Corporate Governance as a Mechanism to Mitigate Financing Constraints on
Investment
Anna Grosman1
This version: 16 May 2013
Abstract
This paper addresses whether adopting good governance standards alleviates constraints on financing
investment via improved internal efficiency or access to external capital. The Russian context serves as an
appropriate setting for studying the longitudinal effect of governance. I use transparency and disclosure score
computed by Standard and Poor’s (S&P) as the primary proxy for corporate governance, supported by
shareholder ownership structure and shareholder characteristics. I find that corporate governance has a
positive and significant impact on capital investment. I find also that state owned enterprises (SOEs) are more
sensitive than oligarch-owned enterprises to improved governance, but less sensitive to the changes in
internal funds (cash-flows). SOEs are poorly governed judging by their S&P transparency and disclosure scores,
therefore, any improvements to their governance might have a significant effect on the level of their capital
expenditures. On the other hand, it might be that SOEs are subject to soft budget constraints.
I find also that governance of financially constrained firms positively and significantly affects investment.
Governance, therefore, is a valid mechanism for reducing financing constraints on investment. The empirical
analysis is conducted on a 10-year panel dataset containing observations on the largest Russian companies,
publicly listed in Russia or abroad. Robustness checks and controls for endogeneity include dynamic panel
generalized method of moments (GMM) and vector autoregression (VAR) techniques.
Keywords
Corporate governance, transparency and disclosure, investment, SOEs, oligarchs
1
Imperial College Business School. Email: a.grosman09@imperial.ac.uk. I thank Mario Amore, Erkko Autio, Bat
Batjargal, Gilles Chemla, Ciaran Driver, Maria Guedes, Sergei Guriev, Philip Hanson, Jonathan Haskel, Bersant
Hodbari, Alan Hughes, Anna Kochanova, Aija Leiponen, Ekaterina Lyahovetskaya, Janus Lim, Anita McGahan,
Nigel Meade, Andrea Mina, Bruce Tether, Stefano Rossi, Ammon Salter, Alex Settles, Bruce Tether, Mike
Wright and Todd Zenger for helpful comments and discussions. Participants at SOAS, the European School of
New Institutional Economics (ESNIE), Druid Academy, London Business School Trans-Atlantic Doctoral
Conference, Royal Economic Society (RES) job market, European Bank for Reconstruction and Development,
Academy of Natalya Nesterova (Moscow), BASEES (the British Association for Slavonic and East European
Studies), CEELBAS, Techniques in Management Research Workshop and Imperial College Research Festival also
provided useful comments. I also thank Standard and Poor’s for providing the transparency and disclosure
scores. This research is supported by EPSRC. All views expressed and errors are my own.
1
1. Introduction
This paper researches the influence of corporate governance, measured by transparency
and disclosure practices, on investment behaviour. I address two primary and distinct
questions. First, does good corporate governance help internal capital markets to channel
the right amounts of capital to each project within the firm? In other words, can corporate
governance improve firm transparency and the efficiency of internal capital budgeting so
that the funds are allocated appropriately among investment projects? And second, does
good corporate governance help external capital markets to channel the right amount of
capital to each firm? In other words, by being better governed, are firms able to attract
adequate amounts of external capital to lift financing constraints on investment projects?
Although these two questions are logically distinct - in the sense that the workings of the
external and internal capital markets appear to be quite different - an overarching goal of
this research is to emphasize how governance regulates capital allocations across and within
firms. Corporate governance is one of the curative mechanisms that arise endogenously to
mitigate the effect of financing constraints on investment outcomes.
Both research questions addressed in this paper have been subject to prior empirical and
conceptual study although the first question, on the impact of governance through internal
capital markets, has been less well studied relative to the second question. The theoretical
and empirical literature focuses on explaining capital budgeting practices on the basis of
information and agency problems within the firm. This literature identifies cases where
certain governance structures, through their impact on internal capital markets, can lead to
either increases or reductions in the efficiency of capital allocation. This literature includes
works by Rajan et al. (2000), Williamson (1975), Scharfstein and Stein (2000), to name a few
(for a more extensive review, see Stein, 2003). Research on the impact of governance
through external capital markets is more mature and includes seminal works by Hubbard
(1998), Schleifer and Vishny (1997) and Johnson et al. (2000).
Corporate governance, through firm transparency, plays a crucial role in giving firms access
to external capital and, thus, mitigating financing constraints on investment. Arguably, the
2
importance of governance is underestimated by managers of funds-rich state-owned firms,
but adopting high governance standards might still improve their firms’ internal efficiency
and financing structure. I test the effect of corporate governance on financing constraints
through investment, a factor that has not been tested extensively in the Russian context;
most of the literature examines the effect of governance on firm value, profitability and
performance, which are very widely used indicators of shareholder value. In summary, the
research gap addressed by this paper is the relationship between transparency and
disclosure practices, financing constraints, and investment behaviour in an emerging
economy context.
Related studies on corporate governance in Russia include Black et al. (2006, 2001),
Goetzmann, Spiegel and Ukhov (2003), and Kuznecovs and Pal (2012); and Durnev and Kim
(2005), and Klapper and Love (2004) (cross-country). These studies tend to focus on one
year (in case of surveys) or a relatively short time period. Thus the access to the unique data
computed by Standard and Poor’s (S&P) on transparency and disclosure (TD) rules in the
period comprising reforms of governance code in 2002-2004
2
allows me to update the
effect of governance on investment in Russia. I go beyond this literature in that my analysis
highlights the different investment behaviours of the state and oligarchs. The present paper
contributes also to the literature on financing constraints (Hubbard, 1998; Moyen, 2004;
Raith et al., 2007; Brown and Petersen, 2009).
Countries vary substantially in their predominant mode of firm governance. For simplicity, I
compare two models: an external, diffuse shareholder model, and an internal concentrated
blockholder model. In the presence of weak protection of shareholder rights, weak law
enforcement, as well as less efficient capital markets, the predominant ownership structure
is concentrated. The main risks that beset emerging economies, including Russia, are
principal-principal conflicts, that is, conflicts between the ‘controlling’ and the minority
shareholders. These conflicts centre on transparency and disclosure, dilution (through share
issuance, mergers, etc.), asset stripping and transfer pricing, bankruptcy, limits on foreign
2
A new Code of Conduct was issued by the Federal Securities Commission (FSC) in February 2002, effective in
2003, reflected in firms’ accounts from 2004.The code’s major objective was to provide a framework for
corporate governance in Russian firms (Puffer and McCarthy, 2003).
3
ownership, management attitude to shareholders, registrar risk, dividend policy and
dividend payout.
I contribute to the literature on concentrated blockholder models (Grossman and Hart,
1980; Shleifer and Vishny, 1986; Leland and Pyle, 1977; Aghion and Tirole, 1997; Claessens
et al., 2000) and, more specifically, to work on politically connected firms, since state
controls between 50 and 70% of Russian listed firms (Fisman, 2001; Faccio, 2006; Frye and
Iwasaki, 2011; Okhmatovskiy, 2010). Russia’s unique institutional context allows longitudinal
study of the effects of corporate governance on investment behaviour. In developed
countries, governance is stable over time. The improvements to Russian governance over
the last 10-20 years provide enough variation to test the relationship between corporate
governance and investments, in fixed effects and instrumental variable frameworks. Only a
few studies examine governance in the Russian context over an extended period.
