-0.0248**	-0.0433***
	(0.0113)	(0.0139)
Year 2010	0.0101	0.0117
	(0.0120)	(0.0121)
Year 2011	0.00131	0.00855
	(0.0103)	(0.00920)
Year 2013	-0.0264	0.0852
	(0.0224)	(0.112)
Number of offshores	-0.0122**	-0.0176*
	(0.00524)	(0.00908)
Offhore intermediate dummy	-0.119*	-0.0769
	(0.0615)	(0.109)
Number of offshore countries	0.0988***	0.116**
	(0.0374)	(0.0474)
Undisclosed voting rights	-0.0658	0.109
	(0.0803)	(0.109)
State ultimate owner	0.0444	-0.0764
	(0.0627)	(0.101)
Dispersed ultimate owner dummy	0.00559	-0.103
	(0.0332)	(0.0744)
Insiders dummy	0.106	0.130*
	(0.0680)	(0.0676)
Lagged performance	0.513***	0.464***
	(0.168)	(0.0899)
_cons	-0.389**	-0.118
	(0.178)	(0.185)
N	1164	1167

Size of a company influences ROS positively and significantly, which means that investment opportunities and economies of scale outweigh agency and monitoring costs of bigger companies. The influence is also positive but insignificant for ROA variable. Age has now significant influence on both ROS and ROA measures.

There are two interesting patterns in the results for year dummies. Firstly, the results reflect the growth period of 2005-2007 years as year dummies for this period significantly and positively influence both company's performance measures, while there is a negative significant influence of 2009 year, which indicates the recession stage.

Among the control ownership indicators, the only significant one is the insider's ownership dummy, which has a positive effect on ROA value. This may be explained by the fact that insider owners seeking for long-term (in the form of increased stock values) and short-term (dividends) income from their shareholdings, may either affect the strategic decisions in the company as well as 'manipulate' with accounting measures.

What is more important, we obtained some interesting results on offshore ownership variables. Offshore ownership dummy has positive and significant influence on ROS measure, which corresponds to our initial assumptions that offshore ownership has negative influence on company's performance through the asymmetry of information problem among market agents. The effect of offshore dummy on ROA is also negative, but insignificant, corresponding to the results for Bulgarian firms obtained by Mueller, Dietl, Peev (2003). The total number of offshores has negative and significant effect on both performance measures. As we proposed, this relationship is explained by the increase in 'fuzziness' of ownership which, in turn, make is easier to to hide activities and take out corporate belongings, assets or profits for the ultimate owners' own benefit.

Still, there is one unexpected result which is connected with the total number of offshore countries indicator. Initially, we expected that number of countries and number of offshores has similar reasons to affect the performance negatively, however, empirical models for our sample shoed the significant positive effect of number of offshore countries where the company's owners are registered on ROS and ROA measures. One potential explanation of this relationship may be the diversification and the effect of each particular offshore country special features like development of financial infrastructure, secrecy regime, development of property protection legislation, etc. So, the positive effect may arise from the fact that Russian companies with intermediate offshore owners in several countries may gain these particular positive effects of each geography.

In order to study special features of different offshore countries deeper, we will then analyze the performance-ownership relationship only for offshore-owned firms.

3.2Regression analysis for firms with offshores in ownership structure

For offshore-owned firms we estimated the set of specifications including FSI index as an explanatory variable, number of offshores and number of countries and dummies on particular offshore countries, namely, Cyprus (being the most popular offshore location in the sample) and Cayman Islands (possessing the highest FSI Index out of all presented locations in the sample). The model specifications chosen for analysis are Fixed Effects panel data regressions (in comparison to previous section, here Hausman test rejected Arellano Bond models due to the large number of instruments compared to the sample size). (See Appendices 6 and 7 for all specifications).

Table 5 Fixed effects models for the offshore-owners subsample

Independent variable	ROA	ROS	ROS
Dependent variable	All	FSI	All
2005	0.0478	0.0587	0.125***
	(0.0629)	(0.0626)	(0.0443)
2006	0.135	0.154	0.124***
	(0.108)	(0.108)	(0.0451)
2007	0.234	0.251	0.0914**
	(0.156)	(0.156)	(0.0447)
2008	0.317	0.332	0.0761
	(0.208)	(0.208)	(0.0511)
2009	0.308	0.327	0.0359
	(0.260)	(0.260)	(0.0616)
2010	0.449	0.469	0.0683
	(0.312)	(0.312)	(0.0698)
2011	0.523	0.544	0.0580
	(0.364)	(0.364)	(0.0803)
2012	0.594	0.616	0.0411
	(0.416)	(0.416)	(0.0896)
2013	0.685	0.706	-0.00440
	(0.467)	(0.468)	(0.0975)
own_unknown_vote	0.0269	0.0162	-0.113**
	(0.0395)	(0.0382)	(0.0455)
State Ultimate Owner	-0.170***	-0.169***	-0.0911
	(0.0647)	(0.0645)	(0.0744)
Insider Dummy	0.0817	0.0455	0.0748
	(0.0589)	(0.0551)	(0.0678)
LN(FSI)	-0.0153**	-0.0170***	-0.00847
	(0.00638)	(0.00626)	(0.00735)
Liquidity	0.0100***	0.0101***	0.0108***
	(0.00259)	...

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