The findings in this paper suggest that governance impacts on investment through internal
capital markets and also that private ownership concentration could replace good
governance. Specifically, I find that governance does not impact significantly on investments
in the case of oligarch firms. This reinforces the theoretical and empirical findings of a
curvilinear relationship between governance and concentration of ownership (Guriev et al.,
2003). Up to a certain limit, ownership concentration has a positive effect on corporate
governance. Above that threshold, the relationship becomes negative. Empirical findings
suggest that the threshold is at about 50%, which reinforces the importance of corporate
control market. I find also that governance matters most for fixed investment by SOEs. State
companies make up 80% of the value of stock market in China, 62% in Russia, and 38% in
Brazil.3
Another important finding is related to the zero sensitivity of cash flow to investment for
SOEs. In emerging economies context, zero sensitivity of cash flow to investment is
interpreted as evidence of a weak form of soft budget constraints (Lizal and Svejnar, 2002).
Firms face soft budget constraints if there is willingness of government or some other
institution to provide additional resources or to otherwise bail them out.4 A positive
3
Sources: Deutsche Bank, Fortune and The Economist
4
See Kornai (1979, 1986, 1998) for an introduction to and discussion of the concept of a soft budget
constraint.
4
sensitivity of cash flow to investment can be interpreted as evidence of financing constraints
(Fazzari et al., 1988). When estimating the impact of governance on capital investment
through external capital markets, I find that corporate governance matters for investment in
firms that are a priori financially constrained.
While corporate governance influences investments, the opposite could be argued that
capital-intensive firms might decide to improve transparency and disclosure levels in order
to obtain more investment (reverse causality). More importantly, the relations between
governance and investment are likely to be dynamic (unobserved heterogeneity and
simultaneity). For example, current investment will affect future governance choices and
these, in turn, may affect future firm investment. I address endogeneity using dynamic
panel data (Arellano-Bond Generalized Method of Moments – GMM - version) and vector
autoregression (VAR) techniques.
The dynamic panel GMM estimator incorporates the dynamic nature of governance
relationships to provide a valid and powerful instrument that controls for unobserved
heterogeneity and simultaneity (Wintoki et al, 2012). The use of GMM estimator is also
strictly required where the lagged dependent variable introduces Nickell’s bias (Arellano,
2003). The dynamic modelling approach has been used in other areas of finance and
economics where the structure of the problem suggests a dynamic relation between the
dependent and independent variables. Examples include governance and research and
development (R&D) (Driver and Guedes, 2012), external finance constraints and investment
(Whited and Wu, 2006), internal finance and investment (Bond and Meghir, 1994),
economic growth convergence (Caselli et al., 1996), labour demand estimation (Blundell and
Bond, 1998), and economic growth (Beck et al., 2000). The remainder of the paper is
organized as follows. Section 2 presents the theoretical framework; Section 3 describes the
data and the empirical proxies for corporate governance. Section 4 demonstrates the
empirical relationships between governance, financing constraints and investment; Section
5 reports robustness checks. Section 6 concludes.
2. Theoretical Framework
Financing constraints affect all publicly listed companies and particularly in emerging
markets where debt and equity capital markets are underdeveloped or illiquid (La Porta et
5
al., 1997, 2000). Modigliani and Miller’s seminal work on the irrelevance of the financing
structure has given rise to various models generally without reference to the possible
influence of financing factors. However, by relaxing Modigliani and Miller’s assumptions,
especially those related to the symmetry of information, firms experience financing
constraints in the presence of imperfect and inefficient markets. The higher the asymmetry
of information or the lower the standards of governance, the higher is the effect of
governance on financing constraints (Hubbard, 1998) where such constraints exist. This
effect of governance on financing constraints is illustrated in Figure 1. For constrained firms,
good governance extends the horizontal part of the supply curve or flattens the angle of the
slope as shown in Figure 1. Unconstrained firms will still benefit from good governance since
it improves their operational transparency and efficiency.
Figure 1: Financing constraints
Rate of return
Demand
Demand
(unconstraine (constrained
d firm)
firm)
Supply of
financing
Good governance
demand shift
Supply of
financing
(good
governance
discount )
R1
R0 (=cost of
internal funds)
C0
Internal funds up to C0
C1 C1’ with
good
governance
Capital
By improving governance or reducing the asymmetry of information between agent and
principal (external shareholder or debtholder), I hypothesize that the firm could mitigate
financing constraints in the following (non-exhaustive) ways:
6
Hypothesis One. Internal capital market channel: Good TD may improve the firm’s internal
efficiency making cash-flows more visible and controllable, and promoting more efficient
allocation of internal funds stimulating investment expenditure; and
Hypothesis Two. External capital market channel: the firm’s greater transparency will
attract external investors and provide greater access to local and global financial markets,
enabling the firm to raise more funds externally via capital markets mitigating financing
constraints on capital investments.
Since the influential work of Fazzari, Hubbard and Petersen (1988), researchers have used
the sensitivity of capital investment to cash-flow as a metric for gauging the severity of
financing constraints. The intuition behind this test is straightforward. If a firm cannot
obtain outside finance, then its investment should respond strongly to movements in
internal funds. Implementing this idea requires controlling for investment opportunities;
otherwise cash flow might capture movements in investment opportunities rather than
movements in internal funds. Investment opportunities or demand are most often proxied
by Tobin’s Q, although some studies suggested Tobin’s Q contains measurement error
(Ericksson and Whited, 2000; Hennessy et al., 2007). The standard approach to measuring
financing constraints continues to be based on fixed effects regression of capital investment
on cash-flow and Tobin’s Q. Most studies find that firms that are a priori more likely to face
binding financing constraints exhibit greater sensitivity of investment to cash-flow, for
example, Fazzari et al. (1988, 2000), Hubbard (1998), Whited and Wu (2006) and Bond and
Van Reenen (2007), but Kaplan and Zingales (1997, 2000) criticize this view.
Referring back to Figure 1, the steeper the upward-sloping portion of the supply curve, the
higher the cost of capital. Whether governance has the effect of making this slope shallower
or steeper can be investigated by testing its interaction effect with a measure of access to
external capital. In the extreme case, where there are no costs associated with the
asymmetry of information between lender and borrower, the slope would be horizontal,
and there would be no financing constraints on investment. Further analysis of the
interaction effects is conducted in the section 4 ’Empirical Specification’.
Conceptually, the claim that governance will positively affect investment cannot be
straightforwardly justified. Jensen (1986) argues that managerial opportunism leads
7
managers to overinvest in ‘pet’ projects that do not create shareholder value; therefore,
good governance might stop managers from investing and ultimately reduce investment.
Jensen’s argument is based on events in the late 1970s and early 1980s related to US oil
companies, which wasted a lot of money on pet projects and diversification. These
acquisitions were unsuccessful partly because of the absence of managerial expertise
outside the oil sector. The oil industry had large increases in free cash flow. However, the
evidence shows that the oil firms continued to invest in exploration and development even
though the rate of return probably had a negative net present value (NPV) (McConnell and
Muscarella, 1985), rather than paying out the excess cash to shareholders. The
overinvestment problem is likely to be more pronounced in stable, cash-rich companies in
mature industries with few growth opportunities.
The Russian institutional context allows me to hypothesize that there are no reasons to
expect Russian managers to overinvest. Most Russian firms already have old and fully
amortised assets, due to the legacy of the soviet regime and heavy use of outsourcers rather
than reliance on own production. The current average longevity of equipment in Russia is
twice its desired levels. In spite of the prolonged recovery in 1999-2008, capital investment
by Russian firms is still low and of poor quality. Russian firms are characterized by
inseparability of management and control due mainly to the influence of large shareholders
over management. This influence over management is not unusual for firms with
concentrated ownership (R. Murdoch, R. Branson and C. Koch preside respectively over
News Corp, Virgin Group and Koch Industries and control their management entirely). But
unlike in Europe and the US, there are legal loopholes in Russia allowing the majority
shareholders (with the consent of managers) to funnel funds out of firms rather than to
invest in long term assets and infrastructure. They seek to maximize short-term rents
because of the uncertain legacy of their assets acquired in the 1990s’ privatisations, often
through crime and bribes, which undermines investment and leads also to conflicts between
majority and minority shareholders. The majority of Russian firms consider their productive
assets to be underinvested and obsolete in the face of growing competition and market
demand (Aganbegyan, 2008, Dzarasov, 2011). This institutional impediment to investment
growth supports my hypothesis of good governance improving investment.
8
3. Data
The mechanisms of governance can be assessed according to board and management
structures, to shareholder rights, to transparency and disclosure of information, etc. Table
Table 8 on page 55 in the appendices compares the methodologies of different institutions
used to measure levels of corporate governance. All are based on more or less similar
criteria. The TD score is the most frequent measure of governance standards and appears in
the methodologies of four out of five institutions. I use unique corporate governance
variables from the TD rankings of S&P.
Black et al. (2006) analyse the various measures of corporate governance in Russia and
conclude that sophisticated governance indices are not necessarily better predictors. They
found the S&P TD scores to be more useful measures of governance, in that they predicted
Tobin’s Q and, therefore, may correspond to the elements of governance that matter to
investors. Black et al. (2006) state that, in this respect, TD scores outperform the more
complex S&P governance indices.
Based on previous research findings, I decided to use S&P TD scores as my proxies for
corporate governance. The TD scores produced by S&P for 90 companies consist of three
components: ownership structure and shareholder rights; financial and operational
information; and board and management structure and process. These three sub-scores are
positively correlated with one another. The checklist methodology consists of searching for
110 TD attributes relating to the three components (for the full list of attributes see Table
12 in the appendices). Each attribute is scored on a binary basis to ensure objectivity, and
the scores for the three components are based on the scores for individual attributes.
Scoring accounts for information included in three major sources of public information annual reports, web-based disclosures, and public regulatory reporting (such as publicly
available statutory documents filed with the Federal Financial Markets Service (FFMS), the
Russian financial markets regulator, the Frankfurt Stock Exchange, UK Listing Authority
(UKLA), the UK Financial Services Authority (FSA), and the US Securities and Exchange
Commission (SEC), available on the web sites of companies and stock exchanges or the
respective regulatory authority). According to the weighting system, public disclosure regardless of its source - yields 80% of the maximum score for each item in the check list
used for data compilation. The remaining 20% of points (10% each) are awarded if this
9
information is also available from the other two sources. This methodology reflects the
notion that replication of information in various sources represents value for investors since
it makes the information more easily accessible. The value of replication, however, is
incremental relative to the fact of disclosure.
The S&P methodology is constructed from the perspective of an international investor,
which is reflected in the list of attributes.
Table 1: Example questions, survey of transparency and disclosure
The strengths of the TD score lie in its usage and applicability. First, there is enough interfirm and temporal variation among scores to make the TD score an interesting variable for a
longitudinal study. 2009 scores range from 20% for the lowest scored company to 80% for
the highest one. There is room for improvement in future Russian TD scores. In 2009, the
transparency index, calculated as the average score for the 90 Russian companies, was only
56%; in 2003, the last year that this survey was conducted in the UK, France and US the
scores for these countries were respectively 71%, 68% and 70%.
Second, TD scores matter for investors, especially foreign ones, since they are willing to pay
the highest premium for Russian firms with the best governance practices relative to firms
from other countries (McKinsey, 2002 - Global Investor Opinion Survey). Examples of these
10
firms include MTS, Vimpelcom and Wimm-Bill-Dann, which have higher trading multiples
relative to their counterparts (Shekshnia, 2004).
Third, transparency and disclosure are integral to corporate governance (Patel et al., 2002).
TD practices are an important component of the corporate governance framework (OECD,
1999) and a leading indicator of corporate governance quality. Beekes and Brown (2006)
find that firms with higher corporate governance standards make more informative
disclosures. Transparency and full disclosure of information is important for emerging
markets and particularly Russia, where external capital is necessary to sustain the high
growth rate and the biggest agency problem centres on asymmetric information and
expropriation by majority shareholders (Aksu and Kosedag, 2006).
However, the TD scores produced by S&P used in this paper have not been analysed
previously in connection with financing constraints on investment in Russia or other
emerging markets. Most research that uses TD scores focuses on their interaction with firm
value. Patel et al. (2002) provides a series of bi-variate correlations of price-to-book ratios to
TD scores for six emerging countries,5 and finds that for five markets, this correlation is
positive. Aksu and Kosedag (2006) provide evidence that firm size, financial performance
and market-to-book equity best explain the variation in TD scores of the firms listed on the
Istanbul Stock Exchange. Doidge et al. (2007) test a model of how country characteristics,
such as legal protections for minority investors and the level of economic and financial
development, influence firms’ implementation of measures to improve the transparency of
their Profit and Loss Statement (P&L). The authors find that country characteristics explain
much more of the variance in TD ratings (39%-73%) than observable firm characteristics
(4%-22%). Patel and Dallas (2002), in one of the first studies on TD, highlight that firms with
good TD practices have lower costs of capital. In their seminal research, Gompers et al.
(2003) included TD as one of the components of corporate governance index and found that
stronger rights in the US have led to better firm performance. Similarly, Klapper and Love
(2004) and Black et al. (2006) considered TD parameters along with other corporate
governance practices. Black et al. (2006) have used alternative governance indices provided
by other agencies and investment banks and shown that governance positively influences
firm’s value.
5
Kuznecovs and Pal (2012) find that TD scores significantly boosted the
Does not include Russia.
11
performance of firms, particularly for utility firms. The general consensus in this literature is
that better TD practices tend to lower the cost of capital, boost performance and increase
firm value.
The TD score gives a ranking for a firm based on publicly disclosed information. It is an
objective measure in the sense that the information is either disclosed or not. The accuracy
of the information, however, is not entirely assessed. To an extent, the disclosure of audited
accounts, especially if they accord with IFRS or US GAAP standards and/or are conducted by
a top-tier auditor, is some measure of the accuracy of the information disclosed. However,
for more ’subjective’ governance measures, Russian companies tend to engage in ‘window
dressing’, and good governance practices are limited to a box ticking paper exercise. Is an
independent director really independent? Or, vice versa, is he so remote from the firm’s
operations as to be purely a public relations figure? Nevertheless, TD scores by S&P are
more reliable and complete than other measures of governance for Russian listed firms (for
methodology on other governance measures, see Table 8 on page 55 in the appendices).
TD and governance standards in Russia are improving gradually as more and more Russian
firms participate in international capital markets. In 2009 over 60 Russian companies were
listed abroad. S&P TD scores show that companies listed on the main markets of the London
Stock Exchange (LSE) and the New York Stock Exchange (NYSE) were substantially more
transparent than those not listed.6 The data also show that companies electing independent
directors to their supervisory boards have higher levels of transparency.
Table 2 on page 13 lists number of observations, mean, median, standard deviation from
mean, minimum and maximum observations for the raw variables used in empirical
specifications. The TD total score is broken down into three sub-scores: financial and
operational disclosure, ownership and shareholders rights, and board and management
structure. The total score is an algebraic sum of the three sub-scores. Financial data are
from the Compustat Global database for all Russian firms listed in Russia or abroad in the
period 2000-2010. Investment is defined as annual capital spending on tangible assets.
6
Companies traded only in Russia had an average transparency index of 50%, whereas the transparency index
for companies listed on the LSE was 63% and 74% for NYSE-listed firms.
12
Ownership data were collected from annual reports, web-based disclosures and business
publications.
Average annual sales for the sample are 2.1 billion euros7 which is quite high due to some
large outliers and high standard deviation. Median annual sales are 486 million euros.
Average capital investment amounts to 347 million euros and median investment to 57
million euros. The averages for the three sub-scores over the entire period are 51, 49 and 44
percent respectively.
Table 2: Summary statistics
There is significant correlation between firm size (proxied by sales) and its corporate
governance (Table 3). Larger publicly listed firms tend to be more attentive to appropriate
governance levels. Investment is strongly correlated with gross cash-flow, which is in line
with mainstream finance theory. New debt is strongly correlated with governance; the
higher the governance standards, the lower the cost of capital due to reduced moral hazard
and, therefore, more access to external financing.
7
Exchange rate EUR/RUR of 40.8 over 01/01/2000-31/12/2010 period.
13
Table 3: Correlations
4. Empirical Specification
Let us define the empirical specification. Investment equations can be run as ‘Q’, Euler or
accelerator models. Q models use Tobin’s Q based on the idea that if Q is greater than 1,
then a firm has an incentive to invest. Euler models are dynamic disequilibrium models that
are estimated indirectly. Accelerator models can take many forms and relate to sales on the
assumption of some constancy of the investment to sales ratio (which can be modified by
including other variables). Both Q and accelerator models can be run in error correction
form because they are both based on the idea of a target that may not be in equilibrium so
disturbances from the target can be modelled.
I start with an accelerator model of investment in error correction form. Most research
shows that investment is non-stationary so the error correction model is preferred. Error
correction models have been used for capital investment and R&D investment in a number
of papers, such as Becker and Hall (2009), Bond et al. (2003), Mairesse et al. (1999), Bond et
al. (1997), Blundell et al. (1992) and Bean (1981).
∆𝑖𝑖𝑡 = 𝛽0 + 𝛽1 ∆𝑖𝑖,𝑡−1 + 𝛽2 (𝑖𝑖,𝑡−2 − 𝑠𝑖,𝑡−2 ) + 𝛽3 ∆𝑔𝑖𝑡 + 𝛽4 ∆𝑔𝑐𝑓𝑖,𝑡−1 +𝛽5 𝑋𝑖𝑡 + 𝑑𝑡 + 𝜖𝑖𝑡
(1),
14
where ∆𝑖𝑖𝑡 is the first difference of the logarithm of investment. An alternative proxy for
investment used in the literature is the ratio
𝐼𝑖𝑡
, where 𝐼𝑖𝑡 is investment and 𝐾𝑖,𝑡−1 is
⁄𝐾
𝑖,𝑡−1
the capital stock (fixed assets) at the end of the period t-1.8
However, by using ∆𝑖𝑖𝑡 , I avoid reliance on 𝐾𝑖𝑡 which contains biased fixed assets valuation.
In other words, 𝐾𝑖𝑡 is based on the assumption of depreciation of fixed assets and, hence, is
less accurate than 𝐼𝑖𝑡 . 𝑋𝑖𝑡 represents a vector of the variables which have been emphasized
as determinants of investment from a variety of theoretical perspectives. It includes first
difference of sales in logarithms ∆𝑠𝑖𝑡 . It also includes a debt term to control for possible
omitted variable biases and to evaluate the changing role of external finance on investment
(Bond and Meghir, 1994; Brown and Petersen, 2009).
The rationale for the inclusion of lagged investment term, based on formal models of
investment behaviour, is the presence of adjustment costs of investment (Brown and
Petersen, 2009). ∆𝑔𝑐𝑓𝑖,𝑡−1 which is the first difference of the logarithms of the firm’s gross
cash flow at the end of period t-1 is defined as the sum of net income and depreciation and
amortisation charges. The coefficient of ∆𝑔𝑐𝑓𝑖,𝑡−1 represents the potential sensitivity of
investment to fluctuations in available internal finance and could reflect the presence of
financing constraints on investment.
The term (𝑖𝑖,𝑡−2 − 𝑠𝑖,𝑡−2 ) is the error correction term, where 𝑖𝑖,𝑡−2 is natural logarithm of
capital investment and 𝑠𝑖,𝑡−2 is natural logarithm of sales at the end of period t-2. The error
correction term represents the long-run properties of this model. ‘Error correcting’
behaviour requires that 𝛽1 < 0, so that investment above the desired level is associated
with lower future investment, and vice versa. ∆𝑔𝑖𝑡 is the first difference of governance
(natural logarithm), proxied by the TD score. 𝑑𝑡 controls for year fixed effects, and 𝜖𝑖𝑡 is a
random error term. The unit of analysis is firm i at time t. Firm fixed effects are removed by
first-differencing.
8
𝐼𝑖𝑡
𝐼
∆𝐾𝑖𝑡
. Proof: 𝑖𝑡⁄𝐾
≈
≈
⁄𝐾
⁄𝐾
𝑖,𝑡−1
𝑖,𝑡−1
𝑖,𝑡−1
𝐾
= log ( 𝑖𝑡⁄𝐾
− 1) = ∆𝑘𝑖𝑡 = ∆𝑖𝑖𝑡 , where 𝑘𝑖𝑡 is the (natural) log of
𝑖,𝑡−1
∆𝑖𝑖𝑡 can be approximated by the ratio
∆𝑙𝑜𝑔𝐾𝑖𝑡 = log 𝐾𝑖𝑡 − 𝑙𝑜𝑔𝐾𝑖,𝑡−1
the desired capital stock for firm i in the period t.
15
Hypothesis One: Good TD can improve the internal efficiency or transparency of the firm,
cash-flows become more visible and controllable, and there is more efficient allocation of
internal funds stimulating investment expenditure.
I believe that transparency and disclosure facilitates accountability of boards and managers,
and, in a broader economic context, stimulates economic competitiveness and capital
investment. In Table 4 Model (1) I test for the direct effect of governance on investments.
Table 4: Investment and direct impact of governance, fixed effects
Governance is statistically significant in Model (1). The t-test on the error correction term
ect shows that ect’s coefficient is different from 0 and significant in all three models, which
means that cointegration exists. In other words, the error correction model is a
representation of the short-run dynamic relationship between sales and investment, in
which the error correction term incorporates in the model long-run information on sales
and investment. I test for heteroscedasticity in the error terms with White’s test (White,
1980) by reporting White’s robust standard errors.
16
If I control for firm size with first difference in total assets, in addition to controlling with
first and second differences in sales, the results of model (1) are unchanged. If I split the
panel into two parts - according to the existence of controlling majority shareholder (50%
plus 1 share) or not, governance remains a significant and positive factor for investment in
the presence of a majority shareholder, whether this is a state entity or a private investor.
The results for a dispersed ownership sub-panel are inconclusive partly due to its smaller
size.
I then analyse the companies owned by majority shareholders and look at the sub-panel
with an oligarch as the controlling shareholder, and at the sub-panel with the state as the
controlling shareholder (SOEs). I find that governance significantly and positively influences
investment in the latter case (cf. Table 4 Model (3)). This might be interpreted as greater
transparency and disclosure being a positive factor for investment in SOEs. SOEs are poorly
governed; their TD scores are much lower than the average score or scores of non-SOEs,
therefore, any improvement to their governance might have a significant impact on the
level of their capital expenditure. The coefficient of governance in the state-sub panel (0.35)
is double that for the full panel (0.18).
While governance is important for SOEs, I notice that the coefficient of gross cash flow is
insignificant. The absence of sensitivity of investment to internal funds in the case of SOEs
could be evidence of soft budget constraints, which means that cash-rich Russian SOEs are
not financially constrained. Another explanation of non-sensitivity of investment to cash
flow would be that SOEs operate in frictionless markets, i.e., it is cheaper to issue external
than internal funds. Asymmetrically, oligarch-owned firms are more sensitive to the levels of
internal funds (significance in the ∆gcfi,t−1 coefficient), while governance matters less to
them than to SOEs. I lag the difference in gross cash-flow by one year to avoid reverse
causality since once the firm has invested, it might become financially constrained.
The state is involved in many Russian firms whether through direct shareholdings, board
membership or other shareholders being members of the Parliament (Duma), etc. To
account for any state involvement, I split my panel into two sub-panels: with (any share
ownership, board directorship, etc.) and without any kind of state involvement. The results
for the sub-panel of firms with state involvement are less significant (coefficient range of
17
0.185-0.250) than for those firms with a strictly controlling state ownership stake (table not
reported). Companies with some state involvement as opposed to state control might be
better governed so that investment reacts less strongly to changes in governance.
To validate my results with panel fixed effects, I run ordinary least squares (OLS) on the
means of the explanatory variables (cf. Table 13 on page 69 in the appendices) and control
for fixed effects (industry, listing, foreign shareholder). I confirm that governance has a
significant and positive impact on investment. I find that firms in the metals and mining, oil
and gas and diversified telecommunications sectors have a higher propensity to invest than
in other sectors. I find significance in neither the ownership nor listing variables.
Hypothesis Two: By being more transparent, the firm attracts external investors, has greater
access to local and global financial markets, and is able to raise more external funding via
capital markets, which in turn mitigates financing constraints on investment.
I argue here that transparency towards investors and the general public is essential for
accessing local and international capital markets and, thus, diversifying sources of funding,
which mitigates financing constraints on investment.
A recent stream in the finance literature (see Brown and Petersen (2009) for a
comprehensive review) argues for the inclusion of external finance in investment studies.
Including debt issues would control for possible omitted variables bias and evaluate the role
of external finance on investment. Carpenter (1995) argues that, in the investment
equation, the focus is not only on cash-flow but also on the fundamental difference in the
role of debt finance in the firm’s financing decision. For constrained firms, debt is a source
of external finance used to fund profitable investment projects. When these firms issue new
debt, it represents a relaxation of the constraint and there should be a large and positive
change in investment. Goergen and Renneboog (2001) justify the inclusion of debt by saying
that debt captures potential bankruptcy costs and the tax advantages of debt. They find that
a high level of leverage leads to a reduction in investment as the bond market and banks
require high premiums to compensate for the bankruptcy risk if internally generated funds
do not suffice for investment spending.
18
Moyen (2004) constructs two models: an unconstrained model in which firms have access to
external financial markets, and a constrained model in which firms have no access. She does
not include debt in the regression and finds that it magnifies cash flow sensitivity of
unconstrained firms, i.e., she implies that omitting debt from ICF regressions may lead to
upward biased cash flow coefficients. Raith et al. (2007) model investment and internal
funds and show that the relationship between the two variables is a convex U-shape: in
particular, for sufficiently low levels of internal funds, a further decrease leads to an
increase in the firm’s investment. If internal funds are sufficiently negative, a further
decrease in internal funds might make it optimal to increase borrowing to such a degree
that there may be a negative correlation between investment and cash flow. Brown and
Petersen (2009) control for external finance and instrument cash flow to eliminate the
contemporaneous correlation between external finance and the cash flow variable. For the
reasons stated above, I control for the use of debt finance in the investment equation.
Table 5: Investment and external finance, fixed effects
19
Here, I only analyse long term debt, while about 50% of debt on the sampled firms’ balance
sheets is short term debt. The firms are still not highly leveraged; on average, they have 1.5x
Debt/EBITDA multiple. There is a broad range of firms contracting new long term debt, from
firms controlled by the state to firms controlled by private shareholders.
Governance is significant in models (1) and (2) in Table 5. New long term debt positively and
significantly affects capital investment.
A common approach in the financing constraints literature is to separate firms into groups
according to a priori criteria related to the presence of financing constraints. Researchers
have used a variety of criteria to categorize firms (see Hubbard, 1998). Some split the
sample by firm size (Carpenter et al., 1998, Chevalier and Scharfstein, 1995), some by
dividend payouts, both are proxies for net worth. Other a priori groupings are based on
more direct proxies for information costs, e.g., firm’s underwriting costs (Calomiris and
Himmelberg, 1995).
Another popular approach that I use in Table 5 Models (2) and (3) is to measure financing
constraints directly and interact them with the variables of interest (cash-flow, governance,
etc.). Goergen and Renneboog (2001) construct financing constraints as a dummy variable
that is set to 1 if a company issues new equity, reduces dividend payments, omits dividends,
or is financially distressed (files for bankruptcy). Gertler and Hubbard (1989) interact cash
flow with a dummy for periods of recession showing that, between 1970 and 1984, US
manufacturing firms exhibited a higher sensitivity of investment to cash flow during
recessions. Amore et al. (2012) defines as financially constrained those firms that are either
young or credit dependent (have an S&P credit rating).
My sample is rather small to separate the firms into groups. Instead, I create a variable for
financing constraints that categorizes firms as constrained and unconstrained and interact
this variable with governance. Financing constraints is a dummy variable, taking the value 0
for unconstrained firms, i.e., firms that paid common or preferred dividends (following
Fazzari et al., 1988; Bond and Meghir, 1994) and did not issue common equity
(Angelopoulou and Gibson, 2007) and 1 for constrained firms, i.e., those firms that issued
new common equity. Seifert and Gonenc (2010) found that firms in emerging economies
20
finance their investment needs mainly with equity, the opposite of what would be expected
under the pecking order theory. Statistically, Russian firms have low leverage and in this
situation, I could argue that external equity is raised before debt to finance their deficits. If
firms want to raise equity, being transparent is more important to them since they are
facing public investors hence the improvement in TD scores will give them better chances in
raising equity and drive investment. While for the banks, TD scores are less important: they
know the information already (whether through secondary issue or not), since they monitor
the firms through covenants. I lag financing constraints by one year to avoid reverse
causality since once the firm has invested, it might become financially constrained.
In model (3) of Table 5, the interaction term between financing constraints and governance
is significant and positive. This is evidence that companies with financing needs suffer from
underinvestment, and it provides support for Hypothesis 2 when I control for constrained
firms. Corporate governance matters and has positive impact on investment for firms that
are a priori financially constrained.
5. Robustness Checks
In this section, I address some potential concerns about the data and econometric
methodology. One such is endogeneity. Endogeneity problems are frequent in studies that
analyse corporate governance practices at firm level (Klapper and Love, 2004). This is
because it is generally difficult to find exogenous factors or natural experiments with which
to identify the relations being examined (Wintoki et al., 2012). First, there is the problem of
reverse causality. A fast growing firm, for example, may adopt better governance practices
in order to ensure access to external financing at lower cost. These growth opportunities
will be reflected in the market valuation of the firm, inducing a positive correlation between
governance and Tobin’s Q. It is more questionable whether, after controlling for growth
opportunities with Tobin’s Q, I would still find reverse causality between investment and
corporate governance.
If I assume that firms and investors care about governance, and TD scores in particular, then
a positive change in investment or cash flows might improve the TD score. Would firms try
to influence S&P in order to get a higher TD score through the increase in investment or
21
cash flow? This seems rather cumbersome, since there are other direct ways of improving
the score, such as publicly disclosing more information. So if reverse causality between
investment and TD scores exists, it is not very intuitive. If I use another proxy for corporate
governance, such as board structure, reverse causality is more obvious because the board
structure is the firm’s decision ultimately and not that of an external institution.
Dynamic panel data (Arellano and Bond version) are often used to address the issues of
causality, unobserved heterogeneity and simultaneity (Wintoki et al, 2012). Investment data
are not as fat-tailed as high frequency share data for example, so GMM is unlikely to be
spurious. Here, I use difference GMM and system GMM with collapsed instruments
(Roodman, 2009). I make a small sample adjustment and report t-statistics and the Wald 𝜒 2
test. I also report robust standard errors which are consistent with panel-specific
autocorrelation and heteroscedasticity.
I produce a Hansen test for over-identification. The dynamic panel GMM estimator uses
multiple lags as instruments. This means that the system is over-identified, and provides me
with the opportunity to carry out the over-identification test. Table 6 and Table 7 show the
results of the Hansen test for the GMM estimates, difference and system respectively. The
Hansen test yields a J-statistic which is 𝜒 2 distributed under the null hypothesis of the
validity of my instruments. The results in Tables 6 and 7 reveal a J-statistic with a p-value in
the range of 0.1-0.3, meaning I cannot reject the hypothesis that my instruments are valid. I
tested also for exogeneity of a subset of my instruments using difference-in-Hansen test of
exogeneity (not reported). The results also showed that I cannot reject the hypothesis that
the additional subset of instruments used in the GMM estimates is indeed exogenous.
Taken together, the specification tests provide empirical verification for my argument that
my instruments for governance are exogenous with respect to investment.
Models (1) in Table 6 and Table 7 show that governance is significant and positive for
investment. However, when controlling for gross cash-flows and Tobin’s Q in Model (3) in
Table 6, neither governance, nor gross cash-flow are significant for investment. Could it be
that improved governance coupled with growing cash-flows is still not enough to reverse
the trend of allocation of funds to short term projects, due to risks related to uncertain
property rights? This might be one explanation for why Russian firms have underinvested in
22
assets while not always being financially constrained. According to the pecking order
theory9, this could be interpreted as an absence of financing constraints. In other words, it
could be that demand for investment funds by managers is so low that it does not intersect
the upward-sloping portion of the supply curve (
Figure 1, page 6). This might indicate that managers are colluding with major shareholders
to hold investment low irrespective of cash flow, because of the risk of asset extraction by
the state. A manager-controlling shareholder tandem might decide to spend the extra cash
on short term projects or dividends, rather than to invest in capital assets. An alternative
empirical explanation could be that the number of GMM instruments is getting large
relative to the number of groups of observations and as a result standard errors provide an
inefficient estimation. The absence of significance in TD might then reflect the data
limitations and further robustness checks are needed.
Model 5 in Table 6 provides support for Hypothesis 2 - the interaction term between
financing constraints and governance is positive and significant, meaning that firms
classified as financially constrained benefit from better governance.
Table 7 presents the results for the system GMM. Both gross cash flow and Tobin’s Q are
positive and significant for investment, providing support for the findings in the classical
investment literature. Again, for firms that are a priori financially constrained, greater
transparency and disclosure mean greater investment (Model 5).
In addition to GMM, I also apply VAR to my panel data (for full details on the methodology,
see Love and Zicchino, 2006) to address endogeneity. By analysing orthogonalized impulseresponse functions I can separate the response of investment to shocks coming from
governance or other variables. My results (cf. Figure 7 on page 70 in the appendices)
indicate that the impulse response of investment to a one standard deviation shock in
governance has a positive impact.
A second problem might arise due to omitted variables which serve as pre-determinants for
differences in sales, assets, or gross cash-flows, which, in turn, are shown to be correlated
with investment. I use panel data techniques with fixed effects, which partially addresses
9
Pecking order theory states that firms prefer to finance investments with internal funds first, then debt and
finally, equity.
23
omitted variables bias for variables such as industry, region, legal framework, etc. I also use
lagged dependent variables.
A third potential concern is selection bias. The companies in the sample were selected
according to their size and liquidity. In 2009, they amounted to 90, 76 of which were
included in a 2008 study. The liquidity of stocks is generally positively correlated with firm
size, but there are exceptions, especially in cases of minor free-float. There are more than
300 public companies in Russia, and the S&P sample may not be representative of all
Russian public companies. As larger companies tend to be more transparent than smaller
ones, the sampling method is likely to cause an upward bias in assessments of the
transparency of the entire population of public Russian companies. On the other hand, since
the companies included in the sample account for some 80% of the cumulative market
capitalization of the Russian stock market, they represent a majority of the Russian
economy in terms of assets and operations.
Russian small and medium-sized enterprises (SME) have different priorities. They are
operating in situations where there is less need for transparency, similar to other SMEs in
the rest of the world. The costs of transparency and disclosure are quite high, including
accounting and information technology (IT) expenses, and they can be an obstacle to
pursuing good governance standards.
24
Table 6: Investment and corporate governance, difference GMM
25
Table 7: Investment and corporate governance, system GMM
26
6. Conclusion
I conclude this paper with a brief summary of my main findings and a discussion of the main
implications. I tested the significance of capital investment sensitivity to governance
through internal capital markets. I found governance to be a significant and positive factor
for investment, using both fixed effects and GMM estimators.
I analysed the companies owned by majority shareholders, and looked at the sub-panel with
an oligarch as the controlling shareholder and at the sub-panel with the state as the
controlling shareholder. I found that governance significantly and positively influences
investment in the latter case. This might be interpreted as greater transparency and
disclosure being a positive factor for investment in SOEs.
Private ownership concentration might replace good governance. Specifically, I found that
governance does not significantly affect investments when looking at firms controlled by
oligarchs. When estimating the impact of governance on capital investment through
external capital markets, I found that corporate governance matters and has a positive
impact on investments for firms that are a priori constrained.
External capital has a significant and positive influence on investment. Firms that raised
additional debt were (are) subject to more scrutiny from banks and were (are) applying
better governance rules to maximize the use of additional cash flows in investment projects.
An unexpected result consisted of finding gross cash flow to be not always significantly
related to investment. This might indicate that managers are colluding with major
shareholders to hold investment low irrespective of cash flow because of the risk of
expropriation. Another reason for gross cash flow-investment insensitivity might be the
presence of soft budget constraints. In an emerging economies context, zero sensitivity of
cash flow to investment is interpreted as evidence of a weak form of soft budget
constraints.
27
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Appendices
Table 8: Measuring corporate governance risks in Russia
55
Figure 2: Distribution of companies by total TD score, 2008-2009
Distribution of companies by transparency score, 2008-09
Share of companies in the sample
40%
35%
30%
25%
20%
2008
15%
2009
10%
5%
0%
<30%
30%-40%
40%-50%
50%-60%
60%-70%
>70%
T&D Score
Note: The gap in transparency between the highest and the lowest scorers in the ranking remains considerable
and even increased over 2008-2009. In 2009, the scores ranged from a high of 80% (78% in 2008) for the top
ranked company to a low of 20% (28% in 2008) for the lowest ranked company. Some companies in the middle
tier of 2008 dataset have improved their performance, while for some other companies from the middle tier
and some outsiders transparency has worsened significantly. As a result, the score dispersion within the rank
has increased. The average gap between the neighbouring companies amounts to almost 0.68 percentage
points which is higher than in 2008 (0.56 percentage points) but almost the same as in the 2007 dataset. The
average TD index for the top 10 companies remained virtually unchanged from 2008 to 2009 at 75.6%.
56
Figure 3: TD sub-scores distributions graphs


Histogram Fin. and operational
information score
Frequency
30
0
0
10
20
20
Frequency
40
40
50
60
Histogram Ownership Structure and
Shareholder rights score
0

.2
.4
.6
.8
Ownership structure and shareholder rights score
1
0
.2
.4
.6
.8
Financial and Operational Information score
1
0
20
Frequency
40
60
Histogram Board and Management
structure score
0
.2
.4
.6
Board and management structure and process score
.8
Note: Ownership and Shareholder rights score and Board and Management score show a normal distribution
shape with highest frequency of 50% score. A number of firms have high Financial and Operational information
score in the range of 60%-80%.
57
Figure 4: TD sub-scores correlations graphs
Board management structure score to fin.
and operational information score
0
1
.8
.6
.4
.2
0
0
.2
.4
.6
.8
1
Financial and operational information score
Ownership str. & s/holder rights score to
financial and operational information score
.2
.4
.6
.8
Ownership structure and shareholder rights score
Fitted values
1
0
.2
.4
.6
Board and management structure and process score
95% CI
Fitted values
95% CI
Correlation between log of capex and
total T&D score
0
0
.2
5
.4
.6
10
.8
1
15
Board management structure score to
ownership and s/holder rights score
.8
0
.2
.4
.6
Board and management structure and process score (%)
Fitted values
95% CI
.8
0
.2
.4
Total TD Score (%)
Log of capex in RUB
Fitted values
.6
.8
95% CI
Note: The 3 sub scores are correlated. If a firm has high reporting of financial and operational information, it
will likely report sufficient information on board, management and ownership structures. The log of capital
expenditure (investment) is correlated with Total TD score.
58
Sahre of the aggregate MCap
Figure 5: Distribution of aggregate market capitalization by transparency, 2008-2009 10
60%
50%
40%
30%
2008
20%
2009
10%
0%
<30% 30%-40% 40%-50% 50%-60% 60%-70% >70%
TD Score
Figure 6 : Relationship between firm market capitalization and transparency, 2009
70%
60%
TD Score
50%
40%
30%
20%
10%
0%
<200
200-400
400-1000
1000-5000
>5000
MCap, USDmln
Note: The 2009 TD dataset shows that, similar to previous years, a major portion of total market capitalization
is represented by companies with high levels of transparency. In 2009 (see Figure 5) this relationship was
especially pronounced. Companies with TD scores higher than 60% represent about 78% of aggregate market
capitalization. This effect can be explained both by the generally greater transparency of big companies (Figure
6) compared to smaller ones, and by the positive impact of greater transparency on the company’s market
value.
10
Market capitalization numbers were computed based on the market capitalization of companies averaged for the period
1.1.2009 to 18.5.2009 (Source: S&P)
59
Disclosure to investors of some important elements remains low, especially in relation to information on the
terms of employment contracts with CEOs, details of directors’ and executives’ remuneration and information
on auditors’ provision of non-audit services (Table 9: Weakest areas of disclosure by the largest Russian
companies, 2007-2009).
Table 9: Weakest areas of disclosure by the largest Russian companies, 2007-2009
Component
2007
2008
2009
23
43
29
42
26
48
24
29
34
22
15
42
27
36
44
4
16
11
15
26
14
19
7
8
19
19
12
23
39
11
8
7
3
23
23
2
24
32
1
29
37
33
6
30
6
20
6
1. Ownership Structure


Number and indemnity of all the shareholders holding more than
10%
Disclosure of share of beneficiary owners more than 75%
2. Shareholder rights



Evidence of existence of code of business conduct and ethics
Announcement of recommended dividends before the record date
Calendar of important future shareholder dates
3. Financial Information




Detailed earnings forecast
Disclosure of whether auditor renders non-audit services
Non-audit fees paid to the auditor
Indication that related-party transactions are made on market or
non-market terms
4. Operational information

Social reporting
5. Board and management information



Details of CEO’s contract
Record of attendance at board meetings
Information on ratio of in person and in absentia board meetings
6. Board and management remuneration


Specifics of directors’ pay
Specifics of managers’ pay
60
Table 10: TD Scores of 10 Most Transparent Russian Companies
61
Table 11: List of 90 Russian companies with S&P TD scores (in 2009)
(Arranged alphabetically)
Acron
OGK-1
Aeroflot
OGK-2
AFI Development
OGK-3 (WGC-3)
AvtoVAZ
OGK-4
Baltika
OGK-6
Bank Saint Petersburg
Open Investments
Bashneft
Pharmstandard
C.A.T. oil
PIK
Central Telecommunications Co.
Polymetal
Cherkizovo
Polyus Gold
Comstar-UTS
Raspadskaya
СТС Media
RAZGULAY Group
Dixy Group
RBC Information Systems
Enel OGK-5
Rosneft
Eurasia Drilling
Rostelecom
Evraz Group
Sberbank
FGC UES
Seventh Continent
Fortum (formerly TGC-10)
Severstal
Gazprom
Sibir Energy
Gazprom Neft
Sibirtelecom
GlobalTrans
Sistema
IDGC Holding
Sistema-Hals
IDGC of Centre
Slavneft-Megionneftegaz
IDGC of North-West
SOLLERS (formerly Severstal-Avto)
IDGC of Urals
Surgutneftegas
IDGC of Volga
Tatneft
IDGC of Centre and Volga Region
TGC-1
INTER RAO UES
TGC-4
62
Irkutskenergo
TGC-5
RusHydro
TGC-6
KAMAZ
TGC-9
LSR Group
TGC-14
LUKOIL
TMK
M.Video
TNK-BP Holding
Magnit
Uralkali
Mechel
Uralsvyazinform
MMK
Veropharm
Mobile TeleSystems
Vimpelcom
Mosenergo
Volga TGC (TGC-7)
MOESK
VolgaTelecom
Norilsk Nickel
VSMPO-AVISMA
North-West Telecom
Bank Vozrozhdenie
NOVATEK
VTB
Novolipetsk Steel
Wimm-Bill-Dann Foods
Novorossiysk Commercial Sea Port
X5 Retail Group
63
Table 12: Criteria for TD survey
Block 1: ownership structure and shareholder rights
Component 1. Ownership structure
Disclosure of:
1. The number and par value of issued ordinary shares.
2. The number and par value of issued other types of shares disclosed.
3. The number and par value of authorized but unissued shares of all types.
4. The identity of the largest shareholder.
5. The identity of holders of all large stakes (blocking: > 25%; controlling: > 50%).
6. The identity of shareholders holding at least 25% of voting shares in total.
7. The identity of shareholders holding at least 50% of voting shares in total.
8. The identity of shareholders holding at least 75% of voting shares in total.
9. The number and identity of each shareholder holding more than 10%.
10. Indication that management is not aware of the existence of any stake exceeding 5%
other than those reported.
11. An update on shareholder structure after the record date.
12. Shareholding in the company by individual senior managers.
13. Shareholding in the company of individual directors.
14. Description of share classes.
15. Shareholders by type.
16. Percentage of cross-ownership.
17. Information on exchange listings.
18. Information on indirect ownership (e.g., convertible instruments).
Component 2. Shareholder rights
Disclosure of:
19. Corporate governance charter or corporate governance guidelines.
20. Evidence of existence of a code of business conduct and ethics.
21. Content of code of business conduct and ethics.
22. Articles of association (including changes).
23. Voting rights for each voting or non-voting share.
24. How shareholders nominate board members.
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25. How shareholders convene an extraordinary general meeting (EGM).
26. Procedure for initiating inquiries with the board.
27. Procedure for putting forward proposals at shareholders meetings.
28. Formalized dividend policy.
29. Announcement of recommended dividends before the record date.
30. Review of last shareholders meeting.
31. Full minutes of general shareholder meeting (GSM).
32. Calendar of important shareholder future dates.
33. GSM materials published on the web site.
34. Detailed press releases covering last corporate events.
35. Policy on information disclosure.
Block 2: Financial and Operational Information
Component 3. Financial information
Disclosure of:
36. The company’s accounting policy.
37. The accounting standards it uses for its accounts.
38. Accounts according to local standards.
39. Annual financial statements according to an internationally recognized accounting
standard (IFRS/U.S. GAAP).
40. Notes to annual financial statements according to IFRS/U.S. GAAP.
41. Independent auditor’s report on annual financial statements according to IFRS/U.S.
GAAP.
42. Unqualified (clean) audit opinion on annual financial statements according to IFRS/U.S.
GAAP.
43. Audited IFRS/U.S. GAAP financial statements published before 30 April.
44. Unaudited IFRS/U.S. GAAP financial statements published before 30 April.
45. Audited IFRS/U.S. GAAP financial statements published before annual general meeting.
46. Unaudited IFRS/U.S. GAAP financial statements published before 30 June.
47. Disclosure of related-party transactions (RPTs): sales to/purchases from, payables
to/receivables from related parties.
48. Indication that RPTs are made on market or non-market terms.
49. Exact RPT terms.
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50. Interim (quarterly or half yearly) financial statements according to an internationally
recognized accounting standard (IFRS/U.S. GAAP).
51. Notes to these financial statements.
52. Whether these financial statements are audited or at least reviewed.
53. Consolidated financial statements according to local standards.
54. Methods of asset valuation.
55. List of affiliates in which company holds a minority stake.
56. Ownership structure of affiliates.
57. A basic earnings forecast of any kind.
58. A detailed earnings forecast.
59. Segment analysis (results broken down by business line).
60. Revenue structure (detailed breakdown).
61. Cost structure (high degree of detail).
62. Name of auditing firm.
63. Whether audit firm is a top-tier auditor.
64. Auditor rotation policy.
65. How much the company pays in audit fees to the auditor.
66. Whether auditor renders non-audit services.
67. Non-audit fees paid to the auditor.
Component 4. Operational information
Disclosure of:
68. Details of the firm’s type of business.
69. Details of products or services company produces or provides.
70. Output in physical terms.
71. Description of functional relationships between key operating units within the group.
72. Industry indicators that allow comparison with peers.
73. Other financial indicators.
74. Characteristics of fixed assets employed (including licences).
75. Efficiency indicators.
76. Discussion of corporate strategy.
77. Plans for investment in future years.
78. Detailed information about investment plans in the coming year.
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79. Output forecast of any kind.
80. Overview of trends in the relevant industry; regulatory environment with regard to
industry.
81. Market share for any or all of the company’s businesses.
82. Social reporting (e.g., Global Reporting Initiative).
83. Overview of compliance with ecology law.
84. Principles of corporate citizenship.
Block 3: Board and Management Structure and Process
Component 5. Board and management information
Disclosure of:
85. List of board members (names).
86. Details of directors’ current employment and position.
87. Other details: previous employment and positions, education, etc.
88. Date of appointment to the board.
89. Name of chairman.
90. Details on role of company board of directors.
91. List of matters reserved to the board.
92. List of board committees.
93. Names of all members of current committees.
94. Bylaws relating to other internal audit functions than the audit committee.
95. Information on ratio of in absentia and in person board meetings.
96. Record of attendance at board meetings.
97. List of senior managers not on board of directors.
98. Senior managers backgrounds.
99. The non-financial details of CEO’s contract.
100. The number of shares held by managers, in other affiliated companies.
101. Policy on assessment of board of directors and training provided to them.
Component 6. Board and management remuneration
Disclosure of:
102. Decision-making process related to directors’ pay.
103. Specifics of directors’ remuneration, including salary levels.
104. Form of directors’ salaries, such as in cash or in shares.
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105. Specifics of directors’ performance-related pay.
106. Decision-making process to determine managerial (not directors’) pay.
107. Specifics of managers’ (not directors’) emuneration, such as salary levels and bonuses.
108. Form of managers’ (not directors’) pay.
109. Specifics of managers’ performance-related pay.
110. Level and composition of CEO’s remuneration.
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Table 13: Investment and direct impact of governance, cross section Ordinary Least
Squares
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Figure 7: Impulse responses - VAR model
Note: Errors are 5% on each side generated by Monte-Carlo with 1000 reps. VAR model with three variables:
Log of investments (lncapxr), log of sales (lnsaler) and log of total TD score (lntotal)
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Acronyms
CEE
Central and Eastern Europe
CIS
Commonwealth of Independent States
EBIT
Earnings before Interest and Tax
EUR
Euro
FDI
Foreign Direct Investments
FE
Fixed effects
FFMS Federal Financial Markets Service
FSA
Financial Services Authority
FSC
Federal Securities Commission
GDP
Gross Domestic Product
GAAP Generally Accepted Accounting Principles
GMM Generalized Method of Moments
IFC
International Finance Corporation
IFRS
International Financial Reporting Standards
IMF
International Monetary Fund
IT
Information Technology
LSE
London Stock Exchange
MBA
Master of Business Administration
MSc
Master of Science
NPV
Net Present Value
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NYSE New York Stock Exchange
OECD Organisation for Economic Co-operation and Development
OLS
Ordinary Least Squares
P&L
Profit and Loss Statement
PhD
Doctor of Philosophy
PPA
Principal-Principal Agent
PPP
Purchasing Power Parity
RDT
Resource Dependence Theory
RPT
Related Party Transactions
RUIE
Russian Union of Industrialists and Entrepreneurs
RUR
Russian Rouble
S&P
Standard and Poor’s
SCA
Securities and Exchange Commission
SME
Small and Medium Enterprises
SOAS School of Oriental and African Studies
SOE
State Owned Enterprise
TD
Transparency and Disclosure
TFP
Total Factor Productivity
UK
United Kingdom
UKLA UK Listing Authority
US
United States
VAR
Vector Auto-Regression
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